Search is not available for this dataset
article
stringlengths 4.36k
149k
| summary
stringlengths 32
3.35k
| section_headings
listlengths 1
91
| keywords
listlengths 0
141
| year
stringclasses 13
values | title
stringlengths 20
281
|
|---|---|---|---|---|---|
The Neotropics contains half of remaining rainforests and Earth's largest reservoir of amphibian biodiversity . However , determinants of Neotropical biodiversity ( i . e . , vicariance , dispersals , extinctions , and radiations ) earlier than the Quaternary are largely unstudied . Using a novel method of ancestral area reconstruction and relaxed Bayesian clock analyses , we reconstructed the biogeography of the poison frog clade ( Dendrobatidae ) . We rejected an Amazonian center-of-origin in favor of a complex connectivity model expanding over the Neotropics . We inferred 14 dispersals into and 18 out of Amazonia to adjacent regions; the Andes were the major source of dispersals into Amazonia . We found three episodes of lineage dispersal with two interleaved periods of vicariant events between South and Central America . During the late Miocene , Amazonian , and Central American-Chocoan lineages significantly increased their diversity compared to the Andean and Guianan-Venezuelan-Brazilian Shield counterparts . Significant percentage of dendrobatid diversity in Amazonia and Chocó resulted from repeated immigrations , with radiations at <10 . 0 million years ago ( MYA ) , rather than in situ diversification . In contrast , the Andes , Venezuelan Highlands , and Guiana Shield have undergone extended in situ diversification at near constant rate since the Oligocene . The effects of Miocene paleogeographic events on Neotropical diversification dynamics provided the framework under which Quaternary patterns of endemism evolved .
Tropical regions contain more than half of biological diversity on just 7% of the Earth's surface [1 , 2] . Differences in biodiversity between tropical and temperate regions have been attributed to contrasting speciation and extinction rates [3] . Within the Neotropical realm , the Amazon Basin and the Chocoan region contain half of Earth's remaining rainforests and one of the largest reservoirs of terrestrial biodiversity . However , the impact of pre-Quaternary ecogeographic constraints on Neotropical biodiversity is largely unknown and the mechanisms contributing to species richness are unclear [3 , 4] . For example , the well-documented high α−diversity ( species richness ) of the flora and fauna of the Amazon rainforest [5] is usually attributed to local geoclimatic dynamics that promote monotonic accumulation of lineages [6 , 7] . However , the lower β−diversity ( species turnover relative to distance ) within the Amazon Basin is puzzling [8] and vastly underestimated . Current hypotheses are based on restricted , mostly Quaternary , spatiotemporal scales involving paleogeographic or ecological events ( e . g . , riverine barriers , Pleistocene climate change ) [3] , persistence of conservative niches [9] , and analyses of phylogeography and endemicity [10] . In addition to speciation/extinction processes [3] , major paleogeological events promote diversification , yielding complex phylogenetic patterns of vicariance , dispersal , and secondary sympatry [6] . Using phylogeographic analyses of the endemic and diverse clade of poison frogs ( Dendrobatidae ) , we reconstructed Neotropical biogeography from the Oligocene to the present and revealed a widespread and highly dynamic pattern of multiple dispersals and radiations during the Miocene . Major geoclimatic events have shaped the Neotropics . The most important include the isolation and reconnection of South America , the uplift of the Andes , the extensive floodbasin system in the Amazonian Miocene , the formation of Orinoco and Amazon drainages , and the dry−wet climate cycles of the Pliocene−Pleistocene ( Figure 1 ) . The Panamanian Land Bridge ( PLB ) between the Chocó and Central America , which formed progressively until the Pliocene [11] , was an important biogeographic catalyst of dispersal and vicariance events at the Miocene−Pliocene boundary ( e . g . , Alpheus shrimps and freshwater teleost fishes ) [12 , 13] . Similarly , the uplift of the Andes advanced the formation of the Amazon River , converting a widespread , northwest-flowing Miocene floodbasin into the current eastward-running Amazon Basin [14 , 15] . Two Miocene marine incursions into this wetland system isolated several aquatic taxa as living relicts , including the Amazon River dolphin , lineages of marine-derived teleosts and stingrays , and brackish water mollusks [16 , 17] . However , controversies exist about the magnitude and duration of these geoclimatic events [18] . Although well known for its megadiversity , no studies of the Neotropics have examined diversification patterns in highly speciose and widespread lineages over broad temporal and spatial scales . A general explanation that associates rates of speciation with paleogeographic events is lacking . Here , we test two general hypotheses about the spatial configuration of biogeographic areas on the origin of Neotropical diversity ( Figure 1 ) . First , under the center-of-origin hypothesis , lineages from the currently most diverse area ( i . e . , Amazon Basin ) dispersed to other areas ( Figure 1 , HA: SM1 ) . Second , under the stepping-stone hypothesis , paleogeographic events constrained the patterns of lineage diversification in the Neotropics among geographically adjacent areas ( Figure 1 , HA: SM2 ) . Using a recently developed maximum likelihood ( ML ) procedure that estimates geographic range evolution , we tested both hypotheses against a null biogeographic model ( Figure 1 H0: SM0 ) using a well-sampled Neotropical clade , the poison frogs ( Dendrobatidae ) . We sampled 223 of the ∼353 ( 264 described and 34–89 undescribed ) species , distributed from Central America and Guiana Shield to southeast Brazil and from Andean páramos ( 4 , 000 meters above sea level [masl] ) to lowland rainforests ( <300 masl ) . However , ∼40% of the species diversity remains unsampled ( Table S11 ) . Because the true diversity ( i . e . , described , undescribed , and extinct species ) cannot be accurately assessed [19 , 20] , macroevolutionary inference should account for missing diversity . Our goals are to ( 1 ) infer how geographic range evolution yielded current species distributions , ( 2 ) estimate the general patterns of speciation and extinction under necessarily incomplete taxon sampling , and ( 3 ) synthesize these findings with paleogeographic events to explain current patterns of species richness .
The patterns of spatial and temporal distribution of poison frogs were inferred using dispersal-extinction-cladogenesis ( DEC ) modeling [26] . We compared three DEC models ( i . e . , a null and two alternatives ) for ten areas ( Figures 1 and S6 ) . The null model ( SM0 ) assumes that spatial structure has no effect on biogeographic patterns of evolution . The alternative models either favor the Amazon Basin as a center-of-origin ( SM1 ) , or patterns of dispersal/vicariance that reflect the spatial arrangement or connectivity of biogeographic areas ( a stepping-stone model; SM2 ) . Our results strongly support the SM2 model over SM1 ( large phylogeny Δln[L] = 140 . 5 , p < 0 . 001; chronogram Δln[L] = 48 . 6 , p < 0 . 001 ) and SM0 ( large phylogeny Δln[L] = 17 . 7 , p < 0 . 001; chronogram Δln[L] = 0 . 5NS , p > 0 . 05 ) . The nonsignificant comparison of SM2 and SM0 for the chronogram alone is likely due to its reduced taxon sample . The chronogram and a summary of the significant biogeographic events with confidence limits ( Tables S8 , S9A , and S9B ) from the stepping-stone model are superimposed on the four major clades ( Figure 2 ) . The most recent ancestor of Dendrobatidae was distributed in regions that correspond to the current Venezuelan Highlands and Northern Oriental Andes at 40 . 9 ± 5 . 4 million years ago ( MYA ) . This ancestral range split into a Venezuelan Highlands ancestor of clade A and an Andean ancestor ( clade B + C + D ) . The most recent ancestor of each major clade occurred during the Oligocene at 34 . 9 ± 5 . 4 MYA ( clade A ) , 30 . 9 ± 3 . 9 MYA ( clade B ) , 27 . 1 ± 3 . 2 MYA ( clade C ) , and 29 . 9 ± 4 . 0 MYA ( clade D ) . We inferred 14 dispersals into and 18 from the Amazon Basin to adjacent areas , including three major radiations and a single lineage extinction within Amazonia . We also found five cross-Andean dispersals , five dispersals from Northern to Central Andes , six dispersals from Northern Andes to Chocó , four dispersals from Chocó to the Andes , and three temporal phases of lineage dispersal with two interleaved periods of vicariant events between the Chocó and Central America ( Figure 2 and Table S12 ) . The diversity of Amazonian poison frogs ( >70 species ) resulted from 14 separate dispersals into this region , in three phases ( Figure 2 ) . First , the two oldest dispersals originated independently from the Guiana Shield ( 23 . 8 MYA ) and from the developing Andes ( 21 . 1 MYA ) , just before and during the existence of the Amazonian Miocene floodbasin . Second , a single dispersal from the Guiana Shield occurred 15 . 5 MYA , during a low sea-level period associated with reduction of the Miocene floodbasin system . Third , the 11 remaining dispersals from the Andes took place between ( 1 . 6–7 . 3 MYA ) during the formation of the modern Amazon Basin river system . Ancestral area reconstructions using a Bayesian multistate procedure similarly support the recent multiple dispersals to the Amazon Basin ( Figure 2 ) . Thus , our results suggest that much of the extant Amazonian biodiversity results from relatively recent immigration of distinct lineages followed by in situ radiation during the last 10 MYA . At least 18 dispersals from the Amazon Basin to other areas were found in three temporal phases . First , the earliest dispersals to the developing Chocoan lowlands ( 21 . 8 MYA ) and the Andes ( 15 . 2 MYA ) occurred during the establishment of the Miocene floodbasin system . Interestingly , for the present Chocoan lineage of Dendrobates pumilio ( Figure 2 ) , our results suggest a Miocene overwater dispersal from Chocó to the developing Central America archipelago and the extinction of the Amazonian lineage ancestor at ∼19 . 5 MYA during the formation of the Miocene floodbasin system . Second , dispersals to the Guiana Shield ( 1 ) , the Venezuela Highlands ( 1 ) , and the Andes ( 1 ) took place after the Miocene floodbasin system receded ( 8 . 8–10 . 8 MYA ) . Third , the 12 remaining dispersals were very recent ( 0 . 7–6 . 0 MYA ) , to the Guiana Shield ( 7 ) , Andes ( 4 ) , and Brazilian Shield ( 1 ) . Thus , 16 out of 18 occurred <11 MYA , after the establishment of the current Amazonian geomorphology ( Figure 2 ) . At least four major diversifications occurred within Amazonia: ( 1 ) the Allobates trilineatus complex ( 26 species ) is the oldest ( 14 . 0–15 . 1 MYA ) ; the three remaining are more recent ( 5 . 4–8 . 7 MYA ) , ( 2 ) the Amazonian Ameerega ( 19 species ) ; ( 3 ) the Dendrobates ventrimaculatus complex ( 15 species ) ; and ( 4 ) the Allobates femoralis complex ( nine species ) . Moreover , all four lineages entered the Guiana Shield area in the Pliocene ( Figure 2 ) . Species diversity in the Andes ( 71 species ) resulted from a continuous diversification process since the late Eocene ( Figure 2 ) . However , several Andean events were contemporaneous with establishment of the Amazon Basin . Five cross-Andean dispersals from Oriental to Occidental Andes ( 2 . 0–25 . 4 MYA ) , five dispersals from Northern to Central Andes ( 14 . 9–30 . 9 MYA ) , and six dispersals from the Northern Andes to the Chocoan lowlands ( 8 . 3–29 . 9 MYA ) ( Figure 2 ) took place before the establishment of the Andes as a major geographic barrier during the Miocene–Pliocene boundary [27] . We also found five dispersals from the Chocó to North and Central Andes ( 1 . 1–6 . 6 MYA ) ( Figure 2 ) that took place mostly in the Pliocene when the Andes were already established as a barrier . Our results indicate that lower montane transition zones between Andean and lowland environments ( Chocó and Amazonia ) promote diversification , as exemplified by the Amazonian Ameerega [28] and the Chocoan Epipedobates . Central American and Chocoan species ( >40 ) also show a complex pattern of diversification at the end of the Miocene . Ten dispersals from Chocó to Central America suggest a pattern of recurrent colonization and isolation in three phases ( Figure 2 ) . First , the two oldest dispersals ( 8 . 3–12 . 1 MYA ) from the Chocó overlap with a proposed earlier exchange of faunas during the late Miocene [29] . A single vicariant event at 6 . 8 MYA isolated the first wave of immigrants ( i . e . , ancestors of Phyllobates and Colostethus 1 ) . Interestingly , the contemporaneous divergence of the Trinidad and Tobago species ( Mannophryne trinitatis and M . olmonae ) from Venezuelan relatives at 8 . 3 MYA suggests a global period of high sea level . Second , six Pliocene dispersals from South America ( 3 . 2–5 . 4 MYA ) , immediately followed a proposed low sea-level period after 6 . 0 MYA [11] . Six vicariant events in the middle Pliocene ( 1 . 1–3 . 6 MYA ) are concomitant with a second high sea-level period ( 1 . 5–3 . 0 MYA ) that separated Central America from the Chocó [11] . Third , two dispersals in the late Pleistocene ( 0 . 5–2 . 2 MYA ) are contemporaneous with the Great Faunal Interchange at 1 . 2 MYA [11] . Likewise , the endemic poison frog of Gorgona Island ( Epipedobates boulengeri ) , located 50 km off the Pacific coast , was derived from a Chocoan ancestor 2 . 4 MYA during the same period . Our results strongly support the repeated dispersal of poison frogs into Central America across the PLB before its final Pliocene closure . Similar results for the ancestral area reconstruction were obtained by dispersal-vicariance analysis ( DIVA ) [30] . However , DIVA provided unrealistic ancestral reconstructions for basal nodes ( Figure S7 ) , and a large number ( i . e . , ∼16 × 106 ) of equally parsimonious reconstructions ( see Material and Methods ) . Therefore , DIVA analyses were considered exploratory due to its algorithmic limitations [26 , 31] . We estimated diversification rates of the chronogram ( i . e . , dendrobatid family clade ) using the adjusted γ statistic to account for incomplete taxon sampling [32 , 33] . The γ statistic compares the relative position of the nodes in a chronogram to that expected under the pure birth model , and different values of γ characterize whether the diversification rate has increased ( γ > 0 ) , decreased ( γ < 0 ) , or remained constant ( γ = 0 ) over time [32] . However , we emphasize that the γ statistic is an indirect estimation of the rate of diversification [32] , it should only be applied to monophyletic groups , and our inferences from the γ statistic should be considered relative measures of the diversification . Our result from the chronogram of the adjusted γ statistic is −0 . 662 , and we failed to reject ( p = 0 . 508NS ) the null of the pure birth expectation of exponential growth , γ = 0 . Simulations have suggested that significant positive values of γ have been associated with two alternatives , either increasing diversification or high extinction through time [34] . The general absence of Tertiary frog fossils from the lowland Neotropics is intriguing [35] , but it does not provide evidence for or against increases in extinction/diversification , as suggested by our estimated γ statistic for the dendrobatid family clade . However , our results of the ancestral area reconstructions strongly suggest that the bulk of recent diversification in poison frogs might be due to rapid radiations in the Amazon Basin and the Central American-Chocó super-regions in the late Miocene . Therefore , our inference of recent dispersals to Amazonia and the recent geological origin of the modern Neotropical rainforest [18] might weigh in favor of a recent increase in diversification . Additional data from other Neotropical biota might be crucial to validate our inferences . We further evaluated our claim of a significant increase in diversification in the Amazon Basin and the Central America-Chocó super-regions . We tested for significant changes in diversification rate at the genus-supraspecific level ( GSPF chronogram and Figure 3; see Material and Methods ) under incomplete taxon sampling using ML approach [36] with two extreme values of the extinction/speciation ratio ( i . e . , extinction rate fraction α = μ/λ , α = 0 , and α = 0 . 99 ) ( Table 2 ) . The GSPF chronogram rejected the constant-rate model ( all lineages with equal diversification rate ) in favor of a variable rate model ( at least one lineage has a significant higher or lower diversification rate ) ( Table 2 ) . Additionally , the GSPF chronogram favored the variable-rate model with diversification rate change in one or more lineages ( Figure 3; Tables 3 , S13 and S14 ) over an alternative of retained elevated ancestral diversification rate ( Table 2 ) . However , we found possible spurious significant rate increases ( i . e . , nodes 2 and 9 of Figure 3 and Table 2 ) that might be dependent on more inclusive lower nodes ( i . e . , 1 and 5 , respectively ) . This “trickle-down effect” artifact can be explained by a significant rate increase detected in daughter clade being carried over to the adjacent parent clade . The diversification rate increase within Ameerega is 3 . 23-fold ( α = 0 ) to 7 . 55-fold ( α = 0 . 99 ) higher than the rest of the GSPF chronogram ( Figure 3; Tables 3 , S13 and S14 ) . Interestingly , Ameerega corresponds the most recent ( i . e . , 8 . 7 MYA ) widespread radiation of poison frogs in the Amazon Basin after dispersal from the Andes ( Figures 2 and 3 ) in the late Miocene . Other significant increases in the diversification rate include two in Amazonia , two in the Chocoan region , and one in the Andes ( Figure 3 and Table 3 ) . Significant decreases in the rate of diversification correspond to the rare Guiana Shield endemic Allobates ( 0 . 0008-fold reduction ) and the mostly Andean endemic Clade B ( 0 . 4851-fold reduction ) ( Figure 3; Tables 3 , S13 , and S14 ) . Therefore , we found that Amazonian and Central American-Chocoan lineages significantly increased their diversification rate since the late Miocene , while the diversification rate in the Andean and Guianan-Venezuelan-Brazilian Shield lineages have been near constant with a tendency to slow down since the Oligocene . However , we acknowledge that these super-regions ( i . e . , the Andes and the Guiana-Venezuela-Brazilian Shield ) might be undersampled ( Table 1 ) and conclusions about their near-constant rate of diversification need further validation .
The unstable coexistence of lineages within a large community for extended periods of time has been hypothesized as a cause of Neotropical diversity [7] . However , our results suggest that such a model is incomplete; rather , the complex pattern of diversification is strongly intertwined with paleogeographic events . Our inferences about the past history of the poison frogs using ancestral area reconstructions and diversification analyses provide new insights on speciation and extinction patterns in the Neotropics . Three species richness patterns are potential explanations for the extant diversity differences among regions of the Neotropics: ( 1 ) high immigration into one region after suitable geoclimatic conditions are established; ( 2 ) gradual in situ diversification of old endemic clades , regardless of the geoclimatic conditions , promoting species accumulation; or ( 3 ) rapid in situ diversification of endemic clades after favorable geoclimatic conditions are established . We found that all three patterns might apply to different areas depending on historical context . All extant Amazonian species descended from 14 lineages that dispersed into the Amazon Basin , mostly after the Miocene floodbasin system receded . The recurrent immigrations that originated mostly in the adjacent Andes ( species-richness pattern 1 ) , combined with an increased rate of diversification , explain the high α–diversity of Amazonia . Later , from the Miocene-Pliocene boundary to the present , a rapid in situ diversification ( pattern 3 ) gave rise to the extant Amazonian endemic biota . Therefore , most species in Amazonia originated in the last 10 MY . Moreover , lineages immigrating into Amazonia at <8 . 0 MYA radiated rapidly , resulting in widespread species and young clades ( e . g . , Ameerega , Allobates trilineatus , A . femoralis , and Dendrobates ventrimaculatus groups ) . The diversity in the Chocoan-Central American super-region derived from scattered immigrations ( pattern 1 ) from Andes to the early Chocoan rainforest during the late Miocene . However , starting at the Miocene-Pliocene boundary , significant orogenic events gave rise to the Central American archipelago [11 , 37] followed by sea level fluctuations [38] , which provided the conditions for repeated dispersal and vicariance events in pre-PLB islands . Evidence of rapid in situ diversification ( pattern 3 ) is supported by the high genetic diversity observed among poison frogs and other lineages especially between Western and Eastern Panamá [12 , 39 , 40] . Interestingly , our results might explain the high β–diversity of other endemic clades within the Chocó-Central America super-region [8] as originating initially from long-distance dispersals between disconnected islands , with diversification later during isolation by high sea levels . The Andes have undergone extended in situ diversification ( pattern 2 ) since the late Eocene . However , our analyses also provided evidence of decline in the diversification rate since the middle Oligocene , which has important implications for history and conservation of the endemic Andean fauna . First , the Andes uplift at the Miocene–Pliocene boundary caused significant changes in the rate of diversification in the lowland transition zone . We found that several poison frog lineages distributed on one or both sides of the Andes had dispersed repeatedly before the Miocene uplift ( i . e . , five cross-Andean and five Northern to Central Andes migrations ) . Paleogeological evidence supports introgression of shallow seas across the northern Andes during the Miocene [14] , suggesting a historical connection between the Amazon Basin and the Chocó . Second , the Pliocene Andean uplift ( >2 , 000 m above sea level ) [27] formed a significant barrier to dispersal , because no other cross-Andean dispersals were found . The uplift also was associated with dramatic ecological changes [27] and a decrease in diversification rates . These results suggest a role for niche conservatism [41 , 42] , in that some lineages may have gone extinct because of failure to adapt . Alternatively , despite greater sampling effort in the Andes region than in other areas , we failed to find some previously common Andean species ( e . g . , Hyloxalus jacobuspetersi and the Ecuadorian H . lehmanni ) . Consequently , it is difficult to separate a natural decrease in diversification rates from the current trend of amphibian species extinctions at high altitudes due to anthropogenic habitat alteration [43] , increased UV radiation [44] , climate change [45] , or pandemic infection [46] . In contrast , the montane transition zones of the Andes and adjacent lowlands ( Chocó and Amazonia ) have become centers of rapid cladogenesis ( pattern 3 ) , and species richness in these transition zones might be underestimated because many Neotropical lineages have been shown to contain several cryptic species [47] . Therefore , dispersals within or across the Andes diminished during the Pliocene , but diversification has intensified in the Andes-lowlands interface . Although some of the oldest lineages of poison frogs originated in the Guiana Shield and the Venezuelan Highlands ( >30 species ) , our results suggest extended in situ diversification ( pattern 2 ) followed by a decline in the rate of diversification of endemic clades in both areas since the early Miocene . Along the same lines , the Guiana Shield has high poison frog endemism , which is mostly restricted to the summits of the sandstone tepuis [48] , while recent Amazonian poison frog immigrants occupy lowlands adjacent to the tepuis . Our results suggest that the decline of endemic Guianan diversity might be associated with ecological changes in habitat due to the collapse of the ancient tepuis [4] and repeated dispersals from Amazonian lineages since the Pliocene . However , the diversity of poison frogs in the Guiana Shield is only beginning to be revealed [48] . In contrast , diversification in the Venezuelan region most likely reflects the oldest vicariant event in Dendrobatidae , at 40 . 9 MYA . The costal ranges of Mérida , Cordillera de la Costa , and Paria peninsula are species rich but their total area is less than 5% of that of the Amazon Basin . No lineage of this endemic fauna has dispersed out to other regions since the early radiation of the poison frog family in the late Eocene . However , Eocene floristic paleoecological reconstruction of the Venezuelan Highlands area showed that it was more diverse than at present [49] , suggesting that the ancestral habitat of the first poison frogs might have been lowland . The depauperate dendrobatid fauna of the Venezuelan llanos and Brazilian Shield plateau is puzzling , but might be related to Holocene aridity [50] . The recurring dispersals to Amazonia suggests that a large part of dendrobatid diversity results from repeated immigration waves at <10 . 0 MYA , followed by a rapid in situ diversification after geoclimatic conditions suitable for a rainforest ecosystem were present . The biota of Amazonia was not isolated during the process of diversification , but finely intertwined with the development and export of biodiversity across the entire Neotropical realm . Poison frog diversity in the Chocoan-Central American super-region was significantly associated with formation of the PLB in the Pliocene . Repeated dispersals between disconnected islands followed vicariance by cyclic high sea-level periods , promoted rapid in situ diversification and endemism of poison frog lineages . The extant Andean , Guianan , and Venezuelan Highlands fauna most likely originated after prolongated in situ diversification since the origin of the poison frog clade , but the pace of species formation within these areas has slowed down . Phylogenetic analyses on tropical biota such as birds [51] and the species-rich genus Inga [52] as well as models of diversification [3] , have argued that the Amazon might accumulate older lineages; however , the origin of those lineages is not clear . Our results are the first to provide evidence , to our knowledge , of the major involvement of the Andes as a source of diversity of both the Amazon and the Chocó–Central America region . Because 23 . 5% of endemic Amazonian amphibian species are dendrobatids ( i . e . , ∼70 of 298 ) [53] , our results may generalize to other Neotropical terrestrial biota with similar distribution . Moreover , these results provide a crucial broad spatiotemporal framework that , coupled with realistic phylogeny-based explanations of the current richness in Neotropics , explains why species occur where they do and how they came to get there . Thus , the major patterns of dispersals and radiations in the Neotropics were already set by the Miocene–Pliocene boundary , but the ongoing process of Neotropical radiation is occurring now , in the Chocó–Central America region and especially in the Amazonian rainforest .
Because there are no fossil poison frogs , a large phylogeny of amphibians was constructed to calibrate the age of the root of the dendrobatid tree . We used a total of 89 terminals including 80 species of anurans ( 30 families ) , three species of salamanders ( three families ) , three species of caecilians ( three families ) , and three outgroups ( lungfish , human , and chicken ) ( Figures S1 and S2; Table S2 ) . The amphibian classification partially follows that of Frost et al . [54] . Conflicts with Frost et al . [54] are indicated as paraphyletic families ( e . g . , Dicroglossidae 1 and 2 ) . Molecular data include the mitochondrial rRNA genes ( 12S and 16S sequences; ∼2 , 400 bp ) and the nuclear protein-coding gene RAG-1 ( approximately 495 bp ) . Sequences were retrieved from GenBank ( 74 terminals ) or sequenced ( 15 terminals ) from total genomic DNA ( Table S2 ) . The primers and protocols for amplification , purification , and sequencing of PCR products are provided in previous studies [22 , 55 , 56] . PCR products were sequenced in both directions and compared to GenBank sequences using BLAST ( http://www . ncbi . nlm . nih . gov/BLAST/ ) . By this procedure , we were also able to validate sequences in GenBank and exclude contaminated or mislabeled submissions . GenBank accession numbers are given in Table S2 . A total of 406 individuals for described ( 137 ) and undescribed ( 34–89 ) species of poison frogs and 12 outgroups ( from Hyloidea ) were used to estimate the phylogeny ( Figure S3A–S3D; Table S3 ) . The estimate of undescribed species ( 34–89 spp . ) corresponds to the estimated minimum and maximum number of new species . Therefore , the described diversity of poison frogs ( 264 species ) plus the diversity discovered by our analysis yields a maximum of 353 ( 264 known + 89 maximum number of undescribed species ) , which is a better estimate of the true extant diversity . Of the 127 described poison frog species not sampled ( Table S11 ) , we were able to identify their closest relative or species group in 92 . 1% of the cases ( 117 species ) . Thus , we are confidant that we have not missed any crucial lineage and that our conclusions will hold as more data are incorporated . Furthermore , the conservation status of the unsampled species is based on the Global Amphibian Assessment [57] is as follows ( Table S11 ) : 51 . 3% are data deficient , 16 . 6% have been described recently ( no category ) , 28 . 9% are in one of four “threatened” categories , and 3 . 2% are of least concern . The classification here partially follows that of Grant et al . [23] . Species placements that conflict with this taxonomy are indicated as paraphyletic genera ( e . g . , Colostethus 1 and 2 ) . Proposed taxonomic changes and corrections are explained in “Corrections to Taxonomy” ( Text S1 ) . Our taxon sample included species from throughout the distribution of dendrobatid frogs , with regions of higher diversity sampled more extensively ( Table S3 ) and the 117 species that were not sampled were assigned to a taxonomic group ( Tables S11 and S13 ) and included in the Materials and Methods section “Speciation and extinction patterns under incomplete taxon sampling . ” Molecular data were generated using the same protocols indicated in the first paragraph of Materials and Methods . We included only the mitochondrial rRNA genes ( 12S and 16S sequences; ∼2 , 400 bp ) , from which 374 individuals ( 121 species ) have complete sequences . GenBank sequence accession numbers are given in Table S3; because some outgroup sequences will appear in other papers currently under review , their numbers are not listed . Although sequences from other genes are available in GenBank , these were not used because data from the data are highly incomplete; complete data for only 80 species were available . Moreover , simulation studies have indicated that large phylogenetic analyses including many terminals with incomplete sequences might bias branch length estimation , and cause topological inconsistencies due to unrealistic estimates of the rate of evolution [58] . Contigs were assembled using Sequencher 4 . 7 [59] . Sequences were initially aligned using ClustalX 1 . 81 [60] and manually adjusted to minimize informative sites using MacClade 4 . 08 [61] . The final matrices included 2 , 688 characters ( i . e . , 2 , 192 from mitochondrial genes , 495 from RAG-1 ) for the Amphibian Tree Matrix ( ATM ) and 2 , 380 characters ( mitochondrial genes ) for the Poison Frog Tree Matrix ( PFTM ) . A total of 228 ( ATM ) and 113 ( PGTM ) ambiguously aligned characters were excluded . For the ATM , we divided the data into mitochondrial gene and RAG-1 partitions . For the PFTM , we used 12S rRNA , tRNA-Valine , and 16S rRNA partitions . The best model of nucleotide substitution for each partition was determined using ModelTest 3 . 7 [62 , 63] . For the ATM , GTR + Γ + I and TrN + Γ + I were selected for the mitochondrial genes and RAG-1 segments , respectively . For the PFTM , GTR + Γ + I was selected for the 12S and 16S rRNA segments and the GTR + Γ for the tRNA-valine segment . ATM and PFTM were analyzed with ML methods under a genetic algorithm in GARLI 0 . 951 [64] and with Bayesian sampling of tree space with MrBayes 3 . 1 . 2 [65 , 66] . For the ATM analyses , the amphibian species were constrained to be monophyletic . For the GARLI analyses , a total of 40 independent runs were used to infer the best tree and 200 nonparametric bootstrap searches were used to estimate support for the nodes . For the MrBayes analyses , tree topology estimation , branch lengths , and Bayesian posterior probabilities ( PP ) were determined from five independent runs of four incrementally heated chains . Runs were performed for 35–45 × PFTM 106 generations under partitioned models using default settings as priors; the sampling frequency was 1 in 1 , 000 generations . The convergence of the runs and the optimal burn-in was determined to be 1 . 238 × 106 ( ATM ) and 4 . 282 × 106 ( PFTM ) generations using MrConverge [58] . This program estimates the point where the likelihood score becomes stationary and the overall precision of the bipartition posterior probability is maximized . For molecular dating analyses the strict molecular clock model was rejected from both ATM ( χ2 = 4 , 762 . 6 , df = 87 , p < 0 . 001 ) and PFTM ( χ2 = 3822 . 6 , df = 404 , p < 0 . 001 ) datasets using a likelihood ratio test ( LRT ) that compared the best unconstrained GARLI trees to those estimated under a strict molecular clock . Therefore , a relaxed molecular clock Bayesian method in MULTIDIVTIME [21] was used to estimate chronograms for the Amphibian Tree and the Poison Frog Tree . For chronogram estimation , all taxa in the ATM dataset ( Amphibian Chronogram ) and a pruned PFTM dataset ( Poison Frog Chronogram ) were used . The pruned PFTM dataset excluded multiple individuals of the same species to improve computational efficiency . For each analysis ( ATM and pruned PFTM ) , the aligned matrix and the rooted ML topologies without branch lengths were input into MULTIDIVTIME [21] . Branch length estimates under the F84 + Γ model of molecular evolution and variance/covariance matrices were calculated using the BASEML and ESTBRANCHES components of PAML 3 . 15 [67] and MULTIDIVTIME [21] , respectively . Calibration points ( Figures S4 and S5; Tables S4–S6 ) , relaxed-clock model priors , and variance/covariance matrices were then input into MULTIDIVTIME [21] . For the Amphibian Chronogram , three different sets of time constraints were used to assess the robustness of the dating estimates; these were based on paleogeography , vertebrate fossils , and amphibian fossil records ( Table S4 ) . The ingroup tip-to-root distances needed for the estimation of the MULTIDIVTIME rtrate and rtratesd priors [21] were calculated using TreeStat v1 . 1 [68] . The relaxed-clock model priors were 344 MYA for the expected age between tip and root ( rttm ) and 20 MYA for its standard deviation ( rttmsd ) . The rttm prior corresponds to the divergence of Amniota and Amphibia and is based on fossil and molecular analyses [69–72] . The expected molecular evolution rate at the ingroup root node ( rtrate ) prior and its standard deviation ( rtratesd ) were estimated at 0 . 00345 substitutions/site/MY by dividing the median of ingroup tip-to-root distances by the rttm prior as suggested by the MULTIDIVTIME documentation [21] . The priors for the expected value of the Brownian motion constant υ ( nu ) ( brownmean ) and its standard deviation ( brownsd ) were estimated to be 0 . 058 by setting rttm * brownmean equal to 2 . 0 ( on-line suggestion of Frank Rutschmann [73] ) . The bigtime parameter was set to twice the estimated time divergence of Amniota and Amphibia ( i . e . , 700 MYA ) . Markov chain ( newk , othk , thek ) and beta ( minab ) priors were set to default values . Each MCMC chain was run for 1 × 106 generations with sampling frequency of 1 per 100 generations and burn-in of the first 100 , 000 generations . All analyses were run twice to ensure convergence of the time estimates . The divergence time estimated for each node of the amphibian chronogram was described by its mean age and 95% confidence interval ( CI ) ( Table S7 ) . The Poison Frog Chronogram was estimated from the pruned PFTM dataset that included 224 individuals from 157 poison frog species ( 131 described and 26 undescribed ) and 12 outgroups . The fossil record of Tertiary Neotropical frogs is minimal and no fossils of poison frogs have been found [35] . For this reason , we used a three-part strategy to date the Poison Frog Chronogram . First , the expected ages and 95% CIs of the split of the Dendrobatidae from other Hyloidea and the age of the most recent common ancestor of this clade were estimated from the Amphibian Chronogram . Second , a list of geological time constraints ( Table S5 ) was developed based on paleogeological evidence ( Table S6 ) . Third , ancestral area reconstruction was inferred and explained in the Materials and Methods section “Ancestral area reconstruction” . To test the overall accuracy of these approaches , three sets of progressively less inclusive time constraints were used ( Table S5 ) . The relaxed-clock model priors were 71 . 4 MYA for the expected age between tip and root ( rttm ) and 18 . 6 MYA for its standard deviation ( rttmsd ) . This value , estimated from the Amphibian Chronogram , corresponds to the mean age of the split between Dendrobatidae and its sister clade . The expected value ( rtrate ) and standard deviation ( rtratesd ) priors were set to 0 . 0056 substitutions/site/MY . The priors for the expected value ( brownmean ) and its standard deviation ( brownsd ) of the Brownian motion constant υ ( nu ) were set to 0 . 2 . These priors ( rtrate , rtratesd , brownmean , and brownsd ) were obtained similarly as in the Amphibian Chronogram ( see above ) . The bigtime parameter was set to three times the estimated age of the Dendrobatidae node from the Amphibian Chronogram ( 40 × 3 = 120 MYA ) . Markov chain priors ( newk , othk , thek ) , beta prior ( minab ) , and MCMC chain parameters were the same as for the Amphibian Chronogram . All analyses were run twice to ensure convergence of the time estimates . The divergence time estimated for each node of the Poison Frog Chronogram was described by its mean age and 95% CI ( Table S8 ) and major taxonomic events ( Table S9 ) . We assessed the robustness of the calibrations ( Table S5 ) with three approaches . First , we recalculated the chronogram by using penalized likelihood approach ( PL ) [74] implemented in r8s [75] . Because the penalized likelihood method requires at least one fixed node age , the nodes were fixed for root of the poison frog tree at 42 . 9 MYA or for the major split between clades B + C + D and A at 36 . 3 MYA . These values correspond to the mean age for each node obtained by averaging the ages from the three estimates of the Amphibian Chronogram ( Table S7 ) . We calculated the Poison Frog Chronogram with all constraints and then by removing one calibration constraint at a time ( Tables S9 and S10 ) . We use five different smoothing parameters ( 1 , 10 , 100 , 500 , and 1 , 000 ) , an additive scale for rate penalty , and 20 random starts to estimate the chronogram . The average of all runs without the respective constraint was used as the estimate of node age ( Table S9B ) . Second , we recalculated the chronogram using the same parameters of the relaxed-clock Bayesian method but removing one calibration constraint at a time ( Table S9A ) . Finally , we recalculated the Poison Frog Chronogram to determine the effect of the combination of bigtime and rttmsd priors , which have been shown to have the substantial impact [76] , by changing the estimates of nodes to make them older or younger . We used the same rttmsd prior ( 18 . 6 MYA ) and two bigtime prior values of 60 and 80 ( Table S9A ) . Based on all tests , the estimates of 36 exemplar nodes that correspond to major phylogenetic events in the family ( Table S9A and S9B ) and the differences in MYA from the Poison Frog Chronogram node mean are provided ( Table S10 ) . We found that all time estimates from each test were within two standard deviations of the mean of poison frog chronogram ( Table S10 ) . The time estimates obtained by using the penalized likelihood method mostly were within one standard deviation from those estimated in the Poison Frog Chronogram . In the case of the tests done using the relaxed molecular clock Bayesian method , the removal of constraint A ( 2 . 4–15 . 0 MYA , which was assigned to nodes that correspond to dispersals between South America and Central America ) was found to provide older time estimates for all nodes , especially those close to the root . The effect of changing the rttmsd/bigtime priors combination was negligible . The reconstruction of ancestral areas of the poison frog clade was determined by three methods: a maximum-likelihood inference of geographic range evolution [26 , 31 , 77] , DIVA [30] , and Bayesian analysis of ancestral areas [78–80] . Ten areas ( designated by letters ) were delimited on the basis of geological barriers , areas of endemism [81–83] , and distribution maps [57] ( Figure 1; Table S3 ) . The Andes were divided into four adjacent regions: Central Occidental Andes ( H ) , Central Oriental Andes ( G ) , North Oriental Andes ( E ) , and North Occidental Andes ( F ) . Northern and Central Andes are divided perpendicular to the southern limit of Carnegie Ridge ( parallel 2 °S ) in southern Ecuador [84 , 85] . The Oriental and Occidental Andes are divided along the Interandean valleys that separate both parallel mountain chains . The Guiana Shield ( B ) and Brazilian Shield ( K ) regions are located in the eastern shoulder and center of South America , respectively . Both regions are ancient Precambrian plateaus with lowland tropical rainforest , dry forest , and cloud forests from 200–2 , 800 m . The Venezuelan Highlands ( D ) region includes the current Caribbean costal cordilleras of Venezuela ( Mérida , Cordillera de la Costa , and Paria peninsula ) and Trinidad and Tobago Islands . Paleontological and stratigraphical evidence suggests that Venezuelan Highlands region had strong similarities to the western Amazonian Tertiary fossil fauna [86–89] . However , the Venezuelan Highlands biogeographic distinctiveness is evidenced by the Miocene uplift [90] , episodic Miocene floodings , and the formation of the Llanos [91] , the separation from the Guiana Shield region by the current Orinoco river drainage and from the northern Andes by the Táchira depression . The Amazon Basin ( C ) region includes the river drainage and its extensive lowland tropical rainforest <300 m . The Central America ( J ) region corresponds to the lowlands and highlands of western side of the PLB to southern Nicaragua ( northernmost distribution of poison frogs ) . The Chocó ( I ) region includes the eastern side of the PLB and the costal lowland tropical forest below 500 m of the Pacific Coast of Ecuador and Colombia on the western side of Andes . The Magdalena river drainage and the Gorgona Island were also included in the Chocó region based on the paleontological and biotic resemblance to the Chocó [92–94] . Ancestral area reconstructions , time of diversification , and rate of diversification estimates require a fully bifurcate tree [95–98] . We used the best GARLI tree to reconstruct the ancestral areas . Each species in the phylogeny was assigned to one or more regions based on distribution ( Table S3 ) . For the first method , we estimated a DEC model using Lagrange package [26 , 77] . The DEC model is a continuous-time stochastic model for geographic range evolution in discrete areas , with ML parameters estimated for rates of dispersal between areas ( range expansion ) and local extinction within areas ( range contraction ) . The DEC model considers geographic scenarios of lineage divergence ( including scenarios involving within-area speciation ) , allowing a widespread ancestral range to persist through a cladogenesis event as ancestral states at internal nodes on a phylogeny with observed species ranges at the tips . In all cases , ancestral ranges were assumed to include no more than two areas , the maximum observed for extant species . Moreover , spatial and temporal constraints ( e . g . , area distances , dates of geological origin ) may be imposed in the DEC model estimation , providing a more accurate estimation of the ancestral areas and hypothesis testing of specific geographic scenarios . We tested three biogeographic scenarios based on the hypothesized origin of the group ( Figure S6 ) . First , the SM0 null model has all areas as equiprobable ancestral ranges and assigns them to be adjacent pairs ( i . e . , separated by one step in the matrix ) , plus the individual areas themselves . Second , the SM1 ( center-of-origin model ) favors an Amazon Basin origin of the poison frog clade and assigns all non-Amazonian areas to be adjacent to the Amazon Basin ( i . e . , separated by one step in the matrix ) and nonadjacent to each other ( i . e . , separated by two steps ) . However , the one exception was to make the Chocó and Central America adjacent , because it is not possible to reach Central America without passing through the Chocó . Third , the SM2 model ( stepping-stone model ) assigns the rates of dispersal between areas to be inversely scaled by their relative distance and connectivity ( e . g . , the distance between Guiana Shield and Central America is four steps , so the rate of dispersal was constrained to be 0 . 25 ) . Each analysis estimated the global rate of dispersal and local extinction on the phylogeny with species ranges at the tips , considering all possible range inheritance scenarios ( ancestral states ) at internal nodes without conditioning on any particular values . Then , the global rates were used to calculate and rank the likelihoods of all ancestral states at every internal node on the phylogeny . The ranking is done by calculating the likelihood at the root of the tree , given the global rates , with one node fixed at one ancestral range scenario , and all other nodes free to vary . The ML scores for all nodes are compared to the overall ML score of each geographic scenario under a “global” method of ML ancestral state reconstruction [99] . The likelihood of each model was optimized against the observed ranges of species and their phylogenetic relationships , with differences greater than 2 log-likelihood units considered significant; the reconstruction with the worst score was rejected [99] . Tests of the three hypotheses ( SM0 , SM1 , and SM2 ) were repeated in both the complete ML phylogeny and chronogram ( reduced number of taxa ) . All tests were repeated in both large ML phylogeny and chronogram including and excluding Allobates alagoanus whose phylogenetic position at the base of clade A is not well supported ( Figure S3A ) . In all cases similar results were found , so the results excluding A . alagoanus are presented . For the second method , we performed ancestral area reconstruction by DIVA using DIVA 1 . 1 [30] . DIVA assumes that speciation occurs as a consequence of vicariance and reconstructs these events at no cost . Therefore , the ancestral geographic distributions are reconstructed by minimizing the number of dispersal or extinction events necessary to explain the actual distribution pattern . Because we assigned all nine regions to favor dispersals equally , no single solution for the large tree was found ( ∼16 × 106 possible reconstructions ) . Hence , this analysis is considered exploratory due to its limitations . DIVA , which is parsimony-based , only optimizes historical events on cladograms without regard to relative or absolute time ( i . e . , branch lengths ) , and is less flexible about geographic lineage divergence scenarios ( e . g . , widespread ancestors are assumed to undergo vicariance ) ; specific biogeographic hypotheses cannot be tested . In consequence , the strict consensus of the dispersal/vicariance events on >75% of all possible reconstructions was used as the best solution and mapped on the ML poison frog phylogeny ( Figure S7 ) . For the third method , estimates of the values of states at ancestral nodes were derived by point estimates ( log-likelihoods ) using an ML approach in BAYESMULTISTATE , a component of BAYESTRAITS [79 , 100] . The reduced chronogram ( 236 terminals ) described in the Materials and Methods section “Determination of divergence times” was used for the analysis . We coded all terminal distributions under two alternatives , Amazonian Basin ( C ) origin , and non-Amazonian Basin origin ( all other areas ) . We calculated the proportion of likelihood of both alternatives at each node with 10 , 000 samples under default parameters . The results were mapped onto the chronogram ( Figure 2 ) . We also tested whether an Amazonian origin was present at each node by constraining the node to this state using the option “fossilizing” in BAYESMULTISTATE [100] . The likelihoods of the constrained and unconstrained reconstructions were compared , and a difference of ≥2 log-likelihood units was considered significant; the model with the worse score was rejected [99] . We calculated the tree imbalance of the ML poison frog phylogeny , the reduced-taxon chronogram , and the tree of each super-regions: the Amazon Basin ( region C ) , the Andes ( regions E–H ) , Chocó-Central America ( regions I and J ) , and Guiana Shield-Venezuela-Brazilian Shield ( regions B , D , and K ) . We used conservative imbalance metrics [25 , 101]: IC [102] , IS [103] , and s [104 , 105] . All standardized indices and probability of rejecting the null model of each branch having the equal probability of splitting ( Equal Markov Rate or ERM ) were calculated using functions colless . test , sackin . test , and likelihood . test , implemented in the R package apTreeshape [106] . An indirect estimate of diversification rates assuming incomplete taxon sampling was explored using the γ statistic [32] and adjusted for actual phylogenies by excluding the distance between the most recent node to the present ( i . e . , gn node of a phylogeny of size n ) , which does not come from the same distribution [33] . The adjusted γ statistic value was obtained from the Poison Frog Chronogram ( family level ) . The adjusted γ statistic [32 , 33] was calculated using following functions of the R package LASER [107]: ( 1 ) the gn node was excluded from the chronogram ( i . e . , entire family and major region subtrees ) by using truncateTree function; ( 2 ) the γ statistic of the truncated tree was calculated using gamStat function; ( 3 ) a null-distribution of γ under a pure birth model was obtained by 10 , 000 Monte Carlo replicates using mccrTest . Rd . This function generates full size trees at the species level and prunes randomly terminals to the actual size of the empirical tree ( i . e . , simulates incomplete taxon sampling ) ; ( 4 ) the empirical γ statistic was adjusted by subtracting the mean value of the simulated null-distribution of γ , which is expected to be 0 [33]; and ( 5 ) the p-values of the adjusted γ statistics were computed from a normal distribution; values outside the ±1 . 96 standard deviation boundaries are significantly different ( alpha = 0 . 05 ) from the null pure birth expectation . We tested for significant changes in diversification rate within the poison frog clade using a ML methodology under the assumption of incomplete taxon sampling [36] . First , we produced a GSPF level chronogram by pruning all but a single lineage per taxonomic group ( Figures 3 and S8; Table S13 ) from the Poison Frog Chronogram , yielding a tree of 78 terminals . The total species richness per taxonomic group was assigned to each terminal based on previous taxonomic and phylogenetic studies [22 , 23 , 108–112] . Second , we estimated a constant diversification rate r ( i . e . , the difference between speciation λ and extinction μ rates ) across the phylogeny using a ML estimator that incorporates both known taxonomic diversity and phylogenetic data [36] . We calculated the constant-rate model fit statistics ( log likelihood and AIC score ) and r using the fitNDR_1rate . Rd function of LASER [107] . Third , we tested for shifts in diversification rate within the poison frog phylogeny by comparing likelihood of the GSPF chronogram under constant and rate-flexible diversification models [36] . Two alternative hypotheses for rate-flexible model may explain the shifts in rate of diversification: ( 1 ) an increase within a particular clade ( rCL ) from the ancestral diversification rate r ( flexible-rate model ) or ( 2 ) a clade-specific decrease ( rCL ) from the ancestral diversification rate r ( rate-decrease model ) [36] . We calculated both rate-flexible alternative model fit statistics , r and rCL values using the fitNDR_2rate . Rd function of LASER [107] . The best fitting model was determined using a likelihood ratio test ( LRT ) between the constant-rate and the flexible-rate models ( nested ) , and by ΔAIC scores between the flexible-rate and rate-decrease models ( not nested ) . All analyses were performed under two extremes of the relative extinction rate ( α = μ/λ , α = 0 and α = 0 . 99 ) as a fixed parameter to determine the robustness of the results to variation in the extinction fraction [113] . GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) sequence accession numbers mentioned in this paper are EU342502–EU342745 ( see Tables S2 and S3 ) .
|
The Neotropics , which includes South and Central America , contains half of remaining rainforests and the largest reservoir of amphibian diversity . Why there are so many species in certain areas and how such diversity arose before the Quaternary ( i . e . , more that 1 . 8 million years ago [MYA] ) are largely unstudied . One hypothesis is that the Amazon Basin was the key source of diversity , and species dispersed from there to other areas . Here , we reconstruct a time-calibrated phylogeny and track , in space and time , the distribution of the endemic and species-rich clade of poison frogs ( Dendrobatidae ) during the Cenozoic ( more than 65 MYA ) across the continental Neotropics . Our results indicate a far more complex pattern of lineage dispersals and radiations during the past 10 MY . Rather than the Amazon Basin being the center of origin , our results show that the diversity stemmed from repeated dispersals from adjacent areas , especially from the Andes . We also found a recurrent pattern of colonization of Central America from the Chocó at 4–5 MY earlier than the formation of the Panamanian Land Bridge at 1 . 5 MYA . Thus , the major patterns of dispersals and radiations in the Neotropics were already set by ∼5–6 MYA ( the Miocene–Pliocene boundary ) , but the ongoing process of Neotropical radiation is still happening now , especially in the Chocó–Central America region and Amazonian rainforest .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"ecology",
"computational",
"biology",
"evolutionary",
"biology"
] |
2009
|
Amazonian Amphibian Diversity Is Primarily Derived from Late Miocene Andean Lineages
|
Cystic echinococcosis is a worldwide distributed helminth zoonosis caused by the larval stage of Echinococcus granulosus . Human secondary cystic echinococcosis is caused by dissemination of protoscoleces after accidental rupture of fertile cysts and is due to protoscoleces ability to develop into new metacestodes . In the experimental model of secondary cystic echinococcosis mice react against protoscoleces producing inefficient immune responses , allowing parasites to develop into cysts . Although the chronic phase of infection has been analyzed in depth , early immune responses at the site of infection establishment , e . g . , peritoneal cavity , have not been well studied . Because during early stages of infection parasites are thought to be more susceptible to immune attack , this work focused on the study of cellular and molecular events triggered early in the peritoneal cavity of infected mice . Data obtained showed disparate behaviors among subpopulations within the peritoneal lymphoid compartment . Regarding B cells , there is an active molecular process of plasma cell differentiation accompanied by significant local production of specific IgM and IgG2b antibodies . In addition , peritoneal NK cells showed a rapid increase with a significant percentage of activated cells . Peritoneal T cells showed a substantial increase , with predominance in CD4+ T lymphocytes . There was also a local increase in Treg cells . Finally , cytokine response showed local biphasic kinetics: an early predominant induction of Th1-type cytokines ( IFN-γ , IL-2 and IL-15 ) , followed by a shift toward a Th2-type profile ( IL-4 , IL-5 , IL-6 , IL-10 and IL-13 ) . Results reported here open new ways to investigate the involvement of immune effectors players in E . granulosus establishment , and also in the sequential promotion of Th1- toward Th2-type responses in experimental secondary cystic echinococcosis . These data would be relevant for designing rational therapies based on stimulation of effective responses and blockade of evasion mechanisms .
Helminths are metazoan parasites currently infecting a quarter of the world population [1] . The high prevalence of helminthiasis reflects one outstanding feature of parasite infections: chronicity . This fact could be read into helminths having special skills to adapt to defense mechanisms triggered by infected hosts , in order to survive for long periods of time . Thus it is not surprising that in most cases host immune responses are ineffective in parasite elimination . Chronicity observed in the context of parasite infections often associates with polarized cytokine responses . In this respect , although helminths belong to a highly divergent animal group , they induce polarized and stereotyped Th2-type responses , with rare to no levels of Th1-type components [2] . Cystic echinococcosis is a zoonotic disease caused by the larval stage of the cestode Echinococcus granulosus , and shows a cosmopolitan distribution with a worldwide prevalence of roughly 6 million infected people [3]–[5] . Parasite cysts are able to live for very long periods of time within the infected host , and thus it is thought that E . granulosus evades or modulates the host immune response through still unknown mechanisms . In this regard , the experimental model of secondary infection has been used to study host-parasite interactions . This model is based on the intraperitoneal ( ip ) inoculation of viable protoscoleces into susceptible and immunocompetent mice [6] . Using Balb/c mice strain , secondary cystic echinococcosis can be divided into two stages: an early stage ( until day 20–30 pi ) in which the infection establishes ( protoscoleces develop into hydatid cysts ) [7] , followed by a late or chronic stage in which already differentiated cysts grow and eventually become fertile cysts . There is scarce information regarding early immune responses in the peritoneal cavity of infected mice [8]–[10] . Breijo et al . by means of ip implanted diffusion chambers found that the initially triggered local inflammation resolves and completely disappears by day 30 pi [11] . These results suggest that infection establishment is associated with a strong local control of inflammation during the initial phase of protoscoleces differentiation into hydatid cysts . Cytokines key roles during E . granulosus experimental infection were first analyzed by Rogan [12] . Focusing on chronic infections , the author suggested that systemic Th2-type cytokine responses would be an actively induced mechanism used by the parasite in order to suppress the expression of potentially harmful Th1-type cytokines [12] . Regarding early stages of infection , Dematteis et al . showed that there is an early and systemic Th2-type cytokine response which is unrelated to protective immunity [13] . On the other hand , immune mechanisms associated with IFN-γ effects seem to be relevant to the development of protective immune responses [14] , [15] . Therefore , it has been suggested that the development of an early Th2-type cytokine response could be read into a mechanism modulated and/or actively induced by the parasite to favor its survival . Due to the lack of information about early and local immune responses in experimentally infected mice , the present work reports a deep and detailed analysis of early cellular and molecular immunological phenomena taking place in the peritoneal cavity of infected mice .
Animal experiments were performed in compliance with Comisión Honoraria de Experimentación Animal ( CHEA ) - Universidad de la República according to the Canadian Guidelines on Animals Care and the National Uruguayan Legislation No . 18 . 611 since 2009 ( “Animales en Experimentación e Investigación” ) . Protocols were approved by Comité de Ética - Facultad de Química - Universidad de la República ( Uruguay ) and were given the Protocol Approval Number 030510 ( http://csic1 . csic . edu . uy/chea ) . Adult female Balb/c mice were obtained from DILAVE ( Uruguay ) and housed at the animal facility of Instituto de Higiene ( Montevideo , Uruguay ) . Protoscoleces were obtained by aseptic puncture of fertile bovine hydatid cysts , and were washed several times with phosphate buffered saline ( PBS ) pH 7 . 2 containing gentamicin ( 40 µg/mL ) [13] . Protoscoleces viability was determined by means of eosin exclusion and flame cell activity [13] . Only those batches with over 90% viability were used . For experimental infections , mice were inoculated ip with 2000 viable protoscoleces in 200 µL of PBS ( infected mice ) , or 200 µL of PBS alone ( control mice ) . At different times post-inoculation ( pi ) , infected and control mice ( numbers are shown under each figure ) were sacrificed by cervical dislocation prior inhalatory anesthesia . Peritoneal cavities were extensively washed with cold RPMI 1640 supplemented with 2% FCS . Red blood cells were lysed by treatment with RBC Lysing Buffer ( Sigma ) and remaining leukocytes were counted using Neubauer chamber and trypan blue staining . Individual control mice were always highly homogeneous , thus they were grouped to increase sample space for statistical analyses . Cell suspensions were pre-incubated with anti-mouse CD32/CD16 mAb ( Mouse Fc Block , Becton Dickinson ) for 30 min at 4°C and then were incubated during 30 min at 4°C with the following anti-mouse MoAb: CD19-PE , CD19-FITC , CD3-PE , CD3-FITC , CD4-FITC , CD8-FITC , panNK-PE , CD138-PE , CD25-PE y CD69-FITC , and their specific isotype controls ( Pharmingen , Becton Dickinson ) . Cellular viability was analyzed by propidium iodide staining ( Pharmingen , Becton Dickinson ) . Acquisitions and analyses were performed using a FACScalibur flow cytometer ( Becton Dickinson ) and Cell Quest® software ( Becton Dickinson ) , respectively . RNA extraction was performed using TRIzol® ( Invitrogen ) and DNA contamination was eliminated by DNasa I treatment ( Invitrogen ) following manufacturer's recommendations . cDNA was then obtained by 1 µg RNA reverse transcription using M-MLV-RT ( Invitrogen ) at 42°C for 50 min . qPCR reactions were performed using mouse specific primers available under request for FoxP3 , Pax5 , Blimp-1 , Bcl-6 , TNFα , IFN-γ , IL-2 , IL-12_p35 , IL-15 , IL-4 , IL-5 , IL-6 , IL-10 , IL-13 , TGF-β and β-actin . qPCR were performed using QuantiTect SYBR Green PCR Kit ( QIAGEN ) following manufacturer's instructions and 0 . 9 µM of each specific primer in a Rotor-Gene 6000 ( Corbett Life Science ) . The cycling used for every reaction was 95°C for 15 min , 40 cycles at 95°C for 15 sec and 60°C for 1 min , followed by a melting curve rising from 72°C to 90°C . β-actin was used as a normalizing gene . Relative mRNA amounts were calculated using the 2−ΔΔCt method [16] , and samples fold increase/decrease was referred to control group values . Peritoneal cells from 5-days infected mice were cultured without stimulus in complete RPMI 1640 ( 10% heat inactivated FCS , 100 µg/mL streptomycin , 100 U/mL penicillin , 10 mM L-glutamine and 50 µM 2-mercaptoethanol ) during 72 h at 37°C and 5% CO2 . Anti-PSA ( protoescolex somatic antigens ) specific antibodies were measured by ELISA in individual supernatants according to Dematteis et al . [17] . In equally diluted samples , specific IgM , IgG1 , IgG2a , IgG2b and IgG3 were determined using appropriate goat or rabbit anti- ( mouse Ab isotype ) antibodies labeled with peroxidase ( Sigma ) . Peroxidase activity was detected using O-phenylendiamine as chromophore ( Sigma ) , and absorbance values were recorded at 492 nm . Statistical analyses were assessed by non-parametric Mann-Whitney U test and differences were regarded as significant with p<0 . 05 .
Firstly , we analyzed peritoneal cells behavior in Balb/c infected mice . It is worth mentioning that some parasite-adhered peritoneal cells ( e . g . macrophages ) may have been unintentionally discarded due to technical limitations of peritoneal washings . Thus , whenever peritoneal cells are mentioned throughout this work , we strictly refer to parasite-non-adhered peritoneal cells . Results in Figure 1 . A . show that peritoneal leukocytes number rapidly increased , reaching a 3-fold peak by day 7 pi . Then , by means of size ( FSC ) and complexity ( SSC ) flow cytometry parameters we analyzed the contribution of lymphocytes and non-lymphoid cells to this behavior . Non-lymphoid cells number remained unchanged until 5 days pi and showed a 2-fold increase by day 7 pi ( Figure 1 . A . ) . Contrarily , peritoneal lymphocytes number showed an early and significant increase by day 3 , which was sustained until day 9 pi . Because of such peritoneal lymphocytes behavior , we further analyzed the kinetics of the three main lymphocyte populations . Figure 1 . B . shows disparate behaviors among peritoneal lymphocytes . Both T cells ( CD3+ ) and NK cells ( pan NK+ ) number rapidly increased by day 3 pi , peaking at day 5–7 pi ( Figure 1 . B . ) . On the contrary , B cells ( CD19+ ) behavior was very different . CD19+ cells number significantly decreased by day 5 pi , and then recovered reaching a 2-fold increase by day 7–9 pi ( Figure 1 . B . ) . Overall , these results show that infection by E . granulosus induces significant and early changes mainly in the peritoneal lymphoid compartment of infected mice . Initial analyses , aimed at explaining possible causes of B cells decrease , dismissed massive cell death phenomena ( no propidium iodide staining among peritoneal cells ) and terminally differentiated plasma cells ( CD19−CD138+ cells ) ( data not shown ) . Thus , we further analyzed in depth the presence of local antibody secreting cells ( ASC ) . Qualitative flow cytometry analyses showed a quick rise in large and CD19low lymphocytes ( data not shown ) , suggesting the existence of a local ASC differentiation process [18] , [19] . Because ASC differentiation is tightly regulated at the molecular level by specific transcription factors , we next analyzed Pax5 , Bcl-6 and Blimp-1 local expression at different time points . Results in Figure 2 . A . and 2 . B . show a consistent expression profile related to ASC differentiation . Additionally , functional evidence of local ASC was obtained by analyzing specific antibodies titers in culture supernatants of non-stimulated peritoneal cells from 5-days infected mice . Interestingly , only IgM and IgG2b anti-PSA titers were increased ( Figure 2 . C . and 2 . D . ) . Overall , results reported here reveal the existence of a peritoneal ASC differentiation process in early stages of infection , which is characterized by large CD19low cells and an active transcriptional program of plasma cell differentiation . Moreover , local ASC are a source of specific IgM and IgG2b antibodies . Information regarding NK cells role in helminth infections is scarce and partially controversial . In the experimental model of E . granulosus infection there has been no reports to our knowledge on NK cells behavior so far . Phenotypic characterization of peritoneal NK cells showed a rapid increase in activated NK cells ( CD69+panNK+ cells ) peaking at day 5 pi ( Figure 3 . A . ) . Although this value represents a 12-fold increase respect to control animals , it is interesting to note that only a 40% of peritoneal NK cells showed an activated phenotype . Since activated NK cells increase their cellular size [20] , we also analyzed it by flow cytometry observing a significant increase in median FSC values on peritoneal NK cells at day 5 pi ( Figure 3 . B . ) . Peritoneal T lymphocytes ( CD4+ and CD8+ ) were also analyzed . Kinetic analyses reported here showed a rapid increase in CD3+CD4+ cells by day 3 pi reaching an 8-fold increase by day 5–7 pi , and a slower increase in CD3+CD8+ cells from day 5 pi reaching a 6-fold peak by day 7 pi ( Figure 4 . A . ) . We also analyzed the presence of Treg cells within the CD4+ T cells peritoneal compartment . Results shown in Figure 4 . B . indicate a 15-fold increase in CD4+CD25+ T cells by days 5–7 pi . It is well known that CD4+CD25+ phenotype is not exclusive of Treg cells , being also shared by activated CD4+ T cells [21] , [22] . Therefore , to confirm the local and early presence of Treg cells we further analyzed Foxp3 mRNA expression level . Figure 4 . C . shows a rapid and significant increase in Foxp3 transcripts from day 3 pi , peaking by day 5 pi . To our knowledge , this is the first report on Treg cells within the peritoneal cavity of E . granulosus infected mice . Overall , results reported here suggest an active role for peritoneal T cells subpopulations during early experimental infection by E . granulosus . Although cytokine responses during murine E . granulosus infection have been analyzed to some extent , early and local cytokine profile in infected mice has not been described . Hence , we performed a qRT-PCR analysis for several cytokines in peritoneal cells from mice at early stages of infection . Expression analysis of Th1-type associated cytokines ( TNFα , IFN-γ , IL-2 , IL-12 and IL-15 ) showed that IFN-γ , IL-2 and IL-15 are quickly induced ( Figure 5 . A . , 5 . B . and 5 . C . ) . Their expression levels were significantly higher than control animals as soon as 3 days pi . IL-15 transcript levels returned to baseline values by day 5 pi , whereas IFN-γ and IL-2 induction was sustained until day 5 of infection . In contrast , IL-12 and TNFα showed a slight but significant decrease in their transcript levels at day 5 and 7 pi , respectively ( Figure 5 . C . ) . On the other hand , Th2-type cytokines ( IL-4 , IL-5 , IL-6 , IL-10 and IL-13 ) were induced later in time ( Figure 5 . D . , 5 . E . and 5 . F . ) . Expression levels of IL-4 , IL-6 and IL-13 increased significantly from day 5 pi , while expression of IL-5 did not augment until day 7 pi . Interestingly , IL-10 showed a sustained induction over time from day 3 pi until the end of the experiment . TGF-β did not increase its transcript levels throughout the analyzed time points ( data not shown ) . Results reported here show a biphasic kinetic behavior in cytokine expression profile . Th1-type cytokines seem to predominate from infection beginning until day 5 pi followed by a switch towards a Th2-type response . These results are in accordance with previously obtained data using pooled peritoneal cells from infected and control mice at each time point ( our own unpublished results ) .
Studies regarding early stages in natural secondary infections are virtually absent from current literature . Although ip inoculation of protoscoleces into immunocompetent mice is a dissimilar situation from natural secondary infections , due to differences mainly in the immune status of hosts with a primary infection , this model may pose at least some hypotheses on the early interaction between protoscoleces and the immune system . Early and local immune responses elicited during experimental secondary infection by E . granulosus have been scarcely studied [8]–[11] . Susceptibility of E . granulosus protoscoleces to immune effectors is thought to be greater during early stages of infection , being only a small percentage of parasites able to develop into defined hydatid cysts [14] , [23]–[25] . Protoscoleces become cysts in a relative short period of time [7] , thus we focused our work on the characterization of early immunological events taking place at the anatomical site of infection establishment , e . g . peritoneal cavity . Peritoneal leukocytes number in infected mice increased quickly due to proliferation of resident cells and/or systemic recruitment ( Figure 1 . A . ) . Non-lymphoid cells number showed no early changes . However , it is worth noting that analyses were performed from day 3 pi and non-lymphoid cells are usually recruited very rapidly , tending to disappear just as fast . For example , in experimental infections by Brugia malayi and B . pahangi neutrophils are the main peritoneal population 24 h pi , completely disappearing by day 3 [26] , [27] . Therefore , results reported here may not completely exclude earlier ( e . g . neutrophils ) or later ( e . g . macrophages ) roles for non-lymphoid cells during E . granulosus establishment in the murine model of secondary cystic echinococcosis . In contrast , peritoneal lymphocytes showed a very different behavior ( Figure 1 . B . ) . While B cells number dropped at day 5 pi , T and NK cells number increased from day 3 peaking both populations at days 5–7 pi . These results are very different to those reported for experimental Taenia crassiceps infection , where peritoneal lymphocytes peak at 6–8 weeks of infection [28] . Therefore , this conflicting lymphocyte kinetics in similar models of ip cestode infections suggests that lymphocytes behavior might be largely dependent on parasite species . Peritoneal B cells were shown to undergo an early plasma cell differentiation process in E . granulosus infected mice ( Figure 2 ) . Plasma cell differentiation induces migration to anatomical sites of final maturation and survival niches , probably explaining the local drop in B cells counts [19] . Plasma cell differentiation is a tightly regulated molecular process; characterize by up- and down-regulation of specific transcription factors [29] . Pax5 and Bcl-6 keep mature B cells in its naïve state by suppressing ( among other functions ) the expression of Blimp-1 , the master transcription factor for plasma cell differentiation . Such process can be evidenced by decreased levels of Pax5 and Bcl-6 , with a concomitant increase in Blimp-1 expression . Our analyses proved the existence of an active molecular process of plasma cell differentiation during the early stages of experimental infection , which was functionally confirmed by the local secretion of anti-PSA IgM and IgG2b antibodies ( Figure 2 ) . Such isotype profile was similar to that observed in T-independent humoral responses [30] . In fact , similar results have been described in E . granulosus infected CD4KO mice at the systemic level [31] . Interestingly , CD40−/− mice infected with E . multilocularis produce specific IgM , IgG2b and IgG3 antibodies after 2 months of infection [32] . Thus , early T-independent antibody responses could be a common phenomenon in experimental infection by Echinococcus spp . Moreover , regarding the isotype restriction reported here it is interesting to note that tegument and suckers of E . granulosus protoscoleces have Fc-like receptors able to bind human IgG1 and IgG3 antibodies trough their specific Fc portions [33] . Such isotypes structurally and functionally correspond to murine IgG2a and IgG2b , respectively [34]; and therefore local IgG2b specific induction in early stages of infection could be seen as a possible evasion strategy exploited by E . granulosus . NK cells have been shown to play major roles in early infections by viruses , bacteria and protozoa [35]–[37] . However , there are very few reports referring to their role in helminth infections [38]–[42] . Here , we have shown that peritoneal NK cells not only rapidly rise in number but also display an activated phenotype ( Figure 3 ) . Interestingly , the peak in NK cells number ( day 5 pi ) is associated with a 40% of cells exhibiting an activated phenotype . Our results are in agreement with reports on murine Litomosoides sigmodontis natural infection showing local expansion and activation of NK cells [41] . Although E . granulosus and L . sigmodontis infections are dissimilar systems at the parasitological and immunological levels , such a common behavior in NK cells suggests that they may play important roles in helminth infections . Peritoneal T cells analyses showed differential kinetics among T cells subsets ( Figure 4 ) . CD4+ T cells number showed a faster increase than CD8+ T cells , being the former the main contributor to total T cells increase . Rises in the number of local CD4+ T cells have been described in several helminth infections [43] , [44] . However , there are very few reports regarding CD8+ T cells behavior . For example , E . multilocularis chronically infected humans display an increase in oligoclonality and activation status of their CD8+ T cells [45] . Similarly , murine chronic infections by E . granulosus have shown rises in splenic CD8+ T cells [46] , and E . multilocularis protoscoleces are able to induce suppressor CD8+ cells in naïve splenocytes cultures [47] . Some reports have also shown that locally expanded CD8+ T cells seem not only to play inefficient roles in helminth infections , but also are probably responsible for suppressive immune responses [48] , [49] . On the other hand , CD4+CD25+Foxp3+ Treg cells have been shown to play key roles in animals and humans with helminth infections [50]–[54] . Experimental data have verified an old hypothesis stating that helminths actively suppress or reduce immunopathological conditions in their hosts . These effects are now partially attributed to recruitment and/or induction of CD4+CD25+Foxp3+ Treg cells [53] . Results reported here are an initial and rough characterization of Treg cells behavior in the peritoneal cavity of early E . granulosus-infected mice , showing a nice correlation between Foxp3 expression and CD4+CD25+ T cells ( Figure 4 ) . Although this is the first report on peritoneal Treg cells in early infection by E . granulosus , Mejri et al . have recently reported the presence of a subpopulation of peritoneal CD4+CD25+ Treg cells in E . multilocularis infected mice [55] . It is worth noting that while they reported an increase in peritoneal Treg in 6-weeks infected mice , our results have shown a much earlier rise ( 5–7 days pi ) . Overall , these results suggest that CD4+CD25+Foxp3+ Treg cells could be involved in the modulation of immune responses within the peritoneal cavity of Echinococcus spp . infected mice . Initial studies on the early immune response in E . granulosus infected mice showed a defined Th2-type systemic cytokine profile , suggesting that immune polarization is an early event [13] . Here , we have shown that the expression of some Th1-type cytokines is quickly induced at the local level ( Figure 5 ) . However , the decline observed for IL-12 and TNFα transcripts suggests that the initial inflammatory response is weak . In addition , the absence of IL-12 induction could explain the short duration of the Th1-type cytokine profile because IL-12 is a key mediator in early Th1-type bias [56] . Interestingly , this set of cytokines seems to be related to peritoneal NK cells behavior because they are factors involved in their proliferation ( IL-15 ) , activation ( IL-2 ) and effector functions ( IFN-γ ) [57] . Early NK cells increase and activation phenotype could be partially explained by the quick IL-15 and IL-2 local induction . Moreover , the highest number of activated NK cells correlated well with the greatest increase in IL-2 and IFN-γ transcripts . Regarding IL-2 , it also has been described as an important autocrine factor for T cells proliferation ( including Treg cells ) , being therefore a probable explanation for the early peritoneal T cells rise [56] . On the other hand , Th2-type cytokines showed a later induction ( Figure 5 ) except for TGF-β which did not increase its transcript levels ( data not shown ) . TGF-β activity has been shown not to be dominantly regulated by classical transcriptional ( expression ) and/or translational ( production ) means , but mainly through enzymatic conversion of latent TGF-β expressed on cell surfaces [58] . Thus , TGF-β analyses through qRT-PCR not always derive in conclusive results . IL-4 , IL-6 and IL-13 transcripts significantly increased from day 5 pi , and IL-5 did it only at day 7 pi . Interestingly , IL-10 showed a sustained induction from day 3 of infection . Most of these cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) are involved in plasma cell differentiation [19] , and their induction correlated well with B cells drop and plasma cell transcription factors profile . Moreover , early IL-10 induction could be attributed to the rapid increase in local Treg cells , because they are well known to secrete large amounts of IL-10 [59] . Regarding Treg cells , it is worth noting that Foxp3 expression increased at day 5 pi ( Figure 3 . C . ) as well as IL-6 ( Figure 5 . D . ) . Although IL-6 has been shown to impair Foxp3 induction in Treg cells [60] , it has been also reported that this is not always the case at least in vitro [61] . On the other hand , our results showed that Foxp3 induction precedes IL-6 up-regulation . Indeed , Foxp3 transcripts are already up-regulated at day 3 pi , peaked at day 7 and then down-regulated its mRNA levels ( Figure 4 . C . ) , while IL-6 is induced at day 5 pi being its transcripts level quite constant thereafter ( Figure 5 . D . ) . Quickly up-regulated IL-10 ( Figure 5 . F . ) could be responsible for the early Foxp3 induction , which shuts itself down once IL-6 is up-regulated . Thus , our results are not absolutely conclusive about the relationship among cytokine expression profiles and Treg cells behavior . Results reported here have clearly shown biphasic kinetics in local cytokines expression . While Th1-type cytokines initially prevail and return to baseline values by day 7 pi , Th2-type cytokines are later induced ( day 5 pi ) and become predominant by day 7 pi . Our results are in accordance with those reported for murine cysticercosis due to T . crassiceps infection [62] . In early stages of experimental cysticercosis a clear but transient Th1-type immune response develops ( high levels of IL-2 and IFN-γ ) , but as infection time progresses a permanent Th2-type response follows ( high levels of IL-4 , IL-6 and IL-10 ) . Thus , authors suggested that the sequential activation of Th1- and Th2-type responses would favor parasite reproduction [62] . In secondary cystic echinococcosis , the early switch from a Th1- to a Th2-type local cytokine response , could favor developing protoscoleces to reach the immune resistant cystic stage . Finally , our results pose a possible explanation for the early and local events in E . granulosus experimental infection ( Figure 6 ) . It has been well documented that approximately only 10% of inoculated protoscoleces finally develop into defined hydatid cysts [14] , [23]–[25] . Therefore , early immune responses although not optimal to prevent infection establishment , are probably not entirely ineffective . Based on preliminary results obtained from infected Balb/c mice treated with polyI:C we hypothesized that NK cells could play an important role in very early infection ( our own unpublished results ) . Activated NK cells through IFN-γ production could be partially responsible for the activation of resident macrophages inducing their known protoscolicidal activity [14] . We are currently performing several experiments in order to verify such hypothesis . On the other hand , it has been reported that in the presence of specific antibodies on the protoscoleces surface , the tegumental membrane depolarization due to complement activation is faster and stronger than that observed in the absence of specific antibodies [63] . Therefore , IFNγ-activated macrophages as well as local antibodies recognizing protoscolex antigens and able to activate the complement system could be partially responsible for the elimination of approximately 90% of parasite inoculum . From day 5–7 pi E . granulosus would be able to bias the cytokine response towards a Th2-type profile and induce/recruit cells with regulatory activity ( e . g . Treg cells ) . These phenomena would block the initial effects triggered by IFN-γ , allowing at last the infection establishment . It is worth highlighting the absence of early IL-12 induction since it has been shown that forced IL-12 production in experimentally infected Balb/c mice negatively influence the infection outcome , suggesting that Th1-type responses could generate some protective effects [15] . In conclusion , our results open new ways to investigate the involvement of several immune effector players in E . granulosus protoscoleces killing , and also in the sequential promotion of Th1- towards Th2-type immune responses in the model of secondary cystic echinococcosis .
|
Cystic echinococcosis is a zoonotic disease caused by the larval stage of the cestode Echinococcus granulosus and shows a cosmopolitan distribution with a worldwide prevalence of roughly 6 million infected people . Human cystic echinococcosis can develop in two types of infection . Primary infection occurs by ingestion of oncospheres , while secondary infection is caused by dissemination of protoscoleces after accidental rupture of fertile cysts . Murine experimental secondary infection in Balb/c mice is the current model to study E . granulosus-host interaction . Secondary infection can be divided into two stages: an early stage in which protoscoleces develop into hydatid cysts ( infection establishment ) and a later stage in which already differentiated cysts grow and eventually become fertile cysts ( chronic infection ) . During infection establishment parasites are more susceptible to immune attack , thus our study focused on the immunological phenomena triggered early in the peritoneal cavity of experimentally infected mice . Our results suggest that early and local Th2-type responses are permissive for infection establishment .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"humoral",
"immunity",
"medicine",
"immune",
"cells",
"cytokines",
"immunity",
"to",
"infections",
"immunology",
"microbiology",
"host-pathogen",
"interaction",
"parasitic",
"diseases",
"helminth",
"infection",
"animal",
"models",
"adaptive",
"immunity",
"model",
"organisms",
"immune",
"defense",
"neglected",
"tropical",
"diseases",
"immunoregulation",
"echinococcosis",
"infectious",
"diseases",
"t",
"cells",
"biology",
"mouse",
"immune",
"response",
"immune",
"system",
"antibody-producing",
"cells",
"b",
"cells",
"nk",
"cells",
"immunity"
] |
2011
|
Early Peritoneal Immune Response during Echinococcus granulosus Establishment Displays a Biphasic Behavior
|
Ribosome queuing is a fundamental phenomenon suggested to be related to topics such as genome evolution , synthetic biology , gene expression regulation , intracellular biophysics , and more . However , this phenomenon hasn't been quantified yet at a genomic level . Nevertheless , methodologies for studying translation ( e . g . ribosome footprints ) are usually calibrated to capture only single ribosome protected footprints ( mRPFs ) and thus limited in their ability to detect ribosome queuing . On the other hand , most of the models in the field assume and analyze a certain level of queuing . Here we present an experimental-computational approach for studying ribosome queuing based on sequencing of RNA footprints extracted from pairs of ribosomes ( dRPFs ) using a modified ribosome profiling protocol . We combine our approach with traditional ribosome profiling to generate a detailed profile of ribosome traffic . The data are analyzed using computational models of translation dynamics . The approach was implemented on the Saccharomyces cerevisiae transcriptome . Our data shows that ribosome queuing is more frequent than previously thought: the measured ratio of ribosomes within dRPFs to mRPFs is 0 . 2–0 . 35 , suggesting that at least one to five translating ribosomes is in a traffic jam; these queued ribosomes cannot be captured by traditional methods . We found that specific regions are enriched with queued ribosomes , such as the 5’-end of ORFs , and regions upstream to mRPF peaks , among others . While queuing is related to higher density of ribosomes on the transcript ( characteristic of highly translated genes ) , we report cases where traffic jams are relatively more severe in lowly expressed genes and possibly even selected for . In addition , our analysis demonstrates that higher adaptation of the coding region to the intracellular tRNA levels is associated with lower queuing levels . Our analysis also suggests that the Saccharomyces cerevisiae transcriptome undergoes selection for eliminating traffic jams . Thus , our proposed approach is an essential tool for high resolution analysis of ribosome traffic during mRNA translation and understanding its evolution .
Understanding the dynamics of protein translation is a fundamental question in biology , and has been extensively studied using experimental and computational methods in recent years [1 , 2] . During translation , multiple ribosomes may translate the same mRNA . The density of ribosomal traffic across the transcript poses several open questions , such as how often a ribosome’s path is blocked by a second ribosome , do queues of multiple ribosomes typically form on mRNAs and what is their effect on the overall translation rate of an mRNA . Computational and mathematical modeling of translation dynamics was first proposed in the 1960s [3–8] . A fundamental aspect of these models is ribosome queuing . Specifically , the totally asymmetric simple exclusion process ( TASEP ) and variants thereof have been widely used to study ribosome traffic [2 , 9] . This model assumes that the movement of the ribosomes is only in one direction , from the 5′ end to the 3′ end , a ribosome can only hop to the next position on the transcript at some known rate , and only if it is free from other ribosomes . Thus , it enables a direct study of ribosome queuing . In an early study , Zhang et al . studied the effect of clusters of slowly translated codons on the forming of queues across a synthetic transcript , as well as a set of endogenous mRNAs [10] . Mitarai et al . approximated codon translation rate using 3 categories and showed that ribosome collisions in the E . coli lacZ operon are more frequent than previously thought , where about 5% of ribosome translation time is wasted because of collisions along the mRNA [11] . In a later study , Mitarai and Pedersen compared the ribosome traffic on 87 wild-type sequences and sequences where the synonymous codons were swapped randomly , and showed that wild-type genes tend to reduce ribosome collisions [12] . Chu et al . performed two studies , accompanied by experiments expressing CFLuc , that analyzed ribosome traffic under different codon sequence variants , and demonstrated the effect of codon usage and queuing of ribosomes in the 5’-end region on translation initiation [13 , 14] . In recent years , dozens of additional studies based on variants of the TASEP model have been employed to answer fundamental questions in molecular evolution , functional genomics , synthetic biology , and more [2] . However , performing an accurate transcriptome-wide assessment of ribosome queuing remains a challenge due to the need for careful calibration of the model [2] . A number of studies in recent years have suggested that translation is rate-limited by the initiation rate of a gene [15–17] . In this regime , traffic jams are less likely to form due to the low density of ribosomes on the mRNA , and are expected to have a weaker relation with codon usage [18] . However , an ongoing debate remains over the relative contribution of different factors to the control over translation rate , which is also related to the rate limiting nature of initiation vs . elongation [14 , 19–22] . In recent years , ribosome profiling ( Ribo-seq ) has been the state-of-the-art technology for monitoring the transcriptome-wide distribution of translating ribosomes at high-resolution [23] . Ribosome profiling is based on deep-sequencing of ribosome-protected mRNA fragments ( RPFs ) from living cells , such that the sequence of each fragment indicates the position ( footprint ) of a translating ribosome on the transcript . Ribo-seq has been used to detect positions with significantly enriched reads . Strong peaks in the reads density have been interpreted as ribosome pausing sites and were linked to various possible causes [24–32] . If indeed ribosome pausing is common , we expect queues to form around such pauses . It should be noted that typically , Ribo-seq protocols filter fragments that are longer than a single ribosome ( approximately 30bp ) , with few exceptions [33] . Thus , adjacent ribosomes and regions where pairs ( or more ) of ribosomes are frequent , are expected to be under-represented in most Ribo-seq datasets . This may limit the analysis of ribosome stalling and queuing if indeed a significant portion of ribosomes are paired . However , there are currently no comprehensive estimates of the extent of ribosome queuing . In this study , we provide for the first time , quantitative estimates for the portion of pairs of ribosomes and analyze the dynamics of ribosome queuing using experimental and simulated data .
The probability that two ribosomes are adjacent to one another is affected by a long list of variables such as the number of ribosomes in the cell , the length and number of translated mRNAs , the initiation rates , the distribution of elongation rates along the mRNA , etc . Thus , it is not trivial to estimate this value without a direct experiment . To estimate the quantity of ribosomes that are adjacent to one another in the transcriptome–which is related to the extent of queuing / traffic jams on transcripts–the first steps of the ribosome profiling protocol in yeast were performed as previously described [23 , 34] ( details in the Materials and Methods section ) . Ribosome protected footprints ( RPFs ) of various sizes were generated , followed by sucrose gradient fractionation . The resulting profiles were analyzed , along with previously reported footprint gradients [23 , 33 , 35] , by employing Gaussian mixture to model the different components within the profile ( Fig 1 , details in the Materials and Methods section ) . Previous studies focused mostly on isolating the RPF fraction with footprints protected by a single ribosome ( referred to here as mono-RPFs ) , with some exceptions [33] . Here , we estimated the size of two fractions: mono-RPFs ( mRPFs ) and di-RPFs ( dRPFs ) , the latter containing footprints protected by two ribosomes ( a disome ) . The area of each fraction in the profile is proportional to the number of ribosomes in each fraction . Thus , the ratio between the dRPFs fraction and the mRPFs fraction can provide an estimation for the ratio between the number of ribosomes that are directly adjacent to one another and the number of isolated ribosomes on the transcript . The ratio obtained ranged from 0 . 196 to 0 . 352 in the four datasets , suggesting that at least 1 in 6 ribosomes is adjacent to another and form a pair . When considering the existence of larger clusters of ribosomes , however , in smaller quantities , the probability of observing a pair of adjacent ribosomes is expected to be even higher , at least 1 in 5 as justified by the computational model in the next sections . We obtained similar results using different methods ( S1 Fig ) . The variance between different studies may reflect the condition in which the experiment was performed , such as the type and amount of RNase used ( which may cut dRPFs at some rate and relocate them to the mRPF fraction ) , but possibly also additional conditions that affect translational efficiency in cells . The high fraction of dRPFs detected in all the experiments , however , suggests that ribosome queuing is more prominent and widespread than typically assumed , and that the distribution and determinants of traffic jams in the transcriptome are important fundamental questions . This may also suggest that due to the large extent of queuing , previous results that attempted to answer these questions by sequencing only mRPFs were based on limited data . In order to study the locations where queues form , we sequenced both fractions of mRPFs and dRPFs ( details in the Materials and Methods section ) . Next , the distribution of mRPFs and dRPFs in the transcriptome was analyzed using our data and Guydosh and Green’s dataset [33] . RPKM ( reads per kilobase per million ) values of genes according to both RPF types showed a strong correlation between the two ( r = 0 . 88 , p<10−307 , n = 6 , 664; Spearman’s rho and its asymptotic p-value; Fig 2A ) , however still lower than typically observed between replicates of the mRPF fraction [36] . We expect differences between the distribution of mRPFs and dRPFs to reflect the location of ribosomal traffic jams . Meta-gene profiles of normalized footprint counts ( NFC , i . e . read counts divided by the transcript’s mean read count ) were calculated for mRPFs and dRPFs ( Fig 2 , S2 Fig and S3 Fig for the separate datasets ) . When examining the 5’-end of genes ( Fig 2B ) , an enrichment of dRPFs compared to mRPFs was observed . In addition , it can be seen that the profile of dRPFs decreases into the gene . Genes were divided into two sets of highly and lowly expressed ( top / bottom 50% according to their protein abundance ) . In order to control for the number of reads in the sets , the highly expressed genes were downsampled to have the same total number of reads as the lowly expressed genes . We found that both mRPF and dRPF profiles had higher normalized counts in the set of highly expressed genes compared with lowly expressed genes ( p = 3x10-13 / 7x10-11 , respectively , rank-sum p-value ) . Both mRPFs and dRPFs had a median normalized count significantly higher than the background ( 1 . 24–1 . 35-fold for mRPFs and 1 . 58–1 . 61-fold for dRPFs , p<0 . 01 , empirical p-value based on random sampling of positions within the same set of genes ) . This may imply that ribosome queuing is frequent in the 5’-end region , and specifically in highly expressed genes . Peak calling was performed in order to detect regions with significant ribosome pausing , and study its association with the traffic around the peak . Peaks were defined as positions that are at least 5 standard deviations above the ORF’s mean ( ignoring positions empty of reads ) . In the vicinity of detected peaks in the mRPF profiles a dRPF peak can be observed , distanced 9 codons upstream ( Fig 2C ) . These di-footprints mark the position of two ribosomes–a ribosome positioned 9 codons upstream to the mRPF peak , and a second one approximately where the mRPF was detected . Unlike in the 5’-end region , mRPF peaks were more extreme in lowly expressed genes ( p = 9x10-52 , 3 . 44-fold above background compared with 1 . 92 in top genes ) . Similarly , the associated dRPF peaks were mostly related to lowly expressed genes ( p = 3x10-8 , 2 . 05-fold above background compared with 1 . 28 ) . Correspondingly , in the vicinity of peaks detected in dRPF profies an mRPF peak was detected 9 codons downstream ( Fig 2D ) . Furthermore , the dRPF peaks were more extreme in lowly expressed genes ( p = 2x10-17 , 6 . 66-fold above background compared with 4 . 49 ) , and the associated mRPF reads were enriched in lowly expressed genes ( p = 0 . 005 , 1 . 61-fold above background compared with 1 . 09 ) . One hypothesis that may explain this result is that when traffic jams form in lowly expressed genes , they tend to be more severe . Traffic jams are expected to form in regions with high ribosomal density–for example , in highly expressed genes with high initiation and translation rates . On the other hands , highly expressed genes are under selective pressure to increase translation efficiency and to reduce traffic jams , for example by developing a ramp in their 5’-end [37 , 38] . Due to this , lowly expressed genes may still contribute significantly to the observed queues despite being less dense . Whenever ribosomes form a queue , the traditional ribosome profiling protocol ( which sequences mRPFs ) is expected to fail to fully capture the present ribosomal density . We tried to estimate the percentage of queued positions and locations of such queues by detecting missing mRPFs in the profiles . The following rule was employed: a position had a significant number of mRPFs missing if the number of mRPFs in that position was in the bottom 10% of that gene , while the number of dRPFs in that position was in the top 10% of that gene . For each gene with at least 10% dRPF reads coverage , the percentage of positions with missing mRPFs was calculated according to this rule ( median: 9% ) . In addition , the calculation was repeated for 3 regions of the gene: the first 100 codons , the last 100 codons and the rest of the gene ( Fig 3A ) . The percentage of positions with missing mRPFs was significantly higher in the first / last regions ( p = 4x10-5 / 0 . 03 , respectively; rank-sum p-value ) , compared with the mid-region , suggesting that these regions tend to contain more traffic jams . Finally , the correlation between the percentage of positions with missing mRPFs in a gene and various features related to its expression and translational efficiency was computed , while controlling for dependencies between the features using partial Spearman correlations . Significant negative correlations were observed with the gene’s adaptation of the coding sequence to the tRNA pool as measured via tAI [39] ( r = -0 . 21 , p = 1 . 4x10-6 ) , and its protein abundance ( PA , r = -0 . 39 , p = 3x10-55 ) . Significant positive correlations were observed with the initiation rate of the gene ( r = 0 . 10 , p = 10−4 ) , the PA per RNA copy ( r = 0 . 33 , p = 8x10-39 ) , and ORF length ( r = 0 . 15 , p = 2x10-9; Fig 3B ) . Initiation rates for the aforementioned test were inferred using a simulation of ribosome traffic , as discussed in the next section and the Materials and Methods , with ribosomal densities and RNA copies estimated from Ribo-seq and RNA-seq data . Similar results were observed using different thresholds for detection of missing mRPF and appear in S4 Fig . The test was repeated for the dRPF-to-mRPF ratio with similar results ( Fig 3B ) . We further discuss these results below . Simulating ribosome movement enables a study of traffic on the mRNA in greater detail . A simulation of translation in 5 , 450 yeast genes was performed based on the totally asymmetric simple exclusion process ( TASEP ) model , with parameters calibrated using Arava et al . ’s experimental measurements of the number of ribosomes on each transcript as well as RNA copy numbers [40] ( details in the Material and Methods section , simulation results reported in S1 Table ) . Translation initiation rates of genes were fitted to achieve the measured ribosomal density . In addition , RNA and ribosome numbers were scaled globally to achieve the total quantities of transcripts and translating ribosomes that are generally accepted in the literature ( details in the Materials and Methods section ) . The resulting initiation rates ( median: 0 . 09s-1 , with rates ranging from 0 . 019s-1 in the 10th percentile to 0 . 29s-1 in the 90th percentile ) were in good agreement with previous results ( r = 0 . 70 , p<10−307 , n = 5 , 162; asymptotic p-value for Spearman’s rho ) [41] . The correlation between the resulting translation rates and PA per RNA copy was moderate ( r = 0 . 32 , p = 2 . 2x10-16 , n = 5 , 132 ) , and similar to their correlation with the ratio of Ribo-seq RPKM to RNA-seq RPKM ( r = 0 . 37 , p = 1 . 3x10-170 , n = 5 , 384 ) . To test the simulated profiles at the codon level , density profiles of the 1 , 000 densest genes in a high-resolution ribosome profiling dataset were utilized [42] . A median correlation of 0 . 10 was observed between simulated and measured ribosome density profiles of genes , smoothed using a 10-codon wide sliding window . This value is comparable to the correlation between two mRPF profiles from different experiments ( r = 0 . 16 for Brar et al . vs . Guydosh et al . ; this is similar to previous reports [36] ) . We generated a simulated distribution of the fractions of RPFs in the simulation ( ribosomes were taken to be 10 codons long ) , counting the number of isolated , pairs , triplets of ribosomes etc . throughout the transcriptome ( S4 Fig ) . First , only directly adjacent ribosomes were considered , with no codons between them , resulting in a dRPF-to-mRPF ratio of 0 . 11 , which is lower than reported in the section above . However , when considering simulated RPFs comprising of ribosomes up to 1–4 codons apart on the footprint , the observed ratio increased to 0 . 17–0 . 32 –results that are in good agreement with the ones above ( Fig 3C ) . Thus , it is possible that some of the RPFs that are typically collected in a ribosome profiling experiment contain ribosomes with 1–4 free codons between them . Other parameters that may affect this ratio are the growth conditions , and the existence and frequency of ribosome pausing , which was not simulated here [25] . Similarly , the simulation was utilized to estimate the size of the fraction of tri-RPFs ( containing 3 ribosomes ) , which was 3- to 5-times smaller than the dRPF fraction , suggesting that dRPFs represent the majority of queued ribosomes ( Fig 3C , S4 Fig ) . Moreover , the model enables to extrapolate and estimate the fraction of total ribosomes that are not isolated since they are adjacent an additional ribosome , and therefore are unlikely to be detected via mRPF sequencing . We estimate that this fraction ranges from 14% ( 0 codon separation between neighboring ribosomes ) to 34% ( up to 4 codon separation ) ( Fig 3C ) . We analyzed the distribution across the transcriptome of queued ribosomes and their waiting times in the simulation , that is , ribosomes that cannot complete an elongation cycle due to the next codon being covered by another ribosome . The probability of observing a queue in a gene was 0 . 21 ( median over genes , with probabilities ranging from 0 . 024 in the 10th percentile to 0 . 58 in the 90th percentile ) . Typically , 3 . 67% of the translating ribosomes on an mRNA were delayed due to traffic jams ( median over genes , with the 10th and 90th percentiles being 0 . 9% and 14 . 2% ) , and 5 . 9% of all translating ribosomes in the cell were delayed at any given time . This fraction is related to the number of observed dRPFs in the previous section , however it is expected to be smaller than the latter since some of the ribosomes that are within a footprint’s range from one another may not interfere with another ribosome’s progress at all times ( only at time intervals when the two are directly adjacent and the downstream ribosome is elongating more slowly than the upstream ribosome ) . The total delay time of a ribosome during the synthesis of a complete gene was 3 . 5s ( median over genes , with delay times ranging from 1 . 07s in the 10th percentile to 11 . 65s in the 90th percentile ) . Next , the meta-gene analyses in the above section were repeated for simulated profiles . In windows surrounding observed Ribo-seq peaks ( the ones detected in Fig 2 ) , a significant enrichment was observed in the queued fraction of ( simulated ) ribosomes ( QFR ) ( 1 . 09-fold compared with background around mRPF and dRPF peaks , Fig 3D ) . Similarly , peaks were detected in the profiles of the QFR values per position , with significant enrichment of mRPF and dRPF reads observed in their vicinity ( Fig 3D ) . Thus , the simulated traffic has a significant relation with the measured RPFs . Differences between the experimental data and simulation can be partially explained by the high level of inter-experiment variance , and by the fact that our model does not include long translational pauses , which may affect a significant portion of ribosomal traffic [25] . We tested the relation between ribosome delay times per codon and various sequence/expression-related features of the gene , including the inferred initiation rate , tRNA Adaptaion Index ( tAI ) score , protein abundance ( PA ) , PA per RNA copy , and ORF length . Partial Spearman correlations were computed between ribosome delay times per codon and each feature given the other 4 features ( Fig 3E ) . A strong positive correlation with initiation rates ( r = 0 . 69; p<10−307; n = 5 , 080; asymptotic p-value ) and a strong negative correlation with tAI scores ( r = -0 . 50 , p<10−307 , n = 5 , 080 ) were observed , implying that genes that contain codons recognized by less abundant tRNA species tend to exhibit ribosome congestion and traffic jams across their sequence . These results are similar to the ones reported above based on Ribo-seq data ( Fig 3B ) . Furthermore , weak yet significant correlations were observed with PA ( r = -0 . 13; p = 1 . 4x10-21; n = 5080 ) , PA/RNA ( r = 0 . 13; p = 3 . 2x10-32; n = 5 , 080 ) and gene length ( r = -0 . 07; p = 10−7; n = 5 , 080 ) . Next , QFR was computed in 3 different regions of the ORF–the first 100 codons ( median: 2 . 8% ) , the last 100 codons ( 2 . 25% ) and the rest of the sequence ( 2 . 48% ) –for 4 , 204 genes with appropriate length ( Fig 3F ) . On average , the percentage of queued ribosomes in the first region was 1 . 9/2 . 1-fold higher in the first region compared with the middle/last region ( respectively ) in that gene; this is with agreement with the 5’-end enrichment observed in Ribo-seq data . The simulation was repeated for 100 random genomes where the order of synonymous codons within each gene was permuted , as well as for 100 random genomes where the global rates of codons were shuffled ( without altering the gene sequence , details in the Material and Methods section ) , and employed them to calculate two empirical p-values: p1 controlling for codon order , and p2 controlling for the general decoding rates of the different codons . We found that traffic jams were less prevalent in the real genome according to the various properties discussed above , including: ribosome delay time ( p1<0 . 01 , p2<0 . 01 ) , queue probability ( p1<0 . 01 , p2 = 0 . 01 ) and QFR ( p1<0 . 01 , p2<0 . 01 ) , as well as the ratio of simulated dRPF to mRPF ( p1<0 . 01 , p2<0 . 01 ) . Interestingly , the percentage of queued ribosomes in the first 100 codons was higher than in permuted sequences ( p1<0 . 01 ) , while in other regions of the gene it was lower than in permutations ( p1<0 . 01 ) . This is in agreement with previous suggestions that selection acts on codon order to decrease congestion downstream by delaying ribosomes when entering the ORF [38] . P2 values were also utilized to test the simulation calibration . The simulation results were in better agreement with experimental data for the selected set of codon decoding rates than for random assignments of the same 61 rates . For example , the correlation between simulated and measured footprint density profiles was significantly higher for the selected parameters ( p2<0 . 01 ) , as was the correlation between simulated translation rates and PA/RNA ( p2 = 0 . 01 ) , and the correlation between simulated translation rates and Ribo-seq/RNA-seq RPKM ratio ( p2 = 0 . 01 ) . We studied gene sets that exhibited extreme traffic conditions , either with respect to the rest of the genes or with respect to randomized variants of these genes . The resulting z-scores from comparing an observed property of a gene ( e . g . , its translation rate ) with the distribution of that property in variants with shuffled synonymous codons and the same ribosomal load on the transcript can be regarded as an optimality score for the sequence ( details in the Materials and Methods section ) . The set of genes with the highest translation rate compared to its respective random set ( top 5% ) has a significant subset of common genes with the set of genes with the lowest fraction of queued ribosomes compared to random ( bottom 5% , p = 5x10-50 , hypergeometric p-value ) , and vice versa ( p = 10−88 , Fig 4A ) . This is expected , as genes with an optimized codon order ( and reduced traffic jams ) can reach a higher translation rate while occupying the same average number of ribosomes ( see for example [37] ) . In addition , a significant negative correlation was observed between the z-scores of QFR and the inferred initiation rates of genes reported in the sections above ( r = -0 . 13 , p = 6 . 2x10-21 , n = 5 , 450 ) . This result is in agreement with the hypothesis that highly expressed genes are expected in many cases to be under selective pressure to optimize their sequence as to minimize queuing ( see for example [37] ) . Functional enrichment was performed for the above sets of genes ( Fig 4B and 4C , sets appear in S2 Table ) , detecting a number of biological processes and pathways over-represented in genes with a low QFR z-scores , such as cytoplasmic translation ( p = 0 . 0305 , hyper-geometric p-value adjusted for FDR , light brown in Fig 4B ) , and glucose fermentation ( p = 0 . 004 , light brown in Fig 4B ) . The largest subset of QFR-optimized genes was associated with transmembrane transport ( p = 0 . 0215 , light brown in Fig 4B ) . Interestingly , the same process was also enriched in genes with high z-scores of dense queuing in the first 100 codons compared with the middle codons of the genes ( p = 0 . 0216 , light blue in Fig 4C ) . Many of these genes are not highly expressed , as discussed below , and may be selected for these properties for reasons other than translational efficiency , such as co-translational folding [43] . Next , functional enrichment was performed for sets of highly and lowly ranked genes , according to their properties ( sets appear in S2 Table ) . A number of pathways were enriched both in highly translated genes ( top 5% , dark green in Fig 4C ) , as well as in rate-optimized genes ( top 5% of z-scores , light green in Fig 4C ) according to translation rate , namely cytoplasmic ribosomal proteins ( prank = 2 . 8x10-41 , pzs = 0 . 0422 for the highly ranked and highly optimized sets , respectively ) , glycolysis ( prank = 7 . 1x10-3 , pzs = 0 . 0285 ) and glucose fermentation ( prank = 4 . 2x10-4 , pzs = 0 . 0382 ) . A number of pathways were enriched in highly translated genes ( dark green in Fig 4C ) , as well as in QFR-optimized genes ( light brown in Fig 4B ) , namely cytoplasmic translation ( prank = 2 . 5x10-39 , pzs = 0 . 0305 ) , carbon metabolism ( prank = 1 . 2x10-3 , pzs = 5 . 2x10-3 ) , cell wall organization ( prank = 0 . 0385 , pzs = 0 . 0424 ) , glycolysis ( pzs = 4 . 1x10-3 ) and glucose fermentation ( pzs = 4 . 4x10-3 ) . This is expected , as highly translating genes also tend to have higher ribosomal densities and therefore their codon usage may be under selection to decrease traffic jams .
Ribosome traffic jam is a phenomenon that has been mentioned many times in the context of genome evolution , biophysics of translation , translation modeling , biotechnology , and more . To the best of our knowledge , this study provides the first comprehensive experimental quantification of traffic jams during translation . We estimated that at least 20% of the ribosomes in the budding yeast S . cerevisiae are positioned close enough to another ribosome on the transcript to be collected as a single footprint in a ribosome profiling experiment . This is a direct indication for the level of queuing in transcripts . Our estimates using sucrose gradient analyses have been consistent across experiments , and with simulated ribosome traffic based on empirical parameters . The observed variance in results obtained from different datasets may have been related to the experiment’s particular conditions and protocol . The current experimental resolution limits the accuracy of analysis to footprints containing a single ( mRPF ) or a pair ( dRPF ) of ribosomes , but future Ribo-seq experiments may enable the analysis of larger footprints . The results reported here suggest that queuing is not a negligible phenomenon . It appears to have important effects on organism evolution and fitness . Since translation is the process that consumes most of the energy in the cell [44] , an increase of x percentage in ribosome density should be related to a similar increase in growth rate or fitness ( as it is directly related to the energy consumed by the cell ) . It is clear that even a change of 1% or even 0 . 1% ( as was estimated in Fig 3D ) in the fitness of a micro-organism should have a very strong effect on its evolution ( for example , in the case of S . cerevisiae , an organism with a doubling time of 90min , a mutant with 0 . 1% increase in fitness is expected to practically be 97% of the population after 8 months ) . Moreover , increase in traffic jams may have an even stronger impact when considering its potential effect on ribosome abortion , that can be related again to energy waste , as well as to the production of nonfunctional and even toxic truncated proteins . Thus , from a biological/evolutionary point of view these values are potentially significant . We believe that further comprehensive experimental and computational efforts should be spent in the future to better understand and quantify the effect of local increase/decrease in ribosome densities/queues on organism fitness . Due to these implications , ribosome queuing should be considered when designing Ribo-seq experiments ( or using any other technology for studying translation ) , when analyzing mRNA translation , or when modeling mRNA translation . Specifically , the results reported here support the usage of the dozens of TASEP based models in studies that have been published in recent years [2 , 9 , 45] . These results also support the idea that the genomes of various organisms are under selection for generally minimizing queuing ( see , for example , [10 , 38 , 41 , 46] ) . Our approach has enabled the detection of transcript properties that are associated with high/low levels of queuing; for example , we showed that higher initiation may tend to increase queuing while higher adaptation to the tRNA pool may tend to decrease queuing . Thus , our methodology can be used for developing better models of translation and for designing synthetic genes with increased/decreased queuing levels . In summary , we believe that the computational-experimental approach demonstrated here will enable the analysis of translation at a higher resolution . It is essential to perform similar studies in various organisms and in different conditions to understand the significance of queuing across the tree of life . In the current study , we focused on translation under standard growth medium for yeast . It is important to emphasize that it is generally believed that under stress condition there are more traffic jams due to non-typical expression of tRNA species [47]; thus , it is possible that the estimates reported here serve as a lower bound for the ribosome traffic levels observed in the environment and evolution of yeast and other micro-organisms .
Ribosome profiling was performed as previously described [23 , 34] , with minor modifications . rRNA was depleted using Ribo-Zero by EpiCentre . The small RNA sequencing kit from New England BioLabs [48] was utilized . Sucrose gradient fractionation was employed for filtering reads containing one or two ribosomes . Additional filtering of DNA was performed with BluePippin to select the relevant length prior to sequencing . A Gaussian mixture model was fitted to the sucrose gradient profiles of ribosome footprints . To this end , an iterative expectation-maximization algorithm was used ( implemented in MATLAB’s fitgmdist function ) . As input to the algorithm , 500 equally spaced points were sampled from the X-axis of the profile ( which is related to particle size ) , with their representation in the input set ( the number of times each point appears ) proportional to their absorbance value ( the point of maximum absorbance arbitrarily set to appear 100 times in the input set ) . Model fitting was run multiple times using a varying number of components ( 4 to 8 ) , using random seeding of the algorithm , as well as seeding with components centered around peaks in the profile ( detected using MATLAB’s findpeaks ) , until reaching an optimal solution . The quality of models was assessed based on the Bayesian information criterion and manual inspection of candidate solutions with scores distanced at most 0 . 5% from the optimum . Transcript sequences of Saccharomyces cerevisiae were obtained from Ensembl [49] ( R64 release 87 ) , and UTR lengths were based on previously reported major transcript isoforms ( mTIF , selecting the longest one available for each transcript , and using a default length of 500bp for missing UTRs ) [50] . Adaptors ( AGATCGGAAGAGCACACGTCT in our dataset ) were trimmed from reads using Cutadapt [51] ( version 1 . 12 ) . Bowtie [52] ( version 1 . 2 ) was employed to map them to the transcriptome . In the first phase , Ribo-seq reads that mapped to rRNA and tRNA sequences were discarded using Bowtie parameters ‘-n 2—seedlen 21 -k 1—norc’ . In the second phase , the remaining Ribo-seq reads , as well as RNA-seq reads , were mapped to the transcriptome with Bowtie parameters ‘-v 2 -a—strata—best—norc -m 200’ . When the 3’ adaptor contained polyA alignments were extended to their maximal length by comparing the polyA with the aligned transcript until reaching the maximal allowed error ( 2 mismatches ) . Unique alignments were first assigned to the ribosome occupancy profiles . For multiple alignments , the best alignments in terms of number of mismatches were kept . Then , multiple aligned reads were distributed between locations according to the distribution of unique ribosomal reads in the respective surrounding regions . To this end , a 100nt window was used to compute the read count density RCDi of unique reads in vicinity of the M multiple aligned positions in the transcriptome , and the fraction of a read assigned to each position was RCDi/∑j=1MRCDj . The location of the A-site ( the site of the upstream ribosome in the case of dRPFs ) was estimated for each read length by performing meta-gene analysis of the distribution of 5’-end of reads upstream to the start codon of ORFs . If a global maximum was detected in the range of 5-20bp upstream to the start codon ( no larger peak observed down-/upstream from this range ) , this distance was selected as the shift to the P-site for this read length , otherwise these reads were discarded . For 2 datasets where peaks were hard to detect , a default shift of 15bp was employed . Mapping statistics and accession numbers for all datasets used appear in S3 Table and S5 Fig . An aggregated dataset of our experimental data and Guydosh et al . ’s was analyzed in Figs 2 and 3 , and each dataset was analyzed independently in S2 Fig and S3 Fig . Protein abundance data for dividing genes into highly / lowly expressed sets was obtained from PaxDB ( GPM_2012_09 ) [53] . Ribo-seq reads were analyzed in Python 3 . 5 using custom scripts and Biopython 1 . 6 . 8 , SciPy 0 . 19 . 0 , and NumPy 1 . 12 . 1 . Translation was modelled stochastically using a totally asymmetric simple exclusion process ( TASEP ) model , as performed in previous studies [54–56] . An mRNA with N codons is modeled as a lattice of N sites . At any time t , each site can be occupied by a single ribosome . Each ribosome translates a single site , as well as covers L sites in total , so that other ribosomes are excluded from these sites ( cannot occupy or translate them ) . The model dynamics consist of 3 possible events: ( 1 ) A free ribosome will attach to site i = 1 with rate λ , provided that the first L/2 codons on the mRNA are empty; ( 2 ) An attached ribosome located at site i will move to the next site i + 1 with rate λi , provided codon i + L/2 is not covered by another ribosome ( otherwise we refer to this ribosome as “queued” or “delayed” ) ; ( 3 ) In the case of i = N the ribosome movement is in fact a termination step . It is assumed that the time between initiation attempts is distributed exponentially with rate λ . In addition , it is assumed that the time between jump attempts from site i to i + 1 is exponentially distributed with translation rate λi , which is inversely proportional to the mean translation time ti of the codon . It follows , that the time between any consecutive events is exponentially distributed ( the minimum of exponentially distributed random variables ) with the rate μ , given by: μ ( {ni}i=1N ) =λ+∑i=1Nniλi , where ni ∈ {0 , 1} denotes the translation state of site i ( equal to 1 while being translated , and 0 otherwise ) . The probability that a specific event was an initiation attempt is given by λ/μ ( {ni} ) , while the probability that a specific event was a jump attempt ( or termination event ) from site i to site i + 1 is given by niλi/μ ( {ni} ) . In each simulation step , an event and the time that passed between events are sampled according to the above distributions . Next , state variables are updated according to the rules for each event . A pseudo-code of the simulation is given in S1 Text . In order to bring the system to a steady state , the simulation was initiated with an mRNA empty of ribosomes , followed by simulation steps until reaching 100 terminations . The system was then simulated for additional 10 , 000 terminations while keeping track of various properties of the system ( details in the Monitored features section below ) . For example , the steady state rate of protein production was determined by dividing the number of termination events by the total time that has passed . For this number of simulation steps , the difference between two simulation runs in the translation rate for a gene was small , typically 0 . 6% ( median , ranging from 0 . 1% in the 10th percentile of differences between genes to 2% in the 90th percentile ) . The simulation was implemented and analyzed in MATLAB R2016b . During simulation , steady state distributions were generated for a number of variables related to the translation process . The state variables {ni} were integrated over the simulation time , and divided by total simulation time to get a vector of ribosomal densities for each site on the transcript ( the fraction of time a site was occupied by a ribosome ) . The sum of which is equal to the mean number of ribosomes on the transcript in steady state . Similarly , density profiles were generated for simulated footprints of any feasible size k ( the number of ribosomes in the footprint ) by assigning , at each simulation step , every ribosome to a footprint set according to its distance from neighboring ribosomes . If the distance between two ribosomes was smaller than the maximal allowed spacing , the pair was assigned to the same footprint . After completion of assignments , state variables {mik} were set to 1 ( translated ) / 0 ( otherwise ) according to the position of the upstream ribosome in each footprint of size k . The simulated footprint density profiles were obtained by integrating {mik} over time . Finally , queuing and delay of ribosomes were monitored during the simulation . First , a state variable was defined per site {di} , which was set to 1 once a ribosome in site i failed at a jump event due to the next codon being covered by another ribosome , and was set back to 0 once the ribosome successfully jumped . The density of queued ribosomes was obtained by integration similarly to above; the sum of which gives the average number of queued ribosomes on the transcript ( or the queued fraction of ribosomes , QFR , when normalized by the number of ribosomes in the gene/region ) . Second , the total time that each ribosome spent translating the RNA , as well as the total time spent in queued state , were averaged over the simulated terminations to obtain the average translation and delay times . Finally , a state variable q was defined for the transcript , which was set to 1 as long as any ribosome was delayed across the transcript , and utilized it to obtain the probability for a queue . In order to keep track of the quantities of ribosomes / RPFs in the cell , the RNA copy numbers measured by Arava et al . [40] were employed , while taking into account the estimated occupied ( actively translated ) RNA copies and the simulated ribosomal / RPF densities described above . For the purpose of computing the correlation between the simulated profiles and Ribo-seq read count profiles ( as reported in S5 Fig ) , reads were sampled from this simulated RPF distribution until reaching an equal number of reads to those in the Ribo-seq experiment . Codon translation times were obtained from [57] , where they were fitted using a whole-cell translation simulation model with finite cellular resources and Ribo-seq data ( S4 Table ) . Initiation rates were selected to optimize the fit of the number of ribosomes on each transcript in steady state to the experimental data of Arava et al . [40] . In this study , the authors measured a transcriptome-wide profile of ribosome association with mRNAs , including the number of ribosomes per-transcript ( a total of 51 , 819 ribosomes on 5 , 700 transcripts , S3 Table in the original paper ) , their RNA copy numbers ( a total of 12 , 534 molecules ) and an estimation of the fraction of actively translated RNA copies of each type . As estimations for the total number of ribosomes ( 187 , 000 ± 56 , 000 , of which 70–85% are engaged in active translation ) and RNAs ( ranging from 12 , 500 to 60 , 000 ) in a yeast cell vary in the literature [40 , 58 , 59] , we considered 3 major scenarios: ( 1 ) A “balanced” scenario , taking the number of RNAs to be in the mid-range of the above estimates ( 26 , 000 [60] ) , as well as taking the number of ribosomes to be in the mid-range ( 187 , 000 , of which 85% actively translating ) ; ( 2 ) A “dense” scenario , taking the number of RNAs to be in the lower range ( 12 , 500 ) and the number of ribosomes to be in the upper range ( 243 , 000 , of which 85% actively translating ) ; ( 3 ) A “sparse” scenario , taking the number of RNAs to be in the upper range ( 60 , 000 ) and the number of ribosomes to be in the lower range ( 131 , 000 , of which 70% actively translating ) . In the first scenario , all but 194 genes converged to feasible initiation rates ( that successfully induced the number of ribosomes in steady state as reported by Arava et al . , after scaling ) , and the median initiation rate was identical to a previous study ( 0 . 09s-1 [41] ) . In the second and third scenarios 2 , 341 and 4 genes failed , respectively; the median initiation rate was 0 . 13s-1 and 0 . 02s-1; and the fit of the model to experimental data was generally less accurate ( S5B Fig ) . The model was tested with varying termination times ranging from 1-fold to 500-fold the typical translation time of the fastest codon , and did not find significant improvement for longer termination times and therefore set it to be as fast as the fastest codon ( S5C Fig ) . Ribosome size was set to be 10 codons , based on the typical footprint length of mRPFs . Results for various choices of ribosome size appear in S5D Fig . It can be seen that results do not change considerably in the range of 9–15 codons , with marginally better fitting with experimental data in the range of 10–12 codons . Finally , the model was tested with different footprint spacing parameters ( allowing up to 0–4 codons between ribosomes on a single footprint ) , and found a slight increase ( but no clear maximum ) in correlation with Ribo-seq double footprints ( dRPFs ) and single footprints ( mRPFs ) when allowing a higher number of free codons between ribosomes within a footprint ( S5E Fig ) . Initiation rates were also fitted to Ribo-seq and RNA-seq data using the TASEP model , for the purpose of analyzing their relation with missing mRPFs and the dRPF-to-mRPF ratio ( Fig 3B ) . To this end , the number of ribosomes on each transcript was estimated using the ratio of Ribo-seq ( dRPF ) to RNA-seq RPKM values; the number of copies of each transcript was estimated using the RNA-seq RPKM values . Ribosome and RNA quantities were then scaled to have the same total numbers as described in the first scenario above . We generated simulations for 100 genomes according to each of two models . In the first model , we permuted the rates of codons globally , i . e . by changing the translation rate of all positions of a codon in the transcriptome at once . The table with permuted codon rates was not constrained by codon synonymy , hence this model may serve as a validation for the calibration of the simulation . In the second model , we kept the original codon rates , but shuffled the order of synonymous codons across the sequence of a gene . Thus , this model controls for codon order in each transcript . In both randomizations , we selected initiation rates resulting in the same average number of ribosomes on the transcript as measured in real genes . We utilized annotations of GO terms ( accessed 04/09/15 ) obtained from SGD [61] and mapped them to generic slim definitions of the biological process ontology [62] . In addition , we obtained pathways from Wiki Pathways ( accessed 29 April 2016 ) [63] .
|
During translation , multiple ribosomes may translate the same mRNA . The density of ribosomal traffic across the transcript poses several open questions , such as how often a ribosome’s path is blocked by a second ribosome , do queues of multiple ribosomes typically form on mRNAs and what is their effect on the overall translation rate of an mRNA . However , this phenomenon hasn't been quantified yet at a genomic level . Nevertheless , methodologies for monitoring translation are limited in their ability to detect ribosome queuing . On the other hand , most of the models in the field assume and analyze a certain level of queuing . Here we present an experimental-computational approach for studying ribosome queuing based on sequencing of RNA footprints extracted from pairs of adjacent translating ribosomes , and a computational model of translation dynamics . Our data shows that ribosome queuing in Saccharomyces cerevisiae is more frequent than previously thought , suggesting that at least one to five translating ribosomes is in a traffic jam; these queued ribosomes cannot be captured by traditional methods . Our analysis also suggests that the S . cerevisiae transcriptome undergoes selection for eliminating traffic jams , while specific regions and genes may possibly be under selection for increased queuing .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"messenger",
"rna",
"simulation",
"and",
"modeling",
"genome",
"analysis",
"molecular",
"biology",
"techniques",
"cellular",
"structures",
"and",
"organelles",
"genetic",
"footprinting",
"research",
"and",
"analysis",
"methods",
"genomics",
"gene",
"expression",
"molecular",
"biology",
"ribosomes",
"biochemistry",
"genetic",
"fingerprinting",
"and",
"footprinting",
"rna",
"biochemical",
"simulations",
"cell",
"biology",
"nucleic",
"acids",
"protein",
"translation",
"genetics",
"transcriptome",
"analysis",
"biology",
"and",
"life",
"sciences",
"computational",
"biology"
] |
2018
|
The extent of ribosome queuing in budding yeast
|
In various fields , statistical models of interest are analytically intractable and inference is usually performed using a simulation-based method . However elegant these methods are , they are often painstakingly slow and convergence is difficult to assess . As a result , statistical inference is greatly hampered by computational constraints . However , for a given statistical model , different users , even with different data , are likely to perform similar computations . Computations done by one user are potentially useful for other users with different data sets . We propose a pooling of resources across researchers to capitalize on this . More specifically , we preemptively chart out the entire space of possible model outcomes in a prepaid database . Using advanced interpolation techniques , any individual estimation problem can now be solved on the spot . The prepaid method can easily accommodate different priors as well as constraints on the parameters . We created prepaid databases for three challenging models and demonstrate how they can be distributed through an online parameter estimation service . Our method outperforms state-of-the-art estimation techniques in both speed ( with a 23 , 000 to 100 , 000-fold speed up ) and accuracy , and is able to handle previously quasi inestimable models .
Models without an analytical likelihood are increasingly used in various disciplines , such as genetics [1] , ecology [2 , 3] , economics [4 , 5] and neuroscience [6] . For such models , parameter estimation is a major challenge for which a variety of solutions have been proposed [2 , 1 , 7] . All these methods have in common that they rely on extensive Monte Carlo simulations and that their convergence can be painstakingly slow . As a result , the current methods can be very time consuming . To date , the practice is to analyse each data set separately . However , considering all the calculations that have ever been performed during parameter estimation of a particular type of model , for each different data set , one cannot help but notice an incredible waste of resources . Indeed , simulations performed while estimating one data set may also be relevant for the estimation of another . Currently , each researcher estimating the same model with different data will start from scratch , and can not benefit from all the possibly relevant calculations that have already been performed in earlier analyses by other researchers , in other locations , on different hardware , and for other data sets , but concerning the same model . Hence , we propose an estimation scheme that dramatically increases overall efficiency by avoiding this immense redundancy . Most current algorithms are inherently iterative and ( slowly ) adjust their window of interest to the area of convergence . Instead , we propose to generate an all-inclusive and one-shot prepaid database that is capable of estimating the parameters of a particular model for all potential data sets and with almost no additional computation time per data set . Our approach starts with the extensive simulation of data sets across the entire parameter space . These data are then compressed into summary statistics , after which the relation between the summary statistics and the parameters can be learned using interpolation techniques . Finally , global optimization methods can be used on the previously created ( hence , prepaid ) database for accurate and fast parameter estimation on any device . This results in a mass lookup and interpolation scheme that can produce estimates to any given dataset very quickly . In Fig 1 we present a graphical illustration of the prepaid parameter estimation method . First ( panel A ) , for a sufficient number of parameter vectors θ , large data sets are simulated , compressed into summary statistics ( i . e . , ssim ) and saved—creating the prepaid grid . This prepaid grid is computed beforehand and the results are stored at a central location . Second ( panel B1 ) , the observed ( data ) summary statistics ( sobs ) are compared to the simulated ( data ) summary statistics ( i . e . , ssim ) using an appropriate objective loss function d ( ssim , sobs ) and a number of nearest neighbor simulated summary statistics are selected . The loss function is related to the loss function used in the generalized method of moments [8] and method of simulated moments [9] . Third ( panel B2 ) , interpolation methods are used to find the relation s = f ( θ ) between the parameter values and the summary statistics for the selected points of the previous step [10 , 11] . In this paper , we use tuned least squares support vector machines , LS-SVM [12] . Finally ( panel B3 ) , the objective loss function d ( spred , sobs ) , now using predicted summary statistics spred , is minimized as a function of the unknown parameter values using an optimizer . A number of important aspects of the prepaid method deserve special mention . First , the parameter space is required to be bounded . If this is unnatural for a given parametrization , then the parameters have to be appropriately transformed to a bounded space . Second , we typically start from a uniform distribution of parameter vectors in the final parameter space . This choice reflects on the uniformity of the grid’s resolution , but has no further implications provided the grid is sufficiently dense . Bayesian priors can be implemented without recreating the prepaid grid , since the prior can be taken into account in the loss function . Third , often a user is not interested in a single instance of a model , but rather has data from several experimental conditions that share some common parameters but assume other ones to be different . Also in these cases the prepaid grid does not need to be recreated , as the parameter constraints can be included through priors with tuning parameters ( i . e . , penalties ) . Fourth , the creation of the prepaid database is a fixed cost and usually takes from a couple of hours to one or more days , depending on the complexity of the model of interest ( see below for a number of examples ) . Once its prepaid database is created , the parameters of the model can be estimated for any data set , with any amount of data ( number of observations ) . The prepaid method can be studied theoretically in simple situations . For example , in Methods , we apply the prepaid idea for estimating the mean of a normal distribution and study some of its properties for two different summary statistics . In what follows , the prepaid method will be applied to three more complicated , realistic scenarios .
In a first example , we apply our prepaid method to the Ricker model [13 , 2] which describes the dynamics of the number of individuals yt in a species over time ( with t = 1 to Tobs ) : y t ∼ Pois ( ϕ N t ) N t + 1 = r N t e - N t + e t ( 1 ) where e t ∼ N ( 0 , σ 2 ) . The variables Nt ( i . e . , the expected number of individuals at time t ) and et are hidden states . Given an observed time series { y t } t = 1 T obs , we want to estimate the parameters θ = {r , σ , ϕ} , where r is the growth rate , σ the process noise and ϕ a scaling parameter . The Ricker model can demonstrate near-chaotic or chaotic behavior and no explicit likelihood formula is available . Wood [2] used the synthetic likelihood to estimate the model’s parameters . In the original synthetic likelihood approach ( denoted as SLOrig ) , the assumed multivariate normal distribution of the summary statistics is used to create a synthetic likelihood . The mean and covariance matrix of this normal distribution are functions of the unknown parameters and are calculated using a large number of model simulations . The synthetic likelihood is proportional to the posterior distribution from which is sampled using MCMC and a posterior mean is computed . Wood’s synthetic likelihood SLOrig approach is compared to the prepaid method , where we create a prepaid grid of the mean and the covariance matrix of a similar set of summary statistics . Prepaid estimation comes in multiple variants , depending on the use of an interpolation method . The first , which uses only the prepaid grid points and chooses the nearest neighbor ( maximum synthetic likelihood ) as final estimate , will be called SL ML Grid . The second , SL ML SVM , uses LS-SVM to interpolate between the parameters in the prepaid grid to increase accuracy . The differential evolution algorithm ( a global optimizer; [14] ) is used to maximize this interpolated synthetic ( log ) likelihood . Additional details on the implementation of the synthetic likelihood can also be found in Methods . Fig 2 shows both the accuracy of parameter recovery ( as measured with the RMSE ) and computation time for the three methods under comparison: ( 1 ) SLOrig as in [2] , the prepaid method ( 2 ) with interpolation ( SL ML SVM ) , and ( 3 ) without ( SL ML Grid ) interpolation . As can be seen in Fig 2 , the prepaid estimation techniques lead to better results than the synthetic likelihood for Tobs = 1 , 000 , both in accuracy and speed . The SLOrig method leads to some clear outliers ( see Methods ) which testifies to possible convergence problems ( probably due to local minima ) . The prepaid method suffers much less from this problem . Most striking is the speed up of the prepaid method: The SL ML Grid version of the prepaid estimation is finished before a single iteration of the 30 , 000 iterations in the synthetic likelihood method has been completed—100 , 000 times faster . In addition , it is demonstrated that the coverages of the prepaid method confidence intervals are very close or exactly equal to the nominal value ( we look at 95% bootstrap-based confidence intervals ) . SVM interpolation is mainly helpful for large Tobs , where one expects a higher accuracy of the estimates and the grid is too coarse . The analyses with large Tobs could only be completed in a reasonable time using the prepaid method ( See Methods for more detailed information ) . In the application above , the tacitly assumed prior on the parameter space is uniform . In addition , there is only one data set for which a single triplet of parameters ( r , σ , ϕ ) needs to be estimated . In Methods , we show how both limitations can be relaxed . First , it is explained how different priors for the Ricker model can be implemented . Second , it is discussed what can be done if there are two data sets ( i . e . , conditions ) for which it holds that r1 = r2 and σ1 = σ2 but ϕ1 and ϕ2 are not related . Finally , we also tested our estimation process on the population dynamics of the Chilo partellus , extracted from Fig 1 in Taneja and Leuschner [15 , 16] . Here we found that r = 1 . 10 ( 95% confidence interval 1 . 06–1 . 34 ) , σ = 0 . 43 ( 95% confidence interval 0 . 30–0 . 54 ) and ϕ = 140 . 60 ( 95% confidence interval = 43 . 94–208 . 19 ) . We found similar results using the synthetic likelihood method ( see Methods ) , but our estimation was 4000 times faster . A second example we use to illustrate the prepaid inference method is a trait model of community dynamics [17] used to model the dispersion of species . For this model ( see also Methods section ) , there are four parameters to be estimated: I , A , h , and σ . As with the first application , there is no analytical expression for the likelihood [17] . As an established benchmark procedure for this trait model , we apply the widely used Approximate Bayesian Computation ( ABC ) method [18 , 19 , 20 , 21] as implemented in the Easy ABC package and denoted here as ABC PM Orig ( PM stands for posterior means , which will be used as point estimates ) [22] . As priors , we use uniform distributions on bounded intervals for log ( I ) , log ( A ) , h and log ( σ ) ( see Methods for the exact specifications ) , but this can be easily changed as explained for the first example . To allow for a direct comparison with the ABC method ( ABC PM Orig ) , and to illustrate the versatility of the prepaid method , we have also implemented three Bayesian versions of the prepaid method . The first , SL PM Grid , creates a posterior proportional to the prepaid synthetic likelihood . The second method , ABC PM Grid , saves not only the mean and covariance matrix of the summary statistics for every parameter in the prepaid grid , but also a large set of uncompressed summary statistics . Using these statistics we are able to approximate an ABC approach . The third , ABC PM SVM , again interpolates between the grid points to achieve a higher accuracy . All methods result in accuracies of the same order of magnitude as can be seen in Table 1 . The main difference is again the speed of the methods: ABC PM Grid is about 23 , 000 times faster than traditional ABC . For small sample sizes , all ABC based methods achieve good coverage . However , for large sample sizes , ABC PM Orig cannot be used anymore ( because of the unduly long computation time ) . For the prepaid versions and large samples , it is necessary to use SVM interpolation between the grid points to get accurate results . In a third example , we apply our method to stochastic accumulation models for elementary decision making . In this paradigm , a person has to choose , as quickly and accurately as possible , the correct response given a stimulus ( e . g . , is a collection of points moving to the left or to the right ) . Task difficulty is manipulated by applying different levels of stimulus ambiguity . A popular neurally inspired model of decision making is the Leaky Competing Accumulator ( LCA [23] ) . For two response options , two noisy evidence accumulators ( stochastic differential equations , see Methods section ) race each other until one of them reaches the required amount of evidence for the corresponding option to be chosen . The time that is required to reach that option’s threshold is interpreted as the associated choice response time . For different levels of stimulus difficulty , the model produces different levels of accuracy and choice response time distributions . The evidence accumulation process leading up to these choices and response times is assumed to be indicative of the activation levels of neural populations involved in the decision making . As in the first two examples , there is no analytical likelihood available that can be used to estimate the parameters of the LCA . Moreover , the LCA is an extremely difficult model to estimate . To the best of our knowledge , only [24] systematically investigated the recovery of the LCA parameters , but for a slightly different model ( with three choice options ) and with a method that is impractically slow for very large sample sizes , making it difficult to show near-asymptotic recovery properties with . For an experiment with four stimulus difficulty levels , the LCA model has nine parameters . However , after a reparametrization of the model ( but without a reduction in complexity ) , it is possible to reduce the prepaid space to four dimensions ( see Methods ) and conditionally estimate the remaining subset of the parameters with a less computationally intensive method . Three variants of the prepaid method have been implemented: taking the nearest neighboring parameter set ( based on a symmetrized χ2 distance between distributions ) on the prepaid grid ( CHISQ NN Grid ) , averaging over the grids nearest neighboring parameter sets of 100 non-parametric bootstrap samples ( CHISQ BS Grid ) , using SVM interpolation for every bootstrap estimate ( CHISQ BS SVM ) . A nearest neighbor or bootstrap averaged estimate completes in about a second on a Dell Precision T3600 ( 4 cores at 3 . 60GHz ) , an SVM interpolated estimate requires a couple of minutes extra . Fig 3 displays the mean absolute error ( MAE ) of the estimates for four of the nine parameters as a function of sample size , separately for three estimation methods . The results for the other parameters are similar and can be consulted in the Methods section . It can be seen that with increasing sample size , MAE decreases . The SVM method pays off especially for larger samples . Fig 4 shows detailed recovery scatter plots for a subset of the parameters for 1 , 200 observed trials , which is the typical size of decision experiments . To get better recovery , larger sample sizes have to be considered ( see Methods section ) . In general , recovery is much better than what has been reported in [24] . The coverage of the method , based on non-parametric bootstrapping , is satisfactory for all sample sizes , provided SVM interpolated estimates are used for Tobs > 100000 . In addition , we do not find evidence for a fundamental identification issue with the two option LCA , as has been stated in [24] .
In three examples , we have demonstrated the efficacy and versatility of the prepaid method . The prepaid method is at least as accurate as current methods , but many times faster ( 23 , 000 to 100 , 000-fold speed up ) . Besides the improvements at the level of speed and accuracy , the prepaid method has a number of other distinct advantages . First , the prepaid method can be used for a very large number of observations , contrary to the synthetic likelihood or ABC methods . The use of very large simulated data sets allows a practical investigation of large-sample properties of the estimator , which is a problem for the synthetic likelihood and ABC . Second , because of the enormous speed improvement and having data sets available across the whole parameter space , the prepaid method allows for fast yet extensive testing of recovery of simulated data across this space—the recovery of every single parameter set can be evaluated . Such a practice leads to detailed internal quality control of the used estimation algorithm . Although the idea behind the prepaid method is fairly simple , we want to anticipate a few misconceptions that might arise . First , as has been demonstrated in the context of the Ricker model ( the first example ) , the prepaid method can easily deal with different priors and with equality constraints on parameters , without the need to recreate the underlying prepaid grid . Second , the observed data based on which the model parameters have to be estimated can be of any size , again without the need to recreate the prepaid grid for each and every sample size . In the first two examples the synthetic likelihood [2] is used , but its exact effect on likelihood based model selection techniques , such as information criteria , is not known . For users interested in model selection , we propose cross-validation as its implementation is straight forward . The main draw-back of this resampling method , its computational burden , is mitigated by the use of the prepaid method . Ideally , the prepaid databases and the corresponding estimation algorithms will be constructed and made available by a team of experts for the model at hand . Subsequently , a cloud based service can be set up to offer high quality model estimations to a broad public of researchers . As an example , we created such a service for the Ricker model in Eq 1: http://www . prepaidestimation . org/ , where we allow the user to estimate the parameters of the Ricker model for personal data as well as 4 example data sets including one real life data set [15 , 16] . By using such a cloud based service , researchers that need their data analyzed with computationally challenging models , can avoid many of the pitfalls they would otherwise encounter venturing out on their own . This practice will also lead to increased reproducibility of computational results . As the need for reproducibility and transparency is ( fortunately ) increasingly recognized by the broader scientific community , critical model users will want to see proof of robust estimation across the entire parameter space , and be able to test this themselves . The current standard of simply sharing the code of a procedure , still grants developers of complex models/methods a layer of protection from public scrutiny , because the level of knowledge and infrastructure required to check the work is considerable and not many are called to take up the challenge . The prepaid method , however , allows any user with a basic grasp of statistics to check the consistency of the model and method , using data they have simulated themselves . In the future , we expect a natural evolution towards a situation where stakeholders in certain models ( the developers and/or heavy users ) will provide an estimation service or outsource this endeavor to a third party . The infrastructure required for hosting such a service is orders of magnitude lighter than what is required for the calculation of the database itself or a thorough simulation study for that matter . We are currently hosting the Ricker model on a very modest system ( medium level desktop ) . A first possible objection to the prepaid method is the considerable initial simulation cost ( for the examples discussed , prepaid simulations took up to a couple of days on a 20-core processor ) . However , this overhead cost will dissipate entirely as increasingly more estimates are sourced from the same prepaid database . Moreover , the initial prepaid cost can be easily distributed across multiple interested parties . Further , because the database can be used for internal quality control , additional simulation studies investigating the recovery of parameters are made redundant . Indeed , whenever a new model and associated parameter estimation method are proposed , a recovery study is needed to study how well the parameters of the model can be estimated using the method . When such a simulation study is set up in a rigorous way , the prepaid grid will have been ( partially or completely ) constructed . For the first and the second example , the time to create the prepaid grid was of the same order as that of the parameter recovery study included for the estimation techniques the prepaid grid was compared with . Note however that the parameter recovery study of the traditional techniques was only partial , as data sets with more observations , for which the parameter estimation would take an excessively long time using only traditional methods , were excluded . If those would be included , a parameter recovery study would be at least 10 times slower than the creation of the prepaid grid . The fact that a parameter recovery study takes at least as much time as the creation of the prepaid grid makes sense . A recovery study should test the estimation of parameters in the whole realm of possible data sets . The prepaid grid exactly covers this realm . The argumentation above shows that a parameter recovery study and a prepaid grid are very related . In fact , Jabot , saw the necessity of reusing ABC simulations to reduce computation time in his recovery study for the model of the second example [17] . More broadly , we are convinced that other researchers also have used similar tricks to avoid redundant simulation within their own research context . For example , a reviewer of this manuscript noted that s/he uses a prepaid grid ( although not named so ) when trying models in which the parameters change across trials . The main difference with prepaid estimation is that we propose to reuse these simulations to facilitate future estimations . A second possible objection is that the prepaid grid , unsurprisingly , does not escape the curse of dimensionality: The grid size grows exponentially with the number of parameters . The prepaid method is most effective for highly nonlinear models with substantively meaningful parameters , as they appear in various computational modeling fields . For these models , all simulation based estimation techniques struggle with the curse of dimensionality . For the prepaid method , this limitation can be alleviated in a number of ways . First , the use of interpolation techniques allows for a substantial reduction of the number of prepaid points ( by a factor of five for the same accuracy in the trait model example; see Methods section ) . Second , as is shown in the LCA example , it is possible to only partially apply the prepaid method and combine it with traditional estimation techniques . In this way , the less challenging parameters can be estimated conditionally on a prepaid grid of the more intricately connected ones . Third , as shown by tackling three challenging examples , current storage and/or memory technology can accommodate realistically sized prepaid databases . A last possible objection is the risk , that once the prepaid grid is created for a certain model , researchers will be biased towards using this particular model . They may prefer the relatively easy prepaid estimation of this model over the use of other models without a prepaid grid . We hope however that also the creation of the prepaid grid is manageable enough for any model to prevent such scenarios . A possible improvement of the prepaid method lies in a smarter construction of the prepaid grid . First , there is a straightforward theoretical angle: spreading the grid points out according to Jeffrey’s prior rather than a naïve parameter based prior , would lead to a more evenly distributed estimation accuracy , and therefore a smaller database size will suffice for a given minimum accuracy . Additionally , the database could be improved based on the actual queries of users . If the simulation grid proves a bit thin around the requested area ( not a lot of unique grid points ) , more grid points can be added there . This way more detail is added where it matters . Finally , the prepaid method also offers exciting opportunities for future research . First , another typical case where the same model has to be estimated multiple times , arises in a multilevel context ( where several individual analyses are regularized by a set of hyperparameters defined on the group ) . Although extremely useful , multilevel analyses typically come with an additional computational burden . Because the synthetic likelihood , as any likelihood , can be extended to a multilevel context , the prepaid method should be too . Further research is needed to develop this idea . Second , the prepaid philosophy can also be used to choose a good set of summary statistics , which are necessary for simulation based estimation techniques . During the creation of the prepaid grid many summary statistics can be saved , with no additional simulation cost . The effectiveness of combinations of summary statistics are then easily tested in parameter recovery studies as the prepaid estimation is so quick . It is our strong belief that this method will massively democratize the use of many computationally expensive models , which are now reserved for people with access to specific high-end hardware ( e . g . , GPUs , HPC ) . Apart from such democratization , this approach could significantly impact the current work flow of scientific modeling , in which every part of the estimation is carried out locally by an individual researcher .
For a very simple setting , we want to study the performance of the prepaid methods analytically . Assume yi ∼ N ( μ , s2 ) ( i = 1 , … , Tobs ) with the mean μ unknown ( and to be estimated and the standard deviation s known ( so number of parameters K = 1 ) . The observed mean is denoted as y ¯ . We will explore two situations . In the first situation , y ¯ will be our summary statistic sobs ( hence number of summary statistics R = 1 ) to estimate μ ( y ¯ is also a sufficient statistic for μ ) . In the second situation , we will study what happens if s obs = y ¯ 2 is chosen to be the summary statistic . The basic model equations of the Ricker model is given in Eq 1 . A second model we will apply our prepaid modeling technique to , is a stochastic dispersal-limited trait-based model of community dynamics [17] . The data that will be modeled , are the abundances of species ( hence a vector of frequencies , in which each component is a different species ) . Each species in the local environment is assumed to have a competitive value dependent on its trait u , given by the filtering function F ( u ) = 1 + A e - ( u - h ) 2 2 σ 2 . ( 17 ) Here A is the maximal competitive advantage , h is the optimal trait value in the local environment and σ describes the width of the filtering function . At each time step , one individual from the local community dies . It is then replaced with a probability 1 - I I + J + 1 by a random descendant from the local pool . Here , J is the size of the local community and I is the fourth parameter to estimate , related to the amount of immigration from the regional pool into the local community . The probability that this descendant comes from a certain individual in the local community , is proportional to the competitiveness of this individual . With a probability of I I + J + 1 , the dead individual is replaced by an immigrant from the regional pool . The distribution of traits u of the individuals in the regional pool is assumed to be uniform over u . It is noteworthy that Jabot saw the necessity of reusing ABC simulations to reduce computation time in his recovery study [17] . The model was simulated using the C++ code from the Easy ABC package [22] where a regional pool of S = 1000 species was defined evenly spaced on the trait axis ( i . e . , the resolution ) and J = 500 was the size of the local community . Elementary decision making has been studied intensively in humans and animals [31] . A common example of an experimental paradigm is the random-motion dot task: the participant has to decide whether a collection of dots ( of which only a fraction moves coherently; the others move randomly ) is moving to the left or to the right . The stimuli typically have varying levels of difficulty , determined by the fraction of dots moving coherently . Assuming there are two response options ( e . g . , left and right ) , the Leaky Competing Accumulator consists of two evidence accumulators , x1 ( t ) and x2 ( t ) ( where t denotes the time ) , each associated with one response option . The evolution of evidence across time for a single trial is then described by the following system of two stochastic differential equations: d x 1 = ( v + Δ v i - γ x 1 - κ x 2 ) · d t + c · d W 1 d x 2 = ( v - Δ v i - γ x 2 - κ x 1 ) · d t + c · d W 2 , ( 26 ) where dW1 and dW2 are uncorrelated white noise processes . To avoid negative values , the evidence is set to 0 whenever it becomes negative: x1 = max ( x1 , 0 ) and x2 = max ( x2 , 0 ) . The initial values ( at t = 0 ) are ( x1 , x2 ) = ( 0 , 0 ) . The evidence accumulation process continues until one of the accumulators crosses a boundary a ( with a > 0 ) . The coordinate that crosses its decision boundary first , determines the choice that is made and the time of crossing is seen as the decision time . The observed choice response time is seen as the sum of the decision time and a non-decision time Ter , to account for the time needed to encode the stimulus and emit the response . Eq 26 describes the evolution of information accumulation for a two-option choice RT task , given the presentation of a single stimulus . For all stimuli , the total evidence is equal to v , but the differential evidence for option 1 compared to 2 is 2Δvi , which is stimulus dependent and reflects the stimulus difficulty . In this example , we assume the stimuli can be categorized into four levels of difficulty , hence i = 1 , … , 4 . The model gives rise to two separate choice response time probability densities , p1i ( t ) and p2i ( t ) , each representing the response time conditional on the choice that was made . Integrating the densities over time will result in the probability of choosing the response options: ∫ 0 ∞ p1 i ( t ) d t =Pr ( option1forstimulusi ) and ∫ 0 ∞ p 2 i ( t ) d t =Pr ( option2forstimulusi ) . Obviously , when taken together , p1i and p2i sum to one . All parameters in the parameter vector θ = ( v , Δv1 , … , Δv4 , κ , γ , a , Ter ) can take values from 0 to ∞ . This parametrization is known to have one redundant parameter [24] , so we choose to fix c = 0 . 1 .
|
Interesting nonlinear models are often analytically intractable . As a result , statistical inference has to rely on massive , time-intensive , simulations . The main idea of our method is to avoid the redundancy of similar computations that typically occur when different researchers independently fit the same model to their particular dataset . Instead , we propose to pool computational resources across the researchers interested in any given model . The prepaid method starts with an extensive simulation of datasets across the parameter space . The simulated data are compressed into summary statistics , and the relation to the parameters is learned using machine learning techniques . This results in a parameter estimation machine that produces accurate estimates very quickly ( a 23 , 000 to 100 , 000-fold speed up compared to traditional methods ) .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"ellipses",
"statistics",
"random",
"variables",
"geometry",
"covariance",
"simulation",
"and",
"modeling",
"interpolation",
"regression",
"analysis",
"mathematics",
"artificial",
"intelligence",
"research",
"and",
"analysis",
"methods",
"statistical",
"distributions",
"computer",
"and",
"information",
"sciences",
"mathematical",
"and",
"statistical",
"techniques",
"support",
"vector",
"machines",
"probability",
"theory",
"linear",
"regression",
"analysis",
"physical",
"sciences",
"statistical",
"methods",
"machine",
"learning",
"numerical",
"analysis"
] |
2019
|
Prepaid parameter estimation without likelihoods
|
Over a century since Ronald Ross discovered that malaria is caused by the bite of an infectious mosquito it is still unclear how the number of parasites injected influences disease transmission . Currently it is assumed that all mosquitoes with salivary gland sporozoites are equally infectious irrespective of the number of parasites they harbour , though this has never been rigorously tested . Here we analyse >1000 experimental infections of humans and mice and demonstrate a dose-dependency for probability of infection and the length of the host pre-patent period . Mosquitoes with a higher numbers of sporozoites in their salivary glands following blood-feeding are more likely to have caused infection ( and have done so quicker ) than mosquitoes with fewer parasites . A similar dose response for the probability of infection was seen for humans given a pre-erythrocytic vaccine candidate targeting circumsporozoite protein ( CSP ) , and in mice with and without transfusion of anti-CSP antibodies . These interventions prevented infection more efficiently from bites made by mosquitoes with fewer parasites . The importance of parasite number has widespread implications across malariology , ranging from our basic understanding of the parasite , how vaccines are evaluated and the way in which transmission should be measured in the field . It also provides direct evidence for why the only registered malaria vaccine RTS , S was partially effective in recent clinical trials .
Mosquito-to-human malaria transmission occurs when sporozoites from the salivary gland of the mosquito are injected into the skin during blood-feeding . Parasites then pass to the liver where they replicate , each sporozoite yielding many thousands of merozoites which go on to cause patent infection . Relatively little is known about the population dynamics of malaria between the bite of the infected mosquito and patency . It is currently assumed that the probability of mosquito-to-human transmission is determined simply by the presence of salivary gland sporozoites and not the total number of parasites . For example malaria transmission intensity and the human force of infection are measured using the entomological inoculation rate ( EIR ) the average number of infectious mosquito bites per person per year [1] . EIR is calculated by multiplying the human biting rate by the proportion of mosquitoes with salivary gland sporozoites and therefore does not explicitly consider how heavily infected the mosquitoes are . Controlled human malaria infection ( CHMI ) trials are increasingly being used to evaluate new vaccines [2] and offer a new way to examine the basic biology of the parasite . Volunteers are deliberately infected through the bite of a blood-feeding mosquito or from the direct syringe injection of titrated cryopreserved sporozoites harvested from laboratory reared mosquitoes [3] . The number of syringe injected sporozoites has been shown to correlate with the probability of infection , though the number of viable/infectious sporozoites inoculated is unknown [3 , 4] . Therefore it is unclear whether the dose response is caused by an increased probability of the person being injected by a rare viable sporozoite , or whether it is the higher number of viable sporozoites which increases the chance of infection . Mosquito-delivered sporozoites more reliably recreate the natural infection process than syringe inoculation [4–6] , here the probability of infection increases with the number of infectious bites [7 , 8] . However a review of the CHMI trial literature and model-system data indicates that no study has thoroughly examined the impact of the number of sporozoites within those bites . The limited number of studies on the subject have given inconsistent results [5 , 9–11] , potentially resulting from small sample sizes and the use of insensitive statistical methods [12] . Many highly infected mosquitoes fail to inject sporozoites during blood feeding [13–15] so it is again unclear whether the dose response is caused by more bites having a higher probability of injecting a viable sporozoite , or whether the increased quantity of sporozoites injected increases transmission . Currently it is not possible to accurately determine the salivary gland sporozoite burden prior to blood feeding nor the quantity inoculated into the host , which current estimates suggest may vary by multiple orders of magnitude [14–16] . Consequently CHMI studies estimate parasite challenge by dissecting mosquitoes after blood-feeding , counting the number of sporozoites remaining in the salivary glands using microscopy ( here referred to as the number of residual-sporozoites , which is scored on a logarithmic scale ) [9] . Residual-sporozoite score has been shown to correlate with the number of sporozoites injected into the skin [15] . Time to detectable blood-stage infection is used in CHMI studies to differentiate the efficacy of vaccines which did not elicit sterilising immunity , with longer pre-patent periods indicating a smaller number of parasites passing from the liver to the blood and therefore higher intervention efficacy . Historically , shorter pre-patent periods have been correlated with volunteers receiving more infectious bites [8 , 17 , 18] but recent analyses of the Plasmodium vivax malaria datasets using more appropriate statistical techniques suggest evidence for such an association is weak [12] , though once again the impact of sporozoite intensity was not thoroughly investigated . The relationship between pathogen dose and disease transmission may have important implications for the epidemiology of the disease , how transmission is measured in the field , and the impact of vaccination [19] . It appears intuitive that transmission will increase with the size of the inoculum though there is little empirical evidence to support this assumption and no direct evidence from human malaria . The dose required to infect a host varies dramatically between diseases [20] so the importance of sporozoite load and its relationship with the probability of transmission needs to be clarified . This study combines results from novel and previously published experimental infections and analyses them with new statistical methods to rigorously determine the impact of the number of residual-sporozoites on the probability of infection . In the CHMI trials analysed here volunteers are bitten by infected mosquitoes until they have received 5 bites from mosquitoes with >10 residual-sporozoites [21] . This ensures that all control volunteers develop malaria [22] though precludes these volunteers being used to investigate factors influencing the probability of naïve human infection . We therefore analyse the results of a recent CHMI study where volunteers were given a partially effective pre-erythrocytic vaccine ( PEV ) which targets the circumsporozoite protein ( CSP ) [23] . The analysis is extended to the Plasmodium berghei–A . stephensi mouse model system [24] where the importance of the number of sporozoites in a mosquito bite can be assessed more thoroughly , with or without the presence of an anti-CSP antibody . To investigate whether sporozoite load influences the size of the liver-to-blood inocula in hosts which become infected ( and not just whether or not blood-stage infection develops ) the relationship between residual-sporozoite number and the time to patency is assessed . This allows the impact of parasite load to be investigated in a larger human dataset with and without vaccine where most volunteers became infected ( S1 Table ) . For the sake of brevity both humans pre-administered a pre-erythrocytic vaccine and mice pre-administered the anti-CSP 3D11 antibody are referred to as being given a PEV , whilst those that did not are called naïve .
The association between the number of residual-sporozoites and the probability of infection was assessed in 47 vaccinated volunteers ( challenged with Plasmodium falciparum using Anopheles stephensi mosquitoes ) , 9 of which became infected ( Fig 1A ) . Though there was no difference in the mean ( or total ) residual-sporozoite number in mosquitoes biting persons who became infected or remained uninfected ( S2 Table ) a binomial model [25] shows that infection was significantly more likely from mosquitoes with >1000 residual-sporozoites ( Fig 1B ) . The best fit model indicates that mosquitoes harbouring >1000 sporozoites had a per bite transmission probability of 9 . 2% ( 4 . 5%-16 . 0% ) whilst those with a lower number of parasites did not measurably contribute to transmission ( Fig 1B , see S3 Table for the results of the full model comparison ) . This model is used to estimate the probability that a volunteer became infected according to the number of residual-sporozoites in the bites they received ( Fig 1C ) . Though there is still considerable variation , the predicted probability of infection is significantly higher in infected volunteers ( p = 0 . 040 ) supporting the original analysis that mosquito parasite load influences the ( per bite ) probability of mosquito-to-human transmission . None of the 13 volunteers who received 0 or 1 bites from mosquitoes with >1000 residual-sporozoites became infected . The analysis is extended to the mouse model system [24] where the importance of the number of sporozoites in a mosquito bite can be assessed more thoroughly , with or without the presence of PEV . A total of 844 mice were included in the analysis , 99 of whom were given a PEV ( Fig 1D ) . Results show a clear dose-dependency , with mosquitoes with a higher number of residual-sporozoites being more likely to transmit malaria both in the naïve mice , and those given a PEV . The probability of infection in a naïve host from a single bite is 32% ( 19%-46% ) from mosquitoes with 1–10 sporozoites and 78% ( 53%-93% ) from those with >1000 sporozoites ( Fig 1E ) . The approximately linear increase in transmission probability with the log-sporozoite number suggests that the per-sporozoite transmission probability is a negative density-dependent process . Model predictions of the probability of infection for each mouse ( according to the number of residual-sporozoites of the bites they received ) are compared to whether or not they became infected ( Fig 1F ) . There is a significant association between infection status and the model-derived probability of infection ( p<0 . 0001 ) . The model is able to predict infection relatively accurately ( for example 95 . 4% of mice with a >95% probability of infection became infected ) . Greater variability is seen in the probability of infection for mice than for humans . This is because in the rodent dataset the number of bites a mouse receives varies ( in addition to the number of residual-sporozoites within those bites ) whereas in the human dataset all volunteers receive 5 bites from mosquitoes with >10 residual-sporozoites . Mosquitoes with no detected residual-sporozoites by microscopy can , very rarely , infect mice . In total , 291 naïve mice were bitten by mosquitoes with no detectable salivary gland sporozoites following blood-feeding . Of these 13 ( 4 . 5% ) went on to develop a blood stage infection . The lack of observable sporozoites could be caused by mosquitoes injecting all sporozoites during blood-feeding , or due to measurement error in the counting process . If all mice bitten exclusively by mosquitoes with no detectable sporozoites were removed from the analysis then each additional bite from mosquitoes with no detected residual-sporozoites reduces the probability of infection by 4 . 6% ( 0 . 5%-7 . 4% ) . Comparing this model to a reduced version where zero scores have no contribution to transmission shows that including the negative impact of these bites does significantly improve model fit ( Akaike information criterion , AIC , with 0 = 445 . 9 , AIC without 0 = 452 . 4 ) . The cause of this negative impact on transmission is unknown and requires further investigation though it could result from a non-specific immune response initiated by an uninfected mosquito bite[26] . As expected the PEV significantly reduces the probability of infection , reducing the per bite transmission probability by 29 . 1% . The most parsimonious model indicates that the PEV is more effective against lower sporozoite challenges . It reduced the probability of infection by 70 . 5% for bites with <101 residual-sporozoites and 16 . 3% in mice bitten by mosquitoes with >100 sporozoites ( AIC constant efficacy model = 623 . 5 , variable efficacy model = 622 . 5 , Fig 1E ) . The number of residual-sporozoites significantly influences the time to patency in both naïve volunteers and those given different PEVs . Data from a total of 267 CHMI volunteers who developed malaria were analysed , 192 of these had received a PEV candidate ( Fig 2A and 2B ) . The best fit survival analysis model was unable to differentiate between mosquitoes with no detectable salivary gland sporozoites and those with 1–10 post-feeding perhaps because the numbers of bites with 1–10 sporozoites was relatively low . A full description of the time invariant components of the survival analysis are given in S4 Table . Importantly , the model predicts that naïve volunteers who were infected by 5 mosquitoes with >1000 sporozoites would have detectable parasitemia on average >2 days earlier than those infected by 5 mosquitoes with 11–101 sporozoites . There is a significant association between observed time to patency and survival analysis model predictions based on the number of residual-sporozoites ( Fig 2C , p<0 . 0001 ) . Time to patency analysis was repeated with those P . berghei challenges of mice which developed infection ( Fig 2D–2F ) . Data from 429 mouse experimental challenges that generated blood-stage infection were used in the survival analysis , 31 of which had received a PEV . These results are consistent with the human data , with naïve mice and those given PEV reaching patent parasitemia earlier if they were bitten by mosquitoes with higher number of residual-sporozoites . Again there is a significant association between observed time to patency in mice and model predictions ( Fig 2F , p<0 . 0001 ) . In both the human and rodent system the number of bites and the associated residual-sporozoite loads explains some of the variability in time to patency but not all . This is why the variation seen in observed time to patency is substantially greater than that predicted by the model . The causes of this additional variation are unknown though in the human dataset it could be due in part to the different efficacies of the PEV candidates administered . There is greater variability in the predicted time to patency in the rodent system as the number of times the mice were bitten had a bigger range ( between 1 to 10 bites ) than the human dataset where all volunteers were bitten by 5 mosquitoes with >10 residual-sporozoites . Human volunteers bitten by ‘additional’ mosquitoes that had ≤10 residual-sporozoites ( in addition to the other 5 bites of >10 residual-sporozoites ) had a shorter time to patency ( Fig 3 ) . These lightly infected mosquitoes are currently not considered infectious in CHMI trials though they appear to have a significant impact on transmission ( S4 Table ) . Similar results were seen in the mouse data , with each additional bite with ≤10 residual-sporozoites reduced the time to patency in infected mice . In time-to-patency data of both humans and mice the best fit model did not differentiate between bites from mosquitoes with no detectable and 1–10 residual-sporozoites . The occurrence of mosquito bites with ≤10 sporozoites were relatively common in CHMI trials . On average each infected volunteer received 1 . 6 bites from mosquitoes with ≤10 residual-sporozoites , with 2/3 of the volunteers receiving one or more ( Fig 3 ) . The impact of these additional bites depends on the other bites that the volunteer received . For example , if a volunteer was bitten by 5 mosquitoes with 101–1000 salivary gland sporozoites then the additional bites reduced the time-to-patency by on average 1 . 5 days ( Fig 3 ) .
Together the human and rodent data provide strong evidence that mosquitoes with more salivary gland sporozoites post-feeding are more infectious . Though this cannot currently be tested directly in vivo , the most plausible explanation is that that mosquitoes with a higher number of residual-sporozoites injected more sporozoites into the skin during blood-feeding which increased the probability and the speed of the ensuing blood-stage infection . Whilst the probability of infection analysis could only be tested in humans who had been given a PEV , the consistent association between residual-sporozoites and the time to patency in naïve and vaccinated volunteers and the mouse experiments suggest that parasite intensity , and not just the number of infectious bites , is key to understanding the underlying biology of malaria transmission . The mouse experiments indicate a continuum of infection: with every sporozoite injected into the vertebrate increasing transmission . The relationship between the number of parasites in the salivary gland and the size of the inoculum is poorly understood . Early laboratory work proffer apparently contradictory results [9 , 13 , 16 , 27] . The thin diameter of the mosquito proximal duct means that only one or two sporozoites can pass down it at the same time [13] which supported the hypothesis that inoculum size , if confined to the void volume of the salivary duct , would be independent of sporozoite gland burden . Nevertheless more recent experiments which better resemble natural transmission show a clear positive association between residual-sporozoite load and the number of sporozoites injected into mice [15] . On blood feeding the majority of sporozoites appear to be injected during the first few seconds [13 , 27] suggesting their presence in the duct prior to the bite being taken . The number in the ducts will be dependent upon the density of sporozoites in the glands , the time over which they have accrued in the duct since last feeding ( on blood or sugar ) and the available volume in the duct . There could be additional reasons other than the number of parasites injected which could explain why heavily infected mosquitoes are more infectious . These could include increased ( per sporozoite ) infectivity [28] or more frequent mosquito probing [29] and further work will be required to determine the causes of the observed infectivity . Previous studies that failed to find a relationship between residual-sporozoite number and infection used relatively insensitive statistical methods [5 , 11] which may have caused them to be underpowered to detect a difference due to the wide variability in the size of the inoculum from similarly infected mosquitoes . The linear increase in the probability of infection on the logarithmic scale ( Fig 1E ) shows that for every additional residual-sporozoite the increase in transmission declines ( i . e . mosquito-mouse transmission is a negative density-dependent process ) . This has been observed by others [13] and could be modulated by the limited diameter of the duct restricting passage of sporozoites in highly infected mosquitoes . The linear increase in the probability of infection on the logarithmic scale ( Fig 1E ) shows that for every additional residual-sporozoite the increase in transmission declines ( i . e . mosquito-mouse transmission is a negative density-dependent process ) . This has been observed by others [13] and could be due to the limited diameter of the duct restricting passage of sporozoites in highly infected mosquitoes . This analysis allows vaccine efficacy estimates to be standardized from mosquito delivered CHMI trials . It is very technically difficult to homogenize the number of salivary gland sporozoites within a mosquito before blood-feeding , making it hard to fully control the infection challenge from CHMI mosquitoes . Including sporozoite intensity information in the analysis of CHMI studies could help reduce between volunteer variability ( lowering sample sizes ) [21] and improving the characterisation of immune responses [30] . The number of volunteers used in CHMI trials is understandably small . This study shows that failing to account for the ( random ) variability in parasite challenge will make PEV candidates harder to compare , both within and between studies . The results from the human and murine systems are consistent . In the rodent model a naïve mouse would require 2 bites from mosquitoes with >1000 residual-sporozoites or 4 bites from mosquitoes with 11–100 residual-sporozoites to ensure a 95% probability of infection . This is in line with the human data where all naïve volunteers became infected having received 5 bites from mosquitoes with >10 sporozoites . Naïve humans could not be included in the analysis as the parasite challenge was so high that all became infected ( so there was no variability in that dependent variable ) . Nevertheless , the consistent pattern seen in mice with and without PEV suggests that residual-sporozoite number would have been associated with the probability of infection in naïve volunteers had parasite challenge been lower . By comparison with published estimates on the infectivity of P . berghei sporozoites [31] , the observed probability of infection is high suggesting current arrangements for laboratory transmission have increased overall transmission probability . The epidemiological importance of parasite intensity will depend on the distribution of sporozoites in wild mosquitoes and whether similar trends are observed in people with a prior history of malaria infection . All the volunteers analysed here were malaria naïve and received all infections within minutes of each other . It will therefore be important to repeat the experiments on people from malaria endemic regions who may have acquired a degree of immunity to infection . It will also be necessary to confirm the dose effect in singly bitten humans as it is unlikely that many people in malaria endemic Africa will be bitten by 5 infectious mosquitoes in such a short time period . There are relatively little data on the number of sporozoites in naturally infected mosquitoes [32–35] though laboratory reared and infected A . stephensi have been suggested to inject a similar number of sporozoites to natural infections [9] . In a very low transmission site on the Thai–Myanmar border a recent study reported a geometric mean of 57 sporozoites per mosquito ( range 9–11 , 428 ) [33] . This compares to earlier studies in Africa and PNG where endemicity was higher and geometric means were >4000 ( range 150–10 , 000 ) [32 , 34] . In these datasets the distribution of sporozoites between mosquitoes appears highly over dispersed ( aggregated ) , with a large number of lightly infected mosquitoes and few with very heavy infections . Notwithstanding the above , more than 45% of infectious mosquitoes caught in a low transmission site in Kenya had >1000 salivary gland sporozoites [35] , the same number of residual-sporozoites that was associated with successful infection in the anti-CSP CHMI . In this study mice were given anti-CSP antibody by passive transfer ( i . v ) , whilst humans were vaccinated with the anti-CSP RTS , S/AS01B . RTS , S is thought to induce T cell responses as well as antibody . Though we see a similar relationship between both anti-CSP interventions disentangling the impact of the different immune responses is beyond the scope of this study . In both the human and mouse data analysed here the anti-CSP interventions were more effective against bites from mosquitoes with a lower residual-sporozoite number . This may explain why recent Phase II and III trials of RTS , S/AS01 ( which targets CSP ) were partially effective as the vaccine may only be providing sterilizing immunity against lightly infected mosquitoes [36] . Further work is needed to establish whether the anti-CSP antibodies/immunity provided sterilizing immunity against all bites from mosquitoes with a low number of residual-sporozoites ( a “threshold” type of protection ) or whether it prevents infection from a certain percentage of inoculated sporozoites ( a “leaky” type of vaccine ( see S1 Fig for a graphical explanation of the two hypotheses ) . The RTS , S/AS01 Phase III trial was not powered to detect a difference between sites but evidence suggests that it had a higher efficacy against clinical malaria in low transmission settings [37] . Though this is likely to be caused in part by different levels of immunity in the human population variable parasite challenge could also have contributed . It is unknown whether mosquito parasite challenge will diminish as the disease is successfully controlled ( and therefore vaccine efficacy might be expected to rise ) or whether the sporozoite dose may remain broadly the same [38] . The work shows that lightly infected mosquitoes ( with ≤10 residual-sporozoites ) contribute to transmission though they have a lower chance of causing onwards infection than more heavily infected mosquitoes . In areas approaching local elimination a large proportion of mosquitoes are thought to be infected by people with low density infections [39] . The contribution of different infection classes to this reservoir of infection has been assessed by their ability to infect mosquitoes [39] ( human-mosquito-transmission ) but has failed to consider how onwardly-infectious these mosquitoes might be ( mosquito-to-human transmission ) . Work presented here strongly suggests that not all mosquitoes are equally infectious and therefore low density human infections might have a smaller epidemiological impact if they result in less infectious lightly infected mosquitoes [33] . Further work is needed to investigate this hypothesis as it will have consequences for the required sensitivity of malaria diagnostics and the effectiveness of different drug-based control strategies [40] . Malaria transmission intensity in the field is currently measured using estimates of the number of infectious mosquito bites per person per year ( the entomological inoculation rate , EIR ) [1] . This metric does not explicitly distinguish between light and heavy infections and therefore fails to describe accurately the onwards infectivity of mosquitoes . This will reduce its sensitivity and potentially generate additional bias in different settings [1] . The proportion of infectious mosquitoes in EIR estimates is typically measured by ELISA which is relatively poor at detecting infection in mosquitoes with fewer than 150 sporozoites [41] . The work presented here shows that these infections contribute to transmission thus causing the human force of infection to be underestimated ( particularly in areas approaching local elimination [33] ) . The EIR is central to our understanding of malaria and how control interventions are compared . The influence of the number of parasites on mosquito-to-human transmission could therefore have wide reaching implications across malariology .
All human data analysed here has previously been published elsewhere . See individual publications for specific ethical statements . All animal procedures were performed in accordance with the terms of the UK Animals ( Scientific Procedures ) Act ( PPL 70/7877 ) and were approved by the Imperial College Animal Welfare and Ethical Review Body ( AWERB ) LASA guidelines were adhered to at all points . The Office of Laboratory Animal Welfare Assurance for Imperial College covers all Public Health Service supported activities involving live vertebrates in the US ( no . A5634-01 ) . Recovery anaesthesia and terminal anaesthesia was performed during the course of this study as per PPL 70/7877 . Anaesthesia used was Ketamine/Rompun , and Schedule 1 was performed by overdose , exsanguination or cervical dislocation as part of Home Office approved regulated procedures . ” A full outline of the volunteer selection , procedure and ethical considerations of the humans challenge studies is given by Sheehy et al . [42] whilst the mouse data was generated using methods outlined in Blagborough et al . [24] . Briefly laboratory reared Anopheles stephensi mosquitoes were fed on Plasmodium infected blood and maintained for ~21 days to enable them to develop salivary gland sporozoites . P . falciparum NF54/3D7 strain was used to infect humans , P . berghei clone 2 . 34 was used in the mouse studies . Mosquitoes were considered to have delivered sporozoites only if they ingested red blood cells in the midgut . Fed mosquitoes were immediately dissected and the number of sporozoites remaining in the salivary glands categorized on a logarithmic scale: 0 ( no sporozoites ) , 1 ( 1–10 ) , 2 ( 11–100 ) , 3 ( 101–1000 ) , 4 ( >1000 ) [9] . In the human trials the feeding procedure was repeated until each volunteer had received 5 bites with >10 residual-sporozoites ( in studies carried out before 2004 this value was of score 3 or above , S1 Table ) . Mice were bitten 1 , 2 , 3 , 4 , 5 or 10 times irrespective of the residual-sporozoite score . All bites were received in a short period of time so it is assumed that all mosquitoes contribute equally to the probability of infection and the time to patency ( i . e . bite order is not important ) . Historical CHMI challenge data were collated by returning to the original paper records of each volunteer . Infections where information on the full parasite challenge was unavailable were excluded from the analysis . The CHMI trials were originally carried out to test different potential malaria PEV candidates ( S1 Table ) . All human control volunteers developed patent infection so could not be used in the probability of infection analysis . Instead data from a single study where 33 volunteers received a three dose regime of RTS , S/AS01B ( 18 ) that provided sterilizing immunity to 14 patients ( who were subsequently re-challenged 6 months later ) . There was no significant difference in the impact of the two intervention arms or between initial challenge and re-challenge infections so all data were pooled making a total of 47 experimental infections . In the mouse study data was collated from population studies to evaluate the impact of compounds inhibiting malarial transmission from mouse-to-mosquitoes which are not present during mosquito-to-mouse transmission ( atovaquone [24] , primaquine [43] , NITD609 [43] , artemether + lumefantrine [43] and OZ439 [43] ) . Other populations of mice were administered the monoclonal antibody 3D11 which targets the same homologous circumsporozoite protein ( PbCSP ) as the human vaccine RTS , S ( PfCSP ) which is the first malaria vaccine to be licensed [44] . A number of the human vaccines presented in S1 Table also target PfCSP . Asexual parasite density was regularly measured to determine infection status and time to patency ( every day from day 4 to 10 for the mouse model and twice daily from day 6 to 15 and daily up to day 21 in the human volunteers ) . Volunteers were given a curative anti-malarial treatment on the detection of blood-stage parasites by microscopy . Up to this time-point samples were analyzed by using quantitative real-time PCR ( qPCR , analyzing 150μl blood ) [42] whilst parasiteamia in mouse samples were quantified by microscopy ( by reading 4 microscopy fields , each with approximately 300 red blood cells ) [24] . The mean residual-sporozoite score ( the average score across all mosquito bites ) and the total residual sporozoite score ( the sum of the scores across all bites ) were calculated for all hosts . To disentangle the effect of the number of bites and the residual-sporozoite scores within those bites a linear-mixed effect model is used to test whether hosts that got infected were bitten by mosquitoes with a higher residual-sporozoite score . The mean and total residual-sporozoite score is taken as the dependent variable whilst a binary value denoting whether or not the host developed infection is included as a fixed effect . The number of bites received is incorporated as a random effect allowing the difference in mean and total residual-sporozoite scores to vary between biting groups . Models with and without the fixed effect were compared using a likelihood ratio test to see whether there was a significantly different residual-sporozoite scores between infected and uninfected hosts . It is likely that the relationship between the probability of infection and residual-sporozoite score is non-linear so a more advanced binomial statistical model is tested [25] . It is assumed that all bites are independent , i . e . the transmission probability of a bite from a mosquito with a certain residual-sporozoite score is the same irrespective of the number of other bites received . The overall probability of infection is described by a binomial distribution allowing data from multiply bitten hosts to be accurately included ( i . e . the probability of infection from two bites each of which have an infection probability of 50% is 75% , not 100% ) . Sporozoite score is categorised into bins on the logarithmic scale so the probability of infection is estimated for each group independently ( i . e . not using a continuous function ) as the distribution of parasite numbers within these bins is unknown . Let ϕ denote the probability of a susceptible host becoming infected and bj be the per bite transmission probability ( the proportion of bites by an infected mosquito with a residual-sporozoite score j which go onto develop patent infection ) . The probability of transmission can then be estimated using a binomial distribution , ϕ=1−∏g=1 . . q ( 1−[bS ( g ) ( 1−ν ) +bS ( g ) ν ( 1−εS ( g ) ) ] ) , ( 1 ) where q is the number of bites received and S ( g ) signifies the residual-sporozoite score in the gth bite ( i . e . S ( g ) = 0 , 1 , 2 , 3 or 4 ) . Parameter v is a binary variable denoting whether the vertebrate host received a PEV or not whilst εj indicates the efficacy of that PEV against a bite with sporozoite score j ( the proportional reduction in the probability of infection of a single bite caused by the PEV ) . Let i denote a group of hosts which receive the same parasite challenge ( i . e . the same number of bites with the same residual-sporozoite scores and either a PEV vaccine or not ) , ni the number of hosts which receive exposure i and xi the number of these that become infected . Using the notation x¯ to denote the corresponding list of observations , under a binomial model the likelihood is proportional to , L ( x¯ , n¯|b0 , b1 , b2 , b3 , b4 , ν ) ∝∏i=1 . . zϕxi ( 1−ϕ ) ni−xi , ( 2 ) where z is the number of combinations of different parasite exposures . This likelihood can be maximised to obtain estimates of the transmission probabilities of mosquitoes with different residual-sporozoite scores from the infection data . A suite of nested models were fit to the human and mouse data and the most parsimonious were selected using the Akaike information criterion ( AIC , lowest value giving the most parsimonious model ) . These nested models ranged from the full model outlined above ( model 6 in S3 Table where mosquitoes with each of the different residual-sporozoite scores has a different contribution to transmission ) down to models where all sporozoites positive scores were pooled together ( i . e . transmission was independent of sporozoite load but dependent on the number of mosquito bites , model 2 ) or infection is independent of the number of bites ( model 1 ) . Residual-sporozoite scores of zero were also included to determine whether they had an impact on transmission . To test whether a bite with a certain score ( or a range of scores ) had a significant impact on the transmission metrics each model was systematically reduced to determine whether setting the contribution of a bite to zero improved the parsimony of the model . The full range of different models investigated is outlined in S3 Table . The impact of the PEV was subsequently assessed by initially assuming that vaccination had a different impact on the probability of infection of each sporozoite score before the model was systematically collapsed , grouping the impact of the vaccine on adjacent sporozoite scores together , until the most parsimonious combination was found . Multivariate 95% confidence intervals for each of the parameters were generated from the likelihood profile using a likelihood ratio test . Residual-sporozoite scores ( or groups of ) whose 95% confidence intervals spanned 0 were further collapsed to determine whether setting them to zero improved the parsimony of the model . To investigate the full impact of ( apparently ) uninfected bites the datasets were reduced , removing all hosts that only received bites with a residual-sporozoite score of zero . The best fit model was used to predict the likelihood of infection for each experimental infection according to the number of bites they received , the number of residual-sporozoites within those bites and whether or not they received a vaccine candidate . The association between these predicted values and the observed infectious status was evaluated using simple logistic regression ( with null and full models compared using a likelihood ratio test ) . The pre-patent period is typically defined as the first time-point at which infected red blood cells are detected . In this study it is estimated in humans using qPCR [45 , 46] and microscopy in mice [24] . The appearance of the first detectable parasite is relatively variable as some PCR runs generate false positive ( low ) values [47] and the number of parasites in the sample will vary by chance at low densities [48] . This is shown in the dataset by high heteroscedacity when linear random effects models are fit to the individual parasite growth rate curves [47] . Instead studies measure a time to a low parasite density threshold as this reduces sampling variability and the influence of false positive results . Here a density of 1000 parasites per μl for humans and 2% infected red blood cells for mice are used though the same qualitative results are seen with different thresholds . Therefore throughout the manuscript the time to patency specifically refers to the time to the threshold parasite density . The influence of residual-sporozoite score on the time to patency is analysed using a semi-parametric additive hazard model [49] implemented in the “timereg” R package [50] . This type of survival analysis enables the covariates to act additively on the baseline hazard instead of multiplicatively ( as done in standard proportional hazard model ) allowing the number of bites to act on an absolute scale rather than a relative one [50] . As there is a minimum time between parasite challenge and patency the baseline hazard is allowed to vary over time whilst the covariates ( the number of bites with a score of 0 , 1 , 2 , 3 or 4 and whether the host was given a vaccine ) are assumed to be time invariant . In the human dataset a variety of different vaccine candidates were tested . For simplicity it is assumed that all vaccines have an equal impact on the time to patency as the number of volunteers for each vaccine type is very small ( typically 3–8 volunteers per candidate , S1 Table ) . Parasite challenges which did not result in infection were removed from the analysis to ensure that the influence of sporozoite number on the time to patency is independent of whether the host became infected . Infected hosts who had not reached the parasite density threshold ( 42 out of 429 mice ) were classified as being censored . The mean time to patency was estimated by integrating over the survivorship function and the association between model derived prediction and the observed time to patency was tested using simple linear regression . Likelihood estimates are not available for the additive hazard model so the significance of the different covariates ( from zero ) is estimated by resampling [50] . Without more formal model selection procedure the full model is collapsed ( using the model combinations presented in S3 Table ) until all covariates are significantly different from zero ( p values of <0 . 05 were significant ) . For example , if the model collapses to model 2 then this suggests that the time to patency is independent of residual-sporozoite score but is significantly associated with the number of mosquito bites . For some mice only a binary outcome for infection was available instead of a parasite density estimate . To enable the whole dataset to be utilized a dummy variable was added denoting whether parasite density estimates were measured or not , providing an estimate of the time between the first detection and 2% red blood cell infection . Point estimates of the 95% confidence intervals were generated by resampling [50] . Raw data used in the mouse work is given in S1 Dataset . The size of the Liver-to-Blood parasite Inoculum ( LBI ) can be estimated using linear regression from the parasite growth rate in the vertebrate host [47] . LBI estimates has been used to disentangle the impact of vaccine in CHMI trials and may be associated with residual-sporozoites number . Unfortunately its estimation is beyond the scope of this work as it requires accurate measures of asexual parasite density over multiple timepoints . This information was also unavailable for the mouse dataset as parasitemia was estimated by microscopy ( which has high measurement error , particularly at low densities ) and because relatively few data points are available ( on average <3 per host above the density threshold ) . In the human data information from different vaccines is pooled as each study is relatively small . This would make LBI estimates highly uncertain due to differences in the parasite multiplication and variability in PCR heteroscedacity between studies [47] .
|
Malaria is transmitted to humans by the bite of an infectious mosquito though it is unclear whether a mosquito with a high number of parasites is more infectious than one with only a few . Here we show that the greater the number of parasites within the salivary gland of the mosquito following blood-feeding the more likely it is to have transmitted the disease . A clear dose-response is seen with highly infected mosquitoes being more likely to have caused infection ( and to have done so quicker ) than lightly infected mosquitoes . This suggesting that mosquito-based methods for measuring transmission in the field need to be refined as they currently only consider whether a mosquito is infected or not ( and not how heavily infected the mosquito is ) . Novel transmission reducing drugs and vaccines are tested by experimentally infecting people using infectious mosquitoes . This work indicates that it is important to further standardise infectious dose in malaria experimental infections to enable the efficacy of new interventions to be accurately compared . The work also provides direct evidence to suggest that the world’s first licenced malaria vaccine may be partially effective because it fails to provide protection against highly infected mosquitoes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"invertebrates",
"parasite",
"groups",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"immunology",
"tropical",
"diseases",
"parasitic",
"diseases",
"animals",
"parasitic",
"protozoans",
"parasitology",
"vaccines",
"preventive",
"medicine",
"apicomplexa",
"protozoans",
"vaccination",
"and",
"immunization",
"insect",
"vectors",
"digestive",
"system",
"public",
"and",
"occupational",
"health",
"malarial",
"parasites",
"epidemiology",
"exocrine",
"glands",
"pathogenesis",
"disease",
"vectors",
"insects",
"arthropoda",
"mosquitoes",
"anatomy",
"host-pathogen",
"interactions",
"salivary",
"glands",
"biology",
"and",
"life",
"sciences",
"malaria",
"sporozoites",
"organisms"
] |
2017
|
Probability of Transmission of Malaria from Mosquito to Human Is Regulated by Mosquito Parasite Density in Naïve and Vaccinated Hosts
|
Place cells in the hippocampus of higher mammals are critical for spatial navigation . Recent modeling clarifies how this may be achieved by how grid cells in the medial entorhinal cortex ( MEC ) input to place cells . Grid cells exhibit hexagonal grid firing patterns across space in multiple spatial scales along the MEC dorsoventral axis . Signals from grid cells of multiple scales combine adaptively to activate place cells that represent much larger spaces than grid cells . But how do grid cells learn to fire at multiple positions that form a hexagonal grid , and with spatial scales that increase along the dorsoventral axis ? In vitro recordings of medial entorhinal layer II stellate cells have revealed subthreshold membrane potential oscillations ( MPOs ) whose temporal periods , and time constants of excitatory postsynaptic potentials ( EPSPs ) , both increase along this axis . Slower ( faster ) subthreshold MPOs and slower ( faster ) EPSPs correlate with larger ( smaller ) grid spacings and field widths . A self-organizing map neural model explains how the anatomical gradient of grid spatial scales can be learned by cells that respond more slowly along the gradient to their inputs from stripe cells of multiple scales , which perform linear velocity path integration . The model cells also exhibit MPO frequencies that covary with their response rates . The gradient in intrinsic rhythmicity is thus not compelling evidence for oscillatory interference as a mechanism of grid cell firing . A response rate gradient combined with input stripe cells that have normalized receptive fields can reproduce all known spatial and temporal properties of grid cells along the MEC dorsoventral axis . This spatial gradient mechanism is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections . Spatial and temporal representations may hereby arise from homologous mechanisms , thereby embodying a mechanistic “neural relativity” that may clarify how episodic memories are learned .
Navigating the world requires the brain to learn and maintain memory of spatial positions within various environments . Place cells in the hippocampal areas CA1 and CA3 demonstrate a neural code for position in large spaces that higher mammals inhabit [1] and thereby play a critical role in spatial navigation . CA3 receives major projections from layer II of the medial entorhinal cortex ( MEC ) [2] , where grid cells are predominant [3] , [4] . Unlike place cells , individual grid cells fire at multiple positions . When an animal navigates in an open field , these positions form a regular hexagonal grid uniformly covering the entire navigable environment . These cells are found throughout the length of MEC with the spatial period of their firing fields increasing from the dorsomedial to the ventrolateral end [4]–[6] . In particular , Brun and colleagues [6] recorded from a total of 143 grid cells within layers ( II , III , V/VI ) of MEC located between 1% to 75% the distance along the dorsoventral axis , while rats ran back and forth on a 18 m long linear track . The recorded cells were divided into three groups based on their anatomical location with respect to the postrhinal border of MEC; namely , dorsal , intermediate and ventral . The one-dimensional periodic spatial responses of these cells in the two running directions were processed separately to estimate characteristic properties of grid cells , such as grid spacing , grid field width , peak firing rate , and mean firing rate . The main finding was that both grid spacing and field width increased from dorsal group to ventral group , for either running direction . Interestingly , distributions of these variables increased not only in mean but also in variability with distance along the dorsoventral axis . However , the peak firing rate decreased from dorsal group to ventral group , and there was a negative trend for mean firing rate . The presence of multiple spatial scales on the dorsoventral axis of MEC has important implications for the development of the hippocampal place cells [7]–[9] . Several self-organizing map ( SOM ) models have been proposed that show how signals from grid cells of multiple spatial scales can together induce the learning of hippocampal place cells that are capable of representing position in the larger spaces that higher mammals navigate ( e . g . , [10] , [11] ) . In particular , Gorchetchnikov and Grossberg [11] showed this expansion of the scale of the spatial representation from grid cells to place cells arises due to the fact that the SOM is sensitive to the most frequent coactivations of grid cells across multiple scales , which on a linear track occur with a spatial period equal to the least common multiple of the inducing grid spacings . But how do grid cells learn to fire at multiple positions that form a hexagonal grid in two-dimensional open environments ? And how does the spatial scale of grid cells increase along the dorsoventral axis of MEC , enabling their target place cells to represent ever-larger spaces ? Recent data and modeling provide some clues , forming the basis for the current work . Excitatory projections to the hippocampal formation from layer II of MEC are primarily from stellate cells [12] . That makes them the most likely candidates for grid cells . In vitro whole-cell patch clamp recordings [13] , [14] have shown that these stellate cells exhibit subthreshold membrane potential oscillations ( MPOs ) in response to steady current injection . The temporal period of these oscillations increases from the dorsomedial to the ventrolateral end of MEC , thereby correlating with the observed gradient in spatial period and size of the firing fields of grid cells . In addition , voltage-clamp recordings in these cells demonstrated that the time constants of the hyperpolarization-activated cation current decreases along the dorsoventral axis of MEC [15] , [16] . Knockout of the HCN1 subunit in the hyperpolarization-activated cyclic nucleotide-gated ( HCN ) channels , which control kinetics [17] , flattens the dorsoventral gradient of MPO frequency [18] . In addition , the rise and fall times of excitatory postsynaptic potentials ( EPSPs ) in these cells progressively become longer along the dorsoventral axis [19] . The variation in EPSP kinetics was attributed to differences in the membrane conductance mediated by HCN and leak potassium channels . Combined , all these results suggest a correlation between the rate of intrinsic dynamics in MEC layer II stellate cells and the spatial scale of grid cells . This article develops a SOM neural model , called Spectral Spacing for reasons summarized below , to explain the above data . This model shows how a gradient of cell response rates along the dorsoventral axis of MEC can control the development of grid cells whose hexagonal grid firing fields exhibit a gradient of spatial scales and whose MPOs exhibit a gradient of frequencies . These results combine several conceptual and technical advances . First , these results are part of an emerging general entorhinal-hippocampal model architecture ( see also [20] ) , which shows that , despite their different receptive field structures , both grid cells and place cells may be learned using the same SOM laws . Thus , both grid cell periodic hexagonal firing fields and place cell unimodal firing fields , despite their different appearances , may arise from the same neural mechanisms due to the different inputs that they receive at their respective stages in the entorhinal-hippocampal hierarchy . Second , these SOM laws have been proposed to control development and learning in many other parts of the brain , notably the visual cortex . Thus , both grid and place cells may develop using general SOM principles of brain map organization . Third , the linear velocity and angular velocity path integration inputs that drive model learning are derived from realistic trajectories of rats in spatial learning and memory experiments . Fourth , these linear velocity and angular velocity estimates can both be transformed into position codes by ring attractors . Fifth , the rate gradient mechanism for spatial learning in the MEC pathway and its hippocampal projections is homologous to a rate gradient mechanism that has been used to model temporal learning in the lateral entorhinal cortex ( LEC ) pathway and its hippocampal projections . Spatial and temporal representations in the medial and lateral processing streams may hereby arise from homologous mechanisms , thereby embodying a mechanistic “neural relativity” in the entorhinal-hippocampal system . This homology may clarify why spatial and temporal representations both occur in hippocampus , and provides new clues about how episodic memories may be learned . In summary , this model system exhibits parsimony and unity , both in its use of similar ring attractor mechanisms to code the linear and angular velocity path integration inputs that drive learning , and in its use of a rate gradient mechanism that can support the learning of both spatial and temporal codes . Even more striking is the fact that both grid cell and place cell receptive fields emerge by detecting , learning , and remembering the most frequent and energetic coactivations of their inputs . This co-occurrence property is different from the property of oscillatory interference that some other models have proposed ( e . g . , [21] ) . Oscillatory interference models have , to the present , been used to explain properties of grid cells , without showing how they can be learned , or how such a learning process can generate the different grid spatial scales along the dorsoventral extent of MEC . Moreover , several articles ( e . g . , [13] , [22] ) have interpreted the gradient of subthreshold MPO frequencies in MEC layer II stellate cells as strong evidence for an oscillatory interference-based firing of grid cells . In sharp contrast , the grid cells in the Spectral Spacing model exhibit the gradient of MPO frequencies as an epiphenomenon of SOM learning mechanisms , thereby showing that this gradient can occur in the absence of an oscillatory interference mechanism . In order to better understand what aspects of the Spectral Spacing model are needed to explain how spatial and temporal properties of grid cell firing change along the dorsoventral extent of MEC , several model and input variations were simulated ( see Simulation Settings ) . These simulations demonstrate that , at least among these variations , only a response rate gradient , combined with input cells that have normalized receptive fields , can explain all the data mentioned above . The input cells to the grid cells are called stripe cells [23] . They are called stripe cells because each cell fires with a preferred movement direction and spatial period , thereby giving rise to stripes of activation ( Figure 1A ) . Suggestive data about these cells in deeper layers of MEC were reported in [4] . In addition , Krupic , Burgess , and O'Keefe [24] have reported data showing stripe-like spatial firing profiles for a group of cells in the dorsal parasubiculum , which projects to layer II of MEC [25] , [26] . In the GRIDSmap model [23] and the Spectral Spacing model simulations , the stripe cells process linear velocity inputs that are modulated by head direction as the model animal navigates a realistic trajectory that was reported in the data of [4]; see Figure 1B . These signals are assumed to be computed in vivo from vestibular estimates of linear and angular acceleration , which are generated in the otolithic organs and semicircular canals , respectively , of the inner ears [27] . In addition to its preferred direction and spatial scale , each stripe cell is assumed to have a preferred spatial phase ( Figure 1C ) . A set of stripe cells for a given direction and spacing , which differ only in spatial phase , can be represented by cells constituting a one-dimensional ring attractor ( Figure 1D ) . In such a ring attractor , linear velocity projected onto the preferred direction moves an activity bump around the ring of stripe cells ( see Figure 1D and Equations 1 . 11 . 4 ) . One revolution of the activity bump corresponds to traversal of a length equal to the associated stripe spacing along the direction ( Figure 1A ) . The spatial firing of a stripe cell as the animal moves at a constant speed on a straight path is assumed to have a Gaussian profile , for simplicity , with different stripe cells in the ring having different spatial offsets for their peak firing . The movement of the activity bump depends on the component of linear velocity along the associated direction . As a result , the spatial firing pattern of a given stripe cell in a two-dimensional environment resembles Gaussian-modulated oriented stripes with a fixed spacing that uniformly spread across the entire environment ( Figure 1A ) . Because of the periodic boundary condition , each stripe cell operates over a limited spatial scale equivalent to the spacing between its adjacent stripe fields . As noted above , each stripe cell ring attractor includes cells that are sensitive to a given spatial scale , both spatial period and spatial phase , and movement direction . The set of all stripe cells , across all spatial periods , spatial phases , and directions , taken together , implicitly represent the spatial position of the animal . In particular , stripe cells of different spacings can represent the animal's position at multiple spatial resolutions . The firing of a stripe cell with a prescribed directional preference is modulated by a head direction signal via a cosine law that projects the current direction of the navigating animal at each time onto the stripe cell's preferred direction ( see Equation 1 . 1 ) . Head direction estimates have been modeled by ring attractors that are sensitive to angular velocity signals [28]–[35] . Both linear velocity and angular velocity signals in the Spectral Spacing model are thus assumed to be transformed into movements of activity bumps in ring attractors in order to perform linear and angular path integration , respectively ( cf . [23] , [36] ) . Adult-like head direction cells are already present in the parahippocampus by P16 when rat pups begin to explore their environments for the first time [37] , [38] . If both stripe cells and head direction cells are indeed computed by ring attractors , then this provides a plausible explanation of how stripe cells could be ready at this developmental stage to support the learning of grid cells . Stripe cells with multiple directional preferences and spatial phases for a given spatial period initially project with random adaptive weights to cells in the category learning layer of a SOM . SOM cells obey membrane , or shunting , equations and interact in a recurrent on-center off-surround network . Self-excitatory feedback enables the resolution of competition among the map cells in order to choose one or a few winners . The self-excitatory feedback does this by contrast-enhancing the activity of winning category cells [39] , but it can also cause perseveration of activity in the winning cells , even after their bottom-up inputs shut off . A perseverating cell could inhibit other map cells , via the recurrent off-surround , that would be needed to represent different combinations of inputs that arise as an animal continues to navigate . Activity-dependent habituative gating of the positive feedback signals causes a collapse of such persistent self-activation , and thereby allows different map cells to become active and learn at different times as the bottom-up stripe cell input pattern changes with the animal's navigational movements in space . In other words , habituative gating helps to “whiten” the learned spatial fields of the map cells . Habituative gating has been used in SOM models of other parts of the brain since being introduced in [40] . It has helped , for example , to simulate complex properties of map development in visual cortical area V1 ( e . g . , [41]–[43] ) . Signals from winning map cells trigger learning in the abutting synapses of pathways from the stripe cells . The adaptive weights in these synapses track a normalized time-average of the signals in the pathways from the stripe cells while their target map cells are active . After learning , the bottom-up signals can efficiently activate map cells that exhibit hexagonal grid fields . In addition to these basic SOM ingredients , the current model investigates how a gradient of response rates in the map cells can lead to learning of a gradient of model grid cell spatial scales whose properties match neurophysiological data from multiple experiments about grid cells along the dorsoventral axis of the MEC . See the subsection below on the Scale selection problem . The learning law is called a competitive instar learning law because it selectively strengthens the adaptive weights from coactive stripe cells to active map cells while it competitively self-normalizes the total adaptive weight abutting each map cell [40] , [41] , [44] , [45] . This learning law enables each grid cell to arise as a learned spatial category in a SOM . The competitive aspect in the learning law may be interpreted in terms of how developing axons abutting a target neuron compete for limited target-derived neurotrophic factor support in order to survive [46]–[48] , and its conservation of total synaptic weight is consistent with neurobiological data ( e . g . , [49] ) . Such a competitive instar learning law is different from a purely Hebbian learning law , which allows adaptive weights to increase but does not allow them to decrease . The instar learning law permits both weight increases ( long-term potentiation ) and weight decreases ( long-term depression ) . It hereby enables the weights to adapt to the spatial pattern of signals from the stripe cells . This pattern sensitivity enables grid cell learning to become sensitive to temporal co-occurrences of stripe cell firing . Simultaneously active stripe cells are more likely to strongly activate map cells whose bottom-up weight patterns closely match their activity pattern . Adaptation of the weights to a map cell occurs only when its activity is above a threshold ( see in Equation 1 . 6 ) . This postsynaptic activity-based gating ensures faster adaptation of incoming weights for more active map cells . During each learning episode , the weights tend towards the average normalized pattern of the inputs . Thus , the likelihood of the map cells becoming tuned to particular sets of inputs , which consistently succeed in driving them , gradually increases . Note that the bottom-up connections from stripe cells to grid cells remain adaptive for the lifetime of the animal , and not just during the development period . The GRIDSmap model [23] learned grid cells in response to a wide choice of stripe cell directional preferences . For example , hexagonal grid firing fields were learned even when stripe cell directions differed by 7 , 10 , 15 , 20 , 60 , or random numbers of degrees . GRIDSmap hereby overcame a problem of the oscillatory interference models of grid cells ( e . g . , [21] , [22] ) , which created a hexagonal grid spatial firing pattern using hard-wired inputs from exactly three band cells ( a similar concept to stripe cells , proposed earlier by [21] ) with directional preferences differing by 60° . Band cells in oscillatory interference models , unlike stripe cells , are defined by the interference of two theta frequency MPOs . SOM models are , in contrast , able to select among multiple possible combinations of stripe cell inputs to learn only a subset of combinations that are favored in terms of both frequency and total activation . Why hexagonal grid patterns are favored can be explained in terms of a simple trigonometric property of two-dimensional space to which a SOM is sensitive as an animal navigates [20] , [23] . By this property , among all possible subsets of coactive stripe cells experienced during two-dimensional navigation , the ones that are most frequent and energetic are those comprising three stripe cells whose directional preferences differ from each other by 60° [20] , [23] . These favored coactivations of stripe cells occur at positions that form a regular hexagonal grid when the model animal navigates in an open field . Until recently , SOM models of place cell learning used idealized or hand-crafted grid cells ( e . g . , [10] , [11] ) . Pilly and Grossberg [20] proposed the GridPlaceMap model to show how grid and place receptive fields , despite their different characteristics , can emerge simultaneously at different levels in a SOM hierarchy , obeying the same laws for neuronal dynamics and synaptic plasticity , by responding to the most frequent and energetic coactivations of their corresponding input neurons . This medial entorhinal-hippocampal hierarchy of stripe , grid , and place cells enables the brain to represent increasingly large spaces , and provides increasingly large spatial information per cell in predicting the spatial position of an animal . Both the GRIDSmap and the GridPlaceMap models learn hexagonal grid firing fields whose spatial scale is derived from that of the input stripe cells . In particular , stripe cells with the same period were used to learn grid fields of a given spatial scale . Stripe cells of different spatial scales were assumed to activate different locations along the dorsoventral axis in layer II of MEC , thereby giving rise to grid cells with different spatial scales . But how is the selection of just one spatial scale of stripe cells realized for each grid cell scale ? What would happen if stripe cells of multiple scales initially projected to the map layer before grid cell learning began , as in Figure 2 ? In other words , how do grid cells learn to select among , not only multiple directional preferences and spatial phases , but also among the multiple spatial scales , of their stripe cell inputs ? What properties of the dynamics of a map cell can select the spatial scale to which it will learn to respond as a grid cell ? This article shows that the rates at which the category cells and their corresponding habituative transmitters respond , called the response rate ( parameter in Equation 1 . 5 ) and habituation rate ( parameter in Equation 1 . 7 ) , respectively , can help to select the spatial scale of the stripe cells to which the category cells will learn to respond , and thus the spatial scale of the learned hexagonal grid firing fields , as well as the MPO frequencies with which these grid cells respond in vitro to a steady current input . Whereas a dorsoventral gradient in either response rate or habituation rate can explain the corresponding gradient in learned spatial scale and MPO frequency of grid cells , only a gradient in response rate was found to be consistent with data regarding the associated dorsoventral gradient in peak and mean firing rates of grid cells [6]; see the Results section for details . Different cell response rates also indirectly alter the rates at which the habituative transmitters inactivate and recover ( see Figure 3D ) . Remarkably , this response rate gradient for spatial learning is computationally homologous to a rate gradient that was proposed over 20 years ago to explain hippocampal data about temporal learning [50]–[52] . The model for temporal learning was called a Spectral Timing model because its different cell populations respond with a “spectrum” of different rates . The current model may therefore be called a Spectral Spacing model . Whereas the rate gradient for spatial learning is proposed to occur in MEC and its hippocampal projections , the rate gradient for temporal learning is proposed to occur in LEC and its hippocampal projections . This homology may provide new clues about how episodic memories are learned . See the Discussion section for further comments about this predicted form of “neural relativity” in the entorhinal-hippocampal system .
The 100 cm×100 cm environment was divided into 2 . 5 cm×2 . 5 cm bins . During each learning trial , the amount of time spent by the navigated trajectory in the various spatial bins was tracked . The output activity of each category cell in every spatial bin was accumulated as the trajectory visited that bin . The occupancy and activity maps were smoothed using a 5×5 Gaussian kernel with standard deviation equal to one . At the end of each learning trial , smoothed and unsmoothed rate maps for each category cell were obtained by dividing the cumulative activity variable by cumulative occupancy variable in each bin . Peak and mean firing rates for a category cell in a given trial were obtained by considering all spatial bins in the corresponding rate map . For each category cell , six local maxima with and closest to the central peak in the spatial autocorrelogram of its smoothed rate map were identified . Gridness score , related to rotational symmetry , was then derived using the method described in [38] , and grid spacing was defined as the median of the distances of these six local maxima from the central peak [5] . Grid orientation was defined as the smallest positive angle with the horizontal axis made by line segments connecting the central peak to each of these local maxima [5] . Grid field width was estimated by computing the width of the central peak in the spatial autocorrelogram at which the correlation equals zero or there is a local minimum , whichever is closer to the central peak [37] . Further , inter-trial stability of each category cell for a given trial was computed as the correlation coefficient between its smoothed rate maps from the current and immediately previous trials , considering only those bins with rate greater than zero in at least one of the two trials [38] . A gridness score greater than 0 was used to classify map cells as having hexagonal grid-like spatial firing fields . In vitro experiments by [13] and [14] were simulated by injecting steady current input into the category cells in the absence of bottom-up inputs and local recurrent inhibitory interactions . The membrane potential of each category cell in this paradigm was obtained using Equation 1 . 5: ( 1 . 8 ) The habituative transmitter gate was defined once again by Equation 1 . 7 . The membrane potential trace of each cell for the duration of the current injection was used to estimate the underlying frequency of the MPO as the one maximizing its power spectrum . The power spectrum was calculated using the Fast Fourier Transform ( FFT ) of the potential trace after subtracting its mean . We considered two variations of the model equations to clarify what combination of mechanisms best explains neurobiological data . Stripe cells were simulated with two , or three , spatial periods ( two: = 20 cm , = 35 cm; three: = 20 cm , = 35 cm , = 50 cm ) , four spatial phases ( = [ , , , ] for the stripe period ) , and nine direction preferences ( −80° to 80° in steps of 20° ) . Stripe cells were activated in response to linear velocity and head direction inputs derived from a realistic rat trajectory of ∼10 min in a 100 cm×100 cm environment ( data: [4] ) ; see Figure 1B . The trajectory was interpolated to increase its temporal resolution to match with the time step of numerical integration of model dynamics ( 2 ms ) , and it was assumed that the head direction was parallel to the trajectory at any moment . In each of the Cases 2–11 below , 40 learning trials were employed . For these simulations except those in Case 3 , the model animal ran along the trajectory shown in Figure 1B in each trial . For Case 3 , a novel trajectory was created for each trial by rotating the original trajectory by a random angle about the origin . In order to ensure that such derived trajectories go beyond the square environment only minimally , the original trajectory was prefixed by a short linear trajectory from the origin to the actual starting position at a running speed of 15 cm/s . The remaining minimal outer excursions were bounded by the environment's limits . For each map cell , properties of grid cell firing like grid spacing , grid field width , gridness score , grid orientation , peak rate , mean rate , and inter-trial stability were computed for each trial; see Post-processing subsection in the Methods section . The mean and standard error of mean ( SEM ) of these properties within each independent population of map cells were obtained to observe various trends along the temporal rate gradient .
Figure 3 shows the results of the single cell simulation of Case 1 when that cell is given different response rates in Equation 1 . 5 in response to a stripe cell-like input ( Figure 3A ) . Figure 3B shows the cell responses when the on-center feedback term is removed . As noted previously , self-excitatory feedback helps to contrast-enhance cell activity ( compare Figures 3B and 3F ) . However , if the habituative gate in Equation 1 . 5 is held constant at the value of one , then the outputs perseverate through time ( Figure 3C ) . When transmitter gating is restored , the gates respond more slowly along the dorsoventral axis as their controlling cell activities do ( Figure 3D ) , even if the habituation rate is the same across response rates , due to the activity-dependent term in Equation 1 . 7 . When the properties in Figures 3C and 3D are combined multiplicatively in the on-center feedback term , it has a unimodal form that grows and decays more slowly as the cell response rate is decreased along the dorsoventral axis ( Figure 3E ) . The cell output signals along the axis inherit this variable-rate unimodal form ( Figure 3F ) . In particular , cells exhibit a temporally delayed and broader response with a smaller peak activity for lower response rates . The higher the response rate , the faster is the activation of the membrane potential , allowing the cell activity to buildup to a higher level that is then gated off as quickly by the correlated change in the effective depletion rate of the transmitter . In this way , the habituative transmitter gating mechanism plays a role akin to a slow negative current that is activated by cell activity , much like the h-current [60] , and AHP currents [61] . The results of this simulation clarify how scale selection occurs ( Cases 2–11 ) . For a cell to respond with contrast-enhanced , or above-threshold , activity at any moment with the help of its self-excitatory feedback signal , its habituative transmitter needs to be at a sufficient high level . But each time the cell responds intensely , there is a collapse of the transmitter ( Figure 3D ) , which takes longer to recover for slower response rates because of the increased duration of cell activity . This implies that , the slower the response rate , the longer the minimum temporal duration before the cell can again respond with above-threshold activity . In other words , ventral MEC cells , which have slower response rates in the model , favor periodic inputs that are presented with a longer temporal interval , and dorsal MEC cells , which have faster response rates , favor those that are presented with a shorter temporal interval . This property directly explains learned scale selectivity for the case of a rat running forward at a constant speed on a linear track . Then dorsal MEC cells in the model respond better to inputs at periodic positions with relatively smaller spacings , while ventral MEC cells respond better to those with relatively larger spacings . However , the situation is more complicated when the rat navigates along the type of two-dimensional real trajectory used in our simulations , for which the running speed of the rat through time varies between 0 cm/s and 146 . 6 cm/s with a mean of 14 . 03 cm/s , a standard deviation of 9 . 8 cm/s , and a mean length of piecewise linear segments of only 0 . 9 cm . How different response rates selectively learn different spatial scales in response to such realistic trajectories is discussed in the next subsection . Figure 4 compares neurophysiological data [6] with simulation results for Case 2 regarding the distribution of grid spacing at different anatomical locations along the dorsoventral axis of MEC . MEC grid cells exhibit periodic spatial firing fields whose spacing increases from the dorsal to the ventral ends ( data: Figures 4A and 4C ) . Also , the spacing increases in variability along this axis . Brun and coworkers [6] remarked that the rat brain seems to allocate most of the grid cells to represent space at smaller scales , based on data that both intermediate and ventral MEC also have cells exhibiting periodic spatial responses with smaller spacings . Emergent properties of model simulations ( Figures 4B and 4D ) emulate these data . Figure 4B plots grid spacing ( mean +/− SEM ) of learned map cells with gridness score >0 ( see blue curve ) and of those with gridness score >0 . 3 ( see red curve ) as a function of response rate , or equivalently the distance along dorsoventral axis , in the last trial . Figure 4D shows the distribution of spacing of all map cells as a function of response rate . Learned map cells with gridness score >0 . 3 are identified by red squares , and those among the remaining with gridness score >0 are identified by blue squares , and the rest by black ones . These results indicate that , despite non-stationary variations in running speed and in heading direction along a realistic trajectory in the open field , the response rates of the map cells select the spatial scale of input stripe cells to which the learned hexagonal grid firing fields maximally respond . Faster response rates can more effectively sample smaller stripe cell spatial periods , whereas slower response rates can do the same for larger stripe cell spatial periods , for reasons that are stated more precisely in the next paragraph . In this way , faster/dorsal MEC cells learned grid fields with smaller spacings , and slower/ventral MEC cells developed preference for larger grid spacings . As noted earlier , for each input stripe scale considered separately , the most frequent and energetic activations of grid cells occur when sets of three stripe cells are coactivated whose preferred directions differ by 60° [20] . Now consider a dorsal map cell that becomes intensely active for the first time at some spatial position . During this first learning episode , the synaptic weights of its connections from stripe cells begin to get pruned to slowly match the normalized average input pattern . Given the faster dynamics of dorsal cells , this cell can again respond intensely to consistent stripe cell activations from either spatial scale at nearby positions as the animal moves around . Given the higher number of fields for a small-scale grid structure in a limited environment , and given the relatively lower peak activity of large-scale stripe cells , this dorsal cell has a higher likelihood of developing tuning to an appropriate set of stripe cells from the small scale . On the other hand , the slower dynamics of ventral cells prevents them , on average , from developing tuning to stripe cell coactivations from the small scale , because they tend to recur faster than the recovery rate of the ventral habituative transmitters . As a result , ventral cells that develop grid-like spatial selectivity gradually prefer stripe cell coactivations from the large scale . Increased variability in grid spacing for ventral cells may be understood as a manifestation of their weaker and temporally prolonged signal levels ( Figure 3F ) , which cause broader regions of space to be incorporated into their developing selectivities . These results clarify how a gradient of temporal response rate leads to selective learning of the gradient of grid spatial scale , and are thus consistent with a recent study using HCN1 knockout mice regarding how manipulation of the anatomical gradient in intrinsic properties of stellate cells affects the gradient in grid scale [62] . Figure 5 shows neurophysiological data [6] and simulation results for Case 2 regarding the distribution of grid field width at different anatomical locations along the dorsoventral axis of MEC . MEC cells exhibit periodic spatial firing fields whose width increases from the dorsal to the ventral ends ( data: Figures 5A and 5C ) . As for grid spacing , the grid field width also increases in variability along the axis . Model simulations ( Figures 5B and 5D ) match these data . An estimate for grid field width was obtained by computing the width of the central peak in the autocorrelogram where the correlation crosses zero . Figure 5B plots grid field width ( mean +/− SEM ) of learned grid cells as a function of response rate , or the distance along the dorsoventral axis , in the last trial . Figure 5D shows the distribution of field width of all map cells as a function of response rate . Learned grid cells are identified by red squares , while others by black ones . Figure 6 shows neurophysiological data [6] and simulation results for Case 2 regarding the peak and mean firing rates of grid cells at different anatomical locations along the dorsoventral axis of MEC . Unlike grid spacing and grid field width , the peak firing rate of MEC cells decreases from the dorsal to the ventral ends ( data: Figure 6A ) . There is also a negative trend for mean firing rate along the axis ( data: Figure 6C ) . The model simulates and explains these data too by using the response rate gradient and normalized grid cell receptive fields , respectively . Figures 6B and 6D plot ( mean +/− SEM ) peak and mean firing rates , respectively , of learned grid cells as a function of response rate , or the distance along the dorsoventral axis , in the last trial . As we have already seen , faster response rates of map cells result in higher peak output activities ( see Figure 3F ) . Given that the total area of the grid firing fields is roughly constant , or normalized , across spatial scales , a decrease in peak firing rate along the dorsoventral axis explains a decrease in mean firing rate . Figure 7 shows how ( A ) gridness score , ( B ) inter-trial stability , ( C ) percent , and ( D ) grid orientation of learned grid cells in the last trial vary as a function of response rate for Case 2 . Error bar plots ( mean +/− SEM ) are shown for gridness score , inter-trial stability , and grid orientation . Due to the regular hexagonal structure of grid cell spatial fields , grid orientation varies between 0° and 60° . Moreover , since grid orientations of 0° and 60° are identical , circular mean and standard deviation were calculated over the range of [0° , 60° ) . The hexagonal and periodic quality of the learned spatial firing fields , measured by the gridness score , decreases with response rate . Similarly , the spatial stability of the learned grid-like firing fields between consecutive trials , called the inter-trial stability , tends to decrease for slower response rates , with relatively poorer stability for the most ventral of the model MEC cells . The decrease in gridness score with distance along the model's dorsoventral axis coincides with the decrease in the proportion of learned grid cells . These three simulation results together suggest poorer and less stable pattern learning for ventral cells . Given the temporally delayed and broader output responses of ventral cells , the periods when the postsynaptic learning gate in Equation 1 . 6 is positive do not correlate temporally as well with the activities of the triggering coactive stripe cells; compare the black curve in Figure 3A with the blue curve in Figure 3F . This situation results in a persistent recoding of the incoming weights for ventral cells as the trajectory is traversed , explaining their weaker inter-trial stability and gridness score measures . Fyhn and colleagues [3] have reported consistent data showing lower spatial stability for cells in ventromedial MEC compared to dorsolateral MEC ( see their Figure 4J ) , but the recording enclosures used were relatively small to appropriately sample the large spatial scale of the ventral cells . Model grid cells in each of the MEC local populations along the dorsoventral axis did not learn exactly the same grid orientation . However , given the recurrent inhibition among the category cells , the different hexagonal grid fields that are learned as a result of self-organization have minimal overlap among them , because of which all possible grid orientations are not equally likely . This can be understood as a consequence of how two sets of hexagonal grid fields of the same scale can have the least total overlap only when they share the same orientation . In SOM model simulations , clustering around a dominant orientation is often observed [20] . This occurs despite the lack of excitatory coupling among neighboring category cells , which helps to prevent a topographic map of grid spatial phases from being learned ( data: [5] ) . Existing data on grid orientation at various dorsoventral locations are preliminary ( Figure 2e in [5]; Supplementary Figure 4 in [63] ) , but seem to suggest a narrowly tuned distribution for grid cells recorded on the same tetrode . In our simulations , we observed that in general the spread of the orientation distribution is inversely correlated with the number of learned grid cells in the local population ( see Figure 11H below for an example of a narrow learned orientation distribution ) . More systematic work aimed at ascertaining how the mean and spread of the grid orientation distribution vary along the dorsoventral axis is needed . The learned mean grid orientations along the response rate gradient , for Case 2 , have a circular standard deviation of 9 . 87° , suggesting that grid orientations of different scales may not be similar . This is expected as the different local populations in our model do not mutually interact . The standard deviation of learned mean grid orientations for various response rates was 12 . 05° when a novel trajectory was used in each trial ( see Figure 11G below ) , and was 12 . 76° when three input stripe cell spatial scales ( 20 , 35 , and 50 cm ) were employed ( see Figure 12F below ) . Figure 8 presents simulation results for Case 2 regarding how various measures of learned grid cells vary as a function of number of learning trials , for two representative response rates ( dorsal: ; ventral: ) . Reported measures are ( A ) grid spacing , ( B ) grid field width , ( C ) gridness score , and ( D ) inter-trial stability . Despite having to learn in response to two input stripe spatial scales , dorsal MEC cells ( green curves in the four panels ) pick out their spatial scale ( grid spacing , grid field width ) quickly and do not change their preference through time ( Figure 8A ) . There is not much change in the inter-trial stability measure either ( Figure 8D ) . Average hexagonal gridness quality of the learned grid firing fields for these model dorsal cells , however , shows gradual improvement over trials ( Figure 8C ) . This is consistent with developmental data from rat pups regarding how emerging grid cells show significantly more change ( improvement ) in gridness score than in grid spacing [37] . Both the gradual improvement in gridness score of the grid cells with faster rates ( Figure 8C , green curve ) and the more rapid selection of grid spatial scales ( separable curves in Figures 8A and 8B ) reflect the tuning of bottom-up weights from stripe cells to grid cells . The rapid separation during learning of fast and slow rate grid cell properties can occur as soon as the different rates preferentially select stripe cells of compatible scale . The more gradual development of the gridness score for the faster response cells requires , in addition , detection and selection of the subset of projections from stripe cells of the smaller scale that are most frequently and energetically coactivated , and the suppression of less favorable correlations . The ventral MEC cells ( blue curves in the four panels ) exhibit lower gridness scores ( Figure 8C ) and inter-trial stability ( Figure 8D ) measures that do change much through time , but show more fluctuation in their spatial measures through time ( Figures 8A and 8B ) , although they exhibit higher values overall . As we have already discussed above , the slower dynamics of ventral cells explains their poorer learning and lower stability . The variability through time of their spatial scale may also be related to their energetically smaller and temporally broader signal levels ( Figure 3F ) . Figure 9 shows Case 2 simulations of learned spatial fields and synaptic weights from stripe cells of two representative model grid cells , ( A ) one from a ventral location , and ( B ) the other from a dorsal location , in the last trial . The spatial autocorrelograms of the rate maps ( see top right in each panel of Figure 9 ) make clear the underlying spatial scale of the grid fields . Consistent with the exhibited spatial scales , only the maximal adapted weights from each stripe cell ring attractor for the corresponding scale show local peaks whose preferred directions differ by 60° . These results are consistent with the explanation given for the scale differences in Figure 4 . Once again , the temporal response rate constrains the spatial scale of the stripe cells that can succeed in shaping and driving the emerging grid cells . Figure 10 shows simulation results for Case 2 regarding the learned spatial fields of two representative model cells , ( A ) one from a ventral location , and ( B ) the other from a dorsal location , across learning trials . These illustrate in greater detail the relatively poorer inter-trial stability and higher preference for larger spatial scales for ventral MEC cells . Figure 11 presents simulation results for Case 3 in which the model animal runs along a novel realistic trajectory in each trial . Several measures of learned map cells in the last trial are shown as a function of response rate ; namely , ( A ) grid spacing , ( B ) grid field width , ( C ) gridness score , ( D ) inter-trial stability , ( E ) percent of grid cells , ( F ) peak rate , and ( G ) grid orientation . Additionally , panel ( H ) shows the grid orientation distribution of learned map cells for the dorsal most MEC population . These results demonstrate that the ability of the Spectral Spacing model to solve the stripe scale selection problem ( Figures 4–7 ) is not tied to the particular navigation trajectory ( see Figure 1B ) that was used for Case 2 . The main quantitative differences with Case 2 are a relatively higher gridness score and proportion of learned grid cells , but lower inter-trial stability . These can be interpreted as consequences of , respectively , experiencing more hexagonal grid exemplars , and undergoing more persistent recoding of synaptic weights from stripe cells as a result of denser environmental coverage [20] . Figure 12 presents simulation results for Case 4 in which the category cells receive projections from input stripe cells of three spacings ( 20 cm , 35 cm , and 50 cm ) . These stripe spacings were chosen such that their ratio ( 1∶1 . 7∶2 . 5 ) matches that of the smallest three grid spacings across rats [63] . Several measures of learned map cells in the last trial are shown as a function of response rate : ( A ) grid spacing , ( B ) grid field width , ( C ) gridness score , ( D ) inter-trial stability , ( E ) percent of grid cells , and ( F ) grid orientation . These results demonstrate that the Spectral Spacing model can also select from among three scales of input stripe cells for grid scale gradient learning . Development of even larger grid scales will require realistic trajectories of rats in much bigger environments ( i . e . , much greater than 100 cm×100 cm ) . The main quantitative differences with Case 2 are a relatively lower gridness score and proportion of learned grid cells , but higher inter-trial stability . More input stripe cells , due to the additional scale , reduce the effective rate of change in the bottom-up weights to map cells ( see Equation 1 . 6 ) . This reduced plasticity correlates with more stability , but slows down the improvement in hexagonal gridness of the spatial fields of the developing map cells . Figure 13 summarizes for other model and input variations the learned grid spacing ( Figures 13A and 13C ) and grid field width ( Figures 13B and 13D ) of grid cells in the last learning trial as a function of response rate . These model and input variations include injection of noise into membrane potential dynamics of map cells ( Case 5 ) ; changes to the learning law and how the habituative gating mechanism operates ( Case 6; Equations 2 . 1–2 . 3 ) ; a different signal function governing output activities of map cells ( Case 7; Equations 3 . 1–3 . 3 ) ; stripe cells with the same peak activity between the two scales ( Case 8 ) ; stripe cells with the same field width between the two scales ( Case 9 ) ; and stripe cells with both the same peak activity and field width between the two scales ( Case 10 ) . In all model variations but Case 8 , which we discuss below , learned grid spacing and field width vary as in the data along the dorsoventral axis of MEC . Simulations for Cases 5 , 6 , and 7 are shown in Figures 13A and 13B by blue , green , and red curves , respectively , and simulations for Cases 8 , 9 , and 10 are shown in Figures 13C and 13D by blue , green , and red curves , respectively . In Case 8 , unlike the data , dorsal cells learned hexagonal grid fields derived from large-scale stripe cells . This case imposes the same peak activity across both small-scale and large-scale stripe cells . Thus , the stripe cell receptive fields are not normalized across scales , and the large-scale stripe cells have a competitive advantage since they are sampled by map cells for a longer time . This advantage seems to be sufficient for them to win over small-scale stripe cells with the same peak activity , despite the lower frequency of their favored coactivations across space ( 7 for the stripe spacing of 35 cm , compared to 23 for the stripe spacing of 20 cm , in a 100 cm×100 cm field ) , in driving the learning of large-scale grid cells even for faster response rates . Thus , if stripe field widths increase with stripe spacing , similar to grid cells [6] , this result suggests a need for a concomitant decrease in peak activity for stripe cells; in other words , stripe cell receptive fields need to be normalized . Normalized receptive fields occur in many other examples of multi-scale processing in the brain , and may be a general principle of brain design . The general design theme is how to achieve selective processing across multiple scales , so that the largest scales do not always win the competition to represent incoming data . Normalization ensures that the degree of brain commitment covaries with the amount of evidence for that choice [64] . In particular , normalized multiple scales help to ensure: speed-selective processing of visual motion , with larger scales responding selectively to faster speeds [65]; depth-selective perceptual grouping wherein larger oriented filters can represent nearer depths but smaller filters only represent farther depths [66]; and length-selective processing of speech wherein longer sequences of items stored in working memory can selectively activate list chunks that represent these longer sequences , which in turn suppress the activity of list chunks that respond to shorter sequences [64] , [67] , [68] . Figure 14 shows the simulation results for Case 11 in which it is only the habituation rate that is varied . Several measures of the learned grid cell firing are shown , namely , ( A ) grid spacing , ( B ) grid field width , ( C ) gridness score , ( D ) inter-trial stability , ( E ) percent of grid cells , and ( F ) peak firing rate , as a function of habituation rate . All measures except the peak firing rate are consistent with those obtained from a response rate gradient ( Figures 4 , 5 , and 7 ) . The peak firing rate increases as the habituation rate decreases with distance from the dorsal end ( Figure 14F ) , in contrast with Figure 6B , where the peak firing rate decreases with response rate . The increase in peak output activity for map cells with habituation rate decrement can be understood as follows: With the response rate fixed , slower habituation rates result in slower collapses of transmitter , which are thus increasingly unable to counter the amplifying effect on grid cell activity of the self-excitatory feedback signal . These observations allow us to single out , in our model , the response rate as the parameter that most likely enables the learning of the dorsoventral gradient in grid cell spatial scale . Experimental studies [62] , [69] have reached a similar conclusion that relatively slower temporal summation by ventral MEC cells most likely accounts for their increased spatial scale . As noted in the Introduction section , in vitro studies have showed that layer II MEC stellate cells exhibit subthreshold MPOs , in response to steady current injection , whose temporal period increases from the dorsal to the ventral end of MEC ( Figure 15A ) , thereby correlating with the observed gradient in spacing and field width of grid cell spatial responses [13] , [14] . In our model , when a steady current is injected into each category cell in the absence of any intercellular interactions ( Equation 1 . 8 ) , an MPO is generated with a frequency that tends to covary with both the response rate ( Figure 15B ) and the habituation rate ( Figure 15C ) for various current amplitudes . Our results suggest that , although there is a correlation between the gradient of MPO frequency and the gradient of grid cell spacing and field width , there is no direct causal link between them . The MPO frequency gradient is just an emergent property that results from model dynamics that control grid cell learning and activation . In particular , when a model category cell depolarizes in response to current injection , the positive feedback signal amplifies cell activity . This amplification increases the activity-dependent rate of inactivation of the habituative gate ( Equation 1 . 7 ) , which thereby gates off the amplification , causing the cell to become less active . Since the habituative gate is activity-dependent , it then recovers , and the cycle repeats leading to oscillations in the membrane potential . A faster response rate leads to faster amplification , habituation , and recovery; thus , to a faster oscillation ( Figure 15B ) . A faster habituation rate , even for fixed response rate , has a similar effect ( Figure 15C ) because the habituative gate again collapses more quickly , thereby gating off the amplification more quickly , which in turn enables the transmitter to recover more quickly . Figures 16A , 16C , and 16E summarize simulations of membrane potential and habituative transmitter traces in response to current injections of different amplitudes for a ventral MEC cell with a slow response rate , and Figures 16B , 16D , and 16F summarize simulations for a dorsal MEC cell with a fast response rate . Note the faster MPO for the faster response rate . Yoshida and coworkers [14] studied the effect of depolarization on the frequency of subthreshold MPOs within single MEC layer II stellate cells at different locations on the dorsoventral axis ( Figure 15A ) . They reported that the MPO frequency of dorsal cells tends to increase with depolarization , and that of ventral cells tends to decrease . However , these positive and negative effects at ventral and dorsal locations are statistically significant only if the low-power broadband MPOs at the most hyperpolarized levels are included in the analysis . These data are consistent with our simulations of the effect of current amplitude on MPO frequency , presented in Figures 15B and 15C . In the Spectral Spacing model , increased current amplitude tends to cause a faster recovery of the cell potential in each MPO cycle after the phases of amplification and habituation . However , larger current amplitudes , with their resultant higher mean membrane potentials and lower mean habituative transmitters , cause relatively lower amplitude oscillations about the mean levels ( Figure 16 ) . This happens because the habituatively gated self-excitatory feedback term , which controls the oscillatory dynamics , decreases with increasing current amplitude ; see Equation 1 . 8 . Cellular noise begins to obscure the general positive effect of current amplitude on the frequencies of such oscillations , especially for slower response rates and habituation rates . This explains the saturation effect of depolarization on MPO frequency at all locations along the dorsoventral extent of MEC , and the apparent negative trend of MPO frequency with depolarization for ventral cells . The simulation results in Figures 4–16 together clarify how all the observed gradient properties of grid cells can be explained as emergent properties of a gradient of response rates in a suitably defined SOM .
Our model , and [23] before us , propose that stripe cells and head direction cells use 1-D ring attractor networks to perform path integration in response to linear and angular velocity inputs , respectively . This proposal suggests that the brain parsimoniously uses a similar design to integrate both types of velocity inputs . Different stripe scales may , for example , result from different gains of linear velocity in controlling the speed of revolution of the activity bump along the ring of cells . It remains an open experimental question as to how many spatial scales of stripe cells may exist . The current simulations show how the dorsoventral gradient of grid cell spatial scales may self-organize in response to either two or three stripe cell scales . In principle , it is possible that there are as many scales of stripe cells as there are scales of grid cells . In particular , are the stripe cells , in parasubiculum [24] or another parahippocampal subregion , arranged with respect to spatial scale in a manner similar to the grid scale gradient in layer II of MEC ? It is also an open question as to whether the seemingly constant ratio ( 1∶1 . 7∶2 . 5 ) of the three smallest grid spacings across rats [63] is mirrored in the stripe cell layer , or emerges through learning from the response rate gradient across grid cells . Even if there are as many stripe cell scales in vivo as grid cell scales , the problem of how entorhinal cells learn to select their spacing from various scales of input stripe cells needs to be addressed , since they would likely receive inputs from a significant portion of the stripe cell gradient , comprising at least a few scales if not all , similar to how principal cells at an arbitrary dorsoventral location in the hippocampal formation receive projections from about a quarter of the dorsoventral extent of superficial MEC [70] . Our model proposes how path integration information is hierarchically processed in the medial entorhinal-hippocampal system ( stripe cells to grid cells to place cells ) to convert a stripe cell population code that implicitly represents an animal's position using multiple small spatial scales into a place cell code in which a single place cell can explicitly represent spatial position in large environments . The intermediate stage of grid cells converts input stripe cell signals into a form conducive to the learning of such unimodal place cell spatial fields , which thereby significantly increase the scale of spatial representation compared to the inducing grid cells . Simulations in [11] illustrate the possibility that the hippocampal spatial scale may be as large as the least common multiple of the inducing grid cell scales . The Spectral Spacing model shows , in turn , how the gradient of inducing grid cell spatial scales can be learned as a result of a response rate-based selection process . Can place cells be learned directly from stripe cells , without the intervention of hexagonal grid fields ? The presence of the animal at a given spatial position strongly activates just one or few stripe cells in each directional ring attractor . So , a unimodal spatial field at that position could be learned , in principle , if a map cell could become tuned to the combination of all these coactive stripe cells across directions and scales . However , such input combinations are not favored by the self-organization process because they occur only at single positions in the environment , as opposed to the multiple positions at which the stripe cell combinations suitable for hexagonal grid fields are activated . As we mentioned above , map cell learning at both the grid cell and place cell levels is naturally sensitive to both the energy and frequency of input coactivations . How , then , are place cells learned , given they are activated only at single positions in the environment ? If inputs to a SOM come from comprise grid cells of multiple spatial scales , then sets of co-active grid cells involving a greater number of scales are more likely to gain control of hippocampal map cells [20] . However , grid cell coactivations from two or more scales do not occur more than once in typical-sized environments [11] , especially because grid scales differ by non-integer ratios [63] . The spacings of grid fields in our model are adaptively selected based on cell response rate , which is inversely correlated with the minimum temporal duration between two episodes of intense activity . Therefore , it is important to discuss how the learned grid cells may respond if an adult animal were to run around an environment with a mean speed that is higher or lower than when the grid cells are learned during development . However , these extreme speed cases may be relevant only for theoretical purposes because of two reasons; namely , the distribution of running speeds in the realistic trajectory , used for our simulations , is relatively broadly tuned with a standard deviation of 9 . 8 cm/s , and existing relevant data indicates that the average running speed of rats increases by just ∼2 cm/s from P16 to adulthood ( see Supplementary Figure 1F in [37] ) . In both data and model ( Figures 4C and 4D ) , neighboring grid cells exhibit a spectrum of spacings in their spatial responses , especially with more distance along the dorsoventral axis , and our simulations show that only a subset of them can be classified as grid cells ( Figure 4D ) . It may thereby be that high mean speeds favor learned map cells with larger spacings at a given dorsoventral location in order to express an appreciable hexagonal grid spatial activity pattern , whereas low mean speeds may favor those with smaller spacings . This possibility in the model is related to how the excitability of a map cell is dependent on the level of the habituative transmitter , whose depletion and recovery dynamics are in turn controlled by the response rate variable . The firing rate [4] and inter-burst frequency [71] of grid cells are known to vary in proportion to running speed . These data suggest that the response rates of MEC layer II cells in vivo may be modulated by running speed , because of which the slope and intercept of the dorsal-ventral gradient in grid spacing may not be significantly altered in response to very fast or slow running speeds . It would be instructive to explicitly test this prediction . Our model simulations illustrate how gradients in intrinsic properties such as membrane potential oscillation frequencies of stellate cells along the dorsoventral axis of MEC layer II may arise from the same response rate mechanism that constrains the learning of the gradient of grid cell spatial scales . This prediction is consistent with data of [72] , which showed that the anatomical gradient in intrinsic properties of MEC layer II stellate cells exists before the rats begin to explore their spatial environments for the first time . Boehlen and colleagues [72] also reported , using sharp microelectrode recordings , that the peak frequency of subthreshold MPOs in the MEC increases as juvenile rats age into adults ( see their Figure 3B ) , though such an age-dependent change was not seen in patch clamp recordings ( see their Figure 3D ) . In contrast , studies that investigated the development of grid fields from postnatal ( P16 ) to adult stage [37] , [38] did not report any age-dependent variation in spatial periods of grid cells . This lack of change in spatial scale could be due to mechanisms that dynamically stabilize grid fields after they form . In particular , the spatial stability of grid cell receptive fields may require top-down feedback from place cells [73] . Such top-down interactions , among other memory-stabilizing processes , may dynamically buffer previously learned connections in the entorhinal-hippocampal hierarchy against the effects of a response rate change . Indeed , place cell selectivity can develop within seconds to minutes , and can remain stable for months [74]–[77] . Such a combination of fast learning and stable memory is often called the stability-plasticity dilemma [44] , [78] . Grossberg [40] showed that SOMs , by themselves , cannot solve the stability-plasticity dilemma in environments whose input patterns are dense and non-stationary through time , as occurs regularly during real-world navigation . In response to such inputs , learned categories can be persistently recoded by new inputs . However , SOMs augmented by learned top-down expectations that focus attention upon expected combinations of features can do so . Adaptive Resonance Theory , or ART , was introduced in [79] to show how to dynamically stabilize the learned memories of SOMs . In ART , learned top-down expectations match bottom-up input patterns to focus attention upon expected combinations of critical features , drive fast learning of new , or refined , recognition categories that incorporate these critical feature patterns into their learned prototypes , and dynamically stabilize established memories . Grossberg [80] proposed how such attentive matching mechanisms from hippocampal cortex to MEC may stabilize both learned grid and place cell receptive fields . Besides helping to account for why the spatial scales of grid cells are maintained despite changes in intrinsic cellular properties as development proceeds [72] , the incorporation of top-down connections from place cells to grid cells may also help to improve the spatial stability of learned grid fields ( Figures 7B and 11D ) . Experimental data about the entorhinal-hippocampal system illustrate how the predicted properties of top-down expectations and attentional matching may play a role in spatial learning and memory stability . Kentros and colleagues [81] reported that “conditions that maximize place field stability greatly increase orientation to novel cues . This suggests that storage and retrieval of place cells is modulated by a top-down cognitive process resembling attention and that place cells are neural correlates of spatial memory” , and that NMDA receptors mediate long-lasting hippocampal place field memory in novel environments [82] . Morris and Frey [83] proposed that hippocampal plasticity reflects an “automatic recording of attended experience . ” Bonnevie and colleagues [73] showed that hippocampal inactivation causes grid cells to lose their spatial firing patterns . In summary , our model here and in [20] of grid and place cell learning uses self-organizing maps ( SOMs ) . Every SOM can exhibit catastrophic forgetting in response to a dense non-stationary input environment . ART top-down matching and attentional focusing mechanisms can dynamically stabilize learning in any SOM; that is , they solve the stability-plasticity dilemma . It is known that grid and place cells solve the stability-plasticity dilemma . Thus , our SOM model is incomplete , but because the model uses SOMs , there is a clear path for completing it , unlike other kinds of grid cell models , such as oscillatory interference and 2-D attractor models , which have not yet shown how the learning of their grid cells happens , and further how this learning may be dynamically stabilized ( see subsection below on Other grid cell models ) . The nature of our model's incompleteness clarifies data about how and when deformations in grid cell receptive fields do occur [73] . Finally , there are important data from several labs ( e . g . , Berke , Kandel , and Morris ) showing the kinds of attentional , learning , and oscillatory dynamics that ART predicts for the stabilization of place cell learning . Our model hereby clarifies an important conceptual link between these data about place cells and data about attention , learning , memory , and oscillations in grid cells . More work needs to be done to study how the response rate gradient and the habituative gating mechanism in our model relate to the HCN and leak potassium channels , which control the varied temporal integrative properties of MEC layer II stellate cells [19] , [84] , [85] . However , the manner in which MPOs arise in our model category cells is similar to how subthreshold MPOs in these stellate cells are known to occur based on the concerted action of a positive and a negative current [86]; in particular , persistent sodium ( NaP ) current and hyperpolarization-activated cation current , respectively [60] . The habituative gating mechanism is similar to how AHP currents control adaptation and refraction in proportion to recent cell activity . Indeed , the proposed gradient in cell response rates , which modulates habituative gate dynamics , is consistent with data showing an increase in the recovery time constant of mAHP currents along the dorsoventral axis of MEC [87] . The model suggests several predictions regarding the development of grid cells at different anatomical locations along the dorsoventral axis of MEC as young animals begin to navigate for the first time . These predictions are tempered by the awareness that the model does not yet incorporate various known mechanisms , such as top-down matching and attentional mechanisms from hippocampus , that may influence model properties , notably their malleability after the predicted dynamical stabilization of grid field structures sets in due to attentive matching . Existing empirical studies on the development of grid cells [37] , [38] have not looked for differences in the learning dynamics of grid cells across spatial scales . Model simulations suggest that lower proportions of grid cells , lower gridness scores , lower spatial stability , and higher variability in grid spacing through time may be found at more ventral locations of MEC . The Spectral Spacing model illustrates how control by a single rate parameter can determine a gradient of grid cell spatial scales in response to inputs from multiple stripe cell spatial scales . Multiple small grid cell scales can then be adaptively combined in the hippocampus to generate place cell scales that are large enough to support spatial navigation [11] , [20] . A similar strategy for temporal coding seems also to occur in the brain: Previous modeling [50]–[52] has shown how control by a single rate parameter can determine a gradient of small temporal scales that can be adaptively combined in the hippocampus to generate temporal scales that are large enough to bridge temporal gaps between stimulus and response , such as those that occur during trace conditioning and delayed non-match to sample experiments . As we noted earlier , this latter type of model is called a Spectral Timing model . In support of this prediction , MacDonald and coworkers [88] have reported hippocampal “time cells” that have all the properties required to achieve spectral timing; in particular , “… the mean peak firing rate for each time cell occurred at sequential moments , and the overlap among firing periods from even these small ensembles of time cells bridges the entire delay . Notably , the spread of the firing period for each neuron increased with the peak firing time …” The correlation of the peak firing time with the spreading of the firing period is called a Weber law , and is one of the dynamical signatures of spectral timing . It remains to be shown whether the spectrum of time cells arises from a gradient in a single rate parameter . A biophysical interpretation of this rate parameter in terms of calcium dynamics in the metabotropic glutamate receptor system has been given for the case of spectral timing in the cerebellum [89] . The most parsimonious prediction is that a similar mechanism holds in all cases of spectral timing throughout the brain . To the present , spectral timing has been modeled in the hippocampus , cerebellum , and basal ganglia [90] . Thus , dorsoventral gradients in single rate parameters within the entorhinal-hippocampal system may create multiple small spatial and temporal scales that can be fused into larger spatial and temporal scales in the hippocampal cortex that are large enough to control adaptive behaviors . The mechanistic homology between these spatial and temporal mechanisms suggests why they may occur side-by-side in the medial and lateral streams through entorhinal cortex into the hippocampus . In particular , spatial representations in the Where cortical stream go through postrhinal cortex and medial entorhinal cortex on their way to hippocampal cortex , and object representations in the What cortical stream go through perirhinal cortex and lateral entorhinal cortex on their way to hippocampal cortex [2] , [91]–[94] , where they are merged . This unity of mechanistically homologous space and time representations may be summarized by the term “neural relativity” . The existence of such computationally homologous spatial and temporal representations in the hippocampus may help to clarify its role in mediating episodic learning and memory . Indeed , investigators since Tulving [94]–[98] have suggested that each episode in memory consists of a specific spatio-temporal combination of stimuli and behavior , and discussed evidence supporting this claim . This subsection highlights and justifies differences between the GRIDSmap model [23] and the current Spectral Spacing model . First , we introduced a threshold in the signal function that transforms membrane potentials of map cells into their output activities , which both govern the recurrent inhibitory interactions and gate the competitive adaptation of corresponding bottom-up weights ( see parameter in Equations 1 . 5 and 1 . 6 ) . This helps to ensure the following properties [20]: Second , we initialized the pre-development synaptic weights of the connections from stripe cells to grid cells ( in Equation 1 . 6 ) using a uniform distribution between 0 and 0 . 1 . The mean of these initial weights ( 0 . 05 ) is higher than that ( 0 . 0075 ) used in [23] . This helps to ensure that each entorhinal map cell in a larger population ( >>5 in [23] ) is activated at least somewhere in the environment , and thereby participates in activity-dependent learning to likely emerge as a grid cell [20] . Map cells with initial weights from stripe cells that are low , or with those that do not closely match any input pattern during spatial navigation , cannot adapt enough to contribute towards spatial representation . Third , the inactivation and recovery dynamics of the habituative transmitter depend only on the self-excitatory feedback signal ( see Equation 1 . 7 ) in the equation governing membrane potential dynamics ( Equation 1 . 5 ) , and not also on bottom-up excitatory inputs ( see Equation 2 . 3 ) . This gating is sufficient to prevent persistent firing of map cells that become intensely active and thereby allow other cells to participate in activity-dependent plasticity . Case 6 simulation results presented in Figures 13A and 13B ( green curves ) show that grid cell spatial scale gradient can be learned even when the habituative gating operates on both the weighted stripe cell inputs and the recurrent on-center feedback ( see Equation 2 . 1 ) . This is in part due to model robustness , and in part due to the relatively weaker driving force of bottom-up inputs compared to the self-excitatory feedback signal ( see Figures 3B and 3C ) . Fourth , the adaptive weights from stripe cells to category cells use a different version of the instar learning law ( Equation 1 . 6 ) that more robustly enabled category cells to become tuned to coactivations of stripe cells [20] . The instar learning law used in GRIDSmap ( Equation 2 . 2 ) could sometimes allow a category cell to get tuned to just one strong or sustained input neuron when its adaptive weight exhausts the weights available for learning in the other stripe cell pathways via term in Equation 2 . 2 . As a result , stripe-like , rather than hexagonal , firing fields of grid cells could arise in two situations: more correlated activations of stripe cells when stripe cells exist with smaller separations between stripe directions , or more sustained activations of stripe cells with larger stripe fields ( see Figures 8 and 9 in [23] ) . Instead , the current learning law allows each weight to track the ratio of stripe cell activities , time-averaged during intervals when the learning gate is open . In the GRIDSmap model , the stripe cells of different spacings were assumed to have the same maximal firing rate but different field widths ( i . e . , and in Equation 1 . 4 ) . In other words , the total firing in a stripe field was different across scales , so that the stripe cell receptive fields are not normalized . In contrast [6] , reported that the peak firing rates of grid cells decrease from the dorsal to the ventral end of MEC while grid field widths increase , and Spectral Spacing model simulations show that normalized stripe cell receptive fields are needed to simulate all the data about how spatial and temporal properties of grid cell firing changes along the dorsoventral axis . Several models exist to explain the generation of grid cells , but the Spectral Spacing model differentiates itself by providing for the first time a principled explanation of how grid cells learn not only their characteristic hexagonal grid firing patterns [5] , [37] , [38] but also their spatial scale gradient along the dorsoventral axis of MEC [4] , [6] , and how this self-organization process relates to intrinsic cellular properties along the same axis [19] . These contributions represent significant breakthroughs , especially considering that few prior works address aspects of how grid cells may be learned in a self-organized manner [20] , [23] , [99] . Prior grid cell models can be generally classified into two categories based on whether the linear velocity path integration happens before or at the level of grid cells . In addition to the SOM type of model , the former possibility has been modeled using mechanisms of oscillatory interference [21] , [22] , [100] and ring ( 1-D periodic ) attractors [36] , [99] . In the family of models based on oscillatory interference , the inputs to grid cells at which path integration occurs have been called band cells [21] . Although band cells use different mechanisms than the stripe cells of SOM models [23] , they also generate 1-D periodic spatial firing patterns ( see Figure 1A ) . Models which implement path integration at the level of grid cells include toroidal ( 2-D periodic ) attractor networks [8] , [9] , [101] . Oscillatory interference models [21] , [22] , [100] propose that the grid cell firing pattern forms from interference between membrane potential oscillations in different compartments within a single cell . These compartments include the cell soma , whose oscillation has a baseline theta frequency , and various dendritic compartments , whose oscillation frequencies are sensitive to linear velocity and head direction . In this way , displacement information can be implicitly encoded in the phase differences between the baseline oscillation and the different active oscillations . The dendritic oscillations are controlled by input band cells , which exhibit periodic firing with frequencies proportional to the linear velocity component along their preferred directions . The interference models assume each grid cell receives inputs from exactly three band cells whose preferred directions are 60° apart from each other in order to generate hexagonal grid spatial firing fields . Grid firing patterns different from hexagonal patterns , and which are not observed in vivo , result if this constraint is not met [22] . The interference models assume that the right input combination of band cells is selected through some self-organization process [21] , but this has not yet been demonstrated . The existence of subthreshold oscillations in dMEC layer II stellate cells [12] , [53] and their dorsoventral gradient [13] , [14] are interpreted as strong evidence for an oscillatory interference-based mechanism for grid cells [13] , [21] , [22] . However , the Spectral Spacing model not only learns grid cells of multiple spatial scales without invoking oscillatory interference , but also accounts for their MPOs and , in particular , the gradient in oscillation frequencies along the dorsoventral axis of MEC as an epiphenomenon . The 2-D attractor models [8] , [9] , [101] propose that grid cell properties result from network-level dynamics in a two-dimensional sheet of neurons . In the absence of any translational movement , persistent localized firing of grid cells is ensured by a recurrent on-center off-surround connectivity with symmetric weights between the cells . However in response to non-zero linear velocity signals , the connections among cells are activated in a directionally asymmetric manner to cause the activity pattern , or bump , to shift accordingly for the direct encoding of displacement information . 2-D spatially periodic firing fields arise from toroidal boundary conditions . While these models do not require an additional stage for the purpose of linear velocity path integration , it has not been demonstrated how a non-topographic periodic 2-D attractor network can be self-organized in the brain . A previous proposal for entraining such a network by a topographic aperiodic 2-D attractor network [9] has been suggested to be not feasible [101] . Moreover , 2-D attractor models have not yet provided a functional role for the gradient in the rate of temporal integration along the dorsoventral axis of MEC layer II [19] . They also do not yet account for the gradient in the frequency of subthreshold MPOs that are elicited in response to steady current injections [13] , [14] , and in the peak and mean firing rates [6] . While how a stripe cell ring attractor network self-organizes has also not yet been shown , it should be noted that [35] have shown how learning can adaptively calibrate vestibular , visual , and motor inputs to ring attractors that code head direction .
|
Spatial navigation is a critical competence of all higher mammals , and place cells in the hippocampus represent the large spaces in which they navigate . Recent modeling clarifies how this may occur via interactions between grid cells in the medial entorhinal cortex ( MEC ) and place cells . Grid cells exhibit hexagonal grid firing patterns across space and come in multiple spatial scales that increase along the dorsoventral axis of MEC . Signals from multiple scales of grid cells combine to activate place cells that represent much larger spaces than grid cells . This article shows how a gradient of cell response rates along the dorsoventral axis enables the learning of grid cells with the observed gradient of spatial scales as an animal navigates realistic trajectories . The observed gradient of grid cell membrane potential oscillation frequencies is shown to be a direct result of the gradient of response rates . This gradient mechanism for spatial learning is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections , thereby clarifying why both spatial and temporal representations are found in the entorhinal-hippocampal system .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"computer",
"science",
"social",
"and",
"behavioral",
"sciences",
"mathematics",
"chemistry",
"biology",
"engineering"
] |
2012
|
How Entorhinal Grid Cells May Learn Multiple Spatial Scales from a Dorsoventral Gradient of Cell Response Rates in a Self-organizing Map
|
As a consequence of the accumulation of insertion events over evolutionary time , mobile elements now comprise nearly half of the human genome . The Alu , L1 , and SVA mobile element families are still duplicating , generating variation between individual genomes . Mobile element insertions ( MEI ) have been identified as causes for genetic diseases , including hemophilia , neurofibromatosis , and various cancers . Here we present a comprehensive map of 7 , 380 MEI polymorphisms from the 1000 Genomes Project whole-genome sequencing data of 185 samples in three major populations detected with two detection methods . This catalog enables us to systematically study mutation rates , population segregation , genomic distribution , and functional properties of MEI polymorphisms and to compare MEI to SNP variation from the same individuals . Population allele frequencies of MEI and SNPs are described , broadly , by the same neutral ancestral processes despite vastly different mutation mechanisms and rates , except in coding regions where MEI are virtually absent , presumably due to strong negative selection . A direct comparison of MEI and SNP diversity levels suggests a differential mobile element insertion rate among populations .
Retrotransposons are endogenous genomic sequences that copy and paste into locations throughout host genomes [1]–[3] . Most mobile elements annotated in the human reference genome are remnants of ancient retrotransposition events and are no longer capable of active retrotransposition . However , a fraction of mobile elements remain active and contribute to variation between individuals in the human population . These active elements belong almost exclusively to the Alu , L1 , and SVA families of non-LTR retrotransposons [4] . The Alu family is the most common mobile element in primate genomes , with more than 1 . 1 million copies in Homo sapiens [5]–[7] . The sequence of a full-length Alu element is 300 bp long . Alu elements are classified into a range of sub-families which have different propensities for retrotransposition , and are identified according to sequence alterations . Several AluY sub-families are currently active and are responsible for the bulk of mobile element insertion variation in Homo sapiens . The human reference genome contains over 140 , 000 annotated AluY elements . After Alus , L1 insertions are the next most prevalent family of mobile elements . There are over 500 , 000 L1 elements annotated in Homo sapiens . A full-length L1 is a sequence of roughly 6 kb in length and the most active L1 sub-family in the human lineage is L1HS [8] , [9] . There are a little more than 1 , 500 L1HS annotated elements in the human reference . A third family of mobile element are SVA retrotransposons [10] . SVAs are hybrid elements of SINE , VNTR and Alu components that range in size up to several Kb , with more than 3 , 600 annotated SVA elements in the human reference genome . SVA elements are thought to be the youngest family of retrotransposons in primates [11] . Other less common classes of mobile elements , such as DNA transposons , and endogenous retroviruses are not the focus in this study . Mobile element insertions ( MEI ) are known to generate significant structural variation within Homo sapiens [12] , [13] and have diverse functional impacts [14]–[16] . In vitro experiments identified key features of Alu [17] and L1 [18] elements responsible for retrotransposon activity . The identification of MEI variant loci in humans initially began with disease-causing insertion events ( e . g . hemophilia [19] , breast cancer [20] ) . Experimental approaches were based upon library screening and small-scale PCR based display assays [21] . These approaches have been augmented by comparisons of the NCBI and the HuRef genomes [22] , [23] , large scale fosmid-end sequences [24] , and targeted sequencing of element-specific PCR products [25]–[28] . The dbRIP database of MEI polymorphisms [29] currently contains 2 , 691 polymorphic loci , enabling early estimates for the total number of segregating events [25] and per-generation mutation rates [23] . MEI polymorphisms can be detected either as insertions or as deletions in samples relative to the reference genome . Mechanistically , however , both types of observations are due to retrotransposon insertion; precise excisions of mobile elements are essentially non-existent [1] . Therefore MEI detected as deletions are , in fact , retrotransposon insertions in the reference DNA and can be verified as such by comparison with ancestral genomes . Detection and genotyping properties of MEI detected as insertions ( “non-reference MEI” ) and as deletions ( “reference MEI” ) are substantially different . We present their respective properties separately before combining the two detection modes into a unified MEI analysis . The deletion detection methods and properties of the full set of 1000GP deletions have been extensively described in the 1000GP CNV companion paper [30] . This allows us to focus on specific properties of the reference MEI subset of those deletions . Effective computational algorithms using second-generation sequencing data exist for identifying deletions [27] , [31] , [32] , and have been used to find MEI in particular [33] . Detecting non-reference MEI directly as insertions from whole genome shotgun sequence data poses a more challenging problem , owing to the inherent difficulties associated with accurate mapping of sequenced reads derived from highly repetitive regions of the genome . Only recently have methods been developed for the purpose of non-reference MEI detection from second-generation whole genome shotgun data including published studies of L1 element insertions [34] and of Alu insertions [35] . These studies adopted similar computational approaches to one of our insertion detection methods ( the read pair method , see Materials and Methods ) and have different detection properties ( Text S2 Comparisons , Figures S8 , S9 , S10 ) . Relative to previous studies , we present a broad analysis of MEI variation in the human population; with more variant loci detected , from the three major mobile element families , using multiple detection methods , each with comprehensive experimental validation ( Table 1 ) . The present study represents the combined efforts of the MEI sub-group of the 1000 Genomes Project and has been prepared as a companion to previous 1000GP pilot publications [30] , [36] . The MEI analyzed in this study were included the 1000GP variant call release of July 2010 ( ftp://ftp-trace . ncbi . nih . gov/1000genomes/ftp/pilot_data/release/2010_07 ) , also provided as Table S1 ) . The specific purpose here is to provide a more detailed description of the methods , validation experiments , and properties of the 1000GP catalog of MEI events , and to extend the analysis by adding genotype information , population allele frequencies , and population specific mutation rates .
We analyzed two whole-genome datasets produced by the 1000GP , the low coverage pilot dataset consisting of 179 individuals sequenced to ∼1–3X coverage and the trio pilot dataset consisting of two family trios sequenced to high , ∼15–40X coverage ( Table S2 , Figure S4 ) . These datasets included samples from three continental population groups , 60 samples of European origin ( CEU ) , 59 African ( YRI ) , and 60 Asian samples from Japan and China ( CHBJPT ) . The two pilot datasets were produced and analyzed for complementary purposes . The trio dataset was used for assessing detection methods in high coverage samples and for the purpose of finding candidate de novo insertions in the trio children . The high coverage dataset was used to assess population properties of MEI . Both datasets contributed to the overall catalog of events . We developed two complementary methods for the detection of non-reference MEI , a read-pair constraint ( RP ) method applied to Illumina paired-end short read data , and a split-read ( SR ) method applied to the longer read data from Roche/454 pyrosequencing ( Materials and Methods: non-reference MEI detection ) . Figure 1a and 1b shows the respective detection signatures and examples of event displays . Candidate MEI events were formed as clusters of supporting fragments . A limitation specific to RP detection arises from annotated elements within a characteristic read pair fragment length of candidate MEI ( Figure 1a ) . Read pairs spanning from a uniquely mapped anchor into an annotated mobile element with a fragment length consistent with the given library fragment length distribution ( Figure S5 ) are characteristic of the reference allele and are not evidence for non-reference MEI . These “background” read pairs occasionally have fragment lengths on the extreme tails of the library distribution and can potentially be misclassified as evidence for non-reference MEI . For this reason RP detection criteria required at least two supporting fragments spanning into the insertions from both sides of the insertion . We also masked insertion positions within a fragment length around each annotated element of the corresponding family from RP detection in order to achieve a low false detection rate . The SR method was not dependent on the fragment length distribution in the 454 data so these additional detection criteria were not required . We applied the two methods to both 1000GP pilot datasets ( Table 1 ) separately , yielding a total of 5 , 370 distinct genomic MEI loci , 33% of which were found by both SR and RP methods ( Figure 1c ) . The overall level of detection overlap between SR and RP methods is limited by detection sensitivity and specificity ( see below ) and the number of samples sequenced by both 454 and by Illumina read pairs . In addition to the 5 , 370 non-reference MEI , we identified 2 , 010 reference MEI detected as deletions of mobile elements in samples . The reference MEI events were selected from the full release set of 1000GP pilot deletions ( n = 22025 ) [30] , [36] based on matching deletion coordinates to RepeatMasker 3 . 27 Alu , L1 , and SVA annotations [6] , and the requirement that the mobile element is absent in the chimpanzee genome [37] ( 6x pan Trogodytes-2 . 1 assembly ) at the corresponding positions in hg18 ( Materials and Methods: Reference MEI selection ) . Figure 1e shows an example event display of an AluYb8 reference MEI , detected as a deletion in the trio pilot data . All but one of the reference MEI were found by one or more of the RP or SR deletion detection algorithms that were part of the released 1000GP deletion call set [30] , [38]–[42] with a small overlapping contribution from algorithms based on assembly or read depth methods [43] , [44] ( Figure 1d , Table S3 ) . The complete set of 7 , 310 MEI calls is simply the combined set of reference and non-reference MEI over both pilot datasets ( summarized in Table 1 , complete list in Table S1 ) . Insertions occurring at the same locus from different call sets were merged using a 100 bp window for matching positions , choosing the SR insertion coordinate when available to represent the merged event . Similarly for reference MEI , deletion merging was accomplished among the 23 separate 1000GP call sets using a precision-aware algorithm described in detail in the 1000GP SV companion paper [30] . The full catalog of MEI loci appear to be distributed randomly across the genome ( Figure 2b ) with a characteristic spacing of 0 . 4 Mb between MEI loci , except for an apparent MEI hotspot in the HLA region of chromosome 6 where 19 MEI loci are clustered in a 1 Mb region ( 8 times the genomic average density for MEI , Figure S11 ) . Accurate read mapping in the HLA region is complicated by a high density of variation [36] , however , we see no evidence of falsely detected MEI here . The balance between reference and non-reference MEI , proportions of RP and SR detected loci , the fraction of previously identified MEI loci , and the validation rate are all consistent with genomic averages; only the density of MEI is significantly increased . The genomic proportions of the three mobile element families are 85±2% Alu , 12±2% L1 , and 2 . 5±1% SVA ( Figure 2b ) for both reference and non-reference MEI . Most non-reference MEI loci were detected from the low coverage pilot data ( Figure 2c ) while the reference MEI were more evenly distributed between the low coverage and trio pilot data ( Figure 2d ) . As described in the 1000GP main pilot paper [36] , more than 80% of the non-reference MEI were newly identified loci not detected by previous studies [23]–[28] , [34] , [35] , [45] . However , in the mean time , several published studies have produced new lists of non-reference MEI loci including L1 insertions [34] and Alu insertions [28] , [35] . Half of the non-reference MEI loci from this study have not yet been reported elsewhere ( Figure 2e , Figure S8 ) . Table 1 of the 1000GP paper lists 5 , 371 MEI , two of these events were subsequently merged into one to form the present count of 5 , 370 MEI detected as insertions . For reference MEI , we find that 76% of our events matched deletion coordinates listed in the dbVAR ( 28 January 2011 ) structural variation database or a deletion identified in the HuRef genome [22] , [46] , leaving 24% of the reference MEI unreported prior to 1000GP publications . The 1000GP catalog of MEI variant sites includes all 7 , 310 detected loci , including those matching MEI from other publications . Further comparisons among the recent MEI studies are provided in Text S2 . We benchmarked each of the four non-reference MEI call sets ( separate SR and RP call sets for the low coverage and trio pilot datasets ) to assess detection sensitivity and specificity . As MEI are currently not suitable for microarray validation due to their highly repetitive sequence , all validations were done by locus-specific PCR . 200 loci were randomly selected from each of the four insertion call sets . Using an automated pipeline [32] , primer design was possible for 746 loci ( Table S4 ) . In addition to the randomly selected loci , other candidate loci were selected for validation experiments in order to confirm SVA insertions ( n = 7 ) , to test potential de novo insertions from the pilot 2 trio ( n = 1 ) , and gene-interrupting events ( n = 86 attempted ) , as well as for algorithm training and testing purposes ( n = 386 ) . These additional PCR results ( Table S4a ) were not used to assess false detection rates , except for the special case of SVA insertions , which were under-represented in the random loci selection since SVA insertions are relatively rare . All candidate loci with successful primer design were tested on two different population genetic panels ( Materials and Methods: Validation ) one with DNA of 25 individuals from the low coverage pilot , and one with DNA from all samples of the trio pilot dataset . In addition to other human samples from populations not represented by the pilot datasets , DNA of a chimpanzee was also included on the panel to confirm that the identified insertion is indeed human-specific . An example of typical results for a low coverage locus is shown in Figure 3a . Through additional primer design for loci with inconclusive results and PCRs using a primer residing within the 3′ end of a retrotransposon , in particular within SVA elements , more than 98% of the tested candidate loci were successfully genotyped . The validation experiments revealed overall insertion false discovery rates for each dataset of less than 5% ( Table 1 ) . Among the different retrotransposon families ( L1 , SVA , and Alu elements ) , false discovery rates varied noticeably ( Figure 3b ) , with Alu insertions showing the lowest false-positive rate ( 2 . 0 [1 . 1–3 . 4] % , followed by L1s ( 17 [10]–[27] % ) , and SVAs ( 27 [8]–[55] % ) with 95% confidence intervals . This is not entirely unexpected as polymorphic Alu insertions tend to be low divergence full-length AluY elements , unlike L1 or SVA insertions which tend to be truncated and may be accompanied by adjacent transduced genomic DNA sequences . Although the SR and RP detection methods are very different , the overall detection specificities were remarkably consistent . Following the validation of non-reference MEI , we assessed detection sensitivity . The primary challenge here was to find suitable gold standard non-reference MEI that should be present in our samples from which to assess sensitivity . We estimated sensitivity in three different ways , as a consistency check . First , we estimated sensitivity by using the high quality non-reference MEI from HuRef [23] as a gold standard and found that 74% of the 650 Alu , L1 , or SVA insertions in HuRef matched MEI insertion loci in our catalog ( Table S5 ) . This represents a lower limit for insertion detection sensitivity since not all MEI in the HuRef genome are necessarily present in the 1000GP pilot samples . Next we looked at the overlapping insertion detection between the RP and SR methods in the trio children samples ( Figure 3c , Figure S6 ) , which were the samples sequenced to the highest depth for both Illumina and 454 data . Based on the detected loci overlap ( see Materials and Methods: Detection sensitivity ) , we estimate 67%±3% and 70%±7% sensitivities respectively for RP and SR insertion detection in the trio children ( Table S6 ) , with a combined SR+RP detection sensitivity exceeding 90% in the CEU trio child ( see Materials and Methods , Eq . 4 ) with high coverage data from both 454 and Illumina reads . A third approach to estimate for the non-reference MEI detection sensitivity is based on the validation PCR genotypes in the low coverage dataset . Since the PCR loci were selected as random subsets for each RP and SR call set independently , the validated sites selected from SR events can be used as a gold standard to assess RP detection sensitivity , and vice-versa . Detection sensitivity as a function of allele frequency ( Figure 3d ) was estimated for each method from PCR genotypes at those loci randomly selected for validation of the complementary method . PCR genotypes provided the allele frequency estimate on the abscissa . Statistical errors at high allele frequency are large because the limited number of tested MEI loci at higher allele frequencies . Detection sensitivity of the RP method saturates close to 70% at high coverage and the SR method sensitivity exceeds 70% at high coverage ( Figure S6 ) . The corresponding trend is apparent in Figure 3d . The combined detection sensitivity approaches 90% for common alleles ( Materials and Methods , Eq . 4 ) . However , since relatively few of the low coverage samples were sequenced with 454 , a realistic estimate for the detection sensitivity to common MEI insertions is between 70% and 80% . This is consistent with 75% derived from the HuRef gold standard comparison and the sensitivity estimate from the trio pilot overlaps . Equivalent estimates for Alu , L1 , and SVA specific sensitivities for common MEI alleles are 75%±10% , 50%±10% , and 50%±20% respectively ( Table S9 ) . Regarding reference MEI detected as deletions , the overall validation rate from PCR and local assembly for the MEI component of deletions was 96% . This does not imply that the remaining 4% were false , only that the released set of deletions contained reference MEI detected by two high specificity algorithms with characteristic false detection rates less than 10% . These algorithms did not require additional validation evidence in the 1000GP release . A rough estimate for the false detection rate for the MEI component of deletions is therefore 0 . 4% . The number of algorithms supporting a given call is another indicator of call quality . The average number of separate deletion calls ( out of a maximum of 23 call sets ) supporting events in the MEI subset was 7 . 8 while the average over all other deletions was 2 . 3 ( Figure S2 ) . The high validation rate and high consensus among detection algorithms indicate that this subset of deletions is relatively free of detection artifact . The practical limitation on the specificity of these events as reference MEI is the subsequent MEI selection criteria . Only a small fraction the 2 , 010 selected events were ambiguous in terms of matching coordinates to an annotated mobile element with corresponding gap in the chimpanzee genome assembly ( e . g . Figure S3 , bottom panel ) . The 1000GP CNV paper identified 2029 reference MEI variants using the BreakSeq algorithm . Overlap between the respective lists is 89% . We estimate 10% as an upper limit on the false discovery rate for reference MEI . Detection sensitivity for reference MEI was estimated from the fractions of gold standard reference MEI identified by Xing et al . from HuRef [22] , [23] , [46] , and reference MEI identified by Mills et al . [4] , [47] from 1000GP samples NA12878 and NA12156 matched to any of our 2 , 010 reference MEI ( Table S5 ) . In each case the fraction of those MEI deletions found in this study exceeded 90% . This level of detection sensitivity is considerably higher than the bulk deletion detection sensitivity reported in the SV companion paper [30] , indicating that the RP and SR deletion detection methods developed for the 1000GP were particularly well suited for reference MEI detection . We characterized each detected MEI event ( Table S1 ) by the insertion position , which algorithm ( s ) detected the event , number of fragments supporting the insertion and reference alleles , insertion length ( Figure S12 ) , element family , bracketing homology ( Figure 4a ) , and assembled sequence . MEI have a characteristic “target site duplication” region of homology bracketing the insertion . The target site duplication length distributions for the MEI detected by different methods , as well as for different element families , peaked at 15 bp with a standard deviation of 7 bp ( Figure 4a ) . The full insertion sequence from reference MEI is readily extracted from the reference , but non-reference MEI require local de novo assembly to reconstruct the inserted sequence . For this we used 454 data to reconstruct 1 , 105 Alu insertions ( Tables S1 and S7 ) from our event list based on the PHRAP assembly program [48] . We then used BLAT [49] to map assembled contigs back to the build 36 . 3 human reference to identify the boundaries of the inserted sequence . The inserted sequence was then mapped back to the RepeatMasker mobile element sequences using the RepeatMasker web server ( http://www . repeatmasker . org ) to identify the sub-family ( Figure 4b ) . The accuracy of Alu sub-family classification was assessed by comparison to matched 359 Alu insertions from dbRIP [29] and nine fully sequenced Alu insertions from PCR validation experiments . 272 of the assembled Alu sub-family classes were identical ( 74% ) . The most active Alu sub-families are AluYa5 and AluYb8 . AluY sub-families account for essentially all Alu variation . The relative proportions among Alu sub-families are consistent among reference and non-reference MEI , as well as consistent with the Alu sub-families observed in HuRef [23] . The Alu sub-family breakdown differs from that reported by Hormozdiari et . al . [35] who identified more than 10% of their set of insertions from AluJ or AluS sub-families . The authors of that study point out that these ‘older’ Alu events could arise from mechanisms other than retrotransposon insertions . Genotyping of non-reference MEI ( Materials and Methods: Genotyping ) was based on counts of fragments supporting the reference allele and fragments supporting the insertion allele at each locus for each sample . Heterozygous MEI sites are identified by roughly equal amounts of reference and alternate allele supporting fragments spanning an insertion locus , while homozygous sites should have all fragments supporting one or the other allele . For reference MEI , we used genotypes produced by the Genome STRiP package [39] , which was developed for 1000GP deletion genotyping [30] , [39] and incorporates Beagle [50] imputation based on linkage with local SNPs . Both genotyping methods provide phred-scaled [51] genotype quality ( GQ ) metrics at each site that reflect confidence in the given call based on supporting evidence , GQ = 0 to a total lack of genotype evidence and GQ = 10 indicating that the genotype should be 90% accurate . The GQ metric depends on the number of fragments found to support the MEI and non-MEI alleles for a given locus and sample ( Text S2: Genotyping methods ) . As in most issues of sensitivity vs . specificity , there is a trade-off between high genotype efficiency and genotyping accuracy . The drop-off in genotyping efficiency vs . GQ threshold is more severe for non-reference MEI ( Figure S13 ) . For subsequent genotype-based analysis of non-reference MEI sites and samples we required GQ≥7 , which corresponds to roughly 40% genotyping efficiency in the low coverage pilot data . For reference MEI we required GQ≥10 , which corresponds to an efficiency of 80% . Genotyping efficiency improves with increased sample read coverage ( Figure S13 , bottom panel ) , particularly for non-reference MEI . Genotyping accuracy for non-reference MEI is assessed by direct comparison to PCR validation genotypes in the same samples , and by testing for Mendelian errors in the trios and violations of Hardy-Weinberg Equilibrium in the low coverage data ( Text S2 Genotyping tests , Figures S13 and S14 ) . Validation genotypes are listed in Table S4 ( also as the “VG” field of the released MEI insertion genotyped VCF files ) . Genotype contingency tables for the low coverage data ( Table 3 ) show an 87% agreement between sequenced genotypes and PCR genotypes for sites with GQ≥7 . Genotyping accuracy improves with increasing GQ threshold ( Figure S13 ) but never exceeds 90% in the low coverage data . Non-reference MEI genotyping performance for high coverage trio data ( Table 3 , Table S8 ) was considerably better than for the low coverage data . However , for population analyses we used only low coverage data in order to minimize the potential for coverage biases . The accuracy of GenomeSTRiP genotypes ( for reference MEI events ) with GQ≥10 was estimated at 99% in the full 1000GP deletion call set [30] , [36] , [39] . We estimated MEI allele frequencies from the count of high quality ( GQ≥7 non-reference and GQ≥10 for reference MEI ) genotyped insertion alleles for each MEI locus . Allele frequencies were estimated from loci with at least 25 high quality genotypes for each continental population group . The two MEI detection modes ( i . e . reference and non-reference insertions ) have very different allele frequency spectra ( Figure 5a–5c ) . Since the non-reference MEI and reference MEI components have very different powers of detection and genotyping , the two components were corrected separately ( Materials and Methods: Allele frequency spectra ) before being combined into the full MEI spectrum ( Figure 5d–5f ) . We estimated correction factors for each population group , each element type , and each detection mode ( Table S9 ) . Non-reference MEI correction factors are larger than reference MEI factors because of the lower detection sensitivity and genotyping efficiency . The allele count spectra were compared to the standard neutral model [52]–[54] , θ/i , where θ is an MEI diversity parameter and i is the allele count in a fixed number of samples . The value of θ is fit from the MEI allele count spectrum for each population group and the fitted model is the gray dotted line appearing in Figure 5d–5f . Only allele count bins in the range 0 . 15<frequency<0 . 95 were used in the fit ( bins marked with error bars in Figure 5d–5f ) to avoid regions of poor detection sensitivity . The corresponding gray dashed lines superimposed on Figures 5a–5c also represent the neutral model expectation , modified to account for the respective ascertainment conditions , ( θ/2N ) for reference MEI , ( θ/i ) ( 2N−i ) / ( 2N ) for non-reference MEI , where N = 25 is the number of samples in the spectra . These ascertainment condition expressions are based on the assumption that the reference genome represents a random sample from the given population , which is admittedly simplistic but nevertheless explains much of the difference between the allele spectra of reference and non-reference MEI . A coalescent simulation ( Text S2 Coalescent , Figure S17 ) for MEI variation also shows this behavior using standard population history parameters [55] . Fitted values of the diversity parameter θ for each of three population groups and each element family are listed in Table 4 , along with rough estimates for the corresponding MEI mutation rates based on the neutral model ( μ = θ/ ( 4·Ne ) ) with an effective population size Ne of 10 , 000 [56] , [57] . Confidence intervals for μ and θ ( Table 4 ) take into account Poisson noise and uncertainties in the correction factors , but do not reflect the degree to which the model assumptions are valid . All three element families have been combined into the allele count spectra shown in Figure 5 , although the Alu family is the dominant component . Allele frequency spectra for different element families have similar shapes ( Figure 6a ) . We know from SNP studies that the shape of the allele frequency spectrum is modulated by demographic history , and that this shape is characteristically different for European , African , and Asian populations [56] , [57] . When compared to SNP allele frequency spectra from the same datasets ( Figure 6b ) , the MEI and SNP frequency spectra show similar trends among the corresponding populations . Among the three population groups , the CHBJPT spectrum shows relatively few low frequency allele loci . This was also apparent in comparison with the neutral model ( Figure 5e ) . We also analyzed population differentiation by applying principal component analysis to the matrix of allele counts across the low coverage pilot samples and loci ( Figures S15 and S16 ) . Some structure is immediately apparent in the matrix of allele counts , e . g . increased heterozygosity in the YRI samples , but PCA reveals population specific patterns of MEI that result in tight clusters of samples according to geographic origin ( Figure 6c ) ; again similar to population patterns for SNPs [58] , CNVs [59] and deletions [30] . As few as 39 of the 5 , 370 non-reference MEI loci were located in exonic sequence , mostly in untranslated regions , and only 3 were found in coding exons ( Table 2 ) . These numbers are much lower than expected from random placement ( Materials and Methods: Functional calculation ) , indicating strong selection against MEI disrupting gene function . The suppression factor for an MEI to occur in a coding region compared to the genome-averaged rate is 46x , a much stronger suppression than is observed for coding SNPs ( Table S10 , suppression factor = 3 . 9x ) , and is similar to SNPs that cause the loss of a stop codon ( 42x , derived from Table 2 of [36] ) . Two of the MEI interrupting coding regions were PCR-validated . These two MEI appear to be of little functional consequence: ZNF404 is a member of a highly paralogous zinc finger gene family and C14orf166B is a predicted gene without functional annotation . These findings suggest very strong negative selection against MEI interrupting coding regions . Although it is obvious from first principles that insertions in functional regions should be deleterious , the observed suppression factor in a large catalog of MEI in populations quantifies the effect . The high-coverage trio data allows for the most precise estimates of the total number of MEI variants between pairs of individuals because of the high detection sensitivity . The number of pair-wise variant loci is calculated as the presence or absence of an insertion at a given locus , combining reference and non-reference MEI . We selected the two trio children ( NA12878 and NA19240 ) for comparison between CEU and YRI individuals and the trio parents for comparison of individuals within the CEU and the YRI population groups . After corrections for detection sensitivity and false detection ( Text S2 and Table S6 ) , we found 2 , 034±120 MEI variant loci between the African and the European trio children , 1 , 442±120 between the YRI parents , and 663±140 MEI between the CEU parents . The pair-wise event numbers scale linearly with coalescent time derived from SNPs ( Figure 6d ) in these samples ( Text S2: Coalescent [60]–[64] ) . Previous estimates for the de novo mobile element insertion rate and our own estimate of the MEI mutation rate are one event per 20 births in the human population [23] . Accordingly , we did not expect to find de novo insertions in our sample of two trio children . Among all MEI events detected in the trio offspring against the reference ( 1 , 778 in NA12878 and 1 , 971 in NA19240 ) , we did identify a single de novo candidate insertion in NA12878 , not detected in either parent or in any other sample ( Table S6 , de Novo ) . A subsequent PCR validation experiment revealed that this insertion was , in fact , present in one of the trio parents , but not detected from the sequence data . All in all , our study found no direct evidence for de novo MEI events in the two trio samples . MEI genotyping allows us to estimate MEI heterozygosity within each sample . We define heterozygosity as the count of heterozygous loci across the individual's genome . In a manner similar to the allele frequency analysis , heterozygosity is corrected for detection and genotyping efficiencies ( Materials and Methods: Heterozygosity ) such that it represents the true number of heterozygous loci in the sample . Heterozygosity , π , and the diversity parameter , θ , fit from the allele count spectrum , are related population metrics that depend on the MEI mutation rate , μMEI , and demographic history . In the neutral model ( under mutation-drift equilibrium in the limit of infinite segregating sites and a constant effective population size , Ne ) the two metrics should be approximately equal [65]: ( 1 ) Deviations can be interpreted as evidence for selection pressure , changing demographic parameters , or possibly as changes in the mutation rates . These metrics were originally developed as a framework for SNP analysis but can also be applied to MEI variants . It is this property of heterozygosity that we wish to exploit . A comparison MEI and SNP heterozygosity within the same samples allows a direct comparison of the corresponding mutation rates , because the impact of long-term demography ( here simplified in terms of Ne ) is identical for both variant types . Consequently , the MEI mutation rate can be estimated as: ( 2 ) Given constant mutation rates we would expect proportionality between πSNP and πMEI in samples from different population groups , however a scatter plot of πMEI vs . πSNP over the low coverage pilot samples ( Figure 7a ) shows some deviation . Heterozygosity for the Asian sample group is systematically elevated above the proportionality line ( dashed line ) . Also shown on the scatter plot is a grey region corresponding to SNP and MEI differences between the human and chimpanzee reference genomes [37] , [66] . The MEI insertion rate is known to be roughly 2 . 5 times higher in the human than in the chimpanzee lineage [66] , however , the time dependence of the MEI mutation rate during human evolution is not yet known . For this , we re-expressed the SNP and MEI heterozygosities for each sample in terms of μMEI vs . coalescent time ( Figure 7b ) based on equation ( 2 ) , a constant SNP mutation rate ( μSNP∼1 . 8×10−8 mutations per site per generation [67] ) , and the coalescent time estimated from the SNP heterozygosity . Characteristic MEI mutation rates for each population group were derived from Eq . ( 2 ) with <πMEI> and <πSNP> averaged over the samples in the group . Values of μMEI for each population and each element family are compared to μMEI derived from θ fitting ( Figure 7c ) and are listed in Table 4 with 95% statistical confidence intervals . Confidence intervals from the allele frequency fits ( error bars in Figure 7c ) are larger than statistical errors from the averaged heterozygosities over samples ( error bars within the circles on Figure 7c ) because each sample provided independent observations for the average heterozygosity , whereas in the allele frequency spectra fits all samples were combined . Both estimates are subject to systematic errors that may arise from the detection , genotyping , and correction procedures . To test for systematic biases in μMEI we re-processed both allele frequency spectra and heterozygosity estimates over a range of genotype selection thresholds ( Text S2: Stability , Figure S18 ) and found consistent trends in μMEI among the population groups and element families , although the overall scales of the mutation rates are uncertain to 20% . Values of the element specific mutation rates in Table 4 and Figure 7c are consistent with previous reports [23] , [25] , [68] . In summary , careful error analysis led us to believe that the differences in the mutation rate observed between the different population sample groups are likely to result from biological processes , rather than measurement or analytical artifacts .
MEI alleles propagate within population groups much like other predominantly neutral polymorphisms . MEI allele frequency spectra from the low coverage samples are in general agreement with expectations from the standard neutral model for allele drift in a population . The major differences in allele frequency spectra between non-reference and reference MEI ( Figure 5a–5c ) are explained by the ascertainment condition that the derived MEI allele occurs in a given sample ( the reference ) and are in agreement with expectations based on a coalescent simulation of MEI population drift ( Figure S17 ) . MEI allele frequency spectra among the three population groups exhibits a similar trend to SNPs ( Figure 6b ) , although the MEI spectrum in the Asian samples is a poor fit to the θ/i form ( χ2/d . f . ∼2 from Table 4 ) with an excess of high frequency alleles and a deficit at low frequency ( Figure 5e ) . MEI allele frequencies were based on MEI detected and genotyped across three element families ( Alu , L1 , and SVA ) , from both non-reference and reference MEI , and multiple detection methods ( RP and SR ) , each with characteristic detection sensitivities and false detection rates . Corrections for these effects , as well as genotyping efficiencies , were included in the allele frequency spectra . Measurements of MEI heterozygosity offer a more direct method to estimate MEI insertion rates . Like the allele frequency spectrum , heterozygosity is dependent on accurate genotyping and includes corrections for efficiency losses , but in this case the corrections were made on a per sample basis , which is more specific since sample coverage is the dominant limitation for detection and genotyping power ( Figure S6 ) . The heterozygosity measurement also has an advantage in that each sample is an independent estimate of the population average <πMEI> and <πSNP> . The heterozygosity measurements revealed evidence for differential MEI mutation rates among the three population groups . The probability that the Asian population samples have the same MEI mutation rate as the other two population groups is very low ( paired t-test p-value<10−6 ) . We tested the stability of this result by varying the genotype selection criteria across a range of threshold ( Figure S18 ) and found that the differential MEI rate effect is indeed stable . Sequence coverage in the 1000GP low-coverage pilot data was roughly the same for all three continental population groups ( Table S2 ) , so we do not expect coverage differences to generate significant systematic biases in these population comparisons . The question remains whether the differential MEI mutation rate between populations is driven by a shared increase of μMEI within Homo sapiens , as suggested by Figure 7b , or simply by varying insertion rates among different populations . The pilot data is consistent with either interpretation , so data from more populations ( more than 30 population groups from five continents are planned for the full 1000GP ) will be needed to discriminate between the two hypotheses . Based on the global values for the diversity parameters θMEI and πMEI ( Table 4 ) , and the neutral model , our rough estimate of the total number of MEI segregating sites in the human population with allele frequency>10% is 4500 , and 9000 for frequency>1% , with 20% uncertainty arising from parameter estimates . Counting only those sites with a sufficient number of genotypes to measure allele frequency , our dataset contains more than half of the segregating human MEI sites with frequency>10% . This study of the 1000GP pilot datasets is a sizable step toward a complete population-based catalog of common human MEI polymorphisms , made possible by targeting both non-reference and reference MEI events in the human genome . We identified 7 , 380 polymorphic mobile element insertions from the Alu , L1 , and SVA families . Based on experimental validation of random subsets of loci we estimate that the false discovery rate in this study is less than 5% . Detection power for common alleles ( allele frequency>10% ) varies between non-reference MEI ( 70%–80% ) and reference MEI ( >90% ) . We were also able to assemble the inserted sequence for more than 1 , 000 non-reference Alu MEI and found consistent proportions of Alu sub-families in comparison to MEI identified in HuRef . This comprehensive variant discovery and genotyping effort allowed us to directly compare the segregation properties of different variant types from the same dataset . Our analysis revealed that , to a first approximation , the evolution of MEI variants is similar to SNPs and consistent with neutral models [52] , [53] , except in exonic regions where they are subject to negative selection on the scale that acts against SNP variants resulting in stop codon loss . An intriguing finding from our data , however , is the detection of signals suggesting a recent increase in MEI rates in humans .
Both the SR and the RP methods were based on identification of non-reference MEI as clusters of mapped DNA fragments in which one end mapped to the consensus sequence of a mobile element while the other end was uniquely mapped to the reference genome in a location inconsistent with a known mobile element location in the reference ( Figure 1a–1b ) . The RP method required at least two MEI supporting fragments across both the 5′ and 3′ insertion breakpoints for each candidate MEI from the pooled datasets ( the low coverage and trio pilot data were pooled separately ) . The SR method required only one MEI supporting fragment across either the 5′ or 3′ breakpoints for candidate events . We used 52 consensus element sequences from Repbase [69] ( www . girinst . org , version 14 . 03 , Table S11 ) to identify reads mapping to mobile elements . The RP method used Mosaik [70] ( bioinformatics . bc . edu/marthlab/Mosaik , version 0 . 9 . 1176 ) for read pair mapping of Illumina paired-end data to the NCBI36 human reference ( build 36 . 3 ) and the Spanner [40] program to identify non-reference MEI by clustering supporting fragments [40] , [71] , [72] . The SR method also used Mosaik to align 454 data , for full read mapping as well as for split-read mapping . We used extensive simulation experiments [73] to optimize detection methods , algorithm parameters , and post-process MEI candidate event selection filters ( further details are provided in Text S2 ) . The 2 , 010 reference MEI events are a subset of the 1000GP pilot release of 22 , 025 deletions [30] . 95% of the MEI sites detected as deletions were found by more than one algorithm but the dominant mapping algorithms were Mosaik , and Maq [74] , with detection algorithms Spanner , Pindel [41] , BreakDancer [38] , and GenomeSTRiP [39] . Two selection criteria ensure that a given deletion corresponds to a true variant MEI inser a given deletion corresponds to a true variant MEI inserted in the reference genome: Non-reference MEI detected by the SR and RP methods were merged according to a 100 bp matching window around the leftmost insertion coordinates . To assess call set intersections between this study and other published lists of non-reference MEI , we used a matching window of 200 bp around each insertion position . We adopted the ‘leftmost’ coordinate convention ( Figure S1 ) , in keeping with 1000GP call sets , whereas other studies used rightmost or unclear coordinate conventions . The respective scales of the matching windows were dictated by the characteristic position resolutions of the algorithms ( Figure S7 , Figure S10 ) , which varied considerably from study to study . Redundant loci from recent publications were not counted multiple times in Figure 2e . To identify matching reference MEI to other studies we required at least 50% reciprocal overlap between the starting and ending NCBI36 deletion coordinates . For SR detection the relevant coverage statistic is 454 base coverage , counts of aligned reads covering a given base , averaged across the accessible genome . For RP detection the driving coverage statistic is Illumina read-pair spanning coverage , counts of fragments in which the non-sequenced segment of the fragment between the reads cover a given base , averaged across the genome ( Table S2 ) . The four non-reference MEI event lists ( Table 1 ) were submitted to the 1000 Genomes Structural Variation subgroup for validation experiments to assess false detection rates . 200 loci from each list were randomly selected for primer design and subsequent PCR validation . Primers were designed as described previously [32] , [36] to span across the insertion breakpoint and to guarantee unique mapping to build 36 . 3 . In addition to the estimation of the false detection rates , validation genotypes were derived from gel-band size comparison for each sample and site tested by PCR . We also used the validation data to estimate detection sensitivity based on the overlap of events called between the two independent sequence data platforms and algorithms . For loci with ambiguous PCR results , no amplification , or amplification of only the empty insertions site , a second primer pair was designed . For the primer design , 600 bp of flanking sequence on either side of the insertion site was retrieved from genome . ucsc . edu using Galaxy . Alu elements within the flanking sequence were masked to “N” using RepeatMasker ( repeatmasker . org ) . Primers were designed with BatchPrimer3 v2 . 0 in the flanking sequence , leaving at least 100 bp before and after the predicted insertion site . Next , all primers were tested with BLAT to determine the number of matches in the human genome . If one primer of a primer pair matched several times and the other primer was unique , a virtual PCR was performed . Primer combinations with one predicted PCR product were tested on our panel . Otherwise primers were designed manually ( if possible ) after repeat-masking the flanking sequence with the complete repeat library . In addition , for L1 and SVA loci without unambiguous PCR amplification , primers were designed , placing one primer within the 3′ end of the mobile element sequence [75] . The primers were designed to match the consensus sequences of the youngest L1 and SVA sub-families . All PCR primers were ordered from Sigma Aldrich , Inc . ( St . Louis , MO ) . All LSU-designed PCR primer sequences used in this project can be found at http://batzerlab . lsu . edu . The two non-reference MEI detection methods use independent DNA libraries . So the overlap between the RP and SR are governed by the respective detection sensitivities , statistically akin to the Lincoln-Peterson method [76] used in ecological studies to estimate the size of a population based on two random capture and recapture samplings . This estimate assumes that the two algorithms are sensitive to the same type of events and that the difference between the event lists is a sampling issue . The expression for the detection respective detection sensitivities ( εRP and εSR ) depends on the false detection rates ( fRP and fSR ) provided by the validation experiments , the counts of loci detected by each method ( nRP and nSR ) , and the count of loci detected by both methods ( nRP . SR ) : ( 3 ) Given detection sensitivities εRP and εSR from independent datasets and methods , the combined detection sensitivity ( RP+SR ) becomes: ( 4 ) for samples in which both types of data were available ( e . g . trio samples NA12878 and NA19240 ) . For reference MEI we used available genotypes calculated by GenomeSTRiP [39] for the 1000GP deletion call set . GenomeSTRiP results were not readily available for non-reference MEI so we developed a simple Bayesian framework to estimate the posterior probability for each possible genotype . The posterior genotype probability is: ( 5 ) where NALT and NREF are the counts of fragments supporting the alternate and reference alleles respectively; g is the genotype ( i . e . homozygous reference allele , heterozygous , homozygous insertion allele ) ; P ( g ) is the prior probability for the genotype g ( a flat prior was used , P ( g ) = 1/3 ) ; pg is the expected fraction of insertion fragments given a genotype g ( i . e . pg = 0 . 5 for heterozygous insertions , pg∼0 for homozygous reference , and pg∼1 for homozygous insertions ) ; Pbin ( NALT , NALT+NREF , pg ) is the binomial probability that NALT+NREF fragments will fluctuate to NALT , given an expected fraction pg . The called genotype for a given site is the genotype with the maximum posterior probability . The Bayesian framework also provides genotype likelihoods , which are used to construct genotyping quality metric ( GQ ) for each site and sample . The GQ value adopts the “phred” quality convention: ( 6 ) Where P ( g|NALT , NREF ) is the posterior probability for the called genotype from Eq . ( 5 ) . GQ is highly dependent on the total number of supporting fragments ( reference plus insertion ) . A selection of sites at GQ = 7 should correspond to roughly to 80% genotyping accuracy and corresponds to sites with 2 or more supporting fragments . MEI loci with at least 25 genotyped samples per population ( 50 samples for the combined population spectra ) were included in allele frequency spectra . Sites of GQ≥7 non-reference MEI and of GQ≥10 reference MEI were included . For loci with more than 25 genotyped samples , a random subset of 25 was used for the allele count spectra ( Figure 5 ) . For the allele frequency spectra ( Figure 6a–6b ) we projected down to 25 samples according to the hypergeometric distribution [56] , [57] which smooths the spectrum while retaining all available information from loci with more than 25 genotyped samples . Hypergeometric projection was not used to build the allele count spectra used for fitting purposes because it introduces correlation among allele count bins . We constructed the allele count spectra for MEI events detected as insertions and those detected as deletions separately to account for the distinct ascertainment conditions before combining them into the aggregate spectrum . The combined spectrum includes corrections for respective detection and genotyping efficiencies: ( 7 ) where nREF ( i ) and nNREF ( i ) are the counts of genotyped loci for reference ( e . g . Figure 5b ) and non-reference MEI ( Figure 5a ) at allele count i , KREF and KNREF are scaling factors for each detection mode ( not dependent on i ) , and nMEI ( i ) is the net count of MEI variant loci at a given allele count i ( Figure 5c–d ) . The correction factors depend on the detection sensitivity ( εDET ) and genotyping efficiency ( εGEN ) as K = ( εDET·εGEN ) −1 . Genotyping efficiency is simply the fraction of detected sites with 25 more genotyped samples ( Table S9 ) . Detection efficiency is described above ( Detection specificity and sensitivity , Figure 3d ) . SNP allele frequency spectra ( Figure 6b ) were based on the 1000GP release VCF files ( ftp://ftp-trace . ncbi . nih . gov/1000genomes/ftp/pilot_data/release/2010_07/ ) with no corrections . SNP allele frequency spectra were projected down to 50 samples using the hypergeometric distribution [56] , [57] . Only non-reference MEI with insertion position confidence intervals entirely within annotated regions ( Gene , UTR , CDS ) were counted . No MEI that were subsequently invalidated were counted . Relative to random placement across the genome the MEI suppression or boost factor is defined as: ( 8 ) where Ntot is the total number of MEI loci , Ltot = 2 . 85×109 bp is the length of the accessible genome , Lobs is the size of the region ( 1 MB or the sum of coding regions ) where the number of observed MEI is Nobs . The null model for MEI placement results in a binomially distributed Nobs , which is generally not far from what we observe , except in the case of functional regions ( suppressed ) and HLA ( hotspot ) . For the calculation of MEI inserted in CDS regions , only non-reference MEI were considered , since an embedded reference MEI precludes annotation as a coding sequence . MEI and SNP heterozygosity for each sample were calculated from the counts of genotyped heterozygous sites . For MEI , the total numbers of genomic heterozygous sites were estimated with corrections for genotyping efficiency and detection sensitivity . The genotyping efficiency for a given sample is the fraction of detected loci with high quality ( GQ≥7 non-reference , of GQ≥10 reference MEI ) genotypes . There is also a sample specific correction for genotyping bias against heterozygotes at sites with limited fragment coverage: ( 9 ) where the sum is over genotyped loci passing the GQ threshold for the given sample , Nloci is the count of such sites , NF is the count of supporting fragments ( both reference and insertion allele ) at the site and binopdf is the binomial probability density function that a heterozgygous site will randomly produce only reference supporting fragments . The KHET correction was applied only to the non-reference MEI component because , for reference MEI detected as deletions , GenomeSTRiP used not just supporting fragment information for genotype likelihoods , but also used Beagle to impute missing data from linkage with local SNP haplotypes to identify heterozygous deletions . For each sample ( s ) the number of heterozygous MEI in the genome is estimated as: ( 10 ) where πMEI ( s ) is the heterozygosity for sample s , πREF ( s ) and πNREF ( s ) are the raw counts of heterozygous sites for reference and non-reference MEI , εDET is the detection sensitivity , and εGEN ( s ) is the fraction of detected sites genotyped in the given sample ( Figure S13 , Table S9 ) . SNP heterozygosity is derived from the raw counts of heterozygous sites . All values of heterozygosity are in units of heterozygous sites per genome , and the length of the genome is considered to be the accessible genome ( 2 . 85 Gb ) [36] . The SNP heterozygosity values are transformed to rough estimates of the corresponding coalescent time ( Figure 7b ) [77]: ( 11 ) where , μSNP = 1 . 8×10−8 mutations per site per generation , and TGEN∼25 y is the average time between generations .
|
We embarked on this study to explore the 1000 Genomes Project ( 1000GP ) pilot dataset as a substrate for Mobile Element Insertion ( MEI ) discovery and analysis . MEI is already well known as a significant component of genetic variation in the human population . However the full extent and effects of MEI can only be assessed by accurate detection in large whole-genome sequencing efforts such as the 1000GP . In this study we identified 7 , 380 distinct genomic locations of variant MEI and carried out rigorous validation experiments that confirmed the high accuracy of the detected events . We were able to measure the frequency of each variant in three continental population groups and found that inherited MEI variants propagate through populations in much the same way as single nucleotide polymorphisms , except that MEI are more strongly suppressed in protein coding parts of the genome . We also found evidence that the MEI mutation rate has not been constant over human population history , rather that different populations appear to have different characteristic MEI mutation rates .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"functional",
"genomics",
"genetic",
"mutation",
"genome",
"evolution",
"genome",
"scans",
"neutral",
"theory",
"population",
"genetics",
"genome",
"sequencing",
"mutation",
"genome",
"analysis",
"tools",
"genome",
"databases",
"mutation",
"types",
"mutation",
"databases",
"genetic",
"polymorphism",
"biology",
"genetics",
"genomics",
"computational",
"biology",
"genetics",
"and",
"genomics",
"human",
"genetics"
] |
2011
|
A Comprehensive Map of Mobile Element Insertion Polymorphisms in Humans
|
U87MG is a commonly studied grade IV glioma cell line that has been analyzed in at least 1 , 700 publications over four decades . In order to comprehensively characterize the genome of this cell line and to serve as a model of broad cancer genome sequencing , we have generated greater than 30× genomic sequence coverage using a novel 50-base mate paired strategy with a 1 . 4kb mean insert library . A total of 1 , 014 , 984 , 286 mate-end and 120 , 691 , 623 single-end two-base encoded reads were generated from five slides . All data were aligned using a custom designed tool called BFAST , allowing optimal color space read alignment and accurate identification of DNA variants . The aligned sequence reads and mate-pair information identified 35 interchromosomal translocation events , 1 , 315 structural variations ( >100 bp ) , 191 , 743 small ( <21 bp ) insertions and deletions ( indels ) , and 2 , 384 , 470 single nucleotide variations ( SNVs ) . Among these observations , the known homozygous mutation in PTEN was robustly identified , and genes involved in cell adhesion were overrepresented in the mutated gene list . Data were compared to 219 , 187 heterozygous single nucleotide polymorphisms assayed by Illumina 1M Duo genotyping array to assess accuracy: 93 . 83% of all SNPs were reliably detected at filtering thresholds that yield greater than 99 . 99% sequence accuracy . Protein coding sequences were disrupted predominantly in this cancer cell line due to small indels , large deletions , and translocations . In total , 512 genes were homozygously mutated , including 154 by SNVs , 178 by small indels , 145 by large microdeletions , and 35 by interchromosomal translocations to reveal a highly mutated cell line genome . Of the small homozygously mutated variants , 8 SNVs and 99 indels were novel events not present in dbSNP . These data demonstrate that routine generation of broad cancer genome sequence is possible outside of genome centers . The sequence analysis of U87MG provides an unparalleled level of mutational resolution compared to any cell line to date .
Grade IV glioma , also called glioblastoma multiforme ( GBM ) , is the most common primary malignant brain tumor with about 16 , 000 new diagnoses each year in the United States . While the number of cases is relatively small , comprising only 1 . 35% of primary malignant cancers in the US [1] , GBMs have a one-year survival rate of only 29 . 6% , making it one of the most deadly types of cancer [2] . Recent clinical studies demonstrate improved survival with a combination of radiation and Temozolomide chemotherapy , but median survival time for GBM patients who receive therapy is only 15 months [3] . Due to its highly aggressive nature and poor therapeutic options , understanding the genetic etiology of GBM is of great interest and therefore , GBM has been selected as one of the three initial cancer types to be thoroughly studied in the TCGA program [4] . To that end , numerous cell line models of GBM have been established and used in vast numbers of studies over the years . It is well recognized that cell line models of human disorders , especially cancers , are an important resource . While these cell lines are the basis of substantial biological insight , experiments are currently performed in the absence of genome-wide mutational status as no cell line that models a human disease has yet had its genome fully sequenced . Here , we have sequenced the genome of U87MG , a long established cell line derived from a human grade IV glioma used in over 1 , 700 publications [5] . A wide range of biological information is known about this cell line . The U87MG cell line is known to have a highly aberrant genomic structure based on karyotyping , SKY [6] , and FISH [7] . However , these methods neither provide the resolution required to visualize the precise breakpoint of a translocation event , nor are they generally capable of identifying genomic microdeletions ( deletions on the order of a megabase or less in size ) in a whole genome survey of structural variation . SNP genotyping microarrays can be used to detect regions of structural variation in the forms of loss of heterozygosity ( LOH ) and copy number ( CN ) based on probe intensity , but do not reveal chromosomal joins . To assess the genomic stability of U87MG , the genome was genotyped by Illumina Human 1M-Duo BeadChip microarray . In spite of being cultured independently for several years , the regions of LOH and the CN state of our U87MG genome matched exactly with data retrieved from the Sanger COSMIC database for U87MG [8] , which had been assayed on an Affymetrix Genome-Wide Human SNP Array 6 . 0 . This suggests that although U87MG bears a large number of large-scale chromosomal aberrations , it has been relatively stable for years and is not rapidly changing . This suggests that prior work on U87MG may be reinterpreted based on the whole genome sequence data presented here . The first draft of the consensus sequence of the human genome was reported in 2001 [9] , [10] . The first individual human diploid sequence was sequenced using capillary-based Sanger sequencing [11] . Since then , a few additional diploid human genomes have been published utilizing a variety of massively parallel sequencing techniques to sequence human genomes to varying degrees of coverage , variant discovery , and quality typically costing well over $200 , 000 and several machine months of operation [12]–[16] . For the sequencing of U87MG , we utilized ABI SOLiD technology , which uses a ligation-based assay with two-base color-encoded oligonucleotides that has been demonstrated to allow highly accurate single nucleotide variant ( SNV ) and insertion/deletion ( indel ) detection [17] . Additionally , long mate-paired genomic libraries with a mean insert size of 1–2kb allowed higher clone coverage of the genome , which improved our ability to identify genomic structural variations such as interchromosomal translocations and large deletions . While longer insert sizes would improve resolution of some structural variants , during genomic shearing the highest density of large fragments occurs at 1 . 5kb , allowing a sufficiently complex library to be generated from only 10 micrograms of genomic DNA while still being well powered to identify structural variations . Here , we demonstrate that aligning the two-base color-encoding data with BFAST software and decoding during alignment allows for highly sensitive detection of indels , which have in the past been difficult to detect by short read massively parallel sequencing . For cancer sequencing , it is important to assess not only SNVs , but indels , structural variations and translocations , and it is preferable to extract this information from a common assay platform . A major characteristic of the U87MG cell line that differentiates it from the samples used in other whole genome sequencing projects published thus far is its highly aberrant genomic structure . Due to its heavily rearranged state , we thoroughly and accurately assessed each of these major classes of mutations and demonstrated that small indels , large microdeletions and interchromosomal translocations are actually the major categories of mutations that affect known genes in this cancer cell line . These analyses provide a model for other genome sequencing projects outside major genome centers of how to both thoroughly sequence and assess the mutational state of whole genomes .
From ten micrograms of input genomic DNA , we performed two and a half full sequencing runs on the ABI SOLiD Sequencing System , for a total of five full slides of data [17] . Utilizing the ABI long mate-pair protocol , we produced 1 , 014 , 984 , 286 raw 50bp mate-paired reads ( 101 . 5Gb ) . In some cases the bead was recognized by the imaging software for only one read , thereby producing an additional 120 , 691 , 623 single end reads ( 6 . 0Gb ) . In aggregate , we generated a total of 107 . 5Gb of raw data ( Table 1 ) . We also performed an exon capture approach designed to sequence the exons of 5 , 253 genes ( 10 . 7Mb ) annotated in the Wellcome Trust Sanger Institute Catalogue of Somatic Mutations in Cancer ( COSMIC ) V38 [8] , Cancer Gene Census , Cancer Genome Project Planned Studies and The Cancer Genome Atlas ( TCGA ) [4] GBM gene list using a custom-created Agilent array . This approach used the Illumina GAII sequencing system [18] to sequence captured DNA fragments using a paired end sequencing protocol . This resulted in 9 , 948 , 782 raw 76bp paired end reads ( 1 . 51Gb ) , and a mean base coverage of 29 . 5× . These reads were used to calculate concordance rates with the larger whole genome sequence dataset . The Blat-like Fast Accurate Search Tool ( BFAST ) [19] version 0 . 5 . 3 was used to align 107 . 5Gb of raw color space reads to the color space conversion of the human genome assembly hg18 from University of California , Santa Cruz ( http://hgdownload . cse . ucsc . edu/goldenPath/hg18/bigZips/ , based on the March 2006 NCBI build 36 . 1 ) . Duplicate reads , typically from the same initial PCR fragment during genomic library construction , were inevitable and accounted for 16 . 4% of the total aligned data . These were removed using the alignment filtering utility in the DNAA package ( http://dnaa . sourceforge . net ) . A total of 390 , 604 , 184 paired end reads ( 39 . 06Gb ) , 266 , 635 , 829 ( 13 . 33Gb ) unpaired reads , and 62 , 336 , 824 ( 3 . 12Gb ) single end reads were successfully mapped to a unique location in the reference genome with high confidence for a total of 55 . 51Gb of aligned sequence ( Table 1 ) . For the exon capture dataset , we uniquely aligned 8 , 142 , 874 paired end reads ( 1 . 2Gb ) and 1 , 097 , 000 ( 83Mb ) unpaired reads for a total of 1 . 32Gb of raw aligned sequence ( Table 2 ) . Using the ABI SOLiD reads , we identified small insertions and deletions ( indels ) , single nucleotide variants ( SNVs ) , and structural variants such as large-scale microdeletions and translocation events . The exon capture Solexa reads were used to validate SNVs identified in the SOLiD sequencing . The overall pattern of base sequence coverage from the shotgun reads changes across the genome , and as expected is highly concordant with the copy number state as determined by Illumina 1M Duo and Affymetrix 6 . 0 SNP analysis ( Figure 1 ) . Regions of two normal copies , such as chromosome 3 , showed even base sequence coverage across their entire length ( 12 . 4 reads/base , excluding centromeric and telomeric regions which are not represented accurately in hg18 ) . Meanwhile , regions with one-copy state according to the SNP chip , such as the distal q-arm of chr11 and the distal p-arm of chr6 , show about half the base sequence coverage ( 7 . 2 reads/base ) as a predicted two-copy region . Likewise , predicted three-copy state regions , such as the distal q-arm of chr13 , show about 1 . 5 times the base sequence coverage of a predicted two-copy region . A complete deletion spanning the region on chromosome 9 that includes the CDKN2A gene is also seen in both the SNP chip and ABI SOLiD base sequence coverage . These data show at a very large scale that sequence placement is generally correct and supports the copy number state calls from the array based data . Single nucleotide variants ( SNVs ) and small insertions and deletions ranging from 1 to 20 bases ( indels ) were identified from the alignment data using the MAQ consensus model [20] as implemented in the SAMtools software suite [21] . SAMtools produced variant calls , zygosity predictions , and a Phred-scaled probability that the consensus is identical to the reference . To improve the reliability of our variant calls , variants were required to have a Phred score of at least 10 and further needed to be observed greater than or equal to 4 but less than 60 times and at least once on each strand . In total , we identified 2 , 384 , 470 SNVs meeting our filtering criteria . Of these , 2 , 140 , 848 ( 89 . 8% ) were identified as exact matches to entries in dbSNP129 [22] . Exact matches had both the variant and observed alleles in the dbSNP entry , allowing for the discovery of novel alleles at known SNP locations . In total , 243 , 622 SNVs ( 10 . 2% ) were identified as novel events not previously recorded in dbSNP 129 . This rate of novel variant discovery is consistent with other whole human normal genome sequences of European ancestry relative to dbSNP [12] . These SNVs were further characterized based on zygosity predictions from the MAQ consensus model , separating SNVs into homozygous or heterozygous categories ( Table 3 ) . The observed diversity value for SNVs ( θSNV , number of heterozygous SNVs/number of base pairs ) across autosomal chromosomes was 4 . 4×10−4 , which is generally consistent with the normal human genome variation rate . For small ( <21bp ) insertions and deletions , 191 , 743 events were detected with 116 , 964 not previously documented in dbSNP 129 . The same criteria as used for SNVs was used for determining if an indel was novel and they were further classified as homozygous or heterozygous using the SAMtools variant caller ( Table 4 ) . The observed diversity value ( θindel , number of heterozygous indels/number of base pairs ) across autosomal chromosomes was 0 . 38×10−4 . A subset of 38 variants meeting genome-wide filtering criteria , including a 20-base deletion , was tested by PCR and Sanger sequencing with 34 being validated . In summary , 85 . 2% of SNVs ( 23/27 ) , and 100% of small insertions ( 3/3 ) , deletions ( 4/4 ) , translocations ( 3/3 ) and microdeletions ( 1/1 ) were validated in this manner ( Table S1 ) . While this is a small sample , it demonstrates an overall low false positive rate . The size distribution of indels identified in U87MG is generally consistent with previous studies on coding and non-coding indel sizes in non-cancer samples [23]–[25] . Small deletion sizes ranged from 1 to 20 bases in size and their distribution approximates a power law distribution in concordance with previous findings [23] ( Figure 2A ) . There is a small deviation from the power law distribution with an excess of 4-base indels in U87MG's non-coding regions ( Figure 2A , red bars ) [11] , [26] . A similar trend is seen with insertions in non-coding sequence with the maximum observed insertion size of 17 bases ( Figure 2B , red bars ) . The maximum insertion size observed is less than the maximum deletion size because it is easier to align longer deletions than it is to align insertions . Some small insertions and deletions are likely to be larger than the upper limit of 17 and 20 bases actually observed , but the 50-base read length limits the power to align such reads directly . In coding regions , there is a bias towards events that are multiples of 3-bases in length that maintain the reading frame despite variant alleles , suggesting that many of these are polymorphisms ( Figure 2A-deletions , Figure 2B-insertions , blue bars ) . In non-coding regions , only 10 . 8% of indels are a multiple of 3 bases in size , while in coding regions , 27 . 0% are 3 , 6 , 9 , 12 or 15 bases in size . This trend is expected based on past observations of non-cancer samples [11] , [26] . Observed SNV base substitution patterns were consistent with common mutational phenomena in both coding sequences and genome wide . As expected , the predominant nucleotide substitution seen in SNVs is a transition , changing purine for purine ( A<->G ) or pyrimidine for pyrimidine ( C<->T ) . Previous studies have observed that two out of every three SNPs are transitions as opposed to transversions [27] , and we observed that 67 . 4% of our SNVs were transitions , while 32 . 6% were transversions , a 2 . 07∶1 ratio . ( Figure 3 ) However , in coding regions , there appears to be an increase in C->T/G->A transitions and a decrease in T->C/A->G transitions , whereas genome-wide these transitions were approximately equivalent . To assess the coverage depth of the U87MG genome sequence , we followed Ley at al . [13] and required detection of both alleles at most positions in the genome . We utilized the Illumina 1M-Duo BeadChip to find reliably sequenced positions in the genome with an understanding that this may lead to bias towards more unique regions of the genome . In order to best use the SNP genotyping array data , we included only those regions that are diploid based on normal frequency of heterozygous calls and copy number assessment . This effectively permitted us to use the heterozygous calls for assessing accuracy of the short read data for variant calling ( Figure 1 ) . Only SNPs both observed to be heterozygous and that the Illumina genotyping chip called ‘high quality’ were used , which provided a total of 219 , 187 high quality heterozygous SNPs for comparison . 99 . 71% of these were sequenced at least once . After applying variant detection filtering criteria ( see Materials and Methods ) and assessing concordance between the sequence calls and genotyping array calls , 93 . 71% of the genome was sequenced at sufficient depth to call both alleles of the diploid genome . This is roughly equivalent to the likelihood of sufficient sampling of the whole genome when repeats and segmental duplications are excluded . Notably , a variant allele was observed at every position called heterozygous by SNP chip , while a reference allele was observed at 201 , 414 ( 97 . 94% ) positions . In other words , the SNV detection algorithm uniformly miscalled the homozygous variant allele . Filtering for quality causes a bias toward identifying SNVs at sites that have higher coverage . That said , after SNV quality filtering , diploid coverage of the cytogenetically normal portions of the genome was 10 . 85× for each allele , which is clearly adequate for calling over 90% of the base variant positions on each allele at high accuracy . Because the positions of the genome included on SNP arrays is not a random sampling of the genome , we also assessed mapping coverage genome-wide . Of all bases in the haploid genome , 78 . 9% of the whole reference genome was covered by at least one reliably placed read . Of that portion of the genome , 91 . 9% of all bases were effectively sequenced based on passing variant calling filters ( Phred>10 , >4× coverage , <60× coverage ) . Thus , a total of 72 . 5% of the whole genome was sequenced , including repeats and duplicated regions , which is typical of short sequence shotgun approaches . 10 . 9Mb of genomic sequence was targeted consisting of the amino acid encoding exons of 5 , 235 genes and were sequenced to a mean coverage of 30× using the Illumina GAII sequencer . Given the larger variability of coverage from the capture data , only a subset of these bases ( 8 . 5Mb ) was evaluable to determine the false positive variant detection rate from the complete genomic sequence data . This region contained 1 , 621 SNPs present in dbSNP129 . Within the 8 . 5Mb of common and well-covered sequence in the genomic sequence data and the capture sequence , there were 1 , 780 SNVs called from the genomic sequence . The same non-reference allele was concordantly observed at 1 , 631 positions within the capture data . At 149 positions , the non-reference allele was not observed in the capture data , but the reference allele was detected . However , the mean coverage at these 149 positions was significantly lower than that of the other 1 , 631 positions ( p = 0 . 0003 ) , suggesting that the non-reference allele was not adequately covered and is under called in the capture data . Moreover , of the 1 , 621 dbSNPs in the region , the capture adequately covered only 1 , 515 . In these data there was a bias for the pull down data to under observe the non-reference allele ( Figure S1 ) . The 106 dbSNP positions detected in the ABI whole genome sequence dataset were observed to all call the reported alternate allele from dbSNP . In theory , if these were errors , then non-reference base calls should be randomly distributed to the three alternate base calls . Thus , no discrepancies are reliably identified within the dbSNP overlap when a variant was called in the ABI genomic sequence data . There were a total of 100 novel SNVs detected in the ABI genomic sequence dataset that were also very well evaluated in the Illumina pull down data with at least 20 high quality Illumina reads , such that the ABI sequence could be well validated . Of these , 2 of the 100 discovered variants in the genomic sequence dataset were not observed in the Illumina pull down sequencing dataset . Thus , of the entire 8 . 5mb interval there are 2 unconfirmed variants for an estimated false positive error rate of about 3×10−7 for the whole interval . Alternatively viewed , there were 100 novel SNVs , with a 2% error rate in those novel positions . Thus , the de novo false discovery rate may be as high as 2% . Extrapolating to the whole set of 243 , 622 novel SNVs , we expect up to 4 , 872 false positives SNVs . These observations are roughly concordant with a sampling of 37 novel SNVs ( not in dbSNP ) in the whole genome set selected for testing by Sanger sequencing . Of these , 34 out of 37 ( 92% ) were validated . There are now several publicly available complete genomes sequenced on next generation platforms . We compared the SNVs discovered in U87MG to two of these published genomes: the James D . Watson genome [12] and the first Asian genome ( YanHuang ) [14] . Further , we simultaneously compared each of these to dbSNP version 129 [22] . Compared with dbSNP , 10 . 2% of U87MG SNVs , 9 . 5% of Watson SNVs , and 12 . 0% of YanHuang SNVs were not present within dbSNP ( Figure 4 ) . As U87MG was derived from a patient of Caucasian ancestry , which is confirmed by genotyping , it is unsurprising to see a higher overlap with dbSNP for U87MG than for YanHuang . Between the three genomes themselves , 44 . 7% of U87MG SNVs overlapped with Watson SNVs while 60 . 0% of SNVs were in common with YanHuang SNVs . Only 8 . 5% of dbSNP SNVs were shared between Watson and U87MG , while 11 . 3% of them were shared between YanHuang and U87MG . Thus , there is not a substantially higher amount of SNVs in the U87MG cancer genome relative to normal genomes . We utilized the predictable insert distance of mate-paired sequence fragments to directly observe structural variations in U87MG . Our target insert size of 1 . 5kb gave us a normal distribution of paired end insert lengths ranging from 1kb to 2kb with median around 1 . 25kb and mean around 1 . 45kb in the actual sequence data ( Figure S2 ) . We identified 1 , 314 large structural variations , including 35 interchromosomal events , 599 complete homozygous deletions ( including a large region on chromosome 9 containing CDKN2A/B , which commonly experience homozygous deletions in brain cancer ) , 361 heterozygous deletion events , and 319 other intrachromosomal events ( Table 5 ) . The 599 complete microdeletions summed up to approximately 5 . 76Mb of total sequence , while the 361 heterozygous microdeletions summed to 5 . 36Mb of total sequence . Most of the microdeletions were under 2kb in total size . Because of the high sequence coverage and mate pair strategy each event was supported by an average of 138 mate pair reads . Mispairing of the mate pairs did occur occasionally due to molecular chimerism in the library fabrication process , but such reads occur at a low frequency ( <1/40 of the reads ) . Thus , the true rearrangement/deletion events were highly distinct from noise in well-mapped sequences . Interchromosomal events included translocations and large insertion/deletion events where one part of a chromosome was inserted into a different chromosome , sometimes replacing a segment of DNA . All together , these structural variations show a highly complex rearrangement of genomic material in this cancer cell line ( Figure 5 ) . All identified structural variants are summarized in Table S2 . We note as well that even when breakpoints are within genome-wide common repeats there can be sufficient mapping information to reliably identify the translocation breakpoint ( Figure S3 ) . The thirty-five interchromosomal events often coincided with positions of copy number change based on the average base coverage ( Figure 5 ) . Figure 6 shows two interchromosomal events between chromosomes 2 and 16 . The events on chromosome 16 are less than 1kb apart while those on chromosome 2 are about 160kb apart . Based on the average base coverage , there appears to be a loss of genomic material between the event boundaries on their respective chromosomes , shifting from two to one copy . Although we are unable to determine the origin of such an event , it appears that there was an interchromosomal translocation between chromosomes 2 and 16 with a loss of the DNA between the identified regions on each chromosome . A subset of 3 translocations were confirmed by amplifying DNA from the breakpoint-spanning region by polymerase chain reaction and sequencing by dideoxy Sanger sequencing ( Table S1 ) . Each confirmed the predicted breakpoint to within 100 nucleotides of the correct position . In a subset of cases , unmapped short read fragments could be identified from the shotgun short read data that span the breakpoint and are concordant at base resolution with Sanger sequencing of PCR amplified product spanning the breakpoint The SNVs and indels identified in U87MG were assessed for their potential to affect protein-coding sequence . We considered variants predicted to be homozygous and to affect the coding sequence of a gene through a frameshift , early termination , intron splice site , or start/stop codon loss mutation as causing a complete loss of that protein . We chose to focus on homozygous null mutations for two major reasons . First , this is an interesting set of genes that we can predict from the whole genome data are non-functional within this commonly used cell line . Although heterozygous mutations can certainly affect gene products in multiple ways , it is difficult to assess their effect from genomic data alone . Second , by cross-referencing such null mutations with known regions of common mutation in gliomas we can pick out specific candidates that are of interest to the glioma community . Of the 2 , 384 , 470 SNVs and 191 , 743 small indels in U87MG , a total of 332 genes are predicted to have loss-of-function , homozygous mutations as a consequence of small variants ( Table S3 ) . Of these , 225 genes contained variants matching alleles annotated in dbSNP ( version 129 ) , while 107 contained novel variants not observed in dbSNP . We further divided these homozygous mutant genes by variant type . Of genes mutated by SNVs , 146 contained variants present in dbSNP while only 8 were knocked out by variants not in dbSNP . The ratio of known SNPs causing loss-of-function mutations to total known SNPs ( 146/2 , 140 , 848 = 6 . 82×10−5 ) was not significantly different from the ratio of novel SNVs causing loss-of-function mutations over total novel SNVs ( 8/243 , 622 = 3 . 28×10−5; p = 0 . 04 ) . This indicates that many of the possible de novo point mutations may indeed be rare inherited variants made homozygous by chromosomal loss of the normal allele . In contrast to the trend in SNVs , small indels that homozygously mutated genes were more often novel . There were 79 genes predicted homozygously mutated by indel variants reported in dbSNP while 99 were predicted mutated by novel indels . Despite this trend , however , there was not a significant enrichment of deleterious indels among the novel indels ( 99/191 , 743 = 5 . 16×10−4 ) compared to the known indels ( 79/116 , 964 = 6 . 75×10−4; p = 0 . 08 ) This suggests that the difference in ratios of novel versus documented SNVs ( 8 vs . 146 ) and indels ( 99 vs . 79 ) is the result of compositional bias in dbSNP129 , which contains a far greater number of SNPs compared to indels . We also assessed the structural variants in U87MG for whether or not they were likely to affect a gene . Two different criteria were used to determine if translocations and microdeletions impacted a coding region , both predicted to produce an aberrant or nonfunctional protein . Using the UCSC known gene database , we identified 35 genes affected by interchromosomal translocations , 145 affected by complete deletions , 91 affected by heterozygous deletions and 166 affected by other intrachromosomal translocations ( Table 4 ) . Interchromosomal translocation events were significantly enriched for occurring at positions where they would affect genes with 32 out of 35 events ( 91 . 4% ) occurring within 1kb of a gene ( p<0 . 0001 ) , while only 44 . 1% of the reference genome is within 1kb of a known gene . In total , intrachromosomal events did not display this enrichment with 145/319 ( 45 . 5% ) falling within 1kb of a gene ( p = 0 . 67 ) . However , we ran a set of simulations to assess whether microdeletions were enriched to overlap exons because we noted that 585 of our 599 complete microdeletions were less than 10kb in length with a mean size of 1 . 8kb . We ran 100 , 000 simulations randomly placing 600 microdeletions of 2kb lengths and determined how many times a microdeletion spanned an exon . In this way , we demonstrated that complete ( homozygous ) microdeletions under 10kb in size spanned exons slightly more often than by chance with a simulated p-value of . 046 . Similar assessment of microdeletions greater than 10kb in size did not find evidence of enrichment . These findings suggest that small microdeletions may preferentially occur within genes as opposed to being randomly distributed across the genome , but the signal is not strong from the available data . Genes affected by structural variations are summarized in Table S4 . The annotation tool DAVID was used to further examine the biological significance of the list of likely knockout mutations ( including genes affected by SNVs , indels , microdeletions and translocation events ) using the EASE analysis module . After gene ontology ( GO ) analysis , 18 GO terms were nominally enriched and associated with the mutated gene with a p-value < = 0 . 01 ( Table S5 ) . These GO enrichments include cell adhesion ( GO:0007155 and GO:0022610 ) , membrane ( GO:0044425 ) , and protein kinase regulator activity ( GO:0019887 ) . The list of genes was also compared to the list of cancer-associated genes maintained by the Cancer Gene Census project ( http://www . sanger . ac . uk/genetics/CGP/Census/ ) . For SNVs and small indels , eight were observed in the census list , but this is not unexpected given the large number of mutations found in this cell line ( p = 0 . 21 ) . Two CGC genes were affected by complete microdeletions ( CDKN2A and MLLT3 ) , and one gene each was affected by heterozygous microdeletions ( IL21R ) and interchromosomal translocations ( SET ) . These included genes previously annotated as mutated in instances of T cell prolymphocytic leukemia ( TCRA and MLLT3 ) , glioma ( PTEN ) , endometrial cancer ( PTEN ) , anaplastic large-cell lymphoma ( CLTCL1 ) , prostate cancer ( ETV1 & PTEN ) , Ewing sarcoma ( FLI1 and ETV1 ) , desmoplastic small round cell tumor ( FLI1 ) , acute lymphocytic leukemia ( FLI1 and MLLT3 ) , clear cell sarcoma ( FLI1 ) , sarcoma ( FLI1 ) , myoepithelioma ( FLI1 ) , follicular thyroid cancer ( PAX8 ) , non-Hodgkin lymphoma ( IL21R ) , acute myelogenous leukemia ( SET ) , fibromyxoid sarcoma ( CREB3L2 ) , melanoma ( XPC ) , and multiple other tumor types ( PTEN and CDKN2A ) . We also explored the overlap of genes with mutations in GBMs according to the Cancer Genome Atlas ( TCGA ) with those we predicted are homozygously loss-of-function mutated in U87MG ( Table S5 ) . Seven genes mutated in U87MG by SNVs or indels were also found mutated within the TCGA sample ( PTEN , LTF , KCNJ16 , ABCA13 , FLI1 , MLL4 , DSP ) . This overlap is not statistically significant ( p = 0 . 16 ) . Ten additional genes overlapped , including two genes mutated by interchromosomal translocations ( CNTFR , ELAVL2 ) , three genes mutated by intrachromosomal translocations ( ANXA8 , LRRC4C , ALDH1A3 ) , and five by homozygous microdeletions ( CDKN2A , CDKN2C , MTAP , IFNA21 , TMBIM4 ) . Finally , in order to place the homozygous mutations of U87MG in context relative to GBM mutational patterns as a whole , the Genomic Identification of Significant Targets in Cancer ( GISTIC ) method [28] was applied to 293 glioblastoma samples with genome wide copy number information available from the TCGA . This yielded a list of significant , commonly deleted regions present across glioblastomas as a group and highlights genes commonly mutated in GBMs . These data indicate that all or parts of chromosomes 1 , 6 , 9 , 10 , 13 , 14 , 15 , and 22 are commonly deleted within GBMs as a group . In total , these regions comprise 915 , 306 , 764 bases , covering roughly 30 percent of the genome . In order to highlight genes homozygously mutated in U87MG that are within the regions of common loss , we cross-referenced these lists and found that 62/332 ( 19% ) are within the GISTIC defined regions . This does not suggest a significant overlap of homozygously mutated genes in U87MG with commonly deleted regions , but those mutated genes that do overlap may be of increased relevance to cancer . Two of the 62 genes are also in the Cancer Gene Census: PTEN and TCRA . We propose that a subset of the genes mutated in U87 within these commonly deleted regions may be the specific targets of mutation and should be assessed on larger sample sets . ( Table S5 and Figure S4 ) .
Reported individual human genome sequencing projects using massively parallel shotgun sequencing with alignment to the human reference genome clearly indicate the practicality of individual whole genome sequencing . However , the monetary cost of data generation , data analysis issues , and the time it takes to perform the experiments have remained substantial limitations to general application in many laboratories . Here we demonstrate enormous improvements in the throughput of data generation . Using a mate-pair strategy and only ten micrograms of input genomic DNA , we generated sufficient numbers of short sequence reads in approximately 5 weeks of machine operation with a total reagent cost of under $30 , 000 . We believe this makes U87MG the least expensive published genome sequenced to date signaling that routine generation of whole genomes is feasible in individual laboratories . Further , the two-base encoding strategy employed within the ABI SOLiD system is a powerful approach for comprehensive analysis of genome sequences and , in concert with BFAST alignment software , is able to identify SNVs , indels , structural variants , and translocations . Of particular interest in whole-genome resequencing studies such as this one is how much raw data must be produced to sequence both alleles using a shotgun strategy . Here , 107 . 5Gb of raw data was generated . Of this , 55 . 51Gb was mapped to unique positions in the reference genome . In effect , this results in a mean base coverage of 10 . 85× per allele within non-repetitive regions of the genome . Repetitive regions are of course undermapped , as their unique locations are more difficult to determine . This level of oversampling is adequate for high stringency variant calling ( error rate less than 5×10−6 ) at 93 . 71% of heterozygous SNP positions . There may be some biases in library generation resulting in bases that are not successfully covered even if they are relatively unique , but solutions to this may be found in performing multiple sequencing runs with varied library designs , as suggested in other studies [17] . With rapid advances in the generation of massively parallel shotgun short reads , one of the major computational problems faced is the rapid and sensitive alignment of greater than 1 billion paired end reads needed to resequence an individual genome . We demonstrate a practical solution using BFAST , which was able to perform fully gapped local alignment on the two-base encoded data to maximize variant calling in less than 4 days on a 20-node 8-core computer cluster . Comparing U87MG SNVs with the James Watson [12] and YanHuang [14] genome projects' SNVs displays differences in SNV detection between the three projects . Being derived from a Caucasian individual , U87MG and James Watson are expected to share more SNVs than U87MG and YanHuang . However , when we compared SNVs between U87MG and these two genomes , more SNVs were actually shared between U87MG and YanHuang . Meanwhile , the YanHuang project called significantly more SNVs in total than both our U87MG sequencing project and the James Watson project . These results stress that utilizing different sequencing platforms ( U87MG-ABI SOLiD , James Watson-Roche 454 , YanHuang-Illumina Solexa ) , alignment tools ( U87MG-BFAST , James Watson-BLAT , YanHuang-SOAP ) and analytical approaches results in finding different quantities of SNVs . The higher genomic coverage in our U87MG sequence relative to James Watson and the increased sensitivity of BFAST relative to BLAT and SOAP were counted on to find highly robust variants . This is particularly important when sequencing a cancer genome because of the interest in finding novel cancer mutations as opposed to common polymorphisms . The genomic sequence demonstrates global differences in variant type across the coding and non-coding portions of the human genome . By increasing the sensitivity of indel detection , we revealed that small indels have mutated genes at a higher rate than SNVs . A larger proportion of the indels identified are predicted to cause a protein coding change compared to SNVs ( 178/191 , 743 indels vs . 154/2 , 384 , 470 SNVs ) . In U87MG , there is a relative increase in 4-base indels genome-wide , which has been observed in other normal genomes [23]–[25] ( Figure 2 , red bars ) . However , indels found in coding regions exhibit a bias toward events that are multiples of 3-bases in length ( Figure 2 , blue bars ) presumably selected to maintain reading frame . Thus , many of these events are likely to be polymorphisms and not disease related genomic mutations [25] . Similarly , the nucleotide substitution frequencies demonstrate a bias in coding regions compared to non-coding . Two-thirds of the substitutions were transitions genome-wide , as expected [27] , but there was an enrichment of CG->TA transitions in coding regions ( Figure 3 ) . It is well established that the most common source of point mutations and SNPs in primates is deamination of methyl-cytosine ( meC ) , causing transition to a thymine ( T ) [16] , [29] , and there is circumstantial evidence of that in U87MG's genome as well . The resolution of genome-wide chromosomal rearrangements is substantially improved by the mate-pair strategy , coupled with sensitive and independent alignment of the short 50-base reads ( Figure 5 ) . Based on published SKY data , we anticipated 7 interchromosomal breakpoints [6] . However , whole-genome mate-paired sequence data revealed the precise chromosomal joins of 35 interchromosomal events , which account for previously observed chromosomal abnormalities in U87MG but at additional finer scale resolution ( Figure 5 , Figure 6 , Figure 7 ) . The translocation events were enriched in genic regions with 32/35 ( 91 . 4% ) occurring within 1kb of genes . A weaker , but still noticeable enrichment over genes occurs with microdeletions as well , which are generally missed by other experimental techniques like DNA microarrays . Thus , within the overall mutational landscape of this cancer cell line , translocations and structural variants preferentially occurred over genes , supporting a model where cancer mutations occur via structural instability rather than novel point mutations . Delving into the functional effects of the mutations in U87MG through gene ontology and cross-referencing the literature , we found a large number of known and predicted cancer mutations present in the cell line . There is always a concern when dealing with a cancer cell line that mutations will be more related to its status as a cell line than to the cancer it was derived from . While this remains a concern , the large number of predicted and known cancer genes present in U87MG suggests other genes mutated in it have relevance to cancer as well . Using GISTIC to find regions with common deletions in glioma samples , we highlight 60 genes that are mutated in U87MG and are located in regions that are commonly deleted in GBMs that are not included within the Cancer Gene Census list as potential candidate mutational targets in GBMs ( Table S5 ) . Cancer cell lines are commonly used as laboratory resources to study basic molecular and cellular biology . It is clearly preferable to have complete genomic sequence for these valuable resources . U87MG is the most commonly studied brain cancer cell line and is highly cytogenetically aberrant . While this made the sequencing and mutational analysis more challenging , it serves as a model for future cultured cell line genomic sequencing . Through custom analyses , we found that the mutational landscape of the U87MG genome is vastly more complicated than we would have expected based on the variants discovered in previously published genomes . It is our hope that the increased genomic resolution presented here will direct researchers and clinicians in their work with this brain cancer cell line to create more effective experiments and lead to a greater ability to draw meaningful conclusions in the future .
The NCBI reference genome ( build 36 . 1 , hg18 , March 2006 ) , genome annotations , and dbSNP version 129 were downloaded from the UCSC genome database located at http://genome . ucsc . edu . A local mirror of the UCSC genome database ( hg18 ) was used for the subsequent analysis of variants using included gene models and annotations . The Watson genome variants were downloaded from Cold Spring Harbor Laboratory ( http://jimwatsonsequence . cshl . edu ) with bulk data files available from ftp://jimwatsonsequence . cshl . edu/jimwatsonsequence/ . The YanHuang variants were downloaded from the Beijing Genomics Institute at Shenzhen ( http://yh . genomics . org . cn/ ) with bulk data files available from http://yh . genomics . org . cn/download . jsp . U87MG cells were ordered from ATCC ( HTB-14 ) and cultured in a standard way . Genomic DNA was isolated from cultured U87MG cells using Qiagen Gentra Puregene reagents . DNA was stored at −20C until library generation . Long-Mate-Paired Library Construction: The U87MG genomic DNA 2× 50bp long mate-paired library construction was carried out using the reagents and protocol provided by Applied Biosystems ( SOLiD 3 System Library Preparation Guide ) . A similar protocol was reported previously [17] . Briefly , 45ug of genomic DNA was fragmented by HydroShear ( Digilab Genomic Solutions Inc ) to 1 . 0–2 . 5kb . The fragmented DNA was repaired by the End-It DNA End-Repair Kit ( Epicentre ) . Subsequently , the LMP CAP adaptor was ligated to the ends . DNA Fragments between 1 . 2–1 . 7kb were selected by 1 . 0% agarose gel to avoid concatamers and circularized with a biotinylated internal adaptor . Non-circularized DNA fragments were eliminated by Plasmid-Safe ATP-Dependent DNase ( Epicentre ) and 3ug of circularized DNA was recovered after purification . Original DNA nicks at the LMP CAP oligo/genomic insert border were translated into the target genomic DNA about 100bp by nick translation using E . coli DNA polymerase I . Fragments containing the target genomic DNA and adaptors were cleaved from the circularized DNA by single-strand specific S1 nuclease . P1 and P2 adaptors were ligated to the fragments and the ligated mixture was used to create two separate libraries with 10 cycles of PCR amplification . Finally , 250–300bp fragments were selected to generate mate paired sequencing libraries with average target genomic DNA on each end around 90bp by excision from PAGE gel and use as emulsion PCR template . Templated Beads Preparation: The templated beads preparation was performed using the reagents and protocol from the manufacturer ( Applied Biosystems SOLiD 3 Templated Beads Preparation Guide ) . SOLiD 3 Sequencing: The 2×50b mate-paired sequencing was performed exactly according to the Applied Biosystems SOLiD 3 System Instrument Operation Guide and using the reagents from Applied Biosystems . We used an array pull-down capture strategy established in our lab [30] . An Agilent custom array for capturing 5 , 253 “cancer-related” genes was designed through Agilent e-array system ( www . agilent . com ) . Only the amino acid encoding regions were targeted with 60mer oligos spaced center-to-center 20–30bp . The probes were randomly distributed across two separate 244K arrays . The library for cancer gene capture sequencing was generated following the standard Illumina paired-end library preparation protocol . 5ug of genomic DNA was used for the starting material and 250–300bp fragments were size-selected during the gel-extraction step . In the last step , 18 cycles of PCR were performed in multiple tubes to yield 4ug of product and mixed with 50ug of Human Cot-1 DNA ( Invitrogen ) , 52ul of Agilent 10× Blocking Agent , 260ul of Agilent 2× Hybridization Buffer and 10× molar concentration of unpurified Illumina paired-end primer pairs custom made according to the sequences provided by Illumina ( Oligonucleotide sequences , 2008 , Illumina , Inc: available on request from Illumina ) . The mix was then diluted with elution buffer for the final volume of 520ul and then incubated at 95°C for 3 min and 37°C for 30min . 490ul of the hybridization mix was added to the array and hybridized in the Agilent hybridization oven ( Robins Scientific ) for 65 hrs at 65°C , 20rpm . After hybridization , the array was washed according to the Agilent wash procedure A protocol . The second wash was extended to 5 minutes to increase the wash stringency . After washing , the array was stripped by incubating it in the Agilent hybridization oven at 95°C for 10min , 20rpm with 1 . 09× Titanium Taq PCR Buffer ( Clonetech ) . After the incubation and collection of the solution , 4 tubes of PCR were performed with each tube containing 96ul of the collected solution , 1ul of dNTPs ( 10mM each ) , 1ul of Titanium Taq ( Clonetech ) and Solexa primers , 1ul each . 15 cycles of PCR was performed at the following condition: 30sec at 95°C , ( 10 sec at 95°C , 30 sec at 65°C , 30 sec at 72°C ) ×18 cycles , 5 min at 72°C and hold at 4°C . The amplified product was purified using QIAquick PCR Purification Kit and eluted in 30ul of EB . After confirming the size of the amplicon on 2% agarose gel and measuring the concentration , the amplicon was diluted to 10nM , the working concentration for cluster generation . The Illumina flowcell was prepared according to the manufacturer's protocol and the Genome Analyzer was run using standard manufacturer's recommended protocols . The image data produced were converted to intensity files and were processed through the Firecrest and Bustard algorithms ( 1 . 3 . 2 ) provided by Illumina to call the individual sequence reads . We used Blat-like Fast Accurate Search Tool version 0 . 5 . 3 ( BFAST http://bfast . sourceforge . net ) [19] to perform sequence alignment of the two-base encoded reads off the ABI SOLiD to the NCBI human reference genome ( build 36 . 1 ) . Utilizing the local alignment algorithm included in BFAST [31] , we were able to simultaneously decode the short reads , while searching for color errors ( encoding errors ) , base changes , insertions , and deletions . We found candidate alignment locations ( CALs ) for each end independently . We utilized ten indexes to be robust to up to six color errors , equating to a 12% per-read error rate: 1111111111111111111111 111110100111110011111111111 10111111011001100011111000111111 1111111100101111000001100011111011 111111110001111110011111111 11111011010011000011000110011111111 1111111111110011101111111 111011000011111111001111011111 1110110001011010011100101111101111 111111001000110001011100110001100011111 We also set parameters to use only informative keys when looking up reads in each index ( BFAST parameter -K 8 ) , and to ignore reads with too many CALs aggregated across all indexes ( BFAST parameter -M 384 ) . If reads mapped to greater than 384 locations , then they were categorized as ‘unmapped’ . We then performed local alignment for each of the returned CALs , simultaneously decoding the read from color space searching for color errors ( encoding errors ) , base changes , insertions , and deletions [31] . We choose the “best scoring” alignment , accepting an alignment only if it was at least the equivalent edit distance of two color errors away from the next best alignment . This is approximately similar to a ‘mapping quality’ of 20 or better from the MAQ program output , for reference . We removed duplicate reads using the alignment filtering utility found in DNAA ( http://dnaa . sourceforge . net ) . For single-end and mate-paired reads where only one end mapped , we removed duplicates based on reads having identical stat positions . For mate-paired reads , we removed duplicates where both ends had the same start position . Illumina generated sequence was aligned to the NCBI human reference genome ( build 36 . 1 ) using BFAST with the following parameters applied . Each end of the fragment library was mapped independently to identify CALs , utilizing ten indexes to be robust to errors and variants in the short ( typically 36bp ) reads: 1111111111111111111111 1111101110111010100101011011111 1011110101101001011000011010001111111 10111001101001100100111101010001011111 11111011011101111011111111 111111100101001000101111101110111 11110101110010100010101101010111111 111101101011011001100000101101001011101 1111011010001000110101100101100110100111 1111010010110110101110010110111011 We also set parameters to use only informative keys when looking up reads in each index ( BFAST parameter -K 8 ) , and to ignore reads with too many CALs aggregated across all indexes ( BFAST parameter -M 1280 ) . We then performed a standard local alignment for each CAL . Reads were declared mapped if a single unique best scoring alignment was identified within the genome . Duplicate reads were filtered out in the same manner as for the ABI SOLiD data . To find SNVs including SNPs and small indels , we assumed the MAQ consensus-calling model [20] utilizing the implementation in SAMtools [21] . We used a value of 0 . 0000007 for the prior of a difference between two haplotypes ( -r parameter ) . This was chosen based on ROC analysis of a test dataset ( data not shown ) . Structural variations were detected using custom algorithms designed to comprehensively search for groups of mate-pair reads with aberrant paired-end insert size distributions that are consistently identifying a unique structural variant in the genome . We utilized the “dtranslocations” utility in the DNAA package ( http://dnaa . sourceforge . net ) for the primary structural variation candidate search . The utility first selected all pairs for which each end is uniquely mapped to a single location in the human genome and for which the mate-pair reads are not positioned in the expected size range relative to the consensus genome . Then we filter out false positives that are not consistent with a chromosomal difference on an allele . Briefly , the genome was divided into 500-base bins sequentially stepped 100-bases apart from their start positions . Each bin was then paired with other bins on the basis of containing similar ‘mismapped’ mate-pair reads . The aberrant mate-paired reads were defined as reads that were mapping less than 1000 or greater than 2000 bases apart within the reference genome sequence , which is selected based on the insert size distribution calculated from the aggregate dataset ( Figure S2 ) . These were then rank-ordered based on the number of mate-pairs meeting criteria , and the destination bin with the most reads within it was paired with a given source bin to create a ‘binset’ . Binsets containing less than 4 reads were filtered out , removing 98 . 3% of the candidates based on having too little evidence supporting them . The resulting list of filtered binsets was then scanned for clusters of binsets . Binset clusters are groups of binsets where the source bins occur within 2000 bases of each other and the destination bins occur within 2000 bases of each other . Redundant binsets were combined and those binset clusters that contain too few ( less than 9 binsets spanning at least 1000 bases ) or too many binsets ( greater than 29 binsets spanning at most 3000 bases—higher is impossible given our insert size distribution ) were removed as artifacts . The resulting binset clusters represent the reads immediately flanking structural breakpoint events . This detection process is currently being automated as Breakway ( http://breakway . sourceforge . net ) , but was done using custom scripts at the time of analysis . The structural variations were then separated into interchromosomal and intrachromosomal events . Intrachromosomal events of less than 1Mb are assessed for deletion status by averaging base coverage within the bounds of the event and comparing it to base coverage 200kb outside the event on both sides . Those that have average interior base coverage less than 25% of the average exterior base coverage are classified as “complete” deletions . Those with average interior base coverage between 25% and 75% that of average exterior base coverage are classified as “heterozygous deletions” ( deletions of at least one copy of the region , but with at least one copy remaining ) . Variant calls from the SAMtools pileup tool were first loaded into a SeqWare QueryEngine database and subsequently filtered to produce BED files . This filtering criteria required that a variant be seen at least 4 times and at most 60 times with an observation occurring on each strand at least once . For SNVs we further enforced the criteria that SNVs should only be called in reads lacking indels and the last 5 bases of the reads were also ignored . This reduced the likelihood that spurious mismappings were used to predict SNVs and eliminated the lowest quality bases from consideration . For small indels ( <21bp ) we enforced a slightly different filter by requiring that any reads supporting an indel were only allowed to contain one contiguous indel and these reads were not considered if the indel occurred on either the beginning or end of the read . These criteria , like the SNV criteria , were used to reduce the likelihood of using mismapped reads or locally misaligned reads in the variant calling algorithm . The elimination of reads with indels at the beginning or end of the read was intended to remove potential alignment artifacts caused by ambiguous gap introduction due to lack of information at the ends to guide proper alignment . Together , these filtering criteria reduced the likelihood that sequencing errors were identified as SNV or indel variants . We used scripts available in the BFAST toolset and SeqWare Pipeline to filter and annotate the variant calls . Variants passing these filters were further annotated by their overlap with dbSNP version 129 . Variants were required to share the same genomic position as a dbSNP entry along with matching the allele present in the database to be considered overlapping . Mapping to dbSNP allowed us to filter out known SNPs from de novo variants . Filtered SNV and indel variants were then analyzed for their affect within the genome that is annotated with gene models . This analysis used scripts from the SeqWare Pipeline project and gene models downloaded from the UCSC hg18 human genome annotation database . Six different gene model sets from hg18 were considered: UCSC genes ( knownGene ) , RefSeq genes ( refGene , http://www . ncbi . nlm . nih . gov/RefSeq ) , Consensus Coding Sequence genes ( ccdsGene , http://www . ncbi . nlm . nih . gov/CCDS ) , Mammalian Gene Collection genes ( mgcGenes , http://mgc . nci . nih . gov ) , Vertebrate Genome Annotation genes ( vegaGene , http://vega . sanger . ac . uk ) , and Ensembl genes ( ensGene , http://www . ensembl . org ) . Each variant was evaluated for overlap with genes from each of the 6 gene models . If overlap was detected the variant was examined and tagged with one or more of the following terms depending on the nature of the event: “utr-mutation” , “coding-nonsynonymous” , “coding-synonymous” , “abnormal-ref-gene-model-lacking-stop-codon” , “abnormal-ref-gene-model-lacking-start-codon” , “frameshift” , “early-termination” , “inframe-indel” , “intron-splice-site-mutation” , “stop-codon-loss” , and/or “start-codon-loss” . The variant was also tagged with the gene symbol and other accessions to facilitate lookups . This information was loaded into a SeqWare QueryEngine database to allow for querying and filtering of the variants as needed . Genes affected by structural variations were assessed in two ways depending on the structural variation type . For interchromosomal translocation events , a gene was considered “affected” when either end of an interchromosomal translocation event fell in a genic region ( including the entire coding region plus 1kb up- or down-stream of the gene's coding region ) . The same criteria were used for all intrachromosomal translocation events . For events that were classified as complete or heterozygous deletions , a gene was considered affected if all or part of a coding exon was deleted . Homozygous SNVs , small indels , large deletions , and translocation events for variants that included predicted coding sequence changes were tallied . This became a reference list of variants with serious homozygous mutations that likely completely disrupted , or “knocked out” , the normal function or synthesis of the target protein . For the SNVs and small indels , a “knockout” variant was defined as a homozygous call by the SAMtools variant caller where the variant was predicted by the SeqWare Pipeline scripts to change coding sequence with one or more of the following annotations: “early-termination” , “frameshift” , “intron-splice-site-mutation” , “start-codon-loss” , and/or “stop-codon-loss” . The “early-termination” event represented a stop codon introduced upstream of the annotated stop codon . The “frameshift” represented an indel that resulted in a shifting of the reading frame of the gene resulting in , typically , early termination and non-sense coding sequence . The “intron-splice-site-mutation” referred to a mutation in the two consensus splice site intronic bases flanking exons ( GT at the 5′ splice site and AG at the 3′ splice site ) . Finally , “stop-codon-loss” and “start-codon-loss” simply refer to variants that interrupt the stop or start codons . We chose to not include “coding-nonsynonymous” and “inframe-indel” annotations in this list of knocked out variants because , while potentially serious as these mutations are , they are not guaranteed to result in an unexpressed or non-functional protein . However , homozygous frameshift , early termination , splice site , and stop/start codon loss mutations are very likely to interrupt a gene's expression and translation to functional protein . As described above , large microdeletions that removed all or part of an exon and interchromosomal translocation events that fell within 1kb of a gene's coding region were also classified as mutated genes . Once suspect knockout variants were identified , a mapping process was used to translate one or more variants to the gene symbol . This mapping allowed us to condense multiple variants affecting multiple gene models to a more abbreviated list of gene symbols likely to be affected by these knockout mutations . The mapping from variants to gene symbols used variants identified with gene models from the refGene and the knownGene tables in the UCSC hg18 database and mapped these variants to gene symbols using queries against the name field of the knownGene table and the alias field of the kgAlias table . The UCSC table browser was used to accomplish these queries and map the knownGene identifiers to gene symbols via the kgXref table . A similar approach was used for homozygous large-scale microdeletions and translocation events . The list of knockout genes was uploaded to the Database for Annotation , Visualization , and Integrated Discovery ( DAVID , version 2008 ) to identify enriched Gene Ontology ( GO ) terms [32]–[33] . Overlap with GO terms from the biological process , cellular component , and molecular function ontologies were considered . The default parameters were used and a p-value cutoff of < = 0 . 01 was considered significant . The overlap between the Cancer Gene Census genes and those identified as knockouts in U87MG were compared . The Cancer Gene Census project is an ongoing effort to catalog genes with mutations that have been implicated in cancer [34] . It is a highly curated list that includes annotations for each gene including tumor types , class of mutations , and other genetic properties . We used the gene symbol list from the September 30th , 2009 complete working list , which includes 412 gene symbols . The overlap between mutations in the Cancer Genome Atlas ( TCGA ) and those identified as knockouts in U87MG was analyzed . TCGA is an ongoing effort to understand the molecular basis of cancer through large-scale copy number analysis , expression profiling , genome sequencing , and methylation studies among other techniques [4] . It provides information on mutations found by Sanger sequencing on many patient samples . For glioblastoma this includes sequence data aberrations detected in 158 patient samples and 1 , 177 genes . The Genomic Identification of Significant Targets in Cancer ( GISTIC ) method was used to find significant areas of deletion in 293 samples from the TCGA [24] . The GISTIC technique was designed to identify and analyze chromosomal aberrations across a set of cancer samples , based on the amplitude of the aberrations as well as the frequency across samples . This approach produced a series of commonly deleted regions across the set of TCGA GBMs . To calculate the areas of deletion , we used 293 Affymetrix SNP 6 . 0 samples segmented using the GLAD SNP analysis module [35] . Default parameters of GISTIC were used . GISTIC produces peak limits , wide peak limits , and in addition broader region limits . These commonly deleted broader regions were then scanned for predicted knockout genes in U87MG . The distribution of small indel sizes was examined for both deletions and insertions . Indels classified as affecting coding-sequence by the SeqWare Pipeline ( see above ) were compared to those outside coding regions . Raw counts were collected , recalculated as percents of total , and compared directly . Similarly , nucleotide substitution frequency was examined for SNVs from U87MG both genome-wide and only in coding regions . Once binned appropriately , the SNV nucleotide substitutions were counted , tallied in a table , and graphed as percents of total . Variants from the Watson and Yan Huang genome were downloaded from each respective project from the following URLs: ftp://jimwatsonsequence . cshl . edu/jimwatsonsequence/watson-454-snp-v01 . txt . gz and http://yh . genomics . org . cn/do . downServlet ? file=data/snps/yhsnp_add . gff . These files contained variant calls for each genome along with annotations describing the variant as novel or occurring in dbSNP . The Watson genome only contained SNV calls so our comparison was limited to just SNVs . The Yan Huang genome also contained calls indicating heterozygous or homozygous . However , a variant was considered to match between genomes regardless of zygosity state . We compared the overlap of the U87MG genome , dbSNP and each of these genomes in turn . SNVs from U87MG that were considered for comparison had to meet our criteria; variants had to be observed at least 4 times , at most 60 times , at least once per strand , and with a minimum phred score of 10 . SNVs in the three-way comparison were said to match if the position and allele matched between the genomes . If both variants matched between U87MG and the other genome and one was annotated in dbSNP , then the other was considered in dbSNP as well . If neither contained annotations from dbSNP the variant was considered novel . A similar process was carried out for variants distinct to each genome . The results were recorded as Venn diagrams showing the overlap between dbSNP , U87MG , and the Watson or Yan Huang genome . Genomic DNA from U87MG was submitted to the Southern California Genotyping Consortium to be run on the Illumina Human 1M-Duo BeadChip , which consists of 1 , 199 , 187 probes scattered across the human genome . The Illumina Beadstudio program was used to analyze the resulting intensity data . Loss of heterozygosity was determined by analyzing B-allele frequency as determined by the Beadstudio program . Normal two-copy regions of the genome are represented by long stretches of probes with B-allele frequencies of 0 , 0 . 5 or 1 . Regions of LOH , on the other hand , deviate from this pattern significantly . Copy number was determined by looking at probe intensity . Primers for validation were designed by targeting regions immediately flanking the event predicted by our whole genome sequence analysis using the Primer3 tool ( http://frodo . wi . mit . ed/primer3/ ) . Polymerase chain reaction was performed following standard protocols using Finnzymes Phusion Hot-Start High Fidelity polymerase . Products were run on 2% agarose gel electrophoresis and product purity and size was assessed by staining with ethidium bromide . Sanger sequencing was performed at the UCLA Genotyping and Sequencing core facility using an ABI 3730 Capillary DNA Analyzer . Sequence trace files were analyzed using Geospiza FinchTV . Validation status and PCR primers are listed in Table S1 . Intensities , quality scores , and color space sequence for the genomic sequence of U87 SOLiD were uploaded to the Sequence Read Archive under the accession SRA009912 . 1/Sequence of U87 Glioblastoma Cell-line . Intensities , quality scores , and nucleotide space sequence for the exon capture U87 Illumina sequence were also uploaded to the Short Read Archive under the same accession . For both datasets , alignment files have been uploaded to the Short Read Archive as additional analysis results . Variant calls for both datasets are available via a SeqWare QueryEngine web service at http://genome . ucla . edu/U87 . This tool allows for querying the variants using a variety of search criteria including coverage , mutational consequence , gene symbol , and others . SeqWare QueryEngine produces results in both BED and WIG format making it compatible with the majority of genome browsers such as the UCSC genome and table browsers . Variant data will be uploaded to SRA as metadata along with the raw sequences . For the whole genome SOLiD alignment , small indels ( <21bp ) , SNVs , large deletions , and translocation events can be queried . For the exon capture Illumina alignment , small indels and SNVs can be queried . Most software used for this project is open-source and freely available . We created two software projects that were instrumental in the analysis of the U87MG data: BFAST and SeqWare . The color- and nucleotide-space alignment tool BFAST can be downloaded from http://bfast . sourceforge . net and many of our alignment filtering as well as the primary step in structural variation detection can be found in the DNAA package at http://dnaa . sourceforge . net . The SeqWare software project was used throughout the analysis of variant calls . We used the SeqWare LIMS tool for sample tracking , the SeqWare Pipeline analysis programs for annotating variants with dbSNP status and mutational consequence predictions , and SeqWare QueryEngine was used to database and query variant calls and annotations . This software and documentation can be downloaded from http://seqware . sourceforge . net .
|
Glioblastoma has a particularly dismal prognosis with median survival time of less than fifteen months . Here , we describe the broad genome sequencing of U87MG , a commonly used and thus well-studied glioblastoma cell line . One of the major features of the U87MG genome is the large number of chromosomal abnormalities , which can be typical of cancer cell lines and primary cancers . The systematic , thorough , and accurate mutational analysis of the U87MG genome comprehensively identifies different classes of genetic mutations including single-nucleotide variations ( SNVs ) , insertions/deletions ( indels ) , and translocations . We found 2 , 384 , 470 SNVs , 191 , 743 small indels , and 1 , 314 large structural variations . Known gene models were used to predict the effect of these mutations on protein-coding sequence . Mutational analysis revealed 512 genes homozygously mutated , including 154 by SNVs , 178 by small indels , 145 by large microdeletions , and up to 35 by interchromosomal translocations . The major mutational mechanisms in this brain cancer cell line are small indels and large structural variations . The genomic landscape of U87MG is revealed to be much more complex than previously thought based on lower resolution techniques . This mutational analysis serves as a resource for past and future studies on U87MG , informing them with a thorough description of its mutational state .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"computational",
"biology/literature",
"analysis",
"genetics",
"and",
"genomics/genomics",
"oncology",
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/genome",
"projects",
"computational",
"biology/genomics",
"genetics",
"and",
"genomics",
"genetics",
"and",
"genomics/bioinformatics"
] |
2010
|
U87MG Decoded: The Genomic Sequence of a Cytogenetically Aberrant Human Cancer Cell Line
|
One root cause of the neglect of rabies is the lack of adequate diagnostic tests in the context of low income countries . A rapid , performance friendly and low cost method to detect rabies virus ( RABV ) in brain samples will contribute positively to surveillance and consequently to accurate data reporting , which is presently missing in the majority of rabies endemic countries . We evaluated a rapid immunodiagnostic test ( RIDT ) in comparison with the standard fluorescent antibody test ( FAT ) and confirmed the detection of the viral RNA by real time reverse transcription polymerase chain reaction ( RT-qPCR ) . Our analysis is a multicentre approach to validate the performance of the RIDT in both a field laboratory ( N’Djamena , Chad ) and an international reference laboratory ( Institut Pasteur , Paris , France ) . In the field laboratory , 48 samples from dogs were tested and in the reference laboratory setting , a total of 73 samples was tested , representing a wide diversity of RABV in terms of animal species tested ( 13 different species ) , geographical origin of isolates with special emphasis on Africa , and different phylogenetic clades . Under reference laboratory conditions , specificity was 93 . 3% and sensitivity was 95 . 3% compared to the gold standard FAT test . Under field laboratory conditions , the RIDT yielded a higher reliability than the FAT test particularly on fresh and decomposed samples . Viral RNA was later extracted directly from the test filter paper and further used successfully for sequencing and genotyping . The RIDT shows excellent performance qualities both in regard to user friendliness and reliability of the result . In addition , the test cassettes can be used as a vehicle to ship viral RNA to reference laboratories for further laboratory confirmation of the diagnosis and for epidemiological investigations using nucleotide sequencing . The potential for satisfactory use in remote locations is therefore very high to improve the global knowledge of rabies epidemiology . However , we suggest some changes to the protocol , as well as careful further validation , before promotion and wider use .
Rabies is a viral zoonotic encephalomyelitis transmitted to humans after exposure to infected mammals , mainly dogs , through bites , scratches or licks on damaged skin or mucous membranes . This disease still continues to represent a public health concern worldwide , with an estimate of 60 , 000 human deaths per year , mainly in low income countries . Because of limited control measures in many countries and a lack of governmental concern , rabies remains a neglected tropical disease . This neglect is especially deplorable given the entirely preventable nature of the disease through vaccination of dogs and timely adherence to post-exposure prophylaxis ( PEP ) of exposed victims [1 , 2] . In this way , human deaths due to this zoonotic disease could be reduced by over 95% [3 , 4] . Lack of surveillance represents one major element of negligence , leading to missing data on disease incidence , imprecise estimates of the economic impact and a general underestimation of the true worldwide burden of rabies [4 , 5 , 6 , 7] . This means that advocacy for rabies control cannot be supported with solid evidence and the necessity for action is not perceived at the decision maker level , for instance governmental authorities [8] . Poor surveillance is primarily a result of lack of political commitment and resource attribution for the control of rabies , and thus a cycle of neglect perpetuates . The vicious cycle is reinforced by the disregard of disease control in domestic dogs , which constitute negligible economic value and the fact that rabies affects largely marginalized communities with difficult access to healthcare . Deficiencies in basic healthcare do not only contribute to hinder access to PEP but also lead to the misdiagnosis of rabies in the face of other causes of encephalitis , such as cerebral malaria , as reported in Malawi [9] . Surveillance is fundamental to accurate burden of disease measures , for advocacy of disease control and also a prerequisite for disease elimination [10 , 11] . Currently , rabies is believed to be underreported at the extent of 1:60 in humans and this rate could even be much higher for animal rabies incidence [6] . One possible point of leverage to break the cycle of underreporting and neglect is the reinforcement and simplification of diagnostic capacities and tools . Infrastructures required for the current standard diagnostic tests are expensive , and their methodologies and interpretation need thoroughly experienced personnel . Antigen detection of RABV using the direct fluorescent antibody test ( FAT ) is the World Health Organization ( WHO ) and World Organisation for Animal Health ( OIE ) reference test [6 , 12 , 13] and is routinely performed in many developed countries . However , it is difficult to establish in developing countries because fluorescence microscopes are expensive and the required maintenance is demanding . Also , the immunofluorescence conjugate necessary for the test is costly and has to be transported and stored refrigerated . Finally , accurate reading of the test needs stringent quality control of the test performance and very experienced personnel . Similar constraints are encountered with the direct rapid immunohistochemical test ( DRIT ) [14 , 15] . Although the DRIT can be read using a light microscope , the test methodology requires a meticulous protocol which currently lacks commercialized biotinylated anti-rabies antibodies and has to be carried out by trained personnel . For rabies diagnosis however , a simpler field test is desirable for various reasons . In the current situation the benefits of rabies diagnosis are not well perceived by the public and rabies suspicious animals are often killed immediately and rapidly disposed [16] . Also , a short time lag between suspicion and confirmation of a rabies case is important for early adherence to PEP or the cost savings in case of a negative diagnostic result . Finally , transport of samples over long distances in climatically warm settings increases the risk of poor sample quality , which adversely affects FAT test results [17] . Proximity to the public through decentralized laboratory facilities is therefore vital for good sample quality , as well as rapid detection and response . A rapid immunodiagnostic test ( RIDT ) based on the lateral flow principle was first described and evaluated in 2007 on a limited panel of RABV samples [18] . The same study reported on the detection limit and potential risk of cross reactivity . Further laboratory evaluation was conducted more recently on the use of this RIDT for the detection of RABV circulating in Europe , and extended to the detection of other species of lyssaviruses [19 , 20] . Both studies showed positive results regarding sensitivity and specificity of such tests compared to the FAT . To date , only two studies have been conducted under field conditions , both suggesting positive results for the use of RIDT [21 , 22] . In our study , we evaluated the practicability and the performance of this RDIT identified as Anigen Rapid Rabies Test ( Anigen test ) ( Bionote Inc . ) in different settings: under field conditions with its application to the surveillance of rabid animals in N’Djamena , Chad and in laboratory settings with a panel of selected RABV isolates . Lastly , we evaluated this tool for a novel application in rabies surveillance , with its use as a vehicle for viral RNA storage and conservation , and demonstrated that recovery and detection of RNA present on the strip of positive samples was possible . The Anigen test appears as a promising tool for the post-mortem diagnosis of animal rabies , and the molecular detection and genotyping of positive test strips .
During June 2012 , the RIDT Anigen test , a chromatographic immunoassay-based on lateral flow technology manufactured by BioNote , Inc ( Gyeongi-do , Republic of Korea ) [23] , was added into the routine diagnostic procedure of the rabies laboratory of the Institut de Recherche en Elevage pour le Développement ( IRED ) in N’Djamena , Chad . It was utilized in parallel with the FAT test , which had been used since 2001 . Rabies-suspect animals were presented to the IRED by their owners or by the bite victim . No active surveillance was initiated throughout the study . However , awareness was intensified prior to the study period , during May 2012 , by a poster campaign sensitizing the public in N’Djamena to seek medical treatment after a dog bite and to send the biting animal to the IRED in case of rabies suspicion . For the validation of the Anigen test in the field , diagnostic results from June 2012 to February 2015 were included . Only samples originating from dogs were considered for inclusion according to the manufacturer’s recommendation [23] . During the 33 months of the study period , a total of 49 rabies -suspect dog heads were submitted to IRED for diagnostic testing . The origin of the samples is detailed in Table 1 . Most were in fresh condition on arrival and upon testing ( 85% , n = 42 ) . Five of the samples were decomposed , while in one case , the sample quality was not noted . Only one sample was so decomposed that it was impossible to analyse and was excluded from the study . The final sample size of field isolates at IRED was 48 ( Table 1 ) . The Anigen test was further validated at the National Reference Centre ( NRC-R ) and WHO Collaborating Center for Rabies at the Institut Pasteur in Paris , France , on 73 samples selected from the collections housed in both of these centers , from 12 different species originating from various countries and belonging to different phylogenetic clades ( S1 Table ) . All these samples were previously analysed by FAT . Thirty of them were negative and the remaining 43 were positive . The positive samples represented a large diversity of RABV . All these 73 samples were stored at -80°C for archive before analysis . In addition , the limit of detection of the RIDT was evaluated at NRC-R using a panel of 8 different isolates of RABV adapted and amplified on baby hamster kidney cells ( BSR cells ) . Viral suspensions were titrated on the same cells using 5-fold serial dilutions in cell culture medium and expressed as fluorescent focus units per mL ( FFU/mL ) . For the RIDT evaluation , titrated RABV suspensions were first tested at several concentrations using the buffer available from the RIDT kit as a diluent . The FAT , the gold standard technique for post-mortem diagnosis of rabies [12] was performed at the NRC-R under quality assurance ( accreditation ISO/IEC 17025 ) , as previously described [13] . In the rabies laboratory of N’Djamena , the FAT was performed with some deviations regarding the standard procedure: lack of positive and negative control samples inclusion , absence of routine quality assessments , and storage of the immunofluorescent conjugate past the expiration date . In this setting , two microscopic slides were prepared , with two brain impressions per slide . If no viral characteristic fluorescent inclusions were observed on all four impressions , the sample was considered negative . Doubtful results were declared positive due to the potential fatal consequences of a false negative result for the bite victims . However , due to some deviations regarding the standard procedure , it was not possible to consider FAT performed ad IRED as the gold standard for the specificity and sensitivity analysis . The Anigen test is a simple and rapid diagnostic tool , presenting as an all-in-one included kit . Once the brain is extracted , it is used without additional material and equipment except for one dilution step requiring an additional vial of phosphate-buffered saline ( PBS ) prepared according to the manufacturer’s recommendation . However , for our study , we omitted the first dilution step , only using the vial with buffer provided by the kit to simplify the test procedure in view of future application under realistic field conditions . The same procedure was used for the Anigen test at NRC-R and at IRED . If it was possible to anatomically identify the regions of the brain in a sample , the test was performed with a small section of the brainstem ( approximately 0 . 1 g ) , otherwise the same amount of material was taken from different parts of decomposed brain samples . The brain sample was mixed directly in the tube containing the buffer with the swab , all included in the kit , for about one minute , until most of the brain material was well dissolved and then put on to the test plate using the transfer pipette provided in the kit . Four drops were deposited on the strip ( corresponding to nearly 100 μL ) . The test could be interpreted when the coloured liquid reached the top of the test and the purple indicator colour had vanished from the filter paper background . As described by the manufacturer , a positive test result was indicated by two purple lines , one in the test zone and the other in the control zone . If a line only appeared in the control zone , the test was considered negative . In cases where only the test line was coloured rather than the control band , the test was declared invalid and was performed once again . The test took approximately 5 to 10 min after deposit of sample and the interpretation was not be performed after 10 min , according to manufacturer recommendations . Following these recommendations , the test was suitable for dog , raccoon dog and cattle samples ( animals which were used originally in the validation of the method [18] , and should be tested immediately after collection . In this study , two different batches were used for the validation of the RIDT assay , with batches n°1801076 and n°1801111 for the field and the laboratory validation , respectively . Brain impressions were performed directly on FTA Whatman cards , a support dedicated to the storage and preservation of RNA [24] . Prior to use , the cards were stored at room temperature in a sealed plastic bag in a dry and clean area . The samples were prepared by diluting a small section ( approximately 0 . 1 g ) of the brain in 1 ml PBS ( 10% ) . After thorough mixing , the brain homogenate was loaded onto the card with a pipette until the sample indicator circle on the filter was covered . The cards were then dried 24 hours at room temperature before being put separately in transparent plastic bags for transportation . To prepare the samples on FTA Whatman filter paper , 1 cm2 was cut-off from the area containing the brain impression and incubated during 1 hour in Tri-Reagent LS ( Molecular Research Center , Cincinnati , Ohio , USA ) or overnight in cell culture medium ( DMEM ) ( Life Technologies , Saint Aubin , France ) , then placed in Tri-Reagent . To obtain viral RNA directly from the Anigen test strip , the cassettes were opened , the filter paper was removed and the area where the sample was deposited was collected and placed into 1 mL of Tri-Reagent LS . For both FTA and Anigen test supports , total RNA extraction was performed as previously described , following manufacturer recommendations [25] . Viral RNA detection was performed using a one-step dual combine pan-lyssavirus RT-qPCR assay recently described [26] , targeting a conserved region among the polymerase . Briefly , this assay includes a pan-RABV RT-qPCR probe-based technique , able to detect all representatives of the broad genetic diversity of RABV , using two degenerated TaqMan probes . In parallel , a SYBR Green RT-qPCR assay is able to detect all the other lyssaviruses tested , in addition to RABV isolates . Both of these assays , which were optimized to a final reaction volume of 20 μL , were performed using 5 μL of RNA template ( previously diluted 1:10 in nuclease-free water ) . For each assay , appropriate controls were used . Details of the combined pan-lyssavirus RT-qPCR assay are in S2 Table and in reference [26] . A selected panel of RNA extracts from Anigen test , which were found positive with the dual combine RT-qPCR assay , were evaluated with RT-PCR to generate amplicons suitable for genotyping by sequencing ( at least 500 nt in length ) . Briefly , a volume of 6 μl of total RNA extraction was used for reverse transcription as previously described [25] . RNA was incubated at 65°C for 10 min with 2 μL of pd ( N ) 6 random primers ( 200 μg/mL; Roche Diagnostics ) and 2 μL of sterilized distilled water and then were stored on ice . Each tube was incubated with 200 U of Superscript II RT ( Invitrogen ) , 80 U of RNasin ( Promega ) , and 10 nmol of each nucleotide triphosphate ( Eurobio ) , in a final volume of 30 μL for 90 min at 42°C , for reverse transcription . Two microliters of complementary DNA ( cDNA ) were then amplified by PCR targeting the nucleoprotein gene of RABV , as described in [27] . The RIDT assay was evaluated by the NRC-R in an inter-laboratory trial organized during 2015 by the European Union reference laboratory for rabies , which is located in Nancy , France [28] . The FAT technique was also evaluated in parallel in this trial . The test panel consisted of nine anonymous samples of freeze-dried homogenized brains , either uninfected or infected with various lyssavirus species . Details of this trial have been provided elsewhere [28] . Results obtained with FAT and Anigen techniques were compared using the McNemar and Kappa statistic tests in Stata , and were analyzed to determine the intrinsic parameters of the RIDT assay . However and conversely to the FAT technique done at the NRC-R , the immunofluorescence assay performed at the IRED could not be considered as the reference technique due to several deviations compared to the standard procedure . In case of discrepancy between RIDT and FAT , samples were tested for RNA detection with RT-qPCR assay performed on FTA Whatman cards impregnated with the brain of the corresponding sample . For the determination of the sensitivity and the specificity of RIDT , true positivity and true negativity was defined according to the result that was shared by at least two tests among FAT , RIDT and viral RNA detection .
For the majority of the total sample size ( n = 121 ) tested at IRED and at NRC-R , the RIDT was successfully performed , with the presence of a line clearly visible in the control zone after 5 to 15 min of migration once the sample was deposited ( Fig 1A and 1B ) . For only a few samples ( n<10 ) , the test was repeated , due to abnormal or incomplete migration ( absence of the line in the control zone ) . When they scored positive with RIDT , most samples exhibited a line with strong intensity in the test zone ( Fig 1B ) . In a few cases the test bands showed even higher intensity than the control band . Also , for some samples tested at NRC-R , the line in the test area was only faintly visible , despite a strong intensity of the line in the control zone ( Fig 1A ) . A total of eight titrated suspensions from different RABV adapted to cell culture was selected to determine the limit of detection of the RIDT ( Table 2 ) . A volume of 100 μL of each of them , diluted or not , were tested . The lowest number of fluorescent focus-forming units ( FFU ) detected with this assay was 105 FFU , and was obtained for RABV 9704ARG and 04030PHI . Isolates 9147FRA and 9508CZK exhibited a positive signal with 106 FFU . Lastly , no positive signal was obtained with the initial viral suspension for virus 8743THA and 9001FRA , indicating that the limit of detection was > 8 . 1 x 106 FFU and > 2 . 4 x 105 FFU , respectively . Seventy-three samples from NRC-R , including forty-three positive samples representing a large diversity in term of host species , geographical location and genetic diversity ( S1 and S3 Tables ) were tested . Compared to the gold standard FAT , the RIDT demonstrated an accordance of 95% . The specificity was 93 . 3% with only two false positive results among the 30 FTA-negative specimens , noticed for samples 150057 and 150125 which were originated from a dog and a cat , respectively ( Table 3 , S1 and S3 Tables ) . The sensitivity of the RIDT was 95 . 3% , with only two false negative results observed for isolates 9217ALL and 9312MAU , a red fox from Germany and a dog from Mauritania , respectively ( Table 3 , S1 and S3 Tables ) . Among the 48 samples included for evaluation at IRED only 3 were not concordant between FAT and RIDT , yielding an accordance of 94% ( Table 3 ) . Two of the discordant samples ( samples 343 and 389 ) were decomposed and the quality remained unknown for the last one ( sample 362 ) ( Table 1 ) . The FAT was impossible to perform on one ( sample 389 ) of the 3 and for the two others ( samples 343 and 362 ) , the result was positive ( S4 Table ) . For all these 3 specimens , RIDT tested negative ( S4 Table ) . In these three cases , where RIDT and FAT did not yield the same result , viral detection performed by RT-qPCR on the FTA Whatman card could not detect viral RNA after multiple attempts confirming the negative RIDT result ( S4 Table ) . For sensitivity and specificity of RIDT and FAT under field conditions , true positivity and true negativity was defined according to the result that was shared by at least two tests among FAT , RIDT and viral RNA detection on the FTA Whatman card . The RIDT showed a higher specificity ( 100% ) than the FAT ( 78 . 5% ) at IRED . Accordance with the overall true test results of the 48 samples from IRED was 100% for RIDT and 94% for FAT . The McNemar test showed no significant difference between the FAT and RIDT ( Exact McNemar significance probability = 0 . 5 ) on samples test at IRED and the Kappa value was 0 . 86 , indicating excellent agreement between the tests . The exact McNemar significance probability for the comparison of FAT and RIDT performed at NRC-R was 1 and the Kappa value was 0 . 89 . Overall , of the 121 samples analysed at NRC-R and IRED , the McNemar significance probability was found to be 0 . 45 and the Kappa value was 0 . 87 . A total of 51 samples were tested at NRC-R for viral RNA detection using RT-qPCR on the Anigen test strip , which were previously found positive for the post-mortem diagnosis of rabies ( Table 4 , S1 and S4 Tables ) . The FAT scored also positive for all of these specimens . Among them , 32 originating from IRED were used during the field evaluation of the RIDT , whereas 19 were obtained in NRC-R during the laboratory evaluation . Positive detection was obtained for 26 ( 81 . 2% ) , 18 ( 94 . 7% ) and 44 ( 86 . 3% ) samples from IRED , NRC-R and the two combined , respectively ( Table 4 ) . In parallel , detection of viral RNA was also performed at NRC-R on FTA Whatman cards for 31 samples , which were positive after analysis with both FAT and RIDT at IRED ( S4 Table ) . In this case , a perfect concordance ( 100% ) was noticed . In addition , viral RNA from 2 and 6 samples found previously negative with RIDT were not detected after RT-qPCR analysis performed on the Anigen test strips and FTA Whatman cards , respectively ( Table 4 ) . When compared to the FTA Whatman card , RT-qPCR performed on the Anigen test strip exhibited a sensitivity of 80 . 6% ( Table 5 ) . A limited panel of viral RNA samples extracted from Anigen test strips , which were previously confirmed positive by RT-qPCR , were secondarily tested for genotyping . A total of 14 samples ( 4 originating from IRED and 10 from NRC-R ) were analyzed , among them 13 ( 93% ) provided PCR amplicons ( at least 500 nucleotides in length ) targeting regions of the nucleoprotein gene commonly used for genotyping after sequencing ( S3 and S4 Tables ) . The only sample found negative ( isolate 9702IND ) was weakly positive after FAT and RIDT tests , which could then probably explain the absence of PCR amplification . Finally , we evaluated the RIDT Anigen test in an inter-laboratory trial , in parallel of the FAT on nine anonymous samples . The results obtained were concordant with those expected ( Table 6 ) [28] . In particular , we were able to detect three different RABV isolates ( strains CVS27 14–10 , GS7 and DR627 ) constituting the panel , as well as 4 other lyssavirus species , including Duvenhage virus ( DUVV ) , European bat lyssavirus 1 ( EBLV-1 ) and 2 ( EBLV-2 ) , and Bokeloh bat lyssavirus ( BBLV ) .
Specificity and sensitivity of the evaluated Anigen test are only slightly reduced compared to the known reference tests for rabies virus detection in brain samples . The results are promising for field use , where the test could help to establish rapid preliminary diagnostic results , which would be further confirmed using WHO and OIE recommended tests at central laboratories . However , we suggest important changes to the test protocol: skip the dilution step of brain biopsy in PBS and perform the brain homogenate with the swab directly into the specimen tube containing 1 ml of assay diluent , both provided in the kit . We also recommend to provide a more precise sketch depicting the brain sampling method . Rapid rabies tests cannot substitute for the current reference tests , but are crucial for the success of rabies surveillance systems in developing countries . Further , we demonstrated here that the test cassettes can be used as a vehicle to ship viral RNA to reference laboratories for further laboratory confirmation of the diagnosis and for epidemiological investigations .
|
The high fatality and burden of rabies stands in contrast to the very low performance of laboratory-based surveillance in resource-challenged countries . The absence of reliable human and animal rabies incidence data ultimately result in neglect of disease prevention and control and the perpetuation of RABV transmission despite the existence of powerful management tools . Rapid , easy to perform rabies diagnostic tests that do not require expensive equipment or special storage conditions , which can be reliably performed by trained ordinary veterinary professionals , are needed urgently for use in low income countries . Such novel methods will help to accurately assess the global rabies burden and are necessary to monitor rabies control and elimination . The present study evaluates the performance and reliability of a rapid , easy to use rabies diagnostic tool . Overall , the validated test was in high accordance with the standard reference method for the detection of RABV by immunofluorescence microscopy and showed even higher reliability when applied in resource poor laboratory conditions . The obtained results support the high potential for the use of this test in the field but suggest a change of the original technical protocol and a need for wider validation .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"tropical",
"diseases",
"rna",
"extraction",
"microbiology",
"vertebrates",
"animals",
"mammals",
"dogs",
"viruses",
"rabies",
"rna",
"viruses",
"neglected",
"tropical",
"diseases",
"molecular",
"biology",
"techniques",
"genotyping",
"extraction",
"techniques",
"veterinary",
"science",
"research",
"and",
"analysis",
"methods",
"rabies",
"virus",
"infectious",
"diseases",
"lipids",
"veterinary",
"diseases",
"zoonoses",
"fats",
"medical",
"microbiology",
"microbial",
"pathogens",
"molecular",
"biology",
"biochemistry",
"lyssavirus",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"amniotes",
"organisms"
] |
2016
|
Validation of a Rapid Rabies Diagnostic Tool for Field Surveillance in Developing Countries
|
Nuclear landmarks and biochemical factors play important roles in the organization of the yeast genome . The interaction pattern of budding yeast as measured from genome-wide 3C studies are largely recapitulated by model polymer genomes subject to landmark constraints . However , the origin of inter-chromosomal interactions , specific roles of individual landmarks , and the roles of biochemical factors in yeast genome organization remain unclear . Here we describe a multi-chromosome constrained self-avoiding chromatin model ( mC-SAC ) to gain understanding of the budding yeast genome organization . With significantly improved sampling of genome structures , both intra- and inter-chromosomal interaction patterns from genome-wide 3C studies are accurately captured in our model at higher resolution than previous studies . We show that nuclear confinement is a key determinant of the intra-chromosomal interactions , and centromere tethering is responsible for the inter-chromosomal interactions . In addition , important genomic elements such as fragile sites and tRNA genes are found to be clustered spatially , largely due to centromere tethering . We uncovered previously unknown interactions that were not captured by genome-wide 3C studies , which are found to be enriched with tRNA genes , RNAPIII and TFIIS binding . Moreover , we identified specific high-frequency genome-wide 3C interactions that are unaccounted for by polymer effects under landmark constraints . These interactions are enriched with important genes and likely play biological roles .
Understanding the spatial organization of the genome in the cell nucleus is essential to gain insight into important nuclear activities such as repair , recombination , and replication of DNA , as well as the control of the transcriptional status of genes [1 , 2] . The overall organization of genome has been shown to be compartmentalized in the form of chromosome territories [3] , topologically associated domains [4 , 5] , and spatial localization of individual gene loci [6] . Such compartmentalization affects the expression levels of genes among eukaryotes from yeast [2] to mammals [1] . With its well understood nuclear architecture and transcriptional machineries [2] , budding yeast provides an excellent model system for investigating how eukaryotic cellular activities are related to genome organization . Furthermore , there is now clear evidence that important nuclear events such as cancer-promoting chromosomal translocations observed in human nuclei and relocation of genomic elements upon breaks of double stranded DNA observed in budding yeast originate from analogous cellular machineries [2] . Studies using electron microscopy techniques revealed detailed structures of architectural landmarks of budding yeast nucleus . These include the spindle pole body ( SPB ) , the nucleolus , and the nuclear envelope ( NE ) [6–14] . SPB is functionally equivalent to centrosome in mammalian nuclei , where all heterochromatic centromeres are attached throughout interphase [11] . The nucleolus , where ribosome synthesis and assembly take place , contains clusters of ribosomal DNA ( rDNA ) repeats [6 , 10 , 12–15] . The NE , where telomeric regions of yeast chromosomes are anchored , facilitates silencing of telomeric genes [7–10] . In addition , microscopy experiments further revealed the dynamics behavior of important genes of budding yeast [6] . The spatial organization of the yeast genome likely results from both generic polymer effects such as self-avoiding polymer chains confined in the cell nucleus [2 , 25 , 27 , 32 , 33] as well as effects of biochemical factors such as transcription factor binding [2] . With genome-wide studies using Chromosome Conformation Capture ( 3C ) technique [16 , 17] , large-scale long-range chromatin looping interactions across the budding yeast genome have been identified [16] . Studies of polymer models of both human [18–25] and yeast [26–30] genomes have revealed important insight into the principle of genome folding . For example , two recent seminal computational studies demonstrated that chromosomes of budding yeast behave as randomly folded flexible self-avoiding polymer chains that are subject to the constraints of nuclear landmarks and nuclear confinement [26 , 27] . It was shown that tethering of genomic elements such as centromeres and telomeres to the nuclear landmarks gives rise to the preferential localization of chromosomes and functional loci in the nucleus [26 , 27] . In addition , modeled interactions are found to have excellent correlation with experimentally captured interactions at the chromosome level , with intra-chromosomal locus-locus interactions well reproduced at 32–75 kb resolution [26 , 27] . However , modeled and experimentally captured inter-chromosomal interactions are only modestly correlated . Furthermore , these volume exclusion models [26 , 27] may be capturing only interactions arising from generic polymer effects , with strong correlation ( R>0 . 90 ) found only at lower resolution that requires binning of the interaction frequency data . A recent study showed that after correction of measured interaction frequencies using a statistical null model , the budding yeast genome no longer exhibits properties of a randomly folded polymer under constraints [31] . The important issue whether the organization of the yeast genome is dictated by physical tethering of landmarks and the excluded-volume effects as discussed in [26 , 27] , with specific protein-mediated interactions playing negligible roles , remains uncertain . Overall , the precise roles of nuclear landmarks , volume confinement , biochemically mediated interactions , as well as their relative contributions to the overall organization of yeast genome are unclear . In this study , we explored computationally the structural properties of budding yeast genome under different combinations of landmark constraints and nuclear confinement . Our goal is to answer the following questions: ( 1 ) how does the confinement of the cell nucleus affect the organization of the yeast genome , ( 2 ) to what extent the genome organization determined by the physical architecture of the nucleus , ( 3 ) what are the relative contributions of the individual nuclear landmarks to overall genome organization , ( 4 ) how can we distinguish chromatin looping interactions arising from biochemical factors from those arising from generic polymer properties . Our study is based on the multi-chromosome Constrained Self-Avoiding Chromatin ( mC-SAC ) method and the generation of ensembles of ∼150 , 000 model genomes using the geometrical Sequential Importance Sampling technique ( g-SIS ) [32 , 33] . In agreement with previous studies [26 , 27] , our results showed that indeed the overall patterns of chromatin interactions of the budding yeast genome are well captured when only polymer effects under the spatial confinement of cell nucleus and landmark constraints are considered , with now good correlation for both intra- and inter-chromosomal interactions at the improved resolution of 15 kb ( row-based Pearson correlation coefficient R of 0 . 91 ) . Our study further specified the roles of individual landmark constraints , and showed that the size of the nuclear confinement is the key determinant of intra-chromosomal interactions , while centromere tethering is responsible for much of the observed inter-chromosomal interactions and correlation of pairwise telomere distances to chromosomal arm lengths . Our study also shed some light on the origin of the spatial locations of eight important genes , as they can be determined by their genomic distances to the centromeres . In addition , we report a number of additional new findings . We found that chromosomal fragile sites where double-stranded DNA breaks upon DNA perturbation are clustered in three-dimensional space . Furthermore , novel chromatin interactions undetected in experimental studies [16] are uncovered from our computational ensemble models of yeast genomes . These novel chromatin interactions are enriched in tRNA genes and are found to be stabilized by binding of the transcription factors TFIIS and RNA polymerase III . Our results further indicate that the clustering of tRNA genes , to a large extent , is likely a consequence of the spatial clustering of centromeres to the SPB . In addition , we found there are interactions between specific genomic elements enriched with known important genes that were not captured by polymer properties , but are detected in experimental studies [16] . This was made possible by removing expected interactions due to polymer effects from experimental measurements . Overall , our findings define the specific roles of confinement and individual landmarks , and can uncover likely biologically relevant interactions from genome-wide 3C measurements that are beyond polymer effects .
The nuclear architecture of budding yeast is composed of Nuclear Envelope ( NE ) , Spindle Pole Body ( SPB ) , nucleolus , and 16 chromosomes . The locations of SPB , NE , and nucleolus are placed according to measurements from imaging studies ( Fig 1A ) [6–14] . The locations of the 16 chromosomes are modeled as independent but interacting polymers . Each monomer of the polymer chain is modeled as spheres that corresponds to a 3 kb of DNA [34 , 36] . The entire budding yeast genome is modeled a total of 3990 monomers and are divided into 16 chromosomes . The mC-SAC model is developed based on our single C-SAC chain growth model [32 , 33] . First , we mapped the locations of centromeres , telomeres and rDNA repeats onto the polymer chains that corresponds to each chromosome . Each chromosome is divided into right and left arms from their centromeres , except Chr 12 ( Fig A in S1 Text ) . The polymer chain representing Chr 12 is divided into three segments to accommodate the nucleolus ( see S1 Text and Fig A in S1 Text ) . The budding yeast genome is therefore composed of 33 chromosomal arms , each represented by a polymer chains . The genome γ = ( x1 , x2 , … , x33 ) is a collection of chromosomal arms , where each arm xk consists of n units as x k = ( x 1 k , x 2 k , . . . , x n k ) . The three-dimensional location of the i-th unit of the k-th chromosome arm is denoted as x i k = ( a i k , b i k , c i k ) ∈ R 3 . To generate a chromosomal arm , we grow the mC-SAC chain one unit at a time ( each unit contains 5 beads , i . e . , 15 kb DNA ) , ensuring the self avoiding property along the way , namely , x i k ≠ x j l for all i ≠ j . We use a s = 1640-state off-lattice discrete model ( see [32 , 33 , 37 , 51] for more details ) . The new unit added to a partial chain is placed at x t + 1 k , taken from one of the unoccupied s-sites neighboring x t k , with a probability of growth g ( x ) , which is the trial distribution . This selection introduce a bias away from the target distribution π ( x ) , and this bias is corrected by assigning each successfully generated genome a proper weight w ( x ) = π ( x ) /g ( x ) . Details can be found in references [32 , 33 , 37 , 51] . The multiple chain growth process starts with a random selection of a chromosomal arm and placement of its corresponding centromere at a random location in the SPB . We then employ the chain growth strategy to grow chromosomal arms until the telomere of the corresponding arm reaches to the target location , i . e . NE . In the case of Chr 12 , we select a random location on the nucleolus to place the rDNA repeats and grow the chain towards to its targeted location ( i . e . NE or SPB ) . We repeat this process until all 33 chromosomal arms are completely generated ( see S1 Text for details ) . The relative positions of the genes with respect to the SPB is defined as the ratio between the median location of the gene and the median location of SPB in the ensemble of model mC-SAC genomes , namely , median ( gene ) / median ( SPB ) = μ 1 / 2 gene / a SPB ( 1 ) where μ 1 / 2 gene is the median agene coordinate of the three-dimensional coordinates of xgene = ( agene , bgene , cgene ) calculated using the coordinates of ensemble of model genomes . The median coordinate of the SPB , aSPB , is pre-determined from the imaging experiments and depicted in Fig 1 . This calculation is adopted from the original imaging study [6] , where the three–dimensional coordinates were projected to two principal axis as ( ρgene , zgene ) , where ρgene corresponds to the projection of ( bgene , cgene ) and zgene corresponds to agene ( Fig A in S1 Text ) .
We model the chromatin fiber of budding yeast as chained beads , where each bead corresponds to 3 kb of DNA with a diameter of 30 nm in accordance with the experimental and theoretical suggestions [34–36] . Following previous studies [26 , 27 , 33] , we used light microscopy data to model the architecture of yeast nucleus . The nucleus is modeled as a sphere of a diameter of 2 μm and contains the Spindle Pole Body ( SPB ) , the Nuclear Envelope ( NE , modeled as a shell of thickness of 50 nm following [26] ) , the nucleolus , and 16 chromosomes ( Fig 1A and Fig A in S1 Text ) [33] . Chromosomes all reside inside the nucleus as independent but interacting self-avoiding chromatin fibers . The entire budding yeast genome is represented by a total of 3 , 990 beads divided into 16 different chromosomes ( Fig 1B ) . An ensemble of ∼150 , 000 independent model genome structures are generated that are subject to the nuclear confinement , centromere clustering at SPB , telomere attachment at the NE , and rDNA repeat clustering at the nucleolus . This is achieved by sequentially growing self-avoiding chromatin chains one unit ( 5 beads ) at a time , where each unit corresponds 15 kb of DNA using the technique of geometrical Sequential Importance Sampling ( g-SIS ) [32 , 33 , 37 , 38] . We call this the fully-constrained ensemble of mC-SAC chains . In addition , we examined the effect of landmark constraints by generating separate ensembles of ∼150 , 000 independent model genomes . All of these ensembles are subject to nuclear confinement , excluded volume effect , and two or less constraints from nuclear landmarks ( see Table 1 ) . In total , we have 5 additional ensembles: ( 1 ) The ensemble of without telomere is subject to all landmark constraints except the telomere attachment to the NE , ( 2 ) the ensemble of without nucleolus is subject to all landmark constraints except the exclusion of chromatin in nucleolus , ( 3 ) the ensemble of without centromere is subject to all landmark constraints except the centromere tethering to the SPB , ( 4 ) the ensemble of with only centromere is subject to only centromere tethering to the SPB in addition to nuclear confinement and excluded volume effects , and ( 5 ) the random ensemble is only subject to nuclear confinement and excluded volume effects ( see Table 1 ) . Recent genome-wide Chromosome Conformation Capture ( 3C ) studies have quantified the frequency of chromatin looping interactions of budding yeast genome , which can be summarized by an interaction frequency matrix [16] . Two recent seminal studies showed that interactions at whole chromosome level , as well as intra-chromosomal locus-locus interactions at 32–75 kb resolution are well accounted for by polymer effects [26 , 27] . To examine how well our model can capture the overall genome organization , we first calculated Pearson correlation between chromosome-pair interaction frequencies in the fully-constrained model ensemble and those detected in genome-wide 3C experiment [16] . The result of R = 0 . 99 at p < 7 . 08 × 10−92 is similar to those of previous studies [26 , 27] . We then calculated the correlation between interaction frequency matrices following previous studies [26 , 27] . The interaction frequency matrices obtained from our predicted ensemble ( Fig 1D and 1F ) and from genome-wide 3C experiments ( Fig 1E ) are strongly correlated , with an R = 0 . 83 at 15 kb resolution ( Fig 1C , p-value ¡ 0 . 001 , see also S1 Text and Fig B in S1 Text ) following the calculation procedure of [27] . This is an improvement over R∼ 0 . 50 as reported in Figure S4 C of [27] at the same 15 kb resolution . The row-based R of 0 . 94 at 32 kb as calculated in [26] is also comparable with the reported R of 0 . 94 [26] . Importantly , the calculated inter-chromosomal interaction frequencies in the fully-constrained ensemble and those observed in genome-wide 3C experiments are also in agreement , with an R of 0 . 75 at 15 kb resolution . This compares favorably with previously reported R of 0 . 54 at a 2× lower resolution of 32 kb [26] . The heat maps obtained from experiments [16] and from mC-SAC ensemble have nearly identical patterns ( Fig 1E–1F ) . To eliminate the effect of proximity interactions and non-specific interactions arising from nuclear confinement of self-avoiding chromatin chains , we used our random ensemble as the null model to calculate the propensity ( observed/expected ) of each interaction in both fully-constrained ensemble ( Fig 1H ) and the genome-wide 3C data ( Fig 1G ) . After exclusion of non-specific interactions , the propensities from the fully-constrained ensemble and propensities from genome-wide 3C measurements have strong correlation , with an R of 0 . 96 at 15 kb resolution and an R of 0 . 97 at 32 kb resolution ( Fig 1I , see also S1 Text ) . Overall , our results obtained from the fully-constrained models of budding yeast genome show that model genomes generated under the constraints of nuclear confinement and all three nuclear landmarks can capture much of the experimentally measured intra- and inter-chromosomal interactions at 15 kb resolution . Both experimentally measured and mC-SAC inter-chromosomal interactions are dominated by interactions between pericentromeric regions , hence a cross-like pattern originating from centromeres is observed ( Fig 1D , 1E and 1F ) . These results suggest that nuclear confinement and nuclear landmarks play key roles in determining the overall organization of yeast genome . Here we studied the effects of landmark constraints on the organization of yeast genome , through analyses of additional ensembles in Table 1 . The overall correlation between the interaction frequencies from each ensemble and from experimental measurements is strong ( R > 0 . 75 , Table 1 ) , suggesting again nuclear confinement and excluded-volume effects that are common to all four ensembles are the dominant factors in determining the overall interaction patterns of the budding yeast genome . Inter-chromosomal interactions in most of ensembles are also highly correlated with experimentally captured inter-chromosomal interactions , except the ensembles in which centromere tethering is turned off . When the constraint of centromere tethering is removed , the correlation deteriorate from 0 . 75 to 0 . 30 . These findings suggest that models with the constraint of centromere tethering to the SPB imposed in addition to the volume confinement can capture inter-chromosomal interactions observed in genome-wide 3C experiments . Indeed , we see an increase in the inter-chromosomal correlation with a negligible compromise in the intra-chromosomal correlation when we imposed only the centromere tethering as the constraint ( Table 1 ) . This also suggests a nonlinear correlation relationship between the number of constraints and the agreement with the experimental observation . Specifically , when one or more constraints are removed while nuclear confinement and centromere tethering are maintained ( Table 1 , column 2–4 and column 6 ) , the correlation between experimental and model data of inter-chromosomal interactions fluctuate somewhat , but all have high values ( 0 . 75–0 . 86 ) . When the centromere constraint is removed , the correlation R deteriorates significantly to 0 . 30 . Upon additional removal of nucleolus and telomere constraints , R further deteriorates to 0 . 25 ( row 2 , col 7 ) . For intra-chromosomal interactions ( row 3 ) , models with different constraints removed all show overall similar correlation ( R = 0 . 87 – 0 . 95 , col 2–6 ) , and R = 0 . 89 when only the confinement and self-avoiding conditions are imposed ( col 7 ) . These slight fluctuations may be due to different sampling efficiencies , as it is easier to satisfy the constraints when the number of constraints decrease . Our findings show that specific landmark constraint affects the organization of budding yeast genome differently . The nucleolus constraint has effects only on the configurations of chromosome 12 ( Fig D in S1 Text ) . We further examined the importance of centromere tethering on the pairwise distances between telomeres . When the centromeres are not attached to the SPB , the linear relationship between pairwise telomere distances and chromosomal arm lengths that was observed in fluorescence imaging experiments disappears ( Fig C in S1 Text ) . Overall , these results showed that centromere attachment to the SPB largely determines the chromosome-chromosome interactions , hence the chromosomal positioning in the nucleus . The folding landscape of individual chromosomes , on the other hand , is largely determined by the nuclear confinement and volume exclusion . Furthermore , our results show that not all constraints contribute equally to the overall organization of the budding yeast genome . Indeed , the removal of nucleolus constraint alone has minor influence on the correlation between experimentally measured and computed interactions . In contrast , our results showed that spatial confinement and centromere attachment play key roles in the genome organization of budding yeast . The spatial locations of genes affect their transcriptional status [1] . The relative positions of seven important genes of the budding yeast and the left telomere of Chr 7 with respect to the SPB were measured in a fluorescence imaging study [6] . These genes include HMO1 on Chr 4 , GAL2 on Chr 12 , SNR17A on Chr 15 , RPS5 on Chr10 , GAL1 on Chr2 , URA3 on Chr5 , and RPS20 on Chr 8 . Previous computational models showed an agreement between relative positions of modeled genomes and experimentally observed locations [26 , 27] . We compared the positions of these genes measured from our fully-constrained ensemble with experimentally observed relative positions and found an agreement ( R2 = 0 . 95 , Fig 3A ) . Specifically , the relative positions of these genes are found to be inversely correlated with their genomic distances to corresponding centromeres , similar to a previous study [6] ( Fig 3B and 3C ) . In the original imaging study , a gene located at ( agene , bgene , cgene ) in the three–dimensional space is projected to two principal axes with coordinates of ( ρgene , zgene ) , where ρgene corresponds to the projection of ( bgene , cgene ) , and zgene corresponds to the cartesian location agene ( Fig A in S1 Text ) . The relative position of a gene is calculated as the ratio of agene/aSPB . Since the centromeres are located in the SPB , which is near the nuclear envelope ( towards ( a , b , c ) = ( −0 . 7 , 0 , 0 ) in Fig 1 ) and furthest away from the origin , a gene with genomic location away from the centromere would have its projected z-coordinate closer to that of the origin ( a , b , c ) = ( 0 , 0 , 0 ) . For example , a gene with agene = −0 . 1 will have a ratio of −0 . 1/ − 0 . 7 , which is smaller than the ratio of a gene that is located on SPB , as its ratio would be −0 . 7/ − 0 . 7 . That is , the relative position of a gene to the SPB decreases as it becomes closer to the origin and its genomic distance to the centromere increases . We hypothesize that the relative positions are determined by the genomic distances of these genes to centromeres . To test this hypothesis , we generated two artificial genomes that have the same overall genome size and architecture as the budding yeast nucleus . Artificial Genome 1 ( AG1 ) has the same number and lengths of chromosomes as the budding yeast genome , but with randomized locations of the centromeres . Artificial Genome 2 ( AG2 ) has only 12 chromosomes , with the locations of centromeres also randomized . We found the same cross-like pattern in the interaction frequency heat map as the budding yeast genome for AG1 and AG2 ( Fig 3D and 3E ) , suggesting that the number and the length of the chromosomes have little effects on the overall pattern of yeast genome organization . However , when the genomic locations of the eight genes were mapped to the artificial genomes , their relative positions deviate significantly from the experimentally measured positions ( R2 = 0 . 16 and R2 = 0 . 11 for AG1 and AG2 , respectively , Fig 3F ) . Surprisingly , the inverse relationship between the genomic distance to the corresponding centromere and the relative positions of these genes observed in wild type yeast is well preserved ( R2 = −0 . 87 and R2 = −0 . 91 for both artificial genomes , respectively , Fig 3G ) . We further compared experimentally measured relative positions of these genes with their positions obtained from the ensembles of “with only centromere” and “without centromere” to examine the roles of centromere tethering on genome organization . The ensemble of “with only centromere” captured the relative spatial positions of these genes quite well ( R2 = 0 . 88 , Fig 3H ) , whereas the relative positions in the ensemble of “without centromere” do not correlate well with experimental measurements ( R2 = 0 . 11 , Fig 3I ) . Overall , these results strongly suggest that centromere tethering is a key determinant of the folding of yeast genome and the positions of several important genomic elements are largely determined by their genomic distances to their corresponding centromeres . In eukaryotes , chromosomes can break at specific locations when DNA replication is perturbed [41] . These specific locations are called fragile sites . A recent genome-wide study of mapping of fragile sites showed that they are associated with sequence and structural motifs that pause or stall the DNA replication forks [41] . Fragile sites were also found to be associated with the origin of replication [42] . We mapped all 201 experimentally identified fragile sites to beads in our polymer model of yeast genome and calculated the mean interaction frequencies among them . Only non-local interactions between fragile sites that are more than 45 kb apart are considered , and proximity effects are eliminated in our consideration . Overall , the mean interaction frequency between the 95 mapped beads containing fragile sites is 35 . 9 . The random probability of observing similar or higher frequency is p < 0 . 001 ( Fig 4A ) , as estimated by bootstrapping 10 , 000 sets of 95 random beads that are at least 45 kb apart , and most of these interactions are found to be between different chromosomes . These results showed that fragile sites have a high propensity of clustering spatially together in the nucleus ( Fig 4B and 4C ) , indicating that the underlying mechanism of double-stranded DNA breaks coming together in 3D space to create a repair foci [43] may be facilitated by the centromere tethering and the confinement of the cell nucleus . This is not surprising as majority of the fragile sites are located within 200 kb of the centromeres ( Fig 4D ) and is likely a result of centromere co-localization on the SPB . While genome-wide 3C technique has identified many long-range pairwise chromatin interactions in budding yeast [16] , these interactions are incomplete due to the distribution of restriction enzyme sites and lack of full mappability of the fragments . Our fully-constrained ensemble can be used to predict novel interactions that are not captured by genome-wide 3C experiments . In addition , it is also important to identify biologically specific interactions captured in genome-wide 3C studies but are unaccounted for by polymer effects under landmark constraints and nuclear confinement .
Eukaryotic genomes reside within the confined space of cell nucleus , and its organization is also directed by interactions with substructures called nuclear landmarks . Previous studies [26 , 27] have already shown that random configurations of tethered chromosomes can reproduce measured interaction patterns [16] in the budding yeast genome , although the reported correlation between modeled and measured inter-chromosomal interactions is at the modest resolution of 32 kb , which is not strong . The direct effects of individual nuclear landmarks on genome folding , as well as the origin of inter-chromosomal interactions are unknown . A major technical challenge is the extreme difficulty in obtaining an adequate sampling of multiple chromatin chains subject to both landmark constraints and the confinement of the cell nucleus . The mC-SAC model developed in this study is based on a novel sampling technique [32 , 33] to achieve this . It enables the generation of large ensembles of model genomes with different combinations of landmark constraints under nuclear confinement . Our results showed that nuclear confinement and excluded-volume effects alone largely determine intra-chromosomal interaction patterns of individual yeast chromosomes , without the requirement of centromere tethering to the SPB and telomere attachment to the NE . This is in agreement with the results from polymer-diffusion studies [50] . Our results also highlight the importance of nuclear size on the patterns of interactions of genomic elements , as the experimentally captured interaction patterns disappeared , when the nuclear size is enlarged . Our results further demonstrated that centromere tethering to the SPB , along with the nuclear confinement and excluded-volume effect , are sufficient to capture the patterns of inter-chromosomal interactions . Furthermore , measured inter-chromosomal interactions are dominated by interactions between pericentromeric regions , hence a cross-like pattern originating from centromeres is observed . Our results also showed that , when only the landmark constraint of centromere tethering to the SPB is introduced , observed patterns of inter-chromosomal interactions are reproduced . Our results suggest that gene-regulatory systems involving long-range chromatin interactions might have been inherited from the telophase of budding yeast . Furthermore , the key difference in the regulatory machinery between the telophase and the interphase cells might be the silencing of telomeric genes through attachment to the NE . Such attachment , however , has no significant effects on the overall genome organization of budding yeast ( Fig D in S1 Text ) . Previous studies showed the presence of co-localization and clustering of important genomic elements such as early replicating sites or tRNA genes [16 , 26] . However , the origin of such clustering remained unclear . Our results demonstrated that this clustering is largely due to the attachment of centromeres to the SPB . Except genes on Chr 12 and telomeres , positions of genomic elements on the chromosomes relative to the SPB are strongly correlated with their genomic distances to their corresponding centromeres . We also showed that the relative positions of genes can be reproduced , when the location of centromeres are randomized , and even when the total number of chromosomes artificially altered , as long as their genomic distances to the corresponding centromeres are given . This finding may be useful for predicting spatial positions of important genes from their genomic locations . For example , the spatial distances between tRNA genes decrease as their genomic distances to the centromeres decrease ( Fig 5C ) . Our results are consistent with the suggestion that genomic locations of important elements in budding yeast were selected by evolutionary pressure [26] . Our model of budding yeast can be used to infer the biological details of the organization of yeast genome . The fully constrained ensemble can not only reproduce the pattern of spatial interactions from genome-wide 3C studies , but can also provide additional details by filling in the gaps in the sparse interaction matrices . Interactions arising from landmark constraints but absent in the genome-wide 3C data are enriched with transcription factors TFIIS as well as RNAPIII . These are located in pericentromeric regions of chromosomes , and contain significant amount of tRNA genes . In addition , we found that chromosomal fragile sites are clustered together in three–dimensional space , most likely as a result of their location at pericentromeric sites and a consequence of centromere clustering at the SPB . The proximate clustering of fragile sites suggest a machinery for DNA double break repair to repair multiple break sites , even those located on different chromosomes . It further suggests that these sites might experience less selective pressure to maintain resistance to perturbations . As SPB functionally corresponds to centrosome in mammalian cell nuclei , where the centromeres are attached during metaphase , our results may suggest that fragile sites of human genome could form spatial clusters and also be in genomic proximity to the centromeres . It is further possible that translocations due to the errors during mitosis in the human genome might be cancer promoting may also be related to centromere clustering . Because of the dominant effects of landmark constraints and confinement on the folding patterns of the budding yeast genome , it is challenging to uncover the specific spatial interactions that are due to biological factors . One approach to identify such interactions is to generate ensembles of model genomes that are subject to landmark constraints . Taking this ensemble as a null model , one could in principle remove polymer effects from the interactions captured in genome-wide chromosome conformation capture study . However , current polymer models are inadequate for such a task , as they cannot reproduce the inter-chromosomal interaction patterns , and hence will introduce many false positives [26 , 27] . Previous studies also suggested that volume exclusion models capture only expected interactions when such expected interactions were removed , as there were no significant correlations between model genomes and experimental measurements [31] . Our results suggest that such correlations can be improved significantly with better sampling techniques . To further understand whether the budding yeast genome organization is dictated by landmark constraints , we removed the interactions arising from excluded-volume effects , chain connectivity and nuclear confinement from both experimental measurements and our fully-constrained computed ensemble , and compared the remaining interaction frequencies . Our results suggest that overall experimentally measured interactions are in agreement with the remaining interactions of the fully-constrained ensemble of modeled genomes . Furthermore , there exists a set of interactions that occur at high frequency in the genome-wide 3C data but are almost absent in the fully-constrained ensemble . These interactions involve several important genes . Overall , we were able to extract interactions of potential biological interest from the interaction frequencies of genome-wide 3C data , a challenging task due to the dominance of polymer effects in experimental measurements . These interactions are found to be between some of the tRNA genes as well as landmark genes . With improved mC-SAC sampling technique , our computed 3D ensembles of budding yeast genome recaptures the observed intra- and inter-chromosomal interactions at the finer resolution of 15 kb , a resolution higher than those of previous studies [26 , 27] . Our study also reveals a number of novel findings that were not previously seen [26 , 27] . First , our results showed that spatial confinement and excluded volume effects alone can account for measured intra-chromosomal interactions . Second , attachment of centromeres to SPB is a major determinant of inter-chromosomal interactions , which was not accounted for in previous studies ( R = 0 . 75 in this study vs . R = 0 . 54 in [26] ) . Third , spatial locations of eight important genes can be determined by their genomic distances to the centromeres , as genomic distance of loci to centromeres and their spatial locations are now shown to be highly correlated . Fourth , chromosomal fragile sites , defined as double-stranded DNA breaks upon DNA perturbation , are found to be cluster in three-dimensional space . Fifth , we predicted novel long-range chromatin interactions not present in genome-wide 3C study that are mediated by RNAPIII and TFIIS , all involving tRNA genes . Sixth , our results confirm recent finding of tRNA gene clustering largely from centromere attachment to SPB . Finally , we succeeded in removing expected interactions from experimental measurements and identified important biologically specific genome-wide 3C interactions beyond any polymer effects . While these are important findings , our model is still limited , as it does not contain sufficiently detailed spatial information , because of the coarse-grained nature of both the mC-SAC model and the available genome-wide 3C data on budding yeast genome . Inferring the structural details of gene regulation for just a few kilo-bases requires chromatin models of much finer resolution . This finer resolution awaits advances in theory , model , and experimental measurements .
|
The architecture of the cell nucleus and the spatial organization of the genome are important in determining nuclear functions . Single-cell imaging techniques and chromosome conformation capture ( 3C ) based methods have provided a wealth of information on the spatial organization of chromosomes . Here we describe a multi-chromosome ensemble model of chromatin chains for understanding the folding principles of budding yeast genome . By overcoming severe challenges in sampling self-avoiding chromatin chains in nuclear confinement , we succeed in generating a large number of model genomes of budding yeast . Our model predicts chromatin interactions that have good correlation with experimental measurements . Our results showed that the spatial confinement of cell nucleus and excluded-volume effect are key determinants of the folding behavior of yeast chromosomes , and largely account for the observed intra-chromosomal interactions . Furthermore , we determined the specific roles of individual nuclear landmarks and biochemical factors , and our analysis showed that centromere tethering largely determines inter-chromosomal interactions . In addition , we were able to infer biological properties from the organization of modeled genomes . We found that the spatial locations of important elements such as fragile sites and tRNA genes are largely determined by the tethering of centromeres to the Spindle Pole Body . We further showed that many of these spatial locations can be predicted by using the genomic distances to the centromeres . Overall , our results revealed important insight into the organizational principles of the budding yeast genome and predicted a number of important biological findings that are fully experimentally testable .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"transfer",
"rna",
"chromosome",
"structure",
"and",
"function",
"centromeres",
"telomeres",
"fungi",
"model",
"organisms",
"non-coding",
"rna",
"materials",
"science",
"experimental",
"organism",
"systems",
"epigenetics",
"macromolecules",
"structural",
"genomics",
"chromatin",
"materials",
"by",
"structure",
"saccharomyces",
"research",
"and",
"analysis",
"methods",
"polymers",
"polymer",
"chemistry",
"chromosome",
"biology",
"gene",
"expression",
"chemistry",
"yeast",
"biochemistry",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"yeast",
"and",
"fungal",
"models",
"saccharomyces",
"cerevisiae",
"genomics",
"physical",
"sciences",
"organisms",
"chromosomes"
] |
2017
|
Spatial organization of the budding yeast genome in the cell nucleus and identification of specific chromatin interactions from multi-chromosome constrained chromatin model
|
The ability to effectively modify behaviours is increasingly relevant to attain and maintain a good health status . Current behaviour-change models and theories present two main approaches for ( healthier ) decision-making: one analytical/logical , and one experiential/emotional/intuitive . Therefore , to achieve an integral and dynamic understanding of the public perceptions both approaches should be considered: community surveys should measure cognitive understanding of health-risk contexts , and also explore how past experiences affect this understanding . In 2011 , community perceptions regarding domestic source reduction were assessed in Madeira Island . After Madeira’s first dengue outbreak ( 2012 ) a unique opportunity to compare perceptions before and after the outbreak-experience occurred . This was the aim of this study , which constituted the first report on the effect of an outbreak experience on community perceptions regarding a specific vector-borne disease . A cross-sectional survey was performed within female residents at the most aegypti-infested areas . Perceptions regarding domestic source reduction were assessed according to the Essential Perception ( EP ) -analysis tool . A matching process paired individuals from studies performed before and after the outbreak , ensuring homogeneity in six determinant variables . After the outbreak , there were more female residents who assimilated the concepts considered to be essential to understand the proposed behaviour . Nevertheless , no significant difference was observed in the number of female residents who achieved the defined ‘minimal understanding’’ . Moreover , most of the population ( 95 . 5% ) still believed at least in one of the identified myths . After the outbreak some myths disappeared and others appeared . The present study quantified and explored how the experience of an outbreak influenced the perception regarding a dengue-preventive behaviour . The outbreak experience surprisingly led to the appearance of new myths within the population , apart from the expected increase of relevant concepts’ assimilation . Monitoring public perceptions is therefore crucial to make preventing dengue campaigns updated and worthy .
Most of the 2011 worldwide major causes of death ( MCD ) , rely on behaviour changes for their prevention [1] . Increasing physical activity , fruits/vegetables intake , hand-washing , use of condoms , and decreasing not only fat , salt and sugar intake but also smoking habits , are crucial in the control of heart disease ( 1st MCD ) , stroke ( 2nd MCD ) , chronic obstructive lung disease ( 4th MCD ) , diarrhoea ( 5th MCD ) , HIV ( 6th MCD ) , or diabetes ( 8th MCD ) . Behaviour changes are increasingly relevant to attain and maintain a good health status , especially when facing health threats for which there is no efficient or timely treatment . This is the case for dengue fever that such as other mosquito-borne diseases , requires a good compliance to certain preventive , protective or therapeutic actions . Moreover , since there is no vaccine or treatment for dengue fever , neither 100% effective insecticides , community behaviour has a huge impact on its prevention and control [2] . It is still not widely understood how to effectively promote behaviour changes [3] . During several decades , many behaviour impact campaigns were shown to be fruitless . In the past 50 years , literature extensively presented theoretical models which tried to clarify cognitive ways of ( healthier ) behaviour acquisition [4 , 5 , 6 , 7 , 8 , 9] . Recently , the concept of ‘past experiences’ was stated as being crucial in determining ( healthier ) decision-making . Many authors claimed that , due to the type of emotions and intuition that they produced , ‘past experiences’ could strongly encourage or discourage a particular action [6 , 7 , 8 , 9 , 10] . Altogether , these contributions seem to present two different approaches by which humans perceive decision-making and then make decisions: one analytical and one experiential [11] . In order to improve the efficacy of the behaviour-promoting messages , the authors firmly suggested that messages should be not only meaningful but also emotionally adequate for the targeted community . This way , the assessment of community’s cognitive and emotional perceptions , is hence useful in the guiding of effective health-seeking messages . However , few studies explored emotional experience-driven perceptions but rather frequently only focused on the assessment of the cognitive ones [12] . Some evidence suggested that experience can influence public perceptions and reactions in two ways [13] . In one aspect , it can over-estimate the risk perception [10 , 13] ( i . e . alert-feeling , referred to as ‘availability bias’ [14] ) and consequently promote protective/preventive actions . It can also underestimate the risk perception [12 , 15] ( i . e . habituation effect also called ‘gambler’s fallacy’[14] ) which can discourage protective/preventive actions . Only few studies have explored this issue in real situations . Besides the scientific interest of scrutinizing the complex process of ( healthier ) decision-making , the monitoring of public perceptions and behaviours contributes to the continuous and adequate update of the behaviour-promoting messages concerning their ( rational and emotional ) content . This is the case of any chronic and endemic disease , where the ( health ) risk is maintained during time such as dengue epidemic and endemic areas [12] . In 2005 , a dengue vector species , Aedes aegypti was reported in Madeira archipelago . In 2012 , the first dengue outbreak was recorded in the territory [16] . Community perception regarding preventive behaviours ( domestic source reduction ) was assessed and described in details by the current investigators , before the outbreak had been declared [17] . At the end of the outbreak , a unique opportunity to explore and compare community perception before and after the outbreak appeared . The aim of this study was thus to re-assess community perceptions regarding the same preventive behaviour ( domestic source reduction ) just after the dengue outbreak in order to compare how it has altered with the outbreak experience . This constitutes to our knowledge the first report of this kind describing the effect of an outbreak experience on community perceptions regarding a specific vector-borne disease .
Out of the several municipalities which were covered by the PRE-outbreak study area , only some were selected to be included in this POST-outbreak study ( Fig . 1 ) . In order to decrease the sample size , ‘Câmara de Lobos’ was excluded since it was the sole rural municipality . Facing the impossibility of including both urban and rural municipalities , the urban ones were preferred based on two main reasons: ( i ) they presented a dengue incidence rate greater than 200 during the outbreak ( S1 Fig . ) ; and ( ii ) they comprise the capital city of the archipelago , Funchal , and thus an important point of aegypti-dispersion . Part of the Funchal municipalities: ‘Sé’ , ‘Santa Maria Maior’ and ‘Imaculado Coração de Maria’ , were also included in the POST-outbreak study area besides those considered in the PRE-outbreak study ( ‘São Pedro’ and ‘Santa Luzia’ ) . These extra-included area were also covered by the 2012’s most aegypti-infested area ( presenting a density level of 31% or over along the year ) , thus ensuring a homogeneous level of natural exposure to the A . aegypti among the studied residents [18] . The geographic area covered in the present study will be mentioned as ‘Extended-AEGYPTI area’ and consists , thus , in part of five Funchal’s municipalities from the 2012 most aegypti-infested area . Due to the mentioned unfeasibility to include a representative sample of the resident population of the study area , an intentional sample of exclusively female subjects who lived in the study area—‘Extended-AEGYPTI area’ aged 18 years old or over and who didn’t integrate the previous PRE-outbreak survey was selected from customers of central hairdressers and pharmacies , placed in the selected area . The women selection was based on three main reasons: ( i ) before the outbreak women were significantly less aware of domestic source reduction than men ( S2 Fig . and S1 Table ) ; ( ii ) women are the majority within the studied population [19]; ( iii ) women above 15 years-old were the age/gender-group more affected by the disease during the outbreak [20]; and ( iv ) culturally , in Madeira Island , women are more related to the main dengue-preventive behaviour proposed than men do ( see details about the behaviour proposed in ‘Essential-Perception’ subsection ) . All women who entered in the establishment and who met the inclusion criteria were invited to participate . The type of establishment were chosen in order to allow the study to cover the most possible heterogeneous women sample , in what concerns their age groups , education levels and socio-economic background . In order to stimulate participation of women from all the included municipalities , two central establishments of each service were chosen to participate in the study according to their convenient geographical location , being one placed in the east and the other in the west region of the studied area . The study area has a population density which can be as high as 1433 . 5 habitants per square kilometers [21] . The sample size was calculated using Epitools’ sample size calculators ( 2014 , AusVet Animal Health Services ) in order to perform a comparison of two means using the t-test ( 2-tailed ) where a 1 point is relevant in Essential-Perception Score difference ( variation between −10 , 10 ) [22] . The sample size calculation considered a 95% confidence level , a power of 80% and a pooled variance equal to 10 ( S = √10 = 3 . 16 ) . The obtained n was 157 ( S2 Table ) . Finally , this sample size was inflated in 30% to account for incomplete interviews . A cross-sectional survey was performed to assess residents’ perceptions through face-to-face interviews . Before data collection , establishments’ managers/participants gave their written/oral informed consent respectively . Previous to the beginning of this survey , the questionnaire was pre-tested in a non-selected establishment placed in the selected area . During the interview , a questionnaire comprising 21 questions was applied , covering dengue-preventive issues and personal-socio-demographic characteristics . In agreement with what was inquired in the PRE-outbreak study , questionnaire covered five main topics: ‘Medical Importance’ ( two questions ) , ‘Local Context’ ( two questions ) , ‘Domestic Attribute’ ( three questions ) , ‘Mosquito Breeding’ ( three questions ) and ‘Control Measures’ ( three questions ) [17] . Besides the variables ‘gender’ , ‘education level’ , ‘age group’ , and ‘geographical area’ , two other variables were assessed: ‘travels to dengue endemic countries ( DEC ) ’ which measures who had already been to any dengue endemic country and ‘admitted mosquito exposure ( AME ) ’ which measures who recognized to had been bitten by mosquitoes . The survey was performed by trained personnel from the local health authority from 22nd of March until 16th of April , 2013 . In each establishment ( pharmacies/hairdressers ) , interviews were performed during a Monday-to-Saturday week , between 9am and 7pm ( according to establishments’ opening hours ) The study was approved by Instituto de Higiene e Medicina Tropical Ethics Committee , Instituto de Higiene e Medicina Tropical , Universidade Nova de Lisboa , Lisbon ( reference: 09-2013-TD ) . Populations studied in both PRE/POST-outbreak surveys were matched into pairs , ensuring homogeneity in six critical socio-demographic variables . Resulting matching population comprised thus pairs of individuals composed of an individual from the PRE-outbreak study and an individual from the POST-outbreak study with equivalent personal-socio-demographic characteristics . Matching pairs of individuals were equal in ( or “blocked” on ) gender , education level , age group , geographical area , travels to DEC and AME variables , already shown to be determinants to the individual perception [17] . This sampling methodology can also be called as randomized block design , and the latter variables as blocking factors [23] . For comparative purposes , the criteria ‘geographic area’ was applied in two different ways , generating two different matching approaches . In one matching approach , herein called ‘adjusted matching’ , the ‘geographic area’ criteria distinguished only residents living in urban areas from residents living in rural areas . According to this criteria , ‘Câmara de Lobos’ ( covered exclusively in PRE-outbreak study area ) was the sole rural municipality . In the other matching approach , herein called ‘restricted matching’ , the geographic criteria besides the previous distinction between urban and rural areas also differentiated urban municipalities covered in both PRE-outbreak and POST-outbreak studies ( ‘Santa Luzia’ and ‘São Pedro’ ) from the remaining urban ones which were exclusively included in the POST-outbreak study ( ‘Sé’ , ‘Santa Maria Maior’ and ‘Imaculado Coração de Maria’ ) . The other criteria ( gender , education level , age group , travels to DEC and AME ) were strictly applied in both matching approaches , i . e . only individuals who were equal in this variables were matched . Both matching procedures were built in Excel ( Microsoft Office , Windows 8 ) , and guaranteed that individuals were randomly selected within those that were personal-socio-demographically equivalent . Moreover , matching procedures were optimized in order to re-include all the non-selected individuals in the subsequent matching rounds . The assessment of the community perception was performed using the Essential-Perception analysis ( EP-analysis ) , as described below . Essential-Perception analysis assesses community perception regarding a particular behavioural proposal: the domestic aegypti’s source reduction , considered the most critical dengue-preventive practice by the World Health Organization [24] . This corresponds to the elimination ( emptying , covering or removing ) of water-containers present inside or around residential buildings . The EP-analysis’ considers ten essential concepts which assimilation by individuals was revealed to be determinant to the performance of the proposed behaviour ( Table 1 ) [17] . Essential-Perception analysis allows the characterization and estimation of the community’s perceptions through four different approaches , all of them used here: ( i ) score of Essential-Perception , ( ii ) concept assimilation , ( iii ) topic understanding and ( iv ) myth identification and estimation . The first measures the number of concepts that were assimilated ( out of those defined to be ‘essential’ ) and thus how far-off is the studied population from achieving the complete ‘Essential Perception’ ( EP-Score = 10 ) . The second describes how much those ‘essential’ concepts were assimilated or not-assimilated by the community . The third , organizes the ‘essential concepts’ in topics and describes how topics are/not being understood . Residents who have acknowledged both topic-related concepts are according to this tool considered to have ‘completely understood the topic’ , the acknowledgement of only one out of the two topic-related concepts is considered as a ‘partial understanding of the topic’ , and residents who did not perceive any of the two topic-related concepts are considered to have ‘not understood the topic’ . Finally the fourth , by analysing the concept assimilation , identifies erroneous beliefs which may persist in the community ( herein mentioned as ‘myths’ ) and estimates their putative frequency in the studied population ( see example in S3 Fig . ) . All collected information was introduced and records were double-checked . Statistical analysis was performed using Statistical Package for Social Sciences 19 . 0 ( SPSS , Inc . , Chicago , IL , USA ) . Answers obtained from the questionnaires were re-coded to obtain other categorical variables implicit in the EP-analysis . The personal-socio-demographic feature of the studied population presented in Table 2 was described in what concerns the gender , age group , education level , municipal division , travels to dengue endemic countries ( DEC ) and AME . The age groups were categorized in ten-year intervals and the education level was divided into five categories starting from ‘never studied’ until ‘upper graduation’ . This categorization allow that groups were similar in number of individuals . Comparison of the two urban municipalities covered in both PRE-outbreak and POST-outbreak studies ( ‘Santa Luzia’ and ‘São Pedro’ ) confirmed a priori the validity of the criteria ‘geographic area’ in the restricted matching . In this matching those municipalities were considered equivalent . S3 Table shows that despite the previous observed differences observed in their EP-score level , when “blocking” the education level there are no significant differences between the two municipalities . Analysis of the demographic data of the extra-included areas compared the new added municipalities ( ‘Sé’ , ‘Santa Maria Maior’ and ‘Imaculado Coração de Maria’ ) and the previously studied ( ‘Santa Luzia’ and ‘São Pedro’ ) . Comparison is presented in S4 Table showing that there were no relevant differences between them in what concerns the two critical socio-demographic determinants: age group and education level supporting thus a priori the validity of the criteria ‘geographic area’ in the adjusted matching . Comparisons of EP-score medians between populations from PRE/POST-outbreak studies were made using the non-parametric Wilcoxon Test ( Table 3 ) , after rejecting the normality of Essential-Perception score difference through Kolmogrov-Smirnov test . Additionally , the number of individuals who achieved an EP-score equal to or higher than seven ( EP-score≥7 ) was calculated in both studies and differences were compared , using the McNemar Test ( Table 4 ) . This cut-off was chosen due to lack of subjects that achieved an EP-score equal to ten ( EP-score = 10 ) . In order to evaluate the methodology used during the matching process , comparisons between paired and non-paired samples were performed , according to their EP-score and their socio-demographic characteristics . In order to ensure that restricted and adjusted matching sample sizes ( n = 47and n = 90 ) were satisfactory , the power associated to Wilcoxon test ( the non-parametric alternative to t-test ) was calculated a posteriori using free statistical power analysis program , G*Power 3 . 0 [25] .
Statistical tests were performed in order to explore the differences between medians of the EP-score from studied populations in both PRE/POST-outbreak studies , confirming a significant increase in the EP-Score median after the outbreak ( p<0 . 001 , Table 3 ) . An increase in the number of individuals who achieved an EP-score equal to or higher than seven ( EP-score≥7 ) in the POST-study population was also statistically confirmed ( p<0 . 001 , Table 4 ) .
In general , the community perception regarding preventive domestic practices improved within female residents of most aegypti-infested areas in Madeira Island after they experienced a dengue outbreak . By analysing how and how much assimilation of each 'Essential-concept’ has changed , crucial information can be retrieved regarding people´s perceptions about this experience and their future role in its prevention . For many Madeira residents , the experience of this dengue outbreak was probably the first contact with a mosquito borne disease ( it was the first in almost a hundred years in Europe , [26] . This can explain the observed increase in the assimilation of the idea that ‘mosquitoes can transmit diseases’ ( MI1-concept ) . Moreover , before experiencing the outbreak , the community's worst incident with mosquitoes were allergic reactions , which could be considered as the sole health consequence of mosquito bites . After the outbreak , it was not surprising that the percentage of residents that were aware of ‘the kind of diseases that mosquitoes can transmit ( such as dengue , yellow fever and malaria ) ’ ( MI2-concept ) almost doubled . Therefore , in the POST-outbreak study there were a higher percentage of people who rightly appraised the impact of mosquitoes in health . Since no fatal cases occurred during the dengue outbreak , some beliefs such as , ‘dengue disease does not kill’ and ‘dengue in Madeira is less aggressive’ may be present in the community . These questions should be clarified within the community due to the possibility of a different virus serotype reaches the Madeira territory , increasing the risk of dengue haemorrhagic cases to occur . Even though assimilation of both ‘Local Context’ concepts increased after the outbreak , the majority of residents still ignored that ‘there is a high possibility for a ( second ) dengue outbreak in Madeira’ ( LC2-concept ) . The acknowledgement of this concept was expected to increase after the outbreak , assuming that the previous identified myth which states that ‘Madeira were not at risk of have dengue’ would be opposed with the experience of a dengue outbreak . However , its assimilation merely increased 10% . Even though people had probably realized that Madeira was at risk and that several dengue cases occurred , two erroneous interpretations could explain this 10% result . Firstly , the false belief that the ‘dengue outbreak have ended due to the eradication of the disease or the mosquito’ ( alleged myths 6a/6b , Table 5 ) . Secondly , gambler’s fallacy , the invalid belief that when something happens more frequently than normal during a period of time , the probability of happening again in the future decreases ( alleged myth 3 , Table 5 ) [14] . People who believe in these alleged myths underestimate the probability of another dengue epidemics occur in Madeira Island . Improvements in DA1-concept , DA2-concept , LC1-concept and MB1-concept can be attributed to the “boom” of educational information transmitted during the outbreak . This information was transmitted by the news , by official reports , and most importantly by the exhaustive door-to-door campaign that was rapidly implemented in the areas where most dengue cases were being reported during the outbreak period . In the latter , trained personnel of the health-authorities entered in residential buildings and supported the residents in performing correct and extensive elimination of mosquito breeding sites inside and in the surroundings of their houses ( i . e . aegypti source reduction ) . This provided a useful opportunity for residents to realize ‘the existence of larval forms/mosquitoes in their own houses’ ( DA 1-concept ) , to ‘recognize containers that were serving as breeding sites’ ( MB1-concept ) , to emphasize the idea that ‘domestic control could be efficient in the A . aegypti control’ ( DA2-concept ) , and finally to comprehend that their ‘residential area had ( indeed ) vector-mosquitoes’ ( LC1-concept ) . In contrast to the improvement in the above stated concepts , the percentage of people who believed in ‘false mosquito breeding inducers , such as , animals or food debris’ augmented after the outbreak and thus , MB2-concept was the sole concept of which assimilation had declined after the outbreak . Female residents may have ‘erroneously indorsed A . aegypti’s proliferation to dirty environments’ ( with food debris or animals ) . This assumption could be interpreted as an intuitive explanation for the appearance/establishment of the A . aegypti and dengue disease in the Island . As stated in psychology in the attribution theory , humans need to “attribute” causes to events which are not understood [27] . Female residents , who agreed with latter belief , and believe to live in clean households , will not feel responsible to perform domestic source reduction . Finally , almost all the female residents agreed with the efficacy of domestic source reduction in the control of mosquitoes ( CM1-concept ) . However , the majority still erroneously consider ‘insecticide application or flyswatter usage’ as effective measures to control mosquito population ( CM2-concept ) . In fact , these practices are protective ( i . e . can , in some manner , avoid the mosquito bite ) but are not preventive ( i . e . are able to control the mosquito proliferation ) . This mistake is determinant because people that believe in it tend to focus their efforts on these easier but less efficient practices and to disfavour the truly efficient ones , which are more difficult to implement ( such as , domestic source reduction ) . Moreover , previous studies had shown that the local A . aegypti population , present in Madeira Island , was resistant to the most common insecticides , which raised questions about the reasonability of its application , even when used with protective objectives [28] . Overall , there were only three Essential Concepts that were still not considered by the majority of the studied population ( LC2-concept , MB2-concept and CM2-concept ) . Under the assumptions of the EP-analysis , the individual minimal understanding and putative subsequent compliance to the proposed behaviour , requires the assimilation of all the ten concepts defined as ‘essential' . Consequently , the weak integration of one of these concepts by the community can compromise the usefulness of the behaviour impact campaigns . It is worth pointing out that , even though concept assimilation had generally improved after the outbreak , only 4 . 5% of the studied population achieved the referred ‘minimal understanding’ ( EP-Score equal to ten ) . Consequently , there were still very few residents that are ready to engage in the proposed behaviour . Along with the observed improvement of essential concept assimilation , myths believed by the community also changed . Even though the community is now closer to the needed ‘minimal understanding’ , the task of local authorities is still difficult since after the outbreak they have to cope with new/different beliefs , following ideas such as ‘Madeira is immune to suffer a second outbreak’ ( alleged myth 3 and 6 ) . In reality , myths could subtly persist in the community , weakening the effect of strategies aimed at behaviour changes . Therefore , an adequate monitoring of public perceptions is undoubtedly crucial to ( more quickly ) detect them , allowing preventive campaigns to be planned accordingly . Apart from the here observed public erroneous interpretations ( probably caused by their short contact with the vector and the disease ) community can provide other enriching contribution such as technical hitches in implementing proposed behaviours , pointing out messages or expressions difficult to understand , and suggesting housewives-friendly solutions [29 , 30] . The similarity found between paired and non-paired samples , regarding their EP-score levels supported the validity of the criteria used in the adjusted matching approach . Moreover , the observed equivalent results between the adjusted and the restricted matching procedures corroborated the validity of the geographical adjustments . Furthermore , the calculated power value supported the strength of the results albeit the apparently small size of the sample . In fact , prior sample size estimations indicated a minimal amount of 157 subjects required to fulfil the objectives of this study ( as mentioned in Methods section ) , assuming a minimal difference ( 1 point ) between the EP-score levels from PRE/POST-outbreak studies . However , since a difference of 2 point was observed , only 40 pairs of subjects were needed to detect it fulfilling the same objectives ( S2 Table ) . The studied sample size was thus higher than the required to the aimed analysis . Therefore , the power associated to Wilcoxon test is also high as described in Table 6 . It is worth noticing that considering the studied sample , only women from urban areas were covered , and therefore results may not be equivalent in male subjects , rural communities or in long-term dengue regions . In conclusion , after experiencing a dengue outbreak in Madeira Island , female community perception towards the aimed preventive engagement improved in some aspects ( as intuitively expected ) but also deviated in other aspects , particularly by the emergence of new myths . The most frequent myths may be used in the future to outline appropriate priority messages . Subsequent health-messages tailored according to present findings could strengthen community engagement in dengue-preventive behaviours . Monitoring public perceptions ( before/after an intervention or an outbreak ) revealed a great value , not only for public health professionals but also for researchers who may be interested in investigating the complex interplay between experiences , perceptions and decision-making . Thus , lessons taken from this work can be useful not only for local authorities but also for all professionals who are engaged in dengue preparedness in endemic or epidemic countries , as well as , to those interested in strengthening tools for other behaviour-based preventable diseases .
|
Since there is no vaccine or treatment available for dengue fever , its prevention relies on community participation . Residents are asked to remove from their houses and gardens all receptacles where mosquitoes can breed . Exploring the public perception regarding dengue prevention is crucial for detecting obstacles to their participation in the proposed preventive activities . The authors explored and compared the community's perceptions before and after the first dengue outbreak in Madeira Island . For the first time it was possible to study the effect of a dengue outbreak in the public perceptions regarding its prevention . After the dengue outbreak , the authors found an improvement in the perception of the community . However , even after experiencing an outbreak , the majority of the residents still did not understand their role in the dengue prevention and , thus were not ready to adhere to it . Moreover , the authors also observed some new myths within the community after the outbreak ( which were not present before the outbreak ) . The improvement of community perceptions was expected . However this search also revealed that this experience can surprisingly promote the emergence of new myths which may hamper the community engagement in the dengue prevention .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Impact of a Dengue Outbreak Experience in the Preventive Perceptions of the Community from a Temperate Region: Madeira Island, Portugal
|
Leishmania is transmitted by female sand flies and deposited together with saliva , which contains a vast repertoire of pharmacologically active molecules that contribute to the establishment of the infection . The exposure to vector saliva induces an immune response against its components that can be used as a marker of exposure to the vector . Performing large-scale serological studies to detect vector exposure has been limited by the difficulty in obtaining sand fly saliva . Here , we validate the use of two sand fly salivary recombinant proteins as markers for vector exposure . ELISA was used to screen human sera , collected in an area endemic for visceral leishmaniasis , against the salivary gland sonicate ( SGS ) or two recombinant proteins ( rLJM11 and rLJM17 ) from Lutzomyia longipalpis saliva . Antibody levels before and after SGS seroconversion ( n = 26 ) were compared using the Wilcoxon signed rank paired test . Human sera from an area endemic for VL which recognize Lu . longipalpis saliva in ELISA also recognize a combination of rLJM17 and rLJM11 . We then extended the analysis to include 40 sera from individuals who were seropositive and 40 seronegative to Lu . longipalpis SGS . Each recombinant protein was able to detect anti-saliva seroconversion , whereas the two proteins combined increased the detection significantly . Additionally , we evaluated the specificity of the anti-Lu . longipalpis response by testing 40 sera positive to Lutzomyia intermedia SGS , and very limited ( 2/40 ) cross-reactivity was observed . Receiver-operator characteristics ( ROC ) curve analysis was used to identify the effectiveness of these proteins for the prediction of anti-SGS positivity . These ROC curves evidenced the superior performance of rLJM17+rLJM11 . Predicted threshold levels were confirmed for rLJM17+rLJM11 using a large panel of 1 , 077 serum samples . Our results show the possibility of substituting Lu . longipalpis SGS for two recombinant proteins , LJM17 and LJM11 , in order to probe for vector exposure in individuals residing in endemic areas .
The Leishmaniasis is a widely distributed disease , caused by Leishmania protozoans and transmitted by sand fly vectors . Infected sand flies inject parasites when attempting to take a blood meal . In this process , vector saliva is inoculated together with Leishmania into the host skin . This saliva is composed of molecules that modulate the host's hemostatic , inflammatory and immune responses [1] . Some of these molecules are immunogenic and stimulate strong immune responses in animals including humans [2] , [3] . Importantly , the humoral response against sand fly saliva has been proposed as a potential epidemiological marker of vector exposure in endemic areas of Leishmaniasis [4] , [5] . Sand fly populations tend to be clustered [5] leading to unequal exposure of human populations . Screening of human antibodies to sand fly saliva could be a useful indicator of the spatial distribution of sand flies in a particular region . Pinpointing areas of high exposure to sand fly bites may be helpful in directing control measures against Leishmaniasis . Large-scale serological studies to detect vector exposure have been limited by the difficulty in obtaining large amounts of saliva . Additionally , the use of salivary gland sonicate inherits the limitation of potentially considerable variability in stocks of sand fly saliva due to differences in the feeding source and time of collection after feeding [6] . Salivary protein content varies along the feeding cycle and is influenced by the source of feeding used by sand flies [6] . Another limitation of using SGS is a potential lack of specificity of the salivary proteins due to immunogenicity of proteins present in different species . The use recombinant proteins may reduce such a problem by using proteins which exhibit predominant species-specificity . Two recombinant molecules , rLJM17 and rLJM11 , from Lutzomyia longipalpis saliva , were recognized by sera of men , dogs and foxes from endemic areas for visceral Leishmaniasis ( Teixeira et al . , unpublished data ) , represent good candidates for large scale testing of human exposure to Lu . longipalpis bites . In this study , we tested a large cohort for exposure to Lu . longipalpis , and validated the results obtained using the recombinant proteins with total sand fly saliva .
Informed consents were obtained from all participants and all clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki . The project was approved by the institutional review board from Centro de Pesquisas Gonçalo Moniz-FIOCRUZ/BA . Lutzomyia longipalpis , Cavunge strain , were reared at Centro de Pesquisas Gonçalo Moniz-FIOCRUZ , as described elsewhere [7] . Salivary glands were stored in groups of 20 pairs in 20 µl NaCl ( 150 mM ) Hepes buffer ( 10 mM , pH 7 . 4 ) , at −70°C . Immediately before use , salivary glands were disrupted by ultrasonication . Tubes were centrifuged at 10 , 000×g for 2 min and the resultant supernatant ( Salivary Gland Sonicate - SGS ) was used for the studies . This study was divided in three phases ( Figure 1 ) . Different sets of serum samples were randomly selected from three independent epidemiological surveys previously performed in two endemic areas for Leishmaniasis ( one for VL and the other for cutaneous Leishmaniasis ) . The selection criteria of the subjects enrolled in each survey are published elsewhere [5] , [8] , [9] . In the first phase , 26 serum samples were obtained from an epidemiological survey of VL in children less than 7 years old living in a region of São Luis , Maranhão State , in northeastern Brazil , where VL is endemic and Lu . longipalpis is prevalent [5] . These samples were selected based on presenting seroconversion against the Lu . longipalpis SGS after a follow up period of six months . The cut off value of the anti-SGS ELISA was established as the mean plus three standard deviations ( SD ) of the mean optical density ( OD ) of serum samples of 26 individuals from an urban non-endemic area for both human Leishmaniasis and Lu . longipalpis . Serum samples with OD above this cut-off ( 0 . 073 ) were considered SGS-positive . The same methodology was applied to assess the cut-off values for the recombinant proteins . The objective was to primary set the cut-off values and to verify the concordance of seroconversion against the SGS and the recombinant proteins . In the second part of the study , we attempted to check if the recombinant proteins were useful to discriminate anti-SGS positivity . To do this , we randomly selected another 80 individuals from the same endemic area , 40 being positive and 40 being negative for anti-SGS , and performed serology against the recombinant proteins . Receiver-Operator Characteristic ( ROC ) curves were built for each protein separately and for the combination of both . New cut-offs combining highest sensitivity and specificity and the highest likelihood ratio for this discrimination were determined based on the ROC curves . The ROC curves lead us to identify the effectiveness of these proteins for the identification of anti-SGS positivity . In addition , to evaluate specificity regarding reactivity to other sand flies , we used 40 serum samples obtained from an epidemiological survey conducted in a region endemic for American cutaneous Leishmaniasis ( Canoa , a rural village , located near Santo Amaro , Bahia , Brazil ) . In this region , Lu . intermedia represents the major sand fly species , with L . braziliensis being the main Leishmania species in the area . Both Lu . longipalpis and Lu . intermedia normally live in different ecosystems and only rarely individuals are exposed to both of them . Details of the area , patients and anti-Leishmania delayed type hypersensitivity skin test are described elsewhere [8] . We used data from 40 individuals exposed to Lu . intermedia in a previous investigation [9] and addressed the cross-reactivity to the whole SGS or the recombinant salivary proteins from Lu . longipalpis . The third part of the study was the validation of the serology for detection of antibodies against the Lu . longipalpis salivary recombinant proteins as a marker of vector exposure . We used a larger panel consisting of 1 , 077 sera from another population survey done through home visits . Therefore , serum samples were obtained from children residing in two endemic areas for visceral Leishmaniasis ( Vila Nova and Bom Viver ) , in Raposa county , Maranhão State , Brazil . Vila Nova and Bom Viver have an approximate population of 2 , 600 and 4 , 307 inhabitants , respectively . Within this population , a total of 1 , 297 children under 10 years old were identified and , of these , 1 , 077 children were enrolled in the study ( 220 individuals withdrawn consent ) . The flow chart of the third phase of the study is illustrated in Figure S1 . Antibodies to sand fly saliva from endemic area humans , dogs , or fox sera recognize mostly salivary proteins in the range of 15 to 65 kDa . Based on this information we selected nine transcripts coding for salivary proteins from Lu . longipalpis falling in this molecular range ( LJM17 [AF132518] , LJM111 [DQ192488] , LJM11 [AY445935] , LJL143 [AY445936] , LJL13 [AF420274] , LJL23 [AF131933] , LJM04 [AAD32197 . 1] , LJL138 [AY455916] , and LJL11 [AF132510] ) [10] . From the range of recombinat salivary proteins tested rLJM17 and rLJM11 were the best candidates recognized by sera from all three hosts ( Teixeira et al . unpublished data ) . Recombinant proteins were produced by transfecting 293-F cells ( Invitrogen ) with plasmids ( VR2001-TOPO ) coding for these different salivary proteins following the manufacturer's recommendations ( Teixeira et al . unpublished data ) . The concentrated supernatant was added to a HiTrap chelating HP column ( GE Healthcare ) that was then connected to a Summit station HPLC system ( Dionex , Sunnyvale , CA ) consisting of a P680 HPLC pump and a PDA-100 detector . ELISA was performed as described before [4] . Briefly , ELISA plates were coated with Lu . longipalpis SGS , equivalent to 5 pairs of salivary glands/mL ( approximately 5 ug protein/mL ) , or with 1 ug of each recombinant protein/mL ( when used independently or in combination ) in carbonate buffer ( NaHCO3 0 . 45 M , Na2CO3 0 . 02 M , pH 9 . 6 ) overnight at 4°C . After three washes with PBS- 0 . 05% Tween , the plates were blocked for 1 hour at 37°C with PBS-0 . 1% Tween plus 0 . 05% BSA . Sera were diluted 1∶50 with PBS-0 . 05% Tween and incubated overnight at 4°c . After further washings , the wells were incubated with alkaline-phosphatase-conjugated anti-human IgG ( Sigma , Sr . Louis , MO ) at a 1∶1 , 000 dilution for 45 minutes at 37°c . Following another washing cycle , the color was developed for 30 minutes with a chromogenic solution of p-nitrophenylphosphate in sodium carbonate buffer pH 9 . 6 with lmg/mL of MgCI2 . The concentrations of saliva or recombinant proteins used were determined in a dose- response experiment to assess the optimum signal without loosing specificity . In all experiments , values obtained were subtracted from those obtained the background ( i . e . OD values observed in well with only buffer and without SGS or recombinant antigens ) . The serological experiments were repeated twice with similar results . The laboratory personnel who performed the assays using the recombinant proteins were blinded about the results of ELISA assays for anti-SGS . The statistical analyzes were performed using the GraphPad prism software 5 . 0 ( GraphPad Prism Inc . , San Diego , CA ) . Data regarding antibody levels before and after SGS seroconversion were compared using the Wilcoxon signed rank paired test . Kruskal Wallis with Dunn's multiple comparisons test was performed to estimate differences of OD values between three or more groups . ROC curves were used to establish the cut-off values based on the identification of the serology value , which presented the highest sensitivity and specificity in the prediction of anti-SGS positivity . Correlations between the antibody titers against SGS and those against rLJM17 and rLJM11 recombinant proteins were checked using the non-parametric Spearman test . For the validation of the serology in the third phase of the study , the calculation of sensitivity , specificity and predictive values were done through contingence tables . In all instances , differences presenting p<0 . 05 were considered statistically significant .
In the first part of this study , we tested whether rLJM17 and rLJM11 are associated with Lu . longipalpis exposure we measured the reactivity of total SGS , rLJM17 , rLJM11 using serum samples from 26 children that seroconverted to SGS-positive in a period of six months . Figure 2A shows anti-SGS antibody levels at time 0 and 6 months , demonstrating a significant increase in the optical density of the samples . Using the same serum samples , assays were then performed with rLJM17 , rLJM11 or a combination of both proteins as antigens . Both rLJM17 and rLJM11 were able to reflect the SGS-seroconversion in a variable number of samples ( Figure 2B–C ) . Combining both proteins considerably increased the detection of anti-SGS seroconversion , as only four samples were negative within those positive for anti-SGS ( Figure 2D ) . In addition , in agreement with these findings , the OD values fold increases were lower for both anti-rLJM11 and anti-rLJM17 compared with anti-SGS ( Figure 2E ) . The serology using the combination of the recombinant proteins displayed a higher fold increase , similar to the pattern observed for anti-SGS ( Figure 2E ) . Western blot analysis performed for a small number of sera showed that some samples that recognize LJM17 do not recognize LJM11 and vice versa ( data not shown; Teixeira et al . unpublished data ) , reinforcing the use of combined antigens to enhance sensitivity . We further evaluated the effectiveness of the recombinant proteins in predicting anti-SGS seroconversion using a larger sample of individuals ( n = 80 ) , in which 40 were negative and 40 were positive for anti-SGS . The combination of both rLJM17 and rLJM11 as antigens incremented effectiveness by 8% compared to rLJM17 tested alone and by 17% compared to rLJM11 , estimated by the area under the curves ( Figure 3A ) . Thus , serology using these two combined salivary antigens is suitable to discriminate individuals exposed to Lu . longipalpis saliva ( AUC: 0 . 89; p<0 . 0001; cut-off 0 . 054; likelihood ratio: 8 . 34 ) compared with the use of LJM17 ( AUC: 0 . 81; p<0 . 0001; cut-off: 0 . 022; likelihood ratio: 5 . 69 ) or LJM11 ( AUC: 0 . 72; p = 0 . 035; cut-off: 0 . 063; likelihood ratio: 2 . 16 ) separately ( Figure 3B ) . Before SGS or the salivary recombinant proteins could be validated as markers of exposure to Lu . longipalpis , it was necessary to assess the specificity by evaluating reactivity towards individuals exposed to other sand flies . Hence , we tested serum samples from an endemic area for cutaneous Leishmaniasis ( Canoa , Bahia , Brazil ) , in which the major species of sand flies is the Lutzomyia intermedia . Both Lu . longipalpis and Lu . intermedia normally live in different ecosystems and only rarely individuals are exposed to both of them . We used data from 40 individuals exposed to Lu . intermedia in a previous investigation [9] and addressed the cross-reactivity to the whole SGS or rLJM17 and rLJM11 from Lu . longipalpis . Furthermore , 40 individuals who lived in an endemic area for Lu . longipalpis were tested for anti-SGS from Lu . intermedia serology ( Figure 4 ) . Almost all individuals from the Lu . longipalpis endemic area presented positive serology for this vector , but none of them were positive for anti-SGS for Lu . intermedia ( Figure 4 ) . Thirty-eight out of 40 individuals from Lu . intermedia endemic area displayed positive serology for the saliva of this vector , six also recognized Lu . longipalpis SGS ( Figure 4 ) , one recognized rLJM11 , two recognized rLJM17 and the same two recognized the combination of the two recombinant proteins . Thus , to the end of the second phase of the study we found that both SGS and the recombinant salivary proteins present very low cross-reactivity against a different and wide distributed sand fly . In addition , we established the combination of the recombinant proteins as a potential good predictor of exposure to Lu . longipalpis , since ROC curve analysis showed a sensitivity of 91% and a specificity of 76% with the cut-off value of 0 . 054 OD , with a likelihood ratio of 8 . 34 . The final step was to validate the use of these salivary antigens as a reliable marker of exposure to Lu . longipalpis . To do so , we tested a panel of 1 , 077 samples of unknown anti-SGS status from children from another visceral Leishmaniasis endemic area . Sera positive against rLJM17+rLJM11 displayed a positive correlation with anti-SGS IgG levels ( Spearman r = 0 . 379 , p<0 . 0001; Figure 5A ) . Additionally , when considering only individuals who seroconverted to SGS ( n = 200 ) , this correlation became stronger ( Spearman r = 0 . 491 , p<0 . 0001; Figure 5B ) . The overall performance of the serology using the combined recombinant proteins was satisfactory , with a sensitivity of 77% ( 95% CI: 70 . 5–82 . 6 ) , a specificity of 88% ( 95% CI: 85 . 7–90 . 1 ) , a positive predictive value of 60% ( 95% CI: 53 . 2–65 . 5 ) , a negative predictive value of 94 . 4% ( 95% CI: 92 . 6–95 . 9 ) , and a likelihood ratio of 6 . 43 ( Figure 6 ) . We then stratified SGS positive cases in quartiles according to optical density values in order to verify if the efficiency of the serology would increment in those individuals with higher antibody titers against SGS ( Figure 6A ) . Concordant and discordant results from the combined rLJM17 and rLJM 11 serology were calculated for each quartile ( Figure 6B ) . The assay using combined salivary antigens presented a general trend with increased effectiveness of prediction in individuals with higher anti-SGS antibody titers ( Figure 6B ) . Thus , the use of the recombinant salivary proteins was effective in the estimation of exposure to the Lu . longipalpis saliva ( Figure 6C ) . Age could be an important factor influencing exposure in the endemic areas and thus we tested whether the concentrations of anti-SGS antibodies were influenced by age . In the age range included in the study , there was no difference in the age distribution of positive individuals within stratified quartiles , ( Kruskal Wallis test p = 0 . 065 ) , indicating that the antibody titers may likely represent exposure to the sand fly .
Antibodies against the salivary gland components of blood sucking insects [11] , [12] , can be used as epidemiological markers of vector exposure [13] , as has been shown for Leishmaniasis [4] , [5] . Large epidemiological investigations using salivary gland antigens are hampered by the limitation of obtaining large amounts of highly reproducible salivary glands sonicate . Herein we report on the detection of sera reactive to whole SGS using two recombinant proteins from Lu . longipalpis saliva , rLJM17 and rLJM11 and show a positive correlation between the results obtained using SGS and those obtained using rLJM17+rLJM11 as antigens . Besides the possibility of being produced in large amounts , recombinant salivary proteins bring another advantage to serological tests as they can produce in a highly reproducible fashion . It is known that sand fly saliva protein profile , as well as its relative content , varies at different stages after a meal [6] , [14] , [15] , and this cannot be totally controlled even in standardized colonies . The combined use of different recombinant proteins is justified since not all SGS-positive sera recognize the same protein bands [5] . In the present study , some serum samples recognized either rLJM17 or rLJM11 when tested by ELISA and this was further confirmed by western blot ( data not shown ) . Such differential recognition may explain the better performance of the test when samples highly reactive to SGS were employed . Other immunogenic salivary proteins are likely candidates to be tested in conjunction with rLJM17 and rLJM11 , which may increase the test sensitivity . Importantly , recombinant molecules selected for use in serology should not cross-react with salivary proteins from other non-vector sand fly species , which may lead to false positive results . In order to test for specificity , we have evaluated sera from one area where Lu . longipalpis is highly predominant to one area where this species is not found . In a large survey in the whole São Luis island , including the three municipalities which comprise areas 1 and 3 of the present report , with the capture of 22 , 581 specimens Lu . longipalpis ( 66 . 4% ) of the captured specimens . It was followed by Lutzomyia whitmani ( 24% ) and Lutzomyia evandroi ( 5 . 9% ) , with the remaining 29 species represented 3 . 7% of the total sample [16] . On the other hand , in the Canoa village ( area 2 of the present study ) a phlebotomine survey performed at the time of sera collection evidenced a marked predominance of Lu . intermedia , representing 94% of the captured specimens , with a small number of Lutzomyia migonei and Lutzomyia ( Nyssomyia ) sp . [17] . Comparative sequence analysis of LJM17 from Lu . longipalpis and Lu . intermedia LJM17-homologue showed some areas of high aminoacid conservancy ( Teixeira et al . unpublished data ) . However , cross reactivity with Lu . longipalpis SGS was not observed in animals experimentally exposed to Lu . intermedia SGS [9] . Likely , the tertiary conformation of the LJM17 protein from Lu . longipalpis , is distinct from that of Lu . intermedia accounting for the specificity of the assay . In conclusion , we have shown here that ELISA employing two recombinant proteins derived from Lu . longipalpis saliva is a powerful tool for detecting specific exposure to vector sand flies in populations . These proteins represent a promising epidemiological tool that can aid in implementing control measures against Leishmaniasis .
|
During the blood meal , female sand flies ( insects that transmit the parasite Leishmania ) inject saliva containing a large variety of molecules with different pharmacological activities that facilitate the acquisition of blood . These molecules can induce the production of anti-saliva antibodies , which can then be used as markers for insect ( vector ) biting or exposure . Epidemiological studies using sand fly salivary gland sonicate as antigens are hampered by the difficulty of obtaining large amounts of salivary glands . In the present study , we have investigated the use of two salivary recombinant proteins from the sand fly Lutzomyia longipalpis , considered the main vector of visceral leishmaniasis , as an alternative method for screening of exposure to the sand fly . We primarily tested the suitability of using the recombinant proteins to estimate positive anti-saliva ELISA test in small sets of serum samples . Further , we validated the assay in a large sample of 1 , 077 individuals from an epidemiological survey in a second area endemic for visceral leishmaniasis . Our findings indicate that these proteins represent a promising epidemiological tool that can aid in implementing control measures against leishmaniasis .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/neglected",
"tropical",
"diseases"
] |
2010
|
Using Recombinant Proteins from Lutzomyia longipalpis Saliva to Estimate Human Vector Exposure in Visceral Leishmaniasis Endemic Areas
|
Understanding the cellular mechanisms that ensure an appropriate innate immune response against viral pathogens is an important challenge of biomedical research . In vitro studies have shown that natural killer ( NK ) cells purified from healthy donors can kill heterologous cell lines or autologous CD4+ T cell blasts exogenously infected with several strains of HIV-1 . However , it is not known whether the deleterious effects of high HIV-1 viremia interferes with the NK cell-mediated cytolysis of autologous , endogenously HIV-1-infected CD4+ T cells . Here , we stimulate primary CD4+ T cells , purified ex vivo from HIV-1-infected viremic patients , with PHA and rIL2 ( with or without rIL-7 ) . This experimental procedure allows for the significant expansion and isolation of endogenously infected CD4+ T cell blasts detected by intracellular staining of p24 HIV-1 core antigen . We show that , subsequent to the selective down-modulation of MHC class-I ( MHC-I ) molecules , HIV-1-infected p24pos blasts become partially susceptible to lysis by rIL-2-activated NK cells , while uninfected p24neg blasts are spared from killing . This NK cell-mediated killing occurs mainly through the NKG2D activation pathway . However , the degree of NK cell cytolytic activity against autologous , endogenously HIV-1-infected CD4+ T cell blasts that down-modulate HLA-A and –B alleles and against heterologous MHC-Ineg cell lines is particularly low . This phenomenon is associated with the defective surface expression and engagement of natural cytotoxicity receptors ( NCRs ) and with the high frequency of the anergic CD56neg/CD16pos subsets of highly dysfunctional NK cells from HIV-1-infected viremic patients . Collectively , our data demonstrate that the chronic viral replication of HIV-1 in infected individuals results in several phenotypic and functional aberrancies that interfere with the NK cell-mediated killing of autologous p24pos blasts derived from primary T cells .
Natural killer ( NK ) cells are important effectors of innate immune responses and are capable of providing cellular immunity against tumor-transformed and virally-infected cells , without prior antigen sensitization [1] , [2] . Among the several NK cell effector-functions , spontaneous killing of non-self targets was the first to be described and is the reason they were named “natural killer” cells [3] . NK cell cytolytic machinery is modulated by a delicate balance between opposing signals delivered by two heterogeneous families of inhibitory and activating NK cell receptors . Under physiological conditions , cytotoxicity against normal autologous cells is blocked by the specific recognition of different MHC class –I ( MHC-I ) molecules by inhibitory NK cell receptors ( iNKRs ) . Interactions between iNKRs and MHC-I , tolerance to self , and determination of the extent of cytolytic activity are achieved through a complex process that educates NK cells to ensure self-recognition [4] . Diminution or absence of expression of HLA-I alleles on a cell surface following viral infection or tumor transformation results in the reduced engagement of iNKRs and allows activating NK receptors and co-receptors to trigger NK cell-mediated cytolysis [5] . Several studies have already described numerous aberrancies of NK cell phenotype and function in chronically HIV-1 infected patients with high levels of ongoing viral replication . These abnormalities include aberrant expression and function of several iNKRs and natural cytotoxicity receptors ( NCRs ) , markedly impaired cytolytic activity against tumor cell targets , defective production of important antiviral cytokines [6] , [7] and defective interactions with autologous dendritic cells ( DCs ) [8] . All of these phenotypic and functional perturbations are particularly pronounced in an unusual CD56neg/CD16pos ( CD56neg ) NK cell subset that is preferentially expanded in HIV-1 infected viremic patients [9] , [10] , [11] . Because the frequency of peripheral blood CD4+ T cells that harbor replication-competent virus is extremely low in HIV-1 infected patients [12] , [13] , it remains to be determined whether highly dysfunctional NK cells from patients with high levels of ongoing viral replication are able to eliminate autologous and endogenously HIV-1 infected CD4+ T cells . In order to further understand the direct effects of HIV-1 on CD4+ T cells and other cell types , several models of in vitro infection with different HIV-1 strains have been developed . Through these experimental methods , several reports show that HIV-1 selectively down-modulates HLA-A and -B alleles in both cell lines and CD4+ T cell-derived blasts [14] , [15] , [16] . It has been also demonstrated that the ability of NK cells from healthy donors to kill autologous and exogenously infected CD4+ T cell blasts is influenced by modulation by the exogenous virus of ligands for inhibitory and activating NK cell receptors on these primary T cell blasts [15] , [17] . Even though these approaches using in vitro infection have significantly contributed to understanding the cellular interactions between NK cells and autologous HIV-1 infected CD4+ T cell blasts , it is still unclear what role , if any , NK cells obtained from HIV-1 infected viremic patients play in the clearance of endogenously infected autologous CD4+ T cells ex vivo . In the present study , we describe the killing of endogenously infected CD4+ T cell blasts by autologous NK cells from HIV-1 infected viremic individuals . In addition , we describe several mechanisms involved in the regulation of this NK cell-mediated killing . Finally , we characterize phenotypically the endogenously HIV-1 infected CD4+ T cell blasts used as targets in this experimental system .
