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
|
|---|---|---|---|---|---|
In metazoans , apoptotic cells are swiftly engulfed by phagocytes and degraded inside phagosomes . Multiple small GTPases in the Rab family are known to function in phagosome maturation by regulating vesicle trafficking . We discovered rab-35 as a new gene important for apoptotic cell clearance from a genetic screen targeting putative Rab GTPases in Caenorhabditis elegans . We further identified TBC-10 as a putative GTPase-activating protein ( GAP ) , and FLCN-1 and RME-4 as two putative Guanine Nucleotide Exchange Factors ( GEFs ) , for RAB-35 . We found that RAB-35 was required for the efficient incorporation of early endosomes to phagosomes and for the timely degradation of apoptotic cell corpses . More specifically , RAB-35 promotes two essential events that initiate phagosome maturation: the switch of phagosomal membrane phosphatidylinositol species from PtdIns ( 4 , 5 ) P2 to PtdIns ( 3 ) P , and the recruitment of the small GTPase RAB-5 to phagosomal surfaces . These functions of RAB-35 were previously unknown . Remarkably , although the phagocytic receptor CED-1 regulates these same events , RAB-35 and CED-1 appear to function independently . Upstream of degradation , RAB-35 also facilitates the recognition of apoptotic cells independently of the known CED-1 and CED-5 pathways . RAB-35 localizes to extending pseudopods and is further enriched on nascent phagosomes , consistent with its dual roles in regulating apoptotic cell-recognition and phagosome maturation . Epistasis analyses indicate that rab-35 acts in parallel to both of the canonical ced-1/6/7 and ced-2/5/10/12 clearance pathways . We propose that RAB-35 acts as a robustness factor , defining a novel pathway that aids these canonical pathways in both the recognition and degradation of apoptotic cells .
During the development of metazoans , cells that undergo apoptosis are internalized and degraded by other cells that are referred to as engulfing cells or phagocytes [1–3] . The phagocytic removal of apoptotic cells is an evolutionarily conserved event that supports normal tissue turnover and homeostasis , facilitates wound resolution and tissue regeneration , and prevents inflammatory and auto-immune responses induced by the release of dead cell contents [3 , 4] . Throughout the development of Caenorhabditis elegans hermaphrodites , 300–500 germ cells and 131 somatic cells undergo apoptosis [5–7] . The temporal and spatial patterns of these cell death events are highly consistent between embryos [5] . Apoptotic cells exhibit a “button-like” and highly refractive morphology under the Differential Interference Contrast ( DIC ) microscope , and are rapidly engulfed and degraded by multiple types of neighboring cells [5–8] . Genetic screens and further characterizations of mutations that result in the “cell death abnormal” ( Ced ) phenotype , characterized by the accumulation of persistent cell corpses , have identified a number of genes that act in the recognition , engulfment , or degradation of cell corpses [9 , 10] . The Rab family of small GTPases play critical roles in membrane trafficking events , including endocytosis and exocytosis , autophagy , and phagosome maturation [11 , 12] . A well-known function of Rab GTPases and their effectors is to serve as docking factors that facilitate the attachment and fusion of different membrane compartments and/or vesicles [11] . Multiple mammalian and C . elegans Rab proteins play essential roles for phagosome maturation by facilitating the incorporation of intracellular organelles to phagosomes , an action that delivers digestive enzymes to the phagosomal lumen and that might also facilitate the acidification of the lumen [13 , 14] . C . elegans and mammalian RAB-5 are required for the recruitment and incorporation of early endosomes to phagosomes [15–17] , while C . elegans and mammalian RAB-7 are critical for the incorporation of lysosomes to phagosomes [18 , 19] . In C . elegans , both RAB-5 and RAB-7 function downstream of a signaling pathway that promotes phagosome maturation; this pathway is initiated by the phagocytic receptor CED-1 and mediated by the large GTPase DYN-1 [8 , 15 , 18 , 20] . C . elegans RAB-2 and RAB-14 also make important contributions to the phagosomal degradation of cell corpses [21–23] . The signaling pathway led by CED-1 initiates phagosome maturation not only by recruiting Rab proteins to phagosomal surfaces , but also by initiating the production of PtdIns ( 3 ) P , a phosphorylated phosphatidylinositol species and an important second messenger , on phagosomal membranes [16 , 18] . PtdIns ( 3 ) P recruits multiple effectors to phagosomes , including membrane remodeling factors and docking factors that facilitate the recruitment and fusion of intracellular vesicles [13 , 24] . Consequently , phagosome maturation events are largely dependent on the presence of PtdIns ( 3 ) P and certain Rab GTPases [15 , 16] . Interestingly , the presence of RAB-5 and PtdIns ( 3 ) P on phagosomal surfaces displays a co-dependent relationship [15 , 16] . Two phosphatidylinositol 3-kinases , PIKI-1 and VPS-34 , catalyze the production of PtdIns ( 3 ) P on phagosomal surfaces [16 , 25] . PIKI-1 and VPS-34 are functionally opposed by MTM-1 , a phosphatidylinositol 3-phosphatase that dephosphorylates PtdIns ( 3 ) P and in this manner counteracts phosphatidylinositol 3-kinase activities [16] . Throughout the phagosome maturation process , PtdIns ( 3 ) P is present on phagosomal surfaces in a two-wave oscillation pattern , a pattern coordinately regulated by PIKI-1 , VPS-34 , and MTM-1 [16] . MTM-1 is recruited to the surface of extending pseudopods as an effector of PtdIns ( 4 , 5 ) P2 , another phosphorylated phosphatidylinositol species that is enriched on the surface of growing pseudopods during engulfment [25] . The initial appearance of PtdIns ( 3 ) P on phagosomes correlates not only with the recruitment of PIKI-1 to phagosomal surfaces by the CED-1 pathway [16] , but also with the simultaneous disappearance of MTM-1 from nascent phagosomes , which is implicated to be a result of the disappearance of PtdIns ( 4 , 5 ) P2 from phagosomal surfaces [25] . Whether the CED-1 signaling pathway also regulates the turnover of PtdIns ( 4 , 5 ) P2 has not yet been tested . The phagocytic receptor CED-1 provides a link between the engulfment of apoptotic cells and the subsequent maturation of nascent phagosomes [8 , 18] . During engulfment , CED-1 recognizes phosphatidylserine ( PS ) , an “eat me” signal exposed on the surface of apoptotic cells , and defines one of the two canonical parallel pathways that stimulate pseudopod extension and cell corpse internalization [26–28] . Several other key components act in this engulfment pathway alongside CED-1: CED-7 , an ABC transporter homolog that exposes PS on the surface of apoptotic cells; CED-6 , a cytoplasmic adaptor for CED-1; and DYN-1 , an ortholog of the large GTPase dynamin that promotes “focal exocytosis” during pseudopod extension and stabilizes the cytoskeleton underneath extending pseudopods in response to CED-1 activation [8 , 28 , 29 , 30] . In the other canonical engulfment pathway , CED-2 regulates the activity of the CED-5/CED-12 complex , presumably through its N-terminus that contains SH2 and SH3 domains [31–35] . The CED-5/CED-12 complex , in turn , functions as a bipartite nucleotide exchange factor to activate the Rac GTPase CED-10 [31 , 33] . CED-10 promotes the reorganization of the actin cytoskeleton and the extension of pseudopods around cell corpses [34 , 35] . However , residual engulfment activity persists after inactivating both the ced-1/-6/-7/dyn-1 and ced-2/-5/-10/-12 pathways , suggesting that there are yet unknown pathways that play significant roles in cell-corpse engulfment [8 , 36] . Although other proteins–such as alpha and beta integrins–are also reported to contribute to cell corpse engulfment , their effects are mild in comparison to the CED-1 and CED-5 pathways [37 , 38] . In addition to the putative missing pathways , many other questions also remain regarding the molecular mechanisms that control apoptotic cell clearance . For example , although many Rab GTPases have been implicated in the regulation of clearance events , it remains unclear whether this is an exhaustive list . The C . elegans genome contains 30 genes that encode close homologs of mammalian Rab GTPases [39 , 40] . To determine which of these rab genes function in apoptotic cell clearance , we screened for Rab GTPases that participate in cell corpse clearance by analyzing the phenotype of each candidate after RNAi knockdown or gene deletion , excluding rab-2 , -5 , and -7 , which were reported to act in cell corpse clearance . We discovered that inactivation of rab-35 , which encodes a homolog of mammalian Rab35 , reduces the efficiency of apoptotic cell clearance . Further characterizations revealed novel features and functions of RAB-35 . Unlike RAB-5 and RAB-7 , which facilitate specific maturation events , RAB-35 regulates multiple steps throughout apoptotic cell clearance . Our findings further indicate that RAB-35 represents a clearance pathway that functions in parallel to the CED-1 and CED-5 pathways , yet in many ways resembles the mechanisms and functions of the CED-1 pathway . We thus propose that RAB-35 acts as a robustness factor , defining a novel pathway that ensures the stability of apoptotic cell clearance .
The C . elegans genome contains 30 genes that encode close homologs of mammalian Rab GTPases , 23 of which have been assigned gene names [40] . Among these putative rab genes , rab-2 , rab-5 , rab-7 , and rab-14 have been reported to act in the clearance of apoptotic cells [13] . To determine if any other Rab proteins are involved in the same process , we individually knocked down the expression of 17 rab genes in C . elegans using the RNA interference ( RNAi ) treatment , and scored the number of germ cell corpses in the gonad of adult hermaphrodites ( Materials and Methods ) ( Fig 1A ( a ) ) . Moreover , we scored the number of germ cell corpses in deletion mutants for additional 8 rab genes ( Materials and Methods ) ( Fig 1A ( b ) ) . RNAi of rab-1 and rab-11 . 1 cause lethality before the worms develop into adults thus could not be scored . Of the Rabs subjected to RNAi treatment or putative loss-of-function deletions , only 6 had more than four times the number of germ cell corpses compared to the wild-type control ( Fig 1A ) . Of these 6 , rab-35 ( RNAi ) worms exhibited the highest number of persistent germ cell corpses , indicating the strongest defect in the clearance of germ cell corpses , characteristic of the Ced phenotype ( Fig 1A ) . To verify the Ced phenotype produced by rab-35 RNAi , we examined two putative rab-35 null alleles , the nonsense mutation b1013 and the deletion allele tm2058 ( Fig 1B ) [41] . Both rab-35 ( b1013 ) and rab-35 ( tm2058 ) mutants exhibit identical Ced phenotypes in embryos in mid- ( 1 . 5-fold , ~420 min-post 1st cleavage ) and late- ( late 4-fold , 700–800 min-post 1st cleavage ) stage embryos and the 48 hour post-L4 adult gonads ( Fig 1C and 1D ) , confirming the RNAi results . To determine whether the button-like objects observed under DIC optics in rab-35 ( b1013 ) mutants are actually cell corpses , we probed them for the exposure of PS on their surfaces , a distinct characteristic of cells undergoing apoptosis [27] . Using MFG-E8::mCherry–a secreted PS-binding reporter [27] , we detected bright mCherry signal specifically on the surface of the button-like objects ( Fig 1E ) , indicating that they are indeed apoptotic cells . In addition , we expressed the rab-35 cDNA , as an N-terminal GFP-tagged form , specifically in engulfing cells under the control of the ced-1 promoter ( Pced-1 gfp::rab-35 ) [26] , and found that it completely rescued the Ced phenotype in rab-35 ( b1013 ) mutants ( Fig 1F ) . This result suggests that RAB-35 acts in engulfing cells to promote cell corpse-clearance . RAB-35 is known to act in receptor-mediated endocytosis and endocytic recycling in C . elegans [41 , 42] . In rab-35 mutant adult hermaphrodites , we confirmed the presence of the characteristic excess of yolk in the pseudocoelom due to their previously reported defects in trafficking yolk into oocytes [41 , 42] ( S1 Fig ) . Using time-lapse microscopy , we monitored the localization of GFP::RAB-35 expressed in engulfing cells ( Pced-1gfp::rab-35 ) , which rescues the Ced phenotype of rab-35 ( b1013 ) mutants . We tracked the clearance process of three apoptotic cells on the ventral surface: C1 , engulfed by ABplaapppa; C2 , engulfed by ABpraapppa; and C3 , engulfed by ABplaapppp , using our previously established protocol ( Fig 2A ) [43] . C1 , C2 , and C3 undergo apoptosis shortly after the initiation of ventral enclosure , an event that occurs at ~320–330 minutes post-first cleavage [43] . GFP::RAB-35 labels the extending pseudopods throughout engulfment; moreover , GFP::RAB-35 exhibits an ephemeral burst of enrichment on nascent phagosomes that lasts merely 2–4 minutes ( Fig 2B ) . Afterwards , the phagosomal GFP signal rapidly declines to the background level by approximately 15–20 minutes after the initiation of engulfment ( Fig 2B and 2C ) . This dynamic enrichment pattern suggests that RAB-35 might participate in multiple events during apoptotic cell clearance . We introduced S24N and Q69L , two point mutations previously established to convert Rab GTPases into the GDP-locked and GTP-locked forms [41] , respectively , individually into the Pced-1 gfp::rab-35 reporter constructs . Overexpression of RAB-35 ( S24N ) produced a Ced phenotype in the wild-type background as strong as that displayed by rab-35 null mutants ( Figs 2D and S2A ) , verifying its predicted dominant-negative effect [41] . Moreover , GFP::RAB-35 ( S24N ) failed to enrich on the surfaces of extending pseudopods or nascent phagosomes ( Fig 2E ) , suggesting that it is a non-functional form . Remarkably , overexpression of RAB-35 ( Q69L ) , the presumed GTP-locked form , failed to rescue the Ced phenotype of rab-35 mutants ( Figs 2D and S2A ) , although it displayed persistent enrichment on the phagosomal membrane ( Fig 2E ) . Together , the altered localization patterns and the lack of rescuing activity observed in both mutant forms of RAB-35 suggest that the ability to switch between GTP- and GDP-bound forms is required for its proper dynamic localization pattern and function during apoptotic cell clearance . To better understand how the cycling of RAB-35 between the GDP- and GTP-bound forms is regulated , we examined C . elegans orthologs of known GAPs and GEFs of mammalian Rab35 to determine which ones function in the context of apoptotic cell clearance . We first studied the loss-of-function alleles of rme-4 and flcn-1 , which encode the C . elegans orthologs of the mammalian GEFs connecdenns 1/2/3 and folliculin , respectively [44] ( S3 Fig ) . The flcn-1 ( ok975 ) null mutation resulted in a Ced phenotype that is slightly weaker than that of rab-35 ( b1013 ) mutants ( Figs 3A and S2B ) . Furthermore , the flcn-1 ( ok975 ) ; rab-35 ( b1013 ) double mutants exhibited a Ced phenotype identical to that of rab-35 ( b1013 ) mutants , placing both flcn-1 and rab-35 in the same genetic pathway ( Figs 3A and S2B ) . These results suggest that flcn-1 might act as a GEF for RAB-35 , but also that it may be working in tandem with another GEF . RME-4 , a homolog of the connecdenns ( S3 Fig ) , was previously reported to act as a GEF for RAB-35 for its function in endocytic trafficking [41] . Interestingly , although rme-4 ( tm1865 ) single mutants did not exhibit any statistically significant Ced phenotype , the flcn-1 ( ok975 ) ; rme-4 ( tm1865 ) double mutants exhibited a Ced phenotype more severe than that displayed by the flcn-1 ( ok975 ) single mutants and identical in severity to that of rab-35 ( b1013 ) mutants ( Figs 3A and S2B ) , suggesting that RME-4 might also function as a GEF for RAB-35 in the context of apoptotic cell clearance . Compared to FLCN-1 , however , the contribution of RME-4 to apoptotic cell clearance is relatively minor and redundant . We subsequently probed tbc-7 ( RNAi ) and deletion mutant alleles of the genes tbc-10 , and tbc-13 [39] , which encode the C . elegans orthologs of TBC1D24 , TBC1D10A/B/C , and TBC1D13 , known GAPs for mammalian Rab35 , respectively [41] ( S3 Fig ) . We found considerable evidence that TBC-10 acts as the sole GAP for RAB-35 in the context of cell corpse clearance . Firstly , loss of function of tbc-10 , but not those of tbc-7 or tbc-13 , produces a Ced phenotype identical to that of rab-35 ( b1013 ) mutants ( Figs 3B and S2C ) . Such results are consistent with our observation that RAB-35 must switch between its GTP- and GDP-bound forms to function ( Fig 2D ) , as inactivating its putative GAP ( tbc-10 ) appears to disable RAB-35 . If the GTP-locked form of RAB-35 were its active form , tbc-10 mutants would instead lock RAB-35 in a constitutively active form and thus fail to exhibit a Ced phenotype . Secondly , when the localization of GFP::RAB-35 throughout the clearance of C1 , C2 , and C3 was monitored in tbc-10 mutants , we found that–relative to wild-type–RAB-35 was enriched on extending pseudopods and nascent phagosomes normally , but its removal from phagosomal surfaces was delayed ( Fig 3C ) . This pattern is similar to that of GFP::RAB-35 ( Q69L ) in a wild-type background ( Fig 2E ) , indicating that GFP::RAB-35 is locked in the GTP-bound form in tbc-10 mutants . Finally , the tbc-10 ( tm2790 ) ; rab-35 ( b1013 ) double mutants did not enhance the Ced phenotype over either single mutant , confirming that tbc-10 is in the same genetic pathway as rab-35 , as would be expected for a putative GAP for RAB-35 ( Figs 3B and S2C ) . To monitor the subcellular localization of FLCN-1 and TBC-10 , we constructed GFP-tagged FLCN-1 and TBC-10 reporters that are expressed specifically in engulfing cells ( Materials & Methods ) . GFP::FLCN-1 is primarily localized to the cytoplasm of the engulfing cells ( Fig 3D ) . In addition , a weak yet visible enrichment is observed on the surface of extending pesudopods and nascent phagosomes ( Fig 3D ) . Like GFP::FLCN-1 , TBC-10::GFP also displays a transient enrichment pattern on extending pseudopods and nascent phagosomes , only the enrichment is much more distinguished ( Fig 3E ) . Both the FLCN-1 and TBC-10 reporters colocalize with mKate2::RAB-35 during engulfment and the closure of a phagosome , yet disappear sooner from nascent phagosomes than RAB-35 ( Fig 3D and 3E ) . These localization patterns are consistent with the model that TBC-10 and FLCN-1 act as regulators of RAB-35 . C . elegans RAB-2 , RAB-5 , and RAB-7 all play important roles in the maturation of phagosomes that contain apoptotic cells [13] . To determine whether RAB-35 is involved in this process , we measured how fast the cell corpses C1 , C2 , or C3 inside phagosomes were degraded in rab-35 mutant embryos . The lifetime of a phagosome is measured using a combination of a GFP::moesin reporter , which specifically labels the polymerized actin filaments underneath the extending pseudopods [28] , and CTNS-1::mRFP , a lysosomal membrane marker that is enriched on the surface of phagosomes during maturation [18] . These two reporters are co-expressed in engulfing cells under the control of the Pced-1 promoter [18 , 28] . GFP::moesin is used to determine when pseudopods fuse to form a nascent phagosome , providing a way to determine the time point when phagosomal maturation begins and to measure the initial diameter of a nascent phagosome ( Fig 4A ) . CTNS-1::mRFP is used to measure the diameter of a phagosome throughout maturation ( Fig 4A ) . We defined phagosomal lifetime as the period it takes for the phagosome to shrink to one-third of its original radius . Using this assay , we found that rab-35 ( b1013 ) mutants exhibited a significantly longer phagosomal lifetime than their wild-type counterparts; 75% of phagosomes in rab-35 mutants had a lifetime longer than 60 minutes , compared to only 13 . 3% of phagosomes in wild-type embryos ( Fig 4B and 4E ) . These results indicate that RAB-35 is important for the efficient degradation of phagosomal contents . During the degradation of cell corpses , two kinds of intracellular organelles–early endosomes and lysosomes–are known to be recruited to the surface of phagosomes and subsequently fuse to the phagosomal membrane , depositing their contents into the phagosomal lumen [13] . The recruitment and fusion of early endosomes to the phagosome was probed using HGRS-1::GFP , an established early endosomal surface marker expressed in engulfing cells [8] . Starting from the birth of a nascent phagosome , HGRS-1::GFP begins to appear on the surface of a phagosome as puncta ( Fig 4D ) . The continuous accumulation of the puncta over time generates a GFP ring around the phagosomal surface ( Fig 4D ) . In rab-35 mutant embryos , this GFP ring appears much slower than in wild-type embryos; in rab-35 mutant embryos , ~80% of the GFP rings are formed later than 10 min after a nascent phagosome forms , whereas in the wild-type background , 100% of the rings appear within 10 min . This observation suggests that RAB-35 is required for the efficient recruitment of early endosomes ( Fig 4C and 4D ) . CTNS-1::mRFP was used to visualize the recruitment of lysosomes to the surface of phagosomes . CTNS-1::mRFP also first appears on phagosomal surfaces as puncta , gradually accumulating and forming a mRFP ring on the phagosomal surface ( Fig 4E ) [18] . Time-lapse recording found that rab-35 ( b1013 ) mutants had no significant delays in the recruitment of lysosomes ( Figs 4E and S4B ) . To further determine if the fusion of lysosomes to phagosomes was normal in rab-35 mutants , we monitored the entry of a NUC-1::mRFP reporter ( expressed in engulfing cells ) ( Materials and Methods ) into the phagosomal lumen . NUC-1 is an endonuclease that specifically resides in the lysosomal lumen [45] . Similar to CTNS-1::mRFP , NUC-1::mRFP is recruited to phagosomal surfaces as mRFP+ puncta; however , unlike CTNS-1 , the fusion of lysosomes to the phagosome causes NUC-1::mRFP to enter the phagosomal lumen ( S4A Fig ) . In rab-35 ( b1013 ) embryos , both the recruitment of NUC-1::mRFP to phagosomal surfaces and entry of the mRFP signal occurred on an timescale identical to that of wild-type embryos , suggesting that inactivating rab-35 has no effect on phagolysosomal fusion ( S4B and S4C Fig ) . The rapid enrichment of RAB-35 during the formation of a nascent phagosome ( Fig 2B ) suggests that RAB-35 may function during the initiation of phagosome maturation . At this same time , the predominant phosphatidylinositol species on the phagosomal surface switches from PtdIns ( 4 , 5 ) P2 to PtdIns ( 3 ) P , a process necessary for the progression of phagosome maturation and cell corpse degradation [16 , 18 , 28] . We monitored the dynamic localization pattern of RAB-35 relative to those of PtdIns ( 4 , 5 ) P2 and PtdIns ( 3 ) P . We co-expressed , in engulfing cells , the RAB-35 reporters with previously established reporters for PtdIns ( 4 , 5 ) P2 ( PH ( hPLCγ ) ::GFP ) or PtdIns ( 3 ) P ( 2xFYVE::mRFP ) [18 , 28] ( Fig 5A and 5B ) . We observed that RAB-35 enrichment corresponded exactly with both the loss of PtdIns ( 4 , 5 ) P2 and the gain of PtdIns ( 3 ) P on the surface of a nascent phagosome ( Fig 5A and 5B ) . Because PtdIns ( 3 ) P is essential for phagosome maturation ( see Introduction ) , and because the disappearance of PtdIns ( 4 , 5 ) P2 from phagosomal surfaces is correlated with the production of PtdIns ( 3 ) P on phagosomes ( Fig 5A and 5B ) [25] , we examined whether RAB-35 regulates the dynamic pattern of PtdIns ( 4 , 5 ) P2 and PtdIns ( 3 ) P on phagosomes . We first monitored PtdIns ( 4 , 5 ) P2 dynamics on the surface of phagosomes in a series of mutant embryos ( Fig 5C ) . We found that in rab-35 ( b1013 ) and ced-1 ( n1506 ) mutants , but not ced-5 ( n1812 ) mutants , PtdIns ( 4 , 5 ) P2 persists longer on phagosomal surfaces ( Fig 5C and 5D ) . ced-1 ( n1506 ) mutants exhibited a longer delay in PtdIns ( 4 , 5 ) P2 disappearance than rab-35 ( b1013 ) mutants , and rab-35 ( b1013 ) ; ced-1 ( n1506 ) double mutants exhibited a much more severe delay than either single mutant ( Fig 5D ( a ) ) . These results suggest that RAB-35 and CED-1 act in a parallel and partially redundant fashion for the removal of PtdIns ( 4 , 5 ) P2 from phagosomal surfaces . The ced-5 ( n1812 ) null mutation , on the other hand , does not affect PtdIns ( 4 , 5 ) P2 ( Fig 5C and 5D ( b ) ) . The phagocytic receptor CED-1 was previously demonstrated to play an essential role in initiating PtdIns ( 3 ) P synthesis on the surface of nascent phagosomes [18] . In rab-35 mutant embryos , the appearance of the initial peak of PtdIns ( 3 ) P was significantly delayed compared to wild-type , although the defect was not as strong as that observed in ced-1 mutant embryos ( Fig 6A and 6B ( a ) ) . rab-35; ced-1 double mutants have a more severe delay in the PtdIns ( 3 ) P appearance compared to either single mutant ( Fig 6B ( a ) ) , again suggesting that RAB-35 and CED-1 act in parallel for the generation of PtdIns ( 3 ) P on phagosomal surfaces . On the other hand , because ced-5 mutants fail to exhibit any delay in PtdIns ( 3 ) P production , and because the severity of the delay of PtdIns ( 3 ) P displayed by the rab-35; ced-5 double mutants is equivalent to that of the rab-35 single mutants , we conclude that ced-5 is not involved in the regulation of PtdIns ( 3 ) P production ( Fig 6A and 6B ( b ) ) . PtdIns ( 3 ) P typically appears on the phagosomal surface in two distinct waves [16] , as exhibited in Fig 6A ( note the white and yellow arrows ) . In contrast to wild-type embryos , where every phagosome exhibited both waves , in rab-35 ( b1013 ) and ced-1 ( n1506 ) mutants , the first wave of PtdIns ( 3 ) P was not observed on 21 . 7% and 33 . 3% of phagosomes , respectively ( Fig 6C ) . In addition , in ced-1 mutants , the second wave of PtdIns ( 3 ) P was not observed 33 . 3% of phagosomes ( Fig 6C ) . rab-35 ( b1013 ) ; ced-1 ( n1506 ) double mutants exhibited more severe defects than either single mutant ( Fig 6C ) . Together , our observations indicate that RAB-35 and CED-1 act in parallel to promote the production of PtdIns ( 3 ) P on the surface of nascent phagosomes . To further explore how inactivation of rab-35 delays PtdIns ( 3 ) P production , we performed time-lapse recording of phagosomes containing C1 , C2 , and C3 in order to characterize the localization of PIKI-1 , the PI-3 kinase that has been reported to localize to and functions on the surface of nascent phagosomes , as well as that of the PI-3 phosphatase MTM-1 that largely antagonizes PIKI-1 activity [16 , 25] ( see Introduction ) . rab-35 ( b1013 ) mutants exhibited a normal recruitment pattern of PIKI-1::GFP; the enrichment of PIKI-1::GFP was observed on the surface of every phagosome , and the level of enrichment was comparable to that observed in wild-type embryos ( S5 Fig ) . In contrast , MTM-1::GFP persists on the surface of phagosomes approximately twice as long in rab-35 ( b1013 ) as in wild-type embryos , and more than thrice as long in ced-1 ( n1056 ) mutants ( Fig 7A ) . These results indicate that both RAB-35 and CED-1 are required for the timely removal of MTM-1 from phagosomal surfaces . Furthermore , rab-35 ( b1013 ) ; ced-1 ( n1506 ) double mutants display an even longer delay in MTM-1::GFP removal , indicating that rab-35 and ced-1 function in parallel to regulate MTM-1 removal ( Fig 7C ) . The timing of PtdIns ( 4 , 5 ) P2 disappearance and MTM-1 removal from phagosomal surfaces are similar in all backgrounds analyzed ( Figs 5D and 7C ) , consistent with the fact that MTM-1 is a PtdIns ( 4 , 5 ) P2 effector [25] . In addition , it suggests that , in rab-35 mutants , the persistent presence of PtdIns ( 4 , 5 ) P2 on phagosomal surfaces might cause MTM-1 to remain on phagosomes . Sorting nexins SNX-1 and LST-4 , two PtdIns ( 3 ) P effectors and membrane remodeling factors , are recruited to phagosomal surfaces by PtdIns ( 3 ) P [46] . SNX-1::GFP and LST-4::GFP were visualized using time lapse microscopy to characterize their localization to phagosomal surfaces . SNX-1::GFP is found on 100% of phagosomes in wild-type embryos; moreover , it forms a continuous ring on the surface of more than 90% of phagosomes , a pattern suggesting the presence of a large enough number of the SNX-1::GFP molecules to cover the entire surface of a phagosome ( Fig 7B and 7D ( a ) ) . In contrast , only 73 . 3% of phagosomes rab-35 ( b1013 ) mutants recruit SNX-1::GFP; of these phagosomes , more than half recruit SNX-1::GFP as isolated puncta instead of as a continuous ring ( Fig 7B and 7D ( a ) ) . These results suggest that RAB-35 is important for the efficient recruitment of SNX-1::GFP to phagosomes , consistent with the defects in PtdIns ( 3 ) P production previously observed in rab-35 mutants . However , continuous rings of LST-4::GFP were observed on 100% of phagosomes in both rab-35 ( b1013 ) mutants and wild-type embryos ( Fig 7D ( b ) ) . Given that the recruitment of LST-4 is mediated in part through DYN-1 [46] , the mild defect in PtdIns ( 3 ) P production characteristic of rab-35 ( b1013 ) mutants may not be sufficient to significantly influence the recruitment of LST-4 on phagosomes . Given that rab-35 ( b1013 ) mutants are defective in production of PtdIns ( 3 ) P , and that the recruitment of RAB-5 and the production of PtdIns ( 3 ) P on phagosomal surfaces are co-dependent processes [16] , we investigated the functional relationship between RAB-35 and RAB-5 . We made a number of observations that indicate that RAB-35 functions upstream of RAB-5 in the regulation of phagosome maturation . Firstly , the enrichment of mKate2::RAB-35 on the surface of nascent phagosomes precedes that of GFP::RAB-5 by approximately 30–60 seconds ( Fig 8A ) . Secondly , inactivation of rab-5 using RNAi treatment results in the presence of extra cell corpses in an otherwise wild-type background; moreover , the rab-35 null mutation does not further enhance the Ced phenotype caused by rab-5 ( RNAi ) treatment ( Figs 8C and S6 ) , suggesting rab-35 and rab-5 act in the same genetic pathway . Thirdly , rab-35 ( b1013 ) mutants exhibit a delay in the recruitment of RAB-5 to the phagosome ( Fig 8B and 8D ) . This delay resembles that caused by the ced-1 mutation ( Fig 8B and 8D ) , although it is not as severe . Additionally , rab-35 ( b1013 ) ; ced-1 ( n1506 ) double mutants display a delay stronger than either single mutant ( Fig 8B and 8D ) , suggesting that rab-35 and ced-1 function in parallel to recruit RAB-5 to the phagosome . The initial enrichment of GFP::RAB-35 on extending pseudopods ( Fig 2B ) suggests that in addition to phagosome maturation , RAB-35 might function in earlier steps of cell corpse clearance . To determine whether RAB-35 plays any role in the recognition and/or the engulfment of cell corpses , we took advantage of CED-1ΔC::GFP , a GFP tagged and truncated form of CED-1 that is missing its C-terminal intracellular domain [26] . This engulfing cell-specific transmembrane receptor first clusters to the contact site between the engulfing and dying cell , is further enriched to the extending pseudopods , and , when engulfment is complete , forms a ring around the nascent phagosome ( Fig 9A ) [28] . Unlike the full-length CED-1 , CED-1ΔC::GFP stays on the surface of a phagosome until it is completely degraded [28] . Thus , cell corpses labeled with CED-1ΔC::GFP rings must have been previously engulfed , while cell corpses in the middle of being engulfed are labeled with partial GFP+ rings that represent phagocytic cups ( Fig 9A ) . Cell corpses that are not recognized by an engulfing cell fail to be labeled by a GFP signal ( Fig 9B ) . We first analyzed all cell corpses in mid-stage ( 1 . 5-fold stage ) embryos . In rab-35 ( b1013 ) embryos , a significantly lower percentage of engulfed cell corpses were observed than in wild-type embryos ( Fig 9C ) , suggesting that the rab-35 mutation causes defects in the internalization of cell corpses . Such a defect might result from defects in the recognition or the actual engulfment of the cell corpse . To distinguish which is the case , we monitored the recognition and engulfment of dying cells C1 , C2 , and C3 using the CED-1ΔC::GFP reporter . To further discern the precise moment that pseudopod extension initiates , we took advantage of the temporal consistency of C . elegans development between embryos , choosing the moment when the two ventral hypodermal cells ABplaapppp and ABpraapppp begin to extend towards the ventral midline as the “0” time point ( Fig 9A ) . Because Pced-1 ced-1ΔC::gfp is expressed in embryonic hypodermal cells and localizes to the plasma membrane , the GFP signal allows us to accurately record this moment ( Fig 9D ) . The moment of recognition is thus the time point when the clustering of GFP signal at the dying- and engulfing- cell contact site is observed . We found that the recognition of cell corpses was delayed in rab-35 ( b1013 ) mutant embryos ( Figs 9D and S7A ) . In wild-type embryos , 40% of the cell corpses are recognized within the first 10 minutes of ventral enclosure , yet in rab-35 ( b1013 ) mutants , only 6 . 7% cell corpses are recognized within the same time period ( S7A ( a ) Fig ) . Additionally , in rab-35 ( b1013 ) mutants , 20% of the cell corpses are recognized between 21–30 minutes after the start of ventral enclosure , whereas only 6 . 7% of cell corpses take that long to be recognized in wild-type embryos ( S7A ( a ) Fig ) . Conversely , rab-35 null mutation causes no obvious delays in pseudopod extension and the consequential engulfment once the cell corpse is recognized ( Fig 9D and S7B ) . Together , these results indicate that during the cell-corpse internalization process , RAB-35 specifically regulates the recognition of cell corpses . We found that null mutants of ced-1 and ced-5 , two engulfment genes that each represents one of the two parallel pathways for engulfment [8 , 47 , 48] , display significantly greater delays in cell corpse-recognition than rab-35 ( b1013 ) null mutants ( Figs 9D and S7A ( a-c ) ) , indicating that both CED-1 and CED-5 are essential for the timely recognition of apoptotic cells . The ced-1; ced-5 double mutant embryos suffer greater recognition delay than each single mutant strain ( S7A ( b-f ) Fig ) , indicating that for the step of cell corpse-recognition , ced-1 and ced-5 act in two parallel pathways . We further observed that in double mutant combinations , rab-35 ( b1013 ) mutants enhanced the recognition delay of both ced-1 ( n1506 ) and ced-5 ( n1812 ) mutants ( S7A ( b-c , e ) Fig ) , suggesting that rab-35 acts in a pathway separate from the ced-1 or ced-5 pathways in the context of cell corpse recognition . The ced-1; rab-35; ced-5 triple null mutants suffer a stronger recognition delay than any of the respective double mutants , with 63 . 2% of cell corpses not being recognized within 40 minutes after ventral enclosure started ( S7A ( d , f ) Fig ) , further supporting our hypothesis . The rab-35 null mutation does not enhance the delay in engulfment caused by the ced-1 or ced-5 null mutations ( S7B Fig ) , again demonstrating that rab-35 does not regulate pseudopod extension around cell corpses . We have identified the functions of RAB-35 in two distinct cell-corpse clearance events: ( I ) the recognition and ( II ) the degradation of cell corpses . In both of these events , RAB-35 appears to function independently of both CED-1 and CED-5 . To further determine whether rab-35 represents a novel pathway in addition to the ced-1 and ced-5 pathways to regulate apoptotic cell-clearance , we performed a thorough epistasis analysis between rab-35 and the ced-1/ced-6/ced-7 pathway and the ced-2/ced-5/ced-10/ced-12 pathway [48] by quantifying the number of cell corpses in double mutant combinations between the putative null allele rab-35 ( b1013 ) and null alleles of representative genes in both pathways . Remarkably , rab-35 was found to be parallel to multiple components of both the ced-1/ced-6/ced-7 pathway ( ced-1 and ced-6 ) and the ced-2/ced-5/ced-10/ced-12 pathway ( ced-5 , ced-10 , and ced-12 ) , as rab-35 loss of function substantially enhances the Ced phenotype of each of these mutants in embryos and adult gonads ( Figs 10A , 10B and S8 ) . Furthermore , in rab-35 ( b1013 ) ; ced-1 ( n1506 ) ; ced-5 ( n1812 ) triple mutants , the number of cell corpses is further increased relative to each of the double mutant strains ( Figs 10A , 10B and S8 ) , again suggesting that rab-35 contributes to the clearance activity by acting in a pathway independent of either the ced-1 or ced-5 pathways . To search for candidate phagocytic receptors that function in this new engulfment pathway , we examined genes encoding integrins , transmembrane receptors that have been previously implicated in apoptotic cell clearance in both mammals and C . elegans [37 , 38 , 49 , 50] . C . elegans contains two α integrin subunits ( ina-1 , pat-2 ) as well as a single β integrin subunit ( pat-3 ) , RNAi knockdown of each of which generates a modest Ced phenotype as previously reported [37 , 38 , 49 , 50] ( Fig 10C ) . Furthermore , knockdown of ina-1 , pat-2 , or pat-3 fail to enhance the Ced phenotype caused by the rab-35 ( b1013 ) mutation ( Fig 10C ) . On the other hand , knockdown of ina-1 , pat-2 , or pat-3 all significantly enhance the Ced phenotypes caused by the ced-1 or ced-5 null mutations ( Fig 10C ) . Collectively , these data indicate that integrins act in parallel to both the ced-1 and ced-5 pathways , but might function in the same pathway as rab-35 .
For many Rab small GTPases , such as Rab7 , the GTP- and GDP-bound forms are their active and inactive forms , respectively [18 , 70 , 71] . Other small GTPases , such as RAB-5 , need to switch between the GTP- and GDP-bound forms in order to function [72] . Our characterization of the presumed GTP- and GDP-locked mutant forms of RAB-35 has revealed that the specific function of RAB-35 in apoptotic cell clearance requires it to cycle between the GTP- and GDP-bound forms , resembling the dynamics observed in RAB-5 . Among three C . elegans homologs of mammalian proteins known to act as Rab35 GAPs [73] , we have identified TBC-10 as the GAP for RAB-35 in the context of apoptotic cell clearance . In tbc-10 deletion mutants , where RAB-35 is supposedly locked in the GTP-bound state , GFP::RAB-35 persists on the surfaces of phagosomes . However , apoptotic cell clearance is defective to a degree akin to rab-35 null mutants , supporting the hypothesis that RAB-35 must switch between its GTP- and GDP-bound forms to function properly . TBC-10 is strongly enriched on the extending pseudopods and developing phagosome and colocalizes with RAB-35 . Furthermore , the disappearance of TBC-10 from phagosomal surfaces coincides in time with the burst of RAB-35 enrichment observed shortly after engulfment . TBC-10 , as a GAP for RAB-35 , might thus regulate multiple aspects concerning the localization and function of RAB-35 , although further research is necessary . Although in vitro GEF activity of folliculin for mammalian Rab35 has been detected [74] , and folliculin was reported to activate Rab35 to mediate EGF receptor recycling in a cancer cell line [75] , the functional relationship between folliculin and Rab35 in an animal context has not been reported . We found that flcn-1 , the C . elegans homolog of folliculin , acts in the same genetic pathway as rab-35 does to promote cell corpse clearance . This result , together with the observed pseudopod and phagosome enrichment of FLCN-1 , suggest that FLCN-1 might act as a GEF for RAB-35 during cell corpse clearance . This is the first time that folliculin is implicated as a GEF for RAB-35 during animal development . We have further revealed that C . elegans RME-4 , a homolog of the connecdenns DENND1A-C and a GEF for RAB-35 in yolk receptor recycling [41] , activates RAB-35 alongside FLCN-1 during apoptotic cell clearance . However , the rme-4 null mutation does not cause a significant defect in clearance by itself , suggesting that FLCN-1 acts as the predominant GEF for RAB-35 in the context of apoptotic cell clearance , while RME-4 only plays a minor role . Our results are consistent with the observation that , as a multifunctional GTPase , RAB-35 is regulated by different GEFs in each cellular event that it is involved [44] . Phosphorylated forms of phosphatidylinositol species are second messengers that play essential roles leading the formation and degradation of phagosomes [24] . During apoptotic cell clearance in C . elegans , a process known as PtdIns ( 4 , 5 ) P2 to PtdIns ( 3 ) P switch occurs immediately after the sealing of pseudopods and the formation of nascent phagosomes . During this switch , PtdIns ( 4 , 5 ) P2 –which has been enriched on extending pseudopods–rapidly disappears from phagosomal surfaces . PtdIns ( 3 ) P , which is essential for the initiation of phagosome maturation , subsequently appears on phagosomal surfaces at a high level and oscillates in a biphasic pattern [16 , 25 , 28] . We have observed that once engulfment starts , GFP::RAB-35 becomes enriched on the surface of extending pseudopods . The pseudopod localization pattern overlaps with that of PtdIns ( 4 , 5 ) P2 and might be a result of RAB-35’s membrane-anchoring prenylation motif typical of Rab GTPases [76] and its evolutionarily conserved polybasic region that has a high affinity for negatively charged phosphatidylinositol species such as PtdIns ( 4 , 5 ) P2 [77–79] . On the surfaces of nascent phagosomes , the further initiation of RAB-35 enrichment coincides perfectly with both the turnover of PtdIns ( 4 , 5 ) P2 and the appearance of PtdIns ( 3 ) P . This unique pattern is consistent with a role of RAB-35 in the switch of phagosomal phosphatidylinositol species from PtdIns ( 4 , 5 ) P2 to PtdIns ( 3 ) P . Furthermore , we found that rab-35 mutants suffer significant delays in both the disappearance of PtdIns ( 4 , 5 ) P2 and the appearance of the first wave of PtdIns ( 3 ) P on phagosomal membranes . Interestingly , we found no defects in the recruitment of the PI 3-kinase PIKI-1 . However , we have discovered that the PI 3-phosphatase MTM-1 , a PtdIns ( 4 , 5 ) P2 effector that dephosphorylates PtdIns ( 3 ) P and in this way counteracts the function of phosphatidylinositol 3-kinases [16 , 25] , persists on the surface of nascent phagosomes much longer in rab-35 mutants . Together , the above evidence indicates that RAB-35 , through an unknown mechanism , promotes the turnover of PtdIns ( 4 , 5 ) P2 on phagosomal membranes , which in turn leads to the loss of MTM-1 from phagosomal membranes and the increase of PtdIns ( 3 ) P production . We have also found that RAB-35 contributes to RAB-5 recruitment on phagosomal surfaces . As RAB-5 promotes the production of phagosomal PtdIns ( 3 ) P on phagosomal surfaces [16] , we propose that RAB-35 facilitates the robust production of PtdIns ( 3 ) P on phagosomal surfaces through two separate activities , the removal of PtdIns ( 4 , 5 ) P2 and the recruitment of RAB-5 ( Fig 11B ) . This delay of PtdIns ( 3 ) P production observed in rab-35 mutants is associated with numerous defects in phagosome maturation: ( I ) the degradation of cell corpses is delayed; ( II ) the phagosomal recruitment of early endosomes , an intracellular organelle that is essential for phagosome maturation [13] , is also delayed; and ( III ) SNX-1 , a sorting nexin and PtdIns ( 3 ) P effector known to promote phagosome maturation [46] , is recruited to the phagosomal surface less efficiently . Given that SNX-1 is necessary for the recruitment of early endosomes to phagosomes [46] , we propose that this defect might be a cause for the defects in the recruitment of early endosomes . Our findings thus uncover a molecular mechanism employed by RAB-35 for cell corpse degradation and delineates a novel pathway led by RAB-35 for regulating the PtdIns ( 4 , 5 ) P2-to-PtdIns ( 3 ) P switch , an event essential for the initiation of phagosome maturation . What we report suggests that RAB-35 promotes phagosome maturation primarily through the removal of PtdIns ( 4 , 5 ) P2 on phagosomal surfaces . Inactivation of mammalian Rab35 results in the accumulation of PtdIns ( 4 , 5 ) P2 on intracellular vacuoles and other structures [57 , 60] , suggesting that the timely turnover of PtdIns ( 4 , 5 ) P2 is a conserved activity of Rab35 . The level of PtdIns ( 4 , 5 ) P2 is known to be determined by two antagonizing activities , the phosphorylation of PtdIns ( 4 ) P or PI ( 5 ) P by PI kinases and the dephosphorylation of PtdIns ( 4 , 5 ) P2 by PI phosphatases [80] . Whether RAB-35 targets any of the PI kinases or phosphatases awaits further investigation . The phagocytic receptor CED-1 and its adaptor CED-6 play a separate role in initiating phagosome maturation in addition to recognizing cell corpses and initiating engulfment [13 , 18] . The CED-1 pathway promotes PtdIns ( 3 ) P production on–and Rab GTPase recruitment to–phagosomal surfaces [13 , 18] . Here we observed that all of the defects in phagosome maturation displayed by rab-35 null mutants , including the persistence of PtdIns ( 4 , 5 ) P2 and its effector MTM-1 on nascent phagosomes , the delay in phagosomal PtdIns ( 3 ) P production , the various defects in the recruitment of SNX-1 and RAB-5 , and the delay of the incorporation of early endosomes to phagosomes , are also displayed by ced-1 null mutants [8 , 18 , 20 , 46] ( Figs 4–8 ) . However , all of these defects are much more pronounced in ced-1 mutants; for instance , phagosomal PtdIns ( 3 ) P production , the incorporation of early phagosomes , and phagosome degradation are frequently blocked in ced-1 mutants [8 , 18] , whereas in rab-35 mutants they are merely delayed . In fact , this is the first time that the function of CED-1 in promoting phagosomal PtdIns ( 4 , 5 ) P2 removal is revealed . Remarkably , rab-35; ced-1 double mutants display enhanced defects relative to each single mutant in all of the assays mentioned above , indicating that RAB-35 initiates phagosome maturation in a manner independent of the CED-1 pathway . Given that CED-1 controls other events such as the incorporation of lysosomes to phagosomes in addition to those events regulated by RAB-35 [18] , our observations suggest that this novel RAB-35 pathway acts in parallel to the CED-1 pathway in some but not necessarily all phagosome maturation events . The recognition and engulfment of cell corpses are known to be controlled by two parallel pathways , the ced-1/-6/-7 and ced-2/-5/-10/-12 pathways [18 , 48 , 81] . The rab-35 ( b1013 ) null mutation also causes a delay in this process , but this defect is not as severe as that observed from either the ced-1 or ced-5 null mutations . The rab-35 null mutation does not affect pseudopod extension , yet it enhances the defect in cell corpse recognition observed in ced-1 or ced-5 mutants , suggesting that RAB-35 functions in parallel to both CED-1 and CED-5 in the recognition step . When the overall clearance defects were measured by epistasis analysis , we found that rab-35 defines a third pathway by acting in parallel to both the ced-1/-6/-7 and ced-2/-5/-10/-12 pathways in both the recognition and degradation of cell corpses . INA-1 , one of the two C . elegans integrin α subunits , has been proposed to act as a phagocytic receptor because of its enrichment on pseudopods and in vivo binding to cell corpses [37] . It was further placed in the ced-2/-5/-10/-12 pathway through epistasis analysis [37] . PAT-2 , the second integrin α subunit , and PAT-3 , the β integrin subunit , were also proposed to act as phagocytic receptors , and the small GTPase CDC-42 was reported to mediate the effect of PAT-2 [38 , 50] . However , in the literature there is some discrepancy regarding whether PAT-2 signaling acts upstream of the CED-10/Rac1 GTPase [38 , 50] . Our observation that ina-1 , pat-2 , and pat-3 act in the same pathway as rab-35 and apparently in parallel to the ced-1/-6/-7 and ced-2/-5/-10/-12 pathways , together with the nature of integrins as transmembrane receptors , suggest that rab-35 might function downstream of ina-1 , pat-2 , and pat-3 , a hypothesis that is reflected in Fig 11 . They further suggest in which pathway the integrins act might be more complex an issue than previously reported . Future investigation will determine whether and how RAB-35 mediates integrin signaling for the recognition of apoptotic cells . Also , whether the integrins participate in cell corpse-degradation needs to be explored . Given that all phenotypes observed in rab-35 single mutants are relatively modest , is the contribution of the rab-35 pathway important for apoptotic cell clearance ? We propose that RAB-35 acts as a robustness factor that provides a “buffer” to maintain the stability and effectiveness of cell corpse clearance . Robustness factors are important in maintaining system stability when animals encounter genetic or environmental changes . Indeed , after rab-35 mutant embryos are subject to heat treatment , the Ced phenotype is further enhanced ( S9 Fig ) , indicating that RAB-35 helps to keep the mechanisms behind apoptotic cell clearance stable when the system is stressed . When both the CED-1 and CED-5 pathways are intact , missing RAB-35 activity only causes modest defects , much weaker than missing either of the two canonical pathways; however , when one or both of the two canonical pathways is inactivated or when under stress , the RAB-35 pathway provides the necessary activity to support cell corpse clearance . Considering that diseases can be regarded as a subversion of the “robust yet fragile” nature of optimized and complex biological systems [82–84] , we postulate that RAB-35 plays a critical role in health . This effect is likely enhanced in aging individuals that experience an increased incidence of autoimmunity and cancer [85 , 86] , which are associated with defects in apoptotic cell clearance and RAB-35 function [3 , 65 , 73 , 87] . Further exploring this physiological role for RAB-35 will broaden our view of the function of Rab GTPases in both development and diseases .
C . elegans strains were grown at 20°C as previously described [88] . The N2 Bristol strain was used as the reference wild-type strain . Mutations and integrated arrays were performed as described previously [39 , 89] , except when noted otherwise: LGI , ced-1 ( n1506 ) , ced-12 ( n3261 ) , rab-10 ( ok1494 ) , unc-75 ( e950 ) ; LGII , flcn-1 ( ok975 ) , C56E6 . 2 ( ok1307 ) ; LGIII , ced-6 ( n2095 ) , rab-35 ( b1013 , tm2058 ) , tbc-10 ( tm2790 ) , Y71H2AM . 12 ( ok1989 ) ; LGIV , ced-5 ( n1812 ) , ced-10 ( n1993 ) , 4R79 . 2 ( tm2640 ) ; LGV , unc-76 ( e911 ) , F11A5 . 3 ( tm3628 ) , F11A5 . 4 ( tm3630 ) ; LGX , rme-4 ( ns410 ) , tbc-13 ( ok1812 ) , rsef-1 ( ok1356 ) , K02E10 . 1 ( tm2564 ) . All ok alleles were provided by the C . elegans Gene Knockout Consortium and distributed by Caenorhabditis Genetics Center ( CGC ) . All tm alleles were generated and provided by the National Bioresource Project of Japan . Transgenic lines were generated by microinjection as previously described [90] . Plasmids were injected alongside the coinjection marker pUNC76 [unc-76 ( + ) ] into unc-76 ( e911 ) mutant adult hermaphrodites as previously described [91] , with non-Unc animals being identified as transgenic animals . The cDNAs for rab-11 . 1 , -18 , -19 , -30 . -33 , -35 , glo-1 , nuc-1 , tbc-10 , and flcn-1 were amplified from a mixed-stage C . elegans cDNA library using polymerase chain reaction ( PCR ) . The cDNAs for rab-11 . 1 , -18 , -19 , -30 . -33 , -35 , glo-1 were cloned into RNAi-by-feeding vector L4440 to generate RNAi constructs . Pced-1 gfp::rab-35 was constructed by cloning the rab-35 cDNAs [41] into the XmaI and KpnI sites of pZZ956 ( Pced-1 5’ gfp ) . Pced-1 mKate2::rab-35 was constructed by replacing the gfp cDNA in Pced-1 gfp::rab-35 with mKate2 cDNA [92] . The ( S24N ) and ( Q69L ) mutations were introduced into Pced-1 gfp::rab-35 using the QuickChange Site-directed Mutagenesis Kit ( Stratagene , La Jolla , CA ) to generate Pced-1 gfp::rab-35 ( S24N ) and Pced-1 gfp::rab-35 ( Q69L ) , respectively . Using the same kit , the S33N mutation was introduced into Phsp-16/2 rab-5 and Phsp-16/41 rab-5 . Phsp-16/2 gfp::rab-5 ( S33N ) and Phsp-16/41 gfp::rab-5 ( S33N ) were produced by inserting the gfp cDNA into Phsp-16/2 rab-5 ( S33N ) and Phsp-16/41 rab-5 ( S33N ) , respectively . The nuc-1 cDNA [93] was inserted into the BamHI and XmaI sites of pZZ829 ( Pced-1 gfp ) to generate Pced-1 nuc-1::gfp . Pced-1 nuc-1::mcherry was generated by replacing the gfp cDNA with the mcherry cDNA . Pced-1 gfp::flcn-1 was constructed by cloning the flcn-1 cDNA into the XmaI and KpnI sites of pZZ956 ( Pced-1 5’ gfp ) . Pced-1 tbc-10::gfp was constructed by cloning the tbc-10 cDNA into the AgeI and BamHI sites of pZZ829 ( Pced-1 3’ gfp ) . All plasmids contain an unc-54 3’ UTR . RNAi screen of the candidate rab genes was performed using the feeding protocol as previously described [94] . The RNAi feeding constructs for rab-11 . 1 , -18 , -19 , -30 , -33 , -35 , and glo-1 were produced by our lab , while the remaining constructs came from a C . elegans RNAi library [95 , 96] . Mid-L4 stage hermaphrodites were placed on plates seeded with E . coli containing the RNAi feeding construct . After 48 hrs , the numbers of germ cell corpses per gonad arm were scored using a DIC microscope . RNAi of tbc-7 , rab-5 , ina-1 , pat-2 , and pat-3 was performed using the same feeding protocol as above using constructs from the same library except that , 24 hrs after L4-stage hermaphrodites were placed on RNAi feeding plates , these adults were transferred to a second RNAi plate . After an additional 24 hours , the numbers of cell corpses in 1 . 5-fold and late 4-fold stage embryos were scored using a DIC microscope . DIC microscopy was performed using an Axionplan 2 compound microscope ( Carl Zeiss , Thornwood , NY ) equipped with Nomarski DIC optics , a digital camera ( AxioCam MRm; Carl Zeiss ) , and imaging software ( AxioVision; Carl Zeiss ) . Previously established protocols were used to score cell corpses under DIC microscopy [8 , 43] . Somatic embryonic cell corpses were scored in the head region of embryos at various developmental stages ( comma , 1 . 5-fold , 2-fold , late 4-fold , and early L1 ) . Germ cell corpses were scored in one of the two gonadal arms of adult hermaphrodites 24 or 48 hrs after the mid-L4 stage . Yolk analysis was performed by characterizing the amount of yolk found in the pseudocoelom near the gonads of adult hermaphrodites 24 or 48 hrs after the L4 stage . An Olympus IX70-Applied Precision DeltaVision microscope equipped with a DIC imaging apparatus and a Photometris Coolsnap 2 digital camera was used to capture fluorescence and DIC images , while Applied Precision SoftWoRx software was utilized for image deconvolution and processing [43] . To quantify the number of engulfed cell corpses in 1 . 5-fold to 2-fold stage embryos expressing CED-1ΔC::GFP , both DIC and GFP images of 40 serial z-sections at a 0 . 5-μm were recorded for each embryo . Engulfed cell corpses were those labeled with a full GFP+ circle . Unengulfed cell corpses were those that display the refractive appearance under DIC optics yet were either labeled with a partial GFP+ circle or not labeled at all . The dynamics of various GFP , mRFP , mKate2 , and mCherry reporters during the engulfment and degradation of cell corpses C1 , C2 , and C3 were examined using an established time-lapse recording protocol [18 , 43] . Ventral surfaces of embryos were initially monitored 300–320 minutes post-first cleavage . Recordings typically lasted 60–180 minutes , with an interval of 30 secs to 2 mins . At each time point , 10–16 serial z-sections at a 0 . 5-μm interval were recorded . Signs such as embryo elongation and embryo turning prior to comma stage were closely monitored under DIC to ensure that the embryo being recorded was developing normally . The moment of cell corpse recognition is the time when CED-1ΔC:GFP first clusters to the region where an engulfing cell contacts a cell corpse , measured relative to the moment ventral enclosure begins; the initiation of ventral enclosure is defined as the time point when hypodermal cells ABplaapppp and ABpraapppp begin to extend across the ventral surface . The time period of pseudopod extension is the time interval between when budding pseudopods labeled with CED-1ΔC::GFP are first observed and when the two pseudopods join and seal to form a nascent phagosome . The life span of a phagosome is defined as the time interval between when pseudopods seal to form the nascent phagosome and when the phagosome shrinks to one-third of its original radius .
|
After apoptosis , cell corpses must be promptly recognized and phagocytosed by engulfing cells . These nascent phagosomes then undergo a maturation process that results in the degradation of the apoptotic cell corpse . Phagosome maturation is enabled and coordinated through Rabs , small GTPases that recruit specific sets of effectors to the phagosome . The ced-1/6/7/dyn-1 and ced-2/5/10/12 pathways represent the two major canonical pathways in apoptotic cell clearance . However , substantial apoptotic cell clearance persists even when both of these pathways are inactivated . We show that the small GTPase RAB-35 promotes the recognition of cell corpses necessary for engulfment , as well as the initiation of phagosome maturation after engulfment . Specifically , RAB-35 localizes to nascent phagosomes and promotes simultaneous PtdIns ( 4 , 5 ) P2 turnover and PtdIns ( 3 ) P production , a recently discovered process that we refer to as the PtdIns ( 4 , 5 ) P2-to-PtdIns ( 3 ) P switch . We also found that RAB-35 aids in the recruitment of RAB-5 and early endosomes to the nascent phagosome . Although CED-1 also performs many of these same functions , we found that rab-35 acts in parallel to both the ced-1/6/7/dyn-1 and ced-2/5/10/12 pathways . Therefore , we claim that RAB-35 defines a third pathway that functions throughout apoptotic cell clearance in order to make it more robust .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"cell",
"death",
"invertebrates",
"vesicles",
"rna",
"interference",
"caenorhabditis",
"enzymes",
"cell",
"processes",
"enzymology",
"animals",
"animal",
"models",
"developmental",
"biology",
"caenorhabditis",
"elegans",
"guanine",
"nucleotide",
"exchange",
"factors",
"model",
"organisms",
"phagosomes",
"experimental",
"organism",
"systems",
"epigenetics",
"embryos",
"cellular",
"structures",
"and",
"organelles",
"research",
"and",
"analysis",
"methods",
"embryology",
"genetic",
"interference",
"proteins",
"gene",
"expression",
"guanosine",
"triphosphatase",
"biochemistry",
"rna",
"signal",
"transduction",
"eukaryota",
"hydrolases",
"cell",
"biology",
"nucleic",
"acids",
"phenotypes",
"apoptosis",
"genetics",
"nematoda",
"biology",
"and",
"life",
"sciences",
"cell",
"signaling",
"organisms",
"signaling",
"molecules"
] |
2018
|
The small GTPase RAB-35 defines a third pathway that is required for the recognition and degradation of apoptotic cells
|
The morphogenetic transition between yeast and filamentous forms of the human fungal pathogen Candida albicans is regulated by a variety of signaling pathways . How these pathways interact to orchestrate morphogenesis , however , has not been as well characterized . To address this question and to identify genes that interact with the Regulation of Ace2 and Morphogenesis ( RAM ) pathway during filamentation , we report the first large-scale genetic interaction screen in C . albicans . Our strategy for this screen was based on the concept of complex haploinsufficiency ( CHI ) . A heterozygous mutant of CBK1 ( cbk1Δ/CBK1 ) , a key RAM pathway protein kinase , was subjected to transposon-mediated , insertional mutagenesis . The resulting double heterozygous mutants ( 6 , 528 independent strains ) were screened for decreased filamentation on Spider Medium ( SM ) . From the 441 mutants showing altered filamentation , 139 transposon insertion sites were sequenced , yielding 41 unique CBK1-interacting genes . This gene set was enriched in transcriptional targets of Ace2 and , strikingly , the cAMP-dependent protein kinase A ( PKA ) pathway , suggesting an interaction between these two pathways . Further analysis indicates that the RAM and PKA pathways co-regulate a common set of genes during morphogenesis and that hyper-activation of the PKA pathway may compensate for loss of RAM pathway function . Our data also indicate that the PKA–regulated transcription factor Efg1 primarily localizes to yeast phase cells while the RAM–pathway regulated transcription factor Ace2 localizes to daughter nuclei of filamentous cells , suggesting that Efg1 and Ace2 regulate a common set of genes at separate stages of morphogenesis . Taken together , our observations indicate that CHI–based screening is a useful approach to genetic interaction analysis in C . albicans and support a model in which these two pathways regulate a common set of genes at different stages of filamentation .
Candida albicans is a member of the resident flora of the gastrointestinal tract and is the most common fungal pathogen in humans . The most severe manifestations of candidiasis occur in immunocompromised patients and include debilitating mucosal disease such as oropharyngeal candidiasis as well as life-threatening disseminated infections of the bloodstream and major organ systems [1] . Animal studies have shown that the pathogenic potential of C . albicans is associated with its ability to transition between three morphological states: yeast , pseudohyphae , and hyphae [2] , [3] . Further insights into the contributions of the different morphotypes to pathogenesis have emerged from elegant studies with C . albicans strains that allow the conditional induction of filamentation in vivo [4] . For example , C . albicans genetically restricted to the yeast form by constitutive expression of NRG1 are able to establish infection in mice but no disease results until the expression of NRG1 is repressed and the organism is able to form filaments . The relationship between morphogenesis and virulence in C . albicans is , however , not a simple one . Many mutants that are unable to undergo morphogenesis also display other phenotypes . For example , many transcription factors that are required for morphogenesis regulate a host of other genes and display pleiomorphic phenotypes . The complicated nature of the relationship between morphogenesis has been further highlighted by the elegant study recently reported by Noble et al . [5] . Noble et al . generated a bar-coded collection of homozygous deletion mutants and used it in a signature-tagged mutagenesis study of infectivity in a mouse model [5] . Mutants with defects in morphogenesis were more likely to have decreased infectivity; however , a significant portion of mutants with severe morphogenesis defects retained the ability to cause infection . It is important to note that Noble et al . assayed for infection and not for disease . Thus , their results are not necessarily in conflict with studies discussed above that indicate that morphogenesis is required for disease progression in animal models [4] . Furthermore , their work serves to highlight the fact that additional studies will be required to fully understand the complex relationship between morphogenesis and pathogenesis in C . albicans . Given the close association of morphogenesis with C . albicans pathogenesis , the genetic and cell biologic analysis of this process has been the subject of intensive study [6] . Consequently , many genes have been shown to affect filamentation , and , correspondingly , a number of regulatory pathways have been shown to play a role in the orchestration of the morphogenetic program in C . albicans [7] . The PKA , CPH1 , HOG1 , RIM101 , CHK1 , and CBK1/RAM pathways are among those that regulate morphogenesis under a variety of conditions [6] , [7] . Although much remains to be learned about how individual pathways and genes contribute to morphogenesis , an important question that has not been extensively studied is how these various pathways interact to regulate morphogenesis . In the model yeast S . cerevisiae , relationships between regulatory pathways can be readily characterized using recently developed systematic , genome-wide genetic interaction strategies [8]–[10] . These approaches have yielded a wealth of information regarding the mechanisms through which cells regulate complex biological processes [11] . However , because C . albicans is diploid and lacks a classical meiotic cycle , the mating-based genetic strategies used to create genome-wide libraries of double mutant strains in S . cerevisiae are not applicable . Consequently , genetic interaction studies in C . albicans have been limited to gene-by-gene analyses . Despite these limitations , such studies have proven quite informative and suggest that large scale interaction studies could represent a powerful approach to studying regulatory networks in C . albicans . For example , Braun et al . carried out a thorough , systematic epistasis analysis of three transcriptional regulators ( EFG1 , TUP1 and CPH1 ) and showed that each played a distinct role in the regulation of filamentation [12] . Recent advances in the genetic analysis of C . albicans have greatly facilitated the development of innovative approaches to the study of this important human pathogen [13] . Among these important developments is the application of transposon-based mutagenesis strategies [14] to the creation and study of large-scale libraries of heterozygous [15] , and homozygous [17] , [18] C . albicans mutants . Similarly , large collections of homozygous null [19] and conditional mutants [20] have been created in a targeted manner and analyzed for a variety of phenotypes including morphogenesis , virulence and drug susceptibility . To our knowledge , one area that has not been explored is the development of approaches to large-scale synthetic genetic interaction analysis in C . albicans . Here , we describe the first large-scale synthetic genetic interaction screen in C . albicans . Our strategy builds on pioneering yeast genetics approaches developed in both S . cerevisiae and C . albicans and is based on the concept of complex haploinsufficiency ( CHI ) . CHI is a special case of a genetic phenomenon referred to as unlinked non-complementation in the context of yeast genetics and as dominant enhancers or dominant modifiers when applied to Drosophila [21] . Unlinked non-complementation occurs when a cross between two haploid strains containing single recessive mutations located in separate loci results in a diploid strain ( complex heterozygote ) that retains the phenotype of a parental strain . In yeast , the construction of such mutants was used to great advantage in the genetic analysis of cytoskeletal genes such as tubulin [22] and actin [23] . CHI , which is a special case of unlinked non-complementation , occurs when strains containing heterozygous mutations at two separate loci display a more severe phenotype than strains that contain heterozygous mutations at the single loci alone [21] . In essence , CHI can also be called synthetic haploinsufficiency . Recently , a genome-wide CHI-based strategy was developed in S . cerevisiae and successfully used to create a genetic interaction network for the essential gene , ACT1 [21] . As described in the seminal work of Uhl et al . [15] , large scale haploinsufficiency-based screening was first applied to C . albicans in the transposon-mediated , insertional mutagenesis analysis of filamentation and , thus , haploinsufficiency-based screening has excellent precedence in this system . Whereas Uhl et al . carried out their haploinsufficiency screen starting with a “wild type” strain [15] , we reasoned that transposon mutagenesis of a parental strain containing a heterozygous mutation at a locus of interest would represent an expedient approach to the generation of a large library of complex heterozygotes that could then be the basis of a CHI screen for genes that interact with the parental mutant . In principle , CHI-based screening has a number of attractive features . First , CHI allows one to identify genes that function within the pathway affected by the parental or query mutation including upstream and downstream components of the pathway , transcriptional outputs of the pathway , and substrates of pathway enzymes . Second , CHI-based screening can also identify genes or pathways that function in parallel with the query pathway and , therefore , allow one to identify pathways that co-regulate a given process . Third , CHI is ideal for the study of essential genes because only heterozygous mutations are generated . We developed a CHI-based screening strategy ( Figure 1 ) and applied it to the identification of genes that interact with the RAM signaling network during C . albicans filamentation [24]–[27] . The RAM network has been extensively studied in S . cerevisiae [28] and is required for a variety of cellular processes in both S . cerevisiae and C . albicans including polarity , cell wall synthesis , cell separation and filamentous growth . Cbk1 is the key serine/threonine protein kinase [24] , [27] that mediates many of the functions of the RAM network through its regulation of the transcription factor Ace2 [24] , [27] . RAM pathway mutants in C . albicans show two distinct filamentation phenotypes: CBK1 null mutants are unable to form filaments on Spider Medium ( SM ) or serum-containing medium [24] , [27] while ACE2 null mutants are constitutively pseudohyphal and form true hyphae on serum [25] . Although our understanding of the RAM network in C . albicans has increased in recent years [24]–[27] , many questions remain , including: how does it interact with the many other regulatory pathways during morphogenesis and what genes and proteins are regulated by Cbk1 and/or its downstream transcription factor Ace2 ? Through this novel application of a CHI-based screening strategy , we have identified RAM/Ace2 transcriptional targets and generated genetic evidence for an interaction between the RAM and PKA pathways during morphogenesis . Follow-up studies of the screening results further suggest that a balance between RAM and PKA-pathway activity is required for cells to establish a normal distribution of morphotypes during nutrient-induced filamentation . Taken together with previous work on these two pathways , our observations support a model where PKA-regulated transcriptional activity is most important in the transcription of RAM/PKA co-regulated genes early in morphogenesis , while the RAM pathway is more important as daughter nuclei accumulate within the hyphal structure .
An outline of the CHI-based screening strategy is presented in Figure 1 . In preparation for the CHI screen , we first constructed a transposon suitable for large-scale insertional mutagenesis in C . albicans . To enable efficient mutagenesis with limited transposition bias , we generated a donor plasmid derived from the bacterial element Tn7 . The Tn7 system has been used extensively for in vitro mutagenesis [29] , [30] with low reported insertion site specificity [31] . For purposes of this screen , the Tn7 element was modified to contain a recyclable allele of the CaURA3 gene; specifically , we inserted the URA3-dpl200 allele into Tn7 sequence encoded in the donor plasmid pGPS3 . The URA3-dpl200 allele was designed by Wilson et al . [32] to allow recombinational excision of the URA3 gene under counter-selection with 5-fluoro-orotic acid ( 5-FOA ) . Subsequently , we performed in vitro mutagenesis of the genomic library pEMBLY23 ( Materials and Methods ) derived from C . albicans strain WO-1 . Non-specific Tn7 transposition was achieved using the TnsA , TnsB , and TnsC* proteins paired with the TnsAB transposase and appropriate cofactors [29] . The genomic library was mutagenized to yield an estimated 20 , 000 independent insertions . The resulting insertional library was recovered in E . coli , and genomic DNA inserts were released by enzyme digestion for introduction into the C . albicans Ura- parental strain , cbk1Δ/CBK1 ( CAMM292 , see Table S1 for strain table ) . By homologous recombination , the mutagenized genomic DNA fragment will replace its native chromosomal locus , thereby generating a heterozygous insertion mutant in the parent cbk1Δ/CBK1 strain . DNA transformations were performed nine times , yielding a total of 6528 independent Ura+ transformants . The C . albicans double heterozygotes were isolated and screened for decreased filamentation as follows . The cbk1Δ/CBK1 mutant was originally studied in C . albicans by McNemar and Fonzi [24] and was found to be haploinsufficient with respect to filamentation on a variety of media . Uhl et al . also isolated a heterozygous cbk1 insertion mutant in their haploinsufficiency screen [15] . As shown in Figure 2A , cbk1Δ/CBK1 colonies show a decreased area of central wrinkling and a more prominent ring of peripheral filamentation on SM at 37°C . The haploinsufficiency of this parental strain was advantageous for two reasons . First , it provided increased sensitivity in that the strain was already deficient for filamentation . Second , it could also improve specificity because weak phenotypes of non-interacting , transposon-derived mutants would not be apparent due to masking by the cbk1Δ/CBK1 phenotype . As described above [24] , [25] , [27] , RAM pathway mutants show two distinct phenotypes depending on the conditions used to induce filamentation , but both phenotypes are apparent on solid Spider Medium ( SM ) . In order to identify mutations that potentially interacted with both general functions of the pathway , we , therefore , screened for decreased filamentation on SM at 37°C . All subsequent experiments were conducted under these conditions unless otherwise indicated . The library of 6528 complex heterozygous mutants was spotted in 96-well format and scored for decreased peripheral invasion and altered colony wrinkling relative to a Ura+ derivative of the parental cbk1Δ/CBK1 strain ( 11 , Figure 2A ) . Clones showing both phenotypes were re-tested using two independent colonies . A total of 441 complex heterozygous mutants with decreased peripheral invasion and altered colony wrinkling were re-confirmed on both SM and SM containing uridine to control for positional effects of the URA3 gene ( Figure 2A ) . We specifically selected mutants with decreased zones of peripheral filamentation and less pronounced central wrinkling relative to the parental strain ( Figure 2A and 2B ) . All mutants showed some degree of peripheral filamentation . The most common composite phenotype indicated a small zone of peripheral agar invasion with a broad region of moderate wrinkling ( Figure 2B ) . The transposon insertion sites for approximately one-third of the mutants ( 139 strains ) showing potential synthetic genetic interactions were identified using a 3′-RACE/sequencing approach ( see Materials and Methods ) , yielding 42 unique transposon-derived mutations as putative CBK1-interactors . Since 8 insertion sites were identified in at least 5 separate clones ( Figure 3A and 3B ) , the screen appeared to be saturated to the limits of the library and the mutagenesis technique . Therefore , we did not sequence the remaining two-thirds of the mutants and focused on evaluating the initial set of 42 mutants . It is , however , important to note that the screen itself is unlikely to be saturated for all possible CBK1 interactors , as the library almost certainly did not contain insertions in all predicted C . albicans genes . The URA3 marker was recycled from the heterozygotes by 5-FOA mediated recombinational excision [32] . Following phenotypic re-testing to confirm that homozygosis was not responsible for curing the URA3 marker , CBK1 was re-integrated at its chromosomal position using plasmid pMM4 [24] . The phenotypes of 41 of 42 candidate CHI strains were modified by re-integration of CBK1 ( Figure 2A ) , indicating that the observed phenotypes were dependent on the cbk1 mutation and were likely due to a synthetic genetic interaction between cbk1Δ/CBK1 and the transposon insertion . The high percentage of CBK1-dependent phenotypes may be due to the fact that the parental cbk1Δ heterozygote is itself haploinsufficient on SM and most non-interacting insertion mutations that are themselves haploinsufficient do not have sufficiently strong phenotypes to appreciably change the phenotype of the double heterozygote relative to the parental strain . To confirm these interactions further , a subset of ten complex heterozygous mutants was independently constructed from CAMM-292 by single gene-replacement [33] . All ten double mutants recapitulated phenotypes displayed by the transposon-derived mutants and showed distinct phenotypes relative to strains with single deletions of the interacting genes . Representative images from this analysis are shown in Figure 2B . To further characterize the morphologies of the mutants , we determined the proportion of yeast , pseudohyphae and hyphae after 3 hours induction in liquid SM at 37°C . The interacting mutants consistently showed an increased proportion of pseudohyphal cells relative to wild type and cbk1Δ/CBK1 strains ( Figure 2C ) . Similarly , examination of cells scraped from SM plates showed that the filaments of double mutants had constricted septal areas characteristic of pseudohyphae ( Figure 2D ) . Importantly , all of the mutants were indistinguishable from wild type and the parental strain when serum was used as the inducer of filamentation ( data not shown ) . Since ace2Δ/Δ mutants also show decreased peripheral invasion , decreased central wrinkling , increased levels of pseudohyphae , and normal filamentation on serum ( 25 ) , we conclude that the majority of the CBK1-interacting genes isolated in the screen appear to affect the Ace2-dependent functions of the RAM pathway . Literature analysis of the set of CBK1-interactoring genes revealed that approximately one-half are involved in glycolysis/respiration , biosynthesis , or cell wall metabolism ( Figure 3B ) , cell processes consistent with established functions of the RAM pathway [24]–[28] . An important interactor in terms of validating the screen is SSD1 because it is a likely Cbk1 substrate in S . cerevisiae [34] , displaying well-characterized genetic interactions with CBK1 in both S . cerevisiae [35] and C . albicans [27] . Comparison of our dataset with that generated by the haploinsufficiency screen of Uhl et al . revealed no overlap [15] . As discussed above , we suspect that this lack of overlap is also related to the fact that our parental strain is haploinsufficient for filamentation and , thus , non-interacting transposon-derived mutations causing simple haploinsufficiency were , in effect , masked by the phenotype of the parental strain . In principle , the Ace2-deficient phenotypes displayed by the double heterozygous mutants could result from mutations that interfere with the activation of Ace2 or from mutations that affect a key transcriptional target of Ace2 . We isolated three mutants that could cause a CHI-interaction with CBK1 through the former mechanism . First , we isolated orthologs of two genes that regulate mitotic exit in S . cerevisiae , CDH1 [36] and SLK19 [37] . Ace2 is well known to localize to the nuclei of daughter cells in both S . cerevisiae [38] and C . albicans [25] , [39] . Since Cdh1 and Slk19 regulate mitotic exit , the point in the cell cycle when Ace2 localizes to the nuclei [38] , we suggest that disruption of mitotic exit through the loss of these proteins may further decrease the overall activity of Ace2 . In addition , NSP1 , a key component of the nuclear import machinery , was isolated . Studies in S . cerevisiae [40] have indicated that decreased NSP1 gene dosage leads to inhibition of nuclear import , and it seems plausible that a strain lacking an allele of NSP1 could have decreased nuclear import of Ace2 which would further decrease the overall Ace2-mediated transcriptional activity of the cbk1Δ/CBK1 mutant . The larger class of CBK1-interacting mutants that relate to Ace2 function is the set of genes that appear to be part of the transcriptional output of the RAM pathway ( Figure 3C and 3D ) . To identify such genes in our data set , we searched the promoter regions of CBK1-interactors and found 22 genes that contain a C . albicans Ace2-consensus binding sequence [MMCCASC , 26] . Of these genes , 11 have been shown to display decreased expression in ace2Δ/Δ mutants during hyphal induction as reported in a recent transcriptional profiling study [26] . To further confirm that our screen identified genes regulated by Ace2 , we examined the binding of Ace2 to the promoters of 5 CBK1-interactors with consensus binding sites ( ACT1 , ADH1 , ENO1 , HGT6 , & RGD3 ) during both yeast and hypha-phase growth by chromatin immunoprecipitation ( ChIP ) . Consistent with ChIP data for Ace2 reported by Wang et al . [39] , the absolute enrichment was relatively low , most likely due to its cell cycle regulation and our non-synchronous experiments ( Figure 4 ) . Nevertheless , all five promoters were bound by Ace2 at levels comparable to those observed for the well-established Ace2 target CHT3 and to those reported by Wang et al . [39] during yeast growth . In addition , three promoters were bound in hyphal phase ( Figure 4 ) . Taken together , the presence of Ace2 binding sites , the transcriptional profiling data , and ChIP data support the notion that many of the CBK1-interacting genes are transcriptional targets of Ace2 . Comparison of the set of CBK1-interactors with data from a variety of transcriptional profiles of C . albicans morphogenesis indicated that a substantial subset of CBK1-interactors ( 14 interactors , 34% ) are regulated by the cAMP/PKA pathway through the transcription factor Efg1 [41] . Indeed , 10 CBK1-interactors contain consensus binding sites for both Ace2 and Efg1 ( Figure 3C and 3D ) , suggesting that these two transcription factors may regulate a common set of genes . Further supporting this notion are previous studies indicating that both Ace2 and Efg1 induce glycolytic genes and repress genes involved in oxidative respiration [26] , [41] . Indeed , we searched the C . albicans genome and found that the promoters of 384 genes contain consensus binding sites for both Ace2 and Efg1 ( Table S2 ) . Consistent with previous studies of the two pathways , the set of putatively co-regulated genes is enriched for genes contributing to glycolysis , biosynthesis , and cellular stress responses . Recently , Wang et al . have also shown that the promoters of Ace2-regulated cell wall and cell separation genes are bound by both Efg1 and Ace2 during morphogenesis [39] . Taken together our genetic data strongly support the notion that genes regulated by the PKA pathway may also be important components of the transcriptional output of the RAM pathway during morphogenesis . In addition to transcriptional targets of the PKA pathway , three other CBK1 interactors ( MAF1 , SLF1 & ACT1 ) have connections to the PKA pathway ( Figure 3A ) . MAF1 and SFL1 are both orthologs of PKA-regulated transcriptional regulators in S . cerevisiae [42] , [43] , suggesting that proper PKA-mediated transcriptional control is important in the absence of full RAM pathway activity . Further suggesting that the activity of the PKA pathway is important in RAM pathway mutants , we isolated ACT1 as a CBK1-interactor . Although ACT1 is , of course , a crucial part of the cell cytoskeleton , it also plays an important role in activation of the cAMP/PKA pathway . The Sundstrom lab has shown that actin dynamics regulate PKA activity [44] and , recently , Zou et al . have elegantly demonstrated that actin functions as part of a PKA sensor/activator complex during hyphal development [45] . Indeed , decreased G-actin levels lead to decreased PKA pathway activity and , in turn , decreased filamentation in C . albicans [45] . As such , one explanation for the interaction between ACT1 and CBK1 is that the lowered ACT1 gene dosage in the act1Δ/ACT1 cbk1Δ/CBK1 mutant exacerbates the filamentation defects of decreased RAM pathway activity by concomitantly limiting PKA activity . This explanation also implies that the PKA pathway may compensate for decreased RAM pathway activity during morphogenesis . To test the hypothesis that the RAM and PKA pathways regulate a common set of genes during morphogenesis , we examined the expression of two CBK1-interacting genes containing both Ace2 and Efg1 binding sites in ace2Δ/Δ and efg1Δ/Δ mutants after 3 hours of hyphal induction with SM . As shown in Figure 5A , the expression of the transcripts increased in both strains relative to wild type by quantitative RT-PCR . These observations suggest either that Efg1 and Ace2 are functioning as transcriptional repressors or that compensatory responses are occurring to maintain expression of these genes during morphogenesis when one of the two pathways is disabled . To test the latter hypothesis , total cell lysates of the RAM pathway mutant ace2Δ/Δ were prepared and the level of PKA enzymatic activity determined after 3 hours exposure to hypha-inducing conditions ( Figure 5B ) . At this time point , PKA activity has reduced to low levels in wild type cells [45] , but there is clearly increased PKA activity in the ace2Δ/Δ mutant . This suggests that the PKA pathway is hyperactive in RAM pathway mutants and is consistent with the hypothesis that the PKA pathway may compensate for decreased RAM pathway activity . To further test the interaction between the RAM and PKA pathways , we deleted one allele of CBK1 in strains containing homozygous null mutations in one of the catalytic subunits of the PKA enzyme [46] to yield the mutants cbk1Δ/CBK1 tpk1Δ/Δ and cbk1Δ/CBK1 tpk2Δ/Δ . The two triple mutants along with wild type and the parental mutants were incubated in SM for 3 hours at 37°C to induce filamentation . As shown in Figure 5C , deletion of TPK1 in the cbk1Δ/CBK1 background decreases the proportion of pseudohyphae formed by the cbk1Δ/CBK1 mutant , while deletion of TPK2 has no effect ( data not shown ) , suggesting that the increased proportion of pseudohyphae formed by cbk1Δ/CBK1 is dependent on TPK1 . The phenotypic differences evident upon deleting the two isoforms of PKA are consistent with previous data indicating that they have distinct and redundant roles in filamentation [46] . Interestingly , cultures of cbk1Δ/CBK1 tpk1Δ/Δ in SM contained significant numbers of filaments that showed characteristics of both pseudohyphae and hyphae ( Figure 5D ) . This hybrid morphology was not observed in cultures of wild type , cbk1Δ/CBK1 , or tpk1Δ/Δ cells . Similar hyphae-pseudohyphae hybrid morphologies were recently observed by Carlisle et al . in cells expressing an intermediate level of UME6 [47] , suggesting that concurrent disruption of both RAM and PKA pathways interferes with the ability of the cell to commit to one morphotype . These observations also suggest that a balance between the activities of the PKA and RAM pathway is required for normal morphogenesis . Increased and/or dysregulated PKA pathway activity has been linked previously to increased pseudohyphae formation . For example , Tebarth et al . have shown that overexpression of EFG1 induces constitutive pseudohyphae [48] . We , therefore , hypothesized that elevated PKA activity might be responsible for the constitutively pseudohyphal phenotype displayed by ace2Δ/Δ as well as the increased proportion of pseudohyphae observed with cbk1Δ/CBK1 heterozygotes showing CHI . Three observations support this hypothesis . First , treatment of ace2Δ/Δ cells with the substrate-based PKA inhibitor MyrPKI [49] , under non-inducing conditions , significantly increased the number of yeast-like cells and decreased the number of mature pseudohyphae ( Figure 6A ) , strongly supporting the notion that increased PKA activity is involved in the constitutive pseudohyphal phenotype of ace2Δ/Δ . Second , EFG1 expression is elevated in both RAM pathway mutants and cbk1Δ heterozygotes relative to wild type over the time course of hyphal induction ( Figure 6B ) . Densitometric analysis of three replicates of the 180 min time point indicates that the EFG1 levels are 2–4 fold higher in each of the mutants relative to wild type ( p<0 . 02 , Student's t test ) . To further confirm this elevation , we compared the levels of EFG1 in wild type and the double heterozygote cbk1Δ/CBK1 pgk1Δ/PGK1 . Consistent with the semi-quantitative data , EFG1 is elevated in cbk1Δ/CBK1 pgk1Δ/PGK1 relative to wild type ( 4 . 8 log2 , std . dev . 0 . 9 , p = 0 . 01 , Student's t test ) . Third , deletion of both alleles of EFG1 in the cbk1Δ/CBK1 background decreases expression of ENO1 by a modest 1 . 5-fold and PGK1 a more significant 8-fold relative to the parental strain ( Figure 6C ) , indicating that at least a portion of the increased expression of putatively co-regulated genes in RAM mutants is mediated by the PKA-Efg1 pathway . Taken together , these experiments suggest that some of the CBK1-interacting genes isolated in our screen are part of the transcriptional output of both the PKA and RAM pathways and that decreased RAM function in the CBK1 double heterozygotes leads to a compensatory increase in PKA pathway activity which , in turn , manifests as a phenotype of increased pseudohyphal growth due to increased EFG1 levels [45] . Although our results strongly suggest that the RAM and PKA pathways interact during morphogenesis and that the PKA pathway may be hyper-activated in the absence of RAM activity , it remained to be determined how these pathways interact during normal morphogenesis . As discussed above , one of the best characterized functions of Ace2 in both S . cerevisiae and C . albicans is as a daughter cell-specific transcription factor [26] , [38] , [39] . Two other laboratories [26] , [39] have previously shown that in C . albicans , Ace2 localizes to daughter nuclei in actively dividing yeast-phase cells as well as in serum-induced filaments; our results confirm those findings in SM ( Figure 7A ) . We , therefore , hypothesized that the relative contributions of Ace2 and Efg1 to gene regulation during the course of hyphal development may correspond to the timing of their nuclear localization . To our knowledge , the nuclear localization of Efg1 during filamentation had not been described previously . To test this hypothesis , we used indirect immunofluorescence to compare the proportion of cells with nuclear Efg1 at the initiation of hyphal development to the proportion in hyphal cell nuclei . As shown in Figure 7B , Efg1 is present in the nuclei of 50–60% ( n = 100 cells ) of cells prior to shifting to SM . In contrast , Efg1 is detectable in only ∼10% of hyphal nuclei . Correspondingly , Efg1 occupancy of the promoter regions of ENO1 and PGK1 is also higher at the initiation of hyphal development by ChIP analysis ( Figure 7C ) . This suggests that Efg1 may be more important at the onset of , or early in , the filamentous transition , while Ace2 contributes to Efg1/Ace2 co-regulated gene transcription as daughter cell nuclei accumulate within the hyphal structure . Consistent with this model , ACE2 expression increases over the 3-hour time course of hyphal induction ( Figure 7D ) ; this finding is also consistent with its role in gene expression within daughter cell nuclei . Interestingly , the promoter region of ACE2 has five Efg1 consensus binding sites , suggesting that the PKA pathway may contribute to the regulation of ACE2 expression . However , treatment with the PKA inhibitor MyrPKI reduced levels of ACE2 expression only modestly after 3 hours in SM ( Figure 7E ) . Although this observation supports a possible direct link between the PKA and RAM pathways , it suggests that PKA-Efg1 is not the sole , or even dominant , regulator of ACE2 expression . As a whole , these data support a model in which Efg1 plays a more important role at the initiation of hyphal development in SM , and Ace2 plays a more important role once daughter nuclei accumulate within the hyphal structure . Since EFG1 expression is maintained throughout the time course of hyphal development ( Figure 6B ) and Efg1 is present in some hyphal nuclei ( Figure 7A ) , it is unlikely that the relationship between Ace2 and Efg1 represents an “either/or” type of scenario . Instead , it seems more likely that a balance exists between the relative contributions of the two transcription factors to gene expression and that this balance varies during hyphal development .