It has been reported previously that activation in vitro with different stimuli of either total PBMCs or purified CD4+ T cells from HIV-1 infected individuals induces viral expression and replication [18] , [19] , [20] , [21] . The number of HIV-1 virions produced by these endogenously infected activated blasts was detected through real-time PCR , while the amount of p24 HIV-1 core antigen released in culture supernatant was determined by ELISA . Using an experimental approach similar to that for activating primary T cells in vitro ( Figure S1 ) , we sought to isolate and characterize these productively infected cells starting from highly enriched CD4+ T cells purified from HIV-1 infected viremic patients . As shown in Figure 1A , 5–7 days of stimulation with phytohemoagglutinin ( PHA ) and recombinant IL-2 ( rIL2 ) were required to observe a variable but consistent percentage of endogenously HIV-1 infected CD4+ T cell blasts , characterized by the presence of intracellular viral p24 core antigen . In order to determine the peak of maximal expansion of these infected cells , we tested the amount of p24 antigen in CD4+ T cell blasts every 3 days for 3 weeks . Within our cohort of HIV-1 infected viremic donors , the highest percentages of p24pos expression in activated CD4+ T cell blasts were detected , on average , after 12 days of activation ( median: 9 . 43%; SD = ±6 . 6 ) and started to decrease progressively after this time point ( Figure 1B ) . Of the other several stimuli used to expand HIV-1 infected CD4+ T cell blasts , only PHA plus rIL-2 and rIL-7 achieved similar and sometimes better results after 12 days in culture compared to stimulation with PHA and rIL-2 ( Figure S2 ) . We then analyzed whether p24pos CD4+ T cell blasts were able to proliferate during the period of maximal expansion . As expected , the ability of unfractionated CD4+ T cell blasts from HIV-1 infected patients to undergo proliferation was significantly lower compared to that of unfractionated blasts from healthy donors ( Figure 2A ) . Even though the positive expression of Ki67 nuclear antigen by p24pos fractions of CD4+ T cell blasts indicated that these endogenously infected cells were able to enter the cell cycle ( Figure 2B ) , it has been shown both in vitro and ex vivo that they are arrested in G2/M stage and do not complete the cell cycle [22] , [23] . In order to correlate the kinetics of expansion of these endogenously infected blasts with cell proliferation and CD4 expression , we analyzed the dilution of the vital dye carboxyfluorescein diacetate succinimidyl ester ( CFSE ) in CD4+ T cell blasts using a multicolor flow cytometric approach . After 12 days of stimulation , a subset of proliferating CFSE-labeled blasts showed active intracellular viral replication ( p24pos cells ) with a simultaneous down-modulation of cell surface CD4 ( Figure 2C ) . Therefore , the loss of CD4 is associated with a productive infection in either endogenously ( Figure 3A ) or exogenously infected CD4+ T cell-derived blasts [17] , [24] , [25] . We also visualized the intracellular HIV-1 p24 core antigens in endogenously infected CD4+ T cell blasts by fluorescence microscopy ( Figure 2D ) . It has also been reported that HIV-1 infection in vitro results in a selective down-modulation of MHC-I molecules in cell lines and in exogenously infected primary CD4+ T cells [14] , [15] , [16] . In order to determine whether this phenomenon also occurs in endogenously infected CD4+ T cell blasts expanded from HIV-1 infected individuals , we analyzed the expression of classic and non-classic HLA molecules on p24pos and p24neg blasts . We found that surface levels of HLA-A and -B alleles , calculated as mean fluorescence intensity , were significantly down modulated on p24pos/CD4neg blasts compared to p24neg/CD4pos blasts ( p = 0 . 004 for HLA-A alleles; p = 0 . 018 for HLA-BW4 and –BW6 alleles ) . In contrast , the expression of HLA-C and HLA-E molecules did not substantially differ between p24neg and p24neg blasts ( Figures 3B–C and Table 1 ) .
In the present study we demonstrate the ability of NK cells from HIV-1 infected viremic patients to kill endogenously HIV-1-infected autologous CD4+ T cell blasts derived from viremic patients . We show that , subsequent to the selective down-modulation of MHC-I molecules , infected p24pos blasts become partially susceptible to lysis by rIL-2 activated NK cells , while p24neg blasts are spared from killing . This NK cell-mediated killing occurs mainly through the NKG2D activation pathway . However , decreased NK cell expressions of NCRs contribute to the low level of NK cell cytolytic activity . In addition , the unusually high frequency of dysfunctional CD56neg NK cell subsets among HIV-1 infected viremic patients was shown to strongly correlate with the low degree of NK cell cytolytic responses against infected p24pos cell blasts . The ability of NK cells to kill autologous HIV-1 infected target cells mainly through the NKG2D pathway has been previously demonstrated in vitro with exogenously infected CD4+ T cell blasts from healthy donors [15] , [17] . In contrast , we examined NK cells from HIV-1 viremic individuals and autologous target cells that were expanded from endogenously infected CD4+ T cells . Given the reported abnormalities in phenotype and functions of NK cells from HIV-infected viremic individuals [26] , [27] , [28] , the present study was designed to determine the function of NK cells in killing HIV-1 infected target cells under conditions that more closely mimic the in vivo situation in HIV-infected individuals . Our experimental system relied on rIL-2 activated NK cells instead of freshly purified NK cells . We previously reported that prolonged activation with rIL-2 did not restore phenotype and function of highly dysfunctional NK cells from HIV-1 infected viremic individuals . Only CD56 expression was recovered upon activation with rIL-2 after 3 weeks of culture . Despite the reversion of the CD56neg to a CD56pos phenotype , rIL-2 stimulated CD56neg-derived NK cell populations still expressed very high percentages of iNKRs and extremely low levels of NCRs , similar to results from experiments performed with freshly purified CD56neg NK cell subsets . Even after 21 days of activation with rIL-2 the cytolytic potential of these highly dysfunctional NK cells from HIV-1 infected viremic patients did not improve and remained significantly lower compared to that of NK cells from uninfected individuals[7] , [8] , [11] . Given the fact that stimulation with rIL-2 does not substantially reverse the pathologic characteristics of freshly purified NK cells from HIV-1 infected viremic donors , we designed an experimental system using rIL-2 activated NK cells . This yielded NK cells as effector cells against autologous and endogenously HIV-1 infected CD4+ T blasts at the day of their maximal expansion . Circulating CD4+ T cells from HIV-infected individuals harbor very low frequencies of replication-competent virus [12] , [13] . This has made it very difficult to adequately characterize endogenously infected CD4+ T cells from HIV-infected individuals . Several studies showed that activation in vitro with several and multiple stimuli enhanced viral replication in CD4+ T cells from HIV-1 infected patients . The in vitro induced replication of HIV-1 was measured by the release of p24 HIV-1 core antigen in culture-supernatant [18] , [19] , [20] , [21] . Using a similar approach for cell activation , we stimulated freshly purified CD4+ T cells from HIV-1 infected viremic patients in order to expand and isolate these endogenously infected T cell blasts detected by intracellular p24 staining . Activation with PHA plus rIL2 ( with or without rIL-7 ) was the most effective in expanding a population of endogenously infected CD4+ T cell blasts . The peak maximal expansion of HIV-1 productively infected blasts was reached , on average , 12 days after activation and the rate of expansion of p24pos cell blasts differed among samples from the 30 HIV-1 infected viremic patients analyzed in the present study . The reasons for such heterogeneous results are unclear and many variables such as cellular or soluble suppressive factors , different numbers of circulating latently HIV-1 infected cells , different viral strains , culture conditions or other factors could contribute to this variability . Further investigations are needed to extensively characterize the kinetics of HIV-1 in endogenously infected cells and the replication cycle of these p24pos/CD4neg cell blasts . Our aim in the present study was restricted to an examination of the interactions between endogenously HIV-1 infected CD4+ T cell blasts and autologous NK cells from HIV-1 infected viremic patients . In the preparation of target cells , we separated infected from uninfected cells on the basis of the lack of expression of CD4 together with intracellular expression of p24 on certain cells ( infected ) and the expression of CD4 and lack of intracellular expression of p24 on other cells ( uninfected ) . It has been reported that HIV-1 is able to down-modulate the expression of CD4 on T cell surfaces , a phenomenon induced either by Nef , which enhances the internalization and degradation of CD4 , or by Vpu and Env , which interfere with the transport of newly synthesized CD4 to cell surface [24] , [25] . Even though the physiological relevance of CD4 down-regulation is not fully understood , the absence of CD4 on cell surfaces represents another marker of cells productively infected with HIV-1 . We confirmed that endogenously infected CD4+ T cell blasts harboring replication-competent virus down-modulate CD4 expression and , on the basis of surface levels of this molecule , we were able to separate infected p24pos from uninfected p24neg T cell blasts . In line with results previously reported with HIV-1 infection in vitro [15] , endogenously HIV-1 infected CD4+ T cell blasts selectively down-modulated HLA-A and –B alleles while the expression of HLA-C and HLA-E molecules was conserved . The selective down-regulation of these MHC-I molecules should render p24pos/CD4neg cell blasts susceptible to NK cell-mediated killing . In fact , although the surface levels of HLA-C and -E may still protect infected cell blasts from the cytolysis exerted by autologous NK cells expressing iNKRs specific for these conserved alleles of MHC-I [14] , [29] , this is not the case for NK cells that express iNKRs specific for HLA-A and –B [15] . We show that the degree of NK cell-mediated lysis of p24pos/CD4neg blasts was significantly higher compared with that of p24neg/CD4pos blasts . Moreover , masking experiments highlighted the important role of the selective down-modulation of HLA-I molecules , because only the complete blocking of all MHC-I alleles rendered infected p24pos and uninfected p24neg cell blasts equally susceptible to NK cell-mediated lysis . Other studies reported that conserved or even up-regulated levels of HLA-E on HIV-1 infected cells are able to inhibit NK cell-mediated cytolysis of HIV-1 infected cells through binding to its specific inhibitory receptor NKG2A [14] , [30] . In our study , we used two different mAbs ( 3D12 and 4D12 ) in order to detect the surface levels of HLA-E . Despite the fact that there was some variability among different donors , we detected no significant differences in the high levels of HLA-E expression between HIV-1 infected and uninfected CD4+ T cell blasts from HIV-1 infected viremic patients . Moreover , given that the frequency of the NKG2Apos NK cell subset is greatly decreased in chronic HIV-1 infected viremic patients compared to that of healthy donors [7] , [31] , it is unlikely that the interaction between HLA-E and NKG2A can explain the decreased NK cell mediated killing of HIV-1 infected blasts . In fact , our masking experiments demonstrated that the complete blocking of NKG2A did not increase NK cell cytolysis of autologous p24pos blasts ( data not shown ) . The reason for the discrepancy in the role of NKG2A/HLA-E interactions between these previous studies and our data may be the fact that the effector cells used in those previous studies were heterologous NK cell lines expressing high levels of NKG2A against HLA-E transfected target cell lines . As mentioned above , the engagement of activating NK cell receptors should be able to trigger the cytolytic activity in NK cells expressing iNKRs specific for HLA-A and -B and lacking iNKRs for HLA-C and-E . In this regard , NK cell-mediated killing of infected p24pos/CD4neg cell blasts was found to be mainly NKG2D-dependent . These results are in line with the highly conserved expression of NKG2D on NK cells from HIV-1 infected viremic individuals and with the relatively high percentages of p24pos blasts expressing NKG2D ligands . The direct effect of HIV-1 on the positive or negative modulation of NKG2D ligands on the surfaces of primary CD4+ T cells infected in vitro with HIV-1 is controversial [17] , [32] . We show that masking the binding of NKG2D to its ligands clearly resulted in a marked reduction of the NK cell-mediated lysis of infected blasts . These results suggest that the NKG2D ligands , expressed at high levels in p24pos/CD4neg blasts , play an important role in NK cell-mediated killing of autologous infected cells . Several groups previously reported that HIV-1 viremia affects several functions of NK cells and dramatically influences their phenotype [26] , [27] , [28] . Interestingly , the surface expression and activation pathway of NKG2D are among the few NK cell characteristics spared from the deleterious effects of HIV-1 infection . In order to understand better how HIV-1 affects NK cell cytolytic responses , it would be important for future investigations to address the molecular mechanism ( s ) underlying the resistance of NKG2D , compared to other important activating and inhibitory NK receptor pathways , to the dysfunction associated with HIV viremia . Although defective in HLA-A and –B expression , p24pos/CD4neg blasts remain still poorly sensitive to killing exerted by autologous NK cells . This is partly the result of inhibitory interactions between iNKRs and conserved HLA-C molecules , as demonstrated by the relatively low levels of killing of both infected and uninfected autologous blasts even in the presence of anti-MHC-I mAbs which completely block the interactions between MHC-I molecules and iNKRs . The relatively low degree of NK cell cytolytic activity against endogenously HIV-1 infected CD4+ T cell blasts might be secondary to aberrancies in NK cell triggering through important activating receptors other than NKG2D . This concept is further supported by the finding that NK cells from HIV-1 infected viremic patients were markedly impaired , compared to that from healthy donors , in their ability to kill highly susceptible target cells such as K562 and 221 tumor cell lines that do not express MHC-I molecules . Moreover , if we compare these experimental data with our previously reported results obtained with HIV-1 infection in vitro [17] , the degree of killing of autologous , exogenously HIV-1 infected CD4+ cell blasts by NK cells from healthy donors appears to be markedly higher compared to killing by of highly dysfunctional NK cells obtained from HIV-1 infected viremic individuals . In this context , the low levels of NKp46 and NKp30 on NK cells from HIV-infected individuals significantly correlated with NK cell-mediated killing of MHC-Ineg K562 and 221 cell lines ( data not shown ) and of endogenously HIV-1 infected autologous CD4+ T cell blasts . These data suggest that the negative effect of HIV-1 viremia on NKp46 and NKp30 expression interfere with the NK cell lysis of endogenously HIV-1 infected autologous CD4+ T cell blasts . HIV-1 infected autologous CD4+ T cell blasts . Our finding of the negative contribution of NCRs in the killing of endogenously infected targets in HIV-infected viremic individuals differs from the findings of a previous study in which we described that NKG2D was important in NK lysis of infected targets , but that NCRs played no demonstrable role [17] . This discrepancy may result from the fact that the study in question used NK cells from normal individuals and target cells that were infected in vitro with several viral strains , whereas the present study employed NK cells from HIV-infected viremic individuals and endogenously infected target cells . It is well known that HIV-1 viremia induces a CD4+ T cell depletion that leads to immunodeficiency and correlates with disease progression . However , it has also been reported that the majority of CD4+ T cells dying during the infection are not productively infected with HIV-1[33] . One possible explanation is that these uninfected CD4+ T cells are eliminated through a mechanism not directly linked to viral replication . It has been demonstrated both in vitro[17] and ex vivo ( Figure 5B ) that HIV-1 replication can modulate the expression of ligands for NKp46 , NKp30 and NKp44 on uninfected p24neg/CD4pos T cell blasts . In particular , an highly conserved motif of HIV-1 gp41 envelope protein can induce the expression of NKp44 ligand on uninfected CD4+ T cell blasts and render these cells susceptible to NK cell-mediated killing via NKp44 activation pathway[34] . A recent report showed that is possible to prevent the expression of NKp44 ligand on CD4+ T cells , thus providing new insight for both preventive and therapeutic HIV-1 vaccine strategies[35] . In conclusion , the present study shows that NK cells from HIV-1 infected viremic patients display a variable although generally low ability to lyse endogenously HIV-1 infected autologous CD4+ T cell blasts derived from peripheral blood . The selective down-modulation of HLA-A and -B molecules makes p24pos/CD4neg cell blasts susceptible , at least in part , to autologous NK cell-mediated lysis mainly through the NKG2D activation pathway . Several other factors including the decreased NK cell expression of NCRs , low levels of NCR-specific ligands on p24pos CD4+ T cell blasts and the high frequency of the dysfunctional CD56neg NK cell subset also contribute to the low levels of NK cell-mediated killing of HIV-1 endogenously infected autologous CD4+ T cell blasts . In fact , the defective killing through the NCR activation pathways and the presence at very high levels of a markedly anergic CD56neg NK cell population substantially impair the ability of NK cell to kill endogenously HIV-1 infected autologous CD4+ T cell blasts . Understanding the mechanisms by which HIV-1 is able to negatively modulate the expression and function of NCRs on NK cell and of their ligands on HIV-1 infected CD4+ T cells will certainly give us new insights for improving the NK cell-mediated lysis of infected cells and for enforcing the innate immune control of HIV-1 infection .
Thirty HIV-1 infected viremic individuals were studied . The median CD4+ T cell count was 373 cell per ml ( SD = ±193 ) and the median viremia was 32 , 677 HIV-1 RNA copies ( SD = ±80 , 734 ) per ml of plasma as detected by an ultrasensitive branched DNA ( bDNA ) assay ( Chiron ) with a lower limit of detection of 50 copies per ml . Patients were either naïve to antiretroviral therpay ( ART ) or had formerly been receiving ART , but were not receiving therapy at the time of the study . Leukapheresis was conducted in accordance with protocols approved by the Institutional Review Boards ( IRBs ) of the University of Toronto , Ontario , Canada and the National Institute of Allergy and Infectious Diseases ( NIAID ) , National Institutes of Health ( NIH ) , Bethesda , Maryland , USA . Each patient signed a consent form that was approved by the above IRBs . As negative controls , cells from 30 healthy donors seronegative for HIV-1 were obtained by apheresis generously provided by the Transfusion Medicine Department of the Mark O . Hatfied Clinical Research Center of the NIH as a part of IRB approved clinical studies . PBMCs were obtained from leukapheresis packs by Ficoll-Hypaque density gradient centrigugation ( LSM , MP Biomedicals ) . CD4+ T cells and NK cells were freshly isolated by negative selection ( Stem Cell Technologies ) according to the protocol provided by the manufacturer . The purity of CD3+/CD4+ T cells was ≥97% . Purified NK cells contained ≤ 3% contamination with other PBMC subsets , as determined by expression of CD3 , TCR-a/b , TCR-g/d , CD19 or CD14 . In order to expand CD4+ T cell blasts productively and endogenously infected with HIV-1 , we activated freshly purified CD4+ T cells ( 2×106/ml ) with different stimuli , as shown in Figure S1 . Briefly , cells were cultured with RPMI medium 1640 supplemented with antibiotics ( Gibco ) and FCS ( HyClone ) as previously described[7] and stimulated with phytohemoagglutinin ( PHA ) ( Sigma-Aldrich ) at 3 µg/ml for 24 hours plus recombinant IL-2 ( rIL-2 ) ( Roche ) at 50 IU/ml with or without recombinant IL-7 ( rIL-7 ) ( R&D Systems ) at 10ng/ml for 21 days . We also activated freshly purified CD4+ T cells with rIL-7 with or without rIL-2 or with soluble anti-CD28 mAbs at 5 µg/ml on tissue culture plates coated with anti-CD3 mAbs at 10 µg/ml ( BD-Pharmingen ) for 21 days in the presence of rIL-2 . Freshly purified NK cells were activated in vitro for 12 days with rIL-2 at 200 IU/ml at 2*106/ml . The following panel of anti-human monoclonal antibodies ( mAbs ) were used in this study: mAbs 289 ( IgG2a anti-CD3 ) , C218 and A6-220 ( IgG1and IgM anti-CD56 , respectively ) , KD1 ( IgG2a anti-CD16 ) , AZZ20 and F252 ( IgG1 and IgM anti-NKp30 , respectively ) , BAB281 and KL247 ( IgG1 and IgM anti-NKp46 , respectively ) , Z231 and KS38 ( IgG1 and IgM anti-NKp44 , respectively ) , ON72 and Bat221 ( IgG1 anti-NKG2D ) , MA127 and ON56 ( IgG1 and IgG2b anti-NTBA , respectively ) , pp35 and Co54 ( IgG1 and IgM anti-2B4 , respectively ) , Ma152 and CER1 ( IgG1 and IgM anti-NKp80 , respectively ) , KRA236 and F5 ( IgG1 and IgM anti-DNAM-1 , respectively ) , L14 ( IgG2a anti-Nectin 2 ) , L95 ( IgG1 anti-poliovirus receptor ) , Z270 ( IgG1 anti-NKG2A ) , Y9 ( IgM anti-CD94 ) , EB6 ( IgG1 anti-p58 . 1/KIR2DL1 ) , Gl183 ( IgG1 anti p58 . 2/KIR2DL2 ) , Z276 ( IgG1 anti-p70/KIR3DL1 ) , F278 ( IgG1 anti-LIR-1ILT2 ) and A6 . 136 ( anti-MHC class I molecules , IgM ) . FITC- , PE- or APC-labeled anti-CD3 , anti-CD4 , anti-CD8 , anti-TCRα/β , anti-TCRγ/δ , anti-CD14 , anti-CD19 , anti-CD56 , anti-MICA/B and anti-CD48 mAbs were purchased from BD Biosciences . Soluble fusion proteins for NKp30 , NKp46 , NKp44 and NKG2D with the Fc portion of human IgG and anti-human ULBP-1 , -2 and -3 mAbs were purchased from R&D Systems . PE-labeled anti-human Fc fragment mAb was purchased from Jackson ImmunoResearch Laboratories . FITC- and PE- anti human anti-HIV-1 p24 mAb ( clone KC57 ) used for intracellular flow cytometry staining was purchased from Coulter Clone . PE-labeled anti-HLA-A mAbs were purchased from Lab Vision Corporation . Anti-human HLA-C mAb ( clone L31 ) was kindly provided by Dr . Patrizio Giacomini ( Regina Elena Cancer Institute , Rome , Italy ) and used in flow cytometry as previously described[36] . Anti-human HLA-Bw4 ( clone 116 . 5 . 28 ) and HLA-BW6 ( clone 126 . 39 ) mAbs were kindly provided by Dr . Keith Gelsthorpe ( National Blood Transfusion Service , Sheffield , UK ) . Anti-human HLA-E mAbs ( clones 3D12 and 4D12 ) were kindly provided by Dr . Dan Gerarthy ( Fred Hutchinson Cancer Research , Seattle , WA , USA ) . For one- , two- or three-color cytofluorimetric analysis ( FACS Calibur , BD ) , cells were stained with the appropriate FITC- , PE- or APC-labeled mAbs . For indirect staining , cells were stained with appropriate unlabeled mAbs followed by FITC- or PE-conjugated isotype-specific goat anti-mouse second reagent ( Southern Biotechnology Associates ) . Second appropriate anti-isotypic mAbs stained with FITC and/or PE and/or APC were used as negative controls . For intracellular staining , samples were fixed and permeabilized by cytofix/cytoperm solution and washed with perm-wash solution 1X ( BD-Pharmigen ) according to the protocol provided from the manufacturer . The data were analyzed using FlowJo software ( Tree Star Inc . ) . The percentages of HIV-1 infected CD4+ T cell blasts were detected by intracellular flow cytometry with an anti-p24 core virus antigen mAb . Cells undergo cell cycling were evaluated by detecting the intra-nuclear expression of Ki67 ( BD-Pharmigen ) . CD4-derived T cell blast proliferation was detected by 3[H]thymidine uptake assay ( 16 hours ) . Cellular proliferation was also evaluated by dilution of the vital dye CFSE ( Molecular Probes ) according to the supplier's instructions . After 10–12 days of activation with PHA and rIL-2 , unfractionated CD4+ T cell blasts were permeabilized by cytofix/cytoperm solution and stained with an PE-labeled anti-p24 HIV-1 core antigen mAb ( Coulter Clone ) followed by a biotin conjugated mouse anti R-PE mAb ( BD ) . Infected p24pos CD4+ T cell blasts were detected by a Pacific Blue fluorescent-dye conjugate of streptavidin ( Molecular probes ) according to the supplier's instructions . Fluorescent cells were then washed in PBS , suspended in medium , and sealed on the slides with cover slips . Images were collected on a Leica TCS-NT/SP confocal microscope ( Leica ) using a 63x oil immersion objective NA 1 . 32 . Pacific Blue was excited using an Argon laser at 364 nm . DIC ( differential interference contrast ) images were collected simultaneously with the fluorescence images using the transmitted light detector . Images were processed using Leica TCS-NT/SP software ( version 1 . 6 . 587 ) , Imaris 3 . 3 . 2 ( Bitplane AG ) , and Adobe Photoshop 7 . 0 ( Adobe systems ) . In line with the timeframe of maximal expansion of p24pos/CD4neg blasts , after 12 days of stimulation we removed by negative selection all contaminant cells from CD4+ T cell blast cultures ( MACS , Milteny Biotec ) . As a result , we obtained a highly purified population of CD4+ T cell-derived blasts containing ≤5% contamination of other lymphocyte subsets ( TCRg/d+ , CD8+ , CD56+ , CD16+ and CD19+ cells ) . HIV-1 infected CD4+ T cell blasts were then separated from uninfected blasts on the basis of CD4 surface expression through magnetic microbeads conjugated with an anti-human CD4 mAb ( MACS , Milteny Biotec ) , according to the protocol provided by the manufacturer . The purities of fractions of uninfected CD4pos and infected CD4neg blasts , as assessed by intracellular staining with HIV-1 p24 core antigens , was ≥97% and ≥70% , respectively . p24neg/CD4pos and p24pos/CD4neg cell blasts were then used as target cells against autologous rIL-2 activated NK cells in a 4-hour 51Cr release assay as described previously[37] . Saturating concentration ( 10 µg/ml ) of specific mAbs blocking NK cell receptors or MHC-I molecules were added for the masking experiments . The NK cell:T cell blast ratio was 10∶1 ( Figure S1 ) . Polyclonal NK cells were also tested in a 4-hour 51Cr release assay against MHC-Ineg erythroleukemia K562 and MHC-Ineg B-EBV cell line 721 . 221 ( thereafter termed 221 ) . E/T ratios are indicated in the figures . 51Cr release cytolytic assay were performed on cells from 15 HIV-1 infected viremic patients . Immune response distributions between healthy donors and HIV-1 infected viremic patients were compared using the Mann-Whitney test . The phenotypic and functional differences between p24pos and p24neg blasts from HIV-1 infected individuals were evaluated using the Wilcoxon signed ranks test . The functional differences between NK cell-mediated baseline lysis and lysis in masking experiments were evaluated using the Wilcoxon signed ranks test . All p-values are 2-sided and unadjusted . All statistical associations between different immune parameters were determined by the Spearman rank test for correlation . To estimate the time of maximal infection , the mean outcome of infected p24pos over time was modeled as a polynomial function of time and estimated using least squares . The maximum of this function was then determined and a bootstrap procedure was used to provide a confidence interval ≥95% for the maximum .
|
Natural killer ( NK ) cells represent an important line of defense against viral infections . In vitro studies with exogenously infected CD4+ T cell blasts from healthy donors have demonstrated that NK cells can kill autologous HIV-1-infected target cells . However , the ability of NK cells from HIV-1-infected viremic patients to kill autologous , endogenously infected CD4+ T cells had never been examined and remains uncertain . Given the reported abnormalities in phenotype and functions of NK cells from HIV-infected viremic individuals , we determined the function of NK cells in killing HIV-1-infected target cells under conditions that more closely mimic the in vivo environment in HIV-infected individuals . We show that NK cells from HIV-1-infected viremic patients display a variable although generally low ability to selectively eliminate autologous and endogenously HIV-1-infected CD4+ T cell blasts expanded ex vivo from peripheral blood . Various factors , including the markedly defective engagement of important NK cell activation pathways and high frequencies of the pathologic CD56neg/CD16pos NK cell subset in HIV-1-infected viremic patients , influenced NK cell–mediated cytolysis of endogenously infected CD4+ T cell blasts .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/hiv",
"infection",
"and",
"aids",
"immunology/immunity",
"to",
"infections",
"immunology/innate",
"immunity"
] |
2008
|
Lysis of Endogenously Infected CD4+ T Cell Blasts by rIL-2 Activated Autologous Natural Killer Cells from HIV-Infected Viremic Individuals
|
Trypanosoma brucei gambiense causes 97% of all cases of African sleeping sickness , a fatal disease of sub-Saharan Africa . Most species of trypanosome , such as T . b . brucei , are unable to infect humans due to the trypanolytic serum protein apolipoprotein-L1 ( APOL1 ) delivered via two trypanosome lytic factors ( TLF-1 and TLF-2 ) . Understanding how T . b . gambiense overcomes these factors and infects humans is of major importance in the fight against this disease . Previous work indicated that a failure to take up TLF-1 in T . b . gambiense contributes to resistance to TLF-1 , although another mechanism is required to overcome TLF-2 . Here , we have examined a T . b . gambiense specific gene , TgsGP , which had previously been suggested , but not shown , to be involved in serum resistance . We show that TgsGP is essential for resistance to lysis as deletion of TgsGP in T . b . gambiense renders the parasites sensitive to human serum and recombinant APOL1 . Deletion of TgsGP in T . b . gambiense modified to uptake TLF-1 showed sensitivity to TLF-1 , APOL1 and human serum . Reintroducing TgsGP into knockout parasite lines restored resistance . We conclude that TgsGP is essential for human serum resistance in T . b . gambiense .
Throughout their evolution in sub-Saharan Africa , humans have been under assault by a range of different pathogens . One defining challenge is that posed by African trypanosomes , a species complex of blood-borne protozoan parasites transmitted by tsetse flies [1] . The principle pathogenic species in Africa are Trypanosoma brucei , T . congolense and T . vivax , although only Trypanosoma brucei sub-species are able to infect humans . A key feature of these parasites is the ability to undergo antigenic variation by modifying the variant specific glycoprotein ( VSG ) enveloping the cell that renders the mammalian adaptive immune system largely ineffective [2] . Components of the innate immune system therefore contribute significantly to defence against these organisms [3] . Critical to these defences is the serum protein apolipoprotein L1 ( APOL1 ) found in some catarrhine primates , including humans [4] , [5] . The protein is able to kill the majority of trypanosome species in a dose-dependent manner [5] . APOL1 is delivered to parasites in two fractions of the high-density lipoprotein ( HDL ) component of serum , termed trypanolytic factor 1 and 2 ( TLF-1 and TLF-2 ) [6] . TLF-1 binds to the parasite through an interaction between the haptoglobin-related protein ( HPR ) surrounding the TLF-1 particle and the haptoglobin haemoglobin receptor ( HpHbR ) in the flagellar pocket of the parasite [7]–[9] . Under the acidic conditions found in the lysosome , APOL1 changes conformation and embeds in the lysosomal membrane , forming pores in the organelle , leading to cell death [5] , [10] . A proportion of TLF-2 similarly enters trypanosomes via HpHbR , although an alternate route also contributes to uptake [11] . Although TLF-1 and 2 kill the majority of trypanosome species , two sub-species of T . brucei have evolved to overcome this innate immunity . T . b . rhodesiense and T . b . gambiense are both resistant to lysis by APOL1 and establish bloodstream infections in humans [1] . T . b . rhodesiense causes an acute form of the disease and is found in East Africa whereas T . b . gambiense is found in West and Central Africa . T . b . gambiense causes a more chronic form of the disease and is responsible for 97% of all human cases of trypanosomiasis [12] . The mechanism of human serum resistance for T . b . rhodesiense involves the expression of a truncated VSG , termed serum resistance associated ( SRA ) protein [13] , [14] . SRA binds to APOL1 in the lysosome , preventing lysis [14] . However , the SRA gene is absent from T . b . gambiense , the more prevalent human infective sub-species [15] . The T . b . gambiense subspecies consists of two sub-groups ( 1 and 2 ) that differ in phenotype , including their associated pathology . Group 1 T . b . gambiense parasites are the most prevalent of the human infective trypanosomes and are responsible for the vast majority of cases [16] . Group 1 T . b . gambiense can be distinguished by both their reduced efficacy of HpHbR for binding TLF-1 , due to a conserved single nucleotide polymorphism [17]–[19] and also by the presence of a specific truncated VSG , TgsGP [20] . The TgsGP gene is present in all group 1 isolates examined to date but not in T . b . brucei , T . b . rhodesiense or group 2 T . b . gambiense [20]–[23] . The specificity of TgsGP to group 1 T . b . gambiense and its resemblance to SRA , in that it is a truncated VSG gene , led to a suggestion that this gene may confer human serum resistance to group 1 T . b . gambiense [20] . The gene was transfected into T . b . brucei where it did not confer increased resistance to human serum . It was hypothesized that if TgsGP was involved in human serum resistance other factors would also be required to confer the phenotype in T . b . brucei [20] . Efforts to delete the gene from T . b . gambiense were unsuccessful and the function of TgsGP remained unknown [20] . Here we have successfully deleted the TgsGP gene from T . b . gambiense and demonstrated that it is essential for human serum resistance and requires a T . b . gambiense genetic background in order to function .
To assess whether TgsGP is involved in human serum resistance in T . b . gambiense , the gene was deleted from the genome of a group 1 T . b . gambiense strain . All strains of T . b . gambiense investigated so far are hemizygous for TgsGP , allowing a complete knockout with just one round of transfection [20]–[22] . Although it was postulated that TgsGP was an essential gene and could not be deleted [20] , several TgsGP−/0 clones were generated in this study . One of the clones was selected for analysis and used for subsequent assays . The deletion of TgsGP from the clone was confirmed by PCR ( Figure 1A ) . The TgsGP−/0 T . b . gambiense clones was unable to survive in the presence of normal human serum ( Figure 2 ) or recombinant APOL1 ( Figure 2 ) , with significantly fewer surviving cells compared to wild-type T . b . gambiense ( human serum t-test p = 0 . 001 , APOL1 t-test p<0 . 001 ) . The clone grew in the presence of non-lytic serum in a similar manner to wild-type T . b . gambiense ( t-test p = 0 . 145 ) . This indicates that TgsGP is involved in protecting against the trypanolytic protein APOL1 . The clone was able to grow in the presence of TLF-1 and the number of cells after 24 hours does not differ significantly from that of the wild-type T . b . gambiense strain ( t-test p = 0 . 511 ) . Wild-type T . b . gambiense is resistant to lysis by TLF-1 due to reduced efficacy of their HpHbR for binding TLF-1 . Thus lethal amounts of the lytic particle are not internalised by the parasites [18] , [19] . It is likely that TgsGP−/0 T . b . gambiense clones are able to grow in the presence of TLF-1 because it possesses the T . b . gambiense HpHbR allele that is less efficient at binding TLF-1 . As previously detailed , group 1 T . b . gambiense is characterised by a non-functional HpHbR which results in a reduced uptake of TLF-1 and to a lesser extent TLF-2 [17]–[19] , [24] . To investigate the effect of the loss of TgsGP in combination with TLF-1 uptake , a T . b . gambiense strain expressing a functional T . b . brucei HpHbR ( TbbHpHbR ) and lacking TgsGP was created ( termed TbbHpHbR−/+ TgsGP−/0 ) . Expression of both wild-type and ectopic TbbHpHbR alleles was confirmed by RT-PCR ( Figure 1B ) . An allele-specific HpyCh4V restriction site present in the open reading frame of TbbHpHbR , but absent in TbgHpHbR , was used to distinguish between the alleles ( Figure 1B ) and demonstrated that both alleles were expressed , although the TbgHpHbR allele exhibits lower expression relative to the TbbHpHbR allele . The strain expresses a fully functional HpHbR and hence takes up TLF-1 to a degree similar to T . b . brucei , confirmed by fluorescence microscopy ( Figure 3 ) . TbbHpHbR−/+ TgsGP−/0 T . b . gambiense clones were killed in the presence of normal human serum , recombinant APOL1 or , unlike TgsGP−/0 clones , physiological levels of TLF-1 ( Figure 2 ) . The number of remaining cells at 24 hours was significantly lower than wild-type T . b . gambiense ( human serum t-test p = 0 . 001 , TLF-1 t-test p<0 . 001 , APOL1 t-test p<0 . 001 ) . However , the cells were able to grow in the presence of non-lytic serum in a similar manner to wild-type T . b . gambiense ( t-test = 0 . 690 ) . A T . b . gambiense clone with TgsGP and the functional TbbHpHbR was able to grow in the presence of human serum and APOL1 ( Figure 2 ) with cell number not significantly differing from wild-type T . b . gambiense ( human serum t-test p = 0 . 936 , APOL1 t-test p = 0 . 465 ) or in the presence of non-lytic serum ( t-test p = 0 . 972 ) . However , the clone displayed a trypanostatic growth effect in physiological levels of purified TLF-1 with significantly fewer surviving cells compared to wild type ( Figure 2 ) ( t-test p = 0 . 001 ) . To confirm that the loss of resistance to human serum , APOL1 and TLF-1 in TbbHpHbR−/+ TgsGP−/0 T . b . gambiense was due to the loss of TgsGP , the gene was re-introduced into this background . Resistance to human serum , TLF-1 and APOL1 was rescued by the re-introduction of TgsGP , confirming that this gene is essential for resistance to lysis ( Figure 2 ) . When the same TgsGP add-back construct was transfected into a human serum sensitive T . b . brucei , it did not confer resistance to any lytic component ( Figure 2 ) , confirming earlier work [20] . Previous work has shown that TgsGP localises to the flagellar pocket in T . b . gambiense and this is likely to be the site of interaction between TLF and TgsGP [20] . A possible hypothesis for the observation that when TgsGP is transfected into in TbbHpHbR−/+ TgsGP−/0 T . b . gambiense background it restores human serum resistance but does not confer resistance in T . b . brucei [20] ( figure 2 ) is that the protein is not trafficked correctly to the flagellar pocket . In order to verify localisation , TgsGP was transfected into wild-type T . b . brucei with the addition of a TY tag into a HindIII restriction site at position 1130 of the TgsGP ORF , upstream of the predicted GPI anchor sequence [25] , [26] . Immunofluorescence with anti-TY antibodies shows clear localisation of TY-TgsGP adjacent to the kinetoplast and co-localization with fluorescent Concanavalin A , which acts as a marker for the flagellar pocket [27] , ( Figure 4 ) . However , these cells were killed in human serum , TLF-1 or APOL1 ( Figure S1 ) . A similar localisation is observed when the TY-tagged TgsGP protein is expressed in TbbHpHbR−/+ TgsGP−/0 T . b . gambiense ( Figure 4 ) , with strong signal close to the kinetoplast and a more diffuse signal closer to the nucleus . In this case , the capacity to grow in human serum , TLF-1 and APOL1 was restored by the reintroduction of the TY-tagged TgsGP ( Figure S1 ) . As an identical construct was used in both transfections , it is probable that group 1 T . b . gambiense possess a protein or mechanism complementing TgsGP that is absent in T . b . brucei .
This study demonstrates that the TgsGP gene is essential for resistance to human serum in the most clinically important T . brucei sub-species , group 1 T . b . gambiense . Previous work has shown that TgsGP did not confer resistance to human serum when ectopically expressed in T . b . brucei [20] , which was confirmed here . As originally hypothesized [20] , it appears likely that this is due to other factor ( s ) or mechanism ( s ) that works in concert with TgsGP , which are absent in T . b . brucei . By removing TgsGP from T . b . gambiense itself , we have demonstrated that the gene is necessary for resistance to human serum . Elucidation of a gene essential to human serum resistance in group 1 T . b gambiense unlocks new avenues for future treatment of human African sleeping sickness . These include peptide screens that neutralise the TgsGP protein , targeted antibodies or the possibility of using TgsGP as a vaccine candidate , as expression is required for parasite survival in humans . Additionally , there exists the potential that variants of APOL1 may offer protection against T . b . gambiense . Sera from individuals possessing certain APOL1 alleles has been shown to affect the growth of T . b . rhodesiense and it has been suggested that these alleles may be protective against T . b . rhodesiense [28] , [29] . However , this has yet to be confirmed in a case control study . Nevertheless , it is likely that there are variant APOL1 alleles that protect against group 1 T . b . gambiense in resistant individuals , such as the reportedly resistant Bambuti people of the Mbomo region in the Democratic Republic of the Congo [30] or recently described asymptomatic and self-cured cases from Côte d'Ivoire [31] . One other benefit of our study is the trypanosome research community now possesses a representative group 1 T . b . gambiense strain that is easily cultured , is no longer human serum resistant , yet only differs from the wild-type by a single gene . This is a powerful biological resource that could replace T . b . brucei as the common laboratory model for the human disease , which maybe useful , particularly as several drugs display different efficacies between sub-species [1] . As such , identifying TgsGP as a gene essential for resistance to human serum in group 1 T . b . gambiense will likely be important to future control of the disease .
Bloodstream form T . b . brucei Lister 427 ( MITat 1 . 2 ) was grown at 37°C under 5% CO2 in HMI9 medium supplemented with 20% foetal bovine serum ( Sigma-Aldrich ) and 20% Serum-Plus ( Sigma-Aldrich ) . The bloodstream form group 1 T . b . gambiense strain ELIANE ( MHOM/CI/52/ELIANE ) was isolated from a patient infected while in Côte d'Ivoire [22] . It was cultured in modified HMI9 [32] supplemented with 20% serum plus ( SAFC Biosciences Ltd . ) . Similar to other group 1 T . b . gambiense strains , ELIANE is consistently resistant to lysis by human serum , despite repeated passage . T . b . gambiense and T . brucei strains were transfected using the protocols outlined in [33] . For ectopic expression of TgsGP in T . b . brucei and reinsertion into the TgsGP−/0 T . b . gambiense strains , the TgsGP ORF was inserted into the pURAN vector [34] using G418 for selection . Ectopic expression of TbbHbHpR in T . b . gambiense was achieved using the tubulin-targeting TbbHbHpR pTub-phelo construct , using phleomycin for selection [17] . For deletion of TgsGP from the genome of T . b . gambiense and TbbHbHpR−/+T . b . gambiense , 500 base pairs from both the upstream and downstream regions of TgsGP ( sequence AM237444 . 1 , http://www . genedb . org ) were inserted into a vector containing a hygromycin resistance cassette . Insertion of TY-tagged TgsGP into the deletion strain T . b . gambiense and T . b . brucei was performed by inserting a TY tag into a HindIII restriction site at position 1130 of the TgsGP ORF . This sequence was ligated into the pURAN vector [34] , [35] and transfectants were screened using a G418 selection marker . This insertion site is upstream of the predicted GPI anchor site identified using the big-PI software package [25] and a GPI prediction protocol validated for trypanosomes [26] . Correct integration for constructs was assessed by PCR and/or RT-PCR . All primers used in the studies and their targets , are listed in Table S1 . Total RNA was isolated from cells using RNeasy kit ( Qiagen ) according to manufacturers' instruction , with additional DNase steps . 2 µg RNA was subject to a second round of DNase treatment ( Invitrogen ) prior to cDNA synthesis using Superscript III ( Invitrogen ) , according to manufacturers' instructions . RT-PCR was performed using Taq DNA polymerase and the primers are described in Table S1 . For RFLP analysis of HpHpR , the amplified product was cleaned using GeneJet PCR purification column , digested with HpyCh4V and the digested products separated on a 2% agarose gel . TLF-1 purification , labeling and survival assays were performed as previously described [17] , [36] . APOL1 synthesis and purification was performed as previously described [36] . Protein purity was estimated using a Nanodrop spectrometer ( Nanodrop ) and SDS-PAGE . A Western blot using an antibody raised against an APOL1 peptide ( Sigma-Aldrich ) was used to verify that the bands present were APOL1 . To assess survival in human serum , trypanosomes were diluted to 5×105 per ml in HMI9 and incubated for 24 hours with 20% human serum or 20% non-lytic foetal bovine serum ( FBS ) . The number of surviving trypanosomes in each well was recorded after 24 hours using a haemocytometer . To assess survival in TLF-1 and APOL1 , trypanosomes were diluted to 5×105 per ml in HMI9 with FBS . Cells were incubated with a physiological amount of TLF-1 ( 10 µg/ml ) . For the recombinant APOL1 assays , a concentration of 50 µg−1 ml was used as this had previously been determined to kill 100% of T . b . brucei cells in a 24-hour assay [36] . The number of cells in each well was counted with a haemocytometer at 24 hours . There were four replicates for each data point . The number of surviving cells for each treatment were compared between each of the T . b . gambiense clones and wild-type T . b . gambiense using the unpaired 2-tailed t-test function of the Minitab 14 Statistics Package ( Minitab ) . TLF-1 immunofluorescence assays were performed as previously described [17] , [36] . Immunofluorescence localisation of TY-TgsGP was performed with approximately 106 bloodstream-cultured parasites in mid-log phase . Cells were incubated with 5 mg/ml FITC conjugated Concanavalin A in serum-free HMI9 for 20 minutes at 4°C . The Concanavalin A binds to glycoproteins in the flagellar pocket but is not endocytosed due to the reduced temperature , thus labeling the flagellar pocket [27] . Cells were then fixed by immersion in chilled methanol for 30 minutes . Slides were incubated for 1 hour with 1∶500 primary mouse anti-TY antibody ( Iain Johnston , University of Glasgow ) , washed with PBS and then incubated with 1∶1000 of AlexaFluor568 anti-mouse secondary ( Invitrogen ) . The slides were mounted using 50% glycerol , 0 . 1% DAPI and 2 . 5% DABCO . Parasites were imaged using a Deltavision Core system and SoftWorx package ( Applied Precision ) . Images were composited using the ImageJ software package [37] .
|
Trypanosoma brucei gambiense causes 97% of all cases of African sleeping sickness , a fatal disease of sub-Saharan Africa . Most species of trypanosome , such as T . b . brucei , are unable to infect humans due to trypanolytic factors in human serum . Understanding how T . b . gambiense overcomes these factors and infects humans is of major importance in the fight against this disease . Previous work indicated that a failure to take up some trypanolytic factors by T . b . gambiense contributes to resistance , although other mechanisms are involved . Here , we have examined a T . b . gambiense specific gene , TgsGP , for involvement in resistance to human serum . We show that TgsGP is essential for resistance to lysis as deletion of TgsGP in T . b . gambiense renders the parasites sensitive to most trypanolytic factors . TgsGP deletion in T . b . gambiense modified to overcome the sub-species trait to reduce uptake of some trypanolytic factors resulted in sensitivity to all trypanolytic factors . Reintroducing TgsGP into these knockout parasite lines restored resistance . We conclude that TgsGP is essential for human serum resistance in T . b . gambiense .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
The TgsGP Gene Is Essential for Resistance to Human Serum in Trypanosoma brucei gambiense
|
Plasmodesma ( PD ) is a channel structure that spans the cell wall and provides symplastic connection between adjacent cells . Various macromolecules are known to be transported through PD in a highly regulated manner , and plant viruses utilize their movement proteins ( MPs ) to gate the PD to spread cell-to-cell . The mechanism by which MP modifies PD to enable intercelluar traffic remains obscure , due to the lack of knowledge about the host factors that mediate the process . Here , we describe the functional interaction between Tobacco mosaic virus ( TMV ) MP and a plant factor , an ankyrin repeat containing protein ( ANK ) , during the viral cell-to-cell movement . We utilized a reverse genetics approach to gain insight into the possible involvement of ANK in viral movement . To this end , ANK overexpressor and suppressor lines were generated , and the movement of MP was tested . MP movement was facilitated in the ANK-overexpressing plants , and reduced in the ANK-suppressing plants , demonstrating that ANK is a host factor that facilitates MP cell-to-cell movement . Also , the TMV local infection was largely delayed in the ANK-suppressing lines , while enhanced in the ANK-overexpressing lines , showing that ANK is crucially involved in the infection process . Importantly , MP interacted with ANK at PD . Finally , simultaneous expression of MP and ANK markedly decreased the PD levels of callose , β-1 , 3-glucan , which is known to act as a molecular sphincter for PD . Thus , the MP-ANK interaction results in the downregulation of callose and increased cell-to-cell movement of the viral protein . These findings suggest that ANK represents a host cellular receptor exploited by MP to aid viral movement by gating PD through relaxation of their callose sphincters .
Plasmodesma ( PD ) is a cell-wall spanning channel that interconnects plant cells and is unique for plants . This structure is characterized by a neck-like constriction at its each end , is lined by the plasma membrane , and is traversed by a strand of appressed endoplasmic reticulum , providing endomembrane and cytoplasmic connections to adjacent cells . PD mediate direct macromolecular exchange between the connected cells , and this transport is highly regulated ( for review , see [1] , [2] , [3] ) . PD are also utilized by plant viruses for infection . After initial inoculation and replication in the infected cell , plant viruses spread to the neighboring cells and throughout the entire plant . For this transport , viruses utilize their cell-to-cell movement proteins , MPs [3] , [4] , [5] , [6] , [7] , [8] , [9] . The traffic of viral MPs represents a paradigm and a model system for macromolecular transport through PD . For example , Tobacco mosaic virus ( TMV ) MP , the archetype of many viral MPs , presumably associates with the viral genomic RNA to form a movement ribonucleocomplex [10] , targets this complex to PD [11] , [12] , [13] , [14] , and increases the PD size exclusion limit to translocate the movement complex through the PD channel [15] , [16] . To date , two cellular factors , actin filaments and callose have been implicated in control of transport through PD [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] . A recent study suggested that the Cucumber mosaic virus and TMV MP may sever the actin filaments to aid their cell-to-cell transport [25] , and callose , which accumulates at the neck region of PD [18] , represents a molecular sphincter that restricts cell-to-cell transport of macromolecules [18] , [19] , [20] , [21] , [22] , [23] , [24] . The level of callose in the cell wall is primarily determined by a balance between the enzymatic activities of callose synthases and β-1 , 3-glucanases [26] , [27] , [28] . Callose deposits presumably affect transport through PD , because their elevated accumulation delays local and systemic movement of different viruses [21] , [22] , [23] , [24] . Thus , it would make a biological sense if plant viruses had evolved a mechanism to regulate , via viral MPs and as yet unknown host factor ( s ) , callose deposits to allow their own movement through PD . So far , however , the existence of such a mechanism has not been demonstrated . To date , several and very diverse host proteins have been shown to bind viral MPs . For example , cytoskeletal elements , calreticulin , pectin methylesterases , and DnaJ chaperones have all been shown to interact with TMV MP [11] , [29] , [30] , [31] , [32] , [33] , [34] . Yet , none of these factors had any effects on callose deposits at PD . The role of one class of host proteins , ankyrin repeat-containing proteins ( ANKs ) — also known as TIP1-3 , and initially reported to bind MP of the Potato virus X ( PVX ) in vitro [35] — in viral movement has received no attention since their discovery . Here , we investigated the potential involvement of ANK in regulation of mobility of TMV MP through PD . Our data indicate that ANK interacts with MP and promotes reduction in PD callose deposits and subsequent MP cell-to-cell transport .
To evaluate TMV spread within infected plant tissues , it is useful to employ a plant host that does not develop necrosis upon infection with this virus . Thus , we chose the cultivar of tobacco ( Nicotiana tabacum cv . Turk ) , which lacks the N-gene and , therefore , does not produce a hypersensitive cell death response to TMV [22] , [36] , [37] . Because the ANK homolog of this cultivar has not been isolated , we cloned its cDNA and compared the predicted amino acid sequence with several known ANK homologs from tobacco and Arabidopsis . Figure 1 shows that the ANK sequence is well conserved in these plants , with the highest degree of identity found in the C-terminal ankyrin repeats ( underlined in blue ) , which represent the protein domains typically involved in protein-protein interactions [38] , [39] . N-termini of ANK proteins also carry a loosely-defined PEST domain ( underlined in red ) [35] , [40] , [41] , which often , but not always , serves as signal for proteolysis [42] , [43] , [44] . We then utilized reverse genetics to gain the first insight into the possible involvement of ANK in viral movement . We generated transgenic tobacco plants with RNAi-based suppression of the ANK gene . For specific suppression , we targeted the sequences of ANK that encode the N-terminal part of the protein , rather than its more conserved C-terminal ankyrin motifs ( Figure S1 ) . Based on quantitative real time PCR ( qPCR ) analysis of several independently-transformed lines , we identified severe and moderate suppressors , in which the ANK gene expression levels were reduced to 5% and 15–40% , respectively , of the wild-type expression level ( Figure S2A ) . The severe suppressors also produced a markedly chlorotic phenotype ( line RNAi ANK3 , Figure S2B ) , consistent with the known involvement of ANK homologs in chloroplast biogenesis [45] . The moderate suppressors , however , appeared healthy and did not develop any detectible morphological or developmental phenotypes ( line RNAi ANK1 , Figure S2B ) . Two such transgenic lines , RNAi ANK1 and RNAi ANK2 , were selected for further analyses . First , to confirm the specificity of the RNAi-based ANK suppression , we examined the expression levels of two genes , ASPARTATE AMINOTRANSFERASE ( AATF ) and MAGNESIUM PROTOPORPHYRIN IX ( MgPP ) , which are unrelated to ANK , but contain a short , 17-bp nucleotide sequence that is also found in the ANK sequence used for the RNAi suppression ( Figure S1 ) . No differences were detected in the expression levels of AATF and MgPP in both RNAi ANK lines and in the wild type plants ( Table S1 ) , indicating that the ANK suppression in our RNAi ANK lines was specific . We then used RNAi ANK1 and RNAi ANK2 to examine the effect of the reduction in the endogenous ANK expression on MP cell-to-cell movement . To this end , MP was tagged with YFP and the encoding constructs introduced into the wild-type and RNAi transgenic lines by microbombardment . The MP-YFP movement was determined by confocal microscopy two days after bombardment . Figure 2A shows that , in the wild-type plants , approximately 60% of the signal was distributed between 2 to 3 cells . These observations were consistent with the known rate of the MP movement in plant tissues [46] , [47] , and typical for this tobacco cultivar . In contrast , 70–75% of the signal remained confined to a single cell in RNAi ANK1 and RNAi ANK2 lines , indicating substantial decrease in the MP movement . Statistical evaluation of these data by the unpaired two-tailed Student's t-test confirmed that the MP movement capacities in the RNAi ANK1 and RNAi ANK2 plants were similar to each other , but different from those in the wild-type plants ( Figure 2A ) . MP was found associated with PD in a punctate pattern characteristic for PD localization in both RNAi ANK1 ( Figure 2B , D–F ) and in RNAi ANK2 plants ( not shown ) , as in wild-type ( Figure 2C ) [46] , [48] , [49] , [50] , [51] . Importantly , the MP colocalized with a PD marker , the PD callose binding protein ( PDCB ) [52] ( Figure 2D–F ) , further confirming the targeting of the viral protein to PD in RNAi ANK background . MP localization to PD was also confirmed in both wild-type and RNAi ANK backgrounds at different time periods after bombardment ( Figure S3A , B , D , E ) ; furthermore , the expression levels of MP-YFP in these plants were essentially the same , indicating that the alteration of the ANK amount in the cell does not affect the accumulation of MP ( Figure S3G ) . Thus , the ANK suppression most likely affected MP translocation through PD , rather than its PD targeting or level of expression . MP is known to associate with the host cell ER [53] , [54] , [55] , [56] , [57] , [58] . Thus , we examined whether ANK is also involved in cell-to-cell transport of ER-associated plant proteins . Specifically , we tested the cell-to-cell diffusion of two of such proteins , the Arabidopsis calnexin ( CNX ) and calmodulin-regulated Ca2+-ATPase ER membrane protein ( ACA2 ) , which move between cells presumably due to membrane diffusion through the PD-spanning ER [53] . Table 1 shows no statistically significant effects of ANK suppression on this cell-to-cell diffusion of YFP-tagged CNX and ACA2 as well as free YFP that diffuses through cytoplasm . Thus , the effect of ANK on the MP movement is specific . That the lack of ANK negatively affects MP movement is suggestive of ANK's involvement in this transport process . To support this notion further , however , it is useful also to demonstrate that overproduction of ANK enhances movement . To this end , we generated another series of transgenic lines , this time , expressing ANK under the control of a constitutive promoter . We then selected two of these lines , ANK1 and ANK2 , which exhibited moderate levels of the ANK transcript overexpression ( 1 . 5–2 fold , see Figure 3 ) . We could not measure directly the levels of the ANK protein in these lines due to the lack of a specific anti-ANK antibody . Instead , we produced transgenic lines overexpressing ANK tagged with a short ( 1 kDa ) StrepII epitope [59] , [60] and demonstrated close correlation between the levels of the ANK-StrepII mRNA and the ANK protein ( Figure S4 ) , suggesting that the increased levels of ANK transcription lead to the increased accumulation of ANK protein . When the ANK1 and ANK2 plants were examined for their ability to support cell-to-cell movement of MP-YFP , the movement was enhanced in both lines . Specifically , whereas 58% of the signal moved in the wild-type plants , the extent of movement in the ANK transgenic plants reached a total of 84–86% ( Figure 3 ) . In addition , the extent of the movement was also slightly , but consistently enhanced , with 7% of the signal found in three cell-clusters in the wild-type tissues , and in 11–17% in the ANK1 and ANK2 plants . The unpaired two-tailed Student's t-test confirmed the statistical significance of the differences in cell-to-cell movement of MP between the wild-type and the ANK transgenic plants ( Figure 3 ) . As shown in Figure S3 , the PD localization pattern or the accumulation level of MP were not affected in these plants . Collectively , these data indicate that the ANK may represent a plant factor that facilitates the MP cell-to-cell movement . Biologically , suppression of MP mobility should lead to delayed infection , while increase of the MP trafficking through PD is expected to enhance the viral spread . To examine this possibility , we inoculated the ANK overexpressor and suppressor lines as well as the wild type plants with a recombinant virus that carries an autofluorescent tag DsRed ( TMV-DsRed ) in the place of its coat protein . The local movement of this virus was detected by the appearance and spread of the DsRed signal . Figure 4 shows that whereas the viral transport became visible in all plants approximately at the same time after inoculation ( panels A , B ) , the subsequent viral spread occurred faster in the ANK1 and ANK2 lines than in the wild type plants ( panels A , C , E ) . In contrast , in RNAi ANK1 and RNAi ANK2 lines , the virus moved significantly slower than in wild type plants ( Figure 4A , C , D ) . Importantly , the viral movement in the ANK suppressor lines was delayed , but not arrested , and it reached the wild-type levels late in the infection process ( Figure 4F , G ) . It is important to assertain whether the positive effect of ANK overexpression and negative effect of ANK suppression on the viral movement are not due to increased or decreased replication of the virus in the ANK overexpressor or suppressor lines , respectively . To this end , we produced protoplasts from ANK1 , ANK2 , RNAi ANK , RNAi ANK2 , and wild type plants and infected them with TMV-DsRed . Figure 4H shows that no significant differences were detected in the replication levels of the virus between any of the tested plant lines , suporting the idea that the effect of ANK on the spread of the virus was due to its bona fide effect on the cell-to-cell movement capacity of the viral MP . To gain insight into molecular mechanism of the ANK function in MP movement , we first examined the subcellular localizations of both proteins . When MP tagged with YFP and ANK tagged with CFP were transiently coexpressed in tobacco leaves following agroinfiltration , MP displayed the typical punctate pattern of PD localization ( Figure 5A , B ) , whereas ANK was largely cytoplasmic with characteristic transvacuolar strands ( Figure 5C , D ) , consistent with the known cytoplasmic localization of its homologs [41] , [45] . Thus , the majority of the ANK population does not colocalize with MP . Furthermore , the presence of ANK did not detectibly alter the MP localization . Indeed , the MP localization pattern in the coexpressing cells ( indicated with single asterisks ) was essentially identical to that in the neighboring cells ( indicated with double asterisks , Figure 5A–D ) . Taken together with the data in Figure 2 ( panels B–F ) and Figure S3 ( panels A–F ) , these observations indicate that ANK does not affect PD targeting of MP . Although the expression pattern of ANK is different from that of MP , MP is known to possesses cytoplasmic domain [56] , [58] , providing physical basis for potential interaction between a proportion of the ANK population and MP . Thus , we tested whether ANK can directly recognize and bind MP in vivo . To this end , we utilized the bimolecular fluorescence complementation ( BiFC ) assay in planta [61] . To date , BiFC represents one of the best assays for protein-protein interactions and subcellular localization of the interacting proteins within living cells . In this approach , proteins are tagged with N-terminal ( nYFP ) and C-terminal ( cYFP ) halves of YFP , neither of which fluoresces on its own . Interaction of the tagged proteins results in reconstruction of the YFP signal [62] . In addition , to avoid potentially non-specific effects on protein overexpression on the interaction [63] , MP-cYFP was expressed from a relatively weak nopaline synthase promoter [64] . Under these conditions , coexpression of MP-cYFP and nYFP-ANK in tobacco leaf epidermal cells resulted in appearance of strong YFP signal , indicating protein interaction ( Figure 5E–G ) . Importantly , this YFP signal faithfully colocalized with the PDCB-mCherry ( Figure 5F–H ) , clearly demonstrating that the MP-ANK interaction occurs at PD . The specificity of the MP-ANK interaction was verified in negative control experiments , for which we chose two unrelated cytoplasmic proteins similar in size to ANK ( 37 kDa ) , the Arabidopsis cytoplasmic NADH kinase ( NADK3 , 36 kDa ) [65] , and a fragment of the bacterial ß-glucuronidase ( GUS , 37 kDa ) . Neither NADK3 ( Figure 5I ) nor the GUS fragment ( not shown ) promoted reconstruction of the YFP signal when they were tagged with nYFP and coexpressed with MP-cYFP . Thus , the in vivo interaction between MP and ANK at PD was specific . This BiFC data were confirmed by an independent approach , using a renatured gel blot overlay assay for protein interaction [33] . Figure 5J shows that ANK tagged with StrepII specifically interacted with the membrane-immobilized recombinant MP tagged with glutathione S-transferase ( GST ) ( lane 1 ) , whereas no such binding was observed to the immobilized unfused GST ( lane 2 ) . Binding of MP to ANK was specific because it did not occurred between MP and NADK3 ( Figure 5J , lanes 3 , 4 ) or between MP and CNX or ACA2 ( not shown ) . Thus , MP can specifically recognize and bind ANK both in vivo and in vitro . Arabidopsis ANK2 is involved in ROS scavenging through its interaction with ascorbate peroxidase [40] , and ROS may , in turn , affect PD transport [66] , [67] . Thus , we tested whether expression of ANK alone or together with the MP alters the ROS content of plant tissues . Figure 6A shows histochemical staining for ROS revealed no detectible differences in ROS content between the control , mock-transformed tissues and those expressing ANK or ANK and the MP . Also , the ROS levels in transgenic plants , both ANK overexpressor and suppressor lines , were not significantly different from wild-type lines ( Figure S5 ) . Most likely , therefore , ANK facilitates MP movement through the mechanism independent from ROS . Another possibility is that ANK is involved in regulation of callose sphincters of PD . Callose deposits are known to affect viral movement through PD [20] , [21] , [22] , [23] , [24] , presumably functioning as a sphincter which physically restricts PD mediated macromolecular trafficking . Thus , ANK may facilitate MP movement by reducing the PD callose deposits . To test this idea , we transiently expressed ANK and MP separately or together with each other in tobacco leaves . The amounts of callose in the expressing tissues were assayed by immunostaining using anti-callose antibody with previously demonstrated specificity [68] , [69] , followed by quantitative confocal microscopy . Figure 6 shows that this technique readily visualizes callose deposits in a typical distribution pattern specific for PD with virtually no background signal ( panels B and C ) , and no signal at all in the absence of the primary , anti-callose antibody ( panel D ) . Quantification of the callose-specific signal ( Figure 6E ) revealed that expression of ANK alone or MP alone resulted in virtually no reduction in callose levels as compared to control tissues . However , when both ANK and MP were coexpressed in the same tissues , the callose-specific signal decreased substantially ( Figure 6C , E ) , to as low as 37% of the control . These results were statistically significant as demonstrated by the unpaired two tailed Student's t-test ( Figure 6E ) . Thus , ANK most likely potentiates MP movement through PD by reducing PD callose deposits , and this down-regulation of the callose content requires the presence of both ANK and MP .
The molecular mechanism by which MP gates PD for viral trafficking remains obscure even two decades after the discovery of this activity of MP [15] . This is mainly due to the lack of knowledge about the host factor ( s ) with which MP interacts during the PD gating process . Once such factor , actin , is suggested by a recent study showing that several viral MPs may sever actin filaments to relax PD during their cell-to-cell movement [25] . Here , we characterized another cellular factor , a tobacco cytoplasmic protein ANK , which likely facilitates MP transport through PD . Our data demonstrate direct interaction between ANK and MP in vivo and in vitro and indicate that this interaction results in reduction of callose deposits at PD and enhanced cell-to-cell movement of MP . Conversely , ANK deficit results in reduced movement of MP and slower spread of viral infection . Mechanistically , ANK promotes increase in PD transport by reducing callose deposits at PD and , by implication , relaxing the callose sphincter around the PD channel . There are two interesting aspects to this activity of ANK . First , it binds and affects PD transport of MP , which is known to target specifically to PD and gate and traverse them [13] , [15] , [16] . On the other hand , it does not bind to or affect PD transport of proteins , such as CNX , ACA2 and free YFP , that do not specifically localize to PD and move through them presumably by simple diffusion which does not involve PD gating [53] . Thus , ANK most likely is involved in active PD gating , but not in mere endomembrane-mediated diffusion through PD . Second , ANK cannot elicit its effects on PD transport alone , but does so in concert with MP . Thus , MP does not simply use the existing PD transport pathway , but actively interacts with the host components and participates in the gating activity . MP , thought to associate with endomembranes [54] , [55] , [57] , [70] , interacts with ANK most likely through its cytoplasmic domains [56] , [58] . Because MP specifically targets to PD [13] , it may help direct ANK , a predominantly cytoplasmic protein , to PD . ANK is a multifunctional protein , the roles of which in different plant species are just beginning to emerge . In Arabidopsis , there are two highly conserved ANK proteins , both of which are involved in chloroplast biogenesis by binding to its outer envelope membrane proteins and delivering them to their destination [45] . The Arabidopsis AKR2 also participates in ROS scavenging via interaction with ascorbate peroxidase [40] . Also , both Arabidopsis and tobacco ANKs are involved in disease resistance against Pseudomonas syringe [40] , [41] , and ANK/HBP1/TIP1 from the N-gene containing N . tabacum cv . Xanthi in hypersensitive cell death response to TMV [41] . ANK/HBP1/TIP1 and two ANKs from N . tabacum cv . Samsun , i . e . , TIP2 and TIP3 , may bind MP of PVX; these interactions , however , were shown only in the yeast two-hybrid system , and potential involvement of these ANKs in potexviral movement has not been explored [35] . Finally , also in the yeast two-hybrid system , ANK/HBP1/TIP1 , TIP2 and TIP3 interact with vacuolar β-1 , 3-glucanases [35] , [71]; because these ANKs and β-1 , 3-glucanases reside in different subcellular compartments , this interaction , presumably , is not biological [72] . Furthermore , all known β-1 , 3-glucanases that might degrade callose are predicted to be either vacuolar or secreted [28] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] and are not known to possess cytoplasmic domains . The predicted subcellular localization suggests that it is physically impossible for these enzymes to interact with cytoplasmic ANK . One possibility is that ANK may be crucially involved during the targeting of β-1 , 3-glucanases containing ER-derived vesicles by MP to PD , as proposed recently [3] , [53] , by directly binding to MP . Another possible target for the ANK activity is cellular callose synthases . Callose is presumed to have a rapid turnover [81] , and active callose synthases are likely critical to maintain the PD callose deposits . These enzymes usually contain several subunits , many of which have not been sufficiently characterized; however , at least some of them have been predicted to represent transmembrane proteins with cytoplasmic domains [82] , [83] , [84] . This topology of callose synthases would allow interactions with ANK and/or ANK- MP complexes . Regardless of its potential downstream targets in the molecular pathway of PD gating , ANK most likely represents a cellular factor that recognizes MP and acts synergistically with it to gate PD and mediate MP transport through these channels .
Total RNA was extracted from the N . tabacum cv . Turk plants by Tri-Reagent ( Ambion ) , and its cDNA was synthesized using the RevertAid First Strand cDNA Synthesis Kit ( Fermentas ) with the oligo-dT primer . The full-length ANK cDNA was obtained by PCR using the primer pair 5′GCGAATTCTATGTCTGAGGGAGAGAAAGTTTTGCC3′/5′TCCCCCGGGTCACAGAAACACATCTTTCTCGAGCAG3′ and the total cDNA as template . For pANK , the PCR-amplified ANK cDNA was inserted into the EcoRI-SmaI sites of pSAT4A-35SP-MCS-35ST [85] . For pRNAi-ANK , the ACTIN intron 11 sequence from pSK-int [86] was first inserted into the HindIII-EcoRI sites of pSAT4A-35SP-MCS-35ST , resulting in pRNAi . To suppress the ANK gene specifically , we designed RNAi constructs that target the unique ANK cDNA sequences located between positions 1 and 406 . These sequences of ANK were obtained from pANK by digesting it with BamHI-HindIII and BamHI-EcoRI and cloned into the BglII-HindIII and EcoRI-BamHI sites , respectively , of pRNAi . The resulting pRNAi-ANK construct carried the inverted repeat of the first 406 base pairs of the ANK cDNA , flanked by the ACTIN intron 11 sequences . The entire expression cassettes from pRNAi-ANK and pANK were then cloned as I-SceI fragments into the binary vector pRCS2-nptII , which contains a kanamycin resistance selection marker [87] , resulting in pRCS-RNAi-ANK and pRCS-ANK , respectively . For pTMV MP , the MP coding sequence was PCR-amplified using the primer pair 5′AAGAATTCATGGCTCTAGTTGTTAAAGGAAAAGTG3′/5′TGCATCCCGGGTTAAAACGAATCCGATTCGGCGACAGT3′ and pTMV004 [88] as template . The amplified product was digested with the EcoRI and SmaI and inserted into the same sites of pSAT4A-nosP-MCS-nosT , which was constructed by transferring the nosP-MCS-nosT expression cassette as an AgeI-NotI fragment of pSAT2A-nosP-MCS-nosT [85] into the same sites of pSAT4-EGFP-C1 [87] , replacing the EGFP-C1 expression cassette . Then , the expression cassette from pTMV MP was cloned as an I-SceI fragment into pRCS2-nptII , resulting in pRCS-TMV MP . To construct pCFP-ANK and pnYFP-ANK , the PCR-amplified ANK cDNA was digested with EcoRI and SmaI , and the resulting fragment cloned into the same sites of pSAT4-ECFP-C1 and pSAT4-nEYFP-C1 [61] , respectively . pSAT4-ECFP-C1 was made by transferring the ECFP-C1 expression cassette as an AgeI-NotI fragment of pSAT6-ECFP-C1 [87] into the same sites of pSAT4-EGFP-C1 [87] , replacing the EGFP-C1 expression cassette . Then , the expression cassettes from pCFP-ANK and pnYFP-ANK were cloned as I-SceI fragments into pRCS2-nptII , resulting in pRCS-CFP-ANK and pRCS-nYFP-ANK . For pTMV MP-YFP and pTMV MP-cYFP , the TMV MP-encoding sequence was first PCR-amplified using the primer pair 5′AAGAATTCATGGCTCTAGTTGTTAAAGGAAAAGTG3′/5′GGCCGGTACCGAAACGAATCCGATTCGGCGACAGT3′ and pTMV004 as template . The amplified fragment was digested with EcoRI and KpnI , inserted into the EcoRI-XbaI sites of pSAT4A-nosP-MCS-nosT by double-ligation with the YFP and cYFP coding sequences excised with KpnI-XbaI from pSAT4-EYFP-N1 and pSAT4-cEYFP-N1 [61] , respectively . pSAT4-EYFP-N1 was made by transferring the EYFP-N1 expression cassette as an AgeI-NotI fragment of pSAT6-EYFP-N1 [87] into the same sites of pSAT4-EGFP-C1 [87] , replacing the EGFP-C1 expression cassette . Then , the expression cassettes from pTMV MP-YFP and pTMV MP-cYFP were cloned as I-SceI fragments into pRCS2-nptII , resulting in pRCS-TMV MP-YFP and pRCS-TMV MP-cYFP . For pnYFP-NADK3 , NADK3 cDNA sequence was obtained by PCR using primers 5′GCGAATTCATGGCGATTAGGAAGCTTTTGCTTCTTTTG3′/5′CATCCCGGGCTAGTACCTTGATCTGATCTGAG3′ and Arabidopsis cDNA library [89] as a template . The PCR-amplified NADK3 cDNA was digested with EcoRI and SmaI , and the resulting fragment was cloned into the same sites of pSAT4-nEYFP-C1 [61] . Then , the expression cassette from pnYFP-NADK3 was cloned as an I-SceI fragment into pRCS2-nptII , resulting in pRCS-nYFP-NADK3 . For pCNX-YFP and pACA2-YFP , the CNX and ACA2 cDNAs were amplified from the Arabidopsis cDNA library [89] using the respective sets of primers , 5′GCGAATTCTATGAGACAACGGCAACTATTTTCCG3′/5′GGCCGGTACCATTATCACGTCTCGGTTGCCTTTTGC3′ , and 5′GCGAATTCTATGGAGAGTTACCTAAACGAGAAT3′/5′GGCCGGTACCAACGGGAATCGTCTTCAGTCCAGCG3′ . The amplified products were digested with EcoRI and KpnI , and cloned into the same sites of pSAT4A-YFP-N1 . The PDCB-mCherry expression construct was kindly provided by Dr . Maule [52] . The StrepII coding sequence [59] was included in the reverse primer of the primer pair 5′ GCGAATTCTATGTCTGAGGGAGAGAAAGTTTTGCC 3′/5′GCGGCCGCTTATTTTTCAAACTGCGGATGGCTCCAGGTACCCAGAAACACATCTTTCTCGAGCAG3′ and pANK as template . The StrepII coding sequence was also included in the reverse primer of the primer pair 5′GCGAATTCATGGCGATTAGGAAGCTTTTGCTTCTTTTG3′/5′GCGGCCGCTTATTTTTCAAACTGCGGATGGCTCCAGTACCTTGATCTGATCTGAGA3′ and used to amplify the NADK3 cDNA from the pnYFP-NADK3 . The amplified products were digested with EcoRI and EagI and inserted into the same sites of pET28c ( + ) ( Novagen ) , producing pET28-ANK-StrepII and pET28-NADK3-StrepII , respectively . To produce GST-MP , the MP coding sequence was excised from pTMV MP by digesting it with EcoRI and SmaI inserted into the same sites of pGEX-5X-1 ( GE Healthcare Life Sciences ) , producing pGEX-TMV MP . All PCR reactions were performed using a high-fidelity proofreading Pfu Turbo DNA polymerase ( Stratagene ) , and their products were verified by DNA sequencing using an Applied Biosystems 3730 Genetic Analyzer . pRCS-RNAi-ANK and pRCS-ANK were introduced into the disarmed Agrobacterium strain EHA105 , which was then used to transform N . tabacum cv . Turk as described [90] . The resulting transgenic plants were selected on kanamycin-containing media , and maintained according to our standard protocol [22] . The transgenic plants were vegetatively propagated , and the suppression or overexpression of ANK in the ANK and RNAi ANK lines , respectively , was monitored by reverse transcription ( RT ) followed by quantitative ( q ) PCR . Control , wild-type tobacco plants were grown as described [22] . All plants were transferred to soil and grown in an environment-controlled chamber at 22–24°C under long day conditions of 16 h white light ( 70–80 µmol photons m-2 s-1 ) and 8 h dark . All experiments utilized 5–6-week-old plants with 6–8 leaves . Total RNA was extracted from tissue samples using Tri-Reagent ( Ambion ) , treated with RNase-free DNase I ( Fermentas ) , and 0 . 5-µg samples of the resulting preparations were reverse-transcribed using the RevertAid First Strand cDNA Synthesis Kit ( Fermentas ) , according to the manufacturer's instructions . RT reaction products were amplified by qPCR in a 7300 Real-Time PCR System ( Applied Biosystems ) with Maxima™ SYBR Green qPCR Master Mix ( Fermentas ) using primer pairs specific for ANK ( 5′AGGCTGCACTAACTGCTGGT3′/5′TTACAGCGGCTCCATTCTCT3′ ) , MP ( 5′AAAGATTTCAGTTCAAGGTCGTTCC 3′/5′TCCGTCTCTCACGTTTGTAATCTTC3′ ) , ACTIN ( 5′TCACTGAAGCACCTCTTAACC3′/5′CAGCTTCCATTCCAATCATTG3′ ) , and TUBLIN ( 5′TACACAGGGGAAGGAATGG/CTCGAAACCAACGCTTATC3′ ) . Relative abundance of the ANK or MP mRNA-specific products was normalized to the amount of the product specific for ACTIN and TUBLIN , respectively , which represented an internal control of a constitutively expressed gene . The absence of potential residual genomic DNA contamination was confirmed by control qPCR reactions performed without RT . For agroinfiltration , binary vectors were introduced into the Agrobacterium EHA105 strain as described [91] , grown overnight at 28°C , and infiltrated into intact N . tabacum leaves as described [92] , [93] . Microbombardment experiments were performed as described [46] . Briefly , 100 µg of DNA preparations was adsorbed onto 10 mg of 1-µm gold particles , which were then microbombarded into the lower leaf epidermis at a pressure of 80–110 psi , using a portable Helios gene gun system ( Model PDS-1000/He , Bio-Rad ) . All experiments were repeated at least three times . Leaves with the length of 18 cm , excluding petiole , from 4–5 week-old plants were selected for all experiments . Constructs expressing YFP-tagged tested proteins , i . e . , pTMV MP-YFP , pCNX-YFP and pACA2-YFP , were microbombarded into the lower epidermis at the equivalent locations on each leaf . When movement of two proteins was compared , their expression constructs were introduced at the symmetrical locations relatively to the mid-rib of the leaf . At least 120 YFP-expressing clusters in each microbombarded tissue were observed under a Zeiss LSM 5 Pascal confocal microscope . After 24 h , all expression clusters were represented by single cells and considered to indicate the absence of movement . After 48 h , the number of fluorescent cells in each expression cluster varied due to cell-to-cell movement . At this time , the number of cells in each expression cluster was recorded , followed by statistical evaluation of resulting data by the unpaired two-tailed Student's t-test . Differences between sets of measurements with p-values less than 0 . 001 , corresponding to the statistical probability of greater than 99 . 9% , were considered statistically significant . pTRBO-DsRed ( kindly provided by Dr . Jens Tilsner , University of Edinburgh ) was agroinfiltrated into 18-cm , excluding petiole , leaves from 4–5 week-old plants . Four to 14 days after inoculation , at least 40 DsRed-expressing clusters in each infiltrated tissue were analyzed by confocal microscopy , followed by statistical evaluation of the DsRed-expressing surface area measurements by the unpaired two-tailed Student's t-test . Differences between sets of measurements with P-values <0 . 001 , corresponding to the statistical probability of greater than 99 . 9% , were considered statistically significant . Protoplasts were prepared from the 3–8-cm , excluding petiole , leaves from 4–5 week old plants . The leaves were sterilized in 50% bleach with 0 . 1% SDS for 5 min , followed by 5 rinses in distilled water and overnight incubation at room temperature in the protoplasting enzyme mixture ( 40 mg/ml cellulase Onozuka R-10 ( Phytotechnology Laboratories ) and 1 . 5 mg/ml macerozyme ( MP Biomedicals ) in 500 mM D-sorbitol , 1 mM CaCl2 , 5 mM MES , pH 5 . 5 ) . The protoplasts were collected by centrifugation at 600 x g for 5 min , resuspended in the MMC buffer ( 0 . 7 M mannitol , 10 mM CaCl2 , 5 mM MES , pH 5 . 8 ) at 2×107 cells/ml . For transformation with the viral infectious clone , 5 ml of 30% PEG 8000 in the MMC buffer and 4 µg of the pTRBO-DsRed DNA were added to 2 ml of protoplast suspension and mixed gently . After 15-min incubation at room temperature in the dark , 40 ml of the culture medium ( 3% sucrose , 500 mM D-mannitol , MS salts , 5 mM MES , pH 5 . 7 ) was added to the protoplast suspension . The incubation was continued for an additional 45 min , after which the protoplasts were collected by centrifugation , resuspended in 35 ml of the culture medium , and incubated further to allow replication of the virus . Protoplast samples ( 5 ml ) were harvested 16 , 28 , 40 , 52 , and 64 h after transformation , and their total RNA was extracted by the TRIzol ( Invitrogen ) method according to the manufacturer's instructions . For subcellular localization , pTMV MP-YFP and pCFP-ANK were expressed in tobacco leaves following microbombardment , and the subcellular fluorescence pattern was analyzed by confocal microscopy . For BiFC , pRCS-nYFP-ANK and pRCS-TMV MP-cYFP , or pRCS-nYFP-ANK and pRCS-nYFP-NADK3 binary constructs were expressed in tobacco leaves following agroinfiltration , and BiFC was detected by confocal microscopy as described [61] . For the combination of pRCS-nYFP-ANK and pRCS-TMV MP-cYFP , we used Agrobacterium cells at OD600 = 0 . 0015 . For negative controls , bacterial cultures at OD600 of up to 0 . 6 were used to confirm the absence of reconstructed YFP signal even at very high inocula . Recombinant GST-MP , unfused GST , ANK-StrepII , and NADK3-StrepII were produced as described [94] in BL21 ( DE3 ) Escherichia coli strain ( Novagen ) from the pGEX-TMV MP , pGEX-5X-1 , pET28-ANK-StrepII and pET28-NADK3-StrepII vectors . The identity of these proteins was confirmed by western blot analysis using anti-GST ( Santa Cruz ) and anti-StrepII ( Genscript ) polyclonal antibodies , and the amounts of these proteins in the total bacterial extracts were estimated by scanning densitometry of the corresponding bands on SDS-polyacrylamide gels stained with Coomassie Brilliant Blue R-250 , using the known amounts of BSA as standard . Protein extracts containing 1 µg of GST-MP or unfused GST were resolved on 15% SDS-polyacrylamide gels , followed by eletrotransfer to a nitrocellulose membrane . The membrane-immobilized proteins were incubated with 0 . 5 µg/ml of ANK-StrepII or NADK3-StrepII and processed as described [32] . Binding of the tested proteins to the immobilized proteins was detected by probing the membranes with anti-StrepII rabbit polyclonal antibody ( Genscript ) , followed by anti-rabbit IgG+M secondary antibody conjugated to HRP . Three days after agroinfiltration with binary constructs expressing the tested proteins , i . e . , pRCS-ANK and/or pRCS-TMV MP , leaf disks from the agroinfiltrated areas were excised and transferred to the 10 mM MES ( pH 5 . 6 ) containing SIGMAFAST 3 , 3′-diaminobenzidine ( DAB ) ( Sigma-Aldrich ) . DAB was vacuum-infiltrated into the tissues for 20 min , followed by incubation of the leaf disks in the DAB staining solution for 4 h in the light and 16 h in the dark . After four brief washes in double-distilled water , the stained leaf disks were transferred to 95% ethanol and heated at 55°C to remove chlorophyll . The tissue was rehydrated , and imaged using an EPSON Perfection 4490 photo scanner . Three days after agroinfiltration with pRCS-ANK and/or pRCS-TMV MP , tissue samples ( <2×5 mm ) from the agroinfiltrated areas were fixed by vacuum infiltration with 4% paraformaldehyde in the PIPES buffer ( 100 mM PIPES , 5 mM EGTA , 2 mM MgCl2 , pH 6 . 9 ) followed by overnight incubation at 25°C with gentle agitation . The samples were then washed twice in double-distilled water and depleted of chlorophyll by incubation for 2 h at 25°C with gentle agitation in a graded series of ethanol ( 25 , 50 , 75 , and 95% ) . For permeabilization , the dehydrated samples were air-dried , transferred to the PIPES buffer , and freeze-shattered as described [95] . Finally , the samples were transferred to a microscope slide , blocked in 1% BSA in PBS , and reacted with 1/200 dilution of anti-callose mouse monoclonal antibody ( Biosupplies , Parkville , Australia ) in 1% BSA in PBS , followed by the Alexa-488-conjugated anti-mouse IgG+M secondary antibody ( Jackson ImmunoResearch , 1/200 dilution in 1% BSA in PBS ) . After three 10-min washes in PBS , the immunostained samples were mounted on microscope slide , using BioMount ( Electron Microscopy Sciences ) , with the abaxial side of the leaf facing up and observed under a confocal microscope . To avoid measuring callose induced by the wounding made by the initial step of the sample preparation , the section in the middle of the tissue piece was selected for the observation . For each experiment , the confocal microscopy analysis included five samples immunostained independently . The total signal intensity in a 318×318 µm-area was measured using the fluorescence intensity quantification function of the LSM 5 Pascal software for least five fields per sample or a total 25 fields per experiment . To measure background signal intensities , mock-transformed samples , i . e . , tissues infiltrated with Agrobacterium carrying empty expression vector , were immunostained in the absence of the primary antibody . These background signal values were subtracted from the signal values obtained from the samples , the average signal intensities with standard error for the each experiment were calculated and evaluated statistically by the unpaired two-tailed Student's t-test as described for analyses of the cell-to-cell movement data . All the procedures for the experiments documented in Figures S1 , S2 , S3 , S4 and S5 and Table S1 are presented in File S1 ( Supplemental Methods ) . The GenBank accession number for the ANK sequence reported in this paper is GU320195 . The accession numbers for the ANK homologs are: NtANK1/HBP1 from N . tabacum cv . Xanthi; AAK18619/AAN63819 , NtTIP2 and NtTIP3 from N . tabacum cv . Samsun; AAO91861 . 1 and AAO91862 . 1 , AtAKR2B and AtAKR2 from Arabidopsis thaliana; NP_179331 and NP_849498 . The accession numbers for the two genes that contains 17-bp identical to the sequence found in ANK are: N . tabacum AATF; AB126259 . 1 , and MgPP; AF014052 . 1 .
|
During infection , plant viruses utilize their cell-to-cell movement proteins ( MPs ) to gate plant intercellular connections , the plasmodesmata ( PD ) , and spread between the host cells . The mechanism by which MPs facilitate their cell-to-cell translocation remains elusive . We have identified a tobacco ankyrin repeat-containing protein , ANK , that interacts with MP of Tobacco mosaic virus ( TMV ) both in vivo and in vitro . When coexpressd with MP , ANK promoted intercellular transport of MP and , by implication , gated PD for its movement and facilitated the viral spread . Conversely , RNAi-based knock-down of the ANK gene expression resulted in reduced MP movement and attenuated viral infection . Coexpression of ANK with MP resulted in reduction of callose deposits at PD , which are known to function as PD channel sphincters . Interestingly , expressing ANK alone did not affect callose deposits . Thus , ANK most likely represents a cellular factor that recognizes MP and acts synergistically with it to gate PD and mediate MP transport through these channels .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"plant",
"biology/plant-biotic",
"interactions",
"plant",
"biology/plant",
"cell",
"biology",
"virology/host",
"invasion",
"and",
"cell",
"entry"
] |
2010
|
ANK, a Host Cytoplasmic Receptor for the Tobacco mosaic virus Cell-to-Cell Movement Protein, Facilitates Intercellular Transport through Plasmodesmata
|
Dengue virus ( DV ) infection is one of the most common mosquito-borne viral diseases in the world . The innate immune system is important for the early detection of virus and for mounting a cascade of defense measures which include the production of type 1 interferon ( IFN ) . Hence , a thorough understanding of the innate immune response during DV infection would be essential for our understanding of the DV pathogenesis . A recent application of the microarray to dengue virus type 1 ( DV1 ) infected lung carcinoma cells revealed the increased expression of both extracellular and cytoplasmic pattern recognition receptors; retinoic acid inducible gene-I ( RIG-I ) , melanoma differentiation associated gene-5 ( MDA-5 ) and Toll-like receptor-3 ( TLR3 ) . These intracellular RNA sensors were previously reported to sense DV infection in different cells . In this study , we show that they are collectively involved in initiating an effective IFN production against DV . Cells silenced for these genes were highly susceptible to DV infection . RIG-I and MDA5 knockdown HUH-7 cells and TLR3 knockout macrophages were highly susceptible to DV infection . When cells were silenced for only RIG-I and MDA5 ( but not TLR3 ) , substantial production of IFN-β was observed upon virus infection and vice versa . High susceptibility to virus infection led to ER-stress induced apoptosis in HUH-7 cells . Collectively , our studies demonstrate that the intracellular RNA virus sensors ( RIG-I , MDA5 and TLR3 ) are activated upon DV infection and are essential for host defense against the virus .
Pathogen associated molecular patterns ( PAMP ) trigger innate immunity against pathogens and this response represents the first line of defense against various microorganisms [1] . Double strand RNA ( dsRNA ) , a viral replication intermediate , is sensed by cytoplasmic RNA helicases retinoic acid-inducible gene I ( RIG-I ) and melanoma differentiation-associated gene 5 ( MDA5 ) as well as by toll-like receptors-3 ( TLR3 ) [2] . TLR3 and RNA helicases interact with different PAMP during the proximal signaling events triggered by the dsRNA . However , these two parallel viral recognition pathways converge at the level of IFN regulatory factor-3 ( IRF3 ) . Phosphorylation of IRF3 initiates antiviral responses , including the activation of type I interferon ( IFN ) , interferon stimulating genes ( ISGs ) and proinflammatory cytokines [3] , [4] . While TLR3 is primarily responsible for recognizing viral components such as viral nucleic acid and envelope glycoproteins in the extracellular and endosomal compartments [5] , DExD/H box–containing RNA helicases - RIG-I , MDA5 - recognize intracellular dsRNA and they constitute the TLR-independent IFN induction pathway . Although both RIG-I and MDA-5 share high degree of functional and structural homology , they were observed to respond to different dsRNA moieties and RNA viruses . They contain caspase-recruiting domains ( CARD ) that allow them to interact with Interferon Promoter Stimulated 1 ( IPS-1 ) ( otherwise known as Virus-induced Signaling adapter ( VISA ) ; mitochondrial antiviral signaling protein ( MAVS ) or Cardif ) [6] . Similar to TLR3 , IPS-1 mediates activation of TBK1 and IKKε which in turn activates/phosphorylates IRF3 . Phosphorylated IRF3 then homodimerises and translocates to the nucleus [7] to stimulate the expression of type I interferons – IFN-α and IFNβ . IFN-α/β , together with an array of other interferon stimulated genes ( ISGs ) and cytokines , lead to the establishment of an antiviral state which restricts virus spread in the host cells . Dengue virus was reported to induce type I IFN even in RIG-I or MDA5 null cells [8] . The same is observed with West Nile virus [9] , another Flavivirius . Japanese encephalitis virus [10] and Hepatitis C virus [11] , also belonging to the Flavivirdae family , on the other hand , are recognized only by RIG-I . These results suggest that Flaviviruses , despite their common genomic features and replication strategies , are differentially recognized by the host . Despite having the IFN pathway activated in response to viral infection , pathogenic viruses have evolved ways to manipulate the IFN system to favor their survival in the host cell . Reports have shown that DV can antagonize the IFN pathway via their non-structural proteins such as NS2A , NS2B , NS4B and NS5 [12] , [13] . A recent microarray analysis of dengue virus type 1 ( DV1 ) -infected lung carcinoma cell line , H1299 , showed upregulation of a number of innate immune response genes . In particular , RIG-I , MDA5 and TLR3 were up-regulated more than 8- , 5- and 2-fold respectively [14] . Furthermore , Sumpter and colleagues [11] reported that inactivation of RIG-I in HUH-7 cells resulted in permissiveness of hepatitis C virus ( HCV; a flavivirus ) RNA replication . Since HUH-7 cells have low basal level of Toll-like receptor 3 ( TLR 3 ) gene expression , this cell line would be a good in vitro model system to investigate RIG-I-dependent signaling in DV1 infection [15] . In this study , we present evidence that DV1 infection results in the upregulation of RIG-I , MDA5 and TLR3 expression in HUH-7 cells . This is the first study that shows the role of all three viral RNA sensors – RIG-I , MDA5 , TLR3 – in the same cellular system . Previous studies have shown the role of these sensors in different cell lines which may not take into consideration the differences in the genetic make-up of the cell lines . We show , in this study , how RIG-I , MDA5 and TLR3 signaling pathways play a role in DV1-infected cells .
Wild type and TLR3 knockout macrophages were kind gifts from Dr . Xu Shengli , Bioprocessing Technology Institute , Singapore . Macrophages , HUH-7 and shRIG-I cells were cultured in DME medium containing 5% fetal bovine serum ( FBS ) and 1% penicillin and streptomycin antibiotics ( PSA ) . One set of uninfected macrophages , HUH-7 and shRIG-I cells served as a control , while another set was infected with the Singapore strain of dengue type 1 virus at a multiplicity of infection ( MOI ) of 1 and incubated at 37°C for 2 h . The supernatant was replaced with fresh DME containing 1% FBS , and infected and uninfected cells were harvested after 3 days . Mock represents cells incubated/transfected with cell lysates/vector for a period of time similar to the infected cells . Total RNA was extracted using the Trizol reagent ( Invitrogen , USA ) and RNA concentrations quantified via UV spectrophotometry at 260 and 280 nm . RNAs with an OD260 nm∶OD280 nm absorbance ratio of at least 1 . 9 with intact ribosomal 28S and 18S RNA bands were used in this study . DV1 was inactivated by exposing the virus to a UV lamp ( wavelength , 254 nm ) at a distance of 5 cm for 1 h . UV-inactivation was confirmed by the inoculation of C6/36 cells before use and , in individual experiments , by monitoring the exposed cells for synthesis of viral non-structural protein , NS3 , at 72 h . The supernatant fluids from the inoculated cells were also monitored for the presence of infectious virus . TLR3−/− mice have been described previously [16] and maintained in the C57BL/6 background . Wild type C57BL/6 mice were from the Jackson Laboratory ( Bar Harbor , Me ) . Bone marrow-derived macrophages ( BMDMs ) were generated by culture of bone marrow cells in DMEM containing 20% FCS and 30% L929 conditioned medium ( DMEM-C ) for 6 days . BMDMs were harvested and tested for purity by flow cytometry with antibodies specific for F4/80 and Mac-1 ( BD Pharmingen ) . The purity of BMDMs was typically 90–95% Total RNA ( 5 µg ) from DV1 infected and uninfected cells were reverse-transcribed . Real-time RT-PCR was carried out for the selected genes using gene-specific primers and the LightCycler-FastStart DNA MasterPLUS SYBR Green 1 reaction mix ( Roche Molecular Biochemicals , Mannheim , Germany ) . The LightCycler system was used to monitor the SYBR Green signal at the end of each extension period for 40 cycles . The threshold cycle ( CT ) for each gene of interest and for the GAPDH housekeeping gene , and the difference between their CT values ( ΔCT ) were determined . The relative expression values ( 2−ΔΔCT ) between uninfected and infected samples for the selected genes were determined by using the uninfected sample as the reference with its ΔCT value subtracted from the ΔCT value of the infected sample ( i . e . ΔΔCT ) . Relative fold difference values shown in figures are average of at least two independent experimental results . A two-step semi-quantitative RT-PCR method was used to measure gene expression in the DV1 infected and uninfected samples . Random hexamers ( Qiagen Inc . ) was used as primer in the first step of cDNA synthesis . Total RNA ( 5 µg ) was combined with 2 µl of random hexamers , 200 µM dNTPs and H20 and preheated at 65°C for 10 min to denature secondary structures . The mixture was then cooled rapidly in ice and then 5 µl 5×RT Buffer , 10 mM DTT , 0 . 5 µl RNAse inhibitor ( Roche ) , 1 . 0 µl ( 10 mM ) dNTP and 200 U reverse transcriptase ( Roche ) were added for a total volume of 20 µl . After pulse spinning , the RT mix was incubated at 43°C for 90 min and then stopped by heating at 95°C for 5 min . The cDNA stock was stored at −20°C . The yield of cDNA was measured according to the PCR signal generated from the internal standard house-keeping gene GAPDH amplified 30 cycles with 1 µl of the cDNA solution . Gene-specific PCR amplifications were carried out by adding 5 µl of 10 x PCR buffer , 1 µl ( 5 U/µl ) Taq Polymerase , 1 . 0 µl ( 10 mM ) dNTP , 1 µl of each primer , 1 . 5 µl ( 50 mM ) MgCl2 , 3 µl of the first strand cDNA and double-distilled water to 50 µl . The PCR products were loaded onto ethidium bromide-stained 1% agarose gels . A 100 bp DNA ladder molecular weight marker ( Fermentas ) was run on every gel to confirm expected molecular weight of the amplification product . The Primer pairs used were: OAS2: sense , TGAGAGCAATGGGAAATGGG , anti-sense , AGGTATTCCTGGATAAACCAACCC; RIG-1: sense , TGTGGGCAATGTCATCAAAA , anti-sense , GAAGCACTTGCTACCTCTTGC; MDA5: sense , GGCACCATGGGAAGTGATT , anti-sense , ATTTGGTAAGGCCTGAGCTG; IFNβ: sense , CTCTCCTGTTGTGCTTCTCC , anti-sense , GTCAAAGTTCATCCTGTCCTTG; ISG15: sense , TGGTGGACAAATGCGACGAA , anti-sense , CAGGCGCAGATTCATGAAC; ISG56: sense , TCTCAGAGGAGCCTGGCTAAG , anti-sense , CCACACTGTATTTGGTGTCTAGG; XBP1: sense , CTGGAAAGCAAGTGGTAGA , anti-sense , CTGGGTCCTTCTGGGTAGAC; GAPDH: sense , GACAACTTTGGTATCGTGGAA , anti-sense , CCAGGAAATGAGCTTGACA . TUNEL assay . TdT-mediated dUTP-biotin nick-end labeling ( TUNEL ) was performed using ApoAlert DNA Fragmentation Assay kit ( Clontech ) according to the manufacturer's instructions . Briefly , the cells were fixed with 4% formaldehyde/PBS and resuspended in 0 . 2% Triton X–100 and incubated on ice for 5 min . The cells were labeled by adding 50 µl TUNEL mix . The samples were then resuspended in PBS prior to flow cytometry ( FACS Calibur; Becton-Dickinson , San Jose , CA ) and results displayed using WinMDI 2 . 8 software program . Subgenomic content . To measure subgenomic content , cells were fixed with 70% ice-cold ethanol and stained with 50 µg/ml propidium iodide ( PI ) containing RNase and subgenomic content was evaluated by a flow cytometer . Statistical analysis . Error bars in figures represent data expressed as the mean ± S . D . of at least three independent experiments . Z-test for two simple means was used to calculate P-value . Cells were harvested up to 72 hpi , and lysed in RIPA buffer containing protease inhibitors . The lysates were subjected to immunoblotting using primary and HRP-conjugated secondary antibodies followed by the ECL-Plus chemiluminescence substrates ( Amersham ) . Mouse monoclonal DV1 E and NS3 antibodies used were prepared in-house . Anti RIG-I and anti-MDA5 ( Axxora ) ; anti-actin ( Santa Cruz ) ; anti-calreticulin ( BD Transduction Laboratories ) ; anti-IRF3 ( Santa Cruz ) and anti-myc ( Sigma Aldrich ) . As shRNA target in the RIG-I sequence , 5′-AATTCATCAGAGATAGTCA -3′ was chosen HUH-7 cells were transfected with shRNA constructs using Lipofectamine 2000 reagents ( Invitrogen ) . Clones were selected in the presence of G418 and screened for reduced RIG-I expression . For over-expression studies , RIG-I and MDA5 coding regions were cloned into a mammalian expression vector . The identities of the clones were confirmed by DNA sequencing . Synthetic dsRNA polyI:C was purchased from Sigma Aldrich . Cells were transfected with 1 µg/ml polyI:C for various times using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer's instructions . For gene silencing of RIG-I: sense , CAGAAGAUCUUGAGGAUAAUU was used . siTLR3 was purchased from Sigma Aldrich . HUH-7 cells ( 1×105 per well ) were plated in six-well plates . At 24 h after incubation , cells were washed , replenished with medium without serum and transfected , at 40–60% of confluency , with specific siRNA or control siRNA by using a siRNA transfection reagent ( Lipofectamine RNAi Max reagents , Invitrogen ) according to the manufacturer's instructions . After 8 h incubation at 37°C , the liposome suspension was removed and complete culture medium was added . After 24–48 h , cells were infected with DV1 and then harvested at 48 h for analysis . Cells that were untreated or treated with control siRNA served as controls . Cells were lysed in buffer containing 50 mM Tris HCl ( pH 7 . 4 ) , 150 mM NaCl , 1 mM EDTA , 1% NP-40 , protease inhibitors and phosphatase inhibitors for 30 min at 4°C . Proteins were separated by electrophoresis in 8% non-denaturing polyacrylamide gels containing 1% sodium deoxycolate in the cathode buffer . IRF3 monomers and dimers were detected by Western blot using polyclonal antibodies against the full-length IRF3 ( Santa Cruz ) . Cells were grown on 24-well plates . After infection with DV1 for the indicated times , cells were fixed in cold methanol for 10 min , washed in PBS and blocked in normal goat serum for 1 h . DV1 E antibody was then applied for 1 h followed by FITC-conjugated secondary for another 1 h . The cells were then washed 4 times with PBS and examined under a fluorescence microscope or quantified using a flow cytometer . Cells transiently transfected or cells infected with virus were cultured for up to 48 h . IFN-β in the supernatants was measured using VeriKine Human IFN-β ELISA kit ( PBL Biomedical Laboratories ) according to the manufacturer's instructions .
To study if RIG-I influences DV1 infection and understand the relationship between innate antiviral responses and virus replication , we used short-hairpin RNA interference ( shRNA ) technology to knockdown RIG-I in HUH-7 cells . A clonal population of stably transfected cells that showed decreased expression for RIG-I was selected . RIG-I knock-down cells ( shRIG-I ) were infected with DV1 for up to 72 hours and subjected to immunoblotting analysis . RIG-I expression increased over time in DV1-infected wild type HUH-7 cells and there was minimal RIG-I up-regulation in the shRIG-I cells ( Fig . 1A ) . Since Loo and colleagues [8] demonstrated that dengue virus type 2 triggered both RIG-I and MDA5 , we probed for MDA5 expression in DV1-infected cells . Minimal MDA5 expression was observed in DV1-infected shRIG-I cells while increased MDA5 expression was observed in HUH-7 cells ( Fig . 1A ) . Although MDA5 seems to be upregulated faster than RIG-I in HUH-7 cells , the basal level ( see mock infection ) of the two proteins are not the same . It appears that HUH-7 cells have a higher basal level of MDA5 compared with RIG-I . Mock-infected HUH-7 cells showed expression of MDA5 , whereas shRIG-I cells did not . To investigate if this MDA5 down regulation also occurred at the transcriptional level , RT-PCR was performed on RNA extracted from DV1-infected cells . MDA5 expression at the mRNA level was visibly low in shRIG-I cells as compared to HUH-7 cells ( Fig . 1B ) . To define if this MDA5 down regulation was due to clonal specificity , another clone of shRIG-I cells were infected with DV1 and probed for MDA5 expression . Minimal MDA5 expression was observed in the second clone as well ( data not shown ) . It was important to ensure that this phenomenon is not due to DV1 infection , which may not be activating or could be inhibiting MDA5 expression in these cells . A synthetic analog of viral dsRNA , polyinosine-polycytidylic acid ( polyI:C ) , that can bind and activate MDA5 expression was used to investigate the level of MDA5 expression in shRIG-I and HUH-7 cells . MDA5 expression in shRIG-I cells was reduced even with polyI:C transfection ( Fig . 1C ) . Quantitative RT-PCR showed that the MDA5 expression was decreased 8-fold in shRIG-I cells as compared to HUH-7 cells upon DV1 infection ( Fig . 1D ) . To understand if MDA5 expression was related to RIG-I expression in mammalian cells , we transfected RIG-I siRNA ( which had a different target sequence to that of RIG-I shRNA ) in A549 and HUH-7 cells . We observed that transfection of RIG-I siRNA significantly knocked down both RIG-I and MDA5 in HUH-7 cells but only RIG-I in A549 cells ( Fig . 1E ) . Knock down of RIG-I in both A549 and HUH-7 cells showed an increase in DV1 propagation . Since the sequences of RIG-I shRNA and RIG-I siRNA are different , it is thus unlikely that these siRNAs knocked down a common off-target gene such as MDA5 . To show that this response is not cell-type specific , A549 cells were transfected with siRIG-I or/and siMDA5 . We observed that knockdown of both RIG-I and MDA5 showed an increase in DV1 propagation as noticed in shRIG-I cells ( Fig . 1F ) . This result confirms that the observations in HUH-7 and shRIG-I cells are not cell-type specific . To rule out the possibility that contamination of cellular nucleic acids in the virus preparations that could activate RIG-I and MDA5 , the same virus preparations were UV-treated and used to infect HUH-7 and shRIG-I cells . Infection of cells with UV-treated DV1 did not result in any significant increase in RIG-I or MDA5 expression ( Fig . 1G ) . DV1 infection of HUH-7 and shRIG-I cells over a period of 72 hours showed that the latter was highly permissive for DV1 propagation as noted by high levels of viral proteins ( Fig . 1A ) . Antibodies against DV1 E ( structural ) and NS3 ( non-structural ) proteins were used to show effective and efficient DV1 propagation in shRIG-I cells . Furthermore , knockdown of RIG-I , using siRNA duplexes , in A549 and HUH-7 cells also showed an increase in DV1 propagation ( Fig . 1E ) . HUH-7 and shRIG-I cells were infected with DV1 and fixed at 48 and 72 h for immunofluorescence assay using a monoclonal antibody directed against DV1 E protein . Enhanced fluorescence intensity was observed in DV1-infected shRIG-I cells as compared to infected HUH-7 cells ( data not shown ) . This fluorescence intensity was quantified by flow cytometric analysis using monoclonal antibody directed against DV1 E protein . A significant increase in DV1-infected cells in shRIG-I cells as compared to infection in HUH-7 cells was observed ( Fig . 2A ) . To define if this enhancement effect was due to increase in viral replication , quantitative RT-PCR ( real-time RT-PCR ) for negative strand DV1 RNA level , which is indicative of viral replication , was performed . Real-time RT-PCR showed more than 3-fold increase in negative strand DV1 RNA level in shRIG-I cells compared to HUH-7 cells ( data not shown ) . An immunofluorescent-based TCID50 assay was used to titrate the amount of infectious particle in HUH-7 and shRIG-I cells ( Fig . 2B ) . DV1 propagated significantly higher in shRIG-I cells than in HUH-7 cells . Recently , Loo and colleagues [8] demonstrated that RIG-I and MDA5 are equally essential for IFN-β production induced in response to infection with dengue virus . To determine whether DV1 induces IFN-β promoter activity in HUH-7 and shRIG-I cells through a RIG-I and MDA5-dependent pathway , we infected these cells with DV1 for up to 72 h . Total RNA extracted from DV1-infected cells were subjected to qualitative ( Fig . 2C ) and quantitative ( Fig . 2D ) RT-PCR to detect mRNA levels of 2′ , 5′-oligoadenylate synthetase 2 ( OAS2 ) , interferon stimulated gene ( ISG ) 15 , ISG56 and IFN-β genes . These genes were chosen for detection because a recent microarray analysis of DV1-infected H1299 cells ( human non-small lung cancer cells ) showed increased expression of OAS2 , ISG15 and ISG56 [14] . RT-PCR assays showed a significant increase in mRNA levels of IFN-β and IFN stimulated genes ( OAS2 , ISG15 and ISG56 ) in DV1-infected shRIG-I cells as compared to infected HUH-7 cells . Qualitative RT-PCR for negative strand viral RNA was performed to show level of virus replication in the infected cell lines ( Fig . 2C ) . Virus replication was much pronounced in shRIG-I cells . Quantitative RT-PCR showed at least 200- , 50- , 700- and 4-fold difference for OAS2 , ISG15 , ISG56 and IFN-β gene expression respectively between DV1- infected HUH-7 and shRIG-I cells ( Fig . 2D ) . IFN-β production was assayed using ELISA . DV1-infected shRIG-I cells produced more significant levels of IFN-β compared with DV1-infected HUH-7 cells ( Fig . 2D; lower panel ) . When dsRNA is sensed in the cytoplasm , RIG-I and/or MDA5 recruit the adaptor protein IPS-1 [17] , [18] resulting in the downstream activation ( dimerization ) and nuclear localization of IRF3 which acts as a transcriptional factor for IFN-α/β expression . IRF3 dimerization was assessed by native PAGE with anti-IRF3 antibody as a probe . Figure 2D shows stronger IRF3 dimer formation in DV1-infected shRIG-I cells compared to infected HUH-7 cells . Ratio of IRF3 dimer/monomer shows greater dimer formation in shRIG-I cells ( 0 . 92 ) as compared to HUH-7 cells ( 0 . 59 ) at 72 h . Increased permissiveness for DV1 infection in shRIG-I cells could have lead to induction of other stimulators of IRF3 , such as TLR3 . These results , collectively , show that factors other than RIG-I/MDA5 may play a role in DV1-induced immunity . Since shRIG-I cells are highly permissive for DV1 replication , this might lead to accumulation of viral RNA and perhaps an overload of the cellular protein synthesis machinery triggering host response to the infection [19] . Viral protein overload in the endoplasmic reticulum could result in unfolded protein response ( also known as ER stress ) and activation of apoptotic cell death in DV1-infected cells . We investigated if this proliferation of viral antigens in shRIG-I cell would lead to increased ER stress and eventually apoptosis/cell death . ER stress leading to apoptosis in dengue virus-infected cells has been reported previously [20] . As a signal of ER stress , X box binding protein 1 ( XBP1 ) gene expression was detected . XBP-1 is up-regulated as part of the endoplasmic reticulum ( ER ) stress response , the unfolded protein response ( UPR ) [21] . During ER stress , IRE-I/XBP1 pathway is activated by cleavage of a 26-nucleotide intron from unspliced XBP1 ( uXBP1 ) mRNA resulting in XBP1 in its mature form ( sXBP1 ) [20] . Infection of shRIG-I and HUH-7 cells by DV1 resulted in splicing of XBP1 ( Fig . 3A ) . Splicing of XBP1 in DV1-infected shRIG-I cells occurs as early as 24 hpi as compared to 48 hpi in infected HUH-7 cells . Our results conform to that of Umareddy et al . , [22] whereby the authors showed that XBP1 is spliced upon dengue virus replication . Since most DV1 proteins are localized on the luminal side of the ER membrane prior to cleavage and processing into mature forms , we investigated ER stress by also detecting the expression of calreticulin , a multifunctional , multi-compartmental protein most abundant in the ER lumen . Calreticulin interacts and assists in the folding of various glycoproteins , including viral proteins [23] . The DV1 envelope protein which is produced in large quantities during infection is an N-linked glycoprotein [24] . More pronounced increase in calreticulin protein expression was observed in DV1-infected shRIG-I cells as compared to HUH-7 cells over a period of 72 hpi ( Fig . 3A ) . Taken together , these results show that ER stress is more profound in shRIG-I cells due to increased permissiveness to DV1 replication . Apoptosis/cell death is the final outcome of dengue virus infections [25] . Although UPR is necessary for cell survival and viral replication , prolonged UPR can lead to cell death [22] . Furthermore , activation of RIG-I upon virus infection had been reported to activate the apoptotic cascade [26] . Interferon-β promoter stimulator-1 and IRF-3 were shown to be required for efficient apoptosis following reovirus infection , suggesting a common mechanism of antiviral cytokine induction and activation of the cell death response [26] . Since DV1 infection of HUH-7 and shRIG-I cells showed activation of UPR , IRF-3 dimer formation and IFN- β activation , cell death induced by DV1 infection in HUH-7 and shRIG-I cells was investigated . DNA fragmentation , a hallmark of apoptosis , was observed in both DV1-infected HUH-7 and shRIG-I cells in varying degrees ( Fig . 3B ) . In situ DNA fragmentation was investigated using TUNEL assay , which relies on the specific binding of terminal deoxynucleotidyl transferase ( TdT ) to exposed 3′-OH ends of the fragmented DNA . The signal is then amplified by avidin-peroxidase , enabling detection of in situ DNA fragmentation by flow cytometry . The results of this assay on DV1-infected cells showed an approximately 10% increase in cell death in shRIG-I cells as compared with HUH-7 cells after 72 hpi ( Fig . 3B , upper panel ) . Cell death in DV1-infected cells was further investigated by analysis of sub-G1/DNA content , indicative of cell death , in HUH-7 and shRIG-I cells . Analysis of the changes in the DNA content distribution showed 20% more sub-G1 DNA in DV1-infected shRIG-I cells as compared to DV1-infected HUH-7 cells ( Fig . 3B , lower panel ) . To investigate if RIG-I , MDA5 or both helicases influence the initiation of IFN response and to understand their antiviral potencies , we overexpressed these genes in HUH-7 cells . Upon infection with DV1 for up to 48 h , less DV1 propagation was observed in cells transfected with both helicases ( Fig . 4A ) . Real-time RT-PCR showed more than 5-fold decrease in negative strand DV1 RNA level in cells transfected with both RIG-I and MDA5 ( 0 . 13 ) compared to infected HUH-7 cells ( 1 . 0 ) . Significant increase in IFN-β gene expression was also noted in infected cells overexpressed with both RIG-I and MDA5 compared to infected HUH-7 cells ( Fig . 4B ) . When transfected individually , RIG-I and MDA5 were not able to efficiently suppress DV1 replication but induced elevated amounts of IFN-β expression . These results clearly show that RIG-I and MDA5 synergistically mediate an antiviral response during DV1 infection . Inspite of knocking down RIG-I and MDA5 , the level of IFN-β produced seems to increase upon DV1 infection in shRIG-I cells . Furthermore , an increase in cytokine production was noted ( Fig . 2C ) and an increase in IRF3 dimerization was also observed in native PAGE analysis ( Fig . 2E ) . This phenomenon could be due to an increase in virus replication in the knock down cell line or due to other intracellular receptors such as Toll-like receptor 3 ( TLR3 ) that recognize double-stranded RNA synthesized during DV1 replication . TLR3 has been shown to recognize double-stranded RNA [27] and RIG-I/MDA5 and TLR3 signaling pathways converge to phosphorylate IRF3 [28] . We investigated if TLR3 could recognize DV1 infection . Since the basal level of TLR3 is low in HUH-7 cells , we used wild type ( WT ) and TLR3-knockout ( TLR3ko ) macrophages . Furthermore , TLR3 is abundant on macrophages [29] . While this work was in progress , Tsai et al . , [30] reported that TLR3 in HEK293 cells recognizes dengue virus type 2 and induces cytokine production . In our study , WT and TLR3ko macrophages were infected with DV1 for 48 h and cell lysate and RNA were collected for analysis . Immunoblotting analysis and both semi-quantitative and real-time RT-PCR showed that DV1 virus propagation was more efficient in TLR3ko macrophages ( Fig . 5A ) . Negative strand viral RNA , indicative of DV1 replication , was quantitated using real-time RT-PCR . Approximately 2 . 5 fold increase in viral RNA was noted in DV1-infected shRIG-I cells as compared to infected HUH-7 cells . These experiments show that TLR3 recognizes DV1 double stranded RNA intermediate . DV1 infected HUH-7 and shRIG-I cells showed increase in TLR3 expression over 72 h ( Fig . 5B ) . This result was confirmed by real time RT-PCR analysis ( data not shown ) . To determine if TLR3 could modulate DV1 replication , we overexpressed TLR3 in HUH-7 cells and infected with DV1 for up to 48 h . Semi-quantitative RT-PCR and immunoblotting show that over-expression of TLR3 plasmid greatly inhibited DV1 replication in cells ( Fig . 5C ) . IFN-β gene expression was quantified for the same set of experiments . Cells over-expressing TLR3 showed significant increase in IFN-β production ( Fig . 5C ) . To gain deeper understanding of the role of TLR3 in DV1 infection , siRNA silencing of TLR3 in HUH-7 and shRIG-I cells was carried out . HUH-7 and shRIG-I cells were transfected with siTLR3 and infected with DV1 for 48 h . Since the basal level of HUH-7 is low , real-time RT-PCR analysis was used to determine TLR3 knock down efficiency in cells ( Fig . 6 ) . Control siRNA transfected cells showed increase in TLR3 expression upon DV1 infection as compared to siTLR3 transfected cells . Semi-quantitative and real-time RT-PCR analysis for DV1 negative strand RNA showed increase in DV1 replication in siTLR3 transfected cells with reference to GAPDH expression ( Fig . 6 ) . IFN-β production was quantified by ELISA for the same set of experiments , showing significant increase in IFN-β expression in shRIG-I cells transfected with siTLR3 and infected with DV1 , as compared to control siRNA transfected and infected cells for the same ( Fig . 6 ) . Quantitative real-time RT-PCR for negative strand viral RNA shows a higher fold increase in DV1 replication in shRIG-I cells ( 2 . 14 ) as compared to HUH-7 cells ( 1 . 39 ) . These data are evidence that DV1 RNA is recognized by TLR3 and such recognition modulates DV1 replication by eliciting IFN-β production .
Innate immune defenses are the first line of host anti-pathogen mechanisms . Among them , type 1 IFNs play a critical role in host defense against virus infection . IFN pathway is triggered through host recognition of viral macromolecular motifs also known as pathogen-associated molecular patterns ( PAMP ) . In this study , we show that IFN-β production in response to DV1 was enhanced in RIG-I and MDA5 down-regulated cells . Knockdown of RIG-I and MDA5 enhanced cellular permissiveness to DV1 RNA replication and also increased virus propagation . Consequently , higher expression of IFN-related and stimulated genes , such as OAS2 , ISG15 , ISG56 , as well as strong activation of IRF3 was observed in the cell line . Analysis of ER stress responses , such as splicing of XBP1 and activation of calreticulin , also showed earlier splicing of XBP1 and accumulation of more calreticulin in RIG-I- and MDA5-knockdown cells . Prolonged ER stress , probably due to increase in viral load , resulted in stronger induction of apoptosis in these cells . Further analysis of the functional roles of RIG-I and MDA5 demonstrated that RIG-I and MDA5 synergistically suppressed DV1 infection with induction of IFN-β production in wild type HUH-7 cells over-expressing the two helicases . Meanwhile , using siRNA against TLR3 showed that down-regulation of TLR3 resulted in higher DV1 infection , and over-expression of TLR3 inhibited DV1 infection significantly by inducing high levels of IFN-β production . When siTLR3 was transfected in shRIG-I cells ( down regulated for RIG-I and MDA5 ) , increased permissiveness to DV1 infection was observed . TLR3 knockout macrophages also showed increased permissiveness to DV1 infection than wild type cells . Collectively , our results showed that RIG-I , MDA5 and TLR3 are able to recognize DV1 infection and establish a strong antiviral state in these cells . Available evidence reveals that RIG-I and MDA5 play differential roles in host antiviral defence against viral infections . RIG-I was shown to respond to RNA bearing a triphosphate at their 5′ end [31] and a wide variety of RNA viruses including Japanese encephalitis viruses , influenza viruses and paramyxoviruses [8] , [32] , while MDA5 responds primarily to poly ( I:C ) , a dsRNA mimetic , and piconaviruses [33] . Saito et al . [34] and Uzri and Gehrke [35] identified the Hepatitis C virus polyuridine motif of the 3′ untranslated region ( UTR ) and its replication intermediate as the PAMP substrate of RIG-I . Uzri and Gehrke [35] also showed that the dengue virus full length 5′ UTR and 3′ UTR activated IFN-β to moderate levels . On the other hand , despite the innate immune system's surveillance and detection of PAMP , pathogens like DV1 still establish infection in the presence of a robust innate immune system , as viruses have evolved different mechanisms to counteract the host anti-viral response . For example , DV1 proteins such as NS2A , NS2B , NS4A , NS4B and NS5 could inhibit IFN production [36] , [13] , thus establishing infection in the presence of a functioning innate immune system . Other DNA and RNA viruses have been shown to comprise of IFN inhibitors as well . For example , paramyxovirus V proteins induce STAT degradation or block STAT phosphorylation , inhibiting IFN signaling [37] . Adenovirus E1A proteins and large T antigen proteins of murine polyoma virus also inhibit IFN signaling [38] , [39] . NS1 protein of influenza A virus inhibits IFN-β production [40] . RIG-I-mediated IFN response was shown to play a critical role in restriction of virus infection in cultured cells . Hepatitis C virus [11] , herpes simplex virus-1 and adenovirus [41] were shown to replicate to much higher titers in RIG-I mutant human hepatoma cells HUH-7 . 5 . 1 compared to wild type HUH-7 cells . Modulation of dengue virus infection by IFN has been clearly demonstrated in other studies [42] , [43] . However , despite experimental evidence , the roles played by individual pattern recognition receptors in restriction of dengue virus infection are still not clearly understood . Using a microarray in our earlier study , we demonstrated IFN-related gene induction in DV1-infected cells [14] . A majority of genes , including RIG-I , MDA5 and TLR3 , that were strongly up-regulated were IFN-related genes . Previous studies have shown that RIG-I and MDA5 are necessary for establishing an antiviral state against dengue virus [8] . Evidence presented in this study lends further support that both RIG-I and MDA5 are involved in induction of IFN response in dengue virus-infected cells . Interestingly , knockdown of RIG-I gene expression in HUH-7 cells in isolated stable clones showed down-regulation of both RIG-I and MDA5 expression . RIG-I and MDA5 share a limited homology in their overall primary structure [6] . The shRNA used in this study was against a target sequence of RIG-I , as described by Seth and colleagues [44] and no homology to MDA5 sequences was noted during sequence alignment . Thus , it is unclear why silencing RIG-I in HUH-7 cells would also suppress the expression of MDA5 . Further investigations are required to investigate if MDA5 expression in HUH-7 cells is RIG-I-dependent or the shRNA sequence is silencing MDA5 expression in unknown ways . The observation that RNA silencing of RIG-I and MDA5 in HUH-7 resulted in a significant increase in DV1 replication and IFN-β production raised a possibility that the third RNA sensor , TL3 , may also play an important anti-DV1 role in this cell system . As the basal level of TLR3 in HUH-7 cells is low [15] , its role in induction of IFN and in restriction of virus infection in this type of cells is not fully appreciated . The fact that knockdown of TLR3 by siRNA in RIG-I- and MDA5-knockdown cells ( shRIG-I cells ) further enhances dengue virus infection and reduces IFN-β response demonstrates that all the three pattern recognition receptors are implicated in host innate immune response to the same virus in a same infected cell , and may play a synergistic role . Although , as discussed above , DV1 produces IFN antagonists , no viral protein has been shown to interfere in the upstream sensing of viral double stranded RNA by RIG-I , MDA5 or TLR3 . As these receptors are inducible in DV1-infected cells [14] , the enhancement effect on viral replication by RIG-I- and MDA5-knockdown , in turn , enhances the TL3 induction and IFN-β response . This may explain why higher levels of IFN-β response were observed in RIG-I- and MDA5-knockdown cells . It also suggests that the presence of minute amounts of a specific receptor in a particular cell type would play an important restriction role . On the other hand , as these three receptors show distinct substrate specificities , it would be reasonable to assume that they may recognize different parts of the viral components . A clear advantage for the host is that the invading pathogens can be effectively captured even though mutations occurred in a certain part of the viral genome . If the virus evades one pathway of detection , another could detect and trigger innate immunity against the virus . In summary , our data show that RIG-I , MDA5 and TLR3 synergistically activate innate immune response against DV1 infection in HUH-7 cells . Our study shows the involvement of RIG-I , MDA5 and TLR3 in innate immune response against dengue virus in the same cellular system ( HUH-7 ) . The results also show that over-expression of RIG-I or MDA5 individually induces weak IFN-β expression as compared to when both genes are over-expressed . However , over-expression of TLR3 alone could recognize DV1 and initiate strong IFN-β response . This study , thus , contributes to ongoing characterization of the innate antiviral response to dengue virus infection in cells . Understanding and identifying the molecular patterns that trigger innate immune signaling may lead to targeted and specific antiviral strategies against dengue virus infection .
|
Dengue fever , dengue haemmorhagic fever and dengue shock syndrome , which are caused by dengue virus infection , are a major public health problem in many parts of the world , especially South East Asia . The investigation of host cell transcriptional changes in response to virus infection using DNA microarray technology has been an area of great interest . In our previous study , we used microarray technology to study expression of individual human genes in relation to dengue virus infection . Most of the genes that were upregulated were type 1 interferon related genes . To gain a better understanding of the innate immune response to dengue virus , we knocked down RIG-I , MDA5 and TLR3 genes in HUH-7 cells . Silencing these genes using siRNA technology resulted in significant increase in viral replication . This increase in viral load induced ER stress leading to apoptosis . This study demonstrates a synergistic role for RIG-I , MDA5 and TLR3 in restricting dengue virus infection .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"immunology/immune",
"response",
"immunology/immunity",
"to",
"infections",
"immunology/innate",
"immunity",
"microbiology/innate",
"immunity",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis"
] |
2011
|
RIG-I, MDA5 and TLR3 Synergistically Play an Important Role in Restriction of Dengue Virus Infection
|
Model simulations indicate that the response of growing cell populations on mechanical stress follows the same functional relationship and is predictable over different cell lines and growth conditions despite experimental response curves look largely different . We develop a hybrid model strategy in which cells are represented by coarse-grained individual units calibrated with a high resolution cell model and parameterized by measurable biophysical and cell-biological parameters . Cell cycle progression in our model is controlled by volumetric strain , the latter being derived from a bio-mechanical relation between applied pressure and cell compressibility . After parameter calibration from experiments with mouse colon carcinoma cells growing against the resistance of an elastic alginate capsule , the model adequately predicts the growth curve in i ) soft and rigid capsules , ii ) in different experimental conditions where the mechanical stress is generated by osmosis via a high molecular weight dextran solution , and iii ) for other cell types with different growth kinetics from the growth kinetics in absence of external stress . Our model simulation results suggest a generic , even quantitatively same , growth response of cell populations upon externally applied mechanical stress , as it can be quantitatively predicted using the same growth progression function .
Mechanotransduction is the mechanism by which cells transform an external mechanical stimulus into internal signals . It emerges in many cellular processes , such as embryonic development and tumor growth [1] . Cell growth in a confined environment such as provided by the stroma and surrounding tissues increases cell density and affects the balance between cell proliferation and death in tissue homeostasis [2 , 3] . Tumor spheroids have long been considered as appropriate in vitro models for tumors [4] . While the dynamics of freely growing spheroids has been extensively studied both experimentally [5] and numerically ( e . g . [6 , 7 , 18] ) , more recent experiments have also addressed the growth of spheroids under mechanical stress . Helmlinger et al . ( 1997 ) and later Cheng et al . ( 2009 ) and Mills et al . ( 2014 ) [8–10] experimentally investigated the growth of spheroids embedded in agarose gel pads at varying agarose concentration as a tunable parameter for the stiffness of the surrounding medium . Other approaches such as the application of an osmotic pressure determined by a dextran polymer solution have also been developed to investigate the impact of external pressure on spheroid growth [11] . In all cases mechanical stress was reported to slow down or inhibit spheroid growth . Delarue et al . [12] suggested that growth stagnation is related to a volume decrease of the cells . However , a quantitative relation between pressure and cell fate is not reached yet . The works of Helmlinger et al . [8] and their follow-ups have inspired a number of theoretical papers aiming at explaining the observations , either based on continuum approaches considering locally averaged variables ( e . g . for density and momentum , for overview see [13] ) [3 , 14–17] , or by agent-based models ( ABMs ) representing each individual cell [19 , 20] belonging to the class of models , which are extended and refined in the presented work . For example , the growth kinetics of multicellular spheroids ( MCS ) embedded in agarose gel as observed by Helmlinger et al . [8] could be largely reproduced , if cell cycle progression was assumed to be inhibited either above a certain threshold pressure or below a certain threshold distance between the cell centers , whereby growth inhibition occurred at different spheroid sizes for different densities of extracellular material [19] . However , the model developed in that reference has no precise notion of cell shape , hence does not permit definition of cell volume , thus pressure and compression cannot be physically correctly related [21] . Here , we first establish a computational model to quantitatively explain the growth kinetics and patterns found for CT26 ( mouse colon carcinoma cell line ) multi-cellular spheroids constrained by a spherical elastic capsule , partially based on data previously published [26] and partially based on new data introduced below . This novel experimental technique , called the “cellular capsule technology” [26] allows to measure the average pressure exerted by the cell aggregate onto the calibrated capsule by monitoring the radial expansion of the shell once confluence is reached . Pressure can be recorded over periods as long as a week and the histological data collected and analyzed on fixed and sliced spheroids can provide snapshots of the spatial multicellular pattern . We refer to this experimental technique as “Experiment I” . The thickness , and thus the stiffness of the capsule , was varied to mimic different mechanical resistance conditions . Delarue et al . ( 2014 ) [12] investigated the effect of mechanical stress on MCS growth using the same cell line in a different experimental setting . We exploit these results to challenge our model and determine whether the same computational model designed to match experiment I is capable to quantitatively explain also this experiment ( referred to as “experiment II” ) . In experiment II , mechanical compression was imposed using the osmotic effects induced by a dextran solution . The main difference between those two experiments is that whereas the pressure gradually increases with increasing deformation of the elastic capsule in experiment I , in experiment II a constant stress is applied due to osmotic forces in the absence of any obstructing tissue ( see Fig 1A ) . In this paper , we aim to decipher and quantify certain mechanisms of spheroid growth altered by mechanical stress . At this stage , we establish a robust computational approach that can be applied to various systems ( cell lines and experimental procedures ) and that allows to recapitulate the growth dynamics and the observed cellular patterns . We will show that this can be reached with a minimal number of hypotheses without having to explicitly integrate specific molecular pathways . Gaining insight in the molecular mechanisms would require additional challenging experiments in which the pathways are selectively inhibited or enhanced in a three-dimensional environment , and would add further parameters to the model . To the best of our knowledge , a specific mechanotransduction molecular pathway has been highlighted once , demonstrating the impact of cell volume change on the expression of the proliferation inhibitor p27Kip1 [12] . As modeling technique we here developed an agent-based model . Simulations with ABMs provide a computer experiment representing an idealized version of the true wet-lab experiment [77] . ABMs naturally permit accounting for cell to cell variability and inhomogeneities on small spatial scales as they represent each cell individually . Center-Based Models ( CBM ) are a prominent representative in the class of ABMs in which forces between cells are calculated as forces between their centers . Center-based models for multicellular systems were derived from conceptual anologies to collodial particle dynamics by re-interpretation of parameters and addition of growth and division processes [53 , 75] . The model developed here is fully parameterized in terms of physical parameters , which makes each component possible to validate . However , it circumvents difficulties that standard center-based models have at large compression ( see [21] ) establishing a hybrid modeling strategy to compute the mechanical interaction forces by so-called three dimensional ( 3D ) Deformable Cell Models ( DCMs ) [70 , 79] . A DCM displays cell shape explicitly at the expense of high computational cost ( see Fig 2 ) . In our hybrid strategy the parameters of the CBM that considers the cell shape only in a statistical , “coarse grained” sense thereby permitting simulations of large cell population sizes , are pre-calibrated from a finer scale DCM . This strategy permits to combine the advantages of the DCM with the short simulation time of the CBM . Both CBM and DCM are parameterized by measurable quantities to identify the possible parameter range of each model parameter and avoid non-physiological parameter choices . We studied the series of experimental settings in the works [26] and [12] as both utilize a common cell line , and exert stress on growing MCS of that cell line in different experimental settings . The model is then further tested with experiments on other cell lines as provided in the second work . To unravel the dynamics of MCS subject to compression , our modeling strategy is to postulate and implement hypotheses on cell growth , quiescence and death , and iteratively adapt or extend them in case the model simulations are falsified by comparison with the experimental data . Pursuing a similar strategy enabled us to obtain predictions of subsequently validated mechanisms in liver regeneration [27 , 28] . Based upon analysis of the relation between pressure , cell density and cell compressibility in the two different experiments , our findings suggest that contact inhibition can be regarded as a robust continuous process imposed by a reduction of cell volume as a consequence of increasing pressure and individual cell compressibility ( see Fig 3 ) . In addition , the high-resolution model shows that potential effects of micro-mechanics at the interface with the capsule may depend on the mechanical properties of the cells . For the sake of clarity , we below start to first present the minimal model that was able to explain the data , before discussing in which ways simpler models with other hypotheses failed .
Experiment I: Following microfluidics-assisted encapsulation of CT26 cells into alginate capsules , the growing aggregates of cells were monitored by phase contrast microscopy ( see [26] for details ) . After the tumor cells reached the inner border of the elastic alginate capsule corresponding to a radius of about 100 μm ( t = 0d in Fig 1B ) , they were observed to further induce a dilatation of the capsule , which is an indicator of the exerted pressure . The capsule expansion was measured from the point of confluence over several days , while histological data of the spheroids were collected at the stage of confluence and at 48h past confluence . Capsules have been designed to generate shells with two different thicknesses . The thin ones ( H/R0 ≈ 0 . 08; H = 8μm ) are the softer while the thick ones ( H/R0 ≈ 0 . 25; H = 30μm ) mimic a larger mechanical resistance against growth . Besides the data extracted from [26] , we have also exploited and analyzed unpublished data corresponding to new sets of experiments in order to critically test the reliability of the method ( see Fig 4 ) . We extract four main observations from these experiments . ( EI . OI ) In the absence of a capsule , an initial exponential growth stage was observed with doubling time Tcyc = 17h [26] . The growth kinetics however starts to deviate from exponential growth for spheroid size R ≈ 175 μm , ( see Fig 1B ) . ( EI . OII ) In the presence of a capsule , the exponential growth is maintained until confluence , i . e . R = R0 ≈ 100 μm , which shows that the capsule is permeable to nutrients and allows normal growth . Once confluence is passed , the time evolution of the capsule radius exhibits two regimes: i ) an initial “fast” growth stage T1 ( t < 1day ) , crossing over to ii ) a “slow” quasi-linear residual growth stage T2 ( t > 1 day ) that at least persists as long as the capsules are monitored , i . e . up to one week . The transition happens roughly at a pressure of ∼ 1 . 5 kPa , see Fig 4C . The observed long-time growth velocities were ∼ 2 μm/day for the thin capsules ( Fig 4A ) and 0 . 7 μm/d for the thick capsules ( see Fig 5 ) . ( EI . OIII ) The nuclei density , obtained from cryosections , increases from ∼ 1 nucleus / 100 μm2 before confinement , to roughly 2 nuclei / 100 μm2 after confluence , with a relatively higher number near the center of the spheroid ( 1 . 2 times more compared to the outer regions ) , and a local increase at the border of the capsule . The distribution and shape of cell nuclei reported in [26] suggests that cells near the capsule border are deformed with a flatened shape , while those in the interior look compact shaped . ( EI . OIV ) Most of the cells in the core of the spheroid are necrotic after 48h of confinement , while the cells located in a peripheral viable rim of roughly two cell layers thickness ( λI ≈ 20 μm ) , show viability and proliferative activity during the whole time course of the experiment , including period T2 . ( EI . OV ) Fibronectin staining indicates there is ECM present during free growth; staining after 48h indicates more ECM regions near the capsule border and a weak signal inside the spheroid . Experiment II: in the work of Delarue et al . ( 2014 ) [12] , CT26 spheroids ( initial radius ∼ 100 μm ) were grown in a dextran polymer solution . To recover osmotic balance , water expulsion out of the spheroid generates osmotic forces exerted to the outer cells that are transferred as compressive stresses to the interior ( bulk ) cells . The concentration of dextran regulates the applied pressure . ( EII . OI ) The growth speed at p = 5 kPa is significantly lower than in control spheroids where no pressure is exerted . ( EII . OII ) The spheroid free growth data does not show an initial exponential phase found in ( EI . OI ) ( Fig 1B ) . This surprising discrepancy might result from the different culture conditions between both experiments . In experiment I , the medium has repeatedly been refreshed [26] , while in experiment II this has not been done so often ( private communication ) , leading to lower concentrations of nutrients and other molecular factors in experiment II . During the whole course of osmotic stress application , an over-expression of the kinase inhibitor p27Kip1 together with an increased number of cells arrested in the G1 phase was observed , but no significant change in apoptosis rates after 3 days was reported . ( EII . OIII ) Delarue et al . ( 2014 ) also considered the stress response for other cell lines ( AB6 , HT29 , BC52 , FHI ) performing steps EII . OI and EII . OII for each cell line . These data will be used to validate our model despite less information concerning cell size and cycling times is available for these cell lines . As a first step we proposed a number of hypotheses for the growth dynamics common to experiments I and II . ( H . I ) In both experiments a linear growth phase was observed after exposing the MCS to external stress . The growth of the cell population that is not constrained by either mechanically-induced growth inhibition , nutrient , oxygen or growth factor limitations is exponential [4] . We assumed that deviation of growth from an exponential indicates restriction of proliferation to a rim . This may have different reasons , for example necrosis that has been only reported for experiment I ( EI . OIV ) , or of cells being quiescent . Both necrosis and quiescence can result from a lack of nutrients or other factors [6 , 29] , that may indirectly be promoted by pressure , e . g . in case the compression of the cell layer squeezed between the capsule shell and the inner cell layers leads to the formation of an obstructive barrier for some nutrients ( as glucose ) to the cells located more deeply in the interior of the tumor . However , cell quiescence ( or cell death ) may also be a direct consequence of mechanical pressure , e . g . if cells subject to compression cannot advance in cell cycle for too long and then undergo apoptosis [6 , 29] . We do not specify the origin of the rim here , we take it into account through the definition of a thickness λk ( k = I , II is the experiment index ) . In Exp . I , λI distinguishes the necrotic cells from viable ones . In Exp . II , λII separates the quiescent cells from the ones that can still proliferate . Necrotic cells as observed in experiment I can undergo lysis , in which they steadily lose a part of their fluid mass . The decrease of mass is limited to about 70%–90% of the total initial mass of the cell [30 , 31] . ( H . II ) Cell growth rate may be declined or inhibited by pressure [8] . The authors of a recent study [12] hypothesized that the growth rate may be down-regulated if the cell volume is reduced as a consequence of pressure . We here test the hypothesis that growth rate is dependent on the volumetric strain ( “true strain” , commonly used in case of large strains ) , ϵ V = - log ( V / V r e f ) , ( 1 ) where V is the actual compressed volume and Vref is the volume of the cell in free suspension . The volumetric strain can be related with the pressure by integration of the relation dp = −KdϵV . K is the compression modulus of the cell and depends on the actual volume fraction of water , and the elastic response of the cytoskeleton [42] . It may also be influenced by the permeability of the plasma membrane for water , the presence of caveolae , and active cellular responses [32 , 78] . As such , the timescale at which K is measured is important . In our final model ( presented here first ) we further assume that the cell exhibits strain hardening effects , and hence K depends on the volumetric compression of the cell ( see Section Models ) . In our simulations , we regarded K as the long timescale modulus of cell , as growth and divisions are slow processes . We studied constant and a volume-dependent compression moduli ( the calculation of growth , volume and pressure for each cell in the model is explained in Section Cell growth , mitosis , and lysis , Eq 8 ) . On the molecular level , volume reduction correlates with over-expression of p27Kip1 which progressively decreases the proliferating potential . Other molecular players such as the transcriptional regulators YAP/TAZ were also reported to be mechano-sensitive [33] . In the scope of the present work , these reports suggest that quiescence , and perhaps also apoptosis , may be controlled by either pressure or cell volume . Experimental studies [34–37] mainly measured the growth rate of dry mass or size . These indicate that the growth rate α varies within the cell-cycle , yet a unique relationship is difficult to infer . We propose as general form for growth rate α a Hill-type formula defined as ( 1—Hill function ) : α = α 0 ϵ V t r n ϵ V n + ϵ V t r n , ( 2 ) where α0 is the growth rate of the unconstrained cell , ϵ V t r is a threshold value1 , and n is an integer . The parameter ϵ V t r is the value where the cells have lost 50% of their initial growth rate . Note that for ϵ V t r → ∞ we retrieve a constant growth scenario , whereas increasing n from 1 to ∞ modifies the curve from a smooth decrease to a sharp pressure threshold ( see Fig 3A ) . The use of a Hill-type function thus makes a variety of growth scenarios possible . Hill formulas have been used in the past to simulate contact inhibition in epithelial tissue and tumors [17 , 38 , 39] . We discuss the generality of this approach in the Discussion section . ( H . III ) It is generally accepted that cells that have passed the G1 checkpoint ( also known as restriction point ) are committed to divide , else they go into quiescence ( G0 ) . In our model we assume this checkpoint is situated after 1/4 of the total cell cycle time [40] . The transition criterion to the quiescence state can be defined as the one at which the growth rate “stalls” , i . e . α/α0 < αqui ( see Fig 3A ) . “Sizer versus Timer”: According to hypothesis H . II growth rate depends on the compression of the cells , hence the volume doubling time can locally vary and is larger than for uncompressed cells . Limiting cases would be that division occurred after volume doubling at a variable time [6] ( “sizer” ) , or after a pre-defined time ( “timer” ) often mentioned in developmental biology [41] . We therefore also compared the effect of constant time vs . doubling of volume criterion in cell division on the cell population behavior . Also mentioned in H . II , the unconstrained growth rate α0 itself may vary during the cell cycle . To study the potential effect of these variations we performed comparative runs considering constant growth rate as well as exponential growth rate during the cell cycle ( details in Cell growth , mitosis , and lysis ) . For the model development and parameterization we pursued a multi-step strategy sketched in Fig 2 ( see also Tables 1 and 2 ) . The model parameters for the “model I” to mimic experiment I , { P M 1 } , and “model II” to mimic experiment II , { P M 2 } , were step-wise calibrated from experiments I and II , and in each case first for growth in absence of external mechanical stress on the growing population , then in presence of stress . They can be categorized by separating between cell line-specific parameters { P C = j } , where j ∈ {CT26 , AB6 , HT29 , BC52 , FHI} , determines the cell line , and experiment-specific parameters { P E x p = k } with k = I , II characterizing the experimental setting . The simulations were performed with a center-based model ( CBM ) . As the model is parameterized by measurable physical and bio-kinetic parameters , parameter ranges could readily be determined within narrow limits ( Table 2 , [27] ) . First { P M 1 } was identified in three steps ( 1 ) - ( 3 ) ( Table 1 ) . ( 1 ) As the “standard” CBMs are inaccurate in case of high compression [21] , the cell-cell interaction force in the CBM in this work was calibrated using computer simulations with a deformable cell model ( DCM ) , resulting in an effective stiffness E ˜ for every cell in the CBM for every cell at high compression , that increases with increasing compression , see Calibration of the CBM contact forces using DCM . E ˜ belongs to { P C = C T 26 } of the CBM . The DCM could not be directly used for the growth simulations , as it is computationally too expensive to run simulations up to the experimentally observed cell population sizes of ∼ 104 cells . Next , the experimental information was taken into account ( Fig 2 ) . ( 2 ) Comparing simulations of the CBM with the data from the stress-free growth control experiment of multicellular CT26 spheroids ( MCS ) in experiment I permits determining those parameters of { P C = C T 26 } that were are unaffected by the presence of the elastic capsule ( Table 2 ) , see Model setup and parameter determination . ( 3 ) Adding a thin elastic capsule specifies the set of experimental parameters { P E x p = 1 } ( Young modulus , Poisson ratio and thickness of the capsule , etc . ) , and permits identifying those cell line specific parameters that respond on the presence of the capsule . In experiment I these are the parameters characterizing cell cycle entrance and cell growth ( 2 ) . Finally , model I is characterized by the conjunction of the cell-specific and the experiment-specific parameter sets { P M 1 } = { P C = C T 26 } ∪ { P E x p = 1 } . Replacing the thin by a thick capsule in the simulations by changing the experimentally determined thickness parameter for the thin capsule in { P E x p = 1 } by that for the thick capsule leads to a predicted simulated growth dynamics that matches well with the one experimental data without any additional fit parameters ( Fig 5B ) . Experiment II has been performed with CT26 , AB6 , HT29 , BC52 , FHI cells . For CT26 cells , the cell-line specific parameter set remains the same in experiment II as in experiment I . Differently from experiment I , stress-free growth in experiment II is not exponential but linear , reflecting different growth conditions that limit cell proliferation to a “proliferating” rim . This determines the proliferating rim size λII as the experimental parameter of set { P E x p = 2 } that summarizes the impact of growth medium under the conditions of experiment II in stress-free growth . In presence of dextran , { P E x p = 2 } is expanded by only the measured pressure exerted by dextran , which as it is experimentally determined , is no fit parameter ( λII remains unchanged ) . With the parameter set { P M 2 } = { P C = C T 26 } ∪ { P E x p = 2 } , the simulation model predicts a growth dynamics that quantitatively agrees with the one experimentally found indicating that the growth response only depends on the exerted pressure , not on any other parameter ( Fig 1B ) . In a last step , the stress responses of the other cell lines , j = {AB6 , HT29 , BC52 , FHI} have been modeled for the experimental setting of experiment II , again in two steps ( Fig 1D–1G ) . The first step was to adjust the cell cycle time Tcyc of the cell line to fit the stress-free growth leading to replacement of that one parameter in passing from { P C = C T 26 } to { P C = j } , the second was predicting the growth subject to dextran-mediated stress without any parameter fitting i . e . , using { P E x p = 2 } for the experimental parameters . Summarizing , almost the entire parameter determination is done by adjusting the model parameters to experiment I for a thin capsule . After this step there is only one fit parameter for each cell line , summarizing the cell-line specific effect of growth conditions of experiment II for the stress-free growth ( i . e . , the control experiment ) . The step to simulate population growth subject to external stress , both in the thick capsule for CT26 as well as in experiment II with dextran for the cell lines CT26 , AB6 , HT29 , BC52 and FHI is performed without parameter fitting .
By establishing a quantitative model of growing multicellular spheroids ( MCS ) subject to compressive stress calibrated with data on growth in an elastic capsule we were able to demonstrate that the stress response of a growing tumor is quantitatively robust and reproducible even if cells grow under different conditions and if the pressure is exerted by different experimental methods . Given the enormous complexity of intracellular processes involved in the control of MCS growth this is fascinating as it might open the possibility that largely separated robust functional modules may be identified and studied in separation without the need to analyze all interactions of the components of one module with the components of other modules , and without incorporating all interactions at the molecular level . In particular , we first developed a model to study CT26 cells grown in an elastic thin and thick capsule , and then modified this model in a minimal way by taking into account the remarkably different growth behavior of freely growing tumor spheroids ( i . e . not subject to compressive stress ) to simulate the tumor growth response of CT26 and other cell lines in a dextran solution . We show that the mechanical stress response is quantitatively the same despite significantly different culture and protocol conditions . Without the model , it would have been very difficult to identify this equivalence . The key results of our analysis are: ( R . I ) With increasing compression the cell growth rate decreases . This relation could be well captured by a Hill-type function for the growth rate α that depends on the volumetric strain ( Eq 2 ) , and a transition into quiescence if the growth rate dropped below a threshold value . A sharp volume or pressure threshold below which no cell cycle entrance would occur anymore , is not compatible with the data . Together with the strain hardening assumption of cells during compression , this overall points to a nonlinear increasing growth resistance of the cells upon mechanical stress . ( R . II ) Cells divide when their dry mass has doubled during the cycle . A “timer” as a decision mechanism for dividing could not explain the data . A particular point of concern in many studies of spheroids is the appearance of cell death . Our work is based on the observations of Alessandri et al . ( 2013 ) , who observed necrosis ( CT26 cells , using FM4-64 ) in capsule confined cells , while their free growing spheroids exhibited the normal exponential growth for R < 150 μm . Helmlinger et al . ( 1996 ) [8] observed a decrease in apoptotic ( LS174T cells , using TUNEL ) events during compression , and reported little necrosis ( not quantified ) for spheroids with R < 150 μm . They concluded that the haltered growth of the spheroids is mainly due to the increasing compressed state , which can be partially confirmed by our simulations . In the work of Delarue et al . ( 2014 ) [12] , no increase of apoptosis ( HT29 cells , using cleaved-caspase 3 ) was observed after 3 days for spheroids with R ∼ 100 μm . Contrary , earlier Montel et al . ( 2012 ) [11] did report increased apoptosis using cleaved-caspase 3 for CT26 cells , while Cheng et al . ( 2009 ) [9] did observe an increase of necrosis ( 67NR cells , using propidium iodide ) even in very small spheroids R ∼ 50 μm , yet mainly for the interior cells . At the periphery , cells were still dividing . Whether necrosis and apoptosis occurs may well be dependent on the cell type and experiment , but overall it seems that the peripheral cells are unaffected . Another issue that deserves attention is that despite recent significant advances in exploring the relations between the cell mechanical parameters and cell responses during an externally applied mechanical stress , a coherent consensus has not been reached . One issue in this discussion is the cell compression ( bulk ) modulus . For instance , in Delarue et al . ( 2014 ) [12] , one concludes that cells are compressible reporting a rapid cell volume reduction at the level of the MCS ( Multicellular Spheroids ) under compressive stress . Another work of Delarue et al . ( 2014 ) [43] indicates bulk moduli of the order of 10 kPa . Both works consider the long-term effects ( > 1h ) of compression on spheroids . The work of Lin et al . ( 2008 ) [44] seems to concur with this as they measure cell bulk moduli of about 10 kPa with measurements on a timescale of minutes . On the other hand , the Monnier et al . ( 2016 ) [78] report individual cell compression moduli of several orders of magnitude higher ( 1 MPa ) than the ones reported above , also on short time periods of minutes . Yet they state in their paper that on longer timescales , the cell response may become more complex due to intracellular adaptations . We emphasize that in our paper we are considering timescales of larger than one hour as cells are doubling their volume in about a day so that the rate of percentage of the volume increase is about 0 . 07%/min . As such , the compression moduli of the cells that we find should be regarded as long-term values , where the cell can respond differently as compared to short timescales . For instance , the cell may respond by expelling fluid through aquaporins . In the work Tinevez et al . ( 2009 ) [42] , the cytoplasm bulk modulus is estimated as ±2500 Pa . Despite not being the modulus of the whole cell , it indicates that if cells are able to expel water through the aquaporins on longer timescales , their resulting bulk moduli agree with our values . Our modeling strategy is based on in silico experiments i . e . , abstracted experiments on the computer , where each individual cell was represented as modeling unit with those properties , actions and interactions that were considered as necessary to quantitatively explain the cellular growth response on mechanical compression . The implementation of cell-cell and cell-environment interaction directly accounts for physical laws with ( in principle ) measurable physical parameters that permit straightforward limitation of parameter ranges to those physiologically relevant . This made it possible for us to largely confine the parameter values to published or directly observed relatively narrow ranges , and introduce free fit parameters only for the cell cycle progression . A particular challenge was to construct an individual agent-based model that permits stable and robust simulations up to several tens of thousands cells under high compression . Under these conditions cell displacements may have to be minimal , which rules out models operating on lattices unless the lattice size would be chosen a very small fraction of the cell diameter ( in which case they would lose their computational advantage ) . Thus , the requirements of constraining the parameters , and providing realistic simulation trajectories in time favored models operating in lattice-free space implementing a dynamics simulated by equations of motion ( as opposed to a Monte Carlo dynamics , which under some condition mimics a master equation ) . The prototype of lattice free models are center-based models that calculate the forces between cells as forces between cell centers . However , as mentioned above and explained in more detail elsewhere [21] this model type has significant problems in dealing with cell populations under large compressive stress i . e . , with exactly the situation we are faced with in this work . To solve this issue , we developed a deformable cell model , which represents each individual cell in much greater detail as in center-based models but at the expense of much longer simulation times . As simulations with that model up to several thousands of cells were not feasible , we performed simulations with this model of characteristic MCS configurations under large compressive stress and used the results to establish a new interaction force model within center-based models that permit to mimic large cell populations under large compression . Furthermore , we mention that despite their limit on cell numbers , simulations with DCM can give valuable information on micro mechanics . In our study , we found that stiffer cells in a scaled capsule model more likely could cause a gradient in cell pressure from the border to the center of the spheroid than soft cells ( section Cell deformation and pressure distribution during in a compressed spheroid in DCM ) . These potential effects are difficult to investigate with center-based models and prove the necessity of further development of high resolution models , and perhaps running them on high performance computers . Finally , we discuss briefly how to include the effect of extracellular matrix ( ECM ) into the model more explicitly . The quantity of ECM that is produced may depend on the cell type . For instance , fibroblast generally produce more ECM than epithelial cells . In the capsule experiment by Alessandri et al . ( 2009 ) [26] the sparse ECM signal suggests that ECM is sparse in the compressed spheroids . In case ECM would be present at higher volume fractions , a more important part of the compression might be attributed to ECM , which might change the growth response of multicellular spheroids subject to externally applied mechanical stress . There are several ways how this can be included in our model which , despite it was not in the scope of this paper , would be a natural future step to perform . This can be either a detailed model of ECM [81] , taking into account ECM in a global calibration approach similar to the global approach detailed in absence of ECM ( see S1 Text ) , or a composite material approach , where instead of considering as basic modeling unit a single cell , it is regarded as a cell plus its embedding ECM ( for the concept in agent-based models , see Drasdo et al . ( 2007 ) [53] ) . A more detailed description can be found in S1 Text .
In CBMs cells are approximated as simple geometrical objects capable of active migration , growth and division , and interaction with other cells or a medium [53] . In CBMs the precise cell shape is not explicitly modeled but only captured in a statistical sense . Here , the cells are represented by homogeneous isotropic elastic , adhesive spheres . Agent-based models permitting large deformations and representing cell shape explicitly are generally called Deformable Cell Models ( DCMs ) [21–23 , 25 , 60 , 71 , 74] . In a basic DCM the cell surface is discretized with nodes which are connected by viscoelastic elements . Nodes and their connecting elements represent a flexible scaffolding structure . The discretization can be extended to the entire cell cytoplasm and even organelles be represented , yet here we regard the cell interior as a homogeneous matter . The nodes at the boundary form a triangulated structure , accounting for the mechanical response of the membrane and cortical cytoskeleton . The total force on each node consists of cell-cell interaction and intracellular interaction forces , the latter describing membrane and cortex mechanical behavior , and cell volumetric compressibility . The basic equations of motion in DCM is formally the same as for the center-based model ( Eq 4 ) , but is now applied to each node i of a cell2: Γ n s , i v → i + ∑ j Γ n n , i j ( v → i - v → j ) = ∑ j F → e , i j ︸ in-plane + ∑ m F → m , i ︸ bending + F → v o l , i ︸ volumechange + ∑ T F → T , i ︸ areacorrection + F → r e p , i + F → a d h , i ︸ contact ( 13 ) with the matrices Γns and Γnn representing node-substrate friction and node-node friction , respectively . v → i denotes the velocity of node i . The first and the 2nd term on the rhs represent the in-plane elastic forces and bending force , the third term on the rhs a volume force controlled by the cell compressibility . The fourth term is a force that avoids excessive triangle distortion . The two last terms ( F → a d h , i , F → r e p , i ) describe the adhesion and repulsion forces on the local surface element in presence of nearby objects as e . g . another cell or the capsule in experiment I ( for details see S1 Text ) . Different from CBMs , the cell bodies in contact do not overlap and therefore triangles belonging to different cells will be repelled upon approaching each other . For consistency with the CBM we chose the model components of the DCM such that cells are inherently isotropic . As the DCM directly represents cell compartments , the range of its parameters can readily be determined ( Table 1 . For further details see S1 Text ) . During the process of compression , cells rearrange and deform to a closer packing . As discussed above , common models to model the interactions between cells ( such as Hertz , JKR , extended Hertz , Lennard-Jones , etc . ) are based on pair-wise interaction force calculations and do not take into account the effect of volume compression emerging from the simultaneous interaction of many cells [21 , 53] . In simulations using these interaction force models , the apparent volume ( as seen in the simulation ) that the spheroid occupies upon strong compression , may become much smaller than consistent with the material parameters; even incompressible cells having Poisson ratio ν = 0 . 5 reduce their volume [21 , 62] . Simulations of spheroid growth in a capsule performed with an uncalibrated model result in an unrealistic capsule dilatation ( see S1 Text ) . The deformable cell model ( DCM ) does not suffer from such shortcomings , but is not amenable to the amount of cells observed in experiments I and II in reasonable computing time on standard desktop computers . For this reason we here chose a hybrid strategy: we corrected the interaction force in the CBM based upon numerical compression experiments performed with the DCM , and used the so calibrated CBM to perform simulations reminiscent of virtual computer experiments in the experimental settings I and II ( Fig 8 ) . In order to estimate the repulsive contact forces in case of many cell contacts , we have constructed a DCM spheroid computer experiment with ∼ 400 cells initially positioned in a closest sphere packing . In this computer experiment , the outer cells were then pushed towards the spheroid center quasi-statically to avoid friction effects , using a shrinking large hollow rigid sphere encompassing the cells ( see Fig 8A ) . All cells have the same size but taking into account a moderate variable cells size were found to not affect the results significantly . Interestingly we observed in the calibration simulations , that cell shape of isotropic cells in the calibration compression simulations with the deformable cell model appear distorted near the capsule border in agreement with the shapes one would infer from the position of the cell nuclei in the capsule experiments [26] . The capsule is made of an quasi-incompressible alginate gel exhibiting a strain hardening behavior . The stress-strains relationship was measured in a stretching experiment of an thin alginate cylinder . Strain hardening behavior was observed for strains >15% . In case of a thick walled capsule , the expansion strain is low and hence linear elasticity can be applied . We refer to the hollow sphere example as described in [64] to compute the radial displacement of the capsule from the internal pressure . If on the other hand the capsule has a thin wall , strains can become large , and the linear elasticity hypothesis fails . For this case , in line with ref . [26] the original young modulus is modified instead of employing nonlinear elasticity theory . The nonlinear relationship in stress and strain ( ϵcap ) was phenomenologically characterized in ref . [26]: E c a p = E c a p , 0 ( 1 + a ϵ c a p ) ( 15 ) where ϵcap is the strain and a = 1 . 5 to obtain an optimal fit with the experiment . The capsules have an initial inner and outer radius Rin , 0 and Rout , 0 respectively , whereby typically H = Rout , 0 − Rin , 0 > 0 . 2Rin , 0 for thick capsules , H being the capsule thickness . The pressure difference along the capsule wall can be related to the change in radii by [26]: p c a p = 4 3 E c a p s R ′ u ( R i n ) R i n ( 16 ) Where Ecaps is the Young modulus of the capsule material , Rout is the outer radius , and u ( Rin ) = Rin − Rin , 0 is the displacement at the outer radius . Furthermore , R ′ = ( 1 + 1 1 + Δ R 0 3 / R i n 3 ) , in which the outer radius is related to the inner radius Rin by Δ R 3 = R o u t 3 − R i n 3 = R o u t , 0 3 − R i n , 0 3 , assuming incompressibility of the elastic shell . To simulate the radius evolution of the capsule , one computes pressure pcap by dividing the sum of all contact forces of the cells with the capsule by the actual inner surface area . Taking into account the damping by the alginate material , we arrive at an ODE , formally similar to Eq 10: γ c a p R i n ( t ) d R i n ( t ) d t = p c a p ( t ) - 4 3 E c a p s R ′ u ( R i n ( t ) ) R i n , ( 17 ) with a lumped material damping parameter γcap . It was shown in [48] that the viscosity of the capsule material is low and does not influence the much slower dynamics of the spheroid . Accordingly , in our model γcap was chosen low to reflects the material’s ability to rapidly adapt to a change in spheroid radius while not affecting the slow growth dynamics . We here explain the determination of the mechanical model parameters starting from the thin capsule experiment . A large fraction of the parameters are fixed from direct observations or published references , see Table 2 for more details . Within parameter sensitivity analysis simulations the parameters that could not be fixed by experimental observations , were varied within their physiological ranges to study their impact on the simulation results . Some parameters turned out to only negligibly affect the simulations results , see S1 Text . As the simulation time was too long to determine the parameters within their physiological ranges based on a maximization of a likelihood function , or to perform a parameter identifiability analysis , we identified plausible parameters by a two-step procedure . We first determined those model parameters that determine the simulated growth behavior in case of free growth by comparison to the experimental data for CT26 cells in experiment I . In the next step the parameters relevant for the specific experiment were fixed . After this , two remaining parameters , namely K and Tlys were calibrated by the thin capsule simulations , yielding a model without a growth rate adaptation ( see Cell-specific parameters K and Tlys during stress conditions ) . Each simulation result was compared to the experimentally observed spheroid diameter of the growing spheroid prior to confluence , and the slope of the residual growth curves after 48h , thereby retaining the parameters that are physically plausible and can best explain the data at the same time .
|
The effect of mechanical resistance on the growth of tumor cells remains today largely unquantified . We studied data from two different experimental setups that monitor the growth of tumor cells under mechanical compression . The existing data in the first experiment examined growing CT26 cells in an elastic permeable capsule . In the second experiment , growth of tumor cells under osmotic stress of the same cell line as well as other cell lines were studied . We have developed an agent-based model with measurable biophysical and cell-biological parameters that can simulate both experiments . Cell cycle progression in our model is a Hill-type function of cell volumetric strain , derived from a bio-mechanical relation between applied pressure and cell compressibility . After calibration of the model parameters within the data of the first experiment , we are able predict the growth rates in the second experiment . We show that that the growth response of cell populations upon externally applied mechanical stress in the two different experiments and over different cell lines can be predicted using the same growth progression function once the growth kinetics of the cell lines in abscence of mechanical stress is known .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Models"
] |
[
"cellular",
"stress",
"responses",
"classical",
"mechanics",
"cell",
"cycle",
"and",
"cell",
"division",
"cell",
"processes",
"biological",
"cultures",
"radii",
"mechanical",
"stress",
"geometry",
"simulation",
"and",
"modeling",
"mathematics",
"research",
"and",
"analysis",
"methods",
"compression",
"cell",
"lines",
"glucans",
"physics",
"biochemistry",
"polysaccharides",
"ht29",
"cells",
"dextran",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"glycobiology"
] |
2019
|
Quantitative cell-based model predicts mechanical stress response of growing tumor spheroids over various growth conditions and cell lines
|
The extracellular matrix ( ECM ) provides physical scaffolding for cellular constituents and initiates biochemical and biomechanical cues that are required for physiological activity of living tissues . The ECM enzyme ADAMTS5 , a member of the ADAMTS ( A Disintegrin-like and Metalloproteinase with Thrombospondin-1 motifs ) protein family , cleaves large proteoglycans such as aggrecan , leading to the destruction of cartilage and osteoarthritis . However , its contribution to viral pathogenesis and immunity is currently undefined . Here , we use a combination of in vitro and in vivo models to show that ADAMTS5 enzymatic activity plays a key role in the development of influenza-specific immunity . Influenza virus infection of Adamts5-/- mice resulted in delayed virus clearance , compromised T cell migration and immunity and accumulation of versican , an ADAMTS5 proteoglycan substrate . Our research emphasises the importance of ADAMTS5 expression in the control of influenza virus infection and highlights the potential for development of ADAMTS5-based therapeutic strategies to reduce morbidity and mortality .
Influenza A virus infection is responsible for substantial global morbidity and mortality ( >500 , 000 deaths each year [1] ) and largely afflicts high-risk groups , including the very young and elderly . There are currently two countermeasures employed to control influenza virus infection: vaccines and antivirals . Although generally effective , the imperfect proofreading capacity of the RNA-dependent RNA polymerase drives constant genetic drift . Moreover , a segmented genome facilitates rapid genetic shift , resulting in the need for reformulation of seasonal vaccines or the emergence of resistance following administration of antivirals , leading to suboptimal prophylactic or therapeutic intervention [2] . T cells are a vital component of the adaptive immune response following influenza virus infection . Critically , trafficking of activated influenza-specific T cells from draining lymph nodes ( including the mediastinal lymph node [MLN] ) to the site of primary infection in the lung requires direct contact and interaction with the extracellular matrix ( ECM ) [3] . The ECM provides adhesive substrates , such as proteoglycans and collagen , to encourage and facilitate lymphocyte trafficking [4] . Expression and remodelling of ECM components is strictly regulated to control movement of immune cells . Therefore , it is not surprising that perturbations in substrate availability and ECM remodelling significantly impact granulocyte and lymphocyte migration in a number of model systems [5–7] . The A Disintegrin-like and Metalloproteinase with Thrombospondin-1 motifs ( ADAMTS ) family are a group of secreted metalloproteinases found within the zinc-dependent metzincin super-family that also consists of matrix metalloproteinases ( MMPs ) and ADAMs [8] . The ADAMTS family comprises 19 mammalian ADAMTs enzymes [9] . ADAMTS5 is one of the most highly characterised and well-known proteinases in this family and has been shown to cleave the hyalectan class of chondroitin sulphate proteoglycans ( CSPGs ) , including aggrecan , brevican , neurocan , and versican [10–13] . Hyalectans/CSPGs are large aggregating macromolecules that hydrate tissue and confer rigidity to the extracellular space . ADAMTS5 has become a major drug target for arthritis therapy as ADAMTS5 knockout mice ( Adamts5-/- mice ) are resistant to aggrecan cleavage in articular cartilage and are thus protected from experimentally induced arthritis [14 , 15] . Aside from the documented role in arthritis , ADAMTS5 has been shown to play a role in embryonic development , including limb and cardiac morphogenesis , and skeletal muscle development through its versican remodelling properties [11 , 16 , 17] . Importantly , its role in viral immunity is currently undefined . Versican , a substrate of ADAMTS5 , is a widely expressed tissue proteoglycan involved in cell adhesion , proliferation , and migration [4] . The two predominant splice-variants of versican that harbour ADAMTS cleavage sites in their shared glycosaminoglycan ( GAG ) -β domain are V0 and V1 [18] . GAG chains provide interactive points for antigen recognition receptors ( Toll-like receptor 2 and 4 ) , chemokines ( MCP-1 , MCP-2 , CCL5 ) , and cell surface markers ( CD62L , CD44 ) , some of which are directly linked to immune cell migration [19–21] . Furthermore , in vitro studies have shown that Poly I:C induced versican expression can restrict CD4+ T cell migration by preventing ECM adhesion [22] . Given the fact that ADAMTS5 is a versicanase [11] , we hypothesised that it would play a key role in viral immunity . Our data demonstrates that host expression of ADAMTS5 is required to help ameliorate disease following influenza virus infection . Adamts5-/- mice clearly show increased weight loss and higher viral titres throughout the course of influenza virus infection along with impaired CD8+ T cell migration and immunity .
ADAMTS enzymes are widely distributed in human adult tissues and play a key role in normal cellular function . Although Adamts5-/- mice are viable and phenotypically normal , based on gross analysis of histological samples [14 , 15] , detailed characterisation has revealed decreased interdigital web regression leading to fused digits [11] , cardiac valve maturation [17] , and abnormal formation of multinucleated myotubes required for skeletal muscle development [16] . In contrast , the homeostatic immune cell composition of naïve Adamts5-/- mice has yet to be determined . Antibody staining and flow cytometric analysis of immune cell populations performed prior to infection suggested that the proportion of dendritic cells ( CD11c+MHCII+ ) , alveolar macrophages ( CD11c+F480+ ) , and interstitial macrophages ( CD11b+F480+ ) were comparable in the lungs of C57 . BL/6 ( wild-type [WT] control mice ) and Adamts5-/- mice ( Fig 1A–1C ) , as were the number of CD4+ and CD8+ T lymphocytes and B cells in the spleen ( Fig 1D–1F ) . Furthermore , we analysed immune cell populations ( dendritic cells , macrophages , NK , T and B cells ) in the lung ( S1 Fig ) , spleen ( S2 Fig ) , and thymus ( S3 Fig ) and found no observable differences between Adamts5-/- mice and C57 . BL/6 controls . As such , we concluded that Adamts5-/- mice were immunologically “normal” prior to infection . We also carefully analysed the gene expression level of related ADAMTS family members that have “versicanase” activity to determine if compensation of enzymatic activity was evident . Quantitative reverse-transcriptase polymerase chain reaction ( QRT-PCR ) data demonstrated similar gene expression levels of Adamts1 , 4 , 8 , 15 , and 20 in the lungs of WT C57 . BL/6 and Adamts5-/- mice ( Fig 1G ) . However , increased expression of the Adamts9 versicanase was observed in Adamts5-/- mice when compared to WT controls , although this was not statistically significant ( p = 0 . 067 ) . To investigate the role of ADAMTS5 in influenza virus infection , we initially examined in vivo weight loss and lung virus replication kinetics following infection . Influenza virus titres normally peak 3 d post infection ( p . i . ) , and virus is cleared by day 7–10 p . i . [23] . We intranasally infected Adamts5-/- mice and C57 . BL/6 controls with 104 pfu/mouse-adapted X31 ( H3N2 ) influenza virus , and observed enhanced weight loss ( p < 0 . 05 on day 8 p . i . ) in Adamts5-/- mice across the experimental infection period when compared to C57 . BL/6 controls ( Fig 2A ) . At the peak of viremia ( day 3 p . i . ) , Adamts5-/- mice showed higher virus titres in the lung when compared to C57 . BL/6 controls by both plaque assay ( Fig 2B ) and QRT-PCR analysis of influenza virus Matrix-1 gene expression ( Fig 2C ) . Similar observations were recorded at 7 d p . i . ( Fig 2D and 2E ) , suggesting Adamts5-/- mice do not clear influenza virus as effectively as C57 . BL/6 controls . Additionally , a qPCR time-course analysis of ADAMTS enzyme expression in the lungs of influenza-infected WT and Adamts5-/- mice was also performed and shown in S4 Fig for general reference . As there was evidence of delayed viral clearance in Adamts5-/- knockout mice , we set out to determine if this observation was associated with perturbations in cellular immunity . We initially enumerated total CD4+ and CD8+ T cell numbers in the lungs and spleens of Adamts5-/- and C57 . BL/6 control mice to determine if the delay in viral clearance observed in Fig 2 correlated with functional differences in T cell populations . We observed decreased numbers of total CD4+ and CD8+ T cells in the spleen and lung of Adamts5-/- mice at days 7 and 10 following infection ( Figs 3A , 3B , 4A and 4B ) . In the C57 . BL/6 mouse model of influenza virus infection , influenza-specific CD8+ T cell immunity is first detected 4–5 d p . i . and peaks at day 10 p . i . [23 , 24] . In our study , influenza-specific CD8+ T cells were enumerated using tetrameric complexes that recognised the immunodominant DbNP366-372 ( ASNENMETM ) or DbPA224-233 ( SSLENFRAYV ) CD8+ T cell epitopes [25] . Fewer DbNP366-372 and DbPA224-233 CD8+ T cells were detected in the spleen and lung of Adamts5-/- mice at both day 7 and 10 p . i . when compared to C57 . BL/6 controls ( Figs 3C , 3D , 4C and 4D ) . The intracellular cytokine staining ( ICS ) assay was then used to assess the functionality of the CD8+ T cell response in the spleen and lung at multiple time points following infection . Adamts5-/- mice had fewer DbNP366-372+IFNγ+CD8+ and DbPA224-233+IFNγ+CD8+ T cells in the spleen and lung at days 7 and 10 p . i . ( Figs 3E , 3F , 4E and 4F ) . The lack of influenza-specific CD8+ T cells in the periphery of Adamts5-/- mice suggested possible accumulation of cells in the draining lymph nodes of the lung , such as the MLN . Careful analysis revealed increased numbers of total CD4+ and CD8+ T cells and DbNP366-372+ and DbPA224-233+ tetramer+ CD8+ T cells in the pooled MLN of Adamts5 -/- mice when compared to controls 7 and 10 d p . i . ( Fig 5A–5D ) . These observations were further validated using ICS of pooled MLN ( Fig 5E and 5F ) and suggested that the ECM remodelling by ADAMTS5 contributes to migration of effector T cells from the MLN to peripheral tissue . ADAMTS5 is an important enzyme involved in the remodelling of the ECM , and its actions are thought to contribute to the trafficking of key immune cell populations , such as macrophages [26] . While the hyalectans ( aggrecan , brevican , and neurocan ) are tissue specific , V0/V1 versican isoforms are widely expressed throughout the body [4 , 27] . We therefore reasoned that the lack of ADAMTS5 enzymatic activity in the MLN of Adamts5-/- mice would result in an accumulation of the versican substrate . It is also important to note here that the presence of versican has previously been associated with inhibition of lymphocyte migration [22 , 28] and may result in T cell accumulation in the MLN ( Fig 5A ) . Moreover , versican upregulation has also been associated with inflammatory stimuli [4 , 29] . As such , versican and versican cleavage fragment ( versikine ) expression in the MLN of influenza virus infected Adamts5-/- mice was assessed by immunohistochemistry . MLN tissue from infected animals was paraffin embedded , sectioned , and stained with anti-GAGβ ( V0/V1 versican side chains ) and anti-DPEAAE ( versikine ) antibodies to define expression . Confocal microscopy of sections revealed increased levels of versican in the MLN of Adamts5-/- mice when compared to C57 . BL/6 controls ( Fig 6A and 6B ) . Our data also suggested that versican was expressed within the T cell areas of the lymph node following immunohistochemical staining as T cells and versican co-localised in the MLN ( S5 Fig ) . In support of this data , decreased versikine was observed in the MLN of Adamts5-/- when compared to C57 . BL/6 controls ( Fig 6C and 6D ) . Adamts5-/- mice used in these studies were generated via the insertion of a Lac-Z allele into the catalytic site of the ADAMTS5 gene , and so Lac-Z expression can be used as a surrogate reporter for ADAMTS5 expression . X-gal staining of the MLN of Adamts5-/- mice showed expression throughout the organ ( Fig 6E ) . Additionally , we stained influenza-infected lung with antibodies specific for versican and versikine . Bronchioles ( S6A Fig ) and arteries ( S6B Fig ) in the lung were stained with DAPI and anti-Gagβ ( versican ) to compare versican expression and cleavage to that found in the MLN . No differences were observed in staining between C57 . BL/6 and Adamts5-/- mice ( S6 Fig ) . This suggests that the absence of ADAMTS5 in the MLN prevents efficient cleavage of versican and results in accumulation of T cells in the draining lymph node . In contrast , the absence of ADAMTS5 in the lung does not influence versican cleavage . Given the accumulation of versican in the MLN of Adamts5-/- influenza virus infected mice ( Fig 6 ) , we examined if the absence of ECM remodelling was linked to impaired CD8+ T cell migration . Ex vivo transwell assays were employed to assess migration of CD8+ T cells as previously described [30 , 31] . The surface of the upper transwell chamber was initially coated with versican-enriched conditioned media from transfected HEK293T cells [16 , 32] , prior to the addition of a T cell chemoattractant ( CXCL12 ) to the lower transwell chamber to encourage T cell migration . Enriched CD8+ T cells from influenza virus infected Adamts5-/- or C57 . BL/6 mice were then added to the upper chamber of the transwell and migration assessed . The data clearly demonstrates ( p < 0 . 05 ) that CD8+ T cells isolated from Adamts5-/- influenza virus infected mice show impaired migratory capacity through a versican-overlay transwell system when compared to C57 . BL/6 controls expressing functional ADAMTS5 enzyme ( Fig 7A ) . Furthermore , the introduction of exogenous versicanase ( ADAMTS5/ADAMTS15 conditioned media from HEK293T cells ) resulted in improved migration of CD8+ T cells through the versican overlay ( Fig 7A ) . We also assessed if we could replicate these observations using human T cells . In these assays , we assessed the migration of a human immortalised CD4+ T cell line ( JURKAT cells ) following inhibition of ADAMTS5 with antibody . The data demonstrates that ADAMTS5-inhibited JURKAT cells do not migrate as efficiently as their uninhibited counterparts ( S7C Fig ) . We also determined that CD8+ T cells from C57 . BL/6 mice express ADAMTS5 using qRT-PCR ( Fig 7B ) . The expression of other versicanases ( ADAMTS1 , 4 , 9 , 15 ) in CD8+ T cells extracted from C57 . BL/6 and Adamts5-/- mice was also assessed and showed increased expression of ADAMTS4 , 9 , and 15 following influenza virus infection ( S8 Fig ) . We also confirmed the expression of ADAMTS versicanases in JURKAT cells and peripheral blood lymphocytes ( S7A and S7B Fig ) and found that ADAMTS5 and 15 are highly expressed in both cell types . Additionally , we assessed the ability of CD8+ T cells from C57 . BL/6 and Adamts5-/- mice to cleave versican . Our data indicates that versican was not cleaved as effectively by CD8+ T cells from Adamts5-/- mice ( S9 Fig ) . These results indicate that ADAMTS5 is indeed expressed by CD8+ T cells and establishes that ADAMTS5-mediated cleavage of versican is necessary for T cell migration . Given the accumulation of versican observed in the MLN of Adamts5-/- mice ( Fig 6B ) and inhibition of CD8+ T cell migration in transwell assays ( Fig 7A ) , we wanted to assess if reducing versican expression would result in a rescue of T cell function . To achieve reduced versican levels , we crossed Adamts5-/- mice with versican reduced mice ( Vcan+/hdf ) . It should be noted that disruption of both versican alleles is embryonic lethal [33] . We infected C57 . BL/6 , Adamts5-/-Vcan+/hdf ( versican reduced ) , and Adamts5-/-Vcan+/+ mice with 104 pfu X31 ( H3N2 ) influenza virus and assessed CD8+ T cell immunity in the spleen and MLN at day 10 p . i . Adamts5-/-Vcan+/hdf ( versican reduced ) mice showed increased numbers of total CD8+ T cells in the spleen at day 10 following infection when compared to the Adamts5-/-Vcan+/+ control group ( Fig 8A ) . Increased influenza-specific DbNP366-372 and DbPA224-233 CD8+ T cell numbers were also detected by tetramer staining in the spleen of Adamts5-/-Vcan+/hdf versican reduced ) mice at day 10 p . i . when compared to Adamts5-/-Vcan+/+ controls ( Fig 8B ) . This was also reflected in functional assays where Adamts5-/-Vcan+/hdf ( versican reduced ) mice showed improved IFNγ production for both T cell specificities ( Fig 8C ) . We also assessed CD8+ T cell numbers in MLN of influenza-infected Adamts5-/-Vcan+/hdf ( versican reduced ) mice to determine resumption of egress . Careful analysis revealed comparable numbers of total CD8+ T cells and influenza-specific CD8+NP366-372+ and DbPA224-233+ ( by tetramer and ICS ) in the MLN of Adamts5-/-Vcan+/hdf ( versican reduced ) and C57 . BL6 control mice ( Fig 8D–8F ) . We also assessed influenza-specific immunity in influenza virus-infected Vcan+/hdf and C57 . BL/6 mice and found that NP366-372-specific CD8+ T cells were increased in the lungs of Vcan+/hdf mice ( S10 Fig ) . Concurrently , NP366-372-specific CD8+ T cell numbers were decreased in the MLN of Vcan+/hdf mice when compared to C57 . BL/6 controls ( S10 Fig ) . These important and highly novel findings highlight the importance of the ADAMTS5 enzyme-versican substrate interaction as a key process in the regulation of virus-specific immunity .
Increasing evidence in the literature highlights the importance of zinc-dependent metzincins in the regulation of immune responses . MMPs and ADAMs have been strongly associated with neutrophil , macrophage , dendritic cell , and lymphocyte migration [6 , 34–36] . Here , we show for the first time that ADAMTS5 , a member of the ADAMTS family , has a key role in influenza virus-specific immunity through a mechanism that involves ECM remodelling . Adamts5-/- mice had higher peak viremias and showed signs of delayed influenza virus clearance when compared to C57 . BL/6 controls ( Fig 2 ) . The defect contributed to fewer total CD4+ and CD8+ T cells in the periphery and an accumulation of these cells in the MLN ( Figs 3–5 ) . Results from our transwell migration assays and Adamts5-/-Vcan+/hdf mouse studies further support our hypothesis that the absence of ADAMTS5 reduces ECM proteoglycan cleavage and impedes ( but does not entirely block ) the movement of influenza-specific lymphocytes to effector sites , such as the lung or to the periphery ( Figs 7 and 8 ) . Migration of CD8+ T cells from draining lymph nodes to the periphery is critically important for the establishment of full effector function and eventual clearance of pathogens , such as influenza virus . Our research suggests that the lack of ADAMTS5 enzymatic activity in influenza virus-infected Adamts5-/- mice results in accumulation of the large extracellular proteoglycan V0/V1 versican ( Fig 6A ) . Increased V0/V1 versican expression has also been noted in the developing limb [11] and heart valves [17] of Adamts5-/- mice . We believe that the accumulation in the MLN shown in this current study prevents lymphocyte trafficking and results in the exacerbation of disease following influenza virus infection . Furthermore , corroborating evidence by others in the field demonstrates that an epitope in the N-terminal globular domain of versican promoted CD4+ T cell migration and lymphocyte rolling [22] . Additionally , versican overexpression was associated with decreased infiltration of CD8+ T cells in stromal compartments of cervical cancer [28] . Further studies have suggested that the related zinc-dependent metzincins , the MMPs , are essential for immune cell trafficking . Like ADAMTs enzymes , MMPs contain a catalytic domain that utilises a conserved zinc binding sequence ( HEXXHXXXGXX ) for catalysing reactions [8] and have a broad range of cleavage substrates . This is in contrast to the highly specific cleavage moieties associated with ADAMTS enzyme activity . It is therefore not surprising that the MMPs have been identified in a vast number of physiological processes [37] . For example , MMP9 , a highly characterised extracellular metalloproteinase associated with immune cell trafficking , has been detected in neutrophils , macrophages , dendritic cells , and T cells [31 , 38] . MMP9 and related MMPs ( MMP2 , 7 , 10 , 14 ) have been shown to degrade ECM roadblocks associated with immune cell trafficking in a similar fashion to what we have proposed in our study . Specifically , MMP9 and MMP2 expressing Th1 T cells demonstrate increased motility through collagen in a transwell migration assay [39] . Supporting in vivo data has suggested that a blockade of the MMP9 and MMP2 signalling pathway ( Wnt ) is associated with impaired T cell extravasation in an experimentally induced skin inflammation model [6] . Moreover , lipopolysaccharide-stimulated macrophages isolated from Mmp10-/- mice fail to migrate efficiently in transwell studies when compared to C57 . BL/6 control macrophages [5] . In these studies , ECM components , such as collagen and elastin , inhibited immune cell migration . Collagen and elastin form key ECM components of basement membranes , and so dampened MMP activity would , in turn , lead to accumulation of these components and inhibition of immune cell migration and tissue infiltration . In contrast to the abovementioned studies , versican , a key ADAMTS5 substrate , is widely expressed in tissues and is not predominately associated with the basement membrane ( as are MMP substrates ) . ECM components , such as versican , provide a “sticky” surface for T cell adherence . Versican GAG chains interact directly or indirectly with molecules on the T cell surface , such as CD62L and CD44 [20 , 21 , 40] , both of which are known to contribute to T cell trafficking . Increased levels of versican , such as those observed in Adamts5-/- mice , may therefore prevent T cell interaction with the ECM , leading to perturbations in T cell migration . Thus , we propose that cleavage and removal of versican blockades via the action of proteoglycanases , such as ADAMTS5 , is required for efficient T cell interaction with the ECM to encourage migration to effector sites in the periphery and for the subsequent resolution of infection ( Fig 8G ) . Our hypothesis is further strengthened by data demonstrating that reduction of versican restores normal T cell function in Adamts5-/-Vcan+/hdf mice ( Fig 8 ) . It is important to note that the migration of influenza-specific CD8+ T cells was not fully impeded in our experimental model . ADAMTS5 may therefore be working in concert with other metalloproteinases to facilitate T cell migration . The proteoglycanases , ADAMTS1 , 4 , 8 , 9 , 15 , and 20 , as well as MMP1 , 2 , 3 , 7 , and 9 , are capable of producing versican fragments in a similar fashion to ADAMTS5 [9 , 16 , 32 , 41 , 42] . It is reasonable to suspect that there is redundancy built into the trafficking system , as related family members , such as ADAMTS9 ( Fig 1F ) , may be providing some compensatory function in the absence of ADAMTS5 , allowing some T cell migration ( although highly restricted ) to occur into the periphery ( Figs 3 and 4 ) . The cooperative requirement of versican cleavage by ADAMTS9 with ADAMTS5 has been observed in embryogenesis , and so its presence in regulation of immune cell migration cannot be discounted [11 , 43–45] . Furthermore , the transwell migration assay indicated that multiple ADAMTS enzymes can mediate T cell migration ( Fig 7A ) . Further studies with related family members are required to ascertain their specific contribution to influenza-specific immunity . Our findings would suggest that overexpression of ADAMTS5 or reduced versican expression could restore and improve immunity . Evidence from MMP9-related influenza studies suggests that a more circumspect approach may be required . MMP9 has been shown to be involved in the repair of lung tissue following influenza virus infection and can prevent bleomycin-mediated lung fibrosis by remodelling the ECM and degrading cytokines [46] . However , MMP9 overactivity in MMP9 transgenic mice has been associated with excessive neutrophil infiltration following influenza virus infection , leading to poor survival [38] . An inhibitor targeting ADAMTS5 has already undergone clinical trial as an osteoarthritic therapeutic ( https://clinicaltrials . gov/show/NCT00454298 ) . Administration of ADAMTS5 inhibitors for osteoarthritis may therefore be contraindicated in elderly patients , as they are more susceptible to influenza infection . Careful dissection and characterisation of metalloproteinase expression may therefore be required to determine the contribution of these enzymes to overall tissue repair and immunity . In summary , our data show that the ADAMTS5 ECM enzyme activity is critically important for lymphocyte trafficking following influenza virus infection ( especially CD8+ T cell immunity ) . In conclusion , interventions that facilitate increased ADAMTS5 expression used in conjunction with current approved antivirals and/or vaccines offer a new approach for combating unexpected emerging influenza virus pandemic threats .
All animal experiments were approved by the Deakin University Animal Ethics Committee ( under G38-2013 and G34-2015 ) and were conducted in compliance with the guidelines of the National Health and Medical Research Council ( NHMRC ) of Australia on the care and use of animals for scientific purposes . Six-to-twelve-week old Adamts5 ( B6 . 129P2-Adamts5tm1Dgen/J ) and Vcan ( Vcan+/hdf ) male and female mice ( Jackson Laboratory ) , backcrossed eight times on a C57 . BL/6 background , were bred at the School of Medicine , Deakin University [11 , 47] . Adamts5-/- mice and Vcan+/hdf mice were crossed for three generations to generate Adamts5-/-Vcan+/hdf and Adamts5-/-Vcan+/+ mice . The animals were housed at 20°C on a 12-h day/night cycle in sterilised cages ( Techniplast ) and provided food and water ad libitum . Mice were sex and age matched for experiments . C57 . BL/6 ( WT; wildtype ) mice were purchased from the Animal Resource Centre , Perth , Australia . Eight–to-ten-week old male or female naïve C57 . BL/6 , Adamts5-/- , Adamts5-/-Vcan+/hdf and Vcanhdf/+ mice were anaesthetized by isoflurane inhalation and infected intranasally ( i . n . ) with 104 plaque forming units ( pfu ) X-31 ( H3N2 ) in a 30 μl volume , diluted in PBS . All mice were weighed throughout the course of infection and euthanized at days 3 , 7 or 10p . i . We also detail experiments using Adamts5-/- and WT littermate controls in S11 Fig . Spleen , lung , and MLN samples were aseptically removed from mice at various time-points following influenza virus infection . Lungs were digested with collagenase ( Sigma ) , whilst spleens and MLNs were disrupted with glass microscope slides to generate single-cell suspensions . Spleen cell suspensions were enriched for T cells following B cell panning on plates coated with goat anti-mouse IgG and IgM antibody ( Jackson ImmunoResearch , West Grove , PA ) for 1 h at 37°C . Lungs from influenza virus-infected mice were removed and homogenised in RPMI medium 1640 ( Life Technologies ) , containing 40 μg/ml gentamycin and 10 , 000 μg/ml penicillin/streptomycin . Viral titres ( pfu/ml ) were determined by plaque assay on Madin-Darby Canine Kidney ( MDCK ) cell monolayers , as previously described [48] . Spleen , lung , and MLN single cell suspensions from naïve and infected mice were stained with conjugated monoclonal antibodies targeting murine CD3 , CD8 , CD4 , CD314 , CD11c , CD11b , MHCII , F4/80 , and B220 for 30 min at 4°C and analysed on a BD-LSRII ( BD-USA ) . The following antibodies were purchased from BD Pharmingen: CD8α-PERCP and CD8α-FITC ( 53–6 . 7 ) , CD4α-FITC ( RM4-4 ) , CD3-APC ( 145-2C11 ) , F4 . 80-PE ( T45-2342 ) CD314-PE ( CX5 ) , CD11c-APC ( HL3 ) , MHCII-PERCP ( M5/114 ) , CD11b-FITC ( M1/70 ) , and CD45r-PERCP ( RAB3-6B2 ) . Results were analysed using Flowjo software version 7 ( Flowjo; Ashland , USA ) . CD8+ T lymphocyte populations from the spleen , lung , and MLN were enumerated following staining with fluorescently labelled tetrameric complexes directed against the two immunodominant influenza-specific CD8+ T cells epitopes ( DbNP366-372-PE or DbPA224-233-PE ) for 1 h at room temperatures in 0 . 1% BSA/ 0 . 02% sodium azide in PBS , as previously described [49 , 50] . Cells were then washed and stained with anti-CD8α-FITC and analysed on a BD-LSRII . DbNP366-372 and DbPA224-233 CD8+ T function was then assessed using ICS . Briefly , cells were cultured for 5 h in 96-well round bottom plates with influenza NP366-372 ( ASNENMETM ) or PA224-233 ( SSLENFRAYV ) peptide in the presence of Golgi-plug ( BD , USA ) and IL-2 , permeabilised and stained for the presence of CD8α and IFNγ ( BD , USA ) as previously described [51] . Data was acquired on a BD-LSRII and analysed using Flowjo software . RNA was extracted from CD8+ T cells in the lung and spleen of C57 . BL/6 and Adamts5-/- mice , immortalised CD4+ T cells ( JURKAT cells ) , and human peripheral blood lymphocytes as per the manufacturer’s instructions using an RNeasy kit ( Qiagen ) . One microgram of total RNA was reverse transcribed using the Superscript III cDNA synthesis kit ( LifeTech ) . QRT-PCR was undertaken on cDNA using iQ SYBR Green Super mix ( Bio-Rad ) and oligonucleotide primers for ADAMTS proteoglycanases 1 , 4 , 5 , 8 , 9 , 15 , and 20 with the following qRT-PCR parameters: 94°C for 2 min followed by 40 cycles of 94°C for 15 s and 58°C for 1 min . Influenza M1 cDNA levels were measured using probes as previously described ( Life Technologies ) [52] . The quant-iT OliGreen ssDNA Assay Kit ( Invitrogen ) was used to quantitate total cDNA input following manufacturer’s instructions . Changes in mRNA levels in lungs were calculated using 2-ΔΔCt method [16] . Human embryonic kidney ( HEK ) 293T cells ( ATCC , Manassas , VA ) were grown in DMEM ( Gibco ) containing 10% FCS in 5% CO2 at 37°C . Cells were transfected using Lipofectamine 2000 ( Invitrogen ) with pcDNA3 . 1MycHisA+ ( Invitrogen ) constructs encoding mouse full length V1 versican ( kindly provided by Professor Dieter Zimmerman ) , full-length ADAMTS5 , catalytically inactive ADAMTS5 ( ADAMTS5EA ) , full-length ADAMTS15 and catalytically inactive ADAMTS15 ( ADAMTS15EA ) , and empty vector control according to the manufacturer’s instructions . Serum-free conditioned medium was collected at 48 h post transfection as previously described , and expression was detected by western blotting using an anti-GAG antibody ( Merck Millipore ) and anti-myc ( Merck Millipore ) antibody for transfection with ADAMTS constructs . CD8+ T cells from influenza virus infected Adamts5-/- and C57 . BL/6 mice were incubated with HEK293T conditioned media containing full-length V1 versican and IL2 ( 20 U/mL ) for 16 h at 37°C . Cleavage was detected by western blotting using an anti-GAG ( versican ) antibody ( Merck Millipore ) and anti-DPEAAE ( versikine ) antibody ( Abcam ) . Immunoblots were analysed using ImageJ software . Migration assays were performed in 12-well chamber inserts ( 5 μM ) ( Corning Inc . ) as previously described [39] . Inserts were coated with V1 versican conditioned media [16 , 32] , and recombinant mouse CXCL12 ( 10ng/ml ) ( R&D ) was added to the lower chamber of the transwell to promote migration . Adamts5-/- or C57 . BL/6 magnetically-enriched ( Stemcell ) CD8+ T spleen cells ( 105 ) were loaded to the upper chamber in migration media containing ADAMTS5FL , ADAMTS5EA , ADAMTS15FL , or ADAMTS15EA conditioned media from tramsfected HEK293T cells or serum-free migration media . Additionally , ADAMTS5 antibody ( 1000 ng/mL ) was added to the upper chamber of JURKAT cells ( 105 ) in the versican transwell chamber and migration assessed . These cells were allowed to migrate for 4 h at 37°C at 5% CO2 . Following removal of non-migrating cells in the upper chamber , the transwell membrane was stained with haematoxylin ( Sigma-Aldrich ) to determine the number of migrating cells . MLNs from infected C57 . BL/6 and Adamts5-/- mice were removed at day 10p . i . and fixed in 4% paraformaldehyde prepared in β-gal wash buffer ( 0 . 1 M phosphate buffer pH 7 . 4 , 2 mM MgCl2 , 0 . 02% NP-40 , 0 . 01% Na deoxycholate ) . MLNs were then washed in this buffer and incubated overnight in β-gal staining solution ( 5 mM potassium ferricyanide , 5 mM potassium ferrocyanide , 1 mg/ml X-gal in DMSO ) at 37°C . The next day , MLNs were rinsed in wash buffer and fixed in 4% paraformaldehyde ( prepared in wash buffer ) at 4°C overnight . After a brief rinse in wash buffer , tissues were imaged then embedded in paraffin for sectioning and eosin staining . MLNs from infected C57 . BL/6 and Adamts5-/- mice were fixed in 4% paraformaldehyde at 4°C overnight and then paraffin-embedded and sectioned . Seven micrometre sections were stained with anti-versican ( Merck-Millipore ) or anti-DPEEAE ( versican cleavage fragment ) ( Thermo-Fischer ) antibodies at 4°C overnight . The following day , tissues were washed in 10% Triton-X ( Astral ) to remove excess antibody and then incubated with Alexa-fluor594 goat-anti-mouse antibody ( Life-technologies ) . Sections were then washed in 10% X Triton-X ( 3 x 10 min ) and stained with DAPI ( Thermo-Fischer ) . Slides were then viewed under a Leica SP5 confocal microscope at 400 x magnification . As data were normally distributed , they are presented as grouped data expressed as mean ± standard deviation ( SD ) ; n represents the number of mice . Statistical differences between two groups were analysed by Student's t test . Statistical differences between more than two groups were determined by two-way analysis of variance ( ANOVA ) , followed by a Bonferroni multiple-comparison test . All statistical analyses were performed using GraphPad Prism 5 for Windows . In all cases , probability levels less than 0 . 05 ( *p < 0 . 05 ) were indicative of statistical significance .
|
Movement of immune cells is critical for effective clearance of pathogens . The response to influenza virus infection requires immune cell trafficking between the lung , mediastinal lymph node and other peripheral lymphoid organs such as the spleen . We set out to assess the contribution of a specific extracellular matrix enzyme , ADAMTS5 , to migration of lymphocytes and overall pathogenesis following infection . In our studies , we demonstrate that mice lacking Adamts5 have fewer influenza-specific lymphocytes in the lung and spleen following infection . These observations correlated with an accumulation of influenza-specific lymphocytes in the mediastinal lymph node and increased virus titres . This work suggests that ADAMTS5 is necessary for immune cell migration to the periphery , where lymphocyte function is required to fight infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"blood",
"cells",
"cell",
"motility",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"spleen",
"pathogens",
"immunology",
"cell-mediated",
"immunity",
"microbiology",
"orthomyxoviruses",
"viruses",
"developmental",
"biology",
"rna",
"viruses",
"cytotoxic",
"t",
"cells",
"infectious",
"disease",
"control",
"infectious",
"diseases",
"white",
"blood",
"cells",
"animal",
"cells",
"medical",
"microbiology",
"t",
"cells",
"microbial",
"pathogens",
"cell",
"biology",
"influenza",
"viruses",
"immunity",
"viral",
"pathogens",
"physiology",
"cell",
"migration",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"organisms"
] |
2016
|
ADAMTS5 Is a Critical Regulator of Virus-Specific T Cell Immunity
|
The sea lamprey ( Petromyzon marinus ) represents one of the few vertebrate species known to undergo large-scale programmatic elimination of genomic DNA over the course of its normal development . Programmed genome rearrangements ( PGRs ) result in the reproducible loss of ~20% of the genome from somatic cell lineages during early embryogenesis . Studies of PGR hold the potential to provide novel insights related to the maintenance of genome stability during the cell cycle and coordination between mechanisms responsible for the accurate distribution of chromosomes into daughter cells , yet little is known regarding the mechanistic basis or cellular context of PGR in this or any other vertebrate lineage . Here we identify epigenetic silencing events that are associated with the programmed elimination of DNA and describe the spatiotemporal dynamics of PGR during lamprey embryogenesis . In situ analyses reveal that the earliest DNA methylation ( and to some extent H3K9 trimethylation ) events are limited to specific extranuclear structures ( micronuclei ) containing eliminated DNA . During early embryogenesis a majority of micronuclei ( ~60% ) show strong enrichment for repressive chromatin modifications ( H3K9me3 and 5meC ) . These analyses also led to the discovery that eliminated DNA is packaged into chromatin that does not migrate with somatically retained chromosomes during anaphase , a condition that is superficially similar to lagging chromosomes observed in some cancer subtypes . Closer examination of “lagging” chromatin revealed distributions of repetitive elements , cytoskeletal contacts and chromatin contacts that provide new insights into the cellular mechanisms underlying the programmed loss of these segments . Our analyses provide additional perspective on the cellular and molecular context of PGR , identify new structures associated with elimination of DNA and reveal that PGR is completed over the course of several successive cell divisions .
The sea lamprey ( Petromyzon marinus ) represents one of the few vertebrate species known to undergo large-scale programmatic elimination of genomic DNA over the course of its normal development [1–4] . Programmed genome rearrangements ( PGRs ) result in the reproducible loss of ~20% of the genome from somatic cell lineages and a reduction of chromosome number from ~198 to ~164 ( 2N ) [4–6] . Previous studies have shown that DNA is physically eliminated during the transition between gastrula and blastula stages: between the second and third day of development [4] . Given that most aspects of lamprey’s developmental and cellular biology are conserved with other vertebrates [7–10] , PGR holds the potential to provide novel insights related to maintenance of genome stability and interactions between various cellular mechanisms responsible for the proper segregation of chromosomes . Lampreys are by no means the only organisms that undergo large-scale programmed rearrangement of their genomes . Organisms known to undergo PGR include diverse protozoan , invertebrate and vertebrate taxa , and the mechanisms underlying PGR are thought to be similarly diverse [11–19] . Studies of these independent acquisitions have revealed common themes that speak to the underlying logic of PGR and its integration with other epigenetic silencing pathways [11–14] . In many taxa PGR is known to occur early in development and results in the targeted elimination of specific genomic segments from essentially all somatic cell lineages , with targeted segments being retained exclusively by the germline . Studies in lamprey and the nematode Ascaris suum have shown that eliminated DNA encodes genes that are expressed in mature gonads and embryonic germ cells [6 , 20] , supporting the interpretation that PGR likely serves as an irreversible mechanism of silencing genes within somatic cell lineages . Studies performed on diverse taxa suggest that PGR-mediated silencing may often interact cooperatively with other silencing pathways . In the ciliates both DNA methylation/hydroxymethylation and methylation of histone H3 at lysine 9 ( H3K9me ) are associated with programmed elimination [11 , 21] . In sciarid flies embryonic elimination of the paternal X chromosome is associated with retention of H3S10 hyperphosphorylation ( H3S10P ) during late anaphase , which may contribute to silencing by preventing decondensation and access to H3K9 by methyltransferases [12 , 13] . Similarly , in zebra finch a single germline-restricted chromosome is heavily marked by both trimethylated H3K9 ( H3K9me3 ) and acetylated H4K16 in meiotic testes ( the chromosome is eliminated at the end of male meiosis and only transmitted by oocytes , although embryonic elimination has not been directly observed ) [14] . Little is known regarding the mechanistic basis or cellular context of PGR in any vertebrate lineage . Given the high fecundity of lampreys and the fact that fertilization and all stages of embryonic development occur externally , lamprey provides a powerful system for observing and manipulating cells during the process of PGR . Here we describe epigenetic correlates of PGR and the spatiotemporal dynamics of DNA elimination in lamprey . In situ analyses revealed that the earliest DNA methylation events target specific extranuclear structures ( micronuclei ) that contain DNA eliminated by PGR . The spatiotemporal resolution of these analyses also permitted the discovery of other reproducible subcellular features that are associated with the differential segregation of retained vs . eliminated DNA and the packaging of eliminated DNA into micronuclei . Specifically , eliminated DNA appears to be packaged into chromatin that does not migrate with somatically retained chromosomes and is superficially similar to lagging chromosomes that are observed in some cancer subtypes [22–24] . Closer examination of “lagging” chromatin reveals distributions of repetitive elements , cytoskeletal contacts and apparent chromatin contacts that provide new insights into the cellular mechanisms underlying the programmed loss of these segments .
Programmed DNA elimination is sparsely distributed across the tree of life and likely arose several times over metazoan evolution [19] yet in several species programmed elimination of DNA has been shown to act cooperatively with other , more conserved , epigenetic silencing pathways [11–14] . To investigate possible interactions between PGR and early gene silencing events , we applied indirect immunofluorescence labeling using antibodies against 5-methylcytosine ( 5meC ) , histone 3 trimethylated at lysine 9 ( H3K9me3 ) and histone 3 trimethylated at lysine 27 ( H3K27me3 ) to characterize the distribution of these modifications during early embryogenesis . In general these repressive modifications were essentially absent at the earliest developmental time points and increased in abundance during the first week of development . Similar patterns have been observed for several vertebrate and invertebrate species , reflecting reprogramming events that are involved in the initial establishment of pluripotency following fertilization ( i . e . global demethylation ) and the subsequent onset of zygotic genome activation [25 , 26] . However , the subcellular localization of two modifications ( 5meC and H3K9me3 ) deviated from the typical pattern that has been described for other taxa . During the first two days of development , days post fertilization ( dpf ) 5meC and H3K9me3 immunofluorescence localized almost exclusively to DAPI-positive extranuclear structures ( micronuclei–MNi , Fig 1A ) . To more thoroughly test whether micronuclei are associated with the elimination of DNA via PGR , we performed in situ hybridization with the germline-enriched repetitive element Germ1 . This sequence is highly abundant within the germline and only localizes to two somatically retained chromosomes [6] . These analyses revealed that a majority of MNi , though not all , contain the Germ1 repeat , consistent with the interpretation that these micronuclei contain material destined for elimination from somatic lineages via PGR ( Fig 1C and 1D , S1 Table ) . Two-color immunolabeling of 5MeC and H3K9me3 revealed that these heterochromatic marks occur in largely non-overlapping sets of MNi and vary in prevalence over the first several days of embryogenesis ( Fig 1A and 1B , S2 Table ) . At 1 dpf , 5MeC was essentially absent from both nuclei and micronuclei , whereas ~60% of micronuclei showed strong immunolabeling for H3K9me3 . At 1 . 5 dpf the proportion of H3K9me3 positive MNi remained relatively stable , and the first 5MeC positive MNi were observed , albeit at a relatively low frequency ( ~7% of MNi ) . At 2 dpf the proportion of 5MeC positive MNi significantly increased and the proportion of H3K9me3 positive MNi significantly decreased , with localization of H3K9me3 transitioning to the primary nucleus ( Fig 1A , S1D Fig , S2 Table ) . Similar patterns of 5MeC and H3K9me3 immunolabeling were observed at 2 . 5 and 3 dpf . Coordinate with changes in the distribution of epigenetic modifications , the abundance of MNi also changed dynamically over the first week of embryogenesis , rising sharply at 1 . 5 dpf , peaking at 2dpf and approaching zero by 7 dpf ( Fig 1B and 1D ) . The developmental profile and subcellular localization of H3K9me3 and 5MeC marks suggest that these epigenetic modifications may mark MNi in different phases of elimination . Micronuclei with elevated levels of H3K9me3 were predominantly located in close proximity to the primary nucleus , whereas MNi with elevated levels of 5MeC were typically located at more distal sites ( Fig 1A , S1C and S1D Fig ) . The timing and location of MNi with chromatin repressive marks suggest that H3K9 tri-methylation marks recently formed MNi and that 5MeC may mark older MNi . It seems plausible that DNA methylation might act to ensure transcriptional silencing of material in MNi prior to its complete elimination . Notably , similar interactions between H3K9 and DNA methylation have been observed during heterochromatin formation and chromatin-remodeling in organisms that do not undergo PGR ( fungi [27] , plants [28] , and mammals [29] ) . In comparison to 5MeC , the repressive histone mark H3K9me3 showed a somewhat more complex pattern over the course of the cell cycle . This mark localizes to condensed chromosomes during metaphase and persists through telophase/cytokinesis but is essentially absent from interphase nuclei ( S1A–S1C Fig , Fig 1A ) . The presence of H3K9me3 in newly formed micronuclei suggests that micronuclear H3K9me3 marks are remodeled more slowly than their primary nuclear counterparts following M phase . Cell cycle-dependent changes in histone H3 methylation have been reported for mammalian systems , which do not undergo PGR , and appear to be necessary for proper mitotic segregation [30–32] . Moreover , studies in both mammalian and non-mammalian systems have shown that H3K9 methylation is critical for anchoring heterochromatin to the nuclear envelope [33] . Immunolabeling of nuclear envelope markers lamin B1 , and nuclear pore o-linked glycoprotein in rearranging embryos reveals that both of these proteins localize to interphase nuclei , but are absent from micronuclei ( S2A and S2B Fig ) . In human , depletion of LMN-B1 and pore complex proteins are associated with nuclear membrane defects in the context of cancer [34] . Taken together , these studies indicate that retention of H3K9me3 in newly formed MNi might play functional roles in maintaining chromatin compaction , positioning eliminated chromatin , or recruiting other structural components of MNi . Our in-depth analyses of MNi and their associated chromatin modifications revealed other cellular features that appear to be associated with PGR . The most striking among these were numerous anaphases with large amounts of lagging chromatin ( Fig 2 ) . Although these lagging anaphases were often visible in sections , the spindle apparatus often spanned more than 50 micrometers in rearranging embryos . As such , wax sections rarely permitted observation of entire anaphases ( Fig 2F ) . To study detailed morphology of lagging anaphases we adapted the passive CLARITY technique ( PACT ) to whole lamprey embryos [35] . This approach increases the permeability of cells with minimal impact on the morphology of the embryos and effectively eliminates autofluorescence associated with yolk platelets ( e . g . Figs 1 and 2 ) . To complement this clearing method , we also optimized methods for DNA staining , fluorescence in situ hybridization , and β-tubulin immunolabeling of cleared lamprey embryos . Altogether , these analyses provide critical perspective on the developmental context of PGR and the dynamic behavior and packaging of eliminated DNA within rearranging cells . We were able to establish a timeline for the onset and completion of PGR by examining PACT-cleared embryos across the first several days of development , leveraging natural variation in cell division rate during the first day post fertilization . Lagging anaphases were essentially absent during the first five to six cell divisions ( e . g . in embryos with 30–60 cells ) but abundant in embryos with more than 64 cells , suggesting PGR is initiated at approximately the onset of the seventh cell division ( Fig 3 ) . Lagging anaphases were also present at similarly high abundance at 2 dpf but dramatically decreased in frequency thereafter ( S3 Table , S3A and S3B Fig ) . Notably , lagging chromosomes are observed earlier in development than MNi and peak in abundance at earlier developmental stages ( Fig 3F; S3B and S3C Fig ) . We interpret the earlier appearance of lagging anaphases relative to MNi , as indicative of eliminated material initially slated for elimination during metaphase or early anaphase , and secondarily packaged into MNi . Detailed examination of embryos at 1–3 dpf also revealed a graded series of cellular morphologies that appear to track the progression of DNA loss both within and between cell cycles . These morphological features provide additional perspective on the cellular and mechanistic details of elimination . Below we describe several salient features of eliminated chromatin , including its subcellular organization across the cell cycle and its association with cytoskeletal components . Within a cell cycle , eliminated chromatin is first identifiable as thread-like structures that are situated between groups of poleward-oriented chromosomes immediately after the metaphase/anaphase transition . As anaphase progresses , eliminated material begins to exhibit distinguishable differences in its apparent motion relative to retained chromosomes . Lagging chromatin is typically oriented parallel to the interpolar microtubules and appeared to be tightly associated with spindle filaments ( Fig 4A; S4 Fig ) . As cells enter telophase , retained sister chromatids begin to decondense and adopt lobate structures consistent with decondensation of somatic chromosomes and recruitment of nuclear envelope proteins . Notably , lagging chromatin does not appear to decondense at this same time and associates with tubulin prior to being packaged into compact MNi ( S4 Fig and S5 Fig ) . In situ hybridization with Germ1 and other repetitive sequences ( Cot1 and 2 DNA ) revealed that lagging chromatin was distributed symmetrically across the metaphase plane . Hybridization with Cot1 DNA revealed that the polar ends of migrating ( retained ) chromosomes are enriched in highly repetitive DNA ( S6 Fig ) , consistent with the interpretation that Cot1 DNA strongly hybridizes to centromeres , as has been observed for other species [36–38] . Notably , labeled Cot1 DNA also localized to the distal ends of some lagging fragments , suggesting that these segments contain active centromeres that are capable of engaging the kinetochore microtubules and ( slower ) poleward motion [36 , 37] ( Fig 4B , S1 Movie ) . Moreover , poleward-oriented regions of lagging chromatin are highly enriched in H3K9me3 ( S1B Fig ) , which is considered a hallmark of constitutive pericentromeric heterochromatin [39 , 40] . We interpret the symmetry of labeling and polar orientation of centromeric regions of lagging chromosomes as indicating that a substantial fraction of eliminated material was replicated in the previous cell cycle , packaged into sister chromatids at metaphase and drawn poleward at anaphase , albeit at a slower rate than somatically retained chromosomes . Direct confocal imaging of fluorescently stained chromosomes ( Fig 4C , S2 Movie ) and in situ hybridization of Cot2 DNA ( Fig 4D ) revealed that the equatorial ends of symmetrically stretched sister chromatids often lay in close proximity to one another throughout anaphase . These apparent contacts between sister chromatids exhibit enhanced hybridization to Cot2 DNA , suggesting the possibility that an as-yet undefined class of repetitive sequences may contribute to PGR by anchoring sister chromatids to one another during anaphase ( Fig 4D ) . Notably , Germ1 is not present at these points of contact and is generally located in regions closer to the presumptive centromeres ( Fig 4E ) . Taken together , these observations indicate that some of the eliminated material consists of entire chromosomes or large chromosomal segments and suggest that chromatin/chromatin ( or DNA/DNA ) contacts between telomeric segments of sister chromatids might contribute to the decelerated migration of these large eliminated fragments . In addition to these large and longitudinally stretched segments , we also observed globular ( presumable acentric ) conglomerates of chromatin localized to the equatorial region ( Fig 2C , see also Fig 5C ) . The presence of these conglomerates lends support to the idea that recombinational processes ( intra- or inter-chromosomal ) or DNA breakage contributes to PGR [4] . It seems plausible that these acentric fragments could be driven toward the equatorial region by the same polar ejection forces that normally act to orient chromosome arms during cell division [41] . The observation that eliminated material consists of both entire chromosomes and smaller chromosomal fragments mirrors observations from hagfish and parasitic nematodes , wherein both entire chromosomes and chromosomal fragments are lost from somatic lineages [2 , 19 , 42] . To shed further light on patterns of DNA breakage during PGR , we performed immunolabeling with an antibody to the histone variant γ-H2AX , which binds double stranded DNA breaks and recruits repair machinery [43 , 44] , and employed fluorescent TDT-mediated dUTP nick-end labeling ( TUNEL ) labeling to more generally detect DNA breaks . Although all other histone variants yielded interpretable signals , attempts to immunolabel γ-H2AX yielded no signal in embryos at 1–5 dpf . The absence of γ-H2AX immunolabeling could reflect either a paucity of double stranded breaks or failure to react with a lamprey γ-H2AX homolog . On the other hand , TUNEL labeling yielded strong and reproducible staining that was localized exclusively to MNi ( S7 Fig ) . Given evidence that MNi represent the last visible sites of eliminated DNA , it seems plausible that TUNEL labeling reflects the degradation of germline-specific DNA within MNi . Taken together these observations indicate that DNA elimination proceeds through an ordered series of events , wherein germline-specific sequences 1 ) are initially slated for elimination during early anaphase ( perhaps metaphase ) , 2 ) exhibit slower poleward movement in comparison to retained chromosomes and 3 ) condense to form MNi where they are methylated and ultimately degraded . Thus far , analyses of PGR in lamprey have revealed that patterns of gene loss are indistinguishable among diverse somatic cell lineages , which might be interpreted as supporting a simplistic model wherein all germline-specific sequences are eliminated during a single cell cycle [4 , 6] . However , in situ hybridization of intact cells with Germ1 appears to support a somewhat more complex model . As mentioned above , lamprey somatic cells possess a single pair of chromosomes that hybridize to the Germ1 probe ( S6A Fig ) , whereas Germ1 hybridizes to several additional chromosomes in germ cells and embryonic cells that have not completed PGR [4] . As such , this marker can be used to track the progression of PGR . In early cell divisions ( at 1 dpf ) anaphases were observed that contained multiple Germ1 signals interspersed among retained ( normally migrating ) chromosomes and relatively small amounts of lagging material , consistent with partial elimination of germline-specific sequences ( Fig 5A ) . Variation in the process of elimination is also apparent in later developmental stages , as some anaphases possess two somatic Germ1 signals and small amounts of Germ1-negative lagging chromatin ( Fig 5B and 5C ) . These patterns suggest that cells had undergone at least one previous cycle of DNA elimination , over which they lost all germline-specific copies of Germ1 , and were engaged in eliminating additional material at the time of fixation . The interpretation that PGR plays out over several cell cycles is further supported by the frequent observation of lagging chromatin and peripheral MNi within the same cell ( S8 Fig ) . Presumably these peripheral MNi contain material that was eliminated in the previous cell cycle ( s ) . In this context , it is also worth noting that the earliest elimination events ( 1 dpf ) appear to be associated with subcellular structures that are not observed at later stages . These appear as dense , presumably heterochromatic , structures located near the cleavage furrow with filamentous extensions oriented toward the enveloped nuclei ( Fig 4D; S9 Fig ) . In general , these morphological features seem consistent with the interpretation that some ( early ) elimination events are characterized by persistent chromatin/chromatin or DNA/DNA contacts and that many of these same segments maintain an association with spindle microtubules . While it is possible that this variation is related to the fact that PGR is occurring in cells of vastly different sizes at 1 vs . 2 dpf ( Fig 5E and 5F ) , it seems possible that the unique structures observed at 1 dpf might also reflect variation in the underlying mechanisms of PGR across early development . Our analyses underscore the fact that evolution can arrive at diverse solutions to a common problem . Multicellular organisms employ a diversity of epigenetic silencing pathways , including covalent chemical modification of DNA or histones , expression of DNA binding factors ( chromatin proteins and noncoding RNAs ) that mediate the accessibility of DNA for transcription , and expression of short RNAs that promote degradation or prevent translation of transcripts . In general , these pathways are distributed broadly across diverse eukaryotic lineages , although individual pathways are evolutionarily labile [45–50] , being retained in most lineages but absent from others . Programmed DNA elimination is sparsely distributed across the tree of life and likely arose several times over metazoan evolution [19] , and in some cases PGR has been shown to act cooperatively with other silencing pathways ( e . g . ciliates [11] , sciarid flies [12 , 13] and zebra finch [14] ) . It seems likely that each of these independent lineages has evolved its own approaches to achieve the reproducible elimination of DNA , prevent the loss of retained segments , and integrate these mechanisms with existing silencing pathways . As such , each of these lineages holds the potential to provide unique insights into a diversity of conserved ( and derived ) cellular mechanisms , including those that contribute to the proper segregation of chromosomes , epigenetic silencing , reconstitution of the nuclear envelope , and the maintenance of genome stability . One notable feature of lamprey PGR is the variability in the content and form of eliminated chromatin across the first three days of development . Observations suggest that elimination events occurring ~1 . 5–2 dpf often target large regions ( entire chromosomes ) and appear to involve physical interactions between homologous chromosomes or sister chromatids . Earlier and later elimination events appear to target smaller fractions of the genome . The presence of variability across development raises several questions with respect to the mechanisms and outcomes of lamprey PGR . For example , does DNA loss involve a fixed number of steps/cell cycles ? Do all elimination events share a common mechanism , or do new mechanisms/interactions arise later in development ? Are later events uniform , or do they result in minor genetic variation across somatic cell lineages [6] ? The variability observed over the time course of lamprey PGR is somewhat reminiscent of chromosome elimination in Acricotopus lucidus ( Diptera , Chironomidae ) [51] . In most cases , all germline-limited chromosomes are lost in a single mitosis , but rarely , one or several chromosomes escapes elimination and segregates with the somatically-retained chromosomes . Based on these observations , it has been suggested that a threshold exists wherein a certain number of hypothetical marks are necessary to drive elimination of A . lucidus chromosomes . As yet , it remains to be determined whether the observed variation apparent among lamprey elimination anaphases is programmatic , cell lineage specific , inherently noisy , or explained by threshold effects . The analyses presented here reveal several new cellular and molecular details related to developmentally programmed genome rearrangements in lamprey , a species that undergoes PGR in the context of a developmental and cellular biology that is largely conserved with other vertebrates [7–10] . Our analyses indicate that individual segments are slated for elimination during metaphase and are ultimately packaged into compact structures ( micronuclei ) , a subset of which are enriched for repressive chromatin marks . These studies also demonstrate that PGR is initiated at an earlier developmental stage than was previously indicated via PCR-based assays [4] and strongly indicate that PGR is a more protracted process , being completed over the course of several successive cell divisions . Based on our these new findings , we suggest that efforts to further dissect the mechanisms underlying lamprey PGR should include studies aimed at defining 1 ) the sequence of , and interactions between , repetitive sequences that occur in regions of contact between some eliminated chromosomes , 2 ) the role of epigenetic modifications ( particularly silencing ) in PGR and 3 ) interactions between eliminated DNA and components of the spindle apparatus/cytoskeleton . In addition to providing critical insights into the cellular and mechanistic basis of PGR , such studies are expected to aid in translating this information to systems wherein large-scale rearrangements and DNA losses are less programmatic and generally deleterious .
Clearing procedure was performed according to Yang et al . [35] . Paraformaldehyde fixed embryos were incubated in hydrogel monomer solution with 5% acrylamide supplemented 0 . 5% VA-044 overnight . Polymerization was performed at 37°C for 2 . 5 hours then embryos were washed briefly with PBS , and incubated in 8%SDS , 1x PBS for 5 days at 37°C with gentle shaking . FISH and immunolabeled samples were washed in 1x PBS with 5 buffer changes over the course of a day and transferred into staining solution ( 1x PBS , pH = 7 . 5 , 0 . 1 Triton X-100 , 0 . 01% sodium azide ) . Spreads of somatic metaphase chromosomes were generated from embryos at 11 dpf . After overnight treatment with 0 . 1% colchicine , embryos were ground in Dounce homogenizer , incubated with 0 . 075 M KCl hypotonic solution for 45 minutes at room temperature and fixed in methanol:acetic acid ( 3:1 ) . Cell suspensions were placed on glass slides and air-dried . Embryos for this and other experiments were produced under the University of Kentucky IACUC protocol number 2011–0848 . Paraffin sections were prepared for immunolabeling and FISH as follows . Sections were deparaffinized in two changes of xylene , gradually rehydrated in a dilution series of ethanol ( 100 , 80 , 70% in water ) , rinsed in water , and placed for overnight incubation in 10 mM sodium citrate buffer ( pH 6 . 0 ) at 37°C to reduce auto-fluorescence and aid in antigen retrieval . Slides were then washed in PBS before hybridization and immunolabeling . Probes for in situ hybridization were labeled by nick-translation using direct fluorophores Cyanine 3-dUTP ( Enzo Life Sciences , ENZ-42501 ) or Fluorescein-12-dUTP ( Thermo Scientific , R0101 ) as described previously [52 , 53] . Germ1 repeat was obtained from a previously characterized BAC-clone [4] using extraction with Qiagen Large Construct kit ( Qiagen Science , 12462 ) . Cot1 and Cot2 fractions were isolated from genomic DNA according to kinetics of reassociation [54] , using S1 nuclease to digest single stranded ( low copy ) DNA [38 , 55] . Cot DNA isolation was performed in 1 . 2XSSC solution as follows: 120°C heating for shearing and denaturing , reannealing at 60°C , and S1 nuclease digestion for 1 hr at 42°C [55] . Whole embryo FISH was performed using modified procedure for cryosections [56] . Briefly , embryos were incubated in 10 mM sodium citrate buffer , pH = 6 . 0 overnight at 37°C in a rotating incubator , washed in 1x PBS for 1 hour , and then placed in 50% formamide in 2XSSC for 2–3 hours . For hybridization , formamide/SSC solution was replaced with 30 μl hybridization mix consisting of 50% formamide , 10% dextran sulfate , 0 . 01% sodium azide , and 150 ng labeled DNA-probe . Embryos were pre-incubated for overnight at 37°C to permit penetration of probes , after which probe and target DNA was denatured by heating samples to 75°C for 3 minutes . Following overnight incubation at 37°C samples were washed in 50% formamide in 2XSSC and in 0 . 4XSSC , 0 . 3% IGEPAL® CA-630 ( Sigma Cat . no . I8896 ) at 45°C for 10 min each , then in 2XSSC , 0 . 1% IGEPAL for 10 min at room temperature . DAPI and SYTO-24 counterstain was performed in staining solution at room temperature for , at least , 1 hour for embryos at 2–3 dpf and overnight for 1 dpf . Fluorescence in situ hybridization of embryonic sections and mitotic spreads was carried out according to standard protocols [56 , 57] with minute modifications [52] . Deparaffinized section slides were incubated in 8% sodium thiocyanate solution overnight , pretreated with 10 μg/ml RNase and 0 . 01% pepsin solutions , denatured in 70% formamide with subsequent dehydration in ethanol series ( 70 , 80 , 100% ) and hybridized with 100–200 ng of probe overnight in humid chamber at 37°C . For chromosome spreads prehybridization treatments with sodium thiocyanate , RNase , and pepsin solutions were skipped . Primary antibodies for immunolabeling were as follows: monoclonal anti-5-Methylcytosine ( Epigentek , A-1014 ) , polyclonal anti-Histone H3-K9 Trimethyl ( Epigentek , A-4036 ) , polyclonal anti-Histone H3-K27 Trimethyl ( Epigentek , A-4039 ) , monoclonal anti-Beta Tubulin ( Abcam , ab179513 ) , polyclonal anti-Lamin B1 ( Boster , PB9611 ) and monoclonal anti-Nuclear Pore-O-linked Glycoprotein ( Thermo , MA1-071 ) . Primary antibodies were diluted 1:100 in 1x PBS , applied to slides , and incubated overnight at 4°C . After washing in PBS and PBST twice for 10 min each , slides were incubated with secondary antibodies to their respective host species at a 1:100 dilution using the following antibodies: Alexa Fluor 488 F ( ab' ) 2 fragment of rabbit anti-mouse ( LifeTechnologies , A-21204 ) , Alexa Fluor 594 F ( ab’ ) 2 fragment of goat anti-mouse , Alexa Fluor 488 chicken anti-rabbit ( LifeTechnologies , A-21441 ) . After washing as described in PBS and PBST solutions , slides were mounted using VectaShield-DAPI media ( Vector Laboratories , H-1400 ) . Whole embryo immunofluorescence labeling was carried out according to methods previously described for single cell phenotyping [35] . Briefly , PACT-cleared embryos were incubated with primary antibodies ( 1:100 , in PBS containing 10% normal serum of secondary antibody host species ( rabbit ) , 0 . 1% Triton X-100 and 0 . 01% sodium azide ) for 3 days , replacing antibodies daily . Unbound antibody was removed via PBS washes , and samples were incubated with secondary antibodies ( 1:100 ) for 2–3 days then washed for 1 day in PBS prior to incubation with DAPI ( 50 ng/ml ) and imaging media ( RIMS: 88% Histodenz ( Sigma , D2158 ) in PBS with 0 . 1% tween-20 and 0 . 01% sodium azide , pH to 7 . 5 ) . All staining and mounting steps were conducted at room temperature with gentle shaking . TUNEL reactions were performed on paraffin sections from 2 dpf embryos . Slides were deparaffinized , treated with sodium citrate solution as above , and labeling was performed using the Click-iT Plus TUNEL Assay ( Life Technologies Cat . no . C10617 ) , according to manufacturer instructions . Samples were permeabilized with proteinase K for 30 min at 37°C . A positive control was generated by treatment with a 1:50 dilution of DNAseI ( ThermoFisher Scientific Cat . no . EN0525 ) in reaction buffer , followed by incubation at room temperature for 30 min . TUNEL assays were performed on experimental and positive control slides simultaneously , then slides were mounted with VectaShield-DAPI media ( Vector Laboratories , H-1400 ) . After FISH and immunolabeling , slides were analyzed with an Olympus-BX53 microscope using filter sets for DAPI , TexasRed , and FITC . Images were captured using CellSence software . For thicker samples , such as sections and embryonic cells after PACT clearing , we used Extended Focal Imaging ( EFI ) function in order to generate a single deep-focus image . Three-dimensional images of anaphases were obtained using a scanning confocal microscope ( Nikon C2 ) equipped with NIS-Elements AR software . Three-dimensional images were converted in two-dimensional format in NIS Element Viewer . Pseudocolor corrections were performed using Adobe Photoshop CS6 . Video recordings were made in NIS Element Viewer using QuickTime media player “Screen recording” function . The frequency of MNi in paraffin sections was assessed by counting DAPI-stained primary nuclei and small extra-nuclear DAPI-positive structures . For FISH and immunolabeling experiments , MNi were counted as signal-positive when they yielded visible DAPI emission and fluorescence in the specific wavelength corresponding to the fluorophore used for detection . Between 20 and ~200 primary nuclei were counted per slide , depending on stage of development . Fewer nuclei were counted for earlier stages due to the fact that these embryos consist of smaller numbers of larger cells . Counts of MNi , anaphases and lagging anaphases were performed after hybridization of whole embryos with fluorescently-labeled Cot2 DNA in order to improve visualization of eliminated DNA . Frequencies of MNi , anaphases and lagging anaphases were compared between adjacent time points using Pearson’s chi-square test and by calculation of Bayesian central confidence intervals [58] .
|
Lampreys possess a fascinating genome biology wherein large portions of the genome , including large numbers of genes , are programmatically deleted during development . The lamprey therefore represents a uniquely informative system with respect to several broad areas of biology , including genome stability/rearrangement , epigenetic silencing , and the establishment and maintenance of pluripotency . However , little is known regarding the cellular context or mechanism of deletion , partly due to the challenges of observing rearrangements in situ . Here we present analyses and new techniques that significantly advance our understanding of the subcellular context of programmed rearrangements and interactions between programmed deletion and canonical DNA silencing mechanisms . These analyses demonstrate that DNA elimination occurs earlier in embryogenesis than was previously recognized and reveal several new cellular and molecular aspects of programmed DNA loss . Specifically we show that eliminated DNA exhibits a unique migration pattern during cell division , is packaged into discreet subcellular structures later in the cell cycle , and undergoes epigenetic silencing through DNA and histone methylation . These observations provide new insight into the mechanisms underlying programmed DNA loss and suggest a functional link between programmed DNA loss and other , more conserved gene silencing pathways .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"anaphase",
"micronuclei",
"molecular",
"probe",
"techniques",
"cell",
"cycle",
"and",
"cell",
"division",
"cell",
"processes",
"microbiology",
"vertebrates",
"animals",
"lampreys",
"developmental",
"biology",
"epigenetics",
"molecular",
"biology",
"techniques",
"embryos",
"chromatin",
"research",
"and",
"analysis",
"methods",
"embryology",
"probe",
"hybridization",
"fishes",
"chromosome",
"biology",
"gene",
"expression",
"molecular",
"biology",
"agnatha",
"dna",
"hybridization",
"cell",
"biology",
"cyclostomata",
"genetics",
"biology",
"and",
"life",
"sciences",
"protozoology",
"organisms",
"chromosomes"
] |
2016
|
Cellular and Molecular Features of Developmentally Programmed Genome Rearrangement in a Vertebrate (Sea Lamprey: Petromyzon marinus)
|
Mimulus guttatus and M . nasutus are an evolutionary and ecological model sister species pair differentiated by ecology , mating system , and partial reproductive isolation . Despite extensive research on this system , the history of divergence and differentiation in this sister pair is unclear . We present and analyze a population genomic data set which shows that M . nasutus budded from a central Californian M . guttatus population within the last 200 to 500 thousand years . In this time , the M . nasutus genome has accrued genomic signatures of the transition to predominant selfing , including an elevated proportion of nonsynonymous variants , an accumulation of premature stop codons , and extended levels of linkage disequilibrium . Despite clear biological differentiation , we document genomic signatures of ongoing , bidirectional introgression . We observe a negative relationship between the recombination rate and divergence between M . nasutus and sympatric M . guttatus samples , suggesting that selection acts against M . nasutus ancestry in M . guttatus .
While speciation is often depicted as a simple event in which a single species splits into two , there is increasing evidence that this process is often more complex . In particular , speciation reflects a tension among divergence , the assortment of ancestral variation , ecological interactions and in some cases introgression that play out across the environment of the incipient species . Historically , a population genetic view of the process of speciation has been limited to few loci , where stochasticity in ancestral processes can prevent strong inferences about isolation and gene flow . By contrast , whole genome resequencing ( even of only a few individuals ) reveals many genealogical histories across contiguous genomic regions to provide well-resolved views of population history , divergence and introgression [1]–[5] . Here , we present a population genomic investigation of the speciation history of two closely related species of yellow monkeyflowers , the primarily outcrossing Mimulus guttatus , and the self-pollinating M . nasutus – an evolutionary model system for which the genetic and ecological basis of reproductive isolation is reasonably well characterized [6] . In flowering plants , speciation often involves a shift in pollinator ( e . g . , [7]–[9] ) or mating system ( e . g . , [7] , [10]–[12] ) , with concomitant divergence in key floral traits causing reproductive isolation between lineages . The evolutionary transition from outcrossing to self-fertilization , as occurred in M . nasutus , is of particular interest because the expected reduction in both the effective population size and effective recombination rate [13] , [14] can dramatically alter population genetic processes and patterns of genomic variation [15] , [16] . Recent evidence for elevated levels of putatively deleterious alleles in selfing taxa [17]–[19] is consistent with the idea that inbreeding reduces the effectiveness of purifying selection ( due to a lowered effective population size ) . However , we still have few examples of the effects of self-fertilization on patterns of diversity across the genome , particularly in the context of recently diverged and potentially hybridizing species . Genomic datasets from young selfing species can uniquely inform the process of mating system divergence by allowing us to compare regions of the genome that share a common ancestor before or after the origin of self-fertilization and thus understand the assortment of ancestral variation [20] . The M . guttatus – M . nasutus species pair is an excellent model for investigating the causes and consequences of mating system evolution and species divergence . M . guttatus is primarily outcrossing ( although the outcrossing rate varies across populations [21]–[23] ) with large , bee-pollinated flowers and occupies diverse ecological habitats throughout western North America . M . nasutus is highly selfing with reduced , mostly closed flowers . Although these species are often found in different microhabitats , the range of M . nasutus is broadly nested within that of M . guttatus and the two species do co-occur . In sympatry , M . nasutus and M . guttatus are partially reproductively isolated by differences in floral morphology , flowering phenology , and pollen-pistil interactions [24]–[26] . Although early-generation hybrids occur in nature [24] , [27] , numerous intrinsic hybrid incompatibilities decrease hybrid fitness [28]–[30] . Based on the most detailed population genetic analyses of Mimulus to date ( two and six nuclear loci , respectively [30] , [31] ) , M . nasutus exhibits reduced diversity compared to M . guttatus , and some M . guttatus sequences are nearly identical to M . nasutus , suggestive of historical introgression . However , this limited view of the genome cannot resolve the timing and genomic consequences of divergence between Mimulus species , nor can it inform the extent or consequences of introgression between them . We present the first population genomic analysis of M . guttatus and M . nasutus , spanning diverse ecotypes collected from throughout the species' ranges . We use these dense and contiguous population genomic data to estimate the population-split time , quantify rapid loss of ancestral variation accompanying the transition to selfing in M . nasutus , and identify ongoing , bidirectional introgression . Additionally , we observe a negative correlation between the recombination rate and interspecific divergence between M . nasutus and sympatric M . guttatus , a result best explained by selection against introgressed M . nasutus ancestry in M . guttatus . Our approach provides a detailed view of differentiation and introgression in a tractable ecological , genetic , and evolutionary model system .
Overall patterns of genomic differentiation show deep population structure in M . guttatus , with M . nasutus diverging from a central Californian M . guttatus population approximately 200 kya . To visualize pairwise relatedness , we constructed a rate-smoothed neighbor-joining ( nj ) tree ( see METHODS for a discussion of the nj approach in population genetics ) . This tree clearly displays a deep phylogeographic split within M . guttatus , roughly corresponding to northern and southern parts of its range; however , geography is an imperfect predictor of genetic structure within M . guttatus ( e . g . , DUN is from a northern latitude yet clusters with our southern M . guttatus samples ) . The tree places all M . nasutus samples as a node within the southern M . guttatus cluster ( Figure 1B ) . The fact that M . guttatus is paraphyletic suggests that M . nasutus budded from within a structured ancestral M . guttatus population . A principle component analysis ( PCA , Figure 1C ) also reveals the genetic structure within M . guttatus – PC2 differentiates northern and southern M . guttatus groups . Consistent with the single origin of M . nasutus , PC1 separates M . guttatus from the strongly clustered M . nasutus , presumably as a consequence of a shared history of genetic drift among these M . nasutus samples . Down-sampling to any one M . nasutus sample controls for this shared drift , and places M . nasutus within southern M . guttatus ( Figure S1 ) . To support these qualitative inferences we generated a quantitative description of genetic structure within M . guttatus , focusing on our high-coverage ( focal ) samples . Pairwise sequence diversity at synonymous sites within northern ( πS AHQT×CACG = 3 . 97% [3 . 89%–4 . 06%] ) and southern ( πS DPR×SLP = 4 . 45% [4 . 39%–4 . 52%] ) M . guttatus samples is significantly lower than that within M . guttatus overall ( πS = 4 . 91% [4 . 85%–4 . 96%] ) , and between the north and south ( πS = 5 . 26% [5 . 20%–5 . 30%] , Figure 1D ) . Diversity within the northern and southern clades is consistent with a very large effective population size ( Ne ) of approximately one and a half million chromosomes for both groups ( assuming the per generation per base mutation rate , μ = 1 . 5×10−8 [following Koch et al . 2001] ) . As a simple estimate of the population split time ( τ generations ) , ignoring possible introgression , we assume that the divergence between populations is the sum of pairwise diversity ( π ) within an ancestral population and the product of the per-generation mutation rate , μ , and two times the split time [34] . Using this relationship , and representing ancestral diversity by the southern M . guttatus samples , we set τ = ( πS NorthGut×SouthGut−πS SouthGut ) /2μ and estimate a split between northern and southern Mimulus populations more than a quarter of a million years ago ( 265 ky [251 ky–280 ky] , assuming an annual life history ) . As above , this estimate assumes μ = 1 . 5×10−8/bp/generation but can be linearly rescaled by alternative estimates of μ . For example , readers can multiply divergence time estimates by a factor of two if they prefer the estimate of μ = 7×10−9/bp/generation [35] . Interspecific divergence between M . guttatus and M . nasutus ( dS = 4 . 94% [4 . 88%–5 . 00%] ) is comparable to overall M . guttatus diversity , and exceeds diversity within northern or southern M . guttatus collections ( Figure 1D ) . We derive a simple estimate of split time between M . guttatus and M . nasutus as we did above to estimate the split between focal northern and southern M . guttatus samples . Using the difference between divergence of M . nasutus from the southern , allopatric M . guttatus sample ( to minimize the influence of recent introgression and historical divergence between M . guttatus’ genetic clusters ) and a proxy for diversity in an ancestral population ( southern M . guttatus ) , we estimate that M . nasutus and M . guttatus split approximately 200 ky ago , τ = ( πS Nas×AlloSouthGut−πS SouthGut ) /2μ = 0 . 5875%/2μ = 196 ky [181 ky–212 ky] . As a complementary inference of historical patterns of divergence within M . guttatus and between species , we applied Li and Durbin's implementation of the pairwise sequentially Markovian coalescent ( PSMC ) [36] to pairwise combinations of focal haploid genomes ( Figures 1E and S2 , S3 , S4 , S5 , S6 ) . The PSMC analysis infers large population sizes within both northern ( CACG×AHQT ) and southern ( SLP×DPRG ) samples , with an apparent bout of strong recent population growth . However , as we have sampled from a structured population the inferred larger recent population sizes likely represent reduced coalescent rates caused by population structure , rather than dramatic recent increases in Ne . Likewise we infer a very low rate of coalescence ( a very large effective population size ) in the recent past between northern and southern M . guttatus ( SLP×AHQT ) compared to within these groups ( SLP×DPRG and CACG×AHQT , Figures 1E and S2 ) likely reflecting the strong genetic structure within range-wide M . guttatus . We also use this PSMC analysis for an additional estimate of the approximate split time , by assessing when the inferred coalescent rate between species decreases ( i . e . , the population size estimate increases ) relative to the rate within M . guttatus [see 36] . In doing so , we focus on the southern M . guttatus samples that fall closest to M . nasutus in our nj tree . The inferred coalescent rate between M . nasutus and southern M . guttatus ( SLP×KOOT , gray line ) decreases relative to the rate within southern M . guttatus ( SLP×DPRG , dark blue/navy line ) , i . e . , the lines diverge , from ∼500 to ∼300 kya , suggesting either a gradual split between species over that time span , or a hard split sometime within that range ( Figures 1E and S3 ) . This result , which represents an upper bound on time since speciation , is qualitatively similar to our lower estimate based on synonymous nucleotide variation among these samples . We note that for both analyses , historical introgression of M . nasutus into SLP would make this split seem more recent than it actually was . Patterns of genomic variation within M . nasutus reflect the genomic consequences of a recent transition to selfing . Synonymous diversity within M . nasutus ( πS = 1 . 09% [1 . 03%–1 . 14%] , Figure 1D ) is one fifth that observed within M . guttatus , consistent with a high rate of genetic drift since M . nasutus’ origin . Moreover , most ancestral variation in M . nasutus has been homogenized: of the fixed differences between M . nasutus and M . guttatus , 90% are derived in M . nasutus and 10% are derived in M . guttatus ( when polarizing by M . dentilobus ) . Although M . nasutus has lost much of its ancestral variation , shared variants still constitute a much higher proportion of its polymorphism ( 50% ) relative to an equally sized sample of M . guttatus ( 10% ) . This pattern reflects both the paraphyly of M . guttatus and the incomplete sorting of variation present in M . nasutus’ founders . Consistent with this reduction in nucleotide diversity and incomplete sorting of ancestral variation , PSMC analyses infer a dramatic decline in M . nasutus’ effective population size after it split from M . guttatus ( compare red and black-gray lines in Figure 1E , see also Figure S4 ) , suggesting that the evolution of selfing roughly coincided with M . nasutus’ split from M . guttatus . We caution , however that interpretation of PSMC's estimated population size in M . nasutus is not straightforward . This is because the transition to selfing reduces the population recombination rate more than the population mutation rate [14]; however , Li and Durbin's [36] implementation of the PSMC assumes that both these values change proportionally with the historical effective population size . Relative to expectations under selective neutrality and demographic equilibrium , M . nasutus contains an excess of high-frequency derived synonymous alleles ( Figure S7 ) . We interpret this observation as a reflection of a recent population contraction . This interpretation is in agreement with the decreased synonymous diversity in M . nasutus relative to M . guttatus and our PSMC-based inference of a reduction in Ne . However , population structure within M . nasutus may also contribute to this excess of high frequency derived alleles [37] , [38] . By contrast , in M . guttatus we observe slightly more rare synonymous alleles than expected under a neutral equilibrium model , reflecting recent growth , population structure , and/or weak selection against unpreferred codons . The distribution of synonymous diversity across the genome ( overlapping 5 kb windows with a 1 kb slide , Figure 2A ) bolsters the view that M . nasutus’ genomic diversity is a mixture of closely related genomic regions that rapidly coalesce in the small M . nasutus population , and distantly related regions that do not coalesce until joining a large M . guttatus-like ancestral population . In pairwise comparisons of sequence diversity within M . nasutus , half of the genomic windows are differentiated by πS<0 . 5% ( corresponding to ∼170 thousand years of divergence ) , reflecting recent common ancestry since the species split . On the other hand , one third of such windows are differentiated by πS>2 . 0% , reflecting deep ancestry in a large ancestral population ( Figure 2A , see Figure S8 for different window sizes ) . These findings contrast sharply with comparisons within M . guttatus , as well as between M . nasutus and allopatric M . guttatus samples , for which recent common ancestry since the species split is rare ( πS<0 . 5% for less than 1 . 5% of 5 kb windows ) and deep coalescence is the norm ( mode πS = 4% , Figure 2A , a result roughly consistent across window sizes , Figure S8 ) . Under the neutral coalescent , a pair of lineages will fail to find a common ancestor with each other by generation t with probability e−t/Ne* , where Ne* is the ( constant ) effective number of chromosomes . Therefore , the observation that half of our M . nasutus windows share a common ancestor in the past ∼170 ky , by an admittedly crude calculation , predicts a population size between 150k and 250k effective chromosomes ( compared to the estimated Ne* of 1 . 5 million in M . guttatus from synonymous diversity , above ) . This ten-fold reduction in effective population size as compared to M . guttatus far exceeds both the two-fold decrease in Ne expected to accompany the evolution of selfing and the four-fold decrease calculated by the difference in intraspecific variation . Across the genome , the mosaic nature of ancestry within M . nasutus is apparent as long contiguous regions of recent common ancestry ( colored windows in Figures 2B and S9 ) interrupted by regions of deep ancestry , due to incomplete lineage sorting and/or historical introgression ( white windows in Figure 2B and S9 ) . This block-like ancestry structure results in extensive linkage disequilibrium ( LD ) in M . nasutus . In contrast to M . guttatus , for which the sample pairwise LD drops halfway towards its minimum values within only 15–20 base-pairs , LD in M . nasutus decays much more slowly , not dropping halfway towards its minimum values until 22 kb ( Figure 2C ) . This represents a thousand-fold difference in the decay of LD , as compared to a more modest ten-fold reduction in the effective population size between M . nasutus and M . guttatus . This dramatic difference in the scale of LD between Mimulus species is likely due to a major reduction in the effective recombination rate within the selfing M . nasutus . We use this difference to derive a simple estimate of M . nasutus’ selfing rate using Eq . 1 of Nordborg [14] . Nordborg showed that the ratio of the population-scaled recombination to mutation rate in selfers is reduced by a factor of 1-F compared to the same population if it was outcrossing , where F is the inbreeding coefficient . Substituting in the hundred fold difference in the ratios of effective population size and decay of LD between M . nasutus and M . guttatus , we arrive at 1−F = 0 . 01 . Assuming a constant selfing rate s , F = s/ ( 2−s ) , M . nasutus’ selfing rate is approximately 99% . Patterns of sequence variation suggest a reduced efficacy of purifying selection in M . nasutus , a result consistent with extensive genetic drift and/or linked selection within M . nasutus . All M . nasutus samples contain more premature stop codons than any M . guttatus sample ( M . nasutus: mean 124 . 0 , range = 121–126 , M . guttatus: mean 95 . 5 , range = 86–102 ) , and a large proportion of these premature stops are at high frequency in M . nasutus ( Figure 2D ) . For 27 of the 29 fixed differences for a premature stop codon , M . nasutus carries the premature stop and M . guttatus carries the intact allele . We acknowledge that errors in annotation could underlie some of the excess of premature stop codons inferred in M . nasutus . However , M . nasutus and the focal southern M . guttatus samples are equally diverged from the reference and yet our southern M . guttatus samples do not show this excess . As such , annotation error is likely not a strong contributor to the large and consistent interspecific differences in premature stop codons observed . Additionally , after standardizing by synonymous variation , we observe an excess of putatively deleterious , non-synonymous variation in M . nasutus relative to M . guttatus ( πN/πS = 0 . 197 [0 . 192–0 . 203] and 0 . 157 [0 . 155–0 . 160] , respectively ) . However , this difference is not yet reflected in divergence between the species ( dN/dS = 0 . 156 [0 . 154–0 . 159] ) , presumably because interspecific sequence differences largely reflect variation that predates the origin of selfing in M . nasutus rather than the relatively few mutations accrued within the past ∼170 ky . This pattern of elevatedπN/πS in selfing species but only modest dN/dS between selfers and their close relatives is common [15] , even in genome-wide analyses ( e . g . , [20] ) . We note that elevated πN/πS in selfing species may reflect the faster rate at which nonsynonymous diversity approaches equilibrium after a reduction in diversity compared to synonymous mutations [39] , [40] , rather than a consequence of a reduced efficacy of purifying selection . However , this interpretation cannot explain the absolute excess of premature stop codons in M . nasutus .
Genetically , M . nasutus clusters with central Californian M . guttatus samples , suggesting that speciation post-dated the differentiation of some M . guttatus populations . Thus , speciation in this pair is best described as a ‘budding’ of M . nasutus from M . guttatus , rather than a split of an ancestral species into two . We observe an approximate coincidence between the timing of divergence and the decline in population size in M . nasutus ( as inferred from our PSMC analysis ) . This observation could be a result of the transition to selfing being linked to speciation ( see [11] for phylogenetic evidence of this link in the Solanaceae and [51] , [52] for a likely case in Capsella ) . However , given the misspecification of the PSMC model for the transition to selfing ( see above ) , this observation should be treated with caution . More work is needed to develop methods to test whether split times and changes in selfing rate occur concurrently to see if this is indeed a general pattern . Future genomic analyses across the M . guttatus complex and other species groups will facilitate an in-depth view of the causes and consequences of speciation by the budding of selfing and/or endemic populations from widespread parental species . We note that recent phylogenetic analyses of species' ranges suggest that this mode of speciation is common in Mimulus [53] and other flowering plants [54] . We estimate that M . nasutus split from a M . guttatus population within the last two hundred to five hundred thousand years ( with our estimate of ∼200 ky , inferred from differences in synonymous sequence differences within and between species , and the estimate of ∼500 ky corresponding to conservative estimates of population splits from the PSMC ) . This lies between the ∼50 ky separating selfing Capsella rubella from outcrossing C . grandiflora [20] , [55] and Arabidopsis thaliana which has potentially been selfing for over a million years ( [56] , having split from A . lyrata ∼3–9 Mya [57] ) . Although 200 ky represents a relatively short time evolutionarily , it implies that M . nasutus managed to survive numerous dramatic bioclimatic fluctuations . The transition from outcrossing to self-fertilization in M . nasutus has had clear consequences on patterns of genomic variation . In M . nasutus , linkage disequilibrium exceeds that in M . guttatus by three orders of magnitude . This result suggests a high selfing rate in M . nasutus ( estimated above at 99% ) , consistent with direct estimates from field studies [24] . We observe a four-fold drop in diversity and infer a ten-fold reduction in the recent effective population size in M . nasutus compared to M . guttatus , values far exceeding the two-fold decrease in Ne expected as a direct consequence of selfing [58] , [59] . This more than two-fold reduction in Ne of selfing populations relative to their outcrossing relatives has been identified in other plant [17] , [55] and animal [60]–[62] species pairs , and may be partially due to extreme founding bottlenecks , frequent colonization events and/or demographic stochasticity that further increase the rate of genetic drift [13] , as well as a heightened influence of linked selection in selfing taxa [60] , [63]–[65] . Selfing populations are expected to experience a reduced efficacy of purifying selection accompanying the drop in effective population size and recombination rates [15] , [65] , [66] . Consistent with these predictions , M . nasutus has accumulated numerous putatively deleterious mutations , including nonsynonymous variants and premature stop codons . Presumably , this elevation in radical genetic variants reflects a reduction in the efficacy of purifying selection due to a high rate of genetic drift and linked selection , as well as perhaps the escape of some genes ( e . g . , loci involved in pollinator attraction ) from the selective constraints they faced in an outcrossing population ( e . g . , [17] ) . Despite multiple reproductive isolating barriers , including mating system differences , we find ongoing , bidirectional introgression between M . guttatus and M . nasutus . Evidence of ongoing introgression from the selfer , M . nasutus , into the outcrosser , M . guttatus , is particularly stark . There are numerous evolutionary implications of introgression from selfers to outcrossers . Introgression of deleterious mutations accumulated in selfers may introduce a genetic load to outcrossers . This burden would result in selection against genetic material from selfers in hybridizing outcrossing populations , and could ultimately favor reinforcement of reproductive isolation . Alternatively , such introgression could provide a multi-locus suite of variation facilitating self-fertilization , and other correlated traits ( e . g . , drought resistance and rapid development ) , in favorable environments , as appears to be the case in introgression between wild and domestic beets ( Beta vulgaris , [67] ) . Evidence of introgression from M . guttatus into M . nasutus is subtler , but is potentially critically important . Even relatively low levels of introgression into a selfer may rescue the population from a build up of deleterious alleles , and reintroduce adaptive variation , and so may lower its chances of extinction , a fate considered likely for most selfing lineages [68] , [69] . However , before potentially rescuing a selfing population from extinction , genomic regions introduced from outcrossing species must themselves survive a purging of deleterious recessive alleles . Higher rates of introgression from M . nasutus to M . guttatus would be consistent with the prediction that backcrosses should be asymmetric – because bees preferentially visit plants with larger flowers [70] , [71] and/or larger floral displays [72] , [73] , both features of M . guttatus , visits to M . nasutus and F1 hybrids are likely preceded and followed by visits to M . guttatus [24] , [30] . Consistent with this prediction , direct estimates of hybridization in the DPR sympatric population reveal that F1 hybrids are the product of M . nasutus maternal and M . guttatus paternal parents , respectively [24] . However , we caution that it is considerably more challenging to identify introgression into M . nasutus than into M . guttatus , as the similarity between interspecific divergence and diversity in M . guttatus makes historical admixture difficult to separate from the incomplete sorting of M . nasutus’ ancestral variation . We further note that , although asymmetrical introgression from selfers to outcrossers has been detected in other systems ( Pitcairnia [74] , and potentially in Geum [75] , [76] ) , the relative contribution of selfing vs . other isolating barriers and/or selection is unclear . Dense sampling of sympatric and allopatric populations of outcrossing species experiencing ongoing gene flow with selfing relatives will allow for tests of these hypotheses . Importantly , the number , location and length-distribution of admixture blocks identified from genomic analyses provide information about the longer-term consequences and pace of introgression between selfers and outcrossers . The numerous short blocks ( in addition to long blocks ) of M . nasutus ancestry observed in M . guttatus suggest that M . nasutus ancestry can potentially persist in an M . guttatus background for many generations . Despite this , M . guttatus and M . nasutus are still ecologically and genetically distinct . We identified a genome-wide signature of selection against introgression of M . nasutus ancestry in M . guttatus , in the form of a negative relationship between the local recombination rate and absolute divergence . This relationship was highly significant in both sympatric comparisons , but only weakly significant in parapatry , and insignificant in allopatry . Additionally , we did not find a relationship between recombination and diversity within either species . Moreover , unlike a negative relationship between the recombination rate and relative measures of differentiation , such as FST or the number of fixed differences [e . g . ] , [ 77] , [78 , 79] , this finding cannot also be explained by a high rate of hitchhiking or background selection within populations since the species split [46] , [48] , [49] . Instead , it seems more consistent with M . nasutus ancestry being selected against more strongly in regions of low recombination due to linkage with maladaptive alleles that introgression would introduce . This suggests that the genome has potentially congealed as a barrier to gene flow in low recombination regions . We note that this ‘congealing’ ( sensu Barton [80] , [81] ) requires a threshold density of locally adaptive mutations , measured in recombination distance , and does not require a complex model of multi-locus coadaptation . Previous reports of absolute divergence near the breakpoints of inversions ( e . g . , [82] , [83] ) , or in centromeres relative to telomeres [84] , suggested this result; however , genome-wide evidence for this basic prediction is scarce . Further work , including experiments measuring selection on genetic variants in the wild , and larger sample sizes from both allopatric and sympatric populations , is needed to pinpoint which ( if any ) genomic regions are particularly strongly selected against in hybrids . Genetically mapped loci for adaptive interspecific differences [85] and hybrid inviability and sterility [29] are promising candidates . Indeed , recent analyses of the distribution of Neanderthal haplotype blocks in ∼1000 human genomes has identified genomic candidates for adaptive introgression from Neanderthals to humans and an apparent paucity of introgression at loci putatively influencing male fertility [86] . Our analyses of whole genomes from the M . nasutus - M . guttatus species pair provide a broad view of both the historical divergence in this group and the ongoing processes by which they remain distinct . Less than a half million years ago , a semi-isolated M . guttatus population evolved self-pollination and ultimately transformed into modern day M . nasutus . In the intervening time , this population experienced a contraction in effective population size , and accumulated deleterious mutations while spreading geographically across western North America . More broadly , our work demonstrates that much can be learned about population history from resequencing relatively few samples in a group with an annotated genome and an integrated physical-genetic map . Despite numerous reproductive isolating barriers [24] , [25] , [27]–[29] , sympatric populations of M . guttatus and M . nasutus are still exchanging genes . The low diversity and extensive linkage disequilibrium in M . nasutus facilitates straightforward identification of M . nasutus-like ancestry in M . guttatus , and we use the length-distribution of these blocks to parameterize the recent history of introgression . The many short M . nasutus ancestry blocks suggest that its ancestry can persist in M . guttatus beyond early generation hybrids , and the length distribution of this ancestry is consistent with more than one pulse of introgression . The genomic distribution of introgression is non-random – in sympatry , absolute interspecific divergence is greater in regions of reduced recombination , suggesting selection against long blocks of M . nasutus ancestry in M . guttatus . Additional sequencing of individuals in sympatry will help better parameterize the dynamics and extent of introgression from M . guttatus to M . nasutus and clarify the action of selection for or against admixed ancestry across the genome .
We utilized a combination of existing [downloaded from the NCBI SRA , sequenced by 87] and newly generated whole genome sequence data from 19 different lab and/or naturally inbred Mimulus accessions , including 13 M . guttatus , 5 M . nasutus , and 1 M . dentilobus individual as an outgroup ( Table S1 ) . Samples varied in their geography and life history . Mean sequencing depths range from 2× to 25× , and read lengths include 36 , 76 , and 100 base pair paired end reads . We present SRA accession numbers as well as depth , read length and additional sample information in Table S1 , and note that we obtained the DPRG sequence data directly from the U . S . Department of Energy Joint Genome Institute . Our analysis included newly generated whole genome sequences from five lines ( CACG , CACN , DPRN , NHN , and KOOT ) , and we present details of sequence generation in Text S1 . We aligned paired end reads to the M . guttatus v2 . 0 reference genome [87] using Burrows-Wheeler Aligner ( bwa [32] ) with a minimum alignment quality threshold of Q29 ( filtering done using SAMtools [88] ) . Alignment-processing details can be found in Text S1 . We produced a high quality set of invariant sites and SNPs simultaneously for all lines using the GATK Unified Genotyper , with a site quality threshold of Q40 [89] , [90] . For all analyses described below , we exclusively used genotype calls from reference scaffolds 1–14 , corresponding to the 14 chromosomes in the Mimulus genome . For all analyses ( except PSMC , which requires a consistently high density of data , see below ) , we set also set a strict minimum depth cutoff of 10 reads per site . To assign genotypes at heterozygous sites , we randomly selected one of two alternate alleles . Such heterozygous sites are not concentrated in long genomic regions and account for approximately 1% and 2% of synonymous SNPs in average focal M . nasutus and M . guttatus samples , respectively . This translates to individual synonymous heterozygosity of approximately 0 . 2% and 0 . 5% in M . nasutus and M . guttatus , respectively , even before additional filtering to remove misaligned sites ( see below ) . Because sequence diversity is relatively high in our sample , we also analyzed patterns of pairwise sequence diversity using reads aligned with Stampy [33] ( expected divergence set to 5% ) and an otherwise identical pipeline to that described above . We describe the number of reads mapped with bwa and Stampy for our focal , high coverage lines in Table S7 . To minimize misclassifying mismapped paralogs as SNPs , we then removed triallelic sites and censured genotypes at sites where individual depth was two standard deviations away from mean depth . After these filtering steps , we classified remaining genic loci as zero , two , three , or fourfold degenerate using the Mimulus guttatus v2 . 0 gene annotations provided by phytozome [87] . Compared to bwa , the Stampy pipeline generated quantitatively larger estimates of sequence diversity , but qualitatively similar results ( i . e . , rank order of pairwise πS , Table S2B ) . Because Stampy doubled individual heterozygosity at synonymous sites , and increased πN/πS , we believe that it may have mismapped a greater proportion of our reads . Therefore , although Stampy aligned a greater number of reads to the reference genome than bwa ( Table S7 ) , we conservatively focus on our bwa alignments for our major analyses . As noted above , none of our qualitative conclusions depend on the read alignment pipeline used . In addition to descriptions of our analyses , below and in Text S1 , we recreate many analyses , including our PCA , and HMM in a file submitted to Dryad . These analyses can be run on the processed genotypic data for all samples at SNPs used in nj and PCA analyses as well as comparisons between focal samples in 1 kb windows across the genome , all available from doi:10 . 5061/dryad . vp645 .
|
While speciation is often depicted as a simple population split , in many cases it is likely more complex . Recently , whole genome sequencing and computational methods to interpret patterns of genomic variation have facilitated the inference of complex speciation histories . We present and analyze genomic data to infer the speciation history of an ecological and evolutionary model species pair - Mimulus guttatus/M . nasutus . We infer that M . nasutus split from a central Californian M . guttatus population approximately 200–500 kya , roughly corresponding to M . nasutus’ shift to self-fertilization . We document ongoing gene flow between these species where they co-occur . Finally , we present patterns genomic divergence suggesting that natural selection disfavors introgression of M . nasutus ancestry in M . guttatus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"evolutionary",
"biology",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics"
] |
2014
|
Speciation and Introgression between Mimulus nasutus and Mimulus guttatus
|
Recent attempts to examine the biological processes responsible for the general characteristics of mutualistic networks focus on two types of explanations: nonmatching biological attributes of species that prevent the occurrence of certain interactions ( “forbidden links” ) , arising from trait complementarity in mutualist networks ( as compared to barriers to exploitation in antagonistic ones ) , and random interactions among individuals that are proportional to their abundances in the observed community ( “neutrality hypothesis” ) . We explored the consequences that simple linkage rules based on the first two hypotheses ( complementarity of traits versus barriers to exploitation ) had on the topology of plant–pollination networks . Independent of the linkage rules used , the inclusion of a small set of traits ( two to four ) sufficed to account for the complex topological patterns observed in real-world networks . Optimal performance was achieved by a “mixed model” that combined rules that link plants and pollinators whose trait ranges overlap ( “complementarity models” ) and rules that link pollinators to flowers whose traits are below a pollinator-specific barrier value ( “barrier models” ) . Deterrence of floral parasites ( barrier model ) is therefore at least as important as increasing pollination efficiency ( complementarity model ) in the evolutionary shaping of plant–pollinator networks .
Generalisation is a widespread feature of plant–pollinator interactions [1] , and there is a growing interest in the study of complete communities of interacting plants and flower-visiting insects [2 , 3] . The topology of plant–pollinator qualitative networks ( networks that simply consider whether species pairs interact or not ) follows certain regular patterns [2–6] . The number of interactions ( L ) increases with network size ( S , the sum of the number of plant species , F , and pollinator species , P ) , following a power-law relationship [4] , while the percentage connectivity [C = 100 × L/ ( F × P ) , referred to as connectivity , or C , hereafter] decreases with network size [3] . Furthermore , most plant–pollinator networks are highly nested , i . e . , pollinators that visit a plant species are likely to visit more-generalist plant species as well [3 , 4] . The nestedness ( N ) of a network increases with its C and size , and it is significantly higher than expected by chance for all moderately large pollination networks ( i . e . , with more than 50 species ) that have been studied [4] . The existing studies on plant–pollinator and seed disperser–plant interactions also suggest that the nested structure of mutualistic networks reflects a fundamental difference from antagonistic webs , arising from how specialisation is distributed among interacting species [4–6] . In contrast to mutualistic networks , antagonistic networks ( e . g . , predator–prey , herbivore–plant ) tend to be more compartmentalised , i . e . , characterised by cohesive groups of interacting species with relatively few interactions among groups [7 , 8] . Several authors have suggested that nested patterns of asymmetrical specialisation may be more likely to develop in mutualistic interactions because natural selection specifically favours the convergence and complementarity of traits in interacting species [9] . In contrast , antagonistic interactions may favour greater compartmentalisation through the continual coevolution of defences and counterdefences ( i . e . , evolutionary arm races involving exploitation barriers ) , which generates greater specificity [9] . The idea that nested asymmetries in specialisation are generated by coevolutionary processes has strong links to the concept of “forbidden links” [5] . Under this hypothesis , the topological properties of mutualistic networks result from nonmatching biological attributes of species , such as phenology or morphology , which prevent the occurrence of certain interactions [8] . However , no work to date has explicitly evaluated the extent to which forbidden links can account for network topology , and detailed information about the biology of species interactions is considered a prerequisite for critical tests [10] . The goal of this study is to bridge this gap by considering the extent to which different ecological mechanisms might account for the observed network patterns . Using a set of simple models , we investigated whether linkage rules aimed at representing the two processes outlined above ( trait complementarity and exploitation barriers ) can explain the topological properties of plant–pollination networks . We concentrate on two possible mechanisms . In the first mechanism ( complementarity traits ) , the similarity between the reward that the plant has to offer and the resource that the pollinator seeks determines whether species pairs interact . As an example , consider plant and pollinator phenology: a plant will receive visits only of pollinators that are present and active during their flowering period , and plant and pollinator species can become progressively cospecialised by developing increasingly narrower phenological matches [11] . Other examples may include nectar sugar concentration , in which each major pollinator group prefers a specific range of nectar concentrations or sugar compositions [12–15] ( although other authors have interpreted nectar dilution as a barrier against bee pollination [16] , similar to our second scenario ) ; flower colour , in which flower signals match pollinator perceptual systems ( e . g . , [17–20] , but see also [21 , 22] for a critical view ) ; and specialised scents [23–25] . In the second scenario ( barrier traits ) , what determines whether species pairs interact is not their similarity but rather the ability of the pollinator to reach the reward offered by the flowers . Each flower type conceals its reward behind barriers , and only those pollinators whose traits allow them to overcome the barriers have access to the reward . As an example , consider the length of hawkmoths' proboscises and corolla tubes [26 , 27]: hawkmoths can forage efficiently only at flowers with corolla tubes shorter than their proboscises , and it is mainly these flowers that they visit , as shown by Haber and Frankies [28] for a Costa Rican hawkmoth community . Similar examples , involving proboscis or beak length and corolla-tube depth , have been described for bee- , fly- , and hummingbird-pollinated systems [29–33] . To evaluate whether simple linkage rules lead to mutualistic networks with topological properties similar to those observed in actual networks , we used different sets of rules , derived from the stated mechanisms , to simulate the linkage process . We used complementarity and barrier models based on one , two , or four traits and a mixed model based on two complementarity and two barrier traits . For example , in the model with two complementarity and two barrier traits , each individual ( plant or pollinator ) in a simulated community was assigned two complementarity and two barrier traits at random , and pairs of plants and pollinators interacted if the barrier traits of the pollinator had higher values than the respective traits of the plants and their complementarity traits were “sufficiently” similar . The complementarity models considered three variants ( the narrow , medium , and broad complementarity models ) , depending on how tight the overlap between the plant and pollinator traits had to be for the species to interact . The topology of simulated communities was then compared with the topology of 37 real-world plant–pollinator networks [3 , 4 , 34–36] , including three unpublished matrices kindly provided by J . M . Olesen . For conciseness , we only describe the results of the models providing a closer fit to the data . The assumption that the structure of mutualistic networks results from random interactions among individuals can reproduce certain topological properties of these networks [6 , 10] . To complement our work on mechanistic linkage rules , we used two formulations of the neutrality hypothesis of Vázquez and Aizén [6] to simulate plant–pollinator networks . In these models , the probability that a plant–pollinator pair interacts is proportional to their relative abundance , and relative abundances were drawn from either a uniform ( “uniform neutral model” ) or a lognormal ( “lognormal neutral model” ) probability distribution [37 , 38] .
Most ( 95% ) real-world networks were significantly nested , all exceptions being networks of small size ( network size [S] < 50 ) . Most modelled networks were also highly nested , although a few models showed a small proportion ( up to 11% for the mixed model ) of nonnested networks for small network sizes ( S < 160 and , in most cases , S < 70; Table 1 ) . The scaling properties of networks of increasing species richness ( S ) differed substantially from those of real-world pollination networks for all one-trait models , most two- and four-trait models , and the uniform neutral model ( Table 2; Figures 1 and 2 ) . Only the most restrictive complementarity ( two and four traits , narrow range ) , the four-trait barrier model , the mixed model , and the lognormal-neutral model approached the trends observed in real-world data , and the latter two provided the best fits to most variables . In real-world networks , the connectivity [C] decreases as a power of S ( Figure 1 ) [3] . While five models ( two- and four-trait barrier , four-trait narrow complementarity , mixed , and lognormal-neutral models ) approached this behaviour , all of them failed to predict real-world values across the complete range of network sizes and intersected the empirical fit at the lower , medium , and upper part , respectively , of the data range ( Figure 1 ) . The number of interactions ( L ) increased as a power of S in all models . The exponent of this relationship was greater than the one describing real-world data for all models , and the four-trait barrier , two-trait narrow complementarity , and lognormal-neutral models provided the closest fits ( Table 2 ) . In real-world pollination networks , both the nestedness of the networks ( N ) and the nestedness of random matrices with similar size and average C ( null-model nestedness [NR] ) increased as the logarithm of S ( Table 2; Figure 2 ) . This simultaneous increase of N and NR results in a logarithmic decrease of the relative nestedness [N*] . Five models ( two- and four-trait barrier , two-trait narrow complementarity , mixed , and lognormal-neutral models ) reproduced remarkably well the logarithmic increase in N with network size , but only two of them ( mixed and lognormal-neutral models ) performed well in predicting the nestedness of random matrices ( NR ) and , therefore , the N* ( Figure 2 ) . Bascompte et al . [4] divided their mutualistic networks into two groups , according to whether their number of interactions ( L ) was greater or lesser than expected for their size ( positive or negative residuals , respectively , in the regression between L and S ) . The N* in networks having positive residuals was greater than that in networks with negative residuals ( F = 6 . 59 , df = 1 , 50 , p = 0 . 013 ) [4] . This analysis pooled pollination and seed-dispersal networks , and the results did not reach significance when plant–pollinator networks ( of which there were 25 ) were analysed on their own . However , the addition of 12 networks to the data set used here already resulted in a significant difference ( t = 3 . 51 , p = 0 . 0015 , n = 37 ) . Running this analysis on the networks generated by the models , we observed that four models ( four-trait barrier , two-trait narrow-complementarity , mixed , and lognormal neutral models ) produced comparable results ( Figure 3 ) . Because N* decreases with NR and , in the range of interest ( C < 50% ) , NR decreases with C [39] , the increased N* of positive L-on-S residuals implies that , as the number of connections of mutualistic networks increases , the nestedness of real-world networks does not decrease as fast as it would in a random matrix . A multiple regression analysis showed that the trends described for real-world data ( decreased N and NR and increased N* with increased L , all corrected for the effect of S; Table 3 ) were shown by the mixed , lognormal-neutral , and four-trait narrow-complementarity models .
Recent work has greatly improved our knowledge of the patterns and scaling laws that characterise plant–pollinator networks , and the current challenge is to understand the ecological and evolutionary processes that underlie these regularities . Our work represents a first step in this direction . Given the excellent performance of the lognormal neutral model in predicting network characteristics , what is the use of exploring more complex linkage rules ? The main purpose of null models is to remind us that showing that a given model fits the data well is not necessarily a demonstration that the model is “correct”: more than one mechanism can produce any given pattern [40] . When two alternatives can explain a certain phenomenon , parsimony demands that we provisionally accept the simpler one , and null models are meant to represent the simplest explanation that can be offered . So , if a null model can be used to explain a pattern , there is no good reason to search for a more complex explanation . There are three reasons , however , why the lognormal neutral model can be rejected as a most-parsimonious explanation of network topology: ( 1 ) Assuming random interactions , in itself , is not sufficient to reproduce network topology: the uniform null model provided a very poor fit to the data , and in order to fit the model we had to assume a lognormal distribution of abundances . While the distribution of species abundances in many communities is well described by a lognormal distribution [41] , this is an empirical fit . The interpretation of this empirical fit is problematic because it is not clear whether generalist species are generalist because they are more abundant , or they are more abundant because , being generalists , they have access to more resources . ( 2 ) The neutral model assumes that species abundance determines the frequency of interactions . This assumption has been recently evaluated by Dupont et al . [36] , who found a significant correlation between abundance and generalisation level of the pollinators , but not the plants , in an alpine community . Ollerton et al . [42] , on the other hand , reported an association between interaction frequency and generalisation level but found no significant correlation between the relative abundance of insects in the community and their visitation rate to asclepiad plant species . ( 3 ) The neutral model assumes that network structure results from random interactions between species and “most phenotypic characteristics of interacting species may be irrelevant in determining broad patterns of interspecific interactions” ( e . g . , degree distribution ) [10] . This assumption is at odds with all reported empirical data , which show that phenotypic traits often prevent the interaction between specific pairs of plants and pollinators [8 , 33] . For example , Jordano et al . [8] show how 42% of 65% nonrecorded interactions in a plant–hummingbird subnetwork can be attributed to phenological or phenotypic mismatches between the plant species and the pollinator , indicating that a sizeable fraction of the interactions are “forbidden” and thus individuals cannot interact at random . Given that two key assumptions of the neutral model are not supported by empirical data and that its causal interpretation is problematic , and bearing in mind that more than one mechanism can produce any given pattern , we believe that an examination of alternative models based on phenotypic traits is granted . Let us now turn to the ecological mechanisms ( linkage rules ) that we have considered . Of these , only the multiple-trait models were compatible with the data , and the combination of both barrier and complementarity rules fitted them best . Although we have tried a number of other mechanistic models , such as stochastic versions of the complementarity and barrier models , and models that combined a lower amount of complementarity and barrier traits ( not dealt with in depth for the sake of conciseness ) , we have been unable to produce any version that fitted the data better than the mixed model presented here . Although the optimal fit of the four-trait mixed model does not necessarily imply that it provides a realistic description of the linkage rules responsible for network assembly in the real world [40] , we have shown that simple ecological processes may well lie behind the complexity of these large networks , and this result should encourage us to search for linkage rules in the field . A minimum amount of complexity is nevertheless required to explain real-world networks , since a combination of at least four traits was necessary to reproduce the patterns observed there . The poor fit of the complementarity models to the data shows that the complementarity rule , in itself , cannot be the linkage rule we are looking for . It should be noted that this result can be taken at face value: a model providing a poor fit to the data can be dismissed regardless of whether a better model is known . The complementary and barrier traits had contrasting effects on network characteristics . Plant–pollinator pairs specialised on each other predominate with complementarity models , leading to highly connected networks of low nestedness . To approach the nestedness of real-world networks , we must impose very restrictive conditions ( narrow ranges with several traits ) , and under these conditions , networks are very sensitive to random effects ( high values of NR ) and show excessively low C . With barrier models , on the other hand , specialised plants interact with pollinators that have access to very diverse resources , producing highly nested networks . In mixed models , which provide the best fits , complementary traits relax the trend to excessive nestedness of barrier models , and barrier models relax the too-low connectance and the high dependence of random effects of complementary traits . The demonstration that the complementary rule alone is unable to produce realistic network topologies has important evolutionary implications . It suggests that nested patterns of asymmetrical specialisation observed in mutualistic interactions do not arise because natural selection on mutualisms specifically favours the convergence and complementarity of traits in interacting species . Plant–pollinator coevolution may be the result of selection for plant traits that enhance visitation rates by the most efficient pollinator and/or pollination efficiency by the most common pollinator [1 , 17 , 43] , but floral evolution might also represent a compromise between attraction and defence [44–48] . Although this latter hypothesis has been questioned because deterrent traits will interfere with nectar exploitation by efficient pollinators [49] , recent models incorporating foraging decisions have shown that the evolution of pollinator-deterring traits is possible , provided that deterring parasites ( i . e . , floral visitors that , by reducing pollen availability to more-efficient pollinators , decrease the plant's fitness ) is sufficiently beneficial [50] . Facilitation of most-efficient pollinators predicts an increasing narrowing of the ecological range of plants and pollinators as a consequence of coevolution . In such a scenario , evolution leads to an increasingly tighter match of the coevolved morphological structures ( and/or functional and behavioural traits ) of plants and animals: the complementarity rule . In contrast , parasite deterrence leads to the evolution of floral barriers and pollinator structures allowing them to overcome plant defences: the barrier rule . The inability of the complementarity rule to mimic the topology of real-world networks thus suggests that the most-efficient–pollinator principle is not the main ( or the exclusive ) driving force behind the evolution of floral and pollinator traits . The optimal performance of the mixed model indeed suggests that both the most-efficient–pollinator principle and parasite deterrence operate simultaneously as evolutionary forces in natural communities . Barrier traits also provide an answer to Vázquez's main criticism of Jordano's forbidden interactions: “forbidden links resulting from phenological or morphological constrains are equally likely to affect any species , not just the most connected ones , and it is unclear whether this assembly constraint would necessarily lead to a decay in the tail of the degree distribution” [10] . By its very nature , barrier traits affect specialist and generalist , plant and insect species asymmetrically . While large trait values result in ecological specialisation for plant species ( i . e . , few insects have trait values large enough to access their resources ) , they result in ecological generalisation for insect species ( i . e . , insects with large trait values have access to many plant species ) . The asymmetrical effect of large trait values on plants and insects automatically generates nested patterns in the interaction networks . More important , competition for floral resources results in niche segregation that favours the exploitation of specialised ( i . e . , large trait ) plants by generalist ( i . e . , large trait ) insects , and vice versa [50] , thereby imposing an upper limit to the number of interactions that a generalist can sustain at any given time . The nested pattern seems to be a pervasive feature of mutualistic networks and has also been found in seed-dispersal networks and , more recently , in plant–ant [9] and fish–anemone [51] communities . Our results suggest that these systems may also be characterised by the coexistence of both complementarity and barrier traits . Examples of both types of traits for seed-dispersal networks may respectively include fruiting phenology and disperser availability , on the one hand , and fruit size and gape width , on the other [52] . Nevertheless , it must be noted that our work studies , other than network nestedness , the relationships between network size and various topological properties ( L , C , N , NR , and N* ) . In order to explore the applicability of complementarity and barrier linkage rules to other mutualistic systems , we would need to know how the topological properties of these networks scale with size . Our study represents a first effort to go beyond the description of network topology and into the analysis of the ecological and evolutionary processes behind it , which may complement ( and aid the interpretation of ) other attempts based on empirical studies [8 , 36 , 42] . The results presented in this paper stress the utility of mechanistic , phenotype-based approaches to community-level questions . To elucidate the actual ecological processes responsible for the assembly of plant–pollinator networks , we need detailed quantitative descriptions of the plant–pollinator networks , the relationship between traits of interacting plant–pollinator pairs , and the implications of phenotypic traits for plant and pollinator fitness .
The single-trait complementarity model assumes that plants and pollinators can be described by a single trait . Each species is characterised by a mean trait value and a range of variability , and a pair of species will interact if their traits overlap . Let Vi and Wj be the central trait value for pollinator species i and plant species j , respectively , and let δVi and δWj be their ranges of variability . Then , the value of the interaction matrix corresponding to this pair of species , Iij , will be and We considered three scenarios ( hereafter referred to as broad- , medium- , and narrow-range complementarity models , respectively ) that differed in the average value of δVi and δWj . In all cases , the Vi and Wj were independent random variates with uniform distribution in the interval ( 0 , 1 ) . The δVi and δWj , on the other hand , were random variates with uniform distributions in the intervals ( 0 , 1 ) , ( 0 , 0 . 5 ) , and ( 0 , 0 . 25 ) , for the broad- , medium- , and narrow-range complementarity models , respectively . For many traits , there is a correlation between average value and variability ( J . M . Olesen , unpublished data ) . Including this correlation in the model lead to identical results to the ones with uncorrelated mean and variability , and the results will not be discussed further . In most , if not all , real communities , flowers differ along several dimensions . The two- and four-trait complementarity models consider the possibility that pollinators must fit several floral traits in order to reach the reward offered by flowers . The flowers of the jth plant species are thus characterised by N central trait values and ranges of variability ( N = 2 or 4 for the two- and four-trait complementarity models , respectively ) , Wjk and δWj k , with k = 1 , … N . Similarly , the pollinators of the ith species are characterised by central trait values Vik and ranges of variability δVi k , with k = 1 , … N . Central trait values for plants and pollinators were independent random variates with uniform distribution in ( 0 , 1 ) , and ranges of variability were independent random variates with uniform distribution in the range ( 0 , 1 ) , ( 0 , 0 . 5 ) , or ( 0 , 0 . 25 ) for the broad- , medium- , and narrow-range complementarity models , respectively . The interaction matrix is given by and Like the complementarity model , the one-trait barrier model assumes that flowers and pollinators can be described by a single trait . We ignore variability in this case and simply assume that pollinators of species i will visit flowers of species j if their trait , Vi , is greater than the barrier of the plant , Wj ( assuming that pollinators of species i will visit flowers of species j if Vi < Wj is mathematically equivalent and leads to exactly the same results ) . As a result , the interaction matrix takes the values and The Vi and Wj were independent random variates with uniform distributions in the interval ( 0 , 1 ) . The two- and four-trait barrier models consider the possibility that pollinators must overcome several barriers in order to reach the reward offered by flowers . The flowers of the jth plant species are thus characterised by N barriers ( N = 2 or 4 for the two- and four-trait barrier models , respectively ) , Wjk , with k = 1 , … N , and the pollinators of the ith species are characterised by traits Vik , with k = 1 , … N , where the Vik and Wjk are independent random variates with uniform distribution in ( 0 , 1 ) . The interaction matrix is given by and In real communities , the interactions between flowers and pollinators are likely to combine both complementarity and barrier types of traits ( J . M . Olesen , M . Price , and N . Waser , unpublished data ) . The mixed models combine complementarity and barrier types of traits ( two traits each ) . For this purpose , we selected the barrier model that produced the best fit to real-world data: the narrow-complementarity model . The flowers of the jth plant species are thus characterised by two barrier traits , Wjk , two ranges of variability δWj k ( k = 1 , 2 ) , and two central trait values , Wjk ( k = 3 , 4 ) . The pollinators of the ith species are characterised by two barrier traits , Vik , two ranges of variability δVi k ( k = 1 , 2 ) , and two central trait values , Vik ( k = 3 , 4 ) . All variables are independent random variates with uniform distribution and ranges ( 0 , 1 ) for the barrier traits and central trait values and ( 0 , 0 . 25 ) for the ranges of variability . The interaction matrix is given by and The neutral models assume that interactions between plants and pollinators are determined by the relative abundance of the species . The jth plant species is characterised by its relative abundance , Wj , and the pollinators of the ith species by their relative abundance , Vi , where the Vi and Wj are independent random variates with uniform distribution in ( 0 , 1 ) . The interaction matrix is given by and The lognormal neutral model was similar to the neutral model , except that the Vi and Wj are now independent random variates with lognormal distribution . The interaction matrix is given by and We studied 200 communities generated with the algorithm specified above and used the same parameters to analyse the topology of the networks generated . Models were compared on the basis of their fit to real-world data . We first fitted separate regression lines ( logarithmic for N , NR , and N* , power fit for C and I ) to each data set generated by the various model ( model fits ) . For each model , we then calculated the deviation of the real-world data points from the model fit ( i . e . , the absolute value of the difference between each real-world data point and the expected value , calculated using the model fit ) and compared these deviations with the absolute value of the residuals of the real-world data points to a regression line fitted through these points ( same type of regression as above: logarithmic for N , NR , and N* , power fit for C and I ) . Deviations from model fits and residuals were compared using paired t-tests with sequential Bonferroni correction . We used paired t-tests because we compared pairs of distances for each data point: the deviation from the model fit versus the absolute value of the residual from the empirical fit . We considered as best models all those that did not differ significantly from the empirical fit ( i . e . , those which do not perform significantly worse than an empirical fit ) . Whenever all models performed significantly worse than the empirical fits ( i . e . , for C and I ) , we performed multiple comparisons among all models using paired t-tests ( similar to above but comparing for each real-world data point the deviation from one model fit versus the deviation from the other model fit ) with sequential Bonferroni correction . We considered as best models those with the best fit to real-world data ( i . e . , the lowest sum of squared deviations ) plus all those that did not perform significantly worse ( i . e . , those that did not show significantly higher residuals ) than them .
|
Whether they are antagonistic—as between predator and prey—or beneficial—as between pollinator and flower , interactions among all the key species in an ecosystem follow regular patterns . Connectivity ( the proportion of possible interactions that are actually realised ) , for instance , decreases with network size . The “forbidden links” hypothesis proposes that connectivity decreases because interactions are prevented by a mismatch of biological attributes between certain species . Mismatches could arise from the evolution of complementary traits in mutualistic relationships ( such as insects preferring to pollinate only flowers of a certain colour ) or of traits that prevent exploitation in antagonistic ones ( such as a plant growing a long corolla so that insects without a long proboscis cannot reach the nectar reward ) . We explored the consequences of simple linkage rules based on these two variants on the topology of plant–pollination networks . When compared to data for 37 real plant–pollinator networks , we show that a “mixed” model that combines simple rules from both “complementarity” and “barrier” models best explains the pattern of interactions . This implies , for example , that deterring floral parasites is at least as important as increasing pollination efficiency in the evolution of plant–pollinator networks . Our work emphasises the value of explaining the underlying ecological and evolutionary mechanisms generating such patterns .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"ecology",
"evolutionary",
"biology",
"plants",
"insects"
] |
2007
|
Linkage Rules for Plant–Pollinator Networks: Trait Complementarity or Exploitation Barriers?
|
The accepted model of eukaryotic translation initiation begins with the scanning of the transcript by the pre-initiation complex from the 5′end until an ATG codon with a specific nucleotide ( nt ) context surrounding it is recognized ( Kozak rule ) . According to this model , ATG codons upstream to the beginning of the ORF should affect translation . We perform for the first time , a genome-wide statistical analysis , uncovering a new , more comprehensive and quantitative , set of initiation rules for improving the cost of translation and its efficiency . Analyzing dozens of eukaryotic genomes , we find that in all frames there is a universal trend of selection for low numbers of ATG codons; specifically , 16–27 codons upstream , but also 5–11 codons downstream of the START ATG , include less ATG codons than expected . We further suggest that there is selection for anti optimal ATG contexts in the vicinity of the START ATG . Thus , the efficiency and fidelity of translation initiation is encoded in the 5′UTR as required by the scanning model , but also at the beginning of the ORF . The observed nt patterns suggest that in all the analyzed organisms the pre-initiation complex often misses the START ATG of the ORF , and may start translation from an alternative initiation start-site . Thus , to prevent the translation of undesired proteins , there is selection for nucleotide sequences with low affinity to the pre-initiation complex near the beginning of the ORF . With the new suggested rules we were able to obtain a twice higher correlation with ribosomal density and protein levels in comparison to the Kozak rule alone ( e . g . for protein levels r = 0 . 7 vs . r = 0 . 31; p<10−12 ) .
Gene translation is the central cellular process of sequence decoding to produce a protein . This process occurs in every organism and consumes most of the cellular energy [1]–[3] , thus it has important ramifications to every biomedical field [3]–[9] . Translation consists of three stages: initiation ( the binding of the ribosome to the transcript and the association of the small and large subunits ) , elongation ( the iterative translation of triplets of nucleotides to amino acids by the ribosome ) and termination ( the disassociation of the large and small subunits of the ribosome and the completion of the process ) , which form a recurring cycle of events . In eukaryotes the initiation step usually involves formation of a pre-initiation complex ( consisting of the small subunit , 43S or the 40S subunit , and initiation tRNA ) . According to the accepted scanning model [10]–[14] , this complex accompanied by additional initiation factors scan the mRNA sequence starting from its 5′ end towards its 3′ end , until a start codon is recognized ( usually an AUG that is identified by the initiation tRNA ) , which represents the beginning of the open reading frame ( ORF ) . The recognition of the start codon triggers the association of the large subunit and the beginning of the elongation step [10]–[13] . However , ATG codons are expected to be present in all possible reading frames upstream and downstream the START of the ORF; how thus does the scanning pre-initiation complex recognize the start ATG ? Over 30 years ago Kozak suggested that a specific context ( i . e . the nucleotides before and after a codon ) surrounding the initiating ATG codon is required for its recognition by the pre-initiation complex; asserting that this context should appear only in the vicinity of the initiating ( START ) ATG codon of the ORF [10] . Nevertheless , several deviations from the aforementioned scanning model have been reported based on small scale experiments . For example , there are reported cases of leaky scanning where AUG codons with sub-optimal contexts are skipped and translation initiates at a downstream AUG [14] , and there are cases of translation via internal ribosome entry site ( IRES ) and additional non-canonical mechanisms [15]–[17] , but these have been reported to be relatively rare . In addition , though there is a relatively low number of ATG codons in the 5′UTR as expected by the scanning model [18] , it was shown that there are many cases with non-optimal ATG context scores for the main ATG codon , and cases of ATG codons at the 5′UTR with relatively optimal context scores [19] , [20] , suggesting that the scanning model is an over-simplification of the reality . Previous genomic studies [10]–[13] , that were usually based on a relatively small number of transcripts and\or organisms , were aimed at understanding the nucleotide pattern surrounding the START ATG , with few considering alternative ATGs , and if so usually focusing on those in the 5′UTR and not the ORF . Specifically , amongst others , it was shown that there are a number of preferred nucleotide sequences surrounding the main translation initiation codon in eukaryotic genomes , which may vary among organisms [21] , [22]; there is a relation between sequence features and context scores at the beginning of the ORF and alternative initiation START codons [23] , [24] . In addition various papers studied alternative ORFs in the 5′UTR ( uORFs ) and their effect on translation regulation and re-initiation [17] , [25]–[28] , the effect of mRNA folding on translation and its initiation [17] , [29]–[33] , the relation between alternative ATGs near the beginning of the ORF and protein localization [34] , and the length of the 5′UTRs [27] , [35] . As mentioned , the fact that there are less AUGs at the 5′UTR [18] , and the effect of uORFs on the main ORF downstream , have been previously suggested and are related to the scanning model [17] , [25]–[28] . However , we are the first to quantify the 5′UTR ( and ORF ) region under selection for less ATGs , and the fraction of the protein levels variance that can be explained by these facts . The aim of the current study is to infer new universal rules related to the way in which initiation efficiency and fidelity are encoded in transcripts , to quantify potential alternative translation initiation events , and discern the selection processes maintaining the integrity of translation initiation and the resultant protein product . To this end , we analyse large scale genomic data of dozens of eukaryotes . Based on this analysis we reformulate and refine the translation initiation rules , and improve the understanding of the biophysics of initiation and the evolution of transcripts . Specifically , we show for the first time that there is selection for less ATG codons downstream to the beginning of the ORF; we estimate the length of the region under such selection upstream ( 5′UTR ) and downstream the beginning of the ORF; we report some additional sequence signals related to initiation fidelity such as anti-‘Kozak’ sequences surrounding ATG codons near the beginning of the ORF , and the appearance of stop codons close to them , that are under selection; and we are the first to quantify the partage of the protein levels and ribosomal density variance that can be explained by the different signals near the beginning of the ORF .
First , we re-studied the distribution of nucleotide allocation near the START ATG , focusing on Saccharomyces cerevisiae . As was reported in previous studies [21] , we find that in most of the positions near the START ATG , the distribution of the nucleotide composition is relatively close to uniform in S . cerevisiae genes ( Figure 1A; for example in position −1 the probability to see A/T/C/G is 0 . 45/0 . 21/0 . 18/0 . 16 respectively , and in position +1 the probability to see A/T/C/G is 0 . 3/0 . 27/0 . 13/0 . 3 respectively ) . The meaning of the above result is that if we consider the nucleotide with the highest probability ( e . g . A in positions ±1 ) , there is a very high probability to see a different nt in this position ( probability 0 . 55 and 0 . 7 respectively ) ; even if we consider the two nt with highest probability ( AT in position −1 and AG in position +1 ) , there is still relatively high probability to see a nt different to these two ( probability 0 . 34 and 0 . 4 respectively ) . However , as previously reported [10] , [36] , [37] , some of the positions ( e . g . −3; i . e . 3 nt before the beginning of the ORF ) exhibit a relatively non-uniform nucleotide distribution ( Figure 1A–B , see also supplementary Figure S37 related to Schizosaccharomyces pombe ) . Throughout the paper we use the measure , ( ribosomal density ) · ( mRNA levels ) ( Methods ) , that we named ribosomal-load as a measure of the intensity of gene translation . This measure considers total ribosomal flux over the gene transcripts . As can be seen in Figure 1B , also in the case of highly translated genes with high ribosomal load , the nucleotide distribution near the beginning of the ORF is relatively uniform ( Figure 1B; Methods ) . Is it possible that the nt distribution near the START ATG is related to the gene translation rate ? When we computed the correlation between the optimality of the context score ( a score that is based on the comparison to the context of the most highly translated genes; details in the Methods section ) and the protein levels ( Figure 1C ) , we obtained relatively low but significant correlations; similar results were obtained when we correlated the context score with ribosomal density ( Figure 1D; as we demonstrate later these correlations can be slightly improved when binning the data ) . Similar results were obtained in the 33 eukaryotes that were analyzed ( Figure 2A ) . Specifically , we find that the relative entropy ( Methods ) , a measure of conservation of a nucleotide among the organism's genes , of position −3 upstream of the START codon is the lowest; thus , nucleotide −3 is the most conserved site in all the analyzed organisms ( Figure 2A ) , concordant with previous small scale studies [10] , [36] , [37] . However , as can be seen in Figure 2A , positions −1 , −2 , and 1 are also relatively conserved in many of the analyzed organisms , also in agreement with previous small scale studies [10] , [36] , [37] . Specifically , it seems that the patterns of conserved positions vary along the evolutionary tree . For example , position 1 is relatively conserved in all the vertebrates . This result may reflect co-evolution between the ribosome and the region around the start ATG . The results reported in this subsection are in agreement with previous studies and motivated us to search for additional signals that are under selection near beginning of the ORF . In the previous section we demonstrated that there is a relatively weak signal corresponding to the START ATG in eukaryotes . This result raises the hypothesis that ATG codons near the beginning of the ORF contribute to alternative translation initiation events . If such alternative initiation events are deleterious we expect a selection for less ATGs near the beginning of the ORF . In the rest of the paper , to analyze signals related to such a selection , we considered three reading frames: frame 0 is identical to the reading frame of the gene ORF; frames 1 and 2 represent a frame shift of 1 or 2 nucleotides relative to the main frame ( Figure 2B ) . Indeed , as can be seen in Figure 3 for four model organisms ( S . cerevisiae , Caenorhabditis elegans , S . pombe , and Homo sapiens ) , the number of alternative ATGs is lower near the beginning of the ORF . Similar results were obtained for the 33 eukaryotes that were analyzed ( Figure 4 ) . Specifically , in almost all the organisms and frame shifts there is a decrease in the number of ATGs near the end of the 5′UTR , but also the beginning of the ORF . For example , we found that the genomic region of significant decreased number of ATGs at the beginning of the ORF ( see details in the Methods section regarding the estimation of this region ) is 14/14/11 codons ( i . e . triplets of nt ) for 0 , 1 and 2 nt frame shifts respectively in S . cerevisiae ( Figure 3 ) . Similar analysis was performed for the 5′UTR . The average region under selection for all the analysed organisms is 21/13/15 codons ( 16 across all frames ) for the end of the 5′UTR , and 4/19/10 codons ( 11 across all frames ) for the beginning of the ORF , for the three frames respectively ( Figure 4 ) . In the previous section we showed that there is a universal trend for a significantly lower number of ATGs near the beginning of the ORF . This result raises the question of whether these ATG profiles are selected for . For example , it is possible that the decrease in the number of ATGs near the beginning of the ORF is due to specific amino acid bias . Though the evolutionary selection for the ATG profile can be performed at the amino acid level and by non-synonymous mutations , we performed for the first time , an analysis to show that the signal remains , also when controlling for genomic features such as amino acid bias , codon bias and GC content . Specifically , we compared the ATG profile obtained in each of the analyzed genomes to the one obtained in randomized genomes with identical proteins , total GC content and codon bias ( see more details in the Methods section ) . As can be seen in Figure 5 , the number of ATG codons near the beginning of the ORF indeed tends to be significantly lower than in the randomized genomes , supporting the conjecture that the observed ATG pattern is under selection . Expressly , in the analyzed organisms the 5′UTRs include a lower number of ATG codons than expected , but also fewer ATGs than expected can be found more than five codons downstream of the START codon . Interestingly , there are organisms with positions with more ATGs than expected ( green dots in Figure 5 ) after the beginning of the ORF . This signal may be related ( probably indirectly ) to yet unknown codon bias signals after the beginning of the ORF . Analysis of the genomes of 33 eukaryotes by comparing them to randomized genomes ( Methods ) demonstrates that in most of them a region at the beginning of the ORF is under selection for less ATGs ( Figure 6 ) . Specifically the mean region under selection in the ORF for the analyzed genomes is 3 . 6 and 6 . 8 codons for the first and second frames respectively ( frame 0 is the same for real and randomized genomes as we maintain the original proteins in the random genomes , see Methods and Figure 6 ) , and 29 . 3 , 25 . 3 , and 26 . 8 in the 5′UTR for frame 0 , 1 and 2 respectively . It was previously demonstrated that there is selection for lower folding strength at the beginning of the ORF , presumably to improve the efficiency of translation initiation [30] , [31] . Thus , it is possible that the observed selection for a lower number of ATGs at the beginning of the ORF is a result of the selection for weak mRNA folding in these regions . To examine the relation between the number of ATGs in a short mRNA sequence and the folding energy , we randomized the sub-windows of mRNA sequences of the S . cerevisiae genome maintaining their amino acid content; for each sub-window across all genes we compared the folding energy of variants with at least one ATG codon to variants with no ATG codons ( Methods ) . We found that decreasing the number of ATGs tends to decrease the folding energy ( i . e . increase the folding strength ) ; thus , the decrease in the number of ATG codons at the beginning of the ORF is clearly not a result of selection for weak mRNA folding at the beginning of the ORF ( Figure 7A ) . It was also demonstrated that there is a pattern of slower codons at the beginning ( first 30–50 codons ) of the ORF probably to improve the cost and fidelity of translation [38]; thus , it is possible that the observed signal of decreased number of ATGs at the beginning of the ORF is somehow related to this reported pattern . To show that this is not the case , we sampled randomized genomes that maintain the distribution of codons at the beginning of the ORF ( Methods ) , and demonstrated that the signal of less ATGs at the beginning of the ORF in these randomized genomes is significantly weaker than in the case of the real genome ( see Figure 7B ) . In addition , if indeed translation tends to occur also from alternative ATG codons , and assuming that such an alternative translation initiation is usually deleterious , we expect stronger selection for less alternative ATG codons in highly translated genes ( e . g . genes with higher ribosomal load ) , which potentially have higher effect on the organism fitness than genes with lower translation rates . Indeed , this is exactly the pattern that was observed when we compared the group of 15% top/bottom genes in terms of the ribosomal load; see p-values in figure 7C ) . This result is in agreement with the scanning model , according to which upstream ATGs should exert negative translational control ( e . g . see [14] , [18] ) ; however , we found that such a signal appears also downstream of the start ATG . Finally , our analysis shows that for each of the non-ATG codons there is no region with significantly lower number of codons before/after the beginning of the ORF . Thus , indeed the ATG codon behaves differently from non-ATG codons , supporting the hypothesis that the ATG depletion is related to translation initiation from the alternative ATG codons ( see supplementary Figure S35 ) . In this and the next subsections we report additional signals that are encoded near the beginning of the ORF to prevent alternative initiation of the ribosome , and thus to decrease the cost of translation . At the first step , we considered the context of the alternative ATGs , which is related to the nucleotides in the vicinity of the main ATG codon . It has been demonstrated that the ribosome commences translation more efficiently from ATG codons with a specific context [10] , [11] . Thus , we decided to verify if there is selection for ATG codons with less efficient contexts upstream and downstream the main START ATG . To this end , we developed a context score that is based on the distribution of nucleotides near the main START ATG in genes with a very high ribosomal-load . This score can be computed for every ATG , and higher scores reflect a context that is more similar to the main START ATG context of highly translated genes , and thus it is expected to contribute to a more efficient initiation ( See details in the Methods section ) . We computed this score for each alternative ATG codon , and plotted the mean observed context score profile as a function of the distance from the beginning of the ORF over the entire genomes of S . cerevisiae and S . pombe ( Figure 8 ) . S . cerevisiae and S . pombe were selected as model organisms in this study due to the large evolutionary distance between them ( they are estimated to have diverged approximately 350–1 , 000 million years ago [39] ) . Indeed , as expected , the context score surrounding the ATGs in the vicinity of the main START ATG tends to be lower in the real genomes relatively to the randomized genomes ( Figure 8A; S . cerevisiae: 5′UTR p-value<10−142 , ORF p-value<10−142; S . pombe: 5′UTR p-value<10−142 , ORF p-value<10−142 ) . In addition , the context scores surrounding the ATGs s in the vicinity of the main START ATG in the case of genes that are highly translated , tend to be lower than in the case of genes that consume less ribosomes; specifically the difference between the two groups is larger around the beginning of genes ( S . cerevisiae: 5′UTR p-value = 1 . 6·10−15 , ORF p-value<10−142 ; S . pombe: 5′UTR p-value = 4 . 2·10−142 , ORF p-value<10−142 ; Figure 8B ) . An additional way to decrease the cost of alternative out-of frame undesired initiation events is to introduce a stop codon close to them . In such cases the translated peptide will be shorter and thus its translation and degradation will consume fewer cell resources . To check if indeed there is such a selection , we compared the distances of alternative ATG codons to the closest STOP codon in the same frame obtained for two groups of ATGs: 1 ) alternative ATG codons that are near the START ATG ( less than 6 codons ) and thus have higher probability of being involved in alternative translation initiation based on the scanning model; 2 ) alternative ATGs that are further from the START ATG . Indeed the distances were significantly shorter for the first group when looking across all frames , both when considering ATGs in the 5′UTR ( mean 30 . 8 nt vs . 41 . 2 nt; p-value = 0 . 0008 ) and at the beginning of the ORF ( mean 42 nt vs . 46 . 7 nt; p-value = 0 . 05 ) . Similar results were obtained when we considered the metabolic biosynthesis cost ( instead of peptide length , Methods ) of the peptide potentially translated from alternative ATGs near the START ATG vs . further from the START ATG ( 5′UTR: mean 294 . 8 vs . 404 with p-value = 0 . 0001; ORF: mean 402 . 6 vs . 452 . 3 with p-value = 0 . 04 ) ; or if we also added the translation cost ( Methods; in the case of the 5′UTR: 366 . 6 vs . 500 . 1 with p-value = 0 . 0002 , in the case of the beginning of the ORF: 500 . 6 vs . 561 . 1 with p-value = 0 . 046 ) . In addition , as can be seen in Figure 8C–D - the metabolic cost of translating alternative ATGs is lower than in randomized genomes ( Figure 8C; S . cerevisiae: 5′UTR p-value<10−245 , ORF p-value<10−245; S . pombe: 5′UTR p-value<10−245 , ORF p-value<10−245; the results when considering the translation cost in addition to the metabolic costs were very similar – see the Methods section ) , and in highly expressed genes relatively to lowly expressed genes ( Figure 8D; S . cerevisiae: 5′UTR p-value = 1 . 8·10−16 , ORF p-value = 3 . 6·10−120; S . pombe: 5′UTR p-value = 10−71 , ORF p-value = 10−245 ) . Thus , these results support the conjecture that there is selection for close STOP codons near alternative ATG codons to decrease the metabolic cost of their translation . The current study suggests and surveys a list of transcript features that are related to translation initiation; specifically these ‘rules’ include: 1 ) Fewer ATG codons at the end of the 5′UTR; 2 ) Fewer ATG codons at the beginning of the ORF ;3 ) Optimal Kozak/context sequence at the START ATG; 4 ) Anti-optimal Kozak/context sequence of ATG codons near the START ATG; 5 ) Close stop codon for alternative ATGs . Do the new rules related to translation initiation described in the current paper also have predictive power ? For instance , can they explain the variance in measured protein levels and ribosomal densities ? To answer this question , we designed four computational predictors of protein levels and ribosomal density based on features and rules related to translation initiation described in this study; each of the following predictors was based on more features/rules in comparison to the previous ones ( Methods ) . Four rules/features were considered: ( 1 ) The Kozak sequence in eukaryotes from [10]; ( 2 ) The main START ATG context score; ( 3 ) The number of alternative ATGs less than 30 codons downstream from the main START ATG; ( 4 ) The mean context scores of alternative ATGs less than 30 codons downstream from the main START ATG . We considered four predictors ( A , B , C , and D ) ; each predictor was based on an additional feature relatively to the previous one ( i . e . A is based on feature ( 1 ) ; B is based on feature ( 2 ) ; C is based on features ( 2 ) and ( 3 ) ; D is based on features ( 2 ) , ( 3 ) , ( 4 ) ; see Figure 9B ) . Indeed , as can be seen in Figure 9B , the correlation with binned protein levels and ribosomal density in S . cerevisiae ( Methods ) increases A ) < B ) <C ) < D ) , i . e . this result supports the conjecture that we have designed a better model of initiation: the correlations with protein levels are 0 . 313/0 . 534/0 . 664/0 . 695 ( p-values = 4 . 4·10−3/4 . 6·10−7/<10−12/<10−12 ) respectively , and the correlations with ribosomal density are 0 . 305/0 . 352/0 . 538/0 . 564 ( p-values = 3 . 4·10−6/7 . 8·10−8/<10−12/<10−12 ) respectively . Similar results were obtained for S . pombe ( see Figure 9B ( , or when considering and controlling for the number of features in each predictor ( Methods ) . Thus , the rules reported in this study can be utilized for designing highly expressed heterologous genes with efficient translation initiation .
From a biophysical point of view , the results reported in this study can be used to develop more detailed models of translation initiation . For example , a possible initiation model that has been suggested [45] includes a diffusion/random-walk-type motion of the pre-initiation complex ( note that other possible models may include energy-assisted directional scanning [45] ) . Under this model , the pre-initiation complex at each step has the probability to move forwards but also backwards along the transcript [45] . When the pre-initiation complex approaches an ATG codon it may start translating it with a probability that is related to the context of the ATG , but also to the distance of the ATG from the main START ATG [14] . Specifically , the probability of translating the main START ATG is lower than one , and the pre-initiation complex may continue scanning downstream of the START ATG . However , our analyses suggest that the probability to continue scanning after more than 5--11 codons downstream of the main START codon is negligible ( illustration in Figure 9B ) in terms of its effect on the organismal fitness – the selection signals after this region are not significant . The length of the region under selection is probably related to the biophysical properties of the pre-initiation complex such as its vibrations [46] , its geometry , and the biophysical features of the mRNA molecules . It was previously suggested that the translation elongation step is coupled in various ways with the initiation step via various signals encoded in the codons at the beginning of the coding sequences . For example , it was shown that the first codons of the coding sequences are under selection to induce weak local mRNA folding to improve the efficiency of translation initiation [30] , [31] , [47] . It was also shown that the codons at the beginning of the coding sequences have lower adaptation to the tRNA pool than the codons afterwards [38] , and that the codons ∼10–25 downstream from the main ATG are under selection for strong mRNA folding [29] , probably to improve ribosomal allocation by decreasing the initiation rate and increasing the distances between ribosomes . The current study further demonstrates the coupling between the translation initiation and elongation steps . We suggest that additional signals for efficient and low-cost initiation appear not only at the 5′UTR but also at the beginning of the ORF ( see also Figure 9B ) . These signals include amongst others lower numbers of ATGs and non-efficient ATG context scores in all reading frames . Thus , we suggest that the accepted paradigm that divides the translation process into three stages: initiation ( occurs at the 5′UTR ) , elongation ( at the coding sequence ) , and termination , is inaccurate . We propose an additional step that can be named late-initiation , which occurs at the region near the beginning of the ORF ( Figure 9B ) . This step is part of the elongation stage and its efficiency is affected by signals encoded in the ORF , but is also related to the initiation step . The results reported in this study are also related to the ‘cost’ of translation . It is known that translation is the metabolic process that consumes the largest amount of cellular energy [2] . Here we suggest that to accurately estimate the translation cost we should also take into account the alternative initiation events near the beginning of the ORF . The various signals related to selection against such alternative initiation events suggest , that in the case of genes that do have ATG codons near the beginning of the ORF , there may be a non-negligible number of alternative initiation events . These events presumably consume significant amounts of energy both at the synthesis stage of these short peptides and at their degradation stage . A related result in this context is the fact that the decrease in the in-frame codon frequency at the beginning of the ORF is weaker than in the case of the out-of-frame codons ( see , for example , Figure 4; the effect of in-frame codons is significant in 9 organisms ) . However , we actually do not expect that the signal in the in-frame codons will be as strong as in the case of out-of-frame or the 5′UTR signals due to the following reasons: The analyses described in this study suggest that evolution tends to eliminate events of alternative translation initiation . However , many alternative ATG codons still appear near the beginning of the ORFs in all the analyzed eukaryotes . It is possible that these ATGs still exist since the fitness advantage of eliminating these ATG codons is not high enough . An additional possibility is that at least some of the resultant alternative proteins from these alternative short peptides are functional . Indeed , recently it was demonstrated that many ATGs in the 5′UTR probably initiate translation in two model organisms , Mus musculus [48] and S . cerevisiae [49] . Cases where alternative translation initiation events affect the localization of the produced protein have also been reported in recent years ( see , for example [50]–[56] ) . Our analyses suggest that this phenomenon may be much more widespread and appears in many other eukaryotes . Finally , in agreement with the above paragraph , the observed selection for less ATG codons near the beginning of the ORF reported here does not contradict the fact that the alternative ATG codons that do appear near the beginning of the ORF are relatively conserved [57] . Actually , these two results support each other – the fact that alternative ATG codons may trigger alternative initiation events means that the alternative ATG codons that do appear near the beginning of the ORF probably tend to initiate translation; if they appear there ( and haven't been selected for ) it may mean that they have a ( relatively important ) functional role related to such alternative initiation , and thus should be more conserved relatively to other codons in this region . Analysis of the conservation of the nucleotides near the START ATG for 33 eukaryotes emphasized the importance and conservation of nucleotide position −3 [10] , [36] , [37] ( Figure 2A ) . However , here we clearly show that the pattern of conservation of additional nucleotides varies along the evolutionary tree – different groups of eukaryotes have different conservation patterns . This result may be related to co-evolution between changes in the eukaryotic ribosomes along the evolutionary tree , and the nucleotide sequences that have the optimal interaction with them . Further future studies in this direction may teach us about the evolution of structural changes in the eukaryotic ribosome . We would like to conclude the discussion with a comparison between the transcription and the translation processes . Specifically , do we expect to see similar/analogous signals related to the transcription initiation fidelity as the ones we reported regarding translation initiation ? We believe that the answer to this question is negative – these signals will be weaker in the case of transcription . There are several major dissimilarities making the two processes significantly different in this context . First , only in the case of translation are there three different frames that usually correspond to very different proteins; in the case of transcription there are no out-of frame initiation events , and a small shift should not affect the resultant protein . Second , alternative transcription initiation is also expected to affect the length of the 5′UTR and not the ORF , again not altering the produced proteins . Thus , we believe that the effect of alternative transcription on the organism fitness should be significantly lower than the effect of alternative translation .
|
Gene translation is an important step of the intra-cellular protein synthesis , which is a central process in all living organisms . Thus , understanding how translation efficiency is encoded in transcripts has ramifications to every biomedical discipline . The aim of the current study is to decipher the way translation initiation fidelity is encoded in eukaryotic transcripts , and how evolution shapes the beginning of transcripts . Based on the genomes of dozens of organisms we were able to derive a new , more precise , set of rules related to this process , facilitating a high resolution view of the mechanisms aiding translation initiation fidelity . Among others , we show that there is a universal trend of selection for low numbers of ATG codons upstream , but also in the 5–11 codons downstream of the START ATG , presumably to prevent translation of alternative ORFs over the main one . With the new suggested rules we were able to obtain a twice higher correlation with ribosomal density and protein levels in comparison to the previous translation initiation efficiency rule .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[
"systems",
"biology",
"molecular",
"genetics",
"biology",
"genomics",
"evolutionary",
"biology",
"computational",
"biology"
] |
2013
|
New Universal Rules of Eukaryotic Translation Initiation Fidelity
|
Visceral Leishmaniasis is a deadly disease caused by Leishmania infantum , endemic in more than 98 countries across the globe . Although the most common means of transmission is via a sand fly vector , there is growing evidence that vertical transmission may be critical for maintaining L . infantum infection within the reservoir , canine , population . Vertical transmission is also an important cause of infant morbidity and mortality particularly in sub-Saharan Africa . While vertical transmission of visceralizing species of Leishmania has been reported around the globe , risk factors associated with this unique means of Leishmania transmission have not been identified therefore interventions regarding this means of transmission have been virtually non-existent . Furthermore , the basic reproductive number , ( R0 ) , or number of new L . infantum infections that one infected mother or dam can cause has not been established for vertical transmission , also hampering the ability to assess the impact of this means of transmission within reservoir of human hosts . Canine Leishmaniosis ( CanL ) is enzootic within a U . S . hunting dog population . CanL is transmitted within this population via transplacental transmission with no reported vector transmission , despite many repeated attempts to find infected sand flies associated with these dogs and kennels . This population with predominantly , if not solely , vertical transmission of L . infantum was used to evaluate the critical risk factors for vertical transmission of Leishmania and establish the R0 of vertical L . infantum infection . Evaluation of 124 animals born to eighteen dams diagnostically positive for infection with L . infantum showed that there was a 13 . 84x greater chance of being positive for L . infantum within their lifetime if the mother was also positive within her lifetime ( RR: 13 . 84 , 95% CI: 3 . 54–54 . 20 , p-value: <0 . 0001 ) . The basic reproductive number for vertically transmitted L . infantum within this cohort was 4 . 12 . These results underscore that there is a high risk of L . infantum infection to transmit from mother to offspring . Targeted public health interventions and control efforts that address vertical transmission of L . infantum are necessary in endemic countries to eliminate visceral leishmaniasis .
Leishmaniosis is a disease caused by the obligate intracellular protozoan parasite Leishmania infantum [1–3] . Visceral Leishmaniasis ( VL ) can also be caused by Leishmania donovani which causes anthroponotic human visceral leishmaniasis in many countries including areas of Asia and Africa [4 , 5] . Zoonotic visceral leishmaniasis ( ZVL ) occurs in countries where the disease is endemic/enzootic in both human and animal populations . Within these countries the parasite is transmitted primarily via the phlebotomine sand fly [6 , 7] , although the role of other means of transmission , particularly vertical transmission , is not known . Dogs play an important role in the ecology and control of ZVL as they are the predominant domestic reservoir for the disease , with greater than 10% seropositivity often evident in dogs prior to emergent VL observed in people [8] . Dog ownership is a risk factor of human visceral leishmaniasis in multiple endemic countries with ZVL including Iran , Ethiopia , and Brazil [9–11] . As such , control measures in locations where ZVL is prominent include insecticide treatment or culling of dogs . Dogs remain an important model system for understanding the ecology and epidemiology of VL [12–14] . In recent years vertical , and specifically transplacental , transmission of L . infantum has been shown to be able to maintain infection within population ( s ) of dogs [15 , 16] . Dogs in Brazil have been shown to have infected in utero pups [17–19] . Multiple case reports and case series have identified vertical transmission of VL as an important cause of infant morbidity and mortality [20–22] . Compared to sand fly transmitted infection [23–25] , there is very little known about the risk of vertical transmission in dogs or people [16 , 26–29] . Therefore , understanding the impact and risk factors associated with parasite transmission in utero is important for education and treatment of infected mothers and for control of Leishmania infection within reservoir hosts . In the United States leishmaniosis is enzootic in hunting dogs . CanL was first identified in a dog with no travel outside of the United States in 1980 , but it was not until a large outbreak in a kennel in New York in 1999 that a larger scale study was performed to understand the broad burden of disease in the U . S . hunting dog population [30 , 31] . Further examination found that the primary route of transmission in this population was vertical , from dam to pup [15 , 32] and not via sand fly transmission despite many studies looking for infected sand flies associated with these infected dogs [33 , 34] . Despite experimental studies that indicate that vector transmission of the Leishmania infantum found in US hunting dogs is possible , there is no evidence that vector transmission occurs naturally from the U . S . hunting dog population [34–36] . A decade of surveillance of this hunting hound population found that the prevalence of CanL from vertical transmission was higher than expected and similar to the rates seen in countries where VL is endemic [37 , 38] . The basic reproductive number , R0 , or the number of secondary infections one infected individuals can cause within a susceptible population is an important epidemiological value for public health officials interested in control and elimination of this disease in endemic countries [39] . Previous calculations of the R0 for leishmaniosis have been restricted as these studies did not include vertical transmission as a potential route of transmission or lacked data to assess the true rate of transmission in a population [40–42] . This study examines both L . infantum positive and negative dams their offspring over the course of their lifetime to determine risk factors associated with vertical transmission and the corresponding crude basic reproductive number of vertical transmission . We hypothesize that the crude R0 of vertical transmission will be greater than one: Leishmania will maintain infection by infecting at least one pup from a diagnostically positive dam . Understanding the risk factors associated with vertical transmission remains an important public health concern as elimination and control programs focusing on vector control does not show 100% reduction of VL in endemic countries with zoonotic disease [43–45] , and vertical transmission appears to be a major risk for maintaining disease within an area or population .
A retrospective cohort study based on data collected regarding Leishmania infantum infection and exposure in U . S . hunting dogs since the 1999 outbreak [33 , 34 , 46] was completed . A subset of dams that were diagnostically positive and never diagnostically positive were identified . All pups from these two respective groups , ever positive or never positive , were tracked to determine their Leishmania diagnostic status . All historical data was collected from studies performed by Centers for Disease Control and Prevention [33 , 34] and the our laboratory at Iowa State University and the University of Iowa [15 , 32 , 35 , 47–49] . All dogs were enrolled in this retrospective study with informed consent from their caretakers and all protocols followed were approved by the University of Iowa Institution Animal Care and Use Committee ( IACUC ) an AAALAC accredited institution following the requirements for the US National Institutes of Health Office of Laboratory Animal Welfare Assurances which operates under the 2015 reprint of the Public Health service Policy on Humane Care and Use of Laboratory Animals , under protocol #6041721 . An active surveillance cohort of 4 large ( >50 dogs each ) kennels was established and observed over a 9-year period . Our laboratory visited each of these kennels biannually for at least three years , at which point two of the kennels elected to control visceral leishmaniasis in their kennel via euthanasia . Licensed veterinarians collected 1–5 cc whole blood and serum from all dogs present at these kennels . Demographic information regarding time of pregnancy , sex and age were collected . The active surveillance cohort testing period extended from 2007 to 2017 . This surveillance effort started eight years , or at least one hunting-dog life-span , after the major L . infantum outbreak in 1999 with CanL surveillance performed on these same dogs passively by the CDC as reported in [33 , 34] . DNA was isolated from canine peripheral whole blood samples collected in heparinized or ethylenediaminetetraacetic acid ( EDTA ) via the QIAmp DNA Blood Mini Kit ( Qiagen , Valencia , CA ) per manufacturer protocol . The quality and quanitty of DNA was assessed using a NanoDrop 2000 ( Thermo , Scientific , Waltham , MA ) . Real time quantitative polymerase chain reaction ( RT-qPCR ) was performed as previously described with all samples run in duplicate with positive samples determined as samples with 1 or more positive wells and negative samples with no amplication in any wells [37 , 49–51] . All RT-qPCR included both positive , negative control blood spiked with 106 Leishmania infantum parasites , and negative controls . Between 2007 and 2011 kinetoplastid primer and probe targets were used . The primer and probe sequences were as follows: F 5’-CCGCCCGCCTCAAGAC , R 5’-TGCTGAATATTGGTGGTTTTGG , ( Integrated DNA Technologies , Coralville , IA ) and TaqMan probe , 5’-6FAM-AGCCGCGAGGACC-MGBNFQ , were used ( Applied Biosystems , Foster City , CA ) . From 2012 to present ribosomal primer and probe targets were utilized . The sequences were as follows: F 5’-AAGTGCTTTCCCATCGCAACT , R 5’ CGCACTAAACCCCTCCAA ( Invitrogen , Life Technologies , Grand Island , NY ) , probe: 5’ 6FAM-CGGTTCGGTGTGTGGCGCC-MGBNFQ ( Applied Biosystems , Life Technologies , Grand Island , NY ) . Assays were performed on ABI 7000 systems until 2016 when they were run on ABI 7900 systems ( Applied Biosystems ) . Analysis was performed using ABI 7000 System SDS Software and ABI 7900 HT Sequence Detection Systems Version 2 . 4 . 1 . ( Applied Biosystems ) . Serological status was determined via the Dual Path Platform Canine Visceral Leishmaniasis ( DPP CVL ) assay ( Chembio Diagnostic Systems Inc . , Medford , NY ) or via immunoflourescent anitbody test ( IFAT ) . The DPP CVL assay detects Leishmania-specific anitbodies via rK28 antigen , a Leishmania recombinant antigen . The assay was utilized as previously described with positives determined as dogs with a test and control line appearing at 4 minutes or less [51] . All positives or questionable samples were confirmed using the Chembio microreader system . The system detects the intensity of the control and test lines . Immunoflourescent antibody test ( IFAT ) was utlized on canine samples before 2015 . This test was performed by the Division of Parasitic Diseases at the Centers for Disease Control and Prevention as previously described [33 , 52] . Positive tests were determined as tests where immunofluorescence was reported in 50% of organisms at serum dilutions equal to or above 1/64 . These tests were performed without identifying each dog ( blindly ) and were repeated four times at each dilution to determine positivity . Univariate analyses were performed to determine unadjusted relative risk values for dam’s age at the time of birth , diagnostic status during the year of birth , and other variables . Pearson chi-squared test and Fisher’s exact test were used to assess categorical variables against disease status . Mann-Whitney test was used to compare dam’s age between disease states as age not normally distributed . An unpaired t-test with the Welch’s correction was utlized to compare litter size between infected and uninfected groups . For assessment purposes the dam’s diagnostic status via qPCR or serology during the same year she gave birth was utilized . Feasability restrictions , the fact the gestational period for a dog is two months , prevented the researchers from obtaining information on the dam’s diagnostic status during pregnancy . Multivariable logistic regressions were performed to determine adjusted relative risk . Due to the fact that the dam’s diagnostic status can be determined via qPCR and serology , diagnostitc status was assessed different ways through three models . One model included the overall diagnostic status of the dam ( ever diagnostic positive vs never diagnostic positive ) , the dam’s age at the time she gave birth ( older than six years of age vs younger than or equal to six years of age ) , and the sex of the puppy ( male vs female ) . To further assess the dam’s diagnostic status impact a second model was created with qPCR and serology as separate variables . A third model was created separating the dam’s serology and dam’s PCR status in the year she gave birth into two explanatory variables . P-values of less than 0 . 05 were determined as statistically significant . Each model was fit assuming a binomial distribution with a log link function . Kaplan-Meier time to event analysis was performed to assess whether dam’s diagnostic status altered time to pup diagnostic positive . Basic reproductive number was calculated using dams who were ever diagnostically positive for Leishmania , from which their average proportion of puppies per litter that became Leishmania diagnostic positive was determined . Hunting dogs are a medium size dog with average litter size in the study was between 6–7 [53] . Using the average litter size , the proportion of puppies in a litter that would become positive for Leishmania was determined as the basic reproductive number of vertical transmission in US hunting dogs . For all analyses , as observation of transmission of L . infantum infection was the goal , L . infantum exposure/diagnostic result status for each dog was identified as “ever diagnostically positive” for Leishmania or “never diagnostically positive” for Leishmania . Positivity was determined as qPCR positive and/or serologically positive at any point during the dog’s lifetime . All statistical analyses were performed using SAS 9 . 4 ( SAS Institute , Cary , NC ) and Graph Pad Prism 6 ( GraphPad Software , Inc , La Jolla , CA ) .
Compared to sand fly transmsision , little is known about the risk factors of vertical transmission of Leishmania infantum . Understanding these risk factors and the corresponding likelihood of transmission as measured by the basic reproductive number , R0 , provides valuable information for assessing control and elimination programs for zoonotic leishmaniosis . We hypothesized that a dam’s positive Leishmania diagnostic status during pregnancy would be a risk factor of L . infantum transmission . A retrospective cohort study examined the health records from 130 dogs born to eighteen dams for risk factors associated with vertical transmisison and the corresponding indiviudal level basic reproductive number calculation . Six dogs were removed from analysis due to incomplete data to use in statistical models . There were eight dams identified as Leishmania diagnostic positive at some point in their lifetime and ten dams were diagnostically negative throughout their lives . Most dogs were not multiparous . The average litter size was 6–7 pups ( Table 1 ) . Dogs that ever became diagnostically positive were born to dam’s that were slighly older in age , 5 . 10 years compared to 4 . 04 years ( p-value = 0 . 0004 ) and were more likely to be born to dams who had previously had at least one litter ( p-value <0 . 0001 , RR = 3 . 351 95% CI:2 . 32–4 . 83 ) . There was no significant difference between Leishmania diagnostic outcome in male vs . female dogs . Dogs ever diagnostically positive were more likely to be from large ( r ) litters . This difference have been skewed by on particulalry large litter of fifteen puppies from a dam that was diagnostically positive during her year of pregnancy at six years of age . When this litter is removed the signficance of dam age and litter size is reduced . Additional analysis shows that dogs born to a dam that was qPCR positive for Leishmania infantum at the time of pregnancy had a relative risk of being diagnostically positive during their lifetime 10 . 46x greater than the risk than when the dam was PCR negative at the time of pregnancy ( Unadjusted RR: 10 . 46 , 95% CI: 3 . 57–31 . 82 , p-value <0 . 0001 ) . The dam’s serological status during the year she gave birth was also found to increase the risk of offspring testing diagnostically positive within their lifetime . Pups born to dams seropositve during the year they gave birth were 2 . 69x more likely to test positive for Leishmania within their life ( Unadjusted RR: 2 . 69 95% CI: 1 . 32–5 . 52 , p-value 0 . 0054 , Table 2 ) . A series of three logistic regression models were created to determine the risk factors associated with vertical transmision of L . infantum . The models were labeled as A , B , and C . Whether the puppy became diagnostically positive within their lifetime or not was used as the outcome for these models . Model A assessed a dam’s diagnostic status as ever positive for Leishmania during their lifetime as an explanatory variable along with age at the time of pregnancy , and sex of the dog . When adjusting for all other explanatory variables it is found that dogs born to a dam that was ever positive for Leishmania have a relative risk 13 . 84x greater than dogs born to a dam that was never diagnostically positive ( Adjusted RR: 13 . 84 , 95% CI: 3 . 54–54 . 20 , p-value 0 . 0002 ) . In order to assess the impact of seropositivity/ Leishmania exposure vs detectable parasite infection via qPCR from the blood in transmission two additional models were created; models B and C . Model B utilized a dam’s diagnostic status during the year she gave birth ( positive vs negative ) , age of dam during pregnancy ( older than six vs younger ) , and sex of the puppy as explanatory variables . In this model puppies born to dams diagnostically positive via qPCR or serology during the year of pregnancy were 2 . 27x more likely to become positive for Leishmania compared to dogs born to a dam that was diagnostically negative at the time of pregnancy . Model C used the dam’s qPCR status , serostatus and age during the year of pregnancy and progenys’ sex as explanatory variables . This model allows for the assessment of how parasite infection via qPCR from the blood vs seropositivity/ Leishmania exposure could affect Leishmania transmission . Pups born to a dam that was qPCR positive for Leishmania during pregnancy were 3 . 14x more likely to become positive for Leishmania in their lifetime ( Adjusted RR: 3 . 14 , 95% CI: 1 . 37–7 . 18 , p-value: 0 . 0067 , Table 3 ) . Two dogs born to a dam that was never qPCR or serologically positive for Leishmania were found to be positive during their lifetime . One dog was identified as ever qPCR positive and one as ever serologically positive . A dam’s serological status during the year of pregnancy was not statistically significantly associated with her offspring becoming diagnostically positive . This was an interesting finding as qPCR is a measure of parasite DNA within the peripheral blood . As the transplacental blood supplied each in utero puppy with nutrients , and apparently parasites , this may have increased the risk of the puppy becoming infected with Leishmania parasites . Based on our findings via univariate and logistic regression , we were interested in evaluating the risk of becoming Leishmania diagnostic positive over years of a pup’s life based on it’s mother’s diagnostic status . To better assess when dogs became diagnsotically positive for Leishmania , time to event Kaplan-Meier curves were created . To visualize the overall relationship between age at which offspring became Leishmania diagnostically positive this was compared between the groups of dam Leishmania positve vs negative ever . Dogs born to positive dams ( red ) were statistically significantly more likely to become positive at a younger age than dogs born to negative dams ( blue ) ( chi-square: 40 . 33 , p-value <0 . 0001 , Fig 1 ) . Based on the previous finding that dam qPCR status during the year she was preganant was also highly correlated with the pup becoming Leishmania diagnostic positive , dam’s qPCR status ( negative during year of birth vs . positive ) was utilized . Offspring born to dams who were qPCR positive ( red ) during the year they gave birth were significantly more likely to become positive for Leishmania via qPCR at younger ages than offspring from dams that were qPCR negative ( blue ) ( Fig 2 , chi-squared: 49 . 54 p-value <0 . 0001 ) . This was similar to the relationship between dams who were seropositve during the year they gave birth and the age at which their puppies became seropositive for Leishmania ( Fig 3 , chi-squared 18 . 43 , p-value <0 . 0001 ) . Within this study cohort we found two instances and three litters in which three generations of infected dogs were identified . In these specific families , on average the second generation had evidence of infection in 79 . 2% of dogs ( seropositive or PCR positive at some point of their lives ) . To date , dogs in the third generation were 60 . 4% sero- or PCR positive for L . infantum . It should be noted that one of these litters are dogs currently 3 years old . These younger dogs may become qPCR or seropositive as they age and experience immunosuppressive conditions . A small subset of 20 dogs within the study were more closely followed through their entire lives and cause of death was established . Of the 20 dogs from infected dams for which a cause of death was identified 95% , or 19 of these dogs , died from clinical visceral leishmaniasis . The one dog identified as being diagnostically positive for Leishmania infantum but not dying from clinical visceral leishmaniasis died from a secondary infection with Ehrlichia spp . as identified at necrospy . Neither of the two dogs born to uninfected mothers found to be infected with L . infantum have died from VL , but this is a very small sample size . The basic reproductive number for vertical transmission of Leishmania remains of interest in order to determine effectiveness of control efforts that are in many cases focused on vector transmission . R0 was calculated based on information regarding each litter from this population . On average , 64% of dogs born to a dam who were ever diagnostically positive for Leishmania will become positive in their lifetime . Using the average litter size of our population , between 6 and 7 , we calculate an average R0 of 4 . 16 .
A retrospective cohort study was performed to assess risk factors associated with vertical transmission of Leishmania infantum and a crude basic reproductive number was calculated for the population . The mother’s L . infantum diagnostic status during the year she was pregnant was a statistically significant risk factor for her offspring to be L . infantum positive during their lifetime , with a signficant 13 times greater risk of infection than dogs without maternal expsoure to Leishmania . Despite these dramatic findings in this retrospective cohort study , there is an overall paucity of reported cases of congential VL . This is likely for several reasons; first the diagnostic difficulties of confirming that a case is due to congential transmission vs . expsoure to sand fly transmitted disease in endemic areas . To date there is no way to distinguish L . infantum infection by route of transmission , so in endemic areas the presumption is that cases are vector borne , although this may not be true . The second reason is availability of treatment of mothers for ZVL during pregnancy reducing the maternal parasite load and therefore decreasing transmission to the child/offspring [27 , 54] . This study is the first study to calculate the basic reproductive number and determine risk factors associated with vertical transmission of Leishmania infantum in a population where vertical transmission is the main route of transmission and there is no known vectorial transmission [55 , 56] . Vertical transmission occurs not only in leishmaniosis but other infections as well , such as human immunodeficieny virus ( HIV ) and malaria [57 , 58] . In HIV infection , anti-retroviral treatment during pregnancy and caesarean births have been associated with decreased risk of transmission likely due to a reduced exposure to the mother’s blood and virus [59] . In malaria , mothers with malaria during pregnancy are at risk of vertical transmission [60] . This is similar to CanL where dogs born to mothers that were qPCR positive during pregnancy had a much higher risk of becoming positive for Leishmania . This is likely due to the fact that a positive qPCR test identifies that there was parasite DNA in the blood which is shared between mother and pup across the placenta . The mother’s combined diagnostic status of seropositive or qPCR positive was a significant risk factor in predicting whether a puppy would become positive during their life . This was also reasonable as dogs become immunocompromised there can be increased disease progression and parasite replication with higher serological diagnostic values in dogs with more severe clinical disease [47 , 50] . Within this study there were two sets of three generations of dogs that were followed and data indicating that transmission occurred across these generations . These results provide additional evidence that vertical transmission is capable of maintaining visceral leishmaniasis in a population over multiple generations . Within this study two Leishmania-positive dogs were born to dams that were never qPCR or serologically positive for Leishmania . In the hunting dog community , dogs are commonly drafted or traded between groups and across international borders from endemic to non-endemic areas . Such movement of dogs greatly increases the difficulty of consistent testing across different locations and disease risk levels . This testing limitation may have led to a false negative status for the mother[61] . The two puppies that were identified as serologically/qPCR positive without maternal exposure could also have been exposed to Leishmania via fighting or wound cleaning of infected pen mates as blood to blood contact is possible due to the fact the dogs are housed in communal areas . A small subset of 20 dogs ( 15% of the study population ) were followed until death and a cause of death was identified . 100% of the dogs with an established cause of death were diagnostically positive for Leishmania infantum at some time throughout their life . Of those dogs with an established cause of death in this cohort , 95% died from clinical visceral leishmaniasis . These results highlight that without treatment many of these animals will progress with clinical disease . Therefore , it remains an important public health goal to identify ways to prevent L . infantum transmission from mother to child in both animals and people . The basic reproductive number was calculated via an indivdual level model system , thus the number refers to the number of dogs in each litter that one mother could infect . This calculation provides a direct assessment of the R0 within this cohort . An R0 of approximately 4 ( rounded to the nearest whole number to refer to number of puppies in the litter ) shows that this disease is capable of maintaining at high levels within a population without vector transmission . The R0 of other diseases , such as influenza , which remain important public health concerns across the globe are as low as 2 [62] . Astonishingly , in comparison the R0 identified for an average canine litter coming from an infected dam was greater than 4 , similar to the estimated basic reproductive number of smallpox [63] . As these studies all occur in an area where there is not holoendemic pressure of sand fly transmission , establishing the R0 and effect of vertical transmission in dogs from endemic areas would be valuable . These studies would all be limited by the inability to distinguish sand fly transmitted and vertical transmission once pups are born and it is hard to know the outcome of maternal infection on in utero pups . Current control programs for leishmaniosis in countries where the disease remains endemic in both humans and animals include vector control , vaccination , and dog culling , which has been shown to be ineffective . Based on the data evaluated here , there is a significant need to also address vertical transmission through canine sterilization programs [64–66] . Recent studies have identified vaccination of infected/exposed asymptomatic dogs as safe , so vaccination to boost a better immunity prior to pregnancy may be of value to reduce transmission to the next generation [51] . Larger scale xenodiagnosis studies need to be performed to determine what skin burden of parasites is required to transmit CanL and the effectiveness of vaccination [67] , allopurinol or additional ( immuno ) therapies to reduce parasite load immediately before or during pregnancy . Further analysis using Bayesian compartmental model techniques combining both vector and vertical transmission should be used to better understand the basic reproductive number for the full ecology of Leishmania infection in endemic areas and subsequently model how this number can be altered by public health control and prevention measures to assess elimination potential . The findings of this study underscore the need for risk management through spaying and neutering animals by dog owners to reduce vertical transmision of L . infantum from their dogs . This action would decrease propagation of CanL within the canine reservoir for reduced transmission to people .
|
Canine leishmaniosis ( CanL ) is a deadly disease caused by Leishmania infantum parasite , it is found in animal populations , including people , in more than 98 countries across the globe . CanL was first identified within the US in hunting dogs 1980 and then again in 1999 when a large outbreak in a kennel in New York occurred . As the US is usually not considered a tropical country , there was much debate about how this neglected , vector borne , tropical disease had made its way into these dogs . We found that within the U . S . hunting dog population CanL is transmitted from mom to pup with no reported sand fly transmission in the population , despite multiple attempts to find infected sand flies associated with these dogs . While vertical transmission of this disease has been reported in case reports around the globe , risk factors associated with this unique means of Leishmania transmission are not known . Furthermore , the basic reproductive number , ( R0 ) , or number of new infections that one infected animal can cause has not been reported for vertical transmission of L . infantum . It is important to know the R0 as it helps identify how infectious a route of transmission can be and therefore how easy it might be to control this infection . A cohort of 124 dogs from 18 dams was analyzed from 1999 to 2016 for factors related to vertical transmission . Offspring from dams ever diagnostically positive for infection with L . infantum were 13 . 84x more likely to become positive for L . infantum themselves within their lifetime ( RR: 13 . 84 95% CI: 3 . 54–54 . 20 p-value: <0 . 0001 ) . The basic reproductive number for vertically transmitted L . infantum within this cohort was 4 . 12 . These results underscore that an infected mom is highly likely to infect her offspring if treatment is not started to prevent transmission . There is a need for any public health prevention and control efforts to address vertical as well as vector transmission of canine leishmaniosis in endemic countries .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"maternal",
"health",
"obstetrics",
"and",
"gynecology",
"tropical",
"diseases",
"vertebrates",
"parasitic",
"diseases",
"dogs",
"parasitic",
"protozoans",
"animals",
"mammals",
"protozoans",
"veterinary",
"diagnostics",
"leishmania",
"women's",
"health",
"pregnancy",
"neglected",
"tropical",
"diseases",
"medical",
"risk",
"factors",
"veterinary",
"science",
"veterinary",
"medicine",
"infectious",
"diseases",
"zoonoses",
"epidemiology",
"protozoan",
"infections",
"leishmania",
"infantum",
"eukaryota",
"leishmaniasis",
"biology",
"and",
"life",
"sciences",
"amniotes",
"organisms"
] |
2019
|
Maternal Leishmania infantum infection status has significant impact on leishmaniasis in offspring
|
Plant cells grown in culture exhibit genetic and epigenetic instability . Using a combination of chromatin immunoprecipitation and DNA methylation profiling on tiling microarrays , we have mapped the location and abundance of histone and DNA modifications in a continuously proliferating , dedifferentiated cell suspension culture of Arabidopsis . We have found that euchromatin becomes hypermethylated in culture and that a small percentage of the hypermethylated genes become associated with heterochromatic marks . In contrast , the heterochromatin undergoes dramatic and very precise DNA hypomethylation with transcriptional activation of specific transposable elements ( TEs ) in culture . High throughput sequencing of small interfering RNA ( siRNA ) revealed that TEs activated in culture have increased levels of 21-nucleotide ( nt ) siRNA , sometimes at the expense of the 24-nt siRNA class . In contrast , TEs that remain silent , which match the predominant 24-nt siRNA class , do not change significantly in their siRNA profiles . These results implicate RNA interference and chromatin modification in epigenetic restructuring of the genome following the activation of TEs in immortalized cell culture .
More than half a century has passed since the concept and practice of plant cell culture was first introduced [1] . Unlike most animal cells , plant cells can change from one differentiated state , representing a committed developmental program , to a completely different one via a transition through a dedifferentiated state typical of callus tissue [2] . This process is achieved by varying concentrations and relative proportions of two major plant growth regulators ( auxin and cytokinin ) in the growth medium [3] . Under appropriate conditions , callus cells can continue to grow in “immortalized” suspension cultures , which can be maintained indefinitely without differentiation . Plant cells grown in culture exhibit unprecedented levels of genetic and epigenetic instability [1] . Through changes in gene activity , plant cells are able to respond to the challenges presented by tissue culture conditions and continue to divide according to internal and external cues . Epigenetic regulation plays an important role . For example , hormone habituation is a process during which plant cells in culture shift their requirement for exogenous growth regulators between the auxo- and autotrophic states [4] . The epigenetic nature of this shift is demonstrated by the fact that upon plant regeneration from habituated cell culture and reculturing , the original requirement for growth factors is present again in the reestablished cell culture . In addition to the reversibility , this switch occurs at rates that are orders of magnitude higher than mutations rates in plants [5] . The culturing of plant cells induces heritable variation known as somaclonal variation . It has been observed in numerous instances for a variety of plant species , and given the early promise of cell culture for advances in agriculture , the stochastic nature of somaclonal variation poses a significant challenge [6 , 7] . Genetic analyses of somaclonal variants in maize , rice , tobacco , and other plant species identified novel transposon insertions into genes as an important cause of somaclonal variation [8–14] . Additionally , heritable epialleles are known to occur in plants [15–18] , and there is recent evidence that meiotically heritable epialleles with high reversion rates can be induced in plant cell culture [19] . Plant cells in culture undergo changes in both DNA methylation of the repetitive portions of plant genomes [20–24] , as well as at individual TEs [25 , 26] . Reactivation of retrotransposon Athila , a highly repetitive component of the pericentromeric heterochromatin in Arabidopsis thaliana [27] , was also reported in a cell suspension culture of continuously dividing dedifferentiated Arabidopsis cells [28 , 29] . TEs can serve as insertional mutagens of plant genomes , whereas widespread activation can lead to a wide array of chromosomal rearrangements . These rearrangements can , in turn , lead to misregulation of genes and transposons [1] . Plant genomes contain numerous and diverse TEs [30 , 31] , most of which are transcriptionally silent [27] . In Arabidopsis thaliana , and in other plants , silencing is achieved by both DNA methylation as well as by histone deacetylation and H3 lysine 9 dimethylation , which is targeted by small RNAs ( sRNAs ) in the 21–24-nucleotide ( nt ) range [32–34] . In addition , this mutually enforcing set of chromatin modifications allows for the silenced states of TEs to be inherited throughout normal cell divisions and plant development . When silencing is perturbed by mutations in DNA methyltransferases , RNA interference ( RNAi ) machinery , or histone modifiers , TEs can become reactivated [32 , 35–38] . With the advent of chromatin immunoprecipitation ( ChIP ) , microarray and deep sequencing technologies , it is now possible to map DNA and histone modifications genome-wide [39–41] . In animal systems , these approaches have uncovered histone modifications specific to promoters , such as histone H3 trimethylation at lysine4 ( H3K4me3 ) , to gene coding regions H3K4me2 and H3K36me3 , to polycomb-regulated genes ( H3K27me3 ) , and to repeats and TEs ( H3K9me3 and H4K20me3 ) [42–44] . This epigenomic information has been used to describe chromatin states specific for different stages of differentiation from pluripotent embryonic stem cells , as well as in cancer cell lines [45–48] . Comparable chromosome-wide and genome-wide epigenomic profiles in plants provided the first glimpses into the plant epigenome . DNA methylation in Arabidopsis has a bimorphic distribution: dense cytosine methylation in all three sequence contexts ( CpG , CpNpG , and CpNpN ) characterizes TEs and other repetitive DNA , whereas short clusters of CpG methylation are found in over 25% of gene coding regions [32 , 49–51] . The role of euchromatic , genic DNA methylation is not known , but it is extremely variable between accessions of Arabidopsis thaliana and , as a rule , is not accompanied by heterochromatic histone H3K9 dimethylation or the presence of sRNAs [51] . Additionally , the coding regions of Arabidopsis genes are enriched in H3K4me2 and H3K27me3 [32 , 52 , 53] . In contrast , heterochromatic transposon DNA methylation is strongly correlated with histone H3K9me2 and associated small interfering RNA ( siRNA ) [32 , 54] . Beyond a role in transposon silencing , epigenetic modifications play a role in plant gene regulation and development . Gene imprinting in the endosperm [55] and the regulation of vernalization [56 , 57] are examples of epigenetic control in plant development that are regulated by DNA and histone methylation , respectively . However , epigenetic changes that occur in rapidly dividing dedifferentiated cells during plant cell culture have not yet been described . Using a combination of ChIP and DNA methylation profiling on tiling microarrays , and high-throughput sRNA sequencing , we have mapped the location and abundance of histone and DNA modifications in rapidly dividing , dedifferentiated Arabidopsis suspension cells , and compared them with gene expression in suspension cells and in callus tissue . We have found dramatic DNA hypomethylation and activation of specific TEs , as well as hypermethylation in genic regions that may be responsible for the emergence of epialleles . DNA methyltransferase and most other epigenetic modifiers are up-regulated in cell culture , indicating that loss of targeting rather than modification is responsible for TE activation . Gain of 21-nt siRNA and associated expression of the Argonaute2 gene family member may accompany this loss of targeting .
Chromosome-wide maps of DNA and histone modifications were created by microarray hybridization to a custom Arabidopsis chromosome 4 tiling array [51] . The 22 , 761 probes interrogate most of Arabidopsis chromosome 4 , including the repetitive pericentromeric heterochromatin , as well as the heterochromatic knob on the short arm of chromosome 4 [39] . We profiled covalent modifications of DNA and histones as well as sRNA populations in cultured suspension cells ( Figure S1 ) of the Columbia ecotype that have been in continuous propagation for several years ( I . Meier , unpublished data ) . The cells were cultured in 1× B5 medium with minimal organics supplemented with 3% sucrose and 1 . 1 mg/l 2 , 4-D with a 1:1 and 1:9 final dilution for the 16-h ( cells7 ) and the 96-h ( cells4 ) culture regimens , respectively , every 7 d . Flow sorting of DAPI-stained nuclei at 96 h postculture split showed that a vast majority of these cells were diploid and in the G1 stage of the cell cycle ( Figure S1 ) . In addition , BrdU incorporation results showed that the cells7 sample had close to three times higher BrdU incorporation relative to the cells4 sample ( Figure S1 ) . We also tested the ability of this long-term cell suspension culture to redifferentiate into shoots by growing microcalli from the suspension culture on solid shoot-inducing medium . In contrast to freshly induced calli , which regenerated shoots after 6 wk under the same conditions , cell suspension culture-derived calli continued to proliferate without any differentiation or shoot regeneration . Cytosine methylation was determined by comparing McrBC-digested genomic DNA to an undigested sample of the same DNA by two-color hybridization to the chromosome 4 tiling array [32] . The presence of 5mC was detected as a statistically significant difference in hybridization signal in the untreated sample relative to the McrBC-treated sample , using a linear model that partitions the variation into both biological and technical sources ( Text S1 ) . Samples from two different time points during the cell suspension culture cycle of 7 d ( cells4 and cells7 , as noted above ) were analyzed in quadruplicate . Upon testing for differences between the two DNA methylation profiles , almost no change in DNA methylation ( false discovery rate [FDR] significance level α = 0 . 01 ) occurs during each culture passage . Subsequent statistical analyses benefited from combining these two experiments , thus treating the two DNA samples as biological replicates ( Figure 1 ) . ChIP-chip was performed by hybridization of DNA immunoprecipitated with antibodies against H3K4me2 , H3K9me2 , and histone H3 acetylated at lysine56 ( H3K56ac ) [58] . Each immunoprecipitated and amplified sample was hybridized in triplicate against the input DNA sample , using two-color array hybridization . Statistical analysis ( Text S1 ) identified tiles with significant enrichment of a particular histone modification ( Figure 1 ) . We found significant genome-wide hypomethylation of heterochromatic DNA in cell suspension culture ( Figure 1 ) as compared to Columbia seedling DNA [51] . Significant differences in DNA methylation were also seen in the euchromatic portions of chromosome 4 , in which a gain of methylation in cell suspension culture was the most prevalent trend , typically within the transcribed regions of genes ( “gene-body methylation” ) where CpG methylation occurs . The chromatin maps of the heterochromatic portion of chromosome 4 showed that significant loss of DNA methylation occurred in cell suspension culture relative to differentiated leaf cells ( Figure 1 ) . Although very few regions lost DNA methylation completely , a majority of TEs that comprise heterochromatin became hypomethylated ( Figure 1 ) . Contrary to gene-body DNA methylation , which is correlated with the presence of H3K4me2 , heterochromatic DNA methylation in Arabidopsis is associated with H3K9me2 and with the presence of sRNAs , which direct this histone modification . DNA hypomethylation , loss of H3K9me2 , and in most cases , gain of H3K4me2 , were specific to families of TEs . Most retrotransposons , including Athila , and Copia elements , as well as DNA transposons such as helitrons , AtMu elements , and a few Vandal and AtENSPM elements , lose heterochromatic marks irrespective of their location on the chromosome . In contrast , gypsy class Atlantys and AtGP long terminal repeat ( LTR ) retrotransposons and other DNA transposons such as most of the Vandal and AtENSPM elements retain heterochromatic marks ( Figure S2 ) . This observation is particularly striking when two repetitive elements are found juxtaposed with a sharp boundary between the loss and retention of heterochromatic marks corresponding to the boundaries of coding regions of the TEs ( Figure 1 ) . Transcriptional reactivation of TEs was analyzed by microarray analysis using Affymetrix Arabidopsis ATH1 microarrays . Cells7 samples were compared with 2-wk-old seedlings grown on plates under standard conditions , and 6-wk-old Arabidopsis calli generated from cotyledons grown on MS plates supplemented with a combination of cytokinin and auxin at callus-inducing concentrations . ATH1 arrays , in addition to bona fide genic probes , have close to 1 , 200 probe sets specific to various Arabidopsis TEs , of which approximately 200 map to chromosome 4 ( R . K . Slotkin and R . A . Martienssen , unpublished data ) . A majority of oligonucleotide probes on this array are specific to a single TE , or a small number of closely related TEs , which allowed for precise mapping of TE transcripts to their chromosomal location ( s ) . Weak expression of a few TEs was seen in callus , but a strong up-regulation of a much larger number of TEs was found in suspension cells , relative to both seedling and callus samples ( Figure 2 ) . A total of 142 ( 12% ) out of 1 , 155 TE-specific probes on the array were up-regulated 1 . 5-fold or higher in cell suspension culture . Only 22 and 56 TEs ( <2% and <5% of the total TE probes ) were expressed in seedling or callus samples , respectively ( Figure 2 ) , none of which were in common with cell suspension samples . The transcriptional reactivation of selected TEs located on Arabidopsis chromosome 4 was confirmed by reverse-transcription PCR ( RT-PCR ) ( Figure S2 ) . We further investigated the specificity of epigenomic changes in Arabidopsis cell suspension culture heterochromatin by sequencing sRNA from the two cell suspension culture samples ( cells7 and cells4 ) , as well as from leaf and callus samples . In total , we sequenced 1 , 910 , 836 and 3 , 075 , 388 sRNAs matching the Arabidopsis Col-0 reference genome from the cells7 and cells4 libraries , respectively , as well as 1 , 737 , 719 and 2 , 070 , 923 sRNAs from the leaf and callus libraries , respectively . ( Table S1 ) . We tested the technical reproducibility by resequencing the cells4 library , which showed an excellent match in relative size frequency distribution ( unpublished data ) . The overall size distribution of siRNAs from the four libraries showed a typical distribution with the relative frequency maxima at 21 nt and 24 nt , when known microRNAs ( miRNAs ) were removed from the libraries ( Figure 3 ) . Whereas the 24-nt class was the most abundant size class , an overall decrease in the relative frequency of 24-nt sRNAs was noted in the cell suspension culture library , cells4 . This prompted further analysis of the sRNA data to identify genomic regions responsible for this reduction . Each sRNA sequence from the dataset was associated with a particular Repbase repeat class representing all the major families of TEs in the Arabidopsis genome , and we examined the size distribution of those classes of sRNAs ( Figure 3 ) . This analysis showed that when we compared the relative size frequency of sRNAs specific to particular TE families ( Repbase repeat classes ) , two patterns emerged . The sRNAs specific to those TEs that lose heterochromatic marks and become transcriptionally reactivated in cell suspension culture ( Athila , AtCopia , AtMu , and Helitron elements ) showed a major shift in relative frequency from 24 nt to 21 nt in the cells4 , but also in cells7 libraries , relative to sRNA profiles of the leaf and callus libraries ( Figure 3 ) . In contrast , the sRNAs specific to those TE families that do not lose the heterochromatic marks and remain transcriptionally silent in cell suspension culture ( Atlantys , AtGP , Vandal , and AtENSPM ) retain the relative sRNA size distribution found in the leaves and callus ( Figure 3 ) . We also examined the spatial distribution of sRNAs in cell suspension culture by mapping sRNAs to representative full-length elements representing each of the TE classes listed above . Whereas the distribution and abundance of sRNAs matching a selection of Atlantys2 ( Figure 4 ) , AtGP1 , and Vandal14 elements ( Figure S3 ) on chromosome 4 did not change significantly in the four sRNA libraries , a major shift was observed in both the size and location of sRNAs mapping to Athila2 ( Figure 4 ) . Importantly , the overrepresented 21-nt Athila sRNAs ( and the 24-nt sRNAs to a lesser extent ) were localized to the coding regions of this element ( Figure 4 ) . In addition , the change in relative size frequencies of Athila sRNAs observed in cell suspension culture ( Figure 3 ) was found to be due to massive up-regulation in the production of 21-nt sRNAs , rather than a net loss of 24-nt-size sRNAs ( Figure 4 ) . The same patterns were observed in four other full-length Athila elements located on chromosomes 1 , 2 , 3 , and 5 ( unpublished data ) . Similarly , a Helitron2 element on chromosome 4 was found to gain 21-nt sRNAs specifically at the 3′ end of the coding region ( Figure S3 ) without a loss of 24-nt sRNAs . The only exception to this general trend was the small family of AtMu elements in which the increase in the relative frequency of 21-nt sRNAs was accompanied by a significant loss of the 24-nt sRNAs in cell suspension culture ( Figure S3 ) . One way that TE families might be differentially regulated by sRNAs is through interaction with different Argonaute proteins , which in turn , is determined by the 5′ nucleotide and the sRNA size [59 , 60] . We , therefore , compared the first nucleotide frequencies by size , origin , and TE specificity ( Figure S4 ) . Size , distribution , and 5′ nucleotide specificity were found to be unchanged in leaves and callus , with the majority of TE sRNAs having A as their 5′ base ( Figure S4 ) . However , in the cell suspension culture libraries , the frequency of 5′ A sRNAs is increased in every class of TE that we tested , irrespective of the sRNA size ( Figure S4 ) . In addition , sRNAs from TEs that lose heterochromatic silencing also have a decrease in the relative frequencies of sRNAs with 5′ G as their first nucleotide , irrespective of their size . sRNAs specific to Atlantys , which remains heterochromatic , have no such shift in 5′ nucleotide preference in cell suspension culture ( Figure S4 ) . In contrast to heterochromatic DNA methylation , which is correlated with transcriptionally silent sequences , gene-body methylation tends to occur within genes that are transcriptionally active [49 , 51] . To test whether genic DNA methylation coincides with the sequences enriched for H3K4me2 , a histone modification characteristic of transcribed regions , the chromosomal distribution of cytosine methylation was compared to H3K4me2 using an “average gene analysis” that utilized all genes represented by multiple tiles and lacking sequence homology to known repetitive elements . For each chromatin modification , the number of tiles detecting enrichment was calculated at 10% intervals relative to the length of each gene , with position 1 being the transcription start site . This was compared with the number expected if distribution of significant features was randomly distributed relative to gene position ( Figure 5A ) . These comparisons revealed that the distributions of 5mC and H3K4me2 are spatially correlated along gene coding regions , such that both epigenetic marks are depleted from the sequences flanking the gene coding regions ( positions 0–1 and 9–10 in Figure 5A ) , whereas they are specifically enriched , relative to expected average chromosomal distribution of each mark , over the open reading frames of the gene coding regions ( positions 1–9 in Figure 5A ) . In fact , 69% of genic tiles with significant DNA methylation in cell suspension culture are also associated with H3K4me2 , compared with only 37% of genic tiles without methylation . This correlation is not unique to cell culture-derived chromatin , since a similar correspondence is observed for the distribution of DNA methylation data in seedling [51] and H3K4me2 methylation in leaf tissue ( Figure 5A ) . In contrast to H3K4me2 , H3K9me2 is enriched in intergenic regions ( Figure 5A ) and is negatively correlated with the presence of gene-body DNA methylation . Instead , it is highly enriched in TEs [32] , which are found predominantly in intergenic regions . Strikingly , our “average gene analysis” revealed that H3K56 acetylation was uniquely and strongly enriched 0–100 bp upstream of the coding region ( Figure 5A ) . The association of H3K56ac with gene promoter regions is ubiquitous throughout the chromosome ( Figure 5B ) and does not seem to be restricted to expressed genes . This modification is thus a useful tool for gene-finding algorithms . It is also interesting to note that TEs that gain H3K4me2 are also associated with H3K56ac at their 5′ ends ( Figure 1 ) , indicating they may be actively transcribed . Both hypomethylation of transposon sequences and hypermethylation of euchromatic gene sequences can lead to the generation of epialleles at specific loci , which have the potential to heritably transmit the transcriptional state imposed by the methylation status of its sequences [61] . Since hypermethylation of the euchromatic portion of chromosome 4 was observed in cell suspension culture relative to seedling DNA methylation [51] , we looked at the distribution of this novel DNA methylation with respect to gene coding regions ( Figure 6A ) . Every tile on chromosome 4 was assigned a category depending on whether its sequence was part of a gene , a gene with an associated repeat , or an intergenic region with no known coding potential , and each tile was then counted as methylated if there was any significant DNA methylation detected at that location . The majority of changes in suspension cell culture euchromatin were due to novel DNA methylation relative to seedling DNA methylation [51] in the genic tile classes ( 46% ) , although some genes methylated in seedlings lost methylation in cell suspension culture ( 12% ) ( Figure 6A ) . We tested whether this euchromatic hypermethylation is associated with sRNAs , by comparing the gain or loss of DNA methylation to the frequency of sRNAs from leaves and cell suspension culture from the corresponding genomic regions . We found no significant association between the sRNAs and genic methylation on euchromatic DNA hypermethylation ( p = 0 . 29 ) . By contrast , the association of sRNAs and heterochromatic DNA methylation was highly significant ( p = 0 . 001 ) . A complete list of genes methylated in either cell suspension culture alone or in both the cell culture and seedling is listed in Table S2 . We also compared the DNA methylation profiles and the genome-wide gene expression levels using Affymetrix ATH1 array . This comparison revealed major changes in gene expression , with 41% of genes exhibiting statistically significant changes ( FDR controlled to 0 . 05 ) , between cell suspension culture and seedling , and 58% between cell suspension culture and callus ( Figure 7 ) . However , these expression changes were not correlated with changes in gene-body methylation between cell suspension culture and seedlings . In search of hypermethylated genes that might be silenced in cultured cells , we found that 14% of genic tiles that become hypermethylated in cell suspension also gained H3K9me2 , doubling the number of doubly marked genes found in seedlings . A total of 89% of these tiles were associated with increased sRNA matching these genes , for a total of 28 genes on chromosome 4 ( Table S3 ) that represent candidates for epiallele formation in the immortalized , proliferating cells of the cell suspension culture . An example of a novel epiallele is a member of the IDD family of zinc finger transcription factors At4g02670 , which gains both DNA and H3K9 methylation as well as 24-nt sRNAs in cell suspension culture ( Figure 6B ) . Two Arabidopsis genes on chromosome 4 are well known to be epigenetically regulated: the floral regulator AGAMOUS ( AG ) and the imprinted homeodomain protein gene FWA . A short repeat with limited similarity to the Vandal class of transposons in the second intron of AG is prone to hypermethylation in ddm1 and met1 mutants [17] , whereas a short interspersed nuclear element ( SINE ) element within FWA 5′ UTR is hypermethylated and targeted by sRNAs [32 , 62] . AG and FWA change in their epigenetic status in cell suspension culture , with the second intron of AG gaining a low level of DNA methylation . This is accompanied by methylation of H3K9 in the same region and the appearance of sRNAs 24 nt in size ( Figure 6C ) . The imprinted FWA gene is normally expressed only from the maternal chromosome during endosperm development [55] . Along with other TEs , the SINE element within the FWA 5′ UTR region becomes hypomethylated in cell suspension culture with a concomitant loss of 24-nt sRNA from the same region ( Figure 6D ) . This is accompanied by strong transcriptional reactivation of FWA with a 25-fold increase in cell suspension culture relative to seedling expression and by accumulation of H3K4me2 and low levels of DNA methylation in the FWA coding region ( Figure 6D ) . In order to test whether the heterochromatic DNA hypomethylation and changes in histone methylation patterns seen in cell suspension culture may be due to changes in expression of DNA and histone methyltransferases and other genes affecting chromatin modifications , gene expression levels were examined for a list of 380 chromatin regulation-related genes from the ChromDB database ( http://www . chromdb . org/ ) . A total of 162 out of 380 genes on this list exhibited significant expression differences between cell suspension culture and Col seedling samples . Of these , 121 were up-regulated and 41 were down-regulated in cell suspension culture ( Table S4 ) . DNA cytosine methyltransferases were overexpressed; from a 2-fold increase for CMT3 , DRM1 , and DRM2 to a 5-fold increase for MET1 ( Table S4 ) . The chromatin remodeling gene DDM1 is also required to methylate TEs , and DDM1 transcripts were six times more abundant in cell suspension culture . Another SNF2 class chromatin remodeling gene required for gene silencing , CLSY1 , was also up-regulated in cell suspension culture 3-fold , but DRD1 did not change in expression levels . The VIM1 gene , required to guide CpG DNA methylation during replication , was also up-regulated more than 10-fold . Additionally , the DNA demethylase ROS1 transcript levels were abolished ( Figure S5 ) . In summary , genes involved in DNA methylation are up-regulated in cell suspension culture , whereas those involved in DNA demethylation are down-regulated . This likely contributes to genic hypermethylation , but cannot account for TE activation . Methylation of H3K9me2 in Arabidopsis is under control of histone methyltransferases of the Su ( var ) 3–9 type . The SUVH4 gene ( KYP ) predominates , but SUVH5 and SUVH6 have influence over some heterochromatic sequences . The observed loss of H3K9me2 in cell suspension culture could be explained if the transcripts of H3K9 methyltransferases were down-regulated . However , comparison of transcript levels for these three SUVH genes showed that only SUVH6 was down-regulated in cell culture , whereas the major H3K9 methyltransferase KYP was up-regulated 3-fold in this sample relative to seedling , with H3K9me2 remaining ubiquitous at heterochromatic regions of the genome . No significant difference in SUVH5 transcripts was observed ( Table S4 ) . Three putative and closely related H3K9 methyltransferases ( SUVH1 , SUVH3 , and SUVH8 ) were also up-regulated in cell suspension culture ( Table S4 ) . Histone demethylases and jumonji domain family members JMJ22 and JMJ30 were up-regulated several fold in the cell suspension culture , whereas JMJ24 was down-regulated . The LSD1 family of histone demethylase paralogs in Arabidopsis ( FLD , LDL , LDL2 , and LDL3 ) showed no changes in gene expression ( Table S4 ) . Histone deacetylases followed the same trend , with HDA6 , HDA8 , HDA9 , HDA17 , HDA18 , and HDA19 up-regulated in cell suspension culture , whereas HDA14 was the only histone deacetylase down-regulated . Overall , these results indicate that a preponderance of genes involved in heterochromatic modifications increase in expression levels in cell suspension culture relative to seedling . Changes in the pattern , rather than the extent , of DNA and histone methylation in suspension cell culture might be more readily explained by changes in the targeting machinery , which relies on RNAi . We , therefore , looked at the expression levels of genes from the Dicer-like , RNA-dependent RNA Polymerase , and Argonaute gene families as well as the plant-specific RNA polymerase IV ( NRDP1a ) . Whereas genes involved in miRNA processing , such as AGO1 and DCL1 , were all either down-regulated in cell suspension culture relative to seedling or did not change in expression , those genes that are part of the heterochromatic silencing sRNA pathway , AGO4 , NRPD1a , RDR2 , and DCL3 , were all up-regulated in the cell suspension culture at least 2-fold , with the NRPD1a transcripts being 18 times more abundant in cell suspension culture . Interestingly , additional members of the Argonaute gene family , AGO2 , AGO3 , AGO5 , and AGO9 , were up to 30-fold up-regulated in cell suspension culture , potentially accounting for changes in sRNA abundance .
We found that most genic DNA methylation is associated with H3K4me2 , indicating that gene-body methylation by MET1 might be guided , directly or indirectly , by H3K4me2 ( see below ) . Gene-body methylation in Arabidopsis is dependent on MET1 [32] , is highly variable between Arabidopsis accessions , and is positively correlated with elevated gene expression . In support of this idea , the comparison between the distribution of gene-body DNA and histone methylation patterns revealed that H3K4me2 is colocalized with gene-body DNA methylation for over 75% of methylated genes . In yeast , both H3K4me2 and H3K4me3 mark genic regions , but H3K4me3 is characteristic of transcriptionally active genes [64] , whereas in plant and mammalian genomes , H3K4me3 is found at promoters [43 , 65] . In mammalian cells , de novo methylation via DNMT3 complexes cannot be recruited to nucleosomes containing H3K4me3 , providing a mechanism . perhaps , for exclusion of DNA methylation from promoters [66] . Whereas H3K4me2 is deposited exclusively within coding regions , we showed that promoters are specifically enriched in H3K56ac while gene coding regions were depleted . In addition to its role in genome stability , the acetylation of Lys56 at histone H3 ( H3K56ac ) has been shown recently to associate with gene promoter regions in budding yeast [67 , 68] and in particular with transcriptionally active promoters and the elongating form of RNA Polymerase II in yeast and Drosophila [69] . We show for the first time , to our knowledge , that H3K56ac is also present in gene promoter regions in plants , as well as in TEs that have lost heterochromatic marks in cell suspension culture . Acetylation of this particular amino acid located at the first point of contact between the major groove of DNA and the histone octamer may allow access to the core transcriptional machinery [67] as well as replication-coupled nucleosome assembly [70] . Even though most methylated genes are associated with H3K4me2 , and are actively transcribed , a small percentage of methylated genes are associated with H3K9me2 in leaves , indicating they are epigenetically silenced . This percentage doubles in cell culture , so that 28 genes from chromosome 4 become hypermethylated and associated with H3K9me2 . Close to 90% of these genes are associated with novel 24-nt sRNAs in cell suspension culture , indicating sRNA is the major targeting mechanism for epiallele formation . These genes may also include potential targets of the DNA demethylase ROS1 , whose expression is lost in cell culture and is accompanied by hypermethylation of the promoter region of ROS1 [71] , which contains a helitron TE fragment . A third of these genes are significantly down-regulated or silenced and represent good candidates for epigenetically regulated genes affecting various aspects of somaclonal variation and habituation [4 , 72] . It is important to note that independent plant cell culture lines can vary considerably in their molecular phenotypes . Therefore , analyses of epigenetic changes in independent plant cell culture lines are needed before general conclusions about individual genes can be drawn . Heterochromatin is activated in cultured suspension cells . In contrast to euchromatin , the pericentromeric heterochromatin of chromosome 4 was massively hypomethylated in cell suspension culture . The majority of these heterochromatic regions consists of various classes of TEs [27] , but only some of these classes were hypomethylated , whereas others , such as the LTR retrotransposons ATLANTYS and AtGP , remained methylated . Loss of DNA methylation was often accompanied by loss of H3K9me2 and in some , but not all cases , gain of H3K4me2 . Similar to reactivation of TEs in calli or cell suspension culture of maize , rice , and tobacco [11 , 14 , 26 , 73] , as well as Arabidopsis [28] , we were able to detect transcripts from this group of TEs , though only a subset of the elements with a permissive chromatin landscape was actually transcribed . This very likely reflects the fact that most TEs have undergone genetic changes ( mostly deletions and insertions ) over evolutionary time , diminishing their coding and transcriptional potential . These changes are reminiscent of transposon deregulation caused by mutations in ddm1 , a chromatin remodeling factor [74] . These mutants lose transposon-specific DNA methylation , as well as H3K9me2 , accompanied by gain of H3K4me2 [54] . However , ddm1 mutants affect all families of TEs , whereas cell suspension culture only reactivates specific TEs . Furthermore , 75% of a sample of 380 genes involved in heterochromatin regulation , including DDM1 , are up-regulated in cell culture , indicating that the silencing machinery is largely intact . Instead , the failure to deliver epigenetic marks to some TEs and not others could be the result of perturbations in targeting these modifications by RNAi and sRNA molecules . While sRNAs accumulated from both silenced and reactivated TEs in cell suspension culture , the relative abundance of different size siRNA changed with respect to differentiated tissues . TEs with the strongest reactivation in cell suspension culture , such as Athila retrotransposons , as well as some members of the Helitron , Mu , Vandal , and En/Spm families of transposons , gained 21-nt siRNA , but lost 24-nt siRNA only rarely . In contrast , TEs that remained silent in cell suspension culture , such as most Atlantys and AtGP retrotransposons and some Vandal and EnSpm TEs , produced the 24-nt siRNAs at the same level as they did in vegetative tissue . It would therefore seem that failure to maintain silencing at heterochromatic sequences in cell suspension culture is associated with a shift in relative abundance of 21-nt siRNAs . The novel 21-nt TE sRNA we have detected in cultured cells show a preference for adenosine ( A ) at the 5′ end , and a reduction in guanine ( G ) . These changes are diagnostic of the preferential loading of sRNA onto one or more members of the family of Argonaute proteins found in Arabidopsis . Although the expression levels of AGO4 , the Argonaute protein known to bind 24-nt heterochromatic sRNA , did not change in cell suspension culture , four Argonaute genes whose roles have not yet been defined , AGO2 , AGO3 , AGO5 , and AGO9 , are massively up-regulated in cell suspension culture , along with PolIVa , which has been implicated in heterochromatic sRNA biogenesis [75 , 76] . Arabidopsis AGO2 has recently been shown to bind 21-nt siRNAs , with a strong preference for A as the first nucleotide of the siRNA [59 , 60] . This is precisely the most abundant class of sRNAs in cell suspension culture , and AGO2 is massively up-regulated in cultured cells . If AGO2 is coupled with the biogenesis of the 21-nt siRNA , then it may preclude the proper targeting by and inclusion of the 24-siRNAs into other AGO complexes , resulting in diminished potential of heterochromatic targeting by sRNAs . Additionally , our data support a minor role for a so-far unidentified AGO protein with a preference for G in targeting H3K9me2 and DNA methylation synergistically with AGO4 . Reactivation and hypomethylation of TEs is not unique to plant cell culture . Drosophila Kc and S2 cultures harbor numerous actively transcribed copia and copia-like retrotransposons [77 , 78] capable of transposition [79 , 80] . The reactivation seems independent of the origin of the culture and does not depend on DNA methylation , which is absent from Drosophila [81] . However , components of the piRNA pathway required to silence several classes of transposons are not expressed in Drosophila cell cultures , perhaps accounting in part for this activation [82] . In immortalized cancer cell cultures , non-LTR Alu SINE elements [83 , 84] , as well as LINE-1 elements , lose DNA methylation and are reactivated [85–87] . In mouse , hypomethylation of TEs occurs in mutants in the maintenance DNA methyltransferase DNMT1 , and as in plants , leads to their transcriptional reactivation [36 , 88 , 89] . In tumors , genome-wide hypomethylation also results in TE activation ( [90 , 91] , but tumors express DNMT1 at normal or elevated levels [92–94] . These results indicate that , as in plant suspension cell cultures , targeting rather than maintenance of DNA methylation is defective in mammalian cell cultures and tumors [88] . Whereas the majority of CpG islands in mammalian genomes are normally devoid of methylation [95 , 96] , some CpG islands become hypermethylated in immortalized animal cell cultures [97 , 98] . This methylation often leads to transcriptional gene silencing [95 , 96] , although loss of genic DNA methylation in cell culture can also occur [99–101] . Both the maintenance and de novo DNA methyltransferases are required for CpG hypermethylation in animal cell cultures [102] . Moreover , there is a decline in expression levels of these genes prior to immortalization , which is accompanied by genome-wide DNA hypomethylation , followed by an overexpression in the immortalized cell culture . The resulting hypermethylation [103] is reflected in the gradual increase in CpG hypermethylation of genes in immortalized animal cells [98 , 104] during the transition from senescent to immortalized cell culture growth [105 , 106] . Our results show that in Arabidopsis cell suspension culture , the expression levels of MET1 , the maintenance DNA methyltransferase , as well as those of CMT3 and DRM2 , follow the same pattern , with a 4-fold drop in expression levels of MET1 during callus induction , and a 4-fold increase in expression in cell suspension culture , relative to MET1 expression in seedlings . A corresponding drop in methylation preceding the establishment of the cell suspension culture , would create a need for de novo targeting of methylation , by CMT3 and DRM2 , particularly to repeat-containing genic regions [107] . Mistargeted de novo methylation in subsequent cell suspension culture coupled with high levels of MET1 and down-regulation of DNA demethylase ROS1 would then allow for establishment and perpetuation of genic DNA hypermethylation and for the creation of epialleles . Support for this reprogramming model comes from analysis of the backcross progeny of met1 mutant plants [36] , and subsequent generations [107] , which mimic the loss and reestablishment of DNA methylation in cell culture . In backcross plants , methylation patterns are reestablished only sporadically and depend on RNA-dependent DNA and histone H3K9 methylation , directed by 24-nt siRNA . The 21-nt sRNA were found in met1 mutants from the “TSI” repeat [107] , which is a truncated form of Athila elements , as well as in a broader genome resource sequence of met1 siRNA [71] , although the origin of these siRNA and their persistence in backcrosses was not examined in these studies . Redirection of TE transcripts into a posttranscriptional silencing 21-nt pathway , rather than the transcriptional silencing 24-nt pathway , would account for the failure to reprogram TE DNA methylation in cultured cells . Thus reprogramming of DNA methylation plays a significant role in both animal and plant cell culture , although the role of sRNA in mammalian cell cultures has not yet been examined .
Arabidopsis cell line ( ecotype Columbia ) was maintained in Gamborg's B5 basal medium with minor organics ( Sigma G5893 ) supplemented with 1 . 1 mg/l 2 , 4-D , 3 mM MES , and 3% sucrose . The cells were grown on a shaker at 160 rpm under consistent light condition at 23 °C and were subcultured every 7 d with a 1:10 ( inoculum:fresh medium ) dilution ratio . The wild-type Col seedlings were grown on MS agar plates under standard conditions ( day/light ) for 2 wk . Callus samples were obtained through standard plant tissue culture techniques . Cotyledons of Arabidopsis thaliana , ecotype Columbia , were cut out 7 d after plating on MS plates and transferred onto 1× MS plates supplemented with auxin and cytokinin ( 0 . 15 μg/ml IAA and 0 . 625 μg/ml isopentanyladenine ) and 3% ( w/v ) sucrose . Five weeks later , callus samples 5–8 mm in diameter were collected for gene expression analysis . We used a custom printed genome tiling microarray that comprises approximately 18 . 6 Mb of Arabidopsis thaliana chromosome 4 sequence ( ∼14% of the entire Arabidopsis genome ) . Approximately 22 , 000 tiles , on average 1 kb in size , cover the majority of chromosome 4 , including the entire heterochromatic knob on the short arm of chromosome 4 and several megabases of pericentromeric heterochromatin . Details of array design and production are described in [51] . Genomic DNA was isolated from cell suspension culture using Qiagen Plant DNeasy Mini Kit following the manufacturer's protocol ( except that genomic DNA was eluted with 10 mM TrisCl [pH 8 . 0] ) . Ten micrograms of DNA was sheared to 1–3 kb in size , and half of the sheared DNA was subjected to digestion using McrBC restriction endonuclease , a restriction endonuclease that recognizes 5mC in a variety of sequence contexts ( 5′ ( G/A ) mC ( N ) 40–3000 ( G/A ) mC 3′ ) as described in [51] . Following digestion , both the treated and the untreated DNA sample were briefly run on a 1% agarose gel , and the DNA fragments 0 . 8–3 kb in size were isolated from the gel slices of both the digested and the undigested DNA sample using QiaexII gel extraction kit . Resulting DNA was amplified using a PCR-based approach described in [58] . Three micrograms of each amplified DNA sample was used in a labeling reaction using the Bioprime Array CGH kit and Cy3 and Cy5 fluorescent dyes . Each labeled DNA sample was eluted in 28 μl of 10 mM TrisCl ( pH 8 . 0 ) , and paired DNA samples were combined in a final volume of 70 μl of 4× SSC and 0 . 2% SDS . Each microarray hybridization was carried out at 63 °C overnight ( 14–16 h ) . Following hybridization , microarrays were washed for 8 min in 1× SSC , 0 . 2% SDS ( warmed up to 50 °C ) , then for 5 min in 1× SSC ( at room temperature ) , and finally in two washes of 0 . 2× SSC ( 2 min each ) . Microarrays were quickly dried by compressed air and immediately scanned using an Axon 4000B microarray scanner . DNA methylation levels were measured by cohybridizing differentially labeled McrBC-treated and untreated DNA on a single array . In this way , copy-number differences are not contributing to the difference in hybridization signals on each array . A dye-swap experimental design provided the basis for these chromosome 4 tiling array experiments . DNA methylation was profiled by comparing McrBC-treated samples with untreated samples for both cell culture samples . The experimental design consisted of four replicated dye swaps . Sixteen total arrays were hybridized . Each tile was tested for methylation changes using a simple linear model . Details of the model , hypotheses , statistical test , and multiple testing adjustment are provided in Text S1 . An approximate volume of 5 ml of sedimented cells from cell suspension culture were cross-linked by treating with 1% paraformaldehyde for 15 min . After adding 2 M glycine ( 100 mM final concentration ) and incubating for 5 min to stop the cross-linking , the cells were washed three times with 1× PBS by centrifuging at 1 , 500 rpm for 5 min . The fixed cells were snap-frozen in liquid nitrogen and stored at −80 °C . Chromatin isolation and immunoprecipitation with antibodies against specific histone modifications ( anti-H3K2me2 , anti-H3K9me2 , and anti-H3K56ac ) were done according to [108] with minor modifications ( an extra wash at each washing step ) . Antibodies used were from Upstate/Millipore ( anti-H3K4me2: cat# 07–030 [lot# 26335] , anti-H3Kme2: cat# 07–212 [lot# 27563] , and anti-H3K56ac: cat# 07–677 ) . Immunoprecipitated DNA was amplified , labeled , and hybridized to the custom genome tiling array using the procedures described above for genomic DNA amplification , fluorescent labeling , and microarray hybridization in DNA methylation analysis . For each immunoprecipitation , we hybridized , amplified , and labeled immunoprecipitated DNA against input DNA using two biological and two technical replicates . As with the DNA methylation experiments , a dye swap provided the experimental design . A total of eight microarrays were hybridized for each immunoprecipitation . Details of the model , hypotheses , statistical test , and multiple testing adjustments are provided in Text S1 . Total RNA from 100 mg of 2-wk-old plate-grown seedlings ( Col ) and 100 mg of 4-d cell suspension culture was isolated using Qiagen Plant RNeasy Mini Kit . RNA quality was assessed on an Agilent 2100 Bioanalyzer , RNA 6000 Pico Series II Chips ( Agilent ) . Samples were assessed by an RNA integrity number ( RIN ) score , and those with an RIN score of 7 . 5 or greater were included . The RNA quantity was assessed by Nanodrop ND-1000 ( Nanodrop Technologies ) . Total RNA was amplified by a modified Eberwine Technique , using a Message Amp II kit ( Ambion ) for one round of amplification ( 1 ) . The antisense RNA ( aRNA ) smear analysis for 3' bias was performed on select samples using Agilent 2100 Bioanalyzer RNA 6000 Nano Series II Chips ( Agilent ) . Samples were then prepared for hybridization , hybridized , washed , and then scanned according to the manufacturer's instructions on ATH1 GeneChips ( Affymetrix ) . Affymetrix QC metrics were used for quality control of the image data . The sRNA fractions were isolated from the total RNA sample by size selection on denaturing 15% PAGE gels . RNA molecules between 19 nt and 28 nt in size were eluted from crushed gel slices in four gel volumes of 20 mM Tris ( pH 8 ) , 1 mM EDTA , 0 . 4 M NH4OAc , 0 . 5% SDS overnight , with shaking . sRNA was precipitated with glycoblue ( Invitrogen ) and three volumes of ethanol ( −20 °C overnight ) followed by ligation of the adenylated 3′ adapter ( AMP-5′p = 5′-pCTGTAGGCACCATCAATdideoxyC-3′ ) and then the 5′ adapter ( rArCrArCrUrCrUrUrUrCrCrCrUrArCrArCrGrArCrGrCrUrCrUrUrCrCrGrArUrC ) as described in [109] . After size selection using 15% PAGE gel electrophoresis , sRNA libraries were reverser transcribed and PCR amplified using the forward PCR primer ( 5′-AATGATACGGCGACCACCGAACACTCTTTCCCT ACACGACG-3′ ) and the reverse PCR primer ( 5′-CAAGCAGAAGACGGCATACGATTGATGGTGCCTACAG-3′ ) for 22 cycles using proofreading Taq polymerase . The sRNA libraries were sequenced on an Illumina 1G sequencer using 48 cycles to allow for adequate 3′ adapted masking for precise size determination of the sRNA clones . Total RNA was isolated from young rosette leaves of 2-wk-old Columbia plants grown on plates under standard conditions , 4-d cell suspension culture , as well as from cotyledon-derived callus samples described above using Qiagen Plant RNeasy Mini Kit . Five micrograms of total RNA was treated with DNaseI and then reverse transcribed with SuperScript III with oligo dT primer in a 20-μl reaction . Two microliters of 5-fold diluted cDNA reaction was used as a template in the PCR reaction ( for a final amount of 100 ng of starting total RNA per each PCR reaction ) . Thirty cycles of PCR were performed . The primer sequences used in RT-PCR are listed in Text S1 . General bioinformatics methods . Histone modification , DNA methylation profiles , and sRNA sequencing data were loaded into a MySQL relational database implementing the Bio::DG::GFF schema , thus facilitating intersecting positional , quantitative , and class-based queries and computations . In addition , array data and genome annotations were displayed visually using a Generic Genome Browser , available for public examination at http://chromatin . cshl . edu/epiculture/ . Genes and noncoding RNAs were annotated based on the TAIR version 6 genome release . TEs and known non-TE classes such as the 180-bp centromeric repeat were annotated based on CENSOR analysis [110] of the Arabidopsis genome sequence using version 11 . 10 of the Repbase database [111] . Anonymous tandem repeats were identified using TandemRepeatsFinder [112] , whereas miRNA precursors were localized in the genome by BLAST comparison of precursors sequences provided by Rfam [113] to the Arabidopsis genome . Other regions are assumed to be “Intergenic . ” Adapter sequences were identified and removed using the cross_match algorithm ( http://www . phrap . org/phredphrapconsed . html ) using a minimum match size of 5 and a minimum score of 14 . The nonredundant complement of sequences for the cell culture and inflorescence sRNA libraries was determined by a Perl script that indexed unique masked nucleotide sequences and the frequency with which they were identified . These sequences were aligned to the Arabidopsis genome sequence using BLATN [114] with a word size of 8 , and further computational analysis was restricted to sRNA that perfectly matched the genome . In order to examine sRNA distribution across the genome in an unbiased fashion , copy number-corrected sRNA frequencies were calculated based on BLATN-determined sequence identities for nonoverlapping 100-bp windows . These data were converted to UCSC Wiggle format and displayed in graphical form on our genome browser . Copy number correction was used to partition sequencing frequencies for each sRNA across all instances of that sRNA in the genome . It was performed by calculating the number of discrete , perfect matches for each sRNA from a BLAT query against the Arabidopsis genome as an estimate of copy number , then dividing the sequencing frequencies of individual sRNA matches by that value . sRNA size class analysis was performed by parsing results of a genome-wide BLAT analysis to index sRNA matches by sRNA length and the annotation units within which they fell . To facilitate comparison between libraries , results were plotted in Microsoft Excel as percentages of sRNA matches in each size class relative to all matches from all size classes . Results were summarized for the genome as a whole and then partitioned to examine matches to particular transposable elements and classes of repeats . The physical distribution for each sequence library of sRNAs within representative full-length TEs was determined by performing a BLATN of all sequences in the library against reference sequences . The number of sRNAs on each strand at each base position for the template sequences was computed based on sequencing frequency and the BLATN results and plotted as a histogram using Gnuplot 4 . 0 . Base preference analysis was performed for all sRNA matching the genome in each sequenced library by counting the number of sRNA sequences with a given nucleotide at each position , then dividing values for each nucleotide by the total number of sRNA sequences . Values from the first base were extracted and used for analyses in this paper . These analyses were performed on all sRNA at large , as well as upon a subset of sRNA in which known miRNAs were excluded , since many miRNAs are known to have specific first-base properties [59] . sRNA matches to miRNAs and their precursors were identified by matching the nonredundant complement of sRNA sequences to FASTA files provided by Rfam [113] using the Patscan algorithm [115] allowing up to two internal mismatches . sRNA sequences have been deposited in GenBank and the accession numbers can be found at http://chromatin . cshl . edu/epiculture/ . Affymetrix . CEL files were imported into Agilent GeneSpring GX using the gcRMA algorithm for preprocessing . Significant differences among tissue sources were established using one-way analysis of variance ( ANOVA ) followed by control of the FDR to 5% using the method of Benjamini and Hochberg [116] . Inter-sample comparisons were extracted from the list of statistically significantly different genes using GeneSpring's built-in filtering tools . The GEO accession numbers for the ATH1 microarray data compiled in Table S5 are available at http://chromatin . cshl . edu/epiculture/ .
|
Cultured plant cells are genetically and epigenetically unstable . We investigated the epigenomic consequences of long-term plant cell culture and found that some genes show an increase in DNA methylation , reminiscent of immortalized animal cell lines and cancer cells . By contrast , in the heterochromatic portion of the genome , some transposable elements ( TE ) , undergo dramatic and very precise loss of DNA methylation and transcriptional activation . The reactivated TEs in culture are also accompanied by a production of 21-nucleotide ( nt ) small RNAs . In contrast , TEs that remain methylated and silent retain the predominant class of 24-nt small RNAs , and do not change significantly in their small RNA profiles . Our results implicate RNA interference in epigenetic restructuring of the genome following the activation of TEs in immortalized cell culture .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics"
] |
2008
|
Epigenomic Consequences of Immortalized Plant Cell Suspension Culture
|
Use of cholera vaccines in response to epidemics ( reactive vaccination ) may provide an effective supplement to traditional control measures . In Haiti , reactive vaccination was considered but , until recently , rejected in part due to limited global supply of vaccine . Using Bissau City , Guinea-Bissau as a case study , we explore neighborhood-level transmission dynamics to understand if , with limited vaccine and likely delays , reactive vaccination can significantly change the course of a cholera epidemic . We fit a spatially explicit meta-population model of cholera transmission within Bissau City to data from 7 , 551 suspected cholera cases from a 2008 epidemic . We estimated the effect reactive vaccination campaigns would have had on the epidemic under different levels of vaccine coverage and campaign start dates . We compared highly focused and diffuse strategies for distributing vaccine throughout the city . We found wide variation in the efficiency of cholera transmission both within and between areas of the city . “Hotspots” , where transmission was most efficient , appear to drive the epidemic . In particular one area , Bandim , was a necessary driver of the 2008 epidemic in Bissau City . If vaccine supply were limited but could have been distributed within the first 80 days of the epidemic , targeting vaccination at Bandim would have averted the most cases both within this area and throughout the city . Regardless of the distribution strategy used , timely distribution of vaccine in response to an ongoing cholera epidemic can prevent cases and save lives . Reactive vaccination can be a useful tool for controlling cholera epidemics , especially in urban areas like Bissau City . Particular neighborhoods may be responsible for driving a city's cholera epidemic; timely and targeted reactive vaccination at such neighborhoods may be the most effective way to prevent cholera cases both within that neighborhood and throughout the city .
With the introduction of inexpensive , easy to administer , and effective oral vaccines against cholera , vaccination in response to an epidemic ( reactive vaccination ) may be an effective supplement to conventional control measures . Two safe and internationally licensed oral cholera vaccines are currently available , Dukoral and Shanchol . Both protect against clinical cholera two or more years after vaccination , but neither confers long lasting immunity [1]–[4] . On an epidemic timescale , these vaccines have efficacies ranging from 66 to 86% [2] , [5] . Vaccination against cholera has been used preventatively [3] , [6]–[8] , but before 2012 , we know of only two instances , in The Federated States of Micronesia in 2000 and Vietnam in 2008 , where vaccination commenced during an epidemic [4] , [9] . Vaccine efficacy estimates ranged from 76 to 80% , however , no analysis on how vaccination affected the course of the epidemic was reported for either case [4] , [9] . New data on vaccine performance and the changing epidemiology of cholera prompted the WHO's Strategic Advisory Group to recommend in 2010 that reactive vaccination be considered in specific areas [10] . In order to facilitate rapid procurement and deployment of an oral cholera vaccine , some have proposed the creation of a revolving global stockpile [11] , [12] . While discussions of the global stockpile proceed , countries that use reactive vaccination must contend with a limited supply that may arrive after a significant delay . Spatial heterogeneities may influence how cholera vaccine can best be distributed in a reactive campaign . The effectiveness of a campaign and optimal allocation strategy will depend upon local cholera transmission dynamics , vaccine supply , and logistical delays [12] , [13] . Human movement , water and sewerage infrastructure , and natural waterways facilitate cholera transmission across a city . Within neighborhoods , there can be marked variation in the efficiency of transmission . One country that may benefit from reactive vaccination is Guinea-Bissau , where outbreaks have occurred every three to four years since 1994 . Sector Autónomo de Bissau ( SAB ) , or Bissau City , the capital , consistently reports the most cholera cases within the country ( unpublished data , Guinea-Bissau Ministry of Health ) . In 2008 , 67% of reported cases occurred in SAB while only 25% of the national population live within its boundaries [14] . Reactive vaccination in SAB may be possible in future epidemics given the concentration of cases within the city and the Ministry of Health's experience with vaccination campaigns . Here , we explore the possible effectiveness of different reactive vaccination strategies using SAB as a case study . We fit a neighborhood-based meta-population model to the 2008 cholera epidemic . Using this model , we characterize the spatio-temporal dynamics of cholera transmission within the city and estimate the impact that different reactive vaccination strategies could have had on the course of the epidemic .
During the 2008 epidemic , the Guinea-Bissau Ministry of Health , the WHO , and Mèdecins Sans Frontières implemented a clinic-based cholera surveillance system , which has been described previously [15] . In brief , upon arrival at either the cholera treatment center in the Hospital National Simao Mendes or one of five cholera treatment units ( Figure 1C and 1D ) , health care providers entered patients into a surveillance registry . A patient's age , sex , area of residence , treatment facility , date of presentation , and clinical diagnosis were recorded . Modified WHO cholera case definitions were used [15] . A suspected case was any person suffering from acute watery diarrhea , and a confirmed case was a suspected case with a positive stool sample containing Vibrio cholerae O1 or O139 . We included all suspected and confirmed cases with complete information on their presentation date and home sanitary area in this analysis . The population for each sanitary area within the city was extrapolated from 1991 census data using a constant linear growth rate estimated by the Direcção-Geral Saúde . To estimate the population density in each sanitary area we traced the residential areas using Google Earth ( v6 . 0 . 3 . 2197 ) , then divided each sanitary area's population by its estimated residential area . We fit a discrete-time Susceptible-Infectious-Recovered meta-population model to the confirmed and suspected cases reported during the 2008 epidemic with each of 14 sanitary areas in SAB treated as a distinct population . We assume the epidemic follows a first-order Markov process with a fixed generation time of five days . At each time step , the incidence in each area follows a Poisson distribution with a mean determined by the number infected in the last time step in all areas and the proportion of the area's population remaining susceptible . After infection , individuals were assumed to remain immune for the duration of the epidemic ( See Text S1 for model details ) . We considered models of cholera transmission with and without seasonality assuming ( A ) equal transmission coefficients between and within all areas of SAB; ( B ) different transmission coefficients within each area and equal transmission coefficients between all areas; ( C ) different transmission coefficients within each area and unique symmetric transmission coefficients between each pair of areas; and , ( D ) different transmission coefficients within each area and unique asymmetric transmission coefficients between each pair of areas in the city . We chose the best model based on Deviance Information Criteria ( Text S1 ) . To assess fit we simulated 300 , 000 epidemics predicting five , fifteen , and fifty days ahead drawing new parameters from the posterior distribution every 1000 simulations . Posterior distributions were approximated using Markov Chain Monte Carlo methods using JAGS 3 . 1 . 0 and R 2 . 14 . 0 with non-informative priors [16] , [17] . We ran 3 chains of 400 , 000 iterations with a burn in of 50 , 000 , and assessed convergence using the potential scale reduction factor and through visual inspection [18] . We assume every vaccinated individual receives two doses in a vaccine campaign over a 20 day period and that 75% are fully protected ( [19] ) [3] , [5] , [6] , [20] . In our model vaccinees get no protection until 10 days after the second dose [21] , [22] . Hence , 75% of the susceptible vaccinees are considered immune starting 30 days after their first dose , with no protection before ( Table 1 ) . We considered campaigns with 50 , 000 , 75 , 000 , or 100 , 000 doses ( i . e . 25 , 000 , 37 , 500 , and 50 , 000 individuals vaccinated ) and targeted vaccination at one , two , three , or all ( 14 ) areas ( Table 3 ) . When the proposed number of vaccinees in a specific area exceeded the population size , we distributed vaccine to the other vaccination areas or , in the campaigns with one vaccination area , we dispersed the vaccine throughout the city with each person having equal probability of getting vaccinated . We varied the starting time of the vaccination campaign between 20 and 120 days after the first case was detected . We considered targeted and diffuse ( city-wide ) campaigns . In diffuse campaigns , vaccine was distributed throughout all areas of SAB . In targeted campaigns , we considered three different strategies to select vaccination areas . In the population-based strategy , we selected the areas with the largest population . In the connectivity-based strategy , we vaccinated in areas estimated to be most “connected” to other areas . In the attack rate-based strategy , we chose the areas with the highest attack rate in the 2008 epidemic . We allocated vaccine proportional to population size in all simulations . For each vaccination scenario we ran 5 , 000 simulations calculating the difference between the final epidemic size with and without vaccination . Epidemics were assumed to follow the observed 2008 epidemic course until 30 days after the first dose . In each simulation we drew new parameters from the joint posterior distribution . As a sensitivity analysis , we ran simulations with different generation times ( 3–10 days ) and vaccine efficacies ( 65%–85% ) . Additional simulation study details are available in Text S1 . Original data collection was approved by the Mèdecins Sans Frontières ERB and the National Ethical Review Board of Guinea-Bissau [15] . The analyses presented in this article were conducted on de-identified data and deemed to be non-human subject research by the Johns Hopkins Bloomberg School of Public Health IRB .
The first case in SAB was reported on June 5 , 2008 in Bairro-Militar , the most populated area of the city ( Figures 1A , 1B ) , one month after the first reported case in Guinea-Bissau . Within three weeks , all 14 areas had reported cases ( Figure 1C ) . The Ministry of Health officially declared an epidemic one month after the first case report from SAB . The National Laboratory of Microbiology and the Pasteur Laboratory in Dakar , Senegal identified all positive specimens analyzed as Vibrio cholerae O1 El Tor Ogawa . Nationally , 14 , 226 suspected cases and 228 deaths were reported with 67% ( 9 , 393 ) of cases and 32% ( 73 ) of deaths reported in SAB . The last case in the country was reported in SAB on January 11 , 2009 . Individual-level data in SAB was collected between June 5 , 2008 and October 28 , 2008 , over which time 8 , 024 ( 85% ) suspected and confirmed cases were reported . These analyses focus on 7 , 551 suspected and confirmed cases with complete information on date of presentation , home area , and clinical diagnosis ( Figure S1 ) . In SAB , weekly incidence ranged from 14 to 755 . Within-area attack rates ranged from 9 . 1 to 40 . 6 per 1 , 000 ( Table 2 , Figure 1D ) , with Bandim having both the most cases ( 1 , 816 ) and the highest attack rate . The final model fit both the overall and area-specific epidemic curves well , even when predicting as far as 50 days ( i . e . 10 time steps ) ahead ( Figures 2A , 2B ) . To understand how transmission varied through time , we calculated the odds that an incident case was caused locally ( i . e . attributable to transmission between people in the same area ) for each area throughout the course of the epidemic ( Figure 3 ) . Only Bandim , Plaque , and Santa-Luzia have an odds consistently greater than 1 , suggesting internally driven epidemics in these areas . We define the effective internal basic reproductive number ( ) as the expected number of cases caused within a given area by one infected individual , within the same area , at the beginning of the epidemic . Only areas with can sustain an epidemic absent infections introduced from other areas . The strength of internal epidemics varied with estimates of ranging from 0 . 01 ( 95% Credible Interval ( CI ) 0 . 00–0 . 07 ) in Ajuda to 1 . 17 ( 95% CI 0 . 99–1 . 33 ) in Bandim ( Figure 4 ) . We found no significant correlation between and either estimated population size or population density . Bandim is the only area where we estimate , and it appears to have played a necessary role in driving the epidemic . With Bandim removed , simulated introductions of cases fail to cause epidemics . In contrast , city-wide epidemics occur with removal of any other single area . In simulated epidemics based upon our best-fit model , we find that , on average , at least 10% of cases in each area are caused by cases in other areas ( Figure 2C , Text S1 ) . External transmission coefficients represent epidemic connectivity between areas , and our estimates suggest heterogeneity in inter-area transmission ( Text S1 ) . Based on simulations , we estimate that Bandim contributed over 10% of the cases to over half ( 7/13 ) of the other areas ( Figure 2D ) , highlighting the crucial role it played in the epidemic . The sum of the external transmission coefficients for any area provides an estimate of the effective external basic reproductive number ( ) . This number is the estimated number of cases a single infectious case in that area would cause in all other areas of SAB given the pre-epidemic level of population immunity . Estimates of ranged from 0 . 37 ( 95% CI 0 . 16–0 . 71 ) in Belem to 7 . 32 ( 95% CI 6 . 29–8 . 37 ) in Missira ( Figure 4 ) . Vaccination in the area ( s ) with the highest attack rate leads to larger reduction in cases than all other targeted and city-wide campaigns at all starting times . Targeting vaccination at Bandim only , the area with the highest attack rate , within the first 80 days of the epidemic averts more cases than other strategies regardless of vaccine quantity ( Figure 5 ) . Targeted vaccination in Bandim starting on day 20 is expected to reduce the final size of the epidemic by 41% ( 95% Predictive Interval ( PI ) 0 . 21–0 . 69 ) , 56% ( 95% PI 0 . 30–0 . 85 ) , and 67% ( 95% PI 0 . 40–0 . 89 ) with 25 , 000 , 37 , 500 , and 50 , 000 vaccinees , respectively . In comparison , a city-wide campaign starting on the same day is expected to reduce the epidemic size by 21% ( 95% PI 0 . 07–0 . 34 ) , 30% ( 95% PI 0 . 17–0 . 44 ) , and 40% ( 95% PI 0 . 27–0 . 55 ) for 25 , 000 , 37 , 500 , and 50 , 000 vaccinees ( Tables 4 , S1 , S2 ) . We found wide variability in the outcomes using different targeting strategies , with the differences diminishing as vaccination is delayed ( Figure 5 ) . Under the population-based strategy , only a targeted campaign in the three most populated areas averts more cases than a city-wide campaign ( Figure 5 , Table 4 ) . Targeting the areas estimated to be most “connected” to others averts fewer cases than city-wide campaigns regardless of vaccination starting time and doses . Starting day has a profound impact on the effect of all vaccination campaigns: the sooner vaccination begins , the more cases are averted . With 37 , 500 vaccinees , each day delay in vaccination results in an average of 39 . 5 ( 95% CI 37 . 7–44 . 2 ) fewer cases averted when targeting based on attack rate . Increasing the size of a vaccination campaign early on in the epidemic can significantly improve case prevention , however , the marginal benefit of additional vaccine diminishes as vaccination is delayed . On average , each additional person vaccinated as part of a targeted campaign in Bandim starting on day 20 averts 7 . 5 cases compared to 1 . 7 cases averted per vaccinee in campaigns starting two months later . In simulations , early targeted vaccination leads to fewer cases both within the targeted area and throughout the city when compared to diffuse campaigns . When starting vaccination on day 20 ( Figure 6A ) , targeting Bandim averts more cases both in Bandim ( 1 , 173 ) and in all the other areas combined ( 2 , 265 ) when compared to a city-wide campaign ( 341 averted in Bandim and 1 , 741 in all other areas ) . As the vaccination campaign is delayed , these differences shrink ( Figure 6 ) .
Using a simple spatially explicit model of cholera transmission , we captured the essential dynamics of the 2008 cholera epidemic in SAB , Guinea-Bissau . This model suggests that there was significant transmission between areas in SAB and that one area , Bandim , drove the epidemic . Our simulations show that early distribution of vaccine is the most important determinant of the number of cases prevented . For example , vaccinating 25 , 000 individuals in Bandim on epidemic day 20 would have averted more cases ( 3 , 109 , 95% PI 1 , 475–5 , 198 ) than vaccinating 50 , 000 in the same area just 40 days later ( 2 , 732 , 95% PI 1 , 630–3 , 738 ) . Our simulations suggest that an early vaccination campaign targeted at Bandim alone would have outperformed distributing the same vaccine quantity throughout the city . Not only are more cases prevented overall , but more are prevented in areas outside of Bandim . Our results suggest that rapid small-scale vaccination may be more effective than a delayed larger-scale vaccination campaign . For example , on average , each day delay results in an additional 39 . 5 cases when targeting 37 , 500 people in the areas with the highest attack rate . Applying the average case fatality ratio from the 2008 epidemic ( 1 . 58 per 100 cases [15] ) we estimate that each week delay in vaccination would have resulted in an average of 4 . 4 cholera-related deaths . Transmission hotspots for other infectious diseases have been exploited to devise novel prevention and control approaches [23] , [24] . For example , targeted interventions in hotspots may be key to effective malaria control and elimination [24] . Similarly , cholera hotspots can serve as targets for both reactive and preventative interventions . Identification of hotspots during an epidemic may be challenging . In the case of SAB , Bandim is an area which has had high attack rates in previous epidemics and few improvements in water and sanitation infrastructure . Such historical information may be useful in targeting vaccination; however , more research on combining historical and real-time surveillance data is needed . In our model , vaccination campaigns lasted 20 days , but in reality the duration will vary by the number of vaccinees targeted and the vaccine used . If Shanchol were used with the recommended inter-dose period of 14 days , the campaign would likely exceed 20 days . While this suggests that our results underestimate the speed by which Shanchol vaccination would occur , these differences would be offset by partial immunity conferred before a second dose [22] . As the time to distribute vaccine doses increases , we expect to avert fewer cases . However , there is some evidence that a single dose of oral cholera vaccine may be sufficient for reactive vaccination [22] , [25] . If one dose is sufficient to elicit a strong protective response for the time-scale of an epidemic , more people could be vaccinated quickly . Cholera's generation time is not well characterized and varies widely with the concentration of bacteria in the environment , its survival rate , and the route of transmission [26]–[28] . We ran analyses with alternate generation times of 3 , 7 , and 10 days and got the same qualitative results ( Figures S3 , S4 , S5 , S6 , S7 , S8 and Tables S3 , S4 , S5 ) . We also found that varying the vaccine efficacy to 65% and 85% changed the number of cases averted , but preserved the relative performance of each strategy over time ( Figure S2 and Tables S7 , S6 ) . There are a number of limitations to this work . We focus on a single epidemic in Guinea-Bissau . A longer time series would provide insight into variability in transmission across epidemics . The data came from an intensified surveillance effort from both Mèdecins Sans Frontières and the Guinea-Bissau Ministry of Health , however suspected cases that presented after October 28 , 2008 were only captured by the national surveillance system without details on timing and home sanitary area . There are several possible alternative explanations for the elevated attack rate in Bandim . The cholera case definition used is not 100% specific , and some cholera cases may be false positives . People may be more likely to seek care if their neighbors do , hence clinic visits may cluster even if cholera does not . In addition , Bandim has been the location to several surveillance programs and public health interventions through the Bandim Health Project [29] , perhaps leading to increased awareness . However , if these phenomena were consistent throughout the epidemic they would not lead to elevated estimates of the local transmission rate under our algorithm . We found that how rapidly vaccine can be distributed during a cholera epidemic is the most important determinant of the effectiveness of a reactive vaccination program; and that a single area of SAB was an essential driver of the epidemic . Hence , early targeting of this area would have been the most effective way to reactively distribute vaccine . These results may apply to urban cholera epidemics more generally . It seems reasonable that cholera epidemics in other urban settings , particularly in Africa , may be disproportionally driven by specific parts of the city . If these hotspots can be identified , targeted reactive vaccination may be an effective way to prevent cases both within that area and throughout the city , especially when vaccine supply is limited . Regardless of the distribution strategy used , timely distribution of vaccine in response to an ongoing cholera epidemic can prevent cases and save lives .
|
Cholera remains a major public health threat , causing 3–5 million cases and 100 , 000–120 , 000 deaths each year . In 2010 , data on vaccine performance and the changing epidemiology of cholera prompted the WHO's Strategic Advisory Group to recommend that reactive vaccination be considered in specific areas . We built a spatially explicit stochastic model of cholera transmission and fit it to data from a 2008 epidemic in Bissau City , Guinea Bissau . Using this model we examined the potential effectiveness of reactive vaccination for controlling cholera transmission in Bissau City , comparing strategies for distributing limited vaccine . In simulations , early targeting of a single transmission “hotspot” , Bandim , was the most effective strategy , and led to the greatest reduction in cases both within Bandim and in areas where no vaccine was distributed . This finding has implications for cholera control in urban settings in general: public health officials will often know which areas of a city were hotspots of cholera transmission in the past or where conditions promote efficient transmission . When there is limited vaccine , our work suggests that targeting reactive vaccination at these areas will lead to the greatest reduction in cases both in these areas and elsewhere in the city .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"cholera",
"epidemiology",
"infectious",
"disease",
"epidemiology",
"neglected",
"tropical",
"diseases",
"spatial",
"epidemiology",
"infectious",
"disease",
"control",
"infectious",
"disease",
"modeling"
] |
2012
|
Urban Cholera Transmission Hotspots and Their Implications for Reactive Vaccination: Evidence from Bissau City, Guinea Bissau
|
Viruses have evolved mechanisms of MHCI inhibition in order to evade recognition by cytotoxic CD8+ T cells ( CTLs ) , which is well-illustrated by our prior studies on cowpox virus ( CPXV ) that encodes potent MHCI inhibitors . Deletion of CPXV viral MHCI inhibitors markedly attenuated in vivo infection due to effects on CTL effector function , not priming . However , the CTL response to CPXV in C57BL/6 mice is dominated by a single peptide antigen presented by H-2Kb . Here we evaluated the effect of viral MHCI inhibition on immunodominant ( IDE ) and subdominant epitopes ( SDE ) as this has not been thoroughly examined . We found that cross-priming , but not cross-dressing , is the main mechanism driving IDE and SDE CTL responses following CPXV infection . Secretion of the immunodominant antigen was not required for immunodominance . Instead , immunodominance was caused by CTL interference , known as immunodomination . Both immunodomination and cross-priming of SDEs were not affected by MHCI inhibition . SDE-specific CTLs were also capable of exerting immunodomination during primary and secondary responses , which was in part dependent on antigen abundance . Furthermore , CTL responses directed solely against SDEs protected against lethal CPXV infection , but only in the absence of the CPXV MHCI inhibitors . Thus , both SDE and IDE responses can contribute to protective immunity against poxviruses , implying that these principles apply to poxvirus-based vaccines .
Strategies to leverage strong cytotoxic CD8+ T cells ( CTL ) responses to viral infections are of particular interest as CTLs play essential roles in controlling viral infections [1–5] . Before gaining effector functions , virus-specific CTL precursors must be primed by antigen presenting cells ( APCs ) that present pathogen-derived epitopes via major histocompatibility complex class I ( MHCI ) molecules on the cell surface . If the APC is infected and directly presents endogenously produced antigens , this is known as direct presentation . Alternatively , uninfected APCs may process and cross-present exogenous antigens from infected cells . Cross-presentation is mediated primarily by Batf3-dependent CD103+/CD8α+ dendritic cells ( DCs ) [6–8] , which we refer to as BATF3+ DCs . Peptide-loaded MHCI molecules from infected cells may also be liberated by cell lysis or secreted in exosomes and then transferred onto cross-presenting APCs . When uninfected APCs acquire preformed peptide-MHCI complexes in this manner , they are termed cross-dressed and can drive expansion of CD8+ T cells [9–11] . Induction of CD8+ T cell responses by cross-dressing was previously demonstrated in studies using adoptive transfer of T cell receptor ( TCR ) transgenic ( Tg ) T cells [9–11] and also requires BATF3+ DCs [11] . However , the relative contribution of these processes to non-TCR Tg CTL responses against viral antigens is largely unknown . Upon recognizing cognate antigen on APCs , naïve CTLs are activated to undergo clonal expansion and traffic to the site of ongoing viral infection . There , virus-specific CTLs mediate host resistance by recognizing infected cells via surface MHCI molecules displaying processed viral antigens . Specific T cell recognition activates direct killing of infected cells and production of interferon-gamma ( IFN-γ ) and other cytokines that may have indirect effects . In the later stages of the response , a proportion of CTLs become long-lived memory CD8+ T cells that can provide rapid protection during secondary responses to the viral pathogens . Many viruses display mechanisms that may contribute to evading CTL responses , such as inhibiting MHCI antigen presentation . The effects and mechanisms of MHCI inhibition on CTL responses have been well demonstrated in vitro with herpesviruses [12] . For instance , downregulation of MHCI by murine cytomegalovirus ( MCMV ) prevented MCMV-specific CTLs from killing infected cells , whereas cells infected with an MCMV mutant lacking the viral MHCI inhibitors were lysed by CTLs [13] . However , the in vivo relevance of viral MHCI inhibition in general was previously unclear since herpesvirus-mediated MHCI inhibition had few effects on in vivo CTL responses in murine and nonhuman primate infection models [14–16] . On the other hand , studies of cowpox virus ( CPXV ) by our lab and others indicated that CPXV , uniquely among the orthopoxviruses , mediated mouse and human MHCI inhibition by two open reading frames ( ORFs ) , CPXV012 and CPXV203 [4 , 17 , 18] . CPXV203 retains MHCI molecules in the ER and CPXV012 inhibits peptide loading on MHCI molecules; when combined , these two evasion mechanisms allows CPXV to evade CTL responses . The deletion of intact CPXV012 and CPXV203 from the CPXV genome attenuated viral pathogenesis in vivo [4 , 5] . Furthermore , this attenuation was dependent on the anti-CPXV CTL response since depleting CD8+ T cells restored the virulence of the Δ12Δ203 CPXV mutant , similar to wild type ( WT ) CPXV . Thus , these studies of CPXV established the in vivo importance of viral MHCI inhibition and its effects on antiviral CTL responses . Interestingly , the virus-specific CTL response to CPXV in C57BL/6 mice is dominated by a single antigen ( B8 ) , displaying the immunological phenomenon known as immunodominance , that can impede the development of efficacious vaccines [19] . In theory , removing the IDE ( s ) may circumvent immunity . However , for some viruses , subdominant epitopes ( SDEs ) may compensate and then dominate the immune response [20 , 21] . Such findings revealed that responses against an IDE ( s ) suppress immune responses to SDEs , which is a related yet distinct phenomenon coined immunodomination . CD8+ T cell immunodomination also occurs during secondary responses whereby memory CD8+ T cells can suppress naïve CD8+ T cell responses [22] . CD8+ T cell immunodomination is likely a mechanism that contributes to the immunodominance of the B8 antigen in CPXV infections [23] , but has not been studied in the context of MHCI inhibition . B8R is a highly conserved gene among orthopoxviruses and encodes the secreted soluble B8 protein that binds IFN-γ with broad species-specificity . B8 from ectromelia virus ( ECTV ) is a strong inhibitor of human , bovine , rat , and murine IFN-γ [24] , but VACV and CPXV B8 does not neutralize murine IFN-γ [25] . These differences have been attributed to host-specificity . While the natural host of ECTV is not known , experimentally it is restricted to murine hosts , whereas VACV has a broad host-tropism with an unknown natural reservoir [26] . The natural reservoirs of CPXV are wild-rodent species , but CPXV also has broad host-tropism [27 , 28] . Despite these differences , B8 is the most dominant antigen identified in mice with the H-2Kb MHCI allele , and the B8 CD8+ T cell epitope sequence ( TSYKFESV ) is 100% conserved between ECTV , VACV , CPXV , and other orthopoxviruses [29] . However , it is not clear if B8 is an immunodominant antigen because it is a secreted soluble protein that may be efficiently cross-presented . Previously , we showed that CPXV infection of Batf3-/- mice that selectively lack the main cross-presenting DC subsets ( CD103+/CD8α+ DCs ) [30] display reduced priming of B819-26-specific CD8+ T cells during CPXV infection [5] , suggesting that cross-presentation is a major pathway used to induce CTLs . However , since Batf3–/–mice also lack the capability of cross-dressing , it is also possible that cross-dressing is the main pathway to induce CPXV-specific CTLs . Moreover , it remained unclear whether other CPXV antigens ( i . e . , SDEs ) are efficiently presented by BATF3+ DCs because CPXV B819-26 immunodominates the primary CTL response [5] . Finally , due to the above limitations , it is not known if these processes could be affected by viral MHCI inhibition . Here we studied if transmembrane anchoring of B8 affects its immunodominance , the role of MHCI inhibition in the generation of virus-specific CTLs to SDEs and for the first time , the relevance of cross-dressing in the induction of endogenous antiviral CTL responses .
The immunodominant CPXV B8 antigen is a secreted soluble protein [24] , suggesting that its immunodominance may be due to its property as a secreted molecule , as shown for other antigens [31 , 32] . If this were true , we expect that altering the protein targeting of B8 so that it is no longer secreted from infected cells will affect the acquisition and availability of B8 for APCs , which in turn would affect priming of B819-26-specific CD8+ T cells and its immunodominance . To test these hypotheses and detect subcellular location of B8 , we produced a CPXV mutant expressing B8 fused to mCherry ( B8mC ) and another mutant ( B8TMmC ) expressing B8-mCherry fusion protein with a transmembrane domain ( TMD ) ( Fig 1A ) . We performed subcellular fractionation of infected HeLa cells and analyzed the cytoplasmic extract , membrane extract , and supernatant by Western blot to determine the subcellular location of the B8 variants and if they were secreted . The B8 variants were mainly detected in the membrane extract of both B8TMmC- and B8mC-infected cells , indicating that the infected cells successfully expressed both B8 variants ( Fig 1B ) . We note that the membrane fraction may contain proteins found within the mitochondria and endoplasmic reticulum , but not nuclear proteins , such that the secreted B8 variant detected in the membrane fraction is likely due to proteins localized within the ER and in transit through the secretory pathway . We also detected higher levels of the non-secreted B8 variant in the membrane fraction in comparison to the secreted variant , which is likely due to an accumulation of membrane-associated B8 within B8TMmC-infected cells . Most importantly , the B8 variant was detected in the supernatant of cells infected with B8mC , but not in the supernatant of cells infected with B8TMmC , demonstrating that the B8 variant remains cell-associated in cells infected with B8TMmC ( Fig 1B ) . However , anchoring the B8 antigen did not negatively affect priming of B819-26-specific CD8+ T cells and B819-26 maintained the highest position in the immunodominance hierarchy , as shown in mice infected intranasally ( i . n . ) with B8TMmC or B8mC ( Fig 1C and 1D ) . These data show that secretion of the B8 antigen is not required for priming of B819-26-specific CD8+ T cells or immunodominance during CPXV infection . We also performed kinetic analyses of B819-26-specific CD8+ T cells by staining with H-2Kb tetramers loaded with B819-26 peptide and found that priming by cell-associated B8 resulted in greater expansion of B819-26-specific CD8+ T cells ( Fig 1E ) . These results are consistent with previous findings that cell-associated antigens are cross-presented better than soluble antigens [33 , 34] . When we infected Batf3-/- mice with B8TMmC or B8TM , we found that priming of B819-26 -specific CD8+ T cells was significantly reduced in Batf3-/- mice in comparison to B6 mice ( Fig 1F ) , indicating that the introduced B8 mutations did not alter the dependence on cross-presentation ( or cross-dressing ) in the induction of B819-26-specific CTL precursors . Since priming against the non-secreted B8 protein is still dependent on cross-presenting ( or cross-dressed ) BATF3+ DCs , it is likely that antigens used for conventional cross-presentation by BATF3+ DCs are acquired from infected apoptotic/necrotic donor cells or that BATF3+ DCs are cross-dressed with peptide-loaded MHCI molecules . While we previously reported that priming of CD8+ T cell responses to CPXV is dependent on cross-presenting BATF3+ DCs , others reported that direct priming is the main mechanism to induce CTL responses with VACV infection [35 , 36] . To directly compare these findings , we assessed the CTL response after systemic infection with WT CPXV , Δ12Δ203 ( from here on referred to as ΔMHCIi ) CPXV , or VACV in B6 and Batf3-deficient mice . At 8 days post-infection ( dpi ) , the frequency of splenic CD8+ T cells that produced IFN-γ in ex vivo stimulations with ΔMHCIi-infected DC2 . 4 cells was significantly reduced in WT CPXV- and ΔMHCIi-infected Batf3-/- mice ( Fig 2A ) in comparison to infected B6 mice , confirming the importance of cross-presentation ( or cross-dressing ) in inducing CPXV-specific CTLs , as we showed earlier [5] . Conversely , at 6 or 8 dpi , ex vivo stimulation with a set of 5 VACV/CPXV peptides ( Fig 2B ) or VACV-infected DC2 . 4 ( Fig 2A ) revealed no significant difference in the VACV-specific response between infected B6 and Batf3-/- mice . These results are consistent with the findings that ablation of XCR1-expressing ( CD103+/CD8α+ ) DCs does not completely abolish priming of CD8+ T cells during VACV infection [37] . Thus , the in vivo responses to two highly related orthopoxviruses display distinct requirements for direct presentation ( VACV ) versus cross-presentation/cross-dressing ( CPXV ) . Given that priming of CPXV-specific CTL precursors and cross-dressing of APCs in other settings were both shown to require BATF3+ DCs [11] , we sought to determine if cross-dressing could account for the source of antigen being presented to CD8+ T cells in CPXV infection . To do so , we transferred B6 bone marrow ( BM ) into lethally irradiated Batf3-/--F1 ( Batf3-/--B6 x Batf3-/--BALB/c ) mice ( Fig 3A ) . In B6→Batf3-/--F1 chimeras , donor B6-derived ( Batf3-dependent ) APCs only express H-2b MHCI molecules and should cross-prime CTL responses against H2b-restricted epitopes ( Fig 3A ) . However , priming by H-2d-restricted epitopes would occur only if the H2b APCs in these chimeric mice were cross-dressed with preformed peptide-loaded H-2d class I molecules from the host parenchymal cells , which express both H-2b and H-2d class I molecules . We also produced BALB/c→Batf3-/--F1 chimeras , to analyze the converse situation . The reconstituted mice were infected by i . n . administration with WT CPXV and CTL responses were determined against the immunodominant H-2Kb-restricted B819-26 and the H-2Ld-restricted F226-34 epitopes . As expected , we detected a B819-26 response in B6→Batf3-/--F1 mice that was of similar magnitude to non-chimeric WT-F1 ( B6 x BALB/c ) infected mice ( Fig 3B ) . We also detected a small B819-26-specific response in BALB/c→Batf3-/--F1 , but the frequency of B819-26-specific CD8+ T cells was significantly lower ( ~12-fold ) than in B6→Batf3-/--F1 and WT-F1 mice . A small , yet detectable response to F226-34 was also detected in the lungs of B6→Batf3-/--F1-infected mice , but it was ~3 fold and ~8 fold lower in comparison to WT-F1- and BALB/c→Batf3-/--F1-infected mice respectively . Thus , these data suggest cross-dressing contributes minimally to priming against these peptide determinants . It is possible that cross-dressing by H-2Kb- and H-2Ld-restricted epitopes other than B819-26 and F226-34 , respectively , occurred in infected chimeric mice , so we also performed ex vivo stimulations with ΔMHCIi-infected DC2 . 4 ( H-2b ) and P815 ( H-2d ) cells as these cells present a broad array of naturally derived CPXV peptides ( Fig 3B ) . The frequency of IFN-γ+ CD8+ T cells upon stimulation with ΔMHCIi-infected DC2 . 4 cells was significantly lower in BALB/c→Batf3-/--F1 mice in comparison to B6→Batf3-/--F1 and WT-F1 mice . Similarly , the frequency of IFN-γ+ CD8+ T cells upon stimulation with ΔMHCIi-infected P815 cells was significantly lower in B6→Batf3-/--F1 mice in comparison to BALB/c→Batf3-/--F1 and ~10 fold lower in comparison WT-F1 mice . The frequency of CD8+ T cells that responded to ΔMHCIi-infected P815 cells was also significantly lower in WT-F1 in comparison to BALB/c→Batf3-/--F1 . This was also seen in F226-34 responses ( Fig 3B ) . These findings may be due to the additional epitope diversity from H-2b as well as H-2d expression in WT-F1 , which may compromise responses to H-2d-restricted epitopes during the primary response . Regardless , these results suggest that cross-dressing from non-hematopoietic cells does not generate a vigorous response during primary CPXV responses . We next assessed whether cross-dressing plays a role during secondary responses to CPXV infection since cross-dressed APCs are capable of stimulating memory CD8+ T cells [9] . However , the secondary CPXV response in the B6→Batf3-/--F1 and BALB/c→Batf3-/--F1 mice were similar to what was observed in the primary CPXV response ( Fig 3C ) . Thus , cross-dressing from non-hematopoietic cells also plays a minor role in activating endogenous memory CD8+ T cells following CPXV infection . To test if cross-dressed MHCI could be contributed by the hematopoietic compartment , we reconstituted lethally irradiated Batf3-/--F1 mice with a 1:1 mixture of BALB/c-Thy1 . 1 and Batf3-/--F1 BM ( S1A Fig ) . In these mice , cross-presentation should only be carried out by the donor BALB/c-Thy1 . 1-derived APCs ( H-2d ) . In contrast , cells that are of the donor Batf3-/--F1 ( H-2b x H-2d ) origin will lack BATF3+ DCs and should not carry out cross-presentation , but may serve as a source of cross-dressing peptide-MHCI complexes . We systemically infected BALB/c-Thy1 . 1 + Batf3-/--F1→Batf3-/--F1 mice with WT CPXV and found that the H-2d-restricted response was successfully reconstituted , whereas the H-2b-restricted response was significantly lower than the response in WT-F1 mice and was comparable to Batf3-/--F1→Batf3-/--F1 control mice ( S1B Fig ) . These data indicate that APCs cross-dressed from other hematopoietic cells does not efficiently prime CD8+ T cell responses in the setting of effective viral MHCI inhibition . Taken together , these data suggest that antigens are predominantly cross-presented by BATF3+ DCs during CPXV infection and that cross-dressing plays a minor role , if at all . Insufficient cross-presentation of SDEs may explain the subdominance of other CPXV antigens . To test if cross-presentation of CPXV SDEs alone is capable of inducing a strong CTL response , we mutated the B819-26 epitope anchor residues required for binding to H-2Kb peptide-binding groove , postulating that this will prevent the B819-26 epitope from being presented by H-2Kb . According to the peptide-binding motif of H-2Kb , the B819-26 epitope contains a primary anchor residue ( phenylalanine at position P5 ) and an auxiliary anchor residue ( tyrosine at position P3 ) [38] . To determine whether mutating the primary anchor residue is sufficient to eliminate binding to H-2Kb or if both anchor residues should be mutated , peptide-binding assays were performed using the transporter associated with antigen processing 2 ( TAP2 ) -deficient RMA-S cell line in which addition of peptides capable of binding H-2Kb stabilize its expression on the cell surface [39] . Alanine substitution of the primary anchor residue significantly reduced binding of the B819-26 epitope peptide to H-2Kb as compared to WT B8 , but binding could be increased with increasing concentrations of peptide ( S2A Fig ) . However , alanine substitutions of the primary and auxiliary anchor residues completely abrogated binding of the B819-26 epitope peptide to H-2Kb , even at higher peptide concentrations . Based on these findings , we introduced both substitutions into the WT and the ΔMHCIi CPXV genomes . The CPXV B819-26 epitope mutants B8Y3AF5A ( referred to as ΔB819-26 ) and a B8R deletion mutant ( ΔB8R ) that we generated did not exhibit defects in viral replication in vitro ( S2B Fig ) . Surprisingly , they also did not show attenuated virulence in vivo , as measured by weight loss or lethality , as compared to WT CPXV ( S2C Fig ) . There was no detectable B819-26 response in ΔB819-26- or ΔMHCIiΔB819-26-infected mice ( Fig 4A and 4B , S3A and S3B Fig ) . However , infections with ΔB819-26 or ΔMHCIi-ΔB819-26 generated a robust SDE response . In contrast , as we previously reported [5] , a large proportion of the CPXV-specific CTL response was directed against B819-26 in the lungs of WT- and ΔMHCIi-infected mice . Additionally , there were no significant differences between the overall CTL responses against WT , ΔB819-26 , ΔMHCIi , and ΔMHCIiΔB819-26 ( Fig 4A and 4B ) , despite the loss of the B819-26-specific response . Therefore , the CTL response was completely compensated by SDEs in the absence of a B819-26 response . It was possible that the B819-26 epitope mutation allows CPXV to replicate to higher titers in the lungs of infected mice resulting in higher antigen loads , which could explain the observed compensation . However , the B819-26 epitope mutations did not result in significantly increased viral titers in infected mice ( Fig 4C ) , suggesting that the compensation is unlikely due to increased antigen loads . Immunodomination and priming by SDEs were also not affected by CPXV-mediated MHCI inhibition since there were no significant difference in the SDE response against ΔB819-26 and ΔMHCIiΔB819-26 , as measured by stimulation with ΔMHCIi- ( used to estimate total response ) or ΔMHCIiΔB819-26- ( used to estimate total SDE response ) infected DC2 . 4 cells ( Fig 4A and 4B ) . Additionally , there was no significant difference in the frequency of CD8+ T cells that exhibited an effector T cell phenotype in infected mice ( S3C and S3D Fig ) . Considering that the route of infection can alter antigen levels and immunodominance [23] , we infected mice by intraperitoneal ( i . p . ) injections . Compensation by SDEs was also observed during systemic infection ( Fig 4D and 4E ) , suggesting that compensation was not dependent on antigen levels or the route of infection . However , CTL responses against the panel of subdominant epitopes we tested were not significantly increased in the absence of B819-26 , suggesting that other unidentified or cryptic subdominant epitopes compensated the CTL response . Interestingly , the response against A4288-96 was significantly reduced in the absence of the B819-26-specific response ( Fig 4D ) , suggesting that SDEs were up-ranked in the dominance hierarchy and were now themselves eliciting immunodomination . Furthermore , we found that priming of SDE-specific CD8+ T cells was also dependent on BATF3+ DCs ( S3E Fig ) . These data suggest that the IDE-specific CTL response suppresses cross-priming of SDE-specific CD8+ T cells during primary CPXV infections , indicating immunodomination , but this process was not affected by viral MHCI inhibition . Memory CD8+ T cells also have a capacity for immunodomination and can inhibit naïve CD8+ T cell responses [22] . However , this is not the case for VACV since prior priming with individual SDEs does not alter the immunodominance hierarchy following VACV boost in SDE-primed mice [40] . Considering that the priming mechanisms are different during VACV and CPXV infection ( Fig 2A ) , we tested whether CPXV-specific memory CD8+ T cells can exert immunodomination . We primed mice with WT CPXV , boosted the mice with a low or high dose of ΔB819-26 at 25 dpi , and assessed the CD8+ T cell response in the lungs and spleens 8 days after boosting ( Fig 5A ) . In this group , B819-26-specific memory CD8+ T cells should be present pre- and post-boost , but will not undergo expansion following boost with ΔB819-26 . As expected , we detected B819-26-specific CD8+ T cells in the lungs and spleens of WT CPXV-primed mice after boosting with ΔB819-26 ( Fig 5B and 5C ) and before boosting ( Fig 5D ) . Additionally , we found that WT and ΔMHCIi infection resulted in a similar relative abundance of B819-26-specific CD8+ T cells with a memory phenotype ( CD44+CD62L+KLRG1-CD127+ ) at 25 dpi , suggesting that viral MHCI inhibition does not affect memory T cell development ( S4 Fig ) . In a separate group , mice were primed with SDEs by ΔB819-26 infection and boosted with WT CPXV . In this group , we would expect mice to mount a naïve B819-26 response after boosting with WT CPXV only in the absence of memory CD8+ T cell immunodomination . However , the naïve B819-26 response was significantly inhibited following boost with both a low and high dose of WT CPXV , suggesting that the SDE-specific memory CD8+ T cells immunodominate naïve CD8+ T cells . Alternatively , neutralizing antibodies may have reduced the antigen levels and therefore limited the naïve B819-26 response following boost with CPXV . To assess the potential role of host-protective antibodies , we repeated the above experiments , but this time we depleted CD8+ T cells prior to challenging mice with CPXV ( S5A and S5B Fig ) and then monitored the mice for survival . CPXV-immunized mice that received CD8-depleting or isotype control antibodies survived , whereas naïve mice succumbed to the challenge ( S5C Fig ) . Although this was somewhat expected because CPXV evades CTLs , these results suggest that host-protective antibodies may contribute to protection in the absence of CD8+ T cells during secondary exposure to CPXV . We thus repeated the prime and boost experiments and examined immunodomination in μmT mice , which lack mature B cells . Because CPXV evades CTLs in vivo and μmT mice should not mount a protective antibody response , it is likely that μmT mice are highly susceptible to WT CPXV infection . To avoid this issue , we infected μmT mice with ΔMHCIi CPXV strains as CTLs can effectively control these viruses in WT mice . We primed μmT mice by skin scarification ( s . s . ) infection , which resembles human immunizations with VACV . We then boosted the mice at 25 dpi by i . n . administration , and subsequently assessed the CD8+ T cell response 7 days after boost . Mice primed with ΔMHCIi resulted in expansion of a B819-26-specific CD8+ T cells following i . n . boost with ΔMHCIi ( Fig 5E ) . Mice primed with ΔMHCIiΔB819-26 also mounted a detectable response against B819-26 following i . n . boost with ΔMHCIi , yet this response was significantly reduced by ~9-fold in comparison to mice immunized with ΔMHCIi . Therefore , memory CD8+ T cell immunodomination still occurred in the absence of neutralizing antibodies and viral MHCI inhibition , suggesting that immunodomination may be due to T cell interference . Because memory CD8+ T cells are present at higher frequencies than naïve antigen-specific CD8+ T cells , it is likely that memory CD8+ T cells have a competitive advantage in accessing APC resources [41–43] . For instance , downregulation of MHCI on infected cells may limit the level of antigen presented during CPXV infection , thereby contributing to T cell cross-competition for peptide-MHCI complexes in the secondary response . Indeed , T cell cross-competition for peptide-MHCI complexes during secondary responses has been demonstrated using a heterologous prime-boost strategy [44] , but to our knowledge this has only been directly tested between memory and naïve T cells specific for IDEs . To test if SDE-specific memory CD8+ T cells can cross-compete with naïve B819-26-specific CD8+ T cells , we performed a competition experiment in which we primed mice with ΔB819-26 , adoptively transferred peptide-pulsed BM-derived dendritic cells ( BMDCs ) at 25 dpi , and then assessed the CD8+ T cells responses 6 days after transfer . Transfer of B819-26-pulsed BMDCs into ΔB819-26 -primed mice resulted in a robust B819-26 response ( Fig 5F ) . Likewise , transfer of K36-15-pulsed BMDCs resulted in moderate expansion of K36-15-specific memory CD8+ T cells . However , when BMDCs that were pulsed with B819-26 and K36-15 at the same time were transferred the B819-26 response was inhibited , further supporting the findings that memory CD8+ T cells immunodominate naïve CD8+ T cells . Conversely , B819-26-specific CD8+ T cells dominated the response when BMDCs pulsed with B819-26 and K3L6-15 at the same time were transferred into naïve mice ( Fig 5G ) . If immunodomination is an effect of cross-competition , then providing BMDCs that exclusively present K36-15 and BMDCs that exclusively present B819-26 alone should overcome the effects of immunodomination . When B819-26-pulsed BMDCs were mixed with K36-15-pulsed BMDCs ( pulsed separately ) and transferred into ΔB819-26-primed mice , the B819-26 response was significantly greater than in mice that received BMDCs pulsed with B819-26 and K3L6-15 at the same time , suggesting that cross-competition plays a role in memory CD8+ T cell immunodomination . Interestingly , the B819-26 response in mice that received the 1:1 mixture of K3L6-15-pulsed and B819-26-pulsed BMDCs was significantly lower than in mice that only received B819-26-pulsed BMDCs . Therefore , the partial rescue of the B819-26 response when the epitopes were presented on different APCs suggest that additional factors contribute to immunodomination during secondary responses . In contrast to the secondary response , separating the K36-15 and B819-26 epitopes during primary responses had no effect on immunodomination of B819-26-specific CD8+ T cells ( Fig 5G ) , suggesting that cross-competition for peptide-MHCI complexes contributes to immunodomination mainly during secondary responses . Having demonstrated that SDE-specific memory CD8+ T cells have a capacity for immunodomination , we asked if SDE-specific CD8+ T cells could exhibit immunodomination during primary responses . We reasoned that modulating the immunodominant and subdominant antigen levels may allow SDE-specific CD8+ T cells to immunodominate . To test this , we performed co-infection experiments in which the level of WT and ΔB819-26 input were varied while maintaining the overall viral dose . We first synchronized the infections to limit the variation in the dose by infecting freshly harvested splenocytes with either WT or ΔB819-26 separately . We then mixed WT- and ΔB819-26-infected splenocytes at a ratio of 1:0 , 10:1 , 1:10 , or 0:1 , inoculated mice intravenously ( i . v . ) with a total of 1 x 105 infected cells , and assessed the CTL response at 7 dpi ( Fig 6A ) . A graded B819-26 response was observed with the concurrent increase of ΔB819-26 input and decrease of WT input ( Fig 6B ) , while the overall response as determined by stimulation with ΔMHCIi-infected DC2 . 4 cells remained roughly equal ( Fig 6C ) . These data suggest that SDE-specific CTLs are capable of immunodominating the primary response when the relative abundance of subdominant antigens is increased , even in the presence of the IDE . To confirm that the graded response was not simply due to reduced WT input , we repeated the co-infection experiment using mixtures of WT- and mock-infected splenocytes . Injecting the varying mixtures of WT- and mock-infected splenocytes did not result in a gradation of the B819-26 response ( Fig 6B ) , suggesting that the observed graded B819-26 response was dependent on the subdominant antigen levels . Thus far , our results indicate that CPXV-mediated MHCI inhibition does not affect priming of CD8+ T cells by SDEs . However , we wondered whether SDE-specific CTL responses could provide protection against CPXV infection in vivo . To examine the physiological relevance of SDEs in protecting against CPXV infection , we performed adoptive transfer experiments with CTLs primed with ΔB819-26 or MCMV as a control for antigen specificity ( Fig 7A ) . Mice that received primed CTLs were then challenged by i . n . administration with a lethal dose of ΔB819-26 or ΔMHCIiΔB819-26 . The majority of mice that received MCMV-primed CTLs died following infection with ΔB819-26 or ΔMHCIiΔB819-26 ( Fig 7B and 7C ) . All mice that received ΔB819-26-primed CTLs also died after challenge with ΔB819-26 , whereas all mice challenged with ΔMHCIiΔB819-26 survived . Therefore , CTLs primed by SDEs are capable of recognizing and controlling CPXV only in the absence of CPXV-mediated MHCI inhibition , which is consistent with our previous findings regarding WT CPXV exposure that is dominated by the B819-26 response [4 , 5] .
Here we demonstrate that the secretion of an immunodominant CPXV antigen does not affect immunodominance or cross-priming by the IDE . Intriguingly , we found that the IDE and SDEs are differentially presented by APCs during infection with CPXV and VACV , despite being closely related genetically . We also show that CD8+ T cell immunodomination is not affected by viral MHCI inhibition and can be elicited by SDEs during primary and secondary responses against CPXV infection . Additionally , we show that SDEs alone are entirely capable of generating protective CTL responses , which is dependent on cross-priming by BATF3+ DCs . Cross-priming of CD8+ T cells is important for inducing antiviral CTL responses , especially in settings where direct-presentation is not possible ( e . g . , APCs are not susceptible to infection ) or is evaded ( e . g . , impairing maturation of infected-APCs or inhibiting MHCI presentation ) . Consistent with this notion , herein we showed that the induction of antiviral CTL responses is dependent on cross-presentation in the presence of CPXV-mediated MHCI inhibition . Priming of CD8+ T cell in the absence of viral MHCI inhibition during CPXV infection was also dependent on cross-presenting BATF3+ DCs , albeit to a less extent . While we have not ruled out the possibility that ΔMHCIi-infected BATF3+ DCs prime CTL precursors by direct presentation , CPXV-infected DCs have reduced expression of costimulatory molecules involved in T cell activation [45 , 46] , suggesting that direct-presentation may be limited even in the absence of CPXV012 and CPXV203 . Nonetheless , there are clearly factors other than MHCI inhibition that skew priming of T cells towards cross-priming and further study of CPXV ORFs in the context of ΔMHCIi provides an excellent opportunity to investigate such factors . In this study , we provide evidence that cross-priming is the main mechanism driving CPXV-specific CTL responses . Our studies also indicate that cross-dressing plays no significant role in the T cell response to CPXV infections in vivo . Cross-dressing has been proposed as a mechanism by which APCs can rapidly acquire peptide epitopes for presentation to CTL precursors , thereby eliminating the time spent for antigen processing [9 , 10] . In support of this , DCs can be cross-dressed in vitro by peptide-MHCI complexes from epithelial cells [47] , which are commonly targeted by viruses and thus may serve as a common source of preformed viral peptide-MHCI . Moreover , peptide-MHCI from parenchymal cells cross-dressed DCs in vesicular stomatitis virus ( VSV ) -infected mice and the cross-dressed DCs induced proliferation of memory CD8+ T cells , but not naïve T cells . However , priming of naïve antigen-specific CD8+ T cells by cross-dressed DCs can occur , as demonstrated using DNA vaccination and transfer of adenovirus infected DCs [10 , 11] . In contrast to these studies , we found that cross-dressing does not efficiently prime or drive expansion of endogenous antigen-specific naïve and memory CD8+ T cells during CPXV infection . While previous reports on cross-dressing provide compelling evidence that cross-dressing occurs in vivo , the transfer of TCR tg T cells in these studies may have resulted in non-physiological induction of CD8+ T cells by cross-dressed DCs . Additionally , cross-dressing in these experimental settings may have been promoted due to a potential generation of supraphysiological levels of peptide-MHCI by DNA vaccination or by transfer of adenovirus infected DCs . These factors may explain the difference between previous studies and our results using CPXV infection . Because CPXV encodes an extensive arsenal of immunomodulatory proteins , the possibility that CPXV directly or indirectly inhibits cross-dressing may also explain these conflicting results . For example , downregulation of MHCI cell surface expression by CPXV012 and CPXV203 may prevent transfer of peptide-loaded MHCI molecules by trogocytosis , a process in which intercellular exchange of intact membranes occurs during the formation of an immunological synapse [48–51] . If trogocytosis is required for cross-dressing of APCs in vivo , as been demonstrated in vitro [9] , then cross-dressing dependent T cell responses are expected to be abrogated during CPXV infection . Ultimately , our results suggest that antigens are acquired from necrotic/apoptotic bodies or secreted viral proteins found in the extracellular milieu and are then predominantly cross-presented during CPXV infection . Cross-presentation of peptide epitopes may also be influenced by the nature of the antigens and can affect the extent of CD8+ T cell immunodominance [52–54] . For instance , the secreted immunodominant antigens of M . tuberculosis are likely processed through the cross-presentation pathway [55 , 56] and eliminating bacterial secretion prevents priming of IDE-specific CD8+ T cells during M . tuberculosis infection [31] . Priming of naïve CD8+ T cells against cell-associated subdominant SV40 large tumor antigen ( T Ag ) epitope V is also dependent on cross-presentation , but the response against the V epitope is limited because it is inefficiently cross-presented relative to the T Ag IDE [54] . Our findings suggest that cross-presented CPXV IDEs can be derived from cell-associated antigen since ablating B8 secretion did not negatively affect cross-priming dependent induction of B819-26-specific CD8+ T cells . Moreover , cell-associated B8 elicited a greater B819-26-specific CD8+ T cell response in comparison to secreted soluble B8 , which is consistent with the preferential in vivo cross-presentation previously reported for cell-associated antigens [33 , 34] . However , the underlying mechanisms of immunodominance are complex and are often context dependent as we found that secretion of CPXV B8 is not required for immunodominance and that cross-presentation of CPXV SDEs in the absence of the immunodominant B819-26 epitope stimulated a robust CTL response . The fact that the CTL response to SDEs compensated for the absence of B819-26 suggests that the SDE response is suppressed by the B819-26 response , supporting the concept of immunodomination . In many cases immunodomination occurs as a consequence of T cell competition for limiting APC resources [41 , 43 , 57 , 58] . For instance , competition for peptide-MHCI complexes on APCs during primary CTL responses can occur as a result of antigen abundance [59] . In support of this , we showed that concurrently increasing subdominant antigen levels and reducing immunodominant antigen levels allow SDEs to gain dominance during the primary response to CPXV infection . Similarly , modulating the antigen abundance through different methods during influenza A virus and VACV infection has been shown to influence immunodomination [23 , 60] . In certain models , immunodomination can be overcome when APCs present different epitopes separately [42 , 51 , 58] , indicating that CD8+ T cells of different specificities can cross-compete for peptide-MHCI complexes on APCs . This has been convincingly demonstrated in models where immunodomination occurs when APCs co-present model antigen epitopes . However , epitope co-presentation by APCs does not always influence immunodomination , as we have shown here for primary responses , and the role of cross-competition in inducing antiviral CTL responses is controversial [61] . We found that cross-competition for peptide-MHCI complexes is relevant and that immunodomination occurs during secondary responses as a consequence . Alternatively , the suppressed B819-26 response in our cross-competition experiments may have resulted from K36-15-specific memory CD8+ T cells killing the BMDCs that were pulsed with B819-26 and K3L6-15 at the same time . Nevertheless , we observed partial rescue of the B819-26 response when the epitopes were separated on BMDCs . This partial rescue may be due to peptide exchange between BMDCs that were pulsed separately and adoptively transferred as a mix , which would subsequently result in epitope co-presentation and K36-15-specific memory CD8+ T cell immunodomination . However , additional factors that we did not test such as cross-competition for growth factors , antigen-specific T cell precursor frequencies , or TCR avidity [62] likely contribute to the memory T cell immunodomination as well . Remarkably , immunodomination during the secondary response against CPXV was exerted by SDE-specific memory CD8+ T cell . The capacity for SDE-specific memory CD8+ T cells to inhibit the response to an IDE has been shown with influenza virus [22] , but prior priming with SDE peptides did not result in memory CD8+ T cell immunodomination using VACV , as shown by Wang et al [40] . Here in our study , memory CD8+ T cell immunodomination was clearly evident when SDE-primed mice were challenged with WT CPXV , whereby the naïve B819-26-specific CD8+ T cell response was suppressed . Moreover , memory CD8+ T cell immunodomination was not affected by MHCI inhibition . However , mice were primed by CPXV infection ( in this study ) as opposed to individual SDE peptides ( as done by Wang et al ) . These experimental differences suggest that the priming stimulus and the breadth of the primary response influences immunodomination during secondary responses against poxviruses . Taken together , our findings highlight the need to consider the effects of pre-existing immunity on the outcome of secondary responses and vaccinations . An advantage to using VACV-based vaccines is that in addition to providing protection against heterologous pathogens , the native vector epitopes ( both IDEs and SDEs ) can provide cross-protection against related orthopoxviruses , as supported by our findings here and previous reports [63–66] . However , as a consequence of pre-existing immunity , memory CD8+ T cell immunodomination may limit the target antigen response following immunization with VACV-based vaccines , in turn resulting in non-efficacious vaccinations . For example , native VACV epitopes can mask responses against target antigens expressed by VACV vaccine vectors [19] . Nevertheless , our results support the ongoing evaluation for poxviruses as promising vaccine vectors , and stress the necessity to develop novel vaccination strategies .
Cell lines HeLa , Vero , and P815 were obtained from the American Type Culture Collection ( ATCC ) . DC2 . 4 cells were a kind gift from Dr . Kenneth Rock , University of Massachusetts Medical School . HeLa , Vero , DC2 . 4 , and P815 cells were cultured respectively in Dulbecco’s Modified Eagle Medium ( DMEM ) , Minimum Essential Medium ( MEM ) or RPMI supplemented with 10% FBS ( Mediatech ) , 100 U/ml penicillin , 100 g/ml streptomycin , 1mM sodium pyruvate , and non-essential amino acids ( Gibco ) . VACV-WR was obtained from the ATCC . MCMV Smith strain was a gift from Dr . Herbert Virgin , Washington University . CPXV BAC pBR mini-F construct was kindly provided by Dr . Karsten Tischer , Free University of Berlin . Mutant viruses were generated by en passant mutagenesis [67] using primers listed in S1 Table . Gene fragments were synthesized ( Integrated DNA Technologies ) and assembled using Gibson Assembly ( New England BioLabs ) for cloning of the B8-mCherry fusion contructs ( S2 Table ) . Infectious BAC-derived viruses ( S3 Table ) were reconstituted using a slightly modified method previously described by Xu et al [68] . In brief , ~8x105 Vero cells seeded in 6-well plates were infected with fowlpox virus ( FWPV ) at an MOI of 1 . Transfection of FWPV-infected Vero cells was carried out 1 hour post-infection ( hpi ) with 4 μg of BAC DNA and 5 μL of Lipofectamine 2000 transfection reagent ( ThermoFisher Scientific ) according to the manufacturer’s instruction . Serial dilutions of reconstituted infectious virus were passaged up to four times on Vero cells in order to remove the mini-F vector sequence . Wells harbouring single GFP-negative plaque were isolated and used for preparing virus stocks as previously described [17] . C57BL/6Ncr mice were purchased from the National Cancer Institute . B6 . 129S2-Ighmtm1Cgn/J mice were purchased from the Jackson Laboratory . Batf3-/- mice crossed to the C57BL/6 and BALB/c background were kindly provided by Dr . Kenneth Murphy , Washington University . Growth curves were performed on Vero cells . Supernatant and cells were harvested at 12 , 24 , 28 , and 72 hpi and viral titers were determined by plaque assay using Vero cells . TAP2-deficient RMA-S ( H-2b ) cells were cultured overnight at 28°C in 5% CO2 to accumulate peptide-receptive MHCI molecules at the cell surface . Peptides were then added at various concentrations and the cells were transferred to 37°C . After 6 h of incubation at 37°C , cells were harvested and washed twice in PBS . H-2Kb cell surface expression was then measured by flow cytometry . 1 x 106 HeLa cells were infected at a MOI of 5 . Cells and supernant were collected at 4 hpi and were lysed on ice for 5 min in RIPA lysis buffer supplemented with 1x Halt protease and phosphatase inhibitor cocktail . Cells were further processed for subcellular fractionation using a Subcellular Protein Fractionation Kit ( ThermoFisher ) . Samples were mixed with Laemmli sample buffer ( Bio-Rad ) , incubated at 95°C for 5 minutes , separated by SDS-PAGE , and transferred to PVDF membranes . Immunoblotting was performed using rabbit polyclonal anti-mCherry and rabbit monoclonal anti-EGFR ( Abcam ) followed by horseradish peroxidase ( HRP ) -conjugated goat anti-rabbit IgG ( Cell Signalling ) . 6 weeks of age Batf3-/--F1 ( H2bxH2d ) mice were depleted of NK cells by i . p . administration of 100 μg of PK136 antibody . Two days later , the mice were lethally irradiated with 950 rads and were reconstituted with 1x107 T cell depleted C57BL/6 , BALB/c , or a 1:1 mixture of BALB/c-Thy1 . 1 and Batf3-/--F1 BM cells . BM chimeras were treated with antibiotics for 4 weeks and were allowed to reconstitute for 8 weeks before use . BMDCs were generated by culturing BM cells in the presence of 20 ng/mL GM-CSF and IL-4 ( PeproTech ) for 8 days , as previously described [69] . LPS ( 150ng/nL ) was then added and the cells were allowed to mature overnight . The cells were then pulsed with peptide ( 1g/mL , 45 min ) . Cells were washed extensively in PBS and a total of 2 . 5 x 105 DCs was injected i . v . into recipient mice . Mice were age- and sex-matched for each experiment and used at 8–10 weeks of age . Mice were infected as previously described for i . n . and s . s . infections [5] . For s . s . infections , fur was trimmed with clippers , then a thin layer of Vaseline was applied over the trimmed region and the remaining fur was shaved over with a double-edge razor blade one day before infection . Mice infected by i . p . or s . c . administration were injected with a volume of 100μL or 200μL of virus inoculum per mouse , respectively . For co-infection experiments , splenocytes isolated from B6 mice were infected at an MOI of 5 , harvested 1 hpi , and washed three times with PBS . 1 x 105 infected cells in 200 μL of PBS were transferred intravenously into naïve B6 mice . For the CPXV SDE protection experiment , CD8+ T cells from splenocytes of B6 mice that had been infected 7 days earlier with WT CPXV or MCMV were isolated by positive selection using anti CD8a MicroBeads ( Miltenyi Biotec ) . 3 x 106 CD8+ T cells were transferred intravenously into naïve B6 mice . Mice were infected approximately 24 h after transfer . Single-cell suspensions from the lungs and spleens were prepared at the indicated days post-infection as previously described [5] . 1x106 cells were seeded in 96-well plates and were re-stimulated with peptides or with 1x105 DC2 . 4 cells that had been infected for 4 h with ΔMHCI-i or ΔMHCI-iΔB8 CPXV ( MOI 5 ) . Cells were incubated at 37°C , 5% CO2 . After 1 h at 37°C , GolgiPlug ( BD Biosciences ) was added to each well . Three hours later , cells were stained on ice with Fixable Viability Dye eFlour 506 ( eBioscience ) before staining of cell surfaces for the indicated surface markers . Cells were then fixed/permeabilized and stained for IFN-γ . Background levels were determined using cells from uninfected mice , which usually ranged between 0 . 01–0 . 05% , and were subtracted from the values presented . For intracellular staining of GzmB and tetramer staining , cells were stained ex vivo without stimulation and without incubation with GolgiPlug . H-2Kb-TSYKFESV tetramers were produced in the Immunomonitoring Laboratory within the Center for Human Immunology and Immunotherapy Programs ( Washington University ) . The following monoclonal antibodies were obtained from ThermoFisher , BD Biosciences or eBioscience: H-2Kb ( AF6-88 . 5 ) , CD3 ( 145-2C11 ) , CD8α ( 53–6 . 7 ) , CD8β ( eBioH35-17 . 2 ) , CD4 ( RM4-5 ) , CD44 ( IM7 ) , CD62L ( MEL-14 ) , GzmB ( GB12 ) , KLRG1 ( 2F1 ) , CD127 ( A7R34 ) and IFN-γ ( XMG1 . 2 ) . The data are shown as mean ± SEM and were analysed with an unpaired Student t test or one-way ANOVA followed by Tukey posttest comparison using Prism GraphPad software , asterisks indicate statistical significance and the p values are denoted as *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . Mouse studies were approved by the Animal Studies Committee at Washington University , protocol # A-3381-01 , and adhere to the Institutional Animal Care and Use Committee guidelines .
|
The use of vaccinia virus ( VACV ) to eradicate smallpox is the arguably the most successful demonstration of vaccination . The VACV vaccine also provides cross-protection against related zoonotic orthopoxviruses , including monkey poxvirus ( MXPV ) and CPXV , which circulate between various animal hosts and humans . Interestingly , Edward Jenner first demonstrated the concept of vaccination against smallpox in the late 1700s using CPXV . He also made the curious observation that CPXV vaccination did not always protect against recurrent exposure to CPXV . Jenner’s observations may be explained by the ability for CPXV to evade antiviral CD8+ T cell immune responses . To evade CD8+ T cells , CPXV inhibits MHCI antigen presentation , which is required to prime CD8+ T cells . Importantly , CPXV is the only orthopoxvirus that inhibits MHCI and thus provides a unique opportunity to investigate the effects of viral MHCI inhibition on CD8+ T cell priming . Here , we examine the factors that contribute to priming of CPXV-specific CD8+ T cells and show that viral MHCI inhibition does not affect CD8+ T cell priming , but prior CPXV immunization does inhibit priming during subsequent exposure to CPXV . The effects of pre-existing poxvirus immunity are therefore important to consider if poxvirus-based vaccines against various diseases are to be widely used .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"spleen",
"antigen-presenting",
"cells",
"immunology",
"cytotoxic",
"t",
"cells",
"antibodies",
"research",
"and",
"analysis",
"methods",
"specimen",
"preparation",
"and",
"treatment",
"staining",
"immune",
"system",
"proteins",
"white",
"blood",
"cells",
"memory",
"t",
"cells",
"animal",
"cells",
"proteins",
"t",
"cells",
"immune",
"response",
"biochemistry",
"cell",
"staining",
"cell",
"biology",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types"
] |
2018
|
Cross-priming induces immunodomination in the presence of viral MHC class I inhibition
|
HLA-restricted immune escape mutations that persist following HIV transmission could gradually spread through the viral population , thereby compromising host antiviral immunity as the epidemic progresses . To assess the extent and phenotypic impact of this phenomenon in an immunogenetically diverse population , we genotypically and functionally compared linked HLA and HIV ( Gag/Nef ) sequences from 358 historic ( 1979–1989 ) and 382 modern ( 2000–2011 ) specimens from four key cities in the North American epidemic ( New York , Boston , San Francisco , Vancouver ) . Inferred HIV phylogenies were star-like , with approximately two-fold greater mean pairwise distances in modern versus historic sequences . The reconstructed epidemic ancestral ( founder ) HIV sequence was essentially identical to the North American subtype B consensus . Consistent with gradual diversification of a “consensus-like” founder virus , the median “background” frequencies of individual HLA-associated polymorphisms in HIV ( in individuals lacking the restricting HLA[s] ) were ∼2-fold higher in modern versus historic HIV sequences , though these remained notably low overall ( e . g . in Gag , medians were 3 . 7% in the 2000s versus 2 . 0% in the 1980s ) . HIV polymorphisms exhibiting the greatest relative spread were those restricted by protective HLAs . Despite these increases , when HIV sequences were analyzed as a whole , their total average burden of polymorphisms that were “pre-adapted” to the average host HLA profile was only ∼2% greater in modern versus historic eras . Furthermore , HLA-associated polymorphisms identified in historic HIV sequences were consistent with those detectable today , with none identified that could explain the few HIV codons where the inferred epidemic ancestor differed from the modern consensus . Results are therefore consistent with slow HIV adaptation to HLA , but at a rate unlikely to yield imminent negative implications for cellular immunity , at least in North America . Intriguingly , temporal changes in protein activity of patient-derived Nef ( though not Gag ) sequences were observed , suggesting functional implications of population-level HIV evolution on certain viral proteins .
Escape from Human Leukocyte Antigen ( HLA ) class I-restricted CD8+ T-lymphocytes ( CTL ) in Human Immunodeficiency Virus Type 1 ( HIV ) occurs along mutational pathways that are broadly reproducible based on the HLA alleles expressed by the host [1]–[4] . The opposite phenomenon ( that is , reversion of escape mutations to consensus upon HIV transmission to an individual lacking the restricting HLA ) is somewhat more variable . While some escape mutations revert relatively rapidly following transmission [5]–[7] , others do so more slowly [8] , [9] . Yet others ( perhaps because they harbor no fitness costs , or such costs are rescued by the presence of compensatory mutations ) revert rarely or not at all [10]–[13] . If escape mutations reverted rapidly and consistently , their prevalence in HLA-mismatched persons would remain stably low ( or negligible ) over time [9] . However , escape mutations persisting upon transmission could gradually spread throughout the population [10] , [12] , [14]–[16] . Analogous to the negative impact of transmitted drug resistance mutations on treatment efficacy [17] , acquisition of “immune escaped” HIV by persons expressing the relevant HLA allele could undermine the ability of their CTL to control infection . As such , the spread of HIV strains harboring escape mutations throughout the population could gradually undermine host antiviral immune potential , and potentially diminish the protective effects of certain HLA alleles , as the epidemic progresses [11] , [12] , [18] . The extent to which immune escape mutations are accumulating in HIV sequences over time remains incompletely elucidated – a knowledge gap attributable in part to the scarcity of historic data . Nevertheless , some supportive data exist . It has been suggested that CTL epitopes in European HIV sequences are being “lost” over time through mutational escape , in particular via selection by HLA-B alleles , though this study was limited by the modest number of sequences analyzed [19] . Higher HIV polymorphism frequencies have been reported in modern compared to historic South American HIV subtype B and F sequences , though this study was limited by the lack of host HLA characterization [20] . The high ( ∼75% ) frequency of the B*51-associated HIV Reverse Transcriptase ( RT ) I135X mutation in Japan , a population where B*51 prevalence approaches 20% , is also consistent with escape mutation accumulation [12] , though the possibility that the Japanese epidemic was founded by an HIV sequence containing RT-I135X cannot be ruled out . That certain ( though not all ) escape mutations are capable of spreading in HIV-infected populations has also been demonstrated via mathematical modeling [9] . However , conclusive assessment of the extent to which escape mutants are accumulating in circulation ideally requires large datasets of linked HLA/HIV genotypes from historic and modern eras , combined with ancestral ( founder ) sequence reconstruction of the studied epidemics . The potential pathogenic implications of population-level HIV evolution are also of interest . It has been hypothesized that conflicting selection pressures imposed on HIV by HLA-diverse host populations could lead to ( relative ) viral attenuation over time , while consistent pressures imposed by populations with limited HLA diversity could increase HIV virulence [21] . However , the complex tradeoffs between immune evasion benefits versus fitness costs of escape , and the context-specific nature of these factors with respect to the host genetic milieu , render this a challenging question to address . A recent meta-analysis of HIV clinical prognostic markers ( plasma viral load and CD4+ T-cell counts ) in cohorts from North America , Europe and Australia suggested that HIV could be increasing in virulence [22] , but other reports have been highly conflicting [23]–[30] . Alternatively , pathogenic implications may be investigated , albeit incompletely and indirectly , via assessment of HIV protein function and/or replication capacity of patient-derived viral sequences – though historic data remain scarce . Reductions in replication capacity of recombinant HIV expressing gag-protease sequences from Japanese patients , a population with relatively constrained HLA diversity [12] , [31] , have been reported since the 1990s [32] , while two earlier studies examining replicative fitness of recombinant viruses expressing HIV RT sequences from historic and modern European isolates yielded opposing results [23] , [33] . The goals of the present study are to assess the extent to which HLA-associated polymorphisms are accumulating in HIV sequences over time in a large epidemic region comprising an immunogenetically diverse population ( North America ) , and to investigate whether any genotypic changes have been accompanied by functional implications for the virus . To do this , we genotypically and functionally assessed HIV sequences , linked to host HLA information , from 358 historic ( 1979–1989 ) and 382 modern ( 2000–2011 ) specimens from four key cities in the epidemic ( New York [34] , [35] , Boston [36] , [37] , San Francisco [34] , [38] , [39] and Vancouver , Canada [40]–[42] ) . We performed ancestral phylogenetic reconstructions to infer North America's most recent common ancestor ( MRCA ) HIV sequence , and we defined HLA-associated polymorphisms based on independent published sources [43] . We focused on Gag and Nef , as these are immunogenic HIV proteins whose sequence variability is substantially influenced by HLA [43] and whose function is susceptible to immune-mediated attenuation [44]–[46] . Overall , we observed an HIV epidemic that is steadily diversifying ( in part due to HLA pressures ) , where background frequencies of HLA-associated polymorphisms have , on average , increased by a modest extent over the study period . Notably , HIV polymorphisms selected by protective HLA alleles appear to have increased to a greater relative ( though not absolute ) degree than those restricted by non-protective alleles . Despite these increases , average escape mutation background frequencies remain , in absolute terms , low . As such , we contend that HIV adaptation to host HLA is unlikely to yield imminent negative implications for cellular antiviral immunity , at least in North America . Intriguingly , changes in Nef ( though not Gag ) activity were observed over the epidemic's course , suggesting functional impacts of ongoing HIV evolution on certain viral proteins .
A total of 358 historic HIV sequences spanning 1979–1989 , from observational cohorts of men who have sex with men ( MSM ) established in four key cities in the North American epidemic ( New York [34] , [35] , Boston [36] , [37] , San Francisco [34] , [38] , [39] and Vancouver [40]–[42] ) , were studied alongside 382 modern North American HIV sequences spanning 2000–2011 from untreated persons belonging to various risk groups . High-resolution HLA class I sequence-based typing , aided where necessary by imputation using a published [47] and extensively validated [43] machine-learning algorithm , was successful for 330 ( of 358; 92 . 2% ) historic and 381 ( of 382; 99 . 7% ) modern specimens . The lower success rate for historic samples reflects the use of serum or plasma as a genomic DNA source [48] . A limitation of serum-based typing is the potential overrepresentation of homozygous types due to amplification of only one allele of the pair [48]; indeed , this bias was noted ( e . g . : HLA-B homozygosity was 9% in the historic compared to 5% in the modern cohort , p = 0 . 03 ) . Nevertheless , historic and modern cohorts exhibited comparable HLA allele frequencies ( Pearson's R = 0 . 97 , p<0 . 0001 , and Figure S1 ) , indicating that our analyses of the spread of HLA-associated HIV polymorphisms are unlikely to be majorly confounded by intercohort differences in the frequencies of their restricting HLA alleles . Plasma HIV RNA amplification and bulk sequencing of Gag and/or Nef was successful for the above-mentioned 358 historic specimens ( of an original total of 497 specimens tested , 72 . 0% genotyping success rate ) , yielding 299 Gag and 335 Nef sequences for study . Success rates of historic Gag and/or Nef genotyping , by site , were: New York 73 ( of 94; 77 . 6% ) , San Francisco 32 ( of 75; 42 . 7% ) , Boston 242 ( of 282; 85 . 8% ) and Vancouver 11 ( of 46 , 23 . 9% ) . Infection stage was unknown for most historic specimens , though these included 67 individuals with known or suspected early infection , all from New York . Gag and/or Nef sequencing was successful for 382 modern specimens in total: 358 ( 93 . 7% ) for Gag and 337 ( 88 . 2% ) for Nef , all from individuals with chronic infection . All HIV sequences were subtype B . Estimated maximum-likelihood HIV Gag and Nef phylogenies exhibited star-like shapes typical of HIV sequences sampled from a population [49] ( Figure 1 ) . Despite being a convenience sample , historic sequences exhibited no gross segregation by early ( 1979–1982; N = 28 ) , mid ( 1983–1985; N = 122 ) and later ( 1986–1989; N = 208 ) eras . Moreover , unique historic North American sequences in the Los Alamos National Laboratory ( LANL ) HIV database ( totaling 27 Gag and 56 Nef sequences spanning 1982–1989 ) were interspersed throughout the phylogenies , as were sampled modern LANL sequences spanning 2000-present ( Figure 1 ) . Despite some clustering by city and the predominance of historic sequences in two lineages of a combined phylogeny ( Figure S2 ) , the historic and modern cohort consensus HIV sequences were consistent with one another as well as the LANL North American and global ( worldwide ) subtype B consensus sequences ( Figure S3 ) , with all differences occurring at highly variable residues . Results thus support our HIV sequences as not grossly unrepresentative of the North American epidemic . HIV sequence diversity within the modern cohort was substantially greater than that of the historic cohort ( Figure 1 ) . Grouped by era , the mean ( ±standard deviation [SD] ) patristic ( pairwise ) genetic distances in Gag were 0 . 020±0 . 004 ( 1979–1982 ) , 0 . 027±0 . 009 ( 1983–1985 ) , 0 . 034±0 . 009 ( 1986–1989 ) , and 0 . 074±0 . 012 ( 2000+ ) substitutions per nucleotide site , while those for Nef were 0 . 043±0 . 010 , ( 1979–1982 ) , 0 . 057±0 . 014 ( 1983–1985 ) , 0 . 072±0 . 015 ( 1986–1989 ) , and 0 . 12±0 . 025 ( 2000+ ) substitutions per nucleotide site . Modern HIV cohort sequences ( all sampled during chronic infection ) exhibited comparable mean pairwise distances to modern acute-phase subtype B sequences not included in the previous analysis ( not shown ) , suggesting that infection stage was not a major confounder of our diversity estimates by era . Taken together , results support a diversifying North American epidemic [50] where average intra-subtype Gag and Nef genetic distances have increased approximately two-fold since the 1980s . Before claiming that any highly prevalent HIV polymorphism has arisen as a result of its spread through the population over time , it is important to rule out its presence at the epidemic's genesis ( i . e . founder effect [51] ) . We therefore estimated the founder virus sequence of the North American epidemic by reconstructing the most recent common ancestor ( MRCA ) sequence at the root of the Gag and Nef phylogenies . To this end , we performed ≥50 , 000 MRCA reconstructions per HIV protein on random subsets of the historic sequence data using BEAST ( see methods and [52] ) , and computed a “grand consensus” MRCA reconstruction per protein ( Figure 2 ) . Overall , reconstruction confidence exceeded 80% for all but one codon in Gag ( residue 67 ) and for all but 6 codons in Nef ( residues 15 , 21 , 51 , 152 , 178 and 205 ) , all of which are highly polymorphic sites ( <70% amino acid conservation ) ( Figure 2 ) . The consensus of Gag sequence reconstructions at the MRCA differed from the LANL North American HIV subtype B consensus at only four residues ( A67S , R76K , K91R and E102D ) , while the consensus of Nef MRCA reconstructions was identical to it ( Figures 2 and S3 ) . Note the four ancestor/consensus differences in Gag merit cautious interpretation , as codon 67 was reconstructed with <80% confidence and the remainder are sites with <60% conservation at the amino acid level . MRCA reconstructions undertaken using random subsamples of both historic and modern Gag and Nef sequences were consistent with those computed from historic sequences only ( not shown ) . Finally , the grand mean MRCA date estimate from phylogenetic reconstructions inferred from random subsamples of both historic and modern sequences was 1965 ( range 1962–1967 ) . The consistency of this date with published estimates of a 1960s U . S . epidemic origin [53]–[55] provides additional support for our data as representative of the North American epidemic . A diversifying epidemic will , by definition , feature increasing viral polymorphism frequencies . Thus , to give relevance to our objective of measuring the spread of HLA-driven polymorphisms in HIV sequences over time , it is important to first demonstrate that HIV diversification is driven , at least in part , by HLA pressures . If so , we reasoned that HIV codons known to be under selection by HLA would , on average , have diversified to a greater extent than those not under selection by HLA . To investigate this , we first needed to independently define a list of HIV sites that are known to be under selection by specific HLA alleles . We defined these based on an independent published study of >1800 treatment-naïve individuals with chronic HIV subtype B infection from cohorts in Canada , the USA and Australia [43] , that had no overlap with the historic or modern cohorts studied here . In that study , HLA-associated polymorphisms in HIV were identified using phylogenetically-corrected association testing approaches ( see methods and [43] ) . For the present analysis of HLA selection and HIV diversification , an inclusive definition of “HIV sites under selection by HLA” was warranted; therefore , we defined this as all Gag and Nef codons associated with at least one HLA allele that met a false-discovery rate threshold of <20% ( q-value <0 . 2 ) in the independent study ( see methods and [43] ) . This totaled 95 ( of 500 ) codons in Gag and 99 ( of 206 ) codons in Nef . We began with Gag , by aligning historic and modern amino acid sequences to the HIV reference strain HXB2 and computing changes in Shannon Entropy on a per-codon basis ( 1000 bootstraps ) . This revealed 69 ( of 500; 14% ) codons whose entropies were significantly higher ( p<0 . 001 , q<0 . 01 ) in modern versus historic sequences ( Figures 3A , 3B ) . To minimize circularity of arguments , we next excluded highly ( >99% ) conserved codons from consideration , as these cannot diversify to any great extent ( and as such , are rarely identified as HLA-associated [43] ) – leaving 219 “variable” Gag codons for analysis . Stratifying these sites by their HLA status indicated that , of the 95 Gag sites under selection by HLA [43] , 45 . 2% exhibited significantly higher entropy in modern versus historic sequences , compared to 21 . 0% of the 124 sites not associated with HLA ( p = 0 . 0002 , Figure 3C ) . This indicates that HLA-associated viral sites tend to be those that have diversified the most between historic and modern-era HIV sequences . While entropy approaches strictly investigate the end products of diversification , dN/dS-based approaches provide a more direct way to investigate elevated substitution rates within the phylogeny . As such , we identified sites under significant pervasive positive ( diversifying ) selection in a maximum-likelihood phylogeny comprising historic and modern sequences using the fast unconstrained Bayesian approximation for inferring selection algorithm [56] . As expected , after excluding codons that were >99% conserved , sites under pervasive positive selection were more likely to experience a significant increase in entropy ( p<1×10−5 , not shown ) ( indicating that positive selection is driving some of this diversification ) , and were more likely to be HLA-associated ( suggesting that HLA represents a major source of this selection pressure ) ( p = 0 . 0022 , Figure 3D ) . We repeated these analyses for Nef , revealing trends consistent with those observed for Gag ( Figure S4 ) . Results thus suggest that ongoing HIV diversification is attributable , at least in part , to HLA pressures . We now turn to our major goal of assessing the spread of HLA-associated polymorphisms in the population over time . If escape mutations in HIV are reproducibly selected in individuals expressing particular host HLA ( s ) , but such mutations consistently and rapidly reverted upon transmission , then we would expect their frequencies to be generally higher among individuals expressing the relevant HLA ( s ) , and generally low among individuals lacking them , at levels that remain stable over time . But , if HLA-associated polymorphisms were to persist upon transmission and gradually spread in the population , we would expect polymorphism frequencies among HLA-matched individuals to remain stably higher , but polymorphism frequencies among individuals lacking the restricting HLA ( s ) to increase over time . As such , we stratified our HLA-associated polymorphism frequency comparisons between epidemic eras with respect to persons expressing , versus not expressing , the relevant HLA ( s ) . As before , we defined HLA-associated polymorphisms according to an independent source [43] . Because the present analysis investigated individual viral polymorphisms ( rather than just sites ) associated with HLA , a more specific definition was warranted . As such , we investigated all HLA-associated “adapted” ( escape mutant ) forms meeting a false-discovery rate threshold of <5% in the original study ( see [43] and methods ) . This list comprised specific HLA-associated polymorphisms occurring at 71 Gag and 96 Nef codons [43] . HLA-associated polymorphisms in HIV were additionally stratified based on whether they represented consensus or non-consensus viral residues . Though the vast majority of HLA-associated polymorphisms represent non-consensus residues , a minority represent cases where an HLA allele is associated with preservation of the consensus residue at a given site ( e . g . HLA-B*07:02 is associated with preservation of consensus G357 in Gag ) [43] . We analyzed such cases separately because , under conditions of star-like diversification of a “consensus-like” founder , the null expectation is for polymorphism ( i . e . non-consensus ) frequencies to increase , and consensus frequencies to decrease , over time . Separating them also allows more intuitive interpretation when polymorphism frequencies are summarized as averages . We began by investigating the frequencies of 70 non-consensus HLA-associated polymorphisms , occurring at 60 codons in Gag , between HLA-expressing and non-expressing persons in the historic and modern cohorts ( Figure 4 ) . As expected , individual polymorphism frequencies varied widely , but they were nevertheless enriched among individuals expressing the relevant HLA ( s ) ( Figure 4A ) compared to individuals lacking them ( Figure 4B ) . In accordance with the null expectation , polymorphism frequencies in persons harboring the relevant HLA ( s ) were consistent across historic ( median 18% , Interquartile Range [IQR] 4–54% ) and modern ( median 23% [IQR 7–45%] ) cohorts ( p = 0 . 8; Figure 4A ) . For example , Gag-242N frequency was ≥70% among persons expressing a B58 supertype allele , regardless of era . In persons lacking the relevant HLA ( s ) , we also observed numerous examples of polymorphisms whose frequencies remained stable between historic and modern eras ( e . g . Gag-242N frequency remained <1% in persons lacking a B58 supertype allele ) ( Figure 4B ) . Overall though , the average frequencies of these polymorphisms in persons lacking the relevant HLA ( s ) were modestly , yet statistically significantly , higher in modern ( median 3 . 7% [IQR 2–19%] ) compared to historic ( median 2 . 0% [IQR 0 . 7–10%] ) sequences ( p = 0 . 0002; Figure 4B ) , a result consistent with the spread of many – though not all – HLA-driven polymorphisms in the population . Results remained significant after adjusting for minor inter-cohort differences in HLA frequencies ( as these influence rates of polymorphism transmission ) ( p = 0 . 001 , Wilcoxon one-sample test , not shown ) . Under conditions where HLA-associated polymorphisms are , on average , slowly spreading through the population , we would expect the statistical associations between HIV polymorphisms and their restricting HLA ( s ) to concomitantly weaken . Indeed , this appeared to be the case . The median odds ratios of association between HIV Gag polymorphisms and their restricting HLA ( s ) were modestly lower in modern ( median OR 3 . 1 [IQR 1 . 7–7 . 1] ) compared to historic ( median OR 3 . 8 [IQR 1 . 2–17 . 5] ) cohorts ( p = 0 . 009 , Figure 4C ) . Similar trends were observed for the 89 non-consensus HLA-associated polymorphisms occurring at 77 codons in Nef . Among persons expressing the relevant HLA ( s ) , Nef polymorphism frequencies remained consistently elevated in historic ( median 14% [IQR 3–50%] ) and modern ( median 15% [IQR 3–41%] ) cohorts ( p = 0 . 7; Figure 4D ) . In persons lacking the relevant HLA ( s ) , examples of polymorphisms whose frequencies remained stable across historic and modern cohorts were noted ( e . g . Nef-94E frequency remained ∼1% in persons lacking B*08 , while Nef-135F remained ∼10% in persons lacking A*23:01 and A*24 ) ( Figure 4E ) . Overall though , the average frequencies of these polymorphisms in persons lacking the relevant HLA ( s ) were modestly higher in modern ( median 3 . 4% [IQR 1–12%] ) compared to historic ( median 2 . 0% [IQR 0 . 6–11%] ) sequences , though this did not reach statistical significance ( p = 0 . 054 ) ( Figure 4E ) . Median odds ratios of association between Nef polymorphisms and their restricting HLA ( s ) were also slightly lower in modern ( median 3 . 1 [IQR 1 . 7–7 . 1] ) compared to historic ( median 3 . 8 [IQR 1 . 2–17 . 5] ) cohorts , though not significantly so ( p = 0 . 065 , Figure 4F ) . We also investigated HLA-associated polymorphisms occurring at 11 Gag and 19 Nef codons where the association represented the consensus residue [43] . As expected , we observed higher frequencies of these consensus residues in individuals restricting the relevant HLA ( s ) compared to individuals lacking them ( Figure S5 ) . We also observed trends , though not statistically significant , towards lower consensus frequencies at these sites in modern versus historic sequences , regardless of HLA alleles expressed ( Figure S5 ) . Taken together , our results are consistent with a scenario in which , on average , non-consensus HLA-associated polymorphisms have increased in frequency in North American HIV sequences over time . That said , the observed increases for Nef were not statistically significant , and both proteins harbored numerous examples of HLA-driven polymorphisms with stable background prevalence ( e . g . Gag-242N , Nef-94E , Nef-135F ) . Moreover , although results for Gag attained statistical significance , average polymorphism background frequencies remained notably low , regardless of era . Our results thus indicate that not all HLA-driven polymorphisms are accumulating in circulation . Rather , our results suggest a diversity in accumulation rates , with the majority of nonconsensus polymorphisms spreading slowly ( and others not at all ) – and consensus residues decreasing in frequency overall . These observations confirm slow polymorphism spread predicted by mathematical models [9] and are consistent with an epidemic that is gradually diversifying under selection pressures that include HLA . Our results suggest that , on average , HLA-associated polymorphisms are spreading in the population , albeit slowly . From an immunological perspective , an increasing burden of escape mutations in circulating HIV strains over time could yield a reduction in the ability of individuals to control the virus via cellular responses as the epidemic progresses . We thus asked: if an individual were to be randomly infected by an HIV sequence from the historic or modern eras , to what extent would the latter contain a higher burden of polymorphisms that are “pre-adapted” to their HLA ? To estimate this quantity , we compared each individual's HLA profile against all historic and modern chronic-phase HIV sequences in our dataset , and calculated the percentage of HLA-associated sites in each sequence exhibiting the adapted form specific to each person's total HLA profile . Comparison of the overall per-person averages thus represents the expected extent to which a randomly sampled HIV sequence would be pre-adapted to a given individual , had they been infected by a sequence from that era . Focusing first on non-consensus HLA-associated polymorphisms , our calculations for Gag yielded a median “percentage HIV sites pre-adapted to one's HLA profile” of 14 . 9% [IQR 10 . 1–19 . 5%] for historic versus a median of 17% [IQR 12 . 7–22 . 4%] for modern sequences , an average increase of only ∼2% ( Figure S6 ) . Inclusion of consensus HLA-associated polymorphisms further minimized this gap ( not shown ) . For Nef , the median “percentage of adapted sites” remained consistent across eras ( 19 . 0% in historic versus 18 . 5% in modern ) ( Figure S6 ) ; moreover , inclusion of consensus polymorphisms resulted in lower overall percentages in modern compared to historic sequences ( not shown ) . Results therefore suggest that , despite HIV diversification , an individual's overall expected risk of acquiring escape mutant viruses specific to their HLA allele profile has increased only minimally for Gag , and not at all for Nef , since the 1980s in North America . Broadly speaking , at any given point in time , the average background frequencies of HLA-associated polymorphisms in circulating HIV sequences will generally positively correlate with the frequencies of their restricting HLA alleles in the population [12] . This is because higher absolute numbers of persons expressing the HLA will generally translate to higher absolute numbers of polymorphisms selected and thus transmitted ( though many factors , including the wide-ranging probabilities of polymorphism selection given their location and restricting HLA , the fact that multiple HLA alleles select the same – or opposing – mutations at a given location , the existence of “consensus” HLA-associations , and the timing of polymorphism selection/reversion , will render this correlation less than perfect ) . Nevertheless , such a positive trend is observed in both the historic and modern cohorts , as expected ( Figure S7 ) . However , we are specifically interested in investigating the extent to which HLA-associated polymorphisms are spreading through the population over time . We thus asked: are polymorphisms restricted by certain HLA alleles increasing to a greater extent than others ? To do this , we analyzed all HLA allele groups for which a minimum of three HLA-associated polymorphisms ( regardless of whether they were consensus or non-consensus ) were studied ( 25 alleles total ) . For each HLA-associated polymorphism , we computed its fold-increase in background frequency over time ( for example , a hypothetical polymorphism with a background frequency of 1% in the historic cohort versus 2% in the modern cohort would equate to a two-fold increase ) . For each HLA allele we then calculated the median fold-increase in frequency of all polymorphisms restricted by it . Overall , we observed no significant correlation between the frequency of a restricting HLA allele and the relative extent to which its polymorphisms spread throughout the population between historic and modern cohorts ( Spearman's R = −0 . 35 , p = 0 . 09 ) ( Figure 5A ) . Taken together with the results in Figure S7 , this indicates that , at any given point in time , polymorphisms restricted by common HLA alleles will generally be found at higher absolute frequencies in a population than those restricted by rarer ones , but such polymorphisms do not appear to be spreading in the population to a greater relative extent ( i . e . when expressed in terms of fold-change ) over time . Strong epidemiological links between host carriage of specific HLA class I alleles and HIV disease progression have been demonstrated in natural history studies ( e . g . : [57] ) , with some alleles , notably HLA-B*57 and HLA-B*27 , consistently associated with slower progression [57]–[59] . We therefore wished to investigate the relationship between an HLA allele's “protective” status ( defined as its published Hazard Ratio for progression to AIDS [57] ) and its median fold-increase in polymorphism background frequency between historic and modern eras . Of interest , we observed a significant inverse correlation between these two parameters ( Spearman's R = −0 . 52 , p = 0 . 0076 ) ( Figure 5B ) , suggesting that polymorphisms restricted by protective HLA alleles have , in relative ( fold-change ) terms , spread to a greater extent in the population than those restricted by non-protective HLA alleles . It is nevertheless important to contextualize these results in absolute terms . Of the six HLA-B*57-associated sites studied in Gag , historic sequences harbored a median 0 [IQR 0–1] B*57-associated polymorphisms at these sites , compared to 1 [IQR 0–2] in modern Gag sequences . Of the six B*57-associated sites in Nef ( two of which represent “consensus” associations ) , both historic and modern sequences harbored a median of 2 [IQR 1–3] B*57-associated adapted polymorphisms . It thus remains unclear to what extent these modest absolute increases may compromise the protective effects of certain HLA alleles as the epidemic progresses . We have thus far defined HLA-associated polymorphisms as those identified in independent modern cohorts by statistical association [43] . To investigate the potential existence of novel historic HLA-associated polymorphisms that are no longer detectable in modern sequences due to their spread throughout the population , we applied association testing approaches to our historic dataset directly . Historic patients with known or suspected early infection were excluded ( as these could dilute associations between HLA and HIV polymorphisms due to insufficient within-host evolution ) , and a false-discovery rate ( q-value ) cutoff of 0 . 05 was employed . We were especially interested to see whether HIV codons whose inferred ancestral ( founder ) amino acid differed from the North American consensus ( there were 4 in Gag ) or were reconstructed with <80% confidence ( 1 in Gag and 6 in Nef ) could be explained by the existence of historic HLA-associated polymorphisms at these sites . However , no such evidence was observed ( Figure 6A , 6B ) . Instead , analysis revealed 16 HLA-associated polymorphisms occurring at 10 Gag codons and 28 HLA-associated polymorphisms occurring at 13 Nef codons that , with the exception of an association between B*49:01 and the consensus G at Gag codon 62 , were wholly consistent with published escape pathways [43] and/or were confirmed in the present modern cohort ( not shown ) . In summary , the strongest HLA-associated polymorphisms in historic sequences are consistent with those identifiable today . HIV Gag and Nef are highly immunogenic HIV proteins whose sequence variability is substantially influenced by HLA [43] and whose function is susceptible to immune-mediated attenuation [44]–[46] . As such , we investigated whether the gradual spread of immune escape mutations in North American Gag and Nef sequences may be accompanied by overall changes in the average viral replication capacity and/or protein function of patient-derived HIV sequences . We began with Gag , by generating a recombinant HIV strain expressing the epidemic's inferred Gag ancestral sequence , and another expressing the published global subtype B consensus ( Figure S3 ) in an HIV NL4-3 subtype B reference strain backbone . We also generated recombinant HIV NL4-3 strains expressing a single representative clonal Gag sequence from 108 ( of 120 originally selected; 90 . 0% success rate ) historic and 58 ( of 71 originally selected; 82% success rate ) modern specimens ( Figure 7A ) . A clonal ( rather than quasispecies [60] ) approach was adopted for the patient-derived sequences , as variations in viral stock diversity resulting from differential integrity of historic versus modern specimens could bias replicative measurements . We assayed the in vitro replication capacity of these recombinant viruses using a published reporter T-cell assay [60]–[63] . Replication capacities ( RC ) were normalized to that of parental NL4-3 , such that values >1 and <1 indicate RC greater or less than NL4-3 , respectively . The replication capacities of recombinant viruses encoding the inferred ancestral and global subtype B consensus sequences were comparable to those of parental NL4-3 ( Figures 7B and S8 ) . Recombinant viruses expressing historic or modern Gag clonal sequences displayed a broad range of growth phenotypes , with median RCs approaching that of NL4-3 ( Figure 7B ) . Although there appeared to be a trend towards lower RC among Gag recombinant viruses from early historic ( 1979–1982 ) patients , this was not statistically significant ( Kruskal-Wallis p = 0 . 6 ) . Furthermore , no correlation was observed between the replication capacity of a given Gag clone and its genetic distance from the Gag NL4-3 sequence ( Spearman's R = 0 . 03 , p = 0 . 6 , not shown ) , arguing against confounding effects attributable to our use of a historic lab-adapted sequence ( NL4-3 ) as a viral backbone . Similarly , we cloned the inferred ancestral , global subtype B consensus and a single representative Nef sequence from N = 102 historic and N = 86 modern patients into a GFP-expression vector ( Figures 8A and S8 ) . As modulation of Nef function over the natural history of infection is supported by some [64] , [65] ( though not all [66] ) studies , and a minority of historic Nef clones were derived from persons with known or suspected early infection , we indirectly assessed infection stage as a potential confounder by including Nef sequences from 52 modern chronic and 34 early infection patients not included in previous analyses ( sampled a median of 72 [IQR 48–92] days after infection ) in our comparison group . Following transient transfection into an immortalized T-cell line stably expressing CD4 and HLA-A*02 , we assessed the ability of these Nef clones to downregulate these molecules from the cell surface by flow cytometry [67] , [68] ( Figure 8B ) . The Nef sequence from HIV reference strain SF2 served as a positive control ( SF2 is commonly used as a control in Nef functional studies , as it possesses robust CD4 and HLA class I downregulation activities , e . g . [67] ) ; thus , normalized Nef functions of >1 and <1 indicate activity greater or less than SF2 , respectively . Nef protein expression was verified by Western blot ( Figure S8 ) ; 15 poorly functional Nef clones whose expression could not be detected were excluded ( since in vitro cloning defects or other artifacts could not be ruled out ) , leaving 93 historic and 80 modern clones for analysis . CD4 downregulation activity of ancestral Nef was comparable to that of reference strain SF2 ( Figure 8B ) , while that of global subtype B consensus Nef was ∼3% lower ( not shown ) . Nef clones from historic and modern patients were generally highly functional for CD4 downregulation and exhibited relatively narrow dynamic ranges . Nevertheless , historic patient-derived Nef sequences exhibited significantly lower CD4 downregulation abilities compared to modern sequences ( Kruskal-Wallis p<0 . 0001 ) , with the early ( 1979–1982 ) Nef clones exhibiting the lowest function overall ( Figure 8B ) . Nef-mediated CD4 downregulation of modern Nef clones from individuals in early and chronic infection were comparable ( p = 0 . 9 , Figure 8B and not shown ) , arguing against infection stage as a major confounder of this result . The ability of the ancestral Nef sequence to downregulate HLA-A*02 was ∼3 . 5% higher than reference strain SF2 ( Figure 8C ) , while that of global subtype B consensus Nef was equivalent to SF2 ( not shown ) . Although Nef clones from both historic and modern patients were in general highly functional , historic Nef sequences exhibited significantly lower HLA downregulation abilities compared to modern Nef sequences ( Kruskal-Wallis p<0 . 0001 ) , with the early ( 1979–1982 ) Nef clones displaying the lowest function overall ( Figure 8C ) . HLA downregulation capacities of modern early Nef sequences were on average 1% higher than those from modern chronic Nef sequences ( p = 0 . 14 , Figure 8C and not shown ) , arguing against infection stage as a major confounder . The significantly lower Nef-mediated CD4 and HLA downregulation observed in historic versus modern sequences was robust to inclusion/exclusion of the 15 clones whose Nef expression was not detectable by Western Blot ( not shown ) . Taken together , the lack of significant functional differences between ancestral , subtype B consensus , and median patient-derived Gag clones from historic and modern eras argues against major replicative consequences of HIV Gag diversification during the North American epidemic . In contrast , our Nef results suggest the introduction of a highly functional founder virus to North America in the 1960s , followed by a subsequent decline in average Nef-mediated CD4 and HLA downregulation functions of patient-derived sequences in the 1980s , that were restored to original ( “founder” ) levels by the 2000s . The mechanisms and potential role for host pressures in this phenomenon require further investigation .
The present study examined linked host ( HLA ) and HIV ( Gag/Nef ) datasets from historic ( 1979–1989 ) and modern ( 2000–2011 ) eras in North America to estimate the extent to which HLA-driven polymorphisms may be spreading throughout circulating HIV sequences over time on this continent . Phylogenies inferred from historic and modern samples of HIV Gag and Nef sequence variation were star-like in shape , yielding a reconstructed ancestral ( epidemic founder ) virus sequence that was essentially identical to North American subtype B consensus . Mean pairwise distances between modern HIV Gag and Nef sequences were approximately two-fold greater than those between historic sequences , supporting a diversifying epidemic . Notably , Gag and Nef codons exhibiting the most significant entropy increases over time were enriched for known HLA-associated sites , consistent with a key role of HLA in driving HIV diversification [69] , [70] . Also consistent with an approximate two-fold increase in HIV diversity since the mid-1980s in North America , the average “background” frequencies of HLA-associated polymorphisms ( i . e . in individuals lacking the restricting HLA ) were roughly two-fold higher in modern compared to historic sequences . These differences reached statistical significance for Gag , though not for Nef . As expected , in both historic and modern cohorts , a general positive correlation was observed between the frequency of an HLA allele and the background frequency of its associated polymorphism in the general population . However , the polymorphisms that , over time , appeared to be spreading to the greatest relative extent ( in terms of fold-change ) were not those restricted by common HLA alleles ( Figure 5A ) but rather those restricted by protective HLA alleles [57] ( Figure 5B ) . This observation , along with our lack of identification of novel historic HLA-associated polymorphisms restricted by common HLA alleles , indicates that HIV is not simply adapting to the most frequent HLA alleles in a given host population . Instead , our findings are consistent with protective HLA alleles as those imposing the strongest evolutionary pressures on HIV , an observation that is consistent with previous reports that protective HLA alleles are more likely to induce strong selection at key conserved sites [43] , [71]–[73] . The spread of HLA-associated polymorphisms in circulation could lead to a reduction in host antiviral immune potential over time [12] . We thus wished to interpret our results in terms of the imminence of this potential outcome . First and notably , the extent of HLA-driven polymorphism accumulation in Nef did not reach statistical significance . Second , though observations for Gag did achieve significance , average polymorphism background frequencies remained low in absolute terms ( i . e . 2 . 0% in the 1980s versus 3 . 7% in the 2000s ) – differences that , when expressed in terms of the average estimated extent to which circulating HIV Gag sequences are “pre-adapted” to an individual's HLA profile , translated into an overall increase of only ∼2% between historic and modern eras . Moreover , we observed numerous HLA-associated polymorphisms whose prevalence remained stable in the population ( e . g . B58-supertype-associated Gag-242N , B*08-associated Nef-94E , A*2301/A*24–associated Nef-135F ) , observations that are consistent with their rapid reversion upon transmission [5] , [9] , [74] ( though estimates of the reversion rate for B*08-Nef-94E are somewhat conflicting [9] , [74] ) . That some - though certainly not all - HLA-driven escape mutations are capable of spreading through the population has been demonstrated via mathematical modeling [9] , indicating that the reproducible selection of specific escape mutations in persons harboring the relevant HLA does not always translate into rapid evolution at the population level [9] . That certain HIV sites simultaneously display strong signals for diversifying selection , yet stable polymorphism prevalence , is also consistent with “toggling” between consensus and escape forms [75] as HIV disseminates in a genetically diverse host population . Although our study did not formally attempt to model the dynamics of HLA-driven polymorphism spread in the North American population , our observations suggest that this is happening slowly . Very gradual polymorphism spread is also consistent with mathematical models projecting that , even in the case where an escape mutation never reverts , it could take centuries for it to reach fixation following its initial appearance in the population [9] . Moreover , it has been projected that any reversion ( however slow ) would prevent a polymorphism from ever becoming fixed [9] . Also consistent with slow spread is the near-identity of the reconstructed epidemic MRCA ( founder ) HIV sequence to the North American consensus - which suggests that , between the North American epidemic's genesis and the present day , no polymorphism , HLA-driven or otherwise , has spread to an extent where it now outcompetes that of the original founder residue . Our lack of identification of novel historic HLA-associated polymorphisms at the seven Gag/Nef codons where the inferred ancestor was reconstructed with <80% confidence and the four ( highly variable ) Gag codons where it differed from the modern consensus also argues against the spread of any historic HIV escape mutation in North America to the point where it now defines consensus . Note however that some caution is merited when interpreting the estimated founder viral sequence , since rapid selective sweeps occurring between the epidemic's foundation [54] , [55] and the earliest 1979 sampling date would not have been detected and therefore cannot be ruled out . Acknowledging these caveats , the near-identity between the estimated North American founder virus and modern consensus additionally suggests that statistical associations between particular HLA alleles and the HIV consensus residue at a given site ( e . g . B*07:02 with Gag-G357 ) have not arisen as a result of their selection and subsequent spread in the population to the point where they define the consensus [10] . Rather , these residues were most likely present at the epidemic's foundation - and , if anything , are gradually decreasing in frequency as HIV continues to diversify . We propose that such “consensus HLA associations” represent cases where the founder virus happened to be adapted to certain HLAs ( perhaps because the original founder or earlier hosts expressed them ) , and that these HLAs continue to exert purifying selection on these sites over time . Despite inferred overall slow rates of accumulation , the observation that polymorphisms restricted by protective alleles appear to be spreading to a greater ( relative ) extent than others is potentially important . Indeed , the stabilization of certain protective allele-associated escape mutations by secondary ( compensatory ) substitutions has been documented: the S173A mutation ( which allows the B*27-associated Gag-R264K mutation to persist upon transmission in an HIV subtype B context [11] , [13] ) and the S165N mutation ( which stabilizes B*57-associated mutations within the p24Gag KF11 epitope in a subtype C context [8] ) , are examples . Despite this , we urge caution in extrapolating that the protective effects of HLA alleles will diminish rapidly in North America . Again , it is important to consider that absolute polymorphism background frequencies remain low: modern Gag and Nef sequences together harbor , on average , only one additional B*57-associated polymorphism compared to historic sequences . Similarly , despite polymorphism spread , a B*27-expressing individual still has a >90% chance of acquiring HIV with the immunologically susceptible consensus R at critical Gag codon 264 . Besides , the protective effects of most such alleles ( including , to a certain extent , B*27 [76] ) are attributable to consistent and strong CTL responses against multiple HIV epitopes [43] , [77] , [78] . It is also important to consider that protective HLA-restricted CTL retain activity against polymorphic variants in many cases [79] , [80] , and de novo [81] or cross-reactive [82] CTL responses to in vivo escape variants can , and do , arise . Further integrated evolutionary and molecular studies are therefore required to assess the potential immunologic impact of polymorphism spread on HIV control by protective HLA alleles . Our study also investigated whether HIV evolution in North America has been accompanied by changes in viral replication capacity or protein function . Consistent with previous in vitro assessments of HIV sequences reconstructed using Center-of-Tree approaches [83] , our inferred Gag and Nef ancestral sequences were highly functional . Despite substantial increases in Gag diversity over time , the average replication capacities of recombinant NL4-3 viruses expressing patient-derived clonal Gag sequences from historic and modern eras were comparable to that of NL4-3 expressing the inferred Gag ancestral sequence , arguing against major replicative consequences of HIV Gag diversification during the North American epidemic . These results contrast with reductions in replication capacity of recombinant viruses expressing patient-derived Gag-protease sequences from Japanese patients from the mid-1990s to present [32] , a difference possibly due to the greater homogeneity of HLA alleles in Japanese compared to North American populations , that may exert consistent selection pressures driving the selection of fitness-reducing mutations . In contrast , the average Nef-mediated CD4 and HLA downregulation activities of historic patient-derived sequences were modestly yet significantly lower than modern ones . This is intriguing since the inferred Nef ancestral sequence displayed high function . We therefore speculate that , following the introduction of a functional ancestral Nef sequence into North America , initial HIV adaptation to this new population led to decreases in Nef function that were subsequently rescued upon continued Nef diversification . The higher Nef-mediated HLA class I downregulation function of modern compared to historic sequences , combined with the observation of modest HLA-driven polymorphism spread through the population during this same period , raises the interesting possibility that , compared to viruses circulating in the 1980s , modern North American HIV sequences may exhibit greater immune evasion potential via enhanced HLA class I downregulation [84] function . However , further studies will be required to elucidate the underlying mechanisms and pathogenic implications of these observations . An anticipated criticism is our definition of HLA-associated polymorphisms by statistical association studies of modern cohorts [43] . This approach could underestimate the average extent of polymorphism spread over time , for two reasons . First , such lists could exclude historic escape mutations that are no longer detectable in modern cohorts due to polymorphism spread . To address this we applied statistical association approaches to identify HLA-associated polymorphisms detectable at the population level in the historic cohort . However , all identified polymorphisms save one were consistent with known HLA-associated escape pathways , indicating that the strongest mutations detectable historically remain readily detectable today . A second limitation is that association testing approaches , even those that incorporate phylogenetic correction ( as ours do ) , systematically favor the identification of HLA-associated mutations that escape and revert rapidly [85] , which by definition would not be expected to spread quickly in a population [9] . However , this limitation is somewhat offset by the substantial size ( N>1800 ) of the cohort used to define HLA associations . Mathematical models indicate that at such sample sizes , with phylogenetic correction , significant associations can be detected between HLA alleles and polymorphisms even if these escape and/or revert on a timescale of decades [85] . Moreover we have previously demonstrated that cohorts of this size are powered to detect very rare HLA-associated polymorphisms , as well as those that are nearly universally observed in the population [43] . This study possesses additional limitations , many inherent to convenience sampling and technical challenges of working with historic samples . Although our sequences date back to 1979 , the lack of data from the critical period between HIV's introduction into North America and the late 1970s is a major limitation of this and all other studies undertaken to date . Nevertheless , our historic HIV sequence dataset is 10-fold ( Gag ) and 7-fold ( Nef ) larger than existing data from this era and region , and includes the oldest North American sequences ever published . Another limitation is that specimens were obtained from only four sites in North America , and all historic specimens were derived from observational studies of individuals from a single risk group ( MSM ) [34] , [36]–[42] . As such , our HIV diversity estimates , particularly for the historic era , may represent underestimates . Nevertheless , the dispersion of published North American HIV sequences throughout all phylogenies , the consistency of historic and modern consensus sequences , and our estimated epidemic founder dates that are compatible with published estimates [53]–[55] suggest that our sequences are not grossly unrepresentative of the North American epidemic . Concerns regarding our ability to faithfully amplify the original quasispecies diversity from historic specimens by PCR led us to adopt a single representative clone ( rather than bulk ) approach for our functional assessments of Gag and Nef in order to minimize in vitro bias associated with differences in the diversity of viral stocks . The presence of individuals with known or presumed early infection in our historic cohort and the general lack of clinical staging information are also limitations . To reduce confounding , early sequences were excluded from relevant analyses ( e . g . identification of HLA-associated polymorphisms in the historic cohort and calculation of Odds Ratios of association between HLA and polymorphisms ) , while other analyses verified the appropriateness of pooling data by comparing early and chronic sequences directly to rule out differences between them ( e . g . Nef functional assessments ) . The absence of pVL and CD4 information on historic patients also precluded the investigation of trends in disease markers over time . On the other hand , our development of a sensitive HLA sequence-based typing assay capable of utilizing genomic DNA extracted from plasma/serum [48] allowed us to perform HLA typing of historic specimens , yielding , for the first time , the ability to directly investigate HLA-associated selection pressures over the course of an epidemic . A known limitation of serum-based HLA typing is the overrepresentation of homozygous types due to amplification bias [48] , an effect that was noted in our historic dataset . Though this could lead us to overestimate the historic background frequencies of HLA-associated polymorphisms by erroneously including individuals expressing the relevant HLA into our calculations , the low average background frequencies of HLA-associated polymorphisms in modern sequences indicate that any overestimations would not substantially impact our overall conclusions . A notable strength is the lack of overlap between study cohorts and those from which the reference list of HLA-associated polymorphisms was derived [43] , thus ensuring independence of source and query data . In conclusion , HLA-associated polymorphisms are , on average , slowly spreading throughout North American HIV sequences as the epidemic continues to diversify . This slow adaptation to host cellular immune responses parallels the observed drift of HIV towards a more neutralization-resistant phenotype as a result of population-level viral adaptation to humoral immune pressures [86] , [87] . However , the absolute frequencies of these polymorphisms in circulation remain on average low on this continent , as do the estimated risks of acquiring HIV “pre-adapted” to one's HLA profile . As such , our results are unlikely to translate into major imminent consequences to CTL-mediated control of HIV , at least in the North American region . That said , we acknowledge that even modest changes can have biological implications . Indeed , one could contend that modest increases in the frequency of “pre-adapted” HIV strains are not inconsistent with reports suggesting increased HIV virulence over time [22] . Furthermore , it is important to emphasize that the potential rates , and thus immunologic implications , of HLA-associated polymorphism spread may be substantially greater in populations where HLA diversity is far lower and/or HIV prevalence far higher than North America . Rates and implications of polymorphism spread may also be more profound in populations where transmission tends to occur later in infection , thereby increasing the probability of transmitted escape mutations ( though mathematical models have suggested that realistic differential transmission rates between acute and chronic infection would impact population escape mutation prevalence only minimally [9] ) . As such , we recommend that similar analyses of virus-host adaptation be undertaken to assess the rate of accumulation of immune-driven polymorphisms , and its pathogenic implications , in other epidemic regions where historic specimens are available . In conclusion , though our results remain somewhat open to interpretation , we suggest that they be considered in light of the major advances in HIV treatment and prevention [88]–[92] that have occurred during the timecourse of the present study . Combined with current efforts in prevention and cure research [93]–[95] , these advances give us firm hope that the end of HIV/AIDS will precede the virus' ability to fully subvert host cellular immunity through population-level adaptation .
Research subjects , all adults , were enrolled under REB-approved protocols and provided written informed consent to participate in the original studies for which specimens were collected . Ethical approval to conduct this study was obtained from the Institutional Review Boards at Providence Health Care/University of British Columbia and Simon Fraser University . A total of 497 historic plasma/serum specimens from unique patients enrolled in observational studies of men who have sex with men ( MSM ) at four North American sites between 1979–1989 , were obtained for study . Of these , 94 and 75 were from the New York Blood Center ( NYBC; 1979–1989 ) and the San Francisco Department of Public Health ( SFDPH; 1979–1984 ) , respectively , and represented participants of hepatitis B observational studies whose archived sera were retrospectively tested for HIV [34] , [38] , [39] . A further 282 and 46 were obtained from the Fenway Community Health Clinic in Boston ( Fenway; 1985–1989 ) [36] , [37] and the Vancouver Lymphadenopathy-AIDS Study in Vancouver , Canada ( VLAS; 1984–1987 ) [40]–[42] . With the exception of 67 NYBC patients whose dates of HIV infection were estimated to be within 6 months prior to specimen collection , all other patients were known or presumed to be in chronic infection . Specimen integrity varied by cohort . Whereas sera from NYBC , SFDPH and Fenway were stored at −70°C since collection , VLAS specimens had been stored at −20°C and bore evidence of freeze-thaw cycles . No clinical information ( i . e . plasma viral load , CD4 ) was available for historic specimens; furthermore , sociodemographic and other identifying information were not sought . Our modern comparison cohort comprised 382 individuals for whom HIV Gag and/or Nef sequences were available: 26 were recruited through the Aaron Diamond AIDS Research Center in New York , 91 from Massachusetts General Hospital in Boston and 265 from various cohort studies based at the BC Centre for Excellence in HIV/AIDS in Vancouver , Canada . The modern cohort comprised MSM , injection drug users and individuals with unknown HIV risk group . HIV RNA was extracted from plasma or serum using standard methods . Gag and Nef regions were amplified by nested RT-PCR using sequence-specific primers and amplicons were bidirectionally sequenced on a 3130xl and/or 3730xl automated DNA sequencer ( Applied Biosystems ) . Data were analyzed using Sequencher v5 . 0 ( Genecodes ) or RECall [96] with nucleotide mixtures called if the height of the secondary peak exceeded 25% of the height of the dominant peak ( Sequencher ) or 20% of the dominant peak area ( RECall ) . All HIV sequences were confirmed as subtype B using the recombinant identification program ( RIP; http://www . hiv . lanl . gov/content/sequence/RIP/RIP . html ) . HXB2-alignments were performed using an in-house tool based on the HyPhy platform [97] . Phylogenetic trees were constructed using maximum-likelihood approaches [98] and visualized using FigTree ( http://tree . bio . ed . ac . uk/software/figtree/ ) . Patristic ( pairwise ) genetic distances were computed using PATRISTIC [99] . Intercohort comparisons of Shannon entropy scores ( featuring 1000 randomizations with replacement ) were performed using Entropy-two ( http://www . hiv . lanl . gov/content/sequence/ENTROPY/entropy . html ) . Detection of HIV Gag and Nef codons exhibiting significant evidence of pervasive positive selection ( defined as having a posterior probability ≥0 . 9 that the site-specific nonsynonymous rate exceeds its synonymous rate ) in the combined historic/modern datasets was performed using the fast unconstrained Bayesian approximation for inferring selection algorithm [56] , implemented in Datamonkey [100] , [101] . Consensus sequences were calculated by plurality rule . North American Gag and Nef HIV subtype B consensus sequences were computed from all available Gag and Nef sequences from unique patients annotated with Canada ( CA ) or United States ( US ) country labels in the Los Alamos HIV sequence database ( N = 1624 and N = 1141 Gag and Nef amino acids sequences , respectively , spanning 1983–2011 , accessed June 25 , 2013 ) . Historic plasma HIV RNA Gag and Nef sequences , annotated with year and country of collection , have been deposited in GenBank ( Accession numbers KF701643–KF701941 for Gag and KF701942–KF702276 for Nef ) . HLA class I typing was performed using an in-house sequence-based typing protocol capable of using plasma or serum as a source of genomic DNA [48] and types were assigned using an in-house algorithm . Where necessary , data were imputed to high resolution using a machine learning algorithm trained on a dataset of complete high resolution HLA-A , B and C types from >13 , 000 individuals with known ethnicity ( [47]; http://research . microsoft . com/en-us/projects/bio/mbt . aspx#HLA-Completion ) and assigned the highest-probability allele combination . HLA types could not be imputed when data were missing from more than one locus . Gag and Nef sequences were annotated with sample dates . Putative recombinants were identified using SCUEAL [102] and removed . The most recent common ancestor ( MRCA ) sequences of Gag and Nef were estimated using Bayesian evolutionary analysis by sampling trees ( BEAST ) [52] via 6 ( Gag ) or 5 ( Nef ) replicate chains , each analyzing a different set of 200 sequences selected at random from the dataset , and yielding 10 , 000 ancestral reconstructions per chain , as follows . Trees were sampled at random from the posterior distribution of trees given an exponential relaxed molecular clock [103] , a Bayesian skyline model of effective population size , and a time-reversible nucleotide substitution model determined by an Akaike information criterion-based model selection procedure in HyPhy [97] . Sampling was run for 2×108 steps , with the first half discarded as burn-in and the remainder thinned to 100 trees sampled at intervals of 106 steps in the chain . Convergence of replicate chains was assessed using the Tracer application in the BEAST software package . For each tree , 100 ancestral sequence reconstructions were sampled at random from the posterior distribution defined at the root under a Muse-Gaut codon substitution model in HyPhy . The inferred ancestral sequence was taken as the consensus of these 60 , 000 ( Gag ) and 50 , 000 ( Nef ) reconstructions ( 10 , 000 each per chain for 6 [Gag] and 5 [Nef] chains ) . Timing of each ancestral reconstruction ( tMRCA ) was estimated in BEAST by computing the mean estimate for each replicate chain and then computing a grand mean . The “consensus ancestor” Gag and Nef nucleotide sequences were commercially synthesized ( Invitrogen LifeTech ) for use in functional analyses . The reference list of HLA-associated polymorphisms in modern HIV subtype B sequences was defined in an independent multicenter cohort of >1800 chronically subtype-B infected individuals from Canada , the USA and Australia recruited in the 1990s and 2000s , that did not overlap with historic and modern cohorts analyzed herein , using phylogenetically-informed methods [43] . The same methods [43] were used to identify HLA-associated polymorphisms in the historic dataset , as follows . Briefly , maximum likelihood phylogenetic trees were constructed using Gag and Nef sequences , and a model of conditional adaptation was inferred for each observed amino acid at each codon . Here , the amino acid is assumed to evolve independently along the phylogeny , until it reaches the tree tips ( representing the present host ) . In each host , selection via HLA-mediated pressures and HIV amino acid covariation is directly modeled using a weighted logistic regression , in which the individual's HLA repertoire and covarying amino acids are used as predictors and the bias is determined by the possible transmitted sequences as inferred by the phylogeny [104] . To identify which factors ( HLA and/or HIV covariation ) contribute to the selection pressure , a forward selection procedure is employed where the most significant association is iteratively added to the model , with p-values computed using the likelihood ratio test . Statistical significance is reported using q-values [105] , the p-value analogue of the false discovery rate ( FDR ) . Q-values denote the expected proportion of false positives among results deemed significant at a given p-value threshold; for example , at q≤0 . 05 , we expect 5% of identified associations to be false positives . HLA-associated polymorphisms are grouped into two categories: ( 1 ) amino acids significantly enriched in the presence of the HLA allele in question ( “adapted” forms ) , and ( 2 ) amino acids significantly enriched in the absence of the HLA allele in question ( “nonadapted” forms ) . Second round Gag amplicons were selected from 120 historic and 71 modern patients with known or presumed chronic infection and cloned into the pCR2 . 1-TOPO TA vector ( Life Technologies , Burlington , ON , Canada ) . A single representative clone harboring an intact Gag reading frame that closely resembled the patient's bulk plasma HIV RNA was selected for virus generation [60] , [61] . Gag was amplified by PCR from each clone using 100 bp-long primers matching the NL4-3 sequence upstream and downstream of Gag , designed to facilitate homologous recombination of the amplicon with the pNL4-3Δgag backbone . The plasmid pNL4-3Δgag was developed by inserting unique BstEII restriction sites at the 5′ and 3′ ends of gag using the QuikChange XL kit ( Stratagene ) , followed by deletion of the intervening region via BstEII digestion ( New England Biolabs ) , gel-purification , and re-ligation ( T4 DNA ligase; New England Biolabs ) . PNL4-3Δgag was maintained in Stbl3 E . coli cells ( Invitrogen ) . To generate recombinant viruses , 10 µg of BstEII-linearized pNL4-3Δgag plus 50 µl of 2nd round Gag amplicon ( ∼5 µg ) were mixed with 2 . 5×106 cells of a GFP-reporter T-cell line ( CEM-derived GXR25 cells [106] ) in 125 µl of Mega-Cell medium ( Sigma ) , and transfected by electroporation in 96-well plates ( exponential protocol: 250 Volts , 2000 µF; 25 millisecond pulse duration; BioRad MxCell_Pro ) . Following transfection , cells were rested for 15 min at room temperature , transferred to 25 cm2 flasks containing 1 million GXR cells resuspended in 5 mL of R20+ medium ( RPMI 1640 containing 20% FCS , 2 mM L-glutamine , 100 units/mL penicillin , and 100 µg/mL streptomycin ) , and fed with 5 mL R20+ medium on day 5 and with replacement thereafter . Tat-driven GFP expression , indicating productive HIV infection of GXR cells , was monitored by flow cytometry ( Guava 8HT , Millipore ) starting on day 12 [60] , [61] . Once GFP+ expression exceeded 15% among viable cells , supernatants containing recombinant viruses were harvested and aliquots stored at −80°C . Patient origin of all recombinant viruses was confirmed via sequencing of the Gag region . Viral titers and replication capacity ( RC ) assays were performed using GXR25 GFP-reporter T-cells , as described [60] , [61] . RC assays were initiated at MOI = 0 . 003 , and included one negative control ( uninfected cells only ) and one positive control ( NL4-3 Gag re-introduced into the NL4-3Δgag backbone using identical methods ) per 24-well plate . For each virus , the natural log slope of the percentage ( % ) of GFP+ cells was calculated during the exponential phase of viral spread ( days 3–6 ) . This value was divided by the mean rate of spread of all NL4-3 controls such that RC values <1 . 0 or >1 . 0 indicate rates of spread that were slower than or faster than NL4-3 , respectively . Each virus was assayed in a minimum of two independent experiments and average RC values are reported . First-round Nef amplicons from 102 historic and 86 modern patients were originally selected and amplified using second round primers featuring EcoRI ( forward ) and SacII ( reverse ) restriction sites . Amplicons were PCR-purified ( GeneJET PCR Purfication Kit , Thermo Scientific ) and cloned into the pIRES2-EGFP expression vector ( Clontech ) as described in [67] , [68] . For each patient , a single representative clone harboring an intact Nef reading frame that closely resembled the original bulk plasma HIV RNA sequence by phylogenetic analysis was selected for functional assessment . CD4 and HLA class I downregulation activities for each Nef clone were measured using a CEM-SS derived T cell line that expresses high levels of surface CD4 and HLA-A*02 ( CEM-A*02 ) , constructed as described in [107] . To assess Nef-mediated CD4 and HLA downregulation , 3×105 CEM-A*02 cells were transfected with 5 µg plasmid DNA encoding Nef protein and GFP by electroporation ( BioRad GenePulser MX ) . Twenty hours later , cells were stained with APC-labeled anti-CD4 and PE-labeled anti-HLA-A*02 antibodies ( BD Biosciences ) and cell surface expression was measured in transfected ( GFP-positive ) cells by flow cytometry ( Guava easyCyte 8HT , Millipore ) . For patient-derived Nef clones , the median fluorescence intensity ( MFI ) of CD4 or HLA-A*02 expression in GFP-positive cells was normalized to the MFI of CD4 or HLA-A*02 expression for the negative control ( empty pIRES2-EGFP plasmid ) and positive control ( nef reference sequence SF2 , cloned into pIRES2-EGFP ) to determine the relative CD4 or HLA-A*02 downregulation capacity . As such , a normalized value of 0 . 0 indicates no downregulation activity and a value of 1 . 0 indicates downregulation capacity equivalent to that of the positive control NefSF2 . All assays were performed in triplicate and results are presented as the mean of these measurements . Steady state Nef protein levels were measured by Western blot for the minority of Nef clones that displayed poor ( <50% ) function for either CD4 or HLA-A*02 downregulation activity , alongside 20 randomly-selected clones with activities above this threshold . A total of 5×106 CEM-A*02 cells were transfected by electroporation with 10 µg of plasmid DNA , and cell pellets were collected 20 hours later for preparation of total cell lysates , using a protocol modified from [107] . Lysates were subjected to SDS-PAGE in duplicate and electro-blotted onto PVDF membrane . To ensure detection of patient-derived Nef , duplicate blots were probed using anti-Nef polyclonal antisera developed from rabbit ( NIH AIDS Research and Reference Reagent Program Catalog #2949 , [108] ) or sheep ( ARP 444; NIBSC Center for AIDS Reagents , UK ) . Actin expression was simultaneously assessed . Band intensities were quantified on an ImageQuant LAS 4000 ( GE Healthcare Life Sciences ) . A total of 15 poorly functional Nef clones whose expression could not be detected by Western Blot were excluded from analysis , as in vitro cloning or other defects cannot be ruled out . This left 93 historic and 80 modern Nef clones for analysis .
|
Upon HIV transmission , many – though not all – immune escape mutations selected in the previous host will revert to the consensus residue . The persistence of certain escape mutations following transmission has led to concerns that these could gradually accumulate in circulating HIV sequences over time , thereby undermining host antiviral immune potential as the epidemic progresses . As certain immune-driven mutations reduce viral fitness , their spread through the population could also have consequences for the average replication capacity and/or protein function of circulating HIV sequences . Here , we characterized HIV sequences , linked to host immunogenetic information , from patients enrolled in historic ( 1979–1989 ) and modern ( 2000–2011 ) HIV cohorts from four key cities in the North American epidemic . We reconstructed the epidemic's ancestral ( founder ) HIV sequence and assessed the subsequent extent to which known HIV immune escape mutations have spread in the population . Our data support the gradual spread of many - though not all - immune escape mutations in HIV sequences over time , but to an extent that is unlikely to have major immediate immunologic consequences for the North American epidemic . Notably , in vitro assessments of ancestral and patient-derived HIV sequences suggested functional implications of ongoing HIV evolution for certain viral proteins .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"blood",
"cells",
"sequencing",
"techniques",
"organismal",
"evolution",
"viral",
"vaccines",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"population",
"genetics",
"immunology",
"microbiology",
"immunodeficiency",
"viruses",
"microbial",
"evolution",
"molecular",
"biology",
"techniques",
"hiv",
"epidemiology",
"sequence",
"analysis",
"white",
"blood",
"cells",
"animal",
"cells",
"viral",
"immune",
"evasion",
"medical",
"microbiology",
"hiv",
"epidemiology",
"t",
"cells",
"microbial",
"pathogens",
"pathogenesis",
"viral",
"replication",
"molecular",
"biology",
"immune",
"response",
"viral",
"evolution",
"immune",
"system",
"cell",
"biology",
"host-pathogen",
"interactions",
"virology",
"viral",
"pathogens",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"computational",
"biology",
"evolutionary",
"biology",
"acquired",
"immune",
"system"
] |
2014
|
Genotypic and Functional Impact of HIV-1 Adaptation to Its Host Population during the North American Epidemic
|
MicroRNAs and Argonaute form the microRNA induced silencing complex or miRISC that recruits GW182 , causing mRNA degradation and/or translational repression . Despite the clear conservation and molecular significance , it is unknown if miRISC-GW182 interaction is essential for gene silencing during animal development . Using Caenorhabditis elegans to explore this question , we examined the relationship and effect on gene silencing between the GW182 orthologs , AIN-1 and AIN-2 , and the microRNA-specific Argonaute , ALG-1 . Homology modeling based on human Argonaute structures indicated that ALG-1 possesses conserved Tryptophan-binding Pockets required for GW182 binding . We show in vitro and in vivo that their mutations severely altered the association with AIN-1 and AIN-2 . ALG-1 tryptophan-binding pockets mutant animals retained microRNA-binding and processing ability , but were deficient in reporter silencing activity . Interestingly , the ALG-1 tryptophan-binding pockets mutant phenocopied the loss of alg-1 in worms during larval stages , yet was sufficient to rescue embryonic lethality , indicating the dispensability of AINs association with the miRISC at this developmental stage . The dispensability of AINs in miRNA regulation is further demonstrated by the capacity of ALG-1 tryptophan-binding pockets mutant to regulate a target of the embryonic mir-35 microRNA family . Thus , our results demonstrate that the microRNA pathway can act independently of GW182 proteins during C . elegans embryogenesis .
MiRNAs are highly conserved small non-coding RNAs that orchestrate gene expression in a broad range of developmental processes . The production of miRNA implicates a successive two-step processing involving two RNase III enzymes , Drosha and Dicer , which cleave the primary and precursor miRNA molecules in the nucleus and cytoplasm , respectively . The 21–23 nucleotide RNA products are loaded onto Argonaute proteins to form the ribonucleoprotein complex referred to as microRNA induced silencing complex or miRISC ( Reviewed in [1] , [2] ) . Utilizing sequence complementarity , the miRNAs then guide the miRISC to the 3' untranslated region ( 3'UTR ) of target mRNAs to silence their expression . In humans and Drosophila , the miRISC is associated with a key partner protein , GW182 , which contains glycine-tryptophan ( GW ) repeats . The GW182 N-terminal domain uses these GW repeats to interact with Argonaute [3–5] , while the C-terminal domain recruits the PAN2-PAN3 and CCR4-CAF1-NOT deadenylase complex [6–8] . As a result , the complex triggers mRNA deadenylation and/or translational repression . Despite the difference in their domain organization from that of Drosophila and human GW182 proteins , two related C . elegans proteins , AIN-1 and AIN-2 [9 , 10] , appear to be orthologs of GW182 in C . elegans ( reviewed in [11] ) . Both AIN-1 and AIN-2 are known to interact with Argonautes proteins through their GW repeats , but only AIN-1 interacts with PAN and NOT proteins [12] indicating that AIN-1 is most likely the bona fide functional GW182 ortholog . Thus , the interaction between Argonaute and GW182 proteins is clearly important for miRNA-mediated gene silencing across species , although the domain architectures of GW182 proteins are varied among species . Argonaute proteins have a bilobed structure , each composed of the N and PAZ domains or the MID and PIWI ones ( Reviewed in [13] ) . The PAZ and MID domains are engaged in the guide-RNA recognition at the 3’ and 5’ ends , respectively , while the PIWI domain harbors an RNase H-like active site that catalyzes the endonucleolytic cleavage of nucleic acids . Besides conferring the “slicing activity” on some Argonautes , the PIWI domain has also been reported to be important for the recruitment of silencing factors such as GW182 [4 , 14 , 15] . The crystal structure of human Argonaute2 ( hAgo2 ) identified two hydrophobic pockets on the surface of the PIWI domain that were occupied with free tryptophan residues , suggesting that GW182 proteins could be tethered to hAgo2 via these two pockets on the PIWI domain [16] . The physical interaction between hAgo2 and GW repeats was subsequently validated by NMR studies [17] . Furthermore , analogous binding pockets were also identified on the surface of human Argonaute 1 ( hAgo1 ) [18] , suggesting that these pockets could be a conserved feature for recruiting GW-proteins . Notably , although the molecular interactions between Argonaute and GW182 proteins have become clearer in recent years , the functional importance of this interaction in the context of miRISC-mediated silencing during animal development has yet to be determined . We set out to address the necessity and function of GW182 proteins during miRISC-mediated gene silencing throughout animal development . To achieve these goals , we generated transgenic C . elegans strains expressing an ALG-1 mutant ( ALG-1TPmut ) whose Tryptophan-binding Pockets lost an interaction with AIN-1 and AIN-2 . We have demonstrated that the tryptophan-binding pockets are required for its interaction with AIN-1 and AIN-2 . Surprisingly , loss of the physical interaction between ALG-1 and AINs phenocopied the null allele of alg-1 ( 0 ) , whereas embryonic lethality due to lack of alg-1 and alg-2 was rescued by alg-1 ( TPmut ) alone . These results indicate the existence of a type of miRISC that plays an essential role , without the aid of GW182 proteins , during embryogenesis in animal development .
Recent structural studies of hAgo1 and hAgo2 identified two tryptophan-binding pockets on the exterior of the PIWI domain , which were predicted to serve as the binding site of GW182 proteins [16–18] . Our homology model based on the hAgo structures indicated the presence of two tryptophan-binding pockets on ALG-1 ( Fig 1A–1C; S1 Fig ) . These putative pockets consisted of residues K803 and E838 in the first pocket , and P733 and F802 in the second one , which could recognize tryptophan residues of the bound GW182 protein ( Fig 1A and 1B ) . We tested whether mutations of these residues affected the interaction between ALG-1 and the orthologs of GW182 in C . elegans both in vitro and in vivo . First , we purified a recombinant AIN-1 fragment ( recAIN-1 ) encompassing the previously mapped binding site of ALG-1 [12] along with glutathione-S-transferase ( GST ) -tagged full-length wild-type ALG-1 ( recALG-1 ) . We also created at GST-tagged ALG-1 in which the four key residues of the two Tryptophan-binding Pockets were mutated to alanine ( hereafter recALG-1 ( TPmut ) ; Fig 1C and S2 Fig ) . Consistent with our expectations , the interaction between recALG-1 ( TPmut ) and recAIN-1 proteins in vitro was less than detectable by western blotting ( Fig 1D ) , strongly suggesting that the tryptophan-binding pockets on ALG-1 are essential for the interaction with AIN-1 . To test whether tryptophan-binding pockets mutation also disrupts the interaction with AIN-1/AIN-2 in vivo , we generated an alg-1 ( 0 ) mutant C . elegans strain expressing a single copy of either alg-1 wild-type ( wt ) or alg-1 ( TPmut ) gene under the control of endogenous regulatory elements . We then immunoprecipitated ALG-1 from these animals using a specific antibody [19] . While we could recover both wild-type ALG-1 and ALG-1 ( TPmut ) in our immunoprecipitations , we could only observe an association of AIN-1 and AIN-2 with wild-type ALG-1 ( Fig 1E and S3 Fig ) . These results prove that ALG-1 binds to AINs through the tryptophan-binding pockets , in a manner consistent with the interaction between human Argonautes and GW182 [17] . It is well known that the alteration of miRNA-specific Argonautes causes significant effects on the levels of miRNAs [20] . In C . elegans , the loss of alg-1 gene leads to a dramatic decrease of mature miRNAs along with the accumulation of miRNA precursors , suggesting a role in miRNA processing [21 , 22] . To assess whether ALG-1 tryptophan-binding pockets mutant retains its function in miRNA biogenesis , we investigated the levels of precursor and mature miRNAs in worms carrying null alleles of the alg-1 gene and expressing either wild-type ALG-1 or ALG-1 ( TPmut ) . Quantitative real-time PCR and Northern blotting analyses showed that the level of mature miRNA were reverted to that of wild-type by the expression of ALG-1 ( TPmut ) ( Fig 2A and S4A Fig ) . Accordingly , the levels of miRNA precursors also decreased to those of wild type ( S4A Fig ) . These results indicate that the interaction between ALG-1 and AINs is not necessary for miRNA processing . Next , we assessed whether the mutations of the tryptophan-binding pockets affect binding of ALG-1 to miRNAs . We used 2'-O-methyl RNA affinity columns to purify ALG-1 ( TPmut ) that had been loaded in vivo with miRNAs complementary to the affinity matrix [23 , 24] . Comparable amounts of wild-type ALG-1 or ALG-1 ( TPmut ) were associated with lin-4 and let-7 miRNAs ( Fig 2B ) . Conversely , we also observed a similar level of miRNAs bound to different ALG-1 immunoprecipitated complexes ( S4B Fig ) , suggesting that GW182 proteins do not affect the interaction between the Argonaute and miRNAs . Thus , these results suggest that GW182 proteins are dispensable for miRISC assembly . We next investigated whether AINs-free miRISC can still control gene expression in animals . To this end , we took advantage of developmental phenotypes caused by loss of specific miRNAs . It is known that loss of let-7 miRNA family in C . elegans causes a characteristic phenotype , in which the animal bursts from the vulval opening after L4 moult [25] . This lethal phenotype was observed in a fraction of the population of worms deficient for alg-1 ( Fig 3A ) . The addition of extra chromosomal transgene arrays expressing wild-type alg-1 gene significantly reduced the number of animals that burst ( Fig 3A ) . In contrast , the expression of ALG-1 ( TPmut ) did not rescue alg-1 ( 0 ) animals ( Fig 3A ) . In both the strains , the expression levels of AIN proteins were comparable ( S5 Fig ) . To further characterize ALG-1 ( TPmut ) in miRNA-mediated gene silencing , we monitored the developmental patterning of seam cells . These lateral rows of hypodermal cells undergo a postembryonic developmental program , consisting of patterned rounds of division during each larval stage ( L1 to L4 ) , and ended by terminal differentiation encompassing exit from the cell cycle , cell fusion and production of a cuticular structure ( called alae ) at the adult developmental transition ( Fig 3B ) . This developmental program is controlled at different larval stages by lin-4 miRNA [26] , the let-7 family miRNAs ( miR-48 , miR-84 , miR-241 and let-7 ) [25 , 27] and their targets lin-14 , lin-28 , hbl-1 , daf-12 and lin-41 [25 , 27–33] . In absence of alg-1 , the symmetric seam cell division program that occurs once at the L2 stage is repeated , leading to an increase of seam cell numbers and structural defects in cuticular alae caused by inappropriate terminal differentiation ( Fig 3B–3D; [21] ) . Consistent with the lethality caused by the loss of let-7 family regulation , the seam cell developmental phenotype in alg-1 ( 0 ) mutant animals was rescued by the expression of wild-type ALG-1 but not of ALG-1 ( TPmut ) ( Fig 3D ) . Using transgenic animals expressing a GFP reporter under the control of the lin-41 3'UTR , a known target of let-7 miRNA regulated at the L4-Adult transition [27 , 32] , we observed that the repression of lin-41 by let-7 miRNA is altered in the ALG-1 ( TPmut ) -expressing adult animals ( Fig 3E ) . Interestingly , in all cases the phenotypes observed in ALG-1 ( TPmut ) animals are more severe than the ones caused by a complete loss of ALG-1 proteins . These observations suggest that with its retained capacity of interacting with microRNAs , ALG-1 ( TPmut ) sequester microRNA from ALG-2 , the other functional microRNA-specific Argonaute in worms [19 , 21] . To assess whether the deficiency of miRNA-mediated gene repression in the ALG-1 ( TPmut ) -expressing animals might be resulting from a defect in binding of target mRNAs , we generated a LambdaN ( λN ) /Box-B tethering-based reporter that enables interaction of Argonaute with a target , independently of a miRNA-mRNA interaction . In cultured cells , this has been a conventional system to decipher the molecular basis for gene silencing by Argonaute and GW182 proteins [14 , 34 , 35] . To apply the system to animals , we made a GFP gene reporter where the well-characterized lsy-6 miRNA binding sites in the cog-1 3'UTR were replaced by six Box-B stem loop structures ( Fig 4A ) . We then made a transgenic C . elegans strain co-expressing a single copy of this reporter along with either wild-type or TPmut λN::mCherry-tagged alg-1 gene , both of which were under the control of the alg-1 promoter and 3′UTR regulatory regions . We first confirmed that the presence of the N-terminal tag does not affect ALG-1 function by performing alg-1 mutant rescue . When wild-type λN::mCherry::ALG-1 was co-expressed with the Box-B reporter , a significant decrease of the GFP signal was measured in the pharynx of young adult animals ( Fig 4B to 4G ) . The expression of λN::mCherry::ALG-1 ( TPmut ) protein , however , failed to repress the GFP reporter , suggesting that the interaction with AIN-1 is essential to trigger the repression of the tethering reporter ( Fig 4B , 4H ) . Taken all together , we conclude that the physical contact of ALG-1 to AINs through its tryptophan-binding pockets is important for miRNA-mediated gene silencing in animals . The miRNA pathway is a regulatory mechanism that is essential for the control of various steps during animal development including embryogenesis ( for reviews see [36–38] ) . In C . elegans , this phenomenon is exemplified by the fact that loss of both miRNA-specific Argonaute genes alg-1 and alg-2 leads to embryonic lethality [19 , 21] . We therefore decided to use our ALG-1 ( TPmut ) to test whether the interaction between GW182 proteins and miRISC is essential during embryogenesis . To achieve this , we knocked down alg-2 in alg-1 ( 0 ) or ( TPmut ) animals by feeding them with bacteria expressing dsRNA against alg-2 . Consistent with the phenotype observed in simultaneous RNAi knockdown of alg-1 and alg-2 [21 , 22] , we observed that nearly 70% of the F1 alg-1 ( 0 ) population exposed to alg-2 ( RNAi ) displayed embryonic lethality , while the remaining F1 animals arrested just after hatching ( Fig 5A ) . Surprisingly , the expression of ALG-1 ( TPmut ) in an alg-1 ( 0 ) ; alg-2 ( RNAi ) background is sufficient to rescue embryonic lethality at a level comparable to that of alg-1; alg-2 loss of function animals expressing a wild-type ALG-1 transgene ( Fig 5A ) . Strikingly , nearly all F1 progeny arrest in early stages of larval development ( Fig 5A ) suggesting that GW182 proteins are required for miRISC function during larval development but not during embryogenesis . To directly test the contribution of GW182 proteins for miRNA-mediated silencing during C . elegans embryogenesis , we constructed a balanced strain with loss-of-function alleles of both AIN genes , ( ain-1 ( ku322 ) ; ain-2 ( tm2432 ) /dpy-5 ) , exposed them to dsRNA-expressing bacteria targeting ain-2 ( ain-2 ( RNAi ) ) to remove all maternally loaded ain-2 mRNA , and scored the F1 progeny . Even though AINs were well expressed during embryogenesis ( S3 Fig and S6 Fig ) , the alteration of both AIN genes did not cause significant embryonic lethality but rather led to severe larval developmental arrest of the F1 population as seen for alg-1 ( TPmut ) ; alg-2 mutant animals ( Fig 5B ) demonstrating that the alteration of GW182 function in C . elegans embryos phenocopies the loss of interaction with the miRISC . To further test the importance of AINs in the embryonic miRISC , we tested the capacity of ALG-1 ( TPmut ) to control animal sex determination , an embryonic gene regulatory pathway controlled by the mir-35-41 microRNA family cluster [39] . We therefore utilized the her-1 ( n695gf ) allele that causes a weak derepression of her-1 expression in C . elegans hermaphrodites leading to mild masculinization ( with a low penetrance of intersex and pseudomale; Fig 6 ) . While the loss of alg-1 in this sensitized background significantly increased the number of masculinized animals observed , the expression of ALG-1 ( TPmut ) in her-1 ( gf ) /alg-1 ( 0 ) animals completely reestablished it to the level observed in her-1 ( gf ) animals ( Fig 6 ) demonstrating that the interaction with AINs is not required for the function of the mir-35 microRNA family in this embryonic decision . Taken all together , our findings support that the embryonic miRISC does not necessitate GW182 proteins to silence gene expression .
Most of the reported mutations in Argonaute proteins affect the binding of both miRNA and GW182 proteins [3 , 40 , 41] . Until recently , there have been only two Argonaute mutants reported deficient in the interaction with GW182 protein without affecting miRNA binding [3 , 40] . Interestingly , based on our prediction , two point mutations reported in Drosophila AGO1 ( R771 and F777 ) could also be involved in forming the same tryptophan-binding pockets 1 and 2 in C . elegans . In this study , we have generated such point mutations in ALG-1 and demonstrated the functional importance of these pockets to sustain the interaction even with a different type of GW182 proteins that possess a non-canonical domain architecture . Interestingly , a very recent study using culture cell systems reported that the mutation of these binding pockets in Drosophila AGO1 and human Ago2 abolished the interaction with their GW182 proteins without affecting microRNA binding [42] . These Argonaute variants have been and can continue to be useful to study the mechanism of miRNA-mediated gene silencing independent of GW182 proteins . Thus , application of Argonaute tryptophan-binding pockets mutant variants will provide novel strategies to uncover new types of gene regulation in animals . GW182 proteins have been long thought to be essential for miRNA-mediated gene silencing in animals . Recent observations using Drosophila S2 cells as well as cell-free systems , however , suggest that GW182 is not always necessary for miRISC-mediated gene silencing . For example , Drosophila Ago1 and Ago2 , the latter of which mainly associates with an siRNA-duplex , were both able to repress translational reporters in GW182-dependent and–independent manners [43] . Using miRNA-mediated reporter assays with or without polyA tails , Fukaya and Tomari demonstrated that Drosophila Ago1 could block translation independently of GW182 [44] . More recently , the Carthew’s group reported that in fly , miRISC retains the silencing activity under conditions lacking GW182 protein ( i . e . when nutrients are removed from S2 cell cultured media ) [45] . In these experimental conditions , the absence of GW182 still leads to gene silencing that results from the inhibition of either early translation or elongation . These data implied a possible GW182-independent miRNA repression in cell culture though it remained unclear whether this was the case in animal development . Using C . elegans as a model , we show here that there are two miRNA-mediated gene-silencing pathways that appear to be necessary for specific time windows during development . Our in vivo approach demonstrates that the abrogation of miRISC interaction with GW182 proteins does not cause embryonic lethality as seen in animals lacking miRNA-specific Argonautes , ALG-1 and ALG-2 . These data are in striking contrast with the severe developmental phenotypes observed in those animals after hatching . Given that GW182 proteins are not essential for miRISC-mediated regulation during animal embryogenesis , the miRNA-mediated gene silencing may preferentially block translation , instead of deadenylation and mRNA degradation that requires the recruitment of GW182 proteins to the target mRNAs . This model is reminiscent of the observation that gene silencing can occur independently of mRNA deadenylation during zebrafish embryogenesis [46] . Since GW182 proteins are essential and sufficient for mRNA deadenylation and translational repression , the silencing complex without GW182 must limit the turnover rate of the bound mRNAs during embryogenesis . In this case , the dwell time of miRISC to discriminate the proper target mRNAs would be extended , and could be tested by single molecule studies . Our study discovered that ALG-1 functions , without the aid of AIN-1 and AIN-2 , as an essential factor in early stages of development in C . elegans . This sheds light on the enigmatic miRNA-mediated gene silencing during embryogenesis in animals , which bypasses a regular gene silence pathway that requires GW182 proteins . However , we cannot exclude a possibility that such a GW182 protein-free miRISC plays another unidentified , but critical role during embryogenesis , in addition to gene silencing . We believe that this study lays a strong foundation and experimental context for future studies to understand how and why miRNA-mediated gene silencing pathways are varied at different developmental stages , and how each pathway involves GW182 proteins .
All C . elegans strains were cultured and handled using standard methods . The transgenic strains were generated by MosSCI single insertion method [47 , 48] or by extrachromosomal non-integrated transgene expression [49] . The strategies to build plasmids as well as strains for this study are listed in S1 Text . E . coli BL21 codon+ cells transformed with pGEX plasmids encoding a GST-fusion construct were grown at 37°C . After adding 0 . 1mM Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) at OD600 = 0 . 8 , the E . coli cells were grown at 15°C for 16h . Harvested cells were resuspended in STE buffer ( 10mM Tris pH8 , 150mM NaCl , 1mM EDTA , 5mM DTT , 1mM PMSF ) supplemented with 2 . 5% ( w/v ) of N-Lauryl-Sarkosyl and lysed at 27kPSi in a constant cell disruptor ( One Shot Cell Disruptor , Constant System ) . 1 . 5% ( v/v ) final Triton X-100 was added into the cell lysis . GST-tagged ALG-1 proteins were purified under non-denaturing conditions by affinity chromatography using Glutathione Sefinose matrix and quantified on a 8% SDS-PAGE gel . The pulldown assay was carried out by mixing 100ng of GST::ALG-1 resin coupled protein with 500ng of AIN-1 fragment in binding buffer ( 100mM Potassium Acetate , 30mM Hepes-KOH pH7 , 2mM Magnesium Acetate , 1 . 5% Triton X-100 , 1mM DTT , 1 tablet/10mL Complete Mini Protease Inhibitor without EDTA ( Roche ) ) . The mix was incubated for 1h at 4°C with gentle rotation and washed 2 times with high salt PBS buffer ( 300mM then 500mM NaCl ) followed by a non-stringent final wash with PBS only . Beads were resuspended into 2X denaturing Laemmli buffer and loaded on a 4–15% SDS PAGE gel . The upper part containing GST-tagged ALG-1 was stained with Coomassie Brilliant Blue whereas the lower part was immunoblotted with a primary rabbit polyclonal AIN-1 antibody ( dilution 1:1000 ) . Staged young adults worms were obtained by Alkaline Hypochlorite Solution treatment and plated onto NGM Agar plates seeded with OP50 bacteria . After 4 days at 15°C , animals were harvested in M9 solution and lysed by sonication into ice-cold lysis buffer ( 100mM KAc , 30mM Hepes-KOH pH7 , 2mM Magnesium Acetate , 1mM DTT , 1 . 5% triton X-100 , 1 tablet /10mL Complete Mini Protease Inhibitor without EDTA ( Roche ) ) . Immunoprecipitation and miRISC pull-down assays were carried out as described in [50 , 51] , respectively . The 2′-O-methyl oligonucleotides sequences have been previously described in [23] . Primary rabbit polyclonal ALG-1 and AIN-1 antibodies were used at 1:1000 dilution in PBST supplemented with 5% of milk with overnight incubation at 4°C . AIN-1 and AIN-2 antibodies were generated by injection of two rabbits with either AIN-1 peptide ( EQRAPASTEDYHYS ) or AIN-2 peptide ( GPPDHYYDYSFLG ) and affinity purified using the same epitope ( Feldan ) . RNA preparation and microRNA quantification by quantitative RT-PCR were performed as described in [50] . To quantify the level of microRNA bound to ALG-1 , 4mg of total protein extract was used to immunoprecipitate ALG-1 . A fraction of 10% was mixed to Laemmli denaturing buffer and loaded on 8% SDS-PAGE . 90% of the remaining beads were treated with 20μg of proteinase K and RNA was extracted using TriReagent ( Sigma ) . Samples were spiked and normalized with 50fmol of human synthetic miR-20a as technical control . The RNAi of alg-2 and ain-2 were carried out by feeding using cDNA fragment cloned into RNAi feeding vector L4440 and expressed into inducible IPTG HT115 ( DE3 ) bacterial strain as described in [52] . The oligonucleotides used to generate the plasmids as well as the different plasmids are listed in S1 Table and S2 Table , respectively . DIC Nomarski images and GFP , mCherry fluorescence expressions were collected in animals using a Zeiss AxioCam HRm digital camera mounted on a Zeiss Axio Imager M1 microscope using the same settings for each animal . Intensity of fluorescence in pharynx was measured with Axiovision 4 . 6 software .
|
Animal cells possess different small RNA species capable of precisely controlling the gene expression . Among them , microRNAs form a silencing complex with an Argonaute protein ( known as miRISC ) that abrogates protein production by targeting specific messenger RNAs . While there is a consensus that miRISCs are effective to mediate gene silencing , it is still unclear if they exist in different types in animals . Here we report specific mutations in the C . elegans microRNA-specific Argonaute ALG-1 , which alter its association with the orthologs of GW182 proteins , important factors for miRISC-mediated silencing . Our genetic characterization of this mutant shows that part of miRISCs function without the GW182 orthologs during the embryogenesis . These findings suggest the presence of distinctive miRISC that can regulate gene expression in different ways during animal development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"invertebrates",
"protein",
"interactions",
"caenorhabditis",
"gene",
"regulation",
"animals",
"animal",
"models",
"developmental",
"biology",
"caenorhabditis",
"elegans",
"micrornas",
"model",
"organisms",
"immunoprecipitation",
"genetic",
"engineering",
"research",
"and",
"analysis",
"methods",
"gene",
"silencing",
"genetically",
"modified",
"organisms",
"proteins",
"gene",
"expression",
"genetically",
"modified",
"animals",
"agriculture",
"precipitation",
"techniques",
"biochemistry",
"rna",
"nucleic",
"acids",
"embryogenesis",
"genetics",
"nematoda",
"biology",
"and",
"life",
"sciences",
"non-coding",
"rna",
"agricultural",
"biotechnology",
"organisms"
] |
2016
|
GW182-Free microRNA Silencing Complex Controls Post-transcriptional Gene Expression during Caenorhabditis elegans Embryogenesis
|
Across metazoans , cell cycle progression is regulated by E2F family transcription factors that can function as either transcriptional activators or repressors . For decades , the Drosophila E2F family has been viewed as a streamlined RB/E2F network , consisting of one activator ( dE2F1 ) and one repressor ( dE2F2 ) . Here , we report that an uncharacterized isoform of dE2F1 , hereon called dE2F1b , plays an important function during development and is functionally distinct from the widely-studied dE2F1 isoform , dE2F1a . dE2F1b contains an additional exon that inserts 16 amino acids to the evolutionarily conserved Marked Box domain . Analysis of de2f1b-specific mutants generated via CRISPR/Cas9 indicates that dE2F1b is a critical regulator of the cell cycle during development . This is particularly evident in endocycling salivary glands in which a tight control of dE2F1 activity is required . Interestingly , close examination of mitotic tissues such as eye and wing imaginal discs suggests that dE2F1b plays a repressive function as cells exit from the cell cycle . We also provide evidence demonstrating that dE2F1b differentially interacts with RBF1 and alters the recruitment of RBF1 and dE2F1 to promoters . Collectively , our data suggest that dE2F1b is a novel member of the E2F family , revealing a previously unappreciated complexity in the Drosophila RB/E2F network .
The E2F family of transcription factors was first cloned as a cellular factor that binds to the Early E2 region of the adenovirus genome [1] . Since its discovery , families of E2F transcription factors have been identified in metazoans ranging from nematodes to mammals [2] . One of the important features of E2F transcription factors is their ability to bind to a consensus sequence , TTTCCCGC , which is commonly found in cell cycle-regulated genes [3 , 4] . While most E2Fs heterodimerize with DP to bind the consensus sequence , a subset of E2F proteins bind DNA without the help of DP [5] . Regardless , the ability of multiple members of E2F to bind and regulate the same gene allows fine regulation of target gene expression during development [2] . E2F family proteins can be classified as either “activators” or “repressors” based on their role in transcription [6] . This classification was largely attributed by studies in fruit flies , which have only two E2F genes , de2f1 and de2f2 . Genetic and biochemical assays demonstrated that dE2F1 and dE2F2 have antagonistic functions [7] . While both can directly bind to a common set of genes , dE2F1 promotes whereas dE2F2 represses transcription [8] . As a consequence , the phenotype observed in de2f1 mutant flies can be largely suppressed by inactivating dE2F2 [7] . The simplicity of the Drosophila E2F network provided an example in which the interplay between activator and repressor E2Fs coordinates target gene expression and cell cycle progression . Curiously , while the anti-proliferative effect of dE2F2 was clearly demonstrated in the de2f1 mutant background , de2f2 mutant flies do not display a strong cell cycle defect [7 , 9] . This suggests that in the presence of dE2F1 , dE2F2’s role as a repressor is largely dispensable . Notably , there has been evidence suggesting a repressive role for dE2F1 during development , although the molecular mechanism underling this observation has been elusive [10 , 11] . In mammals , E2F1 , E2F2 and E2F3 are considered to be activator E2Fs while E2F4 to 8 are considered to be repressor E2Fs [2] . Since they have similar DNA-binding specificities , functional redundancies within activator and repressor E2Fs clearly exist . Indeed , all three activator E2Fs must be inactivated in mice to completely inhibit E2F-dependent transcription program and cellular proliferation [12] . In addition , a study by Tsai et al . demonstrated that activator E2Fs can compensate for each other’s function as long as their expression is properly controlled [13] . Specifically , Tsai et al generated mice expressing E2F1 under the control of the E2F3 promoter in the E2F1 to 3 triple-mutant background . These mice are phenotypically similar to E2F1 and E2F2 double knockout mice and not to E2F2 and E2F3 double knockout mice . This study elegantly demonstrated that it is the expression pattern , not the protein sequence , that determines the functional specificity of activator E2Fs during mouse development . Despite the genetic evidence showing similarities between the in vivo functions of activator E2Fs , member-specific activities among mammalian activator E2Fs have been identified . A key molecular difference between activator E2Fs resides in the Marked Box ( MB ) domain . Domain swapping experiments coupled with transcriptome analysis revealed that the MB domain is responsible for generating E2F1- and E2F3-specific gene signatures [14] . This finding was further supported by the observation that the MB domain mediates specific protein-protein interactions . For example , E2F3 was shown to specifically interact with an E-box transcription factor , TFE3 , through the MB domain to cooperatively regulate target gene expression [15] . The MB domain was also shown to be important for the physical interaction between E2Fs and the C-terminal ( C-term ) domain of RB family proteins in mammals [16–18] . Structural studies have identified amino acid residues that are important for member-specific interaction between E2F and RB family proteins . Interestingly , these residues include the key amino acids that mediate an E2F1-specific interaction with the RB tumor suppressor protein , pRB , that allows the pRB/E2F1 complex to silence repetitive sequences [19 , 20] . Overall , the MB domain plays a crucial function , allowing members of activator E2Fs to carry out specific functions . We recently reported that the fruit fly activator E2F , de2f1 , is transcribed from multiple transcription start sites , and that specific promoters are required for proper cell cycle exit during development [21] . Interestingly , the annotated sequences show that an alternatively spliced isoform of de2f1 is associated with the promoter we identified ( http://flybase . org/reports/FBgn0011766 . html ) . The alternatively spliced form , hereon referred as de2f1b , contains an extra exon that adds 48 nucleotides to the de2f1 isoform that has been widely studied for more than two decades , hereon referred as de2f1a ( Fig 1A ) . Notably , the additional exon of de2f1b alters the amino acid sequences within the MB domain . In this study , we provide evidence that dE2F1b is a novel member of the Drosophila E2F family that is functionally distinct from dE2F1a . We demonstrate that dE2F1b is an essential isoform of dE2F1 through molecular complementation tests . While neither dE2F1a nor dE2F1b alone is sufficient to efficiently rescue the early larval lethality of de2f1 mutants , co-expression of both isoforms is . In addition , generation of a de2f1b-specific mutant via CRISPR/Cas9 revealed that dE2F1b is a critical regulator of the cell cycle . We also provide evidence to suggest that dE2F1b is required to negatively regulate E2F target gene expression in specific contexts although dE2F1 has been classified as an activator E2F . Overall , our study identifies dE2F1b as a novel member of E2F and reveals a previously unappreciated complexity in the Drosophila RB/E2F network .
The alternatively spliced isoform of de2f1 , de2f1b , contains an extra exon , exon 3b , that adds 48 nucleotides to the widely studied de2f1 isoform , de2f1a ( Fig 1A ) . As a first step to gain insights into the biological function of de2f1b , we determined the expression level of total de2f1 and de2f1b-specific transcripts at different developmental stages . We performed quantitative RT-PCR ( RT-qPCR ) using RNA isolated from embryos , larvae , pupae , and adult flies , then calculated the copy number per unit of cDNA ( see materials and methods ) . The copy number of total de2f1 per unit of cDNA ranges from 1 . 4 X 105 to 3 . 7 X 105 , the lowest being at the pupal stage and the highest being at the adult stage ( Fig 1B ) . The de2f1b transcript is expressed at a lower level , ranging from 1 . 6 X 104 to 5 . 6 X 104 copies per unit of cDNA . Interestingly , the expression of de2f1b peaks at the larval stage , 5 . 6 X 104 copies , when the total de2f1 level is relatively low , 1 . 6 X 105 copies . Therefore , the de2f1b-isoform represents approximately one third of the total de2f1 at the larval stage , the highest of all stages . We also determined the relative expression level of total de2f1 and de2f1b transcripts in several third instar larval ( L3 ) tissues ( Fig 1C ) . Notably , 5 . 3 X 104 copies of the de2f1b isoform is expressed in salivary glands , representing 56% of the total de2f1 . In eye and wing imaginal discs , de2f1b represents less than 20% of total de2f1 . Taken together , these results suggest that de2f1b expression is developmentally regulated and contributes to a significant fraction of the total de2f1 transcript the salivary gland . Because the only difference between dE2F1a and dE2F1b is the 16 amino acids coded by exon 3b ( Fig 1A ) , we were unable to directly detect dE2F1b at the protein level . Our efforts to raise a peptide antibody against the 16 amino acids did not yield a functional antibody . Nevertheless , we asked whether dE2F1b plays a specific role during development by performing a molecular complementation test in a de2f1 mutant background . de2f1 mutants have severe developmental defects and mostly die at the early larval stage [22 , 23] . We expressed either de2f1a or de2f1b alone or together in a de2f1 mutant background and determined their ability to rescue the early larval lethality ( Fig 1D ) . de2f1a or de2f1b expression alone produces third instar larvae at a much lower frequency than the expected ratio . In addition , the rescued animals are developmentally delayed , only progressing to third instar larvae 10 days after egg laying ( S1C Fig ) . Strikingly , expression of both isoforms results in third instar larvae at the expected frequency with only one day of developmental delay ( Fig 1D and 1E ) . Moreover , a majority of the rescued third instar larvae complete pupariation and became pharate adults ( S1A and S1B Fig ) while none of de2f1a or de2f1b alone larvae progress to pharate adults . Importantly , doubling the amount of de2f1a by expressing two copies of the transgene results in only 23 . 1% of survival at the third larval stage and all surviving larvae fail to progress to pharate adults ( S1D Fig ) . These results indicate that it is the presence of both isoforms that is important for rescue , and not the dosage of the transgenes . Overall , the molecular complementation tests indicate that dE2F1b is a functionally important isoform of dE2F1 and that dE2F1a and dE2F1b likely play distinct roles during development . To determine the in vivo function of de2f1b , a de2f1b-specific mutant line was generated using the CRISPR/Cas9 system . This was achieved by targeted removal of the de2f1b-specific exon and its surrounding splicing acceptor and donor sites ( Fig 2A ) . The de2f1b mutant line was validated by sequencing of its genomic DNA ( S2A Fig ) and through RT-PCR to verify the absence of exon 3b ( Fig 2B ) . In addition , cDNA from de2f1b mutants was sequenced to confirm that this deletion does not result in a frameshift mutation in de2f1a ( S2C Fig ) . At the organismal level , homozygous de2f1b mutant flies develop normally with no apparent defects until the late pupal stage where about 50% of de2f1b mutants fail to complete metamorphosis and die prior to eclosion ( Fig 2C ) . Similar eclosion rates were observed in both de2f1b homozygous mutants and trans-heterozygous mutants of de2f1b and a deficiency covering de2f1 , indicating that this phenotype is specific to the de2f1b mutation . We also observed that de2f1b mutants are female sterile but male fertile , indicating an ovary-specific defect . Indeed , ovaries from well-fed five days old de2f1b females are dramatically smaller than the control ( S2B Fig ) . At the late third instar larval stage ( 105–110 hrs after egg laying , AEL ) , while the sizes of de2f1b and control L3 larvae are comparable , the overall size of the de2f1b salivary gland is consistently smaller than the control ( Fig 2D ) . This observation together with the expression data shown in Fig 1C suggests dE2F1b plays a critical function during salivary gland development . To determine the effect of de2f1b mutation on the overall level of de2f1 transcript , RT-qPCR was performed using RNA isolated from third instar larvae ( Fig 2E ) . Surprisingly , the total level of de2f1 is increased in de2f1b mutant larvae and the expression of E2F target genes , cyclin E ( cycE ) and ribonucleotide reductase small subunit ( rnrS ) , is also increased in de2f1b mutant larvae . Overall , the de2f1b-specific mutation results in tissue specific defects and an overall increase in dE2F1 expression/activity during development . We next examined the de2f1b mutant salivary gland in closer detail . The Drosophila larval salivary gland is an endoreplicating tissue where cells undergo a dramatic period of growth by increasing in ploidy through a variant cell cycle called the endocycle [24] . It consists of repeated cycles of G1 and S phases without intervening mitoses . While other Drosophila tissues grow by endocycle , L3 salivary glands are exceptional in that their cells contain more than 1000 copies of their genome in the form of polytene chromosomes [25] . To determine whether the size defects observed in Fig 2D is related to DNA content , we visualized nuclei using 4′ , 6-Diamidine-2′-phenylindole dihydrochloride ( DAPI ) . During larval growth , the salivary glands grow from the distal-tip to the proximal-end in a coordinated fashion [26] . In control late-stage ( 105–110 hr AEL ) L3 salivary glands , this coordinated growth is apparent since the nuclear sizes are similar across a single tissue ( Fig 3A ) . However , in late-stage de2f1b salivary glands , a striking variation of nuclear size was observed ( Fig 3A yellow arrows ) . Quantification of the overall distribution of nuclear area of three individual salivary glands of control and de2f1b mutants indicates that , on average , nuclear sizes are smaller and are significantly variable in de2f1b mutants than in control salivary glands ( Fig 3B and 3C ) . Notably , quantification of DAPI intensity revealed that de2f1b salivary glands have nuclei with lower DNA content on average than the control ( Fig 3D ) . This result indicates that the smaller nuclear sizes observed in de2f1b salivary glands likely reflects lower DNA content . We next determined the pattern of S-phase cells in late-stage de2f1b salivary glands using ethynyl deoxyuridine ( EdU ) labeling , a thymidine analog . While control late-stage L3 salivary glands have S-phase cells prominently in the proximal region , S-phase cells with variable intensities of EdU are found throughout de2f1b late-stage L3 salivary glands ( Fig 3E ) . This result indicates a failure to properly control endocycle progression in de2f1b salivary glands . Importantly , salivary glands of dDP mutant flies that do not have functional dE2F1 and dE2F2 did not display significant variability in the nuclear size ( Fig 3A–3C ) , suggesting that this phenotype is specific to de2f1b and not a general consequence of deregulating E2F function . Endocycle progression in the salivary gland requires dE2F1-dependent periodic expression of CycE [25] . In G1 phase of the cell cycle , dE2F1 accumulates to promote timely expression of CycE at the G1 to S phase transition . During S phase when CycE level is high , dE2F1 is targeted for ubiquitin-dependent degradation by CRL4CDT2 [26] . Consequently , CycE and dE2F1 expression are coupled to the cell cycle and largely show a mutually exclusive pattern of expression in early-stage ( 80–85 hr AEL ) L3 salivary glands , when cells are actively cycling ( [26] and Fig 4A upper panel ) . This stereotypic pattern of CycE and dE2F1 expression is disrupted in early-stage de2f1b mutant salivary glands . dE2F1 is more broadly expressed and cells expressing extremely high levels of CycE are mostly absent in de2f1b mutant salivary glands ( Fig 4A lower panel ) . In addition , more cells in de2f1b mutant salivary glands co-express CycE and dE2F1 . While only 30% of CycE and 12% of dE2F1 expressing cells co-express the other protein in control salivary glands , these numbers increase to 85% and 45% respectively in de2f1b mutant salivary glands ( Fig 4A ) . We also compared the expression pattern of dE2F1 and a dE2F1 activity reporter , PCNA-GFP ( Fig 4B ) . The PCNA-GFP reporter expresses GFP under the control of a region of the PCNA promoter that contains well-characterized E2F-binding sites [27] . Interestingly , PCNA-GFP shows a pattern of expression that is similar to CycE in control salivary glands . PCNA-GFP expression is highest in cells with a low level of dE2F1 and low in cells with a high level of dE2F1 ( asterisk in Fig 4B upper panel ) . Two interesting differences are observed in de2f1b salivary glands ( Fig 4B lower panel ) . First , the overall intensity of PCNA-GFP is weaker than control although dE2F1 is expressed at a similar level . Second , contrary to what was observed in control salivary glands , PCNA-GFP expression lacks signs of oscillation , being more evenly expressed throughout the salivary gland . Overall , these results indicate that dE2F1b is required for strong activation and periodic expression of its target genes during early L3 salivary gland development . We next examined late-stage L3 salivary glands ( 105–110 hr AEL ) to determine the overall consequences of the de2f1b mutation . In late-stage control salivary glands , dE2F1 is evenly expressed at a lower level than the early stage salivary but higher at the proximal tip where high CycE expression and strong PCNA-GFP activity are also detected ( Fig 4C upper panel ) . In the middle and at the distal tip of the late-stage salivary gland , CycE expression and PCNA-GFP activity is almost undetectable . This indicates that , by this stage , most cells have completed the endocycle and dE2F1 activity is suppressed and limited to the proximal tip of the salivary gland . In late-stage de2f1b salivary glands , we observed what seems to be sustained expression of dE2F1 , CycE and PCNA-GFP throughout the tissue ( Fig 4C lower panel ) . Importantly , dE2F1 and CycE expression is not limited to the proximal region and visible in the middle and distal tip of de2f1b salivary glands ( Fig 4C lower panel ) . Interestingly , we reproducibly detected cytoplasmic dE2F1 signal in many cells of the de2f1b salivary gland ( arrowhead in Fig 4C ) . However , the significance of this observation is currently unclear and further studies will be necessary to elucidate its importance . Nevertheless , this result demonstrates that coordinated downregulation of dE2F1 expression and activity is disrupted in late-stage L3 de2f1b salivary glands . To support this observation , the expression levels of other dE2F1 target genes were determined by RT-qPCR ( Fig 4D ) . We observed approximately a two-fold increase in G1/S-phase target expression such as rnrS and CycE . Notably , the total level of PCNA is not increased in this assay . The PCNA-GFP shows strong expression in the proximal region of control salivary glands , which is lost in de2f1b mutants . Perhaps , higher PCNA expression observed in the middle and distal regions of late-stage de2f1b salivary glands is balanced out by the loss of strong PCNA expression in the proximal region . We also observed a three- to four-fold increase in a number of E2F-regulated G2/M genes such as cyclin B ( CycB ) and fizzy ( fz ) in de2f1b salivary glands . To determine if the sustained expression of dE2F1 target genes is responsible for the salivary gland defects , we reduced the expression level of a key dE2F1 target gene , cycE , via RNAi-mediated knockdown . The RNAi-construct was expressed with the heatshock-Gal4 driver without heat shock , to partially suppresses CycE expression ( S3 Fig ) . Strikingly , the variability of nuclear sizes in de2f1b salivary glands shown in Fig 3 is significantly suppressed by cycE knockdown , indicating that the failure to properly downregulate cycE contributes to this phenotype ( Fig 4E ) . Taken together , our results demonstrate that dE2F1b is an isoform of dE2F1 that is necessary for tight regulation of dE2F1 expression and activity during salivary gland development . Fig 4C and 4D raise the possibility that dE2F1b plays a repressive function at the late-stage of salivary gland development , either by limiting total level of dE2F1 or by directly repressing target gene expression . However , we cannot exclude the possibility that they are an indirect consequence of incomplete endocycle , which is supported by the DAPI quantification ( Fig 3D ) . To gain better insights into dE2F1b’s function , we analysed de2f1b mutant mitotic tissues such eye and wing imaginal discs , which do not have visible developmental defects . Curiously in eye imaginal discs , a number of studies have reported that the dE2F1 protein , despite being an activator , is expressed highest in the morphogenetic furrow where cells are arresting in G1 ( Fig 5A upper panel ) [28] . Strikingly , in de2f1b mutant eye discs , dE2F1 expression in the morphogenetic furrow is greatly reduced ( Fig 5A lower panel ) . In addition , PCNA-GFP expression is ectopically detected in the morphogenetic furrow and the posterior regions of the de2f1b eye disc where it is normally repressed ( Fig 5B left panel ) . A similar change in the expression pattern of a dE2F1 target gene , rnrS , is also observed ( Fig 5B right panel ) . Interestingly , changes in E2F target gene expression do not greatly alter the pattern of S-phase cells in de2f1b eye discs ( Fig 5C ) . However , a reproducible presence of ectopic S-phase cells and CycE expression at the posterior region of de2f1b eye discs are observed ( Fig 5C asterisks ) . Notably , we did not observe any ectopic cell death at the same region , suggesting that ectopic S-phase cells observed in de2f1b mutant eye discs is not a consequence of simply increasing overall dE2F1 activity ( S4 Fig ) [28] . In wing imaginal discs , PCNA-GFP expression is normally repressed in the zone of non-proliferating cells ( ZNC , Fig 5D arrow heads ) , where cells are arrested in either G1 or G2 [29] . Similar to the morphogenetic furrow , dE2F1 expression is highest at the ZNC . Strikingly , dE2F1 expression is also greatly reduced and PCNA-GFP is ectopically expressed at the ZNC in de2f1b wing discs ( Fig 5D ) . Although direct detection of dE2F1b is required as conclusive evidence , our data suggest that dE2F1b is expressed at developmental stages when cells undergo cell cycle arrest , and that it provides a repressive function on target gene expression in this context . To gain molecular insights into how dE2F1b mediates its repressive function , we determined the promoter occupancy of dE2F1 and RBF1 , a negative regulator of dE2F1 . Recruitment of total dE2F1 and RBF1 to known target genes was quantified between control and de2f1b mutant larvae using chromatin immunoprecipitation ( ChIP ) coupled with qPCR . Anti-dE2F1 ChIP revealed increased recruitment of dE2F1 to S-phase genes such as rnrS , and PCNA promoters in de2f1b mutant compared to control larvae ( Fig 6A ) . Contrary to dE2F1 , anti-RBF1 ChIP using the same chromatin extracts showed an overall decrease in the recruitment of RBF1 to the same set of target genes ( Fig 6B ) . Importantly , RBF1 recruitment to a previously identified dE2F2-specific gene , trc8 , is largely unchanged if not increased in de2f1b mutants , indicating that the decrease in RBF1 recruitment is specific to dE2F1 target genes ( Fig 6B ) [30] . A potential explanation is that dE2F1 , whose expression is increased in de2f1b mutants ( Fig 2E ) , competes away dE2F2 that normally forms a stable complex with RBF1 . However , dE2F2 ChIP shows that dE2F2 recruitment is unaltered in de2f1b mutant larvae , indicating that the changes in dE2F1 and RBF1 recruitment are dE2F2-independent ( Fig 6C ) . These observations demonstrate that the lack of dE2F1b promotes dE2F1 recruitment , presumably dE2F1a , and decreases RBF1 recruitment to target promoters , providing a possible molecular explanation of how dE2F1b negatively regulates gene expression . Two functional domains of activator E2Fs physically interact with RB family proteins [16–18] . The transactivation ( TA ) domain interacts with the “pocket” domain of RB , which consists of the A and B domains of RB family proteins , and the MB domain interacts with the C-terminal domain of RB family proteins ( Fig 7A ) . Because the de2f1b-specific exon alters the amino acid sequence of the MB domain , we tested if dE2F1a and dE2F1b differentially interact with RBF1 through GST pull-down assays . We made three GST-fusion constructs ( Fig 7A ) , Large Pocket ( LP ) , which includes A , B and C-terminal domains , Small Pocket ( SP ) , which include A and B domains without the C-terminal domain , and C-terminal domain alone ( C-term ) . Not surprisingly , LP pulls down both dE2F1a and dE2F1b expressed in S2 cells since both isoforms contain the same TA domain ( Fig 7A ) . However , C-term only pulls down dE2F1a and not dE2F1b , indicating that the 16-amino acid insertion interferes with this interaction ( Fig 7A ) . Moreover , the SP reproducibly fails to pull down dE2F1b as efficiently as dE2F1a . These results suggest that the 16-amino acid insertion alters the way that dE2F1 interacts with RBF1 . Because dE2F1a and dE2F1b differentially interact with RBF1 , we examined if RBF1 can equally affect dE2F1a- and dE2F1b-dependent transcription . For this task , dE2F1a or dE2F1b was overexpressed using an eye-specific GAL4 driver , GMR-Gal4 ( GMRG4 ) , and PCNA-GFP was used to monitor their activities . Interestingly , while overexpression of Myc-tagged dE2F1a or dE2F1b in the eye using the GMRG4 driver results in similar levels of expression ( S5 Fig ) , dE2F1b is more efficient at activating PCNA-GFP than dE2F1a ( Fig 7B , second panel ) . dE2F1b can ectopically activate PCNA-GFP in the entire posterior region of the eye disc while the effect of dE2F1a overexpression is limited to several ommatidial rows posterior to the morphogenetic furrow . We also observed a similar effect on CycE expression , showing a stronger induction by dE2F1b than by dE2F1a . Importantly , the fact that dE2F1b is a better activator of transcription than dE2F1a likely explains why PCNA-GFP expression is weaker in early L3 de2f1b salivary glands than the control although the overall dE2F1 expression is relatively unchanged ( Fig 4B ) . To determine the effect of RBF1 on dE2F1a- and dE2F1b-dependent transcription , we co-expressed RBF1 . RBF1 expression strongly suppresses the ectopic PCNA-GFP and CycE expression induced by dE2F1a as well as dE2F1b ( Fig 7B , third panel ) . These results demonstrate that although dE2F1a and dE2F1b differentially interact with RBF1 , their activities can be negatively regulated by RBF1 . We next asked if the ability of dE2F1a and dE2F1b to promote transcription is equally affected by CycE . The MB domain-mediated interaction between different E2F and RB family proteins was recently demonstrated to affect how phosphorylation by cyclin dependent kinases ( CDKs ) disrupt the RB-E2F complex [19] . In addition , we wanted to determine if the increased CycE expression by dE2F1b contributes to its ability in efficiently activating PCNA-GFP . Strikingly , cycE knockdown resulted in different consequences on dE2F1b- and dE2F1a-induced PCNA-GFP expression ( Fig 7B bottom panel ) . While the co-expression of cycE RNAi has little to no effect on the dE2F1a’s ability to ectopically induce PCNA-GFP expression , it strongly suppresses the dE2F1b-induced expression of PCNA-GFP . Moreover , the dE2F1b-induced adult eye phenotype , but not the dE2F1a-induced eye phenotype , is suppressed by cycE knockdown ( Fig 7C ) . Taken together , our results suggest that while both dE2F1a and dE2F1b can activate transcription , dE2F1b’s ability promote transcription is largely dependent on CycE .
Having only one “activator” E2F and one “repressor” E2F , the Drosophila RB/E2F network is widely accepted as a streamlined version of the mammalian RB/E2F network . Here , we present an additional level of complexity to the Drosophila RB/E2F network . Alternative splicing generates two isoforms of de2f1 , one of which was previously uncharacterized . Through molecular complementation tests and generating an isoform-specific mutant , we demonstrated that dE2F1b is an important regulator of the cell cycle during development , particularly in the salivary gland . Curiously , we also found evidence to suggest that dE2F1b may have a repressive function on target gene expression in a context-specific manner . The widely-used de2f1a cDNA was isolated more than two decades ago from an eye imaginal disc cDNA library [31] . Given that the de2f1b contributes to only about 10% to the total de2f1 transcript in the eye disc ( Fig 1C ) , it is not surprising that it is de2f1a and not de2f1b that was originally cloned . Our molecular complementation test ( Fig 1D and 1E ) indicates that dE2F1a and dE2F1b may have distinct roles during development . It will be interesting to revisit some previous experiments where the existence of dE2F1b was not considered . For example , a dominant modifier screen on an dE2F1b-induced eye phenotype may identify a different set of genes from those identified with dE2F1a [32] . Our data also suggest that RNA splicing is an important step of regulating dE2F1 function . Interestingly , splicing factors have been identified as regulators of dE2F2 [33] . Identification of factors involved in the alternative splicing of de2f1 will likely reveal previously unappreciated regulators of the cell cycle . Notably , the difference between the two de2f1 isoforms lies in the MB domain , which is associated with member-specific functions in mammals [14] . Perhaps , Drosophila evolved to express E2Fs with distinct functions through alternative splicing rather than having multiple E2F genes . The de2f1b-specific mutant described in this study has an increased level of de2f1 transcript ( Fig 2E ) . One possible explanation for this observation is that dE2F1b normally limits overall de2f1 expression . At this point , it is difficult to test this hypothesis since a dE2F1b specific antibody does not exist . An alternative explanation is that exon 3b , which was deleted in our study , contains important regulatory sequences that control de2f1 expression at the level of transcription or RNA processing . When we designed the de2f1b-specific mutant , we decided to remove the entire exon 3b sequence instead of mutating splicing donor and acceptor sites . We reasoned that this approach would cleanly remove the de2f1b isoform without causing aberrant alternative splicing . However , if indeed exon 3 contains regulatory sequences , this approach may have resulted in an unintended effect on de2f1 expression . While we did monitor dE2F1 protein expression throughout our study , it will be important to engineer a new set of de2f1b alleles by specifically targeting splicing donor and acceptor sites . These alleles can help determine if the de2f1b mutant defects described in this study are indeed specifically caused by the lack of the de2f1b isoform . Perhaps it is not surprising that de2f1b mutants have defects in salivary glands . Several studies have previously demonstrated that deregulated dE2F1 activity results in endocycle defects . de2f1su89 mutants , which have a point mutation that weakens the interaction with RBF1 , have defects in endocycling tissues in a de2f2 mutant background [34] . Furthermore , de2f1i2 mutants , which have a truncation in the C-terminal transactivation domain , are female sterile due to defects in ovarian follicle and nurse cells , which are also endocycling cells [35 , 36] . It is worth noting that de2f1su89 and de2f1i2 are hypermorphic and hypomorphic mutants of de2f1 respectively , and they both have endocycle defects . Clearly , dE2F1 activity has to be tightly controlled in these tissues . We found evidence to show that dE2F1b is not only a potent activator of transcription ( Fig 7B ) , but also capable of carrying out a repressive function ( Fig 5 ) . In addition , we also showed that dE2F1b can efficiently induce CycE expression ( Fig 7B , second panel ) . We speculate that the dual function of dE2F1b on gene expression and its ability to strongly induce CycE makes dE2F1b an ideal dE2F1 isoform that governs salivary gland development . An unanticipated finding from this study is that while dE2F1b is a potent activator of transcription , it can provide a repressive function in a context-specific manner . The de2f1b-specific exon alters amino acid sequences in the MB domain that make direct contact with the C-term of RB family proteins ( S6 Fig ) . Our GST pull-down experiments showed that while both dE2F1a and dE2F1b are capable of binding to LP of RBF1 , dE2F1b fails to interact with C-term of RBF1 ( Fig 7A ) . This result suggests that dE2F1a and dE2F1b differentially interact with RBF1 and may form different protein complexes . It is plausible that the MB domain of dE2F1b specifically interacts with proteins that can provide a repressive function . Indeed , the MB domain of activator E2Fs is shown to mediate protein-protein interactions in a member-specific manner [15] . Another interesting aspect of the MB domain-mediated interaction is that it influences the way that CDKs affect the stability of the RB-E2F complex [19] . Therefore , it is possible that while both dE2F1a and dE2F1b can interact with RBF1 , dE2F1b is able to form a stable repressive complex with RBF1 when the CDK activity is low . Indeed , the repressive function of dE2F1b is observed in the morphogenetic furrow of the eye disc and in ZNC of the wing discs where cells are arrested in G1 or G2 ( Fig 5 ) . Moreover , dE2F1b-dependent transcription can be efficiently suppressed by cycE depletion ( Fig 7B ) . One of the clear differences between the two dE2F1 isoforms is their relationship with CycE . dE2F1b more strongly induces CycE expression than dE2F1a and is more sensitive to cycE knockdown ( Fig 7B and 7C ) . These results suggest that dE2F1b function is more tightly linked to the cell cycle than dE2F1a . If this is true , what then is the role of dE2F1a during development ? It is conceivable that dE2F1a carries out cell cycle-independent functions associated with activator E2Fs . For example , mammalian E2F1 is shown to specifically regulate cell death genes and its ability to silence repetitive sequences is clearly cell cycle-independent [20] . Interestingly , the MB domain of E2F1 interacts with the C-term of RB in the GST pull-down assay while the MB domains of E2F2 and E2F3 do not [19] . Similarly , we observed that the MB domain of dE2F1a interacts with C-term of RBF1 while the MB domain of dE2F1b does not ( Fig 7A ) . It is possible that dE2F1a has a cell cycle-independent function similar to those associated with E2F1 in mammals . It will be interesting to generate a de2f1a specific mutant to precisely determine its function during development . While dE2F1b is a canonical E2F by structure , its repressive function may be analogous to the function of atypical E2Fs in mammals , E2F7 and E2F8 . First , both dE2F1b and E2F7/8 negatively regulate E2F target genes ( Fig 2E and Fig 4D and [37–40] ) . Second , one of the key genes regulated by dE2F1b and E2F7/8 is an activator E2F ( Fig 2E and [41 , 42] ) . Third , the tissues that are primarily affected by the inactivation of dE2F1b and E2F7/8 are endoreplicating tissues [43 , 44] . E2F7/8 knockout mice have ploidy defects in trophoblasts and hepatocytes . The functional similarity between dE2F1b and E2F7/8 suggests that although the exact mechanism may be different , the interplay between members of E2F proteins may be evolutionarily conserved .
All fly strains and crosses were maintained at 25°C with standard cornmeal medium . w1118 and yw flies were used as controls . The following alleles were used: For de2f1 mutants , de2f1rm729 [22] alleles were crossed to the Df ( 3R ) Exel6186 deficiency allele which lacks the entire de2f1 gene locus ( Exelis collection at the Harvard Medical School ) . dDPa3a1 and dDPa4a3 alleles [7] were crossed together to generate dDP mutants . PiggyBac transposase stock ( #8285 ) was obtained from the Bloomington Stock Center for removal of the ScarlessDsRed cassette for de2f1b mutant generation . For overexpression and rescue experiments , the following GAL4 lines were obtained from the Bloomington Drosophila Stock Center: Ubi-Gal4 , GMR-Gal4 , and hs-Gal4 ( Bloomington , IN , USA ) . For knock-down of CycE , UAS-CycE-RNAi was obtained from the Vienna Drosophila Resource Center ( Vienna , Australia ) . PCNA-GFP was obtained from Dr . Duronio [27] . UAS-FM-dE2F1a and UAS-FM-dE2F1b overexpression constructs were generated by using the Drosophila Gateway collection ( Drosophila Genomic Resource Center ) . The entry clones , pENTR-dE2F1 and pENTR-dE2F1b , were generated then recombined into the pTFM destination vector to be randomly integrated into the Drosophila genome . Minimum of 10 independent transgene lines were screened to identify lines with similar levels of expression . The de2f1b mutant allele was generated in two steps . First , de2f1b-specific exon was replaced by ScarlessDsRed cassette by Wellgenetics , Taiwan . In short , two independent guide RNAs ( gRNAs ) , CRISPR Target Site 1[PAM]: CTCTTTTGCTGCCGAGCGGT[CGG] and CRISPR Target Site 2[PAM]: ACGTTCAAATTGAAGGGGAG[CGG] , were used to target regions flanking the de2f1b-specific exon , were cloned into the pDCC6 vector [45] . For homology-directed repair ( HDR ) , a pUC57-Kan donor plasmid was used containing the upstream and downstream homology arms of de2f1 and a ScarlessDsRed cassette flanked by PiggyBac transposon ends to facilitate screening [45] . The gRNA plasmids and donor plasmid were co-injected into isogenized w1118 embryos . Hatched G0 flies were crossed to balancer stocks . Balanced stocks were screened using a fluorescent microscope and PCR to confirm the insertion of the ScarlessDsRed cassette into the de2f1 gene locus in the right orientation . We received the selected de2f1b-DsRed mutant lines and used PiggyBac transposition to excise the cassette . The de2f1b specific mutation was validated by sequencing of genomic DNA and cDNA isolated from the mutant flies . All molecular complementation crosses were conducted in the de2f1729/Df ( 3R ) Exel6186 mutant background and UAS-FM-dE2F1a , UAS-FM-dE2F1b , and UAS-FM-dE2F1a + UAS-FM-dE2F1b ( recombined ) transgenes were driven by Ubiquitin-Gal4 . To determine rescue efficiency , viability was assessed by counting the total number of L3 larvae , pupae , pharate adults , and adult flies that eclosed . For each rescue cross , minimum of 30 virgin females were use . The rescue was confirmed using RT-PCR specific to endogenous transcript by targeting 5’ UTR ( S1B Fig ) . For every cross , the predicted Mendelian frequency of the rescue genotype is 1 in 9 . This was obtained by first determining the expected frequency of the rescued genotype based on the Mendelian ratio , which is 1 in 16 . When taking into account the homozygous Balancers that die at the embryonic stage , the predicted Mendelian survival frequency becomes 1 in 9 . For each cross , the number of progeny counted is as follows: control n = 710 , dE2F1a rescue n = 625 , dE2F1b rescue n = 545 , dE2F1a+dE2F1b rescue n = 515 , de2f1 mutant n = 572 , and 2XdE2F1a rescue n = 469 . Percentage survival at L3 , for each genotype is the following: control = 100% , dE2F1a rescue = 14 . 4% , dE2F1b rescue = 19 . 8% , dE2F1a+dE2F1b rescue = 100% , de2f1 mutant = 0% , and 2XdE2F1a rescue = 23 . 1% . Percentage survival at pupal stage for each genotype is the following: control = 100% , dE2F1a rescue = 8 . 6% , dE2F1b rescue = 13 . 2% , dE2F1a+dE2F1b rescue = 100% , de2f1 mutant = 0% , and 2xdE2F1a rescue = 23 . 1% . Lastly , percentage survival at pharate stage ( adult for control ) is the following: control = 99% , dE2F1a rescue = 0% , dE2F1b rescue = 0% , dE2F1a+dE2F1b rescue = 83 . 89% , de2f1 mutant = 0% , and 2xdE2F1a rescue = 0% . These values are used to plot a Kaplan-Meier curve to visualize percentage survival ( Fig 1D ) . The following antibodies were used: rabbit anti-dE2F1 ( 1/100 , generous gift from N . Dyson , Massachusetts General Hospital ) , mouse anti-Myc ( 1/200 , Developmental Studies Hybridoma Bank ( DSHB ) , mouse anti-Eya ( 1/200 , DSHB ) , rat anti-ELAV ( 1/200 , DSHB ) , rabbit anti-cleaved Dcp-1 ( 1/100 , Cell Signaling ) , goat anti-CycE ( 1/200 , Santa Cruz sc15903 ) , goat anti-GFP conjugated to FITC ( 1/100 , Abcam ab6662 ) , and secondary antibodies coupled to fluorescent dyes ( 1:500 , Jackson Immunoresearch ) . For Immunostaining , third instar imaginal discs and salivary glands were dissected in PBS and immediately fixed in 4% formaldehyde in PBS for 20 minutes at room temperature with the exception of tissues subjected to anti-dE2F1 staining that were fixed for 30 minutes on ice . Fixed tissues were then washed with 0 . 3% PBST ( 0 . 3% TritonX-100 in 1XPBS ) and 0 . 1% PBST ( 0 . 1% TritonX-100 in 1XPBS ) . Samples were incubated with appropriate amount of primary antibody in 0 . 1% PBST and 1%BSA overnight . Samples were then washed with 0 . 1% PBST , incubated in secondary antibody in 0 . 1% PBST and 1% BSA for 2 hours , followed by several washes in 0 . 1% PBST prior to mounting . To visualize S-phase cells , Ethynyl-2’-Deoxyuridine ( EdU ) cell proliferation assay ( Invitrogen C10339 ) was used according to the manufacturer’s specifications in third instar eye imaginal discs and salivary glands . DNA was visualized with 0 . 1 μg/mL DAPI . All salivary glands were appropriately staged as either early ( 80–85 hours after egg laying , AEL ) or late ( 105–110 hours AEL ) L3 salivary glands for immunostainings . Representative images were selected from a minimum of 10 independent tissues that were appropriately labelled . Adult ommatidial structure was examined by using the nail polish imprinting technique as previously described [46] . In brief , decapitated adult heads were placed in a clear nail polish then quickly removed with the eye side up to dry at room temperature for 1 hour . The nail polish imprint was then carefully peeled off using tungsten needles and mounted for imaging . Minimum of 5 adult heads were analyzed . Image presented depicts the most representative image . Third instar eye imaginal discs were prepared for in situ hybridization as described previously [47] . In brief , the de2f1 and rnrS RNA probes were prepared and anti-DIG antibody conjugated with alkaline phosphatase was used to visualize RNA probes . At least 20 discs were examined and representative images were selected . All fluorescently labelled tissues were mounted using a glycerol-based anti-fade mounting medium containing 5% N-propyl gallate and 90% glycerol in 1XPBS . Images were acquired using a laser-scanning Leica SP8 confocal microscope at the Cell Imaging and Analysis Network , McGill University . L3 salivary glands and L3 wing discs were imaged using the 20x/0 . 7 dry objective . L3 eye discs were imaged using the 40x/1 . 3 oil immersion objective . Representative images are individual slices from z-stacks . Nail polish imprint of the adult eyes were mounted in 100% glycerol and imaged using the DIC channel of the Zeiss AxioImager Z2 . Bright field images of whole larvae , well-fed 5 day old ovaries , salivary glands , and discs subjected to in situ hybridization were imaged using the Canon Powershot G10 and Zeiss SteREO Discovery V8 modular stereo microscope with a conversion lens adaptor . All images were processed using Fiji ( http://fiji . sc/Fiji ) . Nuclear area of DAPI-stained nuclei in the distal region of third instar larval salivary glands was measured using the particle analysis tool in Fiji . A minimum of 35 nuclei that were in the correct focal plane were required for the size distribution analysis . For each genotype , three salivary glands were used for quantification . Each salivary gland nuclear size distribution was plotted on a box and whiskers plot according to its genotype . Average standard deviation was calculated for each salivary gland then averaged to represent a mean value . For DAPI quantification , DAPI-stained salivary glands were imaged using Leica SP8 confocal microscope where each salivary gland was scanned through using 1 μm Z-stack steps . To quantify DNA content , Fiji was utilized where for every Z-stack image , maximum intensity projection was created then mean fluorescence intensity was used as DNA content values . Three late staged ( 105–110 hr AEL ) salivary glands were examined for analysis . Co-expression was quantified using the colocalization analysis tool using Imaris software ( Bitplane ) . 3D colocalization analysis was performed for three independent salivary glands for wildtype and de2f1b mutant early stage ( 80–85 hr AEL ) salivary glands that were scanned through using 1 μm Z-stack steps . For each channel representing either dE2F1 or CycE expression , consistent threshold values were utilized . Values presented represent percentage of dE2F1 expressing cells above threshold that have co-localized with CycE or percentage of CycE expressing cells above threshold that co-localized with dE2F1 . For RT-qPCR , RNA was extracted from different developmental stages including 0-6hr Embryos , L3 whole larvae , 48 After Puparium Formation ( APF ) pupae , and adults ( 5 days old; 3 females and 2 males ) . RNA was also collected from L3 eye and wing imaginal discs , and L3 salivary glands of the appropriate genotype using the miRNAeasy Mini Kit ( Qiagen ) . To eliminate genomic contamination , the RNA was treated with RNAse-free DNase I . 500ng of RNA was used to synthesize cDNA using the DyNAmo cDNA Synthesis Kit ( Finnzymes ) with random hexamer primers . Gene expression was measured using the DyNAmo Flash SYBR Green qPCR Kit ( ThermoScientific ) with the Bio-Rad CFX 96 Real-Time System and C1000 Thermal Cycler . RT-qPCR was performed as described by [21] . For all RT-qPCR experiments , the data was normalized using two housekeeping genes , rp49 and β-tubulin and to the control ( wildtype whole larvae or tissue ) . Each experiment consisted of experimental triplicates and overall , three biological replicates were averaged for the representative figure in this paper . All primers were designed using Primer3 ( Whitehead Institute for Biomedical Research , http://Frodo . wi . mit . edu/primer3/ ) . All primers were tested and run on an 8% acrylamide gel to ensure that there is no genomic contamination in our results . The following primers were used in this paper: Absolute quantification of gene expression for total de2f1 and de2f1b transcripts was performed with RNA isolated from different developmental stages and L3 tissues ( Fig 1B ) . Standard curves for total de2f1 and de2f1b specific primer pairs were generated using specific amounts of plasmid DNA containing de2f1b sequences . Log10 of plasmid copy numbers of a serial dilution were plotted against the experimentally determined qPCR quantification cycle ( Cq ) values of each dilution . The linear regression from each plot was then used to derive an equation to calculate the copy number / unit of cDNA . For each sample , an average Cq value of three independent experiments consisting of biological triplicates was used to determine the copy number / unit of cDNA ( representing 25ng of RNA ) . Error bars indicate standard error of the mean ( s . e . m . ) . Chromatin was collected from 80 third instar larvae from appropriate genotypes . Control or def1b mutant larvae were homogenized in Buffer A1 ( 50mM KCl , 15mM Nacl , 4mM MgCl2 , 15mM HEPES pH7 . 6 , 0 . 5% TritonX-100 , 0 . 5mM DTT , protease inhibitor cocktail , Roche ) , cross-linked in 1 . 8% Formaldehyde followed by addition of 225mM Glycine , followed by three washes in Buffer A1 and one wash with lysis buffer without SDS ( 140mM NaCl , 15mM HEPES pH7 . 6 , 1mM EDTA , 0 . 5mM EGTA , 1% TritonX-100 , 0 . 5mM DTT , 0 . 1% DOC , protease inhibitor cocktail ) . DNA was sheared to 500-100bp fragments by sonicating for 3X30 second intervals with two-minute breaks in Elution buffer 1 ( 140mM NaCl , 15mM HEPES pH7 . 6 , 1mM EDTA , 0 . 5mM EGTA , 1% TritonX-100 , 0 . 5mM DTT , 0 . 1% DOC , 0 . 1% SDS , 0 . 5% N-Lauroylsarcosine , protease inhibitor cocktail ) for the collection of chromatin . Chromatin was immunoprecipitated using rabbit anti-dE2F1 or , mouse anti-RBF1 [8] . Rabbit and mouse IgG were used as non-specific antibodies . Chromatin-antibody complexes were pulled down with Protein A/G-Sepharose Beads and washed four times in lysis buffer ( 140mM NaCl , 15mM HEPES pH7 . 6 , 1mM EDTA , 0 . 5mM EGTA , 1% TritonX-100 , 0 . 5mM DTT , 0 . 1% DOC , 0 . 05% SDS , protease inhibitor cocktail ) , once in LiCl buffer ( 10mM Tris pH8 , 250mM LiCl , 1mM EDTA , 0 . 5% DOC , 0 . 5%NP-40 ) and once in TE pH8 buffer . DNA was eluted from antibody-bound A/G beads using Elution 2 buffer ( 1%SDS , 100mM NaHCO3 ) then treated with RNase A and Proteinase K . DNA was precipitated and cleaned using two rounds of phenol-chloroform extraction followed by a chloroform extraction then resuspended in ddH2O . Presented data are averages of triplicated ChIP experiments which consisted of experimental duplicates followed by quantitative real time PCR reactions . Presented data for target loci enrichment is represented by percentage of input chromatin not subjected to immunoprecipitation . All primers were designed by Primer3 and primers used for ChIP-qPCR analysis are the following: Statistical test performed using two-tailed unpaired t-test assuming equal variance unless otherwise stated . For Figs 3C and 4E , statistical testing was performed using one-way ANOVA with Sidak’s multiple comparisons test , comparing mean of each column to every other column . For all statistical analyses , p ≤ 0 . 05 was considered statistically significant . Drosophila S2 cell culturing was carried out in Schneider’s medium ( Sigma ) supplemented with 2mM L-Glut , 50U Penicillin-Streptomycin , 10% heat-inactivated FBS . Cells were passaged 1:5 three days prior to transfection . A transient transfection was performed using the Effectene Transfection reagent kit ( QIAGEN ) to co-transfect cells with 0 . 8 μg GFP , and 2 . 4 μg of either pAFHW-E2F1a or pAFHW-E2F1b . GFP was co-transfected to visualize transfection efficiency . 48 hours post-transfection , cells were washed in 1X PBS , and re-suspended in 250 μL of protein extraction buffer ( PEB: 20mM Tris-HCl pH8 . 0 , 137mM NaCl , 10% glycerol , 1% TritonX-100 , 2mM EDTA , protease inhibitor cocktail ) . Cells were lysed by vortexing 7 times ( 15s/vortex with 5min breaks ) and crude protein lysate was obtained by centrifuging at 12000 rpm for 20min at 4°C . GST-RBF1-large pocket ( LP ) , GST-RBF1-small pocket ( SP ) , and GST-RBF1-C-terminus ( C-term ) constructs were generated using the pGEX-5x-1 vector ( GE Healthcare Life Sciences ) backbone digested with BamH1/Xho1 ( NEB ) . LP , SP , and C-term segments of RBF1 was generated by using cDNA of full length RBF1 . BamH1 and Xho1 sites were added to primer ends for successful ligation into vector . Primer sequences used for cloning is the following: Successfully ligated vectors with appropriate inserts were then transformed into BL21 cells ( NEB ) . Empty pGEX-5x-1 vector was used as a GST-alone control which was transformed into DH5-alpha cells ( Thermo Fisher ) . 10mL culture of BL21 cells expressing GST-RBF1-LP , GST-RBF1-SP , GST-RBF1-C-term ( Fig 7A ) and GST alone were grown overnight in a 37°C shaker at 2500 rpm . The overnight culture was diluted and grown in the 37°C shaker until an OD600 of 0 . 7 . Next , 1 μL of 100mM IPTG was added per mL culture . Induction for GST-RBF1 C-term was performed by incubating in the 37°C shaker for 3 hrs . For GST-RBF1-SP and GST-RBF1-LP , which have higher molecular weights , induction was performed overnight at room temperature ( 22°C ) . Cells were pelleted , re-suspended in 3mL of PEB , and lysed by sonication . Lysate was centrifuged and supernatant was transferred to fresh tubes where 120 μL of glutathione agarose beads ( 50% slurry , Thermo Fisher ) were added . The tubes were then incubated for 2hrs at 4°C with agitation . Beads were washed 3X in PEB and re-suspended with PEB to make a 50% slurry . For each pull-down assay , 150 μg of S2 cell extracts transfected with dE2F1a or dE2F1b and sepharose beads bound with approximately 2 μg GST-tagged RBF1-LP/SP/C-term or GST alone were used . Beads were incubated overnight at 4°C with agitation , and washed 3X with PEB . Samples were then loaded on an 8% polyacrylamide gel for western blot analysis . All blots were blocked with 10% skim milk powder in 0 . 1% PBS-Tween20 . For the GST-pulldown , 1/1000 dilution of anti-HA rat antibody ( abcam ) was used followed by 1:2000 dilution of anti-rat HRP ( GE Healthcare ) . To measure dE2F1a and dE2F1b transgene overexpression efficiencies , 10 L3 eye discs were dissected from control , GMRG4>FM-dE2F1a , and GMRG4>FM-dE2F1b overexpression groups . Anti-Myc ( 1/1000 dilution , DSHB ) and Anti- β-tubulin ( 1/1000 dilution , DSHB ) were used as primary antibodies followed by anti-mouse HRP ( 1/2000 , GE Healthcare ) .
|
The Drosophila E2F1 ( dE2F1 ) protein has been studied as one of the principal regulators of cell cycle control in both mitotic cells and cells undergoing a variant cell cycle called the endocycle . dE2F1 is the sole “activator” E2F of the highly streamlined Drosophila RB/E2F network . However , there has been evidence suggesting that this simplistic view of the activator E2F may not be true and that dE2F1 can also provide a repressive function . Elucidating the dual role of dE2F1 in transcriptional regulation has been elusive . In our report , we investigate an uncharacterized isoform of dE2F1 that we have termed as dE2F1b . Notably , the evolutionarily conserved Marked Box domain , which is important for target specificity and protein-protein interactions , is altered in this isoform . Our findings suggest that dE2F1b is required for proper cell cycle control in both mitotic and endocycling cells . Strikingly , we show that dE2F1b has repressive functions in a context-dependent manner . Overall , our findings reveal an unanticipated complexity to dE2F1 , providing important insights into the dual function of dE2F1 in transcriptional regulation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"cell",
"cycle",
"and",
"cell",
"division",
"cell",
"processes",
"animals",
"animal",
"models",
"developmental",
"biology",
"drosophila",
"melanogaster",
"model",
"organisms",
"experimental",
"organism",
"systems",
"eyes",
"synthesis",
"phase",
"morphogenesis",
"drosophila",
"digestive",
"system",
"research",
"and",
"analysis",
"methods",
"gene",
"expression",
"life",
"cycles",
"exocrine",
"glands",
"head",
"imaginal",
"discs",
"insects",
"arthropoda",
"eukaryota",
"anatomy",
"cell",
"biology",
"salivary",
"glands",
"genetics",
"biology",
"and",
"life",
"sciences",
"ocular",
"system",
"larvae",
"organisms"
] |
2018
|
An alternatively spliced form affecting the Marked Box domain of Drosophila E2F1 is required for proper cell cycle regulation
|
The tumor suppressor P53 is a critical mediator of the apoptotic response to DNA double-strand breaks through the transcriptional activation of pro-apoptotic genes . This mechanism is evolutionarily conserved from mammals to lower invertebrates , including Drosophila melanogaster . P53 also transcriptionally induces its primary negative regulator , Mdm2 , which has not been found in Drosophila . In this study we identified the Drosophila gene companion of reaper ( corp ) as a gene whose overexpression promotes survival of cells with DNA damage in the soma but reduces their survival in the germline . These disparate effects are shared by p53 mutants , suggesting that Corp may be a negative regulator of P53 . Confirming this supposition , we found that corp negatively regulates P53 protein level . It has been previously shown that P53 transcriptionally activates corp; thus , Corp produces a negative feedback loop on P53 . We further found that Drosophila Corp shares a protein motif with vertebrate Mdm2 in a region that mediates the Mdm2:P53 physical interaction . In Corp , this motif mediates physical interaction with Drosophila P53 . Our findings implicate Corp as a functional analog of vertebrate Mdm2 in flies .
When cells encounter damage to their DNA in the form of DSBs , DNA damage response ( DDR ) pathways are triggered . The ensuing signaling cascades result in cell cycle arrest , induction of DNA repair genes , and in some cases , apoptosis . It is generally thought that , if damage cannot be repaired , cells will undergo apoptosis rather than continue to divide and propagate a damaged genome . If cells with irreparable damage do survive and proliferate , it can result in widespread genomic instability , creating an early state in the progress towards cancer [1–3] . In Drosophila melanogaster , most cells undergo apoptosis in response to irreparable DNA damage , but a few cells escape , continue to divide and exhibit genomic instability [4–6] . In our current study , we aimed to investigate the genetic mechanisms that allow cells to survive in the presence of irreparably damaged DNA . One of the key players that controls the fate of a cell following DNA damage is the tumor-suppressor encoded by the p53 gene , which is found to be mutated in most human cancers [7] . In response to DNA damage , the ATM kinase ( encoded by tefu in Drosophila ) phosphorylates Chk2 ( encoded by lok ) , which in turn phosphorylates and activates P53 [8–10] . Activated P53 is primarily a transcriptional regulator that promotes or inhibits the expression of a large number of target genes that encode a variety of cellular functions such as DNA repair , cell cycle arrest and apoptosis [11–17] . A cell that detects damage and engages the P53 damage response pathway may either repair the damage or experience senescence or death [1–3 , 18] . Humans that lack one copy of p53 are prone to develop cancers , and p53 knockout mice develop cancers at an increased rate [1–6 , 19 , 20] . Similarly , p53-null Drosophila fail to eliminate cells with a DSB in their genome [5 , 7 , 20–23] . The Mdm2 gene is a prominent target of P53 in mammals . It encodes a ubiquitin ligase that negatively regulates P53 and promotes its degradation , constituting a negative feedback loop [8–10 , 24 , 25] . Mediation of apoptosis by P53 is highly conserved throughout metazoa , including Drosophila melanogaster [11–18 , 21–23 , 26] . However , apart from DNA repair genes , targets of p53 that antagonize apoptosis have yet to be reported in flies and no homolog of Mdm2 has been identified . Here , we report the identification of a gene , companion of reaper ( corp ) , whose overexpression mimics the effects of p53 mutants in the soma and the germline . Our experiments indicate that Corp negatively regulates P53 protein levels . The corp gene has been previously identified as a transcriptional target of P53 [14 , 15 , 27] , thus Corp acts on P53 in a negative feedback loop . Furthermore , Corp exhibits similarity to Mdm2 in a region essential for the Mdm2-P53 interaction , and we find that this region of Corp mediates a physical interaction with Drosophila P53 . These similarities lead us to conclude that corp encodes a functional analog of vertebrate Mdm2 in flies and strengthens the similarities between the regulation and the functions of P53 in Drosophila and mammals .
The previously described BARTL ( Bar and Telomere Loss ) assay [20] was used to screen for insertions of a P-element mis-expression element [28] that modify the eye phenotype resulting from the production of an irreparable DNA DSB . In brief , a combination of eyeless-Gal4 ( eyGal4 ) and UAS-FLP is used to drive FLP recombinase expression in proliferating cells of the eye throughout development [29–31] . These flies also carry a Y chromosome with inverted FRT repeats ( DcY ( H1 ) , or simply H1 ) . Recombination between FRTs in inverted orientation on sister chromatids produces dicentric chromosomes which break in the subsequent mitotic division , delivering a chromosome with a single broken end to each of the two daughter cells ( Fig 1 ) . This results in substantial P53-mediated apoptosis and produces flies with characteristic small and rough eyes ( Fig 2B ) . By introducing an EP transposon insertion , which carries UAS elements that can drive expression of a neighboring gene , the flies’ eyes may become larger or smaller , indicating that the EP element in question modifies the fate of cells in these eyes . We identified one such EP insertion ( P{EPgy2}CG1632EY03495 ) that produced nearly wildtype eyes in the BARTL assay ( Fig 2C ) . This insertion was ideally placed to drive expression of the corp+ gene ( CG10965 ) . By qRT-PCR we confirmed that when Gal4 induces the P{EPgy2}CG1632EY03495 element ( hereafter referred to as EP-corp+ ) it drives overexpression of corp+ ( S1 Fig ) . We also constructed a UAS-corp+ transgene , and found that its effect was nearly identical to that produced by EP-corp+ ( Fig 2D ) . When we tested RNAi-mediated knockdown of corp in the BARTL assay the opposite result was obtained: the eye was completely ablated ( Fig 2E; n . b . , ey expression extends beyond the eye proper , accounting for , in some cases , nearly complete ablation of the head ) . A corp mutant was also generated by imprecise excision of a P element located in the 5’ region of the gene . This mutant , corp95B ( S2 Fig ) , is viable in homozygous condition and without obvious phenotype on its own . However , like RNAi-mediated corp knockdown , corp95B completely ablates the eye in the BARTL assay ( Fig 2F ) . This effect can be rescued by the UAS-corp+ transgene ( Fig 2G ) . To verify that the gene CG1632 , in whose intron corp is located and which is transcribed opposite to corp ( S2 Fig ) , is not responsible for these phenotypes we tested RNAi-mediated knockdown of CG1632 in the BARTL assay and found no significant change ( Fig 2H ) . Combined with the observations that a UAS-corp+ construct produces the same phenotype as EP-corp+ and that RNAi against corp has the opposite effect , it is clear that the effects we observe owe to corp and not CG1632 . If EP-corp+ was not induced by Gal4 , and eyFLP was instead used to produce dicentric chromosomes in the eye , we found that the EP-corp+ insertion produced slightly larger eyes than the wildtype control , suggesting that the EPgy2 insertion by itself may have slightly elevated corp expression ( S3B Fig ) . This effect , though statistically significant , is small and only eyGal4-mediated corp+ overexpression can generate the wildtype-like eye phenotype in the BARTL assay ( Fig 2C and 2D ) . To determine whether corp has any influence in the absence of DNA damage we examined wild type or BS flies carrying EP-corp+ , induced or uninduced , and flies carrying the corp95B mutant , but without the induction of dicentric chromosomes . There was no change in eye phenotype in any of these cases , indicating that the effects of altered corp+ expression are seen only after DNA damage ( S3C–S3G Fig ) . EP-corp+-mediated rescue of the eye is not confined to males , or to effects produced by the Y chromosome . We generated XXY females carrying eyGal4 , UAS-FLP and the DcY ( H1 ) chromosome and found that EP-corp+ produced almost wildtype eyes , similar to its effect in males ( S3H and S3I Fig ) . Additionally , we found that corp+ overexpression ameliorated the reduction in eye size produced by dicentric induction on chromosome 3 ( S3J and S3K Fig ) . Therefore , the effect of corp+ overexpression is independent of the sex of the fly or the particular chromosome experiencing damage . We sequenced the corp genomic regions from five laboratory strains and found two allelic variants of corp that differed by 7 nucleotide changes . Four nucleotide changes were found in the second exon: two are silent mutations ( C840T and C891T ) and two ( T725A and T873G ) encode different amino acids ( L96H and L145M ) . These alternate amino acids are also present as polymorphisms in wildtype isolates of D . melanogaster and other Drosophila species ( http://www . dpgp . org ) . There were also three single nucleotide differences ( C277T , A398C and A407T ) in introns . The UAS-corp+ transgene that we constructed and tested ( as mentioned previously ) carries the canonical version , as found in CantonS . The EP-corp+ , y w and w1118 strains all carry the variant allele that differs from the reference CantonS strain . Because the overexpression of either allele produces a similar large eye phenotype in the BARTL assay ( Fig 2C and 2D ) , we conclude that both alleles function similarly , and that the two amino acid differences have , at most , minor effects . One mechanism by which Corp could affect eye size in the BARTL assay is through the suppression of apoptosis , thereby allowing survival and proliferation of cells that would otherwise die . To test this we stained wing imaginal discs for apoptotic cells after treatment with ionizing radiation ( IR ) . Untreated discs show similar low rates of apoptosis in both the y w controls and the corp95B mutants , but IR-induced apoptosis was significantly enhanced in corp95B mutants ( Fig 3A and 3B ) . We then examined the effect of corp+ overexpression . An engrailed-Gal4 element [32] was used in combination with EP-corp+ to drive expression in the posterior compartment of wing discs , which was also marked by co-induction of UAS-GFP . Apoptosis was significantly reduced by corp+ overexpression ( Fig 3C and 3D ) . These results show that Corp is a potent negative regulator of apoptosis following DNA damage in the soma . In the male germline , broken dicentric chromosomes may be healed by de novo telomere addition [33 , 34] . With DcY ( H1 ) these healed chromosomes ( denoted FrY ) may be detected in testcrosses to y w females by the loss of the dominant BS marker that lies distal to the inverted FRTs ( i . e . , by the generation of Bar+ sons ) . To assess the effect of corp on the transmission of broken-and-healed chromosomes we induced expression of FLP by heat shock ( 70FLP10 ) during the first 24 hours of development and used nanosGal4 to drive germ cell-specific overexpression of corp+ . Overexpression of corp+ blocked transmission of FrY chromosomes ( Table 1 ) . We also drove corp+ and FLP expression specifically in the germline using nanosGal4 ( EP-corp+; UAS-FLP nanosGal4 ) and again observed a large decrease in FrY transmission relative to males with unaltered corp expression ( Table 1 ) , confirming that corp+ inhibits transmission of broken-and-healed chromosomes through the male germline . Though it seems surprising that corp+ overexpression produces dissimilar phenotypes in the soma ( survival and proliferation of cells with broken chromosomes ) vs . the germline ( elimination of cells with broken chromosomes ) , there is precedent: the p535A-1-4 loss of function mutation acts similarly . Homozygous p535A-1-4 flies have almost wildtype eyes in the BARTL assay [20] , but strongly reduced transmission of broken-and-healed chromosomes through the male germline [35] . This similarity suggests a functional relationship between corp and p53 . To explore this relationship we generated corp95B; p535A-1-4 double mutants and examined them using the BARTL assay . We found that p53 is epistatic to corp , with the double mutant producing almost wildtype eyes ( Fig 4A ) . In a direct measurement of apoptosis in wing discs following treatment with ionizing radiation ( IR ) we observed the same epistatic relationship: p535A-1-4 suppressed the elevated apoptosis produced by corp95B , and the double mutant was indistinguishable from the p53 single mutant ( Fig 4B and 4C ) . In a complementary experiment , we tested the effect of simultaneously overexpressing corp+ and p53+ . When GMR-Gal4 drives p53+ overexpression in the developing eye , the adults that eclose have very small eyes owing to an elevated frequency of cell death . We found that if corp+ was simultaneously overexpressed , the eyes became significantly larger . Furthermore , when we combined the corp95B mutant with GMR>p53+ , the eyes were much smaller than produced by GMR>p53+ alone ( Fig 4D ) . When apoptosis was directly assayed in eye discs of these genotypes , we observed correlated effects , with corp+ overexpression reducing , and the corp mutant increasing cell death ( Fig 4Fa-c and 4G ) . These results may all be accommodated under the hypothesis that Corp antagonizes P53 , either by suppressing its apoptotic effects or by negatively regulating P53 itself . To determine how Corp might affect P53 we examined P53 levels in eye discs by immunostaining . The GMR promoter was used to overexpress p53+ behind the morphogenetic furrow , providing an easily detected level of expression , which is absent in a p535A-1-4 mutant disc . The corp95B mutant eye discs exhibited a significantly higher level of P53 while EP-corp+ overexpression driven by GMR-Gal4 reduced the level of P53 ( Fig 5A and 5B ) . Thus , we conclude that Corp is a negative regulator of P53 . To further verify our results , we knocked down corp in S2 cells by treating with double-stranded RNA ( dsRNA ) against corp and measured P53 protein levels by Western blot . We found that the quantity of P53 was significantly elevated following corp knockdown ( S4 Fig ) , confirming that Corp promotes P53 downregulation . To determine whether corp regulates p53 at the transcriptional level , we used qRTPCR to measure p53 mRNA in corp mutant or corp+ overexpressing larvae , with and without irradiation . We found that there are no significant or consistent changes in p53 mRNA levels between these genotypes ( Fig 5C ) . We conclude that Corp regulation of P53 occurs primarily at the level of translation or protein stability . The pro-apoptotic reaper ( rpr ) gene is a prominent target of P53 following DNA damage [15 , 21] . If P53 is negatively regulated by corp then we expect that corp overexpression should also reduce reaper induction following IR . To test this , we measured rpr mRNA levels by qRTPCR in corp+-overexpressing and corp95B mutant larvae , with and without irradiation . We found that , following IR , rpr mRNA levels decrease with corp+ overexpression and increase in corp95B mutants ( S5 Fig ) . Although these results were not significant at the 5% level , they are nonetheless consistent with Corp-mediated downregulation of P53 . Recently , it was shown that the P53 transcriptional targets hid and rpr act recursively to increase p53 expression and contribute to the apoptotic program [36] . Given that Corp overexpression results in downregulation of P53 , we expect that it should also suppress the apoptotic phenotype caused by hid and reaper overexpression by attenuating this positive feedback loop . In order to test this prediction , we overexpressed hid or reaper in the eye under control of the GMR-Gal4 driver . This produced adults with small eyes owing to cell death in the eye discs . When corp+ was simultaneously overexpressed the eyes became significantly larger , confirming that Corp interferes with the hid- and reaper-mediated apoptotic programs ( Fig 4E ) . To confirm that this effect was through inhibition of apoptosis , we directly measured cell death in the eye discs of larvae of the above-mentioned genotypes . We found that rate of apoptosis in GMR-hid and GMR-rpr eye discs was significantly decreased in a corp+ overexpressing background ( Fig 4Fd-g and 4H ) . If corp+ overexpression rescues the small eye phenotype of GMR-hid flies by attenuating the feedback amplification loop through p53+ , we expect similar rescue from a p53 mutant . To test this , we measured eye size of GMR-hid+; p535A-1-4 flies and found that they have significantly larger eyes than GMR-hid+ control eyes ( S6 Fig ) . These results are fully consistent with the hypothesis that Corp acts via down-regulation of P53 . In this particular case , corp+-mediated downregulation of P53 blocks the Hid-P53 amplification loop and thereby reduces apoptosis . Mdm2 is the major negative regulator of P53 in vertebrates . However , no homolog of Mdm2 has been found in Drosophila . Given that Corp acts in a negative feedback loop on P53 , we looked more closely at Corp to see whether any similarities to Mdm2 might be identified . We used the domain analysis tool MEME [37 , 38] to search for shared protein motifs between four Mdm2 orthologs ( H . sapiens , M . musculus , G . gallus and D . rerio ) and two Corp orthologs from Drosophila species ( D . melanogaster and D . virilis ) . It identified seven similar motifs , with motifs 4 and 5 shared by Mdm2 and Corp ( Fig 6A ) . Interestingly , motif 4 appears to correspond to the N-terminal region of Mdm2 , the primary P53-interacting domain [39 , 40] . Motif 5 appears to correspond to an additional P53-binding site on Mdm2 [41] . We named the two motifs Corp and Mdm2 Motif-1 ( CMM-1 ) and CMM-2 , respectively . Motivated by the finding of similarities between Corp and Mdm2 in regions of Mdm2 that bind P53 , we asked whether Drosophila Corp and P53 physically interact . We purified GST-DmP53 using a bacterial expression system . C-terminal-tagged Corp-GFP-Flag was then expressed via transient transfection of HeLa or 293 cells . Cell lysates prepared from these cells were incubated with either GST or GST-DmP53 . Corp-GFP-Flag was pulled-down specifically by GST-DmP53 but not by GST ( Fig 6Bii ) , indicating that Corp expressed in mammalian cells interacts with DmP53 . This result suggests either that Corp can interact directly with DmP53 , or that the complex required for their interaction is conserved in mammalian cells . To further probe this , we tested the interaction between GST-DmP53 and in vitro synthesized Corp . We found that GST-DmP53 strongly interacts with in vitro synthesized Corp ( Fig 6Biii ) . Together , these results strongly suggest that Corp can interact directly with DmP53 . To test the role of CMM-1 ( amino acids 58–84 ) and CMM-2 ( amino acids 14–54 ) in mediating the interaction between Corp and DmP53 , we generated deletion constructs that lack CMM-1 ( Δ58–84 ) , CMM-2 ( Δ14–54 ) , or both ( Δ14–84 ) ( Fig 6C and 6D ) . The interaction between DmP53 and Corp mutant was assayed via co-immunoprecipitation ( Fig 6C ) and GST pull-down ( Fig 6D ) . In both systems , proteins that lack CMM-1 have dramatically diminished affinity to DmP53 as compared to full length Corp or Corp ( Δ14–54 ) . This indicates that CMM-1 , the motif shared with the N-terminal P53-interacting domain of Mdm2 , is required for the physical interaction between Corp and Mdm2 .
Several studies have identified transcriptional targets of P53 in Drosophila . Some of these play important roles in DNA damage repair or in triggering apoptosis [14–17 , 42 , 43] . However , the functions of most P53 target genes have yet to be determined . Our discovery that Corp antagonizes apoptosis by negatively regulating P53 is the first demonstration in Drosophila that a P53-regulated gene ( apart from DNA repair genes ) is not solely devoted to apoptosis . Our results show that P53 target genes act in competing pathways , defined by the hid- and reaper-mediated pro-apoptotic pathway and the corp-mediated anti-apoptotic pathway ( Fig 7 ) . Increased or decreased expression of corp+ shifts the balance in favor of survival or death , respectively . In vertebrates , the major negative regulator of P53 is Mdm2 . It binds to P53 and ubiquitinates it , leading to its degradation , and is responsible for restraining P53 activity in unstressed cells . Furthermore , Mdm2 is also a transcriptional target of P53 , and is utilized to turn down the P53 response so that cells that have recovered from the initiating stress , for instance DNA damage , may survive . Though no strict Mdm2 homolog is known in Drosophila , our experiments indicate that Corp provides that function . Similar to Mdm2 , the corp gene is a transcriptional target of P53 , Corp antagonizes the P53-mediated apoptotic program , P53 levels are inversely correlated with corp+ expression and Corp physically interacts with P53 through a motif similar to the region of Mdm2 that mediates P53-Mdm2 binding . These similarities strongly support the idea that Corp regulates P53 by a direct physical interaction , thus leading us to propose that Corp is the functional analog of mammalian Mdm2 ( Fig 7 ) . There are significant differences between Corp and Mdm2 . Mdm2 is an E3 ubiquitin ligase containing a RING domain [44] , but Corp shows no evidence of such a domain . Furthermore , in the mouse Mdm2 mutations can cause recessive lethality . These mutants may be rescued by the additional mutation of p53 [45] , indicating that lethality results from unrestrained P53 activity . In contrast , the corp95B null mutation is not lethal in flies and exhibits no obvious phenotype in the unstressed condition . Corp appears to function only when DNA damage is detected . In Drosophila the normal level of P53 expression is insufficient to cause lethality in the absence of Corp unless P53 is activated by upstream kinases . However , these differences between Corp and Mdm2 may not be as significant as they appear . First , recent findings in mice indicate that the constitutive and induced levels of Mdm2 can be functionally separated . When the P53 Response Element was mutated in the promoter of Mdm2 , so that Mdm2 was still expressed at a basal level but could no longer be induced to high levels by P53 , the resulting mice were viable [46] . Furthermore , when the RING domain of Mdm2 was mutated , so that it no longer functioned as a ubiquitin ligase , but could still interact with its partner Mdmx , the mice were also viable [47] . In both cases the mice were highly sensitive to induced DNA damage , indicating that higher levels of Mdm2 activity are required to recover from DNA damage . Moreover , the latter experiments show that Mdm2 is capable of repressing P53 function without its ubiquitin ligase activity [47] . This may indicate that P53-Mdm2 binding is an ancient mode of regulation , with the ubiquitin ligase activity acquired as a later adaptation . Additionally , recent work has established that Corp physically interacts with the E3 ubiquitin ligase encoded by hyd ( hyperplastic discs ) and with several proteasomal subunits ( S7 Fig ) , suggesting that Corp , like Mdm2 , may participate in proteolytic degradation of P53 [48 , 49] . There is still room for additional explanations for Corp’s phenotypes . If Corp also targeted downstream components of the apoptotic pathway for degradation it might contribute to the phenotypes we observed . Given the existence of a positive feedback loop between downstream pro-apoptotic genes and p53 [36] , Corp might indirectly affect P53 levels by promoting degradation of other components of the apoptotic pathway . However , the physical interaction of Corp and P53 strongly suggests that Corp directly regulates P53 , regardless of whether it may also regulate downstream apoptotic components . Corp is the first reported negative regulator of P53 in Drosophila that is also a transcriptional target of P53 . Although Bonus and Rad6 have been identified as negative regulators of P53 in Drosophila [50 , 51] , neither of them are transcriptional targets of P53 , and are thus less similar to Mdm2 than is Corp . Recent experimental findings from others [15 , 36 , 52 , 53] , and as reported here , indicate that regulation of P53 is complex , with activation by upstream factors and modulation by positive and negative feedback loops . Further investigation of how these pathways are regulated and how they affect these outcomes should greatly improve our understanding of the many functions of P53 . It remains to be understood what benefit might be provided by Corp . If it is normally desirable to eliminate a cell with unrepaired DNA damage to prevent its proliferation , then what purpose could be served by saving such cells ? Unlike mammals , where the function of Mdm2 is needed to restrain P53 in normal cells , Corp appears to function only in cells with damaged genomes . However , previous experiments have shown that in wildtype larvae , many cells with damaged genomes are not eliminated by apoptosis immediately , but rather over a period of a few days [5] . Since corp mutants show increased cell death after irradiation , Corp is clearly one factor that restrains the immediate death of cells with damaged genomes . We have often thought it surprising that flies can survive when dicentric chromosomes are formed , and break , in >90% of their cells during development [5 , 6] . Perhaps if all cells with broken chromosomes immediately succumbed to apoptosis , such flies would not survive . It is easy to imagine that a few remaining survivors , adrift in a sea of dead cells , might not be capable of regenerating a complete imaginal disc . In fact , corp mutants survive poorly after widespread induction of Y chromosome dicentrics ( S1 Table ) . But , if cells with damaged genomes could be eliminated gradually , it might give the surviving cells a suitable matrix to regenerate a disc . Modulating the rate at which cells are eliminated following lethal damage could be the vital function fulfilled by Corp . [54] recently showed that dying cells signal their neighbors to become resistant to damage-induced death . We would not be surprised to find that this pathway acts through Corp .
All flies were maintained at 25°C on standard cornmeal food . Construction of the DcY ( H1 ) and Dc3 ( FrTr61A5 ) 1A chromosomes have been described previously by Kurzhals et al . [20] and p535-A-1-4 by Xie et al . [55] . We obtained the following stocks from the Bloomington , IN ( USA ) Drosophila stock center: P{UAS-FLP1 . D}JD1 ( BL 4539 ) , P{Gal4-ey . H}4–8 ( BL 5535 ) , P{EPgy2}CG1632EY03495 ( BL 15650 ) , P{eyFLP . N}5 ( BL5576 ) , M{3xP3-RFP . attP}ZH-86Fb; M{vas-int . B}ZH-102D ( BL 23648 ) , P{UAS-2xeGFP}AH2 ( BL 6874 ) , nanos-Gal4 [56] , P{GMR-p53 . Ex}3/TM3 , Sb , Ser ( BL 8417 ) , GMR-Gal4 [57] , P{GMR-hid}G1/CyO ( BL 5771 ) , P{GMR-rpr . H}S/TM6B , Tb ( BL 5773 ) and P{Act5C-Gal4}17F01/TM6B , Tb ( BL 3954 ) . Two corp-RNAi stocks: v102751 and v16130 and one CG1632-RNAi stock: v106107 were obtained from the Vienna Drosophila Resource Center , Vienna , Austria ( VDRC ) . The following stocks were obtained from Golic lab collections: heat-shock inducible FLP , P{70FLP}10 , P{UAS-GFP} P{Act-Gal4}/CyO and y w; Sp/CyO; nanosGal4 UAS-FLP ( 95 ) /TM3 , Sb . The engrailed-Gal4 stock was kindly gifted by Mark Metzstein . The coding region of corp from CantonS flies was amplified by PCR with 5’ NotI and 3’ XbaI overhangs ( primers used: Fwd-5’CATATTCGCGGCCGCATGGCCGATATCAGGAGCAG3’ and Rev- 5’CCGCGGGTCTAGACTAGATGCGAATCGAGCGCA3’ ) and cloned into the pUAST-w+-attB vector [58] . Vector plasmid was injected in embryos carrying attP docking sites on chromosome 3 and vasa-ΦC31 integrase on chromosome 4 ( BL 23648 ) . w+ flies were selected for establishing stable transgenic stocks . The corp95B deletion mutation was generated via imprecise excision of the EY03495 P-element insertion ( Baylor College of Medicine Genome Disruption Project ) . The DNA break points were identified by PCR amplification ( primer sets used: Fwd 1: 5’CCAAGCGAACGCATCGCTG3’ , Fwd 2: 5’GAAGAGGTCATCTCCCAAGG3’ , Rev1: 5’CTTAGGAACAATGGTTCAACC3’ and Rev2: 5’GCAGCCGAGGTATGGAAATC3’ and sequencing of genomic DNA obtained from the homozygous mutant . Sequencing of corp+ from the genomic region in five different genotypes , y w , w1118 , EP-corp+ , CantonS and v; Sco/Cy; ry , was carried out by the Core Facilities , University of Utah . Eye photographs were taken using a Nikon D200 digital camera and processed in Adobe Photoshop . Total RNA was extracted from 12–15 adults or third instar larvae using Trizol Reagent ( Sigma Aldrich , MO ) , treated with DNaseI ( Fermentas , PA ) and cDNA was synthesized using RevertAid First Strand cDNA synthesis kit ( Thermo Scientific , PA ) according to manufacturer’s protocol . 1μl of cDNA was used per reaction in triplicates for performing qRT-PCR experiment using Maxima SYBR green/Fluorescein qPCR Master Mix ( Fermentas , PA ) or PerfeCTa SYBR Green FastMix ( Quanta Biosciences , MD ) in an iQ-PCR machine ( Bio-Rad , CA ) . Relative quantification of mRNA levels was calculated using the standard curve method . Relative copy numbers of each gene of interest ( X ) was calculated by normalizing cDNA levels of X over cDNA levels of Ribosomal Protein L32 . Primers that were used are: Fwd-corp: 5’ GCAGCCGAGGTATGGAAATC 3’; Rev-corp: 5’ AAGCCGAGGGTCAGAAGG 3’; Fwd-p53: 5’ GCCGCCTCCTTAATCATGCC 3’; Rev-p53: 5’ GCCGAGACTGCGACGACTC 3’; Fwd-rpr: 5’ CCAGTTGTGTAATTCCGAACGA 3’;[59] Rev-rpr: 5’ GGATCTGCTGCTCCTTCTGC 3’;[59] Fwd-rpl: 5’ CCGCTTCAAGGGACAGTATC3’; Rev-rpl: 5’ ATCTCGCCGCAGTAAACG 3’ . 15–18 wandering third instar larvae were collected in clean 10 mm petri plates and irradiated at 4000 rads using a TORREX120D X-ray generator ( Astrophysics Research Corp , CA ) or a Mark I irradiator ( J . L . Shepherd & Associates , CA ) . These larvae were returned to fresh food and incubated at 25°C until further experimental treatments . For determining eye sizes , the left eye of each fly was measured along the anterio-posterior axis ( A ) and the dorso-ventral axis ( B ) , using a digital filar micrometer ( Lasico , CA ) . These two measurements were used to calculate the area of an ellipse ( i . e . , Π x A/2 x B/2 ) , as the area of the eye , which was then normalized to the mean area of wildtype ( w1118 or y w ) eyes , and was represented as a fraction of wildtype eye size . Flies were allowed to lay eggs and transferred to fresh vials every day . Embryos were collected for 24 hrs , heat-shocked at 38°C for one hour in a circulating water bath and then immediately returned to 25°C . After eclosion , the males were collected and singly mated to 2–3 y w females and their progeny were scored . Alternatively , nosGal4 was used to drive UAS-FLP in the male germline . Wing and eye imaginal discs were dissected from third instar larvae and stained with TUNEL or P53 antibody . To synthesize double stranded RNA for RNA interference experiments with cultured cells , PCR products not more than 700 base pairs , were made of the cDNA of interest flanked by T7 RNA polymerase sites at both ends . After gel purification of the PCR product , it was used as template for in vitro transcription for 6 hours at 37°C in a circulating water bath in 5–6 replicates of 20 μl reaction each for better yield using Ambion Megascript T7 Transcription Kit ( Life Technologies ) , according to manufacturer’s protocol . Then , the reactions were pooled together in a microcentrifuge tube and extracted with phenol-chloroform and chloroform . Finally , dsRNA was precipitated with ispropanol , dissolved in DEPC-treated H2O and quantified in a Nanodrop 1000 spectrophotometer ( Thermo Scientific ) . Primers used for obtaining PCR products were: Fwd_T7corp: 5’TTAATACGACTCACTATAGGGAGAATGGCCGATATCAGGAGCAG3’; Rev_T7corp: 5’TTAATACGACTCACTATAGGGAGACTAGATGCGAATCGAGCGCA3’; Fwd_T7p53: 5’TTAATACGACTCACTATAGGGAGAAGATCCAGGCGAACACGCTG3’; Rev_T7p53: 5’TTAATACGACTCACTATAGGGAGAGGCTTCCGGCACGGACTTG3’; Fwd_T7Pav: 5’TTAATACGACTCACTATAGGGAGAACAACTGCTCTTGGCAGATACC3’; Rev_T7Pav: 5’TTAATACGACTCACTATAGGGAGAAAATCCGTAACGAAACTAACCG3’ . S2 cells were cultured at 25°C in Schneider’s Drosophila Medium ( Invitrogen ) with 10% heat inactivated fetal bovine serum ( HyClone ) and 1X Antibiotic-Antimycotic ( Invitrogen ) . Cells were passaged into fresh medium every 3–4 days and were discarded after passage 25 ( P25 ) . The dsRNA treatment protocol was performed as described [60] . Cells were passaged on day 0 at the rate of 2 x 106 cells/ml . On day 1 they were washed and seeded in 24-well plates at 800 μl/well . 15 μg of dsRNA added to each well . The plates were then returned to the 25°C incubator . On day 4–5 , cells were re-seeded in 6 well plates at 2 ml/well and retreated with 30 μg of dsRNA . As a control of dsRNA uptake rate , cells were treated with Pavarotti dsRNA , which makes them large and multinucleate[61 , 62] . On day 6–7 , cells were collected , lysed and processed for Western blot . S2 cells , with or without dsRNA treatment , were irradiated at 4000 rads to observe any elevation/change in P53 levels from unirradiated cells , with or without dsRNA treatment . No significant changes in P53 levels were observed following irradiation , so treated cells were grouped and categorized as ( I ) no dsRNA control group and ( II ) + dsRNA experimental group for quantification . The Western results were pulled from 3 independent experimental sets in the p53-dsRNA treatment experiment and 5 independent experimental sets in the corp-dsRNA treatment experiment . Out of the 3 p53-dsRNA experimental sets , 1 was following irradiation at 4000 rads and allowing 4 hours recovery before cell lysis . Likewise , out of the 5 corp-dsRNA experimental sets , 2 were following irradiation . Relative P53 levels in each experiment were calculated by normalizing total P53 protein level to total β–tubulin level . The open reading frame of DmP53 was cloned into pGEX and transformed to BL21 DE3 cells . The expression was induced by addition of IPTG to a final concentration of 0 . 1mM in LB/Ampicillin media . Bacterial cells were harvested 4 hours following the induction and resuspended in ice-cold STE buffer ( 10mM Tris-HCl pH8 . 0 , 150mM NaCl , 1mM EDTA and protease inhibitors ) with 1 . 5% Sarcosyl . Cells were then lysed with sonication and subsequently incubated with STE containing 1% Triton X-100 for 30 minutes . Insoluble proteins were removed by centrifugation at 16 , 000g for 5 minutes . Supernatant was then incubated overnight with 50% slurry of glutathione-agarose beads at 4°C . The beads were pelleted by centrifuge at 100g and washed 4 more times with 10 ml of ice-cold PBTP ( PBS with 0 . 1% Triton X-100 and protease inhibitors ) . Washed beads were then resuspended in PBTP with 0 . 01% sodium azide . 15ug of pRK5-corp-gfp-flag plasmid DNA was transfected to 2 million HeLa cells with calcium phosphate . Cells were lysed on plate with 1ml RIPA buffer at 48 hours following transfection . Corp-HA6His was cloned into pCDNA3 . In vitro synthesis were carried out using the TnT Coupled Reticulocyte Lysate Systems ( Promega , Catalog number L4611 ) following manufacturer’s instructions . For in vitro synthesized protein , 1 ug GST or GST-DmP53 bound to Glutathione-agarose beads was incubated with 5 ul of synthesized protein in 500ul Binding buffer ( 50mM Tris-HCl , pH 8 . 0; 2mM EDTA; 150mM NaCl; 0 . 1% NP40; 20uM ZnCl2; 10mM MgCl2; protease inhibitors ) containing BSA ( 0 . 2ug/ul ) . Following 1 hour incubation at RT and 1 hour incubation at 4°C , the beads were washed 4 times with Binding buffer . Beads were then pelleted at 100g , re-suspended and boiled in 30 ul sampling buffer , and resolved on SDS-PAGE gel . Following electrophoresis , the gel was fixed in ( Isopropenol:dH2O:Acitic acid = 25:65:10 ) for 30 minutes and incubated in the Amplify Fluorographic Reagent ( GE Healthcare , NAMP100 ) for 1 hour . The gel was then vacuum dried and processed for autoradiography with an intensify screen at -80°C . For cellular extract , 1 ug GST or GST-DmP53 bound to Glutathione-agarose beads was first incubated for 30 minutes in 500ul binding buffer with 0 . 2ug/ul BSA . 500 ul cell lysate was then added and incubated at 4°C for overnight . Following incubation the beads were washed 4 times with 1 ml of RIPA buffer and pelleted by centrifugation at 100g for 5 minutes . Beads were then resuspended in 30ul sampling buffer and resolved on SDS-PAGE gel . Western analysis was performed with anti-Flag M2 antibody ( Sigma , F1804 ) . Construction of graphs and calculations of statistical significance were performed using Prism 5 . 0 ( Graphpad ) . In box-and-whisker plots the ends of whiskers represent 5th and 95th percentiles , top and bottom of the boxes represent 25th and 75th percentile and the horizontal line in the box represents the median , i . e . , 50th percentile . The Mann-Whitney test was used in Figs 2 , 3 , 4 , 5 and S1 , S3 , S6 Figs; Unpaired t-test in S4C and S5 Figs; and , Paired t-test in S4D Fig . The MEME tool used for searching motif similarity is publicly available software ( http://meme . nbcr . net/meme/ ) . Images were quantitatively analyzed using IMAGE J software from National Institutes of Health ( http://imagej . nih . gov/ij/index . html ) and images were processed using Adobe Photoshop . All line diagrams were composed using Adobe Illustrator .
|
Organisms have exquisitely sensitive mechanisms to detect and respond to DNA damage . If DNA damage in a cell can be repaired , then that cell may resume its normal function . In multi-cellular organisms , if a cell cannot repair DNA damage it usually undergoes programmed cell death . This prevents the proliferation of cells with damaged genomes , which might otherwise differentiate incorrectly or proliferate without limit as cancer . In Drosophila melanogaster we identified corp as a gene that promotes the survival of such cells . Transcription of corp is activated by the P53 tumor suppressor protein , known primarily for its induction of cell death in response to DNA damage . Our experiments show that P53 regulates both pro-death and anti-death genes , and that a competition between these opposing factors determines cell fate . We find that corp functions by down-regulating P53 , establishing a negative feedback loop . In vertebrates an identical mode of regulation is known: P53 up-regulates Mdm2 , which physically interacts with P53 and is its primary negative regulator . We identified a protein motif on Corp that is shared with Mdm2 , and is required for physical interaction between Corp and Drosophila P53 . These results reinforce and strengthen the similarities of the P53 pathways and their regulation in vertebrates and in Drosophila .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Corp Regulates P53 in Drosophila melanogaster via a Negative Feedback Loop
|
Mitochondrial DNA ( mtDNA ) is believed to be highly vulnerable to age-associated damage and mutagenesis by reactive oxygen species ( ROS ) . However , somatic mtDNA mutations have historically been difficult to study because of technical limitations in accurately quantifying rare mtDNA mutations . We have applied the highly sensitive Duplex Sequencing methodology , which can detect a single mutation among >107 wild type molecules , to sequence mtDNA purified from human brain tissue from both young and old individuals with unprecedented accuracy . We find that the frequency of point mutations increases ∼5-fold over the course of 80 years of life . Overall , the mutation spectra of both groups are comprised predominantly of transition mutations , consistent with misincorporation by DNA polymerase γ or deamination of cytidine and adenosine as the primary mutagenic events in mtDNA . Surprisingly , G→T mutations , considered the hallmark of oxidative damage to DNA , do not significantly increase with age . We observe a non-uniform , age-independent distribution of mutations in mtDNA , with the D-loop exhibiting a significantly higher mutation frequency than the rest of the genome . The coding regions , but not the D-loop , exhibit a pronounced asymmetric accumulation of mutations between the two strands , with G→A and T→C mutations occurring more often on the light strand than the heavy strand . The patterns and biases we observe in our data closely mirror the mutational spectrum which has been reported in studies of human populations and closely related species . Overall our results argue against oxidative damage being a major driver of aging and suggest that replication errors by DNA polymerase γ and/or spontaneous base hydrolysis are responsible for the bulk of accumulating point mutations in mtDNA .
Mitochondria are the primary source of energy for cells . Owing to their evolutionary history , these organelles harbor a small , independently replicated genome ( mtDNA ) . Human mtDNA encodes two rRNA genes , 13 protein coding genes that are essential components of the electron transport chain ( ETC ) , and a full complement of 22 tRNAs used in translation of the ETC peptides . The escape of electrons from the ETC can lead to the formation of reactive oxygen species ( ROS ) , which are capable of damaging a variety of cellular components , including DNA . Due to its proximity to the ETC , absence of protective histones , and a lack of nucleotide excision or mismatch repair , mtDNA is thought to be especially vulnerable to ROS-mediated damage and the generation of mutations . Failure to faithfully transmit the encoded information during mtDNA replication leads to the production of dysfunctional ETC proteins , leading to the release of more free electrons and ROS in what has been termed ‘the vicious cycle’ [1] , [2] . Thus , it is not surprising that mutations in mtDNA have been associated with a decline in energy production , a loss of organismal fitness , an increased propensity for a number of pathological conditions , and aging ( reviewed in [3] , [4] ) . Numerous lines of evidence have suggested mtDNA mutations are involved in the aging process . In particular , ETC activity declines with age [5] , [6] , and this decrease is coincident with accumulation of mitochondria with large deletions in their mtDNA [7] , [8] , [9] , [10] . Large , kilobase-sized deletions in mtDNA become more prevalent with age in a variety of tissues , including brain [11] , heart [12] , and skeletal muscle [7] . Furthermore , these large deletions have been shown to increase in frequency in a number of neurodegenerative conditions , including Parkinson's disease [13] , [14] and Alzheimer's disease [15] . In addition , DNA damage , predominantly in the form of 8-hydroxy-2′-deoxyguanosine ( 8-oxo-dG ) [16] , increases with age in both nuclear and mitochondrial DNA [17] , [18] , [19] , [20] . While the role of mtDNA deletions in aging is well established , the role of point mutations remains controversial [21] , [22] . Several previous studies have examined the accumulation of point mutations in human aging and disease [23] , [24] , [25] , [26] . Until very recently , hypotheses that required the observation of rare mutations in mtDNA have been extremely difficult to experimentally validate due to: 1 ) the lack of genetic tools for introducing reporters or selectable markers into mtDNA; 2 ) the high background error rate of most DNA sequencing methods [27] , [28]; and 3 ) the sampling limitations of the few available high-sensitivity mutation assays that screen only a tiny subset of the genome [29] . The mitochondrial genome is 16 , 569 bp , and individual human cells frequently contain hundreds to thousands of molecules of mtDNA; thus , a single human cell typically contains millions of nucleotides of mtDNA sequence . The rate of accumulation of mtDNA mutations has previously been estimated as 6×10−8 mutations per base pair per year [30] . Therefore , reliable study of spontaneous mtDNA mutations requires methodologies that can accurately detect a single mutation among >106 wild-type base-pairs . However , most prior studies of mtDNA mutations and aging have relied upon methods with background error frequencies of 10−3 to 10−4; hence the many reported differences likely reflect changes in mutation clonality or technical artifacts ( e . g . due to increases in DNA damage with age ) rather than true spontaneous mutations . Massively parallel sequencing technologies allow mtDNA to be subjected to ‘deep sequencing’ in order to detect rare/sub-clonal mutations on a genome-wide level . However , these new sequencing methods are highly error prone , with artifactual error rates of approximately one spurious mutation per 100 to 1 , 000 nucleotides sequenced . These high error rates have precluded the study of spontaneous mutations in mtDNA [31] . To circumvent this limitation , we recently developed a new , highly accurate sequencing methodology termed Duplex Sequencing ( DS ) , which has the unique ability to detect a single mutation among >107 sequenced bases [32] . In the study herein , we determined the effect of aging on mtDNA mutation burden by using DS to compare human mtDNA purified from brain tissue of five young individuals ( ages <1 ) and five aged individuals ( ages 75–99 years ) obtained via rapid autopsy ( Table S1 ) . As brain is among the most metabolically active tissues in the human body , we reasoned it to be particularly prone to damage from ROS , and thus , an optimal tissue for comparison between age groups . We assessed the relative frequency , spectrum , and distribution of mtDNA mutations in the two groups . We find that point mutations increase with age , but do so in a non-uniform manner . Furthermore , we find that mutations show a bias in their occurrence with respect to both genome location and strand orientation . The types of mutations we detect are inconsistent with oxidative damage being a major driver of mtDNA mutagenesis .
Point mutations in mtDNA could be the result of maternal inheritance or a de novo mutation event . Maternally inherited mutations or mutations arising during early embryonic development are more likely to be clonal ( i . e . the same mutation being present at the same location in most or all mtDNA molecules ) . Therefore , in order to quantify the frequency of de novo events , we used a clonality cutoff that excluded any positions with variants occurring at a frequency of >1% , and scored each type of mutation only once at each position of the genome . Based on these criteria , the mtDNA from aged individuals show a highly significant ∼5-fold increase in mutational frequency , relative to those obtained from young individuals ( Young: 3 . 7±0 . 9×10−6 vs . Aged: 1 . 9±0 . 2×10−5 , p<10−4 , two-sample t-test ) ( Fig . 2A ) . These mutation frequencies are between one and two orders of magnitude lower than the previously reported values for both young and old individuals using PCR-based methods or conventional next-generation sequencing [24] , [33] , [34] . This discordance likely stems from artifactual scoring of mutations by these latter methods due to misinsertion of incorrect bases at sites of damage in template DNA during the PCR steps . Duplex Sequencing , in contrast , is unaffected by DNA damage [32] . Inspection of the mutation spectra for both the young and old samples reveals that all samples are significantly biased towards transitions ( Fig . 2B ) . Specifically , the most common mutation type , G→A/C→T , is consistent with either misincorporation by DNA polymerase γ or deamination of cytosine to form uracil , as being the largest mutagenic drivers in mtDNA [35] , [36] . The second most common mutation type , T→C/A→G , is consistent with either deamination of adenosine to inosine or a T-dGTP mispairing , the primary base misinsertion mistake made by DNA polymerase γ [37] , [38] , [39] , [40] . Plotting the frequency of each type of mutation as a proportion of total mutations ( Fig . 2C ) reveals that the relative abundance of each mutation type is the same in young and old , suggesting that the mutagenic pressures that result in the observed spectra are constant throughout the human lifespan . Surprisingly , comparison of the mutation spectra of the young and old samples reveals a notable absence of the mutational signature of oxidative damage . A number of studies have shown that oxidative damage to DNA accumulates in both the nuclear and mitochondrial genomes as a function of age , as well as several age-associated pathologies [17] , [18] , [19] , . The most frequent alteration produced by oxidative damage is 8-oxo-dG , which , when copied during replication or repair , results in dA substitutions , yielding G→T/C→A transversions [42] . A number of theories of aging invoke ROS-mediated damage to mtDNA as being a major driver of the aging phenotype ( reviewed in [43] and [44] ) . A key prediction for these theories is that the frequency of G→T/C→A mutations would be expected to increase with time . We failed to find either a preponderance of G→T/C→A substitutions or a proportionally greater increase with age in this type of mutation relative to other types , despite a span of >80 years between our sequenced sample groups ( Fig . 2C ) . Our data indicate that point mutations increase with age and that these mutations are inconsistent with oxidative damage being a primary driver of mutagenesis; we next assessed whether these mutations lead to alterations in the protein coding sequence . We find that in the aged samples , 78 . 3% of mutations are non-synonymous . The incidence of non-synonymous mutations is close to the expected value of 75 . 7% for mtDNA that would occur if non-synonymous and synonymous mutations occur randomly . In contrast , only 62 . 9% of mutations are non-synonymous in the young samples . The reduced mutation load observed in the young samples is consistent with that negative intergenerational selection against such mutations and that this selection is relieved during development and could play a role in the aging phenotype . However , the existence of a high load of non-synonymous mutations does not necessarily mean that the coding changes lead to functional protein alteration . To examine this possibility , we compared the predicted “pathogenicity” of all non-synonymous mutations in both the young and aged samples using MutPred [45] , a software package that calculates the likelihood of a mutation being deleterious based on a number of criteria , including protein structure , the presence of functional protein motifs , evolutionary conservation , and amino acid composition bias . A score between zero and one is assigned to each mutation , with a higher score denoting a higher likelihood of being deleterious . Based on this analysis ( Fig . S1 ) , the predicted pathogenicity of mutations , indeed , increases with age ( p<0 . 02 , Wilcoxon Rank Sum analysis ) , suggesting that mutations acquired during aging may have functional consequences for the electron transport chain . A similar increase in predicted deleterious mutations was also observed using the SWIFT software package ( data not shown ) . The increase in predicted pathogenicity is consistent with mutations causing coding changes occurring randomly and argues against a mechanism by which point mutations are selected against by the cell . Similar finding in clonally expanded mutations were recently reported in colon tissue show a similar increase in predicted pathogenic mutation in mtDNA [46] . The mitochondrial genome can be divided into three different regions: 1 ) protein coding genes , 2 ) RNA coding genes ( consisting of both rRNA and tRNA ) , and 3 ) non-coding/regulatory regions including the origin of replication known as the D-loop . Phylogenetic analysis of both human and other mammalian lineages has shown that population level single nucleotide variants ( SNVs ) tend to cluster in a number of ‘hotspots’ in the mitochondrial genome , most notably in Hypervariable Regions I and II of the D-loop [47] , [48] , [49] . We sought to determine if the distribution of non-clonal mutations within the mtDNA of individuals exhibited a uniform distribution or if certain regions of the genome similarly show variations in mutation frequency . Comparison of the mutation frequencies of the RNA coding genes to the protein coding genes yielded no significant differences in either the young or old samples ( p = 0 . 15 , two-tailed t-test ) . In contrast , we observed a significant increase in mutation frequency of the D-loop ( bp 16024-576 ) relative to the coding regions ( bp 577–16023 ) in both young ( D-loop: 1 . 5±0 . 6×10−5 vs . coding region: 2 . 9±0 . 7×10−6 , p<0 . 01 , two-tailed t-test ) and aged ( D-loop: 5 . 7±1 . 5×10−5 vs . coding region: 1 . 65±0 . 2×10−5 , p<0 . 01 , two-tailed t-test ) samples , suggesting that the D-loop is a mutagenic hotspot . However comparing the relative increase in the mutation frequency of the D-loop between the young and old sample groups ( 3 . 8±1 . 6-fold increase ) to the relative increase seen between the two sample groups in the non-D-loop regions ( 5 . 6±2 . 0 fold increase ) shows no difference . This finding is inconsistent with the idea that the D-loop accumulates significantly more mutations during aging than the rest of the mitochondrial genome . Spectrum analysis shows a similar predominance of transition mutations in both the D-loop and coding regions of the genome ( Fig . 3A ) , with no significant difference in the relative abundance of the different mutation types ( Fig . 3B ) . Taken together , our data suggest that the mutagenic processes of mtDNA are largely uniform across the genome . The human mitochondrial genome has a significant bias in the cytosine/guanine composition between the two strands . Specifically , the light strand ( L-strand ) , which is the coding strand for only nine genes , contains about three-fold more cytosine than guanine , whereas the heavy strand ( H-strand ) codes for the remaining 28 genes and has the opposite composition bias . Human population studies , as well as the comparative analysis of evolutionarily related species , have shown a bias towards the occurrence of G→A and T→C SNPs of the L-strand [50] , [51] , [52] , [53] . These population-level compositional biases are hypothesized to be due to an asymmetric accumulation of mutations between the two strands of mtDNA in the germline; however , to date , the biases have not been observed at the sub-clonal/random level within individuals . To examine this , we compared the frequency of reciprocal mutations occurring on the L-strand ( i . e . G→A on the L-strand vs . C→T mutations on the L-strand ) . By definition , mutations cause complementary sequence changes on both strands of a DNA molecule . Therefore , if a bias does not exist in the orientation of specific mutations towards a particular strand , then the frequency of reciprocal mutations on the same strand would be expected to be equal . Alternatively , the presence of a strand orientation bias would manifest itself in the form of a particular type of mutation occurring more frequently than its reciprocal mutation . We find that the majority of the human mitochondrial genome shows a significant strand orientation bias in the occurrence of transitions , whereas transversions show no apparent asymmetry ( Fig . 4A ) . Specifically , in young samples , G→A/C→T mutations are more likely to occur when the dG base is present on the L-strand and the dC base is in the H-strand , respectively . This pattern is even more pronounced in aged individuals , consistent with this bias being due to ongoing mutagenic process and not the result of maternal inheritance . In addition to the G→A/C→T bias , the aged samples also exhibit a strand orientation bias in the occurrence of T→C/A→G , where dT is more likely to be mutated to a dC when it is located on the L-strand than on the H-strand . Interestingly , this bias , which appears uniformly throughout most of the mtDNA , is uniquely absent in the D-loop region ( Fig . 4B ) . Thus , both the spectrum and strand orientation asymmetry of somatic mtDNA mutation accumulation recapitulates what has been previously recognized in population studies .
The accumulation of somatic mutations in mtDNA has frequently been hypothesized to drive the aging process and its associated pathologies , including neurodegeneration , cancer , and atrophy ( reviewed in [54] ) . The underlying mechanisms by which these mutations occur and accumulate have been the subject of intense study , but remain incompletely defined . One of the major limitations has been the lack of methodologies with sufficient sensitivity to detect rare mutations among a much larger population of wild-type molecules . We recently developed a robust next-generation sequencing methodology , termed Duplex Sequencing , which is able to detect a single point mutation among >107 sequenced bases [32] and has now enabled us to precisely characterize the genome-wide frequency , spectrum , and distribution of somatic mtDNA mutations in aging human brain with unprecedented accuracy . Our data show a significant increase in the load of point mutations as a function of human age , with absolute frequencies 10–100 fold lower than what has been typically reported in the literature using less sensitive assays . Recent work using the Random Mutation Capture assay has reported an age associated increase in mtDNA point mutation frequencies in mice and Drosophila that are on par with the values that we have determined here; however , these studies were limited to only a very small region of the genome [22] ( Leo Pallanck-submitted ) . Of particular interest , despite a ∼1000-fold difference in lifespan , the increase in mutation load with age appears to be highly consistent among multiple species . This surprising finding suggests that the underlying mechanisms behind the age-dependent accumulation of point mutations in mtDNA are conserved between humans , flies , and mice and merit more detailed comparison . Oxidative damage to DNA , most notably in the form of 8-oxo-dG , has long been believed to be a primary driver of mutagenesis in both nuclear and mitochondrial DNA [42] , [55] , [56] . However , our results do not support this hypothesis . In our data , the relative proportion of G→T/C→A mutations is quite low in the young samples examined and , importantly , does not show a disproportionate increase with age relative to other types of mutations . Other recent reports , which used less sensitive methods to detect intermediate frequency sub-clonal mutations , have similarly failed to detect this classic signature of oxidative damage to DNA . For instance , one conventional deep sequencing analysis of aged mice reported no significant burden of G→T/C→A transversions [57] . Even more surprising is the observation that a transgenic mouse strain deficient for both MutY and OGG1 , which are the two primary enzymes responsible for repairing 8-oxo-dG , do not exhibit an increase in mtDNA mutations [58] . Comparison of the spectrum of our reported data ( Fig . 2B ) to that of the clonal SNV's in our data ( i . e . mutations present at >90% ) , as well as those reported in the Mitomap database [59] , reveals an identical bias towards transitions with a minimal number of G→T/C→A transversions ( Fig . S2 ) . Indeed , a similar propensity towards transitions has been noted in numerous animal phylogenies [60] . This consistency in mutational pattern suggests that the mutagenic processes that cause the accumulation of mutations in somatic tissue are also responsible for clonal population variants arising in the maternal germline . In addition to 8-oxo-dG , ROS can also cause a number of other mutagenic lesions , including thymine glycol and deamination of cytidine and adenosine , all of which can induce transition mutations [37] , [61] , [62] . Our data clearly show an excess of transitions relative to transversions , which could be consistent with oxidatively induced deamination events becoming fixed as mutations . It is well established that ROS production and oxidative damage increase with age [17] , [18] , [19] , [20] . Yet , if oxidative damage were the main driver for deamination events , this model would predict that the relative proportion of transitions should be disproportionately higher in aged individuals , which is not the case with our data . While we cannot conclusively rule out a role for ROS in inducing transition mutations , the excess of transitions could additionally be explained by spontaneous deamination of either cytidine or adenosine , especially in single-stranded replication intermediates , or base misincorporation events by DNA polymerase γ during genome replication , which has a known propensity for transition mutations [36] , [39] , [40] . Overall , the absence of key mutagenic signatures of oxidative damage argues against ROS being the major driver of mutagenesis in mtDNA in normal aging brain . Several explanations may account for why we observed few 8-oxo-dG associated mutations in mtDNA despite an extensive literature showing that 8-oxo-dG levels increase with age . First , rapid DNA repair may remove 8-oxo-dG prior to genome replication and this repair capacity may increase with age [63] , thereby keeping 8-oxo-dG mutagenesis to a minimum . Secondly , mitochondrial quality control pathways may simply eliminate mitochondria with damaged mtDNA . Consistent with this idea is the observation that oxidatively damaged mtDNA rapidly disappears from cells treated with H2O2 [64] . In addition , the cellular levels of parkin , a major component of the quality control pathway involved in mitochondrial turnover , increase under conditions of high oxidative stress [65] . Finally , DNA polymerase γ itself may actively discriminate against incorporating 8-oxo-dG [66] . Regardless of the mechanism , our data suggest that cells have evolved one or more strategies to effectively deal with the challenge of replicating mtDNA in the highly damaging environment of the mitochondria . We find a striking excess of mutations in the D-loop in young individuals; however , D-loop mutations accumulate during aging at the same rate as other parts of the mitochondrial genome , consistent with the D-loop not being inherently error prone . Instead , our data suggest that young individuals are born with a higher aggregate mutation burden in the D-loop relative to the rest of the genome , thus preserving the D-loop's disproportionate mutation load as mutations accumulate during life . The higher proportion of low-level/random mutations in the D-loop also offers a likely explanation as to why population level SNVs tend to cluster in a number of ‘hotspots’ in the D-loop . The disproportionate mutation load of the D-loop at birth provides a likely explanation as to why the D-loop has long been considered prone to mutagenesis . Due to the D-loop's higher aggregate burden , the previously used assays were only sensitive enough to detect mutations located in the D-loop . Thus , even though point mutations increase uniformly throughout mtDNA with age , the mutation load in the non-D-loop portions of the genome would likely be below the sensitivity of the previously available assays , thus giving the appearance that the D-loop's mutations increased with age . Human population studies have previously identified a bias in the occurrence of G→A and T→C SNPs of the L-strand , as has comparison of human mtDNA sequences with those of evolutionary related species [50] , [51] , [52] . This imbalance has been hypothesized to be the result of preferential deamination of cytidine and adenosine on the H-strand; however , because this strand orientation bias has only been previously observed at the clonal population scale , the influence of natural selection could not be discounted [50] , [52] . Our data demonstrate that this bias originates from mutagenesis of mtDNA through a process that is continuous throughout life; however , the source of this bias is unclear . One possibility is that this bias arises during mtDNA replication . Replication of mtDNA is thought to occur in an asymmetric manner [67] , [68] . In this model , replication of the H-strand ( G-rich strand of mtDNA ) begins at the H-strand origin , using the L-strand ( C-rich strand of mtDNA ) as its template . During this time , the parental H-strand remains in a single-stranded state until the synthesis of the L-strand is initiated at the L-strand origin . Several studies previously documenting a strand orientation bias of clonal SNVs at the population level hypothesized that the bias is due to an increased rate of spontaneous hydrolytic deamination of cytidine and adenosine on the H-strand spending a greater amount of time in an unprotected single-stranded state during replication [50] , [51] , [52] , [69] . Indeed , there is in vitro evidence that replication past a deaminated dC ( i . e . dU ) by DNA polymerase γ is responsible for the observed asymmetry . Zheng et al . used DNA polymerase γ to amplify small regions of homoduplex mtDNA via PCR and observed the emergence of an excess of G→A/C→T mutations in these reactions [40] . Their experimental conditions involved extended heating of the DNA , which is known to increase the rate of deamination of single-stranded DNA . While our results do not , on their own , provide direct proof that an asymmetry in deamination of single-stranded replication intermediates is responsible for the observed inter-strand mutational skew , our data are consistent the findings and conclusions in Zheng et al . , thus providing strong support for this model [40] . Furthermore , the observation that a similar strand bias exists in the absence of intergenerational selection argues that it is due to an ongoing mutagenic process within the cell and that the bias observed at the population level is likely due to the same process . In summary , Duplex Sequencing is a powerful technology for high-sensitivity study of mtDNA mutagenesis that has enabled us to uncover a number of mutagenic patterns and biases in human brain tissue that have not been previously observed at the somatic level . Taken together , these mutation patterns argue against oxidative DNA damage being a major driver of aging and suggest that replication errors by DNA polymerase γ and/or spontaneous base hydrolysis are responsible for the bulk of point mutations that accumulate in mtDNA . In light of this finding , the central role for oxidative damage to mtDNA in theories of human aging and disease merits re-evaluation .
Mitochondria were isolated from the pre-frontal cortex of either young ( <1 yr ) or old ( >75 yr ) brain tissue with no known brain pathologies ( Table S1 ) . Approximately 500–800 mg of brain tissue was sub-divided into smaller 100–150 mg pieces , with the mitochondria from each piece purified independently from the other pieces of the same tissue sample . Each tissue fraction was dounced in 5 mL of homogenization buffer ( 0 . 32 M sucrose , 1 mM EDTA , 10 mM Tris-HCl , pH 7 . 8 ) with no more than five strokes , as excessive douncing increases the contamination of nuclear DNA . The homogenate was centrifuged at 1000 g for 20 min at 4°C . The supernatant was transferred to a new centrifuge tube and centrifuged at 1000 g for 10 min at 4°C . Samples that exhibited a pellet at the bottom of the tube after the second centrifugation step were centrifuged a third time using the same condition . The supernatant was removed and centrifuged at 13 , 000 g for 30 min at 4°C to give a crude mitochondrial pellet . Each crude pellet was resuspended in Mito-DNase buffer ( 0 . 3 M sucrose , 10 mM MgCl2 , 0 . 15% BSA ( w/v ) , 20 mM Tris-HCl , pH 7 . 5 ) and DNase added to a concentration of 0 . 01 mg/mL and incubated at 37°C for 1 . 5 hrs . This step helps remove contaminating nuclear DNA while not affecting mtDNA protected within intact mitochondria . Mitochondria were then re-pelleted at 13 , 000 g for 30 min and the supernatant discarded . The mitochondrial pellets were washed two times by resuspending the pellets in 1 mL of Mito-DNase buffer followed by centrifugation at 13 , 000 g for 30 min . Finally , the enriched mitochondrial pellets were resuspended in 500 µL Lysis Buffer ( 150 mM NaCl , 20 mM EDTA , 1% SDS ( w/v ) , 10 mM Tris-HCl , pH 7 . 8 , 0 . 2 mg/mL Proteinase K , 0 . 01 mg/mL RNase ) and incubated at 56°C for 1 hr . The mtDNA was extracted using a standard phenol∶chloroform approach . The relative purity of each mtDNA prep from nuclear DNA was determined by qPCR using the following primers sets: nDNA: fwd 5′- TTGCCAGACCATGGGATTGTCTCA , rev 5′-TTCCTACCGAACGAGGACTCCAAA; mtDNA: fwd 5′-ACAGTTTATGTAGCTTACCTC , rev 5′-TTGCTGCGTGCTTGATGCTTG . DNA fractions from the same tissue samples exhibiting a ΔC ( t ) ≥17 . 5 cycles ( corresponding to ∼50% mtDNA by mass ) were pooled and used for library preparation and sequencing . Typical yields were between 100–500 ng of highly pure mtDNA . We carried out synthesis and preparation of the Duplex Tag-labeled adapters and sequencing library preparation as previously described with the following minor mtDNA specific modifications [32]: 1 ) 100–500 ng of mtDNA was sheared using the Covaris AFA system with a duty cycle of 10% , intensity of 5 , cycles/burst 100 , time 20 seconds×5 , temperature of 4°C; 2 ) prior to adapter ligation , the DNA was quantified and a 40∶1 molar excess of adapters was used for the ligation step; 3 ) after adapter ligation and clean up , the library was re-quantified and ∼37 fmol of product was amplified by PCR for a total of 20 cycles . The resulting libraries were subjected to sequencing on an Illumina HiSeq 2000/2500 platform using 101 bp , paired-end reads . This study involved the use of human brain tissue obtained from autopsy . The tissues were collected at University of Washington under the direction of the Alzheimer's Research Center and Seattle Children's hospital under their approved IRB protocols . Our research did not fall under human subjects requirements due to the samples being anonymous .
|
Owing to their evolutionary history , mitochondria harbor independently replicating genomes . Failure to faithfully transmit the genetic information of mtDNA during replication can lead to the production of dysfunctional electron transport proteins and a subsequent decline in energy production . Cellularly-derived reactive oxygen species ( ROS ) and environmental agents preferentially damage mtDNA compared to nuclear DNA . However , little is known about the consequences of mtDNA damage for mutagenesis . This lack of knowledge stems , in part , from an absence of methods capable of accurately detecting these mutations throughout the mitochondrial genome . Using a new , highly sensitive DNA sequencing strategy , we find that the frequency of point mutations is 10–100-fold lower than what has been previously reported using less precise means . Moreover , the frequency increases 5-fold over an 80 year lifespan . We also find that it is predominantly transition mutations , rather than mutations commonly associated with oxidative damage to mtDNA , that increase with age . This finding is inconsistent with free radical theories of aging . Finally , the mutagenic patterns and biases we observe in our data are similar to what is seen in population studies of mitochondrial polymorphisms and suggest a common mechanism by which somatic and germline mtDNA mutations arise .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Ultra-Sensitive Sequencing Reveals an Age-Related Increase in Somatic Mitochondrial Mutations That Are Inconsistent with Oxidative Damage
|
Differentiation of the fish-borne trematodes belonging to the Opisthorchiidae , Heterophyidae and Lecithodendriidae is important from a clinical and epidemiological perspective , yet it is impossible to do using conventional coprological techniques , as the eggs are morphologically similar . Epidemiological investigation therefore currently relies on morphological examination of adult worms following expulsion chemotherapy . A PCR test capable of amplifying a segment of the internal transcribed spacer region of ribosomal DNA for the opisthorchiid and heterophyid flukes eggs taken directly from faeces was developed and evaluated in a rural community in central Thailand . The lowest quantity of DNA that could be amplified from individual adults of Opisthorchis viverrini , Clonorchis sinensis and Haplorchis taichui was estimated at 0 . 6 pg , 0 . 8 pg and 3 pg , respectively . The PCR was capable of detecting mixed infection with the aforementioned species of flukes under experimental conditions . A total of 11 . 6% of individuals in rural communities in Sanamchaikaet district , central Thailand , were positive for ‘Opisthorchis-like’ eggs in their faeces using conventional parasitological detection techniques . In comparison to microscopy , the PCR yielded a sensitivity and specificity of 71 . 0% and 76 . 7% , respectively . Analysis of the microscopy-positive PCR products revealed 64% and 23% of individuals to be infected with O . viverrini and C . sinensis , respectively . The remaining 13% ( three individuals ) were identified as eggs of Didymozoidae , presumably being passed mechanically in the faeces following the ingestion of infected fishes . An immediate finding of this study is the identification and first report of a C . sinensis–endemic community in central Thailand . This extends the known range of this liver fluke in Southeast Asia . The PCR developed herein provides an important tool for the specific identification of liver and intestinal fluke species for future epidemiological surveys .
It is estimated that approximately 17 million people are currently infected with fish-borne trematodes worldwide [1] . In Asia , Opisthorchis viverrini is known to occur in Thailand , Laos , Cambodia and southern Vietnam and Clonorchis sinensis in Korea , China , Taiwan and northern Vietnam [2] , [3] . Liver fluke infection in Thailand is unevenly distributed with a highly endemic focus of infection in the northeast region [4] . Previous parasite surveys have mostly focussed on these communities and frequently found infection with O . viverrini mixed with minute intestinal flukes of the Heterophyidae and Lecithodendriidae [5] , [6] . The heterophyids Haplorchis taichui and less frequently H . pumilio are the most common minute intestinal flukes recovered . Microscopic examination of faecal samples for the presence of eggs using the formalin-ether concentration technique ( FECT ) is currently considered the most sensitive and reliable method for screening liver and intestinal flukes and is therefore the most widely employed technique for fluke parasite surveys [7] . This technique is limited by its capacity to differentiate between the Opisthorchiidae , Heterophyidae and Lecithodendriidae , which have similar egg morphologies . Eggs can therefore only be characterised as ‘Opisthorchis/Clonorchis- like’ [5] , [6] , but no further . A definitive diagnosis to species level requires morphological identification of adult flukes following expulsion chemotherapy [8] , [9] . The ability to differentiate the species of liver and minute intestinal flukes is important from both a clinical and epidemiological perspective . Heavy infections with the minute intestinal flukes are associated with diarrhoea , mucus-rich faeces , dyspepsia , nausea and vomiting [10] , whereas infections with the liver flukes result in mostly biliary and hepatic disease . The frequency and types of pathology and clinical disease among C . sinensis and O . viverrini also seem to differ [7] . For example , cholelithiasis is one of the more serious complications of clonorchiasis , but a rare complication of opisthorchiasis . Although both flukes are implicated as predisposing factors for cholangiocarcinoma , this is more frequent with O . viverrini . From an epidemiological perspective , C . sinensis has a wider definitive host range than O . viverrini [11] , [12] which makes control more challenging . To overcome the diagnostic limitations associated with conventional parasitological methods , a number of PCR-based techniques capable of amplifying species of flukes directly from eggs in faeces have been developed [13]–[16] . An O . viverrini-specific PCR test capable of detecting O . viverrini eggs directly from human faeces was shown to have an analytical sensitivity of 100% , 68 . 2% and 50% compared to the Stoll's egg count containing >1000 , 200 to 1000 and <200 eggs per g of faeces respectively and an analytical sensitivity of 97 . 8% under experimental conditions [16] . This PCR assay proved less reliable however , once evaluated under field conditions with an overall diagnostic sensitivity of 45% compared to the FECT [17] . PCR tests based on amplification of the mitochondrial gene for the identification and discrimination of C . sinensis and O . viverrini [13] and C . sinensis , O . viverrini and H . taichui [15] have been developed and shown to be analytically sensitive under experimental conditions . Amplicons for the targeted fluke species could be obtained in reactions containing 0 . 78 ng of genomic DNA [13] and 10−4 ng of genomic DNA [15] , however the assays have yet to be evaluated and compared to conventional parasitological methods in the field . Here we developed a PCR test capable of specifically amplifying a segment of the internal transcribed spacer ( ITS-2 ) region of ribosomal DNA ( rDNA ) from opisthorchiid and heterophyid flukes directly from eggs in faeces . The ITS-2 has successfully discriminated species from many digenean families and has become the default region of choice for distinguishing species of trematodes [18] . This PCR test is evaluated in terms of both analytical and diagnostic sensitivity and specificity against conventional parasitological methods in a community endemic for liver fluke infection in central Thailand . This study is also the first to demonstrate the occurrence of a C . sinensis endemic community in Thailand .
A rural community consisting of a total population of approximately 5465 people in Nayao village , Sanamchaikaet District , Chachoengsao Province , 150 km east of Bangkok was chosen for this cross-sectional study . The area lies in a low basin of land which is suitable for cultivation of rice which provides the principle income for the province . The dietary habit of eating raw and fermented fish dishes such as ‘koi pla’ , ‘pla som’ and ‘pla ra’ from fish sourced at local ponds is popular among residents of this community . Houses were chosen at random and household members informed of the study by medical students from the Phramongkutklao College of Medicine . After signing human ethics consent forms , single stool samples were collected from a total of 335 individuals of all ages , gender and backgrounds during a 10-day period in mid November 2004 . Participants found positive for gastrointestinal parasites received free anthelmintic treatment from the medical doctors on the research team in order to increase compliance . All samples were qualitatively evaluated and run in parallel for the presence of Opisthorchis-like eggs using a direct faecal smear ( DFS ) , Kato Katz ( KK ) technique and the FECT by experienced parasitology technicians from the Phramongkutklao College of Medicine in the field . Any remaining faecal material was fixed in 20% dimethylsulfoxide ( DMSO ) saturated with salt for transport to the University of Queensland for molecular testing . A single individual found positive for ‘Opisthorchis-like’ eggs in their faeces was treated with a single dose of praziquantel ( 40 mg per kg ) and was then given 30 g magnesium sulfate with as much water as possible to facilitate expulsion of adult flukes . Whole diarrhoetic stools were collected and washed several times before isolating the adult flukes [9] . Adult flukes that had been expelled by this individual were fixed in 70% ethanol for molecular and morphological identification at the University of Queensland . Morphological identification was performed by staining the adult fluke with haematoxylin , dehydrating it in alcohol and clearing it in methyl salicylate before mounting in Canada balsam . This study was approved by the Murdoch University Human and Animal Ethics Committees of Western Australia and the Ethical Committee , the Medical Department Royal Thai Army . Adult flukes of O . viverrini , C . sinensis and H . taichui were extracted using the Qiagen DNeasy Blood and Tissue Kit according to manufacturer's instructions . Those faecal samples found microscopically positive for ‘Opisthrochis-like’ eggs using at least one parasitological test and where sufficient quantities of stool remained , were subjected to DNA extraction and PCR ( n = 31 ) . In addition , 30 faecal samples negative for ‘Opisthorchis-like’ eggs by microscopy were also randomly selected and subjected to DNA extraction and PCR . All PCR reactions were conducted by a single experienced molecular biologist that was blind to the results of the parasitological test results for each sample . It was observed that subjecting ‘Opisthorchis-like’ eggs purified by a saturated salt and glucose gradient to freezing in liquid nitrogen followed by thawing them at 98–100°C resulted in the eggs ‘disintegrating’ to release genomic DNA . Two hundred milligrams of faeces were suspended in 1 . 4 ml ATL tissue lysis buffer ( Qiagen , Hilden , Germany ) and this suspension subjected to 5 cycles of freezing-thawing at liquid nitrogen temperatures . DNA was then isolated from the supernatant using the QIAamp DNA Mini Stool Kit according to manufacturer's instructions . Final elutions of DNA were made in 50 µl of elution buffer instead of 200 µl as recommended by the manufacturer . Sequences of the ITS-2 region of C . sinensis , O . viverrini , O . felineus , H . taichui , H . pumilio and Centrocestus sp . ( GenBank accession nos . EF688144 , EF688143 , AY584735 , DQ513403 , DQ513405 , AY245705 , AY245706 , AY245699 ) were aligned using Clustal W ( http://align . genome . jp/ ) and the primer pair: RTFlukeFa 5′CTTGAACGCACATTGCGGCC-3′ and RTFlukeRa 5′-CACGTTTGAGCCGAGGTCAG-3′ were designed to amplify a 375 bp , 381 bp and 526 bp region of O . viverrini , C . sinensis and H . taichui , respectively , The PCR primers , were also designed with the potential to amplify other species of opisthorchiid and heterophyid flukes . The PCR assay was carried out in a volume of 20 µl containing 1×PCR buffer from Qiagen ( Tris-HCl , KCl , ( NH4 ) 2SO4 , 1 . 5 mM MgCl2; pH 8 . 7 ) additional MgCl2 to give a final 2 . 0 mM concentration , 200 µM of each dNTP , 0 . 25 µM of each primer , and 1 unit Hot Star Taq DNA polymerase ( Qiagen ) . The PCR cycle consisted of an initial stage: 94°C for 15 min , 60°C for 1 min and 72°C for 2 min followed by 35 cycles of 94°C for 30 sec , 60°C for 30 sec , 72°C for 30 sec , a final extension at 72°C for 7 min and a holding temperature of 12°C . PCR products were run on 1 . 5% agarose in 1×TAE buffer at 150V in a Biorad electrophoresis system and were purified using Qiagen spin columns ( Qiagen ) prior to sequencing . Where a multi-banded product was obtained , target bands were excised , frozen and cleaned up with a Quantum Prep Freeze ‘N Squeeze DNA Gel Extraction spin column ( Biorad ) or a Qiaquick Gel Extraction kit ( Qiagen ) . Sequencing was done using an ABI 3130xl Genetic Analyzer ( Applied Biosystems ) using Big Dye 3 . 0 chemistry , after which sequences were edited and assembled using Chromas Pro ( Technelysium Pty Ltd ) . Titration experiments were conducted to determine the analytical sensitivity of the PCR for the detection of C . sinensis , O . viverrini and H . taichui DNA . The assay's ability to detect artificially mixed infections with varying ratios of C . sinensis and O . viverrini with H . taichui were also assessed . Assuming microscopy as the ‘gold standard’ , the diagnostic sensitivity , and specificity together with their 95% confidence intervals were calculated for the PCR using the Wilson method . The assay's ability to detect artificially mixed infections of O . viverrini and C . sinensis was assessed by development of a PCR-RFLP as both species produced PCR products that could not be differentiated by size . Amplified ITS-2 products of RTFlukeFa – RTFlukeRa for C . sinensis and O . viverrini were digested with AcuI ( New England Biolabs ) . According to the restriction profile generated by Nebcutter V2 . 0 ( New England Biolabs ) , O . viverrini does not possess a restriction site for AcuI and remains uncut ( 375 bp ) , whereas C . sinensis has a single AcuI site and gives rise to two bands at 286 bp and 95 bp . Ten microlitres of PCR product were digested with 2 . 5 units of the restriction endonuclease AcuI ( New England Biolabs ) at 37°C for 3 hours in a volume of 20 µl .
Using the primer pair RTFlukeFa and RTFlukeRb , DNA from morphologically identified adults of O . viverrini , C . sinensis and H . taichui gave specific products of 375 bp , 381 bp and 526 bp respectively . The lowest quantity of DNA that could be amplified from individual adults of O . viverrini , C . sinensis and H . taichui was estimated at 0 . 6 pg , 0 . 8 pg and 3 pg respectively . Appropriate sized amplicons were produced in reactions artificially mixing DNA of C . sinensis and O . viverrini separately , with H . taichui , in ratios of 1∶1 , 1∶2 , 1∶3 , 3∶1 and 2∶1 ( Fig . 1A and 1B ) . The PCR however , preferentially amplified O . viverrini when artificially mixed with H . taichui . Weak to negligible bands of H . taichui were produced when mixed in ratios of less than 1∶1 with O . viverrini ( Fig . 1B ) . The PCR-RFLP patterns for differentiating and detecting mixed infections of O . viverrini and C . sinensis are displayed ( Fig . 1C ) . The PCR-RFLP was successful at detecting artificially mixed infections of O . viverrini and C . sinensis in ratios of 1∶1 , 1∶2 . 1∶3 , 3∶1 and 2∶1 . For a diagrammatic guide to the study design and summary of diagnostic results refer to Fig . 2 . A total of 39 ( prevalence 11 . 6% , 95% CI , 8 . 6% , 14 . 92% ) individuals were found positive for ‘Opisthrochis-like’ eggs in their faeces using a combination of all three microscopic techniques ( DFS , KK and FECT ) . The FECT detected ‘Opisthorchis-like’ eggs in more faecal samples ( 25/31 ) than the DFS ( 9/31 ) and KK ( 10/31 ) methods . Using primer pair RTFlukeFa and RTFlukeRb , PCR-positive samples derived from DNA extracted directly from faeces produced a single product corresponding to the expected amplicon size for O . viverrini and C . sinensis ( approximately 380 bp ) . In three cases , non-specific amplicons were produced in addition to the target PCR product , however these amplicons were too weak ( faint ) to subject to DNA sequencing . The results of the PCR analysis of 31 microscopy positive and 30 microscopy negative samples are presented in Table 1 . The PCR test , when compared to the combined microscopy results yielded a sensitivity of 71 . 0% ( 95% CI , 53 . 4% , 83 . 9% ) , and specificity of 76 . 7% ( 95% CI , 59 . 1% , 88 . 2% ) . PCR detected an additional seven samples positive for liver fluke that were negative by microscopy . Mixed infections of O . viverrini and C . sinensis were detected in a single individual by PCR-RFLP . Morphological and genetic characterisation of the fluke species expelled by the human participant Adult fluke specimens isolated from a single human participant in the community were identified by morphology as Clonorchis sinensis [19] . Two adult fluke specimens subjected to PCR demonstrated 100% DNA sequence homology to the ITS-2 region of C . sinensis isolates from Japan and Russia ( GenBank accession nos . EF688144 and EF688143 ) . Phenogram construction of the ITS-2 region of the flukes using the neighbour-joining algorithm and maximum parsimony ( Fig . 3 ) , produced strong bootstrap support for the placement of 15 PCR-positive samples within a single clade corresponding to O . viverrini ( GenBank accession number AY584735 ) and 11 PCR-positive samples corresponding to C . sinensis ( GenBank accession nos . EF688144 , EF688143 ) . Mixed infections with fluke species were not observed by sequencing of the PCR product . Of the 22 individuals found positive for ‘Opisthorchis-like’ eggs by both microscopy and by PCR , 14 ( 64% ) were characterised as O . viverrini and five ( 23% ) as C . sinensis . In addition , three samples ( 13% ) microscopy positive for ‘Opisthorchis-like’ eggs produced amplicon sizes of approximately 410 bp each and upon sequencing , were genetically similar to the didymozoids ( parasites of fishes ) , Rhopalotrema elusiva ( GenBank accession no . AJ224759 ) and Indodidymozoon sp . ( GenBank accession no . AJ224754 ) . Six of the microscopy negative but PCR positive samples were genetically characterised as C . sinensis and a single sample as O . viverrini . No intra-species variation was observed for the O . viverrini isolates obtained from this community relative to those obtained from northeast Thailand ( positive control and published GenBank isolate AY584735 ) . Apart from three isolates of C . sinensis obtained from this community that differed by a transition at a single base , all other isolates of C . sinensis were identical to published ITS-2 sequences of C . sinensis from Japan ( GenBank accession no . EF688144 ) and Russia ( GenBank accession no . EF688143 ) .
A significant finding of this present study was the identification and first report of a community endemic for C . sinensis in central Thailand . It is possible that the humans in this community were infected by imported fish or visited C . sinensis endemic areas , however when questioned about this , villagers reported only eating fish caught in local ponds or from local village markets and had no history of travelling outside Thailand . Previous studies have only reported C . sinensis in Korea , China , Taiwan , Japan , northern Vietnam and the far eastern part of Russia [20] . It is hypothesised that the geographical distribution of clonorchiasis closely parallels the distribution of the snail intermediate host [20] , however this assumption may not be as simple as previously thought . Species of Parafossarulus and Bithynia are most commonly reported to act as first intermediate hosts for C . sinensis . The important species in China , Korea and Japan is Parafossarulus manchouricus and P . anamalospiralis [7] . Other susceptible snails in China are reported as Bithynia fuchsiana , B . longicornis , Melanoides tuberculata and Assiminea lutea [20] . In Thailand , Bithynia siamensis goniomphalos , B . s . funiculate and B . s . siamensis act as hosts for O . viverrini [19] . In a recent survey of freshwater mollusks in Thailand , an intermediate host of C . sinensis , Melanoides tuberculata was isolated in the provinces to the south ( Chanthaburi ) and north ( Nakhon Ratchasima Province ) of our study area [21] . It is possible that this species of snail may be acting as the natural intermediate host of C . sinensis in Thailand . If this is true , then C . sinensis may be as geographically widespread as O . viverrini in Thailand , reflecting the geographical distribution of M . tuberculata , which was isolated from 9/15 districts sampled in the north , east and central regions of Thailand [21] . In saying this however , M tuberculata has been shown to harbour both C . sinensis and O . viverrini in both northern and southern regions of Vietnam , yet surveys to date have found C . sinensis to be restricted to the northern provinces and O . viverrini to the southern provinces [2] . The distribution of potential snail intermediate hosts therefore does not necessarily reflect the distribution of the liver flukes in Southeast Asia . The PCR test developed in this study provides a useful diagnostic tool for further epidemiological surveys to determine the distribution of these liver flukes in human and intermediate hosts . The PCR test developed in this study is capable of amplifying O . viverrini , C . sinensis and potentially the minute intestinal flukes , directly from eggs in faeces . In terms of test parameters , this assay demonstrated a superior sensitivity ( Se ) to the PCR developed by Stensvold et al . ( 2006 ) in the field ( Se = 70 . 9% compared to Se of 45 . 0% ) . It also has the added advantage of being able to amplify fluke species other than O . viverrini . It may be likely that the presence of faecal inhibitors and/or the unsuccessful ‘cracking open’ of these highly resistant eggs during DNA extraction accounted for the false negative results produced by the PCR in this study . The overall specificity ( Sp ) of the PCR evaluated using microscopy negative field samples were inferior to those reported by Stensvold et al . ( 2006 ) ( Sp = 76 . 7% , compared to Sp: 90 . 0% ) , however these assumed ‘false positive’ samples were being compared to the microscopy results ( DFS , KK , FECT ) which are in themselves not ‘gold standards’ . DNA sequences generated from the PCR products of these samples were characterised as either O . viverrini or C . sinensis and therefore the specificity of this PCR may be under-estimated . Three faecal samples microscopy positive for ‘Opisthorchis-like’ eggs were sequenced and identified as being close to the didymozoids Rhopalotrema elusiva and Indodidymozoon sp . This is not the first time that eggs of didymozoid flukes have been recovered in human faecal samples [22] . Flukes belonging to the Didymozoidae parasitize a wide range of species of marine fish and ingestion of these adult flukes by humans during the consumption of fish results in the mechanical passage of the relatively thick-shelled eggs into the faeces . Because of their dimensions ( 35–43×12–28 µm ) and morphology ( oval , operculate ) of the eggs of didymozoid flukes , they can easily be confused with eggs of the Opisthorchiidae , Heterophyidae and Lecithodendriidae . This added confusion may result in further inaccuracies when estimating the prevalence of liver and intestinal flukes in a community using conventional parasitological procedures alone . The apparent absence of H . taichui in the Sanamchaikaet district community was surprising given it is reported commonly in the northeast region of Thailand . It is possible that the PCR failed to amplify eggs from faecal samples with mixed infections of H . taichui and O . viverrini . Under experimental conditions , the PCR showed good analytical sensitivity for detecting H . taichui as a single infection and also when artificially mixed with C . sinensis , but failed to amplify a strong band when artificially mixed with O . viverrini . Since small intestinal flukes have commonly been found as mixed infections with liver fluke species [5] , [23] , the PCR developed in this study may not be successful at detecting these infections . In conclusion , we present data to demonstrate for the first time in Thailand a community endemic for C . sinensis infection . This significant finding undoubtedly opens a new chapter for further research into investigating the distribution , and prevalence of C . sinensis in Thailand and determining the natural intermediate host species capable of supporting its life cycle . Furthermore , the PCR described herein provides a valuable tool for screening and determining the species of liver and intestinal flukes in epidemiological surveys .
|
It is estimated that approximately 17 million people are currently infected with fish-borne flukes worldwide . The fish-borne liver flukes Opisthrochis viverrini and Clonorchis sinensis cause hepatic and biliary disease in humans . The minute intestinal flukes are widely distributed in southeast Asia and are increasingly recognised as an emerging pathogen associated with diarrhoea and gastritis . The most significant finding of this study is the discovery and first report of a C . sinensis–endemic community in Thailand . This finding was aided by the development and application of a new PCR-based technique capable of specifically detecting and characterising O . viverrini , C . sinensis and the minute intestinal flukes , directly from eggs in faeces . Since the eggs are morphologically similar , the fish-borne flukes cannot be differentiated on basis of microscopic examination of stool . This publication also questions the presumption that the distribution of fish-borne liver fluke species in Asia closely parallels the distribution of the snail intermediate hosts . The PCR provides a useful diagnostic tool for further large-scale epidemiological surveys to be carried out in Southeast Asia , which will shed further light on the distribution of these liver flukes in human and snail intermediate hosts with the advantage that targets for more arduous anthelmintic flushing confirmations can be carried out .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/helminth",
"infections",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"microbiology/parasitology",
"infectious",
"diseases/neglected",
"tropical",
"diseases"
] |
2009
|
A New PCR-Based Approach Indicates the Range of Clonorchis sinensis Now Extends to Central Thailand
|
The spatial responses of many of the cells recorded in layer II of rodent medial entorhinal cortex ( MEC ) show a triangular grid pattern , which appears to provide an accurate population code for animal spatial position . In layer III , V and VI of the rat MEC , grid cells are also selective to head-direction and are modulated by the speed of the animal . Several putative mechanisms of grid-like maps were proposed , including attractor network dynamics , interactions with theta oscillations or single-unit mechanisms such as firing rate adaptation . In this paper , we present a new attractor network model that accounts for the conjunctive position-by-velocity selectivity of grid cells . Our network model is able to perform robust path integration even when the recurrent connections are subject to random perturbations .
Responses of grid cells recorded in medial entorhinal cortex ( MEC ) provide accurate population codes for the positions in an environment [1] , and could result from path-integration mechanism [2] . Attractor network models of MEC spatial representations have been proposed , based on two foundations . First , they assume surround-inhibition recurrent connections , such as in Mexican-hat type connection profile , between grid cells [3]–[7] . When sufficiently strong , surround-inhibition connections endow recurrent networks with stationary hexagonal patterns of activity patches even when driven by uniform external inputs [3] , [5] , [8] . In the absence of external cues , this pattern can have an arbitrary spatial phase and orientation , a hallmark of a continuous attractor model . In order to generate individual cells with an hexagonal pattern of firing fields from an hexagonal pattern of network activity , the latter has to flow across the network with a velocity vector proportional to the velocity of the animal , up to a fixed rotation . In [5] , this movement is emerging in the network via a combination of two additional mechanisms: ( i ) each neuron receives a speed-modulated input that is tuned for a particular direction of movement; and ( ii ) the neuron's outgoing connections are slightly shifted in the direction reflecting the neuron's preferred direction . As a result , when the animal is moving in a certain direction , the neurons that prefer this direction are slightly more active than their counterparts , and generate the appropriate flow across the network sheet . While the model of [5] was shown to generate robust grid cells , it cannot account for cells that are strongly directionally tuned ( “conjunctive cells” ) [9] . Moreover , in order to produce stable firing fields , the flow speed should be precisely proportional to the animal velocity , which can only be achieved by the abovementioned mechanism with threshold-linear neurons , not a realistic assumption given strong nonlinearities of neuronal firing mechanism . In order to develop a robust continuous attractor model of grid system , we suggest that MEC networks contain intrinsic representations of arbitrary conjunctions of positions and movements of the animal . To achieve such representations , we construct a network with grid-like activity patterns that are intrinsically moving with different velocities , as opposed to stationary patterns in the earlier models . Individual neurons in the network are labeled with different position/velocity combinations , and connectivity is configured in such a way that activity bumps , when centered on neurons with particular velocity labels , are intrinsically moving at the corresponding speed and direction . The appropriate positioning of the activity bumps is assumed to be achieved by the velocity-dependent input as in [5] . The mapping between the animal movement and the position of the bumps on the velocity axis can be learned by the network during development , such that the velocity of the bumps in the neural space is proportional to the velocity of the animal in the physical space . The network thus performs path-integration and forms stable grid maps in the environment . We demonstrate that this model does not require precise tuning of recurrent connections and naturally accounts for the co-existence of pure grid cells and strongly directional , conjunctive cells .
On a linear track , the network only needs to encode one-dimensional locations and integrate one-dimensional velocity . Each unit is labeled by its coordinates on an abstract 2-dimensional neuronal manifold . is an internal representation of the positions of the environment . For simplicity we assume periodic boundary conditions in . Note that the physical environment has fixed boundary conditions , and the simulated animal can not go beyond the boundaries . Both and are dimensionless quantities , but they reflect physical position and velocity of the animal ( see below ) . We choose the connections between the units such that the network has multiple bumps in the position dimension and a single bump in the velocity dimension . In this paper we consider the simplest choice for the connections from unit to unit in the following form ( but we expect the precise form not to be important for the qualitative behavior of the network ) : ( 1 ) where is uniform inhibition , defines the range of interaction strengths , is the strength of velocity tuning and is an integer , determining the number of bumps in the dimension of . Note that for each value of connections are asymmetric in the position dimension , which results in the bumps moving along this dimension with the speed and direction determined by . Throughout the paper we choose and . The former choice was taken in order to be consistent with the 2D case described below , where several activity bumps are required for the neurons to exhibit triangular grid fields , without endowing the abstract neural tissue with twisted torus boundary conditions necessary for previous models [10] , [11] . The range of is chosen to be , since for values of beyond this range the moving bump solution disappears ( see Eq . 14 and Methods - Speed estimation in the asymmetric ring model ) . The outgoing weight profile of a unit is not centered at its own spatial label , but is shifted by an amount determined by its velocity label ( Fig . 1A ) . The weight profile is broadly modulated in the velocity dimension by the second cosine term of Eq . 1 . The incoming weights of a unit is shifted in the spatial axis by amounts determined by presynaptic units , showing tilted patterns ( Fig . 1B ) , a structure imposed by the term in the first cosine term of Eq . 1 . We first consider the intrinsic activity of the network without a velocity tuned input . The firing rate of the unit at is denoted by . The dynamics of the network activity is described by ( 2 ) where is a uniform input current , is a transfer function typically defined as a threshold-linear function if not stated explicitly: when and 0 otherwise . The notations , and . The coupling can be rewritten as ( 3 ) This model is mathematically similar to the model discussed in [12] , but with bumps and asymmetrical connections in . The properties of the network activity can be characterized by an appropriately chosen set of order parameters . Thanks to the ring connectivity structure used , we introduce five order parameters ( ) to describe the network activity [12] , [13] . The dynamics of the firing rate can be rewritten in terms of these order parameters as ( see Methods - Order parameters for the detailed derivation ) ( 4 ) where , the rescaled input ( see Eq . 27 below ) , is defined by ( 5 ) The dynamics of the order parameters are governed by following equations: ( 6 ) defines the slant of the bumps , is the threshold that sets the size of the bumps , is the amplitude of the bumps in the network . and indicate the peak location of the bumps in and dimensions respectively . The solutions to the system in Eq . 2 show qualitatively different forms depending on the parameters and . If is small , the network activity is uniform ( homogeneous regime , Fig . 2A ) . When increases , the network activity converges to bumps , localized at the arbitrary stationary position in dimension and spanning the whole range in dimension ( static bumps regime; , see Fig . 2B ) . The forces from the units with positive ( negative ) velocity labels in propagating the bumps to right ( left ) balance each other , therefore the bumps are static . For sufficiently large , the bumps become localized also in the velocity dimension at the position ( Fig . 2C ) . Due to the asymmetry of the coupling in the spatial axis , the bumps start to move intrinsically along the spatial axis with velocity dependent on their position on the velocity axis ( traveling bumps regime ) . Since the network forms a continuous attractor manifold in dimension , the bumps are free to be stabilized in the velocity axis and are able to move with a range of possible velocities along the spatial axis . In the traveling bumps regime , the network activity does not have any steady state , but the order parameters and converge to fixed points . should be sufficiently negative in order to keep the network activity from explosion ( amplitude instability regime ) . Throughout the paper , we assume inhibitory connections ( i . e . ) for convenience , although using excitatory connections will lead to similar results . In this section , we analyze the fixed point solutions to the dynamics of the order parameters , and perform simulations to confirm the solutions found . Before analyzing the moving bumps regime , we briefly mention the homogeneous regime and static bumps regime for the sake of completeness . In order to perform path-integration , or equivalently to form stable firing maps , the velocity of the bumps has to be kept proportional to the velocity of the animal ( 15 ) where is the velocity of the animal , is the desired position on the velocity axis , , the spacing between grid fields , is a scaling factor between the velocity of the animal in physical space and the velocity of the bumps in neural space . Eq . 15 means that for any velocity of the animal the time it takes for the animal to travel in physical space between two grid fields is equal to the time it takes for the bumps in neural space to flow for one period with desired velocity . For a given , Eq . 14 , 15 can be solved for ( 16 ) The function therefore tells where the bumps should be located in dimension given the velocity of the animal , i . e . where the velocity tuned external input should be pointing . We choose the velocity-tuned input to the network given by Gaussian tuning ( 17 ) Here is the strength of the velocity tuning , is the sharpness of the tuning , is the amplitude of the input as before . Note that in the brain the function can be implemented by a neural network , the connections of which may be learned during development of the MEC . For simplicity , we assume in this paper that such a network has already been formed during appropriate developmental stages [14]–[16] . In all the simulations presented in this paper , we only consider inputs that are untuned in the spatial dimension , in order to study the ability of the network to perform path integration in the absence of sensory cues . Adding such cues will make the grid fields more robust . In a high-dimensional environment , the neural space is expanded to represent position and velocity in each dimension of the physical space . For a two-dimensional environment , units in the neural space are labeled by coordinates . and jointly represent the four-dimensional space of position and velocity in a two dimensional environment . For mathematical convenience , is assumed to have periodic boundary conditions , i . e . . and are in the range of . The weight matrix between units is defined as an extension of the one-dimensional case ( 19 ) where is the distance on a circle ( 20 ) Here mod ( x , y ) gives x modulo y . As can be seen from Eq . 19 , the velocity label of the presynaptic units in the first cosinus term introduces an asymmetry to the weight matrix in the spatial axis . The second cosinus term is responsible for velocity selectivity . We simulate a network with units uniformly arranged in velocity bins and spatial bins in the neural space , summing up to 50 , 625 units in total in the network . Fig . 7 shows the weights between one example unit at and units with velocity label on the neural tissue . In two-dimensional environments , the state of the network shows similar transitions as in the case of one dimensional environments . For small , the activity of the network is homogeneous . When increases , multiple bumps appear forming a triangular lattice in the spatial dimensions , however the activity of the network is not localized in velocity axis , and the network state is static . When is sufficiently large , the network activity is localized in the velocity axis , and due to the asymmetry in connections , the bumps start to move along the spatial axis . As shown in Fig . 8 , the maximal activity of the units with the same velocity label changes from a homogeneous solution ( light gray lines ) to a localized solution ( black lines ) as increases . The input to the network is given by ( 21 ) where is the component of the velocity vector of the animal in x or y axis of the physical space . , defined in Eq . 16 , gives the location of the bumps on the corresponding velocity axis in the neural space . and are the strength and the width of velocity tuning .
Our model accounts for the conjunctive position-by-movement responses of the cells find in deep layers of MEC [9] . This is because the recurrent weights between units are modulated in the velocity axis ( Eq . 1 ) , so that in each activity patch only units with similar velocity labels and similar position labels are active . In previous models [3] , [5] the incoming weights of the units with the same position labels do not depend on their velocity tuning , therefore they must be active together . Units may gain weak degree of conjunctiveness by scaling up the amplitude of velocity input , so that units that are not driven by strong velocity input will be less active . But since this is not a stable attractor state of the network , strong conjunctiveness will push the network out of the stable regime . In rodent MEC , pure grid cells and conjunctive cells coexist in the same module [18] . Conjunctive cells exist in layer III , V and VI . Pure grid cells are found in layer II , and are mixed with conjunctive cells in deep layers [19] . Overall , the proportion of conjunctive cells among all grid cells is no more than 50% . In our model , the conjunctiveness of a unit is correlated with its absolute velocity label . Grid cells have velocity labels close to the origin ( closer than half the size of the bump ) , hence they are active for all movement directions . Cells that are further away from origin in the velocity axis are only active when the animal moves in a particular direction , thus resulting in head-direction selectivity in addition to position response as pure grid cells . The ratio between the number of pure grid units and the number of conjunctive units depends on the size of the bumps: the larger the size of the bumps , the larger the number of pure grid cells . The model requires precise velocity input indicating the direction and speed of animal movement . MEC may receive velocity-tuned input from posterior parietal cortex and retrosplenial cortex [20]–[24] . These regions integrate multimodal sensory information , such as movement information from vestibular system relayed by thalamus and optical flow information from visual cortex , and play an important role in spatial navigation [25]–[28] . In rodents , many of the cells in posterior parietal cortex have been found to respond to velocity and acceleration [29] . Therefore , posterior parietal cortex can be one possible source of self-motion signal for MEC network [30] . The connections from movement-selective cells in posterior parietal cortex to MEC cells can be tuned during postnatal development , and map animal movement to the position of the activity bumps on the velocity axis ( Eqs . 16 and 21 ) . The possibility to precisely learn such a mapping allows for the flexibility in MEC intrinsic connectivity and neural firing mechanisms . The coupling between units is not necessarily restricted to a cosine shape , as analyzed here . The firing rate of each unit can depend nonlinearly on its input , e . g . as a sigmoid transfer function . The parameter that defines the ratio between the flow of the activity pattern and the velocity of the animal ( see Eq . 15 ) determines the grid scale of a MEC module . From dorsal to ventral , MEC units are arranged into local modules with increasing discrete values of , resulting in discretized grid scales [18] . If there is a bias in the connectivity , e . g . movement-selective cells are systematically connected to MEC cells with larger absolute velocity labels along one velocity axis of the neuronal space , MEC cells will express elliptical grids ( Fig . 12 ) , as observed experimentally [18] . Accumulating experimental evidence shows that mammals adopt two types of navigation . Path-integration is useful when landmarks are not available , e . g . in the darkness or when a cognitive map representation is being learned after entering in a novel environment . Map-based navigation is able to reset the error in path-integration , calibrating the internal spatial representation according to the external landmarks . The dynamics of the spatial representation in the brain depend on the interaction between these two modes of navigation . Integration of these two modes in a network model may better explain the responses of grid cells in novel environments or after environment changes [31] . Several testable predictions can be derived from the model . To verify these predictions , new experiments and analysis should be carried out to examine the selectivity of the responses of MEC principle cells .
The network activity can be characterized by a set of order parameters derived from its Fourier transform ( 22 ) The dynamics of the network activity can be written as ( 23 ) where is the total input to a unit given by ( 24 ) Fourier transforming the firing rate dynamics Eq . 23 reveals the dynamics of the order parameters , and ( 25 ) The solutions of the dynamics can be better described by recombining the order parameters in Eqs . 22 into the following dimensionless quantities ( 26 ) With this set of order parameters and by defining the rescaled gain function ( 27 ) Eq . 23 results in Eq . 4 . Differentiating Eqs . 26 and combining Eqs . 25 lead to the dynamics for the reduced order parameters in Eqs . 6 . Due to the asymmetry of the coupling in is not zero . This can be seen by linear expansion of the integrand at in the first Eq . of 6 and assuming . ( 28 ) does not satisfy the second term of the right hand side of the above equation , since is an even function . In the homogeneous regime , the order parameter vanishes at the steady state . We introduce a new order parameter , being the size of the bumps ( 29 ) and are two free parameters . We choose them to be . It is sufficient to consider the dynamics of and . By using the derivative chain rule , the dynamics of and can be obtained from Eq . 29 and Eqs . 6 , ( 30 ) where . The stability of the homogeneous solution can be inspected by linearizing Eqs . 30 at the fixed point . The matrix governing the linear dynamics of perturbations reads ( 31 ) where is given in Eq . 9 . Therefore , the conditions for the homogeneous solution to be stable are and , as shown in Eqs . 7–8 . We analyze the onset of the freedom of choice of . In this case , the bumps just touch the boundaries of the range . Posing , and the steady state activity at at is ( 32 ) The angle at which this activity is maximal is ( 33 ) So the maximal activity at the boundaries is ( 34 ) For to vanish , ( 35 ) The time constant is set to 10 ms throughout the paper . Differential equations are numerically integrated according to the fourth order Runge-Kutta method . The time-step for numerical integration is ms . For the path-integration simulations on linear tracks , the virtual animal runs back and forth , and changes its running direction only at the two ends of the track . The velocity of the animal is determined according to ( 36 ) where ms is the time constant . is the current running direction . is a piecewise constant function , whose values are sampled uniformly in [0 , 100] cm/s every second . When the animal is approaching close enough to the end of the track ( distance to the end of the track smaller than ) , O ( t ) is set to zero to make sure that the speed of the animal decreases to zero at the end of the track . In this deceleration phase , once the speed of the animal drops below 5 cm/s , the animal reverses its direction and a new value of is chosen randomly . The trajectory in a 2D environment is simulated by two such independent random walkers . For a spacing cm , velocity 100 cm/s in physical space would require the bumps centered at on the velocity axis . In the path-integration simulations , we reduce the range of the velocity label , which is enough to cover the velocities experienced by the rat . The neural space is discretized into equal-sized bins , with each bin occupied by one unit . In the model for linear track , the axis is divided into 200 bins and the axis into 51 bins . The network is composed of 10 , 200 units . In the model for two-dimensional environments , the axes and are divided into 25 bins each and the axes and into 9 bins each , resulting in 50 , 625 units in total . Initially the network is given stronger input for units with specified coordinate to let the network form bumps at the corresponding position of the axis . The input is gradually decreased to uniform . The network is simulated for one second with uniform input , and the center of the bumps on axis and axis are estimated at each time step by ( 37 ) ( 38 ) where is the coordinate of unit in the neural space . is the total number of units in the network . is the activity of unit . Function takes the angle of a complex number . The velocity of the bumps is calculated as ( 39 ) Here , defined in Eq . 20 , measures the distance considering periodic boundary condition . Here we consider a simplified case in which the manifold is reduced to a ring on . On the ring , the units have different labels in but the same label of . The interaction between two units and is ( 40 ) where is an integer . The rate dynamics are defined by ( 41 ) The state of the system can be described in the frequency domain as in the Fourier transform of the activity reads ( 42 ) The mean activity , the magnitude and the phase of the -th component of the Fourier transform are defined as the order parameters of the system . The dynamics in Eq . 41 can be rewritten into a simpler form in terms of the order parameters ( 43 ) where and are given by ( 44 ) ( 45 ) Fourier transforming Eq . 43 results in the dynamics governing the order parameters ( 46 )
|
How do animals self-localize when they explore the environments with variable velocities ? One mechanism is dead reckoning or path-integration . Recent experiments on rodents show that such computation may be performed by grid cells in medial entorhinal cortex . Each grid cell fires strongly when the animal enters locations that define the vertices of a triangular grid . Some of the grid cells show grid firing patterns only when the animal runs along particular directions . Here , we propose that grid cells collectively represent arbitrary conjunctions of positions and movements of the animal . Due to asymmetric recurrent connections , the network has grid patterns as states that are able to move intrinsically with all possible directions and speeds . A velocity-tuned input will activate a subset of the population that prefers similar movements , and the pattern in the network moves with a velocity proportional to the movement of the animal in physical space , up to a fixed rotation . Thus the network ‘imagines’ the movement of the animal , and produces single cell grid firing responses in space with different degree of head-direction selectivity . We propose testable predictions for new experiments to verify our model .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computer",
"and",
"information",
"sciences",
"cognition",
"neural",
"networks",
"computational",
"neuroscience",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"cognitive",
"science",
"neuroscience",
"learning",
"and",
"memory",
"animal",
"cognition"
] |
2014
|
Continuous Attractor Network Model for Conjunctive Position-by-Velocity Tuning of Grid Cells
|
Homologous recombination between the circular chromosomes of bacteria can generate chromosome dimers . They are resolved by a recombination event at a specific site in the replication terminus of chromosomes , dif , by dedicated tyrosine recombinases . The reaction is under the control of a cell division protein , FtsK , which assembles into active DNA pumps at mid-cell during septum formation . Previous studies suggested that activation of Xer recombination at dif was restricted to chromosome dimers in Escherichia coli but not in Vibrio cholerae , suggesting that FtsK mainly acted on chromosome dimers in E . coli but frequently processed monomeric chromosomes in V . cholerae . However , recent microscopic studies suggested that E . coli FtsK served to release the MatP-mediated cohesion and/or cell division apparatus-interaction of sister copies of the dif region independently of chromosome dimer formation . Here , we show that these apparently paradoxical observations are not linked to any difference in the dimer resolution machineries of E . coli and V . cholerae but to differences in the timing of segregation of their chromosomes . V . cholerae harbours two circular chromosomes , chr1 and chr2 . We found that whatever the growth conditions , sister copies of the V . cholerae chr1 dif region remain together at mid-cell until the onset of constriction , which permits their processing by FtsK and the activation of dif-recombination . Likewise , sister copies of the dif region of the E . coli chromosome only separate after the onset of constriction in slow growth conditions . However , under fast growth conditions the dif sites separate before constriction , which restricts XerCD-dif activity to resolving chromosome dimers .
DNA synthesis , chromosome segregation and cell division must be coordinated to ensure the stable inheritance of the genetic material during proliferation . In eukaryotes , this is achieved by coupling the assembly and activity of the cell division apparatus to the assembly and activity of the mitotic spindle , a subcellular structure that serves to separate chromosomes . Yeast and animal cells also evolved a checkpoint mechanism that delays cell scission when chromatin remains trapped in the division plane . No such checkpoint exists in bacteria . Instead , they rely on a highly conserved protein , FtsK , to transport any trapped DNA from one daughter cell compartment to another during septation [1] . FtsK is a bi-functional protein . It includes an integral domain at its amino-terminus ( FtsKN ) , a low complexity ‘linker’ region that lacks any evolutionarily conserved feature ( FtsKL ) and a conserved RecA-type ATPase fold at its carboxyl-terminus ( FtsKC ) [2] . It was initially discovered because of its essential role in cell division in Escherichia coli [3] . However , only FtsKN and FtsKL are implicated in the cell division process [4] . FtsKC serves to transport DNA between daughter cell compartments before final scission [5 , 6] . It assembles into hexamers on double stranded DNA at the initiation of septation and uses the energy from binding/hydrolysis of ATP to translocate on it [7–10] . E . coli harbours a single circular chromosome with a single origin of replication , oriC . A winged helix domain at the extreme carboxyl-terminus of FtsK , FtsKγ , binds to specific 8-bp polar DNA motifs , the KOPS , which orientates its loading [11–13] . KOPS are over-represented on the E . coli chromosome . They point from oriC toward a specific 28 bp site , dif , within the replication terminus of the chromosome , ter . As a result , FtsKC motors are directed towards dif [12] . Daughter chromosomes segregate progressively as they are replicated . However , the delay between the time of replication and the time of separation of sister loci is variable [14] . In particular , it was observed that ter sister copies remained close together at mid-cell until the very end of the cell cycle in different growth conditions [14–17] . This is at least in part explained by the binding of MatP , a protein that interacts with the cell division machinery , to specific DNA motifs within ter [18 , 19] . Microscopic observations of the cellular arrangement of pairs of chromosome loci under slow growth conditions recently suggested that FtsK translocation served to release the MatP-mediated cohesion and/or cell division apparatus-interaction of ter sisters in a KOPS-oriented manner , placing it at the centre of the coordination between the E . coli replication/segregation and cell division cycles [20] . Prior to this observation , FtsK translocation was only considered as a safeguard against the formation of chromosome dimers [21] . Chromosome dimers are generated by homologous recombination events between chromatid sisters during or after replication . They physically impede the segregation of genetic information at cell division , which generates a substrate for FtsK translocation . They are resolved by the addition of a crossover at dif by a dedicated pair of chromosomally encoded tyrosine recombinases , XerC and XerD . Xer recombination-deficient E . coli strains were extensively characterised by microscopy [22 , 23] , growth competition [23] , direct measurements of recombination rates at dif using density label assays derived from the Meselson and Stahl experiment [24–26] and the excision of a DNA segment inserted between two dif sites in direct repetition ( dif-cassette ) at the dif locus [2 , 23 , 27] . These studies demonstrated that chromosome dimers were due to recA-dependent homologous recombination initiated by either the recF or recB pathways and independently of the role of recA in SOS . They also suggested that chromosome dimers formed at a generation rate of less than 20% whatever the growth conditions . However , it was observed that mutations decreasing the processivity of replication forks increased their formation in agreement with the multiple roles played by homologous recombination in replication fork progression [26 , 28 , 29] . Growth competition and dif-cassette excision experiments further indicated that dif only functioned within the ter region , at the zone of convergence of the KOPS motifs [12 , 23 , 30 , 31] . Finally , density label and dif-cassette excision assays showed that recombination at dif took place at a late stage of cell division , after the initiation of septum constriction [25 , 32] . FtsK plays two essential roles in chromosome dimer resolution . First , KOPS-oriented FtsK-dependent DNA transport ensures that the two dif sites of a chromosome dimer are brought together at mid-cell [2 , 12 , 33] . Second , FtsK activates the addition of a crossover by the Xer recombinases via a direct interaction between FtsKγ and XerD [6 , 34 , 35] . The roles played by FtsK in chromosome dimer resolution explains the spatial restriction of the activity of dif on the chromosome to the KOPS convergence zone [12 , 23 , 30 , 31] while the temporal restriction of Xer recombination at chromosomal dif sites suggests that the action of FtsKC is delayed compared to its recruitment to the septum [25 , 32] . Correspondingly , ectopic production of FtsKC was sufficient to activate dif recombination outside of the KOPS convergence zone , independently of cell division and of recA [2 , 27] . Taken together , these results suggested that FtsK normally only acted on chromosome dimers . The mild phenotype of FtsK translocation deficient mutants and their suppression by the inactivation of recA corroborated this hypothesis [33 , 36–38] , in apparent contradiction with the general role of FtsK in the release of MatP-mediated cohesion and/or cell division apparatus-interaction of ter sisters [20] . The dimer resolution machinery is conserved in almost all bacteria [21] . This is notably the case in Vibrio cholerae , which harbours two distinct , non-homologous circular chromosomes , chr1 and chr2 . The dimer resolution sites of chr1 and chr2 , dif1 and dif2 , respectively , differ in sequence . However , we previously showed that V . cholerae FtsK controlled the addition of a crossover by V . cholerae XerC and XerD at both sites [39] . As in E . coli , V . cholerae dif-cassette excision was restricted to the KOPS convergence zone within chr1 and chr2 ter regions and only took place after the initiation of septum constriction [40] . However , dif-cassette excision on both V . cholerae chromosomes was independent of recA [40] . In addition , it was too high to be solely explained by the estimated rate of chromosome dimer formation at each generation [39 , 40] . This observation prompted us to re-visit how the E . coli and V . cholerae chromosomes are managed at the time of cell division using a combination of careful dif-cassette excision assays and newly available fluorescence microscopy techniques . Here , we show that the E . coli recA-dependency and V . cholerae recA-independency of dif-cassette excision are not determined by differences in the dimer resolution machineries of the two bacteria but by differences in the timing of segregation of their chromosomes: whatever the growth conditions , V . cholerae chr1 ter sister copies remain together at mid-cell until the onset of constriction , which increases the chances for FtsK to activate recombination at dif independently of recA . Likewise , we show that in slow growth conditions , E . coli ter sister copies separate after the onset of constriction and dif-recombination is independent of recA . In contrast , our results suggest that MatP does not prevent E . coli ter sister copies from separating away from each other and from mid-cell before constriction in fast growth conditions . We show that separation of ter sisters is independent of FtsK , which explains why recombination at dif becomes dependent on the formation of chromosome dimers by homologous recombination during fast growth .
We first checked whether the respective recA-dependency and recA-independency of dif-cassette excision in E . coli and V . cholerae was not due to differences in the design of the assays that were previously used in the two species . To this end , we created dif- and dif1-excision cassettes by introducing a first copy of these sites in the coding region of the E . coli lacZ gene in such a manner that the produced peptide retained its β-galactosidase activity and a second copy of the sites ahead of the lacZ ORF ( Fig 1A ) . Recombination between the two sites of the cassettes excises a third of the lacZ ORF , which abolishes β-galactosidase production . Plating cells on X-Gal gives the cassette recombination frequency for different time points in growing cultures . We inserted the cassettes at the dif locus in strains in which the endogenous lacZ , xerC and xerD genes were deleted . XerC and XerD were produced from a xerC-xerD operon under the control of the arabinose promoter . The E . coli lacZ promoter and the E . coli lacI repressor gene were added in anti-orientation at the end of the operon to help repress any leaky expression of the recombinases ( Fig 1A ) . In the case of E . coli , the xerC-xerD operon was introduced on a pBAD vector . Due to the instability of the vector in V . cholerae , the xerC-xerD operon was integrated in place of the xerC gene locus in the V . cholerae strains . In a dif-excision cassette experiment , the frequency of recombination per cell per generation ( f ) is deduced from the initial and final Ratios of the non-recombined cells to the total number of cells ( Ri and Rf , respectively ) and the number of divisions ( n ) that occurred during the course of the experiment with the following formula , f = 1-eln ( Rf/Ri ) /n , which can be simplified into f = 1-eln ( Rf ) /n because Ri normally equals 1 . Rf is best monitored when in the 10–90% range . Large n minimises the possible error made on its estimation . Previous work showed that both conditions were reached with overnight cultures of E . coli strains in LB [23 , 27] . Using these conditions for our lacZ dif-cassette , we measured an excision rate in the order of 5% per generation ( Fig 1B; n = 19 . 3±1 . 5 , Rf = 36 . 4±5 . 4 ) . The rate dropped to little more than 1% per generation in ΔrecA cells ( Fig 1B; n = 19 . 9±1 . 1 , Rf = 79 . 8±7 . 3 ) . Previous results indicated that in the case of V . cholerae strains , only 3 h of growth in LB had to be used because of the elevated number of Xer recombination events at both dif1 and dif2 [40] . This short incubation time was large enough for n to reach a 6 to 7 value due to the fast growth rate of V . cholerae . Under these conditions , the excision rate of the dif1-cassette was in the order of 27% ( Fig 1B; n = 6 . 8±0 . 5 , Rf = 10±1 . 8 ) and was not affected by the deletion of recA ( Fig 1B; in the order of 26% , n = 6 . 5±1 . 1 , Rf = 13 . 7±2 . 5 ) . We confirmed that the respective recA-dependency and recA-independency of dif-cassette excision in E . coli and dif1-cassette excision in V . cholerae were not due to a difference in the number of divisions that cells performed during the course of the experiment by monitoring E . coli dif-cassette excision after only 8 h , i . e . at a time when n reached a 7 to 8 value ( S1 Fig ) . Together , these results confirmed the species specificity of the recA-dependency of dif recombination . Cultures of E . coli and V . cholerae recA- strains displayed similar loss of colony forming units ( S2A Fig ) and produced anucleate cells at a similar rate ( S2B Fig ) , suggesting that the E . coli recA-dependence and V . cholerae recA-independence of dif recombination were not linked to any species specific role of RecA in the two bacterial species . On the contrary , V . cholerae XerC and XerD seemed remarkably different from their E . coli counterparts in that they were known to act on sites with highly divergent central regions [39 , 41–43] . To check if the recA-independent cassette excisions observed in V . cholerae were linked to this peculiarity , we swapped the arabinose-inducible xerC-xerD operon and the excision cassettes of the E . coli and V . cholerae reporter strains . We also swapped the C-terminal domains of E . coli and V . cholerae FtsK to create reporter strains harbouring fully heterospecific Xer recombination systems . Cassette excision remained dependent on recA in the two E . coli hybrids and independent from it in the two V . cholerae hybrids , indicating that the FtsK/XerCD/dif systems did not specify the recA-dependency of dif recombination ( Fig 1C ) . As E . coli and V . cholerae dif-cassette excisions depend on the initiation of cell constriction [25 , 32 , 40] and are restricted to the ter regions [25 , 32 , 40] , we wondered if differences in recA-dependency were linked to differences in the timing of segregation of the terminus of the E . coli and V . cholerae chromosomes with respect to the assembly of their cell division apparatus . To test this hypothesis , we compared the position of ydeV , a locus 8 kb away from dif on the E . coli chromosome , and the position of the V . cholerae chr1 dif1 locus in cells under exponential growth in liquid . LB-background fluorescence prevented the use of the exact same growth conditions as those of the cassette excision assays . However , E . coli and V . cholerae dif-cassette excisions remained recA-dependent and independent , respectively , in M9 supplemented with 10% of LB , 0 . 1% casamino acids and 0 . 2% of glucose ( S3 Fig , M9-Rich ) . V . cholerae cells had a generation time of 23 min in this medium , which is only slightly longer than their 22 min LB generation time , whereas the generation time of E . coli cells increased from 24 min in LB to 40 min in M9-Rich . When grown in M9-Rich medium , 37% of E . coli cells displayed two or more ydeV sister loci before a constriction event could be detected ( Fig 2A ) . The proportion of cells with two foci reached 89% when constriction was visible ( Fig 2B , left panels ) . In addition , most of the foci were spatially separated with only 6% of them remaining close to mid-cell , i . e . at a distance from the cell centre of less than 5% of the cell length ( Fig 2B , right panels ) . Thus , only ~8% of the foci , whether single or double , were in the immediate vicinity of the cell division apparatus in constricting E . coli cells with a 40 min generation time . In contrast , a single dif1 spot was observed in 96% of V . cholerae cells that were grown in M9-Rich medium and that presented no visible sign of constriction ( Fig 2C ) . The spot was at mid-cell in the largest cells ( Fig 2C ) . A single dif1 spot was also observed in 59% of the cells in which constriction was visible ( Fig 2D , upper left panel ) . 89% of them were in the immediate vicinity of the cell division apparatus at mid-cell , i . e . at a distance from the cell centre of less than 5% of the cell length ( Fig 2D , upper right panel ) . 71% of the foci observed in the constricting cells with two spots ( Fig 2D , lower left panel ) were also in close vicinity of mid-cell ( Fig 2D , lower right panel ) . Thus , 82% of the foci , whether single or double , were in the immediate vicinity of the cell division apparatus in constricting V . cholerae cells with a 23 min generation time . Finally , we observed that in V . cholerae cells with a single dif1 focus , the spot was located at a pole in the shortest cells , i . e . newborn cells ( Fig 2C ) , whereas the ydeV focus of E . coli cells with a single spot was already positioned around mid-cell in the shortest cells ( Fig 2A ) . Correspondingly , sister dif1 spots remained located at mid-cell in the longest V . cholerae cells , i . e . ready to divide cells ( Fig 2D ) , whereas sister ydeV spots relocated toward the 1/4 and 3/4 positions in the longest E . coli cells ( Fig 2B ) . These observations were consistent with the idea that sister dif sites split and migrated away from mid-cell before the onset of constriction could be manually detected . Sister dif sites separation would then prevent FtsK-mediated Xer recombination activation in E . coli unless a dimer was present . On the contrary , late sister dif1 segregation would permit the action of FtsK on monomeric chromosomes in V . cholerae . The results of Fig 2 seemed to contradict the idea that FtsK drove the orderly segregation of E . coli sister ter independently of chromosome dimer formation [20] . However , the latter phenomenon had been documented for cells under extreme slow growth , with a generation time of 210 min [20] . It led us to suspect that growth conditions influenced sister ter segregation , with fast growth conditions accelerating their segregation . In order to verify this hypothesis , we inspected the localisation of the ydeV and dif1 loci , in snapshot images of cells grown in liquid in minimal medium supplemented with only 0 . 2% of fructose ( M9 ) . Under this condition , E . coli cells presented a generation time of 92 min whereas V . cholerae cells had a generation time of 80 min . In the case of E . coli , 90% of the cells with no visible sign of constriction contained a single ydeV spot , the localisation of which reached mid-cell in the largest cells ( Fig 3A ) . Most of the E . coli constricting cells ( 92% ) still presented two ydeV spots ( Fig 3B , lower left panel ) . However , 14% of them remained in close proximity to the cell centre , i . e . at a distance of less than 5% of the cell length ( Fig 3B , lower right panel ) . Thus , foci , in the order of 18% , whether single or double , remained in the immediate vicinity of the cell division apparatus in constricting E . coli cells with a 92 min generation time . Correspondingly , the single ydeV spot of the shortest cells often located off the cell centre , toward the cell poles ( Fig 3B ) . A similar trend was observed for V . cholerae cells , with a single dif1 spot detected in almost 100% of the cells that presented no visible sign of constriction ( Fig 3C ) and in 78% of the cells in which constriction was visible ( Fig 3D ) . In total , 78% of the foci , whether single or double , were in the immediate vicinity of the cell division apparatus in constricting V . cholerae cells with an 80 min generation time , i . e . at a distance from the cell centre of less than 5% of the cell length . These results suggested that growth conditions influenced sister ter segregation in both E . coli and V . cholerae . We next wondered if the delay in the separation of the E . coli ter sisters that we observed in cells growing with a generation time of 92 min was sufficient to influence dif-cassette excision . Indeed , the rate of dif-cassette excision , as measured in a 16 h experiment , increased from 5% in LB to 20% in M9 ( Fig 4A , left panel ) . In addition , a significant proportion of the observed recombination events ( >60% ) were now independent of recA ( Fig 4A , right panel ) . These results were confirmed with 8 h dif-cassette recombination assays ( S4 Fig ) . As dif-cassette excisions depend on the initiation of cell constriction [25 , 32 , 40] , the results of Fig 4A could be explained if separation of the E . coli chromosome ter sister copies was strictly connected to the onset of constriction under slow growth but that they separated ahead of constriction in fast growth , as suggested by the snapshot results of Figs 2 and 3 . We therefore decided to follow the cell cycle choreography of sister ydeV loci by fluorescent video-microscopy to obtain direct evidence that their separation was strictly connected to the onset of constriction under slow growth but that it took ahead of it in fast growth . In brief , a stack of 32 bright-field images below and above the focal plane of the cells and a single fluorescence image at the focal plane were taken at regular intervals . The bright-field stacks served to reconstruct a high definition image of the cell shapes . Cell genealogy was reconstructed , with cells arising from fully observed division events being oriented from their new pole ( the pole originating from the division of the mother cell ) to their old pole . Each of the individual cell lives were then analysed by two different methods . First , we plotted the long cell axis projections of the fluorescence and reconstituted cell shape as a function of time for cells for which a complete cell cycle was recorded . At each time point , the maximal and minimal intensities of the projections were set to 1 and 0 , respectively . We created images describing the evolution of the fluorescence and cell shape as a function of time by plotting the different projections of individual cells as a function of time using a jet colour code . Individual fluorescence and cell shape images were then compiled into consensus images summarising the results ( Fig 4B ) . In the fluorescence consensus images ( Fig 4B , upper panels , GFP ) , red colouring signals the position of the ydeV loci ( ter ) . In the cell shape consensus images ( Fig 4B , lower panels , BF ) , red colouring corresponds to regions of high bright-field signal with blue colouring at mid-cell signalling cell constriction ( Septa ) . Use of the cell shape projections was as efficient as the use of a fluorescent derivative of the SPOR domain , which specifically labels nascent peptidoglycan at the septum , to detect constriction events ( S5 Fig , [44] ) . Because projections were scaled from 0 to 1 , each of the individual cell lives equally contributed to the consensus images . Consensus images indicated that in slowly growing cells , most sister termini separated and migrated away from mid-cell at the same time as constriction became visible , at approximately 80% of the cell cycle ( Fig 4B , right panels ) . In addition , the ydeV fluorescence signals remained close to mid-cell up to the end of the cell cycle , and lagged at the new pole of newborn cells ( Fig 4B ) , in agreement with the snapshot results of Fig 3 . In contrast , in fast growing cells , sister termini already separated and located away from mid-cell at 40% of the cell cycle whereas constriction became visible only after 60% of the cell cycle had elapsed ( Fig 4B , left panels ) . In addition , they already relocated to the 1/4 and 3/4 positions at the end of the cell cycle ( Fig 4B ) . Second , the position along the cell long axis of each fluorescence focus was manually determined as well as the position of any observed septation trace . This method could give a late estimate of the onset of constriction in cells . Note , however , that septation leads to the formation of a darker line across the cell section in our BF reconstructed images , which helps limit this bias ( S6 Fig ) . We then aligned the cycles of cells for which the initiation of septation was monitored using as a reference the time when septation was first detected ( Fig 4C , dashed vertical line at time 0 ) . We computed the median positions of the fluorescence foci ( Fig 4C , filled circles ) and the frequency of cells with a single focus ( Fig 4C , blue line and radius of blue circles ) , with two foci ( Fig 4C , red line and radius of red circles ) and with 3 or more foci ( Fig 4C , black line and radius of black circles ) . It revealed that in fast growth conditions only 15% of ter sisters were not separated at the time when septation was first detected ( Fig 4C , left panel , blue curve ) . Indeed , most ter sisters were already separated before the onset of constriction could be detected with our BF reconstruction method ( Fig 4C , left panel , red curve ) , with the two foci having already moved away from mid-cell ( Fig 4C , left panel , red spots ) . Finally , cells with 3 or 4 foci could be observed at late stages of the cell cycle , in agreement with the frequent birth of cells with duplicated ter sisters ( Fig 4C , left panel , black spots and black line ) , which explained the decrease in the frequency of cells with two spots at these stages . In contrast , 100% of the slowly growing cells contained a single focus at birth and 66% of them still presented a single spot at the onset of constriction ( Fig 4C , right panel , blue curve ) . In addition , the rate of duplication of sister termini was maximal at the onset of constriction , with 4 . 3% of new duplication events per min ( Fig 4C , right panel , red curve ) . Taken together , these results suggested that separation of sister copies of the E . coli chromosome ter region was connected to the onset of constriction under slow growth but that they separated ahead of constriction in fast growth . It could somehow seem surprising that , in half of the cells grown in M9-Rich , 2 ydeV spots were visible at birth and 3–4 ydeV spots were visible at the time of division ( Fig 4C ) whereas the shortest cells of our snapshot image analysis results only had a single ydeV spot and the longest cells 2 ydeV spots ( Fig 2 ) . We cannot rule out the possibility that differences in the video-microscopy and snapshot image observations are due to differences in the growth condition of the cells in the two sets of experiments . Indeed , the median generation time of cells observed by video-microscopy was 10% shorter than the generation time of cells grown in liquid . However , we wish to emphasize that differences in the methods of analysis of snapshot images and time-lapse experiments are sufficient to explain the observed differences . Snapshot image analysis permits to observe a distorted cell cycle based on cell length instead of cell age because there is a considerable variation in the length of dividing and newborn cells , which is linked to the intrinsic randomness of growth and cell cycle regulation [16 , 45] . In addition , it is difficult to assess during the segmentation of snapshot images if joint cells correspond to ( i ) randomly juxtaposed cells , ( ii ) daughter cells from a recent cell scission event or ( iii ) the two halves of a cell in the process of constriction . It is essential to differentiate the first case from the two others , which implies the use of cell segmentation parameters that overestimate cell scission events , i . e . tend to separate the two halves of a cell in the process of constriction . As a result , some non-constricting cells shown in Fig 2 probably correspond to the two halves of a mother cell before scission . In contrast , time-lapse observations permit to unambiguously determine when cell scission has occurred because once separated the two daughter cells re-orientate and slide along each other . Correspondingly , in our video-microscopy experiments , the median length of cells just before cell scission was found to be in the order of 8 μm whereas the longest cell segments in snapshot images were in the order of 7 μm . The ter domain of the E . coli chromosome harbours MatP-specific DNA binding motifs [19] . MatP interacts with ZapB , an early component of the cell division machinery [18] . As the MatP-ZapB interaction was shown to tether sister copies of ter loci at mid-cell and prevent their separation until the very end of cell division in cells grown on minimal media [18] , we decided to check the action of MatP under our slow ( M9 ) and fast ( M9-Rich ) growth conditions . When compared to the doubling time of matP+ E . coli cells , the generation time of ΔmatP cells increased by almost 60% in M9 and 10% in M9-Rich ( Fig 5A and 5B , ΔmatP ) . Inspection of the cell contour and ydeV fluorescence consensus images further indicated that sister copies of the ydeV locus separated further ahead of the initiation of septation in the ΔmatP cells than they did in the matP+ cells: they separated at 70% of the cell cycle whereas constriction became visible at 80% of the cell cycle in M9 ( Fig 5A , ΔmatP ) ; most sister copies of the ydeV locus were already separated at 20% of the cell cycle whereas constriction was only visible at 60% of the cell cycle in M9-Rich ( Fig 5B and S7 Fig ) . Taken together , these results indicated that MatP participated in chromosome organisation under both slow and fast growth conditions . However , whether in rich or poor growth conditions , the number of oriC to ter foci remained constant during the growth of filaments induced by the addition of cephalexin ( S8 Fig and S1 and S2 Movies ) . Cephalexin drives the formation of smooth filaments by blocking the activity of FtsI without disassembling the cell division apparatus and without impeding new rounds of chromosome replication [46] . These results suggested that MatP maintained ter regions attached to the division machinery for only a limited period of time between each new round of replication . As FtsK translocation was shown to drive the orderly separation of sister copies of the terminus region of the E . coli chromosome under slow growth conditions [20] , we next decided to check the role of FtsK in the separation of ydeV sister copies in M9 and M9-Rich conditions . To this end , we compared when ydeV sister copies separated with respect to the initiation of constriction in cells harbouring an ATPase deficient allele of ftsK ( ftsKATP- ) . We observed a similar 17% increase in the doubling time of ftsKATP- cells in M9 and M9-Rich conditions ( Fig 5A and 5B , ftsKATP- ) . In M9 , sister copies of ydeV only separated at 90% of the cell cycle whereas constriction became visible at 70% of the cell cycle , thereby allowing the management of the terminus region by FtsK ( Fig 5A , ftsKATP- ) . However , sister copies of ydeV separated in cephalexin treated cells , suggesting that FtsK translocation is not absolutely required for sister ter separation under slow growth conditions ( S8 Fig and S1 and S2 Movies ) . In M9-Rich , sister copies of ydeV separated at 40% of the cell cycle whereas constriction became visible at 70% of the cell cycle ( Fig 5B , ftsKATP- ) , demonstrating that ydeV sister copies separation did not require FtsK translocation in rich growth conditions . Careful inspection of individual cell lineages revealed frequent splitting of sister ydeV copies into 3–4 foci at the late stage of the cell cycle , further suggesting that FtsK translocation was not necessary to split the pairs of sister copies ( S9 Fig ) . Indeed , ydeV sister copies seemed to separate 10% of the cell cycle time earlier in ftsKATP- cells than they did in FtsK+ cells in M9-Rich ( Fig 5B , ftsKATP- ) . The phenomenon was not unexpected since cells that require FtsK translocation to complete chromosome segregation during constriction ( such as cells harbouring a chromosome dimer ) are necessarily excluded from the analysis because they fail to complete their cell cycle .
The activity of FtsKC is the sole limiting factor for dif-recombination in WT cells , which suggested that dif-cassette excision could be used to monitor the processing of chromosomes by FtsK [2 , 6 , 39 , 40] . Indeed , restriction of the assembly of FtsK pumps to mid-cell at the onset of cell division [8] was reflected in the spatial and temporal restriction of dif-recombination to the ter region of chromosomes [2 , 23 , 40] and to the initiation of constriction [32 , 40] . Correspondingly , the low excision rate of dif-cassettes integrated at the E . coli dif locus and its dependence on recA were attributed to the low frequency with which the ter regions of monomeric chromosomes remained trapped in the septum at the onset of constriction [2 , 27] . However , this view was contradicted by recent microscopic observations , which suggested that FtsK promoted the orderly segregation of loci within the E . coli ter region , whether chromosome dimers were present or not [20] , which raised the possibility that an as yet unknown mechanism restricted Xer recombination to chromosome dimers in E . coli . The excision of dif-cassettes was not restricted to chromosome dimers in V . cholerae [40] , further suggesting that the E . coli recA-dependence of dif-cassette excision might be due to a specific property of its dimer resolution system . The results we present demonstrate that it is not the case ( Fig 1 ) . Instead , they suggest that recA-dependency and recA-independency are due to the choreography of segregation adopted by the ter regions of chromosomes in the two species ( Fig 2 ) . In particular , the rate of dif-cassette excision increased and became less recA-dependent in conditions in which E . coli ter sister copies remained more frequently located at mid-cell at the onset of constriction ( Figs 3 and 4 ) . Taken together , these results suggest that differences in dif-cassette excision rates report the relative proximity of sister chromosomal regions at the time of constriction in fast and slow growing E . coli and V . cholerae cells . Sister copies of the ter region of chromosomes co-localize at mid-cell until the initiation of cell division in both E . coli and V . cholerae [47 , 48] , at least in part because of the MatP/matS macrodomain organisation system [18 , 19 , 40] . This mode of segregation participates in the coordination between chromosome segregation and cell division . In particular , a nucleoid occlusion factor , SlmA , impedes the assembly of the cell division machinery until a time when the only genomic DNA left at mid-cell consists of the sister copies of the terminus region of chromosomes [49 , 50] . Previous reports suggested that the separation of ter sisters was mediated by FtsK translocation , which stripped MatP off DNA during constriction [20 , 40] . It led to the idea that MatP and FtsK served to coordinate the final stages of chromosome segregation with cell division in bacteria [51] . Our observations of the position of sister termini in V . cholerae cells under slow and fast growth ( Figs 3 and 2 , respectively ) and in E . coli cells under slow growth ( Figs 3 and 4 ) are fully consistent with this idea: sister termini separated at a late stage of the cell cycle , concomitantly with cell constriction ( Figs 2 , 3 and 4 ) ; correspondingly , the chromosomal terminus region lagged at one pole ( the new pole ) of newborn cells ( Figs 2 , 3 and 4 ) . In fast growth conditions , however , we found that the sister termini of the E . coli chromosome separated prior to the initiation of septation ( Fig 4 ) . They even often reached the 1/4 and 3/4 positions before the onset of constriction could be detected , emphasizing that ter segregation was independent from cell division ( Figs 2 and 4 ) . Together , these results suggested that ter segregation was generally independent of FtsK in fast growth conditions , which we confirmed by analysing the choreography of segregation of ter in ftsKATP- cells ( Fig 5 ) . Our observations of ΔmatP cells suggested that MatP still participated in the organisation of the ter region in both slow and fast growth conditions ( Fig 5 ) . What mechanism could explain FtsK-independent sister ter separation in fast growing E . coli cells ( Fig 5 and S8 Fig ) ? We are attracted to the idea that new rounds of replication/segregation reorganise chromosomal DNA and in particular separate sister ter copies by pulling them towards opposite replication machineries . This model readily explains the differences between the action of FtsK in slow and fast growing cells and in cells treated with cephalexin: in slow growing cells , MatP holds sister copies of the terminus together at mid-cell after they are replicated . As the next round of replication/segregation is a long way off , separation of sister ter copies depends on FtsK translocation ( Fig 4 ) . Note , however , that FtsK translocation is not essential ( Fig 5 ) . In fast growing cells , overlapping rounds of replication/segregation break sister ter copies apart before the onset of constriction ( Fig 4 , [52] ) . The resulting 1/4 and 3/4 sister foci are separated or not depending on the advancement of the next/overlapping rounds of replication/segregation in each cell ( Fig 4 ) . In cephalexin treated cells new rounds of replication/segregation can likewise permit sister ter copies separation in the absence of constriction ( S8 Fig ) . In contrast , overlapping replication rounds are absent and/or limited to one in V . cholerae cells under both slow and fast growth ( Figs 2 and 3 , [53] ) , which explains why FtsK always acts on sister ter regions ( Fig 1 ) . Future work will need to assess what are the different mechanisms that participate to the reorganisation of the bacterial nucleoid during growth and what is their relative contribution in different modes of growth [54] .
Bacterial strains and plasmids used in this study are listed in S1 and S2 Tables , respectively . V . cholerae strains are derivatives of El Tor N16961 strain rendered competent by the insertion of hapR by specific transposition and constructed by natural transformation . E . coli strains are derivatives of MG1655 , constructed by P1 transduction and/or integration/excision . Engineered strains were confirmed by PCR . Growth media: LB ( Luria-Bertani broth ) , M9-Rich ( M9-MM supplemented with 0 . 2% glucose , 0 . 1% CAA , 10% LB and 1 μg/ml thiamine ) and M9 ( M9-MM supplemented with 0 . 2% fructose and 1 μg/ml thiamine ) . For the in vivo recombination assays , V . cholerae strains were grown in LB ( generation time 22 min ) and M9-Rich ( generation time 23 min ) , and E . coli strains were grown in LB ( generation time 24 min ) , M9-Rich ( generation time 40 min ) and M9 ( generation time 92 min ) . For microscopy experiments V . cholerae strains were grown in M9-Rich and M9 ( generation time 80 min ) , and E . coli strains were grown in M9-Rich and M9 . V . cholerae: reporter cells were grown overnight in LB supplemented with 0 . 2 mM IPTG . Cultures were diluted in the morning in LB and grown at 37°C until they reached an OD600 comprised between 0 . 2 and 0 . 5 . They were then diluted to an OD600 of 0 . 02 in LB supplemented with 0 . 1% L-Arabinose and grown at 37°C for 3 h . Serial dilutions of the cells were spread on LB agar plates supplemented with X-Gal ( 80 μg/ml ) and IPTG ( 0 . 2 mM ) before and after the induction of recombination . E . coli: chemically competent reporter cells ( rubidium chloride ) were transformed with pCM165 or pCM166 , inoculated in fresh media ( LB , M9-Rich or M9 ) supplemented with 0 . 1% L-Arabinose and 100 μM ampicillin and grown at 37°C for 8 h or 16 h . Serial dilutions of the cells were spread on LB agar plates supplemented with ampicillin ( 100 μM ) , X-Gal ( 80 μg/ml ) and IPTG ( 0 . 2 mM ) before and after the induction of recombination . Cells are not growing exponentially over the entire course of the experiments because ( i ) adaptation to the new growth conditions leads to a lag period at the beginning of the experiments and ( ii ) nutrient depletion leads to a stationary period at the end of the experiments . During both periods , cells do not divide . Therefore , recombination events can be almost entirely attributed to the intermediate exponential growth phase because dif-cassette excision takes place during cell division . The number of divisions ( n ) that took place during the exponential phase is deduced from the initial and final Number of cells in the cultures ( Ni and Nf , respectively ) by the formula n = ln ( Nf/Ni ) /ln ( 2 ) . For snapshot analyses , cells grown to exponential phase in M9 and M9-Rich were spread on 1% ( w/v ) M9 or M9-Rich agar pads , respectively ( ultrapure agarose , Invitrogen ) . Phase contrast and fluorescence images were acquired using a DM6000-B ( Leica ) microscope . For time-lapse analyses , cells grown to exponential phase in M9 and M9-Rich were spread on a 1% ( w/v ) M9 or M9-Rich agar pads , respectively . Images were acquired using an Evolve 512 EMCCD camera ( Roper Scientific ) attached to an Axio Observe spinning disk ( Zeiss ) . Pictures were taken every 2 min for cells grown in M9-Rich medium and every 4 min for cells grown in M9 . At each time point , we took a stack of 32 bright-field images covering positions 1 . 6 μm below and above the focal plane . Cell contours were detected and cell genealogies were retraced with a MatLab-based script developed in the lab [49] . After the first division event , the new pole and old pole of cells could be unambiguously attributed based on the previous division events . A lacO array was inserted next to dif1 and was detected using a LacI-mCherry fusion produced from the lacZ locus of V . cholerae chr1 . A parST1 motif was inserted at the ydeV locus and detected by production of a YGFP-ParBpMT1 from plasmid pFHC2973 . A lacO array was inserted 15 kb from the E . coli oriC locus . For the joint detection of oriC and ydeV loci , LacI-mCherry and YGFP-ParBpMT1 fusions were produced from plasmid pAD16 . Under these conditions , the tags did not interfere with the localisation of the loci [18 , 40 , 47] . The SPOR domain of FtsN was fused to mCherry and to the DsbA signal sequence to efficiently export it into the periplasm , the fusion was expressed from a PBAD promoter induced with 0 . 01% L-Arabinose . SPOR localisation was inspected in M9 . Cephalexin ( 10 μg/ml , final concentration ) was added directly to the agarose slide . If not stated otherwise , leakiness of the promoter was sufficient for signal detection . Wild Type and recA- cells were grown to exponential phase , serial dilutions spread on plates and CFU calculated . Experiments were performed as triplicates of triplicates .
|
DNA synthesis , chromosome segregation and cell division must be coordinated to ensure the stable inheritance of the genetic material during proliferation . In eukaryotes , this is achieved by their temporal separation and the existence of checkpoint mechanisms that delay certain steps until others are completed . In contrast , replication , segregation and cell division are interconnected in bacteria . For instance , studies in slowly growing Escherichia coli cells revealed that sister copies of the replication terminus of its chromosome are tethered together at the division site by the binding of a protein , MatP , which interacts with the cell division machinery , to be orderly segregated by a cell division protein , FtsK , which assembles into oriented DNA pumps at the time of constriction . Here , we show using both genetic and fluorescent video microscopy that it is not the case when E . coli cells undergo multiple rounds of replication .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"dimers",
"(chemical",
"physics)",
"fluorescence",
"imaging",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"cell",
"cycle",
"and",
"cell",
"division",
"pathogens",
"cell",
"processes",
"vibrio",
"microbiology",
"vibrio",
"cholerae",
"dna",
"translocations",
"bacteria",
"bacterial",
"pathogens",
"homologous",
"recombination",
"research",
"and",
"analysis",
"methods",
"imaging",
"techniques",
"chromosome",
"biology",
"chromosomal",
"aberrations",
"medical",
"microbiology",
"microbial",
"pathogens",
"chemistry",
"genetic",
"loci",
"physics",
"biochemistry",
"cell",
"biology",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"dna",
"recombination",
"chemical",
"physics",
"organisms",
"chromosomes"
] |
2017
|
Fast growth conditions uncouple the final stages of chromosome segregation and cell division in Escherichia coli
|
During active behaviours like running , swimming , whisking or sniffing , motor actions shape sensory input and sensory percepts guide future motor commands . Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and , it has been argued , for perceptual processes . This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood . Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and , consequently , change endogenous neural fluctuations and responses to sensory input . We support this theory with modeling and data analysis in two vertebrate systems . First , in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex . We argue this suppression provides an appealing account of a brain state transition ( a marked change in global brain activity ) coincident with the onset of whisking in rodents . Moreover , this mechanism suggests a novel signal detection mechanism that selectively accentuates active , rather than passive , whisker touch signals . This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input . We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator . We show , as predicted by this theory , that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour . More generally we argue that our results demonstrate the dependence of neural fluctuations , across the brain , on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone .
Neural response are strongly sensitive to behavioural state . The onset of movement such as running and whisking is coincident with prominent modulations in neural activity in sensory areas [1–3] . The rodent whisker system has become a key model system within which to investigate these changes [4–6] . The onset of active whisking in a previously quiet but attentive rodent is correlated with a marked reduction in endogenous synchronous neural activity of neurons in sensory areas; quantified as a reduction in low frequency fluctuations and a decrease in correlations between the membrane potentials of neurons in the barrel cortex [4] . Furthermore , membrane potential responses to experimentally induced perturbations of the whisker are also reduced by the presence of whisking [6] . These changes suggest that movement reduces neural gain [7 , 8] in the barrel cortex suppressing neural fluctuations and sensory response . Several internal pathways have been implicated in this gain regulation including various neuromodulatory pathways [9 , 10] , intracortical feedback modulation by motor areas [11] or they could be directly triggered by changes in sensory input [12 , 13] via thalamo-cortical projections [14] . Despite this gain reduction , robust responses to sensory input occur during active contact events when the whisker collides with an object placed in the whisk field [5 , 6] . Thus , a whisking-induced gain reduction cannot by itself account for the difference in sensory responses to whisker perturbations and active contact events without appeal to additional mechanisms [15] . The reafference principle ( RP ) [16] also does not straightforwardly explain these differences . The RP explains the amplitude of sensory response by a mismatch between the actual sensory input and its prediction , where the prediction is based on an efference copy ( an internal copy of motor command ) . But the RP does not explain why sensory responses to whisker perturbations , which are always unpredicted , are suppressed during movement . Active behaviours are defined by closed-loop feedback interactions between brain/body/environment which are central to motor control and , it has been argued , pivotal to account of perceptual processes [17–19] . During active whisking reafferent sensory input ( sensory input resulting from one's own actions ) conveys information about proprioceptive sensory feedback of whisking and which informs the subsequent motor control of the vibrissae [20 , 21] . Repeated cycles of reafferent sensory input followed by motor output constitute a closed-loop feedback interaction between cells in the barrel cortex and the vibrissae [22] . In this work , we show that in this system closed-loop feedback mediated by whisking vibrissae can: 1 . Suppress synchronous endogenous neural fluctuations and passive sensory responses , 2 . Account for large response to active touch events because of a transient interruption of this feedback . The results provide a nuanced view of predictive coding where neurons represent predictions errors about consequences of motor actions rather than the difference between the predicted and actual sensory input . More generally these results strongly support the centrality of closed-loop interaction in perceptual apparatus [17] by suggesting a specific role they play in event detection . To support a key prediction of this theory we examine how closed-loop interactions in a motor control behaviour impact on neuronal fluctuations . Specifically , we re-analysed data from a second system , a larval zebrafish behaving in a virtual reality where fictive water flow is simulated by a grating ( striped image ) drifting across the fish retina [23] . In this set up zebrafish larvae are immobilised with a neuromuscular blocker . The fish's attempted movements relative to the grating are monitored through motor neuron activity and translated into appropriate modulation of the velocity of the grating [23] . With data from this setup we show that the presence of closed-loop interactions between neurons and fictive swim speed causes the suppression of synchronous neural fluctuations across the fish brain in a manner analogous with the rodent whisker system . Further we show that the amount of this suppression for each neuron is correlated with the strength of its involvement in the optomotor signaling . Together , these results suggest that understanding changes in neural activity across the brain caused by the onset of movement requires the study of closed-loop brain/body/environment interactions beyond open-loop sensory paradigms . Thus we strongly support the argument that a full understanding of phenomenology of neural circuits during active behaviors requires moving away from the idealisation of the brain as an input/output information processor toward its role as a dynamic control system regulating behaviour [19] .
In moving animals , the brain receives sensory input that originates in the external environment , or exafferent sensory input ( Fig 1A , blue arc ) . In addition , efferent motor commands ( Fig 1A , green arc ) drive the body and environment and induce reafferent ( self-generated ) sensory input ( Fig 1A , red arc ) [16 , 24] . To develop an intuition of how closed-loop feedback , mediated by reafferent input , could impact on neural activity we introduce two model conditions . First , we assume that when an animal is not moving the brain receives only exafferent input , we describe this as an open-loop condition ( Fig 1B , top ) . Second , when the animal begins to move the brain interacts with the environment coupling motor action and reafferent sensory input , we refer to this as a closed-loop condition ( Fig 1C top ) . Note: it is likely that some reafferent input is always present but our focus here is on the effect that the onset of a previously absent reafferent sensory pathway could have on neural activity . We examine these two conditions in a simple idealized model , see [17] for a similar idealisation , where brain variable B ( which describes collective neural activity , e . g . , membrane potential activity ) receive input from , or interacts with , the body and environment . In the open-loop condition the collective neural activity , Bo ( t ) , is assumed to be described in term of a first-order linear differential equation , dBo ( t ) dt=−Bo ( t ) τ+I ( t ) +ξo ( t ) , ( 1 ) where ξo is white noise of instantaneous variance σ2 generated inside the brain , t is time , τ is the time constant of the system and I ( t ) is exafferent input . Essentially , in the absence of input , we represent collective neural activity as a simple leaky integrator system with leak timescale τ driven by endogenous noise ( see Fig 1B , bottom , for traces ) . Of interest here is the magnitude of fluctuations which can be calculated as the autocorrelation peak ( instantaneous variance ) of variable Bo which is Peako = σ2 τ/2 , and the gain of the response to sensory input ( calculated as the ratio between a static input and an equilibrium response ) , which is simply Gaino = τ . Thus in this simple system both the gain and the fluctuations are determined by the timescale of the endogenous dynamics . However , during the closed-loop condition we write the dynamics of the brain variable , dBc ( t ) dt=−Bc ( t ) τ+wBc ( t ) +I ( t ) +ξc ( t ) , ( 2 ) where we have idealised reafferent input as a simple self-feedback signal with strength w , i . e . , we have assumed this feedback is linear and instantaneous ( we will relax this assumption later ) . In this condition , the continuous cycles of reafferent input constitute a closed-loop feedback signal to the brain . The presence of this feedback changes the effective time constant to τeff = τ/ ( 1−wτ ) . The magnitude of the fluctuations is now characterized by autocorrelation peak Peakc = Peako/ ( 1−wτ ) and the effective gain of the system is Gainc = Gaino/ ( 1−wτ ) . In particular , if this feedback is negative ( w < 0 ) , it will suppress both fluctuations and the gain of sensory responses , see Fig 1B and 1C ( bottom panels ) . This very simple model suggests that , in principle , closed-loop feedback mediated by the body/environment could have a direct impact on neural activity . One way to accentuate sensory responses is described in Fig 1D . Here the brain initially has low closed-loop gain ( Gainc = τ/ ( 1−wτ ) ) and thus exhibits suppressed fluctuations . However , if during a sensory event ( Fig 1D , grey bar ) closed-loop feedback is interrupted , e . g . , if whisking is interrupted by contact with an object ( see below ) , then brain will have temporarily high open-loop gain ( Gaino = τ ) . Thus the combination of a large sensory response and suppressed background fluctuations prior to sensory event can accentuate signal-to-noise ratios . In the following , we explain how these three conditions can be realized in the rodent whisker system .
In this study we proposed the idea that negative closed-loop sensory feedback during active behavior reduces network gain , which in turn , suppresses synchronous neural fluctuations and modulates sensory responses . We supported this with modelling and data analysis in the whisker system and in a behaving zebrafish , see summary Fig 7 . The formal component of our theory , i . e . , that closed-loop sensory feedback can modulate a system's gain , is well documented in dynamical systems theory and control theory [32 , 33] . This gain control occurs even though the pathways mediating feedback are purely additive ( c . f . Eqs 1 and 2; i . e . , effectively repeated cycles of feedback accumulate over time and produce a multiplicative effect ) . Thus , a constitutively active closed-loop feedback that mediates action-perception cycles is essential for the form of gain control we propose . This means that discrete and intermittent involvement of reafferent input does not imply gain modulation . For example , the classical reafference principle explains neural responses by a one-time detection of the mismatch between an efference copy ( predicted ) and reafferent ( actual ) input [16] . However , this situation is likely an inaccurate idealization to describe the closed-loop systems studied here . For example , in the zebrafish system , swim bouts typically occur every 700 ms and this interval closely overlapped with the peak of the estimated sensory feedback interaction ( Fig 6B ) . Hence , the neural responses in the fish experiment suggest a more dynamic system , where neural activity evoked by many cycles of action and sensation are continuously and mutually interacting . The idea that closed-loop feedback is central to cognition is not new and has early precedents in behavioral psychology [19] , resonate with a movement in embodied cognitive science [18 , 34 , 35] and has recently been proposed as concrete alternative to input/output conception of perceptual processing [17 , 36] . Our work shares the view of these proposals and provides a specific example where brain function is contingent on closed-loop interactions between brain/body/environment . Furthermore , we provided a mathematical model showing why neural dynamics underlying cognitive states cannot be recapitulated even if the sensory input during active behavior is identically repeated , i . e . , a replay condition [37] . The presence of continuous negative closed-loop sensory feedback during active behavior is fundamental for our theory . In our rodent study we assumed negative closed-loop sensory feedback was mediated directly by a cortical-whisker circuit . However , our theory is agnostic to the detail of the neural implementation and several other schemes are possible ( see S2 Appendix ) . This assumption is consistent with the idea that the barrel cortex comprises a nested set of servo control loops that regulate various aspects of whisker dynamics [22] . At the level of the whole vibrissa system multiple parallel and nested feedback loops both positive and negative most likely exist [22] . In zebrafish , the presence of negative feedback during swimming behavior is a priori necessary for optic-flow stabilization behavior because the fish must act in opposition to perceived optic flow in order to minimize horizontal displacement [38 , 39] . Interestingly , neurons that received strong negative feedback and were substantially stabilized were located in the cerebellum ( Fig 6D ) . This is consistent with the theoretical viewpoint that the cerebellum is strongly involved in the action-perception cycle [40–42] . We suggest that closed-loop sensory feedback plays a major role in brain state control . However , importantly , we do not propose this mechanism is mutually exclusive with other mechanisms , such as thalamo-cortical input [25] or neuromodulation [10 , 43 , 44] because brain state transitions also occur in the absence of sensory feedback e . g . , the onset of running that does not change the visual input [3 , 45] , during sleep [46 , 47] , or by dissection of the sensory nerve [5 , 25] . Mechanisms underlying brain state transitions are likely to be redundant and occur even in the absence of mechanisms , such as thalamo-cortical input [25] or corollary discharge [26] , albeit involving further delay ( see S1 ) . Such functional redundancy may help to maintain the stability of brain state [44 , 48 , 49] . Furthermore , the relative importance of internal and external mechanisms might adaptively change in an experience-dependent manner [50] . In the whisking model , we proposed that the regulation of cortical gain by closed-loop sensory feedback could explain enhanced active touch . Specifically , negative sensory feedback during whisking reproduces suppressed fluctuations and reduces responses to passive whisker stimulation ( see Figs 2 and 3 ) . Moreover , robust neural response to active touch events could be explained by the interruption of this feedback when the whisker is driven into an external object . These interruptions transiently release the cortex from a low gain state and enhancing sensory responses to salient sensory stimuli . This mechanism for active touch contrasts with the account of sensory processing suggested by the reafference principle [16] , which postulates that motor efference is discounted from sensory input , allowing animals to sense exafferent signals ( externally caused sensory input ) without being confounded by the consequences of their own motor actions . In contrast , our theory suggests that the sensory system is insensitive to pure exafference during active sensing [4]; see Fig 3 , but sensitive to the interruption of reafference which may allow animals to focus attention on the consequences of their own motor actions . This idea is supportive of other work that has cast doubt on the role of efference copy during active sensing [51] . This mechanism is also distinct from the most common form of predictive coding [52] , where neural activity represents the error between the actual and the brain’s prediction of sensory input . Instead our suggestion could be viewed as a more specific form of predictive coding where neurons represent predictions errors about consequences of motor actions , in this sense it is closer to the idea of active inference [53 , 54] . While it is straightforward to generalize this sensory mechanism to other tactile systems , its implication for other modalities is less clear . However , in theory , closed-loop sensory feedback could be interrupted anywhere along the action-perception cycle , thus dynamically regulating neural gain . The timely interruption of this feedback , possibly related to transient freezing of behavior , could serve as a general mechanism for temporarily accentuating neural responses against a background of reduced noise . For example , closed-loop sensory feedback could be gated by the frequency of miniature eye movements [55] a hypothesis that complements a previous proposal suggesting such movements are under active closed-loop control [56] . Furthermore , cerebellum neurons , which are strongly involved in the sensory-motor cycle , could be suppressed in anticipation of salient sensory events by a relevant brain area , such as the reticular formation [31 , 57] . The importance of using naturalistic sensory stimuli to study and manipulate brain state dynamics is widely demonstrated [58] . However , an important prediction of our theory ( Fig 4 ) , supported by our experimental findings is that brain dynamics during active sensing cannot be fully recapitulated or re-encoded , even if the same sensory input is precisely recorded and replayed back into a passive brain . These results provide evidence that brain state during active behaviors can only be accurately understood by a quantitative account of ongoing brain-environment interactions [18] .
To investigate the ‘in principle’ feedback between barrel cortex and whiskers we model a simple cortical circuit that interacts with a single whisker , see Fig 2A . Our cortical circuit comprises of N excitatory and N inhibitory neurons ( i = 1…N are excitatory and i = N+1 , … , 2N are inhibitory , N = 100 ) modeled as a linear dynamical system by , x . i=−xi+∑j=12Nwijxj−ai−wxθθp+ξi+I , which is numerically simulated by a Euler forward integration method with time-bin dt = 0 . 5 ms . Hereafter , all time derivatives are taken to represent single-step differences divided by dt ( e . g . x . ( t ) =[x ( t+dt ) −x ( t ) ]/dt ) , but we omit the ms time unit . wij is the synaptic strength from neuron j to i , ai is an adaptation current described below , θp is the whisker protraction angle interacting with neurons with weight wxθ = 0 . 002 , I is exafferent input that takes I = 0 . 035 upon whisker stimulation and otherwise zero , and ξi is independent white noise of unit variance added to each neuron . We interpret xi as both the firing rate and membrane potential , assuming a roughly linear relationship between the two . Entries in the connectivity matrix are assigned as wij = bijJ + b′ijg for excitatory synapses ( j = 1… , N ) and wij = −b″ijg for inhibitory synapses ( j = N + 1 , … , 2N ) , where bij , b′ij , b″ij are all random binary values that take b0 > 0 with probability p = 0 . 1 and 0 with probability 1 − p , respectively . The weights are scaled by J=1pN and g=g0√2Np ( 1−p ) , so that dynamics are insensitive to the parameter values of p and N . Note that the eigenvalue spectrum of the connectivity matrix wij is centered around b0 and spread with the radius b0g0 in the limit of large N . Hence , the network is excitation dominated . The variability of weight values across neurons is controlled by the magnitude b0g0 of the excitatory-inhibitory-balanced component and this variability is controlled by the parameter g0 = 0 . 05 , which reproduces highly synchronized up/down-like fluctuations during the quiet state . To promote significant network fluctuations observed in the barrel cortex we scale of the connectivity matrix b0 such that the lead eigenvalue of this matrix is close to unity ( ≈ 0 . 975 and the dynamics are close to instability . We include an adaptation current that gives these fluctuations a low frequency ( ca . 1 Hz ) component modelling up/down-like oscillations [59–61] in the absence of neuron/whisker interactions . The adaptation current is integrated as ai˙=−0 . 07ai+0 . 008xi Over time , the adaptation variable slowly builds upon neural activity and suppresses neurons , resulting in the ca . 1-Hz oscillation . Consequently , in the absence of interactions with the whisker , implemented by setting wxθ = 0 , this simple network reproduces the power spectrum and cross-correlogram of neurons in the barrel cortex [5 , 6] , see Fig 2B and 2C . We model a simple flexible vibrissa as two hinged sections ( with bending angle θh ) connected at the base ( with protraction angle θp to the body ) of unit length which are constrained by simple torsion springs with spring constant k1 and k2 respectively , see Fig 2A . We assume the whisker is light and frictionless and simulated it by numerically minimising the energy of the system , E=k1 ( θp−θeq ) 2+k22θh2 , where θeq equilibrium value of the base spring . Here , only the ratio k1/k2 is important for the results and , without losing generality , we set k1 = 1 . The central hinge spring has an equilibrium value of zero angular displacement and thus tends to align both sections . Whisking is driven both by the cortex and a central a pattern generator ( CPG ) [62] . Specifically , the equilibrium value of the base spring , θeq is set as , θeq˙=−0 . 93θeq+wθxN∑i=1Nxi+u , where the second term on the right-hand side is the sum of activity in the cortical excitatory population and the third term is the external CPG activity . Here u is modeled as simple stochastic oscillator , given by u˙=− . 98u+2πFwhiskv+ξu v˙=− . 98v−2πFwhisku+ξv , where Fwhisk = 10Hz is the frequency of the oscillator and ξu , ξv are independent Gaussian white noise . wθx = 0 . 085 describes the relative strength of the cortex versus the CPG in driving the whisker variable . With this parameter , the whisker is mainly driven by the CPG and is only modulated by cortical activity . In this model , most excitatory neurons respond to whisker retraction and drive whisker protraction . Adding a separate counterpart population that responds to whisker protraction and drives whisker retraction in a similar manner does not change the model’s behavior . We simulate a passive deflection of the whisker by a brief injection of input of I = 0 . 035 to the cortical neurons for c . a 25 ms . The magnitude of this input approximately matches the evoked change over the standard deviation of the membrane potential ( ΔVm/σVm ) in response to magnetic whisker deflection during the whisking condition [5] . Contact events are simulated by simulating a horizontal solid wall is placed above the whisker ( 1 unit length away ) . To simulate contact with the wall we solve the energy equation subject to the length constraint in the vertical direction , sin ( θp ) +sin ( θp−θh ) <1 . Thus , as the whisker collides with the wall it deforms accordingly , see Fig 2A . By adjusting the relative stiffness of each torsion spring ( i . e . k2/k1 ) , we can control the degree to which the protraction angle is affected by contact events , e . g . , if the whisker is very flexible , the protraction angle will change continuously , despite contact of the tip . During contact we also inject an input ( I = 0 . 035 ) to the cortical neurons for the duration of the contact event , but for no longer than 25 ms to simulate contact-detection signal that results from the stereotypical response of pressure sensitive cells in the trigeminal ganglion [27] . The model was run for 200 s in the closed loop , open-loop , and sustained period of active touch to calculate all quantitative measures . To quantify the discriminability of whisker contact events we calculated an information theoretic measure of generalized signal-to-noise-ratio . Specifically , we calculated the Chernoff distance [63–65] between probability distributions , p1 ( x ) and p0 ( x ) , in the presence or absence of a sensory event , respectively . Specifically , this measure Ψ ( p1∥p0 ) ≡−min0<λ<1log∫p1λ ( x ) p01−λ ( x ) dx summarises the detectability of whisker stimulation based on population responses and , unlike a naive calculation of signal-to-noise ratio , is applicable even when p1 ( x ) and p0 ( x ) are very different distributions . For our model , the probability distribution for each condition is well described by a Gaussian distribution , p0/1 ( x ) =|2πC0/1|−1/2exp ( −12 ( x−μ0/1 ) C0/1−1 ( x−μ0/1 ) ) , where C0/1 and μ0/1 are covariance matrix and vector of means , respectively , in the presence ( with subscript 1 ) or absence ( with subscript 0 ) of a sensory event . By substituting this into the expression for Chernoff distance and employing the Gaussian integral identity and expressing the Chernoff distance in terms of C0 , C1 , and μ0 , μ1 , we calculate the covariance and mean between a small number of neurons ( here three ) , randomly selected from the cortical network described above . We calculate covariance’s across ensembles of 500 networks every 10 ms for a period of 1 s , starting at the onset of the sensory event . Minimization with respect to λ is computed numerically . In a transgenic fish expressing the calcium indicator GCaMP2 brain-wide calcium activity was monitored using a two-photon microscope to scan single planes in the brain . We analyzed the calcium signal ( ΔF/F ) at various sample frequencies ( ca . 2−3 Hz ) across 1908 cells in 32 fish , see [23] and electrical recordings of swim power . We analyzed data taken from a 6-min recording of 1−6 prominent calcium sources per fish , putative neurons , across 600 trials . In the first 3 min , the fish performed the closed-loop optomotor behavior . For the subsequent 3 min , each fish was presented with the stimulus received in the closed-loop stimulus which is a repeat of what the animal experienced in the previous 3 min , the replay condition . In the original study , the gain ( i . e . , the multiplicative factor between fictive swim power , and the speed of visual feedback ) was alternated between a high and low gain condition every 30 s . This gain alternating protocol is not relevant to the current study . To reduce this variability in data , we subtracted the mean activity level in each gain setting in our analysis ( from both brain and behavior variables ) . Notably , our main results were qualitatively the same , even without such subtraction of the means . We distinguish variables in the closed loop condition ( Bc and Ec ) and replay condition ( Br and Er ) , see Fig 5B . Specifically , we assume that the closed-loop dynamics in the frequency domain are described by the following equations , Bc ( ω ) =F ( ω ) Ec ( ω ) +RBc ( ω ) ( 4 ) Ec ( ω ) =G ( ω ) Bc ( ω ) where F ( ω ) is an afferent filter describing the interaction from the environment to the brain ( i . e . , the Ec → Bc filter , see Fig 5B dashed blue arrow ) and G ( ω ) is an efferent filter from the brain to the environment ( i . e . , the Bc → Ec filter , see Fig 5B dashed green arrow ) , respectively , and RBc ( ω ) is the residual inputs not accounted of by the filters . Note: we have assumed that the noise on the environment is negligible , this is a reasonable assumption given that visual flow is directly modulated by motor nerve activity . Similarly , we also write the replay dynamics in the frequency domain as , Br ( ω ) =F ( ω ) Ec ( ω ) +RBr ( ω ) ( 5 ) Er ( ω ) =G ( ω ) Br ( ω ) . In the replay condition , neurons are driven by the recorded visual stimulus in the closed-loop condition , which is determined by fish’s motor activity in the closed-loop condition Ec . Note: we have made the assumption that F ( ω ) and G ( ω ) are the same filter in the both conditions ( i . e . , the interactions with the same color in Fig 6A have the same property ) because the sensory and motor circuits in the brain remain the same between the conditions . We use Eq 5 in the replay condition to fit the linear filters F ( ω ) and G ( ω ) because the computation would be more involved in the closed-loop condition than the replay condition . We first calculate linear filter F ( Fig 6A , solid blue arrow ) that minimizes the mean square error between the observed variable Br and the convolution F * Ec over time . Next , we determine G ( t ) by first calculating the residual variability of neural activity in the replay condition that cannot be accounted for by the closed-loop environment , i . e . , RBr ( ω ) =Br ( ω ) −F ( ω ) Ec ( ω ) and subsequently calculating how RBr drives the environment in the replay condition Er , effectively determining the Br → Er interaction ( Fig 6A , solid green arrow ) . The filters were constrained as a superposition of Laguere functions . We use Laguere functions up to the order that best satisfied the Akaike Information Criterion [66] . Almost all filters had an order that was mid-range between 1 and 15 . The Ec → Br → Er interaction ( Fig 6A solid orange arrow ) is then straightforwardly computed by the convolution of both filters , H ( ω ) = F ( ω ) G ( ω ) . Based on the assumption that the filters are the same in the two conditions , we assume that self-feedback in the closed-loop condition ( Fig 6A , dashed orange arrow ) is the same as H ( ω ) . In our investigation , we calculated the ratio of the low frequency power of neural fluctuations between the closed-loop and replay conditions . We then compare this empirical ratio with the theoretically expected ratio based on the estimated filters . To derive this theoretically expected ratio , we write the dynamics of neural activity in the closed- and replay conditions in the frequency domain as , Closed‑loop:Bc ( ω ) =H ( ω ) Bc ( ω ) +RBc ( ω ) = ( 1−H ( ω ) ) −1RBc ( ω ) Replay:Br ( ω ) =H ( ω ) Bc ( ω ) +RBr ( ω ) , where H ( ω ) = F ( ω ) G ( ω ) is the estimated combined filter in the frequency domain and we assume the noise in the closed- and replay conditions have the same power spectrum , i . e . , RBc ( ω ) 2=RBr ( ω ) 2 . The ratio of the power between each condition is then , Bc ( ω ) 2Br ( ω ) 2=1H ( ω ) 2+1−H ( ω ) 2 . We also investigated the effect of accumulative cycles of feedback on brain dynamics by comparing the full closed-loop effect with a control effect that includes only one-time feedback . Namely , we can expand the contribution of each cycle in a geometric series as Bc ( ω ) = ( 1−H ( ω ) ) −1RBc ( ω ) = ( 1+H ( ω ) +H2 ( ω ) +H3 ( ω ) ⋯ ) RBc ( ω ) where the O ( Hn ) term in the above Taylor expansion describes the effect from signal propagation along the feedback loop for n times . By neglecting the contributions with n>1 , we can write the effect of a single cycle of feedback effect as , B1 ( ω ) = ( 1+H ( ω ) ) RBc ( ω ) . This yields an alternative expression for the ratio of the power between each condition that only includes one-time effect of feedback as , B1 ( ω ) 2Br ( ω ) 2=1H ( ω ) 2+1+H ( ω ) −2 . To further investigate how the effective interaction between the brain and the environment depends on the closed-loop feedback , we compare Ec → Br filter in the replay condition and the Ec → Bc filter in the closed-loop condition naively computed by neglecting closed-loop effects . Notably , the naïve Ec → Bc filter in the closed-loop condition generally has an acausal component , because the brain Bc and the environment Ec are mutually interacting ( see below ) . Thus to calculate these filters we use Hermite rather than the Laguere functions to capture the acausal ( t<0 ) side of the filter . To quantify the difference between these filters , using Eq 4 , we write Ec ( ω ) = ( 1−H ( ω ) ) −1 ( G ( ω ) RBc ( ω ) ) , and thus the naïve Ec → Bc filter in the closed-loop condition is Bc ( ω ) Ec* ( ω ) Ec ( ω ) Ec* ( ω ) =F ( ω ) + ( Ec ( ω ) RBc* ( ω ) Ec ( ω ) Ec* ( ω ) ) *=F ( ω ) + ( G ( ω ) 1−H ( ω ) ) *|RBc ( ω ) |2|Ec ( ω ) |2 where * describes complex conjugate . Hence , this filter is different from the corresponding filter F ( ω ) in the replay condition by the second term . To predict the second term without knowing RBc , we again assume |RBc ( ω ) |2≈|RBr ( ω ) |2 , where the latter spectrum is based on the residual RBr computed in the replay condition .
|
Animals actively exploring or interacting with their surroundings must process a cyclical flow of information from the environment through sensory receptors , the central nervous system , the musculoskeletal system and back to the environment . This closed-loop sensorimotor system is essential for an animal's ability to adapt and survive in complex environments . Importantly , closed loop feedback signals also regulate brainwide neural circuits for behavior . Specifically , the activity of coherent populations of neurons inform motor behaviours and in turn are influenced by sensory feedback signals mediated by the environment . We develop a theory that suggests that this feedback can explain the marked changes in large-scale neural dynamics and sensory processing ( together referred to as brain state ) that coincide with the onset of active behaviours . This feedback may contribute to flexible context dependent neural computations in brain systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"methods"
] |
[
"control",
"theory",
"medicine",
"and",
"health",
"sciences",
"fish",
"swimming",
"engineering",
"and",
"technology",
"membrane",
"potential",
"vertebrates",
"electrophysiology",
"social",
"sciences",
"neuroscience",
"animals",
"biological",
"locomotion",
"motor",
"neurons",
"control",
"engineering",
"animal",
"anatomy",
"systems",
"science",
"mathematics",
"zoology",
"computer",
"and",
"information",
"sciences",
"animal",
"cells",
"touch",
"animal",
"physiology",
"cellular",
"neuroscience",
"psychology",
"eukaryota",
"cell",
"biology",
"vibrissae",
"physiology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"sensory",
"perception",
"organisms"
] |
2018
|
A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback
|
Regulatory regions maintain nucleosome-depleted , open chromatin status but simultaneously require the presence of nucleosomes for specific histone modifications . It remains unclear how these can be achieved for proper regulatory function . Here we demonstrate that nucleosomes positioned within accessible chromatin regions near the boundaries provide platforms for histone modifications while preventing the occlusion of regulatory elements . These boundary nucleosomes were particularly enriched for active or poised regulatory marks in human , such as histone acetylations , H3K4 methylations , H3K9me3 , H3K79me2 , and H4K20me1 . Additionally , we found that based on a genome-wide profiling of ∼100 recombinant yeast strains , the location of open chromatin borders tends to vary mostly within 150 bp upon genetic perturbation whereas this positional variation increases in proportion to the sequence preferences of the underlying DNA for nucleosome formation . More than 40% of the local boundary shifts were associated with genetic variation in cis- or trans-acting factors . A sizeable fraction of the identified genetic factors was also associated with nearby gene expression , which was correlated with the distance between the transcription start site ( tss ) and the boundary that faces the tss . Taken together , the variation in the width of accessible chromatin regions may arise in conjunction with the modulation of the boundary nucleosomes by post-translational modifications or by chromatin regulators and in association with the activity of nearby gene transcription .
Open chromatin provides access to a wide spectrum of DNA binding proteins for genetic regulation processes such as transcription , repair , recombination , and replication . In this regard , open chromatin profiling has been widely used to identify the location of regulatory regions , including promoters , enhancers , insulators , silencers , replication origins , and recombination hotspots [1]–[6] . Regulatory DNA elements are made accessible upon histone depletion . Thus , nucleosome remodelling and modification should be intimately coupled with open chromatin formation and regulation . While chromatin opening is required at regulatory regions , promoters and enhancers carry specific histone modifications that are associated with regulatory activity and particular functionality [7] , [8] . For example , H3K4me3 can mark active promoters along with H3/H4 acetylations or mark poised promoters in concert with H3K27me3 [9]–[11] while the combinations of H3K27ac , H3K4me1 , and H3K9me3 can differentially mark active and inactive/poised enhancers [12]–[15] . Based on such knowledge , the identification of different regulatory states , including active promoters , poised promoters , weak promoters , strong enhancers , and weak enhancers , was made possible through genome-wide analyses of the distribution of those histone modifications [16] . To understand the mechanisms by which various histone modifications specifically mark regulatory regions that should be in nucleosome-free states , we set out for integrative analyses of recent data generated as part of the ENCODE project , including chromatin accessibility , histone modifications , histone variant H2A . Z , in vivo nucleosome positioning , and transcription factor ( TF ) binding in the GM12878 lymphoblastoid cell line . Chromatin accessibility was measured based on next-generation sequencing of DNA isolated by two different methods , namely the DNase I hypersensitivity assay [17] , [18] and formaldehyde-assisted isolation of regulatory elements ( FAIRE ) technique [19] . Chromatin immunoprecipitation sequencing ( ChIP-seq ) was used to obtain the profile of ten different histone modifications , positioning of the histone variant H2A . Z , and binding sites of ∼90 transcription factors . Nucleosome occupancy was measured based on micrococcal nuclease ( MNase ) digestion ( MNase-seq ) . We also used open chromatin ( FAIRE-seq ) data and MNase-seq data for a set of yeast recombinants generated by a cross between laboratory ( BY ) and wild ( RM ) yeast strains [20]–[22] . To understand the contribution of DNA sequences to chromatin structure , we also employed data for the positioning of the nucleosomes that were reconstituted in vitro purely based on naked yeast and human DNA [23] , [24] .
By using deep sequencing technology , we previously identified 4 , 897 open chromatin loci in yeast [25] based on the FAIRE assay [19] . In this work , we profiled in vivo nucleosomes by means of MNase-mediated purification of mononucleosomes ( see Methods ) . Unexpectedly , we discovered the presence of boundary nucleosomes just inside of open chromatin ( black curve in Figure 1A ) , a pattern which also appeared with 46 , 080 open chromatin regions identified in the GM12878 human lymphoblastoid cells by the ENCODE project ( black curve in Figure 1B ) . This evolutionarily conserved feature was commonly found for promoter and non-promoter regulatory regions . In vitro nucleosomes that were reconstituted purely based on naked DNA [23] , [24] also peaked within open chromatin in both yeast and human ( gray shade in Figure 1 ) . In yeast , the corresponding DNA sequences displayed an increase in the C/G dinucleotide frequency ( red dots in Figure S1 ) and a decrease in the A/T dinucleotide frequency ( blue dots in Figure S1 ) , exhibiting nucleosome-favouring features near the boundaries of accessible chromatin . In yeast , >60 . 8% of open chromatin regions had sequence-directed ( in vitro ) nucleosome positioning whereas >25 . 6% had nucleosome positioning in vivo ( Table S1 ) . In human , the fraction of nucleosome-possessing chromatin sites is lower than in yeast but the same tendency ( higher in vitro than in vivo occupancy ) is maintained ( Table S1 ) . Although there was a difference in the peak position between the in vivo and in vitro nucleosomes particularly in human , the relative distance was consistent between promoter and non-promoter regions . Therefore , we propose that nucleosome-encoding sequences are more associated with the boundary in vivo nucleosomes rather than the center of regulatory regions as previously observed [24] , [26] . The in vitro nucleosomes in non-promoter regions appeared to be positioned at the center of open chromatin because the average size of non-promoter regions , as estimated by the location of the inside FAIRE peak ( blue curve in Figure 1 ) , was smaller than that of promoters . Indeed , the in vitro nucleosomes peaked at the center of small-sized ( <500 bp ) open chromatin regions while forming a bimodal peak in longer regions ( >1 kb ) ( Figure S2 ) . On the other hand , the in vivo nucleosomes formed a bimodal peak regardless of the size of the region ( Figure S2 ) . When examined according to TF binding sites ( TFBSs ) in the human cells , two strongly positioned nucleosomes were found 200 bp away on average from empirical TFBSs ( based on the ChIP-seq of ∼90 TFs ) , and periodic nucleosome phasing was observed in the surrounding regions ( see black curve in Figure 2A ) . A less stable positioning of the flanking nucleosomes and less distinct phasing of the surrounding nucleosomes were obtained when sequence-predicted TFBSs ( based on the Transfac database ) were used ( gray curve in Figure 2A ) . Intriguingly , sequence tags from DNase I hypersensitive sites ( DHSs ) were confined within the 400 bp region centered on the TFBS ( black curve in Figure 2B ) . The coincidence between the position of the two flanking nucleosomes ( yellow lines in Figure 2A ) and the edges of the DHS tag cluster ( yellow lines in Figure 2B ) was not observed when DHS tags were aligned by Transfac sequence motifs ( gray curve in Figures 2B ) . This implies that the boundary nucleosome positioning and the nucleosome phasing may be dependent on in vivo TF binding events . We then sought to examine nucleosome organization across defined open chromatin domains . As illustrated in Figure 2C , the nucleosomes positioned within open chromatin near the boundaries may carry specific histone modifications while DNA-binding factors may bind in between the flanking nucleosomes . Maintaining nucleosome signatures at the borders may help to prevent occlusion of regulatory elements by histones . The boundary positioning of nucleosomes was confirmed by the genome-wide average patterns ( black solid lines in Figure 3 ) . Notably , different histone modifications showed different patterns across open chromatin ( coloured lines ) and H2A . Z-containing nucleosomes ( black dotted line ) were observed in between the boundary nucleosomes . TF binding was concentrated in between the two flanking boundary nucleosomes ( Figure S3 ) . Histone marks associated with active gene transcription such as H3K9ac , H3K27ac , H3K4me2 , and H3K4me3 coincided with H2A . Z distribution across open chromatin ( Figure 3A ) . While the acetylation patterns ( red and orange lines ) were well overlapping with H2A . Z positioning , there was a slight dip on the methylation levels ( violet and blue lines ) . By using comprehensive chromatin data in human T cells , encompassing H2A . Z occupancy , histone methylation and acetylation marks , and MNase-digested nucleosomes [9] , [10] , [27] , we calculated relative H2A . Z levels across the genome and compared them with histone modification levels . H2A . Z incorporation positively correlated with most histone acetylations , in particular with H3K9ac and H3K27ac , but not with histone methylations except H3K4me3 and H3K4me2 ( Figure S4 ) . Those active histone marks are expected to decrease nucleosome stability and this may explain the low occupancy of the H2A . Z-enriched central nucleosomes . Nucleosome purification in low salt conditions revealed the enrichment of H2A . Z nucleosomes at the nucleosome-free region of the promoter as defined in high salt conditions [28] . Histone methylations such as H3K4me1 , H3K9me3 , H4K20me1 , and H3K79me2 were absent on the central H2A . Z nucleosomes but present on the flanking nucleosomes ( Figure 3B ) . Enhancer elements marked by H3K4me1 alone are inactive or poised until they turn into active enhancers in the wake of H3K27ac modifications [13] . H3K9me3 is also associated with poised enhancers . High levels of H3K9me3 are found in enhancers that are inactive in one cell type but become active in another under the control of the stimulus-induced demethylase Jmjd2d [15] . H4K20me1 was found to be associated with transcription activation in the context of canonical Wnt signaling [29] and with specific classes of enhancers that are deprived of H2A . Z: certain classes of enhancers are enriched in H2A . Z but not H4K20me1 while others are enriched in H4K20me1 but not H2A . Z [30] . Promoter H3K79me2 was linked to active transcription in flies [31] and in humans [32] but in another study it did not show any preference toward either active or silent genes [9] . A role for H3K79me2 in enhancer regulation remains to be elucidated . Taken together , histone modifications related to inactive or poised enhancers or other regulatory states occur on the nucleosomes at the borders of open chromatin . Unlike the above histone modifications , H3K27me3 and H3K36me3 are not concentrated in specific regions but spreading across multiple nucleosomes [9] . H3K36me3 forms a broad domain of enrichment across the body of genes as a regulator of alternative splicing [33] . While H3K27me3 typically shows a domain-like profile similarly to H3K36me3 , it can also form a peak around the transcription start site of bivalent genes [34] or appear at poised enhancers [14] . Both marks ( red and green line in Figure 3C ) were present on nucleosomes ( black solid line in Figure 3C ) that were distant from open chromatin , as opposed to the other marks that were absent on these nucleosomes ( Figures 3A and 3B ) . A higher level of H3K27me3 ( red line ) was observed on the boundary nucleosomes as compared with H3K36me3 ( green line ) , maybe indicating the association of H3K27me3 with poised promoters or enhancers . To examine the positional changes in the borders of open chromatin according to genetic variation , we identified open chromatin loci in 96 different yeast strains [25] consisting of the parental strains ( BY4716 and RM11_1a ) and the descendants resulted from their crossing [20]–[22] . We aligned all open chromatin sites in the laboratory strain ( BY4716 ) by the 5′ boundary , center , and 3′ boundary , and then mapped the relative locations of nearby open chromatin loci in the other strains , resulting in the cluster of homologous regions falling within a certain distance ( Figure 4A ) . While the central location changes within 25 bp upstream or downstream , the border shifts by ∼75 bp away probably giving rise to changes in the size of the region ( Figure 4B ) . The effect of technical variation or inherent data structure could be ruled out in general ( Figure S5 ) . Importantly , the borders with a higher intrinsic propensity for nucleosome positioning showed a higher degree of deviation , clearly separating those with the in vitro occupancy score [23] <0 and >0 . 5 ( Figure 4C ) . We used the score of 0 . 5 as the threshold for a positioned in vitro nucleosome . To identify genetic determinants of the local boundary shifts , we carried out quantitative trait locus ( QTL ) mapping for the end-to-end distances of the open chromatin boundaries that were identified in BY4746 and were <100 bp away from their homologous sites in all the other strains . At a false discover rate ( FDR ) of 0 . 01 , 39 . 2% of the boundary shifts were significantly associated with at least one genetic marker in trans . About 5 . 4% were associated with cis-acting elements located within 100 kb . In terms of the number of associations , the trans- and cis-associations accounted for 84 . 3% and 15 . 7% , respectively . Genetic markers with >5 trans-linkages included chromatin remodelers and transcription regulators ( Table S2 ) . The largest number of associations was found for IES6 , which encodes a protein that associates with the INO80 chromatin remodelling complex . INO80 is an ATP-dependent nucleosome spacing factor that is involved in nucleosome positioning and mobilization with a role in transcription and DNA repair [35] . Not only general transcription factors such as SRB2 , a subunit of the RNA polymerase II mediator complex , but also several sequence-specific transcription factors were identified ( Table S1 ) . Three of the subunits of the MCM2-7 complex , which is involved in DNA replication , were also associated with multiple regulatory regions ( Table S1 ) . While 42% of boundary shifts were associated with genetic variation , perturbation in cellular environment caused by combinatorial or secondary effects of multiple genetic alterations may underlie other local changes . We then compared the results of the boundary QTL mapping with those of the QTL mapping for chromatin accessibility as previously performed for the same dataset [25] . The fraction of the cis-associations in the boundary QTL mapping ( 15 . 7% ) was two times higher than that in the accessibility QTL mapping , implying that underlying DNA sequences play a significant role in the regulation of open chromatin boundaries . Sixty-six boundary shifts were associated in cis with 226 genetic markers while 853 boundaries were in trans with 431 genetic markers . Interestingly , only for 4 . 5% of the 66 cis-associated boundaries and 5 . 0% of the 853 trans-associated boundaries , the relevant chromatin region was also identified in the accessibility QTL mapping . This supports that the variation in boundary locations does not simply reflect the variation in chromatin accessibility despite a possible mechanistic correlation between peak size and peak width . While different target chromatin regions were identified in the two QTL mappings , there was a considerable overlap of responsible regulatory loci . Among the 431 regulatory loci that were associated in trans with boundary variations , 52 . 4% were also responsible for chromatin accessibility in trans , and 58 . 0% of these dual chromatin QTLs were trans-expression QTLs as well . On the other hand , 15 . 0% of cis-QTLs for boundary variations were cis-QTLs for chromatin accessibility . The overlapping fraction is low because a single marker cannot usually cover multiple different chromatin regions in cis . However , 97 . 1% of these dual chromatin QTLs were cis-expression QTLs . This cross-confirmation suggests that the regulatory loci identified in each QTL mapping may be functional with many of them exerting effects on transcription regulation . To investigate the functional effect of boundary shifts on gene transcription , we examined the pattern of boundary variations in relationship with the transcription pattern of the gene whose expression level is associated with the same genetic marker and whose tss is located within 1 kb from the open chromatin of question . For example , in the locus illustrated in Figure 5A , the expression level of TAT1 ( Figure 5B ) and the boundary location of the upstream open chromatin peak ( Figure 5C ) are both associated with common local genetic markers . In this case , the gene is transcribed from right to left , and the left boundary ( orange box in Figure 5A ) , but not the right boundary , of the chromatin peak was genetically associated . The strains with the RM genotype at this locus tend to have the left boundary farther from that in the BY strain and closer to the tss ( Figure 5C ) and have higher expression levels of the gene ( Figure 5B ) . In fact , the distance of the left boundary to the tss was correlated with the expression level ( Figure 5D ) . We found that in all cases in which a boundary location is associated with a local or distant genetic marker in common with the expression level of a gene located within 1 kb from the chromatin peak , only the boundary that faces the tss , but not the boundary on the other side , has been identified in the QTL mapping . Therefore , the example provided in Figure 5 is a general feature of the relationship between chromatin border regulation and gene expression regulation . This is a novel finding and it is currently unclear by what mechanism the border of accessible chromatin can affect or be affected by the transcription of the gene it faces . Active histone modifications on the boundary nucleosome or an active physical interaction of TFs and RNA polymerase II may result in an extension of chromatin borders towards the tss . Our results reveal an evolutionarily conserved feature of nucleosome positioning within accessible chromatin . The nucleosomes residing at the boundaries of open chromatin seems to play a role in demarcating functional regulatory regions such that DNA binding events take place in between these flanking nucleosomes in the middle of the accessible chromatin area . We also found that the positioning of these demarcating nucleosomes is coupled with in vivo TF binding events and that the sequence preferences of the underlying DNA for nucleosome formation are proportional to genetic variation in the size of the accessible region . Therefore , the variation in the width of accessible chromatin regions caused by the locational changes of the open chromatin borders may arise in concert with the modulation of the boundary nucleosomes by post-translational histone modifications and by chromatin regulators and in association with the activity of nearby gene transcription .
We obtained 46 , 080 genomic regions enriched for DNase I hypersensitivity as identified by F-Seq [36] that were validated by enrichment for FAIRE signals as called using ZINBA ( Zero Inflated Negative Binomial Algorithm ) [37] , from the ENCODE Open Chromatin Synthesis track of the UCSC Genome Browser ( http://genome . ucsc . edu ) for the GM12878 lymphoblastoid cell line . Chromosomal coordinates of the validated DNase I peaks were refined by interrogating the base-pair resolution map of DHS tags obtained from the UCSC Genome Browser ( “DNase I Digital Genomic Footprinting” track ) . Specifically , the average number of the DHS tags mapped outside of the peak boundaries across all the validated DNase I peaks was obtained and then the end positions of each DNase I peak were adjusted such that the maximum number of the DHS tags mapped outside of the adjusted ends would not exceed the expected ( average ) number obtained . To identify open chromatin in yeast , we obtained the BY-RM cross strains from the original authors [20]–[22] . We profiled 94 yeast segregants by high-throughput sequencing of the FAIRE libraries , resulting in 4 , 897 open chromatin loci [25] . In vivo nucleosome occupancy in the GM12878 lymphoblastoid cells was obtained from the UCSC Genome Browser ( Nucleosome Position by MNase-seq from ENCODE/Stanford/NYU ) . The MNase-seq reads were extended to 147 bp and then mapped across the boundary of open chromatin . We used the NPS package [38] to identify 498 , 270 positioned nucleosomes . In vitro nucleosome positioning was identified in a previous study [24] . A total of 616 , 856 positioned nucleosomes with stringency >0 . 4 were used . For in vivo nucleosome profiling in yeast , the MNase-mediated purification of mononucleosomes was carried out . The mononucleosomal DNA fragments were sequenced by Illumina Genome Analyzer , subjected to 36 cycles of single-read sequencing . We used Genetrack software [39] to identify the location of 50 , 285 mononucleosome [40] . Log-normalized occupancy scores for in vitro nucleosomes in yeast [23] were downloaded from the authors' website . A positive score indicates enrichment of nucleosome tags relative to the genome-wide average . Based on the patterns in Figure 4 , a score of 0 . 5 was used as the threshold of in vitro nucleosome positioning . Histone modification data for the GM12878 lymphoblastoid cell line were downloaded from the ENCODE Histone Modification Tracks . Data for H3K4me1 , H3K4me2 , H3K4me3 , H3K9ac , H3K9m3 , H3K27ac , H3K27me3 , H3K79me2 , and H4K20me1 , and H2A . Z were downloaded . The raw reads were extended to 200 bp and the number of the extended reads mapped to the body and flanking regions of open chromatin was obtained . To handle different sizes of open chromatin , the body regions were divided into the same number of bins with varying lengths . The profiles of transcription factor binding were obtained from the ENCODE Transcription Factor Binding Tracks . All the data available for the GM12878 cell line were generated by either HudsonAlpha Institute for Biotechnology ( HAIB ) or Stansford/Yale/USC/Harvard ( SYDH ) . The peaks of transcription binding were identified by the MACS software ( HAIB ) or the PeakSeq algorithm ( SYDH ) . The number of peaks was obtained for the body and flanking regions of open chromatin in a similar manner as the histone modification plots . The Human/Mouse/Rat Conserved Transcription Factor Binding Sites track of the UCSC Genome Browser provided 3 . 8 million evolutionarily conserved binding sites of 250 transcription factors as inferred based on the Transfac Matrix Database ( v7 . 0 ) . To predict actual binding sites of the transcription factors , we first identified enriched regions for transcription factor binding by using the peak finding functionality of the HOMER package [41] , located the peak summit as overlapping with the maximum number of ChIP-seq tags within the give region , and then discarded the peaks in which <80% of the ChIP-seq tags covered the peak summit . In this manner , we selected the peaks that were likely to contain the focused binding site of a single transcription factor . The summit positions of the filtered peaks were used as the GM12878 TFBSs . For the 4 , 897 open chromatin loci identified in the BY strain , the end-to-end distances to the nearest open chromatin sites in the other strains were obtained . A total of 918 boundaries were less than 100 bp away from the closest homologous site in all the other 95 strains . The nearest end-to-end distances for these 918 boundaries across the 95 strains were used as the quantitative trait . The genotypes of the genetic markers from the original study [21] were used for QTL mapping . As previously suggested [42] , the adjacent markers with no more than two genotypic mismatches across the 96 samples were merged into one average profile , resulting in a total of 1 , 533 markers . To identify potential regulators , we first identified the genes that are located within 10 kb upstream or downstream of the genomic region covered by a genetic marker and then performed the functional annotation of the genes by using the Gene Ontology term ‘DNA binding’ and by using the list of genes known to be involved in transcription and chromatin regulation . For QTL mapping , we measured associations between the genotypes represented as a categorical variable ( 0: RM , 0 . 5: missing , 1: BY ) and the end-to-end distances of the chromatin boundaries identified above . False discovery rates ( FDRs ) were computed based on the permutation test . The matrix of the end-to-end distances was shuffled by resampling the label of the yeast strains , resulting in a total of randomized matrices , P values were determined by comparing the observed association with the expected associations from the permuted data as , where is an interpretation function . FDRs were obtained by adjusting the P values for multiple testing as previously suggested [43] . A total of 1 , 882 marker-trait associations were identified at an FDR of 0 . 01 . The distance of 100 kb between the marker and trait was used to differentiate cis- and trans-associations . Regarding the human T cell chromatin data [9] , [10] , [27] , histone methylation/H2A . Z occupancy data , histone acetylation data , and MNase-digested nucleosome data ( in resting T cells ) were obtained from http://dir . nhlbi . nih . gov/papers/lmi/epigenomes/hgtcell . aspx , http://dir . nhlbi . nih . gov/papers/lmi/epigenomes/hgtcellacetylation . aspx , and http://dir . nhlbi . nih . gov/papers/lmi/epigenomes/hgtcellnucleosomes . aspx , respectively . MNase-seq nucleosomes and H2A . Z-containing nucleosomes were identified by using the NPS package [44] . Histone modification levels were estimated for individual positioned nucleosomes based on overlapping sequence read counts and the relative enrichment of each type of histone modification on H2A . Z nucleosomes was computed . All the data used in this work is summarized in Table S3 . The nucleosome occupancy data in yeast have been made available at the GEO database with accession number GSE34923 .
|
Open chromatin formation and regulation are intimately coupled with nucleosome remodelling and modification . Regulatory regions such as promoters and enhancers maintain nucleosome-free , open chromatin states whilst at the same time the presence of nucleosomes is required for specific histone modifications . In this work , we carried out detailed analyses of our data of open chromatin maps for ∼100 different yeast strains and whole-genome nucleosome occupancy along with the public data of open chromatin and nucleosome positioning in human generated in the ENCODE project . We observed nucleosomes positioned within accessible chromatin regions near their boundaries . These boundary nucleosomes appeared to carry various histone methylations without hampering the binding of DNA regulators and sequence preferences for these nucleosomes were associated with variation in the width of accessible chromatin . The end positions of open chromatin domains , particularly with high intrinsic preferences for nucleosome formation , were more flexible than the middle point , changing mostly within 150 bp upon genetic perturbation . By using quantitative trait loci ( QTL ) mapping , we identified genetic variants that are associated with the variation in the width of open chromatin and examined its relationship with nearby gene expression .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] |
[] |
2013
|
Regulation of the Boundaries of Accessible Chromatin
|
Cruzain , the major cysteine protease of Trypanosoma cruzi , is an essential enzyme for the parasite life cycle and has been validated as a viable target to treat Chagas' disease . As a proof-of-concept , K11777 , a potent inhibitor of cruzain , was found to effectively eliminate T . cruzi infection and is currently a clinical candidate for treatment of Chagas' disease . WRR-483 , an analog of K11777 , was synthesized and evaluated as an inhibitor of cruzain and against T . cruzi proliferation in cell culture . This compound demonstrates good potency against cruzain with sensitivity to pH conditions and high efficacy in the cell culture assay . Furthermore , WRR-483 also eradicates parasite infection in a mouse model of acute Chagas' disease . To determine the atomic-level details of the inhibitor interacting with cruzain , a 1 . 5 Å crystal structure of the protease in complex with WRR-483 was solved . The structure illustrates that WRR-483 binds covalently to the active site cysteine of the protease in a similar manner as other vinyl sulfone-based inhibitors . Details of the critical interactions within the specificity binding pocket are also reported . We demonstrate that WRR-483 is an effective cysteine protease inhibitor with trypanocidal activity in cell culture and animal model with comparable efficacy to K11777 . Crystallographic evidence confirms that the mode of action is by targeting the active site of cruzain . Taken together , these results suggest that WRR-483 has potential to be developed as a treatment for Chagas' disease .
American trypanosomiasis , or Chagas' disease , is the third largest parasitic disease burden in the world , and largest in the Western hemisphere . [1] The disease is endemic in Central and South America , and approximately 16 million people are currently afflicted . Patients with Chagas' disease develop flu-like symptoms during the acute stage , followed by gastrointestinal lesions [2] and cardiopathy [3] in the chronic stage . The etiological agent of Chagas' disease is the protozoan parasite , Trypanosoma cruzi , which is commonly transmitted to the human host through the bite of the blood sucking triatomine beetle , transfusion of infected blood , or mother-to-child transmission . Nifurtimox and benznidazole , the two drugs used for treatment of Chagas' disease , have significant drawbacks , as they are at best moderately effective in the chronic stages of the infection and cause severe side effects . [4] , [5] Hence , the development of novel therapeutics to effectively treat Chagas' disease is essential . The major cysteine protease of T . cruzi , cruzain , is an attractive target for the development of trypanocidal agents . Cruzain is expressed throughout the parasite life cycle and plays important roles in the survival of the organism , including immunoevasion , acquisition of nutrients , and parasite differentiation . [6] In addition , the lack of redundancy of this enzyme makes the parasites vulnerable to cruzain inhibition . In recent years , K11777 ( 1 , Figure 1 ) , a selective cruzain inhibitor , has been demonstrated to eradicate T . cruzi infection in cell culture , mouse , and dog models . [7] , [8] , [9] These studies prove that cysteine protease inhibitors could serve as a viable agent for chemotherapeutic intervention . X-ray crystal structures of cruzain in complex with reversible [10] , [11]and irreversible inhibitors [12] , [13] , [14] , [15] , [16] , [17] have been reported , and the overall folding pattern and structure of the active site is highly homologous to papain . Seven substrate binding sites , four ( S4 , S3 , S2 , and S1 ) on the acyl side and three ( S1′ , S2′ , and S3′ ) on the amino side of the scissile bond , form a cleft between two structural domains of the enzyme . The catalytic triad of Cys25 , His159 , and Asn175 , as well as the highly conserved Trp177 , is contained within this cleft . Like most other papain-like cysteine proteases , the interaction of the S2 site of the enzyme with the complementary P2 residue is the key specificity determining factor . Cruzain is able to accommodate phenylalanine or arginine in the P2 position of the ligand due to the presence of Glu208 ( cruzain numbering ) found at the base of the S2 pocket , which can form a salt bridge with the positively charged arginine side chain . [13] , [18] A variety of cysteine protease inhibitors have been reported in the literature . [19] , [20] , [21] , [22] In one of our group's strategies in designing parasitic cysteine protease inhibitors , we have developed peptidyl vinyl sulfones based on the pioneering work by Hanzlik [23]and Palmer . [24]The vinyl sulfone warhead acts as a Michael acceptor for the active site Cys25 , while the sulfone unit and the peptide framework provide several hydrogen bond acceptors that interact favorably with complementary residues in the active site . [14] In our earlier reports , we investigated the structure-activity relationship of these inhibitors with variations on the vinyl sulfone substituent , the P1 side chain , and the P3 group to generate a series of highly potent cruzain inhibitors . [25] Further studies led to the identification of compounds that effectively disrupted T . cruzi infection in cell culture assays; [26]however , most of these compounds proved to be too weak to be effective drugs in the in vivo mouse model . To date , all of our compounds contain a hydrophobic group at the P2 site . Herein , we report the synthesis of WRR-483 ( 2 ) , the arginine variant of K11777 , its remarkable biological properties , and a crystal structure of WRR-483 bound to cruzain .
All reaction solvents were of reagent grade and used as received . Tetrahydrofuran , dichloromethane , diethyl ether , and toluene were purified by passing through a solvent column composed of activated A-1 alumina . Unless indicated otherwise , all reactions were conducted under an atmosphere of nitrogen using flame-dried or oven-dried ( 170°C ) glassware . Proton nuclear magnetic resonance ( 1H NMR ) spectra and carbon-13 ( 13C ) NMR spectra were recorded on commercially available NMR spectrometers at 400 MHz and 100 MHz , respectively . The proton signal for residual , non-deuterated solvent ( δ 7 . 26 ppm for CHCl3 , δ 2 . 50 ppm for DMSO , and δ 3 . 31 ppm for MeOD ) was used as an internal reference for 1H NMR spectra . For 13C NMR spectra , chemical shifts are reported relative to the δ 77 . 0 ppm resonance of CDCl3 , δ 23 . 0 ppm for DMSO , or the δ 49 . 0 ppm resonance of MeOD . Coupling constants are reported in Hertz ( Hz ) . Mass spectra were recorded at the University of Michigan Mass Spectrometry Laboratory . Analytical thin layer chromatography ( TLC ) was performed on Kieselgel 60 F254 glass plates pre-coated with a 0 . 25 mm thickness of silica gel . The TLC plates were visualized with UV light and/or by staining with either Hannesian solution ( ceric sulfate and ammonium molybdate in aqueous sulfuric acid ) or permanganate solution ( potassium permanganate in aqueous sodium hydroxide ) . Column chromatography was generally performed using Kieselgel 60 ( 230–400 mesh ) silica gel , typically using a 50-100:1 weight ratio of silica gel to crude product . Cruzain[27] , [28] , rhodesain [29] , and tbcatB [30] were recombinantly expressed as described previously . TbcatB assays were performed as described previously . [31] Cruzain ( 2 nM ) or rhodesain ( 3 nM ) was incubated with 0 . 5 to 10 µM inhibitor concentration in 100 mM sodium acetate at pH 5 . 5 , containing 5 mM DTT ( buffer A ) for 5 min at room temperature . Then buffer A containing Z-Phe-Arg-AMC ( Bachem , KM = 1 µM ) was added to enzyme inhibitor to give 20 µM substrate concentration in 200 µL , and the increase in fluorescence ( excitation at 355 nm and emission at 460 nm ) was followed with an automated microtiter plate spectrofluorometer ( Molecular Devices , Flex station ) . Inhibitor stock solutions were prepared at 20 mM in DMSO , and serial dilutions were made in DMSO ( 0 . 7% DMSO in assay ) . Controls were performed using enzyme alone and enzyme with DMSO . IC50 values were determined graphically using inhibitor concentrations in the linear portion of a plot of inhibition versus log[I] ( seven concentrations tested with at least two in the linear range ) . For pH dependence studies , cruzain was tested at 4 nM in 5 µM Z-Phe-Arg-AMC in 0 . 15 M citrate phosphate buffer at pH 5 . 5 and pH 8 . 0 with 5 mM DTT , and 0 . 01% Triton-X 100 . Enzyme was added to wells of a 96-well microtiter plate containing substrate and inhibitor diluted in DMSO ( 0 . 5% final concentration ) , or DMSO control . Final inhibitor concentration ranged from 0 . 01 µM to 10 µM . Experiments were done in triplicate . Assays were run at 25°C in an automated microtiter plate spectrofluorometer , with robotic delivery of enzyme and readings every 1 . 52 seconds throughout the assay , before and after enzyme addition . Inhibitor dilutions which produce simple exponential progress curves over a wide range of kobs , were used to determine kinetic parameters . The value of kobs , the rate constant for loss of enzyme activity , was determined from an equation for pseudo first order dynamics using Prism 4 . 0 ( GraphPad ) . When kobs varied linearly with inhibitor concentration , kass was determined by linear regression analysis . If the variation was hyperbolic , indicating saturation inhibition kinetics , kinact and Ki were determined from an equation describing a two-step irreversible inhibitor mechanism [kobs = kinact [I]o/ ( [I]o+Ki* ( 1+[S]o/KM ) ) ] and non-linear regression analysis Prism 4 . 0 . [32] All values were corrected for substrate concentration . CA-I/72 T . cruzi parasites were isolated from a chronic Chagasic patient , cloned , and maintained as previously described . [33] For growth inhibition studies , J774 macrophages cultured in RPMI-1640 medium with 5% heat inactivated fetal calf serum ( FCS ) were irradiated ( 9000 rad ) to arrest the cell cycle and plated onto 12-well tissue culture plates for 24 h at 37°C . After infection with 105 T . cruzi trypomastigotes per well for 2 hours , monolayers were washed with RPMI medium and treated with the inhibitors at 10 µM in RPMI medium ( triplicate wells per inhibitor ) . Inhibitor stocks were made to 10 mM in DMSO and diluted prior to use . All assays include untreated , K11777-treated , and uninfected macrophage controls . Fresh medium with or without inhibitor was replaced every 48 h and inhibitor efficacy was monitored daily . Survival time was defined as the time before the cell monolayer was destroyed by the infection . Under these culture conditions , T . cruzi completed the intracellular cycle in 6 days in untreated controls . Treatment duration was up to 27 days as such regime results in cure of macrophages treated with 10 µM K11777 ( positive control ) . Macrophages were subsequently cultured in normal medium for up to 40 days to elucidate if the effective inhibitors were trypanocidal ( cure host macrophages ) or trypanostatic ( delay intracellular cycle of the parasite ) . For dose-response studies , bovine embryo skeletal muscle ( BESM ) cells were infected with T . cruzi as previously described with minor modifications . [34] Briefly , 150 µL of RPMI medium containing 1000 BESM cells were seeded per well in a 96-well plate and incubated for 4 h at 37°C to allow cell attachment . Monolayers were then infected with 1000 trypomastigotes/well of CA-I/72 T . cruzi for 2 h at 37°C . Cells were washed once with 200 µL of sterile PBS and medium was replaced with WRR-483 at the following concentrations: 20 , 10 , 7 . 5 , 5 , 2 . 5 , 1 . 25 , 0 . 6 and 0 . 3 µM . Cultures were then incubated for 72 h at 37°C in a humidified atmosphere with 5% CO2 . Cultures were next fixed in 4% fresh paraformaldehyde in PBS , stained with DAPI , and counted under a fluorescence microscope ( 400× ) . Mean numbers of parasites per cell ( ±SE ) were calculated as previously reported ( n = 3–4 per concentration ) . Controls consisted of untreated wells and cultures similarly treated with 10 µM K777 and 0 . 1 µM posaconazole . Cruzain was expressed and purified as described previously . [27] Activated cruzain was incubated overnight with molar excess amounts of inhibitor dissolved in DMSO . This prevented further proteolytic activity and lack of activity was confirmed via fluorometric assay against the substrate Z-Phe-Arg-AMC ( Bachem , KM = 1 µM ) . After passage over a MonoQ column , fractions containing pure inhibited cruzain were concentrated , to a final concentration of 10 mg/mL , with a Viva-Spin ( Viva Science ) column ( MWCO 15 kDa ) . Simultaneous with concentration , buffer exchange to 2 mM bis-tris at pH 5 . 8 was performed . Crystals of maximum size were obtained after approximately 1 week via the hanging drop method , from a precipitating agent of 1 . 26 M ( NH4 ) 2SO4 , 0 . 2 M LiSO4 , at pH 6 . 0 . Crystals were flash-cooled in liquid nitrogen after a 5 second soak in 20% ethylene glycol and loaded into a SAM ( Stanford Auto Mounter ) cassette for crystal screening . [35] All diffraction data were collected at the Stanford Synchrotron Radiation Laboratory ( SSRL ) , beamline 9-1 , using monochromatic radiation of 0 . 98 Å , after selecting an optimal crystal from screening performed with the robotic SAM system . An ADSC Quantum 315 3x3 CCD array detector was used with low temperature conditions of 103 K at the crystal position . Data processing was completed with MOSFLM [36] and SCALA . The structure was solved via molecular replacement using the MOLREP program of the CCP4 suite [37] with a model derived from cruzain bound to a different vinyl-sulfone containing inhibitor ( PDB ID 1F2A ) , with inhibitor and water molecules removed from the search model . The topmost solution contained two unique monomers related by an NCS two-fold axis of symmetry . The solution was 82 . 2 σ above noise level , with an Rfactor of 0 . 493 . Iterative rounds of manual model building and refinement were completed with COOT and Refmac5 with isotropic temperature factors . The inhibitor molecule was manually placed and fit to electron density using COOT . Clear and representative density for the entirety of both inhibitor molecules in the asymmetric unit was observed at better than 1 . 5 σ above the noise level . Water molecules were placed with COOT and manually assessed . Molecules of the cryoprotectant ethylene glycol and the crystallization precipitant ammonium sulfate were also discernable in final electron density maps of this structure and were placed using COOT and refined with Refmac5 . All statistics for data collection , structure solution and refinement are given in Table 1 . The coordinates and observed structure factor amplitudes for the refined structure have been deposited in the Protein Data Bank under accession code 3LXS .
The synthesis of WRR-483 is summarized in Figure 2 . Esterification of commercially available Nα-Fmoc-Nω- ( 2 , 2 , 4 , 6 , 7-pentamethyldihydrobenzofuran-5-sulfonyl ) -L-arginine ( 3 ) with benzyl alcohol , followed by Fmoc removal gave amine 4 . Amine 4 was converted to an isocyanate by treatment with triphosgene;[38] subsequent addition of N-methylpiperazine afforded urea 5 . Deprotection of the carboxylic acid by hydrogenolysis gave the carboxylic acid 6 . The tert-butyl carbonate blocking group of vinyl sulfone 7[24] was removed and the resulting amine was coupled with acid 6 to give vinyl sulfone 8 in 84% overall yield . Finally , the 2 , 2 , 4 , 6 , 7-pentamethyldihydrobenzofuran-5-sulfonyl ( Pbf ) group was removed by treatment of 8 with trifluoroacetic acid in dichloromethane to provide WRR-483 ( 2 ) . WRR-483 , a K11777 analog , which replaces phenylalanine with arginine at the P2 position , was previously reported as an effective inhibitor of the Entamoeba histolytica cysteine protease 1 ( EhCP1 ) . [39] EhCP1 displays a high preference for arginine at P2 that is unusual among the papain-like protease family . In addition , this inhibitor was also demonstrated to successfully reduce amebic invasion in the human colonic xenograft model by 95% . [39] Cruzain is a dual-specific protease that binds to substrates containing phenylalanine or arginine in the P2 site , with a preference for phenylalanine over arginine . [13] , [18] Hence , we anticipated that WRR-483 would inhibit cruzain , but with lesser efficacy compared to K11777 . The inhibitors were also assayed against rhodesain , a closely related enzyme in the protozoan parasite , T . brucei . Rhodesain lacks the critical glutamate residue at the S2 site for arginine binding , and was reported to be inactive against substrates with arginine at P2 . [29] Therefore , selectivity for cruzain over rhodesain was anticipated . In the study of irreversible inhibitors , IC50 values are highly dependent on assay methods and enzyme concentration , hence the second-order rates of inhibition were determined . As summarized in Table 2 , WRR-483 was a modest inhibitor of cruzain , with a kobs/[I] value of 4 , 800 s−1M−1 In comparison , K11777 was more effective than WRR-483 by over 20-fold . As expected , WRR-483 showed no inhibition of rhodesain , even at 10 µM concentration . K11777 and WRR-483 were also assayed against tbcatB , the putative essential protease of T . brucei , [30] but both compounds were weak inhibitors . The low rate of inactivation of cruzain by WRR-483 is most likely because cruzain has ca . 35% of its maximal activity for arginine containing ligands at pH 5 . 5 . [13] , [18] Thus , WRR-483 was re-evaluated in a different buffer condition at a range of pH values . By switching from acetate to a citrate-phosphate buffer , a slight improvement in enzyme activity and inhibitor potency was observed at pH 8 . 0 . The IC50 value of WRR-483 showed an almost 10-fold improvement at pH 8 . 0 than at pH 5 . 5; however , there was only a 4-fold increase in the second-order rate of inactivation . On the other hand , K11777 showed a consistent IC50 value at pH 5 . 5 and 8 . 0 , while kinact/[Ki] decreased from 1 , 030 , 000 to 234 , 000 ( Table 3 ) . WRR-483 was then tested for its potency on arresting the intracellular growth of T . cruzi infection in J774 macrophages . The effectiveness of the inhibitor was determined by the number of days the lifetime of the treated infected cells were prolonged compared to the control ( Table 4 ) . In the absence of the inhibitor , parasite-infected macrophage controls lysed in six days , due to the completion of the parasite intracellular replication cycle . Surprisingly , at 10 µM , WRR-483 was as effective as K11777 in eliminating the parasite from the host cells , as the cultures with the vinyl sulfones stayed intact until the experiment was terminated . Even after inhibitor treatment was ceased on the 27th day , no re-emergence of parasite infection was observed for two weeks thereafter , at which point the experiment ended . Determination of dose response of the activity of WRR-483 against T . cruzi infection in BESM cells demonstrated that WRR-483 had an EC50 of 0 . 2 µM , which was more effective than K11777 and comparable to posaconazole ( EC50 = 4 . 2 and 0 . 2 µM for K11777 and posaconazole , respectively ) . [34] Encouraged by the anti-parasitic activity in the macrophage assay , WRR-483 was further evaluated in vivo . In a murine model of acute Chagas' disease , mice were exposed to 106 trypomastigotes of the virulent CA-I/72 clone ( Table 5 ) . All untreated control mice infected with T . cruzi died within 21–60 days while all mice treated with WRR-438 or K11777 survived the acute infection and appeared healthy and normal . Tissues inspected histologically including heart and skeletal muscle were normal and free of parasites in 3/5 mice treated with WRR-483 and 3/5 mice treated with K11777; 2/5 mice in each group had 1–3 nests of amastigotes visible in skeletal muscle while heart tissue was normal or presented mild inflammatory infiltrate . In contrast , 5/5 untreated controls had T . cruzi amastigotes and intense or moderate inflammation in heart and/or skeletal muscle . Hemocultures were negative for all inhibitor treated mice and positive for untreated controls . To identify any possible off-target activity , and to assess selectivity , WRR-483 and K11777 were screened against a panel of 70 proteases ( Reaction Biology Corp ) . The proteases that WRR-483 and K11777 inhibited with IC50 values less than 10 µM are shown in Table 6 . The inhibitors were active against only papain and a few of the papain-like members of the cathepsin family , particularly cathepsins B , C , and S . However , potency of WRR-483 against papain , cathepsins L and V were moderate , and no inhibition of other cysteine proteases , including calpain-1 , cathepsins H and K , was observed . Overall , the selectivity profile of WRR-483 was comparable to K11777 , but WRR-483 had a lower affinity for all proteases except for cathepsin B . A 1 . 5 Å crystallographic structure of cruzain with WRR-483 was determined . Two unique complexes ( A and C ) of cruzain covalently bound to the inhibitor WRR-483 comprise the crystallographic unit ( Figure 3 ) . The two cruzain molecules are nearly identical in structure , with a RMS distance of 0 . 17 Å when superimposed upon one another . The two inhibitor molecules are also nearly identical in conformation and placement within the active site of cruzain , with the exception of the conformation of the N-methyl-piperazine group at the P3 position . The packing of the asymmetric unit reveals that the opening of the active site cleft of each cruzain molecule points toward the other , such that the two WRR-483 molecules within the asymmetric unit are directly adjacent to one another . The homophenylalanine moiety of the inhibitor molecules come within a 3 . 6 Å distance of one another at the point of closest proximity . This packing appears to be an artifact of crystal packing in this space group , with these crystallization conditions . The WRR-483 inhibitor molecule is covalently bound to the active site cysteine via Michael addition , at a distance of 1 . 84 Å in complex A and 1 . 83 in complex C ( Figure 3b ) . Other points of contact between cruzain and the inhibitor are fairly consistent between complex A and C . The vinyl sulfone moiety makes several constructive interactions to the protein . One of the vinyl sulfone oxygens forms hydrogen bonds with His162 Nδ1 ( 3 . 39 Å in complex A , 3 . 43 Å in complex C ) , Gln19 Nε2 ( 3 . 09 , 2 . 90 Å ) and with Gln 19 Oε1 ( 2 . 94 Å , complex A ) while the other oxygen makes a 2 . 98 Å hydrogen bond to an ethylene glycol molecule in complex A . This nexus of interactions is consistent with what was observed in several other X-ray crystal structures of cruzain with bound vinyl sulfone inhibitors . [14] , [16] When the cruzain-WRR-483 structure is superimposed on several other structures of cruzain bound to vinyl-sulfonyl containing inhibitors ( PDB 1F2A , 1F2B , 1F2C , 2OZ2 ) , the positioning and orientation of the vinyl sulfone moieties are nearly perfectly aligned ( data not shown ) . Hydrogen bonding interactions between cruzain and the peptide backbone of WRR-483 are consistent with the previously reported complexes . These include hydrogen bonding between the peptidyl oxygen of Asp161 and the amide nitrogen of the inhibitor ( 2 . 89 , 2 . 86 Å ) , the peptidyl nitrogen of Gly66 with the amide oxygen of WRR-483 ( 3 . 00 , 3 . 02 Å ) , and the peptidyl oxygen of Gly66 with the urea nitrogen of WRR-483 ( 2 . 85 , 2 . 91 Å ) . Hydrogen bonding interactions are also formed between the urea carbonyl oxygen of the inhibitor and two water molecules in complex A at distances of 2 . 83 Å and 3 . 36 Å . In complex C , there is one such interaction at this point , at a distance of 2 . 83 Å . Like the other vinyl sulfone inhibitors , the homophenylalanine side-chain extends into the solvent without making any favorable contacts with the enzyme . The most interesting of interactions , because they are located within the bottom of the S2 pocket , the site known to impact substrate preference profiles for papain-family cysteine proteases , are with the Arg moiety of WRR-483 . The Nε of the Arg moiety of WRR-483 makes contacts with the Oε2 of Glu208 at distances of 2 . 84 Å and 2 . 81 Å in the two complexes . The Nη of the inhibitor arginine moiety binds to the Oε1 of Glu208 , at distances of 2 . 89 and 2 . 89 Å . The remaining hydrogen bond contacts with the arginine group of the inhibitor are to the amide nitrogen . In complex A , there is a hydrogen bond formed to a water molecule at a distance of 2 . 99 Å .
Kinetic studies indicate that WRR-483 is a modest inhibitor of cruzain . This compound is more potent at higher pH levels , but it still relatively weak when compared to K11777 . Yet , in the in vitro cell assay , this compound demonstrates trypanocidal activity comparable to the lead compound , K11777 , and is unexpectedly effective in curing acute T . cruzi infection in mice . Crystallographic evidence indicates that WRR-483 indeed binds to cruzain in a similar fashion compared to other vinyl sulfone analogs , with the addition of hydrogen bonding interactions between Glu208 in the S2 pocket and the arginine side chain of the inhibitor . The structure of cruzain bound to another irreversible inhibitor containing an arginine in the P2 position has been solved previously . [13] In this structure , two conformations of the arginine side chain of Z-Arg-Ala-FMK have been modeled at partial occupancies , with the one identified as more physiologically relevant at ca . 70% occupancy ( PDB ID 2AIM ) . In the major conformation , the NH1 and NH2 of the arginine moiety form a substrate-directed salt bridge to the glutamate in the base of the S2 pocket . The glutamate ( Glu 205 ) side chain is oriented such that it points into the S2 pocket , towards the guanidine group . This is in contrast to how this residue is often found , swung out towards solvent , in structures that contain a hydrophobic P2 moiety which is not conducive to forming a salt bridge , hydrogen bond or another constructive electrostatic contact . [10] , [12] The overall structure of 2AIM and the currently described structure are globally similar . Superimposition of the two structures results in an RMS distance of 0 . 469 Å . However , the two structures have distinct interactions between the P2 arginine and the S2 glutamate . While the arginine side chains are similarly oriented at the Cβ and Cγ positions , Cδ is positioned differently , as a result of a rotation of ca . 180° about the Cβ-Cγ bond . This results in a subsequently different positioning of the remaining atoms in the moiety . The arginine Nε of WRR-483 sits approximately where NH1 of the Z-Arg-Ala-FMK was positioned , and one terminal nitrogen of WRR-483 is approximately in the same position as the corresponding nitrogen of the FMK inhibitor . The other terminal nitrogen of WRR-483 is directed towards solvent ( Figure 4 ) . The conformation of the S2 glutamate is also different between the two structures . While still largely directed into the S2 pocket , Glu208 in cruzain-WRR-483 is less fully anchored to the arginine and therefore is not as well contained . There are several interpretations for the rather unexpected anti-parasitic properties of WRR-483 . It is conceivable that WRR-483 is inhibiting more than one parasitic protease . Other potential targets include the cathepsin B-like protease[40] and cruzipain-2 , an isoform of cruzain which was reported to be active against substrates containing an arginine group in the P2 position . [41] Protease profiling studies indicate that WRR-483 is only active against a few members of the cathepsin family . Also , we have previously demonstrated that WRR-483 is a potent inhibitor of the cathepsin L-like cysteine protease , EhCP1 , of E . histolytica , and is much more effective against EhCP1 than K11777 . [39] Thus , it is possible that WRR-483 is targeting an as yet unidentified cathepsin L-like cysteine protease in T . cruzi as well . Another possibility is that the WRR-483 inhibits cruzain either located on the cell membrane or released by the parasite , and not in the lysosome . The inhibitor is hydrophilic in nature , primarily due to the guanidine group , and is more active at physiological pH , making it very favorable in the extracellular environment . Parasite cysteine proteases function in a broader pH profile than their host orthologues , which makes them more susceptible to the inhibitor . As a part of the host cell invasion mechanism , cruzain is secreted by T . cruzi trypomastigote to release an invasion factor from the parasite membrane . [42] Selective inhibition of the released extracellular cruzain , in effect , could lead to inhibition of host invasion . This was also observed in the study of invasion by E . histolytica . In this case , WRR-483 was found to be effective in reducing host invasion by inhibiting the cysteine proteasethat is released by the organism . [39] In this study , we have identified WRR-483 , as an effective agent to treat acute Chagas' disease in a murine model . This compound was as effective as the lead compound , K11777 , in eliminating parasite proliferation despite displaying modest potency against cruzain . The crystal structure of WRR-483 complexed to cruzain was solved and established the binding mode of the inhibitor to the enzyme . This structure was highlighted by the formation of a salt bridge between the guanidine moiety of the inhibitor and Glu208 , but in a different conformation when compared to another structure of cruzain bound to a different arginine-containing inhibitor . WRR-483 has recently completed pharmacokinetic and toxicology studies ( SRI international ) and was shown to possess a decent pharmacokinetic profile ( Cl = 27 . 5 mL/min/kg , Vd = 15 . 1 L/kg , t1/2 = 6 . 4 h at 10 mg dose iv ) but low oral bioavailability due to the polar arginine residue ( %F<1% ) . The no observed adverse effect level ( NOAEL ) is higher than 100 mg/kg . These data suggest that WRR-483 may be useful for treating parasitic infections like T . cruzi as an IV agent , or E . histolytica infections of the intestinal tract . Further studies to understand the precise mechanism of trypanocidal action of WRR-483 and design of analogs with improved bioavailability are currently underway .
|
Current drugs for Chagas' disease , caused by Trypanosoma cruzi infection , are limited in efficacy and are severely toxic . Hence the development of novel chemotherapeutic agents targeting T . cruzi infections is an important undertaking . In recent years , there has been considerable interest in cruzain , the major protease in T . cruzi , as a target to treat Chagas' disease . Herein , we present the synthesis of WRR-483 , a small molecule designed as an irreversible cysteine protease inhibitor , and an assessment of its biological activity against cruzain and T . cruzi infection . This compound displays pH-dependent affinity for cruzain and highly effective trypanocidal activity in both cell cuture and a mouse model of acute Chagas' disease . The crystal structure of WRR-483 bound to cruzain elucidates the details of inhibitor binding to the enzyme . Based on these results , this inhibitor is a promising compound for the development of therapeutics for Chagas' disease .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"chemistry/organic",
"chemistry",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"pharmacology/drug",
"development",
"biochemistry/biomacromolecule-ligand",
"interactions",
"infectious",
"diseases/protozoal",
"infections",
"biochemistry/small",
"molecule",
"chemistry",
"biochemistry/drug",
"discovery"
] |
2010
|
In Vitro and In Vivo Studies of the Trypanocidal Properties of WRR-483 against Trypanosoma cruzi
|
The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation . However , little is known of the role of dendrites in neuronal gain control . New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function , including the role of dendrites in gain control . We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain , incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable . To investigate how different topologies of the neuronal dendritic tree affect the neuron’s input-output characteristics we generate branching geometries which replicate morphological features of most common neurons , but keep the number of branches and overall area of dendrites approximately constant . We found a relationship between a neuron’s gain modulability and its dendritic morphology , with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship . The theory was then tested and confirmed on two examples of realistic neurons: 1 ) layer V pyramidal cells—confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons , and 2 ) stellate cells . In addition to providing testable predictions and a novel application of dual-opsins , our model suggests that innervation of all dendritic subdomains is required for full gain modulation , revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves . Finally , our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functions , such as gain modulation .
Neuronal gain modulation occurs when the sensitivity of a neuron to one input is controlled by a second input . Its role in neuronal computation has been the subject of much investigation [1–4] , and its dysfunction has been implicated in a range of disorders from attention deficit disorders , through to schizophrenia , autism and epilepsy [5–8] . Neocortical neurons vary in modulability , with gain modulation having been observed in cortical pyramidal cells from layers 2/3 , 5 and 6 [9 , 10] , whereas input-output relationships in some other cell types , such as entorhinal stellate cells , appear to be much less modulable [11] . Despite their role as the principal excitatory neuronal class within the cortex , it is unknown which properties of pyramidal cells are necessary in order to modulate their gain . Gain modulation is signified by a change in the gradient of the input-output function of a neuron , in comparison to an overall change in excitability , which is instead evident as a lateral shift . There have been several proposed mechanisms for how a neuron alters the relationship between its input and output , including the use of shunting inhibition to shift the input-output curve [12 , 13] , and varying the rate of background synaptic noise , decreasing the ability of the neuron to detect target input signals [14 , 15] . A subsequent theoretical study posits that both mechanisms may be necessary [16] , which is supported by experimental evidence from intracellular in vivo recordings [17] , indicating that these processes are not mutually exclusive and may instead operate in different regimes . Notably , theoretical studies have used point neuron models , while experimental studies have injected current into the soma . However , as in situ the processing of individual synaptic inputs occurs within the dendrites rather than somatically , this raises another possibility: that gain modulation may involve dendritic processing . The modulation of gain is affected by the balance between excitation and inhibition , and as dendrites act to integrate inputs from throughout their arbors , their capacity for mediating between attenuation and saturation is highly dependent upon the local configuration of dendritic segments and synaptic inputs [18–24] . This suggests the possibility that the morphology of the dendritic tree itself is sufficient for managing attenuation and saturation of inputs , thereby facilitating a neuron’s capacity for gain modulation . To date , technical limitations in observing and manipulating activity at multiple locations throughout the dendritic arbor have made experimental studies of the dendritic contribution to neuronal gain control infeasible . While recording from single or a small number of dendritic locations is possible [25] , this technique is not suited to manipulating activity over multiple locations , mimicking the thousands of inputs a pyramidal cell receives in vivo . However , optogenetics may prove to be a better method for manipulating dendritic activity , as light-activated opsins can be expressed throughout the entire membrane of the neuron—including the dendrites . The existence of both excitatory [26] and inhibitory opsins [27 , 28] suggests the possibility of altering the balance of excitatory and inhibitory currents locally in dendrites , to act as a synthetic substitute for the effect of excitatory and inhibitory presynaptic input . This raises the prospect of a viable experimental method with which to investigate the mechanisms of neuronal gain modulation in the whole cell , as opposed to studying somatic effects alone . Here we demonstrate through a computational model that neuronal gain modulation can be determined by cell morphology , by means of a set of dendritic morphological features which mediate between attenuation and shunting to modulate neuronal output . The local interaction of competing excitatory and inhibitory inputs is sensitive to the placement of dendritic sections . This indicates that gain modulation can be achieved by altering the overall balance of excitation and inhibition that a neuron receives , rather than being dependent on the statistical properties of the synaptic input . As experimental validation of our work would require optogenetics , we tested our hypothesis in detailed , biophysical models of opsin-transfected neurons , using experimentally fitted models of channelrhodopsin-2 ( ChR2 ) and halorhodopsin ( NpHR ) , which when activated produced excitatory and inhibitory photocurrents . This study proposes a new perspective on the contribution of dendritic morphology to the characteristics of neurons , relating the shape of their dendritic arbors directly to their functional role within the neural circuit . We show that all dendritic subdomains are required for gain modulation to occur , suggesting that distinct innervation through synaptic targeting by discrete presynaptic populations could be an effective mechanism by which a neuron’s output can be quickly gated between high gain and low gain modes . By incorporating kinetic opsin models within our detailed , compartmental neuron modeling approach , we make predictions which are directly testable by optogenetic experiments . In particular , our model leads to the proposal of a new illumination protocol for a more naturalistic method of neuronal photostimulation—in which rather than simply imposing spikes or shutting down neuronal activity entirely , it is possible to increase or decrease the gain of a neuron’s response to existing inputs .
We begin with a modified ball-and-stick model , comprising of a single soma and approximately 126 dendritic sections with only passive properties . We systematically rearranged the dendrites to vary polarity via the number of primary branches np and the branching patterns via the number of sister branches at each bifurcation nb of the resulting arbor ( see Methods ) such that they were symmetrical around the soma ( Fig 1A ) . The total number of branches is controlled by the number of bifurcation stages ( nℓ , this number includes the creation of primary branches ) . This enabled us to generate 20 distinct dendritic morphologies . As the number of sections remained approximately constant , thus fixing the spatial extent of the dendritic arbor , this allowed us to identify if a neuron’s capacity for gain modulation could be determined by the dendritic arrangement , independently of total dendritic area , and if so , to establish which specific morphological features contribute . Effects of the total dendritic area have been investigated in [29] . The contribution of branching has been investigated previously , and found to attenuate both voltage signals [30] and membrane resistance [20] . To understand the interaction between neuron-wide activation ( as provided by the photocurrent ) and a single point input ( such as current injection or presynaptic input ) , we evaluated first the steady-state response of the abstract models when photocurrents were induced throughout the entire dendritic arbor , before considering separately the current injection in a single distal branch . Excitatory and inhibitory opsins ( ChR2 and NpHR , respectively ) were included throughout the dendritic tree in addition to the soma , and generate excitatory and inhibitory photocurrents when photoactivated . We set the opsin expression ( defined by gphoto in Eq ( 1 ) ) to be inversely proportional to the area of each compartment , and fixed the irradiance to be equal across the entire neuronal surface , thus ensuring the resulting photocurrent induced for each section is constant . Measuring the net photocurrent locally along the length of the dendritic arbor while shifting the ratio between NpHR and ChR2 ( xNpHR ) whilst keeping all other conductances constant , different dendritic morphologies summate the photocurrents such that the voltage measured at the soma is influenced by branching and polarity ( Fig 1C ) , where morphologies with no branching show a small amplitude for the photocurrent . This relationship between net amplitude and branching was consistent across all arbor shapes we tested ( Fig 1B ) , providing the first indication that different neuron types will sum the photocurrents differently . Like [30] , we injected current at a single point on a distal , terminating branch and measured the membrane voltage across the path between the input site and soma , along with sites at sister branches , which indicated the amount of voltage attenuation that occurred without photocurrents included ( Fig 1D , note differing voltage scales ) . For a fixed amount of current injected on a single terminating branch , the perturbation when measured at the soma followed an identical trend as to that observed for the photocurrents in Fig 1B , due to the symmetry of the dendritic configuration , varying however in magnitude . This suggests that the magnitude of depolarization from photoactivation has to be matched to that obtained from the point input; mismatch will result in the neuron’s output being determined by the dominant term . Thus , if there is a fixed point input , this requires the amount of photoillumination to be matched to the dendritic morphology . We additionally measured the depolarization when both photoillumination and current injection were included , and found that it was a linear sum of the responses we observed separately for both types of input , as expected as there were only passive ion channels in the dendrites . Whether the input was dendrites-wide or a single point , these dual methods of driving the neuron illustrate their respective effects: that for both methods , depolarization is largest and most effective for branched structures . For sustained whole cell photoactivation , the induced photocurrent acts to raise or lower the effective resting membrane potential , upon which the depolarization from a single ( or multiple ) distal point can further drive the membrane at the soma to threshold . These results also indicate that the effect of the photoactivation can dominate the neuron’s response if not matched to the relative level of activation induced by current injection at a single point . To contrast and compare these results with the impact of the dendritic tree when the input is located at the soma , we investigated the four characteristic dendritic morphologies from Fig 1A ( see also S1 Fig ) . This situation has been previously extensively investigated , e . g . Eyal et . al . [29] , and was found that the shape and size of dendritic arbors strongly modulate the onset of action potentials by regulating the impedance load attached to the soma . The dendritic tree acts as a current sink in this case , and its impedance affects the soma depolarisation . We then quantified the transient response by driving the neuron with spiking input , mimicking excitatory postsynaptic potential ( EPSP ) events included at set of locations at a terminating branch distal to the soma . By changing the rate of presynaptic events and then measuring the neuron’s firing rate we get the background firing rate when not illuminated , before repeating with irradiance ( Fig 1E ) , we were able to measure the gain of the neuron while varying the E:I balance by changing the ratio of ChR2 to NpHR ( Fig 1F ) . For irr = 0 . 02 mW/mm2 , we found that gain modulation was achieved in a subset of dendritic morphologies , marked by an increase in the gradient of the response as opsin activation moved from being dominated by inhibition to excitation ( Fig 1F ) . To identify whether there was a consistent trend between dendritic configuration and gain modulation , we define a new measure we term the gain modulation index ( M ) , as the relative change in gradient of the response curves ( from Fig 1F ) when dominated by ChR2 and NpHR respectively , i . e . M = ( θChR2 − θNpHR ) /θbalanced for the difference in angles for the two responses ( the slope for θbalanced is approximately tan ( θbalanced ) ≈ 1 ) . An M ≃0 indicates that no gain modulation occurred , whereas increasing M indicates an increasing degree of gain modulation . We found that there was a small region for which modulation was substantial , and correlated to dendritic structures that were multipolar with a small degree of branching ( Fig 1G , point np = 4 , nc = 2; note that a discrete set of measurements is additionally presented as a continuous colourmap for the purpose of better visualisation ) . Following our earlier indication that photoactivation has to be matched to dendritic structure , we measured the modulation for irradiance values an order of magnitude smaller and greater . When irr = 0 . 002 mW/mm2 we observed no gain modulation for all dendritic configurations ( not shown ) , as their responses were dominated by the current injection . Increasing the irr = 0 . 2 mW/mm2 expanded the region of dendritic configurations which displayed gain modulation , which was now most prominent for bipolar morphologies ( Fig 1H ) . To obtain better intuition as to how irradiance affected modulation , we charted M for our four example neurons over four magnitudes and observed a clear trend , with preferred irradiance values for which a neuron will display maximal modulation ( Fig 1I ) . For irr = 2 mW/mm2 some configurations ( e . g . ( 4 , 1 , 31 ) ) start entering tetanic stimulation for full illumination with ChR2 , for which the modulation M starts decreasing . Following the predictions made by our abstract models , we investigated whether these principles still hold for detailed neuronal models , using a highly detailed Layer 5 pyramidal cell ( Fig 2A ) , previously published in [31] . Its reconstructed morphology is roughly bipolar with moderate branching , which , from the abstract models we tested , demonstrates a strong capacity for gain modulability . However , the model also contained 9 additional ion channel types heterogeneously distributed throughout the soma , apical and basal dendrites . These included multiple variants of Ca2+ and Ca2+-gated channels , which introduced non-linearities as well as significantly longer time constants , which may alter the capacity of a neuron to generate spikes and thus indirectly alter its capacity for gain modulability . To reproduce experimental tests , we began by driving our L5PC by injecting current at the soma , in a similar manner to a typical in vitro electrophysiological experiment , and compared the firing rates upon illumination against the background firing rate ( Fig 2B ) . This revealed that IF curves were co-located ( Fig 2C ) , indicating no gain modulation . However , this was consistent with findings from our abstract models where we observed that gain modulation was site specific for the driving input . Consequently , we moved the injection site to a distal location on an apical dendrite . This time , we observed clear changes to the gradient of the IF curve as increasing amounts of current were used to drive the cell while varying the E:I balance ( Fig 2D ) . While this demonstrated that the gain of this pyramidal neuron may be modulated in an in vitro scenario , neurons in situ are instead driven by thousands of excitatory and inhibitory synaptic inputs located throughout their dendritic arbor . Thus we repeated our simulation , but changed the input to mimic PSPs , by identifying 384 sites for excitatory inputs , and 96 sites for inhibitory inputs , throughout the apical and basal dendrites ( Fig 2E ) -two bottom panels . We observed that gain modulation was still clearly evident ( Fig 2F ) , although the firing rates saturated for input firing rates greater than 20 Hz . To examine the effect of dendritic morphology , we also investigated gain modulation in stellate cells , which are also present within cortical circuits , but whose morphology is very different from pyramidal cells . We used a Layer II hippocampal stellate cell model previously published by [32] , based on reconstructions from [33] . Morphologically , it is multipolar with a small degree of branching ( Fig 3A ) , which places it near to the abstract models for which we observed little to no gain modulation . Unlike L5PCs , the response to an in vitro input of injecting current at a dendritic location ( Fig 3B ) revealed that stellate cells do perform divisive gain modulation ( Fig 3C ) . In the case of current injection at the soma the same effect was observed as for the L5PC cell shown in Fig 2C: an approximately co-linear response ( S2 Fig ) . We then drove the cell by supplying synaptic inputs throughout the dendritic tree to mimic in vivo conditions ( Fig 3D ) , and observed no gain modulation but rather a linear shift as xNpHR was varied ( Fig 3E ) . This suggests that while stellate cells presumably play an important role within the neural circuit , the gain of their input-output functions is unlikely to be modulated in vivo , but are instead likely to be subject to shifts in overall excitability through changes in the amount of excitation or inhibition . From our results , it is clear that for some neurons , such as pyramidal cells , it is possible to retune their output by applying whole-field photoactivation . However , by measuring the output firing rate , we ignore spike train structural characteristics such as the timing of the spikes . To examine how spike timing was affected by optogenetically altering the balance of excitation to inhibition , we considered a L5PC’s spike train in response to frozen noise input for an in vivo-like scenario and define a period during which we wish to increase or decrease the firing rate while the driving input remains fixed ( Fig 4A ) . High-level illumination , which is commonly used experimentally , dramatically reshapes the spike train as the membrane potential is either completely hyperpolarized or the neuron fires with a high-frequency , regular rate ( Fig 4B ) . Thus while this technically reprogrammes the gain of the cell , it does so by artificially rewriting the output spike times . Instead , preserving subthreshold dynamics that arise from the hundreds of presynaptic events should allow the spiketrain characteristics to remain naturalistic , retaining rather than overwriting existing information processing functionality . To test this , we used a significantly lower level of illumination and found that the resulting spiketrains are qualitatively similar to the original response ( Fig 4C ) . To what extent can we perturb the neuron through external photoactivation before spiketrain characteristics are destroyed ? To quantify how the intrinsic spike timing of a neuron is altered by increasing levels of optogenetic activation , we measured the interspike interval ( ISI ) and then calculated the coefficient of variation of the interspike interval sequence ( CVISI ) , and the Fano factor ( FF ) , which describes the variance of the spiketrain normalized by the mean firing rate . We compared the CVISI and FF during the period where the firing rate was altered , as both the strength of illumination and the balance of excitation to inhibition was varied , for different levels of intrinsic activity . We observed that FF ( Fig 4D ) and CVISI ( Fig 4E ) could be maintained in the same range as the unperturbed spiketrain , but that this was dependent on level of illumination , suggesting that the artificial drive has to be matched to the level of the input the neuron already receives . The best matched level was for irr = 0 . 002mW/mm2 , which closely matched the intrinsic CVISI and FFISI values of the neuron . Using this irradiance , we observed a smooth transition from the original response as the optogenetic drive moved from NpHR-dominated to ChR2-dominated ( Fig 4F ) . Our findings for both biophysically detailed and abstract models demonstrate that dendritic morphology greatly contributes to determining a neuron’s capacity for gain modulability . Up until this point , we have only considered scenarios with equal illumination for every dendritic subdomain . Experimentally , however , this is not guaranteed due to unequal expression of opsins throughout the cell membrane as well as uneven light scatter as photons move through tissue . Thus we investigated how gain modulation was affected when dendritic subdomains were unequally photoactivated . Mechanistically , this is relevant for gain modulation as synaptic input to a neuron is not likely to be uniformly distributed throughout the entire dendritic tree , but may instead be organized by presynaptic origin [21 , 34] . Could it be that such organization is present to allow the coordinated activation of dendritic subdomains , which is required for modifying the neuron’s output ? We began by examining partial illumination in abstract models , illuminating only one dendritic subdomain ( pole ) to examine how this altered gain as ChR2:NpHR was varied . We first want to pick the case with the strongest modulation gain during full illumination and we find that by looking at results in Fig 1H ) : that is a bipolar model np = 2 , nb = 6 , now illustrated in Fig 5A ) and the output frequency is shown in Fig 5B ) . By illuminating only one pole instead ( Fig 5A ) , we observed that partial illumination abolished gain modulation ( Fig 5C ) . Measuring the voltage along both the illuminated and non-illuminated branches revealed that during partial illumination , the non-illuminated pole/branch acts as a current sink . In this scenario , only 50% of branches were illuminated: perhaps gain modulation was still possible with an increased but incomplete set of dendritic subdomains ? To test this , we need a multipolar abstract neuron ( rather then the bipolar shown in Fig 5A ) and we chose one with np = 4 ( and nb = 2 , nl = 5 ) , and successively activated additional subdomains ( poles ) until all branches were illuminated . We found that M increased as successive poles were illuminated ( Fig 5D ) . As this principle would hold for all dendritic morphologies , our findings demonstrate that partial illumination incapacitates gain modulation and illustrates that coordinated activation between dendritic branches is necessary for full gain modulation . We then tested partial illumination in our detailed neuron model of a L5PC by targeting the apical dendrites , reflecting a realistic scenario in which light from a superficially located source would be more likely to penetrate the apical rather than basal dendrites ( Fig 6A ) . Similarly to abstract models with two primary branches , we found that partial illumination abolished gain modulation in L5PC when driven by current injection at a site in the apical dendrites ( Fig 6B ) . A more realistic experimental scenario is one in which the likelihood of photons scattering rises with increasing depth , corresponding to a continuous gradient for the effective irradiance that decreases with distance from the surface ( Fig 6C ) . Furthermore , as opsin activation occurs by illumination using a wavelength that is normally chosen optimally for each opsin ( subject to available laser lines ) , and longer wavelengths proportionally penetrate distances , we examined the penetration gradients for ChR2 and NpHR independently ( Fig 6D ) . Previously , we had only considered full-illumination of both ChR2 and NpHR with no graded illumination , which was equivalent to a gradient value of 0 . 0 ( Fig 6D , top-left corner , purple circles ) . Now , by fixing xNpHR and irradiance while activating the L5PC without any additional driving stimulus , we could chart how independently varying each gradient impacted on the firing rate . We observed three trends that are consistent with our earlier observations: ( i ) increasing the xNpHR factor increased the contribution of the NpHR gradient , which decreased the firing rate; ( ii ) a ChR2 gradient = 0 . 0 ( signifying full ChR2 illumination ) and a NpHR gradient of 1 . 0 resulted the largest firing rates; ( iii ) higher irradiance values led to higher firing rates , increasing the range of ChR2 gradients for which the neuron fired . However , we were interested in cases that correspond to the realistic scenario in which longer wavelengths penetrate through tissue further . As the preferential activation wavelengths for ChR2 and NpHR are λ = 475nm and 590nm respectively , this manifests as a bias towards NpHR-dominated regimes . Introducing a small degree of graded illumination reduced the firing rate; the neuron was further silenced by increasing the NpHR gradient from a slight bias ( Fig 6E , blue circles ) to a significant relative difference between ChR2 and NpHR gradients ( Fig 6E , green circles ) . The modulation by graded illumination was ubiquitous , although dependent on the irradiance , xNpHR value and scatter gradients for each wavelength . Experimentally , these effects can be easily overcome by prior calibration to compensate for the effects of scattering , but serve to highlight the sensitivity of a neuron to deviations from unequal innervation .
Previous work [12–14 , 16] examined what input properties are required to alter the output gain of the neuron . Critically , these studies took a somatocentric viewpoint , concentrating on the output of the neuron for a given input , but bypassing the computation performed by the neuron itself . In this work , we addressed the contribution of the dendrites directly , by considering how their configuration may help or hinder modulation of the neuron’s activity and thus explain why some classes of neurons , but not others , contribute to setting the gain in a neural circuit . We established that the configuration of dendrites can affect a neuron’s capacity for gain modulability , with a centrally placed soma and a moderate amount of branching being most receptive to gain modulation . As this shape closely matched pyramidal cells , this reinforces that their role within neural circuits is to act not only as the primary excitatory neuron but also as a key element in the setting of the gain of the circuit . Thus , in addition to the influence of dendrites on firing patterns [24 , 35] and their role in dendritic computation [36 , 37] , our results demonstrate a new aspect to dendrites that directly relates the morphological properties of an individual neuron to its functional role within a network . We explored the relation between a neuron’s dendritic morphology and capacity to alter its firing rate by using excitatory and inhibitory photocurrents locally input to each dendritic section . The use of photocurrents , as well as making the study relate more closely to putative optogenetic validation experiments , was intended to mimic the local excitatory and inhibitory currents induced by the numerous presynaptic inputs located throughout the entire dendritic tree , with the notable difference in that while postsynaptic potentials are transient , the photocurrents we induce were typically close to steady-state . Further input was additionally applied that mimicked in vitro or in vivo input . We made no specific assumptions as to the specific type of stimulus representation of the input , such as visual contrast [38] , orientation [10] or other stimulus traits; our results hold for the general case in which a driving input at discrete set of location is modulated by neuron-wide distributed drive . Using this framework allowed us to identify that dendritic branching and the relative location of the soma were the most important morphological characteristics , as dendritic branching allowed balance between saturation and attenuation while a centrally located soma avoided it acting as a current sink . As previously established in [30] , these effects become crucial when considering the compounded local input that is applied to each dendritic section ( here , the non-driving input i . e . , the photocurrents ) . For L5PC neurons , the stratified output for illumination suggests that if net drive to the dendrite is able to sufficiently cover the entire arbor , then L5PCs will be gain-modulable independent of the location of the driving input , which has been shown to govern the input-output relationship for single inputs [18 , 39] . In this respect , our findings suggest that gain modulation should be achievable regardless of the specific input location . However , there is one critical caveat: that the input must be dendritic . The absolute abolition of gain modulation when the driving input was located at the soma in a pyramidal cell reinforces the role of dendrites in processing input and their contribution to modulating gain . It also highlights the difficulties associated with experimentally unraveling neuronal mechanisms which involve dendritic processing . While recording at the soma gives us an exact measure of the cells output , injecting input directly to the soma bypasses the dendrites , rendering their contribution invisible . Instead , techniques such as dendritic patching or extracellular drive are more suitable for this purpose , despite their respective technical challenge or lack of control for the number and locations of synaptic sites . The future development of holographic methods in combination with optogenetics provides potential solution to both limitations , although it is currently limited by the tradeoff between number of distinct sites that can be targeted and the frequency of their stimulation [40] . We note here that we attempted to use only passive conductances in biophysical models , in order to have better comparison with abstract neurons in interpreting the role of dendritic morphology , since their models include some active conductance ( although in relatively low concentrations ) . However , due to their configuration and complexity we were unable to obtain normal neuronal responses i . e resting potential did not equilibrate in the experimentally observed range , due to different parameters required for the L5PC and stellate cells to operate in the passive regime . Quantifying the effect of partial illumination revealed that gain modulation also requires the participation of all dendritic subdomains . Removing background input from dendritic subdomains resulted in the unactivated arbors becoming a current sink , and reduced the ability of the neuron to modulate gain . Tracing studies have hinted that within pyramidal cells in sensory areas , there is synaptic targetting with feedforward presynaptic input from the thalamus tends to synapse onto basal dendrites , while input from higher cortical areas instead connects within apical dendrites [34 , 41] . This arrangement of separate innervation to distinct dendritic subdomains would very easily allow for the same mechanistic process as we have observed here , whereby both feedforward and feedback connections are required for full gain modulation . Removal of one of these sources , such as the feedback input from higher cortical areas , would quickly act to shunt any background drive to corresponding dendritic subdomain , thus providing a mechanism for rapid switching between full gain modulation and no gain . As we observed that the modulation of gain approximately scales with the fraction of the dendritic subdomains that receive background driving input , the change between full gain and no gain can be most effectively controlled with two dendritic subdomains , as increasing the number of subdomains requires greater coordination between distinct input areas . An important feature of computational models of neuronal information processing is that they be experimentally testable . Traditionally , for biophysically detailed , compartmental models of neurons , this has involved making predictions that can be confirmed by intracellular recording ( whole cell patch clamp or sharp microelectrode ) experiments . However , recent years have seen new optogenetic experimental paradigms come to the forefront of neuroscience , which are likely to form the basis of many experimental designs to test principles of neuronal function . Computational modeling of neuronal function should incorporate simulation of experimental predictions made by the model; whereas in the past this was largely electrophysiological , this now includes both electrophysiological and optogenetic predictions . We envisage that computational optogenetic modeling is likely to assist in bridging the gap between computational and experimental studies in areas ranging from neuroscience [42 , 43] to cardiac electrophysiology [44] . For this reason , in the current study we incorporated kinetic models of opsin into the biophysically detailed neuron models described here . Optogenetic illumination protocols in current use can generally be classified as “hard control” , in which the output of a cell is written directly by using high levels of illumination to induce either spiking or hyperpolarization [45 , 46] . The problem with such approaches is that they effectively reprogram the output of the neuron , disrupting/eliminating the information processing operation that it is performing on its inputs . We suggest that a more refined method of optogenetic modulation would preserve the cell’s ability for its outputs to be affected by its inputs , but altering the gain of this input/output transformation . Our findings demonstrate the feasibility and support the development of such optogenetic control of individual neuronal gain . In this approach , using whole-field , low-level illumination allows for subthreshold dynamics to dominate , and the neuron remains driven by its presynaptic input , with the gain of its input-output function modulated by activation of a mixture of opsins . In the current work , we demonstrated that a combination of channelrhodopsin and halorhodopsin can provide a suitable opsin mix , with effect dependent upon target cell morphology . For the general purpose of optogenetic gain control , step-function opsins ( SFO ) [47] and stabilized step-function opsins ( SSFO ) [48] may be suitable , as they do not required continuous illumination to be active , and have their suitability for loose control has already been demonstrated when driven by inputs located at the soma [47] . SFOs and SSFOs have already been proposed for use in the study of plasticity and homeostatic mechanisms during development . Our results support their suitability for application to gain modulation in vivo but also predict a restriction to their usage that will be dependent on the class of neurons to be targeted . More generally , our findings suggest that smaller , rather than larger , photocurrent amplitudes are desirable for the purpose of modulating gain . Unequal or incomplete optical activation of the entire dendritic arbor also has significant implications for experiments that include optogenetics . We used optogenetics specifically as opsins are expressed on the surface membrane , and therefore can generate photocurrents locally within the dendrites . Experimentally , however , opsins may be non-uniformly distributed throughout the neuronal membrane , while optical point sources incompletely illuminate the entire membrane surface area , which is further compounded by scatter effects as light moves through tissue . We quantified the impact of optical scattering by examining how graded illumination alters the gain modulation curves of a L5PC , for the scenario when the scattering was equal but also for the more realistic scenario where it is unequal . For instance , ChR2 is activated at λ = 475nm , while NpHR is preferentially activated at λ = 590nm , which penetrates further through tissue . From our results , approximately equal attenuation for both wavelengths only slightly decreases the firing rate; as this imbalance increases and shifts towards longer wavelengths , we found a substantial decrease in firing rate due to this physical constraint that biases in favor of NpHR . Additionally , although ChR2 and NpHR have different peak absorption wavelengths , they both are activated over a larger range of wavelengths , and consequently there is low-level activation of NpHR at λ = 475nm , further biasing dual activation towards NpHR-dominated regimes . These effects can be experimentally compensated for by calibrating curves as the ratio of ChR2 to NpHR is varied , but require explicit measurement for individual experiments . The issue of precise co-activation of both excitatory and inhibitory opsins can be addressed by development of better dual opsins , as well as of better techniques for controled 3D illumination . Several options already exist , such as holographic spatially patterned illumination technology [49] and individually addressable LED micro-arrays [50] . More generally , identifying the impact of experimental effects is critical for the improvement and refinement of optogenetics . While optogenetics offers new possibilities for precise spatial and temporal targeting of distinct neural populations , practical hurdles such as optimally designing illumination protocols are more difficult to identify through experimental means . Additionally , as new opsin variants with differing kinetics becoming available , the task of identifying which opsin is best suited to match the intrinsic dynamics of a target neuron class becomes increasingly impractical to test . For these aims , the use of computational models of opsins will become increasingly significant [51] , from the level of channel kinetics , to the level of a single neuron [52 , 53] , and beyond to the level of the network .
Our model of a co-activated opsin utilizes our previously published 6-state models of channelrhodopsin-2 ( ChR2 ) [43 , 52] and halorhodopsin ( NpHR ) [53] . A 6-state model was chosen for both ChR2 and NpHR , which includes two open states , two closed states and two inactivated states that are coupled together with by rate constants . Only the open state contributes to the generation of the photocurrent iphoto for each opsin type , which is calculated per compartment and is additionally proportional to the area A and maximal conductance for each opsin g ¯ photo in combination with two terms related to the irradiance ϕ and the membrane voltage Vm: i photo = A g ¯ photo ψ ( t , ϕ ) f ( V m ) ( 1 ) Critically , these models accurately capture the ion concentration kinetics and so allow accurate modelling of subthreshold dynamics , and can be tuned to provide a faithful reproduction of the temporal courses induced by opsin activation . Throughout this work we refer to the ratio xNpHR as the relative strength of NpHR illumination in reference to a fixed value for ChR2 illumination . Each of the abstract neuron models we created included a soma and approximately 125 dendritic sections that were arranged with varying degrees of branching . Dendritic morphology was described as the number of primary branches np , the number of sister branches nb and the number of branching stages nℓ . The total number of branches ( Ntotal ) is given by: N total = n p · ( ∑ k = 0 n l - 1 n b k ) . ( 2 ) Altogether , we generated 31 different dendritic configurations that , despite their geometrical configuration , had approximately equal surface areas and volumes . Dendritic tapering was excluded to conserve total surface area . For the biophysical properties , the values for the soma were C = 1pF , diameter = 10μm and length L = 10μm , while each dendritic section had parameters diameter = 0 . 4μm , C = 2pF and length L = 50μm . All sections and soma had passive membrane properties ( g_pass = 0 . 00005 , e_pass = -75mV ) while the soma additionally had Hodgkin-Huxley channels , the NEURON built-in hh mechanisms were used , with conductances: gnabar_hh = 0 . 25 , gkbar_hh = 0 . 1 , gl_hh = 0 . 000166 , el_hh = -60mV , ek = -70mV . Each soma and dendritic section had ChR2 and NpHR models inserted , with constant expression throughout the dendrites and soma . Constant driving input for the steady state response was modeled by injecting constant current in the last segment of a single distal segment , and normalizing the distance from total length to soma . Synaptic locations were chosen randomly from all distal sections . Synapses themselves were modeled using NEURON’s ExpSyn model , with input spiketimes drawn from independent Poisson process . As different dendritic morphologies had different electrotonic distances from synapse location to soma , synaptic weights were chosen where possible such that the output firing rate was approximately equal to the input firing rate , enforcing a loose version of synaptic democracy . For some arbor configurations , the length from soma to distal dendrites was greater than the electrotonic distance and a transient response could not be obtained . The dendrite tapering was not included in our simulation for abstract neurons , but it is included for two realistic biophysical neuron models . Inclusion of tapering for abstract neurons creates a problem of keeping approximately the same total dendritic tree area for various morphologies , which was the more relevant factor with respect to our results .
|
New experimental techniques based on optogenetics allow neuronal activity to be manipulated with a high degree of spatial and temporal precision . This opens up new prospects for testing computational models of neuronal function , including questions such as the role of dendrites in neuronal gain control . However , compartmental models in computational neuroscience have not , until now , incorporated the kinetic models of opsins that are required in order to directly match the predictions of a computational model with observed optogenetic experimental results . Here , we introduce an approach for computational optogenetic modeling to test hypotheses , demonstrating it with application to the role of dendrites in neuronal gain control . We find that gain modulability is indicated by dendritic morphology , with pyramidal cell-like shapes optimally receptive to modulation . All dendritic subdomains are required for gain modulation—partial illumination is insufficient . Due to the simulation framework used , these results are directly testable through optogenetic experiments . Computational optogenetic models thus can be used to improve and refine experimental protocols for direct testing of theories of neural function .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"action",
"potentials",
"medicine",
"and",
"health",
"sciences",
"optogenetics",
"membrane",
"potential",
"electrophysiology",
"neuroscience",
"ganglion",
"cells",
"brain",
"mapping",
"bioassays",
"and",
"physiological",
"analysis",
"neuronal",
"dendrites",
"research",
"and",
"analysis",
"methods",
"animal",
"cells",
"biophysics",
"neurophysiological",
"analysis",
"physics",
"cellular",
"neuroscience",
"neuronal",
"morphology",
"cell",
"biology",
"pyramidal",
"cells",
"physiology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"neurophysiology"
] |
2018
|
Neuronal gain modulability is determined by dendritic morphology: A computational optogenetic study
|
In West Africa , Trypanosoma brucei gambiense , causing human African trypanosomiasis ( HAT ) , is associated with a great diversity of infection outcomes . In addition to patients who can be diagnosed in the early hemolymphatic phase ( stage 1 ) or meningoencephalitic phase ( stage 2 ) , a number of individuals can mount long-lasting specific serological responses while the results of microscopic investigations are negative ( SERO TL+ ) . Evidence is now increasing to indicate that these are asymptomatic subjects with low-grade parasitemia . The goal of our study was to investigate the type of immune response occurring in these “trypanotolerant” subjects . Cytokines levels were measured in healthy endemic controls ( n = 40 ) , stage 1 ( n = 10 ) , early stage 2 ( n = 19 ) , and late stage 2 patients ( n = 23 ) and in a cohort of SERO TL+ individuals ( n = 60 ) who were followed up for two years to assess the evolution of their parasitological and serological status . In contrast to HAT patients which T-cell responses appeared to be activated with increased levels of IL2 , IL4 , and IL10 , SERO TL+ exhibited high levels of proinflammatory cytokines ( IL6 , IL8 and TNFα ) and an almost absence of IL12p70 . In SERO TL+ , high levels of IL10 and low levels of TNFα were associated with an increased risk of developing HAT whereas high levels of IL8 predicted that serology would become negative . Further studies using high throughput technologies , hopefully will provide a more detailed view of the critical molecules or pathways underlying the trypanotolerant phenotype .
Human African trypanosomiasis ( HAT ) , or sleeping sickness , caused by Trypanosoma brucei gambiense ( T . b . gambiense ) is classically described as a chronic infection characterized by an early hemolymphatic stage ( stage 1 ) associated with nonspecific symptoms such as intermittent fevers and headaches , followed by a meningoencephalitic stage ( stage 2 ) in which the parasite invades the central nervous system and causes neurological disorders and death if left untreated . Long considered as invariably fatal , observations are increasingly indicating that infection by T . b . gambiense can result in a wide range of clinical outcomes in its human host [1]–[3] . Recently , self-cure processes have been described in HAT patients refusing treatment in Côte d'Ivoire [4] . Furthermore , individuals with ( i ) high responses to the card agglutination test for trypanosomiasis ( CATT ) ; ( ii ) serological positivity to the highly specific T . b . gambiense immune trypanolysis test ( TL ) and ( iii ) negative parasitological results ( SERO TL+ ) have been reported from a number of endemic foci in West Africa [5] , [6] . Noteworthy , follow-up studies showed that only some of these subjects develop HAT ( parasite can be detected by microscopy in body fluids ) while others are able to maintain high and specific serological responses over long periods of time [7] . These observations suggest that SERO TL+ individuals have been in contact with T . b . gambiense and that some of them are able to control infection to levels that cannot be detected by microscopy . This hypothesis is supported by the fact that parasite DNA can be detected by PCR in this category of subjects [7] , [8] and that direct microsatellite typing of trypanosomes from blood samples detected the same genotypes as those found in HAT patients [9] . Overall , these field observations are in line with the idea that trypanotolerance exists in humans , too , as demonstrated in some West African taurine breeds and in inbred mice models displaying differential susceptibility toinfection [10] , [11] . While several studies were designed to investigate the immune response in HAT patients at different stages of disease [12] , [13] , almost nothing is known about the response occurring in SERO TL+ subjects who are apparently able to control infection . In this study , we evaluated the levels of 10 cytokines ( IL12p70 , IL2 , IL4 , IL5 , IL8 , IL1β , IL6 , IL10 , tumor necrosis factor ( TNF ) α , and interferon ( INF ) γ ) in HAT patients , SERO TL+ subjects and endemic controls recruited during medical surveys which were conducted in active HAT foci in Guinea . In addition , SERO TL+ subjects were followed up for at least 2 years in order to analyze the prognostic value of cytokine levels determined at study inclusion on the subsequent evolution of the serological and parasitological status: ( i ) development of HAT; ( ii ) maintenance of high antibody responses , and ( iii ) progressive decrease in antibody responses .
The study was carried out in three active HAT foci ( Dubreka , Boffa , and Forecariah ) located in mangrove areas of coastal Guinea [14] . Most of the population is from the Soussou ethnic group and lives in small villages scattered along mangrove channels . Main occupations are rice cultivation , fishing , wood cutting , and salt extracting , all activities that bring the population into close contact with Glossina palpalis gambiensis which is the only vector of T . b . gambiense in these areas [15] , [16] . Other diseases such as tuberculosis , leprosy or cholera are still present and malaria is highly endemic . All subjects included in this study were identified during medical surveys organized by the National Control Programme ( NCP ) between November 2007 and May 2011 , according to the WHO and NCP policies , as described previously [7] . During the surveys a total of 41 , 311 individuals were screened , blood ( 5 ml ) was collected in heparanized tubes from all individuals who tested positive to the CATT mass screening test , and a twofold plasma dilution series was tested to determine the CATT end titer . All individuals with end titers of 1/8 or greater were submitted to microscopic examination of lymph node aspirates whenever swollen lymph nodes were present; 350 µl of buffy coat was then examined by using the mini-anion exchange centrifugation test which has shown to have a positive threshold of 10 trypanosomes/ml of blood [15] . When trypanosomes were detected , lumbar puncture was performed and the disease stage determined by searching for trypanosomes using the modified simple centrifugation technique [17] of cerebrospinal fluid ( CSF ) and by white blood cell ( WBC ) counts . HAT patients were classified as stage 1 ( 0–5 WBC/µl ) , early stage 2 ( 6–20 WBC/µl; or ≤20 WBC with trypanosomes in CSF ) , or late stage 2 ( >20 WBC/µl ) and treated according to the NCP recommendations . In addition to HAT patients ( n = 108 ) and CATT plasma-positive subjects ( SERO , n = 84 ) , 5 ml of blood was also taken from CATT-negative individuals ( n = 42 ) selected from the same CATT series as the HAT and SERO subjects and subjected to the same tests as described above . For all subjects , aliquots were made with leftover plasma and with CSF for HAT patients when available . Collected samples were then frozen directly in the field in a liquid nitrogen container and stored at −80°C at CIRDES until use . For each individual , an aliquot of plasma was used to perform the immune trypanolysis test that detects Litat 1 . 3 and Litat 1 . 5 variable surface antigens specific for T . b . gambiense [6] . The 24 SERO that were negative and the two endemic controls that were positive were excluded at this stage of the study; all HAT patients who were positive and those for whom both plasma and CSF samples were available ( n = 52 ) were included in the study: 10 , 19 , and 23 were classified as stage 1 , early stage 2 , and late stage 2 , respectively . The study sample phenotypic and demographic characteristics are summarized in Table 1 . All SERO TL+ individuals were then followed up at their home for 2 years . When present , serological and parasitological tests were repeated as described above . Out of the 60 SERO TL+ , 40 could be followed up for at least 2 years ( on average 3 visits ) and were included in the analysis of the prognostic value of cytokine levels in the evolution of the serological and parasitological status . Plasma cytokine levels ( IL12p70 , IL2 , IL4 , IL5 , TNFα , INFγ , IL8 , IL1β , IL6 , and IL10 ) were determined for all study subjects with the human Th1/Th2 and human inflammation Cytometry Bead Array ( CBA ) cytokine kits according to the manufacturer's instructions ( BD , Biosciences ) . For CSF samples we used only the human inflammation CBA kit to quantify IL8 , IL1β , IL6 , IL10 , IL12 , and TNFα levels . After acquiring sample data by flow cytometry ( BD FACSCanto ) and the BD FACSDiva software , results were generated in a graphical and tabular format using the Flow Cytometric Analysis Program Array software ( FCAP Array , BD Biosciences ) . Univariate analysis of cytokine levels between groups was performed by using the nonparametric Wilcoxon signed-rank test ( Kruskal-Wallis ) . The association between cytokine levels and the risk to develop HAT in SERO TL+ individuals was also evaluated by stepwise multivariate logistic regression . The covariates included in these analyses were age ( in years ) , gender , and cytokine levels . Cytokine levels were assigned to two classes of equal size using the median cytokine value as the threshold . The most significant covariates ( P<0 . 1 ) were then entered one by one until no significant improvement in the model likelihood ratio was observed . The JMP5 ( SAS Institute ) software was used for univariate analyses and multivariate logistic regressions and the R software was used for the construction of box-plots . Normed principal component analysis ( PCA ) was performed using the ade4 package in the R environment [18] with cytokines data ln ( 1+x ) transformed . The association between each of the study recorded covariates and the individuals x and y coordinates on the first factorial plan was assessed by logistic regression for qualitative covariates ( HAT status , disease stage in HAT patients , gender and disease geographic focus ) and by linear regression for age . This study was performed as part of medical surveys conducted by the NCP according to the national HAT diagnostic procedures and was approved by the Ministry of Health in Guinea . All participants were informed of the objectives of the study in their own language and signed a written informed consent form . For participants under 18 years of age , a written informed consent was obtained from the parents . This study is part of a larger project aiming to improve HAT diagnosis for which approval was obtained from the World Health Organization ( WHO , Research Ethics Review Committee ) and IRD ( Comité Consultatif de Déontologie et d'Ethique ) ethical committees .
Mean cytokines levels measured in the CSF and plasma samples of HAT patients in the different disease stages are shown in Table 2 . In the CSF , TNFα , IL1β and IL12 levels were similar in all stages . Patients in the second stage of disease had significantly higher IL10 ( p = 0 . 0003 ) , IL8 ( p = 0 . 001 ) , and IL6 ( p = 0 . 01 ) levels in CSF than patients in stage 1 or early stage 2 . In contrast , none of the cytokines measured in the plasma displayed a significant association according to the disease stage although a trend was observed for IL1β and INFγ levels to be increased along with disease severity ( Figure S1 ) . On the basis of these results , we gathered all patients into one single group ( HAT patients group ) in order to further compare the plasma levels of cytokines with those of SERO TL+ and endemic controls . With the exception of IL5 , significant differences were observed between HAT patients , SERO TL+ and endemic controls for all other cytokines measured in plasma ( Figure 1 ) . The lowest plasma levels of all cytokines except for IL12 were observed in endemic controls . IL1β , IL10 , and INFγ cytokine levels were significantly higher in both HAT patients ( p<0 . 0001 , p<0 . 0001 , p = 0 . 01 , respectively ) and SERO TL+ individuals ( p = 0 . 007 , p = 0 . 004 , p = 0 . 03 respectively ) than in endemic controls . IL2 ( P<0 . 0001 ) and IL4 ( p<0 . 0001 ) were significantly higher in HAT patients specifically , whereas IL8 ( P<0 . 0001 ) , IL6 ( p = 0 . 001 ) , and TNFα ( p = 0 . 005 ) were significantly higher in SERO TL+ individuals only . SERO TL+ individuals were also characterized by very low levels of IL12 ( p<0 . 0001 ) as compared to controls and patients . In order to get an overview of the cytokine response in SERO TL+ and HAT patients and to explore similarities between individuals or groups of individuals for all the cytokines in a single analysis , we performed a Normed PCA including all 10 cytokines . As shown in Figure 2A the two first components resume more than 50% of the total variance of the data set . A representation of the first factorial plan is given in Figure 2B . None of the recorded covariates ( HAT status , disease stage in HAT patients , age , gender , or disease focus ) appeared to be correlated with the individuals coordinates on the x-axis . This part of the variance in cytokine levels is likely explained by the occurrence of other pathologies or diseases such as malaria , highly prevalent in the area , but that were not recorded . Nevertheless the second component , which accounted for 24 . 8% of variation ( y-axis ) , resulted in the separation of study subjects according to a HAT/SERO TL+ gradient . The main cytokines contributing to the variance of the second component were IL8 , IL6 , IL12 , and TNFα , indicating that SERO TL+ are mainly characterized by an inflammatory response , which is not present in HAT patients ( Figure 2C ) . In order to evaluate the prognostic value of the cytokine levels determined at study inclusion on the subsequent evolution of the parasitological and serological status in SERO TL+ individuals , longitudinal follow-up were initiated . According to the results of parasitological and serological tests performed during the follow-up visits we could classify SERO TL+ into three distinct groups ( Figure 3 ) . The first group ( SERO TL+/HAT ) comprised 12 individuals in whom the parasite was detected in body fluids during follow-up . At study inclusion these individuals were presumably in the early stage of the infection process but trypanosomes were not detected at that time . The second group , ( SERO TL+/CATTneg ) comprised 15 individuals in whom trypanosomes were never detected but who displayed decreasing CATT responses ( with end titer becoming <1/8 ) . Similarly decreasing CATT responses were also observed in treated HAT patients in Guinea [19]; they were also observed in confirmed HAT patients refusing treatment in Côte d'Ivoire and in whom parasitological testing became subsequently negative [4] , suggesting that these SERO TL+ subjects were engaged in a process of self-cure . The third group ( SERO TL+/CATT≥1/8 ) was composed of 13 individuals who maintained elevated CATT responses throughout the follow-up period and who can be considered as asymptomatic carriers of parasite with parasitemia below the detection limit of parasitological tests . Univariate and multivariate analysis of cytokine levels in these SERO TL+ showed that those individuals with the highest IL10 levels ( p = 0 . 003 , OR = 13 . 09 [2 . 19–124 . 29] ) and undetectable TNFα ( p = 0 . 009 , OR = 10 . 49 [1 . 72–101 . 12] ) had a markedly increased risk of developing HAT ( Table 3 , model I; Figure 4 ) . In contrast , the highest levels of IL8 ( Table 3 , model II; Figure 4 ) were significantly associated with the group of SERO TL+ in whom decreasing antibody responses were observed ( p = 0 . 006 , OR = 8 . 32 [1 . 79–53 . 44] ) . We did not observe any significant association of cytokine levels with the maintenance of high CATT responses ( Table 3 , model III ) .
Late-stage HAT develops when trypanosomes cross the blood-brain barrier , inducing a neuroinflammatory process associated with leukocyte infiltration into the central nervous system [25] . As previously observed , elevated levels of cytokines with both inflammatory ( IL8 and IL6 ) and counterinflammatory ( IL10 ) properties were found in the CSF of late-stage patients [12] , [13] , [26] , [27] . These results are in line with the proposal of using these cytokines or other molecules intervening in the neuroinflammatory process [28]–[30] as late-stage diagnostic tools . In this study we failed to show any significant association between plasma cytokine levels and the different disease stages although trends were observed for IL1β and INFγ to be slightly increased in late stage patients . However we had to rely on limited numbers of subjects for this analysis and this may have precluded finding evidence of small differences in cytokine levels according to the disease stage . Noteworthy , weak association of plasma cytokine levels with disease severity were also observed in other T . b . gambiense endemic areas from central Africa [12] , [13] although in these studies IL8 levels were slightly higher in early-stage patients . This is in contrast to T . b . rhodesiense infections , in which plasma concentrations of TNFα and INFγ were clearly shown to be correlated with disease severity [31] , [32] . This may be related to the fact that T . b . rhodesiense infections are known to be acute , progressing to late-stage disease in several months , which is in clear contrast to the chronic nature of T . b . gambiense . Nevertheless , important differences in plasma cytokine levels were observed in HAT patients as compared to endemic , uninfected controls , with highly significant differences ( p<0 . 0001 ) for IL1β , IL2 , IL4 , and IL10 and to a lesser extent for INFγ ( p = 0 . 01 ) . These results suggest that , in HAT patients , T-cell responses are activated involving both Th1 and Th2 subsets . Although the various animal model systems used have provided conflicting evidence regarding the immunological factors that influence the magnitude of resistance to African trypanosomes , the overall picture in mice is that the host response requires the contribution of both VSG-specific B- and T-cell responses and a proper activation of the macrophage/monocyte phagocyte system to control infection [33] . Type-1 cytokine responses ( INFγ , TNFα ) , leading to macrophage activation to produce trypanotoxic NO [34] , are observed during the early stage of infection in both susceptible and resistant mice . However , in resistant mice , the cytokine profile switches to a type-2 response ( IL4 , IL10 ) during the late/chronic stages of infection , presumably restricting prolonged and exaggerated inflammatory responses [35] . One has to note that the term “resistant” in mouse models is often exaggerated as these mice are characterized more by chronic infection and delayed mortality , which seems to parallel the infectious course in gambiense HAT . In contrast , early mortality in highly susceptible mice is caused by an excessive activation of the macrophage system , associated with an excessive production of INFγ and a systemic inflammatory syndrome [36] , a picture that appears more closely related to human T . b . rhodesiense infections . In SERO TL+ individuals the cytokine profile was clearly different from that in HAT patients , although some features were shared such as the induction of IL1β , IL10 , and INFγ , although to lower levels than in HAT patients . As shown by the lower levels of IL2 and IL4 and the almost absence of IL12 , a cytokine that plays a pivotal role in driving Th1-mediated cellular immunity , T-cell responses appeared to be less activated in SERO TL+ . In contrast , SERO TL+ subjects were characterized by a marked inflammatory response with elevated levels of IL8 , IL6 , and TNFα . In these subjects , this inflammatory process appears to be Th1 independent and thus more likely results from the innate activation of the immune system , possibly through parasite-derived , macrophage-activating molecules interacting with pattern recognition receptors [37] , [38] . As previously reported in Guinea [7] , follow-up of SERO TL+ individuals over a 2-years period showed that this group is heterogeneous . Interestingly , SERO TL+ with the highest IL10 levels and none detectable TNFα at study inclusion had a markedly increased risk of being confirmed by microscopy as HAT patients during their follow-up ( OR = 13 . 09 [2 . 19–124 . 29] and OR = 10 . 49 [1 . 72–101 . 12] respectively ) . Increased IL10 and low TNFα levels were also observed in HAT patients confirming their association with disease susceptibility . Interestingly a line of evidences indicates that the production of TNFα is involved in the control of parasite growth but also in the development of pathogenesis in experimental trypanosomiasis [39] . The mechanisms by which TNFα interacts with trypanosomes ( via direct versus indirect actions ) are still controversial and differ in the various experimental models [40] , [41] . In the light of our results , one can note that the production of TNFα was shown to be induced by T . b . gambiense in human macrophages [42] and that IL10 is well known for its macrophage deactivation properties . Finally , an important finding of this study was that high levels of IL8 ( >30 pg/ml , OR = 8 . 32 , p = 0 . 006 ) were associated with the group of SERO TL+ becoming negative in serology , suggesting that this cytokine is a marker of the host immune response able to eliminate infection . IL8 is a major cytokine involved in innate immune responses . Its main function is to be a chemoattractant for neutrophils , suggesting that these cells play an important role in resistance to infection . In contrast to mice , neutrophils represent 50–70% of leukocytes in humans; they are essential effectors of innate immunity notably through the production of antimicrobial peptides [43] , among which the cathelicidins were shown to be typanotoxic [44] . Whereas the analysis of several cytokines only provides a limited view of the host immune response to infection and precludes drawing firm conclusion on the precise mechanisms controlling parasite growth and or pathogenesis in human , for the first time this study shows that contrasted cytokine profiles ( summarized in Figure 5 ) are associated with the diversity of infection outcomes observed in T . b . gambiense endemic areas [1] . Unraveling the mechanisms underlying human resistance/susceptibility to T . b . gambiense and identifying the key controlling elements will require further studies . Understanding how the human host is naturally able to control and even cure infection is an exciting issue with the potential of identifying novel therapeutic targets . Ongoing blood mRNA profiling and whole genome association studies comparing HAT patients and SERO TL+ individuals [45] will hopingly help unraveling the mechanisms underlying human resistance/susceptibility to T . b . gambiense . As part of the HAT elimination goal , increasing attention is being paid to asymptomatic carriers . As available treatments are expensive , still toxic , and require long-term hospitalization in specific treatment centers , unconfirmed serological suspects are usually not treated and sent back home by the mobile teams screening the population . Regarding this situation , an important finding of the present study is that markers of disease development ( high IL10 , low TNFα ) or self-cure processes ( high IL8 ) exist in the blood of SERO TL+ . These cytokines , or other biomarkers yet to be discovered , could thus be used to develop simple tests that help make the appropriate therapeutic decision for this particular category of subjects . Further studies based on larger samples from different endemic areas will have to be performed to confirm the prognostic value of these cytokines or other markers and define the most appropriate concentration threshold to consider in order to reach optimum specificity . To our knowledge this study is the first to show that human individuals able to resist T . b . gambiense are characterized by a marked inflammatory cytokine profile , pointing out innate immunity and possibly neutrophils as potential actors involved in the control of infection . Future and ongoing studies using high throughput methods to compare HAT patients and SERO TL+ will provide a more precise picture of the immune response occurring in these individuals and hopingly help identifying the critical molecules or pathway controlling resistance/susceptibility to T . b . gambiense HAT . We believe such studies will provide new insights into the identification of novel therapeutic or prophylactic targets and enable the design of new tools to improve the diagnosis and management of parasitologicaly unconfirmed seropositive individuals , an important challenge in the perspective of HAT elimination .
|
Whereas immunological mechanisms involved in the control of trypanosome infections have been extensively studied in animal models , knowledge of how Trypanosoma brucei gambiense interacts with its human hosts lags far behind . In this study we measured cytokine levels in sleeping sickness patients and individuals who were apparently able to control infection to subdetection levels over long periods of time or who were engaged in a process of self-cure as demonstrated by the disappearance of specific antibodies . In contrast to patients , trypanotolerant subjects were characterized by a strong inflammatory response with elevated levels of IL8 , IL6 , and TNFα . This study indicates that both protective immune responses and markers of disease development exist in human T . brucei . gambiense infection and constitute an important step forward to identify new diagnostic or therapeutic targets in the fight against sleeping sickness .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"parasitology",
"biology",
"and",
"life",
"sciences",
"immunology"
] |
2014
|
Unravelling Human Trypanotolerance: IL8 is Associated with Infection Control whereas IL10 and TNFα Are Associated with Subsequent Disease Development
|
Switchgrass ( Panicum virgatum L . ) is a perennial grass that has been designated as an herbaceous model biofuel crop for the United States of America . To facilitate accelerated breeding programs of switchgrass , we developed both an association panel and linkage populations for genome-wide association study ( GWAS ) and genomic selection ( GS ) . All of the 840 individuals were then genotyped using genotyping by sequencing ( GBS ) , generating 350 GB of sequence in total . As a highly heterozygous polyploid ( tetraploid and octoploid ) species lacking a reference genome , switchgrass is highly intractable with earlier methodologies of single nucleotide polymorphism ( SNP ) discovery . To access the genetic diversity of species like switchgrass , we developed a SNP discovery pipeline based on a network approach called the Universal Network-Enabled Analysis Kit ( UNEAK ) . Complexities that hinder single nucleotide polymorphism discovery , such as repeats , paralogs , and sequencing errors , are easily resolved with UNEAK . Here , 1 . 2 million putative SNPs were discovered in a diverse collection of primarily upland , northern-adapted switchgrass populations . Further analysis of this data set revealed the fundamentally diploid nature of tetraploid switchgrass . Taking advantage of the high conservation of genome structure between switchgrass and foxtail millet ( Setaria italica ( L . ) P . Beauv . ) , two parent-specific , synteny-based , ultra high-density linkage maps containing a total of 88 , 217 SNPs were constructed . Also , our results showed clear patterns of isolation-by-distance and isolation-by-ploidy in natural populations of switchgrass . Phylogenetic analysis supported a general south-to-north migration path of switchgrass . In addition , this analysis suggested that upland tetraploid arose from upland octoploid . All together , this study provides unparalleled insights into the diversity , genomic complexity , population structure , phylogeny , phylogeography , ploidy , and evolutionary dynamics of switchgrass .
In the past decade , switchgrass ( Panicum virgatum L . ) has been targeted as a prime candidate energy crop . As a C4 grass , switchgrass has high biomass production with minimal field-based inputs . Its adaptability allows it to be grown productively in large areas of the USA , including marginal lands . In addition , propagation by seed and the perennial growth habit of switchgrass enable relatively effortless establishment , field management and harvest . Although switchgrass shows great promise as a bioenergy feedstock , it would never be considered a model species for genetic or genomic research . Most of the fundamental characteristics of its biology render switchgrass a difficult taxon for the genetic dissection of even the simplest of its useful biofuel-related traits . Switchgrass is a largely self-incompatible and highly heterozygous species [1] . In contrast to species with inbred lines , both forward and reverse genetics are difficult to conduct in switchgrass . In addition , there is evidence of extensive chromosome-number variation , including multiple ploidy levels , as well as aneuploidy [2] . Moreover , switchgrass has a relatively large genome size [2] , [3] and lacks a reference genome , both of which hamper the development of an effective marker system . Overall , these challenges are not unique to switchgrass: there are thousands of key species with similar characteristics , and we need tools that can be applied to all of them . Many of the challenges posed by switchgrass can be overcome through genotyping by sequencing ( GBS ) . This protocol is a multiplexed , high-throughput , and low-cost method to explore the genetic diversity in populations [4] . It employs a reduced representation library ( RRL ) strategy [5] to target a fraction of the genome for sequencing , thereby decreasing cost and increasing the SNP-calling accuracy . GBS is the simplest of the RRL approaches developed thus far [6]–[9] , and has already seen extensive application in a wide diversity of taxa , i . e . , in barley and wheat [10] , as well as , maize [4] , [11] , rice , grape and cacao ( many publications in progress ) . Currently , the RRL strategy has been used for diversity evaluation in various species , resulting in the discovery of hundreds of thousands of SNPs . In most of these cases , the libraries were sequenced on the Illumina platform , and the SNP calling relied on having a reference . The reference could be a high-quality genome sequence [12]–[16] , de novo assembly from deep sequencing [17]–[20] or transcriptome sequences [21] . The reference ( ideally a reference genome ) not only physically orders the SNPs , but also provides the sequence context for paralogs , assigning them to different sites . This reduces the false SNP calls from paralogs , especially in wholly or partly duplicated , or transposon-saturated genomes . However , in the absence of a reference genome , SNP calling may be much less accurate with short-read sequencing technologies , because true SNPs , sequencing errors and SNPs between paralogs can be difficult to distinguish . The Illumina platform and Roche GS-FLX are an effective combination to call SNPs when lacking a reference genome [17]–[20] , but additional labor , time and cost are required to build a rough reference with GS-FLX . Therefore , we designed a universal and unconditionally reference-free SNP calling approach to analyze short sequence data from RRLs of any species , especially for the majority which lack a reference genome . To enable genome-wide association studies ( GWAS ) and genomic selection ( GS ) in switchgrass , we developed both linkage and association populations . Phenotypic data from these populations were collected over three field seasons . All 840 individuals in the linkage and association populations were genotyped with GBS . To overcome the inherent difficulties of the lack of a reference genome , multiple ploidy levels and high heterozygosity , a bioinformatics pipeline for SNP discovery based on a species-wide network approach called the Universal Network-Enabled Analysis Kit ( UNEAK ) was developed . This pipeline was validated in maize and then successfully applied to switchgrass GBS data . High density SNPs were generated to enable future GWAS and GS . Further analysis of the SNP data sets provided unparalleled insights into the diversity , genomic complexity , population structure , phylogeny , phylogeography , and evolutionary dynamics of switchgrass .
When a reference genome is available , SNP discovery can be easily performed by aligning reads to the physical map . However , when there is no reference genome , as is the case for the majority of species , significant challenges arise . The UNEAK pipeline overcomes many of these challenges . The general design of UNEAK is as follows ( Figure 1 ) : Reads are trimmed to 64 bps . The trimmed parts of the reads are ignored because the sequencing errors are enriched at the ends of reads . Identical 64-bp reads are collapsed into tags . Pairwise alignment identifies tag pairs having a single base pair mismatch ( Figure 1C ) . These single base pair mismatches are candidate SNPs . Because of the complexity of the genome , many of the tag pairs form networks ( Figure 1D and Figure 2 ) . A network filter is employed to discard complicated networks , which are usually a mixture of repeats , paralogs and error tags ( Figure 1E and Figure 2 ) . Ideally , after application of the network filter , the only networks remaining are composed of reciprocal tag pairs , which can then be used for SNP calling . To account for sequencing errors , we introduced a parameter called the error tolerance rate ( ETR ) to improve our initial network filter ( see Methods ) . Without this feature ( ETR = 0 ) , sequencing errors can have a substantial negative impact upon the number of retained SNPs , especially when the depth of coverage is high . When sequencing errors occur and error detection is not employed , affected tag pairs are no longer reciprocal and therefore are removed from the data set ( Figure 1E ) . By employing an appropriate ETR , the edges between error tags and real tags are cut . In this manner , complicated networks can be separated into different sub-networks , and only those sub-networks composed of reciprocal real tag pairs are kept ( Figure S1 ) . Hence , the SNPs with higher coverage , the most valuable part of the data set , are more likely to be retained ( Figure 1E and Figure 2 ) . To validate the UNEAK pipeline , we tested it with GBS data from a single RIL family ( B73×B97 ) from the maize nested association mapping ( NAM ) population [22] . The large and complex genome of maize [23] makes this a useful test . The 199 inbred lines were processed using the GBS protocol applied in switchgrass . The only difference was that the maize samples were sequenced on the Illumina Genome Analyzer which has about 10% of the throughput of an Illumina Hiseq 2000 . To evaluate the effectiveness of the network filter , we ignored the existence of the maize B73 reference genome and called SNPs at two stages in the pipeline , before and after application of the network filter . The first data set had 336 , 020 SNPs , which were composed of all tag pairs with a 1 bp mismatch . The second data set was comprised of the 92 , 951 SNPs that passed the network filter . Only 23 . 3% of the SNPs in the first data set aligned to a unique site in the maize reference genome . In contrast , after application of the network filter , 78 . 6% of the SNPs aligned to unique positions . Here , for a uniquely mapped SNP , one tag had a single perfect match to the reference; the other had a single best match at the same site . For the other 21 . 4% , either or both tags of a SNP aligned equally well to more than one site . Among them , 48 . 6% SNPs aligned to two sites . Considering that ApeKI is a partially methylation sensitive enzyme [4] , and that potential tags from long restriction fragments are generally absent from GBS data due to PCR bias , some of the tags that aligned to multiple sites in fact may have come from a single site . To quantify this effect , we performed an in silico ApeKI digest of the maize reference genome and identified 8 , 420 , 424 potential B73 GBS tag loci . All of the 6 , 994 , 161 tags from the B73×B97 family were then aligned to the reference genome; 2 , 966 , 692 of these matched perfectly , and thus were B73 tags . These B73 tags accounted for only 35 . 2% ( 1 , 045 , 475 ) of the potential B73 tags from the in silico digest . Hence , there is a strong possibility that a large proportion of the 21 . 4% of SNPs from reciprocal tag pairs that align to multiple positions in fact derived from a single genomic position . For example , those SNPs that aligned to two sites ( 10 . 4% of total SNPs ) had only a 35 . 2% chance of originating from two genomic positions . Therefore , we estimated that the SNPs essentially aligned to unique positions should be greater than 85% . The marked difference between the allele frequency distributions before and after application of the network filter demonstrates that this filter substantially improves the quality of the data . In contrast to the pre-network filter distribution , in which only low and high frequency error peaks were discernible , the post-network filter distribution was dominated by a central peak around the expected allele frequency of 0 . 5 . At the same time , the two error peaks located at the two ends of the distribution were significantly reduced ( Figure S2 ) . The 92 , 951 SNPs were also validated by both linkage disequilibrium ( LD ) analysis and sequence alignment . First , we calculated LD ( r2 ) between these SNPs and the 1106 Illumina Golden Gate SNP markers developed in NAM [22] , based on the assumption that valid SNPs should be in LD with adjacent markers . For the 20 , 402 SNPs with call rates >0 . 3 , the average r2 with the four adjacent markers were calculated . The results showed that 92 . 8% of the GBS SNPs were in LD with a flanking NAM SNP with an r2 greater than 0 . 2 ( Figure S3 ) . Second , we aligned the non-B73 tags of these SNPs to the B97 whole genome shotgun sequences from maize HapMap2 data [24] , which were sequenced at 4 . 2× and supposedly covered the majority of the B97 genome . The results showed that 93 . 2% of the GBS SNPs corresponded to HapMap2 SNPs from B97 . To enable GWAS and GS in switchgrass , we created a full-sib linkage population ( n = 130 ) , a half-sib linkage population ( n = 168 ) and an association panel ( 66 diverse populations , n = 540 ) ( Table S1 and Table S2 ) . Using GBS , approximately 350 Gb of sequence were generated from an Illumina HiSeq 2000 . The UNEAK pipeline called 400 , 107 , 476 , 005 , and 700 , 236 SNPs from the full-sib , half-sib , and association populations , respectively . All together , about 1 , 242 , 860 putative SNPs were discovered in switchgrass . All of these SNPs had minor allele frequencies ( MAFs ) greater than 0 . 05 . There were 29 , 838 ( 6 . 9% ) , 69 , 605 ( 12 . 8% ) and 112 , 099 ( 13 . 8% ) SNPs with MAFs less than 0 . 05 in the three populations , respectively . Because we cannot distinguish low frequency SNPs from sequencing errors , SNPs with a MAF less than 0 . 05 were removed from further analysis . The average coverage of the three data sets was less than 1× , but for some SNPs the coverage was as greater than 6× ( Figure S4 ) . The SNP calls can be found at http://www . maizegenetics . net/snp-discovery-in-switchgrass . The parents of the full-sib population are upland tetraploids . In general , a stretch of DNA sequence should have four orthologous copies in tetraploids . Therefore , when considering the allele frequency distribution of an F1 population , we expected to see seven peaks , representing all possible allele frequency ratios of two parents ( e . g . , 1∶7 , 2∶6 , 3∶5 , etc . ) . However , only three peaks were observed ( 1∶3 , 1∶1 and 3∶1 ) after the network filter was implemented ( Figure S2D ) , the signature of an F1 population of a heterozygous diploid . From this , we infer that tetraploid switchgrass is thoroughly diploidized . After the network filtering step , a second filter is implemented in UNEAK to remove remaining sequencing errors and paralogs . This filter is a goodness-of-fit χ2 test ( α = 0 . 05 ) based on the null hypothesis that , in diploid species , the counts of the two paired tags of a SNP are equal in all heterozygous individuals . A substantial number of incorrect SNP calls were removed from the data set of the F1 full-sib population ( compare Figure S2D to Figure 3 ) . The three peaks of the allele frequency distribution for the remaining SNPs ( Figure 3 ) represent the crosses of AA×Aa ( expected allele frequencies of 0 . 25 and 0 . 75 , with 1∶1 segregation of AA and Aa genotypes ) , AA×aa ( no segregation ) , and Aa×Aa ( expected allele frequencies of 0 . 5 with genotypic segregation ratios of 1∶2∶1 ) . The diploid nature of tetraploid switchgrass can also be recognized in individual plants . The tetraploid parents of the full-sib linkage population , U518 and U418 , were sequenced at a high coverage of 6× . We ran UNEAK to call SNPs from loci that were heterozygous in both parents . The results showed that the two alleles at heterozygous loci have equal read frequencies within each tetraploid of 0 . 5 ( Figure S5A and Figure S5B ) , providing more evidence that tetraploid switchgrass is diploidized . To compare the distribution pattern of octoploid and tetraploid , we sequenced one octoploid switchgrass , K101 , also at 6× . In contrast to the tetraploids , the read frequency distribution within K101 had three peaks , at 0 . 25 , 0 . 5 and 0 . 75 ( Figure S5C ) . This result indicated that octoploid switchgrass behaves more like an autotetraploid . GBS is a low coverage genotyping approach , especially when the genome is digested with ApeKI , which is a frequently cutting restriction enzyme . Before constructing a high-density linkage map , we first evaluated the quality of the switchgrass genotypic data set . The low depth of sequencing , relative to the number of restriction fragments within GBS size range , has two effects on genotypic data quality . The first is a large amount of missing data . The SNP call rate increases with coverage ( Figure S4 ) . Across the 400 , 107 markers discovered in the switchgrass full-sib linkage population , we achieved a median coverage of 0 . 54× , which translated into a median SNP call rate of 40% . The second effect of low coverage is that heterozygous SNPs can be miscalled as homozygotes , even at markers with high call rates . To quantify the rate of miscalled heterozygotes , we selected markers with expected allele frequency ratios of 1∶1 ( MAF>0 . 45 in the full sib progeny ) that appeared , based upon high coverage GBS data from the parents , to be homozygous in both parents ( AA×aa ) . These markers should be heterozygous in all of the full-sib progeny . As expected , the proportion of miscalled heterozygotes is very high at low coverage markers and declines substantially as coverage increases ( Figure S6 ) . In the subset of markers with the highest coverage ( >4× coverage , or >90% SNP call rate ) we estimate that <30% of heterozygotes were miscalled as homozygous . Due to the large amount of missing data and miscalled heterozygotes , traditional methods to detect linkage based on the LOD score might not be applicable . Therefore , we used the modulated modularity clustering ( MMC ) method [25] to construct linkage groups . Unlike the agglomerative hierarchical clustering methods used in other genetic map software [26] , the MMC is a coherent clustering approach seeking objective groups in the data . Because it does not require input parameters to decide the group number , this approach is completely data driven . Consequently , this clustering method is useful for obtaining linkage groups in a species . To construct linkage groups , we only used the most informative markers ( 0 . 2<MAF<0 . 3 ) that should be heterozygous in only one of the parents . A subset of these markers , specifically two sets of 3 , 000 SNPs with a call rate >0 . 9 , or >4× coverage ( Figure S4 ) and with <30% miscalled heterozygotes ( Figure S6 ) , were selected for constructing paternal and maternal linkage groups , using the pseudo-testcross [27] mapping strategy ( Figure S7 ) . The MMC method was used to group markers based on the Spearman's rank correlation coefficient ( r ) between marker pairs . This method clustered 3 , 000 paternal SNPs into 18 linkage groups , which perfectly matches the haploid chromosome number of tetraploid switchgrass ( Figure 4 ) . Using the same method , the 3 , 000 maternal SNPs clustered into 19 linkage groups ( Figure S8A ) . Based on their synteny to foxtail millet ( see next section ) , two of these linkage groups were subsequently merged . The next objective of this research was to use pairwise r2 to order the markers within each of the linkage groups . This is an example of the travelling salesman problem ( TSP ) , with the additional complication of missing data and that a proportion of the heterozygotes were miscalled as homozygote as a result of the low depth of coverage . We tried several combinatorial optimization methods ( e . g . the genetic [28] and ant colony [29] algorithms ) to find the optimal order , but none of these resulted in a reasonable marker order . Ultimately , however , we were able to order these SNPs based on the synteny of switchgrass with other grasses , since the grass family has a remarkably conserved genome [30] . Foxtail millet ( Setaria italica ) is the closest relative of switchgrass with an available reference genome ( 490 Mb ) [31]–[33] . It is estimated to have diverged from switchgrass roughly 3–7 million years ( Myr ) ago and is a diploid species with nine haploid chromosomes , half of the haploid chromosome number of tetraploid switchgrass . We hypothesized that tetraploid switchgrass was formed by a genome duplication after its divergence from the common ancestor ( n = 9 ) of the two species . Thus we expected that each of the chromosomes of foxtail millet should align with two linkage groups of switchgrass . By aligning the 3 , 000 markers in the 18 paternal linkage groups , we found that 299 , or nearly 10% , mapped to unique locations in the foxtail millet genome . As expected , the linkage groups of switchgrass matched very well with chromosomes of foxtail millet , indicating that the original linkage group clustering in switchgrass was correct ( Figure 5 ) . This result also indicated that strong synteny has been maintained between the two species , in spite of the genome duplication event . Similarly , 339 out of 3 , 000 ( 11 . 3% ) markers in the 19 maternal linkage groups also aligned to the foxtail millet genome . In most cases , each foxtail millet chromosome matched two linkage groups , except for chromosome 1 . This chromosome had three matches to switchgrass linkage groups . Specifically , linkage groups 1 and 3 were aligned to two separate parts of chromosome 1 ( Figure S8B ) . We hypothesized that the two linkage groups represented one chromosome , but were not successfully clustered together using MMC . Therefore , we merged maternal linkage groups 1 and 3 , and thus both the paternal and maternal markers formed 18 linkage groups . To make high density linkage maps , we used the 6 , 000 markers from 36 linkage groups ( 18 paternal linkage groups and 18 maternal linkage groups ) as the seed and then attempted to fit as many SNPs as possible into these groups . However , the large proportion of missing data may have a major impact on the clustering . Therefore , markers with call rates of 0 . 2 , 0 . 5 and 0 . 9 were used to check the clustering quality ( Table S3 ) . Via alignment to the foxtail millet genome , the uniquely aligned markers were identified and clustered into the 36 linkage groups based on the Spearman's rank correlation coefficient . For the data sets with call rates of 0 . 2 , 0 . 5 and 0 . 9 respectively , the results showed that 60 . 2% , 76 . 8% and 90 . 8% of the SNPs aligned to physical chromosomes that were syntenic to their linkage groups . Assuming that 90 . 8% represents the actually degree of synteny conservation between foxtail millet and switchgrass , then it appears that 30% and 14% of the SNPs from the data sets with call rates of 0 . 2 and 0 . 5 were assigned to the wrong linkage group , respectively . To strike a balance between the quality and number of SNPs , the 88 , 217 SNPs with MAFs between 0 . 12 and 0 . 38 and call rate >0 . 5 were chosen to add to the 36 linkage groups to construct high density linkage maps . Out of these 88 , 217 SNPs , 9 , 437 could be aligned to unique positions in the foxtail millet genome; physical and genetic chromosomal assignments agreed for 7 , 245 of these 9 , 437 . Based on the strong synteny between switchgrass and foxtail millet , we were able to order the 7 , 245 uniquely aligned SNPs , resulting in paternal and maternal framework maps consisting of 3 , 244 and 4 , 001 ordered markers , respectively . To check the quality of the synteny based order in the framework maps , we calculated the pairwise Spearman's rank correlation coefficients for the markers . High coefficient values were distributed along the diagonal in the heat map ( Figure S9 ) . This indicates that the synteny between switchgrass and foxtail millet is high enough to provide a reasonable order for switchgrass SNPs . The remainder of the 88 , 217 SNPs was then placed on the framework maps according to the r2 within their assigned chromosomes . A paternal linkage map ( 18 chromosomes , 41 , 709 markers ) and a maternal map ( 18 chromosomes , 46 , 508 markers ) were constructed . Both framework maps and the high density linkage maps can be found at http://www . maizegenetics . net/snp-discovery-in-switchgrass . In addition to the genomic analysis of the bi-parental populations , phylogenetic analysis was performed using the SNPs discovered in the diverse association panel . We selected all of the markers with call rates greater than 0 . 5 in 540 individuals , which included both tetraploid ( 4× ) and octoploid ( 8× ) plants . Because the size of the 8× genome is approximately twice the size of the 4× genome , the SNPs may be biased towards 8× specific SNPs . Furthermore , the octoploid plants may have half the sequencing depth of the tetraploids . Both of these factors have the potential to affect the phylogeny reconstruction . Hence , this data set was evaluated for ploidy specific SNPs as well as for coverage of 4× and 8× switchgrass . Based upon a χ2 test , 2 . 4% and 6 . 6% of the SNPs had a significantly larger number of genotype calls in 4× and 8× switchgrass , respectively ( p<0 . 05 ) , which is similar to the expected type I error rate of 5% . Moreover , the sequencing depth for the 4× and 8× plants was similar , specifically 1 . 60× and 1 . 55× coverage for the 4× and 8× switchgrass , respectively . This analysis indicated that the SNPs were suitable for phylogenetic analysis across different ploidy levels . Using 29 , 221 markers with call rate greater than 0 . 5 , a neighbor- joining ( NJ ) tree was constructed based on the pairwise genetic distance among the 540 individuals ( Figure S10 ) . To avoid the problem of ploidy specific SNPs mentioned above or the different amount of missing data in individuals , only the sites having genotype calls on both individuals were used while calculating pairwise distance . The phylogeny showed that the upland and lowland ecotypes were clearly separated , with further geographically based subgroups found within each ecotype . Most individuals from the same population were clustered together in the phylogenetic tree . Ploidy variation in switchgrass is ecotype-specific: plants of the lowland type are tetraploid , whereas those of the upland ecotype are primarily tetraploid ( 4× ) or octoploid ( 8× ) . We estimated ploidy level in at least one clone per population in the diversity set using flow cytometry ( Table S1 ) . This ploidy information was mapped onto the marker-based phylogeny of switchgrass , indicating that ploidy level also resolves into distinct groups ( Figure S11 ) . Isolation by distance is also clearly indicated by geographic analysis . A Mantel test showed that genetic and geographic distance were significantly correlated ( r = 0 . 51 , P-value<0 . 001 ) . A direct comparison of the groups indicated by the phylogeny with their geographic origins ( Figure 6 ) further illustrated the strong influence of geography on the distribution of genetic diversity in this widespread species . Clearly , the phylogeny of switchgrass concurs well with ecotypes , ploidy level and geographic distribution . However , what does the phylogeny tell us about the evolutionary origin of the upland octoploid ? Is it an allopolyploid , formed by a wide hybridization between an upland ecotype 4× and a lowland ecotype 4× ? Or is it the product of a combination of two upland 4× , resembling more of an autopolyploid origin ? The first scenario is not likely , because the upland 8× is not intermediate between the ecotypes , but more closely related to the upland 4× ( Figure 6 ) . To address the second scenario , we first identified an appropriate outgroup . Foxtail millet , which proved to be highly informative for linkage mapping in switchgrass , was an ideal outgroup for this study . As demonstrated , it is possible to uniquely align approximately 10% of switchgrass SNPs to foxtail millet . Of the 29 , 221 markers used for the phylogeny analysis , 3 , 144 aligned to the foxtail millet genome . Comparing these SNPs to the foxtail millet genome , we identified the ancestral alleles of switchgrass and assigned 3 . 1 kb of foxtail millet sequence as the outgroup . Next , a NJ tree was constructed with 500 bootstrap replicates ( Figure S12 ) . Upland and lowland ecotypes were well separated . However , even 3 , 144 markers were unable to resolve the sub-groups within the upland ecotype with high bootstrap values . Nevertheless , all of the lowland individuals formed one clade , with a bootstrap value of 100% . For the next stage of the analysis , the lowland ecotype was designated as the outgroup for upland switchgrass . Taking the lowland ecotype as the outgroup , we bootstrapped the tree based on 29 , 221 markers ( Figure 7A ) . The results showed that within the upland ecotype , 8× East and 4× North constitute distinct groups . However , the 8× West clade has a low bootstrap value of 15% . We inferred that this is because Upland 8× West group contains admixed individuals that overlap genetically with Upland 8× East and Upland 4× North . Because the Upland 4× North is an inner branch of Upland 8× West , it is unlikely that the upland 4× gave rise to upland 8× . In fact , our analysis suggests the opposite: upland 4× arose from upland 8× . The upland south clade is the outgroup of three other clades of upland switchgrass with a bootstrap value of 100% ( Figure 7A ) . Further evidence supporting this came from the multiple dimensional scaling ( MDS ) plot ( Figure 7B ) . The MDS was based on the kinship matrix of individuals of the upland ecotype . The Upland 4× North clade has clearly reduced diversity compared to the upland 8× groups . To formally test the two competing scenarios , namely of ( 1 ) upland 4× arising from upland 8× , versus ( 2 ) upland 4× arising from lowland 4× , we constructed an alternate , constrained topology consistent with scenario two ( Figure S13 ) . Using the program MEGA , we calculated the likelihood of the two topologies using a random subset of 5 , 000 out of the original 29 , 221 markers . The original topology , corresponding to scenario one , was 10590 times more likely than the alternate , constrained topology , strongly supporting the scenario where upland 4× arose from upland 8× .
Genomic selection and GWAS have the potential to substantially improve the efficiency of breeding programs [34] . The decreasing cost and increasing throughput of next generation sequencing have enabled large scale SNP discovery efforts in many species , particularly those that are important in the current agricultural economy , are well-characterized genetically , and already have reference genomes . However , the growing demands for energy and environmental conservation require the breeding of an increasingly diverse set of species . Many of these species , including switchgrass , currently lack reference genomes . While significant gains have been made since the inception of switchgrass breeding in the 1950s [35] , genome-based selection methods offer significant opportunities to increase the rate of gain [36] . SNP discovery from next-generation sequencing data is particularly challenging in the absence of a reference genome . Our SNP calling pipeline , UNEAK , was developed specifically in response to this challenge . Unlike most non-reference SNP calling protocols , UNEAK does not require a partially sequenced genome , contigs from additional sequencing platforms [17]–[20] , or a transcriptome to serve as a pseudo reference genome [21] . By constructing networks of tags , UNEAK mimics the processes of replication and mutation of paralogous sequences . Filtering out the more complex networks resolves the paralog and repeat issues which hinder SNP discovery efforts in species with large genomes , multiple ploidy levels , or without reference genomes . Starting with high throughput GBS reads from the Illumina platform , UNEAK provides a time- and cost-efficient way to generate hundreds of thousands of markers for population evaluation , linkage map construction , quantitative trait loci ( QTL ) mapping , GWAS and GS , in species with limited genetic resources . These high density markers will greatly facilitate genomic selection in biofuel species or in species with agricultural , ecological , or medical importance . Continuing efforts have been made to call SNPs in species lacking a reference genome . For example , the restriction-site associated DNA ( RAD ) [7] method yields high coverage SNPs using the 8 bp cutter , SbfI , that can be successfully used for phylogeographic study [37] and genetic map construction [38] , [39] . However , the RAD analysis pipeline , Stacks [40] , requires high coverage sites and assumes that the species under investigation is diploid . In contrast , UNEAK can perform well for both high and low coverage genotyping methods . Moreover , UNEAK can be used in polyploid species , which are becoming more economically important . In addition , the high density panel of SNPs discovered by UNEAK provides an opportunity to conduct GWAS and GS to accelerate the breeding process . Even though UNEAK was designed for SNP discovery in species without reference genomes , it can also be used for species with a reference genome . In fact , most reference genomes generally do not cover the whole genome of a species , for two reasons . The first is technical: a reference genome derived from a single individual is usually incomplete because of technical difficulties . In other words , some genomic regions are “technically missing” . The second is biological: one individual's genome does not completely represent the whole genome of that species , because of presence and absence ( PAV ) variation . Regions containing PAVs can be “biologically missing . ” In either case , the genetic variation in the missing genome is basically inaccessible when using SNP discovery methods that rely on the reference genome . UNEAK makes it possible to gain access to those missing regions . In this study , maize was used to validate the UNEAK pipeline . Maize is a large and complex genome , which experienced multiple genome duplication events [41] , [42] , has a large amount of repetitive sequence , numerous PAVs [23] , [43] , [44] , with only about 50% overlap in sequence content between any two unrelated inbred lines [45] , [46] . Results from maize convincingly validated the UNEAK pipeline . Of the 92 , 951 SNPs discovered by UNEAK , 78 . 6% aligned to unique positions in the maize reference genome . Because not all potential GBS tag loci in the maize reference genome are accessed by GBS , the actual proportion of unique SNPs should be above 85% . Validation by either LD or alignment suggested that >92% of the SNPs with MAFs greater than 0 . 05 were legitimate . The other 8% were probably due to sequencing errors or paralogs . However , these false positives can be significantly reduced through use of a minor allele frequency filter in biparental populations . For example , for SNPs with a MAF>0 . 3 , the validation rate reaches 96 . 2% . The UNEAK pipeline was designed to perform SNP discovery in a broad range of species , therefore only the network filter , which is the key to UNEAK , was implemented and evaluated in the maize test . Essentially , based on the tag count file output from UNEAK , end users can design filters specific to the biology of their study population . For example , repulsion of alleles in inbred lines , or equal tag count of alleles at heterozygous sites in diploid species can be tested to filter for higher quality SNPs . Although UNEAK generates reliable SNPs , it cannot guarantee high quality genotype calls when sequencing coverage is low . Coverage has a major impact on genotype quality ( Figure S4 and Figure S6 ) . Call rate increases with coverage , but with diminishing returns ( Figure S4 ) . In inbred lines or haploid germplasm , especially in species with a reference genome , too much coverage is a waste of sequencing resources . On the other hand , in highly heterozygous germplasm , high rates of coverage may be required to distinguish heterozygous sites from homozygous , depending on the desired level of error tolerance . In this case , to obtain high quality genotypes from GBS , it may be helpful to use enzymes with longer recognition sequences than ApeKI . A high quality linkage map is essential for QTL mapping and the assembly of whole genome sequence . The average coverage of the SNPs in the switchgrass full-sib population is only 0 . 95× . Even so , we were able to identify linkage groups . Using the 3 , 000 SNPs with the highest call rate ( >0 . 9 ) , 18 linkage groups were successfully identified and confirmed by alignment to the foxtail millet genome , for both paternal and maternal markers . However , without relying on synteny with foxtail millet , we were unable to order the markers within the linkage groups , even after trying many different algorithms . Our inability to order the markers stemmed from the low sequencing coverage; this led to high amounts of missing data , and heterozygotes often being miscalled as homozygotes , even for SNPs with high call rates ( Figure S4 and Figure S6 ) . For the same reason , the high density linkage maps ( 41 , 709 SNPs in the paternal map and 46 , 508 SNPs in the maternal map ) we developed based on the lower call rate ( >0 . 5 ) had approximately 14% markers grouped to wrong chromosomes . This percentage was largely reduced for the SNPs uniquely aligned to the foxtail millet genome and clustered into the one of the syntenic linkage groups of switchgrass . These SNPs comprise the framework maps , which were ordered based on the strong synteny between the two species [33] . Both high density maps ( 41 , 709 and 46 , 508 SNPs ) and high quality framework maps ( 3 , 224 and 4 , 001 SNPs ) are available at our website . These linkage maps should be useful for the current switchgrass genome assembly effort by the Joint Genome Institute . For example , they can be used to differentiate contigs derived from the two subgenomes as well as to order contigs on a chromosome . The GBS protocol generally provides low coverage if the enzyme ApeKI is used , but higher coverage can be obtained by choosing enzymes with longer recognition sites [4] . There is a tradeoff between coverage and number of SNPs . For linkage map construction , where the number of recombination events is limited , thousands of SNPs usually provide sufficient resolution . However , in breeding applications , a higher density of SNPs should provide a better chance to find SNPs that are tightly associated with QTLs . Therefore , further development of UNEAK will focus on linkage map construction using a six base cutter such as PstI in GBS , which is expected to result in at least 8 times higher coverage than ApeKI and thus should provide a better balance between coverage and total number of SNPs . Ploidy level variation significantly complicates genetic research in switchgrass . The F1 full-sib population of switchgrass in this study was made by crossing two upland tetraploids . Although seven peaks were expected in the allele frequency distribution in the F1s , the three peaks clearly indicated that the tetraploid switchgrass behaves like a diploid ( Figure 3 ) . We also observed nearly equal intra-individual allele read frequencies of ∼0 . 5 at heterozygous loci within individual tetraploid plants . In addition , the MMC method successfully clustered paternal and maternal markers into 18 and 19 linkage groups respectively , with the extra maternal linkage group later merged with another via synteny . The correlations within the linkage groups are visibly higher than between groups ( Figure 4 ) . Construction of an SSR-based linkage map for switchgrass also indicated that chromosomes pair preferentially in meiosis [47] . These four lines of evidence indicate that tetraploid switchgrass shows disomic inheritance and has undergone diploidization over the past one million years [48] . The diploid nature of tetraploid switchgrass will greatly simplify genetic research and whole genome sequencing efforts . Based on 29 , 221 markers , the phylogenetic analysis in this study provides high resolution to cluster the 540 individuals from 66 populations . As reported by previous studies using either sequence from chloroplast genomes [49]–[51] or SSR markers in nuclear genomes [51] , [52] , early divergence of the upland and lowland ecotypes was also observed in this study . In most cases , individuals from the same population were grouped together . Additionally , we found distinct subgroups within each ecotype based on their geographic distribution . This result clearly indicates isolation by distance , which could not be detected by random amplified polymorphic DNA ( RAPD ) markers [53] . The fact that subgroups of different ploidy form distinct clades indicates that the two ploidy levels are reproductively isolated [1] . To investigate if non-random patterns of shared missing genotypes between individuals affected the tree topology , we evaluated the ploidy specific SNPs and coverage of the 4× and 8× switchgrass . Only 6% of the SNPs ( slightly higher than the type I error rate ) were octoploid specific , and sequence coverage was quite similar in the two ploidy groups . These observations indicate that octoploid switchgrass behaves like an autotetraploid ( Figure S5 ) . Hence , it does not contain many private genomic regions relative to tetraploid switchgrass . Moreover , only the sites with genotype calls in both individuals were used to calculate the genetic distance , which should minimize the impact of differential amounts of missing data . In addition , we reconstructed multiple trees at different call rates ranging from 0 . 15 to 0 . 9 , and the overall topology with respect to the main groups , for example Upland 8× West , Upland 4× North , Upland 8× East , Upland 8× South , Lowland South and Lowland Northeast , was stable regardless of call rate ( data not shown ) . When call rate was greater than 0 . 9 , there were only about 800 SNPs in the data set , many of which were repetitive paralogs . When the call rate is less than 0 . 15 , there were not enough shared sites between individuals to calculate genetic distances . Thus , in spite of the low coverage of GBS , we concluded that the phylogenetic relationship constructed in this study is reliable . These results also suggest that missing genotypes do not alter the performance of phylogenetic analysis , provided that large numbers of SNPs are used . Our results suggest that the upland 4× arose from upland 8× . We used a stepwise method to designate the lowland ecotype as the outgroup of the upland ecotype . Phylogenetic analysis indicated the upland 8× is closer to this external , lowland branch . Additionally , upland 4× showed less diversity than upland 8× . Both lines of evidence support the hypothesis that upland 8× gave rise to upland 4× . This conclusion is contradictory to the accepted evolutionary trajectory of higher ploidy level being derived from either auto- or allo-polyploid events involving lower ploidy taxa . A reversion to the lower ploidy could occur via apomixis , whereby an unfertilized haploid ( 4× ) gamete becomes a viable embryo . This is a well-documented phenomenon in perennial grasses [54] . Confirmed haploidy of switchgrass has been observed in two laboratories , in both cases at extremely low frequencies , on the order of 10−4 to 10−2 , from the 2n = 4x = 36 to the 2n = 2x = 18 ploidy level [49] , [55] . The relatively high frequency of tetraploid accessions in the northern USA ( Figure 6 ) cannot be explained simply by this phenomenon , suggesting that selection may play a role in favoring upland tetraploid genotypes in certain northern environments . According to the phylogeny ( Figure 7 ) and the geographic distribution of upland switchgrass ( Figure 6 ) , we confirmed a south to north migration path of upland switchgrass , which agrees with a previous study [51] . Our data also indicated a loss of diversity during the migration , manifested largely by the shift in ploidy from 8× to 4× in the north . For the lowland switchgrass , our phylogenetic analysis cannot tell the migration direction . However , it is very likely that the common ancestor of upland and lowland ecotypes came from the southern area , then migrated to the north , not vice versa . Therefore , we inferred a general south to north migration path of switchgrass ( Figure S14 ) . The natural barrier of the Appalachian Mountains split the northern spread of switchgrass into two subgroups . To the west of the mountains , the most recent common ancestor ( MRCA ) of the Upland ecotype was 8× . During migration to the north , a ploidy level shift occurred and the Upland 4× emerged . To the east of the Appalachian Mountains , the lowland ecotype was favored , and continued spreading northward along the coastal plains . Subsequently , the Lowland 4× Northeast subgroup diverged both geographically and genetically from the Lowland 4× South . The switchgrass germplasm used for this phylogenetic study derived mainly from northern-adapted populations , with very restricted sampling of populations from southern and central regions . Based on the geographic distribution and deep divergence of the upland and lowland ecotypes [51] , [52] , we expect the switchgrass from southern regions , particularly in Mexico , Texas and Florida , to be clustered with the lowland ecotype . Switchgrass from the central regions might provide useful information about the divergence of switchgrass into two ecotypes , the origin of upland ecotypes , and how the ploidy level shifted during the migration . More extensive sampling of switchgrass from all regions of North America will undoubtedly improve our understanding of switchgrass evolution .