Methods for the large-scale genetic analysis of Candida albicans have advanced tremendously in recent years , leading to a number of important and informative studies [14]–[20] . To our knowledge , however , no large-scale synthetic genetic analyses have yet been reported . Here , we present the first such screen . Our approach was based on a CHI strategy , and , like other large-scale genetic analyses of C . albicans , we employed transponson-mediated insertional mutagenesis to generate a large collection of double heterozygous mutants derived from a parental strain containing a heterozygous null mutation of the RAM pathway kinase CBK1 . This library was then used to screen for genes that interacted with CBK1 during SM-induced morphogenesis . First and foremost , our data establishes that CHI-based genetic interaction screening is a useful method for the genetic analysis of the obligate diploid yeast C . albicans . A priori , CHI-based genetic screening of a signaling network such as the RAM pathway would be expected to identify genes that interact with the query gene through a variety of mechanisms . Inspection of our dataset confirms these expectations in that it includes transcriptional targets of the RAM pathway ( e . g . , ENO1 , PGK1 ) , genes that likely affect the function of pathway components ( e . g . , NSP1 , SLK19 ) , and genes that function in parallel pathways ( e . g . , MAF1 , SLF1 ) . In the specific case of screening a protein kinase mutant , it should also be possible to identify substrates of that kinase . Although no bona fide substrate of Cbk1 has been confirmed in C . albicans , our screen identified a very likely candidate in Ssd1 . Ssd1 is a well characterized Cbk1 substrate in S . cerevisiae [34] and has been shown previously to interact genetically with CBK1 in both S . cerevisiae [35] and C . albicans [27] . A consensus Cbk1 phosphorylation sequence has recently been identified in S . cerevisiae [34] . Supporting the possibility that CaSsd1 is a substrate of CaCbk1 is the presence of this consensus phosphorylation sequence . Of the remaining CBK1-interactors , RGD3 , an uncharacterized potential Rho GTPase , and VPS13 , a protein involved in vacuolar protein sorting , also have sequences that match the consensus phosphorylation sequence for ScCbk1 ( data not shown ) . Studies directed towards confirming these putative Cbk1 substrates are in progress . The CBK1-derived double heterozygous mutants isolated in our screen displayed phenotypes indicative of defects in the Ace2-dependent functions of the RAM pathway in that they were only observed on SM [25]; mutations in genes affecting Ace2-independent functions would be expected to display filamentation defects on both SM and serum [27] . Since many of the interacting genes appear to be transcriptional targets of Ace2 , we propose that the effect of partially disabling the RAM pathway by deletion of one allele of CBK1 is exacerbated by further deletion of one allele of a gene regulated by the CBK1-dependent transcription factor Ace2 . The cumulative effect of these two mutations results in phenotypes ( increased proportion of pseudohyphae ) consistent with a further decrease in Ace2-mediated RAM transcriptional activity . By this analysis , Ace2-transcriptional targets that display CHI interactions with CBK1 would , therefore , appear to be particularly important components of the transcriptional output of the RAM pathway during morphogenesis on SM . A particularly powerful feature of synthetic genetic analysis is the ability to identify interactions between regulatory pathways and , in this regard , our CHI screen of cbk1Δ/CBK1 was quite informative , highlighting the interplay between the RAM and PKA pathways during morphogenesis . Although no components of the PKA signaling pathway were identified as CBK1-interactors , analysis of the dataset revealed that many of the interactors were regulated by the PKA pathway . Indeed , the similar transcriptional characteristics of the PKA-regulated transcription factor Efg1 and Ace2 in C . albicans have been previously noted [50] and , while our work was in progress , Wang et al . reported that Efg1 and Ace2 bound to the promoters of C . albicans genes involved in cell separation [39] . In addition , the PKA and RAM pathways have been linked genetically in S . cerevisiae through experiments showing that ectopic over-expression of the PKA kinase subunit TPK1 suppresses growth and budding defects of RAM pathway mutants in an Ace2-independent manner [51] . Our data suggest that the PKA and RAM pathway interact in C . albicans with respect to Ace2-dependent functions . Consistent with this model , consensus binding sites for both Efg1 and Ace2 are located in the promoter regions of a significant proportion of CBK1-interactors . A genome-wide search identified 384 putative Efg1/Ace2 co-regulated genes , suggesting that the two pathways interact to modulate the expression of a substantial subset of genes . The interaction of these two pathways is further supported by our isolation of two PKA-regulated transcriptional modulators ( MAF1 & SLF1 ) as CBK1 interactors as well as by the synthetic genetic interactions between CBK1 and TPK1 observed in our follow-up studies . The simplest manifestation of a model in which the PKA and RAM pathways co-regulate a set of genes would be that deletion of either ACE2 or EFG1 results in the decreased expression of co-regulated genes . As shown in Figure 5A , this is not the case as the expression of putatively co-regulated genes is increased in both ace2Δ/Δ and efg1Δ/Δ mutants . This suggested that the two pathways may compensate for one another when the other pathway is disabled . Supporting this notion , the activity of the PKA pathway is increased in RAM pathway mutants ( Figure 5B ) , and EFG1 mediates a substantial portion of the increased expression of co-regulated genes in the absence of full RAM pathway activity ( Figure 6B ) . Accordingly , the level of EFG1 expression is also increased ( Figure 6B ) and , since inappropriately high levels of EFG1 promote pseudohyphal growth ( 48 ) , this observation provides an explanation for the increased amounts of pseudohyphae displayed by RAM pathway mutants . We , therefore , propose that the increased PKA activity in RAM pathway mutants represents a compensatory response that maintains expression of Ace2/Efg1 co-regulated genes in the absence of a fully functional RAM pathway . However , constitutively elevated levels of PKA activity represent a dysregulated state and , consequently , the expression levels of the genes are not returned to normal but are elevated . Thus , it appears that a balance between the activity of the PKA and RAM pathways is required to maintain properly regulated expression of co-regulated genes . Maintaining a balance between the activities of the two pathways appears to be required for normal hyphal development because: 1 ) loss of EFG1 leads to a failure to form filaments; 2 ) loss of ACE2 leads to the accumulation of pseudohyphae; and 3 ) concurrent partial disruption of both pathways leads to the formation of filaments with characteristics of both hyphae and pseudohyphae ( Figure 5D ) . If , as our results suggest , a balance between PKA and RAM pathway-mediated transcription is required for the cell to normally undergo filamentation , then how is this balance established and maintained ? Although further work will be required to determine the molecular mechanism of this interaction , the cell cycle-regulated nature of both Efg1 and Ace2 suggests that the pathways might be active at different times during morphogenesis . Ace2 , for example , localizes to the nuclei of daughter cells in both yeast and filamentous C . albicans [26] , [39] . Efg1 , on the other hand , has been shown to be rapidly down-regulated soon after hyphal induction in some conditions [48] . These considerations led us to propose that Efg1 may be more important in the expression of co-regulated genes earlier in morphogenesis , while Ace2 is the dominant regulator later in morphogenesis when daughter nuclei appear within the filament . Consistent with that model , we showed that more nuclei contain Efg1 at the initiation of morphogenesis than later in the process . Ace2 , on the other hand , is absent from the vast majority of nuclei at the initiation of morphogenesis but is found in daughter nuclei as they accumulate within the filament ( Figure 7A ) . Consistent with its role later in morphogenesis , overall expression of ACE2 also increases as the cells are exposed to inducing condition for longer periods of time ( Figure 7D ) . Since Efg1 remains detectable in hyphal nuclei ( Figure 7B ) , it is unlikely that Ace2 replaces Efg1 entirely but rather Ace2 may become relatively more important as daughter nuclei accumulate within the filament and undergo mitosis . Thus , it seems that a balance between the PKA and RAM pathways exists and that this balance is important for smooth morphogenesis . A potential illustration of the importance of this balance is provided by the morphologies displayed by the tpk1Δ/Δ cbk1Δ/CBK1 mutant in which single filaments show characteristics of both hyphae and pseudohyphae . This model is also consistent with the observations of Wang et al . , who reported that Efg1 represses the expression of Ace2-regulated cell separation genes during hyphal development [39] . They found that in wild type strains , the Ace2-regulated expression of chitinase CHT3 occurs approximately 3 hours post-hyphal induction , a point at which multiple septa and daughter nuclei have formed within the hyphal filament . The 3-hour time point also corresponds to the time when we observed high levels of ACE2 expression . In EFG1 mutants , on the other hand , Wang et al . found that CHT3 is inappropriately expressed within the first hour of induction and is expressed at higher levels at 3 hours [39] . Our observations regarding the timing of Efg1 nuclear localization correlate well with these expression data in that Efg1 is present early when it suppresses Ace2-mediated CHT3 expression but is absent when CHT3 expression is induced . It is important to note that Efg1 has previously been proposed to function as both a transcriptional activator and repressor during hyphal morphogenesis [39] , [41] and , taken together with the observations of Wang et al . , our data are consistent with such a role . At this point , further work will be required to understand the molecular mechanisms by which the RAM and PKA pathway interact . As noted above , ACE2 does possess a number of Efg1 consensus binding sites within its promoter . This suggests a possible feed-forward mechanism by which Efg1 activates the expression of ACE2 , which , in turn , takes over transcription of co-regulated genes . However , chemical inhibition of the PKA pathway only modestly reduced expression of ACE2 during hyphal induction ( Figure 7E ) . Similarly , efg1Δ/Δ mutants also exhibit very slight changes in ACE2 expression ( data not shown ) . Although there may be an operative component of this feed-forward mechanism , it seems to be a relatively minor contributor to the crosstalk between these pathways . In summary , we have shown that CHI-based genetic interaction screening is a useful approach for the analysis of complex phenotypes in C . albicans . The application of this approach to the RAM pathway has provided insights into the mechanisms by which the PKA and RAM signaling pathways function together during the transition from yeast to filamentous cells in C . albicans .
All strains are derived from CAI4 ( ura3Δ::imm434/ura3Δ::imm434 ) . CAMM-292 ( ura3Δ::imm434/ura3Δ::imm434/cbk1-Δ1::hisG/CBK1 ) [24] was used as the parental strain for transposon mutagenesis . A complete list of strains and genotypes is provided in Table S1 . Yeast peptone dextrose supplemented with 80 mg/L uridine , synthetic dextrose medium lacking uracil , and SM were prepared using standard recipes [15] , [52] . Induction of filamentation was carried out using SM plates ( 37°C , 3D ) or liquid SM ( 37°C , 3 h ) . All phenotypes were confirmed on SM plates supplemented with uracil to control for possible positional effects of URA3 expression . Proportions of yeast , pseudohyphae and hyphae in liquid cultures were determined by light microscopy using morphological scoring criteria described by Sudbery et al . [53] . C . albicans strain WO-1 pEMBLY23 genomic DNA library ( NIH AIDS Research & Reference Reagent Program ) was mutagenized ( 9 independent reactions ) in vitro using the GPS3-Mutagenesis system from New England Biolabs ( Beverly , MA ) and a donor plasmid ( pGPS3 ) containing the CaURA3-dpl200 cassette [16] inserted at the Spe I restriction site . Mutagenized genomic fragments were released by PvuII digestion and transformed into CAMM-292 using a lithium acetate-protocol with heat shock at 44°C for 20 min [54] . The library is available upon request from the Kumar laboratory ( akumar@umich . edu ) . Transposon insertion sites were amplified by 3′ RACE ( rapid amplification of cDNA ends ) using primers complementary to the ends of the transposon construct , cloned into a TA vector , and sequenced . Insertion sites were then identified by BLASTN searches using the Candida Genome Database ( www . candidagenome . org ) . Ten double heterozygotes that showed CHI were independently constructed from the Ura- parental strain cbk1Δ/CBK1 ( CAMM292 ) using fusion PCR methods to generate URA3-based knockout cassettes [33] . The cassettes were used to transform CAMM292 to Ura prototrophy , and correct integration was confirmed by PCR . Two independent isolates were evaluated for all phenotypes . Total RNA was isolated using the RiboPure Yeast Kit ( Ambion , Austin , TX ) and reverse transcribed using the SuperScript III First Strand Synthesis Kit ( Invitrogen , Carlsbad , CA ) . Changes in transcript levels of target genes were analyzed using the Platinum SYBR Green Mix ( Invitrogen ) and normalized to ACT1 levels using the 2−ΔΔCt method [55] . ChIP assays were performed as described previously [56] using Ura+ CAI4-dervatives containing ACE2-TAP and EFG1-MYC alleles . Protein kinase A activity was measured in total cell lysates using the PepTag cAMP-dependent protein kinase assay kit ( Promega , Madison WI ) following a protocol previously developed for C . albicans [57] . Lysates were prepared from wild type and ace2Δ/Δ cells that had been exposed to SM for 3 h . Phosphorylation of the PepTag substrate was determined by agarose gel electrophoresis; the unphosphorylated substrate migrates in the opposite direction as the phosphorylated substrate . Images of the gel were captured on a gel-doc imaging system and processed using Adobe PhotoShop software . Identical contrast and levels were used for each image . Light and fluorescence microscopy was performed using a Nikon ES80 epi-fluorescence microscope equipped with a CoolSnap CCD camera . Images were collected using NIS-Elements Software and processed in PhotoShop . Indirect immunofluorescence was performed as previously described using anti-Myc ( Invitrogen ) primary- and TexasRed-conjugated ( Molecular Probes ) secondary- antibodies [58] . DAPI and Calcofluor white staining was performed as described [52] .
|
Candida albicans is the most common cause of fungal infections in humans . As a diploid yeast without a classical sexual cycle , many genetic approaches developed for large-scale genetic interaction studies in the model yeast Saccharomyces cerevisiae cannot be applied to C . albicans . Genetic interaction studies have proven to be powerful genetic tools for the analysis of complex biological processes . Here , we demonstrate that libraries of C . albicans strains containing heterozygous mutations in two different genes can be generated and used to study genetic interactions in C . albicans on a large scale . Double heterozygous mutants that show more severe phenotypes than strains with single heterozygous mutations are indicative of genetic interactions through a phenomenon referred to as complex haploinsufficiency ( CHI ) . We applied this approach to the study of the RAM ( Regulation of Ace2 and Morphogenesis ) signaling network during the morphogenetic transition of C . albicans from yeast to filamentous growth . Among the genes that interacted with CBK1 , the key signaling kinase of the RAM pathway , were transcriptional targets of the RAM pathway and the protein kinase A pathway . Further analysis supports a model in which these two pathways co-regulate a common set of genes at different stages of filamentation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"biology",
"microbiology"
] |
2011
|
A Large-Scale Complex Haploinsufficiency-Based Genetic Interaction
Screen in Candida albicans: Analysis of the RAM Network during
Morphogenesis
|
Humans are protected against infection from most African trypanosomes by lipoprotein complexes present in serum that contain the trypanolytic pore-forming protein , Apolipoprotein L1 ( APOL1 ) . The human-infective trypanosomes , Trypanosoma brucei rhodesiense in East Africa and T . b . gambiense in West Africa have separately evolved mechanisms that allow them to resist APOL1-mediated lysis and cause human African trypanosomiasis , or sleeping sickness , in man . Recently , APOL1 variants were identified from a subset of Old World monkeys , that are able to lyse East African T . b . rhodesiense , by virtue of C-terminal polymorphisms in the APOL1 protein that hinder that parasite’s resistance mechanism . Such variants have been proposed as candidates for developing therapeutic alternatives to the unsatisfactory anti-trypanosomal drugs currently in use . Here we demonstrate the in vitro lytic ability of serum and purified recombinant protein of an APOL1 ortholog from the West African Guinea baboon ( Papio papio ) , which is able to lyse examples of all sub-species of T . brucei including T . b . gambiense group 1 parasites , the most common agent of human African trypanosomiasis . The identification of a variant of APOL1 with trypanolytic ability for both human-infective T . brucei sub-species could be a candidate for universal APOL1-based therapeutic strategies , targeted against all pathogenic African trypanosomes .
African trypanosomes continue to exert a significant barrier to agricultural production and rural development across sub-Saharan Africa [1] . Due to a primate-specific innate trypanolytic mechanism , the majority of trypanosome species are unable to infect man . However , two sub-species of Trypanosoma brucei , T . b . rhodesiense and T . b . gambiense , have evolved distinct processes to resist this lysis and cause the debilitating and often fatal human form of African trypanosomiasis , known as sleeping sickness . The West African T . b . gambiense parasite typically causes a chronic disease profile , while the zoonotic T . b . rhodesiense sub-species , located in Eastern and Southern Africa , results in a more rapidly progressing , acute infection [2 , 3] . Seventy-million people over an area of 1 . 55 million km2 are at risk of contracting either of the two human-infective sub-species [4] . Current anti-trypanosomal drugs for medical and veterinary administration are largely unsatisfactory due to high toxicity , difficult treatment regimens , and emerging resistance [5–7] . Decades of drug development for African trypanosomiasis has produced safer refinements of existing therapies [7 , 8] and a number of promising novel drug candidates [9–11] , but as yet no new anti-trypanosomal therapy has successfully passed phase III clinical trials . Furthermore , the adaptive immune response of vertebrates is rendered largely ineffective by the trypanosome’s ability to cyclically evade detection through variant surface glycoprotein ( VSG ) -mediated antigenic variation [12 , 13] , placing a significant hurdle in the path of vaccine development . Broad-spectrum , safe , easily administered , and effective therapies to treat African trypanosomiasis are therefore still needed . The recent discovery of primate serum proteins that are able to kill both animal and human-infective trypanosomes is now offering opportunities for novel therapeutic approaches [14 , 15] . It has been known for over a century that the serum of humans and a small number of other Catarrhine primates are highly toxic to most African trypanosome species [16 , 17] . The molecular basis of this innate immunity in man has been elucidated and centres on two trypanolytic serum complexes , Trypanosome Lytic Factor 1 ( TLF-1 ) [18 , 19] and TLF-2 [20 , 21] , which share the same core protein components: haptoglobin-related protein ( HPR ) and apolipoprotein L1 ( APOL1 ) . HPR bound to haemoglobin mediates TLF-1 endocytosis via the haem-scavenging , haptoglobin-haemoglobin receptor ( HpHbR ) on the trypanosome’s surface [22–25] . Difficulty in purifying TLF-2 ex-vivo , has hindered discovery of exactly how this complex is bound and internalised by the parasite but it is known that it does not require HpHbR [21 , 26] . Despite differences in uptake , both TLF-1 and TLF-2 utilize the same lytic component in the form of the ionic channel-forming protein , APOL1 [22 , 27 , 28] . Following internalization , APOL1 undergoes a pH-dependant conformational change in the endolysosome pathway which releases it from the TLF complex [29 , 30] , and promotes insertion into parasite membranes [31 , 32] . The exact mechanism of APOL1-mediated lysis that follows remains to be elucidated . In one recent model APOL1 insertion was found to disrupt both lysosomal and mitochondrial membranes , inducing an apoptosis-like cell death [33] . In contrast , an alternative model proposes that endosome recycling of APOL1 to the neutral environment of the parasite’s plasma membrane accelerates cation-selective channel activity and promotes lysis by osmotic swelling [34] . The Trypanosoma parasites responsible for animal trypanosomiasis are rapidly killed by this innate defence system , whereas the human sleeping sickness parasites , T . b . rhodesiense and T . b . gambiense , are able to resist lysis . In T . b . rhodesiense , resistance is effected by the VSG-derived , serum resistance associated ( SRA ) protein [35 , 36] which binds to the C-terminal domain of APOL1 in the endolysosome pathway preventing channel-mediated lysis [27 , 37–39] , plausibly by impeding correct membrane insertion of APOL1 [34 , 40] . The mechanism of human serum resistance in T . b . gambiense has taken longer to unravel . T . b . gambiense typically grows to very low parasitemia and is difficult to adapt to laboratory models . An additional complicating factor is that T . b . gambiense shows two distinct "groups" that differ in genotype and phenotype [41–44] . The classic , clonal T . b . gambiense type [45] , labelled “group 1” and found in West and Central Africa , is the predominant human-infective sub-species , responsible for 97% of all reported human cases [46] . T . b . gambiense group 1 strains are invariably resistant even after prolonged passage in laboratory rodents [42 , 47] and the mechanism underlying this resistance appears multifactorial , with at least three independent contributing components so far identified . Firstly the reduction of TLF-1 uptake through reduced expression and polymorphism of the HpHbR receptor that reduces binding affinity [48–50]; secondly , expression of a VSG-related T . b . gambiense-specific glycoprotein ( TgsGP ) which is essential , but not sufficient , for resistance [51] and which may increase resistance to APOL1 pore-mediated lysis by stiffening trypanosomal membranes [52]; and thirdly , faster APOL1 degradation has been proposed , through the action of cysteine peptidase [52 , 53] . A second , more virulent type of T . b . gambiense was identified in Cote d’Ivoire and Burkina Faso in the 1980’s [42 , 44] and defined as “group 2” , but has since virtually disappeared and may now be extinct . Studies of the limited number of group 2 strains that have been isolated indicate that these parasites are closely related to West Africa T . b . brucei [41 , 43 , 44 , 54] and exhibit a variable human serum resistance phenotype , in a manner superficially similar to T . b . rhodesiense [42 , 47 , 48] . Although the underlying resistance mechanism remains elusive it does not appear to involve a reduction in TLF-1 uptake [48] or the SRA [55] or TgsGP gene [56 , 57] . Unlike humans and gorillas [58 , 59] , from which they diverged around 25 million years ago [60] , several members of the Cercopithecidae ( Old World monkey ) family appear intrinsically resistant to T . b . rhodesiense [58 , 59 , 61] . Both serum and APOL1 from the East African baboon species , Papio hamadryas , has been demonstrated to effectively lyse human-infective T . b . rhodesiense [14 , 58] . This difference in innate immunity between Homo sapiens and P . hamadryas , has been pinpointed to the position of a single amino acid in the baboon APOL1 C-terminus which prevents the parasite’s SRA protein from binding and neutralising APOL1 lytic activity [62] . Furthermore , a nearly identical mutation has now also been detected in the C-terminus of APOL1 variants of some humans with African ancestry whose serum exhibits lytic activity against T . b . rhodesiense but not T . b . gambiense [63] . This led to the hypothesis that as T . b . gambiense is found only in West Africa , another variant of APOL1 may exist in some West African primates that is able to kill T . b . gambiense . In this study we examined the serum and APOL1 protein of a West African baboon species , Papio papio , suggested to be refractory to T . b . gambiense infection , with the ability to eliminate parasites in a laboratory infection [64] . Here we demonstrate that serum and recombinant protein from the P . papio APOL1 ortholog lyses representative strains of all sub-species of T . brucei in an in vitro assay system . The identification of an APOL1 variant with broad trypanolytic ability against T . brucei sub-species , including the most prevalent T . b . gambiense type , may provide a potential reagent for the development of universal APOL1-based therapeutic agents .
Representative bloodstream form cell lines were selected for each subspecies from a collection at the University of Glasgow and have been previously described . STIB247 is a T . b . brucei strain originally isolated from a hartebeest in Serengeti , Tanzania in 1971 [65] . The T . b . rhodesiense strain EATRO98 was isolated by the East African Trypanosomiasis Research Organization ( EATRO ) from a human in Nyanza , Kenya in 1961 [66] . T . b . gambiense group 2 strain STIB386 ( MHOM/CI/78/TH114 ) was originally isolated in 1978 from an infected patient in Côte d'Ivoire [67] . ELIANE ( MHOM/CI/52/ITMAP 2188 ) is a T . b . gambiense group 1 strain isolated from a human in Côte d’Ivoire in 1952 [68] . Additional T . b . gambiense group 1 strains tested were human isolates , PA ( MHOM/CG/80/ITMAP1843/PA ) from Republic of the Congo in 1975 [43] , BIM ( MHOM/CM/75/ITMAP1789/BIM ) from Cameroon in 1975 [43] , and TOBO ( MHOM/CI/83/DAL596/TOBO ) and S1/1/6 RI from Côte d'Ivoire in 1983 [69] and 2002 [70] , respectively . All bloodstream form culture lines were maintained in vitro in modified HMI9 medium [71] supplemented by 1 . 5 mM glucose , 1 mM methyl cellulose , 250 μM adenosine , 150 μM guanosine and 20% foetal bovine serum ( FBS ) . Expression of the SRA human serum resistance gene in T . b . rhodesiense EATRO98 was maintained under selection with 1% normal human serum . Ectopic expression of functional T . b . brucei HpHbR in ELIANE was previously generated using the tubulin-targeting TbbHpHbR pTub-phelo construct ( strain ELIANE TbbHpHbR-/+ ) [51] , and maintained under phleomycin selection . Bloodstream form isolates BIM and S1/1/6 RI were grown from stabilate in donor BALB/C mice ( Harlan , United Kingdom ) and trypanosomes purified from blood by differential centrifugation as previously described [72] . Cells were maintained as for bloodstream culture cells lines at 37°C in 5% CO2 for up to 24 hours until use . All animal procedures were carried out in accordance with the Animals ( Scientific Procedures ) Act of 1986 . Subspecies classification for T . b . gambiense group 1 strains was confirmed by a positive PCR result for the T . b . gambiense specific glycoprotein ( TgsGP ) gene and T . b . rhodesiense by a positive PCR result for the subspecies-specific serum resistance-associated ( SRA ) gene , as previously described [48] . T . b . brucei and T . b . gambiense group 2 strains were confirmed by a combination of negative SRA/TgsGP PCR results , the human serum sensitivity phenotype and their microsatellite genetic profile [73] . Sera Laboratories International , UK , provided pooled adult P . papio baboon serum derived from two individuals . Additional P . papio baboon serum , derived from a single adult male individual , was provided by Matrix Biologicals , UK . Normal human serum was obtained from a consented human donor and subject to appropriate ethical approval . The APOL1 protein levels in all serum samples are unquantified . Trypanosomes were diluted to 5 x 105 parasites per ml in modified HMI9 , with the addition of human serum or P . papio serum serially diluted in foetal bovine serum ( FBS ) , or FBS only , to a total concentration of 20% . Assays were performed in a final volume of 200 μl in a standard 96 well plate at 37°C in a CO2-equilibrated incubator . The number of viable motile trypanosomes was quantified at 24 hours by haemocytometer counts under the microscope in triplicate , for at least three independent experiments . The percentage viability of parasites in human or P . papio serum was normalised relative to the FBS control for each cell line to account for inherent differences in strain growth rate . Dose–response curves and IC50 values were determined using GraphPad Prism software ( version 7 . 0 ) . The H . sapiens ( accession no . CCDS13926 . 1 ) or P . papio ( accession no . KC197810 ) APOL1 open reading frame ( ORF ) was synthesised and supplied by GeneArt life technologies in an Invitrogen Gateway-compatible entry vector . The entry vector containing the APOL1 cDNA sequence , minus the N-terminal signal peptide ( H . sapiens , residues 28–398; P . papio , residues 28–288 ) was cloned into pDest17 destination vector , which added an N-terminal 6xHis-tag , and transformed into BL21- AI competent E . coli . Protein expression was induced using 0 . 2% L-Arabinose for 16 hours at 37°C . Cells were lysed with urea lysis buffer ( 8 M urea , 20 mM Tris-HCl , 0 . 5 M NaCl , 5 mM imidazole , pH 8 ) and the cellular detritus removed by centrifugation at 5000g for 15 minutes . A small aliquot was removed for analysis by SDS-PAGE and Western blot with 1:5000 HRP-conjugated mouse anti-His antibody ( Qiagen ) and the remainder was used for protein purification under denaturing conditions . Denatured 6x His-tagged APOL1 protein was purified by passing the cell lysate through a gravity-flow Ni-Sepharose column ( Gravitrap , GE Healthcare ) , and washing several times with urea lysis buffer pH 8 supplemented with increasing concentrations of Imidazole ( 5 mM-50 mM ) . Finally , bound protein was eluted with urea lysis buffer pH 8 containing 500mM imidazole . The eluate was dialyzed overnight against 20mM acetic acid and 0 . 05% tween and concentrated using 10 , 000 MW Vivaspin columns ( Sartorius ) . Purity and concentration of the final purified protein was checked using a Qubit fluorometer ( Thermo fisher ) and SDS-PAGE ( S1 Fig ) , then the concentration adjusted to 1 mg/ml and stored in aliquots at 4°C . To assess survival in recombinant APOL1 , trypanosomes were diluted to 5 x 105 parasites per ml in modified HMI9 containing 20% FBS and incubated with a dilution series of recombinant human or P . papio APOL1 . The recombinant APOL1 was formulated in protein-free buffer ( 20mM acetic acid , 0 . 05% tween ) and added in a volume of 10 μl to a final assay volume of 200 μl in a standard 96 well plate . A control containing an equivalent volume of protein-free buffer was also prepared . Assays were performed at 37°C in a CO2-equilibrated incubator , and the number of viable motile trypanosomes in each well was quantified at 24 hours by haemocytometer counts under the microscope in triplicate for at least three independent experiments . Cell counts in recombinant APOL1 were compared to control wells containing protein-free buffer only to determine percentage survival . In each assay , cells were incubated in 20% normal human serum as a positive control . Dose–response curves , IC50 values and one-way ANOVA were determined using GraphPad Prism software ( version 7 . 0 ) . Where indicated , trypanosomes were pre-incubated with 10 mM ammonium chloride ( NH4Cl ) , a weak base , for 30 minutes at 37°C to reverse acidification of the endolysosome system prior to the addition of recombinant APOL1 . Samples for IFA were prepared as follows . All incubation steps unless stated otherwise were performed in a humidor at room temperature . Bloodstream form trypanosomes were diluted in HMI9 medium containing 20% FBS at a concentration of 106 parasites/ml and incubated with 50 μg/ml purified recombinant H . sapiens or P . papio APOL1 for two hours at 37°C . After this period , cells were washed once in serum-free HMI9 medium , and settled onto glass slides before fixing in 1% paraformaldehyde for 10 minutes . Samples were permeabilised using 0 . 1% Triton X-100 in PBS for 20 minutes then incubated in blocking solution ( 2% BSA in PBS ) for 20 minutes . After washing three times in PBS , slides were incubated for 40 minutes with 1:500 mouse anti-p67 antibody ( gift from Jay Bangs , Department of Microbiology and Immunology , University at Buffalo , NY , USA ) in blocking solution . Washes were repeated and then primary antibody was detected using 1:1000 goat anti-mouse AlexaFluor594 secondary antibody ( Life technologies ) incubated for 40 minutes in blocking solution . To detect His-tagged APOL1 slides were washed three times in PBS and then incubated for 40 minutes with 1:500 AlexaFluor488 mouse anti-penta-His antibody ( Molecular Probes , Invitrogen ) in blocking solution . Following a final three washes the cells were treated with 50% glycerol , 0 . 1% DAPI , 2 . 5% 1 , 4-diazabicyclo [2 . 2 . 2] octane ( DABCO ) in PBS , protected with a coverslip and sealed with acetone . Slides were imaged using the Deltavision Core system and SoftWorx package ( Applied Precision ) with standard filter sets ( DAPI/FITC/Texas-Red and Light transmission ) . Approximately 30 serial sections through each trypanosome were taken for each filter . The images were composited and the brightness , contrast and color levels normalised between samples and exposures using the ImageJ software package ( US National Institute of Health ) . The University of Glasgow ethical review board approved the use of human serum in this study . The human serum volunteer gave written informed consent .
Trypanolytic activity against the human-infective East African T . b . rhodesiense sub-species has been demonstrated for sera from several members of the Cercopithecidae family , including baboons , mandrills and sooty mangabeys [14 , 37 , 58 , 59] . To date however , no primate has been identified with lytic activity against West African T . b . gambiense parasites . To determine the trypanolytic ability of serum from the West African Guinea baboon , P . papio , representative examples of the different T . brucei sub-species , were incubated for 24 hours in vitro , with a dilution series of P . papio or human serum . The strains selected included five different isolates of classic T . b . gambiense group 1 , the cause of 97% of reported HAT cases [46] , from a number of different disease foci in West Africa . As illustrated in Fig 1A , normal human serum efficiently lysed T . b . brucei bloodstream parasites ( IC50; 0 . 0005% ) in a 24 hour assay , but not strains of the human-infective T . b . rhodesiense or T . b . gambiense subspecies . In contrast , P . papio ( pooled sera ) was completely lytic to all tested strains , including both T . b . gambiense group 1 and 2 isolates , at concentrations ≥ 10% ( Fig 1B ) . The sensitivity of T . b . brucei to P . papio pooled serum ( IC50; 0 . 00035% ) was comparable to that of T . b . rhodesiense ( IC50; 0 . 00038% ) . T . b . gambiense group 1 and 2 strains however , were killed significantly less potently , with an IC50 approximately 70-fold ( IC50; 0 . 024% serum , T . b . gambiense group 2 ) or 2000-fold ( IC50; 0 . 46–1 . 68% serum , T . b . gambiense group 1 ) higher than that of the other sub-species , although still at a sub-physiological concentration . The trypanolytic activity of P . papio was also confirmed against a smaller collection of T . brucei strains using an alternative source of P . papio sera derived from a single male individual , which killed T . b . gambiense at a lower concentration > 2% ( S2 Fig ) , presumably reflecting variation between individual animal samples . APOL1 has been demonstrated to be the lytic factor in normal human serum [22 , 27 , 28] , and T . b . rhodesiense-lytic orthologs of APOL1 have now been identified in the serum of a number of Old World monkey species , including species of the Papio baboon genus [14 , 37 , 58] . Furthermore this lytic activity of Papio APOL1 against T . b . rhodesiense has been demonstrated to be the result of a single polymorphism [62] . We therefore hypothesize that the broad lytic ability of P . papio may be attributable to a functional variant of this protein . Sequenced APOL1 cDNA was used as a template for the production of recombinant variants of P . papio and human APOL1 protein ( S3 Fig-Amino acid alignment ) . Representative strains of the different T . brucei sub-species were incubated in the presence of purified P . papio and human recombinant protein to determine if APOL1 alone had demonstrable trypanolytic ability . Titrated human recombinant APOL1 protein completely lysed T . b . brucei parasites after 24 hours ( IC50; 1 . 013 μg/ml ) , at concentrations comparable to the physiological levels of APOL1 reported for normal human serum [74–76] , but had no lytic effect on strains of the human serum resistant parasites , T . b . rhodesiense , T . b . gambiense group 1 or T . b . gambiense group 2 ( Fig 2A ) . In contrast , recombinant P . papio APOL1 protein exhibited trypanolytic activity against representative strains of all T . brucei sub-species ( Fig 2B with additional T . b . gambiense group 1 strains assays provided in S4 Fig ) . Furthermore , strains of all sub-species tested appeared equally susceptible to the effect of recombinant P . papio APOL1 , with no significant difference in IC50 observed ( one-way ANOVA , F ( 3 , 24 ) = 1 . 741 , p = 0 . 19 ) . Notably , as has been observed for human APOL1 , this lytic activity is inhibited by the addition of the acidotropic agent ammonium chloride to the assay ( Fig 2A and 2B ) . Ammonium chloride is a weak base that raises endolysosomal pH , thereby preventing pH-dependant conformational changes to APOL1 that are predicted to be essential to efficient ion-channel mediated lysis [32 , 34 , 77] . This corresponding inhibition of APOL1-mediated lysis for both orthologs is further indicative of a conserved mechanism of action . In summary these assays demonstrate that the P . papio APOL1 ortholog in isolation exhibits trypanolytic ability against all tested examples of the human-infective T . brucei sub-species . Although there may be other , as yet uncharacterized factors that contribute to the lytic ability of P . papio serum , the APOL1 ortholog is a significant trypanolytic component . A reduced sensitivity to lysis was observed for both the predominant T . b . gambiense group 1 and minor group 2 strains , relative to T . b . brucei and T . b . rhodesiense , when incubated with P . papio serum , but not recombinant APOL1 protein . We postulated that for T . b . gambiense group 1 , this difference might be the result of disparity in the rate of uptake of APOL1 versus APOL1–containing trypanolytic factors by these parasites . In normal human serum , HPR bound to haemoglobin , acts as ligand to facilitate TLF-1 uptake via the T . brucei HpHbR receptor [23 , 78] . However , a defining feature of T . b . gambiense group 1 strains is a decrease in TLF-1 internalisation as a result of reduced HpHbR expression and a conserved L210S substitution that reduces the binding affinity of HpHbR for its ligand [50 , 79] . Reduced TLF uptake via HpHbR contributes to the invariant human serum resistant phenotype of these parasites , although alone is insufficient to impart resistance to human serum [78] due to the existence of other speculated receptors for TLF-1 [80 , 81] , and the additional TLF-2 particle in human serum for which the uptake mechanisms remain unknown [21 , 82 , 83] . In contrast , recombinant APOL1 is internalised by non-specific fluid phase endocytosis and trafficked through the endolysosome pathway , thus completely circumventing the HpHbR receptor [27 , 48] . The number of molecules in the TLF complex and its exact structural composition in baboon serum is currently unresolved , but a representative baboon species , P . hamadryas , has been demonstrated to have similar constitutive components ( HPR and APOL1 ) to human TLF [14] . As T . b . gambiense group 1 parasites have a reduced uptake of human TLF-1 but the other subspecies do not we postulated that a similar mechanism could reduce the uptake of P . papio TLF particles by T . b . gambiense group 1 strains , which is corrected by direct incubation in recombinant APOL1 protein . To investigate this we repeated the serum resistance assays using a T . b . gambiense ELIANE strain expressing a functional T . b . brucei HpHbR receptor ( ELIANE TbbHpHbR -/+ ) , that was previously generated by our laboratory and demonstrated to take up comparable amounts of TLF-1 to T . b . brucei [51] . As previously observed , expression of the functional T . b . brucei HpHbR receptor alone was insufficient to convert the phenotype of T . b . gambiense to human serum sensitivity and this clone ( TbbHpHbR -/+ T . b . gambiense ) retains full resistance to normal human serum ( Fig 3A ) . However , it exhibits a 1000-fold increased sensitivity to P . papio serum ( relative to the wild-type T . b . gambiense group 1 ELIANE strain ) , producing an IC50 value ( 0 . 0005% ) comparable to that observed for the T . b . brucei and T . b . rhodesiense sub-species ( Fig 3B and S2 Fig ) . Taken together , the serum and APOL1 assays indicate that diminished TLF uptake via the HpHbR receptor , rather than higher innate resistance to P . papio APOL1-mediated lysis underlies the increased resistance to P . papio serum observed for T . b . gambiense group 1 strains . In T . b . gambiense group 2 , in contrast , an as yet uncharacterised HpHbR–independent mechanism/s determines human infectivity . T . b . gambiense group 2 strains , including the STIB386 isolate used in this study , have been shown to express the HpHbR gene at level comparable with T . b . brucei , with no demonstrable reduction in TLF-1 uptake [48] . Consequently , the reduced sensitivity to P . papio serum lysis , but not APOL1 protein , also observed for these HpHbR-functional parasites , further indicates that important differences exist in the cell biology of between T . b . gambiense group 2 and T . b . gambiense group 1 strains that determine sensitivity to these primate lytic factors . Human recombinant APOL1 is taken up by fluid phase endocytosis and trafficked through the endocytic pathway to the endolysosome , the initial activation site of APOL1 , in all T . brucei sub-species [27 , 48] . This results in lysis of T . b . brucei but not of T . b . rhodesiense or T . b . gambiense [48] , which each possess mechanisms to resist the lytic effects of APOL1 [35 , 48 , 51 , 52] . To determine if P . papio APOL1 is localised through the parasite endolysosome pathway in a similar manner to that demonstrated for human APOL1 , uptake of both recombinant proteins was compared in T . b . brucei and T . b . gambiense group 1 parasites using a fluorescent antibody to detect the His-tagged recombinant APOL1 protein . The cells were then examined by microscopy , in conjunction with the lysosomal marker p67 . In order to achieve images of APOL1 uptake we used high concentrations of APOL1 ( material and methods ) to counteract possible degradation of APOL1 in the lysosome . Consistent with previous experiments of serum and APOL1 uptake in our laboratory [48 , 49 , 51] , no lysosomal swelling was observed . As shown in Fig 4 , both human and P . papio APOL1 are internalised by T . b . brucei and T . b . gambiense after a two hour incubation and are observed to co-localise with an antibody directed against the lysosomal membrane protein p67 , indicative of the parasite endolysosome pathway [84 , 85] . These observations , in parallel with the ablation of lysis observed after co-incubation with acidotropic agent , ammonium chloride in APOL1 lysis assays , suggest that as previously demonstrated for human APOL1 , exposure of the protein to the low pH of the endolysosomal pathway is also a requirement for trypanolytic activity of the baboon APOL1 ortholog .
The ancient co-evolutionary engagement of African trypanosomes with their mammalian hosts has shaped an innate lytic molecule in man that protects from infection with most African trypanosomes . In response , the extensive antigenic repertoire of T . brucei [86] has provided a rich resource from which to evolve counter-measures to APOL1-mediated lysis on at least two occasions; SRA in T . b . rhodesiense in East Africa [35 , 36 , 87] , and TgsGP in T . b . gambiense group 1 in West Africa [51 , 52 , 88] . In this study we present a novel APOL1 variant from a species of West African baboon that killed examples of all T . brucei sub-species , including T . b . rhodesiense , T . b . gambiense group 2 , and T . b . gambiense group 1 , the agent of most current cases of human African trypanosomiasis . The identification of such genetic variants , capable of killing both animal and human-infective parasites presents new opportunities for unconventional approaches to disease treatment and control , using APOL1-based biological therapies . Previous studies have identified APOL1 orthologs in a subset of Old World monkeys [14 , 62] , and an APOL1 variant with a key similarity in some humans with African ancestry [63] , that encode proteins lytic to T . b . rhodesiense . In both variants , evidence suggests protection is mediated by the position of a single lysine residue in the C-terminal protein domain that obstructs coiled-coil interactions with SRA , thus allowing APOL1-directed lysis to proceed unimpeded [62] . Unfortunately in humans , the two amino acid deletion that alters the SRA-binding region in this APOL1-G2 variant come with an associated fitness cost: a 7–29-fold increased risk of developing a wide spectrum of kidney disorders in individuals carrying two copies of a variant allele [63 , 89–92] . The exact biological mechanism underlying this APOL1-associated nephropathy is not yet known but appears to be specific to the human variants . Engineered versions of the human APOL1 variant transiently expressed in a mouse model caused significant toxicity to the organ of expression ( liver ) , which was not observed with baboon APOL1 or human APOL1 modified to introduce only the protective baboon lysine to the C-terminus [62] . This is an encouraging result , and such baboon-like APOL1 variants are now the focus of efforts to create suitable mechanisms of delivery , such as the conjugation of APOL1 protein to an antibody fragment targeted to parasite surface antigens [93] and an ambitious project to create targeted transgenic cattle expressing variant APOL1 [15] . These variants could be used to protect the reservoir host species from zoonotic T . b . rhodesiense sleeping sickness in addition to animal trypanosomiasis , which places severe restrictions on agricultural production and rural development in Sub-Saharan Africa [1] . Unfortunately , they will have a limited effect on the overall burden of human sleeping sickness . None of the APOL1 variants used in these experiments are able to kill the major human pathogen T . b . gambiense group 1 which places a population of 57 million people in West and Central Africa at risk of disease [4] , less than 5% of whom are currently under surveillance [94] . Furthermore , there is a risk that the proposed interventions could result in the creation of a vacant ecological niche that increases the incidence of T . b . gambiense group 1 in domestic livestock through selective removal of susceptible competitor species such as T . b . brucei , T . congolense , T . vivax and T . b . rhodesiense . We have addressed these concerns directly in this study by examining the serum of a West African baboon species P . papio that overlaps in distribution with that of T . b . gambiense , and which had been suggested to self-cure T . b . gambiense group 1 infection [64] . In that study primates infected with T . b . gambiense group 1 parasites exhibited a serological response that decreased throughout the course of the experiment and had no detectable parasitemia , consistent with an initial infection , followed by rapid parasite clearance and self-cure . In our study P . papio serum is able to lyse T . b . gambiense in 24 hours in vitro . The difference in timing of parasite killing between the in vivo and in vitro experiments , which could be due to a number of different factors such as parasite sequestration , is a well-recognised phenomenon . It is possible that parasites avoid lysis by residing in sites of low APOL1 concentrations ( for example at the bite site in the skin ) in the animal before eventually being cleared . This factor must be taken into account when attempting to develop APOL1-based therapies as in vitro assays do not always reflect the complexity of in vivo cell biology . The introduction of improved bioluminescent imaging to quantify parasite burden could be used to test in vivo for complete parasite clearance . We have shown that the lytic effect of P . papio serum can be reproduced with an ortholog of the trypanolytic primate defence protein , APOL1 , which demonstrates the uptake and localisation characteristics of other previously identified APOL1 proteins [27 , 48] . The trypanolytic action of this P . papio APOL1 variant against T . b . rhodesiense can be attributed to the C-terminal lysine mutation that is conserved among several members of the Cercopithecine subfamily that includes baboon , mandrills and mangabeys [62] . However the mechanism by which it counters T . b . gambiense , which has evolved multiple contributing mechanisms of human serum resistance , remains more elusive . All T . b . gambiense group 1 parasites share a mutated HpHbR with reduced affinity for one of the human APOL1-containing particles ( TLF-1 ) via the HPR ligand [48–50] , although a second particle , TLF2 , appears to have alternative , as yet unresolved , mechanism ( s ) of internalisation [24 , 80 , 81 , 83] . The exact composition of TLF in baboon serum has not been clarified . However analysis of an HPR-affinity purified HDL sub-fraction from P . hamadryas baboon serum detected a TLF-equivalent particle that contains the same structural components as human TLF [14] . Furthermore , when transiently expressed in mice , all three components were required for maximum lytic activity against T . b . rhodesiense [14] , suggesting HPR-HpHbR may play a role in uptake of baboon TLF . Here we show that T . b . gambiense group 1 , although still fully susceptible to sub-physiological concentrations of P . papio serum , was 1000-fold less sensitive than T . b . brucei sub-species . This difference was ablated when functional T . b . brucei HpHbR was restored to the T . b . gambiense parasite , supporting a role for P . papio TLF uptake via both HpHbR-mediated endocytosis as well as unidentified alternative mechanisms , possible shared with those already proposed for human TLF [24 , 80 , 81 , 83] . Secondly , the TgsGP gene has been demonstrated to be essential for human serum resistance in T . b . gambiense group 1 , as gene deletion renders the parasites sensitive to human serum lysis [51 , 52] . In contrast to the T . b . rhodesiense SRA protein , TgsGP and APOL1 do not appear to interact directly . Instead , TgsGP is proposed to bolster T . b . gambiense resistance to human APOL1 pore-forming activity through a process of plasma membrane stiffening [52] . A third mechanism by which T . b . gambiense might resist the actions of NHS , through enhanced APOL1 degradation within the endolysosomal system , has also been proposed [52] . Modulation of expression levels of the cysteine protease Cathepsin L and its inhibitor ( ICP ) has demonstrated an important role for cathepsin-mediated degradation of APOL1 in human serum resistance [53] . Difference in expression levels of these genes has not been detected in T . b . gambiense , however a lower pH is observed within the early endosomes that is predicted to accelerate their proteolytic activity relative to T . b . brucei [52] . Intriguingly , we observed equal sensitivity of all strains tested to P . papio APOL1-directed lysis , suggesting that the activity of TgsGP , and APOL1 degradation by cysteine peptidases , that effectively hinders human APOL1 in T . b . gambiense , poses no such barrier to the P . papio variant . This raises interesting questions about how exactly P . papio APOL1 is able to overcome these factors ? Many details of the action of the TgsGP protein in particular remain cryptic . Despite its essential role in human serum resistance in T . b . gambiense , ectopic expression of T . b . gambiense TgsGP alone in T . b . brucei is insufficient to confer resistance to human serum [51 , 88] . There is evidently a role for other , as yet unidentified processes , in T . b . gambiense human serum resistance , which are absent or incomplete in T . b . brucei . Sequence analysis has revealed that baboon and human APOL1 orthologs share only 58% amino acid sequence identity [14] . Despite this , in the recently elucidated example of baboon serum lysis of T . b . rhodesiense it was demonstrated that a single amino acid substitution conserved between baboon species is responsible for APOL1 evasion of SRA binding [62] . Uncovering the mechanism by which P . papio has developed its broad trypanolytic ability may offer further insights into the workings of T . b . gambiense human serum resistance , as well as aid in the design of an improved APOL1 therapy that could target all pathogenic trypanosomes across Sub-Saharan Africa . Such universal therapies that can treat both animal and human pathogens are particularly appropriate to the “one health” approach , currently advocated by WHO , FAO , and OIE , that integrates medical and veterinary health policy and research for addressing zoonotic diseases . The Guinea baboon P . papio is found only in a limited area of western equatorial Africa , where its range overlaps with that of T . b . gambiense group 1 . Five other baboons are represented in the Papio genus of which serum for only one , the east African P . cynocephalus ( yellow baboon ) has been previously tested against T . b . gambiense parasites , and was reported to be non–lytic [37] . Unfortunately APOL1 sequence is currently unavailable for comparative analysis with this species or the southern African P . ursinus ( Chacma baboon ) and P . kindae ( Kinda baboon ) species from Central Africa . Of the remaining Papio species , APOL1 sequences from cDNA have been successfully obtained for P . hamadryas ( Hamadryas baboon ) from North East Africa , and Central African P . anubis ( Olive baboon ) [62] , the closest related species to P . papio in a recent phylogenetic study of mitochondrial DNA [95] . Amino acid alignments of P . papio APOL1 with these available sequences indicate ~98 . 5% identity to P . hamadryas and 93 . 5% to P . anubis ( S3 Fig ) . A study in which C-terminal polymorphisms of P . anubis were incorporated into human recombinant APOL1 were observed to be lytic to T . b . rhodesiense but not T . b . gambiense [37] , however full length APOL1 transcripts , unavailable at the time of the study , have not been tested . For P . hamadryas , serum and APOL1 have not yet been tested against T . b . gambiense , however a laboratory infection of two individual baboons with a strain of T . b . gambiense group 1 suggested hamadryas baboons to display a level of trypanotolerance to infection [64] . Future studies in which the sensitivity of T . brucei subspecies to serum and APOL1 from the other baboon species , followed by the construction of chimera mutants are now needed to help resolve the crucial polymorphisms responsible for T . b . gambiense lysis , as has been successful for T . b . rhodesiense .
|
African trypanosomes are protozoan parasites that affect both humans and animals in poor rural areas of sub-Saharan Africa , and are a major constraint on health and agricultural development . Disease control is principally dependent on the administration of drugs , which are old and largely unsatisfactory . Humans are naturally resistant to infection by most African trypanosomes species because of a lytic protein component in their blood , called APOL1 . However , human-infective trypanosomes , T . b . rhodesiense in East Africa , and T . b . gambiense in West Africa , have evolved separate mechanisms to disarm this lytic protein and cause disease . Recently , variants of APOL1 were discovered in some primates that are able to kill the East African human disease-causing sub-species . These APOL1 variants form the basis of current attempts to create novel therapeutic interventions that can kill both animal and human-infective trypanosomes . In this study , we show that another variant of the same protein from a West African baboon species is able to kill , not only East African human-infective trypanosomes , but also the West African parasites , which causes the majority of human African trypanosomiasis cases . This new APOL1 variant could be a potential candidate for anti-trypanosomal therapies targeted at all pathogenic trypanosome species .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"vertebrates",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"animals",
"mammals",
"primates",
"physiological",
"processes",
"protozoans",
"old",
"world",
"monkeys",
"infectious",
"diseases",
"tissue",
"repair",
"zoonoses",
"monkeys",
"baboons",
"proteins",
"recombinant",
"proteins",
"protozoan",
"infections",
"trypanosomiasis",
"lysis",
"(medicine)",
"biochemistry",
"trypanosoma",
"physiology",
"biology",
"and",
"life",
"sciences",
"trypanosoma",
"brucei",
"gambiense",
"amniotes",
"serum",
"proteins",
"organisms"
] |
2016
|
A Primate APOL1 Variant That Kills Trypanosoma brucei gambiense
|
We have investigated in vivo the role of the carboxy-terminal domain of the Bacillus subtilis Single-Stranded DNA Binding protein ( SSBCter ) as a recruitment platform at active chromosomal forks for many proteins of the genome maintenance machineries . We probed this SSBCter interactome using GFP fusions and by Tap-tag and biochemical analysis . It includes at least 12 proteins . The interactome was previously shown to include PriA , RecG , and RecQ and extended in this study by addition of DnaE , SbcC , RarA , RecJ , RecO , XseA , Ung , YpbB , and YrrC . Targeting of YpbB to active forks appears to depend on RecS , a RecQ paralogue , with which it forms a stable complex . Most of these SSB partners are conserved in bacteria , while others , such as the essential DNA polymerase DnaE , YrrC , and the YpbB/RecS complex , appear to be specific to B . subtilis . SSBCter deletion has a moderate impact on B . subtilis cell growth . However , it markedly affects the efficiency of repair of damaged genomic DNA and arrested replication forks . ssbΔCter mutant cells appear deficient in RecA loading on ssDNA , explaining their inefficiency in triggering the SOS response upon exposure to genotoxic agents . Together , our findings show that the bacterial SSBCter acts as a DNA maintenance hub at active chromosomal forks that secures their propagation along the genome .
Maintaining genome integrity is a permanent challenge for all organisms , particularly during genome duplication , when accidental replication fork arrests expose the genome to damage . Numerous mechanisms have evolved to counteract the deleterious consequences of fork arrest ( reviewed in [1] , [2] ) . The multiplicity of these fork repair mechanisms reflects the need to respond appropriately to a variety of damaged fork structures . A key question is therefore how these multiple rescue pathways are appropriately and efficiently triggered and coordinated in the cell . Bacteria can manage chromosomal replication fork arrest without necessarily interrupting other key cell cycle events . Their genome is generally composed of one circular DNA molecule ( of several Mbp ) replicated by a single pair of divergent forks fired at a fixed origin , oriC . Thus , effective repair of accidentally arrested replication forks is vital to bacteria . In addition to a requirement for removal and repair of the damage originally responsible for a particular replication fork arrest , the cell possesses the machinery necessary for re-assembling the replication machinery ( the replisome ) at these rescued forks [3] . An emerging model is that components of the replisome determine the recruitment of accessory proteins at the forks to assist their progression . One of these is DnaN , a dimeric protein that forms a ring around double-stranded DNA ( dsDNA ) and clamps the replicative DNA polymerase [4] , and also interacts with several proteins involved in DNA replication and repair ( reviewed in [5] ) . Another protein of the replisome , the Single-Stranded DNA Binding protein ( SSB ) , is also known to interact with accessory proteins at the fork . The primary role of SSB at the fork is to facilitate the activities of replisomal enzymes by preventing the formation of ssDNA secondary structures ( for a review , see [6] ) . SSB is composed of two domains: an N-terminal ssDNA binding domain and a C-terminal domain , SSBCter , enriched in glycine and acidic amino-acids . A short hexapeptide motif with a consensus signature D-D-D-I/L-P-F emerges from the end of the protein [7] . The SSBCter is dispensable for SSB tetramerisation and interaction with ssDNA [8] , [9] but permits interaction with many proteins of the DNA recombination , repair and replication machineries . The E . coli SSB ( EcSSB ) interactome is currently estimated to include 14 proteins ( reviewed in [6] ) . Many of the SSB partners are involved in distinct replication fork repair pathways . Thus , SSB might be responsible for coordinating recruitment of these repair proteins at active replication forks . As judged by the analysis of SSB localization in B . subtilis and E . coli [10]–[12] , active forks are the subcellular sites where SSB accumulates in replicating cells grown without genotoxic stress . We previously provided strong support for the idea that SSB acts as a protein recruitment platform at active replication forks by localizing in living B . subtilis cells three conserved DNA helicases as GFP fusions . These were PriA , the primary restart protein , which directs replisome re-assembly on branched DNA originating from arrested forks [13]–[15] , and RecG and RecQ , two recombination proteins involved in the maintenance of the genome and of chromosome forks [16] , [17] . These three proteins accumulate at chromosomal forks in an SSBCter-dependent manner , a discrete localization that does not depend on accidental fork arrest [8] . In addition , we have characterized a B . subtilis PriA mutant unable to interact with SSB , which was no longer targeted to active chromosomal forks and did not support replication restart unless overproduced . This underlines the direct benefit of pre-recruitment and targeting of PriA by SSB on active chromosomal forks: in anticipation of a requirement of PriA repair action , which can occur at any stage of genome replication [8] . Thus , an hypothesis raised by such a preparatory mode of PriA action at replication forks would also apply to the other SSB protein partners . In this study , we have further defined the B . subtilis SSBCter interactome at active chromosomal forks . First , using cytological and biochemical approaches we have extended the number of B . subtilis SSB partner proteins targeted at forks to twelve , including RarA , SbcC and XseA , which are also present in E . coli but not previously known to interact with EcSSB . Among the other proteins identified were key effectors of the RecFOR loading machinery for RecA . In addition , 3 others , including the DNA polymerase DnaE , appear to be specific to the B . subtilis SSB interactome . Paradoxically , although DnaE is one of the two essential B . subtilis DNA polymerases , this interaction is not essential since we have been able to delete SSBCter while retaining cell viability [8] . In parallel to this screening , we have undertaken detailed analysis of the multiple defects caused by the deletion of the SSBCter in vivo . Based on these results we propose an integrated model for replication fork rescue in which SSB coordinates the multiple processes potentially involved in a cascade-like manner . In an initial response to replication fork blockage , the system would first attempt to repair the damage and restart the stalled fork by the coordinated action of proteins present at the fork prior to its blockage . Failure to circumvent the blockage in this way would lead to a second set of responses , in particular the de novo loading of RecA on ssDNA at arrested forks by specific SSB-associated proteins . This would facilitate fork remodeling by homologous recombination ( reviewed in [2] ) . Failure at this step would then lead to a more robust response by induction of the SOS system to provide increased levels of repair proteins to repair damaged forks .
A remarkable feature of the B . subtilis SSB protein is that deletion of its C-terminal end is not lethal to the cell , in sharp contrast to that of E . coli [9] . This enabled the demonstration that PriA , RecG and RecQ proteins are targeted to active chromosome replication forks in B . subtilis in a manner which depends on the C-terminal region of SSB [8] . To identify additional proteins targeted to active chromosomal forks in the same way , we have extended these studies by using two B . subtilis ssb alleles truncated for the last 35 ( ssbΔ35 ) or 6 ( ssbΔ6 ) codons . Candidate proteins were chosen according different criteria . Some were E . coli homologues already shown or proposed to interact physically with the EcSSBCter ( reviewed in [6] ) . Others were selected because of their sequence or functional homology with known partners of B . subtilis SSB . A third group included those already known to be present at active chromosomal forks . The final group comprised proteins selectively purified with B . subtilis SSB using the Tap-tag procedure [18] and identified by mass spectrometry . All candidates were screened for localization at active replication forks as GFP fusions , expressed ectopically from the amyE locus , in SSBCter deletion strains and in the isogenic wild type strain ( ssb3+ ) . Most candidates were also screened for physical interaction with SSB as purified proteins in vitro or by Tap-tag analysis with the use of the SPA motif fused to their C-terminal end at their original genetic locus . The combination of these three approaches identified 9 additional proteins which together represent an extended view of the SSBCter interactome targeted to active B . subtilis chromosomal forks . The results of this screening are compiled in Table 1 and described in the following sections . B . subtilis and closely related bacteria encode two RecQ homologues [19] . The first , initially annotated as YocI , was renamed RecQ since it is highly homologous to the single RecQ protein generally encoded in bacterial genomes ( including E . coli ) . B . subtilis RecQ co-localizes with active replication forks in an SSBCter-dependent manner and Tap-tag analysis of RecQ provided further evidence for its ability to interact unaided with SSB [8] . The second RecQ homologue , RecS ( also annotated as YpbC ) , is smaller . The RecQ family is typified by 8 helicase motifs . In both RecQ and RecS , they are located towards the N-terminal ( Nter ) region and are followed by a more divergent C-terminal ( Cter ) region . In EcRecQ , the latter region carries the site of interaction with SSB [20] , [21] . As shown in Figure 1A , Tap-tag analysis of RecS revealed a prominent interaction with SSB and a protein of unknown function , YpbB , encoded immediately upstream of recS . The stop codon of ypbB overlaps the start codon of recS , suggesting translational coupling of the two proteins ( Figure 1B ) . In the few bacterial species encoding a ypbB homologue , this invariably appears upstream of a recS homologue in a common operon ( see Figure S1 ) . In the Tap-tag experiments , RecS and YpbB appear in almost equimolar amounts after purification , as does the co-captured SSB ( Figure 1A ) . In addition , the cellular concentrations of RecS and RecQ appear similar ( between 150 and 300 copies per cell ) as judged by western-blotting of total protein extracts of cells expressing the RecS-SPA or RecQ-SPA fusions and probed with anti-Flag antibodies against the SPA motif ( not shown ) . To test whether RecS and/or YpbB are targeted to chromosomal replication forks , we first constructed N-ter GFP fusions of each gene individually at amyE . Both GFP-RecS and GFP-YpbB fusion proteins appeared largely dispersed throughout the cell ( Figure 1C ) although some cells were found to exhibit tiny foci on their nucleoid ( Table S1 ) . In view of their tandem genetic configuration , it seemed possible that both might be required for correct targeting . We therefore inserted a construction ( GFP-ypbB/recS ) including both genes at the amyE locus , to retain potential translational coupling . As shown in Figure 1D , all ssb3+ cells carrying this construct exhibit a regular GFP focus pattern identical to that observed previously with GFP-recQ ( [8]; Table S1 ) . The simplest explanation for this , as implied by the Tap-tag analysis ( Figure 1A ) , is that YpbB and RecS assemble into a single complex able to interact with SSB , resulting in its targeting to active chromosome replication forks . Biochemical evidence for physical interaction between YpbB and RecS comes from our attempts to purify them from E . coli ( described in Text S1 ) : YpbB could not be prepared as a soluble protein alone but only as a stable complex with RecS . Furthermore , the YpbB/RecS complex , but not RecS alone , interacts physically with SSB ( Figure S2 ) . Finally , no localization of GFP-YpbB was observed in ssbΔ35 and ssbΔ6 cells ( Figure 1D and Table S1 ) . Altogether , these results show that YpbB is targeted to active chromosomal forks in an SSBCter-dependent manner . They also indicate that the GFP-YpbB foci depend on RecS , with which YpbB forms a stable complex . Reciprocally , RecS could also be present at forks via its association with YpbB . The detection of RecS in the Tap-tag of SSB argues for this is the case ( see below ) . To test whether localization at active forks is a property shared by other DNA helicases known to act in repair of arrested replication forks , we analyzed PcrA [22] . A functional GFP-PcrA fusion did not form foci in growing cells but appeared to localize non-specifically to the nucleoid ( Figure S3A ) . In addition , SSB was not detected among the proteins that co-purified with PcrA in Tap-tag experiments ( Figure S3B ) . The Tap-tag is nevertheless validated by the recovery of 2 known partners of PcrA with the functional PcrA-SPA fusion: RNA polymerase [23] , and RecA [24] , [25] . Furthermore , we did not detect an interaction between purified SSB and PcrA ( active as a DNA helicase ) in a specific SSB pull-down assay ( Figure S4C ) detailed below . A lack of discrete targeting in the cell was also observed for the widespread DinG DNA helicase ( Figure S3A ) , recently demonstrated in E . coli to function in concert with Rep or UvrD ( the functional equivalents of PcrA; [26] , [27] ) in resolving accidents caused by collision between the replication and transcription machineries . In addition , SSB was not detected in the Tap tag of DinG ( Figure S3B ) . Most of the proteins co-purified with DinG were ribosomal proteins , indicative for a putative role of DinG in translation and/or ribosome biogenesis in B . subtilis . Thus , anchorage to active chromosomal forks visualizable by focus formation would not be a hallmark of all effectors of DNA replication rescue . We could not exclude , however , that specific interactions of PcrA and DinG with one component of the fork might occur without being strong or cumulative enough to generate a detectable focus . We previously showed that DnaX , a homologue of the E . coli Holopolymerase III ( EcPolIII ) τ subunit , still formed foci in ssbΔ35 dividing cells [8] . This is also true for other components of EcPolIII conserved in B . subtilis , i . e . HolA , HolB and DnaN , as well as for the replicative DNA helicase , DnaC ( Table S1 ) . We could not test the primase , DnaG , since neither N- nor C-terminal GFP fusions gave rise to discrete foci in wild-type B . subtilis cells [10] . B . subtilis encodes two DNA polymerases essential for genome duplication , PolC and DnaE [28] . Both are homologous to the single essential E . coli DNA polymerase , EcDnaE . GFP fusions to PolC and DnaE were both shown to localize at active chromosomal forks [28] , [29] . Remarkably , and in sharp contrast with PolC-GFP , we found that DnaE-GFP did not form foci in ssbΔ35 cells ( Figure 2A and Table S1 ) nor in ssbΔ6 cells ( Table S1 ) . This suggests that DnaE accumulates at active chromosomal forks via a physical interaction with SSB whereas PolC does not . Tap-tag analysis of DnaE was not informative , since only the DnaE-SPA prey was recovered ( not shown ) . We therefore explored the potential interaction between DnaE and SSB in vitro with purified recombinant proteins . We used a pull-down assay based on magnetic beads coated with ssDNA fully bound by purified SSB or SSBΔ6 ( or SSBΔ35 , which behaved as SSBΔ6; Figure S5 ) to test specific interaction between a protein and the SSBCter . This assay was validated with RecQ and RecG ( see Figure S4A and S4B; Figure S5 ) . Purified DnaE was found to interact with the SSBCter in this assay and poorly with SSBΔ6 and SSBΔ35 ( Figure 2B and Figure S5 ) , supporting the notion that the interaction between DnaE and SSB is direct and accounts for the accumulation of DnaE-GFP at active chromosomal forks . We next examined the localization of other proteins known or expected to co-localize with B . subtilis chromosomal forks but not essential for their propagation . These included SbcC , a subunit of the heterodimeric SbcCD nuclease that acts specifically on ssDNA palindromic structures [30]; YabA , a negative regulator of initiation of DNA replication at oriC in B . subtilis [31]; RarA , which , in E . coli , is required for RecA loading on arrested chromosomal forks [32]; and RecO and RecJ , which are also involved with RecA loading at arrested forks in concert with RecF , RecR and RecQ . Among these , only YabA was found to localize in ssbΔ35 cells ( Figure 2C , Table S1 ) . Since YabA localizes at forks in a DnaA- and DnaN-dependent manner [33] , this result implies that DnaN and DnaA act in ssbΔ35 cells as in wild-type cells . GFP-SbcC was previously shown to co-localize with B . subtilis replication forks [10] . Here we find that this localization is dependent on the C-terminal domain of SSB ( Figure 2C and Table S1 ) . The three others candidate proteins , i . e . RarA , RecO , and RecJ , which did not localize in ssbΔ35 cells in contrast to wild type ssb3+ cells ( Figure 2C and Table S1 ) , were found to interact physically with SSB . This was demonstrated , by Tap-tag analysis in the case of RecJ ( Figure 2D ) , by pull-down and gel filtration assays for RarA ( Figure 2E and Figure S6 ) and by pull-down assays for RecO ( Figure 2F ) . The precise targeting of the functional GFP-RecO fusion to active forks ( and not to ssDNA gaps that could be formed elsewhere on the genome ) was confirmed by its co-localization with the replisome protein DnaX ( Figure S7 ) . These results imply that although RecA is not normally present at active forks [34] , these are equipped with key components of the RecFOR machinery ( i . e . RecO , RecJ , RecQ and RarA ) permitting recruitment of RecA at replication forks upon accidental arrest [34] . To identify a more complete repertoire of SSB partners , we used SSB as a prey in Tap-tag analysis . However , in contrast to EcSSB [35] , we were unable to construct an SSB-SPA fusion at the ssb locus suggesting that capping SSBCter with the SPA motif inactivates SSB and leads to cell lethality . We therefore inserted the ssb-SPA construct under the Pxyl promoter at amyE to generate mixed SSB complexes composed of both SSB-SPA and wild-type SSB subunits . Their selective capture , via the SSB-SPA component should permit the co-capture of protein partners interacting with the uncapped wild-type SSB subunits . Ectopic expression of SSB-SPA had no observable negative effect on cell growth ( not shown ) . As shown in Figure 3 , wild type SSB and SSB-SPA subunits were recovered in equal amounts from cells grown with ( lane 3 ) or without ( lane 1 ) D-xylose induction of SSB-SPA expression . As expected , the total yield of the hetero-tetrameric SSB/SSB-SPA complexes was higher with than without D-xylose . Many proteins were observed to co-purify with SSB/SSB-SPA complexes ( Figure 3 ) . These included those reproducibly recovered in other Tap-tag experiments performed with other B . subtilis proteins ( e . g . GyrA and many ribosomal proteins ) and were not considered further . The others appeared to be specific partners of SSB . They were not observed in control experiments where the ectopic ssb-SPA was replaced with a wild type ssb allele ( lane 2 ) . In addition , their levels were increased when SSB-SPA expression was induced ( compare lanes 1 and 3 ) . Many of these proteins ( e . g . RecJ , RecQ , RecG , and RarA ) were identified in the experiments described above as direct partners of B . subtilis SSB . Very few peptides of RecS were unambiguously detected by mass spectrometry . As reported above , RecS alone could not stably interact with SSB in vitro but could do so in a complex with YpbB ( Figure S2 ) . YpbB was not detected in the Tap tag of SSB , possibly because the number of YpbB molecules recovered was below the level of detection by mass spectrometry , RecS being very close to this limit . The Ung protein was identified in the Tap tag of SSB . Ung was identified previously as a partner of EcSSB [36] . However , some known SSB partners , such as RecO , PriA , DnaE , SbcC and YpbB , were absent . This is probably due to differences in affinity between SSB and each of its partners and to variation in their natural cellular levels since some are known to be present in very low amounts ( for instance , ∼50 copies of PriA per cell; [15] ) . The same dual explanation may also account for the differential yield of some SSB partners recovered in the experiment e . g . RecJ which is by far the most abundant protein co-purified with the SSB/SSB-SPA complex ( Figure 3 ) . We also identified several new candidate partners . These included XseA , the large subunit of ExoVII , and YrrC , a protein of unknown function conserved in gram positive bacteria and predicted to be a helicase/nuclease by sequence analysis . GFP fusions to XseA and YrrC were also both found to form foci on the nucleoid of ssb3+ cells but not in ssbΔ35 cells ( Table S1 ) . This screening identified a repertoire of 12 proteins belonging to the B . subtilis SSB interactome escorting active chromosomal forks . Together , these proteins fulfill a large variety of functions concerned with DNA processing . As a result , the SSBCter emerges as a central hub of DNA processing functions at chromosomal replication forks , where SSB naturally concentrates in the cell . While deletion of the SSBCter is not lethal , ssbΔ35 cells show viability defects . They exhibit a ∼5-10 fold lower plating efficiency during growth in rich medium , i . e . under fast growing conditions ( Figure 4A ) , as well as in minimal medium ( not shown ) , and smaller colonies on solid medium ( Figure 5A ) . The reduced viability was also directly inferred by observation of exponentially growing cells ( in rich medium ) under the microscope where up to 15% of ssbΔ35 cells show various kinds of cellular and/or nucleoid morphological defects ( i . e . distribution , shape , length , segregation; see Figure S8 ) . Similar observations were made with the ssbΔ6 strain ( Figure S9A and S9B and not shown ) . The SSBCter interactome includes many proteins involved in maintaining genome integrity . The importance of the SSBCter might therefore be expected to be more pronounced under growth conditions that are stressful for the genome . Indeed , ssbΔ35 and ssbΔ6 cells are nearly as sensitive to UV irradiation as recA− cells ( Figure 4B and Figure S9C ) . Similarly , both mutants are also more sensitive to Mitomycin C ( MMC ) than the ssb3+ strain ( Figure S9D ) . To investigate further the intracellular role of the SSBCter , we examined the effect of complementation of the defects of ssbΔ35 cells by ectopic expression of wild-type SSB at amyE in a controlled manner from the Pxyl promoter . Upon induction with D-xylose production of SSB from the Pxyl promoter was ∼10% that of the natural SSB level ( Figure 4C; compare lane 2 with lane 6 ) . This low concentration was , however , sufficient to fully suppress the plating defect of the ssbΔ35 strain ( not shown ) . It also fully restored UV resistance at doses up to 10 J/m2 ( Figure 4D ) . Above this dose , the cells exhibited sensitivity intermediate between that of ssb3+ and ssbΔ35 cells indicating that the intracellular concentration of SSBCter is determinant for an optimal response to DNA damage . We next investigated whether SOS , a well known cellular response to DNA damaging agents , could be triggered in ssbΔ35 cells . In B . subtilis , the SOS system is regulated by RecA-induced auto-cleavage of the LexA repressor . We used a PlexA:lacZ construct as a reporter of SOS activity and another DNA damaging agent , MMC , as an inducer [37] . The MMC-induced SOS response was dramatically reduced in ssbΔ35 cells compared to ssb3+ cells ( Figure 4E ) . These results therefore underline a pivotal role for the SSBCter in triggering the SOS response . Another notable defect of ssbΔ35 strains is their temperature-sensitive growth , as measured by plating assay ( Figure 5A ) . This lethality is fully corrected by SSB expression induced from the ectopic Pxyl:ssb ( not shown ) . Since some SSB partners are independently important for cell viability , we tested whether the temperature sensitivity of ssbΔ35 cells could be corrected by increasing individual expression of these partners placed under the Pxyl promoter from the amyE locus . Overexpression of DnaE or PriA , the two most important components of the SSB interactome for cell viability , did not alleviate the temperature sensitivity of ssbΔ35 cells ( not shown ) . Unexpectedly , however , induced expression of RecO ( as a functional RecO-SPA fusion ) did so ( Figure 5B ) . In contrast , the plating defect characteristic of the ssbΔ35 strain observed at permissive temperature is not corrected by RecO overexpression ( Figure 5B ) . RecO is a recombination mediator protein that , with RecR and RecF , directs loading of RecA onto ssDNA coated by SSB [38] . We therefore tested whether suppression of ssbΔ35 temperature sensitivity by overexpression of RecO was dependent on RecA . We introduced the recA::tet allele into the ssbΔ35 and ssb3+ strains carrying the Pxyl:recO-SPA cassette . Inactivation of recA in the ssb3+ strain provoked weak temperature sensitivity ( Figure 5C ) . Disruption of recA prevented RecO suppression of ssbΔ35 temperature sensitivity ( Figure 5C ) . This implies that RecA loading on ssDNA is needed for this suppression . The formation of a RecA-ssDNA nucleofilament is the pre-synaptic intermediate of homologous DNA recombination and the inducing signal of SOS , which we have shown above to be defective in MMC-treated ssbΔ35 cells ( Figure 4E ) . However , individual overexpression of RecO neither restored SOS induction by MMC in this mutant , nor suppressed its sensitivity to MMC ( not shown ) . Thus , the suppression is not solely due to RecO action , but also relies on RecA , leading to the proposal that it proceeds through the RecO-dependent loading of RecA on ssDNA . The previous experiments demonstrated that SSBCter was required for repair of lesions throughout the genome . They did not address the question of whether the SSBCter specifically assists chromosomal fork progression . To specifically stress the replication fork , we used two B . subtilis strains bearing temperature sensitive alleles , dnaN5 and dnaX51 , whose products are exclusively associated with the replisome [39] and analyzed how deletion of the SSBCter affects their viability . The ssbΔ35 allele , genetically linked to the erythromycin resistance marker ( EryR ) , was introduced by transformation at low temperature into strains carrying the replication mutations . An isogenic EryR-linked ssb3+ allele was also used as a control . Viable EryR clones were obtained upon transformation at 30°C of the dnaN5 and dnaX51 strains with the ssbΔ35 and ssb3+ alleles . The ssbΔ35 dnaN5 and ssbΔ35 dnaX51 recombinants exhibited the characteristic plating and growth defects of the ssbΔ35 strain ( Figure 6 ) . Interestingly , they were both significantly more temperature sensitive for growth than their ssb3+ counterparts ( Figure 6A–6C ) . Thus the SSBCter is crucial for growth of ssb3+ dnaN5 and ssb3+ dnaX51 cells at semi-permissive temperatures . In addition , we also found that ssbΔ35 cells were markedly more sensitive than ssb3+ cells to DNA replication stresses induced either by hydroxyurea , which diminishes the dNTP pools , or by HPUra , an antibiotic that specifically inactivates the essential DNA polymerase , PolC ( not shown ) . The exact nature of the defects provoked by a dysfunction of the replisomes made with the mutated DnaN or DnaX proteins is not known . These defects could be either fork arrest or lesions left by continuing forks . These results provide evidence that the SSBCter is crucial for ensuring the proper duplication of a genome damaged by stresses that specifically impair the replisome .
SSB is not the only source of accessory proteins at the replication fork . DnaN and the replicative helicase also act as anchors for distinct replication accessory proteins ( for reviews , see [5] , [40] ) . While DnaN and the replicative helicase are expected to be confined to replication forks , the spectrum of SSB activity on the genome could be larger since its localisation is primarily determined by availability of ssDNA . Assembly of the SSB interactome at a precise site on the genome is nevertheless expected to be qualitatively and quantitatively modulated by the length of the ssDNA available for SSB polymerisation . Indeed , the local concentration of SSBCter will increase with the length of the SSB-ssDNA nucleofilament . With an average length of 1 kb for single strand DNA on the lagging strand template at an active bacterial DNA replication fork and a binding mode of SSB of ∼65 nts per tetramer , a minimum of ∼60 copies of SSBCter may be present at each fork . This would generate a filament capable of attracting many molecules of the different SSB interactome members . Consequently , chromosomal DNA replication forks constitute permanent subcellular sites for assembling the SSB interactome in dividing cells . In addition , all DNA processes that generate stretches of ssDNA accessible to SSB tetramers are expected to produce such centers for the SSBCter interactome anywhere on the genome ( as in the case of the repair of DNA double-strand breaks ) . Thus , the DNA toolbox associated with the SSBCter should not be considered as exclusively devoted to the progression of replication forks . In contrast to DnaN and the replicative helicase , the SSBCter may therefore be a general determinant for the maintenance of genome integrity . Comparison of the SSBCter interactome of E . coli ( compiled in [6] ) and that determined here for B . subtilis provides several important general conclusions concerning SSBCter function . Homologues of prominent EcSSB partners such as PriA , RecQ , RecG , RecJ , RecO and Ung which are conserved in B . subtilis ( and generally widespread in bacteria ) have also been demonstrated to interact with B . subtilis SSB . This points to a strong selective pressure in maintaining such a conserved and abundant SSBCter interactome . In this study , we have identified additional B . subtilis SSB partners ( i . e . RarA , SbcC and XseA ) also widely conserved in bacteria ( including E . coli ) but not yet identified as part of the EcSSB interactome . Conversely , we have not yet identified other known conserved EcSSB partners , such as DnaG primase and Topoisomerase III [41] , [42] , in the SSB interactome of B . subtilis . Further experiments will be required to determine whether these proteins are indeed members of the SSB interactome . B . subtilis SSB partners that are not widely conserved in bacteria have also been identified , e . g . YrrC ( a putative helicase/nuclease encoded in the genomes of gram positive bacteria also annotated as RecD ) and the YpbB/RecS complex ( see Figure S1 ) . Conversely , EcSSB partners , such as the χ subunit of the EcHolopolymerase III [43] , [44] , have no equivalent in B . subtilis . Thus , the interactome of SSB also includes some specific proteins representative of subgroups of bacteria . This reveals particular needs in genome metabolism , suggesting that not all bacteria require these functions and/or have evolved distinct alternative strategies to execute identical functions . Another specific part of the B . subtilis SSBCter interactome is the replisomal DNA polymerase DnaE [28] . Neither its homologue PolC , nor any other known proteins of the B . subtilis replisome , depend on the SSBCter for their targeting to active chromosomal forks ( Table S1 ) . DnaE remains essential for viability of ssbΔ35 cells ( not shown ) . Interestingly , it has recently been shown that the essential role of DnaE in the replisome is to elongate a short DNA stretch on the RNA primers synthesized by the DnaG primase before a hand-off to the bona fide replicative polymerase , PolC [45] . Thus , DnaE has presumably evolved distinct interactions with the replisome to functionally link the activities of DnaG and PolC . However , these interactions are not strong enough to produce detectable fluorescent foci with the DnaE-GFP fusion at active forks in ssbΔ35 cells . In line with this reasoning , the DnaE interaction with the SSBCter might serve an additional role . E . coli encodes a single DNA polymerase of the DnaE family , which is not part of the EcSSB interactome [6] . In contrast , the EcDNA polymerase II ( EcPolII ) has been found to interact with the EcSSB [46] . EcPolII is not essential for the cell , is involved in distinct pathways of replication re-activation and belongs to the E . coli SOS system [47] . Remarkably , B . subtilis dnaE also belongs to the SOS regulon [48] . This raises the possibility that B . subtilis DnaE might also be involved in certain fork maintenance pathways , as demonstrated for many members of the SSB interactome . A central piece of the SSB interactome is RecO , which acts with RecR and RecF to direct the loading of RecA on SSB-coated ssDNA [49] . Temperature sensitivity of ssbΔ35 cells can be suppressed by RecO overexpression , and in a RecA-dependent manner . This shows that ssDNA accessible to RecA is generated in ssbΔ35 cells at high temperature , and that RecA then mediates cell rescue . It also reveals a RecO dysfunction in the ssbΔ35 strain , which can be compensated by increasing its cellular concentration . This parallels the results obtained previously with a PriA mutant unable to interact with SSB , whose inefficiency in directing replication restart was compensated by its overexpression [8] . Thus , a consequence of deleting SSBCter is a reduction in the activities of certain of its partners , resulting from loss of SSBCter-assisted and targeted recruitment to their sites of action on the genome . Importantly , RecO overexpression does not suppress the growth defect of ssbΔ35 cells observed at permissive temperature . This points to the importance of the other SSB partners for sustaining optimal growth of wild-type cells . The growth defect of ssbΔ35 cells could not be corrected by the overexpression of DnaE or of PriA alone; these are the two proteins of the SSB interactome known to be essential for growth ( in rich medium in the case of PriA; [15] ) . Thus , it is possible that more than one SSB partner must be overexpressed to circumvent this defect , if this stems solely from the loss of their SSB-assisted targeting in the cell . Clearly , more work is needed to understand the growth defect caused by the deletion of the SSBCter . Another marked defect of ssbΔ35 cells at permissive temperature is their inefficiency in inducing the SOS response upon treatment by MMC . This reflects a failure to generate the RecA-ssDNA filament which would normally act as a triggering signal . The RecFOR apparatus is needed for the MMC-mediated SOS induction in B . subtilis [50] . However , RecO overexpression in MMC-treated ssbΔ35 cells did not lead to SOS induction ( not shown ) . Conversely to what is observed at non permissive temperature , this strongly indicates that other members of the SSBCter interactome are needed for generating and/or stabilizing the ssDNA template for the loading of RecA upon MMC treatment . Obvious candidates are the RecQ and RecJ proteins , a helicase/exonuclease couple known to generate the ssDNA from damaged DNA or inactivated replication forks , onto which the RecFOR machinery mediates RecA delivery ( reviewed in [2] ) . The SOS response defect in ssbΔ35 cells has an important bearing on the results of a recent study on RecA localization in B . subtilis cells . GFP-RecA focus formation on the genome provoked by DNA damaging agents ( including MMC ) was shown to depend on replisome activity although RecA does not appear to be pre-recruited at active forks [34] . Our results suggest a mechanism to explain this conditional RecA localization . We propose that the active fork itself has the potential to load RecA directly onto ssDNA already available or produced de novo via the SSBCter . Together , these results support a model in which the SSBCter interactome associated with active forks provides a series of solutions for promoting their restart upon accidental blockage ( Figure 7 ) , as well as for dealing with errors left behind the passage of the fork . A key step is replisome assembly on the branched DNA backbone of the fork . The PriA protein and its interaction with the SSBCter are central to this event [8] . This could be the only response necessary if arrest is due to replisome dismantling . In more complex situations , other actions aim at protecting and/or clearing the fork , via individual or concerted actions of the many members of SSBCter interactome . DNA repair is obviously crucial . This could be handled either immediately by the proteins already present , or delayed via RecA loading that could then act in two ways . RecA may reconstruct the fork by homologous recombination , and may induce the SOS response to provide more effectors for DNA repair . Amongst the new effectors coming into play are the error-prone DNA polymerases . Interestingly , it has been shown that the recruitment of E . coli PolV at the 3′end of a DNA gap flanked by RecA filaments is increased by an interaction with the EcSSBCter [51] . A distinct class of repair pathways not drawn in the model of Figure 7 , are those acting on lesions caused by the replisome but not accompanied by fork arrest . ssDNA gaps are prominent examples of such lesions . In such cases , ssDNA gaps are expected to remain coated by several copies of SSB still interacting with or attracting the proteins that will promote repair . In conclusion , the SSBCter emerges as a general maintenance pivot of bacterial genome integrity . Long stretches of ssDNA are intimately associated with the functioning of active bacterial forks . These form primary targets of SSB in living cells and , consequently , of its interactome . One consequence is that replisomes of chromosomal forks are escorted throughout their progression along the bacterial genome ( generally for more than 2 Mbp per fork , as in the case of E . coli and B . subtilis model bacteria ) . In addition and in a reciprocal way , the forks behave as vehicles for many DNA repair proteins , providing also a convenient way to scan DNA integrity during genome duplication .
B . subtilis strains used in this study , all based on the 168 or L1430 derivatives , are listed in Table S2 along with the strategies used for their construction . They were propagated in LB medium supplemented , unless otherwise indicated , with appropriate antibiotics ( erythromycin , 0 . 6 µg/ml; spectinomycin , 60 µg/ml; chloramphenicol , 5 µg/ml; tetracycline , 15 µg/ml , phleomycin , 2 µg/ml ) . ssb3+ , ssbΔ35 , ssbΔ6 and all strains carrying a gene tagged with the SPA motif at its locus were maintained with IPTG ( 1 mM ) . Expression of a gene under the Pxyl promoter was achieved by adding 0 . 2% of D-xylose to the medium . All new chromosomal structures were verified by PCR using appropriate pairs of primers . In case of insertions at the amyE locus , these were also verified by the loss of amylase activity on starch containing media plates . E . coli strains used were MiT898 [15] for plasmid constructions and ER2566 from NEB , Rosetta ( DE3 pLys ) or BL21-Gold ( DE3 ) from Novagen for protein expression and purification . All plasmids used in this study are listed in Table S3 . Details of their construction are presented in Text S1 . Microscopy analyses were done as described previously [8] . Cells , grown at 30°C until mid-exponential growth phase in LB medium supplemented with appropriate antibiotics and 0 . 2% D-xylose , were examined with a Leica DMRA2 microscope equipped with a ×100 magnification oil-immersion objective and a COOLSNAP HQ camera ( Roper Scientific , USA ) . Images were captured and processed with METAMORPH V7 . 5r5 . Except for SSB , the SPA purification tag [52] was joined to the 3′ end of each gene candidate at its original locus . Tandem affininity purifications were performed as previously described [8] with slight modifications , which are detailed in Text S1 . Purification procedures of all the proteins produced in E . coli used in this study are described in Text S1 . 25 µl of Dynabeads M-280 streptavidin per assay ( Invitrogen ) were incubated 15 min at 4°C in 20 mM Tris-HCl pH 7 . 5 , 2 M NaCl with 50 pmol of a 65-mer oligonucleotide 5′-CGTCGTTTTACAACGTCGTGACTGGGAAAACCCTGGCGTTACCCAACTTAATCGCCTTGCAGCA-3′ biotinilated ( with biotin TEG; Genecust ) . Beads were washed with 200 µl of the same buffer , resuspended in 200 µl of buffer B ( 20 mM Tris-HCl pH 7 . 5; 200 mM NaCl ) supplemented with 80 pmol of purified SSB or SSBΔ6 and incubated at 20°C under agitation ( 800 rpm ) in a 96 wells plate in a Thermomixer ( Eppendorf ) . Beads were washed in 200 µl of buffer B and resuspended in 200 µl of the same buffer supplemented with various quantities of purified DnaE , PcrA , RecO , RecG , RecQ , or RarA proteins as indicated in the figures . After 30 min of incubation at 800 rpm and 20°C , beads were washed in 200 µl of buffer B , drained and resuspended in 10 µl of SDS-PAGE loading buffer . The proteins were separated on 14% SDS-PAGE and revealed by Coomassie blue staining . Spot assays were used to measure the viability of B . subtilis strains used in this study . O/N cultures , incubated at 30°C or 37°C , as indicated in the figure legend , were diluted in fresh LB medium at the same temperature supplemented as indicated in the figure legend with erythromycin , IPTG and with or without D-xylose . At mid-log phase ( A650nm≈0 . 3 ) , 10 µl of 10-fold dilutions ( 100 to 10−5 in Figure 5 and 10−1 to 10−6 in Figure 6 ) were spotted on LB agar plates containing the same antibiotics and inducers as those used in the liquid culture . Plates were then incubated O/N at different temperatures ( as indicated in the figure legends ) . In UV resistance assays , plates were exposed to UV irradiation at the indicated doses prior to O/N incubation at 37°C . Colonies were counted after 24 or 48 hours of growth ( depending on their growth rate and/or the incubation temperature ) . Cell survival was expressed as the ratios of the CFU ( Colony Forming Units ) of UV-treated to untreated cells or of CFU obtained at the tested temperature to CFU obtained at 37°C ( Figure 5 ) or 30°C ( Figure 6 ) for each strain . O/N cultures of strains containing the PlexA:lacZ cassette at amyE were propagated at 37°C in LB medium supplemented with erythromycin , spectinomycin and IPTG . Cells in exponential phase ( A650nm≈0 . 03 ) , obtained by inoculating O/N cultures in fresh LB medium supplemented with erythromycin and IPTG , were treated or not with 40 ng/ml MMC to induce or not the SOS response respectively . Sample of ∼0 . 5 ml per unit of A650nm were taken from cultures every 30 min and treated as described previously [53] for the determination of β-galactosidase activity , expressed in nmol of ONP produced per minute and per mg of protein . Whole protein extracts and western blot analysis were done as previously described [8] with slight modifications as reported in Text S1 .
|
Cell multiplication relies primarily on the complete and accurate duplication of the genome . Thus , all organisms have evolved multiple mechanisms to protect , repair , and re-activate the DNA replication forks . A large body of research is currently aimed at deciphering the mechanisms that precisely direct the proteins involved in these rescue pathways towards the chromosome replication forks . Here , we have used the model bacterium Bacillus subtilis to demonstrate that the active chromosomal DNA replication forks are pre-equipped with many such rescue effectors via their direct physical interaction with the carboxy-terminal end ( Cter ) of the Single-Stranded DNA Binding protein ( SSB ) . A detailed analysis of the multiple defects of viable B . subtilis mutants deleted for the Cter of SSB ( SSBCter ) revealed the vital role of this domain for the maintenance of genome integrity and fork propagation . The inability to grow at high temperature is a major defect of the ssbΔCter mutant . We show that this lethality can be specifically suppressed by overexpression of RecO , one of the numerous partners of SSB , apparently by mediating the loading of the RecA recombinase on ssDNA .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/dna",
"repair",
"molecular",
"biology/recombination",
"molecular",
"biology/dna",
"replication",
"genetics",
"and",
"genomics/chromosome",
"biology"
] |
2010
|
The C-Terminal Domain of the Bacterial SSB Protein Acts as a DNA Maintenance Hub at Active Chromosome Replication Forks
|
In this work we investigate , by means of a computational stochastic model , how tumor cells with wild-type p53 gene respond to the drug Nutlin , an agent that interferes with the Mdm2-mediated p53 regulation . In particular , we show how the stochastic gene-switching controlled by p53 can explain experimental dose-response curves , i . e . , the observed inter-cell variability of the cell viability under Nutlin action . The proposed model describes in some detail the regulation network of p53 , including the negative feedback loop mediated by Mdm2 and the positive loop mediated by PTEN , as well as the reversible inhibition of Mdm2 caused by Nutlin binding . The fate of the individual cell is assumed to be decided by the rising of nuclear-phosphorylated p53 over a certain threshold . We also performed in silico experiments to evaluate the dose-response curve after a single drug dose delivered in mice , or after its fractionated administration . Our results suggest that dose-splitting may be ineffective at low doses and effective at high doses . This complex behavior can be due to the interplay among the existence of a threshold on the p53 level for its cell activity , the nonlinearity of the relationship between the bolus dose and the peak of active p53 , and the relatively fast elimination of the drug .
The p53 gene is an important oncosuppressor gene , and its product is heavily involved in the control of both cell proliferation and cell differentiation . It is well known that p53 triggers cell cycle arrest and even apoptotic pathways in response to moderate and , respectively , strong stress signals [1] . Concerning cell differentiation , p53 suppression induces a strong increase of the probability of symmetric division in breast stem cells [2] , and drug-driven activation of p53 induces rapid differentiation of human embrionic stem cells [3] . Underexpression of p53 is often observed in tumors carrying wild-type p53 [1] , [4] . This phenomenon is caused by overexpression of Mdm2 protein , the main competitor of p53 [1] , [4] . For example , this may occur when the gene p14 , which inhibits Mdm2 by sequestering it into the nucleus , is deleted , as observed in breast , brain and lung cancers [1] . Another cause leading to the underexpression of wild-type p53 is given by Mdm2 amplification [1] , [4] . Moreover , binding to viral proteins in infected cells causes underexpression of p53 in chronic infection-related cancers [1] , [4] . Underexpressed wild-type p53 is seen as a primary candidate target for antitumor therapies based on chemical molecules [1] or siRNA [5] . Among the p53-targeting drugs , a prominent role is played by Nutlins [6] , a family of small molecules able to bind Mdm2 exactly in the "binding pockets" where p53 binds , so impeding the formation of p53-Mdm2 complexes and inducing a rapid p53 level increase . Since the activation of p53 may cause the triggering of both the apoptotic pathways and the differentiation of tumor stem cells , Nutlins are regarded as potentially important antitumoral agents . In their study , Vassilev et al . [6] showed a potent antitumor activity of Nutlins on wild-type p53 tumor cell lines , such as HCT116 , RKO and SJSA-1 cells , whereas only a marginal effect on mutant p53 cell lines ( such as SW 480 , MDA-MB-435 , PC3 ) was observed . The same research group [7] later found that different Nutlins subtypes may have a differential action on different tumor cell lines . A number of other preclinical studies reported that Nutlin is an effective antitumor drug for important types of cancers carrying dysfunctional wild-type p53 . The antineoplastic action of Nutlin on chronic B-cell lymphocytic leukemia with wild-type p53 has been shown [8] , documenting a series of synergies with doxorubicin . Nutlin is active against prostate cancer cells retaining wild-type p53 and androgen receptor signaling [9] , and works by inhibiting their proliferation via cell cycle arrest and apoptosis . Nutlin-3a is also active in Hodgkin lymphoma , where p53 is rarely mutated [9] . In Ewing's sarcoma cells , Nutlin-3 restores wild-type p53 functions , with cancer growth inhibition and apoptosis induction , whereas no effect was observed for cells with mutated p53 ( the mutation , however , affects only of those tumors ) . In addition , Nutlin is active against human glioblastoma multiforme [10] , where of patients carry amplifications of Mdm2 . In this case , Nutlin was active in the wild-type p53 glioblastomas , where it also caused cell senescence . In [10] , it has been explicitly noticed that cell lines can significantly differ in their apoptotic response to similar levels of p53 activation . We may observe that the Nutlin-mediated restoration of p53 levels does not automatically guarantee beneficial effects if other modules of p53-related pathway are dysfunctional . For example , Ma et al . [11] showed that Nutlin-3 is unable to induce p53-related apoptosis in cells where p53-Ser46 phosphorylation is defective . In retinoblastoma p53 is intact , but it is silenced by MDMX overexpression [12] , [13] . A preclinical study [13] has reported strong activity of locally-administered Nutlin-3a against retinoblastoma , and synergy with topotecan . Recently , it has been shown that Nutlin overcomes resistance to Vemurafenib in melanoma lines [14] , and to Cisplatin in ovarian cancer cells [15] . Finally , in both the above-mentioned studies concerning the role of p53 in the differentiation of stem cells [2] , [3] , Nutlin was the drug used for p53 activation . The above experimental findings on the effect of Nutlin on wild-type p53 tumors can be roughly summarized as follows: the binding of Nutlin to Mdm2 , by inactivating the main antagonist of p53 , leads to increasing the p53 level , which negatively influences the tumor growth , in part because of the onset of cell arrest and apoptosis , in part – for stem cell-based tumors – by establishing in cancer stem cells a more physiological pathway of asymmetric cell division . However , the design and implementation of efficacious therapies requires going beyond a mere descriptive approach , which disregards the kinetics and the quantitative features of the phenomena . Valid tools can be provided by Systems Biology , which is able to integrate information from multiple sources in a coherent quantitative model by using mathematics and bioinformatics [16] . Indeed , the role of p53 has elicited the interest of many computational biologists since the seminal experimental/modeling work conducted by Alon et al . [17] , where the onset of oscillations in p53 concentration during the response to radiation stress was shown . A major contribute was given by Ciliberto et al . [18] , who stressed the role of both negative and positive feedbacks in p53/Mdm2 interplay . Other authors [19] , [20] , [21] , [22] stressed the role of delays in the p53/Mdm2 network , although recently it has been noticed [23] , [24] , [25] that the use of explicit delays to explain p53 oscillations ( and other dynamical features ) may be avoided by including in the model the complexes formed by p53 and Mdm2 . Sturrock et al . [26] , [27] and Dimitrio et al . [28] proposed models where the intra-cellular spatial diffusion of p53 is represented and used as causative agent for the onset of the observed oscillations . Laise et al . [29] recently proposed a model of the hypoxia-related apoptotic pathway p53/HIF-1/p300 networks . Apart from [29] , all the above-mentioned mathematical models have focused only on the p53/Mdm2 network . However , PTEN protein plays a major role in p53 regulation [30] , [31] and should not be neglected . A stochastic model of p53/Mdm2/PTEN interplay during environmental stresses was proposed by Puszynski et al . [32] , and that model forms the basis of our work . Zhang and colleagues in [33] included the p53/PTEN/Akt/Mdm2 positive feedback in their deterministic model , although not directly including PTEN among the state variables , showing its relevance in the interplay between p53/Mdm2/PTEN network and p21 network . Moreover , in [34] they more directly analyzed the role of the delicate trade-off between the p53/PTEN/Akt/Mdm2 and the ATM/p53/Wip1 feedbacks during the process of DNA damage response . The present work is aimed at building – and , to some extent , comparing with data – a quantitative model of Nutlin pharmacodynamics at the single-cell level that explicitly includes the main biochemical network regulating p53 , along with the transcriptional feedbacks . These chemical reactions , mostly following the mass-action law , are converted into a hybrid stochastic model , i . e . , a model including both differential equations and birth-and-death stochastic processes . The model , besides Mdm2 , includes PTEN and ubiquitins as two other major players shaping the dynamics of the p53 network . Indeed p53 is a transcription factor for PTEN , which in turn ( through PIP and Akt ) induces Mdm2 phosphorylation by means of a positive feedback [30] , [31] . Since these processes are enacted with timescales not substantially different to those typical of Mdm2-p53 interactions , PTEN cannot be eliminated from the model via a quasi-steady state approach . On the contrary , since we are interested in analyzing the dynamics of cell response to time-varying Nutlin concentration , PTEN is a primary actor and its subnetwork has to be explicitly represented . The role of p53 ubiquitination in the dynamics of p53 has been emphasized in [18] . Moreover , we trace p53-Mdm2 complexes , as suggested in [23] , [24] . Concerning the pharmacodynamics of Nutlin , which is the key issue of our study , the competition of Nutlin with p53 for Mdm2 binding is coupled in our model with a simple linear cell uptake . We show by simulations that the stochasticity of gene-switching may account for the observed inter-cell variability of the response . Moreover , our simulations suggest that dose-splitting could reduce the anti-tumoral effect of Nutlin in vivo . Considerations on the limits of the model , the clinical applicability of the drug , and the future research direction conclude this work .
As in [32] , we follow the experimental evidence [37] , [38] , [39] that transcription factors regulate the probability that a given gene is ON or OFF , rather than the mRNA transcription rate . The ON/OFF random gene-switching results in a burst production of mRNA molecules , and introduces ( also in the idealized case of the absence of downstream sources of randomness ) a large level of stochasticity in the dynamics of cell regulation networks [40] , [41] , [42] , [43] . Denoting by the number of gene copies of a generic gene , the number of copies of gene active at time is . We assume that the deactivation rate of the single gene copy is constant , and that the deactivation events are independent , so that: As far as the activation rates are concerned , we recall that the p53 protein is a transcription factor for both Mdm2 and PTEN [44] , and that p53 phosphorylation enhances p53 activity in transcription [45] . Moreover , p53 is involved in co-translational dimerization and in post-translational dimerization of dimers [46] . Although such tetramerization appears to be rather inefficient in solution , p53 dimers exhibit high cooperativity in DNA binding , with a Hill coefficient of 1 . 8 [47] , and mutated p53 with impaired tetramerization binds DNA with an affinity six-fold less than the affinity of the wild-type protein . Thus , we assume that p53 in the cell is mainly present as a dimer , and that its activity in transcription requires tetramerization at the level of DNA binding . These assumptions yield towhere . We assume that when a gene copy is active , transcription proceeds at a constant rate . We remark that although all the other chemical reactions of the model are described by ordinary differential equations , the time-courses of all the chemical species will be actually given by stochastic processes since all the reactions are ultimately driven by gene activation . Finally , note that the random fluctuations of the gene activation may be seen as a bounded stochastic process [48] perturbing the system constituted by proteins and transcripts , some of which , in turn , feedback on the dynamics of this peculiar kind of noise . As mentioned above , Nutlin perturbs the p53-Mdm2 system by binding to Mdm2 and occupying the p53 binding pocket on the Mdm2 molecule . As a consequence , Mdm2 cannot form complexes with p53 , and p53 ubiquitination is impaired [6] . So , in our model , we assume that each of the Mdm2 forms ( unphosphorylated in cytoplasm , and phosphorylated in cytoplasm and nucleus ) can be in two states . The first is Mdm2 free from Nutlin , which can bind to p53 and then is called "active" . The second one is Mdm2 coupled to Nutlin , so that it cannot form p53-Mdm2 complexes and it is called "inactive" . Note that both such states can be phosphorylated or unphosphorylated , and that , when phosphorylated , they can be translocated to or from the nucleus . The accumulation of cytoplasmic inactive Mdm2 is caused by Nutlin binding as well by dephosphorylation of Nutlin-bound phospho-Mdm2 . Conversely , its loss derives by Nutlin dissociation , phosphorylation and degradation . These processes yield the equation ( 1 ) where denotes the number of free Nutlin molecules in the cell , the number of phosphorylated AKT molecules , and the parameters have an obvious meaning . The dynamics of the amount of cytoplasmic inactive phosphorylated Mdm2 is ruled by similar processes , to which nuclear import and export must be added: ( 2 ) In the nuclear compartment , similarly , Nutlin binds to nuclear-phosphorylated Mdm2 which is inactivated according to the following equations: ( 3 ) The dynamic equations for the Nutlin-free cytoplasmic unphosphorylated and phosphorylated Mdm2 , as well as for nuclear phosphorylated Mdm2 , are reported in S1 Text . Although cell uptake of Nutlin appears saturable , the saturation seems to be achieved for rather high extra-cellular concentrations ( in HCT116 cells , no clear saturation up to extra-cellular concentrations of 50 microM has been found [57] ) . In view of the moderate values of the extra-cellular concentration of free Nutlin usually attained in the experiments , we assume a linear uptake . Concerning Nutlin efflux , the export rate is assumed linear for simplicity . Binding of Nutlin to Mdm2 and the dissociation of their complexes also contribute to the change of the intra-cellular amount of free Nutlin . Taking into account all these processes , for the cell amount of free Nutlin we can write the following equation: ( 4 ) where denotes the extra-cellular concentration of free Nutlin . To simulate in vivo Nutlin treatments , we exploited the pharmacokinetics data for oral delivery in mice reported in [58] to compute the extra-cellular Nutlin concentration . In particular , we have chosen parameters that fit the measured Nutlin concentration in retina , since the time profile of such a concentration is similar to those in plasma and spleen [58] , and then such profile can be taken as an approximation of the pharmacokinetics in the whole organism . It is important to remark that a substantial binding of Nutlin to plasma proteins has been demonstrated [58] , so that the free Nutlin concentration in plasma is only a small fraction of the total Nutlin concentration . In [58] , the binding data were fitted to the equilibrium equation ( 5 ) where denotes the protein-bound Nutlin concentration , is the concentration of total plasma protein binding sites , and is the equilibrium association constant . From their data , Zhang et al . [58] estimated M , and M . Denoting by the concentration of total Nutlin , we haveand can be expressed in terms of , obtaining: ( 6 ) Protein binding is likely to occur also in the retina , and , as suggested in [58] , we may assume that in this tissue the binding is the same that in plasma . Therefore , assuming that ( i ) drug distribution occurs in a single compartment , ( ii ) only free Nutlin is eliminated , ( iii ) elimination is linear , and ( iv ) protein binding is in quasi-steady state , the simplest pharmacokinetic equation for Nutlin reads: ( 7 ) where is the drug dose rate ( in , e . g . , mg Kg−1 sec−1 ) , is the initial time of delivery , is a factor accounting for the conversion from mg Kg−1 to moles , divided by the distribution volume , and is given by ( 6 ) . will be the input of Eq . ( 4 ) for the intra-cellular Nutlin . Usually , in representing oral delivery , the gastro-enteric release is assumed exponential so that Eq . ( 7 ) , in case of administration of a single dose at time , can be rewritten as ( 8 ) where is the dose ( in mg Kg−1 ) . An easy modification of the above equation accounts for the case of repeated administrations .
In [6] , Vassilev et al . reported experimental in vitro measurements of cell viability as a function of different concentrations of Nutlin . Different tumor cell lines ( HCT116 , RKO , SJSA-1 ) were exposed to Nutlin for five days , and thereafter cell viability was assessed by MTT assay . Since the MTT assay measures the activity of intra-cellular enzymes that reduce the tetrazolium dye , and therefore in a broad sense it measures the cellular metabolic activity , the loss of "viability" according to this assay indicates either cell cycle arrest or cell death . Successive investigations [59] , [60] with different experimental techniques demonstrated that Nutlin induces cell arrest in all the considered cell lines , whereas it induces substantial apoptosis ( revealed by Annexin V positivity ) only in SJSA-1 cells , even though apoptosis is not absent in the other cell lines , particularly in RKO cells . These findings suggested that Nutlin-dependent activation of p53 leads to different outcomes ( cell arrest or apoptosis ) because of different downstream alterations [59] . The data in [6] show a decrease of the viability when the drug concentration increases , the viability being suppressed at M Nutlin concentration ( see Fig . 2 ) . Surprisingly , such a dose-response pattern is quantitatively quite similar for all the considered cell lines despite the fact that the Mdm2 gene is 25-fold amplified in SJSA-1 cells and not amplified in HCT116 and RKO cells [59] , [61] . Our stochastic simulations , performed with the parameter values reported and discussed in S3 Text ( see Tables 1 and 2 in S3 Text ) show that the proposed model is nicely able to reproduce these experimental curves , as illustrated in Fig . 2 , by assuming that the loss of "viability" is caused by the rising of p53 level over a certain threshold [62] . More precisely , we assume that if the amount of nuclear-phosphorylated p53 exceeds a threshold value for at least a time , then cell arrest or apoptosis is triggered , and the viability of that cell is lost . The adequacy of this strict assumption in fitting the data in [6] might be questioned when the response of a substantial fraction of cells consists only in cell cycle arrest ( as for HCT116 and RKO cells ) , since there are several evidences that the Nutlin-induced cell cycle block is reversible after the end of the stimulus , and that the kinetics of this recovery is different in different cell lines [60] , [63] , [64] , [65] . Concerning RKO cells , it has been suggested that in cells not undergoing apoptosis the cell cycle block may be quite long [60] . On the contrary , contrasting results on the action of Nutlin on HCT116 cells have been found [60] , [64] , [65] , with evidences that the time needed to recover proliferation after treatment may be very short ( full proliferation was observed three days after the removal of Nutlin [60] ) or rather long ( colony formation was totally suppressed by Nutlin treatment for seven days after drug removal [65] ) . Therefore , taking into account the lack of consensus on the recovery kinetics of arrested HCT116 cells , we restricted ourselves to fit only the data from RKO and SJSA-1 cells . To obtain Fig . 2 , we tried and 1 h with few changes in the numerical results , and selected h for the final fitting . To predict the fraction of viable cells at each concentration , the individual response of 500 cells was simulated ( see S2 Text for details on the simulation algorithm ) . The number of Mdm2 gene copies was assumed equal to 2 when data from RKO cells were fitted , and equal to 50 in the case of data from SJSA-1 cells . Different values of the p53 threshold were allowed for Mdm2-amplified and non-amplified cells . The values of the parameters of Table 2 of S3 Text , together with the threshold values , were adjusted by a trial-and-error procedure . As expected , in the case of SJSA-1 cells , a threshold value much lower than the value set for RKO cells ( vs . molecules/cell ) was needed , to compensate for the lower p53 levels imposed by the abundance of Mdm2 molecules . Actually , other causes such as differences in the p53 transcriptional activity or in the abundance of downstream molecules can contribute to set the threshold value , but for sake of simplicity and since there are no clear experimental evidences , they are not included in the present model . Note that the dose-response data reported in [6] are given as a function of the total Nutlin concentration in the medium . Some degree of Nutlin binding to the culture medium proteins , however , has been demonstrated in [58] , and from such measurements we could estimate the equilibrium association constant and the concentration of medium binding sites ( see Fig . 1 and Table 3 of S3 Text ) . Supposing that the binding capability of the medium employed in [6] and of the medium employed in [58] be the same , we computed for each total concentration the corresponding concentration of free Nutlin by means of ( 6 ) . These values were used in Eq . ( 4 ) to calculate the intra-cellular free Nutlin amount . We also predicted the cell response when the PTEN feedback was disabled ( keeping all the other parameters unchanged ) , to mimic tumor cells in which PTEN is not expressed . The expected reduction of Nutlin efficacy occurs only when no amplification of Mdm2 is present , when , instead , the effect of PTEN deletion is very limited . It is interesting to compare the dynamics of nuclear-phosphorylated p53 and nuclear-phosphorylated Mdm2 after the exposure to different Nutlin concentrations among those of Fig 2 . In Fig . 3 , we show stochastic simulations of RKO cells for the exposure to total concentrations of M and M ( panels A–D ) . The median number of p53 molecules grows after the start of Nutlin exposure and tends to stabilize after some oscillations to a value higher than the baseline value . When the PTEN feedback is disabled , the increase of p53 amount is reduced . In the panels E–H of Fig . 3 , showing the simulation of SJSA-1 cells , we may note that the number of nuclear Mdm2 molecules is larger than in the case of RKO cells , and the number of p53 molecules is smaller , in agreement with the presence of a robust Mdm2 gene amplification . Fig . 4 shows the corresponding dynamics of total and free intra-cellular Nutlin . We recall here that molecules in a cell of volume equal to m3 correspond to a concentration of M . Note that most of the intra-cellular Nutlin is bound to the Mdm2 molecules . In Vassilev's experiments [6] , the extra-cellular Nutlin concentration was maintained constant . This is not the case of in vivo delivery , when the drug is given as boli , i . e . , computationally speaking , in impulsive doses . By fitting available data of Nutlin pharmacokinetics [58] , we identified the parameters of the pharmacokinetic model ( 8 ) ( see Fig . 5 , panels A and B , and Table 3 of S3 Text ) . By means of that model , we have simulated realistic oral deliveries of Nutlin in mice . Since during bolus delivery cells are transiently exposed to the drug , the possible cell recovery from cycle arrest is expected to have a remarkable influence on the fraction of cells still blocked at the assessment time . Some exploratory simulations ( see S4 Text ) , where recovery is allowed to occur a random time since active p53 level drops below the threshold , confirm the extent of this impact , showing the great importance of the mean recovery time and of the time at which "viability" is assessed . Thus , on the basis of our simple hypothesis on the cell response to Nutlin , we can predict the fraction of cells that do not respond during the time of simulation , whereas we cannot predict in principle the fraction of cells that are blocked ( or are in apoptosis ) at the end of simulation . Of course , these quantities coincide if cells preferentially undergo apoptosis ( SJSA-1 cells ) , or if the recovery time is larger than the interval between the start of treatment and the assessment time , as it should be for RKO cells . In Fig . 6 , we show the simulated dose-response curves in the case of a dose given as a single bolus ( solid lines ) , and when the dose is split in four boli ( dashed curves ) , administered with 24 h ( panel A ) , 12 h ( panel B ) , and 6 h breaks ( panel C ) . The dose-response curves are deeply affected by the splitting . Indeed: i ) both in RKO cells and in SJSA-1 cells , splitting the dose causes a larger viability up to about 200 mg/kg; ii ) in SJSA-1 cells , doses larger than 200 mg/kg guarantee almost zero viability for both the single and the split dose delivery; iii ) in RKO cells , for doses larger than 200 mg/kg split doses are more effective than the single dose , which keeps a residual fraction of non-responding cells of about at 400 mg/kg; iv ) when the fractionated doses are delivered with intervals of 6 h , the viability is generally larger than in the case of 24 h intervals , i . e . , the therapeutic response is disadvantaged; v ) after a single dose , SJSA-1 cells appear more responsive than RKO cells . Some insights into the above behavior can be obtained by analyzing the dynamics of extra-cellular free Nutlin and intra-cellular Nutlin , both total and free , and the time-courses of nuclear p53/Mdm2 . Figs . 7 and 8 report such profiles in RKO cells when the total Nutlin dose is 50 mg/Kg and 400 mg/Kg , respectively . In Figs . 7B and 8B , the response to the single doses is shown by plotting the profiles of nuclear-phosphorylated p53 and of nuclear-phosphorylated Mdm2 . Note that although immediately after the dose delivery the Mdm2 amount reduces close to zero , after a short time-lag the number of molecules rapidly recovers and a high peak is reached 10 hours after the drug administration . Concerning p53 , the peak is reached before Mdm2 regrows over the baseline value , and ultimately also p53 is restored to its pre-delivery value . Note , moreover , that the p53/Mdm2 response is initiated by the first small peak of the total intra-cellular Nutlin amount ( well visible in Fig . 8A ) corresponding to the peak of extra-cellular free Nutlin concentration , and not by the delayed and dominant peak of intra-cellular Nutlin . Due to the rapid drug elimination , the splitting with 24 h breaks results in four almost independent dynamics . In such a case , there is only a slight accumulation of the total intra-cellular Nutlin , more visible at 400 mg/kg ( see Fig . 8C ) , which is mirrored in the nuclear p53 peaks ( see Fig . 8D ) . When the interval among split doses is 6 h , instead , there is a clear accumulation of the total intra-cellular Nutlin both at doses of 50 and 400 mg/Kg . Quite surprisingly , the p53 peaks , although rather merged together , have heights on average smaller than the peaks achieved by 24 h breaks ( compare Fig . 7D and F , and Fig . 8D and F ) . The figures also evidence a nonlinear relationship between the dose injected and the total amount of intra-cellular Nutlin , and between the dose injected and the peak of nuclear p53 ( measured from the baseline value ) . When the dose of 400 mg/Kg is split with 24 h breaks , the first median p53 peak is over of the median p53 peak after a single dose , whereas the dose injected is only ( compare Fig . 8B and D ) . The nonlinearity is present also at the dose of 50 mg/kg but it is less pronounced: after splitting with 24 h breaks , the median p53 peaks are about of the peak after the single dose ( compare Fig . 7B and D ) . The complex p53 regulation network , may be the sources of this nonlinearity . The inefficacy of dose-splitting at low doses and the opposite behavior at high doses ( found in simulating RKO cells ) can be explained by the contrasting effects of the following factors: a ) the existence of a threshold triggering the cell-arrest/apoptotic response penalizes the dose fractionation , unless the dose is very high . In fact , if the p53 level exceeds the threshold after a single dose delivery , the same is not granted for split doses; b ) the nonlinearity between dose and intra-cellular Nutlin amount ( above described ) rewards fractionated schedules by advantaging small doses . The necessity that the level of crosses a threshold to elicit the cell response also explains the general increase of viability observed in our simulations with 6 h breaks , since in this case the p53 peaks were smaller than the p53 peaks found with 24 h-break splitting . A finer inspection of the simulated response shows that in the case of splitting with 6 h breaks , the PTEN amount reaches the largest value ( not shown ) , likely because the level of active p53 is rather sustained over the whole 24 h period of Nutlin administration ( we remind that , according to the model , the overall transcription activity of p53 is substantially related to the time integral of its concentration ) . The high level of PTEN causes a reduced Mdm2 phosphorylation and then accumulation of Nutlin-bound non-phosphorylated Mdm2 in the cytoplasm . In this way the accumulation of total intra-cellular Nutlin during drug delivery with 6 h breaks may not translate into a corresponding increase of the peak level of nuclear active p53 . Although the response of RKO and SJSA-1 cells to a continuous Nutlin exposure is quite similar ( see Fig . 2 ) , SJSA-1 cells are predicted to be more responsive than RKO cells after a single bolus delivery ( Fig . 6 ) . We may advance an explanation based on the role of PTEN . First , note that the relationship between the dose-response curves of the different cell lines with PTEN OFF in Fig . 2 is similar to that of Fig . 6 , single dose , i . e . , SJSA-1 cells result more responsive . With PTEN ON and continuous exposure to the drug , the slow positive PTEN feedback loop has time to play its role and , as a result , the active p53 level increases in the nucleus . This favors the RKO "mortality" but not that of SJSA-1 cells . Indeed , the strong Mdm2 overexpression makes the positive feedback promoted by PTEN less important . Thus , we can see a viability difference between PTEN ON and OFF for RKO , but not for SJSA-1 cells . In the case of a dose delivered with a single bolus , the input signal does not last long enough to trigger the positive PTEN feedback , so RKO cells do not exhibit Mdm2 blocking in cytoplasm and the consequent increase of nuclear p53 . Their viability thus remains greater than that of SJSA-1 cells .
Although deterministic models give valuable information on the average behavior of a biochemical system , they are by definition unable to reproduce statistical behavior differences , both intra-cellular ( i . e . , possible random changes in the response of single cells when observed for a long time ) and inter-cellular ( i . e . , different responses of two "identical" cells ) . The experimentally observed dose-response curves mirror this inter-cellular variability of the response to drug delivery . In recent years a vast body of research has focused on the randomness affecting biomolecular networks . Two kinds of stochasticity are usually considered . The first kind is caused by the interplay between cells and their microenvironment . This stochasticity is termed extrinsic noise . Another kind of randomness comes from the intrinsic stochastic nature of chemical reactions , and its effect becomes more evident when the number of transcripts or proteins is low . In such a case , differential equations do not allow an accurate representation of the dynamics of those transcripts and/or proteins . Interestingly , even when a differential equation model appears to be feasible , in some conditions the average behavior of its stochastic counterpart can diverge significantly from the deterministic prediction ( see e . g . , [66] ) . However , another internal source of noise is often neglected: the randomness of the process of gene activation/deactivation . Actually this kind of noise might be one of the major sources of random fluctuations in intra-cellular protein concentrations . If the switching rates of the genes are very large , one can neglect this noise because it is 'filtered' by the network itself . Still , this is not possible in many cases as it has been experimentally shown [37] , [38] , [39] and theoretically confirmed [40] , [41] , [42] , [43] . In reproducing the original Vassilev's experiments [6] in-silico , our numerical simulations have shown that the stochasticity in p53 , Mdm2 and PTEN regulation , introduced at the level of gene transcription , leads to a large variability of the cell response to Nutlin , with the fraction of responding cells growing with the Nutlin dose . As far as the feedbacks are concerned – feedback is the other key concept of Systems Biology – our simulations suggest that the positive feedback enacted by means of PTEN-Akt-PIP might be essential to reproduce the dose-response of RKO cells , whereas it might only have a negligible effect in SJSA-1 cells . This would imply that the ability of the loop in sequestering Mdm2 in the cytoplasm is no longer sufficient to significantly impact on the Mdm2-p53 dynamics when Mdm2 is strongly amplified . Our condition about the loss of viability , based on [62] , depends on the phosphorylated p53 level in nucleus , which implies we consider only transcription-dependent mechanism of p53-dependent apoptosis . Indeed , p53 can trigger apoptosis also in a transcription-independent manner . Mono-ubiquitinated p53 can be transported to the mitochondria where it interacts with BCL2 family proteins , so to activate Bak [67] or release Bax from the complexes with BCL-2/XL [68] . This in turn leads to the mitochondrial membrane permeabilization , cytochrome-c release , and caspase cascade activation . However , in Nutlin-based mono-therapy , where p53 activation is achieved by a mechanism different from that triggered by DNA damage , we can expect that the dynamics of mono-ubiquitinated p53 closely follows the dynamics of phosphorylated nuclear p53 . So , transcription-independent apoptosis should likely parallel transcription-dependent apoptosis . In case of combined therapy , instead , the uniqueness of such a dynamics is expected to be broken , and our model should be completed with the mitochondrial function of p53 , following e . g . [67] . Concerning the clinical applicability of Nutlin , our simulations of boli-based drug delivery suggest that remarkable effects on cell viability may be observed only when large doses are administered , and that dose-splitting generally worsens the response at low-medium doses . Of course , these indications are only preliminary , and simulations of a wider set of patterns of fractionated delivery would be valuable . More importantly , the study should be completed by the evaluation of the toxicity of different schedules . The reported results , in addition to the relatively short clearance time of Nutlin , might cast some doubts on the use of Nutlin in clinics as a monotherapy for cancers associated to the cell lines here considered . However , the recently proposed role of Nutlin as a "neutralizer" of chemoresistance to other antitumor drugs [13] , [14] , [15] might open a new way for the clinical use of this agent . Furthermore , we also wish to stress that viability predictions when cells are mainly affected by cell cycle arrest should require accurate information on the kinetics of the possible return into the cell cycle . In such a case , the model predictions on the efficacy of fractionated schedules could actually be experimentally validated , provided that careful experimental assessment of the pharmacokinetics and of the binding properties of the drug be available . Small agents different from Nutlin that reversibly inhibit the capability of Mdm2 to bind p53 have been recently discovered . Examples of such agents are MI-219 [69] , RG7112 [70] and RG7388 [71] . After suitable parameter tuning , our model could be applied to describe the action of all these new anticancer chemicals . Although quite detailed , the present investigation has some limitations and misses a number of possibly important points . We wish to discuss some of them in the following . First , the role of MDMX in p53 regulation has not been explicitly described in our model . MDMX protein is an antagonist of p53 with some similarity to Mdm2 [72] . When overexpressed , MDMX impairs the activity of Nutlin in increasing the p53 level [73] . MDMX binds p53 , with the consequent possible inactivation of that protein , and the p53 binding is not inhibited by Nutlin [73] . Although MDMX does not directly exert E3 ligase activity , it forms heterodimers with Mdm2 enhancing the efficiency of Mdm2 in p53 polyubiquitination [74] . However , p53 is not a transcription factor for the MDMX gene [72] , so that , strictly speaking , there is no negative feedback in the p53 regulation through MDMX . As a consequence , the impact of the relative abundancy of MDMX in different cell lines may be ( and implicitly is ) assessed in our model by adjusting the values of some parameters , such as the p53 ubiquitination rate . It is worth mentioning that in both HCT116 and SJSA-1 cell lines , MDMX is poorly expressed [75] , [76] . Anyway , including MDMX among the players of the model would increase its flexibility , also allowing for the quantification of the action of MDMX-targeting drugs [77] , [78] . An additional negative feedback involving p53 can be mediated by Wip1 . Wip1 is transcriptionally dependent on p53 and other factors like CREB and NF-kB , and its role is to shut down the p53 regulatory unit after the repair of double strand breaks ( DSBs ) is successfully completed [79] , [80] . Basically , it dephosphorylates p53 and returns Mdm2 to the active form after its deactivation by ATM which is induced by DSB occurrence . Since Wip1 is mainly produced after DSB formation while it remains at low level when DNA is intact [79] , it should not significantly influence the pharmacodynamics of Nutlin in the case of monotherapy . This guess has been experimentally confirmed in the U2-OS cell line , comparing the response to Nutlin of Wip1-silenced and normal cells ( K . Szoltysek and P . Janus , personal communication ) . Thus Wip1 was not included in the model . The extension of the model to include the Wip1 feedback would be on the contrary very important to describe the effects of the combination of Nutlin treatment and radiotherapy . In recent years , an increasing number of miRNAs [81] have been shown to be involved in shaping p53 functions and p53 regulation , and a very complex network of interactions is likely to be unveiled [82] , [83] . A large part of such miRNAs , e . g . , the miR-34 family and the miR-15/16 family , are involved in the downstream functions of p53 , such as cell arrest and apoptosis , but miRNAs also appear to exert some control on the p53 expression . For instance , it has been observed that miR-125b [84] and miR-504 [85] , when overexpressed , keep the p53 level low by direct binding to the p53 mRNA . In our model , under the assumption of a constant level of the above-mentioned miRNAs during Nutlin treatment , their action should just result in the modulation of the values of parameters such as the p53 translation rate and the p53 mRNA degradation rate . Other miRNAs may act more subtly by establishing positive feedbacks , e . g . , the loop connecting miR-34a , SIRT1 and p53 , or miR-192/194 , Mdm2 and p53 [83] . By impairing the expression of Mdm2 , overexpression of miR-192/194 has been reported to potentiate the efficacy of the Mdm2 inhibitor MI219 [86] . Although these findings are of great interest , we believe that further studies are needed to elucidate the relative importance of these controls for p53 biology before including miRNA-mediated feedbacks in our model of p53 regulation . Moreover , we remark that up to now only a limited number of investigations is available to assess the values of the biochemical parameters characterizing the activity of miRNAs . We also mention that the same miRNA may exert opposite functions in different tumors: for instance , the overexpression of miR-125b that down-regulates p53 in colorectal cancers [87] , seems to induce cell cycle arrest and apoptosis in Ewing sarcoma cells , possibly by p53 activation through down-regulation of PI3K and phospho-AKT [88] . Turning now to model features more directly linked with Nutlin activity , the cellular influx and efflux of Nutlin might be modeled in more detail . For example , there is preliminary evidence that Nutlin can be a substrate for ABC transporters like p-glycoprotein [89] , which suggests the possibility of a nonlinear efflux rate . Further quantitative experimental information , however , is required to implement this effect in our model . We finally remark that a biomolecular network is never isolated , and its dynamics may be deeply affected by its interactions with many other ( often unknown ) networks , as well as by various random signals coming from the extra-cellular environment . These "extrinsic noises" may substantially synergize with the intrinsic and the gene-regulation stochasticity [90] . For example , since it is not clear the mechanism setting the p53 threshold triggering the cell response – such a mechanism can actually depend on the transcriptional activity of p53 and on the interplay of a number of other genetic networks – it would be of interest to model this threshold as an average value perturbed by bounded extrinsic noise . In addition , the pharmacokinetics of many drugs can be perturbed by random fluctuations of the clearance rate , affecting sometimes in turn the pharmacodynamics , as investigated in [91] for generic non-targeted chemotherapies in macroscopic solid tumors . Moreover , the dynamics of biomolecular networks can be influenced by the spatial diffusion of chemicals in the intra-cellular space . Diffusion may lead to peculiar phenomena of significant biological impact [92] , [26] , revealing important synergies with the temporal stochasticity [93] . These issues are worth investigating in connection with the cell response to Nutlin , and we intend to face them in the near future .
|
P53 is an antitumor gene regulating vital cellular functions such as repair of DNA damage , cellular suicide , and cell proliferation: in many tumors p53 is lowly expressed and/or mutated . Drugs targeting the biomolecular network of p53 are becoming important . The network includes the key proteins Mdm2 and PTEN , whose production is regulated by p53 , and which , in turn , enact positive and negative feedbacks on p53 . Drug Nutlin , inhibiting the p53 inhibitor Mdm2 , might be important for tumors where p53 is underproduced but unmutated . We investigate the cellular mechanism of action of Nutlin . The basic concept of our mathematical model is that the experimentally observed cell-to-cell variability of Nutlin efficacy is caused by the randomness of gene activation/deactivation of Mdmd2 and PTEN . Indeed , the abundance/scarceness of p53 regulates the probability that the relative genes are active or inactive . The model reproduced the experimental cell-specific response to different doses of Nutlin ( dose-response curves ) in some types of tumor cells . Much clinical research focus on 'metronomic' drug delivery regimens , where instead of giving large doses with long intervals , smaller doses are frequently delivered . In our simulations , dose-splitting of Nutlin produced a response generally worse than the response to a single dose .
|
[
"Abstract",
"Introduction",
"Models",
"Results",
"Discussion"
] |
[
"systems",
"biology",
"biochemistry",
"biochemical",
"simulations",
"medicine",
"and",
"health",
"sciences",
"clinical",
"medicine",
"pharmacodynamics",
"biology",
"and",
"life",
"sciences",
"pharmacology",
"computational",
"biology",
"pharmacokinetics"
] |
2014
|
The Pharmacodynamics of the p53-Mdm2 Targeting Drug Nutlin: The Role of Gene-Switching Noise
|
Human African trypanosomiasis ( HAT ) manifests in two stages of disease: firstly , haemolymphatic , and secondly , an encephalitic phase involving the central nervous system ( CNS ) . New drugs to treat the second-stage disease are urgently needed , yet testing of novel drug candidates is a slow process because the established animal model relies on detecting parasitemia in the blood as late as 180 days after treatment . To expedite compound screening , we have modified the GVR35 strain of Trypanosoma brucei brucei to express luciferase , and have monitored parasite distribution in infected mice following treatment with trypanocidal compounds using serial , non-invasive , bioluminescence imaging . Parasites were detected in the brains of infected mice following treatment with diminazene , a drug which cures stage 1 but not stage 2 disease . Intravital multi-photon microscopy revealed that trypanosomes enter the brain meninges as early as day 5 post-infection but can be killed by diminazene , whereas those that cross the blood-brain barrier and enter the parenchyma by day 21 survived treatment and later caused bloodstream recrudescence . In contrast , all bioluminescent parasites were permanently eliminated by treatment with melarsoprol and DB829 , compounds known to cure stage 2 disease . We show that this use of imaging reduces by two thirds the time taken to assess drug efficacy and provides a dual-modal imaging platform for monitoring trypanosome infection in different areas of the brain .
Human African trypanosomiasis ( HAT ) , also known as sleeping sickness , is endemic to sub-Saharan Africa [1] , [2] and is almost always fatal if untreated . Although its prevalence fell to a position of near control in the 1960s , a breakdown of surveillance and treatment allowed its re-emergence , with an estimated 300 , 000 cases annually by 1998 . Over the past decade there has again been a steady decline in disease incidence most likely due to increased surveillance , distribution of free drugs and implementation of several clinical trials , but previous experience suggests that eradication is by no means assured [3] , [4] . Development of a vaccine for HAT is unlikely due to the process of antigenic variation [5] , [6] and the control of the tsetse fly responsible for disease transmission is problematic [7] . Chemotherapy is thus fundamental to efforts to eliminate HAT [8] . However , current drugs for HAT are highly unsatisfactory; with varying degrees of toxicity , a need for costly parenteral administration , efficacy below 100% and resistance a growing problem [9] , [10] . To address these issues , a “pipeline” of new compounds has now emerged , although only two compounds ( fexinidazole and SCYX-7158 ) are in clinical trials with a third , CPD-0802 ( DB829 ) in advanced preclinical trials [8] . However , these novel drugs may not fulfil the requirements of potency and pharmacokinetic and safety profiles needed to eradicate the disease . A major confounding factor in the development of new drugs for HAT is the lack of an efficient model for the second stage of the disease , when trypanosomes have become manifest in the central nervous system ( CNS ) . The current model involves infecting mice with the GVR35 strain of Trypanosoma brucei brucei which become established in the CNS by 21 days [11] . Candidate drugs are then given and possible recrudescence of infection monitored in blood samples taken over a period of 180 days . The time delay in obtaining results is clearly a hindrance to defining structure activity relationships in iterative rounds of chemical synthesis . In recent years , the improved sensitivity of in vivo imaging has expanded its application to a broad range of basic biological questions including disease modeling and drug screening . In infection biology , successful models to screen for drugs and infection patterns have been established for a number of microbes including Mycobacterium tuberculosis [12] , Streptococcus pneumoniae [13] , Leishmania [14] and Trypanosoma brucei [15] , [16] . To determine whether use of in vivo imaging could yield a shortened method for screening candidate stage two drugs , we generated transgenic strains of brain invasive , chronically infecting T . brucei that stably express firefly luciferase . We show here that bioluminescence imaging of infected mice can radically shorten the period required to assess the in vivo efficacy of candidate drugs for stage 2 trypanosomiasis .
All animal experiments were performed in accordance with the Animals ( Scientific Procedures ) Act 1986 and the University of Glasgow care and maintenance guidelines . All animal protocols and procedures were approved by The Home Office of the UK government and the University of Glasgow Ethics Committee . Adult female CD-1 mice ( 20–30 g body weight ) were purchased from Charles River Laboratories and maintained under specific pathogen-free conditions . Mice were infected with 5×104 Trypanosoma brucei brucei 427 ( WT , -LUC2 or –Rluc ) , or with 3×104 T . b . brucei strain GVR35 ( WT , -LUC2 or -mCherry ) trypanosomes by intraperitoneal injection and monitored for parasitemia by counting trypanosomes , in blood taken from the tail vein , using a haemocytometer ( sensitivity of 2×104 parasites/ml ) . To generate the backbone construct ( pTbAM ) that would be used to integrate reporters into the ribosomal DNA ( rDNA ) loci , a 250 bp sequence corresponding to the T . b . brucei rDNA promoter was amplified from genomic DNA using a forward primer to introduce a SacI restriction site and a reverse primer that introduced MluI and NotI sites ( Table S1 ) . In addition , a 563 bp fragment of the rDNA non-transcribed spacer sequence was also amplified from genomic DNA using a forward and reverse primer that introduced ApaI and KpnI restriction sites . Both amplicons were sequentially digested with SacI and NotI or ApaI and KpnI respectively and then ligated either side of a puromycin resistance cassette flanked by αβ Tubulin intergenic regions into a pBluescript backbone to create pTb-R . An upstream 5′-untranslated region ( UTR ) gene regulatory element corresponding to the T . b . brucei GPEET2 5′UTR , was amplified using primers to introduce NotI and XhoI restriction sites . The GPEET2 5′UTR amplicon and pTb-R backbone were digested with NotI and XhoI and ligated to create pTbAM . The enhanced firefly luciferase gene , luc2 , was amplified from pGL4 . 14 ( Promega ) using forward and reverse primers to introduce an XhoI site before and a BamHI site after the gene , and cloned into pGEMT . Following digestion with these restriction enzymes the luc2 gene was cloned into the XhoI/BamHI digested pTbAM vector to create pTbR-LUC2 ( pGL2116 ) . The mCherry gene was amplified using primers to add HindIII and BamHI sites and cloned into pGEMT . HindIII/BamHI digested mCherry was cloned into pHD1034 ( from C . Clayton , [17] ) to generate pHD1034-mCherry ( pGL2160 ) , and into p2628 ( from M . Carrington [18] ) to generate pGL2036 . T . brucei GVR35 WT had previously only been passaged through mice and in order to generate a bioluminescent line the cells had to be adapted to in vitro culture . Mice were infected with the mouse-passaged GVR35 WT line and monitored for parasitemia daily . During the first peak of parasitemia while trypanosomes were still dividing , blood was harvested and added to flasks containing different types of media and a range of fetal calf serum concentrations at 37°C , 5% CO2 . GVR35 trypanosomes grew only in IMDM ( Iscove's Modified Dulbecco's Medium , Gibco ) supplemented with 20% heat-inactivated fetal calf serum ( PAA ) , 20% Serum Plus , 0 . 75 mM hypoxanthine in 0 . 1 N NaOH , 4 . 1 mM glucose , 0 . 12 mM thymidine , 1 . 5 mM sodium pyruvate , 0 . 037 mM bathocuproine disulphonic acid , 0 . 2 mM β-mercaptoethanol , 1 . 1 mM L-cysteine , 0 . 38 mM adenosine , 0 . 38 mM guanosine , 0 . 83 g . L−1 methylcellulose , 0 . 04 mM kanamycin , 75 units . ml−1 penicillin and 0 . 075 mg . ml−1 streptomycin ( all Sigma-Aldrich ) . Trypanosomes were fully adapted to culture and growing at a constant rate of 3-fold overnight after a month of culture . At this stage stabilates were made in supplemented IMDM medium containing 10% glycerol and frozen in liquid nitrogen for future use . All genetic modifications were done on culture-adapted GVR35 WT cells within a week after defrosting stabilates . For both WT and transgenic GVR35 , in vitro culturing was kept to a minimum to avoid effects on virulence . Culture-adapted Trypanosoma brucei brucei strain GVR35 bloodstream forms were grown in vitro at 37°C , 5% CO2 in supplemented IMDM medium ( as described in Culture-adaptation of GVR35 WT ) . For generation of bioluminescent GVR35 lines 20 µg of KpnI/SacI-linearized pTbR-LUC2 plasmid was transfected into 3×107 mid-log GVR35 WT trypanosomes using the Human T-cell Solution and Amaxa Nucleofector ( Lonza ) set on program X-001 . GVR35-mCherry lines were generated by transfection using NotI-linearized pHD1034-mCherry plasmid . After recovery for 24 hours transformed clones were selected by limiting dilution in the presence of 0 . 15 µg . ml−1 puromycin ( Calbiochem ) . T . brucei Lister 427 bloodstream form cells were grown in HMI-9 medium supplemented with 20% heat-inactivated fetal calf serum ( PAA ) , 50 units . ml−1 penicillin and 50 µg . ml−1 streptomycin ( Sigma ) at 37°C , 5% CO2 . Reporter 427 lines were generated by transfecting 1×107 mid-log 427 WT cells with 20 µg of linearized plasmid using the Human T-cell Solution and Amaxa Nucleofector ( Lonza ) . NotI-digested pGL2036 plasmid was used to generate 427-mCherry while NotI-digested Rluc-pHD309 [15] and KpnI/SacI-digested TbR-LUC2 was used to generate bioluminescent lines . After recovery for 6 hours transformed clones were selected by limiting dilution in the presence of appropriate antibiotics 5 µg . ml−1 hygromycin B ( Calbiochem ) or 0 . 5 µg . ml−1 puromycin ( Calbiochem ) . Mid-log bloodstream form trypanosomes , grown in vitro , were centrifuged at 1500 g for 10 minutes . Cells were resuspended in 100 µl of RPMI and added to 100 µl reconstituted luciferase assay reagent ( Promega ) . For the analysis of clones , bioluminescence from 1×106 cells was measured at different times after addition of substrate using an EnVision plate reader ( PerkinElmer ) , and expressed as relative light units . For the in vitro detection limit assay , bioluminescence from 10–107 cells was measured in 96-well plates using an IVIS Spectrum ( Caliper Life Sciences ) and expressed as total flux in photons per second . The IC50 values for trypanocides were determined by using a modified Alamar Blue assay . Cells were grown in IMDM medium supplemented as described earlier and assays performed in duplicate on three independent occasions . Briefly , mid-log strain GVR35 WT and GVR35-LUC2 trypanosomes were added to a dilution series of drugs in IMDM at a final density of 3×104 cells/ml . After incubation for 48 hours at 37°C , 5% CO2 Alamar Blue reagent ( 20 µl , 0 . 49 mM resazurin in PBS , pH 7 . 4; Sigma-Aldrich ) was added to each well and plates incubated for another 48 hours . For DB829 assays the final cell density was adjusted to 1×104 cells/ml . Alamar Blue was added after 72 hours and incubated for a further 48 hours . Fluorescence ( excitation of 530 nm and emission of 590 nm ) was measured using a FLUOstar OPTIMA microplate reader ( BMG LABTECH ) and IC50 values determined using GraFit5 ( Erithacus Software ) . Mice infected with strain GVR35-LUC2 were treated with trypanocidal compounds after bioluminescence imaging from 21 days post-infection . For diminazene aceturate ( Sigma-Aldrich ) , mice were injected intraperitoneally with a single dose of 40 mg/kg diluted in distilled water . Melarsoprol gel was prepared as described previously [19] and 0 . 1 ml ( containing 3 . 6 mg melarsoprol ) was applied topically to the back of the neck for 3 consecutive days . DB75 [2 , 5-bis ( 4-aminidinophenyl ) furan] and DB829 ( CPD-0801 , both provided by Rick Tidwell , University of North Carolina , Chapel Hill ) dissolved in DMSO were diluted in distilled water before injecting intraperitoneally into mice . DB75 was injected at 20 mg/kg for 5 consecutive days while four different doses of DB829 were used: 40 mg/kg for 3 days followed by 20 mg/kg for 1 day , 25 mg/kg for 2 days followed by 40 mg/kg for 2 days , 25 mg/kg for 5 days and 20 mg/kg for 4 days . Prior to bioluminescence imaging , mice were injected with the relevant substrate and imaged under isoflurane anaesthesia using an IVIS spectrum ( Caliper Life Science ) . For LUC2 , 150 mg/kg of D-luciferin was injected intraperitoneally 10 minutes prior to imaging while for Rluc , mice were injected intravenously with 15 µg coelenterazine h ( Caliper Rediject Coelenterazine h ) immediately before imaging . Images were acquired using 10–60 seconds exposure and small or large binning depending on the light produced , 1 f/stop , and an open filter . For whole body images field of view E ( 25 . 6×25 . 6 cm ) and for head images field of view A ( 4×4 cm ) was used . Living image software ( Caliper Life Sciences ) was used for all image acquisition and data analysis . For ex vivo imaging of organs mice were perfused using phosphate-buffered saline containing 15 g . L−1 glucose . The organs were then removed and soaked in D-luciferin for 5–10 minutes before imaging . It has been reported that exposure of the dura by local craniotomy impairs brain function [20] , [21] . The skull was therefore thinned without removing it , and a two-photon microscope used to image with high resolution through the remaining bone [22] . This technique also enables imaging of objects immediately beneath the skull . Excitation light came from a Ti-sapphire femtosecond laser tunable from 700 to 1050 nm ( Chameleon Ultra II , Coherent , Santa Clara , USA ) . To extend the wavelength range , the output of the Ti-S laser passed through an optical parametric oscillator ( OPO , Coherent ) : when pumped by the Ti-S laser at about 800 nm , outputs up to 1200 nm were obtained . It was possible to use part of the pump wavelength ( 800 nm ) simultaneously with the OPO output . The intensity of the Ti-S beam bypassing the OPO was regulated by an acousto-optical modulator controlled by the imaging program ( Zen 2010 , Zeiss ) . The scan head ( Zeiss LSM7 MP ) had a maximum rate of 8 frames per sec . Almost all the imaging was done with a 20× water immersion objective , NA 1 . 0 . ( W Plan-Apochromat , Zeiss ) . The dichroic mirror in the microscope nose split the beam at a wavelength of 690 nm except when quantum dots emitting at 705 nm were used , in which case a dichroic splitting at 740 nm was used . Five detectors of non-descanned fluorescence were available , three multialkali photodiodes , and two GaAsP detectors . Image files were analysed , and videos prepared , using Volocity ( Perkin-Elmer ) . Initial anaesthesia of mice was achieved by a low dose of Hypnorm/Hypnovel ( VetaPharm/Roche 5 ml/kg body weight , intraperitoneally ) and was reinforced as necessary with isoflurane in oxygen . Core temperature was maintained at 36 . 8–37 . 0°C by a heating mat ( De-Icers ( MHG ) Ltd , Cheltenham , UK ) . The parietal skull was exposed and glued to a plate with a hole of 5 mm diameter and the bone within the hole was thinned to about 20 µm [23] . During the thinning and imaging , the skull was superfused with a Tris-buffered saline containing 2 mM CaCl2 . After imaging , the mouse was humanely killed by an overdose of anaesthetic . Blood plasma was labeled by intravenous injection of dextran 70 kD , 50–70 µl of 100 mg . ml−1 , conjugated with either fluorescein isothiocyanate or rhodamineB isothiocyanate ( both Sigma-Aldrich ) , or with quantum dots ( QTracker , Invitrogen , emission peak at 705 nm , 20–30 µl ) . For mice infected with WT or LUC2 trypanosomes , a fluorescent diamidine was injected with the vascular marker , usually DB75 [2 , 5-bis ( 4-aminidinophenyl ) furan] or , occasionally , DB829 ( CPD-0801 , both provided by Rick Tidwell , University of North Carolina , Chapel Hill ) at a final concentration of 10 mg/kg body weight . Extravascular trypanosomes in the meninges moved too fast to be imaged in three dimensions . However , there was a marked maximum in the population in a layer less than about 10 µm thick . To obtain approximate values for the number of trypanosomes per unit area , areas were chosen at random , and while focused at the depth of maximum trypanosome population , a time series at the maximum scan rate for 100 cycles ( giving a total time of 12 s ) was acquired . The video was played back at reduced speed and the trypanosomes counted . The size of the imaged area was chosen to include fewer than 20 trypanosomes , and was usually 212 µm2 or 143 µm2 . At least 8 non-overlapping areas were counted in each mouse .
To allow in vivo tracking of T . b . brucei over the full course of infection we generated trypanosomes stably expressing bioluminescent proteins . The optimal reporter vector was determined by employing the monomorphic T . b . brucei 427 strain , a well-established laboratory strain that can be genetically modified in vitro with ease , and induces a high level of infection in mice within 2–3 days . Bloodstream form 427 trypanosomes transfected with Rluc-pHD309 [15] plasmid , to express Renilla luciferase , or with the TbR-LUC2 ( Figure S1 ) , to express firefly luciferase were analyzed in vitro and in vivo for sensitivity of detection . The majority of 427-Rluc clones exhibited high luciferase activity in vitro ( ∼106 relative light units ) , similar to the bioluminescent 427 lines generated by Claes et al [15] . While 427-LUC2 showed lower luciferase activity in vitro ( ∼103–104 relative light units ) these trypanosomes were more readily detected in vivo ( Figure S2 ) , presumably because the emission wavelength was longer [24] . TbR-LUC2 vector was therefore chosen for generation of the reporter lines to be used in the bioluminescence imaging model . The infection of mice by T . b . brucei GVR35 is the standard model for assessing drugs against stage 2 HAT [25] , [26] . In order to generate bioluminescent GVR35 trypanosomes the TbR-LUC2 plasmid was transfected into culture-adapted bloodstream form GVR35 parasites . GVR35-LUC2 clone 3 showed the highest luciferase activity ( ∼103 relative light units ) when tested in vitro ( Figure 1A ) and this line was used for further in vitro and in vivo analyses . To determine the limit of detection , a dilution series of GVR35-LUC2 cells ranging from 10 to 107 trypanosomes were imaged by an in vivo imaging system ( IVIS ) ( Figure 1A ) . Wells containing 106 GVR35 WT trypanosomes were used to determine the background bioluminescence . The minimal trypanosome number reliably detected above background was 5×103 parasites ( P = 0 . 015 ) . As seen in Figure 1A , bioluminescence increased with trypanosome number . A high level of luciferase expression is advantageous for sensitive imaging but might lead to alterations in virulence and disease progression . Bloodstream form GVR35-LUC2 cells were indistinguishable from the GVR35 WT parent line with regards to growth in vitro confirming that the pTbR-LUC2 plasmid had no impact on proliferation in culture . To confirm parity between WT and GVR35-LUC2 infections in vivo , brains were analysed at 7 , 14 , 21 and 28 days post-infection for trypanosome load , as assessed by real-time quantitative PCR ( qPCR ) of parasite DNA [27] and for neuropathology ( both described previously in [28] and [29] ) . No significant difference in parasite burden was detected between WT and GVR35-LUC2 at days 14 , 21 or 28 post-infection , although a significant increase in parasite burden was observed at day 7 with GVR35-LUC2 ( Figure 1B ) . Using a well-established neuropathology grading scale [29] , WT and GVR35-LUC2 lines produced similar disease scores over the course of the infection . The results suggest that GVR35-LUC2 infections are comparable to WT in respect to brain parasite load and the neuropathological response during the CNS stage of disease . To ensure that the genetic modification of GVR35 trypanosomes did not influence their sensitivity to trypanocidal compounds the inhibitory concentration ( IC50 ) for different trypanocides against WT and GVR35-LUC2 lines was determined using a modified Alamar Blue assay ( Table 1 ) . There were no significant differences between WT and GVR-LUC2 in the IC50 values for the compounds tested ( diminazene , DB75 , DB829 and melarsoprol ) , indicating that LUC2 expression did not influence the sensitivity to these trypanocides in vitro . We next used GVR35-LUC2 to monitor trypanosome burden using IVIS through the full course of an infection . Mice developed fluctuating blood parasitemia [30] and survived up to day 35 . Strong bioluminescence was present by day 7 and the signal increased and disseminated to the heads as well as other body regions as the infection progressed ( Figure 2 ) . While intermittent decreases in blood parasitemia were observed , bioluminescence increased over time , indicating that the extravascular trypanosome population expands during the course of infection and contributes to the overall bioluminescence . To confirm that GVR35-LUC2 established a CNS infection , brains from infected mice were removed , soaked in D-luciferin and imaged ex vivo . Perfusion of mice prior to removal of the brain reduced the bioluminescence by ∼5-fold compared to brains from non-perfused mice , indicating that trypanosomes in the blood serving the brain contribute significantly to total brain bioluminescence ( Figure 3A , bottom panel ) . Therefore , to limit the analysis to extravascular trypanosome loads , mice were routinely perfused before removal of the brains . Imaging over the course of infection revealed the presence of extravascular bioluminescent trypanosomes in brains at day 7 with an increase in bioluminescence , and thus trypanosome loads , over time ( Figure 3A , top panel ) . We further examined whether this method could be used to detect live trypanosomes in other organs . Mice infected for 35 days were perfused and various organs including brain , spleen , liver , heart , lungs , inguinal- , axilliary- , brachial- , cervical- , mesenteric- and popliteal lymph nodes , were removed and imaged after soaking in D-luciferin ( Figure 3B ) . Bioluminescence was detected in all organs evaluated , indicating the widespread distribution of trypanosomes at the late stage of infection , but also the potential use of this method to evaluate trypanosome burdens in organs during infection . To validate bioluminescence imaging as a method for evaluating the efficacy of trypanocidal compounds , GVR35-LUC2-infected mice were treated with known trypanocides . We firstly used two well-established drugs: diminazene aceturate ( berenil ) , a first stage drug known to be ineffective for stage 2 [25] , and melarsoprol , a drug that clears trypanosomes from the CNS [19] , [31] . Mice were imaged at day 21 to confirm that the infection was established , then treated with the relevant compound and imaged weekly . In the first 2 weeks after diminazene treatment most of the bioluminescence detected pre-treatment disappeared and trypanosomes were undetectable in the blood ( Figures 4A , S3A and S4A ) . Head images , however , revealed low bioluminescence , indicative of persistent trypanosomes . Bioluminescence in the heads increased by day 41 with possible localisation in the forebrain and cervical lymph nodes , and spread to the rest of the mouse , and in particular to the spleen in the following weeks . Trypanosomes became detectable in the blood 1–3 weeks after bioluminescence detection by IVIS imaging ( Table 2 ) . While bioluminescence was consistently found in the heads of treated mice , the position of the signal varied and often appeared to be located in the rostral area early after treatment . To confirm that trypanosomes did in fact survive in the brain following treatment mice were perfused and brains imaged ex vivo ( Figure 4Aii ) . Strong bioluminescence was observed in in vivo head and ex vivo brain images of untreated mice at day 21 indicating a high level of infection before treatment . Head images of the same mice 1 week after diminazene treatment showed either no signal ( mouse nr 3 ) , signals in the rostral area towards the eyes and nose ( mouse nr 4 , 5 , 6 ) or signals emanating from the inner ear ( mouse nr 5 and 6 ) . Ex vivo brain images from these mice clearly indicated that bioluminescent trypanosomes were still present in all the brains after treatment , and were predominantly located in the olfactory bulb and cerebellum region . Treatment with melarsoprol led to the loss of all bioluminescence within a week ( Figures 4B , S3B and S4B ) and mice remained aparasitemic and clear of bioluminescence until the end of the experiment at day 184 . Quantitative PCR analysis of brains from these mice also showed that they were cleared of trypanosome DNA . Thus the method reliably reported clearance of trypanosomes in cured mice and confirmed previous findings that 3 doses of topical melarsoprol administered during stage 2 disease results in 100% cure rate ( Table 2 , [19] ) . The efficacy of two experimental diamidines was then tested in the GVR35-LUC2 imaging model . DB75 clears trypanosomes from blood but is unable to cure mice during stage 2 disease , while its aza-analog DB829 is effective at curing CNS infections [32] , [33] . When GVR35-LUC2-infected mice were treated on day 21 post-infection with the maximum tolerated dose of DB75 ( 20 mg/kg for 5 days ) trypanosomes were cleared from the blood at day 28 but in vivo bioluminescence was still detected in the heads and tails ( Figures 4C and S3C ) . By day 36 most of these bioluminescent signals disappeared but remained at a low level in the heads of 50% of treated mice . Bioluminescence reappeared in the heads of the remaining mice over the following weeks ( Table 2 and Figure S4C ) . The bioluminescent signal subsequently increased and spread: at first located in the front of the head , then in the cervical lymph nodes and finally the spleen and rest of body ( Figure S3C ) . Trypanosomes were not detected in the blood until day 85 or later ( Figure 4C and Table 2 ) . In DB75-treated mice a bioluminescent signal showing the presence of trypanosomes was detected a full 7 weeks before parasites were first detected in blood . Dose-dependent in vivo activity was observed for DB829 ( Table 2 and Figures 4D , S3D , S4D and S5 ) . Treatment with the maximum tolerated dose for this drug ( 40 mg/kg for 3 days followed by 20 mg/kg for 1 day ) resulted in a 100% cure rate . At day 26 trypanosomes could no longer be detected in the blood of treated mice ( Figures 4D and S3D ) but bioluminescence was still visible in the heads and in the areas of lymph nodes and spleen . Most of this was abolished by day 41 although residual bioluminescent spots ( <3×105 photons/sec ) were observed in the heads of some mice . In the following weeks mice were cleared of all bioluminescence and no trypanosomes were detected in blood by the endpoints at day 98 or day 197 . Lower doses of DB829 were unable to cure all mice: 2/6 relapsed after treatment with 25 mg/kg for 2 days followed by 40 mg/kg for 2 days , 1/6 mice relapsed after treatment with 25 mg/kg for 5 days ( Figure S5 ) and 3/4 relapses occurred at the lowest dose of 20 mg/kg for 4 days ( Table 2 ) . A comparison between bioluminescent images of mice that relapsed and those that remained aparasitemic indicated that bioluminescence of 3×105 photons/sec or higher for head regions reliably predicted relapse . This value offers the means to provide a quantitative cut-off with a head signal below 3×105 photons/sec defined as negative . For animals treated with sub-curative doses of DB829 bioluminescence was detected between 3 and 7 weeks before trypanosomes were identified in the blood ( Table 2 ) . Trypanosome infection causes meningitis [34] , [35] and the presence of trypanosomes in the meninges , and also the superficial parenchyma , has been reported [36]–[38] . If trypanosomes were present in these locations they would contribute to the in vivo IVIS signal from the head . Intravital multi-photon microscopy through the thinned skull allowed the identification of individual intravascular and extravascular parasites in the meninges , and below that to a depth of about 150 µm into the parenchyma . The presence of parasite populations in these different regions , and their susceptibility to drug treatments shown by IVIS imaging to be effective against stage 1 or stage 2 disease , could therefore be assessed . Intravascular trypanosomes were detected through the thinned parietal skull to a depth of about 150 µm below the pia mater . In mice infected for 3 days with T . b . brucei strain 427 expressing fluorescent mCherry , trypanosomes were visible in blood vessels , but virtually none were extravascular ( Figure 5A and Video S1 ) demonstrating that the surgery itself did not give trypanosomes access to extravascular spaces . Mice were then infected with T . b . brucei strain GVR35 modified to express mCherry and the parasites were imaged between days 13 and 41 . Motile GVR35 trypanosomes were observed in extravascular spaces in the meninges . To localize them , we focussed first on the underside of the skull and then imaged deeper into the meninges . The trypanosomes tended to be present in a thin layer , and in each microscope field the depth of greatest trypanosome density was estimated . The mean depth of this peak density was 28 . 4±6 . 7 µm ( mean ± SD , n = 8 mice ) below the skull ( Figure 5B and Video S2 ) . To compare results of multi-photon imaging with IVIS imaging it was necessary to use bioluminescent GVR35-LUC2 and WT ( as control ) trypanosomes . Addition of a fluorescent stain was required to allow detection of trypanosomes using the multi-photon approach and since DB75 accumulates within circulating trypanosomes and has intrinsic fluorescent properties [39] this compound could be used to label trypanosomes . We tested the GVR35 strain and found that , following intravenous injection of DB75 ( 10 mg/kg ) , fluorescence-labeled extravascular trypanosomes were clearly visible in the meninges . Within 25 min of DB75 administration , the nucleus and kinetoplast were visible ( Figure 5C inset and Video S3 ) , as were nuclei of host cells in the meninges ( Figure 5 , C and D , and Video S4 ) . Trypanosomes could be unambiguously identified by virtue of their size and motility , which was not affected by DB75 during the maximum of 3 hours imaging per mouse . Furthermore , moving , labeled , trypanosomes were still observed 24 hours after DB75 administration ( Figure 5C and Video S5 ) . By comparing the distributions of trypanosomes that expressed fluorescent proteins , and those that were labeled by DB75 , we concluded that all the extravascular trypanosomes imaged by multi-photon microscopy were accessible to this compound . Trypanosomes were detected in the meninges from day 5 post-infection with no marked difference in the meningeal population numbers between GVR35 WT , -LUC2 and -mCherry ( Figure 5E ) . To determine the susceptibility of meningeal trypanosomes to stage 1 drugs , GVR35-infected mice were treated with 40 mg/kg diminazene [25] at day 21 . Moving trypanosomes could not be detected in the meninges two days after treatment , indicating that trypanosomes had been cleared from this site ( Video S6 ) . Another group of treated mice were imaged on day 44 ( 23 days after treatment ) first by IVIS then by multi-photon imaging . Although no trypanosomes could be detected in the blood of these mice , IVIS imaging revealed bioluminescence in the brain ( Figure 5F ) . However , no trypanosomes were observed by multi-photon microscopy , indicating that IVIS-detected bioluminescence did not originate from meningeal trypanosomes ( Figures 5G and S6 ) . At later times , trypanosomes of the various lines used were detected in the meninges but only after their reappearance in the blood ( Figure 5E ) .
Effective treatment of stage 2 HAT requires a drug that crosses the blood-brain barrier in a concentration sufficient to kill CNS-resident trypanosomes . Many trypanocidal drugs , such as those used for stage 1 treatment , clear trypanosomes from the vasculature and peripheral compartment , but parasites that remain viable in the brain will eventually re-establish infection in the blood [11] , [25] , [40] . Current pre-clinical mouse models of stage 2 HAT assess the efficacy of drugs by the detection of trypanosomes in blood following treatment . In the case of failure , the re-emergence of blood trypanosomes often occurs several months after treatment and the standard model requires 180 days post-treatment follow up to declare a drug curative in stage 2 . Here , we describe an infection model that uses bioluminescence imaging to detect trypanosomes in the brain following drug treatment , which is a significant improvement upon the classical 180 day model . Bioluminescence imaging has been widely used for in vivo tracking of tumour cells in cancer and the distribution of pathogens in infected animals [12] , [14] , [41]–[43] . LUC2 , a modified firefly luciferase , has so far provided the best sensitivity for in vivo detection [44] , [45] and its substrate luciferin is known to cross the blood-brain barrier , allowing successful imaging in the brain [43] , . The gene has been optimized for cytosolic expression by the removal of a peroxisomal targeting sequence , which is present at the C-terminal end of the native luciferase gene and shown to target native luciferase to the glycosomes in T . brucei [48]–[50] . We found LUC2 firefly luciferase to be superior to Renilla luciferase [15] , [16] for in vivo detection of T . b . brucei , most likely due to its longer wavelength emission ( 562 nm ) [24] . Crucially for the success of the model , the expression of LUC2 from the ribosomal locus in GVR35 remained stable over the course of infection . The TbR-LUC2 plasmid could also be used for the generation of other bioluminescent Trypanosoma spp . making it possible to extend the analysis of promising compounds to tests on field isolates . The GVR35-LUC2 model was validated initially using two widely used trypanocidal drugs: diminazene aceturate , a veterinary drug that can cure stage 1 but not stage 2 disease [25] , and melarsoprol , a drug used for stage 2 human disease that cures 100% of treated mice when appropriately dosed [19] . As expected , relapse was observed in the case of diminazene while melarsoprol cured mice , thus providing a benchmark for compounds that cure stage 1 and stage 2 disease . In cases of relapse after treatment with diminazene , bioluminescence was first detected in the heads of mice before disseminating throughout the body . Surprisingly , early signals were often located in rostral areas of the heads in in vivo imaged mice , while ex vivo brain images clearly showed bioluminescence in the brain . It is feasible that in vivo rostral signals are linked to the strong bioluminescence in olfactory bulbs , and the inner ear signals to the cerebellum but this hypothesis requires further investigation . The discrepancy between signal strength detected by in vivo and ex vivo imaging suggests that much of the brain bioluminescence is lost during in vivo imaging , as observed by others [51] , or that the substrate D-luciferin does not accumulate in the brain in sufficient amounts with the 150 mg/kg dose used . In vivo sensitivity may be improved for future studies with the use of red-shifted luciferases [52] , red-shifted luciferin analogues [53] or by increasing the D-luciferin dose [54] . The bioluminescence detected in ex vivo brains is consistent with previous observations that trypanosomes survive in the CNS after sub-curative treatment [11] , [25] but it does not exclude the possibility that trypanosomes may survive in other extravascular regions . The use of GVR35-LUC2 and ex vivo imaging of different tissues early after treatment should be helpful in identifying areas where parasites survive . For further refinement and testing of the model , groups of mice were treated with two experimental diamidine compounds: DB75 , a compound that failed to cure stage 2 models of disease even at the highest tolerated dose , and DB829 , shown to have dose-dependent activity against CNS trypanosomes [33] . Importantly , after DB75 treatment , IVIS imaging rapidly revealed that trypanosomes remained viable in the head , even though the blood was cleared . The time taken to identify DB75 as an unsuccessful stage 2 compound was thus reduced by seven weeks using our bioluminescence imaging approach compared to the standard model relying on relapse in the blood . Although bioluminescence remained visible in some mice within the first 2 weeks after treatment it disappeared in others to re-emerge in the following weeks . To distinguish between cured and non-cured mice it is thus necessary , with the model described here , to extend the imaging period to 60 days post-treatment . While all bioluminescent signal was cleared one week after melarsoprol treatment , bioluminescence remained visible in mice treated with DB829 . This observation may relate to differences in the in vivo killing rate and distribution of compounds in extravascular tissues . In vitro , melarsoprol kills trypanosomes more rapidly than DB829 [55]–[58] . In addition to providing information about relapse , the in vivo imaging model applied here can therefore distinguish in vivo between fast-acting compounds , which may be more favourable , and those with slower activity . Despite having a higher IC50 value than DB75 in in vitro testing ( Table 1 , [33] , [58] ) , DB829 was more active against CNS trypanosomes in vivo . This discrepancy between in vitro and in vivo efficacy may be explained by different distribution of these two compounds in vivo . Studies using the prodrug of each compound have shown higher systemic levels of DB829 compared to DB75 [59] but it is not known whether this accounts for its greater efficacy in the brain . Differences in the uptake of diamidines by trypanosomes grown in vitro and in vivo have also been described [58] . Such observations highlight the need for in vivo assessment of compounds under development as stage 2 drugs , and extension of pharmacokinetic studies to assess distribution of drugs in the CNS and their uptake by resident trypanosomes . Using multi-photon imaging , which provides optical resolution of individual parasites , we observed invasion of the meninges as early as day 5 post-infection [37] , [38] . These parasites are likely to be responsible for some of the bioluminescent signal emanating from the head . However , clearance by diminazene indicates that this population is not protected by the blood-brain barrier , and does not significantly contribute to bioluminescent signals emanating from the head following chemotherapy . The meningeal trypanosomes are thus not responsible for the later relapse observed in mice treated with stage 1 drugs . These findings extend our understanding of CNS-associated trypanosomes to include populations that enter the CNS during earlier stages of the infection and are accessible to stage 1 drugs . Trypanosomes in the superficial meninges of mice may be equivalent to the so-called ‘intermediate stage’ of infection in some patients with HAT . This intermediate stage of infection has been suggested following successful treatment of patients presenting with raised CSF white blood cell counts with stage 1 drugs [60]–[62] , though the concept and treatment of this clinical scenario remains controversial . The multi-photon imaging approach used in this study revealed a location in the brain where trypanosomes are accessible to stage 1 drugs , but we were unable to identify parasites within parenchyma beyond the blood-brain barrier , protected from stage 1 treatments , that were responsible for the eventual recrudescence of parasitaemia . Further investigations , utilising this powerful technique , will provide invaluable information about trypanosome interactions and distribution throughout the brain . For example with the use of fine optical probes such as gradient index ( GRIN ) lenses the range of multiphoton imaging could be extended to reach several millimetres into the brain [63] , [64] . In summary , we have developed in vivo bioluminescence imaging to extend the capability of the currently employed T . brucei strain GVR35 model of stage 2 disease . The model provides a substantial improvement in time taken to establish whether a compound is curative in stage 2 disease . The time taken to assess drug efficacy in stage 2 trypanosomiasis has been a major bottle neck in the drug development process and the profound reduction in screening time we present here , cutting the post-treatment follow up time by two thirds , represents a significant advance that should expedite drug discovery for stage 2 HAT .
|
Trypanosoma brucei , a parasite transmitted by the bite of tsetse fly , is responsible for the disease human African trypanosomiasis ( HAT ) . In advanced stages of HAT , trypanosomes invade the central nervous system ( CNS ) , resulting in an array of neurological symptoms , and eventually death . Existing drugs for treatment of HAT are highly unsatisfactory and new safe drugs are urgently needed . Currently , potential drugs for HAT are screened in a mouse model that relies on the emergence of trypanosomes from tissues and their detection in blood . This can take up to 200 days , making selection and further development of new drugs slow and costly . Here , we employ in vivo imaging and genetically modified trypanosomes to monitor parasite distribution throughout the body in live infected mice . Our bioluminescence imaging approach provides sensitive detection of trypanosomes at sites of infection , allowing more rapid and more effective in vivo screening of candidate HAT drugs . Higher resolution intra-vital microscopy was used to investigate trypanosome dynamics in the brain and their accessibility to drugs during infection . These approaches allow more sensitive real time tracking of trypanosomes during chronic infections and will provide new insights about trypanosome pathogenesis in future experiments .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"drugs",
"and",
"devices",
"african",
"trypanosomiasis",
"host-pathogen",
"interaction",
"microbiology",
"parasitic",
"diseases",
"parasitology",
"parastic",
"protozoans",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"infectious",
"diseases",
"infectious",
"diseases",
"of",
"the",
"nervous",
"system",
"biology",
"infectious",
"disease",
"modeling",
"pathogenesis",
"drug",
"discovery",
"trypanosoma",
"drug",
"research",
"and",
"development",
"protozoology"
] |
2013
|
In Vivo Imaging of Trypanosome-Brain Interactions and Development of a Rapid Screening Test for Drugs against CNS Stage Trypanosomiasis
|
Antagonistic coevolution between hosts and parasites can involve rapid fluctuations of genotype frequencies that are known as Red Queen dynamics . Under such dynamics , recombination in the hosts may be advantageous because genetic shuffling can quickly produce disproportionately fit offspring ( the Red Queen hypothesis ) . Previous models investigating these dynamics have assumed rather simple models of genetic interactions between hosts and parasites . Here , we assess the robustness of earlier theoretical predictions about the Red Queen with respect to the underlying host-parasite interactions . To this end , we created large numbers of random interaction matrices , analysed the resulting dynamics through simulation , and ascertained whether recombination was favoured or disfavoured . We observed Red Queen dynamics in many of our simulations provided the interaction matrices exhibited sufficient ‘antagonicity’ . In agreement with previous studies , strong selection on either hosts or parasites favours selection for increased recombination . However , fast changes in the sign of linkage disequilibrium or epistasis were only infrequently observed and do not appear to be a necessary condition for the Red Queen hypothesis to work . Indeed , recombination was often favoured even though the linkage disequilibrium remained of constant sign throughout the simulations . We conclude that Red Queen-type dynamics involving persistent fluctuations in host and parasite genotype frequencies appear to not be an artefact of specific assumptions about host-parasite fitness interactions , but emerge readily with the general interactions studied here . Our results also indicate that although recombination is often favoured , some of the factors previously thought to be important in this process such as linkage disequilibrium fluctuations need to be reassessed when fitness interactions between hosts and parasites are complex .
Host-parasite interactions have the potential to produce rapid co-evolutionary dynamics . If host genotypes are favoured that resist infection by the most common parasites and parasite genotypes are favoured that thrive on frequent hosts , this will produce selection against common genotypes and hence may result in cyclically fluctuating genotype frequencies in both interacting species . Such ‘Red Queen’ dynamics have been the focus of several theoretical studies [e . g . , 1]–[3] and are also documented empirically . For example , analysing ‘archived’ Daphnia hosts and their Pasteuria parasites in a pond sediment , Decaestecker et al . [4] observed rapid co-evolutionary change over time and temporal adaptation of parasites to hosts . Based on Red Queen dynamics is the Red Queen Hypothesis ( RQH ) for the maintenance of sexual reproduction and recombination [5] , [reviewed in 6] . Despite being costly in many important respects , sexual reproduction is very widespread and common among eukaryotes , and many hypotheses have been put forward to explain this pattern through a selective advantage of recombination [7]–[9] . The RQH states that an advantage to sexual reproduction arises because Red Queen dynamics lead to deleterious statistical associations ( linkage disequilibria , or LD ) between alleles in the hosts that are involved in defence against parasites . According to the RQH , recombination is then favoured because it breaks up these associations ( i . e . , reduces LD ) , and a modifier allele that increases recombination rate can spread in the population through hitchhiking with disproportionately fit genotypes . Previous theoretical work has established several key results regarding the conditions under which the RQH works as well as the underlying mechanisms . It has been demonstrated that selection on loci modifying recombination rates can be partitioned into a long-term and a short-term effect [10] , [11] . The long-term effect arises from increasing the additive genetic variance for fitness so that selection operates more efficiently . The short-term effect is determined by the relative fitness of the combinations of alleles generated through recombination . A characteristic of the RQH is that the short-term effect can be positive and it has recently been shown that it can be responsible for a substantial part of the selection for recombination in the RQ [12] . Rapid fluctuations in epistasis are a necessary condition for selection for increased recombination through the short-term effect . In particular , Barton [10] showed that epistasis needs to change its sign every 2–5 generations if high recombination rates are to evolve . To produce such rapid fluctuations in epistasis , selection on either the host or the parasite must be strong [13] , a requirement that is in accord with the predictions of a number of different Red Queen models [14]–[17] . One of the most important factors influencing both the coevolutionary dynamics and selection for recombination is the type of interaction model that defines fitness values for hosts and parasites [16] . One of the most widely used interaction models is the matching allele ( MA ) model and derivations thereof [e . g . , 3] , [5] , [11] , [14] , [17] , [18] . In the MA model , it is assumed that parasites can infect the host if all alleles at a number of parasite interaction loci match the alleles at corresponding loci in the host . In this case , the parasite fitness is maximal and the host fitness is reduced by a certain amount that corresponds to the virulence of the parasite . Conversely , if none of the parasite alleles matches the host alleles , the parasite cannot invade and has its fitness reduced , and the host fitness is maximal . If only a subset of alleles match , fitness is affected in a variety of ways in different version of the MA model , and the fitness values for these semi-matching interactions are crucial for whether recombination is favoured or disfavoured [3] , [14] , [16] . Interaction models other than MA models include the gene-for-gene ( GFG ) model [19] , [20] and the Nee model [21] . A common feature of all interaction models that have been used to date is that they are defined by only few parameters . For example , interactions in the simplest case of a two-locus/two-alleles system are in general described by two 4×4 matrices that give the fitness for each host genotype when interacting with each parasite and vice versa . Nevertheless , even the most general matching allele models utilise at most three parameters to fill these 32 matrix entries [e . g . , 14] . As a consequence , the interactions models that have been used previously are simplistic in several ways , usually assuming , for instance , equal fitness effects at the two loci involved . Although these standard interaction models have been invaluable in assessing the plausibility of the RQH and identifying the population genetic forces that are at work , they explore but a very limited and probably unrealistic set of possible host-parasite interactions in general . Agrawal & Lively [22] addressed this problem by investigating models that lie on a continuum between MA and GFG models . Here , we go a step further and study interactions in two-locus/two allele models in their most general form . We construct large numbers of randomly generated interaction models and analyse the resulting dynamics . Specifically , we investigate how properties of the fitness matrices affect the co-evolutionary dynamics , and how the dynamics in turn influence selection for or against recombination . One important property of interaction matrices that we identify is the ‘antagonicity’ of the interaction , which we define as . Our results indicate that whilst some of the previous results on the RQH appear to be fairly robust with respect to interaction models ( including the requirement for strong selection on hosts or parasites ) , other predictions – in particular those concerning LD fluctuations – need to be qualified based on the results with our generalised interaction models .