The association panel consisted of 66 diverse switchgrass populations grown from seed in the greenhouse of the USDA-ARS Dairy Forage Research Center in Madison WI in 2007 . This panel was mainly composed of northern adapted upland populations ( Table S1 ) . In addition , two tetraploid F1 linkage populations were propagated at the same time . One was derived from the bi-parental cross of two upland accessions from the germplasm collection of MDC , named U518 and U418 . The other was a half-sib population whose maternal parent is U601 . The numbers of individuals in these two populations were 130 and 168 , respectively . For the association panel , ten clones from each population were initially planted in replicated field plots in Ithaca , NY and Arlington , WI in 2008 . All genotyping was conducted on plants from the Ithaca site . The reduced representation libraries were constructed and sequenced according to the published GBS protocol [4] with one modification . Specifically , a titration experiment showed that ∼0 . 1 pmol of each adapter was appropriate for switchgrass ( rather than ∼0 . 06 pmol ) , and that amount was used with 100 ng of genomic DNA . DNA samples were digested with the restriction enzyme ApeKI , which has a 4 . 5 bp cut site ( CGWGC , where W = A or T ) . The resulting libraries were sequenced on the Illumina HiSeq 2000 . Ninety five samples ( plus a blank negative control ) were sequenced per lane . The non-reference pipeline UNEAK ( http://www . maizegenetics . net/gbs-bioinformatics ) was developed for SNP discovery and genotyping in species like switchgrass ( Figure 1 ) . In UNEAK , Illumina reads are trimmed to 64 bp and stored in bit format , which greatly reduces the amount of storage space and enables relatively fast computation . About 40GB of data from one lane of an Illumina HiSeq 2000 can be processed to SNP genotypes in 20 minutes on a personal computer with 2 . 67 GHz CPU and 8 GB memory . More technical details of UNEAK are described in Text S1 . The network filter is the key step for identifying and removing paralogs . The simplest networks , reciprocal tag pairs , are more likely to be real SNPs than tag pairs that are part of complicated networks . Rare tags , with read counts below a specified error tolerance rate ( ETR ) , are assumed to result from sequencing error . For two tags ( t1 and t2 ) with a 1 bp mismatch and read counts c1 and c2 , if c1/ ( c1+c2 ) <ETR , then t1 is assumed to be a sequencing error of t2 . The edges connecting real tags and error tags are then sheared , dividing the network complexes into parts . Remaining reciprocal , real tag pairs are then identified as SNPs ( Figure S1 ) . According to the frequency distribution of tag pairs in structured populations ( Figure 2A and Figure S2C ) , we found the frequencies of most sequencing errors were less than 3% . Therefore , the ETR was specified as 0 . 03 in this study . To calculate the coverage in the SNP data sets , we calculated the sum of the read counts of all of the sites across all the individuals . This sum is then divided by the number of sites and number of individuals . So the measurement of coverage in this study is reads/site/individual . The maternal and paternal parents of the full-sib linkage population , U518 and U418 , were sequenced via GBS at about 6× , i . e . , at six times higher coverage than the F1 individuals in the full-sib population . SNP markers that were homozygous in one parent and heterozygous in the other and which had a minor allele frequency ( MAF ) in the progeny between 0 . 2 and 0 . 3 were chosen for linkage analysis via the pseudo-testcross mapping approach [27] ( Figure S7 ) . We selected two sets of 3 , 000 markers for paternal and maternal linkage groups , respectively . Initial linkage groups were constructed based upon the 6 , 000 markers with the highest call rate ( >0 . 9 ) . The MMC method [25] was used to cluster the markers into linkage groups . In the MMC input file , homozygous genotypes were assigned a value of 0 or 2 , and heterozygous genotypes and missing data were assigned a value of 1 . Spearman's rank correlation coefficient was used by MMC . Markers representing the cross of AA×Aa segregate from either a paternal or maternal linkage group . Using the 36 linkage groups produced by MMC from these initial 6 , 000 markers as seeds , 88 , 217 markers ( 0 . 12<MAF<0 . 38 , call rate >0 . 5 ) were then assigned to linkage groups based upon their Spearman's rank correlation coefficient to each seed linkage group . To order the markers within each linkage group , we relied upon extensive synteny between the switchgrass and foxtail millet genomes . Markers were mapped to the foxtail millet genome ( http://www . phytozome . net/foxtailmillet . php ) via Basic Local Alignment Search Tool ( BLAST ) [56] with a P-value cutoff of 1e-5 . The 7 , 245 markers that mapped to a single site of the foxtail millet genome , and clustered with one of the syntenic linkage groups of switchgrass , were used to construct the synteny based framework linkage maps . To construct the high density linkage maps , the rest of the 80 , 972 markers were mapped to the framework marker with highest value of Spearman's rank correlation coefficient within their assigned linkage groups . A pairwise genetic distance matrix between individuals was calculated and an un-rooted NJ tree constructed using TASSEL [57] . All of the 29 , 221 markers with a call rate greater than 0 . 5 in the diverse populations were used in this analysis . To assess the robustness of the topology of the tree , 500 bootstrap replicates were performed using MEGA [58] . To address the evolutionary trajectory of upland switchgrass , a two-step phylogenetic analysis was performed . In the first step , foxtail millet was used as an outgroup . A NJ tree was reconstructed based on the 3 , 144 SNPs that could be aligned to unique positions in the foxtail millet genome . This first step identified the lowland ecotype as ancestral to the remaining switchgrass ecotypes studied herein . The second step omitted foxtail millet and used the lowland ecotype as the outgroup . This second NJ tree was reconstructed based on 29 , 221 markers ( alignment to foxtail millet not required ) . An MDS plot was also generated based upon the kinship matrix of individuals calculated from the 29 , 221 markers in TASSEL [57] .
|
Recent advances in sequencing technologies have enabled large-scale surveys of genetic diversity in model species with a wholly or partly sequenced reference genome . However , thousands of key species , which are essential for food , health , energy , and ecology , do not have reference genomes . To accelerate their breeding cycle via marker assisted selection , high-throughput genotyping is required for these valuable species , in spite of the absence of reference genomes . Based on genotyping by sequencing ( GBS ) technology , we developed a new single nucleotide polymorphism ( SNP ) discovery protocol , the Universal Network-Enabled Analysis Kit ( UNEAK ) , which can be widely used in any species , regardless of genome complexity or the availability of a reference genome . Here we test this protocol on switchgrass , currently the prime energy crop species in the United States of America . In addition to the discovery of over a million SNPs and construction of high-density linkage maps , we provide novel insights into the genome complexity , ploidy , phylogeny , and evolution of switchgrass . This is only the beginning: we believe UNEAK offers the key to the exploration and exploitation of the genetic diversity of thousands of non-model species .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"genome",
"evolution",
"plant",
"biology",
"population",
"genetics",
"ploidy",
"crop",
"genetics",
"genetic",
"maps",
"algorithms",
"genome",
"sequencing",
"genome",
"analysis",
"tools",
"plant",
"science",
"linkage",
"maps",
"plant",
"genomics",
"sequence",
"analysis",
"genome",
"complexity",
"plant",
"genetics",
"comparative",
"genomics",
"biology",
"agriculture",
"computer",
"science",
"genetics",
"biofuels",
"genomics",
"evolutionary",
"biology",
"genomic",
"evolution",
"computational",
"biology",
"genetics",
"and",
"genomics"
] |
2013
|
Switchgrass Genomic Diversity, Ploidy, and Evolution: Novel Insights from a Network-Based SNP Discovery Protocol
|
High-dose chemotherapy has long been advocated as a means of controlling drug resistance in infectious diseases but recent empirical studies have begun to challenge this view . We develop a very general framework for modeling and understanding resistance emergence based on principles from evolutionary biology . We use this framework to show how high-dose chemotherapy engenders opposing evolutionary processes involving the mutational input of resistant strains and their release from ecological competition . Whether such therapy provides the best approach for controlling resistance therefore depends on the relative strengths of these processes . These opposing processes typically lead to a unimodal relationship between drug pressure and resistance emergence . As a result , the optimal drug dose lies at either end of the therapeutic window of clinically acceptable concentrations . We illustrate our findings with a simple model that shows how a seemingly minor change in parameter values can alter the outcome from one where high-dose chemotherapy is optimal to one where using the smallest clinically effective dose is best . A review of the available empirical evidence provides broad support for these general conclusions . Our analysis opens up treatment options not currently considered as resistance management strategies , and it also simplifies the experiments required to determine the drug doses which best retard resistance emergence in patients .
Antimicrobial resistance is one of greatest challenges faced by modern medicine . There is a widely held view that the evolutionary emergence of drug resistance is best slowed by using high doses of drugs to eliminate pathogens as early and quickly as possible . This view , first expounded by Ehrlich [1] ( ‘hit hard’ ) and later Fleming [2] ( ‘if you use penicillin , use enough’ ) , is today encapsulated in the advice to administer ‘the highest tolerated antibiotic dose’ [3 , 4] . The rationale is two-fold . First , a high concentration of drug will eliminate drug-sensitive microbes quickly and thereby limit the appearance of resistant strains . Second , a high concentration of drug will also eliminate strains that have some partial resistance , provided the concentration is above the so-called mutant prevention concentration ( MPC ) [5–12] . This is an intuitively appealing idea , but several authors have recently questioned whether high-dose chemotherapy is , as a generality , defensible in terms of evolutionary theory [13–16] . This is because the use of extreme chemical force comes at the cost of maximizing the selective advantage of the very pathogens that we fear most; namely , those which cannot be eradicated by safely administered doses of drug . Some experimental studies have also shown that lighter-touch chemotherapy not only better prevents the emergence of resistance but it restores host health just as well as high-dose chemotherapy [15–17] . Here we use principles from evolutionary biology to provide a general and comprehensive theoretical framework for studying the effects of different drug treatment strategies . The analysis shows that high-dose chemotherapy gives rise to opposing evolutionary processes . As a result , the optimal therapy for controlling resistance depends on the relative strengths of these processes . High-dose therapy can , in some circumstances , retard resistance emergence but evolutionary theory provides no support for using this strategy as a general rule of thumb , nor does it provide support for focussing on the MPC as a general approach for resistance prevention . More broadly we find that the opposing evolutionary processes lead to a unimodal relationship between drug concentration and resistance emergence . Therefore the optimal strategy is to use either the largest tolerable dose or the smallest clinically effective dose . We illustrate these general points with some simple models that show how a seemingly minor change in parameter values can alter the outcome from one where high-dose chemotherapy is optimal to one where using the smallest clinically effective dose is best . A review of the empirical evidence provides broad support for these conclusions .
Determining a patient treatment regimen involves choosing an antimicrobial drug ( or drugs ) and determining the frequency , timing , and duration of administration . The impact of each of these on resistance emergence has been discussed elsewhere [9 , 18] . Here we focus solely on drug concentration because it has historically been the factor most often discussed and because it is the source of recent controversy [10 , 12–14 , 16] . We seek to understand how the probability of resistance emergence changes as a function of drug concentration . For simplicity we assume that drug concentration is maintained at a constant level during treatment and refer to this concentration as ‘dose’ . This assumption is not meant to be realistic but it serves as a useful tool for gaining a better understanding of how drug resistance evolves . After laying the groundwork for this simple case we show in the Supporting Information that allowing for more realistic pharmacokinetics does not alter our qualitative conclusions . Drug resistance is a matter of degree , with different genotypes having different levels of resistance ( measured , for example , as the minimum inhibitory concentration , MIC ) . Our main focus is on what we call high-level resistance ( HLR ) . This will be defined precisely below but for the moment it can be thought of as resistance that is high enough to render the drug ineffective ( so that its use is abandoned ) . We begin by supposing that the HLR strain is one mutational step away from the wild type but we relax this assumption in the Supporting Information . Why is it that resistant strains reach appreciable densities in infected patients only once drug treatment is employed ? The prevailing view is that there is a cost of resistance in the absence of the drug but that this cost is compensated for by resistance in the presence of the drug . It is not the presence of the drug per se that provides this compensation; rather , it is the removal of the wild type by the drug that does so [13 , 19] . This implies that the presence of the wild type competitively suppresses the resistant strain and that drugs result in the spread of such strains because they remove this competitive suppression ( a process called ‘competitive release’; [19] ) . To formalize these ideas , consider an infection in the absence of treatment . The wild type pathogen enters the host and begins to replicate . As it does so , it consumes resources and stimulates an immune response . We use P ( t ) to denote the density of the wild type and X ( t ) to denote a vector of within-host state variables ( e . g . , density of immune system components , resources , etc ) . Without loss of generality we suppose that the vector X is defined in such a way that pathogen replication causes its components to decrease . For example , if a component of the state vector represents some element of an immune response , then we can define this component of X ( t ) as the inverse of this immune cell density . This decrease in X , in turn , makes the within-host environment less favorable for pathogen replication . If X is suppressed enough , the net replication rate of the wild type will reach zero . Thus X can be viewed as the quality of the within-host environment from the standpoint of pathogen replication . As the wild type replicates it gives rise to the HLR strain through mutation and the initial infection might include some HLR pathogens as well . But the HLR strain is assumed to bear some metabolic or replicative cost , meaning that it is unable to increase in density once the wild type has become established . Mechanistically this is because the wild type suppresses the host state , X , below the minimum value required for a net positive replication by the HLR strain [19] . Thus , we ignore the effect of the HLR strain when modeling the joint dynamics of P ( t ) and X ( t ) in the absence of treatment ( see Appendix 1 in S1 Text for details ) . At some point ( e . g . , the onset of symptoms ) drug treatment is introduced . Provided the dosage is high enough the wild type will be driven to extinction . We use c to denote the ( constant ) concentration of the drug in the patient . We distinguish between theoretically possible versus feasible doses . Theoretically possible doses are those that can be applied in vitro . Feasible doses are those that can , in practice , be used in vivo . There will be a smallest clinically effective dose that places a lower bound on the feasible values of c ( denoted cL ) and a maximum tolerable dose because of toxicity ( denoted cU ) . The dose range between these bounds is called the therapeutic window [20] . Once treatment has begun , we use p ( t;c ) and x ( t;c ) to denote the density of the wild type strain and the within-host state . This notation reflects the fact that different dosages ( i . e . , concentrations ) will give rise to different trajectories of p and x during the remainder of the infection . We model the dynamics of p and x deterministically during this phase . As the wild type is driven to extinction it will continue to give rise to HLR microbes through mutation . The mutation rate is given by a function λ[p ( t; c ) , c] that is increasing in p and decreasing in c . We suppose that limc → ∞ λ[p , c] = 0 because a high enough drug concentration will completely suppress wild type replication and thus mutation . Any HLR microbes that are present during treatment will no longer be destined to rarity because they will be released from competitive suppression [19] . We use π[x ( t; c ) , c] to denote the probability of escaping initial extinction when rare . The function π is increasing in x because it is through this state that the HLR strain has been competitively suppressed [19] . And π is decreasing in c with limc → ∞ π[x , c] = 0 because a high enough dose will also suppress even the HLR strain . We can now provide a precise definition of high-level resistance ( HLR ) . Although limc → ∞ π[x , c] = 0 , the concentration at which this limit is reached can lie outside the therapeutic window [cL , cU] . We define HLR to mean that π[x , c] is very nearly equal to π[x , 0] over the therapeutic window . Biologically this means that , in terms of clinically acceptable doses , significant suppression of HLR is not possible . We focus on HLR because , for genotypes that do not satisfy this property , there is then no resistance problem to begin ( since one can always use a high enough dose to remove all pathogens ) . For instance , there is evidently no resistance problem in HIV and Hepatitis C when patients are fully compliant with recommended combination therapy regimens [21] . That is because at clinically acceptable doses of those combination therapies , mutations conferring HLR do not arise . We are here interested in cases in which significant suppression of HLR is not possible even at the upper end of the therapeutic window . With the above formalism , we focus on resistance emergence , defined as the replication of resistant microbes to a high enough density within a patient to cause symptoms and/or to be transmitted [19] . In the analytical part of our results this is equivalent to the resistant strain not being lost by chance while rare . With the above assumptions the host can be viewed as being in one of two possible states at any point in time during the infection: ( i ) resistance has emerged ( i . e . , a resistant strain has appeared and escaped ) , or ( ii ) resistance has not emerged . We model the transition between these two states as an inhomogeneous birth process . Appendix 1 in S1 Text then shows that the probability of resistance emergence is approximately equal to 1−e−H ( c ) where H ( c ) = D ( c ) + S ( c ) ( 1 ) and D ( c ) = ∫ 0 a λ [ p ( s ; c ) , c ] π [ x ( s ; c ) , c ] d s ( 2 ) S ( c ) = - n ln 1 - π [ x ( 0 ; c ) , c ] ( 3 ) We refer to H ( c ) as the resistance ‘hazard’ , and a is the duration of treatment with s = 0 corresponding to the start of treatment . The quantity D ( c ) is the de novo hazard—it is the hazard due to resistant strains that appear de novo during treatment . It is comprised of the integral of the product of λ[p ( s; c ) , c] , the rate at which resistant mutants appear at time s after the start of treatment , and π[x ( s; c ) , c] , the probability of escape of any such mutant . The quantity S ( c ) is the standing hazard—it is the hazard due to a standing population of n resistant microbes that are already present at the beginning of treatment ( see Appendix 1 in S1 Text ) . To minimize the probability of resistance emergence we therefore want to minimize the hazard H ( c ) , subject to the constraint that the dosage c falls within the therapeutic window [cL , cU] .
The opposing evolutionary processes explained above are the reason why intermediate doses yield the largest hazard [16] . First note that the functions λ and π will typically be such that D ( 0 ) ≈ 0 . In other words , the HLR strain does not emerge de novo within infected individuals if they are not receiving treatment . Mechanistically , this is because any resistant strains that appear tend to be competitively suppressed by the wild type strain [19] . Although , S ( 0 ) need not be zero ( see S2 Fig ) , the rate of change of S ( c ) with respect to c ( i . e . , the third term in Eq 4 ) is positive at c = 0 . Therefore the maximum hazard cannot occur at c = 0 . Second , for large enough doses we have π[x ( s; c ) , c] ≈ 0 for all s because such extreme concentrations will prevent replication of even the HLR strain . This makes both the de novo hazard D ( c ) and the standing hazard S ( c ) zero . Furthermore , for large enough c we also have λ[p ( s; c ) , c]≈0 for all s as well if HLR can arise only during wild type replication , because such extreme concentrations prevent all replication of the wild type . This is an additional factor making the de novo hazard D ( c ) decline to zero for large c . Therefore limc → ∞ H ( c ) = 0 and so the maximum hazard cannot occur for large values of c either [16] . Thus , the maximum hazard must occur for an intermediate drug dosage . Although this prediction is superficially similar to that of the mutant selection window hypothesis [5–9] , there are important differences between the two as will be elaborated upon in the discussion . We have seen that the maximum hazard occurs for an intermediate dose . Although in principle the hazard function might be quite complex , in practice our models have never produced anything other than a unimodal relationship between H ( c ) and c ( i . e . , a single maximum ) . Furthermore , because the maximum hazard must always occur at an intermediate dose , even if the theoretical hazard curve is multimodal the existence of error in drug delivery and other sources of noise will tend to make the empirical hazard curve unimodal ( Appendix 1 in S1 Text ) . As will be seen in the Discussion , an extensive body of empirical work also shows that measured hazard curves always appear to be unimodal . As a result , the drug dose which best reduces the probability of resistance emergence is always at one of the two extremes of the therapeutic window . This means that it is best to use either the smallest clinically effective dose or the largest tolerable dose depending on the situation , but never anything in between ( Fig 1 ) . To illustrate the general theory we now consider an explicit model for the within-host dynamics of infection and resistance . We model an acute infection in which the pathogen elicits an immune response that can clear the infection . Treatment is nevertheless called for because , by reducing the pathogen load , it reduces morbidity and mortality ( see Appendix 3 in S1 Text for details ) . We begin by considering a situation in which the maximum tolerable drug concentration cU causes significant suppression of the resistant strain ( Fig 2a ) . We stress however that if this were true then , by definition , the resistant strain is not really HLR and thus there really is no resistance problem to begin with . We include this extreme example as a benchmark against which comparisons can be made . Not surprisingly , under these conditions a large dose is most effective at preventing resistance ( compare Fig 2b with 2c ) . This is a situation in which the conventional ‘hit hard’ strategy is best . Modern treatment of HIV is an example of this . With combination therapy and good patient compliance , it is evidently possible to completely prevent virus replication and thus the emergence of resistance [18] . Now suppose that the maximum tolerable drug concentration cU is not sufficient to directly suppress the resistant strain ( Fig 3a ) . In this case the only difference from Fig 2 is a change in the resistant strain’s dose-response curve . Now there really is a potential resistance problem in the sense that , from a clinical standpoint , the drug is largely ineffective against the resistant strain . Under these conditions we see that a small dose is more effective at preventing resistance emergence than a large dose ( compare Fig 3b with 3c ) . This is a situation in which the conventional or orthodox ‘hit hard’ strategy is not optimal . Eq ( 4 ) provides insight into these contrasting results . The only difference between the models underlying Figs 2 and 3 is that ∂π/∂c and ∂π0/∂c are both negative for Fig 2 whereas they are nearly zero for Fig 3 ( that is , at tolerable doses , the drug has negligible effects on resistant mutants ) . As a result , the negative terms in Eq ( 4 ) outweigh the positive terms for Fig 2 whereas the opposite is true for Fig 3 . These results appear to contradict those of a recent study by Ankomah and Levin [12] . Although their model is more complex than that used here , Eq ( 4 ) and its extensions in S1 Text show that such additional complexity does not affect our qualitative conclusions . Ankomah and Levin [12] defined resistance evolution in two different ways: ( i ) the probability of emergence , and ( ii ) the time to clearance of infection . For the sake of comparison , here we focus on the probability of emergence . Ankomah and Levin [12] defined emergence as the appearance of a single resistant microbe . As such their emergence is really a measure of the occurrence of resistance mutations rather than emergence per se . In comparison , we consider emergence to have occurred only once the resistant strain reaches clinically significant levels; namely , a density high enough to cause symptoms or to be transmitted . There are two process that must occur for de novo resistant strains to reach clinically relevant densities . First , the resistant strain must appear by mutation , and both our results ( Fig 3d ) and those of Ankomah and Levin [12] show that a high dose better reduces the probability that resistance mutations occur ( this can also be seen in Eq 4 ) . Second , the resistant strain must replicate to clinically significant levels . Ankomah and Levin [12] did not account for this effect and our results show that a high concentration is worse for controlling the replication of resistant microbes given a resistant strain has appeared ( Fig 3d ) . This is because higher doses maximally reduce competitive suppression . In Fig 3 the latter effect overwhelms the former , making low-dose treatment better . In Fig 2 these opposing processes are also acting but in that case the drug’s effect on controlling mutation outweighs its effect on increasing the replication of such mutants once they appear . More generally , Fig 4 illustrates the relationship between drug concentration and the maximum size of the resistant population during treatment , for the model underlying Fig 3 . In this example a high concentration tends to result in relatively few outbreaks of the resistant strain but when they occur they are very large . Conversely , a low concentration tends to result in a greater number of outbreaks of the resistant strain but when they occur they are usually too small to be clinically significant . One can also examine other metrics like duration of infection , total resistant strain load during treatment , likelihood of resistant strain transmission , etc . but the above results are sufficient to illustrate that no single , general , result emerges . Whether a high or low dose is best for managing resistance will depend on the specific context ( i . e . , the parameter values ) as well as the metric used for quantifying resistance emergence . In Appendices 3–6 of S1 Text we consider cases where there is pre-existing resistance at the start of infection , strains with intermediate resistance , other measures of drug dosing and resistance emergence , a model of chronic infection based on resource competition , and more general pharmacokinetics . None of these factors alters the general finding that the optimal strategy depends on the balance between competing evolutionary processes .
Our framework makes a number of empirical predictions that are consistent with existing data . First , the resistance hazard will be maximized at intermediate drug concentrations . This is well-verified in numerous studies . In fact a unimodal relationship between resistance emergence and drug concentration ( often called an ‘inverted-U’ in the literature ) is arguably the single-most robust finding in all of the empirical literature [23–41] . Second , the position and shape of the hazard curve will vary widely among drugs and microbes , depending on how drug dose affects mutation rates and the strength of competition . Such wide variation is seen [23 , 24 , 28 , 29 , 35 , 38 , 39 , 42 , 43] , presumably reflecting variation in the strength of the opposing processes highlighted by Eq ( 4 ) . Third , the relationship found between drug concentration and resistance evolution in any empirical study will depend on the range of concentrations explored . At the low end , increasing dose should increase resistance evolution; at the high end , increasing dose should decrease resistance evolution . Examples of both cases are readily seen , often even within the same study [15 , 23–41 , 44–50] . It is important to note that there are clear examples for which low-dose treatments can better prevent resistance emergence than high doses [15 , 39 , 42 , 44–47 , 49–51] , despite an inherent focus in the literature on experimental exploration of high-dose chemotherapy . The theory presented here argues that uniformity is not expected and the bulk of the empirical literature is consistent with this prediction . An important and influential codification of Ehrlich’s ‘hit hard’ philosophy is the concept of the mutant selection window , and the idea that there exists a mutant prevention concentration ( MPC ) that best prevents resistance evolution [7–9] . The MPC is defined as ‘the lowest antibiotic concentration that prevents replication of the least susceptible single-step mutant’ ( see p . S132 in ref . [8] ) . When drug concentrations are maintained above the MPC , ‘pathogens populations are forced to acquire two concurrent resistance mutations for replication under antimicrobial therapy’ ( see p . 731 in ref . [52] ) . Below the MPC lies the ‘mutant selection window’ , where single-step resistant mutants can replicate , thus increasing the probability that microbes with two or more resistance mutations will appear . Considerable effort has been put into estimating the MPC for a variety of drugs and microbes [4] . The relationship between these ideas and the theory presented here is best seen using the extension of Eq ( 4 ) that allows for strains with intermediate resistance . Appendix 2 in S1 Text shows that , in this case , Eq ( 4 ) remains unchanged except that its first term ( the mutational component ) is extended to account for all of the ways in which the HLR strain can arise by mutation through strains with intermediate resistance ( see expression 2–3 in Appendix 2 of S1 Text ) . A focus on the MPC can therefore be viewed as a focus on trying to control only the mutational component of resistance emergence . And as the theory embodied by Eq ( 4 ) shows , doing so ignores the other evolutionary process of competitive release that is operating . The use of the MPC therefore cannot be supported by evolutionary theory as a general rule of thumb for resistance management . If evolutionary theory does not support the use of MPC as a general approach then why does this nevertheless appear to work in some cases [34 , 53] ? The theory presented here provides some possible explanations . First , if HLR strains can appear only through mutation from strains with intermediate resistance , and if feasible dosing regimens can effectively kill all first step mutants , then such an approach must necessarily work since it reduces all mutational input to zero . But for most of the challenging resistance management situations in medicine , achieving this is presumably not possible . For example , if the MPC is not delivered to all pathogens in a population because of patient compliance , metabolic variation , spatial heterogeneity in concentration , etc , then the mutational input will not be zero . Also , if HLR strains can arise in ways that do not require mutating through strains with intermediate resistance ( e . g . , through lateral gene transfer; [54] ) then again the mutational input will not be zero . In either case , one must then necessarily account for how the choice of dose affects the opposing evolutionary process of competitive release in order to minimize the emergence of resistance . S3 Fig illustrates this idea by presenting a numerical example in which the MPC is the worst choice of drug concentration for controlling HLR . Second , the theory presented here suggests that the MPC can be the best way to contain resistance if this concentration happens to be the upper bound of the therapeutic window ( although see S3 Fig for a counterexample ) . If , however , the MPC is less than the upper bound then even better evolution-proofing should be possible at either end of the therapeutic window . If the MPC is greater than the upper bound , as it is for example with most individual TB drugs [55] and levofloxacin against S . aureus [28] , the MPC philosophy is that the drug should then be abandoned as monotherapy . But our framework suggests that before doing so , it might be worthwhile considering the lower bound of the therapeutic window . Researchers have tended not to examine the impact of the smallest clinically effective dose on resistance evolution , perhaps because of an inherent tendency to focus on high-dose chemotherapy . It would be informative to compare the effects of the MPC with concentrations from both ends of the therapeutic window on resistance emergence experimentally . The MPC has yet to be estimated for many drug-microbe combinations [4] and it can be difficult to do so , especially in a clinically-relevant setting [52 , 54] . Given the uncertainties involved , and the need to make clinical decisions ahead of the relevant research , some authors have suggested the working rule of thumb of administering the highest tolerable dose [3 , 4] . Our analysis shows that evolutionary theory provides no reason to expect that this approach is best . By reducing or eliminating the only force which retards the emergence of any HLR strains that are present ( i . e . , competition ) , Eq ( 4 ) makes clear that a hit hard strategy can backfire , promoting the very resistance it is intended to contain . If the relative positions of the HLR hazard curve and the therapeutic window are known , rational ( evidence-based ) choice of dose is possible . If the therapeutic window includes doses where the resistance hazard is zero , then those doses should be used . However , by definition , such situations are incapable of generating the HLR which causes a drug to be abandoned , and so these are not the situations that are most worrisome . If the hazard is non-zero at both ends of the therapeutic window , the bound associated with the lowest hazard should be used ( Fig 1b and 1c ) . If nothing is known of the HLR hazard curve ( as will often be the case ) , then there is no need to estimate the whole function . Our analysis suggests that the hazards need be estimated only at the bounds of the therapeutic window . These bounds are typically well known because they are needed to guide clinical practice . Estimating the resistance hazard experimentally can be done in vitro and in animal models but we note that since the solution falls at one end of the therapeutic window , they can also be done practically and ethically in patients . That will be an important arena for testing , not least because an important possibility is that , as conditions change , the optimal dose might change discontinuously from the lowest effective dose to the highest tolerable dose or vice versa . There is considerable scope to use mathematical and animal models to determine when that might be the case and to determine clinical predictors of when switches should be made . Our focus has been on the evolution of resistance in the pathogen population responsible for disease . Looking forward , an important empirical challenge is to consider the impact of drug dose on the broader microbiome . Resistance can also emerge in non-target micro-organisms in response to the clinical use of antimicrobials [45] . Resistance in those populations can increase the likelihood of resistance in future pathogen populations , either because of lateral gene transfer from commensals to pathogens , or when commensals become opportunistic pathogens [9 , 56 , 57] . For instance , aggressive drug treatment targeted at bacterial pneumonia in a rat model selected for resistance in gut fauna . Lower dose treatment of the targeted lung bacteria was just as clinically effective and better managed resistance emergence in the microbiota [51] . It is unclear just how important these off-target evolutionary pressures are for patient health , but if they are quantitatively important , this raises the interesting and challenging possibility that the real hazard curve should be that of the collective microbiome as a whole , weighted by the relative risk of resistance evolution in the components of the microbiome and the target pathogen . It will be challenging to determine that , but our focus on either end of the therapeutic window at least reduces the parameter space in need of exploration . Our analysis suggests that resistance management is best achieved by using a drug concentration from one edge of the therapeutic window . In practice , patients are likely treated more aggressively than the minimum therapeutic dose ( to ensure no patients fail treatment ) and less aggressively than the maximum tolerable dose ( to ensure no patients suffer toxicity ) . This means that medical caution is always driving resistance evolution faster than it need go , particularly when the maximum hazard lies within the therapeutic window ( Fig 1b and 1c ) . From the resistance management perspective , it is important to determine the level of caution that is clinically warranted rather than simply perceived . For many years , physicians have been reluctant to shorten antimicrobial courses , using long courses on the grounds that it is better to be safe than sorry . It is now increasingly clear from randomized trials that short courses do just as well in many cases [58–60] and they can reduce the risk of resistance emergence [58 , 61 , 62] . We suggest that analogous experiments looking at the evolutionary outcomes of lowest clinically useful doses should be the next step . Such experiments in plants have already shown unambiguously that low dose fungicide treatment best prevents the spread of resistant fungal pathogens [63] . How generally true this is for other pathogens , or pathogens of other hosts , remains to be seen . We also note that our arguments about the evolutionary merits of considering the lowest clinically useful doses have potential relevance in the evolution of resistance to cancer chemotherapy as well [64] .
|
The evolution of antimicrobial resistant pathogens threatens much of modern medicine . For over one hundred years , the advice has been to ‘hit hard’ , in the belief that high doses of antimicrobials best contain resistance evolution . We argue that nothing in evolutionary theory supports this as a good rule of thumb in the situations that challenge medicine . We show instead that the only generality is to either use the highest tolerable drug dose or the lowest clinically effective dose; that is , one of the two edges of the therapeutic window . This approach suggests treatment options not currently considered , and simplifies the experiments required to identify the dose that best retards resistance evolution .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2016
|
Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance?
|
Understanding the relationship between brain structure and function is a fundamental problem in network neuroscience . This work deals with the general method of structure-function mapping at the whole-brain level . We formulate the problem as a topological mapping of structure-function connectivity via matrix function , and find a stable solution by exploiting a regularization procedure to cope with large matrices . We introduce a novel measure of network similarity based on persistent homology for assessing the quality of the network mapping , which enables a detailed comparison of network topological changes across all possible thresholds , rather than just at a single , arbitrary threshold that may not be optimal . We demonstrate that our approach can uncover the direct and indirect structural paths for predicting functional connectivity , and our network similarity measure outperforms other currently available methods . We systematically validate our approach with ( 1 ) a comparison of regularized vs . non-regularized procedures , ( 2 ) a null model of the degree-preserving random rewired structural matrix , ( 3 ) different network types ( binary vs . weighted matrices ) , and ( 4 ) different brain parcellation schemes ( low vs . high resolutions ) . Finally , we evaluate the scalability of our method with relatively large matrices ( 2514x2514 ) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest . Our results reveal a nonlinear structure-function relationship , suggesting that the resting-state functional connectivity depends on direct structural connections , as well as relatively parsimonious indirect connections via polysynaptic pathways .
The last decade has witnessed considerable progress towards understanding how the brain structural connections constrain and enable brain functional networks and the resulting behavior [1–3] . Structural connectivity ( SC ) refers to the ‘hard-wired’ white matter anatomic connections between brain areas and can be constructed with current noninvasive neuroimaging technologies , such as diffusion tensor imaging ( DTI ) and diffusion spectral imaging ( DSI ) [4] , whereas functional connectivity ( FC ) generally refers to the temporal correlation between nodal activities observed in functional neuroimaging data such as functional magnetic resonance imaging ( fMRI ) blood oxygenation level–dependent ( BOLD ) signals that are acquired during task performance or the resting state [5] . In particular , resting-state fMRI has become an important basis for functional network analysis , after the discovery of intrinsic spatially distributed low-frequency fluctuations of BOLD signals [6] . Structural and functional networks each provide different , yet complementary information about brain organization and function . There is no simple relationship between structure and function . For example , brain areas not directly connected by structural links can be functionally connected [7] . Therefore , the question of how diverse functional networks arise from a relatively fixed structural architecture remains unanswered in neuroscience . Several attempts have been made to relate the brain structure to function . One is to simply calculate the SC-FC correlation [8–13] or compare the graph theory based topological features between structural and functional networks [14–18] . An emerging approach is to use large-scale computational modeling to understand the link between structural and functional brain connectivity [19–22] . Alternative methods focus on directly performing the topological mapping of structure-function connectivity using the regression framework [23–24] . These methods are either largely descriptive , or limited to relatively small connectivity matrices and therefore difficult to build realistic whole-brain models . It remains a challenge to fully elucidate the intricate relationship between brain structure and function . In this paper , we present a whole-brain data-driven approach to the structure-function mapping . We show that the predicted functional matrix can be represented as a weighted sum of the powers of the structural matrix , consisting of both direct and indirect effects along varying paths . We find a stable solution by exploiting the regularization procedure to cope with large matrices . We further introduce a novel measure of network similarity based on persistent homology [25–27] for assessing the goodness of fit for the mapping; such a measure enables the complete comparison of network topological changes across all possible thresholds , thereby effectively circumvents the problem of arbitrarily selecting the threshold on edge weights of the resulting functional networks . On three connectivity datasets , we demonstrate that our approach can fully uncover the direct from the indirect structural paths in predicting functional connectivity . We examine the structure-function relationship systematically by comparing ( 1 ) the regularized vs . non-regularized procedures , ( 2 ) a null model of the degree-preserving random rewired vs . original structural matrices , ( 3 ) binary vs . weighted network types , and ( 4 ) low- vs . high- resolution parcellation schemes . Finally , we evaluate the scalability of our method with relatively large matrices ( 2514x2514 ) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest [28] . Our results indicate a nonlinear structure-function relationship , suggesting that the resting-state functional connectivity is mainly mediated by direct structural connections , and is also contributed by relatively parsimonious indirect connections via polysynaptic pathways .
Structural connectivity from five healthy right-handed male participants was obtained using diffusion spectrum imaging ( DSI ) data and tractography [7 , 14] . The gray matter was partitioned into 66 cortical regions according to anatomical landmarks , and further subdivided into 998 regions-of-interest ( ROIs ) . The 998 ROIs were chosen to provide approximately equal size such that their boundaries aligned with those of the 66 cortical regions [14] . As a result , two different parcellations were generated in the same dataset: the low-resolution of 66 regions and the high-resolution of 998 ROIs . White matter tractography was used to estimate the fiber tract density connecting each pair of ROIs , averaged across subjects . The dataset are available from the open source Connectome mapper [29] . The empirical resting-state functional connectivity was also obtained for the same subjects by measuring the corresponding fMRI BOLD signal during 20 min in absence of stimulation or task . This empirical functional connectivity matrix reflects the correlation of the BOLD activity between different brain areas at rest . The correlations of BOLD activity were computed using the ROI time series that were down-sampled to the 66-region map by averaging across all ROIs within each region . Fig 1A shows the low-resolution anatomical and resting-state functional connectivity matrices ( 66 x 66 ) . Each row or column of the matrix shows a region of interest or ROI of the cortex , and its entries represent structural or functional connectivity with all the other ROIs . The matrices represent both the left and right hemispheres , with the top half of the rows/columns representing one hemisphere and the bottom half the other . The structural and functional connectivity were extracted from same-subject diffusion tensor imaging ( DTI ) and fMRI data acquired non-invasively from 12 healthy human subjects while at rest . It consists of a parcellation of 2514 ROIs; the dataset can be downloaded from the Brain Hierarchical Atlas NITRC page ( https://www . nitrc . org/frs/ ? group_id=964 ) . Further details are available in [28] . These are relatively large matrices ( 2514 x 2514 ) gathered from the same subjects . Given the structural and functional connectivity matrices S ∈ ℝN×N and F ∈ ℝN×N , with N being the total number of network nodes , our goal is to find a general matrix function f that maps S onto F ( Fig 1A ) : F=f ( S ) ( 1 ) For any analytic function f , the mapping can be approximated via the Taylor series as a polynomial in S of degree at most N-1 due to the Cayley-Hamilton theorem [30 , 31]: F^=∑k=0N−1ckSk ( 2 ) where the first term ( k = 0 ) is an offset for fitting the diagonal elements of the matrix F , reflecting local , recurrent self-coupling within individual nodes; the second term ( k = 1 ) represents the direct contribution of S to F ( e . g . , the middle path between nodes 1 and 5 in Fig 1B ) , and other higher-order terms ( k ≥ 2 ) provide indirect contributions of various path lengths ( e . g . , S2 term or the length 2 of the upper path between nodes 1 and 5 via the intermediate node 2: 1–2–5; S3 term or the length 3 of the lower path between 1 and 5 via 3 and 4: 1–3–4–5 in Fig 1B ) . For S being a weighted structure matrix , Sk contains the same path information as the binary matrix at the path length of k , but additionally incorporates weight information into the structure . Therefore , the observed functional matrix can be represented as a weighted sum of the powers of the structural matrix , consisting of both direct and indirect effects along varying paths . This model has an underlying assumption that the maximum absolute eigenvalue of the structural matrix is smaller than one , which can be satisfied by normalizing the coupling strength of the matrix ( see below ) . The coefficients in ( 2 ) are typically found with the least-squares method , yet the matrix mapping becomes numerically ill-posed and rank-deficient for very large networks . To improve the model fitting , we first introduce a global brain signal as a common shared input to all the nodes , which can be represented as a constant matrix whose values are all the same . Spontaneous BOLD signal can exhibit coherence both within discrete brain networks and over the entire brain [32] . A global signal , typically defined as signal averaged across all voxels , can induce high correlations across the brain , which is often removed to better isolate functional networks . Second , we address the rank-deficient issue by solving the Tikhonov regularized least squares problem , which is given as follows: c^=argmin‖F−∑k=0KckSk−G‖2+μ‖c‖2 , with c = ( c0 , c1 , … , cK ) T , and the summation term is the estimated functional connectivity matrix of maximum path length K , which can be determined by the goodness of fit ( See below ) . G is a constant matrix denoting the global shared input . The regularization parameter μ can be determined by the generalized cross-validation ( GCV ) [33 , 34] . GCV provides a robust estimate of how well a given model would fare under cross-validation testing ( particularly how overfit or underfit the model is ) without the demanding computational burden for performing every cross-validation . We have used the GCV as described in [35] . The statistical significance of the estimated coefficients can be assessed with the bootstrap resampling procedure . Unless otherwise stated , S is a binary structural matrix ( where connections are either absent or present ) that is converted from the complete weighted matrix; it hence has the same number of edges as the original structural matrix . When solving the above optimization problem , a couple of important practical issues ought to be considered . First , only the diagonal and the lower triangular part of the matrices are needed since all the matrices involved are symmetric . Second , with the increasing path length , the matrix powers of S rapidly blow up , which becomes a rather severe issue for large matrices . To avoid the numerical overflow of the power series , before the mapping is performed , we normalize each matrix power by dividing every element by a normalization factor , which is defined as the maximum absolute element of the matrix , i . e . , Sk/max ( |Sk| ) . We then restore the same normalization factor to the corresponding estimated coefficient to obtain the coefficients conforming to the original matrix power . As a result , the estimated coefficients at higher path lengths are typically rather small due to the large values of the higher matrix powers . To compare the empirical and predicted functional networks , we introduce a new measure of network similarity based on persistent homology [25–27 , 36] . Calculating persistent homology generally requires a measure of distance or dissimilarity between nodes . We can convert the correlation-based connectivity matrix to a dissimilarity matrix via 1– |correlation| . To fix the idea , we first construct a binary network from the dissimilarity matrix: two nodes are connected by an edge if their distance is less than a parameter λ ( called the filtration value in persistent homology ) . At λ = 0 , the number of connected components ( also known as the zeroth Betti number , β0 ) is equal to the number of nodes , since no edge links are included . As λ is increased , the number of connected components declines . As a result , the persistence represented by λ measures how long node pairs , hence path-connected components , stay connected as the filtration value λ varies . Observing the topological evolution over changing filtration values leads to a network filtration , which allows the construction of a minimum spanning tree [37 , 38] . The changes in connectedness can be quantified by a barcode summarizing the zeroth Betti number , which counts the number of path-connected components , across a range of λ [27] . The so-called barcode , as illustrated in Fig 2 ( top ) for the low-resolution dataset , consists of a plot tracking the fate of connected components as the filtration value λ changes , with connectivity maps of functional network at some selected λ shown at the bottom of Fig 2 . We measure the goodness of fit of the mapping as the sum of squared errors ( SSEβ ) between the barcodes for the target ( β0 ( λ ) ) and predicted ( β^0 ( λ ) ) networks , which is defined as follows: SSEβ=1N2∫01 ( β^0 ( λ ) −β0 ( λ ) ) 2dλ The normalization by N2 , where N is the total number of network nodes , renders this measure independent of the network size , thus facilitates the comparison between different networks . As the path length grows , the SSE of the mapping decreases . The maximum path length K is selected where the SSE stops descending precipitously . A key advantage of using persistent homology is that one can examine a graph filtration generated by all possible thresholds and systematically analyze the persistence of network topological changes across these thresholds [39 , 40] . Therefore , our new measure is ideal for comparing brain network topologies as it does not rely on any fixed threshold , but instead encompasses all the detailed changes in network architecture in the filtration .
We first carried out network mapping for low-resolution connection matrices with a size of 66 x 66 ( N = 66 anatomical subregions , see Methods and Fig 1A ) . Fig 3 in the top row provides the snapshots of the structural matrices sequentially with direct connection , length-2 and length-3 indirect connections , whereas in the middle row shows the inferred functional connectivity matrices at maximum path length of 1 , 2 and 3 , respectively . The inferred functional matrix is a weighted sum of the powers of the structural matrix , shown in the bottom of Fig 3 , consisting of both direct and indirect pathways in addition to the local , recurrent self-coupling within nodes and the global shared input . Next , we performed persistent homology analysis to assess the quality of the network mapping . Fig 4A shows the barcodes of the inferred functional matrix at different path lengths and the target functional connectivity matrix ( red curve ) , where we see across the full scales that the inferred networks approach the target network as the path length increases . The goodness of fit , indexed by SSEβ , is shown in Fig 4B , where we see the SSEβ decreases as the path length increases , with the maximum path length of 5 ( the elbow point ) containing most of the structural information needed to infer functional connectivity . Fig 4C displays the estimated global shared input and model coefficients up to K = 5 , with the error bars indicating the 95% confidence interval obtained via the bootstrap procedure . We note that the coefficients alone are not a reliable indicator for determining the influence of each path length as the matrix powers at the longer path lengths can have very high values , particularly for large matrices . To demonstrate that our regularization method is able to handle large matrices , we scale up the mapping matrices to high-resolution parcellation ( 998 x 998 ) , which is a refinement of the low-resolution ( 66 x 66 ) surface partition . Importantly , by comparing the low- and high-resolution matrices on the same dataset , we can examine the extent to which the mapping is influenced by the choice of different parcellation schemes . Fig 8A and 8B display , respectively , the structural and functional connectivity matrices , with a size of 998 x 998 . Fig 8C shows the predicted functional connectivity matrix at the maximum path length of 4 , which is determined by the SSEβ curve ( Fig 8E ) based on the barcodes shown in Fig 8D . The estimated coefficients , together with their 95% confidence intervals , are listed in Table 1 , where we see that the large weight in the direct pathway relative to the indirect paths , in addition to remarkably strong local , inhibitory self-coupling within nodes and the modest global shared input to all the nodes . Note that the differential coupling of positive and negative coefficients at different path lengths , although the exact nature is unknown , could indicate the direction of the signal change in the underlying neuronal events . The comparison of the correlation between the observed and predicted functional networks for the low- and high-resolution structural connections is shown in Fig 8F . Clearly , the anatomical connectivity is better reflected by FC at low spatial resolution and is only weakly correlated with FC at high resolution , a finding consistent with previous studies [7] . In addition , regardless of whether the parcellation is of low or high spatial resolution , we observe that the paths of length up to 5 in the brain graph contain most of the structural information needed to predict functional connectivity . This observation is striking with important implications for understanding the structure-function relationship , suggesting that the functional interactions are mediated by rather parsimonious polysynaptic anatomical connections . As a final test , we evaluate the generalizability and scalability of our method using a new dataset consisting of relatively large matrices ( 2514x2514 ) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest . Fig 9A–9C , respectively , show the structural connection , functional connectivity matrices and the predicted functional matrix at the maximum path length of 5 , as determined by the goodness of fit shown in Fig 9E . Table 2 shows the estimated coefficients and their 95% confidence intervals . We observe very strong local self-coupling and modest global shared input . Similar to other datasets analyzed above , the direct pathway again shows much stronger influence than the indirect pathways; the influence is gradually fading with increasing path length . The barcodes derived from the persistent homology analysis for the inferred functional matrices at the path length ranging from 1 to 10 are presented in Fig 9D . Though the correlation between structural and functional matrices increases as more indirect paths are involved ( Fig 9F ) , the overall correlation values however are rather modest , as compared to the small matrices . These results suggest that finer parcellation of anatomical connectivity may be more vulnerable to scanning noise and fiber reconstruction errors . It is intriguing to note that the structural paths of length up to 5 seem to be highly robust across various sizes of the matrices for achieving optimal prediction of functional connectivity . These results together provide the compelling evidence that resting-state functional connectivity depends on direct underlying structural connections within a relatively economical polysynaptic pathway .
In this paper , we have presented a whole-brain data-driven approach to the structure-function mapping . Such a mapping allowed the inferred functional matrix to be represented as a weighted sum of the matrix powers of the structural connections , containing both direct and indirect pathways . This representation unveils a nonlinear relationship between the underlying structural connections and the observed functional networks , as confirmed by all three connectivity datasets used in this study . We have further introduced a novel measure of network similarity based on persistent homology for assessing the quality of the network mapping; such a measure enabled the complete comparison of network topological changes across all possible thresholds , and thus effectively circumvented the problem of selecting the arbitrary threshold for the resulting functional networks . We demonstrated that our approach could uncover the direct as well as the indirect structural paths in predicting functional connectivity . We provided a more detailed characterization of our approach in four different aspects . First , we compared our regularized verse the non-regularized procedures . For small matrices , both methods yielded consistent results , which provided a validation for our regularization procedure . The non-regularized method , however , failed for large matrices such as 998 x 998 due to rank deficiency problems . In essence , our whole-brain structure-function network mapping is just to solve a large-scale optimization problem with Tikhonov regularization , also known as l2 regularization , which perhaps is the most commonly used method of regularization of ill-posed problems . There is a rich body of literature on sparse network models such as compressed sensing , graphical-LASSO , sparse partial correlations [43–49] . It is conceivable that such regularization can be replaced with the l1 regularization or LASSO [43] in the objective function . Unlike the Tikhonov regularization , the LASSO can set some coefficients to zero . As such , the interpretations can be different , which would require some additional justifications relative to the path length . Second , we performed a null model analysis with the degree-preserving randomly rewired structural topology . The random rewiring allowed us to assess whether our observations were directly shaped by the fixed structural network or could be simply explained by a random process . We found that the observed structure-function relationship cannot be reproduced with the rewired structural matrices , suggesting that structural networks impose critical constraints on functional networks . Therefore , the null model is an important control to assay the dependence of functional networks on structural connections . Additionally , it can be used as a benchmark value for statistical significance test . The null model has been increasingly used to assess what network features are important in separating different types of networks [50 , 51] . The best way to generate random networks , however , is still an actively debating topic for brain network studies [52 , 53] . Determining what topological information in the structural data is driving the structure-function network mapping will be important for understanding the nature and relationships between networks . Although we have used a null model in which the degrees of all nodes are strictly preserved , other null models such as a minimally wired network [63] are possible . Further studies are needed in terms of the more biologically meaningful null models as benchmarks . Third , we examined the impact of different network types ( binary vs . weighted matrices ) on the mapping . The key observation was that the weighted structural network seemed always to provide more information for the structure-function mapping than the binary unweighted network , with the caveat of proper weight rescaling . The rescaling was due to the fiber counts straddling several orders of magnitude; it was not realistic to have such a large range for inter-areal physiological efficacies [7] . As we observed , the model did not perform well on the raw fiber data , especially for large matrices where the model coefficients rapidly blew up with the increasing path length . Thus , it is crucial to resample the fiber values into a Gaussian distribution to reduce the their large range before the mapping is performed or simply use the binary structural matrix . Fourth , we determined how the mapping was influenced by different parcellations of brain regions by comparing the fine and coarse structural matrices , i . e . , high and low spatial resolutions , on the same dataset . In general , we observed that ( 1 ) for either low- or high-resolution parcellation , the structure-function correlation grows with the increasing paths of length up to 5 , the direct structural path of which has the strongest influence on the resulting functional connectivity , and ( 2 ) the overall structure-function correlation values are somewhat modest for large matrices , as compared to the small matrices . Our observation seems rather general , albeit relatively small sample size , as confirmed for large matrices ( 2514 x 2514 ) from a different dataset of 12 subjects . This analysis suggests that the resting-state functional connectivity is primarily mediated by direct structural connections , with incremental contributions from relatively short indirect pathways up to length 5 in the underlying structural connections . The elbow-point selected at the path length 4–5 in our analysis is visually determined . The choice of an optimal path length K is often ambiguous , with interpretations depending on the data . Knowing the exact number of path length is desirable , but does not add fundamentally to our main conclusion that the resting-state functional connectivity is in part dependent on direct structural connections , but also indirect connections via polysynaptic pathways . Nonetheless , examining how much correlation explained by each path length may offer a hint to the relative importance of structural paths of different length in the mapping . For example ( Fig 5B ) , at the path length of 1 , there is a correlation of 0 . 516 when only direct structural path is considered . The correlation jumps up to 0 . 555 , which is 3 . 9% increase of correlation ( i . e . , 0 . 555–0 . 516 = 0 . 039 ) when structural path lengths of 2 are considered . As we consecutively add structural path length of 3 to 7 into the mapping , the correlation increase at each corresponding path length are , respectively , 1 . 3% , 1 . 9% , 3 . 0% , 0 . 6% , and 0 . 4% . Notice how the incremental improvement is gained by including each path length , becoming negligible for K ≥ 5 . It is also worth mentioning that the path length up to 5 does not necessarily related directly to the diameter of the structural network [2]; the maximum path length is generally shorter than the network diameter ( e . g . , the diameter is 6 for 998-node network , and is 9 for the 2524-node network ) . Nonetheless , it seems that the diameter of the structural brain network provides a much tighter upper bound than the network size for the length of the paths used in the Taylor-series approximation ( Eq . 2 ) . We also note that the overall low structure-function correlation observed in the finer parcellation of anatomical connectivity , which could be due to ( 1 ) the noise potentially introduced in the scanning , and ( 2 ) the low quality of structural connectivity for long interhemispheric fiber tracts through the corpus callosum , which were not detected because of the limited resolution of the imaging/tractography techniques [14] . The structure-function topological mapping introduced in this study is a general method developed within the framework of matrix function [31] . To determine the coefficients in the model ( Eq . 2 ) , there are several options that can be deemed as the special cases . One option is to simply consider the coefficients as some specific constants so that a closed-form solution can be achieved . For example , when ck = 1 , we have F ≈ 1/ ( 1−S ) , which forms the basis of network deconvolution algorithm [54] . Similarly , when ck = 1/k ! , we have F=∑k=0N−1Skk ! ≈eS , whereby the k ! in the denominator indicating that longer paths contribute disproportionately less compared to shorter paths . Such a closed-form solution leads to the well-known measure of network communicability [55] . In this work , we have focused upon mapping structural connections to functional networks . Similarly , we can infer functional connectivity from structural connections [56] by simply inversing the role of F and S . It can be done from the algorithmic viewpoint [24] , but anatomical verification is preferred . In addition , unlike the structure-to-function mapping , the interpretation of the inverse mapping becomes less straightforward . Persistent homology is a method used in topological data analysis to characterize topological structures such as connectedness and holes in high-dimensional data [25–27] . Traditionally , it has been applied to point-cloud data , though recently it has become increasingly prominent in network neuroscience to uncover the topological structure of data [36–39 , 57–62] . A unique feature of persistent homology is that it allows one to examine the changes in network architecture over a full spectrum of possible thresholds rather than just at a single fixed threshold value . To assess the quality of the network mapping , we analyzed the topological structures of these inferred functional networks against the target empirical functional network , and examined the persistent homology by calculating the zeroth Betti number ( β0 ) , i . e . , the number of connected components . Earlier work has showed that , β0 , the persistence of components , can be used to classify pediatric attention deficit hyperactivity disorder ( ADHD ) , autism spectrum disorder ( ASD ) and control subjects [38] . Similarly , we found that our β0-based network similarity measure was sensitive enough to capture the changes in network topology with different path lengths , and compared favorably to other existing methods . Though our measure is based only on the zeroth Betti number , it is important to note that other Betti numbers such as β1 ( the first Betti number ) and β2 ( the second Betti number ) , corresponding to topological circles and trapped volumes in the data , respectively , capture other higher-order homologies in the data and therefore can also be used to measure more complex structures of the network . As recently demonstrated [63] , relative persistent clique patterns can be identified in the brain via higher-order Betti numbers . We note that , however , the interpretation of weighted network architecture via persistent homology does not in itself account for the presence of spurious network links which can be characterized via appropriate null networks . When measuring the functional connectivity , we have used the Pearson correlation coefficient . Other measures such as partial correlation or Granger causality can also be used [64–66] . For example , partial least squares method has been recently used to investigate the association between structural networks and functional networks [67] . Likewise , conditional Granger causality , which statistically removes the common input and the indirect influences between a given ROI pair , has been shown to be more appropriate to quantify the strength of the functional interaction enabled by the fibers linking the a pair of ROIs [13] . A detailed comparison at the whole-brain scale of each measure’s performance against well-characterized connectivity data would serve to identify their relative strengths and weaknesses .
|
One of the major challenges in neuroscience is to understand how brain structure is related to function . In this work , we present a whole-brain method to quantify the structure-function relationship . Our data-driven approach allows the inferred functional connectivity matrix to be represented as a weighted sum of the powers of the structural matrix , containing both direct and indirect pathways . We further introduce a novel measure of network similarity based on persistent homology for assessing the goodness of fit for the mapping; such a measure enables the complete comparison of network topological changes across all possible thresholds , and thus effectively circumvents the problem of selecting the arbitrary threshold for the resulting functional networks . Our results show that our approach is able to uncover both direct and indirect structural paths for predicting functional connectivity in all three connectivity datasets , suggesting that the resting-state functional connectivity is at least in part mediated by indirect pathways , in addition to direct structural connections . The finding of a nonlinear relationship between brain structure and function is conceptually new , thus advances our understanding of how structural networks shape functional networks . This work demonstrates the potential utility of our approach in a rapidly growing field of network neuroscience .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"diagnostic",
"radiology",
"functional",
"magnetic",
"resonance",
"imaging",
"neural",
"networks",
"signaling",
"networks",
"neuroscience",
"magnetic",
"resonance",
"imaging",
"brain",
"morphometry",
"cognitive",
"neuroscience",
"brain",
"mapping",
"network",
"analysis",
"neuroimaging",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"imaging",
"techniques",
"radiology",
"and",
"imaging",
"diagnostic",
"medicine",
"diffusion",
"tensor",
"imaging",
"biology",
"and",
"life",
"sciences",
"cognitive",
"science"
] |
2017
|
Structure-Function Network Mapping and Its Assessment via Persistent Homology
|
Trypanosome lytic factor ( TLF ) is a high-density lipoprotein ( HDL ) subclass providing innate protection to humans against infection by the protozoan parasite Trypanosoma brucei brucei . Two primate-specific plasma proteins , haptoglobin-related protein ( Hpr ) and apolipoprotein L-1 ( ApoL-1 ) , have been proposed to kill T . b . brucei both singularly or when co-assembled into the same HDL . To better understand the mechanism of T . b . brucei killing by TLF , the protein composition of TLF was investigated using a gentle immunoaffinity purification technique that avoids the loss of weakly associated proteins . HDL particles recovered by immunoaffinity absorption , with either anti-Hpr or anti-ApoL-1 , were identical in protein composition and specific activity for T . b . brucei killing . Here , we show that TLF-bound Hpr strongly binds Hb and that addition of Hb stimulates TLF killing of T . b . brucei by increasing the affinity of TLF for its receptor , and by inducing Fenton chemistry within the trypanosome lysosome . These findings suggest that TLF in uninfected humans may be inactive against T . b . brucei prior to initiation of infection . We propose that infection of humans by T . b . brucei causes hemolysis that triggers the activation of TLF by the formation of Hpr–Hb complexes , leading to enhanced binding , trypanolytic activity , and clearance of parasites .
African trypanosomes are blood parasites of mammals in sub-Saharan Africa that cause chronic wasting diseases in both humans and domestic animals [1] . The three subspecies of Trypanosoma brucei are defined by their host range , geographical distribution , and course of disease [1–3] . Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense infect humans and cause African sleeping sickness , while Trypanosoma brucei brucei infects non-primate mammals and causes nagana in cattle . All African trypanosomes are able to evade the host adaptive immune system through a process called antigenic variation , which is a consequence of periodic changes in the variant surface glycoprotein that covers the entire parasite [4] . T . b . brucei does not cause human disease because of its susceptibility to an innate immune activity in human serum . This protection is conferred by trypanosome lytic factor ( TLF ) , a minor subclass of human high-density lipoprotein ( HDL ) [5–7] . TLF contains apolipoprotein A-I ( ApoA-1 ) , a protein found in all subclasses of HDL , and two proteins , haptoglobin-related protein ( Hpr ) and apolipoprotein L-1 ( ApoL-1 ) that are unique to primates [8–19] . Both Hpr and ApoL-1 have been reported to be toxic to T . b . brucei [8 , 14] . The cellular pathway for TLF killing of T . b . brucei initiates with binding of TLF to high-affinity receptors located in the flagellar pocket of the parasite [20 , 21] . Bound TLF is endocytosed via coated vesicles and traffics to the parasite lysosome . Within the acidified lysosome , TLF is activated and causes parasite lysis [22–24] . ApoL-1 and Hpr have been proposed to have different mechanisms of toxicity and may act synergistically . ApoL-1 is a colicin-like protein that kills trypanosomes through the formation of ion pores [10 , 25–29] . Hpr is a hemoglobin ( Hb ) -binding protein that has been proposed to induce an iron-dependent , Fenton-like reaction within the acidic lysosome of T . b . brucei that leads to the formation of free radicals and peroxidation of the lysosomal membranes [15 , 24 , 30] . When ApoL-1 and Hpr are present in the same HDL particle , the specific activity for T . b . brucei killing is enhanced 800-fold [14] . Hpr is 91% identical to haptoglobin ( Hp ) , an abundant ( ∼0 . 45–3 mg/ml in normal human serum ) acute phase serum protein , possessing very high affinity for Hb [31] . Complexes of Hp and Hb that form when Hb is released from erythrocytes undergoing intravascular hemolysis are removed from the circulation by the CD163 scavenger receptor [32] . In contrast to Hp–Hb , the Hpr–Hb complex does not bind CD163 [31 , 33] , and the Hpr serum concentration appears to be unaffected by hemolysis [32 , 33] . A role for Hb in trypanolysis has previously been speculated but this view was subsequently debated [12 , 15 , 30] . In light of this , the biological significance of the recently reported high-affinity binding of recombinant Hpr to Hb remains enigmatic . In the present study we have re-investigated a potential function of Hb in relation to TLF and trypanolysis . Hb was absent from TLF purified from freshly collected human plasma using a gentle immunoabsorption protocol . Nevertheless , Hb bound to purified native Hpr , as well as TLF , with high-affinity and stimulated T . b . brucei killing in vitro . The association of Hb with Hpr significantly increased TLF binding to T . b . brucei . Furthermore , both an iron chelator and a free radical scavenger inhibited cell lysis , suggesting a direct role for Hb-derived iron in T . b . brucei killing by TLF . A combination of the ion pore–forming activity of ApoL-1 and free radical production of Hpr–Hb may account for the synergy observed in HDLs containing these proteins .
In order to define the protein composition of native TLF , freshly prepared human plasma was fractionated by “one-step” immunoaffinity chromatography with antibodies against Hpr or ApoL-1 . Unlike the “two-step” method that initially uses high-salt density ultracentrifugation followed by immunoaffinity chromatography to purify TLF , the one-step method eliminates the possible loss of apolipoproteins during the high-salt centrifugation step [34] . These methods allowed us to make two comparisons: First , a comparison of proteins purified by the one-step purification protocol with proteins purified by the two-step purification protocol . Second , it allowed us to compare the composition of TLF purified by the one-step purification method using antibodies to ApoL-1 or Hpr . Previous analysis of the protein composition of TLF , purified by the two-step procedure , demonstrated the presence of ApoA-1 , ApoL-1 , Hpr dimer , and Hpr tetramer ( Figure 1A ) [14] . Comparison of proteins purified by the two procedures by SDS-PAGE and western blot analysis showed that the protein compositions were similar in both cases ( Figure 1 ) . This shows that high-salt density ultracentrifugation used in the two-step protocol does not dramatically change the protein composition of the TLF particle . Two differences were noticed . First , the ratio of Hpr dimer to tetramer was higher in samples purified using the single-step immunoabsorption method . Second , there was a reduction in sub-stoichiometric proteins in preparations of TLF purified by the two-step purification method . Specifically , a significant amount of Hp , albumin , and transferrin was detected by SDS-PAGE , western blotting , and liquid chromatography/mass spectrometry/mass spectrometry ( LC-MS/MS ) in the one-step purified material ( Figure 1A–1C , Table 1 ) . In addition to these serum proteins , other human serum proteins , including angiotensinogen and intellectin , were detected at low abundance by LC-MS/MS analysis ( unpublished data ) . Notably , no Hb was detected in any of the TLF preparations . In order to determine whether the protein composition of HDLs containing Hpr or ApoL-1 differed significantly , samples were purified from human plasma by the one-step immunoaffinity method using antibodies to Hpr , ApoL-1 , or ApoA-1 . All samples were analyzed by Coomassie staining , western blot , and LC-MS/MS ( Figure 1; Table 1 ) . The protein composition of samples purified by anti-Hpr or anti-ApoL-1 absorption was indistinguishable . The similar ratio of Hpr and ApoL-1 , in the samples purified with anti-Hpr and anti-ApoL-1 , indicates that most serum Hpr and ApoL-1 are assembled into the same HDL . The major proteins present in samples purified by anti-ApoA-1 immunoaffinity chromatography were ApoA-1 , Hp , and transferrin . Only trace amounts of ApoL-1 and Hpr were detected in these samples , consistent with TLF being a minor subclass of human HDL [6] . These findings rule out the possibility that a significant amount of human plasma Hpr or ApoL-1 is free in the circulation and suggests a pathway for co-assembly of these two apolipoproteins into the same HDL particle . The specific activity of TLF purified by the one-step method using antibodies to either Hpr or ApoL-1 was similar . In our standard lysis assay , 0 . 5 μg resulted in 40% lysis in 2 h at 37 °C ( Figure 2A ) . Human plasma samples purified with anti-ApoA-1 were also trypanolytic , but the specific activity for killing was reduced over 10-fold ( Figure 2A ) . The low specific activity of the ApoA-1-purified samples was due to the relatively low ratio of TLF to non-lytic HDL in this preparation . This is consistent with previous estimates of TLF abundance , suggesting it represents less than 0 . 1% of total human serum HDL [6 , 14] . When plasma samples were purified by the two-step protocol that included high-salt density ultracentrifugation followed by either anti-ApoL-1 or anti-Hpr immunoaffinity purification , the specific activity for trypanosome killing was approximately 10-fold higher than samples purified by the one-step methods ( 0 . 025 μg and 0 . 04 μg giving ∼50% lysis , respectively ) ( Figure 2B ) . The increased lytic activity of the samples purified by the two-step procedure may be due to the increased purity of these samples . This is because gradient centrifugation employed in the two-step purification process reduces the amount of contaminating , high-abundance serum proteins , such as albumin and transferrin ( Figure 1A ) . In addition , Hp , a potent inhibitor of TLF killing of T . b . brucei [9] , is weakly associated with all human HDL and is effectively removed by high-salt density ultracentrifugation ( Figure 1A ) . The mechanism of Hp inhibition of human serum lysis of T . b . brucei is not known; based on results presented later , inhibition may be a direct consequence of Hp binding to free Hb in serum . Two different morphological phenotypes have been reported for human HDL killing . One is defined by lysosomal membrane breakdown , cell swelling , and trypanosome death [13 , 24 , 30 , 35 , 36] . The other , recently reported by Pays and co-workers , involves extensive lysosome swelling , creating a large cytosolic vacuole that is proposed to exert enough pressure on the plasma membrane that the cell ruptures [8 , 10 , 25] . Plasma samples purified by either anti-Hpr or anti-ApoL-1 immunoaffinity produced identical morphological changes in T . b . brucei preceding lysis ( Figure 2C–2F ) . After a lag phase of approximately 20 min , the overall morphology of the cells changed . Swelling was accompanied by an overall change in the shape of the cells , first resulting in a kite-like appearance followed by continued swelling and rounding of the cells until the cells ruptured . The formation of a large cytoplasmic vacuole was not observed . In live cell imaging , the nucleus , nucleolus , flagella , and other organelles are visible ( Figure 2D and 2F ) . The morphology of T . b . brucei treated with intact human plasma produces the same morphological changes , leading to T . b . brucei lysis , like those seen with purified TLF ( Figure 8C ) . The reason for the discrepancies between the morphological changes observed by the Pays lab and those observed by us and others following treatment with either purified TLF or intact human serum/plasma remains unresolved . Previous studies suggested Hpr , despite being highly homologous to Hp , did not bind Hb [12 , 30] . It is likely that Hpr–Hb complexes were not detected in these studies because of the presence of mild , non-ionic detergents in the immunoprecipitation assays . More recently , it has been shown that native and recombinant Hp and recombinant Hpr bind Hb with high affinity and that Hb-coupled Sepharose can precipitate Hpr-containing HDLs from plasma [31] . Although our analysis of TLF revealed the presence of Hpr , no Hb was detected in any of the purified HDL preparations . In order to determine whether native TLF and purified native Hpr could bind Hb , surface plasmon resonance ( SPR ) analysis was performed with purified preparations of native TLF , Hpr , and Hb . TLF and Hpr both bound immobilized Hb with high affinity ( the Kd for the Hpr–Hb interaction was 2–5 nM as estimated by SPR ) ( Figure 3A ) . The horizontal progress of the curves representing the dissociation phase ( after arrows in Figure 3A ) indicates an almost irreversible binding of Hpr as well as binding of TLF to Hb . To ensure that the SPR response observed upon incubation with TLF ( Figure 3A , right panel ) could not be attributed to binding between Hb and Hpr released from the TLF particle , binding of purified TLF to Hb was also studied in a pull-down assay . As revealed in Figure 3B , Hb-coupled Sepharose beads specifically bound the entire TLF particle containing Hpr as well as ApoL-1 and ApoA-1 . Consistent with the recent finding that recombinant Hpr in complex with Hb does not bind the human Hp–Hb receptor CD163 , the complex between native Hpr and Hb did not bind to immobilized purified human CD163 in the SPR analysis ( unpublished data ) [31 , 33] . The observation that Hb binds to purified Hpr and TLF led us to re-investigate the role of Hb in TLF-mediated killing of T . b . brucei . Since fetal bovine serum ( FBS ) , a component of our standard trypanosome assay , typically contains small amounts of Hb released by hemolysis during serum preparation , we modified the standard trypanosome lysis assay to eliminate FBS ( Figure 4A ) . The killing activity of TLF was drastically reduced when bovine serum albumin ( BSA ) was substituted for FBS in our in vitro lysis assay . Addition of FBS restored maximal tyrypanosome killing activity in the assay ( Figure 4B and 4C ) . To determine whether Hb was the co-factor supplied by the FBS in the in vitro lysis reactions , we titrated Hb into the modified serum-free assay ( Figure 5A ) . Hb restores the killing activity of TLF , while the addition of Hb alone is non-toxic to T . b . brucei even at very high concentrations ( Figure 5A; Figure S1 ) . Furthermore , if the Hb-binding protein , Hp , is added to the reactions , it inhibits lysis in a concentration-dependent fashion ( Figure 5B ) . Together , these results indicate that Hb is a necessary co-factor for maximal TLF killing of T . b . brucei and that direct binding of Hb to Hpr may be required . Hb could stimulate trypanosome killing either a ) by increasing the affinity of TLF for the trypanosome receptor , b ) by playing a direct role in the lytic process , or c ) by a combination of the above . To test whether Hb was necessary for trypanosome binding , Alexa Fluor 488-labeled TLF was incubated with trypanosomes at 4 °C for 1 h , cells were washed , and then cell-associated fluorescence was measured by flow cytometery ( Figure 6A ) . Addition of Hb increased binding of TLF to T . b . brucei . Maximum stimulation of binding was reached at a concentration of approximately 30 μg/ml , which represents a 1:10 molar ratio of TLF to Hb , assuming an average molecular mass of 500 kDa for TLF [6] . Addition of an equimolar concentration of Hp to Hb severely inhibited binding of TLF to cells , resulting in binding levels similar to the TLF binding observed in the absence of Hb , either by sequestering all available Hb or by competition for the trypanosome TLF–Hb receptor . These experiments suggest that Hb binding to TLF stimulates receptor-mediated binding to T . b . brucei , and that TLF–Hb binding to the receptor is specific and saturable . Killing of T . b . brucei by TLF requires trafficking of the toxin to the acidic trypanosome lysosome [22 , 24] . To determine whether both TLF and Hb co-localize to the lysosome , Alexa Fluor 488–conjugated TLF and Alexa Fluor 594–conjugated Hb were incubated with trypanosomes . When TLF or Hb alone was incubated with cells , no intracellular fluorescence was observed ( Figure S2A ) . Co-incubation of TLF and Hb resulted in the rapid uptake and intracellular co-localization to the lysosome ( Figures 6B and S2B ) . These results illustrate that TLF needs to be associated with Hb in order to be endocytosed by T . b . brucei , and that both TLF and Hb traffic to the lysosome of the cell . Heme is toxic to trypanosomes presumably through production of free radicals that lead to lipid peroxidation of trypanosome membranes [37] . Previous studies suggested that the toxicity of TLF might involve lysosomal membrane peroxidation [15 , 24 , 30] . Since Hb traffics with TLF to the trypanosome lysosome , we asked whether Hb could initiate a Fenton-like reaction . Fenton chemistry requires ferrous iron , hydrogen peroxide , and low pH to produce hydroxyl radicals that peroxidate lipid and can lead to membrane degradation and cell death [38] . All of these conditions are present if TLF transports Hb to the trypanosome lysosome . To test whether TLF–Hb induces a Fenton reaction , we performed lysis assays in the presence of the iron chelator deferiprone and in the presence of the free radical scavenger N , N′-diphenyl-1 , 4-Benzenediamine ( DPPD ) ( Figure 7A and 7B ) . Both iron chelation and the scavenging of free radicals inhibited lysis by about 50% . To further investigate the mechanism of TLF killing , we returned to morphological analysis of TLF–Hb-treated trypanosomes . We have shown that TLF killing of T . b . brucei results in cell swelling , changes in overall cell morphology , and eventually lysis ( Figure 2C–2F ) [24] . Consistent with earlier electron microscopy of cells treated with gold-conjugated TLF [24 , 36] , these results suggest that TLF–Hb causes the breakdown of lysosomal membranes . To determine whether TLF–Hb caused extensive lysosomal membrane breakdown , T . b . brucei was incubated with fluorescein-labeled 500-kDa dextrans , which trafficked to the lysosome by bulk phase endocytosis ( Figure 7C ) . After the cells were pre-loaded with dextrans for 30 min , TLF–Hb or human plasma was added to the cells . After 30 min of exposure , the fluorescent dextrans were found predominately in a single vesicle co-localizing with the lysosomal membrane marker p67 . By 120 min post-treatment with either TLF–Hb or human serum , the dextrans were visible throughout the cell , while the p67 remained predominantly associated with the singular lysosome . Since the fluorescently labeled dextrans escape the lysosome in the presence of TLF , the lysosomal membrane must be open to allow release of the large dextrans . The increased intensity of the fluorescein-labeled dextrans at 120 min post TLF treatment is likely due to their release into the neutral pH of the cytoplasm [39] . Incubation with normal human plasma also resulted in release of pre-loaded dextrans into the cytoplasm prior to trypanosome lysis ( Figure 7C ) . These results show that TLF–Hb causes lysosomal membrane breakdown prior to cell lysis , as does normal human serum .
A major question concerning the killing of T . b . brucei by human serum has centered around whether the toxin was Hpr , ApoL-1 , or both of these proteins [10 , 14 , 25 , 40] . In this paper , the protein composition of human TLF was determined by western blot and LC-MS/MS . While several studies have previously identified protein components of this subclass of trypanolytic human HDL , all have used samples that have been subjected to lengthy purification protocols involving high-salt density ultracentrifugation [6 , 34] . We were concerned that these procedures would dissociate proteins from TLF that might provide information on the native composition of this innate killing factor and on the mechanism of killing . When TLF was isolated directly from freshly prepared human plasma by immunoaffinity chromatography with antibodies against either human ApoL-1 or Hpr , the major protein components were identical . The specific activity and morphology of T . b . brucei lysis was also identical . This analysis indicates that Hpr and ApoL-1 are assembled into the same HDL particle and that the amount of Hpr or ApoL-1 found free in the human circulatory system is extremely low . Thus , the native toxin in humans is not ApoL-1 or Hpr alone but is an HDL particle containing both of these apoliproteins . This is consistent with previous studies showing that Hpr and ApoL-1 were present in the same HDL and both were required for maximal trypanosome killing [14] . In addition , recent analysis of the trypanolytic activity of human serum from individuals deficient in either ApoL-1 or Hpr suggests that both proteins play distinct and important roles to achieve maximal lytic activity [40] . Several early studies reported that TLF did not bind Hb [12 , 30] , but recently it was shown that recombinant Hpr bound Hb with high affinity [31] . Consistent with these results , we found that purified native TLF and purified native Hpr bound Hb with high affinity ( Figure 3 ) . Nevertheless , MS analysis failed to detect Hb as a component of purified TLF , most likely because the low levels of Hb that are released into the plasma of individuals with normal erythrocyte turnover are instantly bound by Hp , which is present in large excess compared to Hpr ( the concentration of Hp is roughly 10- to 100-fold higher than Hpr in normal human serum ) [12 , 31] . The rapidly formed Hp–Hb complex is then removed by CD163 . Hence , despite its high affinity for Hb , Hpr may only compete well for free Hb under physiological conditions where Hp levels were low . As we discuss below , infection by trypanosomes causes declines in Hp levels in animals [41–44] . In order to evaluate the role of Hb as a co-factor in TLF-mediated killing of T . b . brucei , we developed an in vitro lysis assay without FBS , a standard component of our in vitro trypanosome lysis assays , and a source of contaminating free Hb ( Figure 4A ) . We found that substitution of BSA for FBS in lysis assays dramatically reduced the activity of TLF , but that maximal activity could be restored by addition of human Hb to the assays ( Figure 5A ) . Hb stimulation of T . b . brucei killing by TLF is due to both enhanced binding to the trypanosome cell surface receptor for TLF and direct cytotoxicity of lysosomally localized Hb ( Figures 6 and S2B ) . The basis for the enhanced trypanosome receptor recognition of the TLF–Hb complex is unknown , but the mammalian CD163 receptor shows a similar binding preference for the Hp–Hb complex [32 , 33] . Our studies show that not only is trypanosome binding of TLF stimulated by Hb association but that the trafficking of both TLF and Hb to the lysosome is enhanced ( Figures 6 and S2B ) . The mechanism of TLF-induced cell lysis is controversial , but studies from our lab have shown that both freshly prepared normal human serum and TLF produce peroxidated lipids in T . b . brucei , suggesting a mechanism of killing [30] . Based on the findings reported here , we propose that Hb , bound to Hpr within the TLF particle , contributes directly to the established ApoL-1-mediated toxicity of TLF [8 , 10 , 28 , 29 , 40] . Following binding and lysosomal localization , Hb may contribute iron that reacts with H2O2 in a Fenton-based reaction that leads to free radical formation and lysosomal membrane breakdown ( Figure 7 ) [9 , 15 , 24 , 45] . Interestingly , earlier studies using an in vitro TLF assay containing 0 . 2% BSA , and no Hb , did not kill T . b . brucei by a Fenton-based mechanism ( [46] ) . The morphological analysis of T . b . brucei treated with TLF–Hb showed cells swelling into a kite-shape prior to the bursting . We were able to directly test the permeability of the lysosomal membrane following TLF–Hb treatment by pre-loading cells with defined size ( 500 kDa ) fluorescein-conjugated dextrans then adding TLF–Hb ( Figure 7C ) . Prior to lysis , dextrans were seen throughout the cytoplasm indicating that TLF–Hb caused the lysosomal membrane to breakdown , releasing the large fluorescein-conjugated dextrans . Based on the pharmacological inhibition of TLF–Hb killing by deferiprone and DPPD , we postulate that membrane lipid peroxidation plays a major role in lysosomal membrane breakdown followed by cell lysis . This does not argue against a role of ApoL-1 in TLF killing of T . b . brucei [8 , 10 , 19 , 25–28] . Recombinant and native ApoL-1 are toxic to T . b . brucei and the evidence that it creates ion pores in trypanosome membranes is convincing [10] . Does Hb stimulation of TLF binding and killing of T . b . brucei occur in humans , and is this important in protection against these parasites ? In vitro studies reported here leave little doubt that Hb association with Hpr enhances TLF binding to and killing of T . b . brucei . However , when TLF is isolated from normal human donors , Hb is absent . This apparent paradox can be explained when one carefully considers the early physiological events associated with trypanosome infection . During infection of animals by African trypanosomes , there is substantial hemolysis , and in some cases , the hematocrit can drop by as much as 50% [42–44] . The exact cause of hemolysis is not known , but blood cell production increases during infection , so the decrease in hematocrit is not due to decreased erythrocyte production but rather to hemolysis [47] . Hemolysis results in the release of large quantities of Hb , most of which will be bound and removed by circulating Hp [42 , 48] . During a trypanosome-induced acute phase response in calves , levels of Hp are reduced to undetectable levels 8 d post-infection , presumably due to removal of Hp from the circulation after Hp–Hb complexes have formed [42] . In mice , Hp levels initially increase after trypanosome infection , and then decline [48 , 49] . Consistent with the studies in animals , human trypanosome infections also result in substantial hemolysis and release of free Hb [50] . While Hp initially binds all free Hb , the clearance of the Hp–Hb complex would result in a significant drop in Hp levels to the point where Hpr would become a major Hb-binding protein . Thus , as Hp levels decrease and TLF–Hb levels increase , there may be a substantial stimulation of TLF activity . A second factor may influence the formation of TLF–Hb complexes in the circulation of humans . Previous studies have reported the incidence of both genetic and phenotypic ahaptoglobinemia in African populations , perhaps due to reduced severity of malaria infection in Hp-negative individuals [51–53] . In some regions where malarial infections are prevalent , the frequency of ahaptoglobinemic individuals can be as high as 48% [51 , 54 , 55] . In these individuals , Hpr-associated TLF could be the primary Hb-binding protein . We have previously postulated that ApoL-1 and Hpr act synergistically in T . b . brucei killing [14] . We now propose an avenue by which TLF activity is increased when Hpr and ApoL-1 are present in the same HDL particle . First , Hb binding to TLF stimulates endocytosis of TLF–Hb by increased receptor affinity . Second , TLF–Hb provides iron that initiates Fenton chemistry , leading to lysosomal membrane breakdown while ApoL-1 forms pores in membranes , disrupting the ability of the cell to regulate osmosis . The results presented here support the hypothesis that formation of the Hpr–Hb complex within the TLF particle plays a role in human serum killing of T . b . brucei . Hb seems to induce increased binding of TLF to the parasite and contributes directly to toxicity in the lysosome . This suggests that parasite-induced hemolysis stimulates innate immunity against the parasite in humans .
Blood was collected from healthy human volunteers and was maintained on ice or at 4 °C throughout the fractionation procedure . Plasma was separated from blood cells by centrifugation at 3 , 500 rpm in a SLA-3000 rotor ( Sorvall , http://www . thermo . com/ ) for 10 min at 4 °C . The plasma supernate was removed and re-centrifuged in an SS-34 rotor ( Sorvall ) at 9 , 500 rpm for 10 min at 4 °C to remove any residual blood cells . Plasma was kept on ice for no more than 3 h prior to antibody affinity chromatography . Monoclonal antibodies were raised against TLF particles , and the specificity of the antibodies for Hpr and ApoL-1 has been previously described [14] . The monoclonal antibodies were purified from mouse ascites by Protein G affinity chromatography according to the manufacturer's recommendations ( Pierce Biotechnology , http://www . piercenet . com/ ) . Affinity-purified polyclonal antibodies against human ApoA-1 were purchased from Rockland Immunochemicals ( http://www . rockland-inc . com/ ) . The three antibodies ( 200 μg ) were coupled to Affigel 15 ( 50 μl ) ( Bio-Rad , http://www . bio-rad . com/ ) according to the manufacturer's recommendations and 100 μl of the AffiGel slurry ( corresponding to approximately 200 μg of antibody ) was transferred to 1 . 5-ml microfuge tubes . Human plasma ( 1 ml ) was added to each antibody containing tube and incubated for 30 min at 4 °C on a rotating platform . AffiGel/antibody resin was recovered by centrifugation for 90 s at 1 , 000g at 4 °C and the immuno-depleted plasma discarded . Additional 1-ml samples of plasma were added to each sample and immuno-depletion steps were repeated until 5 ml of plasma had been treated . The AffiGel/antibody resin was then washed seven times with 1 ml of PBSE ( 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 10 mM KH2PO4 , 3 mM EDTA ) . Following the washes , the AffiGel/antibody resin was treated with 200 μl of 100mM glycine ( pH 2 . 0 ) for 5 min at 4 °C to elute bound human plasma proteins . The eluate was dialyzed against PBSE at 4 °C prior to analysis for protein composition and trypanolytic activity . Following elution from anti-ApoL-1 , anti-Hpr , and anti-ApoA-1 AffiGel resin , samples were precipitated with 100% ice-cold acetone and proteins were separated on a 10% SDS-polyacrylamide gel under non-denaturing conditions . Gels were either Coomassie stained or western blotted onto nitrocellulose . For western blot analysis , membranes were blocked with 5% milk in TTBS ( 20mM Tris , 500mM NaCl , 0 . 05% Tween-20 ) and were probed with antibodies diluted with 5% milk in TTBS: Hpr ( 1:10 , 000 ) , ApoL-1 ( 1:10 , 000 ) , ApoA-1 ( 1:5000 ) , Hb ( 1:2000 ) , and transferrin-Hrp ( 1:2000 ) . After incubation with secondary antibodies ( goat anti-rabbit and goat anti-mouse at 1:10 , 000 in all cases except transferrin ) , ( Pierce Biotechnology ) proteins were detected following reaction with ECL Plus ( GE Healthcare , http://www . gelifesciences . com/ ) and were visualized by autoradiography . Protein concentrations were determined by Bradford assay ( Bio-Rad ) . For mass spectroscopy analysis , protein bands were excised from Coomassie-stained gels , trypsin digested , and fractionated by reverse phase chromatography ( C-18 PepMap100 , 75 um ID × 15 mm , 3-um particle size , LC Packings/Dionex , http://www . dionex . com/ ) . The column eluate was introduced onto a QSTAR XL mass spectrometer ( Applied Biosystems , http://www . appliedbiosystems . com/ , and MDS Sciex , http://www . mdssciex . com/ ) by electrospray ionization . Ions were selected and fragmented using a standard information dependent acquisition method . An ion had to be assigned a charge in the +2 to +4 range to be considered a candidate for fragmentation . Identification of proteins was performed using ProID software ( Applied Biosystems ) and experimental spectra were matched against in silica trypsinizations of the NCBI non-redundant database . Bloodstream T . b . brucei ( Lister strain 427 , MiTat 1 . 2 ) were cultured in HMI-9 media supplemented with 10% FBS ( heat inactivated , 58 °C for 30 min ) . Trypanolytic activity of fractionated HDLs was determined by in vitro microscopic assays as previously described [6] . All samples were dialyzed exhaustively against PBSE , at 4 °C , prior to incubation with the parasites ( 1 × 107 cells per 300-μl assay ) for 2 h at 37 °C in HMI-9 media containing 10% FBS . To determine whether FBS contributed to TLF-mediated killing of T . b . brucei some lysis assays contained 1% BSA and 1% glucose without intact FBS . Hb A0 and Hp 1–1 were obtained from Sigma-Aldrich ( http://www . sigmaaldrich . com/ ) . In this paper and in previous papers , we define a lytic unit of activity as the amount of material necessary to lyse 50% of the parasites , in a 300-μl assay containing 1 × 107 cells after incubation for 2 h at 37 °C [8] . The iron chelator 1 , 2-dimethyl-3-hydroxypyrid-4-one ( deferriprone ) was used in the chelation inhibition studies and DPPD was the antioxidant used . Lysis of live parasites was visualized by the addition of TLF to T . b . brucei imbedded in 1% low melting point agarose ( Sigma ) made with PBSE containing 1% glucose following 2 h of incubation at 37 °C . Samples were prepared for microscopy by the addition of 10 μl of low melting point agarose to 10 μls of T . b . brucei at a final concentration of 1 × 107/ml . Both the agarose and trypanosome samples were maintained at 37 °C , gently mixed , and transferred to a microscope slide . To further reduce the motility of trypanosomes , the slide was chilled at 4 °C for 10 min and then imaged using a motorized Zeiss Axioplan2 and an MRm camera interfaced with the Axiovisions 4 . 4 software ( Zeiss , http://www . zeiss . com/ ) . Methanol-fixed samples were also visualized . Cells incubated with TLF samples as above were washed with PBSE containing 10% FBS , then resuspended and smeared on a microscope slide . Slides were rapidly air dried , and cells were fixed for 5 min in methanol ( −20°C ) . Following methanol fixation , slides were air dried and mounted with 4' , 6-diamidino-2-phenylindole ( DAPI ) containing antifade reagent ProlongGold ( Invitrogen , http://www . invitrogen . com/ ) and imaged as above . Trypanosomes were resuspended at a concentration of 1 × 107/ml in F12-FBS and were incubated with 300 μg/ml 500-kDa fluorescein-labeled dextrans ( Invitrogen ) for 30 min at 37° C . Cells were then washed three times with 1X PBSE and were incubated at 3 × 106/ml in F12-FBS with either 2 units of normal human serum ( 1 . 7% total human serum ) or 2 units of purified TLF ( 0 . 017% TLF ) , and cells were examined after 30 min or 2 h . Aliquots were treated with 0 . 001% formaldehyde on ice for 5 min , rinsed with PBS , and resuspended in PBS-10% FBS . Cells were then smeared onto a slide and air dried . For co-localization with anti-p67 , cells were rehydrated in PBS-10% FBS and incubated in 1:1000 anti-p67 ( a gift of Jay Bangs , University of Wisconsin , Madison , Wisconsin , United States ) for 1 h at room temperature , followed by staining with a goat anti-mouse secondary antibody labeled with Alexa Fluor 594 ( Invitrogen ) . Slides were rinsed in PBS containing 1% glucose , air dried , and viewed using a motorized Zeiss Axioplan2 and an MRm camera interfaced with the Axiovisions 4 . 4 software ( Zeiss ) . SPR analysis was conducted essentially as described in [31] except that native TLF and Hpr were used in the binding assays instead of recombinant Hpr . Hpr and TLF were purified from fresh human plasma using the two-step purification method [14] . In the case of Hpr purification , human HDLs were solubilized using 10mM CHAPS and purified using an anti-Hpr column as described [14] . The Kd for the Hpr–Hb interaction was estimated by the BIAevaluation 4 . 1 software ( http://www . biacore . com/lifesciences/index . html ) using a Langmuir 1:1 binding model . For precipitation experiments , purified TLF was incubated with Hb-coupled Sepharose , BSA-coupled Sepharose , or underivatized Sepharose . After extensive washing in a solution containing 2 mM CaCl2 , 1 mM MgCl2 , 10 mM Hepes , and 140 mM NaCl ( pH 7 . 8 ) , bound proteins were eluted in SDS-containing sample buffer and visualized by SDS-PAGE . Binding studies of TLF to trypanosomes was conducted with Alexa Fluor 488 ( Invitrogen ) labeled TLF , and Hb and Hp noted above . Trypanosomes were incubated with labeled TLF , unlabeled Hb , and Hp at 4 °C for 1 h . The cells were then washed three times at 1 , 400g for 7 min at 4 °C . The cells were resuspended in 2% formaldehyde at 4 °C until measured by flow cytometry using the CyAn ADP ( Dako , http://www . dako . com/ ) and analyzed using FlowJo software ( TreeStar , http://www . treestar . com/ ) . Immunofluoresence microscopy was employed to localize TLF and Hb in the cell . Alexa Fluor 488 ( Invitrogen ) –labeled TLF and Alexa Fluor 594 ( Invitrogen ) –labeled Hb were incubated with trypanosomes pre-incubated with 50 μM chloroquine for 30 min . TLF and Hb were added at the concentrations noted in Figure 7 . LysoTracker Red DND99 ( Invitrogen ) was used to identify the lysosome . These studies were done on a Zeiss Axioplan and the images analyzed using IPLab Spectrum version 3 . 9 . 4r2 from Scanalytics–BD Biosciences ( http://www . scanalytics . com/ )
|
African trypanosomes are parasites that can infect a wide range of mammals , including domestic animals and humans . Several hundred thousand humans are infected with African sleeping sickness , but this number would be much higher if not for a natural defense molecule found in human blood . The trypanosome lytic factor ( TLF ) is a minor subclass of high-density lipoprotein that contains two proteins found only in primates , apolipoprotein L-1 and haptoglobin-related protein ( Hpr ) . In this paper , we show that Hpr contributes to TLF toxicity to trypanosomes because it binds hemoglobin ( Hb ) . We found that when Hb is bound to TLF , it is rapidly taken up by the parasite and activated within the acidic environment of the parasite's digestive organelle , the lysosome . Within the lysosome , Hb releases iron , inducing a chemical reaction that produces free radicals that damage membranes and contributes to trypanosome killing . Usually , free Hb is rapidly cleared from the circulation of mammals because of the organ damage free Hb can cause . Trypanosome infection results in breakage of red blood cells and the release of large amounts of Hb . We postulate that trypanosome infection causes increased vascular levels of Hb , resulting in the formation of TLF–Hb complexes that may be important in “arming” the human innate immune system to clear the circulation of certain African trypanosomes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"infectious",
"diseases",
"in",
"vitro",
"immunology",
"eukaryotes",
"molecular",
"biology"
] |
2007
|
Hemoglobin Is a Co-Factor of Human Trypanosome Lytic Factor
|
The soil-dwelling bacillus Burkholderia pseudomallei is the etiological-agent of the neglected and life-threatening emerging infection melioidosis . The distribution of B . pseudomallei in West Africa is unknown . In the present study we aimed to determine whether B . pseudomallei and B . thailandensis are present in the environment of central Sierra Leone . In June-July 2017 , we conducted an environmental surveillance study–designed in accordance with existing consensus guidelines—in central Sierra Leone . A total of 1 , 000 soil samples ( 100 per site ) were collected and cultured . B . pseudomallei was not identified in the soil , but we identified seven novel B . thailandensis sequence types with multi-locus sequence typing ( MLST ) and 16S rRNA gene sequence analyses . The presence of B . pseudomallei was not demonstrated , however , multiple novel B . thailandensis sequence types were identified . More environmental and sequencing studies are needed to further understand the genetic diversity , evolution and virulence of these emerging organisms .
The Gram-negative environmental bacterium Burkholderia pseudomallei is the etiological agent of melioidosis , an emerging but neglected infectious disease . Disease presentations vary from abscess formation to fulminant sepsis [1] . Melioidosis has a mortality up to 50% in low resource settings and is predominantly found in Southeast-Asia and northern Australia [1] . Infection with B . pseudomallei primarily occurs in people who are in regular contact with soil and water [1 , 2] . B . thailandensis is a member of the B . pseudomallei complex , is considered a-virulent [3 , 4] and rarely causes disease in humans [5–10] . Knowledge about the global distribution of B . pseudomallei and B . thailandensis , however , is limited . Patients from sub-Saharan Africa reported with melioidosis are few and isolated ( e . g . the The Gambia , Burkina Faso , Nigeria and Gabon ) , which most probably is the result of under-recognition and under-reporting . These cases may represent the ‘Tip of the Iceberg’ [1 , 11] . From the West African country of Sierra Leone , only one case of melioidosis has been reported [12] . Modelling studies , however , estimate that in Sierra Leone annually hundreds of patients suffer from melioidosis , of which the vast majority will die [13] . The tropical climate , heavy rains and abundant rice farming in central Sierra Leone all contribute to the high pre-odds likely-hood for the presence of B . pseudomallei and B . thailandensis in its soils [13] . In the present study we aim to determine whether B . pseudomallei and B . thailandensis are present in the soil of central Sierra Leone .
Oral informed permission was obtained from landowners and written informed permission from the paramount chief of Yele , Sierra Leone , prior to soil sampling .
A total of 1 , 000 samples ( 100 per site ) were collected at ten sampling sites in the Tonkolili and Bombali District , central Sierra Leone ( see Table 1 ) . Initial identification methods [14 , 16] , led to the isolation of 32 Burkholderia strains from 25 soil samples . Four Burkholderia strains showed a negative latex-agglutination test; the rest showed ( possible ) positive latex-agglutination test results . Two main clusters are presented in a phylogenetic tree based on the concatenated sequences of the seven household genes of all B . thailandensis STs available in the PubMLST database ( Fig 2 ) . Cluster I contains exclusively isolates from Asia and Oceania , while cluster II comprises isolates from all isolates from Sierra Leone and the one from Gabon ( ST1126 ) . One isolate with ST537 was an outlier . Interestingly , ST1126 , ST696 and ST101 were identified to express a B . pseudomallei-like capsular polysaccharide ( BTCV ) [18] possibly explaining why many of the isolated B . thailandensis showed cross-reactivity with the B . pseudomallei latex-agglutination test . MLST data and microarray based comparative genomic hybridization revealed earlier that there is a separate subgroup of B . thailandensis isolates ( ST696 , ST101 and ST73 ) containing BTCV strains , which are genetically different from the other B . thailandensis isolates [17] . In this study , two clearly separated B . thailandensis clusters ( I and II ) were observed . Various studies have reported human infections by B . thailandensis belonging to both cluster I ( ST77 , ST80 and ST345 ) and cluster II ( ST73 and ST101 ) ( https://pubmlst . org/bpseudomallei/ ) [5–10] . It has been postulated that B . thailandensis isolates within cluster II are more virulent than those in cluster I [17] , but evidence has not been reported [6 , 17] . Clinical characteristics are indistinguishable from B . pseudomallei infection and include soft tissue infection , abscess formation , pneumonia and sepsis [5–10] . The most recently described B . thailandensis case occurred in a 29-year old diabetic woman with an infected wound and swelling of her forearm after a car incident in Arkansas , US [6] . Our study has several limitations , including the lack of standard blood culture services for febrile patients across Sierra Leone . This limits targeted soil sampling studies centred around an index case . In addition , we cannot dismiss the possibility of sampling-error , although consensus guidelines were followed [14] . More knowledge about the exact composition of soils in which B . pseudomallei and B . thailandensis reside could help to determine which sites to study in future environmental surveillance studies . Furthermore , bacteria could have been present in a viable , but not cultivable state . It remains difficult to differentiate B . thailandensis and other members of the B . pseudomallei complex from B . pseudomallei by methods commonly available in clinical labs , even in developed countries , which may result in diagnostic confusion . Therefore , improving the detection and differentiation of members of the B . pseudomallei complex to improve patient care and appropriate public health responses is desired . Taken together , B . pseudomallei was not cultured from the soil of central Sierra Leone , but B . thailandensis with novel genotypes were found . B . thailandensis infection in humans have been sporadically reported in the literature in both the US and Asia [5–10] . B . thailandensis in general is considered a-virulent . As a result , clinical disease attributed to B . thailandensis is important . This also holds true for to the melioidosis research community , because no strict biocontainment conditions for B . thailandensis are required . The true clinical relevance of this soil-dwelling bacillus , however , remains to be elucidated . We encourage further environmental and sequencing studies on both B . pseudomallei and B . thailandensis to further understand the genetic diversity , virulence and evolution of these emerging organisms .
|
The environmental bacterium Burkholderia pseudomallei is the cause of melioidosis , an often-fatal but neglected infection prevalent across tropical areas . B . thailandensis is a member of the B . pseudomallei complex , rarely causes disease in humans and is considered a-virulent . Modelling studies have estimated a high prevalence of B . pseudomallei in western Africa . In this study , we performed an environmental surveillance study in the West African country of Sierra Leone . Remarkably , we could not demonstrate the presence of B . pseudomallei in the soil of Sierra Leone . However , by both culture and sequencing methods , we identified multiple B . thailandensis strains and novel genotypes . Patients with debilitating B . thailandensis infection have been occasionally reported in among others Southeast Asia and the US . Environmental and sequencing studies on both B . pseudomallei and B . thailandensis are essential to further understand the genetic diversity and evolution of these neglected but emerging organisms .
|
[
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] |
[
"taxonomy",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"burkholderia",
"pseudomallei",
"pathogens",
"melioidosis",
"burkholderia",
"infection",
"geographical",
"locations",
"microbiology",
"bacterial",
"diseases",
"phylogenetics",
"data",
"management",
"phylogenetic",
"analysis",
"bacteria",
"bacterial",
"pathogens",
"africa",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"infectious",
"diseases",
"computer",
"and",
"information",
"sciences",
"bioinformatics",
"medical",
"microbiology",
"microbial",
"pathogens",
"sierra",
"leone",
"evolutionary",
"systematics",
"evolutionary",
"genetics",
"people",
"and",
"places",
"database",
"and",
"informatics",
"methods",
"biology",
"and",
"life",
"sciences",
"burkholderia",
"evolutionary",
"biology",
"organisms"
] |
2019
|
Identification of Burkholderia thailandensis with novel genotypes in the soil of central Sierra Leone
|
Many picornaviruses cause important diseases in humans and other animals including poliovirus , rhinoviruses ( causing the common cold ) and foot-and-mouth disease virus ( FMDV ) . These small , non-enveloped viruses comprise a positive-stranded RNA genome ( ca . 7–9 kb ) enclosed within a protein shell composed of 60 copies of three or four different capsid proteins . For the aphthoviruses ( e . g . FMDV ) and cardioviruses , the capsid precursor , P1-2A , is cleaved by the 3C protease ( 3Cpro ) to generate VP0 , VP3 and VP1 plus 2A . For enteroviruses , e . g . poliovirus , the capsid precursor is P1 alone , which is cleaved by the 3CD protease to generate just VP0 , VP3 and VP1 . The sequences required for correct processing of the FMDV capsid protein precursor in mammalian cells were analyzed . Truncation of the P1-2A precursor from its C-terminus showed that loss of the 2A peptide ( 18 residues long ) and 27 residues from the C-terminus of VP1 ( 211 residues long ) resulted in a precursor that cannot be processed by 3Cpro although it still contained two unmodified internal cleavage sites ( VP0/VP3 and VP3/VP1 junctions ) . Furthermore , introduction of small deletions within P1-2A identified residues 185–190 within VP1 as being required for 3Cpro-mediated processing and for optimal accumulation of the precursor . Within this C-terminal region of VP1 , five of these residues ( YCPRP ) , are very highly conserved in all FMDVs and are also conserved amongst other picornaviruses . Mutant FMDV P1-2A precursors with single amino acid substitutions within this motif were highly resistant to cleavage at internal junctions . Such substitutions also abrogated virus infectivity . These results can explain earlier observations that loss of the C-terminus ( including the conserved motif ) from the poliovirus capsid precursor conferred resistance to processing . Thus , this motif seems essential for maintaining the correct structure of picornavirus capsid precursors prior to processing and subsequent capsid assembly; it may represent a site that interacts with cellular chaperones .
Picornaviruses comprise a large family of non-enveloped RNA viruses that includes important human and animal pathogens . Examples include poliovirus ( PV ) ( genus: Enterovirus ) , hepatitis A virus ( Hepatovirus ) , encephalomyocarditis virus ( Cardiovirus ) and foot-and-mouth disease virus ( FMDV ) ( Aphthovirus ) . In picornavirus particles , the RNA genome ( ca . 7 , 100–8 , 900 nt ) is surrounded by a protein shell ( capsid ) consisting of the four structural proteins VP1 , VP2 , VP3 and VP4 [1] , with the exception of parechoviruses and kobuviruses in which the VP0 ( the precursor of VP2 and VP4 ) remains uncleaved ( reviewed by [2] ) . The capsid is composed of 60 copies of each of these structural proteins; VP1 , VP2 and VP3 are exposed on the surface of the particle while VP4 is entirely internal [3–5] . Translation of the positive-sense RNA genome is dependent on the internal ribosomal entry site ( IRES ) within the 5′ untranslated region ( UTR ) that directs cap-independent translation initiation [6] . During and after translation of the single open reading frame , processing of the newly synthesized polyprotein occurs ( reviewed in [2] ) . Usually three or four primary products are formed , namely the Leader ( in many picornaviruses ) , the capsid precursor P1 or P1-2A ( depending on the genus ) and the precursors of the non-structural proteins , namely P2 and P3 . Many of these viruses , e . g . members of the Cardiovirus , Hepatovirus and Aphthovirus genera , have a Leader protein at the N-terminus of the polyprotein , i . e . upstream of the capsid precursor . In the Aphthoviruses , the Leader protein is a protease ( Lpro ) , which cleaves itself from the N-terminus of the P1-2A precursor , see Fig 1 . Cleavage of the junction between the structural and non-structural proteins , at either the VP1/2A or the 2A/2B junction , is usually mediated by the 2A protein , but the function of the 2A protein varies between the genera [7] , see Fig 1A . In the cardio- and aphthoviruses cleavage at the 2A/2B junction ( at the C-terminus of 2A ) is protease independent and happens during translation by a process termed “ribosomal skipping” [8] or “StopGo” [9] . In this case , the 2A protein remains attached to the precursor of the structural proteins ( as P1-2A ) until it is removed by the 3C protease ( 3Cpro ) , see Fig 1B . In the enteroviruses , the cleavage at the VP1/2A junction ( i . e . at the N-terminus of 2A ) , to release P1 , is mediated by the 2A protein that is a chymotrypsin-like protease [10 , 11] . The P2-P3 junction and the other protein junctions within these precursors are cleaved by 3Cpro to produce the mature non-structural proteins . However , the P1 capsid precursor of enteroviruses requires the 3CD protease ( 3CDpro ) for its processing [12 , 13] whereas for the cardio- and aphthoviruses the 3Cpro is sufficient to cleave the P1-2A precursor into three structural proteins ( VP0 , VP3 and VP1 ) plus 2A [1 , 14] , see Fig 1B . During capsid assembly , VP0 is cleaved ( in most picornaviruses ) to generate VP2 and VP4 by a process that is currently not understood . There are seven different serotypes of FMDV: O , A , C , SAT 1 , SAT 2 , SAT 3 and Asia 1 . There is a high level of sequence variation between the surface exposed structural proteins of these different serotypes . The internal VP4 protein is the most conserved of the capsid proteins with 81% of the residues being invariant [15] . In contrast , only 26% of the VP1 protein residues are invariant and furthermore it ranges in size ( 209–213 aa ) between serotypes [16] . VP1 is the most surface exposed capsid protein [3] and has been one of the most studied FMDV proteins due to its antigenic importance and role in virus attachment [17] . One of the antigenic sites in VP1 is located on the G-H loop ( including residues 141–160 ) , which contains an arginine-glycine-aspartate ( RGD ) motif that is involved in the attachment of the virus to cellular integrin receptors [18 , 19] . Surprisingly , previous work has demonstrated that a cell-culture adapted FMDV , lacking part of this G-H loop ( aa 142–154 ) , is still able to replicate and grow normally in cell culture through the use of heparan sulfate proteoglycans ( HSPG ) as receptor [20] . Viruses have only a very limited coding capacity within their genomes and thus they rely on cellular factors and pathways to complete their life cycle . Several studies have suggested that cellular chaperones , including various different heat shock proteins ( Hsps ) , are required to facilitate virus entry , genome replication , protein expression and protein assembly for a variety of viruses , including picornaviruses . Viral proteins , like cellular proteins , are dependent on such chaperones for their correct folding and assembly [21–24] . Studies on the role of Hsp90 , using specific inhibitors , have shown that these agents reduce the replication of diverse viruses in vitro . The Hsp90 appears to be involved in the regulation of viral polymerase function in the case of herpesvirus [25] and hepatitis B virus [21] , whereas this chaperone seems to be required for capsid processing and assembly in different picornaviruses [23 , 26] . Hsp90 and Hsp70 have been reported to interact with the PV capsid precursor , P1 [23 , 27] . The interaction between PV P1 and Hsp90 ( possibly together with Hsp70 ) , and likely in conjunction with its co-chaperone p23 , is believed to protect the P1 from degradation by proteasomes ( which remove misfolded proteins ) and is also involved in the folding of P1 allowing it to be correctly processed by the 3CDpro [23] . Recently , we have shown that impeding the processing of one of the cleavage sites within the FMDV P1-2A , at either the VP0-VP3 or the VP3-VP1 junctions , did not block processing of the other cleavage sites , indicating that processing of these junctions is mutually independent [28] . However , in an earlier study , it was shown that truncation of VP1 ( removing the C-terminal 42 amino acids of VP1 ) completely blocked processing of the residual capsid precursor at both the VP0-VP3 and the VP3-VP1 junctions by 3Cpro in a cell-free system [29] . Similarly , truncating the PV P1 precursor , by removing 50 aa from the C-terminus of VP1 ( 302 residues in length ) , blocked cleavage of the 2 junctions within the P1 precursor in vitro [30] . The basis for these effects has not been explained . However , taken together , these results suggest that the C-terminus of VP1 is important in relation to the processing of the entire capsid precursor of picornaviruses . In this study , we have now identified a short region within the C-terminus of VP1 that is critical for the processing of the FMDV capsid precursor . This region contains a stretch of five amino acids that are very highly conserved amongst all FMDVs . Furthermore , this region is also strongly conserved between most other picornaviruses , including PV , suggesting a shared role for this motif for capsid processing and assembly within the picornavirus family .
Previous studies have shown that truncation of the FMDV P1-2A , by removal of the 2A peptide and the C-terminal 42 residues of VP1 , completely abrogated processing by 3Cpro in vitro [29] even though the cleavage sites between VP0 and VP3 and between VP3 and VP1 were unmodified . To confirm these observations , within cells , stop codons were introduced at different positions within the P1-2A coding sequence . Transient expression assays were used to express the FMDV A22 Iraq P1-2A capsid precursor and its derivatives , within BHK cells , both in the absence and presence of the FMDV 3Cpro . The plasmids encoding both the P1-2A ( wt ) and the P1 alone ( truncated to the first amino acid of the 2A peptide ) served as positive controls . Both of these controls yielded the expected products corresponding to the P1-2A precursor and the P1 precursor ( approximately 85 kDa ) , respectively in the absence of 3Cpro ( Fig 2 , lanes 1 and 3 ) . When these plasmids were co-transfected with a plasmid that expresses the 3Cpro , both of these products were efficiently processed as indicated by the production of VP0 ( approximately 37 kDa ) ( Fig 2 , lanes 2 and 4 ) . Thus , the absence of the 2A peptide did not affect processing of the capsid precursor by 3Cpro ( as observed previously [14 , 26] ) . Plasmids encoding mutant precursors , truncated to residue 205 in VP1 and 199 in VP1 ( VP1 being 211 aa in length in FMDV A22 Iraq ( wt ) ) , generated products of approximately 85 kDa in the absence of 3Cpro ( Fig 2 , lanes 5 and 7 ) , and these were efficiently processed in the presence of 3Cpro ( Fig 2 , lanes 6 and 8 ) . The four additional mutants , P1 ( VP1 Y185Stop ) , P1 ( VP1 L158Stop ) , P1 ( VP1 A107Stop ) and P1 ( VP1 L53Stop ) all yielded products corresponding to their expected size in the absence of 3Cpro ( Fig 2 , lanes 9 , 11 , 13 and 15 ) , however it is noteworthy that these truncated products accumulated to a lower level in the cell lysates . Strikingly , no processing of these truncated precursors was detected for any of these four mutants in the presence of 3Cpro ( Fig 2 , lanes 10 , 12 , 14 and 16 ) although each of these products contained the unmodified VP0/VP3 and VP3/VP1 junctions . As expected , no products were detected in the negative control ( no DNA ) ( Fig 2 , lane 17 ) . In order to map the determinants of capsid processing more precisely , plasmids were constructed to express mutant forms of the P1-2A precursor with fairly small internal deletions within the C-terminal portion of VP1 . To serve as positive controls , both the P1-2A ( wt ) and a mutant form with a deletion within VP1 , designated P1-2A ( VP1 Δ142–154 ) , were included . The latter deletion is tolerated by the infectious virus [20] and thus it was expected that 3Cpro should be able to fully process all of the junctions in this deletion mutant . As expected , expression of both the P1-2A ( wt ) and the P1-2A ( VP1 Δ142–154 ) led to the synthesis of products corresponding to the P1-2A precursor ( approximately 85 kDa ) ( Fig 3 , lanes 1 and 13 ) . Furthermore , both the P1-2A ( wt ) and the P1-2A ( VP1 Δ142–154 ) products were efficiently processed in the presence of 3Cpro ( Fig 3 , lanes 2 and 14 ) . Notice that the VP1 product derived from the P1-2A ( VP1 Δ142–154 ) mutant migrated faster than the VP1 produced from the P1-2A ( wt ) ( ( approximately 28 kDa ) due to the internal deletion ( note that these antibodies do not recognize VP3 [31] , but presumably this was also made ) . Five different short deletions were introduced into the region of VP1 spanning residues 185–199 ( the region found to be critical by the truncation analysis ) , namely P1-2A ( VP1 Δ185–199 ) , P1-2A ( VP1 Δ185–189 ) , P1-2A ( VP1 Δ188–192 ) , P1-2A ( VP1 Δ191–195 ) and P1-2A ( VP1 Δ194–199 ) . Each of these constructs generated products that were very similar in size as the wt P1-2A in the absence of 3Cpro ( Fig 3 , lanes 3 , 5 , 7 , 9 and 11 ) . However , in the presence of 3Cpro the mutant having the largest deletion , P1-2A ( VP1 Δ185–199 ) could not be processed ( Fig 3 , lane 4 ) . The same product , corresponding to the P1-2A precursor , was observed both in the absence and presence of 3Cpro . Similarly , the mutants P1-2A ( VP1 Δ185–189 ) and P1-2A ( VP1 Δ188–192 ) were also not processed in the presence of 3Cpro ( Fig 3 , lanes 6 and 8 ) . It is again noteworthy that the mutant P1-2A products that could not be processed accumulated to a lower level in the cell lysates than the P1-2A precursors that could be processed ( c . f . lanes 3 , 5 , 7 and 1 , 9 , 11 , 13 ) . In contrast , co-expression of 3Cpro with the P1-2A ( VP1 Δ191–195 ) and P1-2A ( VP1 Δ194–199 ) led to production of VP0 indicating that processing of these mutant precursors had occurred ( Fig 3 , lanes 10 and 12 ) . However , it is noteworthy that no product corresponding to VP1 was detected , when P1-2A ( VP1 Δ191–195 ) was co-expressed with 3Cpro ( Fig 4A , lane 4 ) . Furthermore , unexpectedly , when the P1-2A ( VP1 Δ194–199 ) was co-expressed with 3Cpro a major product corresponding to the intermediate VP3-VP1 ( approximately 49 kDa ) was detected ( Fig 4A , lane 6 ) . Only a weak signal corresponding to the mature VP1 was detected indicating severe inhibition of processing at the VP3/VP1 junction in this mutant ( Fig 4A , lane 6 ) , n . b . this cleavage site is located over 190 residues away in the linear sequence . No products were detected in the negative control lane . ( Fig 4A , lane 9 ) . Due to inefficient detection of VP1 from some of the mutant precursors , an extra modification that blocks processing of the VP1/2A junction ( 2A L2P ) [32] was introduced into the plasmids that express P1-2A ( VP1 Δ191–195 ) , P1-2A ( VP1 Δ194–199 ) and the positive controls; P1-2A ( wt ) and P1-2A ( VP1 Δ142–154 ) . The additional modification ( 2A L2P ) ensured that the 2A peptide remained fused to the VP1 ( as VP1-2A ) . Each of these constructs generated products corresponding to the P1-2A precursor in the absence of 3Cpro ( Fig 4B , lanes 1 , 3 , 5 and 7 ) . The 2A L2P substitution increased the sensitivity of VP1 detection when using the anti-FMDV A-Iraq antibody . This showed that the P1-2A ( wt + 2A L2P ) and the P1-2A ( VP1 Δ142–154 + 2A L2P , positive control ) precursors were fully processed to yield VP0 and VP1-2A in the presence of 3Cpro as expected ( Fig 4B , lanes 2 and 8 ) . It also verified that cleavage at the VP3-VP1 junction in the P1-2A ( VP1 Δ194–199 +2A L2P ) occurred at a slower rate compared to wt , since the VP3-VP1-2A intermediate was far more abundant for the P1-2A ( VP1 Δ194–199 + 2A L2P ) than for the P1-2A ( wt +2A L2P ) in the presence of 3Cpro ( compare lanes 6 and 2 in Fig 4B ) . It should be noted that some mature VP1-2A could be detected from the P1-2A ( VP1 Δ194–199 + 2A L2P ) and thus cleavage of the VP3/VP1 junction was not completely blocked ( Fig 4B , lane 6 ) . Furthermore , the P1-2A ( VP1 Δ191–195 + 2A L2P ) could be processed to generate VP0 and VP1-2A ( Fig 4B , lane 4 ) . However , the VP3-VP1 intermediate produced from the P1-2A ( VP1 Δ191–195 +2A L2P ) mutant was also more abundant than the intermediate seen with the P1-2A ( wt + 2A L2P ) indicating that this mutant also had a slower processing at the VP3/VP1 junction ( Fig 4B , lane 4 ) . The cleavage of the unmodified VP1/2A junction in the P1-2A precursors with different internal deletions , was investigated using an anti-2A antibody . As expected , both the P1-2A ( wt ) and the positive control P1-2A ( VP1 Δ142–154 ) generated products of approximately 85 kDa corresponding to the P1-2A precursor in the absence of 3Cpro ( see supplementary material S1 Fig , lanes 1 and 13 ) . In the presence of 3Cpro , no products were detected by the anti-2A antibodies from either the P1-2A ( wt ) or the positive control P1-2A ( VP1 Δ142–154 ) indicating that the VP1/2A junction had been processed ( S1 Fig , lanes 2 and 14 ) ; note the 2A peptide itself is only 18 residues long and is not detected by immunoblotting . The two mutants , P1-2A ( VP1 Δ191–195 ) and P1-2A ( VP1 Δ194–199 ) that showed slower processing of the VP3-VP1 junction also generated products corresponding to the P1-2A precursor in the absence of 3Cpro ( S1 Fig , lanes 9 and 11 ) . However , in the presence of 3Cpro , no products were detected by the anti-2A antibodies ( S1 Fig , lanes 10 and 12 ) , indicating that these two deletions in VP1 did not affect processing of the VP1/2A junction . Surprisingly , the non-processable precursors , i . e . P1-2A ( VP1 Δ185–199 ) , P1-2A ( VP1 Δ185–189 ) and P1-2A ( VP1 Δ188–192 ) , could not be detected using the anti-2A antibody , either in the absence or presence of 3Cpro , and thus we cannot conclude whether cleavage of this junction was affected by the deletions ( S1 Fig , lanes 3–8 ) . No products were detected in the negative control ( No DNA , S1 Fig , lane 15 ) . Alanine-scanning mutagenesis was employed to identify individual residues within the C-terminal region of VP1 ( between residues 185 and 199 of VP1 ) that are required for 3Cpro processing of the P1-2A precursor . The wt and mutant precursors were expressed alone and also in the presence of the FMDV 3Cpro as above . As expected , the P1-2A ( wt ) and all 15 of the single amino acid substitution mutants each generated products corresponding to the P1-2A precursor in the absence of 3Cpro ( see Figs 5 , 6 and S2 ( supplementary material ) , odd numbered lanes ) . The wt and some 13 different mutant P1-2A precursors , excluding the mutants P1-2A ( VP1 Y185A ) and P1-2A ( VP1 R188A ) , were processed by 3Cpro to yield VP0 and VP1 ( Figs 5 , 6 and S2 ( supplementary material ) even numbered lanes ) . In contrast , the P1-2A ( VP1 Y185A ) and P1-2A ( VP1 R188A ) mutants were highly resistant to cleavage by the 3Cpro ( Fig 5 , lanes 4 and 10 ) . Furthermore , it was again apparent that the accumulation of these mutant P1-2A products in the cell lysates was lower than for the wt precursor and for the other mutants that could be processed ( Fig 5 , lanes 3 and 9 ) . Thus , the single amino acid substitutions VP1 Y185A and VP1 R188A were individually able to severely inhibit processing at both the VP0/VP3 and the VP3/VP1 junctions within the P1-2A precursor and had a deleterious effect on the level of the unprocessed product generated within cells . Surprisingly , none of the single alanine substitutions in the VP1 194–199 region had any effect on the processing of the junctions within the P1-2A precursor ( S2 Fig , lanes 4 , 6 , 8 , 10 , 12 and 14 ) . None of these produced the severe block on cleavage of the VP3-VP1 junction that was detected with the P1-2A ( VP1 Δ194–199 ) mutant ( Fig 4 , lane 6 ) . However , interestingly , the P1-2A ( VP1 V193A ) was processed more slowly at the VP3-VP1 junction compared to the P1-2A ( wt ) and the other alanine mutants ( Fig 6 , lane 12 ) . The cleavage of the VP1/2A junction of the P1-2A precursors with different alanine substitutions , was also investigated using the anti-2A antibody . As expected , the P1-2A ( wt ) generated a product of approximately 85 kDa corresponding to the P1-2A precursor in the absence of 3Cpro ( see supplementary material S3 Fig , lane 1 ) . However , in the presence of 3Cpro , no product ( containing 2A ) was observed from the P1-2A ( wt ) showing that VP1/2A junction had been processed ( S3 Fig , lane 2 ) . The two mutants , P1-2A ( VP1 C186A ) and P1-2A ( VP1 P187A ) that were correctly processed at the VP0/VP3 and the VP3/VP1 junction also generated products corresponding to the P1-2A precursor in the absence of 3Cpro ( S3 Fig , lanes 5 and 7 ) . However , as with the wt protein , in the presence of 3Cpro no products including 2A could be detected , indicating that these two substitutions individually did not prevent processing at the VP1/2A junction . Neither of these mutant precursors , with single amino acid substitutions , which were highly resistant to cleavage at the VP0/VP3 and the VP3/VP1 junctions , i . e . P1-2A ( VP1 Y185A ) and P1-2A ( VP1 R188A ) , could be detected by the anti-2A antibody , either in the absence or presence of 3Cpro . Thus , we cannot conclude whether this junction was affected by these substitutions ( S3 Fig , lanes 3 , 4 , 9 and 10 ) . These results are consistent with the inability to detect the mutant capsid precursors P1-2A ( VP1 Δ185–199 ) , P1-2A ( VP1 Δ185–189 ) and P1-2A ( VP1 Δ188–192 ) , with the anti-2A antibody , as shown in S1 Fig ( see above ) . To confirm the importance of the YCPRP motif in the context of the virus itself , specific mutations have been introduced into the full-length FMDV cDNA , that encode single amino acid substitutions ( to Ala ) within the YCPRP motif . In addition , a deletion of the sequence encoding residues VP1 185–190 from the full-length FMDV cDNA was also made . RNA transcripts were prepared in vitro from each of the mutant plasmids and introduced into BHK cells . The initial harvests , prepared after 24h , were passaged onto fresh BHK cells and the appearance of cytopathic effect ( CPE ) observed . Clear CPE was observed with the wt transcript and from the mutants encoding the VP1 C186A and P189A substitutions . In contrast , no CPE was apparent for the mutants encoding the VP1 Y185A , P187A and R188A substitutions or with the mutant lacking residues VP1 185–190 ( see Table 1 ) . Sequencing of the P1-2A coding region from the rescued viruses ( FMDV VP1 C186A and FMDV VP1 P189A revealed that the introduced mutations were retained and that no secondary mutations had occurred . These results verified the critical importance of residues Y185 and R188 in VP1 for P1-2A processing ( Fig 5 ) and for virus infectivity . It is noteworthy that the P187A mutant was also non-infectious ( Table 1 ) although the capsid precursor processing could be observed in the transient expression assay ( see Fig 5 , lane 8 ) .
The FMDV 3Cpro is able to cleave a variety of different junction sequences in the virus polyprotein [33] . We have shown previously that blocking cleavage of one junction in the FMDV P1-2A did not affect processing of the other junctions [28] . In the current studies , it has been shown that modifications that modify or delete a short motif in the C-terminus of VP1 , can prevent processing of the FMDV capsid precursor P1-2A at each of the usual cleavage sites , which are far separated , in the linear sequence , from the site of the modifications . The VP0/VP3 cleavage site is more than 400 amino acids away from the modified motif in the linear sequence while the VP3/VP1 junction is almost 200 amino acids away . It seems very likely that this reflects a major change in protein conformation for these mutant proteins . Viral proteins , like cellular proteins , are dependent on cellular chaperones for correct folding , assembly and function [24] . The viral capsid precursor must fold to a conformation that is soluble and recognizable by the viral protease to be processed . After the cleavage of the precursor , the mature capsid proteins assemble around the viral genome to form the protein shell , which contains 60 copies of each of the subunits . These structures must be stable both within , but also outside , the host cells to permit virus spread . Moreover , the virus particle must also be able to disassemble upon entry into cells to deliver the viral genome to initiate a new infection . Thus , the core structure of the capsid proteins ( as distinct from the antigenic loops ) is probably tightly constrained . Within the picornavirus family , the general structure of the capsid proteins are very similar [2] . Several chaperones are known to facilitate folding of picornavirus capsid proteins [23 , 26] . The mature picornavirus capsid proteins are generated by cleavage of the P1 , P1-2A or L-P1-2A precursors . Both Hsp90 and p23 , a co-chaperone of Hsp90 , have been reported to be required for processing of the PV P1 precursor into the mature structural proteins [23] . Similarly , inhibitors of Hsp90 have been shown to impede processing of the wt FMDV capsid precursor in cell-free assays [26] . However , interestingly , hepatitis A virus ( HAV ) is not sensitive to the inhibition of Hsp90 function [34] . This indicates that HAVs might employ other strategies for correct folding of the capsid precursor . However , it is noteworthy that HAV also has several unique characteristics that distinguish it from most other members of the picornavirus family , e . g . slow growth rate , lack of capsid protein myristoylation and use of only a single viral protease ( 3Cpro ) for polyprotein processing [35–38] . An earlier study showed that Hsp90 mediates PV P1 folding in cells . Inhibition of this chaperone lead to misfolding of P1 , which resulted in the targeting of the PV P1 for degradation by the cellular quality-control system ( proteasome pathway ) , and thus the level of the PV P1 was strongly reduced [23] . These observations are consistent with the results presented here on the FMDV P1-2A . All of the FMDV P1-2A precursors that cannot be processed by 3Cpro accumulated to a lower level than the P1-2A ( wt ) . This was apparent for the truncated precursors ( VP1 Y185Stop , VP1 L158Stop , VP1 A107Stop , VP1 L53Stop ) , precursors with small internal deletions ( VP1 Δ185–199 , VP1 Δ185–189 , VP1 Δ182–192 ) and two precursors with single amino acid substitutions ( VP1 Y185A and VP1 R188A ) . Thus , it may be that the mutant precursors , which cannot be processed , are misfolded and therefore targeted for degradation , hence the reduced level of these products within cells . Interestingly , Geller et al . , [23] showed that inhibition of the Hsp90 chaperone in a cell-free system ( rabbit reticulocyte lysate ) , where the proteasomal degradation system is inhibited by free hemin , did not reduce the yield of P1 [23] . However , even in the absence of proteasomal degradation , the Hsp90 was still required for P1 to fold into a processing-competent conformation , since the PV P1 precursor , in the absence of Hsp90 , adopted a misfolded conformation that could not be recognized by the 3CDpro and thus could not be processed into the mature capsid proteins [23] . The clear resistance to processing of certain mutant FMDV P1-2A proteins ( in which the YCPRP motif is modified or deleted ) and their reduced accumulation within cells is entirely consistent with these results ( see Fig 2B , Fig 3 , Fig 4 and Fig 5 ) . As indicated above , a critical region that is required for the correct processing of the FMDV capsid precursor has now been identified . This motif ( YCPRP ) is very highly conserved among FMDVs . Indeed , the YCPR sequence was found to be completely conserved in over 100 FMDV strains , with representatives from all 7 serotypes [15] ) ; only variation to YCPRA has been observed ( Fig 7 ) . However , previously , no function for this conserved sequence had been identified . The YCPRP motif is also highly conserved among other picornaviruses as well , e . g . it exists as FCPRP in cardioviruses and WCPRP in enteroviruses , see Fig 8 . Indeed , both Y to F and Y to W are very conservative amino acid substitutions , since all three amino acids have similar properties with non-polar , aromatic side chains . This high conservation likely reflects its importance for correct folding of the capsid precursor . The high resistance to cleavage of the junctions between the structural proteins following substitution of residues VP1 Y185 and VP1 R188 individually indicates that correct cleavage may be dependent on the interaction with several amino acids in this region and thus the whole motif seems to be of high importance for correct folding and subsequent processing of the capsid precursor . Furthermore , these results are consistent with the observations that the substitutions VP1 Y185A and VP1 R188A , that each prevent P1-2A processing by 3Cpro in cells ( Fig 5 ) , also block FMDV infectivity ( Table 1 ) . It is interesting to note that the VP1 P187A mutant was also non-infectious ( Table 1 ) even though processing of the P1-2A could still be observed ( Fig 5 , lane 8 ) . The high conservation of this motif clearly reflects its sensitivity to modification . An earlier study has shown that removing 50 residues from the C-terminus of the PV VP1 prevented cleavage of the two junctions , VP0/VP3 and VP3/VP1 , within the capsid precursor in vitro [30] . Significantly , these 50 amino acids include the highly conserved motif ( WCPRP ) identified here , and thus indicates the importance of this motif , not only for FMDV , but also more widely within the picornavirus family . Similarly , as indicated above , removal of 42 residues ( including the YCPRP ) from the C-terminus of the FMDV VP1 protein completely prevented cleavage of the capsid precursor by the 3Cpro in a cell-free system [29] . Recently , we have shown that blocking cleavage of one of the junctions within the FMDV P1-2A precursor did not block the cleavage of the other junction within the capsid precursor [28] . Thus , the severe inhibition of cleavage of both junctions likely reflects a changed overall structure of the capsid precursor , thereby preventing cleavage of both junctions . It is interesting to note that in HAV , the equivalent region of VP1 has the sequence YFPRA , perhaps the two substitutions together account for the lack of sensitivity of HAV assembly to Hsp90 inhibitors [34] . It can be proposed that the conserved motif serves as a binding site for an important chaperone , e . g . Hsp90 ( or its partners ) , that is necessary for correct protein folding . A proposed model for this interaction is shown in Fig 9 . A co-chaperone of Hsp90 , called p23 , also seems to be involved in the correct folding of the PV P1 . It has been reported that treatment with geldanamycin ( GA ) did not affect the PV P1-Hsp90 interaction , but abolished the P1-p23 interaction and thereby affected P1 maturation [23] , thus indicating different possibilities for chaperone interaction at this specific site . Picornaviruses are able to adapt very rapidly since they have an RNA dependent RNA polymerase with a high error rate and no error correction mechanism . However , Geller et al . , [23] showed that PV was unable to adapt to an Hsp90-independent P1 folding pathway during several passages in cells in culture or in PV-infected mice when the function of Hsp90 was inhibited by the presence of GA . Thus it seems that for the virus to adapt to a folding pathway without the involvement of Hsp90 requires extensive change [23] . It is interesting that the deletion VP1 Δ194–199 strongly inhibited cleavage at the VP3-VP1 junction , without affecting the cleavage of the VP0-VP3 junction ( Fig 4 , lane 6 ) . Surprisingly , the alanine scanning substitutions through this specific region did not identify any individual residue that affected cleavage of any of the junctions ( S2 Fig , lanes 4 , 6 , 8 , 10 , 12 and 14 ) . However , interestingly the P1-2A ( VP1 V193A ) mutant , modified at a residue adjacent to the deletion , also displayed a slower processing rate of this VP0/VP3 junction compared to the wt and the other alanine mutants ( Fig 6 , lane 12 ) . However , this VP1 V193A mutant does not seem to affect the processing of the VP3-VP1 junction to the same extent as the VP1 Δ194–199 mutant . In addition , the VP1 Δ191–195 mutant also showed a lower processing of this VP3/VP1 junction ( Fig 4B , lane 4 ) as judged by the elevated level of the VP3-VP1 product . These results indicate that the cleavage of the VP3-VP1 junction , may be dependent on the interaction with several amino acids and that residues within the VP1 aa 193–199 region are important for optimal processing of the VP3-VP1 junction , more than 190 aa away from the site . We have noted previously that the K210E change in VP1 that severely limited processing at the VP1/2A junction also enhanced the yield of VP3-VP1-2A [14] . These studies also identified a genetic link between the processing of the VP1/2A junction and the substitution E83K in VP1 . Furthermore , Escarmis et al . , [39] showed that the substitution M54I within the VP1 of serotype C FMDV resulted in less efficient processing at the VP3/VP1 junction . Thus , there are multiple , complex , interactions , some of which operate “at a distance” , that govern picornavirus capsid protein processing and assembly .
The plasmid pO1K/A22 contains a T7 promoter upstream of a full-length FMDV cDNA with the A22 Iraq capsid coding sequence within an FMDV O1K backbone as previously described [28 , 40 , 41] . To investigate the effect of different modifications within the P1-2A , the FMDV cDNA was digested with ApaI and then religated to remove most of the sequence encoding the non-structural proteins ( including the 3Cpro ) downstream of the 2A-peptide , as described previously [28] . These constructs contained a modified form ( W52A substitution ) of the Lpro to overcome the negative effect of the L protease on protein expression in cells . The modified Lpro with the W52A substitution retains the L/P1 cleavage activity but is defective at inducing cleavage of the translation initiation factor eIF4G [42]; the primer sequences used to make this modification are listed in S1 Table . This parental plasmid is referred to throughout as P1-2A ( wt ) and all modifications were made in this background . Variants of the plasmid , with in-frame stop codons introduced to truncate the capsid precursor at different sites either at the start of the 2A sequence or within the VP1 coding sequence , were generated using site-directed mutagenesis [43] . Briefly , fragments were amplified in PCRs using Phusion High-Fidelity DNA Polymerase ( Thermo Fisher Scientific ) , according to the manufacturer’s instructions , to create mega-primers , using the P1-2A ( wt ) plasmid as template and reverse primers specifying the introduction of STOP codons , together with the forward primer; 14TPN9_F , see S1 Table . The PCR products ( between 350 and 900 bp in length depending on where the modification was made ) were gel purified using the GeneJET Gel purification kit ( Thermo Fisher Scientific ) . These PCR products were used as megaprimers for a second round of PCR ( 500 ng megaprimer , and 100 ng template ) , using the P1-2A ( wt ) as template to produce the modified plasmids . After the PCR and subsequent DpnI digestion of the template plasmid , the products were transformed into chemically competent Escherichia coli ( E . coli ) cells . Plasmids were amplified from individual colonies , purified using the GeneJet Plasmid Miniprep Kit ( Thermo Fisher Scientific ) and screened by Sanger Sequencing using the BigDye Terminator v . 3 . 1 Cycle Sequencing kit and a 3500 Genetic Analyzer ( Applied Biosystems ) . Plasmids encoding the desired modifications were amplified and purified using the QIAGEN Plasmid Midi Kit ( Qiagen ) . All of these constructs encoded P1 that was truncated at different sites by introducing two STOP codons . The plasmids ( listed in S1 Table ) were labelled to denote the location of the Stop codons as follows; P1-2A ( 2A L1Stop ) , P1-2A ( VP1 I205Stop ) , P1-2A ( VP1 D199Stop ) , P1-2A ( VP1 Y185Stop ) , P1-2A ( VP1 L158Stop ) , P1-2A ( VP1 A107Stop ) and P1-2A ( VP1 L53Stop ) ; for primer sequences , see S1 Table . Variants of the P1-2A plasmid that express mutant proteins with various different deletions in the C-terminal region of VP1 between residues VP1 185 and VP1 199 were created using site-directed mutagenesis , essentially as described above; for primer sequences see S1 Table . The plasmids were labelled as follows: P1-2A ( VP1 Δ185–199 ) , P1-2A ( VP1 Δ185–189 ) , P1-2A ( VP1 Δ188–192 ) , P1-2A ( VP1 Δ191–195 ) and P1-2A ( VP1 Δ194–199 ) . Furthermore , an additional positive control was included in which 13 amino acids within VP1 were deleted , this construct was called P1-2A ( VP1 Δ142–154 ) . An earlier study had shown that FMDV with this deletion is able to replicate [20 , 44] . Alanine substitutions were introduced at the codons for each residue individually between VP1 185 and VP1 199 , with the exception of VP1 A192 , where the original alanine codon was substituted by one encoding a serine . The mutations were produced using site-directed mutagenesis , as described above; the primer sequences are listed in S2 Table . These modifications resulted in 15 different plasmids: P1-2A ( VP1 Y185A ) , P1-2A ( VP1 C186A ) , P1-2A ( VP1 P187A ) , P1-2A ( VP1 R188A ) , P1-2A ( VP1 P189A ) , P1-2A ( VP1 L190A ) , P1-2A ( VP1 L191A ) , P1-2A ( VP1 A192S ) , P1-2A ( VP1 V193A ) , P1-2A ( VP1 E194A ) , P1-2A ( VP1 V195A ) , P1-2A ( VP1 S196A ) , P1-2A ( VP1 S197A ) , P1-2A ( VP1 Q198A ) and P1-2A ( VP1 D199A ) . Baby hamster kidney ( BHK ) cells ( originally obtained from the ATCC ( CCL-10 ) ) were grown in 35-mm wells to about 90% confluence , when they were infected with the recombinant vaccinia virus , termed vTF7-3 [45] that expresses the T7 RNA polymerase . All the various P1-2A plasmids and the 3C plasmid ( pSKRH3C [46] ) express the FMDV cDNA under the control of a T7 promotor . After one hour incubation at 37°C , the vaccinia virus was removed and the cells were transfected with the specified plasmid DNA using FuGENE 6 ( Promega ) , as described previously [47] . To obtain the highest levels of processed capsid protein expression , 1000 ng of the P1-2A plasmid alone or with 10 ng of the 3Cpro plasmid were used for each transfection of cells [28] . The cells were incubated in a CO2 incubator , at 37°C overnight and then , after removal of the medium , lysed with 500 μl Buffer C ( 20mM Tris-HCl ( pH 8 . 0 ) , 125 mM NaCl and 0 . 5% NP-40 ) ; the cell extracts were clarified by centrifugation at 18 , 000 x g for 10 min at 4°C . Immunoblotting was performed using clarified cell lysates mixed with 2 x Laemmli sample buffer ( Bio-Rad ) ( containing 25 mM DTT ) . The proteins were separated by SDS-PAGE using a 12% Bis-Tris gels ( Bio-Rad ) and transferred to PVDF membranes ( Milipore ) , by wet blotting , at 200 mA for 1 . 5 hours . PBS containing bovine serum albumin ( BSA ) ( 5% ) and Tween20 ( 0 . 1% ) was used as blocking buffer ( 1 hour at room temperature ( RT ) ) and dilution buffer for the guinea pig anti-FMDV O-Manisa ( Man ) antisera ( prepared “in house” , as used previously [28] ) ( overnight at 4°C ) and their corresponding secondary antibodies ( 2 hours at RT ) . Guinea pig anti-FMDV O-Man antisera was used for detection , since it is very efficient in detecting the denatured capsid proteins from various FMDV serotypes . PBS containing skimmed milk powder ( 5% ) and Tween20 ( 0 . 1% ) was used as blocking buffer ( 1 hour at RT ) and dilution buffer for the primary guinea pig anti-FMDV A-Iraq antisera ( prepared “in house” , as used previously [28] ) and the anti-2A-peptide antibody ( overnight at 4°C ) and their corresponding secondary antibody ( 2 hours at RT ) . The proteins were detected using the following primary antibodies: guinea pig anti-FMDV O-Man antisera ( 1:1000 ) , guinea pig anti-FMDV A-Iraq antisera ( 1:500 ) or FMDV anti-2A-peptide antibody ( 1:1000 ) ( Rabbit , ABS31 Merck Millipore ) . Appropriate HRP-conjugated secondary antibodies ( Dako ) and a chemiluminescence detection kit ( Pierce ECL Western Blotting Substrate , Thermo Fisher Scientific ) were used to detect the proteins bound by the primary antibodies . Images were captured using a Chem-Doc XRS system ( Bio-Rad ) . The plasmid pO1K/A22 contains a full-length cDNA corresponding to a chimeric FMDV genome as previously described [28] . It includes the capsid coding sequence from FMDV A22 Iraq and the rest of the genome from FMDV O1K . Briefly , fragments were amplified in PCRs , using the pO1K/A22 plasmid as template , together with primers specifying the desired mutations , see S1 and S2 Tables . The PCR products were gel purified and used as megaprimers for a second round of PCR , again using the wt pO1K/A22 as template to make full-length plasmids of approximately 11 , 000 bp . After the PCR and subsequent DpnI digestion of the template DNA , the products were introduced into E . coli cells . Plasmids were amplified from individual colonies and sequenced . The plasmids , containing the full-length wt or mutant FMDV cDNAs , were linearized by digestion with HpaI and then transcribed in vitro using the MEGAscript T7 Transcription Kit ( Thermo Fisher Scientific ) . An aliquot ( 1 μL ) of each RNA sample was visualized following agarose gel electrophoresis to check yield and integrity and the rest ( 19 μL ) was introduced into BHK cells by electroporation as described previously [28] . The cells were transferred to Falcon flasks and Eagle’s medium containing 5% calf serum was added . The cells were incubated overnight at 37°C and then harvested . Aliquots ( 1ml ) of the harvest were inoculated onto fresh BHK cells and the appearance of cytopathic effect monitored at 1 and 2 days post-inoculation . For samples displaying CPE , viral RNA was isolated using the RNeasy Mini Kit ( Qiagen ) and reverse transcribed using Ready-To-Go You-Prime First-Strand Beads ( GE Healthcare Life Sciences ) together with random primers . The cDNA corresponding to the P1-2A region was amplified as four overlapping fragments of around 1000 bp by AmpliTaq Gold DNA Polymerase ( Thermo Fisher Scientific ) as described previously [28] and then sequenced . For each RNA , a negative control , lacking the reverse transcriptase , was included in the RT-PCRs to verify that the PCR products were obtained from viral RNA and not from residual plasmid template .
|
The picornavirus family includes clinically important human and animal pathogens , for example: poliovirus , rhinovirus ( causing the common cold ) and foot-and-mouth disease virus ( FMDV ) that infects cloven-hoofed animals . Picornaviruses contain a positive-sense RNA genome surrounded by a protein shell , also called a capsid . The capsid proteins are made from a precursor and correct processing and assembly of these capsid proteins is necessary in the virus life cycle to create new infectious virus particles . In this study , we have identified a short motif ( just 5 amino acids long ) within the capsid precursor , which is highly conserved among picornaviruses . Deletion of this motif inhibited processing of the junctions between the mature structural proteins within this precursor , with one junction being more than 400 amino acids away from this region . This motif also seems to be required for the optimal accumulation of the capsid precursor in cells . We hypothesize that the motif may be involved in binding to a cellular protein , such as a chaperone , to stabilize the capsid precursor and promote its correct folding to allow it to be processed by the viral protease prior to capsid assembly .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"blood",
"serum",
"animal",
"diseases",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"foot",
"and",
"mouth",
"disease",
"microbiology",
"precursor",
"cells",
"vertebrates",
"animals",
"mammals",
"viruses",
"animal",
"models",
"rna",
"viruses",
"experimental",
"organism",
"systems",
"sequence",
"motif",
"analysis",
"zoology",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"bioinformatics",
"animal",
"cells",
"proteins",
"animal",
"studies",
"structural",
"proteins",
"viral",
"packaging",
"viral",
"replication",
"picornaviruses",
"guinea",
"pigs",
"biochemistry",
"rodents",
"eukaryota",
"blood",
"cell",
"biology",
"anatomy",
"virology",
"database",
"and",
"informatics",
"methods",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"immune",
"serum",
"amniotes",
"organisms"
] |
2019
|
Identification of a short, highly conserved, motif required for picornavirus capsid precursor processing at distal sites
|
Onchocerciasis in Yemen is one of the most neglected diseases , where baseline estimates of onchocerciasis and monitoring of the impact of ivermectin regularly administered to the affected individuals on its transmission are lacking . Therefore , this study aimed to determine the anti-Ov16 IgG4 seroprevalence among local communities of Hodeidah and Al-Mahwit governorates of Tihama region . The factors possibly associated with previous exposure to infection were also studied . This cross-sectional study was conducted in two ivermectin-targeted districts endemic for onchocerciasis in Hodeidah and Al-Mahwit and two untargeted districts with unknown previous endemicity in Hodeidah between February and July 2017 . For 508 residents sampled by a multi-stage random approach , data were collected and blood specimens were screened for anti-Ov16 IgG4 using the SD BIOLINE Onchocerciasis IgG4 rapid tests . The study revealed an overall anti-Ov16 IgG4 rate of 18 . 5% ( 94/508 ) in all surveyed districts , with 10 . 2% ( 12/118 ) of children aged ≤10 years being seropositive . Moreover , rates of 8 . 0% ( 4/50 ) and 6 . 1% ( 4/66 ) were found in districts not officially listed as endemic for the disease . Multivariable analysis confirmed the age of more than ten years and residing within a large family as the independent predictors of exposure to infection . Onchocerciasis transmission is still ongoing as supported by the higher anti-Ov16 IgG4 seroprevalence rate among children aged ≤10 years compared to that ( <0 . 1% ) previously set by the World Health Organization as a serologic criterion for transmission interruption . Further large-scale studies combining serologic and entomologic criteria are recommended for the mapping of O . volvulus in human and blackfly populations in endemic foci and their neighboring areas of uncertain endemicity . In addition , ivermectin distribution , coverage and impact on disease transmission need to be continually assessed .
Onchocerciasis is a neglected tropical disease of the skin and eyes caused by the filarial nematode Onchocerca volvulus and transmitted by the bites of infected Simulium blackflies . It is endemic in 31 countries in sub-Saharan Africa and in some foci in Latin America and Yemen , with approximately 187 million people being exposed to potential transmission [1 , 2] . In addition , over a million disability-adjusted life years have been recently estimated to be lost due to onchocerciasis [3] . Promising strides towards the control and elimination of the disease have been made since the introduction and donation of the microfilaricide ivermectin ( Mectizan ) through Mectizan Donation Program ( MDP ) in the late 1980s [4–7] . Ivermectin administration at intervals interrupts transmission and incidence of new infections with O . volvulus in endemic foci in the long run [8 , 9] . Effective efforts through mass drug administration ( MDA ) campaigns at repeated rounds undertaken by control programs have led to the successful elimination of the disease in four countries in Latin America as certified by the World Health Organization ( WHO ) between 2013 and 2016; namely , Colombia , Ecuador , Mexico and Guatemala [10] . Yemen is the only country endemic for onchocerciasis in Asia , where the disease mainly affects the rural communities residing near the flowing streams of main seasonal watercourses ( locally referred to as wadis ) in western governorates [11 , 12] . Clinically , onchocerciasis in Yemen is a unique form of localized , hyper-reactive onchodermatitis referred to as "sowda" [13] , which is difficult to diagnose in the laboratory by skin snip examination as a result of the scarcity of microfilariae [14 , 15] . Although the epidemiology of onchocerciasis in the country lacks clear mapping and national burden estimates , its focal endemicity has been documented in 33 districts of eight governorates; namely , Taiz , Ibb , Hodeidah , Dhamar , Raymah , Al-Mahwit , Sana’a and Hajjah [12] . In the early 1990s , ivermectin donated by the MDP was first distributed for treating the clinical manifestations of sowda in Wadi Al-Ghail , Taiz [16] , where onchocerciasis had been reported to be endemic by Büttner et al . [11] . Its use at three-month intervals was then recommended as a control strategy , desirably through national campaigns [16] . Ivermectin has then been distributed to patients in a few affected communities , mainly through the National Leprosy Elimination Program in Taiz and the Charitable Society for Social Welfare ( CSSW ) , a non-governmental organization ( NGO ) committed to Mectizan distribution to the affected populations since 2000 . Several campaigns have been implemented in endemic areas following the approval of donating Yemen 91 , 000 Mectizan treatments on a quarterly basis by the Mectizan Expert Committee of the MDP [6] . The political crisis and war in the country since the Arab Spring revolutions in the region in 2011 have dashed the hope raised by the development of a national action plan in 2010 to eliminate the disease by 2015 [17] . The major mainstays adopted as part of the onchocerciasis elimination plan involve a combination of MDA with ivermectin to at-risk populations together with consolidating the clinic-based management of infected cases . In addition , the plan involves vector control and strengthening surveillance , including serologic and entomologic surveys ( Ministry of Health and Population , personal communication , 2018 ) . However , the current situation led to a number of challenges to the implementation of the elimination plan , including the insecurity , financial and logistic restrains besides the humanitarian priorities . In January 2016 , however , the first MDA with ivermectin was implemented in Hodeidah and Al-Mahwit , targeting over 162 , 000 children and adults [18] . Although the disease is of focal nature and its baseline mapping in the targeted governorates is lacking , an ivermectin coverage rate of 94 . 8% has been reported in four targeted districts in the two governorates of Tihama region ( Ministry of Health and Population , personal communication , 2018 ) . It is worth mentioning , and to the best of our knowledge , that there are no published studies on the serostatus of onchocerciasis in the targeted areas of the country . Defining areas to be targeted by MDA with ivermectin and post-MDA surveys are key components to the success of the proposed elimination plan . This , in turn , highlights the importance of the present pilot study in providing a zoomed image to a part of the epidemiologic scene from its serologic attribute . Serologic markers are now widely used to determine the recent exposure to infection with O . volvulus because of the possibility of their early detection in infection before skin snips become positive . In this regard , immunoglobulin G4 ( IgG4 ) response to the Ov16 antigen expressed by the third ( L3 ) and fourth ( L4 ) larval stages of the parasite is the most specific marker of recent infection [19] , confirming ongoing disease transmission . This marker is highly sensitive and provides evidence for recent transmission when detected among young children . Accordingly , the negativity of anti-Ov16 IgG4 has been recently used to confirm the interruption of disease transmission in foci following extensive rounds of MDA or community-directed treatment with ivermectin ( CDTI ) campaigns in a number of countries in Latin America and Africa [20–24] . When tested against skin microfilaria status , a lateral flow rapid diagnostic test ( RDT ) for detecting anti-Ov16 IgG4 antibodies against the parasite showed sensitivity and specificity levels of 98 . 0% compared to levels of 94 . 0% and 96 . 0% , respectively , for enzyme-linked immunosorbent assay ( ELISA ) [25] . This , in turn , makes the use of RDTs for detecting anti-Ov16 in sera of children in endemic settings a useful and cost-effective tool for the long-term monitoring of disease transmission in the community following MDA campaigns [26 , 27] . In 2014 , the SD BIOLINE Onchocerciasis IgG4 RDT was launched as a surveillance tool for identifying exposure to infection by detecting anti-Ov16 IgG4 [28] . It is noteworthy that the quality of such RDTs during field use has been successfully ensured with the use of recombinant human anti-Ov16 IgG4 antibody-based positive controls [29] . In line with the efforts to eliminate onchocerciasis from the country , there is a need to evaluate the status of its ongoing transmission after ivermectin distribution campaigns in endemic foci . Therefore , the present study aimed to determine anti-Ov16 IgG4 serostatus among rural residents in ivermectin-targeted endemic areas and neighboring untargeted areas with unknown endemicity in Hodeidah and Al-Mahwit governorates of Tihama region , west of Yemen . The factors possibly associated with previous exposure to O . volvulus infection were also studied .
This cross-sectional study ( S1 Checklist ) was conducted in four districts in Hodeidah and Al-Mahwit in the period from February to July 2017 . Hodeidah is located on the Red Sea at the coordinates of 14°48' N and 42°75' E , whereas Al-Mahwit is bordering Hodeidah and located at the coordinates of 15°28' N and 43°32' E ( Fig 1 ) . Both governorates are characterized by the presence of fast-flowing seasonal streams and perennial watercourses ( wadis ) , where Wadi Surdud is the most famous one traversing the two governorates to drain into the Red Sea . It is well-known that the people of rural areas residing alongside these watercourses are mainly engaged in agricultural activities . Of the four districts surveyed during the present study , two are endemic for onchocerciasis; namely , Ad Dahi alongside Wadi Surdud and its tributaries in Hodeidah and Bani Sa'ad alongside Wadi Dayan and its tributaries in Al-Mahwit , where breeding sites of the vector exist . The first CDTI campaign in Tihama region was implemented in both districts in 2016 ( CSSW , personal communication , 2017 ) . Moreover , ivermectin distribution campaigns targeting symptomatic patients have been conducted three times a year since 2002 in Bani Sa'ad . Meanwhile , Al Marawi'ah and As Sukhnah districts of Hodeidah in the vicinity of the surveyed endemic districts , which are not listed as onchocerciasis-endemic districts ( Ministry of Public Health and Population , personal communication , 2017 ) , were included in the study . Al Marawi'ah is traversed by Wadi Siham and its tributaries , while As Sukhnah is traversed by Wadi Malih , which may be potential breeding sites for the vector . In accordance with the criteria set by the WHO for the determination of sample size in health studies [30] , a minimum sample size of 384 was calculated at an expected onchocerciasis prevalence of 50 . 0% ( due to the lack of prevalence data in the country ) , a confidence level of 95 . 0% and an accepted margin of error of 5 . 0% . Yet , 392 individuals were recruited from the surveyed onchocerciasis-endemic districts . To avoid the effect that might be introduced as a result of the heterogeneity in infection prevalence and the sparse distribution of rural communities in the study areas , multi-stage sampling was adopted to obtain the best representative sample , where endemic districts and sub-districts of the studied governorates were considered as the clusters . In the first stage , Ad Dahi and Bani Sa'ad were randomly selected from a list of endemic districts in Hodeidah and Al-Mahwit , respectively . In the second stage , two ( Upper Grabeh and Lower Grabeh ) and four ( Al Wahaweh , Bani Ali , Gaaferat Alh and Utmah ) sub-districts were randomly selected from Ad Dahi and Bani Sa'ad , respectively . It is to be noted that cluster sampling might not always have a relationship to streams and possible breeding sites , which could yield a somewhat biased sample close to breeding sites to ensure negativity at the source of the infection . Households were then randomly selected from each sub-district , and all family members were invited to participate , ensuring the proportionality of the sample size of each sub-district to its population size . In addition to the individuals sampled from onchocerciasis-endemic districts , 116 individuals were randomly selected from Al Marawi'ah and As Sukhnah districts with unknown previous endemicity , totaling the sample size to 508 . Data on the district of residence , gender , age , clinical signs of onchocerciasis , source of drinking water , durables of households and history of ivermectin intake were collected using a pre-designed questionnaire . Presence of nodules and the subjective reporting of itching that usually disturbs sleeping or interferes with working capacity were recorded according to the guidelines of the WHO [31] . However , it was almost impossible to observe the nodules in body parts that are considered to be private by the participants , particularly women . Finger-prick blood was screened for anti-Ov16 IgG4 using the SD BIOLINE Onchocerciasis IgG4 RDT ( Standard Diagnostics , Inc . , Gyeonggi-do , Republic of Korea ) according to the manufacturer’s instructions . Negative and three concentrations of positive controls ( high , middle and low concentrations of anti-Ov16 IgG4 ) , supplied by PATH ( www . path . org ) , were used to ensure the quality of each lot of RDTs at the points of testing in the field prior to blood screening . Data were analyzed using IBM SPSS Statistics for Windows , version 23 . 0 ( IBM Corp . , Armonk , NY , USA ) . The socioeconomic status ( SES ) was determined using the principal component analysis ( PCA ) of durables owned by households [32] . The constructed PCA-based scores of households were divided into five wealth quintiles and three SES categories , where households’ residents with the lowest 40% , the middle 20% and the highest 40% of household wealth quintiles were classified as being of low , middle and high SES , respectively [32] . Associations or differences between categorical variables were tested using Pearson’s chi-square test in bivariate analysis . The crude odds ratios ( ORs ) and the associated 95% confidence intervals ( CIs ) of the proportion of seropositive individuals were also calculated to measure the strength of association between each independent categorical variable and the anti-Ov16 IgG4 seropositivity status . Multivariable analysis using logistic regression was performed to determine the adjusted ORs with their associated 95% CIs so as to identify the independent predictors of anti-Ov16 IgG4 seropositivity . P values of < 0 . 05 were considered statistically significant . The study protocol was reviewed and approved by the Ethics Committee of the Faculty of Medicine and Health Sciences , University of Science and Technology , Sana’a , Yemen ( Ref . 2016/14 ) . Participation in the study was on a voluntary basis after explaining its purpose to the heads of households and participants . In this respect , written informed consent was obtained from adults and the heads of households before recruiting their children .
The overall anti-Ov16 IgG4 seroprevalence rate was 18 . 5% ( 94/508 ) , with a higher rate in Ad Dahi ( 23 . 7% ) than Bani Sa’ad ( 20 . 4% ) , but there was no statistically significant difference ( χ2 = 0 . 61 , P = 0 . 435 ) . On the other hand , lower rates of 8 . 0% and 6 . 1% were observed in Al Marawi'ah and As Sukhnah , respectively ( Table 2 ) . In Bani Sa’ad , the prevalence of anti-Ov16 IgG4 among participants aged ≤10 years was significantly lower ( χ2 = 5 . 9 , P = 0 . 015 ) than among those aged >10 years , being 9 . 1% and 24 . 5% , respectively . In contrast , no statistically significant difference ( χ2 = 0 . 27 , P = 0 . 610 ) was observed in the prevalence of anti-Ov16 IgG4 between the participants of the two age groups in Ad Dahi , being 20 . 4% and 24 . 4% for children aged ≤10 and >10 years , respectively . With the exception of anti-Ov16 IgG4 positivity in a seven-year-old participant from Al Marawi’ah , all participants tested positive for anti-Ov16 IgG4 in Al Marawi’ah and As Sukhnah were >10 years ( Table 2 ) . Bivariate analysis showed that only the age and family size were significant predictors of anti-Ov16 IgG4 seropositivity , where those aged >10 years were at about a twice higher risk of exposure to O . volvulus infection than those aged ≤10 years ( OR = 2 . 18; 95% CI: 1 . 10–4 . 31 , P = 0 . 024 ) . In addition , participants from large families were more than twice as likely to be exposed to infection compared to those from small families ( OR = 2 . 6; 95% CI: 1 . 08–6 . 31 , P = 0 . 028 ) . The occupations of farmers ( OR = 2 . 93; 95% CI: 1 . 25–6 . 88 , P = 0 . 014 ) and housewives ( OR = 2 . 15; 95% CI: 1 . 16–3 . 96 , P = 0 . 015 ) were significantly associated with anti-Ov16 IgG4 seropositivity . However , district of residence ( OR = 1 . 21; 95% CI: 0 . 75–1 . 95 , P = 0 . 435 ) , gender ( OR = 1 . 03; 95% CI: 0 . 63–1 . 68 , P = 0 . 914 ) , education status ( OR = 2 . 69; 95% CI: 0 . 78–9 . 27 , P = 0 . 117 ) , SES ( OR = 1 . 0; 95% CI: 0 . 58–1 . 73; P = 1 . 00 ) source of water ( OR = 1 . 00; 95% CI: 0 . 61–1 . 64; P = 0 . 994 ) , history of ivermectin intake ( OR = 1 . 01; 95% CI: 0 . 74–1 . 57; P = 0 . 693 ) , presence of nodules and/or itching ( OR = 1 . 06; 95% CI: 0 . 59–1 . 92; P = 0 . 839 ) was not found to be significantly associated with anti-Ov16 IgG4 seropositivity . On the other hand , multivariable analysis further confirmed that farmers ( adjusted OR = 2 . 67; 95% CI: 1 . 11–6 . 44 , P = 0 . 029 ) , housewives ( adjusted OR = 1 . 92; 95% CI: 1 . 01–3 . 64 , P = 0 . 046 ) and being a member of a large family ( adjusted OR = 2 . 62; 95% CI: 1 . 07–6 . 45 , P = 0 . 063 ) were the independent risk factors associated with anti-Ov16 IgG4 seropositivity among residents of endemic rural areas of Hodeidah and Al-Mahwit ( Table 3 ) .
Onchocerciasis is focally endemic in eight governorates of Yemen . Nevertheless , neither estimates of O . volvulus burden in the country nor studies on the impact of regular ivermectin campaigns or CDTI on its transmission in targeted areas are encountered published in the literature . Because of the failure to achieve the goal of eliminating the disease by 2015 , the WHO paid attention to its elimination from the country by 2020 [33] . In Hodeidah and Al-Mahwit , control activities have been carried out by the CSSW since 2000 , mainly through the distribution of ivermectin donated by the MDP to infected individuals . The last activity was the MDA to endemic districts in Hodeidah and Al-Mahwit through involving local populations in CDTI campaigns in 2016 . To the best of our knowledge , the impact of campaigns in interrupting the transmission of the parasite in targeted areas has not been assessed in Yemen . The present study revealed an anti-Ov16 IgG4 seroprevalence rate of 18 . 5% among local residents of the four study districts in Hodeidah and Al-Mahwit and seropositivity among young children , providing serologic evidence for ongoing O . volvulus transmission following regular ivermectin distribution to infected individuals and CDTI campaigns in such districts . This is in contrast to the success of ivermectin MDA campaigns to interrupt onchocerciasis transmission and its elimination from a number of countries and certain endemic foci in some countries of Africa and Latin America [10 , 21 , 22 , 34–40] . However , it could be unrealistic to compare between the successful high-coverage efforts devoted by onchocerciasis elimination programs over a long time in such countries and the low-coverage campaigns implemented by an NGO in the governorates of Tihama . Moreover , the political upheaval and wars in the country since 2011 negatively impacted the efforts of disease elimination , rescheduling the expected disease elimination from the country by 2015 [17] . The present study is the first to unveil the transmission of the infection in districts with unknown disease endemicity , where prevalence rates of 8 . 0% and 6 . 1% were observed in the districts of Al Marawi'ah and As Sukhnah . This , in turn , indicates that the disease is probably more widespread than historically and anecdotally anticipated . However , infection needs to be confirmed by a skin-snip polymerase chain reaction ( PCR ) . In addition , serologic assessment of infection among children according to the WHO guidelines is required to assess the ongoing transmission of onchocerciasis in such new foci . PCR screening of blackfly pools for the parasite could also augment post-MDA elimination mapping in areas alongside wadis and their tributaries . The lower anti-Ov16 IgG4 prevalence in the latter districts compared to Bani Sa’ad and Ad Dahi could be explained by the fact that endemicity levels of onchocerciasis vary between geographic areas as a result of the interaction between several factors related to the parasite , vector , host and environmental conditions . Thus , comprehensive mapping of endemic areas is needed to geostatistically determine the level of disease endemicity and the foci of top priority for targeting with ivermectin MDA campaigns . Meanwhile , the high proportion ( 21 . 7% ) of asymptomatic infected patients in the present study reveals that symptom-free microfilaria carriers could be a potential reservoir of infection and contribute to the ongoing transmission of the parasite in such areas , particularly with the fact that they may deny the use of ivermectin in absence of symptoms . It is worth mentioning that asymptomatic O . volvulus infections are not uncommon events among Yemeni patients , possibly representing a third of cases in some endemic areas [11] . In contrast , hyper-reactive sowda patients have very low-density microfilariae in the skin and usually comply with ivermectin treatment . The low rate of nodule carriers in the studied districts ( 11 . 5%; 45/392 ) is consistent with the published literature about the uncommon presentation of subcutaneous nodules among Yemeni patients with sowda in earlier studies [15 , 41 , 42] . For instance , Anderson et al . [41] reported that 14 . 3% ( 5/35 ) of skin snip-positive patients from the southwestern region of Yemen were nodule carriers . In addition , Büttner and Racz [42] reported that only 15 . 0% ( 16/104 ) of patients with onchocerciasis in Taiz were nodule carriers , with nodules being mostly observed over the calf or thigh . The lack of baseline anti-Ov16 IgG4 seroprevalence rates makes it difficult to accurately understand the extent to which ivermectin distribution had impacted the disease epidemiology . However , seropositivity rate among children ≤10 years in the studied districts confirms recent exposure and continuing transmission . It is noteworthy that anti-Ov16 IgG4 of <0 . 1% among children <10 years old is the criterion set by the WHO to confirm the interruption of disease transmission and its elimination [33 , 43] . In the present study , the significantly lower rate among children aged ≤10 years compared to those >10 years ( 9 . 1% vs . 24 . 3% , respectively ) in Bani Sa’ad raises promise regarding a partial impact of the ivermectin on onchocerciasis transmission . This could reflect the accumulative impact of the regular three-month-interval distribution of ivermectin to the affected individuals in Bani Sa’ad since 2000 prior to the last campaign in 2016 . On the other hand , the single campaign in Ad Dahi in 2016 did not lead to significant changes in the prevalence of anti-Ov16 IgG4 between children aged ≤10 years ( 20 . 4% ) and those >10 years ( 24 . 4% ) . The early start and repeated distribution of ivermectin in Bani Sa’ad could probably maintain drug coverage for the entire reproductive life span of O . volvulus adult worms , which may extend between 9 and 14 years [44] . It is to be noted that ivermectin is a long-acting microfilaricidal drug that has a little effect on the adult worms and , therefore , controls the disease by killing microfilariae , reducing clinical manifestations and interrupting transmission by the vector but does not cure the disease completely [45] . This , in turn , justifies for the rare exposure to O . volvulus among children born by the end of MDA implementation in endemic areas and the utility of screening such children for anti-Ov16 IgG4 as an indirect indicator for determining transmission interruption [33] . Mathematical modeling demonstrates the utility of anti-Ov16 IgG4 seropositivity among children aged <10 years as a marker for the post-MDA interruption of onchocerciasis transmission [46] . Yearly MDAs significantly reduce human infection rates and , hence , reduce seroconversion rates in newborns and young children proportionately to the MDA duration and coverage , but adults who seroconverted before the start of MDA remain seropositive [46] . Moreover , a lower force of infection ( FOI ) is usually associated with younger age groups [47] , where FOI is an epidemiologic measure of the rate of infection acquisition by susceptible individuals and is usually used in mathematical modeling to compare the rate of disease transmission between two different groups [48] . Continuing transmission of O . volvulus after MDA with ivermectin has been suggested in three Senegalese districts after more than ten years of 45–90% coverage , where the anti-Ov16 IgG4 was prevalent among 6 . 9% of the population and 2 . 5% of five- to nine-year-old children [49] . On the other hand , the interruption of onchocerciasis transmission following years of MDA has been evidenced , among other criteria , by seronegativity or <0 . 1% seroprevalence of anti-Ov16 IgG4 among children aged ≤10 years in certain foci of endemic countries , including Guatemala [33 , 34] , Uganda [36] , Mexico [37] , Sudan [21 , 38] , Ecuador [22] , Nigeria [39] and Equatorial Guinea [40] . In Yemen , skin snip examination for sowda is challenging due to the rare presence of microfilariae that may require repeated collection and examination of skin snips [14 , 41] . This was evident in the present study , where all skin snips from nodule carriers were negative for microfilariae . This is attributed to both the poor sensitivity of skin snip microscopy for the detection of the low microfilarial load in the nodules of sowda patients due to their degeneration by the hyper-reactive immune response [42] and the possible impact of distributed ivermectin in killing microfilariae . As a rule of thumb , however , examination of skin snips should not be used to evaluate the impact of MDA with ivermectin on the interruption of onchocerciasis transmission or to determine the time of stopping such MDA campaigns [33] . In the present study , individuals aged >10 years are at a twice higher risk of exposure to O . volvulus infection compared to those aged ≤10 years . Repeated risk of contact with the vector could explain the higher exposure by increasing age . Moreover , the impact of ivermectin on reducing the infection exposure rate among young children and seroconversion of adults before the start of ivermectin administration could not be ruled out [46] . Although large family size was an independent risk factor of anti-Ov16 IgG4 positivity in the endemic districts , the reason behind this association is not clear but it could be attributed to the fact that more family members are engaged in agricultural activities and spend most daytime outside houses compared to small families with younger members . The significantly higher seropositivity rates among farmers and housewives compared to those not working could be explained by the increased exposure of these population categories to the bites of blackflies . In such rural communities , farmers and housewives are usually engaged in agricultural activities in the farms along the breeding sites of the vectors for long periods during the daytime . In addition , housewives are mainly responsible for bringing water from seasonal watercourses to their homes , usually several times a day based on the amount of the water needed by their household members . It is noteworthy that the vector of O . volvulus in Yemen belongs to Simulium damnosum complex , which is a distinctive subspecies referred to as S . rasyani and bites outdoors during the daytime with two peaks of biting activity , in the morning until 09 . 00 and after 16 . 00 [50 , 51] . It should be stressed that there is a need for a detailed study of the factors affecting the extent of human contact with blackfly populations and the intensity of exposure to infection , including the distance of human dwellings from probable breeding sites . Although the present study is limited by adopting only Ov16 serology as a criterion , its primary aim was to provide baseline seroprevalence of anti-Ov16 IgG4 in endemic areas of the country because of the lack of published studies in this regard . Moreover , skin snip microscopy is not suitable for the evaluation of transmission status and is absolutely insensitive for the detection of microfilariae in case of sowda . On the other hand , it was rather difficult to meet the entomologic criterion set by the WHO [33] , which recommends a minimum sample size of 6000 blackflies to determine the prevalence of infective flies by PCR . In fact , this is in accordance with the recommendations of the WHO Guideline Development Group [33] , which declared that resources , cost , feasibility and acceptability should be considered when choosing the tests to be used to demonstrate the interruption of onchocerciasis transmission . It has to be acknowledged is that there is no prior validation of RDTs in the study area against Ov16 ELISA as a reference method , and this comes in part from the unavailability of commercial ELISA kits for this purpose . Nevertheless , the quality of RDT performance has been assured by the inclusion of serial dilutions of anti-Ov16 IgG4 positive and negative controls supplied by PATH ( www . path . org ) . Moreover , the feasibility of integrating the use of anti-Ov16 IgG4 RDTs into onchocerciasis surveillance activities instead of the use of traditional skin snip microscopy has been recently demonstrated from Senegal [52] . Another issue to be considered is that the numbers of investigated cases are few when allocated over the four districts of the study . However , this could be justified by the fact the total sample size was statistically calculated to address the study objective . In conclusion , onchocerciasis is still being transmitted in the Tihama region of Yemen despite ivermectin distribution to the affected individuals and the implementation of CDTI in 2016 . This is supported by the recent exposure of children aged ≤10 years in the region to the parasite , who were positive for anti-Ov16 IgG4 . This could be attributed to the insufficient coverage rate with the drug and its distribution without having baseline infection rates in the targeted endemic areas and their neighboring localities . Moreover , onchocerciasis transmission has also been found in two districts not previously categorized as endemic for the disease and had never been targeted by ivermectin . Therefore , there is a need to establish valid baseline data for onchocerciasis mapping in endemic or potentially endemic areas before being targeted by ivermectin MDAs , with continuous monitoring with respect to elimination mapping . Despite the absence of onchocerciasis interruption , there is a decline in disease transmission in Bani Sa’ad district of Al-Mahwit as reflected by the significantly lower anti-Ov16 IgG4 seroprevalence among children aged ≤10 years . Therefore , interruption of disease transmission and its elimination is most likely a future task if good coverage with regular ivermectin MDA campaigns is achieved and its impact on disease transmission is continually monitored and evaluated .
|
Onchocerciasis is endemic in certain foci in the western governorates of Yemen . Monitoring the impact of the regular ivermectin administration to affected individuals on the transmission status and providing baseline onchocerciasis estimates in endemic areas are crucial for planning effective elimination strategies . We found that the disease transmission is still ongoing in Hodeidah and Al-Mahwit governorates of Tihama region as indicated by the anti-Ov16 IgG4 seropositivity among children aged ≤10 years . In Bani Sa'ad , where affected individuals had been regularly targeted with ivermectin over the last 15 years , we found that the anti-Ov16 IgG4 seroprevalence rate was significantly lower among children aged ≤10 years ( 9 . 1%; 5/55 ) compared to those >10 years ( 24 . 5%; 37/151 ) , reflecting a possible decline in disease transmission . We also revealed onchocerciasis transmission in districts with unknown previous endemicity for the first time , with rates of 8 . 0% and 6 . 1% being found in Al Marawi'ah and As Sukhnah districts of Hodeidah . Large-scale surveys are recommended for mapping of O . volvulus in human and blackfly populations in endemic foci and neighboring untargeted areas of uncertain endemicity as a forward step towards the elimination of the disease from the country .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"children",
"medicine",
"and",
"health",
"sciences",
"pruritus",
"onchocerca",
"volvulus",
"pathology",
"and",
"laboratory",
"medicine",
"helminths",
"tropical",
"diseases",
"geographical",
"locations",
"social",
"sciences",
"parasitic",
"diseases",
"animals",
"onchocerca",
"neuroscience",
"age",
"groups",
"neglected",
"tropical",
"diseases",
"onchocerciasis",
"infectious",
"disease",
"control",
"families",
"infectious",
"diseases",
"serology",
"yemen",
"people",
"and",
"places",
"helminth",
"infections",
"psychology",
"eukaryota",
"asia",
"nematoda",
"biology",
"and",
"life",
"sciences",
"population",
"groupings",
"sensory",
"perception",
"organisms"
] |
2018
|
Onchocerca volvulus infection in Tihama region - west of Yemen: Continuing transmission in ivermectin-targeted endemic foci and unveiled endemicity in districts with previously unknown status
|
The brain contains a complex network of axons rapidly communicating information between billions of synaptically connected neurons . The morphology of individual axons , therefore , defines the course of information flow within the brain . More than a century ago , Ramón y Cajal proposed that conservation laws to save material ( wire ) length and limit conduction delay regulate the design of individual axon arbors in cerebral cortex . Yet the spatial and temporal communication costs of single neocortical axons remain undefined . Here , using reconstructions of in vivo labelled excitatory spiny cell and inhibitory basket cell intracortical axons combined with a variety of graph optimization algorithms , we empirically investigated Cajal's conservation laws in cerebral cortex for whole three-dimensional ( 3D ) axon arbors , to our knowledge the first study of its kind . We found intracortical axons were significantly longer than optimal . The temporal cost of cortical axons was also suboptimal though far superior to wire-minimized arbors . We discovered that cortical axon branching appears to promote a low temporal dispersion of axonal latencies and a tight relationship between cortical distance and axonal latency . In addition , inhibitory basket cell axonal latencies may occur within a much narrower temporal window than excitatory spiny cell axons , which may help boost signal detection . Thus , to optimize neuronal network communication we find that a modest excess of axonal wire is traded-off to enhance arbor temporal economy and precision . Our results offer insight into the principles of brain organization and communication in and development of grey matter , where temporal precision is a crucial prerequisite for coincidence detection , synchronization and rapid network oscillations .
Brains , like electronic networks , face a hard design problem: how to pack very many , yet highly interconnected , discrete computing devices within the least possible space while simultaneously preserving efficient communication [1] . Neocortex , for example , is densely packed and composed mostly of axonal and dendritic ‘wire’ [2] , [3] originating largely from massive intracortical interconnectivity [4]–[6] . Each intracortical axon arbor , which can extend over many millimetres , transmits electrical activity from one neuron to thousands of others in its vicinity [6]–[8] . Therefore , each arbor represents a network design problem with at least two distinct communication costs ( e . g . [9] ) : the amount of wire used to connect with all its postsynaptic targets ( spatial or construction cost , in the sense of network design ) , and the time taken for an action potential radiating from the presynaptic cell to reach these targets ( temporal or routing cost ) . Ramón y Cajal [10] proposed that distinct laws conserving material or ‘wire’ ( space ) , conduction delay ( time ) , and brain volume govern neuronal design , and that from these laws physiological inferences could be made . However , Ramón y Cajal [10] , [11] did not attempt to quantify the relative importance of these conservation laws nor how these distinct laws might interact to reproduce neuronal morphology . In recent years , attention has concentrated on material conservation as proposed in the ‘wiring minimization principle’ [12]–[16] , which alone is claimed to explain many key features of brain organization including the intracortical wiring underlying functional maps in neocortex [14] , [16] . Yet whether intracortical axonal trees in grey matter conform to the wiring minimization principle remains empirically untested and its consequences on temporal cost have not been explicitly considered . Here , we empirically investigated , to our knowledge for the first time , the spatial and temporal cost optimality of whole three-dimensional intracortical axon arbors .
Mammalian neocortex is composed of two main morphological classes of neuron: spiny ( ∼80% of all neurons ) and smooth or sparsely-spinous ( ∼20% ) [17]–[20] . We examined intracortical axon arbors of ten putative excitatory ( morphologically-identified spiny stellate and pyramidal cells ) and nine putative inhibitory cells ( morphologically-identified large basket cells ) , which we three-dimensionally reconstructed after labelling in vivo in adult cat primary visual cortex and are described in more detail elsewhere [21]–[23] . Pyramidal and spiny stellate axons target the dendrites of spiny and smooth neurons [3] , [6] , [24]–[28] , while basket cell axons target the soma and proximal dendrities of both smooth and spiny neurons [8] , [20]–[22] . Together these neurons are representative of the majority ( 85–90% ) of neuronal types present in cat visual cortex [6] , [8] , [18] , [29] and their axons are thought to form the long-range networks underlying functional maps [6]–[8] , [21]–[23] , [30] . The morphology of spiny cell and basket cells analysed here are shown in relation to cortical lamina in Figure 1 . To test for spatial cost minimization , we used a minimum spanning tree ( MST ) algorithm [31] to find the least amount of wire required to connect together axon origin with all boutons present in a given axon arbor [12] . Total wire length may be further shortened if additional ( Steiner ) vertices ( akin to axon bifurcations – nodal point where the axon divides to produce at least two child branches ) are inserted to produce an Euclidean Steiner minimal tree ( ESMT ) [32] . However , Steiner tree problems are considered computationally intractable Non-deterministic Polynomial time ( NP ) -hard [32] , so we used the only available ( heuristic ) algorithm for finding large vertex set ESMT [33] , which has proved successful with other 3D datasets . Wire length economy ( ε ) was calculated from the ratio of minimum to actual total axon wire length . To test for temporal cost minimization , we approximated temporal cost from the total distance travelled ( path length ) by a notional axon potential from the axon origin to each bouton . Here the minimum-cost graph is a star tree , a single-source shortest path tree with a parallel branch from axon origin to each bouton vertex [9] . To estimate axonal latency , we divided path length by a uniform conduction velocity ( see Methods ) . Axon conduction velocity varies with axon thickness , branching , ion channel density and variety , and myelination [34] , so latency estimates here are approximations only . Yet realistic numerical simulations of intracortical axon arbors suggest path length is the main determinant of latency [35] . Current estimates of mean intracortical axonal conduction velocity in adult cat visual cortex vary ( range = 0 . 1–0 . 6 m s−1 [36]–[38] ) but are typically slower than , for example , the main type of thalamic afferent axon innervating visual cortex ( e . g . X-type geniculate axons , range = 8–20 m s−1 [39] ) . Path length economy ( γ ) was computed from the ratio of minimum to actual average path length , though similar results were obtained for total path length . A simple example illustrating the distinction between wire and path length cost minimization [9] , [31] is shown in Figure 2 . Here , connecting nearby boutons in serial fashion to minimize wire length tends to increase time delay ( Figure 2A , middle ) , whereas dedicating parallel branches to each bouton to minimize time delay dramatically increases wire length ( Figure 2A , right ) . The difference between a 3D MST and an ESMT is shown in Figure 2B . We now consider whether this relationship extends to biological axon arbors and whether biological axons are wire-length optimized . To investigate wire length economy , we contrasted the total length of intracortical axon arbors to minimum-length graphs ( Figures 3–7; see Table S1 ) . Spiny cell axon arbors were not optimized for wire length ( p<0 . 001 , Wilcoxon signed rank test , one-sided; εspiny = 0 . 86±0 . 04 , mean ± sd ) with on average 5 . 66±2 . 93 mm excess wire per axon or 14±4% of total wire length ( Figure 3 ) . For example , a minimum-length graph connecting the same bouton set as a layer III pyramidal ( spiny ) axon arbor used 6 mm less wire or 15% of total axon length ( Figure 4 ) . Basket cell axons also were suboptimal for wire length ( p<0 . 005 , Wilcoxon signed rank test , one-sided; εbasket = 0 . 76±0 . 02 ) and even significantly less economical than spiny cells ( εspiny vs . εbasket: p<0 . 0005 , Mann-Whitney U test , one-sided ) ( Figure 3 ) with on average 10 . 33±4 . 13 mm excess wire per axon or 24±2% total axon length . For instance , a minimum-length graph of a large layer III basket cell axon arbor used nearly 14 mm less wire or 24% of total axon length ( Figure 5 ) . In comparison , star graphs used around 40–50 times more wire than axons ( εstar = 0 . 02±0 . 01 and 0 . 02±0 . 02 , respectively; see Figure 3 ) . Both wire and path length economy measures were uncorrelated with either total arbor length or bouton number ( Figure 6 ) , suggesting they are scale-invariant measures and robust to incomplete axon arbor reconstruction . Inserting additional vertices ( akin to axon bifurcations ) did not significantly reduce total arbor length . The ESMT algorithm inserted typically double or more ( Steiner ) points than actual axon bifurcations per arbor ( spiny axons: 130±73 axon nodes vs . 621±518 Steiner points; basket: 632±348 axon nodes vs . 1662±528 Steiner points ) but only marginally shortened total wire length ( see Table S1 ) . Rarely were these additional vertices co-located with actual axon bifurcation nodes ( ≤2 . 5 µm distance: spiny , 2 . 92±1 . 90% per arbor; basket , 8 . 45±4 . 62% ) . Here , axon internal ( aperture ) branching angles were distributed normally ( spiny angle distribution , 82 . 7±34 . 4° , n = 1298 nodes; basket , 85 . 8±33 . 0° , n = 6192 nodes; see Figure 7 ) . Regardless of algorithm , Steiner points require a 120° internal angle [13] , [32] , [33] . Yet few axon bifurcations met this condition ( spiny , 12% and basket , 14% in range 120±10°; see shaded region , Figure 7 ) . This discrepancy cannot be explained by , for example , local junction volume optimization [13] because while nearly three-quarters of all spiny axon diameter branching ratios were unambiguously of equal volume cost ( 74% , 965/1298 ) few of these matched the 120° prediction for equal volume cost ( 11% , 106/965 ) . These results suggest the branching properties of intracortical axonal trees do not match those of wire-minimized Steiner minimal trees . Crucially , if the purpose of axon bifurcations was to shorten arbor wire length ( as predicted from the wire minimization principle ) then supplying them as additional vertices for the MST algorithm ( “MST nodes” results ) should guarantee a wire-minimized arbor [31] . Yet in all cases this critical test resulted in longer not shorter arbors ( spiny , +0 . 61±0 . 30 mm , p<0 . 005; basket , +2 . 46±1 . 23 mm , p<0 . 005 , both Wilcoxon signed rank test , one-sided; see Figure 3 ) , implying that the positioning of intracortical axon bifurcations is not consistent with shortening wire length . Overall , these results , invariant to reconstruction completeness , suggest that individual excitatory and inhibitory intracortical axon arbors are not optimized for wire length and their branching behaviour does not match the predictions of the wire or local volume minimization principles . To investigate potential sources of excess wire , we first used Strahler ordering [40]–[42] to characterise the branching structure of each axonal tree ( for an example , see Figure 8 ) . The Strahler ordering scheme has been widely used to quantify natural tree-like branching hierarchies including dendritic as well as axonal arbors [41] , [42] . We chose this particular scheme to permit a direct comparison with previous work on the structure of intrinsic cortical axon arbors in cat visual cortex [41] . This centripetal ordering scheme gives a purely topological description of the axonal tree by labelling terminal branches as first-order ( k = 1 ) and then incrementally ascending the tree hierarchy until reaching the root branch ( axon origin ) , which has maximum order [42] . Here , spiny cell axonal trees had maximum order of 5 or 6 except for one arbor of 4 , while most basket cells had maximum order 7 except for one of order 5 and one of 6 ( c . f . 5–6 spiny & 5–7 smooth , [41] ) . In addition , for each arbor we classed internodal axon branches ( k≥2 ) as either ‘bouton-laden’ ( sections directly supporting one or more boutons ) or ‘bouton-free’ ( sections lacking any boutons ) . One source of excess wire was the typically short ( a few µm ) distance between the last ( most distal ) bouton and the tapering tip of the axon branch , the terminal axon segment ( see Figure S2 ) . This source accounted for around 2% excess wire ( spiny: 0 . 74±0 . 35 mm per arbor or 1 . 7±0 . 9% excess wire; basket: 1 . 28±0 . 31 mm per arbor or 2 . 3±0 . 6% excess wire ) . All subsequent analyses subtracted this source of excess wire . When examining how different wire-related arbor properties varied with branch order , we discovered that while the proportion of total axon length and bouton number , and bouton density all decreased with branch order , conversely , the proportion of internodal bouton-free axon length increased ( see Figure 9 ) . For example , first- and second-order branches accounted for the vast majority of boutons ( spiny , 88 . 9±7 . 2% & basket , 97 . 1±2 . 1%; c . f . grouped 92±5% [43] ) and axonal wire ( spiny , 80 . 6±6 . 5% & basket , 76 . 1±3 . 7%; c . f . length uncorrected & grouped 82±6% [43] ) ( see Figure 9AB ) . In addition , mean bouton density ( bouton-laden sections only ) fell as branch order increased with , for example , basket cell first- and second-order branches having a greater density than spiny cells axons ( e . g . at first-order: spiny , 0 . 07±0 . 01 & basket , 0 . 18±0 . 03 boutons per micron , or interbouton interval ( ibi ) 14 . 1 & 5 . 7 microns per bouton , respectively; c . f . grouped ibi 3–11 microns per bouton [43] ) , though thereafter bouton density declined similarly to zero by fifth-order ( see Figure 9C ) . Importantly , we found whole arbor wire length economy was negatively correlated with the proportion of total boutons per arbor located on first- and second-order branches ( Spearman rank correlation , rs = −0 . 84 , p<10−6 , one-sided; linear regression , slope = −109 . 21 , intercept = 183 . 35; see Figure 10A ) suggesting wire length economy improved when boutons were more evenly spread over an arbor . Internodal axon branches of basket cells are often myelinated and so lack boutons [8] , [21] though this appears less prevalent in spiny cell axons [24] , [44] . Here , we found internodal bouton-free length per arbor increased on average from a fraction of second-order total branch length ( spiny , 5 . 5±2 . 7%; basket , 25 . 7±11 . 2% ) to 100% by fifth-order ( see Figure 9D ) . Whole arbor wire length economy was negatively correlated with the proportion of total axonal length that was bouton-free ( Spearman rank correlation , rs = −0 . 94 , p<10−6 , one-sided; linear regression , slope = −1 . 93 , intercept = 1 . 75; see Figure 10B ) , indicating that arbors with a lower proportion of bouton-free internodal wire tended to be more economical . Recall ‘bouton-free’ wire length here refers to complete internodal sections lacking any boutons , which therefore might be myelinated , and does not count interbouton gaps on bouton-laden sections . Together these results suggest intracortical axon higher-order branches ( k≥3 ) support fewer boutons per length and have proportionately more whole internodal sections devoid of boutons than lower-order branches . To investigate the relationship between wire economy and axonal branching , we computed the wire economy of each subtree grouped by branch order ( origin of parent branch became subtree root vertex ) but excluding root branch ( whole arbor ) . Recall wire economy was uncorrelated with bouton number or axon length ( see Figure 6 ) . Here , we found that as subtree branch order increased , starting from terminal branches ( which after tip length correction were optimal ) towards whole arbor , so the average subtree wire length economy progressively decreased ( Figure 10C ) . Correspondingly , as subtree branching complexity increased so the proportion of excess wire increased asymptotically in increments of between 2–4% for spiny cell and 1–6% for basket cell axons ( Figure 10D ) . Individual arbor rate of decline in wire economy between branch order levels was scaled by whole arbor economy level ( spiny: r2 = 0 . 82 , p<10−6; basket: r2 = 0 . 92 , p<10−6 ) . Thus , branching itself appears to cost wire , which may explain why basket cell axon arbors generally have poorer wire economy than spiny cell axons . Wire-minimization algorithms aim to shorten total wire length by simplifying a geometric problem without regard to any other objective function [32] . So it is understandable why the nature of the bouton distribution over an axonal tree , both in terms of local density and spaces of the arbor lacking any boutons ( bouton-free sections ) , determines wire economy . A low economy spiny axon arbor , for example , directly links bouton-rich terminal patches instead of following the path of the actual but bouton-free main axon collateral , which runs tangentially to the cortical surface ( see Figure 11A ) . Yet for the most economical spiny axon fewer shortcuts exist because boutons were more evenly spread over its arbor ( Figure 11B ) . Particular to basket cell axon morphology , shortcuts ‘zig-zag’ between unmyelinated bouton-rich terminal branches avoiding myelinated bouton-free collaterals ( Figure 11C , upper ) , a feature absent in spiny cell arbors ( Figure 11C , lower ) . Moreover , in our sample the most economical axon had the lowest branch order ( 4 ) , most boutons on its higher-order branches ( 28% ) , and nearly the least bouton-free wire ( 3 . 3% c . f . 3 . 1% ) . In contrast , the least economical arbor had the highest order ( 7 ) , least boutons on its higher-order branches ( 1% ) , and most bouton-free wire ( 35% ) . The ESMT algorithm performed similarly in relation to the MST algorithm ( see Steiner ratios in Table S1 ) suggesting economy was not related to algorithm performance . Together these results suggest that the origin of wire economy involves a combination of factors that constrain spatial ( geometric ) bouton distribution in particular the degree of branching complexity , the proportion of bouton-free internodal length , and the relative distribution of boutons over an arbor . In cerebral cortex , a low degree of temporal dispersion of synaptic input arrival times ( standard deviation of latencies ) is critical for the synchronization of distributed responses [45] , rapid network oscillations [46] , and coincidence detection within the millisecond range [47] . The degree of temporal precision is dependent on the anatomical and physiological characteristics of axonal wiring interconnecting cortical neurons . Hence , the minimum width of the postsynaptic temporal integration window is at least partly dependent upon the precision of intracortical architecture . An interconnected network of star trees , for example , would be expected to provide optimal temporal precision by ( i ) minimizing temporal dispersion , and ( ii ) preserving the distance-time relationship , so that signals from co-active neurons equally distant from a postsynaptic neuron they both innervate arrive simultaneously . In visual cortex , for example , these properties are believed to be important in promoting the temporal binding of spatially distinct co-linear visual stimuli [30] . Here , we investigated the temporal economy of axon arbors compared to wire-length minimized graphs ( Figures 12–14; see Table S1 ) , assuming a uniform conduction velocity at each part of the arbor . Example spiny and basket cell axon arbors demonstrate that wire length minimization increased both average path length ( average axonal latency ) and path length variance ( temporal dispersion ) ( Figures 12AB ) . In general , average path length from axon origin ( root vertex ) to bouton for biological arbors ( median ± sd , spiny , 1 . 13±0 . 41 mm; basket , 0 . 61±0 . 15 mm ) was much shorter than wire-minimized MST ( spiny , 2 . 04±1 . 30 mm; basket , 1 . 21±0 . 69 mm ) . Hence , spiny axon arbors were suboptimal for path length ( p<0 . 005 , Wilcoxon signed rank test , one-sided; γspiny = 0 . 67±0 . 06 ) as were basket cell axonal trees ( p<0 . 005; γbasket = 0 . 66±0 . 07 ) . Yet axon average path length was significantly shorter than corresponding MSTs for both spiny ( p<0 . 001 , Wilcoxon signed rank test , one-sided; γ = 0 . 41±0 . 10 ) and basket cells ( p<0 . 001; γ = 0 . 34±0 . 05 ) ( Figure 12C ) . Path length variance of axon distributions was significantly less than MST distributions ( spiny & basket , p<10−6 , Brown-Forsythe modified Levene test ) . In contrast to wire economy , there was no difference in path economy between axon classes ( γspiny vs . γbasket: p = 0 . 91 , Mann-Whitney U test , two-sided ) indicating that intracortical arbor temporal cost may be class-independent . Inserting additional vertices did not significantly improve path length economy with ESMT ( spiny , p = 0 . 09 and basket , p = 0 . 47 , Wilcoxon signed rank tests , two-sided; γspiny = 0 . 42±0 . 11 , γbasket = 0 . 35±0 . 05 ) though supplying axon nodes led to a small increase for MSTs ( spiny , p = 0 . 17; basket , p = 0 . 07; both Δγ = +0 . 02 ) ( see Figure 12C ) . Thus , with or without additional vertices and regardless of cell class , wire minimization yielded worse temporal economy than real axons . Figure 13 illustrates that for axons the relationship between cortical distance from axon origin to bouton and path length diverged only slightly from optimal ( slope = path length ratio = 1 ) albeit with a small offset ( spiny , regression slope = 1 . 17 , intercept = 0 . 25 mm , r2 = 0 . 89 , see Figure 13A top; basket , slope = 1 . 06 , intercept = 0 . 18 mm , r2 = 0 . 87 , see Figure 13B top ) . In contrast , the path length of wire-minimized MST arbors , with or without axon bifurcations as additional vertices , diverged sharply from optimal with distance ( spiny , slope = 2 . 24–3 . 03 , intercept = 0 . 11–0 . 44 , r2 = 0 . 45−0 . 48 , see Figure 13A middle & bottom; basket , slope = 2 . 01−2 . 11 , intercept = 0 . 31−0 . 34 mm , r2 = 0 . 70−0 . 75 , see Figure 13B middle & bottom ) . Individual path lengths in axon arbors were typically less than twice the optimum length ( 82% spiny axonal boutons , n = 22 , 344 total boutons; 78% basket axonal boutons , n = 44 , 064 total boutons ) while far fewer MST path lengths fell within this range ( spiny MSTs , 33–34%; basket MSTs , 12–13% ) ( see shaded region in Figure 13CD ) . Axonal boutons with path length ratios of 2 or above were confined mostly to within 0 . 5 mm of parent cell body yet for MSTs such ratio values were found at nearly all distances . The similar , positively skewed shape of both spiny and basket cell axons path length ratio distributions ( median ± sd , spiny = 1 . 45±0 . 77; basket = 1 . 53±0 . 86; see Figure 13CD ) suggests high ratios were increasingly penalised compared with MST distributions which typically peaked later with a longer tail ( spiny = 2 . 44±1 . 77 with bifurcations & 2 . 57±1 . 96 without; basket = 2 . 79±1 . 31 with & 2 . 94±1 . 58 without ) . These results suggest a common temporal cost mechanism may regulate intracortical axon morphology to preserve the distance-time relationship , which is especially important for the most distant connections within functional maps . To predict the effect of wire minimization on temporal dispersion , we estimated axonal latency deviation about the regression lines ( green lines shown in Figure 13AB ) for both axon and MST data ( Figures 14 and 15 ) . Independent of conduction velocity , spiny cell axon temporal dispersion was 5 . 7 times less than MSTs with axonal bifurcation vertices and 8 . 2 times less than MSTs without axonal bifurcations ( Figure 14A , left ) . For basket cell axon temporal dispersion , the corresponding values were 2 . 9 ( with ) and 3 . 4 ( without bifurcation vertices ) times less ( Figure 14A , right ) . For instance , at 0 . 15 m s−1 conduction velocity latencies covered a narrower temporal window than MSTs ( spiny , ±5 vs . ± >20 ms , Figure 14B left; basket , ±2 vs . ±8 ms , Figure 14B right ) , which was maintained when conduction velocity doubled to 0 . 30 m s−1 ( spiny , ±2 vs . ±12 ms , Figure 14C left; basket , ±1 vs . ±4 ms , Figure 14C right ) . Figure 15 illustrates that the relative temporal dispersion of spiny cell arbors was double that shown by basket cell axons , which generally have greater branching complexity than spiny cell axons . Moreover , this difference is likely to be enhanced from the postsynaptic somatic targeting by largely myelinated basket cell axons [8] , [21] compared to the postsynaptic dendritic targeting by mainly unmyelinated spiny cell axons [6] , [17] . These results suggest the design of intracortical axonal arbors supports a low degree of temporal dispersion and a close relationship between distance and latency , prerequisites for intracortical synchronization [45] , fast network oscillations [46] , and coincidence detection [47] , yet wire-minimized arbors ( with or without branch points ) demonstrate much poorer temporal precision making them ill-suited for these functions . Based on these results , we hypothesized that both spatial and temporal costs simultaneously constrain intracortical axon arbors: empirical data here suggests that the least amount of wire was used to ensure that most axon path lengths were less than twice the minimum conduction delay . In classical network design problems , however , simultaneously minimizing both construction and routing costs is considered intractable because they are conflicting objective functions [9] , [48] . Instead , approximation algorithms are used to find graphs representing a continuous trade-off between these two costs [9] , [48] . To test this hypothesis and investigate the relationship between spatial and temporal arbor costs , to each axon arbor we applied the light-approximate spanning tree ( LAST ) algorithm [48] , which strictly limits path length ratio through a single parameter , αLAST . Depending on αLAST value , the algorithm can generate at one extreme an MST ( αLAST≫1 ) or at the other a shortest path tree ( star tree ) ( αLAST = 1 ) , with intermediate αLAST values generating hybrid MST-star graphs; for example , αLAST = 2 ensures that all path lengths are less than twice the minimum . To obtain a baseline for comparison , we generated for each axon 250 independently randomized trees spanning the same vertex set [49] and calculated their wire length and path length economy . For both spiny and basket cell populations , as αLAST increased so path economy decayed from unity to around half , while simultaneously wire economy rose rapidly from near zero to approach unity asymptotically ( Figure 16A ) . The results support the hypothesis that wire and path length economies are generally opposing costs at least for this type of arbor . Around αLAST = 1 . 9 costs were balanced , ε = γ≈0 . 79 ( Figure 16A ) . Combined in the ε–γ plane these curves created a continuous cost trade-off: commencing with star trees there was a gradual decline in γ with increasing ε until reaching the equilibrium point , where γ fell sharply down towards MST parameter values ( Figure 16B ) . Hence , the trade-off gain in path economy becomes far more expensive in terms of wire cost to the left of equilibrium ( Figure 16B ) . Importantly , the economy parameters of both basket and spiny cell class axons fall mostly on or around these trade-off curves ( Figure 16B ) , suggesting that LAST algorithm offers a reasonable approximation to the underlying cost constraints on axon wiring . While a few axons were close to equilibrium , most had economy parameter values biased towards wire minimization . In comparison , randomized arbors gave simultaneously extremely poor both wire length economy ( spiny , 0 . 017±0 . 006; basket , 0 . 014±0 . 004 ) and path length economy ( spiny , 0 . 015±0 . 004; basket , 0 . 009±0 . 003 ) demonstrating the effectiveness of spatial and temporal cost optimization ( Figure 16B ) . These results offer support for the hypothesis that neocortical axon arbor design represents a trade-off between spatial and temporal communication costs .
For over a century , Ramón y Cajal's [10] conservation laws have guided research aimed at understanding the functional principles of neuronal morphology , a topic dominated recently by wire-length minimization of 2D arbors [12]–[16] . To our knowledge , this study is the first quantitative empirical test of Cajal's laws for whole 3D neocortical axon arbors within grey matter . Here , we discovered that neocortical axonal trees are not globally minimized for either wire length ( material ) or path length ( conduction delay ) . Instead their three-dimensional branched design represents the trade-off of a modest amount of excess axonal wire ( ∼10–20% total arbor length , equivalent to roughly 3% extra grey matter volume [3] ) to obtain a roughly two-fold gain in overall temporal economy and three-fold or more gain in temporal precision . In contrast , algorithms used here suggest wire length minimized arbors would significantly impair the temporal precision of neuronal network communication ( Figures 12–14 ) whereas path length minimized arbors would demand at least an order of magnitude larger neocortex ( Figure 3 ) . Specifically , it appears axon bifurcations function to preserve the relationship between conduction time and cortical distance ( Figure 13 ) and to tightly regulate the degree of temporal dispersion in transmission of axonal signals ( Figures 14 and 15 ) . From these axonal tree properties we infer that the highly interconnected intracortical network architecture , thought to underlie functional maps [7] , [8] , [21]–[23] , [25] , [30] , is designed to be capable of operating with a high degree of temporal precision ( e . g . for coincidence detection ) . In particular , inhibitory basket cell axon transmission seems capable of double the degree of temporal precision of excitatory spiny cell axon arbors ( Figure 15 ) , consistent with the notion that in cerebral cortex precise somatic inhibition sharpens coincidence detection of more broadly tuned excitatory signals [50] . Therefore , these results have implications for our understanding of neuronal communication and coding within cerebral cortex [1] . The graph optimization algorithms were used here to measure the degree of optimality of single axons to investigate Cajal's laws of neuronal morphology and should not be considered as models of cortical circuit development ( see ‘Developmental Considerations’ ) . This type of approach is consistent with previous analyses of wiring economy [12] , [14] , [51]–[53] that have also relied on global information , mostly based on empirical data as done here . The rationale for wiring optimization is that it is the result of evolutionary pressure to maximize an organism's survival by selecting developmental mechanisms capable of generating the most efficient brain wiring [1] , [12] , [13] , [16] . Minimizing total wire length and minimizing path length are distinctly different problems [9] , [31]–[33] , [48] . Although we cannot completely exclude the possibility that for a given vertex set other algorithms might find an arbor simultaneously optimal or very near optimal wire and path length economy ( same connection topologies ) , we think in general it is unlikely because of the different objective functions and problem geometry . Consider , for example , a star tree where the optimal path length of a given vertex is a direct connection to the root vertex . To begin shortening the total wire length of this arbor requires that another vertex , whether fixed or inserted , be included in the path between root and the given vertex . Because of the triangular inequality of the Euclidean metric , a detour via this additional vertex will in general increase path length [31] , [48] . So it follows that for any algorithm to further reduce total wire length implies that individual paths will become less direct and so longer . Therefore , we suggest the nature of the problem geometry will in general force any algorithm to trade-off wire and path length objective functions [9] , [48] , although other algorithms may achieve a better degree of trade-off . In any case such improved results would only serve to emphasize the suboptimality of cortical axon arbors . The morphological and topological similarity of our sample with the only larger comparable studies of axonal trees [6] , [41] , [43] suggests it is representative of intracortical neurons in adult cat visual cortex . Since spiny and basket neuronal types are conserved [10] , [11] , [17] we would expect results here should generalise to other cortical areas across species , though it is possible the trade-off may vary according to functional requirements ( e . g . enhanced temporal precision in auditory cortex ) . By labelling adult axon arbors in vivo we were able to analyse relatively stable , long-range axon arbor connectivity , which would not have been possible using axons obtained in vitro from neonatal brain slices ( e . g . [20] ) . Our sample did not include any non-basket GABAergic cell types [17] , [19] , [20] . But we would expect to obtain comparable results from analysis of these missing cell types because they have similar properties to axons studied here such as total axon arbor length and internal axon branching angle [6] , [43] , [54] . Other known costs might affect the results . Metabolic cost , for instance , is generally considered a major resource limitation for brain organization and function [1] . Of the grey matter energy budget , signalling accounts for more than three-quarters with a smaller fraction ascribed to maintaining ionic equilibrium [55] . Because wire and path length costs should correspond with metabolic costs for ion channels and transporters at rest and when signalling , respectively , energy costs may be considered as implicit within the current approach . During development , material transport costs from soma towards the growing tips during axon extension [56] , for example , are likely proportional to path length . Finally , for reasons of combinatorial complexity , we did not explicitly consider axon volume as a variable cost for optimization . Yet failure to find evidence supporting either whole arbor wire length or local junction volume minimization [13] here argues against whole arbor volume optimization . The current analysis , though not a complete description of all constraints , appears to represent a reasonable approximation to the main costs of neocortical axon arbor design . To compare tortuous axon trajectories with straight graph edges , we measured the direct rather than actual distance between fixed points in the axon reconstructions ( see Figure S1 ) . It is reasonable to suppose that the difference in wire length between artificial and neuropil spaces would allow the graph extra length to avoid other neuronal processes and larger cellular obstacles such as capillaries [2] , [3] . Dendritic processing also contributes to the signalling latency between presynaptic and postsynaptic cells [57] . Dendritic conduction delay is likely to be proportional to distance along dendritic branches from an axodendritic synapse to the cell body with conduction velocity dependent upon branch thickness and active and passive ionic currents [57] . Currently , it is impractical to trace in vivo each of the thousands of connections made by a whole axon arbor to their respective position on postsynaptic neurons , so anatomically-based estimates of dendritic delay must be based on knowledge obtained from previous work on individual cell pair tracings . Basket cell axons , for instance , invariably contact the cell bodies of postsynaptic neurons [8] , [20]–[22] so it is likely that the temporal dispersion of basket cell axonal connections may not be significantly delayed by dendritic processing . In contrast , individual spiny cell axons mostly contact the proximal , medial or distal parts of the dendritic tree of other spiny neurons [3] , [6] , [23]–[28] , [37] , so here delay might be significant . However , there is evidence for a spatial segregation of synaptic inputs from different presynaptic sources on spiny cell dendritic trees [26] , so it is possible that this delay might not greatly broaden temporal dispersion between two particular cell populations but simply provide an average timing offset between them , which may have a functional significance [58] . Thus , dendritic processing may increase the temporal dispersion of spiny cell signalling relative to basket cells ( see Figure 15 ) . Current results suggest that for neocortical axonal trees material conservation prevails over conduction delay conservation ( see Figure 16 ) . But here because of practical limitations we assumed a constant conduction velocity across the whole arbor ( see Methods ) , which might underestimate temporal economy . For example , myelinated primary axon collaterals [34] could reduce latency to child branches without altering wire length , so shifting the trade-off closer to equilibrium . Recall many of the primary and secondary axon branches lacked any boutons ( see Figure 9D ) and so might be myelinated , which in the case of basket cell axons is most likely correct [8] , [21] , [22] . Moreover , there is evidence that evolution uses myelination to reduce conduction delay as wire length increases with brain size [59] . More accurate temporal costing might , therefore , reveal the two conservation laws are equally important . Though the lack of wire optimization of single arbors here does not necessarily imply intracortical networks are suboptimal for wire length it does cast doubt on the applicability of the principle by itself to grey matter [12]–[16] , especially given highly stereotyped connectivity patterns within neocortex [3] , [6] , [17] , [19]–[23] , [27] . Yet models claiming support for global wire minimization typically lack axonal branching and instead employ direct , parallel connections ( star trees ) between planar lattice points [14] . Hence , these models in fact optimize path length not wire length , which questions their validity to explain the organization of intracortical wiring functional maps in visual cortex according to wire length minimization only [14] , [16] . Moreover , recent work suggests the only completely mapped nervous system ( C . elegans ) is not globally minimized for wire length ( [51] , [52] c . f . [53] ) . Independently , Kaiser and Hilgetag [52] , using published gross connectivity matrices , recently reported non-optimal wire minimization in white matter between parallel pathways interconnecting visual cortical areas , which they too attributed to reducing communication delay . However , their study was concerned with unbranched axon bundles within white matter whose mean lengths were inferred not measured [52] . By contrast , here we traced and measured the length of the actual 3D trajectories of individual branched axon arbors within grey matter . If the results of Kaiser and Hilgetag [52] are later validated by empirical measurements of actual 3D individual axon lengths then this could imply the existence of a universal principle of cortical organization used both within ( grey matter , this study ) and between cortical areas ( white matter ) to optimize neuronal network communication . While evidently correct for unbranched axons , the implication that axon arbor material conservation also leads to conduction delay minimization [11] , [13]–[16] requires axonal branching to simultaneously save wire and path length , contrary to results from classical network design [9] , [32] , [48] . Isolated Steiner Y-junctions would appear to meet this requirement [13] provided the spatial arrangement of connections is compliant . Yet here the angle condition for Steiner junctions was rarely met by axon bifurcations ( Figure 7 ) . Moreover , linking together a set of individual Steiner junctions would not be expected to improve temporal economy because minimizing path length is not part of the objective function of Steiner minimal tree algorithms [32] , [33] , a point supported by ESMT results ( see Figure 12C ) . Axon bifurcations in fact tend to worsen spatial economy ( see Figures 3 and 10C ) but improve temporal economy ( see “MST nodes” results , Figure 12C ) . Basket cell axons , for instance , typically have a greater degree of branching complexity , less temporal dispersion but poorer wire economy than spiny cell axons . Indeed , if conserving wire length was the main determinant of axon morphology why do neocortical axons exceed third order branching when typically first and second-order axon branches account for virtually all boutons ? Therefore , there is evidence from algorithms used here that axonal branching ( increased parallelism ) enhances temporal economy at the cost of spatial economy . Intriguingly , Ramon y Cajal [10] did note some examples of neuronal morphology “sacrificing economy of matter in favour of economy of time” ( p . 105 ) though we suggest this is the general rule in grey matter . To optimize intracortical axon communication , we conclude that faced with a similar ( neuronal ) network design problem evolution has selected a trade-off where the spatial cost of arbor wiring is minimized subject to temporal cost limits . Before considering how the developing axon arbor might be shaped by material and conduction delay conservation principles to attain its mature morphology , we need first to briefly outline the in vivo development of intracortical axonal trees and the different factors regulating axon morphology during cortical development . Intracortical axonal trees examined here follow a characteristic pattern of development in vivo [60]–[72] . Spiny intracortical axons , for instance , begin with the main descending axon trunk emitting numerous collateral side branches that extend radially for up to a millimetre or so typically without branching ( outgrowth phase ) ( for further details , see [60]–[68] ) . As these long primary collaterals gradually lengthen they then start to add distal branches but mostly interstitial secondary and tertiary branches at intervals along their length which then form crude clusters of collateral branches until reaching their maximum extent ( elaboration phase ) . Finally , activity-dependent mechanisms are believed responsible for the increased branching frequency at some arbor locations and branch elimination at others to refine clusters ( remodelling phase ) [60] , [61] , [69]–[71] . Basket cell arbors similarly begin with the gradual extension of primary unbranched collaterals from the main axon shaft followed by the sprouting of distinctive interstitial side branches [72] though it is unclear whether or not these arbors are extensively remodelled . Thus , during cortical development both spiny and basket cell axon arbors increase in branching complexity . Without tracking the development of individual whole cortical axons in vivo , it is not possible to directly determine how spatial and temporal communication costs might constrain local arbor growth . Yet evidently the initial structure of long unbranched axon collaterals radiating from the main axon trunk [60]–[61] , [63] , [66] , [68] , [72] , similar to a star tree ( see Figure 2A , right ) , implies that in the early stages of axon development minimizing conduction delay may take priority over material cost . The purpose of this initial radial outgrowth may be to rapidly cover the cortical space around the cell body to maximise potential connectivity and form a cortical scaffold with a precise distance-time relationship . Since the mature arbor appears more frugal with material cost this raises the possibility that during axon arbor development there may be a shift from temporal to spatial cost minimization . To investigate this idea , we need to understand how axon arbors develop . During cortical development , the role of axon growth and branching is to find and synapse with numerous appropriate target neurons in order to construct a functional neuronal network . To find target neurons , the axon growth cone , the locomotory tip of the nascent axon , locally integrates multiple extracellular molecular signals via receptor activation to determine its direction and rate of outgrowth [56] . Extracellular ligands , which can trigger attractive or repulsive responses , include various growth factors ( e . g . neurotrophins ) , short-range nondiffusible cell adhesion molecules ( e . g . neural cell adhesion molecule , NCAM ) and extracellular matrix molecules ( ECM ) ( e . g . laminin ) , and long-range diffusible ( e . g . netrins ) and membrane bound concentration gradient cues ( e . g . ephrins ) [56] . At the growth cone , ligand bound receptors transiently increase the concentration of intracellular Ca2+ via influx through calcium permeable channels and/or release from internal stores [73] , [74] . The frequency and spatial gradient of Ca2+ transients dynamically reorganize the growth cone's actin cytoskeleton ( via calcium-dependent enzymes and Rho GTPases signalling pathways ) to determine whether it extends , turns , retracts , splits ( branch to create two growth cones ) , collapses , or pauses [74] . Axon morphology is determined by the organization of actin filaments , microtubules , and neurofilament cytoskeleton components [75]; though required later for axon calibre enlargement neurofilaments are not essential for axon elongation [76] . When moving slowly or paused , for example , the growth cone is enlarged with numerous sensing thin antenna-like processes ( filopodia ) that actively explore the local environment in a highly efficient manner [77] without affecting axon shaft orientation [78] . Yet when rapidly advancing , the growth cone is small and dome-like , lacking filopodia [73] . Mechanical tension generated by actin-related changes in the growth cone extend the axon in short straight sections between adhesive points [79] , [80] with the rate of extension proportional to the degree of mechanical tension whether applied artificially by towing [81] or induced by extracellular signalling such as growth factors [82] . In a homogeneous growth medium lacking any guidance cues , a single axon through its intrinsic stiffness maintains an essentially straight course albeit with some oscillation [78] , without growth-inducing extracellular signals no axon outgrowth occurs [83] . Individual growth cones can act independently [84] , interact with others through long-range cAMP signalling [85] , are modulated by global neuron state [86] , and avoid contact with their own axonal processes [87] . Thus , a growing axon arbor can be described as performing a constrained parallel search of the developing neuropil guided by extracellular signals . The overwhelming majority of cortical axon branches are interstitial rather than the result of growth cone splitting [88]–[90] . Delayed interstitial branching in cortical axons is strongly associated with earlier growth cone pausing behaviour [91] , [92] while de novo interstitial cortical axon branching can be induced by local extracellular signals from diffusible chemoattractants like netrin-1 and a range of growth factors [91]–[94] . Interstitial branches are formed following Ca2+ transients that locally disrupt the actin cytoskeleton to reorganize actin filament and microtubule arrays [92] , [95] . Other extracellular molecules such as diffusible chemorepellent Sema3A can , however , stop collateral branching by inhibiting growth cone pausing [93] . Hence , calcium signalling is implicated in axon extension , branching , and turning though these responses can be modified or reversed downstream in the signalling pathways [73] , [74] . The initial stages of cortical axon development do not appear to depend on electrical activity [96] , [97] , [98] but on Ca2+ transients [73] , [92] , [94] , which can also be triggered by electrical activity . For instance , Ca2+ transients originating from intracellular stores induced by either strong depolarization or receptor activated signals such as by growth factor ligands regulate neurotrophin secretion [99] . Later cortical axon remodelling does , however , depend on patterned electrical activity [61] , [96] , [98] , [100] . Thus , intracellular calcium signalling appears central to controlling axon arbor development . Growth factors are necessary for neuronal outgrowth , differentiation , and survival ( see [101] ) . Multiple growth factors contribute to neuronal development including the neurotrophic family of molecules structurally related to nerve growth factor ( NGF ) such as brain-derived neurotrophic factor ( BDNF ) and structurally unrelated growth factors such as basic fibroblast growth factor ( FGF-2 ) [101] . A target-derived growth factor signal travels from the distal axon to the cell body via both slow retrograde axonal transport of internalised ligand bound receptor complex endosomes and faster direct signalling cascades [102] . At the cell body , these signals through gene expression control the synthesis of neuronal proteins required for growth and inhibit programmed cell death [101] . Neuronal survival does not generally depend on a single growth factor and dependency can switch according to developmentally regulated changes in ligand availability , receptor expression or pathway response [101] , [103] . In addition , specific growth factors can produce differential effects on axon outgrowth rate and branching probability for the same neuronal type [101] , [104]–[108] . Importantly , axon branches receiving and supplying growth factor to the cell body survive along with the cell body itself but those that do not wither [109] , suggesting growth factors are capable of selectively maintaining those axon branches important to neuronal survival . Therefore , extracellular growth factor molecules can selectively regulate axon arbor morphology . Competition for resource-limited growth factors could explain how material and delay conservation principles drive or at least influence intracortical axon arbor development . To compete with other cortical neurons for survival , axons must rapidly obtain growth factors from target sources and then transport their signals back to the cell body as quickly as possible . To obtain growth factors rapidly , the existing axon must extend directly towards a source , an imperative that might drive material conservation . For example , calcium-dependent de novo axon branch induction by a variety of growth factors including FGF-2 is directed towards a localised source [106] , and the incremental extension of an axon branch directly between discrete sources of growth factor [106] , [110] produces morphology similar to bouton strings observed on cortical axon branches . Over longer distances and even in complicated spatial environments , pathfinding using local chemical gradients or contact based cues can yield the shortest trajectory to target sources [111] , [112] . Furthermore , growth cones may be optimal at sensing growth factor concentration gradients in vitro [113] . To quickly transport growth factor signals from distal axon to cell body , an axon branch gains a competitive advantage over all others if the retrograde axonal transportation delay is shorter than for other axon branches regardless of whether they derive from the same ( intra-axonal competition ) or a different neuron ( inter-axonal competition ) . Competition for the shortest transportation delay for growth factor signals to the cell body might drive conduction delay conservation and resource limitation would lead to pruning branches with longer delays . Taken together , these forces naturally lead to a trade-off between axon extension directly towards a growth factor source and retrograde signalling delay because while , for example , the axon of one neuron may extend a shorter branch to a target source than another , it will only gain a competitive advantage if the overall transportation distance is shorter . The stages of intracortical axon development may be explained within this framework by the differential effect of multiple growth factors acting on the same growing axon . For example , secreted insulin-like growth factor 1 ( IGF-1 ) facilitates neuronal survival and axon outgrowth of unbranched corticospinal tract axons towards distant targets while BDNF promotes their branching and arborization but not outgrowth [114] , suggesting that multiple growth factors may act in concert on the same extrinsic axon to coordinate the different phases of arbor formation . It is possible that intrinsic axons develop in a similar manner but not necessarily using these particular growth factors in the same roles , even though both are expressed in neocortex postnatally [115] , [116] . One type of growth factor ( or combination of growth factors ) might , for instance , support the initial rapid extension of long unbranched primary axon collateral to create the ‘spokes’ for a rapid transport system for growth factor signalling . Next , the dominance of another type of growth factor might then promote greater branching in the elaboration phase . Provided subsequent axon additions do not curve back towards the cell body , new connections formed by these secondary and tertiary branches will inherit a low path length ratio ( see Figure 13CD ) . Finally , in the remodelling phase , homeostatic regulation may , as suggested for cortical dendritic arbors [117] , maintain a total cost budget ( derived from resource limits of available growth factor ( s ) ) so that expansion in one part of the arbor may result in pruning elsewhere in the same arbor . Indeed , evidence exists for a push-pull branching mechanism during cortical axon arbor development based on the relative difference in local Ca2+ transient frequencies between branches [118] . Thus , within this competitive framework the stages of in vivo intracortical axon formation might be explained by developmentally regulated phases in neuronal dependency on multiple growth factors . During its growth a cortical axon will encounter obstacles in the neuropil including others axons , dendrites , glia , and blood vessels [2] , [3] and extracellular signals [56] , both of which may constrain its trajectory . Current evidence suggests that dendritic tree ( e . g . [119] ) and astrocyte and oligodendrocyte glial cell maturation lags behind axonal development [120] , [121] while capillary blood vessels , typically 2–3 µm diameter during early postnatal development , co-develop with intrinsic axons through common molecular guidance cues [122]–[124] . These observations suggest that the majority of neuropil obstacles may either be arranged to suit axonal tree development or avoided by small deviations in axon trajectory including axon-axon encounters . Recall before our analysis here we took into account axon trajectory deviations ( see Materials & Methods ) . In any case , according to the growth factor mechanism proposed above , any large obstacles leading to grand excursions of axon length between neurons during development would typically be eliminated through growth factor competition and so would not appear in the adult arbors analysed here . Similarly , a neuron whose axon arbor becomes too restricted by local neuropil inhomogeneity might not survive into adulthood because of insufficient growth factor . Extracellular signals limiting axon growth patterns may also provide anatomical constraints on arbor economy [56] . Recent work in both visual and barrel cortex suggests , however , that inappropriate branches formed by initially exuberant arbors are later eliminated to produce the precise laminar or topographic specificity of mature intracortical arbors [68] , [125] , suggesting that arbors might , at least in some cases , be established first according to economy principles and later pruned according to the expression of laminar or spatial delimiting cues ( e . g . [126] ) without greatly affecting arbor economy . Regardless of whether growth cones might be optimal at finding target sources in vivo ( see [113] ) , it is unknown whether , subject to anatomical constraints above , the competitive mechanism proposed here for the regulation of branch extension and pruning is optimal or not . It is difficult to test this because we know of no existing algorithm guaranteed to find the global optimum for this type of dynamical system problem . In addition , there are no published quantitative data for any species concerning the amount of intrinsic axonal wire used over the course of cortical development for comparison with the proposed mechanism . Therefore , at present the most we can conclude is that we would expect axon arbor development based on growth factor competition to be highly efficient . How might the growth factor mechanism be affected by histological differences between cortical areas [127] ? Cortical regional specification is itself controlled by extracellular signalling patterns [128] , [129] . If area-specific histological differences occur postnatally during intrinsic axon outgrowth , such as in barrel formation in rodent somatosensory cortex [130] , then growth factor competition will most likely ensure that the neurons with the most economical axons are retained while others are lost independently of changes in the composition of the neuropil . Otherwise cortical specification signals might compensate for differences in neuropil composition by altering the pattern and/or level of multiple growth factor signals to regulate the phases of axon development , e . g . increased neuronal density may require an increased levels of available growth factor [101] . In summary , we have proposed a potential mechanism based on growth factor competition to explain how and why material and conduction delay conservation principles might shape the development of intracortical axon arbors independent of cortical region . This proposal could be tested by tracking and manipulating the development of single intrinsic cortical axon arbors in vivo ( e . g . [90] ) to discover whether competition between axonal processes and subsequent pruning is related to these two conservation principles in the manner described .
All surgical procedures followed the German Welfare Act and were in accordance with European Communities Council Directive 86/809/EEC . Nine adult cats ( 8–14 months old ) underwent anatomical labelling experiments . The surgical and anatomical labelling methods used here have been reported previously [21]–[23] . Briefly , anaesthetised , paralysed animals were used for intrinsic signal optical imaging and subsequent labelling of visual cortical neurons using intracellular and bulk injection of biocytin or biotinylated dextran-amin ( ABC , Vector Laboratories , Burlingame , CA , USA ) . After completion of the in vivo recordings and injections the animals were perfused with a fixative and tissue blocks of region of interest were sectioned on vibratome . The labelling was revealed with the avidin-biotin complexed horseradish peroxidase technique [131] and the sections were dehydrated and embedded in resin on slides . The entire axonal and dendritic trees of well-labelled cells were reconstructed in 3D at ×1000 magnification using the computer-aided Neurolucida reconstruction system ( MicroBrightField , Colchester , VT , USA ) . All statistical analyses were performed with aid of R statistical package [132] . Statistical tests of significance for pairwise comparisons were performed with Wilcoxon signed rank test and for unrelated design with Mann-Whitney U test with 1% significance level .
|
Within the grey matter of cerebral cortex is a complex network formed by a dense tangle of individual branching axons mostly of cortical origin . Yet remarkably when presented with a barrage of complex , noisy sensory stimuli this convoluted network architecture computes accurately and rapidly . How does such a highly interconnected though jumbled forest of axonal trees process vital information so quickly ? Pioneering neuroscientist Ramón y Cajal thought the size and shape of individual neurons was governed by simple rules to save cellular material and to reduce signal conduction delay . In this study , we investigated how these rules applied to whole axonal trees in neocortex by comparing their 3D structure to equivalent artificial arbors optimized for these rules . We discovered that neocortical axonal trees achieve a balance between these two rules so that a little more cellular material than necessary was used to substantially reduce conduction delays . Importantly , we suggest the nature of arbor branching balances time and material so that neocortical axons may communicate with a high degree of temporal precision , enabling accurate and rapid computation within local cortical networks . This approach could be applied to other neural structures to better understand the functional principles of brain design .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/cognitive",
"neuroscience",
"neuroscience/theoretical",
"neuroscience",
"neuroscience/sensory",
"systems",
"physiology/sensory",
"systems",
"computational",
"biology/computational",
"neuroscience",
"physiology/morphogenesis",
"and",
"cell",
"biology",
"computational",
"biology/systems",
"biology"
] |
2010
|
Neocortical Axon Arbors Trade-off Material and Conduction Delay Conservation
|
Identifying microbial pathogens with zoonotic potential in wild-living primates can be important to human health , as evidenced by human immunodeficiency viruses types 1 and 2 ( HIV-1 and HIV-2 ) and Ebola virus . Simian foamy viruses ( SFVs ) are ancient retroviruses that infect Old and New World monkeys and apes . Although not known to cause disease , these viruses are of public health interest because they have the potential to infect humans and thus provide a more general indication of zoonotic exposure risks . Surprisingly , no information exists concerning the prevalence , geographic distribution , and genetic diversity of SFVs in wild-living monkeys and apes . Here , we report the first comprehensive survey of SFVcpz infection in free-ranging chimpanzees ( Pan troglodytes ) using newly developed , fecal-based assays . Chimpanzee fecal samples ( n = 724 ) were collected at 25 field sites throughout equatorial Africa and tested for SFVcpz-specific antibodies ( n = 706 ) or viral nucleic acids ( n = 392 ) . SFVcpz infection was documented at all field sites , with prevalence rates ranging from 44% to 100% . In two habituated communities , adult chimpanzees had significantly higher SFVcpz infection rates than infants and juveniles , indicating predominantly horizontal rather than vertical transmission routes . Some chimpanzees were co-infected with simian immunodeficiency virus ( SIVcpz ) ; however , there was no evidence that SFVcpz and SIVcpz were epidemiologically linked . SFVcpz nucleic acids were recovered from 177 fecal samples , all of which contained SFVcpz RNA and not DNA . Phylogenetic analysis of partial gag ( 616 bp ) , pol-RT ( 717 bp ) , and pol-IN ( 425 bp ) sequences identified a diverse group of viruses , which could be subdivided into four distinct SFVcpz lineages according to their chimpanzee subspecies of origin . Within these lineages , there was evidence of frequent superinfection and viral recombination . One chimpanzee was infected by a foamy virus from a Cercopithecus monkey species , indicating cross-species transmission of SFVs in the wild . These data indicate that SFVcpz ( i ) is widely distributed among all chimpanzee subspecies; ( ii ) is shed in fecal samples as viral RNA; ( iii ) is transmitted predominantly by horizontal routes; ( iv ) is prone to superinfection and recombination; ( v ) has co-evolved with its natural host; and ( vi ) represents a sensitive marker of population structure that may be useful for chimpanzee taxonomy and conservation strategies .
Foamy viruses ( also termed spumaviruses ) are complex retroviruses that naturally infect numerous mammal species , including primates , felines , bovines and equines , but not humans [1]–[4] . Simian foamy viruses ( SFVs ) have been identified in a wide variety of primates , including prosimians , New World and Old World monkeys as well as apes , and each species has been shown to harbor a unique ( species-specific ) strain of SFV [5]–[13] . Moreover , closely related SFVs have been isolated from closely related primate species: a comparison of phylogenies derived from SFV integrase and primate mitochondrial DNA sequences revealed highly congruent relationships , indicating virus-host co-evolution for at least 30–40 million years [13] . This ancient relationship may be responsible for the non-pathogenic phenotype of SFV: Although highly cytopathic in tissue culture , the various SFVs do not seem to cause any recognizable disease in their natural hosts [2] , [3] , [14] . SFVs are highly prevalent in captive primate populations , with infection rates ranging from 70% to 100% in adult animals [2] , [3] , [5] , [15]–[19] . Transmission is believed to occur through saliva because large quantities of viral RNA , indicative of SFV gene expression and replication , are present in cells of the oral mucosa [3] , [20]–[22] . However , little is known about the prevalence and transmission patterns of SFV in wild-living primate populations . Although there is no human counterpart of SFV , humans are susceptible to cross-species infection by foamy viruses from various primate species . Indeed , the first “human foamy virus” [23] isolated from a Kenyan patient with nasopharyngeal carcinoma more than three decades ago was subsequently identified to be of chimpanzee origin [7] , [8] . Since then , SFV strains from African green monkeys , baboons , macaques and chimpanzees have been identified in zookeepers and animal caretakers who acquired these infections through occupational exposure to primates in captivity [19] , [24]–[27] . More recently , about 1% of Cameroonian villagers who were exposed to primates through hunting , butchering and the keeping of pet monkeys were found to be SFV antibody positive , and genetic analysis of three such cases documented infection with SFV strains from DeBrazza's monkeys , mandrills and gorillas [10] . Finally , a large proportion of individuals ( 36% ) who were severely bitten and injured while hunting wild chimpanzees and gorillas had detectable SFVcpz or SFVgor sequences in their blood [28] . Thus , humans are susceptible to a wide variety of SFVs and seem to acquire these viruses more readily than other retroviruses of primate origin , such as simian immunodeficiency viruses ( SIVs ) or simian T-lymphotropic viruses ( STLVs ) . Interestingly , these infections appear to be non-pathogenic and thus far exhibit no evidence of onward transmission by human-to-human contact; however , additional studies will need to be conducted to fully characterize the natural history of SFV infections in humans [10] , [24] , [28]–[30] . Among wild primates , chimpanzees ( Pan troglodytes ) are of particular public health interest since they harbor SIVcpz , the precursor of the human immunodeficiency virus type 1 ( HIV-1 ) [31]–[34] . There are four proposed chimpanzee subspecies which have been defined on the basis of geography and differences in mitochondrial DNA ( mtDNA ) sequences [35] , [36] . These include P . t . verus in west Africa , P . t . vellerosus in Nigeria and northern Cameroon , P . t . troglodytes in southern Cameroon , Gabon , Equatorial Guinea and the Republic of Congo , and P . t . schweinfurthii in the Democratic Republic of Congo and countries to the east ( Figure 1 ) . Two of these , P . t . troglodytes and P . t . schweinfurthii , are naturally infected with SIVcpz , but only P . t . troglodytes apes have served as a reservoir of human infection [31]–[34] . It is now well established that SIVcpzPtt has been transmitted to humans on at least three occasions , generating HIV-1 groups M , N and O . Moreover , two of these cross-species infections ( groups M and N ) have been traced to distinct P . t . troglodytes communities in southeastern and southcentral Cameroon , respectively [33] . The reason for the emergence of SIVcpzPtt strains , but not SIVcpzPts strains , in humans is unknown , but could reflect regional differences in the types and frequencies of human/chimpanzee encounters . Thus , examining humans for SFVcpz infection might be informative as to the location ( s ) where human/chimpanzee contacts are most common; however , no information exists regarding the prevalence , geographic distribution and genetic diversity of SFVcpz in chimpanzees in the wild . In this study , we sought to develop an experimental strategy that would allow us to identify and molecularly characterize SFVcpz infection in wild-living chimpanzees by entirely non-invasive means . The rationale for this approach was two-fold . First , we wished to explore whether large scale screening of endangered primates for infectious agents other than primate lentiviruses was feasible . Second , we wished to examine whether SFVcpz could serve as a test case in efforts to develop suitable early warning systems for pathogens that might infect humans exposed to wild animals . To this end , we tested whether fecal based methods previously developed for SIVcpz could be adapted to the non-invasive detection and molecular characterization of SFVcpz . Our results show that this was indeed possible . Using these newly developed methods , we determined the prevalence of SFVcpz infection in wild chimpanzee communities throughout equatorial Africa , molecularly characterized 120 new SFVcpz strains , examined the subspecies association and phylogeography of SFVcpz , documented numerous instances of SFVcpz co-infection and recombination , investigated the routes of SFVcpz transmission in the wild , and examined the frequency of SFV cross-species transmissions from prey species . Our results reveal important new insights into the molecular ecology and natural history of SFVcpz infection that could not have been gained from studies of captive chimpanzees , and show more generally how endangered primates can be studied by non-invasive molecular approaches to elucidate the circumstances and mechanisms of pathogen transmission .
SFV infection of primates and humans is generally diagnosed by documenting virus specific anti-Gag antibodies in serum or plasma using ELISA and/or Western blot approaches [8] , [10] , [18] , [19] . The infecting SFV strain is then molecularly characterized by amplifying viral DNA from peripheral blood mononuclear cell ( PBMC ) or other tissue DNA [8] , [10] , [11] , [13] , [16]–[18] , [24] , [26] , [28] . Since collecting blood from wild chimpanzees is not feasible , we sought to develop methods of SFVcpz detection that are entirely fecal-based . To accomplish this , we examined whether existing methods of SIVcpz fecal antibody and nucleic acid detection [33] , [37] , [38] could be adapted to the non-invasive identification and molecular characterization of SFVcpz . Western blot strips were prepared from sucrose purified SFVcpz virions and used to test 40 fecal extracts from 23 SFVcpz infected chimpanzees from the Yerkes Primate Research Center ( Table 1 ) . Reactivity with the two SFVcpz Gag proteins p74 and p71 was scored positive , following interpretive guidelines established for serum antibody positivity [8] , [10] , [18] . The absence of viral bands was scored negative , and samples that did not meet either criterion were classified as indeterminant . Using this approach , SFVcpz specific IgG antibodies were detected in 29 of 40 fecal extracts from infected chimpanzees ( Table 1 ) . All samples reacted with the Gag doublet and a subset also recognized the accessory Bet ( p60 ) protein ( Figure 2A ) . In contrast , none of 21 fecal extracts from uninfected human volunteers exhibited false-positive or indeterminant Western blot reactivities ( Figure 2A; Table 1 ) . We also investigated whether SFVcpz nucleic acids could be detected in fecal samples using primers designed to amplify a conserved 425 bp fragment ( pol-IN ) in the viral pol gene ( Figure 3 ) [11]–[13] , [18] . In vitro studies have shown that foamy viruses , in contrast to other retroviruses , reverse transcribe their RNA genome before they assemble into virus particles and bud from infected cells [39] , [40] . Thus , infectious foamy virus particles have been reported to contain mostly viral DNA , while productively infected cells contain mostly viral RNA [1] , [40] , [41] . Using nested PCR to analyze fecal samples from the 21 infected chimpanzees , we found SFVcpz DNA in only 2 of 40 samples ( Table 1 ) . However , RT-PCR of fecal RNA from these same specimens yielded amplification products for 30 samples . Sequence analysis confirmed the authenticity of the amplification products and identified 11 distinct SFVcpz strains ( Table 1 ) . Omission of the cDNA synthesis step during the RT-PCR procedure failed to yield detectable amplification products . These results thus indicate that SFVcpz is present in chimpanzee fecal samples mostly as viral RNA , the source of which ( cell associated , cell free , or both ) remains to be determined . The sensitivities of SFVcpz antibody and viral nucleic acid detection in fecal samples from captive chimpanzees were determined to be 73% and 75% , respectively ( Table 2 ) . Assay specificities were 100% ( Table 1 ) . Interestingly , not all fecal vRNA positive chimpanzees were also fecal Western blot positive ( and vice versa ) . Two SFVcpz infected apes ( CPZ6 , CPZ23 ) , each of whom had detectable RNA in at least two independent stool samples , were repeatedly fecal antibody negative ( Table 1 ) . Since both individuals had high titer antibodies in their blood , this was not due to a recently acquired SFVcpz infection . Two other apes ( CPZ7 , CPZ20 ) were fecal antibody positive , but virion RNA negative ( Table 1 ) . Thus , antibody or virion RNA screening alone would have missed SFVcpz infection in these individuals . Nonetheless , Western blot together with RT-PCR correctly diagnosed SFVcpz infection in 21 of 23 captive chimpanzees , suggesting that the newly developed assays were of sufficient sensitivity and specificity for field surveys , especially when used in combination . To determine to what extent chimpanzees are infected with SFVcpz in the wild , we tested 724 fecal samples from 25 different field sites across equatorial Africa for virus specific antibodies and/or viral RNA ( Table 3 ) . Samples were selected from existing specimen banks based on their geographic representation , available host genetic information ( mtDNA , microsatellite and sex markers ) , and remaining quantities of material . Figure 1 depicts the geographic location of the sites with respect to the ranges of the four proposed chimpanzee subspecies . Except for P . t . verus , all other subspecies were sampled at multiple sites . Specimens from the Taï Forest ( TA ) as well as from Gombe ( GM-MT , GM-KK ) and Mahale Mountains ( MH ) National Parks were collected from individually known ( habituated ) chimpanzees under direct observation . Samples from the Goualougo Triangle ( GT ) , several field sites in Cameroon ( DP , EK , BB , MB , LB ) , and the Kalande community ( GM-KL ) in Gombe National Park were obtained from non-habituated chimpanzees , but were subsequently genotyped using mtDNA , microsatellite and sex markers and thus also represent known numbers of individuals [33] ( B . Keele and B . H . Hahn , unpublished ) . Samples from the remaining field sites in Cameroon ( MF , MP , WE , MT , BQ , DG , CP ) , Gabon ( LP ) , the Central African Republic ( ME ) , the Democratic Republic of Congo ( BD , WL , WK ) , Rwanda ( NY ) and Uganda ( KB ) were derived from an unknown number of chimpanzees . All samples were previously screened for SIVcpz antibodies and/or vRNA [33] , [37] , [42] ( F . van Heuverswyn and M . Peeters , unpublished ) and their integrity was confirmed by mtDNA analysis ( Table S1 ) . Of 724 fecal samples included in the analysis ( Table 3 ) , 706 were tested by Western blot analysis and 211 were found to be SFVcpz antibody positive ( 18 samples were of insufficient quantity for immunoblot analysis but were used for RT-PCR amplification ) . All of these reacted with the Gag p74/p71 doublet and a small number also recognized the p60 Bet protein ( Figure 2B ) . Interestingly , two samples from the DP site reacted only with the Bet protein and were thus classified as indeterminant ( not shown ) . The remaining 493 fecal extracts exhibited no detectable bands and were thus classified as antibody ( SFVcpz IgG ) negative . A subset of samples ( n = 392 ) was also examined for SFVcpz nucleic acids ( Table 3 ) . RT-PCR of fecal RNA yielded pol-IN ( 425 bp ) amplification products for 175 samples , all of which were shown to contain SFVcpz sequences ( two samples were RT-PCR positive using LTR and pol-RT primers , respectively ) . In contrast , amplification of fecal DNA from these same samples failed to yield viral sequences ( not shown ) , providing further evidence for the presence of SFVcpz RNA , and not DNA , in fecal material . A breakdown of antibody and RNA positive samples for each field site is shown in Table 3 . The results revealed SFVcpz infected chimpanzees at all field sites . We next sought to determine the prevalence of SFVcpz infection at each of the 25 field sites . To accomplish this , we examined whether fecal antibody and vRNA detection tests yielded similar data for captive as well as wild communities . Inspection of Table 3 indicated that this was not the case . For example , at the TA field site all of 16 fecal samples were SFVcpz antibody positive ( 100% ) , but only 7 contained vRNA ( 44% ) . In contrast , at the ME field site none of 21 fecal samples contained antibodies ( 0% ) , while 16 were vRNA positive ( 76% ) . Importantly , the latter was not due to a lack of antibody cross-reactivity since other P . t . troglodytes samples ( e . g . , 11 of 20 GT samples ) were Western blot positive using the same antigens ( Table 3 , Figure 2B ) . To examine this further , we re-calculated test sensitivities using only samples from SFVcpz infected wild chimpanzees ( Table 2 ) . This yielded surprising results: not only did test results vary extensively between different field sites , the sensitivities of antibody and vRNA detection were also inversely correlated ( Figure 4 ) . To account for this in prevalence estimations , we decided to calculate a “field sensitivity” for each test by averaging values across all collection sites . The rationale for this was that the strong negative correlation between the two assay sensitivities would predict that if the sensitivity of one test was underestimated , the sensitivity of the other test would be overestimated to a roughly similar degree . Thus , if samples were subject to an equal number of both tests , these effects would tend to even out . While many samples were not subject to equal numbers of the two tests , this nonetheless seemed to represent the most reasonable approach . For both Western blot and RT-PCR assays , the average sensitivities across all sites were around 56% . Therefore we pooled results from all tests to obtain a general sensitivity value ( 56 . 3% ) that was then used to calculate the prevalence rates . Table 3 summarizes the prevalence of SFVcpz infection at 25 different field sites . For 11 sites , these values were calculated based on the proportion of infected individuals . For the remaining sites , prevalence rates were estimated based on the proportion of antibody and/or vRNA positive fecal samples , but correcting for repeat sampling and sample degradation ( see Methods ) . The results revealed uniformly high infection rates for all sites , similar to previously reported values for captive primate populations [15]–[19] . The highest prevalences ( >90% ) were found at a P . t . verus field site in Cote d'Ivoire ( TA ) ; two P . t . vellerosus field sites in central Cameroon ( MF , MP ) ; four P . t . troglodytes field sites in Cameroon ( DG , CP ) , Gabon ( LP ) and the Central African Republic ( ME ) ; and three P . t . schweinfurthii field sites in the DRC ( BD ) , Uganda ( KB ) and Tanzania ( GM-MT ) . The lowest infection rates ( <60% ) were identified at three P . t . troglodytes field sites in southern Cameroon ( BQ , MB , LB ) . Given that the confidence intervals for the prevalence estimates across the various sites showed extensive overlap ( Table 3 ) , these differences are unlikely to be significant . These data thus indicate that SFVcpz is widely distributed and infects chimpanzees at very high prevalence rates throughout their natural habitat . The fact that all 724 fecal samples had independently been tested for SIVcpz antibodies and/or viral nucleic acids provided an opportunity to compare the two viruses with respect to their relative infection frequencies and geographic distribution . As reported previously , natural SIVcpz infection has been identified only in two of the four chimpanzee subspecies ( P . t . troglodytes and P . t . schweinfurthii ) , and then only in a fraction of sampled communities [33] , [37] , [42] . Thus , SIVcpz is clearly much less common and widespread among wild chimpanzees than is SFVcpz . Nonetheless , the current survey included field sites where both SFVcpz and SIVcpz infections were endemic . To examine whether the two infections were epidemiologically linked , we selected seven sites with known numbers of SFVcpz and/or SIVcpz infected chimpanzees . Four of these were located in Cameroon ( MB , LB , EK , DP ) , while the other three were located in Gombe National Park in Tanzania ( GM-KK , GM-KL , GM-MT ) . Table 4 summarizes the results: Of a total of 130 chimpanzees tested , 55 were infected only with SFVcpz , 7 were infected only with SIVcpz , and 15 harbored both viruses . Thus , SFVcpz/SIVcpz co-infections are not uncommon at locations where both viruses are endemic; however , examination of the relative frequencies of single and dual infections at individual sites , or sites in combination , revealed no association between SFVcpz and SIVcpz ( Fisher exact test; P>0 . 2 ) . Thus , there was no evidence that infection with one of these viruses increased or decreased the likelihood of infection by the other . To determine under what circumstances chimpanzees acquire SFVcpz in the wild , we screened members of two habituated communities for evidence of infection . The Kasekela and Mitumba communities are located in Gombe National Park and have been under human observation since the 1960s and 1980s , respectively [43] . Chimpanzees from both communities are followed daily ( with particular individuals selected for all-day observation ) and their reproductive states and social interactions are recorded . Thus , for many Mitumba and Kasekela apes , especially the more recent offspring , the date of birth is known . This provided an opportunity to compare the frequency of SFVcpz infection among individuals representing different age groups . Testing the most recent fecal sample available , we found no evidence of SFVcpz infection in four infants age 2 years or younger . In addition , only three of ten chimpanzees ages 2 . 1 to 9 years were found to be SFVcpz antibody and/or viral RNA positive . In contrast , all of 13 adult chimpanzees ages 14 to 45 years were SFVcpz infected ( Figure 5 ) . Thus , there was a significant increase of SFVcpz infection with age , suggesting horizontal rather than vertical ( perinatal ) transmission as the predominant route of infection in these communities . To investigate whether perinatal transmission was responsible for at least some of the newly acquired infections , we tested longitudinal samples from the three SFVcpz positive offspring and their infected mothers . As shown in Table 5 , Fansi ( born in November 2001 ) was fecal Western blot positive in June 2004 ( 2 . 6 years of age ) , but both fecal antibody and viral RNA negative two years earlier in August of 2002 . Similarly , Flirt ( born in July 1998 ) was fecal Western blot positive in October 2001 ( 3 . 2 years of age ) , but antibody and viral RNA negative one year earlier in November 2000 . Although false negative results at the earlier timepoints cannot be excluded , these data suggest that the two infants acquired SFVcpz after their first and third year of life , respectively . Analysis of the third mother/offspring pair also failed to provide evidence for perinatal transmission . Although Tarzan ( born in October 1999 ) was SFVcpz fecal antibody positive at the earliest timepoint ( 2 . 6 years of age ) and harbored a virus that was identical in its pol-IN sequence to that of his mother's , the same pol-IN sequences were also recovered from three other chimpanzees , including Flirt and one unknown individual from the neighboring Kalande community . Thus , it is unclear whether Tarzan acquired his SFVcpz infection from his mother during or shortly after birth , or whether he became infected later by another route . Taken together , none of these three mother/offspring pairs provided conclusive evidence for vertical transmission of SFVcpz in the wild . To determine the evolutionary relationships of SFVcpz strains infecting wild chimpanzees in different parts of equatorial Africa , we selected 392 fecal samples for RT-PCR analysis . Using primers designed to amplify a conserved 425 bp pol-IN fragment [11]–[13] , [18] , we recovered SFVcpz sequences from 175 samples ( one sample yielded only LTR and another only pol-RT sequences; not shown ) . Pol-IN sequences were also amplified from two P . t . vellerosus apes housed in a Cameroonian sanctuary ( SA161 and SA163 ) as well as eight wild-living P . t . schweinfurthii apes who were sampled at different locations within the Democratic Republic of Congo ( BA432 , BF1167 , EP479 , EP486 , KS310 , UB446 , WA466 , WA543; Figure 1 ) . The phylogenetic relationships of these SFVcpz sequences to each other and to subspecies specific SFVcpz reference sequences from the database are shown in Figure 6 . The analysis revealed three well-defined SFVcpz clades for viruses from P . t . verus , P . t . vellerosus , and P . t . schweinfurthii apes , respectively , each supported with very high posterior probabilities . In contrast , SFVcpz strains from P . t . troglodytes formed two distinct ( well-supported ) groups in the maximum clade credibility ( MCC ) tree: ( i ) one major group which comprised the great majority of the newly identified P . t . troglodytes strains , and ( ii ) one minor group which included only four strains from the Lope Reserve in Gabon and which formed a sister clade to SFVcpz from P . t . schweinfurthii ( Figure 6 ) . Since the placement of the Lope group apart from the other P . t . troglodytes strains was not supported by a high posterior probability , we wondered whether its unexpected position in the MCC tree might be due to the short length of the pol-IN ( 425 bp ) fragment . To clarify these relationships , we amplified additional gag ( 616 bp ) and pol-RT ( 717 bp ) fragments from a subset of samples . Indeed , phylogenetic analysis of these larger fragments placed a representative of the “Lope variant” ( LP29 ) together with the other SFVcpzPtt strains within a single cluster . In the gag region , this clade was supported with a highly significant posterior probability ( Figure 7 ) . In the pol-RT region , where the posterior probability was not high , the MCC tree nevertheless placed all P . t . troglodytes sequences in a monophyletic clade ( Figure 8 ) . Moreover , an analysis of combined pol-IN and pol-RT data ( not shown ) yielded a monophyletic P . t . troglodytes SFVcpz clade , with 100% posterior probability . Thus , SFVcpz strains from wild chimpanzees grouped into four major lineages according to their subspecies of origin . To examine further the evolution of SFVcpz at the subspecies level , we obtained mitochondrial DNA sequences ( hypervariable D loop region ) from all SFVcpz vRNA positive fecal samples and performed a Bayesian Markov chain Monte Carlo ( BMCMC ) phylogenetic analysis ( Figure S1 ) . The topology of this tree was similar to previous mtDNA phylogenies in several key features [33]: ( i ) P . t . verus and P . t . vellerosus as well as P . t . troglodytes and P . t . schweinfurthii clustered together , forming two highly divergent lineages; ( ii ) P . t . verus and P . t . vellerosus formed two well separated sister clades; and ( iii ) P . t . schweinfurthii fell within the P . t . troglodytes radiation . Comparison of this mtDNA phylogeny with those of SFVcpz pol-IN , pol-RT and gag regions ( Figures 6 , 7 , 8 ) revealed a number of differences . Most notably , SFVcpz strains from P . t . vellerosus were much more distant from SFVcpz strains infecting P . t . verus than would have been predicted based on mtDNA phylogenies of their respective hosts . In both gag and pol-RT trees , P . t . vellerosus viruses shared a most recent common ancestor with strains from P . t . troglodytes rather than with strains from P . t . verus ( as with the placement of the Lope strains , the gag pattern was mirrored in the MCC tree from the pol-RT analysis , albeit without significant support ) . In addition , SFVcpz from P . t . troglodytes apes formed a single clade ( Figures 7 and 8 ) , while their corresponding mtDNA sequences were paraphyletic , being separated by the P . t . schweinfurthii clade ( Figure S1 ) . In many cases , chimpanzees with highly divergent mtDNA haplotypes harbored closely related SFVcpz strains , and vice versa . Finally , one fecal sample ( WE464 ) collected north of the Sanaga River contained SFVcpz sequences from P . t . vellerosus , but mtDNA sequences from P . t . troglodytes ( boxed in Figures 6 , 7 , 8 , S1 ) . While the latter finding is most simply explained by the migration of a P . t . troglodytes ape across the Sanaga River some time in the past , followed by infection of her progeny with the local variety of SFVcpz , the other discordances are more difficult to interpret . It is clear that SFVcpz is not strictly maternally inherited , since its evolutionary history shows differences with the mtDNA tree . Moreover , the mtDNA phylogeny ( Figure S1 ) offers only a limited perspective on the ancestral relationships of chimpanzee populations , even setting aside any possible inaccuracies due to the short fragment analyzed . Thus , deciphering chimpanzee evolution in the more recent past will require additional study . However , the fact that 120 naturally occurring SFVcpz strains clustered in strict accordance with their mtDNA-defined subspecies of origin provides compelling evidence for virus-host co-evolution . As shown in Figure 1 , three of the four chimpanzee subspecies were sampled at multiple locations . This provided an opportunity to examine whether viruses from P . t . vellerosus , P . t . troglodytes and P . t . schweinfurthii apes clustered according to their collection sites of origin , as previously reported for SIVcpz [33] , [42] . Inspection of Figures 6–8 revealed that this was generally not the case . Although each of the major SFVcpz clades exhibited considerable structure , the great majority of sublineages were comprised of viruses from multiple field sites . Moreover , geographic distance did not predict viral diversity . For example , viruses from the single DG field site in southern Cameroon exhibited as much pol-IN inter-strain diversity ( 0% to 5 . 8% ) as did viruses collected hundreds of kilometers apart at the CP and LB/MB field sites ( 0% to 4 . 1% ) . Nonetheless , there were some notable exceptions . Significant geographic clustering was observed for ( i ) P . t . troglodytes viruses from the ME and GT field sites in the Central African Republic and the Republic of Congo ( Figures 6 , 7 , 8 ) ; ( ii ) P . t . troglodytes viruses from the LP field site in Gabon ( Figure 6 ) ; and ( iii ) P . t . schweinfurthii viruses from the BD field site in the Democratic Republic of Congo ( Figure 6 ) . Interestingly , all of these were associated with potential barriers to chimpanzee movement . GT and ME were the only P . t . troglodytes field sites east of the Sangha River; LP was separated from all other P . t . troglodytes sites by the Ogooue River; and BD was the only P . t . schweinfurthii collection site north of the Uele River ( Figure 1 ) . Thus , in addition to delineating the subspecies ranges , major rivers and other biogeographical barriers appear to also have influenced the dispersal of SFVcpz within existing subspecies ranges . GENECONV analyses and inspection of phylogenetic trees inferred for each independently amplified gene fragment ( Figures 6 , 7 , 8 ) identified several SFVcpz strains with a strong signal of distinct evolutionary histories in different parts of their genome . For example , MF1269 was most closely related to other MF strains in gag and pol-RT regions ( Figures 7 and 8 ) , but clustered with MP and WE viruses in the pol-IN region ( Figure 6 ) . Such discordant branching patterns can be indicative of viral recombination but also of co-infection with divergent viruses [44]–[47] . Similarly , DG534 , DP157 , and CP470 were all found by GENECONV to be members of sequence pairs with globally significant ( P<0 . 05 ) evidence of mosaic evolution . In the case of DG534 and DP157 , highly significant putative recombination breakpoints were detected at or near the junction of independently amplified sequence fragments , strongly suggesting that some were due to the amplification of two or more variants from the same sample rather than intramolecular recombination per se . To differentiate between these possibilities , we selected five such samples ( DG534 , DP157 , MF1279 , MF1269 , CP470 ) for additional RNA extraction , RT-PCR and sequence analyses . Comparison of independently amplified gag , pol-IN , and pol-RT sequences yielded unequivocal evidence of SFVcpz co-infection in three of the five fecal samples . As shown in Figures 6 and 8 , DP157 harbored two clearly distinct variants ( DP157A and DP157B ) . The observation that these sequences fall into different , highly supported clades ( P = 1 . 0 ) within the P . t . troglodytes SFVcpz radiation leaves little doubt that this chimpanzee was co-infected with more than one strain . The alternative , i . e . , that the divergent sequences trace back to a single infection that diversified extensively within a single chimpanzee , is inconsistent with the fact that sequences from other apes are interspersed between the DP157 variants . Similarly , DG534 and MF1279 each exhibited ( at least ) two distinct SFVcpz strains as determined by phylogenetic analysis of independently amplified ( and directly sequenced ) pol-RT sequences ( Figure 8 ) . To follow up on these observations , we subjected two of these samples ( DP157 and MF1279 ) to single genome amplification ( SGA ) . This approach amplifies single viral templates , precludes Taq polymerase errors and in vitro recombination , and provides an accurate representation of the viral population in vivo [48]–[50] . Targeting both pol-IN ( Figure 9A ) and pol-RT regions ( not shown ) , we generated SGA derived sequences for MF1279 and DP157 . Phylogenetic analysis of these sequences confirmed co-infection of MF1279 with two SFVcpz strains , and revealed the presence of at least four distinct SFVcpz strains in DP157 ( Figure 9A ) . In contrast , re-amplification experiments indicated that MF1269 and CP470 each harbored only a single identifiable virus . To determine whether MF1269 was truly mosaic , we first used GENECONV to examine its concatenated gag , pol-RT and pol-IN sequences for evidence of recombination . Pairwise analyses identified a potential recombination breakpoint in MF1269 near the pol-IN/pol-RT overlap ( although this comparison fell marginally below significance according to the global P-value , which is corrected for multiple comparisons ) . We then amplified the corresponding pol fragment as a single genetic unit ( Figure 3 ) . The resulting L-pol sequence was identical to the concatenated MF1269 pol-IN and pol-RT sequences , indicating that the apparent signal of recombination could not be explained , in this case , by co-infection . Moreover , SGA amplification of the MF1269 L-pol fragment ( which yielded four amplicons that differed from each other and the direct sequence by two or less nucleotide substitutions ) excluded the possibility that the recombination breakpoint was a Taq polymerase induced PCR artifact [48] . Given the GENECONV evidence and more importantly , the 100% posterior probability support for MF1269 clustering on a different P . t . vellerosus SFVcpz lineage in pol-IN than in gag or pol-RT ( Figures 6 , 7 , 8 ) , we concluded that this sequence is a bona fide SFVcpz recombinant . The CP470 case offered perhaps even stronger evidence of natural SFVcpz recombination . Having confirmed by repeated amplifications that this sample was not coinfected , we observed that the most parsimonious explanation for its inclusion by GENECONV in a sequence pair with a globally significant fragment ( P<0 . 03 ) was that it was a “parent” , rather than a “daughter” ( recombinant ) sequence . GENECONV identified EK522 as the other sequence in the pair . Figure 9B indicates that it is this sequence and its close relatives ( EK511 , EK505 , EK506 ) that appear to move from being closely related to CP470 ( as well as MB191 and MB318 ) upstream of the identified breakpoint , to sharing a most recent common ancestor with the clade of ME viruses downstream of the breakpoint . We did not seek to reproduce the observed breakpoints in the EK clade because the fact that the sequences move across the tree together straightforwardly indicates that they evolved from a common recombinant ancestor . It is worth noting that EK522 was the only one of this group with a globally significant P-value when compared with CP470; the other EK sequences all had highly significant pairwise P-values , but non-significant global values . Since they are all clearly closely related , this indicates that the global P-values represent a rather conservative measure of statistical significance for recombination in SFVcpz . It is thus highly likely that several of the numerous fragments that were significant in pairwise , but not global GENECONV comparisons also reflect recombination ( data available upon request ) . Indeed , the strong phylogenetic evidence for recombination in MF1269 indicates that this is the case . Taken together , these results show for the first time that chimpanzees can be superinfected by different SFVcpz strains and that such superinfection can lead to recombination . They also suggest that recombination may occur rather frequently in SFVcpz . Chimpanzees are avid hunters and frequently prey on smaller monkeys [51]–[53] . Since exposure to primates through hunting promotes acquisition of SFV by humans [10] , [28] , we wondered whether this was also the case in chimpanzees . Using conserved pol-IN primers previously shown to amplify divergent SFV strains [11]–[13] , [18] , we uncovered one simian foamy virus that did not fall within the SFVcpz radiation ( Figure 10 ) . This virus , termed LB309 , was identified in a male member of a group of nine P . t . troglodytes apes sampled at the LB field site [33] . Phylogenetic analysis indicated that this unusual virus was most closely related to SFV strains previously identified in captive DeBrazza's ( Cercopithecus neglectus ) and mustached ( Cercopithecus cephus ) monkeys housed in an African primate facility ( W . Switzer , unpublished ) . Since LB309 was identified in only a single fecal sample , we considered the possibility that it represented a mixture of chimpanzee and monkey fecal material; however , the fact that previous host genetic studies had yielded unambiguous microsatellite , sex and mtDNA data ( Table S2 in [33] ) rendered this scenario highly unlikely . We also looked for co-infection with chimpanzee foamy virus since four other chimpanzees from the LB site harbored SFVcpz ( Table 3; Figure 6 ) ; however , repeated failure to amplify SFVcpz specific gag and pol-RT sequences suggested LB309 was the only ( productively ) infecting SFV strain . Although the species origin of LB309 could not be determined , this represents the first documented case of a monkey-to-ape transmission of SFV in wild P . t . troglodytes apes .
A primary objective of this study was to explore whether non-invasive detection methods previously developed for SIV could be adapted to the identification of other infectious agents in endangered primates . We selected SFV infection of wild chimpanzees as a test case for several reasons: First , SFVs can infect humans who come in contact with primates and may thus represent suitable markers of human zoonotic exposure risks [8] , [10] , [13] , [19] , [24]–[28] . Given that chimpanzees are naturally infected with several known human pathogens [33] , [54] , [55] , determining the prevalence and genetic diversity of SFVcpz represented a first step toward examining the utility of this virus as a sentinel for human zoonoses . Second , although seemingly non-pathogenic in natural and non-natural hosts , SFVs could alter the course of SIV and HIV infections since dual SFV/HIV infections have been documented both in sex worker and blood donor cohorts in Africa [22] , [56] . Thus , screening chimpanzees for both infections provided an opportunity to examine whether SIVcpz and SFVcpz are epidemiologically linked . Finally , foamy viruses are being explored as vaccine and gene therapy vectors for various human diseases [57]–[59] . It thus seemed prudent to study at least one member of this virus group in its natural host . To this end , we developed new SFVcpz specific fecal detection methods and used these to conduct a large-scale molecular epidemiological survey of wild chimpanzees throughout equatorial Africa . Our results indicate that non-invasive screening strategies can be extended to other infectious agents and show more generally how endangered primates can be studied by non-invasive molecular approaches . Although both SIVcpz and SFVcpz infected chimpanzees secrete antibodies and nucleic acids into their feces , we found marked differences in the detection sensitivities of these viral markers between the two infections . The most striking difference was the extreme variability with which fecal antibodies and/or viral RNA were detected in SFVcpz infected apes from different communities ( Table 2 ) . For example , at the TA and MH field sites nearly all SFVcpz infected chimpanzees were antibody positive ( Western blot sensitivities of 100% and 92% , respectively ) , but only very few had detectable viral RNA in their feces ( 44% and 9% , respectively ) . In contrast , at the BB and LB field sites nearly all SFVcpz infected chimpanzees were vRNA positive ( 100% and 80% , respectively ) , but only very few had detectable antibodies in their feces ( 8% and 13% , respectively ) . A comparison of test sensitivities across all field sites indicated that these values were inversely correlated ( Figure 4 ) . To determine whether this was due to a technical artifact , we re-analyzed nearly 300 antibody negative samples ( including 74 specimens containing SFVcpz RNA ) using newly produced Western blot strips and freshly prepared fecal extracts . Except for 17 weakly reactive samples , all others remained antibody negative . We also analyzed 34 IgG negative fecal samples for the presence of SFVcpz specific IgA . None of these were positive , consistent with the absence of SFVcpz specific IgA in other chimpanzee mucosal compartments [60] . Finally , we repeated RT-PCR analysis on a select number of RNA negative samples , but failed to uncover new SFVcpz sequences . Thus , the observed differences in fecal antibody and vRNA detection sensitivities cannot be explained by uneven test performance . Instead , SFVcpz infected chimpanzees appear to shed virus specific antibodies and nucleic acids only intermittently . Whether these fluctuations reflect true temporal differences in fecal antibody secretion and virus replication , or are the consequence of generally lower production levels that sometimes fall below the limits of detection , will require further study . However , in light of the data in Figure 9 , it is tempting to speculate that the observed inverse correlations reflect , at least in part , different stages of recurring SFVcpz superinfection cycles where high titer viral replication at mucosal sites elicits an effective humoral ( and possibly also cellular ) immune response which reduces fecal viral load until the next infection cycle ensues . Regardless of the underlying mechanism ( s ) , the observed fecal antibody and viral RNA fluctuations are in stark contrast to chronic SIVcpz infection where fecal antibodies are detected at all times with high sensitivity ( 92% ) , and where vRNA is amplified from virtually all antibody positive ( non-degraded ) fecal samples especially when different PCR primer sets are used [33] , [42] . Thus , a screening algorithm consisting of an initial fecal antibody test followed by RT-PCR of only antibody positive samples ( which is the standard approach for non-invasive SIVcpz surveys ) is clearly not suitable for molecular epidemiological studies of SFVcpz . Instead , reliable non-invasive SFVcpz prevalence estimates require the use of both vRNA and antibody detection tests . SFV infection is latent in most tissues , except for lung and tissues of the oral pharynx which express large quantities of viral RNA ( up to 104 copies per cell ) and thus represent primary sites of SFV replication [20]–[22] . SFV replication has also been observed in the mesenteric lymph nodes and small intestine of SIVmac infected macaques [22]; however , even in these severely immune compromised animals , there was no evidence of SFV replication in the large intestine [22] . In light of these data , our finding of SFVcpz RNA in a large number of fecal samples comes as a surprise . Passage through the stomach would be expected to degrade both cell and virion associated SFVcpz RNA . It is thus highly unlikely that the fecal RNA that we observe is produced in the oral mucosa . Instead , it seems more likely that gut epithelial cells represent a primary site of SFV replication , at least at some stage during natural infection . Given the apparent fluctuations in fecal RNA shedding , it is easy to envision how this could have previously gone unrecognized [22] . We did not determine the copy number of SFVcpz RNA in the feces and thus cannot estimate how many cell equivalents are required to account for the detected amounts . However , in addition to SFVcpz , we also amplified SFV RNA from a limited number of bonobo , gorilla and mandrill stool samples , all of which were collected in the wild ( Figure 10 ) . It is thus clear that fecal RNA shedding is a common property of this entire group of viruses . It will be interesting to determine whether SFV RNA containing stool samples are infectious . This could explain why some zoo workers and animal handlers who never had direct physical contact with non-human primates were found to be SFV infected [8] , [19] . In addition to its production site , the source of the SFVcpz RNA in stool samples remains a mystery . Unlike in other retroviruses , reverse transcription of the SFV genome takes place during budding and virion assembly , resulting in the production of SFV particles that contain both viral DNA and RNA [39] , [40] . The viral RNA that we detect may thus derive from cell free virions and/or from mRNA and genomic RNA present in productively infected cells that are sloughed off into the feces . However , since SFV particles often bud at intracellular membranes [61] , we would expect to also detect viral DNA . Instead , we found SFVcpz DNA in only 2 of 40 fecal samples from captive chimpanzees , and in none of 173 samples ( including 87 SFVcpz RNA positive specimen ) from wild chimpanzees . Thus , it remains unknown whether the SFVcpz RNA present in fecal samples is cell-derived , particle-derived , or a combination of both . Given our findings , it may also be of interest to determine whether currently used in vitro culture systems accurately reflect SFV replication in vivo . Our survey of 25 different chimpanzee communities revealed high prevalence rates of SFVcpz infection across equatorial Africa . This observation , together with the lack of geographic clustering of most SFVcpz strains , and the obvious propensity of SFVcpz to superinfection and recombination , indicates that SFVcpz is a highly transmissible virus . Previous studies have indicated horizontal routes as the primary mode of SFV transmission [2] , [17] , [62] . Our findings in Gombe National Park are consistent with these observations . The fact that we detected SFVcpz in each of 13 adult chimpanzees , but in only 3 of 14 infants and juveniles indicates a clear increase of SFVcpz prevalence with age . In addition , we found no conclusive evidence for perinatal transmission . Two of the three infected offspring were SFVcpz negative at the time of first analysis , and the third one harbored a virus that was genetically indistinguishable in the pol-IN region from viruses infecting unrelated chimpanzees . Thus , perinatal transmission of SFVcpz , if it occurs at all , appears to be uncommon in wild-living chimpanzees . Instead , chimpanzees appear to acquire SFVcpz by horizontal routes , most likely by exposure to saliva ( or feces ) , as has been proposed for other primates [17] , [20] , [62] . Indeed , young chimpanzees stay with their mothers until they are 8 or 9 years old and often share food . Thus , infants and juveniles are frequently exposed to their mother's saliva , which may constitute a common source of infection . In contrast , SIVcpz appears to be transmitted primarily by sexual ( and sometimes perinatal ) routes ( [37]; Keele et al . , unpublished ) . In light of these differences , the absence of an epidemiological link between SIVcpz and SFVcpz infections is perhaps not too surprising . Examining seven different communities , we found no indication that infection with one of these viruses increased or decreased the likelihood of infection by the other . Simian foamy viruses are believed to have co-evolved with their respective primate hosts for millions of years [13] , and our finding of subspecies-specific SFVcpz lineages is consistent with this hypothesis . Remarkably , all of the 120 newly characterized SFVcpz strains clustered according to their subspecies of origin . This included one strain from a site ( WE ) just north of the Sanaga River ( i . e . , within the range of P . t . vellerosus ) infecting an individual with P . t . troglodytes mtDNA , indicating gene flow , but not viral flow , across a subspecies boundary . This monophyly of SFVcpz strains from each subspecies contrasts with the mtDNA phylogeny where P . t . schweinfurthii sequences lie within the P . t . troglodytes radiation . While the validity of classifying chimpanzees into subspecies has been questioned [63] , the SFVcpz phylogeny corroborates the existence of four geographically isolated chimpanzee populations and the absence of SFVcpz transmission between subspecies argues that they are effectively separated , especially since such transmissions are frequently observed in captive settings ( e . g . , see DEB and MUS SFVs in Figure 10 ) . The SFVcpz and mtDNA phylogenies ( Figures 6 , 7 , 8 , S1 ) differed with regard to the relationships among the four subspecies . However , these differences do not undermine the co-evolution hypothesis . When successive speciation events occur over a relatively short timescale , persistence of polymorphism from one event to the next means that any one genetic marker may not have the same phylogeny as the species [64]; this phenomenon is even more likely with recent subspeciation events . Thus , even if there has been complete co-evolution of SFVcpz with chimpanzees , discordance between the SFVcpz and mtDNA phylogenies may appear because either , or both , differ from the true historical relationships among the subspecies . In fact , the apparently shorter coalescence time of SFVcpz indicated by the reciprocal monophyly of P . t . troglodytes and P . t . schweinfurthii viruses suggests that SFVcpz could be less susceptible to this problem than mtDNA . Thus , SFVcpz may emerge as a more sensitive marker of population structure that may be useful for chimpanzee systematics as well as conservation strategies . Phylogenetic analyses identified discordant branching orders for several SFVcpz strains , suggesting co-infection or recombination [44]–[47] . To examine whether this was indeed the case , we selected a subset of samples for repeat RT-PCR analyses , including single genome amplification ( SGA ) of re-extracted fecal viral RNA . SGA amplifies single viral templates , is not subject to Taq polymerase induced nucleotide substitutions and recombination , and thus provides an accurate representation of the viral population in the individual [48]–[50] . Adapting this approach to fecal RNA provided new insights into SFVcpz biology . SGA analysis formally documented infection with more than one virus in two chimpanzees . One of these apes ( MF1279 ) was infected with two distinct SFVcpz strains , while the other ( DP157 ) harbored at least four genetically diverse viruses . In both cases , predominant viral forms were identified by bulk RT-PCR ( red in Figure 9A ) , but SGA was required to characterize the full extent of viral diversity in the sample , including the relative proportion of different variants . Repeat RT-PCR and SGA analyses also documented mosaic genome structures in several SFVcpz strains and demonstrated that these did not represent PCR artifacts . Although preliminary , these results suggest that superinfection and recombination occur rather frequently . As mentioned above , successive superinfection cycles may account at least for some of the observed fluctuations in fecal antibody and viral RNA detection in different chimpanzee communities . It will be interesting to test this hypothesis in chimpanzees from Gombe National Park where longitudinal samples from SFVcpz infected apes are available . Because they are avid hunters , chimpanzees are also frequently exposed to SFV strains from other primate species . Testing 392 fecal samples for SFVcpz viral RNA , we found one male chimpanzee to harbor an SFV strain ( LB309 ) that was closely related to viruses previously identified in captive DeBrazza's and mustached monkeys ( Figure 10 ) . The finding of LB309 RNA indicated a productive viral infection in the chimpanzee host . Similar findings were recently reported for chimpanzees from the Taï Forest where 3 of 12 apes studied harbored SFV strains from sympatric western red colobus monkeys [65] . Interestingly , these apes ( all males ) were also coinfected with SFVcpz; however , it was not determined whether the dual infections were productive since viral DNA ( and not RNA ) sequences were amplified from spleen necropsy specimens using strain specific PCR primers [65] . Since we did not use strain specific primers , it is likely that our data grossly underestimate the frequency of SFV cross-species transmission in the wild . Moreover , the failure of these cross-species infections to initiate secondary spread suggests that their replication ( and thus fecal detection ) may be limited . However , the examples demonstrate that chimpanzees , like humans , are susceptible to SFVs from other primate species , and the fact that all cross-infected apes were males ( who hunt more frequently and eat more meat than females ) strongly suggest that these transmissions occur in the context of predation . These findings may be of use to primatologists interested in chimpanzee hunting behavior and prey preferences in the wild . Finally , SFVs are of public health interest because people in sub-Saharan Africa are routinely exposed to these viruses in the context of primate bushmeat hunting [10] , [28] . We show herein that SFVcpz infection is highly prevalent in wild chimpanzee populations throughout their natural range . Thus , monitoring humans for SFVcpz infection should be informative as to the locations where human/chimpanzee encounters are most frequent and where additional cross-species transmissions should be anticipated . One such area is southern Cameroon where chimpanzees are endemically infected with SIVcpz strains that have already crossed the species barrier to humans , in one case ( HIV-1 group M ) with devastating consequences [33] . Screening humans for SFVcpz infection may also provide new insight into the environmental circumstances that underlie cross-species transmissions . For example , if the frequency of human SFVcpz infection were significantly lower in east compared to west central Africa , this would argue for lower exposure rates and , in turn , provide a reason why SIVcpz strains from P . t . schweinfurthii apes have not emerged as human pathogens . Thus , human SFVcpz infection should be formally investigated a sentinel for ape-derived pathogens , including new SIVcpz/HIV-1 outbreaks .
Fecal samples ( n = 40 ) were collected from 23 captive chimpanzees housed at the Yerkes National Primate Research Center ( Table 1 ) , all of whom were known to be chronically infected with SFVcpz [8] , [66] . Fecal samples were also obtained from nine P . t . vellerosus apes housed in a Cameroonian sanctuary ( SA ) who were of unknown SFVcpz infection status . Samples were preserved in RNAlater , shipped and processed as described [33] , [37] . All studies were carried out in strict accordance with international guidelines for the ethical scientific use and humane care of primates in research ( the Yerkes National Primate Research Center is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ) . Blood and fecal samples were collected from 21 human volunteers who had no previous contact with primates or primate tissues ( informed consent was obtained and the study protocol was approved by the University of Alabama at Birmingham Committee for Human Research ) . All of these individuals were seronegative for SFVcpz antibodies as determined by Western blot analysis ( Table 1 ) . The fecal samples ( n = 732 ) used in this study were selected from existing banks of specimens previously collected for molecular epidemiological studies of SIVcpz [33] , [37] , [67] , [68] . The great majority ( n = 724 ) were collected from chimpanzee communities in Cote d'Ivoire , Cameroon , the Central African Republic ( CAR ) , Gabon , the Republic of Congo ( RC ) , the Democratic Republic of Congo ( DRC ) , Uganda , Rwanda , and Tanzania ( white circles in Figure 1 ) . Eight additional samples ( BA432 , BF1167 , EP479 , EP486 , KS310 , UB446 , WA466 , WA543 ) were collected at various locations in the DRC ( black circles in Figure 1 ) . All samples were collected in the wild , and their species and subspecies origin were confirmed by mitochondrial DNA analysis . All samples were also screened for SIVcpz antibodies and/or nucleic acids . Of the 732 samples , 87 were collected from habituated chimpanzees under direct observation . These included members of the North and South communities in the Taï Forest ( TA ) , Cote d'Ivoire [69] , the Mitumba and Kasekela communities in Gombe National Park ( GM-MT , GM-KK ) , Tanzania [43] , [70] , and the M group in Mahale Mountains National Park ( MH ) , Tanzania [71] . At seven additional field sites , the number of sampled individuals was retrospectively determined by microsatellite analysis [33] . These included the DP , EK , LB , MB and BB field sites in southern Cameroon , the non-habituated Kalande ( GM-KL ) community in Gombe National Park , and a site in the Goualougo Triangle ( GT ) , Republic of Congo [72] . At the remaining locations , the number of sampled chimpanzees remained unknown . These included the MF , MP , WE , MT , DG , BQ and CP field sites in Cameroon , the ME site in the Central African Republic , a site in the Lope National Park ( LP ) in Gabon , the BD , WL and WK field sites in the DRC , and a site in the Nyungwe Forest Reserve ( NY ) in Rwanda . Finally , samples were also obtained from the Ngogo community in Kibale National Park ( KB ) . Although the Ngogo chimpanzees are habituated [52] , the particular individuals sampled for this study were not identified . Fecal samples were also obtained from a wild-living gorilla ( LP5 ) and mandrill ( LP47 ) in the Lope National Park as well as a wild-living bonobo ( LM183 ) in the DRC . The species origin of these samples was confirmed by mtDNA analysis ( Table S2 ) . In addition , SFV pol-IN sequences were amplified from uncultured PBMC DNA from a wild-caught DeBrazza monkey ( 99CM-CNE1 ) previously reported to also harbor SIVdeb [73] . Fecal samples were examined for the presence of SFVcpz specific antibodies using an enhanced chemiluminescent Western immunoblot assay modified for RNAlater preserved specimens as described [33] . RNAlater is a high salt solution ( 25 mM Sodium Citrate , 10 mM EDTA , 70 g ammonium sulfate/100 ml solution , pH 5 . 2 ) that preserves nucleic acids , but precipitates proteins , including immunoglobulin . To prepare extracts suitable for Western blot analysis , fecal/RNAlater mixtures ( 1 . 5 ml ) were diluted with PBS-Tween-20 ( 8 . 5 ml ) , inactivated for 1 hr at 60°C , clarified by centrifugation ( 3500×g for 30 min ) to remove solid debris , and then dialyzed against PBS overnight at 4°C to resuspend fecal immunoglobulin . Reconstituted extracts were subjected to immunoblot analysis using SFVcpz antigen containing strips . For Western blot strip preparation , an infectious SFVcpz proviral clone ( pMod-1 ) was transfected into BHK21 cells and the resulting virus expanded in Cf2Th cells [61] , [74] . Briefly , Cf2Th cells ( 2×106 cells per 150 mm dish ) were inoculated using a multiplicity of infection of 0 . 1 , harvested at 75–100% CPE , pelleted , and resuspended in PBS ( 30 ml ) . Virions were released from cells by repeated freezing and thawing ( 4 cycles ) , purified by ultracentrifugation through a 20% sucrose cushion ( 23 , 500×g , 2 hrs ) , analyzed for protein content using a protein assay kit ( Pierce , Rockford , Ill . ) , denatured in Reducing Sample Buffer ( Piece , Rockford , Ill . ) , boiled , and run on a 7 . 5% Criterion Ready Gel ( BioRad , Hercules , Calif . ) . Proteins were transferred to polyvinylidene difluoride ( PVDF ) membranes ( BioRad , Hercules , CA ) , which were cut into strips ( 4 mm width ) , incubated with blocking buffer ( 5% nonfat dry milk , 3% fetal bovine sera , 0 . 5% Tween-20 in PBS ) , and then reacted overnight at 4°C with fecal extracts as described [33] . Protein-bound antibody was detected with goat-anti-human IgG-HRP ( Southern Biotech , Birmingham , AL ) and Western blots were developed using an enhanced chemiluminescence ( ECL ) detection system ( GE Healthcare Bio-Sciences , Piscataway , NJ ) . Fecal RNA was extracted using the RNAqueous-Midi kit ( Applied Biosystems/Ambion , Austin , TX ) and subjected to reverse transcriptase polymerase chain reaction ( RT-PCR ) amplification using different sets of SFVcpz specific primers . In each case , cDNA was synthesized using the outer reverse primer ( R1 ) , followed by nested PCR using forward ( F1/F2 ) and reverse ( R1/R2 ) primers . Fecal DNA was extracted using the QIAamp DNA Stool Mini Kit ( Qiagen , Valencia , CA ) and subjected to nested PCR . Previously described sets of nested primers were used to amplify subgenomic pol-IN ( 425 bp ) , gag ( 616 bp ) and LTR ( 260 bp ) regions [8] , [10]–[13] , [18] . In addition , nested pol-RT primers were designed to amplify a 717 bp reverse transcriptase ( RT ) fragment that extended the pol-IN fragment by 580 bp to the 5′ end ( F1: 5′-AGCAGGATATGTAAGATATTATAATGA -3′; R1: 5′-TCTCATATTTGGCCACCAATAAAGG -3′ F2: 5′- TTTCATTATGATAAAACCTTACCAGAA -3′; R2: 5′- TCCGGTGTGAGCCAAATTGTGGGCTTG -3′ ) . For a subset of samples , we also used forward pol-RT primers in combination with reverse pol-IN primers to amplify a 1 , 005 bp L-pol fragment . The positions of these primers in the SFV genome are shown in Figure 3 . PCR conditions included 60 cycles of denaturation ( 94°C , 20 s ) , annealing ( 50°C , 30 s ) , and elongation ( 68°C , 1 min ) for the first round . Second round conditions included 55 cycles of denaturation ( 94°C , 20 s ) , annealing ( 52°C , 30 s ) , and elongation ( 68°C , 1 min ) . Amplified products were gel purified ( Qiagen ) and sequenced directly without interim cloning . Population sequences were analyzed using Sequencher version 4 . 6 ( Gene Codes Corporation , Ann Arbor , MI ) and chromatograms were carefully examined for positions of base mixtures . For a subset of samples suspected to harbor SFVcpz recombinants or mixtures of distinct viral strains , the complexity of the SFVcpz viral population within individual hosts was independently analyzed by single genome amplification ( SGA ) . Fecal RNA was extracted from additional aliquots and cDNA synthesized as described above . cDNA was endpoint diluted in 96-well plates such that fewer than 29 reactions yielded an amplification product . According to a Poisson distribution , the cDNA dilution that yields PCR products in no more than 30% of wells contains one amplifiable cDNA template per positive PCR more than 80% of the time . PCR conditions and primers were as described above . All amplicons were sequenced directly , and sequences with ambiguous positions excluded from further analysis . The sensitivities of SFVcpz antibody and viral nucleic acid detection were determined for captive ( YK ) as well as wild-living chimpanzees ( TA , DP , EK , BB , MB , LB , GT , GM , MH ) of known SFVcpz infection status . Captive chimpanzees were diagnosed as SFVcpz infected by demonstrating virus specific antibodies in their blood ( Tables 1 and 2 ) . Wild-living chimpanzees were identified as SFVcpz infected by demonstrating virus specific antibodies or viral RNA in at least one fecal sample ( Table 2 ) . For each site , sensitivities were calculated as the fraction of positive tests per total number of samples tested , with confidence limits determined given the assumption of binomial sampling . For these calculations , it was assumed that successive test results from the same individual were not correlated . The specificity of fecal antibody detection was calculated using test results from SFVcpz antibody negative human volunteers ( Table 1 ) and determined to be 1 . 00 ( 0 . 87–1 . 00 ) . The specificities of vRNA and vDNA detection in fecal samples were also 1 . 00 , since all amplification products were sequence confirmed . For sites where the number of sampled chimpanzees was known ( TA , DP , EK , BB , MB , LB , GT , GM-KL , GM-MT , GM-KK , MH ) , SFVcpz prevalence rates were estimated based on the proportion of infected individuals . For each chimpanzee , the probability that it would be detected as being infected , if it was truly infected , was calculated taking into consideration the sensitivities of the types of assays performed as well as the numbers of specimens analyzed . Since the sensitivities of antibody and viral RNA detection varied extensively between captive and wild chimpanzees as well as different collection sites ( Table 2 ) , test sensitivities were averaged across all field sites . These “field sensitivities” were then used to calculate SFVcpz prevalence rates , with 95% confidence limits determined based on binomial sampling . For field sites where the number of sampled individuals was not known ( MF , MP , WE , MT , DG , CP , BQ , LP , ME , BD , WL , WK , KB , NY ) , prevalence rates were estimated based on the number of fecal samples collected and tested . Based on results from previous field studies [33] , it was assumed that a fraction ( 17% ) of all fecal samples was partially degraded and that any given chimpanzee was sampled on average 1 . 72 times . Using these corrections , the proportion of SFVcpz infected chimpanzees was estimated for each field site , again taking into account the “field sensitivities” of the different tests as well as the numbers of specimens analyzed . In addition , the number of unique mtDNA haplotypes served as an indicator of the minimum number of chimpanzees analyzed . From these determinations , prevalence rates and their confidence limits were calculated . The species and subspecies origin of all chimpanzee fecal samples used in this study was determined by mitochondrial ( mt ) DNA analysis ( Table S1 ) . A 498-bp region of the mtDNA genome ( D loop ) was amplified using primers L15997 ( 5′-CACCATTAGCACCCAAAGCT-3′ ) and H16498 ( 5′-CCTGAAGTAGGAACCAGATG-3′ ) , and all amplification products were sequenced directly . The resulting sequences were aligned and identical sequences grouped into mtDNA haplotypes . A subset of these haplotypes has been reported previously [33] . The remainder were classified based on their phylogenetic relatedness to subspecies specific mtDNA reference sequences . Haplotypes and corresponding GenBank accession numbers are listed in Table S1 . Nucleotide sequences of SFVcpz gag , pol-RT and pol-IN fragments were aligned using Se-Al ( A . Rambaut , distributed by the author at http://tree . bio . ed . ac . uk/software/seal/ ) . Several previously characterized SFVcpz strains were included as reference sequences ( GenBank accession numbers: SVFpvrc679 , AY195683 and AY195708; SFVprvc1138 , AY195682; SFVpvlcpz2 , AY195686; SFVpvlcpz4 , AY195687; CpzCam32 , AY639133; CpzCam19 , AY639141; CpzCam21 , AY639122; SFVpsc925 , AY195676 and AY195702; HFV , NC001795; SFVptr1040 , AY195673 and AY195699; SFVptr1436 , AY195700; SFVptrb1 , AY195681 and AY195707; SFVcase6 , AY195712; SFVpsc5126 , AY195701 and AY195675; SFVpvra101 , AY195678; SFVpvra055 , AY195677; SFVpts-No , AJ627552; SFVpts-Ni , AJ627553; SFVcase14 , AY195719; SFVcase13 , AY195718; SFVpvra182 , AY195706; SVFcase10 , AY195716; SFVcase8 , AY195714; SFVpvra136 , AY195705; SFVcase9 , AY195715; SFVcase12 , AY195717; SFVcase7 , AY195713; SFVpvrc941 , AY195685 and AY195709; SFVcpz , NC001364; CpzCam44 , AY639136; CpzCam30 , AY639128; CpzCam15 , AY639138; CpzCam35 , AY639130; PanGabNto , AY639123; PanGabNte , AY639124; PanGabBel , AY639126; Ppan1935 , AJ627551; SFV-6 , X83296; SFV-7 , X83297 ) . Very few insertions or deletions were required to align the data , and the resulting gaps were treated as unknown characters . All the alignments are available from the authors upon request . We initially used the Bayesian Markov chain Monte Carlo ( BMCMC ) method implemented in MrBayes v3 . 1 [75] to infer phylogenies for the mtDNA and SFV data . However , for some data sets , most notably the pol-IN alignment , we observed that the MCMC samples were dominated by trees that exhibited clearly spurious branching patterns , with long branches leading to distantly-related clades often breaking up the monophyly of closely related groups of sequences ( not shown ) . We therefore employed the ‘relaxed molecular clock’ BMCMC method implemented in BEAST [76] , so-called because it relaxes the assumption of a constant rate of evolution across the tree , allowing different lineages to evolve at different rates . Although our interest was not in estimating divergence dates , Drummond and colleagues found that using a model that falls between the extremes of assuming either a strict molecular clock or no molecular clock appeared to improve both the accuracy and precision of topology inference across a wide range of taxa [77] . Our results provide further support for this conclusion , since the artifacts described above for the MrBayes analyses were not observed in the BEAST results . All the BEAST runs were performed under an uncorrelated lognormal relaxed molecular clock model with a constant population size coalescent tree prior , using a general time-reversible nucleotide substitution model with heterogeneity among sites modeled with a gamma distribution . For each mtDNA and SFVcpz data set , simultaneous sampling times were assumed since the small intervals between sampling dates are expected to be negligible given the long time span of evolution represented not only by the mtDNA but also the SFV data sets [13] . For each analysis , two independent runs of 5 to 20 million steps were performed . Examination of the MCMC samples with Tracer v1 . 4 ( A . Rambaut & A . J . Drummond , http://beast . bio . ed . ac . uk ) indicated convergence and adequate mixing of the Markov chains , with estimated sample sizes in the 100s or 1000s . After inspection with Tracer , we discarded an appropriate number of steps from each run as burn-in , and combined the resulting MCMC tree samples for subsequent estimation of posteriors . We summarized the MCMC samples using the maximum clade credibility ( MCC ) tree ( including branch lengths ) found using TreeAnnotator v1 . 4 . 7 [76] , with posterior probabilities indicated ( as percentages ) for nodes with P>0 . 90 . All trees were saved with branch lengths measured in substitutions per site rather than time . In order to investigate the possibility of recombination in SFVcpz , and to map any putative recombination breakpoints , we conducted a recombination detection analysis using GENECONV [78] . GENECONV performs a series of comparisons between all pairs of sequences in an alignment and asks whether certain fragments are unusually alike ( available from the author at http://www . math . wustl . edu/sawyer/geneconv/ ) . For example , if two sequences are nearly identical over one stretch of sequence , but are highly divergent across the remainder , the similar fragment might be detected by GENECONV as a putative mosaic region . If , after statistically correcting for multiple comparisons , that fragment still appears to be unexpectedly similar , it will be flagged as a globally significant fragment by GENECONV . A simple follow-up analysis with phylogenetic trees inferred from the different regions detected by GENECONV can then confirm whether certain sequences contain regions with conflicting evolutionary histories ( i . e . supporting significantly discordant topologies ) . GENECONV results on a concatenated alignment of strains for which gag , pol-IN , and pol-RT sequences were available indicated several globally significant fragments; however , because many of the inferred breakpoints were at the gag-pol concatenation junction , we investigated the possibility that the putative “recombinants” detected with these data set actually represented co-infected samples in which different variants had been amplified for the distinct regions comprising the concatenated data set . Because this appeared to be the case , we restricted subsequent recombination analyses to individually amplified gene regions . All newly obtained SFVcpz and mtDNA D-loop sequences have been submitted to GenBank , and accession numbers are listed in Tables S1 and S2 , respectively .
|
Cross-species transmissions of infectious agents from primates to humans have led to major disease outbreaks , with AIDS representing a particularly serious example . It has recently been shown that humans who hunt primates frequently acquire simian foamy virus ( SFV ) infections . Thus , these viruses have been proposed as an “early warning system” of human exposure to wild primates . In this study , we have tested this concept by developing non-invasive methods to determine the extent to which wild chimpanzees are infected with SFV . We analyzed more than 700 fecal samples from 25 chimpanzee communities across sub-Saharan Africa and obtained viral sequences from a large number of these . SFV was widespread among all chimpanzee subspecies , with infection rates ranging from 44% to 100% . The new viruses formed subspecies-specific lineages consistent with virus/host co-evolution . We also found mosaic sequences due to recombination , indicating that chimpanzees can be infected with multiple viral strains . One chimpanzee harbored an SFV from a monkey species , indicating cross-species transmission in the wild . These data indicate that chimpanzees represent a substantial natural reservoir of SFV . Thus , monitoring humans for these viruses should identify locations where human/chimpanzee encounters are most frequent , and where additional transmissions of chimpanzee pathogens should be anticipated .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"infectious",
"diseases/hiv",
"infection",
"and",
"aids",
"microbiology/microbial",
"evolution",
"and",
"genomics",
"ecology/evolutionary",
"ecology"
] |
2008
|
Molecular Ecology and Natural History of Simian Foamy Virus Infection in Wild-Living Chimpanzees
|
Although immunopathology dictates clinical outcome in leprosy , the dynamics of early and chronic infection are poorly defined . In the tuberculoid region of the spectrum , Mycobacterium leprae growth is restricted yet a severe granulomatous lesion can occur . The evolution and maintenance of chronic inflammatory processes like those observed in the leprosy granuloma involve an ongoing network of communications via cytokines . IL-10 has immunosuppressive properties and IL-10 genetic variants have been associated with leprosy development and reactions . The role of IL-10 in resistance and inflammation in leprosy was investigated using Mycobacterium leprae infection of mice deficient in IL-10 ( IL-10−/− ) , as well as mice deficient in both inducible nitric oxide synthase ( NOS2−/− ) and IL-10 ( 10NOS2−/− ) . Although a lack of IL-10 did not affect M . leprae multiplication in the footpads ( FP ) , inflammation increased from C57Bl/6 ( B6 ) <IL-10−/−<NOS2−/−<10NOS2−/− . While IL-10−/− mice exhibited modest FP induration compared to B6 , NOS2−/− and 10NOS2−/− mice developed markedly enlarged FP marking distinct phases: early ( 1 month ) , peak ( 3–4 months ) , and chronic ( 8 months ) . IFN-γ-producing CD4+CD44+ cells responding to M . leprae cell wall , membrane , and cytosol antigens and ML2028 ( Ag85B ) were significantly increased in the evolved granuloma in NOS2−/− FP compared to B6 and IL-10−/− during early and peak phases . In 10NOS2−/− FP , CD4+CD44+ and especially CD8+CD44+ responses were augmented even further to these antigens as well as to ML0380 ( GroES ) , ML2038 ( bacterioferritin ) , and ML1877 ( EF-Tu ) . Moreover , fragmented nerves containing CD4+ cells were present in 10NOS2−/− FP . The 10NOS2−/− strain offers insight on the regulation of granuloma formation and maintenance by immune modulators in the resistant forms of leprosy and presents a new model for investigating the pathogenesis of neurological involvement .
Leprosy is a neglected tropical disease that is still diagnosed in >200 , 000 new patients every year [1] . Its clinical spectrum is associated with a diverse and often dynamic immune response ranging from strong cell mediated immunity ( CMI ) at one end to complete anergy toward Mycobacterium leprae antigens at the other . As patients are often not diagnosed until years post-infection , the early stage determinants of disease resolution or progression are not yet understood . Likewise , much remains unknown regarding the immunopathogenesis of leprosy neuropathy which can occur even after successful antimicrobial therapy . Several global research collaborations are actively endeavoring to develop effective vaccines and new diagnostic methods [2]–[10] , but considerable additional effort is needed to ultimately eliminate leprosy . The majority of leprosy patients are classified into the borderline area of the spectrum [11] where there appears to be a partial immunity of an undefined nature which allows neither complete anergy nor resolution of disease . Borderline leprosy can be immunologically unstable , permitting upgrading and downgrading responses due to immunological fluctuations or acute reactional episodes that may cause significant tissue destruction . In an effort to investigate this broad range of responses within the lesion , we have evaluated the M . leprae-induced footpad ( FP ) granuloma in a number of mouse strains with immune defects [12]–[16] , including inducible nitric oxide synthase knockout mice ( NOS2−/− ) [17] , [18] . NOS2−/− mice respond to M . leprae infection in a manner that resembles borderline tuberculoid disease in that bacterial growth is restricted and they develop a large granulomatous response , composed of epithelioid macrophages and numerous lymphocytes , which infiltrates surrounding tissue . IL-10 is an anti-inflammatory and immunosuppressive cytokine produced primarily by macrophages and T cells . IL-10 polymorphisms have been associated with leprosy resistance or susceptibility in several endemic populations [19]–[25] , and variations in IL-10 expression have been noted in relation to the occurrence and treatment of reactions [26]–[28] . In addition , Toll-like receptor 2 polymorphisms have been linked to susceptibility and increased production of IL-10 [29]–[31] . Therefore , IL-10 appears to play a major role in the course of this chronic infectious disease although the precise mechanisms of action within the site of infection are unknown . A better understanding of its involvement could benefit our ability to control the pathological consequences of leprosy . Based upon these collective observations , we hypothesized that mice having an IL-10 deficiency would exhibit a more robust immune response toward M . leprae . Furthermore , a lack of IL-10 in the absence of NOS2 would further intensify the NOS2 immunopathological response , conceivably driving that model toward a more inflammatory or “reactional” state . Therefore , we examined M . leprae infection in IL-10−/− and NOS2−/− mice , as well as double knockout mice ( 10NOS2−/− ) , and evaluated three issues directly in the FP granulomas over the course of long term infection: 1 ) growth of the bacilli and histopathology , 2 ) host cellular dynamics evoked by M . leprae infection in the FP lesion , and 3 ) characterization of M . leprae antigen-specific T cells . Results show that while NOS2 is largely responsible for regulating the magnitude of T cell infiltration into the granuloma , a concomitant lack of IL-10 results in intensified M . leprae antigen responsiveness and T cell invasion of nerves .
These studies were performed under a scientific protocol reviewed and approved by the National Hansen's Disease Programs Institutional Animal Care and Use Committee ( Assurance #A3032-01 ) , and were conducted in accordance with all state and federal laws in adherence with PHS policy and as outlined in The Guide for the Care and Use of Laboratory Animals , Eighth Edition . M . leprae strain Thai-53 was propagated in athymic nu/nu mice ( Harlan Sprague-Dawley , Inc . , Indianapolis , IN ) . M . leprae were harvested from the FP and viability was assessed by radiorespirometry , which measures the oxidation of 14C-palmitic acid to 14CO2 [32] and vital staining , which utilizes Syto9 and propidium iodide to determine cell wall integrity ( Bacterial Viability Staining Kit , Life Technologies , Grand Island , NY ) [33] . M . leprae bacilli were stored at 4°C and used within 24 hours of harvest . Female mice 5–7 weeks old were obtained from Jackson Laboratories ( Bar Harbor , ME ) from the following strains: IL-10−/− ( B6 . 129P2-IL-10tm1Cgn/J ) , NOS2−/− ( B6 . 129P2-NOS2tm1Lau/J ) and C57BL/6J mice ( B6 ) . IL-10−/−/NOS2−/− double knockout mice ( 10NOS2−/− ) were generated by crossing IL-10−/− and NOS2−/− strains to produce offspring bearing both knockout mutations . Mice were housed under pathogen-free conditions in laminar flow animal isolators , in sterile cages , and maintained on sterile food and water . Like the parental strains , the 10NOS2−/− mice thrived in these conditions and were good breeders . They exhibited no overt differences in phenotypic or clinical features . Mice were infected with freshly harvested , viable M . leprae via inoculation of a low dose of 6×103 bacilli in 0 . 03 ml phosphate buffered saline ( PBS ) into each hind FP . To assess bacterial multiplication , M . leprae were harvested from the FP and acid fast bacilli ( AFB ) were counted as previously described [32] . M . leprae were enumerated at 3 months to verify early bacterial growth , at 6 months because maximum growth is reached in the control strain at this time , and at 12 months to check for clearance or continued growth in the knockout strains . FP tissue was examined by histology . Feet were fixed in 10% buffered formalin , decalcified in 10% ( w/v ) sodium citrate and 22 . 5% ( v/v ) formic acid , and embedded in paraffin [15] . Cross-sections ( 4 µm ) from the distal , mid and proximal areas of the metatarsus were stained with hematoxylin and eosin . An Olympus BX53 microscope ( Center Valley , PA ) equipped with a Retiga 2000R Firewire Digital camera and cellSens Imaging Software was used to assess and capture images from the stained tissues . Mice were infected with M . leprae via inoculation of a high dose of 3×107 bacilli in 0 . 03 ml PBS into each hind FP [14] , [15] . Induration was measured weekly using a Vernier caliper . To characterize the granulomatous response , FP were harvested at 1 month ( early ) , 4 months ( peak induration ) , and 8 months ( chronic ) post-infection and evaluated for cell phenotypes , cytokine expression , and M . leprae antigen responsiveness . O . C . T . medium ( Tissue Tek , Inc . , Torrence , CA ) embedded FP tissue was snap frozen in liquid nitrogen and stored at −70°C . Serial 4 µm sections ( Frigocut 2800E , Leica Microsystem , Inc . , Bannockburn , IL ) were fixed in cold acetone and , after blocking with avidin and biotin ( Vector Laboratories , Burlingame , CA ) , stained with rat anti-mouse CD4 ( BD Biosciences , San Jose , CA ) . Biotin-labeled mouse anti-rat F ( ab ) 2 fragments ( Jackson ImmunoResearch Laboratories , Inc . , West Grove , PA ) was applied , and immunohistochemical visualization was achieved with Vectastain Elite ABC kit ( Vector ) , the AEC Substrate kit ( Vector ) , and hematoxylin counterstain . RNA was prepared from FP tissue of individual mice as described previously [15] . cDNA was generated from 0 . 5 µg RNA using random hexamers and an RT-for-PCR kit ( Clontech , Palo Alto , CA ) at 42°C for 1 hr in a thermocycler ( 9600 , Perkin-Elmer Corp . , Norwalk , CT ) . Controls for DNA contamination were prepared from RNA samples using the reverse transcription reagents minus the reverse transcriptase . All sample and control preparations were aliquoted and stored at −70°C . Real-time RT-PCR for IFN-γ transcripts were carried out via Taqman technology using specific primer sets and probes and Universal Master Mix ( Applied BioSystems ) in an ABI PRISM 7300 Sequence Detection System ( Applied BioSystems ) . Semiquantitative analysis of the data was performed using the ΔΔCT method and expressed as a fold increase in cytokine expression over uninfected FP . Data were normalized for template variation using the GAPDH RT-PCR value for the same template . FP tissue , which was fixed in 70% ethanol and stored at −20°C until processing , was rehydrated in water and suspended in TRIzol reagent . RNA and DNA were extracted using the FastPrep FP 24 instrument ( MP Biomedicals , Solon , OH ) as described previously [34] , [35] . The number of M . leprae was determined on the DNA fraction of each specimen via Taqman methodology using the standard curve method , primers , and a probe specific for a common region of the repetitive element , RLEP [36] . Based on this RLEP count , the RNA equivalent of 3×103 M . leprae from the purified RNA fraction was converted to cDNA using an Advantage RT-for-PCR kit ( Clontech , Mountain View , CA ) . As a control for possible DNA contamination , “mock” cDNA was prepared using an equivalent amount of RNA , polymerase mix , and primers without the reverse transcriptase . The viability of the M . leprae in each sample was determined on cDNA , using Taqman technology , the standard curve method , and primers and probes specific for esxA ( encodes the ESAT-6 protein ) [35] . FP tissues were aseptically minced and digested using collagenase and DNase to generate single cell suspensions as previously described [37] . Cells were counted , treated with Fc Block ( CD16/CD32[Fcγ III/II receptor]; BD Biosciences ) and stained with the appropriate isotype control antibodies or one or more of the following: anti-CD3 ( clone 17A2 ) , anti-CD4 ( clone RM4-5 ) , anti-CD8a ( clone 53-6 . 7 ) , anti-CD44 ( clone IM7 ) , anti-CD244 . 2 ( clone 2B4 ) , anti-CD11b ( clone M1/70 ) , anti-I-Ab ( clone 25-9-17 ) , and anti-Ly-6G ( clone 1A8 ) ( BD Biosciences ) . Data was collected using a FACS Aria interfaced with Hewlett Packard 4100 running FACS Diva software ( BD Biosciences ) . FP cells were enriched for lymphocytes by adherence and non-adherent cells were plated in 96 well plates at 2×105 cells in 200 µl medium ( RPMI 1640 [Life Technologies] , 10% fetal bovine serum [HyClone Laboratories , Logden , UT] , 25 mM HEPES buffer [Life Technologies] , 0 . 2% NaHCO3 [Life Technologies] , 2 mM glutamine [Irvine Scientific , Santa Ana , CA] , 100 µg/ml ampicillin [Sigma-Aldrich , St . Louis , MO] ) containing 1×10−5 M 2-mercaptoethanol ( Sigma-Aldrich ) . Cells were incubated overnight at 33°C with PBS ( Irvine Scientific ) , anti-CD3 ( BD Biosciences ) or 10 µg/ml of the following M . leprae antigens: Membrane , Cell wall antigen ( CWA ) , Cytosol , and five recombinant proteins which previously had been shown to be immunologically important and/or recognized by either cell mediated or serological responses in leprosy patients [9] , [10]: ML2028 ( Ag85B , belongs to the mycolyltransferase Ag85 family and is an essential enzyme involved in cell wall biogenesis [38] ) , ML0050 ( CFP-10 , culture filtrate protein which can elicit potent early cell mediated immune responses ) , ML0380 ( GroES , a chaperonin protein involved in protein folding , it is one of the most highly expressed native proteins of the leprosy bacillus [39] and is recognized by one third of all M . leprae reactive T cells in tuberculoid leprosy patients and healthy household contacts [40] ) , ML2038 ( BfrA , bacterioferritin is a membrane protein involved in iron uptake [41] ) , and ML1877 ( EF-Tu , elongation factor involved in protein synthesis ) . These antigens were provided through the NIH/NIAID Leprosy Research Contract N01 AI-25469 from Colorado State University and are currently supplied through BEI Resources ( Manassas , VA ) . Cells were collected and stained for CD4 , CD8 , CD44 and intracellular IFN-γ ( clone XMG1 . 2 ) using BD Cytofix/Cytoperm Plus Kit with BD GolgiPlug according to kit instructions . Cytokine and chemokine concentrations in the supernatants were determined using a Bioplex Mouse Cyto 23Plex Kit and analyzed on the Bio-Plex System with Luminex xMap Technology ( Life Science Research , Hercules , CA ) . Analytes targeted were: IFN-γ , IL-1α , IL-1β , IL-2 , IL-3 , IL-4 , IL-5 , IL-6 , IL-9 , IL-10 , IL-12p40 , IL-12p70 , IL-13 , IL-17 , Eotaxin , KC , G-CSF , GM-CSF , TNF , CCL-2 , CCL-3 , CCL-4 , and CCL-5 . Growth of M . leprae was analyzed using the GLM procedure ( SAS 9 . 3 ) with an analysis of variance in a factorial arrangement of group vs . time , followed by post hoc comparisons with pairwise t-tests of least square means and non-parametric Mann-Whitney tests . All other data were analyzed using unpaired t-tests with or without Welch's correction ( SigmaPlot 12 . 0 , SyStat Software ) . Data were considered significant at p≤0 . 05 [14] , [15] .
We first assessed growth of M . leprae in NOS2−/− mice using the standard mouse FP growth assay , which exploits the fact that a minor inoculum of M . leprae ( ≤104 ) into the FP of immunocompetent mice can initially evade immune-mediated killing , grow locally for approximately 6 months , and peak at 105–106 bacilli when growth is halted by the immune response [42] . As shown in Fig . 1A , multiplication of the bacilli was similar in the B6 and NOS2−/− strains and peaked on the order of 105 AFB per FP by 6 months post infection . Extended observation demonstrated that M . leprae growth did not continue past the 6 months peak in the NOS2−/− but showed a trend toward improved clearance at 12 months ( p = 0 . 0049 ) . The M . leprae growth assay is limited in that the minimal infection site that develops in immunocompetent mice ( see Fig . 2B , [B6] ) is insufficient for in depth investigation of the granuloma . Therefore , we utilize the FP induration assay , where adequate numbers of M . leprae are administered in the initial inoculum to induce a granuloma which can then be monitored via induration and at the cellular level throughout chronic infection . As shown in Fig . 1B , B6 FP reached peak induration of 0 . 72±0 . 13 mm at 3 months post infection and maintained this level for several months . In contrast , induration in NOS2−/− FP was significantly augmented by 1 month ( p<0 . 001 ) , continued to increase until it peaked at 2 . 36±0 . 51 mm at 4 months , then declined over the next 8 months . In order to investigate the immune modulators within the site of infection , FP lymphocytes were harvested at 4 months and stimulated with M . leprae membrane antigen in vitro . Striking differences between the B6 and NOS2−/− strains were seen in cytokine and chemokine production . Large amounts of Th1 cytokines ( IFN-γ and IL-2 ) , chemokines ( CCL-3 and CCL-4 ) , and TNF ( Fig . 1C ) , as well as other analytes associated with inflammation ( IL-1α , IL-1β , IL-3 , IL-9 , IL-13 , IL-17 , CCL-2 CCL-5 , G-CSF and GM-CSF [data not shown] ) were generated by the NOS2−/− cells compared to B6 FP cells . In contrast , similar levels of IL-6 , IL-12p40 , IL-12p70 , KC , and eotaxin ( data not shown ) and very low levels of the Th2 cytokines , IL-4 and IL-5 ( Fig . 1C ) , were produced by both strains . Interestingly , significantly elevated levels of IL-10 ( Fig . 1C ) were generated by the NOS2−/− FP cells compared to B6 ( p = 0 . 004 ) . Because IL-10 has immunosuppressive properties , we questioned whether its generation in the NOS2−/− mice may be an attempt to temper the inflammatory response generated at the site of infection . If so , we postulated that inhibiting both IL-10 and NOS2 could push the model to a more inflammatory state . Our initial attempts to inhibit both IL-10 and NOS2 involved supplementing the drinking water of M . leprae-infected IL-10−/− mice with L-NIL , a selective inhibitor of NOS2 . These mice developed an enlarged FP induration over an 8 week period ( data not shown ) . In order to perform longer term studies , however , we generated a NOS2 and IL-10 double knockout strain ( 10NOS2−/− ) by cross-breeding mice with the individual knockouts . 10NOS2−/− , alongside IL-10−/− , NOS2−/− and B6 mice , were inoculated with 6×103 viable M . leprae in both hind FP and tissues were harvested at 3 , 6 and 12 months post inoculation to assess growth of the bacilli . As shown in Fig . 2A , AFB counts from all knockout groups demonstrated a pattern of growth that was similar to the B6 strain in that they controlled infection and peaked at 6 months on the order of 105 AFB per FP , albeit counts were somewhat higher in NOS2−/− ( p = 0 . 0477 ) and 10NOS2−/− ( p = 0 . 0146 ) . Again , there was improved clearance in the NOS2−/− ( p = 0 . 0069 ) at 12 months , as well as in the 10NOS2−/− ( p = 0 . 0413 ) . Although a restriction of growth of M . leprae was achieved by all groups , histological assessment indicated significant variations in the granulomatous responses ( Fig . 2B ) . Mild interstitial mononuclear cell infiltrates were observed in B6 FP . More extensive cellular infiltration was observed in IL-10−/− , and the most extensive infiltrates were seen in NOS2−/− and 10NOS2−/− . In all strains , the infiltrates were composed of mononuclear cells , with few granulocytes . In both NOS2−/− and 10NOS2−/− , the inflammatory infiltrates replaced muscle bundles in the FP , but no active necrosis of muscle was seen and no epidermal changes were observed . In order to further investigate granuloma dynamics , we assessed induration , cellular recruitment , IFN-γ expression , and bacterial viability over the course of long-term infection of the FP . As shown in Fig . 3A , induration in B6 FP reached a peak of approximately 0 . 5 mm at 1 month post infection and was maintained at this level for several months . FP thickness in IL-10−/− was greater than in B6 mice throughout infection ( p<0 . 05 ) . A strongly amplified pattern of induration was observed in NOS2−/− and 10NOS2−/− permitting division into three phases for investigation: early ( 1 month ) , peak ( 3–4 months ) and chronic ( >8 months ) . By 1 month , both NOS2−/− and 10NOS2−/− FP were significantly more indurated than either IL-10−/− or B6 FP ( p<0 . 0001 ) , and by peak phase both of these strains similarly exhibited >4 times the induration of IL-10−/− or B6 FP ( p<0 . 0001 ) . During the later phase of chronic infection , 10NOS2−/− showed a slower decline than NOS2−/− in FP induration and eventually diminished into a final plateau of approximately twice that of IL-10−/− and B6 FP ( p<0 . 0001 ) . As shown in Fig . 3B , all strains demonstrated robust IFN-γ expression in response to M . leprae infection . B6 and IL-10−/− mice exhibited a >1 log increase in IFN-γ expression over uninfected FP throughout infection . In NOS2−/− FP , IFN-γ expression was increased more than 2 log over that in B6 FP at 4 months ( p<0 . 001 ) . Expression of IFN-γ was significantly greater in 10NOS2−/− FP compared to all other strains at 1 month , and this high level of expression was maintained throughout infection . To verify that the augmented induration and IFN-γ expression seen in NOS2−/− and 10NOS2−/− FP was not due to enhanced bacterial growth in these strains , we determined M . leprae counts and viability in this high dose model . Compared to initial counts at 1 day , the number of M . leprae remained relatively constant in B6 and IL-10−/− FP or decreased slightly in NOS2−/− and 10NOS2−/− FP at 4 months post infection ( Fig . 3C ) . Moreover , M . leprae exhibited high viability in all strains at 1 day ( Fig . 3D ) but bacterial viability declined significantly in all strains by 4 months , again indicating that the lack of NOS2 and/or IL-10 did not compromise immune-mediated killing of the bacilli . M . leprae-infected FP were evaluated at 4 months by immunohistochemistry for the presence and distribution of CD4+ T cells . As shown in Fig . 4A , CD4+ cells in B6 FP were distributed throughout the lesion and characteristically surrounded intact nerves . A similar pattern was seen in the IL-10−/− FP ( Fig . 4B ) . NOS2−/− and 10NOS2−/− had substantially more CD4+ cells in the FP . In NOS2−/− FP ( Fig . 4C ) , CD4+ cells surrounded the nerves in a pattern similar to B6 and IL-10−/− . In contrast , in 10NOS2−/− FP ( Fig . 4D–F ) , CD4+ cells were observed inside fragmented nerves . Single cell suspensions were prepared from the FP tissues of each group during the three phases of infection . As shown in Fig . 5A , on the order of 106 cells were recovered from B6 FP at each stage of infection . All knockout strains had significantly more cells infiltrating the FP at 1 month compared to B6 mice , and NOS2−/− and 10NOS2−/− maintained higher numbers of infiltrating cells into the peak and chronic stages . Cellular phenotypes at the site of infection determined by flow cytometry showed that ∼50–70% of the recruited cells were found in the myeloid gate ( Fig . 5B ) and ∼20–40% segregated to the lymphoid gate in all strains of mice ( Fig . 5C ) . Throughout the infection period , the majority of the myeloid cells in the FP from all strains was macrophages ( Fig . 5D ) and expressed IAb ( Fig . 5G ) . Neutrophils accounted for <10% of the FP cells ( Fig . 5E ) . In the lymphoid gate , B cells and NK cells comprised <2% of the FP cells ( data not shown ) . Major differences were seen , however , in the T cell populations ( Fig . 5F ) where NOS2−/− and 10NOS2−/− FP yielded a significantly increased percentage of CD3+ cells . In all strains , CD4+ cells ( Fig . 5H ) comprised the majority of the CD3+ cell population as compared to CD8+ cells ( Fig . 5I ) . Both CD4+ and CD8+ cells , however , were augmented in the NOS2−/− and 10NOS2−/− FP , especially during the peak and chronic phases . As previously reported for B6 mice [14] , the majority of the CD4+ and CD8+ cells infiltrating the FP in all strains expressed the activation phenotype of CD44+CD62L− ( data not shown ) . In order to assess the nature of the T cells recruited to the site of infection , FP granuloma lymphocyte populations from each strain were stimulated with various M . leprae crude and purified antigens in vitro and evaluated by flow cytometry for IFN-γ production by CD4+CD44+ ( Fig . 6 ) and CD8+CD44+ ( Fig . 7 ) cells . In general , all strains demonstrated an IFN-γ response by CD4+CD44+ ( Fig . 6 ) and CD8+CD44+ ( Fig . 7 ) cells to the crude M . leprae antigens ( membrane , CWA , cytosol ) and to ML2028 ( Ag85B ) at one or more points during long-term infection . In comparison to B6 , NOS2−/− and 10NOS2−/− mice exhibited a significantly higher percentage of CD4+CD44+ and/or CD8+CD44+ cells that recognized these antigens . M . leprae membrane antigen evoked the strongest response of all the antigens screened . It was detected by 10NOS2−/− in 15 . 8±3 . 1% of CD4+CD44+ ( p = 0 . 0041 ) at 1 month post infection , a response similar to that seen in NOS2−/− FP cells ( Fig . 6A ) . The effect of both IL-10 and NOS2 deficiencies in recognizing the crude antigens was even more sharply demonstrated in CD8+CD44+ cells of 10NOS2−/− ( Fig . 7A ) . M . leprae membrane antigen was recognized by 29 . 1±2 . 4% of CD8+CD44+ cells ( p = 0 . 0002 ) which was a 5–6 fold increase over all other strains , including the NOS2−/− cells ( p<0 . 0001 ) . Interestingly , the 10NOS2−/− FP T cells also exhibited an enhanced response to the purified antigens , ML0380 , ML2038 and ML1877 . ML2038 and ML1877 were recognized early by 10NOS2−/− CD4+CD44+ ( Fig . 6A ) and CD8+CD44+ ( Fig . 7A ) cells but not significantly at 4 months ( Fig . 6B and 7B ) or 8 months ( Fig . 6C and 7C ) . ML0380 was recognized at 1 month ( Fig . 6A ) and at 4 months ( Fig . 6B ) by CD4+CD44+ cells . Control mice were not particularly responsive to ML0050 , a finding previously reported in both M . tuberculosis- [43] and M . leprae- [5] infected B6 mouse models , and a lack of IL-10 and/or NOS2 did not enable responsiveness to this antigen .
Nerve damage in leprosy can be the consequence of immunological or inflammatory responses induced by the organism [11] , [44] , and the nerve injury seen at the tuberculoid end of the spectrum is thought to be due to destructive granulomatous inflammation . A variety of mechanisms have been proposed [45]: the M . leprae-infected Schwann cell can act as an antigen-presenting cell and signal destruction of the infected cells [46]; the inflammatory response can injure the nerve as an “innocent bystander”; a cell mediated immune response may become destructive when T cells necessary for the protective response induce tissue damage depending on the type and quantity of the local cytokine response , as during Type 1 reactions . We did not see infection of Schwann cells by M . leprae in the B6 or any of the knockout strains; hence , the first mechanism for nerve damage is unlikely in our model . While the second mechanism may play a role , the elevated IFN-γ response in the absence of IL-10 and appearance of T cells inside damaged nerves in the 10NOS2−/− FP would suggest the latter mechanism . Thus , a lack of IL-10 appears to result in a neuropathologic response in the inflammatory NOS2−/− model . IL-10 up-regulation can suppress lymphocyte-driven immunity , and in leprosy IL-10 is detected in multibacillary lesions [47]–[50] . This M . leprae-induced IL-10 could contribute to the development of the T cell anergy seen at the Th2-dominant lepromatous end of the leprosy spectrum by steering the immune response toward a phagocytic rather than antimicrobial program [51] . In vitro , M . leprae infection stimulates monocytes to produce IL-10 [52]–[54] , and IL-10 supplementation of infected macrophage cultures extends maintenance of M . leprae viability [55] , [56] . In an experimental model , IL-10 expression is elevated in M . leprae-infected IFN-γ knockout mice which exhibit less resistance to infection compared to control mice [13] . Interestingly , IL-10 is also detected in paucibacillary leprosy lesions [48] . Its presence along with various Th1 cytokines has been suggested to be a mechanism for tempering immunopathology [57] . Iyer et al [58] proposed that the observation of both IL-10 and TNF in Type 1 and Type 2 reactions indicated the simultaneous suppression of proinflammatory pathways and activation of regulatory pathways to control tissue damage from excessive inflammation . This study represents the first time IL-10 deficiency has been investigated 1 ) in vivo , 2 ) at the site of infection and 3 ) over acute and chronic infection in an experimental leprosy model . Similarities in M . leprae growth profiles across the strains suggest that an absence of IL-10 , either alone or combined with a NOS2 deficiency , did not provide a more growth restrictive environment in the already highly resistant B6 strain . M . leprae-infected IL-10−/− mice exhibited slight exacerbations compared to B6 in histopathology , FP induration , and antigen responsiveness with no evidence of survival or resumed M . leprae growth over the course of infection . In contrast , IL-10−/− mice infected with BCG or M . tuberculosis exhibited high Th1 cytokine responses and enhanced inflammation with persistent granulomas containing reduced bacterial loads in short-term lung studies compared to wild type mice [59]–[62] . Nevertheless , a longer term study [63] , demonstrated eventual morbidity due to M . tuberculosis regrowth . In our studies with NOS2−/− mice ( [17] and this paper ) , we found that M . leprae infection of the FP resulted in a large granuloma composed of numerous epithelioid cells and dense collections of lymphocytes . The granuloma infiltrated muscle bundles and partly destroyed them , with recruited cells exhibiting elevated levels of Th1 cytokines and chemokines , as well as IL-10; yet bacterial growth remained restricted as compared to B6 . Similarly , previous studies using M . avium infection of NOS2 deficient mice have shown increased granuloma formation and cellularity and an up-regulation of Th1 cytokine , chemokine and IL-10 expression in the absence of bacterial replication [64] , [65] . Thus , we questioned whether disruption of both IL-10 and NOS2 would exacerbate the NOS2 model further toward a more inflammatory or even “reactional” state . Infection of 10NOS2−/− FP with M . leprae resulted in a markedly enhanced accumulation of M . leprae-responsive CD4+ and CD8+ T cells in the granuloma and a CD4+ T cell infiltration into the local nerves , an outcome not seen in the single knockout mice nor previously reported in mouse models for leprosy . It is noteworthy that peak inflammation developed within 3–4 months after M . leprae inoculation , a time when the bacteria have been killed . While ∼15% of reactions are identified at the time of leprosy diagnosis , reactions and neuropathy continue to occur during the first year of treatment ( Scollard et al . , submitted for publication ) and even years after successful multidrug therapy ( MDT ) [66]–[68] . It has been hypothesized that antimicrobial therapy which kills the bacilli may cause release of increased or different antigens thereby heightening the risk for subsequent immunological complications in some patients . No M . leprae-specific antigens have yet been identified in relationship to this phenomenon . Thus , the three induration phases revealed interesting contrasts among the mouse strains regarding which M . leprae antigens were recognized , which lymphocytes targeted them , and when they were targeted over the course of long term infection . In the 10NOS2−/− FP , there was a significantly stronger IFN-γ response to the crude antigens and ML2028 ( Ag85B ) , as well as to ML0380 ( GroES ) , ML2038 ( bactoferritin ) , and ML1877 ( EF-Tu ) by both CD4+ and especially CD8+ T cells at the site of infection . Interestingly , the GroES , Ag85B , and EF-Tu antigens of M . tuberculosis have been reported to function as plasminogen receptors [69] . Activation of the mammalian plasminogen-plasmin system has been proposed to be a mechanism whereby pathogens can contribute to bacterial dissemination and tissue damage [70] . Whether these M . leprae homologues promote tissue damage via this pathway is an interesting prospect for investigation . Despite 30 years of global MDT programs and redefined elimination targets , leprosy remains a persistently reported disease in a number of endemic countries . However , even if all current active cases of leprosy were detected and provided MDT today , a significant portion of those patients would still experience subsequent progressive disease complications for years to come due to immunological perturbations . Leprosy reactions are a major cause of permanent neuropathy and disability especially in patients with borderline disease . Our findings present the 10NOS2−/− strain as an interesting model for investigating lymphocyte recruitment into the granuloma and nerves throughout long term infection in the resistant forms of leprosy . Determining the antigens recognized by T cells at the site of M . leprae infection could significantly further understanding of immunopathogenesis . This in turn , could provide stepping stones for the development of M . leprae-antigen specific diagnostics to improve detection , monitoring , and treatment of leprosy patients .
|
Despite effective antimicrobial therapy , 30–50% of leprosy patients develop immunological complications called leprosy reactions before , during or even years after being cured . Leprosy reactions are a major risk for neuritis that leads to peripheral nerve damage , disfigurement and disability . Unfortunately , why and how leprosy reactions occur is not well understood . Based on the latest human genetic leprosy susceptibility research and mouse infection models , we generated a double knockout mouse strain ( 10NOS2−/− ) which has deficiencies in two key immune factors , interleukin-10 ( IL-10 ) and inducible nitric oxide synthase ( NOS2 ) . We investigated the dynamics of the immune response to Mycobacterium leprae infection and chronicled the types of immune cells recruited to the site of infection . 10NOS2−/− mice developed a substantial induration in response to infection , as well as an increased interferon-gamma response to components of the leprosy bacillus . Interestingly , these animals also exhibited CD4+ T cell infiltration into the nerves , a phenomenon which has not been previously reported in leprosy mouse models . This new model provides insight into potential mechanisms whereby immune modulators may regulate leprosy reactions and neuritis and could aid the development of tests for monitoring and treatment of leprosy patients .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"intracellular",
"pathogens",
"pathogens",
"tropical",
"diseases",
"microbiology",
"bacterial",
"diseases",
"population",
"modeling",
"neglected",
"tropical",
"diseases",
"bacterial",
"pathogens",
"neuropathy",
"infectious",
"diseases",
"mycobacteria",
"medical",
"microbiology",
"microbial",
"pathogens",
"infectious",
"disease",
"modeling",
"pathogenesis",
"peripheral",
"neuropathy",
"host-pathogen",
"interactions",
"neurology",
"leprosy",
"biology",
"and",
"life",
"sciences",
"computational",
"biology"
] |
2014
|
IL-10 and NOS2 Modulate Antigen-Specific Reactivity and Nerve Infiltration by T Cells in Experimental Leprosy
|
Scabies and skin infections are endemic in many Australian Aboriginal communities . There is limited evidence for effective models of scabies treatment in high prevalence settings . We aimed to assess the level of treatment uptake amongst clinically diagnosed scabies cases and amongst their household contacts . In addition , we aimed to determine the likelihood of scabies acquisition within these households over the 4 weeks following treatment provision . We conducted an observational study of households in two scabies-endemic Aboriginal communities in northern Australia in which a community-based skin health program was operating . Permethrin treatment was provided for all householders upon identification of scabies within a household during home visit . Households were visited the following day to assess treatment uptake and at 2 and 4 weeks to assess scabies acquisition among susceptible individuals . All 40 households in which a child with scabies was identified agreed to participate in the study . Very low levels of treatment uptake were reported among household contacts of these children ( 193/440 , 44% ) . Household contacts who themselves had scabies were more likely to use the treatment than those contacts who did not have scabies ( OR 2 . 4 , 95%CI 1 . 1 , 5 . 4 ) , whilst males ( OR 0 . 6 , 95%CI 0 . 42 , 0 . 95 ) and individuals from high-scabies-burden households ( OR 0 . 2 , 95%CI 0 . 08 , 0 . 77 ) were less likely to use the treatment . Among 185 susceptible individuals , there were 17 confirmed or probable new diagnoses of scabies recorded in the subsequent 4 weeks ( 9 . 2% ) . The odds of remaining scabies-free was almost 6 times greater among individuals belonging to a household where all people reported treatment uptake ( OR 5 . 9 , 95%CI 1 . 3 , 27 . 2 , p = 0 . 02 ) . There is an urgent need for a more practical and feasible treatment for community management of endemic scabies . The effectiveness and sustainability of the current scabies program was compromised by poor treatment uptake by household contacts of infested children and high ongoing disease transmission .
Skin infections are a significant cause of morbidity in disadvantaged settings around the world [1] , [2] . Scabies and pyoderma are endemic in many Aboriginal communities in northern Australia . These conditions cause local morbidity and contribute substantially to clinic workload and costs [3] . Moreover , the primary bacterial pathogen underlying most pyoderma in these communities is Group A Streptococcus ( GAS ) , which can cause a myriad of debilitating secondary complications [4] , including acute nephritis . Recent evidence also suggests a link between GAS skin infection and rheumatic fever , and consequently rheumatic heart disease [5] . Post-streptococcal disease rates in Aboriginal Australians are among the highest in the world [6] . Scabies is thought to underlie the majority of bacterial skin infections in these communities [7] , thus controlling scabies is critical to improving skin health and reducing the burden of GAS secondary complications . Scabies is a disease that often accompanies poverty , with high prevalence being consistently associated with crowded living conditions [8]–[10] . Previous experience suggests community-based mass-treatment approaches are likely to be the most effective for control of scabies and skin sores in remote Aboriginal communities [1] , [11]–[13] . Similar models are used to control endemic parasitic and other diseases around the world [14]–[16] . Substantial reductions in the prevalence of scabies in endemic settings have previously been described with mass community treatment using oral ivermectin [17] and topical permethrin [18] , [19] . However , sustainability has been difficult to achieve [2] , [12] , particularly where there is high mobility between communities and households . A high level of community participation is critical to the success of community-based programs such as this . To maximise treatment uptake during both mass distribution and routine screening , the treatment must be acceptable and feasible in the setting to which it will be applied [20] . A skin health program incorporating mass annual distribution of permethrin cream , routine scabies screening and treatment at the clinic and in homes , has been operating in the remote Aboriginal communities of the East Arnhem region of the Northern Territory since 2004 . Over this time , scabies prevalence amongst children in the region remained unchanged at 13% [21] . Due to the ongoing scabies burden in these communities we aimed to assess levels of treatment uptake among households in which one or more members was seen with scabies during routine screening . We also sought to investigate acquisition of scabies among household members during the month after the initial visit .
The study was conducted in two Aboriginal communities participating in the East Arnhem Regional Healthy Skin Program in remote northern Australia . A review of presentations to the community health clinics in 2006 revealed that 63% of infants had been diagnosed with scabies and 69% with skin sores in the first year of life [3] . The skin program included annual mass community treatment for scabies ( a designated “Healthy Skin Day” ) , combined with ongoing screening , treatment and follow-up of children aged <15 years in each community . On Healthy Skin Day , all individuals were encouraged to apply topical permethrin 5% ( Lyclear ) , which was distributed directly to all households in the community . Residents were advised to apply the cream all over the body and leave it on overnight or for a period of at least 8 hours . Healthy Skin Day was held within the month prior to the commencement of data collection for this study . The East Arnhem program included routine skin assessments of children under 15 years throughout the year , which aimed to identify and treat reinfestation arising after the mass community treatment . These were conducted at the community clinic , during school screening programs and on home visits . Where a child with scabies was identified , permethrin cream was provided for all household members . Locally-relevant resources were produced and used by local workers to teach people about scabies and skin health during the assessments . The program team included doctors , nurses , dermatologists , Aboriginal health workers and locally employed community workers . A Darwin-based team visited regularly to support the local community workers . The program had been operating in these communities for 2 . 5 years when this nested study commenced . The nested study population was households where one or more children had been diagnosed with scabies during routine home visits . Data were collected between December 2006 and June 2007 . Informed written consent was given by all participating households . This study was approved by the Human Research Ethics Committee of the Northern Territory Department of Health and Menzies School of Health Research ( Approval number 04/11 ) . A member of the Darwin-based team visited each community every two weeks during the 6-month study period to assist the community workers in recruitment and follow-up of households . Children with scabies were identified during home visits for routine skin screening undertaken as part of the existing skin health program ( Day 0 ) . The first child from a given household seen with scabies became the index child for that household . In line with normal skin program practice , the mother/carer of this child was provided with permethrin cream for all household members , together with standardised verbal instructions for cream use . Local guidelines for scabies treatment indicate that all household members should be treated , regardless of individual scabies status . Household members included the index case and all individuals considered by the index child's mother/carer to be living in the house at that time , as occurs during normal skin health program procedures . All household members other than the index case are referred to as the index cases' household contacts . Instructions were given for the index child and all other household members to use the cream that night . As noted , provision of treatment was according to normal skin health program procedures , and was not dependent on participation in this study . The date of birth , sex , scabies and skin sore status of each household member was recorded . Household members with skin sores were referred to the clinic for treatment . Recommendations regarding environmental measures to eradicate scabies from the household were also provided , which included washing all clothing and linen . Participation in these activities was not necessary for an individual or household to be considered to have participated fully in treatment . Evidence indicates that the role of fomites in transmission of scabies is minimal [22] , [23] . However we consider these environmental recommendations to be positive public health messages , particularly in a highly overcrowded setting such as this . On Day 1 we revisited the home and asked the mother/carer which household members had used the treatment . Treatment uptake was defined at the individual and household level . For an individual , we accepted either self-report or report from the primary carer that the household member had used the cream . Complete household treatment uptake occurred if all household members were reported to have met the definition for individual treatment uptake . If household treatment participation was incomplete , the mother/carer was asked if she could say why household members had not used the cream . Subsequent home visits were undertaken on Days 14 and 28 to screen household members for scabies and skin sores . Additional permethrin cream was provided as required at these visits . Utilisation of cream provided after Day 1 was not assessed . Scabies was diagnosed clinically by specifically-trained staff , using accepted criteria and based on the nature and distribution of characteristic scabies lesions [24] , [25] . This is the standard and widely accepted approach to scabies diagnosis in an endemic setting such as this [17] , [26] , [27] . Skin sores were also diagnosed clinically , based on the presence of any crusted , purulent or dry sores . Where a household member was not present during the home visit , we asked the primary carer in the household to report whether the individual had scabies and/or sores . Findings are described according to whether a trained Healthy Skin Worker ( HSW ) screened the individual or if skin status relied on family member report . When measuring scabies acquisition ( defined as an individual who was scabies-free at Day 0 and had a clinical diagnosis of scabies within 28 days ) we classified incident cases as either confirmed or probable . “Confirmed” scabies acquisition required an individual to be seen by a HSW both at baseline ( with a clinical diagnosis as scabies-free ) and again at a follow-up ( clinical diagnosis of scabies ) Report of scabies acquisition by a family member was considered “probable” . These were individuals who were: a ) clinically diagnosed as scabies-free at baseline by a HSW and then reported to have scabies by a family member at the follow-up visit or; b ) were not seen by a HSW at baseline but were reported to be scabies-free by a family member and were subsequently diagnosed as scabies by a HSW at follow-up or were reported to have scabies by a family member at the follow-up visit . Data were analysed using Intercooled Stata 9 ( Stata Corporation , College Station , Texas , USA ) . Comparisons of continuous variables across two levels of a binary variable were conducted using the Mann Whitney U test . For comparison of proportions between two binary variables , relative risks and 95% confidence intervals ( CI ) were calculated . Chi square test or Fisher's Exact test were used to test the significance of associations where appropriate . To assess the relative contribution of independent ( explanatory ) variables to individual treatment uptake , logistic regression analysis was implemented using the method of marginal models estimated using Generalised Estimating Equations ( GEE ) with information sandwich estimates of variance . GEE was used due to the potential for clustering among households . An exchangeable correlational structure was used . These analyses were performed in Stata using the xtgee command . In exploring differences between susceptibles who did and did not acquire scabies over the one-month follow-up period , GEE was also employed given the potential for clustering by household . Since subgroups contained small numbers of individuals , there was insufficient power to test all explanatory variables by multivariate GEE model . Therefore , simple ( univariate ) GEE was performed for each independent variable of interest . This provided a measure of the significance of the association between acquiring scabies and each independent variable while controlling for the potential influence of household clustering .
Forty households participated , involving 596 individuals ( 40 index children , 556 household contacts ) . Median household size was 15 . 5 persons ( IQR 12 , 20 ) . At baseline ( Day 0 ) , a median of 23 . 6% ( IQR 11 . 0 , 44 . 7 ) of individuals in each household had scabies as determined by either HSW screening or family member report ( Table 1 ) . All households in which a child with scabies was identified during normal skin program activities agreed to participate in the study .
Our study is the first to investigate levels and determinants of treatment uptake in a scabies endemic setting . Treatment uptake among index children was over 70% , suggesting that it is possible to achieve high levels of individual treatment use . However , treatment uptake among the household contacts of these children was poor and there were very few households in which all members were reported to have used the treatment . We are likely to have achieved a higher level of uptake in this study sample than would otherwise have occurred , given that householders were aware we would return the following day . Routine Healthy Skin program procedures do not include this home visit the day after treatment provision . In addition , self-reported treatment uptake is likely to be subject to reporting bias , which may also have resulted in an overestimation of treatment uptake . Therefore , even under circumstances of increased motivation to use the treatment and potential over-reporting of uptake , observed rates of treatment use were still very poor amongst household contacts . This low participation in treatment has important implications for the likelihood that sustained reductions in scabies burden can be achieved with the community-based model employed here . Our findings support current recommendations for universal treatment of close contacts where scabies is present , with the odds of scabies acquisition being greatest among young children and individuals in households with incomplete treatment uptake . Even in an endemic setting characterised by high mobility between households , universal treatment among family members in households where scabies was present significantly reduced the likelihood of acquisition among susceptible individuals . This is critical to protect young children , who are most at risk of infection . However , we have also demonstrated that the current approaches to achieving universal treatment are neither feasible nor effective in this setting . Although there was an established community-based control program in the two communities in which this study was conducted , we observed a very high level of secondary transmission . While the risk of secondary transmission remained unacceptably high in both communities , it was five times higher in Community A than Community B . The former had both a comparatively higher household burden of scabies and lower levels of treatment uptake amongst household contacts , which helps to explain the differential . Overall , reported treatment uptake was poorest in settings with a high scabies burden . This was observed at both the community level and at the household level . As noted , Community A had a higher median household scabies burden compared to Community B , and significantly less treatment uptake . In addition , irrespective of community of residence , an individual from a household with a high scabies burden was less likely to use the treatment than an individual from a household with a low scabies burden . The depressing reality for many households in these communities may well be that scabies has become part of life . At the individual level , treatment failure , or failure of others to adequately treat , creates an ongoing cycle of scabies transmission within both the household and the community . This may well contribute to the likelihood that those households with high scabies burden become increasingly less likely to treat . A number of community-based initiatives have documented a sustained reduction in scabies burden in endemic settings using topical permethrin . In Panama , directly observed permethrin treatment of all inhabitants of an island community , together with ongoing surveillance and treatment of new cases , resulted in a significant reduction in scabies prevalence [18] . Similar results have previously been reported in Australian Aboriginal communities without the requirement of directly observed treatment [12] , [13] . That the achievements of previous programs have not been observed here may reflect community-specific factors , such as social or environmental characteristics , which may not be generalisable to other communities or regions . Indeed , even in the current study we identified marked differences in treatment participation and household scabies burden between the two participating communities . Furthermore , given the low levels of treatment participation observed here , it seems unlikely that complete participation would have been achieved during the mass community treatment event that took place in the weeks prior to our study . If a substantial and rapid reduction in community scabies prevalence could be achieved through complete participation in mass treatment , the subsequent management of new cases at the household level may be a more feasible and effective approach . However , the high level of movement within and between households and communities would still present significant challenges to maintaining a low prevalence of scabies . The difficulty in achieving a sustained reduction in scabies burden in these settings has previously been recognised [2] , [12] , [18] . In Australia and many other countries , topical preparations such as permethrin cream are the only approved treatment for community management of scabies . The practicality of topical treatment for the community management of endemic scabies has been questioned [9] , [25] . Indeed , several characteristics of the treatment and setting give reason to doubt that a sustained reduction in scabies prevalence can be achieved with this approach . Environmental factors make total-body topical treatment impractical . These factors include the large number of people in each house , high heat and humidity , limited opportunities for privacy to apply the cream [1] , and poor infrastructure for washing it off [28] . When complete treatment uptake does occur , there is a strong likelihood of rapid reinfestation due to the high prevalence of scabies , overcrowding and frequent movement between households and communities . A realistic consequence of this is low motivation to repeat the treatment process . The likelihood of individual participation in treatment for the community management of endemic parasitic infection has been linked to expected personal benefit and expected personal cost [29] . It seems likely that the low level of uptake observed here , particularly where there is a high burden of scabies , is symptomatic of low motivation to participate in a treatment regime that is onerous ( high time and inconvenience ) and has been seen to have limited effectiveness ( low personal benefit ) . This is supported by the reasons cited for non-participation in treatment , the most common of which was that using the treatment wasn't a priority . Many also reported the inconvenience and unpleasantness of the treatment to be a key barrier to use . Of additional concern is the potential for the development of drug resistance [30] , [31] when such long-running community disease control programs achieve only limited participation and disease reduction . Concerns regarding mite resistance to permethrin have recently been described in a number of Aboriginal communities in northern Australia [32] , [33] . Thus it is possible that even if greater levels of treatment participation could be achieved , resistance to this treatment may undermine any potential impact on disease burden . These findings demonstrate an urgent need for a more suitable treatment for scabies to reduce the burden in endemic settings . Oral ivermectin has demonstrated success in the community management of endemic scabies [17] , [26] , [27] . For example , in the Solomon Islands , mass ivermectin administration to all residents of a small number of islands , combined with treatment of any individuals subsequently entering or returning to the communities , reduced scabies prevalence from 25% to <5% after four months , and was subsequently sustained at <1% [17] . Ivermectin is also widely used in community control of other parasitic infestations . Since 1987 , it has been used extensively in community-based mass treatment programs in Africa and Latin America to control endemic onchocerciasis [34] . The Global Programme to Eliminate Lymphatic Filariasis recently reported that between 2000 and 2007 , 149 million treatments of ivermectin had been given through community-based mass drug administration in 12 African countries [35] . Ivermectin is also effective against other parasitic infestations that can occur in high-scabies burden settings , such as strongyloidiasis [26] , which is endemic in many Australian Aboriginal communities [36] . Ivermectin is not currently approved for the mass community management of scabies in Australia . Notwithstanding , a growing body of literature indicates it is safe and effective when used in mass drug treatment programs and is ideally suited for use in the community as it is a single oral dose that is easily administered . In 2003 , it was estimated that 6 million people worldwide had taken ivermectin for various parasitic infestations with no serious drug-related adverse events reported [37] . Contributing to the safety profile is the accumulation of non-event data among pregnant women [38]–[40] . While ivermectin presents a viable alternative for the management of scabies , especially where compliance with topical treatment is improbable or impractical [34] , [37] , the provision of a more practical treatment alone is unlikely to completely resolve the low treatment participation seen here . Many households cited reasons for non-participation that may not be readily resolved simply with a more practical treatment . For example , there is an enduring perception that individuals without scabies do not need to be treated . Factors that have been identified as important for treatment participation and program success in mass ivermectin administration include strong community education and awareness-raising , engaging well-trained health workers who are trusted and respected by the community , and community involvement and ownership in the program [41]–[43] . It will be critical to consider such factors in the implementation of any initiative to reduce the burden of scabies in these communities , and to work together with communities to better understand household and individual barriers to program participation . Our study has several limitations that are inherent to research in this setting . With large and transient family groups , the concept of a household unit is tenuous and follow-up of individuals is compromised . While complicating data collection , this also further highlights the challenge of infectious disease management at the household level . In our study , almost half of the susceptible individuals were lost to follow up . We must therefore consider the possibility that those individuals who were followed up represent a biased sample of the population at risk . Those who were lost to follow up were significantly less likely to belong to a household in which all members used the treatment , and less likely to use the treatment themselves . This suggests that we have missed a segment of the susceptible population that may indeed be at a higher risk of acquiring scabies , and we may therefore have underestimated acquisition . Where a household member was unwilling or unavailable to participate in skin screening , a family member reported scabies status for that individual . Ideally , all household members would have been available for skin screening . The presence of itching and visible skin lesions in a household where scabies is known to be present has been reported to be highly specific [11] . Notwithstanding , here we have distinguished between scabies cases diagnosed by a trained worker ( confirmed case ) and those reported by a family member ( probable case ) in order to mitigate any potential impact of this on the validity of our data . There is limited evidence for the effective , long-term management of scabies in high prevalence areas [44] . Achieving a significant reduction in infectious disease burden in an endemic setting requires a high level of treatment coverage among those exposed to the disease . Here we have demonstrated a lower likelihood of scabies acquisition when all close contacts participate in treatment . This emphasises the importance of all close contacts of scabies cases being treated , whether symptomatic or not . This is recommended in all settings where scabies is present . However , achieving this high level of participation in treatment can pose a considerable challenge . Our findings indicate that the long-term management of scabies with topical permethrin has not been effective , as evidenced by low rates of treatment uptake and ongoing transmission . These results support the notion that , in controlling infectious diseases such as scabies at the community level , it is critical that the treatment be appropriate , acceptable and feasible in the setting to which it will be applied . It is also fundamental to recognise that the eradication of scabies and other infectious diseases in these settings cannot be achieved by treatment alone . To realise a significant and sustained reduction in disease burden requires that the underlying environmental and social conditions that promote such poor health be addressed .
|
Like many impoverished areas around the world , Aboriginal communities in Australia experience an unacceptably high burden of scabies , skin infections , and secondary complications . Young children are most at risk . Our study investigated scabies in a remote setting with very high rates of skin disease , a high level of household overcrowding , and limited infrastructure for sanitation and preventive health measures . We assessed uptake of scabies treatment and scabies acquisition following provision of treatment by a community-based skin program . In a household where scabies was present , we found that treatment with topical permethrin cream of all close contacts can significantly reduce a susceptible individual's risk of infection . Our findings also demonstrate the challenges of achieving a high level of treatment participation , with limited permethrin use observed among household contacts . This suggests an urgent need for a more practical treatment option . International efforts to reduce childhood morbidity and mortality have demonstrated the efficacy of numerous child health interventions but have also highlighted the deficits in their delivery and implementation . Experiences like this , where the effectiveness of a coordinated local program delivering an efficacious intervention is hampered by poor treatment uptake and ongoing transmission , are an important and timely message for researchers , program managers , and policy-makers .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"pediatrics",
"and",
"child",
"health",
"public",
"health",
"and",
"epidemiology/global",
"health",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"infectious",
"diseases/skin",
"infections"
] |
2009
|
Community Management of Endemic Scabies in Remote Aboriginal Communities of Northern Australia: Low Treatment Uptake and High Ongoing Acquisition
|
The AraC family transcription factor MarA activates ∼40 genes ( the marA/soxS/rob regulon ) of the Escherichia coli chromosome resulting in different levels of resistance to a wide array of antibiotics and to superoxides . Activation of marA/soxS/rob regulon promoters occurs in a well-defined order with respect to the level of MarA; however , the order of activation does not parallel the strength of MarA binding to promoter sequences . To understand this lack of correspondence , we developed a computational model of transcriptional activation in which a transcription factor either increases or decreases RNA polymerase binding , and either accelerates or retards post-binding events associated with transcription initiation . We used the model to analyze data characterizing MarA regulation of promoter activity . The model clearly explains the lack of correspondence between the order of activation and the MarA-DNA affinity and indicates that the order of activation can only be predicted using information about the strength of the full MarA-polymerase-DNA interaction . The analysis further suggests that MarA can activate without increasing polymerase binding and that activation can even involve a decrease in polymerase binding , which is opposite to the textbook model of activation by recruitment . These findings are consistent with published chromatin immunoprecipitation assays of interactions between polymerase and the E . coli chromosome . We find that activation involving decreased polymerase binding yields lower latency in gene regulation and therefore might confer a competitive advantage to cells . Our model yields insights into requirements for predicting the order of activation of a regulon and enables us to suggest that activation might involve a decrease in polymerase binding which we expect to be an important theme of gene regulation in E . coli and beyond .
Transcription factors control cellular protein production by binding to DNA and changing the frequency with which mRNA transcripts are produced . There are hundreds of transcription factors in Escherichia coli and while most of these target only a small number of genes , there are several that regulate expression of ten or more genes . Taken together , such global transcription factors directly regulate more-than half of the ∼4 , 300 genes in E . coli and their regulatory interactions yield important insights into the organization of the genetic regulatory network [1] , [2] , [3] . Because they regulate so many genes , global transcription factors also play a large role in controlling cellular behavior; however , insights into behavior are currently limited by a lack of quantitative information about how transcription factors differentially regulate target genes . One important global transcription factor is MarA , an AraC family protein that activates ∼40 genes ( the marA/soxS/rob regulon ) of the Escherichia coli chromosome resulting in different levels of resistance to a wide array of antibiotics and superoxides ( see [4] for references ) . The effect of MarA at different promoters can vary due to changes in the detailed sequence of the DNA-binding site and its distance from and orientation with respect to the promoter [5] , [6] . These variations can influence the order in which the promoters respond to increasing concentrations of MarA and presumably have important functional consequences for E . coli . To characterize quantitative variations in MarA regulation at different promoters , we recently placed the expression of MarA under the control of the LacI repressor , determined the relationship between isopropyl β-D-1-thiogalactopyranoside ( IPTG ) concentration and the intracellular concentration of MarA , and examined the expression of 10 promoters of the regulon as a function of activator concentration [7] . We found that activation of marA/soxS/rob regulon promoters occurs in a well-defined order with respect to the level of MarA , enabling cells to mount a response that is commensurate to the level of threat detected in the environment . We also found that only the marRAB , sodA , and micF promoters were saturated at the highest level of MarA . In contrast with a commonly held assumption , we found that the order of activation does not parallel the strength of MarA binding to promoter sequences . This finding suggested that interactions between MarA and the RNA polymerase transcriptional machinery play an important role in determining the order of activation , but the data did not immediately reveal what the nature of these interactions might be at the various promoters . Here , we have developed a computational model of promoter activity to understand how interactions between MarA and polymerase activate transcription at the marRAB , sodA , and micF promoters – of the 10 we examined previously , these three promoters are the only ones that exhibited saturation , which provides an important constraint for the modeling . The model was specifically designed to compare a strict recruitment model in which MarA increases polymerase binding but does not increase the rate of post-binding events [8] , [9] , with a more general model in which activator can either increase or decrease polymerase binding , and can either increase or decrease the rate of post-binding events . For each promoter , we evaluated the agreement of both the strict recruitment model and the general model with the data at many points within a physically reasonable region of parameter space . The model successfully explains why the order of promoter activation does not parallel the strength of MarA-DNA binding . For all promoters , the best fit of the general model was better than that of the strict recruitment model . Comparison to the strict recruitment model and full analysis of the goodness-of-fit landscape suggest that MarA does not increase polymerase binding but does increase the rate of post-binding events at these promoters . Moreover , the analysis for the micF promoter suggests that MarA activation can involve a decrease in polymerase binding that is associated with low latency in gene regulation . We discuss the broader significance of these findings .
Our model choice was tailored to the in vivo activity data for the marRAB , sodA , and micF promoters; these data were obtained from batch cultures that were periodically diluted to maintain logarithmic growth [7] . The activity assays were performed after many generations and represent quasi-steady-state levels that are well-matched to a steady-state model of promoter activity . We therefore based our model on a statistical-thermodynamic model that was originally developed to study steady-state transcriptional repression by λ phage repressor [10] . In our model , the promoter exists in a number of distinct states , each of which has a corresponding free energy and activity . The statistical weight of each state in a batch culture ensemble of promoters is given by Boltzmann factors that correspond to thermal equilibrium , and the total promoter activity is calculated as the weighted sum of the individual promoter state activities . Our model considers four promoter states enumerated as follows ( Fig . 1 ) . In State 0 , the promoter is free . This is the reference state with energy and no activity . In State A , MarA is bound at the operator sequence OA , yielding free energy and no activity; in State R , polymerase is bound at the promoter P , yielding free energy and activity aR; and in State X , both MarA and polymerase are bound , yielding free energy , and activity aX . The term er is a recruitment energy that captures the interaction between MarA and polymerase on the DNA: a value er = 0 indicates no influence of MarA on the affinity of polymerase , a value er<0 indicates that MarA increases the affinity of polymerase , and a value er>0 indicates that MarA decreases the affinity of polymerase for the promoter . Unlike a strict recruitment model [11] , to enable us to evaluate the likelihood of alternative mechanisms , our model allows for different activities in the presence or absence of MarA , and even allows for the possibility that the promoter activity might be smaller in the presence of MarA . The free energies of the states with either MarA ( ) or polymerase ( ) bound are defined for 1 M concentrations of free MarA and polymerase , respectively . These free energies are related to corresponding dissociation constants via and where the dissociation constants KA and KR are in molar units . The dissociation constants in turn determine the statistical state weights pi via the following equations: ( 1 ) In Eqs . ( 1 ) , the first three equations follow from the definition of the dissociation constants and free energies , and the last equation follows from the normalization condition . A novel feature of our model is that it considers the interaction between free MarA and polymerase away from the promoter . This interaction is known to be significant from in vitro experimental binding studies [12] , [13]; Heyduk et al . [14] found a similar interaction between CRP and polymerase . The equilibrium between free MarA ( A ) and polymerase ( R ) and the MarA-polymerase complex ( ) is modeled assuming steady-state equilibration characterized by dissociation constant KAR: ( 2 ) To account for other interactions such as nonspecific binding of polymerase to DNA , we also let polymerase be sequestered by a background pool of nonspecific binding partners ( B ) with dissociation constant KBR: ( 3 ) We assume that interactions with the promoter do not significantly influence the equilibrium . This is a reasonable assumption given that the chromosomal lacZ reporter fusions used in Martin et al [7] have a copy number of at most 5 per cell . The model leads to the following equations ( 4 ) where , , and are the total levels of polymerase , MarA , and the background pool in the cell , respectively . Eqs . ( 4 ) yield a cubic equation for with a positive real root ( Text S1 ) . The equation then follows from the first and fourth equations in Eqs . ( 4 ) . Finally , the expressions for [R] and [A] may be used to calculate the state weights in Eqs . ( 1 ) given values of , , and . The total promoter activity is a weighted sum of the activities in each state . No transcription occurs in states 0 or A , in which polymerase is absent from the promoter . Transcription occurs in state R with activity aR , and in state X with activity γaR; polymerase is present at the promoter in both of these states . The equation for the total activity a is therefore ( 5 ) Eq . ( 5 ) represents the general promoter activity model; in the strict recruitment limit , the value of γ is equal to 1 indicating that polymerase activity is the same in the presence vs . the absence of MarA . We assume that the total promoter activity a in Eq . ( 5 ) is proportional to the measured β-galactosidase activity resulting from in vivo lacZ reporter expression . We calibrated IPTG levels against MarA levels using analyses of Western blots in multiple lanes from a single gel [7] . Such calibration is rarely performed even in highly quantitative studies of gene regulation; however , here the calibration is the key to enabling the mechanistic insights that we sought in the modeling . The MarA vs . IPTG data are well-described using the equation ( 6 ) where [I] is the extracellular IPTG concentration , [A]T is the total cellular MarA concentration that appears in Eqs . ( 4 ) , = 20 molecules cell−1 , = 20 , 486 molecules cell−1 , KI = 18 . 98 µM , and h = 2 . 46 ( Figure S1 ) . Due to errors in quantifying small MarA levels , we were unable to obtain a good estimate of from the data; however , we believe that there is some expression of MarA from the plasmid in the absence of IPTG because the basal activity of the lacZ fusions is slightly higher in cells carrying the MarA plasmid than in cells carrying a control plasmid . The value = 20 molecules cell−1 is consistent with the 1 , 000-fold induction of the wild-type lac system and yields reasonable fits to the data . To account for differences between the plasmid expression system and the wild-type system , we tried values as high as = 200 molecules cell−1; however , such models agreed poorly with the promoter activity data . We therefore used = 20 for the modeling studies described below . The experimental data consist of measurements of β-galactosidase activity coupled with standard errors at defined concentrations of external IPTG ( Table S1 ) . To model the data for a given promoter , simulated activity profiles were obtained by calculating the activity at each IPTG concentration using Eqs . ( 1 ) , ( 4 ) , and ( 5 ) . Values of KAR , KBR , KA , KR , er , [B]T and [R]T were sampled from allowed ranges defined with guidance both from the literature and by our measurements ( Methods ) , and values of [A]T for each IPTG level were obtained using Eq . ( 6 ) which was constrained by the calibration . Values of pR and pX were then calculated using Eqs . ( 1 ) and ( 4 ) . The weights pR and pX determine the activity values through Eq . ( 5 ) , which includes additional parameters aR and γ . The values of these parameters may vary among promoters . To simulate promoter activity for a strict recruitment model , the value of γ was set to 1 , and linear regression was used to find the value of aR in Eq . ( 5 ) that minimized a standard χ2 goodness-of-fit statistic calculated between the simulated and measured activity values . To simulate promoter activity for the more general model of activation , we performed a linear regression to simultaneously find the best-fit values of aR and γ . To further sample the fitting landscape , we then randomly sampled five values of γ that differed from the optimum by up to a factor of 100 , finding the best-fit value of aR in each case . At this point it would be typical to seek the combination of parameter values that minimize the value of χ2 and draw some conclusions based on the resulting best-fit model . However , we were concerned about the possibility that many combinations of parameter values might yield reasonable ( if not optimal ) fits to the data and therefore adopted a more rigorous modeling approach . We note that this concern did not come from comparing the number of data points to the number of parameters: the model has 9 parameters , whereas we made multiple measurements of each promoter's activity at 10 or more different IPTG levels ( Table S1 ) . This is adequate to constrain a fit . Rather , our concern was that all of the measured activation profiles have a similar S shape that might be described using ∼4 parameters ( minimal activity; maximal activity; IPTG level at the midpoint; and slope in the regulatable region ) , suggesting that our 9-parameter model might reasonably fit the data for a wide range of parameter values . Instead of drawing conclusions based on the properties of a single best-fit model , we therefore sought more robust results by adopting a Bayesian approach ( Methods ) . In our approach , we began by defining a range of physically reasonable parameter values for KAR , KBR , KA , KR , KX , [B]T , and [R]T , and randomly sampled a large number ( 10 , 000 or more ) of combinations of parameter values from within the allowed range ( Methods; Table 1 ) . For each such combination , as described above , we explored values of γ and aR using either a strict recruitment model or a more general promoter activity model . ( In practice , we found that a certain fraction of the parameter value combinations samples yielded unphysical models in which activation required a negative value of γ; these samples were removed in the analysis . ) We treated the resulting χ2 as an indicator for the quality of a model and used it to define a goodness-of-fit landscape in parameter space . Sampling the landscape in this way permitted us to identify entire regions of parameter space that correspond to reasonable models , and to further determine whether models within the identified region share common mechanisms of activation . This approach therefore enables a much more robust suggestion of activation mechanisms than would conclusions drawn by examining the properties of a single best-fit model . The best-fit activity profiles for the models of each promoter are illustrated in Fig . 2; the parameters of these models are listed in Table 2 . The quality of the fits indicates that the general activation model is entirely consistent with the observed IPTG-dependent activity of the marRAB , sodA and micF promoters: the χ2 values of these fits are 9 . 15 , 6 . 72 , and 2 . 49 , respectively . The strict recruitment model yielded larger χ2 values of 14 . 43 , 11 . 33 , and 622 . 3 , respectively . Overall , the general activation model was more consistent with the promoter activity data; in particular , the strict recruitment model was inconsistent with the micF data whereas the general model was consistent with these data . Table 2 also includes asymmetric errors ( Methods ) that indicate the degree to which parameter values are constrained by the data . These errors indicate that parameter values of the micF model are well-constrained compared to parameter values for the marRAB and sodA models . The magnitude of these errors suggests that analysis of just the best-fit model would not yield robust conclusions concerning mechanisms of activation: for example , the best-fit value of er for the marRAB model is −0 . 44 kBT , but the span of the error includes positive values of er . In the following sections , rather than relying on analysis of the best-fit model , we use analysis of the full fitting landscape to suggest mechanisms of activation of these promoters . To determine whether polymerase activity increases or decreases when MarA is bound to the promoter , we analyzed the parameter γ , which is equal to the ratio of polymerase activity in the presence vs . the absence of activator ( Eq . ( 5 ) ) . It is convenient to perform the analysis using the acceleration energy , ea , defined as ( 7 ) The acceleration energy defined in Eq . ( 7 ) is equivalent to the activator-induced change in the activation energy of a lumped transcription initiation process , under the assumption that initiation follows an Arrhenius law with the same attack frequency in the presence or absence of activator . A value ea = 0 corresponds to an unchanged polymerase activity; this condition is consistent with a strict recruitment model of transcriptional activation , in which activator increases the occupancy of polymerase at the promoter but does not alter polymerase activity [8] , [9] . Models with ea<0 exhibit acceleration and models with ea>0 exhibit retardation of polymerase activity in the presence of activator . For each promoter , the model with the lowest χ2 value has a negative acceleration energy ( Table 2 ) . Scatter plots of χ2 vs . ea for parameter samples indicate that other models with low χ2 values also tend to have negative acceleration energies ( Fig . 3 , left panels ) . To quantify this trend , we used Bayesian methods to estimate cumulative distribution functions C ( ea ) for the posterior probability of ea values ( Methods ) . ( It is important to keep in mind that these distributions do not indicate absolute probabilities as their calculation entails certain assumptions about the likelihood function and the prior distribution of parameter values ( Methods ) ; nevertheless , given these assumptions , the distributions provide a valuable means of interpreting the modeling results . ) The distributions indicate that nearly all of the density lies within the region ea<1 ( Fig . 4A ) : the value of the distribution function at ea = 0 is essentially 1 for the marRAB and micF models , and is 0 . 99 for the sodA model . The modeling therefore suggests that activator increases polymerase activity at the marRAB , sodA , and micF promoters . To determine whether the affinity of polymerase for the promoter changes in the presence vs . the absence of MarA , we analyzed the recruitment energy er . As mentioned above , a value er = 0 indicates no influence of MarA on the affinity of polymerase , a value er<0 indicates that MarA increases the affinity of polymerase , and a value er>0 indicates that MarA decreases the affinity of polymerase for the promoter . For marRAB , the model with the lowest χ2 has er = −0 . 44 kBT ( Table 2 ) . A scatter plot indicates that other models with low χ2 tend to have negative values of er ( Fig . 3A , right panel ) . The cumulative distribution function C ( er ) also shows that most of the probability density corresponds to negative values of er ( Fig . 4B ) : the value is 0 . 978 at er = 0 . The modeling therefore suggests that MarA activation of marRAB involves an increase in the affinity of polymerase at the promoter . It is important to note that an increase in affinity of polymerase for the promoter does not always translate into a significant increase in occupancy . For example , if polymerase is already bound with essentially unit occupancy in the absence of activator , even a large increase in affinity will result in an insignificant increase in occupancy . We therefore analyzed and compared the total occupancy of polymerase at the promoter , ( 8 ) at low ( ) and high ( ) levels of MarA . For marRAB , the basal occupancy for the best-fit model is 0 . 995 , and the occupancy ratio is 1 . 00 . Scatter plots indicate that the fits are relatively insensitive to the precise value of ( Fig . 5A , left panel ) , but that the low-χ2 values of are more sharply centered on 1 . 00 ( Fig . 5A , right panel ) . The cumulative distributions quantify these trends: in , the cumulative probability increases slowly and steadily from about = 0 . 1 all the way to = 1 . 0 , and half of the probability density lies below = 0 . 93 ( Fig . 4C ) . In , there is little density below = 1 . 0 , the distribution increases sharply in the neighborhood of = 1 . 0 , and 79% of the density lies below = 1 . 05 ( Fig . 4D ) . Overall , the model does not strongly indicate whether polymerase is bound or unbound at the promoter in the absence of MarA but it does weakly suggest that MarA does not increase the occupancy of polymerase at the promoter . For both sodA and micF , the models with the lowest χ2 have er>0 ( Table 2 ) . The scatter plot for sodA indicates that other low χ2 models also tend to have positive values of er ( Fig . 3B , right panel ) . In the case of micF , the scatter plot indicates that all models have positive er . This requires some explanation , as our sampling did produce a roughly equal number of models with positive and negative er . As mentioned above , some of the parameter value combinations were eliminated because they yielded unphysical models with a negative optimal value of γ . This is the reason for the different number of points that is apparent for different promoters in Figs . 3 and 5 . In the case of micF , the number of unphysical samples was especially high , and included all those with negative er . The cumulative distributions C ( er ) for these promoters support the trends seen in the scatter plots ( Fig . 4B ) . In the case of sodA , 88% of the density lies within er>0 . In the case of micF , all of the density lies within er>0 . Activation in this model therefore involves a decrease in the affinity of polymerase for both the sodA and micF promoters . Analysis of and suggest that binding of MarA decreases the occupancy of polymerase at both sodA and micF . In the case of sodA , the best-fit model is misleading , as = 0 . 996 and = 0 . 998 for this model ( Table 2 ) , suggesting no influence of activator on polymerase occupancy . However , scatter plots show that the value of is poorly constrained ( Fig . 5B , left panel ) , and that there are many low-χ2 models with <1 ( Fig . 5B , right panel ) . These observations are supported by the cumulative distributions and : there is a relatively steady increase in between 0 . 03 and 1 ( Fig . 4C ) , and 89% of the distribution lies within <1 , with 70% below 0 . 95 ( Fig . 4D ) . In the case of micF the results are more clear-cut: all physically reasonable models have and ( Figs . 4C , 4D , and 5C ) . In addition to the nominal parameter variations in Table 1 , we examined the sensitivity of the results to wider parameter variation ( Methods ) . The variations explored were: changing the value of [R]T to 1 , 000 copies per cell; changing the value of KAR to 0 . 3 , 1 , 10 , and 100 µM; and , instead of fixing the value of KA for each promoter , sampling this parameter randomly between 0 . 25–2 , 000 nM . We also repeated the entire analysis , including these variations , using [B]T = 0 which eliminates the sequestering of free polymerase by other interaction partners . Thus , the above sampling and analysis approach was applied 13 additional times for each of the promoters . For the general model , all of these variations still yielded at least some promoter activation curves with reasonable values of χ2 . With the exception of one variation , the general model yielded significantly better fits than the strict recruitment model for all promoters . The only exception was KAR = 100 µM , which yielded best-fit strict recruitment models of marRAB ( χ2 = 9 . 67 ) and sodA ( χ2 = 8 . 02 ) that were similar in quality to the general model; however , this was not so for micF ( χ2 = 536 ) , and this value of KAR is at least a factor of five higher than the values measured in vitro [12] , [13] . Using [B]T = 0 yielded only poor fits in the strict recruitment limit ( e . g . , χ2 values of 542 , 63 . 8 , and 1071 for the best-fit models of marRAB , sodA , and micF using otherwise nominal parameter values from Table 1 ) , and favored models of sodA in which MarA does not change polymerase occupancy . Given the results obtained for the MarA activation data , we wondered whether our model would yield expected results when applied to a transcription factor that is known to increase the occupancy of polymerase at target promoters . We therefore further validated the model by analyzing published data on transcriptional activation of the lac operon by cAMP-CRP [15] . The cAMP-CRP-dependent relative promoter activity was represented using the equation , where x is the concentration of the active CRP dimer in nM; this expression is consistent with the data published in Bintu et al . [15] . We used a strict recruitment model with γ = 1 , KA = 5 nM , KAR = 0 . 3 µM [14] , and parameters otherwise the same as the nominal values in Table 1 . Consistent with expectations [9] , we found that the recruitment model was entirely consistent with the CRP-dependent lac promoter activity ( Figure S2 ) . Thus , our modeling method is able to distinguish situations where recruitment applies ( e . g . , lac ) from those described here where it does not apply .
The major conclusion of our study is that transcriptional activation can involve a decrease in polymerase binding at the promoter . Our model specifically predicts this is the case for MarA activation of sodA and micF . The model also predicts that MarA does not increase the occupancy of polymerase at the marRAB promoter . For all of these promoters , the model predicts that activation occurs largely through an increase in polymerase activity when MarA is bound . These predictions are consistent with two genome-wide studies of polymerase interactions with the E . coli chromosome [16] , [17] . Grainger and coworkers [16] reported detection of polymerase at the sodA promoter but not the marRAB promoter; that study did not consider interactions at the micF promoter which controls expression of an antisense mRNA transcript . In addition , we cross-referenced the oligonucleotide coordinates of Herring et al . [17] to transcriptional start sites annotated in the EcoCyc database [18] , and found strong-binding 50 bp DNA sequences correctly positioned with respect to sodA ( sequence beginning at 4 , 098 , 720 upstream of 4 , 098 , 780 start ) and micF ( sequence beginning at 2 , 311 , 050 upstream of 2 , 311 , 106 start ) , but not marRAB ( only weakly binding sequences near 1 , 617 , 117 start ) . The presence of polymerase at sodA and micF in uninduced cells strongly suggests that an increase in the affinity of polymerase is not needed to activate these promoters which is consistent with the mechanisms of MarA activation suggested here . Although the possibility of activation involving decreased polymerase binding might at first seem surprising , a decrease in polymerase binding should really be seen as a natural consequence of accelerated polymerase kinetics . Using a Michaelis-Menten equation to describe transcription initiation with a lumped forward rate kf , the value of KM = ( koff+kf ) /kon increases when kf increases and the polymerase binding and dissociation rates kon and koff remain constant . Thus , the apparent affinity of polymerase for the promoter decreases when the forward rate of the reaction increases . Along these lines , it is also important to note a second potentially counterintuitive possibility: activation might involve retardation of polymerase kinetics when accompanied by a sufficiently large increase in polymerase binding . This is also a natural association because an attraction between activator and polymerase at the promoter has the potential to hinder clearance . Mechanisms of transcriptional activation have long been an important subject of research and debate . The textbook mechanism for activation of σ70 promoters is recruitment [8] , [9] , [19] , in which activator merely increases the binding of RNA polymerase at the promoter [20] , and the classic example is activation of the lac operon by cAMP-CRP [8] . The simplicity of the recruitment model is appealing; however , it has long been known that transcriptional activation by λ phage repressor can occur through acceleration of post-binding events leading to promoter clearance [21] , [22] , [23] , and that it is even possible for an activator to directly stimulate polymerase transcription without binding to DNA [24] . In addition , σ54-polymerase binds at promoters and is activated by enhancers that utilize nucleotides to melt DNA , leading to open complex formation [25] . This use of enhancers in activation of σ54-polymerase is reminiscent of activation of stalled polymerase in eukaryotes [26]; the similarity is limited , however , since polymerase stalling in eukaryotes occurs after transcription has already begun [27] . Aspects of the interplay between the energetics of binding and post-binding events and how they govern regulation of transcription have been examined previously [28] . In this regard , the main novel outcome of our work is not the finding that mechanisms other than increasing polymerase binding might be important for transcriptional activation , but rather the suggestion that activation might involve a decrease in polymerase binding . Another important outcome of our work is a model that explains why the strength of MarA binding to promoters does not parallel the order in which promoters are activated with increasing MarA . A critical feature of our model in this respect is explicit consideration of polymerase interactions with MarA and the promoter . Because of these interactions , the shape of the activation profile is not merely governed by KA , the MarA-DNA dissociation constant , but is strongly influenced by er which characterizes the interaction between DNA and the MarA-polymerase complex ( Eqs . ( 1 ) ) . The model therefore quite generally indicates that the strength of activator binding is not expected to parallel the order of activation . This finding not only runs counter to common assumptions in modeling of gene regulation , but also has important implications for prediction of regulon behavior , i . e . , one cannot expect to predict the order of regulon activation in vivo by measuring the affinity of activator for promoter DNA sequences in vitro . By contrast , we expect interactions with polymerase to be less important when a repressor decreases expression by interfering with polymerase binding at the promoter . Such interference corresponds to very large values of er in our model which increases the importance of KA in determining the promoter activity profile ( Eqs . ( 1 ) ) . As a consequence , we expect that it might be possible to exploit in vitro DNA-binding data to predict the order of repression ( or derepression ) of a regulon . It is important to note that our model was developed using data from marRAB- rob- strains [7] , in which the repressor MarR is absent . In wild-type E . coli , MarR not only blocks polymerase binding but also blocks MarA binding at marRAB [29] . We therefore do not expect polymerase to bind at the marRAB promoter in the absence of inducers that relieve MarR repression . On the other hand , in wild-type E . coli , we do expect polymerase to be bound at the sodA and micF promoters in the absence of inducers , as supported by the chromatin immunoprecipitation experiments cited above [16] , [17] . Rob is also missing in the marRAB- rob- strains . Rob is constitutively expressed [30] , [31] and might recruit polymerase to the sodA and micF promoters . However , in the absence of inducers , such as dipyridyl , which bind to Rob and stimulate activation of target promoters [32] , Rob is mostly sequestered in inclusion bodies [33] and cannot access the DNA [34] . Therefore evidence exists that polymerase binds at the sodA and micF promoters in wild-type cells without recruitment by Rob or MarA . Finally we note that activation involving a decrease in polymerase binding decreases latency in both activation and de-activation of gene expression . In the case of activation , as noted in the above argument assuming Michaelis-Menten reaction kinetics , the decrease in polymerase binding is associated with acceleration through an increase in the rate of transcription initiation . In the case of de-activation , a decrease in polymerase binding is associated with acceleration through an increase in the polymerase off rate . Decreases in gene regulation latency can confer a competitive advantage to E . coli in an ecological context [35] . We therefore expect activation involving a decrease in polymerase binding to be an important theme of gene regulation in E . coli and beyond .
We used a wide range of parameter values to model the MarA-dependent activity of the marRAB , sodA , and micF promoters ( Table 1 ) . These values were obtained as follows: The values of χ2 determined for different parameter value combinations represent samples in a fitting landscape . We used Bayesian methods to analyze the fitting landscape , assuming a likelihood function for a sample with parameter combination i . In using this likelihood function , we assume that the errors in measurements of mean promoter activity are independent and normally distributed with widths equal to the standard error of the mean ( error values in Table S1 ) . The probability Pi of the sample i given the data is estimated as ( 9 ) where the index j is summed over all samples . The cumulative distribution function C ( x ) for a parameter x is then given by ( 10 ) where xi is the value of parameter x in sample i , and the sum is restricted to samples i where xi<x . C ( x ) is interpreted as an estimate of the probability that the parameter has a value less than x , given all of the assumptions of the modeling , including the sampling scheme . To quantify the degree of uncertainty in estimated parameter values within the nominal range , we calculated asymmetric errors of parameter values with respect to the optimum ( Table 2 ) . The squared errors for parameter x were calculated using the equation ( 11 ) where xmin is the value of x in the sample with the lowest value of .
|
When environmental conditions change , cell survival can depend on sudden production of proteins that are normally in low demand . Protein production is controlled by transcription factors which bind to DNA near genes and either increase or decrease RNA production . Many puzzles remain concerning the ways transcription factors do this . Recently we collected data relating the intracellular level of a single transcription factor , MarA , to the increase in expression of several genes related to antibiotic and superoxide resistance in Escherichia coli . These data indicated that target genes are turned on in a well-defined order with respect to the level of MarA , enabling cells to mount a response that is commensurate to the level of threat detected in the environment . Here we develop a computational model to yield insight into how MarA turns on its target genes . The modeling suggests that MarA can increase the frequency with which a transcript is made while decreasing the overall presence of the transcription machinery at the start of a gene . This mechanism is opposite to the textbook model of transcriptional activation; nevertheless it enables cells to respond quickly to environmental challenges and is likely of general importance for gene regulation in E . coli and beyond .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biophysics/transcription",
"and",
"translation",
"molecular",
"biology/transcription",
"initiation",
"and",
"activation",
"computational",
"biology/transcriptional",
"regulation"
] |
2009
|
Model of Transcriptional Activation by MarA in Escherichia coli
|
The xanthophyll cycle is involved in dissipating excess light energy to protect the photosynthetic apparatus in a process commonly assessed from non-photochemical quenching ( NPQ ) of chlorophyll fluorescence . Here , it is shown that the xanthophyll cycle is modulated by the necrotrophic pathogen Sclerotinia sclerotiorum at the early stage of infection . Incubation of Sclerotinia led to a localized increase in NPQ even at low light intensity . Further studies showed that this abnormal change in NPQ was closely correlated with a decreased pH caused by Sclerotinia-secreted oxalate , which might decrease the ATP synthase activity and lead to a deepening of thylakoid lumen acidification under continuous illumination . Furthermore , suppression ( with dithiothreitol ) or a defect ( in the npq1-2 mutant ) of violaxanthin de-epoxidase ( VDE ) abolished the Sclerotinia-induced NPQ increase . HPLC analysis showed that the Sclerotinia-inoculated tissue accumulated substantial quantities of zeaxanthin at the expense of violaxanthin , with a corresponding decrease in neoxanthin content . Immunoassays revealed that the decrease in these xanthophyll precursors reduced de novo abscisic acid ( ABA ) biosynthesis and apparently weakened tissue defense responses , including ROS induction and callose deposition , resulting in enhanced plant susceptibility to Sclerotinia . We thus propose that Sclerotinia antagonizes ABA biosynthesis to suppress host defense by manipulating the xanthophyll cycle in early pathogenesis . These findings provide a model of how photoprotective metabolites integrate into the defense responses , and expand the current knowledge of early plant-Sclerotinia interactions at infection sites .
Chloroplasts are not only the factory for photosynthesis , but are also involved in various types of plant-pathogen interactions [1–3] . Indeed , the process of photosynthesis is functionally linked to plant immunity by providing energy , reducing equivalents and carbon skeletons [4–9] as well as producing oxidants and oxidant-derived hormonal messengers with roles in defense responses [10–11] . Light energy absorbed by the harvesting antenna complexes is transferred to reaction centers to drive photochemistry . However , when the rate of excitation energy exceeds the capacity for light utilization , excited-state chlorophyll can be de-excited by thermal dissipation in a process that is commonly assessed as non-photochemical quenching ( NPQ ) of chlorophyll fluorescence [12–15] . Mechanisms involved in thermal energy dissipation include the xanthophylls zeaxanthin and lutein , the photosystem II subunit S ( PsbS ) protein , as well as energetic couplings between the core antenna complexes and LHCII [16–23] . The most rapid component of NPQ is called qE , which is activated by a decrease in thylakoid lumen pH [13 , 15 , 24–25] . In the xanthophyll cycle , low pH activates violaxanthin de-epoxidase ( VDE ) that converts violaxanthin into zeaxanthin via the intermediate antheraxanthin . Conversely , under low light and relatively alkaline conditions , zeaxanthin epoxidase ( ZEP ) catalyzes conversion of zeaxanthin via antheraxanthin into violaxanthin , thus forming an integrated cycle [26] . While there is a school of thought that addressed the zeaxanthin and PsbS-dependent qE as separate mechanisms , the elegant works by Demmig-Adams & Adams group have proposed that these are two parts of the same process , where the xanthophyll cycle generates zeaxanthin , and PsbS triggers the actual engagement of zeaxanthin in thermal dissipation [12 , 27] . At present , although the xanthophyll cycle is well known to be involved in photoprotection , it has not been as deeply characterized in plant disease responses . Several recent studies , however , have shown that there is a correlation between NPQ changes and resistance to pathogens [28–32] . The deletion of PsbS in the npq4-1 mutant was shown to alter jasmonate metabolism and render plant less attractive for herbivores [28–29] . Moreover , NPQ formation is negatively correlated with reactive oxygen species ( ROS ) production under excess light [11 , 15 , 33] , and weakening NPQ may promote 1O2 generation in PSII [26 , 33] . In particular , in the PsbS/vde1 double mutant , treatment with flg22 enhances ROS production and early defense marker gene expression [30] . In addition , the intensity of NPQ was also positively or negatively affected by various pathogen attacks , increasing around the infected regions but decreasing in its core [34–35] . This variability in NPQ might depend on the degree of tissue damage [35] . However , knowledge about the regulatory processes of pathogens on NPQ as well as their impact on plant defense responses is incomplete . The xanthophyll precursor pool plays an important role in the biosynthesis of the phytohormone abscisic acid ( ABA ) [36–38] . De novo synthesis of ABA requires ZEP-catalyzed epoxidation of zeaxanthin to violaxanthin . Subsequently , the violaxanthin-derivatives neoxanthin and xanthoxin are converted into ABA through a series of isomerization and dehydrogenation reactions [39] . In the ABA-deficient mutant aba1 ( an allele of npq2 ) , ZEP is not functional , causes accumulation of zeaxanthinin parallel with decreases in the epoxy-xanthophylls antheraxanthin , violaxanthin and neoxanthin [40–41] . In the xanthophyll cycle , VDE requires ascorbate as a reductant to convert violaxanthin to zeaxanthin [42] . As a result , reduced levels of ascorbate in the Arabidopsis vtc1 ( vitamin C1 ) mutant stimulate ABA production [43] . In contrast , enhanced VDE activity can reduce ascorbate levels and antagonize ABA synthesis [43] . Thus , the regulation of the xanthophyll cycle allows ABA levels to be modified , which could be a subtle mechanism exploited by pathogens to lower plant resistance . NPQ is regulated on a fast timescale by changes in thylakoid lumen pH [15 , 19 , 44] . Disruption of the pH gradient ( ΔpH ) across the thylakoid membrane with the ionophore nigericin can abolish NPQ formation [45] . Conversely , NPQ can be induced in isolated thylakoids by lowering ambient pH [46 , 47] . In the pathogenesis of pathogenic fungi , local pH can be dynamically altered by the pathogen as host colonization advances [48] . In fact , pH value is one of the major features affecting maximal activity of pathogenicity factors , such as the arsenal of cell wall degradative enzymes that display acidic pH-specific expression in the necrotrophic pathogen Sclerotinia sclerotiorum [49 , 50] . Sclerotinia decreases host pH by secreting millimolar quantities of oxalate [51 , 52] . Oxalate exhibits versatile functions in plant infection and fungal development [53]; it triggers plant programmed cell death [54–56] , suppresses plant oxidative burst and callose deposition [57–59] , and inhibits ABA-induced stomatal closure [60] . Functional genetic studies have provided evidence for the relevance of ABA in plant defense against Sclerotinia [61–63] . However , it is still unknown whether the ambient pH changes would affect the xanthophyll cycle and subsequent ABA biosynthesis in the pathogenesis of Sclerotinia . Here , we investigated the interplay of the xanthophyll cycle and plant resistance to the necrotrophic pathogen Sclerotinia . The results show that Sclerotinia caused a dysfunction of the xanthophyll cycle during initial stages of infection , with leaves displaying an abnormal increase in NPQ in a zeaxanthin-related manner even under low light conditions . Further studies revealed that decreases in the precursor violaxanthin were associated with limited ABA biosynthesis , which , in turn , apparently weakened tissue defense responses and eased Sclerotinia colonization of the host plant . These findings present a mechanism of how photoprotective metabolites integrate into the defense work and contribute to understanding the early plant-Sclerotinia interactions at the infection site .
When analyzing the timing and spread of Sclerotinia in its host plant with chlorophyll fluorescence imaging , we identified anomalies of NPQ in plant tissue during early infection . Fig 1A shows images of two conventional fluorescence parameters , Fv/Fm ( maximum photochemical efficiency of PSII ) and NPQ , in randomly selected Arabidopsis leaves . Sclerotinia infection induced a gradual decrease in Fv/Fm , which indirectly reflected the degree of tissue damage [64] . Interestingly , a localized increase in NPQ was observed already 1 h after infection . As inoculation time prolonged , NPQ decreased in the core of the necrotic lesions but increased around the necrosis . Because the infected areas did not behave homogeneously , possibly due to contact spot variances on the uneven foliage , the entire inoculated region and the leading edge were selected for statistical analysis , respectively ( Fig 1B and 1C ) . Although the mean values of NPQ decreased significantly in severely damaged tissue ( as shown at 9 h ) , NPQ remained at high levels from 1 to 3 h after infection ( Fig 1D ) . The increase in NPQ was most pronounced at the leading edge ( Fig 1E ) . Next , changes in NPQ within the context of the penetration of the host by Sclerotinia were assessed . A water-soaked appearance began to emerge at 1 h but exhibited severely at 12 h after infection ( Fig 1F ) . The earlier slight damage might be caused by oxalate in the PDA plug . At early stage , a number of scattered mycelial cells on the leaf surface were observed under scanning electron microscope ( Fig 1G ) . Further results revealed that the infection cushions began forming at 8 h and hyphae were interweaved in the necrotic tissue at later stage ( Figs 1G and S1 ) . These features suggest that the Sclerotinia-induced NPQ increase is an early event that occurs prior to infection cushions formation . A model is proposed to depict how NPQ is related to the infection process ( Fig 1H ) : During the early stage , upper-side cell damage causes a slight decrease in Fv/Fm but greatly enhances NPQ throughout the entire region . As inoculated tissue moves toward necrosis , it exhibits increased NPQ in the leading edge , whereas both Fv/Fm and NPQ are reduced in the necrotic center . This model could help explain why Sclerotinia-induced increased NPQ varies dependent on region and infection stage . We next analyzed the kinetic characteristics of NPQ in the Sclerotinia-infected leading edge area . First , different intensities of actinic light ( levels of photosynthetically active radiation = PAR ranging from 0 to 1465 μmol photons m-2 s-1 ) were used to investigate dynamic changes in NPQ . As light intensity increased , a ring of enhanced NPQ was detectable surrounding the inoculated zone , and then expanded to the entire infected area ( Fig 2A and S1 Movie ) . However , at light intensities exceeding 1175 μmol photons m-2 s-1 , NPQ increased more in un-inoculated regions ( Fig 2A and 2D ) . To analyze formation and relaxation of NPQ under excess light , a light intensity of 725 μmol photons m-2 s-1 was selected . In the Sclerotinia-infected zone , NPQ formed quickly in the first 60 seconds . In contrast , longer illumination led to greater NPQ generation in the un-inoculated control regions and with increased maximum amplitude ( Fig 2B , 2E and S2 Movie ) . Interestingly , when the light switched off , NPQ relaxed more slowly in the infected area ( Fig 2E ) . Because NPQ has a dedicated function in protecting the photosynthetic apparatus against photodamage under excess light , we wanted to ascertain the impact of Sclerotinia infection on NPQ changes at the low light intensity of 133 μmol photons m-2 s-1 , which was close to the natural radiation in our greenhouse . Surprisingly , even at this low light intensity , Sclerotinia infection still rapidly induced NPQ formation ( Fig 2C , 2F and S3 Movie ) . More importantly , the maximal amplitude was approximately 2-fold of that found in un-inoculated regions ( Fig 2F ) . Additionally , differences of NPQ relaxation kinetic between Sclerotinia-infected leaves and the control were still observed ( Fig 2F ) . These results indicate that NPQ , usually seen under excess light conditions , was triggered by Sclerotinia invasion even at low light during early pathogenesis . To explore these effects induced by Sclerotinia under close to natural growth conditions , the low PAR of 133 μmol photons m-2 s-1 was used in the following experiments unless otherwise mentioned . Oxalate is an essential pathogenicity factor for Sclerotinia [52 , 57] . Inoculation with Sclerotinia induced calcium oxalate crystal accumulation in the infection sites ( Fig 3A ) . It is controversial whether or not the dibasic acid oxalate aids in fungal invasion due to direct acidity effects . To explore whether the increased NPQ in Sclerotinia-infected zone was related to positional pH changes , two pH-sensitive fluorescent dyes , lysosensor green DND-189 and acridine orange , were used . The infected plant tissue exhibited a higher level of DND-189 fluorescence upon invasion with wild-type Sclerotinia compared to the oxalate-deficient A2 mutant ( Fig 3B ) . Acridine orange has an emission maximum of 655 nm ( red ) in an acidic environment and of 530 nm ( green ) in a neutral environment [65] . Tissue acidification was determined by measuring the ratio of red-to-green emissions . Results obtained via confocal microscopy show that wild-type Sclerotinia infection induced a higher level of fluorescence emissions of acridine orange in the red channels ( 615 to 660 nm ) compared to the A2 mutant . At the periphery of wild-type Sclerotinia-infected sites , the ratio of red-to-green emissions greatly increased ( Fig 3C ) , indicating a decrease in ambient pH . We then evaluated whether loss of oxalate would affect the Sclerotinia-induced NPQ increase . One leaf was co-infected with wild-type Sclerotinia ( Circle 1 ) and the oxalate-deficient A2 mutant ( Circle 2 ) . Chlorophyll fluorescence imaging revealed that the A2 mutant does not significantly stimulate NPQ as done by wild-type Sclerotinia ( Fig 3D ) . The kinetics of NPQ formation and relaxation are similar in A2-inoculated leaves and controls ( Fig 3E ) . These results suggest that acidification of the plant tissue by Sclerotinia-secreted oxalate might account for the abnormal increase in NPQ . To further investigate the impact of ambient pH changes on NPQ kinetics , leaves were infiltrated with KOX ( potassium oxalate ) buffered to different pH values . A rapid increase in NPQ associated with a lower rate of NPQ relaxation was observed in leaves infiltrated with KOX at pH 3 . 0 , versus pH 7 . 0 ( S2A Fig and S4 Movie ) . These observations confirm that the decrease in ambient pH is responsible for the NPQ increase . Furthermore , inhibition of electron transport with 3- ( 3 , 4-dichlorophenyl ) -1 , 1-dimethylurea ( DCMU ) abolished the enhanced NPQ seen in Sclerotinia-inoculated leaves ( S2B Fig ) , indicating that the Sclerotinia-induced NPQ increase requires photosynthetic electron transport that is presumably coupled with translocation of H+ from stroma to lumen to generate a trans-thylakoid proton gradient ( ΔpH ) . In contrast , in the Sclerotinia-inoculated region , dissipation of the pH gradient using the uncoupler nigericin did not fully abolish the NPQ increase like in the control ( S2C Fig ) . We then measured ATP synthase activity to indirectly reflect the changes of proton motive force . Treatment with KOX at pH 3 . 0 significantly attenuated ATP synthase activity , as represented by decreased inorganic phosphate ( Pi ) at 630 nm ( S2D Fig ) . A down-regulated ATP synthase activity was also confirmed by a bioluminescent luciferase assay detecting ATP generation ( S3 Fig ) . Taken together , these results suggest that the decreased ambient pH caused by Sclerotinia infection might down-regulate ATP synthase activity by limiting proton flux into the stroma and enhancing thylakoid lumen acidification under illumination , thus resulting in increased NPQ . Since lumen acidification is sensed by the PsbS protein during NPQ generation [19] , we next measured NPQ in a Sclerotinia-infected PsbS mutant ( npq4-1 ) . As expected , defective PsbS function greatly attenuated the abnormal increase in NPQ induced by Sclerotinia ( Fig 4A ) . NPQ was induced slowly to a total extent of only 0 . 3 , and most of this NPQ failed to relax during the subsequent period in the dark , consistent with the known NPQ defect of this mutant [18] . In addition to PsbS protein , other factors , such as the violaxanthin de-epoxidase ( VDE ) , are required for full activation of NPQ [26] . A mutant deficient in VDE , npq1-2 , is compared with Col-0 wild-type after inoculation with Sclerotinia in Fig 4B . In the npq1-2 plant , Sclerotinia incubation did not induce increased NPQ , and the kinetic features consisted of a smaller increase and a slower second phase . After infiltration with dithiothreitol ( DTT ) , a known inhibitor of VDE [16] , NPQ formation was strongly inhibited in Sclerotinia-inoculated leaves ( Fig 4C ) . Because VDE converts the carotenoid violaxanthin into zeaxanthin to participate in NPQ , we explored whether NPQ formation as associated with zeaxanthin level . Notably , Sclerotinia infection induced a high level of zeaxanthin-related NPQ ( total NPQ kinetics minus NPQ kinetics + DTT [16 , 20] ) ( Fig 4D ) . Moreover , HPLC analysis showed that zeaxanthin increased 2 . 6-fold in Sclerotinia-infected Col-0 leaves compared to un-inoculated control leaves , with a corresponding decrease in violaxanthin content ( Table 1 ) . Conversely , presumably due to the loss of VDE , Sclerotinia infection did not promote greater zeaxanthin accumulation in the npq1-2 mutant . Moreover , associated with the conversion of most of the violaxanthin to zeaxanthin , one of violaxanthin’s catabolites , neoxanthin , also decreased in Sclerotinia-infected leaves ( Table 1 ) . Collectively , these results indicate that the Sclerotinia-induced NPQ increase was closely correlated with VDE-catalyzed zeaxanthin generation . Because xanthophyll precursors ( i . e . violaxanthin , neoxanthin ) play a key role in ABA biosynthesis [37 , 38] , we explored whether changes in these precursors may affect ABA metabolism . qPCR results showed that expression of ABA biosynthesis genes , except for NCED3 ( 9-cis-epoxycarotenoid dioxygenases3 ) , was not significantly affected after challenge with wild-type Sclerotinia or A2 mutant ( Fig 5A ) . However , ABA immunoassays revealed that leaves incubated with the A2 mutant had elevated levels of ABA ( Fig 5B ) , on average approximately 37% higher than controls ( Fig 5C ) . In contrast , ABA levels in leaves inoculated with wild-type Sclerotinia were approximately 53% lower than those of control leaves ( Fig 5C ) . In such circumstances , the expression of NCED3 may be increased in an attempt to compensate for limitations in ABA biosynthesis . It is worth mentioning that A2 mutant invasion always resulted in tissue yellowing surrounding the necrotic area ( Fig 5D ) . Statistical analysis showed that the yellowing region accumulated a high level of ABA ( Fig 5E ) . We next analyzed the impact of defective of VDE on ABA synthesis . After growth for four weeks in the greenhouse , the npq1-2 mutant showed similar ABA levels as Col-0 . However , upon wild-type Sclerotinia infection , the mutation of VDE enzyme significantly suppressed the ABA levels decrease in the npq1-2 mutant ( Fig 5F ) . These results suggest that Sclerotinia infection could regulate the VDE activity to modify ABA levels . We then evaluated the efficiency of ABA in plant resistance to Sclerotinia . Upon Sclerotinia infection , the average necrotic lesions in leaves pretreated with ABA were significantly smaller than in water-pretreated plants . Likewise , the infected npq1-2 mutant showed a significant reduction in lesion area compared with Col-0 , presumably due to the loss of VDE activity and associated maintenance of violaxanthin and/or neoxanthin levels and ABA synthesis ( Fig 6A and 6B ) . However , due to defects in ABA sensing or ABA biosynthesis , the double mutants npq1-2/abi4-1 and npq1-2/aba2-3 were more susceptible to Sclerotinia than npq1-2 plants . Interestingly , treatment with ABA reverted the phenotype of the npq1-2/aba2-3 mutant but not the npq1-2/abi4-1 mutant ( Fig 6C ) . This result is consistent with previous reports [60] , suggesting that ABI4 is involved in the downstream signaling of ABA in plant resistance to Sclerotinia . Mutation of either the abi4 or the aba2 gene significantly increased the infectious ability of the A2 mutant in npq1-2/abi4-1 or npq1-2/aba2-3 plants ( Fig 6D ) . Together , these results clearly indicate that Sclerotinia could manipulate the xanthophyll cycle and interfere with ABA biosynthesis and signaling to suppress host defense . To gain further insight into the nature of ABA-induced resistance against Sclerotinia , we assessed changes in defense responses , including O2- formation and callose deposition . As shown in Fig 6E , O2- was detectable surrounding the A2 mutant-infected zone that were either sprayed or not with ABA . However , in the wild-type Sclerotinia-inoculated leaves , only pretreatment with ABA was able to induce a ring of O2- accumulation at the periphery of the infection site ( Figs 6E and S4A ) . Upon A2 mutant incubation , callose deposition was also observed in the leading edge of the necrotic regions . Similar results were obtained in the vicinity of wild-type Sclerotinia-induced necrotic lesions after ABA treatment ( Figs 6F and S4B ) . Since the rate of oxalate diffusion in leaf tissue is correlated with plant susceptibility to Sclerotinia [66] , we presumed that ABA-induced local reinforcement of the cell wall by forming callose might be an effective physical barrier to prevent the spread of oxalate and limit mycelial growth .
Light acts as an initial signal that activates the special photoreceptors ( e . g . , phytochromes , cryptochromes and phototropins ) involved in different types of plant-pathogen interactions [6–7 , 67] . In fact , the mechanisms that directly control photosynthetic light reactions also mediate important functions in plant response to pathogens [6 , 68] . Recently , it has been reported that the light-induced reduction of the plastoquinone pool may trigger induction of defense-associated genes and the hypersensitive reaction [2 , 69] . To manipulate such redox signals , pathogens may modulate the activation state of photoprotective mechanisms like changing the extent of NPQ , as an effective way to alter the redox status of the plastoquinone pool and generation of ROS from the photosynthetic electron transfer chain [68] . However , the impact on NPQ by different pathogens seems different , either increasing or decreasing NPQ [34–35] . At present , there is still no clear recognition of these pathogens’ role in manipulating NPQ . The measured NPQ under strong light is just as an indicator for early detection of pathogens in leaves [35 , 64] . Here , our results demonstrate for the first time that Sclerotinia infection induces a localized increase in NPQ in early pathogenesis under the natural and rather low growth light intensity ( Figs 1 and 2 ) . While there are a few reports of pathogens inducing NPQ under such low light intensity , not much is known about an involvement of this effect in the pathogenic process . The present data , however , revealed that an increased NPQ under such low light conditions caused changes in downstream cellular events , such as ABA biosynthesis and ROS generation , which weaken host defense responses during the natural pathogenic processes of Sclerotinia ( Figs 5 and 6 ) . Thus , the localized increase in NPQ in the early stage of infection cannot be regarded as a consequence of the metabolic perturbation of the host cell . Rather , this process is likely proactive in aiding pathogenic success . A more systematic study is required to validate this assumption . Many pathogens are able to actively increase or decrease the surrounding pH at the infection site through alkalization or acidification [48–49] . For Sclerotinia , a low environmental pH may aid pathogenicity by affecting numerous pH-regulated genes and cell-wall-degrading enzymes [49–53] . As an early pathogenic event , the Sclerotinia-induced increase of NPQ demonstrated here in host plant is also closely related to a low ambient pH ( Fig 3 ) . Interestingly , incubation with another oxalate-secreting fungus , Botrytis cinerea , also induced an NPQ increase , although to a much smaller extent than that in Sclerotinia-inoculated regions ( S5 Fig ) . Such differences might result from B . cinerea secreting lower amounts of oxalate than Sclerotinia [70] . However , for pathogens ( e . g . , Pseudomonas syringae ) which cannot secrete acidic or alkaline factors , it is still unclear how they affect NPQ during their pathogenic progress [31–32] . NPQ increases or decreases in response to the level of light utilization in photosynthesis , and any impact of a pathogen on sugar export or sugar consumption is expected to result in changes in NPQ [12 , 71] . In addition , NPQ is directly controlled by the trans-thylakoid proton gradient by at least two processes requiring a low lumenal pH , i . e . , ( i ) the induction of VDE activity ( and resulting conversion of violaxanthin to zeaxanthin ) and ( ii ) the protonation of the PsbS protein leading to the engagement of zeaxanthin in the actual dissipation process [12 , 16–17 , 27] . It is noteworthy that the decrease in lumenal pH that induces NPQ does not have to be generated by photosynthetic electron transport [26 , 45 , 47] . Using isolated thylakoids , NPQ can be induced in darkness by simply lowering buffer pH [46] . While inhibition of linear electron flow with DCMU abolished the NPQ features induced by Sclerotinia , proton gradient collapse by treatment with the uncoupler nigericin did not fully abolish the Sclerotinia-induced NPQ increase ( S2B and S2C Fig ) . Our data suggest that early Sclerotinia infection down-regulates ATP synthase activity and thereby leads to a decreased lumen pH and increased NPQ . Furthermore , infected areas showed NPQ dynamics similar to those of aba1-3 ( S6 Fig ) , a mutant deficient in the ZEP enzyme of the xanthophyll cycle that is associated with constitutive accumulation of zeaxanthin [20 , 72] . Consistent with the features of the latter mutant , our HPLC analyses of Col-0 leaves incubated with Sclerotinia revealed a significant increase in zeaxanthin content ( Table 1 ) . Accumulation of zeaxanthin is necessary to modulate the kinetics of NPQ , enhancing the rate of NPQ formation and retarding the rate of NPQ relaxation [72] . This effect can explain why inhibition of zeaxanthin formation abolished the Sclerotinia-induced NPQ increase in the npq1-2 mutant or wild-type leaves treated with DTT ( Fig 5B and 5C ) . Biosynthesis of ABA begins inside the chloroplast and is limited by xanthoxin synthesized from violaxanthin [36–37 , 39] . In strong light , a low lumen pH activates VDE-catalyzed deepoxidation of violaxanthin to zeaxanthin [17 , 26] . Restraint of VDE activity results in violaxanthin accumulation and promotes ABA synthesis [4] . In the vtc1 mutant with a reduced level of the VDE substrate ascorbate , ABA content increased by 60% compared to that of wild-type [43] . Consequently , a reduction in ABA closely matched the decrease in the amounts of violaxanthin plus neoxanthin after infection with Sclerotinia ( Table 1 and Fig 5 ) . However , qPCR analysis showed that , except for NCED3 , the expression of ABA biosynthesis genes was not significantly affected by wild-type Sclerotinia infection ( Fig 5A ) . This suggests that , in the early stage of infection , regulation of ABA biosynthesis occurs primarily at the substrate level ( violaxanthin ) rather than at the transcriptional level . Since NCED3 is the key enzyme in the ABA biosynthesis pathway [39 , 73] , the increase in NCED3 expression might result from the demand for ABA in the infected tissues . Sclerotinia infection generates the acidic environment that increases activation of VDE , promoting conversion of violaxanthin to zeaxanthin . The decrease in the ABA precursor violaxanthin may be the main reason for the reduced ABA levels in Sclerotinia-infected leaf discs . Taken together , our results suggest that modulation of the xanthophyll cycle provides a mechanism to adjust production of ABA for signaling purposes . To further evaluate this conclusion , it would be interesting to determine the interplay of lumen pH and/or sugar accumulation ( and resulting NPQ changes ) with ABA-mediated defense signaling in many other pathogens that can up-regulate or down-regulate NPQ in their pathogenic processes [32 , 34–35] . Additionally , it is worth mentioning that Sclerotinia infection caused stomatal pores to be more widely open within and around necrotic lesions after dark adaptation [59–60] . Because stomatal movement is tightly regulated by ABA-mediated signaling ( such as ROS generation , Ca2+ permeable cation channels regulation ) [74] , decreased ABA levels might offer an explanation for the inhibitory action of Sclerotinia on stomatal closing . Although ABA’s role in influencing the outcome of plant-pathogen interactions is controversial , functional genetic studies have provided evidence for a positive role of ABA in defense against Sclerotinia [60–63] . In agreement with this view , exogenously applied ABA significantly restricted development of necrotic lesions caused by Sclerotinia . Importantly , the npq1-2 mutant showed more resistance to Sclerotinia compared with Col-0 plants ( Fig 6A and 6B ) . It is likely that Sclerotinia cannot manipulate the xanthophyll cycle in npq1-2 plants due to their deficiency in the VDE enzyme , thus leading to unchanged ABA levels . This hypothesis was confirmed by measuring ABA levels in npq1-2 ( Fig 5F ) . However , the defect in either ABA sensing or ABA biosynthesis weakened the resistance effect in npq1-2/abi4-1 or npq1-2/aba2-3 plants upon Sclerotinia infection ( Fig 6C ) . The oxalate-deficient A2 mutant is less pathogenic than wild-type fungus [52] . Compared to wild-type Sclerotinia , the A2 mutant did not show significantly reduced susceptibility in npq1-2 ( Fig 6D ) , which might result from the already higher levels of ABA induction in the plant response to A2 mutant infection ( Fig 5B–5E ) . This could explain why the abi4 or aba2 mutations increased npq1-2 plant susceptibility to the A2 mutant ( Fig 6D ) . Curiously , although silencing the NPQ machinery with mutant or inhibitor delayed the progression of lesion expansion , both wild-type Sclerotinia and the A2 mutant were still capable of infecting living plant tissue . One reason for this might be the use of PDA agar , which always leads to aggressive growth of Sclerotinia and overwhelms the defense capacity of plant . Actually , disease caused by this devastating necrotrophic fungal has traditionally been difficult to control [53] . Even if situated under unfavorable conditions , Sclerotinia could survive with the aid of sclerotia . Therefore , one can imagine that if the fungus is not directly killed at the source , it can breakthrough an already established defense system . Manipulation of NPQ in the pathogenic process of Sclerotinia is an early event , which primes other invasion processes like suppression of oxidative burst . Thus , we assume that the actual role of the Sclerotinia-induced increasing NPQ may contribute to successful early infection establishment . ROS function as important second messengers in ABA-mediated defense signaling [75] . Manipulation of ROS signals in the pathogenic process of Sclerotinia is particularly intriguing . Sclerotinia-secreted oxalate initially suppressed host oxidative burst , but later promoted ROS generation to achieve pathogenic success [57–58] . Undoubtedly , inhibition of early ROS signaling can contribute to restraining plant activation of defense responses and favor of Sclerotinia invasion . At present , the physiological and molecular regulation mechanism of the initial ROS inhibition is not very clear . Although the previous research reported that the inhibitory effects of oxalate on ROS are largely independent of its acidity , lowering the medium pH indeed led to a greater inhibition of oxidative burst [57] . Tissue acidification is sufficient for Sclerotinia inducing an increase in NPQ , which is known to minimize production of 1O2* in the PSII antenna [15 , 26] . Moreover , NPQ is also correlated with the activation of photosynthetic control , which limits electron transport through the cytochrome b6f complex and alleviates the formation of ROS in PSI [68] . In addition to the effect of thermal dissipation ( leading to NPQ ) in causing de-excitation of singlet-excited chlorophyll , and thereby decreasing ROS formation , there is also a direct effect of zeaxanthin in deactivating ROS and their effect on biological membranes [33] . The Sclerotinia-induced increase in NPQ and in zeaxanthin accumulation was indeed paralleled by suppression of O2- generation ( Figs 1A–1E and 6E ) . In the early infection stage , there seems to be a correlation between the NPQ increase ( and zeaxanthin accumulation ) and oxidative burst inhibition . The Sclerotinia-induced increase of NPQ ( and zeaxanthin accumulation ) under low light should also be expected to attenuate ROS generated in the photosynthetic light reactions . In fact , there is a background level of triplet chlorophyll formation and potential singlet-oxygen formation even under low light level since the fraction of absorbed light converted to photosynthetic electron transport does not exceed about 85% [76] . It is worth noting that exogenous application of ABA reversed the decrease of O2- ( Fig 6E ) , possibly via activation of other cellular ROS-generating systems like NADPH oxidases . The latter mutations were showed susceptible to Sclerotinia in previous report [62] . Further studies are required to test this hypothesis . In summary , our investigation provides evidence about an interplay of the xanthophyll cycle and plant resistance against the necrotrophic pathogen Sclerotinia . The possible correlations are summarized in the model presented in Fig 7 . Initially , Sclerotinia secretes oxalate to acidify the infected tissues , which down-regulates ATP synthase activity and increases NPQ by protonating PsbS protein and activating the VDE enzyme . The latter goes on to convert violaxanthin to zeaxanthin via the intermediate antheraxanthin in the xanthophyll cycle . The decrease in precursor violaxanthin limits ABA biosynthesis and , in turn , affects tissue defense responses including ROS induction and callose deposition , which increase plant susceptibility to Sclerotinia . Additionally , the excellent works of Demmig-Adams & Adams group indicate that elevated NPQ and zeaxanthin accumulation have the potential to affect chloroplast redox status to lower oxidation-derived oxylipins ( like jasmonic acid ) formation [10–11 , 77] , which may weaken callose formation and occlusion of sugar-loading complexes , presumably to facilitate pathogen spread via the phloem . In conclusion , the present study reveals a novel perspective on infection strategies of the necrotrophic fungal Sclerotinia , which provides a model of how photoprotective processes and metabolites are integrated into the plant defense network and thereby contributes to a better understanding of early plant-Sclerotinia interactions at the infection sites .
Arabidopsis Columbia-0 ( Col-0 ) , abi4-1 ( N8104 ) , aba2-3 ( N3834 ) , npq1-2 ( N3771 ) and npq4-1 ( N66021 ) were obtained from the European Arabidopsis Stock Centre . For the generation of crosses npq1-2/abi4-1 and npq1-2/aba2-3 , abi4-1 and aba2-3 mutants were directly crossed to npq1-2 . F2 seeds were germinated on agar plates in the presence of 5 μM ABA . The seedlings with expanded cotyledons were screened via fluorescence video imaging ( PAM-MINI , Walz , Germany ) . F3 lines were re-screened to identify true mutants , and F3 seedlings were used for experiments . Plants were cultivated in growth cabinets at 22°C with a 16-h photoperiod and a light intensity of 120 μmol photons m-2 s-1 . The chemicals ABA , DTT and DCMU were purchased from Sigma-Aldrich; nigericin was obtained from J&K Scientific Ltd . . Detached leaves from 4-week-old plants were inoculated with Sclerotinia at 1 h after vacuum pre-infiltration with DCMU ( 8 μM ) , nigericin ( 50 μM ) or DTT ( 10 μM ) , respectively . For ABA treatment , leaves were sprayed with 100 μM of cis , trans-ABA ( dissolved in 0 . 1% ( v/v ) ethanol ) at 24 h prior to Sclerotinia inoculation . Control leaves were sprayed with water containing 0 . 1% ( v/v ) ethanol . Wild-type Sclerotinia and an oxalate-deficient mutant ( A2 ) were cultivated on potato dextrose agar at 21°C for 3 days . Agar plugs ( diameter 0 . 3 or 0 . 8 cm ) containing the leading edge of growing mycelia were used to inoculate leaves . Infected plants were kept under saturating humidity conditions in a clear plastic box . Photos of necrotic phenotype were captured by a numeric camera ( HDR-XR500E , Sony ) . Lesion size was quantified in at least 10 leaves with a Carl Zeiss system as described by [59] , with photographs captured by a Carl Zeiss AxioCam MRc5 camera installed on a Zeiss inverted microscope . Lesion area was quantified with the measurement tool ‘outline spline’ in AxioVision Rel . 4 . 5 software . A measured example is given in S7 Fig . Chlorophyll fluorescence parameters were measured with an Imaging-PAM Chlorophyll Fluorometer ( PAM-MINI , Walz , Germany ) . The experimental procedures were as described previously [20 , 32] . After inoculation with Sclerotinia , leaves were dark-adapted for 1 h prior to measurement . Parameters Fo ( minimum fluorescence with PSII reaction centers fully open ) , Fm ( maximum fluorescence after dark adaptation ) and Fm’ ( fluorescence level under actinic light-adapted state ) were acquired by the ImagingWin software ( ImagingWin v2 . 0m , Walz ) ( S8 Fig ) . A 0 . 8-s saturating pulse ( 4 , 000 μmol photons m-2 s-1 ) was applied to obtain Fm and Fm’ . Fv/Fm and NPQ were automatically calculated by the ImagingWin software ( Walz ) using the formulas ( Fm-Fo ) /Fm and ( Fm-Fm’ ) /Fm’ , respectively . Actinic light of 725 μmol photons m-2 s-1 was selected as high light , and 133 μmol photons m-2 s-1 was used as low light . Images of the fluorescence parameters were displayed with a false color code , ranging from zero ( black ) to one ( purple ) . After inoculated with Sclerotinia for 1h or 12h , the leaves were immediately fixed in formalin-acetic acid-alcohol for 24 h [78] . Samples were then washed three times in distilled water . Sections were next dehydrated through an ethanol series ( 70% , 80% , 90% , 95% and 100%; 30 min at each step ) . Ethanol-dehydrated samples were processed with critical point drying followed by platinum coating . Coated samples were scanned with a cold field scanning electron microscope at an accelerating voltage of 3 . 0 kV ( S-4800 , Hitachi ) . Positional pH was measured according to [65] . Sclerotinia-infected leaves were stained with LysoSensor Green DND-189 ( 2 . 5 μM ) for 20 min or with acridine orange ( 50 μM ) for 1 . 5 h . Stained leaves were washed twice with a washing buffer ( 10 mM KCl , 10 mM MES , pH 6 . 05 ) . Fluorescence of DND-189 was acquired using a Zeiss LSM510 META system at excitation/ emission wavelengths of 458 nm/505 to 530 nm . Fluorescence emissions of acridine orange in red ( 615 to 660 nm ) and green channels ( 505 to 550 nm ) were obtained after excitation with 488 nm . Tissue acidification was represented by the ratio of the red-to-green emissions of acridine orange . ATP synthase activity was assessed by determining the decrease in the concentration of inorganic phosphate ( Pi ) according to [79] . In brief , 10 mM KOX at a pH of 7 . 0 or 3 . 0 was incubated with 0 . 1 mg Chl ml-1 chloroplasts suspension ( 110 mM sorbitol and 17 mM Hepes-KOH , pH 8 . 0 ) . After adding ADP ( 4 mM ) and Pi ( 50 μM ) , chloroplasts were illuminated with 130 μmol photons m-2 s-1 for the indicated time ( 0–5 min ) and stopped by addition of 4% ( w/v ) cold trichloroacetic acid . Samples were then mixed with 0 . 65 M sulfuric acid and 8 . 5 mM ammonium molybdate , followed by measuring absorbance at 630 nm ( PerkinElmer , Lambda35 , UK ) . Sclerotinia-infected plants were kept under saturating humidity and a light intensity of 130 μmol m-2 s-1 . Infected areas of leaves were obtained with a hole punch at 3 h after inoculation . Pigments were immediately extracted with pre-cooled acetone under dim light condition [80] . After filtering with a 0 . 2 μm filter , the extract was separated and quantified by HPLC with a Waters Spherisorb S5 ODS1 column ( 5 . 0 μm , 4 . 6 mm × 250 mm ) . The solvent system was according to Müller-Moulé et al . [81] . Solvent A ( acetonitrile: methanol: Tris-HCl 0 . 1 M pH 8 . 0 [84: 2: 14] ) was eluted with a linear gradient to 100% solvent B ( methanol: ethyl acetate [68: 32] ) within 15 min , followed by 5 min of solvent B . Relative contents were normalized to 100 chlorophyll a+b molecules . Total RNA was extracted from Sclerotinia-infected zone using RNAiso Plus ( Takara , Dalian , China ) according to the supplier’s recommendation . First-strand cDNA was synthesized with the SuperScript II First-Strand Synthesis System ( Invitrogen ) . qRT-PCR was performed using the lightCycler ( Roche ) real-time PCR detection system . Primer sequences ( S1 Table ) of ABA1 ( At5g67030 ) , ABA2 ( At1g52340 ) , ABA3 ( At1g16540 ) , NCED3 ( At3g14440 ) and AAO3 ( At2g27150 ) were used as described by [82] . Expression of target genes was normalized to ACTIN2 . ABA extraction was according to [83] . Briefly , Sclerotinia-infected leaf discs were collected and frozen in liquid nitrogen . After grinding with a pre-chilled mortar and pestle on ice , the powder was extracted overnight at 4°C in a cold extraction buffer ( 80% methanol and 2% glacial acetic acid ) . The mixture was then centrifuged at 2 , 000 g for 5 min . The supernatant was run through a Sep-Pak C18 Plus Short Cartridge ( Waters Corp ) to remove polar compounds . ABA content was measured by a plant ABA ELISA Kit ( Jiancheng , Nanjing , China ) . Tissue immuno-localization of ABA was according to [84] . Sclerotinia-infected leaves were fixed overnight with 3% ( W/V ) para-formaldehyde in 4% ( W/V ) 1-ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide containing 0 . 1% ( V/V ) Triton X-100 . Tissue cleaning was performed before infiltrating with wax . Sections ( 12 μm ) were obtained with a sliding microtome ( CM1850 , Leica , Germany ) . After dewaxing and blocking , sections were incubated with rabbit anti-ABA primary antibody ( Agrisera , Vännäs , Sweden ) overnight . The fluorescence of Alexa 568 conjugated anti-rabbit secondary antibody was viewed with a confocal microscope ( Zeiss LSM510 META ) . Accumulation of O2- was monitored in situ with nitroblue tetrazolium ( NBT ) as described previously [85] . Images were photographed using a Zeiss inverted microscope with a Carl Zeiss AxioCam MRc5 camera . Callose deposition was stained with 0 . 01% ( w/v ) aniline-blue and observed using a fluorescent microscope [86] .
|
In recent years , the role of the chloroplast in the defense against microbes has been intensively investigated and is of high interest to both plant-microbe interaction and photosynthesis research . The xanthophyll cycle is well known to be involved in dissipating excess light energy to protect the photosynthetic apparatus in a process commonly assessed via non-photochemical quenching ( NPQ ) of chlorophyll fluorescence . Recent studies show that NPQ can be positively or negatively affected by pathogen attack . However , knowledge about the regulatory processes by which pathogens affect NPQ , as well as their impact on plant defense responses , is incomplete . This work characterized the impact of infection of Arabidopsis leaves by the necrotrophic pathogen Sclerotinia sclerotiorum on the xanthophyll cycle . Our research revealed for the first time that Sclerotinia uses a novel strategy involving manipulation of the xanthophyll cycle to weaken host defense responses and increase its successful colonization of host cells . These findings contribute to understanding the plant-Sclerotinia interactions in early pathogenesis , which will provide new sights into the development of strategies to increase Sclerotinia resistance in plants for practical applications .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Manipulation of the Xanthophyll Cycle Increases Plant Susceptibility to Sclerotinia sclerotiorum
|
No efficient vaccine against plague is currently available . We previously showed that a genetically attenuated Yersinia pseudotuberculosis producing the Yersinia pestis F1 antigen was an efficient live oral vaccine against pneumonic plague . This candidate vaccine however failed to confer full protection against bubonic plague and did not produce F1 stably . The caf operon encoding F1 was inserted into the chromosome of a genetically attenuated Y . pseudotuberculosis , yielding the VTnF1 strain , which stably produced the F1 capsule . Given orally to mice , VTnF1 persisted two weeks in the mouse gut and induced a high humoral response targeting both F1 and other Y . pestis antigens . The strong cellular response elicited was directed mostly against targets other than F1 , but also against F1 . It involved cells with a Th1—Th17 effector profile , producing IFNγ , IL-17 , and IL-10 . A single oral dose ( 108 CFU ) of VTnF1 conferred 100% protection against pneumonic plague using a high-dose challenge ( 3 , 300 LD50 ) caused by the fully virulent Y . pestis CO92 . Moreover , vaccination protected 100% of mice from bubonic plague caused by a challenge with 100 LD50 Y . pestis and 93% against a high-dose infection ( 10 , 000 LD50 ) . Protection involved fast-acting mechanisms controlling Y . pestis spread out of the injection site , and the protection provided was long-lasting , with 93% and 50% of mice surviving bubonic and pneumonic plague respectively , six months after vaccination . Vaccinated mice also survived bubonic and pneumonic plague caused by a high-dose of non-encapsulated ( F1- ) Y . pestis . VTnF1 is an easy-to-produce , genetically stable plague vaccine candidate , providing a highly efficient and long-lasting protection against both bubonic and pneumonic plague caused by wild type or un-encapsulated ( F1-negative ) Y . pestis . To our knowledge , VTnF1 is the only plague vaccine ever reported that could provide high and durable protection against the two forms of plague after a single oral administration .
Plague has been one of the deadliest bacterial infections in human history , causing millions of deaths during three major historical pandemics and leaving an indelible mark engraved in human's collective memory . In addition to ancient foci of the disease in Asia and Africa , the last pandemic ( plague of modern times ) , which started one century ago , allowed plague to develop new foci in previously unaffected territories such as Madagascar , Southern Africa , and the Americas . Despite considerable progress in its prevention and cure during the 20th century , plague has recently made a new comeback , causing close to 50 , 000 human cases during the last twenty years [1] , including cases in countries where plague was thought to be extinct [2] . Therefore , plague is categorized by WHO ( World Health Organization ) as a re-emerging disease [1 , 3] . The etiologic agent of plague , Yersinia pestis , is a highly pathogenic Gram-negative bacillus , very recently derived from the much less virulent enteropathogen Yersinia pseudotuberculosis [4] . Transmission of the plague bacillus to humans generally starts with the bite of an infected flea , causing bubonic plague , the most frequent clinical form of the disease . Y . pestis occasionally reaches the airways , and the resulting secondary pneumonic plague is highly contagious due to the emission of infected aerosols , causing inter-human transmission of pneumonic plague . This pneumopathy is systematically lethal in usually less than three days if no treatment is administered . The possible use of the plague bacillus as a bioterrorist weapon is also a serious threat due to its pathogenicity and human-to-human transmission . Y . pestis has been classified by the Centers for Disease Control ( CDC ) of the USA among Tier 1 select biological agents . Different strains of Y . pestis showing resistance to antibiotics currently used to treat patients have been identified in Madagascar [5] . Antibiotherapy can therefore no longer be considered as sufficient against the natural and intentional danger of plague . Facing such a public health risk , vaccines may be one of the only remaining alternatives to limit the death toll in humans . A plague vaccine should confer protection against bubonic plague , the most frequent form of the disease in nature [1] , at the origin of pneumonic plague outbreaks . The vaccine should also protect against pneumonic plague , the most contagious and fatal form of the disease . No plague vaccine is currently licensed . The live attenuated Y . pestis strain EV76 and its derivatives have previously been used in humans [6 , 7] , and were found to confer protection . However , the genetic instability of Y . pestis represents a major obstacle in its use as live vaccine [4 , 8] . Several molecular vaccine candidates have been recently developed , among which two molecular vaccines ( RypVaxtm and rF1Vtm ) are the most advanced in clinical trials [9 , 10] . These vaccines rely on a combination of two peptides: the F1 antigen composing the Y . pestis capsule and the LcrV component of the Type Three Secretion System ( TTSS ) [9 , 10] , which are efficient targets of protective immunity against plague [6 , 11] . Molecular vaccines are generally adjuvanted with alum , and thus are good inducers of antibody production but poor inducers of cellular immune response [12 , 13] . Cellular immunity is , however , important for plague protection [14] , and a weak cellular response could explain why F1-V vaccinated African Green Monkeys were poorly protected despite adequate antibody titers [15] . We recently proposed a vaccine strategy against plague based on an oral vaccination with a live , attenuated strain of Y . pseudotuberculosis [16 , 17] . Because this species is a recent ancestor of Y . pestis , the two species are genetically almost identical , whereas Y . pseudotuberculosis has much lower pathogenicity and much higher genomic stability [4] . Due to their immunogenicity and antigenic complexity , live vaccines generate both humoral and cell-mediated immune responses without addition of adjuvant , and the response is directed against multiple target antigens , thus inducing an immunological response that could not be circumvented by genetic engineering of Y . pestis . In addition , a live vaccine , once developed and validated , could easily and rapidly enter mass production , and would be well suited in response to an emergency need . As a proof of concept , we had reported that oral inoculation of two doses of a live , naturally attenuated Y . pseudotuberculosis could provide 88% protection against bubonic plague [16] . The initial Y . pseudotuberculosis strain that we tested was not genetically defined [16] . To develop a vaccine strain both avirulent and genetically defined , the virulent Y . pseudotuberculosis IP32953 strain , whose genome is known [4] , was irreversibly attenuated by deletion of genes encoding three essential virulence factors ( the High pathogenicity island , YopK , and the pH6 antigen ( PsaA [17] ) . To increase vaccine efficiency , an F1-encapsulated derivative was constructed . This was obtained by cloning the Y . pestis F1-encoding caf operon into a plasmid , and introducing this plasmid into the attenuated V674 Y . pseudotuberculosis , thus producing the V674pF1 strain . Oral vaccination using 108 CFU protected 100% against pneumonic plague caused by a challenge with 33 LD50 Y . pestis and 80% against a high-dose challenge ( 3 , 300 LD50 ) [17] . However , we subsequently found that vaccination with strain V674pF1 protected only 81% of the mice against bubonic plague caused by subcutaneous injection of a moderate dose ( 100 LD50 ) of Y . pestis , a level of protection that was judged insufficient . We also observed that production of F1 was not stable and homogenous in the V674pF1 vaccine strain , possibly accounting for the incomplete protection conferred . The aim of this study was to generate a new vaccine strain that would allow a homogenous production of F1 , and to evaluate the immune response elicited and its protective performances against both bubonic and pneumonic plague .
Animals were housed in the Institut Pasteur animal facility accredited by the French Ministry of Agriculture to perform experiments on live mice ( accreditation B 75 15–01 , issued on May 22 , 2008 ) , in compliance with French and European regulations on the care and protection of laboratory animals ( EC Directive 86/609 , French Law 2001–486 issued on June 6 , 2001 ) . The research protocol was approved by the French Ministry of Research ( N° CETEA 2013–0038 ) and was performed in compliance with the NIH Animal Welfare Insurance ( #A5476-01 issued on 02/07/2007 ) . The Y . pseudotuberculosis and Y . pestis isolates used in this study and their derivatives are described in S1 Table [17] . Bacteria were grown at 28°C in Luria-Bertani ( LB ) broth or on LB agar plates supplemented with 0 . 002% ( w/v ) hemin ( LBH ) . Bacterial concentrations were evaluated by spectrometry at 600 nm and plating on LBH or LB plates . Chloramphenicol ( Cm , 25 μg/ml ) , ampicillin ( Amp , 100 μg/ml ) , kanamycin ( Km , 30 μg/ml ) , spectinomycin ( Spec , 50 μg/ml ) , or irgasan ( 0 . 1 μg/ml ) were added to the media when necessary . All experiments involving Y . pestis strains were performed in a BSL3 laboratory . To introduce the caf operon into the Y . pseudotuberculosis chromosome , the Tn7 transposition tool was used [18] . First , a mini-Tn7 containing a Cm resistance cassette was constructed by cloning the Cm-FRT KpnI-fragment from pFCM1 into pUCR6Kmini-Tn7 digested with KpnI . The resulting plasmid , named pUCR6K-miniTn7-Cm-FRT , was then digested by ApaI and EcoRI and ligated to the 5 kb-PCR fragment containing the entire caf operon from Y . pestis amplified with primers A ( 5’-ATAAGAATGAATTCGTGACTGATCAATATGTTGG-3’ ) and B ( 5’-CGTTAGGGCCCGTCAGTCTTGCTATCAATGC-3’ ) , which added ApaI and EcoRI sites at the extremities of the fragment . To insert the caf locus into the Y . pseudotuberculosis V674 chromosome , plasmids pUC18R6KTn7-caf-CmR and pTNS2 ( transposase provider ) were introduced together into V674 by electroporation . Transposants were selected on LB agar plates containing chloramphenicol and verified for their sensitivity to ampicillin . Presence of the transposon Tn7-caf-CmR at the chromosomal att-Tn7 site was verified by PCR , using two pairs of primers: A ( 5’-CACAGCATAACTGGACTGATTTC-3’ ) and B ( 5’- GCTATACGTGTTTGCTGATCAAGATG-3’ ) for the left junction , and C ( 5’-ATTAGCTTACGACGCTACACCC-3’ ) and D ( 5’- ACGCCACCGGAAGAACCGATACCT-3’ ) for the right junction . The recombinant Y . pseudotuberculosis strain containing the Tn7-caf-CmR region in its chromosome was initially named V674TnF1 , and its use as a vaccine against plague is protected by patent application PCT/IB2012/001609 , issued on August 7 , 2012 . For simplicity , we hereafter refer to this strain as "VTnF1" . To analyze F1 capsule production , bacteria were visualized by optical microscopy in contact with India ink , and an ELISA assay quantifying the F1 capsule on bacteria was performed as previously described [17] . A mini-Tn7 transposon containing a Km resistance cassette was constructed by cloning the Km-FRT SacI-fragment from pFKM1 into pUCR6Kmini-Tn7 digested with SacI . The recombinant pUCR6KTn7-Km plasmid was then digested with ApaI and XmaI , and ligated to the 5878 bp ApaI/XmaI fragment from pGEN-lux ( LuxCDABE provider;[19] ) . The resulting pUCR6KTn7-luxCDABE plasmid was then digested with SpeI and XmaI and ligated to the 109 bp promoter region of ail ( YPO2905 ) , obtained from the Y . pestis DNA template by PCR with primers E ( 5'- CGCACTAGTTGGAATACTGTACGAATATCC-3' ) and F ( 5'- ataCCCGGGccagattgttataacaatacc ) . The resulting plasmid was named pUCR6KTn7-Pail-lux . For transposition of Tn7-Pail-lux into the Y . pestis chromosome , plasmids pUCR6KTn7-Pail-lux and pTNS2 were introduced into CO92 bacterial cells by electroporation . Transposants were selected for with LBH agar plates containing Km . Verification of the CO92::Tn7-Pail-lux recombinant was performed by PCR using the primer pairs A/B and C/D for chromosomal integration , and by measurement of photon emission using a Xenius plate reader ( SAFAS Monaco ) for bioluminescence activity . The virulence of the recombinant CO92::Tn7-Pail-lux derivative upon s . c . injection was checked and was found to be similar to that of the wild-type strain , with a median lethal dose ( LD50 ) of 10 CFU . In vivo imaging was performed with an In Vivo Imaging System ( IVIS 100 , Caliper Life Sciences ) . Mouse vaccinations were performed in a BSL3 animal facility as described previously [17] . Animals were seven-week-old OF1 female mice or ( when specified ) C57BL/6 mice from Charles River France . Bacterial suspensions ( 200 μl in saline ) were given intragastrically ( i . g . ) to mice using a curved feeding needle . Animals were monitored for suffering ( prostration , ruffled hair ) every other day and were weighed to estimate the impact of vaccination . The virulence of the VTnF1 strain by the oral route was tested by infecting i . g . groups of mice ( four per dose ) with increasing doses of bacteria in order to determine the LD50 . The LD50 value was calculated by the Spearman-Karber method [20] . In vivo dissemination of the VTnF1 strain was examined as described previously [17] . Feces ( two fecal pellets ) were collected from live mice and were homogenized in PBS using disposable homogenizers ( Piston Pellet from Kimble Chase , Fisher Sci . ) . Peyer's patches , spleen , liver and mesenteric lymph nodes were collected aseptically from euthanized mice . They were homogenized in sterile PBS using three mm glass beads and an electric mill ( TissueLyser , Qiagen ) . The bacterial load was determined by plating serial dilutions of the homogenates . Mice were challenged either four weeks or six months after vaccination . Y . pestis strains were grown at 28°C on LBH plates , and suspensions in saline were prepared for infections . Mice were infected by s . c . injection ( 100 μl ) in the ventral skin on the linea alba . The LD50 of the CO92Δcaf strain by this route was determined by infecting mice ( six per dose ) with serial dilutions of bacterial suspensions , and was found to be 100 CFU . To induce pneumonic plague , anesthetized mice were infected i . n . as previously described [17] by instillating 10 μl of bacterial suspensions in nostrils ( 5 μl each ) . Animal survival was followed for 21 days . Mouse blood was collected three weeks after vaccination from live animals by puncture of the maxillary artery with a Goldenrod lancet ( Medipoint , USA ) . Microtiter plates ( NUNC ) were coated either with F1 antigen , or a sonicate of the Y . pestis CO92Δcaf strain ( 10 μg/ml ) . The F1 antigen was obtained from Y . pestis as described previously [21] . The sonicate of the Y . pestis CO92Δcaf strain was obtained by sonication of bacteria grown at 37°C on LB agar as previously described [17] . The Yops antigens were purified as previously described [22] , and assays were performed as described before [17] . Briefly , plates were blocked with 5% defatted dry milk and 0 . 1% Tween 20 in PBS . Sera serially diluted in PBS containing 0 . 1% BSA were incubated in wells and bound antibodies were detected using horseradish peroxydase ( HRPO ) –coupled rat antibodies specific for mouse IgG ( Bio-Rad ) . HRPO activity was revealed using TMB substrate ( OptiEIA , BD Pharmingen ) . Antibody ( Ab ) titers were calculated as the reciprocal of the lowest sample dilution giving a signal equal to two times the background . To analyze immunoglobulin isotypes , horseradish peroxidase ( HRP ) -coupled probes directed against mouse IgG1 , IgG2a , IgG2b and IgM ( Caltag ) were used . IgG3 were detected using an uncoupled goat antibody ( Abcam ) , and revealed with an HRP-coupled rabbit antibody against goat IgG ( BioRad ) . For Western blotting , Y . pestis CO92 , wild-type and Δcaf , S1 Table ) were boiled in Laemmli sample buffer ( Thermo ) and were loaded on 12% acrylamide gel for SDS-PAGE separation using a MiniProtean device ( BioRad ) . Migrated material was transferred from the gel onto a PVDF membrane ( Amersham ) . All subsequent steps were performed following the western immunoblotting protocol recommended by Cell Signaling Technology ( USA ) . Membrane strips were incubated overnight in pooled 1/100 diluted sera from naïve mice , or mice vaccinated one month earlier . Bound IgG were revealed using a secondary goat anti-mouse IgG coupled to horseradish peroxidase ( BioRad ) . ECL Plus kit ( Pierce ) was used for peroxidase revelation , and membranes were photographed using a ChemiDoc apparatus ( BioRad ) . To evaluate the cellular memory in vaccinated animals , splenocytes were cultured as described previously [17] . Briefly , spleens from euthanized animals were dissociated and erythrocytes were lyzed using Gey’s hemolytic solution [23] . Cells extensively washed with cold PBS were resuspended in RPMI 1640 + Glutamax ( Invitrogen ) supplemented with 5% fetal bovine serum , penicillin + streptomycin , and 10 mM ß-mercaptoethanol . Cells ( 5x106/condition ) were stimulated with either a sterile Y . pestis CO92Δcaf sonicate ( 5 μg/ml ) , sterile F1 antigen ( 5 μg/ml ) , or Concanavalin A ( 1 μg/ml; Sigma ) as a positive control . After three days , the supernatant was collected and the cytokine content was determined using IFNγ , IL-1β , IL-10 , and IL-17 assays ( Duosets , R&D Systems ) . The Log-rank ( Mantel-Cox ) test was used to compare survival curves . Unpaired Mann-Whitney or Student's t tests were used to compare bacteria numbers , animal weights , antibody titers , and cytokine production . The paired Mann-Whitney test was used for bioluminescence results . Analyses were performed with Prism 6 . 0 software ( GraphPad Software ) .
Strain VTnF1 was constructed by inserting the caf operon encoding F1 [17] into the chromosome of the attenuated Y . pseudotuberculosis V674 , using mini-Tn7 transposon technology [18] . F1 capsule production by recombinant VTnF1 grown at 37°C in LB broth was tested using an F1-specific rapid dipstick test [21] , which was clearly F1 positive ( S1 Fig ) . Microscopic visualization of VTnF1 in India ink revealed that all VTnF1 bacterial cells produced the F1 capsule , as visualized by the repulsion of ink particles with comparable thickness ( Fig 1A ) . This contrasted with V674pF1 cultures , which displayed encapsulated and non-encapsulated bacteria . To quantify F1 capsule production , isolated colonies obtained after growth in LB broth at 37°C were tested using an F1-specific ELISA . All VTnF1 colonies were F1 positive and their F1 levels were homogenous ( Fig 1B ) , whereas V674pF1 colonies exhibited various levels of F1 at their surface , indicating both heterogeneity and instability of F1 production . VTnF1 produced as much F1 as Y . pestis , and this production was temperature-dependent ( Fig 1C ) . Stability of F1 production was additionally tested after VTnF1 growth in vivo in mice . To this aim , VTnF1 was injected i . g . to animals , and five days later , bacteria were recovered from Peyer’s patches . All VTnF1 colonies were positive for F1 by ELISA ( Fig 1B ) . F1 production was homogenous and comparable to F1 levels of colonies obtained after in vitro culture . This demonstrates that VTnF1 stably and homogenously produced F1 after growing in vivo in mice . The V674 strain used to construct VTnF1 was strongly attenuated after intragastric inoculation into mice ( LD50 >1010 CFU;[17] ) . VTnF1 exhibited an attenuation of virulence similar to V674 and the previous vaccine V674pF1 , with an LD50 >1010 CFU . Mice vaccinated with VTnF1 at a dose of 108 CFU presented transient signs of infection ( ruffled hair ) , and a slight delay in weight gain from day three to seven post-vaccination ( average 1 . 7 g ) , but they recovered a normal weight from day 10 ( S2 Fig ) . The F1 capsule is dispensable for Y . pestis virulence in many mouse strains [24–27] , but not in others such as C57BL/6 [28] . When a high dose of VTnF1 ( 4x109 CFU ) was inoculated orally to C57BL/6 mice ( N = 7 ) , no lethality was observed during the three weeks of follow-up . This confirmed that VTnF1 was strongly attenuated , even for C57BL/6 mice that are more susceptible to F1 action . After oral inoculation of VTnF1 ( 108 CFU ) , the bacteria were detected on day five-six in the feces and Peyer's patches of all mice ( Fig 2A and 2B ) , and in the spleen , liver and mesenteric lymph nodes ( MLN ) of most animals ( Fig 2C and 2E ) . However , bacteria were almost completely cleared from the spleen , Peyer's patches and MLN after 15 days , and from the liver after 26 days . Feces tested monthly during the following 5 months remained negative , indicating a vaccine clearance from visceral organs and the gut lumen . The humoral immune response elicited by vaccination with VTnF1 ( 108 CFU orally ) was first evaluated by quantifying serum antibodies against purified F1 at different times post-vaccination over a period of six months . Anti-F1 IgG were detectable as early as four days after vaccination , and reached plateau values after 7 days ( Fig 3A ) . They then maintained at high levels without significant evolution , as shown by the fact that titers observed after six months ( d180 ) were not significantly different from those at d30 ( p = 0 . 08 , 14 mice per group ) . Analysis of the immunoglobulin isotypes revealed that both IgG1 , IgG2a , IgG2b and IgG3 contributed to this humoral response , whereas IgM peaked rapidly after vaccination and then fell to low levels ( Fig 3B ) . IgG recognizing Y . pestis antigens other than F1 were quantified by ELISA using a sonicate of Y . pestis CO92Δcaf as target . All vaccinated mice had high amounts of IgG against Y . pestis CO92Δcaf antigens ( Fig 3C ) . Western blotting analysis ( Fig 3D ) confirmed that in addition to F1 , at least 12 target antigens were recognized by immune sera . Because conformational epitopes were lost due to the denaturing conditions used for electrophoresis , actual targets were probably more numerous . Among antigens strongly recognized are two components of the Caf operon ( absent from the CO92Δcaf strain , Fig 3D ) : the Caf1 and Caf1M antigens ( MW 15 . 6 and 28 . 7 kDa respectively ) . Finally , IgG against purified Yops were also measured because these molecules are essential Y . pestis virulence factors . Such IgG were observed in almost all mice , but with varying levels ( Fig 3E ) . Altogether , our results indicate that the humoral immune response developed rapidly after a single-dose vaccination and involved all main IgG isotypes . The antibodies recognized several antigens other than F1 and were maintained at high levels for extended periods of time without recall vaccination . To evaluate the antigen-specific T cell memory elicited by VTnF1 , splenocytes taken from vaccinated animals were re-stimulated in vitro with either purified F1 antigen , or with a sonicate of the un-encapsulated Y . pestis CO92Δcaf . Cells from mice vaccinated one month earlier with VTnF1 produced IFNγ , IL-17 , and IL-10 in response to F1 ( Fig 4 ) . Although these levels were low , they were significantly higher than those of control mice ( Fig 4 ) , indicating that vaccination mobilized F1-specific memory T cells producing both pro- ( IFNγ , IL-17 ) and anti-inflammatory ( IL-10 ) cytokines . IL-1β production was also measured , but levels were very low ( <0 . 02 ng/ml ) in all conditions . In contrast , cells from VTnF1 vaccinated mice produced high amounts of IFNγ and IL-17 in response to non-F1 Y . pestis antigens , reaching levels at least 10 times higher than those observed for cells from naive mice ( Fig 4A and 4B ) . Strikingly , levels of IL-17 were comparable to those induced by the mitogenic lectin ConA , used as a positive control , indicating that a large proportion of responding splenocytes of vaccinated animals recognized Yersinia antigens and displayed potent pro-inflammatory functions . This cellular response was also much higher than that stimulated by the F1 antigen alone , reflecting the mobilization of T cells directed against multiple Y . pestis antigenic targets . Y . pestis antigens induced production of IL-10 by splenocytes from both naive and vaccinated mice , probably due to the response of innate immunity cells such as macrophages . However , splenocytes from vaccinated mice produced significantly higher levels of IL-10 than cells from naive mice stimulated with both F1 and other antigens , revealing the recall response of Y . pestis-specific memory cells with anti-inflammatory activity ( Fig 4C ) . To evaluate the durability of the cell-mediated response , splenocytes from mice vaccinated six months earlier were also tested . Levels of IFNγ , IL-17 and IL-10 in response to F1 and non-F1 antigens were lower on day 180 than on day 30 post-vaccination , but this difference was not statistically significant . Thus , although slightly reduced , the cell-mediated memory persisted after six months . To determine the protective efficacy of VTnF1 against pneumonic plague , vaccinated mice were challenged intranasally with the fully virulent Y . pestis CO92 strain four weeks after a single oral vaccination ( 108 CFU of VTnF1 ) . 100% of mice challenged with 105 CFU ( 33 LD50 ) of Y . pestis CO92 survived ( Fig 5A ) . Vaccination also protected 100% of the animals exposed to an extremely severe challenge with 107 CFU CO92 ( i . e . 3 , 300 LD50; Fig 5A ) , whereas the previous V674pF1 vaccine strain protected only 80% of the mice infected with this dose [17] . VTnF1 thus appears more protective . To evaluate the protective efficacy of VTnF1 against bubonic plague , vaccinated mice were infected s . c . with Y . pestis CO92 , four weeks after vaccination . A single oral dose ( 108 CFU ) of the VTnF1 vaccine protected 100% of the mice against 103 CFU ( 100 LD50 ) of CO92 ( Fig 5B ) . Compared to V674pF1 which only protected 81% ( 13/16 ) of animals against bubonic plague in these conditions , VTnF1 was again more protective . When animals vaccinated with VTnF1 received a very severe challenge by s . c . injection of 105 CFU CO92 ( 10000 LD50 ) , 93% of mice ( 13/14 ) were still protected . Because the immune memory is known to decline with time , the protection conferred by VTnF1 was evaluated six months ( one third of an OF1 mouse's lifespan [29] after a single-dose oral vaccination with VTnF1 . VTnF1 was completely undetectable in mice's feces at day 30 post vaccination and the following months . Upon s . c . challenge with 100 LD50 of Y . pestis CO92 , 93% of the animals were still protected against bubonic plague . Furthermore , 50% of the mice survived an i . n . challenge with 33 LD50 of Y . pestis CO92 six months after vaccination ( Fig 5D ) . This indicates that the immune memory elicited by the vaccine persisted and provided a long-lasting protection . The capacity of VTnF1 vaccination to confer protection against bubonic plague caused by a non-encapsulated Y . pestis was also evaluated by challenging vaccinated mice with Y . pestis CO92Δcaf . A single oral dose of VTnF1 conferred 100% protection against a severe intranasal challenge with 107 CFU of Y . pestis ( 107 CFU , i . e . 3 , 300 LD50 , Fig 5C ) . The same vaccination protected 93% of vaccinated mice against a severe s . c . challenge with 105 CFU ( 104 LD50; Fig 5D ) . Thus , VTnF1 conferred a strong protection against Y . pestis in the absence of the F1 pseudocapsule , indicating that , even if plague was caused by a natural or genetically modified F1-negative Y . pestis , vaccination with VTnF1 would provide a high level of protection . To determine the stage of the infectious process at which immunity provided by VTnF1 controls Y . pestis proliferation , infection by a bioluminescent Y . pestis strain ( CO92 Tn7ail-lux ) was followed in vivo in live mice . The strain carries in its chromosome the lux operon under the control of the ail promoter , known to be very active during bubonic plague [30] . In unvaccinated animals , Y . pestis multiplied at the site of injection and spread to other organs , causing the death of two mice out of five at 92h post-injection . ( Fig 6A ) . In VTnF1-vaccinated mice , the bioluminescence signal was much lower after 20 hours at the site of injection ( Fig 6B ) and after 44 hours it was no longer visible . Dissemination outside of the injection site was not observed in vaccinated mice . Therefore , vaccination induced fast-acting immune mechanisms that prevented the dissemination of Y . pestis from the site of injection .
To meet the demand for a vaccine able to confer protective immunity against both bubonic and pneumonic plague , we previously constructed the genetically modified Y . pseudotuberculosis V674pF1 candidate plague vaccine , which produces the Y . pestis F1 antigen [17] . Although this vaccine protected against inhalational exposure to Y . pestis , protection against bubonic plague was not complete . This lack of full protection was potentially explained by the observation that production of the F1 antigen was unstable . The objective of the present work was therefore to improve the vaccine by generating a new strain with improved robustness and efficiency . Loss of F1 production by V674pF1 mainly resulted from plasmid instability . Others who used Salmonella as receiver of the caf operon had to apply a sustained antibiotic pressure in vitro to ensure plasmid persistence , but this pressure could not be maintained in vivo [31] . Here , the caf operon was transposed into the chromosome [18] , and we show that production of F1 capsule by VTnF1 was comparable to that of Y . pestis , whereas F1 production by V674pF1 was much more heterogeneous and unstable . The stability of VTnF1 characteristics will allow the large-scale production of the live vaccine according to good manufacturing procedures . VTnF1 provides high protection against both bubonic and pneumonic plague , and is more efficient than V674pF1 used at the same dose ( 108 CFU ) and tested in the same conditions [17] . We had previously reported that only 80% of the mice vaccinated with V674pF1 survived a high-dose pneumonic plague challenge ( 107 CFU CO92 ) , or a moderate bubonic plague challenge ( 103 CFU ) . In contrast , VTnF1 provided complete protection against commonly used bacterial challenges , and almost complete protection against very high challenges . Because V674pF1 and VTnF1 are both derivatives of the V674 attenuated strain , the only possible explanation for this difference of protection is the more homogenous and sustained production of the F1 pseudocapsule by VTnF1 . The F1 antigen can activate macrophages [32] , an adjuvant effect favorable to the adaptive immune response . Therefore , the efficiency of VTnF1 may result from a stronger stimulation of macrophages , and possibly also of dendritic cells which belong to the same lineage , thus fostering immunity more efficiently . The high-level protection observed is especially remarkable as it was obtained with a single oral dose of vaccine . Such a very simple procedure is a key advantage as compared to the repeated injections required by most vaccines to confer a protection extended in time . Difficult to perform in the field , repeated injections are considered by public health authorities as a limitation for mass vaccination . Mice vaccinated with VTnF1 very rapidly control Y . pestis at the skin entry site . This suggests that easily mobilizable effectors such as antibodies and phagocytes reach the infected tissue . Antibodies might play an essential role in protection conferred by VTnF1 , as suggested by the high antibody titers of vaccinated mice . VTnF1 displays much more antigenic diversity than plague molecular vaccines currently under development , which are composed of only F1 and LcrV antigens . The immune response induced by VTnF1 targets various Y . pestis proteins in addition to Caf1 and Caf1M antigens . This target diversity is valuable to protect against bacteria , which can lose target antigens via gene deletions , as observed for the Caf1/F1 antigen [33] . The anti-F1 IgG are known to provide protection by opsonizing Y . pestis , facilitated by F1 abundance and surface localization [25 , 26 , 34] . Whereas the abundant anti-F1 IgG induced by VTnF1 probably play a central protective role against wild type ( F1-encapsulated ) Y . pestis [25 , 27 , 34 , 35] , the resistance of vaccinated mice to plague caused by an F1-negative Y . pestis strain demonstrates that the antibodies directed against other antigens also contribute to protection . Y . pestis F1-specific IgG induced by VTnF1 are detectable as early as four days after vaccination , a fast kinetic comparable to that observed after vaccination with soluble , recombinant F1 [36] . This prompt onset of IgG production indicates that VTnF1 rapidly interacts with lymphoid cells , probably in Peyer's patches , mesenteric lymph nodes and spleen where VTnF1 was observed . The fast switch of the humoral response from IgM toward antibodies of isotypes IgG1 , two and three , generally of high affinity , indicates a strong T-cell dependent response . The presence of abundant IgG3 also indicates that carbohydrate targets are recognized [37] . This diversity is favorable for opsonization via all Fcγ receptors and suggests the involvement of the various B-cells subtypes to the vaccine-induced response [38] . Because IgG levels escalate during the first week to reach the plateau levels observed during the following six months , their contribution to protection against plague at day 30 could already be available at day seven . Cellular immunity plays an important role against plague [39 , 40] and performs critical protective functions during humoral defense against pneumonic plague [41] . Molecular vaccines adjuvanted with alum are poor inducers of this part of the immune response [12 , 13] . In contrast , VTnF1 triggered a strong cell-mediated response without adjuvant . Part of the recall cell response was directed against F1 , but the most important part was directed toward non-F1 antigens . Its intensity , with IL-17 comparable to a mitogenic stimulation by Concanavalin A , indicates the engagement of a high percentage of splenocytes that are likely recognizing multiple antigenic targets . Memory cells produced IFNγ , IL-17 , and IL-10 , composing a mixed Th1-Th17 profile . An IFNγ-dependent type 1 immune response is essential for vaccine-induced protection against plague [39] . IFNγ derived from memory T cells instruct potent innate cell activation , resulting in a fast protective immunity against invading microorganisms [42] . IL-17 producing T lymphocytes ( Th17 cells ) are essential to cure pneumonic plague [43] due to the essential role played by IL-17 in the induction of antimicrobial peptides and attraction of polymorphonuclear leukocytes [44] . The memory response induced by VTnF1 also involves production of the anti-inflammatory cytokine IL-10 , which balances potentially harmful effects of IL-17 [45] . In addition to these direct functions , T cells play an important role in antibody-dependent immunity [40] and thus potentiate the humoral response . Altogether , the immune response induced by VTnF1 , by combining humoral and cellular mechanisms , has the characteristics required to efficiently clear Y . pestis . The sustained humoral and cellular immunity six months after vaccination is unlikely to result from a prolonged stimulation of immunity by live bacteria because VTnF1 was undetectable in feces and organs of most mice one month after vaccination . IgG could be produced by long-lived plasma cells , which differ from so-called memory B cells , and produce abundant IgG without re-stimulation [46] . VTnF1 could have persisted in the gut after one month , for example by durably colonizing the cecum as recently reported [47] , however this is unlikely because only virulent strains cause cecum foci , and they yield high levels of cultivable bacteria in feces . The protective immunity provided by VTnF1 after a six-month period ( 93% against bubonic plague and 50% against pneumonic plague ) is remarkably long-lasting since six months correspond to one third of the mouse life [29] , which is comparable to #30 years in human lifespan . Considering that both antibody titers and cellular responsiveness remained high after six months , the almost full protection against bubonic plague may result from either compartment or more likely a synergy of the two [14] . The lower protection against pneumonic plague indicates that lung immunity is the most demanding and requires one or more components of the immune response , that are mandatory for protection but are the first to decline with time . The nature of this component of acquired immunity is yet to determine , but does not seem related to Ig isotype switching or the decline of antibody or IFNγ/IL-17 production . One possible explanation is that aging causes a modification of alveolar macrophages functions , with spontaneous activation and reduced responsiveness to external stimuli , thus contributing to lung susceptibility to infection [48 , 49] . Live attenuated Y . pestis plague vaccines ( EV76 and subclones ) has been previously used with success in humans , but could generate strong side effects [6 , 7] , and , as all Y . pestis strains , are subject to easy genetic rearrangements due to high numbers of insertion sequences in the genome [4 , 8] . In addition to genetic stability , VTnF1 combines the known advantages of replicating vaccines: elicitation of humoral and cell-mediated immune responses , robustness against mutant microorganisms , easiness of mass production and use , limited cost , etc . , whilst providing guarantees in terms of attenuation , stability , and efficacy against both bubonic and pneumonic plague . Preparedness plans against bioterrorist attacks imply stockpiling millions of vaccine doses . However , stockpiles have a finite lifespan and thus demand regular production of new doses , a rapidly expensive strategy [50] . Live vaccines can be rapidly produced in mass amounts , and are now viewed as a valuable alternative . In conclusion , we propose here a vaccine providing high-level protection against both bubonic and pneumonic plague after a single-dose immunization . VTnF1 is an easy-to-produce , genetically stable and irreversibly attenuated vaccine , providing a long-lasting and highly efficient protection against both wild type and un-encapsulated ( F1-negative ) Y . pestis . To our knowledge , VTnF1 is the only plague vaccine ever reported that could provide high and long lasting protection against both bubonic and pneumonic plague after a single oral administration .
|
Yersinia pestis , the agent of plague , is among the deadliest infectious agents affecting humans . Injected in the skin by infected fleas , Y . pestis causes bubonic plague , which occasionally evolves into the very lethal and contagious pneumonic plague . Y . pestis is also a dangerous potential bioweapon but no plague vaccine is available . The current study describes the development of a vaccine highly efficient against plague in both its bubonic and pneumonic forms . The strategy consists of a live , avirulent , genetically modified Yersinia pseudotuberculosis that produces the capsule antigen of Y . pestis , named F1 . The goal was to propose a vaccine that would be both easy to produce rapidly in large amounts with high quality , and easy to administer to individuals via a single oral dose . The VTnF1 strain described fulfills these demands . The immune response generated is long-lasting , involving both antibodies and memory cells directed against F1 and other antigens . We conclude that VTnF1 is a very promising candidate vaccine against plague .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Complete Protection against Pneumonic and Bubonic Plague after a Single Oral Vaccination
|
The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties . Traditionally , various current waveforms have been injected at the soma to probe neuron dynamics , but the rationale for selecting specific stimuli has never been rigorously justified . The present experimental and theoretical study proposes a novel framework , inspired by learning theory , for objectively selecting the stimuli that best unravel the neuron's dynamics . The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron's dynamics as attested by their ability to generalize well to the neuron's response to novel experimental stimuli . We used this framework to evaluate a variety of stimuli in different types of cortical neurons , ages and animals . Despite their simplicity , a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons . The general framework that we propose paves a new way for defining , evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose .
Ever since the seminal study of Hodgkin and Huxley [1] on the biophysical basis of the squid giant axon action potential , conductance-based models ( CBMs ) have provided a critical connection between the microscopic level of membrane ion channels and the macroscopic level of signal flow in neuronal circuits . Indeed , as we have sought to further our understanding of single neuron and network computation [2] , [3] , CBMs have become one of the powerful computational approaches in Neuroscience [4] , [5] , [6] , [7] . They have been of great assistance in incorporating diverse experimental data under a coherent , quantitative framework and for interpreting experimental results in a functionally meaningful way [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] . Considering the dramatic advancements in our knowledge of single neurons and neural circuits along with the equally impressive increase in computing power during the last decade , CBMs can be expected to become of even greater utility than they already are today [20] , [21] , [22] . The most fundamental difficulty in accurately modeling neurons stems from the fact that their electrical behavior arises from the complex interaction of a large number of non-linear elements – the membrane ion channels [10] , [23] . Furthermore , the identity and density of different ion channels vary from neuron to neuron and cannot presently be directly determined experimentally . Instead , these are treated as free parameters that are typically constrained by an iterative process of comparison between a set of experimental recordings ( e . g . voltage response to current-clamp steps ) and the model's responses until a close resemblance is found . Yet successful matching of model response to a given target experimental data set is not , in and of itself , sufficient to establish the validity of a model , as complex models with numerous parameters run the risk of systematic biases ( or errors ) in the estimation of their parameters , i . e . over-fitting . Specifically , such a bias may not be apparent in the accuracy of matching the response to stimuli used to construct the model , but may be revealed by further testing of the model's generalization to different conditions . One can imagine many different such tests: predicting the response to pharmacological manipulation , examining the stability of the model to small perturbations of model parameter values , etc . Here we describe the application of a particularly intuitive yet powerful measure of generalization: the model's ability to generate an accurate response to a set of current stimuli to which it has not been previously exposed during the model's construction . We favor this form of testing generalization since it gets to the heart of the purpose of conductance-based models ( CBMs ) – to examine whether the model can indeed be considered a valid approximation of the neuron's underlying dynamics . If that were indeed the case , one would expect the response of the model to match the experimental response not only to the stimuli used to constrain it , but also to different , novel inputs . Moreover , measuring the response to different stimuli is experimentally straightforward . Such measurement of generalization has only been sporadically applied in CBM research papers [24] most likely due to the fact that the vast majority of CBMs studies involved hand tuning of parameters [25] , [26] in which clean separation between test sets and generalization sets is difficult due to human involvement . Thus , despite its importance , the quantification of generalization has been lacking from conductance-based neuron modeling . Since there are many different choices for stimuli that can be used to train and test a model , it is crucial to have a clear way of selecting an optimal ( and minimal ) set of stimuli ( and corresponding targets to be preserved ) that will ensure accurate generalization to a wide range of inputs . The present work is the first to have addressed these fundamental principles in order to assess the validity of CBMs in a thorough manner . We experimentally recorded the responses of a variety of cortical neurons to a wide set of different current stimuli ( step , ramp and noise currents ) each with multiple intensities and many repetitions . We then selected a subset of the experimental data ( a training set ) to be employed in generating models of the respective cells , using automated multiple objective optimization algorithms , and reserved another set of stimuli to test the accuracy of these models in generalizing to novel stimuli . We show that , for all neurons tested , CBMs were able to accurately predict stimuli not encountered during the parameter constraining process . Furthermore , by systematically changing the number and type of stimuli used to constrain the models , we determined how each stimulus contributes to the models' predictive power . Notably , models trained solely on responses to step currents were able to accurately predict both the responses to simple stimuli such as ramp current as well as to the responses to physiologically inspired noisy current injections . In contrast , models trained on either ramp or noisy inputs were not as successful in predicting the response to other types of stimuli , i . e . do not generalize well . We discuss the reasons why some stimuli are more successful for estimating the properties of the underlying ion channels than others as well as the implication of this work on the way we understand the process of constraining biophysical neuron models and on the data collection approach required to allow the generation of accurate , predictive CBMs . We believe that our method will become a standard tool for generating in-silico models for a variety of neuron types and that these models could then be used in realistic models of large scale neuronal networks .
The process of constraining ( training ) the CBMs is portrayed in Figure 1 and explained in detail in the Materials and Methods section . Briefly , from the experimental data , voltage responses to suprathreshold current inputs , ( Figure 1a ) we first extract a set of features ( Figure 1b ) . We then obtain the reconstructed morphology of the neuron and assume a set of membrane conductances ( ion currents ) to be present in the neuron's soma ( Figure 1c ) . In the present study we assumed that the modeled cortical cells contain: Transient sodium channel-Nat , Delayed potassium channel-Kd , Slow inactivating persistent potassium channel-Kp , fast non-inactivating potassium channel Kv3 . 1 channel , high-voltage-activated calcium channel Ca , calcium dependent K channel - SK , Hyperpolarization-activated cation current – Ih , M-type potassium channel Im ( for full details see Materials and Methods ) . In the interests of simplicity , and since recordings were performed in the soma , we assumed the neuron's dendrites to be passive . We then run an optimization algorithm ( a Multiple Objective Optimization ( MOO ) algorithm [27] ) to constrain the values of the maximal conductances of these ion channels and of the passive properties of the neuron . The optimization generates a set of multiple models from which we select for further analysis only the models that pass a selection criterion ( Figure 1e ) . These constitute the final set of acceptable models ( Figure 1f ) . The procedure of assessing the models' generalization power is depicted in Figure 2 . In Figure 2a three suprathreshold step currents ( together with the corresponding experimental voltage responses ) were injected to a rat layer V pyramidal cell and used as the training set . Model parameters , maximal conductance values for the eight excitable ion channels modeled and the neuron's passive properties , were automatically constrained until the response of the resulting set of models closely matched the experimental data ( Figure 2b ) . Then , while keeping the model parameters fixed , we applied to the models a new set of stimuli ( ramp currents in this example ) that were not encountered during the parameter constraining procedure , and recorded the models' voltage response to these new stimuli ( Figure 2c , red trace , generalization ) . Finally , we quantified the degree of resemblance of the model response to that of the corresponding experimental response ( Figure 2d ) . This is quantitatively expressed as the model mismatch , or error , as measured by the feature-based distance between model and experiment in units of experimental standard deviation ( SD ) [27] . Figure 3a depicts the ability of models constrained by responses to step currents to predict ( generalize to ) the response to suprathreshold ramp current injections . We find that as the size ( the number of different step currents ) of the training set increases , the average training error between the model and the experimental responses slightly increases ( Figure 3a , blue circles ) . This is expected from learning theory as a model of a given complexity is challenged to fit a growing number of targets [28] . However , the more interesting measure of model accuracy is the error in matching responses to stimuli not encountered during the parameter constraining procedure , the generalization error . This error steeply decreases with the size of the training set ( Figure 3a , red circles , difference between one and four stimuli , P<0 . 0001 ) indicating more accurate , reliable ( better constrained ) models . We next turn to constraining models by ramp current injection ( Figure 3b ) . Surprisingly , when attempting to generalize to step currents using models that were trained on ramp currents , an increase on the size of the training set did not yield better generalization for the response to step currents and the distribution of the generalization errors was very broad ( Figure 3b ) . In order to determine the impact of the nature and number of stimuli used to constrain models on the conductance values of successful solutions , we portray in Figure 3c the spread of parameter values found at the end of the parameter optimization process , as well as simulations of all points on a grid [29] . The spread of solutions consistent with one step stimulus was considerably larger than that of solutions consistent with four ( ratio of areas 0 . 24; Figure 3c , light and dark blue areas for one and four stimuli , respectively , shown in two dimensional space ) . Note that though visualization is difficult beyond three dimensions , the calculation of the consistency of points on a grid with each stimulus can be readily performed on the high dimensional grid . This reduction in area with increasing number of training set stimuli can be seen for most individual conductance dimensions when considered separately as well ( Figure S1a ) . When considering models constrained on ramp currents we again find that for most conductances an increase in the number of stimuli leads to reduction in the spread of successful solutions ( Figure S1b ) . However , the relative size of the area of solutions consistent with four ramp and step current stimuli ( Figure 3d blue areas marked with stimulus icon ) in relation to the area of intersection ( Figure 3d dark blue ) is much larger for ramp currents than step currents . Thus , a solution chosen at random from those consistent with step currents is far more likely to be in the area of intersection , i . e . to be consistent with responses to both ramp and step currents . This is directly in line with the more successful generalization from step current responses to responses to ramp currents than vice versa . We note that the different ways in which stimuli “carve out” zones in parameter space is highly relevant to the problem of solution non-uniqueness and return to this subject in the Discussion . To ensure that the asymmetric generalization is not due to an inherent difficulty with constraining models to match responses to ramp stimuli we quantified the ability of models trained on ramp currents to generalize within stimulus , e . g . , to other ramp current stimuli not encountered during the parameter constraining procedure . We find that , in contrast to the between stimulus generalization , addition of stimuli results in decreased generalization error ( Figure 4a , difference between one and four ramp stimuli , P<0 . 0001 ) . We compared this to the within stimulus generalization in step currents and found it to be qualitatively similar ( Figure 4b , difference between one and four step stimuli , P<0 . 0001 ) . Step or ramp currents are clearly not likely currents for a neuron to encounter in its natural setting . Thus , we consider in Figure 5 the ability of models constrained with these simple stimuli to predict more physiological noise current injections . We employed the gamma coincidence factor ( GCF ) [30] , [31] in order to measure how well-locked is the timing of model APs generated in response to noisy current injection to the APs recorded experimentally in response to the same current ( across multiple experimental repetitions of the current injection ) . Two different noisy currents were used , one with high mean and low standard deviation ( noise type 1 ) and one with low mean and high standard deviation ( noise type 2 ) . We find that models trained on two steps currents and two ramp currents were the best predictors of the experimental AP times that were generated in response to both noisy currents . When comparing the number of APs coincident between the voltage responses derived from two repetitions of the current input , the number of model APs coincident with those of any given experimental repetition was over 90% of the number of APs consistent between two different experimental repetitions ( Figure 5a black traces experimental voltage , black dots experimental AP times , red trace model voltage response , red dots model AP times; GCF 0 . 91±0 . 03 ) . Very similar accuracy was obtained for the second type of noise current ( GCF 0 . 92±0 . 04 , Figure S2a ) . Responses to noise currents can themselves be used to constrain the model by attempting to maximize the temporal fidelity of the model to the experimental AP times . Indeed , models trained on responses to noisy currents achieve a perfect within model accuracy of GCF = 1 . Generalization within stimulus type ( to the other noisy current type ) was also highly successful ( Figure 5b GCF 0 . 95±0 . 09 ) . However , models trained on noisy currents poorly matched responses to step and ramp current inputs ( Figure 5c , 5d average feature error 2 . 58±0 . 85 and 2 . 92±0 . 97 respectively in experimental SD units ) . The discrepancies in feature values fell beyond 2 . 5 experimental SD units , more than twice as much as the between stimulus generalization error of step currents . In addition , the spread of parameter values of successful solutions was very broad ( not shown ) . Thus , the generalization from responses to noise currents to that of simple currents was asymmetric , with the combined step and ramp currents generalizing well to noise currents but not vice versa . We determined the accuracy of generalization from all different training sets to all generalization test sets ( Table 1 ) . We find that the combined set of ramp and step stimuli was the most effective in generalizing to responses of both the simple and noise current injections . Among the single stimuli , the step stimulus was the most successful . Additionally , we find that though adding stimulus intensities improves the generalization error , the added benefit of including additional stimulus intensities of the same type in the training set drops after more than three stimulus intensities . We note that there is no theoretical guarantee that models that generalize well to a certain type of stimulus will also generalize well to different ones . An important class of stimuli are stimuli that continuously sweep through a range of frequencies , sometimes referred to as “chirp” or “zap” stimuli [32] . Though we did not explore the space of such stimuli extensively in our experiments , for the data we have we find that models trained on the combined step and ramp stimuli generalize well to subthreshold frequency sweeps ( Figure S3 ) . Results presented so far pertained to models of a rat layer V pyramidal cell . In order to assess the generality of the results we applied the analyses described above to four additional cells . These cells provided examples of different cell types ( pyramidal , interneuron ) , different ages ( juvenile , adult ) and different animals ( rat , mouse ) . We were able to generate successful CBMs for each of the cells selected ( shown in Figure 6a ) . We found that , in general , the major results highlighted above are consistent across all cells . Namely , the combined set of step and ramp stimuli was the most effective and achieved very high temporal precision values ( Figure 6b ) . The generalization error was reduced as the number of stimuli increased ( Figure 6c ) and the generalization between stimuli was asymmetrical , with this set capable of matching responses to noisy currents , but not vice versa .
Importantly , by considering the ability of CBMs trained on one stimulus type to predict the responses to a set of different stimuli , we provide a simple and valuable way of measuring the utility of a certain stimulus in generating faithful CBMs . Clearly , evaluating the utility of a given stimulus is of great practical importance not only to those directly involved in biophysical modeling but also to experimentalists as it will provide an objective method of selecting which stimuli to be applied experimentally to a neuron in the limited time of stable recording . Notably , despite its centrality to the modeling effort , this subject has evoked little systematic study , perhaps due to the technical difficulty of generating CBMs that generalize well to experimental data ( for surrogate data see ref . [38] ) . Evaluation of the utility of different stimuli has been performed for simpler biophysical models , such as integrate and fire type models [39] . However , the stimuli found are typically closely tied to the specific phenomenological nature of the model assumed ( e . g . , a stimulus tailored to accurately measured the AP threshold ) and are thus not always applicable to models of a different nature ( e . g . models that do not have an explicit parameter for the threshold , such as CBMs ) . For the step and ramp currents studied here , we find that multiple suprathreshold intensities of two second long step and ramp currents are required to generate faithful models . For the number of stimulus intensities studied here additional intensities reduce the generalization error ( Figures 3 , 4 ) . Yet , the added benefit of stimuli beyond three intensities diminishes . For the noise currents , we find that ten second long stimuli were sufficient to generate models that generalize well for different noise currents . For each of the stimuli , we use ten repetitions to estimate the intrinsic variability . A combined set of step and ramp stimuli was able to achieve even better generalization ( Table 1 ) . Thus , training sets combining different stimuli are expected to be more effective than single stimulus sets in their generalization as we indeed find ( see below ) . To what do we attribute the success of step stimuli in generalizing to other stimuli ? More generally , what could make one stimulus more useful than another in generating models that generalize to a wide variety of stimuli ? The intuition behind the success of the step stimulus relies on a combination of the nature of the stimulus itself and single-cell biophysics . The ion-channels expressed by a neuron exhibit a wide range of time constants , from the very brief ( less than a millisecond ) to the very long ( hundreds of milliseconds and more ) . Different stimuli activate these membrane ion channels to different degrees . If a certain channel is only partially activated by a given stimulus , the contribution of this channel to shaping the model dynamics ( and hence the sensitivity of its parameter values ) will not be well estimated . The slow transition through voltage prior to firing an AP elicited by ramp currents strongly inactivates transient currents ( e . g . , fast inactivating Na+ channels ) . Thus , if only ramp currents are present in the training set , the parameter constraining procedure has no opportunity to “learn” of the possibility of transient activation , leading to an underestimate of the sensitivity of parameter values of transient channels . When this model is challenged with the need to generalize to depolarizing step stimuli , in which the degree of inactivation prior to the first AP is much smaller , the full sensitivity of transient channels comes into play and some of the models fail to generate accurate responses . In contrast , white noise ( or noise smoothed by a short correlation time ) is essentially a continuous series of transients . This rapid transition between voltage values is ineffective at activating channels with longer time constants . Hence , the sensitivity of channels with long time constants ( e . g . slow inactivating potassium channels such as Kp ) is underestimated by models trained solely on noise currents . In other words , noise currents are composed only of transient responses and ramp currents lack a strong transient . Step currents , on the other hand , contain both an initial strong transient followed by a sustained level of depolarization . Thus , they are able to activate both transient channels and channels with long time constants , yielding more accurate estimates of their contribution to the overall response of the cell . Note , that had we been dealing with a linear system , white noise would be sufficient to determine its transfer properties and no other stimuli would be required [40] . However , neurons are of course highly nonlinear systems . The intuitive description above is in line with the quantitative results regarding the effectiveness of generalization from different stimuli i . e . , the failure of models trained on ramps to generalize to step currents , ( Figure 3 ) , the failure of models trained on noise currents to generalize to steps and ramps ( Table 1 ) and the spread of acceptable parameter values ( Figure 3 ) . Notably , the intuitions developed are relevant not only to the specific model itself ( as would be the case with phenomenological models ) but also to the general understanding of the function of different ion channels in sculpting neuronal dynamics since the models directly incorporate the experimentally derived dynamics of specific channels . In summary , despite the simple and artificial nature of the step current , it is more successful in constraining the dynamics of the neuron than the synaptic-like noisy stimuli that more closely mimic the conditions a neuron might encounter in-vivo . Thus , we point out that the similarity to natural conditions should not be the only reason for selecting stimuli . Indeed , one must in addition consider how the stimuli might be used to uncover the underlying biophysical dynamics . Mapping the portion of parameter space [29] corresponding to solutions consistent with a given stimulus provides a both intuitive and quantitative view of the effect of different stimuli on model reliability . Different stimuli carve-out different shaped zones in parameter space ( see Figure 3 ) . The degree to which two zones overlap is an indication of how well the models will generalize from one to the other , as only those models found in the intersection area are consistent with both . Thus , if one of the stimuli is chosen to train the model , the portion of the area found outside of the intersection area corresponds to models that will fail to generalize to the other stimulus . By combining different stimuli in the training set we obtain different intersections of these zones . Ultimately , we are interested in finding effective intersections that will reduce the space of solutions as efficiently as possible to the intersection of all stimuli measured . Naturally , as we add more and more stimuli at some point the zones will fail to intersect any longer , indicating that we have tasked our models too far and must either choose a different model or less ambitious requirements . Notably , we believe that this provides a very useful framework for tackling the problem of non-uniqueness in the solution space . Importantly , this will allow more detailed exploration of the spread and composition of different membrane channel conductances for a given neuron type and even comparisons between the same neuron type at a different stage of neuronal maturation , or between different neuron types and different species . In summary , we have demonstrated that , given the experimental response to different stimulus types ( and several repetitions of each ) and based on the theoretical framework presented here , we can construct faithful CBMs of different neuron types that can accurately predict the responses to both simple and noisy current injections that were not used during model construction . This suggests that the models generated indeed capture the neuron's dynamics . We emphasize that modeling studies should report not only the similarity of models to the data used in their generation ( training error ) but should also reserve some of their data for examining the generalization ( or predictive ) quality of the models . We note however that there is no guarantee that models that generalize well to a certain stimulus will also generalize well to other stimuli and this issue requires more careful exploration with many stimuli . Our development of a framework to quantitatively test the utility of different stimuli and our finding that some stimuli are more advantageous in constraining CBMs than other stimuli has prompted us to start exploring experimentally and theoretically the effectiveness of more sophisticated stimulus protocols in constraining neuronal models . Ultimately , the goal is to find the optimal ( and minimal ) set of stimuli that ensure accurate generalization to a wide set of diverse stimuli . There are numerous possible options for the different forms of stimuli that could be injected within a fixed time , for instance frequency sweeps that explore frequency response and resonant properties of neurons [32] , [41] or more complicated noisy stimuli that alternate between different noise parameters [42] , [43] . This is a subject that is presently under active pursuit .
Wistar rats ( 17–19 days old ) and one x98 mouse [44] were quickly decapitated according to the Swiss national and institutional guidelines . The brain was carefully removed and placed in ice-cold artificial cerebrospinal fluid ( ACSF ) . 300 mm thick parasaggital slices were cut on a HR2 vibratome ( Sigmann Elektronik , Heidelberg , Germany ) . Slices were incubated at 37°C for 45–60 min and then left at room temperature until recording . Cells were visualized by infrared differential interference contrast videomicroscopy utilizing a VX55 camera ( Till Photonics , Gräfeling , Germany ) mounted on an upright BX51WI microscope ( Olympus , Tokyo , Japan ) . Cells were patched in slices ∼1 . 8 mm lateral to the midline and above the anterior extremity of the hippocampus ±0 . 8 mm , corresponding to the primary somatosensory cortex [45] , [46] , [47] . Thick tufted layer 5 PCs ( rat and mouse ) were selected according to their large soma size and their apparent large trunk of the apical dendrite . Layer 6 fast-spiking interneurons were selected according to their multipolar soma shape . Care was taken to use only “parallel” slices , i . e . slices that had a cutting plane parallel to the course of the apical dendrites and the primary axonal trunk . The cell type was confirmed by biocytin staining revealed by standard histochemical procedures [48] . Slices were continuously superfused with ACSF containing ( in mM ) 125 NaCl , 25 NaHCO3 , 2 . 5 KCl , 1 . 25 NaH2PO4 , 2 CaCl2 , 1 MgCl2 , and 25 D-glucose , bubbled with 95% O2 – 5% CO2 . The intracellular pipette solution ( ICS ) contained ( in mM ) 110 K-gluconate , 10 KCl , 4 ATP-Mg , 10 phosphocreatine , 0 . 3 GTP , 10 N-2-hydroxyethylpiperazine-N9-2-ethanesulfonic acid ( HEPES ) , and 13 biocytin , adjusted to a pH 7 . 3–7 . 4 with 5 M KOH . Osmolarity was adjusted to 290–300 mosm with D-mannitol ( 35 mM ) . The membrane potential values given were not corrected for the liquid junction potential , which was approximately −14 mV . All chemicals were from Sigma-Aldrich ( Steinheim , Germany ) or Merck ( Darmstadt , Germany ) . Whole cell recordings ( 1–3 cells simultaneously ) were performed with Axopatch 200B amplifiers ( Molecular Devices , Union City , CA ) in the current clamp mode at a bath temperature of 34±1°C during recording . Data acquisition was performed with an ITC-1600 board ( Instrutech Co , Port Washington , NY ) , connected to a Macintosh running a custom written routine under IgorPro ( Wavemetrics , Portland , OR ) . Sampling rates were 10 kHz , and the voltage signal was filtered with a 2 kHz Bessel filter . Patch pipettes were pulled with a Flamming/Brown micropipette puller P-97 ( Sutter Instruments Co , Novato , CA ) and had an initial resistance of 3–4 MW . During recording the series resistance was 10 , 10 , 11 , 17 , or 22 MW and bridge balanced . Miniature excitatory postsynaptic potentials ( mEPSPs ) were blocked with 10 mM CNQX and occasionally with 40 mM AP5 . Three different types of stimuli were applied . Stimuli were scaled with a constant factor k ∈ ( 1 , 2 , 2 , 2 . 5 , 3 ) so that the cells fired with moderate mean frequencies of 2–20 Hz , high enough to obtain enough spikes for analysis , yet low enough not to over stimulate the cells and shorten their life span . Six depolarizing step currents of 2 s duration and increasing amplitudes ( 100–225×k pA ) were applied . Five depolarizing ramp currents , 2 s rising phase ( from 0 to 125–250×k pA ) and symmetrically decaying falling phase , were injected ( only rising phase was used in this study ) . In addition , we apply Ornstein-Uhlenbeck [49] ( OU ) colored noise processes that are considered to represent the current that might arrive at the soma of a cell as a result of the summation of the activation of many synapses in the cell's dendritic arbor [50] . We employ two different 20 s long OU processes with identical correlation time ( 2 ms ) but different statistics . One is generated with a mean of 50×k pA and SD of 100×k pA ( hereby referred to as noise type 1 ) . The other mirrors this process by having a mean of 100×k pA and SD of 50×k pA ( hereby referred to as noise type 2 ) . We repeatedly inject the different currents in order to measure response variability . Each stimulus was repeated 10–20 times . All simulations were performed in the NEURON simulation environment [51] . The morphology of 5 cortical neurons from rat and mouse somatosensory cortex was derived from reconstruction of in-vitro stained cells . The number of compartments employed differed from cell to cell , all cells contained more than a hundred compartments . Specific axial resistance was 150 Ωcm and capacitance was 1 µF . The following ion channels were assumed to be present in the membrane of the modeled soma: Transient sodium channel-Na , Delayed potassium channel-Kd , Slow inactivating persistent potassium channel-Kp , fast non-inactivating potassium channel Kv3 . 1 channel , high-voltage-activated calcium channel Ca , calcium dependent K channel - SK , Hyperpolarization-activated cation current – Ih , M-type potassium channel Im , for full details see ref . [27] . The dynamics of these channels were described using Hodgkin and Huxley formalism [1] . As all the experimental recordings in this work were performed from the cell's somata and for the sake of simplicity , the modeled dendrites were assumed to be passive . The maximal conductance of all eight channels along with the leak reversal potential and leak conductance in the soma and dendrite served as free parameters , yielding a total of eleven free parameters in the model . The allowed range for the conductances can be found in ref . [27] . An overview of the procedure by which we generate conductance-based models ( CBMs ) from an experimental data set is presented in Figure S1 . We begin by recording the responses ( Figure S1a ) of the cell to intracellular current injection . Responses are then analyzed by the extraction of a set of features ( Figure S1b ) , which are used to generate feature-based distance functions ( see below ) . Next , we use the reconstructed morphology ( Figure S1c ) to generate the compartmental model of that cell and assume a set of 8 ion channels to be present in the soma membrane of the model cell . Together the reconstructed morphology and the assumed ion channels compose the model skeleton . When combined with a set of specific values for the free parameters they together constitute a single CBM for that neuron . A stochastic optimization procedure is employed to constrain the parameters of the model in accordance with the experimental data . We employed a multiple objective optimization ( MOO ) algorithm which operates by genetic algorithm optimization [52] . The algorithm evaluates 300 sets of parameter values in parallel and iteratively seeks to reduce the error , which measures the discrepancy between model and experiment ( Figure S1d ) . As the algorithm is stochastic in nature , we repeat the optimization procedure ten times in order to reduce the chance that the optimization procedure fails to converge . Thus , at the end of the optimization procedure , 3000 parameter sets , i . e . 3000 tentative models of the cell are obtained along with their corresponding error values . We then choose only those models that pass the acceptance criterion of a model-experiment mismatch no greater than two SD in each feature ( Figure S1e ) . Ultimately , we end up with a set of models that closely match the experimental voltage responses ( Figure S1f ) . The discrepancy between the target experimental data ( a train of spikes in response to a set of current stimuli ) and model simulation of the response was measured using feature-based distance functions [27] . Features to be fitted were extracted from the firing response of the neuron ( e . g . number of action potentials ( APs ) , spike height ) . The value of each feature was derived from the experimental responses . The model response to the same stimulus was then analyzed in an identical fashion . The model-to-experiment distance value , for this feature , was measured by the distance of the model feature value from the experimental mean , in units of experimental SD . These distance functions have two main advantages . First , they address experimental variability by considering the distance of a model in relation to the experimental SD . Second , they are expressed in well defined , not arbitrary , units . For step pulses , we employ a set of six features: the number of action potentials ( APs ) during the pulse , the time to the first AP from stimulus onset , the accommodation index ( a measure of the accommodation in the rate of APs during the stimulus [27] ) , the width of an AP at half height , the average height of an AP , and the average depth of the after hyperpolarization ( AHP ) as defined by the minimal voltage point . For ramp currents , as the height of APs decreases during the stimulus response , we considered not only the average height of APs and the depth of AHPs but also the slope of a linear fit to the change as an additional feature . For the noise stimuli we do not use feature-based distance functions , but rather the gamma coincidence factor [30] , [31] - an index measuring the coincidence of AP times in relation to the neuron's intrinsic reliability . The index is normalized from 0 to 1 , a value of 0 indicates that a model does no better than a Poisson train and a value of 1 indicates that the model and experimental repetitions have as many coincident spikes on average as do two experimental repetitions . Note , that in this context the objective of optimization is to maximize this value . In order to assess the utility of different stimuli in generating neuron models that generalize well both within stimulus and across stimuli we generate models with training sets that are equally matched in terms of the length of the experimental data . Namely , we consider four different training sets: step current pulses only ( four intensities of two second long step currents ) , ramp current pulses only ( four intensities of two second long ramp currents ) , combined step and ramp currents ( two intensities of two second long step currents and two intensities of two second long ramp currents ) and noise currents ( eight seconds of OU noise process current injection ) . For each of these training sets , we test the generalization of the model to four different conditions: step currents , ramp currents and two different noise currents . Five intensities of step and ramp currents can be potentially employed to both train and test generalization . Stimuli used during the parameter constraining process ( e . g . the four step currents used by the first training set ) are excluded from the generalization test . As we typically employ several feature-based distance functions per stimulus and we often use more than one stimulus for the optimization , we obtain multiple distance function values for each model-experiment comparison . To obtain a single value a weight vector is used to sum all the different distance functions . Here we employ a different approach termed multiple objective optimization ( MOO ) [53] . This approach maintains the multiple distance measures and does not employ a weight vector . Instead , the relation between distance measures is that of domination: solution i is said to dominate solution j if for all distance functions the values of solution i are no greater than those of solution j and for at least one distance function the value of solution i is strictly lower than that of solution j . The purpose of a multiple objective optimization procedure is to find the best possible tradeoffs between the distance functions , termed the Pareto front . We employ a genetic algorithm ( GA ) based optimization algorithm designed for multiple objective optimization named NSGA-II [52] . This algorithm is an elitist ( GA ) with a parameter-less diversity preserving mechanism . We custom implemented this algorithm in NEURON . We find that the algorithm almost always converges after 1000 iterations of evaluation of the full set of parameter values . As a safety factor , 1500 iterations were used . We repeated each given optimization ten times . The spread of successful solutions in parameter space for a given stimulus type can be explored by simply marking the location of each point corresponding to a solution . However , it is difficult to determine in this fashion whether a certain region of parameter space is consistent with more than one stimulus as the points themselves will almost surely not coincide . An additional disadvantage is that many of the solutions are the result of the same optimization run and thus contain artificial correlations due to the closely linked nature of solutions generated by a single optimization run . To overcome these two difficulties we complement our analysis by additional simulation of the response of a large set of points placed on a high-dimensional grid [29] to all ( step and ramp ) stimuli used in the experiments . This approach is extremely computationally expensive . However , it overcomes the above-mentioned difficulties: as the same points are simulated for all conditions , one can easily ascertain which are the conditions consistent with each point . Secondly , as all points are generated on the grid there are no unknown artificial correlations between them . Lastly , this approach allows visualization of projections of the space of solutions consistent with each stimulus ( see Figure 2 ) .
|
Neurons perform complicated non-linear transformations on their input before producing their output - a train of action potentials . This input-output transformation is shaped by the specific composition of ion channels , out of the many possible types , that are embedded in the neuron's membrane . Experimentally , characterizing this transformation relies on injecting different stimuli to the neuron while recording its output; but which of the many possible stimuli should one apply ? This combined experimental and theoretical study provides a general theoretical framework for answering this question , examining how different stimuli constrain the space of faithful conductance-based models of the studied neuron . We show that combinations of intracellular step and ramp currents enable the construction of models that both replicate the cell's response and generalize very well to novel stimuli e . g . , to “noisy” stimuli mimicking synaptic activity . We experimentally verified our theoretical predictions on several cortical neuron types . This work presents a novel method for reliably linking the microscopic membrane ion channels to the macroscopic electrical behavior of neurons . It provides a much-needed rationale for selecting a particular stimulus set for studying the input-output properties of neurons and paves the way for standardization of experimental protocols along with construction of reliable neuron models .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cellular",
"neuroscience",
"computational",
"neuroscience",
"single",
"neuron",
"function",
"biology",
"neuroscience"
] |
2011
|
Effective Stimuli for Constructing Reliable Neuron Models
|
Many of the lipids found on the cuticles of insects function as pheromones and communicate information about age , sex , and reproductive status . In Drosophila , the composition of the information-rich lipid profile is dynamic and changes over the lifetime of an individual . However , the molecular basis of this change is not well understood . To identify genes that control cuticular lipid production in Drosophila , we performed a RNA interference screen and used Direct Analysis in Real Time and gas chromatography mass spectrometry to quantify changes in the chemical profiles . Twelve putative genes were identified whereby transcriptional silencing led to significant differences in cuticular lipid production . Amongst them , we characterized a gene which we name spidey , and which encodes a putative steroid dehydrogenase that has sex- and age-dependent effects on viability , pheromone production , and oenocyte survival . Transcriptional silencing or overexpression of spidey during embryonic development results in pupal lethality and significant changes in levels of the ecdysone metabolite 20-hydroxyecdysonic acid and 20-hydroxyecdysone . In contrast , inhibiting gene expression only during adulthood resulted in a striking loss of oenocyte cells and a concomitant reduction of cuticular hydrocarbons , desiccation resistance , and lifespan . Oenocyte loss and cuticular lipid levels were partially rescued by 20-hydroxyecdysone supplementation . Taken together , these results identify a novel regulator of pheromone synthesis and reveal that ecdysteroid signaling is essential for the maintenance of cuticular lipids and oenocytes throughout adulthood .
Pheromones , chemical communication signals , profoundly influence the behavior of many organisms [1 , 2] . For example , the choice to mate or fight in Drosophila melanogaster is largely dictated by pheromones [3] . In Lepidoptera , a subtle shift of sex pheromone composition alters mate recognition and has been proposed as one mechanism underlying speciation [4–6] . For many insects , hydrocarbons and other lipids on the cuticle function as pheromones . Circadian rhythm [7] , social interactions [8] , diet [9 , 10] , and aging [11] are known to alter the cuticular lipid composition . While the biosynthetic pathways for many pheromones are well-defined , only a few molecular modulators have been identified that systemically modulate pheromone production . Namely , the neuropeptide PDF [7] , juvenile hormone [12] , and insulin-related signals [13] are known to play a role in the regulation of cuticular lipid production . To discover other modulators of the cuticular lipid profile , we performed a genetic screen using Drosophila . In Drosophila , the cuticular hydrocarbon ( CHC ) profiles differ between males and females . The monoene ( Z ) -7-tricosene is the most abundant CHC found on male cuticles and is thought to prevent courtship from other males [14 , 15] . In contrast , female-specific dienes , ( Z , Z ) -7 , 11-heptacosadeine and ( Z , Z ) -7 , 11-nonacosadiene function as aphrodisiacs for males [14] . The CHCs are synthesized in specialized cells called oenocytes which reside beneath the surfaces of the dorsal and ventral abdomen [16] . Two other male-specific sex pheromones , cis-vaccenyl acetate ( cVA ) and CH503 ( 3-O-acetyl-1 , 3-dihydroxyoctacosa-11 , 19-diene ) , are produced in the male ejaculatory bulb [17–19] . Numerous genes encoding enzymes for many of the major CHC biosynthetic steps have been identified including three different desaturases which are needed for carbon-carbon double bond placement [20–23]; elongases [19 , 24 , 25] which extend the carbon backbone; several components of the very long-chain fatty acid synthesis pathway which extend the fatty acyl-CoA precursor [26]; fatty acid synthases which produce methyl-branched CHCs [26 , 27]; and a member of the cytochrome P450 family which catalyzes the final oxidative decarbonylation step common to alkanes and alkenes [28] . To identify novel regulators and components of the pheromone biosynthesis pathway , we carried out a RNA interference ( RNAi ) screen to knockdown the expression of lipid metabolism genes known to be expressed in the oenocytes and assessed the chemical profile of the cuticles by mass spectrometry ( MS ) . Twelve genes were identified which , upon transcriptional silencing , altered CHC profiles . Further characterization of a predicted steroid dehydrogenase gene CG1444 , which we name spidey , revealed a novel role for ecdysteroid-hormones in the development and survival of adult oenocytes .
To identify genes underlying the synthesis of cuticular lipids , we used RNA interference ( RNAi ) together with the Drosophila Gal4-UAS transgene system to silence gene expression within the oenocytes with the oeno-Gal4 or dsx-Gal4 driver . Oeno-Gal4 targets oenocytes while dsx-Gal4 targets a broad range of tissues including the oenocytes and male ejaculatory bulb ( S1 Fig ) . The relative abundances of individual cuticular lipid species or of each chemical class were compared to the profiles of genetic controls . Eighty genes were selected based on known expression in the oenocytes ( from previously published reports ) or predicted functionality in lipid metabolism [29–33] ( S1 Table ) . The abundance and composition of major cuticular lipid species were analysed using two forms of mass spectrometry ( MS ) : Direct Analysis in Real Time ( DART ) MS [34 , 35] and gas chromatography MS ( GCMS ) . The two MS methods provide complementary detection of , respectively , polar and higher molecular weight molecules and alkanes . Fig 1 illustrates the overall screening strategy . Following a preliminary round of screening by DART MS and a second confirmatory analysis by GCMS , we identified 12 genes whose functional suppression by RNAi led to quantitative changes in the CHC profiles of females and/ or males ( Table 1; S2 , S3 , S4 and S5 Tables ) . Three previously known genes underlying hydrocarbon synthesis were identified using our screening strategy: RNAi-mediated gene silencing of desat1 ( CG5887 ) and desatF ( CG7923 ) produced profiles with significantly lower levels of dienes while knockdown of eloF ( CG16905 ) produced profiles with overall shorter length CHCs ( S2 Fig ) . The positive identification of known CHC biosynthesis genes validates the sensitivity of the screen design and data analysis . For several transgenic lines , gene silencing resulted in a shift towards longer CHCs in both males and females ( e . g . , CG7400 ) or overall shortening ( CG11140 , in males ) . In other lines , select chemical classes were altered . For example , the abundance of alkanes increased upon knockdown of CG5162 while silencing of CG2781 , CG1765 , or CG1444 resulted in higher levels of methyl branched alkanes in males . In females , knockdown of CG9102 , CG6300 , or CG17562 led to lower levels of monoenes and a relative increase of long chain alkanes and nonacosadiene . In males , knockdown of CG5162 , CG3961 , CG2781 , or CG1765 resulted in decreased levels of oxygen-containing CHCs . Developmental lethality was observed with two lines: CG6660 ( consistent with its recently reported role in tracheal waterproofing [36] ) , and CG1444 . Because of its dual role in metamorphosis and adult cuticular lipid production , we further investigated the function of CG1444 , which we name spidey . Based on amino acid sequence similarity , spidey is predicted to function as a steroid dehydrogenase , a well-conserved enzyme class that plays a prominent role in converting inactive keto steroids to hydroxy steroids in a diversity of organisms [37 , 38] . To characterize the role of spidey during development , we silenced its expression in the oenocytes of transgenic flies ( oeno>spideyRNAi ) at various time points after embryo fertilization with the use of tubulin-Gal80ts , a ubiquitously-expressed temperature-sensitive transgene . The Gal80ts protein suppresses Gal4 function at 19°C and permits binding to the UAS element at 29°C . Embryos were shifted to the restrictive temperature at successive 24 hour intervals after egg laying ( AEL ) . Knockdown of spidey in oenocytes before the mid-3rd instar stage caused complete pharate lethality of both male and female flies ( Fig 2A and 2B ) . In contrast , spidey knockdown after the mid-3rd instar resulted in successful eclosion of most males and only very few females ( Fig 2A ) . Females failed to eclose during the late pupal stage and 80% of the pupae exhibited a malformed leg phenotype ( Fig 2B; p<0 . 0001 , compared to controls; Fisher Exact Probability test , N = 36 ) , a feature that is also found in flies with defects in ecdysone metabolism [39] . In contrast , male survivors exhibited a 1–2 day delay to pupal formation and a significant overall decrease of many of the major cuticular lipids relative to genetic controls ( Fig 2C ) . Overexpression of spidey in the oenocytes also resulted in developmental lethality . At 29°C , misexpression with 2 different UAS constructs ( oeno>spidey and oeno>spidey . HA ) both resulted in 87% early pupal or pharate lethality ( Fig 2D and 2E ) . At the permissive temperature , all spidey flies and 45% of spidey . HA pupae successfully eclosed . Quantitative PCR of spidey transcript levels in oeno>spideyRNAi and spidey . HA flies confirmed , respectively , a decrease and increase in expression levels at the restrictive temperature ( Fig 2F ) . The phenotypes observed following misexpression or knockdown of spidey were reminiscent of ecdysone pathway mutants . In particular , the malformed legs , partial eclosion , and defective head and spiracle eversion phenotypes have been observed upon overexpression or loss-of-function of the ecdysone inactivating enzyme Cyp18a1 [39] . To determine if manipulation of spidey affects Cyp18a1 expression , we quantified Cyp18a1 transcript levels following spidey knockdown or overexpression . Oeno>spideyRNAi larvae exhibited a 1 . 4-fold decrease in Cyp18a1 expression whereas oeno>spidey . HA larvae showed a 1 . 5 fold increase ( Fig 2F ) . Taken together , these results indicate that spidey expression influences expression of Cyp18a1 , a key component of the ecdysone metabolic pathway . Since the product of spidey is predicted to have steroid dehydrogenase activity and both its knockdown and overexpression result in phenotypic features characteristic of ecdysone signaling disruption , we hypothesized that spidey misexpression alters ecdysteroid titres , resulting in pharate lethality . We examined the steroidal profile of flies in which spidey is silenced with the GeneSwitch system [40] . Control and experimental lines have the same genetic background ( GSoeno>spideyRNAi ) but RNAi expression in the oenocytes ( and hence , knockdown of spidey ) is activated only by exposure to the exogenously applied ligand RU-486 . Hormonal extracts were compared between larvae fed RU-486 ( spideyKD ) and control animals that were not fed the drug ( spideycontrol ) . By comparing flies with the identical genotype , variation in steroid levels due to genetic background is avoided . The analysis of extracts from larval and pupal development stages by liquid chromatography MS/MS ( LC-MS/MS ) showed that the steroid hormones 20-hydroxyecdysone ( 20HE ) , makisterone A ( MA ) , and their respective catabolic products , 20HE-oic acid and MA-oic acid [41 , 42] , were altered in spideyKD flies compared to controls ( Fig 3A ) . While overall levels of 20HE were not changed , both 20HE-oic acid and the ratio of 20HE to 20HE-oic acid were significantly altered in spideyKD 3rd instar larvae , indicating that reduced spidey expression results in the accelerated oxidation of 20HE . A small effect on MA and its catabolic product was also observed in the pupal stage though no significant difference was found in the MA/ MA-oic ratio . In contrast , overexpression of spidey resulted in a substantial drop in 20HE and MA levels in 3rd instar larvae ( Fig 3B ) . Levels of the metabolites 20HE-oic acid and MA-oic acids were below the limits of detection . To test whether spidey-induced changes in ecdysteroid levels could underlie the developmental defects , we supplemented fly food with 20HE , cholesterol , or fatty acids . However , none of these manipulations were able to rescue the larval lethality phenotype of oeno>spideyRNAi flies ( S3 Fig ) . Ecdysteroids in adults are known to play a significant role in organ viability , aging , and stress resistance [43] . We next asked whether the loss of spidey function during adulthood also affects various life history traits . Gene expression was manipulated in spideyKD adults by feeding RU-486 at various time points after eclosion . Interestingly , spideyKD flies showed a decreased resistance to desiccation , lowered starvation resistance , increased susceptibility to oxidative stress , and shortened lifespan ( Fig 4A–4D ) . We noted that approximately 50% of male spideyKD flies exhibited an unusual phenotype at from 14–19 days old . Flies were found attached to the walls of the food vials and could not be displaced by vigorous shaking and knocking ( S1 Movie ) . Flies appeared to die from starvation as a result of not being able to remove themselves from the walls . The “sticky” phenotype was also observed using a second RNAi line ( 11 out of 22 flies , age 25 days; Fig 4E ) . Scanning electron micrographs identified a layer of food-like substance coating the tarsal segments of spideyKD fly legs but not on spideycontrol or wildtype fly legs ( Fig 4F ) . Because of the striking ability of spideyKD flies to adhere to vertical surfaces , we named this gene “spidey” , as an homage to the comic book character , Spider-Man . We hypothesized that the loss of hydrophobicity and increased susceptibility to environmental stressors could be due to a defect of the waxy cuticular surface . Quantitative analysis of CHC amounts by GCMS revealed that first , CHC levels decreased as the duration of spidey knockdown increased; and second , males were more severely affected . At 8 days old , spideyKD adults continuously exposed to RU-486 showed a reduction in overall CHCs levels with an 85% decrease for males and 65% decrease for females compared to age-matched spideycontrol flies ( Fig 5A ) . Notably , the most dramatic effects were observed when spidey knockdown occurred immediately after eclosion: spidey suppression only in the first 0–48 hrs post-eclosion produced as large an effect on CHC loss as continuous suppression of the gene for 8 days ( Fig 5A ) . Moreover , the CHC levels in early adulthood knockdown animals were not restored at 15 days old even though the flies were returned to standard food conditions ( hence , alleviating inhibition of spidey ) from days 3–15 ( Fig 5B ) . Knockdown of the gene for 48 hrs only at 10 days of age produced an intermediate decrease in CHC levels while prolonged suppression for 15 days led to near complete loss of all detectable CHCs ( Fig 5B and 5C ) . These results indicate that spidey is needed throughout adult life for the maintenance of CHCs but is critical in early adult life to establish normal CHC levels . Consistent with this observation , spidey gene expression is maintained at a constant level throughout adulthood ( S4 Fig ) . The loss of CHCs and ensuing sticky phenotype could be due to a defect in biosynthesis or a loss of CHC-producing oenocytes . However , total fatty acid amounts were similar or even higher in both spideyKD males and females compared to controls ( S5 Fig ) , indicating that a reduction in fatty acid precursors is not likely to be the main cause of CHC loss . Ubiquitous knockdown of spidey using a GStub-Gal4 driver also showed no difference in total fatty acid levels or individual fatty acid species compared to genetic controls ( S5 Fig ) . To directly investigate oenocyte viability , we visualized the morphology and quantified the number of adult oenocytes by expressing a membrane-bound green fluorescent protein ( GFP ) . At 8 days old , GFP intensity in spidey-suppressed flies increased significantly due to a swollen appearance of the membrane ( Fig 6 ) . By 15 days old , both female and male flies exhibited a mix of swollen cells , large patches devoid of oenocyte GFP signal , and an overall decrease of GFP expression on the dorsal and ventral abdomen . By 20 days old , large swaths of oenocytes appeared to be missing . Taken together , these data indicate that spidey is required to maintain the viability of oenocytes in adult flies . Silencing spidey expression reduces oenocyte numbers and produces a severe loss of CHC in aged spideyKD flies with concomitant deficits in stress resistance , lifespan , and hydrophobicity . Consistent with this observation , ablation of oenocytes by expression of the pro-apoptotic gene hid during late pupal stage also resulted in the spidey wall-sticking phenotype ( Fig 4E ) . To determine whether the decreased availability of 20HE due to accelerated oxidation underlies the reduction of CHC levels in spideyKD adults , we attempted to rescue the deficits by adding 20HE to the adult diet of spideyKD flies . Interestingly , the CHC levels of spideyKD males but not females were restored to near-control levels upon steroid supplementation at 8 days old ( Fig 7 ) . However , at 20 days old , males still exhibited massive loss of oenocytes indicating that the rescue effects of 20HE are only temporary . While 20HE did not rescue CHC levels in female spideyKD flies , ecdysteroid supplementation appeared to improve oenocyte viability at 20 days old , based on GFP intensity ( Fig 7B and 7C ) . Taken together , the findings show that 20HE is able to partially rescue some aspects of oenocyte function however other signals are likely needed for sustained viability and function .
Pheromone production in insects utilizes many of the same biosynthetic routes and cellular sites integral to lipogenesis [44] . In Drosophila , pheromone-producing oenocytes play essential roles in fat storage and release , similar to some of the roles of mammalian hepatocytes [33] . As such , the chemical composition of the cuticle can reflect changes in lipid metabolism due to aging [11 , 13] , reproductive state [45] , diet [9 , 10 , 46 , 47] , and environmental conditions [48 , 49] . In addition to identifying components of the fatty acid synthesis pathway , our screen also found genes with broad functionality including a master regulator of lipid synthesis ( SREPB ) [50] and the transcription regulatory factor bric à brac ( bab2 ) [51] . These candidate genes could serve as mechanistic links by which environmental and physiological changes modulate lipid pheromone metabolism . In particular , genes that were found to influence levels of oxygenated CHCs will be of interest for future studies since the abundance of these molecules changes in response to age and insulin-related signaling [13] . Our screen also identified three sterol-related genes . Previously , juvenile hormone was shown to be involved in CHC regulation [12 , 52–54] . Our results found that ecdysteroid hormones are important not only for maintaining levels of CHCs in adults but are needed , as well , for the viability of oenocytes . Even a brief abatement of spidey activity during early adulthood was sufficient to cause lasting changes in the cuticular lipid profile late into adulthood . Ecdysteroids have been shown to alter phenotypic and life history traits such as pigmentation , life span , stress resistance [55] , and reproduction [56] in response to environmental cues . Our results indicate that the pheromone profile is another trait subject to steroid control . Precisely timed ecdysteroid release is important for the induction of moulting and metamorphosis and requires careful regulation of biosynthesis and inactivation [57] . Several of the phenotypic features and molecular changes that we observed following spidey misexpression are consistent with a disruption of ecdysteroid metabolism . First , the significant delay to pupation , presence of malformed forelegs , and failure to metamorphosize observed in spidey knockdown flies are reminiscent of other ecdysone-related defects . Second , the early pupal lethality we observed following continuous overexpression of spidey is also consistent with previously characterized terminal phenotypes of ecdysone-deficient mutants . Third , the developmental defects induced by spidey knockdown were accompanied by an increase in 20HE ( but not makisterone A ) catabolism in L3 stage . In contrast , spidey overexpression led to a substantial decrease in both 20HE and makisterone A levels . Lastly , misexpression of spidey alters expression of Cyp18a1 , a member of the Cytochrome P450 monooxidase family that plays an essential role in ecdysone inactivation [39] . Loss of Cyp18a1 function and its overexpression leads to similar spidey misexpression phenotypes , namely lethality during the prepupal or early pupal stage [39 , 42] . It is possible that spidey and Cyp18a1 directly interact with each other in the same metabolic pathway . Alternatively , changes in ecdysteroid levels caused by spidey dysregulation could trigger a change in Cyp18a1 expression through a feedback circuit [58] . The resulting disrupted balance between 20HE and 20HE-oic acid levels or toxicity from excess 20HE-oic acid likely underlies the failure to transition to metamorphosis . While our genetic manipulations and biochemical measurements strongly indicate that spidey has a role in steroid metabolism , ultimately , an enzymatic assay is needed to determine the activity of spidey as a steroid dehydrogenase . We note that females appeared to be more severely affected by the dysregulation of ecdysteroids during larval development . A sex-selective vulnerability to changing levels of 20HE and juvenile hormone-induced pupal wing degeneration has been reported in female moths [59] . Difference in EcR isoform expression or downstream signaling between males and females are postulated to underlie the sexually dimorphic responses to 20HE; however , these mechanisms remain untested . Interestingly , adult female CHC levels were less affected by spidey knockdown compared to males . The release of 20HE from other tissues such as the ovaries could potentially compensate for any loss of function from the oenocytes . Previously published findings alluded to the possibility that , based solely on sequence similarity , spidey encodes a ketoacyl reductase ( KAR ) , a multi-protein elongase complex necessary for the synthesis of long chain fatty acids ( LCFA ) and very long chain fatty acids ( VLCFA ) [36] . We thoroughly investigated this possibility and found no evidence for the role of spidey in fatty acyl elongation . If spidey is involved in fatty acid elongation , we should have seen overall lower FA levels or an enrichment of shorter chain FAs . However , quantitative measurements of LCFA and VLCFA following knockdown of spidey in the oenocytes revealed no significant decrease in FA amounts ( S5 Fig ) . In fact , total FA levels were actually higher in spideyKD flies at 7 days old . Amounts of methyl branched CHCs , which require elongation of malonyl-CoA and acyl-CoA precursors via KAR [60 , 61] , were also significantly higher following spidey knockdown in 8 day old males ( S5 Table ) . Moreover , ubiquitous knockdown of spidey throughout the fly also did not change FA levels or FA lengths indicating that spidey is not likely to function in FA elongation in other tissues . Lastly , we tested the possibility that spideyKD flies exhibit a loss of tracheal waterproofing , a distinct phenotype associated with the loss of LCFAs [36] . We placed spideyKD larvae at 2nd and 3rd instar stages in food stained with blue dye and looked for staining of the tracheal system and hemolymph , indicating loss of waterproofing . No staining was observed in either stage ( N = 10 , each age; data not presented ) . Taken together , the loss of spidey function did not phenocopy either of the prominent phenotypes associated with long chain fatty acid loss function; therefore , these results do not support the role of spidey as a canonical KAR . In Diptera , oenocytes formed during embryonic development disappear during metamorphosis and are replaced during pupal formation by progenitors from a separate population , giving rise to adult oenocytes [62] . Shortly after pupal emergence , Drosophila undergo significant muscle and neural tissue remodelling that is triggered by a carefully-timed drop in ecdysteroid levels [63–67] . Our results show that silencing spidey expression during this period induced long-lasting effects on oenocyte development and CHC production . Taken together , these observations indicate that adult oenocyte viability is likely to be regulated during the early adult stage via spidey-mediated control of ecdysteroid levels . While we did not directly measure levels of 20HE and other ecdysteroids in eclosed adults , the high ratio of 20HE-oic acid to 20HE in 3rd instar larval stage indicates that the unavailability of 20HE due to rapid inactivation is likely to contribute to oenocyte- and CHC-related defects during this early adult window . Silencing spidey expression only in the oenocytes resulted in the eventual loss of oenocytes in older flies and a concomitant decrease in cuticular lipid production . The loss of CHCs has dramatic consequences on life history traits including resistance to desiccation , starvation and oxidative stress , and culminates in a shortened lifespan , consistent with previous work [16 , 28 , 68] . Could oenocyte viability throughout adulthood depend on ecdysteroid signaling ? In larvae , ecdysteroid supplementation across a range of concentrations did not rescue the lethality phenotype ( S3 Fig ) . However , three lines of evidence support a role for 20HE signaling in adult oenocytes . First , our results reveal that 20HE feeding provides a short-term rescue of CHC levels in spideyKD flies . Second , knockdown of the Ecdysone Receptor gene EcR within the oenocytes produced a phenotype consistent with the knockdown of spidey: relatively lower levels of ( Z ) -7-tricosene were observed in both cases ( S3 Table ) . Third , previous reports using an ecdysone response element reporter found that adult oenocytes exhibit high levels of EcR / Ultraspiracle ( EcR/ USP ) reporter activity that increased with age [43] . Taken together , these observations indicate that oenocytes are likely to require 20HE for viability , similar to ovaries in adult females [69] . Our genetic screen led to the identification of several systemic regulators of pheromone production and revealed a new role for ecdysteroid signaling in adult animals . In addition to insulin-related signaling [13] and juvenile hormone [70] , our results show that ecdysone is an essential hormonal regulator of pheromone production . A number of open questions remain about the dynamics and interaction of ecdysteroids and oenocytes . The possible activity of 20HE in other tissues could contribute to the maintenance of oenocyte function . Moreover , it remains to be shown whether the rescue of CHC levels by 20HE feeding takes place directly via EcR/ USP activation in the oenocytes . Lastly , since 20HE feeding resulted in only a partial rescue of CHC synthesis and oenocyte GFP intensity , other molecules are clearly involved . The timing and concentration of 20HE release from the oenocytes might also affect the efficacy of the rescue . Future elucidation of these details will allow us to understand the molecular and cellular mechanisms by which environmental and social conditions shape pheromone production via hormone regulators and , as a consequence , influence behavior , reproduction , and potentially , speciation .
The following lines were used: UAS-RNAi effector lines were obtained from the Vienna Drosophila Resource Centre ( see S1 Table for a complete list ) ; oenocyte-Gal4 , tubulin-Gal80ts ( oeno-Gal4; courtesy of J . C . Billeter and J . Levine , University of Toronto , Mississauga , CA ) [16]; doublesex-Gal4 ( dsx-Gal4 ) , elav-Gal80 ( courtesy of S . Goodwin , University of Oxford , UK ) [71]; GeneSwitch-oenocyte-Gal4 ( GSoeno-Gal4 ) and GeneSwitch-tubulin-Gal4 ( GStub-Gal4; both GS lines courtesy of S . Pletcher , University of Michigan , Ann Arbor , USA ) ; UAS-mCD8-eGFP ( Bloomington Drosophila Stock Center , USA ) ; and UAS-CG1444-3XHA ( spidey . HA; FlyORF , University of Zurich , CH ) [72] . Genetic controls were generated by crossing virgin females of the driver lines to w1118 . For screening , all crosses were raised at 25°C , separated by sex during the pupal stage , and transferred to 29°C to enhance Gal4 production . Flies were kept at 29°C for 5–8 days after collection on a 12 hours light/dark cycle . The ORF of CG1444 was amplified from cDNA synthesized from 3rd instar larvae RNA ( Canton-S ) using primers CG1444-ORF-EcoR1-F ( 5' -ggGAATTCATGGAGGAGAACAACTCGCAAGTGC-3' ) and CG1444-ORF-XbaI-R ( 5'-ggTCTAGACTACTGTTCCTTGGCCAGGCGGCGCA-3' ) and cloned directly into pWalium10-moe vector [73] via the EcoRI and XbaI sites ( underlined ) . Injection of construct into PhiC31-containing attP embryos ( Bloomington #3622 , targeting chromosome II or Bloomington #24749 , targeting chromosome III ) and recovery of transgenic individuals was performed by BestGene Inc . ( Chino Hills , CA , USA ) . GSoeno-Gal4 allows conditional activation of the Gal4 transgene upon exogenous application ( by feeding ) of RU-486 ( 11β- ( 4-Dimethylamino ) phenyl-17β-hydroxy-17- ( 1-propynyl ) estra-4 , 9-dien-3-one ) ( Sigma-Aldrich , MO , USA ) diluted in ethanol . Crosses were raised at 25°C , separated by sex at the pupal stage and fed standard media containing 200 μM of RU-486 upon eclosion . Control flies were raised on standard medium supplemented with ethanol . Flies were kept at 29°C after collection on a 12 hour light/dark cycle and used for assays at 6–8 days old unless otherwise stated . Spidey overexpression lines ( oeno>spidey and oeno> spidey . HA ) were raised continuously at 19°C or 29°C on standard medium and maintained on a 12 hours light/ dark cycle . Fresh food vials were provided daily . Six to 10 flies of each genotype were analysed . Following anesthetization on ice , whole flies were held by their wings with fine forceps and placed between the gas inlet and outlet of the Direct Analysis in Real Time ( DART ) ion source . Cuticular hydrocarbon analysis was performed using a modified version of the method previous described in [74] . Briefly , the DART source was operated in positive ion mode with helium gas with the gas heater set to 200°C . The glow discharge needle potential was set to 3 . 5 kV . Electrode 1 was set to 150 V , and electrode 2 ( grid ) was set to 250 V . The mass spectrometer ( AccuTOF-DART; JEOL USA , Inc . , MA , USA ) was run in the positive-ion mode at a resolving power of 6 , 000 ( FWHM definition ) . Mass spectra were sampled and stored at one spectrum per second with an acquisition range of 60 to 1000 mass to charge ratio ( m/z ) . Mass spectrum of an external reference standard , polyethylene glycol , was acquired in each data file for the calibration of mass measurements . In general , protonated molecules ( [M+H]+ ) are observed . In contrast to gas chromatography MS ( GCMS ) , saturated hydrocarbons are not detected by DART MS ( though under some conditions molecular radical cations may be observed ) [75] . Both methods are capable of detecting alkenes , polar , and apolar molecules . However , DART MS is unable to differentiate between isobaric compounds hence compounds with identical molecular weight but double bonds in different positions ( e . g . , ( Z ) -7-tricosene and ( Z ) -9-tricosene , appear as the same signal in the DART CHC profile ) . The mass-calibrated and centroided mass spectra acquired by MassCenter software ( JEOL USA ) were exported as text files for processing with Mass Mountaineer software ( RBC Software , Portsmouth , NH , available from mass-spec-software . com ) . Peaks assignment was based on m/z values within 0 . 007 u of the theoretical m/z value . To assess changes in the relative abundance of each CHC species , each signal was normalised to the relative abundance of a reference CHC using the following calculation: For males , relative CHC intensity= ( CHC valuetricosene value ) ×100 For females , relative CHC intensity= ( CHC valueheptacosadiene value ) ×100 Male pheromones were further categorised into monoenes , mono-oxygenated , di-oxygenated and tri-oxygenated species and the average of CHC species from all replicates in each category were summed . Candidate lines with CHC profile differences were selected based on normalized values that were 2 standard deviations above or below the pooled average . GCMS samples were prepared by incubating 8 cold anaesthetised flies of each genotype at room temperature for 20 minutes with 120 μL of hexane containing 10 μg/mL hexacosane as an internal standard . 100 μL of the extract was transferred into a fresh glass vial and allowed to evaporate at room temperature . Samples were stored at -20°C . Three replicates were prepared for each genotype . Analysis by GCMS was performed on two systems . A QP2010 system ( Shimadzu , Kyoto , Japan ) equipped with a DB-5 column ( 5%-Phenyl-methylpolysiloxane column; 30 m length , 0 . 25 mm ID , 0 . 25 μm film thickness; Agilent ) was used . Ionization was achieved by electron ionization ( EI ) at 70 eV . One microliter of the sample was injected using a splitless injector . The helium flow was set at 1 . 9 mL/min . The column temperature program began at 50°C , increased to 210°C at a rate of 35°C /min , then increased to 280°C at a rate of 3°C/min . A mass spectrometer was set to unit mass resolution and 3 scans/ sec , from m/z 37 to 700 . Chromatograms and mass spectra were analysed using GCMSsolution software ( Shimadzu ) . In addition , a SCION GCMS ( Bruker Corp . , Billerica , USA ) with a BPx-1701 column ( 60 m x 0 . 25 mm , x 0 . 10 μm; Restek Corp . , Bellefonte , USA ) was used for fatty acid measurements of GStub>CG1444-RNAi adults . The helium flow was set at 1 . 5 mL/min . The column temperature began at 45°C for 4 min , increased to 180°C at a rate of 20°C/min and held at 180°C for 4 min , then increased to 280°C at a rate of 3°C/ min and held for 30 min . Chromatograms and mass spectra were analysed using MS Workstation software ( Bruker ) The relative abundance of each CHC is calculated by dividing the area under the peak by the total area of all peaks detected in the chromatogram . The relative abundance of CHCs from each transgenic line was then compared with that of the control with a Student’s two-tailed t-test ( GraphPad Prism 5 , Graph Pad Software Inc . , CA , USA ) . For total CHC levels , the area under each of the CHC peaks were summed and normalized to the area under the peak for the spiked hexacosane standard . Fatty acid extraction was modified from [76] . Five to nine flies were homogenised in 100 μL of PBS followed by an addition of 600 μL of chloroform: methanol ( 1:2 , v/v ) containing 10 μg/ mL hexacosane or 10 μg/ mL pentadecanoic acid ( Sigma-Aldrich ) . The mixture was vortexed for 1 minute before being shaken at high speed at 4°C for 2 hours . After which , 200 μL of chloroform and 250 μL of water was added to the mixture and vortexed for 1 minute . The samples were then centrifuged at 7500 x g RCF for 2 minutes for phase separation and the lower organic phase was collected . The extraction was repeated twice by adding 400 μL of chloroform to the remaining aqueous phase . The organic extracts were pooled and concentrated by N2 evaporation . The extracts were esterified with the addition of 0 . 5N Methanolic HCl ( Sigma-Aldrich ) and incubation in a water bath at 65°C for 1 . 5 hours with occasional vortexing . The solvents were evaporated under N2 and the samples stored at -20°C until reconstitution with hexane prior to analysis . Peaks corresponding to FA methyl ester ( FAME ) were identified based on retention time and presence of diagnostic ion m/z 74 . The relative abundance of each species was calculated by dividing the area under each peak by the internal standard . Total and individual FA abundances were compared with a Student’s two-tailed t-test ( GraphPad Prism 5 , Graph Pad Software Inc . , CA , USA ) . Quantification of ecdysteroids in larvae SpideyKD and spideycontrol larvae were staged by collecting embryos on grape juice agar for intervals of 1 . 5 hours . Embryos were washed with PBS containing 0 . 05% TritonX-100 then bleached for 30 seconds in 50% sodium hypochlorite followed by 2 washes with distilled water . The embryos were then placed individually into 2 mL polypropylene tubes containing 0 . 5 mL of standard food with or without 200 μM RU-486 . For oeno>spidey and oeno>spidey . HA experiments , crosses were raised continuously at either 19 or 29°C and parental lines were flipped into fresh food vials every 24 hrs . L1 [collected 40 hours after egg laying ( AEL ) ] , L2 ( collected 72 hours AEL for controls and 96 hours AEL for spideyKD to account for delayed maturation; presence of mouthhooks and size were also used as criteria for stage ) , L3 wandering larvae ( collected 4 hours before pupariation , approximately 1–2 hours after emerging from food and becoming inactive on the walls ) and pupae ( 40 hours after pupation ) were collected into 1 . 5 mL polypropylene tubes , placed in liquid nitrogen for 10 sec and stored at -80°C prior to analysis . Steroid quantification was performed as previously described in [77] . Frozen animals were smashed in 1 . 5 mL plastic tubes ( Eppendorf , Hamburg , Germany ) extracted with 1 mL of methanol overnight . Then samples were centrifuged for 5 min at 13400 rpm and the supernatant was collected . The residual pellet was twice re-extracted with 1 mL of methanol . The combined extracts were dried down in a vacuum concentrator , re-dissolved in 1 mL of methanol containing 0 . 25 pmol of muristerone A internal standard and twice extracted with 3 mL of hexane to remove the bulk of di- and triacylglycerols and sterols . The collected lower methanol fraction was dried and re-dissolved in 180 μL of 70% aqueous methanol . Samples were loaded on C18 MicroSpin columns ( Nest group , Southborough , MA , USA ) . Columns were twice washed with 180 μL of 70% methanol to remove the bulk of glycerophospholipids and centrifuged for 1 min at 2000 rpm . Eluates were dried down , re-dissolved in 15% aqueous methanol and transferred to 150 μL plastic vials . HPLC was performed on Agilent 1200 system equipped with a trap column ( OPTI-PAK , 1 μL , C18 ) from Dichrom GmbH ( Marl , Germany ) that was mounted in-line to a 0 . 5 mm × 150 mm analytical column packed with Zorbax SB-C18 5 μm ( Böblingen , Germany ) . The mobile phase consisted of solvent A ( 0 . 1% aqueous formic acid ) and solvent B ( 0 . 1% formic acid in neat acetonitrile ) . The gradient elution program was as follows: delivering 5% of B during first 10 min until the sample is loaded and concentrated on the trap column; ramping from 15% to 30% of B between 11 min to 30 min; increasing up to 100% of B in 1 min and holding for 9 min; stepping down to 5% of B in 1 min and holding for 19 min to equilibrate the column to starting conditions . The flow rate was 10 μL/ min; injection volumes are specified for each experiment . MS spectra were acquired in t-SIM mode on a Q Exactive tandem mass spectrometer ( Thermo Fisher Scientific , Waltham , MA , USA ) . Acquisition settings were set for mass resolution Rm/z = 200 700000; automated gain control target 105; maximum injection time of 200 ms; the width of transmission window of the analytical quadrupole of 1 Da . Extracted ion chromatograms were produced for [M+H]+ molecular ions of ecdysteroids assuming 2 ppm mass accuracy and their quantification was performed using Xcalibur 2 . 2 software . Ecdysteroid quantities were log transformed and analysed using a two-tailed Student’s t-test ( GraphPad Prism 5 ) . Additional measurements of ecdysteroids in oeno>spidey . HA larvae were performed on a different chromatography set up . 25 μL of standard or extract was injected onto a hypersil gold C18 column ( 50 x 2 . 1 mm , 1 . 9 um , Thermo Scientific Inc . ) and separated with a mobile phase consisting of 0 . 1% formic acid in water ( solvent A ) and 0 . 1% formic acid in methanol ( solvent B ) at a flow rate of 350 μL/min with the following gradient: from 0 to 2 min , 90:10 to 80:20; from 2 to 8 min , 80:20 to 20:80; hold at 20:80 for 2 min , then back to the initial condition of 90:10 and equilibrate for 5 min ) . A seven-point calibration curve was prepared covering a range of 0 . 05 ~ 500 ng/ mL . The limit of detection for the analytes is from 1 to 20 pg/ mL . Ecdsyteroid standards were obtained from Santa Cruz Biotechnology , Inc . ( Dallas , USA ) . Total RNA of whole flies ( 5 whole flies per replicate ) or larvae ( 5 larvae per replicate ) was prepared using TRIzol Reagent as per the manufacturer’s instructions . The extracted RNA was treated with TURBO DNA-free Kit ( AM1907 , Life Technologies , USA ) prior to reverse transcription reaction using Superscript III Reverse transcriptase ( 18080–044 , Life Technologies , USA ) to obtain cDNA . Five ng of cDNA was used for qPCR , using KAPA SYBR FAST qPCR Kit ( KK4604 , KAPA Biosystems , USA ) and Applied Biosystems 7900HT Fast Real-Time PCR system . The conditions used were as recommended in the KAPA SYBR FAST qPCR Kit . Relative transcript levels were calculated as 2ΔCt [78] , ΔCt = Ctrp49—Ctsample . Three technical replicates were performed for each genotype , with 3 biological replicates . The following primer sequences were used: spidey forward , 5’- TTCTACCCGGCATGATTAGC; spidey reverse , 5’- GCTCCTTGTACTCCGTCTGC; Cyp18a1 forward , 5’- TACCTGCCCATTACCGAGTC; Cyp18a1 reverse , 5’- ACCCATTGAGTTCCACATCC; rp49 forward , 5’-ATCTCCTTGCGCTTCTTGG; rp49 reverse , 5’-CAAGCCCAAGGGTATCGAC . Electron micrographs of freshly dissected legs were obtained on a JEOL JSM-6360LV scanning electron microscope at 20 kV ( JEOL , Peabody , MA ) . Embryos were collected on grape juice agar for 6 hours and placed in vials containing standard media . Each vial was transferred to restrictive temperature of 29° in 24 hour intervals after egg laying ( AEL ) . The number of intact flies per vial was counted 11 days AEL and the percentage survival was calculated by dividing the number of intact flies by the number of pupae formed . Embryos were collected on grape juice agar and transferred to standard media containing various supplements and kept at 25°C until pupal stage , then separated by sex and transferred to 29°C at 10 days AEL . The number of intact flies was counted 5 days after adult eclosion . The final concentrations of palmitic acid ( C16:0 ) , stearic acid ( C18:0 ) , oleic acid ( C18:1 ) , and linoleic acid ( C18:2 ) were at 50 or 500 μg/ mL . Concentrations for other supplements are as follow: behenic acid ( C22:0 ) , 50 μg/ mL; 20-hydroxyecdysone ( 20HE ) , 7 . 2 , 0 . 72 , or 0 . 072 μg/ mL; and cholesterol , 0 . 14 μg/ mL . For adult ecdysone rescue experiments , food was supplemented with 8 . 33 μg/ mL 20HE . All chemicals were acquired from Sigma-Aldrich . Ten flies were placed in vials containing standard food or standard food with 200 μM RU-486 . Flies were maintained at 29°C on a 12 hour light/dark cycle and transferred into fresh media every 3 to 4 days . The number of dead flies was counted every day . Ten replicates were established for each treatment . 10 flies were transferred to 25 mL vials containing 10 mL of 1% agar or 1% agar with 20 μM RU-486 . The flies were maintained at 29°C and the number of dead flies was counted every 2 hours . Five replicates were established for each treatment . Groups of 10 flies were transferred into an empty 25 mL vial and confined to the lower half of the vial with a foam stopper with approximately 3 g of Drierite dessicant ( Sigma-Aldrich ) in the top half . The vial was sealed with Parafilm and maintained at 25°C . The number of dead flies was recorded every hour . Five replicates were established for each treatment . Flies were starved for 6 hours in empty vials in groups of 10 and transferred to 25 mL vials containing 10 mL of 0 . 8% low melt agarose and 10% sucrose in phosphate buffered saline ( pH 7 . 4 ) containing 20 mM paraquat ( Sigma-Aldrich ) , with or without 200 μM of RU-486 . Ten replicates were established for each treatment . Genetic crosses and control lines were raised at 25°C and switched to 29°C at late pupal stage . Flies were flipped onto new food every 5–7 days and dead flies counted every 2–3 days . The sticky Spider-Man phenotype is defined as flies that are found dead on the wall of the vial ( rather than dead on the surface of the food ) and do not detach despite vigorous shaking and knocking . Dorsal abdominal fillets were prepared according to [79] . Fluorescent images were obtained with a Leica MZ10F fluorescence stereomicroscope fitted with Nikon DXM 1200F camera and Nikon ACT-1 version 2 . 10 imaging software . Fluorescent images were first converted to an 8-bit grayscale image and segmented with the pre-set default threshold setting using ImageJ . The total area occupied by GFP signal within a 9 unit square area consisting of at least 3 bands of oenocytes spanning the medial line was measured .
|
Pheromones are used by many animals to control social behaviors such as mate choice and kin recognition . The pheromone profile of insects is dynamic and can change depending on environmental , physiological , and social conditions . While many genes responsible for the biosynthesis of insect pheromones have been identified , much less is known about how pheromone production is systemically regulated over the lifetime of an animal . In this work , we identify 12 genes in Drosophila melanogaster that play a role in pheromone production . We characterized the function of one gene , which we name spidey , and which encodes a steroid dehydrogenase . Silencing spidey expression during the larval stage results in the rapid inactivation of an essential insect steroid , 20-hydroxyecdysone , and developmental arrest . In adults , spidey is needed for maintaining the viability of oenocytes , specialized cells that produce pheromones and also regulate energy homeostasis . Our work reveals a novel role for ecdysteroids in the adult animal and uncovers a regulatory mechanism for oenocyte activity . Potentially , ecdysteroid signaling serves as a mechanism by which environmental or social conditions shape pheromone production . Exploitation of this conserved pathway could be useful for interfering with the mating behavior and lifespan of disease-bearing insects or agricultural pests .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"chemical",
"compounds",
"organic",
"compounds",
"age",
"groups",
"developmental",
"biology",
"adults",
"steroids",
"lipid",
"signaling",
"lipids",
"chemistry",
"people",
"and",
"places",
"biochemistry",
"signal",
"transduction",
"organic",
"chemistry",
"cell",
"biology",
"genetic",
"screens",
"gene",
"identification",
"and",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"pheromones",
"fatty",
"acids",
"metamorphosis",
"population",
"groupings",
"physical",
"sciences",
"cell",
"signaling",
"larvae"
] |
2016
|
Steroid Hormone Signaling Is Essential for Pheromone Production and Oenocyte Survival
|
Mass drug administration ( MDA ) of ivermectin has become the main intervention to control onchocerciasis or “river blindness” . In Togo , after many years of MDA , Onchocerca volvulus infection has declined dramatically , and elimination appears achievable , but in certain river basins the current situation remains unknown . We have conducted parasitological , serological , ophthalmological , and entomological assessments in northern and central Togo within the river basins of Ôti , Kéran and Mô . Examinations were completed in 1 , 455 participants from 11 onchocerciasis sentinel villages , and O . volvulus transmission by Simulium damnosum sensu lato ( s . l . ) was evaluated . In children ( aged 1–10 years ) , the prevalence of microfilariae ( Mf ) was 2 . 3% and in adults it ranged from 5 . 1 to 13 . 3% . Positive IgG4 responses to O . volvulus adult ( crude ) worm antigen ( OvAg ) and the recombinant Ov16 antigen were in all-ages 48 . 7% and 34 . 4% , and 29 . 1% and 14 . 9% in children , respectively . In the river basin villages of Kéran , Mô and Ôti , the IgG4 seroprevalences to OvAg in children were 51 . 7% , 23 . 5% and 12 . 7% , respectively , and to the Ov16 antigen 33 . 3% ( Kéran ) and 5 . 2% ( Ôti ) . Onchocerciasis ocular lesions ( punctate keratitis , evolving iridocyclitis and chorioretinitis ) were observed in children and young adults . O . volvulus-specific DNA ( Ov150 ) was detected by poolscreen in vector samples collected from Tchitchira/Kéran ( 22 . 8% ) , Bouzalo/Mô ( 11 . 3% ) , Baghan/Mô ( 2 . 9% ) and Pancerys/Ôti ( 4 . 9% ) ; prevalences of O . volvulus infection in S . damnosum s . l . were , respectively , 1% , 0 . 5% , 0 . 1% and 0 . 2% . In the northern and central river basins in Togo , interruption of O . volvulus transmission has not yet been attained . Patent O . volvulus infections , positive antibody responses , progressive ocular onchocerciasis were diagnosed , and parasite transmission by S . damnosum s . l . occurred close to the survey locations . Future interventions may require approaches selectively targeted to non-complying endemic populations , to the seasonality of parasite transmission and national onchocerciasis control programs should harmonize cross-border MDA as a coordinated intervention .
In large parts of Africa , onchocerciasis has been controlled as a public health problem by the Onchocerciasis Control Programme in West Africa ( OCP ) and the African Programme for Onchocerciasis Control ( APOC ) by mass drug administration ( MDA ) of ivermectin , and this intervention has been applied for more than two decades . In a vast part of the initial control areas of the OCP , Onchocerca volvulus infection prevalence and intensity levels have greatly declined [1 , 2] , and currently , the elimination of onchocerciasis appears achievable in certain endemic regions [3–7] . In Togo , the northern territories had been part of the initial OCP anti-vectorial intervention areas since 1976 , whereas the central regions were included into the vector control programme in 1987 , and in both areas , blackfly vector control measures were supplemented since 1988 by MDA with ivermectin . When MDA with ivermectin started , this was implemented mainly by mobile teams and the initial coverage was not very satisfactory [2] . During some years of the early 1990’s , aerial larvicide application was also suspended in several river basins . Regular epidemiological surveys conducted by the National Onchocerciasis Control Programme ( NOCP ) have shown that after nearly three decades of MDA in most of the onchocerciasis hyperendemic districts , the O . volvulus microfilarial prevalence has diminished below 5% in all age groups and below 1% in children aged less than 10 years , suggesting that considerable progress has been made towards the elimination of onchocerciasis according to the operational prevalence thresholds proposed in the Conceptual Framework for Elimination of Onchocerciasis by APOC [3 , 8] . Parasite transmission has never been interrupted completely in central and northern Togo and Benin; the Ôti , Kéran and Mô river basins were “special intervention zones” ( SIZ ) where vector control and intensified ivermectin distribution needed to be continued for years after OCP closure in 2002 [9] . The interventions in the post-OCP period included continued aerial larvicide application for five additional years ( 2003–2007 ) and biannual ivermectin mass treatment was implemented until the end of 2012 [9 , 10] . Despite evidence of approaching elimination in certain regions of Togo , the current situation remains to be assessed by epidemiological and entomological surveys for detection of infection in human and vector population samples according to the recent World Health Organization ( WHO ) guidelines [11] . The WHO guidelines suggest , firstly , that entomological evaluations by Ov-150 PCR poolscreen be conducted to demonstrate interrupted transmission of O . volvulus larvae by female blackfly vectors , and secondly , that serological evaluations by Ov-16 enzyme linked immunosorbent assay ( ELISA ) be carried out to determine the presence of IgG4 antibodies to the O . volvulus-specific Ov-16 antigen in children [11] . The use of skin snip microscopy in parallel with Ov-16 serology is a conditional recommendation , and it may be used in transition during the phase of monitoring and evaluation . The assessment of ocular manifestations in populations where ocular onchocerciasis was present at baseline is considered to be of low priority [11] . In the present work , parasitological , serological , ophthalmological and entomological evaluations were conducted in onchocerciasis sentinel villages in central and northern Togo to assess the current epidemiological situation and to determine whether transmission has been interrupted and ivermectin MDA can be stopped .
The protocol of the study was reviewed and approved by the Togolese Bioethics Committee for Research in Health ( Comité de Bioéthique pour la Recherche en Santé; CBRS , Document #013/2015/CBRS/3 . Septembre 2015 ) , and study authorization and approval were granted by the Ministry of Health of Togo ( Authorization Document #338/2015/MSPS/CAB/SG ) . All specimens ( skin snips and blood samples ) used in this study were collected from study participants who provided written informed consent . The aims of the work , risks , procedures of examination and follow up were explained thoroughly to the respective village population , the village authorities and honorable community members , notably the village chief council . Consent from each study participant was documented and confirmed by signature , and consent for study participation by those younger than 18 years of age was given verbally by each participant ( with written consent and approval for their participation always being obtained from their parents or accompanying responsible adults/guardians ) . For correct and complete understanding , explanations were always given in the local language . Before each follow-up survey , approval was obtained from the appropriate regional ( Direction Régional de la Santé de la Population ) and district-level ( Direction Préfectural de la Santé ) health authorities . Regular epidemiological surveys were conducted in Togo by the OCP and the NOCP , which assessed O . volvulus microfilarial prevalence and intensity , as well as treatment coverage and compliance to ivermectin MDA within the programme area . Such surveys were performed since 1976 during the early rainy season , and around 200 participants were recruited and examined in each selected sentinel village . All sentinel villages are located within less than 3 km of distance to rivers with known breeding sites for the blackfly vector Simulium damnosum sensu lato ( s . l . ) . In Togo , vector control and epidemiological surveys started in 1976 within the OCP-Phase-III-Eastern Extension in the northern river basins of Ôti , Koumoungou and Kara . In 1988 , control measures and epidemiological surveys began for sentinel villages of the OCP-Southern Extension in the river basins of Mô and Mono , and at the same time , also in southern Togo in the river basins of Amou , Anie and Mono . The total number of sentinel villages in Togo included in the epidemiological surveys was 363 , and the endemic populations were repeatedly examined over time . Fig 1 illustrates the temporal trends in microfilarial prevalence from 1976 to 2014 . For this study , the parasitological , serological and ophthalmological surveys were performed in the central and northern regions of Togo ( Régions Savanes and Kara ) , where the total populations according to the latest census , conducted in 2010 , were 776 , 710 and 721 , 504 individuals , respectively . In these regions , 11 villages were selected by the NOCP for an annual survey . Fig 2 shows the selected villages and their location in Togo . Three villages are located in the Région Savanes in the Ôti river basin , i . e . Pancérys , Boutchakou and Koukoumbou . Four villages within the Kara Region are situated along the river Kara , i . e . Goulbi , Tchitchira , Koukoumbou Solla and Kpantiiyagou . Four additional villages from the Région Kara are located in the Mô river basin , i . e . Bawlesi , Mô-Village , Katcha-Konkomba and Saboundi . All sentinel villages are located within less than 1 km of distance to the rivers Ôti , Kara or Mô with known breeding sites for the blackfly vectors . Before ivermectin MDA ( delivered by community-directed drug distributors , CDDs ) , participants gave their informed consent for the collection of skin biopsies to detect O . volvulus microfilariae ( Mf ) . Participation and examination were conducted by family and followed the status: heads of family ( parents ) , children , brothers , uncles , aunts , and grandparents . From each participant , a skin biopsy was taken from each the left and right iliac crest ( for a total of two snips ) with a sterile 2-mm Holth corneo-scleral punch biopsy tool . Immediately , skin snips were placed on glass slides and incubated with physiological saline solution for 30 minutes . Each biopsy was microscopically examined for emerging O . volvulus Mf and their number counted . After this first examination , the two biopsies were transferred separately into a round-bottom well of a 96-well plate containing saline solution , and after a 24-hour incubation biopsies were re-examined as before . The use of two incubation steps for skin biopsies is the standard procedure applied by the NOCP , and this approach makes it possible to detect O . volvulus Mf which may emerge slowly from skin . For each participant , village of residence , family affiliation , age , sex , number of ivermectin treatment rounds received and microfilarial counts in skin biopsies were recorded . Blood drops were collected from fingertips pricked with a sterile lancet on Whatman 903 Protein Saver Cards . The cards were air dried , sealed in plastic bags and stored at 4°C until further use . As per skin biopsies and for each participant , village of residence , family affiliation , age , sex , number of ivermectin treatment rounds received and microfilarial counts in skin biopsies were recorded . All participants gave their informed consent for having an ocular evaluation and were examined by experienced ophthalmologists for ocular onchocerciasis lesions and additionally present ocular pathology . The ophthalmology examinations were performed by MB and TS and ocular pathologies , their grades of evolution and extent were classified as described previously [12] . All participants acknowledged having received ivermectin annually for several years through community directed treatment with ivermectin ( CDTI ) . In the surveyed villages , therapeutic ivermectin coverage of the eligible population had been ≥80% during the past 10 years ( S1 Table ) . Before ophthalmological examination , each participant was asked whether he or she had taken ivermectin annually for several years , and all confirmed having received treatment through CDTI . The anterior eye segment was examined by slit lamp ( Haag Streit 900 ) after participants were asked to sit with their heads bent between their knees for at least two minutes . This position promotes the migration of Mf within the anterior chamber of the eye to be seen and counted . The examination of the posterior segment was done with an ophthalmoscope after pupil dilation with 1% tropicamide and 10% epinephrine hydrochloride . Next to individual data ( age , sex , occupation , village , number of ivermectin treatments ) , the microfilarial load in the anterior eye segment , punctate and sclerosing keratitis and iridocyclitis were recorded . Onchocerciasis cases with punctate keratitis were grouped according to the presence of dead Mf in the cornea ( DMFC ) or living Mf in the anterior chamber ( MFAC ) and further classified as low ( presence of 1–10 Mf ) , moderate ( 11–20 Mf ) or high ( >20 Mf ) . Ocular lesions of the posterior segment were coded as evolving or advanced according to the classification adopted by WHO/OCP [13] . The ocular examinations included the testing of visual acuity eye by eye with an illiterate E chart ( SNELEN ) placed 6 meters away from the patient’s seat , and visual acuity was graded according to WHO/OCP criteria; those with a visual acuity on one or both eyes of <1/20 ( 3/60 or unable to count fingers at 3 meters ) were considered blind; those with impaired vision had a visual acuity on one or both eyes of <3/10 and ≥1/20 ( 3/60 or unable to count fingers at 3 meters ) , and those with good vision had a visual acuity equal to or greater than 3/10 ( 6/18 ) . For the OvAg-IgG4 ELISA , an adult worm antigen extract from male and female Onchocerca volvulus was used [14 , 15]; for the Ov16-IgG4 ELISA , the recombinant O . volvulus-specific antigen Ov16 was applied to measure serological IgG4 responses . The dry blood spot ( DBS ) samples collected as described above were stored refrigerated at 4°C until use . For the ELISA tests , 6-mm wide circles were punched out of the DBS cards and eluted in 200μl of phosphate buffered saline ( PBS ) containing 0 . 05% Tween20 and 5% bovine serum albumin for 2 days at 4°C in deep 96-well polystyrol plates ( NUNC 278605 ) . Microtiter plates ( Costar 3690 , half area ) were coated with OvAg ( conc . 5μg/ml ) or Ov16Ag ( conc . 5μg/ml ) in PBS pH 7 . 4 overnight , after which the coating antigen solutions were discarded , and the plates were blocked with PBS-Tween20 containing 5% foetal bovine serum at room temperature for 1 . 5 hours . Thereafter , plates were washed with PBS-Tween20 ( Sigma P3563 ) , eluted blood samples were added without dilution and the plates were incubated at 37°C for 2 hours . After using PBS-Tween20 ( Sigma P3563 ) for washing , an anti-human IgG4 horseradish peroxidase conjugated monoclonal antibody ( Thermo Fisher Scientific , no . A10654 ) was added ( dilution 1:500 ) for 1 . 5 hours . Plates were washed as above and tetramethylbenzidine ( TMB ) substrate ( Thermo Scientific 34021 ) was added . Plates were then incubated at room temperature for 15 min and the reaction was stopped with 50 μl of 0 . 5M sulfuric acid ( ROTH , K027 . 1 ) . Optical densities ( ODs ) were measured at 450nm with a microplate reader ( EL311 , BioTex Instruments ) . The collection of S . damnosum s . l . was conducted at specific catch points at river sites by trained fly catchers in proximity to sentinel villages in the Ôti river basin ( village Pancérys/ Savanes Region ) , Kara ( village Tchitchira/Kara Region ) , Mô ( village Baghan/Kara Region and Bouzalo/Central Region ) during the rainy season on five consecutive days in late August and beginning of September 2015 . Collections took place from 7am to 6pm alternating the fly catchers every two hours . The blackflies caught daily were first frozen , then suspended in 70% alcohol , and 25 individual S . damnosum s . l . were pooled into a single tube in ethanol and stored below -20°C until DNA extraction and real-time-PCR ( rtPCR ) . In addition , repeated weekly collections of S . damnosum s . l . were continued at the river Mô site , in proximity to the village Bouzalo ( Region Centrale ) until August 2017 . The sampling procedure was the same as above , and this long-term 2015–2017 collection was used to determine the annual biting rate ( ABR ) for the year 2016 , which was calculated by multiplying the average number of blackflies caught daily by the number of days per week in the month to add up to 12 months . From each fly catch location , the daily pooled S . damnosum s . l . flies were processed using the Qiagen DNA Mini Kit ( Qiagen , Hilden , Germany ) . Whole blackflies were used for rtPCR analyses . The pools were first freeze-thawed three times in liquid nitrogen , then ground with a mini grinder in a 1 . 5ml micro-centrifuge tube and digested with proteinase K overnight at 56°C . The eluted DNA concentration for each sample was determined by absorbance at 260 nm and DNA was stored at -20°C before PCR analysis . The DNA concentrations extracted from fly pools ranged from 540 ng/μl to 1200 ng/μl . Real-time PCR primers and probe used were as follows: OvFWD 5'-TGT GGA AAT TCA CCT AAA TAT G-3' , OvREV 5'-AAT AAC TGA CCT ATG ACC-3' , OvProbe 5'-FAM-TAG GAC CCA ATT CGA ATG TAT GTA CCC-TAM-3' ( Eurofins , Genomics ) . Primers and TaqMan probe sequences were designed to amplify a fragment of O . volvulus repeat DNA ( Ov-150 bp , GenBank accession number: J04659 . 1 ) . Taqman Universal PCR Master Mix ( Applied Biosystems , P/N 4304437 ) and nuclease-free water were used with all reactions with the following concentrations and volumes: 2 . 5 μl of 20 μM OvFWD , 2 . 5 μl of 20 μM OvREV primer , 1 . 5 μl of 9 . 2 μM OvProbe , 27 . 5 μl of 2×Master Mix , 50 ng of template DNA from extracted S . damnosum s . l . pools , or 1 ng of genomic DNA isolated from adult O . volvulus , and nuclease-free water was added up to a final volume of 55 μl . Reactions ( 2 × 25 μl per well ) were run with the following cycling conditions: 50°C for 2 min , 95°C for 10 min , ( 95°C for 15 s , 49°C for 30 s , 60°C for 2 min ) × 40 cycles . The Applied Biosystems 7300 Real Time PCR System ( 96-well format ) SDS version 1 . 4 software was used for S . damnosum s . l . pools collection in 2015 and for blackfly pools from 2016 the Corbett Rotor Gene RG-300 , version 6 , software was applied . Duplicate blackfly pool DNA samples with a cycle threshold ( Ct ) value of less than 30 were considered to be positive for O . volvulus DNA . Data were entered in Microsoft Excel and analyses were conducted with the statistical software SAS JMP 11 . 1 . 1 . For Mf prevalence values , the 95% confidence intervals ( 95% CI , Wilson score interval ) were calculated . The sensitivity of the O . volvulus-specific IgG4 ELISA was determined with a contingency analysis . For explorative data analyses , the two-sample Wilcoxon test was applied to evaluate differences between groups . The Chi-square test was used to test differences between examined males and females ( e . g . participation rates ) . Fisher’s exact test ( two-sided ) was applied to compare Mf-prevalence and the ELISA IgG4-OvAg and Ov-16 positive responses between the river basins ( Kéran , Ôti , Mô ) , and the number of Ov-150 DNA positive Simulium damnosum s . l . pools from Ôti/Pancery , Kéran/Baghan , Kéran/Tchitichira and Mô/Bouzalo . One-sided Fisher's exact test was used to evaluate differences in the prevalence of ocular pathologies in patients from the Ôti , Kéran and Mô river basins . Correlations between ophthalmological variables as well as between these and age were explored with Spearman correlation coefficient . For multiple testing , the application of the Bonferroni Holm adjustment ( 11 villages , 3 river basins , 7 age groups , microfilarial prevalences , IgG4 responses ) resulted in an alpha level of 0 . 0023 . For multiple comparisons , and to avoid type I errors , differences between groups were analyzed by the Tukey-Kramer Test .
The onchocerciasis control measures continuously applied from 1976 until 2002 consisted of aerial application of larvicidal compounds into the simuliid vector breeding sites , and from 1990 onwards , annual MDA of ivermectin was introduced , continuing until the present . In 1976 , in most locations the prevalence of O . volvulus infection exceeded 50% , and 20 years later , Mf positivity in the survey populations decreased to below 20% ( median ) ( Fig 1 ) . The O . volvulus microfilarial prevalence in onchocerciasis sentinel villages located in the major river basins of Ôti , Kéran , Kara , Mô , Koumoungou , Anie and Mono declined markedly ( Fig 1 ) , and until the year 2014 , the median prevalence of O . volvulus infections dropped below 5% , but in several locations the Mf-positivity exceeded this level in the river basins of Ôti , Kéran and Mô . In 2015 , a total of 1 , 455 individuals from 11 NOCP sentinel villages gave their informed consent for participation . Of these , the proportions ( and numbers ) of individuals originating from each river basin were: 22 . 3% ( n = 324 ) in Ôti; 37 . 0% ( n = 539 ) in Kéran , and 40 . 7% ( n = 592 ) in Mô . Table 1 summarizes the numbers examined by age and sex and the proportion of positives for skin Mf . Information on age is missing from 41 of the 1 , 455 participants , so data in Table 1 are reported for a total 1 , 414 individuals . Of these , 819 were females and 595 males . The median age in females and males was 30 and 29 years , respectively . Until the age of 15 years , girls and boys were similarly represented in the survey , but participation in examination ( and treatment ) of men aged 16 to 40 years decreased significantly ( Table 1 ) . The Chi-square test was applied to compare differences between female and male survey participation within age groups , indicating a statistically significant difference with greater participation of females ( 16-20y: p = 0 . 0007; age groups 21-25y , 26-30y and 31-35y: for each p<0 . 0001; 36-40y: p = 0 . 04 ) . In the age groups above 40 years , differences in participation between the sexes were not significant ( Table 1 ) . A total of 1 , 455 individuals were examined by skin biopsy for O . volvulus Mf with 83 positives; the overall Mf prevalence in the survey participants was 5 . 7% [4 . 5;6 . 9] ( Table 2 ) . In the Ôti , Kéran and Mô river basins , the ranges of Mf prevalence were 0 . 8–5 . 3% , 7 . 7–13 . 6% and 0–8 . 6% , respectively . The districts in northern and central Togo , where the surveyed 11 villages are located , formed part of the SIZ , and here MDA attained ≥80% ( median ) treatment coverage of the eligible population from 2005 until 2015 ( S1 Table ) . The sensitivities of the OvAg- and Ov16-specific IgG4-ELISAs to detect Mf-positive participants were , respectively , 89 . 2% and 71 . 4% ( Table 3 ) . The all-ages IgG4 seroprevalence values for OvAg and Ov16 were , respectively , 49 . 4% and 34 . 4% ( Table 2 ) . Age-specific serological IgG4 responses to the OvAg and Ov16 antigens are shown in Fig 3 . OvAg and Ov16 sero-prevalence increases with age ( OvAg: Spearman ρ = 0 . 346 , Ov16: Spearman ρ = 0 . 299 , both p<0 . 0001 ) ( Fig 3 ) and it attains a maximum level around the age of 50 years . During the first two decades of age , the mean OvAg- and Ov16-specific IgG4 reactivity was low in most cases; from 16 years onwards an enhanced responsiveness was observed , and from 20 years and older the mean participants' serologic IgG4 responses to OvAg and Ov16 continued to rise steadily until the fifth decade of age ( Fig 3 ) . In children of 5–10 years , 29 . 1% and 14 . 9% showed a positive IgG4 serological response to OvAg and Ov16 ( Fig 3 ) . In the Kéran , Mô and Ôti river basins , IgG4 sero-prevalence values in children ( 5–10 years ) to OvAg were 51 . 7% , 23 . 5% and 12 . 7% , respectively , and to Ov16 the values were 33 . 3% in Kéran and 5 . 2% in Ôti ( Table 2 ) . In total , 5 , 575 blackflies were grouped in 223 pools ( 25 flies each ) . Eight out of 35 pools from Tchitchira were positive for Ov150-DNA ( 22 . 8% ) and the calculated prevalence of O . volvulus infection in S . damnosum s . l . was 1% [16] . In Baghan , three positive pools were detected ( 2 . 9% ) and the calculated infecton prevalence was 0 . 1% . In Mô , five positive S . damnosum s . l . pools were identified ( 11 . 3% ) with a 0 . 5% prevalence of O . volvulus in blackflies ( Table 4 ) . At Pancéry , in the Ôti river basin , two O . volvulus-positive pools ( 4 . 9% ) were found , with a 0 . 2% prevalence of O . volvulus in black flies . O . volvulus-DNA-positive pools from Mô were found from early August 2015 until late October 2015 , suggesting that transmission of O . volvulus occurred during the rainy season . An annual biting rate ( ABR , Jan 2016-Dec 2016 ) was calculated as 15 , 519 bites/person/year at Mô-Bouzalo ( Fig 4 ) . The main ocular pathologies in the examined village populations , reported for the right eye , were papillitis ( 19 . 5% ) , cataract ( 17 . 6% ) , chorioretinitis ( 9 . 8% ) , conjunctivitis ( 7 . 8% ) , tropical limbo-cojunctivitis ( LCET ) ( 6 . 3% ) , iridocyclitis ( 4 . 6% ) , sclerosing keratitis ( 3 . 9% ) and blindness of either eye ( 7 . 4% ) ( Table 5 ) . Of note were punctate keratitis lesions with 1–10 Mf of O . volvulus in the cornea present in children ( aged 12–15 years ) and adults ( 28–52 years ) and sclerosing keratitis in adults ( Table 5 , Fig 5 ) . In four cases , alive or dead O . volvulus Mf were detected in the anterior chamber of the eye . Iridocyclitis in evolution ( n = 7 ) was found in youngsters and adults; retinal lesions ( chorioretinis ) were present in younger adults and older ages ( Fig 5 ) with n = 19 being in evolution and n = 41 at an advanced stage . Evolving cataract ( all causes ) was diagnosed in a few children and mainly in older ages . Cataracts caused by O . volvulus infection and blindness caused by onchocerciasis were observed in individuals aged above 50 years ( Fig 5 ) . Cataract , blindness , chorioretinitis , iridocyclitis , punctate and sclerosing keratitis were observed similarly in female and male participants , while LCET , papillitis , trichiasis and low vision were more often diagnosed in females ( Table 5 ) . Cataract ( Spearman's rank correlation: ρ = 0 . 71; p = 0 . 005 ) , blindness ( ρ = 0 . 75; p = 0 . 002 ) , sclerosing keratitis ( ρ = 0 . 78; p = 0 . 001 ) , iridocyclitis ( ρ = 0 . 71; p = 0 . 005 ) and low visual acuity ( ρ = 0 . 56; p = 0 . 04 ) correlated positively with age disclosing the age-related decline in visual functions ( Table 5 ) . LCET and normal vision were negatively correlated with participants’ age ( Table 5 ) . Several manifestations ( right eye ) , notably cataract , sclerosing keratitis and iridocyclitis , were diagnosed less often ( p<0 . 001 , one-sided Fisher exact Test ) in patients from villages situated in the Mô river basin ( Table 5 ) . Of note were the positive correlations of cataract with sclerosing keratitis ( Spearman's rank correlation: ρ = 0 . 874; p<0 . 0001 ) , cataract with iridocyclitis ( ρ = 0 . 716; p = 0 . 0004 ) , with blindness ( ρ = 0 . 765; p = 0 . 0014 ) and with low visual acuity ( ρ = 0 . 753; p = 0 . 0019 ) ( Table 6 ) . Strongly associated ( ρ = 0 . 8134 , p = 0 . 0004 ) were sclerosing keratitis with iridocyclitis , both pathologies of the anterior segment of the eye , and similarly , vascular retinopathy and druzen correlated positively ( ρ = 0 . 7723 , p = 0 . 0012 ) ( Table 6 ) , both are posterior eye segment lesions .
The present surveys have shown that the northern and central regions in Togo are gradually approaching the elimination of onchocerciasis [7 , 8]; however , the geographical and demographic conditions in the Ôti , Kéran and Mô river basins will require continuous , comprehensively intensified and well-adapted interventions which should reach beyond the operationally standardized MDA . In formerly hyperendemic areas in northern Togo that formed part of the SIZ , biannual MDA attained >80% treatment coverage until 2015 , yet many foci remain positive for onchocerciasis and parasite transmission continues . Here , the future interventional strategy may selectively adapt to the particular characteristics of the endemic populations , notably , to the seasonal migrations in and out of the river basins , to the age and gender profiles of the non-complying groups , and to the seasonal patterns of parasite transmission by the local S . damnosum s . l . vector species . Moreover , national control programmes should harmonize cross-border MDA strategies as a coordinated control measure .
|
Mass drug administration ( MDA ) with ivermectin has become the main tool in the efforts to control and eliminate onchocerciasis ( “river blindness” ) . In some areas , and after many years of MDA , levels of Onchocerca volvulus infection ( the causative parasite ) have declined greatly , and elimination appears achievable . In certain river basins of northern and central Togo , the present epidemiological situation remains unknown . The guidelines of the World Health Organization recommend that before ivermectin MDA can be stopped , interruption of O . volvulus transmission must be demonstrated . To this end , parasitological , serological , ophthalmological , and entomological assessments were conducted in the Ôti , Kéran and Mô river basins . O . volvulus infections and positive antibody responses were found in children aged ≤10 years and adults . Progressive ocular onchocerciasis was diagnosed , and parasite transmission by Simulium damnosum s . l . ( the disease vector ) occurred close to the survey locations . Thus , O . volvulus transmission continues in northern and central Togo , and future interventions may require approaches selectively adapted to seasonal migration of non-complying endemic populations in and out of the river basins , as well as seasonal transmission by the vectors . National control programmes should harmonize cross-border MDA as a coordinated intervention .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"ecology",
"and",
"environmental",
"sciences",
"medicine",
"and",
"health",
"sciences",
"onchocerca",
"volvulus",
"rivers",
"helminths",
"keratitis",
"tropical",
"diseases",
"geographical",
"locations",
"parasitic",
"diseases",
"animals",
"onchocerca",
"aquatic",
"environments",
"bodies",
"of",
"water",
"neglected",
"tropical",
"diseases",
"onchocerciasis",
"eyes",
"infectious",
"disease",
"control",
"africa",
"togo",
"infectious",
"diseases",
"marine",
"and",
"aquatic",
"sciences",
"head",
"people",
"and",
"places",
"eye",
"infections",
"helminth",
"infections",
"eukaryota",
"freshwater",
"environments",
"anatomy",
"earth",
"sciences",
"ophthalmology",
"nematoda",
"biology",
"and",
"life",
"sciences",
"ocular",
"system",
"organisms"
] |
2018
|
Onchocerca volvulus infection and serological prevalence, ocular onchocerciasis and parasite transmission in northern and central Togo after decades of Simulium damnosum s.l. vector control and mass drug administration of ivermectin
|
Stimulator of interferon genes ( STING , also known as MITA , ERIS or MPYS ) induces the activation of TBK1 kinase and IRF3 transcription factor , upon sensing of microbial DNAs . How IRF3 is recruited onto the STING signalosome remains unknown . We report here that silencing of the ER adaptor SCAP markedly impairs the IRF3-responsive gene expression induced by STING . Scap knockdown mice are more susceptible to HSV-1 infection . Interestingly , SCAP translocates from ER , via Golgi , to perinuclear microsome in a STING-dependent manner . Mechanistically , the N-terminal transmembrane domain of SCAP interacts with STING , and the C-terminal cytosolic domain of SCAP binds to IRF3 , thus recruiting IRF3 onto STING signalosome . Mis-localization of SCAP abolishes its antiviral function . Collectively , this study characterizes SCAP as an essential adaptor in the STING signaling pathway , uncovering a critical missing link in DNAs-triggered host antiviral responses .
Microbial infections represent an ever-present threat to host homeostasis and survival . The extracellular and intracellular microbes are dynamically and rapidly sensed by specific Pattern Recognition Receptors ( PRRs ) , including TLRs , NLRs and RLRs [1–3] . Upon recognition of the conserved Pathogen Associated Molecular Patterns ( PAMPs ) , PRRs initiate a myriad of signal transduction pathways , triggering innate and adaptive immune responses to eliminate the microbial pathogens [4 , 5] . DNAs derived from DNA viruses , bacteria or damaged host cells could activate the IRF3 and/or NF-κB signaling pathways , thus inducing the production of type I interferons ( IFNs ) and other pro-inflammatory cytokines [6 , 7] . How cells sense and respond to RNA virus infection is well characterized in the past decade [8–10] . Our understanding of the DNA-triggered signaling is relatively limited . TLR9 detects CpG DNA from endolysosome in the immune cells [11] . Multiple cytosolic sensors are proposed to detect viral or microbial DNAs in cytosol , including cGAS , RNA polymerase III , Mre11 , DNA-PKcs , IFI16 and DDX41 [12–18] . Further studies are needed to clarify the physiological relevance of some of the putative DNA sensors , and to address the biochemical and functional interactions among these sensors . Stimulator of interferon genes ( STING , also known as MITA , ERIS or MPYS ) is characterized as the converging point of the recently identified DNA sensors . STING is an Endoplasmic Reticulum ( ER ) -associated membrane protein , indispensable for inducing the antiviral innate responses triggered by microbial DNAs [19–22] . For examples , STING-deficient cells fail to induce type I IFN production after stimulation of dsDNA or infection with herpes simplex virus 1 ( HSV-1 ) or Listeria monocytogenes [23] . STING knockout mice are highly susceptible to lethal infection by HSV-1 [23] . STING can also bind directly to cyclic dinucleotide ( CDNs ) , including cGAMP , c-di-GMP and c-di-AMP [24 , 25] . CDNs and/or upstream DNA sensors could induce STING dimerization , causing its translocation from the ER , via Golgi , to perinuclear microsome [21 , 23 , 26] . Recently , we have identified the unexpected function of the autocrine motility factor receptor ( AMFR , a . k . a GP78 ) and the insulin induced gene 1 ( INSIG1 ) in innate immunity [27] . AMFR and INSIG1 are ER-resident ubiquitin E3 ligase , responsible for catalyzing the K48-linked poly-ubiquitination of the ER misfolded proteins , a process essential for the ER Associated Degradation ( ERAD ) [28] . We characterize AMFR/INSIG1 to interact specifically with STING , and to catalyze the K27-linked poly-ubiquitination of STING . The K27-linked polyubiquitin chain on STING serves as an anchoring platform for recruiting and activating TBK1 , which then phosphorylates the transcription factor IRF3 [27] . Notably , IRF3 could not bind to the K27- or K63- linked polyubiquitin chain . How IRF3 is recruited onto the STING signalosome remains largely unknown . SREBP cleavage-activating protein ( SCAP ) is a polytopic membrane protein on ER , harboring an N-terminal domain with eight transmembrane helices , and a C-terminal domain with five WD-repeat [29] . It is well established that SCAP interacts with INSIG1 and modulates the lipid homeostasis [30] . In this study , we demonstrate that SCAP interacts with STING independent of INSIG1 , and SCAP is indispensable for DNAs-triggered host antiviral responses . Upon HSV-1 infection , SCAP translocates from ER , via Golgi , to perinuclear microsome in a STING-dependent manner . SCAP thus serves as a scaffold adaptor to recruit IRF3 onto the STING signalosome , which reveals a critical missing link in innate immunity .
Our recent study [27] identified INSIG1 to specifically interact with STING ( Fig 1A ) . Given that INSIG1 interacts with SCAP in lipid metabolism [30] , we wondered whether SCAP was also a component in the STING signalosome . We confirmed the association between INSIG1 and SCAP via the co-immunoprecipitation assay ( Fig 1B ) . We observed that SCAP associated with STING exogenously and endogenously ( Fig 1C and 1D ) . This association was marginally enhanced upon HSV-1 ( Fig 1E left ) , Listeria monocytogenes ( Fig 1E middle ) or ISD ( Fig 1E right ) stimulation . Unexpectedly , silencing of INSIG1 did not affect the association between STING and SCAP ( Fig 1C and 1D ) , indicating that STING associates with SCAP in an INSIG1-independent manner . This suggests that the STING signalosome is physically and functionally distinct from the Lipid Regulatory Complex . The transmembrane region of STING ( 1–175 aa ) was important for its interaction with SCAP ( Fig 1F ) . Likewise , the transmembrane region of SCAP ( 1–735 aa ) was mapped to mediate the same interaction ( Fig 1G ) . Confocal microscope imaging confirmed that SCAP co-localized with STING exogenously and endogenously ( Fig 1H ) . HSV-1 and Listeria monocytogenes infections marginally induced the expression of SCAP ( Fig 1I ) . Taken together , these data suggest that SCAP is a new component in the STING protein complex . To probe the potential function of SCAP in innate immunity , we screened out the specific and effective siRNAs against Scap ( Scap siRNA 3060 , Scap siRNA 3465 for mouse and SCAP siRNA 1302 for human ) , all of which could markedly diminish the expression of exogenous and endogenous SCAP ( Fig 2A and 2B ) . It was observed that knockdown of endogenous Scap inhibited the DNA mimics poly ( dA:dT ) -triggered induction of Ifnb mRNA ( Fig 2C ) . In contrast , poly ( I:C ) - or SeV-triggered RIG-I signaling was marginally affected in Scap knockdown cells ( Fig 2C ) . Knockdown of Scap had no inhibitory effects on the TLRs-mediated activation of the Ifnb mRNA triggered by LPS or poly ( I:C ) added ( Fig 2C ) . These data indicate that SCAP specifically regulates the cytosolic DNA-triggered expression of IFN-β . To substantiate , we explored the effect of Scap knockdown on the expression of IRF3-responsive genes induced by cytosolic DNA challenge , using qPCR ( quantitative PCR ) . As expected , silencing of Scap markedly attenuated the induction of the IRF3-responsive genes ( Ifnb , Ifna4 and Cxcl10 ) in MEF cells , stimulated by the DNA mimics [poly ( dA:dT ) or ISD] ( Fig 2D ) treatment or the DNA pathogens ( Listeria monocytogenes or HSV-1 ) ( Fig 2E ) . However , silencing of SCAP apparently displayed no effect on the Thapsigargin-induced ER stress ( S1 Fig ) . To make it more physiologically relevant , we further investigated the function of SCAP in primary cells . BMDMs ( bone marrow derived macrophage ) were transfected with siRNAs against Scap , followed by HSV-1 or Listeria monocytogenes stimulation . Consistently , silencing of Scap markedly attenuated the expression of IRF3-responsive genes in BMDMs ( Fig 2F and 2G ) . Collectively , these data indicate that SCAP is a positive modulator of the cytosolic DNA-triggered STING signaling pathway . We further delineated the topology of SCAP in the STING signaling pathway . Exogenous expression of cGAS or STING could respectively activate the IFN-β-luciferase reporters , and these activations were obviously impaired when knocking down Scap ( Fig 3A and 3B ) . In contrast , knockdown of Scap marginally affected the expression of IFN-β-luciferase reporter when ectopically expressing TBK1 ( Fig 3C ) . Likewise , Scap knockdown had no effect on the activation of the IFN-β-luciferase reporter , when cells were stimulated with the exogenous IRF3-5D ( Fig 3D ) . Furthermore , ectopic expression of SCAP only or both SCAP and INSIG1 could not activate the IFN-β-luciferase reporter ( S2A and S2B Fig ) . Given the hierarchical relationships among these signaling molecules , we reasoned that SCAP modulates the STING signaling downstream of STING and upstream of IRF3 . Consistently , the expressions of PRDIII-I-luciferase reporters stimulated by cGAS , STING or TBK1 were attenuated in Scap knockdown cells , whereas the expressions of PRDIII-I-luciferase reporters stimulated by IRF3-5D remained intact in Scap knockdown cells ( S3A Fig ) . The ubiquitination of STING or the recruitment of TBK1 was not affected by endogenous SCAP depletion ( S3D to S3G Fig ) . Notably , knockdown of Scap led to an apparent decrease in the phosphorylation of IRF3 , but not that of TBK1 , when stimulating cells with either poly ( dA:dT ) ( Fig 3E and S3B Fig ) or ISD ( Fig 3F and S3C Fig ) . Consistently , ISD-triggered dimerization and nuclear translocation of IRF3 were markedly impaired when silencing SCAP . In contrast , knockdown of Tom20 could not influence the dimerization and nuclear translocation of IRF3 ( S4A and S4B Fig ) . We confirmed via confocal microscopy that STING traffics from the ER to perinuclear/Golgi foci ( also called perinuclear microsome ) upon HSV-1 infection ( Fig 4A ) . We wondered whether this translocation was modulated by SCAP . This possibility was ruled out by the observation that the STING translocation was intact when silencing Scap ( Fig 4C ) . Unexpectedly , HSV-1 infection also triggered SCAP to translocate from ER to the perinuclear microsome ( Fig 4B ) . The Cell fractionation assay further revealed that HSV-1 infection induced both STING and SCAP to be predominantly in the microsome fraction ( S5C Fig ) . Notably , SCAP was co-localized with STING both before and after the HSV-1 infection ( Fig 1H and S5A Fig ) . We reasoned that the SCAP translocation is instead dependent on STING . To test this hypothesis , we monitored the SCAP aggregation in wild-type and Sting-/- MEFs upon HSV-1 infection . As expected , SCAP congregated to perinuclear microsome in wild-type MEFs upon HSV-1 infection ( Fig 4D ) . Interestingly , STING deficiency dramatically reduced the trafficking of SCAP to the perinuclear foci ( Fig 4D ) . This translocation of SCAP was obviously rescued in the Sting-/- MEFs reconstituted with Flag-tagged STING ( Fig 4D ) . In Mavs-/- or Tbk1-/- MEFs , SCAP could also congregate to perinuclear microsome ( Fig 4D ) . Taken together , these data indicate that STING specifically facilitates the translocation of SCAP to the perinuclear microsome . To explore the action of SCAP , we noticed that SCAP interacted strongly with both STING and IRF3 , but not with TBK1 and p65 ( Fig 5A ) . The C-terminal cytosolic domain of SCAP ( 736–1280 aa ) was mapped to bind to IRF3 ( 201–357 aa ) ( S6A and S6B Fig ) , whereas the transmembrane region of SCAP ( 1–735 aa ) was mediated to interact with STING ( Fig 1G ) . So we speculated that SCAP might serve as an adaptor for recruiting IRF3 onto STING . The association of STING and IRF3 was enhanced in the presence of SCAP , whereas silencing of Scap almost abolished this association . In contrast , SCAP did not affect the interaction between STING and TBK1 ( Fig 5B and 5C ) . In addition , ectopic-expression of SCAP promoted the endogenous association of STING and IRF3 in response to the HSV-1 stimulation ( Fig 5D ) . Notably , knockdown of SCAP impaired the endogenous association of STING and IRF3 ( S6C Fig ) . The endogenous interaction between SCAP and IRF3 was also confirmed , and this interaction was enhanced upon HSV-1 stimulation ( Fig 5E ) . In sting-/- MEFs , the interaction of SCAP and IRF3 is markedly attenuated ( S6D Fig ) , which indicates that SCAP recruits IRF3 after its STING-dependent translocation to the microsomes . Notably , IRF3 also congregated to the perinuclear microsome and co-localized with SCAP upon HSV-1 infection ( Fig 5F and S5B Fig ) . Importantly , silencing of Scap blocked the congregation of IRF3 ( Fig 5F ) , whereas TBK1 deficiency did not affect the trafficking of IRF3 to the perinuclear foci ( Fig 5F ) . Taken together , these data indicate that SCAP bridges STING to IRF3 in the perinuclear microsome . To determine the importance of ER localization for SCAP function , we generated three mis-localization mutants of SCAP . SCAP-ΔTM was constructed by deleting the N-terminal transmembrane domain of SCAP . SCAP-Mito was constructed by replacing the transmembrane domain with the mitochondria targeting sequence from Tom70 ( translocase of outer membrane 70 , a mitochondria membrane protein ) . SCAP-NLS was constructed by replacing the transmembrane domain with a nuclear localization sequence ( Fig 6A ) . Confocal microscopy analysis confirmed that SCAP-ΔTM , SCAP-Mito and SCAP-NLS were targeted to whole cell , mitochondria and nucleus , respectively ( Fig 6B ) . As expected , SCAP-ΔTM , SCAP-Mito and SCAP-NLS failed to interact with STING ( Fig 6C ) . SCAP-NLS also failed to interact with IRF3 ( S7 Fig ) . Notably , SCAP mis-localization mutants could not enhance the association of STING and IRF3 ( Fig 6D ) . We further performed rescue experiments to corroborate the functional consequences . MEF cells were first transfected with control or Scap siRNAs , followed by transfection of the control or the RNAi-resistant rSCAP plasmids , respectively . The induction of Ifnb or Ifna4 mRNA was measured after ISD stimulation . Consistently , the induction of Ifnb or Ifna4 mRNA was restored by wild type rSCAP , but not rescued by SCAP mis-localization mutants rSCAP-ΔTM , rSCAP-Mito or rSCAP-NLS ( Fig 6E ) . Taken together , these data indicate that the ER localization of SCAP is essential for its action in the STING signaling . We went on to explore the antiviral function of SCAP in innate immunity . The induction of IFN-β is a hallmark of host antiviral responses . Scap siRNA was transfected into MEF cells , followed by HSV-1 ( Fig 7A ) or Listeria monocytogenes ( Fig 7B ) infection . The supernatants were quantified by ELISA ( enzyme-linked immunosorbent assay ) . As expected , knockdown of endogenous Scap drastically impaired the IFN-β protein production ( Fig 7A and 7B ) . Since IFN-β protects host cells against virus infection , we assessed whether SCAP could restrict HSV-1 infection . MEF cells were respectively pretreated with culture supernatants from ISD-stimulated Scap knockdown cells or control cells , followed by HSV-1 ( Fig 7C ) or Listeria monocytogenes ( Fig 7D ) infection . It was observed that , fresh cells pretreated with culture supernatants from Scap knockdown MEFs were more permissive to HSV-1 or Listeria monocytogenes infection ( Fig 7C and 7D ) . In addition , we investigated whether SCAP attenuated microbial replications by challenging cells with HSV-1-GFP or Listeria monocytogenes . Consistently , knockdown of Scap augmented the levels of HSV-1-GFP-positive cells ( Fig 7E ) , and also markedly enhanced the replication of Listeria monocytogenes ( Fig 7F ) . In contrast , the replication of NDV-GFP was unaffected by depletion of Scap ( S8 Fig ) . Finally , we investigated the in vivo role of SCAP . We delivered into mice , via tail vein injection , the Scap specific or control shRNAs coated with polyethyleneimine ( PEI ) . The efficiency of in vivo ‘knockdown’ was confirmed ( Fig 7G and 7H ) . Next , mice were infected intravenously with HSV-1 , and their survival rates were monitored . As expected , Scap-knockdown mice were more susceptible to HSV-1 infection than control mice . All the Scap-knockdown mice died within 3 days , whereas 50% of the control mice remained alive until 7 days after HSV-1 infection ( Fig 7I ) . Notably , Scap knockdown mice displayed a severer defect in the production of sera IFN-β upon HSV-1 invasion , as compared with the infected control mice ( Fig 7J ) . These data indicate that SCAP is indispensable for protecting mice against HSV-1 infection .
Recent breakthroughs have characterized multiple cytosolic sensors that potentially monitor the cytosolic DNAs ( cGAS , IFI16 , DDX41 , Mre11 and DNA-PKcs ) . STING is established unambiguously as the converging point of the DNA sensors , to further relay the activation signals on ER [31 , 32] . Notably , STING is induced to dimerize and traffic from the ER , via Golgi , to perinuclear microsome [6 , 23] . TBK1 is simultaneously recruited to the same compartment in a STING dependent manner , which then activates the transcription factor IRF3 [23] . It is intriguing to dissect the molecular mechanisms of the DNA-driven assembly of the STING signalosome on either ER or the perinuclear microsome . Our recent study [27] has characterized the AMFR/INSIG1 protein complex as a novel component of the STING signalosome on ER . The E3 ubiquitin ligase AMFR , bridged by INSIG1 , catalyzes the K27-linked polyubiquitination of STING upon microbial DNA challenge . This unique polyubiquitin chain specifically recruits TBK1 and ferries the latter to the perinuclear microsome , along with STING . Notably , IRF3 could bind neither the K27- nor K63- linked polyubiquitin chain . This suggests that IRF3 is recruited onto the STING signalosome via another uncharacterized mechanism . So we speculated that there might be unknown adaptor protein ( s ) on ER to perform this function . An analogy drew our attention regarding the translocations of the STING and SREBP . SREBP is a master transcription factor of the lipid and glucose metabolism on ER , which also translocates from ER to Golgi in response to metabolic stimuli [33] . Notably , SCAP is indispensable for chaperoning SREBP to Golgi , and INSIG1 specifically interacts with SCAP to prevent this translocation [30 , 34] . This inspires us to explore the potential function of SCAP in innate immunity . Both STING and SCAP reside on ER via their N-terminal transmembrane domains . We confirmed the interaction between SCAP and STING , and demonstrated the transmembrane domains of both proteins to mediate this interaction . Unexpectedly , this interaction is independent of INSIG1 , suggesting that the STING signalosome might be physically and functionally distinct from the Lipid Regulatory Complex . To substantiate , there is no obvious difference of IRF3 activation upon HSV-1stimulation , with or without FBS in the cell culture medium ( S9F Fig ) . SREBP1 knockdown did not influence the exogenous DNA-induced IRF3 activation ( S9B and S9C Fig ) . Furthermore , HSV-1 infection could not trigger SREBP1 translocation ( S9D Fig ) . It was well established that the SREBP signaling was dramatically impaired respectively by three SCAP mutants ( SCAP-Y234A , SCAP-Y298C , SCAP-D443N ) [35 , 36] . However , the RNAi-resistant rSCAP-Y234A , rSCAP-Y298C , rSCAP-D443N could rescue the induction of IRF3-responsive genes in SCAP knockdown cells , like that of the wild type rSCAP ( S9E Fig ) . Taken together , these observations indicate that the STING signaling is functionally and physically uncoupled from the SREBP signaling . During revising this manuscript , York et al reported that the cell metabolic reprogramming could also induce the expression of IFNβ and ISGs in a STING-dependent manner [37] . Interestingly , some of the data in this paper suggested that SCAP was also important for this induction . However , they did not address how SCAP could potentially modulate the function of STING . Notably , there are some discrepancies between the two studies concerning the specific effects of SCAP in regulating the induction of IFNβ and ISGs . York’s study was performed mainly in the context of metabolic reprogramming , whereas our study only focused on the microbe-induced activation of the STING signaling . In our experimental setting , we have supplied enough FBS medium to ensure that the cells will not experience metabolic reprogramming even when knocking down SCAP . In contrast , the indicated paper mainly addressed the metabolic reprogramming-induced expression of IFN-β and ISGs , which broadly affects the overall cell metabolism and dramatically influences the basal expression of IFN-β . Although the observation is interesting , we wondered if this cell model directly or indirectly activates the STING signaling , which needs further exploration in future experiments . For example , it is intriguing to address whether perturbing metabolism will lead to the dimerization and translocation of STING to the perinuclear microsome , whether this activation is dependent on cGAS , whether SCAP and IRF3 will congregate in this scenery . We speculate that different stimulating models could potentially lead to the observed discrepancies . In addition , we extensively employed HSV-1 and Listeria Monocytogenes as effective stimuli to address the activation of STING signaling , which are well characterized microbes in the relevant fields . As far as we know , MHV-68 is scarcely employed in elucidating the STING signaling pathway . We do not know why the indicated study did not use HSV-1 or Listeria Monocytogenes . However , MHV-68 could potentially trigger the cGAS-STING signaling [38] , and it was recently reported that some RNA viruses could also activate IRF3 in a STING-dependent manner [32] . We speculate that the different species of viruses might employ subtly different mechanisms to engage the activation of the STING signaling . Functional analyses firmly established the essential role of SCAP in mediating the STING signaling . The phosphorylation of IRF3 stimulated by cytosolic DNAs is markedly impaired when knocking down the endogenous SCAP . Silencing the endogenous SCAP resulted in the impairment of the STING-mediated induction of IFNs and ISGs , and this effect is reversed by exogenously expressing siRNA-resistant SCAP . Silencing of Scap also impairs IFN-β protein production upon microbe infection , thus crippling the host antimicrobial responses against HSV-1 and Listeria monocytogenes . In vivo ‘knockdown’ of Scap induces less interferons and accelerates the death rate of the mice upon the HSV-1 infection . We observed that HSV-1 infection triggers both SCAP and STING to traffic from the ER , via Golgi , to perinuclear microsome . Given that SCAP chaperons SREBP from ER to Golgi via the COP-II vesicle machinery , we had supposed that the translocation of STING is dependent on SCAP . However , knockdown of Scap did not affect the translocation of STING . Instead , the SCAP translocation is dependent on STING , but not on MAVS or TBK1 , as evidenced in the corresponding knockout MEFs . The autophagy-related proteins ( Atg9a and LC3 ) were implicated to modulate STING translocation [26] . It remains to address whether the autophagy proteins could modulate the action of SCAP . Our study further provides a possible mechanism of regulating the STING signaling pathway by SCAP , which York et al . did not address . SCAP interacts individually with either STING or IRF3 , via its N-terminal trans-membrane domains or C-terminal cytosolic domain respectively . The association between STING and IRF3 was respectively enhanced or impaired in response to HSV-1 stimulation , in the presence or absence of SCAP . Mis-targeting of SCAP , to whole cell , mitochondria or nucleus , resulted in its failure to bridge STING to IRF3 . Notably , IRF3 also congregates to the perinuclear microsome after HSV-1 infection; and this congregation is dependent on SCAP , but not on TBK1 . However , it is technologically challenging to demonstrate whether SCAP recruits IRF3 on ER or on perinuclear microsome . Recently , Dobbs et al . [39] have suggested that STING is retained on ER by some unknown inhibitor ( s ) in unstimulated cells . We think that such inhibitor ( s ) could not only block the translocation of STING/SCAP but also mask the IRF3 binding site on SCAP . Microbial infection releases this inhibition and triggers the translocation of STING and SCAP . Consequently , SCAP could recruit IRF3 to the STING signalosome . It is conceptually possible that IRF3 is blocked access to SCAP on ER by some “safe mechanism” , and SCAP on the perinuclear microsome is exposed to IRF3 . We had proposed an evolutionary necessity for assembling the STING signalosome on ER [27] . Arguably , the current characterization of SCAP further substantiates this perspective . We speculate that SCAP was dedicated primarily to mediate the activation of SREBP . Host innate immunity adapted it , along with AMFR and INSIG1 , to integrate into the later-evolved STING signalosome . We predict that future studies will uncover more proteins essential for innate immunity that modulate the ERAD and/or Lipid/Glucose metabolism on ER . Deeper functional links between innate immunity and metabolism are expected to be displayed on the interface of ER . Taken together , this study identified SCAP as the long-sought-after adaptor for recruiting IRF3 onto the STING signalosome . All the evidence favors the STING as an assembly platform , and the translocation of STING is the major cause of the other accompanying congregations . It remains to address what drives the STING translocation and whether the COP-II vesicle is required . The microenvironment of Golgi or microsome is probably favorable to the activation of TBK1 and IRF3 . The underlying mechanisms remain elusive .
C57BL/6 mice 6–8 weeks old were purchased from the Shanghai SLAC Laboratory Animal Company . The mice were maintained under specific pathogen-free ( SPF ) conditions at the Shanghai Institute of Biochemistry and Cell Biology . Animal experiments were carried out in strict accordance with the regulations in the Guide for the Care and Use of Laboratory Animals issued by the Ministry of Science and Technology of the People’s Republic of China . The protocol and the procedures for mice study were approved by the Institutional Animal Care and Use Committee of the Shanghai Institute of Biochemistry and Cell Biology , Chinese Academy of Sciences ( Permit Number: IBCB0027 Rev2 ) . SCAP , STING , TBK1 , IRF3 , INSIG1 , cGAS , p65 were obtained by PCR from the thymus cDNA library and subsequently inserted into indicated mammalian expression vectors . The reporter plasmids ( IFN-β-luciferase , pTK-Renilla ) have been described previously [40] . The SCAP siRNA-resistant form was generated with silent mutations introduced into the siRNA target sequence . All point mutations were introduced by using a QuickChange XL site-directed mutagenesis method ( Stratagene ) . All constructs were confirmed by sequencing . The polyclonal antibody against STING was generated by immunizing rabbit with recombinant human STING ( 221–379 aa ) . The goat polyclonal antibody against SCAP was from Santa Cruz Biotechnology . The rabbit polyclonal antibody against SCAP was a gift from Dr . Xiongzhong Ruan ( Chongqing medical university ) . hemagglutinin ( HA ) , Myc , Ub , SREBP1/2 and IRF3 antibody were purchased from Santa Cruz Biotechnology . TBK1 antibody was from abcam . Tom20 antibody was from BD Biosciences . Flag and β-actin antibodies were obtained from Sigma-Aldrich . Phospho-IRF3 and Phospho-TBK1 antibody was from Cell Signaling Technology . Poly ( dA:dT ) and lipopolysaccharide ( LPS ) was obtained from Sigma-Aldrich . Poly ( I:C ) was purchased from Invitrogen . Wild type HSV-1 and HSV-1-GFP were kindly provided by Dr . Wentao Qiao ( Nankai University ) and Dr . Chunfu Zheng ( Suzhou University ) , respectively . Listeria monocytogenes ( 10403 serotype ) was a gift from Dr . Youcun Qian ( Institute of Health Sciences ) . TBK1 kinase inhibitor BX795 was purchased from InvivoGen . ISD ( Interferon stimulatory DNA ) was prepared by annealing equimolar amounts of sense and antisense DNA oligonucleotides at 95°C for 10 min before cooling to room temperature . Oligonucleotides used are as follows: HEK293 , HEK293T and MEF cells were obtained from the American Type Culture Collection ( ATCC ) . The procedure for generating BMDMs ( bone marrow-derived macrophage ) has been described previously [41] . HEK293 , HEK293T , Sting-/- MEF , Tbk1-/- MEF and Mavs-/- MEF cells were cultured in DMEM medium ( Invitrogen ) plus 10% FBS and 1% penicillin-streptomycin ( Invitrogen ) . Transfection was performed with Lipofectamine 2000 ( Invitrogen ) according to the manufacturer’s instructions . The siRNAs duplexes were synthesized from GenePharma . The sequences of siRNAs are shown as follows: MEF cells were grown on coverslips in 12-well plate . After treatment with or without HSV-1 , coverslips with the cells were fixed for 15 minutes with 4% formaldehyde in PBS and permeabilized in 0 . 25% Triton X-100 in PBS for another 15 minutes , following by using 5% BSA in PBS for 1 hour . Then , cells were stained with indicated primary antibodies followed by incubation with fluorescent-conjugated secondary antibodies . The nuclei were counterstained with DAPI ( Sigma-Aldrich ) . For mitochondria staining , living cells were incubated with 300 nM Mito Tracker Red ( Invitrogen ) for 30 min at 37°C . Slides were mounted with fluorescent mounting medium ( Dako ) . Images were captured using a confocal microscope ( TCS SP2 ACBS; Leica ) with a ×63 ( numerical aperture 1 . 4 ) oil objective . For immunoprecipitation assay , cells extracts were prepared by using lysis buffer ( 50 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 0 . 5% Triton X-100 , 1mM EDTA ) supplemented with a protease inhibitor cocktail ( Roche ) . Lysate were incubated with appropriate antibodies for 4 hours to overnight at 4°C before adding protein A/G agarose beads for another 2 hours . The beads were washed three times with the lysis buffer and eluted with SDS-loading buffer by boiling for 5 minutes . For immunoblot analysis , the immunoprecipitates samples were subjected to SDS-PAGE . The separated proteins were then electrically transferred to a PVDF membrane ( Millipore ) . Immunoblotting was probed with indicated primary and secondary antibodies . The protein bands were visualized by using a SuperSignal West Pico chemiluminescence ECL kit ( Pierce ) . Luciferase reporter assays were performed as described previously [42] . Isolation of Total RNA from indicated cells was performed by using TRIzol reagent ( Invitrogen ) according to the manufacturer’s instructions . Reverse transcription of purified RNA was performed using oligo ( dT ) primers . The quantification of indicated gene transcripts were performed by real-time PCR with using FastStart Universal SYBR GREEN MASTER MIX ( Roche ) , and Gapdh served as an internal control . PCR primers of indicated target genes are shown as below: Cell fraction was performed as described previously [23] . Cells were harvested by centrifugation at 1 , 000g for 10 min . The pellet was resuspended in sucrose homogenization buffer ( 0 . 25 M sucrose , 10 mM HEPES , pH 7 . 4 ) , and cells were lysed by using a dounce homogenizer . Lysed cells were centrifuged at 500g for 10 min , and the supernatant was collected . The supernatant was then centrifuged at 10 , 300g for 10 min . The supernatant was crude microsomal ( microsome and cytosol ) , and the pellet was crude mitochondrial ( MAM and mitochondria ) . The crude microsomal fraction was subjected to ultracentrifugation at 100 , 000g for 60 min . The pellet was microsome fraction . Concentrations of the cytokines in culture supernatants were measured by ELISA kit ( PBL Biomedical Laboratories ) according to the manufacturer’s instructions . The shRNA was delivered into C57BL/6 mice with JetPEI transfection reagent ( PolyPlus Transfection , San Marcos , CA ) according to the manufacturer’s instructions [43 , 44] . The shRNA plasmid and JetPEI was each diluted into 100ml of 5% glucose , then mixed and incubated for fifteen minutes at room temperature at a final N/P ratio of 8 . Finally , the mixture ( 200ml ) was injected into each mouse via tail vein . For analysis of in vivo ‘knockdown’ efficiency , mice were euthanized after forty-eight hours of in vivo shRNA transfection . The mRNA or protein of SCAP was respectively checked in leukocyte from blood or extracts from livers . The control or Scap ‘knockdown’ mice were infected intravenously with HSV-1 . The viability of the infected mice was monitored for 7 days . The mouse serum was collected at six hours after infection to measure cytokine IFNβ production by ELISA . The shRNA was designed targeting mouse Scap 5′- GCT TAG AGC TGC AAG GCA A -3′ sequence . Student’s t-test was used for statistical analysis of two independent treatments . Mouse survival curve and statistics were analyzed with log-rank ( Mantel-Cox ) test . P values of less than 0 . 05 were considered to be statistically significant . The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for the genes and gene products discussed in this paper are: SCAP ( NM_012235 . 2 , NP_036367 . 2 ) ; STING ( NM_198282 . 3 , NP_938023 . 1 ) ; TBK1 ( NM_013254 . 3 , NP_037386 . 1 ) ; IRF3 ( NM_001197122 . 1 , NP_001184051 . 1 ) ; INSIG1 ( NM_005542 . 4 , NP_005533 . 2 ) ; cGAS ( NM_138441 . 2 , NP_612450 . 2 ) ; p65 ( NM_021975 . 3 , NP_068810 . 3 ) .
|
The stimulator of interferon genes ( STING/MITA/ERIS/MPYS ) is characterized as the converging point of the cytosolic DNAs-triggered innate immune signaling , and its function has been well documented in mediating the production of type I interferon and other pro-inflammatory cytokines . It remains intriguing to address how IRF3 is recruited onto the STING signalosome . In this study , we have further identified and characterized the SREBP cleavage-activating protein ( SCAP ) as the long-sought-after adaptor of the STING signaling . Upon microbial DNA challenge , SCAP translocates from ER , via Golgi , to perinuclear microsome in a STING-dependent manner . SCAP thus serves as a scaffold adaptor to recruit IRF3 and facilitate its integration into the perinuclear microsomes . Our study reveals an important missing link in innate immunity , further highlighting the physical and/or functional links between innate immunity and metabolism .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"transfection",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"gene",
"regulation",
"pathogens",
"microbiology",
"light",
"microscopy",
"immunoprecipitation",
"microscopy",
"signalosomes",
"confocal",
"microscopy",
"molecular",
"biology",
"techniques",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"small",
"interfering",
"rnas",
"specimen",
"preparation",
"and",
"treatment",
"microsomes",
"staining",
"proteins",
"medical",
"microbiology",
"gene",
"expression",
"microbial",
"pathogens",
"molecular",
"biology",
"precipitation",
"techniques",
"listeria",
"monocytogenes",
"biochemistry",
"rna",
"immunostaining",
"protein",
"complexes",
"cell",
"biology",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"non-coding",
"rna"
] |
2016
|
ER Adaptor SCAP Translocates and Recruits IRF3 to Perinuclear Microsome Induced by Cytosolic Microbial DNAs
|
Deneddylases remove the ubiquitin-like protein Nedd8 from modified proteins . An increased deneddylase activity has been associated with various human cancers . In contrast , we show here that a mutant strain of the model fungus Aspergillus nidulans deficient in two deneddylases is viable but can only grow as a filament and is highly impaired for multicellular development . The DEN1/DenA and the COP9 signalosome ( CSN ) deneddylases physically interact in A . nidulans as well as in human cells , and CSN targets DEN1/DenA for protein degradation . Fungal development responds to light and requires both deneddylases for an appropriate light reaction . In contrast to CSN , which is necessary for sexual development , DEN1/DenA is required for asexual development . The CSN-DEN1/DenA interaction that affects DEN1/DenA protein levels presumably balances cellular deneddylase activity . A deneddylase disequilibrium impairs multicellular development and suggests that control of deneddylase activity is important for multicellular development .
Conjugation and deconjugation of target proteins with ubiquitin ( Ub ) and related proteins is an important posttranslational regulatory principle to control the stability , activity or location of modified substrates . The neuronal precursor cell expressed developmentally down-regulated gene 8 ( Nedd8 ) is a member of the ubiquitin family and represents the closest relative of ubiquitin ( Ub ) within the group of ubiquitin-like ( Ubl ) proteins [1] . Covalent attachment of Ubls as Nedd8 requires processing of a precursor peptide resulting in a free di-glycine motif at the C-terminus . All Ubls require in addition an ATP dependent activation cascade of an activating E1 , a conjugating E2 enzyme and a substrate specific E3 ligase . Covalent attachment of Nedd8 is termed neddylation , whereas deneddylation is the reverse deconjugation reaction [2] . The major neddylation targets are the cullins which are scaffold proteins within the Cullin-RING ligases ( CRL ) which serve as ubiquitin ligases . Mammals have seven different cullins ( CUL1 , 2 , 3 , 4A , 4B , 5 and 7 ) [3] while orthologs of three of them ( CulA , C and D ) are conserved in Aspergillus nidulans [4] . Studies in mammalian cells showed that the Ub E3 CRL-RING component Rbx1 interacts with the Nedd8 E2 enzyme Ubc12 and acts as a Nedd8 E3 ligase for cullins [5] . Nedd8 induces a conformational change that allows the first Ub to bridge a gap between the Ub-E2 and the substrate to be ubiquitinated [6] . The CUL1 containing CRLs have more than 350 substrates which include a number of factors involved in human tumor formation [7] . Deneddylation of cullins inactivates CRLs and allows the disassembly of the enzyme complex and the binding of CAND1 ( Cullin associated Nedd8 dissociated protein 1 ) [8] , [9] . CAND1 is important for the reassembly of E3 complexes [10] . Thus , deneddylation of cullins has two functions: it blocks ubiquitination and prepares rearrangement of Ub E3 CRLs . The most prominent deneddylases in humans are the CSN ( COP9 signalosome ) complex and DEN1 ( deneddylase 1 ) [11] , [12] . In addition , there are Ub-specific proteases with dual specificity for Ub and Nedd8 such as USP21 and UCH-L3 ( Ub-C-terminal hydrolase ) [13] . The mammalian CSN consists of eight subunits ( CSN1–CSN8 ) which are conserved in the filamentous fungus Aspergillus nidulans [14] , [15] . CSN possesses the deneddylase activity as an intrinsic metalloprotease with a JAMM motif localized on CSN5 [16] . CSN forms functional super complexes with CRLs and removes Nedd8 from cullins via its metalloprotease [17]–[19] . CSN is more than a deneddylase , since it is associated with kinases [20] and the de-ubiquitinating protein USP15 [21] . In addition , it acts as an assembly platform for Ub E3 CRLs [19] , [22]–[24] . In various organisms the CSN is also a key regulator for light dependent cellular processes [25]–[27] . Reduced CSN function results in embryonic lethality in plants [28] , insects [29] or mammals [15] , and an early block of sexual development in A . nidulans [14] , [25] ( Figure 1A ) . CSN up-regulation in various cancers suggests a function in human tumor formation [15] , [30] . The mammalian DEN1 cysteine protease was initially classified as a SUMO specific protease [31] . Its high preference for Nedd8 is determined by a number of key residues responsible for the architecture of the enzyme [32] . Human DEN1 is a bifunctional enzyme which can process the C-terminus of Nedd8 producing the free di-glycine motif and deconjugates Nedd8 from cullins [33] . The developmental function of DEN1 is unclear . The two DEN1 homologs in the fission yeast S . pombe , NEP1 and NEP2 can deneddylate cullins in vitro , but no in vivo function is known [34] . Drosophila DEN1 deneddylates numerous non-cullin proteins which were highly neddylated in corresponding DEN1 mutants [35] . For example , DEN1 targets the regulator MDM2 for degradation by deneddylation , whereas MDM2 is stabilized by neddylation [36] . The Drosophila DEN1 mutation suppresses Nedd8 mutant lethality [35] . Mammalian DEN1 has been shown to be involved in the regulation of apoptosis . Activated caspases can be neddylated by inhibitors of apoptosis ( IAPs ) leading to a block of caspase activity . DEN1 reactivates caspases by deneddylation [37] . In this study we describe the first developmental phenotypes of a denA/DEN1 deletion using the multicellular fungus A . nidulans . DenA/DEN1 and CSN are required for development and physically interact . CSN targets DEN1/DenA for protein degradation and this mechanism is conserved in A . nidulans and human cells .
Mutant strains defective in the DEN1 deneddylase displaying clear phenotypes have not yet been described . Aspergillus nidulans represents a multicellular eukaryotic model which grows as a filament , starting from a fungal spore ( Figure 1A ) . After approximately 20 hours of growth this mold is able to respond to external signals to establish an asexual or a sexual developmental pathway for another round of spore formation [38] . Asexual development is promoted by light and results in mitotic spores ( conidia ) which are released into the air . The sexual pathway is inhibited by light and results in the formation of meiotic spores within closed complex fruiting bodies ( cleistothecia ) [25] , [39] . Defects in the A . nidulans COP9 signalosome deneddylase result in mutant strains which are unresponsive to light and blocked in sexual development [14] , [25] . This defect was compared to the deletion phenotype of the second deneddylase encoded by the DEN1 homolog denA ( AN10456 ) of A . nidulans . The respective 258 amino acid protein and the corresponding open reading frame were identified performing a BlastP search with the human DEN1 amino acid sequence ( UniProt Acc . -No . Q96LD8 ) on the A . nidulans genome [40] applying the NCBI Blast tool ( www . ncbi . nlm . nih . gov ) . The protein is predicted to be a member of the Ulp1 peptidase family with the characteristic catalytic triad histidine ( H ) , aspartate ( D ) and cysteine ( C ) . Close homologs were as well found in other Aspergilli . The human DEN1 protein is 32% identical with A . nidulans DenA ( Figure S1 ) . Rapid amplification of cDNA ends ( RACE ) [41] revealed a transcript with seven exons interrupted by six introns with a total length of 1469 base pairs ( Figure 1B ) . Northern hybridization experiments were performed to monitor expression of denA during different stages of fungal development . The corresponding mRNA was present throughout all stages of fungal life with elevated levels during asexual and sexual development ( Figure 1C ) . In order to figure out whether DenA protein abundance correlates with gene expression western blot experiments were performed for comparable time points . A . nidulans DenA was fused with GFP at the C-terminus ( DenA-GFP ) and the construct was introduced to the endogenous denA locus , expressed from the native denA promoter . The corresponding strain was indistinguishable from wild type indicating that the fusion construct can fulfill denA function ( data not shown ) . The GFP tag applied for these experiments was stable towards protein degradation in fungal cells , whereas the DenA protein fused to it was not . We analyzed the quantitative relation between the DenA-GFP fusion and the remaining GFP tag and applied this as a measure for protein stability of DenA-GFP ( Figure 1D ) . DenA-GFP was present during vegetative growth and early stages of development , but was no more detected at later stages of development ( Figure 1D ) . During vegetative growth the fungal cell produced low amounts of stable DenA-GFP . During sexual and asexual development high amounts of unstable DenA were produced . This was represented by the increase of signal intensity for the remaining GFP tag which is a stable remnant from degradation of the DenA fusion protein ( Figure 1D , GFP ) . Altogether the data showed that denA expression and protein abundance were not correlating . Observations made especially during asexual and sexual development suggest that some kind of post-translational stability control might exist . Additionally we performed fluorescence microscopy with the DenA-GFP which revealed several subpopulations of DenA in vegetative germlings of A . nidulans . Those were localized in the nucleus , at either site of the fungal septum within a ball shaped structure or in the cytoplasm ( Figure 1E ) . In order to explore the role of denA in the fungal cell we generated a knock-out strain . Deletion of the denA coding region resulted in a fungal strain with a significantly reduced growth rate compared to denA wild type ( Figure 2A , 2B ) . Complementation of the ΔdenA strain by denA resulted in a strain indistinguishable from wild type ( Figure 2 ) . Asexual development was almost abolished upon denA deletion , even during constant white light , which normally favors wild type asexual development . Formation of asexual sporulation structures ( conidiophores ) took much longer in the ΔdenA strain and the overall number was marginal . Quantification revealed that spore production corresponds to only 4% of a denA wild type strain ( Figure 2C ) . However , the few conidiophores produced by the denA mutant differentiated in a normal manner . There was no significant difference between sexual development of wild type or the ΔdenA strain in the dark ( Figure 2D , dark columns ) . However , the ΔdenA strain was unresponsive to light and could not inhibit sexual fruit body formation to 20% as wild type in a light-dependent process [42] . The ΔdenA strain formed similar numbers of cleistothecia with normal size and viable ascospores in light or darkness ( Figure 2D , 2E ) . Therefore , fungal DenA is important for light-inhibition of sexual development and is required for asexual spore formation ( Figure 1A ) . Human DEN1 is a dual functional protease in vitro , processing Nedd8 or cleaving the isopeptide bond between Nedd8 and a target protein [31] , [33] , [43] . Yeast-2-hybrid experiments revealed that A . nidulans DenA interacts with the precursor or the mature form of fungal Nedd8 ( Figure 3A ) . We addressed if processing or deneddylation activity of DenA is responsible for the developmental phenotype . Saccharomyces cerevisiae strains deleted for the UCH encoding gene yuh1 cannot produce the processed variant of yeast Nedd8 ( Rub1 ) . Such strains show no obvious growth phenotype but in western experiments neddylated cullins cannot be detected with appropriate antibodies ( Figure 3 , lane 2 ) [44] . We introduced a plasmid into the corresponding yeast deletion strain expressing A . nidulans denA from an inducible GAL1 promoter . After induction by growth on galactose containing medium we prepared crude extracts and performed western blot experiments to monitor protein neddylation . Neither the yeast cullin Cdc53 appeared in its neddylated form nor did the Rub1/Nedd8 antibody detect any other neddylated protein at the size of a cullin ( Figure 3B ) . Thus , A . nidulans DenA was unable to complement the Nedd8 processing deficient yuh1 mutant of S . cerevisiae . To further discriminate between Nedd8 processing and deneddylation an A . nidulans strain was constructed expressing a mature Nedd8 variant ( Nedd8m ) that does not require processing . The resulting strain was viable and competent for asexual and sexual development . Therefore processed Nedd8 can fulfill the functions of the original unprocessed Nedd8 ( Figure 3C ) [4] . When the processed Nedd8 mutant was combined with ΔdenA , it was indistinguishable from the denA deletion phenotype ( Figure 3D ) . Furthermore , in vitro activity tests with recombinant A . nidulans DenA and human DEN1 ( Figure 3E and Figure S2 ) were performed . A linear substrate composed of processed human Nedd8 , C-terminally fused with GFP was efficiently cleaved by the human DEN1 protease but the fungal DenA showed no detectable formation of cleavage product under the tested conditions ( Figure 3E ) . These experiments support that the fungal denA deletion phenotype , which impairs A . nidulans development , does not depend on the Nedd8 processing function . Deneddylation activity of fungal DenA was examined in more detail . GST-DenA purified from E . coli cleaved the isopeptide bond of human CUL1-Nedd8 as efficient as human DEN1 ( Figure 4A and Figure S3 ) demonstrating deneddylase function of the fungal protein . To test DenA deneddylation in vivo we accomplished heterologous expression studies in S . cerevisiae . A yeast strain constitutively expressing A . nidulans culD , the fungal homologue of human CUL4 , was additionally transformed with a plasmid containing a galactose inducible construct of A . nidulans denA . Following induction of denA expression by growth on galactose containing medium CulD neddylation was monitored by western experiments ( Figure 4B ) . When DenA was present CulD could not be detected with the Rub1/Nedd8 antibody and also a LexA antibody generated only a signal for the non neddylated form of CulD ( Figure 4B , right lanes ) . Furthermore , whole cell extracts of A . nidulans wild type , ΔcsnE and ΔdenA were probed with a CulA specific antibody to examine the neddylation state of the cullin . Upon denA deletion the portion of neddylated CulA increased compared to the wild type , corroborating DenA function in cullin deneddylation in vivo . Neddylated CulA species were increased in strains defective in DenA or in the COP9 signalosome deneddylase CsnE . However , the accumulation of neddylated cullin was less pronounced in ΔdenA ( Figure 4C ) indicating that CulA might not be the predominant target of DenA . Nevertheless , these data further support that DenA function in A . nidulans development is rather due to deneddylation than to a Nedd8 processing activity . An A . nidulans csnE/CSN5 deletion strain defective in the deneddylase subunit of CSN is unresponsive to light and blocked in sexual development . This is accompanied by an altered secondary metabolism represented by an aberrant red color accumulating within the hyphae and the surrounding medium [14] , [45] , [46] . The denA deletion strain was also unresponsive to light and impaired in asexual development . The developmental interplay between the two fungal deneddylases was analyzed by constructing a double mutant . The ΔdenA/ΔcsnE strain grew poorly but was viable . It combined the phenotypes of both single deletions including the red color phenotype observed in a ΔcsnE strain ( Figure 5A ) [14] . The A . nidulans double deletion strain was able to grow as vegetative filament . Asexual development was reduced to the marginal level observed in the ΔdenA strain and sexual development was completely abolished ( Figure 5A ) . This corresponds to ΔdenA for asexual development , but goes even further than ΔcsnE which can still initiate but no more complete the sexual cycle [14] , [25] , [45] ( Figure 1A ) . Western experiments revealed that fungal Nedd8 was detected in crude extracts of A . nidulans wild type , ΔdenA , ΔcsnE and ΔdenA/ΔcsnE . Deletion of either deneddylase alone already resulted in a recognizable increase of the Nedd8 signal compared to wild type . In the ΔdenA strain this was mainly caused by an intensification of multiple Nedd8 signals across a wide molecular weight range . The ΔcsnE strain showed a major increase of the Nedd8 signal around 100 kDa which corresponds to the size of neddylated A . nidulans cullins . The ΔdenA/ΔcsnE strain showed a drastic increase of neddylated proteins , not only compared to wild type , but also to the single deletion strains . This is due to an addition of the effects observed for each single deletion strain and accumulation of an additional band ( ∼116 kDa ) in the ΔdenA/ΔcsnE strain , which might be due to multiple neddylation of a cullin ( Figure 5B–5C ) . This correlated with reduction of free Nedd8 compared to wild type which was most pronounced in the ΔdenA/ΔcsnE strain and to a lower extend also visible in the denA and csnE single deletion mutants ( Figure 5B ) . The data suggest that A . nidulans without the deneddylases DenA and CSN has lost almost all developmental competence . The double deletion phenotype of only sporadic asexual sporulation and no sexual development is accompanied by an intensive accumulation of neddylated proteins . This suggests that deneddylation by DenA and CSN plays a prominent role in the regulation of fungal developmental programs beyond filamentous growth . We investigated whether the DenA and CSN deneddylases physically interact within the fungal cell . Yeast-2-hybrid analysis revealed a strong interaction of A . nidulans DenA with CsnG/CSN7 and weaker interactions with CsnA/CSN1 , CsnE/CSN5 and CsnF/CSN6 of A . nidulans ( Figure 6A ) . This suggests an interaction of DenA with the CSN complex . Bi-molecular fluorescence complementation ( BiFC ) studies [9] were conducted to verify the major interaction of DenA with the CsnG/CSN7 subunit . DenA and CsnG/CSN7 were fused to one half of a split yellow fluorescent protein ( YFP ) at their N-termini . Expression of the fusion proteins resulted in a fluorescence signal accumulating in the nucleus ( Figure 6B ) confirming the yeast-2-hybrid result . An interaction between the DenA deneddylase and the COP9 signalosome deneddylase had not yet been described . We investigated whether DEN1 and CSN also interact in human cells . Separation of HeLa cell lysates by density gradient centrifugation and subsequent western analysis revealed co-sedimentation of CSN and a portion of DEN1 ( Figure 6C ) . This corroborates a physical interaction between the two deneddylases in eukaryotic cells . Flag-pull downs with Flag-CSN2-B8 cells verified the interaction . Flag-labeled CSN complex was pulled down and analyzed by western experiments . These pull downs contain all CSN subunits as well as additional associated proteins [18] , [22] . Our analysis demonstrated that DEN1 was co-precipitated with the CSN ( Figure 6D ) . In order to figure out which CSN subunit mediates the interaction with DEN1 in human cells we performed far western experiments . Selected CSN subunits as shown in Figure 6E were produced recombinant , separated by SDS-PAGE and blotted onto a nitrocellulose membrane . Immuno-blotting with the α-DEN1 antibody showed a strong signal with CSN1 and a weak interaction with CSN2 whereas the other CSN subunits included in the test showed no interaction ( Figure 6E , right panel ) . In control experiments the membranes were stripped and probed again with the α-DEN1 antibody demonstrating that the detected interaction was specific . Additional far-western experiments were performed to specify the site of interaction in CSN1 . Wild type human His-CSN1 ( 1–527 ) , an N-terminal fragment of His-CSN1 ( 1–221 ) and a C-terminal fragment of His-CSN1 ( 222–527 ) [22] were applied to these experiments . The results demonstrate a specific binding of DEN1 to His-CSN1wt , as well as to the N-terminal His-CSN1 ( 1–221 ) fragment ( Figure 6F ) . All these data indicate a physical interaction between DEN1/DenA and CSN which is conserved from fungi to man . The functional impact of the CSN-DEN1/DenA interaction on the molecular level in fungal and human cells was determined . DenA protein levels were monitored in csn deficient A . nidulans strains during development and compared to wild type cells . Initial experiments revealed that wild type cells produced low amounts of stable DenA protein during vegetative growth but high levels of very unstable DenA during development ( Figure 1D; unpublished data ) . This effect is most prominent during asexual development . We performed repeated western experiments to quantify the effect . Upon deletion of csnG , the DenA interacting CSN subunit , DenA-GFP protein stability occurred to be significantly increased along asexual development compared to wild type cells of the same developmental time points ( Figure 7A ) . These results indicate a function of CSN for DenA degradation in A . nidulans . We further investigated whether CSN also affects DEN1 stability in human cells as it had been shown for p27Kip1 [47] . DEN1 steady state levels were compared in siGFP and in siCSN1 cells . siGFP cells ( control ) possess normal levels of CSN and siCSN1 cells are characterized by permanently down regulated CSN amounts [48] , [49] . We performed overexpression experiments with transfected constructs of DEN1 ( Xpress-DEN1 ) and CSN1 to study the impact of CSN on DEN1 stability . Ectopically expressed Xpress-DEN1 was much higher in siCSN1 cells as compared to siGFP cells ( Figure 7B ) . Additional overexpression of CSN1 in siCSN1 cells caused a reduction of DEN1 connected with an increase of CSN8 ( Figure 7B ) . We performed inhibitor experiments in order to figure out whether this stability regulation requires proteasomal degradation . Adding the proteasome inhibitor MG132 , in addition to cyclohexamide ( CHX ) treatment , led to a partial accumulation of DEN1 in siGFP cells indicating that DEN1 is degraded at least in part by the proteasome and that the CSN targets it for proteolysis ( Figure 7C ) . CSN1 is the major DEN1 interacting subunit in human cells , which elevates de novo CSN formation [49] . We analyzed whether CSN1 alone can increase DEN1 degradation in HeLa cells . Cells were co-transfected with CSN1wt , with the N-terminal part of CSN1 ( 1–221 ) , the DEN1 binding fragment , and with the C-terminal fragment of CSN1 ( 222–527 ) . As shown in Figure 7D overexpression of CSN1wt and of N-terminal CSN1 ( 1–221 ) but not of C-terminal CSN1 ( 222–527 ) were sufficient to reduce the DEN1 steady state level in HeLa cells significantly . The reduced steady state Xpress-DEN1 level can be restored by MG132 ( Figure 7D , right panel ) indicating proteasome dependence . In summary , these data suggest that the COP9 signalosome supports proteasome-dependent protein degradation of DEN1/DenA in fungi and in human cells .
We show here the interplay between the two deneddylases CSN and DEN1/DenA on a molecular and a developmental level of a multicellular organism . CSN and DEN1/DenA physically interact and CSN is involved in DEN1/DenA protein stability regulation . Therefore , CSN is required to control the balance between cellular amounts of the two deneddylases . The physical interaction of the two deneddylases CSN and DEN1 is conserved between different species . There are differences in the affinity of the CSN subunits to DEN1/DenA between organisms . The major DEN1/DenA binding CSN subunit has been changed from CSN2 in S . pombe ( Figure S4 ) to CsnG/CSN7 in A . nidulans or CSN1 in human cells . This might reflect an evolutionary adaptation to the different complexity of developmental processes in these organisms . Down regulation of CSN results in high steady state concentrations of DEN1 in human cells and fungal cells are unable to reduce DenA protein levels during later phases of asexual development . Elevation of the CSN by overexpressing CSN1 [49] reduced the steady state level of DEN1 in human cells . Since the degradation can be blocked at least in part by MG132 , the process is most likely proteasome-dependent ( Figure 7D ) . It has been shown before that the CSN targets p53 for proteasomal degradation via phosphorylation by the CSN associated kinase CK2 [50] . In addition , p27Kip is phosphorylated by the CSN which accelerates its degradation by the UPS [51] . At the moment the exact mechanism of CSN mediated proteolysis of DEN1/DenA is not known . The Drosophila DEN1 mutation genetically suppresses Nedd8 mutant lethality [35] . A developmental phenotype of a deletion of the DEN1/DenA encoding gene has not yet been described . The filamentous fungus A . nidulans allowed us to dissect the developmental functions of both deneddylases , CSN and DenA/DEN1 . Fungal denA is crucial for asexual development . In addition , denA is required to reduce the sexual program in response to light as an external signal . A . nidulans strains deficient in CSN are also unresponsive to light , but are blocked in sexual development and disturbed in secondary metabolism [14] , [25] , [39] , [46] . Defects in csn normally do not impair asexual development [14] . Our data suggest that fungal DenA is required to initiate asexual development . After the initial phase of asexual development DenA is normally degraded in a CSN-dependent manner . Defects in csn result in stabilized DenA which still allows asexual development . Sexual development and coordinated secondary metabolism require the CSN deneddylase [14] , [25] , [39] . Deletion of both deneddylase genes generates a strain which accumulates a red pigment indicating that the secondary metabolism of the fungus might be defective . The mutant strain growth predominantly vegetative and is highly impaired in multicellular development . The denA/csnE double mutant combines the phenotypes of a ΔdenA strain which forms only marginal amounts of asexual spores and of ΔcsnE which is unable to produce sexual fruit bodies . The closest phenotype , reminiscent to the ΔcsnE/ΔdenA strain , has been recently described for CandA [9] , the fungal homolog of human Cullin-associated Nedd8-dissociated protein 1 ( CAND1 ) [52] . In general , CAND1 interaction only occurs to non-neddylated cullins and requires deneddylation as prerequisite [52] , [53] . Absence of deneddylation activity could stabilize CRL complexes , thereby altering stability , localization or activity of downstream substrates [54] . Given that CSN5 and DEN1/DenA affect various cullin-dependent and independent targets [35] this might result in developmental phenotypes . The CSN complex might function as a mediator between the two deneddylases and their cognate substrates since it also associates to CRLs [19] and other proteins , such as kinases [20] or USP15 [21] . Just like deneddylation , association of CAND1 is required to facilitate adaptor exchange in cullin complexes like the SCFs . Defects in CAND1 can lead to increased stability of certain types of SCF ligase complexes while the formation of others is abolished [10] . This might explain why , in A . nidulans , the defects in sexual differentiation and secondary metabolism of a csnE deletion mutant [14] , [45] , as well as the asexual phenotype of the denA deletion strain appear not only in the corresponding double deletion mutant , but also in the fungal candA mutants [9] . It is also an indication that both the CSN complex and DenA act in a similar pathway which might converge at the molecular level at the conserved physical interaction of both deneddylases . The CSN affects DenA/DEN1 stability in human and fungal cells . To what extend DenA/DEN1 supports or even substitutes the CsnE/CSN5 mediated deneddylase activity of the CSN complex needs to be resolved . However , only a small fraction of DenA/DEN1 seems to interact with the CSN complex in the living cell as indicated by glycerol gradient experiments from human cells ( Figure 6C ) . Additionally this interaction seems to be very transient suggested from different , repeated pull-down experiments in A . nidulans which did not result in co-purification of DenA and CSN subunits with one another ( unpublished data ) . Our results imply that the DEN1/DenA enzyme is needed for cleaving a specific isopeptide bond between Nedd8 and target protein ( s ) required for asexual spore formation which cannot be substituted by CSN . Whether this is one of the three A . nidulans cullins , even a hyperneddylated cullin or a non-cullin protein needs to be elucidated . Only the double mutant strain shows accumulation of an additional band ( Figure 5B ) which does not correspond in size to any of the three fungal cullins modified with a single Nedd8 moiety , but would fit with hyperneddylated cullin species . Another possibility are non-cullin target proteins for DEN1 as it had been shown in Drosophila where MDM2 is a candidate for DEN1-mediated deneddylation [35] . In mammals DEN1 removes Nedd8 and accelerates MDM2 degradation concomitant with p53 activation [36] . Initially human DEN1 was described as SUMO isopeptidase SENP8 [55] but biochemical in vitro experiments showed only marginal activity towards ubiquitin and SUMO substrates [31] , [43] whereas preliminary data suggest that a crosstalk between SUMO and Nedd8 might exist in A . nidulans ( unpublished data ) . We conclude that the significance of the CSN-DEN1 interaction consists in regulating the balance between the two deneddylases which have different developmental functions . In more complex organisms the readouts are presumably more complicated . While disruption of CSN is embryonic lethal in plants [28] , insects [29] and mammals [15] , elevated levels of CSN subunits are connected to certain types of cancer in humans [15] , [30] . Furthermore , effector caspases can be neddylated and are thereby inactivated by inhibitors of apoptosis . DEN1 reverses the inactivation by neddylation and stimulates apoptosis [37] . DEN1 is presumably involved in the regulation of apoptosis as an important differentiation program , because protein levels were increased by chemotherapy [36] . These are exciting starting points to elucidate the interplay between CSN and DEN1 in other organisms and to elucidate the role of deneddylases in human tumor formation .
Strains of A . nidulans used in this study ( Table 1 ) were cultivated on minimal medium [56] and supplements were added as described earlier [57] . Vegetative mycelium was grown in liquid , submerged culture and development was allowed by growth on agar surface at 30°C or 37°C , respectively . Asexual development was induced by enduring white light and formation of sexual fruiting bodies by growth on oxygen limited , tape-sealed plates in the dark [58] . Induction of the nitrate promoter for BiFC was performed on London Medium [1% glucose , 2% salt solution ( 26 g/L KCl , 26 g/L MgSO4 , 76 g/L KH2PO4 , 5% ( v/v ) trace elements ) pH 6 . 5] plus 70 mM NaNO3 for induction , or 5 mM NH4-tartrate for repression , respectively . Media were supplemented with 100 µM pyridoxine-HCl and/or 5 mM uridine . Selection of transformants carrying the respective resistance cassettes was performed by adding 10 µg/ml phleomycine , 100 ng/ml pyridthiamine or 100 ng/ml nourseothricine ( ClonNAT ) . Saccharomyces cerevisiae transformants were grown on synthetic complex medium [ ( 0 . 15% yeast nitrogen base without amino acids and ( NH4 ) 2SO4 , 0 . 5% ( NH4 ) 2SO4 , 0 . 2 mM myo-inositol , 0 . 2% amino acid mix ( 2 g of each standard-l-amino acid except l-histidine , l-leucine , l-tryptophane plus 2 g l-adenine and 0 . 2 g p-aminobenzoate] at 30°C . Carbon sources were 2% glucose or 2% galactose/1% raffinose , respectively . Amino acids were supplemented as required . Escherichia coli strain DH5α was employed for the preparation of plasmid DNA and Rosetta or BL21 cells for expression of recombinant GST::denA . Bacteria were grown in Luria-Bertani ( LB ) medium [1% tryptophane , 0 . 5% yeast extract , 1% NaCl] in the presence of 100 µg/ml ampicillin , and additionally 50 µg/ml chloramphenicol for Rosetta strains . Solid media contained 2% agar . HeLa cells were grown at standard conditions using RPMI1640 media supplemented by 10% ( v/v ) FCS and 2 mM glutamine ( Biochrom ) . HeLa siCSN1 cells exhibiting permanently down regulated CSN and mouse B8 fibroblasts stably expressing Flag-tagged CSN2 were generated and cultured as described earlier [18] , [48] . For transient overexpression cells were transfected using Lipofectamin LTX ( Invitrogen ) according to manufacture specifications . Cells were lyzed and examined as outlined previously [49] , [50] . Plasmids constructed in this study , as well as primer sequences are given in supporting information Table S1 and Table S2 . Details on plasmid construction are described in Text S1 . Aspergillus nidulans strains constructed for this study are listed in Table 1 . Details on A . nidulans strain construction and S . cerevisiae strains are given in Text S1 . Transformation of E . coli and A . nidulans was described previously [59] , [60] . Isolation of plasmid DNA from E . coli was performed using the Qiagen-tip 100 MIDI Kit or Qiagen-tip 20 Plasmid MINI Kit , referring to the producer's manual . Isolation of genomic DNA from A . nidulans was carried out as described earlier [61] . A . nidulans total RNA was isolated from 0 . 5 ml of ground mycelia with the Qiagen RNeasy Plant Mini Kit referring to the manufacturer's instructions . Southern hybridization was carried out with non-radioactive probes using the AlkPhos Direct labeling and detection system from GE Healthcare following the manufacturer's guidelines . Northern hybridization was performed according to standard techniques [62] . DNA fragments for hybridization probes , plasmid construction or sequencing were amplified by PCR with the Taq- ( Fermentas ) , Pfu- ( Promega ) , or Phusion- ( Finzymes ) polymerase , respectively . A . nidulans cDNA was generated from total RNA using the Omniscript RT Kit ( Qiagen ) following the user's manual . Rapid amplification of cDNA ends ( RACE ) [41] was achieved by using the GeneRacer Kit ( Invitrogen ) together with the SuperScriptII reverse transcriptase ( Invitrogen ) following the protocol provided by the company . DNA sequencing was performed by the Göttingen Genomics Laboratory and Eurofins MWG Operon . Radial growth tests and quantification of conidiospores were described previously [45] , [63] . Cleistothecia were quantified from 7 days sexually grown cultures . Surface pictures of plated cultures ( 150 fold magnification ) were collected with an Olympus SZX12 binocular connected to a Kappa PS30 camera . Cleistothecia within a 4×4 field grid of 1 mm2 in size were counted and multiplied to obtain the number per cm2 . Pixel density values for western quantification were obtained from TIFF files directly generated with the Fusion-SL 4 . 2 MP detection system ( Peqlab ) or from digitized X-ray films ( Kodak ) and analyzed with the ImageJ software ( http://rsb . info . nih . gov/ij/index . html ) . Before comparison , sample density values were adjusted according to an appropriate loading control . The ratio of GFP-DenA and the non-degradable GFP-tag was applied as a measure for steady state DenA stability . No loading control was performed for these experiments as the ratio is independent of the amount of total protein . For all other quantitative experiments means of adjusted density values were compared and observed differences between individual samples were verified by statistical analysis using GraphPad Prism 5 . 01 ( www . graphpad . com ) where indicated . Blast searches were made at the National Center for Biotechnology Information ( www . ncbi . nlm . nih . gov ) . Sequence analysis was conducted using the Dnastar Lasergene 8 . 0 software . Protein alignments by ClustalW were carried out at Network Protein Sequence Analysis ( http://npsa-pbil . ibcp . fr ) . Protein motif identification was performed using InterProScan ( http://www . ebi . ac . uk/Tools/InterProScan/ ) . Preparation of whole cell extracts from S . cerevisiae was described previously [64] . Crude extracts from Aspergillus were obtained by grinding mycelia to a fine powder and extraction of soluble proteins with buffer containing 100 mM Tris-HCl , 200 mM NaCl , 20% glycerol , 5 mM EDTA , pH 8 freshly supplemented with Complete protease inhibitor cocktail ( Roche ) , 14 mM ß-mercaptoethanol at 4°C . Protein concentration was estimated using the Bio-Rad assay solution following the manufacturer's guidelines . Proteins were denatured in SDS loading dye by heating at 95°C for 10 min and subjected to SDS-PAGE followed by transfer to a nitrocellulose membrane ( Whatman ) . Detection was carried out using the Enhanced ChemiLuminescence ( ECL ) method described by Tesfaigzi et al . [65] , or by using the Pierce detection kit ( Thermo Scientific ) . Signals were recorded on X-ray films ( Kodak ) , Hyperfilm ECL ( GE Healthcare ) or with a Fusion-SL 4 . 2 MP detection system ( Peqlab ) . Primary antibodies for yeast extracts were directed against Rub1 ( N0580-05 , US-Biological ) , Cdc53 ( sc-6716 , Santa Cruz ) , LexA ( sc-1725 , Santa Cruz ) and the V5 epitope ( R960-25 , Invitrogen ) . GFP fusion proteins were detected using α-GFP antibody ( sc-9996 , Santa Cruz ) and His tagged proteins by α-His-Tag antibody ( 70796-4 , Novagen ) . For A . nidulans experiments α-Actin antibody was purchased from Novus Biochemicals ( NB100-74340 , ) and for experiments with human cells α-Actin and α-γ-Tubulin were purchased from SantaCruz . A Polyclonal antibody directed against Nedd8 was obtained by rabbit immunization with an N-terminal peptide of A . nidulans Nedd8 ( GenScript ) . α-DEN1 , α-CSN1 and α-CSN8 antibodies were purchased from Enzo , anti-Express from Invitrogen , α-Flag from Stratagene . HRP labeled secondary antibodies were purchased from Jackson Immuno Research , Invitrogen or Seramun . pGEX4T3 ( Amersham ) plasmids carrying the respective fusion construct were transformed into competent E . coli BL21 and transformants selected on LB medium containing ampicillin ( 100 µg/ml ) . 10 ml culture was inoculated with a single colony and grown overnight at 37°C on a rotary shaker . 10 ml of overnight-culture were applied to inoculate 250 ml of LB medium . After growth to an OD600 = 0 . 4–0 . 6 at 37°C on a rotary shaker protein expression was induced by adding 1 mM IPTG and further incubation for 2 h . Cells were harvested by centrifugation resuspended in 10 ml of buffer 1 ( 20 mM Tris-HCl , 200 mM NaCl , 5 mM DTT ) and disintegrated by ultrasonification . The lysate was centrifuged for 15 min at 10 . 000×g and GST-DenA/DEN1 bound using Glutathione-agarose from Sigma . Beads and supernatant were incubated for 1 h at 4°C with slow rotation . The bead containing solution was transferred to a poly-propylene column ( BioRad ) and the flow through was discarded . Beads were washed 4 times with 10 ml buffer 1 and proteins were eluted with buffer 2 [50 mM Tris , 200 mM NaCl , 10 mM reduced glutathione pH 8 . 0] . For further concentration of probes and buffer exchange to PBS pH 7 . 4 , AMICON Ultra filter devices ( 10 K ) were used following the manufacturer's guidelines . For glycerol gradient centrifugation cells were lyzed referring to Leppert et al . [49] and density gradient centrifugation was performed in 20 mM Tris; pH 7 . 2; 50 mM KCl; 1 mM β-mercaptoethanol using a glycerol gradient from 5% to 40% and a rotor TLA 100 . 3 ( Beckman ) at 70 . 000 RPM for 1 h . The gradient was calibrated with purified CSN ( about 400 kDa ) and with purified 20S proteasome ( about 700 kDa ) . Flag pull downs were described before [18] . Anti-Flag beads and the Flag peptide were purchased from Sigma . Filter-binding assays ( far-western experiments ) were outlined in detail [66] . The Nedd8-GFP fusion plasmid was kindly provided by Michael Glickman . Protein purification of Nedd8-GFP was carried out under denaturing conditions using Ni-NTA agarose ( Qiagen ) according to the manufacturer's specifications . Deneddylation of Nedd8-GFP was carried out for 30 min at 37°C in buffer containing 30 mM Tris , 10 mM KCl , 5 mM DTT ( pH 7 . 8 ) . The CUL1 plasmid was obtained from BA Schulman and purified according to [67] . In vitro neddylation of the construct consisting of CUL1 associated to RBX1 was performed as described elsewhere [6] and residual unconjugated Nedd8 removed by an additional GST-purification step . Deneddylation was performed in 20 mM Tris HCl , pH 8 . 0 , 200 mM NaCl and 5 mM DTT without prior elution of the Nedd8-CUL1-RBX1 ( approximately 60 kDa ) from GST-beads . Samples were taken after incubation at 37°C for 30 min , boiled at 95°C for 10 min . in sample buffer and analyzed by western experiments . A . nidulans protein interactions were tested with the yeast-2-hybrid based interaction trap [68] following existing protocols [14] , [69] . S . pombe yeast-2-hybrid was performed using the Matchmaker GAL4-based assay ( Clontech ) . A . nidulans colonies , hyphae and structures were visualized by photography with an Olympus CS30 digital camera combined with an Olympus SZX-ILLB2-200 binocular or a Zeiss Axiolab microscope . Pictures were edited and calibrated for magnification with the cellSens software ( Olympus ) . Fluorescent microscopy was performed using a Zeiss Axio Observer Z . 1 system with Zeiss PlanAPOCHROMAT 63×/1 , 4oil or Zeiss PlanAPOCHROMAT 100×/1 , 4oil objective , respectively . Pictures were obtained with a QuantEM:512SC ( Photometrics ) or a Coolsnap HQ2 ( Photometrics ) camera and the SlideBook 5 . 0 imaging software ( Intelligent Imaging Innovations Inc . ) . Membranes were visualized by staining with 1 µm FM4-64 ( Invitrogen ) . Nuclei were stained with 1 µm DAPI ( 4′ , 6-diamidin-2-phenylindol ) . For localization studies exposure times were GFP: 1500 ms , RFP: 25 ms , DIC: 100 ms and in BiFC experiments exposure times were YFP: 2000 ms , DAPI: 20 ms , DIC: 100 ms .
|
The family of small ubiquitin-like ( Ubl ) proteins plays a major role in the control of stability , activity , or localization of modified target proteins in a eukaryotic cell . Lysine side chains are modified by covalent Ubl attachment , and this process can be reversed by specific proteases . Nedd8 is the closest relative to ubiquitin in the Ubl family . We describe here a novel , conserved interplay between two physically interacting deneddylases that are specific for Nedd8 . Increased deneddylase activity had been shown to be associated with human cancers . We convey here specific distinct developmental functions of the two deneddylases in multicellular differentiation of the filamentous fungus Aspergillus nidulans . The physical interaction between both proteins affects protein stability and therefore cellular deneddylase activity . The equilibrium between the two deneddylases and their physical interaction are conserved from fungi to human and seem to be important for normal development of a multicellular organism . These findings open a different angle for future studies of tumor formation in humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cellular",
"stress",
"responses",
"genetic",
"mutation",
"microbiology",
"gene",
"function",
"aspergillus",
"nidulans",
"eukaryotic",
"cells",
"model",
"organisms",
"cell",
"growth",
"molecular",
"genetics",
"gene",
"expression",
"biology",
"systems",
"biology",
"biochemistry",
"cell",
"biology",
"genetics",
"yeast",
"and",
"fungal",
"models",
"cellular",
"types",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] |
2013
|
Control of Multicellular Development by the Physically Interacting Deneddylases DEN1/DenA and COP9 Signalosome
|
Severe influenza A virus infection causes high mortality and morbidity worldwide due to delayed antiviral treatment and inducing overwhelming immune responses , which contribute to immunopathological lung injury . Sirolimus , an inhibitor of mammalian target of rapamycin ( mTOR ) , was effective in improving clinical outcomes in patients with severe H1N1 infection; however , the mechanisms by which it attenuates acute lung injury have not been elucidated . Here , delayed oseltamivir treatment was used to mimic clinical settings on lethal influenza A ( H1N1 ) pdm09 virus ( pH1N1 ) infection mice model . We revealed that delayed oseltamivir plus sirolimus treatment protects mice against lethal pH1N1 infection by attenuating severe lung damage . Mechanistically , the combined treatment reduced viral titer and pH1N1-induced mTOR activation . Subsequently , it suppressed the NOD-like receptor family pyrin domain containing 3 ( NLRP3 ) inflammasome-mediated secretion of interleukin ( IL ) -1β and IL-18 . It was noted that decreased NLRP3 inflammasome activation was associated with inhibited nuclear factor ( NF ) -κB activation , reduced reactive oxygen species production and increased autophagy . Additionally , the combined treatment reduced the expression of other proinflammatory cytokines and chemokines , and decreased inflammatory cell infiltration in lung tissue and bronchioalveolar lavage fluid . Consistently , it inhibited the mTOR-NF-κB-NLRP3 inflammasome-IL-1β axis in a lung epithelial cell line . These results demonstrated that combined treatment with sirolimus and oseltamivir attenuates pH1N1-induced severe lung injury , which is correlated with suppressed mTOR-NLRP3-IL-1β axis and reduced viral titer . Therefore , treatment with sirolimus as an adjuvant along with oseltamivir may be a promising immunomodulatory strategy for managing severe influenza .
Influenza A virus ( IAV ) infection represents a leading threat to global public health . New estimates have indicated that up to approximately 645 , 000 influenza-associated respiratory deaths occur annually [1] . Our previous clinical data showed that critically ill patients infected with influenza A ( H1N1 ) pdm09 virus ( pH1N1 ) is usually accompanied by acute lung injury ( ALI ) and acute respiratory distress syndrome ( ARDS ) , which is characterized by sudden onset of respiratory failure , refractory hypoxemia , and noncardiogenic pulmonary edema , and pathologically by necrosis of bronchiolar walls , diffuse alveolar injury , and substantial inflammatory cell infiltration [2] . Our experimental and clinical studies on severe influenza infection have indicated that virus-induced tissue destruction and dysregulated systemic inflammation are associated with the severity and progression of the disease [2–7] . Combined therapy with antiviral medications and immunomodulators , which not only inhibit viral replication , but also reduce the damaging consequences of host immune responses , has been believed to be beneficial in the treatment for severe influenza pneumonia [8–10] . Rapamycin ( sirolimus ) is an inhibitor of mammalian target of rapamycin ( mTOR ) . It not only blocks host pathways needed for viral replication [11–13] , but also modulates the immune responses during infection [14–16] . Consequently , it may be a promising drug for treating influenza . It was reported that sirolimus combined with oseltamivir and corticosteroid treatment decreases viral titer and improves respiratory function in patients with severe H1N1 virus-induced pneumonia [17] . Furthermore , sirolimus contributes to delay the onset of morbidity in PR8 virus-infected mice [18] . Moreover , everolimus , a derivative of sirolimus , significantly reduces viral titer , lung weight , and hemorrhage score in H5N1 virus-infected mice [19] . However , the mechanisms of action of sirolimus plus oseltamivir in severe pneumonia induced by influenza infection have not been elucidated . mTOR is a serine/threonine kinase that plays pivotal roles in cell survival , proliferation and metabolism [20] . It is composed of two different protein complexes: mTORC1 , which directly phosphorylates S6 kinase ( S6K ) , 4E-binding protein 1 ( 4EBP1 ) , and subsequently activates the downstream target ribosomal protein S6 ( S6RP ) ; and mTORC2 , which phosphorylates AKT [20] . It was reported that influenza virus HA and M2 protein promotes mTORC1 activation , and viral protein expression is mainly relied on mTORC1 , but not mTORC2 [11] . The activation and functions of mTORC2 are not as well characterized as mTORC1 . Previous studies suggested that mTORC2 may contribute to the regulation of apoptosis by the viral NS1 protein during infection [11 , 21] . Aberrant mTOR activation plays critical roles in the pathogenesis of proinflammatory diseases including lipopolysaccharide induced-ALI , sepsis , atherosclerosis and neurodegenerative diseases , which make mTOR an important therapeutic target for these diseases [14] . Whether mTOR activation by influenza virus contributes to pathogenesis has not yet been fully elucidated . NOD-like receptor family pyrin domain containing 3 ( NLRP3 ) inflammasome is a multiprotein complex consisting of NLRP3 , apoptosis-associated speck-like protein with CARD domain ( ASC ) , and pro-caspase-1 . It is a critical cytoplasmic pattern recognition receptor . Once NLRP3 inflammasome is activated , pro-caspase-1 undergoes autoactivation and cleavage to produce an enzymatically mature caspase-1 , which further cleaves pro-IL-1β and pro-IL-18 into mature IL-1β and IL-18 , respectively [22] . The NLRP3 inflammasome plays protective roles by initiating innate immune responses and promoting elimination of virus [23 , 24] . Genetic deficiency of NLRP3 , ASC , or caspase-1 in mice results in low concentrations of IL-1β and IL-18 in the bronchoalveolar lavage fluid ( BALF ) and serum , reduces the infiltration of leukocytes into the lungs , and increases viral titer [23–25] . However , recent studies suggest that excessive NLRP3 inflammasome activity can contribute to IAV-induced lung injury [26] . The use of a specific NLRP3 inhibitor has been noted to significantly protect mice from lethal influenza by reducing the BALF levels of proinflammatory cytokines , such as IL-1β and IL-18 , and decreasing the recruitment of inflammatory cells into the lungs [27–29] . mTOR signaling has been reported to modulate the activity of NLRP3 inflammasome in different in vivo and in vitro models . Couchie et al . reported that the mTORC1/p-S6K signaling axis plays a key role in thioredoxin-80-induced inflammasome activity [30] . Furthermore , it has been shown that NLRP3 inflammasome-induced ischemia reperfusion injury can be averted by mTORC1 inhibition [31] . Additionally , mTORC1 can activate NLRP3 inflammasome in macrophages , whereas sirolimus sufficiently restricts NLRP3 inflammasome via mTORC1 inhibition [32] . Sirolimus can also induce the degradation of pro-IL-1β , and prevent maturation and secretion of cytokines by inhibiting NLRP3 inflammasome activity [33] . However , the effects of sirolimus on severe influenza virus-induced NLRP3 inflammasome activation have not yet been fully researched . In the present study , we investigated the effects and mechanisms of delayed oseltamivir plus sirolimus on lethal influenza infection in mice . Our data showed that the combined treatment remarkably reduced ALI and systemic inflammation . Sirolimus may be a promising adjunctive therapy for severe viral pneumonia .
To explore the therapeutic potential of oseltamivir plus sirolimus , a mouse model of severe pH1N1 infection was established as described in our previous report [34] . As shown in Fig 1A , the mean survival time of mice in the PBS-treated control group was 11 . 1 ± 2 . 6 days , with the survival rate of only 37 . 5% . Sirolimus monotherapy did not result in a protective effect at 2 hours post infection ( hpi ) ; however , a slight protective effect was observed at 2 days post infection ( dpi ) . Furthermore , oseltamivir monotherapy at 2 hpi , and combined therapy with oseltamivir at 2 hpi plus sirolimus at 2 hpi or 2 dpi resulted in a significant increase in survival rate . Additionally , body weight loss was less in mice in these groups compared to the PBS-control group at 14 dpi ( Fig 1A and 1B ) . Unfortunately , patients are usually not administered antiviral medications until 2–4 dpi after the onset of symptoms . Consequently , severe influenza-induced high mortality and morbidity are partially due to the delayed initiation of antiviral therapy . To simulate the clinical situation and real-life scenario , delayed oseltamivir and/or sirolimus treatment at 2 dpi were provided in subsequent experiments . Not surprisingly , delayed oseltamivir or sirolimus monotherapy did not confer any survival rate benefit . However , delayed therapy with both drugs resulted in a significantly higher survival rate than oseltamivir alone ( 87 . 5% vs 37 . 5% , P = 0 . 005 ) or sirolimus alone ( 87 . 5% vs 50% , P = 0 . 018 ) ( Fig 1C ) . Consistent with the survival rate data , body weight loss was less with delayed combined therapy at 14 dpi ( Fig 1D ) . These results demonstrate that combined therapy could provide significant survival benefits against lethal pH1N1 infection even if the treatment is delayed for 2 days . Excessive viral load and immune pathogenesis are important causes of severe lung injury [35–37] . To investigate the protective mechanisms of the combined therapy , pathological damage and viral titer in the lung were assessed . As shown in Fig 2A , PBS-treated pH1N1-infected lungs exhibited extensive lung damage , including desquamation of bronchiolar epithelial cells , diffused alveolar damage , and interstitial inflammatory infiltration at 5 dpi; moreover , lung damage at 7 dpi was more severe . Delayed oseltamivir or sirolimus monotherapy only slightly alleviated pathological damage , whereas delayed combined therapy significantly ameliorated lung pathological injury ( Fig 2A and 2B ) . In addition , the activity of lactate dehydrogenase ( LDH ) in the BALF , which is representative of pathological damage to the lungs , was dramatically decreased in the delayed combined treatment group compared to the mock or monotherapy group ( Fig 2C ) . Surprisingly , at 7 dpi , viral titer was also dramatically lower in the mice that underwent delayed combined treatment than in the PBS-treated mice ( Fig 2D ) . These results suggest that delayed adjuvant sirolimus plus oseltamivir treatment synergistically alleviates lung immunopathological injury and decreases viral titer . Previous studies have proved that mTOR activation plays important roles in diverse animal models of ALI [38–40] . Therefore , we investigated whether sirolimus alleviates ALI by suppressing pH1N1-induced mTOR pathway activation . The activation of mTOR and related molecules was assayed by western blotting and immunohistochemical analyses . The expression of phosphorylated ( p ) -mTOR and downstream p-S6K and p-S6RP were significantly increased in the lung tissues of pH1N1-infected mice ( S1A Fig ) . Histological analysis further revealed the activation of the mTOR signaling pathway , as indicated by the expression of p-mTOR and p-S6RP , which were predominant in both bronchiolar and alveolar epithelial cells after infection with pH1N1 ( S1B and S1C Fig ) . Next , we tested the ability of sirolimus to inhibit mTOR activation in vivo . The results showed that mTOR activation was lower in mice treated with sirolimus only or oseltamivir plus sirolimus than it was in the control mice or mice treated with oseltamivir only ( Fig 3A and 3B ) . Moreover , immunohistochemical analysis of p-mTOR and p-S6RP further revealed that mTOR activity was markedly decreased in both alveolar and bronchiolar epithelial cells after treatment with sirolimus ( Fig 3C and 3D ) . The results suggest that the mTOR pathway is activated during pH1N1 infection . Additionally , the pathway might mediate virus-induced lung injury; however , delayed treatment with sirolimus alleviates immunopathological lung injury via inhibition of mTOR activation . Excessive NLRP3 inflammasome activation contributes to aberrant inflammatory responses and causes severe ALI after influenza infection [26–29] . The NLRP3 inflammasome is a major downstream target of the mTOR signaling pathway [31 , 32] . We next investigated whether the reduced immunopathological injury induced by sirolimus was associated with inhibition of NLRP3 inflammasome activation . As shown in Fig 4A–4E , delayed oseltamivir plus sirolimus treatment significantly decreased both mRNA and protein levels of NLRP3 , ASC , and caspase-1 when compared with oseltamivir or sirolimus monotherapy ( Fig 4A–4E ) . Once NLRP3 inflammasome is activated , pro-caspase-1 undergoes autocleavage to produce active caspase-1 ( p20 subunit ) . The delayed combined treatment also significantly decreased the amount of active caspase-1 p20 in the lung tissue ( Fig 4D and 4E ) . Active caspase-1 can in turn cleave pro-IL-1β and pro-IL-18 into their mature forms . Influenza virus infection can dramatically induce the transcription and translation of pro-IL-1β and pro-IL-18 in the lung . The concentrations of IL-1β and IL-18 in the BALF at 5 dpi were markedly decreased by the combined treatment ( Fig 4F and 4G ) . Additionally , lower IL-1β/IL-18 mRNA and IL-1β protein levels were also observed in the delayed combined treatment group than in the monotherapy group ( Fig 4H–4L ) . These data suggest that the delayed combined therapy alleviates lung immunopathological damage , which is correlated with reduced NLRP3 inflammasome-mediated secretion of IL-1β and IL-18 . The potential mechanisms underlying reduced NLRP3 inflammasome activity by the delayed combined therapy were investigated . NF-κB and ROS are well-recognized factors that cooperatively mediate the activation of NLRP3 inflammasome [41–43] . Influenza virus induced NF-κB activation through phosphorylation of p65 and IκBα . Treatment with sirolimus and/or oseltamivir effectively decreased the levels of p-p65 and p-IκBα in the lungs of the virus-infected mice ( Fig 5A and 5B ) . Moreover , the delayed combined therapy , but not monotherapy , significantly reduced ROS production in the lung homogenates ( Fig 5C ) . Sirolimus can also induce autophagy to inhibit the NLRP3 inflammasome activation [44] . The autophagy-related hallmarks were also measured in pH1N1-infected mice . As shown in Fig 5D and 5E , delayed sirolimus or sirolimus plus oseltamivir treatment could dramatically induce autophagic flux , including decreased expression of autophagy substrate SQSTM1/p62 , as well as increased expression of autophagosomes protein Beclin and LC3-II in the lung tissue at 5 dpi . These data suggest that delayed oseltamivir plus sirolimus treatment reduces NLRP3 inflammasome activity , which is correlated with downregulated NF-κB activity and ROS production , and induced autophagy after pH1N1 infection . We have reported that cytokine storm contributes to pH1N1-induced lung immunopathological damage [4 , 5] . To further elucidate whether the delayed oseltamivir plus sirolimus treatment can repress the cytokine storm after pH1N1 infection , the concentrations of multiple cytokines/chemokines in BALF were measured at 7 dpi using Bio-Plex . The delayed combined therapy significantly decreased the concentrations of eotaxin , IFN-γ , IL-1α , IL-10 , KC , MCP-1 , MIP-1β , RANTES and TNF-α compared with untreated control or monotherapy group; however , the levels of G-CSF , GM-CSF , IL-6 , IL-12 ( p40 ) , IL-12 ( p70 ) , IL-17 and MIP-1α were not noticeably affected ( Fig 6 ) . Abrupt increases in the levels of cytokines and chemokines may contribute to cell recruitment . We then measured the effect of combination treatment on inflammatory cell infiltration after pH1N1 infection . As shown in Fig 7A and 7B , pH1N1 infection caused a large amount of leukocytes infiltration , whereas delayed sirolimus or oseltamivir plus sirolimus treatment significantly reduced inflammatory cell infiltration into the lungs , which was evidenced by low infiltration scores ( Fig 7A and 7B ) . Particularly , the delayed combined therapy significantly decreased macrophage infiltration into the lung tissue ( Fig 7C and 7D ) . Myeloperoxidase ( MPO ) activity , which reflects the number of neutrophils in the lung , was also dramatically decreased after the treatment with sirolimus or oseltamivir plus sirolimus ( Fig 7E ) . The total number of cells and the numbers of different cell types in BALF were also examined . Delayed sirolimus or oseltamivir plus sirolimus treatment significantly reduced the total number of leukocytes , as well as the number of neutrophils , macrophages and lymphocytes at 5 and 7 dpi ( Fig 7D and 7E ) . These data suggest that the delayed combined therapy can alleviate cytokine storm and inflammatory cell infiltration . Airway epithelial cells are the primary targets of influenza virus . We had demonstrated that the viral titers were synergistically reduced in combined treatment group at 7 dpi in vivo ( Fig 2D ) . To further confirm the impact of rapamycin on viral replication , mouse lung epithelial cells ( MLE12 ) were treated with oseltamivir and/or rapamycin after pH1N1 infection in vitro . The copies of viral RNA genomes in the cell-free supernatant were measured in control , oseltamivir , rapamycin , and oseltamivir plus rapamycin treatment groups . As shown in S2 Fig , oseltamivir , as well as rapamycin , could significantly decrease the copies of viral RNA genomes in the cell-free supernatant . Moreover , the combined oseltamivir and rapamycin treatment further decreased the copies of viral RNA genomes at 24 hpi ( S2 Fig ) . The results suggested that rapamycin could decrease viral replication through inhibiting mTOR-mediated initiation and enhancement of viral mRNAs translation in vitro . In addition to hematopoietic cells , NLRP3 inflammasome activation also occurs in non-immune cells , such as primary bronchial epithelial cells , lung fibroblasts , and various epithelial cell lines infected with influenza virus [25 , 35 , 41 , 45 , 46] . To further confirm the effects of the combined treatment on the mTOR-NF-κB-NLRP3 inflammasome-IL-1β pathway , the activation of mTOR and related downstream molecules S6K were assayed by western blotting . As expected , the levels of p-mTOR and p-S6K were dramatically increased after pH1N1 infection in MLE12 cells , whereas oseltamivir and rapamycin reduced mTOR activity synergistically ( Fig 8A and 8B ) . Meanwhile , H1N1 virus-induced NLRP3 inflammasome activation was synergistically repressed by the combined treatment . This was shown by the significantly low levels of NLRP3 and activated caspase-1 p20 ( Fig 8C and 8D ) . Consistently , the combined treatment further reduced NLRP3 inflammasome-mediated maturation and secretion of IL-1β ( Fig 8E and 8F ) . Mechanistically , oseltamivir plus rapamycin synergistically inhibited NF-κB signaling by significantly decreasing p-p65 and p-IκB expression in MLE12 cells ( Fig 8G and 8H ) . Because rapamycin inhibits viral replication , and may further cause decreased immune responses . To rule out the effect of rapamycin induced virus titer depression on immune responses , the MLE12 cells were treated with oseltamivir and/or rapamycin without pH1N1 infection in vitro . As shown in S3 Fig , oseltamivir had no effect on the expression of NLRP3 and IL-1β , however , rapamycin or rapamycin plus oseltamivir significantly decreased the expression of NLRP3 and IL-1β in MLE12 cells . The results suggested that rapamycin could directly modulate the host immune responses . Furthermore , rapamycin induced autophagy-related hallmarks were also measured in vitro experiments . Consistent with the results of the in vivo studies , oseltamivir plus rapamycin treatment also significantly decreased the expression of autophagy substrate SQSTM1/p62 and increased the expression of autophagosomes protein Beclin or LC3-II in MLE12 cells at 24 , 48 or 72 hpi ( S4 Fig ) . All the data demonstrate that combined oseltamivir and rapamycin treatment reduces the NLRP3 inflammasome-mediated secretion of IL-1β in lung epithelial cells , which is correlated with inhibited mTOR-NF-κB signaling and induced autophagy .
Pandemic influenza infection causes substantial mortality . Oseltamivir is most effective against the infection only when it is administered within 48 h from the onset of symptoms . However , in clinical settings , patients usually can’t receive treatment until overt illness symptoms occur after infection , by which time viral load may already be high . Therefore , the high inoculum and delayed therapy used in the presently reported mouse model can better simulate the real clinical situation . Host immune responses that are elicited by IAV infection play important roles in promoting viral clearance . However , aberrant or uncontrolled immune responses , such as cytokine storm and excessive cellular activation , are also important in the pathogenesis of influenza-induced ALI [35 , 37 , 47] . Previous reports have suggested that once the viral infection triggers a cytokine storm , proinflammatory cytokines and chemokines continue to induce immunopathological damage even if viral replication is suppressed [48] . All the results indicated that overwhelming immune responses and viral virulence were the main reasons for the severe disease or even death [2–7 , 37 , 49–53] . Adjuvant corticosteroid therapies are often used in the management of patients with ALI/ARDS , as they are hoped to reduce lung inflammation and improve clinical outcomes . However , clinical data of our study as well as results of other groups have demonstrated that adjuvant corticosteroid therapy may be harmful in patients with influenza pneumonia [54–59] . Novel drugs that attenuate the severity of the infection by reducing the inflammatory response might be promising agents for influenza treatment . In the present study , we found that the delayed oseltamivir plus sirolimus treatment significantly improves survival rate and reduces inflammatory lung tissue damage during pH1N1 infection . We noted that sirolimus potently reduces pH1N1 virus-induced mTOR activation , and subsequently suppresses the NLRP3 inflammasome-mediated secretion of IL-1β and IL-18 . The decreased activation of NLRP3 inflammasome by sirolimus was associated with inhibited NF-κB activation , reduced ROS production , and increased autophagy ( Fig 9 ) . Furthermore , the delayed combined treatment significantly reduced the BALF levels of various cytokines and chemokines . Additionally , inflammatory cell infiltration into the lungs and BALF was reduced after combination treatment . Consistent with the results of the in vivo studies , oseltamivir plus rapamycin reduced mTOR-NF-κB-NLRP3 inflammasome-IL-1β axis signaling in epithelial cells . The mTOR inhibitor sirolimus is widely used in transplant medicine and oncology . It has pivotal roles in immune responses and cellular metabolism . The mTOR inhibition not only leads to the reduction of NF-κB-mediated inflammatory cytokine production [60] , but also results in the potent suppression of CD4+ effector T cells and the promotion of Treg cells [61 , 62] . It has been shown that organ transplant patients who received mTOR inhibitors exhibits decreased efficacy of H1N1 pandemic vaccination [63] . A recent report indicated that influenza virus induces metabolic phosphatidylinositide 3-kinase ( PI3K ) /mTOR changes; however , the PI3K/mTOR inhibitor BEZ235 restores PI3K/mTOR pathway homeostasis and significantly reduces viral titer to alleviate lethal influenza infection [64] . We found that rapamycin significantly decreased the copies of viral RNA genomes in the cell-free supernatant in vitro . Although sirolimus monotherapy could inhibit pH1N1-induced excessive immune responses ( Figs 5 , 6 and 7 ) , which may affect host anti-viral capability , the viral titers were not increased at 5 or 7 dpi in vivo . These results suggested that sirolimus may have the ability to depress viral replication through inhibiting mTOR-mediated initiation and enhancement of viral mRNAs translation . As shown in Fig 2 , although the viral titers were reduced after oseltamivir monotherapy at 5 dpi and 7 dpi , the lung injury was not significantly improved in our animal experiments . Otherwise , although the viral titers in oseltamivir and sirolimus combined therapy group were only slightly lower compared with PBS treatment control group at 5 dpi , the combined therapy still showed significantly improved pathological damage and less body weight loss ( Fig 2 and Fig 1D ) . Meanwhile , the combined therapy significantly decreased the levels of some proinflammatory cytokines and chemokines , as well as cell infiltration ( Figs 6 and 7 ) . The results suggested that the depression of excessive immune responses by sirolimus might be one of important mechanisms for the enhanced protection following infection . In addition , although sirolimus monotherapy partially inhibited the dysregulated immune responses ( Figs 6 and 7 ) , it only slightly improved the survival rate ( Fig 1C ) . The results indicated that antiviral treatment using oseltamivir to depress viral titers and reduce virus-induced cell death is also necessary . Modulating dysregulated immune responses and inhibiting viral titers can ameliorate immunopathological injury of lung . The assembly of the NLRP3 inflammasome results in the activation of caspase-1 and mediates the processing and release of IL-1β . Consequently , it plays central roles in inflammatory responses in diverse human diseases . However , NLRP3 inflammasome signaling must be tightly controlled to avoid inducing a hyperinflammatory state after lethal influenza infection [26–28 , 41 , 65] . The mTORC1 signaling axis plays key roles in regulating NLRP3 inflammasome through multiple mechanisms [30–33 , 44] . Our data showed that sirolimus significantly suppressed the NLRP3 inflammasome-mediated secretion of IL-1β and IL-18 . During IAV infection , two signals are required for NLRP3 inflammasome activation [42] . Firstly , NF-κB activation is needed to increase the expression of pro-IL-1β and pro-IL-18 , as well as inflammasome components , NLRP3 , ASC , and pro-caspase-1 . Secondly , the NLRP3 inflammasome is activated by stimuli including potassium efflux , lysosomal maturation , and ROS production [43] . Several studies have shown that there is a positive crosstalk between mTOR and NF-κB/ROS during inflammation [44 , 66–71] . Consistent with those data , our results showed that sirolimus significantly inhibited NF-κB and ROS by inhibiting mTOR . Furthermore , we found that combination treatment significantly increased autophagy , which may further reduce ROS and inhibit the NLRP3 inflammasome [44] . In conclusion , our results suggest that sirolimus suppresses NLRP3 inflammasome , which is correlated with inhibited NF-κB activation and ROS production , and induced autophagy after pH1N1 infection; however , it is not clear whether other inflammasomes , such as AIM2 , are also inhibited . Our experimental and clinical studies have revealed that dysregulated cytokine storm and excessive inflammatory cell infiltration are associated with disease severity [2–6] . In the present study , our results showed that the combined treatment significantly decreased the concentrations of cytokines and chemokines in BALF . Moreover , immune cell recruitment into the lungs and BALF was also reduced . Inhibition of immune cell infiltration and the expression of inflammatory mediators might have contributed to the beneficial effects of the combination treatment . It has been found that inflamed neutrophils contribute to aberrant pulmonary inflammation and ARDS during influenza infection [52 , 72] . We found that the combined treatment significantly reduces neutrophil count in the BALF and lung tissue . It has been reported that inflamed macrophages contribute to secondary bacterial infections , which ultimately result in morbidity and mortality [73] . We also confirmed that the combined treatment significantly reduces the number of macrophages in the BALF and lung tissue . However , it was not clarified if oseltamivir plus sirolimus can prevent bacterial infections after IAV infection . A few studies have shown that sirolimus increases disease severity after influenza infection . Alsuwaidi et al . reported that sirolimus treatment exacerbates respiratory function by increasing viral titer and worsening lung inflammation [74] . Huang et al . found that sirolimus treatment accelerates disease progression and abolishes the protective effect of oseltamivir treatment by suppressing antigen-specific T cell immunity and impairing virus clearance [75] . However , our results showed protective roles of sirolimus against severe infection when combined with oseltamivir . Possible explanations for this discrepancy include differences in the administration route , dosage , and administration or maintenance time of sirolimus . The prerequisite immune responses to virus infection may be completely suppressed by high dosage of sirolimus ( 3–5 μg/g ) [74 , 75] , and low dosage of sirolimus ( 0 . 03 μg/g ) may have no effect on the severe influenza infection-induced excessive immune response [75] . As an immunosuppressant drug , improper medication is not conducive to achieve the homeostasis of immune response , which not only inhibits excessive immune response , but also does not impede cellular and humoral responses to promote virus clearance . The dosage of sirolimus used in our study ( initial dosage , 0 . 6 μg/g; maintenance dosage , 0 . 3 μg/g ) was similar to those used in a clinical trial ( 2 mg/day ) and previous studies in mice ( 0 . 6–1 μg/g ) which indicated the protective roles of sirolimus [17 , 18] . Moreover , using different virus strains and infective doses may lead to different results . Lethal dose of IAV A/PR/8/34 ( H1N1 ) ( 108 . 1 TCID50 or 1 . 25 × 104 PFU ) [74 , 75] , and sub-lethal dose ( 2 . 5 × 103 PFU ) of IAV A/PR/8/34 ( H1N1 ) [75] were used . The virus strain and infective dose used in our study can better simulate the clinical situation . The detailed mechanisms as to why sirolimus can be either protective or deleterious in lung injury should be further investigated . In conclusion , our findings show that delayed combined oseltamivir plus sirolimus therapy protects against lethal pH1N1 infection by inhibiting lung immunopathologic injury . The combined therapy inhibited viral replication , restored PI3K/mTOR pathway homeostasis , and repressed inappropriate aggressive immune responses . This indicates that sirolimus can be used as a novel treatment alongside existing antiviral therapies in severe influenza . Our findings provided the theoretical and experimental basis for its use in further clinical trials . Future studies investigating detailed medication time and dosage strategies will contribute to the development of sirolimus as an effective adjunct in the treatment of influenza infection .
Specific pathogen free female BALB/c mice , at 6–8 weeks old , were obtained from the Institute of Laboratory Animal Sciences , Beijing , China . Influenza A ( H1N1 ) pdm09 virus was amplified for 3 days at 37°C in the allantoic cavities of 10-day-old embryonated chicken eggs . Clarified allantoic fluid was aliquoted and immediately frozen at −80°C until use . Madin-Darby canine kidney cells ( MDCK ) cells were obtained from American Type Culture Collection ( ATCC ) . MDCK cells were maintained in Eagle’s minimal essential medium ( EMEM ) ( Gibco , Life Technologies , NY ) supplemented with 10% fetal bovine serum ( FBS ) ( Gibco , Life Technologies , NY ) , 100 μg/ml streptomycin and 100 IU/ml penicillin ( Gibco , Life Technologies , NY ) , and were cultured at 37°C in 5% CO2 . Mouse lung epithelial cells ( MLE12 ) ( ATCC ) were maintained in Dulbecco's modified eagle medium ( DMEM/F-12 ) ( Gibco , Life Technologies , NY ) supplemented with 10% fetal bovine serum ( FBS ) , 100 μg/ml streptomycin and 100 IU/ml penicillin , and were cultured at 37°C in 5% CO2 . Mice were anesthetized and inoculated intranasally with 102 50% tissue culture infective doses ( TCID50 ) of virus in a total volume of 50 μl . Control mice were administered with an equal volume of PBS . The TCID50 was determined in MDCK cells after serial dilution of the stock . After infection , mice were weighed and survival of mice was monitored for up to 14 days post infection or until death . Oseltamivir ( Roche , Basel , Switzerland ) and sirolimus ( Pfizer , NY ) were administered by gavage once per day for consecutive 7 days at the indicated days post infection . The administered dosage for oseltamivir was 30 mg/kg . For the mice treated with sirolimus , a loading dose of 600 μg/kg was used on the first day of treatment to facilitate achievement of steady-state blood concentrations , a maintain dose of 300 μg/kg was used during the following 6 days . Control mice were administered with an equal volume of PBS . Five randomly selected mice in each of the groups were euthanized on days 5 and 7 post infection , respectively . BALF and lung tissue samples were collected from these mice for pathologic , immunologic and virologic assays . Pre-treated MLE12 cells with rapamycin ( 100 nM ) ( Cell Signaling Technology , MA ) for 1 h , were re-stimulated with H1N1 virus at multiplicity of infection ( MOI ) = 0 . 01 for 2 h and co-cultured with rapamycin ( 100 nM ) with or without oseltamivir carboxylate ( 10 μg/ml ) for 22 hours . After 24 h of culture , cells were harvested for analyzing the protein expressions . The MLE12 cells were pretreated with or without rapamycin ( 100nM ) for 1 h . After washing with PBS , cells were infected with pH1N1 ( MOI = 0 . 01 ) for 2 h . The infected cells were washed with PBS and incubated with rapamycin ( 100 nM ) with or without oseltamivir carboxylate ( 10 μg/ml ) . Cell-free supernatants were harvested at the indicated hpi . Viral RNA was extracted from the supernatants ( 100 μl ) using RNeasy Mini Kit ( QIAGEN , Germany ) following the manufacturer's instructions . Viral matrix ( M ) genes were measured by the QuantiTect Probe RT-PCR Kit ( QIAGEN , Germany ) . The following primer and probe were used: forward , 5’- GACCRATCCTGTCACCTCTGAC-3’ , reverse , 5’-AGGGCATTYTGGACAAAKCGTCTA-3’ , probe , 5’-FAM-TGCAGTCCT CGCTCACTGGGCACG-BHQ1-3’ . The lung samples were immediately fixed in 10% formalin , embedded in paraffin , and sectioned ( 4μm thickness ) for histopathologic analysis . The sections were stained with hematoxylin and eosin ( H&E ) for examination by light microscopy . Slides were randomized , read blindly , and then scored using a semiquantitative scoring system as described previously [76] . Edema , alveolar and interstitial inflammation , alveolar and interstitial hemorrhage , atelectasis , necrosis , and hyaline membrane formation were each scored on a 0-to 4-point scale: no injury = score of 0; injury in 25% of the field = score of 1; injury in 50% of the field = score of 2; injury in 75% of the field = score of 3; and injury throughout the field = score of 4 . Results were confirmed by an experienced and qualified pathologist . Each slide were assessed the degree of inflammatory cell infiltration of the main bronchus and the surrounding three large vessels , the mean values were obtained [77] . The criteria were no inflammatory cells = score of 0 , a few inflammatory cells = score of 1 , more uneven distribution of inflammatory cells = score of 2 , a large number of inflammatory cells in relatively uniform distribution and rare gathered into a group = score of 3 , a large number of inflammatory cells clump = score of 4 . Results were confirmed by an experienced and qualified pathologist . Paraffin-embedded lung sections were deparaffinized with xylene and hydrated using graded alcohols . The mTORC1 activity was assessed using anti-p-mTOR antibody ( 1:800 , Abcam , Cambridge , UK ) and anti-p-S6RP antibody ( 1:200 , Cell Signaling Technology , MA ) . Macrophage infiltration was detected using anti-CD68 antibody ( 1:200 , Abcam , Cambridge , UK ) [78 , 79] . The antibody was then detected using a standard streptavidin-biotin detection system ( Beijing Zhongshan Biotechnology Co . , Ltd . , Beijing , China ) according to the manufacturer’s instructions . The virus titrations were performed by end-point titration in MDCK cells . Lung samples were homogenized in 1 ml of sterile PBS . MDCK cells were inoculated with tenfold serial dilutions of homogenized lung tissues in 96-well plates . At 1 h post infection , cells were washed once with PBS and incubated in 200 μl of infection medium , consisting of EMEM , 100 μg/ml streptomycin , 100 IU/ml penicillin and 1 μg/ml TPCK-trypsin ( Gibco , Life Technologies , NY ) . At 3 dpi , supernatants of infected cell cultures were tested for agglutinating activity using turkey erythrocytes as an indicator of cellular infection . Infectious titers were calculated from five replicates using the Reed-Muench method [80] . Lung samples were homogenized in 1 ml of sterile PBS and the supernatants were collected . The activity of MPO was measured using myeloperoxidase assay kit ( Nanjing jiancheng bioengineering institute , Jiangsu , China ) . The activity of ROS was measured using ELISA kit ( Bio swamp , Hubei , China ) according to the manufacturer’s instructions . The lungs were lavaged with 4 × 0 . 5 ml of PBS . The BALF was centrifuged 207 g at 4°C for 10 minutes , and the supernatant was collected and stored at -80°C for further analysis . The cells were resuspended in 1 ml of PBS and the total cell numbers were counted with a hemocytometer . Cytospin samples were prepared by centrifuging the suspensions at 350 rpm for 10 minutes using Shandon Cytospin 4 ( Thermo scientific , Cheshire , UK ) and cell differentials were determined by counting at least 300 leukocytes on Giemsa stain according to manufacturer’s instructions . LDH activity in BALF was measured using lactate dehydrogenase assay kit ( Nanjing jiancheng bioengineering institute , Jiangsu , China ) . The IL-1β , eotaxin , IFN-γ , IL-1α , IL-1α , IL-10 , KC , MCP-1 , MIP-1β , RANTES , TNF-α , G-CSF , GM-CSF , IL-6 , IL-12 ( p40 ) , IL-12 ( p70 ) , IL-17 and MIP-1α levels in BALF were measured by Bio-Plex Mouse Cytokine Panel Assay Kit ( Bio-Rad Laboratories , CA ) according to the manufacturer’s instructions . The IL-18 level in BALF was measured by ELISA Kit ( Mouse IL-18 ELISA KIT , Abcam , Cambridge , UK ) . Total RNA was extracted using the RNeasy Mini kit ( Qiagen , Germany ) , and complementary DNA was synthesized with the PrimeScript RT Master Mix ( Perfect Real Time ) ( TaKaRa , China ) . Real-time PCR ( RT-PCR ) was performed using the SYBR Premix Ex Taq II ( Tli RNaseH Plus ) ( TaKaRa , China ) . The following primers were used: NLRP3 , forward: 5’-AATGCTGCTTCGACATCTCC-3’ , reverse: 5’-CCAATGCGAGATCCTGACAA-3’; ASC , forward: 5’-TGAGCAGCTGCAAACGACTA-3’ , reverse: 5’-CACGAACTGCCTGGTACTGT-3’; Caspase-1 , forward: 5’-GTGGAGAGAAACAAGGAGTGG-3’ , reverse: 5’-AATGAAAAGTGAGCCCCTGAC-3’; IL-1β , forward: 5’-GGTGTGTGACGTTCCCATTA-3’ , reverse: 5’-GGCCACAGGTATTTTGTCGT-3’; IL-18 , forward: 5’-ACAGGCCTGACATCTTCTGC-3’ , reverse: 5’-CCTTGAAGTTGACGCAAGAGT-3’; GAPDH , forward: 5’-ATTCCACCCATGGCAAATTC-3’ , reverse: 5’-CGCTCCTGGAAGATGGTGAT-3’ . Protein samples were extracted from lung tissues and MLE12 cells . Proteins were separated by 10% SDS-polyacrylamide gel electrophoresis . After electrophoresis , the proteins were transferred to PVDF membranes by the wet transfer method . Each membrane was blocked with TBST with 5% non-fat dried milk for 1 hour at room temperature , and then incubated overnight at 4°C with primary antibodies directed against mTOR ( 1:1000 , Cell Signaling Technology , MA ) , p-mTOR ( 1:500 , Cell Signaling Technology , MA ) , S6K ( 1:1000 , Cell Signaling Technology , MA ) , p-S6K ( 1:500 , Cell Signaling Technology , MA ) , S6RP ( 1:1000 , Cell Signaling Technology , MA ) , p-S6RP ( 1:500 , Cell Signaling Technology , MA ) , NLRP3 ( 1:500 , Adipogen , CA ) , ASC ( 1:1000 , Cell Signaling Technology , MA ) , pro-caspase-1 ( 1:1000 , Adipogen , CA ) , caspase-1 p20 ( 1:500 , Adipogen , CA ) , pro-IL-1β ( 1:1000 , Abcam , Cambridge , UK ) , IL-1β ( 1:500 , Abcam , Cambridge , UK ) , p65 ( 1:1000 , Cell Signaling Technology , MA ) , p-p65 ( 1:500 , Cell Signaling Technology , MA ) , IκB ( 1:2000 , Cell Signaling Technology , MA ) , p-IκB ( 1:1000 , Cell Signaling Technology , MA ) , SQSTM1/p62 ( 1:1000 , Cell Signaling Technology , MA ) , Beclin ( 1:1000 , Cell Signaling Technology , MA ) , LC3 I/II ( 1:500 , Cell Signaling Technology , MA ) and β-actin ( 1:5000 , Abcam , Cambridge , UK ) . The appropriate HRP-coupled secondary antibody was added and incubated for 1 hour at room temperature . The membranes were then treated with enhanced chemiluminescence western blot detection reagents ( Millipore corporation , MA ) , and the binding of specific antibodies was detected by chemiluminescence . Survival curves were generated by the Kaplan-Meier method and statistical analyses were performed using the log-rank test . The statistical significance between two groups was assessed by Student’s t-tests . For comparisons between ≥ 3 groups , analysis was done by one-way ANOVA . A two-sided p value < 0 . 05 was considered statistically significant . The experiments were performed in biosafety level 3 facilities in compliance with governmental and institutional guidelines . This study was carried out in accordance with the recommendations of the Chinese National Guidelines for the Care of Laboratory Animals and the Institutional Animal Care and Use Committee of the Institute of Laboratory Animal Science , Peking Union Medical College [81] . The protocol was approved by the Institutional Animal Care and Use Committee ( ILAS-PC-2015-016 ) .
|
The severity and lethality of influenza A virus infection are frequently aggravated by virus-induced tissue destruction and overwhelming immune responses . Combined therapy with antiviral medications and immunomodulators , which not only inhibit viral replication , but also reduce the damaging consequences of host immune responses , will be beneficial in the treatment of severe influenza . In the present study , we revealed that pH1N1-induced activation of mTOR promotes lung immunopathological injury , which is correlated with upregulated NF-κB activity and increased reactive oxygen species production . Subsequently , it induces NLRP3 inflammasome activation and the secretion of IL-1β and IL-18 . Combined treatment with oseltamivir and the mTOR inhibitor sirolimus ( as an adjuvant ) not only blocks viral replication , but also suppresses mTOR-NLRP3-IL-1β axis-mediated immune damage , thus protecting mice against lethal pH1N1 infection . Our findings provide the theoretical and experimental basis for the clinical investigation of sirolimus as an adjunct treatment for severe influenza .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"cytokines",
"pathology",
"and",
"laboratory",
"medicine",
"respiratory",
"infections",
"influenza",
"immunology",
"pulmonology",
"animal",
"models",
"developmental",
"biology",
"model",
"organisms",
"signs",
"and",
"symptoms",
"inflammasomes",
"molecular",
"development",
"experimental",
"organism",
"systems",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"inflammation",
"animal",
"studies",
"proteins",
"mouse",
"models",
"immune",
"response",
"immune",
"system",
"biochemistry",
"signal",
"transduction",
"diagnostic",
"medicine",
"cell",
"biology",
"physiology",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"cell",
"signaling"
] |
2018
|
Delayed oseltamivir plus sirolimus treatment attenuates H1N1 virus-induced severe lung injury correlated with repressed NLRP3 inflammasome activation and inflammatory cell infiltration
|
Cryptococcus neoformans is a ubiquitous human fungal pathogen that causes meningoencephalitis in predominantly immunocompromised hosts . The fungus is typically haploid , and sexual reproduction involves two individuals with opposite mating types/sexes , α and a . However , the overwhelming predominance of mating type ( MAT ) α over a in C . neoformans populations limits α–a mating in nature . Recently it was discovered that C . neoformans can undergo same-sex mating under laboratory conditions , especially between α isolates . Whether same-sex mating occurs in nature and contributes to the current population structure was unknown . In this study , natural αADα hybrids that arose by fusion between two α cells of different serotypes ( A and D ) were identified and characterized , providing definitive evidence that same-sex mating occurs naturally . A novel truncated allele of the mating-type-specific cell identity determinant SXI1α was also identified as a genetic factor likely involved in this process . In addition , laboratory-constructed αADα strains exhibited hybrid vigor both in vitro and in vivo , providing a plausible explanation for their relative abundance in nature despite the fact that AD hybrids are inefficient in meiosis/sporulation and are trapped in the diploid state . These findings provide insights on the origins , genetic mechanisms , and fitness impact of unisexual hybridization in the Cryptococcus population .
The level of genetic variation within a species is correlated with evolutionary potential [1] . Hybridization can provide genetic variation within and between populations by yielding progeny more fit in novel or changing environments , and both intra- and interspecies hybridization are a driving force for evolution [2 , 3] . Hybridization is observed in animals , and is especially common in plants [4–8] . Hybrids also occur in microorganisms . For example , Trypanosoma cruzi , the cause of Chagas disease , descends from two ancestral hybridization events [9 , 10]; influenza viruses undergo antigenic variations and host range shifts through hybridization and reassortment [11]; and in the parasite Leishmania , which has no known sexual cycle and a largely clonal population structure , recombinant strains can be generated through interspecific hybridization [12–15] . Because of their morphological and genomic plasticity , fungi are subject to profound genetic changes , including those resulting from hybridization . Indeed , hybridization is one of the most significant biological forces driving fungal evolution , as illustrated by the Saccharomyces sensu stricto complex [16] . This species complex descends from an ancient whole genome duplication event in which two related yeast species hybridized ∼100 million years ago [17–20] . Hybridization can confer novel features; for example , S . cerevisiae–S . paradoxus hybrids exhibit thermal stress vigor [21] . In plant fungal pathogens , hybridization produces novel physiological traits including enhanced virulence [22–24] . By comparison , less is known about the impact of hybridization on the virulence of human pathogenic fungi . Cryptococcus neoformans is a cosmopolitan human fungal pathogen that causes meningoencephalitis in predominantly immunocompromised hosts [25] . Cryptococcal meningitis is the most common fungal infection of the central nervous system and is considered an AIDS defining condition [25–29] . This species is classified into three serotypes based on capsular agglutination reactions [30]: serotype A ( C . neoformans var . grubii , mostly haploid ) , serotype D ( C . neoformans var . neoformans , mostly haploid ) , and AD hybrids ( mostly diploid ) . Serotype A is responsible for the vast majority of human infection ( 95% worldwide ) [25] , but AD hybrids can be fairly common , especially in Europe ( ∼5%–30% ) [31–37] , and are likely more common than currently appreciated [32 , 37–39] . Because this fungus is ubiquitous in nature , and humans are infected through inhalation of infectious propagules from the environment [40–42] , it is important to understand the natural life cycle and its impact on population structure . The bipolar mating system of C . neoformans has been well-defined under laboratory conditions . Mating involves cell–cell fusion of haploids of opposite mating type , a and α , to produce dikaryotic filaments . Nuclear fusion occurs at the tip of the filaments in a fruiting body ( the basidium ) resulting in a transient a/α diploid that immediately undergoes meiosis and sporulation [43 , 44] ( Figure 1 ) . Because clinical and environmental isolates of C . neoformans are predominantly of α mating type ( >98%–99 . 9% ) [25 , 45] , it is difficult to envision that a–α mating is the only significant means by which genetic diversity is generated in nature . C . neoformans serotype D strains undergo monokaryotic fruiting to produce filaments and basidiospores under laboratory conditions [46 , 47] . Fruiting was recently recognized to be a modified form of sexual reproduction occurring between strains of the same mating type [48] ( Figure 1 ) . Because monokaryotic fruiting is commonly observed in serotype D α isolates [46–48] , and the MATα allele enhances fruiting under laboratory conditions [49] , same-sex mating could significantly impact the population structure of this pathogenic fungus in nature . Although the global population of C . neoformans is largely clonal , recombination does occur at a low level [50–54] . Recently , phylogenetic analysis of the sibling species C . gattii has shown that same-sex mating between two different α strains may have given rise to a more virulent strain occupying a new environmental niche and causing the Vancouver Island outbreak [55] . Population genetic studies of C . neoformans serotype A veterinary isolates in Sydney , Australia , also reveal evidence of recombination in a unisexual α population , providing further indirect support for the occurrence of same-sex mating in natural populations ( D . Carter , personal communication ) . In this study , characterization of natural diploid αADα hybrids provides definitive evidence for same-sex mating occurring in nature . Under laboratory conditions , intervarietal matings between strains of serotype A and D lead to cell–cell fusion , but genetic differences between these divergent serotypes ( ∼5%–10% nucleotide polymorphisms ) severely limits meiosis and thus few , if any , viable haploid basidiospores are produced [56 , 57] . Consequently , most natural AD hybrids remain in the diploid ( or aneuploid ) state ( Figure 1 ) , and analysis of these AD hybrids can reveal the genomic nature of their parental strains . For example , most reported AD hybrids are αADa or aADα ( mating type/serotype–serotype/mating type ) [32–36 , 57 , 58] , reflecting their origin from traditional a–α mating between serotype A and D strains . All extant aADα hybrids appear to derive from a cross between African serotype A strains ( Aa ) and serotype D strains ( Dα ) followed by clonal expansion and emigration from sub-Saharan Africa , the only region where serotype A isolates of a mating type are common [53 , 59] . In this study , we identified and characterized natural αADα hybrids that arose from same-sex mating between two α strains of A and D serotypes , providing definitive evidence that the laboratory-defined same-sex mating process occurs in nature . In addition , our analysis reveals a common feature in all aADα and αADα hybrids tested: a C-terminal deletion in the serotype D SXI1α gene located in the MAT locus , which encodes a homeodomain transcription factor regulating mating [60] . Characterization of populations containing the C-terminally truncated SXI1α serotype D ( SXI1Dα ) allele suggests that this mutation may have contributed to the origin of AD hybrids . The common presence of AD hybrids in both clinical and environmental samples may be indicative of hybrid vigor [33 , 35 , 61] . However , unlike clearly documented cases of increased fitness and epidemiological success of plant-pathogenic fungal hybrids [23 , 62–65] , examples of hybrid fitness in human pathogenic fungi have not been well-documented . Previous studies of C . neoformans AD hybrids revealed variable virulence potential [57 , 58 , 66 , 67] . This ambiguity may be due to the analysis of genetically diverse αADa and aADα isolates , which exhibit considerable phenotypic and genotypic variation . The presence of both a and α mating types in a diploid strain may also complicate virulence studies if pheromone sensing occurs during infection [68–70] . Here , αADα hybrids were constructed in defined genetic backgrounds and analyzed for hybrid fitness and virulence . In vitro , laboratory-constructed αADα hybrids exhibited hybrid vigor , and were more UV- and temperature-resistant than either parent . Other virulence attributes of the αADα hybrid were similar to ( e . g . , capsule ) or intermediate between ( e . g . , melanin ) those of the parents . In a murine inhalation model , the laboratory-constructed αADα hybrid exhibited virulence similar to that of the serotype A parent . These observations demonstrate benefits of hybridization in C . neoformans , which may enable less robust serotype D strains to survive both during infection and in the environment .
A report from Litvintseva et al . in 2005 indicated the potential existence of environmental αADα hybrids isolated from North Carolina , USA [35] . To ensure these were indeed AD hybrids , three such isolates and control strains were analyzed by amplified fragment length polymorphism ( AFLP ) analysis . AFLP results using two primer pairs showed that all three isolates generated a banding pattern representing a composite between those of serotype A and D strains , indicative of an AD hybrid ( Figure 2A and 2B ) . These strains also contained twice the cellular DNA content of haploid controls based on fluorescence flow cytometry analysis ( Figure 2C ) , and are therefore diploid . Based on serotype- and mating-type-specific PCR , all three isolates have serotype A– and serotype D–specific genes , both within the mating type locus ( MAT ) and in other genomic regions ( Table 1 ) , further confirming their AD hybrid nature . Sequence analysis suggested the three isolates could be clonal , as PCR-amplified gene sequences were identical ( data not shown ) . The combined sequences for five different serotype A–specific genes ( STE20α , SXI1α , GPA1 , CNA1 , and PAK1 ) were 99 . 9% identical to those of the sequenced serotype A reference strain H99 ( 4 , 226/4 , 230 bp ) [71] . The sequences for four different serotype D–specific genes ( STE20α , GPA1 , CNA1 , and PAK1 ) were 99 . 86% identical to those of JEC21 ( 2 , 886/2 , 890 bp ) , a sequenced serotype D reference strain [72] . Because these AD hybrid isolates contain α mating type genes from both serotype A ( STE20α and SXI1α ) and D ( STE20α ) and lack a mating type genes of either serotype ( Table 1 ) , they are αADα strains that originated from two α parental strains of serotype A and D . This provides the first direct evidence , to our knowledge , of the cell–cell fusion step of same-sex mating occurring in nature . The mating behavior of the natural αADα hybrids was examined in crosses with the reference strains JEC20 ( a ) and JEC21 ( α ) . The αADα hybrids mated with the a reference strain JEC20 to produce mating dikaryotic hyphae with clamp connections ( Figure 3 ) , and did not mate with the α reference strain ( data not shown ) . The two parental nuclei ( diploid α/α and haploid a ) alternated positions in adjacent hyphal cells , a hallmark of compatible matings in basidiomycetous fungi [48 , 73] . Basidial fruiting bodies were also observed in different developmental stages , they contained one or multiple nuclei , and some were decorated with four long chains of spores ( Figure 3 ) . These morphological characteristics are similar to those of matings between haploid α and a cells . However , despite apparently normal morphological differentiation , the spores generated were not viable , and all dissected spores from a cross between the diploid αADα hybrid 6–20 and the a haploid strain JEC20 failed to germinate ( n = 105 ) , indicative of abnormal meiosis , as expected from a triploid . Our observations indicate that the αADα hybrid mates as α , but is unable to complete the final stages of sexual reproduction , including spore germination . Because the SXI1Dα allele could not be amplified from the αADα hybrids with the primers tested ( Table 1 ) , the mating type locus of the αADα hybrids was further analyzed to ascertain whether any genetic alterations were apparent . The MAT locus of C . neoformans is unusually large ( >100 kb ) compared to most fungi and encodes more than 20 proteins [74] . Because of the complex nature of the C . neoformans MAT locus , all genes within the MAT locus of the natural αADα hybrid were examined by comparative genome hybridization ( CGH ) . Mating-type- ( a and α ) and serotype-specific ( serotype A and D and C . gattii ) 70-mer probes for all genes in the MAT locus ( Aα , Aa , Dα , and Da alleles for each MAT gene ) were designed previously for microarray analysis [49] . Here genomic DNA was labeled and hybridized to microarray slides to characterize the mating type locus gene content . Genomic DNA of the natural αADα hybrid 6–20 and the control ( a mixture of H99 [Aα] and JEC21 [Dα] ) were labeled with fluorescent dyes and competitively hybridized to a genomic microarray slide containing the mating-type- and serotype-specific 70-mers . The log2 ratio of fluorescence intensity between the hybrid and the control for all a genes was close to zero regardless of serotypes ( the average log2 ratio of fluorescence intensity was within ± 0 . 4 , meaning that the fold difference between hybrid and control fell into the range of 0 . 76∼1 . 32; data not shown ) , indicating the genetic contents of the control , and sample were similar . Because there were no a genes in the control , this showed that a genes were also absent in the hybrid strain , consistent with the PCR analysis ( Table 1 ) . To ensure that lack of hybridization to Aa or Da probes was not due to failure of the a 70-mers on the microarray slides , hybridizations of Aα/Da , Aa/Dα , and Aa/Da samples using genomic DNA from reference strains were performed . The Aa and Da probes were functional based on this analysis ( Figure S1 ) . As shown in Figure 4 , the overall fluorescence intensity of α genes in the MAT locus from the natural hybrid isolate 6–20 and the control ( Aα + Dα ) was similar for both the serotype A and D alleles ( log2 ratio of fluorescence intensity was within ± 0 . 5 , meaning that the fold difference between hybrid and control fell in the range of 0 . 71∼1 . 41 ) . The only exception was that the SXI1Dα allele appeared to be missing in the natural αADα hybrid , as the fluorescence intensity of the hybrid SXI1Dα was much lower than that of the control ( log2 hybrid/control = −3 . 24 , which means hybrid/control ≈ 0 . 1 ) . This CGH result is consistent with the SXI1α PCR analysis ( Table 1 ) , indicating that hybrid 6–20 contains all α genes from both serotype A and D with the apparent exception of the SXI1Dα allele . However , because the array used was not a tiling array , other potential mutations in the mating type locus , such as indels in regions not covered by the probes and single nucleotide alterations , might not be detected . Because the SXI1Dα gene in the MAT locus of the αADα hybrids did not amplify using SXI1Dα-gene-specific primers ( Table 1 ) , or yield a hybridization signal during CGH analysis ( Figure 4 ) , the structure of the SXI1Dα locus in the natural αADα hybrids was examined by Southern hybridization . Surprisingly , hybridization to the SXI1Dα ORF probe was observed , but the size of the hybridizing band was decreased for the natural αADα hybrids compared to the wild-type serotype D control , suggesting that a shorter version of the SXI1Dα gene was present ( Figure 5 ) . Sequencing of the SXI1Dα allele from the three αADα hybrids revealed a C-terminal truncation of the ORF ( 119 bp ) and a partial deletion of the 3′ untranslated region ( 301 bp ) . Thus , the genomic locus is 420 bp shorter in the αADα hybrids ( Figure 5 ) . The 70-mer oligonucleotide on the microarray slide used to detect the SXI1Dα gene lies within the C-terminal deletion interval , and the sequence of one of the primers ( JOHE15636 ) used to PCR amplify the SXI1Dα-specific gene was also within the missing region ( Figure 5 ) , explaining the apparent absence of the SXI1Dα gene in the PCR and CGH analyses ( Table 1; Figure 4 ) . To test whether the C-terminal deletion in the SXI1Dα gene is unique to the αADα isolates from North Carolina , or is a uniform feature of the aADα and αADα hybrids with a Dα parental origin , additional hybrids were analyzed . Interestingly , all of the aADα and αADα hybrid strains tested share precisely the same C-terminal truncated version of SXI1Dα ( Table 2 ) . Four hypotheses could explain the presence of the truncated SXI1Dα allele in hybrid strains . ( 1 ) The “pre-fusion fitness” model: the truncated SXI1Dα allele may confer an advantage to haploid serotype D strains , and selection for the shorter version of SXI1Dα occurred prior to cell fusion . In this model , the truncated version of SXI1Dα is prevalent in aADα and αADα hybrids simply because it is common in the Dα population . ( 2 ) The “pre-fusion fertility” model: selection for this C-terminally truncated SXI1Dα was prior to cell fusion . This version of SXI1Dα may enhance the fertility of Dα strains and therefore is common in aADα and αADα hybrid strains that result from fusion between Aa or Aα strains and Dα strains with this allele . ( 3 ) The “post-fusion fitness” model: the SXI1Dα truncated version may confer an advantage to AD hybrids such that selection for this allele occurred after hybrid formation . This advantage could involve limiting sporulation , leading to fewer inviable spores in AD hybrid strains . ( 4 ) The “natural variant” model: this SXI1Dα allele is a neutral variant that confers no selective benefit . To test these hypotheses , the prevalence of the C-terminally truncated version of the SXI1Dα allele was investigated in natural Dα isolates . If selection for this SXI1Dα allele occurred prior to the cell fusion events that produced AD hybrids ( “pre-fusion fitness” and “pre-fusion fertility” models ) , this allele should be present in the serotype D α haploid population . If selection for this allele occurred after cell fusion , then it would be expected to be absent in the Dα population ( “post-fusion fitness” model ) . If there was no selection , then this allele need not be common in either the hybrid or the Dα population ( “natural variant” model ) . Twenty-four isolates recorded as serotype D α strains were screened by PCR to detect SXI1Dα size polymorphisms , and four isolates were found to contain the truncated allele , while the remaining 20 isolates contain the wild-type allele ( Table 2 ) . The truncated version of SXI1Dα in these four isolates was sequenced , and the deletion site was identical to that found in the aADα and αADα hybrids . Interestingly , one of five North Carolina Dα isolates , each representing a different genotype [35] , harbors the C-terminally truncated SXI1Dα allele . These five Dα strains were isolated together with the natural αADα hybrids in a previous study [35] . The North Carolina αADα hybrids bearing the C-terminally truncated SXI1Dα allele represent the common AD genotype ( 76% , or 41/54 ) in this region [35] , further supporting the hypothesis that selection for this allele could have occurred . To ensure that these isolates are indeed haploid Dα strains and not unrecognized hybrids , ploidy was analyzed by fluorescence flow cytometry . As shown in Table 2 , with one exception ( isolate 713 ) , all of the serotype D isolates tested were haploid . Isolate 713 showed a diploid nuclear DNA content and was found to be an unrecognized αADα hybrid isolate from Italy ( see below ) . Thus , the truncated SXI1Dα allele is present in the global natural serotype D α isolates , albeit at a relatively low level ( ∼13% , or 3/23 ) , which does not support the “pre-fusion fitness” or “post-fusion fitness” models . The truncated SXI1Dα allele is uniformly present in the aADα and αADα hybrid population ( 100% , or 10/10 ) ( Table 2 ) , which supports the “pre-fusion fertility” or “post-fusion fitness” models . All strains with the truncated SXI1Dα allele harbor an identical SXI1Dα allele , while those strains without the truncation harbor distinct SXI1Dα alleles based on the sequence of the SXI1Dα 5′ region preceding the deletion site . Thus , the novel truncated allele likely arose once in the haploid progenitor population , arguing against the “post-fusion fitness” selection model . These findings support the “pre-fusion fertility” model , in which the SXI1α truncation allele enhances fertility of the serotype D α haploid parental progenitors and , as a result , increases fusion with an Aa or Aα partner to yield the aADα and αADα hybrid populations . During this screening , another αADα hybrid isolate ( 713 ) was identified . This diploid isolate contains α mating type genes from both serotype A ( STE20Aα and SXI1Aα ) and D ( STE20Dα ) , lacks a mating type gene of either serotype type based on mating-type- and serotype-specific PCR ( data not shown ) , and is an unrecognized αADα hybrid . To confirm this , the MAT locus was characterized by CGH . Isolate 713 displayed a CGH MAT profile similar to that of the αADα hybrids from North Carolina ( Figure S2 ) . All of the mating type genes of both the Aα and the Dα alleles were similar to the control ( Aα + Dα ) with the only exception being the SXI1Dα gene , which was also truncated in this natural αADα hybrid . The discovery of an independent αADα isolate from Italy suggests that same-sex mating is not restricted geographically , consistent with the fact that Aα and Dα isolates are globally distributed worldwide in nature and are often sympatric . However , we cannot exclude that an ancestral αADα isolate clonally expanded to distinct locations . To investigate if altered SXI1α alleles also occur in other members of the Cryptococcus species complex , known sequences of the SXI1α gene in the sibling species C . gattii were analyzed [55] . C . gattii and C . neoformans diverged from a common ancestor ∼37 million years ago and are recognized to be separate species [75] . C . gattii is divided into four molecular types: VGI , VGII , VGIII , and VGIV [40] . Although the majority of C . gattii strains are sterile , a significant proportion of VGIII isolates are fertile [55 , 76] . The SXI1α gene sequences in strains of the three molecular types , VGI ( 10/10 ) , VGIV ( 4/4 ) , and VGII ( 10/11 ) appeared wild-type ( data not shown ) with the exception of one VGII strain ( WM178 , 1/11 ) in which the SXI1α gene contains a frameshift mutation ( Figure S3 ) . Interestingly , a premature stop codon is present in the C-terminus of the SXI1α gene in the majority of strains of the VGIII molecular type ( 7/8 , or 87 . 5% ) ( Figure S3 ) . This stop codon truncates the C-terminus of Sxi1α ( corresponding to residue 358 in Sxi1Dα ) seven amino acids N-terminal to the deletion site found in the truncated SXI1Dα allele in the aADα and αADα populations ( 365 aa ) ( Figure S3 ) . Importantly , the homeodomain ( aa 144–205 in Sxi1Dα ) [60] is intact in both truncated SXI1α alleles . The observation of two different mechanisms of C-terminal truncation in the SXI1α gene occurring in subgroups of two different species ( C-terminal deletion in C . neoformans serotype D and AD hybrid strains and premature stop codon in VGIII C . gattii isolates ) indicates that C-terminally truncated versions of the SXI1α gene have arisen independently at least twice in the Cryptococcus species complex . The SXI1α gene is a master regulator of sexual reproduction [60] . Deletion of this gene does not prevent cell–cell fusion , but blocks further sexual morphological differentiation into dikaryotic hyphae , meiosis , and development of basidiospores during a–α mating [60] . The C-terminal deletion of the SXI1Dα gene does not prevent sexual differentiation during mating based on the fact that the two natural Dα strains ( 431 and 434 ) with a C-terminal deletion in the SXI1α gene still produce mating hyphae and abundant basidiospores when crossed with the reference strain JEC20 ( Figure 6 ) . Differences in filamentation and sporulation observed between the wild-type strain JEC21 and the nonisogenic natural strain 431 could be attributable to other genetic differences . Spores dissected from a cross between strain 431 and JEC20 were viable ( germination rate = 83% , n = 72 ) and showed a typical 1:1 Mendelian segregation of mating types ( a:α = 31:29 ) , indicative of normal meiosis . An engineered strain in the JEC21 background with the C-terminally truncated allele of SXI1Dα replacing the wild-type SXI1α allele also mated like wild-type , indicating the truncated SXI1α allele is functional ( Figure S4 ) . These observations indicate that the C-terminal deletion of the SXI1α gene does not impair morphological development or meiosis during mating . The C . gattii SXI1α gene with the premature stop codon at the C-terminus is also functional . Five out of seven VGIII C . gattii strains that contain this SXI1α variant mated with the reference strain JEC20 to form mating hyphae and spores ( Figure 7 ) . Two VGIII isolates were sterile and likely harbor other unlinked mutations that impair fertility ( Figure 7 ) . The C . gattii isolate NIH836 likely harbors a nonfunctional SXI1α gene as an early stop codon occurs after one-third of the coding sequence ( Figure S3 ) ; this isolate was sterile , consistent with the known essential role of SXI1α in mating [60] . Many natural C . gattii strains are sterile under laboratory conditions [77] , whereas the VGIII molecular type contains many of the known fertile C . gattii isolates . Isolate NIH312 , the most fertile C . gattii strain identified thus far [77] , is a member of this group and harbors the SXI1α premature stop codon allele . These findings provide further evidence that changes in the C-terminus of the SXI1α gene may enhance fertility . Previous reports on the virulence of AD hybrids present differing results [57 , 58 , 67] . Reduced virulence of AD hybrid isolates compared to the Aα H99 reference strain was observed by Lengeler et al . [57] , virulence of AD hybrids similar to that of H99 was reported by Chaturvedi et al . [58] , and virulence of AD hybrids intermediate between Aα H99 and Da JEC20 reference strains was presented by Barchiesi et al . [67] . This variation is likely due to both different experimental models and analysis of divergent αADa and aADα isolates , as these isolates differ genotypically and phenotypically . The presence of opposite mating types , a and α , in diploid strains may also have complicated earlier virulence studies , as pheromone production and sensing may occur during infection [68–70] . To avoid these potential complications in virulence studies , AD hybrid strains of only α mating type were constructed based on the H99 ( haploid Aα ) and JEC21 ( haploid Dα ) backgrounds ( see Materials and Methods for details ) . Both parental strains have completed genome sequences and are widely used for genetic and pathogenesis studies [72 , 78 , 79] ( http://cneo . genetics . duke . edu/; http://www . broad . mit . edu/annotation/genome/cryptococcus_neoformans/Home . html ) . The laboratory-generated αADα hybrid was first tested in vitro . As an environmental pathogen , C . neoformans may have evolved and maintained virulence traits through selective pressure in the environment [25 , 56 , 80 , 81] . Defined C . neoformans virulence factors include melanization , capsule production , and the ability to grow at high temperature , all of which confer survival advantages in both animal hosts and the environment . The ability to grow at high temperature ( 37–39 °C ) enables human infection [82–84]; production of a polysaccharide capsule inhibits host immune responses during infection and protects cells from dehydration in the environment [85–88]; production of melanin provides protection from toxic free radicals generated by host defenses during infection and from UV irradiation in the environment [89 , 90] . These virulence properties enable C . neoformans and its sibling species C . gattii to be the only two highly successful mammalian pathogens in the genus Cryptococcus [40 , 56 , 91] . In vitro virulence attributes of the laboratory-constructed hybrid strain were compared to those of the parental strains . Haploid Aα ( H99 ) , haploid Dα ( JEC21 ) , and the laboratory-constructed hybrid αADα ( XL1462 ) strains were examined for sensitivity to UV irradiation , growth at high temperature ( 39 °C ) , capsule production , and melanization ( see Materials and Methods for details ) . Each cell type was capable of capsule production based on microscopic observations ( Figure 8A ) . The diploid αADα hybrid cells were larger than those of the parental Aα and Dα strains , and this was confirmed by forward scatter flow cytometry ( data not shown ) . An association of higher ploidy with larger cell size has also been observed in other organisms [92–94] . The Aα strain H99 was more resistant to UV irradiation than the Dα strain JEC21 , and the αADα hybrid strain was even more resistant to UV irradiation than the Aα parental strain ( Figure 8B ) . Both higher ploidy , which resulted from hybridization , and the interaction of the serotype A and D genomes independently contribute to this enhanced resistance of AD hybrids to UV irradiation , based on the observation that diploid cells ( αAAα or αDDα ) were modestly more UV-resistant than haploid cells ( Aα or Dα ) , but less UV-resistant than αADα hybrids ( Figure S5 ) . The αADα hybrid strain also grew significantly better at 39 °C than the Aα and Dα haploid parental strains , again displaying hybrid vigor ( Figure 8B ) . C . neoformans can produce melanin by oxidizing a variety of diphenolic substrates , including the neurotransmitter L-dihydroxyphenylalanine ( L-DOPA ) [89] . Variation in the rate of melanization yields pigmentation differences . At 22 °C , both the Aα strain and the αADα hybrid were heavily melanized compared to the Dα strain ( Figure 8B ) . At 37 °C , melanization of the hybrid αADα was drastically reduced and was comparable to that of the less melanized Dα parental strain ( Figure 8B ) . This observation indicates a complicated interaction of different virulence attributes ( temperature and melanization ) in the αADα hybrid . In conclusion , the αADα hybrid strain displays hybrid vigor for some virulence factors under defined in vitro conditions , but the effect of hybridization on other virulence factors is complex . As the effects of hybridization on in vitro virulence attributes are complex , the virulence potential of the hybrid was assayed in a murine inhalation model . Animals were intranasally infected with haploid Aα ( H99 ) , haploid Dα ( JEC21 ) , and the laboratory-constructed hybrid αADα ( XL1462 ) strains . Animal survival and fungal burden in the lungs and brains were monitored . The αADα hybrid strain is as virulent as the highly virulent Aα parental strain , based on both survival rate ( Figure 9A ) ( p = 0 . 371 ) and organ burden of fungal cells at the time of sacrifice ( Figure 9B ) . Animals infected with the Dα strain remained viable and showed no symptoms at the conclusion of the study ( day 100 ) . Fungal burden in animals infected with the Dα strain was considerably lower than that of animals infected with the Aα or the αADα hybrid strains . This assay indicates that the Aα and αADα strains are both much more virulent than the Dα strain , and thus hybridization with an Aα partner confers a clear benefit to the less virulent serotype D α strain . Enhanced virulence in animals is not likely to be the selective pressure that gives rise to AD hybrids , as mammalian infection is not an obligate part of the normal life cycle of this environmental pathogen , but it may reflect evolved traits that contribute to the common presence of AD hybrids in nature [80 , 95] .
The same-sex mating process has been observed under laboratory conditions [48] and is hypothesized to occur in nature given that C . neoformans has a largely unisexual population and the α mating type predominates in both clinical and environmental isolates . Population genetic studies also provide evidence that same-sex mating occurs in nature . For example , the Vancouver Island outbreak C . gattii strains are hypothesized to descend from two α parental strains [55] , and serotype A strains from Sydney , Australia , show evidence of recombination in a unisexual α population ( D . Carter , personal communication ) . However , direct evidence for naturally occurring same-sex mating is lacking , probably because of the difficulty of observing this process in nature . By characterizing naturally occurring αADα hybrid strains , we present here conclusive evidence for the cell–cell fusion step in the same-sex mating process . Because of genetic divergence , hybrids have an impaired ability to undergo meiosis and remain in a diploid state where both parental genomes , including the MAT locus , are largely intact . These natural αADα hybrids have α mating type alleles from two parents of different serotypes that can be distinguished by serotype- and mating-type-specific PCR , CGH , and sequencing . All mating type genes ( >20 ) of both serotype A α and serotype D α alleles are present in the AD hybrid , based on CGH , with the exception of the SXI1Dα gene , which bears a unique C-terminal deletion . The fact that αADα hybrids have been found in both the US and Italy suggests either that the same-sex mating process is not restricted to a specific geographic location or that αADα strains clonally expanded and dispersed . Additional AD hybrids of this nature likely remain to be recognized , as the Italian αADα hybrid strain was originally classified as a haploid Dα strain . It can be difficult to recognize αADα strains because ( 1 ) ploidy analysis of strains is not a common laboratory practice , ( 2 ) αADα hybrid strains mate as α strains in mating assays and thus do not behave like aADα or αADa hybrids , which are sterile or self-fertile , and ( 3 ) many AD hybrids are not recognized as hybrid strains by the serotype agglutination test commonly used in ecological and epidemiological studies [32 , 37–39] . Evidence has been presented to advance the hypothesis that some MAT homozygous isolates ( α/α or a/a diploids ) arise via a post-meiotic nuclear fusion event following a–α mating [96] . It is possible that a post-meiotic nuclear fusion event could generate a/a , a/α , and α/α diploid nuclei that are packaged into spores , generating MAT homozygous and MAT heterozygous diploid isolates , as originally proposed by Sia et al . [97] . However , post-meiotic nuclear fusion following a–α mating seems an unlikely explanation for the αADα isolates described here . First , only αADα , and no aADa , MAT homozygous strains were observed . Second , the αADα isolates descend from two α parents of divergent lineages and as a consequence inherited two very divergent alleles of the MATα locus , in contrast to what would be expected for the post-meiotic fusion model , in which the MATα locus alleles would be strictly identical by descent . Third , the genetic distance between serotype A and D isolates precludes efficient meiosis and sporulation , limiting the routes by which the unusual αADα isolates could have arisen . The most parsimonious hypothesis as to the origin of the αADα diploids is same-sex mating between haploid Aα and Dα parents , and further study of the origins of other MAT homozygous strains ( αAAα and αDDα ) is warranted . We hypothesize that such isolates may also have arisen via same-sex mating , based on the findings presented here with respect to αADα isolates . This study provides evidence for the first step in same-sex mating: cell–cell fusion . The natural conditions that stimulate cell–cell fusion events during same-sex mating are still unknown and require further investigation . Furthermore , the current study could not address meiotic reduction of the α/α diploids because none of these isolates was self-fertile under laboratory conditions . Meiosis is similarly precluded in many aADα and αADa hybrids . Only a minority of aADα and αADa hybrids were reported to be self-fertile in a previous study , and only one was observed to produce spores , which germinated poorly ( <5% ) , reflecting a meiotic defect [57] . The extensive DNA divergence between the two serotypes likely triggers a mismatch-repair-system-evoked block to recombination , similar to that in interspecies hybrids in bacteria and budding yeasts [98–101] . In this sense , AD hybrids likely represent a genetic dead end as they cannot complete a normal sexual cycle . They are therefore a source of diversity , but not the source of diversity for the haploid population . While providing direct evidence for α–α same-sex mating in nature , the challenge remains to provide evidence for completion of the α–α sexual cycle , including meiotic reduction and sporulation . This will necessarily entail further studies with natural α/α diploid strains of one serotype ( αAAα , αDDα , or αBBα ) , as the molecular differences are more subtle within each serotype , allowing meiosis . Detailed investigation of such isolates , as has been conducted for laboratory-generated αDDα hybrids [48] , will provide insights on the complete same-sex mating cycle as it may occur in nature . A common feature of the aADα and αADα hybrid isolates is that they all bear a C-terminal deletion in the SXI1Dα gene . Selection for this allele likely occurred prior to the cell–cell fusion events that produced these hybrid strains . Because all of the aADα and αADα hybrids tested bear the same truncated SXI1Dα allele whereas it is uncommon in haploid serotype D α isolates ( ∼13% ) , we favor the hypothesis that this allele enhances the fertility of Dα isolates . This interpretation is further supported by the observation that , unlike a complete deletion of the SXI1α gene , the C-terminally truncated SXI1α is still functional and Dα strains with this allele mate robustly and undergo meiosis normally . This hypothesis is also supported by the observation that C . gattii VGIII strains with a similar shortened version of SXI1α caused by a premature C-terminal stop codon also mate robustly . Because the VGIII group includes most of the fertile C . gattii strains characterized thus far , this shortened allele of SXI1α may also be associated with increased fertility . However , cell fusion between a transgenic strain with only the C-terminally truncated SXI1Dα allele in the JEC21 background and Da or Aa partners was not enhanced compared to wild-type under the laboratory conditions tested thus far ( data not shown ) . It is thus not clear if this truncated allele of SXI1Dα directly promotes the cell fusion step of mating , or is linked to another causative mutation in the MAT locus that was not detected in our study . Another possibility is that the effect of C-terminal truncation of SXI1α is genotype specific and mediated in concert with other unlinked mutations , similar to the observation that the role of the mating type locus in virulence is dependent on genetic background and functions as a quantitative trait locus [49 , 102] . Alternatively , laboratory conditions may not recapitulate the natural environment where cell fusion and mating occur ( pH , temperature , nitrogen source , nutrient , and presence or absence of small molecules such as inositol and auxin indole-3-acetic acid [103] ) . The efficiency of cell fusion varies considerably depending on the isolate and mating medium ( unpublished data ) . The last and , in our view , least likely possibility is that these alterations in the SXI1α gene are neutral variants , and by chance C-terminal truncation and the premature stop codon arose independently in the original ancestors of both the aADα and αADα hybrid populations ( the founder Dα strains ) and the C . gattii VGIII strains . Our study demonstrates the complexity and diversity of the life cycles of C . neoformans and indicates that hybridization is influenced by both environmental and genetic factors . Hybridization between two serotypes may have consequences for pathogenesis , as new strains with altered virulence may arise . The fact that AD hybrids occur at a reasonable frequency in both clinical and environmental samples is possibly indicative of hybrid fitness and an impact of hybridization on C . neoformans infection [33 , 35 , 61] . To test the effect of hybridization on virulence , yet avoid variations caused by natural genotypic differences and potential complications from the presence of both mating types , αADα hybrid strains were constructed in defined genetic backgrounds ( H99 and JEC21 ) , for which complete genome sequences are available and which are widely used in genetic and pathogenesis studies . The constructed αADα hybrid exhibited hybrid vigor under defined conditions , such as growth at high temperature ( 39 °C ) and resistance to UV irradiation . The hybridization effect on melanization is complex and is affected by growth temperature . In most aspects tested in vitro , the αADα hybrid and Aα strains exhibited enhanced fitness compared to the less virulent Dα parental strain . Virulence tests in a murine inhalation model showed that the constructed αADα hybrid is similar in virulence to the Aα parental strain , while the Dα parental strain is almost avirulent . Overall , these observations support the hypothesis that hybridization between serotype A and D enhances the ability of the less virulent serotype D strains to survive both in the environment and in the host . Similar hybrid vigor ( UV resistance and tolerance to high temperature ) has also been observed in natural aADα hybrids , and the increased fitness of these hybrids is hypothesized to have contributed to their worldwide distribution , whereas the parental Aa strains are geographically restricted to Africa [59] . Our findings provide definitive evidence that C . neoformans can undergo same-sex mating in nature . However , a limitation is that natural αADα hybrids have an impaired ability to undergo meiosis and fail to produce haploid progeny , precluding further evaluation with these isolates of the impact of this life style on the haploid population structure and evolution of the C . neoformans species complex . The hybrid vigor displayed by the laboratory-constructed αADα strain , both in vitro and in vivo , offers a plausible explanation for the common presence of hybrids in clinical and environmental isolates . Whether AD hybrids are a source of diversity , are en route to speciation , or are a genetic dead end requires further investigation . The unique α–α unisexual mating cycle that C . neoformans can adopt reflects either an adaptation to the sharply skewed distribution of mating types , or a route by which this disparity arose . It may maximize the advantages of both outcrossing and selfing in this heterothallic fungus that has a largely unisexual population . Similar strategies may also occur in other fungal species . For example , the obligate human fungal pathogen , Pneumocystis carinii , may share a similar life cycle . P . carinii is hypothesized to undergo both asexual and sexual cycles , based on cytological studies [104–106] . Only one mating-type-like region is known in this fungus , and there is no evidence of mating type switching [107] . The life cycle of the filamentous hemiascomycetous fungus Ashbya gossypii may also involve fusion of cells or nuclei of like mating type that then undergo meiosis and sporulation , as only the a allele of the MAT locus has been identified thus far for this species [18 , 108] . Similar inbreeding/selfing reproductive strategies have evolved in other kingdoms . For example , in plants that normally outcross , pseudo-self-compatibility in older flowers allows self-pollenization by a breakdown of self-incompatiblity barriers [109 , 110] , which is conceptually similar to the ability of a heterothallic fungus to engage in same-sex mating . The first gene underlying pseudo-self-compatibility , the S-locus-linked gene PUB8 ( the S locus in plants is functionally similar to the mating type locus in fungi or the sex chromosomes in animals ) , was recently identified [111] . In many insects , parthenogenesis is also conceptually reminiscent of same-sex mating in fungi . Reproduction in parthenogenic strains of the bisexual species Drosophila mercatorum is also analogous to same-sex mating in the heterothallic species C . neoformans [112] . It has been shown in grasshoppers that parthenogenic species can generate a level of variability similar to that in closely related sexual species [113] . Similarly , same-sex mating in C . neoformans could potentially contribute significantly to genetic variation in the largely unisexual fungal population . Elucidating how this life cycle occurs in the genetically tractable fungus C . neoformans , its underlying molecular mechanisms , and its impact on population structure will shed light on similar reproductive strategies occurring in other species .
The congenic strains JEC21 ( α ) , JEC20 ( a ) , H99 , and KN99a were used as mating reference strains . Other strains used in this study are CHY621 ( ura− , NATR ) [114] , sxi1Δ mutants CHY610 [60] and CHY618 ( ura− , NATR ) [114] , XL1462 ( αADα ) , XL1501 ( αAAα ) , XL1620 ( SXI1DαΔC ) , and those listed in Table 2 . Cells were grown on YPD ( 1% yeast extract , 2% BactoPeptone , and 2% dextrose ) or YNB medium ( Difco ) . Mating or cell fusion was conducted on V8 medium ( pH 7 . 0 ) in the dark at 22 °C . To determine mating type , isolates were grown on YPD medium for 1 d at 30 °C and separately cocultured with the reference tester strains , JEC20 ( MATa ) and JEC21 ( MATα ) , on V8 medium in the dark at 22 °C [78] . The isolate and tester strains were cultured alone on the same plate as controls . The mating reactions were examined after a week for mating hyphae formation , which signaled the initiation of sexual reproduction . Mating type was also determined by PCR with SXI1α , SXI2a , and STE20α/a gene primers that yield mating-type- and serotype-specific amplicons . Primers used are listed in Table 3 . Cells were processed for flow cytometry as described previously [48 , 97] . Briefly , cells were harvested from YPD medium , washed once in PBS buffer , and fixed in 1 ml of 70% ethanol overnight at 4 °C . Fixed cells were washed once with 1 ml of NS buffer ( 10 mM Tris-HCl [pH 7 . 6] , 250 mM Sucrose , 1 mM EDTA [pH 8 . 0] , 1 mM MgCl2 , 0 . 1 mM CaCl2 , 0 . 1 mM ZnCl2 ) and then stained with propidium iodide ( 10 mg/ml ) in 0 . 2 ml of NS buffer containing RNaseA ( 1 mg/ml ) at 4 °C for 4–16 h . Then 0 . 05 ml of stained cells was diluted into 2 ml of 50 mM Tris-HCl ( pH 8 . 0 ) and sonicated for 1 min . Flow cytometry was performed on 10 , 000 cells and analyzed on the FL1 channel with a Becton-Dickinson FACScan . Strains were grown in 50 ml of YPD medium at 30 °C overnight with shaking . The cells were washed three times with distilled water and harvested by centrifugation at 4 , 000g for 8 min . The cell pellet was frozen immediately at −80 °C , lyophilized overnight , and stored at −20 °C until genomic DNA was prepared using the CTAB protocol as described previously [115] . The quality of the purified DNA was examined on an agarose gel . AFLPs were generated and analyzed as previously described [53] . Two different EcoRI primer combinations ( EAC and ETG ) were used for the selective PCR , as described previously [68] . Only intense and reproducible bands were scored to identify differences between strains . Genomic DNA was extracted as described above . DNA was digested with restriction enzymes , separated in agarose gels , and blotted to nitrocellulose ( Zeta-Probe , Bio-Rad ) by standard methods . Probes were generated with a Prime-It II kit ( Amersham ) . Hybridization was performed using Ultrahyb ( Ambion ) according to the manufacturer's instructions . Genomic DNA was sonicated to generate ∼500-bp fragments and purified with a DNA Clean and Concentrator kit ( Zymo Research ) . Five micrograms of DNA was used for Cy-3 dUTP or Cy-5 dUTP labeling reactions using the Random Primer/Reaction Buffer mix ( BioPrime Array CGH Genomic Labeling System , Invitrogen ) . Hybridization conditions were as described previously [82] except that the slides contained a C . neoformans whole genome 70-mer oligonucleotide array and serotype- and mating-type-specific 70-mer oligonucleotides for genes in the MAT locus [49] . After hybridization , arrays were scanned with a GenePix 4000B scanner ( Axon Instruments ) and analyzed using GenePix Pro version 4 . 0 and BRB ArrayTools ( developed by R . Simon and A . Peng Lam at the National Cancer Institute; http://linus . nci . nih . gov/BRB-ArrayTools . html ) . Cells were grown on V8 medium in the dark at 22 °C . Hyphae were fixed in 3 . 7% formaldehyde and permeablized with 1% Triton in PBS . Nuclei were visualized by staining with DAPI ( 4′ , 6-diamidino-2-phenylindole , Sigma ) as described previously [47] . PCR products of the SXI1Dα gene were generated using primers JOHE17409 ( GCCGTGCAAGGGTGTAGG ) and JOHE14895 ( GGGCCATTGGAGGAAGCTG ) and template genomic DNA from the strains tested . The PCR products were subjected to agarose gel electrophoresis to reveal different sizes of the SXI1Dα alleles in these strains . To construct the αADα hybrid strain , the auxotrophic strains F99 ( Aα ura5 ) and XL342 ( Dα ade2 ) were cocultured together with strain JEC169 ( Da ade2 lys1 ura5 ) as the pheromone donor on V8 agar medium ( pH 5 . 0 ) in the dark at 22 °C . These three strains are unable to grow on minimal medium without supplementation of uracil , adenine , and uracil + adenine + lysine , respectively . After 24 h of coculture on V8 medium , cells were collected and spread on YNB minimal medium at 37 °C to select for prototrophic fusion products . Two types of fusion products were obtained: the desired diploid αADα hybrid strains and triploid Da/Aα/Dα strains , which were distinguished by mating behavior and ploidy analyzed by flow cytometry analysis . The chosen diploid αADα hybrid strains were further confirmed by mating-type- and serotype-specific PCR analyses using primers listed in Table 3 . Yeast cells were grown in YPD liquid medium overnight at 30 °C . Cells were collected by centrifugation and washed three times with sterile distilled water . Cell density was determined by absorption at 600 nm and cells were 10× serially diluted with sterile water . To examine melanin production , 3 μl of serial dilutions of cells were spotted on melanin-inducing medium containing L-DOPA ( 100 mg/l ) [116] and incubated at 22 °C and 37 °C in the dark for 2 to 4 d . Melanization was observed as the colony developed a brown color . To analyze growth at different temperatures , cells were spotted on YPD medium and incubated at the indicated temperatures . Cell growth was assessed on days 2 , 3 , and 4 . To determine sensitivity to UV irradiation , cells were spotted on YPD medium , air dried for 15 min , and then exposed to UV irradiation ( ∼48 mJ/cm2 ) in a Stratalinker ( Stratagene ) for 0 , 6 , or 12 s . Cells were then incubated at 22 °C , and cell growth was monitored daily from day 2 to 4 . To characterize capsule production , equal numbers of C . neoformans cells were spotted on DMEM ( Invitrogen ) and incubated at 37 °C for 3 d . Cells were scraped from the plates , suspended in India ink , and observed microscopically . The capsule was visualized with light microscopy as a white halo surrounding the yeast cell due to exclusion of the dark ink particles . Mice were infected essentially as previously described [117] . Groups of 4- to 8-wk-old female A/J mice ( ten mice per strain ) were anesthetized by intraperitoneal injection of phenobarbital ( ∼0 . 035 mg/g ) . Animals were infected intranasally with 5 × 104 fungal cells in 50 μl of PBS . The inocula of yeast cells were confirmed by CFU after serial dilutions . To verify strain identity for the inoculation , 100 colonies for the controls and 200 colonies for the hybrid were tested for auxotrophic markers and mating type . Three colonies for each strain were randomly chosen and checked for ploidy by fluorescent flow cytometry . Mice were monitored twice daily , and those showing signs of severe morbidity ( weight loss , extension of the cerebral portion of the cranium , abnormal gait , paralysis , seizures , convulsions , or coma ) were sacrificed by CO2 inhalation . The survival rates of animals were plotted against time , and p-values were calculated with the Mann–Whitney test . The lungs and brains from two animals from each group were removed , weighed , and homogenized in 2 ml of sterile PBS . Serial dilutions of the organ samples were plated on YPD agar plates containing 100 μg/ml chloramphenicol and incubated at 37 °C overnight . Randomly chosen colonies ( 100 for the controls and 200 for the hybrid ) were tested for auxotrophic markers and mating type . Three colonies from each organ were randomly picked and checked for ploidy by fluorescent flow cytometry . Results of auxotrophic marker , mating type , and ploidy analysis of recovered strains were congruent with those for the infecting strains .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/index . html ) accession numbers for the Sxi1α sequences discussed in this paper are as follows: C . neoformans αADα hybrid ( EF471284 ) , C . neoformans wild-type strain JEC21 ( AAN75718 ) , C . neoformans wild-type strain H99 ( AAN75175 ) , C . gattii wild-type VGI strain WM276 ( AAV28797 ) , C . gattii VGII strain WM178 ( DQ096309 ) , C . gattii VGIII strain V28 ( AY973651 ) , C . gattii VGIII strain DUMC140 . 97 ( DQ096306 ) , C . gattii VGIII strain NIH312 ( DQ096307 ) , C . gattii VGIII strain 97/426 ( DQ198312 ) , C . gattii VGIII strain 97/433 ( DQ198313 ) , C . gattii VGIII strain 97/428 ( DQ198314 ) , C . gattii VGIII strain ICB88 ( DQ198315 ) , and C . gattii VGIII strain NIH836 ( DQ198305 ) .
|
Cryptococcus neoformans is a major cause of fungal meningitis , predominantly in immunocompromised individuals . This fungus has two mating types/sexes , a and α , and mating typically requires two individuals with opposite mating types . It is mysterious why the α mating type is overwhelmingly predominant in nature and how the capacity for sexual reproduction is maintained in a largely unisexual population . We postulated that same-sex mating between α isolates may occur naturally , as it does under laboratory conditions . By analyzing natural Cryptococcus diploid hybrid isolates containing two α alleles of different serotypic origins , this study demonstrates that same-sex mating transpires in nature . The observations that Sxi1α , a sex regulator encoded by the mating type locus , is frequently altered in C . neoformans hybrids but rarely in the haploid population , and that Sxi1α is also altered in the fertile VGIII group of the sibling species C . gattii by a different mutation support the hypothesis that these SXI1α mutations may enhance fertility , possibly in concert with other genomic changes . Our study provides insights on the genetic and environmental factors that play important roles in the evolution of the current population structure of this pathogenic fungus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"developmental",
"biology",
"infectious",
"diseases",
"cell",
"biology",
"yeast",
"and",
"fungi",
"microbiology",
"evolutionary",
"biology",
"molecular",
"biology",
"genetics",
"and",
"genomics"
] |
2007
|
αADα Hybrids of Cryptococcus neoformans: Evidence of Same-Sex Mating in Nature and Hybrid Fitness
|
Information processing in neural networks depends on the connectivity among excitatory and inhibitory neurons . The presence of parallel , distinctly controlled local circuits within a cortical network may ensure an effective and dynamic regulation of microcircuit function . By applying a combination of optogenetics , electrophysiological recordings , and high resolution microscopic techniques , we uncovered the organizing principles of synaptic communication between principal neurons and basket cells in the basal nucleus of the amygdala . In this cortical structure , known to be critical for emotional memory formation , we revealed the presence of 2 parallel basket cell networks expressing either parvalbumin or cholecystokinin . While the 2 basket cell types are mutually interconnected within their own category via synapses and gap junctions , they avoid innervating each other , but form synaptic contacts with axo-axonic cells . Importantly , both basket cell types have the similar potency to control principal neuron spiking , but they receive excitatory input from principal neurons with entirely diverse features . This distinct feedback synaptic excitation enables a markedly different recruitment of the 2 basket cell types upon the activation of local principal neurons . Our data suggest fundamentally different functions for the 2 parallel basket cell networks in circuit operations in the amygdala .
Gamma-aminobutyric acid ( GABA ) ergic basket and axo-axonic cells ( AACs ) targeting the perisomatic region of cortical principal neurons ( PNs ) are in a key position to effectively control the firing of their postsynaptic partners [1 , 2] . Consequently , perisomatic inhibitory interneurons are essential for neural computations and thus cognitive processes like learning and memory , perception , and motor control [3 , 4] . The critical role that these inhibitory interneurons play in circuit operation is reflected in the wide variety of neurological and psychiatric diseases that have been implicated in their malfunction , including epilepsy , schizophrenia , and autism [5–7] . In cortical structures , 2 distinct types of basket cells ( BCs ) expressing either a Ca2+ binding protein parvalbumin ( PV ) or a neuropeptide cholecystokinin ( CCK ) and cannabinoid receptor type 1 ( CB1 ) give rise to the main inhibitory input onto the somata and proximal dendrites of excitatory PNs , while their axon initial segment is innervated by PV-expressing AACs ( also referred as chandelier cells ) [8–10] . The 2 BC types are markedly dissimilar in many single-cell features and are thought to generate postsynaptic inhibition with different properties [11–14] . In silico studies suggested that the synaptic connectivity among BCs is a key determinant of neuronal operation , especially during synchronous network activities [15] . Indeed , electrophysiological investigations provide evidence for a role of interconnected BC networks in the generation of gamma oscillations [16] and sharp wave-ripples [17 , 18] , rhythmic activities that are associated with memory acquisition and consolidation , respectively , in the hippocampus [19] . While previous work uncovered that cholecystokinin-expressing basket cells ( CCKBCs ) and parvalbumin-containing basket cells ( PVBCs ) innervate their own kind [20–24] , it is still unclear whether the 2 BC types target each other , giving rise to a “super-network” of BCs that could very efficiently regulate spiking of their target neurons , primarily supervising the local circuit operation . Alternatively , they may form 2 parallel GABAergic networks without any synaptic cross-talk , a circuit organization that could substantially increase the adjustability and computational power in cortical networks [25] . In the latter case , the 2 BC types should receive distinct excitatory input , e . g . , from local collaterals of PNs to fulfill independent operations . To address these fundamental questions , we combined electrophysiological and neuroanatomical techniques with optogenetics in the basal amygdala ( BA ) , a nucleus of the basolateral amygdala complex ( BLA ) that is known to be a site of plastic changes during fear learning [26–28] . This amygdala nucleus together with the lateral nucleus are viewed as nuclear extensions of the neocortex [29] . Although the PNs often with stellate-like appearance in the BA do not form a layered structure , many other features of these neurons match to neocortical pyramidal cells , including their excitatory nature , intrinsic membrane characteristics , connectivity patterns , and plastic properties [27 , 30] . Moreover , the diversity of local GABAergic cells in the BA resembles that observed in neocortical structures [31 , 32] . As in the neocortex or hippocampus , PNs in this amygdala nucleus are innervated by the 3 perisomatic inhibitory cell types [33–36] , providing the structural basis for investigating the synaptic communication between BCs . Our results uncovered the presence of 2 parallel BC networks in the BA . At the single-cell level , both BC types have the same potency to control the spiking of PNs that in turn distinctly excite these 2 interneuron types . Thus , PVBCs and CCKBCs in the BA form 2 separate GABAergic circuits whose activity is driven differentially by local excitatory neurons , which may be a general circuit organizing principle in cortical regions , contributing critically to local information processing .
We focused our investigations on the BA , predominantly on its anterior part ( Fig 1A and 1B ) . To verify that , in the BA , CCKBCs and PVBCs innervate their own kind as in other cortical regions [20–24] , we performed paired whole-cell recordings from 2 interneurons in slices prepared from mice expressing red fluorescent protein under the control of the Cck promoter ( CCK-DsRed ) and from mice which expressed enhanced green fluorescent protein under the control of the Pvalb promoter ( PV-eGFP ) , respectively [37] . In agreement with previous data , a monosynaptic connection from CCKBCs to CCKBCs and from PVBCs to PVBCs could be detected with high probability with a protocol evoking a train of action potentials in the presynaptic interneuron and monitoring the unitary events in the postsynaptic interneuron ( Fig 1E and 1F ) . These observations indicate that both BC types form heavily interconnected networks within their own population . In amygdalar slices containing enhanced green fluorescent protein ( eGFP ) -expressing cells , we also identified a unidirectional connectivity from PVBCs onto AACs ( Fig 1E and 1F ) , in line with previous observations obtained in the hippocampus [21] . The 2 PV-expressing interneuron types were distinguished by their calbindin content , because this Ca2+ binding protein is typically present in PVBCs but not in AACs in the BA ( Fig 1C and 1D; [34] ) . In many cases , the identity of AACs was further strengthened by investigating the close appositions of biocytin-labeled varicosities along the axon initial segments visualized by immunostaining against ankyrin G . This latter method allows unequivocal identification of AACs in cortical structures [34 , 38] . To study the synaptic connectivity between CCKBCs and PVBCs , we crossed the CCK-DsRed and PV-eGFP mice and simultaneous recordings were obtained in slices prepared from the double transgenic mice ( S1A Fig ) . No monosynapic connection could be detected from PVBCs onto CCKBCs ( 0 out of 33 trials , Fig 1C , 1E and 1F ) , while we observed in 2 cases out of 31 trials that CCKBCs gave rise to synaptic input onto PVBCs with a small peak amplitude on average ( 12 . 8 pA and 30 . 6 pA ) ( Fig 1C , 1E and 1F ) . In contrast , unidirectional connectivity from CCKBCs onto AACs was observed with a high likelihood ( 14 out of 26 trials; Fig 1D–1F ) . These in vitro results indicate that CCKBCs and PVBCs typically do not innervate each other but form 2 functionally independent , parallel GABAergic circuits ( Fig 1E ) . This active avoidance of cross-connectivity between the 2 BC types is strengthened by the fact that they both innervated the intermingled population of AACs expressing PV ( Fig 1E and 1F ) . Interestingly , the amplitude of unitary inhibitory postsynaptic currents ( uIPSCs ) in response to the first action potentials in each train was similar irrespective of the nature of the pre- or postsynaptic interneurons ( Fig 1G , S1 Data ) . A marked difference , however , was seen in the short-term dynamics of synaptic transmission . The output synapses of CCKBCs and PVBCs showed strong facilitation and modest depression , respectively ( Fig 1H , S1 Data ) . In addition , we found some differences in other characteristics of synaptic transmission , including the latency and rise times ( S1 Fig , S9 Data ) . In summary , out of 9 possible cases , 4 monosynaptic connections were found to be realized functionally among the 3 types of interneurons , suggesting a highly specific connectivity matrix among GABAergic cells targeting the perisomatic region of PNs ( Fig 1F ) . In other cortical regions , the presence of electrical coupling between interneurons of the same type is characteristic [39 , 40] , possibly promoting synchronous activities [41 , 42] . To test whether gap junction coupling exists between perisomatic inhibitory cells in the BA , we injected hyperpolarizing current steps into an interneuron , and responses were monitored in the other one . We found that interneurons of the same type were often coupled electrically ( S2 Fig , S10 Data ) . Notably , a high probability of gap junction coupling was observed between AACs that are not coupled via synaptic junctions ( Fig 1E , S2 Fig , S10 Data ) . These data strengthen the above observation that CCKBCs and PVBCs form 2 parallel circuits in which these GABAergic cells are massively interconnected via both synapses and gap junctions within their own type . To confirm these in vitro electrophysiological data with an independent method , we investigated the anatomical substrate for the connectivity among interneurons in vivo using immunocytochemistry . The axon terminals of CCKBCs were visualized with an antibody developed against CB1 [34 , 43] , while PV expression detected by immunostaining was used to label boutons of PVBCs [34 , 44] . We noticed that CB1-expressing boutons often contacted the somata of CCKBCs , while PV-containing axonal boutons , separated from other PV-expressing profiles by their co-expression of vesicular GABA transporter ( VGAT ) and glutamate decarboxylase 65/67 ( PanGAD ) , often apposed PV-immunolabeled somata . In both cases , gephyrin , an anchoring protein of GABAA receptors [45] , was present at the soma facing the boutons , implying that both PVBCs and CCKBCs formed synaptic contacts with other interneurons of their own kind ( Fig 2A , S3 Fig ) . In sharp contrast , we found that CB1-containing terminals almost completely avoided PVBC somata , and vice versa; CCKBC cell bodies were only rarely contacted by PV-immunolabeled terminals . In some cases , we could notice CB1- and PV-expressing varicosities in close vicinity to PV- and CCK-containing interneuron somata , respectively . However , gephyrin puncta were characteristically not present between these immunostained profiles but were instead found on the opposite side of the boutons , implying that the connections were established on unlabeled neighboring structures ( Fig 2A , S3 Fig ) . These data support our in vitro results showing that the 2 BC types do not prefer to innervate each other . In contrast , and in line with our electrophysiological observations , the somata of AACs , separated from PVBCs based on their calbindin content ( S3 Fig , [34] ) , received synaptic inputs from both CB1- and PV-containing boutons , as indicated by the presence of gephyrin labeling at the closely apposed boutons expressing CB1 or PV ( Fig 2A , S3 Fig ) . Quantification showed that CCKBCs and PVBCs only occasionally innervate each other , but they targeted other BCs of the same type as well as AACs ( Fig 2B , S2 Data ) . Collectively , these results clearly show that CCKBCs and PVBCs form 2 functionally parallel GABAergic circuits in the BA . Our data raises an intriguing question of whether the BCs belonging to the 2 distinct GABAergic networks provide PNs with similar or distinct inhibitory input . Therefore , we examined the characteristics of the output synapses of PVBCs and CCKBCs and their effects on the spiking of postsynaptic PNs , using paired recordings in vitro . Three action potentials evoked at 30 Hz ( a physiologically relevant spiking activity [33] ) in a presynaptic BC resulted in postsynaptic responses in a PN ( Fig 3A–3D ) . There was a high and similar probability to find a monosynaptically connected pair in the case of both BC types ( CCKBC→PN , 81 . 25% , n = 16 tested pairs; PVBC→PN , 92 . 86% , n = 14 tested pairs; Fisher’s exact test , p = 0 . 34 ) . In addition , we found that many properties of unitary inhibitory postsynaptic currents ( IPSCs ) and potentials ( IPSPs ) evoked by the first action potentials of the trains were also similar ( Fig 3E and 3F , S3 Data , S4 Fig , S11 Data ) . As in the hippocampus [12 , 14] , 3 action potentials evoked in PVBCs resulted in depressing responses in the PNs , while there was no obvious change in the amplitude of IPSC/IPSPs evoked by spike trains in CCKBC→PN pairs at the population level ( Fig 3C and 3D ) . However , this difference in short-term plasticity was not apparent in the IPSP summation , as the area under IPSPs evoked by 3 spikes in the 2 BC types were indistinguishable ( Fig 3F , S3 Data ) . The similarities in the unitary events originating from the 2 BC types implied that these GABAergic cells may have comparable effects on the spiking of PNs . To test this prediction , we injected sinusoidal current trains into the PNs near their firing threshold , and 3 spikes at 30 Hz were evoked in the presynaptic BCs at the peak of a sinusoidal wave , the point where PNs spiked with the highest probability ( Fig 3G ) . This reproducible approach allowed us to calculate the efficacy of inhibition for both GABAergic cell types by comparing the firing probability of PNs during evoked BC activity and during control epochs , during which the presynaptic BC did not fire ( Fig 3G and 3H ) . This analysis fully supported our expectation based on the unitary event properties , namely , that the 2 BC types inhibited PN firing with equal efficacy ( Fig 3I , S3 Data ) , similarly to that observed at a young age ( S5 Fig , S12 Data [37] ) . These results show that in this amygdala network , PVBCs and CCKBCs provide similarly effective synaptic inhibition onto their neighboring PNs . If the 2 parallel BC networks have similar potency to alter the activity of PN populations by controlling their spiking , then , to function largely independently , CCKBCs and PVBCs must receive distinct excitatory inputs , a difference that can be reflected both in the number of synapses and their magnitude . To reveal whether the 2 BC types receive a different number of excitatory inputs , we first estimated the density of glutamatergic axonal varicosities forming bassoon-apposing contacts with the intracellularly-labeled BCs . Here , bassoon , a protein in the active zone of synapses [46] , was used to visualize the presence of functional synapses at the light microscopic level . This analysis showed that the dendrites of PVBCs were covered more than 2 times more densely by vesicular glutamate transporter 1 ( VGluT1 ) -expressing boutons than the dendrites of CCKBCs ( Fig 4 , S4 Data ) . To strengthen these findings that the 2 BC types receive different number of excitatory inputs , we recorded miniature ( i . e . , quantal ) excitatory postsynaptic currents ( mEPSCs ) in the presence of 0 . 5 μM tetrodotoxin and investigated the properties of single events . By analyzing the interevent interval distributions of mEPSCs , we observed a marked difference ( Fig 5A and 5B , S5 Data ) . In CCKBCs , the time intervals between mEPSCs ( i . e . , the reciprocal of the frequency ) were significantly longer than between those events recorded in PVBCs . This data along with the anatomical results ( Fig 4 , S4 Data ) confirms that significantly more glutamatergic inputs are received by PVBCs than by CCKBCs . To assess the potential differences in the magnitude of quantal excitatory inputs of the 2 BCs , we examined the amplitudes of mEPSCs . We found that the mEPSC amplitudes recorded in CCKBCs were substantially smaller than those obtained in PVBCs , resulting in a significant difference in the amplitude distributions of mEPSCs ( Fig 5A and 5B , S5 Data ) . Importantly , mEPSCs in CCKBCs not only had smaller amplitudes but also showed a low variance across recordings ( mini recordings: 12 . 62 ± 0 . 43 pA , n = 5 cells , Coefficient of Variation , [CV] = 0 . 07 ) , indicating that the number of receptors between release sites should vary in a narrow range . In contrast , mEPSCs in PVBCs had overall larger amplitudes and showed a more skewed distribution ( mini recordings: 21 . 19 ± 3 . 01 pA , n = 5 cells , CV = 0 . 31 , Fig 5B , S5 Data ) , showing that a large difference in the number of receptors among individual release sites should be typical for these BCs . To support the conclusions of our mEPSC amplitude analysis , we estimated the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) receptor content at individual synapses along the CCK- and PV-expressing interneuron dendrites using super-resolution microscopy ( 3D-STORM , Fig 5C–5E , S5 Data ) . Although , at present , it is unclear how many localization points ( LPs ) represent a protein , preventing us from quantifying the precise number of receptors in a given synapse , the comparison of the normalized numbers in LPs between 2 samples still provides valuable information of the relative receptor content in both populations . These investigations uncovered that the normalized number of LPs , representing AMPA receptors at individual clusters along the CCKBC dendrites , was significantly lower than those observed along the interneuron dendrites expressing PV ( Fig 5E , S5 Data ) . In addition , there was a significant difference in the area of LP clusters along the dendrites of 2 interneuron types ( Fig 5E , S5 Data ) . These anatomical and electrophysiological results collectively demonstrate that overall , CCKBCs and PVBCs receive both quantitatively and qualitatively distinct excitatory inputs . To directly test whether a defined excitatory input can indeed distinctly target CCKBCs and PVBCs , we have chosen to study the synaptic properties of local afferents of BA PNs , because it is known that PNs in cortical regions readily innervate interneurons in their vicinity [47 , 48] , which is a key determinant of microcircuit operation [15 , 49] . As a first step , we investigated the properties of unitary events from individual PNs onto BCs . We made in vitro paired recordings when current pulses were injected into the presynaptic PN to evoke action potentials , and the postsynaptic responses were detected in the BC . We found that the average potency and failure rate of unitary excitatory postsynaptic currents ( uEPSCs ) were significantly larger and lower , respectively , in PVBCs than in CCKBCs ( Fig 6B and 6C , S6 Data ) , as well as all other synaptic parameters tested ( S6 Fig , S13 Data ) . In addition , single PNs expressing ChR2-mCherry were optogenetically excited and tested the connection between PNs and BCs in PV-eGFP or CCK-DsRed mice . In this latter case , action potentials monitored in loose-patch mode were evoked in the PNs by a spot of blue light illumination , while the postsynaptic responses in the BCs were recorded in whole-cell mode ( S6A–S6D Fig ) . Since action potentials evoked by intracellular current injection and light stimulation resulted in uEPSCs with similar characteristics ( S7E–S7J Fig , S14 Data ) , both methods were used to map the location of PNs that innervate BCs ( S8 Fig , S15 Data ) . We found that excitatory cells were connected to PVBCs with a significantly higher likelihood than CCKBCs ( S6A Fig , S13 Data ) . In addition , we noticed that the connection probability showed distinct distance-dependence ( Fig 6E and 6F , S6 Data ) . The construction of a spatial map for PN→BC pairs uncovered that PVBCs were contacted preferentially by their neighboring PNs ( <200–250 μm ) , but only rarely by more distal excitatory neurons , in line with data obtained in neocortical microcircuits [50 , 51] . In contrast , PNs innervated CCKBCs with lower probability , but the chance to find a connected pair was distance-independent ( Fig 6E and 6F , S6 Data ) . Next , we performed an analysis of contact sites between intracellularly-labelled PNs and BCs using high-resolution confocal microscopy . This investigation revealed that , on average , twice as many contacts could be identified from single PNs onto PVBCs than onto CCKBCs ( Fig 7A–7K , S7 Data ) . However , the location of the contact sites along the somato-dendritic membrane surface of both BCs was similar ( PN→CCKBC pairs: 104 . 34 ± 12 . 67 μm , n = 25; PN→PVBC pairs: 89 . 28 ± 9 . 33 μm , n = 42; p = 0 . 28 , Mann–Whitney U test ) . When the amplitude of unitary events was plotted as a function of the number of identified contact sites , a linear relationship was observed in the case of PN→CCKBC pairs , showing that an increase in the number of contacts results in a proportional increase in the unitary amplitude ( Fig 7M , S7 Data ) . In contrast , no such relationship was found in the case of PN→PVBC pairs ( Fig 7M , S7 Data ) . Moreover , in a distinct set of experiments , we compared the peak amplitudes of unitary events recorded in those pairs , in which only single contacts were detected . This analysis revealed a key difference in the local BC inputs at the level of individual synapses . CCKBCs received small ( 17 . 23 ± 4 . 45 pA [mean ± SD] , n = 7 ) and surprisingly uniform ( CV = 0 . 26 ) synaptic inputs from PNs , while uEPSCs conducted via single synapses in PVBCs were large and highly variable ( 78 . 09 ± 60 . 7 pA [mean ± SD] , n = 9 , CV = 0 . 78 ) . These data are in line with the above results obtained by mEPSC amplitude and 3D-STORM analysis and support the conclusion that the inter-synaptic variance in the EPSC amplitude is much lower in CCKBCs than in PVBCs , which might indicate the distinct plastic properties of excitatory synapses on the 2 BC types . Collectively , our findings show that BA PNs innervate CCKBCs and PVBCs via different principles , because PVBCs receive large and fast uEPSCs from their neighboring PNs that show a profound variance in their amplitude , while uEPSC amplitudes in CCKBCs are small and slightly variable unitary events that originate from PNs distributed more evenly in the BA . These results indicate that the 2 BC circuits in this cortical microcircuit can be distinctly operated by local excitatory drives . To directly evaluate the recruitment of these GABAergic cells selectively by feedback excitation in the BA , we performed electrophysiological recordings in post hoc–identified interneurons combined with optogenetics . First , we produced triple transgenic mice by crossing VGluT1-cre mice with PV-eGFP x CCK-DsRed double-crossed mice , allowing simultaneous investigations of the 2 interneuron types . Then , an adeno-associated virus ( AAV ) carrying channelrhodopsin 2 fused to a red fluorescent protein ( ChR2-mCherry ) expressed in a cre-dependent manner was injected into the BA of these triple transgenic mice , allowing us to selectively excite PNs locally by blue light illumination ( Fig 8A ) . Three to five weeks after injection , acute slices containing the amygdala region were prepared , and simultaneous loose-patch recordings from a PV-containing interneuron and a CCK-expressing interneuron were obtained while the intensity of light illumination was gradually increased , resulting in a cumulative activation of PN populations ( Fig 8B ) . We noticed that spikes could be detected at significantly lower light power in PV-expressing cells in comparison to CCK-containing interneurons ( Fig 8C and 8D , S8 Data , S9 Fig , S16 Data ) . To determine the magnitude of excitatory synaptic inputs necessary to evoke action potentials in these GABAergic cells , the illumination protocol was repeated while the same 2 interneurons were recorded in whole-cell mode ( Fig 8B and 8C , S8 Data ) , allowing the identification of neuron types post hoc ( Fig 8E ) . The results indicated that at the activation level of PNs in which PV-containing interneurons reached their spiking threshold , they received significantly larger evoked excitatory synaptic input than CCKBCs ( S9B Fig , S16 Data ) . This difference was preserved when we separately examined PVBCs ( S9B Fig , S16 Data ) , indicating that PVBCs are driven by lower PN activity levels than CCKBCs . When the PN population activity reached the level required to drive CCKBC firing , PVBCs already discharged multiple spikes ( Fig 8B , number of PVBC spikes at CCKBC firing threshold , 4 . 26 ± 0 . 21 , n = 9 ) . These observations clearly show that the 2 BC types are distinctly recruited by BA PNs , supporting the hypothesis that the functions of PVBCs and CCKBCs are different .
Our study is the first to report a detailed wiring diagram of perisomatic inhibitory interneurons in a cortical structure ( Fig 9 ) . As found in other cortical areas [20–24] , BCs in the BA are mutually interconnected within their own category via both synapses and gap junctions . Our novel findings , that the 2 BC types form parallel inhibitory networks whose distinct feedback recruitment is highly dependent on the local PN activity level , indicate different and independent roles for these GABAergic cell types . Furthermore , the unidirectional connectivity between BCs and AACs may explain the temporal sequence of interneuron firing during synchronous network activities and sensory processing [33 , 52–54] . Our conclusion that CCKBCs and PVBCs form 2 parallel networks is based on the results obtained by 2 independent methods . As CCKBCs and PVBCs receive distinct excitation from PNs ( present study , [11] ) , differ in subcortical inputs , and are endowed with specific sets of receptors [8] , these results imply that the 2 GABAergic networks should function separately for the most part , probably fulfilling distinct roles in neural computation [55] . This hypothesis is supported by in vivo data showing that the 2 BC types fire distinctly during oscillatory activities in the hippocampus [13] , providing temporarily segregated inhibition on the same membrane domain of PNs . Surprisingly , we found that AACs receive inputs from both BC types with high probability and magnitude . Thus , our wiring diagram ( Fig 9 ) suggests that this third perisomatic inhibitory cell type can predominantly , if not exclusively , spike when the 2 BC types are silent . Indeed , hippocampal AACs have been found to be silent during sharp wave-ripple activities when the firing rate of PVBCs is maximal , and in a different phase of theta rhythms in comparison to BCs [9 , 53] . Interestingly , AAC firing precedes firing of other interneuron types upon sensory stimuli [33 , 54 , 56] . These results imply a critical role for AACs at the first stages of sensory processing [57] . The sole communication between AACs can be achieved via electrical coupling ( S2 Fig , S10 Data ) , since these GABAergic interneurons do not form synaptic contacts with each other ( present study , [58] ) . This type of network structure is not unique for AACs in the CNS but has been observed , e . g . , among cerebellar Golgi cells whose synchronous activity can be promoted or reduced via gap junctions depending on the input patterns [59 , 60] . Thus , synaptic inhibition arriving on the axon initial segments may help the synchronization or desynchronization of the activity in PN ensembles as a function of the gap junction strength among AACs , a signal transfer , which might be subject to plasticity [61] . Previous studies investigating the postsynaptic current characteristics generated by the output synapses of BCs in hippocampal slices suggested that PVBCs and CCKBCs gave rise to synaptic inhibition with different properties [11 , 12 , 14 , 62] . In contrast , our studies in the BA show that the magnitude of the synaptic events originating from the 2 BC types is similar both in young and adult mice ( S5 Fig , S12 Data , [37] . The reason for the discrepancy could be 2-fold . First , in distinct cortical structures , the properties of synaptic transmission originating from the 2 BC types might be different . Second , a technical issue might also contribute to the discrepancy . To study synaptic inhibition , experiments are routinely conducted with an intrapipette solution containing high Cl− concentration in postsynaptic neurons , while in our study , we used a low concentration of Cl− , similar to physiological conditions [38] . As the intracellular Cl− concentration may alter some of the parameters of GABAA receptor-mediated synaptic transmission [63] , the results obtained with distinct Cl− concentrations even at the same synaptic junctions can differ , making it hard to compare the synaptic properties , unless the recording circumstances are as identical as possible . Motivated by this potential concern , we compared the inhibitory efficacy of the 3 perisomatic inhibitory cells using the same conditions . In spite of the differences observed in some of the features in synaptic transmission ( present study , [37 , 38] ) , the 3 cell types inhibited PN spiking with equal efficacy . Thus , our results show that the perisomatic region of PNs in the BA receive 3 distinct sources of synaptic inhibition controlling spiking with indistinguishable efficacy . Earlier anatomical studies obtained in the hippocampus [64 , 65] showed that the density of excitatory synapses on the dendrites of PV-expressing interneurons is significantly higher than on the dendrites of CCKBCs . Thus , these 2 GABAergic cell types should be distinctly excited by PN activities , a hypothesis that has been strengthened by slice physiology [11] . Our data confirmed and substantially extended these results with functional implications in the BA by showing that i ) lower activity levels of PNs are sufficient to excite PVBCs than CCKBCs; ii ) the density of VGluT1-expressing glutamatergic inputs is significantly higher on the dendrites of PVBCs than on the dendrites of CCKBCs and , in parallel , the mEPSC frequency in PVBCs is considerably higher than in CCKBCs; iii ) uEPSCs originating from single PNs are larger and faster in PVBCs than in CCKBCs; iv ) PVBCs are preferentially innervated by neighboring PNs , while CCKBCs receive excitation from PNs with lower probability but in a distance-independent manner; and v ) uEPSC amplitude in PN→CCKBC pairs with single contacts , quantal excitatory events ( i . e . , mEPSCs ) in CCKBCs , and AMPA receptor content at individual synapses along their dendrites show a surprisingly uniform distribution , whereas a high variability characterizes uEPSCs and mEPSC amplitude distributions as well as AMPA receptor content at single clusters in PVBCs . The latter results are specifically interesting because they suggest that the excitatory inputs onto CCKBCs from PNs may have little potency to undergo plastic changes in comparison to those excitatory inputs received by PVBCs . Indeed , long-term potentiation ( LTP ) or long-term depression ( LTD ) at the excitatory synapses of hippocampal CCKBCs have not been reported so far [66 , 67] . However , these studies as well as others found long-term changes in excitatory transmission in PVBCs [68 , 69] . Thus , a network of CCKBCs , due to their nonplastic excitatory inputs , may be excited independently of learning-induced changes , while plastic changes at the excitatory inputs of PVBCs might help memory formation in cortical circuits . The generality of our present findings is supported by data obtained in the hippocampus , in which the cross-connectivity between the 2 BC types is low ( if any ) [21] , a significantly lower number of excitatory inputs is received by CCKBCs in comparison to PVBCs [64 , 65] , and PVBCs can be activated more reliably than CCKBCs [11] , similarly to those observed in the BA . Although comparable studies have not been performed in neocortical areas , the presence of analogous cell types and the connectivity patterns [47] as well as the documented distance-dependence in the connection probability between pyramidal cells and PVBCs [50 , 51 , 70] imply that microcircuits in the cerebral cortex may be organized along similar principles as in the BA and hippocampus . In summary , as the same interneuron types are present in all neocortical structures [49 , 71] , the synaptic organizing principles revealed here , in the BA , for the perisomatic inhibitory cells , offer a framework for understanding the temporal dynamics of these 3 interneuron types during oscillatory activities and sensory processing . In addition , the 2 BC networks distinctly excited by PNs in a feedback manner may be a general circuit motif in neocortical areas critical for information processing .
All experiments were approved by the Committee for the Scientific Ethics of Animal Research ( 22 . 1/360/3/2011 ) and were performed according to the guidelines of the institutional ethical code and the Hungarian Act of Animal Care and Experimentation ( 1998; XXVIII , section 243/1998 , renewed in 40/2013 ) in accordance with the European Directive 86/609/CEE and modified according to the Directives 2010/63/EU . Transgenic or double-transgenic mice of either sex ( 3 weeks to 10 weeks old ) expressing eGFP under the control of the Pvalb promoter ( BAC-PV-eGFP , [72] ) , expressing DsRed under the Cck promoter ( BAC-CCK-DsRed , [73] ) , or expressing both eGFP and DsRed were used in in vitro experiments . For acute slice preparation , mice were deeply anesthetized with isoflurane and decapitated . As described before , the brain was quickly removed and placed into ice-cold solution , containing ( in mM ) : 252 sucrose , 2 . 5 KCl , 26 NaHCO3 , 0 . 5 CaCl2 , 5 MgCl2 , 1 . 25 NaH2PO4 , 10 glucose , bubbled with 95% O2/5% CO2 ( carbogen gas ) [37] . Horizontal slices of 200-μm thickness containing the BA were prepared with a Leica VT1000S or VT1200S vibratome and kept in an interface-type holding chamber containing ACSF at 36°C that gradually cooled down to room temperature . ACSF contained the following ( in mM ) : 126 NaCl , 2 . 5 KCl , 1 . 25 NaH2PO4 , 2 MgCl2 , 2 CaCl2 , 26 NaHCO3 , and 10 glucose , bubbled with carbogen gas . After at least 1 hour , incubation slices were transferred to a submerged type recording chamber perfused with 32°C ACSF with approximately 2–2 . 5 ml/min flow rate . Recordings were performed under visual guidance using differential interference contrast microscopy ( Olympus BX61W or Nikon FN-1 ) using 40x or 16x water dipping objective . Neurons expressing eGFP or DsRed were visualized with the aid of a mercury arc lamp or a monochromator ( Till Photonics ) and detected with a CCD camera ( Hamamatsu Photonics or Andor Zyla ) . Patch pipettes ( 4–7 MΩ ) for whole-cell recordings were pulled from borosilicate capillaries with inner filament ( thin walled , OD 1 . 5 ) using a DMZ-Universal Puller ( Zeitz Instruments ) or using a P1000 pipette puller ( Sutter Instruments ) . For loose-patch recordings 3–5 MΩ pipettes were filled with normal ACSF , and an incomplete seal was formed during the recording with the cell membrane of the targeted neuron in order to monitor spiking activity . In whole-cell paired recordings the patch pipette contained a K-gluconate-based intrapipette solution containing the following ( in mM ) : 110 K-gluconate , 4 NaCl , 2 Mg-ATP , 20 HEPES , 0 . 1 EGTA , 0 . 3 GTP ( sodium salt ) , and 10 phosphocreatine adjusted to pH 7 . 3 using KOH , with an osmolarity of 290 mOsm/L . In interneuron ( IN ) →PN paired recordings , the presynaptic intrapipette solution additionally contained 0 . 2% biocytin and 10 mM GABA , and the postsynaptic intrapipette solution additionally contained 100 μM AlexaFluor-488 hydrazide sodium salt ( Invitrogen ) . In PN→IN paired whole-cell recordings , 0 . 2% biocytin and 10 mM glutamate were added to the presynaptic intrapipette solution , and 0 . 1 mM spermine and Cascade Blue hydrazide trisodium salt ( 0 . 1% ) , Lucifer Yellow CH potassium salt ( 0 . 1% ) , or Alexa 594 hydrazide sodium salt ( 100 μM , all from Life Technologies ) was added to the postsynaptic intrapipette solution in order to visualize the recorded cells in different colors . In light stimulation experiments , in which only INs were recorded in whole-cell mode , the intrapipette solution contained 0 . 2% biocytin . In IN→IN paired whole-cell recordings , intrapipette solution for both cells contained the following ( in mM ) : 54 K-gluconate , 4 NaCl , 56 KCl , 20 HEPES , 0 . 1 EGTA , 10 phosphocreatine , 2 Mg-ATP , 0 . 3 GTP ( sodium salt ) , 10 mM GABA , and 0 . 2% biocytin adjusted to pH 7 . 3 using KOH and with an osmolarity of 290 mOsm/L . Recordings were performed with a Multiclamp 700B amplifier ( Molecular Devices ) , low-pass filtered at 2 kHz , digitized at 10–50 kHz , and recorded with an in-house data acquisition and stimulus software ( Stimulog , courtesy of Prof . Zoltán Nusser , Institute of Experimental Medicine , Hungarian Academy of Sciences , Budapest , Hungary ) or Clampex 10 . 4 ( Molecular Devices ) . Recordings were analyzed with EVAN 1 . 3 ( courtesy of Professor Istvan Mody , Department of Neurology and Physiology , University of California , Los Angeles , CA ) , Clampfit 10 . 4 ( Molecular Devices ) , Origin 8 . 6 , or OriginPro 2015 . Recordings were not corrected for junction potential . To test the firing characteristics , neurons were injected with 800-ms–long hyperpolarizing and depolarizing square current pulses with increasing amplitudes from −100 to 600 pA . The broad action potential waveform , accommodating firing pattern , and slow after-hyperpolarization were characteristic for PNs . PN identity was further confirmed by the post hoc morphological analysis of their spiny dendrites . For PVBCs and AACs , fast spiking , nonaccommodating firing pattern together with eGFP expression were typical as well as post hoc analysis of their neurochemical marker profile . CCKBCs were characterized by their accommodating regular firing pattern together with strong DsRed expression as well as post hoc analysis of the expression of CB1 at their axon terminals ( see below ) . For recordings of IPSCs in IN→PN pairs , the presynaptic IN was held near a membrane potential of −65 mV in current-clamp mode and injected by brief square current pulses ( 2 ms , 1 . 5–2 nA ) to evoke spikes . As done before , the PN was clamped at a holding potential of −40 mV [38] . Series resistance was monitored ( range: 8–20 MΩ ) and compensated by 65% . To record IPSPs , the presynaptic cell was stimulated in the same way , and the postsynaptic PN was held in current clamp mode at approximately −55 mV . Bridge balance was adjusted throughout the recordings . Kinetic properties of IPSCs and IPSPs were analyzed on the average of 10 to 20 consecutive events . To test the capability of BCs to suppress PN firing , sinusoidal current pulses at theta frequency ( 3 . 53 Hz ) with peak-to-peak amplitudes of 30 pA and 50 pA were injected into the postsynaptic PN . As done before , the membrane potential of PNs was set ( approximately −55 mV ) to trigger a spike at the peak of the sinusoidal current pulses with the amplitude of 50 pA but not with 30 pA [38] . This adjustment maintained the membrane potential of PNs near the spiking threshold . One trial consisted of 7 sinusoidal current waves ( 5 x 50 pA and 2 x 30 pA ) , repeated 10 to 20 times in each pair . Three action potentials at 30 Hz were evoked in the IN by brief square current pulses ( 2 ms , 1 . 5–2 nA ) before the fourth sinusoidal current wave ( 50 pA ) in each trial . To assess the reduction in spiking probability , the probability of action potential generation in PNs under control conditions was determined from the average of the responses to 50 pA currents ( first , third , fifth , and sixth sinusoidal wave ) , which was compared with that recorded during the fourth cycle . In IN→IN pair recordings , presynaptic INs were held in current clamp mode at approximately −65 mV , and 3 to 10 action potentials were evoked by injection of brief square current pulses ( 2 ms , 1 . 5–2 nA ) at 30 Hz or 40 Hz , while IPSCs in the postsynaptic cell were recorded at the membrane potential of −65 mV . To study the electrical coupling , a hyperpolarizing current of 100 pA or 200 pA in one IN was applied , and the change in the voltage was monitored in the other IN . This experiment was performed bi-directionally . IN pairs were considered electrically coupled when a change in the voltage of one interneuron could also be observed in the other one . For recordings of excitatory postsynaptic currents/potentials ( EPSCs/EPSPs ) in PN→IN pairs , postsynaptic INs were recorded at −65 mV , while 3 to 5 brief square current pulses ( 2 ms , 1 . 5–2 nA ) were applied in the presynaptic PN held at approximately −65 mV in current clamp mode . For constructing the connectivity map , PV-eGFP , CCK-DsRed , or PV-eGFP x CCK-DsRed transgenic mice ( P30–35 ) were injected with 2/5 serotype AAV carrying CaMKII-ChR2-mCherry construct ( Penn Vector Core ) bilaterally into the BA ( anteroposterior: −1 . 5; mediolateral: 3 . 3; dorso-ventral: −4 . 4 mm from bregma , 50–100 nl into each hemisphere ) . At least 3 weeks after the injection , acute slices of 200-μm thickness were obtained from mice expressing ChR2 in PNs , as described above . For mapping the connection probability between PNs and perisomatic inhibitory INs using light stimulation , an eGFP- or a DsRed-expressing IN was recorded in whole-cell mode , while single PNs were sequentially activated by a blue light spot having a diameter of the soma sized ( 15–20 μm ) for 50 ms ( 447 nm blue laser , Roithner Laser Technik ) using a Digital Mirror Device ( DMD ) -based pattern illuminator ( Mightex Polygon 400 ) . PNs were randomly chosen and the light-evoked action potentials were simultaneously monitored with a pipette containing ACSF in loose-patch mode . The connectivity map was created using the XY coordinates of the recorded cells , and the inter-somatic distance was calculated between the tested presynaptic PNs and the postsynaptic IN . Light stimulation intensity was set individually for each tested presynaptic PN to the minimal intensity value sufficient to evoke action potential ( s ) ( S7 and S10 Figs , S14 Data ) . We used 50 ms-long light pulses with low stimulus intensity to minimize the light scattering that might activate some neighboring PNs expressing ChR2 and/or their axons . Since the activation threshold of individual ChR2-expressing PNs highly depends on the ChR2 expression level ( S7 and S10 Figs , S14 Data ) , it is expected that in some cases more than one PN is stimulated by the applied method . To overcome this limitation , we peak aligned the light-evoked action potentials in the PN , recorded in loos-patch mode in order to exclude uEPSCs that originated from the light-activated neighboring PNs ( S7 Fig , S14 Data ) . To test a connection , 50–200 sweeps were recorded , and a peri-stimulus histogram was calculated in order to reveal monosynaptic excitatory connections between the PNs and BCs . In some cases , whole-cell recordings were obtained from the presynaptic PN that was proved to be connected to the BC upon light stimulation , and EPSCs in the postsynaptic BC were recorded upon electrical stimulation of the same PN . This approach allowed us to compare the properties of the unitary events evoked by light and electrical stimulation of the presynaptic PNs . Since no significant difference was found between the results obtained by the 2 methods ( S7 Fig , S14 Data ) , the optical stimulation approach was applicable for mapping the connectivity between PNs and BCs ( Fig 6 and S8 Fig , S6 and S15 Data ) . The amplitude of uEPSCs was measured in an individually defined time window calculated from the peristimulus histogram for each pair , including both events and transmission failure . For the potency of the events , uEPSC amplitudes excluding failures were averaged . For defining the spiking threshold of the INs by population excitation originating from local PNs , double-transgenic mice ( PV-eGFP x CCK-DsRed ) were crossed with VGluT1-cre homozygote mice , and offsprings were injected with 2/5 serotype AAVs carrying DIO-ChR2-mCherry construct bilaterally into the BA ( the same coordinates as described above ) in order to obtain ChR2 expression selectively in PNs . At least 3 weeks after the injection , acute slices were prepared from injected mice . To investigate the excitability of the INs , firing threshold in PVBCs and CCKBCs were always simultaneously measured in loose-patch mode , while the whole area of the BA in the slice was stimulated with 10-ms-long blue ( 447 nm laser ) light pulses with gradually increasing intensity ( typically delta +5% steps ) . Spiking threshold was defined as the lowest light intensity ( % ) , in which INs fired single action potentials in response to the light stimulation of a PC population . After determining the firing threshold in loose-patch mode , INs were repatched in whole-cell mode , and the same protocol was performed to measure the magnitude of the light-evoked currents at the intensity values used for loose-patch recordings . For miniature event analysis , CCKBCs and PVBCs were recorded in whole-cell mode at a holding potential of −65 mV ( i . e . , at the reversal potential of GABAA receptor-mediated conductance using low intrapipette Cl− ) in the presence of 0 . 5 μM tetrodotoxin ( TTX , Hello Bio ) . In these experiments , the intrapipette solution contained ( in mM ) : 110 K-gluconate , 4 NaCl , 2 Mg-ATP , 20 HEPES , 0 . 1 EGTA , 0 . 3 GTP ( sodium salt ) , 10 phosphocreatine , 0 . 2% biocytin , and 0 . 1 spermine adjusted to pH 7 . 3 using KOH , with an osmolarity of 290 mOsm/L . During the offline analysis , individual miniature events were detected automatically using an algorithm , and , after visual inspection of each detected event , the peak amplitude and interevent interval of mEPSCs were measured in Clampfit 10 . 4 ( n = approximately 300 consecutive events/IN ) . BAC-CCK-DsRed mice ( n = 2 ) were intracardially perfused with 4% PFA in 0 . 1 M phosphate buffer ( PB ) . The brain was removed from the skull and resectioned to 50-μm-thick horizontal slices . To reveal cholinergic fibers , rabbit anti-vesicular acetylcholine transporter ( VAChT ) , 1:1000 , Frontier Institute ) was used , which was visualized using an AlexaA488-conjugated donkey anti-rabbit antibody ( 1:500 , Jackson ) . Single-plane fluorescent images were obtained using a Nikon C2 confocal microscope ( Plan Fluor 4x objective , N . A . 0 . 13 , xy: 0 . 8 μm/pixel ) . After the recordings , slices were fixed overnight in a solution containing 4% paraformaldehyde ( PFA ) in 0 . 1 M phosphate buffer ( PB , pH 7 . 4 ) . In the case of the ultrastructural analysis of the connections with electron microscopy , the fixative solution additionally contained 0 . 05% glutaradehyde and 15% picric acid . Biocytin-filled recorded cells were visualized either with Cy3 , Alexa488 or Alexa647-conjugated streptavidin ( 1:3000 , Molecular Probes or Life Technologies ) . In those cells in which the fluorescent dyes Alexa488 or Cascade Blue were used for labeling , the signal was amplified by an immunostaining against the fluorophores ( rabbit anti-Alexa488 ( Invitrogen ) , rabbit anti-Cascade Blue ( Molecular Probes ) , all 1:1000 ) . Confocal images were taken using a Nikon A1R or C2 microscope ( CFI Super Plan Fluor 20X objective , N . A . 0 . 45; z step size: 1 μm , xy: 0 . 31 μm/pixel ) . Using the confocal images , the postsynaptic IN was fully reconstructed in 3D with the Neurolucida 10 . 53 software ( MBF Bioscience ) , and the putative contact sites from the presynaptic PN were labelled . For the detailed analysis of the putative synaptic sites , higher magnification images were acquired using the same microscopes ( CFI Plan Apo VC60X Oil objective , N . A . 1 . 40; z step size: 0 . 13 μm , xy: 0 . 08 μm/pixel ) . The location analysis of the contact sites was obtained by the Neurolucida Explorer software . Values were corrected for shrinkage and flattening of the tissue ( x and y axis: no correction , z axis: 1 . 7 ) . One PN→PVBC and one PN→CCKBC pair was further processed for electron microscopic analysis to approve the presence of synapses at the contact sites determined by confocal microscopy . Biocytin in PNs was revealed using avidin-biotinylated horseradish peroxidase reaction ( ABC; Vector Laboratories ) with nickel-intensified 3 , 3-diaminobenzidine ( DAB-Ni ) , giving a dark reaction product . Rabbit anti-Cascade Blue primary antibody in INs was visualized with biotin-conjugated goat anti-rabbit secondary antibody , with ABC reaction visualized by DAB giving a light brown chromogen . Next , sections were postfixed in 0 . 5% OsO4 with 7% glucose , treated in 10% uranyl acetate , dehydrated in a graded series of ethanol , and embedded in epoxy resin ( Durcupan; Sigma ) . Ultrathin sections of 60-nm thickness were cut , and putative synaptic sites , in which the presynaptic axon formed close appositions with the labelled IN , were examined in serial sections . In both cases , the presence of the synapses could be clearly verified ( Fig 7 , S7 Data ) . Post hoc confirmation of the identity of the INs was performed based on their neurochemical content as follows . After imaging the cell using a confocal laser scanning microscope ( Nikon C2 or A1R ) , only those INs that preserved the axon collaterals were further processed for anatomical identification . For CCKBC identification , an immunostaining using goat anti-CB1 ( 1:1000; Frontier Institute ) was performed , and only those cells in which CB1 receptor expression was found in their boutons were included in the study . To distinguish PVBCs and AACs , an immunostaining against calbindin was performed ( rabbit anti-calbindin , 1:3000 , Swant , see [34] ) . PV-containing cells with calbindin expression in the soma and/or axon terminals were considered BCs , whereas those cells that showed no calbindin expression and displayed characteristic cartridges of terminals surrounding putative axon initial segments ( AISs ) were considered AACs . In some cases , the identity of AACs was strengthened by ankyrin G staining visualizing AISs , as the boutons of biocytin-labeled cells formed close appositions predominantly with ankyrin G-stained profiles [34] . To assess the connection probability between different IN types , we included in the final data set only those recordings in which both cells could be unequivocally identified and had axonal arbor . The estimation of the density of excitatory inputs received by BCs was done as previously described [74] . Biocytin was revealed in in-vitro–filled INs with Alexa488 coupled streptavidin ( 1:3000 , Molecular probes ) , then , after fixation , slices were resectioned into 40-μm–thin sections . Samples were incubated for 4 nights at 4°C in a solution containing the following primary antibodies and reagents: mouse anti-bassoon ( 1:1000 , Abcam ) , guinea pig anti-VGluT1 ( 1:1000 , Millipore ) , 2% normal donkey serum , 0 . 5% Triton-X 100 , and 0 . 05% Na-azide in 0 . 1 M PB . Primary antibodies were visualized with Cy3 conjugated donkey anti-mouse and Cy5 conjugated donkey anti-guinea pig secondary antibodies ( Jackson Laboratory , 1:500 ) incubated for 2 hours at room temperature . Sections were then washed and mounted on slides in Vectashield ( Vector Laboratories ) . Confocal images were taken using a Nikon A1R microscope ( CFI Plan Apo VC60X Oil objective , NA: 1 . 40 , z step size: 0 . 13 μm; xy: 0 . 06 μm/pixel ) , and analyzed with Neurolucida 10 . 53 software . To estimate the content of AMPA receptors at synapses of identified IN dendrites , 3D direct stochastic optical reconstruction microscopy ( 3D-STORM ) was performed . For this study , identified CCKBCs and PV-containing INs were intracellularly filled with biocytin as described above . After the electrophysiological experiments , slices were fixed overnight in 4% PFA and resliced to 30-μm–thick sections . Next , an immunostaining to label AMPA receptors was performed as previously described [75] . Briefly , sections were treated for 10 minutes with a solution containing pepsin 1 mg/ml in 0 . 1 M PB and HCl 0 . 2 N at 37°C for antigen retrieval and then a solution containing 0 . 2% Triton-X and NDS 10% together with BSA 2% in 0 . 1 M PB to prevent unspecific binding of the antibodies . Then , sections were incubated for 5 days at 4°C in a 0 . 1 M PB solution containing 0 . 2% Triton-X , 1% NDS , 0 . 05% Na-azide and the following mixture of primary antibodies: guinea pig anti-pan-AMPAR ( Frontier Institute , 1:200 ) and mouse anti-bassoon ( Abcam , 1:3000 ) . The staining was visualized using Alexa488-conjugated streptavidin ( Molecular Probes , 1:10000 ) , Alexa647-conjugated donkey anti-guinea pig ( Jackson , 1:400 ) and Cy3-conjugated donkey anti-mouse ( Jackson , 1:500 ) . Sections were prepared for dSTORM imaging as follows . After the staining , they were flat mounted on coverslips and stored dried at 4°C . Samples were embedded in freshly prepared imaging medium immediately before the STORM imaging session [76] . Once a dendritic segment of interest was selected , a high-resolution confocal stack ( 512x512 pixels , xy: 0 . 08 μm/pixel , z step size: 0 . 15 μm ) followed by 3D-STORM imaging was performed with an APO TIRF 100x objective , using a Nikon Ti-E inverted microscope equipped with a Nikon C2 confocal scan head , an AndorXion Ultra 897 High Speed EMCCD camera , and a Nikon N-STORM system . For STORM imaging , a 300-mW laser was used ( VFL-P-300-647 , MPB Communications ) . The imaging process for each sample consisted of 5000 cycles of the reporter Alexa647 activation at maximum laser intensity of 30-ms–long frames , with a continuous low-intensity illumination using the 405-nm laser to enhance the activation . To minimize out-of-focus background and focus drift during imaging , all samples were bleached similarly using the 488-nm , 568-nm and 647-nm–laser lines in all the z depth of the sample , and TIRF illumination angle and a Perfect Focus System were applied . Localization points ( LPs ) were collected within 600 nm centered in a focal plane contained in a 1–3 μm range from the surface . The resulting coordinates were acquired using the N-STORM module in the NIS-Elements software , setting the intensity height detection range of the bright points from 1000 to 20000 and the CCD baseline to 100 . Confocal stacks from the imaged areas were deconvolved using Huygens software ( SVI ) , and transformed in ImageJ software . The manual alignment of deconvolved confocal and STORM images , as well as the analysis of the number , 2D Convex Hull area and density of LPs was performed using VividSTORM software [76] . To quantify the number of LPs , a region of interest ( ROI ) was manually drawn around those puncta observed along the dendrite in single focal planes restricted to the central 300 nm , which contained bassoon in close apposition . Puncta depicting AMPA receptors not apposing bassoon were also analyzed ( 11 out of 30 for CCKBCs , 13 out of 53 for PV-expressing interneurons ) , and , as there were no significant differences in the number of LPs compared with the bassoon-apposing puncta ( p = 0 . 16 for PV-containing interneurons and p = 0 . 70 for CCKBCs ) , data were pooled . To avoid biasing the analysis , all images were processed equally , and a normalizing factor was introduced in the number of LPs to account for possible technical differences in the staining or imaging sessions . The normalizing factor was obtained as follows: a cluster analysis was performed in a large area immediately surrounding the analyzed dendrite , using the same parameters used for the analysis of the ROIs . The mean number of LPs from all the automatically identified clusters was calculated for every analyzed image and scored based on the averages obtained in all images . The number of LPs in the ROIs was normalized by dividing the raw data obtained for interneurons by the normalizing factor . In addition , to avoid introducing errors derived from the size/shape of the clusters , analyzed puncta fulfilled 2 additional criteria: ( 1 ) normalized number of LPs higher than 8 , and ( 2 ) puncta area larger than 0 . 07 μm2 . To investigate the connectivity among INs , CCK-DsRed mice ( n = 2 ) and wild-type mice ( n = 2 ) were transcardially perfused with 4% PFA in 0 . 1 M PB , and areas containing the amygdala region were sectioned into 50-μm–thin slices , then freeze thawed above liquid nitrogen in 30% sucrose solution . To label connections between CCKBCs and PV-expressing cells , samples from CCK-DsRed mice were incubated for 6 nights at 4°C in the following primary antibody mixture: mouse anti-gephyrin ( Synaptic Systems , 1:1000 ) , rabbit anti-parvalbumin ( Swant , 1:10 . 000 ) , guinea pig anti-CB1 ( Frontiers Institute , 1:1000 ) , 2% normal donkey serum , and 0 . 05% Na-azide in 0 . 1 M PB . Primary antibodies were visualized with the mixture of the following secondary antibodies: Alexa488 conjugated donkey anti-mouse ( Molecular Probes , 1:500 ) , Alexa647 conjugated donkey anti-rabbit and Dylight405 conjugated donkey anti-guinea pig ( both Jackson Laboratory , 1:500 ) incubated 1 night at 4°C . To label PV→PV cell connections , samples from wild-type mice were used , and besides labeling gephyrin and parvalbumin as described above , guinea pig anti-VGAT ( Frontiers Institute , 1:1000 ) and goat anti-GAD65/67 ( pan-GAD , Frontiers Institute , 1:500 ) primary antibodies were also applied that were visualized with Cy3 conjugated donkey anti-guinea pig and Cy3 conjugated donkey anti-goat secondary antibodies . Sections were then washed and mounted on slides in Vectashield . Confocal images were taken using a Nikon A1R microscope ( CFI Plan Apo VC60X Oil objective , NA: 1 . 40 , z step size: 0 . 13 μm; xy: 0 . 06 μm/pixel ) , and analyzed with Neurolucida 10 . 53 software . Appositions were only identified as contacts if the postsynaptic anchoring protein of GABAA receptors , gephyrin , was localized at the side of the immunolabeled terminal , which faced the somatic membrane , otherwise the terminals were regarded to form contacts on neighboring structures . In case of PV-immunoreactive terminals on PV-immunostained somata , the presence of VGAT and GAD65/67 immunolabeling identified the PV-containing structure as an axon terminal . CCKBC somata were visualized by the expression of the genetically encoded DsRed fluorescent protein . In the case of data with nonnormal distribution according to the Shapiro-Wilk test , the Mann–Whitney U test , Wilcoxon Signed Rank test , Kolmogorov-Smirnov test , and Kruskal–Wallis ANOVA were used for analysis of the data . To correlate variables from normal distributions , the Pearson’s correlation coefficient was used . All statistics were performed using Origin 8 . 6 or OriginPro 2015 . Data are presented as mean ± SEM .
|
The perisomatic region of neurons refers collectively to the membrane surface of the cell body or soma , proximal dendrites , and axon initial segment . This is a unique functional domain in which the activity of a neuron can be controlled in the most effective manner . In the cerebral cortex , the perisomatic region of excitatory principal cells is solely innervated by inhibitory interneurons , which can be divided into 3 functional groups: axo-axonic cells and 2 types of basket cells . The reason why 3 distinct types of inhibitory cells are specialized to control principal cell firing is still unknown . To reveal the possible differences in the role of the 3 interneuron types played in cortical operation , we have investigated the organizing principles of synaptic communication between principal cells and inhibitory cell types in the basal nucleus of the amygdala . In this cortical structure , known to be critical for affective behavior , we revealed that the 2 basket cell types avoid innervating each other but contact axo-axonic cells . Both basket cell types have a similar potency to control principal cell firing , but they receive excitatory input from principal cells with entirely distinct features . Our data suggest fundamentally different functions for the 2 parallel basket cell networks in amygdala operation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"action",
"potentials",
"medicine",
"and",
"health",
"sciences",
"nervous",
"system",
"membrane",
"potential",
"brain",
"electrophysiology",
"light",
"neuroscience",
"electromagnetic",
"radiation",
"nerve",
"fibers",
"interneurons",
"neuronal",
"dendrites",
"animal",
"cells",
"axons",
"light",
"pulses",
"amygdala",
"physics",
"cellular",
"neuroscience",
"cell",
"biology",
"anatomy",
"synapses",
"physiology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"neurophysiology"
] |
2017
|
Differential excitatory control of 2 parallel basket cell networks in amygdala microcircuits
|
The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability . The low success rates of computational design protocols and the extensive in vitro optimization often required , highlight the challenge of designing proteins that perform essential biochemical functions , such as binding or catalysis . One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins . The structural complexity of the functional motif largely determines how readily one can find host protein structures that are “designable” , meaning that are likely to present the functional motif in the desired conformation . One promising route to enhance the “designability” of protein structures is to allow backbone flexibility . Here , we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins—Rosetta Functional Folding and Design ( FunFolDes ) . We performed extensive computational benchmarks , where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein . An observation consistent with several experimental studies that have revealed function-stability tradeoffs . To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo “functionless” fold , which represent two typical challenges where the designability problem arises . The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies , making them valuable candidates for vaccine design endeavors . Overall , we present an accessible strategy to repurpose old protein folds for new functions . This may lead to important improvements on the computational design of proteins , with structurally complex functional sites , that can perform elaborate biochemical functions related to binding and catalysis .
Proteins are one of the main functional building blocks of the cell . The ability to create novel proteins outside of the natural realm has opened the path towards innovative achievements , such as new pathways [1] , cellular functions [2] , and therapeutic leads [3–5] . Computational protein design is the rational and structure-based approach to solve the inverse folding problem , i . e . the search for the best putative sequence capable of fitting and stabilizing a protein’s three-dimensional conformation [6] . As such , a great deal of effort has been placed into understanding the rules of protein folding and stability [7 , 8] and its relation to the appropriate sequence space [9] . Computational protein design approaches focus on exploring two interconnected landscapes related to sampling of the conformational and sequence spaces . Fixed backbone approaches use static protein backbone conformations , which greatly constrain the sequence space explored by the computational algorithm [9] . Following the same principles of naturally occurring homologs , which often exhibit confined structural diversity , flexible backbone approaches enhance the sequence diversity , adding the challenge of identifying energetically favorable sequence variants that are correctly coupled to structural perturbations [10] . Another variation for computational design approaches is de novo design , in which protein backbones are assembled in silico , followed by sequence optimization to fold into a pre-defined three-dimensional conformation without being constrained by previous sequence information [11–13] . This approach tests our understanding of the rules governing the structure of different protein folds . The failures and successes of this approach confirm and correct the principles used for the protein design process [7 , 8] . One of the main aims of computational protein design is the rational design of functional proteins capable of carrying existing or novel functions into new structural contexts [14] . Broadly , there are three main approaches for the design of functional proteins: redesigning of pre-existing functions , grafting of functional sites onto heterologous proteins , and designing of novel functions not found in the protein repertoire . The redesign of a pre-existing function to alter its catalytic activity [15] or improve its binding target recognition [16] can be considered the most conservative approach . It is typically accomplished by point mutations around the functional area of interest and tends to have little impact on global structure of the designed protein . On the other extreme , the design of fully novel functions has most noticeably been achieved by applying chemical principles that tested our fundamental knowledge of enzyme catalysis [17 , 18] . Between these two approaches resides protein grafting . This method aims to repurpose natural folds as carriers for exogenous known functions . It relies on the strong structure-function relationship present in proteins , to endow an heterologous protein with an exogenous function by means of transferring a structural motif that performs such function [3–5 , 19–22] . At the biochemical level , grafting approaches have been used to design high binding affinity protein-protein interactions , by stabilizing binding motifs removing the entropic cost of binding ( e . g . flexible peptides ) [21] , and also by extending the binding interfaces to allow for additional energetically favorable interactions . The extended interfaces also provide opportunities to tune the specificity of the designed proteins [21] . On the practical side , some of the most notable applications of protein grafting thus far , have been the design of novel viral inhibitors [21 , 23] and epitope-focused immunogens for vaccine design [3–5] . Following this strategy one can easily imagine applications to functionalize protein-based biomaterials [24] or to design novel biosensors [25] . The importance of robust grafting approaches to functionalize heterologous proteins is related to the fact that the proteins that naturally perform these functions , may lack the best biochemical properties in terms of size , affinity , solubility , immunogenicity and other application specific factors . Thus far , the most successful grafting approaches are highly dependent on structural similarity between the functional motif and the insertion region in the protein scaffold . When the functional motif and the insertion region are identical in backbone conformation , the motif transfer can be performed by side-chain grafting , i . e . mutating the target residues into those of the functional motif [3 , 5] . In much more challenging scenarios , full backbone grafting may be used in conjunction with directed evolution [19] . Nevertheless , motif transfer is limited between very similar structural regions , which greatly constrains the subset of putative scaffolds that can be used for this purpose . The lack of compatibility between the putative scaffolds and the functional sites has been referred to as a “designability” problem [26 , 27] , which refers to the likelihood of a protein backbone to host and stabilize a structural motif . The designability problem becomes more obvious as the structural complexity of the functional motif grows , drastically limiting the types of functional motifs that can be transferred . Previously , we have demonstrated the possibility of expanding protein grafting to scaffolds with segments that have low structural similarity . To accomplish that task , we developed the prototype protocol Rosetta Fold From Loops ( FFL ) [4 , 21] . The distinctive feature of our protocol is the coupling of the folding and design stages to bias the sampling towards structural conformations and sequences that stabilize the grafted functional motif . In the past , FFL was used to obtain designs that were functional ( synthetic immunogens [4] and protein-based inhibitors [21] ) and where the experimentally determined crystal structures closely resembled the computational models . However , the structures of the functional sites were structurally very close to the insertion segments of the hosting scaffolds . The architecture of FFL was intrinsically limited in the types of constraints available and the grafting of linear , single segment functional motifs . Here , we present a complete re-implementation of FFL with enhanced functionalities , simplified user interface and complete integration with other Rosetta protocols . We have called this new , more generalist protocol Rosetta Functional Folding and Design ( FunFolDes ) . We benchmarked FunFolDes extensively , unveiling important technical details to better exploit and expand the capabilities of the protocol . Furthermore , we challenged FunFolDes with two design tasks of transplanting viral epitopes to heterologous scaffolds , and by doing so probe the applicability of the protocol . The design tasks were centered on using distant structural templates as hosting scaffolds , and functionalizing a de novo designed protein , —FunFolDes succeeded in both challenges . These results are encouraging and provide a solid basis for the broad applicability of FunFolDes as a strategy for the robust computational design of functionalized proteins .
The original prototype of the Rosetta Fold From Loops ( FFL ) protocol was successfully used to transplant the structural motif of the Respiratory Syncytial Virus protein F ( RSVF ) site II neutralizing epitope into a protein scaffold in the context of a vaccine design application [4] . FFL enabled the insertion and conformational stabilization of the structural motif into a defined protein topology by using Rosetta’s fragment insertion machinery to fold an extended polypeptide chain to adopt the desired topology [28] which was then sequence designed . Information from the scaffold structure was used to guide the folding , ensuring an overall similar topology while allowing for the conformational changes needed to stabilize the inserted structural motif . The final implementation of FunFolDes is schematically represented in Fig 1 , and fully described in Materials and Methods . Our upgrades to FFL focused on three main aims: I ) improve the applicability of the system to handle more complex structural motifs ( i . e . multiple discontinuous backbone segments ) ; II ) enhance the design of functional proteins by including binding partners in the simulations; III ) increase the control over each stage of the simulation improving the usability for non-experts . These three aims were achieved through the implementation of five core technical improvements described below . Insertion of multi-segment functional sites . Most functional sites in proteins typically entail , at the structural level , multiple discontinuous segments , as is the case for protein-protein interfaces , enzyme active-sites , and others [29 , 30] . FunFolDes handles functional sites with any number of discontinuous segments , ensuring the native orientations of each of the segments . These new features enhance the types of structural motifs that can be handled by FunFolDes , widening the applicability of the computational protocol . Structural folding and sequence design in the presence of a binding target . Many of the functional roles of proteins in cells require physical interaction with other proteins , nucleic acids , or metabolites [31] . The inclusion of the binder has two main advantages: I ) explicit representation of functional constraints to bias the designed protein towards a functional sequence space , resolving putative clashes derived from the template scaffold; II ) facilitate the design of new additional contact residues ( outside of the motif ) that may afford enhanced affinity and/or specificity . Region-specific structural constraints . FunFolDes can collect from full-template to region-specific constraints , allowing greater levels of flexibility in areas of the scaffold that can be critical for function ( e . g . segments close to the interface of a target protein ) . The type of distance constrains used in the protocol are soft constrains with score penalties if the defined standard deviation is exceeded in the upper and lower bounds . Furthermore , FunFolDes is no longer limited to atom-pair distance constraints [32] and can incorporate other types of kinematic constraints , such as angle and dihedral constraints [33] , which have been used extensively to design beta-rich topologies [8] . On-the-fly fragment picking . Classically , fragment libraries are generated through sequence-based predictions of secondary structure and dihedral angles that rely on external computational methods [34] . We leveraged internal functionalities in Rosetta so that FunFolDes can assemble fragment sets on-the-fly . Using this feature , we can assemble fragment sets based on the structure of the input scaffold . Sequence-based fragments remain an option , however this feature removes the need for secondary applications , boosting the usability of FunFolDes . Lastly , the on-the-fly fragment picking enables the development of protocols with mutable fragment sets along the procedure . Compatibility with other Rosetta modules . Finally , FunFolDes is compatible with Rosetta’s modular xml-interface—Rosetta Scripts ( RS ) [35] . Enabling customization of the FunFolDes protocol and , more importantly , cross-talk with other protocols and filters available through the RS interface . We devised two benchmark scenarios to test the performance of FunFolDes . One of these aimed to capture conformational changes in small protein domains caused by sequence insertions or deletions , and the second scenario assessed protocol performance to fold and design a binder in the presence of the binding target . Typical protein design benchmarks are assembled by stripping native side chains from known protein structures and evaluating the sequence recovery of the design algorithm [9] . The main design aim of FunFolDes is to insert structural motifs into protein folds while allowing flexibility across the overall structure . This conformational freedom allows the full protein scaffold to adapt and stabilize the functional motif’s conformation . This is a main distinctive point from other approaches to design functional proteins that rely on a mostly rigid scaffold [2 , 3 , 11 , 19 , 30 , 36] . For many modeling problems , such as protein structure prediction , protein-protein and protein-ligand docking , and protein design , standardized benchmark datasets are available [37] or easily accessible . Devising a benchmark for designed proteins with propagating conformational changes across the structure is challenging , as we are assessing both structural accuracy as well as sequence recovery of the protocol . To address this problem , we analyzed structural domains found repeatedly in natural proteins and clustered them according to their definition in the CATH database [38] . As a result , we selected a set of 14 benchmark targets labeled T01 through T14 ( Fig 2A ) . A detailed description on the construction of the benchmark can be found in the Materials and Methods section . Briefly , for the benchmark we selected proteins with less than 100 residues , where each test case was composed of two proteins of the same CATH domain cluster . One of the proteins is the template , and serves as a structural representative of the CATH domain . The second protein , dubbed target , contains structural insertions or deletions ( motif region ) , to which a structural change in a different segment of the protein could be attributed ( query region ) . The motif and query regions for all the targets are shown in Fig 2A and quantified by the percentage of overall secondary structure in S1A Fig . To a great extent , these structural changes due to natural sequence insertions and deletions are analogous to those occurring in the design scenarios for which FunFolDes was conceived . Using FunFolDes , we folded and designed the target proteins while maintaining the motif segment structurally fixed , mimicking a structural motif insertion . Distance constraints between residues were extracted from the template in the regions of shared structural elements of the template and the target , and were used to guide the folding simulations . To check whether FunFolDes enhances sequence and structural sampling , we compared the simulations to constrained ab initio ( cst-ab initio ) simulations [33] . As Rosetta conformational sampling is highly dependent upon the fragment set [39] , in this benchmark we also tested the influence of structure- and sequence-based fragments . The performance of the two protocols was analyzed regarding global and local recovery of both structure and sequence . Structural recovery was assessed through two main metrics: ( a ) global RMSD of the full decoys against the target and ( b ) local RMSD of the query region . When evaluating the distributions for global RMSD in the designed ensembles , FunFolDes outperformed cst-ab initio by consistently producing populations of decoys with lower mean RMSD ( mostly found below 5 Å ) , a result observed in all 14 targets ( Fig 2B , S1B Fig ) . This result is especially reassuring considering that FunFolDes simulations contain more structural information of the target topology than the cst-ab initio simulations . The local RMSDs of the query unconstrained regions presented less clear results across the benchmark ( S1B Fig ) . In 13 targets , FunFolDes outperformed cst-ab initio , showing lower mean RMSDs but in some targets with minor differences . When comparing fragment sets ( structure- vs sequence-based ) , both achieved similar mean RMSDs in the decoy populations; nonetheless , the structure-based fragments more often reached the lowest RMSDs for overall and query RMSDs ( Fig 2B , S1 Fig ) . This is consistent with what would be expected from the structural information content within each fragment set . When paired with the technical simplicity of use , time-saving and enhanced sampling of the desired topology , the structure-based fragments are an added value for FunFolDes . We also quantified sequence recovery , both in terms of sequence identity and similarity according to the BLOSUM62 matrix [40] ( Fig 3A ) . In all targets , FunFolDes showed superior recoveries than cst-ab initio , and at the levels of other design protocols using Rosetta [10] ( Fig 3A ) . This type of metrics has been shown to be highly dependent on the exact backbone conformation used as input [9 , 10] . Given that FunFolDes is exploring larger conformational spaces , as a proxy for the quality of the sequences generated , we used the target’s Hidden Markov Models ( HMM ) [41] and quantified the designed sequences that were identified as part of the target’s CATH superfamily according to its HMM definition ( Fig 3B ) . FunFolDes decoy populations systematically outperformed those from cst-ab initio ( Fig 3B ) . The performance of the two fragment sets shows no significant differences . In summary , this benchmark highlights the ability of FunFolDes to generate close-to-native scaffold proteins to stabilize inserted structural motifs . FunFolDes aims to refit protein scaffolds towards the structural requirements of a functional motif . It is thus critical , to explore within certain topological boundaries , structural variations around the original templates . This benchmark points to several variables in the protocol that resulted in enhanced structural and sequence sampling . The computational design of proteins that can bind with high affinity and specificity to targets of interest remains a largely unsolved problem [42] . Within the FunFolDes conceptual approach of coupling folding with sequence design , we sought to add the structure of the binding target ( Fig 1 ) to attempt to bias sampling towards functional structural and sequence spaces . Previously , we used FFL to design a new binder ( BINDI ) to BHRF1 ( Fig 4A ) , an Epstein-Barr virus protein with anti-apoptotic properties directly linked to the tumorigenic activity of EBV [21] . FFL designs bound to BHRF1 with a dissociation constant ( KD ) of 58–60 nM , and after affinity maturation reached a KD of 220±50 pM . BINDI was designed in the absence of the target and then docked to BHRF1 through the known interaction motif . A striking observation from the overall approach was that the FFL stage was highly inefficient , generating a large fraction of backbone conformations incompatible with the binding mode of the complex . To test whether the presence of the target could improve structural and sequence sampling , we leveraged the structural and sequence information available for the BINDI-BHRF1 system and benchmarked FunFolDes for this design problem . As described by Procko and colleagues , when comparing the topological template provided to FFL and the BINDI crystal structure , the last helix of the bundle ( helix 3 ) was shifted relative to the template ensuring structural compatibility between BINDI and BHRF1 ( Fig 4B ) . We used this case study to assess the capabilities of FunFolDes to sample closer conformations to those observed in the BINDI-BHRF1 crystal structure . In addition , we used the saturation mutagenesis data generated for BINDI [21] to evaluate the sequence space sampled by FunFolDes . A detailed description of this benchmark can be found in the Materials and Methods section . Briefly , we performed four different FunFolDes simulations: I ) binding target absent ( no_target ) ; II ) binding target present with no conformational freedom ( static ) ; III ) binding target present with side-chain repacking ( pack ) ; IV ) binding target present with side-chain repacking plus minimization and backbone minimization ( packmin ) . no_target simulations generated a low number of conformations compatible with the target ( <10% of the total generated designs ) ( S2A Fig ) . Upon global minimization more than 60% ( S2A Fig ) of the decoys were compatible with the binding target , at the cost of considerable structural drifts for both binder ( mean RMSD 3 . 3 Å ) and target ( mean RMSD 7 . 7 Å ) ( Fig 4C ) . These structural drifts reflect the energy optimization requirements by the relaxation algorithms but are deemed biologically irrelevant due to the profound structural reconfigurations . In contrast , simulations performed in the presence of the target clearly biased the sampling to more productive conformational spaces . RMSD drifts upon minimization were less than 1 Å for both designs and binding target ( Fig 4C ) . Global structural alignments of the designs fail to emphasize the differences of the helical arrangements ( S2B Fig ) . Thus , we aligned all the designs on the conserved binding motif ( Fig 4A ) and measured the RMSD over the three helices that compose the fold . FunFolDes simulations in the presence of the target sampled a mean RMSD of 3 Å ( lowest ≈ 2 Å ) compared to the BINDI structure ( Fig 4D ) , with the closest designs at approximately 2 Å , while the no_target simulation showed a mean RMSD of 4 . 5 Å ( lowest ≈ 2 . 5 Å ) . While we acknowledge that these structural differences are modest , the data suggests that they can be important to sample conformations and sequences competent for binding . We also analyzed Rosetta energy distributions of designs in the unbound state for the different simulations . We observed noticeable differences for the designs generated in the absence ( no_target ) and the presence of the binding target , -320 and -280 Rosetta Energy Units ( REUs ) , respectively ( Fig 4E ) . This difference is significant , particularly for a small protein ( 116 residues ) . We also observed considerable differences for the binding energies ( ΔΔG ) of the no_target and the bound simulations with mean ΔΔGs of -10 and -50 REUs , respectively ( Fig 4E ) . The energy metrics provide interesting insights regarding the design of functional proteins . Although the sequence and structure optimization for the designs in the absence of the target reached lower energies , these designs are structurally incompatible with the binding target and , even after refinement , their functional potential ( as assessed by the ΔΔG ) is not nearly as favorable as those performed in the presence of the binding target ( Fig 4F ) . These data suggest that , in many cases , to optimize function it may be necessary to sacrifice the overall computed energy of the protein , a common proxy to the experimental thermodynamic stability of the protein [43] . The existence of stability-function tradeoffs has been the subject of many experimental studies [44 , 45] , however , it remains a much less explored strategy in computational design , where it may also be necessary to design proteins with lower stability to ensure that the functional requirements can be accommodated . This observation provides a compelling argument to perform biased simulations in the presence of the binding target , which can be broadly defined as a “functional constraint” . To evaluate sequence sampling quality , we compared the computationally designed sequences to a saturation mutagenesis dataset available for BINDI [21] . The details of the dataset and scoring scheme can be found in the methods and S2 Fig . Briefly , point mutations beneficial to the binding affinity to BHRF1 have a positive score , deleterious mutants a negative score , and neutral score 0 . Such a scoring scheme , will yield a score of 0 for the BINDI sequence . Designs performed in the presence of the binding target obtained higher mean scores as compared to the no_target designs ( Fig 4G ) . The pack simulation , showed the highest distribution mean , having one design scoring better than the BINDI sequence . In some key positions at the protein-protein interface , the pack designs clearly outperformed those generated by the no_target simulation , when quantified by a per-position score ( Fig 4H ) ; meaning that amino-acids productive for binding interactions were sampled more often . This benchmark provides an example of the benefits of using a “functional constraint” ( binding target ) to improve the quality of the sequences obtained by computational design . Overall , the BINDI benchmark provided important insights regarding the best FunFolDes protocol to improve the design of functional proteins . To further test FunFolDes’s design capabilities , we sought to transplant a contiguous viral epitope that is recognized by a monoclonal antibody with high affinity ( Fig 5A ) . For this design , we used the RSVF site II epitope ( PDB ID: 3IXT [46] ) as the functional motif . This epitope adopts a helix-loop-helix conformation recognized by the antibody motavizumab ( mota ) [46] . In previous work we have designed proteins with this epitope , but started from a structurally similar template , where the RMSD between the epitope and the scaffold segment was approximately 1 Å over the helical residues . Here , we sought to challenge FunFolDes by using a distant structural template where the local RMSDs of the epitope and the segment onto which it was transplanted were higher than 2 Å . We used MASTER [47] to perform the structural search ( detailed description in Materials and Methods ) and selected as template scaffold the structure of the A6 protein of the Antennal Chemosensory system from the moth Mamestra brassicae ( PDB ID: 1KX8 [48] ) ( Fig 5A ) . The backbone RMSD between the conformation of the epitope and the insertion region in 1kx8 is 2 . 37 Å ( Fig 5B ) . The A6 protein is involved in chemical communication and has been shown to bind to fatty-acid molecules with hydrophobic alkyl chains composed of 12–18 carbons . Two prominent features are noticeable in the structure: two disulfide bonds ( Fig 5A ) and a considerable void volume in the protein core ( S3 Fig ) , thought to be the binding site for fatty acids . These features emphasize that the initial design template is likely not a very stable protein . In the design process we performed two stages of FunFolDes simulations to obtain a proper insertion of the motif in the topology ( Fig 5C ) . A detailed description of the workflow and metrics used for selection ( S3 Fig ) can be found in the Materials and Methods . A striking feature of our designs , when compared to the starting template , is that they had a much lower void volume , showing that FunFolDes generated structures and sequences that yielded well packed structures ( S3 Fig ) . We started by testing experimentally seven designs . Those that expressed in bacteria were further characterized using size exclusion chromatography coupled to a multi-angle light scatter ( SEC-MALS ) to determine the solution oligomerization state . To assess their folding and thermal stability ( Tm ) we used Circular Dichroism ( CD ) spectroscopy , and finally to assess their functional properties we used surface plasmon resonance ( SPR ) to determine binding dissociation constants ( KDs ) to the mota antibody . Out of the seven designs , six were purified and characterized further . The majority of the designs were monomers in solution and showed CD spectra typical of helical proteins . Regarding , thermal stabilities we obtained designs that were not very stable and did not unfold cooperatively ( 1kx8_02 ) , however we also obtained very stable designs that did not fully unfold under high temperatures ( 1kx8_07 ) ( S4 Fig ) . The determined binding affinities to mota ranged from 34 to 208 nM , which was an encouraging result . Nevertheless , compared to the peptide epitope ( KD = 20 nM ) and other designs previously published ( KD = 20 pM ) [4] , there was room for improvement . Therefore , we generated a second round of designs to attempt to improve stability and binding affinities . Driven by the observation that the native fold has two disulfide bonds , in the second round , we tested eight designed variants with different disulfide bonds and , if necessary , additional mutations to accommodate them . The disulfide bonded positions were selected according to the spatial orientation of residues in the designed models , with most of the disulfide bonds being placed at distal locations from the epitope ( >20 Å ) . All eight designs were soluble after purification and two were monomeric: 1kx8_d2 and 1kx8_3_d1 , showing CD spectra typical of helical proteins ( Fig 5D ) with melting temperatures ( Tms ) of 43 and 48°C ( Fig 5E ) , respectively . Remarkably , 1kx8_d2 showed a KD of 1 . 14 nM ( Fig 5F ) , an improvement of approximately 30-fold compared to the best variants of the first round . 1kx8_d2 binds to mota with approximately 20-fold higher affinity than the peptide-epitope ( KD ≈ 20 nM ) , and 50-fold lower compared to previously designed scaffolds ( KD = 20 pM ) [4] . This difference in binding is likely reflective of how challenging it can be to accomplish the repurposing of protein structures with distant structural similarity . Post-design analyses were performed to compare the sequence and structure of the best design model with the initial template . The global RMSD between the two structures is 2 . 25 Å . Much of the structural variability arises from the inserted motif , while the surrounding segments adopt a configuration similar to the original template scaffold . The sequence identity of 1kx8_d2 as compared to the native protein is approximately 13% . The sequence conservation per-position ( Fig 5G ) was evaluated through the BLOSUM62 matrix , where positive scores are attributed to the original residue or favorable substitutions and negative if unfavorable . Overall , 38 . 5% of the residues in 1kx8_d2 scored positively , and 61 . 4% of the residues had a score equal or lower than 0 . This is particularly interesting , from the perspective that several residues , unfavorable according to BLOSUM62 , yielded well folded and functional proteins . To further substantiate our experimental results , we performed structure prediction simulations of the designed sequences , where we observed that 1kx8_d2 presents a higher folding propensity than the WT protein ( S5A Fig ) . To evaluate if the predicted models presented the correct epitope conformation , we performed docking simulations and observed that they obtained lower binding energies than the native peptide-antibody complex , within similar RMSD fluctuations ( S5A Fig ) . The successful design of this protein is a relevant demonstration of the broad usability of FunFolDes and the overall strategy of designing functional proteins by coupled folding and design to incorporate functional motifs in unrelated protein scaffolds . Advances in computational design methodologies have achieved remarkable results in the design of de novo protein sequences and structures [7 , 8 , 11] . However , the majority of the designed proteins are “functionless” and were designed to test the performance of computational algorithms for structural accuracy . Here , we sought to use one of the hallmark proteins from de novo design efforts–TOP7 [13] ( Fig 6A ) –and functionalize it using FunFolDes . The functional site selected to insert into TOP7 was a different viral epitope from RSVF , site IV , which is recognized by the 101F antibody [49] . When bound to the 101F antibody , site IV adopts a β-strand-like conformation ( Fig 6B ) , which in terms of secondary structure content is compatible with one of the edge strands of the TOP7 topology ( Fig 6C ) . Despite the secondary structure similarity , the RMSD of the site IV backbone in comparison with TOP7 is 2 . 1 Å over 7 residues , and the antibody orientation in this particular alignment reveals steric clashes with TOP7 . Therefore , this design challenge is yet another prototypical application for FunFolDes , and we followed two distinct design routes: I ) a conservative approach where we fixed the amino-acid identities of roughly half of the core of TOP7 and allowed mutations mostly on the contacting shell of the epitope insertion site; and II ) a sequence unconstrained design where all the positions of the scaffold were allowed to mutate . We attempted five designs for recombinant expression in E . coli and two ( TOP7_full and TOP7_partial ) were selected for further biochemical and biophysical characterization , one from each of the two design strategies mentioned above . According to SEC-MALS , both behaved as monomers in solution , with TOP7_partial showing higher aggregation propensity . Both TOP7_full and TOP7_partial ( S6 Fig ) were folded according to CD measurements . TOP7_full showed a CD spectrum ( Fig 6D ) very similar to that of native TOP7 [13] . We observed that TOP7_full was much less stable than the original TOP7 ( Fig 6E ) ( Tm = 54 . 5°C ) . To quantify the functional component of TOP7_full , we determined a KD of 24 . 2 nM with 101F ( Fig 6F ) , within the range measured for the native viral protein RSVF ( 3 . 6 nM ) [49] . Importantly , the KD for TOP7_full is 2400 fold lower than that of the peptide-epitope ( 58 . 4 μM ) [49] , suggesting that productive conformational stabilization and/or extra contacts to the scaffold were successfully designed . Per-residue structural similarity and sequence recovery were evaluated for TOP7_full against TOP7 ( Fig 6G ) . Most conformational changes occur on the site IV insertion region and displacement of the neighboring alpha-helix , with the overall backbone RMSD being 1 . 5 Å . Remarkably , the sequence identity of the most aggressive design ( TOP7_full ) is only 28% , and using the BLOSUM62 based scoring system , we observe that most of the TOP7_full residues were actually favorable , obtaining positive scores . This low conservation is especially relevant considering that intensive studies on TOP7 have revealed the importance of beta-sheet conservation in order to keep its foldability [22 , 50 , 51] . Sequence folding prediction experiments showed that TOP7_full has a similar folding propensity to TOP7 and docking simulations also show lower binding energies as compared to the native peptide-antibody complex , reinforcing the experimental results obtained ( S5B Fig ) , In summary , our results show that FunFolDes repurposed a functionless protein by folding and designing its structure to harbor a functional site , which in this case was a viral epitope . Previously , these computationally designed proteins with embedded viral epitopes were dubbed epitope-scaffolds and showed their medical applicability as immunogens that elicited viral neutralizing antibodies [4] .
The robust computational design of proteins that bear a biochemical function remains an important challenge for current methodologies . The ability to consistently repurpose old folds for new functions or the de novo design of functional proteins could bring new insights into the determinants necessary to encode function into proteins ( e . g . dynamics , stability , etc . ) , as well as , important advances in translational applications ( e . g . biotechnology , biomedical , biomaterials , etc . ) . Here , we present Rosetta FunFolDes , that was conceived to embed functional motifs into protein topologies . This protocol allows for a global retrofitting of the overall protein topology to favorably host the functional motif and enhance the designability of the starting structural templates . FunFolDes has evolved to incorporate two types of constraints to guide the design process: topological and functional . The former entails the fragments to assemble the protein structure and sets of spatial constraints that bias the folding trajectories towards a desired topology; and the latter are the structure of the functional motif and the binding target . Our methodological approach fills the gap between conservative grafting approaches where the structure of the host scaffold is mostly fixed ( Rosetta Epigraft and Motifgraft [19 , 52] ) and the full de novo assembly of non-predefined protein topologies bearing functional motifs [53 , 54] . FunFolDes lies in between , by affording considerable structural flexibility to the host scaffold within the boundaries of its topology . In our view , FunFolDes is the most appropriate tool in situations where the structural mimicry of the functional motif is distant from the receiving scaffold’s site and overall conformational adaptations are necessary to design viable protein structures and sequences . We have extensively benchmarked FunFolDes , leveraging natural structural and sequence variation of proteins within the same fold , as well as deep mutational scanning data for the computationally designed protein BINDI [21] . In our first benchmark , we observed that FunFolDes biases the sampling towards improved structural and sequence spaces . Improved sampling may contribute to solve some of the major limitations in protein design , related to “junk” sampling , where many designs are not physically realistic , exhibiting flaws according to general principles of protein structure . Importantly , higher quality sampling will likely contribute to improve the success rate of designs that are tested experimentally . The BINDI benchmark allowed us to test FunFolDes in a system with extensive experimental data , which included both sequences and structures . Perhaps the most interesting observation was that designs that were theoretically within a sequence/structure space productive for binding , were far from the energetic minimum accessible to the protein fold in the absence of the binding target . This observation resembles the stability-function tradeoffs that have been reported from in vitro evolution experimental studies [44 , 45] . The large majority of the design algorithms are energy “greedy” and the sequence/structure searches are performed with the central objective of finding the global minimum of the energetic landscape . By introducing functional constrains into the simulations , FunFolDes presents an alternative way of designing functional molecules and skew the searches towards off-minima regions of the global landscape . We anticipate that such finding will be more relevant for protein scaffolds that need to undergo a considerable structural adaptation to perform the desired function . If confirmed that this finding is generalized across multiple design problems , it could be an important contribution for the field of computational protein design . Furthermore , we used FunFolDes to tackle two design challenges and functionalized two proteins with two distinct viral epitopes generating synthetic proteins that could have important translational applications in the field of vaccine development . In previous applications , FFL always used three-helix bundles as design templates , here we diversified the template folds and used an all-helical protein that is not a bundle ( 1kx8 ) and a mixed alpha-beta protein ( TOP7 ) , clearly demonstrating the applicability to other folds . For the 1kx8 design series , we evaluated the capability of using distant structural templates as starting topologies as a demonstration of how to functionally repurpose many naturally occurring protein structures available . We obtained stable proteins that were recognized by an anti-RSV antibody with high affinity , showing that in this case , we successfully repurposed a distant structural template for a different function , a task for which other computational approaches [55] would have limited applicability . We see this result as an exciting step forward towards using the wealth of the natural structural repertoire for the design of novel functional proteins . In a last effort , we functionalized a “functionless” fold , based on one of the first de novo designed proteins–TOP7 . For us , this challenge has important implications to understand the design determinants and biochemical consequences of inserting a functional motif into a protein that was mainly optimized for thermodynamic stability . We were successful in functionalizing TOP7 differently than previous published efforts . Previously , TOP7 was mostly used as a carrier protein with functional motifs fused onto loop regions or side chains grafted in the helical regions [22 , 50 , 51] , while our functional motif was embedded in the beta-sheet region . Exciting advances in the area of de novo protein design are also yielding many new proteins [11–13] , which could then be functionalized with FunFolDes , highlighting the usefulness of this approach . Interestingly , we observed that the functionalized version of TOP7 showed a dramatic decrease in thermodynamic stability as compared to the parent protein . While this observation can be the result of many different factors , it is compelling to interpret it as the “price of function” , meaning that to harbor function , TOP7 was penalized in terms of stability , which would be consistent with our findings in the BINDI benchmark and the experimental studies on stability-function tradeoffs . Recently , there have also been several de novo proteins designed for functional purposes [56]; however , these efforts were limited to linear motifs that carried the functions , and the functionalization was mainly accomplished by side-chain grafting [3 , 5] , relying on screening a much larger number of designed proteins . In the light of all the technical improvements , FunFolDes has matured to become a valuable resource for the robust functionalization of proteins using computational design . Here , we presented a number of important findings provided by the detailed benchmarks performed and used the protocol to functionalize proteins in design tasks that are representative of common challenges faced by the broad scientific community when using computational design approaches .
Rosetta Functional Folding and Design ( FunFolDes ) is a general approach for grafting functional motifs into protein scaffolds . It’s main purpose is to provide an accessible tool to tackle specifically those cases in which structural similarity between the functional motif and the insertion region is low , thus expanding the pool of structural templates that can be considered useful scaffolds . This objective is achieved by folding the scaffold after motif insertion while keeping the structural motif static . This process allows the scaffold’s conformation to change and properly adapt to the three-dimensional restrictions enforced by the functional motif . The pipeline of the protocol ( summarized in Fig 1 ) proceeds as follows: To test the ability of FunFolDes to recover the required conformational changes to stabilize a given structural motif , we created a benchmark of 14 target cases of proteins with less than 100 residues , named T01 to T14 . Each target case was composed of two structures of the same CATH superfamily [38] . One of the structures was representative of the shared structural features of the CATH family; we called this structure the reference . The second protein within each target case can present two types of structural variations with respect to the reference: I ) an insertion or deletion ( indel ) region and II ) a conformational change . Direct structural contacts between these two regions make it so that the indel region is likely the cause for the conformational change . We called this second structure the target ( Fig 2 , Table 1 ) . For each template protein we generated approximately 10000 decoys with FunFolDes by folding the target with the following conditions: 1 ) the indel region was considered as the motif , meaning that its structural conformation was kept fixed and no mutations allowed; 2 ) residue-pair distance constraints were derived from the secondary structure elements conserved between reference and the target ( constrained region ) ; 3 ) the region of the protein which showed the largest structural variations ( query region ) was constraint-free throughout the simulation . FunFolDes simulations were compared with constrained ab initio ( cst-ab initio ) simulations , the key difference being that the cst-ab initio simulations allowed for backbone flexibility in the motif region . The comparison between both approaches provides insights on the effects of a static segment in the folding trajectory of the polypeptide chain . In both scenarios a threshold was set after the folding stage where only decoys that had less than 5 Å RMSD from the template were carried to the design stage . The importance of the input fragments was assessed in our benchmark . Both protocols were tested with sequence-based fragments from FragmentPicker and structure-based fragments generated on-the-fly by FunFolDes . Comparison between the two types of fragments provides insight into how to utilize FunFolDes in the most productive manner . Structural recovery was evaluated by RMSD with the target structure . Global RMSD , understood as the minimum possible RMSD given the most optimal structural alignment , was used to assess the overall structural recovery of each decoy population . Local RMSD , was evaluated for the unconstrained ( query ) region and the motif by aligning each decoy to the template through the constrained segments ( excluding the motif ) . This metric aimed to capture the specific conformational changes required to accommodate the motif into the structure ( Fig 2B , S1B Fig ) . Sequence recovery was evaluated through two different criteria , sequence associated statistics and Hidden Markov Model ( HMM ) [41] . For the sequence associated statistics , we quantified sequence identity and similarity according to BLOSUM62 for the core residues of each protein , as defined by Rosetta’s LayerSelector [7] . Motif residues , that were not allowed to mutate , were excluded from the statistics . In the second criteria , position specific scoring matrices with inter-position dependency known as Hidden Markov Model ( HMM ) were used to evaluate fold specific sequence signatures . In this case , the closest HMM to the template structure provided by CATH was used to query the decoys and identify those that matched the HMM under two conditions: I ) an e-value under 10 and II ) a sequence coverage over 50% . Although these conditions are wide , they were within the ranges found between members of CATH superfamilies with high structural and sequence variability like the ones used in the benchmark . To assess the performance of FunFolDes in the presence of a binding target we recreated the design of BINDI as a binder for BHRF1 [21] , the BHRF1 binding motif from the BIM-BH3 protein ( PDB ID:2WH6 [66] ) was inserted into a previously described 3-helix bundle scaffold ( PDB ID:3LHP [3] ) . Four different design simulations were performed , one without the binder ( no_target ) and three in the presence of the binder ( static , pack and packmin ) . The difference between the last three relates to how the binding target was handled . In the static simulations the binding target was kept fixed and no conformational movement in the side chains was allowed throughout the protocol . In the pack simulations the side chains of the binding target were repacked during the binder design stage . Finally , in the packmin simulations the binding target side-chains were allowed to repack and both side-chains and backbone were subjected to minimization . In all cases , the two terminal residues on each termini of the binding motif were allowed backbone movement to optimize the insertion in the 3-helix bundle scaffold . For each of these simulations , approximately 20000 decoys were generated . For the no_target simulations the FunFolDes designs were docked to BHRF1 using the inserted motif as guide to assess their complementarity and interface metrics . In all the simulations , a final round of global minimization was performed , where both proteins of the complex were allowed backbone flexibility . During this minimization , the rigid-body orientation between the design and target was kept fixed . The final ΔΔG of the complexes was measured after the minimization step to enable comparisons between the no_target decoys and the remaining simulation modes . Structural changes related to this minimization step were evaluated as the global RMSD between each structure before and after the process , this measure is referred to as RMSD drift . Structural evaluation includes global RMSD against the BINDI crystal structure ( PDB ID: 4OYD [21] ) as well as local RMSDs against regions of interest in BINDI . In the Local-RMSD calculations the structures were aligned through the inserted motif , as its conformation and orientation relative to BINDI were kept fixed throughout all simulations . The local RMSD analysis was performed over all the helical segments contained in the structures ( all H ) , which provided a measurement of the structural shifts on the secondary structure regions of the designs . To evaluate the sequence recovery we leveraged BINDI’s saturation mutagenesis data analyzed by deep sequencing performed by Procko et al [21] . The experimental fitness of each mutation was summarized in a score matrix where a score was assigned to each amino-acid substitution for the 116 positions of the protein ( S2C Fig ) . In summary , point mutations that improved BINDI’s binding to BHRF1 are assigned positive scores while deleterious mutations present negative values . These scores were computed based on experimental data where the relative populations of each mutant were compared between a positive population of cells displaying the designs ( binders ) and negative populations ( mutants that display but don’t bind ) , these experiments have been described in detail elsewhere [21] . Upon normalization by the BINDI sequence score , a position sequence specific matrix ( PSSM ) was created . Like the original data , this matrix also assigns a positive score to each point mutation if it resulted in an improved binding for the design . This normalization provides a score of 0 for the BINDI sequence , which is useful as a reference score . To experimentally validate the capabilities of FunFolDes and insert functional sites in structurally distant templates , we grafted the 11 residues from the site II epitope from the Respiratory Syncytial Virus ( RSV ) protein F ( PDB ID:3IXT [46] ) , residues 256 to 276 in chain P ( NSELLSLINDMPITNDQKKLMSN ) , into heterologous scaffolds . This is a continuous , single segment , helix-loop-helix conformation epitope . The main objective was to challenge the capabilities of FunFolDes to reshape the structure of the scaffold to the requirements of the functional motif . We searched for insertion segments with RMSDs towards the site II structure higher than 2 Å . The structural searches were performed using MASTER [47] where we used the full-length site II segment as a query against a subset of 17539 protein structures from the PDB , composed of 30% non-redundant sequences included in the MASTER distribution . The RMSD between the query and segments on the scaffolds were assessed using backbone Cαs . All matches with RMSDCα < 5 . 5 Å relative to site II were further filtered by protein size , where only proteins between 50 and 100 residues were kept . These scaffolds were then ranked regarding antibody-binding compatibility , where each match was realigned to the antibody–epitope complex and steric clashes between all glycine versions of the scaffold and antibody were quantified using Rosetta . All matching scaffolds with ΔΔG values above 100 REU were discarded under the assumption that their compatibility with the antibody binding mode was too low . The remaining scaffolds were visually inspected and PDB ID: 1kx8 [48] ( RMSDCα = 2 . 37 Å ) was selected for design with FunFolDes . The twenty-one residues from the site II epitope ( motif ) as present in 3IXT were grafted into a same sized segment ( residues 79–100 ) of 1kx8 using the NubInitioMover . Up to three residues in each insertion region of the motif were allowed backbone flexibility in order to model proper conformational transitions in the insertion points . Atom pair constraints with a standard deviation of 3 Å were defined for all template residues , leaving the motif segment free of constraints . The generous standard deviation was defined to allow for necessary conformational changes to retrofit the motif within the topology . The total allowed deviation from the template was limited to 5 Å to ensure the retrieval of the same topology . In this design series we used sequence-based fragments generated with the 1kx8 native sequence . Three cycles of design/relax were performed on the template residues with the FastDesignMover . A first generation of 12500 designs was ranked according to Rosetta energy . From the top 50 decoys , only one presented the motif without distortions on the edges derived from the allowed terminal flexibility . This decoy was used as template on the second generation of FunFolDes to enhance the sampling of properly folded conformations , with the same input conditions as before . In the second generation , the top 50 decoys according to Rosetta energy were further optimized through additional cycles of design/relax . The final designs were again selected using a composite filter based on Rosetta energy ( top 50 ) , buried unsatisfied polar atoms ( <15 ) , cavity volume ( < 75 Å3 ) and we obtained a final set of 15 candidates from which we prioritized 6 upon the inspection of the computational models . In addition , we also quantified the secondary structure prediction using PSIPRED [64] , all the tested designs had more 65% ( ranging from 65% to 92% ) of the residues with correct secondary structure prediction . The final designs were manually optimized , this process entailed the removal of designed hydrophobic residues in solvent exposed positions , in this designs series we performed between 2 and 4 mutations obtaining 7 designs from the previous 6 . After the initial characterization , designs with added disulfide bridges were generated to improve protein stability and affinity ( S3 Fig , S4 Fig ) . To do so , we use the Rosetta DisulfidizeMover , which screened the designed models for pairs of residues with favourable three-dimensional orientations to host disulfide bonds . Upon the placement of the disulfide bond , the neighbouring residues within 10 Å of the disulfide , were designed to optimize the residue interactions and improve the packing of the designed region . In a second effort to test the design capabilities of FunFolDes we sought to insert a functional motif in one of the first de novo designed proteins–TOP7 ( PDB ID: 1QYS [13] ) Six residues from the complex between the antibody 101F and the peptide-epitope , corresponding to residues 429–434 in chain P ( RGIIKT ) on the full-length RSV F protein [49] , were grafted into the edge strand of the TOP7 backbone using FunFolDes . The choice between epitope and hosting scaffold was made based on the secondary structure adopted by the epitope and the structural compatibility of TOP7 , the RMSDCα between the epitope an the insertion segment was 2 . 07 Å . To ensure that the majority of the β-strand secondary structure was maintained throughout the grafting protocol , the epitope motif was extended by one residue and a designed 4-residue β-strand ( KVTV ) pairing with the backbone of the C-terminal epitope residues was co-grafted as a discontinuous segment into the adjacent strand of the TOP7 backbone . With this strategy we circumvented a Rosetta sampling limitation , where often times extensive sets of constraints to achieve backbone hydrogen-bonds on beta-strands are necessary [8] . After defining the motif consisting of the epitope plus the pairing strand and the sites of insertion on the TOP7 scaffold , FunFolDes was used to graft the motif . Backbone flexibility was allowed for the terminal residues of the functional motif and a β-turn connection between the two strands was modelled during the folding process ( NubInitioMover ) . During the folding process , the 101F antibody was added to the simulation in order to limit the explored conformational space productive for binding . Finally , the NubInitioLoopClosureMover was applied to ensure that a proper polypeptide chain was modelled and no chain-breaks remained , a total of 800 centroid models were generated after this stage . Next , we applied an RMSD filter to select scaffolds with similar topology to TOP7 ( < 1 . 5 Å ) and a hydrogen bond long-range backbone score ( HB_LR term ) to favour the selection of proteins with proper beta-sheet pairing . The top 100 models according the HB_LR score and <1 . 5 Å to TOP7 , were then subjected to an iterative sequence-design relax protocol , alternating fixed backbone side-chain design and backbone relaxation using the FastDesignMover . Two different design strategies were pursued: I ) partial design—amino acid identities of the C-terminal half of the protein ( residues 45 through 92 ) were retained from TOP7 while allowing repacking of the side chains and backbone relaxation; II ) full-design—the full sequence space in all residues of the structure ( with the exception of the 101F epitope ) was explored . No backbone or side chain movements were allowed in the 6-residue epitope segment whereas the adjacently paired β-strand was allowed to both mutate and relax . Tight Cα atom-pair distance constraints ( standard deviation of 0 . 5 Å ) were used to restrain movements of the entire sheet throughout the structural relaxation iterations . From the 100 designs generated , only those that passed a structural filter requiring 80% beta-sheet secondary structure composition after backbone relaxation were selected for further analysis . The 93 designs passing this filter were evaluated with a composite filter based on REU score ( Top 50 ) , hydrogen-bond long-range backbone interactions ( < -113 ) and core packing ( > 0 . 7 ) . The selected designs were finally submitted to human-guided optimisation to correct for hydrophobic residues that were designed in solvent exposed positions ( 1–3 ) and shortening of the connecting loop between the two inserted strands using the Rosetta Remodel application [67] . Interestingly , in an attempt to reproduce the same grafting exercise with MotifGraftMover [55] , this resulted in non-resolvable chain breaks when trying to graft either the two segment-motif or the epitope alone into the TOP7 scaffold . DNA sequences of the designs were purchased from Twist Bioscience . For bacterial expression the DNA fragments were cloned via Gibson cloning into a pET21b vector containing a C-terminal His-tag and transformed into E . coli BL21 ( DE3 ) . Expression was conducted in Terrific Broth supplemented with ampicillin ( 100 μg/ml ) . Cultures were inoculated at an OD600 of 0 . 1 from an overnight culture and incubated at 37°C with a shaking speed of 220 rpm . After reaching OD600 of 0 . 7 , expression was induced by the addition of 1 mM IPTG and cells were further incubated for 4-5h at 37°C . Cells were harvested by centrifugation and pellets were resuspended in lysis buffer ( 50 mM TRIS , pH 7 . 5 , 500 mM NaCl , 5% Glycerol , 1 mg/ml lysozyme , 1 mM PMSF , 1 μg/ml DNase ) . Resuspended cells were sonicated and clarified by centrifugation . Ni-NTA purification of sterile-filtered ( 0 . 22 μm ) supernatant was performed using a 1 ml His-Trap FF column on an ÄKTA pure system ( GE healthcare ) . Bound proteins were eluted using an imidazole concentration of 300 mM . Concentrated proteins were further purified by size exclusion chromatography on a Superdex 75 300/10 GL or a Hiload 16/600 Superdex 75 pg column ( GE Healthcare ) using PBS buffer ( pH 7 . 4 ) as mobile phase . For IgG expression , heavy and light chain DNA sequences were cloned separately into pFUSE-CHIg-hG1 ( InvivoGen ) mammalian expression vectors . Expression plasmids were co-transfected into HEK293-F cells in FreeStyle medium ( Gibco ) using polyethylenimine ( Polysciences ) transfection . Supernatants were harvested after 1 week by centrifugation and purified using a 5 ml HiTrap Protein A HP column ( GE Healthcare ) . Elution of bound proteins was accomplished using a 0 . 1 M glycine buffer ( pH 2 . 7 ) and eluents were immediately neutralized by the addition of 1 M TRIS ethylamine ( pH 9 ) . The eluted IgGs were further purified by size exclusion chromatography on a Superdex 200 10/300 GL column ( GE Healthcare ) in PBS buffer ( pH 7 . 4 ) . Protein concentrations were determined by measuring the absorbance at 280 nm using the sequence calculated extinction coefficient on a Nanodrop ( Thermo Scientific ) . Far-UV circular dichroism spectra of designed scaffolds were collected between a wavelength of 190 nm to 250 nm on a Jasco J-815 CD spectrometer in a 1 mm path-length quartz cuvette . Proteins were dissolved in PBS buffer ( pH 7 . 4 ) at concentrations between 20 μM and 40 μM . Wavelength spectra were averaged from two scans with a scanning speed of 20 nm min-1 and a response time of 0 . 125 sec . The thermal denaturation curves were collected by measuring the change in ellipticity at 220 nm from 20 to 95°C with 2 or 5°C increments . Multi-angle light scattering was used to assess the monodispersity and molecular weight of the proteins . Samples containing between 50–100 μg of protein in PBS buffer ( pH 7 . 4 ) were injected into a Superdex 75 300/10 GL column ( GE Healthcare ) using an HPLC system ( Ultimate 3000 , Thermo Scientific ) at a flow rate of 0 . 5 ml min-1 coupled in-line to a multi-angle light scattering device ( miniDAWN TREOS , Wyatt ) . Static light-scattering signal was recorded from three different scattering angles . The scatter data were analyzed by ASTRA software ( version 6 . 1 , Wyatt ) To determine the dissociation constants of the designs to the mota or 101F antibodies , surface plasmon resonance was used . Experiments were performed on a Biacore 8K at room temperature with HBS-EP+ running buffer ( 10 mM HEPES pH 7 . 4 , 150 mM NaCl , 3mM EDTA , 0 . 005% v/v Surfactant P20 ) ( GE Healthcare ) . Approximately 1200 response units of mota or 101F antibody were immobilized via amine coupling on the methyl-carboxyl dextran surface of a CM5 chip ( GE Healthcare ) . Varying protein concentrations were injected over the surface at a flow rate of 30 μl/min with a contact time of 120 sec and a following dissociation period of 400 sec . Following each injection cycle , ligand regeneration was performed using 3 M MgCl2 ( GE Healthcare ) . Data analysis was performed using 1:1 Langmuir binding kinetic fits within the Biacore evaluation software ( GE Healthcare ) .
FunFolDes is available as part of the Rosetta software suite and is fully documented in the Rosetta Commons documentation website as one of the Composite Protocols . All data and scripts necessary to recreate the analysis and design simulations described in this work are available at https://github . com/lpdi-epfl/FunFolDesData .
|
The ability to use computational tools to manipulate the structure and function of proteins has the potential to impact many facets of fundamental and translational science . Due to our limited understanding of the principles that govern protein function and structure , the computational design of functional proteins remains challenging . We developed a computational protocol ( Rosetta FunFolDes ) to facilitate the insertion of functional motifs into heterologous proteins . We performed extensive in silico benchmarks , and found that when the design of function is required the global energy minima may not be the optimal solution , in line with previously reported experimental studies . Further , we used FunFolDes to design two novel functional proteins , displaying two viral epitopes that can be of interest for vaccine development . The designed proteins were experimentally characterized , showing that functionalization was successfully achieved . These results highlight the capability of FunFolDes to address common challenges on the design of functional proteins . In particular , the reduced structural compatibility between functional sites and host scaffolds , effectively enabling the repurposing of old protein folds for new functions . Overall , FunFolDes provides new means to accomplish the challenging task of functionalizing computationally designed proteins .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods",
"Availability"
] |
[
"crystal",
"structure",
"markov",
"models",
"engineering",
"and",
"technology",
"synthetic",
"biology",
"condensed",
"matter",
"physics",
"synthetic",
"bioengineering",
"mathematics",
"protein",
"structure",
"sequence",
"motif",
"analysis",
"macromolecular",
"design",
"crystallography",
"research",
"and",
"analysis",
"methods",
"bioengineering",
"sequence",
"analysis",
"solid",
"state",
"physics",
"bioinformatics",
"proteins",
"hidden",
"markov",
"models",
"biological",
"databases",
"molecular",
"biology",
"probability",
"theory",
"physics",
"protein",
"structure",
"comparison",
"biochemistry",
"biochemical",
"simulations",
"sequence",
"databases",
"database",
"and",
"informatics",
"methods",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"biology",
"macromolecular",
"engineering",
"macromolecular",
"structure",
"analysis"
] |
2018
|
Rosetta FunFolDes – A general framework for the computational design of functional proteins
|
Differences in gene expression between individual cells can be mediated by epigenetic regulation; thus , methods that enable detailed analyses of single cells are crucial to understanding this phenomenon . In this study , genomic silencing regions of Saccharomyces cerevisiae that are subject to epigenetic regulation , including the HMR , HML , and telomere regions , were investigated using a newly developed single cell analysis method . This method uses fluorescently labeled proteins to track changes in gene expression over multiple generations of a single cell . Epigenetic control of gene expression differed depending on the specific silencing region at which the reporter gene was inserted . Correlations between gene expression at the HMR-left and HMR-right regions , as well as the HMR-right and HML-right regions , were observed in the single-cell level; however , no such correlations involving the telomere region were observed . Deletion of the histone acetyltransferase GCN5 gene from a yeast strain carrying a fluorescent reporter gene at the HMR-left region reduced the frequency of changes in gene expression over a generation . The results presented here suggest that epigenetic control within an individual cell is reversible and can be achieved via regulation of histone acetyltransferase activity .
The silencing domain in Saccharomyces cerevisiae comprises the homothallic mating-type loci HMR and HML , telomeres , and the rDNA locus . Repression of gene expression in HMR , HML , and telomere regions is achieved via binding of a protein complex that includes Sir2p , Sir3p , and Sir4p , whereas repression of gene expression of the rDNA region is achieved by binding of Sir2p [1] , [2] . Silencing at these regions is halted by boundaries that prevent extension along the entire length of the chromosome . Three models of boundary formation have recently been proposed . In these models , boundary formation depends on the DNA sequence [3] , is controlled by histone modification [4] , or depends on the interaction between nuclear pores and chromosomes [5] . Most of the boundaries flanking the regions within the yeast silencing domain have been determined; tRNA is located to the right of the HMR [6]–[8] , while the CHA1 promoter is located to the right of the HML [9] and LB ( Left Boundary ) to the left of the HML [10] , the telomere is flanked by STARs ( subtelomeric antisilencing regions ) [11] , and the rDNA region is flanked by tRNA and Ty-LTR [12] . However , the structure of the boundary positioned on the left of HMR has not yet been elucidated . A previous study demonstrated that insertion of a reporter gene into the telomere boundary region of yeast produces the position effect variegation ( PEV ) phenotype [13] , indicating that the silencing region within the telomere boundary in an individual cell can expand or shrink and that gene expression in this region is regulated by epigenetic control . When the ADE2 gene is used as a reporter to analyze the PEV phenotype , yeast cells bearing a telomere-linked gene produce colonies with both red and white sectors [13] . In previous studies that used the URA3 gene as a reporter to analyze the PEV phenotype , the gene was inserted close to the telomere , HMR , HML , or rDNA region , which are known to comprise the silencing region in S . cerevisiae [6] , [10] , [14] . The degree of repression of the URA3 gene inserted at different sites within the region located to the right of the HML ( HML-right ) is related to the distance between the inserted promoter and the cis-acting I-silencer sequence that flanks the HML region; the PEV phenotype is generated when the URA3 gene is inserted close to the right side of the I-silencer sequence [15] . These data suggest that the state of gene expression can be epigenetically altered in individual cells; however , the studies described above were restricted to examining yeast colonies and therefore could not measure gene expression in individual cells . One way to perform single cell analysis with S . cerevisiae is to conduct a pedigree assay . This technique was previously used to show that Sir1p is involved in the epigenetic control of gene expression [16] and that the deletion of the dpb3 or dpb4 genes , which encode components of DNA polymerase ε , alters the epigenetic switching rate ( the rate of change from the active state to the silent state ) in individual yeast cells [17] . The histone modification enzymes Dot1p and Set1p , and chromatin assembly factor I , also alter the epigenetic switching rate [18] , [19] . Recently , a new approach to single cell analysis of yeast , which uses a fluorescent protein to analyze changes in epigenetic gene expression , was reported . This technique was used to show that the HMR and HML loci behave independently within a single cell , demonstrating that heterochromatin formation is locus autonomous [20] . However , previous studies of single yeast cells using this method were performed over only a few generations . This study describes the development of a new method of single cell analysis that employs protein fluorescence to detect changes in the epigenetic control of gene expression for more than 10 generations of protein in yeast cells . The analysis method was used to demonstrate that epigenetic gene expression within an individual yeast cell is reversible and is regulated by histone acetyltransferase .
The URA3 and ADE2 genes were used as reporters to determine whether silencing from the HMR , HML , and telomere regions in S . cerevisiae occurs in a coordinated manner ( Figure 1A ) . A yeast strain expressing the URA3 gene grew on medium lacking uracil but was unable to grow on medium containing 5-fluoroorotic acid ( 5-FOA ) . By contrast , when URA3 expression was repressed , yeast could not grow on medium lacking uracil but were able to grow on 5-FOA medium ( Figure 1B ( a , b ) ) , as reported previously [21] . Yeast cells in which the URA3 gene was inserted close to the telomere displayed a PEV phenotype , as indicated by growth on both types of medium ( Figure 1B ( c ) ) , as reported previously [13] . White or red colonies were formed when the ADE2 gene was expressed or repressed , respectively ( Figure 1C ( a , b ) ) . Insertion of the ADE2 gene close to the telomere produced a PEV phenotype , as indicated by the growth of yeast colonies with both red and white sectors ( Figure 1C ( c ) ) , as reported previously [13] . The ty5Δ strain , in which the Ty5-LTR in the HMR-left boundary region was replaced with the URA3 gene , was then constructed and a spot assay was performed . The ty5Δ yeast grew on medium lacking uracil but barely grew on 5-FOA plates ( Figure 1B ( d ) ) . The HMR-left PEV strain , in which the URA3 gene was inserted closer to the E-silencer than it was in the ty5Δ strain , and the HMR-left R strain , which contained the URA3 promoter positioned in the opposite direction to that in the HMR-left PEV strain , were then constructed . The HMR-left PEV strain displayed the same PEV phenotype as yeast containing the URA3 gene close to the telomere ( Figure 1B ( e ) ) ; however , the HMR-left R strain did not show this phenotype ( Figure 1B ( f ) ) . The HMR-right PEV and HMR-right R strains were constructed in the same manner as the HMR-left strains; in both of these constructs , tRNA in the HMR-right boundary region was replaced with the URA3 gene . The HMR-right PEV strain , in which the URA3 gene was inserted the same distance from the I-silencer as in the HMR-right R strain , displayed the PEV phenotype ( Figure 1B ( g , h ) ) ; however , the HMR-right R strain did not . The HML-right PEV and HML-right R strains were then constructed by inserting the URA3 gene downstream of the I-silencer in the HML-right boundary region . As expected , the HML-right PEV strain displayed the PEV phenotype ( Figure 1B ( i , j ) ) , but the HML-right R strain did not . These data agree with those reported previously [15] . A set of similar experiments that utilized the ADE2 gene as a reporter instead of URA3 was then performed ( Figure 1C ) . In these experiments , the ty5Δ strain produced a white colony ( Figure 1C ( d ) ) , and the HMR-left PEV strain produced a light pink colony ( Figure 1C ( e ) ) , suggesting an increased frequency of epigenetic switching in this strain . The HMR-left R strain produced a white colony ( Figure 1C ( f ) ) . The HMR-right PEV strain produced a pink colony with a red and white sector , suggesting that these yeast cells retained the same expression state over several generations ( Figure 1C ( g ) ) , while the HMR-right R strain produced a white colony with an inside slightly sectored ( Figure 1C ( h ) ) . The HML-right PEV strain produced a pink colony with an inside sectored ( Figure 1C ( i ) ) and the HML-right R strain produced a white colony ( Figure 1C ( j ) ) . Taken together , these data suggest that the spread of the silencing region in S . cerevisiae differs depending on whether the inserted gene is positioned at the HMR-left , HMR-right , HML-right , or telomere region . The data also suggest that changes in epigenetic expression are regulated in individual cells , which highlights the importance of tracking changes in gene expression within a single cell rather than a mixed population of cells . To examine gene expression changes in single cells , we developed a new analysis system that utilizes expression of fluorescent proteins . Using this method , a single cell is placed in the center of the field of vision of a microscope and changes in epigenetic gene expression that occur during cell division are followed using time-lapse imaging . Five new yeast strains were constructed to precisely measure the fluorescence intensity in a single cell ( Figure 2A ) . In all strains , the mCherry-tagged HTB1 gene ( HTB1-2x mCherry ) was inserted into the euchromatin HIS3 locus on chromosome XV . The control strain ( Euchromatin/Euchromatin , FUY257 ) contained the EGFP-tagged HTB1 gene ( HTB1-EGFP ) inserted into the euchromatin TRP1 locus of chromosome IV . The TEL-VR PEV/Euchromatin ( FUY355 ) strain contained HTB1-EGFP inserted into the telomere on the right side of chromosome V . The HMR-left PEV/Euchromatin ( FUY263 ) and HMR-right PEV/Euchromatin ( FUY356 ) strains contained HTB1-EGFP inserted into the HMR-left region or the tRNA of the HMR-right region on chromosome III , respectively . The HML-right PEV/Euchromatin ( FUY795 ) strain contained HTB1-EGFP inserted into the HML-right region on chromosome III . The EGFP signal was normalized to the mCherry signal to correct for differences in fluorescence intensity caused by gaps in focus . Time-lapse analysis of the Euchromatin/Euchromatin strain revealed that the EGFP and mCherry fluorescent signals were persistent and always coincided with yeast cell division ( Figure 2B , Movies S1 and S2 ) . Time-lapse experiments were then performed using the TEL-VR PEV/Euchromatin ( Figure 2C ) , HMR-left PEV/Euchromatin ( Figure 3A , Movie S3 ) , HMR-right PEV/Euchromatin ( Figure 3B ) , and HML-right PEV/Euchromatin ( Figure 3C ) strains . For these strains , the mCherry fluorescent signal did not disappear upon repeated cell division; however , the EGFP fluorescent signal did disappear , although it returned in some progeny upon continued cell division . Next , the fluorescence intensities of the five strains expressing EGFP and mCherry were measured in all fields of vision at 6 , 8 , 10 , and 12 h after cell division began . Three independent extended time-lapse experiments were performed for each strain and the intensities of the cells within each field of vision were normalized to both the highest level of fluorescence observed at each time-point and the mCherry signal ( Figures 4A−E ) . The fluorescence intensity of the Euchromatin/Euchromatin strain ( Figure 4A ) and TEL-VR PEV/Euchromatin ( Figure 4B ) were fairly stable across the time-course , but a gradual decrease in fluorescence intensity was observed for the HMR-left PEV/Euchromatin ( Figure 4C ) , HMR-right PEV/Euchromatin ( Figure 4D ) , and HML-right PEV/Euchromatin ( Figure 4E ) strains . These data suggest that the spread of gene silencing was altered by repeated cell division and that expression of HTB1-EGFP varied within an individual cell . The HTB1 gene is only active during the S-phase of cell division; therefore , to ensure that the changes in fluorescence observed in the previous experiments were not attributable to properties inherent to the reporter genes , similar experiments were performed using the constitutive URA3 promoter and EGFP as the reporter gene ( NLS-3xEGFP ) . Similar to the results observed for the HTB1-EGFP gene , these experiments also revealed reversible epigenetic changes in gene expression ( Figure S1 ) , suggesting that the changes in fluorescence observed were general phenomena and were independent of the specific reporter gene used . The stability of the expression levels of the two fluorescently labeled reporter genes ( HTB1-EGFP and NLS-3xEGFP ) was measured by exposing cells to cycloheximide to inhibit protein synthesis . When the reporter gene was present in either the euchromatin or HMR-left PEV region , the EGFP signal was reduced by 50% after 2 h treatment with cycloheximide , which corresponds to the doubling time of yeast ( unpublished data ) . This result is similar to those of other reports [20] and suggests that protein turnover was sufficiently rapid to measure transition in the epigenetic state . Changes in gene expression were monitored by measuring the fluorescence intensity of single cells from the TEL-VR PEV/Euchromatin ( Figure 5A , Table S3A ) , HMR-left PEV/Euchromatin ( Figure 5B , Table S3B ) , HMR-right PEV/Euchromatin ( Figure 5C , Table S3C ) , and HML-right PEV/Euchromatin ( Figure 5D , Table S3D ) strains in real time . When HTB1-EGFP was inserted close to the telomere , the same gene expression status ( either ON or OFF ) was maintained for several generations ( Figure 5A ) . However , changes in epigenetic gene expression were less maintained and occurred randomly when HTB1-EGFP was inserted on the left side of HMR ( Figure 5B ) . The results for the strains containing HTB1-EGFP at HMR-right or HML-right regions were more stable across multiple generations than HMR-left PEV/Euchromatin strain ( Figure 5C and 5D ) . These results were similar to those shown in Figure 1 , which were obtained using the ADE2 reporter gene . The phenotype of the cells containing the insert at the HMR-left region was a pink colony , suggesting an increased frequency of epigenetic switching in these cells . Conversely , cells in which the insert was positioned close to the telomere with distinct sector and on the right of the HMR or HML regions produced pink colonies with sectors . These data indicate that the formation of sectors requires maintenance of the same expression status for several generations , whereas pink colonies are produced when the rate of switching of marker gene expression between ON and OFF increases . Statistical analyses were performed to confirm the results of the single cell measurements . The frequencies of transition events between the ON and OFF gene expression states were calculated and a permutation test was used to determine the reproducibility across independent experiments for the HMR-left PEV ( Table S4 ) and TEL-VR PEV ( Table S5 ) strains . Differences in the existence ratio of the ON and OFF states between these independent experiments were observed , but the frequency of change from the ON to OFF state and from the OFF to ON state were reproducible . Therefore , statistical analyses of the data for all four strains ( Figure 5 ) were performed to determine whether a similar or different regularity system governed epigenetic gene expression at an individual region ( Tables 1 and 2 ) . Compared with the HMR-left , HMR-right , and HML-right strains , few TEL-VR PEV cells were in the OFF state and the ratio of cells that varied from ON to OFF was also low . However , the changes from the OFF to ON state were not significantly different across these four strains . In addition , although the ratios of ON and OFF cells , as well as the OFF to ON transition frequencies , were slightly different between the HMR-left and HMR-right strains , these differences were not statistically significant . The change ratio from the ON to OFF state was comparable for these two strains . Furthermore , although the ratio of ON and OFF cells , as well as the change ratio from the ON to OFF and OFF to ON states , differed slightly between the HMR-left and HML-right strains , these changes were not statistically significant . When comparing the HMR-right and HML-right strains , the ratio of ON and OFF cells as well as the change ratio from the ON to OFF states were slightly different; however , these changes were also not statistically significant . The change ratio from the OFF to ON state was comparable for these two strains . In these experiments , we found that the telomere and the HM region had very different epigenetic regularity systems , and that HMR and HML were not perfect much , but they had some similar epigenetic regulation system . The Sas2p protein contributes to silencing of the HMR region [22] , and the spread of the silencing region in the telomere is dependent on the histone modification state . Histone modification is achieved by the histone deacetylase activity of Sir2p and the histone acetyltransferase activity of Sas2p , which acetylates H4 at lysine 16 [23] , [24] . In addition , tRNA and histone acetyltransferase are important for the production of a boundary at the HMR-right region [7] , [25] . Therefore , we analyzed whether the spread of the silencing region at the HMR and HML depends on the histone modification state , as it does at the telomere . Using ADE2 as a reporter gene , the following yeast strains in which the SIR3 gene was disrupted were constructed: HMR-left PEV+sir3Δ , HMR-right PEV+sir3Δ , and HML-right PEV+sir3Δ . The following strains in which the SAS2 gene was disrupted were also constructed: HMR-left PEV+sas2Δ , HMR-right PEV+sas2Δ , and HML-right PEV+sas2Δ . As controls , TEL-VR PEV strains in which the SIR3 or SAS2 gene was disrupted and the ADE2 gene was inserted at the telomere were also generated . Images of the HMR-left PEV strains are shown in Figure 6; images of all other strains are shown in Figure S2 . Disruption of the SIR3 gene in all PEV strains tested produced colonies that were whiter than those produced by the corresponding wild-type PEV strains . By contrast , disruption of the SAS2 gene produced colonies that were redder than the corresponding wild-type PEV strains . These data suggest that , similar to the telomere , the spread of silencing at the HMR and HML regions also depends on the histone modification status [4] , [23] , [24] . To understand why differences in the spread of silencing were observed when the marker gene was inserted to the right or left of the HMR locus , and whether this difference is genetically controlled , we focused on a histone modification enzyme that was previously isolated by our group using genome-wide boundary screening [26] . Single cell time-lapse experiments were performed using the sas2 deletion strains , Euchromatin/Euchromatin+sas2Δ and HMR-left PEV/Euchromatin+sas2Δ ( Figure 7A and Table S6A ) , and then statistical analyses of the data were performed . Small but statistically significant changes in the ratio of ON and OFF cells and the ratio of OFF to ON transitions between the sas2Δ and corresponding wild-type strains were observed ( 5% confidence interval ) . However , the ratio of ON to OFF transitions was not affected by deletion of the SAS2 gene ( Tables 3 and 4 ) . Although the results were not statistically significant , when we focused on the specific mother cell of the lineage tree , the frequency of the change in the epigenetic gene expression state across generations was increased for some cells ( Figure 7A ) . These data suggested that the sas2Δ strain did not undergo a dramatic change in epigenetic regulation , but that SAS2 might be involved in the regulation of the frequency of change in epigenetic gene expression . Gcn5 is a component of the SAGA histone acetylation enzyme complex , Eaf3 is a component of the NuA4 histone acetylation enzyme complex , and Dot1 is the histone methylation enzyme [27] . To investigate the role of these molecules in the silencing effect , Euchromatin/Euchromatin and HMR-left PEV/Euchromatin strains containing deletions of the GCN5 , EAF3 , and DOT1 genes were generated and single cell time-lapse experiments were performed . ( Figure 7B−D and Table S6B−D ) . No epigenetic changes in gene expression were seen in the control strains , which contained HTB1-EGFP inserted into the euchromatin region ( unpublished data ) . Although the ratio of ON and OFF cells was comparable between the gcn5Δ and wild-type strains , the frequency of ON to OFF transitions was significantly lower in the gcn5Δ strain than the wild-type strain . The frequency of OFF to ON transitions was also slightly lower in the mutant strain than in the wild-type strain ( Tables 3 and 4 ) . These results coincided with the results of the lineage tree constructed using single cell time-lapse analyses , which showed that the frequency of changes in the expression state from ON to OFF and OFF to ON decreased over multiple generations of the gcn5Δ strain ( Figure 7B ) . These data suggested that GCN5 is involved in regulating the frequency of changes in gene expression over several generations . The frequencies of the ON to OFF and OFF to ON transitions in the eaf3Δ strain were similar to those observed for the gcn5Δ strain , but the results of the sas2Δ strain did not correlate with those of the gcn5Δ and eaf3Δ strains ( Tables 3 and 4 ) . Deletion of the DOT1 gene increased the number of cells in the ON state and altered the frequency of the ON to OFF transition slightly; however , the frequency of OFF to ON transition was not affected ( Figure 7D , Tables 3 and 4 ) . The impact of deletion of the GCN5 , EAF3 , and DOT1 genes on gene silencing was then examined using ADE2 as the reporter instead of EGFP . Wild-type or gcn5Δ strains containing the ADE2 gene at the telomere , HMR-left , HMR-right , or HML-right region were constructed and colony color assays were performed . The wild-type HMR-left PEV strain produced pink colonies , whereas the HMR-left PEV+gcn5Δ strain produced a mixture of colonies containing white , red , sectored , or red-biased colonies ( Figure 6 ) . In fact , all of the gcn5Δ strains produced the same category of colonies as those produced by the HMR-left+gcn5Δ strain ( Figure 6 , Figure S2 ) . These data suggest that disruption of GCN5 alters the epigenetic control of gene expression at all silencing regions tested . Deletion of the EAF3 gene in the strain containing ADE2 at the HMR-left region produced colonies that were more red in color than those produced by the gcn5Δ strain ( Figure 6 ) , which disagreed with the statistical analyses of the single cell experiments . Similar colonies were also observed for the eaf3Δ strains in which the marker was inserted at the telomere , HMR-right , or HML-right region ( Figure S2 ) . A weakening of the red color of the colonies was observed following deletion of the DOT1 gene in all constructs ( Figure 6 , Figure S2 ) . Taken together , these results indicate that the acetylation status of histones , which is controlled by histone modification enzymes , exerts an epigenetic influence on gene expression in yeast cells . To determine whether the spread of silencing within a single cell correlates with the functioning of the different silencing regions , single cell time-lapse experiments were performed using yeast strains expressing three different fluorescent proteins ( Figure 8A ) , namely H2B-EYFP , H2B-ECFP , and H2B-mCherry . All strains contained HTB1-2x mCherry at the HIS3 locus on chromosome XV . The HMR-left PEV/TEL-VR PEV/Euchromatin strain contained HTB1-EYFP at the HMR-left region on chromosome III and HTB1-ECFP at the telomere on the right side of chromosome V; the HMR-left PEV/HMR-right PEV/Euchromatin strain contained HTB1-ECFP at the HMR-left region of chromosome III and HTB1-EYFP at the tRNA of the HMR-right region of chromosome III; the HML-right PEV/HMR-left PEV/Euchromatin strain contained HTB1-EYFP and HTB1-ECFP at the HML-right and HMR-left regions of chromosome III , respectively; the HML-right PEV/TEL-VR PEV/Euchromatin strain contained HTB1-EYFP at the HML-right region of chromosome III and HTB1-ECFP at the telomere on the right side of chromosome V; and the HML-right PEV/HMR-right PEV/Euchromatin strain contained HTB1-ECFP at the HML-right region of chromosome III and HTB1-EYFP at the tRNA of the HMR-right region on chromosome III . All strains were examined using single cell time-lapse experiments ( Figures 8B , 8C , and S3; Table S7 ) and correlation analyses were performed ( Tables 5 and 6 ) . The correlation between the HMR-right and either the HMR-left or HML-right region was highly significant . A correlation was also observed between the HML-right and HMR-left regions . However , a correlation between the telomere and either the HMR-left or HML-right region was not observed . The significance ( p value ) of the probability of two regions behaving independently was larger than 0 . 1 for comparisons of the TEL-VR PEV and HM regions ( Table 5 ) . By contrast , the probability was extremely low for comparisons of the HMR-left and HMR-right regions , the HMR-left and HML-right regions , and the HMR-right and HML-right regions ( Table 5 ) . These data indicate that the telomere region behaves independently , whereas the HMR and HML regions behave synchronously with high statistical significance . In Table 6 , the left panel displays actual values and the right panel displays the expected appearance frequency under the assumption of no correlation . The actual values exceeded the expected values under no correlation for the ON to ON or OFF to OFF comparisons of the HMR-left and HMR-right regions , the HML-right and HMR-left regions , and the HML-right and HMR-right regions . These data also suggested a positive correlation between the gene expression states of the HMR-left and HMR-right regions , as well as between the HMR and HML regions .
In budding yeast , a colony turns red when expression of the ADE2 gene is repressed . Consistent with a previous report [13] , when ADE2 was inserted close to the telomere , colonies containing red and white sectors were produced [13] . A similar phenotype was also observed when ADE2 was inserted into the HMR-right region , as reported previously [28] . When the reporter gene was changed from ADE2 to URA3 , the transformed yeast grew on medium lacking uracil and on medium containing 5-FOA . These data indicate that , despite the presence of the same DNA sequence , different gene expression states occurred simultaneously within the transformed yeast strains , suggesting epigenetic control of gene expression . The results of the previous studies [13] , [27] did not show the expression status of an individual cell because mixed populations of cells were used . In addition , the production of a sectored colony indicates that gene expression did not change at every division , but rather that the same expression state was maintained over several generations . If the expression status changed every generation or every few generations , a pink colony would have been produced . In this study , we developed a system to monitor changes in epigenetic gene expression in a single cell across many generations; this technique was used to analyze gene expression at the HMR , HML , and telomere regions . The results indicated that the gene expression status can change in all of these genetic regions , even after a cell has maintained the same state for several generations , and that the change in expression from the ON to the OFF state is reversible . Although it did not occur for all regions examined in this study , the tendency for genes to switch from the ON to the OFF state was generally more common than the tendency to switch in the opposite direction , which was also demonstrated previously using a pedigree assay [17] . The HMR-left region tended to be ON or OFF at random , while the expression status of the HMR-right and HML-right regions tended to be maintained over many generations . In addition , expression of the telomere tended to be more stable than that of the HMR-right or HML-right regions . Pink colonies were produced when the ADE2 gene was inserted into the HMR-left region , while sectored pink colonies were produced when the gene was inserted into the HMR-right or HML-right regions . A red and white sectored colony was produced when the gene was inserted into the telomere region . These data suggest that different mechanisms underlie the spread of silencing within each region . Two different fluorescent markers were used to determine whether the spread of silencing was consistent in two different regions of a single cell . A perfect match between the expression statuses of the HMR and HML regions , the HMR and telomere regions , or the HML and telomere regions could not be found; however , correlations between the expression statuses of the HMR and HML regions were observed in many cells . A previous report suggested that the quantity of Sir protein in a single cell might be fixed [29]; therefore , large quantities of Sir protein functioning at one region of the genome may result in a deficit of the protein at other regions . This model would explain the relationship between the rDNA region and the telomere; in other words , it is possible that the silencing level of the rDNA region is inversely correlated with the silencing level of the telomere region [30] . On the other hand , the data presented here demonstrate correlation between the left and right sides of the HMR , as well as between the HMR and HML regions . These data support the results of two previous studies , one of which reported that the E- and I-silencers of HMR can form a loop structure [31] , and another that showed that the bending of chromosome III causes HMR and HML to form a large loop structure that eventually brings HMR and HML close together [32] . Formation of the boundary of the telomere silencing region depends on a balance of acetylated and deacetylated histones; disruption of the SAS2 gene disturbs this balance and allows silencing to spread across a large region of the chromosome [4] , [23] , [24] . We therefore expected the single cell analyses to show a spread of the silencing region and an increased frequency of cells not expressing the marker gene; however , our result in HMR-left region was not perfectly much in this telomere boundary model . Our statistical analysis was not strongly reflected , and the frequencies of the changes in the epigenetic gene expression state from ON to OFF or OFF to ON over multiple generations were increased in lineage of some mother of sas2Δcells ( Figure 7A ) . In future analyses that focus on the age or memory of an individual cell , epigenetic regulation of SAS2 must be considered . Disruption of the GCN5 gene induced a bias of the same gene expression state within a cell . This result was confirmed by an ADE2 colony assay; deletion of the GCN5 gene resulted in the production of two different populations of cells ( red and white colonies ) , which also indicates changes in gene expression and suggests that characteristics differed between individual cells , possibly due to changes caused by deletion of GCN5 . Results of previous studies that used embryonic or induced pluripotent stem cells also suggest that a specific property or characteristic may differ between individual cells [33] , [34] . Elucidating the molecular mechanisms that underlie epigenetic modification of gene expression in yeast could contribute to understanding this problem in other organisms . Eaf3 is important for the formation of the boundary region [7] . In this study , deletion of the EAF3 gene might not affect the epigenetic status of gene expression and a spread of the silencing region was observed in the eaf3Δ strain , as previously reported [7] . Furthermore , single cell analysis showed that the ON state cell increased and altered the frequency of the ON to OFF transition in the dot1Δ strain ( Tables 3 and 4 ) . Similar results were obtained using the ADE2 colony assay , which showed that the dot1Δ strain increased white colony of phenotype of increasing ON state cell ( Figure 6F ) . In fact , dot1Δ cells containing the marker gene in the ON position proliferated according to the anti-silencing mechanism mediated by DOT1 , as previously reported for a DOT1 deletion strain [35] , [36] . On the other hand , a recent paper reported a positive feedback model in which the H3K79 methylation target of Dot1 was enriched at the ON telomere where it caused disruption of transcriptional silencing [37] . Moreover , another group showed that dot1Δ cells establish rapid silencing , and that daughter cells of dot1Δ cells established silencing earlier than mother cells [38] . It is difficult to compare directly these results with our results and draw the conclusion that Dot1 function is different at the HMR and the telomere [35] , [36]; thus , future experiments will be required to determine more precisely the role of Dot1 in epigenetic gene expression . Further analyses are required to elucidate the molecular mechanisms underlying changes in epigenetic gene expression , including how changes in the acetylation state of histones influence epigenetic control . In addition , the mechanisms controlling gene expression fluctuations at individual silencing regions were different , but similar phenotypes were observed for both the telomere and HM regions in an ADE2 colony assay using strains in which genes encoding histone modifier enzymes were disrupted . This result suggests that changes in the histone modification state have a greater influence on the regulation of gene expression fluctuation than the position of the gene . Because conventional large-scale S . cerevisiae cultures comprise mixed populations of cells in various states of gene expression , a system for analyzing epigenetic gene expression using single yeast cells was recently introduced [18] , [20] . However , previous studies of single yeast cells using this system followed the cells for only a few generations . The technique described here enabled monitoring of single yeast cells for more than 10 generations . Using this system , changes in epigenetic gene expression were shown to be reversible and a histone modification enzyme was shown to control these changes . A functional correlation between different epigenetically regulated regions was also identified . In this study , we also analyzed a phenomenon known as PEV . When the HTB1-EGFP gene was inserted into the HMR-left , HMR-right , HML-right , or telomere region , the occurrence of the OFF state of the marker gene expression was much lower than the occurrence of the ON state . Because most conventional epigenetic analyses are performed using a mixed population of cells , it is possible that such experiments are biased towards major phenotypes and potentially miss important minor phenotypes . One of the reasons that the results presented here were not perfectly consistent with those of previous studies may be due to the use of yeast strains of different ages . The abundance of the Sir protein decreases in older cells , while the presence of acetylated histones increases [39] . The results presented here suggest that the epigenetic control of gene expression is not only random but can also be nonrandom . Therefore , new statistical processing methods that enable elucidation of the mechanisms responsible for the epigenetic control of gene expression must be developed . The single cell analysis method described in this study showed that the functioning of the silencing regions differed in individual cells; however , it is unclear why fluctuations in the silencing region are important for controlling epigenetic gene expression . The nature of the genes that control this fluctuation in vivo is also unclear . A set of genes with similar and correlated functions form a cluster near the telomere , and expression of these genes may be controlled by the spreading of the silencing effect . Using the single cell analysis system described here , it is possible to identify genes that are controlled in the same cell at the same time . Future experiments will examine how fluctuations in the silencing domain control gene expression at the molecular level , and why fluctuation of the silencing domain in a cell is important .
The S . cerevisiae strains and the plasmids used in this study are described in Tables S1 and S2 . Cells were grown in YPD medium at 30°C until the early logarithmic phase . For live imaging , cells were placed in a Y2 microfluidic plate ( ONIX ) . Time-lapse imaging was performed using an Axio Observer Z1 ( Carl Zeiss ) microscope fitted with a 40× Plan-Neofluar objective lens ( NA = 1 . 3 ) . To determine the stability of the nonsilenced ( ON ) and silenced ( OFF ) states of individual regions , the frequencies of the following transitions ( T ) between the ON and OFF states were calculated for individual lineages: TON→ON , TON→OFF , TOFF→ON , and TOFF→OFF . The stabilities of the ON ( qON ) and OFF ( qOFF ) states were then quantified as qON = TON→OFF/ ( TON→ON+TON→OFF ) , and qOFF = TOFF→ON/ ( TOFF→OFF+TOFF→ON ) . The total frequencies of the transitions from the ON ( TON ) and OFF ( TOFF ) states were calculated as follows: TON = TON→ON+TON→OFF , and TOFF = TOFF→ON+TOFF→OFF . A permutation test was used to determine the statistical significance of differences in the stability of each state between two lineages; the p value was calculated as shown in Equation 1 . ( 1 ) where T1ON→ON and T1ON→OFF are the transitions for the first lineage , T2ON→ON and T2ON→OFF are the transitions for the second lineage , and mCn is n-combination of m . The same calculations were performed to determine the stability of the OFF state . The data are summarized in Tables 1–6 . The gene expression states of two different regions were observed within a single cell simultaneously . To determine the degree of correlation between activity of the individual regions , the frequencies of the ON and OFF states of each region were calculated as fx ( x ) and fy ( y ) , and the joint frequency for the two regions was calculated as fx , y ( x , y ) ; these data are summarized in Tables 5 and 6 . Assuming that the null hypothesis of two regions behaving independently is correct , fx , y ( x , y ) is expected to be close to fx ( x ) fy ( y ) /N , where N is the total number of cells observed within one lineage . Deviations from this expectation , which are known to follow a Chi-squared distribution with one degree of freedom , were determined as shown in Equation 2 . ( 2 ) The p value associated with χ2 ( ) was then calculated using the Chi-squared cumulative distribution . To test the statistical significance of the correlation between behavior of the HMR-left and HMR-right regions , as well as the HMR and HML regions , was calculated as fx , y ( ON , ON ) +fx , y ( OFF , OFF ) . Assuming that the null hypothesis of independence is correct , the probability of observing ( x , y ) = ( ON , ON ) or ( x , y ) = ( OFF , OFF ) times out of N trials should follow the binomial distribution with a success probability ( q ) equal to [fx ( ON ) fy ( ON ) +fx ( OFF ) fy ( OFF ) ]/N2 . Then , the p value ( psyn ) was calculated as shown in Equation 3: ( 3 )
|
Although eukaryotic gene repression usually acts on individual genes , cells can also repress larger chromosomal regions via the establishment of a high order chromatin structure called heterochromatin . Once initiated , heterochromatin spreads until halted by a boundary , and in this study we focus on how this boundary is formed . Because the mechanism is epigenetic and can differ from cell to cell , we wanted to assess the dynamics of the process by tracking individual cells over multiple generations . Here we develop a novel method employing protein fluorescence to monitor gene expression at the boundaries of several yeast heterochromatic regions simultaneously . This allows us to assess whether different boundaries within a single cell fluctuate in concert or independently of each other . In addition , we use histone modification mutants to probe the specific types of epigenetic regulation responsible for fluctuations in heterochromatin boundary positioning . Using this method , we show that epigenetic gene expression within individual cells is reversible and that this process is regulated by histone acetylation state . Future work will identify connections between variation in boundary positioning and novel transcription control systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"gene",
"expression",
"genetics",
"epigenetics",
"biology",
"chromatin",
"histone",
"modification"
] |
2013
|
Single Cell Visualization of Yeast Gene Expression Shows Correlation of Epigenetic Switching between Multiple Heterochromatic Regions through Multiple Generations
|
Schistosomiasis is a water-related neglected tropical disease . In many endemic low- and middle-income countries , insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases . Hence , modeling is relied upon to predict areas of high transmission and to inform control strategies . We hypothesized that utilizing remotely sensed ( RS ) environmental data in combination with water , sanitation , and hygiene ( WASH ) variables could improve on the current predictive modeling approaches . Schistosoma haematobium prevalence data , collected from 73 rural Ghanaian schools , were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data ( Landsat 8 , Sentinel-2 , and Global Digital Elevation Model ) with fine spatial resolution ( 10–30 m ) . Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites , including applying the models to known human water contact locations . Lastly , measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables . Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured . A water index ( MNDWI ) and topographic variables ( elevation and slope ) were important environmental risk factors , while overall , groundwater iron concentration predominated in the combined model that included WASH variables . The study helps to understand localized drivers of schistosomiasis transmission . Specifically , unsatisfactory water quality in boreholes perpetuates reliance on surface water bodies , indirectly increasing schistosomiasis risk and resulting in rapid reinfection ( up to 40% prevalence six months following preventive chemotherapy ) . Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure .
Schistosomiasis is an important parasitic disease that affects more than 250 million people [1] . Expressed in years lived with disability ( YLDs ) , the impact of schistosomiasis is comparable to that of malaria ( 2 . 9 versus 3 . 2 million YLDs ) [2] . Schistosomiasis is a disease of poverty , with 97% of all infections and 85% of the global at-risk population concentrated in Africa [3] . Ghana has an estimated country-wide prevalence of 23 . 3% , with focal , or localized , prevalence levels >50% [4] . Schistosomiasis is caused by infection with the trematode parasite of the genus Schistosoma [5] . Of the three species that commonly infect humans ( S . haematobium , S . mansoni , and S . japonicum ) , the former two are prevalent in Africa [6] . S . haematobium is the predominant species in Ghana [4] and is the focus of the present study . Schistosomiasis has a complex life cycle that involves the parasite , intermediate host snails , and definitive human host ( and sometimes animal reservoir hosts ) . Transmission occurs in fresh surface water bodies that are contaminated with human waste , provide favorable ecologic conditions for intermediate host snails ( Bulinus species for S . haematobium ) , and sustain human water contact [6] . Human transmission occurs when parasite larvae ( cercariae ) penetrate intact skin during water-based activities and has historically been most common in rural areas with natural slow flowing streams , ponds , and lakes [3 , 6] . To develop and implement effective control strategies against schistosomiasis , accurate data on the geographic and demographic distribution of infections are necessary . Surveillance in endemic low- and middle-income countries is inhibited by limited health infrastructure and cases evading clinical detection due to lower parasite burden and lessened symptoms that result from preventive chemotherapy with the anthelmintic drug praziquantel . Passive health facility-based surveillance and reporting systems are known to severely underestimate the number of infections [7 , 8] . For example , a total of ~25 , 000 schistosomiasis cases were reported into the Ghanaian District Health Information Management System ( DHIMS ) in 2010 ( data received from GHS , 2016 ) . If only ~5 million children ≤15 years of age residing in rural areas ( i . e . , high-risk population ) [9] are considered at the estimated 23 . 3% infection rate [4] , ~1 . 15 million cases would be expected . The reported cases represent only 2 . 2% of this expected number . Some correction for underreporting can be accomplished by predictive modeling , aiming to complement data from surveillance systems and field-based prevalence surveys . Many schistosomiasis predictive modeling studies have been published and reviewed [10 , 11] . Most studies utilized remote sensing ( RS ) and geographic information system ( GIS ) approaches at large spatial extents ( i . e . , national , regional or continental ) [12–14] , with fewer applications of these methods to sub-national mapping [15–17] . Because snail populations , cercarial densities , human water contact patterns , and subsequent schistosomiasis infections exhibit strong spatial heterogeneity [10 , 18 , 19] , further investigation of localized transmission drivers at smaller spatial extents is needed [10 , 11] . Furthermore , most studies included relatively few RS environmental predictors , mainly normalized difference vegetation index ( NDVI ) , land surface temperature ( LST ) , and elevation , whereas many other vegetation- and moisture-related indices and topographic variables are available and should be considered [11 , 20 , 21] . Another important limitation is that most studies utilized point-prevalence data of human infections ( outcome ) typically measured at schools , whereas RS-based environmental data ( predictors ) pertain to water bodies that serve as snail habitats and potential transmission locations . Most models do not account for this spatial mismatch between exposure and outcome measures [11] . A recent study used a more ecologically relevant approach , in which RS variables were extracted from geographically delineated water bodies within a buffer radius around the point-prevalence location [22] . An even more promising approach would be to apply the models to the specific locations along water bodies where human water contacts occur . Further complicating the modeling approach at small spatial extents are socioeconomic and behavioral factors , including water , sanitation , and hygiene ( WASH ) conditions , known to affect individual schistosomiasis risk [23–25] . These factors may have an even greater bearing on the focal nature of disease distribution than the environment [26 , 27] , and should be considered as predictors . While the inclusion of socioeconomic status and metrics of clean water and sanitation access have been advocated [10 , 11] , to our knowledge , WASH variables have not yet been explicitly incorporated into spatial schistosomiasis predictive models . The goal of the present study was to build upon existing predictive modeling approaches using S . haematobium prevalence data from 73 rural communities in the Eastern region of Ghana . We utilized fine resolution RS data ( Landsat 8 and Sentinel-2 ) , expanded the number of predictors ( 15 environmental and four WASH-related variables ) , and explored alternatives for addressing the spatial mismatch between exposure and outcome measures . In this study , primary innovations include the use of a new RS data source ( Sentinel-2 ) , incorporation of field-mapped surface water contact sites into the RS-based environmental modeling approach , and exploration of WASH variables as additional schistosomiasis risk factors .
The study was approved by the Institutional Review Board ( IRB ) at Tufts University in Boston , United States of America ( protocol #11688 ) and Noguchi Memorial Institute for Medical Research in Accra , Ghana ( protocol #1133 ) . Letters of approval were obtained from national and regional offices of Ghana Health Service ( GHS ) and Ghana Education Service ( GES ) . Written informed consent was obtained from the acting head teacher of each school that participated in the schistosomiasis prevalence survey . Verbal assent was sought from the participating children , an accepted ethical and practical approach used in similar low-risk studies [28] . The study was conducted in the tropical Eastern region ( Fig 1 ) , characterized by major and minor peak rainfall periods in June and October , respectively , with dry season lasting from November to February . Four major perennial rivers ( Pra , Birim , Ayensu , and Densu ) drain the region , with an abundance of smaller streams and ponds . Most of these water bodies are used extensively for domestic and recreational purposes ( e . g . , fetching , washing , swimming , and fishing ) . The Pra and Birim rivers , and some of their tributaries , however , are heavily polluted by alluvial gold mining and are no longer used due to high turbidity and presence of toxic compounds [29] . The region is relatively flat with some hilly areas and low mountains ( Atiwa Mountain Range ) reaching an elevation of approximately 750 m above sea level . The study area , spanning 10 administrative districts , was purposely selected outside of a 20-km buffer radius of Lake Volta [29] . Communities situated on its shores are historically known to be endemic for schistosomiasis [30] . However , little information is available about pockets of high transmission along minor rivers and streams that are not easily detected with RS technologies . Prior modeling studies mainly used point-prevalence as outcome data . Prevalence of S . haematobium eggs in urine samples ( or hematuria as a proxy of infection ) is typically measured at schools , while transmission may occur within some distance of this point-prevalence location . With extensive local knowledge from prior community-based studies [29 , 31–33] , the present analysis was conducted at the “community” level . The spatial boundaries of communities were defined by Open Street Map ( OSM ) polygons ( Fig 2 ) abstracted using QGIS software ( version 2 . 12 . 3 ) , an approach validated in a case study [29] . Subsequently , a buffer radius of 1 km was applied to each polygon . The buffer distance was chosen because nearly all known contact with water bodies occurred within 1 km of community boundaries . Throughout the manuscript , the term “community” refers to the OSM polygon + 1 km buffer area ( Fig 2 ) and is used as a unit of analysis , also referred to as grain or support [21 , 34] . Data for this study were obtained primarily from satellite RS sources and field studies , with some additional geographic features digitized from satellite imagery . Surface reflectance , thermal , and elevation data were obtained from RS sources . From these , vegetation and water indices , LST , and topographic variables were derived . WASH variables were obtained from field data available from past studies , namely global positioning system ( GPS ) coordinates of public water sources [29] and data about groundwater quality [32] . The outcome variable , S . haematobium prevalence ( % ) was measured in one school in each of the 73 study communities . Measures of improved and unimproved [35] water access and groundwater quality ( WASH variables ) were combined with RS-based variables to predict schistosomiasis prevalence across the study area . Data processing and analysis steps are described below and outlined in S1 and S2 Figs in Supporting Information . Six environmental indices were calculated from Landsat 8 ( OLI ) and Sentinel-2 surface reflectance data ( Table 2 ) in R software ( version 3 . 3 . 1 ) . In the enhanced vegetation index ( EVI ) equation , L value adjusts for canopy background and C values are coefficients for atmospheric resistance . These enhancements allow for index calculation as a ratio between the red and the near infrared ( nir ) band values , while reducing the background and atmospheric noise and saturation [39] . The values of C1 = 6 , C2 = 7 . 5 , and L = 1 were obtained from the Landsat 8 product guide [40] . In the soil adjusted vegetation index ( SAVI ) equation , L is the soil calibration factor that minimizes soil background conditions that affect partial canopy spectra . The L value of 0 . 5 minimizes soil brightness variation and eliminates the need for additional calibration for different soils [41] . Landsat 8 ( TIRS ) thermal data were processed using ATCOR [42] with a standard emissivity of 0 . 985 to detect water surface temperature , and converted from Kelvin ( K ) to degrees Celsius ( °C ) to represent LST . Elevation data were used to derive stream order and slope . Topographic drainage lines were delineated from the digital elevation model ( DEM ) based on the potential flow direction from higher to lower elevation and accumulation of surface runoff according to topographic conditions using Arc Hydro Tools in ArcGIS ( version 10 . 2 . 2 ) . The resulting stream network was ordered according to Strahler [47] . Slope of the terrain was derived from the DEM as a proxy indicator for potential flow velocity of surface runoff with inclination calculated in degrees . GPS coordinates of public water sources ( standpipes ( SPs ) , boreholes ( BHs ) , protected and unprotected hand-dug wells ( HDWs ) , and surface water access points ( SWAPs ) ) were available from a prior study [29] . SPs , BHs , and protected HDWs that were functional at the time of the study constituted functional improved water sources ( FIWS ) that are not capable of transmitting schistosomiasis . SWAPs constituted unimproved water sources that are capable of transmitting schistosomiasis . Two categorical raster layers were derived from the GPS data using a buffer analysis conducted in ArcGIS 10 . 2 . 2 , which represented improved water access ( within 100–500 m of FIWS ) and surface water access ( within 100–500 m of SWAP ) , to test the hypothesis that locations closer to FIWSs have a lower risk of schistosomiasis transmission and locations closer to SWAPs have higher risk of schistosomiasis transmission [29] . Two additional raster layers of interpolated groundwater iron and total dissolved solids ( TDS ) concentrations ( mg/l ) were also obtained from a prior study [32] . Groundwater quality variables were included because prior studies [29 , 32 , 33] suggested that elevated iron and TDS concentrations in BHs may increase reliance on contaminated surface water bodies , thereby potentially serving as indirect risk factors for schistosomiasis transmission . Lastly , S . haematobium prevalence ( % positive samples ) was calculated from survey data . Prevalence was determined separately for boys and girls in each grade and then adjusted to a gender- and grade- balanced population using direct standardization [48] . Standardized school-level point-prevalence values ( S1 Table , Supporting Information ) were taken to represent community-level prevalence based on the following validated [49] assumptions: ( i ) microhematuria prevalence measured by reagent strip is a reasonable proxy of S . haematobium prevalence in a presumably lightly infected population due to recent preventive chemotherapy; ( ii ) 3rd and 4th grade school children are a representative study population; and ( iii ) where a child lives and attends school are not spatially dependent , inferring that prevalence value at one school is representative of community-level prevalence . A total of 15 environmental and four WASH predictor variables ( Table 3; S3–S21 Figs , Supporting Information ) were derived and resampled to a matching spatial resolution of 10 m . While S . haematobium infection prevalence was represented by point data , predictors were represented by continuous raster data ( Fig 3 ) . Therefore , extraction and aggregation of the raster data within the “community” polygons were necessary . A total of six methods of variable extraction ( masks ) were used ( Fig 3 ) : none {1}–all pixels within the “community” polygon were extracted; unpopulated {2}–data were extracted only for unpopulated pixels as defined by the GUF data; populated {3}–data were extracted only for populated pixels as defined by the GUF data; all water bodies {4}–mask was derived by combining the topographic drainage lines from the DEM , supplemented with ponds , lakes , and gold mining pits that were digitized from satellite imagery; unmined water bodies {5}–mask was derived by removing water bodies that are known to be affected by mining from “all water bodies”; SWAPs {6}–defined as the single pixel GPS points of known surface water contact sites . To understand the spatial linkage between school-based prevalence and the environmental conditions , almost all environmental variables were extracted using masks {1 , 2 , 4 , 5 , and 6} ( Table 3 ) , listed in the order of increasing ecologic relevance . For example , the most ecologically relevant method is to match school-based schistosomiasis prevalence with environmental variables extracted from points within the “community” where known contact with water bodies occurs ( m6 ) . Method 3 ( populated areas ) was not relevant for environmental variable extraction because these locations are not representative of schistosomiasis transmission . Conversely , measures of safe ( FIWS ) and unsafe ( SWAP ) water access apply only to populated areas; hence only method 3 was used to extract these two WASH variables . Unmasked data ( m1 ) were used to extract stream order , iron , and TDS concentrations ( Table 3 ) . For aggregation of environmental variables , primarily the median pixel values were used , except for stream order , where maximum value was used . For aggregation of WASH variables , either median ( iron and TDS ) or mode ( FIWS and SWAP access ) were used ( Table 3 ) . Exploratory analyses included variable summaries and correlations , followed by random forest models . The random forest approach was chosen because it can deal with continuous outcome data , multicollinear predictor variables , and low numbers of training samples , it is the recommended machine learning method for generating predictions [50] , and it has been successfully applied in similar studies [22] . Five non-parametric random forest models were conducted with 15 environmental predictor variables ( Table 3 ) to determine which of the five masks presented the best method of variable extraction . Two versions of the analyses were conducted in parallel ( with Landsat 8 and Sentinel-2 surface reflectance values and environmental indices ) to test consistency of predictive performance of RS data obtained from these two satellites with similar acquisition dates . Explanatory power of random forest models was compared using root-mean-square error ( RMSE ) and R2 values [51] , and relative importance of predictor variables was assessed using the increasing node purity ( “IncNodePurity” ) metric [52 , 53] . All models were applied back to the raster stack of predictor variables to derive continuous predicted S . haematobium prevalence surfaces . Although predicted values were available for all pixels , the same masks used to extract the explanatory variables were applied to the respective predicted prevalence surfaces . After applying the masks , the median predicted values within each “community” were plotted against observed prevalence values . The quality of prediction was assessed using Spearman’s rank correlation between model predicted and observed values , and their fit was compared to the line of equality . Lastly , environmental data extracted using the best performing mask were combined with the WASH variables in a final model to assess the relative importance of the two groups of variables .
As an exploratory analysis , Spearman’s rank correlations were computed between pairs of environmental indices ( S2 Table , Supporting Information ) . The correlation values were consistent across extraction masks and across RS data sources . As expected , correlations among the vegetation indices derived using both Landsat 8 and Sentinel-2 data were generally very high ( 0 . 90–0 . 99 ) . Lower correlation values were observed between the two water indices NDWI and MNDWI ( ~0 . 70 ) . Consequently , negative correlation values between NDWI and the vegetation indices were much higher than those between MNDWI and the vegetation indices ( 0 . 91 versus 0 . 50 ) . To explore the potential reason for this , NDWI and MNDWI were visually compared against a map ( Fig 4 ) . In the first row ( A1 and B1 ) , schematic maps of study communities are shown with populated areas indicated in gray and water bodies , comprised of rivers/streams and dug mining pits , indicated in blue . It appears that the NDWI computed with Landsat 8 data ( A2 and B2 ) results in false detection of water bodies ( i . e . , misclassification of developed surfaces such as settlements and roads ) , essentially serving as an inverse of a vegetation index , which explains the strong negative correlation with vegetation indices . On the other hand , the MNDWI ( A3 and B3 ) more precisely detects water bodies , particularly mining pits . Same conclusions apply to NDWI and MNDWI values derived from Sentinel-2 data ( S13 and S14 Figs , Supporting Information ) . Neither index performed adequately at detecting the SWAPs , shown as + symbols in Fig 4 . Random forest models were first run for each extraction method using environmental variables only ( Table 4 ) . Two versions of the environmental models were run in parallel with Landsat 8 and Sentinel-2 surface reflectance and environmental indices ( in addition to LST and topographic variables derived from a single source ) . The R2 values for all models were relatively low ( <0 . 20 ) , indicating that environmental variables alone were not able to describe more than 15–20% of the variability in S . haematobium prevalence , regardless of RS data source or extraction mask . The predicted prevalence at the pixel level ranged from approximately 5% to 28% ( Fig 4 ) . Aggregated predicted community-level prevalence ranged between 7% and 22% , as compared to the observed prevalence range of 0–40% . Correlations between observed and predicted prevalence values were higher on average for models produced using Landsat 8 environmental data as compared to Sentinel-2 data ( both in combination with LST and topographic variables ) . Models derived using the SWAP mask produced the highest correlation values using both Landsat 8 ( r = 0 . 76 , p < 0 . 01 ) and Sentinel-2 data ( r = 0 . 67 , p < 0 . 01 ) ( Table 4 ) . However , scatter plots of observed versus predicted values still deviated substantially from the line of equality ( S22 and S23 Figs , Supporting Information ) due to the overall low R2 values . From a visual assessment of the predicted prevalence surfaces produced using environmental variables ( Fig 5; S24–S33 Figs , Supporting Information ) , it appears that the SWAP mask resulted in more precise prediction , including correct delineation of water bodies as high-risk locations ( Fig 5 , panel A6 ) . Variable importance was also explored using the IncNodePurity measure from random forest models ( S22 and S23 Figs , Supporting Information ) . MNDWI was an important water index , particularly when environmental data were extracted without knowledge of water contact sites ( masks 1 , 2 , 4 , and 5 ) . Vegetation indices were not commonly observed among the top five important variables in the Landsat 8 models; EVI and NDVI were the most important vegetation indices in the Sentinel-2 models . Slope and elevation were important in many models , whereas stream order was always the least important variable . The final model consisted of environmental variables derived from Landsat 8 data using the SWAP mask in combination with WASH variables . The addition of WASH variables only slightly increased the R2 value from 0 . 15 to 0 . 17 and decreased the RMSE from 9 . 47 to 9 . 03 . However , iron concentration became by far the most important variable . The importance of iron concentration was also evident in the predicted prevalence surfaces , with high values on the western side of the Atiwa Mountain Range ( Fig 6 ) coinciding with high groundwater iron content ( S21 Fig , Supporting Information ) . FIWS and SWAP access indicators were not important in the final model . Of the environmental variables , elevation remained important and stream order remained unimportant ( Fig 6 ) . The correlations between predicted and observed values were not extracted for the final model because multiple masks were used in the model .
In this study , we utilized publicly available environmental data from two multispectral optical sensors in combination with topographic variables and field-collected WASH variables to assess their performance in predicting S . haematobium prevalence at a sub-national spatial extent . Furthermore , we tested five methods of environmental data extraction with varying degrees of ecologic relevance . In epidemiologic literature , schistosomiasis is known as a focal disease , meaning that neighboring villages with seemingly similar conditions can have drastically different transmission profiles and disease prevalence levels [10 , 18 , 19] . This study attempted to characterize some of the sources of spatial heterogeneity at small spatial extents using fine resolution RS data and WASH-related risk factors . We found that knowledge of water contact sites shows promise in schistosomiasis risk prediction at small spatial extents . According to a visual assessment , environmental data extracted using the SWAP mask more precisely delineated water bodies as high-risk locations within communities ( Fig 5 ) . This mask also produced the highest correlation between model predicted and observed prevalence values , depicting heterogeneity in transmission risk among communities ( Table 4 ) . Of the two water indices we explored , MNDWI was the preferred index due to more accurate detection of water bodies . NDWI values were equally high for water and developed pixels ( roads and settled areas ) , indicating false detection of water bodies . Generally , higher values of MNDWI correlated with higher schistosomiasis risk . However , even MNDWI could not detect small streams that sustained most of surface water use ( i . e . , SWAPs ) . Further investigation of these two indices and their utility in water-related disease modeling is recommended . Vegetation indices did not play a major role in prediction . This is not surprising , especially in the SWAP mask models , as these indices are likely characterizing land vegetation cover , rather than aquatic vegetation that affects intermediate host snail abundance [11] . LST did not exhibit a strong influence on schistosomiasis risk , most probably due to the lack of variability in LST values ( 25–32 °C ) , all of which were well within the favorable temperature range for snail and cercariae survival [54 , 55] . Furthermore , because the water bodies in the study area are very small , the spatial resolution of the temperature data ( 100 m ) was likely too coarse to detect water temperature . Slope and elevation were important in prediction . Higher elevation correlated with higher schistosomiasis risk , counter to the literature , likely because the Atiwa Mountain Range is still quite low in elevation , far below the 2 , 000-m above sea level threshold for S . haematobium transmission [18] . Higher slope correlated with lower schistosomiasis risk , potentially due to faster stream flows . At water velocities > 0 . 3 m/s , snails can become dislodged and swept away [55] . Surprisingly , stream order was consistently the least important variable in all models , while it demonstrated a significant positive association with schistosomiasis risk in other studies [17 , 22] . A potential explanation for this is the abundance of small streams throughout the study communities , widespread preference of people for surface water over groundwater , and hence their uniform extensive use . In our study , variables of improved and unimproved water access were not predictive of schistosomiasis risk , consistently with the findings of Lai et al . [4] . However , high iron concentration in groundwater was associated with increased schistosomiasis risk . Our prior studies have provided qualitative support for the hypothesis that unfavorable groundwater quality in improved water sources ( i . e . , boreholes and piped water systems ) for drinking and laundry is a significant driver of increased surface water use , serving as an indirect risk factor for schistosomiasis transmission . The final model results confirmed this hypothesis , with groundwater iron content being the predominant schistosomiasis risk factor with a much higher IncNodePurity value as compared to any of the environmental variables ( Fig 6 ) . Indeed , in Fig 6 , the area with high predicted schistosomiasis prevalence in the center of the image corresponds to the high iron concentration cluster ( S21 Fig , Supporting Information ) . Overall , the models had relatively low predictive power and predicted prevalence values deviated substantially from the observed values , indicating overprediction in the low-prevalence range and underprediction in the high-prevalence range . This is most likely due to the effects of preventive chemotherapy on prevalence measures . With increased treatment frequency , it becomes difficult to detect the effects of environmental conditions on transmission risk [4 , 22] . It would be valuable to apply these approaches in similar geographic extents with a wider prevalence range . Exploring different methods of defining “communities” over which risk factor variables are aggregated ( e . g . , varying the buffer radius within which transmission occurs ) in other geographic , demographic , and cultural contexts is also recommended . We also found that Landsat 8 and Sentinel-2 sensors with similar radiometric resolutions ( 12-bit ) and acquisition dates ( all images were acquired within one week ) , on average , had similar predictive capacities . Cloud cover presented a substantial challenge in RS data acquisition from both data sources , with few cloud-free images available only in the dry season ( December and January ) . Additionally , Landsat 8 data were more affected by haze and ocean spray , as compared to Sentinel-2 data . As RS data algorithms improve , future studies should consider repeating the same environmental models using RS data representative of both dry and rainy seasons to analyze the impact of water stability and dynamics . Synthetic Aperture Radar ( SAR ) data ( e . g . , from Sentinel-1A ) could provide additional information in this and similar cloud-affected regions . Apart from technical challenges associated with using RS data , several logistic challenges may have affected the quality of this study . First , low attendance in some of the study schools ( range 46–95% ) associated with sporting events and market days may have affected the prevalence measures . For example , children from agrarian families who were absent on market days are likely different in terms of socioeconomic status and schistosomiasis exposure profile from those who were present and participated in the study . In a smaller study , it would have been possible to go back and screen absentees; in the present study , this was not possible due to time and scheduling limitations and absence of identifying information about participants . Additional challenges arose from working across 10 administrative districts , especially with securing local GHS personnel to administer praziquantel . Scheduling and coordination efforts were further complicated by the community health workers being on strike in some of the districts during the study . Despite the challenges and limitations , our study makes important contributions to the modeling approaches of schistosomiasis transmission at small spatial extents . First , knowledge of human water contact sites bridges the gap between where prevalence is measured and where transmission may have occurred . This is a critical gap in models that utilize environmental data as predictors of human infection . Second , the impact of groundwater iron concentration on schistosomiasis risk . With prevalence rates up to 40% only six months after preventive chemotherapy and very high rates of fetching surface water ( up to 100% ) and swimming ( up to 90% ) [49] , reinfection is a major concern in the study area . Groundwater quality in improved water sources , more so than improved water access in general , plays a major role in reinfection patterns and can impede schistosomiasis control . While it is well-established that preventive chemotherapy reduces prevalence and worm burden in the short term , with rapid reinfection , it cannot have more than a temporary effect on transmission without complementary improvements in WASH [23 , 24 , 56] . Our extensive experience in the Eastern region of Ghana suggests that it is not only increasing access to WASH resources that matters , but rather increasing utilization of these resources in accordance with local perceptions and preferences . Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure [56] .
|
Schistosomiasis is a water-related neglected tropical disease that disproportionately affects school-aged children in poor communities of low- and middle-income countries . Schistosomiasis transmission risk is affected by environmental , socioeconomic , and behavioral factors , including water , sanitation , and hygiene ( WASH ) conditions . We used fine spatial resolution ( 10–30 m ) remotely sensed data , in combination with measures of local water access and groundwater quality , to predict schistosomiasis risk in 73 rural Ghanaian communities . We found that applying environmental models to specific locations where people contact surface water bodies ( i . e . , potential transmission locations ) , rather than to locations where prevalence is measured , improved model performance . A remotely sensed water index and topographic variables ( elevation and slope ) were important environmental risk factors , while overall , groundwater iron concentration predominated . In the study area , unsatisfactory water quality in boreholes perpetuates reliance of surface water bodies , indirectly increasing schistosomiasis risk and resulting in rapid reinfection ( up to 40% prevalence six months following deworming ) . Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"schistosoma",
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"surface",
"water",
"education",
"helminths",
"sociology",
"tropical",
"diseases",
"geographical",
"locations",
"social",
"sciences",
"parasitic",
"diseases",
"animals",
"health",
"care",
"data",
"mining",
"neglected",
"tropical",
"diseases",
"sanitation",
"information",
"technology",
"data",
"processing",
"africa",
"hydrology",
"public",
"and",
"occupational",
"health",
"computer",
"and",
"information",
"sciences",
"schistosoma",
"haematobium",
"schools",
"people",
"and",
"places",
"helminth",
"infections",
"schistosomiasis",
"environmental",
"health",
"ghana",
"eukaryota",
"earth",
"sciences",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2018
|
Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles
|
Prokaryotic transcription factors ( TFs ) that bind small xenobiotic molecules ( e . g . , TFs that drive genes that respond to environmental pollutants ) often display a promiscuous effector profile for analogs of the bona fide chemical signals . XylR , the master TF for expression of the m-xylene biodegradation operons encoded in the TOL plasmid pWW0 of Pseudomonas putida , responds not only to the aromatic compound but also , albeit to a lesser extent , to many other aromatic compounds , such as 3-methylbenzylalcohol ( 3MBA ) . We have examined whether such a relaxed regulatory scenario can be reshaped into a high-capacity/high-specificity regime by changing the connectivity of this effector-sensing TF within the rest of the circuit rather than modifying XylR structure itself . To this end , the natural negative feedback loop that operates on xylR transcription was modified with a translational attenuator that brings down the response to 3MBA while maintaining the transcriptional output induced by m-xylene ( as measured with a luxCDABE reporter system ) . XylR expression was then subject to a positive feedback loop in which the TF was transcribed from its own target promoters , each known to hold different input/output transfer functions . In the first case ( xylR under the strong promoter of the upper TOL operon , Pu ) , the reporter system displayed an increased transcriptional capacity in the resulting network for both the optimal and the suboptimal XylR effectors . In contrast , when xylR was expressed under the weaker Ps promoter , the resulting circuit unmistakably discriminated m-xylene from 3MBA . The non-natural connectivity engineered in the network resulted both in a higher promoter activity and also in a much-increased signal-to-background ratio . These results indicate that the working regimes of given genetic circuits can be dramatically altered through simple changes in the way upstream transcription factors are self-regulated by positive or negative feedback loops .
The mechanisms that bacteria use to transduce external stimuli into specific responses rely on connected transcriptional factors that shape circuit-like input/output devices [1] . Such networks are comprised of interacting molecular components and can adopt different topologies [2] . The responses of a specific regulatory network to given stimuli are then fine-tuned by the dynamics of its interacting constituents [3] , [4] . Free-living bacteria have evolved to respond and adapt to the perturbations derived from a fluctuating environment by increasing the complexity of their regulatory circuits [5] . The TOL plasmid pWW0 of the soil bacterium Pseudomonas putida mt-2 is a good example . This plasmid encodes two catabolic operons for biodegradation of m-xylene [6] that are subject to an intricate regulatory control involving the interplay among various transcription factors ( TFs ) [7] , [8] . The master regulatory element of the system is the σ54-dependent regulator XylR , which , in the presence of its natural inducers , acts on the Pu promoter of the upper TOL operon . In addition , XylR triggers the expression of the gene that encodes a second regulator , XylS , via the Ps promoter [9] . Due to the divergent character of the Ps and the Pr promoters ( driving expression of xylR , Figure 1 ) , the activation of the Ps promoter not only triggers the expression of xylS but also leads to the down-regulation of xylR transcription [10] . XylR is optimally activated by the primary substrates of the TOL system , such as m-xylene or toluene . However , this TF is not entirely specific for these effectors , as it also responds to a large number of structural analogs . These analogs include both non-substrates as well as metabolic intermediates of m-xylene biodegradation , e . g . , 3-methylbenzylalcohol ( 3MBA ) [6] , [11] , resulting in a degree of naturally occurring effector promiscuity . The transcriptional output produced by XylR on the target promoters Pu and Ps is in turn limited by intracellular concentrations of the TF [12] , [13] and σ54 [14] . This extant configuration of the system not only leads to a quick response to XylR effectors when cells enter the stationary phase , but it also restricts the Pu promoter to low capacity i . e . poor maximum output . The existing characteristics of the XylR-based regulatory network have likely evolved for adjusting the tradeoff between transcriptional efficiency and physiological burden in the natural context , constraining the output of the system . This natural control of the xylR expression loop in the context of the TOL plasmid limits the value of the system as the primary component of whole-cell biosensors [15] , [16] . Previous attempts to increase the performance of XylR/Pu-based biosensing devices have included in vitro evolution of the TF [17] , [18] , construction of regulatory cascades [19] and improvement of the ribosome binding sequence ( RBS ) of the reporter genes [20] . None of these approaches , however , solve the problem of effector promiscuity . The issue at stake is , therefore , whether we can artificially change such an effector-relaxed/low-output circuit regime into a high-signal specificity/high-capacity counterpart without modifying the XylR protein . In this work , we report one strategy to overcome the constraints imposed by the natural architecture of the TOL network on the function of the XylR/Pu regulatory node of the plasmid . To this end , we adopted a Pu-luxCDABE reporter integrated into the chromosome of P . putida for accurately measuring system performance . In this genetic background , we then designed and tested various combinations of translation signals , promoter strengths and regulatory loops aimed at [i] suppressing the effect of effector promiscuity on XylR/Pu output and [ii] enhancing the response to optimal inducers ( e . g . , m-xylene ) . The results herein demonstrate that the working regimes of regulatory nodes , including their signal specificity , can be dramatically altered by changing the upstream connectivity of the TFs involved in the network instead of mutating the structure of the corresponding proteins .
As shown in Figure 1 , the event that triggers the regulatory and metabolic program encoded in the TOL plasmid is the binding of the pathway substrate to the XylR protein [7] and the ensuing activation of the Pu promoter for expression of the upper operon [6] . This results from the interplay of four components: [i] the aromatic effector , [ii] the Pr promoter that transcribes xylR , [iii] the XylR protein itself and [iv] the Pu promoter that is targeted by XylR . Although many other host factors influence the activity of the system in vivo [14] , [21] , [22] , the set comprising the inducer/Pr/XylR/Pu forms the master regulatory device that determines signal specificity , i . e . , the responsiveness of Pu to different aromatic effectors [11] , [23] . The relational map of this node is depicted in Figure 2a . Exposure to aromatic effectors gives rise to a form of the XylR protein that both activates Pu and represses Pr , i . e . , downregulates its own transcription . To have a reliable test system for comparing the inputs and outputs associated with this node , we engineered these components in a strain of P . putida bearing a transcriptional Pu-luxCDABE fusion inserted into its chromosome via a mini-transposon vector ( P . putida Pu·LUX , Figure 2c ) . To ensure a faithful comparison of the input ( i . e . , inducer ) and output ( light emission ) transfer function for each of the configurations tested , we assembled xylR expression in a specialized plasmid called pTn7 Gm FRT [24] . This vector targets any DNA segment inserted therein to a natural attTn7 site present in the genome of P . putida KT2440 [25] in a specific orientation . Furthermore , once inserted , the Gm resistance marker can be excised through site-specific recombination between two flanking FTR sequences , thereby leaving the cells free of antibiotic resistances . To set a benchmark for the subsequent studies , we first produced a strain with the Pr/XylR/Pu regulatory parts connected in the same configuration as the natural TOL plasmid ( Figure 2a ) . To this end , a DNA segment encoding the xylR gene placed under its native promoter was assembled in the aforementioned Tn7 vector to yield pTn7-BX ( Figure 2b ) . The insert was then delivered to the P . putida Pu·LUX chromosome and the Gm marker was deleted as shown in Figure 2c and 2d , thereby generating P . putida BX , which was thereafter the reference reporter strain . Note that all subsequent strains handled below carry the Pu-luxCDABE already described . To quantify the response of the regulatory node of Figure 2a implemented in P . putida BX , the strain was grown in liquid medium and exposed to either optimal inducer vapor ( m-xylene ) or to 1 . 0 mM of a suboptimal effector ( 3MBA ) and the resulting bioluminescence was recorded 5–6 h post-addition . This timing does not significantly affect luminescence ( e . g . see Figure 3 below ) . While the background reading of the Pu output was in the range of 103 luminescence units/OD600 , the m-xylene-induced levels were close to 106 ( Figure 2e ) . These results confirmed the inducibility and strength of the Pu promoter and set a minimum and maximum window of activity for the rest of the work . The addition of 3MBA in the assay produced a luminescence readout that was only approximately 15% of that obtained with the optimal XylR effector but still very high relative to the background , non-induced levels . Such a difference between one inducer and the other is not understood mechanistically , as the apparent binding affinities of both good and bad XylR inducers are similar [26] . In either case , it is plausible that the output of the sensing device as a result of induction by either aromatic compound is limited by the intracellular concentrations of XylR [13] , which curbs the robustness and sensitivity of the system [3] , [4] . With this background , we wondered whether we could exacerbate the difference between optimal and non-optimal inducers ( and thus increase signal specificity ) by artificially increasing some of the parameters of the existing node ( Figure 2a ) , by rewiring its connections or by both methods . Various approaches to this goal are explained below . One theoretical way to modify the sensitivity of a signaling route involves changing the levels of the proteins involved in the process [27] . With this in mind , we entertained the possibility that lowering XylR concentrations could suppress the response of the regulatory device ( Figure 2a ) to 3MBA while preserving the induction of the same system by m-xylene . To test this , we constructed a variant of the node that kept the same relational organization but caused a drop in the levels of XylR by attenuating the protein's translation with tandem , repeated non-overlapping RBSs ( Figure 3b ) [28] . This modification was expected to lower translation of the downstream ORF without any effect on mRNA stability [29] . To implement this change , the Tn7 plasmid pTn7-Pr·RBX was built as explained in the Materials and Methods section and delivered into attTn7 of P . putida Pu·LUX , as explained previously . The resulting strain , P . putida Pr·RBX , was identical to the reference strain P . putida BX except for a translational attenuator at the 5′-upstream untranslated region ( 5′-UTR ) of the xylR gene ( Figure 3b ) . We next verified that the changes in the ( UTR ) of xylR lowered the net expression levels of the regulator without affecting its production kinetics . Thus , in parallel , we grew strains P . putida BX and P . putida Pr·RBX in LB medium and exposed them to 1 . 0 mM of the suboptimal effector 3MBA . Then , we performed Western blot analyses of the cell extracts with an anti-XylR antibody at various times after induction ( Figure 3a and 3b ) . The pattern of induction in the strain with the wild-type 5′-UTR ( P . putida BX , Figure 3a ) was such that expression of XylR reached a maximum during the period 1–2 hours after exposure to the inducer , followed by a decrease at longer times , which was expected from the negative feedback loop that governs xylR expression ( Figure 2a ) . The evolution of XylR in the counterpart strain bearing the modified 5′-UTR ( P . putida Pr·RBX , Figure 3b ) developed similarly , but the net concentration of XylR per cell was clearly lower . To examine the consequences of the different levels of the regulator in the response of the Pu-luxCDABE reporter to the suboptimal inducer , 3MBA was added to cultures of P . putida BX and P . putida Pr·RBX as before and their luminescence measured over time ( Figure 3c ) . The results indicate that the overall output of the strain that expresses lower amount of XylR ( P . putida Pr·RBX ) was 4–7-fold lower than the strain carrying xylR controlled by its natural upstream region ( P . putida BX ) . As a control , we also measured the response of the two strains with the different xylR 5′-UTRs to the optimal effector , m-xylene . As shown in Figure 3d , Pu output in the xylR 5′-UTR-modified strain P . putida Pr·RBX displayed a similar trend ( although at somewhat lower levels ) than the reference counterpart P . putida BX . However , we observed that the response of the cells to each inducer was more divergent in strain P . putida Pr·RBX than in strain P . putida BX . These results suggested that decreasing concentrations of XylR had the effect of widening the relative gap between the induction caused by 3MBA and m-xylene . Yet , the change in the xylR 5′-UTR was insufficient to entirely suppress the response of Pu to the suboptimal inducer . Moreover , lower XylR levels also caused a low-capacity regime with the optimal effector . Therefore , the next question was how to keep and even enhance Pu readout in response to m-xylene while removing the effect of 3MBA . As shown in Figure 3d , the data indicate that decreasing intracellular XylR by changing the xylR 5′-UTR sequence caused a reduction of Pu response to m-xylene by approximately 50% . It is thus plausible that the intracellular levels of the regulator determine the capacity ( i . e . , the maximum output ) of the promoter . As the intracellular level of XylR under its native transcriptional control [13] is small and tends to decrease upon induction with aromatic effectors ( [10] and Figure 3a and 3b ) , we wondered how making xylR transcription subject to a PFL ( instead of the extant negative auto-regulation , Figure 2a ) could affect the sensitivity and the capacity of the regulatory node to m-xylene and 3MBA . Positive auto-regulatory loops are prone to off/on expression patterns [30] , [31] in a very TF concentration-sensitive fashion [32] , [33] . We reasoned it would be possible to find a window of xylR expression that could trigger the on state with the optimal effector and leave the loop with 3MBA in the off state . The first attempt in this direction involved the replacement of the native xylR promoter ( Pr ) by Pu , the promoter that is triggered by effector-bound XylR ( Figure 4a ) . To this end , the same Pu sequence employed to construct the Pu-luxCDABE reporter was amplified with PCR primers and placed in front of a promoterless xylR gene preceded by the modified 5′-UTR [28] discussed above . The resulting expression device was then inserted at the attTn7 site of the reporter P . putida chromosome ( Figure 4b ) [25] , as described in the Materials and Methods to generate P . putida Pu·RBX . Note that this strain is entirely isogenic to P . putida Pr·RBX except that the xylR gene is expressed through Pu and not through Pr . This configuration changes the connectivity of the Pu/XylR node from a negative auto-inhibition device ( Figure 2a ) to a PFL ( Figure 4a ) . To verify that such a modification in fact transforms the expression pattern of XylR in vivo , we used a Western blot to assay the accumulation of the protein in the reference strain ( P . putida BX ) and in P . putida Pu·RBX in the presence and absence of m-xylene . As shown in Figure 4c , the non-induced P . putida BX expressed XylR at low levels with a tendency to accumulate at later growth stages [34] . As expected , exposure to m-xylene under the same conditions resulted in lower XylR levels that appeared to decrease over time . The situation with the strain engineered with a forward loop ( P . putida Pu·RBX ) was similar under non-induced conditions but entirely different when cells were exposed to m-xylene . As shown in Figure 4c ( bottom ) , the intracellular concentration of XylR quickly increased after one hour of induction , reaching very high levels at later growth stages . This experiment demonstrated not only that the positive loop engineered for expression of xylR worked as predicted but also that the effect of m-xylene on Pu was enough to switch the state of the loop to from low to high activity ( see 0 h vs . 5 h of Figure 4c ) . To examine whether expressing xylR through such an artificial regulatory device was translated into a high-capacity XylR/Pu node regime , we quantified the luminescence emitted by cultures of P . putida Pu·RBX induced with m-xylene vapors . As shown in Figure 4d , despite sustaining an attenuated translation of xylR because of the modified 5′-UTR introduced into this strain , the readout of Pu activity in P . putida Pu·RBX was as high as in the strain with the wild-type node ( P . putida BX , Figure 2e ) and more than twofold greater than the strain with the modified 5′-UTR but with wild-type regulatory connectivity ( P . putida Pr·RBX , Figure 3d ) . The result of the creation of the construct discussed above was a circuit ( Figure 4a ) that responded to the bona fide XylR inducer ( m-xylene ) with a transcriptional strength comparable to the wild-type ( Figure 2a ) because the lower level of XylR caused by translational attenuation had been compensated for by a PFL . However , what is the effect of such a change on effector specificity ? To examine this question , we monitored the luminescent response of a culture of P . putida Pu·RBX to 3MBA over time ( Figure 5a ) as well as the sensitivity of the same cells to increasing concentrations of this suboptimal inducer ( Figure 5b ) . As a control , we employed the strain bearing the wild-type architecture of the regulatory node ( P . putida BX ) . The results of our experiments ( Figure 5 ) indicate that the response in the PFL-engineered strain to 3MBA was twofold greater than the response of the wild-type construct . This magnification is expected in such PFL regulatory motifs , which are prone to amplify the response to the trigger signal once it reaches a given threshold [30] , [32] . This scenario was confirmed by the results shown in Figure 5b in which the responses of P . putida Pu·RBX and P . putida BX to 3MBA were followed along with moderate incremental increases of inducer concentrations . While Pu activity derived from the wild-type regulatory motif was only gradually increasing at 3MBA concentrations beyond 0 . 12 mM , the equivalent PFL strain displayed an abrupt change of Pu activity in cultures with 0 . 12 mM inducer ( Pu very low ) vs . those with 0 . 25 mM ( Pu high to very high ) and above . This phenomenon likely reflects the switch-on typically caused by the passing of a threshold in auto-inducing regulatory loops [32] , [35] . While the results noted in Figure 5 did not by themselves elucidate the fundamental mechanism underlying our primary question of interest ( discrimination of two chemically related inducers of Pu , see above ) , they demonstrated that the response of the XylR/Pu node to inducer could be made more digital in a fashion dependent on the concentration of the TF involved . These results suggested that one could keep the node in an entirely off state when XylR levels are below a certain threshold , while triggering a high activity regime once the threshold has been surpassed . On this basis , we recreated the same PFL but pursued a higher limit for XylR auto-induction in a way such that optimal and suboptimal effectors could trigger or not , respectively , a high-activity of the downstream Pu promoter . The results above indicate that making XylR expression subject to a PFL increases the amplitude of the XylR/Pu response to both optimal ( m-xylene ) and suboptimal effectors ( 3MBA ) , which means that both effectors cause XylR ( controlled by the attenuated Pu-based PFL ) to reach the TF threshold imposed by this auto-inducing architecture ( Figure 4a ) [30] . The subsequent question was whether suppression of any response to 3MBA could be brought about by moving the window of effector-induced xylR transcription in the PLF to a range that could still trigger full response to m-xylene but remain impervious to the suboptimal inducer . To check this , we simply replaced the Pu promoter of the P . putida Pu·RBX ( Figure 4a ) with a weaker but still XylR-responding promoter , Ps of the TOL plasmid [36] . As XylR activates Ps in response to aromatic effectors at a lower level than Pu [37] , we hypothesized that a Ps-based PFL would make the switch-on threshold more difficult to reach for a suboptimal inducer . We constructed a P . putida strain ( Figure 6a ) placing xylR and the RBS II [28] downstream of the regulatory region of the xylS gene including its own RBS ( Ps ) , using the same methods employed for construction of other strains ( see Materials and Methods ) . This new strain , which was engineered with a Ps-based PFL ( Figure 6a ) , was named P . putida Ps·RBX ( Figure 6b ) . To examine the response of the new regulatory loop of this strain to either effector , P . putida Ps·RBX was grown in the absence or presence of each aromatic inducer and the intracellular levels of XylR recorded over time along with light emission . Figure 6c reveals that 3MBA failed to trigger the auto-activation loop for XylR expression , suggesting that the levels of the TF were insufficient to switch on the PFL . Consistent with those results , 3MBA also failed to cause any significant activation of the downstream Pu-luxCDABE reporter ( Figure 6d ) . This situation did not change when more inducer was added to the culture ( Figure 6e ) , thereby confirming that the silencing of the PFL could be traced to nothing else but XylR . In contrast , when the same P . putida Ps·RBX cells were induced with m-xylene , the cells exhibited a noticeable accumulation of the XylR protein over time ( Figure 6c ) as well as a strong emission of light ( Figure 6d ) . In fact , the output of the Pu-luxCDABE reporter was twofold higher than that of the wild-type regulatory node of P . putida BX ( Figure 2e ) . These results indicated that , unlike the native effector-responding device of the TOL plasmid , the regulatory architecture implemented in P . putida Ps·RBX could discriminate between optimal and suboptimal inducers in a fashion that was not dependent on their concentration but on their chemical structure alone . Unfortunately , the very low levels of expression of XylR under this PFL made detection of intracellular XylR concentrations difficult in cells exposed to 3MBA ( Figure 6c ) . The mechanistic basis of effector discrimination could therefore be inferred but not really proven . To overcome this uncertainty , we resorted to a further perturbation of the system as explained below . The increase in the signal specificity of the XylR/Pu node reported above could be attributed to a change in the threshold necessary to trigger the response produced by the new regulatory loop of xylR . Should this be the case , any resetting of such a threshold back to its former sensitivity range is predicted to restore the response to the suboptimal effector , 3MBA . How can this be accomplished without varying the architecture of the node yet again ? To solve this conundrum , we decided to replace the wild-type xylR sequence of the Ps-based PFL device with the variant xylRv17 . This mutant encodes a XylR derivative that is responsive to all aromatic effectors of the wild-type protein , but it is also able to trigger low-level activity of target promoters in the absence of any inducer [17] , [26] . The expected result of having xylRv17 expressed under the control of a Ps-based PFL is therefore to downshift the threshold of active TF that is necessary for switching on the auto-inducing device . To test these predictions , we first constructed a control strain P . putida BX17 , which was identical to P . putida BX except that the encoded TF sequence is xylRv17 [26] instead of wild-type xylR . As shown in Figure 7a , P . putida BX17 displayed a basal Pu activity level in the absence of effectors ≥7-fold higher than that of the strain bearing the wild-type xylR gene . The test strain , in contrast , was the same as the P . putida Ps·RBX examined above , but xylR had been similarly replaced by xylRv17 , thus giving rise to P . putida Ps·RBX17 ( see the Materials and Methods for construction details ) . The only difference between the two is the minor semi-constitutive expression of xylRv17 compared with the original TF . This disparity has , however , dramatic consequences in the sensitivity of the regulatory device as a whole to 3MBA . Figure 7b and 7c compares the emission of light of the P . putida BX17 ( xylRv17 under TOL plasmid Pr promoter ) , P . putida Ps·RBX ( wild-type xylR expressed through the Ps-based PFL ) and P . putida Ps·RBX17 ( same but xylRv17 ) strains with and without 3MBA . The results indicate that P . putida Ps·RBX17 was nearly as responsive to this effector as the strain bearing the native configuration of the regulatory system . Consistent with the results reported above ( Figure 6 ) , no light emission above background levels could be detected from P . putida Ps·RBX under the same conditions . Taken together , these data strengthened the notion that up- or downshifting of the auto-activation threshold of the PFL by adjusting the concentration or activity of the TF resulted in a regulatory device whose specificity to given effectors could be drastically changed .
The regulatory networks that control gene expression in cells and organisms have evolved to accurately adjust their reactions to specific stimuli [38] . Traditionally , the well-characterized pieces of these regulatory networks haven been exploited to engineer cells with new responses . Although this approach was useful for constructing a plethora of strains presenting new phenotypes [15] , [20] , [39]–[41] , it was not until the onset of systems and synthetic biology that we began to understand how the output of a specific circuit was conditioned by the shape of the network in which the parts are interconnected . Thus , bottom-up approaches shed light on the intrinsic properties of regulatory networks , allowing for the rational design of newly engineered genetic circuits [3] , [42] . Prokaryotic regulatory systems have been used in the construction of bacterial strains with biotechnological applications , such as whole-cell biosensors to detect environmental pollutants [15] , [41] , [43] , [44] . Such biosensors are generally based on the association of input/output components that usually include one bacterial transcriptional regulator that acts as a sensor module and a reporter gene coupled to its cognate promoter [39] , [45] . The specificity of engineered regulatory networks primarily relies on the responsiveness of the transcriptional factor to the signal of interest [41] . Based on this understanding , the quest for new signal specificities has been generally based on in vitro modification of the sensor module [17] , [18] , [26] , [46] . In this work , we have demonstrated that , by rational rewiring of the architecture of a specific regulatory network , it is possible to modify the input-output function to increase the amplitude or the specificity of the response without modifying the core sensor part of the circuit . To this end , we took advantage of the well-characterized TOL network of P . putida pWW0 [7] , where XylR controls the expression of several genes by binding two target promoters , Pu and Ps [6] . As shown in Figure 2 , XylR responds strongly to m-xylene and , to a lesser extent but still significantly , to 3MBA . As shown above , by modifying the connectivity of the components of the regulatory system , we could [i] increase the general amplitude of the output and [iii] generate a super-specific response to the optimal inducer by filtering the response to the less favorable XylR effector . To accomplish this , we first considered simply lowering the concentration of some of the components of the regulatory system , with the aim of increasing the activation threshold and thus increasing specificity [47] . This approach is not without precedent , as previous studies indicate that controlling the expression levels of MAP kinases in regulatory cascades , through gene expression or post-translational modifications , make it possible to change the activation profile of the system [27] . However , this did not suffice for discrimination between optimal and suboptimal effectors ( Figure 3c and 3d ) . In the natural and translationally attenuated context , the levels of XylR are maintained within limits through a negative feedback loop mediated by the Pr promoter ( Figure 1; [10] , [34] ) . We demonstrated above that replacing this negative auto-regulation by a PFL leads to an amplification of the system output in a fashion typical of bistable switches [30] , [31] , [47] . Furthermore , the combination of a translational attenuator with PFLs endowed with different auto-induction parameters resulted in regulatory devices with activation thresholds far enough apart to discriminate between the two XylR effectors tested . Although other approaches have been used to increase signal sensitivity [19] , [48] , this is , to the best of our knowledge , the first instance that modifies the specificity of a sensor system by simply rewiring the connectivity of the parts involved . We argue this approach is extraordinarily promising for improving the performance of whole-cell biosensors [49] , more so when combined with modification of the core TF [17] or the output modules [28] , [50] in the design of optimized devices . Finally , we have not failed to observe that a large number of regulatory nodes for biodegradative and detoxification operons [51] follow a general architecture that we have designated the master control loop ( MCL , Figure 8 ) . This theme , which is also implicit in many metabolic and regulatory networks [52] consists of an upstream signal ( i . e . , the metabolic substrate or effector ) that both influences expression of the cognate regulator as well as the interaction of the same TF with the downstream target promoter . The 3 components of the motif can interact at 4 sites of the related object and present up to 16 theoretically possible configurations . The native arrangement of the TOL regulatory network , as well as those that have been engineered for the sake of this work , are simply variants of such a general layout . The work above suggests that this motif is endowed with extraordinary plasticity for responding to the specifications of any given regulatory need in terms of capacity , inducibility and signal specificity . We propose such an MCL motif as a frame of reference for the further development of regulatory devices á la carte , as required in contemporary metabolic engineering and other fundamental and biotechnological applications .
P . putida KT2440 [53] , its derivatives and the E . coli strains used in this study were grown in Luria-Bertani ( LB ) medium and handled with standard procedures . E . coli CC118λpir was used as the host for propagating plasmids based on an R6K origin of replication [54] . When required , the media was amended with the specified concentrations of 3-methylbenzylalcohol ( 3MBA ) or saturating vapors of m-xylene . Antibiotics were used at the following concentrations: piperacillin ( Pip ) 40 µg/ml , chloramphenicol ( Cm ) 30 µg/ml , gentamycin ( Gm ) 10 µg/ml , tetracycline ( Tc ) 10 µg/ml and potassium tellurite ( Tel ) at 80 µg/ml . PCR reactions were performed as follows: 50–100 ng of the template indicated in each case was mixed in a 100-µl reaction mixture with 50 pmol of each of the primers specified and 2 . 5 units of Pfu DNA polymerase ( Stratagene ) . The samples were then subjected to 30 cycles of 1 min at 95°C , 1 min at 58°C and 3 min at 72°C . Clones were first verified by colony PCR [55] using 1 . 25 units of Taq DNA polymerase ( Roche ) and later confirmed with DNA sequencing . Other gene cloning techniques and standard molecular biology procedures were performed according to [55] . DNA segments containing the xylR gene under the control of different promoter architectures were cloned in vector pTn7-Gm FRT [24] for their eventual insertion at the native attTn7 site of the P . putida chromosome [25] . Such insertions occur always at the same site and in the same orientation , thus generating entirely equivalent strains [24] . To construct the corresponding mini-Tn7 delivery vectors , we first engineered a series of pUC18Not derivatives [54] carrying the DNA segments at stake as follows . A 2 . 8-kb KpnI-SacI fragment of pBBXylR [56] containing the construct Pr → xylR ( i . e . , the xylR gene expressed through its native promoter of the TOL plasmid ) was inserted in a pUC18Not variant that lacked the EcoRI site , thereby generating pBXe . This plasmid was used as the frame for replacing the native promoter region of xylR with the 3 alternative 5′-upstream sequences employed in this work . In the first case , P . putida mt-2 genomic DNA was amplified with the primersPR 1F ( 5′-CGctcgagGTTAACATAATCGGAGACTGC-3′ ) and PRrbs 2R ( 5′-CCGgaattcCATGCTTAATTTCTCCTCTTTTTGTTTTCCTCTTGTTTTTAT-3′ ) . The resulting 545-bp product contained the native Pr promoter and the adjacent sequence down to the natural RBS ( bold ) but with an added RBSII ( underlined in the sequence [28] between the original RBS and the cognate ATG codon in italics ) . Furthermore , the amplified segment was flanked by the EcoRI and XhoI restriction sites introduced in the primers ( lower case in the sequences above ) . In the second case , primers PU 1F ( 5′-ACGCctcgagCCCGGGAAAGCGCGATGA-3′ ) and PUrbs 2R ( 5′-CGgaattcCATGCTTAATTTCTCCTCT TTTGAAGGGTCACCACTATTTTT-3′ ) amplified a 464-bp segment containing Pu all the way down to the RBS of the xylU gene ( bold ) , which was then followed by the native RBSII ( underlined ) and ATG ( italics ) of the xylR gene , flanked by EcoRI and XhoI sites ( lower case ) . Finally , the primers PS 1F ( 5′-CGctcgagTTGTTTTCCTCTTGTTTTTATCG-3′ ) and PSrbs 2R ( 5′-CGgaattcCATGCTTAATTT CTCCTCTTTAGTTCACGGTTCTCTTATT-3′ ) resulted in a EcoRI-XhoI 256-bp fragment containing the second XylR-responsive promoter Ps of the TOL plasmid ( Figure 1 ) down to the RBS of the xylS gene ( bold ) and followed by the RBSII ( underlined ) and ATG ( italics ) of the xylR gene as before . Each of these 3 EcoRI-XhoI restriction products were cloned into the corresponding sites of the pBXe plasmid , thereby replacing the original xylR upstream region with refactored counterparts and originating pPrRBX , pPuRBX and pPsRBX . These plasmids were separately digested with NotI , which excised DNA segments carrying Pr → xylR , Pr- ( RBSPr RBSII ) → xylR , Pu- ( RBSPu RBSII ) → xylR and Ps- ( RBSPs RBSII ) → xylR . These were cloned in the same orientation into the NotI site of pTn7-Gm FRT [24] generating pTn7-BX , pTn7-Pr·RBX , pTn7-Pu·RBX and pTn7-Ps·RBX . For the constructs bearing the semi-constitutive XylR variant named XylRv17 ( which carries mutations F48I and L222R; [17] ) , a 713-bp EcoRI-AvrII fragment-spanning DNA sequence corresponding to the A domain of xylRv17 was excised from plasmid pBBxylRv17 [17] and recloned into the corresponding sites of pBXe or pPs·RBX , yielding pBX17 and pPs·RBX17 , respectively . These plasmids were then digested with NotI and the fragments encoding Pr → xylRv17 and Ps- ( RBSPs RBSII ) → xylRv17 were cloned , as before , in vector pTn7-Gm FRT [24] , thereby generating pTn7-BX17 and pTn7-Ps·RBX17 . Standardization of the various regulatory devices for xylR expression and measurement of network output required the engineering of a reference Pu-luxCDABE reporter P . putida strain . To this end , we first digested plasmid pattPuLUX [24] with EcoRI/XmaI to delete an internal 86-bp fragment containing the E . coli attTn7 insertion site [57] . The ends of the digested plasmid were blunted with T4 DNA polymerase and relegated to generate the plasmid pPu·LUX . This construct was then digested with NotI , and the fragment containing a Pu-luxCDABE fusion ligated to pJMT6 , a mini-Tn5 delivery vector with a potassium tellurite ( Tel ) resistance cassette [58] , producing pTn5 Tel-Pu·LUX . This plasmid was conjugally transferred to P . putida KT2440 ( see below ) and TelR exconjugants tested for insertion of the hybrid mini-Tn5 Tel element carrying the Pu-luxCDABE fusion . One of these clones was called P . putida Pu·LUX and retained for further use as receptor of the different variants of the mini-Tn7 transposons borne by the plasmids pTn7-BX , pTn7-Pr·RBX , pTn7-Pu·RBX , pTn7-Ps·RBX ( see above ) . Insertion of the corresponding segments in the naturally occurring attTn7 site of P . putida Pu·LUX was confirmed through colony PCR using a primer that anneals within the sequence of the B domain of xylR ( NDoCAvrII: 5′-GCGAATGGCCTAGG CCGTAATACTG-3′ ) and one at the attTn7 insertion site within the glmS gene ( PpuglmS 2R: 5′-GTGCGTGCCCGTGGTGG-3′ ) . The ensuing collection of GmR strains were deleted of this antibiotic marker by transient expression of yeast flippase encoded by the plasmid pBBFLP , which brings about site-specific recombination of the FRT sequences that flank the resistance gene [56] . The same strategy was followed in the case of pTn7-BX17 and pTn7-Ps·RBX17 , although the GmR marker was not removed in these cases . The final outcome of all these manipulations was the isogenic strain collection P . putida BX , P . putida Pr·RBX , P . putida Pu·RBX , P . putida Ps·RBX , P . putida BX17 and P . putida Ps·RBX17 . pTn5Tel-Pu·LUX and mini-Tn7 derivatives ( pTn7-BX , pTn7-Pr·RBX , pTn7-Pu·RBX , pTn7-Ps·RBX , pTn7-BX17 and pTn7-PsRBX17 ) and pBBFLP were conjugally passed from the donor E . coli strain indicated in each case into the different P . putida recipients with a filter mating technique [54] . To this end , a mixture of donor , recipient and helper strain E . coli HB101 ( pRK600 ) was deposited on 0 . 45-µm filters in a 1∶1∶3 ratio and incubated for 8 h at 30°C on the surface of LB-agar plates . Mini-Tn7 derivatives were co-mobilized along with the transposase-encoding genes tnsABCD into the recipient strains by including E . coli CC118λpir ( pTNS1 ) in the mating mixture [59] . After incubation , the cells were resuspended in 10 mM MgSO4 , and the appropriate dilutions plated on M9/succinate amended with suitable antibiotics for counter-selection of the donor and helper strains and growth of the P . putida clones that had acquired the desired insertions . Bona fide transposition was verified in every case by checking the sensitivity of individual exconjugants to the delivery vector marker , piperacillin . To measure bioluminescence production of P . putida cells carrying luxCDABE fusions , 2-ml cultures of the strains under study were first pre-grown in 10-ml test tubes overnight in LB medium at 30°C . The cultures were then diluted to an OD600 of 0 . 05 and grown up to an OD600 = 1 . 0 in 100-ml flasks . At this point , the cultures were exposed to m-xylene or 3MBA , as indicated for each case . When required , 200-µl aliquots of these cultures were placed in 96-well plates ( NUNC ) , and light emission and OD600 were measured in a Victor II 1420 Multilabel Counter ( Perkin Elmer ) . The specific bioluminescence values were calculated by dividing the obtained values of total light emission ( in arbitrary units ) by the optical density of the culture ( OD600 ) . The specific bioluminescence values shown represent the average of at least three biological replicates . Protein analyses were performed according to published protocols [55] . For detection of the XylR ( wild-type and variants ) , 5 µg of whole protein extract of P . putida cells was denatured in a sample buffer containing 2% SDS and 5% ß-mercaptoethanol and run on 10% polyacrylamide gels . These were subsequently blotted onto a polyvinylidene difluoride ( PVDF ) membrane ( Immobilon-P , Millipore ) using a semi-dry electrophoresis transfer apparatus ( BioRad ) . After protein transfer , the membranes were blocked for 2 h at room temperature with MBT buffer ( 0 . 1% Tween and 5% skim milk in phosphate-buffered saline , PBS ) . For detection of XylR , the membranes were incubated with MBT buffer containing a dilution 1/2000 of anti-XylR Phab [13] . The membranes were subjected to 5-min washing steps in 40 ml of MBT buffer alone or MBT with 0 . 1% sodium deoxycholate in the case of the membranes hybridized with Phabs . To detect the anti-XylR Phab bound to the XylR bands , an anti-M13 peroxidase conjugate was utilized ( 1/5000 dilution in MBT ) . The membranes were incubated for 1 h at room temperature with a secondary antibody and washed 5 times in MBT buffer for 5 min each , as before . XylR was developed by reaction of the treated membrane with a chemiluminescent substrate ( ECL , Amersham Pharmacia Biotech ) and recorded on x-ray film .
|
It is generally taken for granted that promoters regulated by transcriptional factors ( TFs ) that respond to small molecules control their specificity to given effectors by tightening or relaxing the intrinsic dual interaction between the TF and the particular inducer . One such promoter is Pu , which drives expression of an operon for the biodegradation of m-xylene by the soil bacterium P . putida mt-2 . While XylR , the chief TF of this system , binds this substrate and activates Pu , the same regulator responds , to a lesser extent , to 3-methylbenzylalcohol and thus also activates the promoter . This work provides evidence that such natural effector promiscuity of the system can be altogether suppressed by replacing the naturally occurring negative autoregulation loop that governs XylR expression with an equivalent positive feedback loop . Based on this result , we argue that signal specificity of a given regulatory device depends not only on the TF involved but also on TF connectivity to upstream signals and downstream targets .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"systems",
"biology",
"environmental",
"biotechnology",
"microbial",
"evolution",
"gene",
"expression",
"genetics",
"synthetic",
"biology",
"biology",
"microbiology",
"gene",
"networks",
"molecular",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
Increasing Signal Specificity of the TOL Network of Pseudomonas putida mt-2 by Rewiring the Connectivity of the Master Regulator XylR
|
Salicylic acid ( SA ) -induced defense responses are important factors during effector triggered immunity and microbe-associated molecular pattern ( MAMP ) -induced immunity in plants . This article presents evidence that a member of the Arabidopsis CBP60 gene family , CBP60g , contributes to MAMP-triggered SA accumulation . CBP60g is inducible by both pathogen and MAMP treatments . Pseudomonas syringae growth is enhanced in cbp60g mutants . Expression profiles of a cbp60g mutant after MAMP treatment are similar to those of sid2 and pad4 , suggesting a defect in SA signaling . Accordingly , cbp60g mutants accumulate less SA when treated with the MAMP flg22 or a P . syringae hrcC strain that activates MAMP signaling . MAMP-induced production of reactive oxygen species and callose deposition are unaffected in cbp60g mutants . CBP60g is a calmodulin-binding protein with a calmodulin-binding domain located near the N-terminus . Calmodulin binding is dependent on Ca2+ . Mutations in CBP60g that abolish calmodulin binding prevent complementation of the SA production and bacterial growth defects of cbp60g mutants , indicating that calmodulin binding is essential for the function of CBP60g in defense signaling . These studies show that CBP60g constitutes a Ca2+ link between MAMP recognition and SA accumulation that is important for resistance to P . syringae .
Plant innate immunity is multi-layered and tightly regulated by a complex signaling network [1] . Defense against biotrophic or hemibiotrophic bacterial pathogens can be thought of as consisting of two branches: the broad and nonspecific defenses triggered by the perception of microbe- or pathogen-associated molecular patterns ( MAMPs or PAMPs ) , and the robust and relatively more specific resistance mediated by resistance ( R ) genes [2] , [3] . MAMPs are proteins and other molecules characteristic of microbes . MAMP-triggered defense is initiated by perception of MAMPs by pattern-recognition receptors ( PRRs ) . Well-characterized examples in Arabidopsis include recognition of flagellin by the receptor kinase FLS2 [4] , of Ef-Tu by the receptor kinase EFR [5] , and of chitin by the LysM receptor kinase CERK1 . Direct binding has been demonstrated for FLS2 and EFR , but not for CERK1 [6] , [7] . FLS2 and EFR require a second kinase , BAK1 , to initiate defense signaling [8]–[10] . Signaling activation results in an oxidative burst produced by the NADPH oxidase encoded by AtrbohD , which is in turn required for deposition of callose at the cell wall [11] . Other responses include closure of stomata , activation of a MAP kinase cascade , and a suite of gene expression changes [12]–[14] . MAMP responses are effective in limiting pathogen growth , as pre-treatment with flg22 , a peptide derived from flagellin , dramatically reduces growth of Pseudomonas syringae pv . tomato DC3000 ( Pst DC3000 ) in an FLS2-dependent manner [15] , efr plants are more susceptible to Agrobacterium tumefaciens [5] , and cerk1 mutants are more susceptible to Alternaria brassicicola [6] , [7] . Bacterial pathogens produce numerous virulence effector proteins that are secreted into the host cytoplasm , where many of them disrupt plant defense responses [2] , [3] , [16] . Plants can counter this if they have one or more appropriate Resistance ( R ) genes . R proteins detect effectors by directly binding effector proteins or by sensing the cellular disturbance caused by effector activity [17] . R protein activation results in induction of additional layers of defenses , including production of reactive oxygen species ( ROS ) and activation of the hypersensitive response ( HR ) , a programmed cell death response thought to limit pathogen access to water and nutrients [18] . R gene recognition of an effector also results in activation of the salicylic acid ( SA ) -dependent defense signaling pathway , which plays an important role in resistance [19] . Several components of the SA signaling circuitry have been identified through genetic analysis in Arabidopsis . ENHANCED DISEASE SUSCEPTIBILITY 1 ( EDS1 ) and PHYTOALEXIN DEFICIENT 4 ( PAD4 ) are physically-interacting proteins that are required for SA synthesis in response to some , but not all , pathogens [20]–[23] . PAD4 and EDS1 are also required for pathogen-induced expression of many SA-independent genes [24] . SALICYLIC ACID INDUCTION DEFICIENT 2 ( SID2 ) , which encodes isochorismate synthase , and ENHANCED DISEASE SUSCEPTIBLITY 5 ( EDS5 ) are required for SA synthesis [25] , [26] . In response to elevated SA , NONEXPRESSOR OF PR GENES 1 ( NPR1 ) undergoes a transition from oligomer to monomer and translocates to the nucleus [27] , [28] . Once there , it interacts with transcription factors to modulate expression of defense genes such as PATHOGENESIS RELATED 1 ( PR1 ) [29] , [30] . Recent studies have shown that SA signaling is an integral part of the MAMP response , as well as of R-gene mediated resistance . Treatment with the MAMPs flg22 or bacterial lipopolysaccharide ( LPS ) caused SA accumulation and systemic acquired resistance , a systemic response associated with SA [31] . Flg22 treatment also induced many canonical SA-related genes , including SID2 , EDS5 , NPR1 , and PR1 [32] . SA was produced in response to flg22 or challenge with Pst DC3000 hrcC , a strain that is unable to transport effectors and thus serves as an elicitor of the MAMP response [33] . Many gene expression changes caused by challenge with Pst DC3000 hrcC were reduced in pad4 or sid2 mutants , demonstrating that MAMP-induced SA plays a role in the MAMP response [33] . Importantly , resistance to Pst DC3000 induced by pre-treatment with flg22 was compromised in pad4 and sid2 mutants , demonstrating that MAMP-induced SA is important for MAMP-triggered resistance [33] . The nature of the link between MAMP recognition and activation of SA signaling remains to be determined . Calcium signaling is another aspect of plant defense responses that has been implicated in both MAMP-triggered and R-gene mediated resistance responses . Rapid influxes of cytosolic Ca2+ have been observed after treatment of Nicotiana plumbaginifolia cells with MAMPs such as LPS , oligogalacturonides ( OGs ) , and cryptogein , a small protein from Phytophthora cryptogea that elicits defense responses and cell death in tobacco [34] . In Arabidopsis , peptidoglycan from gram-positive bacteria acted as a MAMP and induced cytosolic Ca2+ influx , as did flg22 [35] . In the case of treatment of Nicotiana plumbaginifolia cells with cryptogein , blocking calcium influx with La3+ blocked downstream responses including MAP kinase activation , gene expression changes , and the HR , indicating that Ca2+ influx is required for these responses [34] . In Arabidopsis , production of NO in response to LPS required a Ca2+ influx that depended on the CYCLIC NUCLEOTIDE GATED ION CHANNEL 2 ( CNGC2 ) [36] , and flg22 treatment resulted in calcium-dependent phosphorylation of a syntaxin [37] . Calcium signaling also plays a role in R-gene mediated responses . Cytosolic calcium increased in response to treatment of Arabidopsis carrying the R gene RPM1 with P . syringae expressing the cognate effector protein avrRpm1 . Blocking calcium influx with La3+ blocked the hypersensitive response characteristic of this resistant interaction [38] . A mutation in CNGC2 called dnd1 blocks the HR in several cases of Arabidopsis R-gene mediated resistance [39] , suggesting that CNGC2 may be generally important for calcium influx during defense responses . Calcium signals can be transduced by binding of calcium to calmodulin ( CaM ) , a ubiquitous small calcium-binding protein . Binding of CaM to other proteins modulates their activities [40] . The barley MLO protein is a CaM-binding protein that acts as a repressor of defense responses . Mutations that prevent CaM binding reduce the repressing activity of the protein [41] . A number of CaM-binding proteins that are pathogen-inducible have been identified , suggesting that they may participate in the defense response [40] , [42] , [43] . These include members of the CBP60 family in Arabidopsis [44] . The AtCBP60 family consists of seven members ( from AtCBP60a to AtCBP60g: At5g62570; At5g57580; At2g18750; At4g25800; At2g24300; At4g31000; At5g26920; Figure S1 ) that were identified based on their protein sequence similarities to tobacco and maize homologues [45]–[47] . Domains that bind CaM in a Ca2+ dependent manner have been mapped to the C-terminal ends of five family members [44] . AtCBP60 genes were shown to be differentially expressed in response to bacterial pathogens and inducers of defense responses but their biological functions remain unknown [43] . We have studied a member of the Arabidopsis CBP60 CaM-binding protein family , CBP60g ( At5g26920 ) , which lacks the C-terminal CaM-binding domain of other family members . We found that it is inducible by infection with Pseudomonas syringae pv . maculicola strain ES4326 ( Psm ES4326 ) and by MAMPs . Loss-of-function mutants allowed enhanced growth of Psm ES4326 , demonstrating a role of this protein in disease resistance . Characterization of mutant lines revealed a defect in SA signaling following MAMPs treatment , indicating a role for CBP60g in activation of SA signaling by MAMPs . We demonstrated that CBP60g binds CaM , and determined that the CaM-binding domain lies in the N-terminal part of the protein . Mutant proteins that lacked CaM-binding activity failed to complement the defense defects of a cbp60g loss-of-function mutant , indicating that CaM binding is important for the function of CPB60g in defense signaling .
We noticed that , according to microarray data , Arabidopsis CBP60g ( CaM-binding protein 60-like . g; At5g26920 ) was strongly up-regulated in response to infection by the virulent strain Psm ES4326 [48] . We used the real-time quantitative polymerase chain reaction ( qRT-PCR ) to monitor expression of this gene . Figure 1A shows that expression of CBP60g was induced between three and six hours after Psm ES4326 infection , and expression remained high for at least 24 hours . CBP60g expression was also induced between six and nine hours after infection by P . syringae pv . tomato strain DC3000 ( Pst DC3000 ) , but to a lesser extent . We further investigated CBP60g expression after MAMP treatments . We inoculated wild-type plants with Pst DC3000 hrcC , a strain defective in delivery of type-III effectors [49] . By three hours after inoculation , and continuing for at least 24 hours , CBP60g transcript levels were higher than in mock-treated controls . Infiltration with the purified MAMP , flg22 , had an even stronger effect ( Figure 1B ) . These results indicate that expression of CBP60g is induced in response to bacterial pathogens and MAMPs . We studied the function of CBP60g using loss-of-function mutants . We acquired two T-DNA insertion mutants of CBP60g , SALK_023199 and GABI_075G12 , and named them cbp60g-1 and cbp60g-2 , respectively . According to the SIGnAL database ( http://signal . salk . edu/ ) , the T-DNA insertion of cbp60g-1 is located in the third exon of At5g26920 , while in cbp60g-2 it is in the fifth exon , as shown in Figure 2A . Reverse transcription PCR ( RT-PCR ) showed that the CBP60g transcript was absent in cbp60g-1 homozygotes and only partial in cbp60g-2 homozygotes ( Figure 2B ) , suggesting that neither mutant allele produces functional CBP60g protein . To test cbp60g mutants for enhanced susceptibility to P . syringae , wild type ( Col-0 ) , cbp60g-1 , and cbp60g-2 plants were inoculated with Psm ES4326 , and bacterial titer was determined three days later . Figure 2C shows that both mutant lines supported significantly more bacterial growth than wild-type plants , but less than the extremely susceptible pad4 plants [22] . The fact that two independent mutations in CBP60g result in similar enhanced susceptibility phenotypes strongly suggests that these phenotypes result from mutations in CBP60g . This idea was further verified by introducing a genomic clone containing CBP60g and its promoter ( 1093 base pairs upstream of its start codon ) into homozygous cbp60g-1 plants . The progeny of a transformant that was hemizygous for the transgene were infected with Psm ES4326 and bacterial titers in individual plants were determined three days later . The average titer in plants carrying the wild-type transgene was similar to wild-type plants , while the average titer in sibling plants lacking the transgene was significantly higher and similar to untransformed cbp60g-1 homozygotes . Pst DC3000 also grew to higher titers in cbp60g mutants than in wild-type plants , and this phenotype was also complemented by a wild-type CBP60g transgene as shown in Figure 2D . Based on these experiments , we conclude that CBP60g is required for wild-type levels of resistance to both Psm ES4326 and Pst DC3000 . In an effort to understand how cbp60g mutations affect defense responses against bacterial pathogens , we conducted microarray profiling experiments using a customized long-oligonucleotide microarray with probes for 464 pathogen-responsive genes , representing diverse expression patterns [50] . Expression profiling and data analysis using the custom microarray were carried out as described in Methods . First , we compared wild-type and homozygous cbp60g-1 plants 24 hours after inoculation with Psm ES4326 . Other than CBP60g itself , there was only one gene ( COR47 , At1g20440 ) that was significantly different from wild-type by more than two-fold ( Table S1 ) . These results indicated that CBP60g did not have a major effect on gene expression 24 hours after Psm ES4326 infection . Since CBP60g is also inducible by MAMP treatments , we tested the cbp60g-1 mutant for alterations in gene expression following inoculation with Pst DC3000 hrcC . Wild-type and cbp60g-1 plants were mock-inoculated or inoculated with Pst DC3000 hrcC , and samples were collected after three and nine hours , when MAMP-triggered responses generally occur [51] , [52] . At three hours after inoculation with Pst DC3000 hrcC , 31 genes showed differential expression between wild-type and cbp60g-1 plants ( q<0 . 05 ) as shown in Table S2 . At nine hours , 43 genes were differentially expressed ( q<0 . 05 ) . Clearly , the effect of CBP60g on gene expression changes during a MAMP response is larger than it is 24 hours after Psm ES4326 inoculation . To determine in which sector of the defense signaling network CBP60g acts , we compared the effects of cbp60g-1 on the response to DC3000 hrcC to the effects of other mutations that perturb the defense signaling network . We chose pad4 and sid2 , which reduce SA signaling [26] , [53]; coi1 and dde2 , which reduce JA signaling [54] , [55]; ein2 , which reduces ethylene signaling [56] , and mpk3 , which may affect MAMP signaling [57] . Wild-type and mutant plants were inoculated with Pst DC3000 hrcC and wild-type plants were also mock-inoculated . Samples were again collected after three and nine hours . We selected genes with significantly different expression levels in at least one of the seven mutants compared to wild-type , after Pst DC3000 hrcC inoculation ( q<0 . 05; Table S2 ) . Among these , we further selected genes that were induced or repressed by at least two-fold in wild-type plants inoculated with Pst DC3000 hrcC compared to mock-inoculated wild-type plants . For the 88 genes that met these conditions at the three hour time point , the log2 ratios of cbp60g to Col-0 , coi1-1 to Col-0 , dde2-2 to Col-0 , ein2-1 to Col-0 , mpk3 to Col-0 , pad4-1 to Col-0 , and sid2-2 to Col-0 were subjected to complete-linkage agglomerative hierarchical clustering [58] . The same procedure was carried out on the 77 genes that met these conditions at the nine hour time point . Figure 3 shows that the effects of cbp60g most closely resembled those of sid2 and pad4 , which disrupt SA signaling during the MAMP response . At nine hours , the correlations between the cbp60g to Col log2 ratios and the pad4 to Col and sid2 to Col log2 ratios were 0 . 75 and 0 . 68 , respectively as shown in Table 1 . As PAD4 and SID2 function in SA signaling , these strong correlations between the effects of cbp60g and those of mutations known to disrupt SA signaling suggested that CBP60g functions in activation of SA signaling during the MAMP response . The microarray data also revealed that SID2 was induced by Pst DC3000 hrcC inoculation in wild-type plants ( 1 . 74-fold at three hours and 3 . 02-fold at nine hours ) , and that this induction was attenuated in cbp60g mutant plants ( the ratio of SID2 expression in cbp60g-1 to wild-type is 0 . 34 at three hours and 0 . 38 at nine hours ) . The qRT-PCR results shown in Figure 4A confirmed that SID2 expression was induced by DC3000 hrcC inoculation and flg22 treatment , as we have reported previously [33] . SID2 expression was reduced in both cbp60g mutants , with statistically significant differences observed three hours after flg22 treatment and nine hours after DC3000 hrcC inoculation . Since SID2 is required for SA synthesis during the defense response , we suspected that SA accumulation was also compromised in cbp60g mutants . To determine whether SA levels were lower in cbp60g mutants , we measured free ( non-conjugated ) SA levels in wild-type , cbp60g , and sid2 plants following mock treatment , flg22 treatment , and DC3000 hrcC inoculation . Figure 4B shows that SA levels in both cbp60g mutants were significantly lower than in wild-type plants at six and nine hours following flg22 treatment and at nine hours following DC3000 hrcC inoculation ( note the log10 scale ) . SA levels in sid2 plants were very low and did not respond to treatments . We also measured free SA levels in cbp60g-1 following inoculation with Psm ES4326 or the avirulent strain Psm ES4326 avrRpt2 . After Psm ES4326 inoculation , the SA level in cbp60g-1 was only lower than in wild-type plants at nine hours after inoculation ( q = 0 . 002 ) but not 24 , and the extent of the reduction at 9 hours was less than in the case of flg22 or Pst DC3000 hrcC treatments ( Figure 4C ) . To verify that the SA difference we observed in Psm ES4326-inoculated plants was not due to enhanced bacterial growth in the cbp60g-1 mutant , we monitored bacterial titers in the plants used for SA extraction . As shown in Figure S2 , no significant differences in titer were observed among wild type and cbp60g-1 mutants at 9 or 24 hours after inoculation . After Psm ES4326 avrRpt2 inoculation , there were no significant differences ( q<0 . 05 ) in SA accumulation between wild-type and cbp60g mutants ( Figure S3 ) . Taken together , these results show that CBP60g contributes to SA accumulation during the MAMP response and at early times during attack by Psm ES4326 . Having observed that cbp60g mutants were deficient in MAMP-induced SA accumulation , we tested cbp60g mutants for defects in other MAMP-triggered responses . Three characteristic MAMP signaling responses are transient production of reactive oxygen species ( ROS ) , deposition of callose , and inhibition of seedling growth [51] . We monitored flg22-induced ROS production in wild-type , cbp60g-1 , cbp60g-2 , and fls2 plants . FLS2 encodes the flagellin receptor , thus fls2 mutants do not respond to flg22 . There was no difference in production of ROS between wild-type plants and cbp60g mutants , while ROS production was abolished in fls2 plants ( Figure S4 ) . Callose deposition at twelve hours after flg22 treatment was assayed by aniline blue staining and image analysis . No significant differences were observed among wild type and cbp60g mutants ( Figure S5 ) . No callose deposition was observed in pmr4 mutants , which lack a callose synthase [59] . Clearly , cbp60g mutants are not defective in flg22-induced ROS production or callose deposition . Seedling growth is inhibited by flg22 . We tested wild-type , cbp60g , pad4 , sid2 , and fls2 seedlings for inhibition by flg22 . We found that cbp60g , pad4 , and sid2 plants all showed growth inhibition similar to wild-type plants , while fls2 mutants showed very little growth inhibition ( Figure S6 ) . Thus , mutations that reduce MAMP-induced SA production do not have a major effect on inhibition of seedling growth by flg22 . Five of the eight CBP60 proteins have a CaM binding domain ( CBD ) at the C terminus [44] . However , the corresponding sequence of CBP60g is poorly conserved ( Figure S1 ) . In order to test whether CBP60g binds to CaM and identify possible CBD domain ( s ) of CBP60g , we predicted its coiled coil domains using the PredictProtein algorithm [60] , as this protein secondary structure is shared by nearly all known CBDs [61] . Figure 5A shows the positions of the predicted coiled coil domains . We tested the ability of CBP60g to bind CaM by constructing a GST-CBP60g protein fusion and expressing it in Escherichia coli . Western blotting with anti-GST antibody showed that a protein of the expected molecular weight ( approximately 89 kilodaltons ) was produced . A replicate blot was incubated with biotinylated CaM . Bound CaM was then detected with streptavidin-conjugated alkaline phosphatase . Figure 5B shows that full-length CBP60g protein bound to CaM . No binding was observed in the absence of Ca2+ ( Figure S7 ) . We then tested various CBP60g deletion mutants ( Figure 6A ) in an effort to locate the CaM binding domain ( CBD ) . Figure 6B shows that a 76 amino acid fragment from the N-terminus of the protein was sufficient for CaM binding . Further deletions revealed that a fragment of only 45 amino acids retained CaM binding capability ( Figure S8 ) . According to the CaM target database ( http://calcium . uhnres . utoronto . ca/ctdb ) , this amino acid sequence does not contain any of the known CaM-binding motifs . However , as shown in Figure 5c , it does contain a predicted coiled coil domain , and it is amphipathic , a property shared by almost all CBDs [61] . Previous studies showed that disruption of amphipathic properties of CBDs abolished CaM binding [41] , [62] , so we further defined the CBD of CBP60g using site-directed mutagenesis . Based on the helical wheel projection of the CBP60g CBD ( Figure 5C ) , we mutated the codons for all four hydrophobic amino acids ( three valines and one phenylalanine ) on the hydrophobic side to create codons for hydrophilic amino acids ( arginine or lysine ) . As controls we also mutated codons for two amino acids on the hydrophilic side of the CBP60g CBD and one amino acid just outside the CBD . These changes did not affect the amphipathic nature of the predicted helix . Figure 5D shows that loss of any of the four hydrophobic amino acids on the hydrophobic side of the CBD abolished CaM binding , while none of the other mutations had a detectable effect . Taken together , these experiments demonstrated that CBP60g is a CaM-binding protein , and defined the CBD in the N-terminus of the protein . CaM binding often modulates protein function [40] . To investigate whether CaM binding affects the function of CBP60g in defense responses , we engineered transgenic plants carrying mutated CBP60g proteins that no longer bind CaM in the cbp60g-1 mutant background . We then tested them for defects in limiting bacterial growth and SA accumulation . We transformed cbp60g-1 mutant plants with modified genomic constructs including both CaM-binding ( F41K ) and non-CaM-binding ( V28K , V29R ) versions of CBP60g , which were fused to a c-Myc epitope tag at their C-termini . A wild-type version of the CBP60g c-Myc fusion construct ( WT ) was also made as a control . Primary transformants containing single copies of the transgenes were selected by qPCR , and their progeny were used for analyses . First , we tested expression of the modified proteins by immunoblotting using c-Myc antibody . None of the c-Myc fusion proteins were detected in untreated plants , but they were all present in plants inoculated with Psm ES4326 . This was also true for the wild-type CBP60g c-Myc fusion construct ( Figure S9A ) . Thus , the Psm ES4326-induced increase in the CBP60g transcript level is reflected in the protein level . We then measured bacterial growth and SA accumulation in the transgenic plants . Figure 6A shows that 2 days after inoculation with Psm ES4326 , bacterial titers in transgenic lines carrying non-CaM-binding constructs were similar to the titers in cbp60g-1 , while titers in transgenic lines carrying the CaM-binding construct were similar to those in wild-type plants . We assayed four additional independent transgenic lines for bacterial growth , yielding consistent results ( Figure S9B ) . This shows that CaM binding is required for complementation of the enhanced disease susceptibility phenotype of cbp60g-1 . We also measured free SA levels in leaves after treatment with flg22 or infection by Psm ES4326 . Figure 6B shows that the non-CaM-binding proteins , V28K and V29R , failed to complement the SA accumulation defects of cbp60g-1 , while the protein that did bind CaM , F41K , restored SA to wild-type levels . Collectively , these results demonstrate that CBP60g requires CaM binding for its function in disease resistance and MAMP-induced SA accumulation .
Figure 7 shows a model of the position of CBP60g in the defense signaling network . Recognition of MAMPs such as bacterial flagellin by pathogen recognition receptors ( PRRs ) activates a MAP kinase cascade that in turn activates gene expression changes and ethylene production . MAMP recognition also triggers elevation of cytosolic Ca2+ concentration and activates production of reactive oxygen species ( ROS ) by AtrbohD . AtrbohD is required for deposition of callose . Recently , we found that MAMP signaling also activates SA production , and that activation of SA signaling by MAMPs is important for MAMP-induced resistance [33] . SA signaling is also activated in response to recognition of effectors by R genes ( ETI ) . Infection by the virulent strain Psm ES4326 activates SA signaling strongly , and infection by Pst DC3000 activates it to a lesser degree [48] . It is not known whether this activation is due to a weak ETI response that does not result in a hypersensitive response , or to some other mode of pathogen recognition . The increase in cytosolic Ca2+ triggered by MAMP recognition likely affects may aspects of defense signaling , as suggested by the multiple arrows leading out from Ca2+ in Figure 7 . One of these aspects is activation of CBP60g through CaM binding , as we have shown that CaM binding is required for the functions of CBP60g in MAMP-induced SA accumulation and limiting growth of Psm ES4326 . CBP60g contributes to MAMP-induced SA accumulation , as SA levels are reduced in cbp60g mutants . Following Psm ES4326 inoculation , an SA-accumulation defect was observed at nine but not 24 hours . SA accumulation at nine hours likely reflects MAMP signaling , so this data is consistent with the idea that CBP60g is involved in transducing a signal from the MAMP response to SA accumulation . It is likely that there are multiple routes to activation of SA accumulation , with different routes more or less important for different stimuli and/or at different times . This may explain our finding that CBP60g is important for SA accumulation during the MAMP response , but has little effect during the response to Psm ES4326 . While our results show that CBP60g constitutes part of the link between MAMP recognition and activation of SA signaling , we cannot yet determine at what point in the MAMP signaling cascade a signal is transferred to CBP60g . Similarly , the relationship between MAMP recognition and Ca2+ influx is unclear . These uncertainties are indicated by the absence of arrows between PRRs and the MAPK cascade on the one hand , and Ca2+ influx and CBP60g function on the other . Based on examination of the microarray data , we speculate that CBP60g and the MAPK cascade may act independently . As shown in Figure S10 , at 3 and 9 hours after inoculation with Pst DC3000 hrcC , there is no overlap between genes whose expression is affected by mpk3 and those whose expression is affected by cbp60g . If CBP60g function required MAPK activation , or vice versa , we would expect to see some commonly-affected genes . However , it is also possible that if we were able to study a mpk3 mpk6 double mutant ( MAPK3 and MAPK6 are partially redundant , and a double mutant is lethal ) , we might see a different result . One might also ask at what point CBP60g function affects SA signaling . The signal coming from CBP60g must act upstream from SA synthesis , as SA levels are reduced in cbp60g mutants . PAD4 also contributes to SA levels , as pad4 mutants have reduced SA after MAMP treatment and after Psm ES4326 infection [33] . Unlike pad4 , cbp60g does not affect SA levels at late times after infection by Psm ES4326 , and it does not have a substantial effect on gene expression 24 hours after infection [22] , [48] . It may affect SA levels independently of PAD4 , or it may act upstream of PAD4 . This uncertainty is indicated by the dotted circle on the right in Figure 7 . Among the mutants studied by expression profiling , the effect of cbp60g was most similar to that of pad4 , and slightly less similar to that of sid2 . This may be an indication that cbp60g acts upstream of pad4 to activate SA signaling during the MAMP response . Psm ES4326 is a strong inducer of SA synthesis [22] , [48] . In turn , SA-dependent defense responses play a major role in limiting growth of this pathogen . Mutations that seriously compromise SA signaling , including pad4 , eds5 , sid2 , and npr1 , result in increases in bacterial growth on the order of 2–3 log10s [63]–[65] . In cbp60g mutants , we observed reduced SA production following MAMP treatments , and this was reflected in delayed SA accumulation in plants inoculated with Psm ES4326 , evidenced by reduced SA levels nine hours after infection . Growth of Psm ES4326 was enhanced by about 10-fold in cbp60g mutants , a smaller effect than observed in canonical SA pathway mutants . Could the delay in SA accumulation be responsible for the enhanced pathogen growth ? This seems possible . Responses to avirulent and virulent P . syringae strains were shown to be quite similar , with the major differences lying in the relative speed and amplitude of responses , rather than in qualitative effects [66] . Thus , a delay in launching a critical response such as SA signaling could well have a dramatic effect on resistance . Alternatively , CBP60g may have other defense response defects in addition to delayed SA accumulation , which we have not yet detected . These defects , combined with the delay in SA accumulation , may result in enhanced growth of Psm ES4326 . The effect of MAMP responses on resistance can be detected by pre-treating plants with flg22 , and then inoculating with Pst DC3000 one day later . In wild-type plants , this results in a 3-log10 reduction in bacterial growth [15] . In pad4 and sid2 plants , this difference was reduced , with the effect of sid2 being stronger than the effect of pad4 [33] . We tested cbp60g mutants using this assay . While Pst DC3000 grew to higher titers in cbp60g mutants than in wild-type plants , the growth reduction due to flg22 pre-treatment was not significantly different in cbp60g and wild-type plants ( Figure S11 ) . MAMP-induced SA levels are higher in cbp60g mutants than in pad4 , which are in turn higher than in sid2 . It is likely that the reduction of SA in cbp60g plants is not sufficiently severe to compromise flg22-induced resistance . Similarly , systemic acquired resistance to Psm ES4326 was not affected in cbp60g mutants ( Figure S12 ) , suggesting that the reduction in SA produced in response to the Psm ES4325 pre-infection was not sufficiently severe to compromise SAR . We identified a CaM-binding domain near the N-terminus of CBP60g ( Figure S1 ) . This domain is predicted to form a basic , amphipathic helix , but is otherwise unlike known CaM-binding motifs . Plant CaM binding domains are known to be highly polymorphic . Previous studies have identified several motifs that are conserved in some CaM binding proteins . These include the 1–10 and 1–14 motifs described by Rhoads et al [67]; the 1–16 motif described by Osawa et al [68] and the IQ motif described by Cheney et al [69] . Our work adds another defined sequence to the known CaM-binding domains . Mechanisms by which CaM binding can modulate protein function include relieving auto-inhibition , remodeling active sites , and mediating dimerization [70] . The placement of the CaM-binding domain near the N-terminus is consistent with a role of CaM binding in relieving auto-inhibition or in promoting dimerization . It seems unlikely that it remodels an active site , as the central portions of CBP60 proteins show extensive conservation ( Figure S1 ) , yet other CBP60 proteins have C-terminal CaM-binding sites while CBP60g has an N-terminal site . CaM binding is needed for activation of CBP60g function , as mutants lacking CaM binding activity could not complement the SA and pathogen growth defects of cbp60g insertion mutants . Thus , CBP60g is regulated at two levels , elevated mRNA and protein in response to pathogen attack , and Ca2+ in the form of CaM binding . Such a “double check” mechanism may suggest an adverse effect of initiating a CBP60g-dependent defense response . Indeed , we were unable to obtain plants expressing CBP60g under the control of the strong 35S promoter , suggesting that unregulated expression of CBP60g is deleterious . While there is abundant evidence that Ca2+ acts as a signal in the MAMP response , relatively little is known about the effect of this signal . In parsley cell cultures , production of phytoalexins in response to the MAMP Pep-13 requires Ca2+ influx [71] . Here , we provide evidence that Ca2+ affects activation of MAMP-induced SA signaling . CaM binds cbp60g only in the presence of Ca2+ , and CaM binding is required for CBP60g to promote SA signaling . CBP60g thus links Ca2+ to SA signaling . This connection could constitute part of the system plants use to discriminate among pathogens , and between pathogens and beneficial or harmless microbes . Ca2+ has long been considered as a ubiquitous second messenger for many signaling cascades , including defense signaling [40] , [70] , [72] . The complexity of calcium patterns responding to different stimuli led to hypothesis that these patterns encode information that is relayed to downstream signaling components . Germinating spores of Gigaspora margarita ( a beneficial soil fungus that forms a mutualistic association with its plant host ) led to a single transient cytosolic Ca2+ elevation in soybean cell culture that lasted only 20 minutes [73] . Treatment with Rhizobium lipochitooligosaccharide nodulation factors led to rapid periodic cytosolic Ca2+ spikes in alfalfa root hairs without dramatically altering the basal cytosolic Ca2+ concentration [74] . In tobacco cell cultures flg22 led to biphasic cytosolic Ca2+ elevation that lasted several hours [75] . These studies suggested that the calcium signatures in beneficial host microbe interactions may differ from those of pathogenic ones [40] . Perhaps CBP60g is only activated in response to Ca2+ signatures characteristic of a pathogen attack , mediated by various CaM proteins in the plant .
Wild type Columbia ( Col-0 ) , pad4-1 ( At3g52430 ) [76] , sid2-2 ( At1g74710 ) [26] , fls2 ( At5g46330; SAIL_691C4 ) [15] , pmr4-1 ( At4g03550 ) [77] , mpk3 ( At3g45640; SALK_151594 ) [78] , ein2-1 ( AT5G03280 ) [79] , dde2-2 ( AT5G42650 ) [55] , coi1-1 ( AT2G39940 ) [80] , cbp60g-1 ( At5g26920; SALK_023199 ) , and cbp60g-2 ( GABI_075G12 ) Arabidopsis plants were grown on autoclaved BM2 Germinating Mix ( Berger Inc . , Quebec Canada ) in a growth chamber at 22°C and a 12 hours photoperiod under 100 mM m−2 s−1 fluorescent illumination with 75% relative humidity . Plants were 4–5 weeks old at the time experiments were performed . Psm ES4326 , Pst DC3000 and Pst DC3000 hrcC− strains were cultured at room temperature in King's B medium ( protease peptone , 10 mg/ml; glycerol , 15 mg/ml; K2HPO4 , 1 . 5 mg/ml; MgSO4 , 5 mM , pH 7 . 0 ) with 50 µg/µl streptomycin ( Psm ES4326 ) or 25 µg/µl rifampicin ( Pst DC3000 and hrcC− ) . Flg22 peptide ( EZBiolab Inc . , IN , USA ) was used at 1 µM . Psm ES4326 , and Pst DC3000 suspensions in 5 mM MgSO4 of OD600 = 0 . 0002 , and OD600 = 0 . 0001 , respectively , were infiltrated into mature leaves using a needless syringe . Determination of bacterial titers was as described previously [81] . Expression profiling data for plants infected with Psm ES4326 was obtained and analyzed as part of the experiments described in [48] . The data is available from Gene Expression Omnibus ( GEO; http://www . ncbi . nlm . nih . gov/geo/ ) , accession number GSE11009 . The data for cbp60g ( SALK_023199 ) was not included in the Supplemental Tables for Wang et al . , in press , because it was not discussed there . It is provided here as Table S1 . For the experiment using Pst DC3000 hrcC , mature leaves of 4 . 5-weeks-old plants were infiltrated with a bacterial suspension ( OD600 = 0 . 05 , 5×108 cfu/ml ) , or water as mock treatment . Samples were collected 3 and 9 hours post inoculation . Three independent experiments were carried out . RNA was extracted using Trizol ( Invitrogen , CA USA ) described by Sato et al [50] . Expression profiles were analyzed in the R environment with the lme4 package after Stable genes Based Quantile ( SBQ ) -normalization [50] . For comparison of profiles between mock- and Pst DC3000 hrcC-infected Col-0 plants , the data were fitted to a 2-stage mixed effect linear model:where , G , R , , γ , and ε are log2-transformed expression level value , gene , treatment ( mock- and hrcC-infected ) , time ( 3 and 9 hpi ) , experiment group , replicate , residual of the 1st model , and residual of the 2nd model . G , R and T are fixed effects , and γ , and ε are random effects . The contrast of the 2nd model was made to compare Pst DC3000 hrcC-infected and mock-infected values at each time for each gene . For comparison of profiles among different plant genotypes after Pst DC3000 hrcC infection , it was necessary to compensate for the fact that the data for cbp60g-1 was obtained in a separate set of experiments from the experiments using the other mutants . The data from each of the two experiment groups were separately fitted to the above 2-stage model , except that R is genotype ( 8 genotypes ) instead of treatment and that the model contains no . Using the fitted values for the samples common between pairs of experiment groups , calibration values that equalize the fitted values for the same genotype at each time point in different experiment groups were calculated . The calibration values were added to the initial SBQ-normalized data , and the calibrated data were fitted to the above 2-stage model except that R is genotype and that the first model includes an fixed effect . The contrast of the 2nd model was made to compare the value of each genotype with that of Col-0 at each time for each gene . To make the complementation and transgenic site-specific mutagenesis constructs , the genomic coding sequence ( with introns ) of At5g26920 and an additional 1093 base pairs of DNA sequence upstream of its start codon was first amplified by polymerase chain reaction ( PCR ) using KOD Hot Start DNA Polymerase ( Novagen , CA ) and TA-cloned into the pCR8 vector following the manufacturer's protocol ( Invitrogen , CA ) . It was then recombined into the Gateway-compatible pMDC123 binary vector [82] through the LR reaction ( Invitrogen , CA ) . For testing CaM binding , mapping the CBP60g CBD , and identifying crucial amino acids of the CBP60g CBD , full length and various partial cDNA sequences of CBP60g ( without the promoter or introns ) was cloned into the pDEST15 vector ( Invitrogen , CA ) and expressed in E . coli . Site-specific mutagenesis of CBP60g was performed using the Phusion™ Site-Directed Mutagenesis Kit ( New England Biolabs Inc . , MA USA ) . For determination of CaM binding and production of transgenic plants carrying mutated versions of CBP60g , site-specific mutagenesis was carried out beginning with a full-length cDNA clone or a genomic clone , respectively , in pCR8 . Cloning and mutagenesis primers used in these experiments are listed in Table S3 . Arabidopsis transformation was carried out using Agrobacterium tumefaciens stain C58C1 as described by [83] . All cloned DNA sequences were verified by sequencing . 3–4 leaves from each 4-weeks-old transgenic plant were collected and homogenized in liquid nitrogen using a mortar and pestle . 0 . 5 ml of extraction buffer ( 100 mM Tris pH = 8 . 0 , 50 mM EDTA pH = 8 . 0 , 500 mM NaCl , and 10 mM β-mercaptoethanol ) with 35 µl of 10% SDS was then added . Samples were incubated at 65°C for 10 minutes , and DNA was precipitated by adding 130 µl 5 M potassium acetate . Samples were then treated with 10 µ/ml RNase , ethanol-precipitated , washed and quantified before use . The copy number of the BAR transgene relative to that of single copy gene , RMP1 , was determined by qPCR experiments according to Stahl et al . [84] using the SYBR Green JumpStart™ kit ( SIGMA , MO USA ) following the manufacturer's protocol . The thermal cycling program used was 94°C for 2 min , followed by 40 cycles of 94°C for 15 sec , 60°C for 1 min and 72°C for 1 min . Experimental readouts were obtained using ABI7500 Real Time PCR system ( Applied Biosystems , Foster city , CA , USA ) . Copy number of transgenes was determined as described by Bubner et al [85] . Primers used in these experiments are listed in Table S3 . RNA was purified using Trizol ( Invitrogen , CA USA ) . Quantitative RT-PCR experiments were carried out using an ABI7500 Real Time PCR system ( Applied Biosystems , Foster city , CA , USA ) and the SuperScript™ III Platinum® SYBR® Green One-Step qRT-PCR kit ( Invitrogen , CA USA ) , following the manufacturer's protocol . The thermal cycling program was 50°C for 10 min , followed by 40 cycles of 95°C for 15 sec , 60°C for 1 min . ACTIN2 ( At3g18780 ) was used as the internal reference . Relative gene expression and probability values were calculated as described [33] . Primers used in these experiments are listed in Table S3 . Mature leaves of 4 . 5 weeks-old plants were infiltrated with Psm ES4326 ( OD600 = 0 . 01 ) , Pst DC3000 hrcC ( OD600 = 0 . 05 ) or 10 µM flg22 peptide . Determination of SA by solid-phase extraction , isotope dilution GC-MS , and data analysis were performed as described previously [33] . Four weeks old plants were injected with 1 µm of flg22 suspension , and samples were collected 12 hours later . Infiltrated leaves were cleared overnight in alcoholic lactophenol ( 95% ethanol: lactophenol = 2∶1 , lactophenol was made by mixing equal volumes of phenol , glycerol , lactic acid and water ) . Samples were then rinsed in 50% ethanol and then in water . Cleared leaves were stained with 0 . 01% aniline blue in 0 . 15 M phosphate buffer ( pH = 9 . 5 ) . Callose deposits were visualized under ultraviolet illumination using a Nikon Eclipse E600 microscope . Four pictures of different areas were taken of each leaf and callose deposits were counted using the “analyze particles” function of ImageJ ( http://rsb . info . nih . gov/ij/ ) . Six leaves were analyzed for each genotype , and three independent experiments were performed . P values were calculated using the Mann-Whitney U-test . Deletion and site-specific mutagenesis constructs of CBP60g were cloned into the pDEST15 plasmid vector , which creates N-terminal fusions to GST ( Invitrogen , CA USA ) . They were then introduced into competent E . coli BL21 ( DE3 ) pLysS ( Invitrogen ) by electroporation . Colonies were selected on plates containing chloramphenicol ( 34 µg/ml ) and ampicillin ( 50 LB µg/ml ) plates . 200-µl aliquots of 2-ml overnight cultures were added to 4 ml of liquid LB medium containing 50 µg/ml chloramphenicol and 100 µg/ml ampicillin . They were then incubated at 37°C for 2 hr ( OD600≈0 . 4 ) before addition of 20 µl of 200 mM IPTG . After a further 2 hr incubation at 37°C , samples were collected by centrifugation , washed with water , and resuspended in lysis buffer ( 50 mM potassium phosphate pH = 7 . 8 , 400 mM NaCl , 100 mM KCl , 10% glycerol , 0 . 5% Triton X-100 , 10 mM imidazol ) . Prior to loading on SDS gels , samples were frozen and thawed three times with liquid nitrogen and a 45°C water bath , and then mixed with same volume of 2× SDS-PAGE sample buffer ( 0 . 125 M Tris-HCL pH = 6 . 8 , 20% glycerol , 4% β-mercaptoethanol , 0 . 2% bromophenol blue , 4% SDS ) . Protein samples in 1× SDS-PAGE running buffer were separated on 8% acrylamide SDS gels and blotted to PVDF membranes according to the manufacturer's instructions ( Bio-Rad ) . Membranes were incubated with 5% milk and then washed with TBST buffer ( per liter: 2 . 423 g Tris-HCl , 8 g NaCl , and 0 . 1 ml Tween-20 ) before incubating with 10 µl of 0 . 25 mg/ml Rabbit monoclonal anti-GST antibody ( Invitrogen ) in 20 ml of TBST buffer . They were then washed three times with TBST , probed with anti-rabbit IgG conjugated alkaline phosphatase ( AP ) ( Promega , CA ) , and visualized by incubating with 20 ml BCIP/NBT liquid substrate ( Sigma ) . CaM binding assays were carried out using the Affinity® CBP Fusion Protein Detection Kit from Stratagene following the manufacturer's instructions . Some CaM assays were performed in the presence of 0 . 05 M ethylene glycol tetraacetic acid ( EGTA ) in TBST instead of 1 mM CaCl2 , in order to test the Ca2+-dependence of CaM binding . The original ( . gpr ) and normalized data files for the microarray analysis are available from Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) as GSE14237 .
|
Plants respond to attack by microbial pathogens through activation of a battery of defense responses . This activation is controlled by a complex signaling network . Disease resistance depends on rapid activation of plant defense responses . Improved understanding of the signaling network may lead to development of crops with improved disease resistance . Here , we used the model plant Arabidopsis thaliana to study activation of defense responses after infection by a bacterial pathogen , Pseudomonas syringae . We found that a gene not previously known to function in defense signaling , CBP60g , is needed for resistance . By studying plants with mutations in this gene , we found that CBP60g contributes to the increases in levels of the important signaling molecule , salicylic acid , that occur after pathogen recognition . We also found that the CBP60g protein binds calmodulin , a protein that mediates calcium regulation of protein function . Calmodulin binding was necessary for the function of CBP60g in disease resistance . We conclude that CBP60g is a protein that mediates calmodulin-dependent activation of salicylic acid signaling in response to pathogen recognition .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/gene",
"function",
"genetics",
"and",
"genomics/plant",
"genetics",
"and",
"gene",
"expression",
"genetics",
"and",
"genomics/gene",
"expression"
] |
2009
|
Arabidopsis CaM Binding Protein CBP60g Contributes to MAMP-Induced SA Accumulation and Is Involved in Disease Resistance against Pseudomonas syringae
|
Proteins are involved in almost all functions in a living cell , and functions of proteins are realized by their tertiary structures . Obtaining a global perspective of the variety and distribution of protein structures lays a foundation for our understanding of the building principle of protein structures . In light of the rapid accumulation of low-resolution structure data from electron tomography and cryo-electron microscopy , here we map and classify three-dimensional ( 3D ) surface shapes of proteins into a similarity space . Surface shapes of proteins were represented with 3D Zernike descriptors , mathematical moment-based invariants , which have previously been demonstrated effective for biomolecular structure similarity search . In addition to single chains of proteins , we have also analyzed the shape space occupied by protein complexes . From the mapping , we have obtained various new insights into the relationship between shapes , main-chain folds , and complex formation . The unique view obtained from shape mapping opens up new ways to understand design principles , functions , and evolution of proteins .
Proteins are the primary workers in a living cell , involved in transportation , catalysis , signaling , energy production , and many other processes . Classification of protein structures provides fundamental information for our understanding of the principles that govern and determine protein structures , which is one of the essential goals of structural biology and protein bioinformatics . Understanding the repertoire of protein structures is also of practical importance for artificial protein design , which has broad applications in therapeutics such as designing inhibitors [1] and small peptide drugs [2] , as well as the development of biomaterials [3] . Conventionally , protein structures have been classified based on their main-chain conformations and evolutionary history [4–6] . Such classifications led to several important observations including the number of different protein folds in nature [7–9] , distributions of folds in genomes [10 , 11] , and the relationship between sequence and structure conservations [12] . The discovery of the limited number of folds yielded stimulating discussions on the mechanism behind it [13 , 14] . Furthermore , such studies contributed to the birth of a very successful paradigm of threading [15] and more recent fragment-based approaches [16] in protein structure prediction . Some recent studies mapped protein structures into a low-dimensional space to reveal high-level organization of the variety of protein structures . Kim and his colleagues computed structural similarity with DALI , a residue-contact map-based structure comparison method [17] , and mapped representative proteins into a 3D space using multidimensional scaling [18 , 19] . Osadchy and Kolodny represented protein structure domains as a vector indicating the occurrence of fragments in the structure [20] . In both works , the maps exhibited a trend where structures formed clusters according to their fold classes , α , β , α/β , and α+β , and others , which is reasonable but expected . Here , we present a global mapping of 3D surface shapes of single proteins and complexes . In contrast to the previous works [18–20] that considered main-chain conformation to define the structural similarity , the use of surface shape representation led to findings of previously undescribed relationships between protein shape , fold class , and assemblies . We perform a thorough analysis of surface shapes in consideration of the rise of medium- to low-resolution structures determined by electron tomography [21] and cryo-electron microscopy ( cryo-EM ) [22] . Classifying protein structures by shape would be more relevant to functional classes of proteins than using conventional main-chain conformations since protein functions such as binding and catalysis occur at the surfaces of proteins . As shown in our previous study [23] , functionally related proteins often share similar global surface but with low sequence and backbone conformation similarity . An illustrative example is DNA topoisomerase I from human and E . coli . Despite their low sequence identity and structure similarity , both of them share a characteristic pore to encircle DNA double strand . This function similarity can be easily captured by shape descriptors , but not captured by conventional main-chain conformation approach . Protein surface shapes were represented with 3D Zernike Descriptors ( 3DZD ) , mathematical moment-based invariants of 3D functions [23] . 3DZD has been demonstrated efficient for various biomolecular structure comparisons [24] , including comparisons of EM maps [25] . Another critical difference between the current study and the previous works is that we analyzed protein complexes in comparison with single proteins . The shape mapping of single-chain and complex protein structures with 3DZD yielded a unique landscape of protein structure space that was not explored before . Dominant features that characterize protein shape are the eccentricity , which is the degree of elongation of shapes , and the number of domains . Symmetry groups are another feature that affects the shape in the case of protein complexes . A detailed analysis of the principal axis corresponding to the elongation of protein shape has suggested that proteins are required to form multimers if their shape is elongated over a certain degree . Overlapping the shape space occupied by single proteins and complexes identified shapes that are only possible in complexes . The unique view obtained from the current shape mapping leads to a more comprehensive understanding of building mechanisms , evolution , and design principles of proteins .
Fig 1 overviews the 3D space mapping of 6 , 841 representative single-chain protein shapes . The surface shape of each protein was represented with the 3DZD , a rotation-invariant mathematical descriptor of 3D protein surface shape , and mapped to a 3D space using principal component analysis ( PCA ) . 3DZD is based on a series expansion using 3D basis functions , which represents the target 3D shape by a weighted combination of the basis functions . The rotation-invariance is achieved by computing a norm of the coefficient values that are assigned to the basis functions ( see Methods ) . PCA locates similar protein shapes close to each other in the space . The color of points indicates the eccentricity of the shapes , which quantifies how much a shape deviates from a sphere , with a higher value ( red ) assigned for more elongated structures ( the maximum value is 1 ) and 0 for a perfect sphere ( blue ) . A video clip of the 3D distribution ( Appendix , S1 Movie ) is also provided , along with an interactive PyMOL file ( Appendix , S1 Pymol File ) to help readers better understand and further investigate the 3D shape distribution . Many entries of the Protein Data Bank ( PDB ) [26] contain only a fraction of the whole structure; thus , we thought it may be possible that the distribution we see in Fig 1A is biased toward surface shapes of structure fragments . For comparison , we also show in the inset figure of Fig 1A the distribution of 2 , 366 almost complete protein structures , which have at least 95% structure coverage of the whole proteins . The projection was made with PCA independently for this high-coverage dataset . As shown , the distribution of the high-coverage protein dataset is very similar , indicating that partial structures do not bias the distribution of the single-chain dataset . Next , we discuss the shape space of protein complexes ( Fig 6 , S2 Movie , S4 Pymol File ) . The dataset of protein complexes contains 5 , 326 non-redundant structures . We obtained the biological units of complexes from PISA . As in Fig 1 , the color indicates the eccentricity of shapes . The complex shape space is overall very similar to the single-chain shape space ( Fig 1 ) , with the majority of structures located around the globular region near the origin of the axes and a tail region dominated by elongated shapes ( the region with many red points ) . On the other hand , some differences were observed between the complex and single-chain distributions . The protein complexes have more spherical shapes than the single-chain distribution ( data points in dark blue in the mapping ) ( Fig 6A and 6B ) . The eccentricity histograms for the single-chain and complex datasets ( S2 Fig ) verify this observation , which shows that the complex dataset contains highly spherical shapes with a low eccentricity . While there are no single-chain proteins with an eccentricity below 0 . 2 , the complex dataset includes 72 such cases . The differences between the shape spaces of the single-chain proteins and complexes become apparent when they are superimposed ( Fig 7 , S5 Pymol File ) . To compare the size of the spaces occupied by the two datasets , the space was segmented into cubes of 1 axis unit edge length , and cubes were counted if they were occupied by the proteins in the datasets . Among all the cubes ( 3 , 895 cubes ) that were occupied by at least one protein , 24 . 5% were filled by both single-chain and complex structures while 26 . 1% and 49 . 4% were occupied by only single-chain proteins and complex structures , respectively . Thus , the complex structure dataset occupies a larger space than the single-chain protein dataset . Fig 7C shows two example structures each from single-chain specific and complex-specific areas in the shape space . In the single-chain dataset , structures with a flexible tail ( e . g . 3gzrA ) were observed . Another example shown is 3e7kA , a narrow , elongated shape with a single helix , which is obviously very unique in single-chain proteins . On the other hand , highly spherical or symmetrical shapes are unique in protein complexes . 1yzv shown in Fig 7C has a spherical shape with the octahedral symmetry and 4ldm has a two-layer tube-like structure . The wide spread of complex structures suggests that assembling subunits into complexes can increase the range of attainable structures . Fig 6C and 6D annotate representative structures in the complex shape space . The outskirts of the distribution in the first quadrant ( i . e . top right ) in Fig 6C includes shapes of the almost perfect sphere ( e . g . 1yzv , 2y3q ) , two layers of circular ring-like arrangements ( e . g . 1lnx ) , and cube-like shapes ( e . g . 3hsh ) . In the second quadrant ( top left ) several symmetrical “spiky” shapes with multiple protrusions are observed ( e . g . 4fdw , 3r88 ) . Close to the origin ( 0 , 0 , 0 ) , dimeric complexes ( e . g . 1hzt , 2zum ) are observed . Fig 6D views the complex shape mapping from a different direction , showing the tail region occupied by structures with elongated shapes . They include protein structures of different fold classes , e . g . long α helices ( e . g . 4cqi , 3okq ) , β structures ( e . g . 3aqj ) , mixtures of them ( e . g . 1rfx ) , and tube-like shapes ( e . g . 2wie , 2zbt ) . In Fig 10 , we examined how the eccentricity , the size of pockets , and the Vp/Vc ratio distribute relative to the number of amino acids for protein structures in the single-chain and the complex structure datasets . The first panel ( Fig 10A ) shows that very low eccentricity , i . e . highly spherical shapes , are achieved only by complex structures , which confirms the observation in earlier sections . Complex structures tend to have larger pockets as shown in Fig 10B . Naturally , larger protein complexes are capable of having larger pockets . Furthermore , a closer look at the plot around the protein length of up to 1 , 000 residues indicates that complex structures tend to have larger pockets than single-chains even when proteins of the same size are compared . Fig 10C examines the Vp/Vc ratio , the ratio of the protein volume relative to the convex hull of the protein . Overall , single-chain proteins and complex structures show similar distributions , but there are more complex structures observed in the lower end of the Vp/Vc ratio . Panels D , E , F illustrate the difference of shapes with a small Vp/Vc ratio between the two datasets . In the case of single-chains , a small Vp/Vc ratio occurs for flexible proteins such as 3ag3I ( Fig 10D ) while for complexes typical such shapes are symmetrical ones with protrusions ( Fig 10E ) and shapes with a large hollow inside as shown in Fig 10F .
In this study , we have constructed a mapping of the protein structure space for the first time by considering the overall surface shape of both single-chain and complex proteins . The shape space visualized in this work would give an impression that the protein shape space is continuous , but this is not specific to the protein surface shape representation . Indeed , earlier works that mapped protein structures considering main-chain conformations also show continuous structure distributions [17–20]; and moreover , there exists active discussion on the continuity [29] or the many-to-many similarity relationship [30] of the protein structure space . Analogous to well-established protein main-chain structure classifications , such as SCOP [5] and CATH [4] , this work will lead to a new classification for protein shapes at a medium to low resolution , which are being accumulated at an increasing pace by cryo-electron tomography and cryo-EM . By establishing the classification from the distribution of the protein shapes , for example , we will be able to take a census of protein shapes , that is , to count the number of specific protein shapes in organisms and compare across different organisms [31] . The observed variety of protein shapes in this work will also be useful for designing protein representations used in a cell-scale physical simulation of biomolecules [32] . Rather than using an overly simplified molecular representation , as is usual for such a simulation , one could diversify protein shapes in the simulation box by sampling structures from different locations in the shape space ( Fig 1 and Fig 6 ) . Last but not least , this work has strong implications for protein design . Our study indicates that a protein shape can be realized with utterly different backbone conformations that even belong to different fold classes as shown in Table 1 and S1 Fig . Also , the shape mappings of single chains and complexes revealed regions in the shape space that are not occupied by either of them , or are occupied only by complex shapes ( Fig 7 ) . Shapes that correspond to the former may be difficult to construct with proteins , and other materials such as DNAs or polysaccharides may be required , while those in the latter region may be better designed using complexes rather than a single-chain protein . In the coming age of medium- to low-resolution biomolecular structures , protein design needs a novel way of viewing biomolecular shapes . We expect that this work makes a unique and significant contribution by providing a foundation of understanding the protein shape universe .
The representative set of single-chain protein structures was selected from a PISCES culled list with a resolution cutoff of 2 . 2 Å , an R factor cutoff of 0 . 2 , and a pairwise sequence identity cutoff of 25% [33] . From 7 , 260 chains in the list , we removed short chains with less than 40 amino acids . We have also removed proteins that have a large spatial gap , i . e . structures having more than one cluster when Cα atoms were clustered with a 9 Å cutoff . We further removed 82 chains were further removed from the list because their sequences had more than 25% sequence identity to other chains . This process yielded a dataset of 6 , 841 non-redundant protein structures . From this dataset , we prepared another dataset by pruning structures that include less than 95% of residues relative to the whole chain length . The protein lengths were obtained from UniProt [34] . There are 2 , 366 chains in this high-coverage single chain dataset . For each chain , fold class was assigned following CATH . Also , by referring to PISA [27] , we assigned biological unit information . This pruned dataset was shown in inset of Fig 1A and 1B . From PDB , we identified structures that exist as a complex as defined in PISA and downloaded the first biological unit ( BU ) . The same resolution , R factor , and length cutoffs as in the single chain dataset were applied . A complex is considered as redundant if there is another complex with the same number of chains and corresponding chains between them have over 25% sequence identity . Among redundant complex entries , we chose the one with the highest resolution and the lowest R factor . This procedure yielded 5 , 326 complexes . Symmetry information for complexes was obtained from PDB if the BU of the complex considered has the same composition as in PDB . Out of the 5 , 326 complexes , 2 , 876 of them acquired symmetry information . We used 3DZD , mathematical rotation-invariant moment-based descriptors , to represent the surface shape of single-chain proteins and complexes . For a protein structure , a surface was constructed using the MSMS program [35] and then mapped to a 3D cubic grid of the size of N3 ( N was set to 200 ) . Protein size is not explicitly considered in 3DZD calculation . But in our previous study [23] , we have shown that it is rare for proteins with very different sizes to share similar global surface . Moreover , in Fig 4C and 4D , we have also analyzed the chain length distribution in the single-chain shape space . MSMS failed to generate surface for two cases each in the single-chain dataset and the complex structure dataset , for which we used the MSROLL program [36] instead . Each voxel ( a cube defined by the grid ) is assigned either 1 or 0; 1 for a surface voxel that locates closer than 1 . 7 grid interval to any triangle defining the protein surface , and 0 otherwise . This 3D grid with 1s and 0s was considered as a 3D function f ( x ) , for which a series is computed in terms of the Zernike-Canterakis basis [37] that is defined by the collection of functions Znlm ( r , ϑ , φ ) =Rnl ( r ) Ylm ( ϑ , φ ) ( 1 ) with −l<m<l , 0≤l≤n , and ( n−l ) even . Ylm ( ϑ , φ ) are spherical harmonics . Rnl ( r ) are radial functions defined by Canterakis , constructed so that Znlm ( r , ϑ , φ ) are homogeneous polynomials when written in terms of Cartesian coordinates . 3D Zernike moments of f ( x ) are defined as the coefficients of the expansion in this orthonormal basis , i . e . by the formula Ωnlm=34π∫|x|≤1f ( x ) Z¯nlm ( x ) dx . ( 2 ) To achieve rotation invariance , the moments are collected into ( 2l+1 ) -dimensional vectors Ωnl= ( Ωnll , Ωnll−1 , Ωnll−2 , Ωnll−3 , … , Ωnl−l ) , and the rotationally invariant 3D Zernike descriptors Fnl are defined as norms of the vectors Ωnl . Thus Fnl=∑m=−lm=l ( Ωnlm ) 2 ( 3 ) Index n is called the order of the descriptor . The rotational invariance of 3D Zernike descriptors means e . g . that calculating Fnl for a protein and its rotated version would yield the same result . We used 20 as the order because it gave reasonable results in our previous works on protein 3D shape comparison [23 , 38–40] . A 3DZD with an order n of 20 represents a 3D structure as a vector of 121 invariants [23] . The similarity between two proteins X and Y was measured by the Euclidean distance dE between their 3DZDs , dE=∑i=1121 ( Xi−Yi ) 2 , where Xi and Yi represent the ith invariant for protein X and Y , respectively . To illustrate the characteristics of 3DZDs , we compare it against two other structure similarity measures , the Procrustes distance [41] and TM-Score [42] . The Procrustes distance is a root-mean square deviation ( RMSD ) between corresponding points in two objects after an appropriate optimization of translation , rotation , and scaling . The smaller the Procrustes distance , the more similar the shape are . On the other hand , TM-Score is one of the common measures of the similarity of the main-chain conformations of proteins . TM-Score ranges from 0 to 1 , with 1 for identical protein structures . Proteins within the same fold usually have a score above 0 . 5 . The Euclidean distance of 3DZD is usually below 10 for proteins of the same shape [23 , 39] . In S3 Fig , the Euclidian distance of 3DZD and the Procrustes distance were compared in two datasets . Panel A compares pairs of 20 ellipsoids with increasing eccentricities , while panel B shows results on 1 , 278 single-chain protein pairs that have the same number of vertices in the surface representation . The two measures correlated well with a correlation coefficient of 0 . 9784 for the ellipsoid dataset ( S3A Fig ) , because surface points were systematically distributed in the same fashion for all the ellipsoids and thus corresponding points are easily matched for aligning two ellipsoids . On the other hand , the two measures often have very different distances in protein shape cases ( S3B Fig ) , which typically happened when point correspondences do not even allow appropriate scaling of the two structures . In S3B Fig , there are many protein pairs that have different surface shapes with a 3DZD Euclidean distance of over 10 but with a small Procrustes distance of around 0 . 2 . S3C and S3D Fig show such protein pairs . As shown , proteins in these pairs have very different shapes , which indicates that 3DZD performs more reasonably for comparing protein shapes . Indeed , for protein shape comparison , The Procrustes distance has difficulty because corresponding surface points in two proteins need to be determined prior to the distance computation , which are not available in general for protein surface comparison . This is more difficult when two proteins have a different number of surface points to be compared . Apparently , 3DZD does not have such a problem because it does not align points to points . S4A and S4B Fig show the comparison between 3DZD and TM-Score . As shown , these two measures have virtually no correlation . The correlation coefficient was -0 . 1735 for these two measures . Panel B shows the density of the two measures . The highest density ( yellow ) was observed at around 3DZD distance of 5 to 10 and TM-score of 0 . 3 , which is the score range for proteins with similar surface shape but with different main-chain fold . As also shown in Table 1 , there are cases that proteins of the different fold class have a small 3DZD Euclidian distance . S4C and S4D Fig shows two such examples , where two structures have a similar surface shape to each other according to 3DZD but have a very large difference in their main-chain conformations . These results are consistent with our earlier work where we extensively compared 3DZD with conventional protein structure comparison methods [23] . The 3DZD files of the single-chain and the complex datasets are made available at S1 Data . 3DZD can be also computed for PDB files at the benchmark page of 3D-SURFER ( http://kiharalab . org/3d-surfer/batch . php ) [25 , 38] . We used principal component analysis ( PCA ) to project 3DZDs of 121 value vectors of protein structures into 3D . Three eigenvectors were chosen for the mapping because the scree plots ( S5 Fig ) showed that adding more eigenvalues does not contribute much to explaining data variance , and also to be consistent with the previous related works [18–20] . The three eigenvalues explained 52 . 64% and 47 . 76% of the total variation in the single-chain and the complex structure datasets , respectively . In order to quantify how elongated a structure is , we have defined the term eccentricity , which is calculated from the minimum volume enclosing ellipsoid ( MVEE ) of a structure . Given all atoms in a structure , protein MVEE is the ellipsoid with minimum volume that encloses all atoms . From MVEE , the eccentricity is defined as ( 2−b2/a2−c2/a2 ) /2 , where a , b , and c are the length of longest , the second longest , and the third longest semi-principal axes of the ellipsoid , respectively . Elongated structures have an eccentricity close to 1 , while spherical structures have an eccentricity close to 0 . The volume of proteins was computed using MSROLL with a probe radius set to 0 . For 42 cases in the single-chain dataset and 82 cases in the complex dataset where the MSROLL failed , we used the ProteinVolume program [43] instead . The volume values computed by these two programs were very consistent; the difference of volume values for ten randomly selected protein structures was on average 1 . 04% . The convex hull of a protein structure and its volume was computed using the ConvexHull function in the scipy . spatial package [44] . A pocket on a protein surface was identified and its volume was computed with VisGrid [45] . The average size of the pocket volume in the single-chain proteins was 6 , 302 . 9 Å3 . We analyzed the location of proteins with a large pocket whose size is within the top 10% ( 12 , 219 Å3 or larger ) in the single-chain protein surface space ( Fig 1D ) . Donut-shaped structures were identified by first screening structures with genus > 0 and then with the conditions of 0 . 9≤b/a≤1 . 0 and 0≤ ( c2/a2+c2/b2 ) /2≤0 . 6 , where a , b , and c are the parameters of MVEE of the structures . Then , structures that passed the criteria were visually examined . The genus number was computed with the Euler-Poincaré Formula , which states the following relationship between the number of vertices ( V ) , edges ( E ) , faces ( F ) , loops ( L ) , shells ( S ) , and genus ( g ) of a manifold: V + F–E– ( L–F ) = 2 ( S–g ) . To obtain these values of a protein surface , we used triangular meshes computed by EDTSurf [46] . L is equal to F for triangle meshes since triangular faces have exactly 1 loop . S was computed as the number of disconnected groups of faces .
|
Proteins are the major molecules involved in almost all cellular processes . In this work , we present a novel mapping of protein shapes that represents the variety and the similarities of 3D shapes of proteins and their assemblies . This mapping provides various novel insights into protein shapes including determinant factors of protein 3D shapes , which enhance our understanding of the design principles of protein shapes . The mapping will also be a valuable resource for artificial protein design as well as references for classifying medium- to low-resolution protein structure images of determined by cryo-electron microscopy and tomography .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2019
|
A global map of the protein shape universe
|
Neural progenitor cells ( NPCs ) , which are apicobasally elongated and densely packed in the developing brain , systematically move their nuclei/somata in a cell cycle–dependent manner , called interkinetic nuclear migration ( IKNM ) : apical during G2 and basal during G1 . Although intracellular molecular mechanisms of individual IKNM have been explored , how heterogeneous IKNMs are collectively coordinated is unknown . Our quantitative cell-biological and in silico analyses revealed that tissue elasticity mechanically assists an initial step of basalward IKNM . When the soma of an M-phase progenitor cell rounds up using actomyosin within the subapical space , a microzone within 10 μm from the surface , which is compressed and elastic because of the apical surface’s contractility , laterally pushes the densely neighboring processes of non–M-phase cells . The pressed processes then recoil centripetally and basally to propel the nuclei/somata of the progenitor’s daughter cells . Thus , indirect neighbor-assisted transfer of mechanical energy from mother to daughter helps efficient brain development .
Formation of large brains such as the mammalian cerebral cortex depends on continuous and efficient cell production by neural progenitor cells ( NPCs ) [1 , 2 , 3] . NPCs are elongated , spanning a developing brain wall ( up to 300 μm thick in mice ) , and divide at the apical surface [4 , 5 , 6 , 7 , 8] ( Fig 1A ) . They individually move the nuclei and somata in a cell cycle–dependent manner ( apical during G2 and basal during G1 ) , and these nuclear/somal movements are called interkinetic nuclear migration ( IKNM ) [9 , 10 , 11 , 12] ( Fig 1A–1C , S1 Movie ) . Because IKNMs of different NPCs are not synchronized , IKNM-undergoing regions ( 100 μm thick , called the neuroepithelium [NE] or ventricular zone [VZ] ) display nuclei/somata at diverse apicobasal positions , a histological appearance referred to as pseudostratification ( Fig 1A ) . The degree of neuroepithelial pseudostratification ( i . e . , the thickness of NE/VZ ) increases from mice to monkeys [13] to humans [14 , 15] . Extensive pseudostratification is thought to contribute to increasing the frequency of cell divisions per unit apical surface [16] . However , the accompanying dense countercurrents ( i . e . , apical and basal IKNMs ) run the risk of “traffic jams” [17] . For example , experimentally induced failures in basal nucleokinesis lead to near-apical nuclear/somal overcrowding and abnormal brain histogenesis [18] . Despite recent advances in understanding intracellular molecular mechanisms that move the nucleus apically or basally in each NPC [19 , 20 , 21 , 22 , 23] , how heterogeneous IKNMs are safely and efficiently organized ( or horizontally bundled ) into an ordered VZ remains unknown . In the embryonic cerebral cortex , cell production ( i . e . , M-phase NPCs’ divisions ) accompanied by massive flows of the “material” ( somata of G2-phase NPCs ) and the “product” ( those of G1-phase NPCs ) occurs most extensively and continuously among a wide variety of developing tissues . The high pseudostratification of the cerebral cortical VZs has led to it becoming a model system to study “production logistics” in organogenesis . To understand a system-level device to make the intra-VZ nuclear/somal traffic as efficient and energy saving as possible , we sought to determine how active and passive movements are coordinated ( or collaborate with each other ) . Previous quantitative imaging studies revealed that apical nucleokinesis during G2 phase ( Fig 1B ) was faster than basal nucleokinesis during G1 phase ( Fig 1C ) [18–21 , 23–26] and judged to be more directional , with a superlinear mean-squared displacement ( MSD ) pattern , than more fluctuating basal nucleokinesis showing a linear MSD pattern [18 , 21 , 25 , 27] ) . When apical nucleokinesis was inhibited through blocking NPCs’ entrance into G2 phase , basal nucleokinesis was retarded ( Fig 1D ) [25 , 26] . These results therefore suggested that apicalward nuclear flows in the VZ may induce passive basalward nucleokinesis of G1-phase cells ( Fig 1E ) . However , the cell–cell interactions that would mechanically contribute to such a hypothetical passive mechanism remained unknown . Especially , how G2-phase cells’ nuclear/somal movement towards/to the apical surface can induce or lead to such hypothetical passive basal nucleokinesis of early G1-phase cells has not been studied . We here show that mechanical energy provided by an apically dividing ( M-phase ) NPC is transiently stored in the surrounding subapical space ( within 10 μm from the surface ) and elastically returned to its daughter cells , promoting passive basalward nucleokinesis . The elasticity in the subapical space is established by physiological cell crowding , in which neighboring M-phase and non–M-phase cells collaborate mechanically , thereby enabling energy-efficient IKNMs and benefiting the systematic “production logistics” in the VZ .
We aimed at elucidating where and how the hypothetical passive nuclear/somal movements are induced in the mouse neocortical VZ ( Fig 1E ) . One possibility is that nuclei/somata of early G1-phase cells ( i . e . , newly generated daughter cells ) in the subapical space ( within 10 μm from the apical surface ) of the VZ are propelled through direct soma–soma collision or pushing by G2- or M-phase cells ( Fig 2A ) . To evaluate this “direct collision” model that we hypothesized in the subapical space , we first examined the density of somata by visualizing the plasma membranes of all cells ( Fig 2B ) . We found that the subapical space had no extracellular gaps and that apical processes ( which appeared as fine meshes in horizontal sections ) were more abundant ( about 60% ) than somata ( about 40% ) , whereas the process/soma ratio decreased in deeper ( more basal ) VZ regions . Time-lapse observations in horizontal sections of the subapical space ( 5 μm from the apical surface ) ( Fig 2C and 2D , S2 and S3 Movies ) revealed that direct contacts between somata of newly generated daughter cells and those of neighboring G2/M-phase cells , including slight soma–soma contacts , were observed in only 45% of all divisions ( n = 80 ) . Importantly , the time that elapsed from the generation of daughter cells to their nuclear departure from the subapical space was similar between cases in which the nuclei/somata of daughter cells had direct contacts with G2- or M-phase somata ( Fig 2C ) and those without such soma–soma contacts ( i . e . , cases in which the nuclei/somata of daughter cells were neighbored by apical processes , Fig 2D ) ( Fig 2E ) . These results do not support the direct soma–soma collision model in the subapical space . Therefore , we sought to explore an alternative possibility . Even without direct soma–soma collisions , daughter cells’ nuclei/somata could be propelled passively if the surrounding subapical space works like a pressurized air chamber , in which G2-phase cells’ somal influxes and M-phase cells’ voluminous divisions act as an indirect contributor to the displacement of daughter cells’ nuclei/somata ( Fig 3A ) . By analogy , the aorta acts like a compressed air chamber ( “Windkessel” ) to elastically recoil and forward blood ejected by the heart [28] ( S1A Fig ) . If the subapical space were similarly elastic , as predicted by recent simulation studies [18 , 29] , mechanical force provided centrifugally by a given mother M-phase cell while expanding to round up would result in storage of elastic energy in the surrounding process-rich subapical space . The stored elastic energy could then be released and returned centripetally to the nuclei/somata of daughter cells generated by that mother cell , thereby externally inducing the daughter cells’ passive nuclear/somal displacement in the basal direction ( Fig 3A , S1B and S1C Fig , S4 Movie ) , just as the aorta forwards blood in a Windkessel-like manner . Laser ablation on the apical surface resulted in centrifugal recoiling of the surface from the ablated vertex , indicating that it was tangentially contractile ( under tension ) ( Fig 3B and S5 Movie ) . Quantitative monitoring further revealed that the apical meshwork was quite stable even at sites of cell division , with no local tilting or floppiness ( Fig 3C and 3D , S2A Fig , S6 and S7 Movies ) . Inhibition of actomyosin to diminish the contractility of the apical surface changed the original apically concave morphology of cerebral walls to an apically convex shape ( Fig 3E ) . This pharmacologically induced recoiling response ( i . e . , the expansion of the subapical space relative to its original volume ) suggests that the subapical space may have originally been laterally compressed , as previously suggested in the chicken midbrain tube [30] . To further test the possibility of lateral compression in the subapical space , we performed a novel laser ablation test targeting the center of the soma ( Fig 3F ) . We reasoned that if a cell’s soma under centripetal compression by the surrounding space is ablated to reduce its original physical stability , it would lead to shrinkage of the ablated soma and centripetal displacements of elements in the surrounding space . Subapical laser ablation of M-phase cells’ somata in normal/untreated ( apically concave ) cerebral walls resulted in a quick centripetal shift of the surrounding processes ( Fig 3F and 3G , S8 and S9 Movies ) . This soma-directed laser ablation quantitatively showed that the subapical space was under compression and ready to recoil ( Fig 3G and 3H ) . While fiber-like apical processes are flexible and can be bent [18] , they are individually under tension along the apicobasal axis [31] . We observed that apical processes contain microtubules bundles , which provide the structural basis for flexural rigidity [32–35] , most densely in the region near the apical surface ( S2E Fig ) , which was also F-actin rich ( S2F–S2H Fig ) . Horizontal packing of such rubber string–like fibers by the narrowing of the apical surface seemed to contribute to subapical elasticity . Furthermore , imaging in slice culture revealed a novel subapical-specific ( within 5 μm from the surface ) cellular structure; lamellipodia-like protrusions were dynamically extended laterally into the surrounding space from almost all apical processes of non–M-phase VZ cells ( Fig 3I , S2I Fig and S10 Movie ) . They were inhibited by blebbistatin ( S2J Fig and S11 Movie ) . The in vivo existence of this novel structure was confirmed by serial block-face scanning electron microscopy ( SBF-SEM ) ( Fig 3J and S12 Movie ) . The close spatial juxtaposition of this volume-increasing microstructure with the narrowing/contractile apical surface may efficiently increase local subapical elasticity . Together , these results suggest that the subapical space , where basal IKNM ( i . e . , newly generated daughter cells’ nucleokinesis ) begins , is indeed elastic . To evaluate our new model , in which elastic energy stored in local subapical tissue is released to generate a force that lifts up daughter cells’ nuclei/somata , we performed both in silico and slice culture analyses , focusing on the origin and redistribution of mechanical forces during the transition from M phase to early G1 phase . In our mechanical simulation , ( 1 ) the elastic subapical space was depicted in a simplified manner as two springlike strings fixed at the apical end , and ( 2 ) the time-dependent position changes of a single soma ( as of G2 , M , and early G1 phases ) were animated ( Fig 4A and 4B , S3A and S3B Fig , S13 Movie ) . We first applied a constant apicalward force to the center of nucleus/soma in G2 phase and further , until the end of M phase . Reaction forces from the elastic strings generated vertical ( basal ) components , i . e . , a lifting force . Thus , the strings successfully mediated a basal bouncing-like movement of the circle ( daughter cell’s nucleus/soma ) away from the apical surface through sequential transfers of mechanical energy from each laterally expanding M-phase progenitor cell ( providing a pushing force ) to ( 1 ) the subapical space ( storing elastic energy ) and ( 2 ) back to the nuclei/somata of that progenitor cell’s daughters . In silico basalward bouncing of the daughter cell’s soma/nucleus occurred following a timely apicalward force from the M-phase mother cell’s round soma , as if it were pushed by a finger ( S1B and S1C Fig , S4 Movie ) , at its basal pole . Because previous studies have suggested that actomyosin localized basal to the nucleus/soma of G2/M-phase cells may contribute to apical nucleokinesis and rounding up [21 , 36–38] , we speculated that actomyosin provides the cell-biological basis for such transient apicalward pushing ( until the end of M phase ) . To explore this possibility , we live monitored actin dynamics . Interphase cells exhibited F-actin enrichment at the apical junction . During the G2-to-M transition , F-actin accumulated near the basal pole of the soma ( Fig 4C and 4D , S14 and S15 Movies ) , followed immediately by thinning of the basal process and subsequently by rounding up of the soma . F-actin accumulation at the basal side continued during rounding up of the soma and the growth of cleavage furrow in a basal-to-apical direction and then became undetectable after completion of cytokinesis . Immunohistochemistry revealed that the basal F-actin–rich region accumulated phospho-myosin light chain ( Fig 4E ) . To determine whether the basal myosin functions as the apicalward presser of the soma ( i . e . , the squeezer of the cytoplasm ) , we live monitored M-phase cells in cerebral walls treated with blebbistatin ( 20 μm ) . Although this treatment caused medium-immersed ( unembedded ) cerebral walls to freely undergo a concave-to-convex change ( Fig 3E ) , embedding of cerebral wall slices in collagen gel allowed them to fully maintain their original apically concave shape even in the presence of blebbistatin , thereby keeping the subapical space laterally compressed ( at least to a considerable degree ) . We observed abnormal basal bouncing of the blebbistatin-treated M-phase cells’ somata ( Fig 4F , S3C Fig , S16 Movie ) . These data are consistent with our model , in which each M-phase cell’s soma is squeezed from the basal pole during its rounding-up step in an actomyosin-dependent manner and thus laterally pushes the surrounding subapical space . Based on previous studies that well described the IKNM dynamics [18–21 , 23–26] , we extended our analysis on the behaviors of newborn ( ≤3-hr-old ) daughter cells to characterize the relationship between the shapes of their apical cytoplasmic portions and the positions of their nuclei ( Fig 5 ) . Because we reasoned that the initial step would be more susceptible to the hypothetical elasticity-based pushing by the subapical space than later steps , it was also important to determine whether basalward nucleokinesis proceeded constantly or , instead , exhibited transitions from a fast mode to a slower mode . Soon after their generation , most daughter cells remain connected to the apical surface [6 , 39] . Sister daughter cells’ nuclei depart from the subapical space sequentially; one daughter cell inherits the basal process from its mother cell [5 , 6] and initiates basal nucleokinesis more quickly than its sibling ( non–process-inheriting ) cell [18 , 27 , 40] . Despite this morphology-dependent intra-clonal difference in nuclear-departure time , we identified two key rules common to the early-nucleokinetic ( E-IKNM ) and late-nucleokinetic ( L-IKNM ) daughter cells . First , the reduction of the horizontal sectional area of the apical cytoplasm ( at 3 . 5 μm , 5 . 0 μm , and 6 . 5 μm from the apical surface ) was earliest at 3 . 5 μm and later at more basal positions , suggesting an apical-to-basal cytoplasmic thinning , and always preceded the onset of basal nucleokinesis ( Fig 5C–5E ) . Second , basal nucleokinesis , once started , was mostly biphasic , with an initial ( within 30-min ) quick displacement phase occurring in the subapical space and a subsequent slower phase occurring in more basal regions ( Fig 5F and 5G ) . When myosin II was pharmacologically inhibited with 20 μm blebbistatin , both the initial quicker phase and the subsequent slower phases were decelerated ( in both E-IKNM and L-IKNM ) ( Fig 5H , S3D and S3E Fig ) . The results described above support non–cell-autonomous basal nucleokinesis driven by a force externally applied from the subapical space , where actomyosin serves to make the entire apical surface contractile ( thereby contributing to the subapical elasticity ) and to squeeze/push each M-phase cell’s cytoplasm apically from its basal pole ( to begin the lateral storage of elastic energy ) . However , they are also consistent with a cell-autonomous mechanism in which daughter cell–intrinsic actomyosin near the apical surface might transiently squeeze/push daughter cells’ nuclei basally [22] . In silico increase and decrease of the spring constants of the two lines resulted in accelerated and retarded basal nucleokinesis , respectively ( Fig 4B ) . Accordingly , to functionally assess the former ( passive ) mechanism , we performed a set of mechanical experiments to determine whether external forces , as expected to arise in the elastic ( Windkessel-like ) subapical space , are necessary and sufficient for the basal nucleokinesis . Specifically , we locally laser ablated the subapical space that surrounded cells immediately ( <5 min ) after cytokinesis ( Fig 6A ) . Successful ablation of the surrounding space , not the nuclei/somata whose movement should be evaluated , was reflected by tissue irregularity or shrinkage ( Fig 6B , S3F Fig , S18 and S19 Movies ) . Although nuclei of daughter cells in control slices departed from the subapical space in 33 ± 6 min in E-IKNM cases or 58 ± 17 min in L-IKNM cases ( n = 5 pairs ) , nuclei of daughter cells whose neighboring subapical space was laser ablated ( decompressed ) did not move for up to 80 min ( n = 5 pairs ) ( Fig 6C ) . We then asked whether compression to increase the subapical elasticity would accelerate daughter cells’ initial nucleokinesis . To this end , we placed a cerebral wall slice embedded in collagen gel in a silicone-rubber chamber , to which an external force can be applied along a single axis to provide about 14% compression ( and about 8% stretching along the orthogonal axis ) of the subapical space ( Fig 6D–6H , S3G Fig , S20–S22 Movies ) . From this artificially compressed subapical space , both E-IKNM daughter cells’ and L-IKNM daughter cells’ nuclei/somata departed significantly earlier than in control cases ( Fig 6H ) . Next , we sought to determine whether a local external strain , e . g . , a strain provided from the subapical space to the apicalmost cytoplasm of a newly generated cell , can actually move that cell’s nucleus/soma against the expected basal mechanical resistance ( e . g . , intracellular viscosity and/or the extendibility of plasma membrane ) . To mimic the in vivo situation in a more directly manipulable manner , we “imprinted” VZ tissues onto a culture dish so that the bipolar-shaped geometry of VZ cells could be maintained to a considerable degree [41] ( Fig 6I ) . In imprinted cells successfully pushed by a microcapillary from one pole of the nucleus/soma , nuclear/somal displacement to the opposite side was observed either relatively quickly ( within 5 min in 3 out of 4 cases ) or slowly ( more than 15 min in 1 out of 4 cases ) , whereas control uncompressed cells did not show such displacements ( Fig 6J and 6K , S3H Fig , S1 Data ) . These results , together with the aforementioned basalward bouncing of M-phase cells’ somata that occurred through overcoming potential apicalward resistance ( Fig 4F ) , imply that ( 1 ) external forces are necessary and sufficient for the initial passive movement of nucleus/soma in daughter cells , and ( 2 ) the retardation in the initial quick basal nucleokinesis step caused by blebbistatin ( Fig 5H and S3E Fig ) can be most reasonably explained by the absence or reduction of external forces in the Windkessel-like subapical space , although we do not exclude cell-intrinsic actomyosin’s partial contribution to this initial basalward step . Based on these results , we sought to determine the contribution of the initial elasticity-based boosting step provided by the Windkessel-like subapical space to all IKNMs and the formation of highly pseudostratified NE/VZ . For technical reasons , the aforementioned pharmacological and mechanical perturbations did not permit long-term assessments of neuroepithelia . Hence , we aimed at mathematically removing or inhibiting only the initial 10-μm basalward nucleokinesis phase , to which this Windkessel-like mechanism seemed to critically contribute . We developed a new simulation to systematically describe movements of all VZ cells’ nuclei , individually linked with cell cycle progression ( Fig 7A–7D , S4A Fig , S23 Movie ) , based on previously reported parameters for cell cycle progression and cell differentiation [3 , 42] as well as nuclear densities and migration velocities obtained from live-tracked nuclei [18] in the mid-embryonic mouse cerebral VZ . The two major physical forces applied to induce nuclear/somal displacements along the apicobasal axis were ( 1 ) direct soma–soma ( nuclear–nuclear ) interactions ( i . e . , collisions and repulsions ) and ( 2 ) mechanical forces that act in a cell cycle phase–dependent manner , independently of such direct soma–soma repulsions ( S4A Fig ) . The latter ( designated as “non-collision” forces ) included both intracellular forces dependent on microtubules or actomyosin and external forces such as those provided elastically from the cellular processes to the nuclei/somata . For example , the in silico G2-phase cells’ rapid ( highly directional ) apical displacement [18–21 , 23–26] was reproduced by application of the “non-collision” force . Horizontal assembly/bundling of the nuclear/somal movements of individually simulated clonally related cells ( exemplified in Fig 7A , S4A Fig , S23 Movie [left] , showing hairpin loop–like , distally bifurcated “production lines” ) successfully established a virtual ( NE/VZ-like ) pseudostratified tissue ( Fig 7B , S23 Movie [right] ) , which recapitulated the overall IKNM trajectories in vivo ( Fig 7C and 7D , S4B and S4C Fig ) . Consistent with real observations ( Fig 2B–2E ) , soma–soma collisions/repulsions were much less frequent in the subapical ( within10 μm ) part than in more basal parts of the virtual VZ ( S4D Fig ) . Interestingly , basal nucleokinesis in this simulation was functionally biphasic ( S4E–S4G Fig , S24 Movie ) , reminiscent of real observations ( Fig 5F and 5G ) . The simulations showed that G1-phase nuclei/somata existing >10 μm away from the apical surface could be automatically displaced through direct nucleus–nucleus repulsions ( probably between G1-phase cells ) alone , as long as movements of other chronologically different nuclei ( i . e . , apical arrival of G2-phase cells’ nuclei/somata and the initial basal displacement of early G1-phase cells’ nuclei/somata in the subapical [within 10 μm] space ) were secured ( S4B–S4G Fig ) , supporting the previously proposed idea of passive IKNM [9 , 21 , 25 , 26] . By contrast , the subapical ( within 10 μm ) nuclei/somata strongly required a basal acceleration by the “non-collision” mechanism , the absence of which resulted in severe disruption of overall IKNM and the VZ structure ( Fig 7E–7G , S25 Movie ) , suggesting that this initial basalward step may be critical or rate limiting for a high degree of overall pseudostratification . As we experimentally demonstrated in Fig 6 , the initial 10-μm basalward step may strongly depend on an external , elasticity-dependent mechanism . Therefore , it is very likely that the initial basal nucleokinesis step , which is more rapid and directional than the subsequent ( more basal ) nucleokinesis ( Fig 5 ) and requires external elasticity , is critical for the ordered brain histogenesis .
In diverse proliferative cell types , each non–M-phase cell will eventually enter M phase , and the M-phase cell will then generate two non–M-phase ( daughter ) cells . Non–M-phase cells and M-phase cells are therefore in a producer–product relationship . This chronological ( cell cycle ) partnership depends on intracellular chemical reactions , such as the activation and disappearance of cyclins , but a recent study on the Drosophila epithelium showed M-phase cells’ nonchemical ( physical ) contribution to tissue morphogenesis [44] . Following the previously established concept of a mother-to-daughter morphological gift in the developing mouse cortical VZ , i . e . , asymmetric inheritance of each M-phase cell’s basal process/fiber by one daughter cell [6 , 18] , the present study revealed that each M-phase cell also gives mechanical energy to both daughter cells , with elastic assistance from the densely packed apical processes of neighboring non–M-phase cells ( via contractility of the apical surface ) ( Fig 8 ) and other M-phase cells that divide ( to generally increase the pressure of the subapical space ) ( S4H Fig ) . These mother-to-daughter ( intra-clonal ) physical gifts assist daughter cells’ prompt nucleosomal movement away from the subapical space . Thus , such established initial basal nucleokinesis enables non–M-phase cells to have thin and flexible apical processes throughout the subapical space , which is permissive for the voluminous division of new M-phase cells . This permissiveness can be regarded as a mechanical gift from non–M-phase cells to M-phase cells . These bidirectional physical collaborations ( S4H Fig ) may underlie efficient and safe intra-neuroepithelial nuclear/somal logistics and protect the subapical space from local overcrowding . Existence of too many nuclei/somata in the subapical space prevents progenitor cells from freely dividing at the apical surface and induces them to abnormally leave the apical surface , leading to heterotopic divisions and disruption of histogenesis [18] . Thus , give-and-take relationships ( i . e . , mutualism ) exhibited spatially and physically between M-phase cells and non–M-phase cells ( S4H Fig ) are essential for ordered brain development . Previous studies showed that the basal nucleokinesis is mediated by active intracellular mechanisms dependent on kinesin/microtubules [23] and actomyosin [22] . Our IKNM simulation ( virtual VZ ) ( Fig 7 ) showed that collective basalward IKNMs at basal VZ levels ( more than 10 μm from the apical surface ) can occur almost passively using soma–soma collisions between G1-phase cells’ nuclei/somata as a major driving force . Although this result is consistent with a model of the passive basal IKNM suggested by Norden et al . [21 , 25] and Kosodo et al . [26] , the present study cannot address the relative importance of the collision-based passive mechanism compared to the cell-intrinsic ( kinesin- and/or actomyosin-dependent ) basal nucleokinesis mechanisms . In the subapical space ( within 10 μm from the apical surface ) , we found a novel passive IKNM mechanism mediated by tissue elasticity and indirect energy transfer . We cannot precisely determine the relative importance of this boosting mechanism compared to the kinesin- and/or actomyosin-dependent mechanisms in the subapical space . Nevertheless , the existence of the Windkessel-like ( elasticity-based ) boosting mechanism is strongly suggested based on ( 1 ) the elastic property of the subapical space ( Fig 3 ) , ( 2 ) the more directional displacement of nuclei/somata during the initial 30 min than later periods ( with more superlinear MSD curves ) ( Fig 5 ) , and ( 3 ) the decompression and compression experiments that resulted in deceleration and acceleration of initial basal nucleokinesis , respectively ( Fig 6 ) . We speculate that a Windkessel-like boosting mechanism may collaborate with cell-intrinsic basal nucleokinesis mechanisms . For example , centripetal mechanical stimuli applied externally from the subapical space might also trigger intracellular molecular machinery for active nucleokinesis , a possibility to be studied in the future . Similarly , cell cycle progression , which is associated with ( or upstream to ) intracellular molecular mechanisms for nucleokinesis , might also be modulated by external mechanical stimuli—another question to be addressed for better system-level understanding of intra-VZ collective behaviors of NPCs . Atomic force microscopy ( AFM ) revealed that the elastic modulus on/near the apical surface was much greater ( 1 , 400 Pa ) [45] than that in more basal VZ regions ( about 100 Pa ) [46] . The present study showed that an elasticity-based mechanism assists in an initial basalward step in IKNMs . Actually , diverse biological systems for coordinating heterogeneous movements or flows similarly utilize elasticity as a means to minimize total energetic expenditure . In mammalian running [47 , 48] or insect flying [49] , elastic energy stored at one stage in the stride or wingbeat is released at another . Likewise , in blood circulation , the aorta flexibly receives blood ejected from the heart and elastically recoils to forward it ( Windkessel effect [28] ) . Thus , such a commonly used strategy might participate in multiple aspects or phases in the VZ growth or the overall brain development , and perhaps in the neocortical evolution . We previously showed that elasticity ( stiffness ) measured on/near the apical surface of the neocortical VZ by AFM was greater in ferrets than in mice [45] . We also found in slice culture that NPCs’ initial basal nuclear/somal movement was quicker in ferrets than in mice [27] . It is therefore possible that elasticity in the subapical space participates in the differential IKNM behaviors between mice and ferret . Our mathematically IKNM-simulated virtual VZ showed that the initial basalward nucleokinesis step is important for the overall high-degree pseudostratification ( Fig 7 ) . The degree of pseudostratification ( i . e . , the thickness of NE/VZ ) increases from mice to monkeys [13] and even further in humans [14 , 15] . It would be meaningful to study the possible contribution of tissue-level or cell-level elasticity to the thickening of VZ during evolution . Our AFM revealed that single dissociated NPCs are stiffer in mice than in ferrets [45] . Adding such measurable parameters more into our virtual VZ method would be effective to improve our simulation and to expand its feasibility in species other than mice .
The animal experiments were conducted according to Japanese Act on Welfare and Management of Animals , Guidelines for Proper Conduct of Animal Experiments ( published by Science Council of Japan ) , and Fundamental Guidelines for Proper Conduct of Animal Experiment and Related Activities in Academic Research Institutions ( published by Ministry of Education , Culture , Sports , Science and Technology , Japan ) . All protocols for animal experiments were approved by the Animal Care and Use Committee of Nagoya University ( No . 29006 ) . R26-Lyn-Venus transgenic mice ( accession No . CDB0254K ) , R26-H2B-mCherry transgenic mice ( accession No . CDB0239K ) [18 , 50] , and R26-ZO1-EGFP transgenic mice ( accession No . CDB0260K ) [45 , 51] were provided by Toshihiko Fujimori ( NIBB , Japan ) . Pregnant ICR mice were obtained from SLC . Embryos at the mid-embryonic stage ( embryonic day [E] 13 ± 1 ) were used . Image processing , nuclear tracking , and calculation of cross-sectional area and fluorescent intensity were performed using ImageJ . Fluorescent intensity of Lifeact-EGFP at the level of the soma or the basal pole ( as depicted in Fig 4C and 4D ) was determined by measuring the maximal pixel intensity ( i . e . , brightest spots or bands ) on the horizontally sectioned cell cortex . Reconstructed fluorescent 3D images were obtained using Volocity ( PerkinElmer ) . “Departure” of each newborn daughter cell’s nucleus/soma from the subapical space under en face observation ( as discussed in Fig 6 ) was defined in the horizontal sectional plane 5 μm from the apical surface as the time when its diameter became smaller than 3 μm ( including complete disappearance ) . Onset of basal displacement of the nucleus/soma of a newborn daughter cell under cross-sectional observation ( as discussed in Fig 5 , S3D and S3E Fig ) was defined as the time when the mass center of the nucleus/soma moved 1 . 5 μm basally . MSD was obtained based on time-dependent changes of nuclear position along the apicobasal axis , as described previously [18] . To determine whether the apical surface was stable , without exhibiting local tilting or floppiness of apices even where mitosis occurred , we analyzed a 3-min-interval movie obtained by en face observation of the apical surface of a cerebral wall prepared from a R26-ZO1-EGFP transgenic mouse [51] at 0 . 5-μm z intervals . Cell borders at the apical surface level were extracted from the zero-crossing points for first-order derivatives obtained by applying 1D Savitzky-Golay filter along the x , y , and z directions . Peak voxels marked by the filter were then smoothened by morphological operations and skeletonized in 3D space [53] . Individual cell contours were determined by applying modified Dijkstra’s algorithm , which finds the shortest set of edges that represents a distinct cell contour ( S2A Fig , green trajectories in the left panels ) . Normal vectors representing the inclination of each cell apex were estimated by averaging normals for every possible triangular face defined by two neighboring vertices and another vertex in the cell . Angular correlations between the normals of adjacent time points were used to quantify the flatness of the cortical plane ( apical surface ) ( S2A Fig , right panel ) . As previously described [18] , coronal cerebral wall slices ( 250–300 μm thick ) were prepared from E12 mouse embryos and then placed into a polystyrene suspension cell culture dish ( Corning ) . After incubation for 30 min with culture medium containing 1% DMSO ( Sigma-Aldrich ) only or 1% DMSO plus 20 μm blebbistatin ( Calbiochem ) ( Fig 3E ) , slices were fixed with 4% paraformaldehyde prepared in phosphate buffer ( pH 7 . 4 ) and then immunostained with anti-ZO1 mouse monoclonal antibody ( 33–9100 , Invitrogen , 1/500 ) , followed by Alexa Fluor 488-labeled anti-mouse IgG antibody ( A11029 , Thermo Fisher Scientific , 1/1 , 000 ) . Images were acquired on an MZFLIII fluorescent stereomicroscope ( Leica ) equipped with an ORCA-ER digital camera ( Hamamatsu Photonics ) . To examine the effect of blebbistatin on VZ cells’ nuclear movements ( as shown in Fig 4F and S3C Fig [rebounding of M-phase cells’ somata] and S3D and S3E Fig [reduced initial basal nucleokinesis] ) and on the dynamics of the lamellipodia-like protrusions ( as shown in S2J Fig ) , coronal cerebral wall slices ( 250–300 μm thick ) prepared from E13 mouse embryos were embedded in collagen gel prior to incubation in culture medium containing 1% DMSO only or 1% DMSO plus 20 μm blebbistatin . In this collagen-embedded condition , slices , even those treated with blebbistatin , maintained their original apical concavity ( Fig 4F and S3C Fig ) and density of apical endfeet [45] . Consistent with a previous report [54] , progenitor cells in slices treated with 20 μm blebbistatin exhibited cytokinesis . Apices of NPCs were visualized by electroporation with pCAG-EGFP-ZO1 ( gift from F . Matsuzaki ) [39] at a concentration of 0 . 5 μg/μL . Cerebral walls were mounted with the apical side down in collagen gel in a glass-bottomed dish ( Iwaki ) using collagen gel . Laser ablation was performed using a confocal laser-scanning microscope ( FV1000 with 15 mW LD473 , Olympus ) equipped with a UV laser system ( UV-ASU-P2 , Olympus ) and a 60× objective lens ( UPLSAPO60XO , Olympus ) , as described previously [44] . A single pulse of a 349-nm laser illumination was applied to a vertex of cell boundaries simultaneously with the image acquisition [18] . For the ablation of M-phase somata ( Fig 3F–3H , S2B–S2D Fig ) and apical processes around pairs of newborn daughter cells ( Fig 6A–6C ) , cells were visualized by staining with FM4-64 for 30 min ( Figs 3F and 6A–6C ) ; by electroporation with pEFX-LPL-LynN-mCherry and pEFX-Cre ( Fig 3G and 3H , S2B and S2C Fig ) ; or by electroporation with pEFX-LPL-LynN-mCherry , pEFX-LPL-EGFP , and pEFX-Cre ( S2D Fig ) . Laser ablation was performed as described previously [55] on an IX81 inverted microscope ( Olympus ) equipped with CSU-X1 ( Yokogawa ) , iXon3 897 EMCCD camera ( Andor ) , 60× objective lens ( UPLSAPO60XW , Olympus ) , on-stage culture chamber ( Tokai Hit ) filled with 95% O2 and 5% CO2 , and MicroPoint ( Andor ) , operated with the iQ2 live cell imaging software ( Andor ) . A pulse of 365-nm laser illumination at 16 Hz was simultaneously applied to the center of the soma ( Fig 3F–3H , S2B–S2D Fig ) and at 15 points in a circle around the newborn daughter cells ( Fig 6A–6C ) at a horizontal plane 5 μm from the apical surface . Images were acquired at 1-s intervals ( Fig 3F–3H ) , 5-s intervals ( S2D Fig ) , 5-s intervals at the ablation and then 5-min intervals ( Fig 6A–6C ) , or 10-s intervals with z-acquisition ( S2B and S2C Fig ) . In spite of the instantaneous fading of fluorescence , any fragmentation of plasma membrane or cytoplasm was not observed after somal ablation in our experimental condition ( Fig 3G , S2B–S2D Fig ) . On the other hand , the LynN-mCherry–labeled plasma membrane protruded from the apical surface of VZ ( S2B and S2C Fig , S26 Movie ) , suggesting a possibility that the energy from the laser reduced the stability ( or increased the fluidity ) of the cytoplasm . Although the exact reaction of laser-ablated cytoplasm remains unknown , our observations suggested that the ablated cells immediately lost the original physical stability without losing the continuity of the plasma membrane , thereby allowing the cell to passively take a shrinking geometry according to external compressive force in the subapical space . A STB-CH-04 silicone chamber ( dimensions; 20 × 20 × 10 mm , Strex ) was coated overnight at room temperature with 0 . 1% polyethyleneimine ( Sigma-Aldrich ) in 150 mM sodium tetraborate ( pH 8 . 4 ) . The chamber was then unidirectionally stretched about 20% using an STB-100 manual-operated stretcher ( Strex ) . In the pre-stretched chamber , a dissected cerebral hemispheric wall ( 500 × 500 μm ) was mounted in 1 mL of collagen gel , with the apical side down . After gel solidification and addition of medium , the chamber was carefully relaxed to the original shape , thereby compressing the gel-embedded cerebral wall . To compare the subapical space before and after compression , confocal microscopic images were obtained in 5-min intervals at 5 μm from the apical surface on an FV1000 laser scanning confocal microscope ( Olympus ) equipped with a 40× objective lens ( UPLFLN40X , Olympus ) and an on-stage culture chamber ( Tokai Hit ) filled with 95% O2 and 5% CO2 . Bipolar-shaped VZ cells on cell culture dishes were obtained by the cortical imprint method [41] , with partial modifications . Briefly , coronal cerebral wall slices ( 250–300 μm thick ) prepared from E13 mouse embryos were incubated for 10 min at 37 °C in phosphate buffer ( pH 7 . 4 ) containing 10 U/mL papain ( Nacalai Tesque ) and 100 μg/mL DNase I ( Sigma-Aldrich ) . Slices were then placed onto a glass-bottomed dish ( Iwaki ) coated with Cell-Tak ( Corning ) at a concentration of 10 μg/cm2 with a minimal volume of growth medium . After incubation for 20 h , slices were gently moved away from the bottom glass by application of excess growth medium and gentle pipetting . For compression of the soma of VZ cells , sharp microneedles were generated from a GD-1 glass capillary with filament ( Narishige ) using a PN-31 puller ( Narishige ) . To avoid direct association with the bottom glass , the tip of the microneedle was slightly bent using an MF-900 Microforge ( Narishige ) . Compression assay was carried out on an IX71 inverted microscope ( Olympus ) equipped with an MMO-202ND three-axis joystick oil hydraulic micromanipulator ( Narishige ) , an ORCA-ER digital camera ( Hamamatsu Photonics ) , and an on-stage heater ( Tokai Hit ) . The off-center surface of the soma was gently compressed with the lateral part of the microneedle , held in an HI-7 injection holder ( Narishige ) . The indentation depth was ≤3 μm , as estimated by the z axis scale on the micromanipulator . After compression and image acquisition , VZ cells were immediately fixed and immunostained with anti-Sox2 antibody ( ab97959 , Abcam , 1/500 ) . Cross-sectional and en face immunohistochemistry were performed as described previously [18 , 56] , with partial modifications . For immunostaining of phospho-myosin light chain and phospho-vimentin , embryonic brains were fixed for 1 h at 4 °C with periodate-lysine-paraformaldehyde ( PLP ) fixative containing 2% trichloroacetic acid . For immunostaining of microtubules , brains were fixed for 1 h at room temperature by 4% paraformaldehyde fixative containing 1 μm Taxol and 0 . 1% Triton X-100 . Frozen sections ( 16 μm thick ) were treated with the following primary antibodies: anti–α-tubulin ( T6199 , Sigma-Aldrich , 1/1 , 000 ) , anti–phospho-myosin light chain ( ab2480 , Abcam , 1/500 ) , and anti–phospho-vimentin ( D076-3 , MBL , 1/500 ) . Sections were then treated with secondary antibodies conjugated with Alexa Fluor 488 or 546 ( A11029 , A11034 , A10036 , Thermo Fisher Scientific , 1/1 , 000 ) or Alexa Fluor 546–conjugated phalloidin ( A22283 , Thermo Fisher Scientific , 1/1 , 000 ) . Confocal images were obtained on an FV1000 laser scanning confocal microscope ( Olympus ) . Scanning electron microscopy of cerebral hemispheres was performed as described previously [45] . In brief , cerebral hemispheres were fixed in 4% paraformaldehyde in phosphate buffer ( pH 7 . 4 ) and subsequently in 2 . 5% glutaraldehyde in phosphate buffer ( pH 7 . 4 ) . The brains were further postfixed at 4 °C overnight in 2 . 5% glutaraldehyde , followed by incubation with 1% osmium tetroxide in phosphate buffer ( pH 7 . 4 ) and dehydration . The samples were trimmed , coated with carbon , and examined under an S-800S SEM ( Hitachi ) . For SBF-SEM , cerebral hemispheres obtained from E13 mouse embryos were fixed for 3 h at 4 °C in PLP fixative containing 2 . 5% glutaraldehyde . Tissue processing for SBF-SEM was performed as described previously [57] with partial modifications . En bloc heavy metal staining was carried out for 1 h in 2% osmium tetroxide solution containing 150 mM sodium cacodylate , 2 mM calcium chloride , and 1 . 5% potassium ferrocyanide . Hemispheres were then treated with 1% thiocarbohydrazide for 20 min at room temperature , 2% osmium tetroxide for 30 min at room temperature , and 1% uranyl acetate overnight at 4 °C , followed by lead aspartate staining . After dehydration and embedding in Durcupan ACM ( Sigma-Aldrich ) , serial images of the subapical space in the VZ were collected on a scanning electron microscope ( Marlin , Zeiss ) equipped with a 3-View ultramicrotome ( Gatan ) ; z step was 50 nm and acceleration voltage was 1 . 4–2 . 0 kV . Images were captured at 4 , 730× magnification in a field of 8 , 192 × 8 , 192 ( x and y axes ) . Captured images were aligned and reconstructed using the Reconstruct software [58] ( available at SynapseWeb , http://synapseweb . clm . utexas . edu/ ) . To simulate the mechanical behaviors of the nucleus/soma of G2/M-phase or early G1-phase cells in the elastic subapical space , the translocation of the nucleus/soma in question and the potential centripetal mechanical influences from the elastic subapical space to the nucleus/soma were simulated in a cross section containing the apicobasal axis ( Fig 4A and S3A Fig ) , assuming rotational symmetry of the nucleus/soma around the apicobasal axis . The subapical space , which is densely filled with cellular processes and other cells’ nuclei/somata , was represented as two effective elastic strings that tightly sandwiched and held the nucleus/soma . The motion of the center of mass of the nucleus/soma with position vector R at time t can be described by the following equation: ΓdRdt=-∂Urep∂R+Fact . ( 1 ) Here , Γ is the friction coefficient . The right-hand side of Eq 1 represents the force exerted on the center of mass of the nucleus/soma; the first term is derived from the repulsive energy function Urep , which represents the volume-exclusion effect of the nucleus/soma against the strings , and the second term , Fact , is a force vector representing the driving force of apicalward translocation in the period from G2 to M phase . The elastic string is described as N+1 particles connected by N massless springs . The motion of particle i with position vector ri can be described by γdridt=-∂∂ri ( Uels+Urep ) . ( 2 ) Here , γ is the friction coefficient . The right-hand side of Eq 2 , representing the force exerted on the particle , is derived from energy functions . The elastic energy stored in the string , Uels , is expressed by a harmonic potential energy calculated from the positions of each particle: Uels=∑ik2 ( |ri+1-ri|-l0 ) 2 , ( 3 ) where k is the spring constant and l0 is the spring length in the stress-free configuration . The volume-exclusion effect of the nucleus/soma is introduced by a repulsive interaction between the particle i and the nucleus/soma . Assuming that the shape of the nucleus/soma is well approximated by a sphere ( circle in the cross section ) of radius σ , the repulsive potential , Urep , is expressed by Urep=∑iε ( σ|ri-R| ) 12 , ( 4 ) where , ε ( >0 ) is the repulsion energy constant . All model constants are listed in Table 1 . Here , to focus on the bouncing-like basal displacement , both ends of the strings are fixed as the boundary condition . The lateral distance between the fixed ends of the two strings , and the apicobasal distance between the fixed ends of the strings , are represented by d and L , respectively ( S3A Fig ) . We formulated a particle-based mathematical model to reproduce the movements of all VZ cells’ nuclei/somata ( Fig 7A–7G and S4A Fig ) , which is similar to Kosodo et al . [26] . Parameters were set based on our live observation of all VZ cells’ nuclei [18] . At first , we defined the center of the ith nuclei as ri ( note: each bold character represents a 3D vector ) . Next , we defined the movement of the nucleus/soma by the following governing equation: dridt=∑j=1Nf ( rj-ri|rj-ri| ) +g ( ϕi ) +h ( Ri-ri ) . ( 5 ) The left-hand side dridt represents the change in position of the ith nucleus per unit time . The first term of the right-hand side ∑j=1Nf ( rj-ri|rj-ri| ) represents the interaction between nuclei/somata . We assumed that nuclei/somata within a certain distance could repel each other , yielding function f as follows: f ( r ) ={ −β ( α−| r | ) r| r | ( | r |≤α ) 0 ( | r |>α ) . ( 6 ) Here , α represents the size of the nucleus/soma and β represents the stiffness of the nucleus/soma . Note that basal nuclear/somal displacements by ∑j=1Nf ( rj-ri|rj-ri| ) in Eq 5 was minimal at z < 10 ( during the initial 10-μm step ) ( S4D Fig ) , consistent with real observations in slice culture ( Fig 2B–2E ) . The second term of the right-hand side g ( ϕi ) represents the force we applied in the z direction ( either apically or basally ) according to a developmental clock ( mostly corresponding to the progression of the cell cycle , with two exceptions described below ) . ϕi , the time or phase of a given VZ cell’s nucleus/soma , was defined as follows: ϕ = 1 , G2 phase ( 1 h ) ; ϕ = 2 , M phase ( 1 h ) ; ϕ = 3 , G1 phase ( 9 ± 1 h ) of a daughter cell that has inherited the basal process from its mother cell progresses ( i . e . , departs the subapical space earlier than its sibling cell’s nucleus/soma , “early-nucleokinetic” [E-IKNM] ) ; ϕ = 3′ , G1 phase of a daughter cell that has not inherited the basal process from its mother cell progresses ( i . e . , departs the subapical space later than its sibling cell’s nucleus/soma , “late-nucleokinetic” [L-IKNM] ) ; ϕ = 4 , S phase ( 4 h ) ; and ϕ = 5 , a VZ cell that has progressed through G1 phase and then decided not to enter S phase ( cell cycle exited , “differentiated” ) to subsequently leave the VZ . The duration assigned to each cell cycle phase was based on values previously estimated in vivo by Takahashi et al . [3] . According to Takahashi et al . [42] , the probability that a given G1-phase cell enters S phase ( ϕ = 4 ) was set to 67% , whereas the probability of differentiation ( ϕ = 5 ) was 33% . To reproduce the sequential nuclear/somal departure of sister cells ( i . e . , E-IKNM cells and L-IKNM cells ) [18] , a lag time ( 0–3 h , random ) was set between ϕ = 2 and ϕ = 3′ , whereas ϕ = 2 and ϕ = 3 were directly connected ( S4A Fig ) . Although the cell cycle would progress continuously from the end of M phase even in the L-IKNM daughter cells , our IKNM simulation described the sequential departures through a technical delay in cell cycle initiation ( S4A Fig ) . The propelling force can be defined as follows: g ( ϕ ) =−V ( ϕ ) Z1v . ( 7 ) Z1 = ( 0 , 0 , 1 ) represents a unit vector toward the z direction and v the velocity of the nucleus/soma . V ( ϕ ) represents the force that was applied according to the cell cycle phase of each VZ cell ( the owner of the nucleus/soma ) , defined as follows: V ( ϕ ) ={ 8 . 5 ( ϕ=1 ) 0 ( ϕ=2 ) −1 . 6 ( ϕ=3andz<10 ) 0 ( ϕ=3andz≥10 ) −1 . 6 ( ϕ=3′andz<10 ) 0 ( ϕ=3′andz≥10 ) 0 ( ϕ=4 ) 0 ( ϕ=5 ) . ( 8 ) Although basal nucleokinesis is mediated by active intracellular mechanisms dependent on kinesin/microtubules [23] and actomyosin [22] , our IKNM simulation showed that collective basalward IKNM at z ≥ 10 ( VZ levels more than 10 μm from the apical surface ) can occur almost passively ( i . e . , without the second term , depending only on the first term in Eq 5 ) , consistent with a model suggested by Norden et al . [21 , 25] and Kosodo et al . [26] . This passive in silico nuclear/somal movement at z ≥ 10 was possible , however , only when a basalward force was applied during z < 10 ( during the initial 10-μm step ) to both the E-IKNM ( ϕ = 3 ) and L-IKNM ( ϕ = 3′ ) daughter cells using the second term . The density of nuclei/soma in this z < 10 zone is low; therefore , the contribution of the first term is very small . The second term at z < 10 does not distinguish the intracellular mechanisms [22 , 23] from the elasticity-based ( Windkessel-like ) passive mechanism that we propose . The third term of the right-hand side h ( Ri − ri ) represents lateral constraints to the nucleus/soma . This term was set based on our observation that nuclei/somata of VZ cells do not freely move laterally beyond 3 μm [18] . Although not shown in the animation , we required that apical and basal processes attach to the apical and basal surfaces of the virtual cerebral wall . Ri = ( xi , yi , 0 ) represents the positions where these processes were attached to the apical and basal surfaces . h ( Ri − ri ) is defined as follows: h ( Ri−ri ) =γ ( Ri−ri ) ⋅Z2 . ( 9 ) Z2 = ( 1 , 1 , 0 ) is a vector used to limit the movement by structural constraint within the xy-plane , and γ represents the strength of the constraint . Parameters were set as follows: α = 10 , β = 15 , γ = 4 . At the starting point of the simulation , we assigned 96 nuclei/somata ( 10 in G2 phase and 86 in G1 phase ) in 20 × 20 × 100 μm space ( S4A Fig ) with random ( x , y , z ) coordinates . At that ( initial ) moment , a remaining G1-phase duration for each G1 nucleus/soma was set at 9 × ( 100 − zi ) ⁄ 100 h , where zi represents the distance between the center of nucleus/soma and the apical surface . Although the composition of these initial in silico VZ cells ( i . e . , occupied by only G1-phase and G2-phase cells , with no S- or M-phase cells ) did not reflect the real value obtained in vivo [42] , our calculation over about 100 , 000 steps achieved equilibrium with regard to the composition of cells of different cell cycle phases , as follows: 4 . 4 ± 1 . 7% for G2 phase , 2 . 9 ± 2 . 1% for M phase , 43 . 0 ± 7 . 3% for G1 phase , 11 . 7 ± 4 . 7% for S phase , and 24 . 2 ± 3 . 3% for differentiated/cell cycle exited , consistent with Takahashi et al . [3 , 42] . All data analysis was performed between steps 350 , 000 to 400 , 000 of the calculation . A pioneering version of the “virtual VZ” simulation was performed by Kosodo et al . [26] in the neocortical VZ . Based on results of comprehensive monitoring and quantification of all VZ cells’ nuclear movements in slices prepared from H2B-mCherry mice [18] , we made two technical modifications for improvement: ( 1 ) we restricted the horizontal/lateral displacement of all nuclei/somata in VZ ( as shown in S4A Fig ) and ( 2 ) we carefully compared the apicobasal movements of nuclei between the real VZ cells and our virtual VZ using the MSD . Another modification that we made was that while Kosodo et al . [26] described the amount of soma–soma repulsions to be constant , our simulation set the repulsive force to have an inverse relationship with the distance between nuclei/somata ( S4A Fig ) . Because our simulation described the repulsion between nuclei/somata as a first-order approximation in Eq 6 ( see also S4A Fig ) , the physical properties of actual cells derived from cortical surface tension [59] is partially included . However , the measurement of physical properties such as viscoelasticity , which is ( 1 ) an indispensable factor to consider the actual mode of repulsion between nucleus/somata and ( 2 ) highly related with cell-surface tension , cytoskeletal organizations , and , probably , cell shapes in tissue , is still challenging [60] .
|
The development of large brain structures , such as the mammalian cerebral cortex , depends on the continuous and efficient production of cells by neural progenitor cells . Neural progenitor cells are elongated and span the developing brain wall . The nuclei and bodies of these cells move cyclically between the apical and basal surfaces , and they divide every time they reach the apical surface . While we understand how individual cells achieve this cycle , how the movements of several progenitor cells are coordinated with one another remains elusive . By using a combination of live imaging and mechanical experiments , coupled with mathematical simulations , we show that cell crowding at the apical surface , where progenitor cells divide , creates a subapical microzone that is compressed and elastic . We then show that when each mother cell rounds up , preparing for division , it pushes this elastic microzone laterally , thereby storing mechanical energy . After cell division , this mechanical energy is transferred to the daughter cells , propelling them along the axis of movement in the direction of the basal surface , in an energy-saving manner . Our mathematical simulations show that timely departure of newly generated daughter cells is critical for the overall tissue structure of the cerebral proliferative zone .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"g1",
"phase",
"classical",
"mechanics",
"engineering",
"and",
"technology",
"cell",
"cycle",
"and",
"cell",
"division",
"lasers",
"cell",
"processes",
"g2",
"phase",
"mechanical",
"energy",
"molecular",
"motors",
"actin",
"motors",
"stem",
"cells",
"microscopy",
"optical",
"equipment",
"motor",
"proteins",
"research",
"and",
"analysis",
"methods",
"contractile",
"proteins",
"animal",
"cells",
"proteins",
"scanning",
"electron",
"microscopy",
"physics",
"biochemistry",
"cytoskeletal",
"proteins",
"cell",
"biology",
"equipment",
"electron",
"microscopy",
"myosins",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences"
] |
2018
|
Elasticity-based boosting of neuroepithelial nucleokinesis via indirect energy transfer from mother to daughter
|
Ethylene is the main regulator of climacteric fruit ripening , by contrast the putative role of other phytohormones in this process remains poorly understood . The present study brings auxin signaling components into the mechanism regulating tomato fruit ripening through the functional characterization of Auxin Response Factor2 ( SlARF2 ) which encodes a downstream component of auxin signaling . Two paralogs , SlARF2A and SlARF2B , are found in the tomato genome , both displaying a marked ripening-associated expression but distinct responsiveness to ethylene and auxin . Down-regulation of either SlARF2A or SlARF2B resulted in ripening defects while simultaneous silencing of both genes led to severe ripening inhibition suggesting a functional redundancy among the two ARFs . Tomato fruits under-expressing SlARF2 produced less climacteric ethylene and exhibited a dramatic down-regulation of the key ripening regulators RIN , CNR , NOR and TAGL1 . Ethylene treatment failed to reverse the non-ripening phenotype and the expression of ethylene signaling and biosynthesis genes was strongly altered in SlARF2 down-regulated fruits . Although both SlARF proteins are transcriptional repressors the data indicate they work as positive regulators of tomato fruit ripening . Altogether , the study defines SlARF2 as a new component of the regulatory network controlling the ripening process in tomato .
Fruit ripening is a complex , genetically programmed process that is associated with dramatic metabolic and textural transformations including color change , fruit softening , sugar accumulation and production of flavor and aroma compounds [1–3] . The ripening process ultimately leads to fruit withering allowing dispersal of the seeds and based on their ripening mechanism , fleshy fruits are divided into climacteric and non-climacteric types [4] . Climacteric fruit ripening is characterized by the autocatalytic increase in ethylene biosynthesis , and it is widely accepted that this hormone acts as main trigger and coordinator of the ripening process [5] . In support of this view , several genes involved in ethylene metabolism and signaling have been shown to be essential for fruit ripening in tomato and reducing ethylene production via suppression of ethylene biosynthesis genes , ACC synthase ( ACS ) and ACC oxidase ( ACO ) , leads to the inhibition of fruit ripening [6–9] . Likewise , the tomato Never-ripe ( Nr ) mutant , bearing an altered allele of the ethylene receptor gene ETR3 , also shows a non-ripening phenotype due to its reduced ethylene sensitivity [10 , 11] . In line with the ETR receptors being negative regulators of ethylene signaling , silencing of either LeETR4 or LeETR6 with a fruit-specific promoter causes enhanced ethylene sensitivity and early ripening phenotype [12] . On the other hand , repression of tomato EIN3-Binding Factors SlEBF1/SlEBF2 , the downstream component of ethylene signaling F-BOX proteins responsible for the degradation of EIN3 protein , causes constitutive ethylene responses and early fruit ripening [13] . In concert with ethylene , the control of fruit ripening relies on other key regulators , some of which have been functionally characterized . In this regard , silencing of the homeobox protein LeHB1 results in delayed ripening [14] and MADS-box genes like RIPENING-INHIBITOR ( RIN ) and TOMATO AGAMOUS-LIKE 1 ( TAGL1 ) are proved to dramatically affect fruit ripening [15–18] . The COLORLESS NON-RIPENING ( CNR ) , a SQUA-MOSA promoter binding protein ( SBP ) , is shown to directly influence the expression of RIN and other MADS-box genes during fruit ripening [19 , 20] . Moreover , fruits in the rin and cnr mutants remain firm and green for an extended period , and they are deficient in ethylene production and unable to ripen upon exogenous ethylene treatment [19 , 21] . Besides its important role in fruit ripening , ethylene is also involved in several other plant developmental processes [22] . Without minimizing the role of ethylene , it has long been considered that other plant hormones are likely to play a critical role for both the attainment of competence to ripen and the coordination of subsequent steps of the ripening process . In this regard , assumptions that fruit ripening is most likely driven by a complex hormonal balance have been formulated for a long time in the literature , even though clear experimental evidence supporting this hypothesis remained lacking . Auxin is among the first to be assigned a role in the ripening of fleshy fruits because auxin treatment of mature fruit was shown to delay ripening [23–27] . More direct evidence for the involvement of auxin in ripening came recently through the implementation of reverse genetic strategies targeting auxin-dependent transcriptional regulators [28–32] . Auxin signaling is known to regulate the expression of target genes mainly through two types of transcriptional regulators , namely , Aux/IAAs and Auxin Response Factors ( ARF ) . While Aux/IAAs are known to be repressors of auxin-dependent gene transcription , ARFs can be either transcriptional activators or repressors via direct binding to the promoter of auxin-responsive genes [33–39] . In the tomato , 22 ARFs have been identified [39] and the accumulation of some ARF transcripts has been reported to be under ethylene regulation during tomato fruit development suggesting that auxin signaling may influence the control of climacteric fruit ripening [28] . Recently , it was shown that SlARF4 plays a role in fruit ripening mainly by controlling sugar metabolism , and down-regulation of this ARF resulted in ripening-associated phenotypes such as enhanced firmness and chlorophyll content leading to dark green fruit and blotchy ripening [28 , 32 , 40] . The marked ripening-associated pattern of expression of SlARF2 prompted the investigation of its physiological significance and in particular its putative role in fleshy fruit development and ripening . Since two putative co-orhtologs of Arabidopsis ARF2 have been identified in the tomato , named SlARF2A and SlARF2B , transgenic lines were generated that are specifically silenced either in one or simultaneously in the two ARF2 paralogs ( S2 Fig ) . SlARF2 down-regulated lines displayed strong ripening defects and the expression of key regulators of fruit ripening , such as RIN , CNR , NOR and TAGL1 was markedly decreased in SlARF2 under-expressing lines which position ARF2 as a new component of the regulatory network controlling the ripening process in tomato .
Some members of the ARF gene family were shown to play a role in regulating important aspects of tomato fruit ripening [28 , 32] . More recently , expression profiling of tomato ARFs revealed that some members of this gene family display a ripening-associated increase of transcript accumulation suggesting their potential involvement in regulating this process [39] . Among these , the expression pattern of ARF2 is appealing which prompted its molecular and functional characterization . In contrast to Arabidopsis where a single ARF2 gene is present , two putative orthologs are found in the tomato genome with SlARF2A ( Solyc03g118290 . 2 . 1 ) being located in chromosome 3 and SlARF2B ( Solyc12g042070 . 1 . 1 ) in chromosome 12 [39] . The two genomic clones share similar structural organization with , however , SlARF2A being made of 15 exons while only 14 exons are present in SlARF2B . The isolation of full-length cDNAs corresponding to SlARF2A ( 2541 bp ) and SlARF2B ( 2490 bp ) indicated that the deduced protein sizes are 847 and 830 amino acids , respectively ( Table 1 ) , and pairwise comparison of the two SlARF2 protein sequences revealed 83 . 3% amino acid identity . The search for protein domains in Expasy database ( http://prosite . expasy . org/ ) indicated the presence of highly conserved domains typical of ARFs ( Fig 1A ) including the DBD ( DNA Binding Domain ) and the dimerization domains ( protein/protein domain III and IV ) . Moreover , the analysis of a 2 kb promoter sequence using PLACE/signal search tool ( http://www . dna . affrc . go . jp/PLACE/signalscan . html ) revealed the presence of putative Ethylene Response ( ERE ) and Auxin Response ( AuxRE ) elements in both SlARF2A and SlARF2B promoters ( Fig 1A ) . Assessing transcript accumulation by quantitative-RT-PCR confirmed the ripening-associated patterns of expression of the two SlARF2 genes ( Fig 1B ) . SlARF2A and SlARF2B are expressed in all plant tissues tested including root , leaf , stem , flower and fruit with , however , a notably higher transcript accumulation for SlARF2A in both vegetative and reproductive tissues . It is noteworthy that the transcript levels corresponding to the two ARF2 genes undergo a net up-regulation at the onset of fruit ripening ( Fig 1B ) suggesting that SlARF2A and SlARF2B may play an active role in this developmental process . The presence of conserved AuxRE and ERE cis-regulatory elements in the promoter region of SlARF2A and SlARF2B and the expression of both genes in developmental processes known to be regulated by both auxin and ethylene prompted the investigation of their responsiveness to the two hormones . Transcript accumulation assessed by RT-qPCR indicated that SlARF2A , but not SlARF2B , is responsive to exogenous ethylene treatment in mature green fruit ( Fig 2A ) , and that this ethylene-induced expression is repressed by 1-MCP , the inhibitor of ethylene perception ( Fig 2B ) . By contrast , SlARF2B expression was up-regulated by auxin in mature green fruit , while that of SlARF2A showed no responsiveness to auxin treatment ( Fig 2C ) . Genes known to be ethylene ( E4 , E8 ) or auxin ( GH3 , SAUR ) responsive were used as controls to validate the efficacy of the hormone treatment . The subcellular localization of SlARF2A and SlARF2B proteins was then assessed using translational fusion to the Green Fluorescent Protein ( GFP ) in a tobacco protoplast transient expression assay . Microscopy analysis clearly showed that SlARF2A/2B:GFP fusion proteins exclusively localized into the nucleus ( Fig 3A ) , consistent with their putative role in transcriptional regulation activity . The ability of SlARF2A/2B proteins to regulate the activity of auxin-responsive promoters was then evaluated in a single cell system . A reporter construct , consisting of the synthetic auxin-responsive promoter DR5 fused to GFP [41] , was co-transfected into tobacco protoplasts with an effector construct allowing the constitutive expression of SlARF2A or SlARF2B protein . As expected the DR5-driven GFP expression was strongly enhanced by auxin ( 2 , 4-D ) treatment . However , the presence of either SlARF2A or SlARF2B proteins strongly inhibited this auxin-induced activity of DR5 promoter , clearly demonstrating that SlARF2A and SlARF2B act in vivo as strong transcriptional repressors of auxin-dependent gene transcription ( Fig 3B ) . To gain insight into the physiological significance of SlARF2 , transgenic lines under-expressing the two paralogs were generated in the MicroTom tomato genetic background . To this purpose , dedicated RNAi constructs were designed to selectively target either SlARF2A or SlARF2B allowing the generation of transgenic lines specifically silenced in only one of the two SlARF2 genes ( Fig 4A ) . Transgenic RNAi lines in which both paralogs are simultaneously silenced were also obtained . Repression of SlARF2A and SlARF2B in the RNAi lines was confirmed by qPCR analyses in seedlings and fruit tissues showing that the accumulation of SlARF2A or SlARF2B transcripts was selectively reduced in the appropriate silenced lines whereas in the SlARF2A/2B double knockdown lines both SlARF2 genes were significantly down-regulated ( Fig 4B ) . Importantly , the expression of the most closely related ARFs in terms of sequence identity was not reduced in SlARF2A/2B transgenic lines , thus ruling out a lack of specificity of the RNAi strategy ( S2 Fig ) . It is noteworthy that , in the SlARF2A-RNAi lines the down-regulation of SlARF2A seems to be compensated by an increase in SlARF2B expression , while such a compensation mechanism does not occur in the SlARF2B-RNAi lines . To check whether SlARF2A may be directly involved in the transcriptional regulation of SlARF2B , a GFP reporter construct driven by the SlARF2B promoter was co-transfected into tobacco protoplasts with an effector construct allowing constitutive expression of SlARF2A . The data clearly show that the presence of SlARF2A inhibits the expression of the GFP reporter gene driven by the SlARF2B promoter , revealing the ability of SlARF2A to repress in vivo the transcriptional activity of SlARF2B ( Fig 4C ) . SlARF2A/B down-regulated lines displayed multiple auxin-related phenotypes including triple cotyledon formation and enhanced root branching ( S1 Fig ) supporting the idea that the reduced expression of ARF2 might affect auxin responses . To investigate whether SlARF2A and SlARF2B are involved in auxin responses in planta , genetic crosses were performed between the SlARF2 RNAi lines and a tomato line expressing the GUS reporter gene under the control of the DR5 auxin-responsive promoter . In the wild-type background , the basal expression of the DR5-driven GUS was low but displayed a net increase upon exogenous auxin treatment ( Fig 5A ) . By contrast , the basal expression of the GUS reporter gene was dramatically high in the SlARF2AB-RNAi background in the absence of auxin treatment indicating that the under-expression of SlARF2 results in enhanced expression of the auxin-responsive gene . Interestingly , such an increase in GUS expression was not observed neither in SlARF2A-RNAi nor in SlARF2B-RNAi background , suggesting that the two genes are functionally redundant and can compensate for each other ( Fig 5A ) . Assessing GUS transcript accumulation by qPCR confirmed the higher expression of the DR5-driven GUS in the SlARF2AB-RNAi background but not in the SlARF2A and SlARF2B-RNAi lines ( Fig 5B ) . Considering the ripening-associated pattern of both SlARF2A and SlARF2B , we sought to analyze the fruit phenotypes of SlARF2A and SlARF2B single and double knockdown tomato lines . In both SlARF2A and SlARF2B-RNAi single knockdown lines , the fruit exhibited dark green spots at immature and mature green stages , and then displayed a mottled pattern of ripening with yellow/orange spots on the skin remaining till the full mature stage ( Fig 6 ) . The double silenced lines exhibited more severe ripening defects with yellow and orange patches never reaching the typical red color of wild type or out-segregating lines , again suggesting that SlARF2A and SlARF2B may have redundant function in fruit ripening ( Fig 6A ) . Assessing the time period from anthesis to breaker stage revealed a slight but statistically significant delay ( 2 to 3 days delay ) in the onset of ripening between wild type and double knockdown lines ( Fig 6B ) . The fruit color in SlARF2AB-RNAi lines never get fully red ( Fig 6C ) and full ripening cannot be recovered upon exogenous ethylene treatment of the SlARF2A/B RNAi double knockdown fruits which suggests a possible alteration in ethylene perception or response ( Fig 6D ) . The ripening defect phenotype prompted us to monitor the climacteric ethylene production in the SlARF2AB-RNAi line . Ethylene production , assessed either on fruits kept on the plant or detached ( Fig 7 ) , is significantly low throughout ripening and reaches its peak with 3 days delay as compared to wild type ( Fig 7 ) . Assessing the expression of ethylene biosynthesis genes by qPCR ( Fig 8A ) revealed reduced levels of ACO1 , ACS2 , ACS3 and ACS4 transcripts in the SlARF2AB RNAi line at all ripening stages ( Breaker , Breaker+2 and Breaker+8 ) . However , the reduced ethylene production cannot account for the ripening defects because exogenous ethylene treatment failed to reverse the ripening phenotype ( Fig 6D ) . We therefore examined the expression of ethylene receptor genes ( Fig 8B ) . transcript levels corresponding to ETR3 ( NR ) and ETR4 are dramatically low in the transgenic lines compared to wild type at all stages of fruit ripening ( Br , Br+2 , and Br+8 ) and the expression of other receptor genes ( ETR1 , ETR2 , and ETR5 ) is also down-regulated at the breaker+8 stage . The disturbed expression of ethylene receptor genes is likely to result in altered ethylene perception in the transgenic lines . In addition , the expression of EIN2 and two EIN3-like genes ( EIL2 and EIL3 ) , which encode major components of ethylene transduction pathways , was also down-regulated during ripening of SlARF2A/B RNAi fruit ( Fig 8B ) . More striking , the expression of a high number of ERF genes ( Fig 9 ) , known to mediate ethylene responses , was also altered with SlERF . A1 , SlERF . A2 , SlERF . A3 , SlERF . C1 , SlERF . C3 , SlERF . C6 , SlERF . D1 , SlERF . D2 , SlERF . D4 , SlERF . E1 , SlERF . E3 and SlERF . E4 being down-regulated while SlERF . B1 , SlERF . B2 , SlERF . B3 , SlERF . D3 , SlERF . F2 are up-regulated . Altogether , these data strongly suggest that ethylene responses are highly impaired in the transgenic lines . The fruit color saturation assessed by Hue angle , indicative of color intensity , revealed a reduced red pigment accumulation in SlARF2AB down-regulated lines ( Fig 10 ) . Accordingly , the expression of genes involved in the carotenoid pathway was altered . PSY1 , a key regulator of flux through the carotenoid pathway , was significantly down-regulated in the SlARF2AB-RNAi fruits at all ripening stages ( Fig 10 ) . Lower levels of phytoene desaturase ( PDS ) and phytoene synthase ( ZDS ) transcripts were also observed at Br+2 stage in the SlARF2AB-RNAi fruit . By contrast , transcripts corresponding to lycopene beta cyclase genes ( β-LCY1 , β-LCY2 ) displayed higher accumulation than in wild-type at all ripening stages , and those corresponding to lycopene β-cyclases ( CYCB ) were also up-regulated at Br and Br+2 stages in SlARF2AB-RNAi fruit ( Fig 10 ) . On the other hand , SlARF2AB-RNAi fruits maintained higher firmness than wild type throughout ripening ( Fig 11 ) . In line with this delayed softening phenotype , transcript accumulation of PG2A , a major fruit polygalacturonase gene involved in ripening-related cell wall metabolism , was significantly reduced at Br , Br+2 , and Br+8 stages in SlARF2AB-RNAi fruits ( Fig 11 ) . The expression of key ripening regulators assessed at the transcript level was strongly reduced throughout ripening in the SlARF2 RNAi line . Compared to wild type fruit , transcript levels corresponding to RIN and CNR genes were significantly lower at Br , Br+2 and Br+8 stages ( Fig 12 ) . Likewise , the NOR gene displayed reduced expression at Br and Br+8 stages , TAGL1 showed the same tendency at Br and Br+2 stages , FUL1 at Br and Br+2 stages , and FUL2 at Br+2 and Br+8 stages . The altered expression of these genes is consistent with the dramatically altered ripening of SlARF2AB-RNAi fruits . Likewise , the low expression level of E8 and E4 , two ethylene-responsive and ripening- associated genes , is consistent with the altered expression of ethylene biosynthesis and signaling genes . By contrast , mRNA levels of LeHB-1 , another ripening regulator gene , did not display significant change in SlARF2AB-RNAi fruits compared to wild type ( Fig 12 ) .
While ethylene is considered as the key hormone regulating climacteric fruit ripening , the down-regulation of SlARF2 described herein supports the idea that auxin might also play an important role in the control of the ripening process . The altered ripening phenotypes associated with the under-expression of SlARF2 genes are consistent with previous work showing that the coordinated expression of some ARF genes in the tomato is instrumental to normal fruit ripening [28 , 32 , 40] . As depicted in the model proposed ( Fig 13 ) , besides the crucial role devoted to ethylene , the data support a higher order of complexity of the mechanism underlying the control of fleshy fruit ripening which should be rather seen as a multi-hormonal process . Molecular analyses indicate that SlARF2 impacts , either directly or/and indirectly , the expression of master regulators of ripening like RIN , NOR and CNR and components of ethylene biosynthesis and responses ( Fig 13 ) . The data clearly support the idea of SlARF2 being a major component of the regulatory mechanism controlling tomato fruit ripening . It remains however unclear how the knockdown of a transcriptional repressor leads to the down-regulation of a set of genes whose expression is instrumental to climacteric ripening . Given that SlARF2 works as transcriptional repressors , the data imply that their main target could be a negative regulator of the ripening process . While the nature of this putative negative regulator remains to be elucidated , the data indicate that this unknown component has the ability to regulate the key factors controlling fruit ripening such as MADS-Box and ethylene signaling genes . SlARF2 genes are obviously required for climacteric ripening , hence the hypothesis that the rise of their expression at the onset of ripening may inhibit a negative regulator either at the transcriptional or the protein level , thus releasing the expression of key ripening genes ( Fig 13 ) . However , despite their repressor activity on auxin-responsive promoters , it cannot be fully excluded that ARF2A/B may also have the ability to function as activator on the promoter of key genes regulating fruit ripening , such as Rin and Nor . While the expression of SlARF2A and SlARF2B increases during fruit ripening , SlARF2A also displays a high expression level in leaves and flowers suggesting an active role for this gene in vegetative organs . Single knockdown of either SlARF2A or SlARF2B resulted in discreet ripening phenotypes , whereas simultaneous down-regulation of the two genes leads to a severe delay or almost complete inhibition of ripening , indicating that both genes may contribute to tomato fruit ripening . Genetic crosses between SlARF2 RNAi tomato lines and lines expressing the GUS reporter driven by the DR5 synthetic auxin-responsive promoter indicated that single repression of SlARF2A or SlARF2B is unable to significantly affect GUS expression while simultaneous down-regulation of both SlARF2 genes resulted in a strong increase in DR5:GUS expression similar to that observed upon exogenous auxin treatment ( Fig 5 ) . These data indicate that , in planta , SlARF2 acts as a repressor of auxin-dependent gene transcription and clearly suggests that SlARF2A and SlARF2B are functionally redundant . Moreover , down-regulation of SlARF2A is compensated by an up-regulation of SlARF2B suggesting a coordinated expression of the two ARF paralogs . Indeed , transient expression assay revealed the ability of SlARF2A to repress the activity of SlARF2B promoter indicating that the transcription of this latter gene is under direct regulation by SlARF2A . Down-regulation of SlARF2 genes impairs normal fruit ripening likely via altering components of ethylene metabolism , signaling and response . In support of this idea , SlARF2A/B RNAi fruits produce less climacteric ethylene than wild type ( Fig 7 ) and lower expression of ACC oxidase ( ACO ) and ACC synthase ( ACS ) genes whose expression is instrumental to the triggering of climacteric ripening [5 , 9] . It was shown that transition from auto-inhibitory System1 to auto-catalytic System2 is associated with an increased expression of LeACS1A , LeACS2 , LeACS4 , LeACO1 , LeACO3 , and LeACO4 genes [5 , 9 , 42] . Accordingly , repression of genes belonging to ACS and ACO gene families blocked fruit ripening in tomato [6 , 7 , 9 , 43] . In line with the reduced ethylene production in the SlARF2AB-RNAi fruits , the expression of ethylene responsive genes E4 and E8 is also reduced ( Fig 12 ) . The treatment with exogenous ethylene was unable to restore normal fruit ripening suggesting that ethylene signaling is likely impaired in SlARF2 knockdown lines . The expression of ethylene receptor genes NR ( SlETR3 ) , SlETR4 , and SlETR6 is altered in the transgenic lines which may account for the loss of ability to trigger the autocatalytic ethylene production required for normal climacteric ripening even upon exogenous ethylene treatment . It was reported that down-regulation of NR receptor resulted in slight delay in fruit ripening with reduced rates of ethylene synthesis and slower carotenoid accumulation [44] . However , reducing NR expression via RNA antisense strategy has been also reported to result in up-regulation of LeETR4 as a compensation mechanism for the loss of NR [44] . In the SlARF2 under-expressing fruit , both SlETR3/NR and SlETR4 were down-regulated ( Fig 8 ) , which may explain the more severe loss of fruit ripening in SlARF2AB-RNAi lines compared to NR antisense lines . It is widely accepted that modulation of the expression of ethylene-regulated genes is at least partly mediated by ERFs [20 , 45–49] . In particular , it was shown that SlAP2a , a tomato APETALA2/ERF gene , is a negative regulator of fruit ripening [50 , 51] and that SlERF6 plays an important role in tomato ripening and carotenoid accumulation [48] . More recently , the expression of a dominant repression version of another tomato ERF gene , SlERF . B3 , was shown to lead to a dramatic delay in fruit ripening [52] . Interestingly , the expression of a high number of ERFs is disturbed in SlARF2AB-RNAi fruits which may account for the altered ethylene response and contribute to the ripening defect phenotypes . Tomato genes encoding ripening-inhibitor ( RIN ) , non-ripening ( NOR ) and colorless non-ripening ( CNR ) are considered as master regulators of the ripening process and mutations in the corresponding loci dramatically impair fruit ripening [15 , 19 , 21] . Some of the main features of these non-ripening mutants are shared by the SlARF2 knockdown lines such as enhanced fruit firmness , low ethylene production and incapacity to ripen in response to exogenous ethylene . Moreover , the expression of RIN , NOR and CNR genes was significantly down-regulated during fruit ripening in SlARF2AB-RNAi lines ( Fig 12 ) . Considering the crucial role of RIN , NOR , and CNR in the attainment of competence to ripen [53] , the down-regulation of these master transcriptional regulators in SlARF2 under-expressing fruits is likely contributing to the impaired ripening phenotype . SlARF2AB-RNAi fruits showed yellow-orange color associated with a reduced expression of AGAMOUS-like 1 ( TAGL1 ) and FRUITFUL FUL1 and FUL2 orthologs encoding ripening-related MADS domain transcription factors . Accordingly , suppression of TAGL1 was shown to result in yellow-orange fruits and low ethylene levels due to the down-regulation of ACS2 [17 , 18] . Likewise , simultaneous suppression of FUL1 and FUL2 resulted in ripening defects [54] . Strikingly , these phenotypes are similar to those displayed by SlARF2 down-regulated lines . It has been reported that TAGL1 , FUL1 , and FUL2 interact with RIN [55 , 56] forming higher order complexes that regulate tomato fruit ripening [57] . The phenotypes and associated gene expression patterns support the hypothesis that down-regulation of SlARF2 impairs ripening through interfering with the MADS-box regulatory network . This work shows that the expression of SlARF2 is down-regulated in the tomato ripening mutants rin and nor , thus suggesting that these ripening regulators negatively regulate the expression of SlARF2 genes . Taken together , the data support the hypothesis of an active interplay between the major ripening regulators , rin and nor , and SlARF2 which therefore emerges as a new player of the control mechanism of tomato fruit ripening ( Fig 13 ) . It has been suggested that tomato SlARF2 might be involved in auxin and ethylene interplay during the apical hook formation [58 , 59] . This putative role in linking the two hormones signaling is in agreement with the presence of conserved auxin and ethylene-responsive elements in the promoter regions of SlARF2A and SlARF2B . Down-regulation of SlARF2 leads to altered expression of transcription factors known to mediate both ethylene ( ERFs ) and auxin ( ARFs ) responses and results in disturbed expression of auxin and ethylene responsive genes further suggesting the potential involvement of SlARF2A and SlARF2B in the crosstalk between auxin and ethylene . A typical feature of tomato fruit undergoing ripening is the accumulation of lycopene which accounts for the red color whereas β-carotene , conferring an orange color , does not accumulate normally at this stage [60 , 61] . The SlARF2AB-RNAi fruit displayed yellow-orange sectors reflecting increased accumulation of β-carotene and degraded lycopene . The accumulation of lycopene is caused by the up-regulation of the phytoene synthase gene ( PSY1 ) and the down-regulation of LCYB and CYCB [60 , 62–64] . PSY1 is the first rate-limiting enzyme in the plant carotenoid biosynthetic pathway and its transcript accumulation is induced by ethylene [60 , 65] . Repression of PSY1 inhibits total carotenoid accumulation resulting in mature yellow fruit with little lycopene or βcarotene [65] . LCYB and CYCB are responsible for the conversion of lycopene into β-carotene , which turns the fruit orange [61 , 63] . During fruit ripening , transcript accumulations of both genes is repressed by the elevated ethylene levels thus leading to the accumulation of lycopene that is responsible for the red color of ripe fruit [18] . The SlARF2AB-RNAi fruit produced less ethylene than wild type and exhibited low levels of SlPSY1 transcripts and high levels of SlLCYB and SlCYCB , which promotes the accumulation of β-carotene rather than lycopene thus causing the orange-yellow color of SlARF2AB-RNAi fruit . Overall , the work adds another layer to the gene regulatory network underlying fruit ripening reinforcing the concept that the ripening process relies on the interplay between different actors . While the present study is in line with previous reports [28 , 32 , 40] supporting the potential role of auxin in fleshy fruit ripening , there is little doubt that the involvement of other hormones is also likely required for a proper tuning of this complex developmental process . Altogether , the data sustain a high level of complexity of the signaling networks underlying fleshy fruit ripening which may reflect the diversity of the ripening features displayed by different plant species .
Tomato ( Solanum lycopersicum L . cv MicroTom ) seeds were sterilized , washed with sterile water 5 times , and sown in Magenta vessels containing 50ml of 50% Murashige and Skoog ( MS ) medium with 0 . 8% ( w/v ) agar , pH 5 . 9 . The transgenic plants were transferred to soil and grown under standard greenhouse conditions [32] . Conditions in the culture chamber room were set as follows: 14-h-day/10-h-night cycle , 25/20°C day/night temperature , 80% relative humidity , 250 mol . m-2 . s-1 intense light [52] . Three cDNA fragments specific to SlARF2A , SlARF2B and both were cloned into pHellsgate12 vector independently , with primers listed in the S1 Table . Transgenic plants were generated by Agrobacterium-mediated transformation [66] with minor changes: 6 days old cotyledons were used for the transformation; the duration of subcultures for shoot formation was reduced to 15 days; and the kanamycin concentration was 70 mg . L-1 . The constructs were under the transcriptional control of the CaMV 35S and the Nos terminator [32] . The structure of the SlARF2A and SlARF2B were determinated using in silico approaches ( software: Fancy Gene V1 . 4 ) . Protein domains were first predicted on the prosite protein database ( http://prosite . expasy . org/ ) . Promoter sequences of SlARF2A and SlARF2B genes were analyzed using PLACE signal scan search software ( http://www . dna . affrc . go . jp/PLACE/signalscan . html ) . Flower buds of DR5:GUS transgenic plants were emasculated before dehiscence of anthers ( closed flowers ) to avoid accidental self-pollination . Cross-pollination was performed on DR5:GUS emasculated flowers with pollen from wild type , SlARF2A-RNAi , SlARF2B-RNAi , and SlARF2AB-RNAi flowers . For localization of SlARF2A and SlARF2B proteins , the CDS sequences were cloned as a C-terminal fusion in frame with green fluorescent protein ( GFP ) into the pGreen-GFP vector , and expressed under the control of the 35S CaMV promoter . The pGreen-GFP empty vector was used as the control . Protoplasts were obtained from tobacco suspension-cultured ( Nicotiana tabacum ) BY-2-cells and transfected according to the method described previously [67] . GFP localization by confocal microscopy was performed as described previously [38] . For co-transfection assays , the coding sequence of SlARF2A and SlARF2B were cloned into the pGreen vector and expressed under the control of the 35S CaMV promoter . The synthetic DR5 promoter containing AuxRE and the promoter of SlARF2B were cloned in frame with GFP reporter gene in pGreen vector independently . Protoplasts were obtained from suspension-cultured of tobacco ( Nicotiana tabacum ) BY-2-cells and transfected according to the method described previously [67] . After 16 h of incubation in the presence or absence of 2 . 4-D ( 50 μM ) , GFP expression was analyzed and quantified by flow cytometry ( FACS Calibur II instrument , BD Biosciences , San Jose , CA , USA ) as indicated in Hagenbeek and Rock ( 2001 ) . All transient expression assays were repeated at least three times with similar results . To visualize GUS activity , transgenic lines bearing the promoter of DR5 fused with GUS constructs were incubated with GUS staining solution ( 0 . 1% Triton X-Gluc , pH7 . 2 , 10 mM EDTA ) at 37°C overnight . After GUS staining , samples were decolorized using several washes of graded ethanol series [32] . For auxin treatment on light grown seedlings , 21-day-old DR5::GUS seedlings were soaked in liquid MS medium with or without ( mock treatment ) 20 μM IAA for 2 hours . For auxin treatment on fruit , mature green fruits were injected with 20 μM IAA and kept for 6 hours at room temperature . For ethylene treatment on fruit , mature green fruits were treated with air or ethylene gas ( 50 μL . L-1 ) for 5 hours . For 1-MCP treatment , 1 . 0 mg . L-1 1-MCP was applied into the breaker stage fruits for 16 hours . For qPCR expression analysis , the tissues were immediately frozen in liquid nitrogen and stored at -80°C until RNA extraction . Fruits from different developmental stages were harvested and incubated in opened 125-ml jars for 3 hours to remove the wound ethylene production caused by picking . Jars were then sealed and incubated at room temperature for 2 hours , and 1 ml of headspace gas was injected into an Agilent 7820A gas chromatograph equipped with a flame ionization detector ( Agilent , Santa Clara , CA , USA ) . Samples were compared to 1 ml . L-1 ethylene standard and normalized for fruit weight . For ethylene response assay , mature green fruits from wild-type and SlARF2AB-RNAi lines were treated by 10 ml . L-1 ethylene for 3 days , 2 hours and 3 times per day . Fifteen fruits from each line of the SlARF2AB-RNAi and wild type were harvested at the Breaker ( Br ) stage . The firmness was then assessed using Harpenden calipers ( British Indicators Ltd , Burgess Hill , UK ) as described by Ecarnot et al . , ( 2013 ) . After the first measurement , these fruits were kept at room temperature for measuring the firmness day by day . Twenty fruits for each line of the SlARF2AB-RNAi and wild type were harvested at the Br stage . The hue angle values were calculated according to the methods previously described [32] . After measurement , these fruit were kept at room temperature and were measured day by day until fruits got fully red . Different stage fruits were harvested , the pericarp were frozen in liquid nitrogen , stored at -80°C . Total RNA extraction , DNA contamination removing , cDNA generation of tomato tissues ( root , stem , leaves , bud , flower , mature green fruit , breaker fruit , and red fruit ) and qRT-PCR were performed according to methods previously described [38 , 68] . The primer sequences are listed in the S1 Table . Actin was used as the internal reference . Three independent RNA isolations were used for cDNA synthesis and each cDNA sample was subjected to real-time PCR analysis in triplicate . The sequences of genes used for the qPCR can be found at the website ( http://solgenomics . net/ ) under the following solyc numbers: Sl-ERF . A1 ( Solyc08g078180 ) , Sl-ERF . A2 ( Solyc03g093610 ) , Sl-ERF . A3 ( Solyc06g063070 ) , Sl-ERF . B1 ( Solyc05g052040 ) , Sl-ERF . B2 ( Solyc02g077360 ) , Sl-ERF . B3 ( Solyc05g052030 ) , Sl-ERF . C1 ( Solyc05g051200 ) , Sl-ERF . C2 ( Solyc04g014530 ) , Sl-ERF . C3 ( Solyc09g066360 ) , Sl-ERF . C6 ( Solyc03g093560 ) , Sl-ERF . D1 ( Solyc04g051360 ) , Sl-ERF . D2 ( Solyc12g056590 ) , Sl-ERF . D3 ( Solyc01g108240 ) , Sl-ERF . D4 ( Solyc10g050970 ) , Sl-ERF . E1 ( Solyc09g075420 ) , Sl-ERF . E2 ( Solyc09g089930 ) , Sl-ERF . E3 ( Solyc06g082590 ) , Sl-ERF . E4 ( Solyc01g065980 ) , Sl-ERF . F1 ( Solyc10g006130 ) , Sl-ERF . F2 ( Solyc07g064890 ) , Sl-ERF . F3 ( Solyc07g049490 ) , Sl-ERF . F4 ( Solyc07g053740 ) , Sl-ERF . F5 ( Solyc10g009110 ) , Sl-ERF . G1 ( Solyc01g095500 ) , Sl-ERF . G2 ( Solyc06g082590 ) , Sl-ERF . H1 ( Solyc06g065820 ) , PSY1 ( Solyc03g031860 ) , PDS ( Solyc03g123760 ) , ZDS ( Solyc01g097810 ) , β-LCY1 ( Solyc04g040190 ) , β-LCY2 ( Solyc10g079480 ) , CYC-β ( Solyc06g074240 ) , ACS2 ( Solyc01g095080 ) , ACS4 ( Solyc05g050010 ) , ACO1 ( Solyc07g049530 ) , E4 ( Solyc03g111720 ) , E8 ( Solyc09g089580 ) , PG2a ( Solyc10g080210 ) , RIN ( Solyc05g012020 ) , CNR ( Solyc02g077850 ) , NOR ( Solyc10g006880 ) , HB1 ( Solyc02g086930 ) , TAGL1 ( Solyc07g055920 ) , AP2a ( Solyc03g044300 ) , EIN2 ( Solyc09g007870 ) , EIL2 ( Solyc01g009170 ) , EIL3 ( Solyc01g096810 ) , ETR1 ( Solyc12g011330 ) , ETR2 ( Solyc07g056580 ) , ETR3 ( NR ) ( Solyc09g075440 ) , ETR4 ( Solyc06g053710 ) , ETR5 ( Solyc11g006180 ) , ETR6 ( Solyc09g089610 ) , CTR1 ( Solyc10g083610 ) , ACS1 ( Solyc08g081550 ) , ACS3 ( Solyc02g091990 ) , ACS6 ( Solyc08g008100 ) , FUL1 ( Solyc06g069430 ) , FUL2 ( Solyc03g114830 ) , ACO2 ( Solyc12g005940 ) , ACO3 ( Solyc07g049550 ) , ACO4 ( Solyc02g081190 ) . SAUR ( Solyc09g007970 . 1 . 1 ) , GH3 ( Solyc01g107390 . 2 . 1 ) , GUS ( gb|KC920579 . 1| ) . The locus ID numbers of Sl-ARFs can be found in the publication of Zouine et al . ( 2014 ) .
|
The plant hormone ethylene is regarded as the major regulator of fruit ripening but the putative role of other hormones remains elusive . Auxin Response Factors ( ARFs ) are transcriptional regulators modulating the expression of auxin-response genes shown recently to play a primary role in regulating fruit set in tomato , but the potential role of ARFs in the ripening process is still unknown . We show that among all tomato ARF genes , SlARF2 displays the most remarkable ripening-associated pattern of expression , which prompted its functional characterization . Two paralogs , SlARF2A and SlARF2B are identified in the tomato that are shown to be functionally redundant . The simultaneous down-regulation of SlARF2A/B genes leads to a severe ripening inhibition with a dramatically reduced ethylene production and a strong decrease in the expression of key regulators of fruit ripening such as rin and nor . The study defines SlARF2 as a new component of the regulatory network controlling the ripening process in tomato , suggesting that auxin , in concert with ethylene , might be an essential hormone for fruit ripening . While providing a new insight into the mechanisms underlying the control of fleshy fruit ripening , the study uncovers new avenues towards manipulating the ripening process through means that have not been described so far .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Auxin Response Factor SlARF2 Is an Essential Component of the Regulatory Mechanism Controlling Fruit Ripening in Tomato
|
Cutaneous leishmaniasis ( CL ) is caused by Leishmania infection of dermal macrophages and is associated with chronic inflammation of the skin . L . aethiopica infection displays two clinical manifestations , firstly ulcerative disease , correlated to a relatively low parasite load in the skin , and secondly non-ulcerative disease in which massive parasite infiltration of the dermis occurs in the absence of ulceration of epidermis . Skin ulceration is linked to a vigorous local inflammatory response within the skin towards infected macrophages . Fas ligand ( FasL ) and Tumor necrosis factor-related apoptosis-inducing ligand ( TRAIL ) expressing cells are present in dermis in ulcerative CL and both death ligands cause apoptosis of keratinocytes in the context of Leishmania infection . In the present report we show a differential expression of FasL and TRAIL in ulcerative and non-ulcerative disease caused by L . aethiopica . In vitro experiments confirmed direct FasL- and TRAIL-induced killing of human keratinocytes in the context of Leishmania-induced inflammatory microenvironment . Systemic neutralisation of FasL and TRAIL reduced ulceration in a model of murine Leishmania infection with no effect on parasitic loads or dissemination . Interestingly , FasL neutralisation reduced neutrophil infiltration into the skin during established infection , suggesting an additional proinflammatory role of FasL in addition to direct keratinocyte killing in the context of parasite-induced skin inflammation . FasL signalling resulting in recruitment of activated neutrophils into dermis may lead to destruction of the basal membrane and thus allow direct FasL mediated killing of exposed keratinocytes in vivo . Based on our results we suggest that therapeutic inhibition of FasL and TRAIL could limit skin pathology during CL .
Leishmaniasis is a group of parasitic diseases associated with heterogeneous clinical manifestations . Symptoms range from lethal disease with overwhelming infection of the bone-marrow , spleen and liver to localised self-healing ulcers of the skin . Leishmania aethiopica is the main causative agent of CL in the highlands of Ethiopia . Upon infection , parasites reside and replicate within tissue macrophages during an initial silent phase of the infection and the clinical presentation of CL is mainly associated with the infiltration of circulating inflammatory cells into infected tissues . L . aethiopica infection leads to localised cutaneous leishmaniasis ( LCL ) or diffuse cutaneous leishmaniasis ( DCL ) . LCL is characterised by erosive ulcers and a strong T cell mediated response [1] which typically results in spontaneous healing within a year , scar formation and solid protection against re-infection [2] . In contrast , DCL is linked to non-ulcerative chronic nodular disease with abundant parasitic infiltration of the dermal compartment of the skin and antigen specific T cell unresponsiveness [3] , [4] . Structural differences [5] as well as different immunogenic properties [4] , [6] between LCL and DCL causing parasites have been reported . The mechanisms of tissue destruction during ulcerative cutaneous leishmaniasis have not been fully clarified . We have previously reported that dermal FasL and TRAIL expressing cells are present in ulcerative L . major infection and that the number of FasL expressing dermal cells correlate to the level of epidermal apoptosis . Furthermore , in vitro experiments propose FasL and TRAIL as major players inducing apoptosis in keratinocytes during Leishmania induced inflammation [7] , [8] . In the present study expression of FasL and TRAIL within the skin was investigated in ulcerative and non-ulcerative manifestations of L . aethiopica induced CL . More FasL and TRAIL expressing cells were detected in ulcerative self-healing LCL as compared to non-ulcerative chronic DCL . In line with these results , neutralisation of FasL and TRAIL in vivo during experimental leishmaniasis in BALB/c mice led to reduction of ulceration and was not associated with increased infective loads or increased spread of the infection through the lymphatics .
This study was conducted according to the principles expressed in the Declaration of Helsinki . The study was approved by the Institutional Ethical Review Board of Karolinska Institutet ( reference number 31-5427/08 ) and by The National Ethical Clearance Committtee ( NECC ) at the Ethiopia Science and Technology Commission ( reference number: RDHE/78-43/2002 ) . All patients provided written informed consent for the collection of samples and subsequent analysis . All animals were handled in strict accordance with good animal practice as defined by the relevant national animal welfare bodies , and this study was approved by the Regional Animal Studies Ethical Committee , Stockholm North , Sweden ( reference number N72/05 and 305/08 ) . Skin biopsies were collected from healthy controls at St . Paulos General , Specialized Hospital , Addis Ababa , Ethiopia , and from Ethiopian CL patients at the Armauer Hansen Research Institute ( AHRI ) , Addis Ababa , Ethiopia . FasL and TRAIL were visualised in formalin fixed tissue as previously described [7] , [8] and evaluated in Leica fluorescent microscope . Photomicrographs were obtained using a Zeiss Axioskope 2 , AxioVision 4 . 6 ( Zeiss ) and processed using Photoshop CS4 . Apoptosis was assessed through visualizing fragmented DNA using TUNEL ( TdT-mediated dUTP nick end labeling ) kit according to the manufacturer's instructions ( Roche , Penzberg , Germany ) . The number of FasL expressing cells was counted in 25× objective and apoptotic cells were counted in 40× objectives , with more than ten fields evaluated per sample . A scoring system for the wide-spread TRAIL expression found was used as shown in Figure S1 and all samples were evaluated blindly . Leishmania promastigotes propagated from ulcerative and non-ulcerative lesions were used to stimulate healthy peripheral blood mononuclear cells ( PBMC ) for 7 days at 1∶1 ratio and supernatants were collected and cryopreserved . Supernatants were added to cultures of the keratinocyte cell-line HaCaT [9] for 20 hrs and early apoptotic cells were assessed by AnnexinV/Propidium Iodide staining by microscopy . Fas-activating monoclonal antibody ( 1 µg/ml , CH-11; MBL , Nagoya , Japan ) and recombinant TRAIL ( 250 ng/ml , R&D Systems ) were used as positive controls . Fas-blocking monoclonal antibody ZB4 ( 1-2 µg/ml , MBL ) and TRAIL-blocking antibody 2E5 ( 2 . 5 µg/ml , Alexis , KeLab , Gothenburg , Sweden ) were added 30 minutes prior to supernatants . Isotype control antibodies to CH-11 , ZB4 or 2E5 did not affect keratinocyte apoptosis . Apoptosis was assessed by counting 10 to 20 fields under ×40 ocular magnification and expressed as the number of apoptotic cells per 10 fields . Infective-stage metacyclic promastigotes of L . major ( strain Friedlin V1 or LV39 , both gift from David Sacks , NIAID , NIH , Bethesda , USA ) were isolated from stationary cultures ( 4–5 days old ) by negative selection using peanut agglutinin ( Vector Laboratories , Burlingame , CA , USA ) as previously described [10] . Female BALB/c aged 6–8 weeks were infected intradermally with 5×104 metacyclic L . major [11] . Neutralising hamster anti-mouse FasL ( MFL-4 ) [12] , rat anti-mouse TRAIL ( N2B2 ) [13] or isotype-matched hamster or rat IgG control ( Rockland ) were injected i . p . at a dose of 0 . 5 mg twice per week for 4–5 weeks after infection . The evolution of the lesion was monitored weekly by measuring the diameter of the indurations of the ear lesion with a direct-reading vernier caliper ( Thomas Scientific , Swedesboro , NJ , USA ) . After euthanization both ears and retromaxillar lymph nodes were removed . Groups of five mice were infected at three ( MFL-4 and isotype-control ) and two ( N2B2 and isotype-control ) different times . Parasite titrations were performed as previously described [11] . The number of viable parasites in each sample was defined as the highest dilution at which promastigotes could be grown out after 7 days of incubation at 26°C . Single cell suspension of ear and lymph node tissue was prepared as previously described [11] . Cells were stained ex-vivo for Ly6C-PerCP , CD11b-APC , CD11c-FITC , FasL-PE , ( all from BD Biosciences ) and CD45-eFluor 450 ( eBioscience ) Dead cells were excluded by YFD conjugated Live/Dead stain kit ( Molecular Probes ) . To assess IFNγ producing cells , single cell suspension from ear and lymph nodes were cultured over night ( 18 hours ) in the presence or absence of antigen pulsed dendritic cells as previously described [11] . GolgiStop ( BD Biosciences ) was added during the last 4 hours of culture . Cells were stained for TCRβ-FICT , CD8-PerCP , IFNγ-APC ( BD Biosciences ) CD45- eFluor 450 , CD4-PECy7 ( eBioscience ) and Live/Dead ( Molecular Probes ) . 20% ( ex vivo staining ) and 30% ( cytokine restimulation ) of the total number of cells per ear and 500 000 events from lymph nodes were acquired on CyAn ( Beckman Coulter ) and analyzed by FlowJo 8 ( Tree Star Inc ) . Statistical analysis was performed using Prism Graph Pad Software ( Inc . Oberlin Drive , San Diego , USA ) .
Skin biopsies from ulcerative ( n = 19 ) , non-ulcerative ( n = 13 ) and healthy controls ( n = 8 ) were collected at Armauer Hansen Research Institute and at St . Paulos General Specialized Hospital , both Addis Ababa , Ethiopia . Lesions were designated as ulcerative or non-ulcerative according to clinical presentation ( Fig . 1A and 2A ) . The duration of ulcerative and non-ulcerative lesions was partly overlapping with a median clinical history of lesion formation of 6 vs . 44 months at the time of biopsy ( Fig . 1B ) . All included individuals displaying the non-ulcerative phenotype had lesions in several distinct parts of the body whereas ulcerative disease was confined to a single lesion predominately on the face ( Fig . 1A ) . Leishmania infection was verified by detection of amastigotes by May-Grünwald-Giemsa staining or by detection of viable promastigotes in cultures of lesion scrapings . Non-ulcerative lesions contained numerous disorganised macrophages laden with Leishmania amastigotes , few lymphocytes and marked plasma cell infiltration , while ulcerative lesions displayed fewer parasites and organised dermal granulomas with prominent infiltration of lymphocytes and epithelioid cells as previously described [3] . FasL was not expressed in healthy skin ( Fig . 2B ) and FasL was not upregulated in epidermal cells during CL . FasL expressing dermal cells were present in both ulcerative and non-ulcerative leishmaniasis and accumulated close the ulcerated epidermis as shown in Figure 2B . FasL expressing cells were predominately detected in deep dermis of non-ulcerative CL but at significantly lower levels as compared to ulcerative lesions ( Fig . 2B and D ) . The level of TRAIL expression was scored using an arbitrary scale shown in Supplementary figure S1 . Low or moderate TRAIL expression was detected in healthy epidermis . As previously shown [8] , TRAIL expression was increased in epidermis of both LCL and DCL as compared to healthy controls with no significant difference in TRAIL expression between DCL and LCL ( results not shown ) . TRAIL expressing cells were also present in dermis of both ulcerative and non-ulcerative L . aethiopica induced CL with significantly higher expression in dermal inflammatory areas in ulcerative as compared to non-ulcerative lesions ( Fig . 2C and E ) . We were not able to phenotype the TRAIL or FasL expressing cells due to the lack of access of cryopreserved skin tissue . The formalin , paraffin embedded skin biopsies used in this study display abundant auto-fluorescence and could not be used for multi-fluorochrome labelling and detection of double positive cells by confocal microscopy . Previously , Mustafa et al reported that macrophages in L . aethiopica induced CL express FasL [14] . In ulcerative CL caused by L . major , we have previously shown infiltration of FasL expressing T cells and macrophages were present in dermis in cryopreserved skin biopsies [7] . To determine if the increased expression of TRAIL and FasL correlated to increased keratinocyte apoptosis ex vivo , TUNEL staining was performed on biopsies from ulcerative and non-ulcerative leishmaniasis . Previously TUNEL staining on human epidermis showed the same pattern of staining as caspase-cleaved cytokeratin 18 , verifying that TUNEL can be used as a marker of apoptosis in Leishmania infected skin [7] . The number of epidermal apoptotic cells showed great inter-individual variation in all groups examined ( Fig . 3A and B ) and ulcerative lesions did not contain significantly higher numbers of epidermal apoptotic cells as compared to non-ulcerative lesions and healthy skin . However , there was a clear trend to a higher number of apoptotic keratinocytes in the ulcerative group . We have previously shown an increase in the number of apoptotic epidermal cells in L . major caused ulcerative disease as compared to healthy skin in a cohort of young military recruits with a history of ulcerative leishmaniasis of less than three months upon transfer into a hyperendemic Leishmania foci [7] . In the present hospital based study the patient material was collected from a heterogeneous group of patients from endemic areas and the median duration of the disease at the time of tissue collection was longer . Furthermore , infection with L . major typically leads to more aggressive tissue destruction as compared to L . aethiopica caused infection . Due to the immediate and efficient clearance of apoptotic cells in vivo by phagocytic cells such as tissue macrophages , the level of apoptotic cells detected in ex vivo biopsies may not reflect the amount of cell death taking place in the tissue . Thus we utilised an in vitro experimental set-up in which keratinocytes were exposed to Leishmania derived supernatants in the absence of phagocytic cells . It has been postulated that distinct subtypes of L . aethiopica induce ulcerative and non-ulcerative disease through differential immune-activating properties . To test the apoptosis inducing effect of parasites derived from ulcerative and non-ulcerative lesions , parasites from the different clinical manifestations of CL were obtained from clinical lesions and propagated in vitro . Infective promastigotes were used to stimulate PBMC from healthy individuals for seven days and supernatants from such cultures were added to an immortalised keratinocyte cell line sensitive to anti-Fas and TRAIL induced killing ( Fig . 4A and B ) in which Fas blocking and TRAIL neutralising antibodies completely inhibits apoptosis ( Fig . 4D ) . Leishmania promastigotes alone did not induce keratinocyte apoptosis ( results not shown ) and implicating that the immune activation induced by the parasitic infection was necessary to induce killing of keratinocytes . Supernatants derived from LCL stimulated PBMCs induced significantly more keratinocyte apoptosis as compared to unstimulated PBMC or PBMC stimulated with DCL derived parasites ( Fig . 4A and C ) . Furthermore , keratinocyte apoptosis induced by supernatants from LCL infected PBMC could be inhibited by the addition of Fas blocking antibodies ( Fig . 4E ) or TRAIL blocking Abs ( Fig . 4F ) . The isotype controls corresponding to TRAIL and Fas blocking antibodies did not have any effect on keratinocyte apoptosis . No synergistic effect was noted when both FasL and TRAIL were inhibited simultaneously ( Fig . 4G ) . The low level of keratinocyte apoptosis induced by supernatants from DCL infected PBMCs could be reduced with TRAIL blocking antibodies but not with Fas blocking antibodies ( Fig 4 E–F ) . TRAIL but not FasL is expressed on HaCaT and the levels of TRAIL increase during exposure to inflammatory supernatants . Possibly TRAIL , but not Fas , blocking antibodies may prevent keratinocyte-keratinocyte killing in the context of mild inflammation . Current treatment alternatives during active CL are aimed at parasite eradication [2] and have little effect on tissue destruction . On the contrary , current treatment regimes result in exacerbation of inflammation leading to increased tissue destruction and scarring . Targeting specific immune mechanisms has proven to be a promising new approach for the therapy of cancer and autoimmune diseases . We were interested to investigate if such approach could be used to decrease the pathology caused by a protozoan infection such as Leishmania infection . The effect of systemic treatment with FasL and TRAIL neutralising antibodies during the ulcerative process during CL was investigated . L . aethiopica inoculation in mice does not lead to productive infection or ulcerative disease . Thus L . major , causing ulcerative leishmaniasis , was used throughout the in vivo experiments . C57BL/6 inoculated with L . major developed non-ulcerative lesions followed by self-healing and was thus not a suitable model to follow ulcer development . Addition of sandfly salivary gland homogenate to low dose L . major infection in C57BL/6 mice , as previously described [15] , did not cause stable and reproducible ulcer development suitable for our purpose and was not pursued beyond pilot experiments . High dose infection in C57BL/6 ( Jackson strain ) mice bred at Karolinska Institutet caused non-ulcerative lesions followed by necrotic degradation of the ear tissue . Thus , a well-characterised model of ulcerative CL using a low number of metacyclic L . major promastigotes injected intradermally into the ear of BALB/c was chosen [16] despite the Th2 bias and strong IL-4 production associated with this model . Systemic treatment with FasL [12] or TRAIL [13] blocking antibodies was given twice weekly . Hamster and rat isotype control antibodies were given in parallel and there was no difference in ulcer development between the different isotype control antibodies used ( results not shown ) . A clear reduction in the development of ulcers was noted in the treated animals as compared to hamster isotype control treatment ( Fig . 5A–C ) . In spite of reduction of ulceration , neutralisation of FasL or TRAIL was not sufficient to completely inhibit ulcer formation and no synergistic effect was noted by the simultaneous administration of FasL and TRAIL neutralising antibodies . Insufficient clearance of L . major infection has previously been shown in Fas and FasL deficient mice and the treatment strategy used could potentially lead to uncontrolled parasite replication . Impaired control of parasite replication has been shown in Fas and FasL deficient mice [17]–[19] and systemic administration of exogenous recombinant FasL to FasL deficient ( gld ) mice led to elimination of parasites and resolution of cutaneous non-ulcerative lesions [17] . In vitro studies have shown that macrophages infected with L . major up-regulate their surface Fas expression in response to IFN-γ and as a result become susceptible to CD4+ T cell- induced apoptotic death [17] . No data is available on the evolution of Leishmania induced pathology in TRAIL deficient mice . Based on the previous studies in FasL deficient mice , there is a potential risk to exacerbate parasite replication through inhibition of FasL during ulceration . In the model of ulcerative leishmaniasis used in these studies , systemic neutralisation of FasL and TRAIL did not affect increased infectious loads at the primary site of infection ( Fig . 5D ) . Likewise , the infectious load in the draining lymph node was not altered during treatment , suggesting that dissemination of the infection was not enhanced by short-term neutralisation of Fas/FasL and TRAIL-Rs/TRAIL in this model of CL induced by a low dose of infective parasites . Using a different strain of L . major ( LV39 ) did not lead to ulcer development ( Fig . S2 A–C ) despite similar parasite loads in ears and draining lymph nodes . Interestingly , the area of inflamed skin surrounding the site of infection was reduced whereas the parasite loads were not affected in mice treated with FasL neutralising antibodies , thus mirroring the results obtained using the ulcerative model induced by L . major strain Friedlin V1 . There are several potential explanations to why effect of FasL on parasite loads obtained in this report differs from earlier studies . The data previously published was obtained using a different route of inoculation , a different infectious dose and different genetic backgrounds of the host mice . The Fas ( lpr ) deficient transgenic mice show a profound lymphoproliferative phenotype with half the life expectancy as compared to congenic control mice [20] . Due to alterations in the thymic selection of T cells these mice display a skewed T cell repertoire that in itself may affect the ability to combat parasitic disease independently of peripheral FasL signalling during infection . In contrast , we chose to use short-term inhibition of FasL and TRAIL in immuno-competent mice . IFN-γ production by CD4 cells has been ascribed a critical role in parasite clearance during leishmaniasis through activation of infected macrophages . Although the number of CD4+ T cells , and to a lesser extent the CD8+ T cells , were reduced at the site of infection during FasL neutralisation ( Fig . S3A–C ) , the ratio between CD4:CD8 T cells was identical to control infected mice . The percentage of IFN-γ producing CD4+ T cells at the site of infection was not affected by FasL neutralisation as shown in Figure S3D and further antigen stimulation did not enhance the ex vivo production of IFN-γ ( not shown ) , possibly due to the high amounts of parasitic antigen and antigen presenting cells present in the single cell suspension prepared from ear tissue . In line with the finding that FasL neutralisation did not affect the parasitic load , CD4+ T cell antigen-specific IFN-γ production was not affected by FasL neutralisation as shown in figure S3 E–F . It has recently been reported that FasL may potentiate the effect of IFN-γ signalling in macrophages , leading to more efficient parasite eradication [21] . This effect was prevented in the presence of IL-4 and we cannot exclude that the lack of effect , on the infectious load during FasL neutralisation was influenced by the high levels of IL-4 production in BALB/c mice . Taken the lack of a reliable ulcerative leishmaniasis model on a different genetic background , this concern could not be addressed in the present study . The mechanisms behind ulceration during cutaneous leishmaniasis are not understood . Necrotic death due to intense inflammation is probably one cause of ulceration during leishmaniasis , but publications on the subject are scarce . In a therapeutic attempt to administer IFN-γ during human CL to enhance parasite killing , side effects in terms of pronounced inflammation was noted [22] and it has been postulated but not properly proven that inflammation leading to tissues destruction is necessary for treatment control . In addition to the pure apoptosis inducing effect of FasL , proinflammatory effects of FasL signalling has been proposed in a number of different settings and in macrophages resistant to FasL mediated killing , FasL signalling leads to TNFα and IL-8 secretion potentially leading to recruitment of neutrophils into sites of infection . Interestingly , neutrophils are recruited into the site of infection during cutaneous leishmaniasis in humans [23] , [24] and in mice accumulation of neutrophils have been linked to tissue damage [25] . In the latter study , IL-17 was shown to be the major neutrophil chemoattractant during infection . To test if FasL neutralization leads to an impaired recruitment of neutrophils into the infected skin we enumerated the number of neutrophils and macrophages during FasL neutralisation in parallel to control treated mice . A two-fold reduction in the number of neutrophils was found during FasL neutralisation ( Fig 6 ) . Similar results were found in the non-ulcerative model of leishmaniasis obtained by L . major ( strain LV39 ) as shown in figure S2 . It is possible that a complete block of neutrophils into the site of infection , possibly through targeting IL-17 and FasL simultaneously , would further reduce the ulceration . However , in the context of infection potentially tissue damaging cells ( e . g . neutrophils ) may be necessary for parasite control . Neutrophils are rapidly recruited to the site of infection after a sand-fly bite and serve as a first host cell to Leishmania promastigotes . Neutrophils undergo spontaneous apoptosis within days in peripheral tissues and as Leishmania infected , it has been shown neutrophils can facilitate infection [26] . However , in later stages of infection neutrophils probably play a role in controlling the infection through their strong inflammatory , and tissue destructive , function and through activation of Leishmania infected macrophages [27]–[29] . In this report , we show that FasL and TRAIL expressing cells infiltrate dermis and that a higher level of expression is present during ulcerative leishmaniasis as compared to non-ulcerative leishmaniasis . In vitro experiments confirmed that FasL and TRAIL neutralising antibodies directly inhibit keratinocyte apoptosis in the context of Leishmania induced inflammation . The potential role of FasL and TRAIL during Leishmania induced ulceration was further strengthened by the reduction of ulceration during systemic neutralisation of both FasL and TRAIL in a murine model of ulcerative leishmaniasis . FasL neutralisation in vivo led to a reduction in the recruitment of neutrophils into the site of infection , suggesting additional pro-inflammatory mechanisms of FasL signalling during leishmaniasis . The results shown here , obtained from human samples and murine in vivo experiments , suggest at least two different roles for FasL during skin ulceration in Leishmania infection . Firstly , FasL signalling in the inflamed tissue is involved in neutrophil recruitment . Secondly , sFasL induce keratinocyte death . Activated neutrophils are tissue destructive and one can envisage a scenario where neutrophils cause destruction of the basal membrane in areas of intense infection . Through breaking the epidermal-dermal border soluble death ligands get access to keratinocytes leading to direct destruction of the epidermis and ulceration . Fas-FasL interactions have been implicated in the pathogenesis of drug-induced toxic epidermal necrolysis ( TEN ) , a life-threatening disease characterized by extensive destruction of epidermal keratinocytes [12] , [13] . Systemic treatment with intravenous immunoglobulins containing Fas-blocking antibodies limited the ulcerative process during TEN [13] and reduced mortality in several multi-centre analysis . In the case of CL we propose that an adjuvant therapy neutralizing FasL or TRAIL in combination with leishmanicidals could reduce the ulcerative process and subsequent scar formation .
|
Cutaneous leishmaniases are associated with parasite-induced inflammatory lesions of the skin . The degree of clinical pathology is not associated with parasitic burden; on the contrary , ulcerative lesions are associated with low infectious load , and non-ulcerative lesions are associated with an abundant parasite infiltration . Leishmania are intracellular parasites in mammalian hosts and reside in macrophages in the deep layers of the skin , the dermis . The exact mechanism of ulceration in CL is not known and Leishmania parasites do not directly induce destruction of keratinocytes in the most superficial layer of the skin , the epidermis . In this study we investigated if ulcerated lesions were associated with higher expression of FasL- and TRAIL-induced cell-death of keratinocytes . We found a higher expression of FasL and TRAIL in human skin samples from ulcerative as compared to non-ulcerative leishmaniasis . In a mouse model of ulcerative leishmaniasis neutralisation of FasL and TRAIL reduced ulceration . We suggest that FasL and TRAIL participate in the ulcer formation during leishmaniasis both as a chemoattractant of activated neutrophils leading to tissue destruction and through direct killing of keratinocytes . Possible approaches to use this concept in therapeutical interventions with the aim to reduce immunopathology associated with leishmaniasis are discussed .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"and",
"Discussion"
] |
[
"immunology/immunomodulation",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"dermatology/skin",
"infections",
"infectious",
"diseases/skin",
"infections",
"microbiology/parasitology",
"infectious",
"diseases/protozoal",
"infections"
] |
2010
|
Systemic FasL and TRAIL Neutralisation Reduce Leishmaniasis Induced Skin Ulceration
|
Amphiphysin 2 , encoded by BIN1 , is a key factor for membrane sensing and remodelling in different cell types . Homozygous BIN1 mutations in ubiquitously expressed exons are associated with autosomal recessive centronuclear myopathy ( CNM ) , a mildly progressive muscle disorder typically showing abnormal nuclear centralization on biopsies . In addition , misregulation of BIN1 splicing partially accounts for the muscle defects in myotonic dystrophy ( DM ) . However , the muscle-specific function of amphiphysin 2 and its pathogenicity in both muscle disorders are not well understood . In this study we identified and characterized the first mutation affecting the splicing of the muscle-specific BIN1 exon 11 in a consanguineous family with rapidly progressive and ultimately fatal centronuclear myopathy . In parallel , we discovered a mutation in the same BIN1 exon 11 acceptor splice site as the genetic cause of the canine Inherited Myopathy of Great Danes ( IMGD ) . Analysis of RNA from patient muscle demonstrated complete skipping of exon 11 and BIN1 constructs without exon 11 were unable to promote membrane tubulation in differentiated myotubes . Comparative immunofluorescence and ultrastructural analyses of patient and canine biopsies revealed common structural defects , emphasizing the importance of amphiphysin 2 in membrane remodelling and maintenance of the skeletal muscle triad . Our data demonstrate that the alteration of the muscle-specific function of amphiphysin 2 is a common pathomechanism for centronuclear myopathy , myotonic dystrophy , and IMGD . The IMGD dog is the first faithful model for human BIN1-related CNM and represents a mammalian model available for preclinical trials of potential therapies .
Amphiphysin 2 is one of the key factors in muscular membrane remodeling . The gene , BIN1 , has recently been associated with two different muscle disorders: centronuclear myopathy ( CNM , MIM #255200 ) [1] and myotonic dystrophy ( DM , MIM #160900 and #602668 ) [2] . However , the muscle-specific role of the ubiquitous protein amphiphysin 2 and the pathological mechanisms underlying the muscle disorders are not well understood . This is mainly due to the lack of faithful animal models . Centronuclear myopathies are characterized by a generalized muscle weakness , atrophy , predominance of type I fibers , and aberrant positioning of nuclei and mitochondria [3] . The different genetic forms are not or are only moderately progressive . The X-linked and dominant CNM forms result from mutations in the phosphoinositide phosphatase myotubularin ( MTM1 ) and the large GTPase dynamin 2 ( DNM2 ) , respectively [4] , [5] . The autosomal recessive form ( ARCNM ) is caused by mutations in BIN1 , probably involving a partial loss-of-function as the protein level was found to be normal in previously described patients [1] . Amphiphysin 2 , encoded by BIN1 , contains a N-terminal amphipathic helix , a BAR ( Bin/Amphiphysin/Rvs ) domain , able to sense and maintain membrane curvature , a Myc-binding domain and a SH3 domain , both implicated in protein-protein interactions [6] , [7] , [8] . There are at least 12 different isoforms , mainly differing by the presence or absence of a phosphoinositide-binding domain and a clathrin-binding domain encoded by exon 11 and exons 13–16 , respectively [9] , [10] . The clathrin-binding domain is present in the brain isoforms , while the phosphoinositide-binding ( PI ) domain is found almost exclusively in skeletal muscle isoforms [10] , [11] , [12] . Sequencing of cDNA demonstrated that all BIN1 skeletal muscle isoforms contain exon 11 [12] . All ARCNM mutations described to date are in ubiquitously expressed exons [1] , [13] , [14] , [15] , raising the question about the molecular basis of the muscle-specificity of the disease . The BAR domain mutations strongly decreased the amphiphysin 2 membrane tubulating properties when expressed in cultured cells , while SH3 truncating mutations were shown to impair the binding and recruitment of dynamin 2 [1] . Mis-splicing of the BIN1 muscle-specific exon 11 was reported in different forms of myotonic dystrophy ( DM ) [2] . DM is one of the most common muscular dystrophies in neonates and adults , and results from the expression of mutant RNAs with expanded CUG or CCUG repeats leading to the sequestration of splicing factors and subsequent defects in RNA splicing . Splicing alterations of the muscle chloride channel CLCN1 are suggested to be responsible for the myotonia , whereas aberrant splicing of the insulin receptor INSR gene is thought to cause insulin resistance in DM patients . Complete or partial skipping of BIN1 exon 11 in congenital and adult DM was shown to involve structural T-tubule abnormalities and subsequently muscle weakness [2] . However , there are numerous splicing defects in DM . It is therefore challenging to assess the exact contribution of BIN1 exon 11 skipping to the DM phenotype , even though severe hypotonia , respiratory failure and histopathological features such as fiber hypotrophy and centrally located nuclei in the congenital forms of DM show intriguing similarities to CNM . Amphiphysins are key regulators of membrane curvature and trafficking [16] . They can sense membrane curvature and presumably promote the curvature and fission of membranes [17] . Membrane binding occurs via BAR domain dimers , presenting a positively charged concave site that interacts with the negative membrane charges [17] . Amphiphysins also bind and recruit other regulators of endocytosis to sites of plasma membrane inward budding [18] . Amphiphysin 1 expression is restricted to neuronal tissues and the protein regulates synaptic vesicle recycling in the brain [19] . Amphiphysin 2 is highly expressed in adult striated muscle and its expression increases during muscle cell maturation [10] , [11] , [20] , [21] . The polybasic residues encoded by BIN1 exon 11 are required for amphiphysin 2-induced membrane tubulation when exogenously expressed in cultured cells [1] , [22] . In skeletal muscle , amphiphysin 2 is localized at the T-tubules , which are deep sarcolemmal invaginations enabling excitation-contraction coupling [11] , i . e . the process converting an electrical stimulus into mechanical muscle work . This specific localization , together with the membrane tubulation properties of the muscle-specific isoform containing the PI domain , called iso8 or M-amphiphysin , has led to the suggestion that amphiphysin 2 is implicated in T-tubule biogenesis [22] . This is sustained by defects in the localization of nascent T-tubule markers such as caveolin 3 following BIN1 downregulation in cultured cells [23] , and by the abnormal T-tubule structure seen in drosophila with null mutations in amph , the unique ortholog of mammalian amphiphysins 1 and 2 [24] . While faithful animal models were previously characterized for the MTM1 and DNM2 related CNM forms [25] , the perinatal lethality of Bin1-null mice precludes the analysis of the role of amphiphysin 2 in skeletal muscle [26] . Therefore , critical questions concerning the muscle-specific function of amphiphysin 2 in mammals and the pathological mechanism of BIN1-related CNM remain unanswered . The lack of a faithful animal model for autosomal recessive centronuclear myopathy is a hurdle for a better comprehension of the pathological mechanisms and for the development of therapeutic approaches . In this study , we identified and characterized the first human BIN1 mutation affecting the muscle-specific PI domain . We also identified a novel spontaneous canine model reproducing the human pathology and allowing investigations on the physiological role of amphiphysin 2 in skeletal muscle after birth . Characterization of the dog model revealed an important role for amphiphysin 2 in triad structure , and we provide the evidence for a physiological function of the membrane-deforming properties of amphiphysin 2 and its alternative splicing-dependent activity . Our data support the hypothesis that the alteration of the muscle-specific function of amphiphysin 2 on membrane remodeling is a common pathomechanism underlying several canine and human myopathies .
To identify BIN1 mutations affecting its function in skeletal muscle , we sequenced the muscle-specific exon 11 and the adjacent splice-relevant intronic regions in a cohort of 84 patients with various forms of centronuclear myopathy and without mutations in MTM1 , DNM2 , or in the other BIN1 exons . We identified a homozygous BIN1 exon 11 splice acceptor mutation ( IVS10-1G>A ) in two affected members from a consanguineous family from Turkey ( Figure 1A and 1B ) . DNA was not available for the third affected member , who is expected to carry the same homozygous BIN1 mutation as her monozygotic twin sister . The parents are healthy and do not present clinical signs of a muscle disorder . They are first-degree cousins and were found to be heterozygous for the BIN1 exon 11 splice acceptor mutation , confirming autosomal recessive inheritance of the disease . The mutation was not found in unaffected individuals from different origins , including 37 DNAs from an ethnically matched control population , and is not listed in the SNP databases as dbSNP , 1000 genomes , or the NHLBI exome variant server . Patients 1 and 2 are dizygotic twins . Pregnancy and birth , as well as motor and speech development were normal . General muscle weakness was diagnosed at 3 . 5 years . Hypotonia , muscle weakness ( predominantly of the lower limbs ) , respiratory distress ( VC 50% ) and loss of motor skills were rapidly progressive and the twins died from acute pneumonia involving cardiac failure at age 5 and 7 , respectively . Patient 3 is the younger brother , and as for his sisters , pregnancy , birth , motor and speech development were normal . Age of onset was 3 . 5 years and the myopathy was highly progressive , contrasting the slow progression of muscle weakness in the reported CNM cases with BIN1 mutations in ubiquitous exons [1] , [13] , [14] , [15] . Patient 3 presented with predominant proximal muscle weakness of the lower limbs requiring a wheelchair since the age of 5 years , facial weakness , but no respiratory distress . Eye movement defects , as seen in the majority of the MTM1 , DNM2 and BIN1 patients , were not noted . Deep tendon reflexes were absent and the patient had progressive contractures in knees and ankles . Electrophysiological evaluation was normal or showed only unspecific myopathic changes , with normal nerve conduction velocity . Cardiac defects were not noted and CK levels were normal . Patient 3 is now 9 years old and presented at his last medical exam in April 2012 with low MRC grades for both upper and lower limbs . The BIN1 IVS10-1G>A variation changes the AG acceptor splice site into AA , and is predicted to impair exon 11 splicing by various algorithms . The wild-type acceptor site was recognized by NNSPLICE ( score 0 . 84 ) and Human Splice Finder ( 88 . 5 ) , while no acceptor splice site was predicted in the mutated sequence . To confirm an impact on exon 11 splicing , we performed RT-PCR after RNA isolation from a muscle biopsy of patient 1 , amplified a fragment encompassing exons 10 to 12 , and obtained a shorter product compared to the control ( Figure 1C ) . To analyze the transcript ( s ) , we cloned the PCR-products and sequenced the resulting clones . As we and others previously reported , the skeletal muscle BIN1 isoforms contain exon 11 , but lack exons 7 and 13 to 16 . Exon 17 can be either present or absent , corresponding to isoform 8 or M-amphiphysin [10] , [11] , [12] . Among the 30 analyzed clones , only a single clone contained exon 11 . Twenty-nine clones did not contain exon 11 and directly combined exon 10 with exon 12 , demonstrating a major skipping of the in-frame exon 11 in the patient muscle ( Figure 1D ) . The impact of the mutation on the amphiphysin 2 protein level in skeletal muscle was investigated by Western blot ( Figure 1E ) . Using an anti-PI domain antibody , we detected several bands in the control as previously reported [1] , most probably reflecting post-translational modifications of the different isoforms containing exon 11 . In the patient muscle , we found a significant decrease of the level of the amphiphysin 2 isoform containing the PI domain , confirming exon 11 skipping in most BIN1 muscle transcripts . The amphiphysin levels detected with the anti-SH3 domain antibody were similar in patient 1 and control . Together with the genetic data , we conclude that the rapidly progressive CNM form results from a splice mutation involving the skipping of the muscle-specific exon 11 . Previous publications demonstrated the importance of the amphiphysin 2 PI domain in PtdIns ( 4 , 5 ) P2 binding and membrane tubulation [1] , . We transfected C2C12 cells with BIN1 constructs including or excluding exon 11 , and we induced the differentiation of the murine myoblasts into myotubes . Myotubes expressing the exon 11 containing isoform showed tubulation [22] , [27] , whereas the isoform without exon 11 did not induce this effect ( Figure 2 ) . Quantification revealed statistical significance . Immunolabelling of actin , caveolin-3 and RYR1 did not reveal obvious differences between the differentially transfected myotubes ( data not shown ) , suggesting that the amphiphysin 2 PI domain is important for late muscle development or maintenance , rather than for early muscle development . This hypothesis is supported by the fact that the patients were unaffected at birth and during early childhood . The perinatal lethality of Bin1-null mice precludes investigations on the role of amphiphysin 2 in skeletal muscle maintenance [26] . To identify and characterize an animal model for BIN1-related CNM , we analyzed canine pedigrees with molecularly unsolved myopathies . The canine Inherited Myopathy of Great Danes ( IMGD ) is characterized by rapidly progressive muscle atrophy and exercise intolerance with an age of onset of about 6 months . Histological examinations of muscle biopsies from autosomal recessive cases from the UK , Canada and Australia revealed increased nuclear internalization and centralization [28] , [29] , [30] , consistent with centronuclear myopathy . We excluded mutations in MTM1 [31] and PTPLA [32] before sequencing the coding regions and intron/exon boundaries of the canine BIN1 gene ( XM_540990 . 3 ) . We identified a homozygous AG to GG substitution of the BIN1 exon 11 acceptor splice site in five dogs from Canada , US and UK ( IVS10-2A>G; Figures 3A and 3B ) . CK values for the dogs were normal or slightly elevated . Pedigree reconstruction revealed a distant relationship between the US and one UK dog ( Figure 3C ) and a previous publication reported a common ancestor for all IMGD dogs in the UK [29] . The BIN1 IVS10-2A>G mutation was not found in 112 healthy Great Danes and in 35 dogs from 12 other breeds , strongly suggesting its pathogenicity . Like the human BIN1 IVS10-1G>A mutation , the canine BIN1 IVS10-2A>G variation affects the exon 11 acceptor splice site . To assess its impact on splicing , we performed RT-PCR on RNA isolated from skeletal muscle biopsies and found a strong reduction of the BIN1 RNA level compared to healthy controls and compared to a control gene ( MTM1 , Figure 3D ) . We however detected a faint signal of expected size and cloned the amplicon . All three clones contained exon 11 with 27 additional upstream nucleotides , encoding the amino acid sequence ASASRPFPQ ( Figure 3E ) . This in-frame extension results from the disposition of a weak cryptic 5′ acceptor site . The intronic sequence upstream of exon 11 slightly differs between human and dog , possibly explaining the cryptic splicing in dogs versus exon skipping in human patients ( Figure 3F ) . To confirm the impact of the splice mutation on the amphiphysin 2 protein level , canine muscle extracts were probed with an anti-PI domain antibody on Western blot . Compared to the healthy control , amphiphysin 2 was significantly reduced in the affected dog ( Figure 3G ) . Using an anti-SH3 antibody we detected a strong reduction of all skeletal muscle amphiphysin isoforms ( Figure S1 ) in accordance with the RT-PCR data . We conclude that the canine Inherited Myopathy of Great Danes results from a BIN1 exon 11 splice mutation , provoking a strong reduction of the exon 11/PI domain-containing RNA and protein . Vastus lateralis muscle biopsies were performed for patient 1 as well as for patient 3 at the age of 3 . 5 years . H&E staining revealed prominent nuclear centralization ( >60% , arrow ) , fiber atrophy and endomysial fibrosis ( Figure 4 ) , consistent with centronuclear myopathy . Similarly , H&E staining of biceps femoris muscle biopsies from affected dogs revealed nuclear internalization ( >40% ) and fiber atrophy . The central areas devoid of staining reflect perinuclear regions lacking myofibrils . Of note , the transverse muscle sections of patients and affected dogs showed an unusual lobulated appearance with indentations of the sarcolemma ( arrowheads ) . NADH staining of human and canine sections revealed dense central areas in most fibers and “spoke of wheel” appearance in 5% of the fibers . ATPase staining showed no or only a slight predominance of type I muscle fibers as compared to the age matched controls . Gomori trichrome staining did not reveal any further abnormalities ( data not shown ) . Taken together , human and canine histopathologies were comparable . To uncover the pathological defects underlying this highly progressive form of centronuclear myopathy and to validate the canine model , we analyzed human and dog muscle biopsies by electron microscopy . Ultrastructural analysis of the human muscle biopsy revealed centralized nuclei surrounded by an area devoid of myofibrils and containing glycogen granules and other organelles ( Figure 5A , Figure S2 ) , as commonly seen in MTM1 , DNM2 and BIN1-related CNM . Myofibrillar disintegration with occasional Z-band streaming ( arrow , Figure 5A ) was seen in the adjacent sarcomeres . Triad structures were found to be aberrant and we observed frequent enlarged structures , most probably originating from the sarcoplasmic reticulum ( arrow , Figure 5D ) . We also noted other membrane alterations , including accumulations of membranes and tubules , vacuoles containing whorled membranes ( arrow , Figure 5B ) , as well as a high number of myelin-like membranous structures suggestive of autophagosomes ( arrow , Figure 5C ) . Likewise , ultrastructural analysis of muscle biopsies from an affected Great Dane dog showed nuclear internalization , mitochondrial accumulations around the internalized nuclei and myofibrillar disarray ( Figure 5E , Figure S3 ) . We furthermore found membranous whorls ( arrow , Figure 5F ) as reported for the X-linked CNM Labrador retriever model with MTM1 mutation [31] , deep membrane invaginations ( arrowhead , Figure 5F ) , lipofuscin granules ( arrow , Figure 5G ) , and abnormal triads in almost all fibers ( arrow , Figure 5H ) . Sarcolemmal invaginations contained basement membranes and often pointed towards centralized nuclei . Taken together and considering the histological analysis described above , histopathology of IMGD dogs and human patients appear strikingly similar , emphasizing common alterations of membrane structures . To further characterize the pathophysiology of the rapidly progressive human CNM and canine IMGD , we performed immunolocalization experiments on muscle biopsies . Using the R3062 antibody recognizing most amphiphysin isoforms or the PI-domain specific R2405 antibody , signals were detected as an intracellular network in transverse sections of human and canine controls ( Figure 6 ) . Signals were also detected in sections of muscles from patient and affected dog , reflecting the presence of different amphiphysin 2 isoforms as shown by Western blot . Despite the decrease of BIN1 RNA in affected dogs , the remaining mis-spliced in-frame transcripts can explain the detection of amphiphysin 2 on muscle sections , especially because immunohistochemistry is not quantitative . The amphiphysin 2 network appeared however abnormal in patient and IMGD sections . In some fibers we noted central areas without any signal , while in other fibers accumulations around centralized nuclei were observed ( arrows ) . To determine whether these anomalies were specific for the BIN1 exon 11 splice mutation or rather a general BIN1-related CNM feature , we analyzed a muscle biopsy from a patient with the previously reported BIN1 p . Asp151Asn mutation and a classical ARCNM phenotype [1] . We observed similar accumulations of amphiphysin 2 ( Figure 6A ) , suggesting that different BIN1 mutations in humans and dogs lead to similar amphiphysin 2 mis-localization in muscle . Amphiphysin 2 has been proposed to be implicated in T-tubule biogenesis , but the exact link has barely been documented in mammalian skeletal muscle [22] . We therefore examined the skeletal muscle triad using antibodies against the junctional sarcoplasmic calcium channel RYR1 and the T-tubule marker DHPR in human and dog ( Figure 7 ) . Both proteins were profoundly altered , showing focal accumulations or central areas without signal in the fibers . Compared to the control longitudinal sections , the transversal orientation of RYR1-labeled triads was lost in patient and canine muscle . Similarly , the longitudinal sarcoplasmic calcium pump SERCA1 was mislocalized in sections from affected dogs . We next wanted to know whether the aberrant triad structure was concurrent with more generalized membrane defects . Dysferlin and caveolin 3 , key regulators of membrane repair and trafficking [33] , [34] , were found to be mainly located at the sarcolemma in control muscle sections . In contrast , transverse sections of patient 1 and of an affected Great Dane dog revealed striking intracellular accumulations of dysferlin , mainly around central nuclei ( Figure 7 ) . Labeling of the sarcolemmal markers dysferlin , caveolin 3 and dystrophin confirmed the presence of numerous fibers with unusual lobulated and indented sarcolemma , representing deep sarcolemmal invaginations pointing towards the center of the fibers ( arrows , Figure 7 ) . Taken together , our data correlate the highly progressive human CNM and canine IMGD with general membrane alterations at the triad , the sarcolemma and within the fibers . However , these defects did not reflect a general disorganization of the sarcomere , as alpha-actinin labeling appeared largely normal ( not shown ) . Staining of developmental myosin revealed no difference between affected and control dogs , indicating that there is no excessive fiber regeneration in IMGD dogs ( Figure S3 ) . As MTM1 is mutated in X-linked human and canine CNM , we investigated the localization of myotubularin in muscle sections of IMGD dogs . Myotubularin formed an intracellular network in control sections and the signal was stronger in type II fibers labeled with the SERCA1 antibody ( Figure 8 ) . In both analyzed IMGD muscles , myotubularin was mainly located as concentric strands pointing to the center in both type I and type II fibers . We conclude that altered splicing of BIN1 has a strong impact on myotubularin localization in muscle , revealing a potential link between IMGD and X-linked CNM .
Classical BIN1-related ARCNM has been described with neonatal or childhood onset , hypotonia and ptosis and all mutations were found in ubiquitously expressed exons [1] , [13] , [14] , [15] . The muscle weakness was mildly to moderately progressive , and some patients could still walk at older age . In contrast , our patients with a splice mutation affecting the muscle-specific exon 11 showed a rapid disease progression involving strong care-dependence and leading to death within a few years , despite normal motor development and disease-onset not before 3 . 5 years . The histopathological findings of our patients and of the previously reported ARCNM cases partially overlap , including atrophy , prominent nuclear internalization and central dense areas upon NADH-TR staining of muscle sections . However , there is no evidence for type I fiber predominance in the muscle biopsies of our patients . Previous RT-PCR experiments demonstrated a progressive integration of exon 11 in BIN1 mRNA during human skeletal muscle development [2] . We therefore hypothesize that the muscle-specific exon 11 plays a major role in muscle maintenance , rather than in early muscle development . This is in accordance with the highly progressive phenotype of humans and dogs with a disease onset several months or years after birth . Consistently , we detected amphiphysin 2 in muscle tissue , but RNA analysis revealed major skipping of BIN1 exon 11 . This suggests that the patients mainly express an embryonic BIN1 isoform , which might not be able to assume the function of the adult BIN1 isoform , possibly explaining the more progressive phenotype compared to patients with BIN1 mutations in the ubiquitously expressed exons . The characterization of the pathological mechanisms leading to BIN1-related CNM and the development of potential therapeutic approaches is obviated by the lack of a faithful animal model . Bin1-null mice are perinatally lethal [26] , so that a comprehensive analysis of skeletal muscle alterations during disease development is not possible . We sought for dog breeds with molecularly unsolved congenital myopathies and we identified the canine Inherited Myopathy of Great Danes as a disease model reproducing the histological and physiological defects observed in BIN1-related CNM patients . IMGD has been reported for cases in Canada , Australia and UK and is characterized by generalized muscle atrophy , exercise intolerance , exercise-induced tremor and muscle wasting [29] . The disease typically starts before 10 months of age , is highly progressive , and most of the affected dogs are euthanized before 18 months of age due to severe debilitating muscle weakness . Histological examinations revealed internalized or central nuclei without evidence of inflammation , disruption of the sarcomeric architecture with central fiber areas devoid of myofibrils , and central accumulations of mitochondria and glycogen granules ( [28] , [29] , [30] and our data ) . In addition , type I fiber predominance in combination with an increased expression of genes implicated in the slow-oxidative metabolism was described [35] . In this study we demonstrate that IMGD and progressive CNM have a comparable etiopathology and both conditions result from mutations of the AG acceptor splice site of the BIN1 muscle-specific exon 11 . The histopathology and the cellular organization defects of the human and canine muscle disorders are almost identical , we therefore consider IMGD as a faithful mammalian model for BIN1-related centronuclear myopathy . Some dogs of our IMGD cohort were found to be negative for BIN1 mutations , suggesting that IMGD encompasses several disorders with similar clinical and overlapping histopathological features . The proven relationship of two affected Great Dane dogs demonstrates a common origin of the BIN1 exon 11 splice mutation , and it is likely that all five affected dogs described here can be traced back to a common ancestor . As the muscle disorder is inherited as a recessive trait , and as canine pedigrees are generally highly inbred , it is likely that the mutation can be found in Great Dane dog populations from all over the world , as recently demonstrated for another autosomal recessive CNM form in Labrador retrievers [36] . It is therefore of veterinary medical interest to sequence BIN1 exon 11 in Great Dane dogs . Also , veterinarians and veterinary pathologists should consider BIN1 mutations as a possible cause of any unexplained progressive myopathy in dogs , especially when the biopsy displays internal nuclei and lobulated or indented sarcolemma . Detailed immunohistochemical and ultrastructural analyses of muscles from patients and affected Great Dane dogs revealed common membrane alterations and abnormal accumulations of proteins regulating membrane trafficking . Similar findings were observed on biopsies from patients with DNM2 or MTM1 mutations [12] , suggesting that mislocalization of triad proteins reflects common aberrations in CNM and that the amphiphysin 2 muscle-specific isoform plays an important role in triad formation and/or maintenance . This is in accordance with the known biochemical function of amphiphysin 2 and other N-BAR domain proteins to sense membrane curvature and to potentially induce curvature through the insertion of an amphipathic helix into the membrane bilayer . In vitro and cell culture experiments have led to the suggestion that the exon 11 encoded PI-binding motif is essential for membrane tubulation in cultured muscle cells [22] . Indeed , Drosophila mutated for amphiphysin , the ortholog of both amphiphysin 1 and amphiphysin 2 , display an abnormal T-tubule system [24] . T-tubule alterations and muscle weakness were reproduced in murine Tibialis anterior injected with a U7 small nuclear RNA construct harboring an antisense sequence promoting BIN1 exon 11 skipping [2] . However , nuclear centralization and atrophy were not observed , contrasting with the IMGD model . This difference might be species-related , is possibly due to a low efficacy of the AAV-U7 method or alternatively to the examination time point 4 months post injection . As the triad is the membrane structure controlling excitation-contraction coupling , this suggests that impaired excitation-contraction coupling and subsequent calcium homeostasis defects are a primary cause of the myopathy . Of note , abnormal intracellular calcium release was observed in isolated murine muscle fibers after BIN1 shRNA-mediated knock-down [37] . Together with the present characterization of the IMGD model , these data indicate that amphiphysin 2 has an important muscle-specific role in triad structural maintenance , and provide additional evidence that triad modifications are a common defect in centronuclear myopathies , IMGD and myotonic dystrophies [2] , [12] . Triads are not the only membrane compartment affected in patients and dogs harboring BIN1 exon 11 mutations . We also noted central accumulations of caveolin 3 and dysferlin , two key regulators of membrane trafficking in skeletal muscle , numerous membranous whorls , and a peculiar remodeling of the sarcolemma , manifesting an indented fiber perimeter and invaginations towards the center of the fibers . Caveolin 3 regulates membrane tension at the sarcolemma and dysferlin controls membrane exocytosis in sarcolemmal membrane repair [33] , [34] . As both proteins are also present on regenerating T-tubules [38] , their mislocalization resulting from a BIN1 mutation would be in accordance with defective T-tubule regeneration . Moreover , data mainly obtained in cultured cells support a key role of amphiphysins in the formation of endocytic vesicles [16] , and a study in Caenorhabditis elegans suggested a role of amphiphysin in vesicle recycling [39] . Defects in intracellular signaling resulting from calcium defects and impaired transport of ion channels and growth factor might explain the muscle weakness and atrophy in BIN1-related CNM . Our findings on the IMGD model uncovered possible links between BIN1-related and other forms of CNM . Altered triads and the presence of membranous whorls were reported for MTM1 dog , mouse and zebrafish models as well as for patients with MTM1 mutations involving protein loss [12] , [31] , [40] , [41] , [42] . Abnormal triad markers were also reported for MTM1-related and DNM2-related CNM [12] , [43] . Dysferlin localization was not extensively studied in MTM1-CNM but was increased in the cytoplasm of a mouse model and in patients with DNM2-CNM [44] . Moreover , we found myotubularin localization was strongly impaired in IMGD muscles . These findings suggest that myotubularin and amphiphysin 2 are in the same pathway regulating membrane remodeling in skeletal muscle and strengthen the hypothesis of a common pathological mechanism of the X-linked and the autosomal recessive CNM forms . Alternative splicing of BIN1 exon 11 is mis-regulated in patients with myotonic dystrophy [2] . The parallel inclusion of exon 7 was noted , but its impact has not been assessed yet . Here we report the first mutation affecting the muscle-specific exon 11 of BIN1 and having an impact on splicing . The major clinical and histological aspects of the patients and IMGD dogs include general muscle weakness , atrophy and nuclear centralization , consistent with the muscle phenotype in DM patients . Our data therefore support the hypothesis that mis-splicing of BIN1 exon 11 partially accounts for the muscle-specific signs in myotonic dystrophy .
Human sample collection was performed with informed consent from the patients according to the declaration of Helsinki and experimentation was performed as part of routine diagnosis . All dogs were examined with the consent of their owners . Blood and biopsies were obtained as part of routine clinical procedures for diagnostic purposes . Cheek cells were collected by owners or veterinarians using non-invasive swabs . As the data were from client-owned dogs undergoing normal veterinary exams , there was no “animal experiment” according to the legal definitions in Europe and the US . All local regulations related to clinical procedures were observed . Cryopreserved muscle specimens were processed and stored at the University of California , San Diego , under a tissue transfer approval from the institutional Animal Care and Use Committee . Human Genomic DNA was prepared from peripheral blood by routine procedures and sequenced for all coding exons and intron/exon boundaries of MTM1 , DNM2 , and BIN1 as described elsewhere [1] , [4] , [5] . Patient 1 had a normal CTG repeat length at the DMPK locus ( 7 and 13 repeats ) and was therefore excluded for myotonic dystrophy . Control DNAs were from healthy individuals of Turkish origin . Dog DNA samples were extracted from cheek cells , venous blood or muscle biopsy specimens ( cryosections or paraffin embedded tissue ) by routine procedures and sequenced for all coding exons and intron/exon boundaries of canine MTM1 [31] , PTPLA [32] and BIN1 ( primer sequences in Table S1 ) . Control samples were from a world-wide collection of healthy Great Danes as well as from healthy individuals of 13 other breeds . RNA was extracted from muscle biopsies by routine procedures and reverse transcribed using the SuperScript III kit ( Invitrogen , Carlsbad , USA ) . Human and dog amplicons were cloned into the pGEM-T Easy vector ( Promega , Madison , USA ) and transfected into E . coli DH5α cells . Blue/white selection , repeated twice , resulted in 30 clones for the human cDNA and 3 clones for the canine cDNA . Control dog was an unaffected Drahthaar ( German Wirehaired Pointer ) . Primer sequences are listed in Table S1 . Western blot and immunofluorescence were performed using routine protocols . Biceps femoris and tibialis anterior biopsies from two affected dogs ( 14 months and 22 months , respectively ) and from healthy age-matched Golden Retrievers or Belgian Shepherds as controls have been used for the analysis . Following antibodies were used for the study: R2406 ( home-made rabbit anti-BIN1 PI binding domain ) , R2444 ( home-made rabbit anti-BIN1 SH3 domain ) , R3062 ( home-made rabbit anti-BIN1 exon 12 epitope ) , R2867 and R2868 ( home-made rabbit anti-MTM1 ) , mouse anti-GAPDH ( Merck Millipore , Darmstadt , Germany ) , mouse anti-ryanodine receptor 1 ( Affinity BioReagents , Golden , USA ) , mouse anti-SERCA 1 ( Affinity BioReagents , Golden , USA ) , rabbit anti-dysferlin ( Euromedex , Souffelweyersheim , France ) , goat anti-caveolin-3 ( Tebu-BIO , Le-Perray-en-Yvelines , France ) , rabbit anti-caveolin-3 ( Affinity BioReagents , Golden , USA ) , mouse anti-DHPR ( Affinity BioReagents , Golden , USA ) , and mouse anti-dystrophin ( Leica Microsystems , Germany ) . For immunohistofluorescence , transverse cryosections were prepared , fixed and stained by routine methods . Nuclei were stained with Hoechst or DAPI ( Sigma-Aldrich , St . Louis , USA ) . Sections were mounted with slowfade antifade reagent ( Invitrogen , Carlsbad , USA ) and viewed using a laser scanning confocal microscope ( TCS SP2; Leica Microsystems , Wetzlar , Germany ) or a a Zeiss Axio Observer Z . 1 microscope equipped with a 20× , 40× or 63× lens and Axioplan imaging with structured illumination ( Carl Zeiss , Jena , Germany ) . For histochemical analyses , transverse sections of muscle cryosections ( 8 µm ) of vastus lateralis and biceps femoris muscle biopsies were stained with hematoxylin-eosin , modified Gomori trichrome , NADH-TR and myofibrillar ATPase and then assessed for centralized nuclei , fiber morphology , fiber type distribution , cores , protein accumulation and cellular infiltrations . Muscle biopsies were processed for electron microscopy as described previously [45] . Briefly , the tissue was fixed either in 6% phosphate-buffered glutaraldehyde ( human patient ) or in 2 . 5% paraformaldehyde , 2 . 5% glutaraldehyde , and 50 mM CaCl2 in 0 . 1 M cacodylate buffer at pH 7 . 4 ( dog ) , and post-fixed with 2% OsO4 , 0 . 8% K3Fe ( CN ) 6 in 0 . 1 M cacodylate buffer ( pH 7 . 4 ) for 2 h at 4°C and incubated with 5% uranyl acetate for 2 h at 4°C . Samples were dehydrated in graded series of ethanol and embedded in epoxy resin 812 . Ultrathin sections ( 70 nm ) were contrasted with uranyl acetate and lead citrate . Murine C2C12 myoblasts were seeded on coverslips and transfected at 50–60% confluency using Lipofectamine 2000 ( Invitrogen , Carlsbad , USA ) either with GFP-BIN1 isoform 8 ( including exon 11 ) or isoform 9 ( excluding exon 11 , both were a kind gift from Pietro de Camilli , Howard Hughes Medical Institute , USA ) . Cells were differentiated after 24 h by changing to medium containing 2% horse serum instead of FCS and fixed and stained after 5 days of differentiation by routine methods . Nuclei were stained with Hoechst/DAPI ( Sigma-Aldrich , St . Louis , USA ) and sections were mounted with slowfade antifade reagent and viewed using a laser scanning confocal microscope ( TCS SP2; Leica Microsystems , Wetzlar , Germany ) . 1000 genomes - A Deep Catalog of Human Genetic Variation ( URL: http://www . 1000genomes . org/ ) Database of Single Nucleotide Polymorphisms ( dbSNP ) . Bethesda ( MD ) : National Center for Biotechnology Information , National Library of Medicine . ( dbSNP Build ID: 134 ) . ( URL: http://www . ncbi . nlm . nih . gov/SNP/ ) Exome Variant Server , NHLBI Exome Sequencing Project ( ESP ) , Seattle , WA ( URL: http://evs . gs . washington . edu/EVS/ ) Online Mendelian Inheritance in Man ( OMIM ) ( URL: http://www . omim . org/ ) NNsplice: prediction of splice mutations ( URL: http://www . fruitfly . org/seq_tools/splice . html ) Human Splicing finder ( URL: http://www . umd . be/HSF/ )
|
The intracellular organization of muscle fibers relies on a complex membrane system important for muscle structural organization , maintenance , contraction , and resistance to stress . Amphiphysin 2 , encoded by BIN1 , plays a central role in membrane sensing and remodelling and is involved in intracellular membrane trafficking in different cell types . The ubiquitously expressed BIN1 , altered in centronuclear myopathy ( CNM ) and myotonic dystrophy ( DM ) , possesses a muscle-specific exon coding for a phosphoinositide binding domain . We identified splice mutations affecting the muscle-specific BIN1 isoform in humans and dogs presenting a clinically and histopathologically comparable highly progressive centronuclear myopathy . Our functional and ultrastructural data emphasize the importance of amphiphysin 2 in membrane remodeling and suggest that the defective maintenance of the triad structure is a primary cause for the muscle weakness . The canine Inherited Myopathy of Great Danes is the first faithful mammalian model for investigating other potential pathological mechanisms underlying centronuclear myopathy and for testing therapeutic approaches .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"animal",
"types",
"animal",
"genetics",
"genetic",
"mutation",
"gene",
"function",
"animal",
"models",
"histology",
"model",
"organisms",
"molecular",
"genetics",
"veterinary",
"science",
"gene",
"splicing",
"membranes",
"and",
"sorting",
"biology",
"veterinary",
"pathology",
"autosomal",
"recessive",
"congenital",
"hereditary",
"myopathies",
"large",
"animals",
"genetic",
"screens",
"gene",
"identification",
"and",
"analysis",
"genetics",
"human",
"genetics",
"molecular",
"cell",
"biology",
"genetics",
"of",
"disease"
] |
2013
|
Altered Splicing of the BIN1 Muscle-Specific Exon in Humans and Dogs with Highly Progressive Centronuclear Myopathy
|
Existing theories of movement planning suggest that it takes time to select and prepare the actions required to achieve a given goal . These theories often appeal to circumstances where planning apparently goes awry . For instance , if reaction times are forced to be very low , movement trajectories are often directed between two potential targets . These intermediate movements are generally interpreted as errors of movement planning , arising either from planning being incomplete or from parallel movement plans interfering with one another . Here we present an alternative view: that intermediate movements reflect uncertainty about movement goals . We show how intermediate movements are predicted by an optimal feedback control model that incorporates an ongoing decision about movement goals . According to this view , intermediate movements reflect an exploitation of compatibility between goals . Consequently , reducing the compatibility between goals should reduce the incidence of intermediate movements . In human subjects , we varied the compatibility between potential movement goals in two distinct ways: by varying the spatial separation between targets and by introducing a virtual barrier constraining trajectories to the target and penalizing intermediate movements . In both cases we found that decreasing goal compatibility led to a decreasing incidence of intermediate movements . Our results and theory suggest a more integrated view of decision-making and movement planning in which the primary bottleneck to generating a movement is deciding upon task goals . Determining how to move to achieve a given goal is rapid and automatic .
In the reaction time before a movement is initiated , two distinct processes are thought to occur: first , the exact goals of the movement must be decided upon and , second , the actions that will achieve the chosen goal must be selected and/or prepared [1] . Decisions about high-level movement goals have been well-characterized in terms of an accumulation of sensory evidence over time [2 , 3] . The process of selecting and/or preparing the actions to achieve a chosen goal , which we refer to here as movement planning , is classically thought to require further time-consuming computations [4 , 5 , 6] . The relative contribution of goal selection and movement planning to the reaction time remains a matter of considerable debate [7 , 8] . One way to study the process of movement planning is to interrupt it and examine the behavioral consequences . When a reaching movement is released at a lower-than-normal reaction time , movements appear to be biased away from the target stimulus towards a ‘default’ movement [4 , 9 , 10] . As preparation time increases , movements gradually converge on the target . Similar intermediate movements are observed if a target jumps shortly before movement onset [11 , 12 , 13 , 14 , 15] or in tasks that either deliberately or inadvertently create ambiguity about task goals [16 , 17 , 18 , 19] . These intermediate movements have been variously interpreted as reflecting incomplete movement planning or interference between parallel plans to each potential goal . Either interpretation suggests that intermediate movements occur as an unintentional artifact of stressing an underlying planning mechanism . Although generally well accepted , these existing interpretations of intermediate movements are at odds with more contemporary theories of movement execution based on optimal control theory [20] . According to this theory , a single , flexible feedback control policy can be sufficient to generate a wide variety of movements and rapidly switch between them based on new sensory observations [21 , 22] . Such an organization dispenses with the need to re-plan a movement each time the movement goal changes and is incompatible with replanning-based explanations for intermediate movements . Here we show how intermediate movements can be understood within an optimal control framework if the control policy takes into account an evolving decision about the location of movement goals . Our theory , therefore , frames intermediate movements as reflecting a deliberate plan to deal with uncertainty about movement goals , rather than as resulting from erroneous movement planning . A critical prediction of this theory is that intermediate movements should only occur when potential movement goals are compatible , i . e . when they require kinematically similar movements . Given compatible goals , an intermediate movement can bring the hand closer to both goals simultaneously , pending the arrival of further information about which goal to ultimately commit to [23 , 24] . If the compatibility between goals is eliminated , either by separating the goals more widely in space [4 , 25] or by imposing an obstacle between them , intermediate movements no longer offer this advantage and all movements should instead be directed to one target or another . We verified these predictions in three experiments that varied the compatibility between movement goals in two distinct ways .
Subjects were trained to initiate their movements at a precise time during each trial ( Fig . 1A ) and make center-out movements to “shoot” through a target . On a subset ( 30% ) of trials , the target jumped by ±45° , ±90° or ±135° at an unpredictable time ( between 150ms and 550ms ) prior to movement initiation ( Fig . 1B ) . We were interested in how subjects’ behavior ( specifically , the initial direction of their movement ) depended on the amount of re-preparation time ( rPT ) available . That is , the amount of time that elapsed between when the target jumped and when the subject initiated their movement . Fig . 2 shows data from a representative subject . On trials in which the target jumped by 45° , we observed a continuous relationship between rPT and initial reach angle ( Fig . 2A , B ) . For movements initiated less than 200ms after the target jump , movements were directed towards the original target location . Between 200ms and 350ms , the initial reach angle changed gradually from the original to the new target direction as the rPT increased . For movements initiated more than 350ms after the target jump , this subject was consistently able to compensate for the change in target location . A similar pattern held for the behavior in response to 90° jumps , only that the subject adhered to the initial reach direction slightly longer and the transition between targets was steeper ( Fig . 2C , D ) . For the largest target jumps ( 135° change in reach direction ) , there was no clear transitional period between targets . Instead , behavior switched abruptly at around 350ms from movements directed towards the initial target to movements directed towards the post-jump target ( Fig 2E , F ) . All subjects showed the same qualitative pattern of gradual adjustment of reach direction for small target jumps , and more abrupt adjustment for larger target jumps . Interestingly , there were still a small number of intermediate movements generated following large target jumps ( Fig . 2 E , F ) , suggesting a continuous underlying change in reaching direction , albeit so rapid that the transition appeared abrupt . To characterize this behavior quantitatively , we fit sigmoid functions to the relationship between rPT and initial reach direction ( see Methods , Eq . ( 2 ) ) . This yielded two parameters for each subject , for each jump amplitude . The first parameter , τ , reflects the timescale over which this transition occurred . The second parameter , t50 ( s ) reflects the latency of the change in initial movement direction . Comparing the fitted sigmoid parameters for all subjects , we found that the slope of the sigmoid , τ differed significantly across jump amplitudes ( F ( 2 , 18 ) = 22 . 6; p<0 . 0001 ) ( Fig . 3A ) . Post-hoc pairwise comparisons of τ across jump amplitudes were all significant ( p<0 . 05 ) . Thus the transition in initial reach direction was consistently more abrupt for large amplitude target jumps than for smaller amplitude jumps , confirming our predictions . Next , we examined the latency of compensation for the target jump . We found that t50 , the time at which subjects would make an exactly intermediate movement , also depended on jump amplitude ( F ( 2 , 18 ) = 11 . 96; p<0 . 001 ) ( Fig . 3B ) . Visual inspection of the data suggests that the re-preparation time required for complete compensation , i . e . the shortest delay at which movements directed to the post-jump target were reliably observed , seemed to be consistent across jump amplitudes . To test this hypothesis , we considered the time t95 at which each fitted sigmoid reached 95% compensation . This measure showed no significant difference across jump amplitudes ( F ( 2 , 18 ) = 1 . 3; p = 0 . 30 ) ( Fig . 3C ) . Conversely , differences in the times at which compensation began ( t05; 5% of sigmoid height ) were highly significant across subjects ( F ( 2 , 18 ) = 32 . 04; p< . 0001 ) . Thus , target jumps of larger amplitude angles did not require a longer period of re-preparation , despite requiring a larger change in movement direction . The primary difference in behavior across jump amplitudes was in the time at which the change in target location began to be reflected in subjects’ movements . Despite extensive training , all subjects exhibited considerable variability in their movement initiation times . The standard deviation in movement initiation time , averaged across subjects , was 79±21ms . This value was quite large when compared with the timescales over which subjects’ behavior changed ( ∼100ms ) . We considered whether the delay between the target jump and the intended time of movement initiation ( i . e . the delay between the target jump and the fourth tone ) could serve as a better predictor of behavior than rPT . To test this possibility , we repeated our analysis using the absolute time of the target jump instead of the delay between target jump and movement onset ( rPT ) . The total log-likelihood of the sigmoid fit ( including data points classified as outliers ) was significantly worse ( F ( 1 , 6 ) = 48 . 2; p<0 . 001 ) ) . Thus we can conclude that behavior depended specifically on the actual time of movement initiation and not on the intended time of movement initiation . An important feature of Experiment 1 is that although the disappearance of the initial target is unambiguous , the target could have jumped to a number of different locations . We performed a second experiment to determine to what degree , if any , this ambiguity affected the timecourse of compensation for the jump . In Experiment 2 , the target only appeared in two possible locations within each block ( Fig . 4 ) , such that whenever the target jumped , the location it jumped to was always known unambiguously . Despite the difference in paradigms , subject behavior was identical to that seen in Experiment 1 . The initial reach direction changed gradually as a function of rPT for a 45° separation between targets , but changed abruptly when the targets were separated by 135° . As in Experiment 1 , the extent of intermediate movements ( slope of the sigmoid ) differed significantly across jump amplitudes ( F ( 1 , 5 ) = 24 . 66 , p<0 . 01 ) , as did the latency , t50 ( F ( 1 , 5 ) = 36 . 2 , p< . 01 ) . The time at which compensation was complete , t95 , was similar across target separations , although appeared to be slightly earlier for the 135° than 45° separation ( F ( 1 , 5 ) = 5 . 62 , p = 0 . 06 ) . We can conclude from the results of Experiment 2 that ambiguity about the precise location of the post-jump target did not significantly influence behavior . In Experiments 1 and 2 , we showed that decreasing compatibility between goals by increasing their angular separation led to a more rapid transition between movement directions , with a corresponding decrease in the incidence of intermediate movements . This result is consistent with our hypothesis that intermediate movements constitute an intelligent solution to the problem of moving amid goal uncertainty . However , varying the jump amplitude also affected the degree of similarity between the motor commands required before and after the jump . Intermediate movements may have emerged from interference between overlapping movement representations , rather than because of an exploitation of task-level compatibility between goals . In Experiment 3 , we controlled for this possibility by using an alternative approach to reducing the compatibility between the pre-jump and post-jump targets that allowed us to vary the compatibility between goals while keeping the pre-jump and post-jump goals consistent across conditions . We created a series of virtual barriers between adjacent targets ( Fig . 5A ) . Any intermediate movements were penalized by playing an unpleasant rasping tone and withholding points and other success cues from subjects on trials in which they collided with the barrier . We tested the behavior of subjects in response to target jumps of 45° amplitude both with and without these barriers present ( Fig . 5B ) . As before , when the barriers were not present , subjects exhibited a gradual change in movement direction with a large number of intermediate movements . With the barriers present , however , instead of this gradual change in behavior , subjects switched more abruptly from one direction to another . Applying the same analysis as in Experiment 1 , we found a significant difference in the timescales of the change in initial movement direction , τ , across conditions ( F ( 1 , 7 ) = 30 . 75; p< . 001 ) and also in the latency t50 ( F ( 1 , 7 ) = 29 . 98; p< . 0001 ) . The time required to fully compensate for the target jump ( t95 ) was not significantly different across conditions ( F ( 1 , 7 ) = 2 . 57; p = 0 . 15 ) . Importantly , differences in behavior between the barrier and no barrier conditions cannot be attributed to an increase in accuracy demands when barriers were present: variability in initial reach direction on non-jump trials was not strongly affected by the presence of a barrier ( s . d . in initial reach direction without barrier = 4 . 8± . 6° , with barrier = 4 . 4± . 9°; F ( 1 , 7 ) = 3 . 58; p = 0 . 1 ) . Overall , we found that the behavior following 45° jumps with barriers was qualitatively similar to that observed for 135° jumps in Experiment 1 when movement paths were unconstrained . This confirmed our hypothesis that goal compatibility , and not the magnitude of difference in required movement directions , was the key determinant of behavior following a target jump . Our experimental results showed a clear pattern whereby the timecourse of adjustment in behavior following a target jump , and , consequently , the incidence of intermediate movements , dependeds upon the compatibility between the pre-jump and post-jump goals . For nearby targets , which are highly compatible , initial movement direction was adjusted gradually as a function of available re-preparation time . When the potential movement goals were incompatible with one another , initial movement direction switched abruptly at a clear threshold rPT . We formalized our intuition about these results through a mathematical model ( Fig . 6 ) . We suppose that , in the immediate aftermath of the target jump , the subject must determine based on sensory information whether the target has jumped and , if so , where it has jumped to . Although information about the target was presented discretely and unambiguously , the detection of such unambiguous stimuli still may entail significant uncertainty . We modeled this simple decision about the location of the target as a process of noisy evidence accumulation . Critically , this implies that subjects were transiently uncertain as to the true location of the target , but became more confident as more time elapsed following the target jump . We combined this decision-making process with an optimal control model of movement generation . We assume that each potential target is associated with some accuracy cost Jx that rewards movements that pass through the goal region and penalizes movements that do not ( see Methods ) . We augment the usual state of the motor apparatus , xt , with a dynamic stochastic variable rt reflecting accumulating evidence about the true identity of the target . Paralleling standard models of decision-making [3 , 26] , rt represents the log odds ratio of the belief pt that the initial target is the true target location: rt=log ( pt1−pt ) . rt is allowed to vary from −∞ ( certain that the target has not jumped ) to +∞ ( certain that the target has jumped ) . Together with an effort cost Ju , the overall expected endpoint cost is then given by a sum of the accuracy costs associated with each target , weighted by their beliefs ( Equation 9 ) . Optimizing this cost yields a single , fixed control policy ut = π ( xt , rt , t ) that guides responses to fluctuations in both the state of the plant and beliefs about the target . In principle this model could be used to predict full movement trajectories . However , the critical prediction of interest is the decision about what direction to move in at the very start of the movement given the prospect of gaining further evidence later on during movement i . e . whether or not an intermediate movement should be generated . A general solution for this class of control problem is intractable for high-dimensional plants due to the non-quadratic form of the cost function . We therefore examined the behavior predicted by this theory in a simplified one-dimensional model of the center-out reaching task . In this model , the optimal initial reach direction is driven solely by the belief state at movement onset , r0 , and the compatibility between potential goals . We cannot precisely know the timecourse of changes in belief in subjects following the target jump ( i . e . how r0 varies as a function of time since the target jump ) . However , we assume that the change in belief should follow the same monotonic timecourse regardless of the size of the target jump . Thus the model explains differences in behavior across different jump amplitudes as being due to there being different optimal initial reach directions associated with similar belief states . Our aim in the model was to demonstrate that decreasing the compatibility between goals ( by increasing the distance between them ) leads to more abrupt changes in behavior as a function of preparation time . Fig . 6D illustrates the predicted initial reach direction in this simplified model as a function of the belief about the target location at movement onset . As can be clearly seen , the sensitivity of behavior to the belief at the time of movement onset depends significantly on the separation between targets . Our theory also naturally explains the absence of intermediate movements in the presence of a barrier , as seen in Experiment 3 . The presence of the barrier reduces the decision about initial reach direction to a discrete choice , in which case the subject should select the most likely target direction at the time of movement onset , leading to step-like behavior . Although the simulations clearly demonstrate the relationship between goal compatibility and the timescale of the change in reach direction , our simulations also reveal an interesting discrepancy between the theory and the data . In the model , behavior across all target jump amplitudes is aligned for exactly halfway intermediate movements , i . e . where r0 = 0 . Consequently , large-amplitude target jumps are predicted to be fully compensated earlier than small-amplitude jumps . In the data , however , we observed that full compensation for the target jump occurred at similar delays across all jump amplitudes . One potential explanation for this is that in the model , the pre-jump and post-jump targets are treated symmetrically . The fact that the target jumped on only a subset of trials may have biased subjects towards the initial target . Subjects may even possess an innate bias towards their original movement plan in the event that circumstances unexpectedly change . Asymmetry may also have arisen from differences in confidence in the exact target location between pre- and post-jump targets ( although the results of Experiment 2 allow us to largely rule out this possibility ) . There are therefore a variety of asymmetries between the pre and post-jump targets that are not captured by the basic model . We attempted to accommodate these effects within the model through an asymmetric cost function which effectively penalized a miss more heavily when the true target turns out to be the original target . We found that imposing such an asymmetric cost was able to better reproduce the observed pattern of behavior ( Fig . 6E ) . While admittedly post-hoc , these results demonstrate that the basic modeling framework can feasibly be extended to account for this aspect of the data .
Our experiments extend and reinterpret a classic series of studies by Ghez and colleagues , who developed the timed-response paradigm to examine movement planning and preparation [4 , 9 , 32 , 33] . In their experiments , only two movement directions were possible in each trial ( as opposed to 8 in our experiments ) and ambiguity was created by providing no target information at all until shortly before movement onset ( as opposed to jumping an existing target as in our experiments ) . Their results were qualitatively similar to our own , with intermediate movements occurring at low preparation times when targets were narrowly separated but not when widely separated . An advantage of our target-jump approach is that we were able to control the initial belief state of the subject which , along with the low proportion of jump trials and multiple potential targets , made it unlikely that intermediate movements were the result of subjects adopting a deliberate aiming or guessing strategy . Our experimental results therefore reinforce the view that intermediate movements seen at low preparation times reflect an implicit property of the motor system rather than an explicit strategy . Ghez and colleagues suggested that intermediate movements at narrow separations were due to incomplete specification ( i . e . preparation ) of the motor commands required for movement . This theory cannot , however , explain why intermediate movements do not occur for more widely separated targets or in the presence of a barrier . Ghez and colleagues therefore suggested the existence of two distinct mechanisms of movement planning: a discrete mechanism responsible for the abrupt behavior seen at wider separations and a continuous re-specification mechanism operating at more narrow separations ( see also [25] ) . Our theory offers a more satisfying and parsimonious explanation for these contrasting modes of behavior: that they reflect qualitatively different solution regimes to the same optimization problem . Some authors have suggested that intermediate movements following a target jump occur because the target is perceived to be at a location intermediate between goal locations [12 , 13] . Similarly , intermediate movements could potentially reflect an interpolation between movement plans , rather than between perceived goal locations . In either case , such interpolation mechanisms can plausibly account for the pattern of intermediate movements following small ( 45° ) target jumps , and would also predict an abrupt switch in reach directions following 180° jumps . However , interpolation would predict only a relatively modest change in the pattern of intermediate movements as the target separation increased from 45° to 90° to 135° . In particular , this kind of model predicts a far smaller difference in behavior between the 45° case and the 135° case than suggested by our data . In particular , individual subjects tended to show an abrupt transition between movement direction following 135° jumps ( e . g . Fig . 2E ) . This abrupt transition is consistent with the findings of Ghez et al . [4] who reported no intermediate movements when potential targets were separated by 120° . A model based on a direct interpolation between movement plans or goals does not predict such an abrupt transition until the jump amplitude becomes close to 180° and cannot therefore fully account for our findings . Intermediate movements are often interpreted as evidence for interference between parallel movement plans . Tasks that directly manipulate goal uncertainty , either by delaying disclosure of goal information [4 , 16 , 17] , by presenting distractors [18] , or by providing deliberately ambiguous cues [34] , yield intermediate movements . More abstract cognitive decisions can have a similar effect [19] . In all cases , ambiguity about the goals of the movement is believed to lead to interference between associated movement plans , which ultimately leads to errant intermediate movements being generated . The existence of such intermediate movements is therefore thought to offer insights into the underlying mechanism of movement planning . One would expect that such interference , arising from low-level mechanisms , should be unavoidable . This is , however , inconsistent with our result in Experiment 3 in which subjects could easily eliminate intermediate movements in the presence of a virtual barrier . While it may be possible to augment mechanistic models with a means to over-rule the generation of intermediate movements where necessary , this raises serious questions about why intermediate movements should ever be permitted . Our alternative interpretation , analogous to previous proposals by Hudson , Landy and Maloney [24] , is that intermediate movements reflect a single , deliberate movement plan chosen to maximize performance in the task amid ambiguity about the goal , explaining the presence or absence of intermediate movements without requiring any assumptions about the underlying planning mechanism . The basic theoretical framework presented here provides a promising unifying framework for describing perception and action . However , the intractability of obtaining the optimal policy for such models is a severe limitation . Recent advances in solution methods for optimal control problems [35] are inapplicable due to the structure of our control problem . Specifically , efficient solution methods require that noise and control act in the same dimensions . The structure of our control problem violates this requirement , since the decision variable has noisy dynamics but is not controllable . The development of efficient numerical methods applicable to the specific class of problems described here would be valuable in generating more precise predictions of the general theory . Optimal control theory has previously been invoked to account for intermediate movement strategies [36 , 37] . Such theories suggest intermediate movements as a strategy to exploit execution noise: when multiple , equally valid targets are present , it is better to aim for the middle and let execution noise dictate which target to ultimately hit . Although this theory potentially explains the presence of intermediate movements in the presence of multiple targets , we believe it is insufficient to account for our findings since the amount of execution noise required to predict an intermediate movement given a 90° separation between targets is infeasibly large . Although our model accurately accounted for the incidence of intermediate movements , one aspect of the data that was not predicted was the fact that the target jump was fully corrected for at about the same delay across all conditions . The model predicts that perfect compensation will be seen earlier under incompatible conditions compared to compatible ones . This discrepancy could potentially be attributable to our model assuming that the pre-jump and post-jump targets should be treated as equivalent , whereas in reality there are important differences between them . The results of Experiment 2 allow us to rule out the possibility that ambiguity about the location of the post-target jump was a major source of asymmetry . It is possible that deteriorating quality of peripheral vision at more eccentric target locations [38] could account for unexpected differences across conditions , although it would be quite a coincidence for this to lead to such close temporal alignment . Additional sources of asymmetry may arise from intrinsic biases in subjects’ decision-making processes; subjects may be inherently biased against changing their minds [34] . Indeed , given that the target jumped in only 30% of trials , it was more likely a priori that the target would remain in its original location . An alternative explanation for the consistent time at which compensation for the jump became complete is that an underlying mechanistic constraint on movement preparation limited the ability to generate an accurate response to the changed target location . It is unclear how intermediate movements might be reconciled with such a constraint . Nevertheless , our theory provides a rational account of why intermediate movements should ever be allowed to occur , instead of simply always switching abruptly between movement directions . Our normative model suggests an alternative interpretation of a number of well-established neural correlates of movement planning and preparation . High-level movement goals appear to be represented in dorsal [39] and ventral [40] premotor cortex and also posterior parietal cortex [41] . When multiple potential goals are presented , these goals are represented simultaneously [39 , 42] . Conventionally , activity associated with a single goal is construed as representing a specific movement plan; simultaneous responses when multiple targets are present is thought to reflect multiple such plans occurring in parallel [23] . Our theory provides an alternative view: that the overall pattern of activity across this population represents a global belief state ( a multi-target analog of our binary decision variable rt ) over all possible movement goals; details of how to achieve these goals will be determined by a downstream site , possibly primary motor cortex , which is responsible for implementing a single control policy associated with this global belief state . Computational models have suggested that lateral connectivity within a network representing task goals may provide a mechanism whereby intermediate movements are generated [27 , 43 , 44] . Excitatory connections between units tuned to similar movement directions can lead to two peaks of activity becoming merged and thus leading to intermediate movements . These theories therefore explain intermediate movements as a by-product of an underlying planning mechanism . Inhibitory connections between units representing dissimilar movements create winner-take-all dynamics when potential goals are more widely separated . Our results show , however , that intermediate movements are not obligatory; in the presence of a barrier they can be suppressed . This absence of intermediate movements could potentially be explained by inhibition of units representing movement directions that would hit the barrier . However , if it is so easy to eliminate intermediate movements in such a model , it is unclear why they should be permitted in the absence of a barrier . It is currently unclear exactly how control policies are represented in the brain . However , recent theories have suggested that the state of motor cortex at the time of movement onset is sufficient to encode the full sequence of feedforward motor commands required to execute a movement [45 , 46] . Typically , neural activity converges onto a movement-specific preparatory state over a period of around 100ms following stimulus presentation [47 , 48] . It is tempting to interpret this change in neural state as reflecting a form of movement planning . We suggest instead that this observed change in neural state could equally reflect an evolving decision . Indeed , the state of motor areas appears to continuously track belief state during decision-making tasks [49 , 50] . Although these results are often interpreted as reflecting partially formed or blended motor plans , our theory suggests instead that intermediate states might reflect a single control policy that is optimal given a partially formed belief about movement goals—effectively hedging against possible future fluctuations in belief or changes of mind [34] after the movement has begun .
All procedures were approved by the Johns Hopkins University School of Medicine Institutional Review Board . All subjects provided written informed consent prior to participating . 24 adult ( 18–40 y/o , 11 female ) , right-handed , neurologically healthy subjects were recruited for this study . Subjects were seated at a glass-surfaced table . Their right forearm was supported by a plastic cradle equipped with pressurized air vents to allow frictionless planar arm movements . Subjects' arms were obstructed from view by a mirror positioned above the table surface , through which an LCD monitor ( 60Hz ) displayed movement targets and the position of the index finger in a veridical horizontal plane . The index finger was tracked at 130Hz using a Flock-of-Birds magnetic tracker ( Ascension Technology , VT , USA ) . A total of 10 subjects participated in Experiment 1 . On each trial , subjects were required to position the cursor inside a start circle ( 10mm diameter ) . After 300ms , a sequence of four tones spaced 500ms apart was initiated ( Fig . 1A ) . Synchronous with the first tone , a single target ( 25mm diameter ) appeared at one of eight possible target locations , positioned uniformly on a circle of radius 0 . 08m ( Fig . 1B ) . Subjects were required to initiate a ‘shooting’ movement through the target , synchronous with the onset of the fourth tone . Movement onset was detected based on the first time that the tangential velocity of the cursor exceeded 0 . 02m/s . In order to be successful , subjects were required to initiate movement within ±100ms of the onset of the fourth tone and move the center of the cursor through some part of the target region . An on-screen graphic displayed peak velocity after each trial and subjects were asked to keep this above a shown threshold that corresponded to 0 . 9ms-1 . On successful trials , subjects were rewarded with a “success” tone and points towards a cumulative score . On-screen text following each trial indicated whether subjects had initiated their movement too early or too late . 1s after movement onset , subjects were able to begin the next trial by returning to the start circle . In an initial familiarization session prior to the main experiment , all subjects received extensive training ( >500 trials ) at timing their movement initiation accurately . During the main experiment , the target was jumped to a different location in 30% of trials , at a random time ( between 150ms and 550ms ) prior to the fourth tone . The direction of the new target location differed by either ±45° , ±90° or ±135° from the original ( Fig . 1B ) , and this difference was randomly selected on each jump trial . Subjects performed approximately 2000 trials total , divided into blocks of 100 trials . The full experimental session , including occasional breaks , lasted approximately 3 hours . Some subjects performed the main experiment across two separate sessions on different days . Experiment 2 followed the same pattern as Experiment 1 , except that there were only 2 potential target locations . Six new subjects ( 4 Female ) performed 500 trials ( across 5 consecutive blocks ) with a 45° separation between potential target locations ( ±22 . 5° relative to straight-ahead ) , and 500 trials with a 135° separation ( ±67 . 5° relative to straight-ahead ) . The order of target configurations ( 45° separation first or 135° separation first ) was counterbalanced across subjects . The same basic setup was used in Experiment 3 . Eight subjects participated in this experiment ( 3 Female ) , two of whom had also participated in Experiment 1 . Subjects performed two main sessions , each consisting of 8 blocks of 100 trials . The No Barrier session was similar to Experiment 1 , with 8 potential targets and the target jumping on 30% of trials , except that all jumps were ±45° in magnitude . The Barrier session was identical to the No Barrier session , except that a series of virtual barriers was introduced in the workspace ( Fig . 5A ) . These barriers encouraged subjects to move in a straight line towards each target and prohibited intermediate movements . The barrier configuration effectively created a 10mm wide channel within which subjects could move freely . On trials in which subjects entered the barrier region , the barrier turned red , an unpleasant tone was played , and the subject received no score on that trial . Subjects were also verbally encouraged to avoid contacting the barrier . All subjects performed one session with barriers present and one session without and the order of sessions was counterbalanced across subjects . Barriers were also present during the second half of the initial familiarization session in which there were no target jumps . Position and velocity data were smoothed using a 2nd-order Savitzky-Golay filter with half width 54ms . We computed the time of movement onset based on the latest time that the smoothed tangential velocity was less than 0 . 02m/s prior to the peak tangential velocity ( note that this differed slightly from the onset time calculated online that determined the success or failure feedback given to subjects about the timing of their movements during the experiment ) . We expected that the initial direction of movement would depend on the amount of time available to revise the movement plan prior to movement initiation . We therefore computed , for each trial , the re-preparation time ( rPT ) —the duration between the time of the target jump and the time of movement onset . We computed the initial reach direction based on the direction of the tangential velocity 100ms after movement onset . All jump trials were transformed into a common reference frame such that the initial target was located at 0° , and the target jumped in a positive direction . Trials in which the hand failed to move further than 5cm from the start location were excluded from the analysis . In a small number of trials , movements were excessively curved in a way that did not permit a well-defined estimate of the reach direction 100ms after movement onset . This was often associated with failure to keep the hand stationary prior to movement initiation . We identified and eliminated excessively curved movements as follows: we computed the rate of change of estimated movement direction with respect to measurement time , dθ^dt=θ^ ( 100+Δ ) −θ^ ( 100−Δ ) 2Δ , ( 1 ) where θ^ ( t ) is the estimated reach direction at time t and Δ=1130 ms . Based on behavior in non-jump trials , we set a threshold on the absolute value of this rate of 1 . 3°/ms . Of all non-jump trials , 99% fell within this range . As a result of this exclusion procedure , an average of 4 . 5±3 . 1 trials per subject were excluded in Experiment 1 . These excluded trials were distributed similarly across each of the possible jump types ( F ( 2 , 18 ) = 3 . 15; p = 0 . 07 ) . An average of 4 . 7±3 . 3 trials per subject were excluded in Experiment 2 , and an average of 2 . 75±3 . 0 trials per subject were excluded in Experiment 3 , also not depending on the condition ( Experiment 2 , F ( 1 , 5 ) = . 03; p = 0 . 8; Experiment 3 , F ( 1 , 7 ) = 0 . 02; p = . 88 ) . In total , we excluded less than 3% of all trials on the basis of excessive curvature . In order to quantify the timecourse of the change in initial reach direction for comparison across conditions and across subjects , we assumed that the initial reach direction followed a sigmoidal relationship with available re-preparation time: θ=S ( rPT ) =A1+e− ( rPT−t50 ) τ . ( 2 ) We assumed that A was equal to the actual jump amplitude . This function therefore contained two free parameters: a slope parameter τ that characterized the timescale over which gradual changes in reach direction occurred , and a latency parameter t50 which acted to shift the sigmoid along the time axis . An important feature of the data is the presence of uncertainty not just in the estimated reach direction , but also in the estimated rPT . In the presence of such uncertainty , an ordinary least squares fitting approach significantly overestimated τ . We therefore adopted a maximum likelihood approach that specifically accounted for the uncertainty in the rPT . Specifically , we assumed Gaussian noise both in the reach direction , due to either execution variability or measurement noise ( s . d . σθ ) , and in the estimated re-preparation time , due to either variability on the part of the subject , uncertainty in our estimate of the movement onset time , or experimental error in controlling the precise target presentation time ( s . d . σt ) . The likelihood for each observation was consequently given by Li∝∫exp[− ( e−t ) 22σt2− ( θi−S ( e;t50 , τ ) ) 22σθ2]de , ( 3 ) where e reflects possible values for the noise in the measured value of the rPT . We set σθ equal to the mean standard deviation of initial movement directions on non-jump trials across all subjects ( σθ = 10 . 7 ) . We set σt = 10 ms since this value was found to lead to robust performance on pilot data . This likelihood was evaluated by trapezoidal integration over e . Not all subjects consistently generated data with rPTs in the critical slope region of the sigmoid . Behavior for which this data was unavailable was thus equally consistent with a broad range of sigmoid parameters . We resolved this ambiguity by biasing the sigmoidal fit towards more shallow slopes through an additional term added to the log-likelihood: Λ=∑iLi+ατ . ( 4 ) Thus the estimated slope was the shallowest ( longest duration ) slope that was consistent with the data . Importantly , this approach was conservative since this ambiguity tended to occur during larger amplitude target jumps where behavior was expected to be more abrupt . We set α = 0 . 02 , based on fits to synthetic data . Finally , maximizing this likelihood yielded accurate parameter estimates on synthetic datasets , but was quite sensitive to outlying data points in real data . We therefore extended our parameter estimation procedure to make it more robust to outlying data points by supposing that each data point could have been generated by an alternative , uniform distribution , with a fixed likelihood L0 . We identified the sigmoid parameters that maximized the likelihood of this mixture model using an expectation maximization algorithm [51 , 52] . Following this procedure , we rejected an average of 3 . 9%±2 . 3% of data points per subject from Experiment 1 as outliers and 1 . 7±1 . 5% of data points in Experiment 2 , in neither case biased towards any particular condition ( p>0 . 05 ) . In Experiment 3 , the average outlier rejection rate across subjects was 3 . 2%±1 . 9% and was marginally but consistently greater in the barrier condition ( 2 . 5%±1 . 8% No Barrier , 3 . 9%±1 . 8% Barrier; F ( 1 , 7 ) = 5 . 61; p<0 . 05 ) . Here , we consider the problem of selecting optimal actions in order to achieve a goal in the presence of uncertainty . We model the arm through a linear dynamical system in discrete time with state xt , and subject to time-varying controls ut: xt+1=Axt+But . ( 5 ) We characterize the goal of the task through an accuracy cost Jx that penalizes deviations from some goal state g at the end of the movement ( time t = T ) . In addition to this accuracy cost , we assume an effort cost Ju that penalizes large motor commands . It is difficult to say a priori exactly what the form of this cost should be [53] . Following standard approaches [20] , however , we assume that this effort cost is a quadratic function of the overall sequence of motor commands: Ju=∑twtut2 . ( 6 ) In this equation , wt is a potentially time-varying weight . According to the optimal feedback control hypothesis [20] , the motor system selects motor commands ut that minimize the sum of accuracy and effort costs: J=Jx ( xT−g ) +Ju . ( 7 ) The key novelty of our model is that the location of the goal state g is not precisely known . Specifically , we assume that the true goal is at one of two possible locations , g1 and g2 . Supposing that p represents the belief that g1 is the true goal location , and ( 1−p ) the corresponding belief for g2 , we introduce an evidence variable r which reflects the perceived log-odds ratio between the two targets: r=log ( p1−p ) . ( 8 ) Furthermore , we assume that the belief about the state of the target can vary over time . As is commonly assumed in decision-making models [3 , 26] , we model rt as following a Gaussian random walk: rt+1∼N ( rt , σr2 ) . In models of decision-making , the stochastic nature of rt reflects a distribution over stimuli that the subject may have perceived in a given trial . In this case , however , the dynamics of rt reflect the subjects’ subjective prior expectations about how their belief might change in the future , after movement onset . We set rt to follow a random walk with zero drift , reflecting the fact that subjects should expect their beliefs to change , but are not biased to expect that they will change in any particular direction . Note that we do not attempt to explicitly model the actual evolution of rt through the movement in response to presented stimuli . Instead , we focus on the implications that the possibility of future evidence being accumulated during the movement will have for the choice of action at the start of the movement . The overall expected cost depends upon the ultimate belief about the target location , rt , at the end of the movement ( time T ) , i . e . rT: E[J]=pTJx ( xT−g1 ) + ( 1−pT ) Jx ( xT−g2 ) +Ju =11+e−rTJx ( xT−g1 ) +11+erTJx ( xT−g2 ) +Ju . ( 9 ) Our hypothesis is that subjects act to minimize the expected value of this cost . Solving this optimal control problem is not straightforward . Although the overall state of the system ( combining the limb state xt and belief state rt into a single vector ) has linear dynamics and Gaussian noise , the endpoint cost is a non-linear function of that state ( Equation 9 ) . This precludes usual solution methods for optimal control problems which require an endpoint cost that is quadratic in the state . We are therefore forced to rely on a dynamic programming approach [54] which severely limits the dimensionality of problems for which we can obtain a solution . We implemented a simplified model to demonstrate the key features of behavior predicted by this framework . We modeled the center-out reaching task with a single spatial dimension xt representing the angular position of the hand . We assumed that the motor command ut specified the instantaneous angular velocity of the hand , i . e . x˙t=ut . We assumed a fixed time horizon of 200ms . We set wt in Equation 9 , 8 to increase linearly from 0 at t = 0 to wMAX at t = T , to reflect the fact that achieving a given angular velocity requires a higher Cartesian velocity when the hand is further away from the start position and should therefore be more costly . The endpoint cost for each target was given by a step function with width a around the goal region . In order to determine the control policy that minimized the total expected cost ( Equation 9 ) we discretized the state space ( angular position discretization of 0 . 1° , belief discretization of 0 . 5 , and time discretization of 10ms ) and used dynamic programming [54] to find the optimal expected cost-to-go V ( x , r , t ) at each state and time . We used the value function at t = 0 to determine the optimal initial reach angle x0* for each possible initial belief r0 , i . e . We manually selected model parameters ( wMAX = . 001 , a = 1 , σr = 1 ) that yielded qualitatively similar predictions to actual subject behavior . Note that our aim here was not to provide a quantitative fit to the data but to demonstrate the feasibility of our theory to account for our observations—principally the interaction between target separation and the time course of intermediate movements . Finally , based on observed discrepancies between the data and the model ( see Results and Discussion ) , we considered the possibility that the nature of the task may have created an asymmetry between the initial goal and the post-jump goal that is not captured in the basic form of the model . We accommodated some asymmetry within the model through an asymmetric cost function in which the cost function Jx for the initial target location was scaled relative to the post-jump target location: J=α1ptJx1 ( x−g2 ) + ( 1−pt ) Jx2 ( x−g2 ) +Ju . ( 12 ) We set α1 = 10 in order to yield behavior that qualitatively matched observed behavior .
|
Two critical processes need to occur before a movement can be made: identification of the goal of the movement and selection and preparation of the motor commands that will be sent to muscles to generate the movement—in other words , what movement to make , and how to make it . It has long been thought that preparing motor commands is a time-consuming process , and theories advocating this view have pointed to instances where apparently the wrong motor commands are issued if insufficient time is available to prepare them . The usual pattern of these wayward movements is that they are intermediate between two potential targets . In this article we show how such intermediate movements can alternatively be viewed as reflecting an intelligent and deliberate decision about how to move , given uncertainty about task goals . Our theory is supported by experiments that show that intermediate movements only occur in conditions where they are advantageous . The implication of our theory is that the primary bottleneck to generating a movement is deciding on exactly what to do; deciding how to do it is rapid and automatic .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Hedging Your Bets: Intermediate Movements as Optimal Behavior in the Context of an Incomplete Decision
|
Immunocompromised individuals tend to suffer from influenza longer with more serious complications than otherwise healthy patients . Little is known about the impact of prolonged infection and the efficacy of antiviral therapy in these patients . Among all 189 influenza A virus infected immunocompromised patients admitted to ErasmusMC , 71 were hospitalized , since the start of the 2009 H1N1 pandemic . We identified 11 ( 15% ) cases with prolonged 2009 pandemic virus replication ( longer than 14 days ) , despite antiviral therapy . In 5 out of these 11 ( 45% ) cases oseltamivir resistant H275Y viruses emerged . Given the inherent difficulties in studying antiviral efficacy in immunocompromised patients , we have infected immunocompromised ferrets with either wild-type , or oseltamivir-resistant ( H275Y ) 2009 pandemic virus . All ferrets showed prolonged virus shedding . In wild-type virus infected animals treated with oseltamivir , H275Y resistant variants emerged within a week after infection . Unexpectedly , oseltamivir therapy still proved to be partially protective in animals infected with resistant virus . Immunocompromised ferrets offer an attractive alternative to study efficacy of novel antiviral therapies .
During the first 12 months of the 2009 influenza A/H1N1 virus ( pH1N1 ) pandemic an estimated 284 , 000 patients died and hospitalization rates were considerably higher than for seasonal influenza [1] . Although many severe cases were observed in otherwise healthy patients under 50 years of age , most fatal cases during this pandemic were patients belonging to the traditional high risk groups for developing severe disease , like very young children , the elderly and chronically ill patients [2] . In these patients , which in most cases have sub-optimal immune responses , influenza viruses often persists longer and tend to spread more readily into the lower respiratory tract [3] , [4] , [5] , [6] . These observations are in contrast to those in otherwise healthy patients younger than 65 years , for which influenza usually remains a self-limiting upper respiratory tract infection [7] , [8] . It has been recognized that every winter season a significant number of immunocompromised patients are admitted to a hospital with influenza [9] , [10] . For example , of the total 335 influenza A virus infected patients being diagnosed upon admission to ErasmusMC - a tertiary university hospital - between August 2009 and July 2012 , 113 ( 34% ) had an underlying condition that classified them as being immunocompromised [11] . Since immunocompromised patients are more likely to acquire influenza [12] , [13] , showing relatively high influenza-associated mortality [4] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , effective antiviral prophylaxis and treatment protocols are of crucial importance for these patients . Unfortunately , present antiviral strategies are merely based on clinical trials conducted in otherwise healthy patients [25] , since randomized clinical trials in immunocompromised patients are , for both ethical and practical reasons , difficult to perform . Furthermore , the degree and cause of a patient's immunocompromised state is variable and consequently , clinical outcome of infection may vary accordingly . Although antiviral therapy has a documented positive effect on clinical outcome in immunocompromised patients [3] , [26] , [27] , [28] , current antiviral strategies are far from satisfying . This may be explained not only by the lack of evidence based strategies adjusted for immunocompromised patients [4] , [28] , but also by the oral and inhaled administration routes which complicate administration in very young and critically ill patients [29] , [30] , . Furthermore , since physicians may not consider the diagnosis influenza initially , antiviral therapy is often initiated beyond 48 hours [34] , and accompanied by the emergence of an oseltamivir resistant virus [35] . We investigated the incidence of prolonged virus shedding and emergence of antiviral resistance by studying the course of infection of the immunocompromised patients infected with pH1N1 virus treated in our university hospital . These phenomena were studied in more detail in immunocompromised ferrets experimentally infected with pH1N1 virus , that closely mimic immunocompromised patients with influenza . These ferrets all showed prolonged virus shedding and emergence of antiviral resistance . Unexpectedly , the group of immunocompromised ferrets treated with an oseltamivir dose equivalent to a 450 mg dose ( twice daily ) in humans , had a higher survival rate than similarly untreated animals when infected with an oseltamivir resistant virus .
We quantified prolonged virus replication and resistance development in immunocompromised patients retrospectively , who were infected between August 2009 and July 2012 , and hospitalized in our tertiary hospital with influenza A virus . Among the 189 RT-qPCR confirmed influenza A virus infected patients ( median age = 22 . 3 , range = 0–81 ) , 71 ( 38% ) patients were classified as being immunocompromised ( Table 1 ) . These patients were either cancer patients on chemotherapy ( CC ) , solid organ transplant ( SOT ) or patients with an auto-immune disease on immune suppression , HIV-infected patients or patients with another cause of compromised immune status . From 37 ( 52% ) patients , no follow up samples were taken ( physician's choice ) and 18 ( 25% ) patients had cleared the virus within 14 days . Of the immunocompromised patients from whom follow up was available , 11 ( 15% ) had a pH1N1 virus infection and were shedding virus for more than 14 days , despite receiving oseltamivir or oseltamivir/zanamivir therapy ( Figure 1 ) . Prolonged replication of influenza A/H3N2 virus was found in 5 ( 7% ) cases ( data not shown ) . In 5 ( 7% ) of the immunocompromised patients with an influenza pH1N1 virus infection the oseltamivir resistance mutation H275Y in the neuraminidase was detected by RT-PCR during oseltamivir mono or oseltamivir/zanamivir combination therapy . We investigated whether the observations on prolonged virus replication and emergence of antiviral resistance in immunocompromised patients could be mimicked in ferrets receiving a cocktail of immunosuppressive drugs similar to that administered to SOT patients ( combination of mycophenolate mofetil ( MMF ) , tacrolimus and predisolone ) . First , pharmacokinetics were studied following oral administration of ferrets ( n = 4 ) given 20 mg/kg MMF , 1 mg/kg tacrolimus and 8 mg/kg prednisolone ( Table 2 and Supporting information Figure S1 ) . From the concentration over time profiles , peak ( Cmax ) levels for MPA , the active form of MMF , were 65±30 µg/mL with trough ( C12 ) levels becoming undetectable after 8 hours . Peak and trough tacrolimus levels were 86±30 ng/mL and 14±30 ng/mL respectively . We determined area under the curve ( AUC0–12 ) values of 54±14 µg·h/mL for MPA and 438±265 ng·h/mL for tacrolimus . In humans , MPA AUC0–12 values between 30–60 µg·h/mL and tacrolimus trough levels between 5–15 ng·h/mL are proposed [36] , [37] . Because of a shorter MPA half life ( t1/2 ) and high tacrolimus peak levels , we further optimized the ferret regime by administration of the cocktail every 12 hours containing half of the initial tacrolimus dose ( 0 . 5 mg/kg ) . Next , the effects of this cocktail on the ferret immune competence were studied . To this end , 6 groups of ferrets were inoculated intratracheally on day 0 with a wild type or H275Y mutant pH1N1 virus ( Figure 2A ) . Of note , both viruses had been isolated by clonal culturing from the same respiratory sample taken from an immunocompromised patient on oseltamivir therapy [38] . Three days earlier , immunosuppressive therapy was started for all animals , except for the animals in control groups 1 and 4 . On day 13 , blood was collected and influenza antibody titers were determined in the ferret sera ( Figure 2B ) . As compared to day 0 , both the animals in the control groups ( groups 1 and 4 ) and those on immunosuppressive therapy ( groups 2 , 3 , 5 and 6 ) , developed serum hemagglutination inhibiting ( HI ) antibody titers against the inoculated virus . However , antibody titers in the animals on immunosuppressive therapy were significantly lower ( P<0 . 0001 ) ( Figure 2B and S2A–D ) . As an indication of an activated immune response , body temperatures of all animals in the control groups raised from day 2 , as compared to baseline , and remained higher until day 6 ( Figure 2C and S2E–F ) . No significant change in body temperature was detected in the infected animals on immunosuppressive therapy , as is often seen in immunocompromised patients [39] . Finally , pathological examination of lymphoid tissues of animals sacrificed on day 21 revealed deficient lymphoid follicle formation in tracheobronchial lymph nodes and lymphocyte depletion in the tonsils of animals on immunosuppressive therapy ( Figure 2D ) . The animals of groups 1 , 2 and 3 had been inoculated with oseltamivir sensitive ( wild type; H275 ) and the animals in groups 4 , 5 and 6 with oseltamivir resistant virus ( mutant; H275Y ) ( Figure 2A and 3 ) . On day 2 post infection ( p . i . ) , all 6 animals were found positive by virus culture from their throat ( Figure 3 and S3 ) . On day 3 p . i . , virus was detected in the nose of five ( group 1 ) wild type inoculated animals and one ( group 4 ) animal inoculated with oseltamivir resistant virus ( Figure S3A and C ) . In the animals infected with wild type virus , which were treated with oseltamivir ( group 3 ) , replication of virus in the nose was delayed as compared to the oseltamivir treated animals inoculated with mutant virus ( group 6 ) . By day 9 , all animals in the control groups had cleared the virus . The surviving immunocompromised ferrets in the other groups were shedding virus for at least another 7 days , except for those in group 5 . These animals had cleared the virus from the nose and throat by day 13 ( Figure 3C and D ) . In the animals of groups 3 and 6 , oseltamivir therapy ( 10 mg/kg twice daily ) was started 24 hours after infection and continued for 21 days ( Table 2 and S2C ) . Until day 7 , when still 6 animals were alive in each group , the viral loads in the oseltamivir treated animals infected with wild type virus was significantly lower on days 3 , 4 and 5 in the nose and on days 3 to 7 in the throat ( P<0 . 001 ) . Such difference was not observed in the animals infected with oseltamivir resistant virus ( Figure 3C and D ) . Emergence of oseltamivir resistance was studied in the animals infected with wild type virus ( groups 1 , 2 and 3 ) using an RT-PCR assay specifically detecting the H275Y oseltamivir resistance mutation ( Figure 4 ) [40] . From day 8 onward , the H275Y mutation emerged in the virus population of all oseltamivir treated animals in both the nose and throat . The H275Y mutant became the major genotype 2 or 3 days later . No oseltamivir resistant viruses were detected in the immunocompromised wild-type virus infected animals that did not receive oseltamivir . Unexpectedly , oseltamivir treatment appeared to have a positive effect on the proportion of surviving immunocompromised animals and animal body weight loss ( Figure 5 ) . Without oseltamivir treatment , half of the wild type infected group of animals had succumbed by day 11 and none of the remaining animals survived the complete 21 day experiment ( 0/6 ) ( Figure 5A ) . However , when oseltamivir therapy was started 24 hours after infection , half of the animals were still alive at day 16 and these remaining animals all survived until day 21 ( 3/6 ) . Of the immunocompromised ferrets , which were infected with the resistant virus ( Figure 5B ) , half of the untreated animals had died before day 13 , but two of the untreated animals and four of the treated animals survived the complete 21 day experiment . In addition , oseltamivir treatment appeared to have a protective effect on body weight loss of the immunocompromised animals ( Figure 5C and D ) . This trend was observed for both wild type and oseltamivir resistant virus infected groups , although statistical significant differences was found only for the wild type infected animals on day 12 when three animals were still alive ( groups 2 versus 3; P = 0 . 03 ) , and not for the oseltamivir resistant virus infected animals ( groups 5 versus 6; P = 0 . 07 ) .
Here we show that prolonged influenza virus shedding and the emergence of oseltamivir resistance are two phenomena commonly observed in immunocompromised patients during antiviral therapy . As resistance development is low ( 19 out of 874 ( 2 . 2% ) ) treated influenza virus infected patients [41] , the incidence in immunocompromised patients appears to be considerably higher . Of all 71 immunocompromised patients infected with an influenza A virus in our hospital , at least 16 ( 23% ) ( 11 pH1N1 and 5 H3N2 ) showed virus persistence for longer than 2 weeks with 5 of them harbouring oseltamivir resistant virus ( 5/16 ) ( 31% ) . Because 52% of the included patients were sent home before a virus negative follow-up sample had marked the end of infection , final conclusions on the true incidence of prolonged virus shedding and development of oseltamivir resistant virus cannot be made . However , this observation is in line with previously observed high resistance levels ( 33% ) among paediatric cancer patients [35] , and stresses the importance of a thorough evaluation of the currently used antiviral therapies in immunocompromised patients . Since for both logistic and ethical reasons randomized studies are difficult to perform in often critically ill immunocompromised patients , we showed that prolonged influenza virus replication is also a common feature in immunocompromised ferrets . We observed an absence of a rise in body temperature , reduction of lymphocyte proliferation and follicle formation in ferret lymphoid tissues , which are also hallmarks in immunocompromised patients [42] and found a significant reduction of influenza virus specific antibodies in serum . In the 2007/2008 H1N1 virus season an H275Y oseltamivir resistant mutant emerged , which had completely overtaken the circulating virus population by the end of 2008 [43] . This introduction of the H275Y mutation disqualified oseltamivir as the first line antiviral drug . It might happen again with pH1N1 virus if it would also harbour the H275Y mutation without loss of viral fitness . Recent reports on clusters of transmitted pH1N1 H275Y mutant viruses are the first indication that this may not be an unlikely scenario [44] , [45] . Early studies on H275Y pH1N1 viral fitness were performed on viruses isolated shortly after the start of the 2009 pandemic . These viruses were found to be at least slightly compromised in their pathogenicity and replication capacity [46] , [47] , [48] , [49] . We also used an early H275Y virus isolate in our experiment [38] . If virus replication is not tempered by adequate immune responses , duration of virus shedding will eventually be restricted by an exhaustion of susceptible host target cells . This then may explain why replication of this apparently less pathogenic virus lasted longest in our immunocompromised ferrets ( Figure 3D ) . Priority should now be given to study the overall fitness of these recent pH1N1 viruses that appear not to be affected by the H275Y change . The use of our immunocompromised ferrets seems to be a very suitable strategy for this purpose , because subtle fitness costs may be amplified in these animals [50] . In our study , immunocompromised ferrets were treated with an oseltamivir dose of 10 mg/kg twice daily . This dose is equivalent to a much higher human dose than the currently recommended dose of 75 mg twice daily [51] . We observed that , for animals infected with either wild type or H275Y mutant virus , high dose oseltamivir treatment was still beneficial . Currently the World Health Organisation recommends switching to zanamivir when dealing with such a resistant virus [52] . However , in the light of our data , discontinuation of oseltamivir therapy may not always be the best strategy . An increased oseltamivir dose for the treatment of immunocompromised patients may be considered as a future antiviral strategy . However , obviously this will need further clinical investigation first . Of note , an increased dose of oseltamivir is well tolerated in humans [53] . We observed lower mortality in the treated animals infected with H275Y mutant virus without an observed difference in virus titers in the upper respiratory tract . In these ferrets , oseltamivir carboxylate plasma levels peaked ( Cmax ) from 3052 ng/ml in 4 hours to 833 ng/ml 12 hours after administration ( Table 2 and Figure S1C ) . These levels were still about 100 and 30 times higher than the 50% inhibitor concentration of an H275Y mutant virus , which is roughly 30 ng/ml ( ∼100 nM ) [54] . It is therefore plausible that oseltamivir therapy reduced H275Y mutant virus titer in the lungs , but not in the upper respiratory tract . This had been observed before and could explain the lower mortality rates in the treated animals [55] . It will therefore be of interest to study penetration of oseltamivir throughout the ferret respiratory tract in more detail , which may be expected to have a direct impact on the effectiveness of the presently recommended human dose in immunocompromised ferrets . In conclusion , both our clinical observations and ferret experiments show that viral clearance cannot be achieved in the immunocompromised host solely by the use of currently used antiviral therapy . Our immunocompromised ferrets may therefore be an excellent alternative to evaluate and explore novel therapeutic and immunization strategies for immunocompromised patients .
We identified patients hospitalized in the Erasmus Medical Centre ( ErasmusMC ) , a large ( >40 , 000 admissions in 2011 ) tertiary university hospital in the Netherlands , with an influenza A virus positive respiratory specimen taken between August 2009 through July 2012 . Patients had a prolonged virus infection if the virus could still be detected after 14 days . Virological data , patient immune status and administration of antiviral therapy were obtained by reviewing medical records . Immunosuppression was defined as any of the following: receipt of treatment for any cancer , the use of any immunosuppressive medication to prevent transplant rejection or for management of pulmonary or autoimmune conditions , premature birth and below gestational age or a diagnosis of AIDS . Influenza A virus and the H275Y oseltamivir resistance mutation were detected by reverse transcriptase RT-PCR assays . These assays have been described previously [40] , [56] . Informed consent was waived because patient inclusion was performed retrospectively and data were anonymously stored as agreed by the hospital medical ethical board ( MEC-2012-463 ) Two biologically cloned pH1N1 influenza viruses were used in this study . Both viruses were isolated from an oseltamivir treated patient during the first wave of the pandemic in October 2009 . Wild type influenza virus A/Netherlands/1715b/2009 ( genbank ID code: CY065810 ) and H275Y mutant virus were isolated from the original quasispecies by co-cultivation of a respiratory sample in a Madin-Darby Canine Kidney ( MDCK ) cell culture in a single passage [38] . Biological clones were then obtained by 3 additional MDCK passages performed under limiting virus concentrations . As determined by full-genome Sanger sequencing , the mutant virus contained , additional to mutation H275Y in the neuraminidase , an L233M mutation in PB2 and a V541L mutation in HA . Animal were housed and experiments were conducted in strict compliance with European guidelines ( EU directive on animal testing 86/609/EEC ) and Dutch legislation ( Experiments on Animals Act , 1997 ) . The protocol was approved by the independent animal experimentation ethical review committee from the Netherlands Vaccine Institute ( permit number 200900201 ) . All experiments were performed under animal bio-safety level 3 conditions . Animal welfare was observed on a daily basis , and all animal handling was performed under light anaesthesia using a mixture of ketamine and medetomidine to minimize animal suffering . After handling atipamezole was administered to antagonize the effect of medetomidine . All ferrets were eleven-month-old purpose-bred males ( body weights between 1562 and 2362 g ) and were seronegative for Aleutian disease virus and circulating influenza virus ( sub ) types A/H1N1 , A/H3N2 and B virus . The animals were maintained in standard housing and were transferred to negatively pressured glove boxed on the day immunosuppressive therapy was started . They were provided food ad libidum with commercial food pellets and water . Approximately three to four weeks prior to the experiment a temperature logger ( DST micro-T ultra small temperature logger; Star-Oddi , Reykjavik , Iceland ) was placed in the peritoneal cavity of the animals . This device recorded the body temperature of the animals every 10 minutes . From day −8 to day −4 , an average baseline temperature was recorded for each group of 6 animals . The following immunosuppressive drugs were used to suppress the immune system of ferrets: Mycophenolate mofetil ( MMF ) ( CellCept , Roche , The Netherlands ) powder for infusion , tacrolimus concentrate ( 5 mg/ml ) for infusion ( Prograft , Astellas Pharma BV , Leiderdorp , The Netherlands ) and prednisolone sodium phosphate ( 5 mg/ml ) oral solution ( Hospital Pharmacy , UMCN St Radboud , Nijmegen , The Netherlands ) . All ferrets received an antibiotic prophylaxis of amoxicillin supplemented with 62 . 5 mg clavulanic acid ( 250/62 . 5 mg per 5 ml ) oral suspension ( Pharmachemie BV , Haarlem , The Netherlands ) . Prodrug oseltamivir phosphate , used in the ferret experiments , was kindly provided by Hoffman-La Roche LtD . ( Tamiflu , Basel , Switzerland ) . Oseltamivir standards for mass spectrometry , oseltamivir phosphate ( OS ) , oseltamivir-d3 ( OS-d3 ) , oseltamivir carboxylate ( OSC ) and oseltamivir carboxylate-d3 ( OSC-d3 ) were purchased from Toronto Research Chemicals ( Toronto , Canada ) . Mycophenolic acid ( MPA ) standard and internal standards ( MPAC ) were purchased from Sigma Aldrich ( Zwijndrecht , the Netherlands ) and from Hoffman-La Roche LtD . respectively . The tacrolimus standards and internal standards ( Ascomycin ) were purchased from , respectively , Chromsystems and Sigma Aldrich . ULC/MS grade methanol and water containing 0 . 1% formic acid were obtained from Biosolve ( Valkenswaard , the Netherlands ) . Trichloro acetic acid ( TCA ) and formic acid ( >96% , HCOOH ) were obtained from Sigma Aldrich and both were from ACS reagent quality . A schematic of the ferret experiment is presented in Figure 2A . Shortly before gavage , drugs were prepared as follows: MMF was dissolved in a 5% glucose solution ( Baxter , Unterschleisheim , Germany ) to 33 mg/ml . Amoxicillin/clavulanic acid was diluted 5 times in water to obtain a suspension containing 50/12 . 5 mg/ml amoxicillin/clavulanic acid . Oseltamivir phosphate was dissolved in 5% glucose to 20 mg/ml . These intermediate preparations and the ready-to-use tacrolimus and prednisolone solutions were then used to dose the animals orally and twice daily , as follows: four days before infection , all 6 groups received 10/2 . 5 mg/kg amoxicillin/clavulanic acid diluted in 5% glucose to a final administration volume of 4 ml/kg . One day later , antibiotic prophylaxis was supplemented to the regimes of groups 2 , 3 , 5 and 6 with 20 mg/kg MMF , 0 . 5 mg/kg tacrolimus and 8 mg/kg prednisolone retaining the administration volume of 4 ml/kg . On day 1 , 24 hours after infection , therapy of group 3 and 6 was further supplemented with 10 mg/kg oseltamivir phosphate for the remaining 20 days of the experiment . The dose of prednisolone was halved every 7 days from 8 mg/kg in the first to 1 mg/kg in the last week . On day 0 , three days after start of immunosuppressive therapy , ferrets were intratracheally infected with 1×104 TCID50 of wild type ( groups 1 , 2 and 3 ) or mutant virus ( groups 4 , 5 and 6 ) . Each day , pharyngeal and nasal swabs were collected just before administration of the drugs . Swabs were resuspended in 3 ml virus transport medium [57] , and aliquots were made and used either directly for online detection of viral RNA by RT-PCR or stored at −80°C for retrospective virus titration . An electron microscopy counted influenza A virus stock was run in parallel to convert RT-PCR cycle threshold ( CT ) values into a viral particle count . Blood samples for serum and plasma were collected on day 13 after infection . Influenza antibody titers were determined as described previously [58] . Oseltamivir and MMF plasma levels and whole blood tacrolimus levels were determined in a pharmacokinetic pilot study . For 4 days , four groups of ferrets ( n = 4 ) received MMF , tacrolimus , or oseltamivir in combination with amoxicillin/clavulanic acid and prednisolone or as the complete cocktail . On day 4 , blood was collected from these animals after 0 , 10 , 20 , 30 minutes and 1 , 2 , 4 , 5 , 8 and 12 hours after administration of the drugs in order to determine MMF and tacrolimus levels , as described previously [59 , unpublished data] . Ferret oseltamivir plasma levels were determined as described previously with some modifications [60] . Calibrators used for the determination of the calibration curve of OS and OSC were prepared from one single stock solution in plasma each containing 50 µg/ml . Calibrators were then prepared by serial dilutions using drug-free plasma . Calibrators for OS and OSC yielded following concentrations: 5000 , 1500 , 750 , 500 , 250 , 150 , 50 , 12 . 5 , 2 . 5 and 0 ng/ml ( blank ) . Aliquots of 50 µl were spiked with 5 µl internal standard solution containing 50 µg/ml OS-d3 and OSC-d3 . To calibrators and ferret plasma ( K2EDTA ) samples , 5 µl of a 50% TCA solution ( w/v ) was added for plasma protein precipitation . Precipitated plasma proteins were removed by centrifugation for 10 minutes at 2000×g at ambient temperature . De-proteinized plasma samples ( 20 µl ) were 2 . 5 times diluted with ultrapure water and 40 µl of the diluted samples were injected by an auto-sampler ( kept at 4°C ) into the liquid chromatography mass spectrometry ( LC-MS ) system . The calibration curves for OS and OSC showed a linear relationship between the SRM peak area of ratios between OS/OS-d3 and OSC/OSC-d3 , respectively ( OS; r2 = 0 . 9966 and OSC; r2 = 0 . 9970 ) . The LLOQ and LOD were determined according to FDA guidelines and were , respectively , 2 . 5 and 1 . 0 ng/ml for both OS and OSC . The LC-MS system used was an 4000 API triple quadruple mass spectrometer containing a Turbo V electron spray ion source ( ESI ) ( AB Sciex , Concord , Canada ) operating in the positive ionization mode using selected reaction monitoring ( SRM ) in combination with a Dionex Ultimate 3000 UHPLC system ( Amsterdam , the Netherlands ) using an Ascentis Express RP-C18 column ( 100×2 . 1 , 2 . 7 µm , Supelco , Munich ) applying a gradient separation at 30°C . Samples for histological examination of the tonsils and tracheobronchial lymph nodes were taken to evaluate the immune status and were stored in 10% neutral-buffered formalin . Subsequently , these were routinely processed and embedded in paraffin wax , sectioned at 4 µm and stained with haematoxylin and eosin ( HE ) for examination by light microscopy . Data are reported as mean ± standard error of the mean ( s . e . m ) . The P values for comparison of influenza HI antibody titers in figure 2B , virus titers in figure 3 and body weight loss in figure 5C were calculated using Mann-Whitney U test only if at least three animals were remaining in each experimental group . P≤0 . 05 was considered significant .
|
Immunocompromised patients , such as transplant recipients on immune suppressive therapy , are a substantial and gradually expanding patient group . Upon influenza virus infection , these patients clear the virus less efficiently and are more likely to develop severe pneumonia than immunocompetent individuals . Existing antiviral strategies are far from satisfactory for this patient group , as they show limited effectiveness with frequent emergence of antiviral resistance . For ethical and practical reasons antiviral efficacy studies are hard to conduct in these patients . Therefore , we developed an immunocompromised ferret , mimicking an immune suppressive regimen used for solid organ transplant recipients . Upon infection with 2009 pandemic influenza A/H1N1 virus these animals , like immunocompromised patients , develop severe respiratory disease with prolonged virus excretion . Interestingly , all immunocompromised ferrets on oseltamivir therapy excreted oseltamivir resistant viruses ( H275Y ) within one week after start of treatment . Furthermore , high dose oseltamivir therapy still proved to be partially effective against these oseltamivir resistant viruses . These immunocompromised ferrets provide a useful tool in the development of novel antiviral approaches for immunocompromised patients suffering from influenza .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"influenza",
"medicine",
"infectious",
"diseases",
"viral",
"diseases"
] |
2013
|
Prolonged Influenza Virus Shedding and Emergence of Antiviral Resistance in Immunocompromised Patients and Ferrets
|
Mutations in or dys-regulation of the TDP-43 gene have been associated with TDP-43 proteinopathy , a spectrum of neurodegenerative diseases including Frontotemporal Lobar Degeneration ( FTLD ) and Amyotrophic Lateral Sclerosis ( ALS ) . The underlying molecular and cellular defects , however , remain unclear . Here , we report a systematic study combining analyses of patient brain samples with cellular and animal models for TDP-43 proteinopathy . Electron microscopy ( EM ) analyses of patient samples revealed prominent mitochondrial impairment , including abnormal cristae and a loss of cristae; these ultrastructural changes were consistently observed in both cellular and animal models of TDP-43 proteinopathy . In these models , increased TDP-43 expression induced mitochondrial dysfunction , including decreased mitochondrial membrane potential and elevated production of reactive oxygen species ( ROS ) . TDP-43 expression suppressed mitochondrial complex I activity and reduced mitochondrial ATP synthesis . Importantly , TDP-43 activated the mitochondrial unfolded protein response ( UPRmt ) in both cellular and animal models . Down-regulating mitochondrial protease LonP1 increased mitochondrial TDP-43 levels and exacerbated TDP-43-induced mitochondrial damage as well as neurodegeneration . Together , our results demonstrate that TDP-43 induced mitochondrial impairment is a critical aspect in TDP-43 proteinopathy . Our work has not only uncovered a previously unknown role of LonP1 in regulating mitochondrial TDP-43 levels , but also advanced our understanding of the pathogenic mechanisms for TDP-43 proteinopathy . Our study suggests that blocking or reversing mitochondrial damage may provide a potential therapeutic approach to these devastating diseases .
TDP-43 proteinopathy is characterized by the presence of TDP-43 immunoreactive inclusion bodies in the affected tissues . Clinically , TDP-43 proteinopathy manifests as a spectrum of different neurodegenerative diseases , ranging from dementia ( especially fronto-temporal lobar degeneration , FTLD ) and motor neuron disease ( MND ) to traumatic brain injuries [1–4] . FTLD is a prevalent form of dementia with progressive atrophy of the frontal and/or temporal cortices [5–7] . Amyotrophic Lateral Sclerosis ( ALS ) , a common form of MND , is characterized by a progressive loss of upper and lower motor neurons [8–10] . TDP-43 associated neurodegenerative diseases are clinically and genetically heterogeneous . A significant fraction of ALS patients exhibit cognitive impairment [11 , 12]; and ~15% of FTLD patients also show locomotor defects and meet the diagnostic criteria for ALS [12 , 13] . TDP-43-positive lesions are the most frequently identified pathology among FTLD and ALS cases and also present in ~50% AD samples [14–16] . However , the pathogenic mechanisms underlying TDP-43 proteinopathy remain unclear . Mitochondrial damage is associated with a range of neurodegenerative diseases , including Alzheimer’s disease ( AD ) , Parkinson’s disease ( PD ) and MNDs [17–19] . Mitochondrial changes have been detected in cellular and animal models for TDP-43 proteinopathy [16 , 20–27] . It was recently reported that suppressing mitochondrial localization of TDP-43 blocked TDP-43 neurotoxicity [28] . However , mitochondrial morphological changes have not yet been characterized in patient samples , and the effects of TDP-43 on mitochondrial function remain controversial [27–29] . To maintain mitochondrial homeostasis , cells sense and respond to mitochondrial damage by activating a program known as the mitochondrial unfolded protein response ( UPRmt ) , which includes induction of mitochondrial chaperones assisting in proper protein folding , and of proteases promoting clearance of misfolded proteins [30–32] . Recent studies suggest a role of UPRmt in Alzheimer’s disease , Parkinson’s disease and ALS-SOD [33–35] . However , the role of UPRmt in TDP-43 proteinopathy has not been reported . Here , we present a systematic study of TDP-43 proteinopathy combining cellular and animal models with patient samples . Analyses using electron microscopy ( EM ) reveal prominent mitochondrial damage in brain tissues from TDP-43 proteinopathy patients . These mitochondrial impairments include swollen and degenerated cristae or a complete loss of cristae . Similar mitochondrial cristae changes are detected in our cellular and animal models . Consistently , mitochondrial functional impairments are observed , including decreased mitochondrial membrane potential , reduced mitochondrial ATP synthesis and elevated mitochondrial ROS production . Our data show that mitochondrial impairment induced by TDP-43 is an early event , preceding cell death . Furthermore , induced TDP-43 expression leads to the activation of UPRmt in both cellular and fly models for TDP-43 proteinopathy . LonP1 , one of the key mitochondrial proteases in UPRmt , plays an important role in the degradation of mitochondrial TDP-43 . Consistent with the mRNA changes of LonP1 in cellular and fly models , LonP1 protein levels are increased in a fraction of the brain samples of patients affected by FTLD-TDP . Importantly , down-regulation of LonP1 in TDP-43 expressing flies not only induces more severe mitochondrial damage , but also advances disease onset and exacerbates the neurodegeneration phenotype in the animal model . These results suggest that LonP1 plays a protective role against TDP-43-induced neurotoxicity , especially at an early stage of the disease . Together , our data demonstrate that mitochondrial damage is a critical feature of TDP-43 proteinopathy and suggest that protecting mitochondria may have therapeutic potential .
To investigate the role of mitochondria in TDP-43 proteinopathy , we examined mitochondrial morphology in brain samples from patients using transmission electron microscopy ( TEM ) and immuno-electron microscopy ( IEM ) . Following resin-embedding to obtain clear images of mitochondria , we analyzed brain samples from five patients with the pathological diagnosis of either FTLD-TDP or ALS-FTLD-TDP , together with the samples from three control subjects without any TDP-43 pathology ( for details , see S1 Table ) . The majority of mitochondria in the control brain tissues showed normal morphology , with intact mitochondrial membrane and well-organized cristae ( left panels in Fig 1A ) . In contrast , more than 80% of mitochondria in the patient brains exhibited significant mitochondrial damage , especially abnormal cristae structure ( Fig 1 ) . Abnormal mitochondrial cristae presented as either a “vesicular” type with swollen cristae ( marked by the arrows in the middle panels of Fig 1A; as “Swollen” in Fig 1A and Fig 1B ) or a “degenerated” type with a partial to complete loss of cristae ( the right panels of Fig 1A; as “Degenerated” in Fig 1A and Fig 1B ) . Damaged mitochondria were significantly increased in all 5 FTLD-TDP brains as compared with the control brains ( Fig 1B ) . IEM analyses of the brain tissues using a specific anti-TDP-43 antibody revealed that TDP-43 immunostaining signals were clearly detected inside mitochondria in the brain samples of both control and FTLD-TDP patients ( marked by arrows in Fig 1C; with enlarged views in insets ) , demonstrating that the endogenous TDP-43 protein is localized inside mitochondria , consistent with a recent report [28] . Interestingly , electron-dense TDP-43 positive protein aggregates were detected inside ~1% of mitochondria in FTLD-TDP patient samples ( arrowheads in Fig 1D ) , but were not detected in any control samples . These EM analyses demonstrate that mitochondrial damage is a prominent feature in the pathology of brain tissues of TDP-43 proteinopathy patients . To investigate the effects of TDP-43 on mitochondrial morphology and function in living cells , we established tetracycline ( Tet ) inducible HEK293 cell lines , expressing either wild type ( Wt ) or an ALS-associated TDP-43 mutant ( A315T ) . Following Tet-induction for 24 hr , total cell lysates , cytoplasmic fractions and purified mitochondrial preparations were examined by Western blotting . The purity of the mitochondrial preparation was confirmed by the detection of mitochondrial protein TOM20 and the absence of the cytoplasmic GAPDH protein . Consistent with the IEM data from the human brain samples , the endogenous TDP-43 as well as the exogenously expressed Wt or ALS-mutant ( A315T ) TDP-43 were detected in purified mitochondria ( Fig 2A; for a longer exposure , see S1A Fig ) , supporting the mitochondrial localization of the TDP-43 protein . Consistent with previous studies [36 , 37] , expression of the exogenous TDP-43 suppressed expression of the endogenous TDP-43 ( marked by “Endo” in Fig 2A ) . We next performed EM analyses of HEK293 cells expressing TDP-43 to characterize mitochondrial changes . In control cells , the vast majority of mitochondria exhibited normal morphology , with well-organized cristae ( Fig 2B ) . However , in cells expressing the A315T-mutant TDP-43 , severe mitochondrial damage was detected , with significantly reduced mitochondrial sizes and impaired mitochondrial cristae 24 hr post-induction . When Wt TDP-43 was expressed , similar mitochondrial damage was also detected , although to a lesser extent ( Fig 2B; see S1B and S1C Fig ) . These data indicate that expression of Wt or ALS-mutant TDP-43 protein leads to mitochondrial damage in cultured cells . To examine the temporal relationship between TDP-43-induced mitochondrial damage and cell death , we carried out a series of experiments using the Tet-inducible cells expressing Wt- or A315T-mutant TDP-43 proteins at different time points ( 0 , 24 or 36 hr ) following induction of TDP-43 expression . We first measured mitochondrial membrane potential , ROS production and ATP synthesis ( Fig 2C–2E ) . Cells were stained with JC1 ( a mitochondrial membrane potential indicator ) , or mitoSOX red fluorescent dye ( a mitochondrial ROS indicator ) , and then analyzed by flow cytometry . Mitochondrial membrane potential began to show a reduction at 24 hr post-induction in cells expressing A315T-mutant TDP-43; and by 36 hr post-induction , mitochondrial membrane potential reduction was detected in cells expressing either Wt or A315T-mutant TDP-43 ( Fig 2C ) . By 36 hr following the induction of expression of Wt or A315T-mutant TDP-43 , the mitochondrial ROS level was significantly increased ( Fig 2D ) . Total cellular ATP levels and mitochondrial ATP synthesis were measured following published protocols [38 , 39] . Thirty-six hr following induction of TDP-43 expression ( Fig 2E ) , total cellular ATP level did not change ( S2A Fig ) . However , mitochondrial ATP synthesis at this time point was significantly reduced in cells expressing either Wt or A315T-mutant TDP-43 ( with ~20% and ~25% decrease in the Wt and A315T-groups respectively ) , as compared with the control group ( Fig 2E ) . To understand the mechanism by which increased TDP-43 expression suppressed mitochondrial ATP synthesis , we examined which mitochondrial complexes ( complex I through V ) in oxidative phosphorylation were affected . Interestingly , complex I activity was significantly reduced by 24 hr following induction of either Wt or A315T mutant TDP-43 ( Fig 2F ) ; complex IV activity was also reduced by the expression of A315T-mutant TDP-43 ( Fig 2I ) . In contrast , the activities of complexes II , III ( Fig 2G , Fig 2H ) and complex V ( Fig 2J ) were unaffected . These data indicate that increased TDP-43 expression impairs mitochondrial ATP synthesis , possibly by suppression of mitochondrial complex I . TDP-43-induced reduction in the complex I activity was not likely the result of overall suppression of complex I genes by TDP-43 , because quantitative PCR analyses of a number of complex I genes did not show a general reduction in the expression of these genes ( see S2B Fig ) . Future experiments are necessary to elucidate the mechanism by which TDP-43 suppresses the activity of complex I . To examine cell death , cells were stained with an Annexin V-FITC/PI ( propidium idodide ) kit followed by flow cytometry analyses ( Fig 3 ) . Annexin V-positive/PI-negative , Annexin V-negative/PI-positive or Annexin V-positive/PI-positive staining indicates apoptosis , necroptosis or late apoptosis/necroptosis , respectively . Up to 36 hr post-induction , Annexin V-negative/PI-positive or PI/Annexin V double-postive cell populations did not show significant changes in TDP-43 expressing cells compared to the control group . Cells expressing A315T-mutant TDP-43 showed significantly increased cell death only after 36 hr post-induction of TDP-43 expression ( ~2% cells showing Annexin V-positive/PI-negative staining; compared with ~0 . 5% in the control cells ) ; whereas cells expressing wild type TDP-43 showed a less dramatic increase in cell death , also only after 36 hr post-induction ( Fig 3A , Fig 3B ) . It should be noted that at this time point only a small fraction ( <5%; estimated by biochemical fractionation ) of the total TDP-43 was detected in purified mitochondria ( possibly due to the efficient degradation of mitochondrial TDP-43 before the disruption of the balanced mitochondrial proteostasis ) . Because mitochondrial dysfunction was observed at the 24 hr time point , these results demonstrate that TDP-43-induced mitochondrial dysfunction is an early event preceding cell death , suggesting that mitochondrial impairment may contribute to TDP-43 cytotoxicity . To investigate TDP-43-induced mitochondrial damage in vivo , we examined transgenic flies expressing either Wt or A315T-mutant TDP-43 reported in our previous studies [40–42] . Transmission EM analyses of control fly eyes in 3-day old adult animals revealed intact ommatidial structures with seven rhabdomeres , whereas expression of either Wt or A315T-mutant TDP-43 in fly eyes led to severe ommatidial defects , often with a complete loss of rhabdomeres ( Fig 4A ) . Mitochondria in fly eyes expressing either Wt or A315T-mutant TDP-43 showed a significant decrease in size when compared with control flies ( Fig 4 ) . Importantly , more than 85% of mitochondria in the photoreceptors expressing Wt or ALS-mutant TDP-43 exhibited swollen or vesicular cristae , whereas only ~5% of mitochondria in the control group showed damage ( Fig 4A and Fig 4C ) . In this setting , TDP-43 was expressed in photoreceptors under a strong GMR-Gal4 driver from an early stage , leading to rapid and severe mitochondrial damage . By the time of EM examination , >85% mitochondria showed damage in both Wt and A315T-mutant groups , not allowing us to detect differences between the two groups . It is remarkable that mitochondria in fly photoreceptors expressing either Wt or A315T-mutant TDP-43 showed similar mitochondrial cristae damage as those detected in the brain tissues of TDP-43 proteinopathy patients ( see Fig 1A ) . To examine whether the results observed were due to developmental defect ( s ) , we used a system in which TDP-43 expression was induced only in adulthood using a temperature-sensitive tubulin-Gal80ts promoter with the GMR-Gal4 photoreceptor-specific driver or the Elav-Gal4 pan-neuronal driver ( see S3 Fig ) . In this system , flies expressing A315T-mutant TDP-43 in photoreceptors following heat shock induction at the adult stage indeed exhibited progressive mitochondrial damage and retinal degeneration ( S3 Fig ) . The mitochondrion is a major source for the production of reactive oxygen species ( ROS ) [43] . Mitochondrial dysfunction can lead to the accumulation of ROS [44] . Furthermore , excessive ROS production affects neuronal survival and function [45 , 46] . We therefore examined whether TDP-43 expression affected mitochondrial ROS production in vivo using transgenic flies expressing TDP-43 in motor neurons . A fly line expressing mito-roGFP-Grx1 , an in vivo mitochondrial ROS reporter [47] , was crossed with either control RFP or TDP-43-RFP expressing flies . Ratiometric fluorescence confocal imaging was carried out to measure mitochondrial ROS levels in motor neurons expressing control ( RFP ) or TDP-43-RFP using a previously published protocol [47] . Significantly elevated mitochondrial ROS levels were detected in motor neurons expressing either Wt- or A315T-mutant TDP-43 as compared with the control group ( see S4 Fig ) , indicating that TDP-43 expression in motor neurons resulted in mitochondrial dysfunction . Our results presented above showed that increased TDP-43 expression led to mitochondrial cristae damage , reduced activities of mitochondrial OXPHOS complex I and IV , as well as decreased mitochondrial ATP synthesis . In addition , TDP-43 immuno-reactive aggregates were detected inside mitochondria of FTLD-TDP patient brain samples . These observations prompted us to examine if TDP-43 activated the mitochondrial unfolded protein response ( UPRmt ) . Using our inducible HEK293 cells expressing Wt or A315T-mutant TDP-43 , we examined mRNA levels of known genes critical for UPRmt , including ATF5 , HSPA9 ( mtHSP70 ) , HSP60 and LonP1 . Quantitative RT-PCR analyses revealed that by 48 hr post-induction of TDP-43 expression , mRNA levels of ATF5 and LonP1 were increased , and that by 72 hr post-induction , mRNA levels of ATF5 , HSPA9 , HSP60 and LonP1 were all increased in cells expressing either Wt- or A315T-mutant TDP-43 ( Fig 5A ) . To investigate whether TDP-43 expression activated UPRmt in vivo , we induced TDP-43 expression in transgenic flies at the adult stage by heat shock using Elav-Gal4 pan-neuronal driver containing a temperature-sensitive tubulin-Gal80ts element , Elav-Gal4/tubulin-Gal80ts driver [48] ( see S3A Fig ) . At day 15 and day 30 after induction of TDP-43 expression , fly heads were collected for qRT-PCR analyses ( Fig 5B ) . In female flies , by day 15 post-induction , HSP60A mRNA level was significantly increased in A315T-mutant expressing flies; and by day 30 post-induction , mRNA levels of HSP60A , Hsc-70-5 , CG5045 ( encoding ClpP ) and two isoforms of Lon ( the Drosophila ortholog of mammalian LonP1 ) were increased in TDP-43 expressing flies , especially those expressing A315T-mutant TDP-43 . In male flies , the mRNA levels of all four genes were increased in flies expressing A315T-mutant TDP-43 , and to a lesser extent in flies expressing Wt TDP-43 , at 15 day post-induction . However , increased expression of only HSP60A , but not other three genes , was detected by day 30 post-induction of TDP-43 expression ( Fig 5B ) . These data support that UPRmt is activated by TDP-43 expression in the fly model for TDP-43 proteinopathy . Future studies are necessary to understand the significance of and mechanisms underlying the gender different responses observed in TDP-43 flies . We next examined if protein levels of these UPRmt genes are altered in TDP-43 proteinopathy patient samples using a panel of brain samples characterized previously [40] . Western blotting analyses indicate that the average level of LonP1 protein in TDP-43 proteinopathy patient brains was higher than that in the control brains ( Fig 5C , Fig 5D ) . This is consistent with the possibility that UPRmt may be activated in a subset of TDP-43 proteinopathy patient brains . There was no significant difference between patient and control samples in the protein levels of either HSPA9 or HSP60 . Together , these results support the notion that UPRmt is activated in cellular and animal models of TDP-43 proteinopathy as well as a subset of FTLD-TDP patient brains . We further examined the relationship between LonP1 and TDP-43 . LonP1 is a major mitochondrial matrix protease and a member of the evolutionarily conserved superfamily of AAA+ ATPases . LonP1 plays a critical role in mitochondrial protein quality control by preferentially degrading misfolded or oxidized proteins [49] . We first tested whether TDP-43 interacted with LonP1 in a co-immunoprecipitation assay using an anti-Myc antibody in cells expressing Myc-tagged TDP-43 . LonP1 was detected among immunoprecipitated proteins from cell lysates expressing either Wt or A315T-mutant TDP-43 , but not the control lysates ( Fig 6A ) , suggesting that LonP1 interacted with TDP-43 . Further co-immunoprecipitation experiments using a specific TDP-43 antibody showed that the endogenous TDP-43 and LonP1 proteins interacted with each other ( Fig 6B ) . To examine if TDP-43 protein co-localized with LonP1 inside mitochondria , we performed immuno-electron microscopy ( IEM ) using FTLD-TDP brain samples . In these brain samples , TDP-43 immuno-staining signals ( 6-nm gold particles ) were detected in close proximity to LonP1 immuno-staining signals ( 15-nm gold particles ) ( marked by the arrowheads in Fig 6C ) . A number of studies suggest the roles of proteasome and autophagy in degradation of TDP-43 [50–57] . We then tested the effects of a proteasome inhibitor ( MG132 , MG ) and an autophagy inhibitor ( 3-methyladenine , MA ) , and compared them with that of a LonP1 inhibitor [2-cyano-3 , 12-dioxooleana-1 , 9-dien-28-oicacid , CDDO ( CD ) [58] ] in the inducible TDP-43 expressing cells . Interestingly , neither the proteasome inhibitor ( MG ) nor the autophagy inhibitor ( MA ) had an effect on cell viability following induction of TDP-43 expression , whereas the LonP1 inhibitor ( CD ) specifically reduced the viability of cells expressing either Wt or A315T-mutant TDP-43 and enhanced TDP-43 cytotoxicity ( see S5A and S5B Fig ) . At the concentrations used , none of these drugs affected viability of the control cells , indicating that the effect of the LonP1 inhibitor was specifically associated with TDP-43 expression ( Fig 7A; S5A and S5B Fig ) . We next examined whether increasing LonP1 expression suppressed TDP-43 cytotoxicity . Control ( Ctr ) or TDP-43 expressing cells were transfected with a vector control ( - ) or a LonP1-expressing plasmid ( + ) 24hr before Tet-induction; and cells were examined 36 hr post-induction . Increased LonP1 expression suppressed TDP-43 induced cytotoxicity ( Fig 7B ) . Quantification of Western blotting ( WB ) signals showed a ~2-fold increase in LonP1 expression , as normalized by actin levels . The total TDP-43 levels did not show significant changes ( see S5C Fig ) , which is not unexpected because TDP-43 protein is predominantly nuclear , although it is the cytoplasmic/mitochondrial levels of TDP-43 that are correlated with neurotoxicity , as shown by published studies including ours [28 , 59] . We further tested whether down-regulating LonP1 altered TDP-43 induced cytotoxicity . TDP-43 inducible stable cells were transduced with a vector control virus ( Ctr ) or a lentivirus expressing shRNA specifically targeting LonP1 ( KD ) that reduced the LonP1 protein level by ~50% . LonP1 knockdown ( KD ) significantly reduced the viability in cells expressing TDP-43 ( Fig 7C ) . Fractionation experiments demonstrated that LonP1 down-regulation led to an increase in mitochondrial TDP-43 protein level in these cells although the cytosolic levels of TDP-43 were not dramatically affected ( Fig 7D ) , indicating that LonP1 decreases the mitochondrial TDP-43 protein level . To test whether TDP-43 could be directly degraded by LonP1 , we established an in vitro protein degradation assay using purified recombinant LonP1 protein . Our data demonstrated that purified Wt or A315T TDP-43 protein was degraded by the purified recombinant LonP1 protein in a manner dependent on LonP1 concentrations ( Fig 7E ) and dependent on ATP ( see S5D Fig ) . A number of other mitochondrial proteases are involved in mitochondrial proteostasis . The mRNA level of CG5045 , the Drosophila homolog of ClpP , was also increased in transgenic TDP-43 flies ( see Fig 5B ) . We thus examined if TDP-43 also interacted with ClpP . However , no detectable interaction between ClpP and TDP-43 was observed in a co-immunoprecipitation assay ( supplemental S6A Fig ) . Consistently , down-regulation of ClpP did not affect the mitochondrial TDP-43 level , as shown by WB analyses of purified mitochondria from cells following ClpP knockdown ( supplemental S6B Fig ) . Together , these data show that LonP1 reduces TDP-43-induced cytotoxicity , possibly by degrading mitochondrial TDP-43 protein . To investigate whether altering Lon expression in vivo would modify neurodegeneration induced by TDP-43 , we obtained fly lines over-expressing the Drosophila LonP1 ortholog , Lon , or expressing specific siRNA against Lon . Only one fly line overexpressing Lon was available , and it showed ~2-fold increase in Lon mRNA expression compared with control flies when the Elav-Gal4 driver was used ( see S7A Fig ) . However , over-expressing Lon by itself in control flies led to retinal degeneration . This prevented us from testing the effect of over-expressing Lon in TDP-43 flies . On the other hand , two siLon fly lines were obtained , #1 and #2 , which reduced Lon expression to ~30% and ~60% , respectively , of that in the control flies ( see S7A Fig ) . Down-regulating Lon expression by itself in control flies did not cause detectable phenotypes . The siLon#1 fly line showed more robust down-regulation efficiency and was thus used in subsequent experiments . We then crossed siLon flies with TDP-43 transgenic flies and examined retinal degeneration and locomotor function in adult flies expressing TDP-43 in photoreceptors or in all neurons respectively . Using the GMR-Gal4/tubulin-Gal80ts driver , we monitored the progression of retinal degeneration during the adult stage following induction of TDP-43 expression by pulses of heat shock . Retinal degeneration was examined using TEM . By day 20 following TDP-43 induction , flies expressing TDP-43 exhibited profound retinal degeneration . The control flies showed normal photoreceptor organization , and heat shock per se did not affect photoreceptor development or maintenance as previously reported [60] . In contrast , retinae in flies expressing TDP-43 showed ommatidial disorganization with a clear reduction in rhabdomere numbers . The average number of rhabdomeres in flies expressing Wt or A315T-mutant TDP-43 was 6 or 5 respectively , as compared with 7 in the control flies ( Fig 8A , Fig 8B ) . In flies expressing Wt or A315T-mutant TDP-43 , down-regulating Lon expression exacerbated retinal degeneration , reducing the average rhabdomere number to 5 ( Wt; siLon ) or 4 ( A315T; siLon ) , respectively ( Fig 8A and 8B ) . Biochemical fractionation experiments indicate that down-regulating Lon in these flies led to an increase in the mitochondrial TDP-43 level , although there was no significant increase of the TDP-43 levels in the total cell lysates or in the cytosol ( see Fig 8E , Fig 8F; S7B and S7C Fig ) . We further examined solubility of mitochondrial TDP-43 in these flies following sequential extraction using NP-40 , SDS and urea . Down-regulating Lon increased the mitochondrial TDP43 protein level , especially the NP-40 soluble fraction in the Wt TDP-43 group and SDS-resistant/Urea-soluble fraction ( in the urea lanes ) in the A315T-mutant TDP43 group ( see S8 Fig ) . Importantly , Lon knockdown in TDP-43 expressing flies exacerbated mitochondrial damage , with a further reduction in mitochondrial size and an increase in the percentage of damaged mitochondria in the retinae ( Fig 8C , Fig 8D ) , although knock-down Lon by itself in the control flies did not affect rhabdomere or mitochondrial morphology ( see S9 Fig ) . These results demonstrate that mitochondrial TDP-43 accumulation correlates with TDP-43-induced mitochondrial damage and neurodegeneration . Intriguingly , electron-dense aggregate-like structures were detected inside mitochondria in A315T; siLon flies ( Fig 8A , marked by a black arrow in the lower panel in the “A315T; siLon” panel; also see S10 Fig , marked by an arrow ) . These electron-dense aggregate-like structures were not detected in any other groups of flies . Molecular characterization of these electron-dense aggregate-like structures awaits further studies in the future . We also examined the effects of down-regulating Lon on the locomotor function of the flies expressing TDP-43 under the Elav-Gal4/tubulin-Gal80ts driver . Flies expressing TDP-43 showed progressive locomotor defects following induction of TDP-43 expression , with flies expressing A315T-mutant TDP-43 showing more severe defects . Down-regulating Lon expression in flies expressing Wt or A315T-mutant TDP-43 exacerbated the locomotor defects induced by TDP-43 ( Fig 8G ) . In flies expressing Wt TDP-43 , Lon knockdown significantly reduced locomotor function by day 15 onward in males and day 30 onward in females . In flies expressing A315T-mutant TDP-43 , Lon knockdown significantly reduced locomotor function by day 10 onward in males and day 25 onward in females . The exacerbating effect of Lon down-regulation appeared more pronounced in males than in females . The onset of locomotor deficits was advanced in both females and males expressing Wt TDP-43 . By day 40 post-induction of TDP-43 expression , in flies expressing Wt TDP-43 the locomotor index was >30 , whereas down-regulating Lon in Wt TDP-43 flies led to a complete loss of locomotor function in both females and males ( Fig 8G ) . These results show that Lon plays a protective role against TDP-43 induced neurodegeneration in these flies , especially during the early stage of the disease . Together , our data indicate that mitochondrial damage contributes to TDP-43-induced neurodegeneration .
TDP-43 is a multi-functional RNA/DNA binding protein involved in multiple processes of gene regulation , from chromatin remodeling , DNA stability to RNA processing , including microRNA biogenesis , transcriptional and splicing regulation , mRNA trafficking as well as mRNA stability regulation [3 , 4 , 61] . Over a decade ago , TDP-43 was identified as a characteristic protein in the inclusion bodies of tissues from patients affected by TDP-43 proteinopathy , including ALS-TDP and FTLD-TDP [1 , 62] . Since then , a large number of mutations in the TDP-43 gene have been identified in ALS patients , whereas dysregulation of TDP-43 gene expression or its function has been found in patients affected by FTLD and other neurodegenerative disorders [4 , 63 , 64] . Several groups have reported mitochondrial abnormalities in different models for TDP-43 proteinopathy , including abnormal mitochondrial clustering [24 , 26] , and a shift in dynamics toward mitochondrial fragmentation [20 , 22 , 23 , 25] . A recent study reported the accumulation of TDP-43 in mitochondria in TDP-43 proteinopathy brain samples [28] . Of these studies , only one reported ultrastructural changes of mitochondria in mice expressing A315T-mutant TDP-43 [20] . However , it was not clear how widespread this damage was . There has not been , to our knowledge , a systematic morphological characterization of mitochondria in patient samples nor in TDP-43 proteinopathy model systems . Our study builds on these previous results by systematically and quantitatively examining TDP-43 induced mitochondrial damage using EM and other methods across different model systems and in patient samples . Our EM analyses clearly show that mitochondria frequently exhibited severe morphological impairment in TDP-43 proteinopathy patient samples and that such mitochondrial morphological changes are consistently detected across cellular and animal models of TDP-43 proteinopathy ( Fig 1 , Fig 2 , Fig 4 and Fig 8 ) . Interestingly , swollen mitochondrial cristae detected in the TDP-43 expressing cells and animals , and in patient samples , are reminiscent of the mitochondrial abnormality in mice expressing SOD1 mutant [65] . Recent studies indicate that cristae morphology determines the assembly and stability of respiratory chain super-complexes , and affects mitochondrial function [66 , 67] . It is not surprising that mitochondrial cristae are affected in a range of diseases , including neurodegenerative disorders . It has been reported that mitochondrial cristae are disrupted in Alzheimer's disease , showing concentric or parallel stacks [68 , 69] . A previous study from our group revealed that mitochondria in FTLD-FUS brain tissues showed a marked loss or disruption of cristae , with frequent detection of mitochondria in an “onion-like” deformed shape [70] . Data presented in this study demonstrate that vesicular or swollen mitochondrial cristae are a prominent feature not only in our cellular or animal models , but also in patient samples of TDP-43 proteinopathy ( Fig 1 , Fig 2 and Fig 4 ) . Our results together with previous studies support the notion that mitochondrial impairment is a common pathogenic contributor to neurodegenerative diseases , and that distinct ultrastructural changes in mitochondria may reflect different mechanisms leading to mitochondrial damage . Consistent with the morphological changes that we observed , mitochondrial membrane potential and mitochondrial ATP synthesis were reduced upon induction of TDP-43 expression ( Fig 2 ) . Interestingly , TDP-43 expression suppressed the activity of mitochondrial complex I , and to a lesser extent , complex IV , without affecting complexes II , III or V ( Fig 2 ) . The effect of TDP-43 on ATP synthesis and respiratory complexes has been examined in previous studies , but with discrepant results [23 , 27–29 , 71] . Onesto and colleagues observed no change in the total ATP level and reduced mitochondrial membrane potential in fibroblasts from ALS-TDP patients ( carrying the A382T mutation ) , consistent with our results; however , they observed no differences in mitochondrial complex activities . Kawamata and colleagues , on the other hand , reported that there were no mitochondrial bioenergetic defects in fibroblasts or transgenic mice expressing TDP-43 mutants , although mitochondrial calcium handling seemed to be affected [29] . In contrast , Wang and colleagues observed a decrease in ATP synthesis and a decrease in relative levels and activity in complex I from fibroblasts from ALS-TDP patients and HEK293 cells transiently overexpressing wild-type or three ALS-mutants of TDP-43; however , they did not observe changes in the other complexes . Two groups provided evidence for mitochondrial dysfunction , including reduced mitochondrial respiration and ATP synthesis , in NSC-34 cells expressing ALS-mutant TDP-43 [27 , 71] . Further studies are necessary to resolve the discrepancy in these studies . Our data presented here show that TDP-43 increases mitochondrial ROS production both in vitro and in vivo ( Fig 2; S4 Fig ) . Mitochondrion is a major site for ROS production , and excessive ROS accumulation can further damage mitochondria [43 , 72 , 73] . Although there were no detectable effects of TDP-43 on ROS production in cultured fibroblasts in the previous study [23] , data from our cellular model show a clear increase in mitochondrial ROS production induced by TDP-43 ( Fig 2 ) . Furthermore , TDP-43 expression in fly motor neurons significantly increased mitochondrial ROS levels in vivo ( S4 Fig ) . It is interesting to note that the electron-dense TDP-43 positive aggregates detected inside mitochondria in TDP-43 proteinopathy patient brain samples ( Fig 1D ) are reminiscent of the EM findings in lymphoblasts expressing LonP1 mutations of patients affected by cerebral , ocular , dental , auricular , skeletal ( CODAS ) syndrome [74] . The mitochondrial abnormalities reported in these CODAS patients are similar to those detected in our TDP-43 proteinopathy patient samples , including swollen intra- or intercristal compartments , swollen or vesicular cristae and intra-mitochondrial aggregate-like structures ( see Fig 1 ) [74] . Intriguingly , similar intra-mitochondrial aggregates were detected in flies expressing A315T-mutant TDP-43 only when Drosophila LonP1 homolog , Lon , was down-regulated ( see S9 Fig ) . Given that LonP1 is an ATP-dependent mitochondrial protease [49 , 74] , and that mitochondrial ATP synthesis is suppressed by TDP-43 , it is possible that reduced mitochondrial ATP synthesis might affect proteolytic activity of LonP1 , resulting in further TDP-43 accumulation within mitochondria as the disease progresses and eventually leading to irreversible mitochondrial damage and the demise of affected neurons . Our data from both mammalian cells and transgenic flies show that TDP-43 expression elicits UPRmt , a program that is evolutionarily conserved from nematodes to mammals . UPRmt induces expression of mitochondrial chaperones to assist in proper protein folding and proteases to promote clearance of misfolded proteins [30–32 , 75] . A variety of mitochondrial stresses induce UPRmt , including accumulation of misfolded proteins , depletion of mitochondrial DNA , ROS overload , perturbation of OXPHOS or mitochondrial translation , and disruption of the balance between mitochondrial- and nuclear-encoded proteins [30–32 , 76 , 77] . UPRmt has been reported in Parkinson's disease , Alzheimer’s disease and ALS-SOD1 [33–35] . UPRmt activation detected in our cellular and animal models for TDP-43 proteinopathy could be the result of the combined effects of TDP-43 , including mitochondrial accumulation of TDP-43 protein , increased ROS production , decreased membrane potential , impaired respiratory chain function and decreased mitochondrial ATP synthesis . To our knowledge , there were no previous reports of UPRmt in TDP-43 proteinopathy . Consistent with qPCR results from cellular and fly models , the LonP1 protein level was up-regulated in a fraction of patients affected by TDP-43 proteinopathy ( Fig 5 ) . Our data show that LonP1 interacts with TDP-43 and that purified LonP1 degrades TDP-43 ( Fig 6 and Fig 7 ) . More importantly , inhibition or down-regulation of Lon led to increased mitochondrial TDP-43 accumulation and exacerbated mitochondrial damage and neurodegeneration phenotype in vivo ( Fig 8 ) . It is conceivable that balanced protein synthesis and degradation of TDP-43 is critical for ensuring proper function of TDP-43 in the nucleus , cytosol and mitochondria . Recently , a new mechanism of mitochondria-mediated proteolysis , known as “mitochondria as guardian in cytosol ( MAGIC ) ” , was reported for degrading mis-foled proteins [78] . By MAGIC , cytosolic proteins prone to aggregation can be imported into mitochondria for degradation by mitochondria proteases in yeast and human cells , and PIM1 ( encoding yeast Lon protease ) is a major player in this process [78] . The complete machinery for MAGIC remains to be defined . Further studies are necessary to determine whether MAGIC is a major mechanism in mammalian proteostasis . Together , our data led to a working model for the role of mitochondrial degradation of TDP-43 in the pathogenesis of TDP-43 proteinopathy ( Fig 9 ) . Under physiological conditions , TDP-43 is predominantly nuclear , although it shuttles between the nucleus and cytoplasm , with a small amount of TDP-43 transported into mitochondria . When TDP-43 mutations occur , or under certain cellular stresses , the mitochondrial TDP-43 level is increased . Excessive mitochondrial TDP-43 accumulation results in mitochondrial impairment , manifesting as mitochondrial membrane potential loss , mitochondrial ROS increase , and reduced mitochondrial ATP synthesis . Such TDP-43-induced mitochondrial damage triggers UPRmt , allowing the cell to initiate a series of responses to regain mitochondrial proteostasis by up-regulating mitochondrial proteases , including LonP1 . It is likely at this early stage , before mitochondrial damage becomes irreparable , that mitochondrial stress responses enable the cell to reverse mitochondrial dysfunction . However , as the disease progresses , chronic cellular stresses lead to the excessive accumulation of TDP-43 in mitochondria , inducing irreversible mitochondrial damage . For example , persistent increase in the ROS level and severe reduction in ATP synthesis may result in a vicious cycle of suppression of LonP1 proteolytic activity and further accumulation of mitochondrial TDP-43 in spite of an increased protein level of LonP1 , culminating in activation of cell death program ( s ) . Data from our animal model and patient samples , together with our in vitro findings , support the notion that LonP1 may provide a protective mechanism against TDP-43 mediated neurotoxicity . It is noted that the time courses of TDP-43-induced UPRmt gene activation showed differences in male and female flies ( Fig 5B ) . Intriguingly , the exacerbation of locomotor deficits by Lon knockdown appeared to be more pronounced in male flies ( Fig 8G ) . This is consistent with a previous report that expression patterns of Lon protein isoforms were different between male and female flies and that Lon was required for gender-specific responses to oxidative stress [79] . The mechanisms underlying such gender-specific stress responses remain to be elucidated . Further work is necessary to determine whether the gender-specific response ( s ) play a significant role in humans against neurodegeneration . Since the discovery of TDP-43-containing inclusion bodies in ALS and FTLD patient samples , intense efforts have been made to identify proteases capable of degrading TDP-43 . A number of elegant studies have proposed possible involvement of different proteases in degrading TDP-43 , including caspases , calpain and asparaginyl endopeptidase [56 , 80–86] . None of the previously identified proteases have been shown to protect against TDP-43 induced neurotoxicity in vivo . Our biochemical experiments show that the endogenous TDP-43 and LonP1 interact with each other and that TDP-43 is degraded by the purified recombinant LonP1 . Down-regulating LonP1 drosophila homolog , Lon , exacerbates TDP-43 induced mitochondrial damage and neurodegeneration . Together , these data provide previously unknown evidence that the mitochondrial protease LonP1 can protect against TDP-43 induced neurodegeneration in vivo . It will be interesting to investigate in the future whether genetic or epigenetic alterations that affect the expression or function of the human LonP1 gene may influence the onset or progression of TDP-43 proteinopathy . Our study suggests that improving mitochondrial function and reducing mitochondrial damage may provide therapeutic potential for patients affected by TDP-43 proteinopathy .
De-identified postmortem human brain samples from autopsied tissues at the Neuropathology Core of the Cognitive Neurology & Alzheimer's Disease Center at Northwestern University were used following NIH and institutional guidelines . There was no research involving human subjects in this study . All animal studies were performed in accordance with national and institutional guidelines . HEK293 cells were cultured ( 37°C , 5% CO2 ) in DMEM ( Gibco ) , supplemented with 10% FBS ( Gibco ) and transfected as previously described [70] . HEK293-based T-Rex293 cells ( Invitrogen ) were transfected with pcDNA4 TO/myc-His plasmids ( Invitrogen ) expressing either Wt , or A315T-mutant TDP-43 following the manufacturer’s manual . Control cells were transfected with an empty pcDNA4 vector . Individual clones of cells stably expressing TDP-43 were obtained following selection in zeocin ( 400 μg/mL ) . To induce TDP-43 expression , tetracycline ( 0 . 5μg/mL; unless specified otherwise ) was added to the culture medium , and cells were cultured for different periods of time at 37°C until harvesting . Western blotting was used to confirm induction of TDP-43 protein expression . Transgenic flies expressing the human TDP-43 ( Wt or A315T-mutant ) were described previously [40 , 41 , 87] . GMR-Gal4 , OK371-Gal4 , Elav-Gal4 and UAS-Lon-RNAi lines were obtained from the Bloomington Drosophila Stock Center ( BDSC ) . Another UAS-Lon-RNAi fly line was obtained from the Vienna Drosophila Resource Center ( VDRC ) . UAS-dLonOE was from the Kyoto Stock Center . The Tubulin-Gal80ts ( Tub-Gal80ts ) line was kindly provided by Dr . A . Guo ( IBP , CAS ) [48] . The UAS-mito-roGFP2-Grx1 fly lines were kindly provided by Dr . T . Dick [47] . For flies under the Elav-Gal4/Tub-Gal80ts-driver or GMR-Gal4/Tub-Gal80ts-driver , parental flies were crossed and cultured at 18°C , young flies after eclosion were transferred to 28°C for 4 hr every day to induce TDP-43 expression . Other flies were all cultured at 25°C . All flies were raised in standard fly food , 50% relative humidity , and 12hr-12hr light-dark cycles as described previously [41 , 70 , 87] . Antibodies used in this study include polyclonal rabbit-antibodies against TDP-43 , ATP5A1 , LonP1 , HSPA9 , ClpP , TOM20 and IMMT ( ProteinTech Group Inc ) , as well as mouse monoclonal antibodies , anti-actin ( ProteinTech Group Inc ) , anti-HSP60 ( BD Biosciences ) and anti-GAPDH ( CWBIO ) . Rat-anti-dElav antibody is a kind gift from Dr . A . Guo . Brain samples were evaluated for atrophy and for pathology by hematoxylin-eosin staining and immunostaining using corresponding antibodies , as previously described [40] . The brain tissue samples were fixed in 2 . 5% glutaraldehyde ( GA , Electron Microscopy Sciences ) for 2–3 hr at room temperature , after washing with PBS and fixation in 1% OsO4 buffer for 2 hr , the samples were dehydrated with graded ethanol solutions , and then embedded in Epon812 resin ( SPI ) . Ultrathin sections ( 70 nm ) were stained with 2% uranyl acetate for 30 minutes and then lead citrate for 10 minutes before imaging using an electron microscope ( TecnaiTM Spirit , FEI ) . For fly EM samples , fly heads were collected at day 3 , fixed in 4% paraformaldehyde ( PFA , Electron Microscopy Sciences ) and 2 . 5% GA overnight at 4°C . For HEK293 cells , cells were rinsed with PBS and then fixed in 2 . 5%GA overnight at 4°C . TEM sections were prepared following protocols as described previously [88] . Fly heads and cells were then treated in the same manner as the brain tissues described above and sectioned on a Leica EM UC6/FC6 Ultramicrotome . After sections were transferred to copper grids , counter staining was performed with uranyl acetate and lead acetate before EM imaging . Immuno-EM was carried out following our published protocol [70] . Briefly , samples were fixed in 2% PFA and 0 . 2% GA overnight . After rinsing with PBS , samples were embedded in 12% gelatin , dehydrated in 2 . 3M sucrose , subjected to ultrathin sectioning ( 70 nm ) and then mounted on copper grids . After an additional rinse with PBS ( with 1% BSA and 0 . 15% Glycine ) , samples were blocked in 5% goat serum ( Electron Microscopy Sciences , EMS ) for 30 minutes . Immunostaining was performed , incubating with primary antibodies for 2 hr followed by immunogold labeled secondary antibodies ( EMS ) for 1 . 5 hr . Following rinses with PBS , samples were re-fixed with 2 . 5% GA for 10 minutes and stained with 4% Uranyl acetate for 5 minutes , and imaged under a FEI TECNAI SPIRIT electron microscope . Mitochondrial membrane potential was measured in inducible TDP-43 cell lines using the mitochondrial dye JC1 ( Invitrogen ) following a published protocol [89] . Briefly , 48 hr before assay , inducible stable cells expressing the control vector or TDP-43 were seeded in 6-well plates . Tetracycline ( 1μg/mL ) was added to induce TDP-43 expression for 0 , 24 or 36 hr . Cells were detached using Trypsin-EDTA , rinsed in cold PBS and then stained using JC1 ( 5uM ) for 20 minutes at 37°C . Following staining , cells were measured using flow cytometry ( BD FACS Calibur ) and were analyzed by FlowJo software . Data were obtained from four independent experiments . More than 20 , 000 cells were measured per group in each experiment . Image acquisition and analyses of mitochondrial ROS of larval VNC motor neurons were performed according to published protocols with slight modifications [47] . Briefly , OK371-Gal4/UAS-mito-roGFP2-Grx1 flies were crossed with female control or TDP-43 transgenic flies . Third instar wandering larvae were dissected in PBS containing 20mM N-ethyl maleimide ( NEM ) ( Sigma-Aldrich ) , and incubated for 10 minutes . Larvae were then rinsed with PBS and then fixed with 4% PFA before mounting . Fixed larval ventral nerve chord ( VNC ) samples were imaged with a Leica SP8 confocal microscope equipped with a 40X oil immersion objective . Probe fluorescence was excited sequentially at 405 nm ( reduced roGFP ) and 488 nm ( oxidized roGFP ) ( frame by frame ) and detected at 500–530 nm . A ratio image was created by dividing a 405-nm image by the corresponding 488-nm image pixel-by-pixel , resulting in the ratio of reduced to oxidized roGFP . Images were processed and quantified using ImageJ . The total cellular ATP level was measured using a CellTiter-Glo Luminescent Assay ( Promega ) according to the manufacturer’s instruction . Briefly , 48 hr before assay , the control , Wt or ALS-mutant TDP-43 stale HEK293 cells were seeded in 96-well plates . One μg/mL tetracycline was added to induce TDP-43 expression for 0 , 12 , 24 , or 36 hr . Following removal of the culture media and cell lysis , reaction mixtures were transferred to another opaque 96-well plate to measure luminescence . Luminescent signal values were normalized by the protein amount in each group to determine the total cellular ATP levels . Mitochondrial isolation was performed according to published protocols with minor modifications [38 , 70] . Briefly , stable TDP-43-expressing HEK293 cells were suspended in isolation butter [0 . 22M mannitol , 0 . 07M sucrose , 20mM HEPES ( pH 7 . 2 ) , 1mM EGTA] , homogenized with a Glass/Teflon Potter Elvehjem homogenizer ( Bellco Glass Inc ) and then fractionated by sequential centrifugation . Pellets ( the mitochondrial fraction ) were washed twice with wash buffer ( 0 . 25M sucrose , 50mM HEPES , 1mM EGTA , pH7 . 4 ) and were then resuspended in the same buffer . The protein amount was determined by the BCA protein assay ( Pierce ) . Fly mitochondrial purification was performed according to a published protocol with minor changes [90] . Sixty fly heads were collected under a microscope and were transferred into a Glass-Teflon Dounce homogenizer containing 500 μL of cold isolation buffer ( 225 mM Mannitol , 75 mM Sucrose , 10 mM MOPS and 1 mM EDTA , 2 . 5 mg/mL BSA ) and homogenized on ice for 20 strokes . The homogenate was transferred to a 1 . 5 ml tube for centrifugation at 600 g for 10 min at 4°C . The supernatant was centrifuged at 8 , 000 g for 10 min at 4°C to enrich for mitochondria . Mitochondrial pellet was washed with 0 . 5 ml wash buffer ( 225 mM Mannitol , 75 mM Sucrose , 10 mM KCl , 10 mM Tris-HCl and 5 mM KH2PO4 ) and were then resuspended in the same buffer . Mitochondrial ATP synthesis was measured using a published protocol with minor modifications [39] . Briefly , equal amounts ( 30μg ) of purified mitochondria were incubated with reaction substrates ( 0 . 15mM P1 , P5-di ( adenosine ) pentaphosphate; 2mM malate; 2mM pyruvate; 0 . 1mM ADP ) with or without oligomycin at 37°C for 5 minutes . Reaction mixtures were stopped by adding boiling stop buffer ( 100mM Tris-HCl , 4mM EDTA , pH 7 . 4 ) and then an equal amount of CellTiter-Glo reagent ( Promega ) was added to measure ATP using a microplate reader . Mitochondrial ATP synthesis was quantified by subtracting the ATP content in the presence of oligomycin from the ATP content in the absence of oligomycin of the corresponding group . Stable inducible HEK293 cells expressing either the vector control or TDP-43 ( Wt or A315T-mutant ) were established as described above . Mitochondria were purified from these cells 24h following induction with tetracycline ( 1μg/mL ) using a published protocol [70] . Briefly , mitochondria were collected from the boundary between 23% and 40% percoll of gradient centrifugation . Mitochondrial respiratory chain complex activities were measured following the published protocols [39 , 91] . Briefly , 10 μg of mitochondria were applied to a 100μl reaction mixture containing 30 mM KPO4 pH7 . 2 , 5mM MgCl2 , 2 . 5 mg/mL BSA , 0 . 3 mM KCN , 0 . 13 mM NADH , 2 μg/mL antimycin A and 97 . 5 μM ubiquinone-1 . The complex I specific activity was determined by the subtraction of the nonspecific activity in the presence of rotenone from the total NADH oxidase activity in the absence of rotenone . Complex II activity was measured in reaction mixture containing 30 mM KPO4 ( pH7 . 2 ) , 5 mM MgCl2 , 2 . 5 mg/mL BSA , 0 . 3 mM KCN , 50 μM DCPIP , 20mM succinate , 2 μg/mL antimycin A and 65 μM decylubiquinone . The complex II specific activity was determined by subtracting the nonspecific activity in the presence of malonate from the total ubiquinone reductase activity in the absence of malonate . Complex III and IV activities were measured by reduction and oxidation of cytochrome C , respectively , monitoring OD550 respectively , as described previously [91] . Complex V activity was measured by subtracting non-specific activity in the presence of oligomycin following the published protocol [39] . Mitochondrial ROS level was measured as described previously [70] . Briefly , 48 hr before the assay , inducible stable cells expressing the control or TDP-43 were seeded in 6-well plates . Tetracycline ( 1μg/mL ) was added to induce TDP-43 expression for 0 , 24 , 36hr , respectively . Cells were detached using Trypsin-EDTA , rinsed in cold PBS and then stained with mitoSOX-Red for 20 min at 37°C . After washes , cells were fixed with 4% paraformaldehyde for 20 minutes at room temperature . Cells were measured using flow cytometry ( BD FACS ArialI ) within 1 hr with analyses using the FlowJo software . Data were obtained from four independent experiments , with more than 20 , 000 cells were measured per group in each experiment . Cell death was measured using an Annexin V-FITC Apoptosis Detection Kit I ( BD ) according to the manufacturer’s instructions . Briefly , 48 hr before the assay , inducible stable cells expressing the control or TDP-43 were seeded in 6-well plates . Tetracycline ( 1μg/mL ) was added to induce TDP-43 expression for 0 , 24 or 36 hr . Cells were detached by Trypsin-EDTA , rinsed in cold PBS and then stained with Annexin V-FITC and propidium iodide ( PI ) followed by immediate analyses ( within 1 hr ) using flow cytometry ( BD FACS Calibur ) . Data were obtained from four independent experiments , and more than 20 , 000 cells were measured per group in each experiment . Cell viability and cytotoxicity were determined using a CytoTox-ONE Homogeneous Membrane Integrity kit following the manufacturer’s instructions ( Promega ) . Briefly , the activity of lactate dehydrogenase ( LDH ) results in the generation of the fluorescent resorufin product , which was measured using a SPECTRAmax GEMINI XS ( Molecular Device; excitation at 560 nm and emission at 590 nm ) . The cellular LDH activity quantifies the number of viable cells ( cell viability ) ; and the activity of LDH released in the culture media quantifies the number of non-viable cells that have lost membrane integrity ( cytotoxicity ) . A cDNA encoding the human LonP1 protein ( amino acid residues 115–959 ) was cloned into vector pET32M3C [a modified version of the pET32a vector ( Novagen , 69015–3 ) ] , expressed as an N-terminal thioredoxin and 6XHis-tagged protein and purified from E . coli ( Rosetta strain , Novagen ) following the published protocol [92] . Purified human LonP1 was analyzed by SDS-PAGE followed by Coomassie Brilliant Blue staining and by immunoblotting using an anti-LonP1 antibody . Following Tet-induction ( 1μg/mL tetracycline ) of the inducible HEK293 cells for 36hr , MycHis-tagged Wt or A315T-mutant TDP-43 protein was purified using Ni-Sepharose ( GE Healthcare ) . Purified TDP-43 protein was incubated in a 30 μL in vitro degradation reaction system [20 mM Tris-HCl ( pH8 . 0 ) , 20 mM NaCl , 10 mM MgCl2 , 1 mM DTT , 5 mM ATP] with different concentrations of purified LonP1 protein for 90 min at 37°C . The reaction products were analyzed by Western blotting using the corresponding specific antibodies to detect TDP-43 and LonP1 proteins . Total RNA was isolated from HEK293 cells or fly heads using TRizol reagent ( Invitrogen ) as described previously [70] . cDNA synthesis and qPCR were performed as described [30 , 70 , 79] using the corresponding primers ( see S2 Table ) . HPRT-1 and Actin5C were used as reference genes for mammalian cells and fly tissues , respectively . The adult fly locomotor assay was carried out as described previously with minor modifications [41] . Briefly , flies were examined every 5 days with their locomotor index measured as the percentage of flies climbing above a 6-cm line in 15 seconds after they were tapped to the bottom of an empty vial . The experiment was repeated 10 times for each group . The protein solubility was examined as described previously with minor modifications [40] . Briefly , 100 fly heads were collected for mitochondrial purification . 100 μg of the mitochondrial fractions were resuspended in 200 μL RIPA lysis buffer containing 0 . 5% NP-40 , extracted for 20 minutes on ice and then centrifuged at 12 , 000 g to collect the supernatant as the NP-40-soluble fraction and the pellet . The NP-40-insoluble pellet was then resuspended and extracted in 200 μL RIPA buffer containing 2% SDS for 20 minutes on ice . Following centrifugation at 12 , 000 g , the supernatant was collected as the SDS-soluble fraction . The SDS-insoluble pellet was then resuspended and extracted in 100 μL RIPA buffer containing 8 M urea for 20 minutes on ice . Following centrifugation at 12 , 000 g , the supernatant was collected as the urea-soluble fraction . All fractions were then subjected to Western blotting analysis . Data were collected in Excel ( Microsoft ) and analyzed using GraphPad Prism 6 unless specified otherwise . Differences between two groups were analyzed using a Student’s t-test . Multiple group comparisons were performed using a one-way or two-way analysis of variance ( ANOVA ) followed by post-hoc tests . The bar graphs with error bars represent mean ± standard error of the mean ( SEM ) . Significance is indicated by asterisks: * , P < 0 . 05; ** , P< 0 . 01; *** , P< 0 . 001 .
|
TDP-43 proteinopathy is a group of fatal neurological diseases . Here , we report a systematic examination of the role of mitochondrial damage in TDP-43 proteinopathy using patient brain tissues , as well as cellular and animal models . Our data show that TDP-43 induces severe mitochondrial damage , accompanied by activation of UPRmt in both cellular and animal models of TDP-43 proteinopathy . LonP1 , one of the key mitochondrial proteases in UPRmt , protects against TDP-43 induced cytotoxicity and neurodegeneration . Our study uncovers LonP1 as a modifier gene for TDP-43 proteinopathy and suggests protecting against or reversing mitochondrial damage as a potential therapeutic approach to these neurodegenerative disorders .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"brain",
"damage",
"enzymes",
"enzymology",
"toxicology",
"animal",
"models",
"microscopy",
"experimental",
"organism",
"systems",
"mitochondria",
"bioenergetics",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"mitochondrial",
"membrane",
"animal",
"studies",
"proteins",
"gene",
"expression",
"cytotoxicity",
"biochemistry",
"cell",
"biology",
"neurology",
"genetics",
"electron",
"microscopy",
"biology",
"and",
"life",
"sciences",
"proteases",
"energy-producing",
"organelles"
] |
2019
|
TDP-43 induces mitochondrial damage and activates the mitochondrial unfolded protein response
|
NK cells are important antiviral effectors , highly enriched in the liver , with the potential to regulate immunopathogenesis in persistent viral infections . Here we examined whether changes in the NK pool are induced when patients with eAg-positive CHB are ‘primed’ with PegIFNα and importantly , whether these changes are sustained or further modulated long-term after switching to nucleos ( t ) ides ( sequential NUC therapy ) , an approach currently tested in the clinic . Longitudinal sampling of a prospectively recruited cohort of patients with eAg+CHB showed that the cumulative expansion of CD56bright NK cells driven by 48-weeks of PegIFNα was maintained at higher than baseline levels throughout the subsequent 9 months of sequential NUCs . Unexpectedly , PegIFNα-expanded NK cells showed further augmentation in their expression of the activating NK cell receptors NKp30 and NKp46 during sequential NUCs . The expansion in proliferating , functional NK cells was more pronounced following sequential NUCs than in comparison cohorts of patients treated with de novo NUCs or PegIFNα only . Reduction in circulating HBsAg concentrations , a key goal in the path towards functional cure of CHB , was only achieved in those patients with enhancement of NK cell IFNγ and cytotoxicity but decrease in their expression of the death ligand TRAIL . In summary , we conclude that PegIFNα priming can expand a population of functional NK cells with an altered responsiveness to subsequent antiviral suppression by NUCs . Patients on sequential NUCs with a distinct NK cell profile show a decline in HBsAg , providing mechanistic insights for the further optimisation of treatment strategies to achieve sustained responses in CHB .
Chronic Hepatitis B ( CHB ) related cirrhosis and hepatocellular carcinoma ( HCC ) account for approximately 600 , 000 deaths per year [1] . Current treatments for Hepatitis B virus ( HBV ) include Pegylated Interferon-Alpha ( PegIFNα ) and nuleos ( t ) ide analogues ( NUCs ) . Although PegIFNα provides higher rates of off-treatment HBsAg loss , the gold standard treatment endpoint , [2 , 3] this is observed in a small proportion of patients . Alternatively , NUCs require lifelong administration to maintain long-term viral suppression and HBsAg loss as a treatment endpoint is sub-optimal [4–6] . These poor treatment outcomes highlight the limitations of current licensed therapies used in isolation . This is the impetus for the exploration of combination or sequential therapy strategies to improve treatment endpoints , [7 , 8] and importantly provide an immunological and mechanistic rationale to guide future therapeutic strategies . The hallmark of CHB is a dysfunctional immune response; the CD8 T cell repertoire displays an exhausted phenotype , [9 , 10] and similarly the antiviral potential of NK cells is also impaired [11] . NK cells are important innate effector cells making up a significant proportion of the intrahepatic infiltrate . We have previously demonstrated that the immunoregulatory CD56bright NK cell subset is highly enriched in the HBV-infected liver , expressing TNF-related apoptosis-inducing ligand ( TRAIL ) [12] . Their ability to produce cytokines ( IFNγ ) allowing non-cytolytic clearance of HBV-infected hepatocytes has also been shown to be impaired in CHB [11] . IFNα potently activates NK cells and we recently demonstrated that PegIFNα therapy in eAg negative disease led to a dramatic expansion of activated CD56bright NK cells with enhanced antiviral potential , though this effect reduced on treatment cessation [13] . Notably NUC treated eAg negative patients did not show similar NK cell boosting [11] but conversely demonstrated partial restoration of HBV-specific T cells [14 , 15] . Here we investigated whether PegIFNα was able to mediate a similarly potent expansion of functional NK cells in eAg positive CHB as noted in eAg negative disease , and whether any such boosting could be maintained in patients progressing to sequential NUC therapy following PegIFNα . Longitudinal on-treatment NK cell responses were analysed throughout the course of PegIFNα+/- sequential NUCs , and correlated with clinical parameters of treatment response . We report for the first time that functional NK cell responses are restored , upon in vivo administration of PegIFNα , in eAg positive CHB and importantly these effects are preserved on sequential NUCs , with an associated decline in quantitative HBsAg exceeding that seen with either de novo NUC or PegIFNα therapy alone [16] . Insights into this mechanism of innate boosting in patients receiving sequential NUCs provides further scientific rationale to support re-evaluation of future treatment strategies .
We analysed NK cell subsets in patients , pre , during and following the cessation of PegIFNα therapy; 9/18 patients , consecutively studied , progressed to sequential NUC therapy and were studied longitudinally with 3-monthly sampling until viral suppression was achieved . A further 5 patients were studied cross-sectionally during sequential NUCs ( Table 1 , S1 Table , Fig 1 ) . PegIFNα profoundly expanded NK cells in this cohort of patients with eAg positive CHB , as we had previously reported in eAg negative disease [13] . This expansion was more dramatic for the CD56bright immunoregulatory subset of NK cells , which we have shown to be preferentially enriched in the HBV-infected liver; [12] this subset was therefore studied comprehensively ( Figs 2A , 2B and S1A ) . PegIFNα resulted in a depletion of total circulating lymphocytes; however we confirmed that it induced an increase in both percentage and absolute numbers of CD56bright NK cells ( Fig 2A and 2B ) . The PegIFNα-induced expansion of CD56bright NK cells showed a non-significant trend to decrease on sequential NUCs , but notably , their frequency remained significantly higher than baseline . Conversely the CD56dim subset significantly reduced during PegIFNα therapy and tended to return towards baseline on sequential NUCs ( Fig 2C ) ; we therefore focused this study on the CD56bright NK cell subset . ALT normalisation and reduction in HBV DNA was noted , along with a decline in HBsAg levels corresponding to the time-point of viral suppression on sequential NUCs ( Fig 2D ) . The expansion of CD56bright NK cells on PegIFNα could be attributed to their increased proliferation peaking at 9-months after initiation , as assessed by Ki67 expression , a marker of the replicative S-phase of the cell cycle ( Figs 2E and S1B ) . CD56bright NK proliferation remained significantly higher on sequential NUCs than that observed at baseline , whereas their activation ( HLA-DR expression ) peaked at 9 months of PegIFNα and was not maintained on sequential NUCs ( Fig 2F ) . By contrast CD56dim NK cells did not have enhanced proliferation or HLA-DR expression during PegIFNα or sequential NUCs ( S1D and S1E Fig ) . NK cells express a variety of receptors that dictate their activity , [17] hence we analysed their expression of C-type lectin receptors ( NKG2A , NKG2C , NKG2D ) and natural cytotoxicity receptors ( NCRs ) ( NKp30 , NKp44 , NKp46 ) . We noted an increase in NKG2D and NKG2A on CD56bright NK cells throughout PegIFNα therapy , which remained significantly elevated on sequential NUCs ( Figs 3A , 3B , S2A and S2B ) ; no such changes were seen on the CD56dim NK cell subset ( S2C and S2D Fig ) . The expression of the NKG2C receptor was dissimilar . No significant percentage increase in NKG2C was observed during PegIFNα therapy , although an increase in the absolute number of these cells was noted compared to baseline ( S2E Fig ) . Moreover , there was no significant change in the expression of NKG2C during sequential NUCs ( S2F and S2G Fig ) ; all patients except for 1 were CMV seropositive so the known influence of CMV on NKG2C could not be discerned in this cohort . NCRs are involved in the clearance of tumour and virus infected cells [18]; in keeping with this , we noted more striking changes in their expression . A significant increase in the expression of NKp30 , NKp44 and NKp46 on CD56bright NK cells was seen from 6-months of PegIFNα therapy onwards ( Figs 3C and S3A–S3C ) . Importantly further augmentation of NKp30 expression on CD56bright and CD56dim NK cells was observed on sequential NUCs , peaking at viral suppression , with an inverse temporal relationship noted between its expression and HBV DNA ( Figs 3C , 3G , 3H and S3D ) . Upon sequential NUCs , both NKp44 and NKp46 were maintained on CD56bright NK cells at higher levels than baseline , with expression peaking at 6–9 months , in conjunction with the nadir of HBsAg titre , viral load and ALT ( Fig 3G and 3H ) , but these effects were not seen on the CD56dim NK cells ( S3E and S3F Fig ) . The proportion and absolute number of CD56bright NK cells expressing TRAIL increased significantly from 3-months of PegIFNα treatment in eAg positive CHB ( in line with previous findings in eAg negative CHB ) [13] , peaking at 9-months ( Figs 3D and S4A ) , whereas the absolute number of TRAIL+ CD56dim cells did not increase ( S4A Fig ) . TRAIL+ CD56bright NK cells showed a non-significant trend to decrease on sequential NUCs , in line with the reduction of viral load and ALT , but remained significantly higher than pre-treatment levels ( Figs 3D and S4D ) . PegIFNα induced a potent increase in NK cell degranulation of CD56bright NK cells by percent and absolute number , evident within 3-months of therapy initiation ( Figs 3E and S4B ) ; in addition an increase in percent but not absolute number of CD107a+ CD56dim cells was seen ( S4B Fig ) . The ability of CD56bright and CD56dim NK cells to degranulate was maintained on sequential NUCs , peaking at the 6-month time-point ( Figs 3E , 3I and S4E ) . The cytokine producing CD56bright NK cell subset shows limited ability to produce IFNγ in CHB , [11] however patients with eAg positive CHB achieved a striking recovery of the IFNγ producing capacity of their CD56bright NK cells throughout the course of PegIFNα ( Figs 3F and S4C ) . IFNγ production was maintained on sequential NUCs to a variable degree , at levels significantly higher than baseline ( Fig 3F ) . Improvement in clinical parameters ( ALT normalisation , reduction in HBsAg and HBV DNA levels ) on sequential NUCs was associated with significantly higher proportion of IFNγ+ CD56bright NK cells at each sequential NUC therapy time-point when compared to NUC initiation ( Fig 3F–3H ) , effects which were not seen on the CD56dim NK cell subset ( S4C and S4F Fig ) . Maintenance of an expanded population of NK cells with altered receptor expression and enhanced function in patients receiving sequential NUCs was noteworthy , contrasting with published findings in patients receiving de novo NUC therapy [11] . To confirm this , we compared the immunological changes documented following sequential NUC therapy ( Cohort 1 ) ( S1 Table , Fig 1 ) with those in virally suppressed patients at a similar time-point on de novo NUC therapy ( Cohort 2 ) ( S2 Table , Fig 1 ) . In addition we compared them to patients treated with PegIFNα alone who were sampled 9 months post-treatment ( Cohort 3 ) , ( S3 Table , Fig 1 ) , to determine if the changes seen on sequential NUCs were merely delayed effects of PegIFNα . Prior to commencing NUC therapy , patients in cohort 1 and 2 had similar HBV DNA levels , and no statistically significant difference in HBsAg concentrations ( S1 and S2 Tables ) . Previous data have shown that CHB patients virally suppressed with de novo NUCs have reduced levels of circulating CD56bright NK cells , similar to those found in healthy individuals [11] . Instead , we noted that patients on sequential NUCs had higher frequencies of CD56bright NK cells , associated with an increase in their proliferative capacity , than those virally suppressed on NUCs alone ( Fig 4A ) . In addition , sequential NUC patients also had a higher frequency of CD56bright NK cells compared with the PegIFNα therapy only cohort ( Cohort 3 ) , although there was no significant difference in their proliferation between these cohorts ( Fig 4A ) . There was no difference in the expression of HLA-DR ( S5A Fig ) , but we did note a higher proportion of CD56bright NK cells expressing the early activation marker , CD69 , in patients on sequential NUCs compared to those on de novo NUCs or previous PegIFNα therapy only . In addition the increase in CD69 expression on PegIFNα treatment was maintained on sequential NUCs ( Fig 4A ) . Significantly higher levels of NKG2A , NKG2D and NCR expression were observed in the sequential NUC therapy cohort compared with those on de novo NUCs or the PegIFNα only cohort ( Fig 4B and 4C ) , but no difference was seen in NKG2C expression between the cohorts ( S5B Fig ) . Of particular note , the functional potential of CD56bright NK cells was also increased on sequential NUCs compared to those on de novo NUCs or PegIFNα only . ( Fig 4D ) . To further characterise NK cells in the therapy cohorts studied , we analysed their expression of tissue homing/migration and maturation markers . CD62L and CCR7 are implicated in NK cell lymph node homing whereas NK cell recruitment to local tissue sites remains less well understood [19] . However chemokine receptors such as CXCR3 [20] and the selectin CD62L [21] have recently been reported to be important for homing and/or protective roles of NK cells in the liver . We found that the expression of CD62L was localised to the CD56bright NK subset and was significantly higher in patients during PegIFNα therapy and upon viral suppression on sequential NUCs , compared with de novo NUCs and after PegIFN alone , ( Fig 5A ) . We did not , however , see similar findings with the expression of CCR7 or CXCR6 on this subset ( S5C and S5D Fig ) . Patients on PegIFNα therapy and sequential NUCs also expressed CXCR3+ CD56bright NK cells at significantly higher levels than those patients virally suppressed on de novo NUCs or after PegIFNα alone ( Fig 5B ) . The CD56bright NK cells during sequential NUCs were able to express high levels of CD62L , CXCR3 and CD69 ( Figs 4A , 5A and 5B ) , but produced low levels of perforin and granzyme , which localised to the CD56dim NK subset ( S5E and S5F Fig ) . We did not see any change in the proportion of maturation markers on this NK subset , such that there were no differences in CD57 or KLRG1+ CD56bright/dim NK cells in the therapy cohorts , ( S5G and S5H Fig ) but a modest increase in the CD16+ CD56bright NK cell subsets during PegIFNα and sequential NUCs compared to the other therapy cohorts ( S5I Fig ) . Congruent with the enhanced boosting of functional NK cells , patients treated with sequential NUCs achieved a greater decline in HBsAg than those treated for an equivalent duration with de novo NUCs ( Fig 6A ) . To confirm this , we compared the decline in HBsAg achieved after 9–12 months of NUCs in a larger cohort of patients with or without prior PegIFNα; again , the decrease in HBsAg was significantly greater in those on sequential NUCs ( Fig 6B ) . Marked variability in the maintenance of IFN-induced changes in NK cells in patients on sequential NUCs was noted ( Fig 6 ) . We analysed if NK cell functionality was associated with differential clinical outcomes in this small cohort . Patients on sequential NUCs ( Cohort 1 ) were divided according to their HBsAg response; those with any decline in HBsAg from the time of NUC initiation to viral suppression were classified as ‘HBsAg responders’ and those without any decline ( or even an increase ) in HBsAg as ‘HBsAg non-responders’ ( Fig 6C ) . The overall mean HBsAg decline in the responders ( n = 11 ) was 1 . 18 logIU/ml , whereas the non-responders ( n = 3 ) demonstrated a 0 . 80 logIU/ml increase in HBsAg ( Fig 6C ) . A striking increase in the capacity of CD56bright NK cells to degranulate and produce IFNγ was only seen in those considered HBsAg responders . By contrast HBsAg non-responders showed a significant reduction in NK cell CD107a and IFNγ production , highlighting that functional restoration of NK cells was seen only in HBsAg responders ( Fig 6D and 6E ) . In contrast to other NK cell effector functions , NK cell TRAIL declined in these HBsAg responders on sequential NUCs ( Fig 6F ) . The association of HBsAg decline with a decrease in TRAIL expression points to a possible negative impact of TRAIL on immune reconstitution in the setting of sequential NUCs . This is also in keeping with recent studies , where reduced levels of TRAIL are part of an NK cell phenotype associated with immune control in HBV [15] and sustained virological response following DAA therapy in Hepatitis C virus ( HCV ) [22 , 23] . Although the levels of TRAIL remained higher in patients on sequential NUCs following PegIFNα exposure than in the de novo NUC cohort , we noted significant declines in TRAIL+ CD56bright NK cells in the HBsAg responders , with the greatest decline seen in the patient ( Pt . 13 ) who lost HBsAg ( Fig 6F ) .
Here we document for the first time that an expanded population of activated , functional NK cells induced by a course of Peg-IFNα can be maintained for at least 9 months after switching to sequential NUCs . This finding is at odds with the traditional view of NK cells as short lived populations with a rapid turnover and contraction following an acute response [24 , 25] and is instead reminiscent of the prolonged expansion of NK cells reported more recently following homeostatic proliferation in mice [26] and viral infection in humans [27] . Pertinent to this , we have previously shown that PegIFNα treatment of patients with CHB results in sustained induction of IL-15 , [13] one of the cytokines pivotal to long-term maintenance of NK cells with ‘recall’ capacity [26 , 28 , 29] . In addition a recent study has also demonstrated the role of type-1 IFN in promoting NK cell expansion during viral infections , by protecting them against fratricide [30] , which may also be relevant in the setting of sequential NUC therapy . Our data demonstrate that sustained restoration of NK cell responses is associated with an enhanced decline in HBsAg in a cohort of eAg positive CHB patients exposed to PegIFNα who subsequently progressed to sequential NUC therapy . These data are the first immunological characterisation of effects of this treatment sequence , providing a scientific rationale for further examination of this and other combination or sequential approaches . For example , it maybe possible to achieve similar immunological effects using shorter courses of PegIFNα according to current early stopping rules [7] . Previous studies have focused on the impact of these individual antiviral therapies on the innate [11 , 13 , 15] and adaptive [13–15 , 31] arms of the immune response . Recent data show NUC-induced reductions in viral load could prolong the immunostimulatory effects of PegIFNα given in combination , [32] and that NK cells may play a role in the clearance of HBsAg during these combination therapy strategies [8] . However , no immunological data exist on the impact of PegIFNα ‘priming’ followed by sequential NUC therapy for the management of eAg positive CHB . In this eAg positive CHB cohort , PegIFNα was able to induce a marked expansion of activated CD56bright NK cells with antiviral potential , as we have previously described for patients with eAg negative CHB [13] . Unexpectedly , these profound changes in the NK cell compartment were largely maintained for at least 9 months after switching to sequential NUCs , an effect not seen with de novo NUCs therapy . Thus PegIFNα appeared to ‘prime’ NK cells to sustain long-lasting changes , allowing them to respond differently to NUCs; in patients not exposed to PegIFNα , NK cells demonstrated reduced ability to express activating receptors , tissue homing markers/chemokine receptors , produce IFNγ and degranulate . Interestingly in those patients exposed to PegIFNα only , without further therapy , the functionality of NK cells was not maintained at 9 months post cessation of PegIFNα . This indicates that the effect seen on sequential NUCs is not exclusively related to PegIFNα . Furthermore , the differential effects of NUCs on NK cells based on prior PegIFNα exposure are not explained by differences in baseline viral load or HBsAg levels in these cohorts . However , sequential NUCs were associated with significantly greater declines in HBsAg , although no correlation was noted with eAg seroconversion , when compared to de novo NUCs , which may have been attributable to the NK cell reconstitution and/or have contributed to it . These changes merit further study in a larger cohort . It is noteworthy nonetheless , that within this small cohort , reductions in HBsAg were temporally associated with increases in NK cell cytotoxicity and IFNγ production , but with reductions in TRAIL expression , suggesting that the latter may be pathogenic in the setting of sequential NUCs . We have recently reported that TRAIL-bearing NK cells can delete HBV-specific T cells and could therefore constrain antiviral T cell immunity [33] . In keeping with these data , a recent study has shown that NK cell-TRAIL blockade may also lead to recovery of HBV-specific T cells in eAg negative patients virally suppressed on NUCs [15] . Similarly , reduced levels of NK cell TRAIL are associated with sustained virological response following DAA therapy in HCV [22 , 23] . Although we did not see TRAIL levels return to baseline in the sequential therapy cohort , we note that there were significant declines in TRAIL+ CD56bright NK cells in the HBsAg responders , with the greatest decline seen in the one patient who lost HBsAg . Not only were IFNα-induced changes in NK cells maintained on sequential NUCs , but the expression of the activating NCRs , NKp30 and NKp46 were further enhanced on sequential NUCs , showing an inverse temporal correlation with HBV DNA . Recent data from treated cohorts in hepatitis C and delta virus have also demonstrated the modulation of these receptors [22 , 34–38] . NKp30 has been shown to be pivotal in NK/dendritic cell cross-talk [39 , 40] and the regulation of NK cell IFNγ production; [41] consistent with this , we found that enhanced IFNγ production reflected NKp30 expression on sequential NUCs . The correlation we observed between increases in NK cell NKp46 and decline in HBsAg is also in line with data from the HCV field linking this activating receptor with cytotoxic , [42] antiviral and anti-fibrotic activity of NK cells [43–45] . In keeping with this , the increased expression of CXCR3+ CD56bright NKs , seen on sequential NUCs , may be implicated in anti-fibrogenesis [20] in HBV therapies , along with the increased expression of the tissue homing markers , CD62L and CD69 , which may be involved with hepatic NK cell recruitment . Further studies of the ‘on-treatment’ liver compartment would be of interest to fully elucidate this role . Despite the limited number of patients studied , these novel data highlight the potential immunological benefits of PegIFNα-priming as part of a therapeutic strategy . Recent data from the woodchuck hepatitis model show the induction of a T/NK cell signature in the liver correlating with treatment outcome [46] . This highlights the potential therapeutic role for PegIFNα-priming and the resulting modulation of the immune response . Combination or sequential therapy regimes with PegIFNα and NUCs have been postulated to have the capacity to exert complementary or synergistic antiviral and immunomodulatory effects . Previous studies of combination therapies have shown promise in reducing the amount of HBcAg+ hepatocytes and cccDNA loads , [47 , 48] harnessing the different antiviral mechanisms of PegIFNα and NUCs [7 , 8 , 49 , 50] . Such regimes should likewise take advantage of the ability of PegIFNα [51] and NUCs [14 , 15] to reconstitute the innate and adaptive arms of the immune response respectively [7 , 13–15] . Our analysis of the virus specific T-cell response in these patient cohorts , albeit limited , has demonstrated their very low frequency during PegIFNα , with significant recovery on sequential NUCs ( S6 Fig ) . However , future studies will be required to determine whether the capacity of NUCs to induce T cell reconstitution is altered by PegIFNα-priming and its potential effects on NK and T cell interactions [33] . In summary our study supports the capacity of human NK cells to undergo long-lived changes in the context of in vivo IFNα exposure followed by NUC therapy and provides a mechanistic rationale for sequential therapy with PegIFNα followed by NUCs .
Clinical assessment and additional blood sampling were performed during routine hepatitis/treatment clinics at The Royal London Hospital . Written informed consent was obtained and the study was approved by the local ethics committee ( Barts and The London NHS Trust Ethics Review Board ) . Forty-five eAg positive CHB patients undergoing standard HBV treatment regimes were recruited for immunological analysis . Baseline HBV serology was measured , including HBV DNA levels , quantified by real-time PCR ( Roche COBAS AmpliPrep/COBAS Taqman HBV test v2 . 0-dynamic range 20 to 1 . 7x108 IU/ml-Roche molecular diagnostics , Pleasanton , CA ) and HBsAg titre ( Abbott Architect ) . Serum was also tested for HBeAg and anti-HBe with a chemiluminescent microparticle immunoassay ( Abbott Architect , Abbott Diagnostics , Abbot Park , IL ) and CMV IgG . HBV genotype was recorded , along with serum transaminases and Ishak fibrosis stage where liver biopsies were performed . 18 consecutive patients were treated with a 48-week course of Pegylated Interferon-α 2a ( 180μg/week-Pegasys ) as first-line therapy ( Cohort 1 ) . Treatment responses were defined in accordance with national and international guidelines [2–5] and those considered PegIFNα failures/non-responders 6–12 weeks following cessation of PegIFNα ( determined by viral rebound , ALT flare or both ) , ( 14/18 patients ) were offered Entecavir or Tenofovir; defined as sequential NUC therapy for the purpose of this study ( taken up by nine of these patients ) . Detailed longitudinal sampling was carried out on patients from Cohort 1 to characterise temporal immunological changes throughout PegIFNα and sequential NUC therapy . In addition a further 5 patients , deemed treatment failures , following a 48-week course of PegIFNα , progressed to sequential NUC therapy , and were also studied , prior to starting NUC and at viral suppression . ( Fig 1 , Table 1 and S1 Table ) . Immune changes after 9 months of sequential NUCs , in this cohort ( n = 14 ) , were compared cross-sectionally with the following control cohorts sampled at an equivalent time point: Control Cohort 2; 12 patients treated with de novo NUC therapy ( Fig 1 , S2 Table ) . Control Cohort 3; 10 patients treated with PegIFNα for 48 weeks , ( responders , n = 4; non-responders , n = 6 ) , sampled 9 months after cessation of PegIFNα ( Fig 1 , S3 Table ) . For phenotypic analysis of NK cells , PBMC were stained with the following fluorochrome conjugated antibodies or isotype matched controls: CD3-Cy5 . 5/PerCP or CD3/Pe-Cy7 , ( eBioscience , Hatfield , UK ) , CD56-PE Texas Red ( Beckman Coulter , High Wycombe , UK ) , CD56-FITC , CD16-APC Cy7 , HLA-DR V500 , CXCR3-Cy . 5 . /PerCP , CD57-BV605 , NKp46-V450 , TRAIL-PE , ( BD Biosciences , Oxford , UK ) , TRAIL-BV421 , CD62L-AF700 , CXCR6-PE , CD69-APC , CCR7-BV421 , KLRG1-FITC , ( Biolegend , London , UK ) , NKG2A-APC , NKG2C-Cy5 . 5/PerCP , NKG2D-PE ( R&D systems , Abingdon , UK ) , NKp30-APC , NKp44-PE ( Miltenyi Biotec , Surrey , UK ) , in the presence of fixable live/dead stain ( Invitrogen , Paisley , Scotland ) . For phenotypic analysis of T cells PBMC were stained with the fluorochrome conjugated antibodies or isotype matched controls: CD3/Pe-Cy7 , CD8-AF700 , CD4-APC Cy7 ( eBioscience , Hatfield , UK ) , CD38-PE Texas Red , CD14-V500 , CD19-V500 ( Biolegend , London , UK ) . Flourescence minus-one ( FMOs ) were used for gating purposes for all flourochromes; an example of the gating strategy is shown in S1A Fig . Cells were acquired on a FACS LSRII multicolour flow cytometer ( Beckton Dickinson ) and analysed using Flow Jo analysis software ( Tree star , Ashland , OR , USA ) . In addition to percentage , absolute numbers of NK cell subsets were calculated by multiplying their percent by total lymphocyte count . To assess proliferation and further characterisation of the differentiation of NK cells , PBMC were permeabilised and stained with anti-Ki67-PE ( eBioscience , Hatfield , UK ) , Granzyme-B-FITC and Perforin-Cy5 . 5/PerCP ( Biolegend , London , UK ) directly ex-vivo . For intracellular staining for IFNγ production; PBMC were incubated with rhIL12 and rhIL15 ( 10ng/ml ) ( R&D systems , Abingdon , UK ) , for 19 hours at 37°C . 1mM monensin ( Sigma-Aldrich , Gillingham , UK ) was added for the final 3 hours . Cells were then stained with anti-CD3-Cy5 . 5/PerCP or CD3/Pe-Cy7 , CD16-APCy7 , CD56-FITC , and subsequently fixed and permeabilised , followed by intracellular staining for IFNγ-v450 ( BD Biosciences , Oxford , UK ) . Dead cells were excluded by fixable live dead stain . For degranulation , PBMCs were incubated with K562 cells ( 5:1 E:T ratio ) for 3 hours following overnight stimulation with a combination of 50ng/ml rhIL12 and rhIL18 ( Miltenyi Biotech ) . Anti-CD107a-PE mAb ( BD Biociences , Oxford , UK ) was added at the time of stimulation with target-cells and monensin ( 1mM ) added during the last 2 hours of incubation prior to staining and acquisition . Patients were tested for their HLA-A2 status and PBMC from HLA-A2+ patients were stimulated with peptides representing HLA-A2-restricted HBV epitopes ( HBVenv: FLLTRILTI , WLSLLVPFV , LLVPFVQWFV , GLSPTVWLSV; HBVcore: FLPSDFFPSV; HBVpol: GLSRYVARL , KLHLYSHPI ) or the CMV pp65-encoded NLVPMVATV epitope ( Proimmune ) . Virus-specific cells were identified by multicolour flow-cytometry ( BD LSR II ) : surface staining with CD3/Pe-Cy7 , CD8-AF700 , CD4-APC Cy7 ( eBioscience , Hatfield , UK ) , CD38-PE Texas Red , CD14-V500 , CD19-V500 ( Biolegend , London , UK ) in the presence of fixable live/dead stain ( Invitrogen ) . Significance was performed between paired samples; ( pre-treatment , on PegIFNα therapy and on sequential therapy ) , in addition to longitudinal analysis of samples using repeated Anova measurements . P<0 . 05 was considered significant in all cases . ( Prism version 5 , GraphPad Software Inc . , San Diego , Calif . ) .
|
Current therapies for CHB are limited in achieving HBsAg decline and loss leading to a cure . Although PegIFNα may be used , the majority of patients progress to NUC therapy due to treatment failure . PegIFNα and NUCs used in isolation act differentially on the immune response; PegIFNα induces NK cell activation and NUC therapy may partially restore T cell function . Data , however , are limited on the immune effects when these therapies are used in sequence or in combination . Here , we analysed the immune effects of PegIFNα followed by sequential NUC therapy and show this treatment strategy maintains the cumulative expansion of antiviral CD56bright NK cells , following PegIFNα-priming . HBsAg reduction was greater in patients treated with sequential NUCs when compared with de novo NUCs , highlighting the potential benefit of PegIFNα-priming . Such sustained boosting of NK cells on sequential NUCs following PegIFNα-priming has not previously been described , raising the potential of ‘long-lived’ NK cell populations in keeping with their emerging adaptive features . These findings provide a mechanistic and immunological rationale to explore this treatment strategy for CHB whilst awaiting the emergence of new therapies in the field .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"antiviral",
"therapy",
"immunology",
"liver",
"diseases",
"preventive",
"medicine",
"infectious",
"hepatitis",
"hepatitis",
"gastroenterology",
"and",
"hepatology",
"cytotoxic",
"t",
"cells",
"vaccination",
"and",
"immunization",
"research",
"and",
"analysis",
"methods",
"public",
"and",
"occupational",
"health",
"specimen",
"preparation",
"and",
"treatment",
"staining",
"infectious",
"diseases",
"white",
"blood",
"cells",
"animal",
"cells",
"proteins",
"t",
"cells",
"immune",
"response",
"biochemistry",
"cell",
"staining",
"hepatitis",
"b",
"cell",
"biology",
"nk",
"cells",
"interferons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"viral",
"diseases"
] |
2016
|
Interferon Alpha Induces Sustained Changes in NK Cell Responsiveness to Hepatitis B Viral Load Suppression In Vivo
|
Osteogenesis Imperfecta ( OI ) is a human syndrome characterized by exquisitely fragile bones due to osteoporosis . The majority of autosomal dominant OI cases result from point or splice site mutations in the type I collagen genes , which are thought to lead to aberrant osteoid within developing bones . OI also occurs in humans with homozygous mutations in Prolyl-3-Hydroxylase-1 ( LEPRE1 ) . Although P3H1 is known to hydroxylate a single residue ( pro-986 ) in type I collagen chains , it is unclear how this modification acts to facilitate collagen fibril formation . P3H1 exists in a complex with CRTAP and the peptidyl-prolyl isomerase cyclophilin B ( CypB ) , encoded by the Ppib gene . Mutations in CRTAP cause OI in mice and humans , through an unknown mechanism , while the role of CypB in this complex has been a complete mystery . To study the role of mammalian CypB , we generated mice lacking this protein . Early in life , Ppib-/- mice developed kyphosis and severe osteoporosis . Collagen fibrils in Ppib-/- mice had abnormal morphology , further consistent with an OI phenotype . In vitro studies revealed that in CypB–deficient fibroblasts , procollagen did not localize properly to the golgi . We found that levels of P3H1 were substantially reduced in Ppib-/- cells , while CRTAP was unaffected by loss of CypB . Conversely , knockdown of either P3H1 or CRTAP did not affect cellular levels of CypB , but prevented its interaction with collagen in vitro . Furthermore , knockdown of CRTAP also caused depletion of cellular P3H1 . Consistent with these changes , post translational prolyl-3-hydroxylation of type I collagen by P3H1 was essentially absent in CypB–deficient cells and tissues from CypB–knockout mice . These data provide significant new mechanistic insight into the pathophysiology of OI and reveal how the members of the P3H1/CRTAP/CypB complex interact to direct proper formation of collagen and bone .
OI is an inherited disorder of collagen , affecting one in 12 , 000 newborns [1] , [2] . Point mutations in the type I collagen genes COL1A1 or COL1A2 are responsible for the majority of cases , which are typically autosomal dominant . The severity of symptoms of OI is highly heterogeneous , ranging from mild increased risk of bone fractures to neonatal lethality . Recently , mutations in the endoplasmic reticulum ( ER ) -resident proteins Prolyl-3-hydroxylase-1 ( P3H1 , also known as LEPRE1 ) or its binding partner CRTAP were found to be causative in a subset of autosomal recessive forms of OI in humans and/or mice [3]–[5] . P3H1 hydroxylates a single residue ( proline-986 ) of procollagen shortly after secretion into the ER , and this modification is postulated to facilitate the proper folding or stability of collagen trimers [6] . Although CRTAP mutants also show deficient prolyl-3-hydroxylation of collagen , how this protein supports the function of P3H1 has not been elucidated . Cyclophilin B has been found in association with P3H1 and CRTAP , however its function in the complex is completely unknown [5] , [7] , [8] . Cyclophilins form a class of proteins originally discovered by virtue of their high specific affinity for the immunosuppressant drug cyclosporin A [9] . They are thought to accelerate the folding of proteins by catalyzing the isomerization of peptidyl-proline bonds , which are normally constrained in their rotation [10] . Cyclophilins are highly conserved throughout evolution , and the different isoforms display substantial sequence similarity in their central cyclosporine-binding domains . Individual differences at N- and C-termini of the various family members direct their subcellular localizations and protein-protein interactions . The initial cyclophilin discovered , cyclophilin A , is primarily cytosolic , and was shown to mediate the immunosuppressive effect of cyclosporin by creating an inhibitory complex with calcineurin [11] , [12] . Cyclophilin A plays an essential role in blocking the activity of the lymphocyte tyrosine kinase Itk , and mice lacking the Ppia gene develop allergic symptoms due to enhanced Itk-induced TH2 cells [13] . Cyclophilin B ( CypB ) is a highly related family member that is present within the endoplasmic reticulum ( ER ) of all cell types [14] . In vitro studies have previously suggested a possible role for CypB in multiple diverse functions , including immunosuppression [15] , chemotaxis [16] , hepatitis C virus replication [17] , and prolactin signaling [18] . As an ER-resident , it was also postulated to be involved in post-translational folding of secreted proteins , although a requirement for this property has not been clearly established to date . CypB has also been found to associate with collagen [19] . An important feature of patients with OI is the high degree of variability in the severity of their disease symptoms . The causes for this variability are not understood , however it is likely that there are unlinked modifier genes that impact the phenotypic spectrum of disease severity [2] , [20] . In addition , some patients with OI do not have mutations in any known disease-related genes . Thus , it is important to identify additional genes that direct the proper biosynthesis and assembly of procollagen into fibrils . In this report , we identify CypB as a new OI phenotype disease-gene in mice , and explore the function of its interactions with P3H1 and CRTAP .
To determine the role of CypB in vivo , we targeted exon 3 by homologous recombination , and generated mice bearing the knockout allele ( Figure 1A ) . The resulting allele contains an out of frame join between exons 2 and 4 , thus was predicted to completely inactivate the gene . Mating of heterozygote mutant mice gave rise to viable homozygous knockouts at the expected Mendelian ratio . Successful deletion of the gene was verified by Southern blotting ( Figure 1B ) and by Western blotting ( Figure 1C , Figure S1A ) . mRNA generated from the mutated allele accumulated to significantly lower levels than normal , most likely due to nonsense-mediated decay ( Figure S1B ) . Because CypB is expressed in all cell types , and is highly conserved from yeast to humans , we anticipated that homozygous loss might cause developmental abnormalities during embryogenesis . Surprisingly , CypB knockout mice appeared normal at birth , and both sexes were fertile . In addition , Ppib−/− mothers had no apparent difficulties giving birth , feeding , or raising their pups . On the other hand , homozygous CypB knockout mice had reduced body size and weight , in comparison to littermate controls ( Figure 1C and 1D ) , and typically died between 40 and 50 weeks of age of unclear etiology . A striking feature was pronounced kyphosis , noted as early as 8 weeks after birth that progressed in severity with age ( Figure 1F , Figure 2A , Figure S2A ) . Dual-energy x-ray absorptiometry further demonstrated this kyphosis , and suggested that knockout mice had reduced bone density ( data not shown ) . Although rhizomelia has been described in some types of osteogenesis imperfecta [4] , [5] , we did not observe differences in the ratio of femur to tibia lengths in CypB-deficient mice compared to littermate controls ( 0 . 819±0 . 027 vs . 0 . 826±0 . 013 ) ( Figure S2B ) . To study the pathophysiology of altered bone development , Ppib−/− or littermate control mice were euthanized and femurs dissected for analysis by microcomputed tomography . Serial sections of femurs revealed dramatically reduced amounts of trabecular bone in mice lacking CypB ( Figure 2B ) . Average bone volume was significantly reduced and the separation between trabeculae was increased in mutant mice ( Figure 2C ) . Because of the critical role for collagen in directing bone formation [21] , we next analyzed collagen from Ppib−/− mice , as was performed for CRTAP knockout mice [5] . Skin fibroblasts were prepared from knockout and littermate control embryos and maintained in culture . Collagen secreted into serum-free medium was collected and fractionated on SDS-PAGE . Western blotting revealed slightly delayed migration ( Figure 3A ) , consistent with increased post-translational modification , as was noted previously in collagen from LEPRE1 or CRTAP mutant cells [3] , [4] . Samples of bone and cartilage were extracted by limited pepsin digestion , and proteins resolved by SDS-PAGE . Coomassie blue stained bands were excised , subjected to trypsinization , and analyzed by tandem mass spectrometry . The tryptic peptide containing proline-986 ( 975-DGLNGLPGPIGPPGPR-990 ) was identified , and further analyzed by MS2 and MS3 for hydroxylation modifications ( Figure 3B , Figure S3 ) . Remarkably similar to collagen from LEPRE1 mutant humans and CRTAP knockout mice [5] , multiple analyses demonstrated an almost complete absence of 3-hydroxy proline in the peptide at Pro-986 from CypB-knockouts , while collagen from wild type and heterozygote littermates had abundant amounts of peptide containing hydroxy-proline in this position . The known sites of prolyl–4 hydroxylation at residues 981 and 987 were appropriately modified by hydroxylation in the peptides from both wild type and knockout collagen . Similar results were obtained for Type II collagen from cartilage ( Figure 3B ) . 3-hydroxylation of Pro-986 in type I collagen is required for correct fibril formation because mutation of P3H1 or CRTAP causes the accumulation of aberrant fibrils with wider than normal diameter [4] , [5] . We therefore examined subcutaneous collagen fibrils in CypB knockout mice by transmission electron microscopy . The majority of collagen fibrils in these mice were on average 1 . 45 times wider than similar samples from littermate control mice ( 114 . 6 +/− 22 . 4 nm vs . 78 . 6 +/− 12 . 4 nm diameter ) ( Figure 3C ) . Because of the reported interaction between CypB and type I collagen precursors during their transit through the export pathway [19] , we next stained cells for endogenous collagen . In the absence of ascorbic acid , the majority of collagen co-localized with the ER-resident marker PDI ( Figure 4 ) . Upon addition of ascorbic acid for 24 hours , collagen in wild type cells concentrated in the area of the golgi apparatus , as indicated by co-staining with a GM130 antibody [22] . However , in cells lacking CypB , a significant amount of collagen remained in the ER ( Figure 4 ) . Thus , CypB is important for appropriate subcellular localization of collagen within the protein secretion system . Thin skin is a common feature of patients with OI . We noticed that CypB −/− mice were easily identified by their loose skin ( Figure 5A ) . To determine whether this was related to a defect in collagen , sections of skin were fixed and stained by H & E , or Sirrius Red ( Figure 5B and 5C ) . Cellular appearance was normal , however we noted decreased intensity of Sirrius red staining , suggesting lower concentrations of collagen in CypB-deficient skin . Total collagen was extracted from skin samples with pepsin digestion , and tested by ELISA for type I collagen content , verifying this result ( Figure 5D ) . To quantitate the defect further , we performed tensile strength testing of skin samples , as was done previously for Tenascin-X deficient mice , which are a model for Ehlers−Danlos syndrome [23] . Comparison of stress versus strain ( Figure 5E ) revealed several abnormal properties of CypB−/− skin samples , which were very similar to published results from Tenascin-X knockout mice . The ‘toe’ region of the stress-strain curves , representing normal ranges of skin stretching , were much longer than normal ( Figure 5F ) . The steep linear portion of the curves had flatter slopes ( Figure 5G ) , demonstrating dramatically lower stiffness of the aligned collagen fibers , thought to be due to the density and cross-linking of the fibrils [23] . Lastly , much lower forces were required to break CypB-knockout mouse skin ( Figure 5H ) . All three parameters were essentially the same between wild type and heterozygote mice . Lastly , we prepared collagen from skin samples , and analyzed it by mass spectroscopy . As described for bone and cartilage collagen , there was a severe loss of triple-hydroxylated collagen representing the 986-hydroxylated peptide ( Figure S4 ) . Several other investigators have reported that CypB is able to bind to collagen , and to the P3H1 / CRTAP complex [5] , [8] . To verify this in our system , we prepared recombinant CypB as a GST-fusion protein from E . coli , and tested its ability to bind to collagen in the form of gelatin linked to sepharose . GST-CypB indeed bound stably to gelatin-sepharose , and there was no interaction between the control protein GST and gelatin ( Figure 6A ) . Addition of cyclosporine A ( CsA ) completely blocked the CypB-gelatin interaction , indicating that the enzymatic active site of CypB ( which binds to the twisted proline intermediate during the cis-trans peptidyl prolyl isomerase reaction [24] ) is important for affinity to collagen . We investigated whether recombinant CypB would also bind to P3H1 from cells . Lysates from wild type fibroblasts were prepared , incubated with the fusion protein , and then passed over glutathione-agarose resin to recover GST-CypB . We observed a moderate degree of retention of P3H1 specifically to GST-CypB , and none to GST alone , indicating they may indeed interact . ( Figure 6B ) . However , we noted that Type I collagen also was retained specifically in these pull-down experiments , raising the alternative possibility that P3H1 might be indirectly associating with CypB through collagen monomers acting as intermediate bridges . To further explore this latter possibility , we repeated the pull-down experiments in the presence of CsA , to inhibit CypB binding to collagen . However , CsA had only a modest inhibitory effect on the CypB-P3H1 association ( Figure 6C ) , suggesting that the proline-binding site of CypB is probably not involved in interaction with P3H1 . Interestingly , GST-CypB pulled down a much larger fraction of cellular P3H1 when added to lysates from Ppib−/− cells , consistent with the idea that under normal conditions , most P3H1 is already bound to endogenous CypB ( Figure 6C ) . In conducting these studies , we also found that the amount of P3H1 was relatively reduced in CypB-deficient cells ( Figure 6D ) . This effect was specific because there was not a similar reduction in other ER-resident proteins , such as PDI , FKBP65 , HSP47 . However , the residual P3H1 retained its ability to bind to collagen , as determined by affinity to gelatin-sepharose ( Figure 6E ) . Thus , stable accumulation of P3H1 in fibroblasts depends upon CypB , however it does not require CypB in order to associate with collagen . To investigate the reciprocal relationship of P3H1 effects on CypB , several commercial shRNA lentivirus preparations were tested , and two were found to cause significant depletion of P3H1 protein in murine fibroblasts . We found that in both cases , reduction of P3H1 did not have any effect on intracellular levels of CypB ( Figure 7A ) . On the other hand , knockdown of P3H1 severely reduced the amount of CypB that could bind to gelatin-sepharose in vitro , indicating that a substantial portion of CypB-gelatin binding depends upon the P3H1 protein . This result was obtained regardless of whether the cells were grown in the presence or absence of ascorbic acid ( Figure 7B ) , which is known to increase collagen production and hydroxylation [25] , [26] . Like P3H1 , CRTAP has been reported to be mutated in mice and humans with OI . CRTAP does not have a known enzymatic function , and , although collagen from CRTAP−/− mice was shown to have reduced prolyl–3-hydroxylation , its mechanism in this process is not understood . Unfortunately , we were unable to obtain antibodies that recognized the murine CRTAP , thus we turned to a human cell system to explore its potential interactions . HeLa cells were found to have easily detectable CRTAP . CypB was knocked down using a shRNA lentivirus , and lysates were tested as described above . Although CypB was efficiently reduced , there was no impairment of either CRTAP accumulation , nor of the binding of CRTAP to gelatin-sepharose ( Figure 7C ) . Knockdown of CRTAP in HeLa cells , on the other hand , caused a substantial depletion of P3H1 ( Figure 7D ) . In addition , although there was not an effect of loss of CRTAP on CypB intracellular levels , we did observe a dramatic impairment in the ability of CypB from these lysates to bind to gelatin-sepharose ( Figure 7D ) . We conclude that CRTAP maintains the appropriate cellular accumulation of P3H1 , and facilitates CypB binding to collagen .
Collagen makes up the most abundant protein in the body , and is critically important for structural elements of skin and subcutaneous tissue , as well as for providing the osteoid framework on which calcium phosphate precipitates during bone formation . The problem of how microscopic cells synthesize and properly build structures , such as bones , that are many orders of magnitude larger than themselves is an important one in biology and medicine , however the details of this complex process are incompletely understood . It has been of great value to study human and mouse mutants with abnormal bone development , like OI , in order to identify the critical components of this system . Eight types of OI have been described [2] . The initial classification of types I – IV were based primarily on clinical presentation , with type II ( typically lethal in the perinatal period ) being the most severe . All four have subsequently been shown to result from mutations in COL1A1 or COL1A2 genes . Types VII and VIII are autosomal recessive , and are due to mutations in CRTAP and LEPRE1 respectively , while the genetic basis of type V and VI remains unknown . We provide here the first evidence indicating an essential role for CypB in the biosynthesis of bone in mice . Many of the features of OI were recapitulated in Ppib−/− mice , including reduced body mass , kyphosis , and reduced bone density . In addition to the poor quality and quantity of bone mineralization , we found increased laxity and decreased strength of the skin in these animals . Along with the abnormal appearance of collagen fibrils by ultrastructural analysis of subcutaneous tissue , this suggests that CypB provides an important function during the generation of collagen in more tissues than just the skeletal system . The occurrence of kyphosis in these mice may therefore be a function of both decreased strength of connective tissue as well as abnormal vertebral bones , since several genetic conditions with primarily soft-tissue manifestations are associated with spinal deformities [27]–[29] . Although this is the first report of deletion of CypB and its role in bone formation , it is intriguing to note that CypB has been linked previously to a skin disorder in horses known as HERDA , in which affected animals develop hyperextensible skin and scarring along the back [30] . HERDA is an autosomal recessive disorder , and is due to a 39G>R point mutation in CypB , however the molecular mechanism was not previously described [31] . We suspect it may be related to the mechanism we propose in this report , although why it selectively affects the skin in horses is not clear . However , a distinguishing feature of these two genetic conditions is the ultrastructural appearance of collagen fibers , which are reduced in diameter in HERDA-affected horses and larger than normal in CypB and CRTAP mutant mice . This difference may be due to a residual altered chaperone activity or glycosaminoglycan-binding property of mutated CypB in HERDA [31] , which is not present in the knockout mice . Cyclophilins form an ancient , highly conserved group of proteins that are present in all eukaryotic cells , and in some bacteria [32] . The role of CypB has been a mystery , although it has been suggested to mediate prolactin receptor and Interferon Regulatory Transcription factor 3 ( IRF-3 ) signal transduction [18] , [33] , [34] . Given the high degree of conservation of this gene , it was surprising to us that Ppib−/− mice were viable . However , we did observe increased rates of death at approximately 1 year of age . Our preliminary results seem to indicate that providing food and water at a low level in the cages as mice age improves their overall survival . Our molecular studies provide a framework for understanding the relatively complex interrelationships of the members of the P3H1/CRTAP/CypB complex . Intracellular levels of P3H1 were dependent upon both CypB and CRTAP individually . However , P3H1 did not require either partner in order to maintain its ability to bind stably to collagen in the form of gelatin-sepharose . Others have previously shown that the prolyl-3-hydroxylation reaction of P3H1 in the absence of partner proteins is intact [8] . However , we did not observe appropriate prolyl–3-hydroxylation of residue pro-986 in collagen from CypB-deficient cells . This is likely due , in part , to reduced P3H1 levels in these mutant cells . On the other hand , although intracellular levels of CypB did not depend upon either CRTAP or P3H1 , knockdown of either significantly prevented the binding of CypB to gelatin-sepharose . Although CypB has an endogenous collagen-binding property , we observed that the extent of binding is relatively low . CypB in complex with P3H1 was a much better substrate for affinity to gelatin-sepharose . One conclusion from these studies is the observation that in each condition that can cause OI ( i . e . loss of any member of the P3H1/CRTAP/CypB complex ) , there is a significant loss of CypB binding to collagen ( Figure 7 ) . A possible model , therefore ( Figure 8 ) , is that an important role for P3H1 is to stabilize CypB on the newly synthesized procollagen molecule , in order to facilitate its prolyl-isomerization . Proline makes up almost 20% of the residues in type I collagens , so it would not be surprising if these proteins needed special help during folding . CRTAP , according to this model , would provide support by maintaining P3H1 levels . Consistent with this model is the recent finding that the cis-trans peptidyl prolyl isomerase activity of CypB is available and active in the complex with P3H1 and CRTAP [7] . This is in agreement with our finding that CypB/P3H1 binding is not ablated by addition of CsA . Alternative mechanisms for CypB are possible , however . Others have shown that abnormally folded collagen is degraded by proteasomes , which reside in the cytosol , thus would require retrotranslocation [35] . Cyclophilin B has been implicated in retrotranslocation [18] , thus its absence in knockout mice likely compounds the problem in cells with high collagen synthetic rates . Our finding of abnormally localized collagen in the ER of CypB deficient cells is consistent with a delay in their processing or removal to the cytosol for proteasomal degradation . As noted above , there are some OI patients that do not have identified disease mutations . Our results suggested to us the possibility that CypB might be a potential underlying cause in a subset of these people . Indeed , while this manuscript was in revision , several humans with recessive OI of the severe neonatal type were reported to have mutations in the PPIB gene [36] , [37] . The clinical features of the human cases were very similar to findings presented here , although they had increased numbers of bone fractures , possibly due to the relatively greater stress of birth in humans compared to mice . In addition , diseases of other types of collagen , such as Ehlers-Danlos syndrome [38] , may also result from alterations in CypB . Cyclosporin A is a commonly used immunosuppressant . It works by binding to cyclophilin A , which then blocks the interaction of calcineurin with its downstream target NFAT [12] , [39] . It is known , however , that cyclosporine also binds cyclophilin B , and causes it to be released from the cell [40] . Cyclosporin A has multiple toxic side effects in humans , including reduction in bone mineral density [41] . Although the effects of cyclosporin A are complex , this toxicity may in part be mediated through inhibition of CypB , as suggested by this new work . Our findings also raise the possibility that this could turn out to be a side effect of non-immunosuppressive cyclophilin binding drugs , which are currently being tested clinically for anti-viral properties [42] and proposed as a new treatment for muscular dystrophy in order to inhibit cyclophilin D [43] .
All animal procedures were reviewed and approved by the Institutional Animal Care and Use Committee . A targeting plasmid was generated using a BAC clone containing the entire Ppib gene from 129SvJ genomic DNA . Germline transmission in 129 x C56B6 mice was achieved essentially as previously described [44] . The initial mice carried a conditional knockout allele ( Figure 1 ) in case total deletion would be embryonic lethal . Mice were crossed to MMTV-Cre transgenic mice to cause deletion of the floxed exon 3 in female germ cells , after which the Cre transgene was bred out of the offspring , since it was no longer needed . Pups from these females were heterozygous for the KO allele , and were bred as needed to generate heterozygote or homozygote mutant , or littermate control wild type mice . Mouse embryonic fibroblasts ( MEFs ) or skin fibroblasts were generated as previously described [45] . Cells were maintained in DMEM +10% fetal calf serum with 50 µg/ml ascorbate ( sigma ) unless otherwise indicated . For isolation of secreted collagen , cells were cultured in serum free DMEM containing ascorbate . For knockdown in skin fibroblasts , Lentiviral vectors encoding small hairpin interfering RNA sequences ( shRNA ) were obtained from sigma ( LEPRE1 and CypB ) or Open Biosystems ( CRTAP ) . Lentiviral vectors were transfected in 293 FT cells with packaging plasmids ( Invitrogen ) for generating lentiviral particles . Skin fibroblasts were transduced with specific lentiviruses and selected by 2 ug/ml puromycin ( Invitrogen ) . Mice were euthanized and analyzed by radiography ( GE AMX 4 ) of whole body or hind limbs . The femur was cleaned of soft tissue and measured ex-vivo on a uCT 35 micro-CT scanner ( Scanco Medical , Basserdorf , Switzerland ) . Trabecular architecture was measured distally from the growth plate ( 70 kVP , increment 7 um ) at a resolution of 7 um . The analysis region was represented by 100 slices . To analyze modification of secreted procollagen , confluent fibroblasts were stimulated in serum-free conditions containing ascorbate during the 48 hrs . The harvested media were electrophoresed on 5% SDS polyacrylamide gels under reducing conditions and processed for westernblotting with type I collagen antibody ( SouthernBioTech ) . To examine the overall architecture and quantify the collagen content in a given area of mouse skin tissue , sections of paraffin-embedded skin sample from abdomen were stained with H&E or Picrosirius red ( Polysciences , Inc . ) . Type I collagen in the extract from skin samples was quantified with Mouse Type I Collagen Detection Kit ( Chondrex , Inc . ) To determine prolyl 3-hydroxylation in collagen samples from skin , bone and cartilage , collagen was solubilized and extracted by a limited digestion with pepsin ( 100 µg/ml; Calbiochem ) in 0 . 5N acetic acid for 48 hr . This extracts were resolved by SDS-PAGE . For collagen samples obtained from skin fibroblast culture , the harvested culture media were concentrated by Centricon ( Millipore ) and resolved by SDS-PAGE . The Coomassie Blue stained SDS-PAGE gel bands were prepared for mass spectrometry analysis using the following procedures . The gel bands were destained with 50 mM Tris , pH 8 . 1/50% acetonitrile until nearly clear . The bands were then reduced with 30 mM DTT/50 mM Tris , pH 8 . 1 at 55°C for 40 minutes and alkylated with 40 mM iodoacetamide at room temperature for 40 minutes in the dark . Proteins were digested in-situ with 30 ul ( 0 . 004 ug/ul ) trypsin ( Promega Corporation , Madison WI ) in 20 mM Tris pH 8 . 1/ . 0002% Zwittergent 3–16 , at 37°C overnight followed by peptide extraction with 60 ul of 2% trifluoroacetic acid , then 60 ul of acetonitrile . The pooled extracts were concentrated to less than 5 ul on a SpeedVac spinning concentrator ( Savant Intruments , Holbrook NY ) and then brought up in 0 . 15% formic acid/0 . 05% trifluoroacetic acid for protein identification by nano-flow liquid chromatography electrospray tandem mass spectrometry ( nanoLC-ESI-MS/MS ) using a ThermoFinnigan LTQ Orbitrap Hybrid Mass Spectrometer ( ThermoElectron Bremen , Germany ) coupled to an Eksigent nanoLC-2D HPLC system ( Eksigent , Dublin , CA ) . The digest peptide mixture was loaded onto a 250nl OPTI-PAK trap ( Optimize Technologies , Oregon City , OR ) custom packed with Michrom Magic C8 solid phase ( Michrom Bioresources , Auburn , CA ) . Chromatography was performed using 0 . 2% formic acid in both the A solvent ( 98%water/2%acetonitrile ) and B solvent ( 80% acetonitrile/10% isopropanol/10% water ) , and a 5%B to 45%B gradient over 60 minutes at 400 nl/min through a Michrom packed tip capillary Magic C18 75 µm x 200 mm column . The initial LTQ Orbitrap mass spectrometer experiment was set to perform a FT full scan from 375–1600 m/z with resolution set at 60 , 000 ( at 400 m/z ) , followed by linear ion trap MS/MS scans on the top five ions . Dynamic exclusion was set to 2 and selected ions were placed on an exclusion list for 40 seconds . The lock-mass option was enabled for the FT full scans using the ambient air polydimethylcyclosiloxane ( PCM ) ion of m/z = 445 . 120024 or a common phthalate ion m/z = 391 . 284286 for real time internal calibration . The experiment to target the hydroxyproline sites of the peptide DGLNGLPGPIGPPGPR , relied on performing MS3 on the abundant y-ion from fragmentation N-terminal to a proline residue . The Orbitrap full scan used a list of masses for the [M+2H]2+ ions of this peptide without and with 1 to 5 hydroxyprolines to trigger the data dependant ion trap MS/MS scans . The ion trap MS/MS/MS scans were triggered on the most abundant ion of the MS/MS scan . If no masses from the list were detected in the Orbitrap full scan , then the most abundant ions triggered the MS/MS events . The MS/MS raw data were converted to DTA files using extract_msn . exe from Bioworks 3 . 2 and correlated to theoretical fragmentation patterns of tryptic peptide sequences from the Swissprot databases using Mascot™ 2 ( Matrix Sciences London , UK ) . All searches were conducted with fixed modification of carbamidomethyl-cysteine and variable modifications allowing oxidation of methionines for methione sulphoxide , oxidation of prolines for hydroxyproline , and protein N-terminal acetylation . The search was restricted to full trypsin generated peptides allowing for 2 missed cleavages and was left open to all species . Peptide mass search tolerances were set to 10 ppm and fragment mass tolerances are set to±0 . 8 Daltons . All protein identifications were considered when Mascot individual peptide scores were above the 95% percentile for probability and rank number one of all the hits for the respective MS/MS spectra . The MS/MS/MS scans were analyzed manually . Skin tissues taken from lower abdomen were processed for transmission electron microscopy as described [46] . Skin biopsy was fixed overnight in 0 . 1% glutaraldehyde and 4% formaldehyde in 0 . 1-M phosphate buffer at pH 7 . 2 . After primary fixation , tissues were rinsed in 0 . 1-M phosphate buffer , followed by postfixation in phosphate-buffered 1% osmium tetroxide . After rinsing in three changes of distilled water , the tissue was stained en bloc with 2% uranyl acetate at 55°C . The tissues were rinsed in distilled water , dehydrated in progressive concentrations of ethanol and propylene oxide , and embedded in Spurr resin . The resin was polymerized at 65°C . Thin sections were mounted on copper grids for evaluation with a transmission electron microscope ( JEOL JEM-1400; JEOL USA , Peabody , Massachusetts ) . To analyze fibril morphology and density , we selected 5 adjacent regions lying just beneath the epidermal basement membrane from each of 4 sex- and age- matched wild type and CypB knockout littermates . MEFs were cultured on coverslides , fixed with 2 . 5% paraformaldehyde in PBS and permeabilized using 0 . 2% Triton X-100 in PBS for 2 min . Cells were blocked with PBS containing 5% goat serum and stained for intracellular collagen with anti-mouse collagen antibody ( AB765P; Chemicon ) . Cellular localization was verified by costaining with anti-PDI ( stressgen ) or anti-GM130 antibody ( BD ) . Samples were visualized using a Zeiss epifluorescence microscope . Cells were lysed in 1% NP-40 , 20 mM HEPES [pH 7 . 4] , 5 mM NaCl , 5 mM MgCl2 , 1 mM EGTA , 1 mM EDTA , 1 mM PMSF , 10 µg/ml leupeptin , and 45 µg/ml aprotinin . 50 µg of protein lysates were resolved by 8% SDS-PAGE and transferred to charged nylon membranes ( Millipore ) . Western blots were probed for cyclophilin B with rabbit polyclonal antibody ( Affinity BioReagents ) or mouse monoclonal antibody ( k2E2; Abcam ) , type I collagen ( SouthernBioTech ) , P3H1 ( Abnova ) , PDI ( Stressgen ) , Hsp47 ( Stressgen ) , FKBP65 ( BD ) , GST ( Sigma ) , CRTAP ( Abnova ) and beta-actin ( Sigma ) . Reactive bands were visualized with a secondary antibody conjugated with HRP ( Zymed ) and chemiluminescence ( Thermo scientific ) . For pulldowns , cell extracts were mixed with GST-cyclophilin B or GST and treated with GSH-agarose ( GE healthcare ) , washed in lysis buffer and eluted by SDS sample buffer . For binding assays with gelatin-sepharose ( GE healthcare ) , lysates containing approximately 1 mg of total protein were mixed with gelatin-sepharose for 3 h at 4°C with continuous rotation . The resulting precipitate was washed twice with lysis buffer , subjected to SDS-PAGE , and then blotted for the indicated protein . Total RNA was purified with TRIZOL ( Invitrogen ) from splenocytes or MEFs and used as a template for synthesis of cDNA with SuperScript III First Strand RT-PCR kit ( Invitrogen ) . With TaqMAN Gene Expression Assays ( Applied Biosystems ) , Ppib and Actin mRNA levels were measured on a ABI 7900HT Thermocycler ( Applied Biosystems ) . All q-PCR reactions were run in quadriplicate . Ppib level were normalized to constitutively expressed actin . The graph of fold change was generated by ΔΔCt values . Skin from 38-weeks old female mice was biomechanically tested for tensile strength as described [23] . Stress was calculated by normalizing measured force with cross-sectional area . The strain at a stress equivalent to 0 . 05 ( Newtons/mm ) was used as the toe length . Slope was calculated with data in the linear portion ( above 0 . 2 Newtons/mm ) . Highest force was normalized with cross-sectional area for failure force .
|
Osteogenesis Imperfecta ( OI ) , also known as “brittle bone disease , ” is an inherited condition with multiple defects in collagen-containing structures , including the bones , skin , and other connective tissues . Patients with OI suffer from short stature , scoliosis , thin skin , hearing loss , and , most notably , fragile bones that break with little or no trauma . Although many cases are due to dominantly inherited point mutations in the collagen genes , autosomal recessive forms have been described due to defects in the genes for Prolyl-3-Hydroxylase-1 ( LEPRE1 ) and Cartilage-Associated Protein ( CRTAP ) , proteins that modify newly synthesized procollagen . Some patients with OI do not have mutations in any of the known disease-related genes . Here , through the use of newly generated knockout mice , we identify the endoplasmic-reticulum resident prolyl-isomerase cyclophilin B ( CypB ) as a new autosomal recessive OI gene in mice . CypB , P3H1 , and CRTAP were shown to have interrelated effects in maintaining their respective protein levels and ability to bind to collagen . These studies enhance our understanding about how collagen , the most abundant protein in the body , becomes properly assembled to form bones with adequate strength .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"diabetes",
"and",
"endocrinology/bone",
"and",
"mineral",
"metabolism",
"cell",
"biology/extra-cellular",
"matrix",
"biochemistry/protein",
"folding",
"cell",
"biology/developmental",
"molecular",
"mechanisms"
] |
2009
|
Severe Osteogenesis Imperfecta in Cyclophilin B–Deficient Mice
|
Most Cryptococccus neoformans genes are interrupted by introns , and alternative splicing occurs very often . In this study , we examined the influence of introns on C . neoformans gene expression . For most tested genes , elimination of introns greatly reduces mRNA accumulation . Strikingly , the number and the position of introns modulate the gene expression level in a cumulative manner . A screen for mutant strains able to express functionally an intronless allele revealed that the nuclear poly ( A ) binding protein Pab2 modulates intron-dependent regulation of gene expression in C . neoformans . PAB2 deletion partially restored accumulation of intronless mRNA . In addition , our results demonstrated that the essential nucleases Rrp44p and Xrn2p are implicated in the degradation of mRNA transcribed from an intronless allele in C . neoformans . Double mutant constructions and over-expression experiments suggested that Pab2p and Xrn2p could act in the same pathway whereas Rrp44p appears to act independently . Finally , deletion of the RRP6 or the CID14 gene , encoding the nuclear exosome nuclease and the TRAMP complex associated poly ( A ) polymerase , respectively , has no effect on intronless allele expression .
Introns , discovered in 1977 , are genomic sequences that are removed from the corresponding RNA transcripts of genes [1] . First considered just as elements to be removed for correct gene expression , it has since become obvious that they participate in many aspects of gene regulation . Actually , the presence of introns and their splicing by the RNA-protein complex named spliceosome [2] affect gene expression by different means [3] including transcription , polyadenylation , mRNA export , mRNA localisation , translation efficiency and the rate of mRNA decay ( see [4] for review ) . Most eukaryotic genes contain introns although the proportion of genes containing introns is highly variable between organisms . For example , whereas 92% and 78% of the genes in human and plant genomes contain introns , respectively , [5] , [6] introns are found in only 5% of the genes in the yeast Saccharomyces cerevisiae [7] . Furthermore , the influence of introns on gene expression differs from one organism to another and from one gene to another . In mammals , the expression of most of the genes is reduced in the absence of splicing but the effect of introns on gene expression is generally modest [8] . In contrast , the expression of some genes like the β-globin gene or the purine nucleoside phosphorylase gene has been shown to be highly intron-dependent [9] , [10] . Introns act mainly at a post-transcriptional level and their absence reduces nuclear and cytoplasmic mRNA accumulation , alters efficient mRNA 3′end formation and consequently reduces nuclear mRNA export [8] , [11] , [12] . Introns seem also to regulate mRNA translation efficiency [8] , [11] , [12] . Similarly in plants most mutations can be complemented by cDNA sequences suggesting that most genes do not require introns for expression . For a few genes however , IME ( intron-mediated enhancement ) of gene expression has been demonstrated [13] . IME has been shown to act at a post transcriptional level and to be , at least for some genes , independent of splicing per se [14] , [15] . More recently , IME has been shown to regulate 3′UTR formation and , to a lesser extent , translation [15] . However , in both plants and mammals , the pathway by which mRNAs transcribed from intronless alleles are degraded has not been described [16] . In fungi , the information is even more sparse , with most data coming from studies on S . cerevisiae , in which introns are rare and generally not necessary for gene expression [17] , [18] . In a few examples however , introns have been shown to be necessary for gene expression , controlling the export of mRNA from the nucleus [19] , [20] . More recently , introns have been shown to be key modulators of ribosomal protein gene expression in the baker's yeast [21] . In the other hemiascomycete yeasts in which the percentage of intron containing genes goes from 2 . 4% in Candida glabrata to 14 . 5% in Yarrowia lipolytica [22] , introns do not seem to be necessary for gene expression although no specific studies have been reported . Similarly , in Schizosaccharomyces pombe in which 47% of the genes contain introns [23] , these are generally not necessary for gene expression [24] . In filamentous fungi like Aspergillus nidulans or Neurospora crassa , cDNA sequences have been widely used for the production of heterologous or homologous proteins [25]–[27] suggesting only a moderate influence of introns on gene expression . In two cases however , one in Podospora anserina and one in Trichoderma viride , introns were reported to be necessary for gene expression but the mechanisms by which this regulation occurs have not been studied [28] , [29] . Similarly in basidiomycetes , although intron density is generally higher than in ascomycetes [30] , only a few cases of alteration of gene expression by the elimination or the addition of introns have been described [31]–[35] . In Schizophyllum commune , the addition of one intron in a GFP reporter gene has been shown to increase gene expression by altering mRNA accumulation rather than the level of transcription although no further description of the mechanisms by which this regulation occurs has been reported [31] . Cryptococcus neoformans is a capsular basidiomycete yeast mainly studied because it is responsible for opportunistic infections in patients presenting a cellular immune deficiency ( mainly AIDS patients ) that are fatal if left untreated [36] . The presence of an antiphagocytic polysaccharide capsule and the production of the antioxidant melanin are its two major virulence factors [37] , [38] . The genome ( 20 Mb ) sequences of five strains , two of serotype D , one of serotype A , and two of serotype B are now complete [39] , [40] . The sequences of the 14 chromosomes of the serotype D strains were annotated using 21000 cDNA sequences isolated from a normalized library . Of the 6574 predicted genes , 80% had confirmed transcripts associated with them . Interestingly , C . neoformans genes are intron-rich and more than 98% of them have been reported to contain introns . Thus , C . neoformans has probably the intron-richest annotated genome described to date . These introns ( 5 on average per gene ) are very small in size ( 67 bp ) whereas exons have a size ( 250 bp ) close to the human ones [40] , [41] . ) . Alternative splicing has been reported to be very common in C . neoformans and intron retention represents its most common manifestation [40] , [42] . Finally , the fact that the proteome of C . neoformans contains numbers of proteins sharing sequence similarities with known metazoan SR proteins ( [43]; Janbon unpublished data ) as well as the identification of a DEAD-box helicase as a central regulator of multiple virulence factors [44] suggest that intron-dependent regulation of gene expression might play a major role in C . neoformans biology and virulence . In this article , we have addressed the importance of introns for gene expression in C . neoformans . We have shown that introns are necessary for mRNA accumulation for some genes but not for others . We also demonstrated that introns can play a positive or a negative role in this process . Finally , we showed that the nuclear poly ( A ) binding protein Pab2 and the exosome nuclease Rrp44p are implicated in this intron-dependent regulation of gene expression in C . neoformans . Our results also suggested that Xrn2p might act in the same pathway as Pab2p .
We previously reported that the CAS3 gene contains 12 introns , all of them but the last one ( intron 12 ) being located within the CDS [45] . We performed RACE experiments and noticed that among the five 5′end cDNAs sequenced , two were copies of RNA molecules not spliced in the intron 1 whereas the intron 2 was spliced . In order to identify the different types of CAS3 mRNA molecules present in the cell we sequenced a large number of full length cDNAs . Poly ( A ) RNA molecules were purified from C . neoformans var . neoformans cells growing in YPD and used for RT-PCR experiments . After separation by gel electrophoresis and purification , 3 pools of 15 full length cDNA molecules were cloned and sequenced ( see Material and Methods ) . As presented in Figure 1A , a large diversity of CAS3 RNA molecules was identified , ranging from completely spliced molecules to completely unspliced ones . Although these experiments were not quantitative , the pattern of splicing observed revealed that some introns were more rarely spliced than others . Introns 1 and 12 were spliced in only 51% and 18% of the 45 sequenced molecules , respectively . RNA-Seq data alignment pattern analysis confirmed that all the introns from this gene can display a certain level of intron retention ( Janbon , unpublished data ) . With the obvious exception of intron 12 which lies in the 3′UTR , all introns of this gene contain at least one in frame stop codon suggesting that none of these intron-containing mRNA molecules could encode a protein . Evidence for intron retention at the CAS3 locus suggested that at least part of the regulation of this transcript was dependent on introns . So as to analyse the influence of intronic sequences on CAS3 expression we replaced the CAS3 wild type allele by a version without introns ( cas3Δi ) . The co-transformation procedure used here allowed a complete allele replacement at its original locus without any further modification of the local genomic landscape ( see Material and Methods ) . The main phenotype associated with the deletion of CAS3 is a modification of the capsule structure that can be revealed using anti-capsule monoclonal antibodies [46] . As shown in Figure 1B , the capsule structure of the strain bearing cas3Δi was similar to the one in which the gene had been deleted ( cas3Δ ) suggesting that the intronless allele was not functional . Moreover , Northern blot experiments showed that very little CAS3 specific RNA was present in the cas3Δi strain ( Figure 1B ) . Introns are thus necessary for CAS3 expression . To verify whether the importance of introns on gene expression was a general feature in C . neoformans , we cloned cDNAs from the genes UXS1 , CAP10 , UGE1 and CAS4 under the control of their own promoter . These constructs were used to transform the corresponding deletion mutant strains . We then compared mRNA levels of intronless and wild type alleles by Northern blot analysis . As presented in Figure 1C , the influence of introns on mRNA level was gene-dependent . Some genes , like CAP10 , UGE1 and to a lesser extend CAS4 were highly intron-dependent whereas others like UXS1 did not depend on the presence of such sequences to be expressed . The intron-dependent regulation of mRNA accumulation could act at different levels . Thus , the absence of RNA in the cas3Δi strains could be due to the absence or a very low level of transcription or/and to a decrease of the stability of the corresponding RNA leading to a complete or nearly complete degradation of it . To answer this question , we performed nuclear run-on experiments , thus measuring the frequency of transcription initiation of the different alleles largely independently of the effects of RNA stability [47] . The ratio of the CAS3 specific signal versus the ACT1 specific was not altered by the absence of introns ( 1 . 26±0 . 25 ) when compared to the wild-type allele ( 1 . 29±0 . 47 ) ( Figure S1 ) . These results suggested that the absence of introns does not alter the transcriptional activity of the gene but rather greatly alters the stability of RNA molecules transcribed from the cas3Δi allele . So as to better analyse the influence of the introns on gene expression , we constructed a series of alleles bearing different numbers of introns at different positions . These alleles were integrated at the wild-type locus following the same procedure used previously . The level of mRNA was then measured by Northern analysis and confirmed by RT-qPCR ( Figure 2 ) . As previously shown , in the absence of introns , very few transcripts can be detected ( less than 1% of the wild-type ) . Surprisingly , the presence of one intron was not enough to restore any expression of the gene as demonstrated by the analysis done with the strains NE293 , NE295 and NE300 where the introns 12 , 1 , or 11 were present , respectively . Even with 2 introns , the expression of the gene remained barely detectable ( see strains NE294 , NE457 and NE449 ) . The intron 12 and to a lesser extent the intron 1 appeared to play a negative regulatory role in CAS3 expression . Indeed , in strains NE298 and NE299 in which the CAS3 allele lacks the intron 1 and 12 , respectively , the expression of this gene went up 2–3 fold . The negative role of the intron 12 in CAS3 gene expression was confirmed by comparing the expression of the CAS3 alleles from the strains NE454 ( without intron 12 ) and NE453 ( with intron 12 ) . By comparison the absence of intron 2 influenced poorly the level of expression of the gene ( strain NE456 ) . The presence of the other introns regulated CAS3 expression in a positive way . In fact , excepting the regulation by introns 1 and 12 , the more introns were present in CAS3 the better the gene was expressed . The identity of the introns did not seem to be important . Indeed , deletion of introns 2 to 6 ( strain NE296 ) altered CAS3 mRNA level to the same degree as the deletion of introns 7 to 11 ( NE453 ) . Accordingly , the negative effect of the intron 12 appeared to be more dependent on its position than on its sequence as the re-positioning of the intron 2 at the intron 12 position in the cas3Δi12 allele results in a wild type expression of this intron swapped allele ( 127% of mRNA accumulation as compared to the wild type ) ( Figure S2 ) . Finally , dot blot assays using an anti-capsule antibody were performed to see whether CAS3 mRNA levels correlated with the phenotype of the corresponding strains . Results shown in Figure S3 demonstrated that a level of expression of CAS3 of at least 37% ( Figure 2 ) of the wild-type mRNA level is associated with a wild-type capsule phenotype . We aimed to identify elements involved in the degradation of the RNA molecule transcribed from the intronless allele cas3Δi . We constructed and screened an insertional library of C . neoformans mutants using a dot blot assay and the anti-capsule monoclonal antibody CRND-8 . More than 5000 mutant strains were tested and fourteen strains were identified as having a low but detectable reactivity with this antibody ( data not shown ) . Northern blot analysis confirmed that all of them expressed the cas3Δi allele at a low level ( data not shown ) . Analysis of the position of the insertion site in the first mutant strain studied , revealed that it was within the gene CNB04570 coding for a protein of 210 amino acids sharing 49% and 32% of amino acid sequence identity with the nuclear poly ( A ) binding protein of S . pombe [48] and the human one , respectively [49] . Like its human and fission yeast counterparts , the C . neoformans Pab2p sequence presented a single RNA binding domain and an arginine-rich C-terminal domain . However , the poly-alanine domain present in the human protein in which mutations associated with the genetic disease named Oculopharyngeal muscular dystrophy ( OPMD ) have been identified [49] , was absent in both fungal proteins . We deleted PAB2 using a nourseothricin marker ( see Material and Methods ) . As previously reported in S . pombe , the pab2Δ mutants grow less well at 15°C as compared to the wild-type strains . In C . neoformans , we also found that this mutation results in an alteration of the growth rate at 30°C and 37°C ( Figure 3A ) and an increased sensitivity to SDS 0 . 01% as compared to the wild-type . We also studied the classically associated virulence phenotypes and found no evidence of modification of the capsule size or structure and no alteration of the urease production ( data not shown ) . In contrast , we observed a small but reproducible reduction in melanin production ( Figure 3A ) . A pab2Δ cas3Δi strain was constructed by selecting adapted progenies after crossing the single mutant strains . Analysis of the expression of this intronless allele in a pab2Δ genetic background confirmed that this protein regulates cas3Δi expression . Indeed , as shown in Figure 4A , whereas PAB2 deletion did not increase the expression of the CAS3 wild type allele , it restored the expression of the intronless allele cas3Δi up to 12% of the wild type ( Figure 4A , central panel , grey bars ) . We also confirmed by ELISA using another anti-capsule monoclonal antibody ( Mab 302 ) that the level of mRNA accumulation in these strains correlated with the phenotype of the corresponding strains ( Figure 4A , right panel ) . We noticed the presence of two additional bands present in Northern blots for the pab2Δ cas3Δi mutant strain ( Figure 4A , left panel , lane 4 ) . These bands are probably products of partial degradation of the transcript or the result of partial transcription of the gene . Indeed , hybridizing with oriented RNA probes or with probes specific for the 5′ or 3′ends of the gene demonstrated that these additional bands correspond to the 5′end of the sense transcripts ( Figure S4 ) . We also purified RNA from nuclei isolated using a similar protocol as the one used for the run-on experiments ( see Material and Methods ) and compared the accumulation of RNA obtained with this nuclei enriched fraction with the ones obtained with RNA extracted from intact cells . As shown in Figure 4A ( middle panel ) , we observed a more pronounced accumulation of the cas3Δi mRNA in the nuclei enriched fraction ( white bars ) , this more pronounced accumulation being exacerbated by a pab2Δ mutation . In good agreement with the localisation experiment data ( see below ) , these results also suggested a nuclear role for Pab2p in the control of intronless allele expression . Finally , we checked that Pab2p could also modulate the expression of intronless alleles of other genes by constructing a pab2Δ cap10Δi double mutant strain . As shown in Figure S5 , the PAB2 deletion also restored the cap10Δi mRNA level close to the wild type level confirming the role of Pab2p in the control of intronless allele expression . To functionally characterise the biophysical properties of Pab2p , we expressed an N-terminal 6XHis-tagged version in E . coli and purified the recombinant protein ( see Material and Methods ) . We tested the affinity of this recombinant Pab2p towards a 30-mer poly ( A ) RNA coated in a 96-well plate well ( see Material and Methods ) . We found that Pab2p recognized poly ( A ) oligonucleotides in a dose dependent way ( data not shown ) and that this recognition was specific to RNA as the same protein presented very little affinity to a 30-mer poly ( A ) DNA ( Figure 4B ) . Similarly to what has been observed in S . pombe [48] , competition assays suggested also that the binding was specific to poly ( A ) sequences as a poly ( C ) RNA sequence was not able to compete the binding of Pab2p to poly ( A ) ( Figure 4B ) . Next we constructed a GFP::PAB2 allele to localize Pab2p within the cell . We transformed a pab2Δ cas3Δi strain and checked that the transformant selected grew as well as the wild-type strain at all temperatures tested . The functionality of the GFP::Pab2p fusion was confirmed by Northern blot which demonstrated that the fusion protein decreased expression of the allele cas3Δi ( data not shown ) . Examination , by fluorescence microscopy showed a pattern of Pab2p of fluorescence consistent with nuclear localisation ( Figure 4C ) . These results suggested strongly that Pab2p is a nuclear poly ( A ) binding protein . Pab2p has been recently shown to interact with the two nucleases of the exosome ( i . e . Rrp44p and Rrp6p ) to control the synthesis of snoRNAs and the expression of meiotic genes [50]–[52] . It was thus very tempting to hypothesize that this multi-protein complex could regulate the expression of cas3Δi by degrading the RNA transcribed from this allele . We identified the RRP6 ( gene CNC03940 ) and the RRP44 ( gene CND00800 ) homologues in the genome of C . neoformans and constructed corresponding mutant strains . As in the model yeasts S . cerevisiae and S . pombe , RRP6 was not essential and we were able to delete this gene in C . neoformans . The phenotypes associated with the RRP6 deletion were compared with the ones associated with the PAB2 deletion . As presented in Figures 3A and 3B , pab2Δ and rrp6Δ strains presented a similar growth defect at 30°C . However , in contrast to what we observed with the pab2Δ mutant strains , the rrp6Δ mutants growth defect is not exacerbated when the cells are incubated at 15°C or 37°C and no hyper-sensitivity to SDS 0 . 01% was observed . The size and structure of the capsule and the urease production were not affected by the deletion of the RRP6 gene ( not shown ) . Interestingly , we observed the same slight defect in melanin production in pab2Δ and rrp6Δ mutant strains when the cells were grown on Niger medium ( Figure 3B ) . Successive unsuccessful attempts to delete RRP44 suggested that this gene is essential in C . neoformans as it is in S . cerevisiae and S . pombe [53] , [54] . We thus expressed this gene under the control of the GAL7 promoter which has been shown to be strictly regulated by the presence of galactose in the medium and can be used as a regulatable promoter in promoter swap experiments [55] . On galactose , these cells displayed no specific phenotype although RRP44 was clearly over-expressed ( Figure 5B and 5C ) whereas on glucose , the PGAL7::RRP44 strains failed to grow , confirming that this gene is essential in C . neoformans ( Figure 3C ) . To identify the nuclease that regulates expression of intronless CAS3 , the cas3Δi allele was next introduced into the rrp6Δ and rrp44 mutant strains . Moreover , we constructed all possible double mutant strains ( rrp6Δ pab2Δ , rrp6Δ PGAL7::RRP44 and pab2Δ PGAL7::RRP44 ) and we introduced the cas3Δi allele in all of them . Deletion of RRP6 did not restore even partially the expression of cas3Δi ( Figure 5A ) , suggesting that Rrp6p is not the nuclease degrading the RNA transcribed from the cas3Δi allele . Surprisingly , RRP6 deletion in a pab2Δ background resulted in reversion to a complete absence of expression of the cas3Δi allele . We next tested the influence of Rrp44p on the control of cas3Δi expression . To do so , we grew the different strains under the non-restrictive condition ( galactose ) and then transferred them to the restrictive condition ( glucose ) . Preliminary experiments had shown that as early as 2 hours after the transfer of the cells to glucose medium no RRP44 specific mRNA could be detected by Northern blot analysis when this gene was expressed under the control of the GAL7 promoter ( not shown ) . We compared mRNA levels of CAS3 and cas3Δi under non-restrictive conditions and after 10 hours under restrictive growth conditions in the different genetic backgrounds . Whereas CAS3 mRNA levels were similar in all mutant strains tested , incubation of PGAL7::RRP44 cells under restrictive conditions ( glucose ) restored the expression of the intronless allele cas3Δi up to 9% of the wild type ( Figures 5B and 5D ) . Similar results were obtained after shorter ( 6 h ) incubation times . This mRNA level was not increased in the rrp6Δ PGAL7::RRP44 double mutant confirming that Rrp6p is not implicated in this regulation . Interestingly , RRP44 appeared to be up-regulated in the absence of RRP6 suggesting a potential explanation for the absence of cas3Δi mRNA in the rrp6Δ pab2Δ double mutant ( Figure 5A ) . The analysis of the double mutant pab2Δ PGAL7::RRP44 revealed a synergic effect of these mutations . As shown in Figures 5C and 5D , the double mutant strains expressed the intronless allele up to 34% of the wild type . Accordingly , the level of mRNA correlated with the phenotypes of the corresponding strains ( Figure 5D ) . These results strongly suggested that Rrp44p and thus the exosome participates in the degradation of mRNA transcribed from the intronless allele cas3Δi . They also demonstrated that the exosome is acting mainly independently of Pab2p suggesting the existence of a least two pathways regulating intronless expression in C . neoformans . Several RNA species including snRNA , snoRNA , tRNA and rRNA are targeted to degradation by the exosome following polyadenylation by the TRAMP complex [56] . We thus addressed whether this nuclear complex has a role in the regulation of cas3Δi expression . Cid14p has been shown to represent the catalytic subunit responsible for the TRAMP complex poly ( A ) polymerase activity in S . pombe [57] . We deleted the single homologous gene ( CNK02250 ) in the C . neoformans genome . Neither the cid14Δ strains nor the cid14Δ pab2Δ double mutant strains had any growth phenotype at any temperature tested ( 30°C , 37°C , 15°C ) ( not shown ) . Moreover , no alteration of CAS3 or cas3Δi mRNA levels could be observed ( Figure S6 ) . Thus , Cid14p and the TRAMP complex do not seem to be implicated in the regulation of the expression of intronless alleles in C . neoformans . Finally , as Pab2p has been previously implicated in poly ( A ) tail length control [58] , we performed poly ( A ) -tests to examine the length of the poly ( A ) tail in the wild type or in the absence of Pab2p or Cid14p ( see Material and Methods ) . The consequences of these gene deletions on the poly ( A ) tail length of 10 C . neoformans genes including CAS3 were tested . However , we observed no reproducible modification of length of the poly ( A ) tails in any of the mutants tested ( Figure S7 , data not shown ) . In keeping with a primarily nuclear degradation of cas3Δi ( as supported by the nuclear localisation of Pab2p and an enrichment in nuclear fractions ) and given the results obtained for the two exosomal nucleases ( Rrp6 , Rrp44p ) , the major nuclear 5′→3′ exonuclease Xrn2p/Rat1p appeared to be the most promising candidate for Pab2p-assisted degradation of intronless mRNA . This idea was further encouraged by the finding that deletion of PAB2 not only stabilises full-length cas3Δi but also truncated fragments corresponding to the 5′ end of the sense transcript thus suggesting the involvement of a 5′→3′ exonuclease . In addition , Xrn2p and Rat1p have been shown to be involved in the degradation of unspliced transcripts in human and yeast [59] , [60] . Given that XRN2 is likely essential in C . neoformans , we placed the gene ( CNF01810 ) under the control of the GAL7 promoter , similar to the strategy applied for RRP44 . As expected , cells failed to grow under restrictive conditions ( glucose ) showing that XRN2 is indeed essential for C . neoformans viability . However , depletion of XRN2 did not lead to any stabilisation of the cas3Δi transcript neither in a wildtype nor in a pab2Δ context ( Figure 6A ) . Instead a slight decrease of cas3Δi mRNA was observed upon XRN2 depletion in pab2Δ strains ( Figure 6A ) . Accordingly , when we compared the levels of RRP44 expression in wildtype and xrn2 mutant cells , we found an increase in RRP44 expression upon XRN2 depletion ( Figure 6B ) . Likewise expression of XRN2 is elevated in cells depleted for RRP44 ( Figure 6B ) , which might partially explain the rather minor stabilisation of cas3Δi transcripts in rrp44 cells . To circumvent this compensatory effect and thus be able to evaluate the role of Xrn2p , we compared cas3Δi mRNA levels when overexpressing either RRP44 or XRN2 in wildtype and pab2Δ backgrounds . To this end , wildtype , pab2Δ , rrp44 , xrn2 , pab2Δ rrp44 and pab2Δ xrn2 strains were grown in inducing conditions ( galactose ) and the levels of cas3Δi expression were measured by Northern analysis . Note that upregulation of the intronless allele was reproducibly observed when the strains were grown in galactose ( see Figure 5C ) . Overexpression of RRP44 or XRN2 in a PAB2 wildtype context led to the nearly complete degradation of cas3Δi mRNA preserved by growth in galactose suggesting that both nucleases are implicated in the degradation of these mRNA molecules ( Figure 6C ) . On the other hand , overexpression of RRP6 did not lead to any destabilisation of the cas3Δi transcript thus confirming that Rrp6p has no central role in this regulation ( data not shown ) . In the absence of Pab2p , RRP44 overexpression led to a strong decrease of cas3Δi mRNA accumulation and to nearly complete elimination of the two additional bands observed by Northern blot in a pab2Δ single mutant context ( Figure 6C ) . In contrast , XRN2 overexpression in a pab2Δ strain led to a very moderate or no decrease of cas3Δi mRNA accumulation . These results were confirmed by RT-qPCR ( Figure 6D ) although the effect of Xrn2p is probably overestimated in this assay due to the choice of primers specific for the transcript's 5′ end . In conclusion , these overexpression experiments confirm on the one hand that the action of Rrp44p is mainly independent of Pab2p and on the other hand suggest a rather Pab2p-dependent role for Xrn2p in the degradation of the intronless mRNA .