In a strict sense , extinction of genotypes cannot occur in our model , because the population is of infinite size and recurrent mutation will lead to continuous replenishment of genotypes even if these are under strong negative selection . For the following results , we call a genotype ‘extinct’ if the frequency of this genotype does not exceed 10−4 during the 10 , 000 generations that follow the burn-in phase . For comparison , this threshold is approximately reached under mutation-selection balance with a mutation rate of ( as in most of our simulations ) and a selection coefficient of s = −0 . 1 . An inverse relation between host and parasite fitness – corresponding to high antagonicity , A , in our terminology – is one of the key assumptions of Red Queen models ( see Methods for the definition of A ) . Therefore , we have tested how antagonicity affects extinction patterns by creating sets of matrices with a different range of A values and comparing simulation results ( Figure 1 ) . As expected , we observe fewer extinction events as A increases . This makes intuitive sense , because when co-evolution between hosts and parasites becomes less antagonistic ( low values of A ) , increases in host fitness will often also lead to increased parasite fitness and vice versa . Therefore , such interaction matrices often lead to a state where fitness is optimal for both hosts and parasites , in which case all but one genotype become extinct . Aside from antagonicity , genotype extinction is likely to be influenced by the strength of selection acting on hosts and parasites . We therefore compared host allele extinction patterns for sets of interaction matrices that differ by the range values from which the random fitness entries were drawn . The resulting proportions of fitness matrices for which extinction of at least one allele occurred are given in Table 1 . These numbers indicate that extinction becomes more likely when selection pressure on the hosts is high , whereas for small fitness differences ( all fitness values between 0 . 9 and 1 ) , no allele extinctions were observed . The impact of the strength of selection acting on the parasites is weaker and does not show a clear-cut pattern . Thus , even though parasite allele extinction becomes more frequent with increasing strength of selection on parasites ( in line with the symmetry of the model with respect to the two interacting species ) , these extinction patterns in parasites do not seem to translate in a simple way into extinction patterns of host alleles . Although the primary objective of this study is the impact of interaction matrices on host-parasite coevolutionary dynamics , it is also important to assess how the other parameters of the model influence these dynamics . Figure 2 shows some results regarding the impact of the number of parasite generations per host generation ( nPG ) , the recombination and the mutation rate . Increasing nPG increases the proportion of simulations where one or two host genotypes become extinct , but this effect is rather weak . With a recombination rate of rH = 0 . 1 compared to no recombination , the proportion of simulations where one or two host genotypes become extinct is substantially decreased . This makes sense as genotypes that become extinct in the absence of recombination may be continuously produced by recombination if the constituting alleles are present in the population . Interestingly , a high recombination rate of rH = 0 . 5 leads to a greater rate of extinction of three host genotypes , suggesting that recombination may also decrease genetic variation in the population . Finally , low mutation rates or absence of mutation appears to boost extinction of host genotypes . Comparison of genotype dynamics in individual simulations ( not shown ) suggests the following explanation for this phenomenon . Mutation maintains a certain minimum of genotype frequencies even if these genotypes are selectively disfavoured . As a result , when the composition of the parasite population changes , selection for these low frequency host genotypes results in a relatively quick response , which keeps the cyclic dynamics of the system going . By contrast , if mutation is absent or occurs at a very low rate only , genotype frequencies may become so low due to selection that the cyclic dynamics break down and host genotypes become extinct . Since the only effect of recombination is to break down linkage disequilibria ( LD ) , the LD dynamics that result from host-parasite co-evolution are at the core of the RQH . Figure 3 shows the distribution of mean LD and variance in LD , as well as the distribution of minimum and maximum LD for a particular set of interaction matrices . As we have not built in any systematic asymmetry in constructing the random interaction matrices , the distribution is symmetric around a mean LD of zero ( Fig . 3A ) . The stem of the ‘mushroom’ shaped distribution , where mean LD is approximately zero and variance in LD is very low , usually corresponds to extinction or near extinction of one or two alleles . Interestingly , as variance in LD increases , simulations with mean LD close to zero become more rare . Rather , most simulations with high variance in LD show moderate to high absolute values of mean LD . Finally , there are also some simulations with strongly positive or negative means ( close to the maximum value of ±0 . 25 ) and low variance . Surprisingly , we observed that the sign of LD did not change during the 10 , 000 generations of recorded coevolution in the majority of our simulations , i . e . , LD was either always positive or always negative ( compare also the width of the bars in Fig . 4B ) . In Fig . 3B , such instances of LD with constant sign are represented by data points with either positive minimum LD or negative maximum LD . Similarly high incidence of LD dynamics with constant sign were also found in simulations with all other sets of interaction matrices that we tested ( Table 2 ) . The relevance of these observations stems from the intuition that rapid changes in the sign of LD are a prerequisite for selection for increased recombination . As will be demonstrated in the following section , this intuition is misguided . An increase in frequency of the recombination modifier allele M ( i . e . , selection for increased recombination ) was observed with many of our interaction matrices ( Table 3 ) . Figure 4 shows , for a particular set of interaction matrices , how various properties of the dynamics before introduction of M relate to selection for or against M . Extinction of host genotypes has a strong impact on selection acting on M ( Fig . 4A ) . The highest proportion of simulations where M was under positive selection was observed when no genotype became extinct , but M was also selected for in about 20% of simulations when one genotype went extinct . On the other hand , if two genotypes became extinct , M always either decreased in frequency or was selectively neutral . As expected , M was always neutral when one of the alleles became extinct . The proportion of simulations where M increased in frequency was substantially higher when the dynamics exhibited changes in the sign of LD than when LD was of constant sign ( Figure 4B ) . However , even among the simulations where LD was of constant sign we observed selection for recombination in about 20% of the simulations . Figure 5 provides a more detailed picture of how LD statistics relate to the fate of the recombination modifier M . A high variance in LD generally favours selection for M , but LD does not need to fluctuate around a mean of zero for this to happen ( Fig . 5A ) . In the majority simulations where LD did change its sign and both the minimum and the maximum of LD were substantially different from zero , M was under positive selection ( Fig . 5B ) . Conversely , when LD was always strongly negative or always strongly positive , M was usually disfavoured . However , in many simulations either the minimum or the maximum of LD was close to zero , in which case no trend with respect to selection on M was apparent . Examples of the dynamics with selection for or against M in the presence or absence of changes in LD sign are shown in Figure 6 . The strength of selection acting on the two interaction loci is another decisive factor for selection on M ( Fig . 4C ) . With very weak selection on the interaction loci – corresponding largely to extinction of alleles – M is selectively neutral . With increasing strength of selection on the interaction loci , the proportion of simulations where M was under positive selection increases continuously , reaching a maximum of more than 70% of simulations . On the other hand , disregarding the simulations with very weak ( <10−4 ) selection on the interaction loci , the proportion of simulations where selection against M was observed remained more or less constant with increasing strength of selection . These results on the impact of measured selection intensity on the interaction loci are mirrored in the results comparing selection for M with different sets of interaction matrices ( Table 3 ) . We also examined the product of epistasis and LD ( ) in hosts as an indicator for selection for increased recombination ( Fig . 4D ) . This quantity is of interest because if epistasis and LD are of opposite sign ( i . e . , ) , an immediate benefit to recombination is expected ( because disproportionately fit individuals are underrepresented in the population ) . Among the simulations where was negative over most of the 10 , 000 generations prior to introduction of M , M increased in frequency in more than 80% of simulations . When the median of was close to zero , M was largely neutral , and increasingly positive values of median are associated with an increasing proportion of simulations where selection against M was observed . Interestingly , however , even when was mainly positive , M was under positive selection in many simulations . Similar results are obtained when the mean of rather than the median is considered ( results not shown ) . In many of our simulations where M was selectively favoured , we observed that M did not become fixed in the population . Rather , M often remained polymorphic even following the 10 , 000 generations of simulation , with periods of increase and decrease in its frequency . This observation led us to ask whether there exists an evolutionarily stable ( ES ) recombination rate for a particular pair of interactions , i . e . an allele m coding for a recombination rate r that cannot be invaded by alleles coding for other recombination rates . Previous studies have demonstrated the existence of an ES recombination rate [11] , [13] , but it is not clear if this result can be generalized to arbitrary fitness interaction models . To study this question , we screened the entire range of resident recombination alleles m and modifier recombination alleles M for particular pairs of interaction matrices ( Figure 7 ) . In plots 7A and 7B , it appears that there is indeed an allele m associated with a certain recombination rate r>0 which is stable against invasion of all alleles M ( intersections of the white ‘lines’ ) . Plot ( C ) shows a case where recombination is disfavoured . Plot ( D ) gives an example for more irregular patterns of selection on the mutant modifier M , exhibiting bands of neutrality even when the resident recombination rate is much higher than the optimum . An interesting feature of the plots in Figure 7 is that selection for the optimal recombination rate is much stronger when the resident recombination allele codes for a suboptimal recombination rate than when it codes for a superoptimal recombination rate . These results suggest that an in-depth future investigation of ES recombination rates in Red Queen models with arbitrary fitness interactions might be worthwhile .
For this study , we have created large sets of interaction matrices determining host and parasite fitness in specific genotype-genotype interactions . We would like to stress that these randomly generated interaction matrices are by no means intended to represent the distribution of naturally occurring interactions between hosts and parasites , and results like the proportion of matrices for which we find selection for increased recombination are therefore , in themselves , biologically meaningless . Rather , our aim was to investigate to what extent previous results regarding Red Queen dynamics and the RQH depend on the niceties of particular interaction models , to identify informative properties of interaction matrices , and to discover interesting dynamical behaviours that differ qualitatively from the dynamics that arise in standard interaction models . The ‘true’ spectrum of host-parasite interactions found in natural populations is far from being understood . To date , fitness components for interactions between various host and parasite genotypes have been studied for only a few systems [e . g . , 23]–[27] , and even then the underlying genetics are usually poorly understood . The data that are available , however , suggest that fitness interactions are much more complicated in general than in the standard interaction models that have been assumed in previous Red Queen models [e . g . , 23] . One of the most basic questions concerning host-parasite co-evolution is whether and how much polymorphism is maintained at the interaction loci . Different standard interaction models produce both extremes in that respect: extinction of all but one parasite genotype in the simplest ( cost-free ) version of the gene-for-gene model [1] , and generally complete maintenance of all host and parasite genotypes in the various matching allele models and in the Nee model [21] . In the present study , different randomly generated interaction matrices also led to both complete annihilation and preservation of polymorphism , as well as intermediate outcomes ( e . g . , extinction of only one allele ) . We demonstrated that this is determined to a large extent by the level of antagonicity between host and parasite interactions , with decreasing antagonicity leading on average to decreasing polymorphism in the populations . However , even with highly antagonistic interactions , extinction of one or more alleles occurred frequently . This latter result is perhaps not surprising given that the gene-for-gene model is also completely antagonistic ( i . e . , A = 1 ) according to our definition of this term . We also found that moderate selection coefficients favour the maintenance of polymorphism . Based on these results , we predict that polymorphic loci involved in host-parasite interactions observed in natural systems will tend to be characterized by strong antagonicity , but moderate selection coefficients . We would like to caution at this point that ‘antagonicity’ as defined here is only loosely related to the virulence of the parasite or the nature of the species-species relationship in general . Rather , it is a measure for the specific genetic interactions under study . As an example to illustrate this difference , consider a parasite that is highly virulent , i . e . , that leads to strong fitness reduction in infected hosts . This relationship would therefore be described as ‘highly antagonistic’ in the common sense . Let us assume that there are two genotypes of this parasite , P1 that induces optimal levels of virulence ( from the parasite's point of view ) in the host , and P2 that is slightly more virulent than the optimum . ( A classic result in evolutionary epidemiology is that if there are trade-offs between virulence and transmission , intermediate levels of virulence are expected to be evolutionarily stable [reviewed in 28] . ) Everything else being equal , P1 then has a higher fitness than P2 , and hosts infected with P1 will have a higher fitness than hosts infected with P2 . Thus , antagonicity for this simple 1×2 interaction matrix would be , i . e . , the genotypic interaction would be characterized as ‘synergistic’: a mutation from P2 to P1 benefits both parasite and host . Similarly , different host genotypes are conceivable for which fitness differences go into the same direction in hosts and parasites . This shows that there may be genotype-genotype interactions that are not or only slightly antagonistic , even though the host-parasite relationship as a whole is very antagonistic . Whereas our definition of antagonicity refers to interactions between different host and parasite genotypes , antagonicity in terms of the interacting species per se refers to infection versus no infection . In many of our simulations , we observed selection for or against modifier alleles that increase the recombination rate between the interaction loci . As has been reported previously for different interaction models [e . g . , 14] , [16] , strong selection on either hosts or parasites is conducive for selection for higher recombination in the hosts , although strong selection on the hosts appears to be more important . This result holds both for comparisons between different sets of interaction matrices ( where average selection coefficients differ , see Table 3 ) and within single sets of interaction matrices ( where the strength of selection was measured directly , see Fig . 4C ) . Strong selection on the host implies highly virulent parasites , but this is not the only aspect that is important: the parasites must also be very abundant ( if only few hosts in a population are infected , selection to resist parasite infection will be low ) , there must be high levels of genetic variation in hosts to resist the parasites , and resistance must not be too costly . It is important therefore to keep in mind that the fitness values in population genetic models like the one presented here combine all fitness components . To our knowledge there is currently no study that has measured all relevant components of lifetime reproductive success in different host and parasite genotypes , making it impossible to parameterize our models based on real data . Another quantity that appears to be important in determining whether recombination is favoured or disfavoured is the product of epistasis and LD . Negative median values of this quantity usually lead to selection for recombination , whereas sufficiently high , positive values led to selection against increased recombination in the majority of simulations . These results indicate that immediate effects of the recombination modifier ( i . e . , the production of disproportionally fit offspring through recombination ) may have been responsible for selection for the modifier in many of our simulations . However , there are also simulations in which there is selection for recombination despite being mainly positive . We even found instances where the sign of both LD and epistasis was constantly the same ( i . e . , was always positive ) and where recombination was nevertheless favoured . Hence , recombination is sometimes favoured despite an immediate disadvantage of producing disproportionately unfit offspring , indicating that delayed short-term effects and/or long-term effects are also important ( for a classification and analysis of these effects , see Ref . 12 ) . A rather unexpected outcome of our simulations was the distribution of LD statistics and their impact on selection for or against recombination ( see Figures 3 and 5 ) . With most of our random interaction matrices , no change in the sign of LD occurred following the burn-in phase . We suspect that LD fluctuations around a mean of zero that are usually observed with standard interaction models are a result of the intrinsic symmetry of these models . Importantly , constant sign of LD does not imply absence of selection for recombination . LD dynamics appear to be informative about selection for recombination in three extreme cases . First , if LD is constantly zero ( as happened in many simulations because of quasi-extinction of alleles ) , any recombination modifier is selectively neutral . Second , when LD is more or less constant but different from zero , the recombination modifier decreased in frequency . This situation is similar to that of so-called high complementarity equilibria , which have been observed in the multiplicative matching allele model [29] and which are expected from the reduction principle [30] to disfavour recombination . ( According to the reduction principle , in populations at equilibrium in which genotypes of suboptimal fitness are constantly produced through imperfect transmission – e . g . , mutation or recombination – modifier alleles that decrease this imperfect transmission can always spread in the population . ) Finally , when LD fluctuates very strongly around zero , recombination is usually favoured . We would like to stress that in many simulations the LD dynamics could not be assigned to any of these three classes of outcomes , so that the fate of a recombination modifier could not be predicted from LD . We also note that extremely fast fluctuations of either LD or epistasis with sign changes every two to five generations ( the so-called Barton zone ) were never observed in our simulations . Although such dynamics have been predicted to be necessary for fluctuating epistasis to favour high recombination rates ( near 0 . 5; see Ref . [10] ) , our results indicate that at least for the moderately high recombination rates ( 0 . 1 ) that we assumed , this may not be an important requirement for the RQH to work [13] , [17] . A general conclusion from our results is that it is very difficult to predict from empirical data whether recombination is favoured . Even when the dynamics of allele frequencies , LD , epistasis etc . are completely recorded over a long time span and without sampling error , these data do not allow us in general to make accurate predictions with respect to selection acting on a recombination modifier . Given that natural systems will be much more complex in terms of genotypic architecture and population dynamics than our simple , deterministic two-locus model , these conclusion are somewhat dispiriting . Further theoretical investigations into the population genetic mechanism of the RQH and novel , more general theoretical predictions as to when recombination should be favoured or disfavoured in Red Queen models would therefore seem desirable .
We constructed a deterministic discrete time model that is similar to previous models of Red-Queen dynamics [e . g . , 14] , [16] . Both hosts and parasites are haploid and have two interaction loci A and B with two alleles a/A and b/B , respectively , at each locus . In addition , hosts have a third locus M ( recombination modifier ) with two alleles m and M . At each time step , three processes occur in the following order for both hosts and parasites: ( 1 ) reproduction , ( 2 ) selection , and ( 3 ) mutation . A number nPG of parasite life cycles are completed during a single host life cycle , and updating of host and parasite frequencies occurred simultaneously . The three steps of the life-cycle are defined as follows . First , during reproduction , hosts mate and recombine . The order of loci is ABM . Recombination between the two interaction loci A and B is determined by the alleles at the M locus , with recombination rates denoted by rmm , rMm and rMM . Recombination between the B and the M locus takes place at a rate R . Parasites are assumed to reproduce asexually . Second , selection acting on hosts and parasites is determined by a pair of 4×4 interaction matrices , WH and WP . Here , is the fitness of a host with genotype i ( i = ab , Ab , aB or AB ) that interacts with a parasite of genotype j ( j = ab , Ab , aB or AB ) . Likewise , is the fitness of a parasite with genotype j that interacts with a host of genotype i . Interactions between host and parasite genotypes occur proportional to their relative frequencies ( mass-action assumption ) . Note that WH and WP may represent or combine various fitness components of the hosts ( e . g . , parasite virulence , overall parasite prevalence or costs of resistance alleles ) and parasites ( e . g . , infectivity or within-host growth ) . Denoting by pi the frequency of hosts with genotype i and by qj the respective parasite frequencies , the host frequencies following selection are given by ( 1 ) The numerator in equation ( 1 ) can be interpreted as the relative fitness of host i with the present composition vector q of genotypes in the parasite population , and the denominator is the average fitness in the host population . The parasite frequencies following selection are determined analogously , based on host frequencies and WP . Finally , mutation takes place at host and parasite interaction loci . The mutation rate μ is the same for hosts and parasites , for the two interaction loci , and for both directions of mutation . We assume that no mutations occur at the M locus . Host genes involved in defence against parasites as well as parasite genes involved in host invasion are expected to show antagonistic fitness effects . In order to construct random interaction matrices that emulate host-parasite relationships , we therefore defined an ‘antagonicity’ A of a pair of interaction matrices as , the Pearson product-moment correlation coefficient between the corresponding entries of WH and WP . A is a measure of how changes in host fitness relate to changes in parasite fitness . High values of A ( close to 1 ) indicate that in interactions between host and parasite genotypes , a high host fitness implies a low parasite fitness and vice versa . We then constructed the interaction matrices by first filling each entry of the two matrices with a random number drawn from a uniform distribution ranging from ( 1-sH ) or ( 1-sP ) to 1 . Thus , sH and sP determine the average strength of selection on hosts and parasites . If the antagonicity of this pair of interaction matrices fell within a certain range it was added to the set of interaction matrices tested , otherwise it was discarded . Unless stated otherwise , we used different sets of 10 , 000 interaction matrices with a range of in our simulations . The standard protocol for our simulations was as follows . First , we initialized the host population with all individuals carrying the m allele at the M locus and equal frequencies of the four interaction genotypes . Likewise , the parasite population was initialized with equal genotype frequencies . We then allowed the populations to co-evolve for a burn-in period of 10 , 000 host generations . This was followed by another 10 , 000 host generations , during which we recorded the genotype frequency dynamics . From these data , we then calculated several statistics ( e . g . , minimum , maximum , mean , variance ) of a number of properties of the dynamics , including the genotype frequencies themselves , linkage disequilibria , strength of selection and epistasis . Table 4 gives the formulae used to calculate these quantities . We used the additive version of epistasis , but the results are very similar with multiplicative epistasis . Finally , in the simulations where we tested for selection on recombination rate and following the 20 , 000 generations of burn-in and analysis , we introduced a recombination modifier allele M into the host population . The initial frequency of M was 0 . 001 , and M was introduced such that it was in linkage equilibrium with the other two loci . We then simulated for another 10 , 000 generations and recorded the frequency of M . We considered M under positive selection if the final frequency of M was above 0 . 0011 . Conversely , negative selection was assumed if the frequency of M dropped below 0 . 0009 . This 10% increase and decrease in frequency towards the two thresholds within 10 , 000 generations roughly correspond to selection coefficients of and in standard population genetics models with haploid populations under constant selection . If the frequency of M was within the threshold range , M was considered neutral . Unless stated otherwise , we used the following standard set of parameters in our simulations: , nPG = 1 , rmm = 0 , rMm = 0 . 05 , rMM = 0 . 1 , R = 0 . 05 .
|
The Red Queen has become an eponym for rapid and perpetual evolutionary arms races between hosts and parasites . The Red Queen also lends her name to the idea that such arms races are at the core of the question of why sexual reproduction is so widespread among higher-level organisms . According to this view , recombination provides the hosts with an advantage that allows faster adaptation to the parasite population . To date , mathematical models trying to quantify Red Queen dynamics and the Red Queen hypothesis for the evolution of sex have generally made several simplifying assumptions about how host and parasite genotypes interact with each other ( i . e . , how they influence each other's fitness ) . In this article we present a model that allows for arbitrary patterns of fitness interactions between both parties . We demonstrate that the degree of ‘antagonicity’ in these interactions is decisive for whether Red Queen dynamics are observed , and assess the robustness of various previous results concerning the Red Queen hypothesis with respect to fitness interactions . Our results also make clear how difficult predictions of coevolutionary dynamics and selection for recombination are likely to be in real host-parasite systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"biology/population",
"genetics",
"evolutionary",
"biology/animal",
"genetics",
"ecology/evolutionary",
"ecology",
"evolutionary",
"biology/evolutionary",
"ecology",
"ecology",
"evolutionary",
"biology",
"computational",
"biology/evolutionary",
"modeling",
"evolutionary",
"biology/plant",
"genetics",
"and",
"gene",
"expression",
"ecology/theoretical",
"ecology",
"computational",
"biology/ecosystem",
"modeling",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2009
|
Red Queen Dynamics with Non-Standard Fitness Interactions
|
Primary infection with varicella zoster virus ( VZV ) results in varicella ( more commonly known as chickenpox ) after which VZV establishes latency in sensory ganglia . VZV can reactivate to cause herpes zoster ( shingles ) , a debilitating disease that affects one million individuals in the US alone annually . Current vaccines against varicella ( Varivax ) and herpes zoster ( Zostavax ) are not 100% efficacious . Specifically , studies have shown that 1 dose of varivax can lead to breakthrough varicella , albeit rarely , in children and a 2-dose regimen is now recommended . Similarly , although Zostavax results in a 50% reduction in HZ cases , a significant number of recipients remain at risk . To design more efficacious vaccines , we need a better understanding of the immune response to VZV . Clinical observations suggest that T cell immunity plays a more critical role in the protection against VZV primary infection and reactivation . However , no studies to date have directly tested this hypothesis due to the scarcity of animal models that recapitulate the immune response to VZV . We have recently shown that SVV infection of rhesus macaques models the hallmarks of primary VZV infection in children . In this study , we used this model to experimentally determine the role of CD4 , CD8 and B cell responses in the resolution of primary SVV infection in unvaccinated animals . Data presented in this manuscript show that while CD20 depletion leads to a significant delay and decrease in the antibody response to SVV , loss of B cells does not alter the severity of varicella or the kinetics/magnitude of the T cell response . Loss of CD8 T cells resulted in slightly higher viral loads and prolonged viremia . In contrast , CD4 depletion led to higher viral loads , prolonged viremia and disseminated varicella . CD4 depleted animals also had delayed and reduced antibody and CD8 T cell responses . These results are similar to clinical observations that children with agammaglobulinemia have uncomplicated varicella whereas children with T cell deficiencies are at increased risk of progressive varicella with significant complications . Moreover , our studies indicate that CD4 T cell responses to SVV play a more critical role than antibody or CD8 T cell responses in the control of primary SVV infection and suggest that one potential mechanism for enhancing the efficacy of VZV vaccines is by eliciting robust CD4 T cell responses .
Varicella zoster virus ( VZV ) , a neurotropic alphaherpesvirus , is the causative agent of varicella ( chickenpox ) . Following resolution of the acute infection , VZV establishes latency in sensory ganglia , and can reactivate years later , manifesting as dermatomal vesicular lesions known as herpes zoster ( HZ , shingles ) [1] . HZ is a painful and debilitating disease that causes significant morbidity such as post-herpetic neuralgia and HZ opthalmicus [2] , [3] and occasionally mortality in the elderly and immune compromised [4] . HZ affects 1 million people each year in the United States [5] , [6] and persons older than 60 year of age account for 40–50% of HZ cases reported each year [5] , [6] . Given that by 2020 17% of the US population is estimated to be 65 years of age or older ( US Census ) , the incidence of HZ and its associated morbidities is likely to increase . There are currently two FDA approved VZV vaccines available that contain the live attenuated VZV Oka strain: Varivax , directed against chickenpox , and Zostavax , directed against shingles . The introduction of Varivax , and more specifically of the 2-dose regimen , has dramatically reduced the incidence of chickenpox and annual varicella-related hospitalizations and deaths in the US [7] , [8] . Similarly , vaccination with Zostavax reduced the incidence of shingles by 51% in a 3-year study period and resulted in a 61% decrease in the burden of disease [9] , [10] . However , it is important to note that the efficacy of Zostavax was reduced in individuals older than 70 years [9] . Moreover , recent studies showed that the cellular and humoral responses engendered by Zostavax significantly declined 2 years following Zostavax vaccination [11] . Given that the proportion of persons 85 years and older is expected to increase from 14% in 2010 to more than 21% in 2050 ( US Census Bureau , 2010 ) , new vaccination strategies against VZV should be explored . Clinical observations suggest that the successful resolution of acute VZV infection is associated with the development of the host's VZV-specific cellular immunity rather than humoral immunity [1] , [12] , [13] , [14] , [15] , [16] . Specifically , children suffering from congenital immune deficiencies affecting cellular immunity or from hematological malignancies , or undergoing immunosuppressive treatment are at risk for progressive varicella; whereas , children with agammaglobulinemia have uncomplicated varicella episodes [17] , [18] , [19] , [20] , [21] . Transfusion of lymphocytes from immune donors to children with immunodeficiencies can limit VZV infection and replication after the appearance of cutaneous lesions [22] . In contrast , the administration of immunoglobulins with high titers of IgG antibodies to VZV is only protective when administered within 72 hours of exposure [23] , [24] , [25] . While early production of VZV antibodies by HIV-infected children does not prevent progressive varicella [26] , absence of cellular immunity in these children correlates with a risk of prolonged viremic phase , continued formation of skin lesions and dissemination of the virus to the lungs and other organs [16] , [27] . However , these data are confounded by the fact that T cell-deficient patients lack both T cell-mediated and T cell-dependent B cell-mediated responses . Thus , more careful analyses of the contribution of each of the major lymphocyte subsets are required . Both VZV-specific CD4 and CD8 T cells are detected during acute VZV infection [12] , [28] . CD4 Th1 cells , which produce the anti-viral cytokine IFNγ and have cytotoxic potential , predominate the VZV-specific T cell response [12] , [28] , with little or no production of Th2 cytokines [12] . Several studies have shown that VZV-specific CD4 T cells recognize ORF4 [29] , glycoprotein I [30] , and ORF63 [31] . In contrast to CD4 T cells , the specificity and magnitude of VZV-specific CD8 T cell response are only partially understood . Several studies have suggested that VZV-specific CD8 T cells circulate at very low frequencies , possibly due to immune evasion strategies such as MHC class I downregulation [30] , [32] , [33] . However , it is also likely that the CD8 T cell response is under-appreciated because VZV lysate used in the majority of the studies is poorly presented via the MHC-I pathway [34] . Consequently , the role of CD8 versus CD4 T cell immunity in the control of VZV infection remains unclear . Our understanding of the importance of the T and B cell responses to the resolution of VZV infection remains incomplete because of the lack of adequate animal models . VZV infection of small rodent models ( guinea pigs , cotton rats , and mice ) does not result in varicella [35] , [36] , [37] , [38] , [39] . Dr . Arvin and colleagues developed a SCID-humanized ( SCID-hu ) mouse model which has significantly advanced our understanding of VZV pathogenesis [40] . However , the immuno-deficient nature of the animals has precluded the characterization of immune response to VZV infection . Simian varicella virus ( SVV ) is a neurotropic alphaherpesvirus that naturally infects non-human primates ( NHP ) and shares 75% DNA homology and genome colinearity with VZV [41] , [42] , [43] . The exact mode of SVV transmission has not yet been experimentally determined but it is believed to occur through contact with skin lesions of an infected animal , or via exposure to virus-laden aerosolized droplets from infected animal ( s ) , as described for VZV transmission in humans [44] , [45] . We have recently shown that intrabronchial infection of rhesus macaques ( RM ) with SVV recapitulates the hallmarks of acute VZV infection in humans: ( 1 ) generalized varicella rash; ( 2 ) development of cellular and humoral responses; ( 3 ) resolution of acute infection; and ( 4 ) establishment of latency in sensory ganglia [46] . In our model , the introduction of SVV directly into the lungs potentially bypasses the initial stage of VZV replication that occurs in the oropharynx , head and neck regions [47] , which could affect viral amplification within tonsilar memory T cell [40] , [48] , [49] . Despite this potential difference , T and B cell responses develop with similar kinetics relative to the onset of the exanthem in SVV inoculated animals and children infected with VZV , thereby providing a robust animal model with which to study anti-VZV immunity . The goal of this study was to identify the host immune responses essential for protection against primary SVV infection in unvaccinated RM as a model of VZV infection in humans . To this end , we compared disease severity and immune response in four groups of young RM infected with SVV: ( 1 ) control animals; ( 2 ) CD20+ B cell depleted; ( 3 ) CD8+ T cell depleted; and ( 4 ) CD4+ T cell depleted . We show that loss of B cells during primary SVV infection does not alter viral loads or disease severity . In contrast , loss of CD8 T cells led to a slightly higher peak viral load and the loss of CD4 T cells led to significantly higher viral loads and disseminated varicella . These studies suggest that cellular immunity and more specifically CD4 T cell immunity plays a critical role in the control of SVV infection . These observations have important ramifications for the development of novel vaccination strategies to alleviate VZV associated diseases .
The contributions of cellular versus humoral immune responses in the resolution of acute VZV infection have not yet been experimentally addressed . Therefore , using the infection of young RM with SVV as a model of acute VZV infection , we aimed to determine the role of T cell versus B cell responses during acute SVV infection . Prior to intrabronchial SVV inoculation , 16 young RM were divided into four groups of four animals each: ( 1 ) control; ( 2 ) CD8+ T cell depleted; ( 3 ) CD4+ T cell depleted; and ( 4 ) CD20+ B cell depleted . T and B cell depletion regimens were initiated 7 days prior to infection to ensure that the targeted lymphocyte population was not present on the day of infection ( 0 dpi ) . The frequency of CD4+ T cells , CD8+ T cells , and CD20+ B cells in peripheral blood mononuclear cells ( PBMC ) and bronchial alveolar lavage ( BAL ) samples were monitored throughout the study by flow cytometry ( FCM ) . In contrast to peripheral blood , frequency of CD20 B cells is very low in BAL ( Fig . 1 ) . Treatment with CD20 depleting antibody resulted in the loss of B cells beginning at 0 dpi and lasting until 21 dpi in BAL ( Fig . 1C ) and 17 dpi in peripheral blood ( Fig . 1F ) . Administration of CD8-depleting antibody resulted in complete loss of CD8+ T cells between 0 and 14 dpi in BAL ( Fig . 1B ) and between 0 and 17 dpi in peripheral blood ( Fig . 1E ) Administration of anti-CD4 depleting antibody decreased the frequency of CD4+ T cells on day 0 but complete loss was not achieved until 10 dpi ( Fig 1A and D ) . This loss was very transient in BAL samples where the recovery started 14 dpi ( Fig . 1A ) . In peripheral blood , CD4 depletion lasted until 17 dpi ( Fig . 1D ) . In summary , CD20 and CD8 T cell depletions were more profound ( achieving ∼100% loss by day 0 ) and lasted longer than CD4 T cell depletion . Following depletion , recovery was very slow in all three lymphocyte subsets and the frequencies did not return to baseline by the end of the study . To determine the impact of T and B cell loss on SVV replication , we measured viral loads in whole blood ( WB ) , BAL and buccal swabs by qPCR . As previously reported [46] , SVV viral loads in WB were detected in non-depleted control animals 3 dpi , peaked at 7 dpi , and resolved at 21 dpi in WB ( Fig . 2A ) and BAL ( Fig . 2B ) . CD20 depleted animals showed comparable viral loads and viral replication kinetics as those observed in non-depleted animals ( Fig . 2A and 2B ) . In contrast , CD4+ depleted animals showed the highest peak SVV viral loads in WB at 7 dpi ( p = 0 . 05 ) and 10 dpi ( p = 0 . 002 ) ( Fig . 2A ) and BAL at 7 dpi ( p<0 . 001 ) ( Fig . 2B ) compared to non-depleted control animals . They also showed recurrent viremia as evidenced by the detection of SVV viral DNA in WB on days 56 and 70 ( Fig . 2A ) . CD8 depleted animals also experienced higher viral loads 7 dpi in WB ( Fig . 2A ) and 3 dpi in the BAL ( Fig . 2B ) than non-depleted animals but these differences did not reach statistical significance . We also measured SVV shedding in saliva since it is well documented that VZV reactivation results in the shedding of infectious VZV in the saliva [50] , [51] . SVV viral DNA was only detected in the buccal epithelial cells and not the saliva ( Fig . 2C and data not shown ) . CD8 depleted animals experienced higher SVV viral loads in buccal epithelial cells at 7 dpi ( p = 0 . 0418 ) , while CD4 depleted animals showed higher SVV viral loads in buccal epithelial cells at 10 ( p<0 . 001 ) and 14 dpi ( p = 0 . 0182 ) in comparison to non-depleted control animals ( Fig . 2C ) . All 16 animals developed varicella ( Fig . 3 ) , but the severity and duration of the rash varied between groups ( Table 1 ) . As previously reported [46] , control animals developed lesions beginning at 7 dpi that began to heal 14 dpi and were completely resolved 21 dpi ( Table 1; Fig . 3A ) . CD20 depleted animals had comparable disease severity to control animals ( Fig . 3B ) . However , CD8 depleted animals continued to display moderate to severe lesions at 14 dpi that did not resolve until 28 dpi ( Table 1; Fig . 3C ) . CD4 depleted animals experienced severe and disseminated varicella rash ( Fig . 3D ) , which also did not begin to heal until 42 dpi ( Table 1 ) . Taken together , these data show that in contrast to CD20 B cells , the absence of CD8 or CD4 T cells results in higher viral loads and increased disease severity during primary SVV infection . We next investigated the impact of lymphocyte depletion on the proliferative response of B cells and the generation of SVV-specific IgG , IgM , and neutralizing antibody responses . Following antigen encounter , naïve B cells undergo a proliferative burst and acquire the memory marker CD27 . Therefore , we assessed the proliferative response of B cells by measuring changes in the expression of Ki67 , a nuclear protein associated with G2-S cell cycle transition , within two major antigen-experienced B cell subsets: marginal zone-like ( MZ-like ) and memory B cell subsets , using FCM . In BAL from control animals , proliferation of MZ-like B cells began 10 dpi , peaked 14 dpi , and returned to baseline levels 17 dpi ( Fig . 4A ) . Frequency of Ki67+ memory B cells in BAL of control animals increased at 7dpi , peaked 10dpi , and returned to baseline levels 17 dpi ( Fig . 4B ) . In CD20-depleted animals , Ki67+ MZ-like and memory B cell in BAL were detected 14 dpi as the B cell compartment began to regenerate ( Fig . 4A and 4B ) . Interestingly , we detected an earlier onset of proliferation of MZ-like and memory B cells in BAL in CD8 depleted animals compared to control animals ( Fig . 4A and 4B ) . Proliferative burst of the MZ-like B cells peaked 7 days before control animals ( 7 dpi versus 14 dpi ) and reached a higher level ( p<0 . 0001 ) before returning to baseline levels 17 dpi ( Fig . 4A ) . Similarly , the proliferative burst of the memory B cells was detected 4 days earlier ( 3dpi versus 7dpi ) and reached a higher peak 7 dpi compared to control animals ( p = 0 . 0003 ) ( Fig . 4B ) . In contrast , we detected minimal B cell proliferation with no distinct kinetics ( less than 10% ) in both the MZ-like and memory subsets in CD4 depleted animals ( Fig . 4A and 4B , and Table 2 ) . In PBMCs of control animals , MZ-like and memory B cell proliferation was detected 7 dpi , peaked 10–14 dpi , before returning to baseline levels 17 dpi ( Fig . 4C and 4D ) . As described for BAL , Ki67+ MZ-like and memory B cells were detected 14 dpi in CD20 depleted animals ( Fig . 4C and 4D ) , coinciding with the re-appearance of B cells in peripheral blood ( Fig . 1 ) . In CD8 depleted animals the proliferative burst of MZ-like and memory B cells was delayed by 3 days ( 10 dpi compared to 7 dpi , p = 0 . 008 ) ( Fig . 4C and 4D ) but reached higher peak value 14 dpi in the memory subset compared to control animals ( p<0 . 001 ) ( Fig . 4D ) . As observed in BAL , B cells from CD4 depleted animals showed minimal proliferation in response to SVV infection ( p<0 . 05 at all time points examined between 7 and 14 dpi ) ( Fig . 4C and 4D , and Table 2 ) . To assess differences in the magnitude of the proliferative burst , we measured the area under the curve ( AUC ) for MZ-like and memory B cell proliferation between 3 and 17 dpi . This analysis revealed that the magnitude of the proliferative burst of MZ-like and memory B cells was significantly reduced in PBMC in CD4 depleted animals compared to control animals ( p<0 . 001 ) . In CD8 depleted animals , memory and MZ-like B cell proliferative burst was higher in BAL ( p = 0 . 025 ) but MZ-like proliferation was lower in PBMC compared to controls ( p = 0 . 013 ) ( Table 2 ) . We also measured the impact of T and B cell depletion on antibody production . To that end , we measured SVV-specific IgM and IgG titers using standard ELISA and neutralizing antibody titer using plaque reduction assay . In control and CD8 depleted animals , SVV-specific IgM titers peaked 10 dpi ( average titer 1∶2000 and 1∶2465 , respectively ) ( Fig . 4E ) . In contrast , CD20 and CD4 depleted animals had significantly lower average titers of 1∶360 and 1∶190 , respectively ( Fig . 4E , p<0 . 05 ) . Similarly , control and CD8 depleted animals generated a robust SVV-specific IgG response that peaked 21 dpi ( average titer 1∶15800 and 1∶17300 respectively ) ( Fig . 4F ) , whereas CD20 and CD4 depleted animals had significantly reduced IgG antibody production 14 , 17 , 21 , and 28 dpi compared to control animals ( p<0 . 02 for all time points ) . Specifically , SVV-specific IgG titers 21 dpi were on average 1∶6200 and1∶2500 in CD20 and CD4 depleted animals respectively ( Fig . 4F ) . Analysis of the AUC for SVV-specific IgG titers between 0 and 63 dpi showed that both CD20 depleted and CD4 depleted animals have significantly reduced IgG production compared to non-depleted control animals ( p = 0 . 0245 and p = 0 . 0193 , respectively ) ( Fig . 4F ) . We also assessed neutralizing antibody titers following acute SVV infection 14 and 70 dpi in all animal groups by measuring the dilution at which 50% reduction in the number of SVV plaques was achieved ( NT50 , Table 3 ) . NT50 titers were comparable between control and CD8 depleted animals 14 dpi , and by 70 dpi the titers increased 34- to 90-fold and 8- to 17-fold , respectively ( Table 3 ) . In contrast , CD4 depleted animals had considerably lower NT50 titers 14 dpi compared to control animals , which increased 6- to 16-fold by 70 dpi ( Table 3 ) . Plasma obtained from CD20 depleted animals showed no neutralization ability 14 dpi and very negligible NT50 titers 70 dpi ( Table 3 ) . These data suggest that the resolution of varicella during primary SVV infection in unvaccinated animals does not require the presence of neutralizing antibodies . We assessed how loss of T and B cells modulated the kinetics and magnitude of T cell proliferation in BAL and PBMCs in response to SVV infection . Like B cell responses , after antigen encounter , naïve T cells become activated , differentiate into central and effector memory ( CM and EM ) T cells and undergo a robust proliferative burst . We assessed this proliferative burst by measuring frequency of Ki67+ cells within CM and EM T cell subsets in BAL and PBMC [46] . In BAL from control animals , T cell proliferation was detected in all subsets 7 dpi after which the frequency of Ki67+ T cells gradually declined reaching baseline levels 17 dpi ( Fig . 5A–D , and Table 2 ) . In CD20 depleted animals , the kinetics and magnitude of proliferation in all four T cell subsets was similar to that observed in control animals ( Fig . 5A–D ) . The kinetics of the CD4 T cell proliferation in CD8 depleted animals was comparable to that observed in control animals . However , the peak frequency of Ki67+ CD4 CM at 7 dpi for CD8 depleted animals was significantly larger than that observed for control animals ( p = 0 . 0028 ) ( Fig . 5A ) . Similarly , the peak frequency of Ki67+ CD4 EM T cells 10 dpi and 14 dpi was also higher in CD8 depleted animals ( p = 0 . 0054 , p = 0 . 0058 , respectively ) ( Fig . 5B ) . Interestingly the re-appearance of CD4 T cells in the BAL 21 dpi was not accompanied by an increased frequency of CD4 Ki67+ T cells ( Fig . 5A and 5B ) . We detected proliferating CD8 T cells in CD8 depleted animals 17 dpi ( Fig . 5C and 5D ) , which coincides with the re-appearance of CD8 T cells in these animals ( Fig . 1 ) . CD8 CM proliferation was significantly delayed ( 10 dpi versus 7 dpi in control animals ) and reduced in CD4 depleted animals ( p<0 . 001 at 7 dpi; p = 0 . 0059 at 14 dpi ) ( Fig . 5C ) . Similarly , proliferation of CD8 EM in CD4 depleted animals was also delayed ( 10 dpi versus 7 dpi in control animals , Fig . 5D ) . We detected very little proliferation by the CD4 T cells in PBMC from control animals ( Fig . 5E and 5F , and Table 2 ) . On the other hand , CD8 T cell proliferation was easily detected in PBMC in control animals ( Fig . 5G and 5H , and Table 2 ) . The frequency of Ki67+ CD8 CM T cells in control animals peaked 7 dpi and returned to baseline 14 dpi , and the frequency of Ki67+ CD8 EM T cells peaked 10 dpi before returning to baseline levels 21 dpi ( Fig 5G and 5H ) . The kinetics and magnitude of T cell proliferative burst in CD20 depleted animals were comparable to those described for control animals ( Fig . 5E–H ) . As described for the BAL , CD4 T cell proliferation was more robust in CD8 depleted animals compared to controls . Specifically , the frequency of Ki67+ CD4 CM T cells was significantly higher 10 dpi ( p = 0 . 0138 ) ( Fig . 5E ) , and that of CD4 EM T cells was significantly higher 3 , 7 , 10 , 14 , and 17 dpi ( p<0 . 01 for all days ) ( Fig . 5F ) when compared to control animals . Ki67+ CD8 CM and EM T cells were detected 14 dpi ( Fig . 5G and H ) , which coincided with the re-appearance of CD8 T cells ( Fig . 1 ) . As described for the BAL , CD4 depleted animals exhibited a significant reduction of proliferating CD8 CM ( p = 0 . 001 ) and EM ( p< 0 . 01 for 10 , 14 and 17 dpi ) ( Fig . 5G and 5H ) . As previously noted for the BAL , regeneration of the CD4 T cell compartment was not accompanied by an increase in CD4 T cell proliferation in these animals ( Fig . 5G and 5H ) . As described above for B cells , we measured the AUC from 3 to 17 dpi for T cell proliferation to assess the magnitude of the proliferative burst . This analysis showed that in BAL the proliferative burst of: 1 ) CD4 EM T cells was significantly higher in CD8 depleted animals ( p = 0 . 026 ) and 2 ) that of CD8 CM was significantly lower in CD4 depleted animals ( p<0 . 001 ) compared to control animals . In peripheral blood: 1 ) CD8 depleted animals experienced a larger proliferative burst in the CD4 CM ( p = 0 . 08 ) and EM ( p <0 . 001 ) compared to controls and 2 ) the CD4 depleted animals showed a smaller CD8 CM ( p = 0 . 02 ) and EM ( p <0 . 001 ) T cell proliferative burst compared to control animals ( Table 3 ) . In addition to comparing the kinetics and magnitude of the T cell proliferative burst in response to SVV in the different groups , we determined the frequency of SVV-specific T cells within CM and EM subsets by measuring frequency of IFNγ+ or IFNγ+/TNFα+ following stimulation of PBMC and BAL cells with overlapping peptide pools that covered SVV ORFs 4 , 31 , 61 , and 63 using intracellular cytokine staining ( ICS ) ( Fig . 6 , and Table 2 ) . Following stimulation with overlapping peptide pools , SVV-specific CD4 CM and EM T cells in BAL were detected at 7 dpi in control animals , and their frequencies remained stable until 56 dpi ( Fig . 6A and 6B ) . SVV-specific CD8 CM and EM T cells were also detected 7 dpi . Whereas the frequency of SVV-specific CD8 CM T cells increased from 7 to 21 dpi after which it declined , that of CD8 EM T cells remained stable until 56 dpi ( Fig . 6C and D ) . Frequency of SVV-specific CD4 CM T cells were lower in BAL from CD20 depleted animals 7–21 dpi compared to control animals ( p = 0 . 01 , 0 . 008 and 0 . 03 respectively , Fig . 6A ) . The frequencies of SVV-specific CD8 CM in BAL were initially comparable between CD20 depleted animals and controls but decayed faster in CD20 depleted animals 21 dpi ( p<0 . 001 , Fig . 6C ) . The frequency of SVV-specific CD8 EM was also initially lower in BAL in CD20 depleted animals 7 and 14 dpi ( 0 . 03 and 0 . 04 respectively , Fig . 7D ) . After day 21 , frequency of SVV-specific T cells was comparable in CD20 depleted and control animals . In CD8 depleted animals the frequency of responding T cells within CD4 CM subset was lower than that detected in control animals 7–21 dpi ( p = 0 . 04 , p = 0 . 006 and p = 0 . 04 respectively , Fig . 6A ) . Similarly the frequency of responding T cells within CD4 EM was lower 14 dpi compared to controls ( p = 0 . 0413 ) ( Fig . 6B ) . SVV-specific CD8 T cells were first detected in CD8 depleted animals 21 dpi , which corresponds to the re-appearance of the CD8 T cells in these animals , albeit with lower frequency within the CD8 CM subset compared to control animals ( p<0 . 001 , Fig . 6C ) . SVV-specific CD4 T cells appeared in CD4 depleted animals 28 dpi and their frequencies within the CM and EM subsets were comparable to those observed in control animals . It should be noted that 21–28 dpi , the number of CD8 and CD4 T cells is still much lower in CD8 and CD4 T cell depleted animals respectively compared to controls . Therefore , although the relative frequencies of responding T cells within CM and EM subsets may be comparable , the absolute number of SVV specific CD8 and CD4 T cells is much lower in CD8 and CD4 depleted animals respectively compared to controls . In contrast , frequency of SVV-specific CD8 T cells within CM and EM subsets in CD4 depleted animals were lower than those observed in control animals at several time points ( 14 and 21 dpi in CD8 CM p = 0 . 007 and <0 . 001 , respectively; and 7 and 14 dpi in CD8 EM p = 0 . 02 and 0 . 01 , respectively , Fig . 6C and 6D ) . In PBMCs , SVV-specific T cells were detected 7 dpi in control animals and their frequency remained stable until 56 dpi ( Fig . 6E–H ) . In CD20 depleted animals , SVV-specific T cells were also detected 7 dpi , but the frequencies of CD4 EM and CD8 EM were lower compared to control animals ( p = 0 . 03 and p = 0 . 007 , respectively ) ( Fig . 6E–H ) . SVV-specific CD4 T cells in CD8 depleted animals were also detected with similar kinetics and frequencies as seen in control animals ( Fig . 6E and 6F ) . Although SVV-specific CD8 T cells were also detected 7 dpi in CD4 depleted animals , their frequency was initially lower ( 7 dpi p<0 . 001 for both CD8 CM and EM , Fig . 6G and 6H ) . SVV-specific CD8 T cells in CD8 depleted animals were detected 21 dpi ( Fig . 6G and 6H ) , concomitant with the re-appearance of CD8 T cells in these animals ( Fig . 1 ) . SVV-specific CD4 T cells were detected 28 dpi in CD4 depleted animals ( Fig . 6E and 6F ) , corresponding with the re-appearance of CD4 T cells in these animals ( Fig . 1 ) . As stated above , although the relative percentage of responding T cells within CM and EM subsets in the T cell depleted animals may be comparable to those of control animals , the number of and CD8 and CD4 T cells is still much lower in CD8 and CD4 T cell depleted animals respectively compared to controls . To determine the magnitude of the frequency of T cells in response to SVV peptide stimulation , we measured the AUC for SVV-specific T cells in all four animals groups between 7 and 35 dpi . This analysis showed that in BAL the magnitude of: ( 1 ) SVV-specific CD4 CM T cell response is significantly reduced in CD20 and CD8 depleted animals ( p = 0 . 0155 and p = 0 . 0036 , respectively ) ; ( 2 ) SVV-specific CD8 CM response are significantly reduced in CD20 and CD4 depleted animals ( p = 0 . 0146 and p = 0 . 0023 , respectively ) ; and ( 3 ) SVV-specific CD8 EM response is significantly reduced in CD4 depleted animals ( p = 0 . 0445 ) compared to control animals . SVV and VZV infections are associated with the development of cytolytic CD4 and CD8 T cells [46] , [52] . In order to determine how loss of lymphocyte subsets alters frequency of T cells with cytolytic potential , we measured the changes in granzyme B ( grzmB ) expressing CD4 and CD8 T cells ( Fig 7 , and Table 2 ) . In BAL , we detected an increase in the frequency in grzmB+ CD4 CM 7 dpi in all animals ( excluding CD4 depleted animals ) , which was followed by a quick return to baseline 14 dpi ( Fig . 7A ) . Frequency of grzmB+ CD4 EM increased 3 dpi in control and CD8 depleted animals and 7 dpi in CD20 depleted animals ( Fig . 7B ) . This increase in frequency was sustained until 28 dpi after which frequencies return to baseline levels ( Fig . 7B ) . In CD4 depleted animals , grzmB+ CD4 CM and EM were detected 21 dpi ( Fig . 7A and 7B ) coinciding with the reappearance of CD4 T cells in these animals ( Fig . 1 ) and peaked in frequency 35 dpi . As described for CD4 T cells , the frequency of grzmB+ CD8 CM T cells increased 7 dpi in all animals ( excluding CD8 depleted animals ) before returning to baseline 21 dpi ( Fig . 7C ) . However , this increase was significantly reduced in CD4 depleted animals compared to control animals ( p <0 . 001 ) ( Fig . 7C ) . The frequency of grzmB+ CD8 EM increased 10 dpi in all groups ( with the exception of CD8 depleted animals ) , followed by a small decrease and the establishment of a new set point ( Fig . 7D ) . We detected grzmB+ CD8 CM and EM T cells in CD8 depleted animals 14 dpi , after which the frequency remained stable for the duration of the study ( Figs . 7C and D ) . A similar pattern of changes in grzmB+ T cell frequencies was observed in PBMCs . We detected a small increase in frequency of grzmB+ CD4 CM 7 dpi in control animals , followed by a return to baseline levels 10 dpi ( Fig . 7E ) . This increase was significantly lower in CD8 and CD20 depleted animals compared to control animals ( p <0 . 001 in both cases ) ( Fig . 7E ) . The frequency of grzmB+ CD4 EM T cells increased 10 dpi in CD8 depleted animals and 14 dpi in CD20 depleted and control animals ( Fig . 7F ) , before reaching the same peak magnitude 17 dpi followed by the establishment of a new set point 21 dpi ( Fig . 7F ) . In the CD8 CM subset , the frequency of grzmB+ peaked sharply 7 dpi in all groups , albeit at a significantly lower level in CD4 depleted animals ( p<0 . 001 ) , before returning to baseline levels 10 dpi ( Fig . 7G ) . The frequency of grzmB+ CD8 EM increased 10 dpi in all groups , peaked 17 dpi before establishing a new set point ( Fig . 7H ) . We detected grzmB+ CD8 CM and EM in CD8 depleted animals 14 dpi , which coincides with the re-appearance of CD8 T cells in these animals ( Fig . 7H ) . Finally , we measured the AUC between 3 and 21 dpi to assess the differences in grzmB expression between the different groups following SVV infection . In BAL , CD20 depleted animals have a significantly reduced frequency of grzmB+ CD4 EM T cells compared to control animals ( p = 0 . 025 ) . In addition , CD4 depleted animals have a significantly reduced frequency of grzmB+ CD8 CM T cells compared to control animals ( p = 0 . 0147 ) . In peripheral blood , the grzmB+ CD4 CM response was lower in both CD8 ( p = 0 . 0265 ) and CD20 ( p = 0 . 0356 ) depleted animals . Moreover , the magnitude of grzmB expression in the CD8 CM subset is lower in CD4 depleted animals ( p = 0 . 0002 ) .
Several observations suggest that VZV infection is controlled via cellular immunity rather than humoral immunity . Specifically , children afflicted with T cell deficiencies are at increased risk from progressive varicella . In contrast , children diagnosed with B cell deficiencies such as agammaglobulinemia exhibit uncomplicated varicella [17] , [18] , [19] , [20] , [21] . Moreover , whereas VZV antibody administration after contagion but before the appearance of exanthem has been shown to block progression of systemic varicella [53] , administration after the onset of varicella has no effect on disease progression [12] . Finally , despite the presence of high VZV-specific IgG titers , aged persons are at increased risk from VZV reactivation than young adults . However , the exact contributions of T cell and B cell immunity to the resolution of acute VZV infection in unvaccinated individuals have not yet been experimentally elucidated . This knowledge would play a critical role in the design of second-generation vaccines against VZV . SVV is also a neurotropic α herpesvirus and a homologue of VZV that causes varicella in nonhuman primates . We have recently shown that SVV infection of juvenile rhesus macaques recapitulates the hallmarks of primary VZV infection in children including the development of a vesicular rash , the generation of T and B cell responses and the establishment of latency in sensory ganglia [46] . In this model , the animals are infected intrabronchially , which bypasses the early phase of VZV incubation/replication in the head and neck region , resulting in an incubation period of approximately 10 days . Direct inoculation of SVV into the lungs might also result in altered viral amplification since VZV replicates efficiently in tonsilar memory CD4 T cells [48] , [49] . Although , this incubation period is slightly shorter than that of VZV ( 10–21 days ) , the detection of T and antibody responses in SVV-infected macaques in relation to the onset of the rash occurs with comparable kinetics as those described for VZV [12] . Moreover , whereas natural and experimental SVV infection results in significant morbidity and mortality in other nonhuman primate species , infection of rhesus macaques with SVV results in a milder disease that more closely resembles varicella in humans [54] , [55] . These observations suggest that SVV infection of rhesus macaques is a robust model with which to investigate the contribution of CD4 T cell , CD8 T cells and B cell immunity to the control of acute VZV infection . Our results show that depletion of CD20 B cells and loss of early antibody production does not alter viral loads or disease severity . As expected , CD20-depleted animals were significantly compromised in their ability to generate SVV-specific IgM and IgG antibodies . Our data also suggest that the production of neutralizing antibodies is not required for resolution of varicella as the CD20 animals failed to generate a robust neutralizing antibody response yet had uncomplicated varicella . These data are in agreement with clinical observations that the VZV specific antibody response generated during acute VZV infection contributes little to the immune response to VZV [47] . Indeed , children with congenital agammaglobulinemia experience uncomplicated varicella [56] . Moreover , passive antibody therapy is only successful when administered within the first 72 hours following VZV exposure [24] , [25] and has no effect on disease progression if administered after the appearance of the rash [12] . We also observed similar kinetics and magnitude in the frequency of proliferating T cells in BAL and PBMCs of CD20 depleted animals compared to non-depleted controls . Although magnitude and kinetics of T cell proliferation were unchanged in CD20 depleted animals , we detected a lower frequency of SVV-specific T cells 7 and 14 dpi as detected by ICS following peptide stimulation ( ORFs 4 , 31 , 61 and 63 ) in these animals . One possible explanation for these results is that B cells are an important source of antigen presenting cells in this in vitro assay , and their absence results in the detection of a lower frequency of responding T cells . Alternatively the absence of B cells in vivo could lead to a change in immunodominance profile , resulting in T cells in CD20 depleted animals preferentially responding to additional ORFs not included in our peptide pools . The SVV-induced increase in frequency of grzmB+ T cells in CD20 depleted animals was similar in kinetics and magnitude to that observed in control animals with the exception that frequency of grzmB+ CD4 CM T cells in PBMC 7 dpi was significantly lower than control animals . This decrease could be due to the fact that B cells contribute to the activation of CD4 T cells in the peripheral blood . Overall , these data are reminiscent of the documented clinical observations and strongly suggest that the development of antibody response during primary SVV/VZV infection is not critical to the resolution of the acute infection . Our data also suggest a potential role for B cells in modulating the specificity of the T cell response , but more conclusive analyses are required to specifically address that question . Depletion of CD8 T cells resulted in slightly increased viral loads in whole blood and BAL samples but these differences did not reach statistical significance . Interestingly , the time points at which higher viral loads are observed in CD8 depleted animal differ between the BAL and WB , indicative of potentially different mechanisms of control . In the BAL slightly higher viral loads are detected 3 dpi . This is too early to suggest involvement of the CD8 T cell response . Indeed , the antibody that we used for CD8 T cell depletion targets all CD8 + lymphocytes , which in the macaque include NK cells . Thus , it is more likely that the higher viral load detected 3 dpi in CD8 depleted animals is a reflection of NK cell depletion rather than CD8 T cell depletion . In contrast , we observed a higher viral load 10 dpi in WB in CD8 depleted animals , which is more indicative of a defect in the CD8 T cell response . These observations are similar to clinical observations that severe varicella is associated with an absence of NK cells and primed CD8+ T cells responses in children [57] . CD8 depleted animals also experienced a somewhat more prolonged varicella episode and higher number of vesicles . Interestingly , we detected that depletion of CD8 T cells results in an increase in CD4 and B cell proliferation compared to control animals . This compensatory mechanism could have alleviated the consequences associated with loss of CD8 T cell function . Finally , depletion of CD4 T cells resulted in disseminated varicella , higher viral loads and sustained viremia although complete CD4 T cell depletion was not achieved until 10 dpi . The difference in disease severity between CD4 and CD8 T cell depleted animals cannot be explained by differences in the duration of T cell depletion . In fact , complete CD4 T cell depletion was not achieved until 10 dpi and was very transient in the BAL compared to CD8 T cell depletion and lasted 7 days in PBMC ( 10–17 dpi ) , whereas CD8 T cells were depleted for 10 days in BAL and 17 days in PBMC ( 0–21 dpi ) . The severe varicella observed in CD4 depleted animals is in line with clinical observations that HIV+ children fail to generate a T cell response and suffer progressive-disseminated varicella as well as additional complications such as varicella pneumonia , hepatitis , coagulopathy and meningoencephalitis [16] . Additionally , HIV patients are more susceptible to herpes zoster during the phase in which absolute numbers of CD4 T cells decline [12] , highlighting the importance of CD4 T cell responses in controlling VZV infection . It is possible that the use of anti-CD4 depleting antibody resulted in the depletion of CD4+ macrophages . However , we were not able to detect reduced frequency of monocytes/macrophages in CD4 depleted animals using CD14 and HLA-DR as markers ( data not shown ) . It is also possible that the depleting antibody resulted in the loss of CD4+NK T cells . However , a difference in viral loads between CD4 depleted and control animals is not detected until 7 dpi , indicative of a defect in adaptive rather than innate immunity . Indeed , CD4-depleted animals experienced delayed and reduced CD8 T cell proliferative bursts in both peripheral blood and BAL . This delay correlated with a significant decrease in the frequency of SVV-specific CD8 T cells that were detected by ICS . Moreover , the appearance of SVV-induced grzmB+ CD8 CM T cells was also reduced in these animals . Similarly , B cell proliferation and IgG , IgM and neutralizing antibody production was compromised in CD4 depleted animals . Since data from our CD20 and CD8 depleted animals suggests a minimal role for these lymphocyte subsets in the resolution of acute SVV infection , it is very likely that the severe disease experiences by CD4-depleted animals is a consequence of a compromised CD4 T cell response . Taken together , data presented in this manuscript suggest that the CD4 T cell response executes unique effector functions that are critical to the resolution of SVV/VZV infection . Although we cannot discount the possibility of synergism between the loss of B and CD8 T cell responses , our own depletion studies and data from additional studies support the hypothesis that CD4 T cells play a unique role during SVV/VZV infection . For instance , in addition to their role in recruiting CD8 CTL to sites of infection via release of Th1 cytokines [12] , [58] , CD4 T cells can kill VZV-infected cells [31] , [59] , [60] , [61] . Moreover , studies that characterized the VZV-specific T cell response in patients with VZV-induced uveitis revealed that CD4 T cells were responsible for the bulk of the T cell response [62] , [63] . VZV-specific CD4 T cells are also preferentially detected following re-exposure to VZV [64] . Taken together , the data presented in this manuscript and from previously published studies strongly suggest that CD4 T cells play a critical role in coordinating the anti-VZV/SVV response during primary infection that goes beyond providing help for CD8 and B cells . Additionally , a recent study investigating CMV-specific immune responses in CMV-seropositive renal transplant patients reported a positive correlation between low CD4 T cell counts and increased CMV DNA in the plasma of patients , suggesting a role for CD4 T cell immunity in controlling CMV reactivation [65] . This report , along with the data presented in this manuscript , suggest that the role of CD4 T cell responses in controlling herpesvirus infections may have previously been under-estimated . More investigations into this topic are needed to uncover the mechanisms by which CD4 T cells participate in the control of herpes viral infections . In summary , using SVV infection of rhesus macaques as a model of VZV infection [46] , we show for the first time in vivo the contribution of cellular immunity versus humoral immunity during acute SVV infection . Our results indicate that similar to clinical findings regarding VZV control in children , the ability of young rhesus macaques to control acute SVV infection is mediated by T cell immunity rather than humoral immunity . More importantly , our data strongly suggest that CD4 T cells mediate effector functions that are more important that providing help for antibody or CD8 T cell response in the resolution of acute SVV infection . Future studies will dissect the effector functions of CD4 T cells that are important for protection and elucidate the role of CD4 and CD8 T cell immunity in the protection against SVV reactivation .
The study was carried out in strict accordance with the recommendations described in the Guide for the Care and Use of Laboratory Animals of the National Institute of Health , the Office of Animal Welfare and the United States Department of Agriculture . All animal work was approved by the Oregon National Primate Research Center Institutional Animal Care and Use Committee ( IACUC protocol # 0779 ) . The ONPRC has been continuously accredited by the American Association for Accreditation of Laboratory Animal Care since 1974 ( PHS/OLAW Animal Welfare Assurance # A3304-01 ) . All procedures were carried out under Ketamine anesthesia in the presence of veterinary staff and all efforts were made to minimize animal suffering . SVV was propagated in primary rhesus fibroblasts ( 1° RF ) at 37°C in 175 cm2 flasks with DMEM supplemented with 10% FBS . SVV-infected 1° RF were frozen in FetalPlex with 10% DMSO , stored in LN2 , and assayed by plaque assay . SVV cell lysate was obtained by scraping SVV-infected 1° RF at the height of cytopathic effect ( CPE ) followed by concentration , and then sonication using 7 pulses of 70–80W ( Sonicator 3000 , Misonix ) . The sonicated cell-resuspension was pelleted by centrifugation at 2000rpm for 5 min and frozen at −80°C . 16 colony-bred Rhesus macaques ( Macaca mulatta , RM ) 3–4 years of age and of Indian origin were used in these studies . They were housed and handled in accordance with the Oregon National Primate Research Center Institutional Animal Care and Use Committee . To deplete CD4 T cells , 4 RM received the humanized monoclonal antibody OKT4-HulgG on −3 , 0 , and 7 days post-infection ( dpi ) at a dose of 50mg/Kg . To deplete CD8 T cells , 4 RM received the mouse-human chimeric monoclonal antibody cM-T807 on −3 dpi at a dose of 10mg/Kg , and on 0 , 3 , and 7 dpi at a dose of 5mg/Kg . To deplete B cells , 4 RM were administered the mouse-human chimeric antibody Rituxan on −7 , 0 , and 7 dpi at 20mg/Kg . All animals were inoculated intrabronchially with 4×105 PFU SVV as previously described [46] . Blood , bronchial alveolar lavage ( BAL ) , and ∼1 ml saliva samples were collected on days −14 , 0 , 3 , 7 , 10 , 14 , 17 , 21 , 28 , 35 , 42 , 49 , 56 , 63 , and 70 . The absolute numbers of lymphocytes/µL of blood were obtained using a Hemavet ( Drew Scientific , Inc . , Dallas , TX ) . BAL samples were pelleted and resuspended in RPMI supplemented with 10%FBS , streptomycin/penicillin , and L-glutamine . Peripheral blood mononuclear cells ( PBMCs ) and plasma were isolated by centrifugation over a histopaque gradient ( Sigma ) as per the manufacturer’s recommendation . Saliva samples were centrifuged in order to separate the buccal epithelial cells from the saliva . DNA was extracted from heparinized whole blood , BAL cells , saliva and buccal epithelial cells using the Qiagen genomic DNA extraction kit ( Qiagen ) and SVV DNA loads were determined by real-time PCR using primers and probes specific for ORF21 using the ABI 7700 and ABI StepOne instruments ( Applied Biosystems , Foster City , CA ) exactly as previously described [46] . PBMC and BAL cells were surface stained with antibodies against CD8β ( Beckman Coulter ) , CD4 ( eBioscience , San Diego , CA ) , CD28 , and CD95 ( BioLegend , San Diego , CA ) to delineate the naive ( CD28+CD95− ) , central memory ( CD28+CD95+ ) , and effector memory ( CD28−CD95+ ) T cell subsets . PBMC and BAL cells were also surface stained with antibodies against CD20 ( Beckman Coulter , Brea , CA ) , CD3 ( BD Pharmingen , San Diego , CA ) , IgD ( Southern Biotech ) , and CD27 ( BioLegend ) to delineate the naïve ( IgD+CD27− ) , MZ-like ( IgD+CD27+ ) , memory ( IgD−CD27+ ) B cell subsets . Cells were fixed and permeabilized according to manufacturer recommendations ( Biolegend ) before the addition of Ki67-specific antibody ( BD Pharmingen ) . The samples were analyzed using the LSRII instrument ( Beckton , Dickinson and Company , San Jose , CA ) and FlowJo software ( TreeStar , Ashland , OR ) . PBMC and BAL cells were surface stained using antibodies against CD4 , CD8β , CD28 , and CD95 as described for Ki67 staining . Cells were fixed and permeabilized using fixation buffer ( BioLegend ) , and then stained intracellularly using an antibody against granzyme B ( BD Pharmingen ) . Samples were analyzed using the LSRII instrument and FlowJo software . PBMC and BAL cells were either stimulated with SVV lysate ( 1 ug ) for 12 h followed by incubation with Brefeldin A for 6 h , or were incubated with one of the following SVV peptides and Brefeldin A ( Sigma , St Louis , MO ) for 6 h: open reading frame ( ORF ) 4; ORF31; ORF61; or ORF63 . After stimulation cells were surface stained with antibodies against CD4 , CD8β , CD28 , and CD95 as described for Ki67 staining . Samples were fixed and permeabilized using fixation buffer ( BioLegend ) , and then stained using antibodies against IFNγ and TNFα ( eBioscience ) . Samples were analyzed using the LSRII instrument and FlowJo software . ELISA plates were coated with SVV lysate overnight at 4°C , washed three times with 0 . 05% Tween-PBS , and incubated with heat-inactivated ( 56°C , 30 min ) plasma samples in 3-fold dilutions in triplicate for 1 h . After washing three times with 0 . 05% Tween-PBS , horseradish peroxidase ( HRP ) -conjugated anti-rhesus IgG ( Nordic Immunology , The Netherlands ) or anti-rhesus IgM ( Brookwood Biomedical , Birmingham , AL ) was added for 1 h , followed by addition of o-phenylenediamine•2HCl ( OPD ) substrate ( Sigma , St Louis , MO ) . The reaction was stopped with the addition of 1 M HCl . IgG and IgM endpoint titers were calculated using log-log transformation of the linear portion of the curve , and 0 . 1 optical density ( OD ) units as cut-off . IgG and IgM titers were standardized using a positive control sample that was included in every ELISA plate . Neutralizing antibody titers were evaluated as previously described [66] by measuring the plasma dilution at which 50% reduction in SVV plaques was achieved ( NT50 ) . Serial two-fold dilutions of heat-inactivated monkey plasma from 0 , 14 , and 70 dpi were incubated with approximately 150pfu of SVV for 30 minutes at 37°C . Virus/plasma samples were then added to duplicate primary rhesus fibroblast cell monolayers seeded on 12-well plates and incubated for 4 days at 37°C . Monolayers were fixed with methanol and then stained with crystal violet to visualize the SVV plaques . Repeated measures of ANOVA was used to explore differences between groups . Pair-wise comparisons at each time point were performed using contrast t-test . Statistical significance was determined at the level of 0 . 05 . First order autoregressive covariance structure was used to account for within subject correlation . Due to the small sample size , other complicated covariance analyses was not considered .
|
Varicella zoster virus ( VZV ) causes chickenpox and establishes a life-long latent infection in humans . VZV can reactivate years later to cause shingles , a debilitating and painful disease . Vaccines against both chickenpox and shingles are available but not 100% efficacious . Two doses of the chickenpox vaccine are required to provide adequate protection and the shingles vaccine reduces the incidence of this disease by 51% . To improve these vaccines , we must identify the components of the immune system that are important for the control of VZV replication . However , the contribution of T versus B cell responses is unknown . Infection of rhesus macaques with simian varicella virus is a robust model of VZV infection . Here , we used this unique animal model to show for the first time that the absence of B cells does not alter disease severity and that the loss of CD8 T cells only results in a mild increase in disease severity . In sharp contrast , the lack of CD4 T cells leads to disseminated varicella . These data highlight the importance of CD4 T cells and suggest that novel vaccines that focus on engendering a more robust CD4 T cell response against VZV might provide better protection from chickenpox and shingles .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"chickenpox",
"clinical",
"immunology",
"immunology",
"viral",
"diseases",
"infectious",
"disease",
"control",
"immunomodulation",
"immune",
"response"
] |
2011
|
CD4 T Cell Immunity Is Critical for the Control of Simian Varicella Virus Infection in a Nonhuman Primate Model of VZV Infection
|
A high proportion of grade 2 disability ( visible deformity ) is indicative of delay in detection of leprosy and leprosy is one of the major causes of preventable disability . We conducted this study to determine the risk factors associated with disability ( G2D and G1D ) among adult new leprosy cases and to measure their strength of association . A multi-centric case-control study was undertaken in five states of India i . e . Andhra Pradesh , Delhi , Gujarat , Maharashtra and West Bengal ) . Among new adult patients , cases were defined as those with disability ( G2D and G1D ) at the time of diagnosis and controls were defined as those without any disability ( G0D ) . Delays were quantified based on patient recall across a timeline . Patient delay defined as the time period between first noticed symptom by the patient and the first visit to any health care provider ( HCP ) ; HCP delay defined as the time period between patient’s first visit to any HCP and the confirmation of diagnosis of leprosy; and total delay defined as the sum of both patient and HCP delays . A total of 1400 new leprosy patients ( 700 G2D/G1D and 700 G0D ) across five states were interviewed . Among G2D/G1D , the median patient delay was 8 months compared with 4 months among G0D . The median HCP delay was 2 months for G2D/G1D and 1 month for G0D . The median total delay was 14 months for G2D/G1D and 6 . 2 months for G0D; observed median difference between groups was statistically significant ( p<0 . 001 ) . When patient delay was more than 3 months , odds of G2D/G1D at diagnosis were 1 . 6 times higher compared to when patient delay was less than 3 months . When the HCP delay was more than one month , the odds of G2D/G1D were 1 . 4 times higher compared to when the HCP delay was less than one month . When the patient had multi-bacillary type leprosy the odds of G2D/G1D at the time of diagnosis was nine times higher compared to pauci-bacillary type leprosy . Patient delay is the major reason for risk of disability ( G2D/G1D ) among adult leprosy patients . A patient delay of more than 3 months from the notice of first symptom is a significant indicator for the disabilities among adult leprosy patients . Early case detection campaigns like active surveys in endemic spots should be done periodically as this can reduce delays and promote early diagnosis . Additionally , the program should lay greater emphasis on raising community awareness regarding the disease . Also , health care provider delay of more than 1 month have been significant risk factors for disability among adult leprosy cases . Hence , periodical capacitation of all HCPs including private practitioners would significantly contribute to reduce diagnostic delay and promote timely referral and early detection .
Leprosy is an infectious disease caused by the Mycobacterium leprae and is one of the important causes of preventable disability [1] . Early diagnosis and prompt treatment of all new cases of leprosy with World Health Organization ( WHO ) recommended multidrug therapy ( MDT ) remain the key strategies for leprosy control as it would prevent nerve damage and disability [2] . An early diagnosis also provides opportunities for reducing or halting further transmission . Despite that , there are many current reports across the world , showing that people are still diagnosed late for leprosy [3–5] . After the declaration of elimination of leprosy as a public health problem in India in 2006 , leprosy services were integrated within the general health care system for early diagnosis , prompt treatment , and care [6] . Despite increased efforts , reports and studies suggest leprosy is diagnosed late [7–9] . India is one of the countries with the highest leprosy burden with more than 135 , 000 new leprosy patients being detected every year , including 5 , 245 ( 3 . 9% ) new leprosy patients with a visible disability: grade 2 disability ( G2D ) [10] . In the year 2016 , India reported 63% of the world’s new leprosy cases; about 40% of the world’s new G2D among new leprosy cases . India reported an increasing trend of new cases with G2D in the period 2008–2015 from 3 . 1% to 4 . 6% [11] . A total of 3 , 848 new cases with G2D was detected by contact examination in the leprosy case detection campaign in April 2017 to March 2018 [12] . Global Leprosy Strategy 2016–20 , has set a goal to reduce leprosy burden assessed through a target of <1/million rate of newly diagnosed leprosy patients with visible deformities [6] . The number of G2D patients among new leprosy cases in 5 Indian States ( Andhra Pradesh , Maharashtra , Gujarat , Delhi and West Bengal ) was 1 , 913 ( 36% of India ) and 2 , 067 ( 36% of India ) in 2013–14 and 2014–15 respectively [13 , 14] . The number of cases with G2D at the time of diagnosis directly reflects the delay in the early detection; the level of leprosy awareness in the community; the capacity of the health system to recognize leprosy early and to some extent the reach of services [12] . Leprosy presents itself as a public health problem due to the disabilities it causes and the costs associated with its management [15] . Although several factors associated with the development of disabilities are addressed in the literature . However , studying the extent of each factor—alone or associated—empowers managers , health professionals and researchers in the implementation of preventive and curative strategies and would assist the leprosy programmers to prioritise the interventions [16] . The delayed presentation is a recognised risk factor for disability in leprosy and is the result of complex interactions between physical , social , economic and psychological factors [3 , 17] . Assessing the potential factors associated with delays in seeking care and diagnosis of leprosy is essential to identify program impediments and devise appropriate strategies to promote early diagnosis and prevent disability . The present study was conducted to determine the risk factors associated with disability ( G2D and G1D ) among adult new leprosy cases and to measure their strength of association . We hypothesized that adult leprosy cases who have a delay in diagnosis are at a greater risk of developing disability ( G2D and G1D ) . In the present study , we quantified the delay in terms of patient delay and health care provider delay .
Ethical approval was obtained from the Institutional Review Board ( IRB ) of German Leprosy and TB Relief Association ( GLRA ) , India . Informed written consent was obtained from participants . Permission was taken from relevant authorities ( State Leprosy Officers , Directorate of Health Services and District Leprosy Officers ) before conduction of the study . Direct interviews were conducted with the study participants in health facilities by trained research assistants using a structured questionnaire after obtaining their informed consent . To maintain the confidentiality of the study participant’s data , a delinked number was assigned to each study participant . All the filled in written questionnaire was kept in a closed cabinet and will be preserved for a period of five years in GLRA , India office . A case-control study was undertaken during August 2014 to July 2016 ( entire study duration ) in five states in India , Andhra Pradesh , Maharashtra , Gujarat , Delhi and West Bengal ( Fig 1 ) . These five states were chosen based on the feasibility and leprosy burden reported in the preceding years of data collection . The study population were adult leprosy patients ( 18 years and older based on standard definition of adulthood ) at the time of first diagnosis and registered for treatment under the National Leprosy Eradication Programme ( NLEP ) . Cases and control group were identified and selected through the treatment registry , registered after 1st January 2013 ( in order to obtain the required sample size while at the same time attempting to reduce the recall bias ) . Cases were defined as leprosy cases with G2D or G1D , controls were without disability , meaning grade 0 disability ( G0D ) , at the time of first diagnosis [18] . WHO G1D was defined as the patient developing anaesthesia in the palm or sole tested with a ballpoint pen and leprosy eye involvement with preservation of visual acuity better than 6/60 in conformity . G2D was defined as a visible deformity in either the hands , feet or eyes , both according to the WHO disability grading [18] . For each state the sample size required was calculated using Epi Info version 3 . 03 . 17; based on probability of exposure to one of the main risk factors ( poor knowledge/low awareness of disease ) as 20% in the control group and an expected odds ratio of 2 , with power of 80% and an alpha error of 5% for one sided test , the minimum sample size required was 135 cases . Hence , for each state it was decided to select 140 cases and 140 controls . Thus , the total sample size for this study involving five states was 700 cases with disability ( G2D or G1D ) and 700 controls without disability ( G0D ) . In order to select a representative sample of each state , a two-stage cluster sampling procedure was followed . The district was the primary sampling unit , and cases/ controls were the secondary sampling unit . The primary sampling unit ( PSU ) was selected through probability proportion to size ( PPS ) . For each of the selected districts , the secondary sampling unit was a list of G1D and G2D leprosy cases in the order of registration date from the treatment registry . The required number of samples was selected randomly from the list by multistage random sampling . In a situation of not getting a participant ( example: not getting consent for participation or non-availability of the selected participant ) , the next available case or control in the treatment register was enrolled into the study . Controls were selected in the respective blocks ( sub-district level ) of each district where the cases were recruited . Wherever the controls were not available , the next available control based on the leprosy register was approached and recruited . Actual period of data collection was during the period January 2015 and January 2016 using a close-ended structured questionnaire by trained research staffs . The study tool and informed consent form were translated and back-translated into local languages of each of the five states ( Hindi , Marathi , Gujarati , Bengali , and Telugu ) . The questionnaire was pre-tested on 5% of study participants before the actual data collection . The principal investigator supervised every aspect of the data collection to ensure data collected was error-free . The variables collected were educational status , occupation , place of residence , knowledge & awareness of the disease , quantification of patient delay , reasons for the patient delay , first health care provider ( HCP ) met , pathways of health care sought and quantification of HCP delays , etc . Delay in diagnosis was based upon the patient’s recall; as it might be subject to recall bias , the research assistants used months as the measuring unit . Interviewer spent about 40–50 minutes for each interview and recorded the responses ( in written questionnaire ) according to the necessity of each patient . A calendar was offered and provided to the patients , in case this was needed . Also , local or national events or religious festivals were referred for recall . Information on the WHO grading was collected from the existing treatment records and cross-checked with the program staffs whenever it needed clarity . Cases: Cases were adult leprosy patients with G1D or G2D [as categorized by ( WHO] at the time of first diagnosis and who were registered for treatment under the leprosy programme after 1st Jan 2013 ( to reduce recall bias ) . Controls: Controls were adult leprosy patients with grade 0 disability [as categorized by WHO] at the time of first diagnosis and who were registered for treatment under the leprosy programme after 1st Jan 2013 ( to reduce recall bias ) . Data was coded and then entered into EpiData entry software . The de-identified data from all project sites were merged into a centralized EpiData manager database , maintained at the GLRA head office , Chennai . The data were analysed using STATA version 12 . Basic descriptive analyses were done . Mean ( SD ) or median inter-quartile range ( IQR ) was used to describe diagnosis delay . The median difference between cases and controls were analysed using the Mann-Whitney U test . Mann-Whitney U test was used because the delay ( in months ) was not normally distributed . The association between risk factors and disability was inferred using adjusted odds ratios ( adj OR ) and 95% confidence interval ( 95% CI ) . Univariate logistic regression was first fitted and those independent variables which become significant in the univariate regression at p-value of less than 0 . 05 were included in the multivariate logistic regression analysis . Backward stepwise multiple logistic regression was fitted to determine the net effect of each independent variable on late diagnosis of leprosy; using 5% level of significance .
A total of 1 , 400 leprosy patients were enrolled across five states of India in this study , which included 700 cases and 700 controls . In each of the five states , 140 cases ( 70 patients with G2D or 70 patients with G1D ) and 140 controls ( G0D were interviewed . The participant’s socio-demographic characteristics were described in Table 1 . Of the interviewed respondents , 666 ( 95% ) G2D/G1D , and 461 ( 66% ) G0D were of multi-bacillary type leprosy . The first symptom reported was reported as a skin patch with loss of sensation in 58% cases and 88% controls . Numbness in the hand or feet was noticed in 28% G2D/G1D; ulcers/blisters in the hand or feet were reported in 6% G2D/G1D . Symptoms suggestive of claw hand/foot drop/lagophthalmos was reported in 5% G2D/G1D . Non-skin lesions related to leprosy reaction symptoms was reported as first reported symptoms in 12% G2D/G1D and 9% G0D . Though various symptoms were reported by cases and controls , the chief trigger symptom that promoted the health seeking behaviour were skin patch with loss of sensation in 58% G2D/G1D and 88% G0D . Among G2D/G1D , numbness and reaction related symptoms contributed as a trigger symptom in 40% to seek health care from the providers . After noticing the first symptom , 58% G0D first consulted public health facility compared to 42% G2D/G1D . Among the remaining respondents , 42% G2D/G1D and 29% G0D sought care from qualified private practitioners i . e . Modern Medicine ( MBBS & above ) or Indian System of Medicine—Ayurveda Yoga , Unani , Siddha and Homeopathy ( AYUSH ) and 16% G2D/G1D and 13% G0D sought non-qualified practitioners ( Practitioners with no medical degree ) ( Table 2 ) . Data of delay in months was not normally distributed ( using Kolmogorov-Smirnov test ) , hence median duration was used to present the delay instead of mean duration . The median patient delay was 8 months in G1D/G2D and 4 months in G0D; median HCP delay was 2 months in G1D/G2D and 1 month in G0D . The median total delay was 14 months in G1D/G2D and 6 . 2 months in G1D/G2D and about 61% G1D/G2D and 66% G1D/G2D reported nearest public health facility was less than 5 km ( Table 2 ) . The non-linear relationship between patient delay and odds of G2D/G1D among adult leprosy cases at the time of diagnosis in five states of India implies three months patient delay as a significant risk factor for G2D/G1D among adult leprosy patients ( Fig 3 ) . Risk factors for G2D/G1D among adult leprosy cases in 5 states of India , 2014–2016 is shown in Table 3 . Univariate and multivariate logistic regression presented with odds ratio and 95% CI for the variables that predict the delay in leprosy diagnosis among adults with leprosy ( Table 3 ) . Multivariate logistic regression shows that the age of the respondent , leprosy type , occupation as a daily wage labourer , a patient delay of more than three months and HCP delay of more than one month were associated with risk of G2D/G1D at the time of diagnosis . Adult leprosy patients aged more than 60 years were twice more likely to present with G2D/G1D at the time of diagnosis compared with respondents aged less than 30 years ( adj OR = 2 . 2 , 95% CI: 1 . 3–3 . 6 ) and patients with MB type leprosy were nine times more likely to present G2D/G1D at the time of diagnosis compared to PB type leprosy ( adj OR: 9 . 1 , 95% CI: 6 . 2–13 . 3 ) . Respondents who are daily wage labourer/ agriculture workers were 1 . 5 times more likely to present with G2D/G1D at the time of diagnosis compared to salaried ( monthly income generating ) person ( adj OR = 1 . 5 , 95% CI: 1 . 1–2 . 2 ) . The adjusted odds ratio for respondents who preferred public health system against unqualified practitioner had a protective efficacy to present with G0D at the time of diagnosis ( adj OR = 0 . 8 , 95% CI: 0 . 5–1 . 1 ) , but this finding was not statistically significant . When the patient delay was more than three months , the odds of G2D/G1D at the time of diagnosis was significantly higher ( Adj OR = 1 . 6 , 95% CI: 1 . 3–2 . 2 ) among respondents compared to those with patient’s delay of less than three months . This was statistically significant . When the HCP delay was more than one month the odds of G2D/G1D at the time of diagnosis was 1 . 4 times higher compared to those with less than one-month HCP delay ( adj OR = 1 . 4 , 95%CI: 1 . 1–1 . 9 ) and this was not statistically significant ( Table 3 ) . Notably , the majority of cases 554 ( 79% ) and 517 ( 74% ) G0D had not heard/seen/read message related to leprosy/leprosy program before their diagnosis . A total of 652 out of 700 cases reported for not seeking any HCP after the notice of the first symptom . Ninety percent cases reported that they did not know the symptom they experienced was due to Leprosy and felt that the symptom would disappear by itself ( Table 4 ) .
This study was conducted in five states of India selected based on the operational feasibility and leprosy burden during 2012–13 and this could have resulted in selection bias . The study findings might be applicable to southern , eastern , western and northern region of India , however , it should be interpreted with caution while applying the study findings to central and north-eastern part of India due to the non-inclusion of states from these regions . We attempted to adjust for confounding based on leprosy type ( PB/MB ) during analysis but the proportion of PB among the cases was only 5% of the overall sample so we could not do a stratified analysis for PB/MB . Additionally , any possible matching for leprosy type ( PB/ MB ) at subject recruitment phase would not have allowed us to analyse the association of this factor ( PB/MB ) with disability . Further , the study design is a case control design and has an inbuilt reporting bias due to the retrospective nature of data collection but the data collection was carried with as much caution to reduce the recall bias . Further . the study findings did not find a significant difference in the leprosy/program knowledge among G0D ( 26% ) as compared to G2D/G1D ( 21% ) and this is possibly due to small sample size . The matching ratio of cases and controls was 1:1 in this study , but we could not increase the controls due to operational feasibility during the conduct of the study . Our study found that the delay in diagnosis is still a major challenge of leprosy program in India . Both patient delay ( > 3 months ) and healthcare provider delay ( >1month ) have been significant risk factors for disability among adult leprosy cases . In this study , a large proportion of cases with disability ignored the initial symptoms as they thought the symptoms would disappear by itself . To reduce the patient-related delay , the program should lay greater emphasis on raising awareness of the community focusing on key messages like symptoms , the disability consequence of late detection , availability of free treatment , availability of leprosy care in public health facility . Additionally , program should put more emphasis on early case detection campaigns like active survey . On the other hand , efforts should be made to periodically engage and capacitate all HCPs ( all types of private health care providers along with the public health staff ) to have a high index of suspicion of leprosy and facilitate effective referrals . To further understand the specific local factors associated with the patient delay and HCP delay , we suggest that disability audit should be conducted on a routine basis for each leprosy case diagnosed with disability .
|
Leprosy is an infectious disease caused by the Mycobacterium leprae and is one of the major causes of preventable disability . In the recent years there has been an increase in the number of new leprosy patients with disability in India . People affected by leprosy often experience severe stigmatization because of its disabling consequences . This neglected tropical disease continues to pose a major disease burden in India . Despite the availability of health facilities , there continues to be barriers towards leprosy diagnosis and early treatment . Assessment of risk factors for disability is important in the era of leprosy integration with general health services . Risk factors for disability related to the patient and health care provider were identified to help the policy makers to develop appropriate strategies to promote early diagnosis and prevent disability .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"salaries",
"disabilities",
"tropical",
"diseases",
"geographical",
"locations",
"india",
"social",
"sciences",
"health",
"care",
"bacterial",
"diseases",
"health",
"care",
"providers",
"neglected",
"tropical",
"diseases",
"medical",
"risk",
"factors",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"labor",
"economics",
"epidemiology",
"economics",
"people",
"and",
"places",
"diagnostic",
"medicine",
"asia",
"leprosy"
] |
2019
|
Risk of disability among adult leprosy cases and determinants of delay in diagnosis in five states of India: A case-control study
|
Although statin drugs are generally efficacious for lowering plasma LDL-cholesterol levels , there is considerable variability in response . To identify candidate genes that may contribute to this variation , we used an unbiased genome-wide filter approach that was applied to 10 , 149 genes expressed in immortalized lymphoblastoid cell lines ( LCLs ) derived from 480 participants of the Cholesterol and Pharmacogenomics ( CAP ) clinical trial of simvastatin . The criteria for identification of candidates included genes whose statin-induced changes in expression were correlated with change in expression of HMGCR , a key regulator of cellular cholesterol metabolism and the target of statin inhibition . This analysis yielded 45 genes , from which RHOA was selected for follow-up because it has been found to participate in mediating the pleiotropic but not the lipid-lowering effects of statin treatment . RHOA knock-down in hepatoma cell lines reduced HMGCR , LDLR , and SREBF2 mRNA expression and increased intracellular cholesterol ester content as well as apolipoprotein B ( APOB ) concentrations in the conditioned media . Furthermore , inter-individual variation in statin-induced RHOA mRNA expression measured in vitro in CAP LCLs was correlated with the changes in plasma total cholesterol , LDL-cholesterol , and APOB induced by simvastatin treatment ( 40 mg/d for 6 wk ) of the individuals from whom these cell lines were derived . Moreover , the minor allele of rs11716445 , a SNP located in a novel cryptic RHOA exon , dramatically increased inclusion of the exon in RHOA transcripts during splicing and was associated with a smaller LDL-cholesterol reduction in response to statin treatment in 1 , 886 participants from the CAP and Pravastatin Inflamation and CRP Evaluation ( PRINCE; pravastatin 40 mg/d ) statin clinical trials . Thus , an unbiased filter approach based on transcriptome-wide profiling identified RHOA as a gene contributing to variation in LDL-cholesterol response to statin , illustrating the power of this approach for identifying candidate genes involved in drug response phenotypes .