The role of introns on gene expression has been the focus of a large number of studies during the last decades [4] . Most of these studies are coming either from mammals or plants or have been performed using the intron-poor micro-organism S . cerevisiae as a model . In these organisms , the replacement of a wild-type gene by an intronless allele generally has a modest effect on gene expression suggesting that introns are more a source of protein diversity or/and regulation of gene expression than a sine qua non condition for a gene to be expressed [8] , [18] , [21] , [61] . In contrast , expression of some genes like the human β-globin or the plant ERECTA genes is highly dependent on the presence of introns [10] , [15] . Most of the intron-dependent regulation occurs at a post-transcriptional level although the different steps necessary for the production of a mature mRNA , including transcription and splicing are mutually dependant . [62] . In these cases the presence of introns in a transcript can affect 3′end formation , mRNA export from the nucleus and mRNA stability [8] , [63] . However , the pathway ( s ) by which mRNA molecules transcribed from intronless alleles are recognized and degraded remain unknown [64] . The intron density of the pathogenic yeast C . neoformans is probably the highest yet known for an organism having a completely annotated genome . In fact , a recent re-annotation of the C . neoformans var . grubii genome based on RNA-Seq data showed that 99% of the expressed genes have at least one intron ( Janbon , unpublished data ) . Moreover , 11 . 5% and 4 . 1% of genes in C . neoformans have been shown to have 5′ and 3′ UTR introns , respectively [65] . It has also been previously published that alternative splicing is very common in C . neoformans [40] , [42] . More recently , a link between transposon pre-mRNA splicing and RNAi dependent degradation has been demonstrated in C . neoformans [66] . Altogether , these data suggested a central role for intron metabolism in the biology and the virulence of C . neoformans . In this study , using the gene CAS3 as a model transcript , we showed that alternative splicing can affect all introns from a single gene although their spliceability appeared to be intron-dependent . We also demonstrated that introns are necessary for the CAS3 gene expression in C . neoformans . Three other tested genes have the same intron-dependence of gene expression whereas another one ( UXS1 ) can be expressed without introns . This insensitivity to the lack of introns does not appear to depend on the number of introns . Indeed CAP10 has only 3 introns , UGE1 only 4 whereas CAS4 and UXS1 have 9 and 7 introns , respectively . Moreover , it probably does not depend on the presence of an intron in the UTR as CAS3 is the only one of the presently studied genes to possess such an intron . It has to be noted that the fact that intronless bacterial antibiotic resistance genes are commonly used for mutant construction in C . neoformans does not contradict this observation . Indeed , all these genes are expressed under the control of the ACT1 promoter , in which an intron is present [67]–[69] . Our results demonstrated that most introns play a positive role on mRNA accumulation and that the absence of introns does not alter the level of transcription as measured by run-on transcription assay . These results are similar to what has been observed in mammals , in the fungus S . commune or for IME in plants in which the regulation of gene expression by introns acts mainly at a post-transcriptional level [8] , [31] , [61] . In contrast to what has been observed in most cases however , one intron is not enough to restore gene expression . Even with two introns the mRNA level remained below 3% of the wild-type . Most introns played a positive role on gene expression and their action seemed to be more cumulative than specific as previously reported for the ERECTA gene in A . thaliana [15] . The two most external introns ( 1 and 12 ) played a negative role on CAS3 mRNA accumulation . Run-on experiments suggested no transcription rate alteration associated with the deletion of either one of these introns ( data not shown ) suggesting also a post-transcriptional regulatory mechanism . More investigations are obviously needed to understand the role of these introns on mRNA accumulation . The absence of introns results in an important reduction of mRNA accumulation . Deletion of the PAB2 gene partially stabilized mRNA transcribed from the intronless allele . In contrast , the analysis of the cid14Δ strains suggests no apparent role for the TRAMP complex in this regulation [52] . Pab2p has been shown to interact physically with the two nucleases ( Rrp6p and Rrp44p/Dis3p ) from the exosome in S . pombe [50] . In C . neoformans , although the deletion of RRP6 encoding the nuclear exosome nuclease has no effect on the accumulation of mRNA transcribed from cas3Δi , the analysis of the level of cas3Δi mRNA in a RRP44 conditional mutant under a restrictive condition strongly implicates this multiprotein complex in this regulation . The analysis of the double mutant strain pab2Δ rrp44 demonstrated a synergic effect of the two mutations suggesting that these two proteins could act in two independent pathways . [70] , [71] . As suggested in other Pab2p dependent pathways described to date , Pab2p could be a facilitator for the degradation of cas3Δi mRNA through recruitment of another nuclease . Our double mutant strains analysis and overexpression experiments suggest that the nuclear 5′→3′ exonuclease Xrn2p might represent a good candidate . This model could explain the synergic effect of the pab2Δ rrp44 double mutation . Thus , in the single rrp44 mutant strain , XRN2 is over expressed and can partially compensate the effect of Rrp44p depletion whereas in the absence of Pab2p , XRN2 over-expression would have much less effect on the degradation of cas3Δi transcripts . The fact that Xrn2p/Rat1p has been previously shown to be involved in the degradation of unspliced mRNA in human and yeast [59] , [60] sustains this model although no genetic interaction between Xrn2p and Pab2p has been reported to date . Very recently , Pab2p has been shown to be involved in three different RNA processing and degradation pathways in S . pombe . Thus , together with the exosomal nucleases Rrp6p and independently of the TRAMP complex it controls polyadenylation and synthesis of snoRNAs [50] , meiotic gene expression in the Mmi1-dependent pathway [51] , [52] , [72] and targets ribosomal pre-mRNA RPL30-2 [73] . Similarly , in the meiotic gene expression and pre-mRNA RPL30-2 regulation , a synergic effect was observed when RRP44 and PAB2 were mutated suggesting here also that the effect of Rrp44p could be mainly independent of Pab2p although the other elements involved in this Rrp44p-dependent pathway remain to be identified . The role of Pab2p in the intronless gene expression regulation remains mysterious . In S . pombe , Pab2p is recruited to the nascent mRNA before 3′ end formation and polyadenylation and controls the length of poly ( A ) of only a subset of RNAs [48] , [50] , [71] . It also physically interacts both with the exosome nucleases and the poly ( A ) polymerase Pla1p [52] , [71] . In the absence of Pab2p , some cas3Δi mRNA molecules are exported from the nucleus and translated although most of them are still degraded . The subcellular localisation of Pab2p together with the analysis of the accumulation of the mRNA transcribed from the intronless allele in the nucleus , suggested strongly a nuclear role for this protein although Pab2p has been shown to be able to shuttle to the cytoplasm in S . pombe and in Drosophila [70] , [71] . The kinetic of degradation of intronless mRNA in C . neoformans might be the result of a disequilibrium between mRNA export from the nucleus and degradation . Thus , when not enough introns are present the altered dosage of mRNA binding proteins would result in an extended retention time of mRNA in the nucleus giving time to the nucleases to degrade them . The absence of Pab2p would slow down this degradation giving time to some mRNA molecules to be exported in a “take the money and run” strategy [74] ( Figure 7 ) . In terms of evolution , the comparison of S . cerevisiae and C . neoformans provides a fascinating example of opposite evolutionary choices . Whereas S . cerevisiae has lost almost all its introns and has largely simplified its RNA metabolism ( i . e . loss of RNAi pathway , only one SR protein , absence of EJC-like complex… ) , C . neoformans has conserved and maybe increased its intron number and appears to have a very complex RNA metabolism . The selective pressure that has maintained introns in one organism and has eliminated them in another one is unknown . It has to be noted that C . neoformans is not a unique example among basidiomycete fungi . Thus , genes from Coprinus cinereus and Phanerochaete chrysosporium have an intron density close to that of C . neoformans [30] . In two other pathogens , Ustilago maydis for the plants and Malassezia sp . for human for example , the number of genes with introns is small [75] , [76] . This specificity might be related to the fact that C . neoformans is an opportunistic human pathogen living in the environment . As such the diversity of signals to which it can be exposed in the human body or in soil for example is huge . Indeed , this organism has to cope with a large number of different stresses and probably needs a very flexible metabolism . It is tempting to hypothesize that its complex RNA metabolism provides a mechanism to achieve such flexibility .
C . neoformans strains used in this study all originated from the serotype D strain JEC21 [77] and are listed in Table S1 . The strains were routinely cultured on YPD medium at 30°C [78] . Synthetic dextrose ( SD ) was prepared as described [78] . The capsule sizes were estimated after 24 h of growth in capsule-inducing medium at 30°C as previously described [79] . Melanin and urease production were assessed after spotting 105 cells of each strain on Niger or Christensen agar medium , respectively [69] , [80]; the plates were read after 48 h of incubation at 30°C . The bacterial strain Escherichia coli XL1-blue ( Stratagene ) was used for the propagation of all plasmids . Cells were routinely harvested after being grown up to 5·107 cells/mL in YPD . RNA was extracted with TRIZOL Reagent ( Invitrogen ) following the manufacturer's instructions . Total RNA ( 5 µg ) was separated by denaturing agarose gel electrophoresis and transferred onto Hybond-N+ membrane ( Amersham ) and probed with [32P]dCTP-radiolabelled DNA fragments . The banding pattern was quantified with a Typhoon 9200 imager and Image Quantifier 5 . 2 software ( Molecular dynamics ) . Total RNA was extracted from JEC21 cells growing on YPD . mRNA was purified using Oligotex Direct mRNA Mini Kit ( Qiagen ) following the manufacturer's instructions . SMARTer RACE cDNA Amplification Kit ( Clontech ) was used to synthesize the cDNA . CAS3 cDNA was PCR amplified using the primers CAS3a and CAS3AR ( see Table S2 ) and was analysed by agarose gel electrophoresis . The presence of a smeary pattern on the gel suggested the presence of different types of cDNA molecules . The amplified fragments were gel purified in three different pools of sizes and 15 cDNA molecules from each pool were cloned in a pGEMT plasmid and sequenced . An insertional mutant library was constructed in a NE292 ( MATa cas3Δi ura5 ) background using the Agrobacterium tumefaciens strain EHA105 transformed with the plasmid pPZP-NEO1 as previously described [81] . A total of 5796 colonies were transferred from the transformation plates to 96-well plate wells containing 75 µL of capsule inducing medium [79] supplemented with adenine ( 20 mg/L ) and uracil ( 5 mg/L ) . The mutants were then tested with the anti-capsule monoclonal antibody CRND-8 [82] as previously described [83] . Positive mutant strains were isolated and tested a second time using the same strategy . Total RNA was extracted and the presence of CAS3 mRNA was analysed by Northern blot . PAB2 cDNAs were amplified by PCR and inserted into the pQ-30 E . coli expression vector ( Qiagen ) . The E . coli BL21 transformant strains were grown in 50 mL of YT containing ampicillin ( 50 µg/ml ) and kanamycin ( 30 µg/ml ) to an OD600 of 0 . 5; gene expression was induced by adding 1 mM of IPTG and incubation for 4 hours at 37°C . The cells were then disrupted by sonication and centrifuged at 3000·g . The supernatant was recovered and the recombinant proteins were purified by affinity chromatography on a Ni-NTA column ( Qiagen ) following the manufacturer's procedures . The protein solution was adjusted to 20% ( w/v ) glycerol ( final concentration 140 µg/mL ) and stored in aliquots at −80°C . These experiments were conducted in 96-well Streptavidin coated plates ( Nunc ) . For each sample and concentration to be tested one well was washed three times with 200 µl of washing buffer ( Tris 25 mM , Nacl 150 mM , pH 7 . 2 , BSA 1% wt/vol , Tween 20 0 . 05% wt/vol ) . Each well was then incubated for 2 h at room temperature and under agitation ( 700 rpm ) with 100 µl of washing buffer containing 0 . 1 µM of poly ( A ) 30-mers oligonucleotide 5′ biothinylated . Unbound oligonucleotides were then eliminated through three washes with 200 µL of washing buffer . Each well was then incubated with 100 µl of recombinant Pab2p solution ( 0 . 70 mg/mL ) for 30 min at room temperature under agitation ( 700 rpm ) . After three washes with 200 µL of washing buffer , the quantity of poly ( A ) -bound protein was estimated using an anti-His peroxidase linked monoclonal antibody ( Qiagen ) and OPD ( O-phenylenediamine dihydrochloride ) ( Sigma ) following the manufacturer's procedures . After 10 min of incubation at room temperature , the colorimetric reaction was stopped by the addition of H2SO4 4% ( v/v ) and the optic density was measured at 492 nm . For the competition assays , 10 µM of unlabeled poly ( A ) or poly ( C ) ( Sigma ) was added to the protein solution . To localize the Pab2 protein , the PAB2 gene under the control of its own promoter was joined in-frame to a sequence encoding the GFP protein at its N-terminal end . Primers used for amplification are listed in Table S2 . A pab2Δ strain was transformed with a plasmid containing the URA5 marker and the Pab2-fluorescent protein fusion by biolistic delivery [84] . Transformants were grown on minimum medium and analyzed for fluorescence . The pBluescript ( Stratagene ) based plasmid pNE247 contained a 4067 bp DNA fragment containing the complete C . neoformans CAS3 gene PCR amplified using the primers CAS3F and CAS3R ( see Table S2 ) and cloned at the NotI site . This plasmid was used to construct all the CAS3 alleles presented in this study . For the intronless allele , the cDNA from CAS3 was amplified , cloned and sequenced ( see above ) . A completely spliced molecule was digested with SphI and PstI and cloned at the SphI-PstI site of pNE247 , thus replacing the wild type gene by an intronless version under the control of its own promoter . 5 µg of the resulting plasmid pNE254 were NotI digested and mixed with 1 µg of the URA5 containing plasmid pNE10 [79] digested with NotI . This DNA solution was used to transform the strain NE128 ( MATa cas3Δ:ADE2 ura5 ) [46] by biolistic transformation . The transformants were selected on a minimum medium containing adenine at 20 mg/L . After three days at 30°C , the transformation plates were transferred to room temperature and one week after transformation some colonies developed a pink phenotype suggesting that the ADE2 gene had been lost and thus that the cas3Δ::ADE2 allele has been replaced by the cas3Δi allele . The pink colonies were then cultured in liquid YPD so as to loose the unstable pNE10 plasmid [79] . Ura− strains were selected on FOA . The absence of the cas3Δ::ADE2 allele and the correct integration of the intronless allele were confirmed by PCR . The absence of additional integrations in the genome was confirmed by Southern blot . Two independent mutant strains were selected and stored at −80°C . Similar strategies were used to construct the other alleles and the other mutant strains . For nuclei purification , 500 mL of culture ( OD600 = 3 ) were harvested by centrifugation . Spheroplasts were prepared as previously described [85] and re-suspended in 3 mL of lysing buffer ( Pipes 10 mM pH 6 . 9 , sucrose 0 . 5 M , CaCl2 5 mM , MgSO4 5 mM , DTT 1 mM ) containing a complete set of antiproteases ( Roche ) . The spheroplasts were then mechanically disrupted and the intracellular organelles were separated from cellular debris and unbroken cells by centrifugation . Nuclei were purified by differential ultracentifugation ( 1 hour , 161 000·g , 4°C ) through a separation buffer ( Pipes 10 mM , sucrose 2 . 1 M CaCl2 5 mM , MgSO4 5 mM , DTT 1 mM ) containing a complete set of antiproteases ( Roche ) . Nuclei were then washed twice with conservation buffer ( TrisHCl 50 mM pH 8 . 3 , glycerol 40% , MgCl2 5 mM , EDTA 0 . 1 mM pH 8 ) , re-suspended in 500 µl of conservation buffer and stored in aliquots at −80°C . For each run on experiment 100 µL of nuclei suspension was used following a protocol previously described [47] . The radioactive transcripts produced were used to hybridize a serial dilution of DNA spotted on a nylon membrane . The plasmid pNE428 containing the 1558 bp fully spliced JEC21 CAS3 cDNA amplified with the primers CAS3a and CAS3AR and cloned in the pGEMT plasmid ( Clontech ) was used as CAS3 specific DNA . The plasmid pNE435 containing the DNA 519 bp DNA fragment PCR from JEC21 genomic DNA using the primers ACT1F and ACT1R cloned in pGEMT was used as ACT1 specific DNA . The plasmid pGEMT alone was used as negative control . The intensity of the signal was quantified with a Typhoon 9200 imager and Image Quantifier 5 . 2 software ( Molecular dynamics ) . Each experiment was repeated twice using two independent nuclei preparations . The same protocol of nuclei preparation was used to isolate the nuclear RNA fraction . Electrophoretic analysis of these RNA samples showed a clear decrease in the rRNA proportion confirming the quality of our preparation ( data not shown ) . The genes described in this report have been deleted by biolistic transforming a serotype D strain using a disruption cassette constructed by overlapping PCR as previously described [45] . The primer sequences used are given in Table S2 . The transformants were then screened for homologous integration as previously described [46] . The plasmid , pNAT used to amplify the NAT selective marker was kindly provided by Dr Jennifer Lodge ( Saint Louis University School of Medicine ) . The plasmid pPZP-NEO1 used to amplify the NEO selective marker was kindly provided by Dr Joe Heitman ( Duke University ) . Multiple mutant strains were obtained through crosses of single mutant strains on V8 medium as previously described [45] . Progenies were selected on minimum medium to which different amino acids were added . Their genotypes were determined by PCR . The mating types of the strains were determined by testing them on V8 medium in the presence of tester strains of known mating type . A four way overlap PCR gene deletion was used to generate the promoter-specific exchange cassettes of RRP44 and XRN2 , which included a nourseothricin and a neomycin cassette , respectively . The primers used in these experiments are listed in Table S2 . The GAL7 promoter was used as the inducible promoter [55] . The 694 bp upstream the RRP44 ATG and the 601 bp upstream the XRN2 ATG were replaced by the 556 bp present upstream of the GAL7 gene . Transformants were screened for homologous integration as previously described [86] . The ePAT and TVN-PAT reactions were performed using 1 µg of input RNA as previously described [87] . The sequences of the primers used for PCR amplification are listed in Table S2 . The cDNA was column-purified using NucleoSpin Gel and PCR Clean-up columns ( MACHEREY-NAGEL ) . Specific PCR products were analysed by 2% high resolution agarose gel ( Ultra pure 1000; Life Technologies ) pre-stained with sybr safe ( Life Technologies ) and imaged against a 100 bp ladder ( New England Biolabs ) using an LAS 3000 imager and multigauge software ( Fujifilm ) . Total RNA was subjected to an initial DNaseI ( Roche ) treatment to eliminate contaminating genomic DNA . 1 µg of the DNaseI treated RNA was then reverse transcribed using the kit QuantiTect Reverse Transcription ( Qiagen ) following the manufacturer's instructions . Quantitative PCR assays were performed according to Bio-Rad manufacturer's instructions using 96-well optical plates ( Thermo Scientific ) and an iCycler iQ ( 170–8740 , Biorad ) . Each run was assayed in triplicate in a total volume of 25 µL containing the 5 µL cDNA template at an appropriate dilution , 1× Absolute qPCR SYBR Green Fluorescein ( Thermo Scientific ) and 320 nM of each primer . The primers used are listed in Table S2 . PCR conditions were: 95°C/15 min for one cycle , 95°C/30 s for 40 cycles . Amplification of one single specific target DNA was checked with a melting curve analysis ( +0 . 5°C ramping for 10 s from 55°C to 95°C ) . The Ct values obtained in triplicate were averaged and normalised to that of the housekeeping gene ACT1 using standard curves . To verify the absence of genomic DNA contamination , negative controls in which reverse transcriptase was omitted were used . Three independent biological replicates were performed .
|
Cryptococcus neoformans is a major human pathogen responsible for deadly infection in immunocompromised patients . The analysis of its genome previously revealed that most of its genes are interrupted by introns . Here , we demonstrate that introns modulate gene expression in a cumulative manner . We also demonstrate that introns can play a positive or a negative role in this process . We identify a nuclear poly ( A ) binding protein ( Pab2p ) as implicated in the intron-dependent control of gene expression in C . neoformans . We also demonstrate that the essential nucleases Rrp44p and Xrn2p are implicated in two independent pathways controlling the intron-dependent regulation of gene expression in C . neoformans . Xrn2p regulation seems to depend on Pab2p whereas Rrp44p acts independently . In contrast , the other exosome nuclease Rrp6p and the TRAMP associated poly ( A ) polymerase Cid14p do not appear to be implicated in this regulation . Our results provide new insights into the regulation of gene expression in eukaryotes and more specifically into the biology and virulence of C . neoformans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"mycology",
"fungi",
"rna",
"processing",
"genetic",
"screens",
"medical",
"microbiology",
"gene",
"identification",
"and",
"analysis",
"gene",
"expression",
"genetics",
"gene",
"regulation",
"molecular",
"genetics",
"gene",
"splicing",
"biology",
"microbiology",
"microbial",
"pathogens",
"rna",
"stability"
] |
2013
|
Introns Regulate Gene Expression in Cryptococcus neoformans in a Pab2p Dependent Pathway
|
Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions . A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events . In a typical phosphoproteomics study , phosphorylation sites ( substrates ) of active kinases are quantified proteome-wide . By analyzing the activities of phosphorylation sites over a time-course , the temporal dynamics of signaling cascades can be elucidated . Since many substrates of a given kinase have similar temporal kinetics , clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases . Here we present a knowledge-based CLUster Evaluation ( CLUE ) approach for identifying the most informative partitioning of a given temporal phosphoproteomics data . Our approach utilizes prior knowledge , annotated kinase-substrate relationships mined from literature and curated databases , to first generate biologically meaningful partitioning of the phosphorylation sites and then determine key kinases associated with each cluster . We demonstrate the utility of the proposed approach on two time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway . The proposed approach will be a valuable resource in the identification and characterizing of signaling networks from phosphoproteomics data .
Cell signaling controls various aspects of basic cellular processes including homeostasis , proliferation , survival , and cell fate decisions , and defects in mechanisms underlying these processes are associated with a wide range of diseases [1–3] . Protein post-translational modifications ( PTMs ) , which can activate or inhibit protein function/activity , have emerged as key regulators of various signaling pathways [4] . Protein phosphorylation is a common type of PTM that increases the functional diversity of the proteome by altering target proteins between active and inactive forms for signal transduction and integration [5] . It is characterized by the addition of a phosphate group by a protein kinase to a serine , threonine , or tyrosine residue on a substrate protein [6] . Traditionally , protein phosphorylation has been studied largely using in vitro assays and , more recently , protein chip arrays [7] . However , kinase activities are often less specific in vitro compared to in vivo [8] , and , as a result , in vitro analyses often result in a large number of false discoveries . Recent advances in mass spectrometry ( MS ) -based technologies [9 , 10] make it possible to profile proteome-wide phosphorylation events in vivo for investigating signal transduction cascades [11] , understanding complex diseases [12–14] , and develop strategies for therapeuitc intervention [15 , 16] . With isotopic/isobaric labelling techniques and increasingly label-free approach , proteome-wide phosphorylation events can now be identified and quantified at a single amino acid resolution with high precision [17 , 18] . A key goal in a phosphoproteomics study is to identify the set of kinases and their corresponding substrates that underlie key signaling events [19] . Much progress has been made on developing computational tools to predict substrates of a given kinase using consensus sequence recognition motif [20 , 21] and incorporating additional information such as protein structure [22] and colocalization [23] . Conversely , computational approaches have been proposed to identify kinases based on substrate recognition motifs and differentially phosphorylated substrates [16 , 24–27] . It is estimated that there are over 500 kinases in human cells [28] . Most kinases phosphorylate not only many proteins but also many sites on the same protein . By analyzing phosphorylation sites ( substrates ) proteome-wide over a course of time , the dynamics of signaling cascades can be elucidated [29] . Since many substrates of a given kinase have similar temporal kinetics , clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases [8 , 30–33] . To identify the kinases that underlie key signaling cascades , clustering algorithms such as k-means clustering and its variant fuzzy c-means clustering are frequently utilized to partition the phosphorylation sites into clusters with distinctive temporal profiles from which the corresponding kinases and their activity could be inferred [8 , 30–33] . Fuzzy c-means clustering is an extension of the classic k-means clustering that allows a phosphorylation site to be assigned to multiple clusters with probabilistic “membership” scores [34] . While k-means clustering-based algorithms are computationally efficient and provide an intuitive separation and summarization of the temporal profiles [35 , 36] , their performance can be strongly influenced by the user-selection of the parameter k , which dictates the partitioning of the data into exactly k clusters . Thus , estimation of k becomes critical to generating biologically meaningful clusters . An underestimation of k will force unrelated phosphorylation sites to be assigned to the same cluster whereas an overestimation will split related phosphorylation sites across two or more clusters [37] , hence confounding downstream analyses . Numerous methods and metrics have been proposed over the years to estimate the optimal choice of k for k-means clustering-based algorithms . Popular approaches include internal indices such as Dunn index [38] and Connectivity [39] , stability indices such as average proportion of non-overlap ( APN ) , average distance ( AD ) , average distance between means ( ADM ) [40] , and the figure of merit ( FOM ) [41] , and biological indices that measure biological homogeneity ( BHI ) or biological stability ( BSI ) [42] . However , none of these approaches assess the information content of resulting clusters using a formal hypothesis testing framework , nor are they specifically designed for analyzing phosphoproteomics data . Here we propose a knowledge-based CLUster Evaluation ( CLUE ) approach for determining the most informative partitioning of a given temporal phosphoproteomics data using a hypothesis testing approach . Our approach utilizes known kinase-substrate annotations from curated phosphoproteomics databases to first estimate the optimal number of clusters within a dataset and then identifies the enriched kinase ( s ) associated with each cluster . Using simulation studies , we show that CLUE outperforms several alternative approaches in identifying the optimal number of clusters . In addition , we apply CLUE on time-series phosphoproteomics datasets [12 , 43] and identify key kinases associated with human embryonic stem ( hES ) cell differentiation and insulin signaling in 3T3-L1 adipocytes .
Identification of key kinases that control the activation and inhibition of cell signaling is a critical step for characterizing signaling cascades in time-course phosphoproteomics studies . Since many substrates ( phosphorylation sites ) of a given kinase are may have similar temporal profiles , partitioning phosphorylation sites from a proteome-wide time-series study into informative clusters , each with a distinctive temporal profile , becomes vital toward identification of kinases that could explain the observed phosphoproteome . We developed a knowledge-based CLUster Evaluation ( CLUE ) framework that uses existing knowledge , known kinase-substrate annotations from curated phosphoproteomics databases , to guide the generation of biologically meaningful clusters . A schematic overview of CLUE is presented in ( Fig 1 ) . CLUE provides a framework to assess the most informative partitioning of a given temporal phosphoproteomics data . Specifically , CLUE estimates the optimal k for clustering data using k-means clustering-based algorithms ( see Materials and Methods for details ) . To assess CLUE's ability to partition data into meaningful clusters and to assess CLUE's performance against alternative approaches for estimating k , we conducted studies using simulated phosphoproteomics data ( see Materials and Methods for details ) . We generated scenarios where the data were simulated to have varying number of clusters . In each case , the clusters were generated based on a set of randomly selected temporal profile templates ( Fig 2 ) , each representative of a phosphorylation activity profile over seven time points . The goal was to assess how well each method performs in recovering the true number of clusters . We compared CLUE with eight popular approaches including those that use internal indices such as Dunn index [38] and Connectivity [39] , stability indices such as average proportion of non-overlap ( APN ) , average distance ( AD ) , average distance between means ( ADM ) [40] , and the figure of merit ( FOM ) [41] , and biological indices that measure biological homogeneity ( BHI ) or biological stability ( BSI ) [42] . Every method we tested computes an objective score for each k and reports the k with the best score . To facilitate a fair comparison of methods , we transformed the objective scores from each method into the range [0 , 1] by using Min-Max normalization . After the normalization , the scores from methods that seek to minimize the objective function were further transformed into 1 minus the normalized Min-Max scores . First , we compared the performances of CLUE and other commonly used approaches including Dunn index , Connectivity , APN , AD , ADM , FOM , BHI , and BSI in estimating the optimal number of clusters for each of the scenarios with simulated data . In all cases , the fuzzy c-means clustering , an extension of the classic k-means clustering , was used to partition the data , and the results were largely the same even when k-means clustering was used . Results from our simulation studies ( Fig 3 ) reveal that in all cases , CLUE was able to accurately identify the true number of clusters in the simulated datasets whereas other methods were not as accurate . Importantly , the simulation studies also revealed some common biases with some of the methods tested . In particular , BHI , FOM , and AD have a tendency to overestimate the optimal number of clusters . In other words , while these methods are able to capture the lower bound on the optimal number of clusters , they fail to provide a reasonable upper bound . On the other hand , ADM , APN , BSI , Connectivity , and Dunn index appear to suffer from local optima and thus have a tendency to underestimate the optimal number clusters . In all cases , APN , BSI , and Connectivity reported the optimal number of clusters as 2 , severely underestimating the true number of clusters . Although ADM appears to somewhat overcome the bias , it still suffers from local optima . While it is arguable that by observing the pivotal point in the reported scores , several of these methods may help in determining the optimal number of clusters when the true number of cluster is small , such a pivotal point may be less apparent when the number of true clusters is rather large , as one would expect in a high-throughput dataset . Although CLUE , BHI , and BSI utilize known kinase-substrate annotations in aiding their clustering evaluation process , their performances vary significantly perhaps due to how they utilize this information . CLUE's ability to make reasonably accurate predictions on the optimal number of clusters is attributable to it taking advantage of known information and using it to assess and penalize under/over clustering as it attempts to estimate the optimal number of clusters ( see Materials and Methods ) . Similar results were obtained when the classic k-means clustering was used instead of the fuzzy c-means clustering ( S1 Fig ) , indicating that CLUE's performance is not dependent on the type of k-means clustering-based algorithm . Together , these results highlight the advantages of using known kinase-substrate annotations in aiding optimal clustering of phosphoproteomics data . Next , to assess how important the completeness of the known kinase-substrate annotations is in determining CLUE's performance , we simulated data such that only those kinases that had annotations for substrates in g out of the k clusters were considered . The goal of this simulation study was to determine how much known information is sufficient to help guide optimal clustering of the data . The scenario when g = 0 resembles the situation when no existing knowledge is available for use by CLUE . For a method that was designed to rely heavily on existing knowledge to aid clustering , CLUE , as expected , is unable to correctly predict the true number of clusters in the simulated data when g = 0 ( Fig 4A ) . However , as g is set to higher values , CLUE's ability to accurately predict the true number of clusters improves dramatically . Having established how valuable existing knowledge is in aiding correct clustering of high-throughput phosphoproteomics data , we next sought to assess the extent to which incorrect annotations ( noise ) may influence CLUE's performance . To this end , we simulated different levels of noise by requiring 10% , 20% , 40% , 60% or 80% of the substrates to have incorrect kinase assignments , similar to what one might encounter in real-world . As one would expect , CLUE performed poorly when the noise was set at 80% ( Fig 4B ) . However , CLUE was able to consistently recover the true number of clusters even when a substantial percentage , up to ~40% , of the annotation is incorrect . Overall , these simulation results demonstrate that CLUE is robust and powerful in estimating the true number of clusters based on simulated phosphoproteomics data . Given that later time points post stimulus in phosphoproteomics studies capture non-functional phosphorylation [44] , we sought to assess CLUE’s performance as a function of “noisy” data wherein last one or two time points were simulated to be random noise , reflecting non-functional phosphorylation . As expected , we observed a noticable drop in CLUE’s performance with increasingly more time points affected by noise ( S2 Fig ) . This observation highlights the importance of time point selection in phosphoproteomics experimental design . We also assessed CLUE’s performance as a function of the number of profiled time points . In theory , the more the number of time points , the more the chances of capturing the subtle differences in the temporal kinetics , and thus the more the number of clusters one may infer . To test this , we varied the number of time points used for representing temporal patterns in the simulation studies . Specifically , we compared results based on data from all seven time points against those from four ( 1 , 3 , 5 , 7 ) or three ( 1 , 4 , 7 ) time points . Although using data from just four time points correctly predicted the number of true clusters , the levels of uncertainty was noticeably higher ( error bars in S2 Fig , middle panel ) . Using data from fewer ( three ) time points leads to underestimation of the true number of simulated clusters ( S2 Fig , right panel ) . Thus , we conclude that the number of time points required for dissecting various kinases depends on the profiled signaling processes . If the signaling processes have complex temporal features , fewer than sufficient number of time points may not provide the necessary resolution to distinguish them from each other and CLUE will likely group them into a single cluster . To demonstrate how valuable CLUE would be in identifying key signaling events from high-throughput phosphoproteomics data , we applied CLUE on two previously published SILAC-based temporal phosphoproteomics datasets on differentiating human embryonic stem ( hES ) cells ( five time points ) [43] and insulin activation in mouse 3T3-L1 adipocytes ( nine time points ) [12] .
Identification of key kinases that control activation and inhibition of specific signaling events is critical for characterizing signaling networks . In this study , we described a knowledge-based CLUster Evaluation ( CLUE ) approach that enables identification of key signaling events from temporal phosphoproteomics data by utilizing known kinase-substrate annotations . Our simulation studies show that CLUE outperforms many alternative methods in recovering the underlying clusters from temporal datasets . To test how CLUE can be utilized for real-world applications , we analyzed temporal phosphoproteomics datasets generated from hES cell differentiation and insulin activation of adipocytes . The understanding of self-renewal and differentiation of hES cells is a subject of major scientific interest due to its applications in cancer treatment and regenerated medicine [51] . It is widely acknowledged that signaling pathways play critical roles in maintaining the pluripotent state of ES cells [52] and therefore , the identification of kinases that are involved in hES cell self-renewal and differentiation is of great importance . Similarly , the insulin signaling pathway plays a key role in regulating and maintaining the physiology of the adipocytes . Therefore , the characterization of the kinases that are the key components in insulin signaling allows potential clinical application to be targeted at different pathway levels . Using CLUE , we were able to identify and characterize several known and novel kinases that are key regulators in hES cell differentiation and insulin signaling . Furthermore , CLUE can also be used to discover novel substrates for active kinases of interest . For instance , in our analyses of the insulin activation data , many known Akt substrates ( AS160 Ser595 , PFKFB2 Ser469 , and BAD Ser136 ) and mTOR substrates ( FRAP Ser2481 and IRS1 Ser632 ) that have not yet been annotated in PhosphoSitePlus are ranked highly based on the membership score of c-means clustering ( S1 Table ) . Thus , not only does CLUE help in the identification of key kinases but also may facilitate identification of novel substrates of kinases . It is conceivable for a phosphatase to coordinately dephosphorylate a subset of substrates of a given kinase , in which case a subset of substrates of that kinase is expected to exhibit a similar temporal profile and thus clustered together in our analysis . Moreover , increases as well as decreases in substrate phosphorylation levels of a given kinase could be due to elevated ( reduced , resp . ) kinase activity and/or reduced ( increased , resp . ) levels of corresponding phosphatase . Either way , even in the absence of phosphatase-substrate information , as long as substrates that belong to a key signaling cascade exhibit similar temporal profile ( increasing/decreasing ) , CLUE will infer them to belong to a cluster and identify putative kinases associated with the cluster . Depending on whether the phosphorylation levels of the substrates within a cluster over the time-course are up/down , one can infer whether that signaling pathway is activated or inactivated . For example , in our analysis of the hES data ( Fig 5 ) , we identify enrichment of substrates for ERK ( cluster 3 ) and p70S6K ( cluster 6 ) . Based on the temporal profiles , it is evident that ERK signaling is inactivated as hES cells differentiate ( beginning 1hr time point ) , which is consistent with an essential role for ERK signaling in the maintenance of the pluripotent state in hES cells by blocking neuronal , trophectoderm and primitive endoderm differentiation [47] . In contrast , substrates predicted to be that of p70S6K are activated during hES cell differentiation , consistent with the fact that activation of p70S6K alone is sufficient to induce hES differentiation [46] . Thus , CLUE is applicable to analyze both increasing and decreasing phosphorylation profiles and will be useful even when phosphatase-substrate information is unavailable . Other factors such as protein translation rate , degradation rate , and cell cycle progression may affect phosphorylation especially at later time points , and diverse substrates of a given kinase may be modulated with different kinetics . To address these confounding factors , phosphorylation sites and time points may be pre-filtered to select those that are biologically most relevant for capturing a given kinase’s activity when such prior knowledge is available . Our simulation studies reveal that CLUE's performance is dependent on the accuracy of the annotations ( prior knowledge ) that is employed to aid the clustering process . Although CLUE can tolerate reasonable amount of noise/inaccuracies ( up to ~40% ) , using annotations from a high quality source/database is essential for accurate and biologically meaningful clustering of the data . It is worth noting that CLUE's performance is not biased towards larger kinase-substrate annotation groups as Fisher’s Exact test used to test for kinase enrichment is robust to size differences in kinase-substrate annotations . Although we formulated CLUE for analyzing phosphoproteomics data , the general framework of CLUE can also be used to analyse temporal transcriptomics data toward identification of transcription networks and cascades . This can be accomplished by using gene set annotations , as defined by various gene ontology-like databases , or transcription factor-target gene annotations in place of kinase-substrate annotations . While CLUE is designed to perform optimally with k-means clustering-based algorithms , in theory , it can be coupled with other clustering algorithms such as SOM where the cluster enrichment can be evaluated .
Kinase-substrate annotations were compiled from the PhosphoSitePlus database , a curated database of protein post-translational modifications ( PTMs ) including phosphorylation [53] . We compiled mouse-specific and human-specific kinase-substrate annotations and assigned to each kinase its phosphorylation substrates from mouse and human , respectively , based on “KINASE” , “SUBSTRATE” , and “SUB_ORG” columns of the database . The official gene symbols and the phosphorylated residues ( amino acids ) were concatenated together to create unique identifiers for each phosphorylation site . Phosphorylation sites assigned to multiple kinases ( in PhosphoSitePlus ) are classified to multiple kinases in the enrichment analysis . In total , we extracted 206 kinases and 9830 kinase-substrate interactions for human , and 235 kinases and 17532 kinase-substrate interactions for mouse . CLUE relies on annotated kinase-substrate relationships to estimate the optimal k for clustering phosphoproteomics data using k-means clustering-based algorithms ( Fig 1 ) . Given a clustering output from a k-means clustering-based algorithm that partitions the data into exactly k clusters , let i = 1…k be the ith cluster . Let m be the number of kinases annotated in the PhosphositePlus database for the species of interest and j = 1…m be the jth kinase . Let aij denote the number of phosphorylation sites regulated by kinase j that are included in cluster i , bij denote the number of phosphorylation sites regulated by kinase j that are not present in cluster i , cij denote the number of phosphorylation sites in cluster i that are not regulated by kinase j , and dij denote the number of phosphorylation sites that are neither included in cluster i nor regulated by j . Let us define θ as odds-ratio such that θ = ( aij / bij ) / ( cij / dij ) , and under Fisher’s exact test , we can test for the significance of enrichment of j's substrates in cluster i under the null hypothesis that the substrates of j are not over-represented in cluster i ( i . e . H0:θ = 1 ) and the alternative hypothesis that the substrates of j are over-represented in i ( i . e . H1:θ > 1 ) . For a given set of values aij , … , dij , the enrichment can best tested as follows: probij= ( aij+bijaij ) ( cij+dijcij ) ( aij+bij+cij+dijaij+cij ) and the p-value for the test of significance ( i . e . pij ) is obtained by summing the probij values over all combinations of aij , … , dij that return odds-ratio values at least as large as the observed values . By applying the above test for all m kinases against a given cluster i , the significance of the information content of cluster i is determined as follows: p ( clusteri ) =minj=1…m ( pij ) . Then , the p-values for all k clusters are combined using Fisher’s combined probability test: Pk=P ( χd2>−2∑i=1klog ( p ( clusteri ) ) ) , where d = 2k denotes the degrees of freedom . Finally , Pk is converted into an enrichment score Ek = -log10 ( Pk ) , which indicates how informative it is to partition the data into k clusters . The higher the enrichment score , the more informative the resulting clustering is . The enrichment score captures both the information content of each individual cluster while also assessing the overall enrichment of the entire partitioning . Intuitively , with an overestimated k , phosphorylation sites that are substrates of a kinase might be split across two or more clusters , which will be penalized by Fisher’s exact test for lower information content of resulting clusters . In contrast , underestimation of k might group unrelated phosphorylation sites to the same cluster , which will be penalized by Fisher’s combined probability test . By using k-means clustering-based algorithm with a range of different k values to partition the dataset and assessing the enrichment score for each k using CLUE , the optimal k for partitioning can be estimated . To compare CLUE's performance with those of other commonly used approaches for estimating k for k-means clustering-based algorithms , we conducted simulation studies . First , we defined 14 temporal profiles , each with seven time points , representing typical temporal kinetics observed in a time-series study ( Fig 2 ) . Next , time course phosphorylation profiles for individual sites ( substrates ) were simulated by randomly selecting a set of temporal profiles , representing a set of clusters , from the 14 templates and then generating data using the selected temporal profiles with Gaussian noise . Specifically , 500 phosphorylation sites were generated for each temporal profile under a Gaussian distribution with the standard deviation held constant ( σ = 1 ) . For instance , to simulate a 4-cluster dataset , 4 different temporal profile templates are randomly selected and a total of 2000 phosphorylation sites are generated based on the selected temporal profile templates . In the case of simulating a 14-cluster dataset , all temporal profiles templates are used and a total of 7000 phosphorylation sites are generated . Then , we evaluated CLUE's performance using the k-means as well as the fuzzy c-means clustering algorithms . For the purposes of testing , we used values for k ( or c in the case of fuzzy c-means clustering ) ranging from 2 to 20 . In practice , this can be specified by the user . Since the k-means and the fuzzy c-means clustering algorithms randomly initiate centroids , for each k ( or c , respectively ) , clustering was performed 10 times , each time with a different initialization of centroids in order to obtain an estimation of means . The final result is obtained by averaging the results from each individual runs , and the optimal clustering is determined by finding the maximum enrichment score from the final result . For simulating the database of annotated kinase-substrate relationships , we generated 100 kinase-substrate groups , each comprising 50 substrates assigned to a kinase . For evaluation purposes , of the 100 groups , g groups were generated to each contain phosphorylation sites ( substrates ) defined to have the same temporal profile . To assess the extent to which incorrect annotations ( noise ) may influence the performance of CLUE , we set g = 5 and simulated different levels of noise by requiring 10% , 20% , 40% , 60% , or 80% of the substrates from each group to have a temporal profile different from that of the rest of substrates in that group . The remaining 95 kinase-substrate groups were generated to contain substrates that were randomly sampled from all phosphorylation sites in the simulated dataset . The resulting simulated kinase-substrate annotations were used for the evaluation of CLUE , BSI and BHI in estimating the optimal number clusters in the simulation experiments . To demonstrate the utility of the proposed approach , we applied it on two previously published SILAC-based temporal phosphoproteomics datasets on ( a ) human embryonic stem ( hES ) cells differentiation using phorbol 12-myristate 13-acetate ( PMA ) treatment [43] and ( b ) insulin activation in mouse 3T3-L1 adipocytes [12] . The hES cell differentiation data has a total of 14 , 865 unique phosphopeptides containing 23 , 522 phosphorylation sites mapping to 4 , 335 proteins . The phosphopeptides were quantitated over a time-course of five time points during hES cell differentiation ( 0 min , 30 min , 1 hour , 6 hour , and 24 hour ) . For clustering analyses , only those phosphorylation sites that have an associated gene product and at least 2-fold change in phosphorylation levels at any time point during differentiation compared to the initial time point ( 0 min ) were considered . This filtering step resulted in 3 , 416 phosphorylation sites . The insulin activation dataset has a total of 38 , 901 unique phosphopeptides corresponding to 37 , 248 phosphorylation sites mapping to 5 , 705 proteins . The phosphopeptides were quantitated over a time-course of nine time points during insulin treatment of mouse adipocytes ( 0 sec , 15 sec , 30 sec , 1 min , 2 min , 5 min , 10 min , 20 min , and 1 hour ) performed in biological triplicates . For clustering analyses , only those phosphorylation sites that have an associated gene product and are differentially phosphorylated , as determined using a moderated t-test implemented in limma R package [54] with a false discovery rate ( FDR ) of 0 . 05 as cutoff , were considered . This filtering step resulted in 3 , 178 phosphorylation sites . For a given kinase of interest , the amino acid sequences of its substrates annotated in PhosphoSitePlus database is extracted to calculate a position-specific scoring matrix ( PSSM ) as follows: Pa , j=1N∑i=1NI ( xi , j=a ) where N is the number of annotated substrates , j is the amino acid position , a is the set of characters corresponding to the 20 amino acids , and I is the indicator function . Then , a motif enrichment score is calculated for each phosphorylation site by summing the frequency of occurrence of each amino acid in relation to the PSSM . CLUE was implemented as an R package . The source code and documentation are freely available from CRAN ( http://cran . r-project . org/web/packages/ClueR/index . html ) .
|
A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events . Mass spectrometry-based technologies have emerged as a powerful tool to profile proteome-wide phosphorylation events in vivo at a single amino acid resolution with high precision . However , development of algorithms to analyze and identify signaling events from high-throughput phosphoproteomics data is still in its infancy . Here we propose a knowledge-based CLUster Evaluation ( CLUE ) approach for identifying key signaling cascades from time-series phosphoproteomics data . Our approach utilizes known kinase-substrate annotations from curated phosphoproteomics databases to first determine the optimal clustering of the phosphorylation sites and then identify enriched kinase ( s ) . We apply CLUE on time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data
|
Meiosis halves the chromosome number because its two divisions follow a single round of DNA replication . This process involves two cell transitions , the transition from prophase to the first meiotic division ( meiosis I ) and the unique meiosis I to meiosis II transition . We show here that the A-type cyclin CYCA1;2/TAM plays a major role in both transitions in Arabidopsis . A series of tam mutants failed to enter meiosis II and thus produced diploid spores and functional diploid gametes . These diploid gametes had a recombined genotype produced through the single meiosis I division . In addition , by combining the tam-2 mutation with AtSpo11-1 and Atrec8 , we obtained plants producing diploid gametes through a mitotic-like division that were genetically identical to their parents . Thus tam alleles displayed phenotypes very similar to that of the previously described osd1 mutant . Combining tam and osd1 mutations leads to a failure in the prophase to meiosis I transition during male meiosis and to the production of tetraploid spores and gametes . This suggests that TAM and OSD1 are involved in the control of both meiotic transitions .
Meiosis is a central feature in the reproductive program of all sexually reproducing eukaryotes . The process of meiosis involves two rounds of chromosome segregation that follow a single round of chromosome duplication leading to the production of haploid gametes . Meiosis differs from mitosis , somatic cell division , in a number of ways . In meiosis: To generate haploid spores the meiocyte must enter meiosis I , pass through the meiosis I to meiosis II transition and exit meiosis II . Errors in these transitions , are not uncommon and may lead to parthenogenesis or teratoma formation , or to the production of gametes with the somatic number of chromosomes ( 2n gametes ) [3] , [4] . The formation of 2n gametes is thought to be an important mechanism for generating polyploids . Polyploidy has played a key role in the evolution of many fungal , plant , invertebrate and vertebrate lineages , and is particularly frequent in plants [5] , [6] . 2n gametes are also an important tool for plant breeding [7] . Cyclins and cyclin-dependent kinases ( Cdk ) form complexes that are essential for progression through both the mitotic and meiotic cell cycles . The transition from meiosis I to meiosis II requires a fine balance in Cyclin–Cdk activity: it must be sufficiently low to exit meiosis I but must nonetheless be maintained at a level sufficiently high to suppress DNA replication and promote entry into meiosis II [8] , [9] . Precisely how the mitotic machinery is modified for the purpose of meiosis is not fully understood . Most of the knowledge currently available originates from studies carried out in unicellular fungi , Xenopus laevis or mouse oocyte systems . In oocytes , entry into both meiosis I and meiosis II is driven by Cdc2/Cyclin B complexes ( the molecular components of the maturation promoting factor , MPF ) [10] . The rate of Cyclin B synthesis and degradation by the APC ( anaphase promoting complex ) determines the timing of the transitions occurring during meiosis [10] , [11] . At the end of meiosis I , Cyclin B is only partially degraded [12] and the residual , low level of Cdc2/CyclinB activity is essential for entry into meiosis II [13] . This partial Cyclin B degradation is fine-tuned by the Erp1/Emi2 APC inhibitor [14]–[16] . In Schizosaccharomyces pombe , Mes1 is a key player in the meiosis I to meiosis II transition . Like Erp1/Emi2 , the Mes1 protein partially inhibits cyclin degradation by the anaphase promoting complex ( APC ) , thereby allowing entry into meiosis II [17] , [18] . In Saccharomyces cerevisiae , the simultaneous deletion of two of the six B-type cyclins results in a single reductional division during meiosis , with the production of two-spored asci ( dyads ) , suggesting that specialist cyclins may be responsible for mediating the meiosis I to meiosis II transition [19]–[21] . Very little is known about control of the meiotic cell cycle in plants . In maize , the elongate mutant produces diploid female gametes because it is unable to undergo female meiosis II , but the corresponding gene has not been identified [22] . The only gene involved in the meiosis I to meiosis II transition isolated to date is the Arabidopsis OSD1 ( OMISSION OF SECOND DIVISION ) gene , the molecular function of which remains unknown . As osd1 mutants fail to enter the second division in both male and female meiosis , functional 2n gametes and tetraploid progeny are produced [23] . To look for other regulators of meiotic progression , we focused our attention on cyclins . The Arabidopsis thaliana genome contains 10 A-type and 11 B-type cyclins but functions in the cell cycle have been identified for only a few of these molecules , probably because redundancy attenuates the effects of mutations in single cyclin genes [24] , [25] . A noticeable exception to this rule is CYCA1;2 , also known as TAM ( TARDY ASYNCHRONOUS MEIOSIS ) . In the tam-1 mutant , a single substitution of an amino acid ( Thr283-Ile ) slows cell cycle progression during male meiosis [26] , [27] . Here , we revisit the function of CYCA1;2/TAM by isolating a series of alleles , including null alleles , and show that CYCA1;2/TAM is crucial for the meiosis I to meiosis II transition . The tam mutants fail to enter meiosis II , leading to the production of dyads of spores and diploid gametes that have experienced genetic exchange . This demonstrates that the meiosis I to meiosis II transition in plants involves a non redundant Cyclin-cdk activity . Furthermore , combining tam and osd1 mutations leads to a failure of the transition from prophase to the first meiotic division ( meiosis I ) during male meiosis and to the production of single-spore meiotic products and tetraploid pollen grains . This shows that CYCA1;2/TAM and OSD1 are also involved in the prophase to meiosis I transition . The implications of these results for the control of meiotic transitions are discussed . In addition , taking advantage of a genetic strategy we used previously to investigate OSD1 function [23] , we combined tam mutants with mutations that affect homologous recombination ( Atspo11-1 ) and chromosome segregation ( Atrec8 ) , essentially converting meiosis into a mitotic-like division . The tam/Atspo11-1/Atrec8 line , called MiMe-2 , produces diploid gametes that are genetically identical to their parents , mimicking apomeiosis , a key element of apomixis or asexual reproduction through seeds . However , MiMe-2 also resulted in the production of diploid and aneuploid gametes , probably due to the lower penetrance of tam mutations compared to osd1 mutations .
The Arabidopsis TAM ( Tardy Asynchronous Meiosis ) gene has been implicated in the control of meiotic progression [26] , [27] . The tam-1 mutant displays slower cell cycle progression during male meiosis , although it does eventually , like wild type , produce tetrads . However , previous to this study , only one mutant allele with a point mutation leading to a single amino-acid substitution in the protein had been studied . As this point mutation is temperature-sensitive and probably corresponds to a hypomorphic allele , we revisited the role of the TAM gene by isolating and characterizing three independent insertion mutants from public mutant collections and three additional point mutations ( Figure 1A and Figure S1 ) . The tam-2 ( sail_505_C06 [28] , [29] ) T-DNA insertion is in the fourth exon ( ATG+1130 pb ) and is accompanied by a large deletion ( corresponding to half of the TAM coding sequence , the entire TAM promoter region and the first 52 bp of the next gene , At1 g77400 ) , the tam-3 ( SALK_080686 [30] ) T-DNA insertion is in the seventh intron ( ATG+1690 bp ) and the tam-4 ( CSHL_ET12273 [31] ) Ds insertion is in the first exon ( ATG+62 bp ) . The tam-2 and tam-3 mutations are in the Columbia ( Col-0 ) background , whereas the tam-4 mutation is in the Landsberg erecta ( Ler ) background ( Figure 1A ) . Additionally , three point mutations were isolated in an ethyl methanesulfonate ( EMS ) chemical mutagenesis screen . The tam-5 mutation is a G to A transition at nucleotide 378 in the coding sequence that creates a stop codon ( TGG → TAG; Trp77 → stop ) . The tam-6 mutation is also a G to A transition at the -1 position of the 3′ splice site between intron 2 and exon 3 . The tam-5 and tam-6 alleles are both recessive and do not complement one another . The tam-7 mutation is a G to A transition at position -4 of the 3′ splice site between intron 1 and exon 2 . Similar splice site proximal mutations have been observed in other Arabidopsis mutants including det1-1 and det1-3 [32] . The tam-7 allele is dominant and thus could not be used for genetic complementation of the other tam alleles . All three EMS tam alleles are in the Columbia ( Col-0 ) background . The T-DNA alleles were used preferentially for detailed analysis . The tam mutants displayed no defects during somatic development . In Arabidopsis , male meiosis produces a group of four spores , organized in a tetrahedron , called a tetrad ( Figure 1B ) . Each spore gives rise to a pollen grain . In these six independent tam mutants , the products of male meiosis were mostly dyads of spores instead of tetrads ( Figure 1B and Table 1 ) . In addition to balanced dyads the stronger mutants also produced triads and unbalanced products together with a very small number of tetrads . Complementation tests with tam-2 , tam-3 and tam-4 mutants confirmed that these mutations were allelic . Unlike the previously described temperature sensitive tam-1 mutant , these six mutants expressed the dyad phenotype at normal growing temperatures ( 20°C ) . Furthermore , the dyad stage in tam-1 does not appear to be terminal , as meiosis always progresses to tetrad production [27] , whereas the tam-2 , tam-3 , tam-4 , tam-5 , tam-6 and tam-7 systematically produced mostly dyads . This suggests that the tam-1 mutant presents only a delay in the progression of meiosis , whereas the other mutants do not progress beyond the dyad stage ( as confirmed by pollen analysis , see below ) . This phenotype is reminiscent of the phenotype of the osd1 mutant , which produces spores directly after meiosis I . Its gametes are thus diploid and its offspring polyploid . Thus , we determined ploidy levels among the offspring of diploid tam-2 and tam-4 mutants . In the progeny of selfed homozygous mutants , we observed tetraploids , triploids , and occasionally diploid plants ( Table 2 ) . If pollen from tam-2 or tam-4 mutant plants was used to fertilize a wild-type plant , almost all the resulting progeny were triploid , with only a few diploid plants identified ( Table 2 ) . If tam-2 or tam-4 mutant ovules were fertilized with wild-type pollen grains we isolated diploid and triploid plants ( Table 2 ) . Thus , the frequency of diploid spores , resulting in functional gametes , was high in the tam-2 and tam-4 mutants , for both the male ( ∼90% ) and female ( ∼30% ) lineages . Tam mutants produced only slightly fewer seeds than the corresponding wild-type lines ( 36±4 seeds/silique in tam-2; 42±4 in wild type Col-0; 43±4 in tam-4; 52±3 in wild type Ler ) , but many ( >50% ) of the seeds produced by tam-2 mutants and a few ( <10% ) of those produced by tam-4 mutants were shriveled . This finding was not unexpected , because tam mutants produce triploid seeds with an excess of the paternal genome that is associated with the production of shriveled seed , particularly in the Col-0 background [33] . The germination rate of seeds produced by tam-2 was 63% ( n = 264 ) , suggesting that the proportion of triploid seeds may be underestimated in tam-2 progeny . We characterized the mechanisms underlying dyad production in tam mutants , by investigating chromosome behavior during male meiosis using a meiocyte spreading technique ( Figure 2 and Figure 3 ) . In wild type , the ten chromosomes appeared as threads at leptotene , underwent synapsis at zygotene and were fully synapsed , along the SC at pachytene ( Figure 2A ) . After the disappearance of the SC at diplotene the resulting five bivalents condensed , revealing the presence of chiasmata ( Figure 2B ) . The bivalents organized on the metaphase I plate ( Figure 2C ) and homologs segregated to opposite poles at anaphase I ( Figure 2D and 2E ) . The two sets of five homologs aligned on the two metaphase II plates ( Figure 2F ) . The second round of segregation at anaphase II ( Figure 2G ) led to the formation of four sets of five chromosomes , that decondensed to form the spore nuclei ( Figure 2H ) . Meiosis I in tam mutants was indistinguishable from that in the wild type . All stages of prophase were observed , including full synapsis at pachytene ( Figure 3A ) and chiasmata at diakinesis ( Figure 3B ) . The bivalents observed at diakinesis , condensed and aligned on the metaphase plate ( Figure 3C ) . We quantified the chiasma frequency by studying the shape of metaphase I bivalents , as described by [34] , [35] , and found no difference between tam-2 ( 9±0 . 8 chiasmata per cell , n = 70 ) and the wild type ( 9 . 1±1 chiasmata per cell , n = 53 ) . The bivalents segregated at anaphase I and decondensed at telophase I ( Figure 3D–3F ) . However , we found no meiosis II figures ( among >1000 male meiocytes for each tam-2 , tam-3 and tam-4 , from prophase to spore formation ) , consistent with the dyad production in tam mutants resulting from the absence of a second meiotic division . Both typical meiosis I and meiosis II figures were observed during female meiosis ( Compare Figure 4 and Figure 5 ) , consistent with the observed production of ∼70% haploid female gametes in tam mutants and the notion that female diploid megaspores are also produced by skipping the second meiotic division . If diploid gametes are indeed generated by skipping the second meiotic division , any parental heterozygosity at centromeres should be lost in the tam diploid gametes , because sister centromeres cosegregate at division I . Conversely , we would also expect heterozygosity to increase towards telomeres , due to recombination between the locus concerned and the centromere . We tested this hypothesis in two ways , using the fluorescent tagged line ( FTL ) Arabidopsis tetrads system providing direct information about the genetic content of pollen grains ( Figure 6 , Figure 7 , Figure 8 ) and by genotyping male and female tam-2 triploid offspring for polymorphic molecular markers ( Figure 8 ) [23] , [36] , [37] . The FTL system is a visual assay based on the use of reporter constructs encoding fluorescent proteins produced in the pollen of the quartet mutant ( qrt1-2 ) [38] . In this mutant , the pollen grains from each meiosis remain physically attached . We carried out crosses to generate plants with both the qrt1-2 and tam-2 mutations , heterozygous for one or two reporter transgenes conferring pollen fluorescence . As controls , we used both wild type and osd1 mutant plants . We first used an FTL transgene located close to the centromere on chromosome 5 ( FTL3253 encoding AmCyan ) [39] . In wild-type ( qrt1-2 background ) control plants , all tetrads consisted of two fluorescent and two non-fluorescent pollen grains ( Figure 6A ) , reflecting the segregation of the four chromatids , two of which carried the transgene ( n = 120 ) . In the osd1-1 and tam-2 mutants , we observed mostly dyads of pollen ( Figure 6B–6E ) . Furthermore , in both mutants , the vast majority of dyads ( 91% , n = 526 and n = 738 , in osd1-1 and tam-2 respectively ) consisted of one fluorescent pollen grain and one non-fluorescent pollen grain ( Figure 6B and 6C ) . Thus , in these dyads , one of the pollen grains contained the two sister chromatids carrying the transgene , whereas the other pollen grain inherited the other two sister chromatids . This segregation pattern is fully consistent with the absence of a second meiotic division . In a small proportion of dyads ( 9% in both osd1-1 and tam-2 mutants ) both pollen grains were fluorescent , indicating recombination between the transgene and the centromere ( Figure 6D and 6E ) . We then carried out the same experiment with two linked FTL transgenes located at some distance from the centromere on chromosome 5 ( FTL1273 DsRed2 , FTL993 ECFP ) [39] . In the wild type ( qrt1-2 background ) , we observed tetrads without ( Figure 7A ) and with ( Figure 7B ) recombination of the markers as described in a previous study [36] . The frequency of recombination between the two markers , measured as the percentage of non-parental chromatids deduced from the fluorescence distribution , was 32% . In both the osd1-1 and tam-2 mutants , we observed segregation patterns in the dyads reflecting crossing over between the two markers ( 29% ( n = 262 ) and 22% ( n = 367 ) , respectively ) and between the markers and the centromere ( 22% and 22% , respectively ) ( Figure 7C–7J ) . The frequency of heterozygosity at the FTL marker loci in diploid pollen grains was determined ( Figure 8 ) . The frequency of heterozygosity was low next to the centromere and increased with distance from the centromere . All these findings are consistent with the tam and osd1 diploid pollen grains resulting from a single first division of meiosis that includes recombination , with no second division [23] . For confirmation of this genetic analysis , we genotyped triploid offspring generated from male and female gametes from the tam-2 mutant . We first introduced genetic polymorphisms into tam-2 ( Col-0 background ) by crossing this mutant to the No-0 accession . In the F2 generation , we used PCR to select plants homozygous for the tam-2 mutation but heterozygous for a series of microsatellite markers . These plants were crossed , as male or female parents , with plants from a third genetic background ( Landsberg erecta , Ler ) . Genotyping of the resultant triploid progeny for the trimorphic molecular markers revealed the genetic make-up of the 2n gametes produced by the mutant . The Ler allele was present in all the plants ( brought by the wild type Ler haploid gamete ) , while the presence/absence of the Col-0/No-0 allele in the triploids corresponds to the genotype of the 2N mutant gametes . All the diploid gametes tested had the predicted genetic characteristics , similar to those of the diploid gametes produced by the osd1 mutant ( Figure 8 ) . The markers were homozygous at the centromere , but displayed segregation at other loci , due to recombination . These results confirmed that the absence of a second meiotic division was indeed responsible for the production of both male and female 2n gametes in tam-2 . Our tam alleles displayed phenotypes very similar to that of the osd1 mutant , with no second meiotic division , leading to the production of viable diploid male and female gametes . Thus , tam diploid gametes differ from apomeiotic ( mitosis-like ) gametes in that they are genetically different from the mother plant [40] , [41] . We have previously shown that , by combining the osd1 mutation with the Atspo11-1 mutant , which eliminates recombination , and the Atrec8 mutation , which modifies chromatid segregation , it is possible to convert meiosis into a mitosis-like division ( apomeiosis ) . We called this triple mutant MiMe for “mitosis instead of meiosis” [23] . We investigated whether tam mutation could replace the osd1 mutation to convert meiosis into mitosis , by constructing the tam-2/Atspo11-1/Atrec8 triple mutant , which we named MiMe-2 . During male meiosis , MiMe-2 plants mostly generated dyads ( 91 . 2% 446/489 ) , with only a small number of unbalanced products . Observations of chromosome behavior during male and female meiosis showed that the division process resembled mitosis: 10 univalents aligned on the metaphase plate , with the separation of sister chromatids at anaphase ( Figure 9 ) . The selfed progeny of MiMe-2 plants was mostly tetraploid , with some aneuploids ( Table 2 ) . Backcrossing MiMe-2 plants , as the female parent , onto wild-type plants resulted mostly in triploids with a few aneuploid plants ( Table 2 ) , whereas backcrossing MiMe plants , as the male parent , onto the wild type , generated a mixture of mostly triploid plants , diploid and aneuploid plants ( Table 2 ) . Thus , the mitosis-like division observed in MiMe-2 plants gives rise to functional diploid gametes , together with small numbers of haploid and aneuploid gametes , in both the male and female lineages . MiMe-2 plants also displayed lower levels of fertility than either the wild-type or the tam-2 mutant ( wild type: 42±4 seeds/fruit; tam-2: 37±4 , MiMe2: 20±3 ) . This finding was not unexpected , as the tam mutation does not display full penetrance . In meiocytes lacking Atrec8 and Atspo11 that undergo a second division , this division is unbalanced , probably leading to the production of aneuploid gametes in MiMe-2 plants [42] . We analyzed the genetic content of the MiMe-2 diploid gametes , using both the FTL lines and molecular markers ( Figure 7K and 7L and Figure 8 ) . We introduced the same FTL transgenes as described above ( FTL1273 DsRed2 , FTL993 ECFP ) into MiMe and MiMe-2 plants . Almost all the pollen grains of both genotypes displayed both types of fluorescence ( Figure 7K and 7L; 98% and 95% , respectively ) , indicating that they had inherited both transgenes , and confirming the occurrence of a mitosis-like division , rather than meiosis , in both lines . The few cases in which the two pollen grains were not expressing both fluorescent proteins may be explained by chromosome missegregation or occasional extinction of the transgenes . In addition , all the diploid MiMe-2 gametes ( male and female ) systematically retained heterozygosity for each genetic marker tested ( Figure 8 ) . They were thus genetically identical to the mother plant . These results confirm that MiMe-2 plants undergo a mitosis-like division instead of a normal meiotic division , generating gametes genetically identical to the parent plant , but at a lower regularity than MiMe plants [23] . Our tam alleles displayed phenotypes very similar to that of the osd1 mutant , with no second meiotic division , leading to the production of viable diploid male and female gametes . We then combined tam-2 and osd1-1 mutations . The double mutant was almost sterile , producing very few seeds by selfing . Reciprocal crosses with wild type revealed that tam-2/osd1-1 double mutant was female fertile but male sterile . If tam-2/osd1-1 mutant ovules were fertilized with wild-type pollen grains we isolated almost exclusively triploid plants ( Table 2 ) . Observation of female meiosis in the double mutant revealed normal meiosis I but an absence of meiosis II ( Figure 10 ) . The genetic analysis of the female gametes , performed as above , showed that all the diploid ovules tested had the predicted genetic characteristics for an absence of second meiotic division ( Figure 8 ) . These results show that tam-2/osd1-1 double mutants display the same female meiosis phenotype as single mutants , with female meiocytes failing to enter meiosis II , leading to the production of 2n ovules . We investigated the origin of the male sterility phenotype observed in tam-2/osd1-1 double mutant plants , by assessing pollen viability [43] . Figure 11A shows a wild-type anther treated with Alexander stain which produces a red pigment in viable pollen . Anthers of tam-2 and osd1-1 single mutants contained viable pollen grains , although less numerous and slightly bigger than wild type ( Figure 11B and 11C ) . In contrast , tam-2/osd1-1 anther contained very few pollen grains ( 9±11 ) with variable size ( Figure 11D–11F ) . In wild type , meiosis produces four spores ( Figure 12A ) , whereas both tam and osd1 mutants produce dyads of spores ( Figure 12B and 12C ) . In contrast , observation of male meiotic products in tam-2/osd1-1 revealed only “monads” , with a single-spore product ( Figure 12D , n = 498 ) . We then investigated the behavior of male meiotic chromosomes in tam-2/osd1-1 mutants ( Figure 13 ) . Prophase was indistinguishable from wild type: the ten chromosomes appeared as threads at leptotene , underwent synapsis at zygotene and were fully synapsed at pachytene . After the disappearance of the SC at diplotene , the resulting five bivalents condensed , revealing the presence of chiasmata . However , we observed spores with a single nucleus ( Figure 13E and 13F ) , consistent with the observation of monads . Furthermore , only two figures typical of metaphase/anaphase I and no figures of telophase I or meiosis II were found among >1600 meiocytes . This suggests that most male meiocytes skip both meiosis I and II and produce spores directly after replication and prophase , without chromosome segregation . An expected consequence of such a defect is the production of 4n pollen grains . We did not succeed in crossing tam-2/osd1-1 mutants as male but we determined ploidy levels among the seeds produced by selfing and found a large proportion of 6n plants in the progeny of 2n plants ( Table 2 ) . As crosses with wild type showed that tam-2/osd1-1 produce 2n ovules , the occurrence of 6n plants strongly suggests that 4n pollen grains are produced , in accordance with the skipping of both rounds of segregation at male meiosis in tam-2/osd1-1 . A large proportion of 4n plants is also found in the selfed progeny showing that tam-2/osd1-1 also produces 2n pollen grains . These 2n pollen grains likely originate from the few meiocytes that enter meiosis I and produces 2n spores , that later outcompete 4n pollen .
We show here that one of the ten known type A cyclins in Arabidopsis , CYCA1;2/TAM , is required for the transition between meiosis I and meiosis II . No phenotype has been reported for mutants lacking any of the other CYCA , probably due to the high level of redundancy [24] . By contrast , none of the other cyclins were able to compensate for CYCA1;2 in the meiosis I to meiosis II transition . CYCA1;2 may have a specialist function or pattern of expression , required for this transition , that cannot be supplied by any of the other cyclins from Arabidopsis . Alternatively , the lack of CYCA1;2 may decrease generic cyclin/CDK complex activity , causing the meiosis I-meiosis II transition to fail . Both cyca1;2/tam and the previously described osd1 mutants fail to enter meiosis II and produce spores after meiosis I . Remarkably , male meiocytes lacking both OSD1 and CYCA1;2/TAM genes fail to enter meiotic division I , producing spores directly after prophase . This shows that in addition to their crucial function in driving meiosis I to meiosis II transition , these two genes are involved in the prophase to meiosis I transition . This suggests that they both contribute to an activity promoting entry into meiosis I and entry into meiosis II , most likely a CDK activity . The molecular function of OSD1 is currently unknown , however , it has been proposed by analogy to Erp1/Emi2 and Mes1 that it may inhibit APC activity , thus promoting CDK activity [23] . TAM/CYCA1;2 being a cyclin , may directly promote CDK activity . We believe that the meiosis I to meiosis II transition is easily disturbed , because fine regulation of the levels of cyclin/CDK activity is required to ensure both exit from meiosis I and entry into meiosis II [8] . Thus a moderate decrease of CDK activity in osd1 and cyca1;2/tam single mutants may cause failure to enter meiosis II without impairing the prophase to meiosis I transition . In contrast the coincident depletion of OSD1 and CYCA1;2/TAM , may further decrease CDK activity , impairing entry into meiosis I . Unfortunately the direct measurement of CDK activity during meiosis in Arabidopsis is currently not possible . The combination of osd1 and/or cyca1;2/tam mutants with other mutants affecting CDK activity may help to test this model . In S . cerevisiae , the simultaneous deletion of two ( out of six possible ) B-type cyclins ( Clb1 and Clb3 or Clb1 and Clb4 ) leads to a failure to enter meiosis II [20] , [21] . However , although Clb3 activity is specific to meiosis II , this is not the case for Clb1 and Clb4 [19] , and the Clb1 Clb3 Clb4 triple mutant barely sporulates , suggesting that all three proteins have functions in meiosis progression other than the meiosis I-meiosis II transition , similar to OSD1 and CYCA1;2/TAM . In the osd1/tam double mutant , male meiocytes fail to enter meiosis I after prophase , whereas female meiocytes proceed to meiosis I and fail to enter meiosis II revealing a striking difference in the control of male and female meiosis progression . We suggest that other cyclins may partly compensate for the absence of CYCA1;2 , during female meiosis , and that the meiosis I to meiosis II transition may be driven by different mixtures of cyclins in male and female meiosis . This possibility is supported by the cyca1;2/tam single mutants being weakly affected in the female lineage , compared to the male lineage and to osd1 male and female lineage . An analysis of the effects on meiosis of the depletion of other cyclins , either alone or together with TAM/CYCA1;2 , is required to test this hypothesis . Cyclin A proteins specific to male meiosis have already been described in mammals . The mouse and human genomes each contain two different A-type cyclins . One of these cyclins , Cyclin A1 , is restricted to the germ line whereas Cyclin A2 is ubiquitously expressed [44]–[47] . The loss of Cyclin A1 function has no effect on viability and results in male sterility , with male meiosis progressing to the late prophase and then leading to apoptosis; it has no effect on female fertility [48] . Control of male and female meiosis by a different set of cyclins may thus be a general phenomenon . The Arabidopsis genome encodes 50 cyclins or putative cyclins [25] . Two of these cyclins , SDS and CYCA1;2/TAM , have been directly implicated in meiosis . CYCA1;2/TAM is a type A cyclin involved in cell cycle progression [27] [25 and this study] , whereas SDS forms an outgroup , and plays a specific role in recombination with no evidence for any role in cell cycle progression [49] , [50] . SDS is required to bias crossovers between homologous chromosome at meiosis rather than between sister chromatid , as in mitosis [50] . A viable hypomorphic mutant of CDKA displays meiotic defects potentially corresponding to a combination of the sds and tam defects [51] ( e . g . recombination and progression defects ) , consistent with both SDS and TAM/CYCA1;2 being involved in CDKA activation . In wheat , Cdc2/CDKA genes are essential components of the Ph1 locus , which is responsible for preventing recombination between homeologous chromosomes [52] . This suggests that cyclin/CDK activity may finely regulate various meiotic events . Apomixis , or asexual clonal reproduction through seeds , has great potential for agricultural applications [40] , [41] . It can be separated into three developmental components: an absence or alteration of meiosis ( apomeiosis ) , the fertilization-independent development of the embryo from the egg cell ( parthenogenesis ) , and the initiation of endosperm development with or without fertilization [40] , [41] , [53] . We recently showed that fully penetrant apomeiosis can be induced in Arabidopsis , when meiosis is replaced by a mitosis-like division in the MiMe genotype , which combines mutations in three genes , AtSPO11-1 , to eliminate recombination , AtREC8 , to ensure the separation of sister chromatids rather than homologues and OSD1 to abolish the second division [23] . We have now identified a second gene , CYCA1;2/TAM , the mutation of which abolishes the second division of meiosis . The MiMe-2 genotype , which combines the Atspo11-1 , AtRec8 and a newly described tam mutation , gives rise to the same phenotype as the MiMe genotype , with the conversion of meiosis into a mitosis-like division . Thus , apomeiosis can be engineered by combining various mutations . However , as tam mutations have a lower penetrance than osd1 mutations , MiMe-2 plants produce apomeiotic gametes less frequently than MiMe plants . Thus , osd1 mutations may be more suitable than tam mutations for agricultural applications , if the results obtained in Arabidopsis can be extrapolated to crop plants . In addition to apomeiosis in MiMe or MiMe-2 plants , apomixis will required the introduction of parthenogenesis and autonomous endosperm formation .
Arabidopsis plants were cultivated as previously described [54] . For cytometry experiments , Arabidopsis plants were cultivated on Arabidopsis medium [55] , at 21°C , under a 16-h to 18-h photoperiod and 70% relative humidity . The tam-2 ( Sail_505-C06 Columbia accession ) , tam-3 ( Salk_080686 Columbia accession ) , tam-4 ( CSHL_Et12273 Landsberg erecta accession ) , tam-5 , tam-6 and tam-7 mutants were genotyped by PCR . For tam-2 , tam-3 and tam-4 two primer pairs were used . The first pair is specific to the wild-type allele and the second pair is specific to the left border of the inserted sequence . tam-2: N874380U ( 5′ GACTTGATGGATCCACAGC 3′ ) & N874380L ( 5′ CAGAAATCCTCCACTTGCG 3′ ) ; N874380L & LB3Sail ( 5′ TAGCATCTGAATTTCATAACCAATCTCGATACAC 3′ ) . tam-3: N580686U ( 5′ GAAGAGTATAGGCTTTCGCCC 3′ ) & N580686L ( 5′ TGCAACCACATCAGATGAGAG 3′ ) ; N580686L & LBSalk2 ( 5′ GCTTTCTTCCCTTCCTTTCTC 3′ ) . tam-4: ET12273R ( 5′ TAATGGGACCCACTGATGATC 3′ ) & ET12273L ( 5′ ACCTCAGATACACGCAAATGC 3′ ) ; ET12273L & Ds5-1 ( 5′ GAAACGGTCGGGAAACTAGCTCTAC 3′ ) . tam-5 F2P24 . 10_3 ( 5′ CATCGCTTTGGAGCAATTCGGTGT ) & F2P24 . 10_555 ( ACCAAAACCTGCTTTATCTCGCAATT . tam-6 F2P24 . 10_cf ( CACCATGTCTTCTTCGTCGAGAAATCTATCTCA ) & F2P24 . 10_572 ( TGGCCGATCTTATTTGAACAATTCACCTC ) . tam-7 F2P24 . 10_4 ( AAGACACCGAATTGCTCCAAAGCG ) & F2P24 . 10_yjd ( ATGTAATCTAGAGCCGGTCTTTTGTTCAA ) . The PCR products for tam-5 , tam-6 and tam-7 were designed as derived amplified polymorphic sequences ( dCAPS ) [56] using the Sainsbury atPRIMER webtool ( http://www . atprimer . tsl . ac . uk/cgi-bin/form1 . cgi ) . The dCAPS for tam-5 , tam-6 and tam-7 are cleaved with Mfe I ( 125 bp versus 103 bp+22 bp ) , Xba I ( 316 bp versus 136 bp+180 bp ) and Mfe I ( 396 bp versus 365 bp+31 bp ) respectively . The cleaved amplified polymorphic sequences ( CAPS ) used to genotype qrt1-2 were described by Francis et al [39] . The primers used to genotype osd1-1 , Atspo11-1-3 and Atrec8-3 were described in a previous study [23] . The tam-2 and tam-4 T-DNA right border junction was analyzed by sequencing PCR products . For tam-2 , the specific primers used were 77400F ( 5′TTGGCGAATCGTGGCGAGAA 3′ ) and Rb3Sail ( 5′TAACAATTTCACACAGGAAACAGCTATGAC3′ . For tam-4 , the specific primers used were ET12273R and Ds3-4 ( 5′ CCGTCCCGCAAGTTAAATATG3′ ) . The seeds for FTL analysis were obtained from G . P . Copenhaver [36] , [57] . We obtained tam-2/qrt1-2 and FTL3253 +/− plants by selfing double heterozygous tam-2/qrt1-2 , FTL3253 +/− plants . We obtained osd1-1/qrt1-2 and FTL3253 +/− plants by selfing a double heterozygote osd1-1/qrt1-2 FTL3253 +/− plants . We obtained tam-2/spo11-1-3/rec8-3/qrt1-2 and FTL993 +/− FTL1273 +/− plants by selfing a qrt1-2 mutant , triple heterozygous tam-2/spo11-1-3/rec8-3 and FTL993 +/− FTL1273 +/− plant . We obtained osd1-1/spo11-1-3/rec8-3/qrt and FTL993 +/− FTL1273 +/− plants by crossing a qrt1-2 mutant , triple heterozygote osd1-1/spo11-1-3 /rec8-3 FTL993 −/− FTL1273 +/+ plant with a qrt1-2 mutant , triple heterozygous osd1-1/spo11-1-3/rec8-3 FTL993 +/+ FTL1273 −/− plant . Plants of interest were selected by PCR genotyping . For each line , the first pair of primers is specific to the wild-type allele and the second pair is specific to the T-DNA insertion: FTL3253 ( AmCyan , nucleotide position 9304032 on chromosome 5 ) : FTL3253U ( 5′ AACTTAGATGCCGAAGAAATG 3′ ) & FTL3253L ( 5′ GAGATTCTATACAGATTGATCC 3′ ) ; FTL3253U & LB-TDNA_FTL ( 5′ GGCATGCAAGCTGATAATTC3′ ) FTL993 ( CFP , nucleotide position 25731311 on chromosome 5 ) : FTL993U ( 5′ AGTGACAAGAATCCTAGTCG 3′ ) & FTL993L ( 5′GTCTCTACTAAGAGCTCCTC 3′ ) ; FTL993L & LB-TDNA_FTL FTL1273 ( DsRed2 , nucleotide position 18164269 on chromosome 5 ) : FTL1273U ( 5′ TACTTAGTCTAGGGTATACAC3′ ) & FTL1273L ( 5′ TATAATCGTTCGTCAACGAG 3′ ) ; FTL1273L & LB-TDNA_FTL . The I3 line ( qrt1-2 with two insertions on chromosome 3 , FTL1500 CFP at nucleotide position 498916 and FTL1371 DsRed2 at nucleotide position 4319513 ) as described in Francis et al . , [39] was used for EMS mutagenesis as described below . Genetic complementation is typically tested by crossing homozygous mutants , but with the tam mutants this would introduce a complicating variable since the progeny would be tetraploid . Instead we assessed complementation by crossing heterozygous tam plants , selecting double heterozygous progeny with PCR and scoring their phenotype . A tam-2 mutant with Col/No-0 polymorphisms was obtained by crossing a heterozygous tam-2 mutant with a No-0 plant and selfing the F1 generation . We then selected tam mutants heterozygous for several Col/No-0 polymorphisms in the F2 generation and crossed them to wild-type Ler plants . The triploid plants obtained were genotyped to infer the genotype of the tam 2n gametes . We obtained tam-2/spo11-1-3/rec8-3 mutants with Col-0/Ler polymorphisms by crossing a triple heterozygous tam-2/spo11-1-3/rec8-3 mutant ( Col-0 ) with a wild-type Ler and selfing the F1 progeny . Similarly , plants of interest in the F2 generation were crossed with wild-type No-0 and the triploid progeny were genotyped . The trimorphic ( Col-0/Ler/No-0 ) microsatellite markers used to genotype the tam-2 ( Col-0/No-0 ) x Ler population and the tam-2/spo11-1-3/rec8-3 ( Col-0/Ler ) triple mutant x No-0 F1 population have been described previously [23] . Microsatellite The NGA76 microsatellite was amplified ( Tm: 57°C ) with the 5′GGAGAAAATGTCACTCTCCACC 3′ and 5′AGGCATGGGAGACATTTACG 3′ primers ( 35 cycles of 30 sec at 94°C , 30 sec at Tm and 45 sec at 72°C ) . Final meiotic products were observed , chromosome spreads generated , and genome sizes were determined as described previously [23] . Pollen fluorescence was analysed as previously described [36] . Images were acquired with a LEICA DM RXA2 epifluorescence microscope using eCFP and DSRed2 filters ( Chroma Technologies ) and processed with Photoshop 8 ( Adobe Systems Inc . ) . EMS alleles of the TAM locus were generated following the protocol of Weigel and Glazebrook [58] . 0 . 5 grams of seed were imbibed in 30 ml of sterile water for 4 hrs . and then mutagenized with 0 . 2% ethyl methane sulfonate for 16 hrs . at room temperature with gentle agitation . Mutagenized seeds were rinsed with 30 ml of sterile water eight times and then dried before planting . Seeds were harvested from individually collected M1 plants . M2 plants were screened for a pollen dyad phenotype ( background was qrt1-2 which produces pollen tetrads ) .
|
In the life cycle of sexual organisms , a specialized cell division—meiosis—reduces the number of chromosomes from two sets ( 2n , diploid ) to one set ( n , haploid ) , while fertilization restores the original chromosome number . Meiosis reduces ploidy because it consists of two divisions following a single DNA replication . In this study , we identified genes that control the entry into the first and the second meiotic division in the model plant Arabidopsis thaliana . Plants lacking the CYCA1;2 gene execute a single division during meiosis producing functional diploid gametes and polyploid plants in the next generation . By combining this mutation with two others that affect key meiotic processes , we generated plants that produce diploid gametes through a mitotic-like division that are genetically identical to their parents . Furthermore , plants lacking CYCA1;2 and another previously described gene ( OSD1 ) undergo no divisions during male meiosis , producing tetraploid pollen grains .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/gene",
"discovery",
"biotechnology/plant",
"biotechnology",
"cell",
"biology/cell",
"growth",
"and",
"division",
"genetics",
"and",
"genomics/gene",
"function",
"genetics",
"and",
"genomics/plant",
"genomes",
"and",
"evolution"
] |
2010
|
The CYCLIN-A CYCA1;2/TAM Is Required for the Meiosis I to Meiosis II Transition and Cooperates with OSD1 for the Prophase to First Meiotic Division Transition
|
Variability in motor performance results from the interplay of error correction and neuromotor noise . This study examined whether visual amplification of error , previously shown to improve performance , affects not only error correction , but also neuromotor noise , typically regarded as inaccessible to intervention . Seven groups of healthy individuals , with six participants in each group , practiced a virtual throwing task for three days until reaching a performance plateau . Over three more days of practice , six of the groups received different magnitudes of visual error amplification; three of these groups also had noise added . An additional control group was not subjected to any manipulations for all six practice days . The results showed that the control group did not improve further after the first three practice days , but the error amplification groups continued to decrease their error under the manipulations . Analysis of the temporal structure of participants’ corrective actions based on stochastic learning models revealed that these performance gains were attained by reducing neuromotor noise and , to a considerably lesser degree , by increasing the size of corrective actions . Based on these results , error amplification presents a promising intervention to improve motor function by decreasing neuromotor noise after performance has reached an asymptote . These results are relevant for patients with neurological disorders and the elderly . More fundamentally , these results suggest that neuromotor noise may be accessible to practice interventions .
A hallmark of human movement is its variability , recognized in the phrase “repetition without repetition” [1] . Even the world champion in dart throwing does not hit the bull’s eye every time—in fact , it is this remaining randomness that creates the thrill in championships . This variability arises from interactions between a number of computational and physiological processes . Computationally , overt movement variability stems from imperfect error corrections and exploratory actions , and can sometimes be decreased by channeling variability into task-irrelevant dimensions [2–4] . Physiologically , some of the observed variability arises from a large number of processes at all levels of the neuromotor system , from noise in ion channel dynamics and action potential firing rates , to changes in the amounts of neuromodulators , such as serotonin or norepinephrine , that themselves depend on systemic factors , such as arousal [5–7] . While world-class champions may be close to a minimum in both computational and physiological sources of variability , older adults and patients with various neurological diseases show greater variability in their limb movements . For the latter , a behavioral intervention or therapy that decreases this overt variability and its underlying sources could have a major impact on motor function and quality of life . However , it is yet unclear whether or how this intrinsic noise can be accessed through behavioral interventions . Any intervention targeting undesired variability in human movement must consider a fundamental component of motor performance: error . Error information is needed for learning and , consequently , manipulation of error influences learning [8 , 9] . Error is typically manipulated physically or visually . Physically , external forces can guide an individual towards or away from a desired trajectory , causing either error reduction or amplification [10–15] . Visually , non-veridical action outcomes can be displayed that are better or worse than the actual performance . Importantly , studies on visual error amplification have shown that the manipulation accelerates learning and achieves enhanced performance [16–19] . Despite the promise of error amplification , the ways that this manipulation affects the human sensorimotor system remain unclear . Mechanistically , error amplification may influence how individuals correct for errors , i . e . changing the error correction gain ( the proportion of error corrected on each trial ) . It has been proposed that error amplification may simply encourage an individual to make larger corrections , thus improving performance [18 , 19] . While plausible , there may be more explanations for the observed benefits , which may not be apparent without a different approach . Prior work has used the framework of motor adaptation , where externally imposed errors are central and are gradually diminished to reinstate baseline performance . These previous studies have only assessed the effects of error augmentation on the exponential decline of errors , without separating this decay constant from the error correction gain . Further , the error correction gain may depend on the level of neuromotor noise , as the presence of noise may cause instability with a large gain [20] . To achieve a better understanding of the mechanisms underlying performance improvement with error amplification , computational learning models may be of significant value . Van Beers [21] used a stochastic state space model for a pointing task; the model introduced noise during two sequential stages of the error correction process . Modeling of the time course of this pointing task suggested that the optimal correction gain depended on the relative magnitude of the two noise sources . Using the same model to account for learning a throwing task , Abe and Sternad [22] showed that the error correction gain increased with practice . Hence , if one assumed that humans tend to use conservative gains below the optimal values to avoid instability , then it is reasonable to expect that error amplification improves performance by encouraging larger correction gains . Alternatively , the neuromotor system could lower its level of random noise , by changing computational strategies , such as channeling variability into task dimensions that reduce the error sensitivity of movement strategies [2 , 3 , 23 , 24 , 25] . Or , more simply , the system may lower the amplitude of its intrinsic noise via more system-level physiological mechanisms . The present study questions to what degree this is possible . Physiological sources of noise are ubiquitous on all scales , from molecular to cellular to systemic . However , it is yet unknown whether they are modifiable by practice and interventions . This study explores whether error amplification could act as a simple aid to not only alter control strategies , but also lower neuromotor noise . A simple throwing task in a virtual environment was used as an experimental platform to test whether visual amplification of error affects motor performance , not only by changing how errors are corrected , but also , more importantly , by modifying neuromotor noise . Our focus was on improvements after performance has plateaued: by examining “already good” performance , the effects of error amplification are isolated from initial learning transients and sensorimotor calibration processes . To further probe the status of this intrinsic noise , we not only used deterministic error amplification but also used stochastic amplification , where the amplification included a random component . We reasoned that layering additional noise on the amplified error would magnify any effects on intrinsic noise . Amplifying error deterministically makes performance appear worse by proportionally increasing both the magnitude and variability of errors , i . e . the coefficient of variation remains relatively unchanged . By disproportionately increasing variability by adding additional randomness , the coefficient of variation will increase . We conjectured that stochastic error amplification would make subjects perceive themselves as more noisy and variable compared to deterministic amplification alone , which would increase the pressure to reduce noise . The experiment tested the following hypotheses: Hypothesis 1: Amplifying perceived errors improves task performance . Hypothesis 2: Adding noise to the amplified errors improves task performance more than deterministic error amplification alone . Three more hypotheses were tested using three stochastic iterative learning models ( including an error correction gain and noise terms ) to analyze the temporal structure of the task performance data: Hypothesis 3: Error amplification increases the size of error corrections , i . e . it increases the correction gain . Hypothesis 4: Error amplification reduces intrinsic neuromotor noise . Hypothesis 5: Stochastic amplification reduces intrinsic noise more than deterministic error amplification . Finally , we expected that there is an optimal error amplification magnitude that has the greatest effect on task performance and intrinsic noise .
Forty-two right-handed healthy individuals ( 22 male; 20 female; age: 24±5 years ) participated in the experiment . All subjects received an explanation of the experimental task; however , they were not informed about any manipulations . Subjects read and signed an informed consent document approved by Northeastern University’s Institutional Review Board . The task was designed to emulate the skill of throwing an object to hit a target . Specifically , the task was modeled after the table version of the “skittles” pub game ( or tetherball ) . In this game , a ball is suspended from a string or chain attached to the top of a vertical post , and the ball is thrown to hit a target skittle on the other side of the post . The trajectory of the ball is fully determined by the ball’s release angle θR and angular velocity θR˙ . The game is under-determined or redundant , as there are an infinite number of θR and θR˙ combinations that result in a successful hit for a given skittle location [23 , 25] . Participants practiced a two-dimensional version of the skittles throwing task in a virtual set-up with an instrumented lever arm that rotated in the horizontal plane ( Fig 1A ) . A cushioned splint was fixed to the lever arm to secure the participant’s dominant arm in the apparatus . A wooden ball of 6 cm diameter was fixed to the end of the lever arm and subjects grasped this ball with their hand . The distance between the lever arm axis of rotation and the ball was adjustable to the subject’s arm length . The angular position of the skittles lever arm θ was measured with a potentiometer ( Bourns , Inc . , Riverside , CA ) . A force-sensing resistor ( Trossen Robotics , Westchester , IL ) positioned on the surface of the ball was used as a switch to indicate release by converting the analog force signal to a binary open/closed signal using a Schmidt Trigger ( Fairchild Semiconductor Corp . , South Portland , ME ) . The switch was positioned so that the index finger closed the switch when the ball was grasped . The switch opened when the finger was extended and this simulated ball release . Data were sampled at 700 Hz using a personal computer and 16-bit analog-to-digital converter ( DT300 , Data Translation , Inc . , Marlboro , MA ) . A rear-projection screen ( width: 1 . 8 m; height: 1 . 4 m ) was placed 0 . 6 m in front of the subject and the skittles manipulandum . A custom-written acquisition program ( Visual C++ , Version 6 . 0 , Microsoft , WA ) sampled θR and the switch state and gave visual feedback to participants ( Open GL , Version 1 . 2 , Silicon Graphics , CA ) . Angular velocity θR˙ was calculated online using a linear regression over the most recent 10 data samples . At ball release , the slope of the regression was used as the velocity estimate . ( Regression attenuated the influence of measurement noise . ) During task performance the positions of the lever arm , ball , center post , and target skittle were displayed on the rear-projection screen ( Fig 1A ) . The participants saw a top-down projection of the workspace; there were no reports of difficulty with this 90° transformation . When the lever arm was moved and the virtual ball was released ( at the switch opening time ) , the program calculated the ball’s trajectory using the model of Müller and Sternad [23]: x ( t ) =Axsin ( ωt+φx ) e− ( tτ ) ( 1 ) y ( t ) =Aysin ( ωt+φy ) e− ( tτ ) ( 2 ) where τ was the time constant of the decaying trajectory ( τ = 20 s ) , with amplitudes Ax and Ay , given by Ax=xR2+[ x˙Rω+ ( xRτ ) ω ]2 ( 3 ) Ay=yR2+[ y˙Rω+ ( yRτ ) ω ]2 ( 4 ) where xR , yR and x˙R , y˙R are the positions and velocities of the ball at release , respectively , with Ex and Ey denoting the kinetic and spring potential energies determined by Ex=0 . 5 ( mx˙R2+kxR2 ) ( 5 ) Ey=0 . 5 ( my˙R2+kyR2 ) ( 6 ) where m = 0 . 1 kg and k = 1 . 0 N/m . The phases φx and φy of the sinusoidal motions of the two springs were based on the oscillation amplitudes and the x and y release positions ( xR and yR , respectively ) and are given by φx=arccos[ 1Ax ( x˙Rω+ ( xRτ ) ω ) ] ( 7 ) φy=arccos[ 1Ay ( y˙Rω+ ( yRτ ) ω ) ] ( 8 ) and the natural frequency ω was ω=km−1τ2=3 . 16 rad/s ( 9 ) After the ball passed the target skittle , the minimum distance between the ball trajectory and center of the target skittle D-min was calculated ( Fig 1B ) . The color of the target skittle changed from yellow to red , if D-min < 0 . 011 m , signaling a successful hit to the subject . The post ( radius = 0 . 25 m ) was in the center of the workspace at x = 0 , y = 0 m , while the elbow axis of rotation was located at x = 0 , y = -1 . 5 m . The target skittle ( radius = 0 . 05 m ) was positioned at x = 0 . 05 , y = 1 . 05 m . Fig 1B illustrates three different trajectories and the two insets show the coordinates of the arm including the target and error definition . Fig 1C shows the execution space that represents all possible combinations of the execution variables θR and θR˙ and the result variable D-min . Combinations of ball release angle and velocity , θR and θR˙ , that give exact target hits , D-min = 0 , define the solution manifold , which is shown by the red vertical line . For this study , the target position was chosen such that errors depended primarily on θR and only very little on θR˙ , i . e . the solutions were largely insensitive to release velocity . The release angle that yielded a perfect hit was θTarget = 1 . 44 rad . An example of 240 throws produced by an experienced skittles player is shown in the solution manifold , as well as three artificial numbered data points showing different possibilities . Note that other target constellations are associated with nonlinear U-shaped solution manifolds where the D-min depends on both release angle and velocity , θR and θR˙ [25] . Specific to this target constellation was also that the ball could only pass below the target and the smallest achievable distance error ( D-min ) was 5 mm , corresponding to θR = 1 . 44 rad . In Fig 1D , the top left panel illustrates the x-y workspace with the target in red; each black circle represents closest approach of the ball ( D-min ) for a set of exemplary trajectories . Note that subjects could distinguish between the deviations to the left and the right branch from the perfect release angle by the elliptic shape of the ball trajectories . Therefore , error corrections based on visual information were relatively straightforward . The top right panel of Fig 1D shows that the relation between θR and D-min is nonlinear and approximately parabolic . Given the geometry of the workspace and the definition of error D-min as always positive , the errors typically assumed a very skewed distribution ( Fig 1D , lower left panel ) . For statistical analysis , we therefore transformed this data using −1/x2 , based on Tukey's "ladder of powers" [26] . This choice was made after trying several transformations of increasing severity until an approximately uniform distribution was obtained that afforded calculating means ( Fig 1D , lower right panel ) . For the presentation of the results , the errors were transformed back . To manipulate the distance error D-min , only the release angle θR had to be modified , as the solution manifold was insensitive to variations in the release velocity θR˙ . Therefore , the release angle was measured online to calculate the observed ball trajectory and then modified to amplify the error as follows θ˜R=θTarget+A ( θR−θTarget ) ( 10 ) where θ˜R denotes the manipulated release angle , θTarget the release angle that gave a perfect hit , i . e . D-min = 0 , and A the amplification gain . Here , the magnitude of error amplification is held constant ( for a given A ) , and therefore this manipulation is called deterministic error amplification , or DEA . Three magnitudes of A were applied: A = 1 . 5 ( DEA-1 . 5 ) , A = 2 . 0 ( DEA-2 . 0 ) , and A = 2 . 5 ( DEA-2 . 5 ) . Note that the ball trajectory that the participants saw was calculated based on θ˜R , while the angular velocity θR˙ remained unchanged . To stochastically amplify error ( SEA ) , the manipulation was θ˜R=θTarget+ ( A+ξ ) ( θR−θTarget ) ( 11 ) where ξ was uniformly distributed noise on the interval [− ( A−1 ) , + ( A−1 ) ] . These interval boundaries ensured that the noise was centered on A and scaled with A . For example , for A = 2 . 0 the noise interval was [–1 , 1] creating random amplification gains in the range of [1 , 3] . For A = 1 . 5 the range was [1 , 2] , for A = 2 . 5 the range was [1 , 4] . Corresponding to the DEA levels , the three mean or effective gains A¯=A+ξ were: A¯=1 . 5 ( SEA-1 . 5 ) , A¯=2 . 0 ( SEA-2 . 0 ) , and A¯=2 . 5 ( SEA-2 . 5 ) . Errors were only amplified up to a limit; if the manipulated release angle θ˜R fell outside the range of 1 . 00 to 1 . 88 rad ( optimum = 1 . 44 rad ) subjects saw their true error . This prevented subjects from noticing the manipulations . Pilot work had shown that when subjects perceived very large errors , they “discount” them as external perturbations [27] . The 42 subjects were randomly assigned to one of seven groups: there were six experimental groups that received varying types and amounts of error amplification , and one control group without manipulation ( Fig 2 ) . Three groups received DEA and three received SEA . Each amplification type was applied at three levels: 1 . 5 , 2 . 0 , and 2 . 5 . The experiment comprised 6 daily sessions of practice divided into two stages: On the first three days of practice , all participants performed the virtual task without any experimental manipulations . During the second three days , the visual feedback was altered for the six experimental groups ( DEA and SEA ) . Altogether , there were 252 separate practice days with data collection . On each practice day the participants performed four blocks of 60 trials each , to yield 240 trials per day and 1440 total trials for each subject . Participants placed their right forearm into the cushioned splint attached to the lever arm . The height of the lever arm and the distance between the axis of rotation and the ball were adjusted to place the forearm at a comfortable height with the ball firmly grasped . The participants were instructed to hit the center of the target skittle with the ball and to avoid hitting the center post . Each throw typically lasted 1–2 s with about 3 s between the self-initiated throws . The experiment duration in each daily session was about 20 min . For each subject/day the 240 trials were parsed into non-overlapping bins of 20 trials . Participants occasionally hit the center post; these trials were replaced using bootstrap resampling [28] . This method computed an estimate of the sampling distribution of the non-post-hit trials and then replaced the post-hit with a new value randomly drawn from the estimated distribution . Trials with post hits were not eliminated because this would have disrupted the iterative trial-to-trial process , which was the basis for the second set of analyses on the temporal structure of the data using system identification . Within each 20-trial bin the data were averaged to obtain the mean distance error D-min . While the -1/x2 transformed D-min was used for statistical analysis , the figures display the more intuitive untransformed D-min values . A preliminary analysis of D-min for Day 3 revealed one outlier subject in the group DEA-2 . 0 and one subject in the group SEA-2 . 5 , whose means were outside 1 . 5 times the interquartile range ( ±2 . 7 standard deviations or 99 . 3% of a normal distribution ) . These two subjects were excluded from the analysis; thus , for DEA-2 . 0 and SEA-2 . 5 the number of participants was five . Note that the variations in θR were not analyzed , because after initial practice these variables were centered on the optimal release angle and therefore showed the same patterns as the mean D-min . This is consistent with previous work that showed that data distributions centered on the most error-tolerant location early in practice [2] . Due to the extensive experimental protocol , i . e . seven groups with six days of practice for each subject , the number of subjects in each group was relatively small and did not satisfy the assumptions of ANOVA: normality and equality of variances . Therefore , the data were analyzed with permutation tests , which are a subset of non-parametric statistics [29 , 30] . This analysis method uses permutations to create a sample-specific distribution , instead of using an assumed theoretical distribution . A cut-off for a given p-value was obtained from the specific distribution . The permutation analysis involved several steps . First , the data from all subject groups were pooled , based on the null hypothesis that all groups were part of the same population . In the subsequent resampling procedure , the data were randomly shuffled , split into two groups , and the difference between the group means was recorded . This procedure was repeated 1 , 000 , 000 times , resulting in a distribution of group mean differences that represented the probability of obtaining a given difference between two groups randomly selected from the subject population . For all statistical comparisons the difference between the relevant means was compared with the bootstrap distribution . The p-value for each comparison was calculated by dividing the number of bootstrap differences smaller than the actual group difference by 1 , 000 , 000 and multiplying by 2 ( to give the p-value for a two-tailed test ) . The critical threshold for significance was set to p < . 05 , meaning that there had to be 25 , 000 or fewer bootstrap differences below the actual group difference . For example , if only 1000 bootstrap differences were below the tested group difference , the p-value for a two-tailed test was p = . 002 . To evaluate Hypothesis 1—amplifying perceived errors improves task performance—statistical tests were performed to test for differences in D-min between each error amplification group and the control group . These tests were conducted for Day 3 , Day 6 , and the change or difference between Day 3 and 6 . Evaluating D-min on Day 3 tested for between-group baseline differences before error amplification was applied . Evaluating D-min on Day 6 assessed the level reached after three days of practice with amplified error . Examining the change between the days ( Day 6 –Day 3 ) provides a direct assessment of the manipulation effects over time . To test Hypothesis 2—stochastic amplification of perceived errors improves task performance more than deterministic error amplification—the D-min for each pair of DEA and SEA groups ( within an error amplification level , i . e . 1 . 5 , 2 . 0 , or 2 . 5 ) was compared . Finally , to test for possible differences among error amplification levels , i . e . is there an optimal error amplification gain , each DEA group was compared with each other , and each SEA group was compared with each other . The transformed D-min ( −1/x2 ) was used for all statistical tests . To test Hypotheses 3 , 4 , and 5 , which were concerned with the effects of error amplification on the error correction gain and neuromotor noise , we analyzed the error time series across practice using system identification techniques with three different learning models . Results of system identification are evidently dependent on the model used . Therefore , we tested three models that presented a step-wise increase in complexity . All three models included the basic component of any learning model—an error correction gain B . Two of the models quantified intrinsic motor noise via a single noise source , and the third model introduced two independent noise sources . Due to the different model structures they required different methods of system identification . The goal of these analyses was to tease apart contributions in the overt performance change due to the error correction gain or from noise . Note that results revealed that Model 1 and 2 were not as suitable as Model 3 . We nevertheless present all three models to highlight that the model structure significantly determined the results . However , as the data show , one result was invariant across the three model structures . Details about the models and procedures follow . The first model contained two iterative steps with the addition of one noise sample , described by the following equations [21]: θR , i=θPL , i+ηi ( 12 ) ei=θR , i−θTarget ( 13 ) θPL , i+1=θPL , i−Bei ( 14 ) where θPL , i is the planned release angle at trial i , B is the error correction gain , θR , i is the actual release angle , ηi is a sample from a zero-mean Gaussian distribution with variance equal to σ2 , ei is the error between the release angle θR , i and the angle that hits the target θTarget . This model assumes that the actual executed release angle θR , i is equal to the internally planned release angle θPL , i with added motor noise ηi ( the labels “planning” and “execution” should not be taken literally ) . The planned angle θPL , i is updated trial-by-trial according to the visual error and correction gain B . Note that either the actual or manipulated release angle , θR or θ˜R , could be used in the system identification . The interpretation of B was dependent on this choice . Using θR means that if participants fully adjusted the size of their corrections in response to error amplification , then an increase in B should be observed , and this increase should match the error amplification gain . If θ˜R was used , then B remained unchanged if participants increased the size of their corrections in proportion to the amplified errors . For modeling SEA effects , using an amplified θ˜R is non-trivial , because an additional noise term would be needed . To minimize the number of unknown model parameters we used θR in the system identification . To estimate the two unknown parameters B and σ2 , the equations were rearranged and combined into a single equation; θTarget was set to zero . First , Eq 12 was increased by one iteration step: θR , i+1=θPL , i+1+ηi+1 . ( 15 ) Then , Eqs 12 and 15 were inserted into Eq 14 , giving ei+1=ei ( 1−B ) +ηi+1−ηi . ( 16 ) System identification was applied to the time series of execution angles θ with the target angle subtracted , according to Eq 13 . Model validation procedures showed that the system identification of Model 1 was associated with positive biases in σ2 ( see S1 Appendix ) . In addition , system identification of the experimental data showed that estimates for B were close to zero , and prior research has shown that modeling with only one noise source was inadequate [21 , 22] . Thus , we introduced Model 2 that added a second source of noise . This model is a simple extension of Model 1 by adding a second sample of motor noise into the “planning” stage: θR , i=θPL , i+ηEX , i ( 17 ) θPL , i+1=θPL , i−Bei+ηPL , i+1 ( 18 ) K=ηEX , iηEX , i+ηPL , i=ηEX , iηTOTAL , i ( 19 ) where ηTOTAL , i was a sample drawn from a zero-mean Gaussian distribution with variances equal to σ2 . ηTOTAL , i was separated into ηPL and ηEX and their magnitudes were constrained by the ratio K . With this constraint , ηPL and ηEX were not independent . The parameter K was used to describe the noise ratio ( Eq 19 ) . B was the error correction gain; the error in the release angle was given by: ei=θR , i−θTarget . ( 20 ) To estimate the three unknown parameters B , K , and σ2 Eqs 17 to 20 were rearranged into the form of a regression equation as for Model 1: ei+1=ei ( 1−B ) +ηTOTAL , i+1−KηTOTAL , i . ( 21 ) As can be seen , Model 1 is a particular case of Model 2 with K = 1 . Again , system identification was performed on the angle errors as defined in Eq 21 . The constraint K enabled system identification with a linear regression model . More importantly , this constraint “colored” the noise , i . e . the output noise showed long-range correlations with a 1/f distribution that depended on K . A wide range of studies ranging from brain oscillations to motor behavior , such as tapping , posture and walking , have shown that motor noise is colored [31–34] . In contrast to Model 1 , validation of Model 2 showed reliable noise estimates ( S1 Appendix ) . However , simulation results also showed that about 38% of the experimental data rendered negative K values ( S2 Appendix ) . Based on the definition in Eq 19 , K should be positive within [0 , 1] . These results suggested that Model 2 was not appropriate for those blocks of data . Thus , we introduced Model 3 that separated execution and planning noise into two independent quantities . In this model , we assumed that execution and planning noise were independently generated from two random processes . This model was previously shown to account for the observed structure in the variability of human motor actions [21 , 22] . The equations describing the model’s behavior were: θR , i=θPL , i+ηEX , i ( 22 ) θPL , i+1=θPL , i−Bei+ηPL , i+1 ( 23 ) ei=θR , i−θTarget . ( 24 ) where ηEX and ηPL were random samples from two independent zero-mean Gaussian noises with different noise variances; ei denoted the angle error of sample i , and B was the error correction gain . System identification was applied on the experimental data using Models 1 , 2 , and 3 . Consistent with the models , the measured release angle θR was converted to error by subtracting the target angle ( 1 . 44 rad ) from each data point . Note that the error in angle could be zero , unlike the distance error calculated in the x-y workspace . For each subject , the estimations were conducted for each block of 60 trials , yielding four separate estimates per day for each subject . Initial transients were eliminated by excluding the first 10 trials of Block 1 on each day . For Models 1 and 2 the MATLAB System Identification Toolbox ( version 9 . 2 ) was used with the function pem . m ( Prediction Error Method ) to find estimates for the unknown parameters ( for Model 1: B and σ2; for Model 2: B , K , and σ2 ) . This algorithm estimated the parameter vector Θ by minimizing the squared prediction error [35]: Θ^=arg minΘ1tΣi=1tεi2 ( Θ ) ( 25 ) where εi ( Θ ) = θR , i − fi|i−1 ( Θ ) is the prediction error with Θ = {K , B , σ2} and fi|i−1 ( Θ ) is an optimal predictor: fi|i−1 ( Θ ) = ( 1−B ) ei−Kηi−1 ( 26 ) To optimize Eq 25 , we applied a nonlinear least-square curve fitting algorithm with the Levenberg-Marquardt Method using the function lsqnonlin . m of the MATLAB Optimization Toolbox ( version 7 . 2 ) . Since Model 3 included two independent noise sources , a different identification method was needed . Thus , a Maximum Likelihood Estimation ( MLE ) was performed using the Expectation-Maximization ( EM ) algorithm [36] to identify B , σPL2 , and σEX2 . The maximum likelihood estimator of Eqs 22 and 23 was given by: Θ^=arg maxΘ logp ( θR , 1 , ⋯ , θR , t|Θ; e1 , ⋯ , et ) ( 27 ) where Θ≡{−B , σEX2 , σPL2} . ( 28 ) To test the validity of the system identification methods , Monte-Carlo simulations with known parameters were performed for all three models , followed by the identification of the parameters . Details and results of the validation are shown in S1 Appendix . For Models 1 and 2 , the estimation of B and σ2 had a significant positive bias; K was underestimated in Model 2 , especially for higher values . In these cases , we used the Adjusted Yule-Walker ( AYW ) method [37] to provide an unbiased estimate of B ( see S1 Appendix for details ) . On the other hand , validation of Model 3 showed that the estimation provided unbiased estimates of all model parameters . Based on the model validation and estimation results , Model 3 was deemed to be most appropriate . Therefore , only the results for this model are presented below and the results for Models 1 and 2 are relegated to S2 Appendix . Focusing on Model 3 , three dependent variables were analyzed: the error correction gain B , the execution noise variance σEX2 , and the variance of planning noise σPL2 . Each parameter estimate was computed for Day 3 , Day 6 , and the change across the manipulation ( Day 6–3 ) . Hence , these measures received the same statistical treatment as the behavioral measures . To test Hypothesis 3—error amplification increases the size of corrective actions—comparisons were made for B between each experimental group and the control group for Day 3 , Day 6 , and the change from Day 3 to Day 6 . To test Hypothesis 4—error amplification reduces intrinsic neuromotor noise—similar comparisons were made for the two noise estimates . To test Hypothesis 5—stochastic amplification reduces noise more than deterministic error amplification—comparisons were made between DEA and SEA as already described for D-min above . The same tests were also performed to identify differential effects across the different amplification gains .
To further tease apart potential mechanisms underlying the decrease in overt variability , the time series of release variables were examined via stochastic learning models . In particular , three models were used to extricate the contribution of the error correction gain from random noise sources . Model parameters were estimated before and after error amplification using system identification . Parameter estimation was conducted separately for each block of 60 trials; there was a total of 1008 blocks across all days and subjects ( 42 subjects , 6 days , 4 blocks on each day ) . The 60 trials presented a sufficiently long time series that also avoided potential drifts that may have otherwise confounded the parameter estimation . Examples of the raw data of angular error e = θR − θTarget used in the system identification are displayed for three subjects in different groups in Fig 7 . Note this error could be both positive and negative , unlike the distance error D-min . In addition , the deviation of the release angle to the optimal angle could be reduced to zero . The open black circles denote errors in unmanipulated trials; the closed colored circles represent the amplified errors as subjects saw on the screen . The SEA condition had clearly a wider range of amplified errors than DEA . The long sequence of 1440 trials showed a very gradual , almost invisible change in release angle error; nevertheless , the average values showed a clear reduction across days as previously seen in D-min ( Fig 3 ) .
All participants underwent extensive practice over three days ( 720 trials ) until their performance had reached a plateau . Six experimental groups that continued to practice for three more days with visually amplified errors ( another 720 trials ) further decreased their errors , while the control group did not . This improvement was seen for both deterministic and stochastic amplification . Corroborating previous demonstrations [13–19] , these findings present clear evidence that amplifying perceived errors improved task performance ( Hypothesis 1 ) . Extending prior work , three different amplification gains were compared to parametrically assess the effect of error augmentation magnitude . As expected , the lowest gain of 1 . 5 was not effective , neither in stochastic nor in deterministic form . The two larger gains of 2 . 0 and 2 . 5 elicited improvements by virtue of a decreased mean absolute error . Although in some cases the gain of 2 . 0 elicited the greatest improvements , this could have been because this group was worse than others prior to the manipulation . The highest amplification gain 2 . 5 did not produce any “instability” , as speculated previously . Wei et al . [18 , 19] stated that for their perturbed reaching task an error amplification gain of 3 . 1 would lead to overcorrection and instability based on an error updating model [20] , a result that was supported by their experimental data . While the higher amplifications in the present task were successful , one must keep in mind that the specific numerical values are most likely task-dependent . We also posited that the addition of stochastic noise may enhance the effect of amplification ( Hypothesis 2 ) . However , the expectation that the additional noise would add “pressure” to reduce the variability of their performance was not confirmed . We speculate that subjects may simply have “averaged” over the noisy errors , and were therefore insensitive to the immediate error information presented after each trial . For any account of learning , there are two primary options to improve overt errors in the performance variable: optimize the error correction gain and/or reduce the internal noise variance . To tease these two options apart from the overt variability of the task performance , the present study applied system identification procedures based on three stochastic iterative learning models . All three models included two stages , motivated by previous electrophysiological research that identified significant contributions of noise in the planning processes preceding movement , distinct from execution [40 , 41] . A model with two stages of noise was also supported by previous behavioral and modeling studies that assessed structure in the trial-by-trial changes in an aiming task and the skittles task [21 , 22] . The system identification results showed that error amplification elicited a small increase in the correction gain or learning rate B , which supports Hypothesis 3 , and this increase became larger with larger amplification magnitudes ( for SEA , not DEA ) . This result was provided by Model 3 , which was deemed the most appropriate of the three models tested . Although subjects increased the size of their corrections , the increases remained modest , averaging about 5% at most , and fell significantly short of the amplification factors . If participants had fully adjusted the size of their error corrections to the amplification , then there should have been increases of 50% , 100% , and 150% for the three error amplification levels ( 1 . 5 , 2 . 0 , 2 . 5 , respectively ) . This under-compensation agrees with prior work showing that humans typically do not respond proportionately to errors , i . e . they respond to larger errors less than would be expected [42] . The relatively small adjustments in participants’ error correction gains in response to error amplification suggest that this gain adjustment was not the prime driver of the observed improvements in performance . The values for B were mostly between 0 . 1 and 0 . 2 ( 10%–20% of error corrected ) , which is somewhat lower than other studies , which have reported correction gains of about 0 . 38 in an aiming task [21] and in the range of 0 . 20–0 . 50 for a reaching task [36] . The lower values in the present study could arise from the fact that the actions were well-practiced . However , note that B results depend significantly on the choice of the model; therefore , it is important to compare different models to assess their suitability [e . g . 43] . In our study , Model 1 with only a single noise source and Model 2 with two noise sources were not in agreement ( see S2 Appendix ) . By the last day of practice , both models estimated that half of the error amplification groups had higher B values than the control group , but these groups were not the same . For Model 2 the B values were generally large compared to Model 1 where the gains were often less than 0 . 1 . Further , when noise is part of the dynamics , parameter estimation methods may produce a bias [37] . Given these discrepancies and potential estimation problems , we refrained from further interpreting the gains . Note that amplifying the errors in the modeling itself would not alter the relative changes in B , but would only increase the absolute values of B by a factor equal to the amplification gain ( assuming subjects fully respond to the amplified errors ) . The most robust result of the system identification , consistent in all three models , was that error amplification reduced the noise sources , supporting Hypothesis 4 ( see also S2 Appendix ) . While execution noise showed a steady decline after error amplification was applied , planning noise declined more abruptly and reached a plateau ( Fig 9 ) . Although planning noise remained steady in the control group for most of their practice , there was a precipitous drop on the very last practice day . This behavior was consistent across all subjects in this group . Thus , even without error amplification , planning noise may eventually decline , only at a later stage . This observation may also signal that performance improvements due to error amplification may just accelerate internal learning processes , and not introduce new mechanisms . Related work has shown that even without any external rewards , individuals can eventually reach the same performance level as those that are rewarded , but do so at a slower time scale [25 , 44] . While the variance of planning noise was much smaller than execution noise , this does not necessarily imply that planning noise had a negligible impact . For example , for the control group on Day 3 , the two noise variances averaged to about 0 . 00045 and 0 . 011 rad2 . While seemingly small , this equated to standard deviations of about 1 . 2° and 6 . 0° for planning and execution noise , respectively . Thus , the planning noise contributed about 21% to the variability in the release angle . Although it was also hypothesized that that stochastic amplification would reduce noise more than deterministic error amplification ( Hypothesis 5 ) , this hypothesis was not supported . Potentially , participants did not make trial-by-trial adjustments to their actions in response to the visually amplified errors , but instead may average across multiple trials . It should also be kept in mind that stochastic amplification only added one random number to the release angle of each throw; their arm movements leading up to the release were not continuously perturbed . Hence , the added randomness may have been lost on the subjects . A decrease in variability is often achieved by slowing down movement , i . e . a decrease in speed is traded for an increase in accuracy [45 , 46] . The speed-accuracy trade-off has been proposed as a general signature of learning; a more skilled individual can execute movements faster and with greater precision [46] . Further , it is generally believed that signal-dependent noise is a basic property of neuronal signaling [47] . The effects of this noise are readily seen during isometric force production , with the variability of the exerted force increasing as a multiple of the force [48] . For dynamic tasks and trajectories this manifests itself as velocity-dependent noise . Counter to these expectations , participants in the present study did not exhibit systematic decreases in their release velocity across practice , consistent with previous results on a similar task [24] . This suggests that other avenues were available for the neuromotor system to reduce its overt fluctuations . Previous research on the same throwing task highlighted three conceptually different avenues to decrease observed variability . Subjects can: 1 ) find solutions that are error-tolerant , 2 ) co-vary execution variables to minimize the effect of variability on the task result , and 3 ) reduce the variance of noise [2 , 23 , 39 , 49 , 50] . This work showed that error tolerance was achieved very early in practice , while co-variation was a computational strategy that was gradually exploited with practice . Noise remained the component least accessible to even extended practice [2] . In the present study , which included a relatively long practice schedule , tolerance was of little relevance after the first day of practice . Co-variation between the release angle and velocity could have been exploited to channel noise into task-irrelevant dimension , without necessarily reducing the overall amplitude of noise sources . In the present task , release velocity presented such a task-irrelevant dimension . However , counter to expectation , variability in release velocity did not increase concomitant to the decrease in angle variability . Hence , this computational strategy could not explain the lowering of noise . To improve performance subjects could only reduce the noise component . The present target constellation rendered a very specific solution manifold that gives release velocity little contribution to the observed error . However , velocity is not entirely irrelevant . Both release angle and velocity are needed to calculate the ball trajectory . Importantly , the error sensitivity of throws with different velocities changes , as seen by a slight broadening of the neighborhood with low errors at higher velocities ( Fig 1C ) . Subjects can visually distinguish between different release velocities as they lead to ball trajectories with different elliptic paths ( Fig 1B ) . As shown in a previous study , subjects seek those more error-tolerant velocities and do not seek the lowest velocity that might be expected due to the least amount of signal-dependent noise [24] . Finally , small changes in target locations will make release velocity an important determinant of the distance error . Hence , the velocity dimension is not quite as redundant as it appears at first sight . How might subjects have reduced overt noise and possibly physiological noise in response to error amplification ? This question is partly motivated by a known mechanisms in songbird learning , where the nervous system can purposefully inject noise into the learning process during early-learning , and then reduce these self-induced perturbations after learning [51] . Might the human nervous system use noise in a similar way ? Humans can mechanically reduce the influence of noise on task performance with antagonistic co-activation [52–54] . However , while antagonistic co-activation may increase in early learning , it typically reduces with practice [55] . In the present study , signal-dependent noise has been ruled out as an explanation because the release velocity did not systematically decrease with practice ( assuming that velocity presents the “signal” ) [47] . Further , there is some evidence that this noise can change with alterations in the physiological properties of motor units , such as reported in aging muscles [56] , but it remains unknown whether this noise can be altered on a shorter time scale . One final speculation is derived from studies on the effect of neuromodulators , such as serotonin or norepinephrine , on motor neuron excitability [5 , 6 , 57] . Animal studies provided significant evidence that the descending drive to muscle contractions is gain-controlled to modulate the required output force . One study on humans showed that force variability increased after the brainstem–spinal cord neuromodulatory system was up-regulated [6] . A complex interplay of neuromodulators can excite or inhibit spinal cord excitability and thereby match precision demands of motor behavior . It is possible that this gain control is modifiable via error amplification . Evidently , more research is needed to solidify these conjectures . As noted earlier , there could be other factors , such as enhanced perception and correction of errors and motivational factors that may contribute to improved performance with error amplification [18 , 19] . In terms of perception , it could be that increasingly smaller errors become simply too small to detect and amplifying error resolves this problem . However , the present results do not support this conjecture: if the control group had been unable to detect their errors , then their error correction gains should have been close to zero . Counter to this expectation , although small , their gains remained significantly above zero , even after six days of practice . This only remains a possibility if one conjectures that subjects corrected their predicted errors based on internal models of the task . Temporal structure of data can be analyzed with numerous methods , ranging from simple autocorrelation and other linear autoregressive methods to nonlinear methods , such as entropy analysis [58] or recurrence quantification [59] . Autoregressive methods maintain temporal connections and are strictly linear analyses . On the other hand , more sophisticated nonlinear methods , such as multi-scale entropy analyses or recurrence quantification , are useful for more continuous time series , as seen in postural control or heart rate [60–62] . We opted to use iterative learning models to analyze trial-to-trial sequences , where error correction processes explicitly link successive trials and there are explicit parameters of the two processes in focus . However , stochastic iterative models are clearly also not free of limitations . First and common to all analysis is that temporal analyses select one single variable from a complex movement system . While the neuromotor system is unquestionably high-dimensional and has a highly distributed neural network , task success in the current task was described by a single kinematic variable , angle at ball release . This variable lent itself to be mapped on the single model variable . Second , in the task subjects viewed the distance error between their ball trajectory and the target , whereas in the model the error information was the difference in release angle to the optimal angle . Given the parabolic relation between the two , this may have influenced the result , lowering the estimates of B . However , after two days of practice most subjects were close to the optimal release angle , and therefore operated in a regime where the relation was close to linear . Probably more critical is that the mapping of the perceived error in workspace to the error in release angle may include further inaccuracies . Another note of caution relates to the models themselves . The comparative analyses of three basic learning models highlighted that parameter estimates are very sensitive to the structure of the model . For example , all three models assumed additive noise . It is conceivable that the learning mechanisms include multiplicative noise . As we do not know the “true” model , results of these estimations should be interpreted with great care . Further , parameter estimation of a stochastic model has computational pitfalls , and the seemingly straightforward estimates of error correction gains become biased when noise is included [37] , as we showed in S1 Appendix . We therefore only interpreted the changes in noise that was in agreement in all three models . We also tried to rule out alternative explanations and apply caution in the interpretation . Evidently , stochastic learning models with different forms of noise will have to be a topic for further research in motor neuroscience . Finally , there is always the question of generalization: do the observed effects of error amplification extend to other motor tasks ? For example , do the findings also hold for continuous tasks that involve tracking ? How could error amplification be applied without a virtual interface ? Do the results from the virtually controlled task generalize to real-world skittles throwing in 3D ? These critical questions are clearly not confined to our study , but to almost all controlled experimental studies . While it is difficult to draw any definitive conclusions within the scope of a single study , the present results add to existing evidence . Prior studies have shown error amplification benefits in different reaching tasks [15–19] and a pinball-type task [13] . Further , beneficial effects of error amplification have been reported for different sensory modalities , including visual [16–18] and haptic feedback [10 , 12] . Although these studies did not separate improvements due to noise reduction from changes in error correction , it is plausible that similar effects may be found . Of course , further research should substantiate this possibility . The main results of this study was that error amplification elicited continued improvements in performance that otherwise plateaued , and this effect was mainly driven by a reduction in neuromotor noise . As such , error amplification presents a way of stimulating continued improvements in motor performance . The results challenge the assumption that neuromotor noise is invariant and inaccessible to behavioral interventions . Error amplification has the potential to become an effective intervention for improving motor performance in physical therapy and neuro-rehabilitation to improve motor function . This can be of special benefit for those who have reached a performance plateau , ranging from elite athletes , to patients with neuromuscular disorders , and to the elderly .
|
It is widely recognized that neuromotor noise limits human motor performance , generating errors and variability even in highly skilled performers . Arising from many spatiotemporal scales within the physiological system , the intrinsic noise component is commonly assumed to be invariant by most computational models of human neuromotor control . We challenge this assumption and show that after an individual has reached a performance plateau , amplifying perceived errors elicits continued reductions in observed variability . Model-based analyses show that the main driver of this effect is a reduction in the variance of neuromotor noise . Thus , error amplification has the potential to become a key intervention for individuals with increased movement variability due to high levels of neuromotor noise , ranging from children with dystonia , through patients with stroke , to healthy elders .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"learning",
"medicine",
"and",
"health",
"sciences",
"engineering",
"and",
"technology",
"signal",
"processing",
"limbs",
"(anatomy)",
"social",
"sciences",
"neuroscience",
"learning",
"and",
"memory",
"noise",
"reduction",
"regression",
"analysis",
"cognitive",
"psychology",
"mathematics",
"human",
"performance",
"statistics",
"(mathematics)",
"vision",
"research",
"and",
"analysis",
"methods",
"sensory",
"physiology",
"musculoskeletal",
"system",
"behavior",
"mathematical",
"and",
"statistical",
"techniques",
"arms",
"psychology",
"anatomy",
"physiology",
"linear",
"regression",
"analysis",
"biology",
"and",
"life",
"sciences",
"sensory",
"perception",
"physical",
"sciences",
"cognitive",
"science",
"statistical",
"methods"
] |
2016
|
Neuromotor Noise Is Malleable by Amplifying Perceived Errors
|
We have previously shown that the Mycobacterium tuberculosis universal stress protein Rv2623 regulates mycobacterial growth and may be required for the establishment of tuberculous persistence . Here , yeast two-hybrid and affinity chromatography experiments have demonstrated that Rv2623 interacts with one of the two forkhead-associated domains ( FHA I ) of Rv1747 , a putative ATP-binding cassette transporter annotated to export lipooligosaccharides . FHA domains are signaling protein modules that mediate protein-protein interactions to modulate a wide variety of biological processes via binding to conserved phosphorylated threonine ( pT ) -containing oligopeptides of the interactors . Biochemical , immunochemical and mass spectrometric studies have shown that Rv2623 harbors pT and specifically identified threonine 237 as a phosphorylated residue . Relative to wild-type Rv2623 ( Rv2623WT ) , a mutant protein in which T237 has been replaced with a non-phosphorylatable alanine ( Rv2623T237A ) exhibits decreased interaction with the Rv1747 FHA I domain and diminished growth-regulatory capacity . Interestingly , compared to WT bacilli , an M . tuberculosis Rv2623 null mutant ( ΔRv2623 ) displays enhanced expression of phosphatidyl-myo-inositol mannosides ( PIMs ) , while the ΔRv1747 mutant expresses decreased levels of PIMs . Animal studies have previously shown that ΔRv2623 is hypervirulent , while ΔRv1747 is growth-attenuated . Collectively , these data have provided evidence that Rv2623 interacts with Rv1747 to regulate mycobacterial growth; and this interaction is mediated via the recognition of the conserved Rv2623 pT237-containing FHA-binding motif by the Rv1747 FHA I domain . The divergent aberrant PIM profiles and the opposing in vivo growth phenotypes of ΔRv2623 and ΔRv1747 , together with the annotated lipooligosaccharide exporter function of Rv1747 , suggest that Rv2623 interacts with Rv1747 to modulate mycobacterial growth by negatively regulating the activity of Rv1747; and that Rv1747 might function as a transporter of PIMs . Because these glycolipids are major mycobacterial cell envelope components that can impact on the immune response , our findings raise the possibility that Rv2623 may regulate bacterial growth , virulence , and entry into persistence , at least in part , by modulating the levels of bacillary PIM expression , perhaps through negatively regulating the Rv1747-dependent export of the immunomodulatory PIMs to alter host-pathogen interaction , thereby influencing the fate of M . tuberculosis in vivo .
Mycobacterium tuberculosis , the causative agent of tuberculosis ( TB ) , remains a global public health problem , causing , in 2015 alone , over 10 . 4 million new cases and 1 . 8 million deaths worldwide [1] . M . tuberculosis is able to establish an asymptomatic latent infection that can later reactivate to cause active diseases [2–5] . In the latently infected immunocompetent host , the lifetime risk for reactivation is 10% . In those immunocompromised , the risk for recrudescence of latent infection is 10% per year [2–5] . It has been estimated that one-third of the world’s population is infected with M . tuberculosis , and it is generally believed that the majority of these individuals harbor latent bacilli [2–5] . The latently-infected thus constitute a significant reservoir for disease reactivation and transmission . Therefore , latent TB is a major hindrance to the control and eradication of M . tuberculosis . Understanding the mechanisms that regulate tuberculous latency and reactivation may lead to the design of strategies for better TB control . We have previously demonstrated that M . tuberculosis Rv2623 , a universal stress protein ( USP ) homolog , has the ability to regulate mycobacterial growth [6] . Results of transcriptome analysis have revealed that Rv2623 is among the most highly induced genes when M . tuberculosis is exposed to an environment of hypoxia or nitrosative stress [7–9] , conditions that tubercle bacilli are likely to encounter in an infected host [6] . Rv2623 is also induced when M . tuberculosis is internalized by macrophages [10] , in standing cultures [11] , and in the lungs of infected mice during the chronic phase of tuberculous infection [7 , 12] . An M . tuberculosis Rv2623 deletion mutant ( ΔRv2623 ) is unable to establish a chronic infection in mice and Guinea pigs , exhibiting a hypervirulence phenotype [6] . But the growth regulatory property of Rv2623 is not restricted to these in vivo systems . Thus , constitutive overexpression of Rv2623 in both M . smegmatis and M . tuberculosis attenuates bacillary growth in vitro [6] . Biochemical analysis of Rv2623 showed that it has the ability to bind ATP . Mutagenesis studies based on a 2 . 9 Å crystal structure yielded mutants defective in ATP binding . Analysis of these mutants revealed that the growth regulatory property of M . tuberculosis Rv2623 correlates with its ATP-binding capacity [6] . These observations prompted us to propose the possibility that M . tuberculosis Rv2623 may function as a signaling intermediate in a pathway that promotes latency [6] . Investigating the mechanisms by which this USP regulates bacillary growth , the present study provides evidence that ( i ) Rv2623 interacts with Rv1747 , a putative ABC transporter annotated to export lipooligosaccharides [13] , to negatively regulate M . tuberculosis growth; ( ii ) this interaction is mediated via the recognition of a conserved phosphothreonine-containing oligopeptide motif of the USP by the N-terminal FHA domain of Rv1747 ( FHA I ) ; and ( iii ) ΔRv2623 , compared to WT bacilli , exhibits a morphological phenotype that is associated with increased levels of phosphatidyl-myo-inositol mannosides ( PIMs ) , immunologically active molecules that can modulate the host immune response [14 , 15] . Conversely , an Rv1747-deficient mutant produces lower levels of PIMs than WT M . tuberculosis . Further , while ΔRv2623 is hypervirulent in an infected host [6] , ΔRv1747 is attenuated for growth in vivo [16 , 17] . Collectively , the interaction of Rv2623 with the FHA I of Rv1747 to negatively regulate M . tuberculosis growth and the divergent in vivo virulence and PIM phenotypes of ΔRv2623 and ΔRv1747 suggest that this USP may influence mycobacterial growth by modulating Rv1747’s functional activity , raising the possibility that Rv1747 , which has been annotated as a lipooligosaccharide exporter [13] , is implicated in the export of PIMs .
Signaling pathways often mediate their biological functions through interactions of elements involved in the cascade [18–20] . Given the possibility that Rv2623 may be an intermediate in a signaling pathway that promotes persistence [6] , we sought to identify its interacting partners . Indeed , evidence exists that various bacterial USPs interacts with specific interactors to regulate physiologically relevant processes [21 , 22] . Using the yeast two-hybrid screen with Rv2623 as bait , we have identified a set of M . tuberculosis proteins that interact with this USP , among which is the N-terminal FHA I of Rv1747 , the focus of the present study ( Fig 1A and 1B ) . The rationale for focusing on the Rv1747 FHA I derives from the roles of forkhead-associated domains in mediating protein-protein interaction to facilitate a broad range of vital biological processes including signal transduction , transcription , cell cycle regulation , and protein transport [23–27] . Rv1747 is a putative ATP-binding cassette ( ABC ) transporter that harbors two FHA domains [13] ( Fig 1A ) . The screen has identified a fragment spanning amino acids ( a . a . ) 46 to 142 of Rv1747 , which harbors the majority of N-terminal FHA I ( a . a . 29 to 92 ) , to be an interactor of Rv2623 ( Fig 1A and 1B ) . Yeast two-hybrid experiments using a re-cloned full-length Rv1747 FHA I domain validated its specific interaction with Rv2623 ( Fig 1B ) . To further validate the interaction between Rv2623 and the Rv1747 FHA I domain , affinity chromatography experiments were conducted ( Fig 1C ) . For these studies , purified recombinant histidine ( His6 ) -tagged Rv2623 ( His-Rv2623 ) and FLAG-tagged FHA I ( FLAG-FHA I ) expressed in M . smegmatis mc2155 [28] via a pSD series acetamide-inducible expression system were used [29 , 30] . FLAG-FHA I was expressed as a tagged peptide spanning the first 100 a . a . of Rv1747 ( Fig 1A ) . Western blot analyses of the various flow-through , wash , and elution fractions collected in the affinity chromatography studies using anti-FLAG and anti-Rv2623 antibodies revealed that Rv2623 and Rv1747 FHA I co-eluted , demonstrating the interaction of these two mycobacterial proteins ( Fig 1C ) . This interaction was similarly and consistently observed when the affinity chromatography experiments employed recombinant Rv2623 derived from the previously described E . coli-based pQE80L-Rv2623 expression construct [6] , and cMyc-FHA I harbored in the first 120 a . a . of Rv1747 and expressed in the LIC ( ligation independent cloning ) vector pMCSG7 , as well as anti-His antibodies for the detection of Rv2623 ( S1 Fig ) . Of note , results obtained from affinity chromatography experiments showed that Rv2623 does not interact with a pSD vector-expressed recombinant C-terminal Rv1747 FHA II domain ( S2 Fig ) . Together , these results , in conjunction with that of the yeast two-hybrid experiments , strongly suggest that Rv2623 interacts specifically with the Rv1747 FHA I in the in vitro systems studied . The FHA domain , originally discovered in forkhead-type transcription factors [31] , exists in a wide variety of proteins to mediate diverse functions via protein-protein interactions [23–27] . A characteristic property of FHA domains is that these interactions are based on recognition of a phosphorylated threonine ( pT ) residue of the binding partner [24 , 32 , 33] . One prototypic FHA domain that has been examined extensively is that of the Saccharomyces cerevisiae Rad53 protein kinase ( Fig 2A and 2B ) . The Rad53 FHA1 mediates its functions in regulating cell cycle and DNA repair by binding to a pT residue that is embedded within a conserved oligopeptide motif [24 , 32 , 33] . Structural analysis of the Rad53 FHA1 has revealed that specific amino acids critical to the interaction with the pT motif are all located in the conserved region of this modular phosphopeptide recognition domain: Gly69 , Arg70 , Ser85 , His88 , and Asn107 ( Fig 2A , 2B and 2D ) , with Asn107 and Arg70 implicated to play a role in interacting with the conversed phosphopeptide backbone . There is also evidence that Arg70 , together with Ser85 , play a role in hydrogen-bonding with the phosphorylated threonine of the conserved phosphothreonine-binding motif ( Fig 2A and 2B ) [24 , 32] . Parenthetically , the three amino acids critical in mediating binding with the conserved phosphopeptide motif ( Asn 107 , Arg70 , and Ser85 ) are located on the surface of Rad53 FHA1 [24 , 32 , 33] ( Fig 2A and 2B ) . The other two highly conserved non-surface FHA1 residues , Gly69 and His88 , are thought to stabilize the structure of the binding site [24 , 32 , 33] ( Fig 2A and 2B ) . The homology model of the FHA I domain of Rv1747 was generated utilizing the M4T server ver 3 . 0 [34] based on comparative modeling using a combination of 2 templates ( PDB codes 2LC1 and 1UHT ) . The homology model of the Rv1747 FHA domain was then superimposed onto the Rad53 FHA domain [24 , 32 , 33] using Pymol ( www . pymol . org ) and displayed as shown in Fig 2C . This modeling studies of Rv1747 FHA I domain revealed a tertiary structure with 11-stranded β sandwich similar to that of the S . cerevisiae Rad53 protein kinase [24 , 32 , 33] . The conserved Gly , Arg , Ser , His , and Asn that play an important role in mediating the interaction of Rad53 with the pT motif are all present in Rv1747 FHA I ( Gly32 , Arg33 , Ser47 , His50 , Asn69 ) with almost precisely matched spacing except for a one-amino acid difference between Rad53 FHA1 Arg70/Ser85 and Rv1747 FHA I Arg33/Ser47 ( Fig 2C and 2D ) . In addition , tertiary structure modeling has revealed complete superposition of all the conserved a . a . between the S . cerevisiae Rad53 FHA1 and Rv1747 FHA I ( Fig 2C and 2D ) . These results strongly support the feasibility of this M . tuberculosis forkhead-associated domain to interact with phosphorylated threonine-containing peptides . This observation prompted the initiation of study to determine whether putative phosphorylatable threonines are present in Rv2623 . Solvent accessibility analyses based on the crystal structure of Rv2623 revealed that the hydroxyl groups of five of the nine threonine residues of the USP studied are solvent-accessible and therefore potentially phosphorylatable [6 , 35 , 36] ( Fig 3A and S3 Fig ) . The surface locale of these five threonine residues is further confirmed by Pymol display of Rv2623 ( www . pymol . org; Fig 3B ) . Therefore , we examined the structure for additional elements described as part of the conserved pT peptide motif signature contributing to interactions with FHA domains . We focused on the amino acid situated three residues from the corresponding phosphorylatable threonine toward the C-terminus ( pT+3 ) , because it has been observed , based on studies derived from peptide library screening experiments involving a subset of known FHA domains , that specific pT+3 residues contribute to mediating the interaction of this phosphoprotein-recognizing module with phosphorylated threonine-containing oligopeptides by modulating binding and binding selectivity [24 , 32 , 33] . Sequence analysis revealed that four of the five Rv2623 threonines whose hydroxyl group is solvent-accessible ( T90 , T103 , T212 , T237 ) have residues in the pT+3 position that match those found in pT-containing peptides with known FHA domain-binding specificity ( Fig 3A–3C and S3 Fig ) [32] . The above-described experiments provide strong evidence that M . tuberculosis Rv1747 FHA I can interact with Rv2623 through recognition of and binding with potentially phosphorylatable threonine residues in the context of a conserved phosphopeptide motif including the pT+3 residues ( Figs 1–3 ) . To further evaluate the interaction of these two mycobacterial components , we investigated whether Rv2623 is phosphorylated . Lysates of BCG which encodes an Rv2623 orthologue that is 100% identical to the M . tuberculosis counterpart [tuberculist . epfl . ch] , were resolved by two-dimensional ( 2-D ) gel electrophoresis and probed using a monoclonal antibody specific for Rv2623 . This revealed the USP to be detected as three isoforms ( Fig 4A ) , which have comparable approximate molecular mass , but differ based on their isoelectric points , with a shift toward the acidic direction , suggesting that Rv2623 can be post-translationally modified via phosphorylation . Further , Rv2623 affinity-purified from M . tuberculosis and BCG lysates is reactive with antibodies relatively specific for phosphorylated threonines using both a mouse monoclonal antibody and a rabbit polyclonal antibody ( Fig 4B ) . Similarly , affinity purified M . smegmatis-expressed recombinant Rv2623 is also reactive with the anti-phosphothreonine monoclonal antibody ( Fig 4B ) . Together , these results suggest that Rv2623 potentially can be phosphorylated at threonine residues , thus further supporting the possibility that a specific pT-containing peptide of this USP interacts with the FHA I domain of Rv1747 . Results of 2-D gel electrophoretic analyses of Rv2623 and its reactivity with anti-phosphothreonine antibodies ( Fig 4B ) led us to initiate studies to directly examine if any of the threonine residues of Rv2623 can be phosphorylated . For these studies , recombinant Rv2623 purified from M . smegmatis or E . coli that had undergone an in vitro phosphorylation reaction with recombinant M . tuberculosis protein kinase G ( PknG ) was subjected to mass spectroscopic analysis . In pilot experiments , PknG was found to be among a set of serine/threonine protein kinases ( STPKs ) capable of phosphorylating Rv2623 to varying degrees . This observation is in agreement with a previous report noting that M . tuberculosis STPKs are relatively promiscuous in terms of substrate use [37] . PknG was subsequently used for our studies because of its availability ( gift of Dr . J . Blanchard , Albert Einstein College of Medicine ) . The kinased samples were resolved by sodium dodecyl sulfate-polyacrylamide ( SDS-PAGE ) gel electrophoresis , transferred onto nitrocellulose membrane and probed with anti-phosphothreonine and anti-Rv2623 monoclonal antibodies to evaluate for the presence of pT residues in the USP . The results revealed that Rv2623's reactivity to anti-phosphothreonine monoclonal antibody increases with time as the kinase reaction progresses ( S4 Fig ) . For phosphomapping , the band corresponding to phosphorylated Rv2623 was excised from a Coomassie-stained gel containing electrophoretically-resolved kinasing reaction , washed , destained and then subjected to in-gel trypsin digestion . Phosphorylated species in the digest were then TiO2-enriched and analyzed by Liquid Chromatography-Tandem mass spectrometry ( LC-MS/MS ) to identify specific phosphorylated residues . This phosphomapping study identified the Rv2623 threonine at position 237 , one of the solvent accessible residues that also has a conserved pT+3 amino acid known to facilitate binding of FHA domains , as a phosphorylatable residue ( Fig 4C ) . Having demonstrated that the M . tuberculosis Rv1747 FHA I possesses structural attributes and conserved determinants that mediate interactions with interactor proteins , and that the signature forkhead-associated domain-interacting phophothreonine oligopeptide ( together with the appropriate pT+3 residue ) exists in Rv2623 , in silico docking analysis was conducted [38] . Results of the analysis further support the interaction of these two mycobacterial components ( Fig 4D ) . The identification of a phosphorylatable threonine in Rv2623 with a pT+3 residue that can potentially mediate an interaction with Rv1747 FHA I ( Figs 3 and 4 ) prompted studies designed to determine the significance of T237 in the growth-regulatory function of Rv2623 . Because the phosphomapping experiment revealing that T237 is phosphorylatable used in vitro phosphorylated Rv2623 protein ( Fig 4 ) , we investigated , in addition to T237 , the potential contributions of the other four solvent-accessible threonines of Rv2623 ( T90 , T103 , T212 , and T280 ( Fig 3A ) ) to the growth-regulatory function of the USP . All solvent-accessible threonines of Rv2623 were individually mutagenized to alanine to yield five T→A mutants . The ability of the five mutants to attenuate growth in M . smegmatis mc2155 upon pMV261-based overexpression in recipient cells was evaluated using the in vitro BACTEC system as previously described [6] . The results of these studies revealed that , relative to the WT Rv2623 , the ability of the Rv2623T103A and Rv2623T237A mutants to attenuate mycobacterial growth when overexpressed in M . smegmatis mc2155 was significantly reversed ( Fig 5A ) . By contrast , overexpression of the Rv2623T90A , Rv2623T212A , and Rv2623T280A mutants in M . smegmatis mc2155 displayed growth attenuating capacity comparable to that of the Rv2623WT . These results suggest that of the five solvent-accessible threonines , T103 and T237 are the two residues that most likely contribute significantly to the growth-regulatory property of Rv2623 . Expression study , however , revealed that while the levels of overexpression of T237A mutant and WT Rv2623 proteins in M . smegmatis were comparable , Rv2623T103A was poorly expressed for reasons that are currently unknown . This latter result has led us to focus solely on role of Rv2623 T237 in regulating mycobacterial growth ( Fig 5A ) . The observation that the melting temperatures of Rv2623WT ( 41 . 63°C ) and Rv2623T237A ( 42 . 96°C ) are comparable effectively excludes the possibility that the loss of growth-regulatory ability of these threonine mutants is due to protein misfolding or instability ( Fig 5B ) . The fact that mutating the phosphorylatable T237 to a non-phosphorylatable alanine results in the loss of the ability of Rv2623 protein to attenuate growth upon overexpression in M . smegmatis strongly suggests that this threonine residue plays a significant role in regulating mycobacterial growth in a phosphorylation-dependent manner . This , together with the above-described evidence supporting the ability of Rv2623 to interact with the phosphopeptide-recognizing FHA I of Rv1747 , has prompted us to conduct affinity chromatography experiments to examine the capacity of Rv2623T237A to interact with Rv1747 FHA I . The results of these studies using purified differentially-tagged recombinant Rv2623WT , Rv2623T237A , and Rv1747 FHA I revealed that , relative to the WT Rv2623 , the capacity of the T237A mutant USP to bind the M . tuberculosis Rv1747 FHA I , is significantly reduced ( Fig 5C ) . Of note , although results derived from the affinity chromatography experiments for the study of protein-protein interaction are not quantitative , the incomplete ablation of the Rv2623-Rv1747 FHAI interaction ( Fig 5C ) upon T→A mutation of Rv2623 raises the possibility that additional factors may , in concert with T237 phosphorylation , regulate the interaction between these two mycobacterial proteins . Collectively , these results strongly suggest that mycobacterial growth can be regulated through the interaction of the phosphorylated T237 residue of Rv2623 and the FHA I of Rv1747 . To examine whether the Rv2623-Rv1747 interaction plays a role in regulating growth in virulent M . tuberculosis as demonstrated in M . smegmatis , the ability of Rv2623T237A mutant protein to attenuate growth in the Erdman strain upon overexpression was examined using the in vitro BACTEC system [6] . As in the M . smegmatis study , the T237A but not T90A mutation of Rv2623 significantly reversed the ability of WT USP to regulate M . tuberculosis growth ( Fig 5D ) . M . tuberculosis overexpressing the Rv2623T237A mutant protein exhibited growth kinetics comparable to that of the WT Erdman strains lacking pMV261 or harboring the vector control ( pMV261 with no Rv2623 sequence ) . Rv2623WT , Rv2623T90A , and Rv2623T237A proteins were overexpressed to comparable levels in M . tuberculosis Erdman via pMV261 ( S5 Fig ) , thus excluding the possibility that the observed inability of the T237A mutant protein to retard growth of the tubercle bacillus is due to inadequate expression . The growth regulatory property of Rv2623WT , Rv2623T90A , and Rv2623T237A was further examined in M . tuberculosis by growth curve analysis monitoring OD600nm ( Fig 5E ) . In agreement with the BACTEC study , growth curve analysis also indicated attenuated growth in strains expressing Rv2623WT and Rv2623T90A , while that expressing Rv2623T237A mutant grew at a rate similar to Erdman and the Erdman+pMV261 control ( Fig 5E ) . Taken together , these results strongly suggest that T237 of Rv2623 is critical for the growth-regulatory function of this USP through mediating , in its phosphorylated form , the interaction with Rv1747: Binding of pT237-containing Rv2623 to the FHA I domain of Rv1747 negatively regulates M . tuberculosis growth . During the course of manipulation of the M . tuberculosis Rv2623-deficient mutant , it was observed that this strain displays a sedimentation phenotype when grown in Middlebrook 7H9 ( M7H9 ) medium with Tween 80 ( Fig 6A ) . Under this culture condition , WT Erdman and ΔRv2623 grow with similar kinetics monitored over a 2 week-period [6] . Upon standing of suspension of individual 10-day-old bacterial cultures , the ΔRv2623 strain formed a loosely-packed , fluffy sediment compared to WT ( Fig 6A ) . In addition , when plated onto M7H10 agar , these two strains display distinct colony morphotypes , with the Rv2623-deficient strain forming colonies with an apparently smoother , less ruffled appearance ( Fig 6B and 6C ) , which is restored to that of WT with complementation , thus demonstrating Rv2623-specificity of this phenotype ( Fig 6B–6D ) . This observation , together with the functional assignment of Rv1747 as a putative exporter of lipooligosaccharides , prompted us to begin characterizing cell envelope components of M . tuberculosis ΔRv2623 . We initiated studies to evaluate the expression of PIMs , one of the most abundant and bioactive glycolipid families in the M . tuberculosis cell wall [14 , 15] . Two-dimensional thin-layer chromatography ( TLC ) analyses of total polar lipid extracts ( normalized by total protein content ) revealed that the ΔRv2623 mutant displays a PIM profile distinct from that observed in WT Erdman ( Fig 6E and 6F ) . Specifically , ΔRv2623 expresses higher amounts of PIMs , particularly tri- and tetra-acylated PIM2 and PIM6 , compared to WT bacilli ( Fig 6E & 6F ) . In a separate and independent series of experiments , Rv2623-specificity for the hyper-producing phenotype for all the PIM types examined was demonstrated by complementation—using the integrating pMV306 construct that expresses Rv2623 under the control of its native promoter [6]—except for AC1PIM2 ( Fig 6G ) . The apparent unique attribute of this latter PIM species is unclear . Given the observation that Rv2623 interacts with Rv1747 to negatively regulate M . tuberculosis growth , the enhanced PIM levels of ΔRv2623 suggest that Rv2623 may affect in vivo M . tuberculosis growth by modulating the production of PIMs , raising the possibility that Rv1747 ( which has been annotated to be an exporter of lipooligosaccharides [13] ) , could be involved in PIM transport . Consequently , we evaluated the amount of PIMs in an Rv1747-deficient mutant . The results have revealed that M . tuberculosis ΔRv1747 is a hypo-producer of PIMs relative to WT Erdman ( Fig 6H and S6 Fig ) . This latter observation lends support to the notion that Rv1747 can indeed be involved in the transport of PIMs , thereby influencing their levels in the M . tuberculosis cell wall .
Given the remarkable number of people world-wide estimated to harbor dormant tubercle bacilli [2–5] , which physiological state renders treatment challenging [39] , tuberculous latency and reactivation poses a formidable problem for the effective control of M . tuberculosis . The mechanisms by which tuberculosis latency and reactivation are regulated remain incompletely defined [2–5] . We have previously reported that Rv2623 , which encodes a USP and is highly induced by hypoxia and nitrosative stress , can regulate bacillary growth in vitro and in vivo [6] . Importantly , virulent M . tuberculosis deficient in Rv2623 fails to establish a chronic persistent infection and is hypervirulent in an infected host [6] . This latter observation suggests that Rv2623 plays a role in regulating in vivo M . tuberculosis growth , particularly in the context of tuberculous latency [6] . The present study seeks to understand the mechanisms by which Rv2623 regulates M . tuberculosis growth . The results , derived from yeast two-hybrid screen , affinity chromatography studies , crystallographic , as well as bioinformatics and modeling analyses [6 , 24 , 32 , 33] , have provided evidence that this USP interacts with Rv1747 , a putative ABC lipooligosaccharide exporter [13] , to negatively regulate M . tuberculosis growth in concert . This interaction is mediated via the recognition by the Rv1747 FHA I domain , an Rv2623 conserved oligopeptide motif harboring a phosphorylated threonine at position 237 , as assessed by mass spectrometry-based phospho-mapping study . Compared to the WT USP , the interaction of Rv1747 FHA I domain with a mutant Rv2623 protein , which T237 has been mutated to a non-phosphorylatable alanine ( Rv2623T237A ) , is significantly compromised . Significantly , in vivo analyses of specific Rv2623 mutants have shown that the interaction of the USP with Rv1747 has functional consequences: relative to Rv2623WT , the growth regulatory capacity of the mutant Rv2623T237A is markedly attenuated . Together , these results have provided strong evidence that specific elements of Rv2623 and the FHA I domain of Rv1747 mediate the interaction between these two mycobacterial components to negatively regulate M . tuberculosis growth in a phosphorylation-dependent manner . In support of the observation on Rv2623-Rv1747 interaction , evidence exist that bacterial USPs mediate biological functions in concert with interacting molecules . For example , Escherichia coli UspC functions as a scaffolding protein of the KdpD/KdpE two-component salt sensing signaling pathway , and via phosphorylation-dependent mechanisms , regulates the expression of the high-affinity K+ transporter KdpFABC [21] . There is evidence that the Halomonas elongate USP TeaD may regulate the activity of the ectoine transporter TeaABC to maintain osmotic equilibrium [22] . The KdpD/KdpE and TeaD studies thus provide evidence for the interaction between bacterial USP and transporters , supporting our observation that the M . tuberculosis USP Rv2623 interacts with the putative ABC transporter Rv1747 . M . tuberculosis Rv1747 , a 92-kDa integral membrane protein , has been annotated as an ABC transporter [13] . The Rv1747’s nucleotide-binding domain ( NBD ) and its membrane spanning domain ( MSD ) , two major components of a typical ABC transporter , are fused [13 , 40] . This structural organization suggests that the transporter operates as a homodimer [13] . Although its function remains to be formally characterized , Rv1747 harbors elements strongly suggesting that it is an ABC transporter [40] . In addition to the NBD and MSD domains , Rv1747 carries the Walker A and B motifs , which make up the ATP-binding pocket of ABC transporters [40] . Rv1747 also harbors the ABC transporter family signature sequence , a characteristic 12-residue segment located between the two Walker motifs , and the 6-amino acid Linton and Higgins motif downstream of Walker B [40] . In vitro studies have revealed that the Rv1747 NBD displays ATPase activity [41] . A unique feature of Rv1747 is the presence in the NBD of two FHA domains , phosphopeptide-recognizing signaling modules that mediate diverse biologically important processes via protein-protein interactions [23–27] . Of the various M . tuberculosis ABC transporters , Rv1747 is the sole FHA domain-containing member [13] . And Rv1747 , one of the 6 FHA domain-containing M . tuberculosis proteins ( S7 Fig ) , is the only one in this group to harbor two such domains [23 , 26] . The FHA domain , together with the bacterial counterparts of eukaryotic STPKs , is an important component of reversible phosphoregulation pathways that mediates a wide range of biological processes [23–27] . Characterization of FHA-containing M . tuberculosis proteins including EmbR ( Rv1267c ) [42–46] , FhaB ( Rv0019c ) [47] , and GarA ( Rv1827 , glycogen-accumulation regulator A ) [43 , 48–50] has revealed that these molecules , together with other mycobacterial components and STPKs , play important roles in regulating biologically highly significant processes , including synthesis of cell wall components , cell division , drug resistance , virulence levels , and metabolism . The observation that Rv2623 interacts with Rv1747 to regulate bacterial growth adds to the gravity of M . tuberculosis FHA domain-containing components . Rv1747 is required for optimal survival of M . tuberculosis in macrophages , dendritic cells , and in mice [16 , 17] , and this virulence attribute is dependent on its phosphoregulation by PknF [16 , 17] . Thus , a ΔRv1747 M . tuberculosis mutant , while exhibiting no in vitro growth phenotype , is hypovirulent in mice [16 , 17] . This latter hypovirulence phenotype is in stark contrast to the in vivo hypervirulence of ΔRv2623 [6] . Given the observation that Rv2623 and Rv1747 interact to negatively regulate M . tuberculosis growth , the opposing in vivo virulence phenotype of ΔRv2623 and ΔRv1747 suggests that the USP may modulate the growth of the tubercle bacillus in vivo by attenuating the function of the putative transporter . This scenario , together with the divergent PIM phenotype of the Rv2623- and Rv1747-deficient mutants ( with ΔRv2623 and ΔRv1747 being a hyper- and hypo-producer of PIMs; respectively ) , raises the possibility that the putative ABC transporter Rv1747 serves to transport PIM ( s ) , and that these glycolipids may , at least in part , impact mycobacterial growth in vivo through their immune-regulatory attributes [14 , 15] . Lending credence to the above notions , there exist precedents that ABC transporters function as exporters of phospho- and glycolipids [51–53] and that protein phosphorylation serves as a regulatory mechanism of this transport system [52 , 54–56] . As well , it has been reported recently that among the most differentially up-regulated genes in an M . tuberculosis ΔRv1747 strain , relative to WT bacilli , are iniA and iniB [57] , members of the iniBAC operon which are induced by a variety of agents that inhibit mycobacterial cell wall synthesis [58 , 59] . Thus , the differentially enhanced expression of genes of this operon in ΔRv1747 could reflect the bacterial reaction to certain dysregulated cell wall biosynthesis pathways . This latter idea is consistent with the diminished level of expression in ΔRv1747 of PIMs , molecules that are important components of the M . tuberculosis cell envelope , which also serve as precursors for the synthesis of lipomannan ( LM ) and mannose-capped lipoarabinomannan ( LAM ) , the latter a major mycobacterial surface lipoglycan that is essential in the tubercle bacillus [14 , 15 , 60] . Collectively , the ini operon observations , together with annotation of Rv1747 as an ABC transporter that exports lipooligosaccharides , and the diametrically opposed growth and PIM phenotypes of ΔRv2623 and ΔRv1747 , have provided evidence suggesting that Rv1747 may function as a PIM exporter , perhaps translocating certain member ( s ) of this family of glycolipids from the cytoplasmic side to the periplasmic side of the plasma membrane [14 , 60] . The enzymes for PIM biosynthesis and the subcellular locales in which PIMs are produced are incompletely defined [14 , 60] . Evidence exists , however , that synthesis of PIM is compartmentalized , with the lower ( PIM1/2 ) and higher ( PIM5/6 ) order PIMs produced in the cytosolic and periplasmic side of the inner membrane , respectively [14 , 60] . This paradigm suggests that translocation of PIMs ( PIM2 , PIM3 , and/or PIM4 ) across the inner membrane must occur [14] . Nonetheless , bona fide transporter ( s ) of PIMs , their substrate specificity , and how many there are , are unknown [14] . Results of the present study suggest that Rv1747 may function as a PIM transporter . Due to the essentiality of PIM1 , PIM2 , and PIM-derived LM/ManLAM for M . tuberculosis survival , the biosynthesis of PIMs ( including their translocation across the inner membrane ) and related lipoglycans is likely complex and may involve compensatory and/or redundant mechanisms [14] . Indeed , the results of a study on PIM4 channeling protein LpqW suggest that compensatory mechanisms are in place to ensure the maintenance of adequate levels of the essential LAM at the expense of higher order PIMs [61 , 62] . It is possible that this apparent LAM-preserving attribute of M . tuberculosis could be operative in the PIM-hypoproducing ΔRv1747 . This would explain the results of a report noting that a ΔRv1747 strain , despite being a hypo-producer of PIMs ( the precursors for LAM biosynthesis ) , the mutant displays WT level of LAM/ManLAM , notwithstanding the use of a non-quantitative immuodetection approach and the lack of evaluation of PIM expression by the deletion mutant [57] . The present study has provided evidence that M . tuberculosis Rv2623 negatively modulates the transport function of Rv1747 to regulate mycobacterial growth through phosphorylation-dependent mechanisms ( Fig 7 ) . Further , the opposing PIM expression phenotype of the ΔRv2623 and ΔRv1747 mutants suggests that Rv1747 may be a transporter for PIMs , immunologically active molecules that have been shown in vitro to impact M . tuberculosis-host interaction to influence the immune response [14 , 15] . The immune-regulatory properties thus could contribute , at least in part , to the ability of the Rv2623-Rv1747 interaction to regulate M . tuberculosis growth in the host by influencing the anti-tuberculous response [14 , 15] . It is also plausible that the altered levels of expression of these important cell enveloped glycolipids can have an intrinsic effect on mycobacterial growth . These two possibilities are not mutually exclusive . PIMs are downregulated in stationary phase [14] , and it has recently been observed that the production of PIMs by M . tuberculosis is enhanced during infection in primary human macrophages ( S8 Fig ) . This latter result reinforces the concept that the tubercle bacillus can adapt to environmental signals during infection by modulating the cell envelope , including lipid components [63–67] . Whether the level of PIMs in the M . tuberculosis cell envelope can influence in vivo mycobacterial growth and virulence is currently unclear; that it can is supported by our findings that the PIM-hyperproducing ΔRv2623 is hypervirulent in vivo and is unable to establish a chronic infection , while ΔRv1747 , a hypoproducer of PIMs , is attenuated for growth in mice [16 , 17] . Much remains to be learned regarding the precise mechanisms by which Rv2623 modulates mycobacterial growth and by which this USP is regulated . The presence of three apparent Rv2623 isoforms suggests that the regulation of this protein is complex , and that phosphorylation of T237 might not be the only mode of post-translational modification involved . The phosphorylated Rv2623 that was subjected to mass spectrometric analysis was generated via an in vitro phosphorylation reaction mediated by recombinant PknG . Hence it is possible that in vivo , other threonines , in addition to T237 , could serve as additional targets of phosphorylation , thus accounting for the three Rv2623 isoforms observed . It is also possible that other mode of post-translational modification is involved . Equally unclear is the mechanism underlying the previously described dependency of the M . tuberculosis growth-regulating attribute of Rv2623 on its ATP-binding capacity [6] . Is this dependency related to the Rv2623-Rv1747 interaction ? Finally , the apparent function of Rv1747 as a PIM transporter and the mechanisms underlying its regulation remain to be characterized . This will likely not be a straightforward endeavor given the transmembrane nature of Rv1747 . Nevertheless , the collective results of the present study have provided a framework for understanding the mechanisms by which Rv2623 interacts with the putative PIM transporter Rv1747 ( Fig 7 ) to regulate M . tuberculosis growth , particularly in the context of tuberculous persistence , and for potentially advancing knowledge of the biosynthetic pathways of mycobacterial glycolipids and lipoglycans .
Primers , oligonucleotides ( IDT DNA technologies ) and vectors used for generating various constructs are listed in S1 Table . M . tuberculosis strain Erdman and H37Rv and M . smegmatis mc2155 were cultured in supplemented Middlebrook 7H9 ( M7H9 ) medium ( Becton Dickinson , Sparks , MD ) as previously described [6] . Mycobacterial strains overexpressing WT Rv2623 and various mutants of the USP were maintained in supplemented M7H9 broth containing 40 μg/ml of kanamycin . Mycobacterial growth was monitored by measuring absorbance at 600 nm or by the in vitro BACTEC 9000 system as previously described ( Becton Dickinson , Sparks , MD ) [6] . The latter approach involved inoculating stationary phase M . tuberculosis or M . smegmatis in triplicates into BD Myco/F lytic vials ( final bacterial suspension of 104 colony forming units ( CFU ) per ml ) ( BD Bosciences , Sparks , MD ) , whose liquid medium is supplemented with a compound that fluoresces as a result of oxygen depletion due to bacterial growth . The time to detection of fluorescence signals thus reflects the rate of bacterial growth . The pSD series vectors , which direct expression of proteins of interests in M . smegmatis under the control of the acetamide-inducible promoter , were used to generate N-terminal His6-tagged and N-terminal FLAG-tagged recombinant Rv2623 ( His-Rv2623 and FLAG-Rv2623 ) in mc2155 [29 , 30] . Fragments of Rv1747 representing the first 120 amino acids and harboring the FHA I domain ( Fig 1A ) were PCR-amplified to contain the cMyc or FLAG tag and cloned into the pSD vectors for the expression of an N-terminal cMyc-tagged FHA I ( Myc-FHA I ) and N-terminal FLAG-tagged FHA I ( FLAG-FHA I ) [29 , 30] . An N-terminal FLAG-FHA II ( spanning amino acids 201–310 ) fusion ( FLAG-FHA II ) was similarly expressed in the pSD system [29 , 30] . The pQE80L ( Qiagen , Inc . ) -based plasmid pQE-Rv2623 containing an N-terminal His6-tag-Rv2623 fusion construct was also used for the expression of His-Rv2623 [6] . Various M . tuberculosis Rv2623 threonine residue mutants were derived from pQE-Rv2623 via site-directed mutagenesis and similarly expressed ( see below ) . As well , Myc-FHA I and FLAG-FHA I were also expressed in E . coli via the LIC ( Ligation Independent Cloning ) vector pMCSG7 [68] ( pMCSG7-His-TEV-cMyc-FHA I; pMCSG7-His-TEV-FLAG-FHA I ) ( TEV: Tobacco Etch Virus cleavage site ) . The plasmid pSD31-Rv2623 was electroporated into M . smegmatis mc2155 for the expression of His-Rv2623 in this acetamide-inducible system [29 , 30] . Log-phase cultures were induced with acetamide ( final concentration: 0 . 2% ) overnight at 37°C with shaking . Expression of M . tuberculosis Rv2623 based on the pQE80L system was carried out as previously described , following isopropyl beta-D-thiogalactoside ( IPTG; final concentration , 0 . 3mM ) induction in transformed BL21 E . coli E . coli [6] . The threonine residue mutants of Rv2623 were similarly expressed . Protein purification was carried out as described with modification [6] . Auto-induction medium was used for the expression of recombinant proteins via the LIC vector pMCSG7 [68] . Cells were disrupted using hydraulic press or the Matrix B and Fast Prep apparatus ( MP Biomedicals , CA ) for Mycobacterium and sonication for E . coli . Clarified bacterial cell lysates were filter-sterilized and expressed proteins were purified by affinity chromatography using appropriate antibodies against specific tags or Rv2623 followed by gel-filtration chromatography as described previously [6] ( S1 Text ) . M . tuberculosis Erdman strain chromosomal DNA and pGADT7 AD ( Clontech , Mountain View , CA ) cloning vector DNA were used to generate the yeast library for the yeast two-hybrid screen . Construction of the library was carried out by DNA Technologies Inc . ( Gaithersburg , MD ) . Two E . coli libraries ( > 2 x 106 clones per library ) were generated by cloning partially digested , size-fractionated chromosomal DNA consisting of fragments spanning 0 . 5 to 2 . 5 kb as well as those ranging from 2 . 5 to 5 . 0 kb into the BamHI site of the pGADT7AD vector . These two DNA libraries , which were used to screen for Rv2623-interacting prey proteins were cloned in pGADT7AD harboring the GAL4 activation domain . pGADT7AD has the Leu2 nutritional marker for selection in yeast . The gene for the bait protein Rv2623 was cloned into the pGBKT7 vector to generate an Rv2623-GAL4 DNA-binding domain translational fusion . pGBKT7 has the TRP1 nutritional marker for selection in yeast . The screens were conducted using Matchmaker Gold Yeast Two-Hybrid System , according to manufacturer’s instructions . To confirm the interaction between the prey hits with Rv2623 , the putative interactor sequence harbored in pGADT7AD was rescued . pGADT7AD harboring DNA fragments expressing the putative interactor were then individually co-tranformed with pGBKT7 containing the full-length Rv2623 DNA into an auxotrophic reporter strain Y2H Gold ( his- , ade- ) for validation of the interaction . The positive control was Rv2623 interacting with itself , as it forms a dimer in solution [6] . E . coli- and/or M . smegmatis-expressed His-tagged full-length WT Rv2623 ( Rv2623WT ) , the Rv2623T237A mutant , and cMyc-tagged FHA I , and FLAG-FHA II were purified by affinity chromatography using the appropriate antibody followed by gel filtration as described for use in the affinity chromatographic study [6] . Spin columns were packed with 50 μl of Ni-NTA agarose bead slurry ( ~25 μl resin; Qiagen ) and equilibrated with Buffer A ( S1 Text ) . Resins in the equilibrated columns were either left untreated or allowed to react with 150 μg each of either His-tagged Rv2623WT or Rv2623T237A , for 2 hours at 4°C . The columns were then washed to rid of unbound proteins using Buffer B ( S1 Text ) . Appropriate columns were allowed to react with 400 μg each of recombinant cMyc or FLAG-tagged FHA I for 2 hours at 4°C and then thoroughly washed to remove unbound proteins using Buffer A and Buffer B sequentially . The columns were then eluted for bound proteins with 100 ml of Buffer C ( S1 Text ) . The eluates were electrophoretically resolved and subjected to Western blot analysis using antibodies specific for the specific epitope tag or to Rv2623 , followed by an appropriate Horseradish peroxidase-conjugated secondary antibody , and then subjected to detection by chemiluminescence ( Amersham ) . Proteins were densitometrically quantified ( S1 Text ) . The BioRad system was used for the 2-D gel electrophoresis analysis . To prepare samples for isoelectric focusing , 150 μg total protein ( 1 μg for purified protein ) was processed using a ReadyPrep 2-D Cleanup Kit ( Bio-Rad , Hercules , CA ) according to the manufacturer’s instructions . The prepared protein ( final volume: 125 μl ) was allowed to react with a ReadyStrip IPG , pH range 4–7 ( Bio-Rad ) in a rehydration tray for ~16 hours at 20°C . The IPG strip thus prepared was subjected to isoelectric focusing for three hours ( 8kV for 10kvh ( 50μA/strip ) ) . The focused IPG strip was equilibrated and washed according to the manufacturer’s instructions , and loaded onto a 1 . 5 mm-thick 14% SDS polyacrylamide gel with a 4% stacking gel , along with a Precision Plus Protein Dual Color Standard ( Bio-Rad , Hercules , CA ) . The gel was run at 155 V until the dye front migrated to the bottom of the gel ( ~60 min ) . In some cases , the gel was then stained with Sypro Ruby ( Bio-Rad , Hercules , CA ) according to the manufacturer’s instructions . In other cases , the SDS gel was transferred to a PVDF membrane and analyzed for protein separation and detection of Rv2623 by standard Western blotting techniques . The PVDF membrane was reacted with a primary monoclonal antibody against M . tuberculosis Rv2623 ( Advanced Immunochemicals , Inc . , Long Beach , CA; 5-Rv2623-A10 ) followed by a secondary horseradish peroxidase-conjugated anti-mouse IgG antibody . Protein was visualized by chemiluminescence using a Super Signal West Pico Chemiluminescent Substrate ( ThermoFisher Scientific , Waltham , MA ) according to the manufacturer’s instructions and exposed to Kodak scientific imaging film ( BioMax XAR , Eastman Kodak Company , Rochester , NY ) . Relative abundance of protein was quantified using ImageQuant densitometry software ( Molecular Dynamics , Inc; Sunnyvale , CA ) . In some experiments , the PVDF membrane was also stained with Coomassie Brilliant Blue G-250 ( Bio-Rad , Hercules , CA ) according to the manufacturer’s instruction . Recombinant Rv2623 derived from M . smegmatis , as well as the native USP immunoaffinity-purified from M . tuberculosis and BCG cell lysates were analyzed for phosphorylation using a dot blot assay . Briefly , increasing concentrations of Rv2623 protein derived from the various sources were spotted onto nitrocellulose membrane . Phosphothreonine ( Sigma-Aldrich; St . Louis , MO ) served as positive control . The membrane was allowed to dry , followed by blocking with 5% BSA in Tris-HCl ( 20 mM Tris-HCl; pH 7 . 5 ) containing 150 mM NaCl , 0 . 05% Tween 20 ) ( TBST ) . The membrane was probed with anti-phosphothreonine antibodies: clone #42H4 mouse monoclonal or rabbit polyclonal antibodies ( Cell Signaling Technology; Danvers , MA ) , washed thrice with TBST and then reacted with appropriate horseradish peroxidase ( HRP ) -conjugated secondary antibody ( Sigma-Aldrich; St . Louis , MO ) . The blot was developed using ECL reagent ( Amersham; Piscataway , NJ ) . Purified recombinant Rv2623 ( 5 μg ) [6] was subjected to in vitro phosphorylation by M . tuberculosis PknG as described in S1 Text . The reaction product , electrophoretically resolved in a 10% SDS PAGE , was subjected to Western blot analysis using anti-phosphothreonine antibody ( Cell Signaling; Danvers , MA ) . In parallel , a separate gel of electrophoretically resolved reaction mixture was stained with Coomassie Blue to identify the Rv2623 band based on molecular mass . The Coomassie Blue-stained gel band harboring the phosphorylated Rv2623 protein was excised , and the sample sent in chloroform-treated microcentrifuge tubes ( Eppendorf; Westbury , NY ) to the Mass Spectrometry & Proteomics Resource of the W . M . Keck Foundation Biotechnology Resource Laboratory ( Yale School of Medicine , New Haven , CT ) for further processing and mass spectrometric analysis for phosphorylated threonine based on published protocol [69] . Briefly , the gel band was SpeedVac-dried , then solubilized in appropriate buffer . The protein sample was then subjected to dithiothreitol ( DTT ) reduction , IAN-mediated alkylation , and the urea content brought down to 2 M with water prior to trypsin digestion . The digested sample was acidified with 0 . 5% trifluoroacetic acid ( TFA ) , 50% acetonitrile and then subjected to titanium dioxide enrichment using the Top Tips system ( Glygen Corp; Columbia , MD ) . The resulting phosphopeptide-enriched sample , dissolved in 70% formic acid and diluted with 0 . 1% TFA , was then subjected to LC-MS/MS analysis using the LTQ Orbitrap Elite that is equipped with a Waters nanoACQUITY UPLC system , and which uses a Waters Symmetry C18 180 μm x 20 mm trap column and a 1 . 7 μm , 75 μm x 250 mm nanoACQUITY UPLC column for peptide separation . The acquired data was peak-picked and searched using the Mascot Distiller and the Mascot search algorithm , respectively; and quantitatively processed with Progenesis LCMS ( Nonlinear Dynamics , LLC ) as previously described [69] ( S1 Text ) . Protein identification was achieved using Mascot search algorithm ( Matrix Science; Boston , MA ) as described [69] ( S1 Text ) . Manual examination of the MS/MS spectra was conducted to verify the phosphopeptides identified via software programs . Wild-type Rv2623 , with and without a His6 tag , was cloned into the pMV261 vector under the control of the hsp60 promoter using 5’-EcoRI and the 3’-HindIII sites [30] . This construct served as template for the generation of specific Rv2623 single amino acid substitution threonine mutants by mismatched PCR priming using primers designed to direct the incorporation of specific mutations into the Rv2623 coding region following Stratagene Quikchange ( La Jolla , CA ) protocol as described [6] . All constructs were confirmed by sequence analysis . Appropriate constructs were transformed into M . smegmatis mc2155 and M . tuberculosis Erdman to generate mycobacterial strains over-expressing WT Rv2623 and the desired threonine mutants . Transformants were screened by PCR targeting the kanamycin resistance marker ( for M . smegmatis and M . tuberculosis ) and/or Rv2623 ( for M . smegmatis ) . PCR product from M . smegmatis was gel purified ( Qiagen gel extraction kit; Valencia , CA ) and the sequence of the WT and mutant Rv2623 verified . Following sequence verification , expression of Rv2623 and its mutants in M . smegmatis was probed by Western blot , using a mouse anti-Rv2623 monoclonal antibody ( Advanced Immunochemical; Long Beach , CA ) . To distinguish between the native and pMV261-based expression of the USP and its mutants in M . tuberculosis , a mouse anti-His tag monoclonal antibody ( Sigma-Aldrich; St . Louis , MO ) was used . Finally , thermal denaturation curves were determined for purified WT and mutant Rv2623 using an IQ5 Real Time PCR Detection System ( Bio-Rad; Hercules , CA ) following incubation with SYPRO Orange protein gel stain ( ThermoFisher Scientific , Waltham , MA ) as previously described [6] ( S1 Text ) . For analysis of PIMs located in the cell wall of M . tuberculosis strains , bacteria were harvested 12 days after inoculation on M7H11 agar plates and lysed [70] . Protein quantification was performed by the BCA method following the manufacturer’s instructions ( Bio-Rad , Hercules , CA ) . Lysates totaling 10 mg of protein from each strain were delipidated at 37°C for 12 h using CHCl3:CH3OH ( 2:1 , v/v ) followed by CHCl3:CH3OH:H2O ( 10:10:3 , v/v/v ) for an additional 12 h . For 2D-TLC analyses , total crude lipids from each strain were loaded ( 100 μg ) onto the origin of a 10 cm × 10 cm TLC silica gel 60 F254 aluminum plate ( EMD Millipore , Temecula , CA ) based upon equal amounts of protein content from the bacterial lysates and run in the first dimension using CHCl3:CH3OH:H2O ( 60:30:6 , v/v/v ) as a solvent system . The TLC plate was then rotated 90° to the left and run in the second dimension using CHCl3:CH3COOH:CH3OH: H2O ( 40:25: 3:6 , v/v/v/v ) as a solvent system . Plates were dried and sprayed with 10% concentrated sulfuric acid in absolute ethanol and heated at 110°C until lipid bands appeared [71] . The NIH ImageJ program ( http://rsb . info . nih . gov/ij/ ) was used to perform PIM densitometry analysis by calculating mean spot intensities from three independent experiments .
|
Mycobacterium tuberculosis remains a significant public health burden worldwide . The tubercle bacillus can establish a clinically silent latent infection in the host , which can subsequently reactivate to cause diseases , resulting in transmission of the pathogen . We have previously shown that the M . tuberculosis universal stress protein Rv2623 has the ability to regulate mycobacterial growth and may be required for the establishment of latent infection . The present study is undertaken to better understand the mechanisms by which Rv2623 regulates M . tuberculosis growth . Our results have revealed that Rv2623 interacts with Rv1747 , a putative exporter of lipooligosaccharides , to negatively regulate mycobacterial growth . We have defined the molecular elements in these two proteins that mediate their interaction . We have further shown that relative to the wild-type ( WT ) bacillus , an Rv2623 null mutant ( ΔRv2623 ) exhibits a higher content of phosphatidyl-myo-inositol mannosides ( PIMs ) , immunologically active molecules of the M . tuberculosis cell envelope . By contrast , ΔRv1747 produces less PIMs than WT . Of note , while ΔRv2623 is hypervirulent , ΔRv1747 is hypovirulent in mice . These observations link the growth-regulatory attributes of Rv2623 to the function of Rv1747 , suggesting that Rv2623 may regulate M . tuberculosis growth by modulating Rv1747’s export of the immunomodulatory PIMs . Defining how Rv2623 regulates mycobacterial growth will likely provide insight into the mechanisms underlying tuberculous latency and reactivation , processes that play important roles in M . tuberculosis pathogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"phosphorylation",
"yeast",
"two-hybrid",
"assays",
"protein",
"interactions",
"chemical",
"compounds",
"threonine",
"organic",
"compounds",
"protein",
"interaction",
"assays",
"amino",
"acids",
"molecular",
"biology",
"techniques",
"gel",
"electrophoresis",
"bacteria",
"research",
"and",
"analysis",
"methods",
"electrophoretic",
"techniques",
"proteins",
"chemistry",
"actinobacteria",
"molecular",
"biology",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"biochemistry",
"organic",
"chemistry",
"post-translational",
"modification",
"mycobacterium",
"tuberculosis",
"phenotypes",
"library",
"screening",
"genetics",
"hydroxyl",
"amino",
"acids",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"organisms",
"two-hybrid",
"screening"
] |
2017
|
Mycobacterium tuberculosis universal stress protein Rv2623 interacts with the putative ATP binding cassette (ABC) transporter Rv1747 to regulate mycobacterial growth
|
The Bacillus subtilis recH342 strain , which decreases interspecies recombination without significantly affecting the frequency of transformation with homogamic DNA , carried a point mutation in the putative recX ( yfhG ) gene , and the mutation was renamed as recX342 . We show that RecX ( 264 residues long ) , which shares partial identity with the Proteobacterial RecX ( <180 residues ) , is a genuine recombination protein , and its primary function is to modulate the SOS response and to facilitate RecA-mediated recombinational repair and genetic recombination . RecX-YFP formed discrete foci on the nucleoid , which were coincident in time with RecF , in response to DNA damage , and on the poles and/or the nucleoid upon stochastic induction of programmed natural competence . When DNA was damaged , the RecX foci co-localized with RecA threads that persisted for a longer time in the recX context . The absence of RecX severely impaired natural transformation both with plasmid and chromosomal DNA . We show that RecX suppresses the negative effect exerted by RecA during plasmid transformation , prevents RecA mis-sensing of single-stranded DNA tracts , and modulates DNA strand exchange . RecX , by modulating the “length or packing” of a RecA filament , facilitates the initiation of recombination and increases recombination across species .
The bacterial RecA recombinase ( homologue to human RAD51 and DMC1 ) , arranged as higher-order oligomers assembled on tracts of single-stranded ( ss ) DNA , is involved in the DNA strand exchange reaction to warrant genome integrity by recombinational repair ( RR ) , and genetic diversity by genetic recombination ( GR ) . Template-dependent RR preserves the integrity of the genetic information , re-establishes replication and ensures proper chromosomal segregation . In contrast , GR , which occurs in species that can exchange chromosomal DNA segments , is an important mechanism for natural variation among prokaryotes and plays an important role in the dissemination of important traits , such as antibiotic resistance , virulence determinants and metabolic pathways involved in adapting to environmental niches . There are three modes by which bacteria can exchange chromosomal DNA segments: viral-mediated transduction , which may be limited by the viral host range and by the host-encoded restriction system , conjugation and natural transformation . Bacillus subtilis transformation or Escherichia coli conjugation catalyze unidirectional integration of chromosomal ssDNA at a frequency that decreases exponentially with the increasing degree of DNA sequence divergence between donor and recipient reviewed in [1] , [2] . In E . coli the extent of genetic isolation by HFR conjugation is determined by the activity of the mismatch repair system , and requires DNA replication and recombination functions ( specifically requires overproduction of the RecA protein ) [3] , [4] , [5] . B . subtilis natural transformation , which can take DNA of any source , is insensitive to restriction endonucleases and to mismatch repair functions , and shows no obvious requirement for extended DNA replication [2] , [6] . RecA-dependent homologous recombination ( HR ) rather than mismatch repair seems to control the extent of genetic isolation during natural transformation [6] . Here , a specific set of recombination functions , some of which are induced by natural competence ( e . g . , SsbA , SsbB , DprA [Smf or CilB] , RecA , CoiA ) , are mainly located at the cell poles ( namely SsbB , DprA , RecA , CoiA and RecU ) where the DNA uptake machinery is located [7]–[11] . Except for recA and dprA mutations , the B . subtilis chromosomal transformantion frequency with homogamic DNA in single rec-deficient strains , classified within the α ( recF15 or ΔrecO ) , β ( addA5 ) , γ ( recH342 ) , δ ( ΔrecN ) , ε ( ΔruvAB , ΔrecU ) , ζ ( ΔrecQ ) or η ( ΔrecG ) epistatic groups , does not vary more than 3-fold relative to the rec+ value [12]–[15] . The absence of RecA blocks chromosomal transformation , and the absence of DprA results in a 50-fold reduction relative to the rec+ value [10] , [16]–[18] . From those rec-deficient strains tested , the frequency of interspecies gene exchange deviated significantly from the rec+ strain only in the recH342 mutant strains [6] . The frequency of transformation with divergent donor DNA decreased >20-fold in the recH342 strain relative to the rec+ value , without affecting the frequency of transformation by closely related donors [6] , suggesting that HR introduces barriers to genetic exchange , and that the “RecH342” mutation contributes to sexual isolation . Very little is known about the mutation ( s ) present in the recH342 ( BG119 ) strain , but the phenotype ( s ) associated with it suggested that the function ( s ) affected in this strain might act as an accessory factor by regulating the formation of an active RecA filament [19] . Why Does RecA need accessory factors ? The essential single stranded binding ( SSB ) protein ( termed SSB in E . coli , SsbA in B . subtilis or RPA in eukaryotes ) , which is ubiquitous in all living organisms , is involved in multiple pathways of DNA metabolism , including DNA replication , RR and GR reviewed in [20] . The majority of naturally competent bacteria encode a second non-essential protein , termed SsbB [21] . Biochemical studies have shown that the SSBs proteins , which bind to ssDNA and remove secondary structures , limit RecA loading onto ssDNA , as a consequence of the higher affinity and faster binding kinetics , so that the net result is a SSB-coated ssDNA reviewed in [20]–[24] . Furthermore , RecA·ssDNA filament elongation is blocked by DNA secondary structures , whereas assembly of SSB proteins is not , and SSB proteins contribute to the removal of secondary structures upon ssDNA binding , hence the RecA·ssDNA filaments formed on SSB-coated ssDNA after removal of the SSB protein ( s ) are more efficient than those formed by RecA alone reviewed in [20]–[24] . To overcome the effect of a SSB protein on RecA nucleation onto ssDNA , and RecA filament formation , a series of RecA accessory factors regulate such stage reviewed in [22]–[25] . These factors can be divided into two broad classes: those that act before RecA nucleation by promoting assembly of RecA onto SSB-coated ssDNA ( termed RecA mediators ) , and those that act after RecA nucleation and during homology search and strand exchange , by promoting RecA·ssDNA filament assembly and disassembly ( termed RecA modulators ) [25] . Genetic recombination and RR share some accessory factors , but others are specific for each event . The most ubiquitous RecA mediators are RecO and RecR , which are involved both in RR and GR . The role of RecBCD ( counterpart of B . subtilis AddAB ) and RecF as RecA mediators is less conserved and less well-understood in bacteria other than γ-Proteobacteria [26]–[28] . DprA is an ubiquitous RecA mediator that plays a relevant role during GR ( see above ) . The RecA modulators RecF and RecX are widely present in bacteria , but very little is known about their in vivo role [9] , [19] , [22] . In vitro RecXEco destabilizes the RecAEco·ssDNA filaments and RecFEco antagonizes this effect [29] , but RecAEco foci formation ( nucleation onto ssDNA ? ) decreases in the ΔrecXEco , but increases in the recF4115Eco context [30] . The difference between the simplified in vitro system and in vivo could be related to the presence of other RecA modulators in the γ-Proteobacteria , as DinIEco and RdgCEco , whose presence in bacteria of the Firmicutes Phylum is not obvious reviewed in [22] . In B . subtilis , cytological studies have shown that RecN , RecO , RecR , RecA and RecF form a discrete focus on the nucleoid in response to DNA damage . By observing the localization and temporal order of recruitment , we learned that these proteins co-localize to a defined DNA double-strand break ( DSB ) , with RecN localizing first , while RecO , RecR and RecA localize later , followed by RecF [31] , [32 , our unpublished results] . Concomitantly with RecF assembly , the RecA foci are converted onto highly dynamic filamentous structures ( termed threads ) across the nucleoid that are disassembled 120 min later [32] . Biochemical studies suggested that a dynamic RecA·ssDNA filament with an “effectual length” is essential for SOS induction , template-dependent RR and for programmed GR [19] , [24] . Previously , it has been shown that: i ) a subset of RecA functions shows optimal activity at a high ssDNA/protein ratio , which might pack less RecA per unit length of ssDNA , and requires NTP hydrolysis , whereas other catalytic activities are optimal in RecA-saturated complexes that require NTP , but do not hydrolyze it [33] , and ii ) RecA-mediated SOS induction requires an extended filament conformation , but no ATP hydrolysis [34] , [35] . For the SOS induction an extended and saturated RecA·ssDNA filament [33] , [35] , is essential for LexA repressor self-cleavage [36] . The absence of LexA increases the expression of SOS genes reviewed in [37] . In E . coli , RecX inhibits the RecA coprotease activity of RecA in vitro and in vivo , but a null recX mutant ( ΔrecX ) strain shows no obvious phenotype [38] , [39] . In E . coli and B . subtilis the SOS response is reduced and delayed in the absence of RecF , RecO and RecR [40] , suggesting that these products could work as mediators and/or modulators . This is consistent with the observation that certain RecA mutant proteins act as suppressors of the recO , recR or recF defect [41] , [42] . These RecA mutant variants showed the unassisted ability to displace the SSB protein [43] , suggesting that specialized RecA mediators and/or modulators that regulate RecA activities are necessary to avoid the potential hazard that could be caused by miss-regulation of HR [22] . Biochemical studies with protein of E . coli origin , have shown that RecO , alone or in concert with RecR , aids RecA to overcome the limitation imposed by the SSB protein , and loads RecA onto the ssDNA [26] , [44]–[47] . Then , RecX inhibits the strand exchange reaction by blocking RecA·ssDNA filament formation or facilitating RecA filament disassembly [38] , [39] , [48] , [49] , whereas RecF , which physically interacts with RecX , actively participates in the addition of RecA monomers to the nucleoprotein filament , by inhibiting the effect of RecX [29] . These proteins might also modulate the RecA/ssDNA ratios ( packing ) or the length of the RecA·ssDNA filament ( see above ) . During programmed GR in B . subtilis competent cells , the internalized ssDNA should be coated by one of the SSB proteins ( SsbA or SsbB ) . RecO , alone or in concert with RecR , or DprA aids RecA to overcome the limitation imposed by SsbA or SsbB ( or both in concert ) and loads RecA onto ssDNA tracts [18] , [50] . Then , RecA polymerizes on the filament ( RecA threads ? ) and rapidly scans for a homologous dsDNA segment in the recipient that eventually binds to RecA to allow for strand exchange . Here , one strand of the recipient duplex unbinds from its partner and pairs with the internalized ssDNA . Note that henceforward in this paper , and unless stated otherwise , the indicated genes and products are of B . subtilis origin . To gain insight into the initial state of RecA regulation , we have in vivo characterized the function ( s ) impaired in the B . subtilis recH342 strain . We have identified the mutation of the recH342 strain , which maps in the putative recX ( yfhG ) gene , so that the mutation was renamed as recX342 . We have deleted the putative recX gene ( ΔrecX ) , and investigated the in vivo role of RecX to gain insight in the regulation of RecA activity by analyzing its effect in induction of the SOS response , RR and GR . Our work reveals that the absence of RecX reduces the threshold for damage-dependent SOS response , the recF15 mutation delays it , and the effect observed in the single mutants is overcome in the ΔrecX recF15 context . We show that RecA·ssDNA filament necessary for SOS induction is not sufficient for RecA-mediated strand exchange . Here , RecX might act by increasing the stability of the joint molecule or by affecting the length of the minimum efficiently processed segment ( MEPS ) and indirectly removes a barrier for genetic exchange . We propose that RecA exerts a negative effect on plasmid transformation and RecX suppresses it . Our work demonstrates that RecX facilitates HR by modulating RecA activities and plasmid establishment by inhibiting RecA .
The radiation-sensitive rec342 mutant strain , which was isolated in late sixties [51] , bears two separable mutations . One mutation , which leads to methyl methanesulfonate ( MMS ) sensitivity , was termed recH342 ( BG119 strain ) and classified within the γ epistatic group [12] , . To identify the mutation ( s ) present in the recH342 strain ( BG119 ) nucleotide sequence analysis and whole-genome comparisons ( Genome Analyzer , Illumina ) were performed in parallel with the isogenic rec+ ( Reference strain [BG214] ) . The isogenic BG119 ( recH342 ) showed 9 differences with the BG214 ( rec+ ) strain , resulting in 5 amino acid changes , 3 intergenic mutations and 1 silent mutation ( Table 1 ) . The DNA repair phenotype observed in recH342 strain could be attributed to the substitution of Leu for Pro ( L101P ) , in a conserved region of the YfhG protein ( see Figure S1 ) . YfhG shares a low but significant level of identity with genuine RecX proteins [53] ( see below ) . The BG119 strain also carried a point mutation ( P236S ) in a variable region of the DNA translocase SftA ( Table 1 ) . SftA , which is required for coupling chromosomal segregation and cell division , assists the tyrosine recombinases in the resolution of chromosomal dimers reviewed in [54] . Since the mutations present in recH342 did not confer a significant chromosomal segregation defect [55] , [56] , and a plasmid-borne sftA gene failed to complement the MMS-sensitive phenotype of recH342 cells ( data not shown ) , we assumed that the mutation in the sftA gene should not be responsible for the observed phenotype . To test whether putative RecX is involved in RR and/or GR and if it complements the recH342 defect , a null recX ( ΔrecX ) mutant strain was created , and a plasmid-borne recX gene was introduced into the recH342 context . As revealed in Figure 1A , the ΔrecX or recH342 mutation rendered cells sensitive to MMS and H2O2 when compared with rec+ cells . A plasmid-borne recX gene in recH342 cells restored rec+ levels of MMS or H2O2 resistance ( Figure 1A ) , thus the recH342 mutation was renamed as recX342 . This is consistent with the observation that the physical mapping of recX gene , at 79° [57] , is in good agreement with the genetic map of the recH342 mutation , in the tre - glyB region ( 82° interval ) by PBS1 transduction [58] . To ascertain the role of RecX in RR , we transferred the ΔrecX mutation into strains lacking RecA accessory proteins ( e . g . , recO , recF ) and assayed the ability of these strains to resist the acute exposure to MMS or H2O2 . The ΔrecA strain was used as control . Upon exposure to varying concentrations of DNA damaging agents , the ΔrecX strain was moderately sensitive to both drugs when compared with the very sensitive recF15 or ΔrecO strains or the extremely sensitive ΔrecA strains ( Figure 1A and 1B ) . The absence of both RecX and RecO or RecX and RecF , increased the sensitivity of the double mutant strains ( Figure 1B ) to the levels of ΔrecA ( Figure 1A ) . It is likely that ΔrecX is not epistatic with ΔrecO or recF15 ( classified within the α group ) , and that the three functions are essential for RecA-mediated DNA strand exchange . Genes others than recA , which are exclusively involved in HR have been placed into seven different epistatic groups . Except recX342 ( epistatic group γ ) and recF and recO ( α group ) described above , the different epistatic groups and the genes included within them are: addA and addB ( β ) ; recN ( δ ) ; ruvA , ruvB and recU ( ε ) ; recJ , recQ and recS ( ζ ) and recG ( η ) ( Figure S2 ) [19] . The ΔrecX mutation was moved into a representative of each epistatic group [ΔaddAB ( β ) , ΔrecN ( δ ) , ΔrecU ( ε ) , ΔrecJ ( ζ ) or ΔrecG ( η ) epistatic group] ( C . E . C , G . Garaulet , C . Marchisone and J . C . A . , unpublished results ) . The single and double ΔrecX mutant strains were assayed to resist the acute exposure to MMS and H2O2 or the chronic exposure to mitomycin C ( MMC ) and H2O2 . As previously shown for the recX342 mutation [52] , [56] , [59] , ΔrecX was neither epistatic with ΔaddAB ( β ) , ΔrecN ( δ ) , ΔrecU ( ε ) , ΔrecJ ( ζ ) nor with ΔrecG ( η epistatic group ) ( C . E . C . , G . Garaulet , C . Marchisone and J . C . A . , unpublished results ) . The recX gene shows a high ubiquity among Bacteria [53] . It is predicted to be missing only in bacteria of the Cyanobacteria and Chlamydiae Phyla and in some Classes of the Proteobacteria ( e . g . , α-Proteobacteria ) , Firmicutes ( e . g . , Mollicutes ) or Spirochetes Phyla . The RecX orthologues ( 197 orthologues analyzed ) showed only a limited degree of identity among Phyla , but a high degree of identity was observed between the different Classes of the same Phylum [53] , [60]–[62] , suggesting a high divergence or more than one possible evolutionary pathway . The RecX orthologues were classified using a length bias criterium ( Figure S1 ) . With few exceptions ( e . g . , RecX of the Yersinia Genera that are significantly longer , >180-residue long polypeptide ) , RecX of the Proteobacteria Phylum ( 87 orthologues analyzed ) are relatively small proteins ( <170-residue long polypeptides ) , and share a significant degree of identity ( >25% ) among them [53] , [62] ( Figure S1 ) . The structure of RecXEco revealed that it is a modular protein consisting of three tandem repeats of a three-helix motif ( R1α1-3 , R2α1-3 and R3α1-3 ) ( Figure S1 ) [63] . These RecX proteins can be further divided in two subgroups represented by E . coli ( Eco ) and N . gonorrhoeae RecX ( Ngo ) ( Figure S1 ) . The recX gene of the former group is located immediately downstream of recA , forming a single transcriptional unit as in E . coli . RecX of the latter group , which is not part of the SOS response , is located elsewhere in the genome , as in Neisseria ssp . [60] , [61] . RecX of the Actinobacteria Phylum ( 15 orthologues analyzed ) , represented by Mycobacterium tuberculosis ( Mtu ) RecX , are middle size proteins ( 171- to 188-residue long polypeptides ) that share a significant degree of identity ( >40% , ClustalW2 alignment ) among them . RecXMtu shares a higher degree of identity with a large ( e . g . , RecX , ∼20% ) than with a small RecX ( e . g . , RecXEco , ∼11% ) protein , with RecX and RecXEco sharing a very low , ∼15% , overall identity ( Figure S1 ) . RecX of the Firmicutes Phylum ( 83 orthologues analyzed ) , represented by B . subtilis RecX ( Bsu , a 264-residue long polypeptide ) , are large proteins ( 212- to 272-residue long polypeptides ) that share a significant degree of identity ( >30% ) among them . Inspection of the genetic organization around the Firmicutes recX gene , however , discards any conservation on the genome context , even within the closely related Classes of the Phylum . Examination of the amino acids sequence of RecX342 revealed that the conserved L101 ( encircled ) of the predicted α-helix 3 on repeat 1 ( R1 α3 , [63] ) was substituted by P ( L101P ) ( Figure S1 ) . From these data altogether it could be assumed that the small , middle and long proteins are distantly related classes that perform a similar function , and that longer proteins might have an additional uncharacterized function at the C-terminal region . However , secondary structure prediction of Firmicutes RecX revealed that the C-terminal region , which shares no significant identity with any domain of known activity , might fold as three tandem α-helix motifs . Examination of the amino acids sequence of the 43 C-terminal residues of RecX ( residues 221–264 ) revealed significant identity with an internal region of few RecX orthologues ( e . g . , Provotella oulorum , 38% 15/40 residues ) . In P . oulorum RecX , this region aligned with the region of RecXEco that forms the R3α2 and R3α3 motifs ( data not shown ) , suggesting that Firmicutes RecX , which seems to lack R1α1 and R1α2 helices when compared to RecX of different Phyla ( Figure S1 ) , might also consist of three tandems repeats of a three-helix motif . In vivo analyses of B . subtilis cells revealed that: i ) in response to DNA damage , RecA-dependent autocleavage of LexA triggers the SOS induction reviewed in [37] , ii ) expression of recX gene is independent of MMC-induced SOS response [64] , iii ) recA promoter utilization is reduced and delayed in recF15 , recO16 or recR13 cells upon MMC addition [40] , and iv ) the interstrand crosslinks produced by MMC , as most lesions , are removed by nucleotide excision repair ( NER ) prior to DNA replication , but unrepaired damage induces the SOS response and then repair includes translesion synthesis and HR [65] . To determine whether a recX mutation has an effect on the levels of SOS response , the rec+ , ΔrecX , recX342 or ΔlexA cells were exposed to increasing MMC concentrations , the cultures were harvested 30 min later and the levels of RecA protein , expressed from its native locus and promoter , were measured . Equivalent amounts of crude extracts proteins were separated by SDS-PAGE , transferred and blotted against polyclonal antibodies raised against RecA . Serial dilutions of purified RecA were used as concentration standard . The absence of RecX ( ΔrecX ) or the presence of the RecX342 or RecF15 variants did not affect the basal level of RecA when compared with rec+ cells ( estimated to be ∼4 , 500 monomers per cell , or ∼6 µM assuming an average cell volume of 1 . 2 fL ) ( Figure 2A and 2B ) . The absence of RecO , however , slightly reduced it , and the absence of LexA rendered constitutive RecA levels ( Figure 2A and 2B ) . The RecA protein reached its maximal level at ∼0 . 6 µM MMC , and maximal induction caused 4- to 6-fold increase in net RecA in the rec+ context [66] , Figure 2A . As expected , in the absence of the LexA repressor , the level of RecA was comparable to levels detected upon full SOS induction ( ≥0 . 6 µM MMC ) in the rec+ context ( Figure 2A ) . In the absence of RecX , a significant net RecA accumulation ( ∼18 , 000 monomers per cell ) was observed upon exposure to MMC concentrations as low as 0 . 07 µM . Similar results were observed in the recX342 context ( Figure 2A ) . This reduced threshold for SOS response upon MMC addition in recX could be attributed to the lack of negative regulation of RecA . This is consistent with the observation that 0 . 07 µM MMC concentration neither compromised cell proliferation nor cell plating efficiency in the rec+ and ΔrecX context ( data not shown ) . Furthermore , this SOS induction did not significantly contribute to error-prone repair by translesion synthesis ( Text S1 , Annex 1 ) in the ΔrecX context . In vitro studies revealed that: i ) RecXEco blocks RecAEco filament extension reviewed in [22] , and ii ) RecXEco physically interacts with RecAEco and RecFEco [29] , [67] . To test the effect of the absence of the RecX and RecF functions in SOS response , the levels of RecA were measured 30 min after MMC addition . The recF15 mutation reduced net RecA accumulation when compared to rec+ cells and higher concentrations of MMC were needed to reach full induction . The absence of RecX reversed the effect of the recF15 mutation on the level of RecA , with RecA levels comparable to rec+ cells ( Figure 2B ) . It is likely that: i ) the activation of RecA as a coprotease ( RecA filamented onto ssDNA ) , to facilitate self-cleavage of LexA , is modulated by RecX and RecF in response to MMC addition; ii ) in the absence of RecX and RecF there is not net change in RecA induction ( RecA·ssDNA filament formation ) , with one counteracting the activity of the other; and iii ) the RecA filaments formed in the absence of both RecX and RecF are sufficient for SOS response ( Figure 2B ) , but are not proficient for RR ( Figure 1B ) and GR ( Table 2 ) , suggesting that both modulators are necessary to avoid potential hazards that could be caused by miss-regulation of HR . In the absence of RecO addition of MMC slightly increased ( ∼1 . 2-fold ) the RecA levels when compared to the non-induced control ( Figure 2B ) . The absence of RecX slightly increased the RecA levels in the ΔrecO context ( Figure 2B ) . It is likely that in the absence of the RecO mediator there was a marginal increase in the nucleation of RecA protein filaments , and the RecX modulator poorly contributed , if at all , to RecA nucleation in the ΔrecO context ( Figure 2B ) . In response to DNA damage RecO , RecR and RecA form foci at 30 to 45 min , followed by RecF at 60 min after damage [31] , [32] . To gain insight onto the mechanism by which RecX modulates RecA functions , RecX was visualized in cells grown in minimal medium at 25°C ( Figure 3 ) . A strain bearing a C-terminal fusion of RecX to YFP ( RecX-YFP ) was constructed ( Text S1 , Table S1 ) . The RecX-YFP fusion , integrated in its native locus was fully functional . The growth rate ( data not shown ) and the observed survival curve , upon acute exposure to increasing concentrations of MMC for 15 min , of rec+ and recX-yfp cells were similar . As observed with other DNA damaging agents ( Figure 1 ) , the ΔrecX strain was moderately sensitive to varying concentrations of MMC , and the recF15 ΔrecX was extremely sensitive ( Figure S3 ) . Microscopic observation of the strain in exponential growth revealed dispersed localization of RecX-YFP throughout the cells ( Figure 3A ) , whereas this pattern of localization changed dramatically upon MMC ( 0 . 15 µM ) addition . In ∼40% of the cells , RecX was concentrated into distinct foci on the nucleoid ( mostly two per cell , but sometimes up to 5 , 300 cells analyzed ) 60 min after the addition of MMC ( Figure 3B ) , and in 52% after 120 min ( 300 cells analyzed ) ( data not shown ) . Upon DNA damage , RecX was localized as distinct foci after RecA-induced foci formation , which suggests that RecX acts after RecO , RecR and RecA , and concomitant with RecF . The number of RecX foci decreased ( Figure 3C ) 180 min after addition of MMC , until foci were no longer detectable , and growth of cells slowly resumes . Since biochemical [29] , [38] , [39] and structural analysis [63] , [67] have shown that RecXEco interacts with the RecAEco·ssDNA filament , we set out experiments to visualize both proteins in living cells ( Figure 4 ) . Cells bearing a N-terminal fusion of RecA to CFP ( CFP-RecA ) were previously described ( Text S1 , Table S1 ) [32] . Microscopic observation of the strain in exponential growth revealed dispersed localization of RecX-YFP throughout the cells ( Figure 3A and Figure 4A ) , and CFP-RecA throughout the nucleoid [32] , Figure 4A . In response to DNA damage CFP-RecA formed foci at 30 to 45 min , followed by RecX at 60 min . Sixty min after MMC addition the RecA foci started to be more and more condensed , and then formed highly dynamic filamentous structures ( Figure 4B ) . The formation of dynamic thread-like structures of CFP-RecA was maximal at 120 min after addition of MMC , as well as the number of cells containing RecX-YFP foci , which generally co-localized with RecA threads ( Figure 4C ) . From 350 analyzed cells , RecX-YFP foci localized at or near the RecA signals in 41% of the cells ( that is in 91% of all cells showing RecX-CFP foci ) , adjacent to RecA signals in 3% of the cells , or clearly did not co-localize in 1% of the cells; 55% of the cells showed CFP-RecA fluorescence , but no detectable RecX-YFP foci . Between 120 and 180 min , CFP-RecA threads became fewer in number and thus apparently disassembled , until about 180 min , repair was terminated and RecA threads were no longer visible ( Figure 4D , central panels ) . The number of cells containing clear RecX-YFP foci also decreased in a time dependent manner up to 180 min after induction ( still co-localizing with subcellular locations of high CFP-RecA signals ) ( Figure 4D ) , after which foci declined to negligible levels ( 0 . 7% of the cells showed foci , 280 analyzed cells ) . Growth resumed ∼180 min after the initial DNA damage in rec+ cells . In vivo analyses revealed that: i ) RecN is recruited to a defined DSB [32] , [68] , and ii ) RecO , RecR , RecA and RecF co-localize with DNA damage-induced RecN focus [31] , [32] . To test whether RecX was also recruited to a defined DSB , a strain bearing a xylose inducible promoter transcribing the HO endonuclease , an HO cleavage site and a lacO site , both integrated close to the oriC region , and the lacI-cfp cassette ectopically integrated at the threonine locus , was constructed as previously described ( Text S1 , Table S1 ) [32] . After induction of the HO endonuclease a single two-ended DNA break was induced and RecX-YFP foci were observed in about 10% of the cells ( 350 analyzed cells ) . The observed RecX foci were generally not coincident with the oriC ( LacI-CFP ) signal , only 1 out of 34 foci was coincident with an oriC signal ( Figure 4E ) . These experiments show that RecX is not directly recruited to sites of DSBs . Biochemical studies have shown that RecXEco ( RecXNgo ) blocks RecAEco assembly onto ssDNA tracts [29] , [38] , [39] or facilitates a more rapid RecANgo filament disassembly [49] . To test whether RecX affects RecA foci formation ( “nucleation” ) and/or thread assembly or disassembly ( “filament formation” ) , the localization of a functional CFP-RecA fusion in rec+ and in the ΔrecX strain was monitored . For all times points , 350–400 cells were analyzed . During exponential growth , RecA localized throughout the nucleoids in both , rec+ ( Figure 5A ) and ΔrecX cells ( data not shown ) . The absence of RecX neither affected the formation of RecA foci nor the assembly of RecA threads between 60 and 90 min after induction of DNA damage ( data not shown ) , and for the first 120 min following the addition of MMC , no obvious difference in the formation of CFP-RecA threads was detectable between rec+ and ΔrecX cells , 75 to 85% of the cells contained CFP-RecA threads ( Figure 5B and 5C ) . The number of cells showing threads decreased in rec+ cells to less than 50% after 120 min , while 80% of all ΔrecX cells continued to contain RecA threads ( Figure 5C ) . After 180 min only ∼4% of rec+ cells contained visible CFP-RecA threads ( many contained CFP-RecA accumulations at a single cell pole ) , while these structures persisted in 95% of the recX mutant cells ( Figure 5D ) . Even after 210 min , CFP-RecA threads were visible in >50% of mutant cells , while in rec+ cells , RecA was again spread uniformly on the nucleoids , and thread structures were only observed in 1 . 3% of the cells ( Figure 5E ) . These data reveal that RecX is necessary for the down-regulation of RecA threads , which most likely consist of RecA·ssDNA filaments . It is likely that the balance between RecX and RecF governs the dynamics of RecA threads , but at late times , when the DNA damage signal is removed , the thread-stabilizing activity of RecF might be negatively modulated by an uncharacterized function ( s ) , leading to net thread-destabilizing activity of RecX . To gain insight into the chromosomal gene transfer barriers ( see Introduction ) , the fate of RecX during GR was analyzed . At the onset of stationary phase , only 10%–20% of cells stochastically develop time-limited competence in response to specific environmental conditions . Natural competence is a genetically programmed process with a specialized membrane-associated machinery for uptake of exogenous dsDNA that subsequently processes and internalizes ssDNA into the cytosol ( DNA uptake machinery ) [69] . Previously it has been shown that some soluble proteins of the recombination apparatus ( namely SsbB , DprA , RecA , RecU and CoiA ) are located at the cell poles , where they co-localize with the DNA uptake machinery [7]–[11] . To understand the role of RecX on GR its localization was analyzed upon competence induction . Microscopic observation of RecX-YFP in cells grown to competence revealed fluorescent foci in 8 to 10% of cells ( 1260 cells analyzed ) ( Figure S4 ) , suggesting that this is the proportion of competent cells . In the 8–10% of the cells RecX-YFP existed mainly as one focus per cell ( 73% of the cases ) , sometimes localizing to a single cell pole ( ∼27% ) , but mostly at midcell in the nucleoid ( ∼46% ) ( Figure S4 ) . Less often , two ( ∼17% ) foci ( mostly one at the pole and one at the nucleoid ) , three foci ( ∼5% ) , and patched structures ( ∼5% of the cases ) were observed . To investigate the nature of RecX-YFP foci , we performed time-lapse microscopy , capturing images of cells grown to competence without DNA , or 30 min after addition of DNA , with 2 s time intervals ( Figure S5 ) . Irrespective of the presence or absence of DNA , RecX-YFP foci at midcell were dynamic and moved between acquisitions ( note that the signal was weak , so only few frames could be captured ) , while foci at or near the cell pole did not move away from their position . The total number of fluorescent cells ( 7% of total cells in the absence of DNA ) did not change markedly after addition of DNA ( to 10% ) , but the number of cells having one discrete focus increased from 73% to 82% of the cells containing a signal 30 min after DNA addition . With increasing time upon DNA addition ( 0 to 30 min ) the number of RecX-YFP cells with one focus at the pole decreased ( from 27% to 13% ) and the number of cells with one focus on the nucleoid augmented from 46 to 72% ( at least 1000 cells were analyzed for each time point ) ( Figure S5 ) . Upon addition of DNA , the number of cells with more than one RecX-YFP focus and with patched structures became lower to less than 1 . 5% of the competent cells . Figure S5 , movie A , shows a polar focus that moved around the cell pole , but remained there , movie B shows a cell with many foci that moved , and movie C shows a central focus that moved . Thus , polar foci are usually static , possibly representing RecX that is associated with the DNA uptake machinery or any associated recombination protein , and non-polar foci are very dynamic . B . subtilis competent cells can take up DNA of any source and the transfer of chromosomal genes requires HR . The frequency of appearance of met+ chromosomal transformants in single rec-deficient strains , classified within the α ( recF15 or ΔrecO ) , β , γ ( recX342 ) , δ , ε , ζ or η epistatic groups ( Figure S2 ) , does not vary more than 3-fold relative to the rec+ value [12] , [13] , [15] , but is blocked in the ΔrecA context ( Table 2 ) . It is likely that a certain redundancy exists and/or that the critical functions were not studied yet . The absence of RecX severely impaired chromosomal transformation ( ∼200-fold ) with respect to that of the rec+ strain ( Table 2 ) . To establish the potential contribution of RecO and/or RecF in the recX context , the capacity of these cells to be transformed with chromosomal DNA was measured . Chromosomal transformation was inhibited >1 , 000-fold in the recF15 ΔrecX or ΔrecO ΔrecX context , when compared to the absence of RecA that blocked it ( >10 , 000-fold ) ( Table 2 ) . It is likely that distinct effectual length or packing density of the RecA·ssDNA filament is necessary for chromosomal transformation ( Table 2 ) . In the absence of homology with the recipient DNA , a linear ssDNA oligomeric plasmid molecule requires RecO , DprA and RecU for establishment [9] , [10] , [15] , [70] , [71] . Plasmid transformation , however , was only marginally affected relative to the rec+ strain in the ΔrecA context or in single rec-deficient strains classified within the α ( recF15 , ΔrecR ) , β , γ ( recX432 ) , δ , ε ( ruvA2 , ΔruvB ) , ζ or η epistatic groups ( Figure S2 ) [9] , [13] , [15] , [72] . Plasmid transformation was markedly reduced in the absence of RecX ( Table 2 ) . Based on results described above and the observation that the RecA·ssDNA filaments might open an unproductive avenue that is deleterious for plasmid transformation [9] , [71] , it is hypothesized that RecX might be required to dislodge RecA from the internalized ssDNA . If the hypothesis is correct the absence of RecA should suppress the need for RecX . To test this hypothesis the ΔrecX ΔrecA strain was constructed . The absence of RecA partially suppressed the RecX requirement for plasmid transformation , but as expected it remained blocked in chromosomal transformation ( Table 2 ) . It is likely that the RecA·ssDNA filaments might be deleterious for plasmid transformation and RecA modulators , namely RecX ( Table 2 ) and RecU [9] , [71] , are required to catalyze RecA·ssDNA filament disassembly or to block filament assembly . Alternatively , in the absence of both RecA and RecX proteins , an uncharacterized recombination pathway ( specific for plasmid transformation ) becomes operative . To test this hypothesis , the effect of the absence of RecX and RecO , or RecX and RecF in plasmid transformation frequencies was measured after construction of the respective strains . Both chromosomal and plasmid transformation were blocked in the ΔrecX recF or ΔrecX ΔrecO context ( Table 2 ) , it was therefore considered unlikely that an uncharacterized recombination pathway exists . A similar inhibition of GR was observed in the recF15 recH342 ( recX342 ) or ΔrecO recH342 ( recX342 ) strain ( Table 2 ) [15] , [52] .
In all systems with a genuine SOS response system , RecA needs to be recruited onto SSB-coated ssDNA tracts at a blocked replication fork reviewed in [19] , [22] . We show that in the absence of RecX ( presence of RecO and RecF ) , low doses of MMC , which did not significantly affect the cell doubling time , are sufficient to induce the SOS response ( Figure 2A ) . In contrast , in the absence of RecO , high lethal concentrations of MMC were needed to marginally induce RecA synthesis when compared to rec+ cells , and in the absence of both , RecO and RecX , the synthesis of RecA was marginally facilitated when compared to ΔrecO cells ( Figure 2B ) . It is likely that: i ) RecO is essential for RecA nucleation and SOS induction , and the absence of RecX cannot override such defect; ii ) RecF [31] and RecX ( Figure 3 and 4 ) act after RecO as deduced from the cytological studies; and iii ) the RR and GR deficiency in the ΔrecO ΔrecX context could be attributed to decreased RecA·ssDNA nucleation and filament dynamics . This is consistent with the in vitro observation that RecO ( RecOREco ) protein ( s ) contribute to RecA ( RecAEco ) filament nucleation onto SsbA- ( SSBEco ) -coated ssDNA and that RecOREco is unable to counteract the inhibitory effects of RecXEco on RecAEco filaments [22] , [29] . In the absence of RecF ( presence of RecX and RecO ) high lethal concentrations of MMC were needed to marginally induce RecA synthesis when compared to rec+ cells . In the absence of both RecA modulators ( RecX and RecF ) , however , the levels of RecA induction showed a profile similar to rec+ cells ( Figure 2B ) . It is likely that there is a cross talk among RecX and RecF and one might antagonize the effect of the other , and that RecO and RecF contribute to RecA filament formation at different stages ( Figure 2B ) . Conversely , in E . coli the formation of RecA filaments decreased in the ΔrecX strain , and this RecX-mediated destabilization of the RecA·ssDNA filaments was independent of RecFOR [30] , [38] , [73] , and overexpression of RecX [38] or RecF [74] inhibited induction of the SOS response after UV irradiation . The RecA·ssDNA filaments formed in the recF15 ΔrecX context are sufficient for SOS induction , but are not proficient for HR ( Figure 1 and Table 2 ) . It is likely , therefore , that there are different layers of regulation of RecA·ssDNA filament extension with RecF and RecX acting at different levels , and as biological antagonists . How can we rationalize this observation ? It is likely that there are different types of RecA·ssDNA filaments . In the ΔrecX context , a high local RecA concentration may generate a saturated RecA·ssDNA filament , resulting in a higher affinity for LexA . In the rec+ or ΔrecX recF15 cells , sub-saturated RecA·ssDNA filaments may be formed at low MMC dose , and in these conditions RecA equilibrates among the existing DNA lattices lowering its affinity for LexA . Here , a higher MMC dose was required to induce the SOS response when compared to the ΔrecX context . Alternatively , the length of the RecA·ssDNA filament , rather than the packing , is a crucial factor in the rate-limiting step of homologous pairing [34] , [49] . DNA damage-induced RecA formed discrete foci on the nucleoid ∼30 min upon MMC addition [32] . RecX formed discrete foci on the nucleoid and a RecA focus started to be converted into irregular RecA threads after 60 min of MMC addition ( Figure 4 ) . RecA thread formation was demonstrated to be independent of RecF [31] , [32] and RecX ( Figure 4 and Figure 5 ) . Within 60–120 min upon DSB induction the RecX-YFP foci increased in number , and the RecA threads were dynamic filamentous extensions , to dissociate from the DNA and become undetectable at about 180 min in rec+ cells grown in minimal medium at 25°C ( Figure 5 ) . In the absence of RecX , RecA threads persisted for an extended period of time ( Figure 5 ) , revealing that RecX affected the dynamics of RecA threads ( RecA·ssDNA filament disassembly ) rather than RecA foci ( RecA nucleation ) and thread formation ( RecA·ssDNA filament extension ) . Conversely , in response to UV irradiation the formation of E . coli RecA foci decreased in the ΔrecX or recF4115 context [30] . Unlike the RecO , RecA and RecF foci , which co-localize with RecN and with HO endonuclease-generated DSBs , the RecX foci did not co-localize with DNA DSBs ( Figure 4 ) . Consistent with its activity on RecA threads , RecX formed foci after RecA nucleation and co-localized at or near RecA threads ( Figure 4 ) . We propose that RecX has two activities: to counteract the activity of RecF , and to mediate RecA·ssDNA filament dislodging . These activities are essential for rendering a dynamic balance between RecA assembly and disassembly from ssDNA , in order to form an “active RecA·ssDNA filament” and to facilitate the inherent DNA pairing activity of the formed filaments during the search for homology . Plasmid transformation is a RecA-independent process [72] . It was previously postulated that RecA-bound to the incoming oligomeric linear plasmid ssDNA was deleterious [71] . If the working hypothesis is correct , the RecA·ssDNA filaments should be disassembled , either by RecX ( Table 2 ) or by RecU [71] , for plasmid transformation . We have shown that RecX is necessary for plasmid transformation , and such requirement is overcome by the deletion of recA ( Table 2 ) , rather than by the opening of a new and uncharacterized recombination avenue . During natural transformation donor DNA enters the cell as ssDNA molecules , hence end processing is not necessary . Chromosomal transformation is strictly dependent on the presence of a fully functional RecA·ssDNA filament to catalyze intermolecular recombination between the incoming ssDNA and the homologous duplex recipient DNA without significant DNA synthesis [12] , [75] . Competence-induced RecX formed discrete foci at the entry pole and on the nucleoid in the absence of DNA . Upon addition of dsDNA , SsbB ( in concert with SsbA ) at the entry pole protects the incoming ssDNA and limits RecA loading onto SsbA- and/or SsbB-coated ssDNA [18] . RecO ( or DprA ) displaces SsbA and/or SsbB and recruits RecA onto SsbA- and/or SsbB-coated ssDNA leading to RecA foci formation at the entry pole [8]–[10] , [18] . RecX at the entry pole and in the nucleoid should modulate RecA thread formation ( RecA·ssDNA filament extension ) . RecA forms a filament ( thread ) extending from the pole to the centrally located nuclear body [8] . A RecA thread might facilitate the search for a homologous segment and mediate joint molecule formation ( heteroduplex ) with the resident chromosome [9] . The frequency of chromosomal DNA transformation marginally decreased in the recX342 background , but dropped to low levels ( ∼200-fold ) in the ΔrecX context ( Table 2 ) . Studies in other bacteria are less clear , because the absence of RecXDra , results in elevated chromosomal transformation frequencies ( ∼2 . 5-fold increase ) [76] , whereas the absence of RecXNgo also decreases recombination frequencies , although the effect was modest ( ∼5-fold reduction ) [60] . In vitro RecXNgo destabilizes RecANgo·ssDNA filaments by causing its local instability [49] . Although B . subtilis and N . gonorrhoeae RecX proteins might have similar functions , there are also important differences . For example , in the latter species the presence of RecF is not obvious [53] , suggesting that dynamic RecA·ssDNA filament formation is modulated in this bacterium by different partners than those found in B . subtilis cells . Cohan and coworkers [6] , [77] reported that the frequency of unidirectional transfer of DNA between donor and recipient ( chromosomal transformation ) in B . subtilis rec+ cells decreases with increased sequence divergence . Here , each 5% increment in sequence divergence yields ∼ a 10-fold decrease in chromosomal transformation [6] , [77] . The recX342 ( BG119 ) competent cells showed a >20-fold increased sensitive to sequence divergence than when transformed with homogamic DNA [6] . However , there is no measurable effect in preventing inter-species transformation in the recF15 ( BG129 ) or recO16 ( BG107 ) context . Furthermore , the frequency of intra- or inter-species transformation dropped to undetectable levels in the ΔrecX recF15 or recX342 recF15 context ( Table 2 ) , leading to populations with a clonal structural potential . We propose that the RecA·ssDNA filament forms a metastable reversible intermediate , whose dynamic modulation is governed by RecF and RecX and predict that the RecX342 variant makes recombination initiation ( and also termination ) very sensitive to sequence divergence . A RecA·ssDNA filament to initiate recombination between donor and recipient DNA requires a MEPS of 25- to 35-base pairs to initiate recombination between donor and recipient DNA [78] . A similar extent of sequence identity was reported for viral-mediated plasmid transduction [79] , [80] . It is likely that the effectual “length/packing” of the RecA·ssDNA filament and the MEPS needed for proficient inter-specific recombination are more stringent than for intra-specific recombination , and that RecX and RecF prevent the dissociation of potentially unstable heteroduplex intermediates that are essential for HR ( Figure 1 and Table 2 ) . The RecXEco protein contributes modestly to recombinational potential reviewed in [22] . In vitro , the ability of small RecX ( <180 residue long , e . g . , RecXEco ) to inhibit RecAEco-associated activities , i . e . ATPase , strand-exchange , and LexAEco cleavage activities [38] , [63] , [67] , have prompted different groups to categorize RecXEco as a major negative regulator of RecAEco activities . The current model postulates that RecXEco bound to the deep helical groove of the RecAEco nucleofilament blocks RecAEco assembly onto ssDNA , leading to filament destabilization and inhibition of HR . Based on previous data reviewed in [19] and the ones presented here , we propose that RecX is necessary to avoid potential hazards that could be caused by miss-regulation of HR . We propose that SsbA , at the ssDNA in a stalled replication fork , interacts with RecO and loads it ( or RecOR ) onto SsbA-coated ssDNA ( Figure 6 , step b ) [70] . Then , RecO loads few RecAEco monomers onto ssDNA with a limited displacement of SsbA and/or SsbA·RecO ( RecR ) complexes ( Figure 6 , step c ) [47] . The role of RecF in RecA nucleation is unclear , because RecF focus formation is impaired in the absence of RecO [31] , and DNA damage-induced RecF foci , which co-localize with a RecA focus , are clearly detected after RecA focus formation , and concomitantly with RecA thread formation and RecX foci formation ( Figure 5 ) [31] , [32] . In E . coli cells , RecOR or RecFOR , upon interacting with SSB , loads few RecA monomers onto ssDNA with a limited displacement of SSB [46] , [81] , [82] , [83] . Regulation of the cycle of the assembly and disassembly of RecA is achieved through the hydrolysis of ( d ) ATP , whereas the extension ( formation of RecA threads ) is controlled by RecA modulators ( e . g . , RecX , RecF , RecU ) . Based on the data presented here and the in vitro data of Lusetti and co-workers [29] we propose that there might be a crosstalk between RecF and RecX in the modulation of the RecA·ssDNA filament extension ( Figure 6 , step d ) . In the absence of RecX , RecF directly or indirectly could either facilitate RecA·ssDNA filament assembly or slow down disassembly , facilitating RecA·ssDNA filament formation even in the presence of a low DNA damage signal ( Figure 6 , step e ) . These “long” and/or saturated RecA·ssDNA filaments lead to premature induction of the SOS response at low-dose exposure of MMC ( Figure 2 ) , but these RecA·ssDNA filaments are neither proficient for RR nor for GR ( Figure 1 , Table 2 ) . Conversely , in the absence of RecF , RecX directly or indirectly promotes RecA·ssDNA filament disassembly or delays filament assembly , leading to “short” and/or sub-saturated RecA·ssDNA filaments ( Figure 6 , step f ) . In the absence of both RecX and RecF , however , there is no apparent net change in the “activation” of the RecA·ssDNA filament for SOS induction , but RecA-mediated DNA strand exchange ( RR and GR ) is markedly impaired . We favor the view that RecX and RecF , by fine-tuning of the dynamic assembly/disassembly of RecA , facilitate the accumulation of a RecA·ssDNA filament with an effectual length or packing , as long as the DNA damage signal is on . In vitro , RecXEco destabilizes RecAEco·ssDNA filaments by either preventing growth of the filament [39] or by causing its local instability [63] , and RecFEco protects RecAEco assembly by antagonizing the negative modulator RecXEco , specifically during the extension phase [29] . However , in vivo the number of RecAEco foci decreased in ΔrecXEco , but increased in the recFEco context [30] .
B . subtilis strains and plasmids used in this work are listed in Table S1 . All strains were isogenic to BG214 or PY79 and were grown at 37°C on LB rich medium , unless otherwise indicated . E . coli XL1-Blue cells ( Stratagene ) were used for routine cloning . Cells were grown in Luria Bertani ( LB ) medium supplemented with ampicillin ( 100 µg ml−1 Amp ) or chloramphenicol ( 30 µg ml−1 Cm ) as required . All enzymes were purchased from New England Biolabs . Oligonucleotides were ordered from Sigma-Aldrich . To construct the RecX-YFP fusion , the C-terminal region of the recX ( also termed yfhG ) gene was amplified by PCR and cloned into a plasmid carrying a downstream yfp gene . The resulting plasmid was then transformed into a PY79 strain , where it integrated at the original gene locus by single-crossover integration . The ΔrecX strain was constructed by inserting the six-cat-six cassette at the 5′-end of the recX gene as described [59] . In short , oligonucleotides pairs CGGATATCGGATCATCTGG and GTAATCGTTAAGCCTATGGATG and CGACAGCCATTGGACATATGTC and GATAGATATCGCCATCAGCCCAAG were used to PCR amplify with Vent polymerase ( New England Biolabs ) fragments spanning a region 721-bp and 719-bp upstream and downstream from recX , respectively , and overlapping at the start of the recX sequence . StuI digestion of theses fragments , followed by ligation resulted in an EcoRV fragment which contains a deletion involving the 5′ end of recX , and that can be cloned into the same site of a pGEM-T vector ( Promega ) , giving rise to pCB788 plasmid . StuI-cleaved pCB788 was joined to the six:cat:six cassette from vector pCB266 to give rise to plasmid pCB789 . For the construction of strain BG1029 , pCB789 was linearised with NotI and transformed into competent B . subtilis cells . Double mutants were constructed by transformation of the isogenic rec-deficient B . subtilis cells ( recF15 , ΔrecO ) with linear pCB789 DNA with selection for CmR ( Text S1 , Table S1 ) . The cat gene was deleted by β-mediated site-specific recombination to render the BG1065 strain ( Text S1 , Table S1 ) . The ΔrecA mutation was introduced into BG1065 cells ( ΔrecX ) by SPP1 transduction [79] . B . subtilis competent cultures were obtained as described previously [12] . Genomic DNA from the Reference B . subtilis rec+ ( BG214 ) strain and the Test recH342 ( BG119 ) strain were sequenced by high-throughput sequence analyzer ( Illumina ) technology using standard sequencing libraries and filtered sequence data ( BGI ) , of ∼1 gigabases per sample , were used to conduct paired-end nucleotide sequencing with the rec+ BG214 and the BG119 sample as described [84] . Acute survival assays were performed as previously described [66] . Briefly , B . subtilis cells were grown to an OD560 = 0 . 4 at 37°C in LB broth , and exposed to different concentrations of MMS , H2O2 or MMC . After 15 min ( with MMS or H2O2 ) or 30 min ( with MMC ) exposition , cells were diluted and plated on LB agar plates . Colony forming units ( cfu ) were counted and plotted against the concentration of damaging agents , in order to obtain survival curves . For DNA transformation experiments , B . subtilis competent cells were transformed with 100 ng of either SB19 chromosomal DNA to met+ or pUB110 plasmid DNA to kanamycin resistance ( KmR ) . Transformants were plated on minimal medium agar plates containing tryptophan but lacking methionine or on LB agar plates containing Km ( 5 µg ml−1 ) . Transformation efficiencies were normalized to the number of viable cells plated on rich medium without selection , and the values obtained were normalized against those obtained for rec+ cells . B . subtilis strains were grown in LB to an OD560 = 0 . 4 at 37°C with agitation . Then , cells were treated with increasing concentrations of MMC for 30 min . The cells were centrifuged , resuspended in buffer A ( 50 mM Tris HCl [pH 7 . 5] , 300 mM NaCl , 5% glycerol ) and lysed by sonication . Extracts containing equal concentrations of protein from each induction experiment alongside purified RecA protein standard ( 10 to 500 ng ) were separated on 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis . Polyclonal rabbit antiserum was raised against purified RecA protein according to standard protocols . Immunoblots were transferred and probed with anti-RecA antibodies as described previously [66] . RecA protein bands on developed immunoblots were quantified with a scanning densitometer ( Quantity One software ) . Purified RecA protein standards yielded a linear relationship between antibody signal and the RecA protein concentration . The amount of RecA protein in each induced sample was interpolated from the purified RecA protein standard curve . Exponentially growing cells were obtained by inoculating overnight cultures in fresh S7 minimal media and grown to an OD560 = 0 . 4 at 37°C . Cells were then fixed with 2% formaldehyde , 4′ , 6′-diamino-2-phenylindole ( DAPI ) ( 1 µg/ml ) was added for nucleoid visualization , and the cells were analyzed by fluorescence microscopy as previously described [31] . Mid-log phase cells were either untreated or exposed to 0 . 15 µM MMC for variable time and then fixed as described above . To further investigate the in vivo function of RecX in HR , we visualized RecX-YFP or CFP-RecA in living cells as previously described [31] , [32] .
|
This study describes mechanisms employed by the bacterium Bacillus subtilis to survive DNA damages by recombinational repair ( RR ) and to provide genetic variation via genetic recombination ( GR ) . At the center of homologous recombination ( HR ) is the recombinase RecA , which forms RecA·ssDNA filaments to mediate SOS induction and to promote DNA strand exchange , a step needed for both RR and GR . Genetic data presented here highlight the complexity of the network of RecA accessory factors that regulate HR activities , with RecX counteracting the role of RecF in SOS induction . The absence of both RecA modulators , however , blocked RR and GR . Insights into the spatio-temporal recruitment of RecA to preserve genome integrity , to overcome the barriers of gene flow , and its regulation by mediators and modulators are provided . Chromosomal transformation , which declines with increasing evolutionary distance , depends on HR . Indeed , the presence of the RecX modulator decreases the genetic barrier between closely related organisms . The role of RecA mediators and modulators on the preservation of genome integrity and long-term genome evolution is discussed .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"molecular",
"cell",
"biology",
"nucleic",
"acids",
"genetics",
"dna",
"biology",
"dna",
"repair",
"molecular",
"genetics",
"population",
"genetics",
"molecular",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
RecX Facilitates Homologous Recombination by Modulating RecA Activities
|
Progress in mapping loci associated with common complex diseases or quantitative inherited traits has been expedited by large-scale meta-analyses combining information across multiple studies , assembled through collaborative networks of researchers . Participating studies will usually have been independently designed and implemented in unique settings that are potential sources of phenotype , ancestry or other variability that could introduce between-study heterogeneity into a meta-analysis . Heterogeneity tests based on individual genetic variants ( e . g . Q , I2 ) are not suited to identifying locus-specific from more systematic multi-locus or genome-wide patterns of heterogeneity . We have developed and evaluated an aggregate heterogeneity M statistic that combines between-study heterogeneity information across multiple genetic variants , to reveal systematic patterns of heterogeneity that elude conventional single variant analysis . Application to a GWAS meta-analysis of coronary disease with 48 contributing studies uncovered substantial systematic between-study heterogeneity , which could be partly explained by age-of-disease onset , family-history of disease and ancestry . Future meta-analyses of diseases and traits with multiple known genetic associations can use this approach to identify outlier studies and thereby optimize power to detect novel genetic associations .
The common disease—common variant ( CD-CV ) hypothesis has been confirmed by the discovery of thousands of robustly associated loci for a wide variety of complex diseases and quantitative inherited traits [1] . The genetic effects conferred by common susceptibility loci tend to be small ( per-allele disease odds ratios < 1 . 2 or trait variance < 0 . 2% ) [2] with the consequence that they are frequently only reliably detected in association studies based on upwards of tens of thousands of individuals . Such large sample sizes require considerable resources to complete the necessary participant recruitment , phenotyping and genotyping , resources that are unlikely to be available to individual research groups . In response , collaborative networks of researchers have formed consortia in order to assemble large collections of genome-wide association data [3] . Participating studies that were independently commissioned are likely to include specific and varied design features , for instance the precise specification of the phenotype or ascertainment criterion , environmental risk factor profiles or genetic ancestry . These sources of variation could influence the meta-analysis and introduce genetic heterogeneity of effect sizes between participating studies , which would reduce power to detect an overall genetic association . Heterogeneity analysis is currently performed on a variant-by-variant basis , which is potentially sensitive to locus-specific effects , for example specific gene-environment interactions that affect a minority of contributing studies . Furthermore , as the true effect sizes of genetic associations tend to be small with relatively large variances at the individual study level , single variants contain modest information on systematic between-study heterogeneity . Together , these features might mask outlier studies in a meta-analysis that show systematic patterns of heterogeneity due to design features that affect the majority of the associated variants . For example , many diseases have a variety of clinical presentations that could affect the case-mix under alternative recruitment frameworks . In multi-ethnic stroke meta-analysis , the distribution of ischaemic and haemorrhagic cases might differ among populations [4] . Furthermore , sub-phenotypes of disease might have larger or smaller genetic components . For example , although the majority of coronary artery disease ( CAD ) associated loci showed similar effect sizes in analyses based on the subset of cases with myocardial infarction alone versus a broader CAD phenotype ( coronary stenosis >50% , acute coronary syndrome and chronic stable angina ) , discrepant effect sizes were evident at the HDAC9 and ABO loci [3] . Moreover , sampling patients with younger or older age-of-onset of disease or with or without a family-history of disease could affect genetic risk profiles according to the multifactorial liability threshold model [5] . We have therefore developed an analytic approach to search for systematic between-study heterogeneity patterns in genetic association meta-analysis projects . Our approach builds upon the established random-effects meta-analysis method [6] , to combine information from multiple genetic variants into an integrated heterogeneity statistic . We first assess the analytic power of the new method to compare its performance with a conventional method to detect heterogeneity and then confirm the size and further explore the power of the new method in computer simulation exercises . Finally , we apply the method to a recent GWAS meta-analysis of CAD [3] .
To empirically assess the theoretical distributions of M , SPRE statistics for 2 , 10 , 25 or 50 variants were randomly sampled from a Φ ( 0 , 1 ) distribution in 10 , 000 replicates to approximate the null hypothesis of no systematic heterogeneity . The empirical and theoretical distributions of M match very closely irrespective of the number of variants ( S1 Fig and S1 Table ) . The analytic power of M to detect heterogeneity was compared with Cochran’s Q statistic [7 , 8] , a method that is routinely used to detect heterogeneity in meta-analyses and also underpins the I2 inconsistency index [9] . Multiple testing of V variants ( for Q ) and S studies ( for M ) was allowed for by applying Bonferroni’s adjustment to ensure that the family-wise error rates ( FWER ) for each method were equally controlled . Fig 1 shows the comparative power for 10 , 25 and 50 variants in 10 , 15 and 30 studies; the effect sizes for the S-1 “non-outlier” studies were held constant ( loge ( odds ratio ) = 0 . 182 i . e . odds ratio = 1 . 2 ) to model homogeneous effects . The effect sizes for the variants in the outlier study were the product of the “non-outlier” effect size ( i . e . loge ( odds ratio ) = 0 . 182 ) and a parameter ( fold-change ) to model a continuous series of systematic heterogeneity patterns . All studies were equally weighted ( standard error of loge ( odds ratio ) = 0 . 1 ) . It is clear that under all scenarios examined ( Fig 1 ) , that M had greater power than Q to detect systematic heterogeneity patterns . The power of M to detect systematic heterogeneity increased as the fold-change parameter differed from 1 as well as with larger numbers of variants but was slightly attenuated as the number of studies ( and multiple testing burden ) increased . We then used Monte-Carlo computer simulations to empirically assess the type 1 and 2 error rates in a more complex series of “real world” meta-analysis scenarios for differing numbers of variants and studies . Variants were modelled to confer disease risks of varying magnitudes ( S2 Table ) ; the effect size distribution across the variants was inspired by an overview of GWAS findings [10] , which documented the well-established pattern of a progressively larger number of variants with smaller effect sizes . Random variation in effect sizes for the variants in different studies was included by sampling the effect sizes ( i . e . β coefficients scaled as loge ( odds ratio ) ) from a Φ ( β , σ = 0 . 10 ) distribution; this induces a background random heterogeneity pattern that affects all studies upon which we attempt to detect an overlying systematic heterogeneity pattern that only affects a single outlier study . Studies were assigned equal weights in the meta-analysis by fixing the standard errors of the simulated effect sizes based on the median value of standard errors for variants in a recent GWAS meta-analysis [3] ( i . e . SE ( β ) = 0 . 08 ) . Thus each variant was modelled with a background heterogeneity inconsistency index [11] I2 = 100 × 0 . 102 / ( 0 . 102 + 0 . 082 ) = 60 . 5% . Table 1 presents empirical type 1 error rates derived from 1 , 000 replicates to compare with Bonferroni corrected asymptotic p-values < 0 . 05 . The type 1 errors for the M statistics were mostly conservatively controlled in these simulation scenarios . Simulations were then performed to further assess the power of the M statistic to detect outlier studies included in a meta-analysis on a background of random heterogeneity . Table 1 shows the results from simulations where a single outlier study was included in the meta-analysis that showed a random pattern of association ( i . e . the β coefficients for the V variants in the outlier study were sampled from a Φ ( β = 0 , σ = 0 . 10 ) distribution i . e . fold-change = zero ) . The power of M to identify the “null” outlier study increased with the number of variants but there was little impact on power varying the number of studies in the meta-analysis . We then examined scenarios where an outlier study in a meta-analysis was selected to show systematically stronger effects than the other participating studies ( Fig 2 ) . Again the power of M statistic to detect the outlier study increased with the number of variants included in the meta-analysis . Varying the number of studies in the meta-analysis had relatively little impact on the power to detect systematic outliers . Similarly , the power of M statistic to diagnose an outlier study showing systematically weaker effects than other participating studies increased with the number of variants interrogated in the meta-analysis . We also studied the impact of the background level of heterogeneity on power; this showed that it is easier to identify outlier studies with the M statistic if the average level of heterogeneity is low ( S2 Fig ) . The CARDIoGRAMplusC4D consortium has recently reported a GWAS meta-analysis of 60 , 801 CAD cases and 123 , 504 controls assembled from 48 studies [3] . Participants had been recruited from several ancestry groups ( African American , Hispanic American , East Asian , South Asian , Middle Eastern and European ) . The CAD cases included patients with clinical diagnoses of myocardial infarction with or without ST-elevation , other acute coronary syndromes or chronic stable angina , as well as patients who had undergone a revascularization procedure or had angiographic evidence of stenosis ( >50% ) in at least 1 coronary vessel . The majority of the studies recruited CAD cases retrospectively ( i . e . prevalent cases ) , the other prospective studies included a mixture of incident and prevalent disease . The controls included population samples who were unscreened for CAD ( e . g . the UK 1958 Birth Cohort and National Blood Service controls genotyped as part of the Welcome Trust Case Control Consortium [12] ) in addition to samples from volunteers with no personal history of coronary disease or individuals who had undergone coronary angiography but had no radiological evidence of vessel stenosis . Various GWAS SNP arrays had been genotyped by the studies so genotype imputation to the 1000 genomes phase 1 , version 3 haplotype training set was used to facilitate the meta-analysis by maximizing the available mapping information . In an additive-effects-only association analysis , 46 discrete CAD loci surpassed the conventional genome-wide significance threshold ( i . e . P < 5 × 10−8 ) . Variants within the 46 loci were mostly well imputed with 82% of the variants having an imputation quality score > 0 . 9 . A lead variant ( i . e . the variant with the smallest p-value ) for each of these loci was selected for aggregate heterogeneity analysis , 35 of these variants showed some degree of between-study effect size heterogeneity ( i . e . I2 > 0 ) ( S3 Table ) . The 46 lead variants were in linkage equilibrium with each other . Inspection of the M statistics for the 48 studies suggested that some studies showed systematic differences from the average genetic effect ( Fig 3 and S4 Table ) . Notably , studies 9 , 38 and 48 showed significantly stronger effects than average ( Bonferroni corrected p-values < 0 . 05 ) while studies 10 , 19 , 24 and 28 showed significantly weaker effects ( Bonferroni corrected p-values < 0 . 05 ) . An inverse-variance weighted meta-analysis of the M statistics revealed substantial variability in the average effect across studies ( I2 = 85 . 9% ) ( Fig 3 ) . In an attempt to resolve underlying design factors that contributed to this systematic between-study heterogeneity pattern , we applied a random-effects meta-regression method [13] to the M statistics . We examined three potential sources of systematic heterogeneity that might have influenced the CARDIoGRAMplusC4D meta-analysis 1 ) ancestry , 2 ) family-history and 3 ) age-of-onset of disease ( S5 Table ) . The participating studies had been independently commissioned and designed with overlapping disease case ascertainment criteria; accordingly we assigned the studies into earlier-onset ( ≤ 55 years ) and later-onset of disease groups and flagged studies that ascertained cases with a positive family-history of disease ( S5 Table ) . A meta-regression of the M statistics with ancestry coded into 6 groups ( African and Hispanic American , South and East Asian , Middle Eastern and European ) suggested that some of the variability in average effect size could be explained by ancestry ( F5 , 42 = 2 . 52 , P = 0 . 044 ) ( Fig 4A ) . The 3 East Asian studies collectively appear to show stronger than average effects when compared with all other ancestry groups ( F1 , 46 = 4 . 75 , P = 0 . 034 ) . There was no evidence that the average effects for the 38 European studies ( F1 , 46 = 1 . 24 , P = 0 . 271 ) or the 4 South Asian studies ( F1 , 46 = 2 . 99 , P = 0 . 090 ) were systematically different . Meta-regressions of the M statistics suggested that studies that included early-onset cases of disease ( F1 , 46 = 20 . 65 , P = 0 . 00004 ) or included a family-history of CAD in the ascertainment scheme ( F1 , 46 = 29 . 49 , P = 2 . 0 × 10−6 ) showed systematically stronger than average effects ( Fig 4B–4D ) . Finally , a multiple meta-regression analysis of East Asian ancestry , early-onset and family-history of disease showed that these factors jointly explained a significant proportion of the systematic between-study variation of average effect size ( F3 , 44 = 13 . 91 , P = 1 . 6 × 10−6; adjusted R2 = 53 . 2% ) ( Table 2 ) . Additional factors examined as potential contributors to the systematic between-study differences observed included: imputation quality , genotype call rate , Hardy Weinberg equilibrium thresholds , percentage of myocardial infarction cases and case-control ratio . Their contribution to between-study variation of average effect size was negligible . The CARDIoGRAMplusC4D consortium studied an extended list of independently associated variants that define additional discrete loci based upon false discovery rate ( FDR ) criteria [3] ( S6 Table ) . These variants incremented the heritability explained over that conferred by GWAS-significant loci and might offer greater insights into heterogeneity patterns in these data . We therefore repeated the M statistic analysis with 214 variants ( P < 0 . 00005 , FDR < 5% ) , which confirmed the presence of systematic heterogeneity patterns in the 1000 genomes meta-analysis ( S3 Fig ) as well as flagging individual outlier studies ( S4 Fig ) . Four studies , that showed insignificant outlier patterns with 46 GWAS-significant variants showed significant evidence in this analysis of FDR variants ( S7 Table ) and three studies that were outliers in the GWAS 46 are now insignificant . A meta-regression confirmed that East Asian ancestry , early-onset and family-history showed systematically stronger than average effects ( F3 , 44 = 9 . 47 , P = 0 . 0001; adjusted R2 = 44 . 8% ) with family-history as the most important predictor of systematic heterogeneity in this dataset ( S8 Table ) . To compare our M analysis with a conventional single-variant strategy , we re-examined the set of GWAS-significant variants in a series of meta-regressions of three joint predictors , East Asian ancestry , early-onset and family-history . After correction for multiple testing of 46 variants , one variant ( rs2891168 ) detected evidence of stronger associations with early-onset and family-history ( F3 , 44 = 6 . 71 , P = 0 . 0008; adjusted R2 = 44 . 3% ) and another variant ( rs6689306 ) showed stronger associations with East Asian ancestry ( F3 , 44 = 7 . 69 , P = 0 . 0003; adjusted R2 = 71 . 5% ) ( S5 and S6 Figs , S9 Table ) .
We present here a novel statistical approach that integrates information across multiple variants to explore background patterns of systematic between-study heterogeneity in genetic association meta-analyses . Although we have focused on examples drawn from case-control analysis where genetic association statistics have been computed by logistic regression , the method is equally applicable to other normally distributed regression statistics e . g . linear regression analysis of quantitative genetic associations . We hypothesised that design features such as ascertainment criteria for disease cases or genetic ancestry might induce genetic heterogeneity in a meta-analysis . If these design features systematically reduce the average effect size in some of the studies participating in the meta-analysis , then the overall power to detect genetic signals will be reduced . This is an important consideration , since genetic effects for CD-CV are typically small in magnitude requiring very large sample sizes for reliable detection; there is strong pressure to undertake increasingly large meta-analyses . As meta-analysis consortia expand to attain larger sample sizes , the risk that they will become increasingly diverse in terms of underlying design features must surely increase . Analytic and Monte Carlo simulations demonstrate the potential of the proposed M statistic to detect systematic patterns of between-study heterogeneity . These calculations were based on a specified uniform level of heterogeneity for each variant and showed that the conventional approach to detecting heterogeneity ( e . g . Cochran’s Q statistic ) is relatively underpowered to detect systematic patterns . To maximize the power of detecting systematic heterogeneity patterns , we recommend analysing as many independently ( i . e . in linkage equilibrium ) and strongly associated variants as possible . In the future it would be interesting to extend the M approach including variants in linkage disequilibrium ( LD ) as this development might further enhance its power . It is anticipated that lead variants will show varying levels of heterogeneity , indeed several are likely to show little or no statistical evidence of heterogeneity ( i . e . I2 < 25% ) . Such variants do though include some information relevant to detecting systematic weaker or stronger effects than average so we recommend that all firmly associated lead variants are included in the calculation of M statistics . Our simulations also assumed equal weightings for each contributing study , we anticipate that outlier studies with larger sample sizes ( and thus weightings ) will be prominent and outliers with small weightings are likely to be obscure . We also found that the background level of heterogeneity influences the power to detect outlier studies , panels of strongly associated variants that individually show moderate levels of heterogeneity ( 25% < I2 < 50% ) are well suited to this approach . We tested our new methods on data assembled for the CARDIoGRAMplusC4D GWAS meta-analysis of CAD risk [3] . Although there was marked heterogeneity of effect sizes across the participating studies ( Fig 3 ) , all studies showed positive associations with coronary disease risk ( Fig 4 ) and thus made useful contributions to the overall discovery GWAS objective . Meta-regression of the M statistics revealed patterns of systematic heterogeneity that were linked to specific design features , East Asian ancestry , age-of-onset of disease and family-history . The latter two features are predicted by the multifactorial threshold model [5] to induce genetic enrichment [14] . Of note , 50 years ago the early-onset of coronary disease was recognised as a potent risk factor increasing sibling recurrence risks six-fold [15] . Although the magnitudes of the enrichment of average genetic effect size were quite modest ( 14% for East Asian ancestry , 15% for family-history , 11% for early-onset ) , we estimate that this could reduce the required sample size of cases and controls to detect an associated locus by up to 50% . Population genetic diversity , differences in the underlying rates of CAD and the relative contribution of individual risk factors , as well as lower use of preventive therapies in East Asia versus Europe ( and other regions ) may contribute to the enriched genetic signal observed in East Asian studies [16 , 17] . A follow-up meta-regression analysis of individual variants confirmed the role of ancestry , age—of-onset and family-history as significant predictors of systematic heterogeneity . Meta-regression of multiple potential explanatory factors inevitably carries a multiple statistical testing burden , and our present results should be interpreted as an exploration of the substantial systematic heterogeneity patterning evident in Fig 4 . The M statistic approach is advantaged over conventional single-variant methods in that information across multiple variants is aggregated reducing the dimensionality of the multiple comparison problem . Finally , we were unable to detect any systematic heterogeneity patterning attributable to the proportion of CAD cases suffering a myocardial infarction confirming the findings of the CARDIoGRAMplusC4D consortium [3] . There are several potential sources of heterogeneity that might affect genetic association meta-analysis studies . Controls for a common disease might be drawn from unscreened population samples in some studies or screened for the disease and filtered in other studies , this is predicted to dilute genetic signals in studies using population controls [18] . Environmental risk factor profiles might vary from study to study so disease cases sampled from a relatively low risk population would tend to be genetically enriched . Varying levels of LD can also induce heterogeneity [19] , a situation that is particularly important for meta-analyses involving multiple ancestry groups where the lead variant is a tagging rather than the causal variant . For example , African ancestry populations typically show more haplotype diversity and lower levels of LD across the genome than European or in turn East Asian populations [20] . Thus in a multi-ethnic meta-analysis , signals detected by tagging SNPs could show systematic weaker ( in low LD populations ) or stronger ( in high LD populations ) effects that could be detected by the M statistic approach . Given the momentum of the GWAS approach to identify more and more loci with improved genotype imputation training sets [21] , it is inevitable that increasingly large and potentially diverse meta-analysis projects will be conceived . For diseases and traits with multiple known genetic signals , there is now an opportunity to assess the respective contributions of participating studies in newly commissioned meta-analyses . Outlier studies flagged with discrepant M statistics , particularly those with weaker than average effects , can be reviewed as part of the routine quality control of GWAS meta-analysis in case there are design or analytic issues that need attention to maximize power . For design issues that might be difficult to resolve , it would be useful to assess the power of performing meta-analysis in the presence and absence of the studies with discrepant M statistics . Following the final meta-analysis , meta-regression of M statistics including variants tagging previously known as well as newly discovered loci can be used to explore potential design features that might show systematic aggregate effects that are obscured in heterogeneity analyses of individual loci , and influence future study design .
Random-effects meta-analysis is a statistical procedure originally devised by epidemiologists to integrate summary information from multiple independent yet related interventional studies to estimate two parameters , Θ , the average treatment effect across the contributing studies and τ2 , the extent of inter-study variability ( or heterogeneity ) in the treatment effects [22] . The effects evident in each study are assumed to be have been sampled from a normal distribution with two additive variance components , random within-study error σ2 and between-study variation ( i . e . heterogeneity ) τ2 , so that ys , the measured effect in the sth study , can be modeled by: ys = Θ + εs + u where εs ~ Φ ( 0 , σ2s ) , u ~ Φ ( 0 , τ2 ) and Φ denotes the cumulative probability distribution function of a normal random variable . The first step in the analysis is to estimate the magnitude of τ2 , which can be undertaken by several algorithms [22] . This is followed by an inverse-variance weighted ( i . e . 1/ ( τ^2+σs2 ) ) , least squares estimation of the average treatment effect ( Θ ) ( which ignores the study-specific random effects ) and its associated standard error ( E , the “standard error of the prediction” ) . Standardized predicted random effects ( SPRE ) can then be calculated for each of the studies as SPRE= ( ys−θ ) /τ^2+σs2−E2; these are precision-weighted , standard normally distributed statistics ( i . e . SPRE ~ N ( 0 , 1 ) ) that summarize the extent and the direction that individual studies differ from the average treatment effect . If there is no evidence of heterogeneity of effects ( i . e . τ2 = 0 ) , then the SPREs are identical to standardized predicted fixed effects derived from a fixed-effects meta-analysis . A normal probability plot of the SPRE statistics provides a convenient visual way to detect outlier studies that might be unduly influencing the estimate of the average treatment effect that complements inspection of a Forest plot . Consider now a genetic association meta-analysis project comprising S studies with summary-level results for V genetic variants . Genetic effect-sizes ( and their standard errors ) have been estimated in each study by regression modelling to substitute for the treatment effects described above . Assume that the variants selected for heterogeneity analysis are truly associated with the disease or quantitative trait and are in linkage equilibrium ( i . e . uncorrelated ) with each other . Informative variants could include 1 ) published variants that have previously shown strong evidence of association or 2 ) the lead variants at GWAS-significant loci in a post-hoc heterogeneity analysis . The genetic effects need to be synchronized so that the average Θ estimates for each variant are positive ( i . e . all average effects are “in the same direction” consistent with higher disease risks or levels of a quantitative trait ) ; this can be achieved by judicious “flipping” of the regression coefficients submitted by participating studies . For each of V variants , estimate τ2 , Θ and E using the random-effects procedure detailed above and calculate and store SPRE statistics for each of S studies in a regular array SPREsv ( S1 Methods ) . Subsequently , a “mean” aggregate statistic can be calculated that summarizes between-study heterogeneity across multiple genetic variants: Ms= 1V∑v=1VSPREsv . Under the assumption that Ms is a linear combination of V mutually independent , standard normal random variables , then Ms will be normally distributed with expectation ( i . e . mean ) 0 and variance 1/V ( S2 Methods ) . Positive or negative values of Ms indicate that the study shows systematically larger or smaller genetic effects than the average effect , statistically significant deviations are found where |Ms| exceeds an appropriate threshold; we corrected for multiple testing of S studies by applying the Bonferroni procedure to control the family-wise error rate ( FWER ) < 0 . 05 . We developed a Stata script ( getmstatistic ) based on the metareg programme [23] to calculate Ms statistics using τ2 estimates derived from the restricted maximum log-likelihood ( REML ) method . Additionally , an R package ( Rgetmstatistic ) for getmstatistic has been developed . To support the use of this newly proposed statistic , we examined the impact of several systematic heterogeneity scenarios by means of analytic and Monte-Carlo simulation-based power analyses . We first compared our new method with Cochran’s Q statistic , a widely used and computationally simple method to screen for between-study heterogeneity [7 , 8] . Q statistics approximate a chi-squared distribution in large samples [24] , for each scenario non-centrality parameters were equated with calculated Q statistics ( i . e . treating Q as a log likelihood ratio statistic [25]; [26] ) . The non-centrality parameter was then used in standard chi-squared power calculations ( [26] ) , with an allowance for multiple testing of V variants by applying Bonferroni’s correction to control the family-wide error rate ( FWER ) to 5% . Denote the power to detect heterogeneity in a meta-analysis of the vth variant by ωv , then the overall power to detect at least one heterogeneous variant is ω=1−∏1V1−ωv To calculate the analytic power of M , it is convenient to introduce a Wald statistic ( M2 ) , the squared-standardized M statistic i . e . M2= ( MSEM ) 2 where SEM= ( 1V ) 12 , which is approximately chi-squared distributed on 1 degree of freedom . M2 can then be substitute for the non-centrality parameter in standard chi-squared power calculations [26] allowing for multiple testing of S studies by applying Bonferroni’s correction to control the family-wide error rate ( FWER ) to 5% . Denote the power ( ω ) to detect heterogeneity in a meta-analysis for the sth study by ωs , then the overall power to detect at least one heterogeneous variant is ω=1−∏1S1−ωs The above analytic power calculations were performed using scripts and in-built procedures in Stata 10 . 1 . We also carried out Monte-Carlo simulations for scenarios where a systematic heterogeneity pattern is superimposed on a background random heterogeneity pattern , this allows for the possibility that real world heterogeneity patterns have multiple sources and complexity . These simulations allowed the comparison of the distributions of empirical with asymptotic p-values , with empirical p-values calculated using the ( r+1 ) / ( n+1 ) estimator [27] where r represents the rank of the simulated statistic and n the total number of replicates in the simulation exercise . To explore the impact of design features on the magnitude of M that vary between individual studies participating in a meta-analysis , we apply a random-effects meta-regression procedure ( metareg ) in Stata 10 . 1 to regress towards the average deviation of the observed effects of studies . This analysis is based upon study-specific M statistics to summarize the studies’ overall deviation from the average effect with precision weighting ( i . e . 1/SEMs for the sth study—see S2 Methods ) to allow for differing sample sizes in different studies . The studies contributing to the CARDIoGRAMplusC4D study were approved by the ethics committees of the respective medical faculties , and informed consent was obtained from all participants . Summary genetic association data were anonymously meta-analysed and reported here . Software to calculate M statistics is available at the following url: getmstatistic , https://magosil86 . github . io/getmstatistic Supplemental data includes the membership of the CARDIoGRAMplusC4D Consortium , six figures and nine tables .
|
Meta-analysis of genome-wide association studies ( GWAS ) is a valuable tool for the discovery of genes that protect or predispose individuals to common complex diseases . It can though be hampered by excessive heterogeneity among its participating studies . To date , the impact of heterogeneity is assessed locally on an individual SNP basis using Q , I2 and τ2 statistics . Here , we present a new heterogeneity statistic , M that assesses genomic ( multi-SNP ) patterns of heterogeneity in GWAS meta-analysis with enhanced power compared to conventional methods . When applied to a recent GWAS meta-analysis of coronary artery disease , the new statistic revealed substantial patterns of systematic heterogeneity , much of which was attributed to differences in ancestry , age-of—disease onset and family history of disease . The new method can dissect genomic heterogeneity patterns to flag underperforming studies that could comprise the power of the meta-analysis as well as identify influential studies with advantageous design features to inform future meta-analyses of multifactorial disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"genome-wide",
"association",
"studies",
"medicine",
"and",
"health",
"sciences",
"sociology",
"social",
"sciences",
"epidemiological",
"methods",
"and",
"statistics",
"coronary",
"heart",
"disease",
"mathematics",
"statistics",
"(mathematics)",
"genome",
"analysis",
"research",
"and",
"analysis",
"methods",
"cardiology",
"statistical",
"distributions",
"epidemiology",
"mathematical",
"and",
"statistical",
"techniques",
"statistical",
"methods",
"consortia",
"genetic",
"loci",
"probability",
"theory",
"epidemiological",
"statistics",
"meta-analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"genomics",
"vascular",
"medicine",
"genetics",
"of",
"disease",
"computational",
"biology",
"human",
"genetics"
] |
2017
|
Identifying systematic heterogeneity patterns in genetic association meta-analysis studies
|
Intracellular pathogens have evolved diverse strategies to invade and survive within host cells . Among the most studied facultative intracellular pathogens , Listeria monocytogenes is known to express two invasins-InlA and InlB-that induce bacterial internalization into nonphagocytic cells . The pore-forming toxin listeriolysin O ( LLO ) facilitates bacterial escape from the internalization vesicle into the cytoplasm , where bacteria divide and undergo cell-to-cell spreading via actin-based motility . In the present study we demonstrate that in addition to InlA and InlB , LLO is required for efficient internalization of L . monocytogenes into human hepatocytes ( HepG2 ) . Surprisingly , LLO is an invasion factor sufficient to induce the internalization of noninvasive Listeria innocua or polystyrene beads into host cells in a dose-dependent fashion and at the concentrations produced by L . monocytogenes . To elucidate the mechanisms underlying LLO-induced bacterial entry , we constructed novel LLO derivatives locked at different stages of the toxin assembly on host membranes . We found that LLO-induced bacterial or bead entry only occurs upon LLO pore formation . Scanning electron and fluorescence microscopy studies show that LLO-coated beads stimulate the formation of membrane extensions that ingest the beads into an early endosomal compartment . This LLO-induced internalization pathway is dynamin-and F-actin-dependent , and clathrin-independent . Interestingly , further linking pore formation to bacteria/bead uptake , LLO induces F-actin polymerization in a tyrosine kinase-and pore-dependent fashion . In conclusion , we demonstrate for the first time that a bacterial pathogen perforates the host cell plasma membrane as a strategy to activate the endocytic machinery and gain entry into the host cell .
Despite the diversity of virulence factors promoting host cell invasion , only two major mechanisms of entry have been observed [1]–[3] . First , invasins on the bacterial cell surface bind to host cell receptors to activate complex signaling cascades that orchestrate the internalization of the bacterium . Second , some bacteria bypass the requirement for a host receptor by utilizing a secretion system that injects effectors into the host cell . The effectors subvert the host signaling machinery to trigger bacterial uptake into macropinosomes [4] , [5] . Using Listeria monocytogenes as a model intracellular pathogen , we have analyzed a novel entry pathway that is activated in response to host cell perforation by a pore-forming toxin . L . monocytogenes is a foodborne pathogen that causes a large spectrum of clinical manifestations ranging from gastroenteritis to life-threatening meningo-encephalitis and sepsis . Susceptible hosts include the elderly and immunocompromised individuals [6] . In pregnant women , the bacterium can cross the maternofetal barrier causing abortion , stillbirth , and neonatal meningitis or sepsis [6]–[8] . To cross the host barriers and infect various organs including the liver [9]–[12] , L . monocytogenes expresses multiple virulence factors that induce its entry and survival into various nonphagocytic cells [13]–[16] . Two major genetic loci encode the virulence factors responsible for host cell invasion: the internalin operon and Listeria pathogenicity island 1 ( LIPI-1 ) [17] , [18] . The internalin operon encodes internalin ( InlA ) and InlB that bind to E-cadherin and the hepatocyte growth factor receptor ( HGF-Rc/c-Met ) , respectively [19] , [20] . Depending on the receptors expressed by the host cells , L . monocytogenes entry involves one or both internalins [21] , [22] . The internalin/host receptor interactions activate signaling cascades within cholesterol-rich microdomains leading to the internalization of the bacterium [23]–[26] . After internalization , the secreted pore-forming toxin listeriolysin O ( LLO ) and two phospholipases ( encoded by LIPI-1 ) mediate L . monocytogenes escape from the endocytic vesicle into the cytoplasm , where bacteria divide and undergo F-actin based motility to spread from cell to cell [27]–[29] . InlA and InlB were defined as bacterial invasins based upon their critical role in L . monocytogenes invasion of nonphagocytic cells , and the fact that they are sufficient to induce entry of noninvasive bacteria when overexpressed from a plasmid [17] , [30] . It is well established that the internalins are critical for host cell invasion; however , they may not be sufficient for inducing efficient bacterial uptake due to their low levels of expression in L . monocytogenes . Additional virulence factors including LLO have been proposed to regulate L . monocytogenes entry into host cells [31]–[35] . LLO is required for L . monocytogenes pathogenesis [36] and belongs to the family of the cholesterol-dependent cytolysins ( CDCs ) produced by numerous Gram-positive pathogens [37]–[39] . The CDCs are 50–70 kDa proteins synthesized as water soluble monomers that bind to cholesterol in host cell membranes [40] . Three members of the CDCs , intermedilysin , lectinolysin and vaginolysin , have been shown to bind to a host receptor ( the complement regulatory molecule CD59 ) in addition to cholesterol [41]–[43] . Upon binding to host membranes , the CDCs diffuse laterally to form a ring-shaped oligomeric prepore complex . This complex then inserts a large β-barrel pore across the membrane in a cholesterol-dependent fashion [44] . Eukaryotic cells possess sophisticated mechanisms to repair damaged plasma membranes and survive moderate exposure to pore-forming toxins including the CDCs [45] . A growing body of evidence demonstrates that pores formed by pore-forming proteins including perforin , S . aureus alpha hemolysin , and the CDC streptolysin O ( SLO ) , are removed from the plasma membrane through a mechanism that involves membrane internalization [45]–[47] . The ability of CDCs to induce membrane internalization in eukaryotic cells to repair their membrane raised the hypothesis that LLO may affect the internalization of L . monocytogenes . In the present work we explored the link between the formation of pore complexes by LLO and bacterial internalization . Using several experimental approaches , we determined that LLO is a critical invasion factor that perforates the host cell plasma membrane to activate L . monocytogenes internalization into human hepatocytes .
The gentamicin survival assay is commonly used to assess the role of virulence factors in the intracellular survival of bacterial pathogens . This assay enumerates viable intracellular bacteria after killing extracellular bacteria with the cell impermeant antibiotic gentamicin . We measured the relative intracellular survival of wild type L . monocytogenes ( WT ) , along with an isogenic LLO-deficient ( Δhly ) mutant , a double deletion mutant deficient in the expression of InlA and InlB ( ΔinlAB ) , a triple deletion mutant ( ΔhlyΔinlAB ) , and the LLO-complemented mutant ( Δhly + pAM401hly and ΔhlyΔinlAB + pAM401hly ) strains in HepG2 cells . As presented in Fig . 1A , efficient intracellular survival of L . monocytogenes required the expression of the internalins as well as LLO . In the absence of the three virulence factors , intracellular survival was almost completely abrogated . The defect in intracellular survival of LLO-deficient bacteria ( Δhly ) was due to the lack of LLO expression , as the LLO-complemented ( Δhly + pAM401hly ) and WT strains displayed similar intracellular survival . The results obtained with the gentamicin assay reflect the efficiencies of several stages of host cell invasion including L . monocytogenes association with host cells , entry into host cells , escape from the internalization vesicle , and intracellular division . LLO is known to promote L . monocytogenes intracellular survival by mediating bacterial escape from the internalization vesicle; therefore , it was difficult to dissociate the role of LLO in escape from its potential role in bacterial association and/or entry using this assay . To specifically assess the role of LLO during the initial stages of the invasion process , we used an automated fluorescence-based assay that measures the efficiencies of bacterial association with and internalization into host cells [48] . We found that LLO and the internalins did not significantly affect bacterial association with HepG2 cells . However , LLO significantly increased bacterial association when overexpressed from a plasmid ( Fig . 1C ) . More importantly , LLO is critical for L . monocytogenes internalization as we observed a marked decrease in entry of the LLO ( Δhly ) and LLO/internalins ( ΔhlyΔinlAB ) deficient strains relative to the WT strain ( Fig . 1D ) . The LLO-complemented ( Δhly + pAM401hly ) and WT strains displayed similar internalization efficiencies ( Fig . 1D ) . We also tested a second strain of L . monocytogenes ( LO28 ) and its isogenic LLO-deficient mutant ( LO28 hly::Tn917 ) and the human epithelial HeLa cells that are commonly used as a model to study the L . monocytogenes intracellular lifecycle . Again , we found that LLO is a key virulence factor that controls bacterial entry into host cells but does not significantly affect bacterial association with host cells ( Fig . S1 ) . The lack of LLO in the LLO-deficient strains may indirectly decrease the efficiency of bacterial entry by affecting the expression or regulation of other virulence factors . To verify that the defect in entry was solely due to the absence of LLO , we used LLO-deficient bacteria coated with increasing concentrations of six-His tagged recombinant LLO . LLO was noncovalently adsorbed on the surface of LLO-deficient L . monocytogenes using a previously described protocol [49] . We performed a noncovalent coating because the toxin monomers likely need to dissociate from the bacterial surface to freely diffuse within the host cell membrane and form oligomers and pores . To validate the coating procedure , we measured the fluorescence intensity of bacteria coated with increasing concentrations of an Alexa 488 LLO derivative ( Fig . S2 ) . We then measured the entry of LLO-deficient L . monocytogenes coated with increasing concentrations of recombinant LLO and observed that LLO increased bacterial entry into HepG2 cells in a dose-dependent fashion ( Fig . 2A ) . We next determined whether LLO should be localized in the vicinity of the bacteria or whether LLO could act distally to regulate entry . When adding LLO to the culture medium along with the LLO-deficient strain , we observed an increase in the efficiency of bacterial internalization ( Fig . 2B ) . Together , these data show that LLO potentiates internalization of L . monocytogenes into host cells in a dose-dependent fashion by acting locally or from a distance . We determined whether LLO is sufficient to induce bacterial entry into host cells or whether it only potentiates the internalins' activity . We used nonpathogenic and noninvasive Listeria innocua that does not express any of the known L . monocytogenes virulence factors [50] . As shown in Fig . 3A , L . innocua noncovalently coated with six-His tagged LLO were able to enter and survive in HepG2 cells . As a second approach , L . innocua were transformed with a plasmid coding for hly ( L . innocua phly/prfA* ) . As presented in Fig . 3B , LLO secreted from L . innocua phly/prfA* was sufficient to induce bacterial entry and survival into HepG2 cells . The efficiency of host cell invasion by L . innocua phly/prfA* was low compared to what was observed with WT L . monocytogenes and LLO-coated L . innocua ( Fig . 3A ) . Because the activity of LLO in bacterial entry is dose-dependent ( Fig . 2 ) , we determined whether L . innocua phly/prfA* expresses low levels of LLO compared to WT L . monocytogenes . The hemolytic activity of L . innocua phly/prfA* was approximately 40-fold lower than L . monocytogenes ( Fig . 3C ) . L . innocua phly/prfA* also produced low levels of LLO compared with WT L . monocytogenes ( Fig . 3D ) . These results demonstrate that low concentrations of LLO ( below the concentration secreted by L . monocytogenes ) are sufficient to induce bacterial entry into host cells . To assess the role of LLO in the absence of any other bacterial factor , we measured the uptake of 1 µm fluorescent polystyrene beads coated with LLO . The beads were first covalently coated with bovine serum albumin ( BSA ) . The BSA-coated beads were then noncovalently coated with LLO [49] . LLO was sufficient to induce internalization of the beads into HepG2 cells in a dose-dependent fashion; whereas , the beads coated with only BSA were not taken up by the cells ( Fig . 4A and B ) . This result clearly showed for the first time that a pore-forming toxin is a bacterial invasion factor . Cell viability was assessed by measuring the release of lactate dehydrogenase ( LDH ) and by trypan blue exclusion immediately and 24 h following toxin treatment ( Fig . S3 ) . These results show that the concentrations of LLO that lead to the optimal bead uptake by host cells do not compromise cellular integrity . Previous studies have established that pore-formation by LLO is pH sensitive at the host temperature ( 37°C ) , with LLO being more active at acidic pH . At 37°C , soluble LLO is inactivated by a pH-triggered unfurling of the domain 3 twin α-helical bundles that prevents further formation of a pore [51] . We hypothesized that the pH and temperature dependent inactivation would not occur if LLO was incubated in the presence of host membranes . Presumably , LLO would bind to the cell membrane where it would assemble into functional pores rather than unfolding in a nonproductive fashion . This hypothesis was based upon the observation that LLO is active when added to a solution of erythrocytes at 37°C and neutral pH ( all of our hemolytic assays were carried out at 37°C , pH 7 . 4 ) . To test this hypothesis , we pre-incubated LLO for 20 min in the absence of host cell membranes ( in PBS at 37°C , pH 7 . 4 or 5 . 5 ) before performing the hemolytic analysis at 37°C ( pH 7 . 4 or 5 . 5 ) . Consistent with the previous observation of Schuerch et al . [51] , pre-incubating the toxin at neutral pH and 37°C inactivated LLO; whereas , at acidic pH some activity of the toxin was retained ( Fig . 5A ) . If , however LLO was pre-incubated at 4°C for 20 min before performing the hemolytic assay at 37°C , LLO retained its activity at pH 7 . 4 and 5 . 5 ( Fig . 5B ) . These data show that soluble monomers of LLO are inactivated at 37°C and pH 7 . 4 in the absence of membrane , but in the presence of membranes LLO can rapidly bind to the membrane and form pores . In response to membrane perforation by pore-forming toxins , host cells reseal their membranes using a repair process that is activated upon the influx of extracellular calcium [45] . To further demonstrate that low concentrations of LLO efficiently perforate HepG2 cells at physiological temperature and pH , we measured host cell perforation in the presence and absence of extracellular calcium . Membrane perforation was quantified by fluorescence imaging using the membrane impermeant dye ethidium homodimer . In solution this dye is weakly fluorescent , but once host cells are perforated , it enters the cells and associates with nucleic acids , which increases its fluorescence quantum yield . As shown in Fig . 5C and D , a baseline level of fluorescence was detected with host cells exposed to ethidium homodimer in calcium-free buffer at 37°C , pH 7 . 4 ( Videos S1 and S2 ) . LLO induces a massive entry of ethidium homodimer into cells incubated in calcium-free buffer ( Videos S3 and S4 ) , but not in the presence of 1 mM extracellular calcium . The extensive perforation of host cells was not due to the toxicity of ethidium homodimer , as in its absence LLO induces cell swelling and lysis with similar kinetics ( Video S5 ) . In total , these results showed that at 37°C , extracellular LLO efficiently perforates host cells at neutral pH . To determine the importance of toxin oligomerization and pore formation in LLO-induced bacterial and bead entry into host cells , we constructed LLO derivatives locked at different stages of the pore-forming mechanism . Studies performed with the CDC perfringolysin O showed that the introduction of two cysteines at specific locations to form an intramolecular disulfide bond inhibits the hemolytic activity of the toxin [52] , [53] . Depending on its location , the disulfide bond impedes the conformational remodeling required for formation of oligomers and/or pores , while toxin binding to host membranes is unchanged [52] . Based on these studies , we constructed and characterized LLOmL ( monomer-locked ) and LLOpL ( prepore-locked ) . LLOmL was expected to bind to host membranes as a monomer unable to rearrange into a prepore complex . LLOpL was expected to bind to host membranes and oligomerize to form a stable prepore complex that cannot undergo the final transition into a pore . Neither mutant exhibited detectable hemolytic activity up to concentrations of 50 µM ( higher concentrations were not tested ) . The loss of activity was due to the formation of the disulfide bonds , as reduction by dithiothreitol ( DTT ) fully restored the native hemolytic activity of the toxins ( Fig . 6A ) . We next determined the oligomerization state of the toxins associated with erythrocyte membranes . As expected , LLO and LLOpL formed detergent-resistant high molecular weight complexes , whereas LLOmL failed to form such oligomers ( Fig . 6B ) . Importantly , the addition of DTT unlocked LLOmL , as shown by the formation of detergent-resistant high molecular weight oligomers . Finally , the arrangement of the toxins associated with cholesterol-rich lipid layers was analyzed by transmission electron microscopy ( Fig . 6C ) . LLO and LLOpL formed characteristic arc- and ring-shaped oligomers with a diameter of ∼50 nm in the presence and absence of DTT . LLOmL formed short linear assemblies that may be representative of the early stages of toxin oligomerization before formation of the prepore complex . Importantly , LLOmL formed the typical arc- and ring-shaped oligomers when it was reduced by DTT . We determined the role of LLO oligomerization into prepore and pore complexes in bacterial and bead entry into HepG2 cells . Native LLO induced bacterial and bead internalization , whereas neither LLOmL- nor LLOpL-coated beads were taken up by the cells ( Fig . 7A and B ) even though all three bind to host cells with similar efficiency ( Fig . 7C ) . We also used anti-LLO neutralizing antibodies , which were previously shown to prevent formation of LLO pores [54] , to block LLO-mediated uptake of beads . The LLO neutralizing antibodies markedly inhibited LLO hemolytic activity and bead internalization , whereas control anti-LLO antibodies did not ( Fig . 7D and E ) . The prepore to pore conversion of the CDCs is known to require high concentrations of membrane cholesterol [55] . We therefore prevented the formation of membrane pores by depleting host cholesterol using the cholesterol chelating agent methyl β-cyclodextrin ( MβCD ) . Cholesterol depletion completely abrogated LLO-induced host cell perforation and the entry of BSA/LLO-coated beads into HepG2 cells ( Fig . 7F and G ) . To demonstrate that inhibition of bead entry and host cell perforation was specifically due to cholesterol depletion and not to a secondary effect of the MβCD , we show that cholesterol repletion restored bead uptake and membrane perforation by LLO ( Fig . 7F and G ) . We also observed that BSA/LLO-coated beads formed pores in host cell membranes as detected by propidium iodide incorporation ( Fig . S4A and B ) . Membrane perforation is a key event in LLO-induced entry . Therefore , we hypothesized that a heterologous CDC should also increase L . monocytogenes internalization . LLO-deficient L . monocytogenes were coated with recombinant six-His tagged pneumolysin ( PLY ) , the CDC produced by Streptococcus pneumoniae [56] that exhibited a similar hemolytic activity to LLO ( Fig . S5A ) . Like LLO , PLY was able to mediate L . monocytogenes entry into HepG2 cells ( Fig . S5B ) . These data suggested that the LLO structure did not contain unique features that were required to induce bacterial invasion . To demonstrate that host cell perforation by LLO leads to the formation of a micron sized internalization vesicle , we analyzed plasma membrane remodeling at the bead entry site . Scanning electron microscopy images showed the formation of plasma membrane extensions entrapping the BSA/LLO-coated beads after 15 min at 37°C . Within 30 min , we observed an increase in the number of beads completely enveloped by the plasma membrane ( Fig . 8A ) , whereas , the formation of membrane extensions was not induced by the BSA-coated beads . To demonstrate that LLO induces entry of BSA/LLO-coated beads within a membrane-bound internalization vesicle , we quantified intracellular beads that colocalize with the early endosomal marker EEA1 . After 30 min at 37°C , we observed that a large population of intracellular beads was localized in endosomes to which the EEA1 marker had been recruited ( Figs . 8B and C ) . We further characterized the endocytic molecules involved in this pathway . We measured the entry of BSA/LLO-coated beads in cells transfected with clathrin heavy chain silencing RNA and in cells treated with the clathrin inhibitor chlorpromazine , or with dynasore , a dynamin inhibitor ( Fig . 9A and B ) . The uptake of fluorescent transferrin was measured as a control for the inhibition of clathrin- and dynamin-dependent endocytosis ( Fig . 9C ) . The results show that internalization of BSA/LLO-beads is dynamin-dependent and clathrin-independent . Internalization of large particles such as bacteria generally requires the rearrangement of subcortical F-actin to form membrane extensions that engulf the particles [57] . Consistent with this idea , the membrane rearrangements observed by scanning electron microscopy ( Fig . 8A ) were accompanied by the recruitment of F-actin at the bead entry site ( Fig . 9D ) . Furthermore , bead entry was inhibited in cells treated with the F-actin depolymerizing drug cytochalasin D; whereas , microtubule integrity was not required for entry ( Fig . 9E ) . F-actin remodeling involves the activation of the host signaling machinery . As a first approach to determine the transducers involved in entry , we have treated cells with inhibitors of tyrosine kinases ( genistein ) and phosphoinositide 3-kinases ( PI3Ks ) ( LY294002 and wortmannin ) . Tyrosine kinases and PI3Ks are key transducers activated upstream from actin polymerization at the L . monocytogenes entry site [13] . We found that only tyrosine kinase ( s ) activation was critical for LLO-dependent entry ( Fig . 9E ) . We also observed that purified LLO induces membrane ruffling in the 0 . 5 to 2 nM concentration range ( representative Videos S6 and S7 ) . Membrane ruffling started 117±14 . 3 sec after the addition of 1 . 2 nM LLO and was optimal for 555±145 sec ( calculated from 10 movies ) . This provided a convenient experimental model to determine whether pore formation and tyrosine kinases were involved in F-actin polymerization induced by LLO . LLO-induced membrane ruffling was F-actin- and tyrosine kinase-dependent as no ruffling was observed in cells stimulated by LLO in the presence of 0 . 5 µg/ml cytochalasin D or 250 µM genistein ( Videos S8 and S9 ) . Importantly , membrane ruffling was only induced by LLO , but not by 0 . 5 to 50 nM LLOpL ( Video S10 ) . These data provide a link between membrane perforation by LLO and the remodeling of the F-actin cytoskeleton .
These studies revealed the existence of a novel pathway exploited by L . monocytogenes to gain entry into host cells . This pathway is activated in response to membrane perforation by the pore-forming toxin listeriolysin O ( LLO ) . This is the first demonstration that a pore-forming toxin is able to induce the internalization of a bacterial pathogen into host cells . We have used several approaches to show that LLO is crucial for efficient entry of L . monocyogenes into HepG2 and HeLa cells and have used beads coated with LLO to specifically decipher the molecular machinery underlying this novel pathway . LLO is known to mediate L . monocytogenes escape from the endocytic vesicle following bacterial internalization into host cells [27] . The present findings demonstrate that LLO is also critical for L . monocytogenes internalization . Moreover , they show that LLO is sufficient to induce bacterial entry . A role for LLO in bacterial entry into nonphagocytic cells was previously proposed [35] , whereas other studies did not identify such a role for LLO [58] . All of these studies relied upon the gentamicin survival assay that measures intracellular survival . The gentamicin assay is a powerful method , but it exhibits some limitations . First , it reports several stages of the host cell invasion process and cannot distinguish the role of LLO in bacterial entry from its role in intracellular survival . Second , LLO perforates host cells and likely allows gentamicin entry into the cells . As a result , the intracellular survival of WT L . monocytogenes is underestimated in comparison to a LLO-deficient mutant . In the gentamicin assay performed in the present study , a low MOI ( 20 ) was used as higher amounts of bacteria led to substantial entry of gentamicin into the cells ( MW 480 ) , as reported by the incorporation of the cell impermeant dye ethidium homodimer ( MW 857 ) ( our unpublished data ) . Therefore , we developed an automated fluorescence-based assay to specifically and accurately measure bacterial association and entry [48] . With this approach , we demonstrate that LLO plays a critical role in bacterial entry , but not in bacterial association with host cells . Similar to LLO , we found that InlA and InlB did not significantly affect bacterial association , but affected bacterial intracellular survival and entry . This result is not surprising due to the abundance of adhesins expressed by L . monocytogenes , as over ten surface adhesins have been identified [31] , [59]–[65] . When overexpressed , LLO significantly increased L . monocytogenes association with host cells . This result is in accordance with the observation that LLO promotes Bacillus subtilis attachment to epithelial cells [66] . LLO and other CDCs were also shown to remain partially bound to the bacterial cell wall [67] , [68] . Therefore , when overexpressed , the cell wall-associated toxin likely anchors the bacteria to host cells via binding to membrane cholesterol . Indeed , LLO is well known to bind to cholesterol in biological and artificial membranes [40] , [69] . Our study focused on elucidating the role of LLO in bacterial entry into host cells . We first investigated whether host cell perforation by LLO was required for activating this entry pathway . Although pore-formation by LLO is pH-sensitive at 37°C , we demonstrated that extracellular LLO perforates host cells at neutral pH . This finding is supported by a previous study that also concluded that LLO is active at neutral and slightly basic pH values [69] . We constructed LLO variants to determine if LLO binding to host membranes , its oligomerization into a prepore complex , and pore formation are required for LLO-induced bacterial entry . The LLO variant unable to undergo the prepore to pore transition demonstrated that the formation of LLO pore complexes is a key event for bacterial and bead internalization into host cells . How does pore-formation by LLO stimulate bacterial or bead uptake ? The LLO entry pathway appears to be distinct from the canonical bacterial entry pathways . Bacteria are known to induce their entry by activating host receptors or by injecting effectors into the host cell cytosol [1] , [2] , [70] . Gram-positive bacteria do not have a type III secretion system , although , they can produce CDC toxins to mediate the translocation of virulence factors [71] , [72] . The observation that LLO alone was sufficient to induce bacterial or bead entry ruled out this mechanism . The requirement for membrane perforation does not favor the simple model in which LLO acts by activating a signaling host receptor . Indeed , the LLOpL variant that binds to host cells and is able to rearrange into a prepore complex failed to induce bacterial entry . Also , another pore-forming toxin , PLY , could replace LLO in that function . We do not rule out the existence of a yet unknown receptor for LLO that would be shared by PLY , nevertheless , membrane perforation remains a key trigger for entry . LLO induces the formation of internalization vesicles that accommodate large particles ( bacteria or 1 µm beads ) via a cholesterol- , dynamin- , and F-actin-dependent , but clathrin-independent pathway . Several clathrin-independent and dynamin-dependent internalization pathways have been described including lipid raft-dependent pathways [73] . It is important to note that the role of cholesterol in this pathway might be complex , as cholesterol is a structural component of lipid rafts and is critical for LLO binding to host membranes and the formation of LLO pores . However , the observation that LLO associates with and induces the coalescence of lipid raft microdomains favors the hypothesis of a lipid raft-mediated pathway [74] . We found that LLO induces the polymerization of actin at the bead entry site , and that F-actin dynamics and tyrosine kinase activation are required for LLO-mediated bead entry . Using soluble LLO and LLOpL , we observed that membrane perforation by LLO induces F-actin-dependent membrane ruffling . Interestingly , F-actin polymerization within membrane ruffles was tyrosine kinase-dependent . Together , our findings support a model in which host cell perforation by LLO leads to an internalization pathway that involves host tyrosine kinase-dependent stimulation of the actin cytoskeleton and the activity of dynamin . Similar to LLO , PLY promoted bacterial entry into host cells and was shown to induce actin polymerization in neuroblastoma cells [75] . The fact that LLO induces internalization of bacteria in a pore-dependent fashion is reminiscent of the membrane repair pathway observed in eukaryotic cells exposed to pore-forming toxins . In response to the attack by pore-forming proteins , eukaryotic cells undergo membrane endocytosis to remove the pores from their plasma membranes [45] , [47] . We identified host cell effectors , F-actin and dynamin , that are required for LLO-induced particle internalization but are dispensable for membrane repair ( Fig . S6 ) [45] . In conclusion , membrane repair is likely a prerequisite for LLO-induced bacteria/bead entry , but the LLO-mediated entry pathway extends beyond or is distinct from the membrane repair pathway . The mechanisms evolved by L . monocytogenes to gain entry into nonphagocytic cells are complex and involve several invasins such as InlA , InlB , and LLO . The inlA , inlB , and hly genes are controlled by the central regulator of virulence genes , PrfA , and are highly up-regulated in vitro and in vivo during L . monocytogenes infection [76] , [77] . Therefore , LLO is expressed together with InlA and InlB to mediate host cell invasion . Depending on the receptors expressed by host cells , InlA and InlB stimulate bacterial entry individually or in concert [13] , [21] . LLO likely affects bacterial internalization in a large panel of cells because its major host receptor is cholesterol . We used hepatocytes ( HepG2 cells ) that express the InlA and InlB receptors , E-cadherin and the HGF-Rc , respectively [78] , [79] . We also used HeLa cells that do not express E-cadherin and are only permissive to the InlB entry pathway [30] . Our results showed that LLO played a critical role in L . monocytogenes internalization into both cell lines . Therefore , LLO cooperation with InlB alone or with InlA and InlB is critical for bacterial internalization . Our study on LLO and previous studies on InlA and InlB showed that these molecules are individually sufficient to induce bacterial entry with high efficiency when overexpressed from a plasmid or coated on beads [17] , [20] , [80] . However , the expression level of these molecules is low in L . monocytogenes and their concerted activity likely ensures efficient bacterial uptake by host cells . The literature shows that most stages in the infectious lifecycle of a pathogenic bacterium involve numerous factors , all working in concert [21] , [81] , [82] . A recent study demonstrated the importance of the cooperation between InlA and InlB during L . monocytogenes entry into host cells [21] . In this study it was shown that InlA ensured the specificity of bacterial recognition of intestinal cells , whereas InlB increased the InlA internalization rate . Likewise , we speculate that cooperation between LLO , InlA , and InlB occurs during host cell invasion . In this model , the three pathways would contribute simultaneously to the uptake of a given bacteria . Given the identified roles of LLO , InlA , and InlB in stimulating actin polymerization and endocytosis , cooperation between the three invasins likely involves both of these processes . The LLO-dependent entry pathway displays differences and similarities with the InlA and InlB pathways . They differ with respect to the mechanism used to activate the host cells , as membrane perforation is required in the LLO but not in the InlA and InlB pathways . Also , clathrin is involved in the InlA and InlB pathways [83] , but not in the LLO pathway . However , the internalization induced by the three invasins shares common effectors such as host tyrosine kinases , dynamin , cholesterol , and F-actin [13] . Determining how host cells integrate the signals simultaneously generated by each invasin and whether the resulting pathway is the sum of the individual pathways or is a new pathway leading to efficient bacterial uptake constitutes an important goal for future research . Pore-forming toxins are produced by numerous pathogens and may influence their uptake by host cells [84] , [85] . In favor of this hypothesis , while this work was under revision , it has been published that the parasite Trypanosoma cruzi invades host cells by exploiting a host cell membrane repair mechanism in response to membrane damage [86] . Among CDC-producing bacteria , the genera Listeria , Arcanobacterium , Bacillus , and Streptococcus include several pathogenic bacteria that have been shown to invade nonphagocytic cells [87]–[92] . Interestingly , the CDC intermedilysin ( ILY ) is required for internalization of Streptococcus intermedius [88] . However , not all of the CDCs share this property , as a recent study showed that streptolysin O ( SLO ) inhibits internalization of Group A Streptococcus into keratinocytes [93] . Therefore , a fascinating avenue of research is to elucidate the molecular mechanisms underlying the role of pore-forming toxins in the regulation of host cell invasion by intracellular pathogens .
WT L . monocytogenes ( DP10403S ) , isogenic Δhly ( hly is the gene coding for LLO ) ( DPL2161 ) , and ΔinlAB ( DPL4404 ) deletion mutants were gifts from Dr . Dan Portnoy ( U . C . Berkeley , California , USA ) [94]–[96] . To construct the triple deletion mutant ( Δhly ΔinlAB ) we deleted hly in the DPL4404 strain by allelic exchange using the pKSV7 integrational shuttle vector ( a gift from Dr . Nancy Freitag , University of Illinois at Chicago , USA ) [97] . A ∼1000-bp DNA fragment consisting of the upstream ( from bp 962 to 1463 ) and downstream ( from bp 3029 to 3529 ) sequences flanking the hly open reading frame was amplified from L . monocytogenes chromosomal DNA [96] . The primers ( Table S1 ) were designed to generate restriction sites for EcoRI and PstI . The digested fragment was ligated into pKSV7 . The pKSV7 with the 1000 bp fragment was used to perform allelic exchange according to [98] . Nonhemolytic colonies were identified on 5% blood agar plates ( Becton Dickinson ) . The deletion of hly was further ensured by amplification of the chromosomal DNA with the primers used to generate this strain and a second set of primers that amplify the entire hly coding region . WT LO28 L . monocytogenes and the isogenic transposon insertion LO28 hly::Tn917 mutant were gifts from Dr . Pascale Cossart ( Pasteur Institute , Paris , France ) [99] . L . innocua 33090 was purchased from ATCC . Bacteria were grown overnight at 37° C in brain heart infusion ( BHI ) ( BD Biosciences ) . For invasion assays , overnight cultures were diluted 1/20 in BHI and grown at 37°C until OD600 = 0 . 7–0 . 8 . Bacteria were washed three times in phosphate-buffered saline ( PBS ) and diluted to the indicated multiplicity of infection ( MOI ) in the cell culture medium without serum . The plasmids pAM401 and pET29b coding for hly were gifts from Dr . D . Portnoy ( Jones and Portnoy 1994 ) . The plasmid phly/prfA* coding for hly was a gift from Dr . Svetlana A . Ermolaeva ( Gamaleya Research Institute of Epidemiology and Microbiology , Moscow , Russia ) [100] . The plasmid pQE-30 coding for ply was kindly provided by Dr . R . K . Tweten [40] . Human hepatocyte ( HepG2 cells , ATCC HB-8065 ) and cervical epithelial ( HeLa , ATCC CCL-2 ) cell lines were grown in minimum essential medium ( MEM ) ( + ) Earle's salts and L-glutamine ( Invitrogen ) , supplemented with 10% heat inactivated fetal bovine serum ( HI-FBS; Lonza ) , 0 . 1 mM nonessential amino acids , 1 mM sodium pyruvate , 100 U/ml penicillin , and 100 µg/ml streptomycin ( Invitrogen ) . Mammalian cells were maintained at 37°C in 5% CO2 atmosphere . Cells were seeded in 24-well tissue culture plates and grown for 48 h ( HepG2; 1×105 cells/well ) or 24 h ( HeLa; 0 . 5×105 cells/well ) before infection . Bacteria were coated with six-His-tagged toxin [49] . Briefly , 4×108 bacteria were washed twice and incubated for 10 min on ice in buffer A ( 20 mM Hepes pH 7 . 5 , 50 mM NaCl , 1 nM nickel chloride ) . Bacteria were washed and incubated in 200 µl buffer B ( 20 mM Hepes pH 7 . 5 , 50 mM NaCl ) for 10 min with six-His-tagged toxin . Bacteria were then washed once with buffer B and suspended in 200 µl of buffer C ( 20 mM Hepes pH 7 . 5 , 150 mM NaCl ) before infecting HepG2 cells . Carboxylate microspheres ( Alexa 350 , 1 µm diameter; Molecular Probes ) were covalently coated with 5 mg/ml bovine serum albumin ( BSA ) following the manufacturer's instructions . LLO was noncovalently associated to the surface of the BSA coated beads using the same experimental procedure used to coat bacteria . In each experiment , we verified that host membranes were not damaged by LLO , as this could cause the entry of antibodies used to label extracellular bacteria . Following fixation , cells were labeled with an anti-tubulin antibody ( Sigma ) and secondary fluorescent antibodies . We observed that in our experimental conditions antibodies could not enter the cells as microtubules were not labeled . HepG2 cells were infected with the L . monocytogenes strains at MOI 20 , LLO-coated bacteria at MOI 20 , or L . innocua and L . innocua phly/prfA* at MOI 100 . The plates were centrifuged at room temperature for 5 min and incubated for 30 or 60 min at 37°C . Cells were washed and incubated with 15 µg/ml ( for bacteria grown to OD600 = 0 . 8 ) or 100 µg/ml ( For L . innocua grown to OD600 = 0 . 2 ) gentamicin for 1 h or 30 min , respectively . When measuring the intracellular survival of L . innocua in comparison to L . innocua phly/prfA* , the bacteria were grown to OD = 0 . 2 because this bacterial density led to the highest secretion levels of LLO ( data not shown ) . Cells were washed three times with PBS and lysed with 0 . 2% Triton X-100 in H2O . Serial dilutions of cell lysates were immediately performed in PBS and plated on BHI agar . The colony forming units ( CFUs ) were enumerated after 48 h of incubation at 37°C . HepG2 cells ( or HeLa cells ) were infected with bacteria at MOI 20 . The plates were centrifuged for 5 min ( 230 x g ) at room temperature and incubated for 30 min at 37°C . Cells were washed with PBS , fixed with PBS/4% paraformaldehyde ( PFA ) for 15 min at room temperature , and then washed with 0 . 1 M glycine in PBS and incubated for 1 h in blocking solution ( 0 . 1 M glycine , 10% HI-FBS in PBS , pH 7 . 4 ) Following fixation and blocking , extracellular bacteria , total bacteria and host cells were labeled as previously described [48] . To quantitate the numbers of bacteria and mammalian cells , 40 sets of images ( DAPI , Alexa 488 , Alexa 568 , phase contrast ) were automatically acquired for each experimental condition using a 20 X objective . MetaMorph imaging and analysis software was used to enumerate the total number of bacteria ( Nt ) , extracellular bacteria ( Ne ) , and mammalian cells ( Nc ) [48] . The percentage of internalization was calculated as ( Nt - Ne ) /Nt x 100 . Bacterial association with host cells was calculated as Nt/Nc . The results were expressed relative to control ( % control ) . For invasion assays in the presence of soluble LLO , LLO was added to the cell culture medium along with L . monocytogenes . LLO variants were constructed by PCR-based site-directed mutagenesis using pET29b encoding native six-His-tagged LLO as a template . Mutagenic primers ( Table S1 ) were used to construct LLOmL containing the substitutions K344C and I359C; LLOpL containing the substitutions G80C and S213C; and LLO Alexa 488 with the substitutions C484A and D69C . Mutations were introduced by amplifying hly from pET29b using pfu Ultra II fusion polymerase , followed by digestion of methylated template DNA by DpnI ( Stratagene ) . The constructs were transformed in E . coli XL1-Blue and BL21 ( DE3 ) . Mutations were confirmed by DNA sequence analysis at The Ohio State University Plant-Microbe Genomics Facility . Recombinant six-His-tagged LLO and PLY were purified from E . coli BL21 ( DE3 ) as described previously [101] . LLOmL and LLOpL were dialyzed overnight in the absence of reducing agents to allow for disulfide bond formation . Toxins were stored at −80°C in 1 M NaCL , 50 mM phosphate buffer , pH 8 . To obtain LLO Alexa 488 , LLOC484A/D69C was labeled by chemical coupling to Alexa 488-Maleimide ( Molecular Probes ) under conditions that lead to a dye to protein molar ratio >0 . 9 , following the manufacturer's instructions . The fluorescent toxin was separated from the unconjugated dye by gel filtration chromatography . All the recombinant toxins purified in this study contain a six His tag . Sheep erythrocytes ( 10% suspension; Lampire Biological ) were diluted to 0 . 25% in PBS pH 7 . 4 . Serial dilutions of toxins and erythrocytes were co-incubated in PBS at 37°C for 30 min in 96 well plates . Plates were centrifuged and the absorbance of the supernatant ( A540 ) was measured in a PowerWavex340 spectrophotometer . Erythrocytes incubated with 0 . 1% Triton X-100 in PBS or PBS alone served to determine the maximum ( 100% ) and minimum ( 0% ) hemolytic activity , respectively . DTT alone had no effect on hemolysis ( data no shown ) . The hemolytic activities of the bacteria were measured using a similar approach except that the indicated amounts of bacteria were added to the wells . The kinetic hemolytic assay in Fig . 5 was performed according to [102] . In this assay a decrease in absorbance reflects the lysis of erythrocytes . Overnight cultures of L . monocytogenes , L . innocua , and L . innocua phly/prfA* were diluted 1/20 in BHI and grown to OD600 = 0 . 8 ( L . monocytogenes ) or OD600 = 0 . 2 ( L . innocua , and L . innocua phly/prfA* ) in BHI . Bacterial suspensions containing 2 . 0×108 bacteria were collected and centrifuged . The supernatants were collected for protein precipitation and bacterial pellets were washed and lysed as follows . The supernatants ( 0 . 25 ml of L . monocytogenes and 1 ml of L . innocua cultures ) were subjected to trichloroacetic acid ( TCA ) precipitation . One volume cold TCA was added to 4 volumes of supernatant and incubated 1 h on ice . Samples were centrifuged ( 11 , 000 g , 10 min , 4°C ) and precipitates were washed twice with cold acetone . Bacterial and dried protein pellets were suspended in Laemmli's sample buffer . Bacterial lysates ( 107 bacteria/well , ∼600 ng/well ) and precipitates were subjected to SDS-PAGE and western blotting using rabbit anti-LLO ( Abcam ) and horseradish peroxidase-conjugated secondary antibodies ( Cell Signaling ) . HepG2 cells ( 0 . 5×105 ) were cultured in glass bottom culture dishes ( MatTek; 35 mm petri dish , 10 mm microwell ) for 48 h . Cells were washed twice and incubated in the presence or absence of 1 mM CaCl2 in a buffer containing 150 mM NaCl , 1 mM MgCl2 , 5 mM KCL , 20 mM Hepes , 10 mM Glucose , 4 µM ethidium homodimer , pH 7 . 4 . Cells were placed on a temperature controlled microscope at 37°C and phase-contrast and fluorescence images were recorded every 10 s using a 100 X objective for 10 min . LLO ( 0 . 5 nM ) was added and movies were recorded for an additional 28 min . Results were expressed as the average fluorescence intensity measured from at least 5 movies at each time point . HepG2 cells were incubated with LLO ( 1 , 5 , or 20 nM ) or BSA/LLO-coated beads for 30 min at 37°C . Following incubation , supernatants were recovered from each sample and centrifuged at 500 x g at 4°C for 5 minutes to pellet any cells released into the supernatant . 10 µl of each supernatant was diluted into 40 µl of cell culture medium without serum in 96 well plates , and assayed for the presence of lactate dehydrogenase with the TOX7 in vitro toxicology assay kit according to the manufacturer's instructions ( Sigma ) . To assess cell viability immediately or 24 h after LLO treatment , HepG2 cells were detached from wells with 0 . 25% Trypsin-EDTA . Cells were then mixed 1∶1 with 0 . 4% trypan blue for 3 minutes , after which viable ( unstained ) and nonviable ( stained in blue ) cells were enumerated with a hemocytometer . Erythrocyte ghost membranes ( EGM ) were prepared as described previously [44] and stored at 4°C in resealing buffer ( 10 mM phosphate buffer , 5 mM MgCl ) . EGM ( 6 . 75×108 ) were incubated in PBS with 157 nM LLO on ice for 1 min and were transferred to 37°C for 5 min . When indicated , 4 mM DTT was added to reduce the disulfide bond in LLOmL and LLOpL . Samples were centrifuged at 15 , 000 x g for 15 min and the supernatant was removed and replaced with an equal volume of PBS . LLO oligomerization was analyzed using the NativePAGE Novex Bis-Tris Gel electrophoresis system . The samples were mixed with NativePAGE sample buffer containing 1% detergent , and run on a 4–16% Bis-Tris gel as described by the manufacturer ( Invitrogen ) . LLO was detected by western blotting . LLO solutions ( 750 nM ) in buffer ( 20 mM Hepes , pH 7 . 0 , ±2 mM DTT ) were pipetted in Teflon wells as 13 µl droplets and coated with 1 µl of a 0 . 5 mg/ml lipid mixture containing 50 mol% cholesterol ( Avanti ) and 50 mol% 1 , 2 dioleoyl-sn-glycero-3-phosphocholine ( Avanti ) in chloroform . After incubation in a humid chamber at room temperature for 1 h , the LLO complexes were transferred to carbon support films on electron microscopy ( EM ) grids and negatively stained with 1% ( w/v ) uranyl acetate and observed with a FEI Tecnai F20 transmission electron microscope equipped with a Gatan Ultrascan 4K X4K CCD camera . Cells were washed and incubated for 30 min with BSA- , or BSA/LLO-coated beads at a MOI = 20 in MEM . Cells were washed , fixed with PFA and blocked . Extracellular beads were labeled with an anti-BSA rabbit polyclonal antibody ( Sigma ) followed by a goat anti-rabbit secondary antibody conjugated to Alexa-568 . The percentage of internalized beads was determined by fluorescence microscopy based on their unique ( Alexa 350 , intracellular ) or dual fluorescence ( Alexa 350 + Alexa 568 , extracellular ) and expressed as% intracellular beads ( = intracellular beads/total beads * 100 ) . EEA1 was labeled with a primary antibody ( Santa Cruz ) and a fluorescent ( Alexa 488 ) donkey anti-goat secondary antibody in permeabilized cells . When co-labeling of the BSA beads and EEA1 was performed , a fluorescent donkey anti-rabbit secondary antibody was used to label the primary rabbit anti-BSA antibody . For F-actin labeling , cells were fixed and labeled as described in [102] . HepG2 cells were transfected in 24 well cell culture plates with specific human clathrin heavy chain siRNA ( Ambion 43908824 , Table S1 ) or with scrambled siRNA ( Ambion , 4390843 ) ( 50 nM siRNA in 0 . 5 ml/0 . 5×105 cell/well ) using SiPort neoFx transfection reagent according to the manufacturer instructions ( Ambion ) . After 24 h , cell culture medium was replaced and cells were further incubated for 24 h . Clathrin knock-down efficiency was verified in each experiment by western blotting using primary rabbit anti-clathrin heavy chain ( Abcam ) and mouse anti-α-tubulin ( Sigma ) antibodies . Cells were pre-incubated with 0 . 5 µg/ml cytochalasin D ( Sigma ) for 10 min , 33 µM nocodazole ( Sigma ) for 60 min , 250 µM Genistein ( Sigma ) for 60 min , 37 µM LY294002 ( EMD chemicals ) for 60 min , 1 µM wortmannin ( Sigma ) for 60 min , 80 or 120 µM dynasore ( Sigma ) for 30 min , and 10 µM chlorpromazine ( Sigma ) for 30 min before the addition of coated beads , and the drugs were maintained at the same concentrations in the cell culture medium until the cells were fixed . HepG2 cells were washed three times in MEM and were serum starved for 2 h . Cells were incubated in MEM at 37°C with 5 µg/ml iron loaded Alexa 568 conjugated tranferrin ( Molecular Probes ) . After 2 and 10 min of incubation , cells were transferred to ice , washed three times with cold medium and acid washed ( 200 mM NaCl , 50 mM MES , pH5 . 0 ) for 5 min . Four washes were performed with a cold buffer containing 150 mM NaCl , 1 mMCaCl2 , 1 mM PBS , 5 mM KCl , 20 mM Hepes , pH 7 . 4 . Alexa 488-conjugated transferrin was added ( 5 µg/ml ) for 2 h at 4°C . Cells were then washed and fixed . Phase contrast and fluorescence images of the internalized ( Alexa 568-Tf ) and cell surface-associated ( Alexa 488-Tf ) fluorescent transferrin were acquired using a 40X objective . Extracellular and internalized transferrin molecules were measured by quantitative fluorescence microscopy . Briefly , images were first background corrected and the average fluorescence intensities were measured in the cells ( about 1000 cells were analyzed for each experimental condition ) . For cholesterol depletion , HepG2 cells were washed twice with MEM ( without serum ) and incubated at 37°C for 30 min with 5 mM MβCD in MEM . Cells were then washed twice and assayed as described . For cholesterol repletion , cholesterol-depleted cells were washed twice and incubated for 15 min with a solution of 5 mM cholesterol-MβCD in MEM , then washed and assayed as described . A 1∶1∶1 mixture of three monoclonal anti-LLO neutralizing ( H14-3 , B8B20-3-2 , and A4-8 ) or control anti-LLO ( D21-1-4 ) antibodies ( gift from Dr . P . Cossart , Institute Pasteur , France ) was added to purified LLO or to BSA/LLO-coated beads at concentrations of 2 , 10 , or 20 µg/ml . Images were acquired on a motorized inverted epi-fluorescence microscope ( Axio Observer D1 , Zeiss ) equipped with 20 X Plan Neofluar ( N . A . = 0 . 5 ) , 100 X Plan Apo ( N . A . = 1 . 4 ) , and 40X Plan Neofluar ( N . A . = 1 . 3 ) objectives , a high speed filter changer Lambda DG-4 ( 300 Watts Xenon Arc bulb , Sutter Instrument Company ) , an optical emission filter wheel Lambda 10–3 for the fluorescence imaging , and a Smart shutter that controls the illumination for phase contrast imaging ( Sutter Instrument Company ) . The camera ( back-illuminated , frame-transfer EMCCD Cascade II 512 ) was from Photometrics . The filter sets for fluorescence were purchased from Chroma Technology Corporation: DAPI ( 49000 ) , GFP/FITC/Alexa488 ( 49002 ) , Cy3/DsRed/Alexa568 ( 49005 ) . The microscope was controlled by MetaMorph imaging software ( Universal Imaging ) . All movies were acquired on the microscope stage of an inverted fluorescence microscope at 37°C using a 100X objective . Phase contrast and fluorescence images of HepG2 cells were acquired every 10 s for 40 min , LLO was added 10 min after starting recording . In some experiments , cells were incubated in calcium-free buffer as indicated . Movies were accelerated 198 times . Following incubation with BSA/LLO-coated beads , cells were washed in PBS , fixed and processed as described in [23] with the following modifications: Coverslips were sputter coated with gold palladium at 17 mA for 75 sec with a Cressington 108 sputter coater . Samples were examined and photographed with a FEI Nova Nano scanning electron microscope operating at 5 Kv . A minimum of three independent experiments were performed , each in duplicate , unless otherwise indicated . Data were expressed as mean ± Standard Error of the Mean ( SEM ) . P-values were calculated using a standard two-tailed Student's t-test and determined significant if lower than 0 . 05 . In figures , asterisks indicate a significant difference between the indicated experimental conditions ( * p<0 . 05; ** p<0 . 005 ) . The NCBI Reference Sequence ( RefSeq ) accession numbers for the listeriolysin O gene and protein are as follows: hly ( NC_003210 . 1 ) , LLO ( ZP_05237132 ) .
|
Listeria monocytogenes is responsible for the severe foodborne disease listeriosis . During pathogenesis , invasion of nonphagocytic cells by L . monocytogenes is crucial for crossing the host epithelial barriers and colonization of multiple organs including the liver . In this study , we investigated the role of the pore-forming toxin listeriolysin O ( LLO ) in L . monocytogenes entry into human hepatocytes . LLO belongs to the largest family of bacterial pore-forming toxins called the cholesterol-dependent cytolysins and is a major virulence factor of L . monocytogenes . We observed that LLO is required for efficient entry of L . monocytogenes into hepatocytes and shed light on the molecular processes involved in this activity . Using different experimental approaches , we provide the first evidence that LLO is sufficient to induce bacterial internalization into host cells by a pore-dependent mechanism . LLO induces tyrosine kinase ( s ) - , dynamin- , and F-actin-dependent formation of an internalization vesicle . Similar to LLO , the pore-forming toxin pneumolysin regulates bacterial entry into host cells . Together , these findings indicate that host membrane perforation by a pore-forming toxin can be used as an invasion strategy by L . monocytogenes and raise the hypothesis that other bacteria may use a similar entry pathway .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"microbiology",
"molecular",
"cell",
"biology"
] |
2011
|
The Pore-Forming Toxin Listeriolysin O Mediates a Novel Entry Pathway of L. monocytogenes into Human Hepatocytes
|
RNA interference ( RNAi ) is a powerful tool for post-transcriptional gene silencing . However , the siRNA guide strand may bind unintended off-target transcripts via partial sequence complementarity by a mechanism closely mirroring micro RNA ( miRNA ) silencing . To better understand these off-target effects , we investigated the correlation between sequence features within various subsections of siRNA guide strands , and its corresponding target sequences , with off-target activities . Our results confirm previous reports that strength of base-pairing in the siRNA seed region is the primary factor determining the efficiency of off-target silencing . However , the degree of downregulation of off-target transcripts with shared seed sequence is not necessarily similar , suggesting that there are additional auxiliary factors that influence the silencing potential . Here , we demonstrate that both the melting temperature ( Tm ) in a subsection of siRNA non-seed region , and the GC contents of its corresponding target sequences , are negatively correlated with the efficiency of off-target effect . Analysis of experimentally validated miRNA targets demonstrated a similar trend , indicating a putative conserved mechanistic feature of seed region-dependent targeting mechanism . These observations may prove useful as parameters for off-target prediction algorithms and improve siRNA ‘specificity’ design rules .
RNA interference ( RNAi ) is a highly regulated , evolutionarily conserved mechanism of post-transcriptional gene regulation . Small interfering RNAs ( siRNAs ) , the intermediate utilised by this pathway , are 19 base pair ( bp ) long double stranded RNAs ( dsRNAs ) , with 2 nucleotide ( nt ) 3’ overhangs ( Fig 1A ) [1] . When siRNAs are transfected into a cell , one of the siRNA strands ( guide strand ) is incorporated into the RNA-induced silencing complex ( RISC ) while the opposite strand ( passenger strand ) is degraded [2 , 3] . The activated siRNA-containing RISC ( siRISC ) recognises and binds to the target transcript in a sequence-specific manner ( Fig 1B ) . The perfectly complementary region within the target transcript is then cleaved between the 10th and 11th nucleotide relative to the 5’ end of the guide strand [4] . This elegant , endogenous process has been extensively utilised in functional genomics studies and shows potential as a therapeutic platform [5] . When siRNAs were first shown to suppress gene expression in mammalian cells , the process was thought to be highly specific [1] . This belief was later challenged by reports of numerous sequence-specific off-target effects that could potentially induce a toxic phenotype [7 , 8] . The unintended targets were reported to share partial sequence complementarity with the guide strand and were found to primarily arise through a mechanism akin to miRNA targeting [9] . Specifically , 2nd to 7th/8th nt in the 5’ region ( seed region ) of siRNA recognises the unintended target gene within the 3’UTR of mRNA sequence ( Fig 1C ) [10] . While unintended interactions can also arise from the passenger strand entering RISC , in this study , ‘off-target effects’ will refer specifically to the guide strand seed-dependent type . The combined false positive results in genomic studies and potential safety liabilities in clinical application are driving the need to design specific , off-target-free siRNAs . Strategies such as using multiple siRNAs with a shared-single target to dilute out siRNA-specific off-target activity or adjusting the concentration based on on-target potency both have their technical limitations [11 , 12] , particularly in the case of therapeutics . Rational sequence design , with rules based on mechanistic insight , offers a more practical solution . The RISC is a ribonucleoprotein complex composed of multiple RNA-binding proteins , with Argonaute ( Ago ) playing a core role in the silencing process [13] . Ago interacts with the guide strand of an siRNA through two RNA-binding pockets in the PAZ ( bound to 3’ end ) and MID and PIWI ( bound to 5’ end ) domains [14] . We have found that the highly effective siRNA sequence simultaneously satisfy the following four rules; A/U at the 5’ end of the siRNA guide strand , G/C at the 5’ end of the passenger strand , AU richness in the 5’ one-third region of the guide strand , and the absence of long GC stretches [15] . Furthermore , the seed region is exposed on the Ago surface and is most likely mediating the initial target recognition through Watson-Crick base-pairing [16] . We also observed that the off-target efficiency is highly correlated with the thermodynamic stability of protein-free , seed-target duplex [15] . However , knockdown efficiency of seed-dependent off-targets with identical seed sequence but diverse non-seed region ( positions 9–21 ) has shown varying levels of knockdown . While the seed region was confirmed as the primary driving force of off-target activity , our analysis also indicated a possible involvement of the non-seed region and its corresponding target sequences in the observed response [17] . To comprehensively estimate the contribution of sequence-based features to the potency of off-target effects , we correlated the thermodynamic profile ( i . e . melting temperature [Tm] ) of various subsections within siRNA guide strand with experimental results derived from off-target effect screens . Subsequently , we have utilized DNA microarray data generated using siRNAs that satisfy the sequence rules of highly effective siRNAs [15] , to determine features associated with target sequence that influence the efficiency of off-target downregulation . Although little-to-no base-pairing is usually observed between siRNA non-seed region ( nucleotides 9–20 in Fig 1C ) and the corresponding off-target region , it was previously reported with miRNAs that such partial base-pairing is an additional factor that influences the efficiency of seed-dependent knockdown [18] , and was thus included in our analysis . To assess the general applicability of our findings for miRNA silencing , a dataset of experimentally verified miRNA targets was also studied to determine potential conservation of observed features . Understanding and utilization of parameters of thermodynamic control , along with target recognition features , will provide a reliable and practical method to design effective and safe siRNAs , for both research and therapeutic applications .
To investigate the contribution of hybridization thermodynamics to the efficiency of RNA interference in detail , the correlation between melting temperature ( Tm ) of all possible sequence subsections within siRNA duplex and corresponding off-target knockdown measured by luciferase-reporter assays was plotted as two dimensional arrays ( i . e . heatmap ) . Melting temperature has been shown to be a strong predictor of thermodynamic stability of seed-target duplex [17 , 19] and was the measure of choice in this study . The approach allowed us to distinguish three , well separated clusters which sequence composition correlated with RNAi effects ( Fig 2 ) : [1] positively correlated cluster in the siRNA seed region ( nucleotides 2–8 ) , [2] negatively correlated cluster in the non-seed region ( specifically nucleotides 8–15 ) , [3] positively correlated 3’ termini position ( nucleotides 18–19 ) . The correlation coefficient ( r ) of the ‘seed’ cluster exceeded 0 . 7 in results from higher siRNA concentrations ( Fig 2C and 2D ) . The high degree of overlap between plots at different concentrations of siRNAs increases confidence that the observations are significant , and the results confirmed our previous report , which demonstrated strong positive correlation between Tm values of seed-target duplex and off-target silencing efficiency . Longer sequence subsections that overlap a highly correlated shorter region , are more likely to show higher correlation . It is thus appropriate to concentrate on the subsections showing the highest correlation coefficient and the shortest length . Interestingly , nucleotides 2–5 and not the whole seed ( nucleotides 2–8 ) showed the highest positive correlation at all siRNA concentrations ( from r = 0 . 51 at 0 . 05 nM to r = 0 . 78 at 50 nM , all p-values < 0 . 01 ) , suggesting that positions 2–5 may be the most crucial in the interaction between the guide strand and off-target transcripts . The correlation between 3’ termini positions and knockdown efficiencies exceeded r = 0 . 7 in results from assays at 0 . 5 and 5 nM concentrations . Asymmetry in thermodynamics of 3’ and 5’ termini bases in the siRNA duplex determines which strand will be preferential selected and loaded into RISC [17 , 20 , 21] . The 3’ terminal nucleotide is known to be anchored in the PAZ domain of the Ago protein [22] . However , it might be found that the 3’ terminal region is more strongly associated with RISC incorporation compared to 5’ terminal region . While the contribution of seed region binding and 3’ terminal base to silencing potency has been well established [15 , 17] , our results revealed a novel negatively correlated cluster within the non-seed region of siRNA . The correlation between Tm values at non-seed nucleotides 8–15 and off-target knockdown efficiencies was relatively lower but evident at all tested concentrations ( r = ~-0 . 5 , all p-values < 0 . 01 ) . While the thermodynamics analysis of siRNA duplex revealed significant contribution of non-seed sub-region to off-target effect , a question remained regarding the potential effect of off-target sequence ( especially at the off-target sub-section contributing to the observed negative correlation in Fig 2 ) . Although the interaction between guide strand and off-target transcripts is mainly driven by base-pairing at the seed region , there is a likelihood of limited interaction between sequences at the siRNA non-seed region ( Fig 1C ) . To comprehensively test this possibility , we analysed global gene expression changes induced by two siRNAs , siVIM-270 and siVIM-805 designed against human Vimentin ( VIM ) gene , which were previously shown to have potent off-target effects [17] . Putative off-targets were predicted based on perfect complementarity between the siRNA seed ( nucleotides 2–8 ) and 3’UTR sequences derived from the longest transcript of every protein-coding gene . While perfect complementarity between seed and target is not always required for inducing off-target effect , analysis of off-target effects with varied seed binding ( and thus different binding energy ) might introduce additional variability , and further complicate the analysis . First , expression profiles of genes annotated as the putative off-targets were compared to non-off-target genes without seed-matched regions . For both siRNAs , there is a clear separation between the two groups of transcripts , with seed-matched sequences showing significantly lower expression profile relative to the remaining genes without seed-matched sequences ( Fig 3A; siVIM-270 , KS-test d-value = 0 . 21 with p-value < 2 . 2E-16 , Fig 3B; siVIM-805 , KS-test d-value = 0 . 24 with p-value < 2 . 2E-16 ) . The level of downregulation was different among off-target transcripts , which may be partly explained by the variation in the sequence of their non-seed region . While potential and limited base-pairing can be found between siRNA non-seed region and its corresponding target sequences , the currently established calculation procedure of thermodynamic profiles is not applicable for such discontinuous duplexes . The correlation profiles between all possible subsections within target sites corresponding to siRNA non-seed region and off-target effects were thus created based on sequence characteristics measured through GC content ( Fig 3C and 3D ) . While all off-target genes share the same sequence at positions 2–8 , which is complementary to siRNA seed region , the analysis included nucleotide at position 8 as it was found to be important in analysis shown in Fig 2 . The correlation based on luciferase-reporter assay ( Fig 2 ) was calculated using % of repression whereas the results based on DNA microarrays ( Fig 3 ) were calculated using fold change . Lower fold change translates into greater downregulation and thus the correlations in the two analyses are opposite in direction . With the difference between values being much smaller compared to the data shown in Fig 2 , we have ranked and classified them into ‘Low’ , ‘Medium‘ , ‘High’ and ‘Top’ groups using quantiles . The degree ( i . e . strength ) of correlation in both plots is strongest around the bottom region , indicating that sequence subsections encompassing most of the non-seed region show the strongest positive relationship with fold change ( Fig 3C and 3D ) . The patterns observed in global expression data derived from samples treated with siVIM-270 and siVIM-805 confirmed the results from luciferase-reporter assays–siRNA with high GC content in the non-seed region have ‘weaker’ off-target effects . While the top correlations are not as high as those observed with the seed region ( Fig 2 ) , they are both statistically and biologically significant ( siVIM-270 , r = 0 . 23 , p-value = 1 . 2E-13; siVIM-805 , r = 0 . 17 , p-value = 9 . 3E-07 ) . Comparing results from both siRNAs revealed that the common positions with top correlation were 8–15 ( p-values < 0 . 01 ) followed by 9–15 ( p-values < 0 . 01 ) . The former was also identified as the optimal region in the luciferase-reporter assay ( Fig 2 ) and was thus selected as the sequence subsection for all further analyses . The GC content of positions 8–15 was then used to divide off-target transcripts into four groups ( based on quantiles ) , whose expression patterns were plotted as cumulative distributions ( Fig 3E and 3F ) . From left to right , a clear pattern can be seen where targets with lower GC content in the subsection corresponding to siRNA non-seed positions 8–15 , show greater downregulation . For each siRNA , the Kolmogorov-Smirnov test was performed between the ‘Low’ and ‘Very High’ groups , and generated statistically significant results for both siRNAs ( Fig 3E; siVIM-270 , KS-test d-value = 0 . 29 with p-value of 7 . 9E-07 , Fig 3F; siVIM-805 , KS-test d-value = 0 . 2 of p-value = 0 . 003 ) . The higher number of predicted off-target transcripts for siVIM-270 ( particularly for off-target transcripts with 1 or 2 GC non-seed matches ) most likely determines the corresponding distinct profile . As the off-target effects were identified based on perfect complementarity to the seed region ( positions 2–8 ) and both terminal nucleotides of siRNA are known to be incorporated into Ago protein ( positions 1 and 21 ) [22] , the potential base-pairing between siRNA and off-target mRNA was determined via positions 9–20 ( i . e . 3’ region ) . The number of GC and AU base-pairs was calculated for each off-target sites , which allowed the transcript to be placed into five distinctive groups; 2AU , 1AU , None ( i . e . no found matches ) , 1GC and 2GC . Although most of these groups were relatively small , the distribution of each group for both siVIM-270 ( Fig 4A ) and siVIM-805 ( Fig 4B ) showed a clear separation and comparable ordering . The higher numbers of AU matches resulted in greater downregulation while the higher numbers of GC matches showed lower downregulation . The profile of off-target effects without possible non-seed binding was located between the AU and GC groups . To better understand this pattern , average GC content of positions 8–15 was calculated for each group of transcripts , and plotted in the same order as observed in Fig 4A and 4B . A very strong relationship was observed between the average GC contents of sequences corresponding to siRNA non-seed positions 8–15 and the off-target transcripts containing a particular non-seed binding pattern . Off-target transcript with AU matches in region 9–20 have on average lower GC content in region corresponding to the siRNA positions 8–15 , while those off-target transcript with GC matches have on average higher GC content . The ordering based on average GC values of the groups shown in Fig 4C and 4D closely mirrors that in Fig 4A and 4B . To further explore the relationship between off-target effects and GC content in the target region corresponding to the siRNA positions 8–15 , we investigated the cumulative distribution of off-target subgroups according to their GC content . The average GC content for each of the group was used to further divide the transcripts with a particular non-seed binding pattern into two subgroups: one with GC contents less than the mean ( ‘Low’ ) and the other with GC contents more than the mean ( ‘High’ ) . Unfortunately , in majority of cases , the subgroups were too small to perform an accurate analysis . We have thus limited our analysis to the two largest groups ( Fig 4E; siVIM-270 , 1GC match , 133 off-target transcripts , Fig 4F; siVIM-270 , 2GC matches , 151 off-target transcripts ) . In both groups , the results clearly indicated that off-targets with lower GC content in the region corresponding to siRNA positions 8–15 show a greater downregulation . It is difficult to verify whether base-pairing in the siRNA non-seed region occurs in a ‘justified’/canonical ( e . g . binding occurs between nucleotides directly opposite each other , as calculated in Fig 4A and 4B ) or ‘slided’/non-canonical ( e . g . binging occurs between nucleotides not opposite each other , which could explain the pattern observed in all plots in Fig 4 ) fashion . Nevertheless , it was apparent that the high GC contents in the off-target sequences corresponding to siRNA positions 8–15 reduces the potency of off-target effects . Based on the ordering of the groups in Fig 4A–4D it seems as the ‘justified’ base-pairing does not fully occur/affect the seed dependent interaction . Rather , the groups indirectly represent the GC content of the target region as regions with high GC content are more likely to have more GC base-pairs with siRNA non-seed region ( as shown in Fig 4C and 4D ) . Since our present observations demonstrate that siRNA non-seed region may be a novel auxiliary determinant of siRNA-based off-target effect , we extended our investigation to miRNA-based silencing events . While the availability of accurate estimates of the silencing potential of a particular miRNA is severely limited due to their endogenous origin , our analysis took advantage of a database that summarizes their experimentally confirmed targets [23] . The presence or absence of a target in the list formed the basis of our comparison . Twenty five miRNAs with the highest number of experimentally-ascertained targets , including those with perfect and non-perfect seed-complementarity , were selected for the analysis . Putative miRNA targets were predicted as for siRNA ( i . e . perfect complementarity between 3’UTR sequence and seed region ) , and consequently intersected with the list of confirmed hits ( miRNAs with at least 500 predicted targets were considered ) . Predicted off-target genes that were experimentally confirmed were placed in ‘Validated Targets’ group while predicted off-target genes that were not experimentally validated were placed in ‘Remaining Genes’ group . Average GC content of the region corresponding to the miRNA positions 8–15 for all genes was calculated separately for each group and compared ( Table 1 ) . Of the 25 evaluated miRNAs , 21 had lower GC content ( nucleotides 8–15 ) in the ‘Validated Targets’ group compared to the ‘Remaining Genes’ group . While a more thorough approach is needed to accurately explore the relationship ( e . g . target prediction not limited to those with perfect seed complementarity , analysis based on correlation between GC content and quantified silencing potential , comparison between expression profiles ) , the results provide evidence that the phenomenon observed with siRNA might represent a mechanistic feature which also regulate the efficiency in miRNA target silencing .
To uncover the rational design rules of potent siRNAs , several studies have attempted to reveal sequence-dependent features within the siRNA molecule that correlate with efficiency of RNAi [21 , 24] . To better understand the regulatory mechanisms of off-target effects we exhaustively analysed the contribution of each subregion within the siRNA duplex to clarify their relative contributions to potency of off-target silencing . The significance of seed region ( nucleotides 2–8 ) thermodynamic stability in the process of off-target effects has been thoroughly explored [17] . Our detailed study revealed that the siRNA seed region encompassing nucleotides 2–5 has the highest positive correlation with off-target effect ( Fig 2 ) . This result may not be so surprising , since the crystal structure of human Ago2 revealed that nucleotides 2–6 of the guide RNA are splayed out and stably positioned on the MID and PIWI domains in an A-form conformation for base pairing with target mRNAs [25] . This preorganization into A-form like geometry significantly increases affinity for the target RNA [26 , 27] . However , there is a kink between nucleotide 6 and 7 that breaks the A-form structure . These types of conformational change are considered to be related to the importance of base-pairing of nucleotide 7 with off-target transcripts . Furthermore , base-pairing at nucleotide 8 can subsequently stabilise the duplex , although mismatch at this position does not perturb the duplex formation at positions 2–7 . A more recent study has also revealed that initial target scan performed by Ago2 identifies target sequences complementary to nucleotides 2–4 of the miRNA [28] . Thus , the efficiencies of off-target effects might show a stronger correlation with the thermodynamic stabilities of nucleotides 2–5 compared to those of nucleotides 2–8 . The majority of siRNAs used in this study have A/Us at 5’ ends of the guide strands ( positions 1–2 ) and G/Cs at the 5’ end of the passenger strands ( positions 18–19 ) ( see S1 Table ) . The calculated Tm values at nucleotides 1–2 showed no or little correlation with the efficiencies of off-target effects , while those at nucleotides 18–19 showed strong positive correlation ( Fig 2 ) . One of the most widely accepted design algorithms of functional siRNAs for on-target repression is based on the asymmetrical thermodynamics of 5’ end of the guide strand ( position 1 ) and 5’ end of passenger strand ( position 19 ) . The strand with lower base-pairing stability at its 5’ end is preferentially incorporated and retained in the RISC [17 , 20 , 21] . Selective entry of the intended guide RNA strand into RISC significantly increases the efficiency of target gene cleavage , while entry of the opposite passenger strand is undesirable . Furthermore , A/U at the guide strand 5’ terminal shows the 30-fold higher affinity for anchoring in the Ago pocket compared to G/C [29] . While it is possible that 5’ terminal A/U of the guide strand is important for anchoring in the Ago pocket , the thermodynamic profile of nucleotides 1–2 may have little to no correlation with off-target efficiencies . Such an observation is also consistent with previous experiments , indicating no contribution of A/U base pairs at position 1 to the off-target efficiency [9 , 15] . In contrast , the calculated Tm values at nucleotides 18–19 showed a high correlation with off-target silencing efficiencies across all siRNA concentrations ( from r = 0 . 45 to r = 0 . 64 , all p-values < 0 . 01 ) . The results indicate that asymmetrical thermodynamics of both ends of siRNA duplex is predominantly regulated by the 3’ terminal nucleotide rather than the 5’ terminal nucleotide . Alternative explanation can be based on structural analysis , which showed relatively stronger electron density , indicating stronger binding , in the 5’ pocket of the PIWI domain with G/C nucleotide [29] relative to 3’ binding pocket of the PAZ domain [25] . The most striking result of this study was that the siRNA non-seed region is also significantly involved in determining the efficiency of off-target effect . The negative cluster in siRNA analysis ( Fig 2 ) overlapped sequences positioned between nucleotides 8 and 17 , which agrees with structural studies showing that nucleotides 18–21 are held away from the target RNA [30 , 31] . The base-pairing stability in the non-seed region positioned at 8–15 showed the highest negative correlation with off-target activity ( r = ~0 . 5 across all concentrations , all p-values < 0 . 01 ) in duplicated experiments using two different siRNAs , siVIM-270 and siVIM-805 . This tendency of correlation is apparent even at the lowest concentration of 0 . 05 nM ( Fig 2A ) . The non-seed region did not , however , show an increase in correlation coefficient with increasing siRNA concentration . In contrast , the effect of the seed region was dose-dependent , suggesting that the initial interaction through seed-region is a rate-limiting step , with consequent interactions being of lesser hindrance . Interestingly , GC content in target sequence corresponding to siRNA non-seed region ( nucleotides 8–15 ) showed the highest correlation with off-target efficiencies ( Fig 3 ) , which appears to be independent of a ‘justified’ base-pairing within the non-seed region ( nucleotides 9–20 ) ( Fig 4 ) . Nevertheless , results from both analyses showed non-seed region 8–15 as the highest negatively correlating feature , which suggest that interaction between those regions might be behind the effect . It is thus likely that the regions interact via base-pairing with the nucleotides not exactly opposite each other . Such event might disrupt the seed-target duplex by pulling apart both strands . The further away from the seed region , the less effect any potential base pairing may have on the stability of the seed duplex . Alternatively , high GC contents of both regions might interfere with initial recognition ( e . g . binding competition between seed and non-seed ) , affect protein binding ( with RISC or other as yet uncharacterised molecule ) or alter downstream events , which translate into reduced OTE knockdown . A recent comprehensive study of miRNA target interactions reported widespread non-canonical interaction between miRNA non-seed region and its target mRNA sequences [32] . Similar to siRNA , miRNA identifies and binds to its targets primarily through its seed region . Our analysis based on dataset of experimentally validated miRNA targets [23] indicate that validated targets have on average lower GC content in the sequence corresponding to miRNA non-seed region ( nucleotides 8–15 ) relative to predicted off-target effects that were not experimentally validated ( Table 1 ) . Given these combined observations we propose that the non-seed region may function through a conserved mechanism shared by both miRNA and siRNA molecules . We have previously reported that highly functional siRNAs satisfy the following four rules [15]: ( 1 ) A or U at 5’ end of siRNA guide strand , ( 2 ) G or C at 5’ end of siRNA passenger strand , ( 3 ) AU richness at 5’ one-third region of siRNA guide strand , ( 4 ) absence of any GC stretches more than 9 nt in length . Based on our current and previous analyses [17] , we recommend that additional emphasis is placed on three design aspects tailored specifically towards minimising off-target effects ( summarised in Fig 5 ) : ( 3’ ) low Tm in the siRNA seed region ( nucleotides 2–5 to 2–7 ) , ( 5 ) high Tm in siRNA duplex/high GC content in the guide strand within non-seed region ( nucleotides 8–15 ) , ( 6 ) high average GC content for target sequences corresponding to nucleotides 8–15 of the siRNA guide strand . Although the first ‘specificity’ rule ( 3’ ) overlaps with the third functional siRNA rule ( 3 ) the revised recommendation is more precise regarding the location of AU base-pairs ( nucleotides 2–5 to 2–7 ) . While the first two specificity indications , ( 3’ ) and ( 5 ) , will be easily satisfied by calculating Tm values in the selection step of a particular siRNA duplex , the third ( 6 ) is inevitably varied and will require a rigorous off-target effects analysis to be performed to select the most promising designs . This can be accomplished by extracting the sequences upstream of the sites predicted as targets for siRNA seed region and calculating their average GC content . Data presented in Figs 2–4 indicate that adhering to these rules will reduce global off-target effect liability . RNAi is a cascade of protein-RNA interaction events , with reaction rate and efficiency of each step directly affecting the downstream event . The potency of siRNA-mediated knockdown is largely dependent on siRNA sequence , with particular features within the molecule affecting the rate of reaction steps [24] . This phenomenon has led to the general acceptance of several ‘design rules’ of functional siRNA [33] , which may reflect a mechanistic feature of the RNAi . While high on-target knockdown is essential , it is important to address the problem of unintended off-target effects [34–36] . Previously reported factors which contribute to the potency of off- ( strength of base-pairing between seed region and target ) and on-target ( asymmetry in end stability of siRNA duplex ) functions [15] were confirmed in this study . Additionally , Tm in the siRNA duplex at non-seed positions 8–15 and the GC content of target sequence corresponding to the siRNA non-seed positions 8–15 were found to be correlated with the off-target downregulation . The results were also replicated in a database of confirmed miRNA targets suggesting that the contribution of the non-seed region to silencing event arise through a conserved mechanistic feature of seed-dependent targeting mechanism .
Data from our previously reported off-target effect assays of 32 siRNAs was pooled , averaging values for duplicate measurements , setting the maximum luciferase response at 100% and transforming into knockdown % to facilitate interpretation [17 , 19] . The measurements are based on reporter plasmids expressing Renilla luciferase gene with three tandem repeats inserted into the 3’UTR region of the mRNA . The repeat sequence contains a region of perfect complementarity to the seed region of corresponding siRNA ( positions 2–8 in Fig 1C ) , while the remaining regions are intentionally devoid of homology to the non-seed region . The sequence details of all siRNAs , together with corresponding % knockdown at each concentration , are listed in S1 Table . Thermodynamic profiles were created by evaluating every subsection within the guide strand of siRNA ( sequences are listed in S1 Table ) . For every subsection a Tm was calculated using a nearest neighbour model with thermodynamics parameters for RNA-RNA interaction based on Xia et al . [37] . As the comparison is performed for ‘internal’ sequence , the helix initiation factor and symmetry correction were both omitted . The final Tm equation used was: Tm ( °C ) =1000xΔHΔS+InCt4−273 . 15+16 . 6log[Na+] Where ΔH is the sum of enthalpy changes ( kcal mol-1 ) , ΔS is the sum of entropy changes ( kcal mol-1 K-1 ) , Ct is the total molecular concentration of the siRNA ( variable ) and [Na+] is the sodium ion concentration ( 100 mM ) . The microarray analysis was performed according to our previous report [17] . Briefly , HeLa cells were transfected with either of 50 nM siVIM-270 or siVIM-805 . Total RNA was purified and hybridized to Human Genome U133 Plus 2 . 0 GeneChip ( Affymetrix ) . RNA from mock-transfected cells was used as a control . The transcript expression value was calculated using Microarray Suite 5 . 0 ( MAS5 ) [38] with quantile normalization [39 , 40] . The 3’UTR sequence used in off-target transcript prediction and analysis were generated based on gene sequences and coordinate , and probe mapping annotation available in the Ensembl repository [41] . Several steps were taken to ensure accuracy and non-redundancy of the downstream analysis: probes overlapping multiple genes were removed , probes annotated as ‘absent’ or ‘marginal’ by MAS5 analysis suite were omitted , a single probe was selected per gene when several were available , a single 3’UTR sequence was selected per gene ( based on the longest transcript ) in case of multiple splice variants with annotated UTR regions . To reduce potential variability , a gene was treated as a target only when its 3’UTR sequence had a fragment perfectly matching the seed region of the siRNA or miRNA molecule ( positions 2–8 ) . Genes with more than one putative prediction were also excluded from further analysis to ensure independence of analysed samples . A custom script was prepared to locate sites within 3’UTR sequences with perfect match to siRNA seed region and extract the flanking sequences for further analyses . Potential base-pairing in the non-seed region was calculated based on region 9–20 as the nucleotide in position 21 is hidden in the PAZ domain of the Ago protein [22] . GC content was calculated for all possible subsections within predicted targets corresponding to siRNA guide strand non-seed region ( positions 8–21 ) ( Fig 1C ) , mirroring the analysis method of the guide strand described in the ‘Thermodynamics Analysis’ . Results for particular sets of off-target effects were plotted as empirical cumulative distribution functions , which is a convenient method of visualizing differences in distribution and expression patterns between analysed groups . Kolmogorov-Smirnov test ( KS-test ) was used to quantify the separation between plotted cumulative distributions and test its significance . The strength of a linear association between melting temperatures of all possible subsections within siRNA duplexes and their off-target efficiencies measured by Renilla luciferase assay was calculated separately for all siRNA concentration ( 0 . 05 , 0 . 5 , 5 and 50 nM ) . Similarly , the correlation between GC contents of all possible target sequences corresponding to the various siRNA non-seed subsections , and the fold changes derived from DNA microarray experiments , was calculated for siVIM-270 and siVIM-805 . The profiles were plotted as heatmaps , as a convenient method to represent and interpret two dimensional data ( Figs 2 and 3C and 3D ) . The nucleotide position numbering is identical to that used in Fig 1 ( Y axis–start position on the guide strand/target , X axis–end position on the guide strand/target ) . Two different correlation tests were used: Pearson product-moment correlation and Spearman rank correlation coefficient . Though the results from both methods were largely overlapping , the latter was chosen for final plotting as it generated more consistent and defined clusters , most likely due to the monotonic nature of the data . All statistical calculations and plotting were performed in R .
|
Small interfering RNAs ( siRNAs ) are double stranded RNA molecules designed to perfectly match the sequence of a target gene and silence its expression . The function is exerted through the RNA interference ( RNAi ) pathway and has revolutionised biological research due to its ease-of-use and high potency . While siRNAs were initially believed to be highly specific , they have subsequently been observed to interact with other , unintended messenger RNAs . However , the mechanistic details of this process remain poorly understood , and there is a paucity of strategies and guidelines directed toward mitigating this issue . To address this potential safety liability , we performed a comprehensive analysis of sequence characteristics of siRNA duplexes and their target regions . Results from luciferase-reporter assays and global expression data confirmed previous observations that the siRNA seed region is the primary determinant for off-target gene recognition and binding . Furthermore , our analysis revealed the important contribution of siRNA non-seed region , and its corresponding target sequences , to the potency of off-target knockdown . Similar results were observed in an equivalent evaluation of the miRNA-targeting mechanism , suggesting that the correlating features arise through an evolutionary conserved mechanistic factor .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
The siRNA Non-seed Region and Its Target Sequences Are Auxiliary Determinants of Off-Target Effects
|
The deregulation of metabolism is a hallmark of aging . As such , changes in the expression of metabolic genes and the profiles of amino acid levels are features associated with aging animals . We previously reported that the levels of most amino acids decline with age in Caenorhabditis elegans ( C . elegans ) . Glycine , in contrast , substantially accumulates in aging C . elegans . In this study we show that this is coupled to a decrease in gene expression of enzymes important for glycine catabolism . We further show that supplementation of glycine significantly prolongs C . elegans lifespan , and early adulthood is important for its salutary effects . Moreover , supplementation of glycine ameliorates specific transcriptional changes that are associated with aging . Glycine feeds into the methionine cycle . We find that mutations in components of this cycle , methionine synthase ( metr-1 ) and S-adenosylmethionine synthetase ( sams-1 ) , completely abrogate glycine-induced lifespan extension . Strikingly , the beneficial effects of glycine supplementation are conserved when we supplement with serine , which also feeds into the methionine cycle . RNA-sequencing reveals a similar transcriptional landscape in serine- and glycine-supplemented worms both demarked by widespread gene repression . Taken together , these data uncover a novel role of glycine in the deceleration of aging through its function in the methionine cycle .
Aging is characterized by a progressive deterioration of the functional capacity of tissues and organs . Pioneering studies in the nematode C . elegans have identified longevity-associated genes and provided us with great insights into the plasticity of aging [1–3] . In the last few decades , genetic and nutritional interventions have been employed in multiple organisms including Saccharomyces cerevisiae , C . elegans , Drosophila melanogaster , rodents , and more recently fish [4–6] . These models have set the stage for characterizing the genetic basis of physiological aging and for developing efficient strategies to control the rate of aging . To date , metabolic pathways including the mTOR , insulin/IGF-1 , and AMP-activated protein kinase ( AMPK ) signaling pathways have emerged as playing a critical role in aging [reviewed in [7]] . Several studies demonstrate that the levels of specific amino acids effectively influence lifespan by affecting these pathways . For example , the branched-chain amino acids valine , leucine , and isoleucine when administered to C . elegans can function as signaling metabolites that mediate a mTOR-dependent neuronal-endocrine signal that in turn promotes a longer lifespan [8] . Moreover , inhibition of threonine and tryptophan degradation also contributes to lifespan extension by enhancing protein homeostasis in C . elegans [9 , 10] . Additionally , restriction of methionine extends the lifespan of flies in a mTOR-dependent manner [11] . However , supplementation of other amino acids such as methionine , serine , glycine , histidine , arginine , and lysine have been shown to promote lifespan in C . elegans by mechanisms that are to date not known [12] . In addition to the mTOR signaling pathway , alterations in one-carbon metabolism involving the folate and methionine cycles couple amino acid metabolism to the regulation of human health and disease [13] . Glycine , as one of the input amino acids that feeds into one-carbon metabolism , provides a single carbon unit to the folate cycle to yield a variety of one-carbon bound tetrahydrofolates ( THFs ) [14] . These function as coenzymes in methylation reactions including the production of methionine through methionine synthase ( METR-1 in C . elegans ) as well as the universal methyl donor , S-adenosylmethionine ( SAMe ) through S-adenosyl methionine synthetase ( SAMS-1 in C . elegans ) [14] . These output metabolites of one-carbon metabolism support a range of biological functions [14] . In C . elegans , mutations in the metabolic gene sams-1 and the levels of SAMe and S-adenosylhomocysteine ( SAH ) have been implicated in the regulation of aging [15 , 16] . Although the underlying mechanism of how SAMe/SAH status influences aging needs further investigation , studies in vivo have provided evidences that the level of SAMe couples with the trimethylation status of lysine 4 on histone H3 ( H3K4me3 ) and affects gene regulation [17] . Another study in mouse pluripotent stem cells demonstrates that threonine catabolism contributes one carbon to SAMe synthesis and histone methylation through glycine cleavage pathway [18] . Of particular note , several histone methyl-transferases and de-methyltransferases in C . elegans have been identified as longevity regulators [19–21] . Taken together , these studies all suggest that altering one-carbon metabolism is a mechanism that controls the aging process . We recently showed that glycine accumulates with age in a large scale metabolomics study profiling levels of fatty acids , amino acids , and phospholipids across the lifespan of C . elegans , [22] . Another study in human fibroblasts suggested that epigenetic suppression of two nuclear-coded genes , glycine C-acetyltransferase ( GCAT ) and serine hydroxymethyltransferase 2 ( SHMT2 ) which are both involved in glycine synthesis in mitochondria , was partly responsible for aging-associated mitochondrial respiration defects [23] . This study went on to report that glycine treatment rejuvenated the respiration capacity of fibroblasts derived from elderly individuals [23] . However , to date the role of glycine has not been systematically defined in animal models of longevity . In this study , we build upon our previous observations that suggest glycine accumulation in aging animals may play a unique and as-of-yet unexplored role in the regulation of eukaryote lifespan .
We previously measured amino acid levels throughout the life of C . elegans , including four larval phases ( L1-L4 ) and ten days of adulthood from young worms to aged ones ( days 1–10 ) [22] . We reported that the concentrations of most amino acids peaked at the later larval stage or early adult phase and then began declining at different adult stages , reaching low levels by the latest stages of the animals’ life [22] . One stark exception was glycine , which continued to accumulate in aged worms [22] . To determine if the accumulation of glycine with age is due to increased synthesis or reduced degradation , we measured the expression levels of genes directly involved in glycine metabolism in worms collected at different ages ( Fig 1A ) . Specifically , we observed that the expression levels of most genes in glycine degradation and consumption pathways including glycine decarboxylase ( gldc-1 ) , glycine cleavage system H protein ( gcsh-1 ) , phosphoribosylamine-glycine ligase ( F38B6 . 4 ) , and D-amino acid oxidase ( daao-1 ) were dramatically lower at day 9 of adulthood ( D9 ) ( Fig 1B ) . In contrast , the expression levels of most genes involved in glycine synthesis including threonine aldolase ( R102 . 4 ) , serine hydroxymethyltransferase ( mel-32 ) , alanine-glyoxylate aminotransferase 2 ( T09B4 . 8 ) , and alanine-glyoxylate amino transferase ( agxt-1 ) remained unchanged in aged worms ( Fig 1C ) . These data suggest that the accumulation of glycine observed in aged worms is predominantly due to a reduction in the expression of genes required for its degradation . To gain a better understanding of the significance of glycine accumulation in aged animals , we next asked whether this phenomenon is prevalent among long-lived worms such as daf-2 ( e1370 ) and eat-2 ( ad465 ) , the C . elegans models of impaired insulin signalling [2] and dietary restriction [24] , respectively . To characterize the changes in the levels of glycine during aging in the daf-2 ( e1370 ) and eat-2 ( ad465 ) mutant lines , we measured glycine levels in long-lived worms at young and old stages , specifically L3 ( larval stage 3 ) and day 10 ( D10 ) of adulthood . Interestingly , the levels of glycine in daf-2 ( e1370 ) and eat-2 ( ad465 ) at D10 were significantly increased relative to both their levels at L3 ( S1 Fig ) . These results suggest that there might be a generic regulatory mechanism mediating the level of glycine during aging in both wild type and long-lived C . elegans . To confirm the metabolic branch points of glycine metabolism in the control of glycine levels in C . elegans , we subjected worms to RNAi against the genes in glycine metabolism ( Fig 1A ) at the time of hatching , and then measured the level of glycine in D1 worms . Worms treated with RNAi against the genes in glycine synthesis pathways showed no effect on the levels of glycine ( Fig 1D ) . This is perhaps because glycine from bacteria may compensate for the reduction of glycine synthesis in worms . Interestingly , a notable exception to this pattern was found for the knockdown of mel-32 , encoding a worm homologue of mammalian SHMT1 and SHMT2 , which acts to interconvert serine and glycine in one-carbon pathway ( Fig 1A ) [25] . mel-32 RNAi led to a strong increase in the level of glycine ( Fig 1D ) and a concomitant slight decrease in the level of serine ( S2 Fig ) . Thus , these results indicate that MEL-32 is prone to synthesize serine from glycine in C . elegans ( Fig 1D ) . Likewise , RNAi of T25B9 . 1 also led to a subtle increase of glycine in worms , suggesting that T25B9 . 1 tends to degrade glycine in C . elegans ( Fig 1D ) . RNAi knockdown of genes in glycine catabolic pathways including daao-1 , gss-1 , gcsh-1 , glycine cleavage system T-protein ( gcst-1 ) , and dihydrolipoamide dehydrogenase ( dld-1 ) , all significantly increased endogenous glycine compared to control ( Fig 1E ) . Surprisingly , RNAi of phosphoribosylformylglycinamidine synthase ( pfas-1 ) , which is thought to block glycine being used in purine synthesis , was found to lower the level of glycine ( Fig 1E ) . This implies that there are complex metabolic consequences from the perturbation of de novo purine synthesis . Collectively , these data suggest that the majority of glycine in C . elegans is influenced by two metabolic branches , namely one-carbon metabolism via glycine cleavage complex and serine synthesis via MEL-32 . We next verified the effects of glycine on lifespan by administering various concentrations of glycine to worms . To avoid influences of glycine on bacterial metabolism and vice versa , we killed E . coli OP50 with a combination of ultraviolet ( UV ) -irradiation and antibiotic ( carbenicillin ) supplementation . In line with a previously reported observation [26] , worms being fed UV- and carbenicillin-killed E . coli OP50 ( referred to hereafter as “killed bacteria” ) live significantly longer compared to those being fed live E . coli OP50 ( S3 Fig ) . Therefore , to confirm if glycine still accumulates in aged worms after switching to a killed bacteria diet , we again quantified amino acids levels in worms at different stages of C . elegans lifespan including L3 ( larval stage 3 ) , day 1 ( D1 ) , day 3 ( D3 ) , day 6 ( D6 ) and day 9 ( D9 ) of adulthood . We found that most amino acids remained unchanged from L3 to D9 , including valine , tryptophan , lysine , isoleucine , glutamine , asparagine , aspartate , arginine , serine , and proline ( S4A Fig ) . Some amino acids change with age such as leucine , methionine , ornithine , and glutamate ( S4A Fig ) . The levels of these either peak at the L3 stage or at D3 , then decrease with age ( S4A Fig ) . Interestingly , the level of tyrosine peaked at both L3 and D9 , and the level of alanine peaked at D3 , then maintained stable from D3 to D9 ( S4A Fig ) . Although the levels of some of these amino acids in worms fed killed bacteria were more stable with age compared to those in worms fed live OP50 [22] , we consistently found that the levels of leucine , methionine , ornithine , and glutamate decreased and the level of glycine increased in aged worms ( Fig 2A and S4A Fig ) . The results suggest that the changes of these amino acids during aging are robust phenotypes that are independent of the worms being fed live or killed bacteria . On killed bacteria we tested how a range of glycine concentrations from 5 μM to 10 mM affects the lifespan of C . elegans . We observed a significant increase in median lifespan at concentrations of 5 μM , 50 μM , and 500 μM of dietary glycine as compared to untreated controls , with a 7 . 7% ( p < 0 . 0001 ) , 19 . 2% ( p < 0 . 0001 ) , and 19 . 2% ( p < 0 . 0001 ) extension observed , respectively ( Fig 2B ) . Higher concentrations , however , including 5 mM and 10 mM , failed to extend worm lifespan suggesting a dose response where only low concentrations of glycine between 5–500 μM are beneficial to lifespan ( Fig 2B , S4B Fig ) . Our findings are in agreement with previous studies suggesting a dose-effect of amino acids on worm lifespan [12] . Next , we measured amino acid levels in C . elegans at D1 of the adult stage to investigate how glycine supplementations at different doses affect glycine levels in vivo . We did not detect obvious changes of glycine abundance itself or of the other amino acids at any concentration of supplementations such as serine and threonine , the substrates for glycine synthesis ( S5A–S5E Fig ) . These results suggest that in worms , glycine immediately fuels metabolic pathways , likely through glycine cleavage complex and MEL-32 ( Fig 1D and 1E ) . Moreover , the results suggest that the levels of amino acids are tightly controlled in the early adult stage when glycine metabolic genes are actively expressed ( Fig 1B and 1C ) . To test whether a specific timing is required for the beneficial effect of glycine on C . elegans lifespan , we administered glycine to worms at different times in the animal’s life . These times included ( a ) the development phase from the time of hatching to L4 , ( b ) the early adult period from D1 to D3 , ( c ) the adulthood starting from D1 until death , and ( d ) during the entire life ( Fig 2C ) . Intriguingly , the longevity-promotion function of glycine was observed to act exclusively during adulthood , especially the early adult phase , as supplementation from D1 to D3 was sufficient to prolong lifespan by 19 . 0% ( p < 0 . 0001 ) ( Fig 2D ) . In contrast , glycine supplementation during development did not exert a beneficial effect on lifespan ( Fig 2D ) . These data suggest that the first three days of adulthood are important for glycine to confer its longevity effects in C . elegans . RNAi knockdown of mel-32 or the components in glycine cleavage complex ( e . g . gcst-1 ) resulted in pronounced increases of the endogenous levels of glycine in C . elegans ( Fig 1D and 1E ) . As such , we wanted to determine whether such increase by blocking two distinct metabolic branches also impacts lifespan in C . elegans . We hence assayed the lifespan of animals fed RNAi targeting mel-32 and gcst-1 from the time of hatching . Intriguingly , we found that while RNAi of gcst-1 increased the endogenous level of glycine 6 . 9-fold ( Fig 1E ) , it failed to increase lifespan in C . elegans ( Fig 2E ) . In contrast , mel-32 RNAi not only increased the endogenous level of glycine robustly ( 6 . 7-fold ) ( Fig 1D ) , but also improved the survival of wild-type N2 C . elegans significantly ( Fig 2F ) . Thus , these results suggest that elevated endogenous or exogenous glycine levels drive lifespan extension , and that an intact glycine cleavage complex is likely required for this extension . As glycine is an important one-carbon donor and an essential substrate for de novo purine synthesis , we next sought to understand how one-carbon and purine metabolism are influenced by aging . We therefore quantified the expression levels of genes involved in one-carbon metabolism ( Fig 3A ) at five developmental distinct time points ( L3 , D1 , D3 , D6 , and D9 ) in the worm lifespan . We found that with the exception of tyms-1 and dhfr-1 ( thymidylate synthetase and dihydrofolate reductase ) which were upregulated in aged worms , the expression of genes participating in transferring the one-carbon moiety of glycine to form SAMe including gcst-1 ( glycine cleavage system T-protein ) , dld-1 ( dihydrolipoamide dehydrogenase ) , gcsh-1 ( glycine cleavage system H-protein ) , mthf-1 ( methylene tetrahydrofolate reductase ) , metr-1 ( methionine synthase ) , and sams-1 ( S-adenosyl methionine synthetase ) , dropped dramatically in aged animals ( Fig 3B ) [27] . Furthermore , the de novo purine synthesis genes atic-1 ( 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase ) ( Fig 3B ) and F38B6 . 4 ( Fig 1C ) were markedly reduced in aged worms compared to their expression levels in worms at D1 . Together these data suggest a downregulation of both one-carbon and purine metabolism during aging in C . elegans . To resolve in greater detail how glycine supplementation counteracts the age-related changes in one-carbon and purine metabolic genes , we performed next generation RNA-sequencing on D1 worms which were fed control diet ( UV-killed E . coli OP50 ) and 500 μM glycine-supplemented diet from the time of hatching , respectively . In contrast to the changes occurring with age , glycine induced a marked increase in the expression of genes in purine metabolic pathways ( Fig 3C ) . Concomitantly , several genes in one-carbon metabolism ( indicated in red in S6 Fig ) were differentially expressed , including upregulation of atic-1 , F38B6 . 4 , dhfr-1 , tyms-1 , mel-32 , and gcsh-1 , and downregulation of sams-1 , mthf-1 , and gldc-1 ( S6 Fig ) . These results suggest that the expression of purine metabolism genes is subjected to an overall upregulation by exogenous supplementation of dietary glycine , while genes in one-carbon metabolism are under complex regulations upon glycine supplementation . Having determined one of the effects of glycine on worm lifespan and its ability to partly counteract age-related declines in gene expressions in one-carbon and purine metabolic pathways , we next aimed to investigate whether similar gene regulatory events are also present in long-lived mutant worms . To test this , we turned to microarray datasets from three long-lived worm models , daf-2 ( e1370 ) and eat-2 ( ad465 ) [28] or mrps-5 RNAi worms ( reported here ) . We specifically looked at glycine-associated metabolic pathways in the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) database including ‘glycine serine and threonine metabolism’ , ‘one carbon pool by folate’ , ‘cysteine and methionine metabolism’ , and ‘purine metabolism’ . Strikingly , although distinct longevity pathways are known to be active in these long-lived worms [2 , 29 , 30] , all these longevity worm models consistently showed a transcriptional activation of glycine metabolism , folate-dependent one-carbon metabolism , and methionine metabolism ( S7A–S7C Fig ) . This suggests that transcriptional activation of glycine and one-carbon metabolism is a prominent signature of longevity shared by these long-lived worms . In contrast , overall purine metabolism including de novo synthesis , the salvage pathways , and purine degradation was only mildly deactivated across the three long-lived strains ( S7A–S7C Fig ) . To identify prominent transcriptional features in the three long-lived strains , we next examined the expression profile of individual genes belonging to “glycine , serine and threonine metabolism” , as well as folate-mediated one-carbon metabolism and purine metabolism . We found that genes involved in glycine anabolism including T09B4 . 8 , agxt-1 , C15B12 . 1 , R102 . 4 , and T25B9 . 1 were upregulated in daf-2 ( e1370 ) , eat-2 ( ad465 ) [28] and mrps-5 RNAi worms ( S7D–S7F Fig ) . Moreover , a concomitant rise in the expression levels of genes in glycine catabolism and de novo purine synthesis occurred , including gldc-1 , gcst-1 , gcsh-1 , F38B6 . 4 , and atic-1 ( S7D–S7I Fig ) , suggesting a stimulation of metabolic activity of glycine-associated processes in these long-lived worms . To query the expression of genes in the production of SAMe in the long-lived worm models , we specifically checked the expression of five homologues of SAMe synthetases in C . elegans from the microarray data including sams-1 , sams-2 , sams-3 , sams-4 , and sams-5 ( S8 Fig ) . Interestingly , the expression of sams-1 , which encodes the enzyme accounting for the majority of overall SAMe production in C . elegans [31] , was significantly upregulated in all these long-lived worms ( S8 Fig ) . Moreover , sams-5 was increased in daf-2 ( e1370 ) and eat-2 ( ad465 ) [28] , while sams-2 , sams-3 , and sams-4 were suppressed in mrps-5 RNAi and daf-2 ( e1370 ) ( S8 Fig ) . Collectively , the data point to elevated methylation activities in these long-lived worms . In contrast to an overall upregulation of purine metabolism genes in response to dietary glycine treatment , all three longevity worm models specifically induced the expression of genes in de novo purine synthesis compared to control , including F38B6 . 4 , F10F2 . 2 ( pfas-1 , phosphoribosylformylglycinamidine synthase ) , B0286 . 3 ( pacs-1 , phosphoribosylaminoimidazole succinocarboxamide synthetase ) , and atic-1 ( S9A–S9C Fig ) . Taken together , our data reveal that transcriptional activations of glycine and glycine-associated pathways , including one-carbon and de novo purine synthesis , are present in three distinct longevity models . To understand the mechanism of glycine-mediated lifespan extension on a more global scale , we returned to our next-generation RNA-sequencing dataset and performed unsupervised Principle Component Analysis ( PCA ) on the individual libraries . We found a clear separation between glycine-treated versus untreated samples ( Fig 4A ) , corresponding to a large difference in gene expression ( Fig 4B ) . Interestingly , more genes were transcriptionally repressed in response to glycine treatment in which 2629 genes were differentially down-regulated , and 983 genes were up-regulated compared to control ( Fig 4B ) . This suggests an inhibition propensity of glycine on gene expression . To probe the processes changed upon glycine supplementation , we performed gene ontology ( GO ) term enrichment analysis on the differentially expressed genes using the Database for Annotation , Visualization and Integrated Discovery ( DAVID ) bioinformatics resource [32] . A larger number of GO terms were found enriched in the downregulated gene list , among which were GO terms for body morphogenesis , growth regulation , post-embryonic development , molting cycle , cuticle development , multicellular organism growth , and positive regulation of growth rate ( Fig 4C ) . These enrichments are all related to growth control , suggesting a decelerating effect of glycine upon development and growth which is a phenomenon known to be associated to longevity [33] . Additionally , unlike some mutations that confer longevity to the soma at the cost of a reduction in fecundity [34] , supplementation of glycine mildly enhanced the expression of genes in reproduction-related biological processes such as the GO terms of vitellogenesis , meiosis cell cycle , and gamete generation ( Fig 4D ) . While we did not observe clear differences in the number or the size of embryos , the progenies from glycine- and serine-supplemented worms seem to be healthy and normal . Overall , with the observations of the lifespan extending effects of glycine , these data suggest that the beneficial role of glycine slows down pathways that are traditionally ameliorated in healthy aging models . MEL-32 and glycine cleavage complex are major gatekeepers responsible for the flux of glycine into metabolic pathways ( Fig 1A ) . Particularly , although suppression of mel-32 or the components in glycine cleavage complex in C . elegans dramatically elevated the level of endogenous glycine ( Fig 1D and 1E ) , only mel-32 RNAi showed a potent lifespan extension effect on worms likely by favoring the flux of glycine into one-carbon metabolism ( Fig 2E and 2F ) . Additionally , by fueling the one-carbon metabolic network through glycine cleavage complex , glycine contributes to the synthesis of SAMe ( Fig 3A ) , the availability of which has been implicated in the regulation of histone methylation patterns and subsequently gene expressions [35] . Collectively , this led us to hypothesize that the methionine cycle may be required for the lifespan-extending effect of glycine . To test if the methionine cycle is necessary for the longevity effect of glycine , we performed lifespan analyses with the methionine cycle-deficient mutants metr-1 ( ok521 ) and sams-1 ( ok3033 ) . In these mutants , 500 μM glycine failed to promote lifespan ( Fig 5A–5C ) , demonstrating that the effects of glycine on worm longevity depend on the methionine cycle . Serine is another important one-carbon donor and the major precursor for glycine synthesis in vivo [14] . Thus , serine and glycine are closely related to each other in one-carbon metabolism . Given their similarities , we next investigated whether serine can also exert beneficial effects on worm lifespan . Serine supplementation at a concentration from 1 mM to 10 mM has been shown previously to extend worm lifespan [12] . We therefore administered 5 mM serine to worms and measured lifespan . Similar to glycine , we confirmed that serine prolonged the lifespan of worms ( +20 . 8% in median lifespan ) ( Fig 6A ) . Given that the adulthood is important for glycine-mediated lifespan extension , we investigated whether serine increases lifespan in the same fashion as glycine . Similarly , we treated worms with serine at different times in worm’s lifespan including ( a ) developmental stage , ( b ) the beginning of adulthood from D1 to D3 , ( c ) adulthood from D1 until death , and ( d ) during the whole lifetime . Similar to the effects of glycine on lifespan , serine treatment from D1 to D3 is sufficient to recapitulate the beneficial effects on lifespan as did the treatment throughout the entire life or during adulthood only ( Fig 6B ) . In contrast , supplementation during the developmental stage failed to increase lifespan ( Fig 6B ) . This further implies that serine acts on the same downstream longevity signalling pathways to influence aging as does glycine . We further investigated whether the anti-aging effects of serine also rely on the methionine cycle . In agreement with the results observed with glycine supplementation , the lifespan extending effect of serine was also ablated by mutations of metr-1 and sams-1 ( Fig 6C and 6D ) . These results further suggest that serine and glycine prolong C . elegans lifespan via a similar mechanism . To determine the common regulators in both glycine and serine-mediated longevity , we performed RNA-sequencing on serine-supplemented worms . As expected , PCA analysis showed a clear separation between worms treated with either of the amino acids when compared to non-treated worms , and a strong similarity between glycine- and serine-treated worm groups ( Fig 6E ) . Statistical analysis found one significantly differentially expressed gene , F38B6 . 4 ( S10A Fig ) , an enzyme that consumes glycine for purine synthesis . In addition , visualizing the data as a volcano plot showed again a greater number of genes repressed by serine treatment ( 2865 downregulated genes vs 973 upregulated genes ) , in line with the same gene expression suppression pattern of glycine-supplemented worms ( S10B Fig , Fig 4B ) . Likewise , we found a strong overlap between glycine- and serine-treated worms when looking at the up- and downregulated genes , as shown in the Venn diagrams where 82 . 8% ( 2335 ) of the downregulated and 72 . 3% ( 804 ) of the upregulated genes are shared ( Fig 6E–6G ) . Taken together , we suggest a model whereby both glycine and serine supplementation stimulate longevity in a methionine cycle-dependent fashion and through common signaling pathways . Moreover , this seems to be dependent on the expression of sams-1 and metr-1 . This model is illustrated in Fig 6H .
Our work sheds light onto the means by which the amino acid glycine can increase C . elegans lifespan when supplemented to the diet . Using a metabolomics approach , we found that glycine steadily and significantly accumulates in aging C . elegans [22] . Furthermore , we demonstrated that this accumulation is mainly coupled to a decrease in the expression levels of genes in glycine cleavage pathway which control the majority of glycine breakdown in C . elegans . We found that mel-32 RNAi causes a marked rise in the endogenous level of glycine which in turn extends lifespan . Moreover , supplementing dietary glycine extends lifespan at concentrations between 5–500 μM , while mutations in methionine synthase [metr-1 ( ok521 ) ] and S-adenosyl methionine synthetase [sams-1 ( ok3033 ) ] , two enzymes involved in methionine cycle , can fully abrogate this lifespan extension . Furthermore , we found that serine , another amino acid that feeds into one-carbon metabolism , shows similar transcriptional changes , metr-1 and sams-1 dependency , and lifespan extension upon dietary supplementation as does glycine . These results confirm an important role for the methionine cycle in the longevity effects of glycine . Our work reveals a timing requirement of glycine supplementation in the promotion of longevity in C . elegans . Specifically , the first three days of adulthood ( from D1 to D3 ) are crucial for glycine to confer the benefits on longevity . Given that the DAF-2 pathway also acts exclusively during adulthood and throughout the reproductive period to affect lifespan in C . elegans [36] , further investigation is warranted to see if this classical longevity pathway is fully or only partially required for the beneficial effects of glycine . Furthermore , the first three days of adulthood coincide with the reproductive period of worms , raising an interesting question for future studies about the crosstalk between the reproductive system and glycine-activated longevity pathways . Our work identified a counterintuitive biological phenomenon , whereby glycine accumulation was observed during the aging process in worms while supplementation of glycine was nonetheless able to prolong worm lifespan . However , it is not uncommon for changes that occur with age to also benefit lifespan when artificially induced . For example , suppression of IGF1 signaling may extend lifespan in many model organisms [2 , 37 , 38] , while IGF1 levels themselves have been observed to decline with age [39 , 40] . Moreover , methionine restriction is beneficial to lifespan in a variety of model organisms [11 , 41] , while methionine abundance in vivo has been observed to decline with age [observed in this study ( S4A Fig ) and [22]] . Similar to these phenomena , glycine supplementation may activate protective cellular pathways that promote longevity when exogenously applied , while a natural glycine accumulation with age may reflect the organism’s need to upregulate these same cytoprotective pathways to deal with the damage and detrimental changes occurring during aging . Studies in rodents have suggested glycine supplementation to have pro-longevity effects [42 , 43] , anti-inflammatory effects [44] , to be cytoprotective [45] , and to ameliorate metabolic disorders [46] . In humans , glycine supplementation in patients with metabolic disorders has a protective effect against oxidative stress and inflammation [47–49] . In line with these observations in mammalian systems , our data demonstrated that glycine promotes longevity in C . elegans . Furthermore , we show this benefit to occur in a metr-1 and sams-1 dependent manner , implicating the methionine cycle in longevity regulation . Finally , we show glycine supplementation induces widespread suppression of genes including many that are hallmarks of the aging process . Taken together , our findings suggest that dietary glycine is an effective strategy to increase lifespan and warrant further investigation for life- and healthspan studies in humans .
C . elegans strains N2 Bristol , RB2204 sams-1 ( ok3033 ) X , RB755 metr-1 ( ok521 ) II , eat-2 ( ad465 ) , and daf-2 ( e1370 ) were obtained from the Caenorhabditis Genetics Center ( CGC , University of Minnesota ) . Nematodes were grown and maintained on Nematode growth media ( NGM ) agar plates at 20°C as previously described [50] . E . coli OP50 and E . coli HT115 ( DE3 ) with the empty vector L4440 was obtained from the CGC . Bacterial feeding RNAi experiments were carried out as described [51] . RNAi E . coli feeding clones used were mrps-5 ( E02A10 . 1 ) , T09B4 . 8 , agxt-1 ( T14D7 . 1 ) , C15B12 . 1 , R102 . 4 , T25B9 . 1 , gss-1 ( M176 . 2 ) , gcsh-1 ( D1025 . 2 ) , gcst-1 ( F25B4 . 1 ) , pfas-1 ( F10F2 . 2 ) , and dld-1 ( LLC1 . 3 ) . Clones of agxt-1 ( T14D7 . 1 ) , C15B12 . 1 , R102 . 4 , T25B9 . 1 , gss-1 ( M176 . 2 ) , gcsh-1 ( D1025 . 2 ) , gcst-1 ( F25B4 . 1 ) , and pfas-1 ( F10F2 . 2 ) were derived from the Ahringer RNAi library [52]; Clones of mrps-5 ( E02A10 . 1 ) , T09B4 . 8 , and dld-1 ( LLC1 . 3 ) were derived from the Vidal RNAi library [53]; Worms were fed RNAi bacteria from the time of hatching unless otherwise indicated . Glycine and serine were purchased from Merck Millipore ( no . 8 . 1603 . 0250 ) and Sigma ( no . S4500 ) , respectively . A stock of concentration of 1 M glycine and serine was made by dissolving glycine and serine in water . The pH of glycine and serine stock solution was adjusted to 6 . 0–6 . 5 with sodium hydroxide and then sterilized with 0 . 45 μm Millipore filter . The concentrations of 5 μM , 50 μM , 500 μM , 5 mM and 10 mM glycine , and 5 mM serine were used in the present study . Overnight cultures of E . coli OP50 were seeded on standard NGM plates containing carbenicillin ( 25 μg ml-1 ) to prevent the bacterial growth . After drying overnight at room temperature , the bacterial lawn was irradiated with 254 nm UV light using a Stratalinker UV Crosslinker model 1800 ( Stratagene , USA ) at 999900 μJ/cm2 for 5 min . A sample of UV-exposed E . coli OP50 was collected and cultured in LB medium overnight at 37°C to confirm the bacteria were completely killed . Plates seeded with UV-killed bacteria were stored in 4°C and used within 1 week after seeding . Lifespan experiments of amino acids supplementation were performed at 20°C without fluorouracil as described with the exception that UV-killed E . coli OP50 was used [54] . Briefly , for treatment throughout lifespan , worms were cultured on glycine- or serine- supplemented plates from the time of hatching until death; For the treatment during larval development only , worms were cultured on glycine- or serine- supplemented plates from egg until L4 , and then transferred onto control plates until death; For the treatment from D1 to D3 , worms were cultured on glycine- or serine- supplemented plates from D1 ( one day after L4 ) to D3 , and transferred on control plates . For amino acid treatment during adulthood only , worms were cultured on glycine or serine supplemented plates from D1 until death . During the reproductive period ( ≈ day 1–8 ) , worms were transferred to fresh plates every other day to separate them from their progeny . For RNAi lifespan experiments , worms were cultured on NGM plates containing 2mM IPTG and seeded with HT115 ( DE3 ) bacteria transformed with either pL4440 empty vector or the indicated RNAi construct from the time of hatching . Worms were transferred to fresh plates containing 10 μM fluorouracil at L4 larval stage to prevent egg laying . 100–150 worms per condition were used for every lifespan . Survival was scored every other day throughout the lifespan and a worm was considered as dead when they did not respond to three taps . Worms that were missing , displaying internal egg hatching , losing vulva integrity , and burrowing into NGM agar were censored . Statistical analyses of lifespan were calculated by Log-rank ( Mantel-Cox ) tests on the Kaplan-Meier curves in GraphPad Prism . Amino acids were extracted and analyzed as described before [22] and each experiment was performed in three biological replicates . About amino acids profiles change with age in wild type N2 related to Fig 2A and S4A Fig , worms were cultured on UV-killed E . coli OP50 and collected at the desired stage ( L3 , D1 , D3 , D6 , and D9 ) for amino acids extraction; About amino acids profiles change with age in wild type N2 , daf-2 ( e1370 ) , and eat-2 ( ad465 ) related to S1 Fig , worms were cultured on alive E . coli OP50 and collected at L3 and D10 for amino acids extraction; About glycine supplementation experiments related to S5A–S5E Fig , worms were fed UV-killed E . coli OP50 and supplemented with glycine at the desired concentration ( 5 μM , 50 μM , 500 μM , 5 mM , and 10 mM ) or with water control from the time of hatching . Then , D1 worms were harvested for amino acids extraction; About RNAi experiments related to Fig 1D and 1E , and S2 Fig , worms were cultured on NGM plates containing 2mM IPTG and seeded with HT115 ( DE3 ) bacteria transformed with either pL4440 empty vector or the indicated RNAi construct from the time of hatching . Then , D1 worms were harvested for amino acids extraction . Around 1500 synchronized worms at the desired stage were collected , freeze-dried and stored at room temperature until use . Worm lysates were obtained by homogenization and subsequent tip sonication . Amino acids were extracted from worm lysate containing 50 μg protein and measure by UPLC-MS/MS analysis . In Fig 1B and 1C , worms were cultured on live E . coli OP50 and harvested at D1 , D4 , and D9 for mRNA extraction; In Fig 3B , worms were cultured on UV-killed E . coli OP50 and collected at L3 , D1 , D3 , D6 , and D9 for mRNA extraction . Approximately 500 worms were collected in three biological replicates per condition at the desired stage . Total RNA was isolated according to the manufacturer’s protocol . Briefly , samples were homogenized in TRIzol ( Invitrogen ) with a 5 mm steel metal bead and shaken using a TissueLyser II ( Qiagen ) for 5 min at a frequency of 30 times/sec . RNA was quantified with a NanoDrop 2000 spectrophotometer ( Thermo Scientific ) and stored at -80°C until use . Genomic DNA was eliminated , and cDNA was synthesized using the QuantiTect Reverse Transcription kit ( QIAGEN ) . The qPCR reaction was carried out in 8 μL with a primer concentration of 1 μM and SYBR Green Master mix ( Roche ) in a Roche LightCycler 480 system . In all analyses , the geometric mean of two reference genes , eif-3 . C and F35G12 . 2 , was used for normalization and the oligonucleotides used for PCR are listed in S2 Table . daf-2 ( e1370 ) and eat-2 ( ad465 ) worms were fed HT115 E . coli expressing empty vector from the time of hatching . Wild type N2 worms were fed HT115 E . coli expressing dsRNA against mrps-5 from the larval stage 4 of parental worms , and this exposure was continued in the first filial population ( F1 ) . mrps-5 RNAi-treated F1 worms were used for total RNA extraction . Microarray experiment was performed as described [28] . Approximately 500 young adult worms were collected in four replicates per condition and total RNA was extracted as described above . RNA quality and quantity were assessed after DNase clean-up using a 2100 Bioanalyzer ( Agilent Technologies ) . RNA was amplified and labeled using a Low Input QuickAmp Labeling Kit ( Agilent Technologies ) and hybridized using the Agilent Gene Expression Hybridization Kit ( Agilent Technologies ) . An ArrayXS-068300 with WormBase WS241 genome build ( OakLabs ) was used and fluorescence signals were detected by the SureScan microarray Scanner ( Agilent Technologies ) . Data of all samples were quantile normalized using the ranked median quantiles as described previously [55] . Worms were cultured on UV-killed E . coli OP50 upon 500 μM glycine treatment or 5 mM serine treatment from the time of hatching . Approximately 500 worms at D1 were collected in quadruplicates per condition for total RNA extraction as described above . Genomic DNA residues were eliminated with RNase-Free DNase ( Qiagen ) , followed with the cleaning up with the RNeasy MinElute Cleanup Kit ( Qiagen ) . Samples were sent to GenomeScan B . V . ( Leiden , The Netherlands ) for RNA library preparation and sequencing at a 20 million read-depth ( see methods below ) . Samples were processed for Illumina using the NEBNext Ultra Directional RNA Library Prep Kit ( NEB #E7420 ) according to manufacturer’s description . Briefly , rRNA was depleted using the rRNA depletion kit ( NEB# E6310 ) . A cDNA synthesis was performed in order to ligate with the sequencing adapters . Quality and yield after sample preparation was measured with the Fragment Analyzer . Size of the resulting products was consistent with the expected size distribution ( a broad peak between 300–500 bp ) . Clustering and DNA sequencing using the Illumina cBot and HiSeq 4000 was performed according to manufacturer's protocol with a concentration of 3 . 0 nM of DNA . HiSeq control software HCS v3 . 4 . 0 , image analysis , base calling , and quality check was performed with the Illumina data analysis pipeline RTA v2 . 7 . 7 asnd Bcl2fastq v2 . 17 . Reads were subjected to quality control FastQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc ) , trimmed using Trimmomatic v0 . 32 [56] and aligned to the C . elegans genome obtained from Ensembl , wbcel235 . v91 using HISAT2 v2 . 0 . 4 [57] . Counts were obtained using HTSeq ( v0 . 6 . 1 , default parameters ) [58] using the corresponding GTF taking into account the directions of the reads . Statistical analyses were performed using the edgeR [59] and limma/voom [60] R packages . All genes with no counts in any of the samples were removed whilst genes with more than 2 reads in at least 4 of the samples were kept . Count data were transformed to log2-counts per million ( logCPM ) , normalized by applying the trimmed mean of M-values method ( Robinson et al . , 2010 ) and precision weighted using voom [61] . Differential expression was assessed using an empirical Bayes moderated t-test within limma’s linear model framework including the precision weights estimated by voom [61] . Resulting p-values were corrected for multiple testing using the Benjamini-Hochberg false discovery rate . Genes were re-annotated using biomaRt using the Ensembl genome databases ( v91 ) . RNA-seq samples were compared using principal component analysis ( PCA ) and Partial least squares discriminant analysis ( PLS-DA ) using mixomics [62] . Heatmaps , venn diagrams , and volcano plots were generated using ggplot2 [63] in combination with ggrepel ( https://CRAN . R-project . org/package=ggrepel ) , and venneuler ( https://CRAN . R-project . org ) . Data processing and visualization was performed using R v3 . 4 . 3 and Bioconductor v3 . 6 . Gene ontology ( GO ) analyses were conducted using DAVID bioinformatics resource [32] . Genes found to be significantly up- or downregulated with an adjusted p-value < 0 . 05 and | log2 ( fold change ) | > 0 . 5 were subjected to functional annotation clustering . To retrieve significantly enriched GO terms , enrichment threshold ( EASE score ) was set as 0 . 05 for all analyses and the category of each annotation cluster generated by David was curated manually . Heat maps of gene expression profile in S7–S9 Figs were plotted using “R2” , Genomics analysis and Visualization platform ( http://r2 . amc . nl ) . Gene set map analyses were performed on “R2” with defined gene sets from the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathways ( http://www . genome . jp/kegg/ ) [64] . Data were analyzed by two-tailed unpaired Student’s t-test or by one-way ANOVA with Tukey’s post hoc test for multiple comparisons , except for survival curves , which were calculated using the log-rank ( Mantel-Cox ) method . For all experiments , data are shown as mean ± SD and a p-value < 0 . 05 was considered significant .
|
There is a growing number of studies showing that amino acids function as signal metabolites that influence aging and health . Although contemporary -OMICs studies have uncovered various associations between metabolite levels and aging , in many cases the directionality of the relationships is unclear . In a recent metabolomics study , we found that glycine accumulates in aged C . elegans while other amino acids decrease . The present study shows that glycine supplementation increases lifespan and drives a genome-wide inhibition effect on C . elegans gene expression . Glycine as a one-carbon donor fuels the methyl pool of one-carbon metabolism composed of the folate and methionine cycles . We find that the glycine-mediated longevity effect is fully dependent on the methionine cycle , and that all of our observations are conserved with supplementation of the other one-carbon amino acid , serine . These results provide a novel role for glycine as a promoter of longevity and bring new insight into the role of one-carbon amino acids in the regulation of aging that may ultimately be beneficial for humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"invertebrates",
"rna",
"interference",
"chemical",
"compounds",
"aliphatic",
"amino",
"acids",
"caenorhabditis",
"organic",
"compounds",
"animals",
"animal",
"models",
"caenorhabditis",
"elegans",
"model",
"organisms",
"serine",
"experimental",
"organism",
"systems",
"amino",
"acids",
"epigenetics",
"research",
"and",
"analysis",
"methods",
"amino",
"acid",
"metabolism",
"glycine",
"purine",
"metabolism",
"animal",
"studies",
"proteins",
"genetic",
"interference",
"gene",
"expression",
"chemistry",
"methionine",
"sulfur",
"containing",
"amino",
"acids",
"biochemistry",
"rna",
"eukaryota",
"organic",
"chemistry",
"nucleic",
"acids",
"genetics",
"hydroxyl",
"amino",
"acids",
"biology",
"and",
"life",
"sciences",
"nematoda",
"physical",
"sciences",
"metabolism",
"organisms"
] |
2019
|
Glycine promotes longevity in Caenorhabditis elegans in a methionine cycle-dependent fashion
|
In neurogenesis , neural cell fate specification is generally triggered by proneural transcription factors . Whilst the role of proneural factors in fate specification is well studied , the link between neural specification and the cellular pathways that ultimately must be activated to construct specialised neurons is usually obscure . High-resolution temporal profiling of gene expression reveals the events downstream of atonal proneural gene function during the development of Drosophila chordotonal ( mechanosensory ) neurons . Among other findings , this reveals the onset of expression of genes required for construction of the ciliary dendrite , a key specialisation of mechanosensory neurons . We determine that atonal activates this cellular differentiation pathway in several ways . Firstly , atonal directly regulates Rfx , a well-known highly conserved ciliogenesis transcriptional regulator . Unexpectedly , differences in Rfx regulation by proneural factors may underlie variations in ciliary dendrite specialisation in different sensory neuronal lineages . In contrast , fd3F encodes a novel forkhead family transcription factor that is exclusively expressed in differentiating chordotonal neurons . fd3F regulates genes required for specialized aspects of chordotonal dendrite physiology . In addition to these intermediate transcriptional regulators , we show that atonal directly regulates a novel gene , dilatory , that is directly associated with ciliogenesis during neuronal differentiation . Our analysis demonstrates how early cell fate specification factors can regulate structural and physiological differentiation of neuronal cell types . It also suggests a model for how subtype differentiation in different neuronal lineages may be regulated by different proneural factors . In addition , it provides a paradigm for how transcriptional regulation may modulate the ciliogenesis pathway to give rise to structurally and functionally specialised ciliary dendrites .
Once an embryonic cell is committed to a particular fate , it is likely that a precisely ordered progression of gene expression is required to coordinate the complex cell biological events that eventually lead to its terminal differentiation . Determining how this progression is regulated is an important step towards understanding how cells acquire specialised morphologies and functions . In the developing nervous system , cell fate commitment is initiated by the activity of proneural basic-helix-loop-helix ( bHLH ) transcription factors [1] . In vertebrates , atonal ( ato ) -related proneural genes are required for neurogenesis in the spinal cord and cortex ( neurogenin ) , cerebellum ( atoh1 ) , and retina ( atoh7 ) [1] . atoh1 is also required for the formation of mechanosensory cells in the inner ear and in skin [2] , [3] . In Drosophila , ato itself specifies the precursors of several specialised sensory neuron types , including photoreceptors and mechanosensory chordotonal ( Ch ) neurons , which mediate hearing and proprioceptive feedback during locomotion [4] . Whilst proneural genes are intensively studied , little is known of how their function leads to specific programs of neuronal differentiation . ato expression in the ectoderm leads to sense organ precursor ( SOP ) specification in a process that is refined by Notch signalling . After commitment , SOPs divide several times asymmetrically before the 4–5 progeny cells interact and terminally differentiate to form the neuron and support cells of the mature Ch sense organ ( Figure 1A–C ) . The function of ato and other proneural factors in SOP fate determination is relatively well studied . Indeed , known proneural target genes are almost all concerned with SOP specification or fate maintenance [5]–[9] . It is not clear , however , how its function as ‘master regulator’ leads to subsequent neural development . Since ato is expressed only transiently during SOP formation , a likely hypothesis is that it initiates a gene regulatory cascade , which eventually regulates differentiation genes . The nature of this cascade and its regulation have not been elucidated . In contrast to the dearth of knowledge of the regulatory cascade , more is known of Ch neuron terminal differentiation itself . Notably , Ch neurons develop a highly structured dendrite based on a modified cilium [10]–[12] . Ciliogenesis is a conserved , highly ordered process involving the coordinated action of hundreds of proteins [13] . In vertebrates , ciliated cells are widespread , both in the PNS ( e . g . photoreceptors , olfactory neurons ) and other adult tissues ( e . g . kidney , lung ) , and developing cells have a primary cilium that is required for signal transduction for a number of paracrine pathways [13] . In contrast , the only ciliated cells in Drosophila are sensory neurons and sperm . As a consequence , genetic analysis of defective sensory neuron differentiation in Drosophila has enabled the discovery and characterisation of a number of ciliogenesis genes [14]–[16] . These include genes required for the specialised transport process known as Intraflagellar Transport ( IFT ) [16] and homologues of genes disrupted in the human ciliopathy , Bardet-Biedl syndrome ( BBS ) . Ciliogenesis is one of the key differentiation events that must be initiated by Drosophila proneural factors . An important question in the regulation of cellular diversity is how core cell biological pathways are modified to give distinct cell types . Cilia perform a wide variety of specialised functions , but it is poorly known how the core ciliogenesis program is modulated in different cell types . The ciliary dendrite of Ch neurons is anatomically and physiologically distinct from those of other Drosophila sensory neurons ( notably the External Sensory ( ES ) neurons ) ( Figure 1A ) [17] , [18] . Ultimately , these subtype-specific differences in the ciliary dendrite must be regulated by the proneural factors , which have well-known neuronal subtype determining properties in both invertebrates and vertebrates [1] . Whilst ato directs the formation of Ch precursors , another proneural gene , scute ( sc ) , performs this function for ES precursors . sc's function is likely to be mediated partly by the homeodomain factor , Cut , [19] but little is known of Cut's molecular function . Apart from the involvement of cut , it is at present entirely unknown how subtype specification by transiently expressed proneural factors is translated into differences in neuronal phenotype , including the modulation of ciliogenesis . In order to bridge the gap between proneural factor function and the activation of genes required for neural terminal differentiation , we used expression profiling to characterise the progression of gene expression during Ch neuron development . A time course in the onset of differentiation gene expression can be discerned . We then show that ato regulates some of these events through a number of intermediate transcriptional regulators . The gene for Regulatory factor X ( Rfx ) , a well-known and highly conserved regulator of aspects of ciliogenesis , is regulated differently by proneural genes in Ch and ES lineages . We propose that this links proneural subtype specification to differences in ciliogenesis . We also identify a novel forkhead-related factor that is required to regulate genes for specialised aspects of Ch neuron function . In addition , we find that some putative differentiation genes are expressed surprisingly early in neural development and that ato may directly regulate at least one such gene .
For expression profiling during Ch development , ato-expressing cells were isolated from timed collections of embryos . ato-expressing cells were marked by GFP expression from an atoGFP reporter gene construct ( atoGFP cells ) . This reporter gene is expressed predominantly in Ch precursors and their progeny but also in other ato-expressing cells including the developing larval eye ( Figure 1D ) . Embryos from timed collections were dissociated and atoGFP cells isolated by FACS ( Figure S1 ) . Such cells were isolated from embryos at three time points corresponding to the first 3 h of neural development ( t1–t3 ) ( Text S1 ) . t1 coincides maximally with ato expression ( and therefore should include direct target genes ) , whereas later time points reflect subsequent post-ato development as the precursors divide leading up to differentiation ( Figure 1C ) . Expression profiling revealed the number of differentially expressed genes in atoGFP+ versus atoGFP− cells ( referred to as ‘ato-correlated genes’ ) to be 330 , 456 , and 411 at t1 , t2 , and t3 , respectively ( ≥1 . 5-fold enriched , ≤1% false discovery rate ( FDR ) ) . Set analysis of genes enriched in atoGFP cells ( ato-correlated genes ) shows a clear time course of expression changes , with 69 , 141 , and 210 genes unique to t1 , t2 , and t3 , respectively ( Figure 1E; Tables S1 , S2 , S3 ) . This suggests an increase in the complexity of gene expression as development proceeds to differentiation . Manual inspection of ato-correlated genes ranked by fold change shows a high representation of known neurogenesis genes ( Figure 2; Tables S1 , S2 , S3 ) . For instance , among the top ranked genes at t1 are spineless , twin of eyeless , cato , couch potato , dachshund , ato , Rfx , senseless , and BarH1 , all of which are associated with aspects of neural development . Gene ontology ( GO ) analysis shows strong enrichment of GO annotation terms related to PNS development across all three time points ( Tables S4 , S5 , S6 , S7; Text S2 ) . There is a clear progression over time in the representation of genes ( Figure 2 ) and relevant GO terms ( Figure 1F; Text S2 ) . Over-representation of GO terms identified the enrichment in developing Ch cells of genes associated with ciliogenesis ( Text S3; Figure 1F ) . Analysis of over-represented protein domains also highlights domains associated with some classes of ciliogenesis gene ( Text S4; Figure S2; Table S8 ) . To characterise the Ch expression of ciliogenesis genes further , we compared the t3 expression data with collections of genes previously linked to ciliogenesis ( Tables 1 , S9 ) . The Drosophila Cilia and Basal Body database ( DCBB ) has been compiled from a number of genetic and proteomic sources to contain genes , or orthologues of genes , implicated in cilia or basal body structure or function [20] . ato-correlated genes at t3 represent 3 . 0% of the genome but include 10 . 1% of DCBB genes—a highly significant over-representation ( p = 3 . 3×10−19 ) ( Table 1 ) . Another study identified potential ciliogenesis genes from comparative genomic analysis of ciliated and non-ciliated organisms [16] . Strikingly , Ch cells at t3 are 8-fold more enriched than expected by chance for genes implicated in this study ( p = 8 . 7×10−23 ) ( Table 1 ) . Moreover , the subgroup of these genes most associated with compartmentalised ciliogenesis are 27-fold enriched at t3 compared with expected ( p = 3 . 5×10−22 ) ( 23/28 genes ) ( Table 1 ) . For many of these genes , our expression data provide the first confirmatory evidence of a potential role in ciliogenesis . Our data also provide new candidate ciliogenesis genes . Since the atoGFP cells will divide to produce both the Ch neurons and their support cells , ato-correlated genes may include support cell genes in addition to neuronal genes . Few such genes are currently known , but several of these are enriched at t3 ( but not earlier ) , including nompA ( scolopale cell ) [21] , α-tubulin 85E ( ligament and cap cells ) [22] , and Sox15 ( cap cell ) [23] . It is striking that our analyses indicate enrichment for genes required for ciliary differentiation , because terminal Ch differentiation has not yet occurred by the embryonic stage represented at t3 ( approximately Stage 12 ) . This suggests that some aspects of differentiation require the activation of specific differentiation genes prior to overt differentiation . Unexpectedly , a proportion of ciliary genes are already expressed even at t1 ( 8 . 6% of all ciliogenesis genes ( 14/175 ) , 21 . 4% of compartmentalised ciliogenesis genes ( 6/28 ) ; Table 1 ) . At t1 the Ch precursor cells have just been specified by ato and have still to undergo two rounds of division before neuronal differentiation occurs . In situ hybridisation confirmed that mRNAs for several ciliogenesis genes are expressed in Ch precursors or in their first division products . This includes genes required for a wide range of cilia components , such as the ciliary rootlet ( CG6129 – homologue of Rootletin ) , the IFT-B complex ( CG15161 – homologue of IFT46 ) , and the IFT-A complex ( Oseg1 – homologue of IFT122; Oseg4 – homologue of WDR35 ) ( Figure 3A–D ) . Most striking , for instance , is unc , which is thought to be involved in basal body maturation [14] . Although reported to be expressed only upon differentiation [14] , we find that unc RNA is already 9 . 9-fold enriched at t1 ( ranked 11th ) , and early expression is confirmed by in situ hybridisation ( Figure 3E ) . Furthermore , UNC protein is also expressed early and is already localised to the centrosomes in Ch precursor cells ( Figure 3F–I ) . Conversely , many known differentiation genes are not differentially expressed even at t3 , supporting the conclusion that general differentiation has not yet occurred . This includes the Ch-specific TRPV-encoding genes , nanchung ( nan ) and inactive ( iav ) , sensory neuron genes like futsch ( MAP1B ) , and several groups of ciliogenesis gene . Therefore , a specific progression of gene expression can be discerned that defines a temporal program for organised ciliogenesis and neuronal differentiation . A precise program of gene activation implies that transcriptional regulation is important for coordinating the cell biological events underlying ciliogenesis , yet little is known of the gene network underlying this . As a first step in exploring the transcriptional regulation of Ch genes , we characterised expression patterns of a sample of ato-correlated genes by in situ hybridisation ( Table S10; sample chosen based on fold change and lack of previous detailed annotation relating to PNS expression pattern ) . At least 90% of genes tested ( n = 43 ) showed expression patterns that overlap ato-expressing cells , and the vast majority of these showed expression in Ch cells ( Figure S3 ) . Moreover , most of these genes showed expression in the neuronal branch of the sensory lineage , rather than in support cells . Given the nature of the profiling ( Ch cells compared with the rest of the embryo ) , we expected expression in ato-dependent cells , but not necessarily restricted to such cells within the nervous system . Indeed , various types of pattern were observed , including those we categorise as pan-neural ( CNS and PNS ) , pan-sensory ( PNS only ) , or Ch-specific . This distribution of patterns is broadly consistent with the view that the related Ch and ES lineages have both shared and unique properties . Unexpectedly , however , a significant proportion of genes show an intermediate ‘Ch-enriched’ pattern , characterised by strong and early onset expression in the Ch lineage but weak and later onset in the ES lineage ( Figure S3 ) . This includes many differentiation and ciliogenesis genes ( including those mentioned above ) that might otherwise have been expected to be required equally in all ciliated sensory lineages ( pan-sensory ) . We suggest therefore that the subtype differences between the two main neuronal lineages with ciliary dendrites ( Ch and ES ) may partly arise from modulation in timing and level of expression of genes required for a common cellular differentiation program . Since ato/sc proneural genes control the acquisition of Ch/ES subtype identity [24] , the modulation of differentiation suggested above must ultimately result from differences in proneural gene function . In order to link the regulation of differentiation to ato function , we carried out profiling of ato-expressing cells from ato mutant embryos at t1 . In such embryos , atoGFP-expressing cells largely fail to become specified as Ch precursors and remain as ectodermal cells . Comparison with the wildtype expression profile yields 50 genes that are ≥2-fold differentially expressed in wild-type atoGFP+ cells at t1 ( compared with the GFP– cells ) but not in mutant atoGFP+ cells ( compared with the GFP− cells from the same embryos ) ( Table S11 ) . Of these , 11 genes also show a ≥2-fold difference between the fold changes observed in wildtype and mutant embryos , which represent good candidates for downstream targets ( Table S12 ) . Three of these encode transcription factors ( Rfx , cato , and fd3F ) . These genes were investigated as candidate intermediate regulatory factors that link proneural function to differentiation . RFX is a well-known , highly conserved regulator of ciliogenesis and is best known as a proven or predicted regulator of many ciliogenesis genes through binding to an X-box motif ( notably those genes associated with IFT-B ) [20] , [25] . Although required for neuronal differentiation , the Rfx gene is already highly expressed in the earliest atoGFP cells ( 9 . 76-fold enriched at t1 , ranked 12th ) , indicating that it may be responsible for early expression onset of a subset of differentiation genes . Consistent with this , a resampling analysis demonstrates that gene lists for all three time-points are highly significantly enriched for the presence of nearby X box motifs ( Figure S4 ) , indicating the likely presence of Rfx target genes . In addition , of the set of 83 genes in the genome that have a conserved perfect X box motif nearby [20] , 21 . 7% are expressed at t3—a 7 . 1-fold greater frequency than expected by chance ( p = 8 . 23×10−10 ) ( Tables 1 , S9 ) . These include ciliogenesis genes for which experimental evidence has been obtained that they are direct Rfx targets ( such as CG15161 , btv , tectonic , CG6129 , CG4525 ) [20] . Although Rfx is required for both Ch and ES neurons , examination of its expression pattern revealed that , like many of its target genes , it shows a Ch-enriched pattern of expression ( Figure 4A–C ) . It is possible , therefore , that variations in Rfx expression may underlie different subtype-specific programs in Ch and ES cells . In turn , this suggests that Rfx may be regulated differently by ATO and SC proteins in these lineages as part of their neuronal subtype-determining function . Therefore , we examined the regulation of Rfx by proneural factors . Embryonic expression analysis confirmed that Ch expression of Rfx overlaps with that of ato ( Figure 4D ) . In contrast , Rfx expression in ES lineages begins later , only after the termination of sc expression ( Figure 4E ) . By reporter gene analysis , we found that Rfx is regulated through separable Ch and ES enhancers ( Figure 4F ) . The Ch enhancer is activated early in Ch development ( RfxA: Figure 4G ) . This enhancer contains an E box motif whose sequence conforms to that previously shown to respond specifically to ATO activation ( EATO ) [7] . This motif binds ATO in vitro ( Figure S5 ) , and when it is mutated , the early phase of expression in Ch cells is abolished ( Figure 4H ) . Conversely , this enhancer is ectopically activated when ato is misexpressed in the ectoderm ( Figure 4I , J ) , but this ectopic activation is abolished when the E box motif is mutated ( unpublished data ) . In contrast to direct activation by ato , the ES enhancer is active only after sc expression is switched off ( RfxB: Figure 4K , L ) , suggesting that sc only indirectly activates Rfx in ES development . However , we note that the ES enhancer does contain two motifs conforming to the known SC binding site ( GCAGSTG ) and so it is possible that SC directly primes the Rfx gene for later expression in ES lineages . Overall , the evidence suggests that Rfx is a direct target of ato but not of sc , supporting the hypothesis that differences in Rfx regulation may be one means by which proneural factors regulate neuronal subtype characteristics . Interestingly , the ato-related bHLH gene , cato , has a Ch-enriched expression pattern like Rfx [26] . Enhancer analysis revealed that cato too has separable Ch and ES enhancers [27] . The former contains an EATO site that is required for Ch expression , and it is ectopically activated upon misexpression of ato . Mutant analysis of cato reveals roles in cell cycle control and SOP fate maintenance but not in terminal differentiation [27] . Nevertheless , the similar regulation of Rfx and cato suggests that differential regulation of shared intermediate regulatory genes in different neuronal subtype lineages may be a common theme underlying subtype specification by ato and sc . The gene for the predicted Forkhead family transcription factor , fd3F ( CG12632 ) , is highly enriched in atoGFP cells ( 19 . 7-fold at t3; ranked 3rd ) . In contrast to Rfx , fd3F is expressed exclusively in Ch neurons from the precursor stage through to differentiation ( Figure 5A–C ) , suggesting a specific role in Ch neuron specialisation . Its highly specific Ch expression pattern suggests that fd3F may be a direct target of ato . Reporter gene analysis identified an intronic Ch enhancer of fd3F that contains three ato-type E box motifs ( Figures 5D–F , 6I ) . However , reporter expression does not appear strongly altered when these sites are mutated ( unpublished data ) , suggesting that regulation may occur via other E box motifs . At present , therefore , although fd3F is a target of ato , we cannot conclude whether regulation is direct or indirect . To ascertain fd3F's function , we generated a mutation by P-element imprecise excision ( FGN , in prep . ) . Mutant larvae and adult flies exhibit locomotion defects similar to those manifested in ato mutants ( Figure 5G , H; FGN and APJ , in prep . ) [4] , [28] . Given the expression pattern of fd3F , these defects can be attributed to defective Ch neurons , which are required for proprioceptive feedback during locomotion . In ato mutants , such defective behaviour results from loss of Ch neurons . Immunohistochemical analysis suggests , however , that Ch neurons are mostly specified normally in fd3F mutants and little gross structural defect was observed in the neurons ( Figure 5I , J; FGN , in prep . ) . Consistent with this , preliminary analysis of gene expression suggests that most ciliogenesis genes tested are not affected in fd3F mutants ( FGN and APJ , in prep . ) . We hypothesized , therefore , that fd3F regulates specialised aspects of Ch neuronal or ciliary physiology . The transient receptor potential ( TRP ) family of Ca2+ channels are particularly associated with sensory functions in a range of ciliary contexts [29] . In Drosophila , nan and iav encode subunits of a TRPV channel that are uniquely expressed in Ch neurons [17] , [18] . The proteins are located in the Ch ciliary dendrite , where they are required for sensory transduction . We find that the expression of both nan and iav is strongly reduced in fd3F mutant embryos ( Figure 5K–N , and unpublished data ) . Failure in regulation of nan and iav can therefore account for the defective Ch neuron function of fd3F mutants . In conclusion , ato directly or indirectly activates a transcriptional regulator concerned with Ch neuron physiology ( specifically , Ch ciliary dendrite physiological specialisation ) . Whilst many early expressed differentiation genes are known or predicted Rfx targets , not all Ch-specific or Ch-enriched genes ( nor ciliogenesis genes ) have nearby X box motifs , suggesting that other intermediate regulatory factors remain to be discovered . Another possibility is that some early expressed differentiation genes may be directly regulated by proneural factors . Such genes include CG1625 and unc , whose expression depends strongly on ato function ( Table S9 ) . Our analysis ( LM and APJ , in prep . ) shows that CG1625 , which we name dilatory ( dila ) , encodes a coiled-coil protein that localises to the basal body , and dila mutants exhibit defects in ciliary axonemal assembly . Together , these suggest that dila is a not a transcriptional regulator , but instead has a direct function in ciliary dendrite formation . Here , we examined the regulation of dila . The gene is highly expressed in early Ch cells ( 11-fold enriched at t1; ranked 10th ) , and dila RNA exhibits a Ch-enriched gene expression pattern in embryos ( Figure 6A–C ) . However , it has no X box motif within 2 kb of its transcription start site . Its early expression raises the possibility that dila is directly regulated by ato . In vivo reporter gene analysis led to the identification of an enhancer required for dila expression in Ch cells ( Figure 6D , E ) . Conversely , the reporter gene is misexpressed when ato is ectopically activated in the ectoderm ( Figure 6G , H ) . This enhancer contains two sequences resembling EATO motifs , both of which bind ATO/DA in vitro ( Figures 6I , S5 ) . Mutation of these two motifs within this enhancer results in loss of early expression in Ch SOPs ( Figure 6D , F ) and loss of misexpression in response to ectopically activated ato ( unpublished data ) . These data are consistent with direct regulation of dila by ato via one or both of these EATO motifs . We note that in a recent study of potential ato target genes in retinal development , similar evidence was presented to suggest that dila ( as CG1625 ) is regulated by ato via these two motifs [30] . In conclusion , dila represents a differentiation gene that is directly controlled by a proneural factor , despite the gap between proneural factor expression and terminal differentiation .
Numerous genetic and misexpression analyses in a range of organisms have shown that proneural factors influence a neuron's ultimate phenotype ( including its subtype identity ) at an early stage in its development [1] . However , the nature of this influence on the cell biological processes of neuronal differentiation has remained obscure . This study bridges the gap between early specification by the proneural factor , ato , and the differentiation of Ch neurons . The current model in both Drosophila and vertebrates is that proneural factors activate two types of target gene during neural precursor specification: a common target set for shared neuronal properties and a unique target set for subtype-specific properties [31] . Our data suggest that such neuronal subtype differences are ultimately controlled by proneural factors in several ways: by the differential regulation of both specific and common intermediate transcription factors , which in turn regulate genes for aspects of neuronal structural and functional differentiation , and by direct regulation of potential differentiation genes ( Figure 7 ) . The proneural factors ato and sc commit cells to similar but distinct neural precursor fates: Ch and ES neurons are evolutionarily related cell types with similar but distinct structural and physiological properties . Notably , both are characterised by the possession of specialised ciliary-based dendrites [10]–[12] . Thus , ciliogenesis is a key pathway that must ultimately be activated in sensory neurons subsequent to proneural factor function . However , there are important differences between the dendrites of Ch and ES neurons . Ch dendrites have a more prototypically organised axonemal structure and possess a characteristic ciliary dilation—a specialisation that separates the Ch ciliary dendrite into functionally distinct zones [32] . Moreover , there is evidence for an active ‘beat’ of Ch cilia during sensory transduction [33] . In general , ES dendrites appear reduced in structure: although a basal body and short axoneme are present , the tip of the dendrite consists of a ‘tubular body’ of irregularly packed microtubules [10] . Thus the basic ciliogenesis pathway must be modulated differently in Ch and ES differentiation , and ultimately this must reflect a difference in function between ato and sc proneural factors . The ciliogenic regulator Rfx is expressed and required for both ES and Ch lineages , but it is more strongly and more persistently expressed in Ch lineages ( the Ch-enriched pattern ) . This modulation of Rfx expression is at least partly due to differences in its regulation by proneural factors , since it appears to be a direct target of ato but not sc . We hypothesise that differences in Rfx regulation by the proneural factors lead to differences in implementation of a core cilia biogenesis program , thereby directly linking early proneural factor function with key differences of neuronal morphology . Consistent with this idea , our data show that several known or predicted ciliogenesis genes also exhibit this Ch-enriched pattern , and some of these are predicted or known Rfx targets [20] . In this view , the subtype differences between Ch and ES neurons are partly produced by quantitative differences in timing or level of expression of a common differentiation process , which ultimately depends on a qualitative difference in Rfx regulation by the proneural factors . A possible example of this is CG6129 . This gene is a predicted Rfx target gene and is expressed in a Ch-enriched pattern ( Figure S3 ) [20] . The homologous mouse protein ( Rootletin ) localises to the ciliary rootlet and is required for its formation [34] . Thus Ch-enriched expression of CG6129 explains the presence of the ciliary rootlet in Ch neurons but not ES neurons [11] , [12] . One prediction of this hypothesis is that overexpression of Rfx in ES neurons will upregulate Ch-enriched genes , and this is borne out by preliminary experiments that show an increase in CG6129 expression in ES neurons upon Rfx overexpression ( scaGal4/UAS-Rfx embryos; LM and APJ , unpublished data ) . It is notable that differences in IFT activity are proposed to underlie differences in ciliary morphology [35] while RFX class factors have been associated with regulating genes for IFT in a variety of organisms [13] . Our work suggests that variations in Rfx expression level and timing should be explored as a possible factor in cilium diversity . fd3F fits the more conventional view of a proneural target gene that implements a subtype-specific program of differentiation [31] . It is expressed downstream of ato uniquely in Ch neurons and regulates genes required for functional specialisation of the Ch ciliary dendrite . It is likely that Forkhead factors regulate specialisation of ciliogenesis in other organisms . In C . elegans , FKH-2 is expressed widely early in development but is also required specifically for ciliary specialisation of one type of sensory neuron [36] . Foxj1 in mice , Xenopus , and zebrafish appears to be required for the motile cilia of the lung airway and embryonic node , but not for primary cilia [37]–[39] . It remains to be determined whether fd3F regulates the machinery for the active beat that occurs in Ch dendrites as part of sensory transduction [33] . Together , our studies of Rfx and fd3F extend the previously limited knowledge of the gene regulatory network underlying ciliogenesis [13] and provide insight into how the core program may be modified to produce the highly specialised and diverse morphologies that cilia adopt for different functions [36] . Previous to this study , little was known about how ato/sc proneural genes control the acquisition of Ch/ES subtype identity , except that regulation of the Cut homeodomain transcription factor is involved . Mutant and misexpression analyses show that cut is a fate selector switch for ES identity downstream of sc [19] , [40] , but nothing is known of its mode of action or targets . Whereas Rfx and fd3F functions are likely to be confined to neuronal morphology , cut affects the identity of support cells too [41] . As a fate switch in the entire lineage , it appears likely that cut is involved in high-level fate specification ( like proneural genes ) rather than regulating aspects of differentiation directly . However , it is also possible that cut may repress ciliogenesis genes in ES neurons , either directly or by repressing Rfx expression . It will be important to integrate cut into the Ch/ES gene regulatory network in the future . In our temporal expression profiling data , there is a steady increase in the number of known or suspected differentiation genes expressed in developing Ch cells . Many more are not expressed until after our analysis ends . Ciliogenesis is a highly intricate cellular process requiring the coordination of perhaps hundreds of genes [13] , [42] and differences in expression onset may indicate prerequisite steps in the process of differentiation and ciliogenesis . A surprising observation was the significant number of ciliogenesis and differentiation genes that are expressed even at the earliest profiling time point . This is unexpected , since the earliest time point is predicted to be not only before differentiation but also even before cell divisions have generated the neurons . We suggest that further analysis of expression timing may lead to insights into the cell biology of ciliogenesis . The early activation of differentiation genes may reflect the rapid pace of development in the Drosophila embryo . Thus , early expression of ciliogenesis genes may provide components that prime cells for rapid cilium assembly later once differentiation has been triggered . Along these lines , our findings mirror striking observations of retinal ganglion cells , whose rapid differentiation within 15 minutes of the exit from mitosis has been taken to imply that genes required in postmitotic cells must be transcribed before cell division [43] , [44] . A more intriguing possibility is that early expression reflects an orderly time course for ciliogenesis that begins many hours before the final cell division . For example , unc is thought to be required for the conversion of the mitotic centriole to ciliogenic basal body [14] , but we found that the mRNA and fusion protein are expressed even in SOPs , several cell divisions before terminal differentiation . Interestingly , in mammals newly replicated centrioles mature over two cell cycles [45] . It is conceivable that the sensory neuron basal body might similarly need time to mature . Since Rfx and some ciliogenesis genes are expressed in SOPs , what prevents ciliogenesis from being activated in the non-neuronal support cells ? One possibility would be an extension of model recently proposed for the generation of support cell differences , in which Notch signalling between daughter cells confines the function of genes to one branch of the lineage [23] . This would predict that ciliogenesis genes and/or Rfx are Notch target genes . Another possibility is that some of the gene products are asymmetrically segregated . Thirdly , ciliogenesis may not be triggered until one or more key gene products are produced in the neuronal cell . As a corollary , it will be important to explore further the gene regulatory network underlying the temporal and cell-type differences in ciliogenesis genes . Some early expressed differentiation genes are known or predicted Rfx targets [20] . This gives a rationale for the early regulation of Rfx by ato in Ch lineages . However , in both C . elegans and D . melanogaster , Rfx regulates only a subset of ciliogenesis genes ( notably , it does not regulate IFT-A genes ) [20] . Further studies on ato target genes and the ciliogenesis regulatory network in sensory neurons will identify other important regulators ( Figure 7 ) . It remains to be determined how many differentiation genes are , like dila , direct targets of ato . Interestingly , vertebrate proneural factors are hypothesised to regulate directly the transition from cycling neural progenitor ( or neural stem cell ) to postmitotic differentiating neuron . Perhaps ato has retained some part of an ancestral proneural factor function in direct regulation of terminal differentiation despite the subsequent evolution of SOPs that must undergo several divisions before differentiating .
In order to label ato-expressing cells , a 2 . 6-kb fragment upstream of the ato gene was used to drive GFP expression in transgenic Drosophila embryos . After amplification from genomic DNA ( Table S10 for primers ) , this fragment was cloned into pHStinger [46] . The plasmid was used to make transgenic fly lines by microinjection . One viable line , atoGFP . 7 , with high expression levels and lacking detectable ectopic GFP expression , was chosen for embryo dissociation and cell sorting . For expression profiling of ato mutant cells , atoGFP . 7 was introduced into the ato1 mutant background ( a presumed null [4] ) . To minimise genetic background differences , the atoGFP . 7; ato1 line was backcrossed four times to the original atoGFP . 7 stock . The two lines are therefore predicted to be approximately 97% isogenic . In brief , dechorionated atoGFP embryos were dissociated in Shields and Sang ( S2 ) medium ( Sigma ) with 5% fetal bovine serum ( Gibco ) in a Dounce homogeniser with a loose pestle . Cells were pelleted by centrifugation and resuspended in protease solution ( 90% trypsin-EDTA ( Sigma ) in phosphate buffered saline ) . Incubation in this solution for 7 min increased the proportion of viable single cells as judged by Trypan Blue exclusion . Cells were subsequently washed twice in S2 medium . Cell suspensions were separated using a DakoCytomation MoFlo MLS flow cytometer . In each run , 3×105 atoGFP+ and 1×106 atoGFP− cells were collected . Cells were sorted into Schneider medium on ice , then pelleted and homogenised in RNA extraction buffer , and then snap frozen in liquid nitrogen . In all experiments the cell suspension was kept on ice from the time of trypsin treatment until the RNA was extracted from the sorted cells . Quantitation of RNA was carried out using QuantiTect SYBR Green RT-PCR kit ( Qiagen ) and a MJ Research Opticon thermal cycler . rpL32 was used as a control . Using standard techniques recommended by Affymetrix ( http://www . affymetrix . com/support/technical/manual/expression_manual . affx ) , RNA from sorted atoGFP+ and atoGFP− cells was used to probe Affymetrix Drosophila 2 . 0 microarray chips in quadruplicate using independent samples . ∼0 . 5 µg of RNA was converted to cDNA and amplified as cRNA using the 2-cycle protocol , before being biotin labelled and fragmented . The hybridisations were conducted at the Sir Henry Wellcome Functional Genomics Facility , Glasgow , UK . Quality control and normalisation of microarray expression data was performed using the Bioconductor package AffyPLM [47] using the standard RMA method with quantile normalisation . Differentially expressed genes between atoGFP+ and atoGFP− samples were identified using the Bioconductor package limma [48] . Lists of Affymetrix probe-set accessions were extracted from the analysis with the cut-off at a 1% FDR [49] . Affymetrix probe-sets were mapped to genomic locations using the Ensembl database PerlAPI [50] , [51] and only those probe-sets that were not promiscuous ( not mapping to more than one gene ) with ≥50% of their oligomers were considered reliable and used to retrieve stable accessions of ‘trusted genes’ . Protein domain annotations for Pfam , Prosite , Superfamily , and Smart databases were retrieved from Ensembl for all trusted genes in our analyses ( Ensembl v53 March 2009 , Flybase Release FB2008_10 Dmel Release 5 . 13 , Nov . 2008 ) . The resulting data were parsed into genomic frequency tables for each domain from each source . To determine whether any domains were over-represented in our gene lists , we applied a corrected Fisher exact test [52] to the relative domain frequencies between list and genome . All domains that were over-represented with p≤0 . 05 were taken forward for further analysis . Standard methods of whole embryo immunohistochemistry were used . Antibodies used were: anti-Ato 1∶2000 [4] , MAb22C10 1∶100 , MAb21A6 1∶500 , anti-GFP 1∶500 ( Molecular Probes ) , and anti-Pericentrin ( 1∶500 , kindly provided by J . Raff ) . Secondary antibodies were from Molecular Probes . mRNA in situ hybridisation to whole embryos were by standard methods . Primers for antisense RNA probes used are given in Table S13 . For double RNA/protein labelling , the in situ hybridisation was conducted first followed by protein detection . For wild-type embryos , we used the w1118 stock . The fly stock for the uncGFP fusion gene/protein was kindly provided by Maurice Kernan . Fragments were amplified from genomic DNA and cloned into pHStinger . Primers used are given in Table S13 . Transformants were made by microinjection into syncytial blastoderm embryos . In general , at least two independent transformant lines were tested for each construct . For E box site directed mutagenesis , we used the Stratagene Quickchange 2 kit . In each case , CANNTG was altered to AANNTT . In vitro DNA binding assays were performed exactly as previously described using bacterially expressed ATO and DA proteins [7] . DNA probes used are shown in Table S13 . A deletion allele , fd3F1 , was isolated by imprecise excision after P element mobilisation in the line , P{EP}EP1198 . This deletes the 3′ end of the transcription unit and appears to be an RNA and protein null ( FGN , manuscript in preparation ) . Wandering third instar larvae were placed individually on the centre of a layer of 1% agarose in a Petri dish . Larval movement was traced over a period of 2 min . Path lengths were obtained from traces using NIH ImageJ . Larvae tested were from the stocks , ato1 , fd3F1 , and w1118 ( wild type ) . All microarray data from the experiments described are available from the NCBI's GEO database with accession number GSE21520 .
|
Early during development , cells differentiate and take on specialized forms and functions . This requires the activation of specific genes for different cellular pathways . Our study addresses how this activation is regulated in the developing Drosophila nervous system . In this model , it is well known that proneural transcription factors are involved in directing cells to differentiate into various types of neurons . However , the mechanism by which they choreograph the activation of genes for neuronal differentiation is not clear . In this study , we focused on events leading to differentiation of mechanosensory neurons , which have specialized dendritic processes that mediate sensory perception . In these developing neurons we profiled the time course of gene expression that is triggered by the proneural factor atonal . Our analysis revealed the activation of genes required for the formation of these specialized dendrites , called cilia . We then identified several ways in which atonal regulated these genes . First , it activates intermediate transcription factors that regulate different subsets of differentiation genes . Second , in at least one case , atonal activates a differentiation gene directly , one that is involved in the formation of cilia ( ciliogenesis ) . These findings offer new insight into how proneural factors regulate specialized neuronal differentiation pathways .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/cell",
"differentiation",
"neuroscience/neurodevelopment",
"developmental",
"biology/neurodevelopment",
"developmental",
"biology/developmental",
"molecular",
"mechanisms",
"cell",
"biology/gene",
"expression"
] |
2011
|
The Gene Regulatory Cascade Linking Proneural Specification with Differentiation in Drosophila Sensory Neurons
|
Viruses rely on the metabolic network of the host cell to provide energy and macromolecular precursors to fuel viral replication . Here we used mass spectrometry to examine the impact of two related herpesviruses , human cytomegalovirus ( HCMV ) and herpes simplex virus type-1 ( HSV-1 ) , on the metabolism of fibroblast and epithelial host cells . Each virus triggered strong metabolic changes that were conserved across different host cell types . The metabolic effects of the two viruses were , however , largely distinct . HCMV but not HSV-1 increased glycolytic flux . HCMV profoundly increased TCA compound levels and flow of two carbon units required for TCA cycle turning and fatty acid synthesis . HSV-1 increased anapleurotic influx to the TCA cycle through pyruvate carboxylase , feeding pyrimidine biosynthesis . Thus , these two related herpesviruses drive diverse host cells to execute distinct , virus-specific metabolic programs . Current drugs target nucleotide metabolism for treatment of both viruses . Although our results confirm that this is a robust target for HSV-1 , therapeutic interventions at other points in metabolism might prove more effective for treatment of HCMV .
Herpesviruses are large , enveloped , double-stranded DNA viruses , capable of both lytic infection and life-long latency in mammalian hosts [1] . They are major causes of human disease . A majority of adults are infected with herpes simplex virus 1 ( HSV-1 ) and/or human cytomegalovirus ( HCMV ) . An alpha-herpesvirus , HSV-1 infects a wide range of organisms and cells types , causing symptoms ranging from cold sores to encephalitis . The prototypical beta-herpesvirus , HCMV , selectively infects non-transformed human cells . Although frequently asymptomatic , HCMV causes severe disease in neonates and immunocompromised adults . All herpesviruses encode metabolic enzymes in their genomes , primarily ones involved in nucleotide metabolism . The HSV-1 genome encodes a viral thymidine kinase , ribonucleotide reductase , dUTPase and uracil DNA glycosylase , while HCMV encodes a functional form of uracil DNA glycosylase [2] . Like all viruses , however , they rely primarily on the metabolic capabilities of their cellular hosts for replication . Specifically , the host provides the energy , amino acids and lipids , as well as most nucleotides , required by the virus . Improved technologies for measuring both enzymes and metabolites is enabling for the first time in-depth analysis of virus-host cell metabolic interactions . Liquid chromatography coupled to mass spectrometry ( LC-MS ) facilitates direct measurement of a large number of cellular metabolites [3] , [4] . Combined with isotope tracers , metabolic flows ( fluxes ) can also be determined . These new tools have revealed that , rather than passively relying on basal host cell metabolic activity , many viruses actively redirect host cell metabolism [5] , [6] , [7] . For example , hepatitis C virus up-regulates host cell glycolysis and modulates concentrations of specific lipids [8] . Similarly , hepatitis B virus replication perturbs cholesterol metabolism by inducing increased 7-dehydrocholesterol levels [9] . Among herpes viruses , the metabolic effects of HCMV have been the most extensively studied . Infection of a human fibroblast cell line with HCMV leads to two-fold increases in glycolytic activity and nucleotide synthesis , as well as yet greater increases in citric acid cycle flux and lipid biosynthesis [10] . Consistent with HCMV's reliance on the metabolic fluxes that it induces , inhibition of the committed step of fatty acid synthesis and elongation , acetyl-CoA carboxylase , blocks HCMV replication [10] . The virus also induces an increased dependence on glutamine that serves to drive the TCA cycle [10] , [11] . These metabolic changes are only partially accounted for by increased levels of enzyme transcripts , indicating the involvement of multiple regulatory mechanisms [6] . A limitation of studies of virus-host metabolic interactions to date is that they have focused on single virus-host cell pairs . Moreover , they have often employed transformed host cells that differ markedly from the cells usually infected in vivo . This has precluded understanding whether the observed metabolic effects of viruses are relevant in their natural host cells , preserved across host cell types , and conserved within families of related viruses . To address these issues , here we compare and contrast the metabolic effects of HCMV and HSV-1 , across both fibroblast and epithelial host cells . Specifically , we studied the laboratory-adapted AD-169 strain of HCMV , which is restricted to growth in fibroblasts , and whose metabolic effects have been previously studied [6] , [10] . In addition , we examined the epitheliotropic clinical isolate strain TB40/E , which grows in many cell types , to study the infection of epithelial cells [12] . For HSV-1 infections , we chose the highly-passaged , non-neuroinvasive KOS 1 . 1 strain and a prototypical neuroinvasive strain , the F strain [13] , [14] . Both primary human foreskin fibroblasts and the MRC5 fibroblast cell line were analyzed after infection by both viruses . HSV-1 infection was also studied in the Vero African green monkey renal epithelial cell line which is traditionally used for its growth . Given HCMV's propensity to cause retinitis [15] , it was studied in the ARPE-19 retinal pigment epithelial cell line . Using LC-MS to probe core metabolite concentrations and fluxes , we find that HCMV and HSV-1 both trigger major metabolic changes in their cellular hosts , and that these changes are similar across different host cell types and for different strains of the same virus . In contrast , the effects of HCMV and HSV-1 diverge markedly . HCMV most greatly impacts pathways generating substrates for lipid metabolism , whereas HSV-1 most greatly impacts deoxypyrimidine metabolism .
We examined the metabolic changes triggered by infection of fibroblast and epithelial host cells with HCMV and HSV-1 . Fibroblasts ( HFF and MRC5 ) were held at confluence for 3–5 days then serum-starved for 24 hours prior to infection , while epithelial cells ( ARPE19 and Vero ) were infected at 80–90% confluence and maintained in the presence of dialyzed serum at all times . As a consequence the fibroblast host cells were growth arrested at the time of infection , while the epithelial cells continued to replicate after mock inoculation [16] . Consistent with their different growth states , there were substantial differences in the metabolome of the host cells prior to infection , with compounds directly involved in proliferative processes such as carbamoyl-aspartate ( pyrimidine biosynthesis ) , dTTP ( DNA synthesis ) , and S-methyl-5′-thioadenosine ( polyamine synthesis ) markedly higher in the growing epithelial cells than the quiescent fibroblasts ( Figure S1 ) . Other compounds , such as those involved in mitochondrial fatty acid oxidation ( carnitine and acetyl-carnitine ) , were higher in growth arrested fibroblasts compared to growing epithelial cells . Infections were performed at a multiplicity of 3 pfu/cell to ensure near complete exposure of the cell population . Cultures infected with one of the virus strains , or treated with a virus-free mock inoculum , were grown in parallel and sampled at regular time intervals from the beginning of infection until peak virus yields were achieved . Medium was changed every 24 h to ensure a consistent nutrient supply to the cells; lack of media changes in earlier work [6] resulted in some different metabolite patterns from those observed here . In particular , we find that citrate and malate levels increase >10-fold during HCMV infection , compared to the 2-fold change seen in previously published work [6] . Maximum virus output was reached at around 24 h post infection ( hpi ) in HSV-1 infected cells , and around 96 hpi HCMV infected cells ( Figure S2 ) . Over 80 metabolites were identified and detected in all experiments . Relative concentrations of these species , between infected and mock-infected cells , are shown in Figure 1 ( for blue/yellow version of the heat map , see Figure S3; for source data , see Table S1 ) . A third of the compounds were measured by both high resolution mass spectrometry ( orbitrap ) and triple quadrupole mass spectrometry ( QQQ ) . The profile of any single metabolite detected in multiple LC-MS methods was found to be similar , as indicated by co-clustering of the associated data in almost all cases ( Figure 1 ) . Both viruses triggered >4-fold changes in the levels of roughly half of the metabolites assayed . Among the metabolites changing markedly , those increasing outnumbered those decreasing roughly two-to-one . Although the magnitude of the changes in compound levels depended on the host cell , typically being smaller in the growing epithelial cells , the majority of the trends were host cell invariant . This is remarkable given the differing initial growth and metabolic states of the host cells , and it indicates a robust ability of the viruses to re-program metabolism . Extracting major trends from the dataset by singular value decomposition [17] resulted in two characteristic vectors that accounted for >10% of the information in the dataset ( Figure S4A ) . These vectors represent prototypical metabolite response patterns . The first vector accounts for 16% of the variation in metabolite levels over the time courses . In this vector , the signal as a function of time shows a similar trend in each of the time courses , thus representing a generic metabolite concentration response to herpesvirus infection ( Figure S4B ) . The smaller signal in the first and last columns corresponding to the infections of epithelial cells reflects the smaller fold-changes in metabolite levels induced by viral infection in the growing epithelial cells compared to growth arrested fibroblasts . The strongest contributor to the generic infection response is dTTP , whose upregulation is consistent with the shared need of both viruses to replicate their DNA . The second vector , accounting for 12% of the variation in the dataset , represents a virus-specific response with opposing patterns for the HCMV and HSV-1 infection time courses ( Figure S4B ) . Key contributors to this virus-specific response include TCA cycle intermediates , consistent with their rise during HCMV but not HSV-1 infection , and the nucleotides dUMP and dTMP , consistent with their rise during HSV-1 but not HCMV infection . The third most significant vector , which accounts for 6% of the information in the dataset , represents a metabolic response characteristic of Vero cells ( Figure S4B ) . While most of the changes proved to be host cell-independent , the third vector draws attention to the impact of different host cell types on the metabolic effects of viruses . The strongest contributors to this vector are citrate/isocitrate and N-carbamoyl-L-aspartate , due to their depletion in infected Vero cells in contrast to their accumulation in all other cell types . The remaining characteristic vectors account for the other 66% of the information . This large amount of residual information reflects a myriad of metabolite , virus , and cell-type specific dynamics . For example , proline and glycine-betaine showed cell type-specific upward or downward trends . Other metabolites , such as dTTP , showed different dynamic response patterns across different host cell types . In all cell types tested , HCMV infection induced phosphoenolpyruvate , deoxypyrimidine triphosphates , CDP-choline , and acetylated amino acids , as well as a striking and coordinated increase in citrate , malate and other TCA cycle intermediates ( Figure 1 ) . Depleted compounds included glycerophosphoinositol , taurine , and a number of pentose phosphate pathway metabolites . On the other hand , HSV-1 triggered increased levels of pentose phosphate pathway intermediates , as well as glycolytic intermediates , and deoxypyrimidines ( Figure 1 ) . Notably depleted compounds included glycine betaine , taurine , creatine , and NAD+ . The conserved decrease in the osmolyte , taurine , in both HCMV and HSV-1 likely reflects a host cell response to virus-induced increases in cell volume [18] . Glycolysis , the citric acid cycle , and pyrimidine biosynthesis are discussed in greater detail below . Glycolysis and the TCA cycle form the backbone of central carbon metabolism in mammalian cells . Through these two pathways glucose is either oxidized to produce energy in the form of NADH and ATP , or converted to precursors of amino acids , lipids and nucleotides . The levels of glycolytic intermediates are altered in a strikingly different manner during HCMV and HSV-1 infections ( Figure 2A ) . The concentrations of metabolites in lower glycolysis increase during HCMV infection , while levels of upper glycolytic intermediates drop . Conversely , in response to HSV-1 infection the opposite occurs . While these concentration measurements are informative , it is not possible to deduce whether changes in influx , efflux or a combination of both are responsible for the perturbations of the metabolite levels . Neither the turnover rate of a metabolic intermediate , nor the material flow through a pathway , can be predicted based on metabolite pool sizes alone . To understand how material flow , i . e . , flux , is altered , further assays must be employed . In cultured mammalian cells , the enzyme-catalyzed reactions of glycolysis convert the bulk of glucose imported from the extracellular environment to lactate , which gets excreted . Thus , changes in the rate of material flow through glycolysis can be approximated by measuring the rate of glucose consumption and lactate production . We determined the glucose uptake and lactate excretion rates in infected and mock treated HFFs by directly measuring the amount of glucose and lactate in the extracellular medium over time ( Figure 2B ) . HCMV increased the uptake of glucose ( p = 0 . 02 ) and the excretion of lactate ( p = 0 . 0006 ) , in agreement with previously published results on HCMV-infected fibroblasts [10] , [19] , [20] ( Figure 2B ) . On the contrary , in HSV-1 infected cells the glucose uptake ( p = 0 . 21 ) and lactate excretion ( p = 0 . 002 ) rates decreased to a modest extent . In addition to glucose from the medium , glycolysis can also be fueled by glucose acquired from the breakdown of stored glycogen . Moreover , decreased lactate production can reflect increased glycolytic efflux to the TCA cycle , rather than decreased glycolytic flux . To confirm our conclusions based on the glucose and lactate measurements , we also measured the rate of incorporation of isotope-labeled nutrients into downstream metabolites . Following a switch to labeled media , metabolite pools become progressively more labeled , with the unlabeled fraction exhibiting an exponential-type decay . Flux through a metabolite is the product of the rate of this decay and the total pool size of the metabolite [21] . To reliably estimate this decay rate , it is important to obtain samples at early time points where the fractional labeling is changing rapidly . Because label from glucose gets incorporated very quickly into glycolytic intermediates , measurements at later time points are likely to reflect steady-state labeling fractions , not labeling rates per se . At steady state , the amount of labeled metabolite reflects the total metabolite pool size and the fraction of its production from the labeled substrate , but not the rate of labeling . To characterize glycolytic flux , we switched HCMV and HSV-1 infected cells , as well as their mock-treated counterparts , to 13C-labeled glucose containing media , and used LC-MS to monitor the labeled forms of downstream metabolites over time . HCMV infection increased the fractional labeling rate of glycolytic intermediates fructose-1 , 6-bisphosphate and dihydroxyacetone phosphate , while HSV-1 decreased it ( Figure 2C ) . The decrease in the rate of HSV-1 labeling was complemented by a corresponding increase in metabolite concentration . Thus , we can conclude that HCMV significantly increases flux through glycolysis and HSV-1 does not . Interestingly , in HSV-1-infected cells the metabolites upstream of phosphoenolpyruvate build up , while the ones downstream drop ( Figure 2A ) . This suggests a bottleneck in glycolytic efflux at the step catalyzed by pyruvate kinase , the enzyme that converts phosphoenolpyruvate and ADP to pyruvate and ATP . The buildup of glycolytic metabolites upstream of pyruvate is accompanied by increased levels of pentose phosphate pathway intermediates , thus increasing the availability of ribose-phosphate for the synthesis of nucleotides . During hepatitis C infection the levels of most glycolytic enzymes were shown to be elevated , with the notable exception of pyruvate kinase [8] . Such changes in enzyme levels may lead to a similar metabolic outcome as observed in HSV-1 infected cells . However , as the activity of glycolytic flow is under tight allosteric control [22] , direct metabolic analysis of hepatitis C is warranted to confirm this . The metabolites of the TCA cycle showed a particularly interesting difference in labeling patterns when HCMV- and HSV-1-infected fibroblasts were supplied with uniformly labeled 13C- glucose . In the uninfected , growth arrested fibroblasts , citrate was only minimally labeled over a 2 h time period ( Figure 3D , top panel ) . On the other hand , HCMV-infected fibroblasts produced a significant amount of citrate with two labeled carbon atoms ( 13C2-citrate ) ( Figure 3D , center panel ) , while their HSV-1-infected counterparts generated citrate with three labeled carbons ( 13C3-citrate ) ( Figure 3D , bottom panel ) . These two forms of citrate are produced by different pathways , which are selectively up-regulated in a virus-specific manner . Labeled carbon atoms derived from 13C-glucose can enter the TCA cycle via two routes ( Figure 3A ) . In one , pyruvate dehydrogenase and citrate synthase incorporate two carbons from glucose into citrate via acetyl-CoA ( Figure 3B ) . The labeling pattern of citrate during HCMV infection indicates increased influx of glycolytic carbon to the TCA cycle via this route ( Figure 3D ) . This pathway indicates a catalytic use of the TCA cycle , with the two-carbon units originating from glycolysis either oxidized to produce energy by complete turning of the TCA cycle , or diverted from the mitochondria to the cytosol through the citrate shuttle , where the acetyl group is freed for fatty acid synthesis and/or elongation . Global flux analysis on HCMV infection showed that both of these uses of glycolytic carbon are up-regulated by HCMV in MRC5 cells [10] . Our results indicate that HCMV infection of HFFs leads to the same up-regulation . Carbon from glycolysis can also enter the TCA cycle via pyruvate carboxylase , which converts pyruvate to oxaloacetate ( Figure 3C ) . All three labeled carbons in pyruvate are retained in oxaloacetate , which is converted to 13C3-citrate , malate , or aspartate . The labeled forms of TCA cycle intermediates observed in HSV-1-infected cells indicate an up-regulation of carbon influx via pyruvate carboxylase as reflected by the labeling of citrate ( Figure 3D , right panel ) and malate ( Figure S5 ) when cells are supplied with 13C6-glucose . Furthermore , no citrate is detected with two or five labeled carbons in these cells . Thus , unlike in HCMV-infected cells , the use of glucose to drive the citrate shuttle and ensuing fatty acid synthesis is minimal during HSV-1 infection . The previous metabolic analysis of HCMV infection led to the recognition of a potential new drug target by showing that de novo fatty acid biosynthesis is essential for HCMV replication [10] . Pharmacological inhibitors of enzymes in fatty acid biosynthesis were shown to inhibit not only HCMV replication , but also the replication of influenza , an evolutionarily divergent virus [10] . De novo fatty acid biosynthesis does not appear to bear the same importance for the replication of HSV-1 as for HCMV ( Figure 3D ) . This is reflected in the lower sensitivity of HSV-1 replication to 5-tetradecyloxy-2-furoic acid ( TOFA ) ( Figure S6 ) [10] , an inhibitor of acetyl-CoA carboxylase , the first committed enzyme of fatty acid biosynthesis . The reaction catalyzed by pyruvate carboxylase is an anaplerotic reaction that serves to replenish the intermediates of the TCA cycle as they are removed for biosynthetic purposes . However , in spite of its up-regulation during HSV-1 infection , after an initial elevation , the levels of TCA cycle intermediates drop ( Figure 4 ) . This indicates that HSV-1 triggers an even greater increase in TCA cycle efflux . Notably , the concentration of aspartate , which is produced from oxaloacetate , decreases significantly after infection with HSV-1 . In addition to being used for protein synthesis , aspartate is a substrate for pyrimidine nucleotide biosynthesis . Unlike in HCMV infection , in response to HSV-1 infection the rates of total RNA and total protein syntheses drop [23] , [24] . At the same time , viral DNA synthesis increases the demand for deoxyribonucleotides . The nucleotide precursors essential for DNA synthesis can be acquired through salvage reactions or de novo synthesis [25] , [26] . When replicating in quiescent cells as opposed to actively dividing ones , viruses face a greater challenge in acquiring nucleotides for viral DNA replication , because the de novo nucleotide biosynthesis pathways are less active [27] . HSV-1 encodes a set of enzymes addressing this problem and their impact is reflected in increased concentrations of the intermediates of the pyrimidine nucleotide biosynthesis pathway ( Figure 5 ) . HCMV employs an alternative mechanism whereby the host cell is driven from quiescence to the G1/S boundary of the cell cycle [28] , stimulating host cell nucleotide biosynthesis but preventing host DNA replication . Interestingly , in HSV-1 infected serum-starved fibroblasts dTTP levels are not observed to peak and drop after 6 hpi as reported in Vero cells ( Figure 1 ) [26] , and BHK cells [29] . In growth arrested fibroblasts the dTTP pool continues to rise throughout the infection ( Figure 5 ) . Such a trend was previously observed in mutant BHK cells that lack thymidine kinase and deoxycytidine kinase activities [29] . Confluent , serum-starved fibroblasts may present a similar cellular environment , with very low basal activity of DNA-biosynthetic enzymes . Uracil can occur in DNA as a result of cytosine deamination or misincorporation of dUTP [30] . The UL50 and UL2 genes of HSV-1 encode enzymes that address these problems . The viral dUTPase ( UL50 ) serves to reduce incorporation of uracil into viral DNA by decreasing dUTP levels and producing dUMP . Uracil-DNA glycosylase ( UL2 ) participates in base excision repair of the HSV-1 genome , removing uracil from viral DNA [31] , [32] . These two viral enzymes are likely responsible for the increased dUMP and uracil levels during HSV-1 infection ( Figure 5 ) . While there is no known HSV-1 gene that causes the increased production of carbamoyl-aspartate , evidence for the regulation of aspartate transcarbamoylase during adenovirus infections has been presented in the past [33] , [34] . Furthermore , carbamoyl-aspartate levels are observed to rise dramatically in both HCMV and HSV-1 infections ( Figure 1 ) [10] . Carbamoyl-aspartate is produced by the multifunctional CAD protein , which catalyzes the first three steps of de novo pyrimidine biosynthesis in mammalian cells . CAD is highly regulated by growth state-related signaling molecules , such as the epidermal growth factor [35] , [36] . Epidermal growth factor receptor has been shown to play a role in the entry of several different viruses , and it or related signaling pathways might contribute to virally-induced increases in carbamoyl-aspartate levels [37] , [38] , [39] . To confirm that flux from aspartate to pyrimidine nucleotides is up-regulated in HSV-1 infection , we analyzed the labeling pattern of the pathway intermediates after switching cells to medium containing uniformly labeled 13C-glutamine ( Figure 6A ) . As glutamine contributes to anapleurosis in both mock and infected cells , this resulted in labeling of aspartate in both cases , and thus enabled direct comparison of pyrimidine synthesis between these two conditions . Significantly faster labeling of the pyrimidine end-product UTP was observed in infected cells ( Figure 6B ) . As the concentration of UTP is also elevated in HSV-1 infected cells , flux from aspartate to nucleotide synthesis is markedly increased . Taken together , the above observations indicate an upregulation of flux in HSV-1 infected cells from glucose to de novo pyrimidine nucleotide biosynthesis via the pyruvate carboxylase-catalyzed anaplerotic and the aspartate transaminase 2 catalyzed cataplerotic reactions of the TCA cycle ( Figure 7A ) . In agreement with this , small interfering RNA ( siRNA ) mediated knockdown of pyruvate carboxylase and aspartate transaminase 2 inhibit HSV-1 replication , but not HCMV ( Figure 7B–D ) .
Viral replication depends on the energy and biosynthetic precursors supplied by host cell metabolism . Using a mass spectrometry-based metabolomic approach we demonstrate that two closely related viruses , HCMV and HSV-1 , implement divergent metabolic programs ( Figure 1 and Table S1 ) . These programs are robust to host cell type and virus strain . While HCMV enhances glycolytic flux and the delivery of carbon from glucose to the TCA cycle to fuel fatty acid biosynthesis , HSV-1 gears central carbon metabolism toward the production of pyrimidine nucleotide components ( Figure 8 ) . The focus of HSV-1 , but not HCMV , on nucleotide metabolism is interesting in light of nucleoside analogues ( acyclovir and ganciclovir , respectively ) being more effective treatments for HSV-1 than for HCMV [40] . Both compounds depend on phosphorylation by viral kinases for their activation , and the metabolic profile of HSV-1 infected cells reflects the activity of the virally encoded thymidine kinase . On the other hand , the only functional HCMV kinase ( UL97 ) is a protein kinase and has little to no nucleotide kinase activity [41] . This difference is reflected in the metabolome and in the lower efficacy of the nucleoside analogues for HCMV . In contrast , we show that TOFA , an inhibitor of the committed step of fatty acid synthesis and elongation , preferentially targets HCMV over HSV-1 ( Figure S6 ) . The viruses also induce robust changes outside of core metabolism . For example , HCMV , but not HSV-1 , induces a striking increase in acetylated amino acids ( Figure 1 ) . After HSV-1 infection , NAD+ levels dropped by a factor of about 10 , but little decline was evident after HCMV infection . We have recently discovered that this NAD+ depletion is due to elevated poly-ADP-ribose polymerase activity ( L . Vastag , unpublished work ) . The activation of poly-ADP-ribose polymerase has also been observed in HIV-1 and Sindbis Virus infected cells [42] , [43] , [44] . Understanding the significance of such observations requires further study . Why do these two related viruses induce markedly different changes in host cell metabolism ? Both must synthesize viral proteins and nucleic acids and both produce enveloped virions . Perhaps the difference results in part from the markedly different speeds at which the two viruses progress through their replication cycles . HSV-1 produced maximal yields in fibroblasts or epithelial cells within about 24 h , whereas HCMV did not achieve maximal yields until about 96 hpi ( Figure S2 ) . One might speculate , then , that HSV-1 , which accumulates its DNA fairly rapidly , must elevate nucleotide biosynthesis; in contrast , HCMV , which accumulates its DNA over a much longer time frame , does not require such a strong induction ( Figure 1 ) . It is more difficult to suggest why HCMV depends on de novo fatty acid biosynthesis more strongly than HSV-1 ( Figure S6 ) . It is conceivable that HCMV induces the production of new membranes to serve as a source for the virion envelope , while HSV-1 virions are built from pre-existing membranes . Consistent with this view , HCMV-infected cells develop a well-defined , membranous assembly compartment during the late phase of infection [45] , [46] , [47] , but no equivalent structure has been described within HSV-1-infected cells . The metabolic program induced by herpes viruses could be implemented in several ways . One potential strategy involves perturbation of general host biochemical milieu . For example , the HCMV UL37x1 protein elevates free intracellular calcium levels [48] , which could potentially activate glycolysis through the action of calcium-sensitive kinases [19] . Alternatively , virus-coded gene products could modify or interact with pivotal regulators of host cell metabolism , e . g . , the HCMV UL38 protein [49] , or with metabolic enzymes themselves to alter their activity . Yet other strategies could involve modulation of host cell enzyme concentrations through mechanisms involving transcription , translation , or protein stability . A comprehensive systems level analysis , incorporating transcriptomic [50] , [51] , [52] , [53] , proteomic [8] , and metabolic data should help clarify the relative significance of these latter mechanisms . In addition to elucidating the mechanisms underlying host cell metabolic hijacking , an important priority is defining the metabolic programs of other viruses . Among herpes viruses , it will be interesting to see whether most fit either the HSV-1 or HCMV prototype , or whether alternative programs exist . For smaller viruses , it will be interesting to see whether their yet more precious genome space includes instructions for extensive host cell metabolic reprogramming . Such work holds substantial practical value , given overarching importance of enzyme inhibitors as antivirals and the utility of metabolomics for identifying new antiviral targets .
Primary human foreskin fibroblasts ( HFFs ) were collected previously [54] and stored in liquid nitrogen . We used them at passages 8–13 . ARPE19 human retinal pigment epithelial cells , Vero green monkey kidney epithelial cells and MRC5 human embryonic lung fibroblasts were purchased from the American Type Culture Collection . Cells were grown in Dulbecco's modified Eagle Medium ( DMEM ) with 10% fetal bovine serum , 100 µg/mL penicillin and streptomycin ( Invitrogen ) , and 4 . 5 g/L glucose . HSV-1 strain F [55] was kindly provided by B . Roizman ( University of Chicago ) , the HSV-1 KOS 1 . 1 strain [56] was a gift from D . Hargett ( Princeton University ) , and both viruses were grown in Vero cells [57] . BADwt-GFP is a phenotypically wild-type HCMV laboratory strain that was generated from a bacterial artificial chromosome ( BAC ) clone of strain AD169 [58] engineered to express green fluorescent protein [59] . TB40/E-eGFP is a phenotypically wild-type HCMV clinical isolate that was derived from a bacterial artificial chromosome termed TB40-BAC4 [60] containing a green fluorescent protein marker gene under control of the SV40 promoter between US34 and TRS1 . HCMV strains were grown in MRC-5 cells . To prepare virus stocks for both HSV-1 and HCMV , the media of infected cells was layered over a sorbitol cushion ( 20% sorbitol , 50 mM Tris-HCl , pH 7 . 2 , 1 mM MgCl2 ) and virus was pelleted by centrifugation ( 20 , 000 rpm , 1 h , 4°C , Beckman SW28 rotor ) . Virus stocks were prepared in DMEM with 0 . 5% bovine serum albumin and without fetal bovine serum , to avoid serum stimulation of the growth arrested fibroblasts during inoculation . For analysis of metabolites , fibroblasts ( HFFs or MRC5 ) were grown to confluence and maintained in the presence of serum for 5 d . Cells were then washed with serum-free DMEM and maintained in serum-free DMEM for 24 h before infection or mock treatment . Epithelial cells ( ARPE19 or Vero ) were grown to 80–90% confluence in DMEM with 10% dialyzed serum ( Gemini Bio-Products ) before infection . At the time of infection cells were inoculated with virus resuspended in DMEM with or without serum . Mock treated cells were inoculated with equivalent , virus-free DMEM . After a 1 h inoculation fresh DMEM was added to the cells , following two washes with the appropriate medium . For each time point in every experiment an additional mock treated and infected plate was processed for packed cell volume measurement . Approximately 5×105 cells were added to packed cell volume tubes ( Techno Plastic Products ) , which were centrifuged at 2000×g for 5 min before reading [61] . Packed cell volume measurements were used to normalize the metabolite levels between samples . At various times post infection or addition of 13C-labeled glucose- or glutamine-containing DMEM , the media of infected and mock cells was aspirated and −80°C , 80∶20 methanol∶water ( v/v ) was immediately added to quench metabolism . There were no washing steps prior to metabolism quenching , as such steps risk metabolic alterations . Metabolites were then extracted as described previously [21] . The extract was dried under nitrogen and metabolites were resuspended in HPLC-grade water and centrifuged at 15000×g speed for 5 min to remove particulate matter before analysis . To minimize complications due to excessive sample concentration and associated ion suppression during LC-MS analysis , samples were diluted substantially prior to analysis: metabolites collected from 106 cells ( a confluent 60 mm plate of fibroblasts ) were resuspended in 500 µL water . To quantitatively measure the levels of metabolites in extracts prepared from infected or mock treated cultured mammalian cells , two different mass spectrometry methods were employed . Liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) in selective reaction monitoring ( SRM ) mode was used to assay for ∼200 metabolites of confirmed identity from a wide range of metabolic pathways [62] . A Finnigan TWQ Quantum Ultra mass spectrometer was used in the positive ionization mode , and a TSQ Quantum Discovery MAX mass spectrometer in the negative mode , each equipped with an electrospray ionization source ( Thermo Fisher Scientific ) . The SRMs were constructed with parameters acquired through optimizing the collision induced fragmentation of purified standards of the given metabolites . The LC method in positive mode employed an aminopropyl column for separation [62] , while in negative mode the metabolite extracts were passed through a C18 column using tributylamine as an ion pairing agent to achieve longer retention of polar compounds [63] , [64] . In addition , the LC-MS/MS method was complemented with untargeted analysis using liquid chromatography coupled to a stand-alone orbitrap mass spectrometer ( Thermo Fisher Scientific Exactive instrument ) which performs full scans from 85 to 1000 m/z at 100 , 000 mass resolution [65] . In this system , identification of compounds is based on two parameters: the retention time on the LC column and the compound mass measured with less than 2 ppm mass accuracy . Peaks were identified and peak heights exported with the Metabolomic Analysis and Visualization Engine ( MAVEN ) [66] . For glucose uptake and lactate excretion measurements , media samples were collected every 3 h between 45 and 57 hpi for HCMV , and every 2 h between 6 and 18 hpi for HSV-1 . The concentrations of lactate and glucose were measured using a YSI 7100 Select Biochemistry Analyzer ( YSI Incorporated ) . Uptake and excretion rates were determined as the rate of concentration change of these compounds in the media . The values were corrected using the packed cell volume of the infected and mock cells . For experiments involving monitoring the rate of incorporation of 13C-labeled nutrient into downstream metabolites , cells were switched to fresh media 1 h before addition of the labeled nutrient . This minimized the perturbation to the cells when their medium was replaced with isotope containing medium . Cells were then maintained in medium containing the labeled nutrient for different lengths of time . Metabolites were extracted and various isotopically labeled forms quantified by mass spectrometry . The values were corrected for the natural abundance of 13C as described previously [10] . Labeled DMEM was prepared from glucose and glutamine-free DMEM with the addition of U-13C-glucose or U-13C-glutamine ( Cambridge Isotope Laboratories ) . All media were equilibrated to the incubator temperature and gas composition before use . Double stranded siRNA molecules directed against pyruvate carboxylase ( 5′-GACUGUACGCGGCCUUCGATT ) , aspartate transaminase 2 ( 5′-CUAUUGAGAGCUUCACACATT ) , and a Universal Negative Control ( SIC001 ) were purchased from Sigma . Subconfluent MRC5 cells seeded into 96-well plates were transfected with 10 pmol of siRNA using Oligofectamine transfection reagent ( Invitrogen ) according to the manufacturer's instructions . For HCMV experiments , the siRNA transfected cells were incubated for 24 hours and then infected with HCMV strain BADwt-GFP at a multiplicity of 0 . 1 pfu/cell . The cells were further incubated for 96 hours and media containing the infectious virus were harvested . Since HSV-1 replicates with a faster kinetics than HCMV , the transfected cells were incubated for 3 days to allow efficient knockdown of target gene . The cells were then infected with HSV-1 strain F at a multiplicity of 0 . 02 pfu/cell and media were harvested 24 hours after infection . The yield of HCMV and HSV in the media was determined by infectious focus assay . Briefly , fresh MRC5 cells were infected with different dilutions of viruses and fixed 24 hours after HCMV or 4 hours after HSV-1 infection with methanol at −20°C . Foci were identified using mouse monoclonal primary antibodies to HCMV immediate early IE1 protein ( 1B12 ) [54] or HSV-1 immediate early ICP4 protein [67] and a goat anti-mouse Alexa Fluor 488-conjugated secondary antibody ( Invitrogen ) . MRC5 cells were seeded into 6-well dishes and transfected at 70% confluence as described above . HFF cells were grown to confluence , serum starved for 24 hours and infected at 3 pfu/cell with HSV-1 ( F strain ) . At selected times post transfection of MRC5 cells and infection of HFFs , cells were washed with phosphate-buffered saline ( PBS ) , harvested and stored at −80°C . Cells were lysed in RIPA-light buffer ( 50 mM Tris-HCl , pH 8 . 0 , 1% NP-40 , 0 . 1% SDS , 150 mM NaCl , 0 . 1% Triton X-100 , 5 mM EDTA ) with protease inhibitors ( Roche Applied Science ) , and protein concentrations were determined by Bradford assay . Proteins were separated by 10% SDS-containing polyacrylamide gel electrophoresis and transferred to nitrocellulose membranes . Membranes were probed with a primary rabbit polyclonal antibody directed against pyruvate carboxylase ( NBP1-49536 , Novus ) at a dilution of 1∶1000 in PBS-T and 1% nonfat milk . After washing with PBS-T , membranes were probed with goat anti-rabbit HRP-coupled secondary antibodies diluted 1∶5000 in PBS-T containing 1% milk . Proteins were visualized by chemiluminescence using the ECL detection system ( Amersham ) . All p-values were calculated by two-tailed , non-paired T-test . Pyruvate carboxylase ( PC ) : P11498 , aspartate transaminase 2 ( GOT2 ) : P00505 , carbamoyl-phosphate synthetase 2 , aspartate transcarbamylase , and dihydroorotase ( CAD ) : P27708 , HSV-1 dUTPase ( UL50 ) : P10234 , HSV-1 uracil-DNA glycosylase ( UL2 ) : P10186 , HSV-1 thymidine kinase ( UL23 ) : P03176 .
|
Enveloped viruses draw on cellular machinery and materials to generate copies of their genome , structural proteins , and membrane . These biosynthetic processes use the host metabolic network to provide energy and small-molecule precursors . We have investigated how two important enveloped viruses , human cytomegalovirus and herpes simplex virus-1 , alter host metabolism to provide materials for viral replication . We show that rather than passively relying on basal host cell metabolic activity , both viruses actively redirect host cell metabolism , implementing divergent metabolic programs that are robust to host cell type and virus strain . Human cytomegalovirus enhances lipid biosynthesis , while herpes simplex-1 gears central carbon metabolism toward the synthesis of pyrimidine nucleotides . Consistent with these changes , human cytomegalovirus is more sensitive to inhibition of fatty acid synthesis and herpes simplex virus-1 to inhibition of central metabolic reactions leading towards pyrimidine synthesis . As these two closely related viruses have divergent metabolic strategies , and since the metabolic perturbations point to potential drug targets , an important priority is defining the metabolic programs of other viruses .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"virology",
"biology",
"microbiology",
"metabolism"
] |
2011
|
Divergent Effects of Human Cytomegalovirus and Herpes Simplex Virus-1 on Cellular Metabolism
|
The World Health Organization ( WHO ) advises treatment of Mycobacterium ulcerans disease , also called “Buruli ulcer” ( BU ) , with a combination of the antibiotics rifampicin and streptomycin ( R+S ) , whether followed by surgery or not . In endemic areas , a clinical case definition is recommended . We evaluated the effectiveness of this strategy in a series of patients with large ulcers of ≥10 cm in longest diameter in a rural health zone of the Democratic Republic of Congo ( DRC ) . A cohort of 92 patients with large ulcerated lesions suspected to be BU was enrolled between October 2006 and September 2007 and treated according to WHO recommendations . The following microbiologic data were obtained: Ziehl-Neelsen ( ZN ) stained smear , culture and PCR . Histopathology was performed on a sub-sample . Directly observed treatment with R+S was administered daily for 12 weeks and surgery was performed after 4 weeks . Patients were followed up for two years after treatment . Out of 92 treated patients , 61 tested positive for M . ulcerans by PCR . PCR negative patients had better clinical improvement than PCR positive patients after 4 weeks of antibiotics ( 54 . 8% versus 14 . 8% ) . For PCR positive patients , the outcome after 4 weeks of antibiotic treatment was related to the ZN positivity at the start . Deterioration of the ulcers was observed in 87 . 8% ( 36/41 ) of the ZN positive and in 12 . 2% ( 5/41 ) of the ZN negative patients . Deterioration due to paradoxical reaction seemed unlikely . After surgery and an additional 8 weeks of antibiotics , 98 . 4% of PCR positive patients and 83 . 3% of PCR negative patients were considered cured . The overall recurrence rate was very low ( 1 . 1% ) . Positive predictive value of the WHO clinical case definition was low . Low relapse rate confirms the efficacy of antibiotics . However , the need for and the best time for surgery for large Buruli ulcers requires clarification . We recommend confirmation by ZN stain at the rural health centers , since surgical intervention without delay may be necessary on the ZN positive cases to avoid progression of the disease . PCR negative patients were most likely not BU cases . Correct diagnosis and specific management of these non-BU ulcers cases are urgently needed .