Genome-wide association studies ( GWAS ) have been used to identify genetic contributors to a number of common diseases and traits [1] . However , a major problem with this approach is that very large sample sizes are generally required to detect statistically significant associations [2] . This is especially the case for pharmacogenomics , where identification of gene variants associated with drug response may require larger sample sizes than are generally available . Consequently , GWAS has had limited success in the identification of pharmacogenetically relevant single nucleotide polymorphisms ( SNPs ) that survive the stringency of genome-wide multiple testing [3] , [4] . In the largest single statin clinical trial GWAS published to date ( the JUPITER trial of ∼7000 individuals ) only three loci ( ABCG2 , APOE and LPA ) achieved genome-wide significance for association with the magnitude of LDL cholesterol reduction , and in total accounted for only a minor fraction of the overall variation in response [5] . Moreover , GWAS studies are limited by their ability to probe only common genetic variation and thus the limited findings suggest that association studies alone are unlikely to yield the basis for all or even the majority of the genetic variance associated with drug response . In the present report , we describe the use of transcriptome-wide profiling to identify and prioritize genes that may contribute to inter-individual variation in statin-induced plasma LDL-cholesterol lowering . Statins inhibit HMG-CoA reductase ( HMGCR ) , the enzyme that catalyzes the rate limiting step of cholesterol biosynthesis , thus lowering intracellular cholesterol levels [6] . This in turn elicits an increase in expression of cellular LDL receptors that mediate plasma LDL clearance [7] . Since the HMGCR gene is transcriptionally regulated by intracellular sterol content [8] , the magnitude of induction of this gene is a cellular marker of in vitro statin response . We used expression array data from in vitro statin-exposed immortalized human hepatoma cell lines and lymphoblastoid cell lines established from participants of the Cholesterol and Pharmacogenetics ( CAP ) clinical trial of simvastatin treatment [9] to establish a set of “biological rules” for identifying genes whose expression characteristics qualified them as having biologically plausible effects on cholesterol metabolism . RHOA emerged from this analysis , and subsequent functional and genetic studies , as a novel candidate gene contributing to variation in LDL response to statins .
We used a series of filters applied to genome-wide gene expression data from 480 human lymphoblastoid cell lines ( LCLs ) derived from participants in the Cholesterol and Pharmacogenetics study to identify genes that appeared to be biologically plausible candidates for modulating the effects of statins on cholesterol metabolism . The following filter criteria were used ( Table 1 ) : 1 ) expression in normal human liver; 2 ) change in transcript levels in HepG2 ( n = 4 ) and Hep3B ( n = 3 ) human hepatoma cell lines incubated with 2 . 0 µM activated simvastatin versus sham buffer for 24 hr , FDR<0 . 01 , 3 ) change in transcript levels in CAP LCLs incubated with 2 . 0 µM activated simvastatin versus sham buffer for 24 hr ( Q<0 . 05 ) ; 4 ) consistent directionality of statin-induced change in transcripts in hepatoma cell lines and LCLs; 5 ) correlation of statin-induced gene expression change in CAP LCLs with change in expression of HMGCR . After Bonferroni correction for multiple testing ( p<1 . 17e-04 ) we identified 45 genes which passed all filter criteria ( Table 2 ) . When ranked in order of correlation , only two of the top thirteen genes did not encode enzymes in the cholesterol biosynthesis pathway: transmembrane protein 97 ( TMEM97 ) and ras homolog gene family member A ( RHOA ) . Although both had been previously implicated in lipid metabolism [10] , [11] , [12] , neither had been shown to play a role in the cholesterol lowering effects of statin . However , RHOA was particularly intriguing since inhibition of RHOA signaling is thought to be a major mechanism by which statins exert pleiotropic ( or non-lipid lowering ) actions , such as anti-inflammatory effects . Figure 1A demonstrates the strong correlation between statin-induced change in RHOA and HMGCR transcript levels ( p = 7 . 64E-16 , r2 = 0 . 13 ) . To determine if RHOA has a direct effect on markers of intracellular cholesterol homeostasis , we transfected HepG2 cells ( n = 10 ) with siRNAs specific for RHOA or a non-targeting negative control and tested for changes in expression of HMGCR , low-density lipoprotein receptor ( LDLR ) and sterol response element binding transcription factor ( SREBF2 aka SREBP2 ) gene expression . Knock-down reduced RHOA transcript levels by 60-98% ( Figure 1B ) , with no remaining detectable RHOA protein ( Figure 1C ) , and also generated statistically significant reductions in expression of HMGCR ( 0 . 76±0 . 04 fold , p = 0 . 002 ) , SREBF2 ( 0 . 58±0 . 03 fold , p = 0 . 0003 ) , and LDLR ( 0 . 73±0 . 13 fold , p = 0 . 03 ) Figure 1D . RHOA knockdown-mediated reductions in expression of HMGCR , LDLR , and SREBF2 were confirmed in a second hepatoma cell line , Huh7 ( n = 6 ) ; however , the magnitude of the effect was less dramatic than that observed in the HepG2 transfections . To further test the functional role of RHOA , we also measured levels of secreted APOB and APOA1 , the major proteins on LDL and HDL particles respectively , in the culture media 48 hours after knock-down . APOB accumulation in the cell culture media was increased in HepG2 cells after RHOA knock-down ( 1 . 28±0 . 08 fold , p = 0 . 03 , n = 12 ) , while a similar but non-statistically significant trend was observed in Huh7 cells ( 1 . 08±0 . 06 fold , p = 0 . 10 , n = 8 ) , ( Figure 1D ) . No significant changes in secreted levels of APOA1 were observed in either hepatoma cell line . Reduced HMGCR , LDLR , and SREBF2 transcript levels together with increased APOB secretion with RHOA knock-down are all consistent with higher intracellular cholesterol levels , which was documented in the case of cholesterol esters ( 1 . 56±0 . 18 fold vs . controls , p = 0 . 004 , n = 16 ) , ( Figure 1E ) . Although we also detected a trend for elevated free cholesterol after knock-down , this was not statistically significant . A trend of increased intracellular cholesterol ester and free cholesterol was also observed in Huh7 cells after RHOA knock-down ( 1 . 15±0 . 14 fold , p = 0 . 05 and 1 . 06±0 . 15 fold , p = 0 . 27 , n = 8 ) . Lastly , since many genes involved in the maintenance of intracellular cholesterol are transcriptionally regulated in response to changes in intracellular sterol content through SREBF2 , a transcription factor , we sought to test if RHOA was also subject to SREBF2 regulation . Sterol depletion activates SREBF2 , thus stimulating expression of SREBF2 target genes . We confirmed that RHOA mRNA and protein levels were substantially increased by extreme sterol depletion in HepG2 cells with 2 µM simvastatin +10% lipoprotein deficient serum for 24 hr ( Figure 1F and 1G ) . Induction of HMGCR mRNA and protein levels served as a positive control for the effects of cholesterol depletion . Finally , we found small but statistically significant reductions in RHOA transcript levels after SREBF1 knock-down in HepG2 ( 0 . 83±0 . 07 fold , p = 0 . 05 ) and Huh7 ( 0 . 86±0 . 02 fold , p = 0 . 001 ) cell lines ( Figure 1H ) . Although statin-induced changes of RHOA and LDLR mRNA were positively correlated in the LCL panel ( Figure 2A ) , change of the RHOA transcript was inversely correlated with level of LDLR cell surface protein ( Figure 2B ) . Consistent with this relationship , we also identified an inverse correlation of RHOA transcript levels in statin- treated CAP LCLs with absolute changes in plasma total cholesterol ( p = 0 . 02 , r2 = 0 . 01 ) , LDL cholesterol ( p = 0 . 04 , r2 = 0 . 01 ) and APOB ( p = 0 . 007 , r2 = 0 . 01 ) , measured in vivo before and after simvastatin treatment of the individuals from whom these cell lines were derived ( Table S1 ) . In contrast , levels of RHOA in sham-treated LCLs were not significantly correlated with these measures at baseline ( Table S1 ) . Moreover , RHOA transcript levels in statin-treated LCLs were not significantly associated with statin-induced changes in plasma HDL cholesterol levels ( data not shown ) . We next investigated the association of common genetic variation near RHOA with in vivo statin response . Analysis of HapMap3 CEU data [13] with Haploview [14] , revealed that RHOA fell within a large block of linkage disequilibrium spanning almost 500 kb and that there were four major haplotypes when considering markers within 10 kb of the gene , all with frequencies greater than 10% in the CEU population ( Figure S1; Table 3 ) . Haplotypes were inferred based on directly genotyped SNPs ( Table S2 ) or imputed genotypes ( rs11716445 for H3B ) , and the number of copies of each haplotype were tested for association with change in LDL-cholesterol ( delta log ) in response to statin treatment of Caucasian participants in CAP ( n = 580 ) and in the Pravastatin Inflammation CRP Evaluation ( PRINCE: pravastatin 40 mg/day , 24 weeks , n = 1306 ) clinical trial , with adjustment for sex , age , BMI , smoking status , and study population . Of the four haplotypes , H3B showed the strongest association with statin response ( p = 0 . 01 ) , with homozygous H3B carriers having a 29% smaller reduction in the unadjusted percent change of LDL-cholesterol compared to non-carrier controls ( −21 . 8±4 . 5% versus −30 . 7±0 . 4% , Figure 3 ) . When the CAP and PRINCE cohorts were analyzed independently , the directionality of this association was consistent between the two populations ( Figure S2 ) . Haplotype H2 also demonstrated a modest association , with carriers having greater statin-induced changes in LDL-C ( p = 0 . 04 , n = 1886 , Figure 3 ) . There were no significant associations of H3B or H2 carrier status with baseline LDL-cholesterol ( p = 0 . 3 for both ) . We found no association of either H3B or H2 with RHOA transcript levels in CAP LCLs after treatment with 2 µM statin or sham buffer ( n = 115 ) ( Figure S3A ) . However , rs11716445 , the SNP that defines the H3B haplotype , is located in a rare 45 bp cryptic exon ( referred to as RHOA exon 2 . 5 ) that we identified in multiple unique sequences during RNA-Seq analysis of three human hepatoma cell lines ( HepG2 , Hep3B and Huh7 ) , and CAP LCLs ( n = 3 ) , Figure 4A . Expression of the RHOA 2 . 5 exon was validated by Sanger sequencing . Notably , we found that the H3B haplotype showed a very strong association with RHOA exon 2 . 5 levels under both sham ( p = 2 . 7×10−7 , n = 119 ) and statin ( p = 9 . 1×10−13 , n = 115 ) conditions , with carriers exhibiting the highest levels of exon 2 . 5 inclusion ( Figure 4B and Figure S3B ) . The H2 haplotype also exhibited a more modest association with RHOA exon 2 . 5 levels in the opposite direction from H3B ( p<0 . 01 ) , consistent with their in vivo relationships . Using Sanger sequencing , we found evidence of allele-specific expression ( ASE ) at rs11716445 with over 90% of the exon 2 . 5-containing transcripts originating from the H3B chromosome ( Figure 4C and 4D ) . We also observed evidence of ASE at rs2878298 , another SNP found within exon 2 . 5 ( Figure 4D and Figure S3C ) . There was no significant difference in the relative amount of ASE between the statin- and sham-treated states . Finally , to determine if rs11716445 was a general expression quantitative trait locus ( eQTL ) or a splicing QTL , we tested for ASE at rs3448 , a SNP in the 3′ UTR of RHOA , in eight heterozygous carriers ( or H2/H3B ) and found no evidence that the ASE extended beyond exon 2 . 5 ( Figure 4D and Figure S3C ) . These results strongly suggest that rs11716445 is a cis-acting splicing QTL .
We here present results of applying a set of biologically meaningful filters to identify and rank candidate genes associated with inter-individual variation in statin effects on cholesterol metabolism based on their gene expression characteristics . Using unbiased genome-wide screens , we identified genes that were normally expressed in the human liver and changed in response to statin treatment in a manner that was correlated with statin-induced change in HMGCR quantified as an in vitro marker of statin response . From these analyses we identified a number of genes not previously implicated in the lipid-lowering response to statin as potential candidates for future study . We selected RHOA since many of the non-lipid lowering benefits , or pleiotropic effects , of statin treatment have been attributed to its ability to inhibit RHOA activity . Our validation of RHOA as a modulator of cellular cholesterol metabolism , as well as the discovery that genetic variation within RHOA is associated with the magnitude of LDL-cholesterol response to statin treatment , support the continued studies of other novel candidate genes identified through this integrative genomics strategy . RHOA has been previously implicated in cholesterol metabolism through the modulation of ABCA1-mediated cholesterol efflux via two distinct and opposing mechanisms . RHOA inhibition stimulates ABCA1 gene expression via PPARγ and LXR activation [15] , while RHOA activation increases ABCA1 protein stability [11] . Although excess intracellular levels of free cholesterol have been shown to increase RHOA activity [16] , here we demonstrate that RHOA knock-down results in increased levels of secreted APOB , suggesting that RHOA may influence the pool of intracellular cholesterol available for lipoprotein production . Consistent with this hypothesis , we found that knock-down of RHOA in hepatoma cell lines resulted in increased intracellular content of cholesterol esters , the storage form of cellular cholesterol that can be mobilized for lipoprotein secretion . This occurred in conjunction with reduced expression of HMGCR and LDLR , presumably due to cholesterol-induced down-regulation of SREBF2 . Very recently a novel protein , LAMTOR1 ( also known as Pdro/p27RF-Rho ) , was found to both activate RHOA [17] , and to regulate LDL-C uptake and intracellular cholesterol egress from the late endosome/lysosome [10] , further supporting a link between RHOA and cholesterol metabolism . Additional evidence for such a link is provided by the strong correlations that we observed between statin-induced changes in RHOA mRNA levels and both HMGCR and LDLR transcripts . On the other hand , there was an inverse correlation between change in RHOA mRNA and cell surface LDLR protein . While this may appear to be at odds with the change in LDLR transcript level , it is consistent with our finding that greater statin-induced RHOA gene expression was associated with reduced in vivo response lipid response to statin treatment . It is possible that increased RHOA expression directly or indirectly reduces functional LDLR at the cell surface by altering post-translational processing or cellular trafficking , hypotheses that will be tested in future studies . Increased magnitude of this effect may contribute to attenuation of statin-induced plasma LDL lowering . Based on its role in mediating the pleiotropic effects of statin response , RHOA has been proposed as a candidate gene for the study of statin pharmacogenetics; however , genetic variation within RHOA associated with statin response has not been previously identified [18] . Here , we report that a common RHOA haplotype , H3B , is associated with reduced LDL-cholesterol lowering in response to statin treatment in data derived from two independent clinical trials . Within RHOA , this haplotype was defined by a single SNP , rs11716445; however , since rs11716445 is in strong linkage disequilibrium with many SNPs in other genes up to 500 kb away from RHOA , it is possible that its association with statin response may also be due to genetic variation affecting other genes . rs11716445 explained less than 1% of the overall variation in LDL cholesterol response to statin , so neither the H3B haplotype or rs11716445 genotype alone would be a clinically useful diagnostic , but it could be included with other known markers of statin response to improve prediction algorithms . Here we demonstrate that rs11716445 is a cis-acting splicing QTL also associated with allele-specific expression of RHOA exon 2 . 5 , a rare exon found within RHOA intron 2 . The presence of this exon does not disrupt the open reading frame and is predicted to cause a 14 amino acid inclusion in the B3 domain of the RHOA protein , a region with no known interactions [19] , [20] . Although the functional impact of exon 2 . 5 inclusion is unknown , the fact that the two RHOA haplotypes associated with its expression levels , H3B and H2 , are also the only two RHOA haplotypes found to be associated with in vivo variation in statin-induced change in LDL-cholesterol , strongly supports the likelihood that RHOA alternative splicing is functionally relevant . In silico analysis with ESEfinder 3 . 0 identified SRSF2 ( aka SC35 ) , SRSF5 ( aka SRp40 ) , and SRSF1 ( aka SF2/ASF ) binding sites within 20 bp of the exon 2 . 5 splice donor [21] . Notably , the rs11716445 “T” allele is predicted to disrupt an SRSF5 binding motif ( TAGA[T/C]C ) ( Figure S4 ) . This finding is consistent with previous reports demonstrating that SRSF5 and SRSF2 antagonize SRSF1 to promote exon exclusion , as the loss of the SRSF5 binding with the “T” allele would be predicted to result in exon 2 . 5 inclusion [22] . Thus , these results strongly suggest that the rs11716445 “T” ( minor ) allele enhances expression of the RHOA 2 . 5 exon . We also found that the proportion of the expressed RHOA 2 . 5 exon containing the “T” allele was reduced in H1/H3B compared to H2/H3B and H3A/H3B heterozygotes ( Figure S3C ) . Since the H1 haplotype contains the minor allele of the second common SNP within the 2 . 5 exon , rs2878298 , which is predicted to generate a SRSF1 binding site , these findings suggest that there are multiple gene variants that regulate expression of this novel exon; however the functional effects of these SNPs ( rs11716445 and rs2878298 ) as well as the expression of the cryptic RHOA exon remain to be tested . In summary , we here report using a combination of expression array data , functional studies , and genetic analyses that RHOA is a novel candidate gene associated with variation in both in vitro and in vivo response to statin . Although additional studies of statin effects will be required to corroborate these findings , they demonstrate the value of using data from a variety of molecular techniques , including the combination of in vivo and in vitro genetically-regulated phenotypes , as a novel approach for identifying genes involved in drug response .
Lymphoblastoid cell lines ( LCLs ) from 480 Caucasian participants from the Cholesterol and Pharmacogeneomics ( CAP ) clinical trial [9] and HepG2 and Hep3B cell lines were grown under standard conditions and exposed to 2 µM simvastatin or sham buffer for 24 hours as previously described [23] . Although much higher than normal circulating levels of plasma simvastatin , 2–40 nM [24] , this concentration of simvastatin was selected based on previous dose response experiments that were used to determine the amount that elicits a consistent and significant induction of both HMGCR and LDLR mRNA ( Figure S5 ) [23] . Simvastatin was provided by Merck Inc . ( Whitehouse Station , NJ ) and activated to the β-hydroxyacid prior to use [25] . Cell surface LDLR protein was measured in statin and sham treated CAP LCLs as previously described [26] . To confirm statin regulation , HepG2 cells were grown in 6-well plates and incubated with 2 µM activated simvastatin +10% lipoprotein deficient serum ( Hyclone ) for 24 hours . Genome-wide gene expression was measured in RNA from CAP samples and statin and sham treated HepG2 and Hep3B cells . RNA was converted to biotin-labeled cRNA using the Illumina TotalPrep-96 RNA amplification kit ( Applied Biosystems , Foster City , CA ) . cRNA was hybridized to Illumina HumanRef8v3 expression beadchips ( Illumina , San Diego , CA ) . Data were analyzed using GenomeStudio ( Illumina ) . All beadchips had a signal P95/P05>10 . Significance analysis of microarrays ( SAM ) [27] was performed on the 10 , 291 of 18 , 630 probed genes that were expressed in LCLs ( FDR<0 . 05 ) . Expression traits were adjusted for known covariates ( age , gender , exposure batch , cell growth rate as determined by cell count on exposure day , and RNA labeling batch ) and also for unknown sources of variation through adjustment for those principal components that described greater than 5% variance across the dataset [28] . Adjusted data were quantile normalized across each gene to ensure normality . Gene expression in human liver was determined using mean detection p-value as determined by GenomeStudio ( Illumina , San Diego , CA ) from expression profiles measured by Illumina Ref8v2 beadarray on 120 human liver samples ( 2 technical replicates each of 60 samples , GEO accession number: GSE28893 [29] ) . Mean detection p-values across all 120 samples was assessed , and genes with a p<0 . 05 were called expressed . HMGCR , LDLR , SREBF1 , SREBF2 , RHOA ( total ) , and RHOAexon2 . 5 transcript levels were quantified by qPCR with gene expression normalized to CLPTM ( TaqMan Assay number: Hs00171300_m1 , Life Technologies ) as previously described [30] . Primers used for qPCR of total RHOA were F: CGGAATGATGAGCACACAAG and R: TGCCTTCTTCAGGTTTCACC and those used for qPCR of RHOA exon 2 . 5 were F: TATCGAGGTGGATGGAAAGC and R: GCCAACTCTACCATAGTACATTGAAA . RHOA , SREBF1 and SREBF2 knock-down was achieved by 48 hour transfection of 80 , 000 HepG2 or Huh7 cells/well in 12-well plates using either the Ambion Silence Select siRNA ( s759 ) or non-targeting control according to the manufacturer's protocol . Cell culture media was collected from all samples at time of harvest , and APOB and APOAI were quantified in triplicate by sandwich-style ELISA . Samples with a coefficient of variation greater than 15% were subject to repeat measurement . Cholesterol was extracted from the cell pellets with hexane-isopropanol ( 3∶2 , v/v ) and dried under nitrogen . The extracted cholesterol was reconstituted with reaction buffer ( 0 . 5 M potassium phosphate , pH 7 . 4 , 0 . 25 M NaCl , 25 mM cholic acid , 0 . 5% Triton X-100 ) . Total cholesterol content was determined with the Amplex Red Cholesterol Assay Kit ( Invitrogen ) and normalized to total cellular protein quantified by the Pierce BCA Protein Assay Kit ( Thermo Scientific ) . To quantify RHOA protein levels , cells were lysed in Cell Lytic lysis buffer ( Sigma ) , loaded on a 4–12% Tris-Glycine Gel ( Invitrogen ) , and proteins were transferred onto a PDVF membrane using the iBLOT gel transfer system ( Invitrogen ) . The blot was then probed with antibodies diluted 1∶200 to RHOA ( SC26C4 ) , HMGCR ( SCH300 ) and β-actin ( SC ACTBD11B7 ) , all purchased from Santa Cruz Biotechnology . Band densities were analyzed using the Mulitplex Band Analysis tool in Alphaview SA version 3 . 4 . 0 . Haplotypes H1 , H2 , and H3A were assigned using genotype data from tag SNPs ( Table S2 ) , while haplotype H3B was inferred using imputed rs11716445 genotypes . Imputation was performed in BIMBAM using 317K or 610K genotypes in a similar manner as previously described [31] except for use of the HapMap3 and 1KGP CEU pilot data as a reference population . LDL-cholesterol was quantified in self-reported Caucasian American participants of the Cholesterol and Pharmacogenetics ( CAP ) clinical trial twice at baseline and after both 4 weeks and 6 weeks of simvastatin 40 mg/day and in the participants of the Pravastatin Inflammation and CRP Evaluation ( PRINCE ) clinical trial after 12 and 24 weeks of pravastatin 20 mg/day as previously described [9] , [32] . Delta log LDL-cholesterol was calculated as the log average value of LDL-cholesterol on treatment minus the log average of the two baseline measurements , and percent change was the average on-statin value minus the average baseline value over the average baseline value . The CAP trial is registered at ClinicalTrials . gov ( NCT00451828 ) . Informed consent was obtained and approved by the institutional review boards of the sites of recruitment , University of California Los Angeles and San Francisco General Hospital . In addition , all research involving human participants was approved by the Children's Hospital Oakland Research Institute IRB . All haplotypes with a minor allele frequency greater than 5% were identified using Haploview [14] with HapMap3 CEU data . Using an additive genetic model , haplotypes were tested for association with change in delta log LDL-cholesterol using combined results of both clinical trials with adjustment for age , sex , BMI , smoking status , and study population as well as for each trial separately with adjustment for age , sex , BMI , and smoking status . Hep3B , HepG2 , and Huh7 cells were incubated in duplicate under either standard growth conditions ( MEM supplemented with 10% FBS , 1% nonessential amino acids and 1% sodium pyruvate ) or sterol depleted conditions ( MEM supplemented with 1% nonessential amino acids , 1% sodium pyruvate , 2 . 0 µM simvastatin and 10% lipoprotein deficient serum ) for 24 hours . RNA was extracted as previously described and samples from the duplicate experiments were pooled . Sequencing libraries were prepared by isolating mRNA from 7–10 µg total RNA using two rounds of the MicroPoly ( A ) Purist kit ( Ambion ) , fragmenting the mRNA for 20 seconds , synthesizing cDNA using random primers , repairing ends , dA-tailing , ligating adapters , gel purifying fragments , amplifying libraries using indexed primers for 15 PCR cycles , and performing another round of gel purification . Libraries were sequenced to an average depth of 60 million 100 bp reads ( 30 million paired-end fragments ) . Expression of the novel RHOA exon was verified in independent samples through RT-PCR and Sanger sequencing . DNA and RNA was isolated from CAP LCLs after 24 hours of exposure to sham buffer or 2 µM simvastatin . The DNA sequences of exon 2 . 5 and exon 5 were amplified using F: CAAGGCAGGAGAATGGTGTG and R: CCACTGACGATGATTGCTTC and F: GGCCATATTACCCCTTTTCG and R: CCAGAGGGATCTAGGCTTCC , respectively . RT-PCR was performed to amplify the transcript sequences of exon 2 . 5 and exon 5 ( 3′UTR ) using F: TCGTTAGTCCACGGTCTGGT and R: GCCAACTCTACCATAGTACATTGAAA and F: CGGAATGATGAGCACACAAG and R: TTGGAAAAATTAACTGGTACAGAAA , respectively . PCR products were then subject to Sanger sequencing .
|
Statins , or HMG CoA reductase inhibitors , are widely used to lower plasma LDL-cholesterol levels as a means of reducing risk for cardiovascular disease . We performed an unbiased genome-wide survey to identify novel candidate genes that may be involved in statin response using genome-wide mRNA expression analysis in a sequential filtering strategy to identify those most likely to be relevant to cholesterol metabolism based on their gene expression characteristics . Among these , RHOA was selected for further functional study . A role for this gene in the maintenance of intracellular cholesterol homeostasis was confirmed by knock-down in hepatoma cell lines . In addition , statin-induced RHOA transcript levels measured in a panel of lymphoblastoid cell lines was correlated with statin-induced change in plasma LDL-cholesterol measured in individuals from whom the cell lines were derived . Lastly , a cis-acting splicing QTL associated with expression of a rare cryptic RHOA exon was also associated with statin-induced changes in plasma LDLC levels . This result exemplifies the power of applying biological information of well understood molecular pathways with genome-wide expression data for the identification of candidate genes that influence drug response .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"rna",
"interference",
"gene",
"regulation",
"dna",
"transcription",
"gene",
"function",
"molecular",
"genetics",
"personalized",
"medicine",
"gene",
"expression",
"gene",
"splicing",
"biology",
"molecular",
"biology",
"genotypes",
"phenotypes",
"heredity",
"gene",
"identification",
"and",
"analysis",
"genetics",
"human",
"genetics",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics",
"complex",
"traits"
] |
2012
|
RHOA Is a Modulator of the Cholesterol-Lowering Effects of Statin
|
Multistep cell fate transitions with stepwise changes of transcriptional profiles are common to many developmental , regenerative and pathological processes . The multiple intermediate cell lineage states can serve as differentiation checkpoints or branching points for channeling cells to more than one lineages . However , mechanisms underlying these transitions remain elusive . Here , we explored gene regulatory circuits that can generate multiple intermediate cellular states with stepwise modulations of transcription factors . With unbiased searching in the network topology space , we found a motif family containing a large set of networks can give rise to four attractors with the stepwise regulations of transcription factors , which limit the reversibility of three consecutive steps of the lineage transition . We found that there is an enrichment of these motifs in a transcriptional network controlling the early T cell development , and a mathematical model based on this network recapitulates multistep transitions in the early T cell lineage commitment . By calculating the energy landscape and minimum action paths for the T cell model , we quantified the stochastic dynamics of the critical factors in response to the differentiation signal with fluctuations . These results are in good agreement with experimental observations and they suggest the stable characteristics of the intermediate states in the T cell differentiation . These dynamical features may help to direct the cells to correct lineages during development . Our findings provide general design principles for multistep cell linage transitions and new insights into the early T cell development . The network motifs containing a large family of topologies can be useful for analyzing diverse biological systems with multistep transitions .
Cell fate transition , including differentiation , de-differentiation and trans-differentiation , is a fundamental biological process in which the function of a cell gets specialized , reprogrammed or altered . The process often involves significant changes of multiple cellular properties , including the morphology , the self-renewal capacity and the potentials to commit to alternative lineages [1 , 2] . These changes are controlled by the dynamics of interacting transcription factors ( TFs ) and the modulation of chromatin structures , which in term are governed by complex regulatory networks in the cells [3–5] . Interestingly , the fate transitions in many systems are achieved by sequential commitments to a series of cellular states with stepwise changes in their transcriptional profile towards the final stage of the program ( Fig 1 ) [6–11] . The intermediate states between the initial state ( e . g . the undifferentiated state in the case of cell differentiation ) and the final state may be important for multiple purposes , such as facilitating ‘checkpoints’ that ensure appropriate development of cellular behaviors , or allowing the cells to make correct decisions at the lineage branching points [11–15] . One example of these stepwise cell lineage transitions is the development of T lymphocytes in the thymus . The differentiation from multipotent pre-thymic progenitor cells to committed T cells involves multiple cellular states with stepwise changes of their cellular properties and the transcriptional profiles ( Table 1 ) [16–19] . Several lines of evidence suggest that the transition states at an early phase of the differentiation can serve as stable checkpoints for sequential lineage commitments . The progress through these intermediate states is accompanied by stepwise loss of their potentials to differentiate into other cell types: pre-thymic progenitor cells can be converted to a few types of cells , including B cells , natural killer ( NK ) cells , dendritic cells ( DCs ) etc . , whereas the multipotency of the intermediate cell types is more limited but not completely lost [20–26] . In addition , the stability of these intermediate states is substantial because the loss of differentiation signals does not result in de-differentiation of some intermediate states [20] , suggesting restricted reversibility ( or complete irreversibility ) of the multiple transitions . In addition , the lymphoid progenitor cells need to divide for a certain number of times at an intermediate state before committing to the T cell lineage , and the stable activities of the lineage defining transcriptional program at the intermediate stages may be important for the proliferations [27] . Finally , the loss of certain transcription factors ( e . g . BCL11B ) can lead to the termination of the differentiation at some intermediate states , which is often associated with diseases such as leukemia [18 , 20 , 28] . This further suggests that the intermediate states are cellular ‘attractors’ between the initial and the final stages of the differentiation ( Fig 1 , bottom panel ) . Similar stable intermediate states during cell lineage transitions are observed in other systems , such as the epithelial-mesenchymal transition , and the skin development ( Table 1 ) , and those states also serve as regulatory stages for altering cellular properties including self-renewal and migration [10 , 29–37] . Therefore , the multiple intermediate states are involved in diverse normal development and pathological conditions . Understanding the regulatory programs for the sequential cell lineage commitments is a key step towards the elucidation of mechanisms underlying various biological processes involving multistep lineage transitions . Despite the accumulating data and observations on these stepwise lineage commitments , general mechanisms governing these differentiation processes with multiple intermediate cellular states remain unclear . In this study , we explored the strategies in terms of the transcriptional network design that gives rise to stepwise transitions during cell differentiation . We first used a generic form of networks containing three interacting TFs to find network motifs that can produce four attractors ( the minimum number of attractors in the examples of T cell development , epithelial-mesenchymal transition and skin development ) with stepwise changes of transcriptional factor levels . We found two types of network motifs , both involving interconnections of positive feedback loops , which can generate the four-attractor systems . These motifs constitute a large family of gene regulatory networks . We found that there is an enrichment of these motifs in a network controlling the early T cell development . We built a specific model using known interactions among key transcription factors in developing T cells , and the model shows that the transcriptional network governs multistep and irreversible transitions in the development process . To investigate the stochastic dynamics for early T cell development , we mapped out the quasi-energy landscape for the early T cell development . This landscape characterizes the four attractors representing four stages of early T cell development quantitatively . In addition , by calculating the minimum action paths ( MAPs ) between different attractors , we quantified the dynamics of the key factors in response to Notch signal with fluctuations , which are in good agreement with experimental observations . Finally , we identified the critical factors influencing T cell development by global sensitivity analysis based on the landscape topography . Overall , our model for early T cell development elucidates the mechanisms underlying the stepwise loss of multipotency and multiple stable checkpoints at various stages of differentiation . The network topologies for multiple attractors found in this study and our motif discovery strategy combined with the landscape methodology can be useful for analyzing a wide range of cell differentiation systems with multiple intermediate states .
To find transcriptional network topologies that can generate multiple intermediate states during cell fate transition , we first performed random parameter sampling with a network family containing up to 3 nodes ( Fig 2A ) . In this framework of network topology , each node represents a transcription factor ( TF ) that can potentially influence the transcription levels of other two TFs and itself . Topology searching with a 3-node network was used for motif discovery for various performance objectives in previous studies [38 , 39] . We performed exhaustive search for topologies with up to 6 regulations from a total of 9 regulations of the network family , and constructed a mathematical model for each topology ( see Methods for details ) . For each model , we performed random sampling in the parameter space from uniformly distributed values ( S1 Table ) . We selected topologies containing at least one parameter set that is able to generate four attractors with stepwise changes of transcriptional levels . We define the system with four attractors with the stepwise changes of transcriptional levels as the scenario in which there are four stable steady states and they can be consistently ordered by the concentrations of any pairs of TFs . In other words , one TF always monotonically increases or decreases with another TF in these four states , and we term these states ‘ordered’ attractors in this paper . Among the 2114 network topologies that we searched , we found 216 topologies that can produce such behavior . In addition , we found 417 topologies that can only produce four unordered steady states ( TF concentrations are non-monotonically correlated among the states ) ( S11 Fig , S12 Fig ) . To visualize the relationships among these topologies , we constructed a complexity atlas ( Fig 2B ) , in which the nodes represent the network structures that gave rise to four attractors , and the edges connect pairs of topologies that differ by a single regulation ( addition or removal of a transcriptional interaction ) [40] . We define the minimum topologies as those of which the reduction of complexity , or the removal of any regulation from the network , will abolish its capability to generate four attractors ( solid nodes in Fig 2B and examples in Fig 2C ) . We found 29 such minimum topologies which represent the non-redundant structures for producing the four-attractor system . Interestingly , all of the 216 topologies obtained from our search contain three distinct positive feedback loops ( including double-negative feedback loops ) , and they can be categorized into two types of motifs ( Fig 2B , bottom panel ) . The Type I motif contains three positive feedback loops that are closed at a single TF ( red nodes and edges in Fig 2B ) . The Type II motif contains three connected positive feedback loops , two of which do not share any TF but are connected via the third loop ( blue nodes and edges in Fig 2B ) . There is a remarkable diversity of each of the motif types because the interconnected positive feedback loops can share multiple TFs ( S1 and S2 Figs ) . Based on the complexity atlas ( Fig 2B ) , we found that Type II motifs contain 4–6 regulations , and Type I motifs contain 5–6 regulations . Some of the networks with 6 regulations contain subnetworks of both Type I and Type II motifs ( Hybrid type , green nodes ) . The four attractors in the space of two TFs exhibit a variety of patterns of nonlinear monotonic correlations ( Fig 2C , S3 Fig ) , which are governed by intersections of highly nonlinear nullclines in the state space containing the two TFs ( Fig 2D , S1 and S2 Figs ) . The definitions of various types of motifs are listed in Table 2 , and the statistics of the topologies discovered are summarized in Table 3 ( also see S11 Fig for an illustration ) . Overall , this motif family represents a large number of networks that can produce a common type of behaviors: multiple stable intermediate states in terms the transcriptional activity . We next asked whether there is a difference between Type I and Type II motifs in terms of their ability to generate systems with four ordered attractors . We found that with the same number of sampled parameter sets , Type II motifs have greater fractions of parameter sets that give rise to four ordered attractors than Type I motifs do ( Fig 3A and 3B , p-value < 0 . 0001 , Mann-Whitney U test ) . This suggests that Type II motifs may be more robust for governing the four ordered attractors . However , among the 15 Type I and 14 Type II minimum motifs , all Type II motifs are able to generate four unordered attractors ( Some TFs are not monotonically correlated . See S12 Fig ) , whereas there is no Type I motif that has any parameter set that gives rise to four unordered attractors ( Fig 3C ) . This suggests that as compared to Type II motifs , Type I motifs has higher specificity in generating four ordered attractors , which is more relevant to the stepwise cell fate transitions than the unordered ones . Moreover , we observed that the inter-attractor distances between neighboring attractors in the gene expression space were generally more variable with Type I motifs than those with Type II motifs ( Fig 3D , magenta boxes ) . In particular , among the three inter-attractor distances for each model , Type I motifs generated smaller minimum distance than Type II motifs did ( Fig 3D , orange boxes . p-value < 0 . 0001 , Mann-Whitney U test ) . We did not observe any significant difference between Type I and Type II motifs in terms of the stabilities of the attractors and the kinetic paths that they generate ( S13 Fig See Methods for calculation of quasi-energy landscape and kinetic path ) . In addition to the effects of motif types , we also asked whether the fractions of positive or negative regulations in the network can influence the function of generating four-attractor systems . We found that the fraction of positive regulations has a weak but significant correlation with the fraction of successful parameter sets generating four-attractor systems ( S14A and S14B Fig ) . Although negative regulations are slightly less favorable , they might be important to ensure that the levels of some TFs are inversely correlated during multistep cell fate transitions , which is necessary for having at least one highly expressed TFs in each of the initial and the final cell states ( S14C Fig ) . In summary , we found two types of network motifs that generate four attractors with stepwise changes of the transcriptional profile . Two of these attractors represent the multiple intermediate states observed in various biological systems . This exploratory analysis elicits several interesting questions: what are the biological examples of such network motifs ? Can the conclusions with respect to the two types of motifs be generalized to networks with more than three TFs ? Is there any advantage of combining both types of motifs ? How are the transitions among these states triggered deterministically and stochastically ? To provide insights into these questions in a more biologically meaningful context , we will use a specific biological system to describe more detailed analysis of these motifs and their underlying gene regulatory networks in the following sections . We asked whether the motifs that we discovered can be found in any known transcriptional network that potentially control multistep cell differentiation . We used the early T cell differentiation in the thymus as an example to address this question . The differentiation from multipotent lymphoid progenitor cells to unipotent early T cells involves multiple stages at which the cells have differential potentials to commit to non-T lineages and other cellular properties such as proliferation rates . At the early phase of this process , four stages of development T cells ( ETP/DN1 , DN2a , DN2b , DN3 ) were identified experimentally , and the progression through these stages is controlled by a myriad of transcription factors including four core factors , TCF-1 , PU . 1 , GATA3 and BCL11B . These TFs form a complex network among themselves ( see Fig 4A and supporting experimental observations in S3 Table ) , and the stepwise changes in the levels of these TFs were observed in the four developmental stages of T cells [20 , 28] . The interactions involving these core TFs were shown to be critical for the irreversible commitment to the T cell lineage by forming a bistable switch [41] . Among these factors , PU . 1 level decreases as the cells commit to later stages , whereas the levels of other three factors increase in this process . It is unclear , however , whether this transcriptional network can serve as a regulatory unit that governs the multistep nature of the T cell differentiation . We noticed that this T cell transcriptional network contains the motifs that we found in our analysis using the generic form of networks , we therefore hypothesized that the models based on this network can have four attractors with sequential changes of the four TFs . Indeed , using random sampling we were able to find parameter sets that give rise to four-attractor systems similar to what we obtained with the generic 3-node framework . To find the functional components that generate this behavior , we analyzed the subnetworks of the complex T cell regulatory network [42] . We removed the regulations from the network systematically , and we found that out of the non-redundant 1553 topologies ( 2047 subnetworks ) , there are 568 topologies ( 701 subnetworks ) that can generate four attractors with stepwise changes of the TFs ( Fig 4B ) . We used a complexity atlas to visualize the relationships among these subnetworks ( Fig 4C ) . We found that the network can be reduced to one of the 66 minimum topologies ( 97 minimum subnetworks ) which retains the four-attractor property ( solid nodes in Fig 4C ) . Notably , these networks can be classified into the two types of motifs described earlier ( Fig 2B ) . Similar to the networks that we obtained through the generic framework , the two types of minimum motif have 4–6 regulations . Subnetworks with both types of motifs ( green nodes and edges ) start to appear when the number of regulations reaches six . The numbers of motifs and subnetworks obtained for the generic framework and the T cell model are summarized in Table 3 . We next quantified the enrichment of the two motif families in the early T cell transcription network . We first generated random networks by perturbing the existing regulations in the network model and computed the empirical p-values for observing the numbers of different types of network motifs . The T cell network contains a large number of positive feedback loops and the two types of motifs that we described earlier ( Fig 5 , top panel ) . As expected , the network is significantly enriched with positive feedback loops in general ( Fig 5 , middle panel , red bars ) . However , the enrichments of Type I motifs and the combinations of Type I and Type II motifs are even more significant than that of the single positive feedback loops ( Fig 5 , middle panel , red bars ) . To exclude the possibility that this differential significance was observed due to the way we generate random networks which gives low p-values ( <10−4 ) in general , we used another method to generate random networks with an augmented number of regulations ( Fig 5 , middle panel , blue bars ) . Each pair of TFs were assigned with a pair of random regulations ( positive , negative or none ) . Consistent with the previous method , the T cell transcriptional network is enriched with positive feedback loops overall , but the enrichment is more significant for Type I motifs or for the combination of Type I and Type II motifs . Interestingly , motifs that are similar to Type I motif but have higher complexity ( more positive feedback loops ) do not show more significant enrichment than Type I motif does ( S15 Fig ) . In addition , we found that networks controlling switch-like behaviors , but not multistep cell fate transitions , are not enriched with Type I or Type II motif [43–46] . These results suggest the possibility that the network has been evolved to reach more complex performance objectives than those enabled by simple positive feedback loops alone . Since the minimum motifs alone can generate the four-attractor system , we asked whether the combination of these motifs enhances the ability of the network to produce the system . We therefore compared a subnetwork containing only one minimum Type I motif with another one containing multiple such motifs in terms of the performance to generate a particular four-attractor system ( Fig 6A See Methods and S1 Text for details ) . We found that the subnetwork with multiple Type I motifs ( Network 1 ) outperforms the one with only one motif ( Network 2 ) in that Network 1 can give a better fit to a hypothetical four-attractor system ( Fig 6A–6C ) . In this hypothetical ‘target’ system , the four attractors are assumed to be determined by dynamics of PU . 1 with multiple feedback loops . The assumed degradation ( Fig 6B , gray curve ) and production rate functions of PU . 1 ( Fig 6B , green curve ) are specified . The curves of these two functions have 7 intersections , four of which represent attractors . The optimized production function obtained from Network 1 ( Fig 6B , purple curve ) has more robust intersections with the degradation curve than one obtained from Network 2 ( Fig 6B , red curve ) . This difference was observed for production functions of these two categories from multiple runs of optimization ( Fig 6C ) . This suggests the advantage of combining multiple motifs with similar functions to enhance its overall performance . We next compared the 66 minimum motifs ( Fig 4C bottom solid nodes ) and the full topology ( Fig 4C top node ) in terms of the parameter values that gave rise to the four-attractor systems . We found that the full model contains more parameter sets that have low nonlinearity of the regulations than the minimum motifs do ( S16A Fig ) , and the parameter values are distributed in larger regions with the full model than those with the minimum motifs ( S16B Fig ) . We next asked whether the topologies that contain both Type I and Type II motifs have greater probabilities to generate the four-attractor system than the topologies with one type of motifs do . When we explored the parameter space randomly for each topology with a fixed number of samples , a larger number of parameter sets that can generate the four-attractor system were found with the topologies containing both motifs than with those containing either Type I or Type II motifs only ( S5 Fig and Fig 6D ) . This suggests that the combination of both motifs might be a robust strategy to generate the four-attractor system . This pattern was observed for all the topologies in the complexity atlas ( S6 Fig ) as well as those with the same degree of complexity ( Fig 6D , networks with 7 regulations were chosen because they have comparable fractions of the three types of motifs ) . In summary , we found that the core transcriptional network controlling early T cell differentiation are enriched with Type I and Type II network motifs . The network composed of these two types of motifs governs a dynamical system containing four attractors , corresponding to four known stages in the early T cell development . The networks with both types of motifs and greater number of such motifs have more robust capability of generating the four-attractor systems than those networks with fewer types of numbers of motifs do . We next characterized the dynamical features of the four-attractor system of the T cell development model in response to differentiation signals . For this and subsequent analysis , we focused on a model describing the network topology shown in Fig 4A ( the full model ) . We first performed bifurcation analysis of the system to the changes of Notch signaling ( Fig 7A ) . With the increasing Notch signal , the system undergoes three saddle-node bifurcations , at which the stability of the proceeding cellular states is lost ( Fig 7A , black arrows ) . These bifurcation points therefore represent the cell state transitions from one stage to the next . The structure of the bifurcation diagram shows a remarkable robust multistep commitment program governed by the T cell transcription network: the commitment to each stage of the program has restricted reversibility in that the attenuation or withdrawal of the Notch signaling does not result in de-differentiation of the developing T cells ( i . e . the return of the transcription profile to earlier stages that may have greater multipotency ) . It was previously shown that the commitment from DN2a to DN2b is an irreversible process with respect to Notch signaling , and this transition eliminates developing T cells’ potential to be diverted to any other lineages when Notch signaling is abolished [20 , 41] . However , simple toggle-switch models do not explain the observation that the multipotency of the early T cells is lost in a stepwise manner . For example , cells at ETP can be differentiated into B cells , macrophages , dendritic cells ( DCs ) , granulocytes , natural killer ( NK ) cells and Innate lymphoid cellsubset2 ( ILC2 ) , whereas the potentials to commit to many of the lineages are blocked even in the absence of Notch signaling at the DN2a stage , at which the cells can only be differentiated into NK cells and ILC2 [20] . Therefore , the stepwise , irreversible transcriptional transitions revealed by our model is consistent with the experimental observations with respect to the loss of multipotency in the stepwise manner . Although the absence of Notch signal does not allow the reversal of lineage progression , it was previously shown that the absence of BLC11B in lymphoid progenitor cells blocks its ability to progress to DN2b stage , whereas the Cre-controlled knockout of Bcl11b in committed T cells ( e . g . DN3 cells ) reverts its transcriptional profile to DN2a-like cells [28] . Upon blocking the production of BCL11B in our model , we observed the loss of attractors of DN2b and DN3 , and the DN2a state is the only stable stage even in the presence of the strong Notch signaling ( Fig 7B ) . As a result , increasing Notch signaling only triggers one saddle-node bifurcation , representing the transition from ETP to DN2a cell ( Fig 7B black arrow ) , whereas the decrease of the BCL11B production triggers the transition back to DN2a instead of ETP ( Fig 7C ) . These results are in agreement with the previous experimental findings [28] , and they further support the importance of the multistep differentiation system revealed by our model . The bifurcation analysis shows how the lineage progression is influenced by stably increasing or decreasing Notch signal strengths . We next asked how the duration of Notch signal may control the multistep lineage transition . By inducing the differentiation with varying durations of the Notch signaling , we found that cells experiencing transient Notch signals may only commit to intermediate stages of differentiation ( Fig 8A ) . In addition , the system is able to integrate the information of the signal intensity and duration to make decision on the lineage progression . These results suggest that the multistep lineage transition can be triggered by the increasing strength of the signal , the increasing duration of the signal , or the combination of both types of signal dynamics . Earlier experimental studies have shown that transient Notch signaling can irreversibly drive the cells to an intermediate , but committed stage with a definitive T cell identity ( DN2b ) [28 , 41 , 47] . This is in agreement with our results , and our model further suggests that the commitment to other intermediate states is also irreversible with respect to the lineage progression ( note that this irreversibility does not refer to the establishment of T cell identity ) . One possible advantage of the multi-stable system is its robustness of response in facing fluctuating signals . We therefore performed numerical simulations of the dynamical system under increasing Notch signaling with significant fluctuations . Under this condition , transient reduction of Notch signaling halted the progress of the lineage commitment but did not trigger the de-differentiation ( Fig 8B ) . Our model suggests that the design of transcriptional network allows the system to stop at intermediate stages before proceeding to the next ones . This strategy has several potential physiological benefits: 1 ) it protects the cell lineage progression against sporadic fluctuations of Notch signaling; 2 ) it facilitates the ‘checkpoints’ before lineage commitment in the middle of the developmental process and 3 ) it allows the stable storage of differentiation intermediates which can be differentiated into mature T cells rapidly when there is an urgent need of new T cells with a diverse T cell receptor repertoire . With the deterministic modeling and bifurcation approaches , we described the local stability for multi-stable T cell model . However , the global stability is less clear from the bifurcation analysis alone . In addition , it is important to consider the stochastic dynamics for T cell development model because the intracellular noise may play crucial roles in cellular behaviors [48 , 49] . The Waddington landscape has been proposed as a metaphor to explain the development and differentiation of cells [50] . Recently , the Waddington epigenetic landscape for the biological networks has been quantified and employed to investigate the stochastic dynamics of stem cell development and cancer [51–57] . Following a self-consistent approximation approach ( see Methods ) , we calculated the steady state probability distribution and then obtained the energy landscape for the model of the early T cell development ( full model in Fig 4A ) . For visualization , we selected two TFs ( PU . 1 and TCF-1 ) as the coordinates and projected the 4-dimensional landscape into a two-dimensional space by integrating the other 2 TF variables ( Fig 9A ) . Here TCF-1 is a representative T cell lineage TF , and PU . 1 is a TF for alternative cell fates . We also displayed the landscape in a four-dimensional figure , where the three axes represent three TFs ( TCF-1 , BCL11B and PU . 1 ) , and the color represents the energy U ( Fig 9B ) . Note that our major conclusions do not depend on the specific choice of the coordinate ( see S7 and S8 Figs for landscapes with PU . 1/BCL11B and PU . 1/GATA3 as the coordinates ) . In the case without Notch signal ( N = 0 ) , four stable cell states emerge on the landscape for the T cell developmental system ( Fig 9 ) . On the landscape surface , the blue region represents lower potential or higher probability , and the yellow region represents higher potential or lower probability . The four basins of attraction on the landscape represent four different cell states characterized by different TF expression patterns in the 4-dimensional state space ( Fig 9A and 9B provide two types of projections of the whole 4-dimensional landscape ) . These states separately correspond to ETP/DN1 ( high PU . 1/low TCF-1/low BCL11B/low GATA3 expression ) , DN3 state ( low PU . 1/high TCF-1/high BCL11B/high GATA3 expression ) , and two intermediate states ( DN2a and DN2b , intermediate expression for the four TFs ) . The existence of four stable attractors is consistent with experiments [16–19] . As the Notch signal ( N ) increases , the landscape changes from a quadristable ( four stable states coexist ) , to a tristable ( DN2a , DN2b and DN3 ) , to a bistable ( DN2b and DN3 ) and finally to a monostable DN3 state ( S9 Fig ) . These results provide a straightforward explanation for the irreversibility observed in experiments for the stepwise T cell lineage commitment . To check whether our modelling results match experimental data quantitatively , we acquired two sets of gene expression data of the four core TFs for T cell development from previous publications [17 , 47] , and mapped the normalized values ( see Methods ) to the landscape ( Fig 9A and 9B , S7 and S8 Figs ) . Here , the golden balls represent the four steady states ( characterizing four stages of T cell development ) from the models , the red balls represent the gene expression data points ( Data1 ) from Zhang et al . [47] , and the green balls represent the gene expression data points ( Data2 ) from Mingueneau et al . [17] . We found that these gene expression data agree well with our landscape models in the sense that each data point is almost positioned in the corresponding basin ( Fig 9A and 9B , S7 and S8 Figs ) . We found that the landscapes give a better fit to Data2 ( green points ) , since each green data point can be well positioned in one of the four basins , corresponding to four stages of T cell development . In fact , the two sets of data points are not very close to each other or to the steady states ( golden points ) from the models . This is reasonable because these two sets of experimental data are measured separately , probably in different conditions , and these data usually delineate the average of multiple measurements from different samples . Also , the gene expression fluctuations are common in biological systems . Therefore , our landscape pictures provide a natural way to reconcile the two different experimental data , i . e . the gene expression data do not have to be at the positions of steady states . Instead , the gene expression data for each individual stage could be somewhere around that basin because of the fluctuations . This reflects the original spirit of the classic Waddington developmental landscape . To examine the transitions among individual cell types , we calculated kinetic transition paths by minimizing the transition actions between attractors [58 , 59] , obtaining minimum action paths ( MAPs ) . The MAPs for different transitions are indicated on the landscape ( Fig 9 ) . The white MAPs from the ETP state to the DN3 state , correspond to the T cell developmental process while the magenta MAPs from the DN3 state to the ETP state , correspond to reprogramming process . The lines represent the MAPs , and the arrows denote the directions of the transitions . The MAP for T cell developmental process and the MAP for the backward process are irreversible , since the forward and reverse kinetic paths are not identical . This irreversibility of kinetic transition paths is caused by the non-gradient force , i . e . the curl flux [60 , 61] . Here , the solid white lines represent three stepwise transitions from ETP to DN2a , DNa2 to DN2b , and DN2b to DN3 , whereas the dashed white line represents the direct transition path from ETP to DN3 . From the MAPs for T cell development , we found that the direct transition path is very similar to the stepwise transition path ( the white solid line is similar to the white dashed line , Fig 9 , S7 and S8 Figs ) , which indicates that the T cell developmental process needs to go through the two intermediate states ( DN2a and DN2b ) . This confirms the critical roles of the intermediate states for the T cell differentiation . It is worth noting that the MAPs here quantify the most probable transition paths , which suggest the optimal path ( with least transition action ) for cells to switch from one state to another . However , in a realistic gene regulatory system , usually a signal is needed to induce rapid cell state transitions ( e . g . the Notch signaling is used here to induce T cell development ) . To investigate the dynamical developmental process of T cell for multiple TFs , we visualized the 4-dimensional MAP from the ETP to the DN3 state by discretizing the levels of the four TFs . We found that for T cell development , TCF-1 is upregulated first , followed by the activation of GATA3 . This leads to the complete inactivation of the alternative fate TF PU . 1 and the activation of BCL11B ( Fig 10 ) . Interestingly , this temporal order is in good agreement with experimental observations [62] ( Also see the gene expression data of four core TFs for four stages in Fig 9 , S7 Fig and S8 Fig ) . These results suggest that the sequence of switching on or off for different TFs can be critical for the lineage commitment of T cell development . Moreover , under the Bcl11b knockout condition ( kB = 0 ) , the landscape changes from a quadristable ( four stable states coexist ) , to a bistable ( ETP and DN2a ) state ( S10 Fig ) , which is consistent with the bifurcation analysis ( Fig 7 ) and experimental observations [28] . To identify the critical factors ( regulations and TFs ) which determine T cell development , we performed a global sensitivity analysis based on the landscape topography . Specifically , we use the transition action between attractors as a measure to quantify the feasibility of a transition between different attractors . A smaller transition action , corresponding to a smaller energy barrier , means a more feasible transition from one attractor to another . In this way , by changing the parameters each at a time we can identify the critical parameters for T cell development ( we use the transition from ETP to DN3 as an example ) . To do this , we constrict the models within the parameter region corresponding to the four-attractor system , so that we can make comparisons for the changes of transition actions as parameters are varied . We identified some critical parameters of which the variations caused significant changes of transition actions between ETP and DN3 attractor . These parameters include the effective degradation rate of PU . 1 , ( rdP ) , the regulated production rate of PU . 1 ( kP ) , the basal production rate of PU . 1 ( kP0 ) , the threshold of the self-activation of PU . 1 ( KPP ) , and the threshold for the repression of PU . 1 on GATA3 ( KGP ) ( Fig 11 ) . In particular , the increase of the self-activation strength of PU . 1 ( i . e . decreased KPP ) reduces the transition action from DN3 to DN2b ( Fig 11B ) , indicating a less stable DN3 state and a more stable ETP state . This is reasonable because the PU . 1 is a major TF for alternative cell fates ( B-cell , dendritic-cell , and myeloid cell ) , and silencing of PU . 1 is operationally important for T cell commitment [28] . Additionally , the increase of the repression strength of PU . 1 on GATA3 ( decreased KGP ) raises the transition action from ETP to DN2a ( Fig 11B ) , indicating a more stable ETP state and a less stable DN3 state , which is consistent with the observation that GATA3 is a critical TF promoting T cell development . Overall , these results from sensitivity analysis indicate that the PU . 1 synthesis/degradation related parameters , the GATA3 synthesis related parameters , and the regulations between PU . 1 and GATA3 are critical to the dynamics and the cell fate decisions of T cell development . This indicates that the regulatory circuit between PU . 1 and GATA3 plays critical roles for the cell fate determinations during T cell development .
In this study , we identified two types of network motif families that are responsible for generating a four-attractor dynamical system commonly observed in stepwise cell differentiation . Some instances of these motifs were previously described and analyzed in the context of binary or ternary switches during lineage transitions [63–67] , but the systematic analysis for these motifs was not performed to our knowledge . In addition , the design principle for multiple intermediate states was not clear . Our approach provides a comprehensive framework for analyzing systems with a complex dynamical property , a four-attractor system with stepwise transcriptional modulation , and we illustrate the intricate relationships among these motifs with an intuitive visualization method . Previous studies on biological circuits governing irreversible transitions focused on the analysis of toggle switches which generate none-or-all type of responses [68 , 69] . Our work suggests that multistep or graded responses can be associated with irreversible transitions as well . Given the importance of graded response in various biological scenarios [70–72] , we expect the design strategy that we found can be useful for discovery of natural-occurring irreversible graded responses or construction of synthetic biological circuits producing these responses . Our work also suggests that the response to signals , or the progression of lineage transition , may be proportional to the intensity and/or the duration of the signal . This is consistent with the previous observations that the duration of the morphogen signal can be critical for cell lineage choice [73 , 74] . Of note , when signal strength is converted to digital ( none-or-all ) response in early phases of signal transduction , its duration can play an essential role in determining the graded response [75] . In our systematic exploration in the network topology space , we took the assumption that network structure is correlated with its function , i . e . assuming the existence of functional motif structure in transcription regulatory networks . The notion of network motifs is very helpful for understanding many complex biological systems [76 , 77] , but the richness of dynamic behaviors of these motifs is beyond their structures–distinct kinetic rates in the same motif can produce diverse responses [78] . Therefore , it is expected that the motifs that we discovered may be able to generate dynamical behaviors different from the four-attractor system ( we will discuss some of them in the following paragraphs ) . We also expect that some of network motifs can be responsible for multiple functions by themselves , and this multifunctionality may explain the diverse motifs that we found for the four-attractor systems in the biological examples . Future work is warranted to examine the distributions of the diverse functions in the parameter space of the motifs that we found . Nonetheless , it is important to understand the capacity of the network motifs in terms of their functional outputs . Our work provides a holistic view of the potential network motif structures governing multistep cell lineage transitions . Although network motifs with three positive feedback loops closing at a single factor ( Type I motifs discussed in this study ) were not systematically analyzed in previously studies to our knowledge , some simpler versions of Type I motif , e . g . a pair of interconnected positive feedback loops , have been described in various systems such as the epithelial-mesenchymal transition and the cancer progression [65 , 79] . These systems typically govern ternary switches with a single intermediate state . These studies and ours suggest a correlation between the number of positive feedback loops and the number of the intermediate states the system may be able to generate . In fact , early studies on multistable systems have shown the requirement of positive feedback loops for generating multiple steady states [80] , which was proved mathematically [81] . Intriguingly , an ultrahigh feedback system similar to the Type I motifs was shown to govern irreversible transitions with low differentiation rates for adipocytes [82] . It would be interesting to examine whether controlling the low differentiation rate through cell-to-cell variability and controlling the number of intermediate states suggested by our model can be achieved in the same system . Our findings are consistent with the earlier work in that they highlight the importance of this type of signaling motifs in controlling cell differentiation by preventing the direct and homogeneous transition from the initial state to the final one . Near symmetrical parameters in models based on a particular instance of the Type II motif class ( the one with mutually inhibiting TFs ) have been widely used to explain stochastic lineage choice observed in embryonic stem cells , developing hematopoietic cells and CD4+ T cells [83 , 84] . Our findings with Type II motifs complement these studies with newly identified functions of these motifs for cell differentiation . Instead of the stochasticity that breaks the symmetry of this motif , the Notch signal may be responsible for switching the system from one side ( PU . 1 high ) to another ( PU . 1 low ) in a stepwise fashion , and the intermediate states mark the stable stages where the system is relatively balanced in terms of two groups of competing TFs . It was previously suggested that the network consisting of four core transcription factors governs a bistable switch with irreversible transition [41] . Our models based on this network provide explanations for additional experimental observations with respect to the multistep feature of the early T cell development ( see S1 Text for a comparison between our model and the bistable model ) . Although it is possible that interconnection of multiple positive feedback loops simply enhances the robustness of the bistable switches , the observation that several important irreversible transitions in cell cycle progression are primarily controlled by two positive feedback loops implies that the enrichment of the positive feedback loops in the T cell transcriptional network is unlikely due to the intrinsic biophysical limits of positive feedback loops in generating bistable switches [46 , 69] . Instead , other cellular functions , such as generating the multiple intermediate states , might be the performance objectives for the design of this network . Future work with more systematic analysis is warranted to compare the parameter regions corresponding to the bistable , tristable and quadristable systems with ordered and unordered attractors . Our model of early T cell development suggests that the differentiation program may be stopped at multiple locations in the state space of transcription levels of key factors . These multiple attractors may correspond to the lineage branching points at which the progenitor cells are given opportunities to be converted to T cell as well as other types of lymphocytes . As such , it is possible that this dynamical property is exploited to achieve a better control for the fate determination of the lymphoid progenitor cells at systems level . Given that subpopulations of NK cells and DCs are generated by the thymus [85–87] , the multistep lineage transition provides a basis for channeling the lymphoid progenitor to multiple lineages in a precise manner . Based on the recent landscape-path theory and the T cell gene regulatory network model , we investigated the stochastic dynamics of T cell development . We identified four stable cell states characterized by attractors on the landscape including ETP/DN1 , DN3 , and two intermediate states ( DN2a and DN2b ) . These attractors ( cell states ) match two published datasets of gene expression values in a reasonable way [17 , 47] . We also calculated the kinetic transition paths between different cell states from minimum action path approaches . Importantly , from the MAPs of T cell development , we found that different TFs are switched on or off in different orders . For example , TCF-1 needs to be first activated , and then GATA3 is activated , leading to the inactivation of PU . 1 and activation of BCL11B . These predictions agree well with experiments [28 , 62] , which provides further validations for our mathematical model . In our models , we only considered four core factors based on previous published T cell gene regulatory network for simplicity [41] . In the realistic biological system , there are more factors critical to T cell development [28] . It would be interesting to incorporate other important factors into the network and construct a more realistic model for T cell development . By studying the landscape of more comprehensive T cell development network , we will better understand the underlying regulatory machinery and obtain more insights into the intricate mechanisms for T cell development . In summary , we identified a large family of network motifs that can generate four attractors that are observed in various biological systems involving cell lineage transition . We built a mathematical model for transcriptional network controlling early T cell development , and we found that the network underlying this developmental process is enriched with the motifs that we identified . The system with the four attractors has a remarkable irreversibility for transitions to multiple intermediate states when the differentiation signal is varied . We suggest that this multistep process may be useful for precise control of the differentiation of lymphoid progenitor cells towards T cell and other cell types . Our T cell model provides new insights into the complex developmental or regeneration processes , and our combined approaches of comprehensive analysis of network motifs for generating multistable systems and landscape-path framework provide a powerful tool for studying a wide range of networks controlling cell lineage transitions .
We used ordinary differential equations ( ODEs ) to describe the dynamics of the concentrations of transcription factors ( TFs ) . We used Hill function to describe the transcriptional regulation by TFs . Each ODE has the following form: Xi˙ ( t ) =k0 , Xi+kXi∑j=1nβi , j ( 1−θ ) +θ ( XjKi , j ) ni , j1+ ( XjKi , j ) ni , j−rd , XiXi ( 1 ) Here , Xi represents the concentration of a transcription factor ( TF ) . k0 , Xi is the basal production rate of the TF in the absence of any regulator . kXi is the maximum production rate under the control of the transcriptional activators and inhibitors of this TF . βi , j denotes the weight of the influence of the TF j on i . The sum of the Hill functions determines the regulation of the production of this TF by other TFs . In each term of the summation , θ = 1 when the regulating TF ( Xj ) is an activator . θ = 0 when the regulating TF is an inhibitor . Ki , j is the apparent dissociation constant of the regulating TF binding to its regulatory element of the promoter , and it describes the effectiveness of the regulation in terms of the concentration of the TF . n is the total number of regulating TFs . rd , Xi is the effective degradation rate constant . The production rate of the proteins is assumed to be linearly correlated with mRNA production rate . Similar generalized forms of Hill function were previously used for analysis of a variety of gene regulatory networks [52 , 88] . One time unit of our model corresponds to 6 hours , and all the parameters are dimensionless . To exclude the possibility that our conclusions are sensitive to the choice of the form of equations , we used an alternative form of ODE to describe the regulatory networks: Xi˙ ( t ) =k0 , Xi+kXi∏j=1n ( 1−θ ) +θ ( XjKi , j ) ni , j1+ ( XjKi , j ) ni , j−rd , XiXi ( 2 ) In these ODEs , multiplication of Hill functions was used instead of addition . Similar forms of Hill function were also used for modeling a variety of gene regulatory networks [66 , 89] . With this form , the two types of network motifs that generated the four-attractor behavior are the same as those discovered with the additive form of Hill functions ( S3 Fig ) . In fact , using both forms of equations gave rise to the same number of network topologies ( 216 topologies with the steady states shown in both S3 Fig and S4 Fig ) . Therefore , our conclusions are robust in terms of the choice of equation form . During topology searching , random parameters values were chosen from defined ranges ( S1 Table , see below ) . Network topology searching was first performed for all possible topologies involving up to 3 nodes ( TFs ) and 6 regulations that are able to generate four-attractor systems with stepwise changes of TF levels . We sampled n of the 9 arrows in Fig 2A , where 1≤n≤6 . The procedure for the exhaustive search is the following: 1 ) Choose n arrows from the 9 arrows in Fig 2A ( 9 choose n ) , which gives rise to 465 topology templates . 2 ) For each topology template containing n regulations , enumerate all 2n networks ( the number 2 represents positive and negative regulations ) , which gives 12258 networks in total . 3 ) Remove redundant networks that are different in terms of labels of nodes but are otherwise identical in terms of topology ( isometric topologies ) , which produces 2114 non-redundant topologies . Three-node networks were previously used to explore several types of functional dynamics of network motifs [38 , 90] . For each topology , we performed random sampling of 106 parameter sets . For each parameter set , we selected 125 initial conditions in the three-dimensional state space ( ( 0 , 3 . 3 ) for each variable ) using Latin Hypercube sampling , and then solved the ODEs numerically . We stopped the simulations at time point 500 and checked if the 125 ODE systems are stabilized at four or more distinct steady states . We next checked if the changes of the TFs are monotonically coupled . We first ordered the steady states by the levels of one TF , and then we looked for scenarios in which all other TFs monotonically increase or decrease with the ordered TF ( i . e . the attractors with stepwise changes of the TFs ) . We excluded the scenarios in which one TF is not monotonically correlated with others in terms of their levels at the four attractors . Models that generated oscillations at the final time point were also excluded . The parameter sets which produced the stepwise changes of steady state were accepted and their associated network topologies were analyzed . Parameter values for the minimum topologies are listed in S2 Table . To determine whether a network topology contains Type I and/or Type II motifs , we developed a simple algorithm for motif identification . We first enumerated all positive feedback loops ( PFLs ) in the network . A PFL is defined as any unbranched closed loop structure that has even number of negative regulations . We then followed the scheme described in Table 2 to check if Type I and/or Type II motifs exist in a specific network: we enumerated all combinations of three PFLs in the network . If any three PFLs share one or more TFs , then the network contains a Type I motif . If two of any three PFLs do not share any TF , but they ( i . e . at least one TF from PFL 1 and at least one TF from PFL 2 ) are connected via the third PFL , then the network contains a Type II motif . Complexity atlas was plotted for the obtained network topologies as described previously by Jiménez et al [40] ( Fig 2B and Fig 4C ) . We built a model for early T cell development based on the regulations that were previously shown experimentally [91–104] . Information about experimental evidence is described in S3 Table . The form of equations is similar to Eq ( 2 ) . We chose this multiplicative form of Hill functions because earlier experimental study suggested that regulations of Bcl11b gene are combined via an ‘and’ logic gate [105] , which favors the use of multiplication . Although similar detailed information is not available for other TFs , we have shown that our main conclusions with respect to the multistep transitions controlled by a network motif family do not depend on the choice of the form of equations ( Fig 2 and S4 Fig ) . Full list of equations is included in S1 Text . The parameter values were obtained by random searching described above with the four-attractor property as the selection criterion followed by minor manual adjustments . All parameter values are dimensionless . To our knowledge , there is no published experimental measurement that would allow us to directly constrain the ranges of these values except for the degradation rates , which have a unit of the inverse of time . These degradation rates were estimated from a previous study that measured the half-lives ( approximately 4 hours ) of the transcription factors [41] . The parameter values are listed in S4 Table . To explore the subnetworks of the T cell development model that are essential for the four-stage transition , we performed similar exhaustive search in a set of 1553 non-redundant topologies ( 2047 subnetworks ) to find functional circuit in the model . We obtained 568 topologies ( 701 topologies ) from the search , and we analyzed them with complexity atlas . Isometric topologies were removed in the simulations , but they are included in the complexity atlas so that we do not mix isometric topologies with possibly differential biological meanings specific to certain genes . In the bifurcation analysis , the value of the parameter N ( Notch signal strength ) or kBCL11B ( maximum production rate of BCL11B ) is varied and the changes of the steady states of the system were analyzed . We let kBCL11B = 0 to simulate the Bcl11b knockout condition . To simulate the system under various scenarios of Notch signaling , we first varied the strength and/or duration of the Notch signal and checked the steady state distribution of the system under the varying strengths and durations . We tested 200X200 combinations of strengths and durations of Notch signals and obtained the phenotypes of the cells at the steady state . To simulate the fluctuating Notch signals , we divided the time window of the simulation into small intervals ( 0 . 1 unites of time ) . For each interval , we used a random number with a specified mean and an additive noise . The mean of the Notch signal first increased overtime and then became attenuated . To quantify the enrichment of various types of motifs , we used the generic definition of p-value: the p-value for a particular motif is the probability of obtaining at least n number of motifs from a random network population , where n is the observed number of such motif in the T cell network . To compute the p-values , we first counted the frequencies of the positive feedback loop , Type I motif and Type II motif in the T cell model ( i . e . n1 , n2 , n3 , n4 representing the numbers of positive feedback loops , Type I motifs , Type II motifs , and the sum of the Type I and Type II motifs respectively ) . Random networks were generated using two methods: 1 ) for each regulation in the existing T cell model , we randomly reassign its source and target TFs ( referred to as ‘permuted regulations’ ) , and 2 ) for each pair of TFs from the network , we randomly assign a regulation ( positive , negative or none ) ( referred to as ‘permuted regulations’ ) . For each of the two methods , we generated 105 networks , and we calculated the empirical p-values by counting the number of the random networks with the numbers of motifs not less than those of respective motifs in the T cell network . The method with permuted regulations is more biologically relevant because the number of the positive and negative regulations are retained in the random networks . We used the second approach as an alternative to exclude the possibility that the conclusion based on the trend of the p-values is due to the low number of networks containing the extreme amount of the motifs . Due to the difficulty to compare the performances of regulatory circuits with different complexities in general , we selected two specific instances of Type I network motif for comparison . One of them contains only one Type I motif , whereas the other one contains multiple motifs . For each topology , we reduced the system to one ODE with quasi-steady state assumption and defined a continuous production rate function that can produce four attractors as a surrogate function ( see S1 Text ) . Multiple runs of optimization using differential evolution algorithm was used , and 500 converged parameter sets for each circuit were used for comparison . This optimization method was previously used for finding optimum parameter sets and for comparing the performances of regulatory circuits [64 , 106 , 107] . The temporal evolution a dynamical system was determined by a probabilistic diffusion equation ( Fokker-Planck equation ) . Given the system state P ( X1 , X2 , … , XN , t ) , where X1 , X2 , … , XN , represent the concentrations of molecules or gene expression levels , we have N-dimensional partial differential equation , which are difficult to solve because the system has a very large state space . Following a self-consistent mean field approach [52 , 61 , 108] , we split the probability into the products of the individual probabilities: P ( X , t ) =P ( X1 , X2 , … , XN , t ) =∏iNPi ( Xi , t ) and solve the probability self-consistently . In this way , we effectively reduced the dimensionality of the system from MN to MN ( M is the number of possible states that each gene can have ) , and thus made the computation of the high-dimensional probability distribution tractable . Based on the diffusion equations , when the diffusion coefficient D is small , the moment equations can be approximated to [109 , 110]: x¯˙ ( t ) =F ( x¯ ( t ) ) ( 3 ) σ˙ ( t ) =σ ( t ) AT ( t ) +AT ( t ) σ ( t ) +2D ( x¯ ( t ) ) ( 4 ) Here , x¯ ( t ) , σ ( t ) and A ( t ) are vectors and tensors . σ ( t ) denotes the covariance matrix and A ( t ) is the jacobian matrix of F ( x¯ ( t ) ) . AT ( t ) is the transpose of A ( t ) . The elements of matrix A are specified as: Aij=∂Fi ( X ( t ) ) ∂xj ( t ) . By solving these equations , we can acquire x¯ ( t ) and σ ( t ) . Here , we consider only the diagonal elements of σ ( t ) from the mean field approximation . Then , the evolution of the probability distribution for each variable can be acquired from the Gaussian approximation: P ( x , t ) =12πσ ( t ) e− ( x−x¯ ( t ) ) 22σ ( t ) ( 5 ) The probability distribution acquired above corresponds to one stable steady state or the basin of attraction . If the system has multiple stable steady states , there should be several probability distributions localized at each basin with different variances . Thus , the total probability is the sum of all these probability distributions with different weights . From the self-consistent approximation , we can extend this formulation to the multi-dimensional case by assuming that the total probability is the product of each individual probability for each variable . Finally , with the total probability , we can construct the potential landscape by: U ( x ) = −lnPss ( x ) . Here , Pss is the steady state probability distribution , and U is dimensionless potential energy . In this work , we define two quantities based on the landscape theory . One is the energy barrier height , which is defined as the energy difference between the local minimum and the corresponding saddle point . Another quantity is the transition action , which is defined as the minimum action from one attractor to the other . These two quantities both measure the difficulty of the transitions . However , the transition actions are suggested to provide a more accurate description for the barrier crossing between attractors or the transition rate [111] . Therefore , we used the transition actions to quantify the difficulty of the transitions between attractors in this work ( see the following section for the approach of calculating minimum action paths ) . Following the approaches based on the Freidlin-Wentzell theory [58 , 112 , 113] , for a dynamical system with multistability the most probable transition path from one attractor i at time 0 to attractor j at time T , ϕij* ( t ) , t∈[0 , T] , can be acquired by minimizing the action functional over all possible paths: ST[ϕij]=12∫0T|ϕ˙ij−F ( ϕij ) |2dt ( 6 ) Here F ( ϕij ) is the driving force . This optimal path is called minimized action path ( MAP ) . We calculated MAPs numerically by applying minimum action methods used in [58 , 112] . Gene expression data for the four core TFs were obtained from two previous studies on T cell development ( Zhang et al . and Mingueneau et al . [17 , 47] ) . We rescaled these data to the range of the attractors obtained from our models by linearly transforming the expression values so that they match the attractors approximately . This rescaling is necessary because some corresponding expression values from these two datasets differ from each other by more than 10-fold . The corresponding ratios between these two datasets vary significantly as well .
|
The functions of cells are dynamically controlled in many biological processes including development , regeneration and disease progression . Cell fate transition , or the switch of cellular functions , often involves multiple steps . The intermediate stages of the transition provide the biological systems with the opportunities to regulate the transitions in a precise manner . These transitions are controlled by key regulatory genes of which the expression shows stepwise patterns , but how the interactions of these genes can determine the multistep processes was unclear . Here , we present a comprehensive analysis on the design principles of gene circuits that govern multistep cell fate transition . We found a large network family with common structural features that can generate systems with the ability to control three consecutive steps of the transition . We found that this type of networks is enriched in a gene circuit controlling the development of T lymphocyte , a crucial type of immune cells . We performed mathematical modeling using this gene circuit and we recapitulated the stepwise and irreversible loss of stem cell properties of the developing T lymphocytes . Our findings can be useful to analyze a wide range of gene regulatory networks controlling multistep cell fate transitions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"gene",
"regulation",
"immunology",
"notch",
"signaling",
"cell",
"differentiation",
"developmental",
"biology",
"mathematics",
"network",
"analysis",
"computer",
"and",
"information",
"sciences",
"white",
"blood",
"cells",
"transcriptional",
"control",
"network",
"motifs",
"animal",
"cells",
"gene",
"expression",
"t",
"cells",
"signal",
"transduction",
"cell",
"biology",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"topology",
"cell",
"signaling"
] |
2019
|
An enriched network motif family regulates multistep cell fate transitions with restricted reversibility
|
Organismal aging is influenced by a multitude of intrinsic and extrinsic factors , and heterochromatin loss has been proposed to be one of the causes of aging . However , the role of heterochromatin in animal aging has been controversial . Here we show that heterochromatin formation prolongs lifespan and controls ribosomal RNA synthesis in Drosophila . Animals with decreased heterochromatin levels exhibit a dramatic shortening of lifespan , whereas increasing heterochromatin prolongs lifespan . The changes in lifespan are associated with changes in muscle integrity . Furthermore , we show that heterochromatin levels decrease with normal aging and that heterochromatin formation is essential for silencing rRNA transcription . Loss of epigenetic silencing and loss of stability of the rDNA locus have previously been implicated in aging of yeast . Taken together , these results suggest that epigenetic preservation of genome stability , especially at the rDNA locus , and repression of unnecessary rRNA synthesis , might be an evolutionarily conserved mechanism for prolonging lifespan .
Organismal aging is accompanied by the accumulation of damage to DNA and other macromolecules , and a progressive decline in vitality and tissue function . The underlying mechanisms remain unclear , and many models have been proposed to explain the aging phenomenon . Prominent among these models is the “free radical theory of aging” , which posits that the gradual and collective damage done to biological macromolecules ( including DNA and proteins ) by reactive oxygen species ( ROS ) from intrinsic ( e . g . , metabolism ) or extrinsic sources ( e . g . , radiation ) , is the major cause of organismal aging [1] , [2] . Other competing ( although some are overlapping ) models of aging include genetically programmed senescence [3] , [4] , heterochromatin loss [5] , telomere shortening [6] , genomic instability [7] , nutritional intake and growth signaling [8]–[10] , to name a few . In the heterochromatin loss model of aging , Villeponteau ( 1997 ) has proposed that heterochromatin domains , which are set up early in embryogenesis , are gradually lost with aging , resulting in derepression of silenced genes and aberrant gene expression patterns associated with old age [5] . Experimental tests of the role of heterochromatin formation in animal aging , however , have produced controversial results [11] . On the one hand , cellular senescence is associated with an increase in localized heterochromatin formation in the form of Senescence-Associated Heterochromatin Foci ( SAHFs ) , which are a hallmark of replicative senescence of aged cells in culture , and have also been found in the skin cells of aged animals [12]–[14] . On the other hand , it has been shown that premature aging diseases in human and animal models correlate with global heterochromatin loss [15]–[17] . Heterochromatin is important for chromosomal packaging and segregation , and is thus important for genome stability [18] , [19] . Indeed , it has been shown in Drosophila that heterochromatin is essential for maintaining the stability of repeated DNA sequences and of the rDNA locus in particular [20] . Loss of heterochromatin causes disruption of nucleolar morphology and formation of extrachromosomal circular ( ECC ) DNA , which results from an increase in illegitimate recombination at the rDNA locus [20] . Interestingly , disruption of heterochromatin and nucleolar structure , and the consequent increase in ECC DNA , have previously been shown to cause accelerated aging in yeast [21] , [22] . These reports suggest a positive role for heterochromatin formation in promoting longevity . To understand the role of heterochromatin in animal aging , and the underlying molecular mechanisms , we altered heterochromatin levels in Drosophila by genetically manipulating Heterochromatin Protein 1 ( HP1 ) levels and JAK/STAT signaling , and assessed the effects on aging . Our results suggest that heterochromatin formation positively contributes to preventing premature aging and suppresses illegitimate recombination of the rDNA locus and unnecessary rRNA synthesis .
To investigate whether heterochromatin levels are important for longevity , we examined the life spans of flies with reduced or increased levels of HP1 . These flies exhibit reduced or increased levels of heterochromatin , respectively , during development [23] , as HP1 is an integral component of heterochromatin and controls heterochromatin levels [24] , [25] . We found that reducing HP1 levels by half , as in Su ( var ) 2055 heterozygotes , caused a dramatic shortening of life span compared to isogenic controls ( p = 2 . 03−86 ) ( Figure 1A ) . Similar results were found with a second allele , Su ( var ) 2052 ( Figure S1A ) . Conversely , a moderate overexpression of HP1 , caused by basal activity of the hsp70 promoter , significantly extended life span , resulting in a 23% increase in median life span and a 12% increase in maximum life span ( p = 6 . 31−24 ) ( Figure 1A ) . Similarly , at non-heat shock conditions ( 25°C ) , a second ( independent ) line of hsp70-HP1 flies also lived significantly longer than their control flies ( Figure S1B ) . At basal levels of transcription , hsp70-HP1/+ flies exhibited higher heterochromatin levels [26] . By quantitative real-time polymerase chain reaction ( qPCR ) measurements , we found that these flies had approximately 20% higher HP1 mRNA expression than control ( Figure S2A ) . Over-expression of HP1 at higher levels , such as under heat-shock inducible conditions , however , caused developmental abnormality or lethality to the animal . These results suggest that heterochromatin levels significantly influence life span , and moderately higher levels of heterochromatin promote longevity . Since both JAK overactivation and STAT loss reduce heterochromatin levels [26] , [27] , we investigated the effect of altering JAK/STAT signaling on aging . JAK/STAT signaling plays two roles: in the canonical pathway , JAK/STAT directly regulates target gene expression [28] , [29] , while in its non-canonical function , unphosphorylated STAT is essential for heterochromatin formation [26] , [27] and genome stability [19] . In the canonical pathway , loss of STAT has effects equivalent to loss of JAK and opposite to JAK overactivation . However , in the non-canonical function , loss of STAT has the same effects as JAK overactivation , causing heterochromatin destabilization [26] , [30] and genome instability [19] . We examined the life span of Stat92E+/− flies and those heterozygous for gain- or loss-of-function mutations of hop . We found that flies heterozygous for either the gain-of-function hopTum-l or the Stat92E mutation exhibited shortened lifespans compared with wild-type control flies ( p = 8 . 87−23; 2 . 92−53 , respectively ) , while flies heterozygous for a loss-of-function hop allele , hop3 , had longer lifespans ( p = 7 . 34−25 ) ( Figure 1B ) . These results are consistent with the idea that heterochromatin levels influence lifespan . A previous study has shown that Drosophila life span was only slightly reduced in Su ( var ) 205 heterozygotes and was not affected by a chromosomal duplication that encompasses the Su ( var ) 205 locus and many other genes [31] . Using chromosomal duplication , any effects associated with higher levels of Su ( var ) 205 might be masked by higher levels of other neighboring genes . In studies with the loss-of-function heterozygotes , the authors in that study ensured isogenicity of their compared strains by extensively back-crossing the mutations into a common genetic background , and relied on the suppression of position-effect variegation ( PEV ) to determine the presence of the Su ( var ) 205 mutation . PEV results from heterochromatin-mediated gene repression , commonly seen in loss of eye pigmentation [24] . The presence or absence of the Su ( var ) 205 mutation was assumed to correlate 100% with the PEV phenotype . However , we have found that this is not the case . We examined the PEV phenotype of wm4 in the progeny of single pairs of wm4; Su ( var ) 2055/CyO and w flies , and found that the PEV phenotype was not 100% correlated with the Su ( var ) 205 mutation ( Figure S3 ) , rather , there was less PEV than would be expected , suggesting that , with regard to suppression of PEV , either the Su ( var ) 205 mutation is not completely penetrant or that there is an incomplete epigenetic reprogramming at the wm4 locus , or both . On the other hand , it has been shown that many Su ( var ) mutations exhibit maternal-effect suppression of PEV [32] , such that the PEV phenotype can be modified regardless of inheritance of the Su ( var ) mutation . It has also been shown that HP1 mutations disrupt epigenetic reprogramming , causing transgenerational inheritance of epigenetic information [33] , [34] . Thus , in the aging study by Frankel and Rogina ( 2005 ) [31] , the presence or absence of the Su ( var ) 2052 mutation in the test flies may not have been accurately determined . In our current studies , we confirmed the presence of Su ( var ) 205 mutations in the coisogenic strains by both suppression of PEV and homozygous lethality ( see Materials and Methods ) . We found that heterochromatin levels are essential for longevity using both gain- and loss-of-function strategies . To investigate the cause of altered life span in flies with different heterochromatin levels , we observed the behaviors of these flies by video-recording ( see Methods ) . Video playbacks show that aged flies exhibited a gradual loss of mobility and eventually became immobile ( Videos S1 , S2 , S3 ) . By quantifying their mobility ( see Methods ) , we found that , compared with wild-type controls , flies with reduced heterochromatin levels lost mobility much faster , and those with increased heterochromatin levels maintained their mobility for a longer period of time ( Figure 1C; Videos S1 , S2 , S3 ) . It has been shown in C . elegans and Drosophila that old animals die of sarcopenia ( muscle degeneration ) [35] and impaired muscle function precedes aging [36] , similar to the gradual loss of muscle function and frailty in aging humans . Since we found that heterochromatin levels influence Drosophila life span , and since the altered life span was associated with the animals' mobility , we investigated whether loss of heterochromatin is associated with muscle degeneration . We used whole-mount fluorescent immunostaining to examine the integrity of the large intestinal wall muscle , which can be visualized readily in adult flies of different ages after minimal dissection . The fly large intestinal wall muscles consist of longitudinal ( thick ) and circular ( thin ) muscle fibers ( Figure 1D , top left ) . We found that , wild-type flies exhibited progressive muscle degeneration as they aged ( sarcopenia ) , such that the gut muscle fibers gradually showed breakage starting around day 20 , and extensive breakage was seen in 40-day-old fly gut muscles . We found that heterochromatin levels affected the ability to maintain muscle integrity , with 20-day-old Su ( var ) 205+/− flies showing extensive muscle fiber breakage ( Figure 1D , top middle ) , whereas hsp70-HP1 flies maintained their muscle integrity beyond 40 days after eclosion ( Figure 1D , top right ) . We quantified the breakages in longitudinal muscle fibers in a defined area of the midgut and calculated the muscle integrity index for each genotype and age ( see Methods ) . We found that the muscle integrity indices correlate well with the mobility of flies of different genotype and age ( Figure 1D , bottom ) . These results are consistent with the differences in fly motility that we directly observed . Thus , maintenance of heterochromatin levels is essential for the maintenance of muscle structure and function , which consequently affect animal mobility and lifespan . If heterochromatin levels are important for longevity and tissue integrity , then normal aging should be accompanied by gradually decreasing heterochromatin levels . Indeed , it has been shown that normal aging in C . elegans , as well as the premature aging observed in human progeric syndromes , is correlated with changes in nuclear architecture and loss of heterochromatin [15]–[17] , [37] . Since pericentromeric heterochromatin is readily observable in enterocytes , we examined HP1 foci in enterocytes of young and old adult flies . We found that , in contrast to young flies , whose enterocytes had prominent chromocenter enriched with HP1 ( Figure 2A; top ) , old flies had much reduced levels of heterochromatin , with many nuclei in the gut epithelia lacking pronounced HP1 foci ( Figure 2A; bottom ) . Since HP1 is recruited to heterochromatin by binding to histone H3 di- or tri-methylated at lys9 ( H3K9m2 or H3K9m3 ) , heterochromatin-specific chromatin modifications , we further investigated changes in the levels of H3K9m2 in flies of different ages . Interestingly , we found that total histone H3 levels decreased with age when compared with the non-histone nuclear protein HP1 , which remained nearly constant relative to α-tubulin ( Figure 2B ) . However , total levels of H3K9m2 showed a more dramatic decrease with age , and the decrease was obvious even relative to H3 levels ( Figure 2B ) . Total H3K4m3 levels , on the other hand , showed a less dramatic decrease ( Figure S4 ) . Our results are consistent with previous reports that the levels of total histone H3 and heterochromatin marks decrease when animals age [15]–[17] , [38] , [39] . Taken together , the above observations suggest that , total histone H3 levels and their modifications by methylation , especially methylation of K9 , exhibit gradual decline when animals age . The presence of excess HP1 throughout life might help preserving H3K9 methylation , thus delaying its decline . Although HP1 protein levels control heterochromatin levels during development , HP1 is not the sole factor determining heterochromatin formation post-development , especially in aged adult flies , where we have observed a decrease in the levels of H3K9m2 , but not of HP1 . To further confirm that HP1 is not localized on heterochromatin sequences in old flies , we carried out chromatin immunoprecipitation ( ChIP ) experiments to determine HP1 occupancy on the transposable element 1360 , which is highly enriched in constitutive heterochromatin [40] . Transposable element 1360 is present in >300 copies in the fly genome and has been used as a representative sequence for global heterochromatin [26] , [40] . The ribosomal protein 49 ( rp49 ) gene is a constitutively transcribed gene normally not associated with heterochromatin and can be used as a negative control . By performing ChIP experiments using anti-HP1 antibodies followed by PCR amplification , we assessed the levels of HP1 occupancy in these sequences . Indeed , we found that HP1 was found associated with 1360 in young but not old wild-type flies ( Figure 2C ) , whereas in flies carrying the hsp70-HP1 transgene , HP1 was also found associated with 1360 even in old flies . These results are consistent with the idea that heterochromatin levels decrease with aging , and that over-expressing HP1 prevented heterochromatin decline . Taken together , these results suggest that there is a gradual loss of heterochromatin when flies age , as with C . elegans and humans , that the lack of HP1 localization to heterochromatin foci in old flies is likely due to the loss of H3K9 methylation , and that over-expression of HP1 throughout life can prevent or delay heterochromatin loss . Heterochromatin loss could cause re-expression of genes that are normally repressed by heterochromatin . We thus examined the expression of a heterochromatinized lacZ transgene in young and old DX1 flies . These flies carry a tandem array of seven P[lac-w] transgenes , but lacZ ( and white+ ) expression from these transgenes is normally repressed by DNA repeat-induced heterochromatin formation [41] . A reduction in heterochromatin can cause derepression of the lacZ gene contained in the P[lac-w] elements of DX1 flies [27] , [41] . Indeed , we found that old , but not young , DX1 flies expressed lacZ ( Figure 2D , 2E ) , consistent with the idea that heterochromatin levels decline with age . Thus , normal aging is accompanied by a gradual loss of heterochromatin in Drosophila as well . We next investigated the possible mechanism ( s ) by which heterochromatin formation promotes life span extension . It has been shown previously that H3K9 methylation and RNA interference regulate nucleolar stability [20] . Loss of HP1 or Su ( var ) 3–9 levels causes fragmentation of the nucleolus , as revealed by the nucleolar marker Fibrillarin [20] . When we examined the effects of heterochromatin levels on nucleolar morphology , which can be seen most easily in 3rd instar larval salivary gland giant nuclei , we found that conditions that decreased heterochromatin levels , such as JAK over-activation [27] or loss of STAT [26] , were associated with nucleolar instability ( Figure 3A ) . Conversely , conditions that increased heterochromatin formation , such as hop loss-of-function or HP1 over-expression [27] , were associated with a stable nucleolus: the presence of a single , round nucleolus ( Figure 3A ) . Moreover , HP1 over-expression suppressed the nucleolar fragmentation associated with hopTum-l ( Figure 3A ) . These results are consistent with previous findings that JAK overactivation disrupts heterochromatin formation and that heterochromatin formation is important for nucleolar stability [20] , [27] . Nucleolar fragmentation has been attributed to illegitimate recombination of repeated DNA sequences , resulting in instability of the highly repeated rDNA locus . Illegitimate recombination events can be assessed quantitatively by measuring the levels of extrachromosomal circular ( ECC ) DNA [20] . We isolated ECC DNA from flies of different genotypes and quantified ECC levels by calculating ECC index ( see Methods ) . Indeed , we found increased ECC levels in mutants with decreased heterochromatin , such as hopTum-l , Stat92E , and Su ( var ) 205 , and decreased ECC levels in mutants with increased heterochromatin formation , such as hop+/− ( Figure 3B ) . Interestingly , increased ECC formation due to instability of the rDNA locus has previously been shown to cause accelerated aging in yeast [21] , [22] . Taken together with our results from Drosophila , we suggest that the rDNA locus ( or nucleolus ) might be an important cellular target regulated by heterochromatin formation . Finally , we investigated the functional consequences of nucleolar instability . The nucleolus is the site of rRNA biogenesis , where precursor rRNA molecules are transcribed and processed to give rise to 18S , 5 . 8S and 28S rRNAs ( Figure 4A ) . In Drosophila , as well as in mammals , the rDNA locus consists of a few hundred rRNA transcriptional units in tandem repeats . The number of rDNA genes vastly exceeds what is needed for adequate rRNA transcription . Normally only 20 to 25 units ( <10% of the total ) are actively transcribed , while the majority of the rDNA locus is silenced presumably by unknown epigenetic mechanisms [42] . Since it has been shown that loss of heterochromatin , as in Su ( var ) 205 transheterozygotes , leads to illegitimate recombination and thus instability of the rDNA locus [20] , we investigated whether heterochromatin loss also leads to derepression of rDNA transcription . The levels of rDNA transcription can be more sensitively detected by examining the transcription of a class of transposons ( e . g . , R2 elements ) that are specifically inserted into , and are cotranscribed with , the 28S rDNA gene [42] . Normally the host “selects” a region of the rDNA locus free of R2 insertions for transcription and represses the R2-inserted rDNA units by unknown mechanisms [42] . We found that in Su ( var ) 205 transheterozygous mutants , however , transcription of R2 elements was dramatically increased by >40 fold compared to their sibling heterozygous control flies ( Figure 4B ) . This suggests that in preserving the structural integrity of the nucleolus , heterochromatin formation plays a crucial role in silencing the transcription of the majority of rDNA genes , and that loss of heterochromatin causes a dramatic increase in rRNA transcription , which could lead to an increased capacity for protein synthesis , conducive to growth and accelerated aging . To investigate whether the moderately altered heterochromatin levels that have been shown to alter lifespan ( Figure 1 ) , affect the rate of rRNA synthesis , we measured pre-rRNA transcript levels in flies heterozygous for Su ( var ) 205 or carrying an hsp70-HP1 transgene by quantitative real-time PCR ( qRT-PCR ) . Indeed , we found that flies in which HP1 is moderately over-expressed ( by basal activity of the hsp70 promoter without heat shock ) contained >50% less pre-rRNA , whereas Su ( var ) 205 heterozygous flies had levels of pre-rRNA transcripts that were >2 fold higher ( Figure 4C ) . To determine whether these altered rRNA transcription rates affect growth , and thus the body size of the adult fly , which has often been inversely associated with lifespan [43] , we measured the body size and weight of larval and adult flies with altered heterochromatin levels as mentioned above . Indeed , we found that flies moderately over-expressing HP1 had a smaller body weight , and that Su ( var ) 205−/− larvae had a larger body size ( length ) ( Figure 4D ) , and that Su ( var ) 205+/− flies had a larger body weight ( Figure 4E ) . The differences in body weight were not as pronounced as those in rRNA transcription , suggesting that body size may not be solely regulated by the rRNA transcription rate . Nonetheless , these results are consistent with the idea that changes in the rate of rRNA transcription may impact global protein synthesis and thus the growth of the organism . In summary , we have found that heterochromatin formation promotes longevity , genome stability , and suppresses rRNA transcription . The causal relationship between aging and rRNA transcription , however , awaits further investigation . It is interesting to note that factors that promote growth , such as insulin signaling and protein synthesis , usually accelerate aging , whereas inhibition of these pathways extends life span [9] , [44]–[49] . Moreover , it has been shown in yeast that the Sir2 histone deacetylase counteracts aging by inducing heterochromatin formation at the rDNA locus [21] , [22] , thereby suppressing rRNA transcription and maintaining stability of the rDNA locus . In mammals , it has been shown that heterochromatin and Sirt1 epigenetically silence rDNA transcription in response to intracellular energy status [50] . Thus , loss of rDNA silencing due to heterochromatin loss could lead to instability and increased rRNA transcription , which promotes protein synthesis in general . We suggest that suppression of rDNA transcription might be an evolutionarily conserved mechanism essential for animal longevity .
All crosses were carried out at 25°C on standard cornmeal/agar medium unless otherwise specified . Fly stocks of hopTum-l , Stat92E06346 , Su ( var ) 20505 , Su ( var ) 20502 , hop3 , hsp70-Gal4 , and UAS-eGFP were from the Bloomington Drosophila Stock Center ( Bloomington , IN ) . Fly stocks of DX1 and 6-2 mini-white+ ( J . Birchler ) , and hsp70-HP1 ( G . Reuter; L . Wallrath ) were generous gifts . All alleles used for life span analyses were extensively outcrossed before experiments ( see below ) . The following outcrossing schemes were used to minimize genetic background effects . hsp70-HP1 ( line 1; p[hsp70-HP1-eGFP , ry+] carried on the 2nd chromosome; [51] ) flies were outcrossed to a ry506 stock for ten generations , and the ry+ or CyO marker was followed to derive outcrossed hsp70-HP1/+ and CyO/+ flies , respectively . These flies were crossed to establish new “outcrossed” hsp70-HP1 ( p[ry+] ) /CyO; ry506 flies . Su ( var ) 20505/CyO flies were outcrossed to In ( 1 ) wm4 stock for ten generations , and the CyO marker or suppression of In ( 1 ) wm4 PEV was followed to derive outcrossed In ( 1 ) wm4; Su ( var ) 20505/+ and In ( 1 ) wm4; CyO/+ flies , respectively . These flies were crossed in single pairs to derive new “outcrossed” In ( 1 ) wm4; Su ( var ) 20505/CyO stocks ( the presence of Su ( var ) 20505 was confirmed by both suppression of PEV and homozygous lethality ) . For lifespan analysis , the “outcrossed” hsp70-HP1 ( p[ry+] ) /CyO; ry506 flies were crossed to In ( 1 ) wm4 flies , and the “outcrossed” In ( 1 ) wm4; Su ( var ) 20505/CyO flies were crossed to ry506 flies , and the F1 non-CyO flies were collected for lifespan analysis . “Wild-type” control flies were the F1 of In ( 1 ) wm4 and ry506 flies . An independent stock of hsp70-HP1 ( line 2; an unmarked p[hsp70-HP1-lacI] inserted in the 2nd chromosome; [52] ) was outcrossed to In ( 1 ) wm4 stock ( with a CyO chromosome “floating” ) for ten generations . An isogenic line of In ( 1 ) wm4; hsp70-HP1 ( line 2 ) /CyO flies was established from a single male , and the presence of hsp70-HP1 was confirmed by its strong enhancement of PEV . This stock was used for lifespan studies , and a line that did not enhance PEV ( which was considered not carrying the hsp70-HP1 transgene ) was used as wild-type control . To assess life span , 2-day-old females were separated from males and were transferred to fresh vials at 20 flies/vial , and were subsequently transferred to fresh vials every 2–3 days . Dead flies were counted upon each transfer . Only female heterozygotes were analyzed because hop is located on the X chromosome and the hemizygous mutants are not viable . Flies were cultured at 25°C and 70% humidity . Mouse monoclonal anti-HP1 ( C1A9; Developmental Hybridoma Bank , Iowa; 1∶200 ) , rabbit anti-H3 ( di ) mK9 ( 07-212; Upstate Biotechnology; 1∶200 ) , rabbit anti-GFP ( CloneTech; ) , and rabbit anti-Fibrillarin ( Abcam; 1∶500 ) were used as primary antibodies and fluorescent secondary antibodies ( Molecular Probes ) were used in whole-mount immunostaining . Tissues were fixed in 4% paraformaldehyde/PBS and 0 . 3% Triton-X/PBS . Stained tissues were photographed with a Leica confocal microscope . Images were cropped and minimally processed using Adobe Photoshop CS . For chromatin immunoprecipitation ( ChIP ) , adult flies of appropriate ages were snap frozen with liquid nitrogen , and then were cross-linked with 1 . 8% formaldehyde . The flies were homogenized in cell lysis buffer , and ChIP was performed as previously described [26] . Adult flies of appropriate genotypes were split into two groups , one for Hirt ECC DNA isolation as described [20] , and the other for genomic DNA isolation by standard protocols to use as controls . Typically 10 adult males were ground in 500 µl Hirt lysus buffer ( 0 . 6% SDS , 10 mM EDTA , pH 8 . 0 ) for ECC isolation or in 200 µl Buffer A ( 0 . 5% SDS , 100 mM EDTA , 100 mM NaCl , 100 mM Tris-HCl , pH 7 . 5 ) for genomic DNA isolation . Genomic DNA was quantified by spectrometry , and 100 ng of genomic DNA and an equal volume of ECC DNA were used for PCR amplification . Primer sequences used for amplifying ECC and control DNA were as previously reported [20] . The PCR products from ECC sample and genomic control sample were loaded on the same agarose gel . PCR bands were revealed by ethidium bromide staining and photographed . The level of ECC was measured as the ratio of ECC band intensity to that from genomic control run on the same gel . Three independent experiments were done for each genotype . The ECC index was calculated as the sum of each ECC/genomic ratio divided by the total number of ECC bands:Where N denotes the total number of ECC species examined ( N = 8 in this experiment ) . Flies were outcrossed as described in “Life span analysis” to minimize genetic background effects . Virgin female flies of a particular genotype were grouped by 5 in a cornmeal food vial ( without supplementing yeast ) and were passed daily to a fresh vial . Flies of different ages were video recorded for 2 min , around 9:30 AM , with a mounted digital camera . Recording was started after flies fell to the bottom of the vial by knocking the vial on a bench top . Motility scores were assigned to each fly in the video playback according to the speed with which it moved upward in the vial . Adult flies of desired ages were dissected and fixed with formaldehyde . The intestines were stained with phalloidin-fluorescein and observed with an epifluorescence microscope . At least 10 flies of each genotype and age were dissected . Each fluorescein-stained large intestine was assigned a Morphology Score ( MS ) of 0 to 10 based on the number of breakages in the longitudinal muscle fibers in a 3 gut-diameter long stretch of the midgut immediately adjacent to the hindgut . MS = 10−n , where n = the number of breakages in the defined region of the midgut . A score 10 represents prefect morphology: no muscle fiber breakage in the longitudinal muscles ( n = 0 ) . When n≥10 , usually no continuous longitudinal muscle fibers can be identified and accurate counting breakages becomes difficult . In this case , a Morphology Score of 0 was assigned . So MS represents the worst morphology . The muscle Integrity Index is defined as the total MS of each genotype and age divided by the total number of intestines observed ( N ) . Total RNA was isolated from 10 adult male flies ( 2-day-old ) of desired genotypes using the RNeasy Mini Kit ( Qiagen ) or trizol ( Invitrogen ) according to the manufacturer's instructions . One µg of total RNA and primers specific for pre-rRNA and rp49 ( control ) were used to make the first strand cDNA using Superscript III reverse transcriptase ( Invitrogen ) in 50 µl total reaction volume . The cDNA ( at 1∶100 dilution ) was used as template for qRT-PCR analysis using SYBR green based detection on a BioRad iCycler . Reactions were carried out in triplicate , and melting curves were examined to ensure the presence of single products . The levels of pre-rRNA were quantified relative to rp49 transcript levels ( control ) and were normalized to a wild-type control . The following primer pairs ( forward and reverse ) were used . rp49: tcctaccagcttcaagatgac , cacgttgtgcaccaggaact pre-rRNA ( 5′ETS ) : atcggccgtattcgaatggattta , ctactggcaggatcaaccaga
|
Aging is characterized by a progressive decline in vitality and tissue function , leading to the demise of the organism . Many models have been proposed to explain the aging phenomenon . Among the many competing and/or overlapping models is the heterochromatin loss model of aging , which posits that heterochromatin domains ( which are set up early in embryogenesis ) are gradually lost with aging , resulting in de-repression of silenced genes and aberrant gene expression patterns associated with old age . In this paper , we genetically tested the role of heterochromatin in Drosophila aging . We find that heterochromatin levels indeed affect animal lifespan and that heterochromatin represses , among other things , rRNA transcription . Loss of heterochromatin thus leads to an increase in rRNA transcription , a rate-limiting step in ribosome biogenesis and protein synthesis . We suggest that the biological functions of heterochromatin formation include controlling rRNA transcription , which might play an important role in general protein synthesis and animal longevity .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"model",
"organisms",
"genetics",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
Heterochromatin Formation Promotes Longevity and Represses Ribosomal RNA Synthesis
|
Hepatitis C virus ( HCV ) core protein is directed to the surface of lipid droplets ( LD ) , a step that is essential for infectious virus production . However , the process by which core is recruited from LD into nascent virus particles is not well understood . To investigate the kinetics of core trafficking , we developed methods to image functional core protein in live , virus-producing cells . During the peak of virus assembly , core formed polarized caps on large , immotile LDs , adjacent to putative sites of assembly . In addition , LD-independent , motile puncta of core were found to traffic along microtubules . Importantly , core was recruited from LDs into these puncta , and interaction between the viral NS2 and NS3-4A proteins was essential for this recruitment process . These data reveal new aspects of core trafficking and identify a novel role for viral nonstructural proteins in virus particle assembly .
Hepatitis C virus ( HCV ) is a major cause of acute and chronic hepatitis , cirrhosis , and hepatocellular carcinoma . HCV is an enveloped , positive-strand RNA virus classified with the Family Flaviviridae [1] . The viral genome encodes an open reading frame of ≈3011 codons that is translated as a single polyprotein , which is cleaved by viral and host proteases into at least 10 distinct products ( Figure 1A ) . The N-terminal region encodes three structural components: core protein , which forms the viral nucleocapsid , and two envelope glycoproteins ( E1 and E2 ) , which mediate viral attachment and entry . The remainder of the genome encodes the nonstructural ( NS ) proteins: p7 , NS2 , NS3 , NS4A , NS4B , NS5A and NS5B . The NS proteins mediate intracellular aspects of the virus life cycle including RNA replication , subversion of innate antiviral defense , and virus particle assembly . The precise roles of NS proteins in virus particle assembly are not clear but p7 , NS2 , NS3 , NS4A , NS4B , and NS5A all contribute to this process [2] , [3] , [4] . HCV core is a highly basic RNA-binding protein that contains three distinct functional domains [5] . Domain 1 ( amino acid ( aa ) 1–117 ) is hydrophilic and contains determinants for RNA binding and core oligomerization [6] . Domain 2 ( aa 118–177 ) forms a pair of amphipathic helices that mediate the peripheral association of core with cellular membranes [7] , [8] , [9] . Domain 3 ( aa 178–191 ) , which serves as a signal peptide for the translocation of E1 protein into the endoplasmic reticulum ( ER ) lumen , is absent from mature core protein [5] . Core is initially cleaved from the polyprotein by host signal peptidase ( SP ) ; subsequent removal of domain 3 by signal peptide peptidase ( SPP ) then yields mature core protein that forms a homodimer [6] , [9] , [10] . Following cleavage , mature core protein is targeted to lipid droplets ( LDs ) [11] , [12] , [13] . LDs are intracellular storage organelles containing a hydrophobic core of neutral lipids and cholesterol esters surrounded by a phospholipid monolayer embedded with LD-specific proteins [14] . LD biogenesis is not fully understood , but LDs are likely derived from the outer leaflet of the ER and may remain contiguous with this membrane system [15] . LD-associated proteins are presumably loaded onto LDs at sites of ER contact [16] , although vesicular transport mechanisms have not been formally excluded . The best-characterized LD-associated proteins are perilipin , adipocyte differentiation-related protein ( ADRP ) , and tail-interacting protein ( TIP ) 47 , collectively known as the PAT proteins [17] . PAT proteins are thought to regulate the dynamics of lipid acquisition , storage , and release [15] . In addition , the membrane trafficking GTPase Rab18 may associate with a subset of LDs undergoing lipolysis [18] , [19] , [20] , [21] . The role of core trafficking to LDs is not well understood . Prior work has shown that core protein recruits NS proteins and RNA replication complexes to sites adjacent to LDs [22] . Furthermore , core recruits NS5A to the surface of LDs , where they co-localize [23] . Mutations that alter the LD localization of core or that block core's ability to recruit viral NS proteins to LDs inhibit virus production [22] , [24] , [25] , [26] , suggesting that LDs are intimately involved in virus particle assembly . The site of virus budding has not been definitively determined , but the ER retention of E1-E2 [27] , [28] , the complex glycan modifications on secreted virus particles [29] , the differential effects of Brefeldin A ( BFA ) on virus assembly vs . virus secretion [30] , and analogies to closely related flaviviruses ( reviewed in [31] ) , all suggest that virus particles bud into the ER and transit through the secretory pathway . However , it is not yet clear how LD-associated core contributes to this process . We hypothesize that core protein must be trafficked from LDs into nascent virus particles at the LD-ER interface . To understand the dynamics of core protein trafficking during virus assembly , we developed methods to fluorescently label and image functional core protein in live , virus producing cells . We observed core trafficking to static ADRP-positive LDs , forming a cap on the surface . At these sites , core co-localized with the viral E2 glycoprotein and adjacent to NS3 protein , consistent with these being sites of virus assembly . We also observed highly motile ADRP-independent core puncta that represent post-LD form of core . By using pharmacologic inhibitors of virus egress and a panel of mutants blocked in virus assembly , we showed that core is recruited from sites of assembly into these puncta , and that this process requires interaction between viral NS proteins .
To better understand the trafficking of HCV core protein during virus particle assembly , we developed methods to fluorescently label and image functional core in living cells . Traditional live imaging systems often rely on the insertion of fluorescent proteins into a target protein , but the relatively large size of such tags would likely interfere with the function of core protein . We therefore genetically inserted the small tetracysteine ( TC ) peptide tag ( FLNCCPGCCMEP ) near the N-terminus of core ( Figure 1B ) within the context of the HCV Jc1 infectious clone . Importantly , insertion of this tag had only minimal effects on infectious virus production compared to untagged Jc1 ( Figure 1C ) . For both untagged Jc1 and Jc1/core ( TC ) , peak viral infectivity was observed between 48 and 72 h post-electroporation , although the infectivity titers of Jc1/core ( TC ) were slightly reduced ( 2- to 4-fold ) at each time point . This correlated with a 2 . 7-fold decrease in specific infectivity ( 0 . 16 Jc1 infectious units per RNA-containing particle vs . 0 . 06 Jc1/core ( TC ) infectious units per RNA-containing particle ) , suggesting that the small decrease in infectivity titers may have been due to an inefficiency in virus entry rather than in virus assembly . Both Jc1 and Jc1/core ( TC ) had similar biophysical profiles , with peak infectivities and specific infectivities observed in fractions with buoyant density of ≈1 . 10 g/ml ( Figures S1A and S1B ) . Furthermore , the TC-tagged form of core accumulated to similar levels as untagged core within virus-producing cells ( Figure 1D ) , and the TC insertion was retained after six serial virus passages ( data not shown ) . Together , these data indicated that the TC tag insertion was well tolerated and that the TC-tagged core protein was functional for virus assembly . To label core ( TC ) , infected Huh-7 . 5 cells were incubated with FlAsH ( green ) or ReAsH ( red ) under optimized labeling conditions ( as described in Materials and Methods ) during the peak of virus assembly , 48 to 72 h post-infection or electroporation ( Figure 1C ) . As shown in Figure 1E , specific signals were observed in Jc1/core ( TC ) -infected cells , often as bright puncta or crescents ( Figure 1E , arrowhead ) , but not in Jc1-infected cells . Importantly , these FlAsH labeling conditions had no effect on the release of infectious virus particles ( Figure 1F ) , indicating that these methods allowed us to label functional core protein during virus assembly . To further confirm that FlAsH-labeling was specific for core , Jc1/core ( TC ) -infected cells were labeled with FlAsH , fixed , and stained for core by IF ( Figure 1G ) . FlAsH and anti-core IF signal largely co-localized ( Pearson's correlation coefficient = 0 . 715 ) , confirming that FlAsH labeled core ( TC ) protein . However co-localization was incomplete , largely due to the increased signal intensity of the IF signal in a perinuclear , reticular pattern . This difference could be attributable to one or more of the following issues . First , FlAsH signal intensity decreased 4-fold during fixation and processing for IF ( Figure S2 ) , mostly likely because this dye lacks an aldehyde-reactive primary amine and may be washed out during processing . Second , indirect IF is designed to amplify weak signals , whereas FlAsH labeling binds stoichiometrically to the TC tag . Third , FlAsH is less photostable than the Alexa dye used during IF , which could bias the relative signal intensities . Fourth , small dyes and antibodies may differ in their accessibility and efficiency of protein labeling . Nevertheless , these data confirmed that the TC tag can be used to label core protein in live , virus-producing cells . To better clarify the role of core LD-trafficking during virus assembly , we created Huh-7 . 5-derived cell lines that stably expressed ADRP , a marker of storage LDs , fused to green fluorescent protein ( GFP ) or cerulean fluorescent protein ( CFP ) . As ADRP expression levels can influence LD metabolism [32] , we first characterized these cell lines for their ADRP expression , LD content , and ability to support infectious HCV particle assembly . Neither tagged-ADRP protein was overexpressed when compared to endogenous levels of ADRP expression ( Figure S3C ) . There was a modest increase in the number of LDs in Huh-7 . 5/CFP-ADRP cells ( 170 . 8±67 . 89 LDs/cell ) compared to Huh-7 . 5′s ( 128 . 0±53 . 1 LDs/cell ) , although this difference was not statistically significant ( p >0 . 05; unpaired Student's t-test ) . Furthermore , there was no difference in the volume of LDs , either with or without oleic acid supplementation ( Figure S3D ) . Importantly , both cell lines supported infectious virus production at levels comparable to non-transduced cells ( Figure S3A–B ) . These data indicated that the GFP-ADRP and CFP-ADRP cells provided suitable environments to study core-LD trafficking in live cells . To investigate core trafficking , we performed ReAsH labeling and live cell imaging of core ( TC ) in Huh-7 . 5/GFP-ADRP cells . Across multiple experiments , core consistently localized to: i ) a dim reticular pattern , most likely the ER ( Figure S3E ) [13] , [22]; ii ) caps on ADRP-positive LDs; and iii ) bright puncta that were not associated with ADRP-positive LDs ( Figure 2A ) . Notably , the caps of LD-associated core protein frequently faced the ER-like form of core , and core-positive LDs exhibited little directional movement ( Figure 2B , upper panel and Video S1 ) . In contrast , ADRP-positive LDs that lacked core were more motile . Furthermore , core puncta were small and motile ( Figure 2B , lower panel and Video S1 ) . Given the velocity of core puncta , we examined whether they were associated with microtubules by performing live cell imaging in Huh-7 . 5 cells that stably expressed fluorescent protein-tagged ß-tubulin . As shown in Video S2 , core puncta trafficked along microtubules , frequently in a retrograde direction . In contrast , motile core puncta were not associated with fluorescent protein-tagged actin filaments ( data not shown ) . Furthermore the motility of core puncta was inhibited by nocodazole , an inhibitor of microtubule trafficking ( Figure 2C and Video S3 ) . These data indicated that motile core puncta were trafficked on microtubules . To better clarify the interaction of core with LDs , and to determine whether core puncta represent small LDs that lack ADRP , we examined additional LD markers . Staining with a dye specific for neutral lipids confirmed that core localized to semi-spherical caps on the surface of LDs and to small LD-independent puncta ( Figure S3F ) . Furthermore , LD-associated core specifically trafficked to ADRP-positive LDs but not to Rab18-positive LDs ( Figures S3G and H ) , which likely represent LDs undergoing lipolysis [33] . These data confirmed that core trafficked to ADRP-positive LDs and to distinct motile puncta that were not LD-associated . To determine whether the different forms of core corresponded to sites of virus particle assembly , we examined the localization of core with respect to the viral E2 glycoprotein ( a structural component of virus particles ) or NS3 serine protease-RNA helicase ( a NS protein that has been implicated in virus assembly ) . Since we currently lack tools to image functional E2 and NS3 in live cells , these viral proteins were localized by IF in fixed cells . As seen in Figure 2D , the majority of E2 staining was in a reticular pattern , consistent with its ER retention , as well as discrete E2 puncta that co-localized with core ( Figure 2D , arrowheads ) . These core-E2 structures were frequently found adjacent to small LDs and may represent areas where core has been recruited from LDs into nascent virus particles . The frequency of such core-E2 puncta was low ( 2 . 86±0 . 54 per cell , n = 28 cells ) , which could reflect the low burst size of HCV as well as the inherent difficulties with antibody recognition of E2 [34] . In addition , we also observed core puncta that were not labeled with E2 . Similarly , NS3 also showed a reticular staining pattern , but frequently concentrated in regions adjacent to LD-associated caps of core protein ( Figure 2E , arrowheads ) . For further characterization , we looked for co-localization of core with other components of the secretory compartment , including markers for ER exit sites ( Sec16 , Sec23 ) , the ER-Golgi intermediate compartment ( ERGIC-58 ) , and Golgi ( TGN38 ) , but did not reproducibly observe co-localization with these markers ( data not shown ) . Based on these data , we hypothesize that LD-associated core caps likely represent sites of early virus particle assembly , while motile puncta may represent core-containing transport vesicles . To further clarify the relationship between core trafficking and virus particle assembly , we examined core trafficking in a mutant virus , D88 , which is blocked at an early stage of virus particle assembly due to a large in-frame deletion in the E1-E2-p7 genes [35] . The Jc1/core ( TC ) D88 mutant showed a substantial increase in core accumulation on the surface of LDs ( Figures 2F , S3I ) . While a small number of core puncta were observed , they were non-motile ( Video S4 ) , indicating that they were distinct from the motile core puncta seen in virus-producing cells . While it is not yet clear what these non-motile core puncta represent , these data showed that core accumulates on LDs and that motile core puncta were not seen when virus assembly was blocked at an early step . Our previous results suggested that motile core puncta may represent a post LD-form of core . To further investigate this hypothesis , we treated cells with BFA , a fungal metabolite that disrupts ER-Golgi trafficking by inhibiting the activation of ADP ribosylation factor 1 ( ARF1 ) [36] . The timing and dose of BFA treatment were chosen to minimize effects on HCV RNA replication [37] . Consistent with previous findings [38] , BFA treatment inhibited the secretion of virus particles as well as a model secreted protein , causing them to accumulate within BFA-treated cells ( Figure S4A–B ) . As ARF1 can regulate lipid homeostasis [39] , [40] , [41] , we also checked whether our conditions affected LD trafficking . Our BFA treatment conditions had only minimal effects on ADRP protein expression , LD number , and LD volume ( Figure S4C ) , indicating that these conditions could be used to study the trafficking of core protein when ARF1 is inhibited . To observe the effects of BFA on core localization , cells were imaged before and after BFA treatment , as well as after drug washout ( Figure 3A ) . BFA treatment increased both the number of LDs containing core and the amount of core that accumulated on each LD ( Figure 3B–D ) , suggesting a defect in core egress from LDs . After drug washout , both of these effects were relieved ( Figure 3B–D ) . BFA treatment resulted in a slight reduction of LD-independent core puncta at 4 h ( Figure 3E ) ; however , these puncta were not motile when BFA was present ( Video S5 and Figure 3I ) . Upon BFA washout , more core puncta were observed ( Figures 3E , 3H ) and core puncta quickly regained their motility ( Video S6 and Figure 3I ) . These effects were mirrored in the intracellular accumulation of virus during BFA treatment and an increase in virus secretion after BFA washout ( Figures 3F and 3G ) . The absence of motile core puncta during BFA treatment and their re-emergence upon washout suggests that they represent a post-LD form of core . Based on the above results , we expected to observe the trafficking of core from LDs into motile core puncta during live imaging studies . However , these events may be relatively rare , and FlAsH and ReAsH are prone to photobleaching , which greatly limited the number of sequential frames that could be acquired during time course experiments . To better monitor core trafficking over time , we took advantage of the dual labeling capabilities of biarsenical dyes [42] . We reasoned that infected cells could be sequentially labeled under pulse-chase conditions , first with FlAsH to label pre-existing ( “old” ) core , followed by ReAsH to specifically detect newly synthesized ( “new” ) core . Pilot experiments showed that simultaneous labeling of Jc1/core ( TC ) -infected cells with both FlAsH and ReAsH yielded green and red signals that completely overlapped , indicating that both dyes bind TC-tagged core with similar efficiency ( data not shown ) . Next , infected cells were labeled with FlAsH , then labeled with ReAsH after appropriate intervals ( Figure 4A ) . Under these conditions , newly synthesized protein was specifically detected with ReAsH , but not when protein synthesis was halted by cycloheximide treatment during the chase period ( Figure 4B , compare second and third rows ) . Furthermore , newly synthesized core did not traffic to LDs when the maturation of core was blocked by treatment with an inhibitor of signal peptide peptidase but was restored after washout of this inhibitor ( Figure S5 ) . These data confirmed that the dual labeling technique could be used to specifically label and image old and new core under pulse-chase conditions . We used dual labeling to observe the trafficking of core synthesized before and after chase periods of 2 , 8 , and 24 h ( Figure 4B ) . After 2 h , a small amount of newly synthesized core was detected in an ER-like reticular pattern or co-localized with old core in LD-associated caps ( Figure 4B , top row ) . By 8 h , new core and old core had accumulated on LDs to comparable levels , and mixed puncta ( containing both old and new core ) were abundant ( Figure 4B , second row ) . By 24 h , new core was the predominant species in both LDs and motile core puncta ( Figure 4B , bottom row ) . Similar results were also obtained when the order of FlAsH- and ReAsH-labeling were switched ( data not shown ) . In order to observe the trends of core trafficking , we quantitated LDs and motile core puncta that contained old , new , or mixed core protein over time ( Figure 4C ) . These data yielded several interesting results . First , newly synthesized core was targeted to LDs shortly after synthesis . Second , the mixing of old and new core on LDs at 8 h indicated that core-containing LDs maintain communication with the site of core synthesis for extended periods of time ( see Discussion ) . Third , the peak of mixed core in puncta ( 8 h ) was observed after the peak of mixed core on LDs ( 2 h ) , further supporting our hypothesis that motile core puncta represent a post-LD form of core . Fourth , the small proportion of motile puncta containing old and mixed core at 24 h suggested that once core leaves LDs , it is either packaged into virus particles for secretion , or turned over . We next examined the role of NS2 in core trafficking . Prior genetic and biochemical studies indicated that NS2 plays an important role in virus assembly by bringing together the viral E1–E2 glycoprotein and NS3-4A enzyme complexes [4] , [43] , [44] , [45] . We previously identified two classes of NS2 mutants with defects in virus assembly [45] , [46] . Class 1 mutants ( NS2 K27A , W35A , or Y39A ) show reduced interaction between NS2 and NS3 , and their defects in virus assembly can be suppressed by a second-site mutation in the helicase domain of NS3 , Q221L [45] , [46] . A class 2 mutant ( NS2 K81A ) has normal levels of NS2–NS3 interaction but reduced interaction between NS2 and E1–E2 , and its defect in virus assembly can be suppressed by a second site mutation in E1 , E78T [45] , [46] . When the class 1 NS2 mutation K27A was introduced into Jc1/core ( TC ) , the amount of core staining per cell increased , and specifically , core accumulated on LDs ( Figures 5A and 5B ) , similar to what was previously observed with the D88 mutation . The addition of the NS3 Q221L suppressor restored normal core trafficking ( Figures 5A and 5B ) . Based on these results , we tested additional NS2 mutants . All class 1 NS2 mutants showed increased intracellular accumulation of core ( Figure 5C , left panel ) , and specifically , core localized to LDs ( Figure S6A ) . Furthermore , core accumulation was restored to WT levels by the NS3 Q221 suppressor mutation ( Figure 5C , right panel ) . In contrast , the class 2 NS2 mutant , K81A , did not show these effects ( Figure 5C ) . These results were further confirmed by core IF with the untagged Jc1/NS2 ( K27A ) mutant in non-transduced Huh-7 . 5 cells ( Figure S6B ) . Taken together , these data indicated that the genetic interaction between NS2 and NS3 is important for proper egress of core from the surface of LDs . To further clarify whether the interaction between NS2 and NS3 is important for the recruitment of core from LDs into core puncta , we performed FlAsH/ReAsH-dual labeling and quantitated LD-associated core and motile core puncta between 48 and 72 h post-electroporation . The NS2 Y39A mutant was used for these experiments because it showed the greatest accumulation of core ( Figure 5C ) . At all time points , the Y39A mutant showed an abundance of LDs containing old core and few LDs containing mixed or new core ( Figure 5D , left panel ) . In contrast , the Y39A + Q221L double mutant showed LDs containing a mixture of old , new , and mixed core ( Figure 5E , left panel ) , which was qualitatively similar to WT ( Figure 4C , top panel ) . Importantly , the number of motile core puncta , especially those containing new core , was significantly reduced in the Y39A mutant ( Figure 5D , right panel ) but was restored in the Y39A + Q221L double mutant ( Figure 5E , right panel ) . Taken together , we conclude that the interaction between NS2 and NS3 is important for the recruitment of core from LDs into motile core puncta .
We developed methods to image functional core protein in live , virus-producing cells . The small size of the TC tag offered several advantages over fluorescent protein tagging , namely that tagged proteins are more likely to retain native function , and do not require lengthy maturation of protein-encoded fluorophores . Additionally , live cell imaging bypasses the need for fixation , which can alter the localization of LD-associated proteins [47] . In the current study , TC-tagging and labeling with biarsenical dyes had minimal effects on viral replication , core protein expression , or virus particle production . Furthermore , the optimized labeling conditions used here [48] were specific for TC-tagged core protein . Thus , these methods can be used to confidently track functional core protein . Despite repeated attempts , we did not observe FlAsH-labeled extracellular virus particles . This could be explained by several considerations . First , FlAsH label may be inefficiently incorporated into virus particles , perhaps displaced from the TC tag during RNA packaging . Alternatively , incorporated FlAsH label could be quenched or too dim to image reliably . The detection of extracellular virus particles may also be hampered by the low burst size of HCV [34] , the inherent pulse-type labeling with biarsenical dyes , and the propensity of these reagents to photobleach . Nevertheless , we were able to reliably image intracellular forms of HCV core protein . Three forms of core protein were observed in live cells: ER-associated core , LD-associated core , and motile , LD-independent puncta . On LDs , core formed polarized caps that displaced ADRP and were frequently in close apposition to ER-localized core . Similar LD-associated caps of core protein were previously seen in fixed cells by IF staining [24] , [49] , and likely reflect sites of core protein transfer between the ER and LDs . Consistent with this model , newly synthesized core was directed to LDs that already contained older core . These data suggest that core-containing LDs maintain direct communication with the ER for extended periods of time or that LDs containing old or new core can fuse to allow mixing . Large core-containing LDs were immotile during the time periods we observed , which coincided with the peak of virus particle assembly . Prior studies have shown that infection with HCV strain JFH-1 or overexpression of core protein causes microtubule-based trafficking of LDs to the perinuclear region [49] , [50] . In our hands , attempts to image core at significantly earlier times were unfruitful due to low levels of core expression and dim staining . Nevertheless , we did observe core-containing LDs clustered in the perinuclear region , suggesting that they had either formed there or moved there prior to staining . Although LD-associated core has been implicated in virus particle assembly , it has been difficult to demonstrate a direct role for core-LD trafficking in this process . Boulant and colleagues proposed that progressive trafficking of core onto LDs strongly correlates with a rise in infectious virus production [24] . Furthermore , the accumulation of core on LDs inversely correlates with the efficiency of virus production , exemplified by the different localization patterns observed for viruses with high ( Jc1 ) and low ( JFH1 ) infectious titers [22] , [26] . Similarly , we observed dramatic increases in the amount of LD-associated core with an HCV deletion mutant that is unable to assemble virus particles . In addition to LD-associated core , we also observed motile core puncta that were not LD-associated . We propose that motile core puncta represent a form of core relevant for virus assembly based on the following considerations: 1 ) the formation of motile core puncta was blocked in the D88 mutant , which is unable to assemble virus particles ( Figure 2F and Video S4 ) ; 2 ) the formation of motile puncta after BFA washout indicated that they represent a post-LD form of core ( Figure 3 ) ; and 3 ) newly synthesized core trafficked to LDs before it trafficked to motile puncta ( Figure 4 ) . These data suggest that puncta represent transport vesicles containing virus particles or an intermediate in virus particle assembly . For instance , Lai and colleagues recently showed that core may traffic to a compartment containing early endosomal markers during virus particle secretion [51] . It was interesting that core accumulated on the surface of LDs during BFA treatment ( Figure 3B–D ) , suggesting that ARF1 activity is required for the egress of core from LDs . This would be consistent with a defect in virus assembly , as was seen for the D88 mutant ( Figure 2F ) and the class 1 NS2 mutants ( Figure 5 ) . While BFA inhibited virus secretion , intracellular virus particles still assembled during treatment ( Figure 3F–G and S4A–B ) . One possibility is that virus particles made during BFA treatment may utilize the available ER-associated pool of core; presumably then BFA would cause a defect in virus assembly once this pool is depleted . In addition , BFA may slow the egress of core from LDs and reduce the rate of virus assembly . A full accounting of infectious virus production could be used to discriminate between these possibilities . Taken together , our data support the model for core trafficking depicted in Figure 6A . Core protein is synthesized on the ER and trafficked to LDs , which remain in direct or indirect communication with the ER . During virus assembly , core protein is recruited from the surface of LDs and into puncta , which likely represent transport vesicles containing virus particles or possibly a core-containing intermediate . To further explore our model of core trafficking , we utilized our live-cell imaging tools to study the role of NS2 in virus assembly . The class 1 NS2 mutants all showed a large accumulation of core around LDs and fewer core puncta , and core trafficking was restored by the NS3 Q221L second-site suppressor mutation in the viral RNA helicase . In contrast the NS2 K81A mutant showed normal core-LD trafficking . This mutant was previously found to have a distinct defect in virus particle assembly , exhibits normal levels of interaction between NS2 and NS3-4A [45] , and is not rescued by the NS3 Q221L mutation [46] . By using sequential labeling , we showed that a block in virus assembly caused an accumulation of old core on LDs and a vast reduction in newly synthesized core on LDs . Thus , the assembly defect results in either reduced synthesis of core , or increased turnover . Importantly , there was a distinct lack of puncta containing newly synthesized core . Taken together , these data strongly suggest that NS2 , and more specifically , the interaction between NS2 and NS3-4A , is important to recruit core off the surface of LDs and into core-containing puncta . Although NS2 is important for the recruitment of core from LDs , NS2 does not directly interact with core protein [43] , [44] , [45] . How then does NS2 contribute to core trafficking ? NS2 coordinates virus particle assembly by bringing together the viral structural and NS proteins ( Figure 6B ) . We propose that the interaction with NS2 may signal to NS3-4A that it is time to stop replicating viral genomes and to start packaging them . Thus , the NS2-NS3 interaction would supply RNAs for packaging; in the absence of proper interaction between NS2 and NS3-4A , core may accumulate on LDs due to a lack of viral RNA for packaging . In summary , we have developed methods to image functional core protein in live cells . This system allows us to study trafficking of core protein in virus-producing cells , and revealed several novel aspects of core trafficking , including a role for interaction between NS2 and NS3-NS4A in the egress of core from the surface of LDs .
Plasmids pJc1 and pJc1/Gluc2A were recently described [46] . Jc1/core ( TC ) was constructed in multiple steps . First , the Jc1 5′ noncoding region ( NCR ) -core junction was amplified by using KlenTaq-LA ( DNA Polymerase Technology , St . Louis , MO ) with oligos YO-0021 ( 5′-AGA CCG TGC ACC ATG AGC TTT CTC AAT TGT TGT CCT GGC TGT TGT ATG GAA CCT AGC GGA TCC ACA AAT CCT AAA CC-3′ ) and YO-0028 ( 5′-CCA CGT GCA GCC GAA CCA-3′ ) . The amplicon was cloned into pCR2 . 1-TOPO ( Invitrogen , Carlsbad , CA ) and sequenced . The modified 5′ NCR-core junction was then subcloned as a 767-bp ApaLI/AgeI fragment into pJc1 [46] by using common restriction sites . pJc1/core ( TC ) D88 was constructed by ligating a 9193-bp ClaI fragment of pJc1/core ( TC ) and a 1381-bp ClaI fragment from pJc1/NS2 ( AP ) D88 [45] . Plasmids containing NS2 mutations in a Jc1/Gluc2A background were recently described [46] . To facilitate cloning , the Gluc2A gene was inserted into pJc1/core ( TC ) by using common BsaBI and NotI sites . The EcoRI/MluI fragment from this plasmid was then subcloned into the same sites of Jc1/Gluc2A plasmids containing NS2 mutations , which served to simultaneously introduce the TC tag and excise the Gluc2A gene . Plasmids containing suppressor mutations were created in a similar manner . To construct lentiviral vectors containing fluorescent protein-tagged cellular markers , pLenti4/EGFP ( Invitrogen ) was modified to introduce a SpeI site into the multi cloning site . This was done by annealing oligos YO-0183 ( 5′-GAT CCA CTA GTC TGC AGT CCG GAC-3′ ) and YO-0184 ( 5′-CAC TAG TCT GCA GTC CGG ACT CGA-3′ ) and ligating them into pLenti4/EGFP cut with BamHI and XhoI to create pLenti/MCS . The NheI/Bsp120I fragment from pLA4 ( a kind gift of J . McLauchlan [52] ) was subsequently cloned into the SpeI/Bsp120I sites of pLenti/MCS to generate pLenti/GFP-ADRP . To create a CFP-tagged version , the NheI/BsrGI fragment of pCerulean-C1 was subcloned into the XbaI/BsrGI sites of pLenti/GFP-ADRP . Fluorescent protein-tagged ADRP was shown to co-localize with anti-ADRP antibody by IF ( data not shown ) . To construct a fluorescent protein-tagged tubulin vector , the tubulin gene was excised from pmTFP-tubulin ( Allele Biotechnology , San Diego CA ) by using BglII and BamHI and subcloned into the BglII site of pTagRFP-C ( Evrogen , Moscow Russia ) previously modified to contain a S158T mutation that enhances photostability [53] . The mTagRFP ( T ) -tubulin gene fusion was then excised with NheI and SacII and subcloned into the SpeI/SacII sites of pLenti/MCS to create pLenti/RFP-tubulin . To express red fluorescent protein-tagged Rab18 , mTagRFP ( T ) was subcloned into pEGFP-Rab18 ( a gift from I . Derre ) by using common AgeI and BglII sites . A BamHI fragment containing the mTagRFP ( T ) -Rab18 fusion was then subcloned into pLenti/MCS to create pLenti/RFP-Rab18 . For ER-labeling , pmTagRFP ( T ) was modified to introduce a signal peptide by annealing oligos YO-0412 ( 5′-CTA GCC GCC ACC ATG GCC CTG CTG AGC GTG CTG CTG CTG CTG GGC CTG CTG GGC CTG GCC GTG GCC AAG-3′ ) and YO-0413 ( 5′-GAT CCT TGG CCA CGG CCA GGC CCA GCA GGC CCA GCA GCA GCA GCA CGC TCA GCA GGG CCA TGG TGG CGG-3′ ) . The plasmid was further modified to incorporate an ER retention signal ( KDEL ) by annealing oligos YO-0426 ( 5′-GAT CCC GCG CGC CAA GCT TG-3′ ) and YO-0415 ( 5′-AAT TCT CGA GTT ACA GCT CGT CCT TCT T-3′ ) to create pmTag/RFP-KDEL . The signal peptide-RFP-KDEL fusion was then excised by using NheI and XhoI sites and ligated into the SpeI-XhoI sites of pLenti/MCS to create pLenti/RFP-KDEL . pGalT-CFP was a kind gift of N . Altan-Bonnet . Huh-7 . 5 ( a kind gift of C . M . Rice [54] ) and 293T cells were maintained in Dulbecco's modified Eagle medium ( Invitrogen ) containing 10% fetal calf serum ( Hyclone , Waltham MA ) and 1 mM nonessential amino acids ( Invitrogen ) . Huh-7 . 5 cell lines expressing fluorescent-tagged proteins were derived by lentiviral transduction . Briefly , lentiviral vectors were packaged in 293T cells by using the ViraPower packaging system ( Invitrogen ) and used to transduce Huh-7 . 5 cells . Stable cell lines were selected and maintained by using standard growth medium containing 100 µg/ml Zeocin ( InvivoGen , San Diego CA ) . Antibodies used for immunoblotting and IF included anti-β actin ( Sigma , St . Louis MO ) , anti-core C7-50 ( Affinity BioReagents , Waltham MA ) , anti-E2 C1 ( a kind gift of D . Burton [55] ) , anti-GFP ( Invitrogen ) and anti-NS3 ( Virogen , Watertown MA ) . Bodipy 493/503 ( Invitrogen ) was used to label neutral lipids as per manufacturer's instructions . Transient transfections were performed by using TransIT LT1 ( Mirus , Madison WI ) or Fugene 6 ( Roche , Indianapolis IN ) transfection reagents according to manufacturer's instructions . Nocodazole ( Sigma ) was dissolved at 25 mM in dimethylsulfoxide and diluted to 50 µM final concentration . ( Z-LL ) 2-ketone ( EMD BioSciences , Gibbstown NJ ) was dissolved at 100 mM in dimethylsulfoxide and diluted to 100 µM final concentration . Cycloheximide ( MP Biomedical , Costa Mesa , CA ) was dissolved at 100 mM in ethanol and diluted to 30 µM final concentration . Brefeldin A ( BFA; Sigma ) was dissolved at 5 mg/ml in ethanol and diluted to 1 or 5 µg/ml final concentration . The amount of BFA used and the time of BFA treatment were optimized in pilot experiments by measuring Gaussia luciferase secretion ( Figure S3A ) , virus secretion ( Figure S3B ) , and GalT-GFP redistribution ( data not shown ) . Oleic acid ( Sigma; 400 ng/well ) was added to cells 30 min prior to imaging . Cells were transfected with HCV RNA transcripts by electroporation , as previously described [56] and seeded onto coverslips in 24-well plates , or into glass-bottom plates ( MatTek , Ashland MA ) . For infection experiments , cells were seeded onto coverslips or glass-bottom plates , and inoculated with virus at 12–24 h post seeding . Luciferase assays were performed on viral supernatants as previously described [46] . Absolute values of viral infectivity were determined by using an endpoint dilution assay as previously described [57] . Briefly , virus was serially diluted into complete growth medium and each dilution was used to infect multiple wells of a 96-well plate . Fifty percent endpoint dilutions were calculated after wells were immunostained with anti-NS5A 9E10 antibody . To measure intracellular infectivity , cells were harvested by trypsinization at 48 h post-transfection , centrifuged at 1200 rpm for 5 min , resuspended in complete medium and subject to three rounds of freezing ( −196° in liquid nitrogen ) and thawing ( 37°C ) . Cellular debris was removed by centrifugation at 10 , 000 rpm for 5 min and supernatants tested for infectivity as described above . Preformed iodixanol gradients were prepared on a Gradient Master 107 ( BioComp , Fredericton , Canada ) by using 10 and 40% ( w/v ) Optiprep ( Axis-Shield , Oslo , Norway ) in PBS . For equilibrium sedimentation , 1ml of each virus was layered on top of the gradient and centrifuged at 40 , 000 rpm for 14 h at 4°C in an SW-41rotor . Fractions ( 1ml ) were collected from the bottom of the gradient after tube puncture with a fraction collection system ( Brandel , Gaithersburg MD ) . Buoyant densities were determined by refractometry on a handheld refractometer ( Reichert , Depew NY ) . RNA was extracted from fractions or cell culture medium and quantitated using real-time reverse transcription ( RT ) PCR as previously described [46] . Briefly , reactions were run with the LightCycler RNA amplification . HybProbe kit ( Roche Applied Sciences , Mannheim , Germany ) containing 2 µl RNA sample or RNA quantitation standards , 8 mM MgCl2 , 375 nM each primer , 250 nM probe , and 1 U RNase inhibitor ( United States Biochemical , Cleveland , OH ) . Reactions were run on a Roche LightCycler 480 . Specific infectivity was calculated as the infectivity per RNA copy . Cells were labeled with FlAsH or ReAsH reagents ( Invitrogen ) between 48 and 72 h post-infection or post-electroporation with Jc1/core ( TC ) . Immediately prior to diluting , either reagent was mixed with 10 µM 1 , 2-ethanedithiol ( EDT; Sigma ) . The biarsenical dye-EDT mixture was further diluted in Hanks Balanced Salt Solution ( HBBS; Invitrogen ) containing 1 mM sodium pyruvate ( Invitrogen ) , 10 mM glucose ( Sigma ) , 1 mM Patent Blue V ( Sigma ) and 20 µM Disperse Blue 3 ( Sigma ) . Prior to use , Disperse Blue 3 was purified by recrystallization from toluene . After 2 brief washes in HBBS , cells were incubated with FlAsH or ReAsH-containing labeling mixture for 20 min at 37°C , then washed 3 times at 37°C for 10 min with HBSS containing 1 mM 2 , 3-dimercapto-1-propanol ( British anti-Lewisite [BAL]; Sigma ) , and further washed with HBSS prior to imaging . For dual labeling experiments , cells were labeled with 2 µM FlAsH , as described above , for 40 min . Immediately after incubation , cells were briefly rinsed once with HBSS and complete growth medium added to each dish . Cells were incubated for 2 to 24 hours , before being labeled with 0 . 32 µM of ReAsH , as described above , and washed with HBSS containing BAL . Cells seeded on coverslips were fixed with 4% paraformaldehyde ( PFA ) for 20 min at room temperature ( RT ) , then washed twice in PBS containing 100 mM glycine . Cells were permeabilized and blocked in PBS containing 1% BSA ( Sigma ) with 0 . 1% saponin ( Sigma ) . Primary antibodies ( diluted in the same solution ) were added for 1 to 3 h at RT before being washed off with PBS . Secondary Alexa-Fluor antibodies ( 488 or 568; Invitrogen ) were used to label the primary signal . After 1 h incubation and washing , the coverslips were mounted on slides by using Prolong Antifade Gold reagent ( Invitrogen ) . Live cell confocal imaging was performed by using a 60× objective on a Nikon TE2000 PFS-2 microscope ( Nikon , Tokyo Japan ) equipped with a Perfect Focus system ( Nikon ) , Volocity spinning disc confocal unit ( Improvision , Waltham MA ) , and live cell chamber ( 37°C , 5% CO2; Pathology Devices , Westminster MD ) . Fixed cell fluorescence was performed by using the same instrument without the live cell chamber or Perfect Focus system . Images were edited by using Volocity ( Improvision ) , ImageJ ( NIH ) , or Photoshop ( Adobe , San Jose CA ) software . Single particle tracking was performed by using the Volocity Quantitation software package ( Improvision ) . Quantitation of images was performed using the Measurement function in the Volocity software package . To quantify the total number of core particles , the number of objects with volume >0 . 5 µm3 and pixel intensity in the FlAsH or ReAsH channel above background were counted . Background was calculated for each channel as three times the mean pixel intensity of mock-infected cells . Similar methods were used to calculate total LD numbers . LD-associated core was measured by selecting objects with volume >0 . 5 µm3 , and pixel intensities greater than background in both the core and ADRP channels . Total core volume was estimated by summing the volume of core particles within the cell . Total cell volume was estimated by tracing the cell outline and using Volocity volume calculations . Relative core per cell was calculated by dividing total core volume by total cell volume . To calculate the surface area of LDs occupied by core , the surface area of core was determined as the sum of the voxels and expressed as a percentage of the total surface area of the LD . Co-localization was performed in ImageJ by using the Co-localization Finder and PSC Co-localization plugins as previously described [58] . Quantitations performed in Figures 3 through 5 were done in a blinded manner . Huh-7 . 5 cells ( seeded in 6-well plates ) were washed twice with Dulbecco's PBS , lysed in 400 µl sample buffer ( 50 mM Tris [pH 6 . 8] , 100 mM dithiothreitol , 2% [wt/vol] SDS , 10% [vol/vol] glycerol , 0 . 1% [wt/vol] bromophenol blue ) , and homogenized by multiple passes through 22- and 28-gauge needles . Electrophoresis and western blotting were performed as previously described [46] . Statistical analyses ( unpaired Student's t-test ) were performed by using Prism 4 software ( GraphPad , La Jolla , CA ) .
|
Hepatitis C virus ( HCV ) infects almost 200 million people worldwide , causing both acute and chronic liver disease . Although some antiviral treatments exist , they are not fully effective against all HCV genotypes and have serious side effects . In order to develop more effective treatment strategies , a better understanding of how HCV replicates in infected cells is required . In our study , we developed methods to visualize early steps in HCV particle assembly by fluorescently labeling core protein , a structural component of the virus . Soon after protein translation , core trafficked to the surface of large , immobile lipid droplets that were adjacent to sites of virus assembly . Core was also observed in highly motile puncta that traveled along microtubules . By using inhibitors of virus assembly and assembly-deficient viral mutants , we showed that core is recruited from lipid droplets into these puncta , and that this process was mediated by the interaction of HCV nonstructural proteins . Our work describes new methods to study the trafficking of core protein in infected cells , allowing us to better define aspects of infectious HCV particle assembly .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biology"
] |
2011
|
Trafficking of Hepatitis C Virus Core Protein during Virus Particle Assembly
|
In recent years , the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner . One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage , even to highly connected regions . However , these highly connected nodes may not be the most critical regions of the brain network , and it is unclear how the network dynamics are impacted by removal of these key nodes . This work seeks to further investigate the resilience of the human functional brain network . Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest ( ROI ) networks of 5 healthy volunteers . Networks were attacked at key nodes using several criteria for assessing node importance , and the impact on network structure and dynamics was evaluated . The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks , both in terms of network structure and dynamics .
Complex systems may be represented as networks by modeling the system components as nodes and the interactions between components as links , and graph theory methods and dynamical simulations may then be applied to these networks in order to understand their structure and dynamics . The human brain is an example of such a system that can be described as a network . The functional relationships between brain regions , typically measured using imaging techniques such as functional magnetic resonance imaging ( fMRI ) , can be described as a brain network; in particular , nodes represent various brain regions and edges represent strong coherence among the nodes . For a review of the construction and analysis of functional brain networks , we refer the reader to [1] and [2] . An exciting finding since the advent of brain network research was that the functional brain network can withstand extensive damage , even to highly connected regions [3] . In this prior work , regions of the brain network were systematically attacked based on their degree , the number of links to which each region was functionally connected . Regions having the highest degree were eliminated and the associated changes on network topology were evaluated . Then the next highest degree nodes were identified and eliminated and the changes in the network topology were recorded . This process was repeated until all nodes of the network had been removed . This type of systematic removal is referred to as targeted attack , where the most critical hubs are targeted for removal . Additionally , the effect of random failure was studied by selecting nodes for removal with uniform probability . Achard et al . compared the resilience of brain networks to that of two null models , random networks and scale-free networks , since the level of robustness of these networks had been studied previously [4] . Random networks , where the majority of nodes have a similar number of connections ( or degrees ) , proved to be highly resilient to both targeted attack and random failure . Scale-free networks , on the other hand , fragmented rapidly . This may be because a scale-free network is highly vulnerable at a very small number of high-degree nodes , or mega-hubs , which mediate connections among low degree nodes constituting the majority of the network [4] . Functional brain networks , while not as resilient as random networks , were shown to be far more robust than scale-free networks . It is well known that brain networks have characteristics of small-world architecture , that is a combination of high clustering for local specialization and low path length to enable distributed processing [5] , [6] , [7] . Achard et al . proposed that the resilience of the brain network was due to this small-world architecture . Furthermore , Achard et al . observed that the functional brain network degree distribution followed an exponentially truncated power law , meaning that there are fewer mega-hubs and a greater number of mid-degree nodes than would be expected in a scale-free distribution . This exponentially truncated power law distribution also likely contributed to the resilience against targeted attacks of hubs . However , it is possible that the highest degree nodes are not the most critical nodes of the brain network [8] . There are many measures of node importance , or centrality . Each centrality metric has a different consideration for the topological properties that make a node central , and therefore different centrality metrics may be more appropriate for different networks and their specific information flow processes [9] . Furthermore , it is unclear how the removal of these nodes may impact network dynamics in addition to topology . Alstott et al . have taken significant strides towards studying how failure of nodes in the brain network may impact network dynamics [10] . Their study involved simulating neural dynamics on structural brain networks constructed from diffusion spectrum imaging data . These simulated neural dynamics were used to create functional connectivity networks . Network nodes were eliminated based on degree , strength ( weighted degree ) , and betweenness centrality to study the effect on topology . The impact was evaluated by calculating changes in global efficiency and the size of the largest connected component . In dynamical simulations , lesions were simulated by targeting groups of nodes centered on anatomical locations . The impact of a particular lesion was evaluated by simulating neural dynamics on the lesioned networks , and noting changes in the resulting functional networks . They found that betweenness centrality had a considerable impact on network topology , and that the effect on network dynamics is highly dependent on the anatomical location of the lesion . Another study evaluated the effect of brain lesions due to stroke , traumatic brain injury , and brain tumors on functional brain network structure [11] . Specifically , Gratton et al . were concerned with the impact of lesions on brain network community structure , the topological property where network nodes tend to associate into well connected groups . Images from healthy participants and patients with lesions were used to create networks with approximately 90 nodes , in which corresponding nodes in each population were mapped to the same anatomical space . Each network was partitioned into modules ( communities ) using Newman's modularity [12] . Each node was evaluated for its within-module degree , or the number of links connecting nodes in the same module , as well as its participation coefficient , a summary metric of how diversely the node is connected to multiple modules . Gratton et al . discovered that the networks of lesioned patients had lower modularity scores when the lesions were in areas that exhibited higher participation coefficients in normal subjects . There was no statistical relationship between the within-module degree of lesioned nodes and the effect on modularity . They concluded that damage to brain regions linking multiple modules leads to a reorganization of the network that is detrimental to the entire network topology . A large body of previous work on dynamics in complex networks has been focused on artificial networks . Watts studied global cascades in random networks due to small perturbations in the signals embedded in the network [13] . In these networks , each node has a state ( either 1 or 0 ) , and it may choose to change its state based on the states of its neighboring nodes according to a threshold rule . A cascade occurs when a few nodes switch states , causing a large scale propagation of state-switching throughout a large portion of the network . Watts found that as the distribution of threshold values for state-switching was made to be more heterogenous , the system became more prone to producing large cascades . In a similar experiment studying cascades in coupled map lattices , Wang and Xu noted that the size of the cascade is highly dependent on the network structure [14] . They showed that coupled map lattices with small-world architecture or scale-free degree distributions are much more likely to exhibit large cascades due to local shocks than globally coupled ( fully connected ) lattices . Rubinov et al . designed a neurobiologically relevant dynamic model consisting of a computerized network of spiking neurons [15] . They investigated the topological factors necessary for the emergence of self-organized criticality , marked by system dynamics that are self-similar on multiple spatial and temporal scales . They found that the presence of community structure ( groups of nodes that are tightly interconnected ) , low wiring cost ( an estimation of the average distance each wire traverses across the network ) , and synaptic plasticity were all necessary components for producing self-organized criticality . Tanaka et al . studied targeted attack on networks of coupled oscillators [16] . They discovered that the removal of low degree nodes has a large effect on the dynamics of these networks while the removal of high degree nodes does not . They speculate that this is due to the fact that low degree nodes do not interact with a large number of other nodes and therefore have the ability to sustain high levels of activity . As such , the removal of low degree nodes has the potential to alter the overall activity in the system to a great extent . Despite all of the important work on the topological resilience of functional brain networks to targeted attack , and impact on the dynamics of artificial networks , it is still not clear how targeted attacks impact the dynamics in functional brain networks . In this work , we sought to expand our understanding of the resilience of the human functional brain network , both in terms of topology and dynamics . We conducted targeted attack experiments on voxel-based functional brain networks and region-of-interest ( ROI ) networks of 5 healthy volunteers . Networks were selectively attacked using several node centrality metrics to determine which centrality metric best identifies critical nodes . We measured the resulting impact on network topology using three criteria , and utilized two frameworks for assessing the dynamical impact .
All experiments were conducted in accordance with the ethical standards of the Wake Forest University institutional review board and with the Helsinki Declaration of 1975 . Functional brain networks of 5 healthy volunteers were constructed according to [8] . For each subject , 120 fMRI full-brain volumes were acquired over approximately 5 minutes . Images were corrected for motion , normalized to the MNI ( Montreal Neurological Institute ) space , and re-sliced to 4×4×5 mm voxel size using SPM99 ( Wellcome Trust Centre for Neuroimaging , Longdon , UK ) . From these volumes , one time series was extracted for each of the 15 , 996 voxels encompassing all of the gray matter of the cerebrum . Images were corrected for physiological noise by band-pass filtering to eliminate signal outside of the range of 0 . 009–0 . 08 Hz [17] , [18] , and mean time courses from the entire brain , the deep white matter , and the ventricles were regressed from the filtered time series . In the past , the practice of global mean regression has been under scrutiny due to the propensity to produce artificial deactivations , particularly in the white matter and cerebrospinal fluid ( CSF ) [19] . It is important to note , however , that failure to regress the mean signal will prevent detection of true deactivations that are known to occur in the brain . Additionally , the regions that are highly sensitive to these artifacts ( white matter and CSF ) are not considered in the present work . A full discussion on this topic can be found in [20] . The time series in each voxel was correlated with every other voxel using the Pearson's correlation coefficient . These correlation values were then represented in a correlation matrix summarizing the functional relationships between every pair of voxels . A threshold was applied to the correlation matrix , above which voxel pairs were said to be connected . This resulted in a binary adjacency matrix where 1 indicated the presence of a link and 0 indicated the absence . The threshold was defined such that the relationship between the number of nodes N and average number of connections between nodes k was consistent across subjects . Specifically , the ratio of log ( N ) to log ( k ) was the same across subjects [21] . This threshold resulted in a link density of approximately 0 . 0015 , where density is the ratio of the number of links present in the network to the number of possible links . This density is consistent with the size-density relationship of many self-organized networks described in [22] . Moreover , links defined by this threshold represented correlations that are approximately 3 standard deviations above the mean . Figure 1 depicts the process of generating the functional brain networks . Each functional brain network was selectively attacked at the nodes with the highest centrality . In particular , the top 5% highest centrality nodes were removed from the network , along with any links directly connected to those regions . After the removal of the nodes , the respective centrality measure was recalculated and another set of top 5% nodes were identified . This process was repeated until all nodes in the network were removed . Four centrality metrics were utilized , namely , degree centrality , leverage centrality , eigenvector centrality , and betweenness centrality . Degree centrality defines highly central nodes to be those having a high number of links connected to that node . Leverage centrality relates the degree of a node to that of its immediate neighbors . In particular , nodes with higher degrees than their neighbors are considered highly central to their local neighborhood [8] . Eigenvector centrality evaluates centrality based on the centrality of immediately connected neighbors , and therefore a node connected to nodes with high degree is highly central by association [23] . Betweenness centrality defines the importance of a node by the number of shortest paths between pairs of nodes on which the node lies . In this way , high betweenness nodes facilitate the exchange of information along the most efficient trajectories [24] . Formulations for these metrics are provided in [8] . In addition to targeted attacks , we also conducted random attacks by iteratively removing 5% of nodes randomly at each step . After attacking the networks , changes in the network structure were evaluated by assessing three network characteristics: local efficiency ( Eloc ) , global efficiency ( Eglob ) , and the size of the giant component ( S ) . Local and global efficiency are used to infer the efficacy of information exchange through a network by studying its topology [25] . Local efficiency quantifies the extent to which nodes communicate with immediate neighbors and can be thought of as an indication of regional specificity . Global efficiency quantifies the extent to which nodes communicate with distant nodes , and indicates the efficacy of information exchange throughout the entire network . As nodes are removed , the network may fragment into isolated subgraphs . The size of the giant component is defined to be the largest connected subgraph , and may be used to indicate the extent of fragmentation . The impact on dynamics was evaluated using two models . The first is an equation-based spreading activation model described in [26] . This model injects signal into a network , and allows the signal to spread through links and decay according to model parameters . The equation governing the spread of activation is given in Equation 1 below . ( 1 ) If N is the number of nodes in the network , St is an N×1 vector describing the signal at time t , Et is an N×1 vector containing the external signal injected at time t , γ is the relaxation rate of the signal ( 0≤γ≤1 ) , α is the relative amount of activity that flows from a node to its neighbors per unit time ( α>0 ) , and R is the N×N connectivity matrix . R was constructed by eliminating all negative connections in the correlation matrix , setting the diagonal of the matrix to 0 , and normalizing the matrix such that each column sums to 1 . Therefore R contained only weighted ( normalized ) positive connections from the original correlation matrix . External signal , E , was only present at time t = 0 , where the 50 seed nodes were set to 1 , and all other nodes were 0 . The seed nodes for the external signal were randomly selected from the population of nodes that were not deleted . The equation was iterated for 100 time steps . This spreading activation model was tested on the original network and the networks with nodes removed , where 5% through 80% of the nodes were removed in increments of 5% . By examining the total activation in the system over the course of the simulation , we evaluated the impact of removal of highly central nodes on the ability of information to spread through the network . Here , total activation is defined to be the sum of activity values across all nodes in the network at a given time during the simulation . This procedure was performed on 5 subjects . Additionally , the impact of targeting low degree nodes was examined in a single subject in order to further investigate the findings in [16] , where the targeted removal of low degree nodes had a greater impact on the dynamics of a network containing coupled oscillators than high degree nodes . For this experiment , we removed nodes that were the top 5% through 30% highest centrality nodes as well as the 5% through 30% lowest centrality nodes , in increments of 5% . Seed nodes were again randomly selected from the pool of remaining nodes in the networks . Varying the ratio α/γ results in a phase change in the spreading activation model . When α/γ is small , the total activation in the system decays to zero over time ( referred to as Phase I ) , but as α/γ increases , the system enters a regime where the activation builds exponentially in a small component of the system , referred to as Phase II [26] . We chose α = 1 and tuned gamma until the original networks exhibited Phase II behavior , resulting in α/γ = 0 . 96 . Changes in dynamics were also evaluated by embedding a coarser form of each network into an agent-based model called the agent-based brain-inspired model ( ABBM ) described in [27] . An agent-based model is a collection of agents that interact with one another by following simple rules . The rules used here were inspired by the work of Stephen Wolfram [28] , who has been a major contributor to the study of cellular automata . In this case , agents are represented by the nodes of the functional brain network , and links in the network represent communication pathways between agents . Each agent possesses a state , which can be either on or off , and may update its state based on the states of all connected neighbors by following one of Wolfram's Rules . Due to the computational demand of this model , these networks were constructed by parcellating the brain volume of each subject into 90 anatomical regions using the AAL ( automated anatomic labeling ) atlas [29] . The time series of all voxels belonging to a particular ROI were averaged in order to create 90 ROI time series . These time series were cross-correlated to construct a 90×90 ROI correlation matrix containing positive and negative connection weights . A threshold was applied to these networks to preserve only strong positive or negative connections while preventing fragmentation . Therefore , positive and negative weighted links were present in the ROI networks . The process of creating the ROI networks and the mechanisms underlying the ABBM are described in full in a prior publication [27] . These ROI networks were selectively attacked by removing 10% of the nodes ( 9 regions ) with the highest centrality , at random , or with the lowest centrality . Slight modifications to the centrality metrics were necessary in order to calculate these metrics in the weighted , signed correlation matrix . Degree was calculated as the sum of the absolute value of the weights of all links belonging to a node . Leverage and eigenvector centrality , which depend only on the degree of the node and its connected neighbors , were calculated using this definition of degree . The weighted form of betweenness was calculated on the absolute value of the correlation matrix using the MATLAB BGL package ( http://dgleich . github . com/matlab-bgl/ ) . The impact on dynamics was evaluated by testing the ability of the attacked agent-based model to solve the density classification problem , a problem originally utilized to evaluate whether a one-dimensional cellular automaton ( CA ) could support computation [30] . A CA can be thought of as belonging to a class of agent-based models , where agents are spatially embedded as adjacent cells . The goal of the density classification problem is to find a rule that can determine whether greater than half of the cells in a CA are initially in the on state . If the majority of cells are on ( i . e . density >50% ) , then by the final iteration of the CA , all cells should be in the on state . Otherwise , all cells should be turned off . The system should be able to do this from any random initial configuration of node states . The key is that each node receives input from only a few other nodes in the network . Each node must decide based on this limited information whether to turn on or off in the next time step , resulting in network-wide cooperation without the luxury of network-wide communication . The rule and model parameters that must be used in order to perform this task are identified using a search optimization technique known as genetic algorithms . We have demonstrated that the ABBM is able to perform the density classification task with a high level of accuracy across a range of densities , while null models with randomized connectivity are not successful , indicating that the topology of the brain network is amenable to computation . Here we wished to determine how targeted removal of high centrality nodes would impact performance on this task . Table 1 contains a summary of treatments of the functional brain networks used in each procedure for evaluating network structure and dynamics .
The quantities used to analyze the topological changes to the functional brain networks were the size of the giant component , global efficiency , and local efficiency . Each time the highest centrality nodes were identified and eliminated from the network , these three measures were recalculated and plotted along a curve . Figure 2 contains these curves , averaged across the networks of 5 subjects . The size of the giant component , S , was normalized to the size of the giant component of the original network ( S0 ) . As the network nodes were selectively removed , the size of the giant component decreased , but did not show a dramatic reduction until nearly 40% of the nodes were eliminated from the network , regardless of the type of centrality used to identify hubs ( Figure 2 ) . Eliminating these hubs steadily decreased the global and local efficiency of the network as well . When comparing the removal of nodes based on different centrality metrics , removing nodes with high eigenvector centrality had the least effect on the networks . Network metrics declined visibly less for the removal of high eigenvector centrality nodes compared to degree or leverage , when evaluating all three of the network metrics . Targeted attack on high betweenness nodes was not highly different from degree or leverage , but it is notable that betweenness was also not highly different from eigenvector centrality when assessing local efficiency . Table S1 , Table S2 , and Table S3 in Text S1 show the ranges where there was a statistically significant difference in the size of the giant component , global efficiency , or local efficiency depending on the type of attack . Targeted attack and random failure were also evaluated on a network with randomized connectivity . This network was generated using the method described in [31] , where a functional brain network was rewired such that the degree distribution was preserved . The size of the giant component , local efficiency , and global efficiency underwent noticeably steeper declines after targeted attack than the original brain networks . Results from this experiment can be found in Text S2 . Simulations using a spreading activation model were employed to demonstrate changes in network dynamics after targeted attack or random failure . Figure 3 contains the results of the spreading activation model using the original ( intact ) network of one subject , as well as after attacking 20% of the highest degree centrality nodes . The activity within each node was computed at each time step , and during the simulation both the intact network and the attacked network were exhibiting Phase II activity . Recall that the Phase II activity pattern is characterized by a few nodes having activation that is exponentially increasing over time , while all other nodes in the network have activation that decays rapidly to zero . In the case of the original network , 9 nodes were exhibiting exponentially increasing activity . Panel A contains the two-dimensional color map of the time-series for the 9 nodes with exponentially increasing activity . The total activity in the network over the course of the simulation , defined to be the sum of activation across nodes at a given time step , is plotted in panel B . After the network was attacked , the number of nodes with exponential activity increased to 14 , as pictured in panel C . The increase in nodes with building activity caused the total activity in the network , shown in panel D , to increase relative to the intact network . Figure 4 shows the total activity achieved at the end of the simulation ( t = 100 ) , depending on the percentage of nodes removed using the four centrality metrics and for random failure . The original total activity is included , shown at 0% removed . These curves illustrate that the total activity achieved in the network increased depending on the extent of attack when high centrality nodes are targeted . Total activity was maximal when high degree and betweenness nodes were removed . Total activity actually decreased after random failure but removing further nodes had little effect beyond 5% . All targeted attack curves in Figure 4 show a peak in total activity at a certain percentage of nodes removed . The peak in the curves corresponding to removal of high degree nodes occurs at 35% , and the peak corresponding to removal of high betweenness and eigenvector centrality nodes occurs at 40% . However , the curve corresponding to removal of high leverage nodes occurs much sooner , after 20% of nodes have been removed . According to Equation 1 , the signal in a node is the sum of any external signal , previous activity that has not yet decayed , and new activity received from neighbors . The first two factors are not directly impacted by network attacks , as the seed nodes used for initial external signal are held constant as attacks are performed , and the decay factor is not dependent on the connectivity matrix . However , the changes in network connectivity will impact the spread of activity from neighboring nodes . Leverage centrality is designed to identify nodes that are connected to more nodes than their neighbors , and therefore control the content and quality of the information received by their neighbors . Therefore , leverage centrality tends to identify hubs whose directly connected neighbors would be negatively impacted by the loss of those hubs . As high leverage centrality nodes are removed , the remaining nodes that are highly dependent on high leverage nodes are not receiving as much signal . Therefore , leverage has the largest effect on hindering the spread of activation as measured via the peak in the total activation curves . Despite the change in total activation , full activation curves demonstrated that , in the majority of cases , the networks remained in Phase II after targeted attack or random failure ( Figure 5 ) . The exceptions were networks where 70–80% of high degree centrality nodes were removed . These networks exhibited Phase I behavior , in which the total activity in the network decayed to zero . Activation curves are shown for the original network and after removing 20% , 40% , 60% , and 80% of nodes . While targeted attack of high centrality nodes generally increased the total activity in the network ( to a point ) , random failure decreased the total activity for all levels of node removal . One-sample t-tests were performed to compare the final total activity across the 5 subjects after removing 20% , 40% , 60% , and 80% of the nodes . Text S1 contains the resulting statistics . Total activity curves after removing high centrality hubs and low centrality antihubs are shown in Figure 6 for a single subject . Recall that the seeds selected for this experiment were different from those used in previous experiments . Here , seed nodes were randomly chosen from the population of nodes not selected for removal as high centrality hubs or low centrality antihubs . It was necessary that seed nodes not be removed throughout the simulations in order keep the initial external signal , which originated at seed nodes , constant . As the seed nodes for this simulation were unique from the ones used previously ( Figures 3–5 ) , these networks achieved higher total activity values . As hubs were attacked , the total activation increased as in the previous simulation . In this case , the peak and subsequent decrease in final total activity are not captured , as in Figure 4 , although the leverage curve peaked at 20% of nodes removed in the previous experiment . The profiles of the degree , leverage , betweenness , eigenvector , and random curves are similar to Figure 4 . On the other hand , as antihubs were attacked , the activation decreased to a slightly greater extent than random . Spreading activation experiments were also performed with seed nodes selected from the auditory cortex . However , the choice of seed nodes does not appear to change the observed dynamics in the spreading activation model . The results of this experiment can be found in Text S3 . In addition to the spreading activation model , simulations using an agent-based brain-inspired model ( ABBM ) were used to evaluate the impact of targeted attack and random failure on the ability of the ABBM to support global computation . A genetic algorithm was used to identify model parameters that enabled the ABBM to solve the density classification task using the original ( intact ) network ( see Materials and Methods for details ) . The ABBM was then asked to solve the density classification task using the same parameters while operating on the networks with nodes removed . Accuracy curves were generated for each subject in order to evaluate the impact of targeted attack of hubs or antihubs and random failure on the ability of the model to solve this task . Mean accuracy curves , averaged across all subjects , are shown in Figure 7 . On the left half of the density axis , where density <0 . 5 , fewer than half of the nodes were on at the first time step . To the right , where density >0 . 5 , greater than half of the nodes were initially on . All curves have a pronounced decrease in accuracy around density = 0 . 5 , where the classification becomes more difficult . These accuracy curves show that , despite loss of highly central nodes , the ABBM maintains a high level of accuracy in solving the density classification task . This would suggest that the nodes that would be considered to be the most structurally integral components of the network have only marginal importance in information flow . On the other hand , the impact of random failure is greater than any type of targeted attack , specifically in higher density ranges . An ANOVA comparing mean accuracy across attack types revealed that targeting low centrality antihubs resulted in significantly decreased accuracy when compared to targeting hubs in only a select number of cases . Differences were found between leverage antihubs and eigenvector antihubs at density = 0 . 46 ( mean difference 0 . 028 , p = 0 . 019 ) , leverage hubs and eigenvector antihubs at density = 0 . 53 ( mean difference 0 . 064 , p = 0 . 040 ) , degree hubs and eigenvector antihubs at density = 0 . 61 ( mean difference 0 . 046 , p = 0 . 013 ) , and leverage hubs and betweenness antihubs at density = 0 . 61 ( mean difference = 0 . 058 , p = 0 . 019 ) . There were no significant differences in accuracy using the intact network versus any of the attacked networks .
We have presented a study on the topological and dynamical effects of targeted attack and random failure in human functional brain networks . Structural analyses employing local and global efficiency as well as the size of the giant component corroborate the findings presented in [3] in which the authors measured changes in the largest cluster size and the path length in functional brain networks , and further demonstrate that the choice of hub does not change the results appreciably . For any given centrality metric , nearly 40% of the nodes were removed before the size of the giant component qualitatively diverged from the random failure curve , which underwent a steady decrease as nodes were removed ( although statistically significant differences exist much earlier ) . The reduction in local and global efficiency due to targeted attack followed curves only slightly steeper than random failure , with the effect on local efficiency somewhat greater than global efficiency . Global and local efficiency capture characteristics of the network structure that lend themselves to efficiency of information transfer . High local efficiency indicates topology that is conducive to local processing specificity , and topology with high global efficiency is amenable to long range information sharing . The topological characteristics that give the brain networks good local efficiency and reasonably high global efficiency are preserved , even when highly central nodes are targeted . Seemingly , whether high degree , betweenness , leverage , and eigenvector nodes are targeted , the result is the same: the topology of the functional brain network is relatively resilient to targeted attack . Dynamical simulations using the spreading activation model revealed similar findings for information spreading across functional brain networks . Although targeted attack modified the total activity in the system at the end of the simulation , there was no phase transition in the overall behavior . The intact networks displayed Phase II activity , characterized by a limited number of nodes exhibiting exponentially increasing activity , while the activity in all other nodes decayed to zero . Random failure had very little impact on the total activity in the system . In contrast to random failure , the total activity in the network increased initially as high centrality nodes were targeted for removal , indicating that the signal was pooling in a number of nodes to a greater extent than before the attack , driven by the α parameter in the spreading activation model . Subsequently , as an increasing number of nodes were removed from the network , the final total activity decreased . Despite these quantitative changes , there was very little qualitative change in the system across all levels of targeted attack . It is important to note that the overall qualities of the system dynamics did not change . Despite initial expectations based on the work by Tanaka et al . mentioned previously , targeted removal of low centrality antihubs , while decreasing the final total activity , did not have a greater effect than targeted removal of highly central hubs . As Tanaka et al . note , low centrality nodes can maintain higher levels of activity because they do not spread their activity to many other nodes , while high centrality nodes tend to disperse activity to many other nodes . In the spreading activation model , when low centrality nodes are removed , less activity is allowed to pool , and the decaying term ( γ ) in the spreading activation model drives the behavior . On the other hand , removing high centrality nodes and their accompanying links decreases the dispersion of activity , and furthermore allows for increased pooling by simultaneously lowering the centrality of their former neighbors; therefore the total activity in the system increases . The density classification task , rather than modeling the diffusion of information , tests whether a system can support computation . The agent-based brain-inspired model is constructed using the structure of the functional brain network . The agents in the model must make a collective decision ( turn on or turn off ) in order to solve the density classification task . As the network structure changes due to targeted attack or random failure , the information shared between nodes changes . Previously , we demonstrated that randomized connectivity patterns are not well suited to the density classification task , but that the functional brain network is . Therefore , we tested whether changes in network topology would impact the ability of the ABBM to make decisions . While targeted attack of hubs or antihubs impacted the accuracy to some degree , the average accuracy over a range of densities was still high , much higher than the accuracy of null models with randomized connectivity shown in [27] . Random failure resulted in a greater decrease in accuracy than targeted attack . The density classification task is not a trivial problem . Each agent is supplied with a limited amount of local information , and must infer the state of the entire system . Furthermore , simply using the majority rule , where an agent chooses to take the state that the majority of its neighbors have taken , is not effective at solving this task [30] . Rather , the system must evolve a complex , yet simple , rule that can solve this task over just a few time steps , and moreover can accomplish this for any initial configuration . Simply solving this problem alone is notable , but solving it after 10% of the most central hubs and their accompanying links have been removed is an even more impressive feat . The fact that random networks with the same degree distribution as the brain network cannot solve this task would indicate that the network topology that enables the brain network to solve the density classification task remains intact after removal of central nodes . Since the global efficiency of the network remains high after targeted attack , one might be tempted to conclude that the efficient long range communication in the network lends it the ability to support computation . However , random networks , which cannot solve the density-classification task , are also characterized by high global efficiency . On the other hand , elementary cellular automata and other lattice-like networks with high local efficiency have been shown to be able to solve the density classification task with high accuracy [27] , [30] . In these networks , nodes are clustered into well-connected groups and can share information readily , and therefore may be able to synchronize more easily . As high centrality nodes are targeted for removal from the voxel-wise functional brain networks , the networks maintain their high local efficiency to a much greater extent than randomized networks . In the agent-based model simulations , functional brain networks with 10% of the highest centrality nodes removed were still able to perform the density-classification task . These two findings together suggest that functional brain networks are able to perform computational tasks after targeted attack because the networks maintain their efficient local connectivity . The two models we chose to employ for modeling dynamics on functional brain networks are the spreading activation ( SA ) model designed by Shrager et al . [26] , and an agent-based brain-inspired model ( ABBM ) , originally introduced in a prior publication [27] . The SA model and the ABBM simulate the flow of information in two disparate ways . We chose the SA model , a type of diffusion model , due to its application in physiologically relevant settings over the past several decades . Spreading activation has been used in artificial intelligence applications such as studying semantic networks , natural language processing , and information retrieval , as it was designed to be a model for memory associations and recall [32] , [33] , [34] , [35] . While diffusion models are prevalent , agent-based modeling takes a somewhat different approach to simulating information flow . The ABBM is used to examine information sharing dynamics that can produce a collective behavior in the system . While the ABBM does not replicate the exact mechanisms of the brain , the method of agent-based modeling is well suited to producing emergent behaviors , which is almost certainly necessary to produce the most complex human behaviors . In the ABBM , each agent collects and integrates the information received from each of its neighbors , distills the information to a binary signal , and makes a decision on whether to fire based on that signal . Although the ABBM operates on a far coarser scale , this process mirrors action potential generation in a neuron . Other widely used models include artificial neural networks , which consist of a set of nodes which take an input , operate on the input using mathematical functions , and produce an output . The networks are then trained to perform a particular task by allowing connections and mathematical operations to change . Neural networks are used in many pattern recognition applications , such as detecting seizures in EEG data [36] , [37] . The distinction between the ABBM and neural network approaches to modeling brain functions is that the ABBM uses the network architecture determined from human functional brain imaging data , whereas the structure of neural networks is often determined by a set of features and desired outputs . By using functional brain network connectivity , the ABBM is generalized to solve different tasks without the need to re-train the network structure . Alternatively , some researchers model cognitive functions using physical microcircuits . Neural microcircuits are used in applications such as the Blue Brain Project [38] , where brain-like neural structures are modeled using a supercomputer dubbed Blue Gene . The computer consists of a network of 4 , 096 interconnected integrated circuits . The enormous computational power of Blue Gene enables the machine to solve cognitive problems using a brute force approach ( e . g . analyzing the result of any possible move in a game of chess ) . Although the computational capability of Blue Gene is impressive , the advantage of using a combination of genetic algorithms and agent-based modeling is the elimination of the need to evaluate all possible outcomes , but instead search the solution space is a systematic way . The field of network science provides a multitude of measures to capture the characteristics of complex systems , but , paradoxically , the complexity of these systems makes the task of understanding their underlying mechanisms quite challenging . The brain is intrinsically difficult to study . The measures and simulations presented here are surrogates for understanding the structural and dynamic changes that can occur in the brain . One limitation of these simulations is that they do not account for functional specialization of the various brain regions , where specific brain regions are thought to play key roles in specific functions . Certainly , many case studies in history have shown that damage to certain locations in the brain have unique effects due to functional specialization ( e . g . , the famous Phineas Gage [39] ) . The simulations presented here also do not account for neuroplasticity , which enables the brain to remap cortical functionalities in response to sustained injuries . One study by Rubinov et al . examined the impact of random failure and targeted attack of high betweenness nodes in a synthetic neuronal network with neuroplasticity . They showed that allowing for the addition of new nodes through synaptogenesis , even at rates much slower than simulated neuronal death , was able to combat the impact on global and local efficiency [40] . Perhaps incorporating similar components of neuroplasticity into the models used in this work would enable an even greater demonstration of resilience . Despite these limitations , this work progresses our understanding of the resilience of the human functional brain network . Based on the topological and dynamical simulations presented here , we conclude that the functional human brain network is highly resilient to targeted attack , both in terms of network structure and dynamics .
|
Why can the brain endure numerous micro-strokes with seemingly no detrimental impact , until one cataclysmal stroke hinders the ability to perform essential functions such as speech and mobility ? Perhaps various small regions or foci of the brain are highly important to information transfer , and the loss of such highly central foci would be severely injurious to brain function . Identification of such foci , via modeling of the functional brain using network theory , could lead to important advances with regard to brain disease and stroke . In this work , we utilized functional brain networks constructed from human volunteers to study how removing particular regions of the brain impacts brain network structure and information transfer properties . We sought to determine whether a particular measure of region importance may be able to identify highly critical regions , and whether targeting highly critical regions would have a more detrimental impact than removing regions at random . We found that , while in general targeted removal has a larger impact on network structure and dynamics , the human brain network is comparatively resilient against both targeted and random removal .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"fmri",
"biology",
"neuroscience",
"neuroimaging",
"engineering"
] |
2013
|
The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack
|
A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness . As a step towards this goal , we developed a method to examine whether individual genetic variants , or sets of genetic variants , currently influence viability . The approach consists in testing whether the frequency of an allele varies across ages , accounting for variation in ancestry . We applied it to the Genetic Epidemiology Research on Adult Health and Aging ( GERA ) cohort and to the parents of participants in the UK Biobank . Across the genome , we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3 . These results suggest that when large , even late-onset effects are kept at low frequency by purifying selection . Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits , we detected a number of strong signals . In participants of the UK Biobank of British ancestry , we found that variants that delay puberty timing are associated with a longer parental life span ( P~6 . 2 × 10−6 for fathers and P~2 . 0 × 10−3 for mothers ) , consistent with epidemiological studies . Similarly , variants associated with later age at first birth are associated with a longer maternal life span ( P~1 . 4 × 10−3 ) . Signals are also observed for variants influencing cholesterol levels , risk of coronary artery disease ( CAD ) , body mass index , as well as risk of asthma . These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry . We also found marked differences between males and females , most notably at the CHRNA3 locus , and variants associated with risk of CAD and cholesterol levels . Beyond our findings , the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans .
A number of central questions in evolutionary genetics remain open , in particular for humans . Which types of variants affect fitness ? Which components of fitness do they affect ? What is the relative importance of directional and balancing selection in shaping genetic variation ? Part of the difficulty is that our understanding of selection pressures acting on the human genome is based either on experiments in fairly distantly related species or cell lines or on indirect statistical inferences from patterns of genetic variation [1–3] . The statistical inferences rely on patterns of genetic variation in present-day samples ( or , very recently , in ancient samples [4] ) to identify regions of the genome that appear to carry the footprint of positive selection [2] . For example , a commonly used class of methods asks whether rates of nonsynonymous substitutions between humans and other species are higher than expected from putatively neutral sites in order to detect recurrent changes to the same protein [5] . Another class instead relies on polymorphism data and looks for various footprints of adaptation involving single changes of large effect [6] . These approaches detect adaptation over different timescales and , likely as a result , suggest quite distinct pictures of human adaptation [1] . For example , approaches that are sensitive to selective pressures acting over millions of years have identified individual chemosensory and immune-related genes ( e . g . , [7] ) . In contrast , approaches that are most sensitive to selective pressures active over thousands or tens of thousands of years have revealed strong selective pressures on individual genes that influence human pigmentation ( e . g . , [8–10] ) , diet [11–13] , as well as sets of variants that shape height [14–16] . Even more recent still , studies of contemporary populations have suggested that natural selection has influenced life-history traits like age at first childbirth as well as educational attainment over the course of the last century [17–23] . Because these approaches are designed ( either explicitly or implicitly ) to be sensitive to a particular mode of adaptation , they provide a partial and potentially biased picture of what variants in the genome are under selection . In particular , most have much higher power to adaptations that involve strongly beneficial alleles that were rare in the population when first favored and will tend to miss selection on standing variation or adaptation involving many loci with small beneficial effects ( e . g . , [24–27] ) . Moreover , even where these methods identify a beneficial allele , they are not informative about the components of fitness that are affected or about possible fitness trade-offs between sexes or across ages . In line with Lewontin’s proposal to track age-specific mortality and fertility of hundreds of thousands of individuals [28] , we sought a more direct and , in principle , comprehensive way to study adaptation in humans , focusing on current viability selection . Similar to the approach that Allison took in comparing frequencies of the sickle cell allele in newborns and adults living in malarial environments [29] , we aimed to directly observe the effects of genotypes on survival by taking advantage of the recent availability of genotypes from large cohorts of individuals of different ages . Specifically , we tested for differences in the frequency of an allele across individuals of different ages , controlling for changes in ancestry and possible batch effects . This approach resembles a genome-wide association study ( GWAS ) for longevity yet does not focus on an end point ( e . g . , survival to an old age ) but on any shift in allele frequencies with age . Thus , it allows the identification of possible nonmonotonic effects at different ages or sex differences . Any genetic variant that affects survival by definition has a fitness cost , even if the cost is too small to be effectively selected against ( depending on the effective population size , the age structure of the population , and the age at which the variant exerts its effects [30] ) . Of course , a genetic variant can influence fitness without influencing survival through effects on reproduction or inclusive fitness . Our approach is therefore considering only 1 of the components of fitness that are likely important for human adaptation . As a proof of principle , we applied our approach to 2 recent data sets: to 57 , 696 individuals of European ancestry from the Genetic Epidemiology Research on Adult Health and Aging ( GERA ) cohort [31 , 32] and , by proxy [33–35] , to the parents of 117 , 648 individuals of British ancestry surveyed as part of the UK Biobank [36] . We did so for individual genetic variants then jointly for sets of variants previously found to influence 1 of 42 polygenic traits [37–40] .
If a genetic variant does not influence viability , its frequency should be the same in individuals of all ages . We therefore tested for changes in allele frequency across individuals of different ages , while accounting for systematic differences in the ancestry of individuals of different ages ( for example , due to migration patterns over decades ) and genotyping batch effects . We used a logistic regression model in which we regressed each individual’s genotype on their age bin , their ancestry as determined by principal component analysis ( PCA ) ( S1 Fig ) , and the batch in which they were genotyped ( see Materials and methods for details ) . In this model , we treated age bin as a categorical variable; this approach allowed us to test for a relationship between age and the frequency of an allele regardless of the functional form of this relationship . We also tested a model with an interaction between age and sex to assess whether a variant affects survival differently in the 2 sexes . We first evaluated the power of this method using simulations . We considered 3 possible trends in allele frequency with age: ( i ) a constant frequency up to a given age followed by a steady decrease , i . e . , a variant that affects survival after a given age ( e . g . , variants contributing to late-onset disorders ) , ( ii ) a steady decrease across all ages for a variant with detrimental effect throughout life , and ( iii ) a U-shaped pattern in which the allele frequency decreases to a given age but then increases , reflecting trade-offs in the effects at young and old ages , as hypothesized by the antagonistic pleiotropy theory of aging [41] or as may be seen if there are protective alleles that buffer the effect of risk alleles late in life [42] ( Fig 1 ) . In all simulations , we used sample sizes and age distributions that matched the GERA cohort ( S2 Fig ) . For simplicity , we also assumed no population structure or batch effects across age bins ( see Materials and methods ) . For all trends , we set a maximum of 20% change in the allele frequency from the value in the first age bin ( Fig 1 ) . Because of the age distribution of individuals in the GERA cohort ( S2 Fig ) , our power to detect the trend is greater when most of the change in allele frequency occurs in middle age ( Fig 1 ) . For example , for an allele with an initial allele frequency of 0 . 15 that begins to decrease in frequency among individuals at age 20 , age 50 , or age 70 years , there is around 20% , 90% , and 60% power , respectively , to detect the trend at P < 5 × 10−8 , the commonly used criterion for genome-wide significance [44] . We also experimented with a version of the model in which the age bin is treated as an ordinal variable; as expected , this model is more powerful if there is in fact a linear relationship between age and allele frequency . Because we do not know the functional form of the relationship between age and allele frequency a priori in most cases , we used the categorical model for all analyses unless otherwise noted . In the UK Biobank , all individuals were 45–69 years old at enrollment , so the age range of the participants is restricted and our method has low power . However , the UK Biobank participants reported the survival status of their parents: age of the parents if alive or age at which their parents died; following recent studies [33–35] , we therefore used these values ( when reported ) instead in our model . In this situation , we are testing for correlations between an allele frequency and father’s or mother’s age ( if alive ) or age at death ( if deceased ) . This approach obviously comes with the caveat that children inherit only half of their genome from each parent and so power is reduced ( e . g . , [45] ) . Furthermore , the patterns expected when considering individuals who have died differ subtly from those generated among surviving individuals . Notably , when an allele begins to decline in frequency starting at a given age ( Fig 1A ) , there should be an increase in the allele frequency among individuals who died at that age followed by a decline in frequency , rather than the steady decrease expected among surviving individuals ( S3 Fig , see Materials and methods for details ) . In a first analysis , we therefore focused on the majority of participants who reported father’s or mother’s age at death , 88 , 595 and 71 , 783 individuals , respectively . We compared the results of this approach with the results of a Cox proportional hazards model [46] , which allowed us to include individuals who reported their parents to be alive but has the disadvantage of assuming fixed effects across all ages . We further adapted this model to allow us to test for changes in frequency at sets of genetic variants jointly . Many phenotypes of interest , from complex disease risk to anthropomorphic and life-history traits such as age at menarche , are polygenic [47 , 48] . If a polygenic trait has an effect on fitness , either directly or indirectly ( i . e . , through pleiotropic effects ) , the individual loci that influence the trait may be too subtle in their survival effects to be detectable with current sample sizes . We therefore investigated whether there is a shift across ages in sets of genetic variants that were identified as influencing a trait in GWASs ( S1 Table ) . Specifically , for a given trait , we calculated a polygenic score for each individual based on trait effect sizes of single variants previously estimated in GWASs and then tested whether the scores vary significantly across 5-year age bins ( see Materials and methods for details ) . These scores are calculated under an additive model , which appears to provide a good fit to GWAS data [49] . If a polygenic trait is under stabilizing selection ( e . g . , human birth weight [50] ) , i . e . , an intermediate polygenic score is optimal , no change in the mean value of polygenic scores across different ages is expected . However , if extreme values of a trait are associated with lower chance of survival , the spread of the polygenic scores should decrease with age . To consider this possibility , we tested whether the squared difference of the polygenic scores from the mean varies significantly with age ( see Materials and methods for details ) . We first applied the method to the GERA cohort using 8 , 868 , 517 filtered genotyped and imputed autosomal biallelic single-nucleotide polymorphisms ( SNPs ) and indels . We focused on a subset of 57 , 696 filtered individuals who we confirmed to be of European ancestry by PCA ( see Materials and methods , S4 and S5 Figs ) . The ages of these individuals were reported in bins of 5-year intervals ( distribution shown in S2 Fig ) . We tested for significant changes in allele frequencies across these bins . For each variant , we obtained a P value comparing a model in which the allele frequency changes with age to a null model . No inflation was detected in the quantile-quantile plot ( S6A Fig ) , indicating that , for common variants at least , our control for population structure ( and other potential confounders ) is sufficient . To illustrate this point , we looked at the lactose intolerance-linked SNP rs4988235 within the LCT locus , which is among the most differentiated variants across European populations [11]; the trend in the expected allele frequency based on the null model ( i . e . , accounting for confounding batch effects and changes in ancestry ) tracks the observed trend quite well ( S7 Fig ) . By our approach , all variants that reached genome-wide significance ( P < 5 × 10−8 ) reside on chromosome 19 near the APOE gene ( Fig 2A and S8 Fig ) . This locus has previously been associated with longevity in multiple studies [51 , 52] . The APOE ε4 allele is known to increase the risk of late-onset Alzheimer disease ( AD ) as well as of cardiovascular diseases [53 , 54] . We observe a monotonic decrease in the frequency of the T allele of the ε4 tag SNP rs6857 ( C , protective allele; T , risk allele ) beyond the age of 70 years old ( Fig 2B ) . This trend is observed for both the heterozygous and homozygous risk variants ( S9 Fig ) and for both males and females ( S10 Fig ) . No variant reaches genome-wide significance testing for age by sex interactions ( quantile-quantile plot shown in S6B Fig ) . We further investigated the trends in frequency with age for the other 2 major APOE alleles defined by rs7412 and rs429358 SNPs: ε2 ( rs7412-T , rs429358-T ) and ε3 ( rs7412-C , rs429358-T ) , while ε4 is known by rs7412-C and rs429358-C alleles . Unlike the ε4 allele , ε2 allele carriers are suggested to be at lower risk of AD , cardiovascular disease , and mortality relative to the ε3 carriers [51 , 55] . We focused on a subset of 38 , 703 individuals with unambiguous counts of each APOE allele . There is a significant change in the frequency of the ε4 allele with age in this subset ( P~6 . 0 × 10−12 ) , similar to the trend observed for the tag SNP rs6857 ( S11 Fig ) . The ε3 allele shows the reverse trend , with a significant , monotonic increase in frequency beyond the age of 70 years old ( P~1 . 7 × 10−8 ) ( S11 Fig ) . The enrichment of the ε3 allele in elderly individuals can be explained by the corresponding depletion of the ε4 allele and does not necessarily imply an independent , protective effect of the ε3 allele . The frequency of the ε2 allele does not change significantly with age ( P~0 . 21 ) , possibly reflecting low power given its allele frequency of approximately 0 . 06 ( S11 Fig ) . We considered the possibility that some unobserved confounding variable was driving the strength of this signal at APOE . Since there are 2 genotyped SNPs with signals similar to rs6857 within the locus , genotyping error seems unlikely to be driving the pattern ( S8 Fig ) . Another concern might be a form of ascertainment bias , in which individuals with AD are underrepresented in the Kaiser Permanente Medical Care Plan . However , there is no correlation in these data between the amount of time that an individual has been enrolled in this plan and the individual’s APOE genotype ( S12 Fig ) . These observations , along with previously reported associations at this locus , argue that the allele frequency trends in Fig 2B are driven by effects of APOE genotype on mortality ( or severe disability ) . Moreover , the effects that we identified are concordant with epidemiological data on the mean age at onset of AD , given 0 to 2 copies of APOE ε4 allele [53] . This case not only serves as a positive control for our approach , it illustrates the resolution that it provides about age effects of genetic variants . We estimated that we have about 93% power to detect the trend in allele frequency with age as observed for rs6857 ( at a genome-wide significance level , see Materials and methods ) . Using both versions of the model treating age bin as a categorical or an ordinal variable , we have similar power to detect other potential trends considered in Fig 1 for variants as common as rs6857 and with similar magnitude of effect on survival . Yet across the genome , only APOE variants show a significant change in allele frequency with age for both versions of the model ( Fig 2 and S13 Fig ) . Thus , our finding only APOE ε4 allele indicates that there are few or no other common variants in the genome with an effect on survival as strong as is seen in the APOE region . We then turned to the UK Biobank data set . We applied our method to individuals of British ancestry whose data passed our filters; of these , 88 , 595 had death information available for their father and 71 , 783 for their mother . We analyzed 590 , 437 genotyped autosomal variants , applying similar quality control ( QC ) measures as with the GERA data set ( see Materials and methods ) . We tested for significant changes in allele frequencies with father’s age at death and mother’s age at death stratified in eight 5-year interval bins . As in the GERA data set , no inflation was detected in the quantile-quantile plots ( S14 Fig ) . Consistent with recent studies [33 , 34] , the variants showing a genome-wide significant change in allele frequency with father’s age at death ( P < 5 × 10−8 ) reside within a locus containing the nicotine receptor gene CHRNA3 ( Fig 3A ) . The A allele of the CHRNA3 SNP rs1051730 ( G , major allele; A , minor allele ) has been shown to be associated with increased smoking quantity among individuals who smoke [56] . We observe a linear decrease in the frequency of the A allele of rs1051730 throughout almost all age ranges ( Fig 3B ) ( P~1 . 3 × 10−7 and P~2 . 7 × 10−10 , treating paternal age at death as a categorical or an ordinal variable , respectively ) . Although it does not reach genome-wide significance , this allele shows a similar trend with age in GERA ( P~8 . 6 × 10−3 , S15 Fig ) . We note that 30 , 819 of the UK Biobank individuals included in the above analysis were genotyped on the UK BiLEVE Axiom array ( see Materials and methods ) , selected based on lung function and smoking behavior ( while the remaining 57 , 776 samples were genotyped on the UK Biobank Axiom array ) [57] . Expectedly , the frequency of the A allele is significantly higher among UK BiLEVE subjects ( P~2 . 3 × 10−10 ) , but the age effects are similar across both arrays ( P~0 . 72 , see Materials and methods ) . For mother’s age at death , a SNP in a locus containing the MEOX2 gene reached genome-wide significance ( Fig 3C ) . The C allele of rs4721453 ( T , major allele; C , minor allele ) increases in frequency in the age bin centered at 76 years old ( S16 Fig ) , i . e . , there is an enrichment among individuals who died at 74 to 78 years of age , which corresponds to a deleterious effect of the C allele in this period . The trend is similar and nominally significant for other genotyped common SNPs in moderate linkage disequilibrium with rs4721453 ( S16 Fig ) . Also , the signal for rs4721453 remains nominally significant when using subsets of individuals genotyped on the same genotyping array: 44 , 552 individuals on the UK Biobank Axiom array ( P~6 . 6 × 10−5 ) and 25 , 231 individuals on the UK BiLEVE Axiom array ( P~1 . 1 × 10−4 ) . These observations suggest that the result is not due to genotyping errors , but it is not reproduced in GERA ( P~0 . 023 , S17 Fig ) and so it remains to be replicated . Variants within the APOE locus are among the top nominally significant variants ( Fig 3C ) . At the APOE SNP rs769449 ( G , major allele; A , minor allele ) , there is an increase in the frequency of A allele at around 70 years old before subsequent decrease ( Fig 3D , P~1 . 2 × 10−7 ) . This pattern is consistent with our finding in GERA ( of a monotonic decrease beyond 70 years of age ) , considering the difference in patterns expected between allele frequency trends with age among survivors versus individuals who died ( S3 Fig ) . We note that by considering parental age at death of the UK Biobank participants—as done also in [33–35]—we introduce a bias towards older participants , who are more likely to have deceased parents ( S18 Fig ) . We confirmed that our top signals are not significantly affected after adjusting for age of the participants ( among other potential confounders , including participants’ sex , year of birth , and socioeconomic status , as measured by the Townsend deprivation index ) : results remain similar for the SNP rs4721453 near MEOX2 ( P~2 . 1 × 10−9 ) , APOE SNP rs769449 ( P~1 . 5 × 10−6 ) , and CHRNA3 SNP rs1051730 ( P~1 . 8 × 10−6 and P~4 . 3 × 10−9 , treating paternal age at death as a categorical or an ordinal variable , respectively ) . We further tested for trends in allele frequency with parental age at death that differ between fathers and mothers , focusing on 62 , 719 individuals with age at death information for both parents . No variant reached genome-wide significance level ( S19A Fig ) . The rs4721453 near the MEOX2 gene and APOE variant rs769449 show nominally significant sex effects ( P~7 . 2 × 10−8 and P~2 . 2 × 10−3 , respectively ) , with stronger effects in females ( S19B Fig ) . Variants near the CHRNA3 locus are nominally significant when using the model with parental ages at death treated as ordinal variables ( rs11858836 , P~5 . 7 × 10−4 ) , with stronger effects in males ( S19B Fig ) . We next turned to sets of genetic variants that have been associated with polygenic traits rather than individual genetic variants . We focused on 42 polygenic traits , including disease risk and traits of evolutionary importance such as puberty timing , for which a large number of common variants have been mapped in GWASs ( see S1 Table for the list of traits and number of loci ) [37–40] . For each individual and each trait , we calculated a polygenic score based on the genetic variants that reached genome-wide significance level for association and then tested whether this polygenic score , or its squared difference from the mean in the case of stabilizing selection , is associated with survival ( after controlling for covariates , see Materials and methods ) . We first applied the Cox proportional hazards model in the UK Biobank for parental survival , focusing on the participants whose genetic ancestry is British and who reported their father’s or mother’s age or age at death ( 114 , 122 and 116 , 323 individuals , respectively ) . We then compared the results with our approach of testing for changes in the polygenic score across parental ages at death . We further analyzed 2 data sets for replication purposes: participants of the UK Biobank of non-British ancestry ( 29 , 511 and 30 , 372 individuals reporting father’s or mother’s age information , respectively ) and the GERA cohort . Using the Cox model , the scores for several traits show significant associations with father’s survival after accounting for multiple testing ( Fig 4A , Table 1 ) : total cholesterol ( TC , P~4 . 3 × 10−11 ) , low-density lipoproteins ( LDL , P~8 . 1 × 10−9 ) , body mass index ( BMI , P~1 . 8 × 10−8 ) , and coronary artery disease ( CAD , P~9 . 0 × 10−6 ) , consistent with 2 recent studies [34 , 35] . In addition , we uncovered significant association for the polygenic score for puberty timing ( P~6 . 2 × 10−6 ) ; in this analysis , we used age at menarche-associated variants in females , motivated by the high genetic correlation between the timing of puberty in males and females [58] ) . A higher score for puberty timing is associated with longer paternal survival ( per year hazard ratio of 0 . 96 ) ( Table 1 ) , indicating that variants delaying puberty timing are associated with a higher chance of survival , consistent with epidemiological studies suggesting early puberty timing to be associated with adverse health outcomes [59] . For all other traits , a higher score is negatively associated with paternal survival: 1 unit polygenic score hazard ratio of 1 . 09 for TC , 1 . 08 for LDL , 1 . 08 for CAD , and 1 . 22 for BMI ( Table 1 ) . With the exception of lipid traits , the effects on survival are not significantly changed after accounting for the effect of the polygenic score of another trait ( S20 Fig ) . This is especially relevant to BMI and puberty timing , for which there is substantial genetic overlap [38]; the per year hazard ratio is 0 . 97 for the puberty timing score ( P~4 . 8 × 10−4 ) after adjusting for the BMI score . Using our approach instead , i . e . , considering the father’s age at death , led to very similar results . Specifically , all traits significantly associated with paternal survival show a significant change in polygenic score with father’s age at death using the model with parental ages at death treated as ordinal variables ( S21 Fig ) : TC ( P~8 . 8 × 10−9 ) , CAD ( P~3 . 3 × 10−8 ) , puberty timing ( P~1 . 6 × 10−7 ) , LDL ( P~8 . 6 × 10−7 ) , and BMI ( P~3 . 4 × 10−6 ) . In addition , we uncovered significant changes in polygenic score with father’s age at death for asthma ( ATH , P~9 . 4 × 10−5 ) and triglycerides ( TG , P~4 . 4 × 10−4 , the effect of which does not seem to be distinct from other lipid traits , S20 Fig ) . The score for puberty timing increases monotonically with the father’s age at death ( Fig 4B ) , indicative of a protective effect of later predicted puberty timing , whereas all other traits with significant signal show a monotonic decline in score with age ( Fig 4C–4F ) . In a Cox survival model , for mothers as with for fathers , scores for TC , CAD , and LDL are significantly associated with survival , with similar hazard ratios ( Fig 5A and Table 1 ) : 1 unit polygenic score hazard ratio of 1 . 09 for LDL ( P~5 . 2 × 10−8 ) , 1 . 09 for CAD ( P~5 . 2 × 10−6 ) , and 1 . 07 for TC ( P~7 . 8 × 10−6 ) . In addition , the high-density lipoproteins ( HDL ) score is associated with maternal survival ( 1 standard deviation ( SD ) hazard ratio of 0 . 94 , P~8 . 9 × 10−5 ) . Also , suggestive evidence was detected for protective effects of increased predicted age at first birth ( AFB ) ( per year hazard ratio of 0 . 94 , P~1 . 4 × 10−3 ) as well as predicted puberty timing ( per year hazard ratio of 0 . 97 , P~2 . 0 × 10−3 ) ( Fig 5A and Table 1 ) . Other than the LDL and TC , all signals seem to be distinct ( S20 Fig ) , including for puberty timing and AFB , despite the genetic correlation between the 2 phenotypes [39] . In turn , applying our approach to maternal age at death , puberty timing and AFB are the top signals ( P~2 . 2 × 10−4 and P~3 . 1 × 10−3 , respectively , S21 Fig ) . Higher polygenic scores for puberty timing are enriched among longer-lived mothers ( Fig 5B ) , as seen for fathers . Similarly , the score for AFB increases with mother’s age at death ( Fig 5C ) , indicating an association between variants that delay AFB and longer life span . Scores for CAD , LDL , and HDL did not show significant monotonic change across mother’s age at death bins ( P~7 . 7 × 10−3 , P~0 . 058 , and P~0 . 35 , respectively ) ; however , the trends are suggestive of subtle age-dependent effects , with an effect of CAD score in middle age and late-onset effects of LDL and HDL scores ( Fig 5D–5F ) . Testing for age by sex interactions , the TC and CAD score trends with parental ages at death are significantly different between fathers and mothers ( P~4 . 0 × 10−4 and P~7 . 4 × 10−4 , respectively , S22 Fig ) . To further investigate the age dependency of the effects , we plotted polygenic scores among parents who had survived up to a given age as compared to the trends with parental ages at death ( S23 and S24 Figs ) . All traits associated with paternal survival seemingly show more pronounced effects in middle age ( S23 Fig ) . Similar patterns were observed for maternal survival-associated traits except for LDL and HDL , which had more pronounced late-age effects ( S24 Fig ) . We also compared the hazard ratios for ages at death of ≤ 75 and > 75 years ( Materials and methods ) , similar to a recent study [33] . Consistent with trends in scores with parental age , among the traits associated with paternal survival , almost all traits have seemingly stronger effects among younger fathers , particularly for CAD ( S3 Table ) : 1 unit log-odds hazard ratio of 1 . 14 for younger fathers ( P~2 . 6 × 10−9 ) and 0 . 99 for older fathers ( P~0 . 70 ) . Unlike in fathers , in mothers , TC , LDL , and HDL scores had more pronounced late-age effects ( S3 Table ) : for TC , 1 SD hazard ratio of 1 . 03 for younger mothers ( P~0 . 15 ) and 1 . 11 for older mothers ( P~1 . 4 × 10−6 ) and for LDL , 1 SD hazard ratio of 1 . 05 for younger mothers ( P~0 . 03 ) and 1 . 12 for older mothers ( P~3 . 3 × 10−8 ) . Next , we sought to replicate the top associations observed among the UK Biobank participants of British ancestry ( discovery cohort ) in 2 other data sets: participants of the UK Biobank of non-British ancestry and the GERA cohort . Applying the Cox model using parental survival for UK Biobank participants of non-British ancestry , the direction of hazard ratios for all traits ( as well as the estimated values for most traits ) are consistent with the discovery cohort for both fathers and mothers ( S4 Table ) . The congruence of results in 2 cohorts with different ancestries suggests that our top signals are not false positives caused by poor control for population structure . In the GERA cohort , we tested whether polygenic scores change with the age of the participant , similar to our approach for individual genetic variants in this cohort . All top signals except AFB have directionally consistent effects with the discovery cohort ( S5 Table ) . Of particular interest , the strongest signal is an increase in the polygenic score for puberty timing with age of the participants ( P~6 . 7 × 10−3 , S25 Fig ) . In the discovery cohort , we further investigated if there are significant changes in the squared difference of polygenic scores with parental ages at death , as might be expected if the mean value of the trait leads to the highest chance of survival . No trait shows evidence of such stabilizing selection ( S26 Fig ) .
We introduced a new approach to identify genetic variants that affect survival to a given age and thus to directly observe viability selection ongoing in humans . Attractive features of the approach include that we do not need to make a decision a priori about which loci or traits matter to viability and focus not on an end point ( e . g . , survival to an old age ) but on any shift in allele frequencies with age , thereby learning about the ages at which effects are manifest and possible differences between sexes . To illustrate the potential of our approach , we performed a scan for genetic variants that impact age-specific mortality in the GERA and the UK Biobank cohorts . We only found a few individual genetic variants , almost all of which were identified in previous studies . This result is in some ways expected: available data only provide high power to detect effects of common variants ( >0 . 15–0 . 2 ) on survival ( Fig 1 ) , yet if these variants were under viability selection , we would not expect them to be common , short of strong balancing selection due to trade-offs between sexes , ages , or environments . As sample sizes increase , however , the approach introduced here should provide a comprehensive picture of viability selection in humans . To illustrate this point , we repeated our power simulation with 500 , 000 samples and found that we should have high power to detect the trends for alleles at a couple percent frequency in the sample ( S27 Fig ) . Already , however , this application raises a number of interesting questions about the nature of viability selection in humans . Notably , we discovered only a few individual variants influencing viability in the 2 cohorts , most of which exert their effect late in life . On first thought , this finding may suggest such variants to be neutrally evolving . We would argue that if anything , our findings of only a few common variants with large effects on survival late in life suggest the opposite: that even variants with late-onset effects have been weeded out by purifying selection . Indeed , unless the number of loci in the genome that could give rise to such variants ( i . e . , the mutational target size ) is tiny , other variants such as the APOE ε4 allele must often arise . That they are not observed when we have very high power to detect them suggests they are kept at lower frequency by purifying selection . Why might they be selected despite affecting survival only at old ages ? Possible explanations include that they decrease the direct fitness of males sufficiently to be effectively selected ( notably given the large , recent effective population size of humans [60] ) or that they impact the inclusive fitness of males or females . If this explanation is correct , it raises the question of why the APOE ε4 allele has not been weeded out . We speculate that the environment has changed recently , making this allele more deleterious . For example , it has been proposed that the evolution of this allele has been influenced by changes in physical activity [61] and parasite burden [62] . Considering 42 traits that have been investigated by GWASs , we found a number of cases in which the mean polygenic score changes with age . Of course , detecting an effect of age on the traits does not imply that these are the phenotypes under viability selection , as the variants that contribute likely have pleiotropic effects on other traits [37] . Nonetheless , it is perhaps not surprising that we found detrimental effects of higher genetically predicted TC , LDL , BMI , and risk of CAD on survival , as these phenotypes are studied in GWASs precisely because of their adverse health effects . Intriguingly , however , we also found associations for fertility traits , notably , protective effects of later predicted puberty timing and AFB . If these findings reflect life-history trade-offs ( e . g . , longer life span at the cost of delayed reproduction ) , they may help to explain the persistence of extensive variation in such fitness-correlated traits [63 , 64] . Intriguingly , we saw a negative correlation between genetically predicted AFB and number of siblings of the UK Biobank participants , a proxy for the fertility of their parents ( P~4 . 2 × 10−8 , S28 Fig ) , consistent with previous reports of a genetic correlation between AFB and the number of children ever born [21 , 39] . These findings underscore that consideration of survival or fertility effects alone does not allow one to infer whether the net effect of a variant or set of variants is beneficial . Instead , to convert effects on viability such as those detected here or effects on fertility reported elsewhere [22 , 23] into an understanding of how natural selection acts on an allele requires a characterization of its effects on all components of fitness ( including potentially inclusive fitness ) . In this regard , it is also worth noting that while our method is designed to detect changes in allele frequencies ( and in polygenic scores ) caused by genetic effects on age-specific mortality , such changes could in principle also arise from effects on other components of fitness . For example , if the frequency of a genetic variant in a population decreases over decades due to an effect on fertility , its frequency would increase with the age of surviving individuals sampled at a given time ( as in the GERA cohort ) . This confounding is less of an issue when considering effects on the age at death ( what we measured in the UK Biobank ) . Nonetheless , even in the UK Biobank , fertility effects may manifest as effects on age at death; for example , when sampling a cohort of children , parents with later ages at death are possibly born earlier ( S29 Fig ) . To this end , in the UK Biobank , we accounted for changes in allele frequencies with year of birth of the participants themselves ( ideally , we would want to condition on parents born at similar times , which we cannot do; instead , we used year of birth of the participants as an estimator for year of birth of the parents ) . Thus , we believe our results in the UK Biobank not to be confounded by fertility effects . Moreover , a number of our findings in this study are consistent with prior knowledge of effects on survival , such as those for disease risk variants like the APOE ε4 allele . Nonetheless , some caution is required in interpreting trends with age as strictly reflecting viability effects . Also of interest are the marked differences between males and females in our analysis of mothers and fathers of individuals in the UK Biobank . The differences between sexes are most notable at the CHRNA3 locus , which shows a strong effect only in fathers , and sets of genetic variants associated with risk of CAD and cholesterol levels , which exhibit different age-dependent effects between fathers and mothers . Results for the CHRNA3 locus , in which variants are associated with the amount of smoking among smokers , may reflect a gene-by-environment interaction rather than a sex effect per se . Consistent with a more pronounced effect on male than female age at death , smoking prevalence in men has been consistently higher than women over the past few decades in the United Kingdom: from 1970 to 2000 , smoking prevalence decreased from around 70% to 36% in middle-aged men , compared to from around 50% to 28% in middle-aged women [65] . Moving forward , the application of approaches such as ours to the millions of samples in the pipeline ( such as the UK Biobank [66] , the Precision Medicine Initiative program [67] , and the BioVU biobank at Vanderbilt University [68] ) will allow viability effects of rare as well as common alleles to be examined . These analyses will provide a comprehensive answer to the question of which loci affect survival , helping to address long-standing open questions such as the relative importance of viability selection in shaping genetic variation and the extent to which genetic variation is maintained by fitness trade-offs between sexes or across ages .
This study used data sets from the UK Biobank ( application number 11138 ) , as approved by the UK Biobank Board , and the Genetic Epidemiology Research on Adult Health and Aging ( GERA ) , obtained through dbGaP ( request numbers 28113–4 and 57119–2 ) and approved by Columbia University Institutional Review Board , protocols AAAQ2700 and AAAN4411 . We performed PCA using the EIGENSOFT v6 . 0 . 1 package with the fastpca algorithm [76 , 77] for 2 purposes: ( i ) as a QC on individuals to validate self-reported European ancestries ( only in GERA data set ) and ( ii ) to correct for population structure in our statistical model ( for individuals in the UK Biobank of non-British ancestry , we used the PCs provided with the data ) . We downloaded the list of variants contributing to 39 traits ( all traits but age at menarche , AFB , and age at natural menopause ) and their effect sizes recently described in Pickrell et al . [37] from: https://github . com/PickrellLab/gwas-pw-paper/tree/master/all_single . For age at menarche , we used the variants and effect sizes recently identified by Day et al . [38] . We used variants associated with AFB from Barban et al . identified in either sex-specific analyses or analyses of both sexes and used the effect sizes estimated in the combined analysis [39] . We used age at natural menopause-associated variants and their effect sizes from Day et al . [40] . For all traits , we used variants that were genotyped/imputed with high quality in our data ( see S1 Table ) . We ran simulations to determine the power of our statistical model to detect deviation of allele frequency trends with age across 14 age categories mimicking the GERA cohort’s age structure ( 57 , 696 individuals with age distribution as in S2 Fig ) from a null model , which for simplicity was no change in frequency with age , i . e . , no changes as a result of age-dependent variation in population structure and batch effects . For a given trend in frequency of an allele with age , we generated 1 , 000 simulated trends in which the distribution of the number of the alleles in age bin i is Bin ( 2Ni , fi ) , where Ni and fi are the sample size and the sample allele frequency in bin i . We then estimated the power to detect the trend as the fraction of cases in which P < 5 × 10−8 , by a chi-squared test . We ran simulations to investigate the relationship between allele frequency with age of the surviving individuals and the age of the individuals who died in a cohort . We simulated 2 × 106 individuals going forward in time in 1-year increments . For each time step forward , we tuned the chance of survival of the individuals based on their count of a risk allele for a given variant such that the number of individuals dying in the increment complies with: ( i ) a normal distribution of ages at death with mean of 70 years and standard deviation of 13 years , roughly as is observed for parental ages at death in the UK Biobank and ( ii ) a given frequency of the risk allele among those who survive . Specifically , we modeled the survival rate of the population , S , as the weighted mean for 2-alleles carriers , S2 , 1-allele carriers , S1 , and noncarriers , S0: S ( x ) =∑i=02fiSi ( x ) where f denotes the frequency of genotypes in the population and x denotes the age . Si and S are related: Si ( x ) = S ( x ) fi ( x ) /fi , where fi ( x ) is the genotype frequency among individuals survived up to age x . Given a trend in allele frequency with age , we calculated genotype frequencies with age assuming Hardy-Weinberg equilibrium and then estimated genotype-dependent chance of survival , Si ( x ) , taking S ( x ) as the survival function for N ( 70 , 132 ) .
|
Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation , which are sensitive to past selection pressures . We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age ( i . e . , viability selection ) . We applied our approach to the GERA cohort and parents of the UK Biobank participants . We found viability effects of variants near the APOE and CHRNA3 genes , which are associated with the risk of Alzheimer disease and smoking behavior , respectively . We also tested for the joint effect of sets of genetic variants that influence quantitative traits . We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth . We also detected detrimental effects of higher genetically predicted cholesterol levels , body mass index , risk of coronary artery disease ( CAD ) , and risk of asthma on survival . Some of the observed effects differ between males and females , most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels . Beyond this application , our analysis shows how large biomedical data sets can be used to study natural selection in humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"variant",
"genotypes",
"alleles",
"genetic",
"mapping",
"endocrine",
"physiology",
"coronary",
"heart",
"disease",
"families",
"genetic",
"epidemiology",
"cardiology",
"epidemiology",
"endocrinology",
"genetic",
"loci",
"people",
"and",
"places",
"fathers",
"heredity",
"physiology",
"genetics",
"biology",
"and",
"life",
"sciences",
"population",
"groupings",
"mothers",
"puberty",
"vascular",
"medicine"
] |
2017
|
Identifying genetic variants that affect viability in large cohorts
|
Alternative splicing controls the activity of many proteins important for neuronal excitation , but the signal-transduction pathways that affect spliced isoform expression are not well understood . One particularly interesting system of alternative splicing is exon 21 ( E21 ) of the NMDA receptor 1 ( NMDAR1 E21 ) , which controls the trafficking of NMDA receptors to the plasma membrane and is repressed by Ca++/calmodulin-dependent protein kinase ( CaMK ) IV signaling . Here , we characterize the splicing of NMDAR1 E21 . We find that E21 splicing is reversibly repressed by neuronal depolarization , and we identify two RNA elements within the exon that function together to mediate the inducible repression . One of these exonic elements is similar to an intronic CaMK IV–responsive RNA element ( CaRRE ) originally identified in the 3′ splice site of the BK channel STREX exon , but not previously observed within an exon . The other element is a new RNA motif . Introduction of either of these two motifs , called CaRRE type 1 and CaRRE type 2 , into a heterologous constitutive exon can confer CaMK IV–dependent repression on the new exon . Thus , either exonic CaRRE can be sufficient for CaMK IV–induced repression . Single nucleotide scanning mutagenesis defined consensus sequences for these two CaRRE motifs . A genome-wide motif search and subsequent RT-PCR validation identified a group of depolarization-regulated alternative exons carrying CaRRE consensus sequences . Many of these exons are likely to alter neuronal function . Thus , these two RNA elements define a group of co-regulated splicing events that respond to a common stimulus in neurons to alter their activity .
N-methyl-D-aspartic acid ( NMDA ) -sensitive glutamate receptors ( NMDARs ) play important roles in modulating synaptic function in the brain [1 , 2] . Functional NMDARs are heteromeric complexes assembled from one NR1 subunit and one or more NR2 subunits ( NR2A , NR2B , NR2C , and NR2D ) . The NR1 subunit is encoded by a single gene with three alternatively spliced exons that can generate eight different variants . Exon 5 encodes an optional portion of the extracellular N-terminal domain that regulates the pharmacological properties of the receptor . Alternative 3′ splice sites in exon 22 determine the choice of variant intracellular C-terminal domains that affect the rate of export from the endoplasmic reticulum ( ER ) . Regulated exon 21 encodes a peptide cassette that modulates a variety of activities of the protein , including trafficking from the ER to the plasma membrane , phosphorylation by protein kinase C ( PKC ) and protein kinase A ( PKA ) , interactions with yotiao , neurofilament L , and calmodulin , and activation of NMDAR-induced gene expression [3–7] . The splicing of these alternative exons is regulated at different developmental stages and locations , and can be altered by neuronal activity [8–10] . Hence , alternative splicing has a profound effect on the function of NMDARs . The spliceosome is a large complex of small nuclear ribonucleoprotein particles ( snRNPs ) that assemble onto splice sites to bring the intron ends into juxtaposition , and catalyze intron excision and exon ligation [11] . Alternative splicing results from changes in the choice of splice sites by the spliceosome , and is mediated by numerous RNA elements in the pre-mRNA [12 , 13] . These control elements can be either exonic or intronic , and can act as either enhancers or silencers . Individual elements are bound by specific regulatory factors that include members of the serine/arginine-rich ( SR ) family of proteins [14 , 15] and the heterogeneous nuclear ribonucleoprotein ( hnRNP ) group of proteins [16] . The SR proteins generally enhance splicing via recognition of exonic splicing enhancers ( ESEs ) , although they can act in other ways . The hnRNP factors can play diverse roles in enhancement and repression . Some splicing factors are variably expressed in different cell types to generate tissue-specific splicing patterns . Splicing patterns can also respond to stimuli within a particular cell [17 , 18] , yet how this dynamic regulation of splicing occurs is poorly understood . Given its importance to neuronal function , NR1 exon 21 ( E21 ) is a particularly interesting model to study splicing regulation . E21 is included prominently in the forebrain and more frequently skipped in the hindbrain [19] . Several SR proteins as well as the RNA binding proteins NAPOR/CUGBP2 , hnRNP H , and NOVA2 are thought to positively regulate this exon [19–21] . Identified splicing enhancer sequences for E21 include binding sites of the SR proteins ASF/SF2 , SC35 , and SRp40 within the exon , and binding sites for hnRNP H and NAPOR/CUGBP2 in the downstream intron . In contrast , hnRNP A1 can repress the splicing of this exon via its interaction with two exonic UAGG motifs and one intronic G tract [20] . The interactions between these many different proteins and RNA motifs are thought to contribute to the complex patterns of splicing seen for E21 within the brain . In neurons , several exons are known to undergo changes in splicing when the cells are shifted to depolarizing media [10 , 22] . We previously showed that the repression of the BK channel STREX exon after depolarization requires calcium ion influx through L-type calcium channels and a downstream Ca++/calmodulin-dependent protein kinase ( CaMK ) [23] . We found that STREX , as well as the NR1 exons 5 and 21 , were repressed by overexpressed CaMK IV . A CaMK IV–responsive RNA element ( CaRRE ) was identified in the 3′ splice site of the STREX exon [24] . In searches for this element , the 3′ splice sites of NR1 exon 5 and several other exons were found to contain a CaRRE and be subject to repression by CaMK IV . However , the CaRRE was not sufficiently defined to identify many additional CaMK-responsive exons . It was not clear how widespread CaRREs were in the genome , whether there might be additional types of calcium-responsive elements , or whether these elements defined sets of coregulated exons . Moreover , the NR1 E21 3′ splice site did not contain a CaRRE motif . The source of the responsiveness of this exon remained unclear , and we wanted to understand how it could respond to stimuli . In this study , we characterize the response of NMDAR1 E21 to depolarization and activated CaMK IV . We identify two RNA elements within E21 that determine its dynamic regulation . We identify a group of additional exons containing these elements that can also be regulated by depolarization , and define a set of splicing events that likely play important roles in modulating the neuronal activity .
Previously , we showed that splicing of NMDAR1 E21 is repressed by CaMK IV after transient expression of a minigene reporter in HEK 293T cells . [23] . It was not clear whether E21 in endogenous NR1 transcripts could be similarly repressed by depolarization and CaMK IV . To confirm E21 repression in endogenous NR1 transcripts , we examined its splicing in differentiated P19 embryonal carcinoma ( EC ) cells by RT-PCR . Undifferentiated cells were aggregated in retinoic acid ( RA ) and then dispersed and replated [25] . Ten days after RA treatment , 60% of the cells exhibited neuronal morphology with extended fasciculated processes synapsing on other cells . The remainder of the culture consisted of flat adherent cells that resembled either astrocytes or undifferentiated EC cells [26] . The alternative splicing of E21 to E22a or E22b at the 3′ end of NMDAR1 transcript results in four possible different NMDAR1 isoforms . We used E22a- or E22b-specific reverse primers to separately measure the NR1–1:NR1–2 and NR1–3:NR1–4 ratios ( Figure 1A ) . All four NR1 mRNA isoforms were detected in differentiated P19 cells , with 26% of E21 inclusion in the NR1–1/NR1–2 isoform pair ( Exon 22a; Figure 1B , lane 1 ) and 12% inclusion in the NR1–3/NR1–4 isoform pair ( Exon 22b; Figure S1A ) . To examine the effect of depolarization on splicing , these cells were exposed to 50 mM KCl . E21 inclusion was reduced after 6 h of this treatment , and continued to decrease to about 25% of its original level after 24 h ( Figure 1B , lanes 2–5 , and Figure S1A ) . The extent of repression was dependent on the KCl concentration; in 30 mM KCl , E21 splicing was reduced by 50% at 24 h , whereas in 50 mM KCl , E21 splicing was reduced by 75% ( Figure 1B , lanes 5 and 8 ) . These E21 changes were observed in both exon 22a– and exon 22b–containing mRNA . Thus , E21 splicing is indeed repressed by depolarization , and this repression is apparently independent of the choice of alternative 3′ splice site downstream . We next tested whether the CaMK pathway is involved in the depolarization-induced repression , as seen with the BK channel STREX exon . Treatment of the cells with the CaMK inhibitor KN93 prior to depolarization completely blocked the splicing repression ( Figure 1C , lane 3 , and Figure S1B ) . The less-active analog KN92 showed an intermediate effect ( Figure 1C , lane 4 ) . Thus , depolarization-induced splicing repression of E21 is mediated through the CaMK pathway in differentiated P19 cells , similar to what was observed for BK channel splicing in the pituitary cell line GH3 . The observation of a splicing change after a stimulus requires removal of the pre-existing mRNA . Thus , the shift in splicing is observed in a timeframe of hours in response to a continuous application of high KCl concentrations . Although we did not observe a decrease in cell viability from the treatment , this splicing alteration could derive from a permanent change in the cells after chronic depolarization . Alternatively , the E21 skipping could be a reversible adaptation to the depolarizing media . To test the reversibility of this splicing repression , KCl was washed out after 18 or 24 h of depolarization , and then the cells were grown for another 24 h . Removal of the high concentration of KCl from the media restored E21 splicing to its normal level ( Figure 1D , lanes 5 and 6 , and Figure S1C ) . Thus , the splicing repression is reversible and is a dynamic response to environmental cues . To study the sequence elements needed for E21 regulation , we cloned the human E21 exon with flanking intron sequence between two constitutive β-globin exons of the Dup4–1 plasmid ( Figure 2A ) . This reporter was transiently expressed in HEK 293T cells , and the mRNAs were assayed for E21 inclusion by primer extension . The repression of E21 by CaMK IV was examined by co-transfection of plasmids expressing a constitutively active form of CaMK IV ( CaMK IV dCT ) or a kinase-dead mutant control ( CaMK IV dCT K75E ) . E21 was strongly included in the NME21D300 mRNA ( 91% ) when co-expressed with the dead CaMK IV ( dCT K75E ) . In contrast , E21 splicing was reduced by the active CaMK IV ( 67% inclusion; Figure 2B , lanes 1 and 2 ) . To roughly localize the RNA elements responsible for CaMK IV repression , we made several chimeric minigenes containing portions of E21 and portions of the constitutive Dup175 exon , and examined their response to CaMK IV . Deletion of the downstream intron ( NME21 ) had little effect on the overall splicing , but made E21 more repressible by CaMK IV dCT ( Figure 2B , lanes 3 and 4 ) . The deleted downstream intron sequence presumably contains positive elements that counteract the repression . As seen previously , and unlike the BK channel STREX exon or NR1 exon 5 , the E21 3′ splice site does not confer CaMK IV responsiveness when transferred to the constitutive Dup175 exon ( Dup175E21L; Figure 2B , lanes 5 and 6 ) . In contrast , the E21 exon alone , when fused to the Dup175 exon 3′ splice site ( NME21E ) , was highly repressed by CaMK IV ( Figure 2B , lanes 7 and 8 ) . These results indicate that the E21 exonic sequence is sufficient to induce CaMK IV–dependent splicing repression . This is in contrast to other identified exons in which the regulatory element is located in the 3′ splice site . To identify the sequence elements involved in E21 repression , we performed linker scanning mutagenesis across the exon . Blocks of 20 nucleotides within the E21 exon were sequentially substituted with a 20-nucleotide sequence from the IgM gene that is known to lack splicing activity [27] . To maintain the 3′ and 5′ splice sites , the sequence substitutions started at exon nucleotide 3 and ended four nucleotides from the 3′ end of the exon ( Figure 3A ) . Two different substitution mutations had strong effects on the CaMK IV–induced repression . The response was completely eliminated when nucleotides 3–22 ( LS1 ) or nucleotides 33–52 ( LS3 ) were substituted with the heterologous sequence ( Figure 3B , lanes 3 and 4 [LS1] , and 7 and 8 [LS3] ) . Thus , at least two RNA elements , one from each region , are needed together for the CaMK IV–induced repression . Having either one of these RNA elements alone is not sufficient for this response . As described earlier , several ESEs and exonic splicing silencers ( ESSs ) have been identified in the E21 exon , and we found that a substitution that eliminated these ESEs ( LS5 ) did reduce the overall exon inclusion [20 , 28] ( Figure 3B , lanes 11 and 12 ) . However , the CaMK IV response was not affected by this mutation , because the exon was still more excluded in the presence of the activated CaMK IV . We showed previously that the splicing repression of the BK channel STREX exon is mediated by a CaRRE ( CACAUNRUUAU ) located in the 3′ splice site . Sequence analysis of the LS3 region identified a CaRRE-like sequence CACAUUUA within it . Point mutations ( M1 and M2 ) within this CaRRE-like sequence led to significant decreases in CaMK IV repression ( Figure 3C , lanes 7–10 ) . A larger 12-nucleotide substitution of the entire CaRRE motif ( R1 ) nearly eliminated the CaMK IV effect ( Figure 3C , lanes 11 and 12 ) . Interestingly , the LS4 scanning mutation also changes some nucleotides in this region , but only gives a small reduction in splicing repression . Subsequent analysis of this element indicates that by chance , the LS4 mutations only have a small effect on activity ( see below ) . These results identify a functional CaRRE motif within the LS3 region and indicate that this motif can function within an exon as well as the 3′ splice site . In contrast to LS3 , no known ESS was identified in the LS1 region . To map the important elements within this sequence , we mutated a purine-rich element ( M3–2 ) and the adjacent sequence downstream ( M3–3; Figure 3A ) . Although the mutation M3–2 had no effect on CaMK IV repression , the mutation M3–3 totally abolished this response ( Figure 3C , lanes 5 and 6 ) , identifying critical nucleotides in the LS1 region . We called this sequence a CaRRE type 2 motif and will refer to the previous CaRRE as a CaRRE type 1 motif . Recently , exonic UAGG and intronic G track motifs were shown to be important in silencing E21 splicing [20] . The possible roles of these elements in the CaMK IV–dependent repression were examined with mutations M4 and M5 . Consistent with the previous observations , mutation of the UAGG motif promoted E21 splicing in our system ( Figure 3C , lanes 13 and 14 ) . The M4 mutant also exhibited a significantly weaker CaMK IV response . Thus , the reduced exon repression observed in LS6 and LS7 mutants is presumably due to the loss of this element . In contrast , disruption of the G track had no effect on CaMK IV response . These results indicate that CaMK IV–mediated exon repression requires multiple elements , and that there are two critical elements separate from the UAGG and G track motifs . Although the two exonic elements in E21 are both needed for efficient CaMK IV repression , we wanted to test if they could function independently in other exons . For this , we transferred the CaRRE 1 motif to the constitutive Dup175 exon and characterized its response ( Figure 4A ) . Introduction of CaRRE 1 into the 5′ region of Dup175 exon ( Dup175CA10 ) caused slight exon skipping , indicating that CaRRE 1 is a weak splicing silencer . However , co-expression of this clone with CaMK IV strongly repressed exon inclusion ( Figure 4A , lanes 3 and 4 ) . This effect was due to the introduced CaRRE 1 , because a small point mutation in the element reduced its effect , and replacement with a neutral IgM sequence at the same position in Dup175 exon was not able to confer the repression ( Figure 4A , lanes 5–8 ) . Thus , the CaRRE 1 motif can be sufficient to silence splicing within an exonic context . To investigate the effect of position on the activity of CaRRE 1 , the element was moved to the middle of the Dup175 exon ( Dup175CA13 ) . This centrally located CaRRE 1 behaved similarly to an element upstream , but gave a somewhat reduced response to CaMK IV ( Figure 4A , lanes 9 and 10 ) . When two CaRRE 1 motifs were introduced in the Dup175 exon ( Dup175CA10+13 ) , splicing was completely repressed even in the absence of CaMK IV activity ( Figure 4A , lanes 11 and 12 ) . Thus , CaRRE 1 is a splicing silencer whose activity is dependent on both the sequence context and the number of elements in the exon . Similarly , the CaRRE 2 motif was also tested for its ability to function independently . CaRRE 2 was inserted at the +3 position of the Dup175 exon , the same position relative to the 3′ splice site as in E21 ( Figure 4B ) . Similar to CaRRE 1 , CaRRE 2 had weak splicing silencer activity on its own , slightly reducing exon inclusion of the new exon . When co-expressed with CaMK IV dCT , exon skipping was sharply increased , indicating that CaRRE 2 is also sufficient for CaMK IV–dependent repression ( Figure 4B , lanes 1 and 2 ) . To confirm that this effect was due to the same element identified in E21 , the same mutations were tested . As before , M3–2 had a weak effect on the inducible repression ( Figure 4B , lanes 3 and 4 ) . M3–3 , the combined mutation ( M ) , and a replacement sequence ( R ) all eliminated the repression activity , showing that M3–3 mutation covered the minimal CaRRE 2 sequence ( Figure 4B , lanes 5–10 ) . The interaction between the CaRRE 2 and CaRRE 1 motifs was examined by introducing CaRRE 1 to positions downstream of CaRRE 2 ( Figure 4C ) , mimicking the E21 situation . Similar to two copies of CaRRE 1 , one copy of each silencer element led to the complete repression of the exon ( Figure 4C , lanes 1–4 ) . To weaken the activity of CaRRE 1 , we changed the inserted CaRRE 1 sequence to that of the CaRRE found in the 3′ splice site of NR1 exon 5 ( Dup175PR1CA3 ) . This partially restored splicing to 31% inclusion . Overexpression of CaMK IV reduced the splicing to 10% ( Figure 4C , lanes 5 and 6 ) . Similarly , for these exons with both CaRRE 1 and CaRRE 2 , mutations in CaRRE 2 restored splicing in the absence of CaMK ( Figure 4C , lanes 7 and 9 ) . These exons were still repressed by the CaMK IV , and behaved like the exons with a single CaRRE motif ( Figure 4C , lanes 8 and 10 ) . These results indicate that the level of constitutive and inducible repression from the CaRRE's can be finely tuned by the number , position , and exact sequence of the elements present in the exon . Most splicing regulatory elements exhibit considerable degeneracy in the sequence that can bind to a particular regulatory protein and affect splicing . The intronic and the exonic CaRRE 1 have the same CACAY core sequence , but are different in the 3′ half and have different activities when placed at the same position in the Dup175PR1 exon ( Figure 4C , Dup175PR1CA13 and Dup175PR1CA3 ) . Moreover , several CaRRE 1 mutants also have moderate activity , suggesting that CaRRE 1 might have other functional variants , but there is little information on which nucleotides are most important to their function . Similarly , very little is known about the sequence requirements for CaRRE 2 activity . The finding that exonic CaRRE motifs can also affect splicing indicates that there are likely to be many more exons regulated by CaRRE 1 or CaRRE 2 . Thus , we needed to characterize the CaRRE in more detail before searching for them in other exons . To characterize the functional sequence of these two CaRREs , we carried out single nucleotide scanning mutagenesis of CaRRE 1 and CaRRE 2 . We mutated each CaRRE nucleotide to the other three possible nucleotides , one at a time . To facilitate cloning , we first changed the ApaI site in the Dup construct upstream intron to a XhoI site . Interestingly , this change reduced the CaMK IV responses of both the 175CA10-X and 175PR1-X exons compared to their original sequences . The original ApaI site contains a GGG motif that may weaken the exon and increase the silencing activity of the CaRREs . In the absence of CaMK IV , most mutations did not have much effect on the splicing of the exons . When co-transfected with CaMK IV dCT , the different CaRRE mutants exhibited differential responses to the CaMK IV ( Figure 5A and 5B ) . To evaluate these differences , we defined the wild-type silencing activity as 100% and calculated the relative activity of each mutant ( Figure 5 ) . As expected , many mutations reduced the CaMK IV repression to less than 75% of the wild-type activity , and there were four CaRRE 1 mutations and seven CaRRE 2 mutations that only moderately affected silencing activity ( between 150% and 75% of their corresponding wild-type activity ) . Interestingly , there were six CaRRE 1 mutants and six CaRRE 2 mutants that significantly improved silencing activity to greater than 150% of wild type . Among these , a U to G or U to C mutation at position 5 in CaRRE 1 generated a 3-fold increase in silencing activity . In CaRRE 2 , a U to G mutation at either position 2 or position 5 also gave a 3-fold increase in the silencing activity . These data indicate that both CaRRE 1 and CaRRE 2 can function through degenerate sequence elements and are likely more abundant than previously expected . To identify exons that might be regulated by depolarization , we searched for CaRRE 1 and CaRRE 2 alone and in combination in a dataset of 2 , 594 mouse alternative exons . Exons are generally between 50 and 250 nucleotides in length , and only exons of length shorter than 253 nucleotides were examined ( 2 , 461 exons ) . Each exon and 50-nucleotide regions of upstream and downstream intron were separately searched for a list of putative CaRRE motifs . The list of CaRRE sequences included all those with an activity of at least 40% of the wild-type elements . The search identified 151 CaRRE 1 motifs and 104 CaRRE 2 motifs within 248 alternative exons or their flanking introns ( Figure 6A ) . Since NR1 E21 did not pass our automatic expressed sequence tag ( EST ) -genome mapping and filtering for cassette exons ( see Materials and Methods ) , it was not included in the searchable alternative exon set . There were 13 alternative exons that contained two CaRRE motifs . Of these , three alternative exons contain one exonic CaRRE 1 and one exonic CaRRE 2 , as seen in NR1 E21 . About 10% of the alternative exons were found to contain potential CaRRE 1 or CaRRE 2 motifs , suggesting that these elements contribute to the regulation of many alternative exons . To evaluate the significance of these CaRRE motifs in alternative exons , we compared the CaRRE frequencies in alternative exons and constitutive exons . We compiled a database of 10 , 000 constitutive exons , from which 9 , 401 exons with lengths shorter than 253 nucleotides were used in the calculation . The observed CaRRE motif frequencies in these two exon groups were calculated by dividing the number of CaRREs by the number of octamers in the searched region and then normalizing to the frequency of random octamers . The normalized CaRRE motif frequency distributions in alternative exons and constitutive exons are plotted separately for CaRRE1 and CaRRE 2 in Figure 6B . From these calculations , CaRRE 1 motif frequencies were found to be significantly higher in alternative exons than in constitutive exons , and also significantly higher in the introns flanking alternative exons . In contrast , we did not observe a significant enrichment of CaRRE 2 motifs in these regions encompassing alternative exons relative to constitutive exons . Since this element can clearly affect splicing , this lack of enrichment could be due to the CaRRE 2 sequence not being sufficiently defined . We next tested whether the presence of a CaRRE motif in the exon is predictive of depolarization-induced repression . Sixty exons from different categories were chosen for analysis , and of these , expression of 45 exons was detected by RT-PCR in differentiated P19 cells ( unpublished data ) . Most of these exons showed partial inclusion , confirming their categorization as alternative exons . From this set , 27 exons were selected for further analysis . Because every exon has different splicing efficiency in these cells , we used one of two criteria to categorize the splicing as changed after depolarization . If an exon shows an intermediate level of inclusion between 30% and 70% , it is easier to detect change in splicing . For these exons , a 15% change in inclusion was considered significant . In contrast , when an exon is spliced in less than 30% or greater than 70% of transcripts , it is more difficult to detect a large percent change in splicing . For this group , responsive exons were defined as those showing more than a 2-fold difference in exon inclusion or skipping by depolarization . By these criteria , 13 exons were repressed after depolarization ( Figure 7A and 7B ) , and can be classified into two groups by their responses to the depolarization . Group 1 contained six exons whose splicing remained repressed through 24 h , as was seen for NR1 E21 . Group 2 contained seven exons whose splicing was repressed at 12 h , but then began to recover after 24 h under depolarizing conditions . These exons apparently adapt to the condition of chronic depolarization and are initially repressed by the treatment , but may not remain so . Interestingly , three exons showed the opposite response of splicing activation after depolarization ( group 3 ) . These results indicate a range of responses and regulatory mechanisms for exons affected by depolarization . In all , about 60% of the CaRRE-containing exons that were tested showed regulation by depolarization ( Figure 7C and Table 1 ) . As a control , we selected 17 alternative exons that do not contain any of the degenerate CaRRE motifs and tested their response to depolarization . As expected , the splicing of these exons was unaffected by depolarization . The one exception was Adcyap1r1 exon 14 , which is discussed below ( Figure 7A and 7B , group 2 ) . Overall , we find that the presence of CaRRE motifs is highly predictive of depolarization-induced splicing regulation . To confirm that the predicted CaRREs were functioning in these regulated exons , we cloned 11 of these exons into the Dup4–1 splicing reporter and examined their splicing in HEK 293T cells . Six of these exons showed either complete inclusion or exclusion in the HEK 293T cells after transient expression ( unpublished data ) . Five exons showed partial inclusion . Of these , three exons showed CaMK IV–dependent repression in HEK 293T cells ( Figure 8 ) . For these exons , we tested the effect of CaRRE mutation on the CaMK IV–induced repression of these exons . Nf2 exon 16 contains a CaRRE 1 in the 3′ splice site whose deletion greatly reduces the CaMK IV effect ( Figure 8 ) . Rnf14 exon 4 contains an exonic CaRRE 1 whose replacement with the neutral IgM sequence again significantly reduces the CaMK IV repression . For Adcyap1r1 exon 14 , no CaRRE was found in our motif search , but the exon was seen to be repressed by depolarization in differentiated P19 cells . Interestingly , a CACAYNNA sequence very similar to CaRRE 1 is seen in the 3′ splice site of this exon . Deletion of the CACA nucleotides of this element again significantly weakened the CaMK IV response . Thus a CaRRE 1–like element presumably also mediates the repression of this exon . These results confirm that the type 1 CaRREs in these exons are at least partially mediating their regulation . Unfortunately , none of the CaRRE 2–containing exons tested was spliced in the Dup construct in HEK 293T cells , and we were not able to confirm the role of this element for the computationally identified exons . Because the CaRRE 2 sequences were clearly functional in the CaMK IV repression assay ( Figure 5B ) , behaving very similarly to CaRRE 1 , CaRRE 2 is also likely to play a role in this response . However , its correlation with the group of depolarization-dependent exons is not as clear as CaRRE 1 .
NMDAR1 E21 encodes the C1 peptide cassette that modulates a variety of activities of NMDARs . In this report , we show that the splicing of NMDAR1 E21 is repressed by depolarization in neurons . This repression requires CaMK IV activity and is reversible , suggesting that E21 splicing is finely tuned by dynamic inputs from the environment . In mutagenesis analyses , we identify two types of CaRRE motifs required for repression of NR1 E21 and demonstrate that either of these CaRRE motifs can be sufficient to confer CaMK IV–dependent repression on a heterologous exon . In bioinformatic searches , we find that these two CaRRE motifs define groups of co-regulated alternative exons . Thus , these regulatory elements found in NR1 E21 are controlling an ensemble of exons whose splicing is modulated in excitable cells . Many known splicing regulatory motifs are short and degenerate , and we also observed these features in CaRRE 1 and CaRRE 2 [28 , 29] . In single nucleotide mutagenesis experiments , most base changes reduced activity , but some had only moderate effects . We also found that six CaRRE 1 and six CaRRE 2 mutations resulted in an improvement in silencing activity . It is likely that certain combinations of these mutations will also yield active elements . For CaRRE 1 , the eight-nucleotide motif identified in E21 is slightly different from the 11-nucleotide motif located in the STREX 3′ splice site , but these two CaRRE 1 motifs share a CACAYN1–4A core sequence . The definition of these CaRRE motifs as a family of active elements allowed the identification of a group of co-regulated exons by database search . Of 27 exons carrying these motifs , 16 were confirmed as responsive by depolarization in P19 cells . In contrast , only one out of 17 exons not carrying a CaRRE motif showed repression after depolarization . The defined CaRRE motifs are thus highly predictive of exons regulated in this manner . Notably , Adcyap1r1 exon 14 was the only exon tested that did not contain one of the eight-nucleotide CaRRE sequences used in the search , but was repressed after depolarization . Closer inspection identified a CACAYNNA sequence upstream of the AG dinucleotide in the 3′ splice site , and mutation of this element showed that it was important for the repression . Thus , there are clearly additional CaRRE 1 variants that we have not yet defined , and many additional co-regulated exons . In addition to the CaRRE 1 motif , we identified a novel functional element GUGGUAGA in E21 that was also able to mediate the CaMK IV–induced repression . As with CaRRE1 , this element has some intrinsic silencing activity even without CaMK IV , but does not appear to be similar to other identified exonic splicing silencers [29 , 30] . This new CaRRE type 2 motif behaved very much like the CaRRE type 1 motif , and could mediate splicing repression both in the E21 exon and when placed in the heterologous Dup175 exon . However , although the CaRRE 1 is enriched in alternative exons over constitutive exons , the CaRRE 2 motif frequency was similar in both types of exons . Because the presence of a CaRRE 2 was predictive for co-regulated exons , the lack of enrichment in the alternative exon set was puzzling and indicates the need to better define the family of functional type 2 CaRREs . There may be elements related to some portion of CaRRE type 2 that function in constitutive splicing and need to be distinguished from the repressor element itself . For example , one of the CaRRE2 variants included in the search contains a potential A1 binding site UAGG ( Figure 5B , and see below ) . Given that the CaRRE 2 sequence was both essential for E21 repression and sufficient to induce repression on its own , it is clear that these variants of the CaRRE 2 motif play an important role in the response . Recently , two exonic UAGG elements and one downstream intronic G tract were identified as important for the splicing repression of this exon [20] . To assess these elements relative to CaRRE 1 and CaRRE 2 , we made mutations in them and tested their effect on inducible repression ( M2 , M4 , and M5; Figure 3B ) . Consistent with the previous report , mutation of these silencers increased the splicing of E21 . The different strength of splicing response that we observe may result from the different vector and cell systems used . Mutation of the UAGG motifs also reduced the repression by CaMK IV . However , the mutation of CaRRE 1 and CaRRE 2 motifs had much larger effects in the scanning mutagenesis . We also did not observe a correlation between the presence of UAGG and G tract motifs and the response to the depolarization in our other confirmed regulated exons . Although these other silencers may be direct targets of CaMK IV , it is possible that they serve to facilitate the CaMK IV repression by the CaRREs by weakening the overall splicing of E21 . The cooperation of a CaRRE with an auxiliary silencer element was observed in the BK channel STREX exon [23] . This may also be what is seen in comparing the CaRRE1s from E21 and E5 . Both elements mediate CaMK IV–inducible repression , but the element from E21 , which contains a UAGG , is stronger ( Figure 4C ) . Similarly , one of the CaRRE2 mutations that improved its activity created a UAGG . It will be interesting to look at the co-occurrence frequency of these splicing silencers with each CaRRE in regulated exons . Alternative splicing events are generally regulated by complex interactions between multiple cis-acting pre-mRNA elements and trans-acting protein factors . Thus , the responsiveness of a CaRRE will depend on the sequence context of the element and the proteins expressed in each cell . In addition , most RNA elements can mediate either splicing repression or activation depending on their location and surrounding elements [31] . This may also be true for CaRRE 1 , because the CaRRE1-containing exons in Samhd1 , Ktn1 , and Xpo1 all increased in splicing with depolarization . Moreover , an exon that is responsive to depolarization in one tissue may not be in another , due to different protein expression profiles . Our finding that 60% of the CaRRE-containing exons were regulated by depolarization in the P19 cells is likely an underestimate of the percentage that is actually responsive , if one were to test other cells . One determinant of synaptic activity is the number of neurotransmitter receptors present at the synapse [2 , 32] . This is frequently regulated by the protein trafficking systems of ER retention and export , and the transfer of proteins from vesicles to the plasma membrane . For NMDARs , these processes are affected by the splicing of NMDAR1 exons 21 and 22 . Here we show that membrane depolarization in P19 cells represses the splicing of the NR1 E21 , whereas the ratio of E22a to E22b remains the same . NR1 E21 encodes the C1 protein cassette containing an ER retention signal that ensures assembly of the NR1 subunits with the NR2 subunits in the ER [2] . The presence of C1 cassette changes the surface delivery rate of the assembled receptor . By comparing cortical cultures treated with either tetrodotoxin or bicuculline , the choice of E22a or E22b splicing was shown to be altered by neuronal activity [9] . This splicing of E22 also affected the rate of ER export , but the splicing of E21 was not seen to change with these treatments in these cells . Thus , in a variety of systems , alternative splicing appears to be a key mechanism for controlling delivery of NMDARs to the synapse and , presumably , for the regulation of cellular excitation and homeostasis . The presence of a CaRRE is predictive of exons that change upon depolarization , and we used it to define a set of co-regulated exons . The broader identification of these regulated splicing events will be important in understanding the biological impact of depolarization on neurons . The functions of the genes so far identified are listed in Table 1 , and affect a limited number of cell processes , including calcium homeostasis , intracellular signaling , and vesicular transport . Notably , depolarization regulates the splicing of exon 21 in the Atp2b1 transcript . Atp2b1 is a calcium ion pump that mediates the transport of calcium ions out of the cell [33] . Exon 21 of Atp2b1 encodes a calmodulin binding inhibitory domain . Repression of the inclusion of this domain will reduce the autoinhibition of Atp2b1 and hence facilitate the removal of calcium ions from the cell . This regulation of calcium homeostasis fits well with role of depolarization in inducing calcium influx through L-type calcium channels [34] . NMDAR1 and the BK channel are also proteins whose splicing alters ion movement across the membrane . The STREX exon affects the calcium and voltage sensitivity of the BK channels as well as its modulation by PKA [35] , and alterations in its splicing will be affected by calcium homeostasis . Other processes affected by depolarization-mediated splicing are protein and vesicular transport . Pitpnb , Rabggtb , Dnalc4 , and Ktn1 all affect vesicular transport and vesicle movement on microtubules , and Xpo4 and Timm22 affect the trafficking of proteins to different cell compartments . Depolarization also frequently regulates splicing in genes encoding signaling and scaffold proteins ( Shc1 , D4Ertd22e , Samhd1 , Adcyap1r1 , Nf2 , and Spna2 ) . We analyzed the functions of the 248 CaRRE-containing alternative exons using their associated SwissProt keywords ( unpublished data ) . When compared to the parental dataset of 2 , 461 alternative exons , the CaRRE-containing exons showed enrichments of genes associated with the keywords receptor , ion channel , calcium , kinase , protein phosphatase , and endocytosis—all processes likely to affect the regulation of the synaptic activity . However , the confidence level of the enriched functional categories observed in this statistical gene ontology analysis was not high . By identifying more CaRRE-containing alternative exons , we hope to improve this analysis . In the brain , neurons constantly receive signals from other cells and adjust their activity according to the incoming stimuli . In this study , we looked at the splicing response to chronic depolarization and observed several new interesting features of this regulation . We find that the extent of the splicing repression is dependent on the KCl concentration , and that splicing repression is reversible after the removal of the KCl from the medium . Thus , the splicing repression is affected by the strength of the external stimuli , and can respond to the dynamic nature of the cellular environment . We also find that different exons respond to the same treatment with different dynamics . Some exons maintain their repressed level throughout the depolarization time course , whereas other exons adapt to the stimulus and start to recover while still in elevated KCl concentrations . Finally , we find that depolarization not only represses , but also activates , splicing of some exons in neuronal cells . All these properties will allow the fine tuning of individual splicing patterns in excitable cells . The identification of the proteins that bind to the CaRRE motifs within E21 will be needed to understand the mechanism of the inducible splicing regulation . The characterization of the CaRREs in this study will allow the use of biochemical approaches to address this question . Other important questions include what additional molecules are required in the CaMK IV pathway , whether other signaling pathways activated by depolarization also participate in splicing regulation , and how these signaling pathways affect the splicing apparatus . Finally , these responses can look quite different at the single-cell level from the changes observed in bulk culture . Thus , single-cell assays of this splicing regulation are needed . Experiments that address some of these questions will lead to a better understanding of the role of splicing regulation in neuronal cell biology .
HEK 293T cells were grown in DMEM with 10% fetal bovine serum ( FBS ) , and P19 cells were grown in αMEM with 10% FBS . To induce differentiation of P19 cells , the cells were transferred to induction medium containing αMEM with 5% FBS and 0 . 5 μM all-trans retinoic acid ( Sigma-Aldrich R-2625; Sigma-Aldrich , St . Louis , Missouri , United States ) in bacteriological Petri dishes to promote cell aggregation . After 2 d , cell aggregates were transferred to fresh induction medium and cultured for another 2 d . The cell aggregates were then trypsinized and 5 × 106 cells were plated per 100-mm tissue culture plate in αMEM with 10% FBS . Two days after plating , 30 μM arabinocytidine ( ARAC , Sigma-Aldrich C1768 ) was added to the media to inhibit proliferation of non-neuronal ( glial-like ) cells . For depolarization , day 10 cultures of differentiated P19 cells were shifted to medium containing 20 to 50 mM KCl with or without CaMK IV inhibitor KN93 ( 30 μM ) or the inactive analog KN92 ( 30 μM ) . For the recovery experiments , day 9 cultures were used for depolarization and after 18 or 24 h , the KCl-containing medium was replaced with fresh culture medium , and the cells were incubated for another 24 h . One microgram of the cytoplasmic RNA was reverse transcribed with a mixture of ten specific primers against the target exons ( 0 . 1 nmole each ) , and one tenth of the reaction was then amplified in 22–25 cycles of PCR with exon-specific primers , one of which was 32P-labeled . The PCR products were resolved on 8% polyacrylamide/8 M urea denaturing gels . The gel was dried , exposed , and scanned in a PhosphorImager ( Fuji Medical Systems , Roselle , Illinois , United States ) . Exons and partial flanking introns of the regulated exons were amplified using Pfu DNA polymerase from mouse genomic DNA and subcloned between the ApaI and BglII sites of pDUP4–1 . In the single nucleotide scanning experiments , the ApaI site was mutated to XhoI site to facilitate cloning . Mutations were made by PCR using Pfu DNA polymerase . The sequences and mutations were confirmed by DNA sequencing . HEK 293T cells were grown in six-well plates to about 60% confluence and transfected using Superfect ( Qiagen , Valencia , California , United States ) with 1 μg of the minigene reporter and 2 μg of the CaMK IV-dCT or CaMK IV-dCTK75E expression construct . Cytoplasmic RNA was purified after 24 h and one third was used in a reverse transcription reaction using Superscript II ( Invitrogen , Carlsbad , California , United States ) with 32P-labeled DNA oligo DUP3 ( AACAGCATCAGGAGTGGACAGATCC ) . Half of the extension product was then mixed with an equal volume of loading buffer , denatured , and then resolved on 8% polyacrylamide/8 M urea denaturing gels . The gel was dried , exposed , and then scanned in a PhosphorImager . We detected alternative splice forms in mouse by mapping mRNA and ESTs onto genomic sequences as previously described [36 , 37] using the following data: ( 1 ) UniGene EST data [38] from January 2006 for mouse ( ftp://ftp . ncbi . nih . gov/repository/UniGene/Mus_musculus ) and ( 2 ) mouse mm7 genome assembly ( http://hgdownload . cse . ucsc . edu/goldenPath/mm7/chromosomes ) . Internal exons were identified as genomic regions flanked by two splices , and all exon boundaries were confirmed by checking consensus splice site motifs . We identified a total of 5 , 497 cassette exons in 3 , 593 mouse genes . Of these , 2 , 594 cassette exons in 1 , 929 mouse genes had multiple ESTs , supporting both the exon-inclusion and exon-skipped forms , and were used in our subsequent analysis . We also extracted a random set of 10 , 000 constitutive exons as our control . The CaRRE consensus sequences were used to search the exons and a 50-nucleotide region of the upstream and downstream introns in our datasets . This was done with scripts written in Perl . For the frequency analysis , the occurrences of the CaRRE 1 or 2 consensus sequences were compared to the occurrences of random octamers . The p-values were calculated using the Fisher's exact test .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/ ) RefSeq accession numbers for the genes discussed in this paper are as follows: Adcyap1r1 ( NM_001025372 . 1 ) , Atp2b1 ( NM_026482 . 1 ) , D4Ertd22e ( NM_174996 . 2 ) , Dnalc4 ( NM_017470 . 1 ) , Gtl3 ( NM_008187 . 1 ) , Ilf3 ( NM_001042708 . 1 ) , Ktn1 ( NM_008477 . 1 ) , Nf2 ( NM_010898 . 2 ) , Pitpnb ( NM_019640 . 2 ) , Rabggtb ( NM_011231 . 1 ) , Rnf14 ( NM_020012 . 1 ) , Samhd1 ( NM_018851 . 2 ) , Shc1 ( NM_011368 . 3 ) , Spna2 ( NM_001076554 . 1 ) , Timm22 ( NM_019818 . 2 ) , U26 ( NM_173765 . 1 ) , and Xpo4 ( NM_020506 . 1 ) .
|
Multiple mechanisms direct changes in neuronal activity in response to external stimuli , ranging from short-acting modifications of membrane proteins to longer-acting changes in gene expression . A frequently regulated step in gene expression is the pre-mRNA splicing reaction in which the inclusion of exons ( protein-coding sequences ) or the position of splice sites produces alternatively spliced mRNA isoforms encoding functionally different proteins . Here , we study splicing of the NMDA receptor , which responds to the neurotransmitter glutamate to modify neuronal activity . We show that the splicing of an important exon ( E21 ) in the NMDA receptor subunit NR1 mRNA is repressed by cell depolarization and activation of the intracellular signaling molecule , CaMK IV . We find that this splicing repression is mediated by two regulatory sequences within the exon itself . One sequence is similar to a previously described regulatory element that had not been known to function in an exon . The other is a new element . The characterization of these elements as a family of degenerate sequences allowed the identification of a group of exons sharing responsiveness to cell depolarization and CamK IV . These results define a new set of gene expression changes that may occur in modulating neuronal activity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"biochemistry",
"in",
"vitro",
"computational",
"biology",
"neuroscience",
"molecular",
"biology"
] |
2007
|
Depolarization and CaM Kinase IV Modulate NMDA Receptor Splicing through Two Essential RNA Elements
|
Rhabdomyosarcoma is the most commonly occurring soft-tissue sarcoma in childhood . Most rhabdomyosarcoma falls into one of two biologically distinct subgroups represented by alveolar or embryonal histology . The alveolar subtype harbors a translocation-mediated PAX3:FOXO1A fusion gene and has an extremely poor prognosis . However , tumor cells have heterogeneous expression for the fusion gene . Using a conditional genetic mouse model as well as human tumor cell lines , we show that that Pax3:Foxo1a expression is enriched in G2 and triggers a transcriptional program conducive to checkpoint adaptation under stress conditions such as irradiation in vitro and in vivo . Pax3:Foxo1a also tolerizes tumor cells to clinically-established chemotherapy agents and emerging molecularly-targeted agents . Thus , the surprisingly dynamic regulation of the Pax3:Foxo1a locus is a paradigm that has important implications for the way in which oncogenes are modeled in cancer cells .
Rhabdomyosarcoma ( RMS ) is the most common childhood soft tissue sarcoma . Historically , RMS has been thought to arise from muscle because of the expression of myogenic markers . Most childhood RMS falls into one of two biologically distinct subgroups: alveolar ( aRMS ) or embryonal ( eRMS ) . aRMS is the more aggressive variant with a survival rate of less than 20% when metastatic due to chemotherapy and radiation resistance [1] . aRMS is characterized by a frequent t ( 2;13 ) chromosomal translocation , which results in the PAX3:FOXO1A fusion gene , or less frequently by a t ( 1;13 ) mediated PAX7:FOXO1A fusion oncogene [1] . Clinically , the aggressive behavior of aRMS has been attributed to PAX3:FOXO1A transcriptional reprograming because fusion negative aRMS have a more favorable outcome similar to eRMS [2] , [3] , [4] . We previously developed a mouse model of aRMS employing a conditional knock-in approach that expresses Pax3:Foxo1a from the native Pax3 locus in fetal and postnatal myoblasts [5] , [6] , [7] . In this model , Pax3:Foxo1a was necessary but not sufficient for aRMS tumor initiation . Interestingly , cells expressing high levels of Pax3:Foxo1a were more prevalent in metastatic tumors [7] . The heterogeneity of Pax3:Foxo1a expression in primary and metastatic tumors , and enrichment in the latter , suggested that Pax3:Foxo1a might be selectively expressed in a subset of aRMS cells; alternatively , Pax3:Foxo1a expression might be temporally regulated . In the current study we present striking evidence that Pax3:Foxo1a is expressed in a dynamic manner and mediates a G2-specific program enabling checkpoint adaptation and refractoriness to therapy .