Mycobacterium ulcerans disease , commonly called “Buruli ulcer” ( BU ) , is a neglected and emergent tropical disease [1] , [2] , with Africa being the most affected continent [3] . For many years , management of the disease relied mainly on surgical procedures [4] , [5] , [6] . Other treatment strategies included antibiotics alone or followed by surgery [7] , [8] . A proof-of-principle study ( phase-2 trial ) conducted in Ghana evaluated the efficacy of the combination of rifampicin and streptomycin ( R+S ) on early BU lesions ( nodules and plaques ) , and found that after 4 weeks of treatment with these drugs , it was no longer possible to cultivate M . ulcerans from these lesions [9] . This pilot trial led to the World Health Organization ( WHO ) recommendation to treat all BU lesions with R+S , whether followed by surgery or not [10] . WHO guidelines define three categories of treatment based on: ( 1 ) clinical form ( ulcerative or non-ulcerative ) , ( 2 ) lesion size ( lesions less than 5 cm and lesions of 5 cm or more in diameter ) , and ( 3 ) disseminated or mixed forms . Antibiotic treatment of 8 weeks is recommended for all three categories . For lesions ≥5 cm , surgery is recommended , if necessary , after at least 4 weeks of antibiotic treatment [10] . In 2005 , WHO indicated that for very large lesions , antibiotic treatment may be administered for up to 12 weeks [11] . A case-series in Benin showed that of 224 patients treated by the WHO strategy , 215 were successfully treated , with 47% of them receiving antibiotics only . The size of the lesion was the major parameter in deciding to treat by surgery: 73% of patients with lesions of >15 cm in diameter underwent surgery , compared to 17% of patients with lesions of <5 cm [12] . More recently , Nienhuis et al . demonstrated that antimycobacterial treatment alone was effective in 151 patients with early limited BU disease [13] . The efficacy of R+S therapy on large ulcerated forms of BU , which currently are the most common forms of BU in Africa [14] , [15] , [16] remains insufficiently documented . In such cases the efficacy of antibiotics could be compromised by the extent of the necrosis [17] . The objective of the present study was to estimate the efficacy of the standard WHO recommended regimen ( R+S followed by surgery ) in patients with large ulcerated ulcers ( ≥10 cm in longest diameter ) in a rural health zone ( RHZ ) of the Democratic Republic of Congo ( DRC ) . The data obtained allowed us to assess the positive predictive value of the WHO clinical case definition for BU .
This is a prospective observational study that analyzes the response of patients treated with R+S for 12 weeks and usually followed by surgery after the first 4 weeks , a slight adaptation from the 2005 WHO protocol [11] . Study procedures are summarized in the flow sheet ( Figure 1 ) . Patients clinically suspected of BU were recruited from the RHZ of Nsona-Mpangu , Province of Lower-Congo in DRC . This health zone has long been known to be moderately endemic for BU [15] , [18] . Patients were enrolled between October 2006 and September 2007 in 17 Health Centers and one General Reference Hospital . Suspected cases were identified by the head nurse of the Health Centre and later confirmed by two physicians , both co-authors , ( MK , Supervisor of the BU Control Program in the RHZ of Nsona-Mpangu and J-BM , Chief of the RHZ of Nsona-Mpangu ) . After this , the Principal Investigator ( PI ) ( KK ) , personally reviewed all cases . The final decision on classification of cases for this study was reached by consensus of the three physicians ( KK , MK and J-BM ) . For quality assurance , we conducted a post-hoc confirmation of the clinical classification by evaluating photographs of lesions taken at the start of treatment , after 4 weeks of antibiotic treatment , at the end of the antibiotic treatment and at the healed stage . A sample of these photographs was checked retrospectively by three individuals: two other co-authors ( WMM and FP ) and by an African colleague ( Dr . G . Sopoh ) from Benin all of whom are experienced in the clinical diagnosis of BU . The clinical case definition of BU in this study was , “Ulcerative lesions ( maximum diameter ≥10 cm ) , painless or minimally painful , with characteristic undermined edges and a yellowish-white necrotic base surrounded by edematous skin” . All consenting patients fitting this definition were treated in accordance with WHO recommendations . They were assessed by the PI to determine inclusion in the study . Biological samples were simultaneously collected and sent to reference laboratories for case confirmation by 4 laboratory methods: Ziehl-Neelsen ( ZN ) staining , culture , PCR , and histopathology [1] . The study protocol was approved by the Ethical Committees of the Universitair Ziekenhuis Antwerpen , ( N° 6/42/197 ) in Belgium and of the School of Public Health , University of Kinshasa , ( N° ESP/CE/043 ) in DRC . Management of patients was free of charge . Participation in the survey was voluntary and written informed consent was obtained from all participants or their guardian .
Of 94 eligible patients , two were excluded: one refused treatment and one was lost to follow-up . Overall , 92 patients were included in this study with a mean ulcer size of 13 . 81 cm ( SD 16 . 21 ) . The male/female ratio was 0 . 88 . Only patients with ulcers of more than 10 cm in longest diameter were included , thus 90 patients were classified as WHO Category II and two as Category III because of multiple lesions ( Table 1 ) . A total of 61 ( 66 . 3% ) patients were positive by PCR for M . ulcerans ( group I ) and 31 were PCR negative ( group II ) at the start of treatment . Table 1 presents the characteristics of group I and group II patients at start of treatment . There was no age difference noted between the two groups . There were significantly more female patients in group I ( 60 . 6% ) compared to group II ( 38 . 7% ) ( p = 0 . 046 ) . The lower limbs were significantly more frequently affected in group II ( 74 . 1% ) than in group I ( 52 . 4% ) ( p = 0 . 044 ) . The average diameter of the ulcer in group I was 10 . 07 cm ( SD = 1 . 95 ) . In group II , it was 11 . 39 cm ( SD 5 . 82 ) . The difference in initial diameter of the ulcers between groups I and II at the beginning of treatment was not statistically significant ( p = 0 . 320 ) . Of the 61 PCR positive patients ( group I ) , 48 patients ( 78 . 7% ) had a positive ZN and 22 ( 36 . 1% ) were culture positive for M . ulcerans , whereas none ( 0% ) of the 31 PCR negative ( group II ) patients were ZN positive or culture positive ( p<0 . 001 ) . Histopathologic data were available for 49 patient samples ( 20 PCR positive and 29 PCR negative ) . As shown in Table 1 , 95 . 0% ( 19/20 ) of the PCR positive patients had histopathologic features compatible with BU: i . e . contiguous coagulation necrosis of the lower dermis , subcutaneous tissue and underlying fascia; vasculitis in the subcutaneous tissue , and presence of AFB . None of the 29 PCR negative patient samples were positive for AFB . In 25 patients ( 86 . 2% ) histopathologic analysis revealed no characteristic feature of BU and specimens from 12 patients ( 48 . 0% ) showed chronic inflammation . Five patients ( 20 . 0% ) had bacterial suprainfections with gram-positive cocci . Four patients ( 16 . 0% ) had other dermatologic affections ( impetigo , pyogranulomatous dermatitis , chronic dermatitis , hidradenitis ) . Three patients had vascular disorders and one a dermatophytosis . Only four of the 29 PCR negative patients had histopathologic changes compatible with BU but were AFB negative . Clinical responses in the 2 groups after 4 weeks of antibiotic treatment are presented in Table 2 . PCR negative patients had a higher percentage of clinical improvement ( 54 . 8% ) than PCR positive patients ( 14 . 8% ) . This difference was statistically significant ( p<0 . 001 ) . As shown in Table 3 , for the 61 PCR-positive patients , the clinical outcome at the 4th week assessment was related to ZN and culture results at the start of treatment . Indeed , after 4 weeks of R+S , significantly more treatment failures were observed among the ZN positive patients , 75 . 0% ( 36/48 ) , compared to only 38 . 5% ( 5/13 ) of the ZN negative patients ( p = 0 . 013 ) . Treatment successes were obtained in 6 . 3% ( 3/48 ) of the ZN positive patients compared to 46 . 2% ( 6/13 ) of the ZN negative patients . Similarly the outcome was more successful for culture negative patients ( 8/39 or 20 . 5% ) than for patients with positive M . ulcerans cultures ( 1/22 or 4 . 5% ) . The latter difference , however , was not significant . All patients underwent surgical excision after the 4th week except one PCR negative patient who refused surgery . This patient died one month after the end of treatment due to septicemia . After surgical excision of the lesions in the 91 remaining patients , skin grafting was performed when good granulation tissue had formed . Table 2 presents the clinical outcome at the end of the 12th week of treatment . All but one patient in group I were cured ( 98 . 4% success ) . The failed case developed disseminated BU with osteomyelitis . For patients in group II , 25 patients ( 83 . 3% ) were cured; lesions of 4 ( 13 . 3% ) patients deteriorated and one patient remained unchanged . The difference in the outcome at 12 week between group I and group II was statistically significant ( p = 0 . 023 ) . In addition , there was a significant difference ( p = 0 . 026 ) in the average time of scarring of ulcers between group I and group II patients . Indeed , PCR positive patients had a longer average time to scarring ( 10 . 4 weeks ) than PCR negative patients ( 7 . 5 weeks ) . The 4 failure cases were treated with regular dressings only and were cured after seven to 12 months . Two recurrences were observed among the 61 patients with positive PCR . A 7-year-old patient presented with new ulceration at the original site five months after the ulcer had healed . Microbiologic analyses ( ZN and PCR ) of biopsy specimens were negative . After interview , it became clear that the patient's scar had been accidentally traumatized . The lesion completely healed after a few days and thus , this was not a true recurrence . An 8-year-old patient presented with an ulcer associated with osteomyelitis of the humerus at the original site of the lesion ( right elbow ) six months after the end of R+S treatment . This patient also showed functional limitations with contracture and substantial decrease of mobility in the right elbow . No laboratory test was performed at recurrence as the patient and his parents refused surgical biopsy and any surgical prevention of disability treatment at this time . Seven months after the recurrence , the patient accepted surgery but despite interventions performed by plastic surgeons , the patient was left with severe sequelae . No disease recurrence was observed among patients with negative PCR .
This cohort study of large BU-like lesions in rural RDC has two major outcomes: i ) the positive predictive value of the WHO clinical case definition ( i . e . the number of true BU cases among all the BU-like lesions studied ) was low , and ii ) delaying surgical treatment to week 4 of antibiotic treatment may be detrimental for ZN positive cases with large ulcers . The criteria for case ascertainment used in this study to discern whom to treat for BU were primarily clinical and epidemiological . We analyzed clinical outcomes of the patients in our cohort into two distinct groups according to PCR results . PCR analysis was retained because of its high sensitivity compared to the other laboratory tests for the diagnostic confirmation of BU [20]–[21] . Using a clinical diagnosis as reference standard , Chauty et al . [12] obtained a PCR positivity of 57 . 2% . Using the same reference standard , Mensah-Quainoo et al . [22] had a PCR positivity of 72 . 3% and Stienstra et al . [23] had 74 . 8% . In our study , the PCR positivity was 66 . 3% and did not substantially differ from the above mentioned publications in which clinical diagnosis was the only reference standard . The fact that 33 . 7% ( 31/92 ) of our clinically suspected cases of BU were PCR negative raises the question of whether or not these ulcers were really M . ulcerans infections . Clinically suspected cases of BU may be PCR negative if the collection of specimens is not adequate . In our study , the collected specimens were inadequate for histopathologic diagnosis of BU for 5 of 29 PCR negative patients ( biopsy specimens too superficial ) . It is , however , unlikely that the PCR negative results were related to inadequate sampling because for each patient , 2 to 5 specimens were collected and other tests ( ZN stained smears and culture ) were negative for all specimens . Among the 29 PCR negative patients analyzed by histopathology , 25 showed histopathologic features not compatible with BU at start of treatment . Most specimens showed chronic inflammation and some showed bacterial infection due to gram positive cocci . Microbiologic and histopathologic analyses indicate that the PCR negative patients were most likely not BU cases although the clinical aspects of the ulcerated lesions were considered compatible with BU by three physicians who made the diagnosis in DRC before treatment . The histopathologic examinations provided accurate diagnoses for some of these cases ( ulcers due to bacterial infections with gram positive cocci , vascular disorders , dermatophytosis ) [24] , [25] , [26] . Four PCR negative patients , however , showed histopathologic features compatible with BU ( extensive coagulation necrosis in subcutis ) but no AFB were seen . Their clinical status at 4 weeks was deteriorating . Histopathologic changes of these lesions were nonspecific [17] , [25] , [27] . In our opinion , the absence of AFB in histologic examination and negative PCR results make the diagnosis of BU very unlikely for most patients classified in group II , thus we may conclude that the positive predictive value of a clinical case definition of BU was low in this series of large ulcerated lesions . A post-hoc confirmation of the clinical diagnosis of a sample of PCR negative patients , based on retrospective examination of photographs taken before treatment , did not reveal typical BU features . The clinical diagnosis of ulcerated forms of BU may be more difficult than is usually recognized , underlining the importance of confirmation by laboratory tests [1] , [20] . The clinical response after 4 weeks of antibiotic treatment was significantly more successful for PCR negative patients ( 54 . 8% ) than for PCR positive patients ( 14 . 8% ) . If these PCR negative ulcers were due to bacteria other than M . ulcerans it is likely that the antibiotic treatment was efficient against these bacteria ( gram positive coccal infections ) . For PCR positive patients , the clinical outcome after 4 weeks was related to the ZN positivity at the start of antibiotic treatment . Indeed , successful treatment after 4 weeks of antibiotic treatment was observed in 46 . 2% ( 6/13 ) of the ZN negative patients and in 6 . 3% ( 3/48 ) of the patients who were ZN positive at the start of treatment . Deterioration of the ulcers was observed in 87 . 8% ( 36/41 ) of the ZN positive patients and in 12 . 2% ( 5/41 ) of the ZN negative patients . Increase in size of ulcer after 4 weeks , however , does not necessarily imply treatment failure . Paradoxical worsening during treatment was recently reported by O'Brien et al . [28] . As stated by Johnson [29] , these reactions may indeed “contribute to the view that antibiotics are ineffective” . According to Chauty et al . [30] , Nienhuis et al . [31] and O'Brien et al . [28] , these reactions may be characterized by an initial clinical improvement on antibiotic treatment followed by clinical deterioration and by symptoms such as pain and increasing local temperature . Accordingly , histopathologic examination of excised tissue after antibiotic treatment shows florid inflammatory reactions [28] . None of our patients whose lesions enlarged after 4 weeks of antibiotic treatment presented an initial improvement during the first weeks of antibiotherapy or experienced pain or increased local temperature . Moreover , histopathologic examination of tissue excised after 4 weeks of treatment only revealed an increase of the chronic type of inflammatory response in some patients , as previously described following antibiotic treatment [29] . A significant decrease of the positivity for the laboratory tests was , however , observed after 4 weeks of antibiotic treatment indicating that the drugs had some effect on the bacilli ( data not shown ) . Loss of potency of the antibiotics was not an issue since cold chain measures were respected and antibiotics were kept in refrigerators . We , therefore , consider these clinical deteriorations after 4 weeks as probable failures . Although paradoxical reactions during antibiotic treatment should be better documented in patients with large ulcerated lesions , we believe that ZN positive patients should be treated by surgery without delay since previous studies have suggested an association between the ZN positivity of cutaneous lesions and bone dissemination [32] . This concern was illustrated by one of our PCR positive patients who was ZN positive and did not present any clinical evidence of bone involvement at the start of treatment but developed a recurrence with osteomyelitis and severe deformities six months after the end of antibiotic treatment . The need of immediate surgery for ZN positive large ulcers remains , however , speculative and further studies are required to determine its importance in the management of BU . After 12 weeks of antibiotic treatment including surgery after the 4th week , 98 . 4% ( 60/61 ) of the PCR positive patients and 83 . 3% ( 25/29 ) of the PCR negative patients were cured . The 16 . 7% of PCR negative patients who were not cured in our series could have been cases of non-bacterial origin . After a follow-up of 2 years , there was only one recurrence among the 91 patients ( 1 . 1% ) . This recurrence rate falls within the range of <2% published by WHO [1] . Indeed , according to WHO , recurrences , reported in 16–30% of cases after surgical treatment alone , have fallen to <2% following the introduction of antibiotics ( R+S ) [1] . Given our high cure ( 92 . 4% ) and low recurrence rates ( 1 . 1% ) , it seems beneficial to treat large ulcers , whether BU confirmed or not , with antibiotics . The very low relapse rate confirms the efficacy of antibiotics . However , the need and the best time for surgery for large ulcers should be clarified . Further studies are also required to define the type of antibiotic therapy for non-BU large ulcers , and ideally should be based on specific diagnoses . A potential weakness of our study is lack of information on the HIV status of our patients , but at the time of the study there was not yet any regular HIV counseling nor antiretroviral care available . The prevalence of HIV infection in the rural area of Nsona Mpangu is less than 3 . 0% according to the “Programme National Multisectoriel de Lutte contre le VIH/SIDA” [33] . Co-infection with HIV should however be studied in DRC and elsewhere . In Benin , a case-control study comparing HIV-1/HIV2 seroprevalence in BU patients suggests HIV seropositivity increases the risk for BU [34] . HIV infection may also render BU highly aggressive , especially with regard to osteomyelitis . There is also an urgent need for studies to evaluate treatment of HIV positive BU patients with R+S and antiretroviral drugs [35] . The strengths of this study are that: 1 ) the study was performed in a remote rural BU endemic area; 2 ) for the first time the antibiotic treatment of patients with large ulcerated lesions was documented with a follow-up of at least two years; 3 ) all cases were laboratory confirmed by several tests including histology . In conclusion , our study shows that health professionals dealing with BU may have difficulties in recognizing large ulcers due to M . ulcerans on clinical and epidemiologic basis only , hence the importance of microbiologic confirmation by ZN staining at rural health centres . Furthermore , in ZN positive large ulcerated forms of BU ( ≥ to 10 cm in longest diameter ) , the efficacy of antibiotic treatment recommended by the WHO should be better documented and the need and the best time for surgery must be clarified . Finally , our data show that it is possible to successfully treat 92 . 4% ( 85/92 ) of patients suffering from large ulcers ( whether due to M . ulcerans or not ) with low recurrence rates ( 1 . 1% ) by combining an antibiotic treatment with surgery in a rural zone . The data also highlight the need for more specific management of non-BU ulcers .
|
Buruli ulcer ( BU ) disease , a neglected devastating infection caused by Mycobacterium ulcerans , has a huge impact because of the massive necrotizing , disfiguring ulcers that may result if not treated . Therapeutic options are surgery , antibiotics or combinations of both . Since 2004 , the World Health Organization has recommended the use of antibiotics ( rifampicin and streptomycin ) for the management of the disease . The effectiveness of this antibiotic treatment on advanced lesions is , however , not well documented . We evaluated this strategy on large ulcers clinically suspected to be BU , in a rural zone of the Democratic Republic of Congo , and also assessed the outcome of treatment based only on clinical diagnosis . All patients were treated with antibiotics for 12 weeks and surgery was performed after 4 weeks . BU was confirmed by laboratory tests in 67% of the patients indicating that the clinical diagnosis of ulcerated forms of BU may be more difficult than usually reported . Although delayed surgery seemed detrimental in some confirmed cases , it was possible to treat 92% of patients successfully with low recurrence rates ( 1 . 1% ) by combining antibiotic treatment with surgery in a rural zone . However , the need for and the best time for surgery for large Buruli ulcers requires clarification .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases"
] |
2010
|
Response to Treatment in a Prospective Cohort of Patients with Large Ulcerated Lesions Suspected to Be Buruli Ulcer (Mycobacterium ulcerans Disease)
|
Amyotrophic lateral sclerosis ( ALS ) and frontotemporal lobar degeneration ( FTLD ) are two incurable neurodegenerative disorders that exist on a symptomological spectrum and share both genetic underpinnings and pathophysiological hallmarks . Functional abnormality of TAR DNA-binding protein 43 ( TDP-43 ) , an aggregation-prone RNA and DNA binding protein , is observed in the vast majority of both familial and sporadic ALS cases and in ~40% of FTLD cases , but the cascade of events leading to cell death are not understood . We have expressed human TDP-43 ( hTDP-43 ) in Drosophila neurons and glia , a model that recapitulates many of the characteristics of TDP-43-linked human disease including protein aggregation pathology , locomotor impairment , and premature death . We report that such expression of hTDP-43 impairs small interfering RNA ( siRNA ) silencing , which is the major post-transcriptional mechanism of retrotransposable element ( RTE ) control in somatic tissue . This is accompanied by de-repression of a panel of both LINE and LTR families of RTEs , with somewhat different elements being active in response to hTDP-43 expression in glia versus neurons . hTDP-43 expression in glia causes an early and severe loss of control of a specific RTE , the endogenous retrovirus ( ERV ) gypsy . We demonstrate that gypsy causes the degenerative phenotypes in these flies because we are able to rescue the toxicity of glial hTDP-43 either by genetically blocking expression of this RTE or by pharmacologically inhibiting RTE reverse transcriptase activity . Moreover , we provide evidence that activation of DNA damage-mediated programmed cell death underlies both neuronal and glial hTDP-43 toxicity , consistent with RTE-mediated effects in both cell types . Our findings suggest a novel mechanism in which RTE activity contributes to neurodegeneration in TDP-43-mediated diseases such as ALS and FTLD .
RTEs are “genomic parasites”–“selfish” genetic elements that are coded within our genomes and that replicate themselves via an RNA intermediate . After transcription , an RTE-encoded reverse transcriptase generates a cDNA copy , and this cDNA is inserted into a new genomic location at the site of double stranded DNA breaks created by an endonuclease activity encoded by the RTE [1] . Unrestrained RTE activity has been demonstrated to be highly destructive to genomes , resulting in large-scale deletions and genomic rearrangements , insertional mutations , and accumulation of DNA double strand breaks [2] . RTE-derived sequences constitute ~40% of the human genome , a quantity which encompasses a surprisingly large number of functional RTE copies . Although multiple interleaved , highly conserved gene silencing systems have evolved to protect the genome by blocking RTE expression , certain RTEs are nevertheless expressed in some somatic tissues [3 , 4] and can replicate in a narrow window during neural development , leading to de novo genomic insertions in adult brain [5–12] . Moreover , a gradual deterioration of RTE suppression–and resultant increase in RTE activity–has been documented with advancing age in a variety of organisms and tissues [13–20] , including the brain [21] . One or more of the gene silencing systems that normally block genotoxic RTE expression may therefore be weakened with age . We advance the novel hypothesis that a broad and morbid loss of control of RTEs contributes to the cumulative degeneration observed with TDP-43 protein aggregation pathology that is observed in a variety of neurodegenerative disorders , including ALS and FTLD , and that this loss of control of RTEs is the result of a negative impact of TDP-43 pathology on general RTE suppression mechanisms that are most prevalently relied upon in somatic tissue such as the brain . TDP-43 is a member of the hnRNP family that homodimerizes to bind single stranded RNA and DNA with UG/TG-rich motifs [22] . This pleiotropic protein was originally identified as a transcriptional repressor that binds to the TAR element of the HIV-1 retrovirus to repress transcription [23] . TDP-43 is capable of shuttling back and forth from the nucleus to the cytoplasm but is predominantly found in the nucleus in healthy cells . In cells that are experiencing TDP-43 protein pathology , the protein accumulates in dense cytoplasmic inclusions that include full-length protein , caspase cleavage products and C-terminal fragments of TDP-43 , as well as abnormally phosphorylated and ubiquitinated protein [24–26] . TDP-43 protein pathology is currently thought to involve toxicity incurred by cytoplasmic aggregates , interference with normal cytoplasmic function , depletion of normal nuclear TDP-43 stores , or some combination thereof [27] . Functional abnormality of TDP-43 , an aggregation-prone RNA binding protein , is commonly observed in a spectrum of neurodegenerative diseases that spans motor neuron deterioration and progressive paralysis in ALS to dementia and cognitive decline in FTLD [28] . 90% of ALS cases and a large fraction of FTLD cases are considered to be genetically sporadic , in the sense that no known genetic lesion precipitates pathology . However , loss of nuclear TDP-43 and accumulation of TDP-43 immunoreactive cytoplasmic inclusions is observed in nearly all ALS and almost half of FTLD cases [28–30] . The mechanism that initiates the nucleation of TDP-43 protein pathology in apparently genetically normal individuals is not understood [29] . However , TDP-43 contains a low complexity domain in its C-terminal region , which is a common feature of RNA binding proteins that exhibit aggregation pathology in a variety of neurodegenerative disorders . Indeed a recent literature has established that cellular stress can induce such low complexity domain proteins , including TDP-43 , to undergo a concentration dependent phase separation to form liquid droplets that over time can drive fibrilization [31–33] . TDP-43 protein also is known to accumulate in cytoplasmic stress granules in response to cellular stress [34] . Importantly , nuclear TDP-43 protein normally regulates splicing of TDP-43 mRNA , leading to nonsense mediated decay of its own message [35] . Thus the formation of cytoplasmic inclusions and clearance from the nuclear compartment that is observed in patients is also associated with loss of this feedback inhibition onto TDP-43 mRNA , leading to increased accumulation of cytoplasmic TDP-43 mRNA [36] , which likely exacerbates formation of cytoplasmic inclusions . Animal models of TDP-43 related disorders–and neurodegenerative disorders in general–have taken advantage of the concentration dependence of low complexity domain protein aggregation [37] . Most animal models of neurodegenerative diseases therefore have involved transgenic expression to increase protein concentration above endogenous levels , and reproduce many of the signatures of human disease , which in the case of TDP-43 includes aggregation of TDP-43 protein in cytoplasmic inclusions and downstream neurological defects [28 , 38 , 39] . Although such animal models are imperfect representations of what is largely a sporadically occurring disorder , they have enabled the delineation of a myriad of cellular roles for TDP-43 [28 , 40] and have provided the means to uncover genetic interactions between TDP-43 and other genes that are implicated in neurodegenerative disorders [41–44] . TDP-43 pathology in animal models is now understood to cause global alterations in mRNA stability and splicing , de-repression of cryptic splicing , and biogenesis of some microRNAs ( miRNAs ) [28 , 29 , 38 , 39 , 45–47] . In principle , any of the cellular impacts of TDP-43 protein pathology could contribute to disease progression either alone or in combination . However , no clear consensus has yet emerged regarding the underlying causes of neurodegeneration in TDP-43 pathologies . The RTE hypothesis investigated here is motivated by a series of prior observations . First , as mentioned above , LINE 1 RTEs are expressed in some somatic tissue [3 , 4] and can actively replicate during normal brain development , leading to de novo genomic insertions in adult brain tissue [5–12] , although the frequency of de novo insertions per cell is still hotly debated [48 , 49] . Second , increased RTE activity occurs in the brain during aging [21] . Moreover , elevated expression of RTEs has been detected in a suite of neurodegenerative diseases [50–57] and reverse transcriptase biochemical activity of unknown origin has been shown to be present in both serum and cerebrospinal fluid ( CSF ) of HIV-negative ALS patients [58–61] . More recently , a specific RTE , the human ERV HERV-K , was found to be expressed in post-mortem cortical tissue of ALS patients and their blood relatives [50 , 55 , 58] and transgenic expression of the HERV-K Envelope ( ENV ) protein in mice is sufficient to cause motor neuron toxicity [55] . Finally , we have previously predicted via meta-analysis of RNA Immunoprecipitation ( RIP ) and Crosslinked RIP ( CLIP ) sequencing data that TDP-43 protein binds broadly to RTE–derived RNA transcripts in rodent and human brain tissue and that this binding is selectively lost in cortical tissue of FTLD patients [54] . However to date , no studies exist which address whether TDP-43 pathology causes endogenous RTEs to become expressed in vivo , no reports have probed the functional impact of TDP-43 pathology on the natural mechanisms of RTE suppression employed by somatic tissue such as the brain , and no studies have investigated the toxic effects of endogenous RTE activation on nervous system function . The destructive capacity of RTEs has been extensively documented in many other biological contexts , including a wealth of seminal data from Drosophila melanogaster [62–64] . To test whether RTEs play a role in TDP-43 mediated neurodegeneration , we used an established Drosophila transgenic model which afforded the means to examine whether RTE activation causally contributes to TDP-43 mediated toxicity and cell death . We found that several hallmarks of TDP-43-induced degeneration are the result of activation of the gypsy ERV , an RTE that is structurally related to HERV-K , and that this activation leads to DNA damage mediated cell death . Moreover , we uncovered an inhibitory effect of TDP-43 expression on small interfering RNA ( siRNA ) mediated silencing , leading to broad activation of a panel of RTEs . These findings strongly suggest a broad impact of TDP-43 pathology on general RTE activity .
In order to determine whether RTEs contribute to TDP-43 pathological toxicity , we implemented an established animal model in which hTDP-43 is transgenically expressed in Drosophila . As with other animal models , including mouse , rat , fish , and C elegans , such expression reproduces many neuropathological hallmarks of human disease , likely via interference with endogenous protein ( s ) function [27 , 38 , 39 , 65 , 66] . In Drosophila , there is an endogenous putative ortholog of TDP-43 , TBPH . Null mutations in TBPH in flies are lethal [67] , as is the case with mammalian TDP-43 . Hypomorphic loss of TBPH results in neurodevelopmental defects as with the mammalian gene . Overexpression-mediated toxicity has formed the basis of the preponderance of studies on TDP-43 in animal models , and has revealed much of what is known regarding TDP-43 protein function and dysfunction , leading to the dominant hypotheses regarding mechanisms of pathogenesis wherein toxic cytoplasmic aggregates are thought to contribute to disease progression [27–29 , 39 , 40 , 66 , 68] . To test the impact of expressing hTDP-43 on RTE expression , we first used RNA sequencing ( RNA-seq ) to profile transcript abundance . In patient tissue , TDP-43 protein pathology is observed in both neurons and glial cells [29] and an emerging literature has implicated glial cell toxicity in ALS [69–71] . Toxicity of TDP-43 in glia has similarly been documented in animal models , including in Drosophila [72–75] . We therefore examined the effects of transgenic hTDP-43 expression in the neuronal versus glial compartments of the brain . We conducted paired-end Illumina based RNA-seq on head tissue of flies expressing either pan-neuronal ( ELAV > hTDP-43 ) or pan-glial ( Repo > hTDP-43 ) hTDP-43 compared with control flies that carried the hTDP-43 transgene alone with no Gal4 driver ( hTDP-43 / + ) . We generated two independent sequencing libraries for each genotype from a population of animals that were 8–10 days post-eclosion . We generated a total of ~900 million reads , or about 150 million reads per sample ( S1 Table ) , and conducted differential expression analysis ( see methods ) . In order to identify effects both on gene transcripts and RTE transcripts ( Fig 1A–1D; S2A and S3B Tables ) , we included reads that map to repetitive elements using an analysis pipeline that we have previously reported [54 , 76] . Both glial ( Repo > hTDP-43 ) and neuronal ( ELAV > hTDP-43 ) expression of hTDP-43 caused differential expression of a number of cellular transcripts ( Fig 1A and 1C; S2A and S3A Tables ) and transposons , most of which were RTEs or Class I elements ( Fig 1B and 1D; S2B and S3B Tables ) . In the case of differentially expressed genes , a broad spectrum of cellular processes were represented ( see S2A and S3A Tables ) , with both increases and decreases in expression level seen for many genes . This is broadly consistent with previously reported transcriptome analysis using tissue from ALS patients [77] . In fact , the differentially expressed transcripts identified in our RNAseq experiments were significantly enriched for orthologs of genes that are implicated in ALS ( ALS KEGG gene list; S1 Fig and S2C Table ) . In contrast with differentially expressed genes , when examining transposon transcripts the majority of those that were differentially expressed exhibited elevated levels in response to hTDP-43 expression . This was particularly striking for glial TDP-43 expression ( Repo > hTDP-43; Fig 1D ) , where 23 of 29 differentially expressed transposons showed higher levels relative to controls . The majority of the differential effects were observed in RTEs ( Class I elements ) , although a few Class II elements were also represented ( Fig 1B and 1D ) . It is also notable that while some RTE expression was elevated with both neuronal and glial hTDP-43 expression , there were several cases where effects were uniquely detected with only glial or only neuronal hTDP-43 expression . For example , the HeT-A LINE RTE and the mdg3 , HMS-Beagle , gtwin and 3S18 LTR RTEs were elevated with either glial or neuronal expression of hTDP-43 . However , the TART and TAHRE LINE RTEs and the Stalker2 and mdg1 LTR RTEs were only elevated in response to neuronal hTDP-43 expression , while a broad host of RTEs’ expression was elevated specifically in response to hTDP-43 expression in glia . Notable among these is the gypsy element , which we have previously demonstrated to be progressively de-repressed and even actively mobile with advanced age in brain tissue [21] . We cannot formally rule out the possibility that some of the differences between differentially expressed RTEs in Repo > hTDP-43 vs Elav > hTDP-43 may result from variation in copy number of specific TEs between the two Gal4 strains . But we think this is unlikely to be a major contributing factor because all of the strains were backcrossed to the same wild type strain for a minimum of 5 generations prior to the experiments . In the case of gypsy , expression levels are significantly increased in response to pan-glial hTDP-43 expression ( Repo > hTDP-43 ) relative to controls ( hTDP-43 / + ) but no significant effect was observed with pan-neuronal expression of hTDP-43 ( ELAV > hTDP-43 ) ( Fig 1B , 1D , and 1E ) . We selected the gypsy RTE as a candidate of interest to test the functional impact of loss of endogenous RTE suppression in response to hTDP-43 expression for several reasons . First , although gypsy was not the most abundantly expressed RTE in the RNA seq data , this element is known to be one of the most active natural transposons in Drosophila melanogaster , and is responsible for a high fraction of the spontaneous mutations that have been identified . Second , we have previously documented that gypsy is capable of replicating and generating de novo insertions in brain during advanced age [21] . Third , gypsy is an ERV with functional similarity to HERV-K , which is expressed in some ALS patients [50 , 55] . And finally , because of intense prior investigation of the biology of this RTE , extant molecular genetic reagents provided the means to both perturb and detect gypsy function . We began by performing quantitative RT-PCR ( qPCR ) for both ORF2 ( Pol ) and ORF3 ( ENV ) of gypsy on head tissue of flies expressing either pan-neuronal ( ELAV > hTDP-43 ) or pan-glial ( Repo > hTDP-43 ) hTDP-43 . Because disease risk is age dependent and symptoms in ALS patients are progressive , we also examined the compounding effects of age . At two relatively young ages ( 2–4 and 8–10 days post-eclosion ) we observe a dramatic increase in expression of both ORFs ( Fig 2A and 2B ) of gypsy specifically in flies expressing hTDP-43 in glia . In contrast , flies expressing neuronal hTDP-43 experience a wave of gypsy expression at the population level that occurs much later in age ( S2A Fig for ORF3; similar effects seen for ORF2 ) in a similar manner to genetic controls that do not express hTDP-43 ( see also: [21] ) . These flies do not show a significant impact of hTDP-43 expression on gypsy transcript levels . This is entirely consistent with the RNA-seq analyses ( Fig 1B and 1D ) , where gypsy expression was found to be increased in head tissue specifically in response to glial hTDP-43 expression , but not to expression of hTDP-43 in neurons . Importantly , different genomic copy number or basal levels of gypsy expression between the parental Elav-Gal4 and Repo-Gal4 lines are unlikely to underlie the separate effects that we observe on gypsy when driving hTDP-43 expression in either neurons or glia ( S2A . 5 and S2A . 6 Fig ) . Taken together , the RNA-seq and qPCR experiments confirm that gypsy RTE RNA levels are significantly and precociously elevated in response to pan-glial hTDP-43 expression . Whole mount immunolabeling of brains using a monoclonal antibody directed against the gypsy ENV glycoprotein [21 , 78] likewise shows early ( 5–8 days post-eclosion ) and acute accumulation of strongly immunoreactive puncta particularly in brains of flies expressing glial hTDP-43 ( Fig 2C; for quantification see S2B Fig ) . These intense puncta are observed throughout the superficial regions , which contain the majority of cell somata , as well as in deeper neuropil ( Fig 2C ) and persist into older ages . In contrast , we do not observe neuronal hTDP-43 expression to cause elevated gypsy levels above that seen in wild type flies at any time point with either qPCR or immunolabeling ( Fig 2C and S2A Fig ) . Given that effects of glial hTDP-43 expression on gypsy ENV immunoreactivity were so robust in 5–8 day old animals , we examined ENV at earlier time points . We found that in animals expressing hTDP-43 in glia , there is little detectable gypsy ENV protein expression at 0 days ( immediately following eclosion ) . In brains from animals 3 days post eclosion , we observe regional puncta with a variable intensity and spatial location ( S2C Fig ) although this effect was difficult to quantify because of its variability . We next examined the relative impact of glial and neuronal hTDP-43 expression on the physiological health of the animal . As previously documented [73–75] , we see effects with either neuronal or glial expression . However we observe differing severity and time courses , with effects of glial expression being more acute than those observed with expression in neurons . Flies expressing hTDP-43 in neurons exhibit significant locomotor impairment at 1–5 days post-eclosion , and flies expressing glial hTDP-43 show more severe locomotor impairment at this same age . This effect is further exacerbated by 5–10 days post-eclosion; at which point the animals expressing hTDP-43 in glia are largely immobile ( Fig 3A ) . As previously reported [68 , 73 , 74 , 79–82] , flies expressing neuronal hTDP-43 exhibit reduced lifespan in comparison to genetic controls . But flies expressing hTDP-43 in glia display an even more severely reduced lifespan ( Fig 3B ) . We further observe rampant cell death as detected by terminal deoxynucleotidyl transferase dUTP nick end labeling ( TUNEL ) in the brains of flies expressing hTDP-43 in glia as early as 5 days post-eclosion ( Fig 3C ) . Similarly , with transmission electron microscopy ( TEM; Fig 3D ) we observe profuse proapoptotic nuclei in brains of 12 day-old flies expressing glial hTDP-43 . In contrast , driving expression of hTDP-43 in neurons under the OK107-Gal4 driver , which provides high levels of expression in the well-defined and easily imaged population of central nervous system ( CNS ) neurons that constitute the mushroom body , results in little to no increase in TUNEL labeling ( consistent with a previous report: [81] ) even when the flies were aged to 30 days ( S3A Fig ) . The relative expression of hTDP-43 under the two major Gal4 drivers we are using , Repo-Gal4 ( glia ) and ELAV-Gal4 ( neurons ) , does not differ with age , suggesting that divergent age effects on expression level cannot account for the observed differences in toxicity and impact on physical health ( S3D and S3E Fig; respectively ) . Furthermore , we do not observe any effect of hTDP-43 expression on levels of the endogenous fly ortholog , TBPH , regardless of cell type of expression ( S3F Fig; S4 Table ) . Thus , the phenotypes that we observe are not caused by indirect effects on TBPH transcript abundance but instead derive from toxicity of the hTDP-43 transgene itself . The levels of expression of the hTDP-43 transgene relative to the endogenous TBPH gene also are similar to what has been reported in rodent models ( S4 Table ) . As is true in other animal models and in human patients , we cannot readily distinguish whether the effects we observe are due to toxic gain of function , dominant interference with an endogenous protein , or some combination thereof . Importantly , however , we can detect a disease specific phosphorylated isoform of hTDP-43 ( S3B Fig ) as well as cytoplasmic accumulation and nuclear clearance of the protein ( S3C Fig ) , implying that the human protein is being processed in the CNS of the fly as it is thought to be in the disease state in human tissue . Our observation that a panel of RTEs are expressed in response to hTDP-43 transgene expression , along with the extensively documented toxic effects of loss of control of RTEs in other biological contexts [21 , 62–64] and our observation that the gypsy RTE itself can actively replicate and generate de novo insertional mutations in brain tissue during aging [21] , and in response to the hTDP-43 transgene , suggested the possibility that loss of gypsy silencing might in fact account for a portion of the physiological toxicity observed with hTDP-43 expression in glia . To test whether the Drosophila ERV gypsy causally contributes to the harmful effects of hTDP-43 , we used a previously published inverted repeat ( IR ) “RNAi” construct [57] directed against gypsy ORF2 ( gypsy ( IR ) ) that is sufficient to reduce the expression of gypsy by approximately 50% in head tissue of 28-day old animals ( S4A Fig ) . We found that co-expression of this gypsy ( IR ) substantially ameliorates the lifespan deficit induced by glial hTDP-43 expression ( Fig 4A ) . This effect is not observed when a control IR construct is co-expressed with hTDP-43 in glial cells ( Repo > hTDP-43 + GFP ( IR ) ; Fig 4B ) , and neither the gypsy ( IR ) nor the GFP ( IR ) constructs , when expressed alone under Repo-Gal4 ( S4B Fig ) or ELAV-Gal4 ( S4C Fig ) or when present without a Gal4 driver ( S4D Fig ) , has such an effect on lifespan . Therefore activation of gypsy is responsible for a substantial portion of the toxicity that we observe when hTDP-43 is expressed in glia , which results in drastically premature death in these animals . In contrast , co-expression of gypsy ( IR ) does not rescue the lifespan deficit exhibited by animals expressing hTDP-43 in neurons ( Fig 4C ) . This is in accordance with our observations from RNA-seq ( Fig 1B and 1D ) , qPCR ( Fig 2A and 2B; S2A . 1–S2A . 3 Fig ) and immunolabeling ( Fig 2C ) that neuronal expression of hTDP-43 also does not elevate gypsy expression above wild type levels at any given time point over the course of lifespan . The glial specificity of gypsy ( IR ) lifespan rescue is consistent with our observation that gypsy expression is induced specifically when TDP-43 is expressed in glia , lending credence to the conclusion that gypsy is causally participating in the resulting degenerative phenotype We of course cannot rule out the possibility that the gypsy-RNAi construct may also impact gypsy family RTEs that share sequence homology to gypsy . The conclusion that RTEs contribute to TDP-43 toxicity is further supported by the mild but significant lifespan extension that we observe with pharmacological inhibition of the reverse transcriptase activity that is essential for all RTE replication ( S5E , S5F , S5G and S5H Fig ) . RTE replication involves reverse transcription , generation of chromosomal DNA breaks , and integration of the RTE cDNA copy . DNA damage is therefore associated with either abortive or successful attempts at RTE replication and such DNA damage is thought to be a major source of cellular toxicity caused by RTE activity because it activates Chk2 signaling , which leads to programmed cell death . To test whether the harmful effects of hTDP-43 are in fact mediated by DNA damage , we capitalized on the previously documented ability of mutations in Chk2 to mask the toxic effects of RTE-induced DNA damage [83 , 84] . Importantly , mutations in Chk2 do not prevent accumulation of DNA damage; rather they prevent the signaling required for the cell to recognize that DNA damage has occurred and respond by committing to a programmed cell death pathway [85] . We therefore employed an IR construct directed against loki ( loki ( IR ) ) , the Drosophila ortholog of chk2 , which is sufficient to significantly reduce levels of endogenous loki mRNA in head tissue of 28-day old animals ( S5A Fig ) . Remarkably , co-expression of loki ( IR ) with hTDP-43 is able to fully rescue the lifespan deficit caused by hTDP-43 expression in glia ( Fig 5A ) or neurons ( Fig 5B ) . These findings support the conclusion that DNA damage makes a major contribution to the loss of lifespan induced by either neuronal or glial expression of hTDP-43 . This conclusion is supported by our RNA-seq findings , in which we observe that in each case the expression of a panel of RTEs is activated . Although neuronal hTDP-43 expression does not impact the levels of the gypsy RTE specifically , several other RTEs exhibit elevated expression ( see: Fig 1B ) . Importantly , this extension of lifespan is not seen with co-expression of a control IR construct ( Repo > hTDP-43 + GFP ( IR ) ; Fig 4B ) , and neither the GFP ( IR ) nor loki ( IR ) constructs when expressed individually under Repo-Gal4 or ELAV-Gal4 or present without a Gal4 driver ( S4B–S4D Fig ) , have such an effect on lifespan on their own . These data suggest that Loki/Chk2 activity makes a major contribution to the pathological toxicity of hTDP-43 that we observe with both glial and neuronal hTDP-43 expression . The brains of flies expressing hTDP-43 in glia display rampant cell death , seen both with TUNEL staining ( Fig 3C ) and at the level of TEM ( Fig 3D ) . To test whether the decision of cells to commit to a programmed cell death pathway in response to hTDP-43 expression is mediated by Loki ( Chk2 ) , we co-expressed the loki ( IR ) that was so effective in suppressing hTDP-43 toxicity in survival analyses ( Repo > hTDP-43 + loki ( IR ) ) and found that this was sufficient to abolish the dramatic accumulation of TUNEL-positive nuclei induced by glial expression of hTDP-43 ( Fig 5C and 5D ) . Moreover , we found that the gypsy RTE contributes at least in part to the decision of cells to undergo programmed cell death in response to hTDP-43 expression in glia , as knocking down gypsy ( Repo > hTDP-43 + gypsy ( IR ) ) also significantly reduces the TUNEL labeling observed in the CNS of these animals ( Fig 5C and 5D ) . These effects are specific to loki ( IR ) and gypsy ( IR ) as co-expression of an unrelated UAS- ( IR ) construct with hTDP-43 in glia ( Repo > hTDP-43 + GFP ( IR ) ) does not significantly alter the number of TUNEL positive cells compared to brains of flies expressing hTDP-43 alone under Repo-Gal4 ( S5B Fig ) . Importantly , co-expression of the GFP ( IR ) , loki ( IR ) , and gypsy ( IR ) constructs with hTDP-43 under Repo-Gal4 also does not significantly reduce the expression of hTDP-43 ( TARDBP; S5C Fig ) . Differences in the level of hTDP-43 expression between these experimental groups therefore cannot account for the phenotypic rescue observed with loki or gypsy knock down in either the survival or cell death assays . Taken together , these data support the conclusion that the cell death induced by hTDP-43 is mediated predominantly via Loki/Chk2 activity in response to DNA damage , and that this DNA damage is likely induced by RTE activity . For both the physiological toxicity and cell death induced by hTDP-43 expression in glial cells , this effect is in large part due to the activity of one particular RTE , the gypsy ERV . These observations are in agreement with the well-documented accumulation of DNA double strand breaks induced by unleashing RTEs [86] , as well as reports that transgenic expression of the HERV-K ENV protein in mice results in loss of volume in the motor cortex and DNA damage [55] . While the impact of gypsy appears to be restricted to the case where hTDP-43 is expressed in glial cells , our RNA-seq data demonstrate that expression of hTDP-43 causes the induction of a panel of RTEs that normally would be silenced . Such results lead us to postulate that hTDP-43 pathology might be impacting the natural mechanisms by which RTEs in general are normally kept suppressed . We therefore designed a reporter assay to detect the effect of hTDP-43 expression on the siRNA system , which provides the primary silencing mechanism to keep RTEs in check in somatic tissues such as the brain . The major post-transcriptional RTE silencing system available in somatic tissue such as the brain is the siRNA pathway [87–92] . siRNAs with sequence complementarity to RTEs have been detected in many species , including mammals [1 , 88 , 93] , and RTE-siRNA levels have been demonstrated to affect RTE activity [1 , 94–96] . Moreover , disruptions in the siRNA pathway result in increased TE transcript levels [21 , 91 , 97] as well as novel insertions in the genome [21 , 98] . Indeed , we have previously shown that disruption of the major siRNA pathway effector Argonaute 2 ( Ago2 ) leads to precocious gypsy expression in Drosophila head tissue and this is accompanied by rapid age-dependent neurophysiological decline [21] . We therefore engineered a genetically encoded sensor system to inform us as to whether hTDP-43 expression impairs the efficiency of Dicer-2 ( Dcr-2 ) /Ago2-mediated siRNA silencing in the Drosophila nervous system in vivo . Our reporter system relied on three components . We co-expressed a Dcr-2 processed IR construct directed against GFP ( GFP ( IR ) ) with a GFP transgenic reporter . By selecting an effective GFP ( IR ) , we were able to generate substantial silencing of the GFP reporter ( Fig 6A and 6B ) . To test the effects of hTDP-43 on siRNA mediated silencing , we then co-expressed our third component: either hTDP-43 or an unrelated control transgene ( tdTomato ) . This tripartite system was expressed either in all glial cells using the Repo-Gal4 driver ( Fig 6A ) or in mushroom body neurons using the OK107-Gal4 driver ( Fig 6B ) . Brains of young ( 2–4 day ) and middle aged ( 10–12 days ) flies were imaged using confocal microscopy . In the case of neuronal expression we were able to carry the experiment out to old age ( 45–47 days ) , but this was not possible with glial expression of hTDP-43 as it results in dramatic reduction in lifespan ( see Fig 3B ) . What we observed was conspicuously reminiscent of hTDP-43’s impact on gypsy expression . Glial expression of hTDP-43 causes a marked reduction of siRNA silencing efficacy , resulting in easily detectable expression of the GFP reporter . Such expression is dramatic and significant in brains of 2–4 day old flies and persists out to 10–12 days of age ( Fig 6A ) . Brains are obviously deteriorated by the 10–12 day time-point , which likely explains why GFP levels appear to drop off somewhat . Neuronal expression of hTDP-43 in the mushroom body has a similar but more slowly progressing effect on siRNA-mediated silencing of our GFP reporter , with a somewhat later onset ( Fig 6B ) . Indeed , when we perform an analogous experiment using an endogenous reporter of siRNA mediated silencing in a separate structure we observe a similar effect . The GMR-Gal4 driver , which drives high levels of expression in the fly eye , was used to express an IR construct directed against the endogenous white+ pigment gene in place of GFP as a reporter ( Fig 6C ) . As with mushroom body neurons in the CNS , expression of hTDP-43 in the eye causes a progressive de-repression of the silenced reporter . It is noteworthy that the erosion of siRNA efficacy caused by hTDP-43 expression in the eye manifests as clusters of red-pigmented cells , a phenotype which is evocative of the stochastic clusters of ENV immunoreactivity observed early in response to glial hTDP-43 expression ( Fig 6C and S2C Fig ) . In contrast , simply turning on expression of white+ after development results in a uniform darkening of the eye with age ( S6A and S6B Fig ) . Taken together , these findings demonstrate that hTDP-43 expression interferes with siRNA-mediated silencing in several tissue types , resulting in de-suppression of reporter expression . In neurons hTDP-43 expression causes age-dependent progressive erosion of siRNA efficacy , while glial expression of hTDP-43 results in more acute siRNA silencing impairment . Although we have yet to identify which step of the siRNA pathway is disrupted by hTDP-43 expression , it is not simply due to loss of expression of Dcr-2 or Ago2 , the two major effectors of siRNA-mediated silencing in Drosophila [90–93] . qPCR of whole head tissue demonstrated that hTDP-43 expression in both neurons and glial cells does not affect absolute expression levels of Dcr-2 ( S6C . 1 Fig ) or Ago2 ( S6C . 2 Fig ) at either 2–4 or 8–10 days post-eclosion , therefore down-regulation of these molecules is not responsible for the observed de-suppression of gypsy . In fact , in the case of genetic controls and flies expressing hTDP-43 in neurons , Dcr-2 and Ago2 levels actually increase with age beginning at 21–23 days post-eclosion and persisting into old age ( 40–42 days old ) , suggesting that down-regulation of Dcr-2 and Ago2 likewise cannot explain the later elevation of gypsy expression observed in these genotypes ( S6D . 1–S6D . 3 Fig ) . On the other hand , small-RNA seq from head tissue of flies expressing hTDP-43 under the glial Repo-Gal4 driver reveals a relative reduction specifically in levels of antisense siRNAs among the subset that target RTEs whose expression is elevated in the RNAseq data ( S5 Table; S7A and S7B Fig ) . This is suggestive of a defect in either biogenesis or stability of the siRNAs that target these RTEs . We favor a model ( Fig 7 ) in which TDP-43 protein pathology interferes with siRNA biogenesis and/or function , resulting in deterioration of siRNA-mediated silencing accompanied by activation of RTE expression . The resulting increase in RTE expression may lead to accumulation of DNA damage resulting from RTE activity induced by TDP-43 pathology , in turn activating Loki/Chk2 signaling and leading to programmed cell death ( Fig 7 ) .
We previously reported bioinformatic predictions of a physical link between TDP-43 protein and RTE RNAs in rodent and in human cortical tissue [54] . Here we provide in vivo functional evidence in Drosophila that TDP-43 pathological toxicity is the result of RTE activity generally and , in glial cells , expression of the gypsy ERV specifically . This finding is parsimonious with reports of high levels of reverse transcriptase activity in serum and CSF of HIV-negative ALS patients and their blood relatives [59–61] , and of accumulation of transcripts and protein of HERV-K , a human ERV of the gypsy family , in the CNS of ALS patients [50 , 55] . It also is notable that accumulation of virus-like inclusions have been detected by electron microscopy in both neurons and glia of the frontal cortex of one ALS patient with extended prolongation of life via artificial lung ventilation [99] . Furthermore , our findings are complementary to those documenting progressive motor dysfunction in transgenic mice expressing HERV-K ENV protein , one of the three major open reading frames of this human RTE [55] . However , the findings reported here provide the first demonstration that an endogenous RTE causally contributes to physiological deterioration and cell death in TDP-43 protein pathology . Additionally , our findings indicate that reverse transcriptase enzymatic activity contributes to the toxicity of TDP-43 induced RTE expression , and that toxicity is largely mediated by DNA damage-induced cell death . Finally , we demonstrate that TDP-43 pathology leads to erosion of the post-transcriptional gene silencing mechanisms that are broadly responsible for RTE repression , which is accompanied by elevated expression of a panel of RTEs . These findings are in agreement with our previous observations that TDP-43 protein normally exhibits widespread interactions with RTE transcripts in rodent and human cortical tissue and that these interactions are selectively lost in cortical tissue of FTLD patients [54] , as well as a report that knocking out the C . elegans ortholog of hTDP-43 results in broad accumulation of transposon-derived RNA transcripts and double stranded RNA [100] . TDP-43 has frequently been reported to co-localize with the major siRNA pathway components , DICER and Argonaute , in both cell culture and human patient tissue . Indeed such co-localization is commonly detected in stress granules ( SGs ) , cytoplasmic foci for modulating mRNA translation that materialize in response to cellular stress [34 , 47 , 101–104] . SGs are observed in pathological ALS and FTLD patient tissue , and can be induced in neuronal cell culture via overexpression of mutant and wild-type hTDP-43 as well as two other ALS linked genes , SOD1 and FUS , suggesting that they may represent a common downstream mechanism of pathological progression [47] . And SG formation in response to cellular stressors , or overexpression of ALS-linked genes including hTDP-43 , inhibits DICER processing of pre-miRNAs to mature miRNAs [47] . This signature is detectable in both sporadic and familial ALS spinal column motor neurons as a dramatic global reduction in mature miRNAs in comparison to control tissue [47] . These findings are in accordance with previous work by Kawahara and Mieda-Sato ( 2012 ) , which showed that loss of hTDP-43 function itself inhibits cytoplasmic miRNA processing by DICER for at least a subset of miRNAs [45] . In mammals , the same DICER and Argonaute proteins process both miRNAs and siRNAs [105] . Therefore , the effects of SG formation and hTDP-43 manipulation on DICER function may affect siRNAs just as dramatically as miRNAs , however the effects of TDP-43 expression on siRNA function in mammals have as yet to be investigated . In contrast with the mammalian system , siRNAs and miRNAs in Drosophila are processed largely via distinct pathways–Dcr-1/Ago1 and Dcr-2/Ago2 , respectively [105] . This disparity provided an opportunity for us to engineer an in vivo sensor to investigate the effects of TDP-43 on the siRNA system separate from its effects on miRNA biogenesis . While production of some miRNAs is disrupted in both animal models of ALS and human patient tissue , our data clearly demonstrate that in Drosophila , pathological TDP-43 expression disrupts the siRNA function of the DICER/Ago pathway . This finding dovetails with a report that the C . elegans TDP-43 ortholog impacts accumulation of double stranded RNA , which is the substrate of the DICER enzyme [100] . Our findings support the conclusion that the disruption of siRNA silencing contributes to cellular toxicity , dramatic physiological deterioration , and premature death via loss of control of RTEs . Unregulated RTE expression is known to be highly toxic in other biological contexts for a number of reasons , including accumulation of toxic RNAs , creation of harmful mutations , and accumulation of DNA damage . In the case of gypsy we demonstrate that this RTE has a causal impact on cell death and physiological decline in the animal’s health . We also identify DNA damage-induced cell death , mediated by Chk2 activity , as a major contributing factor in the toxicity of TDP-43 both at the cellular and organismal level . Importantly , gypsy is not the only RTE whose expression we found to be increased in response to hTDP-43 expression . We in fact observe a panel of RTEs that exhibit elevated expression , with some variation in this profile when hTDP-43 is expressed solely in neurons or glia . This is consistent with the observation that knocking down gypsy expression only partially suppresses the toxicity of TDP-43 , whereas blocking loki ( chk2 ) expression leads to a near complete suppression of the effects of hTDP-43 expression on cell death and lifespan reduction . The involvement of DNA damage-induced cell death suggest that gypsy and likely other RTEs may be successfully or abortively inserting into genomic DNA , although we are mindful of the fact that increased levels of RTE proteins and RNAs may themselves be cytotoxic , as is observed with the Alu RTE in macular degeneration [52] . In the case of HERV-K , it has recently been shown [55] that TDP-43 binds directly to the LTR at the DNA level , thereby activating transcription of HERV-K . Our results establish , however , that TDP-43 pathology also compromises the siRNA-mediated gene silencing system , which is the major post-transcriptional genomic defense against RTEs in somatic tissues . The mechanisms by which TDP-43 protein pathology disrupts siRNA silencing remain to be investigated , but we favor the idea that it involves direct interactions between TDP-43 and the siRNA protein machinery [45 , 47] , and our previous findings also suggest direct interaction with RTE RNAs [54] . The disruptive impact of TDP-43 on the siRNA system points to a general loss of RTE silencing—as opposed to activation of a specific element such as the gypsy ERV ( or HERV-K ) —as the major contributing factor in hTDP-43-related neurophysiological deterioration . This conclusion is supported by our RNA-seq data which shows a broad and general increase in RTE expression in head tissue of Drosophila expressing either neuronal or glial hTDP-43 , as well as a pronounced reduction specifically in antisense siRNAs which target the RTEs we observe to exhibit increased expression in response to hTDP-43 expression . In accordance with this notion , we have previously shown in Drosophila that mutation of Ago2 , a major effector protein of the siRNA system , results in activation of several different RTEs in brain tissue and causes rapid age-related cognitive decline and shortened lifespan [21] . Like Drosophila , the human genome contains more than one type of functional RTE . In addition to HERV-K , the human genome contains on the order of 100 fully active copies of L1 RTEs , and a far higher number of non-autonomous elements that replicate in trans by capitalizing on the protein machinery encoded by L1s [106] . Moreover , abnormally high levels of expression of several different RTE families has been reported across a suite of neurodegenerative diseases [50–57] , and there is accumulating evidence suggesting RTEs generally become active with advanced age in a variety of organisms and tissues [13–18] , including the brain [21] . Previous studies make the case that this effect may result from age-related loss of transcription-level heterochromatic silencing [16 , 18] . Our finding that TDP-43 erodes post-transcriptional , siRNA-mediated RTE silencing therefore raises an intriguing hypothesis regarding the synergy between age and TDP-43 pathology on RTE activation , particularly when the reinforcing action of siRNAs on heterochromatin is taken into consideration [1 , 18] . This potential synergy , in conjunction with the replicative capacity encoded by RTEs , leads us to posit the “retrotransposon storm” hypothesis of neurodegeneration . We envision that loss of control of RTE expression and replication leads to a feed-forward mechanism in which massive levels of activity drive toxicity and degeneration in the nervous system . Our findings are not in conflict with a wealth of data that have implicated effects of TDP-43 pathology on splicing , RNA stability , translation , and miRNA biogenesis [28 , 29 , 38 , 39 , 45–47] . , and it will be important to conceptually integrate our findings with these other aspects of TDP-43 pathology . But the direct impact we observe on cell death highlights the importance of investigating the contribution of siRNA dysfunction and RTE toxicity in TDP-43-mediated pathogenesis , and may indicate a promising common avenue for novel therapeutic targets in both familial and sporadic cases of ALS .
All transgenic fly stocks used , with the exception of w ( IR ) and GMR-Gal4 , were backcrossed into our in-house wild type strain , the Canton-S derivative w1118 ( isoCJ1 ) [107] , for at least five generations to homogenize genetic background . The GFP , OK107- , ELAV- , and Repo-Gal4 lines [108] , as well as the hTDP-43 [68] and gypsy ( IR ) [57] lines , are as reported previously . The GMR-Gal4 , Gal80ts , w ( IR ) , GFP ( IR ) , and tdTomato lines were acquired from the Bloomington Drosophila Stock Center ( stock numbers: 43675 , 7019 , 25785 , 9331 , and 32221; respectively ) , and the loki ( IR ) line was acquired from the Vienna Drosophila Resource Center [109] ( stock number: v44980 ) . Flies were cultured on standard fly food at room temperature unless otherwise noted . All fly stocks used for lifespan analysis and longitudinal qPCR experiments were double dechorionated by bleach treatment in order to remove exogenous viral infection [21] . Briefly , 4-hour embryos were collected and treated with 100% bleach for 30 min to remove the chorion . Treated embryos were washed and subsequently transferred to a virus-free room equipped with ultraviolet lights to maintain sterility . This was repeated for at least two successive generations and expanded fly stocks were tested via qPCR of whole flies to ensure Drosophila C Virus ( DCV ) levels were below a threshold of 32 cycles . Fly heads were collected for each genotype and total RNA was purified with Trizol ( Invitrogen ) . RNA-Seq libraries were constructed using the NuGEN Ovation Drosophila RNA-Seq kit including DNase treatment with HL-dsDNase ( ArcticZymes Cat . # 70800–201 ) and cDNA fragmentation using the Covaris E220 system according to manufacturer specifications . After amplification , library quality was measured using the Agilent Bioanalyzer system and quantity was determined using Life Technologies Qubit dsDNA HS Assay kit ( for use with the Qubit 2 . 0 Fluorometer ) . Prior to sequencing , pooled libraries were quantified using the Illumina Library Quantification kit ( with Universal qPCR mix ) from Kapa Biosystems and a 1 . 2 pM loading concentration was used for PE101 on the Illumina NextSeq500 platform . Small RNAs were cloned using TruSeq ( Illumina ) approach with modifications described in Rozhkov 2015 [110] . Briefly , all small RNA libraries were constructed from 15–20 ug of TRIzol isolated total RNA . 18–29 nucleotide long small RNAs were size selected on 15% PAGE-UREA gel . After 3’- and 5’-adapter ligation , subsequent gel purification steps and reverse transcription cDNAs were PCR amplified with barcoded primers . PCR products were size selected on 6% PAGE gel , and quantified on Bioanalyzer . Loading concentrations were determined using the NEBNext Library Quant kit for Illumina ( NEB ) . The libraries were sequenced on NEXTSeq platform . RNA-seq libraries were run on an Illumina NextSeq ( paired end 101 ) . Reads were mapped to the Drosophila dm3 genome with STAR [111] allowing up to 4 mismatches and a maximum of 100 multiple alignments . To estimate the pileup along gypsy element , reads were mapped to the gypsy consensus sequence ( GenBank accession: M12927 ) using Bowtie [112] with up to 2 mismatches . Reads were annotated based on genomic locations against ribosomal RNAs , transposable elements ( FlyBase ) , and RefSeq genes ( UCSC genome database RefSeq track ) . The GEO accession number for all RNAseq and smallRNAseq data is GSE85398 ( https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE85398 ) . Reads mapped to ribosomal RNAs were removed from each library . For the remaining reads , expression abundance estimation and differential expression analysis were performed using the TEtranscripts package [76] . Reads for each library were normalized based on library size , e . g . , reads per million mapped ( RPM ) . Statistically significant differences were taken as those genes/transposons ( TEs ) with a Benjamini-Hochberg corrected P-value < 0 . 05 , as calculated by DESeq [113] . Biological replicates were averaged for the purpose of estimating pileup along consensus TEs . The ortholog pairs of fly and human were predicted with the Drosophila Integrated Ortholog Prediction Tool ( DIOPT ) , which integrates the ortholog predictions from 11 existing tools [114] . The pairs with the “high” rank , defining as the best match with both forward and reverse searches and the DIOPT score is at least 2 , were selected . Then we performed Fisher's exact test to check if the differential expression fly genes are enriched in the KEGG ALS human genes with identifiable fly orthologs . Male flies were used for all lifespan assays since the majority of glial-expressing hTDP-43 flies that escape their pupal cases are male . Flies were housed 15 to a vial with a total of 75 flies per genotype and flipped into fresh food vials every other day . All vials were kept on their side in racks for the duration of the experiment . Lifespan experiments were performed blind . Tenofovir disoproxil fumarate ( TDF; Selleck Chemicals , CAS 147127-20-6 ) , Zidovudine ( AZT; Sigma-Aldrich , CAS 30516-87-1 ) , and Stavudine ( d4T; Sigma-Aldrich , CAS 3056-17-5 ) were prepped as solutions using dimethyl sulfide ( DMSO ) as solvent . Standard fly food was melted and cooled to a liquid , and NRTI solutions were added just before solidification to give final concentrations of 0 ( vehicle alone control ) , 1 , 5 , 10 , and 15 μM of each NRTI in a total volume of 0 . 2% DMSO , then stored at 4°C for a maximum of 2 weeks until use . Lifespan was monitored in all male flies with 20 flies per vial for a total of 100 flies monitored per genotype per treatment . Lifespans were performed as above . To quantify and determine the consistency of feeding across treatments , Capillary Feeder ( CAFE ) Assays [115] were performed . Each replicate consisted of five male wild-type flies contained in a 1 . 5 mL microfuge tube chamber with 1% agarose in bottom to maintain humidity and two 5 μL disposable calibrated pipets ( VWR , 53432–706 ) providing the media solution ( 5% sucrose / 5% autolyzed yeast extract , Sigma-Aldrich ) , inserted though the tube cap . Flies were acclimated with untreated solution ad libitum for 24 hours . Measurements were initiated one hour following the switch to experimental solutions and monitored for a total duration of 24 hours . Experimentally treated solutions were prepared to match the concentrations of each NRTI treatment used in solid food for lifespan analysis , as well as an additional solution of high concentration for each NRTI ( 100 μM ) and a solution of vehicle alone control ( 0 . 2% DMSO ) . Displacement due to evaporation was controlled for by subtracting measurements from fly-less CAFE chambers with the vehicle solution set up in parallel to the experimental assays . CAFE assays with untreated solution were also set up in parallel and controlled for evaporation . Locomotion behavior was assayed using the classic Benzer counter current apparatus as in Benzer , S . , 1967 [116] , with the following modifications: freshly eclosed flies were transferred into glass bottles with food and a paper substrate and plugged with foam stoppers . Flies were transferred to fresh bottles every 48 hours until they reached the appropriate age for locomotion assays . The Benzer assay was conducted in a horizontal position with a fluorescent light source to measure phototaxis . Locomotion assays were performed blind . Tissue preparation , cDNA synthesis and qPCR were performed as previously described [21] using the Applied Biosystems StepOnePlus Real-Time PCR System . Heads of 75–100 flies were used for each biological replicate unless otherwise noted . All TaqMan Gene Expression Assays were acquired from Applied Biosystems and used the FAM Reporter and MGB Quencher . The inventoried assays used were: Act5C ( assay ID Dm02361909_s1 ) , Dcr-2 ( assay ID Dm01821537_g1 ) , Ago2 ( assay ID Dm01805433_g1 ) , TARDBP ( assay ID Hs00606522_m1 ) , TBPH ( assay ID Dm01820179_g1 ) , and loki ( assay ID Dm01811114_g1 ) . All custom TaqMan probes were designed following the vendor’s custom assay design service manual and have the following assay IDs and probe sequences: gypsy ORF2 ( assay ID AI5106V; probe: 5’–AAGCATTTGTGTTTGATTTC-3’ ) , gypsy ORF3 ( assay ID AID1UHW; probe: 5’-CTCTAGGATAGGCAATTAA-3’ ) , and DCV ( assay ID AIPAC3F; probe: 5′-TTGTCGACGCAATTCTT-3′ ) . For genomic QPCR , DNA was isolated from 2–4 day old flies ( equal numbers per sample , 50% male/female ) . After RNAase treatment , equal input levels of DNA were used for each PCR reaction , and each reaction was performed in triplicate . The CT values for Gypsy ORF2 were normalized to those for Actin within each sample . The values shown S2A . 5 Fig were then further normalized to the levels in the wild type strain in order to display relative fold change over our wild type strain . Dissection , fixation , immunolabelling , and confocal imaging acquisition were executed as previously described [117] . The ENV primary antibody was used as described in Li , et al . 2013 [21 , 78] . For TDP-43 immunohistochemistry , the primary full length human TDP-43 antibody ( Protein Tech , 10782-2-AP ) was used at a 1:100 dilution , and the primary pSer409 phosphorylated human TDP-43 antibody ( Sigma Aldrich , SAB4200223 ) was used at a 1:500 dilution separately in conjunction with a 1:200 dilution of an Alexa Fluor 488-conjugated secondary antibody ( Thermo Fisher Scientific , A-11070 ) . Repo co-labeling was performed using a 1:200 dilution of primary antibody ( Developmental Studies Hybridoma Bank , 8D12 ) and a 1:200 dilution of a Cy3-conjugated secondary antibody ( Molecular Probes , A10521 ) . DAPI co-staining was performed after a brief wash in 1x PBS immediately subsequent to secondary antibody staining using DAPI Dilactate ( Thermo Fisher Scientific , D3571 ) as per manufacturer specifications . All brains co-stained with DAPI were imaged on a Zeiss LSM 780 confocal microscope using a UV laser and the Zeiss ZEN microscope software package . The gain on the confocal microscope was set using the positive control ( Repo > GFP or OK107 > GFP ) and kept consistent across all subsequent brains imaged . The GFP signal of the median 10 optical sections of the appropriate structures ( either the full brain for Repo or both lobes of the calyx for OK107 , respectively ) was calculated using ImageJ software , as previously described [118] . These ten values were then averaged , and this number used as a representation for each individual brain . 5–10 brains were analyzed per group . For TUNEL staining , the In Situ Cell Death Detection Kit , TMR red ( Roche , 12156792910 ) was used . The same dissection , fixation , and penetration and blocking protocol used for antibody staining was followed [117] , at which point the brains were transferred to the reaction mix from the kit for 2 hours at 4°C followed by 1 hour at 37°C . Brains were then washed , mounted , and imaged as previously described [117] . For imaging , the gain on the confocal microscope was set using the positive control ( Repo > hTDP-43 ) and kept consistent across all subsequent brains imaged . A projection image was generated using the middle 50 optical slices from the z-stack image of the whole brain . This projection image was then thresholded using the maximum entropy technique ( See: [119] ) via the Fiji plug-in for ImageJ software , and the subsequent binary image was subjected to puncta quantification using ImageJ software . Puncta quantification was thresholded for puncta greater than 3 pixels to reduce the likelihood of counting background signal . The total number of puncta counted was then used as a representation for the number of TUNEL-positive nuclei for each brain in subsequent statistical analysis . 7–12 brains were analyzed per group . Quantification of gypsy ENV immunoreactive puncta was performed in the same manner . Flies of the appropriate age and genotype were placed at -70°C for 25 minutes and then kept on ice until immediately prior to imaging . Imaging was performed using a Nikon SMZ1500 stereoscopic microscope , Nikon DS-Vi1 camera and Nikon Digital Sight camera system , and the Nikon NIS-Elements BR3 . 2 64-bit imaging software package . The experiment was designed such that each group is balanced for the number of mini-white transgenes and heterozygous for genomic white+ . Drosophila heads were removed , the cuticle removed and the brains fixed overnight in 2% paraformaldehyde and 2% glutaraldehyde in 0 . 1 mol/L PBS . Samples were rinsed in distilled water and post-fixed for one hour in 1% osmium tetroxide in 1 . 5% potassium ferrocyanide in distilled water . Next , the samples were dehydrated in a graded series of ethanol and the final 100% ethanol was replaced with a solution of absolute dry acetone ( Electron Microscopy Sciences , Hatfield PA ) . The samples were then infiltrated with agitation for one hour in an equal mixture of acetone and Epon-Araldite resin , followed by infiltration with agitation overnight in 100% resin . Samples were transferred to embedding capsules with the posterior head facing towards the bottom of the capsule and the resin was polymerized overnight in a vented 60°C oven . Thin sections were made from the mushroom body region and collected on Butvar coated 2mm x 1mm slot grids ( EMS ) and the sections were counterstained with lead citrate . Thin sections were imaged with a Hitachi H700 transmission electron microscope and recorded on Kodak 4480 negatives that were scanned with an Epson V750 Pro Scanner at 2400 DPI . 3 individual brains processed for Repo / + , 4 individual brains processed for Repo > hTDP-43; many images collected of each brain . For qPCR data , the p-values of all data sets with only two groups were calculated using an unpaired t-test . Where an effect of age for more than two time points within one genotype was determined , a one-way ANOVA was performed , and where multiple ages and genotypes are represented a two-way ANOVA was performed; the results are reported in the figure legends . All pairwise comparisons for qPCR reported in the figures were corrected using the Bonferroni method for multiple comparisons . For both the locomotion data and the GFP quantification , p-values were reported using the Sheffé method; ANOVA results are reported in the figure legends . The CAFE assay is similarly reported , with pairwise comparisons between treatments being made using one-way ANOVA with Tukey’s Multiple Comparisons test . Survival analyses for the lifespan curves were performed using the Kaplan-Meier method , and the Gehan-Breslow-Wilcoxon test were used to compare survival curves . All pairwise comparisons for lifespan curves were corrected using the Bonferroni method . Sample sizes were selected based on standard practices in the literature . No randomization was employed in this study .
|
Functional abnormality of TAR DNA-binding protein 43 ( TDP-43 ) , an aggregation-prone RNA and DNA binding protein , is observed in the vast majority of both familial and sporadic ALS cases and in ~40% of FTLD cases , and mutations in TDP-43 are causal in a subset of familial ALS cases . Although cytoplasmic inclusions of this mostly nuclear protein are a hallmark of the disease , the cascade of events leading to cell death are not understood . We demonstrate that expression of human TDP-43 ( hTDP-43 ) in Drosophila neurons or glial cells , which results in toxic cytoplasmic accumulation of TDP-43 , causes broad expression of retrotransposons . In the case of glial hTDP-43 expression , the endogenous retrovirus ( ERV ) gypsy causally contributes to degeneration because inhibiting gypsy genetically or pharmacologically is sufficient to rescue the phenotypic effects . Moreover , we demonstrate that activation of DNA damage-mediated programmed cell death underlies hTDP-43 and gypsy mediated toxicity . Finally , we find that hTDP-43 pathology impairs small interfering RNA silencing , which is an essential system that normally protects the genome from RTEs . These findings suggest a novel mechanism in which a storm of retrotransposon activation drives neurodegeneration in TDP-43 mediated diseases such as ALS and FTLD .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"invertebrates",
"cell",
"death",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"gene",
"regulation",
"cell",
"processes",
"neuroscience",
"animals",
"invertebrate",
"genomics",
"toxicology",
"animal",
"models",
"dna",
"damage",
"toxicity",
"drosophila",
"melanogaster",
"model",
"organisms",
"experimental",
"organism",
"systems",
"dna",
"drosophila",
"research",
"and",
"analysis",
"methods",
"small",
"interfering",
"rnas",
"animal",
"cells",
"gene",
"expression",
"insects",
"animal",
"genomics",
"arthropoda",
"biochemistry",
"rna",
"cellular",
"neuroscience",
"cell",
"biology",
"nucleic",
"acids",
"apoptosis",
"neurons",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"genomics",
"non-coding",
"rna",
"organisms"
] |
2017
|
Retrotransposon activation contributes to neurodegeneration in a Drosophila TDP-43 model of ALS
|
Lentiviral Envelope ( Env ) antigenic variation and related immune evasion present major hurdles to effective vaccine development . Centralized Env immunogens that minimize the genetic distance between vaccine proteins and circulating viral isolates are an area of increasing study in HIV vaccinology . To date , the efficacy of centralized immunogens has not been evaluated in the context of an animal model that could provide both immunogenicity and protective efficacy data . We previously reported on a live-attenuated ( attenuated ) equine infectious anemia ( EIAV ) virus vaccine , which provides 100% protection from disease after virulent , homologous , virus challenge . Further , protective efficacy demonstrated a significant , inverse , linear relationship between EIAV Env divergence and protection from disease when vaccinates were challenged with viral strains of increasing Env divergence from the vaccine strain Env . Here , we sought to comprehensively examine the protective efficacy of centralized immunogens in our attenuated vaccine platform . We developed , constructed , and extensively tested a consensus Env , which in a virulent proviral backbone generated a fully replication-competent pathogenic virus , and compared this consensus Env to an ancestral Env in our attenuated proviral backbone . A polyvalent attenuated vaccine was established for comparison to the centralized vaccines . Additionally , an engineered quasispecies challenge model was created for rigorous assessment of protective efficacy . Twenty-four EIAV-naïve animals were vaccinated and challenged along with six-control animals six months post-second inoculation . Pre-challenge data indicated the consensus Env was more broadly immunogenic than the Env of the other attenuated vaccines . However , challenge data demonstrated a significant increase in protective efficacy of the polyvalent vaccine . These findings reveal , for the first time , a consensus Env immunogen that generated a fully-functional , replication-competent lentivirus , which when experimentally evaluated , demonstrated broader immunogenicity that does not equate to higher protective efficacy .
The scientific community has aggressively sought after the development of a universal HIV vaccine that can prevail over the extraordinary levels of antigenic diversity in the fight against HIV and AIDS . The considerable extent of genomic variation found between isolates and within clades , and to a larger extent within the circulating recombinant forms , make for an effectual blockade to vaccine protection . Different strategies of vaccine composition and delivery have been proposed that are actively and widely being examined . A majority of these vaccines target the Env protein , as lentiviral antigenic variation is most pronounced in the viral Env proteins that serve as initial primary targets for host immune responses [1]–[5] . Centralized Env immunogens are one of the more promising contemporary approaches to overcoming HIV antigenic diversity [1] , [6] . Centralized sequences attempt to minimize the genetic distance between vaccine proteins and the circulating isolates that pose a threat to public health . The centralized genes are generated through the computational determination of consensus genes ( the most common amino acid at each position ) , ancestral genes ( modelling ancestral states through phylogenetics ) , or center of the tree sequences ( phylogenetic determination of a central isolates ) [1] , [4] , [7] , [8] . Centralized genes have been investigated as effective vaccine approaches in the HIV field both as DNA and/or protein immunogens [6] , [9]–[19] . To date , however , the efficacy of centralized immunogens has not been fully explored in the context of an attenuated lentiviral vaccine model that could provide both immunogenicity data as well as protective efficacy data via virulent challenge in an animal model . Equine infectious anemia virus ( EIAV ) , a macrophage-tropic lentivirus , causes a persistent infection and chronic disease in equids [20] . Infection , transmitted via blood-feeding horse flies , occurs in three stages: acute , chronic and inapparent . Acute and chronic stages are defined by episodes of clinical disease that are triggered by waves of viremia , and distinguished by fever , anemia , thrombocytopenia , edema , and various wasting signs . By one year post-infection animals typically progress to life-long inapparent carriers , but continue to harbor steady-state levels of viral replication in monocyte-rich tissue reservoirs [20]–[22] . Stress or immune suppression of inapparent carriers can induce increases in viral replication and potentially a recrudescence of disease [20] , [23] . Among virulent lentiviruses , EIAV is unique in that , despite aggressive virus replication and associated rapid antigenic variation , greater than 90% of infected animals progress from chronic disease to an inapparent carrier stage , by a strict immunologic control over virus replication [20] . The EIAV system hence serves as a unique animal model for the natural immunologic control of lentiviral replication and disease . Further , EIAV inapparent carriers have proven to be resistant to subsequent virus exposure to diverse viral strains , indicating the development of a high level of prophylactic immunity . Thus , the EIAV system provides a valuable model for identifying critical immune correlates of protection and ascertaining the potential for developing effective prophylactic lentivirus vaccines [24] . While the disease processes for EIAV and HIV have distinguishing dynamics , key similarities between the two virus systems make EIAV an extremely valuable tool and model for AIDS vaccine development [24]–[26] . EIAV and HIV are transmitted parenterally and share a macrophage/monocyte tropism [26] , [27] . EIAV quasispecies also possess high levels of antigenic heterogeneity and their Env proteins share architectural characteristics such as extensive glycosylation and immune decoys [24] , [25] , [28] , [29] . These features , all of which are critical elements associated with initial virus exposure , coupled to a very similar immune maturation process of the EIAV-infected equine to HIV-infected humans , are fundamental factors relevant to vaccine efficacy [24] , [30] . We previously reported serial studies evaluating the efficacy of an attenuated EIAV proviral vaccine containing a mutation in the viral S2 accessory gene ( EIAVD9 ) [31]–[33] . The results of these studies indicate that horses inoculated with the EIAVD9 viral vaccine were 100% protected from disease by virulent , albeit homologous , EIAV challenge . Thus , the EIAV system mirrors other animal lentivirus vaccine models , which consistently identify attenuated vaccines as producing the highest level of vaccine protection . [31]–[38] . Our latest EIAVD9 data demonstrated the effects of challenge virus Env sequence variation on vaccine protection [39]–[41] . We identified a significant , inverse , linear correlation between vaccine efficacy and increasing divergence of the challenge virus Env surface protein , gp90 , compared to the vaccine virus gp90 protein . The study demonstrated that the 100% protection of immunized horses from disease after challenge by virus with a homologous gp90 ( EV0 ) , dropped to approximately 60% protection when a challenge virus gp90 was 6% divergent ( EV6 ) , and nose-dived to less than 50% protection against challenge with a gp90 that was 13% ( EV13 ) divergent from the vaccine strain . Most recently , we demonstrated that the attenuated vaccine strain progressively evolved during the seven-month pre-challenge period and that the observed protection from disease was significantly associated with divergence from the original vaccine strain , not the overall diversity of the vaccine Env quasispecies present on the day of challenge ( DOC ) [39] . Despite numerous studies on the immunogenicity of centralized Env proteins , use of these noteworthy immunogens in an attenuated vaccine model , accompanied by virulent virus challenge , has yet to be reported . In the current study , we sought to directly build upon our current model and the series of described EIAVD9 vaccine studies . Our attenuated vaccine model , coupled with the well-characterized genomic and phylogenetic ancestry of the Env gene of our EIAV strain , enabled a thorough , unparalleled evaluation of centralized sequence vaccine efficacy not as readily modelled in other lentiviral systems . The presented studies evaluated multiple derivatives of centralized Env immunogens , both consensus and ancestral , in our proviral attenuated vaccine backbone . The studies were designed to first , develop and test a consensus immunogen for functionality through examination of replication and pathogenic potential in proviral backbones; second , compare the protective efficacy of the consensus immunogen in an attenuated backbone to attenuated strains containing our ancestral Env [34] as well as a polyvalent Env attenuated strain mixture; and third , to develop and utilize a more stringent challenge model in the form of an engineered quasispecies .
Consensus gene development of the EIAV Env protein focused on the gp90 region of the gene as genomic evolution and antigenic variation in the transmembrane ( gp45 ) protein has been shown to be minimal among characterized longitudinal EIAV isolates [42] , [43] . To engineer a consensus Env , the gp90 genes of approximately 90 naturally occurring isolates from an experimental infection [42] , [44] were aligned . Virus isolates included the inoculum strain Env as well as isolates from all three stages of disease ( acute , chronic , and inapparent ) . Isolates therefore included an ancestral strain ( EV0 ) and its descendant strains that evolved innately between day zero and 1200 days post-infection ( DPI ) . Consensus sequences were derived primarily from codon-aligned nucleotide sequences and secondarily from amino acid alignments . The consensus sequence from the nucleotide alignment was translated , compared to the consensus sequence from the amino acid alignment for congruence and the resolution of ambiguities , and termed ConEnv . To evaluate the veracity of the derived ConEnv sequence and discriminate it against other potential consensus Env proteins , additional consensus sequences were designed . Consensus Envs representing the individual febrile episodes ( six ) and inapparent stage isolates were generated from the isolate amino acid sequences and thereafter a consensus from those engineered sequences was constructed as well . This “consensus from all consensus” method is similar to creating consensus Envs from each HIV clade and subsequently creating a consensus of those clades . The ConEnv consensus sequence was then examined by phylogenetic comparison with these control consensus sequences , the Env sequences involved in ConEnv construction , and sequences targeted as partner Envs for vaccine and challenge strains of this study ( Fig . 1 ) . The “consensus from all consensus” sequence was phylogenetically closely related to the ancestral Env ( EV0 ) emerging on the main ancestral root amongst the acute disease Env genes . ConEnv shared the same ancestral root as the EV6 strain , manifesting ancestrally between EV0 and EV6 . Genetic distance calculations demonstrated the ConEnv gp90 sequence was 4% , 6% , and 11% divergent from the EV0 , EV6 , and EV13 strains , respectively . The majority of the disparate residues occurred within the designated variable regions , specifically V3 through V7 ( Fig . 2 ) . Hence , the ConEnv sequence was a strong consensus sequence representative of the 90 isolates , capturing epitopes broadly characteristic of the family of EIAV isolates . To fully assess the competency of the ConEnv protein to function indistinguishably from a naturally occurring Env protein , ConEnv was evaluated in the context of both attenuated and pathogenic EIAV proviruses . Commercially synthesized ConEnv was cloned into the attenuated EIAVD9 backbone [31] , [32] and the pathogenic EIAVUK3 backbone [45] , with the resultant proviral strains termed ConD9 , and EVCon , respectively . Attenuated and pathogenic proviral clones were sequenced to verify the ConEnv gene , and then transfected into equine dermal ( ED ) cells for production of infectious virus stocks . Virus stocks were titered and characterized for in vitro replication kinetics [46]–[48] . Both proviral strains , EVCon ( pathogenic ) and ConD9 ( attenuated ) demonstrated typical in vitro kinetics , emulating their parental and variant strain counterparts , and peaked in virus production at approximately ten DPI . In vivo analysis of the proviral pathogenic and attenuated ConEnv strains , by experimental infections of equids , confirmed characteristic EIAV clinical and virological profiles of both pathogenic and attenuated infections ( Fig . 3 ) . Typical attenuated and avirulent replication properties were observed for the ConD9 strain . Low level , viral replication kinetics ( averaging between 102–103 copies RNA/ml ) which failed to progress to clinical disease over a 100 day observation period were observed . Conversely , pathogenesis and virulence , characterized by standard viremic replication kinetics ( averaging between103–104 copies RNA/ml and peaking at 106 copies RNA/ml ) , including the induction of acute , and progression to chronic disease were observed with the EVCon strain [20] , [43] , [49] . Hence , for the first time , a synthetic consensus lentiviral Env was demonstrated to not only be fully functional in the context of replication competence in vitro and in vivo , but also capable of inducing traditional and virulent lentiviral disease . The final pre-vaccine trial evaluation was an assessment of ConEnv's immunogenicity . Assays of antibody responses elicited by EVCon , EV0 , EV6 , and EV13 experimental infections , with variant and consensus viruses , indicated primarily distinct neutralization phenotypes for the individual variant Envs; each variant virus was neutralized by immune serum from homologous virus infections , but not from heterologous virus infections , except for marginal neutralization of the EVCon strain by the EV0 heterologous serum ( Fig . 4 ) . However , sera produced by the EVCon virus infections were capable of not only neutralizing its homologous strain , but also neutralized the EV0 and EV6 heterologous strains . Thus , these data demonstrate that the ConEnv was similar to EV0 , EV6 , and EV13 in replication and virulence properties [34] , yet distinct in immune properties as a result of the defined Env sequence variation . Much like the HIV consensus Env recombinant proteins that have been reported [6] , [15]–[18] , this consensus Env , in the context of a fully functional virus , demonstrated immunogenicity induction of neutralizing antibodies with broader recognition of epitopes than that of the naturally occurring isolates from which the ConEnv was derived . To directly evaluate the consensus Env as well as the general premise of centralized immunogens , we compared the consensus Env attenuated strain ( ConD9 ) with an ancestral Env attenuated vaccine strain ( EIAVD9 or D9 ) . Proficiency of the centralized immunogens was further scrutinized by inclusion of a third attenuated vaccine regimen . The third arm of the study , a polyvalent attenuated strain mixture , was chosen as the most rigorous match to the centralized immunogens . The polyvalent attenuated quasispecies was constructed utilizing the D9 backbone . A trivalent attenuated mixture was assembled with the D9 as one of three strains . The EV6 and EV13 Envs were cloned into the D9 backbone to create 6D9 and 13D9 , respectively , the final two strains of the polyvalent mix . The polyvalent attenuated mixture , a 1∶1∶1 mix of D9 , 6D9 , and 13D9 , otherwise termed TriD9 , was tested in vivo for TCID50 dosage verification in a group of eight ponies . Twenty-four ponies of mixed age and gender were divided randomly into groups of eight animals and inoculated intravenously with two , 3×103 TCID50 doses of the ancestral D9 vaccine , the consensus ConD9 or polyvalent TriD9 vaccines , at four-week intervals . The inoculated ponies were monitored daily for clinical signs of EIA , and blood samples were taken at regular intervals for standard measurements of disease , virus replication , and host immune responses , as described previously [31]–[33] , [50] . Figs . 5–7 display the clinical profiles of vaccinated animals . One of the eight animals in the polyvalent TriD9 group developed clinical complications pre-challenge that compromised continued use of the animal in the study and was thus removed from the trial . All twenty-three vaccinates ( 3 trial groups ) exhibited no clinical signs of EIA disease from the attenuated vaccine strains during the seven month observation period , a time frame that allows complete maturation of vaccine immunity prior to virus challenge [22] , [30] , [51] ( Figs . 5A–7A ) . An engineered quasispecies challenge model , EVMX , was developed as a rigorous assessment of immune protection . Based on previous studies [28] , [34] , equivalent ( 1∶1∶1 ) TCID50 dosages of the virulent EV0 , EV6 , and EV13 strains combined to create the virulent , well-defined , swarm . Six months following the second vaccination , the immunized ponies were challenged intravenously every other day with three , 3×103 TCID50 , EVMX inoculations . A control group consisting of six EIAV-naïve ponies was also challenged with EVMX ( Figs . 5B–7B ) . Analyses of vaccinate day of challenge ( DOC ) viral loads demonstrated all three attenuated viral regimens replicated to similar levels , averaging , over the seven month pre-challenge period , between 2×103 and 3×103 copies RNA/ml plasma ( Figs . 5–7 ) . Despite these similarities in viral vaccine replication , however , trial groups displayed markedly different levels of EVMX-induced disease . Four ancestral D9 , six consensus ConD9 , and three polyvalent TriD9 animals displayed clinical signs of EIA disease during the observation period post challenge ( Figs . 5A–7A ) . Chronic disease was observed in the majority of vaccinates that experienced initial acute disease . All six control animals of each variant virus challenge group developed clinical EIA disease , indicating 100% virulence of the quasispecies challenge under the current experimental infection conditions . DOC vaccine immune responses in all groups were also indicative of mature immune responses ( Table 1 , Table 2 , S1 Fig . ) . The polyvalent TriD9 group demonstrated the highest Env-specific serum antibody titer and avidity , although it was only significantly different from the ancestral D9 regimen ( P = 0 . 017 , P<0 . 0001 , respectively ) , and not from the consensus ConD9 regimen ( S1 Fig . ) . While neutralizing antibodies were detectable in all three groups , they could not be associated with protection . Similarly , DOC cellular immunity levels were similar but not correlated with protection levels ( Table 2 ) . For example , measured in vivo cytokine responses were the lowest in the polyvalent TriD9 vaccinates that showed the highest level of protection . The percentage of animals within each trial group protected from clinical EIA was plotted as a function of days post-challenge for survival analysis ( Fig . 8 ) . Both centralized Env vaccine groups had subjects that succumbed to EVMX disease within a typical time frame of 2–3 weeks post-challenge . The polyvalent , TriD9 group , however , demonstrated a delay in the onset of disease with the first animal not breaking until 81 days post-challenge ( Fig . 8 ) . Overall , protection curves of all three vaccine groups were significantly different from one another ( ANOVA , P<0 . 0001 ) . Polyvalent TriD9 vaccinates demonstrated the highest levels of protection that was significantly different from the unvaccinated controls and the consensus ConD9 curves ( P<0 . 0001 , P = 0 . 0002 ) . While the consensus ConD9 group had the lowest level of protection , it was significant as compared to unvaccinated subjects ( P = 0 . 001 ) . Analysis of the trend revealed a significant relationship between the complexity of immunogen and protective efficacy ( P = 0 . 02 ) . Ultimately , the consensus ConD9 strain , while pre-trial appearing to be more broadly immunogenic , demonstrated the lowest level of protection . The polyvalent TriD9 regimen demonstrated the highest level of protection against a quasispecies challenge . The current study not only highlighted important information towards HIV vaccine development and highlighted the importance of rigorous challenge strain engineering .
The collective success of attenuated vaccine regimens makes it the ideal modality to rigorously test novel vaccine concepts . Previous work by our group and others have demonstrated attenuated vaccines to be an extremely useful tool for vaccine development , regardless of the potential for marketable advancement of the attenuated platform due to the safety concerns associated with HIV vaccines . Consequently , we resolved to examine the efficacy of centralized Env immunogens in our well-established EIAV attenuated vaccine model . Results presented here reveal , for the first time , a consensus Env in a fully replication-competent attenuated virus backbone that can confer protective efficacy against virulent virus challenge; however , it does not induce the highest level of protection as compared to ancestral or polyvalent vaccines . A concept not articulated in most reports on centralized Env sequences are the specifics involved in the determination of the consensus sequence . Creation of an ideal consensus sequence requires careful consideration of various parameters . Our original attempts to construct a consensus gp90 gene focused on a more basic approach than what we utilized for the current study . Initial alignments were performed utilizing the three sequences that comprise the EVMX quasispecies challenge strain , EV0 , EV6 , and EV13 [34] . Phylogenetic analysis of this consensus gp90 demonstrated that the consensus sequence was located high on the ancestral root and would not have represented a good consensus sequence as it was highly related to the ancestral , or EV0 gp90 . Likewise , the “consensus of all consensus” method utilized in both the HIV and influenza fields [6] , [9] , [15] , [18] , [19] , [52]–[56] resulted in a gp90 sequence that phylogenetically was more related to early disease isolates on the ancestral root ( Fig . 1 ) than to the consensus gene generated from all 90 isolate sequences ( nucleotide and amino acid ) . In vitro analysis of the virus constructed from the second-generation consensus gp90 , assembled from the 90-isolate alignment and cloned into a proviral backbone , was found to infect equine cells , but was not fully replication competent . Therefore , if we were to analyze this Env in a single-round infection assay it would falsely appear to be fully functional . Hence , the 90-isolate sequence alignment was re-evaluated and a higher level of hand-editing performed , especially in the highly variable V3 region , prior to consensus generation . This thorough computational analysis and consideration of the consensus Env limited the potential sampling bias that obfuscates computational engineering of protein immunogens [1] , [57] . Likewise , our use of an actual ancestoral Env also reduced the potential sampling bias that is problematic to computationally constructed most recent common ancestors [1] , [57] . Additionally , in the absence of replication analysis and study in an attenuated model , the incapability of the consensus Env to functional naturally would not have been observed and a gp90 less representative of a fully functional Env would have been examined . Ultimately , this is the first successful construction of a consensus envelope lentivirus construct with full replication and virulence properties . The highest protective efficacy against disease was observed in the polyvalent TriD9 vaccinates . Survival analysis revealed the polyvalent TriD9 disease curve was significantly different from the naïve controls and importantly , the consensus ConD9 curve . Considering the EVMX challenge quasispecies virus strain composition , the polyvalent TriD9 regimen displaying the highest levels of protective efficacy might be anticipated; however , the ancestral D9 vaccinates also protected to a higher degree than the consensus ConD9 vaccinates . Pre-challenge analysis of clinical and virological factors would not have predicted these results . All three attenuated vaccine regimens displayed similar levels of pre-challenge viral strain replication . Pre-trial evaluation of the capability to induce neutralizing antibodies ( Fig . 4 ) indicated that the ConEnv produced a broader immune response than the ancestral gp90 ( EV0/D9 ) and also the EV6 and EV13 gp90 proteins . Immune analysis of DOC responses did not reveal a direct correlation of protection . Neutralizing antibodies and cellular immune responses were not associated with protective efficacy . Although a significantly higher level of Env-specific antibodies were found in the polyvalent TriD9 vaccinates , the consensus ConD9 vaccinates , similarly , had a higher antibody response as compared to the ancestral D9 . In the case of the consensus ConD9 vaccinates , immune response data is suggestive of potential issues related to the artificial nature of the consensus Env and its ability to induce broadly protective responses . Protection in all three vaccine groups could be due to anamnestic responses that could be related to conserved regions or conformational epitopes that allow for protective response in lieu of sequence identity . Although total antibody binding does not reveal directly correlative data ( S1 Fig . ) , mapping of the reactive epitopes will be key to determining if a region of the gp90 potentially conferred more protective responses . These studies are currently underway . A notable observation of the polyvalent TriD9 vaccinates was the delayed onset of disease ( Fig . 8 ) . The quasispecies challenge virus eventually broke through causing its first case of disease at the late time point of 81 days post-challenge . The nature of this break through is an interesting study of evolution: did the EVMX Env evolve at a faster rate than the polyvalent TriD9 and escape the protective immune responses or did recombination allow the escape . Current studies are being performed to enable future characterization of break-through febrile isolates . As part of the current report we generated an engineered quasispecies challenge virus mix containing different degrees of Env variation . The majority of current lentiviral vaccine studies employ a single strain challenge model . Heterologous challenge models , while more rigorous than homologous challenge models , are commonly single viral strains . Data presented here indicate that more comprehensive challenge models , that include variable Env proteins , should be developed for the study of lentiviral vaccines to better test the vaccine modalities being investigated . The polyvalent TriD9 and EVMX vaccine and challenge strains were at their basic phenotype , a homologous pairing . However , the complexity of a pathogenic , diverse , challenge strain resulted in a notable difference and reduction in the protective efficacy as compared to previous studies where the vaccine and challenge strains were matched in their Env sequences . Improved challenge model development for animal lentiviral studies that comprehensively test protective efficacy are critical tools required for broadly protective vaccine development . The studies presented here demonstrate definitively that polyvalent attenuated vaccine regimens have significantly higher levels of protection as compared to centralized immunogens . Although it is not possible with absolute confidence to extrapolate the results of vaccine studies in any single animal lentivirus system to other animal lentiviruses or to HIV , the data presented here certainly highlight the priority of ascertaining centralized immunogens on HIV vaccine efficacy in the context of higher animal models that include challenge studies which can inform on the true protective nature of the proposed immunogens .
Consensus gp90 Env protein sequence was determined by alignment of nucleotide ( codon alignment ) and amino acid sequences from naturally arisen EIAV isolates originated from an experimental infection ( pony #567 ) [34] , [42]–[44] in the Geneious Pro package of software ( Biomatters , Ltd . ) . Alignments were hand edited where necessary , especially in the highly variable loop region , and ambiguities resolved through partner aligning of nucleotides and amino acids . Phylogenetic characterization of the consensus Env was were constructed by the neighbor-joining method of Jukes Cantor corrected distances with the optimality criterion set to distance as measured in PAUP [58] and implemented in the Geneious Pro 5 . 0 . 4 ( Biomatters Ltd . , NZ ) package of software . Statistical significance of branchings and clustering was assessed by bootstrap resampling of 1000 pseudoreplicates on the complete data set; trees were rooted to the original infectious ancestral Env , EV0 . The trees were edited for publication using FigTree Version 1 . 1 . 2 . Resultant gp90 sequence was synthesized ( GeneArt , Regensberg , Germany ) . The consensus gp90 was cloned into pathogenic and attenuated EIAV backbones using methods and restriction sites previously described [34] , [47] , [48] . Viral stocks were prepared as previously described [34] , [48] . Viral stock titers were determined utilizing our infectious center assay ( cell-based ELISA ) in fetal equine kidney cells , described previously [46] . All equine procedures were conducted in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health at the Gluck Equine Research Center of the University of Kentucky according to protocols approved by the University of Kentucky IACUC ( #01058A2006 ) . The animals were monitored daily and maintained as described previously [31] , [32] , [34] , [43] , [49] . Eight outbred , mixed-breed ponies were separated into two groups of four and experimentally inoculated intravenously with 103 TCID50 of either chimeric strain ConD9 or EVCon . Rectal temperatures and clinical status were recorded daily . Platelet numbers were determined using the IDEXX VetAutoread Hematology Analyzer ( IDEXX Laboratories Inc . , Westbrook ME ) . Clinical EIA ( fever ) episodes were determined on the basis of rectal temperature and platelet count ( rectal temperature >39°C; platelet number <100 , 000/µl of whole blood ) in combination with the viremic presence of infectious plasma virus ( ≥105 ) [20] , [26] , [43] , [49] , [59] . Samples of whole blood , serum , and plasma were collected weekly as well as daily during fever episodes . Plasma samples were stored at −80°C until used to determine plasma viral RNA level . The challenge strain EVMX quasispecies was produced by combining equivalent infectious titers ( TCID50 ) of the variant challenge strains EV0 , EV6 , and EV13 [34] . Viral stocks were prepared as previously described [34] , [48] . Viral stock titers were determined utilizing our infectious center assay ( cell-based ELISA ) in fetal equine kidney cells , described previously [46] . Equine procedures were conducted at the Gluck Equine Research Center of the University of Kentucky according to protocols approved by the University of Kentucky IACUC . Thirty-six mixed age and gender outbred ponies , serognegative for EIAV , were utilized . Daily rectal temperatures and clinical status were recorded . CBC analysis of whole blood was performed using an IDEXX QBC Vet Autoreader . Hematocrit and platelet numbers were monitored weekly . The EIAVD9 stock was produced and vaccinations performed as described [31] , [34] . Twenty-three vaccinated and six naïve ponies were challenged with 3×103 TCID50 of EVMX . The ponies were monitored daily for clinical symptoms of EIA , and blood was drawn at regular intervals ( weekly , daily if febrile ) for assays of platelets , viral replication , and virus-specific immune responses . During the course of these experiments ponies that demonstrated severe disease-associated symptoms resulting in distress as outlined by the University of Kentucky IACUC were euthanized . Plasma samples from all subjects were analyzed for the levels of viral RNA per milliliter of plasma using a previously described quantitative real-time multiplex RT-PCR assay based on gag-specific amplification primers [60] . The standard RNA curve was linear in the range of 101 molecules as a lower limit and 108 molecules as an upper limit . Our in vivo method for assessing immune reactivity to specific peptides , described previously [61] , was used to explore cellular immune responses . Forty-four 20-mer peptides , overlapping by 10 residues , spanning the EIAVD9 gp90 were generated ( GenScript USA Inc . , Piscataway , NJ ) . An additional 15 peptides , specific for the variable regions ConD9 gp90 , were included in the pool for analysis of those vaccinates . Vaccinates were screened for gp90 specific cellular immune responses one week prior to day of challenge . A 2 mm skin biopsy was collected and homogenized and RNA extracted . IFNγ gene expression was determined by real-time PCR , as previously described [61] . Amplification efficiencies were determined using Linreg [62] and samples with amplification efficiencies above 99% were included for further analyses . Beta-glucuronidase ( β-GUS ) was used as housekeeping gene and the ΔΔCT method [63] was used to determine relative gene expression with saline injection site for each vaccinate used as the calibrator . Relative quantity ( RQ ) was calculated as 2−ΔΔCT . Serum IgG antibody reactivity to EIAV envelope glycoproteins was assayed quantitatively ( end point titer ) and qualitatively ( avidity index , conformation ratio ) using our standard concanavalin A ( ConA ) ELISA procedures as described previously [51] . Virus neutralizing activity to the historical reference strain EIAVPV , and vaccine-specific virus stocks EVCon and EVMX , mediated by immune sera , was assessed in an indirect cell-ELISA based infectious center assay using a constant amount of infectious EIAV and sequential 2-fold dilutions of serum [46] , [51] . Significance of protection from disease was performed by survival curve analysis as implemented in GraphPad Prism version 6 . 0d ( San Diego , CA ) . Significance of survival curves were determined utilizing One-way ANOVA with Bonferroni's multiple comparison's test as well as survival analysis of Kaplan Meier plots with Logrank test for trend . All equine procedures were conducted in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health at the Gluck Equine Research Center of the University of Kentucky according to protocols approved by the University of Kentucky IACUC ( #01058A2006 ) .
|
Our best effort for containment of the global HIV epidemic is the development of a broadly protective vaccine . Current research has focused on vaccines that can generate a protective immune response against numerous strains of the virus . For this reason , vaccines with centralized HIV genes as immunogens , which merge HIV genetic information and potentially protect against multiple viral strains in a single inoculation , are an increasing area of interest to the field . Existing published studies have not evaluated centralized immunogens in the context of attenuated vaccines , which to date , have demonstrated the highest level of vaccine protection in lentiviral studies . Furthermore , centralized immunogen studies have also not included protective efficacy findings accomplished through challenge with highly pathogenic virus strains . In this study we not only examine the immunogenicity of these immunogens in an animal model , but we also for the first time evaluate the ability of centralized immunogens to induce protection against virulent virus challenge .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"viral",
"vaccines",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"microbiology",
"retroviruses",
"vaccines",
"viruses",
"vaccination",
"and",
"immunization",
"medical",
"microbiology",
"microbial",
"pathogens",
"attenuated",
"vaccines",
"virology",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"vaccine",
"development",
"lentivirus",
"organisms"
] |
2015
|
Protective Efficacy of Centralized and Polyvalent Envelope Immunogens in an Attenuated Equine Lentivirus Vaccine
|
The differences in demographic and life-history processes between organisms living in the same population have important consequences for ecological and evolutionary dynamics . Modern statistical and computational methods allow the investigation of individual and shared ( among homogeneous groups ) determinants of the observed variation in growth . We use an Empirical Bayes approach to estimate individual and shared variation in somatic growth using a von Bertalanffy growth model with random effects . To illustrate the power and generality of the method , we consider two populations of marble trout Salmo marmoratus living in Slovenian streams , where individually tagged fish have been sampled for more than 15 years . We use year-of-birth cohort , population density during the first year of life , and individual random effects as potential predictors of the von Bertalanffy growth function's parameters k ( rate of growth ) and ( asymptotic size ) . Our results showed that size ranks were largely maintained throughout marble trout lifetime in both populations . According to the Akaike Information Criterion ( AIC ) , the best models showed different growth patterns for year-of-birth cohorts as well as the existence of substantial individual variation in growth trajectories after accounting for the cohort effect . For both populations , models including density during the first year of life showed that growth tended to decrease with increasing population density early in life . Model validation showed that predictions of individual growth trajectories using the random-effects model were more accurate than predictions based on mean size-at-age of fish .
A better understanding of growth will always be an important problem in biology . Somatic growth is one the most important life-history traits across taxa , since survival , sexual maturity , reproductive success , movement and migration are frequently related to growth and body size [1] . Variation in growth can thus have substantial consequences for both ecological and evolutionary dynamics [2]–[4] . Variation in growth can also affect the estimation of vital rates and demographic traits , which may translate to incorrect predictions of population dynamics [5]–[7] . However , the implications of including individual differences in growth in the study of population processes are largely unexplored , in part because of the computational challenges of estimating the determinants and the extent of shared ( i . e . among homogeneous groups ) and individual ( i . e . after accounting for shared component ) variation in growth [5] , [8] . Determining how shared and individual variation in growth emerges may also improve predictions of future growth and size of individuals and populations , which is valuable for conservation and management [9] , [10] . In addition , the ability to reliably predict missing body size data is crucial when testing for size-dependent survival and selection on body size . For instance , when estimating the effects of size on survival , the Cormack-Jolly-Seber model requires the size of individual to be known on the occasions when the individual was alive but not sampled , or , alternatively , it requires individual growth trajectories [11] . Thus , our understanding of growth dynamics and of its consequence on population and evolutionary dynamics can greatly benefit from the use of new computational approaches that are able to tease apart the sources of growth variation . The accurate estimation of parameters of growth models is particularly useful in this regard , since it reduces the information provided by a potentially long series of measurements to a few values that summarize the most relevant process governing growth and can then be used to tease apart individual and shared determinants of growth variation [12] . Multiple processes contribute to the realized growth of organisms , such as individual variation , size-selective mortality , annual and spatial variation in growth , intra- and inter-specific competition . Understanding the nature and contribution of these multiple sources of variation in growth faces a number of methodological challenges . First , to simultaneously estimate shared and individual contributions to the observed variation in growth require longitudinal data . In fact , when data are cross-sectional , it is rarely possible to separate variation in growth that emerges from persistent differences among individuals from variation due to stochastic processes [13] . Second , especially for mobile organisms , it is seldom possible to obtain more than a few observations for an individual throughout its lifetime ( i . e . temporal data are often sparse ) . Thus , data for a particular individual are unlikely to be adequate for the estimation of parameters of the growth model for that individual and additional information may be needed , such as data of other individuals thought to be similar ( i . e . “borrowing strength” or “shrinkage” [14] ) . Models in which the estimate of each effect is influenced by all members in a group are alternatively called hierarchical , random-effects , multilevel , or mixed models [14] . For consistency , in this paper we only use the term random-effects model . Modeling and estimating random effects also have the advantage of addressing the lack of independence between repeated measurements of the same individuals and of individuals in homogeneous groups [15] . In addition , when using parametric growth models , parameter estimates at the individual or shared level as well as their correlation structure can lead to insights on the processes governing the growth of individuals or group of individuals . Third , since no organism can growth without bound , growth models must at some point be non-linear [16] , [17] and the estimation of model parameters is thus computationally demanding . Generally , fast and reliable approaches are needed in order to investigate multiple parameterizations of growth models . In this work , we propose a stable , reliable , and fast parametric Empirical Bayes ( EB ) approach [18]–[20] for estimating shared and individual variation in somatic growth using longitudinal data and random-effects models [8] , [10] , [21] . To illustrate the power and generality of our methods , we consider long-term studies of two populations of marble trout Salmo marmoratus living in Slovenian streams . Both populations have been sampled annually for more than 15 years , and show substantial differences within and among populations in the mean growth of cohorts and in size-at-age of individuals . In [22] , we demonstrated that fast-growing marble trout allow population recovery after massive mortality events , such as those caused by floods and landslides , due to the positive influence of larger size-at-age of fish on recruitment . In addition , because observed variation in growth among individuals is heritable [23] , there is potential for the evolution of growth rates toward faster growth in populations affected by massive mortality events [24] , [25] . However , how variation in growth in marble trout is determined by shared and individual factors is unknown . Within populations of the same species , persistent differences in growth are commonly observed both among groups ( e . g . year-of-birth cohorts , families ) and among individuals within groups . Cohort effects are often induced early in life and have the potential to strongly affect the performance of individuals throughout their lifetime [26]–[29] . These early effects on lifetime growth may reflect either constraints or adaptations [29] , and are often ascribed to climatic vagaries during early development that similarly affect the whole cohort [30]–[32] . In marble trout as well as in other species , population density during the early life stages also has substantial effects on lifetime growth , in particular due a reduction in per-capita food availability [33] or the occupation of spaces of low profitability with increasing population density [34] , [35] . At the population level or within more homogenous groups , among-individual variation may emerge from differences in overall genetic growth potential , metabolic rates , behavioral traits ( e . g . aggressiveness ) , occupation of sites of different profitability , or life-history strategies ( e . g . partition of energy to competing functions , such as growth , storage , reproduction and maintenance ) [36] . In this paper , we show how new computational methods for the estimation of a parameter-rich non-linear growth function using longitudinal data can shed light on the shared and individual determinants of somatic growth in natural populations . Our aim is to expand the toolkit available to biologists rather than proposing a method globally superior to another , since the particular biological problem should play an important role in selecting the tool . The paper is organized as follows . First , we introduce the two populations of marble trout that we used as a motivating example and case study . We then present the Empirical Bayes approach to parameter estimation as implemented in the module ADMB-RE ( Automatic Differentiation Model Builder - Random-Effects ) of the software ADMB [37] and apply the EB approach to the joint estimation of shared and individual variation in growth from longitudinal data using a parameter-rich von Bertalanffy growth function . For the case study of marble trout , we introduce environmental predictors of the von Bertalanffy growth function's parameters k ( rate of growth ) and ( asymptotic size ) such as population density in the first year of fish life and year-of-birth cohort , and test whether their inclusion , in addition to individual random effects , improves model performance . We compare the parameter estimates and resulting estimated growth trajectories for two populations of marble trout living in different habitats and showing different demographic traits , and highlight shared and contrasting results between the two populations . We then test the ability of the growth model to predict unobserved length-at-age of individual fish . We discuss the life-history mechanisms that may generate the observed patterns of growth , as well as their implications for population dynamics .
All sampling work was approved by the Ministry of Agriculture , Forestry and Food of Republic of Slovenia and the Fisheries Research Institute of Slovenia . Original title of the Plan: RIBISKO - GOJITVENI NACRT za TOLMINSKI RIBISKI OKOLIS , razen Soce s pritoki od izvira do mosta v Cezsoco in Krnskega jezera , za obdobje 2006–2011 . Sampling was supervised by the Tolmin Angling Association ( Slovenia ) . Our case study involves two populations ( Gacnik and Zakojska ) of marble trout living in Slovenian streams [38] . These populations are part of a larger study involving 10 marble trout populations [38] . We limit our discussion to two populations showing contrasting demographic traits and characteristics of the habitat to focus on the computational tools and the insights generated by the estimation of the parameters of the growth models . Marble trout is a resident salmonid endemic in Northern Italy and Slovenia that is now endangered due to widespread hybridization with introduced brown trout and displacement by alien rainbow trout . The populations of Gacnik and Zakojska were established in stretches of fishless streams in 1996 ( Zakojska ) and 1998 ( Gacnik ) by stocking age-1 fish that were the progeny of parents from relic genetically pure marble trout populations [39] . Trout in Gacnik and Zakojska are genetically different [39] . Fish hatched in the streams for the first time in 1998 and in 2000 in Zakojska and Gacnik , respectively . Those cohorts are the first included in the analysis . The two populations were sampled annually in June . The geomorphological characteristics of the two streams are different: Zakojska is ( mostly ) a fragmented one-way stream ( i . e . trout can move downstream , but not upstream ) , while Gacnik is a two-way stream , i . e . trout can move in either direction . Fish were collected by electrofishing and measured for length and weight to the nearest mm and g , respectively ( fig . 1 ) . If fish were caught for the first time - or if the tag had been lost – and they were longer than 110 mm they were tagged with Carlin tags [40] and age was determined by reading scales . Marble trout spawn in November–December and offspring emerge in April–May . Underyearlings are smaller than 110 mm in June , thus trout were tagged at age 1 or , in the case of small size , at age 2 . Males and females are morphologically indistinguishable at the time of sampling . The probability of recapture was higher than 80% and we did not find evidence of capture probability varying with age and size for fish older than age 0 [41] . In addition , we found no evidence of size-selective mortality in either stream [42] . The movement of marble trout is limited , and the majority of marble trout were sampled within the same 200 m reach throughout their lifetime . Marble trout females achieve sexual maturity when longer than 200 mm , usually at age 3 or older . The maximum observed age for fish born in the streams was 12 and 9 years in Gacnik and Zakojska , respectively . The last sampling occasion included in the dataset was June 2012 . In Gacnik the last cohort included was the one born in 2010 . Due to a flood that almost completely wiped out the population in 2007 [38] , the last cohort included for Zakojska was the one born in 2008 . Density of fish of age 1 and older ( number m−2 ) was ( mean±sd ) 0 . 05±0 . 04 in Zakojska from 1998 to 2012 and 0 . 16±0 . 07 in Gacnik from 2000 to 2012 . In total , 1 067 unique fish were included in the Zakojska dataset and 4 764 in the Gacnik dataset . Empirical Bayes ( EB ) refers to a tradition in statistics where the fixed effects and variance ( or standard deviation ) of a random-effects model are estimated by maximum likelihood , while estimates of random effects are based on Bayes formula ( e . g . [43] , [44] ) . Although random-effects models can be analyzed using frequentist or Bayesian methods [45] , [46] , the frequentist point of view may have a number of advantages [44] . From a computational perspective the maximum likelihood estimate is relatively inexpensive to calculate and avoids difficulties associated with judging convergence of MCMC samplers when using a full Bayesian approach . Due to these advantages , EB has increasingly been applied in the last few years in the biological sciences in fields including genetics [47] , [48] , disease screening [49] , and genomics [50] . Estimation of fixed effects and variance parameters by maximum likelihood is in widespread use in mixed model software packages , such as the R package “lme4” [51] . ADMB is an open source statistical software package for fitting non-linear statistical models [37] , [53] . ADMB can be used to fit generic random-effects models with an EB approach using the Laplace approximation ( ADMB-RE [54] ) . ADMB is totally flexible in model formulation , allowing any likelihood function to be coded in C++ . Coding in C++ allows also for a great flexibility of functional forms to be used for model parameterization . In terms of computing times , ADMB compares favorably to other software and methods for the estimation of parameters of highly-complex non-linear models [55] ( see also text S2 ) . The gradient ( i . e . the vector of partial derivatives of the likelihood function with respect to model parameters ) provides a measure of convergence of the parameter estimation procedure in ADMB . Although considerations of speed and model complexity may motivate the use of a less strict convergence criterion , by default ADMB stops when the maximum gradient component ( i . e . the largest of the partial derivatives of the likelihood function with respect to model parameters ) is <10−4 . An explicit convergence criterion allows the researcher to systematically move forward in the analysis and thus potentially explore a large number of model parameterizations . A broad range of models describing the physiology of growth have been developed [56]–[62] and recent papers have summarized non-linear growth models along with methods for parameter estimation [16] , [17] . However , it has often been difficult , if not impossible , to estimate parameters for many of the proposed growth models using data on individual growth trajectories in natural settings . Even in the presence of a large amount of data , a highly parameterized model may be only weakly statistically identifiable . We use the growth model due to von Bertalanffy [59] , [63] , [64] . The von Bertalanffy growth function ( vBGF ) has been used to model the growth of organisms across a wide range of taxa , including fish [57] , [65] , mammals [66] , [67] , snakes [68] , and birds [69] , [70] . von Bertalanffy hypothesized that the growth of an organism results from a dynamic balance between anabolic and catabolic processes [59] . If W ( t ) denotes mass at time t , the von Bertalanffy assumption is that anabolic factors are proportional to surface area , which scales as , and that catabolic factors are proportional to mass . If a and b denote these scaling parameters , then the rate of change of mass is ( 3 ) If we further assume that mass and length , L ( t ) , are related by with ρ corresponding to density , then elementary calculus shows that [64] ( 4 ) where and . The linear differential equation in Eq . 4 is readily solved by the method of the integrating factor . Setting to be the asymptotic size ( obtained when we set the left-hand side of Eq . 4 equal to 0 ) and to be the initial size , two forms of the solution are ( 5 ) and ( 6 ) where t0 is the hypothetical age at which length is equal to 0 . In light of Eq . 4 , if , the rate of growth is negative , so that we can think of asymptotic size as the size only attained in the limit of very long times . For a given value of asymptotic size , the parameter k ( in y−1 ) describes how fast the individual or group of individuals reaches the asymptotic size . In this work , we will use the formulation of the vBGF of Eq . 6 , which has 3 parameters: L∞ , k , and t0 . Although the mechanistic definition of asymptotic size in the vBGF introduces an explicit linear relationship on the log scale between k and L∞ ( i . e . ) , in this work we do not explicitly introduce L∞ as equal to , but we let the correlation between L∞ and k at the whole population and at the individual level emerge from data , as it is commonly done [71] . In Supporting Information we provide ( i ) tests of correlation between individual and mean cohort-specific and k using simulated datasets ( fig . S1 and table S3 ) , ( ii ) the mean estimate and confidence intervals for parameters of the best models ( table S2 and table S3 ) , ( iii ) cohort-specific growth trajectories ( fig . S2 ) , ( iv ) derivation of the correlation between parameters of the vBGF under size-dependent mortality and description of potential processes leading to a negative correlation between and k ( text S1 ) , ( v ) confidence bands estimated using a MonteCarlo algorithm ( fig . S3 ) , ( vi ) a comparison with JAGS and the nlme function in R ( text S2 ) , ( vii ) results of a repeatability analysis of body size throughout the lifetime [78] , [79] ( text S3 ) , and ( viii ) details of the Empirical Bayes algorithm ( text S4 ) . All data and code used for the analyses and to produce figures can be found in an online repository at http://dx . doi . org/10 . 6084/m9 . figshare . 831432 .
For each vBGF model we tested , we obtained convergence of the algorithm for parameter estimation in ADMB , and the data used for the estimation of the parameters were well predicted by the models ( for the model with no predictors except individual random effects: Zakojska , R2 = 0 . 97 , MAE = 9 . 58 mm; Gacnik , R2 = 0 . 98 , MAE = 6 . 82 mm ) . We obtained consistent parameter estimates when starting ADMB-RE from different initial parameter values . For each model , the standard deviation of the probability distribution of random effects was larger than 0 . In the vBGF model with no predictors for both and k , the two parameters at the individual level were strongly and positively correlated ( Zakojska; r = 0 . 79 , p<0 . 01; Gacnik , r = 0 . 85 , p<0 . 01 ) ( fig . 3 ) . However , the correlation was inflated by the almost perfect correlation of k and for fish that were sampled just once ( Zakojska; r = 0 . 97 , p<0 . 01; Gacnik , r = 0 . 99 , p<0 . 01 ) . Considering only fish that were sampled more than 2 times , the correlation between k or at the individual level remained positive and highly significant in both populations , albeit weaker ( Zakojska; r = 0 . 48 , p<0 . 01; Gacnik , r = 0 . 59 , p<0 . 01 ) . We also found a strong and positive correlation within cohorts between k and at the individual level in the models that included cohort as predictor in either or both parameters ( for the model with cohort as predictor for both k and , Zakojska [mean r across cohorts ± sd] = 0 . 86±0 . 11; Gacnik = 0 . 86±0 . 12 ) . Tests on simulated data sets showed that when individual trajectories are simulated with positive , negative or no correlation r between k and at the individual level , the estimated correlation between individual random effects estimated with the EB method is very close to the true r ( fig . S1 ) . The CVs of k and at the individual level for the vBGF model with no predictors were 6% and 6% respectively in Gacnik and 2% and 9% respectively in Zakojska . When the model included cohort as predictor for both k and , the range of cohort-specific CV of k and at the individual level were 3–6% ( k ) and 4–7% ( ) for Gacnik and 1–2% ( k ) and 3–13% ( ) for Zakojska . In the model with no predictors , at the population level was greater in Gacnik than in Zakojska , while the opposite was true for k ( mean and 95% confidence intervals , Gacnik: L∞ = 323 . 28 mm [318 . 54–328 . 02] , k = 0 . 24 y−1 [0 . 23–0 . 25] , t0 = −0 . 92 y [−0 . 97- ( −0 . 87 ) ]; Zakojska: L∞ = 298 . 83 mm [289 . 83–307 . 82] , k = 0 . 36 y−1 [0 . 33–0 . 39] , t0 = −0 . 49 y [−0 . 58- ( −0 . 41 ) ] ) . For both populations , k and tended to get smaller with increasing density in the first year of life . The best model according to AIC had cohort as predictor of both k and in both populations ( table 1 , see table S1 and S2 for parameter estimates for Zakojska and Gacnik , respectively ) . Cohort-specific mean k and ( i . e . , with individual random effects uij and vij in Eq . 7 set to 0 ) were negatively correlated ( Zakojska; r = −0 . 81 , p<0 . 01; Gacnik , r = −0 . 87 , p<0 . 01 ) ( figs . 4 and S2 ) . Simulations showed that the estimated correlation between mean cohort-specific k and is not an artifact of the parameter estimation procedure ( table S3 ) . Cohort-specific models with no random effects ( i . e . parameters estimated using nls function in R ) provided consistently greater estimates of and smaller estimates of k than random-effects models ( fig . 5 and table 2 ) , which showed that ignoring autocorrelation among individual measures is likely to upwardly bias estimates of asymptotic length at the group level . In the populations of Gacnik and Zakojska 450 and 62 fish respectively have been sampled more than 3 times during their lifetime . For both populations , the best vBGF model ( i . e . model including cohort and individual random effects as predictors for both k and ) fitted for the fish in the validation samples using only the first observation ( 20% of 450 and 62 fish for Gacnik and Zakojska , respectively ) provided better prediction of the missing observations than mean length-at-age of the respective fish cohort ( fig . 6 , table 3 ) . Finally , when we used no predictors for either model parameter except the individual random effects , the random-effects model provided better predictions of the missing observations than population mean length-at-age ( table 3 ) .
As described above , we found a strong positive correlation between and k at the individual level , as well as very high repeatability of body size in both populations ( text S3 ) . These two results concordantly indicate that size ranks are strongly maintained over time . Two other studies investigated the correlation between the von Bertalanffy growth function's parameters and k at individual level . Using a random-effects model implemented in BUGS , Pilling et al . [80] found a strong negative correlation between and k at the individual level in a sky emperor Lethrinus mahsena population , but they did not discuss any potential processes leading to the estimated negative correlation . In [81] , Alós et al . using a modified five-parameter von Bertalanffy growth function implemented in BUGS found a positive correlation between and two growth parameters ( k0 and k1 ) at the individual level , but they did not discuss the biological and ecological determinants of the observed positive correlation among parameters of the growth function . In text S1 , we discuss the processes that may lead to a negative correlation between and k and here focus on the positive correlation . At the population or group level , the correlation between and k obtained from the Hessian estimated at maximum likelihood estimates of the parameters is usually negative . This correlation does not offer any biological insights , since it occurs because different combinations of and k can basically provide the same fit to the data , in particular when the range of ages is limited [58] , [82] , [83] . In other words , by slightly increasing or decreasing and k in opposite directions , the same likelihood is obtained . Although it is possible to estimate the correlation between random effects within ADMB-RE , this may lead to computational instabilities and possibly to ambiguous interpretation of the correlation parameter when other predictors are taken into account ( we provide the code in the online repository ) . Our simulations confirmed that the observed positive correlation between estimates of and k at the group level ( cohort , as in our case ) and at the individual levels is not a statistical artifact . Multiple non-exclusive and potentially interacting processes may lead to the maintenance of size ranks throughout marble trout lifetime . Specifically , we consider three potential processes: ( i ) among-fish differences in genetic growth potential; ( ii ) habitat heterogeneity; ( iii ) size-dependent piscivory . The best model for both populations included cohort as a categorical predictor for both and k . Within each cohort we found substantial individual variation as well as strong maintenance of size ranks throughout marble trout lifetime ( i . e . the within-cohort correlation of and k at the individual level was strongly positive ) . Models including only density in the first year of life performed distinctly worse than the best model , but better than the model with no predictors . This seems to suggest that other factors , in addition to early density experienced by cohorts , contribute to determine mean growth trajectories of cohorts . Apart from climatic vagaries or particular trophic conditions affecting cohorts in their early life stages , another possible explanation for the emergence of cohort effect is high variance in reproductive success ( e . g . just a few fish contribute to the next generation ) , which is common in salmonids [99] , [100] , combined with ( i ) high heritability of growth and/or ( ii ) heterogeneity in site profitability accompanied by limited movement . The mean growth trajectory of the cohort may thus signal in case of ( i ) the growth potential of the small parental pool , or in case of ( ii ) the profitability of the stream habitat where a large fraction of the cohort lived . Cohort effects on growth were more pronounced in Zakojska than in Gacnik . We found a strong negative correlation between cohort-specific mean and k in both populations . Thus , some of the mean growth trajectories of cohorts were crossing throughout fish lifetime , but within cohorts size ranks were mostly maintained over time . However , the cohort-specific growth trajectories in Gacnik showed very little variation with the exception of a particularly fast-growing cohort , while a richer variety of cohort-specific mean growth trajectories were observed in Zakojska . This may be in part related to the estimation of being particularly sensitive to the presence in the dataset of older individuals [101] . In Zakojska , the dramatic reduction in population size after the flood of 2007 accompanied by the natural thinning of cohorts over time reduced the number of older individuals in the dataset , and this may lead to less accurate predicted mean size of cohorts at older ages . However , we observed the same strong negative correlation even if only including cohorts born up to 2002 ( fig . S2 ) , although the diversity of cohort-specific growth pattern was noticeably smaller and comparable to the diversity observed in Gacnik . Fast-growing cohorts can play a key role in the persistence of small fish populations . Since sexual maturity and egg production in fish are generally size dependent [102] , a higher proportion of fish can reach sexual maturity at younger ages in a fast-growing cohorts than in slow-growing ones . This may be crucial when population size is low and the population is at risk of extinction due to demographic stochasticity . In both Gacnik and Zakojska , the fastest-growing cohorts experienced very low population densities in the first two years of life . Further studies should test whether at the individual or at the cohort level a trade-off between growth and mortality can be observed [103] , and whether fast-growing cohorts had higher lifetime reproductive success than slow-growing ones . A sizable literature on prediction of future growth exists for humans , especially in the context of early identification of pathologies [104]–[107] . An approach similar to that presented in our work for the estimation of lifetime growth trajectories given only information on growth and size during the early stages of life was proposed in [104] and [105] . In particular , in [104] Shohoji et al estimated the lifetime growth of Japanese girls using measurement up to the age of 6 years old . They first adapted to humans a parametric model previously developed to model the growth in weight of savannah baboons . Then , they tested the suitability of an Empirical Bayes approach to estimate model parameters and predict abnormal growth at later stages of life . They found that classification of individuals into proper homogenous groups ( i . e . where the strength is borrowed from ) was necessary in order to obtain accurate predictions of lifetime growth . In [105] , Berkey found that there is a point beyond which the Empirical Bayes method ( but more in general any method ) is no longer robust to missing data , and found - as expected - that growth curve parameters are especially sensitive to the end points of the growth trajectories . Given an appropriate growth model , the prediction of lifetime growth trajectories from early measurements presents further complications - as in our case - when dealing with organisms that still grow after sexual maturity [108] and when homogenous groups ( i . e . cohorts ) may include just a few individuals reaching older ages . In addition , when using the vBGF model with both and k function of cohort and individual random effects , the estimation of cohort effects should be robust to the deletion from the dataset of one-third of the individuals that have been sampled more than 3 times , since the presence of only a few old individuals in the dataset is likely to bias the estimation of [101] . Our results indicate that when strength is borrowed from other individuals , parameters estimated on a single measurement can be used to summarize the growth trajectory of marble trout living in Zakojska and Gacnik and to impute missing observation for the estimation of size-dependent survival . The best vBGF model provided predictions of future growth trajectories in both populations that were consistently ( i . e . for all validation samples ) better than simply using the mean length-at-age of the fish cohort . Clearly , other covariates presently not available or not included in the model , such as sex or position in the stream , may help further improve predictions of lifetime growth and size-at-age . We found better predictions across validation samples for the population of Gacnik than for the Zakojska population . This may be due to a higher number of fish both overall and in each cohort in Gacnik , less variability in growth at the whole population level as well as among fish in the same cohort , as evidenced by the much smaller coefficient of variation of and higher repeatability of body size in Gacnik than in Zakojska , or a lower plasticity of growth trajectories after the first year of life in Gacnik than in Zakojska , which may be caused by more homogenous site profitability in Gacnik . In conclusion , in this work we have shown how the estimation of parameters of a parameter-rich non-linear growth function using longitudinal data can shed light on the shared and individual determinants of somatic growth in natural populations . The estimation method based on the Empirical Bayes approach is readily applicable to different parameterizations of the von Bertalanffy growth function or other growth models , and it provides additional flexibility , speed and ease of use with respect to other approaches [8] . In the case of more frequent sampling of individuals [10] , models with seasonal components may be used and the inclusion of more fine-grained candidate predictors ( such as monthly temperature , flow , trophic conditions ) when available may be tested .
|
Somatic growth is a crucial determinant of ecological and evolutionary dynamics , since larger organisms often have higher survival and reproductive success . Size may be the result of intrinsic ( i . e . genetic ) , environmental ( temperature , food ) , and social ( competition with conspecifics ) factors and interaction between them . Knowing the contribution of intrinsic , environmental , and social factors will improve our understanding of individual population dynamics , help conservation and management of endangered species , and increase our ability to predict future growth trajectories of individuals and populations . The latter goal is also relevant for humans , since predicting future growth of newborns may help identify early pathologies that occur later in life . However , teasing apart the contribution of individual and environmental factors requires powerful and efficient statistical methods , as well as biological insights and the use of longitudinal data . We developed a novel statistical approach to estimate and separate the contribution of intrinsic and environmental factors to lifetime growth trajectories , and generate hypotheses concerning the life-history strategies of organisms . Using two fish populations as a case study , we show that our method predicts future growth of organisms with substantially greater accuracy than using historical information on growth at the population level , and help us identify year-class effects , probably associated with climatic vagaries , as the most important environmental determinant of growth .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"ecology",
"and",
"environmental",
"sciences",
"theoretical",
"biology",
"ecology",
"theoretical",
"ecology",
"biology",
"and",
"life",
"sciences"
] |
2014
|
Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method
|
Mammary epithelial progenitors are the normal cell-of-origin of breast cancer . We previously defined a population of p27+ quiescent hormone-responsive progenitor cells in the normal human breast whose frequency associates with breast cancer risk . Here , we describe that deletion of the Cdkn1b gene encoding the p27 cyclin-dependent kinase inhibitor in the estrogen-induced mammary tumor-susceptible ACI rat strain leads to a decrease in the relative frequencies of Cd49b+ mammary luminal epithelial progenitors and pregnancy-related differentiation . We show by comprehensive gene expression profiling of purified progenitor and differentiated mammary epithelial cell populations that p27 deletion has the most pronounced effects on luminal progenitors . Cdkn1b-/- females have decreased fertility , but rats that are able to get pregnant had normal litter size and were able to nurse their pups implying that loss of p27 in ACI rats does not completely abrogate ovarian function and lactation . Reciprocal mammary gland transplantation experiments indicate that the p27-loss-induced changes in mammary epithelial cells are not only caused by alterations in their intrinsic properties , but are likely due to altered hormonal signaling triggered by the perturbed systemic endocrine environment observed in Cdkn1b-/- females . We also observed a decrease in the frequency of mammary epithelial cells positive for progesterone receptor ( Pr ) and FoxA1 , known direct transcriptional targets of the estrogen receptor ( Erα ) , and an increase in phospho-Stat5 positive cells commonly induced by prolactin ( Prl ) . Characterization of genome-wide Pr chromatin binding revealed distinct binding patterns in mammary epithelial cells of Cdkn1b+/+ and Cdkn1b-/- females and enrichment in genes with known roles in Notch , ErbB , leptin , and Erα signaling and regulation of G1-S transition . Our data support a role for p27 in regulating the pool size of hormone-responsive luminal progenitors that could impact breast cancer risk .
Emerging data indicate that breast epithelial stem cells and progenitors are the normal cells-of-origin of breast carcinomas and factors that influence breast cancer risk ( e . g . , reproductive and hereditary factors ) may alter the number and/or properties of these cells [1] . Besides germline mutations in cancer-predisposing genes including BRCA1 and BRCA2 , the most significant determinants of breast cancer risk are reproductive history and mammographic density . A single full-term pregnancy in early adulthood decreases the risk of estrogen receptor-positive ( ER+ ) postmenopausal breast cancer , the most common form of the disease [2] . By characterizing normal human breast epithelial cells , we previously found that the most significant parity-associated gene expression and epigenetic changes are observed in CD44+ progenitor-enriched cells and the molecular profiles of these cells was also significantly different in BRCA1 and BRCA2 mutation carriers compared to healthy age and parity-matched controls [3] . Thus , alterations of this progenitor-enriched cell population may explain the cellular and molecular basis of the breast cancer-preventive effects of pregnancy . CDKN1B , encoding the p27 cyclin-dependent kinase inhibitor [4] , was among the top differentially expressed genes showing lower levels in parous compared to nulliparous women as well as in controls relative to BRCA1 carriers . The relative number of p27+ mammary epithelial cells was also lower in parous compared to nulliparous and control versus BRCA1-mutant women . Furthermore , the frequencies of p27+ and Ki67+ cells varied and inversely correlated during the menstrual cycle implying that a subset of these cells represents quiescent and proliferating hormone-responsive breast epithelial progenitors , respectively . Our analysis of mammary epithelial cells in mice of different ages and reproductive stage corroborated our findings in humans and also suggested that p27 may be required for the proliferative quiescence of hormone-responsive mammary epithelial progenitors [5] . The breast cancer-protective effect of pregnancy is also observed in rodent models of breast cancer and can be mimicked by hormonal manipulations such as high doses of estrogen , estrogen and progesterone , or human chorionic gonadotrophin ( hCG ) [6–8] . Most of these studies have been conducted in rats using chemical carcinogens ( i . e . , 7 , 12-dimethyl-benz[a]anthracene or N-methyl-N-nitrosourea ) to induce mammary cancer [9] . Carcinogen-induced mammary tumors in rats are ovarian-hormone dependent ( 70% ER+; 30% ER- ) , but these carcinogens are not implicated in human breast cancer although several of the mutations initiated by them , such as those in PIK3CA , are also commonly observed in human breast tumors [10 , 11] . A rodent model of breast cancer that closely resembles the genotype and phenotype of estrogen-dependent human breast tumors involves estrogen ( i . e . 17β-estradiol; E2 ) -induced tumors in the ACI inbred rat strain [12–14] . An attractive feature of the ACI rat model is the generation and characterization of genetic backcrosses with the mammary tumor-resistant Brown Norway ( BN ) inbred strain , which recently allowed for the detailed mapping of mammary tumor genetic susceptibility loci [15 , 16] . Several of these loci are orthologous to regions of the human genome that have been identified as breast cancer susceptibility loci based on GWAS ( genome-wide association study ) studies , highlighting the relevance of the E2-induced ACI rat mammary tumor model to human breast cancer [13] . However , such susceptibility loci were also identified using DMBA-induced mammary tumor models [17] , suggesting that both carcinogen and estrogen-induced models reflect at least some aspects of the human disease , thus , both are useful models for preclinical studies . Genetic deletion of Cdkn1b in mice leads to increase in body size and organs without obvious morphological abnormalities with the exception of dysfunctional ovaries resulting in female sterility [18] . Prior studies in mice investigating the role of p27 in hematopoietic and neural stem and progenitor cells have determined that p27 is a key regulator of quiescence in transit-amplifying progenitors but not in stem cells [19 , 20] . However , the role of p27 in mammary gland development has been controversial . One study reported that p27 is required for mammary gland morphogenesis and function [21] , while another described that loss of p27 has no discernable effects on mammary morphogenesis and differentiation [22] . A potential explanation for these seemingly contradictory findings could be due to the abnormal ovarian function and infertility of female Cdkn1b-/- mice [18 , 23] necessitating mammary fat pad transplantation assays to assess mammary gland development . The mammary gland is a unique organ as most of its development occurs postnatally driven by ovarian and pituitary hormones [24] . Although mammary gland development has most extensively been studied in mice and certain species-specific differences exist , the essential roles for ovarian and pituitary hormones , including estrogen , progesterone , prolactin , and growth hormone are universal [25] . Estrogen ( E2 ) and E2-responsive cells are driving proliferation and ductal elongation during puberty , while in adults progesterone is the more predominant mitogen . Pregnancy-associated lobulo-alveogenesis and subsequent lactation are driven by the combination of high levels of estrogen , progesterone , and prolactin . Some of the actions of these hormones is direct only affecting the cells with receptors for them , while others , especially the proliferative effects , are indirect , mediated by paracrine mechanisms . For example , amphiregulin and RANKL is essential for estrogen and progesterone-induced mammary epithelial cell proliferation , respectively [25] . Rat mammary tumor models are widely recognized as the superior rodent model in reflecting clinical features of human ER+ breast cancer , however , reverse genetics approaches have historically been limited due to the inability to routinely modify the rat genome . While targeted rat genome manipulation technologies have been available since the early 2000s [26] , recent development of the CRISPR-Cas9 gene editing technology has revolutionized the generation of genetically engineered rat models for human traits [27] . We adopted the CRISPR-Cas9 technology to inactivate the Cdkn1b gene in the rat genome . We transferred the loss-of function mutations to the E2-induced mammary tumor-susceptible ACI rat strain , which allowed us to test the role of p27 in hormone-responsive mammary epithelial progenitors in this model with high relevance to the human disease .
First , we assessed if the frequency of p27+ cells is also associated with mammary tumor risk in rats by immunofluorescence analysis of mammary glands of E2-induced mammary cancer-susceptible ACI and resistant BN rats , without and after three weeks of E2 treatment . The frequency of p27+ cells was significantly higher in ACI than in BN rats and it decreased with E2 treatment only in ACI rats . In untreated control animals the relative abundance of proliferative Ki67+ epithelial cells was about the same in the two strains and it was significantly higher following E2 treatment in both strains with ACI rats showing a more obvious increase compared to BN rats ( Fig 1A and 1B ) . These data suggest that ACI rats susceptible to E2-induced mammary tumors might have a higher number of hormone-responsive luminal progenitors with proliferative potential compared to BN rats and that p27 may regulate the quiescence of these cells in this model making it suitable for studying the role of p27 in mammary epithelial progenitors . However , larger numbers of animals would need to be analyzed to conclusively prove strain-specific differences . Next , we utilized the CRISPR-Cas9 system to generate knockout mutations in the rat Cdkn1b gene . The single guide RNA ( sgRNA ) was designed to target exon 2 ( the first coding exon ) of the rat Cdkn1b gene ( S1A and S1B Fig ) . Zygotes were derived from the Sprague-Dawley ( SD ) outbred rat strain , since specific inbred rat strains including ACI are lowly responsive to superovulation procedures , which renders working with ACI zygotes too inefficient . A total of 67 and 19 zygotes were injected in 2 batches , either with a solution containing a plasmid ( pX330 ) expressing the Cdkn1b-targeting sgRNA and Cas9 gene , or with a solution containing purified Cdkn1b-targeting sgRNA and Cas9 mRNA . From the injected zygotes , 47 and 15 were transferred to pseudopregnant females . The first batch resulted in a litter of 12 pups of which 3 showed mutations in Cdkn1b exon2 . The second injection batch yielded a litter of 8 pups of which 1 showed a mutation in Cdkn1b exon2 . Two mutations with the highest potential impact on Cdkn1b function were transferred through the germline showing a deletion mutation by PCR analysis ( S1C Fig ) . Sequencing of the mutations revealed a 32-bp deletion ( DEL-32 ) and a 65-bp deletion ( DEL-65 ) , both disrupting the open reading frame of the Cdkn1b gene ( S1B Fig ) . We bred the selected mutations to the ACI inbred strain for 6 backcross generations . We generated a cohort of Cdkn1b+/+ wild-type and Cdkn1b-/- knockout rats at early ( N1xN1 ) and later ( N6xN6 ) generations for the characterization of the mutant phenotype . Western blot analysis of p27 protein levels using tissues from homozygous DEL-32 animals at 9 weeks of age confirmed the success of p27 knockout in multiple tissues including mammary gland , spleen , and thymus ( S1D Fig ) . Comparison of the average offspring per nest determined by genotype ratios in litters from DEL-32 Cdkn1b+/- x Cdkn1b+/- crosses at early ( N1xN1 ) and later ( N6xN6 ) generations and found allelic ratios that were not significantly different from the expected Mendelian ratios ( p = 0 . 87 and p = 0 . 78 for early and late , respectively; S1E Fig ) , suggesting that p27 deletion does not result in embryonic lethality . We performed comprehensive phenotypic assessment to characterize Cdkn1b+/+ and Cdkn1b-/- females by collecting blood serum , multiple organs , evaluating the weights and histology of two cohorts of female rats , at 4–6 weeks age and 9–16 weeks of age . In the 4–6 week-old cohort , Cdkn1b-/- rats ( n = 5 ) had significantly larger spleen ( p = 0 . 0038 ) , thymus ( p = 0 . 0082 ) and brain ( p = 0 . 0091 ) consistent with the known role of p27 in regulating lymphocyte [18] and neural progenitor cell [28] proliferation . Total body weights , and other organ measurements , including liver , lungs , ovary , and mammary glands were not significantly different compared to Cdkn1b+/+ animals ( n = 7 ) at the 4–6 week timepoint . ( S1F Fig ) . In the 9–16 week-old female cohort , the total body weights of Cdkn1b-/- rats ( n = 16 ) were significantly greater than wild type ( n = 20 , p = 1 . 38x10-5 ) . Similar at the 9–16 week timepoint , all other organs measured , with the exception of ovaries , were significantly ( determined using Welch t-test ) larger in Cdkn1b-/- ( KO ) rats as compared to Cdkn1b+/+ wild type ( WT ) : mammary gland ( n = 17 WT , n = 9 KO , p = 6 . 5x10-4 ) ; spleen ( n = 20 WT , n = 10 KO , p = 2 . 1x10-11 ) ; thymus ( n = 20 WT , n = 10 KO , p = 2 . 2x10-7 ) ; liver ( n = 20 WT , n = 10 KO , p = 2 . 1x10-3 ) ; lungs ( n = 17 WT , n = 7 KO , p = 1 . 4x10-2 ) , brain ( n = 17 WT , = 9 KO , p = 4 . 8x10-7 ) ; pituitary gland ( n = 19 WT , n = 9 KO , p = 3 . 2x10-4 ) . Homozygotes also had cataracts from birth with 100% penetrance . Histologically , we observed hyperplasia and adenomas in the pituitary glands , greatly enlarged follicles and corpora lutea in ovaries and uteruses in Cdkn1b-/- rats at 9 weeks of age , however , the thymus , spleen , and lung appeared histologically normal , despite the significantly increased organ size ( S1G Fig ) . The overall increase of body weight and several organs in Cdkn1b-/- rats is similar to the phenotype reported in Cdkn1b-/- mice [18 , 23] . Unlike the Cdkn1b-/- mice [18 , 23] , however , the ovaries of Cdkn1b-/- rats showed evidence of corpora lutea . As a result , Cdkn1b-/- rats have more normal ovarian function than Cdkn1b-/- mice . Indeed , we found that 1 out of 6 Cdkn1b-/- ( F1xF1 ) females tested was fertile , in contrast to the complete infertility observed in female Cdkn1b-/- mice [18 , 23] . At 12 weeks of age , this Cdkn1b-/- female delivered a normal size litter of 14 pups and lactated sufficiently to nourish her pups , indicating that Cdkn1b deletion in rats does not ablate the ability to complete pregnancy and lactate . For comparison , Cdkn1b+/- and Cdkn1b+/+ ( F1xF1 ) females tested for fertility were 100% fertile , as 4 out of 4 females produced a litter at 12 weeks of age , suggesting that fertility in Cdkn1b-/- is strongly reduced , but not completely absent . To analyze the effects of p27 loss on mammary epithelial cell morphogenesis and differentiation , we characterized the mammary glands of 9-week-old adult virgin females . Whole mount analyses demonstrated that the homozygous knockout mammary glands at this age already showed a large expansion of alveolar buds ( Fig 1C ) , which are the rodent equivalent of the terminal duct lobule unit ( TDLU ) in the human breast [29] . Analysis of hematoxylin and eosin ( H&E ) stained mammary gland tissue sections verified that the Cdkn1b-/- mammary glands have more terminal end bud structures , covering a larger area of the mammary fat pad compared to wild type ( Fig 1D ) . In addition , at this age , the Cdkn1b-/- mammary glands show luminal ectasia ( dilated milk ducts ) , a phenotype previously observed in BN rats resistant to E2-induced mammary tumors [30] . The highly developed mammary gland phenotype observed in 9-week old Cdkn1b-/- rats is in sharp contrast to the hypoplastic mammary glands observed in intact Cdkn1b-/- mice , as well as transplanted Cdkn1b-/- mammary epithelium [21] . Contrary to the pituitary gland adenomas , we did not observe any neoplastic changes in the mammary epithelium of Cdkn1b-/- rats nor any signs of nuclear abnormalities described in fibroblast cultures derived from Cdkn1b-/- mice [31] . To analyze mammary gland morphology and differentiation in further detail , we performed immunofluorescence analysis of smooth muscle actin ( SMA ) myoepithelial cell and Ki67 proliferation marker , as well as milk casein on tissue sections from Cdkn1b+/+ and Cdkn1b-/- rats at 9 weeks of age . The basal epithelial layer , containing SMA+ myoepithelial cells , was intact in Cdkn1b-/- rats , but they had significantly greater total numbers of epithelial cells ( Fig 1E ) Despite having significantly more cells in the mammary epithelium , the relative fraction of Ki67+ cells did not significantly differ across Cdkn1b+/+ and Cdkn1b-/- genotypes ( Fig 1F ) . The morphology of Cdkn1b-/- mammary glands resembled pregnancy/lactation-related changes , a phenotype further supported by high expression of milk protein ( Fig 1E and 1F ) and comparison to mammary glands of ACI rats at multiple stages of gestation ( S2A Fig ) . To assess if the genetic ablation of p27 alters the relative frequencies of mammary epithelial progenitors , we performed Fluorescence Activated Cell Sorting ( FACS ) analysis using cell type-specific cell surface markers on epithelial populations from rats 9 weeks of age . We used Cd31 and Cd45 to visualize endothelial and hematopoietic cells , respectively , while antibodies against Cd24 , Cd29 , and Cd49b , as well as Peanut Agglutinin ( PNA ) were used to quantify the relative frequencies of luminal ( Cd24+Cd29low ) , basal/myoepithelial ( Cd24+Cd29high; also containing mammary stem cells ) , and luminal progenitor ( Cd24+Cd29lowCd49b+ or Cd24+Cd29lowPNA+ ) sub-populations ( S2B Fig ) . The relative frequency of Cd24+Cd29high basal/myoepithelial cells did not differ significantly between Cdkn1b+/+ and Cdkn1b-/- rats , however , the proportion of Cd24+Cd29low mature luminal cells was significantly higher in Cdkn1b-/- rats ( Fig 1G ) . By analyzing the expression of Cd49b , a known marker for luminal progenitors in the mouse mammary gland [32] , we found that Cd49b was expressed mainly in the Cd29low luminal compartment and the fraction of Cd24+Cd29lowCd49b+ luminal cells was significantly reduced in Cdkn1b-/- rats ( Fig 1G ) . The relative fraction of Cd24+Cd29lowPNA+ luminal cells did not statistically differ between the two groups , likely due to the fact that PNA stains a wider range of luminal cells , including alveolar progenitors [33] . We also characterized the mammary glands of 4-6-week-old prepubertal rats to determine if there are any visible differences prior to puberty . Whole mount , H&E , and SMA staining of the mammary glands of 4-6-weeks-old Cdkn1b+/+ and Cdkn1b-/- animals did not reveal any obvious differences ( S2C Fig ) . Similarly , FACS analysis did not detect any significant differences in the relative frequencies of basal , luminal , and progenitor cell populations in this age group ( S2D , S2E , and S2F Fig ) . Taken together , these data suggest that deletion of p27 leads to the expansion of mature luminal cells with a concomitant decrease in the frequency of luminal progenitors quantified from total mammary epithelial cell populations in post-pubertal rats , resulting in an overall more differentiated mammary epithelium . Because these changes are only observed after puberty , they could be due to differences in hormone levels or in the properties of hormone-responsive mammary epithelial cells . To determine if the mammary gland development phenotype is intrinsic to the p27-deficient epithelium or controlled by the host , we conducted mammary gland transplantation assays using rats at 4–5 weeks of age ( a developmental timepoint prior to the onset of puberty ) , allowing 6 weeks for epithelial outgrowth . All of the donor and recipient animals were generated by intercrossing heterozygotes from backcross generation N6 . Both DEL-32 and DEL-65 lines were used as recipients and donors . There was no difference in graft efficiency between genotype-matched ( DEL-32 into DEL-32; or DEL-65 into DEL-65 ) and genotype-unmatched ( DEL-32 into DEL-65 and vice versa ) host-recipient combinations ( p = 1 . 0 ) , indicating that both alleles could be used interchangeably at the N6 backcross generation . Equal numbers of mammary epithelial cells from Cdkn1b+/+ and Cdkn1b-/- donors were grafted on both sides of the inter-scapular white fat pads of recipient rats ( Cdkn1b+/+ or Cdkn1b-/- ) . A total of 10 donors ( n = 5 Cdkn1b+/+ , n = 5 Cdkn1b-/- ) provided cells for 24 recipients ( n = 16 Cdkn1b+/+ , n = 8 Cdkn1b-/- ) . After 6 weeks , the inter-scapular white fat pads containing transplanted cells , along with the endogenous , lower abdominal mammary fat pads , were harvested for whole mount analysis . We quantified the overall outgrowth rate based on the inter-scapular fat pad whole mounts . The outgrowth percentages were 39% , 63% , 50% , 88% for transplant groups ( donor:recipient ) WT:WT ( n = 16 ) , WT:KO ( n = 8 ) , KO:WT ( n = 16 ) , KO:KO ( n = 8 , respectively . Analysis by standard logistic regression of the binary outgrowth data indicated no significant donor ( p = 0 . 503 ) or recipient genotype effect ( p = 0 . 272 ) , and no significant effect of the donor-recipient genotype interaction ( p = 0 . 501 ) on outgrowth incidence ( Fig 2A ) . These results indicate that the fraction of stem/progenitor cells contributing to outgrowth and the mammary fat pad microenvironment are not significantly different between Cdkn1b+/+ and Cdkn1b-/- rats . This is in accordance with the observation that the mammary glands of Cdkn1b+/+ and Cdkn1b-/- females in the prepubertal cohort are identical , as we grafted tissue from donor animals 4–5 weeks of age , a timepoint well in advance of puberty-related changes in these rats . Interestingly , the appearance of the grafted tissue was markedly different between Cdkn1b+/+ and Cdkn1b-/- hosts . We found that the grafts always resembled the phenotype of the host’s endogenous mammary glands , regardless of donor genotype , indicating a strong host effect ( Fig 2B and 2C ) . The host effect was consistent across multiple transplantation experiments . The results indicate that loss of p27 does not significantly affect graft success rate , but it does affect mammary gland development at the onset of puberty . Because the grafted tissues receive the same hormonal regulatory stimuli as the endogenous mammary glands , the endocrine environment in female Cdkn1b-/- rats is likely to be very different from that of the Cdkn1b+/+ animals . To analyze hormonal differences between Cdkn1b+/+ and Cdkn1b-/- rats , we performed serum ELISA for E2 , prolactin ( PRL ) , luteinizing hormone ( LH ) , and follicle-stimulating hormone ( FSH ) in both Cdkn1b+/+ and Cdkn1b-/- animals at 4–6 and 9 weeks of age . FSH levels were undetectable in all samples . The levels of E2 and PRL were significantly higher in the serum of 9-week-old and 4-6-week-old Cdkn1b-/- rats as compared to Cdkn1b+/+ , while LH levels were not statistically different ( Fig 2D ) . These results demonstrate that the endocrine environment is significantly different in Cdkn1b-/- rats , which likely explains the strong host effect observed in the mammary fat pad transplantation assays . The histological abnormalities we observed in the pituitary glands of Cdkn1b-/- animals may underlie the observed hormonal changes . Consequently , the altered endocrine environment may also contribute to the partial infertility observed by lack of reproduction in 5 out of 6 females tested . To characterize molecular mechanisms by which p27-loss leads to altered mammary epithelial cell proliferation and differentiation , we sorted Cd24+Cd29high basal/myoepithelial , Cd24+Cd29lowCd49b-PNA- mature luminal cells , and Cd24+Cd29lowPNA+ and Cd24+Cd29lowCd49b+ luminal progenitors from 9-week-old virgin Cdkn1b+/+ and Cdkn1b-/- females and analyzed their gene expression profiles . Luminal and basal cells had the most divergent gene expression patterns due to lineage-specific differences , while differences between Cdkn1b+/+ and Cdkn1b-/- rats were more pronounced in luminal than in basal cell populations ( S3A Fig ) . Principal Component Analysis ( PCA ) of luminal cell types showed three major clusters , a larger Cdkn1b+/+ mature and luminal progenitor ( LP ) group , while mature luminal and luminal progenitors from Cdkn1b-/- rats clustered by themselves ( Fig 3A ) . The numbers of significantly differentially expressed genes was the highest in the combined Cd24+Cd29lowPNA+ and Cd24+Cd29lowCd49b+ luminal progenitors ( Fig 3B and S1 Table ) . These data suggest that p27 deletion has the most pronounced effects on luminal progenitors and that these changes are driven by the altered hormonal environment of these rats . We performed Metacore analysis [34] of the differentially expressed gene lists to identify pathways and networks that are affected by p27 deletion . In basal cells the top up-regulated pathways were chemotaxis , inflammation and other immune-related processes with many chemokines ( e . g . , Ccl2 , Ccl7 , Ccl13 , Ccl19 , Ccr7 ) overexpressed in Cdkn1b-/- animals , while top down-regulated pathways included regulation of epithelial-to-mesenchymal transition ( EMT ) and Notch signaling driven by decreased tuberin , Smad2 , Dock1 , and Mpdz levels ( Fig 3C ) . Among luminal cells the most significant and highest number of differentially enriched pathways were in the luminal progenitor ( LP ) fraction , which is consistent with our FACS and gene expression data . Top up-regulated networks in LP are almost all cell proliferation and cell cycle related ( e . g . , Cdk1 , cyclin B , cyclin A , Bub1 , Nek2A ) implying that p27-deficient luminal progenitors are more proliferative , while down-regulated networks included protein folding ( Hsp90 , Hsp70 , Hsp27 ) , hedgehog signaling ( Gli3 , Id2 , Fgfr2 ) , and hormone-related pathways ( Esr1 , Creb1 ) ( Fig 3C ) . Top enriched pathways in mature luminal cells were also mostly cell cycle-related consistent with the expansion of this cell population ( Fig 3C ) . Interestingly , lactation and milk protein-related genes Wap , Csn2 , and Csn3 exhibited much higher expression in luminal cells of Cdkn1b-/- rats , while the expression of genes associated with luminal epithelial cell differentiation ( e . g . , Esr1 , Pgr , and Foxa1 ) were reduced in Cdkn1b-/- cells ( S1 Table ) . In line with this , multicolor-immunofluorescence analysis of mammary gland tissue sections demonstrated significantly fewer Pr+ and almost complete absence of Foxa1+ mammary epithelial cells in Cdkn1b-/- rats confirming our RNA-seq data ( Fig 3D and 3E ) . In contrast , the fraction of pStat5+ cells was significantly increased in Cdkn1b-/- mammary glands ( Fig 3D and 3F ) , which is consistent with the pregnancy-related morphogenesis and differentiation in the Cdkn1b-/- mammary gland , since pStat5 is known to be induced by prolactin [35] . Analysis of these same proteins in mammary glands of pregnant ( D11 . 5 ) , lactating ( D9 ) , and involuting ( D4 ) ACI rats showed similar decline in Pr and Foxa1 and increase in pStat5 , however , in contrast to the Cdkn1b-/- glands , proliferation ( Ki67+ cells ) was also decreased in these conditions ( S2A Fig ) . Thus , deletion of p27 in ACI rats leads to pregnancy and lactation-associated changes , but with increased proliferation , implying perturbed differentiation and hormonal regulation of luminal progenitors . Interestingly , estrogen-treated BN rat mammary glands also displayed some of these pregnancy/lactation-related changes ( S2A Fig ) , which could contribute to their relative resistance to estrogen-induced mammary tumors . To further delineate the similarity of Cdkn1b-/- mammary epithelial cells to that of pregnant or hormonally-stimulated animals , we analyzed overlaps between genes high or low in luminal progenitors ( LP ) of Cdkn1b-/- rats and different between virgin and G18 pregnant and lactating ( day 2 ) mouse mammary glands [36] as well as mammary glands of ovariectomized mice before and after acute ( 4–72 hr ) progesterone treatment [37] . We also performed RNA-seq of basal and luminal cells from control and E2-treated ACI and BN rats . All overlaps with genes high in luminal progenitors of Cdkn1b-/- rats were the most significantly enriched in networks related to cellular proliferation including regulation of G1-S and G2-M transition , mitosis and S phase ( Fig 3G ) . In contrast , overlaps with genes low in luminal progenitors of Cdkn1b-/- rats were enriched in networks related to development , hormonal ( estrogen and progesterone ) and growth factor ( Wnt , hedgehog ) signaling , and inflammation . These results further imply that luminal progenitors of virgin adult Cdkn1b-/- rats show pregnancy/lactation-related proliferation and differentiation changes due to their perturbed endocrine environment , and that some of these changes may be due to direct transcriptional regulation by Pr . Interestingly , differences between ACI and BN rats overlapping with differential genes in Cdkn1b-/- LP cells were enriched in hormonal ( estrogen and progesterone ) and growth factor ( Wnt and Hh ) signaling pathways , but not in proliferation-related categories . Based on our analysis of Cdkn1b-/- rats at different ages hormonal and organ-size related changes start to occur at puberty at 6 weeks of age . Thus , to analyze whether p27 deletion causes any molecular differences in mammary epithelial cells in prepubertal animals , we also analyzed the gene expression profiles of Cd24+Cd29high basal/myoepithelial ( BAS ) and Cd24+Cd29high luminal epithelial ( LUM ) cells . We separated this cohort into two age groups , 3–4 weeks ( n = 3 ) , when the mammary ducts just start to extend into the fat pad , and 5–6 weeks ( n = 2 ) old animals when the mammary fat pad extension is completed . In the 3–4 week-old group , Cdkn1b-/- mammary cells did not exhibit clear differences at gene expression level from Cdkn1b+/+ cells , and we did not identify any differentially expressed genes with significant p-value . In the 5–6 week-old cohort we identified 1 , 182 and 2 , 717 genes that were significantly differentially expressed in basal and luminal cells , respectively ( S2 Table ) . Top up-regulated networks in basal cells are apoptosis ( e . g . , Mcl1 , STAT1 , Bcl-3 ) , cell adhesion ( e . g . , tubulin , actin , Has , elastin ) , and cell cycle ( e . g . , Aurora B , Cdc20 ) related , while top down-regulated pathways are associated with protein folding ( e . g . , Hsp70 , Hsp60 ) and translation initiation ( e . g . , 4e-Bbp1 , eif3s7 ) ( Fig 3H ) . Top up-regulated pathways in luminal cells include translational regulation with many genes encoding ribosomal proteins ( e . g . , Rps28 , Rpl23a , Rpl28 , Rack1 , Rps3 , Rpl19 ) and proteins involved in cell adhesion and cell-matrix interactions ( e . g . , Col1a1 , Itga1 , biglycan , lumican , Timp3 , decorin , Itga4 , fibronectin ) . Many of these upregulated genes are known targets ( e . g . , fibronectin ) or modulators ( e . g . , decorin , biglycan ) of the TGFβ signaling pathway and are also upregulated during EMT [38] implying perturbed luminal epithelial cell differentiation . In line with this , top pathways enriched in genes downregulated in Cdkn1b-/- luminal cells include Notch signaling ( e . g . , Notch1 , Hes7 , Smad2 , Gata-3 , Numb ) and regulation of cell proliferation ( e . g . , c-Myc , Gsk3 beta , E2f4 , Cdk6 ) . These data suggest that p27 loss in mammary epithelial cells leads to perturbed luminal epithelial differentiation and altered responsiveness to growth factor signaling pathways , and some of these changes occur early in pre-pubertal rats and prior to significant changes in the systemic hormonal environment . The progesterone receptor is a key transcriptional regulator of mammary epithelial cell differentiation and proliferation [25] . We detected a significant decrease in the fraction of Pr+ cells Cdkn1b-/- rats ( Fig 3D ) and genes induced by acute progesterone treatment in ovariectomized mice overlapped with genes with altered expression in luminal progenitors of Cdkn1b-/- rats ( Fig 3G ) , implying that changes in progesterone signaling may be responsible for some of the phenotypic changes we see in Cdkn1b-/- rats . To test this hypothesis , we performed ChIP-seq for Pr in mammary epithelial cells from 9 weeks old Cdkn1b+/+ and Cdkn1b-/- females . QC analysis of the number of mapped reads ( S4A Fig ) and total peaks above background ( S4B Fig ) confirmed the quality of the ChIP-seq data . De novo motif search also revealed PR consensus binding sequence as top hit ( S4C Fig ) . We identified significant differences in Pr genomic binding between Cdkn1b+/+ and Cdkn1b-/- cells with 3 , 270 and 760 peaks unique to Cdkn1b+/+ and Cdkn1b-/- cells , respectively , while 1 , 972 peaks were common ( Fig 4A , S4D Fig , and S3 Table ) . Id3 and Notch1 are examples for differential peaks present in Cdkn1b-/- and Cdkn1b+/+ cells , respectively ( Fig 4B ) . Most of the Pr peaks were localized to intergenic regions and introns , with smaller fraction in promoters and this relative peak distribution pattern was essentially the same in Cdkn1b+/+ and Cdkn1b-/- cells ( S4E Fig ) . Functional analysis of Pr targets associated with peaks present in both Cdkn1b+/+ and Cdkn1b-/- cells using Metacore revealed that top enriched pathways include Notch , ErbB family , Esr1 , and leptin signaling ( Fig 4C ) . All these pathways are known to play important roles in mammary epithelial cell differentiation and their direct regulation by Pr confirms its role as a key transcriptional regulator of mammary epithelial cells . To investigate if differences in Pr chromatin binding could explain some of the differences in luminal gene expression profiles , we integrated our Pr ChIP-seq data with differentially expressed gene lists . Consistent with the known role of progesterone in progenitor cell proliferation and differentiation , many Pr targets unique to Cdkn1b+/+ cells and differentially expressed between Cdkn1b+/+ and Cdkn1b-/- cells in 9 week-old rats were enriched cell cycle-related genes ( e . g . , Nek2A , Cyclin A , Cyclin D1 , Bub3 ) and BMP/TGFβ signaling ( e . g . , Smad6 , Smad7 , Gli-3 ) ( Fig 4D and S1 Table ) . In contrast , Pr targets unique to Cdkn1b-/- cells and differentially expressed between Cdkn1b+/+ and Cdkn1b-/- cells in 9 week-old rats were enriched in Erα ( e . g . , Pr , Srebp1 ) signaling . Linking Pr targets to genes differentially expressed in 6 week-old animals showed similar results with Cdkn1b+/+ unique targets enriched in cell cycle ( e . g . , Bub3 , Aurora-B , Cyclin G1 , Wee1 ) and cytoskeleton/muscle differentiation ( e . g . , Rock2 , gelsolin , utrophin , tropomyosin , smooth muscle myosin , filamin C ) , while Cdkn1b-/- unique Pr peaks were enriched in Notch ( e . g . , GSK3 beta , MAGP2 , PI3K reg class IA ) and neurohormone ( e . g . , PPAP2 , Lpp3 , Ghr ) signaling ( Fig 4D and S2 Table ) . We also analyzed overlaps between Pr targets and genes induced by acute progesterone treatment or differentially expressed in luminal progenitors of Cdkn1b-/- rats to determine to what degree Pr may directly regulate these expression changes . Genes overlapping among all three groups were enriched in networks related to cell cycle and Fgf and ErbB signaling ( Fig 4E ) emphasizing the role of these signaling pathways in the perturbed mammary gland phenotype in Cdkn1b-/- rats . Consistent with the known role of Pr , progesterone-induced genes that are also direct Pr targets were enriched in Notch , ErbB family , and Wnt signaling . These results imply that a subset of phenotypic alterations in p27 deficient mammary epithelium is due to alterations in Pr signaling , including altered Pr chromatin binding .
p27 is a cyclin-dependent kinase inhibitor and an important negative regulator of cell proliferation suggesting that it may function as a tumor suppressor [39] . However , the role of p27 in tumorigenesis has been controversial and complex [40 , 41] . Cdkn1b is not frequently mutated in treatment-naïve human cancers [41] and its homozygous deletion in mice did not lead to malignant growth [18 , 23] . However , germline mutations in Cdkn1b cause a MEN ( multiple endocrine neoplasia ) syndrome in humans and rats characterized by pituitary and parathyroid tumors [42] . Furthermore , studies using chemical carcinogens and γ-radiation demonstrated that both heterozygous and complete lack of p27 enhanced tumor development in multiple organs implying that p27 may function as a haplo-insufficient tumor suppressor [43] . Correlating with this , genetic crosses of Cdkn1b+/- and Cdkn1b-/- mice with mammary ( MMTV-neu ) [44] and prostate ( Nkx3 . 1and Pten deficient ) [45] tumor models also demonstrated a dosage sensitive effects as Cdkn1b+/- crosses increased tumorigenesis , while complete deletion of p27 decreased it . Other studies suggested that p27 may have both cell-autonomous and cell-nonautonomous functions and some of this can be independent of its role in cell proliferation [46 , 47] . p27 protein levels are prognostic in breast and other cancer types [40] . In breast cancer , lower p27 protein levels are associated with worse overall and disease-free survival in patients with estrogen-receptor positive ( ER+ ) tumors [48] . Besides its role in established tumors , p27 may also influence cancer risk potentially via its effects on regulating body size and progenitor cell functions [49] . In line with this we have previously described that p27 may identify hormone-responsive progenitors with proliferative potential in normal human breast tissues , thus , it could potentially be used for risk prediction [3] . Indeed , our prior data in women demonstrated that high Ki67+/low p27+ and high Ki67+/low ER+ cell frequencies were significantly associated with a 5-fold higher risk of breast cancer compared to low Ki67+/low p27+ and low Ki67+/low ER+ cell frequencies , respectively , among premenopausal women ( Ki67/p27: OR = 5 . 08 , 95% CI = 1 . 43–18 . 1; Ki67/ER: OR = 4 . 68 , 95% CI = 1 . 63–13 . 5 ) [50] . Thus , CDKN1B/p27 may impact both breast cancer risk and disease progression by regulating the proliferation of hormone-responsive progenitors . In this study we analyzed the role of p27 in mammary gland development by deleting Cdkn1b in the ACI rat strain . The rational for generating a Cdkn1b knockout in rats was in part due to the closer relatedness of estrogen-dependent mammary tumors in rats to human breast cancers [14] and that germline mutations of Cdkn1b cause MEN syndrome in rats [42] . The phenotype of Cdkn1b-/- rats showed similarities to that of Cdkn1b-/- mice including increased body and organ size with normal morphology and pituitary tumors [18 , 23] . However , an important difference is the reduced but not completely absent fertility in female Cdkn1b-/- ACI rats compared to female Cdkn1b-/- mice that enabled us to analyze mammary gland development in intact rats in contrast to the mammary transplant studies conducted in mice [21 , 22] . We found that the mammary glands of prepubertal Cdkn1b-/- female rats are essentially indistinguishable from wild type in terms of size , ductal branching , and relative fraction of progenitor and differentiated cells . However , after puberty the mammary epithelium undergoes pregnancy/lactation related changes characterized by extensive proliferation and increase in the total numbers of mammary epithelial cells and milk production . These morphologic changes were accompanied by significant loss of hormone-responsive Pr+ and Foxa1+ cells . Assessing systemic hormone levels and fat pad transplantation studies demonstrated that the observed alterations are not due to the intrinsic properties of Cdkn1b-/- mammary epithelial cells , but caused by the perturbed endocrine environment possibly triggered by pituitary adenomas and hyperplasia . However , our gene expression and Pr chromatin binding pattern profiling by RNA-seq and ChIP-seq , respectively , also suggest changes in the molecular profiles of hormone sensitive cells . Importantly , we detected the most significant differences between Cdkn1b-/- and Cdkn1b+/+ rats in luminal progenitors that demonstrated increased activation of pathways and networks related to cell proliferation , but decreased activity of hormone receptor ( e . g . , estrogen and progesterone ) signaling pathways . We also analyzed the expression of p27 in E2-induced mammary tumor susceptible ACI and resistant BN strains before and after three weeks of E2 treatment . Interestingly the relative frequency of p27+ mammary epithelial cells was significantly higher in untreated ACI compared to BN rats and ACI rats also showed a more obvious increase in proliferation after E2 treatment . These data are in line with our observations in human where we found that women with higher risk of breast cancer ( e . g . , nulliparous women and BRCA1/2 mutation carriers ) have higher fraction of p27+ mammary epithelial cells and also higher fraction of Ki67+ proliferative cells [3] . It would be interesting to test in the future whether deletion of p27 affects E2-induced mammary tumor development in ACI and BN rat strains . In summary , our data demonstrate the power of using genetically engineered rats to study regulators of mammary gland development and breast cancer risk . The ACI rat model particularly could be useful for the functional characterization of genes that influence breast cancer risk due to its susceptibility to estrogen-induced mammary tumors . Our data in Cdkn1b knock out ACI rat support a role for p27 as a key regulator of quiescent hormone-responsive luminal progenitors associated with breast cancer risk .
All animals were housed and maintained in an AAALAC-approved facility . All animal experiments were approved by the MUSC Institutional Animal Care & Use Committee ( IACUC ) . Rat strains Sprague-Dawley ( SD:Hsd; SD ) , ACI ( ACI/Seg ) , and Brown Norway ( BN ) females were purchased from Harlan ( Envigo ) . Mutations were generated on the outbred SD genetic background and introgressed onto the ACI inbred genetic background for six generations . As the mutations have been maintained by backcrossing to the ACI inbred genetic background for more than 10 generations , the current , official nomenclature of these congenic rats is ACI . SD-Cdkn1bem1MUSC and ACI . SD-Cdkn1bem4MUSC . To obtain well-controlled cohorts of Cdkn1b+/+ and Cdkn1b-/- rats , we set up heterozygosity crosses at the N1 ( early ) and N6 ( late ) backcross generations and selected the desired homozygous ( +/+ and -/- ) females by genotyping . At 4 or 9 weeks of age , we harvest ( lymph node-excised ) inguinal/abdominal and all thoracic mammary glands for cellular/molecular analysis . We saved a small section of the inguinal/abdominal mammary gland for histological analysis . These samples were formalin-fixed , paraffin-embedded and processed into histological slides . Another section of the mammary tissue was flash frozen for future molecular analysis . From the remaining fresh tissue ( inguinal/abdominal + thoracic ) single cell suspensions for FACS analysis were generated as described below . For estrogen treatment of ACI and BN rats silasticTM tubing implants empty or containing 27 . 5 mg of E2 ( Millipore-Sigma ) , were made and placed surgically into the interscapular region of 9-week-old ACI and BN rats; these implants release hormone continuously for 3 weeks [30] . The CRISPR-Cas9 technology was implemented following previously established protocols [27] . A sgRNA ( 5’-GAGTCGAAATTCCACTTGCGC- 3’ ) was designed to target the protein coding region of Exon 2 of the rat Cdkn1b gene . The sgRNA was cloned into the pX330 vector ( Addgene ) using previously described protocols [51] . In the first microinjection session , this vector was injected at a concentration of 5 ng/μl into the cytoplasm of SD rat zygotes . For a second microinjection session , the sgRNA was prepared by in vitro transcription using the Megashortscript T7 kit ( Ambion ) from template DNA amplified with PCR primers that included the T7 promoter sequence . Purified sgRNA was co-injected with Cas9 mRNA ( Trilink BioTechnologies ) into the zygotic cytoplasm at respective concentrations of 200 ng/μl and 500 ng/μl . All microinjections were performed at the MUSC Transgenic and Gene Function Core by following Institutional and IACUC-approved animal research protocols . Genotyping was performed using the following primer combinations to detect Cdkn1b+/+ ( WT ) and Cdkn1b-/- ( KO ) alleles , respectively: WT: 5’-CGGGGAGGAAGATGTCAAA-3’ and 5’-TGGACACTGCTCCGCTAAC-3’ , KO: 5’-TGCCGAGATATGGAACAAGC-3’ and 5'-TGGACACTGCTCCGCTAAC-3’ . A mammary gland transplantation assay was done essentially as previously published [33] . Cdkn1b+/- rats of the N6 backcross population ( DEL-32 or DEL-65 ) were intercrossed to obtain Cdkn1b+/+ and Cdkn1b-/- groups . A total of 4 transplantation sessions were done . Donor females were 30–38 days of age , recipients were 30–60 days of age . Mammary organoids were prepared by dissociation of the mammary tissue using Type III Collagenase digestion ( Worthington ) , as previously described [33] . A total of 225K-335K cells , but the same amount for Cdkn1b+/+ ( WT ) or Cdkn1b-/- ( KO ) in a 50% brain homogenate ( derived from Cdkn1b+/+ donor ) were grafted at either side of the interscapular fat pad . Six to ten weeks after surgery , outgrowth was examined by whole mount analysis of the interscapular white fat pad and compared to a whole mount of the endogenous mammary gland . The whole mount slides were photographed using a digital camera . The outgrowth presence data were analyzed as a binary response by logistic regression . The four transplant groups ( WT:WT; WT:KO; KO:WT; KO:KO ) form a 2 × 2 factorial design with donor and recipient genotypes as the main effects . Standard logistic regression with two main effects and an interaction term was done to the binary response data . Cdkn1b+/- rats of the N6 backcross population ( DEL-32 or DEL-65 ) were intercrossed to obtain Cdkn1b+/+ and Cdkn1b-/- groups . The stage of menstrual cycle was determined by vaginal lavage . Only rats in diestrus or metestrus stage were sacrificed . Blood was collected by cardiac puncture ( 4-5ml ) and centrifuged at 2 , 500 RPM for 5 minutes , then kept on ice for 90 minutes . After removal of the fibrin clot , serum was transferred two times and centrifuged at 1 , 200 x g for 5 minutes . The final serum was incubated at 56°C for 30 minutes to deactivate complement and aliquots stored at -80°C . Serum ELISA was performed using undiluted serum and according to manufacturer instructions for Estradiol ( Cayman Chemical #582251 ) , Follicle Stimulating Hormone ( MyBioSource MBS720215S and Luteinizing Hormone ( MyBioSource MBS700807 ) . Serum from wildtype and knockout rats was diluted 1:2 and 1:4 , respectively , to obtain readings within the limits of detection for Prolactin measurement ( CusaBio CSB-E06881r ) . Significance was determined using Student’s t-test . Mammary glands from female Cdkn1b+/+ and Cdkn1b-/- groups ( both N1xN1 and N6xN6 ) were harvested and dissociated as described [5 , 30] . Single cell suspensions were blocked in PBE ( PBS with 0 . 5%BSA , and 2 mM EDTA ) and stained at 4°C for 30 minutes with antibodies: CD24-PE ( HIS50 , BD ) , CD29-PE/CY7 ( HMbeta1-1 , AbD Serotec ) , CD31-APC ( TLD-3A12 , AbD Serotec ) , CD45-V450 ( OX-1 , BD ) , CD49b-PerCP/Cy5 . 5 ( HMalpha2 , Biolegend ) and Peanut Lectin ( PNA ) -FITC ( Sigma ) . Cells were analyzed and sorted using BD FACSAria II SORP UV ( Becton Dickinson ) . Mammary glands from female Cdkn1b+/+ and Cdkn1b-/- groups ( both N1xN1 and N6xN6 ) were harvested and whole mounts were generated as described previously [30] . The inguinal and abdominal mammary glands were collected , fixed , stained in carmine , dehydrated and cleared in xylene . Samples were imaged on a Nikon Ti/E inverted microscope using Nikon Elements software . Histology and multicolor immunofluorescence analyses were performed as described previously [3] . After heat-induced antigen retrieval in TRIS-EDTA buffer ( pH 9 ) , the samples were blocked with 5% goat serum PBS and stained with antibodies against Ki67 ( BD550609 , 1:50 ) , Milk-specific proteins ( Nordic-MUbio , RAM/MSP , 1:200 ) , PR ( ab16661 , 1:100 ) , FoxA1 ( ab23738 , 1:200 ) and phospho-Stat5 ( AbCam ab194898 , 1:250 ) . Images from the stained sections were obtained by Nikon inverted widefield live-cell imaging system . The percentage of cells for each marker was estimated by counting positive cells and total cells per field from 4 to 6 randomly selected regions using ImageJ 1 . 45s software . Luminal ectasia was scored by measuring the percentage area of milk protein to mammary epithelium . Protein lysates preparation and immunoblotting was performed as described previously [30] . Frozen mammary tissues were homogenized with PowerGen Handheld Homogenizer and lysed in RIPA buffer . Proteins were resolved in SDS-polyacrylamide gels ( 4–12% ) and transferred to PVDF membranes by using a Tris-glycine buffer system . Membranes were incubated with 2 . 5% milk powder in 0 . 1% Tween20 in TBS ( TBS-T ) for 1 h at room temperature and incubated overnight at 4°C with primary antibodies: p27 ( 1:1 , 000 , BD Biosciences cat#610241 ) and phospho-Stat5 ( AbCam ab194898 , 1:1 , 000 ) diluted in 2 . 5% milk TBS-T . The CRISPR-Cas9 generated deletion on the coding exon caused a frame-shift leading to premature stop codons at amino acid 47 ( del32bp ) and 39 ( del65bp ) . HRP-conjugated goat anti-mouse IgG ( Thermo Fisher #62–6520 , 1:5 , 000 ) was used as secondary antibody . The membranes were developed with Immobilon substrate ( EMD Millipore ) . All blots were also probed with an antibody to β-actin as reference . The p27 antibody used in this study was a mouse monoclonal antibody generated against the full-length mouse protein amino acids 1–197 . According to consult with the manufacturer , the exact epitopes are unknown . If the deletions were successful , it is unlikely any protein will be detected , since the deletions are predicted to truncate p27 at amino acid 47 and 39 , for DEL-32 and DEL-65 respectively . In addition , aberrant transcripts with premature stop codons are known to be subjected to nonsense-mediated decay , limiting expression of truncated protein products . Total RNA was extracted using the RNeasy Mini Kit ( Qiagen ) . The total RNA was measured by Agilent 2100 Bioanalyzer . RNA-seq libraries were prepared using Clontech Low Input mRNA Library ( Clontech SMARTer ) v4 kit from less than 10ng of purified total RNA according to the manufacturer’s protocol . The concentrations of finished dsDNA library were measured by Qubit Fluorometer , the size of library fragment was measured by Agilent TapeStation 2200 , and RT-qPCR for adapted library molar concentration measurement according to manufacturer’s protocols . Uniquely indexed libraries were pooled in equimolar ratios and sequenced on an Illumina NextSeq500 with Single-End 75bp ( SE75 ) reads by the Dana-Farber Cancer Institute Molecular Biology Core Facilities . Single cell suspensions were obtained from dissociated mammary organoids , 1 × 107 cells were fixed with fixing buffer ( 50 mM HEPES-NaOH ( pH 7 . 5 ) , 100 mM NaCl , 1mM EDTA ) containing 1% paraformaldehyde ( Electron Microscopy Sciences , 15714 ) and crosslinked for 10 min at 37°C . Crosslinking was quenched by adding glycine to a final concentration of 0 . 125 M . The cells were washed with ice-cold PBS , harvested in PBS . The nuclear fraction was extracted by first resuspending the pellet in 1 ml of lysis buffer ( 50 mM HEPES-NaOH ( pH 8 . 0 ) , 140 mM NaCl , 1mM EDTA , 10% glycerol , 0 . 5% NP-40 , and 0 . 25% Triton X-100 ) for 10 min at 4°C . Cells were pelleted , and washed in 1 ml of wash buffer ( 10 mM Tris-HCL ( pH 8 . 0 ) , 200 mM NaCl , 1 mM EDTA ) for 10 min at 4°C . Cells were then pelleted and resuspended in 1 ml of shearing buffer ( 10 mM Tris-HCl ( pH 8 ) , 1 mM EDTA , 0 . 1% SDS ) and sonicated in a Covaris sonicator . Lysate was centrifuged for 5 min at 14 , 000 rpm to purify the debris . Then 100 μl of 10% Triton X-100 and 30 μl of 5M NaCl were added . The sample was then incubated with 20 μl of Dynabeads Protein G ( LifeTechnologies , 10003D ) for 1 h at 4°C . Anti-PR antibody ( H-190 , Santa Cruz , cat# sc-7208 ) were added to each tube and immunoprecipitation ( IP ) was conducted overnight in the cold room . Cross-linked complexes were precipitated with Dynabeads Protein G for 2 hr at 4°C . The beads were then washed in low salt wash buffer ( 20 mM Tris-HCl pH 8 , 150 mM NaCl , 10 mM EDTA , and 1% SDS ) for 5 min at 4°C , high salt wash buffer ( 50 mM Tris-HCl pH 8 , 10 mM EDTA , and 1% SDS ) for 5 min at 4°C and LiCl wash buffer ( 50 mM Tris-HCl pH 8 , 10 mM EDTA , and 1% SDS ) for 5 min at 4°C . DNA was eluted in elution buffer ( 100 mM sodium bicarbonate and 1% SDS ) . Cross-links were reversed overnight at 65°C . RNA and protein were digested with 0 . 2 mg ml−1 RNase A for 30 min at 37°C followed by 0 . 2 mg ml−1 Proteinase K for 1 h at 55°C . DNA was purified with phenol-chloroform extraction and isopropanol precipitation . ChIP-seq libraries were prepared using the Rubicon ThruPLEX DNA-seq Kit from 1 ng of purified ChIP DNA or input DNA according to the manufacturer’s protocol . Raw RNA-seq datasets were aligned to rat reference rn6 genome using the STAR RNA-Seq aligner ( version STAR_2 . 5 . 1b ) as described previously [52] . The read counts for individual genes were generated using the htseq-count script of the HTSeq framework ( version 0 . 6 . 1p1 ) [53] and the rn6 refGene annotation file available at the UCSC Genome Browser . Differentially expressed genes are identified by using DEseq2 [54] with cutoff of p-value < 0 . 05 and log2fold change > 1 , ranked by the statistics . PCA and heatmap visualizations were generated using R software . ChiLin pipeline 2 . 0 . 0 [55] was used for QC and raw ChIP-seq data were aligned to rat genome rn6 by Burrows-Wheeler Aligner [56] . MACS2 [57] was used for peak calling and Deeptools [58] was used for generating heatmap plots . Process networks and pathway analyses were performed using Metacore ( Thomson Reuters ) . Gene expression counts from RNA-seq for lactating versus pregnant mouse mammary gland [36] were obtained from series GSE60450 . Differentially expressed genes were identified by using DEseq2 [54] with cutoff of p-value < 0 . 05 and log2fold change > 1 . Differentially expressed genes from ovariectomized mice treated with progesterone for 4 , 16 , 28 , and 76 hours were gathered from the supplementary information from Fernandez-Valdivia et al . [37] . For comparison with rat , mouse genes were mapped to rat homolog-associated gene names using the biomaRt R package . Statistical analyses of in vitro and in vivo experiments were performed using GraphPad Prism software , using statistical tests indicated in figure legends .
|
The frequency and proliferation of tissue-specific stem and progenitor cells is associated with the risk of malignancy . Thus , regulators of the pool size and proliferation of progenitor cells determine cancer risk . p27 is a key regulator of cellular proliferation and is required for the terminal differentiation of a number of cell types . Here we show that genetic deletion of Cdkn1b in ACI rats susceptible to estrogen-induced mammary tumors decreases the relative fraction of Cd49b+ luminal progenitors identifying p27 as a key regulator of the proliferation and pool size of these cells . Progesterone , acting via the progesterone receptor ( Pr ) , is an important regulator of mammary epithelial cell proliferation and differentiation . Based on ChIP-seq we found that Pr targets different sets of genes in Cdkn1b+/+ and Cdkn1b-/- mammary epithelium and that this is associated with differences in proliferation and differentiation states . Thus , p27 regulates breast cancer risk and tumor development via regulating the pool size and hormonal-responsiveness of luminal progenitors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"breast",
"tumors",
"reproductive",
"system",
"maternal",
"health",
"obstetrics",
"and",
"gynecology",
"cancers",
"and",
"neoplasms",
"cell",
"differentiation",
"hormones",
"epithelial",
"cells",
"oncology",
"developmental",
"biology",
"women's",
"health",
"pregnancy",
"animal",
"cells",
"gene",
"expression",
"breast",
"cancer",
"exocrine",
"glands",
"biological",
"tissue",
"breast",
"tissue",
"biochemistry",
"anatomy",
"cell",
"biology",
"genetics",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"lipid",
"hormones",
"progesterone",
"mammary",
"glands"
] |
2019
|
Deletion of Cdkn1b in ACI rats leads to increased proliferation and pregnancy-associated changes in the mammary gland due to perturbed systemic endocrine environment
|
Whole genome tiling arrays provide a high resolution platform for profiling of genetic , epigenetic , and gene expression polymorphisms . In this study we surveyed natural genomic variation in cytosine methylation among Arabidopsis thaliana wild accessions Columbia ( Col ) and Vancouver ( Van ) by comparing hybridization intensity difference between genomic DNA digested with either methylation-sensitive ( HpaII ) or -insensitive ( MspI ) restriction enzyme . Single Feature Polymorphisms ( SFPs ) were assayed on a full set of 1 , 683 , 620 unique features of Arabidopsis Tiling Array 1 . 0F ( Affymetrix ) , while constitutive and polymorphic CG methylation were assayed on a subset of 54 , 519 features , which contain a 5′CCGG3′ restriction site . 138 , 552 SFPs ( 1% FDR ) were identified across enzyme treatments , which preferentially accumulated in pericentromeric regions . Our study also demonstrates that at least 8% of all analyzed CCGG sites were constitutively methylated across the two strains , while about 10% of all analyzed CCGG sites were differentially methylated between the two strains . Within euchromatin arms , both constitutive and polymorphic CG methylation accumulated in central regions of genes but under-represented toward the 5′ and 3′ ends of the coding sequences . Nevertheless , polymorphic methylation occurred much more frequently in gene ends than constitutive methylation . Inheritance of methylation polymorphisms in reciprocal F1 hybrids was predominantly additive , with F1 plants generally showing levels of methylation intermediate between the parents . By comparing gene expression profiles , using matched tissue samples , we found that magnitude of methylation polymorphism immediately upstream or downstream of the gene was inversely correlated with the degree of expression variation for that gene . In contrast , methylation polymorphism within genic region showed weak positive correlation with expression variation . Our results demonstrated extensive genetic and epigenetic polymorphisms between Arabidopsis accessions and suggested a possible relationship between natural CG methylation variation and gene expression variation .
Epigenetic modification has a profound effect on genome activity . In eukaryotes , DNA methylation of cytosine residues is a common phenomenon [1] that serves as a mechanism to suppress mobile elements [2] , [3] and other nuclear processes such as transcription and recombination [4] . Globally , DNA methylation is closely associated with histone modification and other aspects of chromatin status [5] . DNA methylation within promoter regions can inhibit binding of transcription factors [6] or recruit methyl-CG binding proteins which repress transcription initiation [7]; thus regulates an intrinsic component of growth and development [8] , [9] . Exceedingly dense methylation in intra-genic regions silences transcription by reducing Pol II elongation efficiency [10] , [11] . Evidence of DNA methylation regulating gene expression has accumulated from the study of several epigenetic mutants , or epimutants , such as fwa [12] and superman [13] in Arabidopsis thaliana and agouti [14] in mouse . In these epimutants , affected genes exhibit unusual DNA methylation within promoter regions [12]–[14] . Recent genome-wide analysis of methylation mutants using tiling arrays uncovered the ubiquitous up-regulation of gene expression in hypomethylated regions , especially for pseudogenes and transposons [15] , [16] . It remains unclear , however , how gene expression is regulated by DNA methylation , and specifically how epigenetic polymorphisms contribute to gene expression variation in a natural context . Patterns and inheritance of DNA methylation are substantially different between mammals and plants . In mammals , DNA methylation mostly occurs at CG sites and the whole genome is densely methylated except for CpG islands [17] , [18] . Meiotic inheritance of DNA methylation in mammals is rare [14] . In plants , non-CG methylation at CNG and CNN sites also exist and methylation in plant genomes is relatively sparse outside of heterochromatin [15] , [16] . Meiotic inheritance of DNA methylation is frequently observed in plants [19] , [20] . Several recent studies applied anti-5methylcytosine Chromatin Immuno-Precipitation followed by array hybridization ( ChIP-chip ) and assessed the global patterns of constitutive methylation in A . thaliana [15] , [16] . These studies indicate a significant proportion of DNA methylation in genic regions . Very recently , Vaughn and coworkers reported the study of natural epigenetic variation between A . thaliana Col and Landsberg ( Ler ) accessions using a methylation-dependent McrBC enzyme digestion approach to profile the entire chromosome 4 at a resolution of 1 kb [21] . They found that DNA methylation was highly polymorphic among Arabidopsis strains but that DNA methylation in euchromatin regions had little observable effect on gene expression . In this study , we conducted methyl-sensitive and -insensitive enzyme digestion of genomic DNA from two Arabidopsis accessions , Col and Van , as well as their reciprocal F1 hybrids , followed by hybridization to the Arabidopsis tiling 1 . 0F array [16] , which tiles the whole genome with ∼1 . 7×106 unique array features at 35bp resolution . This approach allows us to precisely locate the genome positions of both constitutive and polymorphic CG methylation , using ∼55 , 000 CCGG-containing features interrogating about half of all CCGG sites of the entire Arabidopsis genome . As this approach preserves the majority of genomic hybridization signals , SFPs can be assessed simultaneously [22] . Furthermore , we compared the methylation and gene expression profiles derived from the same biological samples . Our results demonstrated extensive genetic and epigenetic polymorphisms between natural accessions and a predominantly additive inheritance of CG methylation polymorphisms . Our results also suggested possible contribution of natural CG methylation polymorphisms to gene expression variation . The enzyme methylome approach we present here could be extended to several other isoschizomer pairs such as Sau3AI/MboI for a more complete analysis .
The Arabidopsis Tiling 1 . 0F array ( Affymetrix ) contains 1 , 683 , 620 unique features , which allowed us to survey SFPs between Col and Van accessions at a near saturating resolution . For each genotype , genomic DNA samples from 4 biological replicates were digested with either HpaII or MspI . The differential enzyme digestion can be regarded as pseudo-technical replicates; therefore provided additional detection power . At 1% false discover rate ( FDR ) , 138 , 552 features exhibited significant hybridization differences between accessions . Among them , the Van genotype had a greater signal for 17 , 742 features and the Col genotype showed a greater signal for 120 , 810 features ( Table S1A ) . As the array features were designed from Col genome sequence , SFPs with greater signal in Col suggest sequence polymorphisms ranging from Single Nucleotide Polymorphisms ( SNPs ) to complete deletion of the loci in Van . Features with greater signals in Van are likely due to sequence duplications or represent cross hybridization from regions deleted in Col; thus , the exact genome position of these features is unclear . Therefore these features were removed prior to analysis of genome distribution of SFPs . All SFPs were excluded from transcription analysis described below . Similar to recent reports [23] , [24] , more SFPs occurred in pericentromeric regions than in euchromatin arms ( Figure 1 ) . To assess the genic distribution of SFPs , we calculated the frequencies of SFPs for several annotation categories ( TableS1B ) . As expected , the frequency of SFPs was higher within inter-genic regions than within coding sequences ( χ2 = 7660 , p-value <2 . 2e-16 ) . We then focused on 54 , 519 CCGG-containing features , which interrogate about half of the ∼130 , 000 CCGG sites in the genome , for methylation analysis . These features span the whole genome baring a slight under-representation in the centromeric regions ( Figure S1A ) . Restriction enzymes HpaII and MspI both recognize the CCGG sequence , but HpaII digestion is inhibited by methylation at the internal cytosine while MspI is not . A significantly greater hybridization signal at the target feature in the HpaII sample suggests that the locus is preferentially cleaved by MspI , indicating a quantitative change in methylation of the underlying genomic DNA . For each CCGG-containing feature , we modeled hybridization intensity by testing genotype and enzyme main effects and a genotype×enzyme interaction effect . The genotype effect contrasts two genotypes across enzyme treatments and detects SFPs . The enzyme effect contrasts enzyme treatments across genotypes and detects constitutive CG methylation ( consistent between Col and Van ) as features with significantly greater signal in HpaII sample than in MspI sample . The genotype×enzyme interaction effect compares differential enzyme responses between genotypes , which are CG methylation polymorphisms . For each effect , we calculated a nominal p-value based on 1000 permutations . A total of 4 , 522 features with greater HpaII signal were significant ( p<0 . 05 ) for enzyme effect ( Table 1 ) . We also observed features with a greater signal in MspI sample than in HpaII sample , which was likely due to the conservative quantile normalization procedure . There were 5 , 215 features significant ( p<0 . 05 ) for genotype×enzyme interaction: 3 , 700 corresponding to Col-specific methylation and 1 , 515 corresponding to Van-specific methylation ( Table 1 ) . For this enzyme methylome approach fragment size variation after enzyme digestion could potentially cause variation in labeling . Furthermore , relative position of the CCGG sequence within a feature could affect the detection sensitivity . Evaluation of these aspects , however , demonstrated that the fragment size variation ( Figure S1B ) as well as the relative position of CCGG sequence within feature ( Figure S1C ) did not significantly affect the detection of constitutive or polymorphic CG methylation . To independently validate our tiling array results , we evaluated the false discovery rate ( FDR ) of our methylation polymorphism calls by PCR . Seedlings from the same maternal seed batches ( Materials and Methods ) were grown to the same developmental stage under the same growth condition as in the microarray experiments . Genomic DNA from three independent maternal seed batch replicates was used for each genotype . We randomly selected 41 loci from 3 , 333 features with significant ( p<0 . 03 ) genotype×enzyme interaction . Genomic PCR following differential restriction digest confirmed 24/24 loci as Col-specific methylation ( Figure S2A ) and 17/17 loci as Van-specific methylation ( Figure S2B ) . The confirmation of all 41 loci , however , suggested that our permutation based false positive rate threshold at p<0 . 03 was perhaps overly conservative , thus missing many true positives . For a rough estimation of the false negative rate , we randomly selected 33 loci from all 54 , 519 CCGG-containing features . Genomic PCR indicated 4/33 as constitutive CG methylation and 3/33 as methylation polymorphisms ( Figure S2C ) . By extension , ∼12% or ∼7 , 000 features could contain constitutive methylation site and ∼9% or ∼5 , 000 features would contain methylation polymorphism . Accordingly , we identified 4 , 522 features of enzyme effect and 5 , 215 features of genotype×enzyme interaction at p<0 . 05 for further analysis to balance the false positive and false negative rate . The 54 , 519 CCGG features analyzed covered 20 , 609 genes and 3 , 246 promoters ( defined as transcriptional start site to 500 bp upstream ) . We found that 17% of genes but only 5% of promoters were methylated in both genotypes ( Table 1 ) . Enrichment for genic methylation over regulatory methylation agrees with other recent studies [15] , [16] , [21] . About 19% of genes and notably 13% of promoters contained methylation polymorphism ( Table 1 ) . As this enzyme methylome approach is site-specific , we evaluated the overall cytosine methylation pattern surrounding the detected polymorphic loci by quantitative measurements . Using bisulfite-treated genomic DNA , we typed ( see epityper in Materials and Methods ) 2 regions and sequenced 3 regions spanning 5 loci detected polymorphic for specific CCGG methylation . The epityper experiment quantified the methylation level for all CG sites within ∼300 bp across three independent maternal seed batch replicates for each genotype . In the bisulfite sequencing , we calculated the percent methylation for all cytosine residues within ∼150 bp for a single maternal seed batch for each genotype . All of the 5 polymorphic sites detected by microarray were confirmed by these methods ( Figure S3 , Table S2 ) . Interestingly , the status of CG methylation across the same segment showed a great degree of heterogeneousness , ranging from 0 to 100% methylation ( Figure S3 ) . The level of polymorphism within the same segment was also variable; some CG sites were polymorphic while others were not . Nevertheless , within the same segment sites that were polymorphic seen to be in phase with either Col or Van showing enriched methylation ( Figure S3 ) . Thus the polymorphic sites detected by this enzyme methylome approach in part reflect the local status of methylation variation but also show unique variation . Consistent with a previous report [25] , the majority of non CG sites were not methylated within gene regions . We further compared the constitutive methylation sites detected by our method with two recently published results using ChIP-chip method in A . thaliana [15] , [16] . Comparison with data performed on the same microarray platform [16] showed that 46% of the constitutive CCGG sites detected here were within the methylated regions detected by ChIP-chip ( Table S3 ) . The overlap of the two methods was significant ( χ2 = 107050 , p<2 . 2e-16; Table S3 ) . The remaining 54% of CCGG sites within ChIP-chip regions that were not detected by our method are likely due to different statistical thresholds , truly unmethylated CCGG sites within methylated regions , and/or due to the difference of the biological samples ( developmental stages and growth conditions ) used in these studies . Furthermore , 73% of constitutive methylation sites detected in our study were outside of the methylated regions detected by ChIP-chip ( Table S3 ) . In fact , among the 6 loci validated by quantitative method ( 5 polymorphic and 1 constitutive sites ) , 5 of them were outside of the ChIP-chip regions ( Figure S3 ) , implying that immuno-precipitation by anti-5methylcytosine used in ChIP-chip may depend on relative dense regional methylation . Comparison with the ChIP-chip method using a different microarray platform [15] led to a similar conclusion ( Table S3 ) . The methylated CCGG sites detected by our method showed a slightly higher frequency in larger ChIP-chip segments , in comparison with unmethylated CCGG sites ( Figure S4 ) . We first examined whether constitutive CG methylation showed preference for certain chromosomal regions . The percent CG methylation for each of 1 Mb chromosome bins was calculated . Consistent with a previous report [15] , methylation was generally high around pericentromeric regions and decreased toward chromosome arms ( Figure 2A ) . The sharp decrease of methylation frequency immediately adjacent to pericentromere of chromosome 1 was probably due to high proportion of CNG methylation within this bin which was undetectable by our method ( Figure 2A ) . For both SFPs and constitutive CG methylation , the trend of decreasing frequency from pericentromere toward euchromatin arms suggests potential purifying selection [1] , [15] . Mutations within gene-rich regions are more likely to be deleterious , and based on studies in mammals cytosine methylated positions have a greater mutation rate [1] . In contrast to constitutive methylation , methylation polymorphisms exhibited little variation along chromosomes ( Figure 2A ) . As DNA methylation could affect chromatin structure , such effect likely depends on dense methylation over long distance . To assess whether constitutive methylation sites exhibit co-methylation , i . e . broad regions with consistently methylated or unmethylated sites , we examined the distribution of enzyme effect d scores ( modified t-statistics of enzyme effect ) along chromosome positions by Lowess smoothing . Lowess smoothing performs locally weighted regression on neighboring d scores within an analyzed window ( here 200 kb ) so that each smoothed d score reflects the overall pattern of its neighbors . The smoothed enzyme effect d scores indicates significant regional methylation around pericentromeres , compared with a null distribution of smoothed d scores from random shuffling by 1 kb block ( Figure 2B ) . Within euchromatin regions , however , the real distribution was indistinguishable from null distribution , indicating the lack of regional methylation ( Figure 2B ) . We then evaluated the regional correlation of CG methylation polymorphism . In this context one accession may have increased or decreased regional methylation signal relative to the other strain . Lowess-smoothing of d scores for genotype×enzyme interaction effect revealed very few regional effects of CG methylation polymorphism ( Figure S5A ) , suggesting that between genotypes methylation varies for individual loci rather than for large chromosome blocks . This result may be unique to our enzyme methylome approach which interrogates specific sites rather than anti-5methyl cytosine ChIP-chip which profiles methylation abundance within a ∼1 kb region . We then examined whether methylation sites preferentially accumulated in specific genic intervals of the genome . Features were categorized based on genome annotation ( coding sequence , intron , 5′ and 3′ UTR , and inter-genic regions ) . The percentage of features with constitutive CG methylation was calculated for each class . The extent of CG methylation varied among these categories: highest in coding sequences and introns , moderate in upstream ( 1 kb from transcriptional start site ) , downstream ( 1 kb from transcriptional stop site ) and inter-genic regions , and very low in UTRs , especially 5′ UTR ( Figure 3A , Table S4 ) . Since coding sequences and introns are similar in CG methylation content , we refer to coding sequences and introns as genic regions in the following analysis . To examine the distribution of CG methylation in finer scale , genic regions were binned into ten percentiles based on relative position within the gene , and upstream and downstream sequences were each binned to ten 100 bp intervals and two 1 kb intervals . Percent CG methylation was calculated for each of these intervals . Methylation was extremely low in 5′UTRs and increased gradually until reaching a maximum near the third quarter of genes , and decreased sharply toward 3′UTRs ( Figure 3B ) . Upstream and downstream regions beyond 1 kb showed moderate CG methylation ( Figure 3B ) . Distribution of methylation polymorphisms among annotated sequence categories exhibited a similar pattern to that of constitutive methylation , except that introns seen to contain more polymorphic sites than exons ( Figure 3A , Table S4 ) . Along a typical gene , polymorphic methylation around gene ends was notably higher than constitutive methylation ( Figure 3B ) , implying a potential role of methylation polymorphisms within these regions in regulating gene activity . To examine possible correlation between genic CG methylation and gene size [15] , genes with CCGG-containing feature ( s ) were separated to 4 groups based on gene size . For each gene size group , genic regions were binned to 10 percentiles based on relative position , and the percent CG methylation for each bin was calculated . For genes smaller than 1 kb , methylation was low across the whole gene ( Figure 3C ) . Methylation level generally increased with gene size , especially for the 3′ region of gene , while methylation within the 5′ region of gene adjacent to 5′ UTR maintained at low level ( Figure 3C ) . Similar to constitutive methylation , methylation polymorphism generally increased with gene size ( Figure S5B ) . Considering the large number of polymorphic CG methylation sites within the genome , it is of interest to know how these polymorphic sites are inherited in the next generation . Dominant inheritance indicates that hybrids are more similar to one of the parents , while additive inheritance indicates that hybrids have intermediate phenotypes of parents . Trans methylation effects , perhaps due to differential activity of a cytosine-DNA-methyltransferase between accessions , might result in dominant methylation signatures in the F1 hybrids . Alternatively , cis methylation effects are more likely to be additive in hybrids , affecting a single inherited chromosome at the particular site . In Arabidopsis and likely other flowering plants , MET1-dependent maintenance of CG methylation is thought to be a default pathway , while activation of silenced genes within endosperm by specific demethylation of maternal allele has been observed for MEA and FWA [26] , [27] . To examine these globally , we generated reciprocal F1 hybrids between Col and Van . F1 seedlings were grown together with parental strains , each cross direction with four maternal seed batch replicates . For each CCGG-containing feature , we modeled hybridization intensity by genotype , enzyme and genotype×enzyme interaction effects , where genotype effect was comprised of additive ( contrasting parental strains ) , dominant ( contrasting parental strains and F1 hybrids ) and maternal ( contrasting reciprocal F1 hybrids ) effects . We named the difference between reciprocal F1s as maternal effect , merely because that maternal genotype is expected to have large influence in early development [28] . In the full model , the additive main effect detects differential signals between parental genotypes across enzyme treatments; thus detects SFPs . The enzyme main effect with greater HpaII signal detects constitutive CG methylation , while differential CG methylation between contrasting groups is detected by corresponding interaction terms ( explained in Figure S6 ) . The additive×enzyme interaction again describes methylation polymorphisms between parental strains . With the inclusion of hybrid genotypes , we are particularly interested in the differential methylation between hybrids and parental lines ( dominant×enzyme ) and between reciprocal hybrid lines ( maternal×enzyme ) . These terms reveal hybrid dominance methylation ( Col or Van specific ) or maternal specific methylation ( Col or Van specific ) . Although the additive by enzyme interaction again identified many significant methylation polymorphisms , for dominant by enzyme and maternal by enzyme interaction overall there was little evidence for an enrichment of significant scores for single loci compared with that expected by chance ( Figure S7 ) , indicating that inheritance of CG methylation is predominantly additive and has little or no maternal influence . Nonetheless , certain functional categories were enriched suggesting subtle dominance and maternal effects of methylation may exist ( see below ) . We independently evaluated the CG methylation for F1 hybrids by PCR . F1 seedlings were grown with two maternal seed batch replicates for each reciprocal cross . Genomic PCR was performed using these F1 DNA samples after restriction enzyme digest . Although less quantitative , for the majority of 41 loci with confirmed CG methylation polymorphisms , methylation levels in F1 hybrids was intermediate to that of parental genotypes ( Figure S2A and S2B ) . This is in agreement with our conclusion based on the modeling of array intensity that additive inheritance was predominant for polymorphic loci . In addition , methylation difference between reciprocal hybrids for the majority of these 41 loci was indistinguishable using our genomic PCR condition ( Figure S2A and S2B ) . It should be noted , however , that in our experiment the plants used in the crosses to generate the parental lines and reciprocal F1 hybrid lines had been grown under a well controlled environment , and these plants were at about the same developmental stage at the time of cross ( Materials and Methods ) . Environmental and developmental perturbation could potentially affect the variation and inheritance of methylome profile [19] , [20] . In the microarray experiment , the same seedling samples were split for enzyme methylome analysis and for expression profiling on the same microarray platform , allowing a direct comparison . We first examined the correlation between constitutive methylation and absolute gene expression level . Genes were divided into 20 percentiles according to their absolute expression levels . Within each expression percentile , the number of genes containing constitutive methylation site ( s ) within an analyzed annotation category was divided by the number of genes containing CCGG feature ( s ) within that category . For coding sequences and introns , absolute gene expression level was clearly correlated with degree of constitutive methylation ( Figure 4 ) : weakly expressed genes were the least methylated; methylation gradually increased with expression level then dropped sharply for highly expressed genes . Methylation within upstream/downstream sequences and UTRs was generally low across all expression percentiles ( Figure 4 ) . For the analyzed annotation categories , only UTRs in some cases had a small number of genes with CCGG-containing feature ( s ) in an expression percentile ( Table S5 ) , thus the result was unlikely affected by stochastic error in sampling . We further examined the correlation between methylation variation and expression variation . As the features significant for Van-specific methylation potentially represent duplicated regions within Van genome , we only focused on features significant ( p<0 . 05 ) for Col-specific methylation . Differential gene expression d scores ( modified t-statistics of differential gene expression between Col and Van ) were linearly-regressed against genotype×enzyme interaction d scores . Analysis was performed separately for each of 100 bp intervals within upstream ( Figure S8A ) and downstream ( Figure S8B ) sequences and for genic regions . Significant negative correlation was observed for the 100 bp interval immediately upstream ( r = 0 . 40 , p = 0 . 00027; Figure 5 left panel ) , and for the 100 bp interval immediately downstream ( r = 0 . 51 , p = 0 . 00050; Figure 5 right panel ) . Methylation variation within genic regions showed a very weak , but significant , positive correlation with expression variation ( r = 0 . 056 , p = 0 . 0060; Figure 5 middle panel ) . For a single gene , subtle difference in methylation or expression level between genotypes may not be detectable given the vast number of statistic tests . However , a coordinately regulated gene group may show a significant difference at the level of functional category . Parametric Analysis of Gene set Enrichment [29] , [30] tests groups of genes that may individually exhibit small variation in the same direction and thus be biologically relevant . We applied PAGE to examine selective enrichment in gene ontology categories for constitutive CG methylation ( Table S6A ) and for additive , dominance , and maternal effects of CG methylation polymorphism ( Table S6B ) . As the number of genes containing CCGG feature ( s ) within promoter ( transcriptional start to 500 bp upstream ) was relatively small for PAGE analysis ( 3 , 206 genes for biological process and 3 , 352 for molecular function ) , we focused on genes containing CCGG feature ( s ) within genic region ( 13 , 080 genes for 163 biological processes and 13 , 403 for 119 molecular functions ) . Genes with constitutive CG methylation was significantly enriched in binding activity such as nucleic acid binding , RNA binding and zinc ion binding , motor activity , aminoacyl-tRNA ligase activity , signal transducer activity , and ATPase activity ( Table S6A ) . Comparison of gene set enrichment for additive , dominant and maternal effects of polymorphic CG methylation did reveal a few biological processes exhibiting dominant or parental-origin inheritance ( Table S6B ) . For example , genes in flower development regulation exhibited greater CG methylation in Col parent , but greater CG methylation in Van-mother F1 hybrids ( Table S7A ) . Genes in both cell redox homeostasis and ribosome biogenesis/assembly showed greater CG methylation in Col . In the F1 hybrids however CG methylation of cell redox homeostasis loci was close to Col parent while that of ribosome biogenesis/assembly loci was close to Van parent ( Table S7A ) . Gene set enrichment for gene expression polymorphisms also revealed specific functional categories as coordinately up or down regulated between genotype groups ( Table S6C ) . Interestingly the categories identified included several that were also identified as enriched for methylation polymorphisms between the same genotype groups . For example , heat shock protein binding and microtubule motor activity were significant ( p<1 . 67E-03 and p<7 . 56E-03 , respectively ) molecular functions with greater CG methylation in Col , which were also significant ( p<5 . 84E-6 and p<9 . 64E-04 , respectively ) molecular functions with greater expression level in Col . Chlorophyll biosynthetic process and response to heat were significant ( p<1 . 22E-03 and p<1 . 76E-02 , respectively ) biological processes with greater methylation in Col-mother F1 , as well as significant ( p<3 . 86E-06 and p<4 . 49E-09 , respectively ) biological processes with greater expression level in Col-mother F1 . We did not observe overlap of enriched gene sets between dominance methylation and dominance gene expression . It is likely that for dominance expression , the effect of CG methylome was overly masked by large genetic regulatory effect . Furthermore , we observed many enriched functional categories for differential methylation between mothers that overlapped with enriched functional categories for differential expression between corresponding F1 hybrids ( Table S7B ) . Thus , although individual sites showing genic CG methylation polymorphism had a subtle effect on gene expression , an underlying gene set coordination may exist dually affecting gene set expression and methylation profiles .
The fact that DNA methylation induces chromatin remodeling [5] implies potential DNA co-methylation over long distance . We observed high level of constitutive methylation blocks indicative of co-methylation around pericentromeric regions where transposons and repetitive elements accumulate . In addition , methylation polymorphisms within these regions were relatively low , indicating that constitutive dense methylation blocks might play an indispensable role in suppression of transposon activity . In contrast , euchromatin regions did not exhibit distinguishable blocks of co- methylation or co-regulated methylation polymorphisms , suggesting that effect of methylation within euchromatin regions might be locus-specific . Consistent with our results , methylome profiling in human tissues and cell lines also demonstrated the lack of co-methylation beyond 1 kb distance [18] . Nevertheless , co-regulation of DNA methylation over long distance in euchromatin regions was suggested by Regions of IncreaseD Gene Expression ( RIDGEs ) where physical gene clusters are expressed at high level [31] , [32] . Such epigenetic regulation of large chromosome blocks , however , could depend on spatial or temporal signals [33] , or depend on epigenetic mechanisms other than CG methylation . A recent study by Vaughn et al . did not observe a relationship between differential DNA methylation in euchromatin regions and differential gene expression [21] . In contrast , we found that there is a significant negative correlation between the degree of methylation variation within immediate upstream/downstream regions and the degree of expression variation . The earlier study was based on the analysis of expression data for biological samples grown in different experiment and was limited to 317 genes on chromosome 4 . In this study , we evaluated the correlation between methylation variation and expression variation using a quantitative comparison of expression and methylation profiles for ∼4 , 000 genes and for more than 400 promoters which contained polymorphic CG methylation site ( s ) . In addition , our expression and methylation data were obtained from matched samples grown in the same experiment , eliminating the confounding effects of development stage and environmental condition . Methylation within immediate upstream/downstream regions could interfere with the transcription initiation/termination , which was suggested by our observation of low constitutive methylation levels within these regions . The negative correlation between expression variation and methylation polymorphism within upstream/downstream regions indicates that CG methylation polymorphisms within these regions could play a role in regulating gene expression . Direct repression of basal transcription by DNA methylation within immediate upstream region was also supported by biochemical studies [6] . Methyl-binding proteins could exert a large effect inhibiting gene expression , as seen in human cells for example [7] , but efficient binding of these mediators to methylated promoters may require many methylated sites . Finally , methylation effects of gene expression may not be immediate . Developmentally and/or environmentally induced physiological signals may separate a coordinated response . CG methylation within genic regions is notably high , and exhibits a clear trend , increasing from 5′ to 3′ in longer genes . The exact biological function of genic CG methylation , however , remains elusive . Several biochemical studies demonstrated that intra-genic methylation decreases the efficiency of transcription elongation [10] , [11] , [34] . Nevertheless , in all these studies the examined sequences were methylated at most of their cytosine residues and such dense methylation induced closed chromatin structure [11] . In contrast , genic CG methylation in Arabidopsis occurs at discrete CG clusters [21] , [25] , which has been proposed to prevent transcription from cryptic promoters [15] , [25] . Under this model , weakly expressed genes as well as highly expressed genes are less methylated; the formation of transcriptional initiation complex on cryptic promoters is constrained either by a closed chromatin structure , or by densely occupied DNA strands containing the transcription elongation machinery [15] . In our study , several lines of evidence also implied that genic CG methylation is consistent with increased transcription processability: genic CG methylation increased with gene size and primarily occurred at the 3′ of gene; except for highly expressed genes , correlation between absolute expression level and constitutive CG methylation was positive; although for individual genes positive correlation of expression variation and CG methylation variation was very weak , such correlation was frequently seen at the level of functional categories . The positive effect of genic CG methylation on gene expression , however , is compensated by the fact that dense methylation eventually induces a more closed chromatin structure to impede transcription elongation . It is possible that these two effects jointly decide the efficiency of transcriptional elongation . Gene expression regulatory networks are comprised of cis- and trans-acting factors which exert immediate and large effects on gene expression . Such regulatory networks , however , are exposed to fluctuations stemming from internal and external signals . In contrast , DNA methylation is thought to be relatively stable . Although here we find the direct effect of DNA methylation on expression is subtle , its effect may persist through development directly or indirectly regulating expression and altering whole plant phenotypes . A clear example is epigenetic control of FLC expression which affects flowering time [35] , [36] . In the other hand , life history and environment could accumulatively alter DNA methylation profile [37] . Thus , CG methylation could serves as a memory mechanism in the genome to propagate developmental and environmental influences by modulating gene expression plasticity . The co-enriched functional categories for expression variation and for genic CG methylation polymorphisms further suggest the possible contribution of DNA methylation polymorphisms to natural gene expression variation . Recent epigenetic studies in Arabidopsis have made significant contribution in revealing genome-wide DNA methylation patterns . Nevertheless , more large scale genomic and genetic experiments are essential to understand the dynamics and biological functions of DNA methylome . Particularly , it is of great interest to understand how epigenetic regulation of gene activity directly controls or is affected by developmental programs and environmental responses . Finally the genetic architecture underlying natural variation of DNA methylation is unknown . Our approach for simultaneous profiling of genetic , epigenetic , and transcriptional polymorphisms provides an initial effort toward such an understanding by leveraging a powerful microarray platform .
Seeds of Arabidopsis thaliana accessions Col-0 ( accession number CS22625 ) and Van-0 ( accession number CS22627 ) were obtained from Arabidopsis Biological Resource Center . Seeds were planted in soil , imbibed for 5 days in cold room at 4° , and moved to green house in January 31 , 2005 . Plants were grown in green house with 16 h light ( cool white light supplemented with incandescent ) and 8 h dark at constant temperature of 20° . The first cross experiment was conducted in February 28 , 2005 , and in March 1 , 2005 the second cross experiment was conducted between the same plant pairs as in the first experiment . Both cross experiments began around 9:00am and ended around 5:00pm . In each cross experiment , four replicate crosses for each of Col×Col , Van x Van , Van ( ♀ ) ×Col ( ♂ ) , and Col ( ♀ ) ×Van ( ♂ ) were made . Each replicate cross was between individual paternal and maternal plant and each parental plant was only used once ( 16 Col and 16 Van plants used in total ) . For each replicate cross , the seeds from the two experiments were combined and used as one maternal seed batch . ∼250 seeds from each maternal seed batch were grown on a single petri dish . After gas sterilization for 4 h seeds were plated on a total of 16 , 0 . 7% agar ( Sigma ) plates supplemented with 0 . 5 X Murashige and Skoog salts ( Sigma ) . Seed plates were placed horizontally in a growth chamber ( Percival Scientific Inc . , model E361 ) after stratification for 5 days at 4° . Seedlings were grown for 78 hours under a diurnal mode with 12 h light ( cool white light supplemental with red light ) and 12 h dark at a constant temperature of 20° . Seedlings grown on each plate were split for genomic DNA and RNA preparation . ∼100 seedlings from each plate were pooled and genomic DNA was extracted using DNeasy plant mini kit ( Qiagen ) . About 300 ng DNA was digested with 10 units of HpaII or MspI ( New England Biolabs ) in 50 uL volume at 37° for 16 h . Restriction enzymes were inactivated by heating at 65° for 20 min . DNA was ethanol-precipitated and rinsed with 80% ethanol . DNA was dissolved in 72 uL distilled water and subjected to labeling using BioPrime DNA labeling system ( Invitrogen ) with conditions modified as previously described [38] . About 20 ug total RNA was isolated from an additional 120 seedlings per plate using RNeasy plant mini kit ( Qiagen ) . Poly- ( A ) RNA was enriched from total RNA using Oligotex mRNA mini kit ( Qiagen ) . Poly- ( A ) RNA was mixed with 166 ng random hexamer ( Invitrogen ) and subjected to first-strand cDNA synthesis ( Invitrogen ) as manufacturer recommended in a total volume of 40 uL at 42° for 1 h . The 40 uL first-strand reaction was used in second-strand cDNA synthesis ( Invitrogen ) as manufacturer recommended in a total volume of 300 uL at 16° for 2 h . Samples were then subjected to RNase treatment at 37° for 20 min with 20 units RNaseH ( Epicentre ) , 1 unit RNaseA and 40 units RNaseT ( Ambion ) . Double-stranded cDNA was further purified using Qiaquick PCR purification kit ( Qiagen ) , and then labeled using BioPrime DNA labeling system ( Invitrogen ) as described above . About 30 ug labeling product from enzyme-treated genomic DNA or from double-stranded cDNA was subjected to hybridization to Arabidopsis Tiling 1 . 0F array ( Affymetrix ) using standard gene expression array washing/staining protocol ( Affymetrix ) . Thus we used a total of 32 chips for genomic DNA sample hybridization and an additional 16 chips for RNA sample hybridization . Seeds from the same maternal seed batches used in the microarray experiments were gas sterilized , plated and stratified as described above . Seedlings were grown in the same growth chamber for 78 h under the same condition settings as in microarray experiments . About 100 seedlings from each plate were pooled , froze in liquid nitrogen and stored at -80° till genomic DNA preparation . This growth and harvest procedure was repeated in a separate experiment . For each sample from each growth experiment , genomic DNA was extracted . Genomic DNA samples from one growth experiment were used for genomic PCRs and bisulfite sequencing . For genomic PCR , ∼300 ng DNA sample was digested by HpaII and MspI as described above . 0 . 1 uL digestion reaction or 0 . 1 uL mock digestion reaction without restriction enzyme was used as template in PCR , with 0 . 1 uL extaq ( Takoma , Japan ) in 10 uL volume . PCR condition was set for denature 94° 3 min , 28 cycles of: 94° 15 s , 62° 15 s , 72° 20 s , extend 72° 5 min . 2 uL PCR reaction was separated on 1 . 2% agrose gel ( Invitrogen ) . For bisulfite sequencing , ∼100 ng genomic DNA was converted using EZ DNA Methylation Gold Kit ( Zymo Research ) . Strand-specific PCR was performed as previously described [39] . PCR products were gel purified and cloned using TOPO kit ( Invitrogen ) , and 10-15 clones per template were sequenced . Genomic DNA samples from both growth experiments were submitted to Sequenom for epityper analysis ( http://www . sequenom . com/Seq_methylation . html ) . The microarray data analysis described below used R scripts ( Text S1; also available online http://naturalvariation . org/ccggMethylome ) . Perfect match probes from Arabidopsis tiling 1 . 0F array ( Affymetrix ) were megablasted against Arabidopsis genome release version 7 including mitochondria and chloroplast sequences with word size > = 8 and E-value < = 0 . 01 . Single perfect matches , without a 2nd partial match of>18/25 bp were selected giving a total of 1 , 683 , 620 unique probes . These were mapped to annotated mRNAs as intron , transcription unit ( exon , alternative exons ) , inter-genic region , or flanking probes which span an annotated boundary . Only transcription unit probes were used for expression analysis . For each chip used for genomic hybridization , the CEL intensity of 1 , 683 , 620 unique probes was corrected to remove background effects [22] . Intensity across 32 chips ( 4 genotypes×4 replicates×2 enzymes ) was then normalized by quantile normalization using Bioconductor package Affy . For 1 , 683 , 620 probes , SFPs were detected using Bioconductor package Siggenes [40] . A total of 54 , 519 unique probes contain CCGG within their sequence . For detection of constitutive and polymorphic CG methylation between Col and Van , intensity for each CCGG probe was fit by a mixed linear effect model of genotype+enzyme+genotype×enzyme+random effect ( plant ) . The genotype effect contrasts two lines , and enzyme effect contrasts two enzyme treatments . For each fixed effect , a modified t statistic was calculated for each probe as d = effect coefficient / ( standard deviation+s0 ) , where s0 was a small constant set as the 5% quantile of standard deviations across 54 , 519 CCGG probes and 1000 permutations ( see below ) . The adding of s0 in the denominator makes sure that probes with very small observed errors are not called significant [41] . To evaluate the statistic significance of the d scores for an effect , we calculated a nominal p value based on permutation , where for each probe the p value of the effect was defined as the proportion of d scores , across all CCGG probes and all permutations , which were more extreme than the real d score . For permutation the plant random effect was removed first , then the procedure involved: 1 ) fitting a partial model missing the effect being tested; 2 ) permuting residuals; 3 ) adding permutated residuals to the predicted values; 4 ) fitting that data with a full model; 5 ) calculating a d score; 6 ) repeating step 2 to 5 for 1 , 000 times . The null hypothesis here is that the effect being tested is not significant , thus residuals from partial modeling are assumed to be independent random variables that could be permutated across samples . For analysis of inheritance of CG methylation polymorphisms , intensity for each CCGG probe was linear regressed by the same mixed linear effect model of genotype+enzyme+genotype×enzyme+random effect ( plant ) , where genotype = additive+dominant+maternal . Additive effect contrasts between parental genotypes , dominant effect contrasts between average of parental genotypes and average of F1 reciprocal hybrids , maternal effect contrasts between F1 reciprocal hybrids . To evaluate the statistic significance for each effect , the same permutation approach described above was used . For each chip from cDNA hybridization , CEL intensity of 1 , 683 , 620 unique probes was corrected to remove background effects as described above . SFPs detected from genomic DNA hybridization were removed from transcription unit probes . Intensity for remaining transcription unit probes was normalized across 16 chips by quantile normalization using Bioconductor package affy . For the annotated genes with more than 3 probes , probe intensity from each probe set was modeled by additive , dominant and maternal effect . For each gene , d score was calculated as described above , with s0 set to 50% of standard deviation over 1000 permutations . For the genome and genic distribution of methylation , chromosome position of second cytosine of the first CCGG sub-sequence ( only 1 , 010 probes contain > = 2 CCGG sub-sequence ) of each probe was mapped to annotation categories based on information from TAIR blast_datasets of version 7 . For correlation between absolute expression value and constitutive CG methylation , the expression value for each gene was the mean of exon probe intensity across the probe set and genotypes , and the percent CG methylation in Figure 4 was obtained by three point average for presentation purpose , which doesn't change the result . For correlation between CG methylation polymorphism and gene expression polymorphism , the differential methylation d score was averaged across a gene . For parametric analysis of gene set enrichment , the d scores for effect under study were used as summary statistics . The Gene Expression Omnibus ( GEO ) ( http://www . ncbi . nlm . nih . gov/geo ) accession numbers discussed in this paper are GSE8890 and GSE8891 .
|
The functional expression of DNA sequence depends on the chromatin status . Epigenetic marks at specific loci could affect local chromatin accessibility , thus affect the gene activity of that loci . We applied an enzyme methylome approach to globally detect one type of epigenetic mark , cytosine methylation at CCGG restriction sites . Simultaneous transcriptional profiling allowed gene expression differences to be compared with DNA methylation differences , suggesting functional regulatory regions . Our method reveals natural variation in chromatin patterns which may underlie phenotypic variation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/epigenetics",
"genetics",
"and",
"genomics/plant",
"genetics",
"and",
"gene",
"expression"
] |
2008
|
Global Analysis of Genetic, Epigenetic and Transcriptional Polymorphisms in Arabidopsis thaliana Using Whole Genome Tiling Arrays
|
Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology . While the current structural coverage of proteins is almost exclusively provided by template-based techniques , the modeling of the rest of the protein sequences increasingly require template-free methods . However , template-free modeling methods are much less reliable and are usually applicable for smaller proteins , leaving much space for improvement . We present here a novel computational method that uses a library of supersecondary structure fragments , known as Smotifs , to model protein structures . The library of Smotifs has saturated over time , providing a theoretical foundation for efficient modeling . The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence . This Smotif library is exploited in a fragment assembly protocol to sample decoys , which are assessed by a composite scoring function . Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods , the proposed modeling algorithm , SmotifTF , can employ an exhaustive sampling during decoy assembly . SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods , especially when sequence signal to remote homologs is diminishing . Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem , especially when targeting larger proteins for modeling .
The revolution in DNA sequencing technologies over the last decade has resulted in an enormous , and ever growing , number of gene sequences , which is doubling every ~18 months [1–3] . At the same time , the number of experimentally determined protein structures has increasingly lagged behind due to the inherently slower , more expensive and less predictable outcomes of these experiments [4] . The size of the sequence databases has increased 100 fold between 2000 and 2015 , reaching 60 million entries . At the same time , the rate of protein structure determination has been much slower , with only ~110 , 000 total entries in the Protein Data Bank ( PDB ) [5] . Over the past decade , all structural biology efforts , including structural genomics [6] , have led to an overall increase in the structural coverage of existing proteins from ~30% to 40% at the residue level , despite the huge growth of the underlying sequence database . With existing technologies and strategies , it is projected [7] that it would take 15 years to reach a level of ~55% coverage , which was shown to provide considerable utility for defining large-scale functional characterization of organism-specific properties ( e . g . , the full metabolic network in Thermotoga maritima [8] ) . However , these efforts are now predicted to take twice as long in the expected absence of the Structural Genomics ( SG ) efforts , as SG centers contributed 50–60% of novel coverage despite accounting for less than 10% of all structure depositions [7] . Therefore , the need for reliable methods to model protein structures is stronger than ever before . Computational protein structure prediction can be broadly classified into two categories: ( a ) Homology modeling or template-based modeling ( TBM ) has been successfully used for modeling protein sequences that have an overall detectable sequence similarity over their entire sequence with an experimentally determined protein structure [9] . ( b ) Ab initio or template-free modeling ( TFM ) [10] is required for those proteins that do not have any statistically significant similar protein sequences with known structures . Hence , structure prediction has to be carried out using alternative approaches such as fragment assembly [11–14] or using first principles from physics-based methods [15–17] . Homology modeling approaches have limited applicability , but provide more accurate models when compared to template-free prediction methods and has no size limitations [9 , 18] . Alternately , there are also hybrid modeling methods , which use indirect experimental information , often obtained from automated or semi-automated high throughput experiments , to provide limited structural restraints that can be used for modeling [19 , 20] . Currently , the prospect of increasing structural coverage is tied to the applicability of homology modeling , which provides more than 99 . 5% of the currently observed ~40% structural coverage of protein sequences [7] . Conservation of protein structure is much higher than that of sequence [21 , 22] , which results in a comparatively small number of distinct structural families [23] . The size distribution of protein fold families is very uneven and the most frequently occurring folds ( e . g . , Immunoglobulin , TIM barrel , Rossman fold ) have likely already been identified [24 , 25] . In a typical genome the 10 most populous superfolds cover a third of the protein sequences [26] . Therefore , homology modeling can provide structural models for thousands of proteins in a typical genome using only a few dozen popular folds as templates , and it is currently almost the single source for three-dimensional models [27 , 28] . However , the usefulness of homology modeling is exponentially decreasing as smaller and smaller protein families or singletons need to be modeled . These latter proteins either require a targeted experimental exploration , which is often cost prohibitive , or must be modeled by ab initio or “template-free” style approaches , which do not depend on a detectable sequence similarity to a known experimental structure . However , these approaches are currently suitable to model only relatively small proteins and have a limited success rate [18] leaving much room for improvement . Recent Critical Assessment of Techniques for Structure Prediction ( CASP ) experiments [18 , 29] have also reinforced the fact that efforts need to be concentrated on developing template-free prediction methods that can model structures of proteins with little or no information from other protein structures . Template-free prediction methods can further be classified into two categories: ( 1 ) Ab initio prediction is the modeling of protein structures from first principles without using any information from other existing protein structures [15–17] . ( 2 ) Fragment assembly based methods use a library of protein fragments obtained from known protein structures [11–14] to explore the structure space accessible to the query protein . The fragments themselves may be obtained from remote homologs that share very weak sequence similarity with the query protein and are typically not good enough to be used directly for homology modeling . While the physics-based methods have progressed over the years , they have been less successful than the fragment assembly based methods [10] . The various fragment assembly approaches differ mainly in the type of fragments and the energy functions used for sampling and scoring decoys . I-tasser [12] and Tasser [14] use fragments of aligned segments in varying sizes obtained from various threading methods and follow a replica-exchange Monte Carlo sampling for generating full models . Another variant of Tasser is chunk-Tasser [30] , which uses fragments of supersecondary structures consisting of three consecutive secondary structures as fragments and folds them independently to obtain restrains for modeling . Rosetta [11] uses a library of three and nine residue fragments obtained from remote homologs identified from Psi-BLAST [31] and a simulated annealing Monte Carlo sampling algorithm to obtain protein models . Although most fragment-based methods differ in the type of fragments and the energy functions used for sampling , most of them use a similar approach to score the models . The most commonly used model selection scheme is structural clustering of the sampled decoy structures , to obtain the lowest free energy state , identified as the most populous cluster . Some other methods predict the best model by identifying the consensus conformation from different prediction algorithms [32 , 33] . It has been reported that current template-free prediction methods perform best for small single-domain targets with length up to 120 amino acids [10 , 18] . The quality of prediction drops significantly for larger proteins since the conformational search becomes tedious and less accurate for larger proteins . We have developed a fragment assembly based template-free prediction method using a library of supersecondary structure fragments , known as Smotifs . The concept of using a library of protein structure motifs for structure prediction has been explored earlier using a set of locally defined protein motifs known as I-sites ( invariant or initiation sites ) [34] . I-sites are short sequence motifs of length 3–19 obtained by exhaustive clustering of sequence segments obtained from a non-redundant database of known structures , where each sequence pattern correlates strongly with a recurrent local structural motif in proteins . The I-sites library has been successfully combined with a Hidden Markov Model approach to address various protein sequence and structure related questions such as tertiary and secondary structure prediction , sequence comparison , dihedral angle region prediction and gene identification [35 , 36] . A major difference between those studies and the current method is the definition of the motif fragment , which provides a different conceptual context to our structure prediction approach . We define Smotifs as two secondary structure elements in a protein connected by loop . We have created a library of Smotifs from all known protein structures in the PDB [37] . In our earlier study it was observed that the Smotif fragment library is saturated [38] leading to a hypothesis that all known and yet to be discovered protein folds can be generated using different combinations of the Smotifs already present in the library [39] . Subsequently , the Smotif library was successfully used to develop a hybrid modeling method using chemical shift data from NMR experiments [19] , to classify [40] and model loops in protein structures [37 , 41] and to develop de novo structure based design method [42] . Here , we show that the Smotif library can be used to model protein structures using a fragment assembly method , referred to as “SmotifTF” . The new method , SmotifTF is successful in predicting the overall fold for over 50% of the ab initio test proteins explored in this study .
The SmotifTF method was developed on a randomly selected set of 20 proteins . Table 1 summarizes the accuracy of these predictions in terms of the GDT_TS scores [53] of the top-scoring model with regard to the native structure . GDT_TS score calculates the percentage of structurally equivalent pairs of residues at 1 , 2 , 4 and 8 Å cutoff values upon optimal superposition of the experimentally determined native structure and the computational model . The average GDT_TS score for the predictions in this dataset is 54 . 67 , with 12 protein models above GDT_TS 50% and all proteins with GDT_TS > 30% , indicating correct fold predictions for all proteins . However , this data set is not challenging for template-free predictions , because for many of these cases , good structural templates are available , which make them suitable for homology modeling . To simulate conditions that require template-free structure predictions , the algorithm was repeated by systematically removing high quality templates prior to creating the dynamic Smotif library . All templates with e-values better than 10-10 , 10-5 , 10-1 and 100 were removed , respectively , and the prediction algorithm was repeated ( Table 1 ) . As expected , the quality of prediction depends heavily on the quality of the templates in the Smotif library . The stricter the e-value cutoff for filtering out homologous templates gets , the worse the predictions become ( Table 1 ) . The interplay between the quality of prediction ( Mean GDT_TS ) and the size of the dynamic database ( set of Smotifs obtained from remote homologs ) at different e-value cutoffs is shown in Fig 2 . As the cutoff is made more stringent from “no cutoff” ( all possible templates considered ) to 100 ( all templates with e-value better than 1 . 0 are excluded ) , the average number of Smotifs in the dynamic database ( right Y-axis ) decreases by 50% ( from 1684 . 05 to 826 . 95 ) and the average e-value of the best hit in the dynamic database ( left Y-axis ) increases from barely significant to a random hit value ( from 0 . 006 to 1 . 225 ) , indicating the gradual loss of reliable templates . The quality of predictions drops from a mean GDT_TS of 54 . 67 to 38 . 71 , as the e-value cutoffs get stricter from “no cutoffs” to 100 , respectively . It has been shown earlier that a practical discriminator between ab initio or template-free models and homology models is around GDT_TS 30% and values above GDT_TS 50% indicate high quality homology models [54] . In the current dataset , when a stringent e-value cutoff of 100 is used , 15 of the 20 proteins have GDT_TS > 30% , among which , three have GDT_TS > 50% . All values stay above 20% indicating that the fold , at least partially , has been captured in every case . Even under strict template-free modeling conditions , the SmotifTF prediction method predicts a model above 30% GDT_TS for 75% of the cases , with an overall average GDT_TS of 38 . 71 . Recent CASP experiments show that template-free modeling is still a work in progress and require further methodological developments to be able to provide useful models [29] . Some of the methods that performed the best in the template-free category in recent CASP experiments include I-tasser [12] , HHpred [52] and Rosetta [11] . I-tasser and Rosetta are fragment assembly-based methods that use different kinds of fragments and sampling algorithms as described earlier . HHpred is a template-based modeling method , that uses Hidden Markov Model ( HMM ) profiles and an HMM-HMM comparison algorithm [45] to identify remotely related templates for homology modeling . The HMM-based sequence search is more sensitive and is known to perform better than traditional heuristic sequence search methods . The benchmarks against the above three methods were carried out on a test set of 16 proteins obtained from weekly new releases of the PDB from 10-08-2015 to 12-31-2015 . These were submitted to the I-tasser and HHpred servers online , while Rosetta calculations were carried out using a local installation . In each case , the trivial prediction using the self-template was eliminated . HHpred requires the user to choose the templates for model building , after the HHsearch step . If available , multiple templates were chosen to obtain maximum possible query coverage , which were then submitted to Modeller [55] . In case of Rosetta , 10000 decoys were sampled using the Rosetta algorithm from 100 parallel simulations . The resulting models were then clustered using the algorithm provided in the Rosetta package to identify the largest cluster . The center of the largest cluster was identified as the best model . The results of this analysis are summarized in Table 2 . The mean GDT_TS scores show that I-tasser performs the best with a mean GDT_TS score of 36 . 97 , SmotifTF comes in second with an average GDT_TS score of 33 . 05 and HHpred and Rosetta make the third and fourth positions with GDT_TS 31 . 56 and 30 . 70 respectively . The average GDT_TS is comparable in the four methods and is around 30–35% . Each method has some highlight performances , where its prediction is the best compared to the others . For instance , I-tasser has the best prediction for targets 2mpvA , 3wzsA and 4uzxA whereas Rosetta does better with 4nknA and 4rd5A . HHpred has better models for 4ux3B and 4v1am and SmotifTF has better predictions for 4pqzA , 2mpoA , and 4o7kA . In case of 9 of the 16 proteins in this benchmark test set ( 56% ) , SmotifTF predicts a model with GDT_TS over 30% , indicating an overall correct fold prediction for the ab initio targets . I-tasser , Rosetta and HHpred have predictions above 30% GDT_TS for 9 , 9 and 7 , respectively . The proteins in the table are sorted based on the e-value of best hit in the PDB ( column 4 ) . If one examines the target proteins with the least trivial templates ( only high e-value hits are retained in the Smotif library ) , SmotifTF has an advantage over the other methods as reflected in the mean GDT_TS scores of the last ten entries with e-values > 0 . 1 in the table . For these most difficult targets , SmotifTF has a mean GDT_TS score of 35 . 24 , which is the best along with I-tasser ( 35 . 28 ) . If we consider only the entries with e-values > 2 . 0 ( bottom 4 rows ) , the difference in performance is even more striking with SmotifTF and I-tasser showing the best performance amongst all the methods with an average GDT_TS of 27 . 65 and 27 . 77 , respectively . As expected , the performance of HHpred drops the most ( Mean GDT_TS drops from 31 . 56 to 19 . 00 ) , as this method is explicitly dependent on finding a reasonable overall template , while all other methods are able to combine fragments from a larger variety of possible hits . Overall , there seems to exist a trend , which shows that SmotifTF has a better performance compared to the other methods when the difficulty of prediction is greater as expressed by the e-value of the best template available . While the amount of data is not sufficient to draw statistically conclusive results , nevertheless , from among the 10 the most difficult targets ( with e-values to the best PDB hit above 0 . 1 ) , SmotifTF has the most accurate models amongst the methods compared in four out of five large targets ( sizes 119 , 131 , 182 , 190 , 236 in Table 2 ) , and in the fifth case , it is a close second . We calculated the relative contact order [56] for the target proteins in Table 2 but no apparent correlation could be seen when comparing it with accuracy . We also identified the protein classes for these targets as shown in Table 2 . Among the 16 proteins there are 8 , 2 and 6 cases that are mainly-α , α+β and mainly-β classes , respectively . In terms of the time scales of the four different methods , HHpred server is the fastest , providing results within the order of minutes for all proteins in our benchmark set . I-tasser server , due to its intensive public use and waiting period , provided results within 24–48 hours after submission . SmotifTF and Rosetta were carried out using our in-house linux cluster with 100 computing cores . While SmotifTF completed all jobs within 6–12 hours , Rosetta completed most jobs within 12–24 hours .
New methods for template-free modeling are needed to advance computational techniques of protein structure predictions . We have developed a novel method that uses a fragment library of supersecondary structure motifs to model protein structures . The method follows a fragment assembly protocol using a tailor-made library of supersecondary structure fragments obtained from remotely related proteins using weak sequence signals . The method predicts the core of the protein and the overall fold correctly in over 75% of the cases in the training set and in 50% of the cases explored in the benchmark test set of template-free targets . We have also shown that the current method performs competitively when compared to other existing methods of template-free prediction , which were the best performers in recent CASP experiments . Further , as the difficulty of prediction increases , the Smotif-based template-free prediction method performs better than the other methods compared . The method is relatively simple compared to some other existing approaches , and its good performance is mainly acknowledged to the idea of using an exhaustive set of supersecondary structure fragments . The Smotif-based prediction algorithm is a promising approach to address one of the most challenging problems in molecular biology .
The foundation of the SmotifTF method lies in the representation of protein structures as a set of overlapping supersecondary structure motifs or Smotifs . Smotifs are defined as two regular secondary structure elements ( helices and strands ) in a protein connected by a loop . In a previous study [37] , we had built a library of Smotifs from all known protein structures in the Protein Data Bank [5] . This library consists of over 500 , 000 individual Smotifs classified based on the type of the bracing secondary structure elements ( HH , HE , EH and EE ) and grouped into a few thousand clusters based on their internal geometry . The Smotif library is a backbone-only , geometrically defined fragment library with no side-chain information . The overall prediction method is summarized in Fig 1 . The main aspects of the current method are: ( a ) A dynamic library of Smotifs is built for each query protein using weak sequence signals to remote homologs . ( b ) The weak sequence signals are further used to identify suitable fragments from the dynamic Smotif library for sampling ( c ) Sampling of full protein conformations is explored using exhaustive enumeration of all possible combinations of the fragments chosen earlier ( d ) The sampled full protein structures are scored using a composite energy function to identify the best scoring model . We developed the algorithm on a set of 20 proteins ( Table 1 ) . These were randomly chosen to represent proteins from different folds . Ab initio conditions were simulated by systematically removing high-quality templates from the dynamic Smotif library with e-values better than 10-10 , 10-5 , 10-1 and 100 , respectively ( Table 1 ) . The method was further tested on a new set of 16 proteins ( Table 2 ) . These were specifically chosen to be ab initio targets from new PDB structures released each week starting from 10-8-2014 to 12-31-2014 . The sequences of new PDB releases were obtained each week , clustered using CD-hit [58] at 90% to eliminate similar sequences and then tested using psi-BLAST [31] and HHsearch [45] against the rest of the PDB to eliminate homology modeling targets . The predictions of the SmotifTF algorithm were compared to three other prediction methods: I-tasser [12] , Rosetta [11] and HHpred [52] , which performed well in recent Critical Assessment of Structure Prediction ( CASP ) experiments [18 , 29] . We have chosen new weekly releases from the PDB and focused on those proteins with no templates in the PDB ( other than itself ) to identify ab initio targets . The advantage of doing this is that it mimics blind testing of the methods with minimal intervention from already existing templates in the PDB . SmotifTF is a free software package created using Perl and is distributed under the Artistic license version 2 . 0 ( GPL compatible ) . The complete package can be downloaded from the Comprehensive Perl Archive Network at http://search . cpan . org/dist/SmotifTF/ . The current version supports multiple cores for parallel computing .
|
Each protein folds into a unique three-dimensional structure that enables it to carry out its biological function . Knowledge of the atomic details of protein structures is therefore a key to understanding their function . Advances in high throughput experimental technologies have lead to an exponential increase in the availability of known protein sequences . Although strong progress has been made in experimental protein structure determination , it remains a fact that more than 99% of structural information is provided by computational modeling methods . We describe here a novel structure prediction method , SmotifTF , which uses a unique library of known protein fragments to assemble the three-dimensional structure of a sequence . The fragment library has saturated over time and therefore provides a complete set of building blocks required for model building . The method performs competitively compared to existing methods of structure prediction .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures
|
The capacity of tumour cells to maintain continual overgrowth potential has been linked to the commandeering of normal self-renewal pathways . Using an epithelial cancer model in Drosophila melanogaster , we carried out an overexpression screen for oncogenes capable of cooperating with the loss of the epithelial apico-basal cell polarity regulator , scribbled ( scrib ) , and identified the cell fate regulator , Abrupt , a BTB-zinc finger protein . Abrupt overexpression alone is insufficient to transform cells , but in cooperation with scrib loss of function , Abrupt promotes the formation of massive tumours in the eye/antennal disc . The steroid hormone receptor coactivator , Taiman ( a homologue of SRC3/AIB1 ) , is known to associate with Abrupt , and Taiman overexpression also drives tumour formation in cooperation with the loss of Scrib . Expression arrays and ChIP-Seq indicates that Abrupt overexpression represses a large number of genes , including steroid hormone-response genes and multiple cell fate regulators , thereby maintaining cells within an epithelial progenitor-like state . The progenitor-like state is characterised by the failure to express the conserved Eyes absent/Dachshund regulatory complex in the eye disc , and in the antennal disc by the failure to express cell fate regulators that define the temporal elaboration of the appendage along the proximo-distal axis downstream of Distalless . Loss of scrib promotes cooperation with Abrupt through impaired Hippo signalling , which is required and sufficient for cooperative overgrowth with Abrupt , and JNK ( Jun kinase ) signalling , which is required for tumour cell migration/invasion but not overgrowth . These results thus identify a novel cooperating oncogene , identify mammalian family members of which are also known oncogenes , and demonstrate that epithelial tumours in Drosophila can be characterised by the maintenance of a progenitor-like state .
Cancer cells with significant tumour-propagating potential are increasingly referred to as cancer stem cells . Whilst this refers to the potential of these cells to regenerate the tumour in both in vivo and in vitro assays , it also alludes to the possibility that these cells may have either hijacked self-renewal programmes involved in normal stem cell maintenance , or that they are in fact directly derived from stem or progenitor-like cells . Consistent with either of these possibilities , profiles of tumour cells show increased expression of stem cell factors and associations with progenitor-like cell states [1] , [2] . In Drosophila melanogaster , tumours have long been known to be associated with the retention of stem cell states . Germ line tumours show continual overgrowth of progenitor cells that fail to initiate differentiation , and neuroblast-derived brain tumours are associated with defects in neuroblast ( neural stem cell ) divisions and an expansion of neuroblast numbers [reviewed in 3] . Furthermore , the overgrowth associated with l ( 3 ) malignant brain tumour mutants has been shown to depend upon the acquisition of a stem cell state associated with the germline [4] . Impaired differentiation has also been considered to be a hallmark of Drosophila epithelial tumours [5] , although how differentiation is perturbed and what role this plays in maintaining tumour overgrowth is not yet known . Indeed the epithelial tissues of the imaginal discs are not thought to contain stem cells . Instead it appears that cells become progressively restricted in their developmental potential as patterning mechanisms drive greater elaboration and cell fate commitments across the epithelial field . The sequential nature of these elaborations means that epithelial progenitor-like states are generally associated with earlier developmental times and are not necessarily associated with spatially defined regions of the developing tissue . In the antennal disc , the early progenitor state is yet to be clearly characterised , although the early division between the more distally destined cells that express the homeodomain protein Distal-less ( Dll ) and the more proximal cells expressing the MEIS family transcription factor , Homothorax ( Hth ) , is one of the earliest cell fate divisions to have been described within the developing appendage [reviewed in 6] . Downstream targets of these genes , including atonal ( ato ) , dachshund ( dac ) , distal antenna ( dan ) , and bric-a-brac 2 ( bab2 ) , are subsequently expressed , and gradually define further cell fate divisions along the proximo-distal axis of the appendage [7]–[9] . In the eye disc , the progenitor state has been more fully defined and is thought to be characterised by the expression of Hth , which cooperates with Yorkie ( Yki , or YAP in mammals ) , the transcriptional coactivator of the Hippo tissue growth control pathway [reviewed in 10] , to maintain cells within a proliferative state [11] . The downregulation of Hth coincides with the progressive upregulation of cell fate markers such as dac , eyes absent ( eya ) , dan , ato and embryonic lethal abnormal vision ( elav ) , that define further differentiation [reviewed in 6] . What role , if any , these sequential cell fate restrictions play in mediating the overgrowth of eye and antennal disc tumours has not yet been investigated . Epithelial tumours can be induced in the eye/antennal disc by using a clonal system to combine loss of the cell polarity regulator and tumour suppressor scribbled ( scrib ) with oncogenic Ras or Notch ( N ) signalling . Whilst neither genetic alteration is sufficient to transform cells , in combination they cooperate to drive the formation of invasive tumours that outcompete the surrounding untransformed tissue and massively overgrow [12] . In an overexpression screen , to identify novel cooperating oncogenes that function like oncogenic Ras or Notch , we isolated the BTB-zinc finger ( BTB-ZF ) domain protein Abrupt ( Ab ) . Expression arrays and ChIP-Seq analysis of Ab binding regions and immunohistochemical analysis of the tumours indicates that Ab promotes the retention of a progenitor-like cell state in scrib mutant cells by blocking the expression of dac , eya , dan , ato and elav in the eye disc , and prevents the temporal elaboration of cell fate domains , defined by dac , cut ( ct ) , senseless ( sens ) , dan , bab2 and ato expression , along the proximo-distal axis in the antennal disc . The Hippo tissue growth control pathway transcriptional coactivator , Yki , is both required to promote tumour overgrowth , and sufficient to cooperate with Ab and maintain cells within the progenitor-like state .
We have previously shown how loss of the epithelial cell polarity regulator and tumour suppressor scrib cooperates with oncogenic Ras ( RasV12/RasACT ) or Notch ( Notchintra/NotchACT ) signalling to promote the formation of invasive tumours [12] . To identify novel oncogenes in Drosophila we carried out an overexpression screen to identify additional genes that can cooperate with the loss of scrib to promote tumour overgrowth . This was done using a bank of Gene Search ( GS ) P element lines [13] , which contain UAS sites to ectopically express the flanking genes . By combining this with GAL4-driven expression , we screened independent GS line insertions on the second chromosome for their ability to promote neoplastic overgrowth when combined with the loss of scrib in eye disc clones . Normally the generation of scrib mutant clones in the eye/antennal disc produces adult flies with mildly reduced and necrotic eyes due to Jun kinase ( JNK ) -mediated death of the mutant tissue [12] . We therefore aimed to identify genes that could either cause pupal lethality or , most importantly , act like activated alleles of either Ras or Notch to block larval pupariation and cause massive tumour overgrowth . From screening ∼2000 GS lines , we identified over 50 that caused increased organism lethality when expressed in scrib mutant clones ( Table 1 ) . As the insertion point and expressed genes have been mapped for all GS lines , it was possible to determine that this corresponded to 10 different genes . Using independent transgenes we were able to confirm that overexpression of 6 of them ( abrupt ( ab ) , dorsal ( dl ) , escargot ( esg ) , numb , charlatan ( chn ) and apontic ( apt ) reproduced the lethality of the GS line . For the remaining 4 genes ( kismet ( kis ) , anachronism ( ana ) , CG3363 and CG10543 ) , although we identified multiple independent GS lines for each , independent transgenes were not available at the time to confirm the interaction . For the confirmed interactors , we examined larval eye/antennal discs to determine the extent of clonal overgrowth induced by the transgene alone compared to the amount of overgrowth when combined with the loss of scrib ( Figure 1 and Figure S1 ) . Some of the interactors ( numb and apt ) promoted very little consistent overgrowth phenotypes despite causing organism lethality at the pupal stage of development , whilst chn produced mild overgrowth , and both dl and esg were striking for producing very large antennal overgrowths before the larvae pupated at day 5/6 after egg laying ( AEL ) . However , of the 6 confirmed genes , the strongest interactor was ab . The overexpression of ab in scrib mutant clones was unique amongst the interactors in promoting a block to pupariation and massive tumour overgrowth throughout an extended larval stage . Both GS lines were inserted within the 5′ region of ab , and orientated so as to overexpress ab , and an independent UAS-ab line reproduced the same cooperative effect as the two GS lines . Analysis of scrib−+ab larval eye disc clones at day 5 revealed that differentiation of eye disc tissue was completely abrogated , as judged by the failure to express the photoreceptor differentiation marker Elav , although expression of the antennal cell fate marker , Dll , was retained within the growing tumour ( Figure 1A–H ) . By day 9 , huge invasive tumours had developed and become fused with the brain lobes ( Figure 1I , J ) . In contrast , the overexpression of ab in otherwise wild type eye disc clones promoted antennal disc overgrowth , and sometimes resulted in the formation of ectopic Dll-positive antennal-like structures , however , it did not block photoreceptor differentiation and the larvae pupated , although most died during pupal development ( Figure 1E , F ) . Analysis of proliferation by ethynyl deoxyuridine ( EdU ) incorporation confirmed that whilst ab-expressing clones exhibited a relatively normal pattern of proliferation , scrib−+ab tumours ectopically proliferated and disrupted the normal pattern of cell proliferation within the eye disc ( Figure S2 ) . Furthermore , Terminal deoxynucleotidyl transferase dUTP nick end labeling ( TUNEL ) stains indicated that although scrib mutant cells undergo apoptosis [12] , [14] , cell death in scrib−+ab discs was mainly confined to the wild type tissue surrounding the growing tumours , although interestingly , ab-expressing clones alone were also characterised by increased cell death of wild type cells bordering the clones ( Figure S2 ) . Therefore , similar to RasACT or NotchACT , the overexpression of ab can cooperate with the loss of scrib to block cell death and differentiation , and promote unrestrained and invasive tissue overgrowth , thus acting as a highly potent novel oncogene in Drosophila . Ab is a transcription factor with a BTB protein interaction domain and zinc finger DNA binding domains [15]–[17] . To build a comprehensive picture of how Ab functions in its oncogenic capacity , we employed Affymetrix expression arrays to identify the transcriptional changes induced by ab overexpression both alone and in combination with the loss of scrib , and combined this with Ab Chromatin Immunoprecipitation-Sequencing ( ChIP-Seq ) to identify those genes that were potential direct targets of Ab-mediated regulation . Tissue samples were prepared from mosaic eye/antennal discs overexpressing ab alone , or ab in scrib mutant clones , 5 days AEL . For the expression arrays , samples were compared back to control eye/antennal discs with wild type clones to identify differentially expressed probe sets ( log base 2 fold change >1 , adjusted p value [18] <0 . 05 ) . This analysis indicated that Ab exerts a potent influence on gene expression , with 3028 and 3534 probe sets differentially expressed in ab and scrib−+ab discs respectively , of which 2323 probe sets were shared between the two ( Figure 2A and Dataset S1 ) . The combined 4239 differentially expresssed probe sets encompassed 3549 annotated genes , 183 of which were represented by more than one probe set . The 183 genes with multiple probe sets were largely consistent in their pattern of expression changes in each genotype , although 59 of the 183 genes had probe sets that were both up and downregulated within the same genotype , possibly reflecting the existence of differentially expressed transcripts ( Dataset S2 ) . Quantitative real-time PCR validation of 5 representative genes confirmed the results of the expression array ( Figure S3 ) . To identify genes that could be direct targets of Ab regulation , we performed ChIP-Seq after pulling down Ab-associated chromatin from ab alone expressing mosaic discs and scrib−+ab mosaic tissue . The Ab antibody used for the pull-down has been widely used in the literature [17] , [19] , [20] , and showed good specificity for Ab in eye/antennal disc tissue , as determined by reduced staining of endogenous Ab protein in ab mutant clones , and increased staining in ab over-expressing clones ( Figure S4 ) . Peak enrichments were identified by comparing each sample to input DNA controls ( see Materials and Methods ) . Reflecting the large number of deregulated genes identified from the array , there were many peaks associated with Ab in both contexts ( Figure 2B and Dataset S1 ) . In the ab alone sample , 8881 peaks were identified , associated with the transcriptional start site or introns of 2582 genes; whilst in the scrib−+ab tumourigenic sample , 10 , 892 Ab binding regions were identified , associated with 2746 genes . Validating these data , ChIP and quantitative real-time PCR for 10 candidate genes were consistent with the ChIP-Seq results ( Figure S5 ) , and there was also a high correlation between the ab and scrib−+ab samples ( a correlation coefficient of ∼0 . 86 ) . Of the potential target genes , 2025 were shared between the two samples , whilst 661 new binding site peaks , associated with 721 genes ( some peaks overlapped more than one gene ) , were unique to the scrib−+ab tumourigenic sample ( Figure 2B ) . Shared target genes were enriched for organismal development-related gene ontologies ( GO ) , including “cell differentiation” ( 1 . 42E-70 ) , “imaginal disc development” ( 2 . 39E-64 ) , “imaginal disc morphogenesis” ( 1 . 92E-52 ) and “appendage development” ( 1 . 03E-42 ) ; whilst the 721 scrib−+ab unique genes , were enriched for the GO of “microtubule cytoskeleton” organization ( 4 . 58E-06 ) ( Figure 2D and Dataset S3 ) . DNA recognition sequences for Ab have previously been suggested from its isolation as a protein capable of binding to the Engrailed binding site [15] , and more recently through a bacterial one-hybrid study that defined a consensus Ab binding sequence [16] , however , in vivo we observed no enrichments for these motifs amongst the Ab peak sequences ( data not shown ) . Instead , the most highly represented motif amongst the most significant Ab peaks ( irrespective of genomic location ) from the ab overexpression sample , exhibited significant similarity to the recognition sequence for another BTB-ZF protein and transcriptional activator , the Drosophila GAGA factor , Trithorax-like ( Trl ) ( Figure S6 ) . Other motifs identified within the sequences associated with the most significant Ab peaks exhibited significant similarity to recognition sequences for Scalloped ( Sd ) , which functions with Yki to activate target genes downstream of Hippo pathway signalling [reviewed in 10] , and Brinker ( Brk ) , which is a repressor downstream of the Dpp pathway [reviewed in 21] . We also searched the Ab peak sequences within the promoter regions or introns of potential target genes , for known transcription factor recognition site motifs . This approach also identified the Trl recognition sequence as one of the most highly represented motifs in peaks common to both the ab alone and scrib−+ab samples ( Table S1 ) . Recognition sequences for the mammalian proteins MZF1 ( similar to Drosophila Crooked legs ( Crol ) , a zinc finger , ecdysone-induced gene that is also required for the expression of ecdysone response genes [22] ) and VDR ( similar to Drosophila hormone receptor HR96 and the ecdysone receptor EcR ) were also identified , although the relevance of these sites for Drosophila proteins is not yet clear . In contrast , within peaks unique to the scrib−+ab sample ( and not within the common peaks ) there was significant enrichment of binding sites for AP1 ( the Jun/Fos transcription factor complex that acts downstream of the JNK signalling pathway ) , and REL and NF-KB ( transcription factors that act downstream of the Toll-like receptor inflammatory signalling pathway ) , suggesting that new Ab target genes could be generated through the activation of JNK and associated inflammation pathways within the tumourigenic context . To prioritise these data and focus upon those genes that could be transcriptional regulated by Ab and also critically required for tumour formation , we first removed from consideration all genes identified from the ChIP-Seq results that were not represented by probes on the array , and then combined the results from both analyses to identify those genes that were both associated with Ab peaks and differentially expressed from the microarray analysis ( Figure 2C ) . This revealed that , for the ab alone sample , 27% of the differentially expressed genes ( 687 of 2511 genes ) , and for the scrib−+ab sample , 31% of the ( 915 of 2987 genes ) , were associated with Ab peaks , and thus potentially defined primary targets of Ab-mediated regulation . To identify potential direct targets of Ab that could be key to promoting tumour development , we focussed upon those targets that were both deregulated and associated with Ab peaks in the scrib−+ab tumour sample . Of these , there were two classes of genes that were also associated with Ab peaks in the non-tumour samples , and either deregulated in both ( Class 1 ) , or just deregulated in the tumour sample alone ( Class 2 ) ; and two further classes of genes only associated with Ab peaks in the tumour sample , but either also deregulated in both tumour and non-tumour ( Class 3 ) , or only deregulated in the tumour alone ( Class 4 ) . The largest of the four classes consisted of shared target genes deregulated in both tumour and non-tumour samples ( Class 1 , 450 genes ) , or in the scrib−+ab sample alone ( Class 2 , 269 genes ) , whilst relatively few potential new targets of Ab ( which were also deregulated genes ) were generated in the tumour sample ( 116 genes in Class 3 , and 80 genes in Class 4 ) . In contrast , 2072 genes deregulated in the tumour were likely to be either secondary downstream targets of ab or targets deregulated by the loss of scrib , since they were not associated with Ab peaks ( Class 5 , 1925 genes ) , or were only associated with Ab peaks in the ab overexpression sample alone ( Class 6 , 147 genes ) . Classes 3 and 4 did not exhibit significant GO enrichments , however , amongst the two primary classes of potential Ab target genes ( Class 1 and 2 ) GO categories were identified that were involved in all aspects of tumour formation , from cell fate/differentiation , cell survival/growth/proliferation , and cell migration/invasion ( Figure 2D , and Dataset S3 ) . In contrast , genes deregulated , but not associated with Ab peaks in the tumour ( Classes 5 and 6 ) did not show these GO enrichments . A heat map depicting the relative expression levels of selected genes from Classes 1 to 6 is shown in Figure 2E ( see Dataset S4 for ChIP-Seq peak alignments to the genome for the Class 1–4 targets depicted in this figure ) . The functional significance of these genes will be elaborated upon below . The strongest GO enrichments amongst Ab targets included “multicellular organismal development” ( 1 . 76E-40 in Class 1 and 9 . 83E-22 in Class 2 ) , “cell differentiation” ( 1 . 44E-28 in Class 1 and 7 . 65E-09 in Class 2 ) and “eye development” ( 3 . 72E-12 in Class 1 ) ( Figure 2D ) . Amongst these targets were particular enrichments for ecdysone-response genes , other developmental genes involved with epigenetic control , Notch signalling , and the control of eye/antennal disc differentiation . Eye disc differentiation initiates from the posterior edge of the disc in the late 2nd instar and progresses sequentially towards the anterior edge over a number of days . At the wandering third instar stage , when photoreceptor differentiation has progressed half way across the epithelium ( as marked by Elav staining ) , the eye disc consists of a spectrum of various cellular differentiation states , from the most differentiated posterior cells to the least differentiated anterior cells . The transcription factors Hth , Eyeless ( Ey ) and Teashirt ( Tsh ) are expressed in the most anterior portion of the eye disc within the progenitor domain , whilst more posteriorly , Hth is first downregulated , followed subsequently by Ey and Tsh . The downregulation of Hth marks a transition point whereby cells begin to express Dac , Eya , Dan , Distal antenna-related ( Danr ) , H , Da , Ato and finally Elav [reviewed in 6] ( Figure 4M ) . In the antennal disc , the temporal development of the tissue is not displayed as a spatial distribution of cell fate markers at the third instar stage as it is in the eye disc , however , early to late cell fate transitions have been documented . Initial domains of Hth , Ct and Dll in the 2nd instar larvae establish the early proximo-distal axis of the antenna [23] , [24] , and downstream targets of these genes , including the cell fate markers Ato , Dac , Dan , Bab2 , Spalt major ( Salm ) and Ss , are subsequently expressed throughout the 2nd and 3rd instar to elaborate the proximo-distal axis of the appendage [7]–[9] ( Figure 4M ) . Multiple regulators of eye/antennal disc cell fate were repressed in scrib−+ab tumours . Whilst some were repressed by expression of Ab alone , most of these were substantially further repressed in combination with the absence of scrib . Potential direct targets of Ab involved in regulating cell fate in the eye/antennal disc , and repressed in the tumour state , included dan , eyg el , h and noc ( Class 1 ) , hth , dac , eya , bab2 , pnr , ss ( Class 2 ) , and da ( Class 4 ) . To further validate these results we examined the expression domains of the different cell fate regulators in the tumours . We had already established that scrib−+ab tumours failed to express Elav in the eye disc ( Figure 1G ) , however , examination of other cell fate markers , revealed that Dac , Dan , Eya , Sens and Ato were also repressed within the overgrowing eye disc tumour ( Figure 4A–H and Figure S9 ) . Importantly , all of these proteins ( with the exception of Ato , which was also decreased in scrib mutant and ab-overexpressing clones ) were not strongly downregulated in either scrib mutant clones alone , nor in ab-expressing clones alone , but only in cooperation with both genetic lesions ( summarised in Table 2 ) , thus validating the results from the expression array . In contrast , the expression of the cell fate markers that define earlier states , including Hth , Tsh and Ey were relatively unaffected in scrib−+ab eye disc tumours , despite their domains of expression being enlarged and warped due to the growth of the tumour ( Figure 4I–L and Figure S10 ) , and with the exception of Hth ( see below ) , were not identified as Ab targets . Furthermore , Hth , Tsh and Ey were all downregulated in more posterior tumour cells indicating that they were still being subject to their normal mode of repression . scrib− clones exhibited only minor perturbations in Hth , Tsh or Ey expression , whereas ab-expressing clones showed mildly reduced Hth and Ey , and slightly upregulated Tsh , expression levels . In scrib−+ab antennal disc tumours , most of the tumour tissue expressed Dll , whilst Hth and Ct expression was repressed ( Figure 4I–L , Figure S10 and data not shown ) . Furthermore , the expression of subsequent cell fate markers expressed along the proximo-distal axis , including Dan ( Class 1 ) , Bab2 and Dac ( Class 2 ) , as well as Sens and Ato , were also repressed within the tumours ( Figure 4A–H , Figure S9 and Figure S10 ) . Their expression was only slightly perturbed in scrib mutant clones , whilst ab-expressing clones alone also repressed most of these markers , with the exception of Dac ( summarised in Table 2 ) . The repression of Hth in the Dll-positive tumour mass within the antenna suggested that the tissue was transformed to a more leg-like state [25] , and , consistent with this , the HOX genes Antennapedia ( Antp ) and labial ( lab ) were also upregulated by ab overexpression ( Figure 2E and Dataset S1 ) . The data therefore suggest that whilst scrib−+ab eye/antennal disc tumours are not homogeneous , and consist of a diverse population of cells , they are characterised by the maintenance of an earlier progenitor-like cell state through the continual overgrowth of tissue that , in the eye disc , fails to transition to the expression of Dac , Eya , Ato and Elav , and in the antennal disc , fails to express differentiation markers downstream of Dll that define the elaboration of the appendage along the proximo-distal axis ( summarised in Figure 4M and Table 2 ) . The capacity of Ab to maintain cells within a progenitor-like state suggested that its function might be linked to the eye disc progenitor state transcription factor , Hth . Indeed , the endogenous expression of Ab in the eye disc mirrored the expression of Hth ( Figure 5A ) , and its downregulation in the eye disc heralds the beginning of Dac and Eya expression . Although our analysis of scrib−+ab tumours indicated that Hth expression was not maintained in the tumours , and in fact was repressed in the antennal disc , it was still possible that Hth might be sufficient for tumour formation in combination with scrib mutants or required for scrib−+ab tumour overgrowth . To determine if Hth was sufficient to cooperate with the loss of scrib , we ectopically expressed Hth in scrib mutant clones . However , although this promoted overgrowth of the tumour tissue and pupal lethality , it did not result in a block to pupariation and massive tumour overgrowth throughout an extended larval stage of development , indicating that Hth could not substitute for Ab in a tumour-promoting role ( Figure S11 ) . Conversely , to test for whether Hth was required for scrib−+ab tumour overgrowth , we overexpressed ab in scrib− hth− double mutant clones and assayed for tumour formation . Examination of tumour samples at day 5 revealed that overgrowth was initially confined to regions within the neck and the ventral portion of the eye disc ( Figure 5C ) , regions that correspond to tissue that is least dependent upon hth for cell survival and/or proliferation , and are associated with a role for hth in repressing ventral eye formation [26] , [27] . Indeed , this tissue continued to grow in scrib− hth−+ab tumours , so that whilst overgrowth was substantially delayed compared to scrib−+ab tumours , massive and invasive tumour masses eventually overtook the larvae ( Figure 5D ) . These tumours consisted of characteristic Dll-positive tumour masses within the antennal region , and Ey-positive tumour tissue within the eye disc ( Figure 5E , F ) . Thus , hth is neither sufficient nor absolutely required for Ab-driven tumour formation . To identify potential targets of Ab that could be important for maintaining tumour overgrowth , we analysed Class 1 and 2 genes for GO enrichments associated with cell survival and proliferation . Importantly , the GO categories of “cell death” ( 4 . 84 E-03 ) , “growth” ( 1 . 09 E-06 ) and “cell proliferation” ( 2 . 03 E-05 ) were all enriched within Class 1 targets , which were genes associated with Ab peaks and deregulated in both ab alone and scrib−+ab tumours . Amongst potential cell death targets , the pro-survival Bcl2 homologue Buffy was upregulated , and the cell death inducer Hid ( W ) was downregulated by ab overexpression . Furthermore , klumpfuss ( klu ) and echinus ( ec ) that promote cell death in the pupal retina [28]–[30] , were also downregulated by ab . Notable Class 1 ab targets involved in cell growth and proliferation included the cell growth and G1-S phase driver Cdk4 ( upregulated ) , the inhibitor of the PI3K pathway Pten ( downregulated ) and a number of Hippo pathway components and/or targets , including expanded ( ex ) , fat ( ft ) , thread ( th/DIAP1 ) and diminutive ( dm ) , the Drosophila Myc gene . Whilst th , a survival-promoting effector of Yki activity , was repressed by ab overexpression , which was confirmed by immuno-histochemical analysis of ab-expressing larval discs ( Figure S12 ) , the Yki targets dm and ex were upregulated upon ab overexpression , and ft and hippo ( Class 5 ) , two negative tissue growth components of the Hippo pathway , were repressed . Thus , although multiple genes may contribute to ab-driven tumour overgrowth , ab-mediated impairment to the Hippo pathway could be a key factor . A role for the Hippo pathway in scrib−+ab tumour overgrowth was tested by knocking down yki , a critical downstream transcriptional effector of impaired Hippo pathway signalling . Strikingly , and unlike loss of hth , this substantially restrained scrib−+ab tumour overgrowth and restored pupariation to the tumour-bearing larvae ( Figure 6A–F ) . To determine whether the rescue in tumour overgrowth was accompanied by a restoration to differentiation we examined the expression of cell fate markers . This revealed that whilst knockdown of yki did not restore Elav and Eya expression to scrib−+ab tumours , Dac levels were substantially increased ( Figure 6G , H ) . It was therefore possible that the increased levels of Dac upon yki knockdown could account for the suppression of tumour overgrowth . However , overexpressing dac within scrib−+ab tumours , using a dac transgene , failed to restrain tumour overgrowth and restore pupariation ( Figure S13 ) . Thus , the downregulation of Dac is not a key requirement for continual tumour overgrowth . Furthermore , scrib−+ab tumour cells expressing ykiRNAi , could still be observed with mesenchymal morphology between the brain lobes ( Figure S14 ) , suggesting that whilst Yki activity is required for tumour overgrowth , it is not an essential mediator of tumour cell migration and invasion . The ChIP-Seq and expression array analysis had indicated that ab overexpression was capable of modulating Hippo pathway activity , however , scrib mutant cells also express Hippo pathway reporters , and ectopically proliferate in a Yki-dependent manner [31] . Thus both the overexpression of ab and the loss of scrib each had the potential to promote Yki activity , and either of these could be crucial in driving cooperative tumour overgrowth . To discern which of the two was more critical in mediating cooperation we tested for whether knockdown of wts in either ab-overexpressing clones or scrib mutant clones , was sufficient to elicit cooperative tumour overgrowth throughout an extended larval stage . Whilst knockdown of wts alone in clones did not perturb Elav expression and larvae pupated normally ( Figure 7A ) , ectopically expressing ab in wtsRNAi clones was sufficient to block pupariation of larvae and promote massive overgrowth of the eye/antennal discs . Examination of Elav expression indicated that although at day 5 some wtsRNAi+ab clones were still observed to express Elav , the overgrown clonal tissue that ensued was entirely composed of Elav-negative tissue ( Figure 7B , C ) . Similar cooperation was observed when ab was ectopically expressed within wtsX1 mutant clones ( data not shown ) . In contrast , although knockdown of wts in scrib mutant clones enhanced scrib mutant tissue overgrowth causing pupal lethality , it was not sufficient to completely block Elav expression and drive cooperative tumour overgrowth throughout an extended larval stage of development ( Figure 7D and data not shown ) . Consistent with this interpretation , scrib-mediated impairment to Hippo signalling has been shown to be atypical Protein Kinase C ( aPKC ) -dependent , since it is rescued by expressing a kinase dead dominant negative ( DN ) version of aPKC ( aPKCDN ) within the mutant tissue [31] , and similarly , expressing aPKCDN in scrib−+ab tumours also curtailed tumour overgrowth ( Figure S15 ) . Thus , whilst ab overexpression alone may impair Hippo pathway signalling , the deregulation of the Hippo pathway induced by the absence of scrib is likely to be a key factor in promoting susceptibility to Ab-driven tumour formation . To determine whether expressing ab in wts mutant clones produced tumours that were similar to scrib−+ab tumours , we examined the expression of different cell fate markers in wts−+ab clones . wts mutant clones differentiated normally , apart from a mild downregulation of Dac ( Figure 7E , G and Figure S16 ) . However , although some of the wts−+ab clonal tissue at day 5 expressed normal , or only mildly reduced , levels of Dac , Dan and Eya ( data not shown ) , in older larvae , the overgrown wts−+ab tumours consisted predominantly of eye disc progenitor-like tissue that did not express Dac , Dan or Eya , and antennal-like tissue that ectopically expressed Dll and Dac ( Figure 7F , H and Figure S16 ) . Thus , the wts−+ab tumours retained a progenitor-like state that was similar to scrib−+ab tumours , with the exception that Dac expression was retained within the antennal domain of the wts−-derived tumours , but not in the scrib−-derived tumours . Furthermore , wts−+ab tumours were characterised by the generation of huge , highly-folded epithelial sheets of tissue that remained distinct and did not fuse with the brain lobes , thus indicating that cooperation between wts−+ab was unable to reproduce the invasive properties of scrib−+ab tumours . The invasive properties of RasACT and NotchACT-driven tumours are dependent upon JNK signalling , since blocking Drosophila JNK ( Basket ( Bsk ) ) , within either scrib−+RasACT or scrib−+NotchACT tumours prevents tumour cell invasion [14] , [32] , [33] . The expression array of scrib−+ab tumours indicated that JNK signalling was also likely to be active within these tumours , as evidenced by the upregulation of known JNK-regulated genes such as Matrix metalloproteinase 1 ( Mmp1 ) and scarface ( scaf ) [32] , [34] , which were also identified as potential Ab targets ( Class 1 and 2 , respectively ) . In addition , GO analysis of Class 1 and 2 targets of Ab indicated a significant enrichment for genes within the category of “locomotion” ( 6 . 67 E-12 in Class 1 and 2 . 72 E-07 in Class 2 ) . In the scrib−+ab tumours these included wunen , wunen2 and Trapped in endoderm 1 ( Tre1 ) that are known to promote germ cell migration , and jing and PDGF- and VEGF-related factor 1 ( Pvf1 ) that are involved in border cell migration [reviewed in 35] . Thus , the data suggested that Ab could directly contribute to the invasive capability of scrib−+ab tumour cells by controlling the expression of migration-associated genes , including JNK targets such as Mmp1 . Using the JNK pathway reporter , misshapen ( msn ) -lacZ [36] , we first determined whether JNK signalling was active in scrib−+ab tumours . Indeed , although ab overexpressing clones alone did not upregulate msn-lacZ expression ( Figure 8A , B ) , the reporter was strongly activated within scrib−+ab tumours , most notably within basal portions of the tumour and in cells that appeared to be migrating between the brain lobes , consistent with a role for JNK in promoting invasion ( Figure 8C , D ) . Immunohistochemical analysis also indicated that the JNK target , Mmp1 , was ectopically expressed within scrib−+ab tumours ( Figure 8E , F ) , and , in agreement with the expression array , Mmp1 levels were also slightly elevated in ab alone overexpressing clones ( Figure 8G ) . To next determine whether Ab was capable of promoting invasion , independent of JNK signalling , we then examined scrib−+ab tumours in which JNK signalling was blocked , using a dominant negative JNK transgene ( bskDN ) . Strikingly , the expression of bskDN in scrib−+ab tumours prevented the fusion of the discs to one another and to the brain lobes , thus demonstrating a critical role for JNK in mediating the invasive properties of the tumours ( Figure 8H ) . To confirm the benign nature of the overgrowths we used live cell imaging to monitor the growth of the tumours over time . The scrib−+ab tumour cells were highly motile with individual cells moving rapidly into the brain ( Movie S1 ) . In contrast , the scrib−+ab+bskDN tumours remained compact , despite their massive growth throughout an extended larval stage of development ( Movie S2 ) . Thus , although ab overexpression may contribute to the invasive properties of the tumours by promoting the expression of targets such as Mmp1 , it is not sufficient to promote tumour invasion in the absence of JNK signalling . In this regard , ab-driven tumours resemble RasACT and NotchACT-driven tumours , although , interestingly , expressing bskDN in RasACT and NotchACT tumours additionally restores pupariation to the tumour-bearing larvae , thus curtailing tumour overgrowth [14] , [32] , [33] . In contrast , the formation of massive , albeit benign , scrib−+ab+bskDN tumours during an extended larval stage , indicated that ab blocks pupariation and promotes scrib− tumour overgrowth , even in the absence of JNK signalling . In summary , this comprehensive analysis has identified multiple modes through which the overexpression of ab and loss of scrib cooperate to promote the retention of a progenitor-like cell state and the formation of invasive tumours ( Figure 9 ) . The overexpression of ab modulates the expression of a significant proportion of the genome to block differentiation , repress ecdysone signalling ( potentially through direct association with the ecdysone receptor coactivator Tai ) , and promote cell survival and proliferation; whilst loss of scrib induces aPKC-dependent Yki activity to promote tumour overgrowth , and JNK signalling to promote invasion . Indeed , deregulation of the Hippo pathway is sufficient to cooperate with Ab and drive the formation of large , albeit benign , tumours , although the deregulation of additional pathways in scrib mutants may contribute to the complete spectrum of overgrowth and differentiation defects observed in scrib−+ab tumours .
Repressed ecdysone response genes were enriched amongst potential Ab targets , consistent with the known capacity of Ab to directly associate with the steroid hormone receptor coactivator Tai and repress the expression of ecdysone response genes in the Drosophila ovary [20] . Indeed , we show that Tai is both required for Ab to exert its oncogenic effect in the eye/antennal disc , and sufficient when overexpressed to cooperate with scrib− and promote the formation of large tumours throughout an extended larval stage . Thus , it is possible that Ab also associates with Tai in its oncogenic role , although further work will be required to determine if this is the case . The human homologue of Tai , SRC3/AIB1 , is also a transcriptional coactivator of steroid hormone receptors and an oncogene [reviewed in 40] , although whether it associates with BTB-ZF proteins is not yet known . The repression of multiple ecdysone-response genes within scrib−+ab tumours is striking , yet whether this plays a cell autonomous role in promoting tumour overgrowth is unclear . However , the tumour-bearing larvae also fail to undergo an ecdysone-induced pupariation response , and this non-cell autonomous block in organismal development functions to extend the time frame available for continual tumour overgrowth . Indeed , an extended larval stage is a phenotype elicited by both neoplastic tumour overgrowth [41] and tissue damage [42] , whereby it functions to give time for tissue regeneration before initiating pupariation . A key factor in mediating this delay is Drosophila insulin-like peptide 8 ( Dilp8 ) , which is secreted from tumours or damaged tissues , and acts as a diffusible signal to repress the biosynthesis of ecdysone [43] , [44] . dilp8 expression can be induced by JNK signalling [43] , which is consistent with previous studies indicating that JNK signalling within scrib−+RasACT and scrib−+NotchACT tumours is essential for the failure of the tumour-bearing larvae to pupate [14] , [32] , [33] . In contrast , we show here that ab-driven tumour overgrowth throughout an extended larval stage does not require JNK signalling . Possibly this reflects a capacity of Ab to directly or indirectly regulate dilp8 expression , independent of JNK . Indeed , the expression array indicated that dilp8 was upregulated in both ab-expressing eye/antennal discs , and in scrib−+ab tumours , although the ChIP analysis indicated that dilp8 was only associated with Ab peaks in the ab alone expressing sample ( Class 6 ) . Why ab-expressing larvae pupate ( unlike scrib−+ab larvae ) , despite the elevated levels of dilp8 expression , remains to be determined . Interestingly , known endogenous functions of Ab are also associated with regulating the timing of hormone-induced developmental transitions , including the correct timing of border cell migration in the Drosophila ovary [20] , and neuromuscular junction formation during metamorphosis [45] . In both contexts , and similar to its oncogenic role , Ab expression is associated with earlier developmental states , and its ectopic expression can inhibit temporal progression towards differentiation . The cells of the adult eye are derived from progenitor cells within the 3rd instar eye disc that are characterised by the expression of a number of transcription factors including Hth , Tsh , Ey , ElB and Noc . The endogenous expression of Ab overlaps with Hth , and both Hth and Ab are downregulated prior to the downregulation of Tsh , Ey , ElB and Noc , and coincident with the upregulation of Dac , Eya , So and Dan . In ab-driven tumours , the expression of dac , eya and dan was blocked , as were other downstream posteriorly-expressed differentiation markers ( ato , Elav , sens ) , thereby maintaining cells within an earlier progenitor-like state . The expression array also indicated that elB and noc were repressed in ab-driven tumours , and although these genes are normally expressed within the progenitor region , elB and noc mutants promote overgrowth of Hth-positive progenitor cells [46] . As the ChIP analysis indicated that Ab binding was associated with many of these genes , including eya , dac , dan , elB and noc , we suggest that Ab may promote the maintenance of a progenitor-like state by directly repressing many of these differentiation-promoting genes , although further work will be required to verify this hypothesis . Interestingly , the failure to transition to Eya , Dac and Dan expression and the maintenance of a progenitor-like state may be sufficient to promote over-proliferation of tumour cells in the eye disc , since not only is loss of elB and noc associated with over-proliferation of progenitor cells , but also loss of eya promotes tissue overgrowth in the eye , although this is eventually restrained through the induction of cell death [47] , [48] , and the ectopic expression of hth and tsh can block eya and dac expression and also promote eye disc overgrowth [49] . Ab , however , appears capable of maintaining eye disc tumour overgrowth independent of both hth and tsh , since Hth and Tsh levels are repressed in the posteriorly localised tumour cells , and ab overexpression can promote overgrowth of eye disc tumour tissue , even in the absence of hth . Thus , whilst the repression of multiple differentiation-promoting genes in ab-driven eye disc tumours might cooperate to elicit a default over-proliferative progenitor-like state , this state does not appear to be defined by simply maintaining the expression of the known progenitor state factors , Hth and Tsh . Progenitor cells in the antennal disc are not as well defined as in the eye disc . However , overgrowing tumour tissue in the antennal region was characterised by the expression of Dll and Hth , whilst all other cell fate markers examined were repressed in the tumour , including Ct , Dan , Bab2 , Ato and Sens . A number of other antennal cell fate markers , although not examined by immunohistochemistry , were also identified from the expression array as significantly repressed within the tumour including aristaless ( al ) , brother of odd with entrails limited ( bowl ) , danr , salm and ss . Although the 3rd instar antennal disc , unlike the eye disc , does not present itself as a spectrum of early to late cell fate states marked by the expression of different transcription factors , significant detail is known concerning the temporal development of the appendage from the embryonic stage onwards . The expression of hth and dll , defining the proximal and distal domains respectively , are one of the first divisions to be established in the developing appendage . Neither are required for each others expression [50] , however , the expression of most other cell fate regulators that define the elaboration of the appendage along the proximodistal axis are dependent upon either or both of their activities [7] , [9] , [51]–[53] . Thus , in scrib−+ab tumours , the expression of cell fate markers downstream of dll and hth are repressed resulting in the overgrowth of antennal primordia tissue that has a defined proximo-distal axis that fails to transition towards a further differentiated state . In this regard , the tissue becomes indistinguishable from the developmentally related leg appendages in their early state . Indeed , within the tumours , most of the Dll-positive tissue does not express Hth , making the tumour tissue more characteristic of a leg-like , as opposed to an antennal-like , state . Consistent with this , the two HOX genes Antp , a repressor of hth [25] , and lab were ectopically expressed within the tumours , and both are capable of transforming the antennae to a leg-like fate [54] . We propose that similar to the eye disc , this alteration in cell fate and block in expression of downstream cell fate regulators is directly mediated by Ab , since Ab binding was associated with most of the downstream genes . Whether this block is sufficient for tumour cells to be maintained within a proliferative state is not yet known . However , as in the eye disc , it is possible that multiple mechanisms cooperate to promote the full spectrum of tumour overgrowth . Furthermore , both ab overexpression and loss of bowl ( which was repressed in the tumours ) can induce the development of ectopic antennae within the eye/antennal disc [19] , and whilst the cause of these phenomena is not yet clear , the eye/antennal disc is derived from the fusion of multiple segments , and it has been suggested that ectopic appendages might arise from reawakened appendage primordia that have been cryptically retained within the composite tissue [55] . Thus it is possible that within ab-driven tumours , multiple segmental appendage primordia could be contributing to tumour overgrowth . In summary , parallels emerge between scrib−+ab tumour overgrowth in the eye and antennal disc , in that both are characterised by the maintenance of an earlier developmental state downstream of Hth but upstream of Dac . However , neither Hth activity , nor the downregulation of dac , appear to be essential for tumour overgrowth , thus indicating that further work is required to identify what key transcription factors define the progenitor-like state in ab-driven tumours . As many of the eye/antennal disc transcription factors targeted by Ab have human orthologues that are also implicated in cell fate regulation , organogenesis and tumourigenesis ( eg . Hth ( MEIS family ) , Dac ( DACH family ) , Eya ( EYA family ) , Dll ( DLX family ) ) , it is likely that this work will promote a deeper understanding of how cell fate control also influences the formation of human cancers . The proliferation of progenitor cells within the eye disc is yki dependent [11] , and although the requirement for Yki activity in antennal disc cells has not been examined , yki is required for scrib−+ab tumour overgrowth in both the eye and antennal disc . The requirement for Yki in scrib−+ab tumours could solely reflect a basal need for Yki activity in progenitor cell proliferation , however , loss of scrib impairs Hippo pathway signalling [31] , and we show here that blocking Hippo signalling is sufficient to cooperate with ab and sustain massive tumour overgrowth . Furthermore , the expression array indicated that ab overexpression may also deregulate the Hippo pathway , as can the BTB-ZF protein Trl [56] , which is known to directly associate with Yki [57] . Interestingly , the mode of Hippo pathway deregulation induced by the overexpression of ab is likely to be different to that induced by the loss of scrib . The Yki targets activated in scrib mutants include CycE , DIAP1 , fj-lacZ and ex-lacZ [12] , [31] , however , ab overexpression upregulated the Yki targets dm ( Myc ) and ex , but both th ( DIAP1 ) and fj-lacZ ( data not shown ) were mildly repressed . As both fj and th are also targets of the JAK/STAT pathway [58] , [59] , Ab may additionally function to repress JAK/STAT signalling , as does another Drosophila BTB-ZF protein , Ken and barbie ( Ken ) [60] . Increasing complexity is being recognised in the variety of transcriptional outputs of the Hippo pathway . In the progenitor domain , Yki associates with Hth and Tsh , and instead of promoting th expression , it drives expression of the pro-survival micro-RNA bantam , which represses translation of the cell death inducer hid ( W ) [11] . A similar capacity could be shared by ab overexpression , and potentially it might be the bringing together of two different modes of Hippo pathway deregulation ( both scrib mutant and ab overexpression dependent ) that makes the combining of these two oncogenic forces so potent . Although loss of wts was sufficient to cooperate with ab and promote massive overgrowth of undifferentiated tissue , it was not sufficient to reproduce the entire spectrum of defects in scrib−+ab tumours . The non-invasive nature of wts−+ab tumours is likely to reflect the lack of JNK pathway activity and/or the maintenance of epithelial cell polarity within the tumours . However , whether these additional defects also account for the differences in expression of cell fate regulators is not clear . Whilst overgrowth of wts−+ab tumours was characterised by the failure to express Eya and Dan , Dac was ectopically expressed within the antennal tumours . Interestingly , Dac defines the medial domain of the appendage and is one of the first markers to be expressed downstream of Hth and Dll . Thus it may be the least refractory to inhibition , relative to slightly later acting cell fate regulators . This contrasts with the eye disc in which knockdown of yki in scrib−+ab tumours restored Dac expression , but not Eya or Elav . Whilst this could indicate that , unlike the antennal disc , Dac alone is repressed by Yki activity in the eye , an alternative explanation could be that in both the antennal and eye discs Dac repression requires substantially higher levels of Yki activity than repression of the other cell fate markers . This might make Dac particularly susceptible to restoration when yki is knocked down in the tumours , and conversely , only subject to repression in scrib−+ab tumours when Yki activity is especially high . Even though Hippo pathway mutants are not usually associated with a failure to differentiate , the potential for the Hippo pathway to elicit effects upon cell fate is not without precedence . Impaired Hippo signalling can synergise with loss of Drosophila Retinoblastoma gene ( rbf ) to cause dedifferentiation of photoreceptor cells in the eye disc , independent of effects on cell proliferation [61]; and in the larval brain Yki overexpression can delay differentiation of the neuroepithelia and promote overgrowth of the progenitor cells , although in this case the effects are likely to be a consequence of accelerated cell cycle progression [62] . However , most pertinent to this study , Yki overexpression throughout the eye disc is sufficient to block Eya expression and expand Hth expression [63] . Interestingly , these effects upon Hth levels were specifically linked to exceptionally high Yki activity , since it could not be reproduced by knockdown of wts alone , but only by combining wts knockdown with additional loss of ft and ex [63] . Thus , additive effects that escalate Yki activity elicit qualitatively different effects , and this is likely to be relevant to both our own analysis of ab-driven tumours , as well as more generally to understanding how cooperating pathways synergise to drive tumourigenesis . There are over 40 human BTB-ZF family members , many of which are implicated in both haematopoietic and epithelial cancers , where they act as oncogenes ( e . g . BCL6 , ZBTB7 ) or tumour suppressors ( e . g . PLZF , HIC1 ) [reviewed in 38] . They are key regulators of cell fate , most notably within the immune system [reviewed in 64] , and a number of studies are also consistent with roles in regulating self-renewal and differentiation . A specific orthologue of ab is difficult to ascertain because of the low sequence conservation between Drosophila and mammalian family members , although ZFP161 exhibits the greatest amino acid sequence similarity . ZFP161 is not known to exert oncogenic activity within humans , and indeed its expression in some tumours is more consistent with a potential tumour suppressor role [65] , however , Bcl6 , one of the best-characterised mammalian oncogenic family members , offers striking parallels to the function of Ab . Mainly implicated in lymphomas , Bcl6 expression is also associated with epithelial cancers , and can promote self-renewal and repress differentiation of both B cells [66] and mammary epithelia [67] . A key oncogenic target of Bcl6-mediated repression in lymphomas is blimp-1 , and the two proteins antagonise each other's expression to regulate lymphocyte differentiation [reviewed in 68] . Importantly , Drosophila Blimp-1 is induced by ecdysone [69] , and was also identified as an Ab target , being one of the most highly repressed genes within the tumour . Bcl6 can also repress Notch signalling in Xenopus [70] , similar to the repression of Notch targets by Ab in Drosophila . Although it is not yet known whether Bcl6 associates with the Tai orthologue , the activity of other BTB-ZF proteins are linked with various nuclear hormone receptors and their corepressors including NCoR and SMRT , suggesting that integration with hormone signalling pathways is a feature shared by mammalian family members . Overall , our identification of ab as an oncogene that cooperates with scrib loss of function in tumourigenesis , and analysis of cooperating pathways in Drosophila tumours , have uncovered striking parallels to mammalian tumourigenesis . It highlights the potent oncogenic potential of this class of proteins , supports prevailing views of the importance of impaired differentiation of progenitor cells as key drivers of neoplastic overgrowth , and raises the possibility that mammalian regulators of epithelial cell polarity could also act as important restraints upon the oncogenic potential of the BTB-ZF family of proteins .
The following Drosophila stocks were used: ey-FLP1 , UAS-mCD8-GFP;;Tub-GAL4 , FRT82B , Tub-GAL80 [71]; y , w , hs-FLP; FRT82B , Ubi-GFP; UAS-ab55 [72]; UAS-ab79 [72]; FRT40A , ab1D [17]; UAS-bskDN [73]; UAS-chn [74]; UAS-DaPKCCAAXDN [75]; UAS-dac [76]; UAS-dl [77]; UAS-esg [78]; E ( spl ) m8 2 . 61-lacZ [79]; UAS-hth [27]; hthP2 [80]; msn06946 ( msn-lacZ ) [81]; UAS-numb-GFP [82]; FRT82B , scrib1 [83]; UAS-taiFL [84]; UAS-TaiDB [20]; UAS-taiRNAi ( VDRC #15709 ) ; UAS-tdf ( also known as apt ) [85]; wtsX1 [86]; UAS-wtsRNAi ( NIG #12072R-1 ) ; UAS-ykiRNAi [87] . Clonal analysis utilised MARCM ( mosaic analysis with repressible cell marker ) [88] with FRT82B and ey-FLP1 to induce clones and mCD8-GFP expression to mark mutant tissue . All fly crosses were carried out at 25°C and grown on standard fly media . A random selection of GS ( mini-w+ ) insertions on the second chromosome ( obtained from the NIG stock centre in Kyoto ) , carrying GAL4 sites to overexpress flanking genes , were screened for their oncogenic potential by initially crossing to w−;;TM3/TM6B flies . Male progeny of the genotype w−;+/GS ( mini-w+ ) ;+/TM6B were then selected to cross to virgin w−;+/CyO;FRT82B , scrib1/TM3 , Sb flies . From their progeny , w−;GS ( mini-w+ ) /CyO;FRT82B , scrib1/TM6B male flies were then crossed to ey-FLP1 , UAS-mCD8 . GFP;;tub-GAL4 , FRT82B , tub-GAL80/TM6B virgins to generate progeny containing scrib1 eye disc clones overexpressing the gene ( s ) downstream of the UAS sites and flanking the GS insertion point . The adult flies ( at least 50 ) of the resulting progeny were examined to determine whether the expression of the GS line in scrib1 clones was lethal , or whether it enhanced the scrib1 mosaic adult eye phenotype in the non-TM6B progeny . Any crosses exhibiting lethality were then examined under fluorescent light to determine whether the non-TM6B progeny exhibited GFP-positive tumour overgrowth or a failure to pupate . The insertion point and over-expressed genes of any identified GS lines was obtained by referring to the Drosophila Gene Search Project web site ( http://kyotofly . kit . jp/stocks/documents/GS_lines . html ) . Imaginal discs were dissected in phosphate-buffered saline ( PBS ) from either wandering 3rd instar larvae or from staged lays for larvae of genotypes that failed to pupate and entered an extended larval stage of development . Tissues were fixed in 4% formaldehyde in PBS , and blocked in 2% goat serum in PBT ( PBS 0 . 1% Triton X-100 ) . For the detection of S phase cells , EdU labelling was performed for 30 min at room temperature according to the manufacturers protocol ( Invitrogen ) . TUNEL assays were performed as described in the manufacturers protocol ( Roche Applied Science ) . Primary antibodies were incubated with the samples in block overnight at 4°C , and were used at the following concentrations; rabbit anti-Ab ( S . Crews [17] , 1/200 ) , rabbit anti-Ato ( [89] , 1/1000 ) , rat anti-Bab2 ( [90] , 1/1000 ) , mouse anti-β-galactosidase ( Rockland , 1/400 ) , mouse anti-Br-core ( Developmental Studies Hybridoma Bank ( DSHB ) , 1/200 ) , mouse anti-Ct ( DSHB , 1/100 ) , mouse anti-Dac ( DSHB , 1/10 ) , rat anti-Dan ( [9] , 1/300 ) , mouse anti-DIAP1 ( B . Hay , 1/100 ) , mouse anti-Dll ( [8] , 1/500 ) , mouse anti-Elav ( DSHB , 1/20 ) , mouse anti-Ey ( [91] , 1/20 ) , mouse anti-Eya ( DSHB , 1/20 ) , rabbit anti-GFP ( Invitrogen , 1/1000 ) , guinea pig anti-Hth ( [92] , 1/100 ) , mouse anti-Mmp1 ( DSHB , 1/20 ) , guinea pig anti-Sens ( [93] , 1/1000 ) , rabbit anti-Tai ( [84] , 1/500 ) , rabbit anti-Tsh ( [94] , 1/2000 ) . Secondary antibodies used were; anti-mouse/rat Alexa647 ( Invitrogen ) and anti- rabbit Alexa488 ( Invitrogen ) at 1/400 dilution . F-actin was detected with phalloidin–tetramethylrhodamine isothioblueate ( TRITC; Sigma , 0 . 3 µM , 1/1000 ) . Samples were mounted in 80% glycerol . All samples were analysed by confocal microscopy on an Olympus FV1000 or Leica TCS SP5 microscope . Single optical sections were selected in FluoView software before being processed in Adobe Photoshop CS2 and assembled into figures in Adobe Illustrator CS2 . Eye/antennal discs were dissected from ∼5 day old larvae bearing ab-expressing clones , scrib1+ab-expressing clones , or FRT82B control clones . For the expression array , 20 pairs of discs for the ab and scrib−+ab samples , and 50 pairs of discs for the control FRT82B genotype , were used to prepare RNA . Samples were prepared in triplicate , and the RNA isolated using TRIZOL , before being column purified ( Qiagen ) . Probes were hybridised to GeneChip Drosophila 2 . 0 Genome Arrays ( Affymetrix ) . For ChIP-Seq , eye/antennal discs were dissected and samples cross-linked in a 1 . 8% formaldehyde solution on a rotating wheel for 5 mins , prior to DNA being sheared by sonication ( 15 cycles of 20 seconds , 35% amplitude ) to produce fragments of ∼500 bp [95] . 100 µl of extract ( corresponding to ∼100 discs ) was used for each immunoprecipitation . 35 µl of 50% Protein A-Sepharose CL4B was added to each sample , and cleared after 1 . 5 hours incubation . 2 µl of polyclonal rabbit anti-Abrupt antibody [17] was then added per sample ( or , for the input DNA controls , no antibody was added ) , and incubated overnight with rotation . Immunocomplexes were recovered by adding 35 µl Protein A-Sepharose to each sample , incubating for 3 hours at 4°C , and then harvesting by centrifugation . Chromatin was decrosslinked by RNase and Proteinase K incubations , and the DNA column purified ( Qiagen ) . For each sequencing sample , 3 to 6 immunoprecipitations were pooled . High throughput sequencing was performed for the ChIP-Seq from the ab-expressing ( 8 , 643 , 591 reads ) and scrib−+ab ( 8 , 508 , 640 reads ) samples . ChIP-Seq reads in all cases were aligned to the Drosophila genome ( dm3 genome assembly , BDGP Release 5 ) using Bowtie with default parameters [96] . Correlation between ChIP-Seq experiments was computed with UCSC Table browser [97] . We used PeakSeq [98] to identify the regions significantly enriched on ChIP-Seq reads from each sample in comparison to the normalised input control ( READLENGTH = 40 , MAXGAP = 40 , MINFDR = 0 . 01 and PVALTHRESH = 0 . 0001 ) . The resulting read maps and target lists were visualised as custom tracks in the University of California Santa Cruz ( UCSC ) Genome Browser [97] . Using RefSeq [99] , potential Ab target genes in each ChIP-Seq experiment were identified by the presence of significant peaks either within the promoter region ( from the transcriptional start sites to 500 bp upstream ) or within the introns of each gene . Expression arrays , ChIP-Seq profiles and target regions were deposited in the National Center for Biotechnology Information ( NCBI ) Gene Expression Omnibus ( GEO ) repository as CEL , wiggle ( WIG ) and Browser Extensible Data ( BED ) files , respectively , under the accession numbers GSE42938 ( expression arrays ) and GSE42928 ( ChIP-Seq profiles and target regions ) . Gene Ontology ( GO ) enrichments were identified with the “GO Term Enrichment” v1 . 8 from AmiGo . To annotate the list of putative transcription factor binding sites on each set of ChIP-Seq binding regions ( ab and scrib−+ab ) , we used the MatScan program [100] with the full collection of 827 predictive matrices available in Jaspar and Transfac [101] , [102] . We ranked each matrix on the basis of the number of hits in both experiments , normalizing with the total size for each set of sequences and the number of occurrences identified in the whole genome . To build the list of overrepresented matrices , we selected those models that presented a difference in each ranking of at least 200 positions and a positive normalised fold-change value . To identify a potential DNA recognition sequence for Ab binding we focussed upon peaks with a height of 40 or more reads from the ChIP-Seq profile of the ab overexpression experiment ( irrespective of its genomic location ) , and used MEME [103] to identify enriched motifs . Potential transcription factors capable of recognizing enriched motifs were identified using the TOMTOM program ( MEME suite ) . Total RNA for each genotype was prepared in replicates using TRIZOL and column purification ( Qiagen ) . After cDNA synthesis , qRT-PCR on each replicate was assayed in triplicate using StepOnePlus Real Time PCR System ( Applied Biosystems ) . For the expression array validations , we were unable to use standard house keeping mRNAs to normalise the results since many , such as actin and GAPDH , were changed in the expression profiling . Instead , the expression levels were normalised with respect to CG6044 , which is expressed in the eye/antennal disc but was not significantly deregulated in the expression arrays , and the average for the triplicates determined . The following primers were used: Blimp-1 , forward TTGCGACAAGAAGTACATCAG , reverse GATGGTCTTTATCCAAACACTC; bab2 , forward CAAGTTCGACATACCCATTCC , reverse GATATAGGTACCATGACCCTG; CG6044 , forward ACTCAGCTTCCTCTACTTCC , reverse CGCAATACTAAAGCAATCACAC; Eip78C , forward CTAATAAAGCTGGGCTTCTTCG , reverse GTTGACAAAGTCAGAATCGTAGAG; fru , forward TCAGATACTCAGAGATGCGA , reverse TGTTGTTATCTGTGAGACCA; H15 , forward GTGACTTTGATAGGGATCCCA , reverse AGGAGTCAATTGGGACATCAG . The fold changes for each sample were determined using the 2 ( −Delta Delta C ( T ) ) method [104] . For the ChIP validation of a selection of representative genes , chromatin was immunoprecipitated with the Ab antibody from ab and scrib−+ab samples ( as described above ) , and then used for quantitative real-time PCR , with rabbit IgG immunoprecipitation as a control . Primers were designed around peak regions identified from ChIP-Seq analysis by using PerlPrimer application . The following primers were used: Antp , forward AGGATCACCTATTTAACTGGAC , reverse ATGTACGTGGCATACTTTCAG; Blimp-1 , forward CAAGAACCTGAGACACCTGA , reverse CAAGAACCTGAGACACCTGA; br , forward ACACATTCGCAACCAACAAT , reverse CCCTTCCAGTACCCTACTCT; Buffy , forward GGGATACATTCACCTTATATGCAC , reverse ACCGAAGTTGAAGTAAGCGA; chinmo , forward CATCTTCAACTTCCTTGCTAA , reverse TGAATACGAAATTGAGCGAA; eya , forward CACAGACAACACTCGAATCAG , reverse GCAGCAGAAGAGACAAAGAG; fru , forward GCTCTTCCATTATCGTTCTC , reverse TATACATGTGAATAGGGCAAG: ftz-f1 , forward AGAGATACGAGTATCCGAGTG , reverse GACATGCACATACATATAGACGG; HLHmβ , forward CCTCCCTCCTTATGTATGTG , reverse GCACAATCAGAAGAAGTCAG; Mmp1 , forward GGATAAGTGCCTATTACTAGCTG , reverse GAATAGCTTATTAGCACGGGTC .
|
Cancer is a multigenic process , involving cooperative interactions between oncogenes or tumour suppressors . In this study , in a genetic screen in the vinegar fly , Drosophila melanogaster , for genes that cooperate with a mutation in the cell polarity ( shape ) regulator , scribbled ( scrib ) , we identify a novel cooperative oncogene , abrupt . Expression of abrupt in scrib mutant tissue in the developing eye/antennal epithelium results in overgrown invasive tumours . abrupt encodes a BTB-zinc finger transcription factor , which has homology to several cancer-causing proteins in humans , such as BCL6 . Analysis of the Abrupt targets and misexpressed genes in abrupt expressing-tissue and abrupt-expressing scrib mutant tumours , revealed cell fate regulators as a major class of targets . Thus , our results reveal that deregulation of multiple cell fate factors by Abrupt expression in the context of polarity disruption is associated with a progenitor-like cell state and the formation of overgrown invasive tumours . Our findings suggest that defective polarity may also be a critical factor in BTB-zinc finger-driven human cancers , and warrants further investigation into this issue .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biology"
] |
2013
|
The BTB-zinc Finger Transcription Factor Abrupt Acts as an Epithelial Oncogene in Drosophila melanogaster through Maintaining a Progenitor-like Cell State
|
Network medicine approaches have been largely successful at increasing our knowledge of molecularly characterized diseases . Given a set of disease genes associated with a disease , neighbourhood-based methods and random walkers exploit the interactome allowing the prediction of further genes for that disease . In general , however , diseases with no known molecular basis constitute a challenge . Here we present a novel network approach to prioritize gene-disease associations that is able to also predict genes for diseases with no known molecular basis . Our method , which we have called Cardigan ( ChARting DIsease Gene AssociatioNs ) , uses semi-supervised learning and exploits a measure of similarity between disease phenotypes . We evaluated its performance at predicting genes for both molecularly characterized and uncharacterized diseases in OMIM , using both weighted and binary interactomes , and compared it with state-of-the-art methods . Our tests , which use datasets collected at different points in time to replicate the dynamics of the disease gene discovery process , prove that Cardigan is able to accurately predict disease genes for molecularly uncharacterized diseases . Additionally , standard leave-one-out cross validation tests show how our approach outperforms state-of-the-art methods at predicting genes for molecularly characterized diseases by 14%-65% . Cardigan can also be used for disease module prediction , where it outperforms state-of-the-art methods by 87%-299% .
High throughput sequencing and screening techniques have led to an increasing accumulation of genomic data . Despite this growth , the mechanisms of action through which genomic variants drive disease development are not fully understood . As genomic alleles and malignant mutations are continuously sequenced , most of them still miss a functional annotation [1] . Early approaches to find non-experimental disease gene associations were based on linkage analysis , which establishes likelihood of observing alleles in an organism compared to random chance [2] . However , this type of analysis is highly dependent on linkage disequilibrium , and thus traditionally fails on genetically multifactorial and heterogeneous diseases [3] . Alternative approaches , such as genome-wide association studies , do find gene candidates even for complex diseases . However , they often produce hundreds of candidates , making experimental validation expensive and time consuming . Recent network medicine based approaches bypass the lack of functional annotation by drawing inferences from interaction data . Diseases are seen as perturbations in specific areas of the interactome – the disease modules . Thus the guilt-by-association [4] principle can be applied to find disease genes by prioritizing those close to already known ones . Several approaches have been proposed that exploit this idea and they differ in how they quantify the distance between candidate genes and known disease genes in the interactome . Common measures for the proximity are the number of direct connections , the length of shortest paths and diffusion kernels , including random walkers with restart and propagation flow . For example , Oti et al . [5] use direct neighbours , Köhler et al . [6] use random walkers with restart , and Navlakha et al . [7] include propagation flow and clustering techniques . Previous authors have also shown that diseases with overlapping modules present significant similarities in terms of phenotype and occurrence ( comorbidity ) [8] . Phenotypic data has been suggested to be particularly informative as different perturbations in a single disease module often produce similar phenotypes [9 , 10] , and phenome networks ( where genes are nodes that are connected if they show correlated phenotypic profiles ) strongly correlate with protein-protein interactions and transcriptional regulatory networks [11] . Furthermore , diseases found in distant neighborhoods in the interactome produce different phenotypes [8] . Several methods have been proposed that combine these different types of data to predict disease genes [12] . One group of methods integrates the data into a unique graph that is then used for the prediction . Lage et al . [13] include disease phenotype in the form of clinical features extracted by text mining from scientific papers; Wu et al . [14] create binary networks where nodes represent genes , and these are connected when their BLAST E-values is higher than a predefined threshold; Chen et al . [15] include information from the Gene Ontology [16] , the Mammalian Phenotype [17] and various types of pathway annotations; Li et al . [18] , Vanunu et al . [19] and Mordelet et al . [20] include the van Driel disease similarity information [21] to enhance the network; and other authors use heterogeneous networks where nodes can be either diseases or genes – Xie et al . [22] connect the nodes with Online Mendelian Disease in Man ( OMIM ) [23] and MGI mouse phenotype-gene associations , and Zeng et al [24] use HeteSim [25] scores . Another group of methods carries out inferences for each different type of data separately , and then integrate the results . In particular , Aerts et al . [26] use co-expression networks , metabolic pathways , Gene Ontology , among others; Franke et al . [27] include the Gene Ontology and co-expression networks; Radivojac et al . [28] use the Gene Ontology , the Disease Ontology [29] , and features based on protein sequence; Karni et al . [30] use disease based co-expression networks; and George et al . [31] use metabolic pathways and Pfams [32] . Similar techniques have been used on a related problem , that of predicting disease modules – disease genes can then be found within members of these modules . Liu et al . [33] recover disease modules through the analysis of gene expression data and partitions of co-expression networks; Ghiassian et al . [34] use direct neighbour analysis on protein-protein interaction ( PPI ) networks to iteratively add genes to the modules . All these network methods produce high quality results , but require initial seeds ( i . e . a few known disease genes ) to produce their predictions . In general , results are better when more seeds are available , and several authors have employed disease families ( rather than single diseases ) which were obtained by aggregating phenotypically similar diseases [5–7] , thus increasing the number of initial seeds for their predictions . An important point to be made here is that there are many molecularly uncharacterized diseases , for which no disease gene is currently known – as of 2018 these comprise 3359 diseases in OMIM , i . e . 39% of the entire OMIM database . For these diseases , most of the methods described earlier are not applicable since the initial seeds are not available ( notable exceptions are PRINCE [19] and ProDiGe4 [20] , described in the Methods section ) . We shall refer to molecularly uncharacterized diseases as uncharted , while those diseases for which at least one disease gene is currently known will be referred to as charted . In this paper , we present a disease gene prediction method that predicts disease genes for both charted and uncharted diseases in OMIM , and can also predict disease modules . Our approach , which we have called Cardigan ( ChARting DIsease Gene AssociatioNs ) , is based on a semi-supervised algorithm that propagates labels on the interactome . These labels integrate disease phenotypic information expressed as a similarity measure between diseases , which is obtained by mining and comparing sets of MeSH terms [35] relevant for the diseases . The approach can be thought of as establishing the location for the modules of charted diseases and using these to “triangulate” the location of the modules of uncharted diseases by exploiting disease phenotypic similarities – the intuition for the approach is shown in Fig 1 . We show that Cardigan outperforms state of the art methods in disease gene and disease module prediction .
Our idea exploits the fact that disease modules of diseases with a similar phenotype should be placed close-by on the interactome [9 , 21] . Therefore , genes associated to diseases that are phenotypically similar to a disease of interest should provide useful information to locate its disease module . To predict disease genes for a given disease ( query disease ) , Cardigan begins by calculating its phenotypic similarity to every other disease in OMIM using the approach developed by Caniza et al . [36] . Next , Cardigan assigns a weight to each known disease gene . The weight is related to the Caniza similarity between the query disease and the disease to which the gene is associated ( Fig 2C ) . Weights of disease genes are real values between 0 and 1 and are calculated by rescaling the Caniza similarity through a sigmoid function that is dampened by a multiplicative factor 0<h<1 . ( illustrated in Fig 2B; the motivation for the sigmoid function is presented in Section Significance of the sigmoid in S1 Text ) . If a gene is associated with more than one disease , Cardigan uses the highest similarity value . Genes that are already known to be associated with the query disease , if any , are assigned a weight equal to 1 – in this way , these genes are assigned a weight that is higher than the weight of disease genes of any other disease ( whose value is at most h ) . For a given query disease , we shall call the set of weights assigned to the disease genes the Query Weight Set ( QWS ) for that disease . The parameters of the sigmoid and the dampening factor h were learned using a small training set which we then removed from all subsequent experiments ( the training procedure is detailed in Section Estimation of the default parameters for Cardigan in S1 Text ) . The next step is to propagate the QWS through the graph with a semi-supervised learning procedure ( transition between C and D in Fig 2 ) . Cardigan uses the consistency graph diffusion method from Zhou et al . [37] . This is a graph labelling procedure based on minimizing a cost function that takes into account network weights and an existing set of labels . Let us represent a weighted PPI network with n nodes as an adjacency matrix Wn×n , where each element Wij is the weight between genes i and j ( if the network is binary , then all the values in W are binary , indicating the presence or the absence of an interaction ) . The final labelling vector F ( of size n ) having one element for each gene , whose value is related to the probability of that gene of being associated with the query disease , is obtained by minimizing the following cost function: C ( F ) =12 ( ∑i , j=1nWij‖1DiiFi−1DjjFj‖2+μ∑i=1n‖Fi−Yi‖2 ) where vector Y ( of size n ) is the QWS and μ>0 is a regularization parameter . Let us briefly analyze the cost function in order to get some intuition for the method ( a formal description of the entire procedure is presented in Section Mathematical formulation of Cardigan in S1 Text ) . The cost function being minimized is the sum of two terms . The first term accounts for the consistency of the labels of adjacent nodes ( reflecting the guilt-by-association principle ) –this term is minimized when adjacent nodes have similar labels ( i . e . the difference between Fi and Fj becomes small ) . Also note that the importance of the difference between Fi and Fj is proportional to the edge weight ( Wij ) , i . e . it is related to the probability of the interaction . At the same time , the role of the second term is to conserve the initial labels ( QWS ) , thus it emphasizes the reliability of the initial data for the prediction–this term is minimized when the nodes labels Fi are the same as the initial labels Yi . Finally the μ parameter controls the relative importance of the two terms , while the Dii=∑k=1nWik terms serve as normalization parameters for the node degree . The vector F that minimizes the above cost function can be interpreted as a gene ranking ( Fig 2D ) , and constitutes the output of Cardigan . The minimum of the cost function above has the following closed form [37]: F=β ( I−αS ) −1Y where S = D−1/2WD−1/2 , α=11+μ , and β=μ1+μ . It is important here to note that Cardigan is able to predict genes both for charted and uncharted diseases . In fact , the only input for the procedure is the QWS , which can be obtained for both groups of diseases . The only difference is that charted diseases will contain genes with label equal to one corresponding to disease genes already known for those diseases . Furthermore , the method can be used for the prediction of disease modules , since the top predictions of Cardigan can be interpreted as the disease module for the query disease . We compared the performance of Cardigan against PRINCE , ProDiGe1 , ProDiGe4 and DIAMOnD at predicting disease genes for OMIM diseases ( these algorithms are described in the Methods section ) . PRINCE and Cardigan were run using both binary protein-protein interaction networks ( HPRD [38] , BioGRID [39] , DiamondNet [34] ) as well as weighted networks ( HIPPIE [40] and FUNCOUP [41] ) , while ProDiGe1 , ProDiGe4 and DIAMOnD can run only on binary networks ( see Methods for details ) . As a baseline , we also calculated the performance obtained by a procedure that selects disease genes at random . Following previous authors [19 , 20 , 24] , we evaluated the performance at predicting one gene at a time , measuring how often that gene is found within the first 1 , 10 , 100 , 200 genes output by the different algorithms . We will present the evaluation for charted and uncharted diseases separately , and for each type of disease we will analyze the performance using both time-lapse data and a leave-one-out testing procedure . In time-lapse data experiments , we will attempt to predict genes which have been associated with diseases in the period 2013–17 using data from 2013 . Although these experiments are limited in the size of the test set , they are very important as they provide an evaluation of the system in real-life scenarios . In leave-one-out experiments , we will remove a single disease-gene association and measure how well the system can retrieve it . Time-lapse tests: We begin by presenting the performance of Cardigan at predicting genes that are associated with diseases in 2017 , but were uncharted in 2013 , using data from 2013 . The 2013 OMIM database had 2670 descriptions of uncharted diseases , and 287 of those diseases appear as charted in the 2017 OMIM database . Cardigan is the only method that can make predictions for these 287 diseases . In fact PRINCE and ProDiGe4 , the only other methods that could in principle make predictions for uncharted diseases , are not applicable since their disease kernel does not include any of these diseases [21] . The prediction results are presented in Fig 3A , and show that Cardigan has a good performance which is stable across different networks . Leave-one-out tests: If a given disease has only one known disease gene , then by removing it we obtain a “synthetic” uncharted disease . There are 5707 diseases with a single disease gene in the 2017 OMIM database , and for 3252 of them the disease gene were present in HPRD . For each of these diseases we removed its gene and measured the performance of the methods at predicting it back . Since these are synthetic uncharted diseases , there is no initial set of disease genes , and therefore ProDiGe1 and DIAMOnD cannot be used for this problem . Fig 3B shows that Cardigan clearly outperforms both ProDiGe4 and PRINCE for different number of retrieved predictions . Results using the BioGRID , DiamondNet , HIPPIE and FUNCOUP networks were similar and can be found in Section Other results in S1 Text . Time-lapse tests: In these experiments we tested the performance of the different methods at predicting genes for diseases which were already charted in 2013 and gained further genes by 2017 , using data from 2013 . Out of the 1413 disease gene associations which were new in the 2017 version of OMIM , only 95 of them were added to diseases which were already charted in 2013 . This number further reduced for testing since many of these genes were not contained in the PPI networks ( their number ranges between 64 for HPRD and 78 for FUNCOUP ) . Results for HPRD are shown in Fig 4A , where Cardigan presents a minimum improvement of 8% with respect to the second best method at any threshold . Results using the other PPI networks were similar ( see Section Other results in S1 Text ) . Leave-one-out tests: This is the typical way in which disease prediction methods are tested [7 , 13 , 19 , 20 , 34] . We evaluated the performance of the methods when disease genes were removed one at a time and predicted back . The 2017 OMIM database contains 264 diseases with two or more genes , which result in 970 possible test cases . Fig 4B shows the results for the 826 tests that can be performed using HPRD . We can see how Cardigan outperforms every method at every threshold—the minimum performance improvement is 14% with respect to the second best method at any given threshold . Results using the other PPI networks were similar ( see Section Other results in S1 Text ) . We tested how well Cardigan performed at predicting disease modules , i . e . whether the set of predicted disease genes formed a coherent disease module . To do this , we used the same dataset and followed the same procedure that was previously used by Ghiassian et al . [34] . Their dataset contains 70 diseases and their respective modules , which had been manually curated . In our experiments , we evaluated the performance of Cardigan at reconstructing the module after removing different percentages of genes ( i . e . keeping different percentages of the module ) . The evaluation measure used is the AUC of the ROC curve normalized for the first 200 false positives predictions , thus matching the sizes of disease modules as described by Ghiassian et al . ( for more details see Section Evaluation measure–area under the normalized ROC curve in S1 Text ) . Fig 5 shows that Cardigan outperforms DIAMOnD consistently when keeping different percentages of the module . At each percentage , we performed 10 random selections of the genes that were kept for each disease to avoid biases on the experiments . The minimum improvement is 87% when 95% of the module is kept , and this goes up to 299% when 5% of the module is kept . Note how Cardigan is also able to recover modules even when 0% of the module is kept . Also , as expected , both methods see an increase in performance as the percentage of kept module increases . We present an additional analysis of the modular properties for the predicted modules of uncharted diseases in Section Modular properties of sets of predicted genes in S1 Text .
We have presented Cardigan , a novel network medicine based approach for disease gene prediction . Its key feature is its ability to predict genes for diseases using only their phenotypic description , which allows the method to predict genes for molecularly uncharacterized diseases . We have shown that Cardigan can handle both weighted and unweighted networks of different sizes by testing it on HPRD , DiamondNet , BioGRID , HIPPIE and FUNCOUP . Our experiments show how Cardigan consistently outperforms by a significant margin state-of-the-art methods and is stable on different types of networks . In particular , Cardigan’s performance remains very high on BioGRID where other methods show significant drops in performance . The difference in performance between Cardigan and the other methods is larger in time-lapse experiments than in leave-one-out tests , which are more commonly used in the literature . Here we suggest that time-lapse experiments provide a more realistic evaluation as they mimic more closely the gene discovery process . In fact , looking at the evolution of the OMIM database , we notice that genes for complex diseases are frequently discovered ( and then added ) in groups . The case of adding just one gene at a time , that is portrayed by leave-one-out tests , is much less frequent . Combining the results over all PPI networks from our time-lapse experiments and considering results among the top 200 genes , Cardigan produces the best gene ranking for 80% of the diseases . Table 1 compiles some interesting examples of Cardigan predictions diseases using the 2013 OMIM database , which were later verified . It includes diseases which had been studied for long periods of time and yet , in 2013 , were still missing associated genes–all these diseases have papers in OMIM dated at least from the ‘70s . An interesting question is whether a QWS ( the initial seed set for the diffusion process ) can be thought of as an approximate disease module . To verify this , we checked whether its highest ranking genes share functions and whether they tend to be located in the same neighborhood in the interactome . Our analysis shows that genes with higher weights in the QWS for the different diseases are more likely to share function than expected by random , and that the top genes tend to be located in the same neighborhood ( detailed description of this analysis is presented in Section Analysis of modular properties of gene sets in S1 Text ) . Our method differs from earlier kernel methods approaches for scoring disease genes such as , for example , the Lippert et al . method [46] which requires a clear distinction between known diseases genes , which are labeled , and other genes , which are unlabeled ( more details are provided in Section Relation between Cardigan and the Lippert method in S1 Text ) . In fact , an important difference between Cardigan and other well-known kernelized scoring methods lies in the use of initial labeling for genes other than the known disease genes . Finally , we point out that by including the initial labels , our methodology can be incorporated in a generalized framework , such as , for example , the RANKS tool from Valentini et al . [47] ( a detailed explanation for RANKS is provided in Section Generalization of Cardigan as a methodology to include soft labels in S1 Text ) . Finally , the gene rankings obtained by running Cardigan on the entire OMIM diseases set are provided in the S2 Dataset . We believe that this table constitutes an important starting point for the experimental discovery of disease genes , particularly for uncharted diseases .
Our experiments were carried out using disease data from the OMIM database [23] downloaded in April 2017 . In time-lapse experiments , we also used OMIM data from April 2013 to make predictions which were then verified using the OMIM data from April 2017 . Table A in S1 Text summarizes the differences between these two editions of the database . We also used the Ghiassian et al . [34] diseases module dataset , which encompasses 70 diseases and their modules . These are not necessarily OMIM diseases , and we manually mapped them to OMIM diseases by matching OMIM disease names and taking into account their description . Our mapping from Ghiassian to OMIM diseases is available as a TSV file ( S1 Dataset ) . Protein interaction networks come in two flavours , weighted and binary . In weighted networks , links between two proteins are labelled with a weight whose value is related to the probability of the interaction . In binary networks , links are not labelled and a link is either present or missing ( denoting the existence or the lack of interaction ) . Moreover , interaction data can be experimental or predicted . In order to show the general applicability of our methodology , we performed our tests using different types of protein interaction networks including weighted and binary networks with both experimental and predicted data: HPRD [48] , DiamondNet [34] and BioGRID [39] are binary experimental networks; HIPPIE [49] is a weighted experimental network; FUNCOUP is a large weighted network including both experimental and predicted data . Table B in S1 Text summarizes some of the relevant characteristics of these networks . We compared Cardigan to four methods: ProDiGe1 , ProDiGe4[20] , PRINCE [19] and DIAMOnD [34] . These were chosen because they are state-of-the-art representatives of the disease gene prediction methods and of the disease module prediction methods described earlier . ProDiGe [20] is a family of kernel-based disease gene prediction methods which rank all genes within the protein-protein interaction network for a given disease . The main idea is to learn missing disease-gene associations through a one-class SVM , where known associations are established as positive labels and the other associations are unlabelled . ProDiGe allows gene associations to be shared among separate diseases . Positive labels are produced by multiplying the known disease-gene association matrix and a disease sharing kernel , and the SVM learns using a graph diffusion kernel created from the PPI network . The four methods in the family ( ProDiGe1 to 4 ) differ in the disease sharing kernel: ProDiGe1 does not share genes ( the disease sharing kernel is the identity matrix ) ; ProDiGe2 establishes a uniform low probability to genes from other diseases ( the disease sharing kernel is the identity plus a small constant ) ; ProDiGe3 allows genes to be shared by using a phenotype similarity kernel ( the disease sharing kernel is the van Driel similarity matrix [21] ) ; and ProDiGe4 adds the kernels from ProDiGe1 and ProDiGe3 to give more importance to the genes of the disease of interest . We chose ProDiGe1 and ProDiGe4 as representatives of the disease gene prediction methods as they have been shown to outperform other well-known methods , such as Endeavour [6] , and a multiple kernel learning approach ( MKL1class ) [20] in the top 200 predictions , and they are comparable in performance to newer methods such as BiRW [22] , HSSVM [24] and HSMP [24] when predicting a single disease gene at a time . PRINCE [19 , 50] is a diffusion-based method that uses the Zhou et al . iterative propagation [37] to prioritize genes . It makes use of the disease phenotype information provided by the van Driel similarity matrix [21] to gather additional seeds for the query disease . The phenotype information allows genes from highly similar diseases to be effectively regarded as if they were known genes of the query disease ( in contrast , our method uses a dampening factor to differentiate the weights assigned to genes from diseases other than the query ) . DIAMOnD [34] is a recent disease module prediction method based on direct neighbor analysis which starts from a set of initial seeds and iteratively increases the module by adding new genes . At each iteration , the algorithm evaluates which genes have more connections to the existing disease module than expected by random chance , using the hypergeometric distribution as the null model . The most connected gene according to this model is then added and the authors consider the first 200 to 500 genes as the recovered disease module . Although DIAMOnD is not intended to be a fully-fledged disease gene prediction method , the order in which the genes are added to the module naturally produces a ranking that prioritizes disease genes . In our experiments , we used the implementations of ProDiGe1 , ProDiGe4 and DIAMOnD which were provided in their respective publications . Additionally , we developed our own implementation of PRINCE which uses all the recommended parameters specified in the publication . Caniza et al . [36] recently proposed a measure to quantify the phenotypical similarity between hereditary diseases . Their method begins by collecting , for each disease , the set of MeSH terms assigned to the scientific publications relevant for that disease . The phenotype similarity for a pair of diseases is then quantified by the information content of the term on the MeSH ontology that is the lowest common ancestor between the sets of terms for the two diseases . In practice , the similarity is calculated for the diseases found in OMIM , using the publications that OMIM associates to the diseases . The authors have shown that the similarity between two diseases is correlated with the closeness of their respective disease modules on the interactome . Our method is available as a fast , industrial strength library for Python 2 . 7 which implements sparse matrices and lazy loading for disease similarities to reduce the memory footprint . The code is publicly available from the paper website at http://www . paccanarolab . org/cardigan . Although the execution times of the methods are not the main interest of this work , we point out that our method is very fast–a table comparing the execution times of Cardigan with those of DIAMOnD and ProDiGe for the different types of networks can be found in Section Execution times in S1 Text .
|
The elucidation of the genetic causes of diseases is central to understanding the mechanisms of action of a pathology and the development of treatments . Disease gene prediction methods streamline the discovery of the molecular basis for a disease by prioritizing genes for experimental validation . Although some methods use disease phenotype to aid the prioritization , the great majority use outdated static matrices which limits their disease coverage . Our approach uses an updatable disease phenotype similarity , and employs a non-linear transformation to define a prior probability distribution over the genes that mimics the distribution of disease genes in the interactome . Subsequently , a semi-supervised learning method establishes a prioritization ordering for all genes in the interactome , even for diseases with no known molecular basis . Our method can be used not only to obtain a better prioritization for disease-gene associations , but also for retrieving disease modules .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"interaction",
"networks",
"genetic",
"networks",
"protein",
"interactions",
"statistics",
"applied",
"mathematics",
"protein",
"interaction",
"networks",
"simulation",
"and",
"modeling",
"algorithms",
"mathematics",
"forecasting",
"network",
"analysis",
"genome",
"analysis",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"protein-protein",
"interactions",
"proteins",
"mathematical",
"and",
"statistical",
"techniques",
"statistical",
"methods",
"proteomics",
"molecular",
"biology",
"gene",
"ontologies",
"biochemistry",
"gene",
"identification",
"and",
"analysis",
"kernel",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"genomics",
"gene",
"prediction",
"computational",
"biology"
] |
2019
|
Disease gene prediction for molecularly uncharacterized diseases
|
Age-related changes in DNA methylation have been implicated in cellular senescence and longevity , yet the causes and functional consequences of these variants remain unclear . To elucidate the role of age-related epigenetic changes in healthy ageing and potential longevity , we tested for association between whole-blood DNA methylation patterns in 172 female twins aged 32 to 80 with age and age-related phenotypes . Twin-based DNA methylation levels at 26 , 690 CpG-sites showed evidence for mean genome-wide heritability of 18% , which was supported by the identification of 1 , 537 CpG-sites with methylation QTLs in cis at FDR 5% . We performed genome-wide analyses to discover differentially methylated regions ( DMRs ) for sixteen age-related phenotypes ( ap-DMRs ) and chronological age ( a-DMRs ) . Epigenome-wide association scans ( EWAS ) identified age-related phenotype DMRs ( ap-DMRs ) associated with LDL ( STAT5A ) , lung function ( WT1 ) , and maternal longevity ( ARL4A , TBX20 ) . In contrast , EWAS for chronological age identified hundreds of predominantly hyper-methylated age DMRs ( 490 a-DMRs at FDR 5% ) , of which only one ( TBX20 ) was also associated with an age-related phenotype . Therefore , the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy ageing in later life . We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61 , suggesting that a-DMRs may initiate at an earlier age . We next explored potential genetic and environmental mechanisms underlying a-DMRs and ap-DMRs . Genome-wide overlap across cis-meQTLs , genotype-phenotype associations , and EWAS ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype–phenotype association , where the CpG-site was also an ap-DMR for the same phenotype . Monozygotic twin methylation difference analyses identified one potential environmentally-mediated ap-DMR associated with total cholesterol and LDL ( CSMD1 ) . Our results suggest that in a small set of genes DNA methylation may be a candidate mechanism of mediating not only environmental , but also genetic effects on age-related phenotypes .
DNA methylation is an epigenetic mechanism that plays an important role in gene expression regulation , development , and disease . Increasing evidence points to the distinct contributions of genetic [1] , [2] , [3] , [4] , [5] , environmental [6] , [7] , [8] , and stochastic factors to DNA methylation levels at individual genomic regions . In addition , DNA methylation patterns at specific CpG-sites can also vary over time within an individual [9] , [10] and correspondingly , age-related methylation changes have been identified in multiple tissues and organisms [11] , [12] , [13] , [14] , [15] . Although age-related changes in methylation have been implicated in healthy ageing and longevity , the causes and functional consequences of these remain unclear . Ageing is a complex process , which represents the progression of multiple degenerative processes within an individual . Studies in different organisms have identified many factors that contribute to lifespan and the rate of healthy ageing within an individual . These include components of biological mechanisms involved in cellular senescence , oxidative stress , DNA repair , protein glycation , and others ( see [16] ) . Taking these into account , the concept of biological age has been proposed as a better predictor of lifespan and functional capacity than chronological age alone . Previous studies have proposed that certain traits can be used as measures of biological age [17] and have put forward a stringent definition of an ageing biomarker ( see [18] ) . Here , we examined age-related phenotypes that have previously been considered biomarkers of ageing ( see [19] ) , specifically white cell telomere length , blood pressure , lung function , grip strength , bone mineral density , parental longevity , parental age at reproduction , and serum levels of 5-dehydroepiandrosterone ( DHEAS ) , cholesterol , albumin , and creatinine . Epigenetic studies of age-related phenotypes can help identify molecular changes that associate with the ageing process . Such changes may include both biological markers of accumulated stochastic damage in the organism , as well as specific susceptibility factors that may play a regulatory role . We explored the hypothesis that epigenetic changes contribute to the rate of ageing and potential longevity in a sample of 172 middle-aged female twins , where methylation profiles and age-DMRs were previously characterized in 93 individuals from the sample [14] . We compared DNA methylation patterns with chronological age in the sample of 172 individuals and related epigenetic variation to age-related phenotypes that have previously been used as biomarkers of ageing . We identified phenotype-associated DNA methylation changes and combined genetic , epigenetic , expression , and phenotype data to help understand the underlying mechanism of association between epigenetic variation , chronological age , and ageing-related traits .
We characterized DNA methylation patterns in a sample of 172 female twins at 26 , 690 promoter CpG-sites that map uniquely across the genome . We observed that the majority of autosomal CpG-sites were un-methylated ( beta <0 . 3 , 69% of probes ) , unlike X-chromosome CpG-sites , which were predominantly hemi-methylated consistent with X-chromosome inactivation ( Figure S1 ) . Comparisons of methylation rates within twin pairs indicated that MZ twins had more similar DNA methylation patterns compared to DZ twins , and methylation levels were more similar within co-twins compared to unrelated pairs of individuals ( Figure 1A ) . Correspondingly , intra-class correlation coefficients were significantly greater in MZ twin pairs compared to DZ pairs ( Figure S2 ) indicating evidence for DNA methylation heritability . Estimates of DNA methylation heritability were obtained from CpG-site specific distributions of the MZ and DZ correlation differences . The average whole blood autosomal genome-wide heritability rate was estimated to be 0 . 182 ( genome-wide mean estimate was between 0 . 176 ( 95%CI: 0 . 168–0 . 185 ) and 0 . 188 ( 95%CI: 0 . 180–0 . 196 ) , see Figure S2 ) . We further investigated methylation heritability by identifying genetic associations with DNA methylation , or methylation QTLs ( meQTLs ) . Methylation QTLs have previously been identified in multiple samples and tissues , and the majority of reported associations have been observed in cis and close to the probe [1] , [3] , [20] . Therefore , we restricted our analyses to cis-meQTLs only , that is , SNPs within 100 kb of the methylation probe . At a permutation-based FDR of 5% ( P = 1 . 0×10−5 ) , we identified 1 , 537 probes ( 6 . 3% of probes tested ) that had cis-meQTLs associations involving 22 , 849 SNPs ( Figure 1B ) . The majority of associations were obtained for SNPs within a few kb of the methylation probe ( Figure 1C ) . Altogether , of the 1 , 537 probes with meQTLs identified in this study , 444 ( 28% ) and 61 ( 34% ) were previously reported in brain [3] and lymphoblastoid cell lines [1] , respectively ( Figure 1D ) . Genetic variants that associate with methylation can also have effects on gene expression variation . For the individuals in our sample we also had available gene expression data [21] . We compared the SNPs that were meQTLs in our data with eQTLs from lymphoblastoid cell lines ( LCLs ) in these individuals , as previously defined [21] . We observed that 10% of previously reported eQTLs in LCLs also had significant meQTL signals in whole blood , suggesting shared mechanisms of methylation and gene-expression regulation in a small proportion of genes , which is consistent with previous findings [1] , [3] , [5] . We next compared DNA methylation patterns to age and age-related phenotypes by conducting epigenome-wide association scans ( EWAS ) . We fitted a linear mixed effects model regressing methylation levels at each probe on the chronological age of the individuals and included fixed-effect ( methylation chip and order of the sample on the chip ) and random-effect ( family-structure and zygosity ) covariates . Differentially methylated regions ( DMRs ) associated with age ( a-DMRs ) were identified as those that surpassed the 5% FDR threshold ( P = 3 . 9×10−4 ) . We identified 490 a-DMRs in the 172 females twins ( Table S1 , Figure 2A ) , of which the majority ( 98% ) exhibited increased methylation with age ( hyper-methylated a-DMRs ) . Of the 490 a-DMRs in our study , 75 hyper-methylated a-DMRs were previously reported as hyper-methylated a-DMRs in a subset of these data ( 93 individuals from [14] ) . Furthermore , 36 a-DMRs from our study replicated with the same direction of effect as 88 a-DMRs identified in saliva samples in male twins [11] , and 3 a-DMRs were also in the top 10 reported a-DMRs from multiple brain tissues [13] . The a-DMR probes had similar mean levels of methylation , but significantly greater variability ( Wilcoxon rank-sum test P<2 . 2×10−16 ) compared to autosomal CpG-sites across the genome . The phenotype EWAS DMR analyses focused on the comparison between methylation and age-related phenotypes in the linear mixed effects regression ( LMER-DMRs ) framework . We examined sixteen phenotypes ( Table 1 , Figure S3 , Table S2 ) , which have previously been studied as biomarkers of age . These phenotypes included telomere length , systolic blood pressure ( SBP ) , diastolic blood pressure ( DBP ) , FEV1 and FVC to examine lung function , grip strength , bone mineral density ( BMD ) , serum levels of DHEAS , serum total cholesterol levels , serum high density cholesterol levels ( HDL ) , calculated levels of serum low density cholesterol ( LDL ) , serum albumin levels , serum creatinine levels , maternal longevity ( MLONG ) , paternal longevity ( PLONG ) , maternal age at reproduction ( MREPROD ) , and paternal age at reproduction ( PREPROD ) . For each phenotype we regressed methylation levels against the phenotype and included methylation chip and order on the chip as fixed-effect covariates and family and zygosity as random effects . We also performed the analyses by including or excluding chronological age as a fixed effect covariate . We examined the results using a permutation-based significance threshold , by preserving twin-structure and taking into account missing data patterns for each phenotype and evidence of co-methylation and deviations from normality in the DNA methylation data . We observed that four ap-DMRs for LDL ( cg03001305 in STAT5A with LDL: age-corrected methylation∼LDL beta = 4 . 73×10−3 , se = 8 . 75×10−4 , P = 8 . 72×10−7 ) , lung function ( cg16463460 in WT1 with FEV1: methylation∼FEV1 beta = −0 . 035 , se = 6 . 72×10−3 , P = 5 . 31×10−7; cg16463460 in WT1 with FVC: methylation∼FVC beta = −0 . 0293 , se = 5 . 59×10−3 , P = 4 . 67×10−7 ) , and maternal longevity ( cg09259772 in ARL4A with MLONG: methylation∼MLONG beta = 2 . 11×10−3 , se = 4 . 21×10−4 , P = 1 . 83×10−6; cg13870866 in TBX20: methylation∼MLONG beta = 1 . 10×10−3 , se = 2 . 11×10−4 , P = 1 . 21×10−6 ) were genome-wide significant at a permutation-based FDR of 5% ( Figure 2 , Figure S4 ) . We repeated the LMER-DMR analyses using normalized methylation levels and observed that the reported FDR 5% ap-DMRs ( Table 1 ) also fell in the top-ranked results from the normalized methylation DMR analyses . We compared the 490 a-DMRs to ap-DMRs . Only one of the 490 a-DMRs was also significantly associated with ageing-related phenotypes , specifically ap-DMR for maternal longevity ( TBX20 ) . We examined the genome-wide distribution of ap-DMR association P-values in the set of a-DMRs , but did not observe an enrichment of ap-DMRs in the set of a-DMRs compared to random sets of probes ( Figure S5 ) . We tested for correlation in DNA methylation ( co-methylation ) between nearby CpG-sites both genome-wide and specifically at the 490 a-DMR CpG-sites . We observed evidence for co-methylation , that is , pairs of CpG-sites located within 1–2 kb apart showed greater correlation in methylation patterns compared to pairs of CpG-sites located further apart . The pattern of co-methylation was also observed at the a-DMR CpG-sites , in particular DNA methylation levels at CpG-sites located within 500 bp of an a-DMR were highly correlated with the a-DMR DNA methylation levels compared to CpG-sites located further away from a-DMRs ( Figure S6 ) . To assess if the DMRs identified in our study capture differential proportion of whole blood cell ( WBC ) sub-types we compared DNA methylation levels with WBC sub-type proportions for neutrophils , eosinophils , monocytes , and lymphocytes . Blood count DMR analyses were performed at the 493 a-DMRs and ap-DMRs , and results are presented at a DMR Bonferroni corrected P-value = 0 . 05 ( nominal P = 1×10−4 ) . We did not observe significant associations between DNA methylation at the 490 a-DMR probes with proportion of neutrophils , eosinophils , or monocytes in our data . However , at 19 a-DMRs ( 3 . 9% of a-DMRs ) DNA methylation levels were significantly associated with lymphocyte counts ( Table S3 ) , suggesting that the a-DMR effects at these probes may in part reflect variability in the number of lymphocytes over time . We did not observe significant associations between DNA methylation levels at the four ap-DMRs with any of the blood cell sub-types tested . We conclude that variability in WBC sub-types does not have a major effect on age and age-related DMRs in our study . To explore potential mechanisms underlying a-DMRs and ap-DMRs in our sample , we first considered the hypothesis that DMR effects may mediate genetic-phenotype associations . We focused specifically on the overall set of 493 identified DMRs for age ( 490 a-DMRs ) and age-related phenotypes ( 4 ap-DMRs ) . We observed that 5% of these DMRs also had cis-meQTL effects , which was lower than the genome-wide rate of 6 . 3% of probes on the array with cis-meQTLs . Altogether , the DMRs with cis-meQTLs were located in 26 genes and some of the genes had previously reported genetic associations with longevity ( a-DMR MEFV [22] ) or had been implicated in longevity and ageing ( a-DMRs SMPD3 [23] , GALR1 [24] , [25] , ID4 [26]; see Figure 3A ) . Therefore , genetic and methylation effects may impact age-related phenotypes in a small proportion of genes , either with independent effects or by mediating genetic-phenotype associations through DNA methylation . To explore this hypothesis further on a genome-wide level , we estimated the extent to which cis-meQTLs , genotype-phenotype associations , and ap-DMRs overlapped in our data . We performed genome-wide association scans ( GWAS ) for 12 phenotypes in the set of 172 twins . We assessed the overlap between: ( 1 ) SNPs that were cis-meQTLs and were also phenotype-GWAS-QTLs , ( 2 ) phenotypes with GWAS-QTLs that also had ap-DMRs , and ( 3 ) CpG-sites with meQTLs that were also ap-DMRs . We compared the overlap in the observed data to two genome-wide permutations of the analyses . There were 1 , 537 CpG-sites associated with 22 , 849 cis-meQTLs SNPs in our data . Of the 22 , 849 SNPs , 344 SNPs ( which were originally cis-meQTLs for 111 CpG-sites ) also showed modest suggestive evidence for association in the phenotype-GWAS analyses for each trait ( at P = 0 . 001 ) . Of the 111 CpG sites , 16 CpG-sites ( with 53 SNPs ) also had suggestive evidence for ap-DMR signals ( P = 0 . 01 ) , where the CpG-site was associated with the same phenotype as the GWAS QTL SNPs ( which were also cis-meQTLs for that CpG-site ) . Altogether , we observed 1% ( 16 of 1 , 537 probes ) three-way overlap across the analyses combining the 12 phenotypes , and up to 0 . 2% overlap for individual phenotypes ( for BMD , Cholesterol , DBP , DHEAS , FVC , HDL , and Telomere length; see Table S4 ) . In all cases , a SNP genotype was associated with both CpG-site methylation and phenotype , and the CpG-site methylation was also associated with the phenotype , suggesting that these are likely genotype-phenotype associations that may be mediated through DNA methylation . We estimated the expected overlap of results under the null hypothesis that methylation does not mediate genotype-phenotype associations by permuting the methylation data only , preserving twin structure and patterns of co-methylation , for two genome-wide permutations . We selected the top 1 , 537 CpG-sites that showed most associations with cis-meQTL SNPs in the permutations , and assessed the proportion of CpG-sites that showed suggestive methylation-phenotype associations ( P = 0 . 01 ) and had cis-meQTLs SNPs that showed suggestive genotype-phenotype associations ( P = 0 . 001 ) . In both replicates , we observed minimal overlap of probes across the three sets of the analyses under the null hypothesis ( mean overlap 0 . 36% or 5 . 5 probes of 1 , 537 overlapped under the null ) . Epigenetic variants may also accumulate independent of the genetic sequence , because different lifestyle choices and environments may trigger epigenetic changes . The recently reported association between smoking and methylation levels in F2RL3 is likely to be an example of such effects [6] . Therefore , we next tested for ageing-phenotype associated methylation variants that appeared uncorrelated with genetic variation , by comparing methylation and phenotype differences within monozygotic twin pairs ( MZ-DMRs ) . We limited analyses to 21 MZ twin pairs and 12 phenotypes for which at least 12 of the 21 pairs had phenotype data available for both twins ( Table 1 ) . At a permutation-based FDR of 5% , we observed one MZ-DMR ( cg01136458 , P = 3 . 12×10−7 ) in the promoter of the CUB and Sushi multiple domains 1 gene ( CSMD1 ) that associated with total cholesterol and LDL ( Figure 3B ) . Genetic variants in CSMD1 have previously been associated with several complex traits in multiple studies , but we did not observe an enrichment of ap-DMR or MZ-DMR signals in this gene for the other age-related phenotypes in our data . We pursued replication of the 490 a-DMRs in a sample of 44 younger adult MZ twins ( age range 20–61 , median age 28 ) , who were discordant for psychosis [27] . In the overall set of 44 twins , we replicated 184 a-DMRs ( 38% ) with the same direction of effect at a nominally significant threshold ( P = 0 . 05 ) . In the set of 22 unaffected unrelated individuals alone , 69 a-DMRs ( 14% ) replicated with the same direction of association at nominal significance . Given the relatively modest sample size , we also examined the direction of the association between methylation and age without considering significance thresholds . We observed that 404 a-DMRs ( 82% ) showed consistent effects in the overall set of 44 twins , and 369 a-DMRs ( 77% ) had consistent effects in the set 22 unaffected unrelated individuals alone . The two most significant a-DMRs ( cg22736354 in NHLRC1 and cg05266781 in IRX5 ) showed consistent effects in both discovery and replication samples ( Figure 3C , 3D ) . Both a-DMRs were hyper-methylated with age in the discovery ( cg22736354 methylation∼age beta = 2 . 76×10−3 , se = 3 . 73×10−4; cg05266781 methylation∼age beta = 2 . 00×10−3 , se = 3 . 03×10−4 ) and replication ( cg22736354 methylation∼age beta = 2 . 01×10−3 , se = 3 . 03×10−4; cg05266781 methylation∼age beta = 2 . 00×10−3 , se = 4 . 87×10−4 ) samples . We explored the functional role of a-DMRs by studying their genome localization , by comparing the a-DMR methylation data to gene expression estimates from LCLs , and by searching for gene ontology terms associated with the a-DMR genes . We first characterized the a-DMRs by examining their location with respect to functional genomic annotations and other epigenetic signature marks . We considered functional categories with respect to CpG islands , histone modification marks in LCLs , and DNA binding motifs . For each category we assessed the enrichment or depletion of a-DMR probes relative to all 26 , 690 probes ( Figure 4A ) . We found an enrichment of a-DMRs in CpG islands ( see Figure 4A ) , which is consistent with previous observations for hyper-methylated a-DMRs [14] , [28] . We also observed a depletion of a-DMRs in the presence of histone marks that target active genes in LCLs ( Figure 4A ) . For example , a-DMRs were under-represented in H3K27ac , H3K4me3 , and H3K9ac peaks , which are indicative of enhancers or transcriptional activity , and have been positively correlated with transcription levels . To search for an enrichment of DNA binding motifs in the set of 435 a-DMR genes , we used PSCAN [29] with the JASPAR database [30] . We found a significant enrichment for 28 transcription factor binding sites , many of which could play a role in ageing ( Table S5 ) . The transcription factors associated with enriched DNA binding sites were involved in development ( PLAG1 ) , cellular senescence ( Mycn ) , regulation of cell cycle ( Egr1 , CTCF , E2F1 ) , or had also been associated with ageing ( NF-kappaB ) , age-related processes ( NFKB1 , Klf4 , MIZF , Mafb , ESR1 ) or other established ageing-related genes such as WRN ( SP1 , TFAP2A , Myc , Mycn ) , TERT ( Myc ) , and TORC1 ( HIF1A::ARNT ) . To explore the functional consequences of a-DMRs , we examined gene-expression data at the a-DMR genes , using gene expression estimates obtained in LCLs from the same individuals [21] . We compared whole blood DNA methylation to LCL gene expression in 168 individuals at 348 genes , which had methylation CpG-sites within 2 kb of the transcription start site . We found significant negative correlations between methylation and gene expression in the set of a-DMR genes ( Figure 4B ) , and an overall trend towards low levels of expression at a-DMR genes . One caveat applying to this analysis is that blood methylation corresponds to multiple cell types including lymphocytes . We performed gene ontology term enrichment analyses of biological processes and molecular functions in the set of 435 a-DMR genes [31] . The results indicated strong enrichment for genes involved in the regulation of developmental morphological processes , DNA binding , regulation of cell differentiation , regulation of transcription , and regulation of metabolic and biosynthetic processes ( Table S6 ) .
We identify hundreds of CpG-sites that exhibit age-related directional changes in methylation . The majority of these effects are hyper-methylated with age , a large proportion replicate in an independent sample , and some changes are observed in multiple tissues . These findings indicate that a-DMRs are not likely stochastic events , but instead may associate with biological mechanisms involved in ageing and potential longevity . To address this we compared methylation variants to measures of biological ageing , focusing on markers like telomere length and other age-associated phenotypes that have previously been linked to ageing . However , our phenotype-methylation comparisons identified only a small subset of a-DMRs that also associate with ageing related traits . These findings suggest that although a-DMRs do not appear to be random events , the majority of observed a-DMRs may either be neutral ( or of very small individual effect ) to measures of biological age at later stages in life , or may relate to as yet unknown pathways that correlate with biological ageing . The a-DMRs we detected in blood overlap with previously reported a-DMRs obtained in saliva and brain samples , and previous observations also show that some hyper-methylated a-DMRs occur in both blood and buccal tissues [14] . These results indicate that a proportion of a-DMRs are conserved across tissues in samples of different ages and genders , and raise the question of when such age related methylation changes occur during an individual's lifespan and what their functional role is . Functional annotation of a-DMRs show an enrichment of genes involved in regulation of development , morphology , regulation of transcription , and DNA binding , which has also been previously observed in brain samples [13] . The genes nearest to a-DMRs also showed an enrichment of DNA binding motifs for transcription factors linked to ageing . Functional genomic annotation indicated that a-DMRs tend to associate with epigenetic marks targeting low levels of transcription . Consistent with this , a-DMR genes showed predominantly low levels of expression in LCLs and significant negative correlations between methylation and gene expression . Altogether , we find that a-DMRs are located in regions of the genome that functionally link to development and ageing , and tend to show low gene expression rates in our sample of middle-aged individuals . DNA methylation plays a key role in development and tissue differentiation and therefore , it is plausible that at some a-DMRs differential methylation patterns are established early on in development prior to tissue differentiation and continue to intensify over time . For example , CpG-sites that are methylated during early development may become hyper-methylated over time , either because such sites are more prone to methylation or because cells carrying the methylated variant are more likely to replicate . Our findings indicate that age-related changes in methylation occur throughout life , but the timing of the initial age-related trigger at each CpG-site remains unclear . Our results are consistent with a model where at some CpG-sites the initial change may occur during development and early life , but specifically at an age prior to adulthood . Age DMR studies of younger samples could be useful in establishing the proportion of a-DMRs that are also observed at earlier stages in life . We were able to replicate up to 38% of a-DMRs in a sample of younger adults , but samples from newborns or samples obtained prior to tissue differentiation would help resolve the question of when a-DMRs are established , especially tissue conserved a-DMRs . We tested for methylation associations with age-related phenotypes ( ap-DMRs ) to gain insight into potential mechanisms underlying a-DMRs . We identified four ap-DMRs , of which only one ( cg13870866 in TBX20 ) was also an a-DMR . Two of the ap-DMRs were in genes already implicated in ageing , longevity , or cell senescence , STAT5A [32] and WT1 [33] . Our genotype-methylation-phenotype overlap results suggest that in a small proportion of genes DNA methylation may be a candidate mechanism of mediating genetic association effects on ageing-related phenotypes , however , we cannot exclude the possibility that rare genetic variants in the methylation probe sequence drive some of these associations . We also assayed DNA methylation levels at the four ap-DMR probes in 48 of the individuals in the current study using the new Illumina Infinium HumanMethylation450 BeadChip and obtained significant positive correlations in DNA methylation levels at three ap-DMRs ( cg16463460 , cg09259772 , and cg13870866 ) , indicating evidence for technical validation at these probes . A difficult question in epigenetic studies of phenotypic data is establishing the timing of the epigenetic change relative to trait progression . The age-related phenotype methylation changes identified here may occur prior to the phenotypic change and potentially contribute to phenotype variation , or they may occur as a consequence of ageing processes in the cell . In this cross-sectional study we cannot establish the timing of ap-DMRs with respect to phenotype progression , but can use the findings as potential markers of rate of ageing . Regions that exhibit evidence for DNA methylation heritability , such as the IGF2/H19 region , also exhibit more stable DNA methylation levels over time and tend to occur in functionally important promoter regions [4] . Epigenetic variants in heritable methylation regions are likely to be present at birth , to be more stable over time , and may be involved in regulating the rate of ageing . In our study , 26 a-DMRs also had cis-meQTLs and represent a candidate set of heritable DNA methylation regions that are likely to be more stable and may be involved in longevity . On the other hand , environment-dependent changes in DNA methylation in MZ twins have been reported to occur preferentially in gene-poor regions ( see [34] ) . Here , we identify CSMD1 as the most likely example of an environmentally driven DMR for LDL , but this gene does not fall in a gene-poor locus . The methylation heritability estimates obtained in our data , 0 . 176 and 0 . 188 ( Figure S2 ) , are slightly greater than those previously reported for whole-blood methylation [4] , which may be due to the difference in regions assayed by the two arrays and to the promoter locations of our probes . Correspondingly we identified 1 , 537 CpG-sites with meQTLs . It is possible that a proportion of the meQTLs in our data are due to linkage disequilibrium between the cis-meQTL SNPs and unknown genetic variants in the probe sequence . Obtaining genetic sequences for these individuals will establish the extent to which rare-probe variants exist and affect meQTL findings . However , the overlap across probes with meQTLs across studies and tissues suggests that a significant proportion of the QTLs are conserved across tissues [35] , [36] . These are likely to exhibit stable patterns of methylation across mitosis and meiosis , and may be of functional importance . Many factors will impact the power to detect differential methylation effects related to age and age-related phenotypes . One of these factors relates to the coverage and precision of the methylation assay . In our case , the coverage of methylation sites on the Illumina27k array is relatively sparse and promoter-specific , and therefore limits power to detect age related methylation changes . It is likely that additional age related changes in methylation may be identified using higher resolution methylation assays in larger sample sizes of wider age ranges . In this study , we identified methylation changes associated with chronological age and ageing-related phenotypes and we explored mechanisms underlying ageing-related changes in DNA methylation . Both environmental and genetic factors are thought to contribute to healthy ageing , and epigenetic mechanisms represent a potential pathway of mediating these effects on ageing and age related traits .
All samples and information were collected with written and signed informed consent . The study was approved by the local research ethics committee . Phenotype data were obtained for 172 female twins from the TwinsUK cohort . The TwinsUK cohort ( St Thomas' UK Adult Twin Registry ) comprises unselected volunteers ascertained from the general population [37] . Means and ranges of quantitative phenotypes in Twins UK were similar to age-matched samples from the general population in the UK [38] . The 172 twins in this study included 33 MZ pairs , 43 DZ pairs , and 20 singletons . Phenotypes used in the current study included telomere length , systolic blood pressure ( SBP ) , diastolic blood pressure ( DBP ) , forced expiratory volume in one second ( FEV1 ) and forced expiratory vital capacity ( FVC ) to examine lung function , grip strength , bone mineral density ( BMD ) , serum levels of DHEAS ( DHEAS ) , serum total cholesterol levels , serum high density cholesterol levels ( HDL ) , calculated levels of serum low density cholesterol ( LDL ) , serum albumin levels ( Albumin ) , serum creatinine levels ( Creatinine ) , maternal longevity ( MLONG ) , paternal longevity ( PLONG ) , maternal age at reproduction ( MREPROD ) , and paternal age at reproduction ( PREPROD ) . Phenotype data used in the current study were previously described in the Twins UK sample for the majority of phenotypes , specifically for telomere length [39] , [40] , blood pressure [41] , lung function [42] , grip strength [43] , BMD [43] , [44] , [45] , DHEAS [46] , serum cholesterol [47] , [48] , serum albumin [49] and serum creatinine [50] . Parental longevity data were obtained by questionnaire in 2011 , and included parental age at death and parental age at reproduction for each individual . In cases of missing data , we used co-twin estimates to infer values . In rare cases parental age at death estimates varied across co-twins , and if the estimates were within one year we took the mean , otherwise data were assigned as missing . In 171 of the individuals from our sample we also obtained white blood cell ( WBC ) sub-type counts [51] . WBC counts were derived from fluorescence activated cell sorting of peripheral blood . WBC sub-type specific cell counts were calculated by multiplying the proportion of the WBC count comprised by each cell type by the total WBC cell count ( estimated in thousands of cells per ml ) , for four cell types in our sample: neutrophils , eosinophils , monocytes , and lymphocytes . DNA methylation levels were obtained in 172 middle-aged ( age range 32–80 , median age 57 ) healthy female volunteers who were twins , including monozygotic ( MZ ) twins , dizygotic ( DZ ) twins , and unrelated individuals . DNA methylation patterns were assayed in two batches of 93 [14] and 79 samples . We considered 26 , 690 probes that mapped uniquely to the human genome ( hg18 ) within 2 mismatches ( see [1] ) and discarded probes with missing data , resulting in a final set of 24 , 641 autosomal probes and 959 X-chromosome probes . Methylation values are reported as betas , which represent the ratio of array intensity signal obtained from the methylated beads over the sum of methylated and unmethylated bead signals . We performed principal components analysis ( PCA ) of the methylation values ( normalized to N ( 0 , 1 ) at each probe ) and correlated the first five principal component ( PC ) loadings to covariates ( age , methylation arrays , order of the sample on the methylation array ) to identify potential confounders . We observed that both methylation array and order of the sample on the array were significantly correlated with the first and second PCs and therefore included these two variables as fixed-effect covariates in the linear mixed effects models used in the majority of downstream analyses . Further analyses of DNA methylation patterns within twins were performed using intraclass correlation coefficients ( ICC ) using the R package irr ( v0 . 82 ) . Twin-based DNA methylation heritabilities were estimated as 2 ( ICC_MZ - ICC_DZ ) , and were calculated within each batch of data separately . Direct genotypes were available for 171 samples on a combination of Illumina platforms ( HumanHap300 , HumanHap610Q , 1M-Duo and 1 . 2MDuo 1M custom arrays ) and stringent quality control checks were applied to these genotype data as previously described [21] , [52] . HapMap genotypes were imputed in the set of 171 individuals . Imputation was performed in Impute ( v2 [53] ) using two reference panels , P0 ( HapMap2 , rel 22 , combined CEU , YRI and , ASN panels ) and P1 ( 610K+ , including the combined HumanHap610K and 1M array ) . After imputation , SNPs were filtered at a MAF>5% and an Impute info value of >0 . 8 . Altogether , there were 2 , 054 , 344 directly genotyped and imputed autosomal SNPs used in the QTL analyses . Gene expression estimates and eQTLs from lymphoblastoid cell lines ( LCLs ) in the samples were obtained for 168 individuals in the study [21] . Gene expression levels were measured using the Illumina expression array HumanHT-12 version 3 as previously described [21] . Each sample had three technical replicates and log2 - transformed expression signals were quantile normalized first across the 3 replicates of each individual , followed by quantile normalization across all individuals [21] . We assigned methylation and expression probes to the gene with the nearest transcription start site using Refseq gene annotations . For each gene we obtained the mean methylation ( or gene expression ) estimate , by averaging values over multiple methylation ( or gene expression ) probes if more than one probe was assigned to that gene . There were altogether 435 genes nearest to the 490 age DMRs , of which 348 had transcription start sites within 2 kb of the methylation CpG-sites and for which we also had whole blood methylation data and LCLs gene expression data in 168 individuals . Linear mixed effects models and Spearman rank correlations were used to compare methylation and expression data per gene . We tested for methylation QTLs at 24 , 522 autosomal probes , which had at least one SNP within 50 kb of the probe that passed genotype QC criteria . We fitted a linear mixed-effects model , regressing the methylation levels at each probe on fixed-effect terms including genotype , methylation chip , and sample order on the methylation chip , and random-effect terms denoting family structure and zygosity . Prior to these analyses , the methylation values at each CpG-site were normalized to N ( 0 , 1 ) . Results from meQTL analyses are presented at a false discovery rate ( FDR ) of 5% , estimated by permutation . Here , we permuted the methylation data at the 24 , 522 autosomal probes , performed cis association analyses on the permuted and normalized methylation data , and repeated this procedure for 10 replicates selecting the most associated SNP per probe per replicate . FDR was calculated as the fraction of significant hits in the permuted data compared to the observed data at each p-value threshold . Linear mixed effects models were used to assess evidence for DMRs . In the a-DMR analyses we regressed the raw methylation levels at each probe on fixed-effect terms including age , methylation chip , and sample order on the methylation chip , and random-effect terms denoting family structure and zygosity . To assess the significance of the a-DMRs we compared this model to a null model , which excluded age from the fixed-effects terms . In the ap-DMR analyses we regressed the raw methylation levels at each probe on fixed-effect terms including phenotype , methylation chip , and sample order on chip , and random-effect for family and zygosity , and compared the fit of this model to a null model which excluded the phenotype . We also performed the ap-DMR analyses by including age as a fixed effect covariate in both the null and alternative models . We also repeated both the a-DMR and ap-DMR analyses using normalized methylation levels ( to N ( 0 , 1 ) ) and observed that the reported DMRs were top-ranked in the normalized analyses . To assess genome-wide significance we performed 100 permutations and estimated FDR by calculating the fraction of significant hits in the permuted data compared to the observed data at a specific P-value threshold . Monozygotic twin DMR effects were calculated in the set of 21 MZ twin pairs where both twins were assayed within the same batch of methylation arrays . We estimated MZ-DMRs for 12 phenotypes where data were available in at least 12 MZ pairs . For each phenotype of interest we fitted a linear model comparing phenotype within-pair differences to methylation within-pair differences and reported the P-values obtained from the F-statistics from the overall regression . For the age-corrected analyses we fitted the regression including age as a covariate and compared the results to a null model , which included phenotype differences and age alone . We performed 100 replicates to estimate FDR 5% significant results as described above . At the FDR 5% significance threshold ( nominal P = 2 . 03×10−6 ) , we estimated 35% power to detect the observed correlation ( Pearson correlation = 0 . 83 ) between methylation MZ-differences at cg01136458 in CSMD1 ( mean MZ-beta-difference = 5% ) and LDL MZ-differences ( mean MZ-LDL-difference = 0 . 73 SD ) in 20 MZ pairs . The replication sample comprised 44 MZ twins discordant for psychosis , that were profiled on the Illumina 27K array as previously described [27] . The sample consisted of younger adults ( age range 20–61 , median age 28 ) , including both female and male twin pairs . We compared methylation against age at the 490 a-DMRs both in the entire set of 44 twins and in the set of 22 unaffected unrelated individuals . In the set of 44 twins we fitted linear mixed effect models , regressing the normalized beta values per probe ( normalized to N ( 0 , 1 ) ) against methylation chip , sample order on the chip , sex , and age as fixed effects , and family as random effect . In the set of 22 unaffected unrelated individuals comprising the control twin from each pair we calculated Spearman rank correlation coefficients on the untransformed methylation beta values against age . Genome-wide association scans were performed using linear mixed effects models for 12 phenotypes including telomere length , systolic blood pressure ( SBP ) , diastolic blood pressure ( DBP ) , FEV1 and FVC to examine lung function , grip strength , bone mineral density ( BMD ) , serum levels of DHEAS , serum total cholesterol levels , serum high density cholesterol levels ( HDL ) , calculated levels of serum low density cholesterol ( LDL ) , serum albumin levels , and serum creatinine levels . Linear models were fit as described in the meQTL analyses section substituting phenotype for methylation , using an additive model . SNPs with evidence for association that surpassed P = 0 . 001 , were considered in the overlap across cis-meQTL , genotype-phenotype , and DMR findings . The 26 , 690 methylation probes were assigned to CpG islands according to previous definitions [54] , resulting in 11 , 299 CpG sites that were in CpG islands and 15 , 391 that were outside of CpG islands . Histone modification ChIP-seq data were obtained from the Encode project from one CEPH HapMap LCL ( GM12878 ) in the UCSC genome browser . Peaks in the genome-wide read-depth distribution from ChIP-seq histone modifications H3K9ac , H3K27ac , H3K27me3 , H3K4me1 , H3K4me2 , and H3K4me3 were obtained as previously described ( see [1] ) . Enrichment a-DMR estimates were calculated as the proportion of a-DMRs in each functional category ( CpG islands or histone peaks ) over the proportion of 26 , 690 probe in that functional category . Enrichment 95% confidence intervals were estimated using bootstrap percentile intervals of 1 , 000 re-samplings of the a-DMR data per annotation category . Gene ontology term enrichment analysis was performed using the GOrilla tool for identifying enriched GO terms in the ranked list of a-DMR genes [31] , using Refseq gene annotations in the entire set of 26 , 690 probes as background .
|
Epigenetic patterns vary during healthy ageing and development . Age-related DNA methylation changes have been implicated in cellular senescence and longevity , yet the causes and functional consequences of these variants remain unclear . To understand the biological mechanisms involved in potential longevity and rate of healthy ageing , we performed genome-wide association of epigenetic and genetic variation with both chronological age and age-related phenotypes . We identified hundreds of DNA methylation variants significantly associated with age and replicated these in an independent sample of young adult twins . Only a small proportion of these variants were also associated with age-related phenotypes . Therefore , the majority of age-related epigenetic changes do not contribute to rate of healthy ageing at later stages in life . Our results suggest that age-related changes in methylation occur throughout an individual's lifespan and that a proportion of these may be initiated from an early age . Intriguingly , a fraction of the age differentially methylated regions also associated with genetic variants in our sample , suggesting that DNA methylation may be a candidate mechanism of mediating not only environmental but also genetic effects on age-related phenotypes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome-wide",
"association",
"studies",
"genetics",
"epigenetics",
"biology",
"human",
"genetics",
"genetics",
"and",
"genomics"
] |
2012
|
Epigenome-Wide Scans Identify Differentially Methylated Regions for Age and Age-Related Phenotypes in a Healthy Ageing Population
|
Leptospira ( L . ) interrogans are bacteria responsible for a worldwide reemerging zoonosis . Rodents carry L . interrogans asymptomatically in their kidneys and excrete bacteria in the urine , contaminating the environment . Humans get infected through skin contact and develop a mild or severe leptospirosis that may lead to renal failure and fibrosis . L . interrogans provoke an interstitial nephritis , but the induction of fibrosis caused by L . interrogans has not been studied in murine models . Innate immune receptors from the TLR and NLR families have recently been shown to play a role in the development and progression of tissue fibrosis in the lung , liver and kidneys under different pathophysiological situations . We recently showed that TLR2 , TLR4 , and NLRP3 receptors were crucial in the defense against leptospirosis . Moreover , infection of a human cell line with L . interrogans was shown to induce TLR2-dependent production of fibronectin , a component of the extracellular matrix . Therefore , we thought to assess the presence of renal fibrosis in L . interrogans infected mice and to analyze the contribution of some innate immune pathways in this process . Here , we characterized by immunohistochemical studies and quantitative real-time PCR , a model of Leptospira-infected C57BL/6J mice , with chronic carriage of L . interrogans inducing mild renal fibrosis . Using various strains of transgenic mice , we determined that the renal infiltrates of T cells and , unexpectedly , TLR and NLR receptors , are not required to generate Leptospira-induced renal fibrosis . We also show that the iNOS enzyme , known to play a role in Leptospira-induced interstitial nephritis , also plays a role in the induction of renal fibrosis . To our knowledge , this work provides the first experimental murine model of sustained renal fibrosis induced by a chronic bacterial infection that may be peculiar , since it does not rely on TLR or NLR receptors . This model may prove useful to test future therapeutic strategies to combat Leptospira-induced renal lesions .
Leptospira interrogans ( L . interrogans ) are spirochetal bacteria responsible for a worldwide reemerging zoonosis [1] . Rodents asymptomatically carry the bacteria in their kidneys and excrete them in the urine , contaminating the environment . Humans become infected from contaminated ponds , through direct contact with the bacteria via broken skin or mucosa . Leptospirosis can be mild or severe with respiratory , liver and kidney failure , and constitutes a health problem is East Asia , especially among paddy workers . Chronic kidney disease ( CKD ) is a common feature of numerous renal diseases . A key component of CKD is renal fibrosis , a complex process involving different resident and infiltrated cell types and signaling pathways . Fibrosis results in structural and functional renal alterations characterized by an excessive accumulation of extracellular matrix proteins in scarring tissues , which may lead to organ dysfunction [1] . From human and canine biopsy studies , leptospirosis has been shown to be associated with chronic interstitial nephritis and fibrosis [2] . Recently , it was reported that leptospirosis led to irreversible tubule-interstitial fibrosis in a young male , requiring continuous hemodialysis [3] . In Taiwan , 10% of patients with CKD were seropositive for L . interrogans , although most of them did not have any history record of leptospirosis [4] . Therefore , underestimated leptospirosis could be one of the reasons for the high prevalence of kidney disease in Taiwan [4] and more generally in East Asia . The physiopathology of leptospirosis has been studied in several animal models including rats , gerbils , guinea pigs , and hamsters , and revealed several decades ago that kidney tubulo-interstitial lesions were a hallmark of infection with L . interrogans . A recent study highlighted the importance of iNOS in tubulo-interstitial lesions in mice [5] . However , to our knowledge , the long-term pathophysiological consequences of L . interrogans infection in mice , and in vivo studies about Leptospira-induced fibrosis , have not yet been investigated . Nonetheless , in vitro studies showed that outer membrane components of L . interrogans , among them LipL32 , the major lipoprotein of L . interrogans and TLR2 agonist [6] , activate human cells [7] to produce extracellular matrix components [8] , [9] . We recently developed a mouse model of acute leptospirosis . We showed that , in contrast to C57BL/6J mice that are asymptomatic , mice deficient for both Toll-like Receptor ( TLR ) -2 ( TLR2 ) and -4 ( TLR4 ) ( TLR2/4dko ) are susceptible , and can die from L . interrogans infection with all the features of the severe , acute human disease . We demonstrated that B cells , through both TLR2- and TLR4-mediated signaling , play a crucial role in clearance of the bacteria . Moreover , infected TLR2/4dko mice developed a deleterious inflammation within a few days , associated with renal tubulo-interstitial infiltrates of T cells [10] . We also recently showed that L . interrogans infection triggers a NLRP3-dependent IL1ß secretion in the mouse kidney , as a result of a synergistic effect of two cell wall components , leptospiral LPS and glycolipoprotein , through its downregulation of the Na/K ATPase pump [11] . Preliminary data obtained in surviving mice , several weeks after L . interrogans infection , suggested the presence of fibrotic lesions in mouse kidneys . Innate immune receptors , TLRs and Nod-like receptors ( NLRs ) such as the inflammasome receptor NLRP3 , have recently been shown to play a crucial role in the development and progression of tissue fibrosis of the lung [12] , liver [13] and in a mouse model of kidney fibrosis induced by unilateral ureteral obstruction [14] . Whether innate receptors also play a role in murine L . interrogans-induced renal fibrosis and whether infiltrated inflammatory cells such as T cells or macrophages , already known to promote the renal fibrosis [15] , [16] , are important players in the Leptospira induced fibrosis is currently unknown . Here , we characterized a novel murine model of renal fibrosis induced by bacterial infection , and showed that Leptospira infection of C57BL/6J mice led to a sustained fibrosis , associated with chronic carriage of Leptospira . Using several strains of transgenic mice , we determined that T cells and , unexpectedly , TLR and NLR receptors , were not required to generate Leptospira-induced fibrosis . However , we show that the iNOS enzyme , known to play a role in the interstitial nephritis due to Leptospira , also plays a role in the Leptospira-induced renal fibrosis .
Female C57BL/6J mice ( 8- to 10-wk old ) were purchased from Janvier ( Le Genest , France ) and used as control mice . Mice deficient for TLR2 ( TLR2ko ) , TLR3 ( TLR3ko ) , TLR4 ( TLR4ko ) , TLR5 ( TLR5ko ) , TLR9 ( TLR9ko ) and MyD88 ( MyD88ko ) , originally given by Shizuo Akira ( Osaka University , Osaka , Japan ) , have been further backcrossed eight times into C57BL/6J mice and kindly provided by Michel Chignard ( Institut Pasteur , Paris ) . Double TLR2/TLR4 deficient mice ( TLR2/4ko ) have been previously described [10] . Mice deficient for Nod1 ( Nod1ko ) or Nod2 ( Nod2ko ) , respectively given by John Bertin ( Millenium , Cambridge , MA ) and Jean-Pierre Hugot ( Hôpital Robert Debré , Paris , France ) to Dana Philpott ( University of Toronto , Toronto ) , have been further backcrossed eight times into C57BL/6J mice , before being crossed and genotyped to get double Nod1/Nod2 deficient mice ( Nod1/2dko ) . CD3 deficient mice ( CD3ko ) , B cell deficient mice ( μMT ) , and caspase-1 deficient mice ( Casp1ko ) were kindly provided by Armelle Phalipon ( Institut Pasteur , Paris ) , Claude Leclerc ( Institut Pasteur , Paris ) , and Mathew Alberts ( Institut Pasteur , Paris ) , respectively . iNOS deficient mice ( iNOSko ) in the C57BL/6J background were obtained from the Jackson Laboratory . All protocols were reviewed by the Institut Pasteur , the competent authority , for compliance with the French and European regulations on Animal Welfare and with Public Health Service recommendations . This project has been reviewed and approved ( # 2013-0034 ) by the Institut Pasteur ethic committee ( CETEA #89 ) . L . interrogans serovar Copenhageni strain Fiocruz L1–130 and L . interrogans serovar Manilae strain L495 were used in this study as described earlier [11] . Just before infection , bacteria in early stationary phase ( around 5×108 Leptospira per ml ) , grown in liquid EMJH medium at 28°C , were centrifuged , resuspended in endotoxin-free PBS , and counted using a Petroff-Hauser chamber . The LD50 of the Fiocruz strain in C57BL6/J WT mice is above 109 bacteria/mouse , and WT mice are considered resistant to L . interrogans infection , as are CD3ko , iNOSko , TLR3ko , Casp1ko and Nod1/2ko mice . However , the LD50 of the Fiocruz strain in sensitive MyD88ko , TLR4ko and TLR2/4ko mice is around 107 bacteria/mouse . Therefore , to ensure survival of all mice , Leptospira-resistant mice were infected with 2×108 Fiocruz strain in 200 µl of PBS by the intraperitoneal ( IP ) route , whereas Leptospira-sensitive mice were infected with a lower dose of 2×106 Fiocruz/mouse . Since the LD50 of the Manilae strain in WT mice is around 108 bacteria/mouse , WT mice were infected with 107 Manilae strain/mouse . Mice were sacrificed at different days post-infection ( p . i . ) . Liver , kidneys , and lungs of infected and naive mice were removed . Organs were either rapidly frozen in liquid nitrogen , then stored at −80°C for nucleic acid preparations , or fixed for histology and immunohistochemical studies . Within the first days after experimental IP infection , Leptospira disseminate through blood circulation and reach all the organs , including the kidneys . Then , within one week post-infection , the bacteria disappear from the circulation and settle in and colonize their renal niche , then begin to be shed in the urine . Therefore , penicillin G ( Sigma ) , at the equivalent human dose of 9 million units/60 kg , was administered in 100 µl of endotoxin free PBS ( Biowhittaker ) to 20 g C57BL/6J mice via IP route once a day for 5 consecutive days , either beginning one day p . i . to clear disseminating bacteria , or 3 days p . i . to stop the infection after the renal colonization has started . Bone marrow derived macrophages ( BMDM ) were isolated as described previously and cultured for 7 days in 10% L929-conditioned medium [11] . Mouse BMDM ( 2×105 cells in 200 µl ) were seeded in 96-well plate and stimulated 3 h later with live or killed ( 56°C 30 min ) L . interrogans , at different multiplicities of infection ( MOI ) . Stimulations were stopped 24 h later , and nitric oxide ( NO ) formation was evaluated in supernatants by the measure of nitrites ( NO2− ) via the Griess reaction . Before sacrifice , blood samples ( 200 µl ) were collected by retromandibular puncture into tubes containing 20 µl of heparin ( Choay ) . Samples were centrifuged ( 1500 g , 5 min ) , and the plasma was stored at −80°C . Total serum creatinine was measured in plasma samples using an Olympus AU400 autoanalyzer . The leptospiral burden in urine was determined by quantitative real-time DNA PCR ( qPCR ) . Total DNA was extracted from a drop of urine ( 5 to 100 µl ) using the Maxwell 16 instrument and Cell LEV DNA purification kit ( Promega ) . The qPCR reaction was calibrated using a known number of heat-killed L . interrogans . The DNA concentration was adjusted to around 100 ng in the qPCR reaction . Primers were designed in the peculiar lpxA gene of L . interrogans Fiocruz strain [17] to specifically detect pathogenic Leptospira spp but not other spirochetes or bacteria , using Primer Express 3 software ( Forward ( Fw ) : 5′-TTTTGCGTTTATTTCGGGACTT-3′; Reverse primer ( Rv ) : 5′-CAACCATTGAGTAATCTCCGACAA-3′; Probe: 5′-TGCTGTACATCAGTTTTG -3′ ) . qPCR reactions were run on a Step one Plus real-time PCR apparatus using the absolute quantification program ( Applied Biosystems ) , with the following conditions: 50°C for 2 min , 95°C for 10 min , followed by 40 cycles with denaturation at 95°C for 15 s and annealing temperature 60°C for 1 min , according to the manufacturer's instructions . Results were expressed as the number of Leptospira in 100 µl of urine . Observation and subcultures of Leptospira from fresh urines showed that shed bacteria were mobile and alive ( data not shown ) . Thin transversal sections of kidneys were collected and fixed in Dubosq-Brazil for 2 h then post-fixed in 10% formalin in PBS and embedded in paraffin . Tissue sections ( 5 µm thickness ) were stained with Hematoxylin-Eosin , to evaluate inflammatory changes by light microscopy , or labeled for 30 min with a solution of 0 . 1% ( W/V ) Red Sirius in saturated picric acid , for evaluation of the fibrosis . Picro-Sirius is currently used to stain collagens I and III deposited within the interstitial areas , and is recommended for the diagnosis of chronic renal injury . All sections were examined by two pathologists blinded to the experimental conditions . The degree of interstitial inflammation was graded on a 6-point scale as follows: 0- no inflammation , 1- scattered interstitial mononuclear inflammatory cells , 2- mild diffuse mononuclear cell infiltration , 3- focal nodular mononuclear cell infiltration , 4- diffuse and nodular mononuclear cell infiltration without tubulitis 5- diffuse and nodular mononuclear cell infiltration with significant tubulitis . The degree of interstitial fibrosis was determined using a semi-quantitative scale as previously established [18] as follows: 0- no abnormality , 1- slight increase of interstitial fibrosis affecting less than 25% of kidney samples , with almost normal tubule , 2- moderate interstitial fibrosis affecting less than 25–50% of kidney samples with focal tubular atrophy , 3- severe interstitial fibrosis affecting more than 50% of kidney samples with diffuse extensive tubular atrophy . Morphometry was performed using computerized automatic scan ( Visilog , VFG , Paris ) and expressed as the mean of Red Sirius positive labeling per surface area ( 104 µm2 ) , counted on five different kidney tissue sections for each condition tested . Immunohistochemical studies were performed using avidin-biotin coupled to peroxidase substrate kits ( Vector Laboratories ) according to the manufacturer's instructions . Peroxidase activity was revealed with diaminobenzidine ( brown staining ) ( Dako REAL detection System ) . Antibodies used in this study were: a polyclonal antibody against LipL32 ( a kind gift from David Haake , 1/2000 ) , a monoclonal antibody against CD3 ( Santa Cruz Sc-20047 , 1/100 ) , a rat monoclonal anti-mouse-Gr1 ( Ly-6G/C ) antibody ( CliniSciences 1/100 ) and a monoclonal antibody , anti CD11b ( Clinisciences , 1/100 ) . The number of labeled cells ( T cells with anti CD3 antibody , neutrophils with anti Gr1 antibody , and macrophages/monocytes with anti CD11b ) per surface area ( 104 µm2 ) was counted on five different kidney tissue sections for each of the experimental condition tested . Kidney samples were fixed for 30 min in 2 . 5% glutaraldehyde in 0 . 1 M cacodylate buffer , embedded in Epon , and processed for transmission electronmicroscopy by standard procedures . Total RNA was extracted from kidneys using the RNeasy mini kit ( Qiagen ) . RNA concentration was determined by measuring optical density at 260 nm . 2 µg total RNA was incubated in a final volume of 20 µl containing 1 µl oligo dT ( 100 µM ) ( Fermentas ) , 1 µl dNTP ( 10 mM each ) , 2 µl DTT ( 0 . 1 M ) , 0 . 5 µl Superscript II reverse transcriptase ( 200 U/ µl ) and 4 µl 5×first strand buffer ( Invitrogen ) . RNA with oligo dT was first denatured at 65°C for 5 min , then the enzyme and other reagents were added and maintained at 42°C for 1 h , followed by heat-inactivation at 70°C for 15 min . The generated cDNA was stored at −20°C . After RT , qPCR was performed using cDNA combined with primers , probes and mixed according to the manufacturer's recommendations ( Applied Biosystems ) . qPCR reactions were run on a Step one Plus real-time PCR apparatus ( Applied Biosystems ) , with the following conditions : 50°C for 2 min , 95°C for 10 min , followed by 40 cycles with denaturation at 95°C for 15 s , and annealing temperature 60°C for 1 min . Data were analyzed according to the method of relative gene expression using the comparative CT method also referred to as the 2−ΔΔCT method . PCR data were reported as the relative increase in mRNA transcripts versus that found in kidneys from naive mice , corrected by the respective levels of hypoxanthine-guanine phosphoribosyltransferase ( HPRT ) mRNA used as an internal standard . The sequences of primers and probes for iNOS , IL6 , TNF , and RANTES have already been described [10] . Primers for TGFß ( NM_011577 ) were Fw:5′-TGACGTCACTGGAGTTGTACGG-3′ ( nt1461–1482 ) , Rv: 5′-GGTTCATGTCATGGATGGTGC-3′ ( nt 1610–1630 ) , probe 5′-TTCAGCGCTCACTGCTCTTGTGACAG-3′ ( nt 1522–1547 ) . Validated primers and probes for Mmp2 , ACTA-2 and fibronectin ( Mn_00439498 , Mn_01546133 , Mn_01256744 , respectively ) were from Applied Biosystems . μMT mice lacking B cells were infected IP with 2×107 L . interrogans Fiocruz strain and rescued from death by passive transfer of protective serum , as described [10] . To obtain the protective serum , ten C57BL/6J mice were infected with 5×107 L . interrogans serovar Fiocruz and bled 20 days p . i . After overnight coagulation at 4°C , the sera were collected and heat-inactivated at 56°C for 30 min and kept frozen at −80°C . To be sure the Leptospira will reach the kidney , we let the infection develop for two days before injecting IP 200 µl of protective pooled sera to both infected and naive μMT mice . Survival was monitored and all surviving mice were sacrificed at 15-days p . i . Statistical analysis was performed using GraphPad Prism software . The unpaired t test , ( two-tailed P values ) was used to compare two groups . The distribution of three or more groups was analyzed by One-Way ANOVA . Values are expressed as means , or means + standard deviation ( SD ) . A p value<0 . 05 was considered significant .
C57BL/6J mice infected with 2×108 L . interrogans serovar Copenhageni strain Fiocruz are resistant , and survive infection [10] . One month p . i . , WT mice were sacrificed and their liver , lungs , and kidneys studied by immunohistochemistry . Neither inflammation nor fibrosis could be observed in the livers or lungs of infected mice ( data not shown ) , although infected mice displayed kidney inflammation and fibrosis ( Fig . 1A ) . Indeed , hematoxylin-eosin staining revealed from 1 to 4 inflammatory foci , composed of around one hundred infiltrating cells per kidney section in infected mice , compared to naive mice injected with PBS , that showed no inflammation ( Fig . 1A and Fig . 1B ) . The cellular composition of the infiltrates in naïve and infected mice was investigated by immunohistochemistry . One month p . i . , the renal infiltrates were mostly composed of CD3 positive ( + ) T cells and CD11b+ macrophages/monocytes . In contrast , the few Gr1+ cells ( mostly neutrophils ) detected at day-3 p . i . were no longer detected in kidneys one month p . i . ( Fig . 1C ) . Red Sirius labels collagens I and III , known to accumulate upon fibrosis . The observation and morphometry of Red Sirius staining one month p . i . , revealed mild focal fibrosis in both the cortex and medulla regions of the kidney in Leptospira-infected mice ( Fig . 1A and Fig . 1D ) . To ensure that fibrosis was not peculiar to the Fiocruz strain , C57BL/6J mice were infected with another pathogenic L . interrogans , serovar Manilae strain L495 . One month p . i . , similar mild fibrosis was observed in kidneys from mice infected with the serovar Manilae strain L495 ( data not shown ) . We next thought to establish the kinetics of fibrosis appearance and potential healing in mice . C57BL/6J mice were infected with L . interrogans copenhageni strain Fiocruz and sacrificed at 15 , 30 , 60 , 90 , and 180 days p . i , and their kidneys analyzed by Red Sirius morphometry and qRT-PCR to detect fibrosis and inflammation , respectively . The fibrosis was already present at day-15 p . i . , and persisted at the same mild level for the next 3 months ( Fig . 1D ) . Fibrosis was still present after 6 months p . i . , although a tendency to decrease was observed . Because fibrosis usually occurs upon inflammation , pro-inflammatory chemokines ( RANTES ) and cytokines ( IL6 , TNF ) were monitored by qRT-PCR . The leptospiral infection triggered a marked inflammatory response in day-15 p . i kidneys that decreased over time , but unexpectedly reappeared at day-180 p . i . ( Fig . 1E ) . We also measured the expression of TGF-ß that is usually upregulated upon fibrosis , but we did not find any significant upregulation in the kidneys of infected mice ( data not shown ) . These findings suggest that Leptospira infection triggers an early inflammation , associated with a sustained renal fibrosis for at least 3 months p . i . Since fibrosis is a complex mechanism observed in many inflammatory conditions , most of them unrelated to infectious processes , we wondered whether the presence of Leptospira or their antigens in the kidney were required for inducing fibrosis . Therefore , C57BL/6J mice were infected with L . interrogans serovar Manilae and then treated daily for five consecutive days with penicillin G , the antibiotic used to treat most patients with leptospirosis . Infected mice were either treated from day-1 to day-5 p . i . ( D1–D5 ) , allowing sufficient time for the bacteria to disseminate and reach the kidneys , but be cleared before colonization , or treated from day-3 until day-7 ( D3–D7 ) to allow some Leptospira to colonize the kidneys before the antibiotic treatment . Thereafter mice were sacrificed at 24-day p . i . , and renal fibrosis and inflammation quantified by Red Sirius morphometry and scoring ( data not shown ) . In parallel , the carriage of Leptospira was detected by qPCR of leptospiral DNA in the urine . High amounts of Leptospira were detected in the urine of non penicillin-treated infected mice , and to a much lesser extent in the urine of D3–D7 antibiotic-treated mice , whereas no Leptospira were detected in the urine of D1–D5 antibiotic-treated mice ( Fig . 2A , left panel ) . In parallel , the presence in the kidneys of leptospiral antigens was monitored by immunohistochemistry using an antibody directed against LipL32 , the major lipoprotein of Leptospira ( Fig . 2A , right panel ) . Interestingly , LipL32 was detected in all infected mice , with a more pronounced labeling in the infected , non-antibiotic treated mice , confirming that the timing of penicillin treatment was adequate to allow Leptospira to reach the kidneys . Histological scoring also revealed that the inflammation was only present in infected , non-treated mice and was not observed in D1–D5 or D3–D7 antibiotic-treated mice ( Fig . 2B , left panel ) . Also , qRT-PCR analysis revealed less up-regulation of the inflammatory RANTES mRNA in antibiotic-treated mice compared to the infected mice , not treated with antibiotics . D3–D7 penicillin-treated mouse kidneys exhibited significantly greater levels of RANTES mRNA expression than D1–D5 penicillin-treated mouse kidneys ( Fig . 2B , right panel ) . This suggests that the inflammation observed in the kidney is related to the Leptospira load in the urine , reflecting the renal burden ( data not shown ) . Red Sirius staining ( Fig . 2C , right panel ) and quantification ( Fig . 2C , left panel ) revealed a slight renal fibrosis in non-treated mice compared to naïve mice , although no fibrosis was observed in D1–D5 antibiotic-treated mice , and 2 out of 3 mice exhibited fibrosis in the group of D3–D7 penicillin-treated mice . These results suggest that renal fibrosis occurs in mice colonized by Leptospira in their kidneys and excreting live Leptospira . However , no correlation between the number of Leptospira in the urine and the extent of fibrosis could be observed . Of note , no inflammation or renal fibrosis were observed in the kidneys of mice that cleared the Leptospira infection , but still harbored leptospiral antigens . We next thought to assess the role of the inflammation in Leptospira-induced renal fibrosis . Because inflammatory infiltrates observed one month p . i . with L . interrogans in kidneys were mostly composed of T cells ( see Fig . 1C ) , we questioned whether T cells could be involved in the Leptospira-induced fibrosis . Indeed , T cells have been recently shown to promote renal fibrosis induced by unilateral ureteral obstruction in mice [15] . First , semi-quantitative evaluation by immunohistochemistry of the number of T cells in kidneys of infected C57BL/6J mice revealed that the number of T cell infiltrates decreased over time ( Fig . 3A ) . This suggests that T cells are not directly associated with the observed sustained renal fibrosis . However , to be sure that T cells infiltrates were not the initial trigger of fibrosis , mice deficient for T cells ( CD3ko mice ) and their C57BL/6J counterparts were infected with L . interrogans strain Fiocruz . Mice were sacrificed at day-15 p . i . and their kidneys prepared for immunohistochemistry . Red Sirius morphometric quantification was equivalent in infected WT and CD3ko mouse kidneys , which were both significantly greater compared to the respective naive kidneys ( Fig . 3B ) . Electron microscopy analysis of the kidney of a 4 month post-infected CD3ko mouse revealed a marked interstitial fibrosis , in contrast to the morphological aspect of the kidney of a naive mouse ( Fig . 3C ) . Altogether , these results strongly suggest that T cells do not participate in the induction of renal fibrosis caused by Leptospira . B cells from the adaptive immune response have also been reported to be involved in renal fibrosis through IgG deposition [19] . We already showed that B cells were crucial for clearance of Leptospira , and that transgenic mice deficient for B cells ( μMT mice ) were lethally susceptible to experimental leptospirosis [10] . To test the role of B cells in Leptospira-induced renal fibrosis , μMT mice were infected with Leptospira and rescued with passive transfer of protective serum obtained from Leptospira-infected C57BL/6J mice 20 days p . i . ( Fig . 3D ) , as previously described [10] . Serum-treated naïve and infected μMT mice were sacrificed at day-15 p . i . Kidneys of rescued mice , stained with Red Sirius , showed some mild fibrosis compared to kidneys from the non-infected mice treated with protective serum ( Fig . 3E ) , suggesting that B cells are not involved in Leptospira-induced fibrosis . Collectively , these results suggest that the adaptive immune response to Leptospira from both T and B cells is not directly involved in the induction of renal fibrosis . Innate immunity receptors have recently been linked to fibrosis . Since TLR2 and TLR4 are crucial in mouse defense against Leptospira [10] , and since in vitro experiments suggest that TLR2 stimulation by outer membrane components of Leptospira is important for expression of fibronectin and extra cellular matrix components [9] , we aimed at testing whether TLR2 and/or TLR4 could be involved in Leptospira-induced renal fibrosis in mice . We previously showed that TLR2/4dko mice are sensitive to pathogenic Leptospira and die from the infection . Therefore , groups of WT , TLR2ko , and TLR2/4dko mice were infected with a sub-lethal dose ( 2×106 ) of L . interrogans strain Fiocruz . Mice were then sacrificed at day-90 p . i . , and their kidneys analyzed by Red Sirius morphometry . Unexpectedly , Leptospira-infected kidneys from all genotypes , including TLR2/4dko mice , presented fibrosis when compared to their naïve counterparts ( Fig . 4A ) . Consistent with the marked Red Sirius staining in kidneys from all tested mice , mRNA expression of typical markers classically up-regulated in fibrotic conditions , such as metalloprotease 2 ( Mmp2 ) [20] which is important for the degradation of extracellular matrix components , smooth muscle actin ( ACTA-2 ) over-expressed by activated myofibroblasts [16] and fibronectin , an extracellular matrix glycolipoprotein that binds other fibrillar components such as collagens , were all up-regulated in the kidneys from mice at day-90 p . i . , with no significant statistical differences between the four 4 different mouse genotypes ( Fig . 4B ) . Up-regulation of mRNA expression of the inflammatory RANTES chemokine was also detected in kidneys from all 4 mouse groups , with no statistical differences between groups ( Fig . 4B ) . These rather unexpected findings , showing no differences between Leptospira-induced fibrosis and inflammation in WT versus TLR deficient mice , led us to measure the leptospiral loads in the urine of infected mice . Results from q-PCR of the leptospiral DNA showed that 90 days p . i . WT and TLR2ko mice excreted around 105 and 107 Leptospira per 100 µl of urine respectively , whereas susceptible TLR4ko and TLR2/4dko mice were more heavily infected , with around 108 and 109 Leptospira per 100 µl of urine respectively ( Fig . 4C ) . Of note , quantification of renal burden in the corresponding kidneys by q-PCR of the leptospiral DNA gave the same trend ( data not shown ) . These data are consistent with our previous results obtained at day-3 p . i . , showing an important role of TLR2 and TLR4 in the mouse defense and clearance of Leptospira [10] , and further suggest that the extent of fibrosis is not directly proportional to bacterial excretion , reflecting the renal bacterial loads . To evaluate whether Leptospira infection , and subsequent renal colonization and fibrosis could be deleterious to renal function , the level of serum creatinine , used as a marker of renal function , was measured in the serum of mice at day-90 p . i . Interestingly , the infected WT mice did not show any statistically significant elevation of the serum creatinine when compared to naïve WT mice ( Fig . 4D ) . In contrast , all TLRko mice presented a slight but significant elevation of the serum creatinine levels compared to those of WT mice and corresponding naïve TLRko mice ( Fig . 4D ) . These results indicate that neither TLR2 nor TLR4 were required for Leptospira-induced renal fibrosis , and suggest that the chronic carriage of Leptospira can be associated with a slight alteration of the kidney function , if the Leptospira load is not restricted by the presence of TLR2 and/or TLR4 . Aside from TLR2 and TLR4 , other TLRs that have not yet been studied in the context of leptospirosis , such as TLR5 that senses flagellin , and TLR9 , the receptor of bacterial DNA , could in theory be involved in the murine defense against L . interrogans . MyD88 is the adaptor of most TLRs , except TLR3 . To get insight in the putative role of TLRs other than TLR2 and TLR4 in the Leptospira-induced fibrosis , MyD88ko mice were infected with a sub-lethal dose ( 2×106 bacteria ) of L . interrogans strain Fiocruz . Fifteen days p . i . , a greater burden of Leptospira was detected in the urine ( Fig . 5A ) , and an increased renal inflammatory response measured in kidneys from MyD88ko mice compared to WT mice ( Fig . 5B ) . Red Sirius staining also revealed a fibrosis in kidneys from both infected WT and MyD88ko mice ( Fig . 5C , left panel ) , which was confirmed by qRT-PCR of different markers of fibrosis , whose up-regulation was not statistically different between WT and MyD88ko kidneys from infected mice ( Fig . 5C , right panels ) . Infection of both TLR5ko and TLR9ko mice also confirmed that the Leptospira-induced fibrosis was independent of these TLRs ( data not shown ) . TLR3 is the only TLR using TRIF , but not MyD88 , as an adaptor . TLR3 is known as a viral RNA sensor and is not expected to be involved in Leptospira defense . However , to be sure that none of the TLRs were involved in Leptospira-induced fibrosis , TLR3ko mice were infected with 2×108 L . interrogans Fiocruz and sacrificed at day-15 p . i . Compared to naïve mice , Leptospira infection induced mild renal fibrosis in most of the infected TLR3ko ( Fig . 5D ) . Collectively , these data indicate that none of the TLRs are critically involved in renal fibrosis caused by Leptospira . Since TLRs seem not to be involved in Leptospira-induced renal fibrosis , we wondered whether other innate immune receptors from the NLR family , such as the cytosolic Nod1 , Nod2 , and NLR3 receptors , could be involved . Nod1 and Nod2 are receptors of muropeptides of bacterial peptidoglycan , but their role in defense against Leptospira remains unknown . We recently showed that L . interrogans activates the NLRP3 inflammasome in the mouse kidney [11] . When activated , the NLRP3 inflammasome induces caspase 1 cleavage , which in turns cleaves pro-IL1ß , allowing for its maturation and secretion . To test the role of these NLRs in the induction of renal fibrosis , WT , Nod1/2dko and Casp1ko mice were infected with 2×108 L . interrogans strain Fiocruz ( Fig . 5D ) . Compared to infected WT mice , no reduction in Leptospira-induced fibrosis was observed in kidneys of either Nod1/2dko or Casp1ko mice , showing that Nod1 , Nod2 and NLRP3 are not involved in the Leptospira-induced renal fibrosis . As a whole , these results indicate that the fibrosis induced by Leptospira does not directly rely on TLR and NLR activation . Infiltrating CD11b+ macrophages were detected in kidneys of WT mice one month p . i . ( see Fig . 1C ) . Their role in leptospirosis is difficult to assess since transgenic mice devoid of macrophages were not available . Apart from their phagocytic role , another defense mechanism of macrophages is the production of reactive oxygen species derived from nitric oxide ( NO ) , which is toxic for bacteria and is produced by different nitric oxide synthase enzymes , among them the inducible iNOS . We previously showed that infection with L . interrogans strain Fiocruz induces iNOS mRNA upregulation in WT mouse kidneys at day-3 p . i . [10] . Here , BMDM from C57BL/6J mice were stimulated with L . interrogans strain Fiocruz , and nitrite ( NO2− ) production measured in cell supernatants 24 h later . Both live bacteria and heat-killed Leptospira induced dose-dependent production of NO2− ( Fig . 6A ) , showing that macrophages could be a potential source of NO induced by Leptospira infection . NO production constitutes an innate defense mechanism , but is also known to be responsible for cell toxicity . Recently iNOS was shown to play a deleterious role in Leptospira-induced interstitial nephritis [5] . WT and iNOSko mice were therefore infected with 2×108 L . interrogans Fiocruz strain to test the role of iNOS in the induced fibrosis . Mice were sacrificed at day-15 p . i . and kidneys were processed for immunohistochemistry . No difference could be observed in bacterial loads in urine or inflammatory scores in kidneys between the infected WT and iNOSko mice ( Fig . 6B and 6C ) , although renal fibrosis was slightly reduced , but not abolished , in the infected iNOSko compared to WT kidneys ( Fig . 6D ) . Since the fibrosis at day-15 p . i . was mild , we also compared by qRT-PCR the expression of ACTA-2 , fibronectin and Mmp2 . These markers were statistically slightly less up-regulated in the kidneys of infected iNOSko mice compared to those of WT mice ( Fig . 6E , upper panel ) , although expression levels of transcripts in kidneys of naïve iNOSko mice can not account for the observed down-regulation ( Fig . 6E , lower panel ) . Altogether , these results suggest that upregulation of iNOS mRNA in response to Leptospira infection is deleterious and participates in induction of renal fibrosis . Nevertheless , additional mechanisms appear to be required for Leptospira-induced renal fibrosis .
Wistar rats are currently used as a model of chronic leptospirosis . After being infected , rats carry Leptospira in their kidneys , and persistently excrete them in their urine [21] , [22] . Histopathology studies have revealed the presence of renal tubulo-interstitial lesions in all experimental animal models of acute and chronic leptospirosis [23] . Naturally susceptible C3H/HeJ mice develop acute renal nephritis after experimental infection with Leptospira , but , to our knowledge , chronic carriage of Leptospira for several months in mice has not been described [24] . Despite a few previous studies mentioning renal fibrosis in dogs [2] and rats [23] infected with Leptospira , the putative mechanisms leading to renal fibrosis induced by chronic carriage of Leptospira have not been investigated . In the present study , we characterized a mild , but sustained , renal interstitial fibrosis occurring upon experimental infection of C57BL/6J mice with L . interrogans . A number of immunohistochemical studies have revealed the presence of Leptospira antigens including lipopolysaccharide ( LPS ) , glycolipoprotein and lipoproteins in the renal tubulo-interstitial lesions , although the presence of live Leptospira was difficult to assess because of their long generation time and fastidious in vitro growth . Recent availability of quantitative real-time PCR techniques to evaluate the DNA presence in urine and organs eased the monitoring of Leptospira carriage . Visual and morphometric analysis of the kidneys of infected mice also permitted analysis of the degree of renal inflammation and fibrosis as described earlier [25] . Because fibrosis was found to be highly focal by visual examination , morphometric analysis revealed only an average mild fibrosis . However , this mild fibrosis was reproducibly measured and correlated with the mRNA upregulation of key markers of renal fibrosis . Although Leptospira-induced renal fibrosis appears to be correlated with inflammation as assessed by semi-quantitative scoring and upregulation of inflammatory cytokines , we did not find any significant up-regulation of TGF-ß ( not shown ) , which is considered a key pro-fibrotic factor , usually produced by CD11b+ macrophages infiltrating the infected kidneys . In this line , a recent work showed that TGF-ß , mostly produced by infiltrating macrophages was not mandatory to ischemia/reperfusion induced fibrosis [26] . Acute leptospirosis is characterized by multiple organ failure , including liver , lung , and kidney dysfunctions , and marked inflammation and dissemination of Leptospira in all these organs [10] , [24] , [27] , [28] . Interestingly , fibrosis was not found in the liver and lungs of infected mice that , contrary to the kidneys , were devoid of bacteria ( not shown ) . This suggests that it is neither the initial phase of hematogenous dissemination of Leptospira , nor the initial inflammation , but the leptospiral colonization of the kidneys that triggers the fibrosis . In agreement with this hypothesis , mice that received the early antibiotic treatment ( day-1 to -5 ) were cleared of Leptospira and did not develop renal fibrosis . In contrast , when the antibiotic treatment began later at day-3 p . i . , it did not succeed in totally eliminating the Leptospira , perhaps due to their potential intracellular location in renal epithelial cells or protected niche in the lumen of renal tubules , and some of the mice developed renal fibrosis . These findings suggest that a direct correlation may exist between renal fibrosis and the presence of live Leptospira in the urine . An unexpected and puzzling finding of this study was the lack of correlation between the levels of colonization and the extent of renal fibrosis . Indeed , the sensitive TLR4ko and TLR2/4dko mice , more heavily infected than the WT and TLR2ko mice , did not show any enhanced fibrosis . Together with our results on the antibiotic treated mice , the number of bacteria colonizing the kidney did not correlate with the extent of fibrosis . Therefore , we hypothesize that the initial endothelial insult of live Leptospira penetrating in the kidney may trigger the fibrosis , and that once within its niche in the kidney , colonization by Leptospira would not affect the fibrosis course . Interestingly , one month p . i . , the early antibiotic-treated mice developed neither renal fibrosis nor inflammation , although they harbored LipL32 antigens in the kidneys . LipL32 is the major lipoprotein of Leptospira and was demonstrated to be a TLR2 agonist [6] , and an important component of the outer membrane , involved in vitro in the production of extracellular matrix components by human renal cell lines [8] , [29]–[31] . Our present finding , together with the fact that TLR2 is not involved in Leptospira induced renal fibrosis , strongly suggests that in vivo , LipL32 is not involved in the fibrogenesis process . This discrepancy between the in vivo and in vitro results is striking and may emphasize that the complex phenomenon of fibrogenesis cannot be fully mimicked in vitro , and/or that species specificities of the TLRs may be involved . However , if differences in human and mouse TLR4 specificity towards leptospiral lipid A have already been shown [32] , with only mouse TLR4 recognizing the lipid A , we never noticed such a differential recognition of leptospiral lipoproteins between human and mouse TLR2 . Tissue fibrosis is a very complex dynamic process leading to excessive and pathologic accumulation of matrix components that involves many different cells , differentiation and signaling pathways [16] . In a former study , we showed at day-3 p . i . a protective role of T cells , producing IFN-γ and helping macrophages to fight Leptospira in the mouse kidney [10] . This finding is in accordance with the earlier study of Martha Pereira who showed that depletion of CD4+ and CD8+ T cells in mice worsened interstitial nephritis [24] . A role of CD4+ T cells in promoting the renal fibrosis has been recently described in a mouse model of renal fibrosis induced by unilateral ureteral obstruction [15] . This model generates a progressive fibrosis in the kidney with interstitial infiltrations of macrophages . The question arises as to whether recruited T cells in kidneys of Leptospira-infected mice could , beside their early protective role , have also an adverse effect in promoting the fibrosis at later time points . The use of transgenic mice devoid of T cells ( CD3ko mice ) showed unambiguously that T cells do not take part of the renal fibrotic process in Leptospira-infected mice . Other cells from the adaptive immunity such as B lymphocytes have been associated with renal fibrotic processes by deposition of immunoglobulins , such as IgG4-related disease , showing high level of serum IgG4 and abundant IgG4-positive plasma cell infiltration into the renal interstitium with fibrosis [33] . We previously showed the crucial and protective role of B cells in leptospirosis through early protective , specific IgM production and later IgG production [10] . The experiment using μMT transgenic mice devoid of B cells and rescued from leptospirosis by administration of immune sera , showed that these mice also exhibit some renal fibrosis , suggesting that B cells and/or related antibody production are not involved in the renal fibrotic process . We previously showed the important role of both TLR2 and TLR4 in the murine innate defense against Leptospira [10] , [32] . Surprisingly , we did not find any role of TLR2 nor TLR4 in the induction of renal fibrosis , although recent data indicate a role of TLRs in renal pathologies [34] . Hence , TLR4 activation has been shown to favor kidney fibrosis in the mouse model of unilateral urinary obstruction [14] . TLR2 has also been involved in renal fibrosis after unilateral ureteral obstruction [35] , and has been suggested to be important for Leptospira-induced fibrosis [9] . However , our in vivo results showed that renal fibrosis is still present in TLR2/4 double deficient mice , excluding any major contribution of these receptors , and confirmed the fact that TLR2 agonists such as LipL32 are not major players in triggering renal interstitial fibrosis . Moreover , the fact that Leptospira-infected kidneys from MyD88ko and TLR3ko mice were also fibrotic , excludes any important role for TLRs in the mechanism of Leptospira-induced renal fibrosis . These rather unexpected results are in accordance with the recent work of Anders's group showing that post obstructive renal fibrosis is independent of TLR2 , TLR9 and MyD88 [36] . Apart from the trans-membrane TLR innate immune receptors , the cytosolic family of Nod-like receptors also sense cellular intrusion of pathogens and danger signals . For example , Nod1 and Nod2 detect distinct muropeptides of bacterial peptidoglycan [37] . Leptospira species have a peptidoglycan whose chemical composition is close to the one of Gram-negative bacteria despite some peculiarities in their muropeptide composition [38] . However , infected kidneys from Nod1/2dko mice exhibited interstitial fibrosis , therefore excluding a role for both Nod1 and Nod2 in the induction of renal fibrosis . On the other hand , the inflammasome receptor NLRP3 , shown to participate in the lung fibrosis induced by uric acid [12] , is activated in kidneys from day-3 p . i . with Leptospira [11] . We also reported that the activation of the NLRP3 inflammasome does not occur through reactive oxygen species production , but rather more through the effect of the glycolipoprotein , an outer membrane toxin of Leptospira inhibiting the sodium/potassium pump ( Na/KATPase ) in macrophages [11] . This leads to a potassium dysregulation and activation of NLRP3 . NLRP3 , like most other NLRPs , uses the adaptor ASC to activate caspase1 that in turn cleaves pro-IL1ß , allowing for the IL1ß secretion . Although drugs inhibiting Na/KATPase together with ROS production have been shown to promote renal fibrosis [39] , we neither observed any decrease in fibrosis in Casp1ko mice nor in ASCko mice ( not shown ) . Therefore , our results also strikingly exclude a role for NLRs in the Leptospira-induced fibrosis . We previously reported that Leptospira induce up-regulation of the iNOS mRNA in kidneys at day-3 p . i . [10] . Here we confirmed the production of NO upon stimulation of bone marrow macrophages with live or dead Leptospira . NO production is a potent innate mechanism to eliminate invading bacteria , but upregulation of renal iNOS has also been linked to kidney injury during systemic inflammation [40] . The fact that iNOS deficient mice and antibiotic-treated WT mice were less fibrotic at day-15 p . i . , suggests that early iNOS functions , in response to the initial phase of colonization of the kidneys by Leptospira , would be important for the fibrogenesis process . However , fibrosis is not abolished in the infected iNOSko mice , suggesting that other unknown mechanisms exist , promoting leptospiral-induced renal fibrosis . iNOS has recently been shown by others to participate in Leptospira-induced interstitial nephritis , as measured one month p . i . by a lower histopathological scoring of inflammation in kidneys from iNOSko mice , that were slightly more infected compared to WT mice [5] . Here , we also found a slightly greater number of Leptospira in the urine of iNOSko mice , suggesting that NO participates in bacterial clearance . However , we did not find less inflammation in iNOSko mice , although we found less fibrosis , both by scoring and morphometry of Red Sirius , and , down-regulation of fibrosis markers . Although we demonstrated in a previous study that at day-3 p . i . the up-regulation of iNOS is decreased in TLR2/4dko mice [10] , TLR2/4dko mice still developed renal fibrosis . One explanation for this apparent discrepancy could be that NO , which we previously found to be released from the parenchymal compartment at day 3 p . i . [10] , would be produced by macrophages at later time points through other unknown TLR-independent pathway ( s ) . Analysis of serum creatinine levels , as a marker of renal function , reveals that WT mice do not show elevated levels of serum creatinine 3 months p . i . , confirming their asymptomatic carrier status . However , TLR deficient mice , which harbored more bacteria in their urine , exhibited discretely increased levels of serum creatinine , suggesting that the renal function is slightly affected . Moreover , the renal inflammation in WT mice 6 months p . i . , suggests that chronic carriage of Leptospira in the long-term could be deleterious . Interestingly , since humans do not sense leptospiral LPS through TLR4 [6] , we may hypothesize that chronic carriage of Leptospira , already demonstrated in humans [3] , [41] , [42] , could also be linked to a slightly impaired renal function further favoring development of other kidney diseases as previously suggested by Yang's group in Taiwan [4] . To summarize , the present study provides lines of evidences that renal colonization by Leptospira induces a mild renal fibrosis in mice through TLR- and NLR-independent pathways , and suggests that the activation of iNOS plays a role in the induction of the renal fibrosis . Our work also highlights the fact that future therapeutic strategies should aim at eliminating Leptospira very early after infection , before the renal colonization . Therefore , development of efficient human vaccines against pathogenic Leptospira would be extremely useful to prevent chronic carriage of Leptospira that may , in the long term , alter renal function .
|
Leptospirosis is a bacterial disease transmitted by asymptomatic rodents to humans . The symptoms may be mild , or severe with kidney failure . Renal fibrosis , occurring during inflammatory situations , is characterized by the pathological accumulation of extra-cellular matrix components and can compromise the kidney functions of patients with leptospirosis . Recent research revealed that both innate and adaptive immune responses are involved in the establishment of fibrosis , in several organs and in different pathophysiological situations . In the present study , we characterized a mouse model of chronic infection with Leptospira that provokes mild renal fibrosis . We show that fibrogenesis requires the presence of live Leptospira in the kidney and that B and T cells from the adaptive immune response do not participate in the induction of renal fibrosis . Unexpectedly , we also found that innate immune receptors , TLRs and NLRs , are not involved in the Leptospira-induced fibrosis . Finally , we show that the enzyme responsible for NO production , iNOS , known to participate in renal inflammatory lesions induced by Leptospira , is also involved in renal fibrosis . Our work provides a novel mouse model to study fibrosis occurring due to leptospirosis .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"veterinary",
"science",
"biology"
] |
2014
|
Leptospira Interrogans Induces Fibrosis in the Mouse Kidney through Inos-Dependent, TLR- and NLR-Independent Signaling Pathways
|
The development of effective diagnostic tools will be essential in the continuing fight to reduce schistosome infection; however , the diagnostic tests available to date are generally laborious and difficult to implement in current parasite control strategies . We generated a series of single-chain antibody Fv domain ( scFv ) phage display libraries from the portal lymph node of field exposed water buffaloes , Bubalus bubalis , 11–12 days post challenge with Schistosoma japonicum cercariae . The selected scFv-phages showed clear enrichment towards adult schistosomes and excretory-secretory ( ES ) proteins by immunofluorescence , ELISA and western blot analysis . The enriched libraries were used to probe a schistosome specific protein microarray resulting in the recognition of a number of proteins , five of which were specific to schistosomes , with RNA expression predominantly in the adult life-stage based on interrogation of schistosome expressed sequence tags ( EST ) . As the libraries were enriched by panning against ES products , these antigens may be excreted or secreted into the host vasculature and hence may make good targets for a diagnostic assay . Further selection of the scFv library against infected mouse sera identified five soluble scFv clones that could selectively recognise soluble whole adult preparations ( SWAP ) relative to an irrelevant protein control ( ovalbumin ) . Furthermore , two of the identified scFv clones also selectively recognised SWAP proteins when spiked into naïve mouse sera . These host B-cell derived scFvs that specifically bind to schistosome protein preparations will be valuable reagents for further development of a cost effective point-of-care diagnostic test .
Schistosomiasis is one of the most insidious of all the tropical parasitic infections and threatens the health of hundreds of millions of people worldwide [1] . The last 20 years has seen remarkable progress in disease control through the use of praziquantel ( PZQ ) , but this drug does not protect against re-infection and mass drug administration programmes based around its use are probably untenable long term [2–4] . Recently there has been a major focus on the development of anti-schistosome vaccines , but to date a protective commercial vaccine remains elusive [1] . As mass drug administration decreases worm burdens within endemic areas , the need for improved diagnostic tests should be given research priority [5] . However , in countries where elimination of schistosomiasis has been given precedence , case detection of infected individuals remains problematic as the commonly used methods for diagnosis lack the necessary sensitivity and specificity to accurately determine parasite burden [6] . Although application of modern research laboratory techniques has seen improvements in the diagnosis of helminth infection , uptake has not been uniform and proof of concept studies that show promise have often not been followed through with much needed product development [7] . Currently the Kato-Katz thick smear stool method , based on detection of eggs in faeces , is the test sanctioned by the World Health Organization ( WHO ) for qualitative and quantitative diagnosis of intestinal schistosomiasis [8 , 9] . This test is generally specific , simple and relatively inexpensive , but like many parasitological tests , sensitivity can be insufficient , particularly when worm burdens are low [7] . Consequently , the use of single Kato-Katz measurements can underestimate the prevalence of infection and can confound confirmation of cure assessment following chemotherapy [10] . This is of particular importance in the Peoples Republic ( PR ) of China as the country moves towards programs aimed at the elimination of schistosomiasis japonica [11 , 12] . Since the 1950’s the prevalence of schistosomiasis japonica within Chinese provinces has dramatically decreased [13 , 14] and the requirements for a diagnostic tool has moved from the detection of parasitic infection to the ability to effectively assess disease prevalence [14] . Evaluation of the Gates-funded SCORE project in African countries demonstrated that a rapid , accurate point-of-care ( POC ) diagnostic test that detects a circulating cathodic antigen ( CCA ) could identify S . mansoni antigens [15 , 16] . The CCA and circulating anodic antigen ( CAA ) have been investigated as potential diagnostic candidates and can be detected in the serum and urine of infected individuals [17 , 18] . These antigens are cleared from the serum and urine of schistosomiasis patients within weeks following curative treatment [19] . However , success of these tests has only been validated for areas of high and moderate endemicity [18 , 20] . Whilst CCA and CAA appear to be excellent antigen based tests , we have taken a different approach that may offer advantages for the development of reagents aimed at detecting very low infection levels . Recently McWilliam et al . , demonstrated , in a rat model of schistosomiasis , that the developing schistosome worm can elicit a distinct immune response in discrete tissue sites [21] . Building on this concept we previously published the construction of an scFv-phage library for the detection of larval stage antigens as potential vaccine candidates [22] . However , the S . japonicum larval stages are small , transient and rapidly migrate between tissues . The adult parasites are much larger , more persistent and shed antigen directly into the blood which makes them much more attractive targets for an antigen based diagnostic . Here we describe the construction and characterisation of scFv libraries derived from the portal lymph nodes of S . japonicum infected B . bubalis , and demonstrate their ability to bind to the surface of adult S . japonicum worms and excretory-secretory ( ES ) products . These reagents offer many advantages for diagnostic development , including the ability to affinity mature the reagents , easy selection in a number of modalities , existing detection reagents and strong binding . It is hoped these reagents can be developed into a rapid POC diagnostic to aid in the surveillance and eventual elimination of S . japonicum .
Written approval for animal experiments was provided by the Ethical Review Board of the Hunan Institute of Parasitic Diseases ( approval # 110818 ) and from Monash University Animal Ethics Committee ( approval # 2011-124-FW ) . Animals were maintained and cared for according to the Animal Ethics and Procedures Guidelines of the PR China . Cercariae from S . japonicum were shed from infected Oncomelania hupensis snails collected from an endemic region in the People’s Republic ( PR ) of China using described methods [23 , 24] . Adult S . japonicum worm pairs were collected from infected mice at QIMR Berghofer Institute for Medical Research as described [25] . Extracts of soluble whole adult preparations ( SWAP ) or excretory-secretory ( ES ) products from live adult worms were prepared as described [26 , 27] . Soluble egg antigen ( SEA ) was generated from eggs extracted from infected livers digested in 400 μg/ml collagenase B overnight at 37°C . The digested liver solution was centrifuged ( 400 g , 5 min ) and washed extensively in PBS . The washed solution was then passed though sieves ( 250μm and 150μm , respectively ) and eggs were separated out of the resulting solution using a Percoll gradient and exposed to 10 freeze thaw cycles . Eggs were then homogenised and centrifuged at 10 , 000 g for 2 hr at 4°C and the soluble fraction ( SEA ) collected . Protein concentrations were determined by bicinchoninic acid assay ( BCA; Thermo Fisher Scientific , USA ) . Animal experiments and sample collection were conducted in the PR China as described [28] . Briefly , six mixed sex S . japonicum infected B . bubalis were obtained from an S . japonicum-endemic region in Hunan Province , PR China . Pre-existing infection status was confirmed by faecal egg count to provide animals where natural immunity could be “boosted” by subsequent experimental schistosome infection . All animals were drenched upon arrival with PZQ ( 25 mg/kg ) and randomly assigned into two experimental groups ( n = 3 per group ) . Group 1 was infected with 400 live S . japonicum cercariae percutaneously on the inner thigh . Group 2 was the uninfected control group . Animals were sacrificed 11–12 days post infection ( days p . i . ) . Lymph nodes draining the liver of B . bubalis were collected and cells isolated as described previously [29] . For RNA preparation , 1 x 109 cells were centrifuged and the cell pellet resuspended in 2 ml of QIAzol lysis buffer ( QIAGEN , Netherlands ) and RNA was prepared according to the manufacturer’s recommendations . RNA was further purified using an RNeasy Mini Kit ( QIAGEN , Netherlands ) as per the manufacturer’s recommendations . RNA was quantitated via absorbance at 260 nm using a NanoDrop spectrophotometer ( Thermo Fisher Scientific , USA ) and stored at -80°C until required . The preparation and characterisation of a scFv-phage display library similar to that used in this study has been described previously [22] . Briefly , full length variable light ( VL ) and variable heavy ( VH ) chain genes were amplified by PCR from portal-lymph node RNA derived from buffalo 11–12 days following an experimental S . japonicum infection . The scFv fragment was cloned into a phage display vector ( pAK100; Plückthan Laboratory , University of Zurich , Switzerland ) with the VL and VH genes separated by DNA encoding a flexible linker sequence ( VL- ( G4S ) 4-VH ) . The scFv fragment was directionally cloned into the pAK100 vector using differential SfiI restriction sites and was fused in-frame to the phage gene III coding DNA . This library was transformed in XL-1 Blue Escherichia coli cells and approximately 5 x 107 transformants were recovered following overnight incubation at 37°C . The panning library was amplified by inoculating 50 ml of non-expressing ( NE ) medium ( 2 X Yeast Tryptone ( 2YT ) , 1% glucose , 25 mg/ml chloramphenicol ) with approximately 109 XL-1 Blue E . coli cells containing scFv phagemids and shaken at 37°C . At OD600 = 0 . 5 , 1 x 1011 transducing units per ml ( TU/ml ) M13K07 helper-phage ( New England Biolabs , USA ) and 25 μl 1 M Isopropyl β-D-1-thiogalactopyranoside ( IPTG; Sigma-Aldrich , USA ) solution were added and culture incubated for 15 mins at 37°C without agitation . The culture was then centrifuged at 3500 g for 10 mins and the resulting pellet was diluted in 100 ml low-expression ( LE ) medium ( 2YT , 1% glucose , 25 mg/ml chloramphenicol ( Sigma Aldrich , USA ) and 0 . 5 mM IPTG ) and shaken for 16 hr at 37°C for phage production . Two hours post infection , 30 mg/ml kanamycin ( Bioline , Australia ) was added . Resulting scFv-phage particles were purified and concentrated 100-fold by PEG NaCl precipitation [30] , resuspended in phosphate buffered saline ( PBS ) , and stored at 4°C . Following overnight culture , phage titres of 1 x 1011–1 x1012 TU/ml were typically observed . Plasmid DNA was sequenced by the Micromon Sequencing Facility ( Monash University , Australia ) and aligned to Bos taurus antibody protein sequences using Clustal Omega Multiple Sequence Alignment Software [31] . For selection of high affinity binders , 1 x 1011 scFv-phage particles in 1ml PBS were initially pre-absorbed in 1 . 7 ml microfuge tubes ( Axygen , Corning Life Sciences , USA ) or 96 well microplates ( Corning Life Sciences , USA ) for 1 hr at RT with agitation . This was repeated a total of four times to eliminate scFv-phages that preferentially bind plastic . Following pre-absorption , 1 x 1011 TU/ml scFv-phages were added to 10 ± 2 formaldehyde-fixed adult schistosome pairs ( 10% formaldehyde for 30 mins followed by 3 washes with PBS ) or 1 μg of ES worm products ( 0 . 1 μg coated per well on a on 96 well plate ) and allowed to bind for 2 hr at RT with gentle agitation . Control reactions of scFv-phages without the addition of parasite material were also prepared . Tubes and microplates were then washed 10 times with PBS with 0 . 05% ( v/v ) Tween-20 ( PBS-T ) , followed by an additional two washes with PBS . Bound scFv-phages were eluted with 0 . 2 M glycine/HCl , pH 3 . 0 for 15 min at RT . Supernatant from tubes and microplates was then collected and immediately neutralised with appropriate volume of 1 M Tris-HCL . Eluted scFv-phages ( typically 1 x 104–1 x106 TU/ml ) were amplified as previously outlined and resuspended in PBS . Amplified scFv-phages were used for further panning rounds or parasite binding analysis . Libraries panned against adults or ES are termed Bp-R3-A or Bp-R3-ES respectively and an equal mix of each scFv-pool is termed Bp-R3-AES . Combined BP-R3-AES phage pools were used to minimise amount of S . japonicum material required . Naïve and S . japonicum infected mouse sera ( 21 days p . i . ; kindly supplied by Dr . Patrick Driguez , QIMR Berghofer Medical Research Institute , Australia ) were treated with Affi-Gel Blue to remove albumin as per the manufacturer’s instructions ( Bio-Rad , USA ) . The Bp-R3-AES phage pool was adjusted to 1 x 1011 TU/ml and absorbed against depleted naïve mouse sera ( diluted 1:10 in PBS ) coated onto 96 well microtitre plates ( 100 μl/well; Maxisorb; NUNC , Denmark ) . Pre-absorbed Bp-R3-AES were then panned against depleted infected mouse sera ( diluted 1:10 in PBS ) coated onto 96 well microtitre plates ( 100 μl/well; Maxisorb; NUNC , Denmark ) . Following each panning round phages were eluted and amplified as previously outlined and again absorbed against depleted naïve mouse sera , before being further panned against depleted infected mouse sera . This process was repeated for a total of three rounds . This post infected mouse sera panned library will be termed Bp-R3-post infected mouse sera ( Bp-R3-PIMS ) . Selected scFv coding regions from Bp-R3 phages following infected mouse sera panning ( Bp-R3-PIMS ) were sub-cloned into the E . coli expression vector pAK600 ( Plückthun Laboratory , University of Zurich , Switzerland ) and transformed using electroporation into TOP 10 F’ E . coli cells ( Life Technologies , USA ) as previously described [32] . The recombinant scFv are expressed with an alkaline phosphatase ( AP ) tag to aid in solubility and to facilitate dimerisation and direct detection [33] . The scFv-AP fusions were purified by ion exchange chromatography ( HiTrap Q FF , GE Healthcare , UK ) according to the manufacturer’s recommendations . Purity and correct size of scFv-AP fusion proteins were assessed by western blot . Soluble scFv clones were sequenced by the Micromon Sequencing Facility ( Monash University , Australia ) and aligned using Clustal Omega Multiple Sequence Alignment Software [31] . Soluble scFv fusion proteins were designated Bp-scFv-1 to Bp-scFv-5 , respectively . Microplates ( Corning Life Sciences , USA ) were coated with 100 μl of either S . japonicum SWAP , ES , SEA , ovalbumin ( 0 . 5 μg/well ) or SWAP spiked into uninfected mouse sera ( equivalent to 0 . 5 μg/well in 1:10 diluted naïve mouse sera ) in carbonate coating buffer ( 0 . 05 M carbonate-bicarbonate , pH 9 . 6 ) and incubated overnight at 4°C . The next day microplates were washed three times and blocked in PBS-T for 1 hr at 37°C . The scFv-phage pools ( Bp-pre , Bp-R3-A , Bp-R3-ES , Bp-R3-AES or Bp-R3-PIMS ) diluted 1:5 in PBS-T and soluble scFv-AP clones diluted 1:10 in PBS-T were added to triplicate wells and incubated for 1 hr at 37°C . Specific binding of scFv-phage pools was detected using an anti-M13 pIII monoclonal antibody ( New England Biolabs , USA ) followed by biotin-conjugated anti-mouse antibody ( goat anti-mouse IgG Fc , Jackson Immunoresearch ) , then streptavidin-HRP ( BioRad , USA ) . Reactivity of scFv-AP clones towards schistosome antigens was detected using rabbit anti-alkaline phosphatase ( ABCAM , USA ) , then swine anti-rabbit-HRP conjugate ( Dako , Germany ) All detection antibodies were diluted 1:1000 in PBS-T and incubated for 1 hr at 37°C . Following incubation , all plates were washed three times with PBS-T and developed with 3 , 3’ , 5 , 5’-tetramethylbenzidine ( TMB ) solution ( Life Technologies , USA ) for 15 minutes and the reaction was stopped with 2 M H2SO4 . Antibody or scFv-phage binding was detected using O . D . measurements at 450 nm . Protein preparations ( SWAP or SEA ) were resolved by reducing SDS-PAGE and transferred to nitrocellulose membranes as per the manufactures recommendations ( iBlot Dry Blotting System; Life Technologies , USA ) . Membranes were visualised with Ponceau S ( 0 . 1% ( w/v ) Ponceau S in 5% acetic acid; Sigma-Aldrich , USA ) , cut into individual lanes and blocked for 2 hr at RT in PBS-T . Membranes were incubated overnight at 4°C with Bp-R3-AES phages ( diluted 1:5 in PBS-T ) . Individual lanes were washed three times with PBS-T and Bp-R3-AES phages were detected with anti-M13 pIII monoclonal antibody ( New England Biolabs , USA ) , followed by biotin-conjugated anti-mouse antibody ( goat anti-mouse IgG Fc , Jackson Immunoresearch , USA ) , then streptavidin-HRP ( BioRad , USA ) . All detection antibodies were diluted 1:1000 in PBS-T and incubated for 1 hr at RT . Protein bands were detected using the metal enhanced DAB peroxidase substrate detection system ( Thermo Fisher Scientific , USA ) . Discrete amplified scFv-phages ( Bp-pre , Bp-R3-A or Bp-R3-ES ) were diluted to 1 x 1011 TU/ml and labelled with 120 μg of carboxyfluorescein succinimidyl ester ( CFSE; 10 mg/ml in DMSO ) in a total volume of 1 ml PBS for 1 hr at RT in the dark , with agitation . Excess CFSE was removed by overnight dialysis against PBS at 4°C . Microscopy was conducted using CFSE labelled scFv-phages ( Bp-pre , Bp-R3-A or Bp-R3-ES ) . Briefly , 10 ± 2 formaldehyde-fixed adult worm pairs were reacted with 1 x 109 TU/ml phage particles diluted in PBS for 1 hr at RT in the dark . Parasites were washed five times with 1 . 5 ml of PBS-T and an additional two times with PBS . Parasites were mounted on slides using PBS with 10% glycerol ( Merck-Millipore , Germany ) . Images were taken using a Zeiss fluorescence microscope ( Zeiss Group , Germany ) and a 2 . 5x objective with a consistent exposure time for each sample . Images were processed using Zen software ( Zeiss Group , Germany ) . At least 10 adult parasites were observed for each represented image . Confocal microscopy was performed using a Nikon SMZ 25 stereomicroscope with a motorised stage with CFSE labelled Bp-R3-A phage and formaldehyde-fixed adult parasites . Two hundred images were captured at 10 . 8 μm steps with fixed exposure times and an SHR Plan Apo 2x objective . The images shown were reconstructed and rendered using ImageJ software [34] . A microarray consisting of 232 schistosome specific proteins was prepared as described [35] . Briefly , array pads were hydrated with Whatman Blocking Buffer ( WBB; Whatman , GE Healthcare , UK ) . Phages ( M13K07 helper-phage control , Bp-R3-A or Bp-R3-ES phages ) were pre-absorbed in WBB containing 10% ( w/v ) reconstituted E . coli lysate ( EBB; Mc Lab , USA ) . Binding of scFv-phage particles was detected by addition of anti-M13 pIII monoclonal antibody ( diluted 1:1000 in WBB; New England Biolabs , USA ) , biotin-anti-mouse antibody ( diluted 1:1000 in WBB; Jackson Immunoresearch , USA ) , then streptavidin-conjugated Cy5 fluorophore ( diluted 1:200 in WBB; Surelight P3 , Columbia Biosciences , USA ) . Microarray pads were washed 3 times with TBS-T , 3 times with TBS , then in ultrapure water and dried by centrifugation at RT in a 50 ml Falcon tube ( BD Biosciences , USA ) for 5 min at 500 g . Microarray slides were scanned using a confocal laser microarray scanner ( Genepix 4300A , Molecular Devices , USA ) . The signals were quantified with image analysis software ( Genepix Pro 7 , Molecular Devices , USA ) and the reported feature intensity was calculated by subtracting the median local background signal from the feature signal . Data was analysed using a variation of the “group average” method [36 , 37] . Signal intensity ( S . I . ) greater than the average of the “No DNA” negative controls plus 2 . 5 standard deviations ( S . D . ) and no binding by M13K07 helper-phage was used to determine positive recognition . Binding intensity was designated on a plus minus scale . Sequences of identified antigens were then analysed for developmental expression based on previous work [38] and EST Profile Viewer ( http://www . ncbi . nlm . nih . gov ) . Differences between scFv-phages or soluble scFv clones was determined using a one- or two-way analysis of variance test ( ANOVA ) followed by a Tukey’s post-hoc test . Statistical results were reported when significance was achieved at P < 0 . 05 . Statistical analysis was performed using GraphPad Prism , version 6 . 01 software .
The VH and VL antibody regions were amplified from buffalo portal lymph nodes 11–12 days post infection with S . japonicum and cloned into a scFv-phage vector ( S1A Fig ) . Sequencing of assembled scFv fragments revealed framework similarities and distinct variation in the complementarity determining regions ( CDR; S1B Fig ) . Transformation into XL-1 Blue E . coli cells produced approximately 1 . 5 x 107 colonies . Panning against whole fixed adult worm pairs and ES products resulted in relative enrichment levels of greater than 800 or 80 fold , respectively , ( S2 Fig ) . Libraries generated from this material are prefixed with Bp-R3 . Bp-R3 phages were mixed with formaldehyde-fixed adult worm pairs and binding was assessed by fluorescent staining ( Fig 1 ) . Following three rounds of panning against intact adult worms CFSE labelled Bp-R3-A and Bp-R3-ES phage libraries displayed increased fluorescent binding to adult worm pairs under constant exposure conditions compared with CFSE labelled phage prior to selection ( Bp-pre ) and control M13K07 helper phage ( Fig 1A 3–4 compared to 1–2 ) . To determine where on the adult schistosome the Bp-R3 phage bound we performed confocal microscopy using CFSE labelled Bp-R3 phages and adult schistosomes ( Fig 1B ) . It demonstrated that binding to fixed parasites was primarily restricted to the surface , with no apparent binding to internal structures ( Fig 1B-2 ) . Interestingly , although initially binding appeared to be uniform ( Fig 1A ) , confocal examination indicated there are regions on the surface of the parasite where phage do not bind in sufficient numbers to obtain a positive fluoresce signal ( Fig 1B-1 arrows ) . To quantitate phage binding to schistosome ES products we coated an ELISA plate with ES antigens and added different phage libraries , this showed that the enriched Bp-R3-ES phage library displayed significantly greater binding to ES products than any other phage library ( Fig 1C ) . The Bp-R3-A library selected for adult binding also bound ES antigens more strongly than control phage , indicating the ES and adult surface likely share cross-reactive epitopes ( Fig 1C ) . Screening of a schistosome specific protein microarray with Bp-R3 phages ( Adult or ES ) resulted in significant recognition of ten antigens . All ten of these antigens were recognised by the Bp-R3-ES phages , however only three antigens were consistently recognised by both the Bp-R3-A and Bp-R3-ES phages . These were a hypothetical protein ( NCBI GenBank accession number AY808393 ) , a protein similar to the myosin heavy chain ( SJCHGC09420; AY8153690 ) and the previously defined Sjp40 protein ( AY814158 ) ( Table 1 ) . Other antigens recognised by the Bp-R3 phages included an additional four hypothetical proteins ( AY811797 , AY814150 , AY815838 and AY808749 ) and a further four known proteins , including tropomyosin ( AY809972 ) , a calponin-like protein ( AY813467 ) , a putative dynein light chain-1 protein ( DLC1; AY915388 ) and a protein similar to myosin heavy chain ( AY815690 ) . The tropomyosin antigen ( SJCHGC02287 ) is a 25 kDa protein expressed in a range of tissues at different developmental stages and is associated with muscle contraction and cystoskeletal structure and function [39] . Although tropomysins are not specific to schistosomes , the SJCHGC02287 tropomyosin variant shares less than 50% homology with any other organism outside the schistosome genus . The calponin-like antigen shares homology with a 38 kDa S . japonicum calponin , which has been previously investigated [40] . The DLC1 from S . japonicum has previously been investigated as a potential target for vaccine or drug development [41] . The function of the AY815838 hypothetical protein is currently unknown , but the sequence has some homology to the S . mansoni surface antigens Sm13 ( AAC25419 . 1; 30% ) and Sm25 ( AAA29943; 34% ) . Interestingly , all of the schistosome specific antigens identified using the array show increased binding with the Bp-R3-ES selected phage indicating that these antigens may be excreted or secreted into the host vasculature . It should be noted that the proteins observed in Table 1 were often identified in one life-stage by proteomic analysis , but had expressed sequence tags ( ESTs ) in many life-stages or vice versa . This apparent inconsistency may reflect the incompleteness of the proteomic datasets available for analysis or the fact that mRNA levels do not necessarily correlate with protein expression . We pooled the Bp-R3-A and Bp-R3-ES to widen the pool of potential phage sequences selected for adult and secreted schistosome epitopes and analysed the specificity of the pooled Bp-R3-AES library to bind to different schistosome antigen preparations by ELISA ( Fig 2 ) . The Bp-R3-AES phages showed significant levels of binding towards SWAP ( p < 0 . 05 ) ( Fig 2A ) indicating they recognised adult proteins . There was no observed binding of the Bp-R3-AES pool to the SEA preparation ( Fig 2B ) and background binding by M13K07 helper-phage to any antigen preparation was negligible ( Fig 2A and 2B ) . Western blotting of Bp-R3-AES phages was also performed against S . japonicum derived SWAP and SEA preparations ( Fig 2C ) . The Bp-R3-AES phages bound to a broad range of epitopes within the SWAP preparation , but as observed using ELISA , did not recognise any epitopes within the SEA preparation ( Fig 2C; Lane 2 ) . Although the lack of SEA reactivity was unexpected , for a diagnostic aimed to detect an active adult infections , lack of SEA reactivity could be advantageous . The buffalo derived Bp-R3-AES phages were further panned against infected mouse sera to eliminate scFv-phage sequences that bind epitopes within sera , and enrich for antigens that are excreted into the serum during an experimental infection . This process generated a library designated Bp-R3-PIMS , which displayed similar binding to SWAP proteins when compared to the Bp-R3-AES libraries as determined by ELISA ( Fig 3A ) . Five soluble alkaline phosphatase ( AP ) scFv fusion proteins were generated from the Bp-R3-PIMS library ( Bp-scFv-1 to Bp-scFv-5; S3 Fig ) . All identified scFv-AP proteins ( Bp-scFv-1 to Bp-scFv-5 ) showed significantly higher binding to SWAP relative to an ovalbumin protein binding control as examined by ELISA ( Fig 3C ) . An E . coli lysate control , containing no expressed scFv-AP protein , showed no reactivity against SWAP protein extract ( Fig 3B ) . Two of the scFv-AP clones ( Bp-scFv-1 and Bp-scFv-2 ) were also able to significantly and preferentially recognise SWAP proteins that had been spiked into naïve mouse sera by ELISA ( Fig 3D ) Sequencing of the scFv-AP clones ( Fig 4 ) revealed high conservation of framework sequences within both the VH and VL regions with significant variations only within the complementarity determining ( CDR ) regions . As expected within antibody sequences , the major region of diversity observed was within the CDR 3 of the heavy chain ( CDRH3; Fig 4 ) with amino acid sequence length ranging from 4–16 amino acids .
There has been considerable success at reducing transmission and infection rates of S . japonicum in the PR China [42] , however , there is a need to ensure the parasite life cycle is completely broken and that the national elimination program does not falter due to a significant number of low-level infections not being detected [11] . In areas where schistosomiasis japonica remains a problem there is a need to measure and target treatments as well as to educate communities [43] . If a significant number of low-level infections go undetected , there is the risk that the efforts already employed to control transmission will be in vain [11] . The Kato-Katz stool smear technique has been the backbone of intestinal schistosomiasis diagnosis in epidemiological studies , and in the case of S . japonicum is the only approved measure for diagnosis of current infection [20 , 44] . However , the technique is becoming less useful in regions where control programs have resulted in light parasite infections [45 , 46] . The vast majority of diagnostic measures for schistosomiasis are underestimating parasite burdens [47 , 48] . This is of particular concern for prevention strategies being employed in regions of PR China where accurate diagnosis is crucial for the effective control and surveillance of the disease [49] . The most recent epidemiological surveys suggest that the prevalence of S . japonicum infection in areas where transmission has not yet been controlled is 5 . 1% [49] and , as the country strives for elimination of schistosomiasis , the need for more reliable diagnostic tests to survey parasite prevalence is essential [50] . A number of diagnostic tests that detect the host antibody response to schistosome infection have been developed and are being integrated into national control programs in endemic areas of PR China . These include the circumoval precipitin test ( COPT ) , the silver-enhanced colloidal gold metalloimmunoassay , enzyme-linked immunosorbent assay ( ELISA ) , indirect hemagglutination assay ( IHA ) , dot immunogold filtration assay ( DIGFA ) and dipstick dye immunoassay ( DDIA ) , [44 , 51–53] . These tests provide a more accurate measure of infection than stool based parasitological tests with greater patient compliance as they are less invasive [44] . The DDIA test is now commercially available as a test for S . japonicum in low endemic regions [53] . However , it should be noted that due to the lack of strict approval guidelines in PR China , many poor performing diagnostic tests are also being used [44] . A study by Cai and colleagues further confirmed three of the antibody based diagnostic tests ( IHA , ELISA and DDIA ) successfully identify schistosomiasis infected patients [49]; however none are able to accurately distinguish between current or cured infection status and thus assessment of treatment outcomes is difficult . More recently a number of assays to detect circulating antigen have been described . Detection of two circulating diagnostic antigens , circulating cathode antigen ( CCA ) and circulating anode antigen ( CAA ) , have been developed into diagnostic tests for detection and diagnosis of schistosomiasis . A point-of-care ( POC ) CCA test is now commercially available and has proven successful for detection of intestinal S . mansoni , and has been proposed as an alternative to the replicate Kato-Katz measurements [20 , 54] . The POC-CCA test is effective in endemic regions , yet its efficacy in areas of low intensity infections requires validation [14 , 18 , 55 , 56] . Recent work by van Dam and colleagues demonstrated that a new technique to enhance CAA detection in sera and urine was six fold more sensitive at diagnosing an active schistosome infection when compared to Kato-Katz measurements [14] . The significant advantage of a sensitive and reliable antigen based diagnostic for schistosome infection is the ability to discriminate active infection . Yet , immunological tests are currently still expensive and often require trained technicians for the administration and analysis , which increases cost and demand for infrastructure [57] . Moreover , the ability of these tests to assess drug efficacy and provide information regarding the impact of control interventions stills needs to be evaluated [20] . The zoonotic nature of S . japonicum makes animals , particular water buffaloes , significant infection reservoirs . Water buffalo have been thought to contribute to 75% of human infections [13 , 58] and prevalence of infection within buffalo populations has been reported to be as high as 10% [59] . It is likely that control within humans living in close proximity to water buffalo will require accurate diagnosis of residual infection within these buffalo populations . This will likely require a low cost easy to administer test that accurately determines infection status . The novel phage display approach used in this study incorporates the natural reservoir host of S . japonicum to generate mature antibody fragments that bind to adult schistosomes and ES products . The matured buffalo antibody fragments should show little cross-reactivity with buffalo proteins and may help form the basis of a reagent to diagnose infection in buffalo . Furthermore , we have recently shown that buffalo CDRH3 regions selected for binding to schistosomules using the antibody phage display system are longer than those from non-selected libraries , suggesting that long CDRH3 regions may be important in host parasite antigen recognition [22] . Recent studies have shown that long CDRH3 regions are pivotal in the successful defence against dangerous viral infections such as HIV [60] and although the functional mechanisms for ultra-long CDRH3 regions in cattle has yet to be elucidated [61] they may offer advantages for diagnostic development . Although we did not observe ultra-long CDRH3 regions in the five soluble scFv-AP clones characterised they are only a very small subset of the total number of clones generated . Previous studies have utilised phage display technology to investigate the maturation of displayed antibody or peptide fragments toward specific S . japonicum molecules [62–64] . These studies primarily involved the identification of peptide binders through the use of commercial peptide-phage display libraries . The present study used the immunological status of infected host animals to select from a B-cell repertoire primed to produce variable regions that recognise immune exposed epitopes present in the host at the time of sacrifice , i . e . when adult parasites reach the hepatoportal circulation . Previous studies by McWilliam et al . , have illustrated the ability of selected activated lymph node antibodies to discriminate against particular stages of S . japonicum [21 , 28] . We have expanded on this principle by producing scFv-phage libraries from activated lymph nodes to allow molecular characterisation and easier production of the binding moity , which can be difficult to isolate from lymph node antibody preparations . Following selection against defined antigen pools , the binding profile of Bp-R3 phages against SWAP antigens still displayed reactivity to a broad range of antigens , with significant recognition towards three distinct regions , suggesting affinity maturation of Bp-R3 phages towards specific antigens within SWAP . Examination of Bp-R3 phages by ELISA revealed that Bp-R3 phages selected against adult worms or ES preparations were positively and significantly selected towards the antigenic preparations for which they were panned . This observation indicates the maturation of different antibody paratopes within the two scFv-phage libraries . However , it should be noted that microscopy and ELISA observations did indicate cross-reactivity of the differentially selected Bp-R3-A and Bp-R3-ES phage libraries . When analysed on a protein microarray , specific for schistosome proteins , the Bp-R3 phage libraries bound a number of known and hypothetical proteins . Importantly , the library enriched on ES derived material displayed stronger binding to all detected proteins , suggesting that these epitopes may be secreted by the parasite and are presented within the blood of infected individuals . Interestingly , three of the proteins recognised ( AY814158 , AY815690 and AY915388 ) have previously been observed using an scFv-phage library selected against the larval stages of S . japonicum [22] . Recent work by Silas and colleagues has identified splice variants of tropomyosin ( AY809972 ) within S . mansoni that may be associated with natural immunity within the definitive host [39] . Their work identified high IgE and IgG4 antibody titres following exposure to S . mansoni tropomyosin variants indicating tropomyosin is available to the immune system during a natural infection . This present work also identified and characterised five unique soluble scFv-AP clones selected from the Bp-R3-PIMS phage library , which had been panned against serum obtained from S . japonicum infected mice . This blood panning , in particular the initial selection against naïve sera , was performed to both eliminate scFvs that preferentially bind to blood proteins and enrich for scFv-phages that bind schistosome antigens present in blood following infection . All of the isolated soluble scFv-AP clones ( Bp-scFv-1 to Bp-scFv-5 ) significantly bound to SWAP proteins . Importantly a lysate control , containing no expressed scFv , did not recognise SWAP proteins and no purified scFv-AP clone specifically recognised ovalbumin , a non-related protein binding control . Only two of the five scFv-AP clones were able to significantly detect SWAP above the level of background mouse blood proteins in an ELISA using naïve mouse sera spiked with SWAP antigens . This suggests that the scFv-phage technique can isolate antibody fragments that recognise schistosome antigens in blood . However , further validation and isolation of these scFv antibody fragments is required to ensure they exclusively recognise schistosome antigens in naturally infected animals . The development of a range of approaches to deliver accurate point of care , rapid schistosome diagnosis will be critical in the fight to eradicate schistosomiasis japonica in areas of low but continuing transmission in PR China . Here we describe the use of the natural buffalo host to develop and identify recombinant scFv fragments that bind adult stage and ES antigens from S . japonicum . The isolated soluble scFv clones require further development , purification and testing within a true schistosomiasis japonica infection setting , but once completed could be valuable reagents in the construction of a cost effective point-of-care diagnostic test .
|
Mass drug administration using the highly effective drug praziquantel ( PZQ ) is currently the method of choice to combat schistosomiasis . However , this treatment regime has limitations; in particular , it does not prevent re-infection and sporadic parasite resistance against PZQ is a continuing threat . The path to the successful control of schistosomiasis is highly challenging and must consider , not only the complex nature of the host-parasite interaction , but also the capacity to assess disease burden and parasite re-emergence in communities where successful control has been achieved . Furthermore , control programs must be economically sustainable in endemic countries and despite significant recent advancements the elimination of schistosomiasis may still be some time away . Accordingly , there is a definitive need to formulate innovative approaches for the development of improved diagnostic tools to accurately assess the disease burden associated with active schistosome infections . Here we describe the usefulness of a phage display library to mature antibody fragments derived from lymph node RNA of the natural buffalo host of the Asian schistosome , Schistosoma japonicum , following an experimental infection . These mature antibody fragments were able to bind native parasite proteins and could thus be used to develop a low cost and accurate point-of-care diagnostic test .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Generation of a Novel Bacteriophage Library Displaying scFv Antibody Fragments from the Natural Buffalo Host to Identify Antigens from Adult Schistosoma japonicum for Diagnostic Development
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.