In our genetically-engineered conditional knock-in mouse model of aRMS , eYFP is expressed as a second cistron on the same mRNA as Pax3:Foxo1a ( Figure 1A ) . We have observed heterogeneity of eYFP expression among tumor cells in situ ( Figure 1B ) . To first examine Pax3:Foxo1a expression as a function of time , we flow sorted Pax3:Foxo1alow and Pax3:Foxo1ahigh cells using eYFP signal in two independent murine aRMS primary cultures ( Figure 1C and 1D; Figure S1A and S1B ) . Comparison of Pax3:Foxo1a protein levels for sorted populations showed Pax3:Foxo1alow cells possessed much reduced levels of Pax3:Foxo1a protein ( Figure 1E and Figure S1C ) . However , FACS analysis over time revealed that the eYFP signal of Pax3:Foxo1alow and Pax3:Foxo1ahigh tended towards the mean eYFP fluorescence intensity of unsorted tumor cells with time and/or cell divisions ( Figure 1C and 1D; Figure S1A and S1B ) . Thus , Pax3:Foxo1ahigh cell could dynamically reduce expression of eYFP from the Pax3:Foxo1a locus , and Pax3:Foxo1alow cells could dynamically increase expression of eYFP from the Pax3:Foxo1a locus . We further confirmed that eYFP expression was indeed reflective of Pax3:Foxo1a expression in terms of protein half-life . Figure S1E and S1F shows levels of eYFP signal and Pax3:Foxo1a protein stability after translation inhibition by cycloheximide ( CHX ) . Akin to the strong correlation between eYFP and Pax3:Foxo1a expression at the protein level ( Figure 1 and Figure S1C ) , the protein half-lives of Pax3:Foxo1a and eYFP were roughly similar at 31 . 6 and 44 . 7 hours ( Figure S1E and S1F ) , thereby affirming that eYFP is a reasonable surrogate for transcription of Pax3:Foxo1a from the Pax3 locus ( we do however acknowledge that eYFP is a better marker of the start of Pax3:Foxo1a transcription than the end of Pax3:Foxo1a transcription or protein expression ( i . e . , since eYFP is expressed on the same mRNA as Pax3:Foxo1a , the beginning of fluorescence should coincide with the initial presence of the Pax3:Foxo1a transcript ) . Thereafter , eYFP is susceptible to photo-bleaching and possible proteasomal degradation sooner than the 44 hours observed under conditions of cyclohexamide treatment ( Figure S1F ) ) . To investigate what conditions affect the dynamic alteration of Pax3:Foxo1a expression in aRMS cells , we compared eYFP fluorescence to cell cycle phase as determined by staining with the DNA dye Hoechst33342 . Almost all Pax3:Foxo1alow cells existed in G0/G1 ( 2N ) stage , while to our surprise Pax3:Foxo1ahigh cells were G2/M or hyperdiploid/multinuclear ( ≥4N ) cells ( Figure 1F and Figure S1D ) . We next performed time-lapse experiments of eYFP activity by confocal microscopy . Figure 1G shows in time-lapse images that eYFP activity during cell division is transiently but markedly increased , particularly in pre-mitotic cells . Interestingly , the level of eYFP in some multinuclear cells remained at a high level in cells that appeared to be unable to undergo telophase/cytokinesis ( Movie S1 ) . We next performed QPCR of Pax3:Foxo1a and PAX3:FOXO1A using cell cycle specific sorted mouse and human aRMS cells , respectively . Both mouse and human aRMS cells showed significant differences in the mRNA expression of Pax3:Foxo1a and PAX3:FOXO1A in the transition from 2N ( G1 ) to 3N ( S phase ) and 4N ( G2/M ) cells ( Figure 2A and 2B ) affirming cross-species relevance of the cell cycle dependent mRNA regulation of Pax3:Foxo1a expression . To investigate the transcriptional basis of this Pax3:Foxo1a dynamic expression , we performed QPCR of Pax3 and Foxo1 using cell cycle specific sorted C2C12 mouse myoblast cells of the genotype Pax3 ( wt/wt ) and mouse aRMS primary tumor cells of the genotype Pax3 ( wt/Pax3:Foxo1a ) . C2C12 myoblasts showed significant increases in Pax3 mRNA levels for 4N cells when compared with 2N cells ( Figure 2C ) . Pax3 was not detectable in aRMS cells at the mRNA level ( data not shown ) , which was also reflected in the absence of expression of Pax3 protein in aRMS cells by western blotting ( Figure 2D ) . This result is consistent with our prior studies suggesting that Pax3:Foxo1a causes decreased expression of the wildtype Pax3 locus [5] , [6] . By contrast , Foxo1 mRNA expression did not differ between 2N and 4N in either C2C12 myoblasts or aRMS tumor cells ( Figure 2E ) . Thus , the cell cycle dependence of Pax3:Foxo1a may in some part be attributable to increased Pax3 promoter activity at G2/M versus G1 in C2C12 myoblasts , but Pax3:Foxo1a transcript level is so significantly increased over Pax3 in aRMS cells that other factors related to the chromosomal fusion are likely responsible , e . g . gain of a Foxo1a 3′ cis-enhancer , or loss of a Pax3 3′ cis-repressor repressor . From the design of the conditional knock-in allele [5] , this element ( s ) can be inferred to exist in the 9 . 3 kB of the Foxo1a 3′ region containing exons 2 and 3 and untranslated region ( 6 . 5 kb ) , or exons 8–10 of Pax3 . We also cannot exclude that stabilization of the Pax3:Foxo1a transcript may to some degree play a role , and this stabilization may or may not be related to the Foxo1a cis-elements on the chimeric mRNA . Because Pdgfra [8] and Igf1r [9] are well known direct downstream targets of Pax3:Foxo1a , we determined whether these targets were expressed to any degree in 4N ( G2/M ) cells . We first sorted aRMS tumor cells for Pdgfra or Igf1r positivity versus negativity , then performed DNA content analysis . For both receptor tyrosine kinases ( RTKs ) , the majority of cells with positive RTK surface expression were 2N ( Figure 2F ) . However , nearly twice as many 4N cells are Igf1r ( or Pdgfra ) positive versus Igf1r ( or Pdgfra ) negative , suggesting these Pax3:Foxo1a targets may have a functional role late in the cell cycle , such as the Igf1r-mediated radioresistance seen for other forms of cancer [10] . To determine the role of Pax3:Foxo1a in G2 , M or G2/M checkpoint , we examined markers of each cell cycle phase under non-stress or stress conditions . Immunocytochemistry is presented in Figure 3 is a for Pax3:Foxo1a ( Pax3 ) with phospho-histone H3 ( pHH3 ) , a marker of mitosis , or CDC2-Y15 ( pCDC2 ) , a negative marker of entry into mitosis that is commonly expressed in G2 ( CDC2-Y15 is phosphorylated by Wee1 kinase , which then negatively regulates Cdc2 kinase [11]; CDC2-Y15 is present starting in late G1 then also in S , and G2 phases , but absent in M [12] ) . In murine aRMS primary cultures U23674 and U42369 , pHH3 positive metaphase cells did not express Pax3:Foxo1a protein and yet most pCDC2 positive cells expressed Pax3:Foxo1a very highly ( Figure 3 ) . These results suggest that Pax3:Foxo1a is expressed in the G2 cell cycle phase but not M phase . Human aRMS cell lines Rh3 and Rh41 showed identical results ( Figure 3 ) . Next , we sought to understand the function of Pax3:Foxo1a in G2 . For this purpose we performed genome-wide expression analysis using cells sorted at specific stages of the cell cycle ( 2N vs . 4N ) with or without Pax3:Foxo1a siRNA knockdown ( Figure 4A ) . Because eYFP is expressed as a second cistron in the targeted Pax3:Foxo1a-ires-eYFP allele , we anticipated that siRNA for eYFP would knock down not only eYFP but also Pax3:Foxo1a . Western blotting of Pax3:Foxo1a and native Foxo1a protein 48 hours after siRNA transfection showed that eYFP siRNA efficiently and specifically knocked down Pax3:Foxo1a protein ( Figure 4B ) . Protein expression of the Pax3:Foxo1a transcriptional target Pdgfra was also reduced ( Figure 4B ) . From genome-wide expression analysis of 2N vs . 4N sorted cells with or without Pax3:Foxo1a siRNA knockdown , we found several genes implicated in the process of G2/M checkpoint adaptation to be down-regulated in G2/M ( 4N cells ) when Pax3:Foxo1a was knocked down ( Figure 4C; Table S1 shows all data analyzed by ANOVA ( <0 . 05 ) using the multiple comparison correction method of Benjamini and Hochberg ) . Checkpoint adaptation is the process by which unicellular organisms or cancer cells progress through a delayed cell cycle checkpoint ( G2 or by analogy the mitotic spindle assembly checkpoint ) in lieu of programmed cell death , but before DNA damage is completely repaired [13] , [14] , [15] . Factors implicated in checkpoint adaptation are similar to those involved in checkpoint recovery ( after complete repair of DNA damage ) , but additionally require anti-apoptotic signals [14] . Select G2/M checkpoint adaptation genes implicated in this experiment , the DNA damage sensing/checkpoint progression factors Plk1 , Cdc25b , H2afx and the cell survival factor Birc5 ( Survivin ) , were validated for differential expression by QPCR ( Figure 4D ) . Whether these genes are direct transcriptional targets of Pax3:Foxo1a was investigated by interrogating loci for reported nearby Pax3:Foxo1a binding sites [16] . Most potential regulatory sites were greater than 60 kB away ( Table S2 ) . While regulatory sequences can be hundreds of kBs away from the target gene , it remains possible that these genes may also be regulated indirectly by other Pax3:Foxo1a target genes or miRNAs . As a test of checkpoint adaptation and the permissiveness of aRMS cells to transit from G2 to mitosis despite single- and double-stranded DNA damage , we irradiated tumor cells with or without Pax3:Foxo1a knockdown . Radiation resulted in a higher fraction of DNA breaks amongst mitotic cells ( as represented by dual pHH3 positive , H2AX positive cells ) under conditions of Pax3:Foxo1a expression than its knockdown ( Figure 5A and Figure S2A ) , suggesting that Pax3:Foxo1a does facilitate G2 to M transition , consistent with checkpoint adaptation . Moreover , we performed cell cycle and Annexin V apoptosis detection assay after treatment with 10 Gy radiation for two independent eYFP shRNA knockdown clones compared to two other independent shRNA controls ( as stated early , eYFP knockdown also achieves Pax3:Foxo1a knockdown ) ( Figure S2 ) . Cell cycle analysis of the shRNA clones treated with radiation revealed increasing percentage of cells in cells having ≥4N DNA content after radiation for Pax3:Foxo1a knockdown cells compared to radiated controls ( p<0 . 05 ) ( Figure 5B ) . This result is consistent with a role of Pax3:Foxo1a in overcoming G2 arrest or M checkpoint arrest after radiation . Similarly , the Annexin V apoptosis detection assay showed a lower induction of apoptosis following radiation when Pax3:Foxo1a expression was preserved in shControl clones than shYFP cells ( Figure 5C ) . To test the acute role of Pax3:Foxo1a in tolerization to treatment-related DNA damage in vivo , we used eYFP siRNA to transiently knock down Pax3:Foxo1a in aRMS tumor cells treated with radiation versus non-irradiated controls that were then orthotopically injected into unirradiated host mice . Pax3:Foxo1a mediated a cell survival and tumor re-establishment advantage under the stress condition of irradiation , but not under homeostatic conditions ( p = 0 . 02 , Figure 6A and 6B ) . To assess the extent to which the fusion gene mediates refractoriness to chemotherapy agents , we observed Pax3:Foxo1a to facilitate 2–4 fold refractoriness to clinical agents capable of causing double-stranded DNA breaks and mitotic arrest ( vincristine , actinomycin-D , topotecan ) more so than agent inducing single-strand breaks ( mafosfamide , the active metabolite of cyclophosphamide ) ( Figure S3A–E ) . That a similar role of Pax3:Foxo1a may apply to targeted agents was previously suggested by enriched G2 expression of Pdgfra ( Figure 2F ) and then demonstrated by increased sensitivity to prototypic Pdgfr inhibitor , imatinib , after Pax3:Foxo1a knockdown ( Figure S3F ) . Similarly , Pax3:Foxo1a knockdown sensitized tumor cells to siRNA inhibition of downstream signaling mediators of acquired imatinib resistance ( Figure S3G ) [17] . Thus , these in vitro and in vivo results are consistent with a function of Pax3:Foxo1a in mediating checkpoint adaptation and refractoriness to the established clinical therapies of radiation and chemotherapy , or more contemporary molecularly-targeted agents .
A key finding of this study is that Pax3:Foxo1a expression is dynamic and varies during the cell cycle . To our knowledge this is first report of a translocation-mediated chimeric transcription factor oncogene that is expressed in a cell cycle-specific manner – much less , one that is expressed specifically during G2 . The master transcription factor MYOD is expressed strongly during G1 [18] but is inactivated by phosphorylation during mitosis , which results in deportation from the nucleus [19] . MYF5 is also expressed in a cell cycle-dependent manner , but neither MYOD nor MYF5 expression is increased during G2/M as observed in our study of Pax3:Foxo1a in aRMS . Our findings reveal that Pax3 expression in wildtype C2C12 myoblasts is dynamic and increased during G2/M , but that to account for the dramatic increase in Pax3:Foxo1a expression an additional enhancer effect of Foxo1a 3′ region DNA is likely to be present . This result opens the possibility that co-factors assembled at the Pax3 promoter or fusion gene specific cis-elements might be targeted to suppress Pax3:Foxo1a expression . Cell cycle progression after DNA damage is regulated by checkpoint controls , which prevent continued transit through the cycle until the damage has been repaired , hence protecting the integrity of the genome . Arrest in G1 permits repair prior to replication , whereas arrest in G2 allows repair prior to mitotic chromosome segregation . The p53 tumor suppressor , which is mutated in roughly half of human aRMS , has been shown to be integral to both G1 and G2 damage checkpoint machinery , but some reports found p53 dispensable for the G2 checkpoint [13] , [20] . Checkpoint adaptation is defined as the ability to divide and survive following a sustained checkpoint arrest despite the presence of unrepairable DNA breaks [14] . Cells undergoing checkpoint adaptation will frequently die in subsequent cell cycles if DNA damage goes unrepaired , yet , some cells may be able to survive and proliferate in an aneuploid state [14] . Furthermore , in unicellular eukaryotes and tumor cells , DNA repair can occur at G1 [21] . Here , we reveal that the G2/M adaptation genes ( H2afx , Cdc25b and Plk1 ) were suppressed by Pax3:Foxo1a knockdown in G2 and M cell cycle phases and that fewer cells transited from G2 to M without initiating apoptosis under conditions of Pax3:Foxo1a knockdown in the context of radiation-induced stress . These results suggested that not only cell cycle dependent expression but also a clinically-relevant biology underlying Pax3:Foxo1a expression at the G2-M checkpoint , a critical cell cycle checkpoint following radiation or DNA double strand break inducing-chemotherapy . That a myogenic cancer might utilize genomic instability , aneuploidy or multinucleation as a mechanism of cell survival or tumor cell evolution/progression may not be so unexpected , in retrospect . Normal myofibers are typically multi-nuclear by definition , and genetic conditions predisposing to mitotic disjunction such as Mosaic Variegated Aneuploidy ( MVA ) are strongly associated with the development of RMS [22] . Both aRMS and eRMS have also been documented to be hyperdiploid , tetraploid , polyploid or to even have mixed aneuploid populations [23] , [24] , [25] . At a cellular level , the heterogeneity of cells in rhabdomyosarcoma is notable for the subpopulation of multi-nucleated rhabdomyoblasts which appears with giant nuclei or as multi-nucleated giant cells , often with cross-striations – yet highly mitotic [26] . These rhabdomyoblasts might be compared to the multinucleated stemloid cells in fibrosarcoma , which have a tumor-repopulating ability [27] . Our recent study of aRMS and the PKC iota inhibitor , aurothiomalate , reveals that aRMS cells have a remarkable tolerance to polyploidy , which induces neither apoptosis or senescence [28] . This intrinsic capacity to tolerate aneuploidy as well as this report's observed Pax3:Foxo1a-mediated increase in checkpoint adaptation gene expression may be directly relevant to clinical care , given that decreased expression of these same factors ( i . e . , PLK1 , CCCNB1 , BIRC5 , AURKB ) have been reported to improve sensitivity to mitotic inhibitors [29] . Therefore , the interest generated from chemical screens identifying PLK1 as a potential therapeutic target in RMS [30] is likely warranted . When considering the differences in treatment-related outcomes in RMS subtypes , the role of Pax3:Fox01a in checkpoint adaptation may be our most important clue yet as to how to improve outcome for fusion positive patients: while aRMS are certainly sensitive to standard chemotherapy and radiation , it is the survival of resistant clones which is the cause of disease progression and relapse – which occur to a greater extent in Pax:Foxo1a positive aRMS than fusion negative aRMS or eRMS [31] , [32] , and which we believe to be a result of Pax3:Foxo1a-mediated checkpoint adaptation . These effects on tumor cell sensitivity to radiation , chemotherapy and targeted therapeutics are likely to be cumulative and possibly critically important in defining the otherwise very narrow therapeutic window for fusion positive aRMS , for which the toxicity of chemotherapy and radiation is now dose-limiting [33] . Perhaps the most interesting aspect of this genetically-engineered conditional mouse model of a deadly but rare childhood cancer is that a labor-intensive knock-in approach to modeling the molecular pathophysiology of a fusion gene was beneficial . Successful transgenic tumor models have been generated by constitutive , ectopic expression of translocation-related fusion oncogenes for synovial sarcoma [34] as well as other “driver” oncogene related tumors [35]; similarly , retroviral transfection of oncogenes into hematopoietic cells has enabled this study of translocation-associated leukemia for many years [36] , [37] . However , are these systems driven by non-native or partial-native promoters to be the definitive preclinical platforms for interrogating molecular physiology – or are distal native cis- and trans-regulation temporally critical ? Every experimental system has its advantages and limitations , yet for cell and animal models where translocation-mediated fusion genes have yet to be modeled at the native promoter , we may have an entirely new spectrum of cancer genetics to explore .
The Myf6Cre , Pax3:Foxo1a , p53 conditional aRMS mouse model has been described previously [5] , [6] , [7] , is described as caMOD Model 150064393 , and is publically available through the NCI MMHCC Repository ( MMHCC Strain Codes 01XBL B6; 129-Myf6<tm2 ( Cre ) Mrc> and 01XBM B6; 129-Pax3<tm1Mrc> ) . SHO-PrkdcscidHrhr mice were purchased from Charles River Laboratories ( Wilmington , MA ) and bred/maintained at OHSU . Mouse primary cell cultures ( U23674 , U42369 , U57844 ) were established from tumor samples . Tumors were minced into small pieces and digested with collagenase ( 10 mg/ml ) overnight at 37°C . The dissociated cells were then incubated in Dulbecco's modified eagle's media supplemented with 10% fetal bovine serum ( FBS ) and 1% penicillin-streptomycin in 5% CO2 at 37°C . C2C12 mouse myoblast cells were purchased from ATCC ( Manassas , VA ) . Human aRMS cell lines were a gift from Peter Houghton ( Rh3; Nationwide Children's Hospital , Columbus , OH ) or Patrick Reynolds ( Rh41; COG Cell Culture and Xenograft Repository ) . These cells lines were maintained in the same culture conditions as primary tumor cell cultures: DMEM supplemented with 10% Fetal Bovine Serum ( FBS ) and 1% Penicillin-Streptomycin . All primary cell cultures experiments using cells were carried out at passage 3–7 . For immunofluorescence staining of frozen sections , the polyclonal antibody for green fluorescent protein ( 1∶1000 , AB16901 , Chemicon ) was used with DAPI counterstain . siRNA transfections were carried out using Lipofectamine2000 ( Invitrogen , Grand Island , NY ) according to manufacturer's recommended protocol . siRNA's were diluted between 0 . 1 and 10 nM , and the final concentration of Lipofectamine2000 was 0 . 2% . siYFP Stealth RNAi siRNA Reporter Controls ( cat . 12935-145; Invitrogen ) were used as the eYFP siRNA to knockdown the Pax3:Foxo1a-ires-eYFP bi-cistronic mRNA , whereas Stealth RNAi siRNA Negative Control Med GC #3 ( cat . 12935-113; Invitrogen ) was used as the siRNA control ( siCont ) . To establish shRNA knockdown clones of primary tumor cell cultures , we used MISSION pLKO . 1-puro eGFP shRNA Control Transduction Particles ( cat . SHC005V; Sigma Aldrich ) for Pax3:Foxo1a knockdown and MISSION pLKO . 1-puro Non-Mammalian shRNA Control Transduction Particles ( cat . SHC002V; Sigma Aldrich ) as the control , respectively . shRNA transfections and clonal selection were carried out according to manufacturer's recommended procedures . Mouse RMS primary cell cultures were plated at 1 . 8×106 cells per 150 mm dish . After 24 h , hexadimethrine bromide was added ( 8 µg/ml , cat . H9268; Sigma Aldrich ) , followed by each particle solution ( MOI 0 . 5 ) . After another 24 h , media were removed and fresh media were added . The following day , puromycin was added ( 5 µg/ml , cat . P8833; Sigma Aldrich ) . Puromycin-resistant clones were selected cloning rings at day 14 ( shControl ) and day 17 ( shYFP ) , with continuous puromycin selection at all times . Cells were irradiated on a Trilogy linear accelerator ( Varian , Palo Alto , CA ) with a 10×10 cm AP field . Two centimeter of bolus material was placed on top of the 2 chamber slide or 6 cm dish and the target surface distance to the bolus was at 97 cm . Monitor units on the linear accelerator were then set to deliver 6 Gy or 10 Gy of dose to the cells . Tumors were lysed in radioimmunoprecipitation assay ( RIPA ) buffer or NP40 buffer containing both protease and phosphatase inhibitor ( Sigma ) . The lysates were homogenized and centrifuged at 8000 g for 10 minutes . The resulting supernatants were used for immunoblot analysis . Goat anti-FOXO1A antibody ( cat . Sc-9808; Santa Cruz , Santa Cruz , CA ) , goat anti-GFP antibody ( cat . 600-101-215 , Rockland; Gilbertsville , PA ) or rabbit anti-PDGFRa antibody ( cat . #3164; Cell signaling Technology , Danvers , MA ) . Cells were plated on 8-well CultureSlides ( cat . 354118; BD Falcon , Franklin Lakes , NJ ) , fixed with 4% paraformaldehyde , permeabilized with 0 . 1% or 0 . 25% TritonX100 , washed and incubated with mouse monoclonal anti-skeletal myosin ( FAST ) ( cat . M4276; Sigma ) , rabbit anti-Ki67 ( cat . RM-9106-F; Thermo Scientific , Waltham , MA ) , mouse anti-Pax3 ( cat . MAB2457; R&D Systems ) , mouse anti-phospho Histone H3 ( cat . #9706; Cell Signaling Technology ) , rabbit anti-phospho Histone H3 ( cat . #3377; Cell Signaling Technology ) , mouse anti-phospho Histone H3 ( cat . #9706; Cell Signaling Technology ) , rabbit anti-CDC2-Y15 ( cat . #4539; Cell Signaling Technology ) or rabbit anti-phospho H2AX antibody ( cat . #9718; Cell Signaling Technology ) , overnight , rinsed with PBS , incubated with fluorescein isothiocyanate-conjugated anti-mouse and rabbit IgG ( 1∶200 ) for 1 h , and examined by confocal microscopy with a Zeiss LSM700 instrument . For immunocytochemistry experiments , at least 100 positive cells were scored per specimen . Cells were suspended in Hank's balanced salt solution ( HBSS ) with 2% FBS and 2 mM EDTA . Antibody staining was performed for 20 minutes on ice . Prior to FACS sorting , cells were suspended in 1 µg/ml propidium iodide ( Pi ) and 10 µM calcein blue ( Invitrogen ) to identify viable cells ( Pi−Ca+ ) . Purity checks were performed to confirm that the sorted eYFP+ and eYFP- cell subsets had a purity of >98% using a eYFP expression threshold determined by the background fluorescence of eYFP- C2C12 cells . The following antibodies were used to evaluate receptor tyrosine kinase surface expression: APC-conjugated Pdgfrα antibody ( #17-1401-81 , eBiosciences ) or anti-IGF1 Receptor antibody ( cat . Ab32823; Abcam , Cambridge , MA; 1 in 25 ) . Mouse RMS primary cell cultures were trypsinized and incubated with Hoechst33342 ( final concentration 15 µg/ml ) and Reserpine ( final concentration 5 µM ) . Cells were incubated in the dark for 30 min at 37°C , and analyzed and sorted by flow cytometry using an Influx FACS instrument ( Becton Dickinson , Franklin Lakes , NJ ) . Cell cycle was determined with the FlowJo software ( Tree Star , Inc . , Ashland , OR ) . Mouse primary cell cultures were stained with Annexin V and Propidium iodide using Annexin-V-FLUOS Staining Kit ( cat . 11 858 777 001; Roche ) following the protocol provided by the manufacturer . Briefly , 48 hour after irradiation , 106 mouse primary cell cultures were trypsinized , washed by PBS and resuspended in 100 µl of Annexin-VFLUOS labeling solution , incubated 10–15 min at 15–25°C , and analyzed by FACS Calibur . U23674 cells were subfractionated by FACS sorting as described above . mRNA was isolated using RNeasy spin columns ( Qiagen , Valencia , CA ) and reverse transcribed using Superscript III First-Strand Synthesis System for RT-PCR ( Invitrogen ) . QPCR was performed using an AV7900 PCR system ( Applied Biosystem ) with SYBR-green PCR reagents . Pax3:Foxo1a was detected using the following primer sequences: 5′-AGACAGCTTTGTGCCTCCAT-3′ and 5′-CTCTTGCCTCCCTCTGGATT-3′ . Other primers are Taqman Gene Expression assay , H2afx ( Mm00515990_s1 ) , Cdc25b ( Mm00499136_m1 ) , Birc5 ( Mm00599749_m1 ) , Plk1 ( Mm00440924_g1 ) and Gapdh ( Mm99999915_g1 ) by Invitrogen . RT-PCR data were quantified using the standard curve method , and relative expression of Pax3:Foxo1a per sample was determined by normalization against the quantity of 18 s rRNA and Gapdh within each sample . For each sample , QPCR was performed in technical duplicates and results were averaged . Mouse RMS primary cell cultures were plated at 1×103 cells of each cohort per well in a 96-well plate . After cell incubations , cytotoxic effects were assayed using CellTiter 96 AQueous One Solution Cell Proliferation Assay system ( Promega , Madison , WI ) and SpectraMax M5 luminometer ( Molecular Devices , Sunnyvale , CA ) . IC50 and C . I . were determined with CalcuSyn software ( BIOSOFT , United Kingdom ) . Drugs: Vincristine sulfate salt ( cat . V8879; Sigma ) , Actinomycin-D ( cat . A9415; Sigma ) , Mafosfamide ( cat . sc-211761; Santa Cruz ) , Topotecan hydrochloride ( cat . S1231; Selleck ) , Eribulin mesylate ( NDC 62856-389-01; Eisai ) or Imatinib Mesylate ( cat . S1026; Selleck ) . For these studies , individual siRNA were obtained from Dharmacon ( Lafayette , CO ) , including the mouse siRNA library targeting the tyrosine kinome ( siGENOME ) . These experiments are performed at 100 nM concentration and include non-specific pooled siRNA as a control purchased from Dharmacon . Transfection of siRNA was carried out using Lipofectamine 2000 in Opti-MEM Reduced Serum Media ( Invitrogen ) . After cells were plated in 96-well plates in the presence of inhibitor or siRNA , and incubated for 96 hours , respectively , 20 µL CellTiter 96 AQueous One solution ( MTS ) was added to each well and absorbance values assessed by the BioTek Synergy 2 plate reader ( BioTek , Winooski , VT ) . Labeled target cRNA was prepared from 12 mouse total RNA samples ( 3 independent experiments×4 samples ) . Samples were amplified and labeled using the Ambion MessageAmp Premier RNA Amplification Kit following the manufacturer's protocol . Sample order was randomized . Each sample target was hybridized to an Illumina MouseRef 8 v 2 Expression BeadChip Array . Image processing and expression analysis were performed using Illumina BeadArray Reader and GenomeStudio ( v . 2010 . 1 ) Gene Expression module ( v . 1 . 6 . 0 ) software . Microarray data have been accessioned with the Gene Expression Omnibus ( GEO ) under series GSE41675 . The following link has been created to allow review of record GSE41675 while it remains in in review/under private status: http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=xdajbqeisomcyhq&acc=GSE41675 . aRMS primary cultures ( passage 5 ) were plated in 6 cm dishes . The next day cells were transfected with siYFP Stealth RNAi siRNA Reporter Controls or Stealth RNAi siRNA Negative Control Med GC #3 . Two days later cells were irradiated on a Trilogy linear accelerator with a 10×10 cm AP field with two centimeter of bolus material was placed on top of the 6 cm dish . The target surface distance to the bolus was at 97 cm and monitor units on the linear accelerator were then set to deliver 10 Gy of dose to the cells . Subsequently , cells were trypsinized and 500 , 000 cells were injected into the gastrocnemius muscle of SHO mice that had been pre-injured 24 hours prior with 0 . 85 µg/mouse cardiotoxin intramuscularly . Tumor volumes ( cm3 ) were measured 3-dimensionally with electronic calipers and calculated from formula ( π/6 ) ×length×width×height , assuming tumors to be spheroid . For statistical analysis of disease-free survival , a tumor volume threshold of 0 . 25 cc was applied . The log-rank test was used to contrast treatments . All analyses were performed using R 3 . 0 . 0 ( The R Foundation for Statistical Computing , Vienna , Austria ) .
|
Rare childhood cancers can be paradigms from which important new principles can be discerned . The childhood muscle cancer rhabdomyosarcoma is no exception , having been the focus of the original 1969 description by Drs . Li and Fraumeni of a syndrome now know to be commonly caused by underlying p53 tumor suppressor loss-of-function . In our studies using a conditional genetic mouse model of alveolar rhabdomyosarcoma in conjunction with human tumor cell lines , we have uncovered that the expression level of a translocation-mediated fusion gene , Pax3:Foxo1a , is dynamic and varies during the cell cycle . Our studies support that Pax3:Foxo1a facilitate the yeast-related process of checkpoint adaptation under stresses such as irradiation . The broader implication of our studies is that distal cis elements ( promoter-influencing regions of DNA ) may be critical to fully understanding the function of cancer-associated translocations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Ethics",
"statement"
] |
[
"animal",
"models",
"cellular",
"stress",
"responses",
"model",
"organisms",
"cancer",
"genetics",
"gene",
"expression",
"genetics",
"biology",
"molecular",
"cell",
"biology",
"mouse"
] |
2014
|
Cell-Cycle Dependent Expression of a Translocation-Mediated Fusion Oncogene Mediates Checkpoint Adaptation in Rhabdomyosarcoma
|
Evolutionary theory has produced two conflicting paradigms for the adaptation of a polygenic trait . While population genetics views adaptation as a sequence of selective sweeps at single loci underlying the trait , quantitative genetics posits a collective response , where phenotypic adaptation results from subtle allele frequency shifts at many loci . Yet , a synthesis of these views is largely missing and the population genetic factors that favor each scenario are not well understood . Here , we study the architecture of adaptation of a binary polygenic trait ( such as resistance ) with negative epistasis among the loci of its basis . The genetic structure of this trait allows for a full range of potential architectures of adaptation , ranging from sweeps to small frequency shifts . By combining computer simulations and a newly devised analytical framework based on Yule branching processes , we gain a detailed understanding of the adaptation dynamics for this trait . Our key analytical result is an expression for the joint distribution of mutant alleles at the end of the adaptive phase . This distribution characterizes the polygenic pattern of adaptation at the underlying genotype when phenotypic adaptation has been accomplished . We find that a single compound parameter , the population-scaled background mutation rate Θbg , explains the main differences among these patterns . For a focal locus , Θbg measures the mutation rate at all redundant loci in its genetic background that offer alternative ways for adaptation . For adaptation starting from mutation-selection-drift balance , we observe different patterns in three parameter regions . Adaptation proceeds by sweeps for small Θbg ≲ 0 . 1 , while small polygenic allele frequency shifts require large Θbg ≳ 100 . In the large intermediate regime , we observe a heterogeneous pattern of partial sweeps at several interacting loci .
Rapid phenotypic adaptation of organisms to all kinds of novel environments is ubiquitous and has been described and studied for decades [1 , 2] . However , while the macroscopic changes of phenotypic traits are frequently evident , their genetic and genomic underpinnings are much more difficult to resolve . Two independent research traditions , molecular population genetics and quantitative genetics , have coined two opposite views of the adaptive process on the molecular level: adaptation either by selective sweeps or by subtle allele frequency shifts ( sweeps or shifts from here on ) . On the one hand , population genetics works bottom-up from the dynamics at single loci , without much focus on the phenotype . The implicit assumption of the sweep scenario is that selection on the trait results in sustained directional selection also on the level of single underlying loci . Consequently , we can observe phenotypic adaptation at the genotypic level , where selection drives allele frequencies at one or several loci from low values to high values . Large allele frequency changes are the hallmark of the sweep scenario . If these frequency changes occur in a short time interval , conspicuous diversity patterns in linked genomic regions emerge: the footprints of hard or soft selective sweeps [3–6] . On the other hand , quantitative genetics envisions phenotypic adaptation top-down , from the vantage point of the trait . At the genetic level , it is perceived as a collective phenomenon that cannot easily be broken down to the contribution of single loci . Indeed , adaptation of a highly polygenic trait can result in a myriad of ways through “infinitesimally” small , correlated changes at the interacting loci of its basis ( e . g . [1 , 7 , 8] . Conceptually , this view rests on the infinitesimal model by Fisher ( 1918 ) [9] and its extensions ( e . g . [10] ) . Until a decade ago , the available moderate sample sizes for polymorphism data had strongly limited the statistical detectability of small frequency shifts . Therefore , the detection of sweeps with clear footprints was the major objective for many years . Since recently , however , huge sample sizes ( primarily of human data ) enable powerful genome-wide association studies ( GWAS ) to resolve the genomic basis of polygenic traits . Consequently , following conceptual work by Pritchard and coworkers [7 , 11] , there has been a shift in focus to the detection of polygenic adaptation from subtle genomic signals ( e . g . [12–14] , reviewed in [15] ) . Very recently , however , some of the most prominent findings of polygenic adaptation in human height have been challenged [16 , 17] . As it turned out , the methods are highly sensitive to confounding effects in GWAS data due to population stratification . While discussion of the empirical evidence is ongoing , the key objective for theoretical population genetics is to clarify the conditions ( mutation rates , selection pressures , genetic architecture ) under which each adaptive scenario , sweeps , shifts—or any intermediate type—should be expected in the first place . Yet , the number of models in the literature that allow for a comparison of alternative adaptive scenarios at all is surprisingly limited ( see also [18] ) . Indeed , quantitative genetic studies based on the infinitesimal model or on summaries ( moments , cumulants ) of the breeding values do not resolve allele frequency changes at individual loci ( e . g . [19–22] ) . In contrast , sweep models with a single locus under selection in the tradition of Maynard Smith and Haigh [3] , or models based on adaptive walks or the adaptive dynamics framework ( e . g . [23–25] ) only allow for adaptive substitutions or sweeps . A notable exception is the pioneering study by Chevin and Hospital [26] . Following Lande [27] , these authors model adaptation at a single major quantitative trait locus ( QTL ) that interacts with an “infinitesimal background” of minor loci , which evolves with fixed genetic variance . Subsequent models [28 , 29] trace the allele frequency change at a single QTL in models with 2-8 loci . Still , these articles do not discuss polygenic adaptation patterns . Most recently , Jain and Stephan [30 , 31] studied the adaptive process for a quantitative trait under stabilizing selection with explicit genetic basis . Their analytical approach allows for a detailed view of allele frequency changes at all loci without constraining the genetic variance . However , the model is deterministic and thus ignores the effects of genetic drift . Below , we study a polygenic trait that can adapt via sweeps or shifts under the action of all evolutionary forces in a panmictic population ( mutation , selection , recombination and drift ) . Our model allows for comprehensive analytical treatment , leading to a multi-locus , non-equilibrium extension of Wright’s formula [32] for the joint distribution of allele frequencies at the end of the adaptive phase . This way , we obtain predictions concerning the adaptive architecture of polygenic traits and the population genetic variables that delimit the corresponding modes of adaptation . The article is organized as follows . The Model section motivates our modeling decisions and describes the simulation method . We also give a brief intuitive account of our analytical approach . In the Results part , we describe our findings for a haploid trait with linkage equilibrium among loci . All our main conclusions in the Discussion part are based on the results displayed here . Further model extensions and complications ( diploids , linkage , and alternative starting conditions ) are relegated to appendices . Finally , we describe our analytical approach and derive all results in a comprehensive Mathematical Appendix ( S2 Appendix ) . For the ease of reading , we have tried to keep both the main text and the Mathematical Appendix independent and largely self-contained .
Consider a panmictic population of Ne haploids , with a binary trait Z ( with phenotypic states Z0 “non-resistant” and Z1 “resistant” , see Fig 1 ) . The trait is governed by a polygenic basis of L bi-allelic loci with arbitrary linkage ( we treat the case of linkage equilibrium in the main text and analyze the effects of linkage in S1 Appendix , Section A ) . Only the genotype with the ancestral alleles at all loci produces phenotype Z0 , all other genotypes produce Z1 , irrespective of the number of mutations they carry . Loci mutate at rate μi , 1 ≤ i ≤ L , per generation ( population mutation rate at the ith locus: 2Ne μi = Θi ) from the ancestral to the derived allele . We ignore back mutation . The mutant phenotype Z1 is deleterious before time t = 0 , when the population experiences a sudden change in the environment ( e . g . arrival of a pathogen ) . Z1 is beneficial for time t > 0 . The Malthusian ( logarithmic ) fitness function of an individual with phenotype Z reads W ( Z ) = { s d Z for t < 0 s b Z for t ≥ 0 . ( 1 ) Without loss of generality , we can assume Z0 = 0 and Z1 = 1 . We then have W ( Z0 ) = 0 . Furthermore , W ( Z1 ) = sd < 0 , respectively W ( Z1 ) = sb > 0 , measure the strength of directional selection on Z ( e . g . cost and benefit of resistance ) before and after the environmental change . For the basic model , we assume that the population is in mutation-selection-drift equilibrium at time t = 0 . We extend the basic model in several directions . This includes linkage ( S1 Appendix , Section A ) , alternative starting conditions at time t = 0 ( S1 Appendix , Section B ) , diploids ( S1 Appendix , Section C ) , and arbitrary time-dependent selection s ( t ) ( S2 Appendix , Section M . 1 ) . Here , we describe how we relax the assumption of complete redundancy of all loci . Diminishing returns epistasis , e . g . due to Michaelis-Menten enzyme kinetics , will frequently not lead to complete adaptation in a single step , but may require multiple steps before the trait optimum is approached . In a model of incomplete redundancy , we thus assume that a first beneficial mutation only leads to partial adaptation . We thus have three states of the trait , the ancestral state for the genotype without mutations , Z0 = 0 ( non-resistant ) , a phenotype Zδ = δ ( partially resistant ) for genotypes with a single mutation , and the mutant state Z1 = 1 ( fully resistant ) for all genotypes with at least two mutations , see Fig 1 ( b ) . For diminishing returns epistasis , we require 1 2 ≤ δ < 1 . The fitness function is as in Eq ( 1 ) . A model with asymmetries in the single-locus effects is discussed in S1 Appendix , Section D . For the models described above , we use Wright-Fisher simulations for a haploid , panmictic population of size Ne , assuming linkage equilibrium between all L loci in discrete time . Selection and drift are implemented by independent weighted sampling based on the marginal fitnesses of the ancestral and mutant alleles at each locus . Due to linkage equilibrium , the marginal fitnesses only depend on the allele frequencies and not genotypes . Ancestral alleles mutate with probability μi per generation at locus i . We start our simulations with a population that is monomorphic for the ancestral allele at all loci . The population evolves for 8Ne generations under mutation and deleterious selection to reach ( approximate ) mutation-selection-drift equilibrium . Following [6 , 37] , we condition on adaptation from the ancestral state and discard all runs where the deleterious mutant allele ( at any locus ) reaches fixation during this time . ( We do not show results for cases with very high mutation rates and weak deleterious selection when most runs are discarded ) . At the time of environmental change , selection switches from negative to positive and simulation runs are continued until a prescribed stopping condition is reached . We are interested in the genetic architecture of adaptation—the joint distribution of mutant frequencies across all loci—at the end of the rapid adaptive phase . Following [31] , we define this phase as “the time until the phenotypic mean reaches a value close to the new optimum” . Specifically , we stop simulations when the mean fitness W ¯ in the population has increased up to a proportion fw of the maximal attainable increase from the ancestral to the derived state , W ( Z 1 ) - W ¯ W ( Z 1 ) - W ( Z 0 ) = f w . ( 2 ) For the basic model with complete redundancy , this simply corresponds to a residual proportion fw of individuals with ancestral phenotype in the population . Extensions of the simulation scheme to include linkage or diploid individuals are described in S1 Appendix , Sections A and C . Parameter choices: Unless explicitly stated otherwise , we simulate Ne = 10 000 individuals , with beneficial selection coefficients sb = 0 . 1 and 0 . 01 , combined with deleterious selection coefficients sd = −0 . 1 and sd = −0 . 001 for low and high levels of SGV , respectively . ( The corresponding Wrightian fitness values used as sampling weights in discrete time are 1 + sb and 1 + sd . ) We investigate L = 2 to 100 loci . We usually ( except in S1 Appendix , Section D ) assume equal mutation rates at all loci , μi = μ and define Θl = 2Ne μ as the locus mutation parameter . Mutation rates are chosen such that Θbg ≔ 2Ne μ ( L − 1 ) ( the background mutation rate , formally defined below in Eq ( 10 ) ) takes values from 0 . 01 to 100 . We typically simulate 10 000 replicates per mutation rate and stop simulations when the population has reached the new fitness optimum up to fw = 0 . 05 . In the model with complete redundancy , we thus stop simulations when the frequency of individuals with mutant phenotype Z1 has increased to 95% . Different stopping conditions are explored in S1 Appendix , Section G . We partition the adaptive process into two phases ( see Fig 2 for illustration ) . An initial stochastic phase , governed by selection , drift , and mutation describes the origin and establishment of mutant alleles at all loci . We call mutants “established” if they are not lost again due to genetic drift . The subsequent deterministic phase governs the further evolution of established alleles until the stopping condition is reached as described above . While mutation and drift can be ignored during the deterministic phase , interaction effects due to epistasis and linkage become important ( in our model , they enter , in particular , through the stopping condition ) . We give a brief overview of our analytical approach below; parameters are summarized in Table 1 . A detailed account with the derivation of all results is provided in the Mathematical Appendix S2 Appendix . During the stochastic phase , we model the origin and spread of mutant copies as a so-called Yule pure birth process following [38] and [39] . The idea of this approach is that we only need to keep track of mutations that found “immortal lineages” , i . e . derived alleles that still have surviving offspring at the time of observation ( see Fig 2 for the case of L = 2 loci ) . Forward in time , new immortal lineages can be created by two types of events: new mutations at all loci start new lineages , while birth events lead to splits of existing lineages into two immortal lineages . For t > 0 ( after the environmental change ) , in particular , new mutations at the ith locus arise at rate Neμi per generation and are destined to become established in the population with probability ≈ 2sb . Similarly , birth of new immortal lineages due to split events in the Yule process occur at rate sb ( because the selection coefficient measures the excess of births over deaths in the underlying population ) . For the origin of new immortal lineages in the Yule process and their subsequent splitting we thus obtain the rates p mut , i ≈ N e μ i · 2 s b = Θ i s b ; p split ≈ s b . ( 3 ) Extended results including standing genetic variation and time-dependent fitness are given in the Appendix . Assume now that there are currently {k1 , …kL} , 0 ≤ kj ≪ Ne mutant lineages at the L loci . The probability that the next event ( which can be a split or a mutation ) occurs at locus i is k i · p split + p mut , i ∑ j = 1 L ( k j · p split + p mut , j ) = k i + Θ i ∑ j = 1 L ( k j + Θ j ) . ( 4 ) Importantly , all these transition probabilities among states of the Yule process are constant in time and independent of the mutant fitness sb , which cancels in the ratio of the rates . As the number of lineages at all loci increases , their joint distribution ( across replicate realizations of the Yule process ) approaches a limit . In particular , as shown in the Appendix , the joint distribution of frequency ratios xi ≔ ki/k1 in the limit k1 → ∞ is given by an inverted Dirichlet distribution P inDir [ x | Θ ] = 1 B [Θ] ∏ j = 2 L x j Θ j - 1 ( 1 + ∑ i = 2 L x i ) - ∑ i = 1 L Θ i ( 5 ) where x = ( x2 , … , xL ) and Θ = ( Θ1 , … , ΘL ) are vectors of frequency ratios and locus mutation rates , respectively , and where B [ Θ ] = ∏ j = 1 L Γ ( Θ j ) ∑ j = 1 L Γ ( Θ j ) is the generalized Beta function and Γ ( z ) is the Gamma function . Note that Eq ( 5 ) depends only on the locus mutation rates , but not on selection strength . After the initial stochastic phase , the dynamics of established mutant lineages that have evaded stochastic loss can be adequately described by deterministic selection equations . For allele frequencies pi at locus i , assuming linkage equilibrium , we obtain ( consult S2 Appendix , Section M . 1 , Eq ( M . 2a ) , for a detailed derivation ) p ˙ i = p i ( W ( Z 1 ) - W ¯ ) = s b p i ( Z 1 - Z ¯ ) , ( 6 ) where W ¯ and Z ¯ are population mean fitness and mean trait value . For the mutant frequency ratios xi = pi/p1 , we obtain x ˙ i = d d t ( p i p 1 ) = p ˙ i p 1 - p i p ˙ 1 p 1 2 = 0 . ( 7 ) We thus conclude that the frequency ratios xi do not change during the deterministic phase . In particular , this means that Eq ( 5 ) still holds at our time of observation at the end of the rapid adaptive phase . This is even true with linked loci . Finally , derivation of the joint distribution of mutant frequencies pi ( instead of frequency ratios xi ) at the time of observation requires a transformation of the density . In general , this transformation depends on the stopping condition fw and on other factors such as linkage . Assuming linkage equilibrium among all selected loci , we obtain ( see S2 Appendix , Theorem 2 , Eq ( M . 20 ) ) P f w [ p | Θ ] = δ ∏ j = 1 L ( 1 - p j ) - f w B [ Θ ] ∏ j = 1 L p j Θ j - 1 ( ∑ i = 1 L p i ) - ∑ i = 1 L Θ i ( ∑ j = 1 L f w p j 1 - p j ) ( 8 ) for p = ( p1 , … , pL ) in the L-dimensional hypercube of allele frequencies . The delta function δX restricts the distribution to the L − 1 dimensional manifold defined via the stopping condition f w = ∏ j = 1 L ( 1 - p j ) . Further expressions , also including linkage , are given in S2 Appendix and in S1 Appendix , Section A . In general , the joint distribution corresponds to a family of generalized Dirichlet distributions . We assess the adaptive architecture not as a function of time , but as a function of progress in phenotypic adaptation , measured by fw , Eq ( 2 ) . Hence , fw rather than time t plays the role of a dynamical variable in the joint distribution , see Eq ( 8 ) . In the special case fw → 0 ( i . e . complete adaptation , enforcing fixation at at least one locus ) , this distribution is restricted to a boundary face of the allele frequency hypercube and Eq ( 8 ) reduces to the inverted Dirichlet distribution given above in Eq ( 5 ) . In the Results section below , we assess our analytical approximations for the joint distributions of adaptive alleles , Eqs ( 5 ) and ( 8 ) , and discuss their implications in the context of scenarios of polygenic adaptation , ranging from sweeps to small frequency shifts .
For our biological question concerning the type of polygenic adaptation , the ratio of allele frequency changes of minor over major loci is particularly useful . With “sweeps at few loci” , we expect large differences among loci , resulting in ratios that deviate strongly from 1 . In contrast , with “subtle shifts at many loci” , multiple loci contribute similarly to the adaptive response and ratios should range close to 1 . Our theory ( explained above ) predicts that these ratios are the outcome of the stochastic phase , and their distribution is preserved during the deterministic phase . They are thus independent of the precise time of observation . For our results in this section , we assume that the mutation rate at all L loci is equal , Θi ≡ Θl , for all 1 ≤ i ≤ L . This corresponds to the symmetric case that is most favorable for a “small shift” scenario . Results for asymmtric mutation rates are reported in Appendix S1 Appendix , Section D . Consider first the case of L = 2 loci . There is then a single allele frequency ratio “minor over major locus” , which we denote by x . For two loci , the joint distribution of frequency ratios from Eq ( 5 ) reduces to a beta-prime distribution . Conditioning on the case that the first locus is the major locus ( probability 1/2 for the symmetric model ) , we obtain for 0 ≤ x ≤ 1 , P β ′ [ x | Θ l ] = 2 Γ ( 2 Θ l ) ( Γ ( Θ l ) ) 2 x Θ l - 1 ( 1 + x ) - 2 Θ l , ( 9 ) Fig 3 compares the expectation of this analytical prediction with simulation results for a range of parameters for the strength of beneficial selection sb and for the level of standing genetic variation ( SGV implicitly given by the strength of deleterious selection sd before the environmental change ) . There are two main observations . First , the simulation results demonstrate the importance of the scaled mutation rate Θbg ≡ Θl ( for two loci ) . Low Θbg leads to sweep-like adaptation ( heterogeneous adaptation response among loci , E[x] ≪ 1 ) , whereas high Θbg leads to shift-like adaptation ( homogeneous response , E[x] near 1 ) . Second , the panels show that the selection intensity given by sd and sb has virtually no effect . Both results are predicted by the analytical theory ( Eq ( 9 ) ) . In S1 Appendix , Section A , we further show that these results hold for arbitrary degrees of linkage ( including complete linkage ) . For more than two loci , L > 2 , one-dimensional marginal distributions of the joint distribution , Eq ( 5 ) , generally require ( L − 1 ) -fold integration , which can be complicated . However , it turns out that the key phenomena to characterize the adaptive architecture can still be captured by the 2-locus formalism , with appropriate rescaling of the mutation rate . For the general L-locus model , we broaden our definition of the summary statistic x above to describe the allele frequency ratio of the first minor locus and the major locus . To relate the distribution of x in the L-locus model to the one in the 2-locus model , we reason as follows: For small locus mutation rates Θl , the order of the loci is largely determined by the order at which mutations that are destined for establishment originate at these loci . I . e . , the locus where the first mutation originates ends up as the major locus and the first minor locus is usually the second locus where a mutation destined for establishment originates . The distribution of the allele frequency ratio x is primarily determined by the distribution of the waiting time for this second mutation after origin of the first mutation at the major locus . In the 2-locus model , this time will be exponentially distributed , with parameter 1/Θl . In the L-locus model , however , where L − 1 loci with total mutation rate Θl ( L − 1 ) compete for being the “first minor” , the parameter for the waiting-time distribution reduces to 1/ ( Θl ( L − 1 ) ) . We thus see from this argument that the decisive parameter is the cumulative background mutation rate Θ b g = ( L - 1 ) Θ l ( 10 ) at all minor loci in the background of the major locus . In Fig 3 ( orange dots ) we show simulations of a L = 10 locus model with an appropriately rescaled locus mutation rate Θl → Θl/9 , such that the background rate Θbg is the same as for the 2-locus model . We see that the analytical prediction based on the 2-locus model provides a good fit for the 10-locus model . A more detailed discussion of this type of approximation is given in S1 Appendix , Section F . While the distribution of allele frequency ratios , Eqs ( 5 ) and ( 9 ) , offers a coarse ( but robust ) descriptor of the adaptive scenario , the joint distribution of allele frequencies at the end of the adaptive phase , Eq ( 8 ) , allows for a more refined view . In contrast to the distribution of ratios , the results now depend explicitly on the stopping condition ( the time of observation ) and on linkage among loci . We assume linkage equilibrium in this section and assess the mutant allele frequencies when the frequency of the remaining wildtype individuals in the population has dropped to a fixed value of fw = 0 . 05 . In S1 Appendix , Section G , we complement these results and study the changes in the adaptive architecture when fw is varied . Fig 4 displays the main result of this section . It shows the marginal distributions of all loci , ordered according to their allele frequency at the time of observation ( major locus , 1st , 2nd , 3rd minor locus , etc . ) for traits with L = 2 , 10 , 50 , and 100 loci . Panels in the same row correspond to equal background mutation rates Θbg = ( L − 1 ) Θl , but note that the locus mutation rates Θl are not equal . The figure reveals a striking level of uniformity of adaptive architectures with the same Θbg , but vastly different number of loci . For Θbg ≤ 1 ( the first three rows ) , the marginal distributions for loci of the same order ( same color in the Figure ) across traits with different L is almost invariant . For large Θbg , they converge for sufficiently large L ( e . g . for Θbg = 10 , going from L = 10 to L = 50 and to L = 100 ) . In particular , the background mutation rate Θbg determines the shape of the major-locus distribution ( red in the Figure ) for high p → 1 − fw = 0 . 95 ( the maximum possible frequency , given the stopping condition ) . For Θbg < 1 , this distribution is sharply peaked with a singularity at p = 1 − fw , whereas it drops to zero for high p if Θbg > 1 ( see also the analytical results below ) . As predicted by the theory , Eq ( 8 ) and below , simulations confirm that the overall selection strength does not affect the adaptive architecture ( see S1 Fig for comparison of simulation results for sb = 0 . 1 and sb = 0 . 01 ) . As discussed in S1 Appendix , Section A , sufficiently tight linkage does change the shape of the distributions . Importantly , however , it does not affect the role of Θbg in determining the singularity of the major-locus distribution . This confirms the key role of the background mutation rate as a single parameter to determine the adaptive scenario in our model . While Θbg = 1 separates architectures that are dominated by a single major locus ( Θbg < 1 ) from collective scenarios ( with Θbg > 1 ) , the classical sweep or shift scenarios are only obtained if Θbg deviates strongly from 1 . We therefore distinguish three adaptive scenarios . To complete our picture of adaptive architectures , we investigate the robustness of our model assumption against relaxation of redundancy . As explained above ( Model extensions and Fig 1 ) , we implement diminishing returns epistasis , such that an individual with a single mutation has fitness δsb/d , while individuals carrying more than one mutation have fitness sb/d . With small deviations from complete redundancy ( e . g . δ = 0 . 9 , stopping at 5% ancestral phenotypes , see Fig S2 Fig ) we obtain basically no differences in the genomic patterns of adaptation . With larger deviations ( e . g . δ = 0 . 5 ) quantitative differences appear . However , the qualitative picture concerning the scenario of polygenic adaptation remains the same . Fig 5 shows the marginal frequency distributions of major and minor loci for a trait with relaxed redundancy with δ = 0 . 5 that is sampled when the population has accomplished 95% of the fitness increase on its way to the new optimum , Eq ( 2 ) . Given the fitness function , this is not possible with adaptation at only a single locus . At least two loci are needed . The Figure compares the simulation data for the relaxed redundancy model ( colored dots ) and the full redundancy model ( dots in back and gray ) . As in Fig 4 , traits in the same row have the same background mutation rate Θbg . However , the background rate for the model with relaxed redundancy is redefined as Θ b g relax = ( L - 2 ) Θ l , ( 15 ) where Θl is the locus mutation rate ( equal at all loci ) . We thus define the background rate , more precisely , as the combined population-scaled mutation rate of all loci that are not essential to accomplish adaptation of the phenotype and , thus , are truly redundant . With this choice , the adaptive architecture of the relaxed redundancy model reproduces the one of the model with full redundancy—up to a shift in the number of the loci due to an extra locus that is needed for adaptation with relaxed redundancy . The Figure captures this by comparing traits with relaxed redundancy with L = 3 , 4 , 11 , and 101 loci to fully redundant traits with one fewer locus . The inset figures in the column for L = 4 loci show the same scenario , but with an averaged marginal distribution for the two largest loci with relaxed redundancy ( in green ) . In summary , our results show that relaxing redundancy leads to qualitatively similar results , but with a reduced “effective” background mutation rate that only accounts for “truly redundant” loci .
For any polygenic trait , the multitude of possible adaptive architectures is fully captured by the joint distribution of mutant alleles across the loci in its basis . Different adaptive scenarios ( such as sweeps or shifts ) correspond to characteristic differences in the shape of this distribution , at the end of the adaptive phase . For a single locus , the stationary distribution under mutation , selection , and drift can be derived from diffusion theory and has been known since the early days of population genetics ( S . Wright ( 1931 ) , [32] ) . For multiple interacting loci , however , this is usually not possible . To address this problem for our model , we dissect the adaptive process into two phases . The early stochastic phase describes the establishment of all mutants that contribute to the adaptive response under the influence of mutation and drift . We use that loci can be treated as independent during this phase to derive a joint distribution for ratios of allele frequencies at different loci , Eq ( 5 ) . During the second , deterministic phase , epistasis and linkage become noticeable , but mutation and drift can be ignored . Allele frequency changes during this phase can be described as a density transformation of the joint distribution . For the simple model with fully redundant loci , and assuming either LE or complete linkage , this transformation can be worked out explicitly . Our main result Eq ( 8 ) can be understood as a multi-locus extension of Wright’s formula . For a neutral locus with multiple alleles , Wright’s distribution is a Dirichlet distribution , which is reproduced in our model for the case of complete linkage , see S1 Appendix , Section A . For the opposite case of linkage equilibrium , we obtain a family of inverted Dirichlet distributions , depending on the stopping condition—our time of observation . Note that ( unlike Wright’s distribution ) the distribution of adaptive architectures is not a stationary distribution , but necessarily transient . It describes the pattern of mutant alleles at the end of the “rapid adaptive phase” [30 , 31] , because this is the time scale that the opposite narratives of population genetics and quantitative genetics refer to . In particular , the quantitative genetic “small shifts” view of adaptation does not talk about a stationary distribution: it does not imply that alleles will never fix over much longer time scales , due to drift and weak selection . On a technical level , the transient nature of our result means that it reflects the effects of genetic drift only during the early phase of adaptation . These early effects are crucial because they are magnified by the action of positive selection . In contrast , our result ignores drift after phenotypic adaptation has been accomplished—which is also a reason why it can be derived at all . To capture the key characteristics of the adaptive architecture , we dissect the joint distribution in Eq ( 8 ) into marginal distributions of single loci . As explained at the start of the results section , these loci do not refer to a fixed genome position , but are defined a posteriori via their role in the adaptive process . For example , the major locus is defined as the locus with the highest mutant allele frequency at the end of the adaptive phase . ( Since all loci have equal effects in our model , this is also the locus with the largest contribution to the adaptive response , but see S1 Appendix , Section D . ) This is a different way to summarize the joint distribution than used in some of the previous literature [26 , 28 , 29] , which rely on a gene-centered view to study the pattern at a focal locus , irrespective of its role in trait adaptation . In contrast , we use a trait-centered view , which is better suited to describe and distinguish adaptive scenarios . For example , “adaptation by sweeps” refers to a scenario where sweeps happen at some loci , rather than at a specific locus . This point is further discussed in S1 Appendix , Section F , where we also display marginal distributions of Eq ( 8 ) for fixed loci . The theme of “competition of a single locus with its background” relates to previous findings by Chevin and Hospital ( 2008 ) [26] in one of the first studies to address polygenic footprints . These authors rely on a deterministic model of an additive quantitative trait to describe the adaptive trajectory at a single target QTL in the presence of background variation . The background is modeled as a normal distribution with a mean that can respond to selection , but with constant variance . Obviously , a drift-related parameter , such as Θbg , has no place in such a framework . Still , there are several correspondences to our result on a qualitative level . Specifically , a sweep at the focal locus is prohibited under two conditions . First , the background variation ( generated by recurrent mutation in our model , constant in [26] ) must be large . Second , the fitness function must exhibit strong negative epistasis that allows for alternative ways to reach the trait optimum—and thus produces redundancy ( due to Gaussian stabilizing selection in [26] ) . Finally , while the adaptive trajectory depends on the shape of the fitness function , Chevin and Hospital note that it does not depend on the strength of selection on the trait , as also found for our model . A major difference of the approach used in [26] is the gene-centered view that is applied there . Consider a scenario where the genetic background “wins” against the focal QTL and precludes it from sweeping . For a generic polygenic trait ( and for our model ) this still leaves the possibility of a sweep at one of the background loci . However , this is not possible in [26] , where all background loci are summarized as a sea of small-effect loci with constant genetic variance . This constraint is avoided in the approach by deVladar and Barton [42] and Jain and Stephan [31] , who study an additive quantitative trait under stabilizing selection with binary loci ( see also [43] for an extension to adaptation to a moving optimum ) . These models allow for different locus effects , but ignore genetic drift . Before the environmental change , all allele frequencies are assumed to be in mutation-selection balance , with equilibrium values derived in [42] . At the environmental change , the trait optimum jumps to a new value and alleles at all loci respond by large or small changes in the allele frequencies . Overall , [42] and [31] predict adaptation by small frequency shifts in larger parts of the biological parameter space relative to our model . In particular , sweeps are prevented in [31] if most loci have a small effect and are therefore under weak selection prior to the environmental change . This contrasts to our model , where the predicted architecture of adaptation is independent of the selection strength . Thus , in our model , weak selection does not imply shifts . This difference can at least partially be explained by the neglect of drift effects on the starting allele frequencies in the deterministic models . In the absence of drift , loci under weak selection start out from frequency x0 = 0 . 5 [42] . In finite populations , however , almost all of these alleles start from very low ( or very high ) frequencies—unless the population mutation parameter is large ( many alleles at intermediate frequencies at competing background loci are expected only if Θbg ≫ 1 , in accordance with our criterion for shifts ) . To test this further , we have analyzed our model for the case of starting allele frequencies set to the deterministic values of mutation-selection balance , μ/sd . Indeed , we observe adaptation due to small frequency shifts in a much larger parameter range ( S1 Appendix , Section B ) . Generally , adaptation by sweeps in a polygenic model requires a mechanism to create heterogeneity among loci . This mechanism is entirely different in both modeling frameworks . While heterogeneity is ( only ) produced by unequal locus effects for the deterministic quantitative trait , it is ( solely ) due to genetic drift for the redundant trait model . Since both approaches ignore one of these factors , both results should rather underestimate the prevalence of sweeps . Indeed , heterogeneity increases for our model with unequal locus effects ( see S1 Appendix , Section D ) . Both drift and unequal locus effects are included in the simulation studies by Pavlidis et al ( 2012 ) [28] and Wollstein and Stephan ( 2014 ) [29] . These authors assess patterns of adaptation for a quantitative trait under stabilizing selection with up to eight diploid loci . However , due to differences in concepts and definitions there are few comparable results . In contrast to [31] and to our approach , they study long-term adaptation ( they simulate Ne generations ) . In [28 , 29] , sweeps are defined as fixation of the mutant allele at a focal locus , whereas frequency shifts correspond to long-term stable polymorphic equilibria [29] . With this definition , a shift scenario is no longer a transient pattern , but depends entirely on the existence ( and range of attraction ) of polymorphic equilibria . A polymorphic outcome is likely for a two-locus model with full symmetry , where the double heterozygote has the highest fitness . For more than two loci , the probability of shifts decreases ( because polymorphic equilibria become less likely , see [44] ) . However , also the probability of a sweep decreases . This is largely due to the gene-centered view in [28] , where potential sweeps at background loci are not recorded ( see also S1 Appendix , Section F ) . We have described scenarios of adaptation for a simple model of a polygenic trait . This model allows for an arbitrary number of loci with variable mutation rates , haploids and diploids , linkage , time-dependent selection , new mutations and standing genetic variation , and alternative starting conditions for the mutant alleles . Its genetic architecture , however , is strongly restricted by our assumption of ( full or relaxed ) redundancy among loci . In the haploid , fully redundant version , the phenotype is binary and only allows for two states , ancestral wildtype and mutant . Biologically , this may be thought of as a simple model for traits like pathogen or antibiotic resistance , body color , or the ability to use a certain substrate [45 , 46] . Our main motivation , however , has been to construct a minimal model with a polygenic architecture that allows for both sweep and shifts scenarios—and for comprehensive analytical treatment . One may wonder how our methods and results generalize if we move beyond our model assumptions . Key to our analytical method is the dissection of the adaptive process into a stochastic phase that explains the origin and establishment of beneficial variants and a deterministic phase that describes the allele frequency changes of the established mutant copies . This framework can be applied to a much broader class of models . Indeed , in many cases , the fate of beneficial alleles , establishment or loss , is decided while these alleles are rare . Excluding complex scenarios such as passage through a fitness valley , the initial stochastic phase is relatively insensitive to interactions via epistasis or linkage . We can therefore describe the dynamics of traits with a different architecture ( e . g . an additive quantitative trait with equal-effect loci under stabilizing selection ) within the same framework by coupling the same stochastic dynamics to a different set of differential equations describing the dynamics during the deterministic phase . This is important because , as described above , the key qualitative results to distinguish broad categories of adaptive scenarios are due to the initial stochastic phase . This holds true , in particular , for the role of the background mutation rate Θbg . We therefore expect that these results generalize beyond our basic model . Indeed , we have already seen this for our model extensions to include diploids , linkage , and relaxed redundancy . Vice-versa , we have seen that other factors , such as alternative starting conditions for the mutant alleles , directly affect the early stochastic phase and lead to larger changes in the results . As shown in S1 Appendix , Section B , however , they can be captured by an appropriate extension of the stochastic Yule process framework . Several factors of biological importance are not covered by our current approach . Most importantly , this includes loci with different effect sizes and spatial population structure . Both require a further extension of our framework for the early stochastic phase of adaptation . Unequal locus effects ( both directly on the trait or on fitness due to pleiotropy ) are expected to enhance the heterogeneity in the adaptive response among loci , as confirmed by simulations of a 2-locus model in S1 Appendix , Section D . The opposite is true for spatial structure , as further discussed below . Although our assumptions on the genetic architecture of the trait ( complete redundancy and equal loci ) are favorable for a collective , shift-type adaptation scenario , we observe large changes in mutant allele frequencies ( completed or partial sweeps ) for major parts of the parameter range . A homogeneous pattern of subtle frequency shifts at many loci is only observed for high mutation rates . This contrasts with experience gained from breeding and modern findings from genome-wide association studies , which are strongly suggestive of an important role for small shifts with contributions from very many loci ( reviewed in [1 , 15 , 47–49] , see [12 , 50 , 51] for recent empirical examples ) . For traits such as human height , there has even been a case made for omnigenic adaptation [8] , setting up a “mechanistic narrative” for Fisher’s ( conceptual ) infinitesimal model . Clearly , body height may be an extreme case and the adaptive scenario will strongly depend on the type of trait under consideration . Still , the question arises whether and how wide-spread shift-type adaptation can be reconciled with our predictions . We will first discuss this question within the scope of our model and then turn to factors beyond our model assumptions .
|
It is still an open question how complex traits adapt to new selection pressures . While population genetics champions the search for selective sweeps , quantitative genetics proclaims adaptation via small concerted frequency shifts . To date the empirical evidence of clear sweep signals is more scarce than expected , while subtle shifts remain notoriously hard to detect . In the current study we develop a theoretical framework to predict the expected adaptive architecture of a simple polygenic trait , depending on parameters such as mutation rate , effective population size , size of the trait basis , and the available genetic variability at the onset of selection . For a population in mutation-selection-drift balance we find that adaptation proceeds via complete or partial sweeps for a large set of parameter values . We predict adaptation by small frequency shifts for two main cases . First , for traits with a large mutational target size and high levels of genetic redundancy among loci , and second if the starting frequencies of mutant alleles are more homogeneous than expected in mutation-selection-drift equilibrium , e . g . due to population structure or balancing selection .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"quantitative",
"trait",
"loci",
"epistasis",
"mutation",
"genome",
"analysis",
"trait",
"locus",
"analysis",
"crystallographic",
"techniques",
"research",
"and",
"analysis",
"methods",
"genetic",
"loci",
"phenotypes",
"natural",
"selection",
"heredity",
"evolutionary",
"processes",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"evolutionary",
"biology",
"phase",
"determination",
"computational",
"biology"
] |
2019
|
Polygenic adaptation: From sweeps to subtle frequency shifts
|
Kaposi's sarcoma associated herpesvirus ( KSHV/HHV-8 ) is the causal agent of all forms of Kaposi sarcoma . Molecular epidemiology of the variable K1 region identified five major subtypes exhibiting a clear geographical clustering . The present study is designed to gain new insights into the KSHV epidemiology and genetic diversity in Cameroon . Bantu and Pygmy populations from remote rural villages were studied . Antibodies directed against latent nuclear antigens ( LANA ) were detected by indirect immunofluorescence using BC3 cells . Peripheral blood cell DNAs were subjected to a nested PCR amplifying a 737 bp K1 gene fragment . Consensus sequences were phylogenetically analyzed . We studied 2 , 063 persons ( 967 females , 1 , 096 males , mean age 39 years ) , either Bantus ( 1 , 276 ) or Pygmies ( 787 ) . The Bantu group was older ( 42 versus 35 years: P<10−4 ) . KSHV anti-LANA seroprevalence was of 37 . 2% ( 768/2063 ) , with a significant increase with age ( P<10−4 ) but no difference according to sex . Seroprevalence , as well as the anti-LANA antibodies titres , were higher in Bantus ( 43 . 2% ) than in Pygmies ( 27 . 6% ) ( P<10−4 ) , independently of age . We generated 29 K1 sequences , comprising 24 Bantus and five Pygmies . These sequences belonged to A5 ( 24 cases ) or B ( five cases ) subtypes . They exhibited neither geographical nor ethnic aggregation . A5 strains showed a wide genetic diversity while the B strains were more homogenous and belonged to the B1 subgroup . These data demonstrate high KSHV seroprevalence in the two major populations living in Southern and Eastern Cameroon with presence of mostly genetically diverse A5 but also B K1 subtypes .
Human herpesvirus-8 ( HHV-8 ) or Kaposi's sarcoma associated herpesvirus ( KSHV ) is a Gammaherpesvirus , first identified in a tumor biopsy from an AIDS-related Kaposi's sarcoma ( KS ) [1] . It is considered as the causing agent of all forms of KS [2] , [3] , [4] ( epidemic , iatrogenic , classic and endemic ) as well as Primary Effusion Lymphoma [5] , [6] , and most Multicentric Castleman Diseases [7] , [8] , [9] . KSHV global distribution is heterogeneous . Areas of high endemicity , corresponding to areas of classic and endemic KS have been reported [10] , [11] , [12] , [13] , [14] , [15] . The epidemiological determinants are quite different depending on the level of endemicity [3] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] . Saliva is considered as the main vector of KSHV infection [13] , [21] , 22 . Interestingly , KSHV infection can be unevenly distributed from one region to another [14] , [19] , [23] , [24] , [25] , [26] , [27] suggesting non-uniform specificities in transmission modes [28] , [29] . Molecular epidemiology studies on KSHV have mainly focused on the variable K1 region ( ORF-K1 ) . This has lead to the identification of five main viral subtypes ( A , B , C , D , E ) that exhibit a geographical clustering [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] . There are two highly variable K1 regions ( VR1 and VR2 ) , which encode the areas usually targeted by the immune system on the K1 protein [30] , [41] . Subgroup A1–4 and subtype C are predominant among populations of European descent [30] , [32] , [36] , [42] , [43] and in some regions of Asia [44] , [45] , [46] . Subgroup B1–4 and clade A5 are predominant in Sub-Saharan Africa [31] , [34] , [35] , [47] , [48] , [49] , [50] . The present work aimed at gaining new insights into the KSHV epidemiology and genetic diversity in Cameroon , in Western Central Africa . Although endemic and epidemic KS are frequent in Cameroon , KSHV genetic polymorphism is nearly unknown in this country with only three K1 sequences published so far [34] .
Ethical approval was given in Cameroon by the Ministry of Public Health in Cameroon: D30-295/AR/MINSANTE/SG/DROS/CRC/CEA1 , the National Comity of Ethics in Cameroon: N° 034/CNE/MP/06 . In France by the Comité de Protection des Personnes ( CPP ) : 2011/01NICB , the Commission Nationale pour l'Informatique et les Libertés ( CNIL ) : EGY/FLR/AR111711 . Prior to field sampling , community and individual written informed consent were sought and provided by participants after detailed information on the study were provided . This study was carried out in rural areas of Cameroon ( figure 1 ) . The present study was performed on a large population of Bantus and Pygmies , living in remote rural villages or settlements of the rain forest area of South and East Cameroon . Study populations were sequentially sampled over different time periods . Samples from the South were mostly collected from 1994 through 2000 . A complementary series was collected from 2006 through 2010 . Samples from the Centre and the East areas were collected from 2004 through 2010 . Populations and collection procedures have been previously described [51] , [52] and comprise diverse Bantu groups from the three study areas and two Pygmy groups . The Baka Pygmies , by far the most important Pygmy group in Cameroon is found in Eastern and Southern Cameroon . The Bakolas are the second most important group and have their settlements exclusively in the Southern part of the country and the Bedzams are the less numerically important and less accessible . This group was not included in the current work . A systematic approach for the enrolment was carried out in all reachable villages and settlements , scattered alongside roads and tracks across the forest . A standardized questionnaire was used to collect personal demographic data . Collected data included the name , age , sex , location , ethnicity , family links . A 5 to 10 ml whole blood sample was collected in EDTA K2 vacuum tubes , from all consenting individuals meeting the inclusion criteria . Plasma and buffy-coat were obtained 48 to 72 hours after sampling and kept frozen at −80°C . A simple clinical examination was performed when requested by participants in the study . Treatment for common local ailments was given if available . A transfer to an appropriate medical facility was advised for severely ill individuals encountered on site . The Ministry of Public Health and the National Comity of Ethics approved the study in Cameroon . In France , approval was obtained from the “CPP” and the “CNIL” . Prior to field sampling , community and individual written informed consent were sought and provided by participants after detailed information on the study were provided . Serologic detection of anti-LANA antibodies was done by indirect fluorescent assay using KSHV positive and EBV negative BC3 cell line expressing only Latent-associated Nuclear Antigen as described , [37] , [53] using diluted plasma ( 1∶40 , 1∶80 , 1∶160 ) deposited on BC3 cells . Positivity was considered for presence of nuclear dotted reactivity at 1∶80 dilution . Statistical analyses were realized with “Stata” software version 11 . 1 ( Statacorp , Colledge Station , Texas ) . Between groups characteristics were compared using the Student t test for continuous variables and the Fisher exact test for categorical variables . Adjustment for age was performed using a logistic regression model for KHSV prevalence , and a linear regression model for log-transformed titers . Test for trends were used to study changes of KHSV prevalence or antibody titres over age . Conditions and procedures for DNA extraction from blood buffy coats in all positive plasmas on BC3 serological assays , as well as amplification method have been previously described [37] . Amplified products for 29 samples were directly sequenced and phylogenetic analyses were conducted . All new sequences are deposited in GenBank under accession numbers: JX290272 ( O350805 ) , JX290273 ( Mezi5 ) , JX290274 ( AKO107 ) , JX290275 ( Lobak80 ) , JX290276 ( Mezi1 ) , JX290277 ( Ako381 ) , JX290278 ( BAD54 ) , JX290279 ( BAD84 ) , JX290280 ( BAD230 ) , JX290281 ( BAD337 ) , JX290282 ( CHAS59 ) , JX290283 ( CHAS80 ) , JX290284 ( Lobak42 ) , JX290285 ( lozi5 ) , JX290286 ( Lozi6 ) , JX290287 ( MEBAK55 ) , JX290288 ( MEZI8 ) , JX290289 ( MEZI11 ) , JX290290 ( O290107 ) , JX290291 ( AKO205 ) , JX290292 ( O300153 ) , JX290293 ( O300186 ) , JX290294 ( O300189 ) , JX290295 ( O300137 ) , JX290296 ( O300227 ) , JX290297 ( O300237 ) , JX290298 ( O300265 ) , JX290299 ( O300275 ) , JX290300 ( O323101 ) . The recombinant analysis was performed by boot scanning with the Simplot software v3 . 5 . 1 [54] . We deposited all 29 new nucleotide sequences in GenBank under accession numbers JX290272 to JX290300 .
The current study tested 2063 individuals ( 967 females , 1096 males ) originating from rural areas of the Center , the South and the East regions of Cameroon ( table 1 ) . Of note , no Pygmies populations were studied from the Center area . The mean and median age for the overall population was 39 years; however , the mean age of Pygmies was 35 . 4 years , and it was higher in Bantus ( 41 . 5 years , p<10−4 ) ( table 1 ) . The overall KSHV sero-prevalence in the study was high ( 37 . 2% , 768/2063 ) and significantly increased with age ( p<10−4 ) , but was not different according to sex , ( 37 . 6% ( 413/1096 ) in males and 36 . 7% ( 355/967 ) in females ( p = 0 . 68 ) . KSHV prevalence in Bantus ( 43 . 2% , 551/1276 ) and in Pygmies ( 27 . 6% , 217/787 ) was significantly different ( p<10−4 ) . While the KSHV prevalence increased with age among Pygmies ( p = 0 . 001 ) , the increase was less pronounced among Bantus ( p = 0 . 04 ) ( figure 2 ) . Anti-LANA-1 antibody titres were globally high in infected people and ranged from 80 ( 1 . 9 log ) to 20 , 480 ( 4 . 3 log ) with a geometric mean value of 2 . 6 log . Significantly higher anti-LANA-1 titres were found in infected Bantus compared to infected Pygmies ( geometric means of 2 . 7 versus 2 . 4 log , respectively , p<10−5 ) , and this difference was independent of age . In a multivariate analysis , we observed higher anti-LANA1 titres in Bantus p<10−4 in the South and the East regions compared to the Center ( p<10−4 ) . DNAs extracted from peripheral blood buffy-coats from 461 persons including 56 living in the Center , 190 in the South and 215 in the East were all amplifiable by the β-globin PCR and then subjected to KSHV K1 PCR . Finally , 29 sequences ( 29/461 = 6% ) of 730 bp of the K1 gene ( ORF-K1 ) were generated from 18 men and 11 women ( median age = 35 years and range 6–75 years ) . All sequences originated from apparently healthy individuals ( 24 Bantus and 5 Pygmies ) . Twenty-seven sequences were unique . The isolates from two couples were found identical . Five of the sequences were of the B subtype while 24 were of the A5 subgroup . Intratype and intertype polymorphism were observed among the 29 new K1 sequences . Pairwise comparison of the 27 unique sequences revealed an overall intertype nucleotide polymorphism of up to 20% and a 37 . 5% amino acid polymorphism . The 22 unique A5 sequences exhibited a 0 . 2% to 6 . 9% nucleotide divergence while the five unique subtype B sequences showed a 0 . 3% to 6 . 6% divergence in their nucleotides composition . The initial phylogenetic analyses were performed on 633 nt-long sequences , including the 29 new strains , together with 61 K1 prototype sequences . The analyses were based upon 2 different phylogenetic methods ( neighbor joining and maximum likelihood ) , which gave similar phylogenetic topologies . The 5 major K1 molecular subtypes ( A , B , C , D , E ) were supported by high bootstrap values in the NJ analysis ( figure 3 ) . The 29 new strains did segregate in the 2 separate groups , previously described as sub-Saharan taxa . Most of the strains ( 24/29 = 83% ) belonged to the paraphyletic A5 clade , which contains also the 3 Cameroonian sequences previously obtained from AIDS-KS [34] . The remaining sequences ( 5/29 = 17% ) clustered with the B1 subgroup , with sequences originating from Central African Republic , Uganda and Zimbabwe . Interestingly , the 29 new sequences exhibited neither geographical nor ethnic group aggregation . Indeed , 4 out of the 5 strains originating from Pygmies belonged to the A5 clade . The proportion was the same for the Bantus strains ( 20/24 = 83% ) . We also performed phylogenetic studies separately on the sequences encoding the variable regions ( VR , 258 nt-long sequences ) , which are the major target of the immune system [30] , [41] and the rest of the sequence , that is less susceptible to the immune system as an evolutionary driving force ( 375 nt ) . With both subsets , the 5 major subtypes could be defined ( figure 4 ) . We confirmed that the 29 new K1 sequences did segregate in 2 groups: one belonging to the A subtype and the other one to the B subtype . Of note , the definition of the A1–4 monophyletic group was possible when analyzing the VR regions: a high boostrap value was found at the root of the group . Interestingly , such a group was not distinguishable when considering the rest of the sequence: one could not differentiate the strains from this clade from sequences of the A5 group .
The present epidemiological report shows a very high KSHV seroprevalence in the two rural populations studied . This confirms previous findings on a smaller population of rural Bantus from South Cameroon [19] and extends it to Bantus living in other areas , as well as , for the first time to the remote Pygmy populations . Furthermore , our study demonstrated that KSHV is highly prevalent in children . This is consistent with a non-sexual acquisition of the virus . Indeed , in highly endemic population of African origin , studies have demonstrated a high level of familial aggregation , with transmission between children of the same family and from mother to child [19] , [20] . In central , and mostly East Africa , endemic KS can also occur in young children . We previously hypothesized that this peculiar KS form may be related to an early and massive KSHV infection in genetically susceptible individuals [14] . In some classical KS in children , diverse genetic defects have been reported [59] , [60] , [61] . Similar studies need to be performed in children suffering from endemic KS in central Africa . We found that KSHV prevalence was similar in men and women in both groups and increased with age , especially in Pygmy groups . This is comparable to the data found on rural general populations of central and East Africa [14] , [19] , [49] , [55] , [57] , [62] . While in African population , non-sexual transmission of KSHV is considered as the major mode of viral acquisition , sexual transmission is likely to contribute to further viral spread in adults [3] , [13] , [63] . However , this feature appears to greatly differ to that of industrialized/occidental countries where most of the infection seems to be acquired after adolescence , especially in high-risk groups [3] , [63] , [64] . KSHV seroprevalence was quite surprisingly found higher in Bantus than in Pygmies . Indeed , we expected a higher prevalence in Pygmies as they have a lower “living standard” than the surroundings Bantus . As demonstrated for EBV , studies have indeed suggested that KSHV prevalence , in Africa , may also be related to the socio-economic level of the studied populations [65] , [66] . Furthermore , other works show that populations that kept a traditional way of life show high prevalence for KSHV [13] , [33] , [67] . However , other studies are necessary to appreciate the different items ( environmental co-factors , specificities in ways of life influencing transmission modes , or even genetic features ) , which can lead to the apparent differences found here between Pygmies and Bantus . Our present sero-epidemiological report was based on anti-LANA-1 antibodies detection while several of the performed studies in Africa used assays detecting anti-lytic antibodies . While both assays perform very well in epidemiological studies , the latter are generally considered less specific than the anti-latent ones [3] , [13] , [68] , [69] . This implies that seroprevalences are frequently lower in studies using anti-latent assays [69] , [70] , [71] , [72] . This is well illustrated by a work performed in 292 persons from a North Cameroon hospital using anti-latent or anti-lytic immunofluorescence assays ( IFA ) . While the anti-lytic IFA prevalence was 51% with a clear increase with age , the anti-latent IFA prevalence was of 25% without any increase with age [55] . Our study may have some limitations . HHV-8 between-group prevalence difference is generalized and assumed to Bantus and Bedzams in the Centre area despite no data were available from the Bedzam Pygmies . We have shown here that the 29 obtained KSHV K1 sequences ( 5 from Pygmies and 24 from Bantus ) are all sub-Saharan A5 or B variants . In our report , we did not observe any specific geographical or ethnical subtype or subgroup segregation . Both Bantus and Pygmies were represented in A5 , and B1 subgroups that appear to be distributed throughout the studied areas . This suggests an ancient origin of these strains in these areas and a genetic exchange between both populations . Of note , the prevalence of A5 sequences in our study is higher than prevalence observed in Zimbabwe ( 45% of 64 KS patients ) [50] , in Uganda ( 53% of 31 KS patients ) [73] , in West and other Central African countries ( 8 of 21 KS patients ) [34] . Interestingly , the B monophyletic group is , so far , composed exclusively of sequences isolated from individuals with African origins , suggesting a geographical isolation of the infected populations and an ancient speciation . In contrast , the African sequences from the A5 paraphyletic group are closely related to viruses found mostly in populations , which form the A1–4 subgroup . The origin of the A5 group is thus quite intriguing . We first envisioned that the A5 clade could have emerged upon recombination , and would therefore form an intermediate group . However , by Simplot analysis , we found no evidence for such a genetic event . Therefore , we speculate that the divergence between the A1–4 and A5 groups rose from natural genetic drift and speciation . It would have been very interesting to date the separation of the viral populations . Unfortunately , the molecular clock analysis we performed was not conclusive . Indeed , to perform such study , one would like to focus on segments that have comparable mutation rates . Usually , when considering coding regions , we focus on the divergence of the 3rd nucleotide codon . Considering this limitation , the sequence we considered was too short , not informative enough . Thus , we studied separately the two VR genetic regions , which are the major targets of the immune system on K1 , and the rest of the sequence . When considering the VR genetic regions , the A5 and A1–4 subgroups were still defined . In contrast , these groups were undistinguishable when considering the rest of the sequence . These data suggest that the separation between the 2 groups is not ancient enough to have accumulated mutation through genetic drift on the entire sequence; the separation between the A1–4 and A5 groups is thus , probably , more recent than the emergence of the C or B subgroups . This conclusion was previously suggested . Indeed , White et al . have shown that viral strains from the B subtype have accumulated more non-synonymous mutations when compared to strains from the A5 group , which they interpreted as the hallmarks of an older divergence of the B subtype [50] . This conclusion is strengthened by the fact that non-synonymous mutations were observed throughout the B strains sequences , while they were limited to the VR regions for the A5 clade . This suggests that the immune pressure for the 2 groups could have been different . The difference between the A1–4 and A5 group is suspected to be mainly shaped upon immune pressure on the VR regions . As for the origins of the A5 group , we hypothesize that the A group has African origins and upon immune selection ( maybe associated with specific HLA ) a monophyletic A1–4 group has emerged , mostly in Caucasian populations , but also described in individuals of African origin [30] . The remaining sequences would then form the A5 clade . Cameroon is a good candidate for further phylo-geographic studies of KSHV subtype distribution and polymorphism as the country is inhabited by a multitude of ethnic groups of divergent historical origins .
|
Kaposi's sarcoma associated herpesvirus ( KSHV/HHV-8 ) is the causal agent of one of the most frequent skin tumors found endemically or epidemically associated to HIV in Central and Eastern Africa . This highly variable virus tends to cluster geographically according to specific major subtypes . Its prevalence is high in that area and increases with age . Despite its association to all forms of Kaposi sarcoma and high prevalence described in some low income populations in Cameroon , KSHV arouses limited interest , and only few focused previous studies have looked into prevalence and modes of transmission , especially in families . Extended molecular epidemiology is unknown both in healthy individuals and in Kaposi patients , which led to looking for new insights among Bantu and Pygmy populations from rural villages in three regions of Cameroon sharing a quite similar living environment but yet genetically , socially , and culturally different . The present study is designed to describe variations of molecular subtypes in each of these population groups regarding their geography in rural areas of southern , central , and eastern Cameroon .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"molecular",
"epidemiology",
"infectious",
"disease",
"epidemiology",
"viruses",
"and",
"cancer",
"virology",
"epidemiology",
"disease",
"surveillance",
"biology",
"and",
"life",
"sciences",
"microbiology",
"viral",
"disease",
"diagnosis"
] |
2014
|
Epidemiology and Genetic Variability of HHV-8/KSHV in Pygmy and Bantu Populations in Cameroon
|
The conserved protein kinase Sch9 is a central player in the nutrient-induced signaling network in yeast , although only few of its direct substrates are known . We now provide evidence that Sch9 controls the vacuolar proton pump ( V-ATPase ) to maintain cellular pH homeostasis and ageing . A synthetic sick phenotype arises when deletion of SCH9 is combined with a dysfunctional V-ATPase , and the lack of Sch9 has a significant impact on cytosolic pH ( pHc ) homeostasis . Sch9 physically interacts with , and influences glucose-dependent assembly/disassembly of the V-ATPase , thereby integrating input from TORC1 . Moreover , we show that the role of Sch9 in regulating ageing is tightly connected with V-ATPase activity and vacuolar acidity . As both Sch9 and the V-ATPase are highly conserved in higher eukaryotes , it will be interesting to further clarify their cooperative action on the cellular processes that influence growth and ageing .
In Saccharomyces cerevisiae , Sch9 is part of the highly conserved TORC1 pathway which plays a central role in the nutrient-induced signaling network , thereby affecting many aspects of yeast physiology such as stress resistance , longevity and cell growth [1–3] . The rapamycin-sensitive TORC1 mediates these effects mainly via two key branches . In the first branch , activated TORC1 phosphorylates and inhibits Tap42 , which in turn controls the activity of type 2A and type 2A-like protein phosphatases [4 , 5] . In the second branch , TORC1 contributes to Sch9 activation by phosphorylating multiple residues in its C-terminus [6] . In addition to TORC1-mediated activation , Sch9 can be phosphorylated by the sphingolipid-dependent Pkh1-3 kinases on the conserved PDK1 site , and this phosphorylation is indispensable for its function [6 , 7] . Although Sch9 is a downstream effector of the TORC1 complex , the protein kinase can also function independently of TORC1 [3 , 8 , 9] . Moreover , it has been proposed that Snf1 , the orthologue of mammalian AMP kinase , also modulates Sch9 activity by phosphorylation [10] . As three different kinases modulate Sch9 activity in response to diverse stimuli , Sch9 appears to act as a major integrator that regulates various aspects of yeast physiology . A prime example of this is the control of lifespan by Sch9 . Indeed , both tor1Δ and sch9Δ strains display increased lifespan as compared to the WT strain [2 , 11] , and downregulation of nutrient signaling via the TORC1-Sch9 branch seems to be part of an evolutionary conserved mechanism that extends lifespan across a wide range of eukaryotic species [12 , 13] . The V-ATPase is a highly conserved proton pump that mediates the luminal acidification of multiple organelles of the biosynthetic and endocytic pathway , thereby regulating numerous cellular processes including vesicle trafficking , autophagy , pH- and ion- homeostasis ( reviewed in [14–16] ) . These V-ATPases are multi-subunit protein complexes consisting of a membrane-embedded V0 sector containing the proton translocation pore , and an attached peripheral cytosolic V1 sector responsible for ATP hydrolysis to fuel proton transport . Although higher eukaryotes often exhibit tissue- and/or organelle-specific expression of multiple isoforms of one subunit , in yeast only the V0 sector subunit a is encoded by organelle specific homologues: VPH1 encodes the isoform localized at the vacuole , while STV1 encodes the isoform that cycles between the Golgi apparatus and endosomes [17] . In both yeast and higher eukaryotes , V-ATPase activity is tightly regulated by reversible assembly of the V1 and V0 sector [14 , 18–20] . Although the exact molecular mechanism governing glucose-dependent reversible assembly is still a matter of debate , recent reports shed some light on the signaling mechanisms by which yeast might control this assembly process . Addition of glucose to carbon starved cells triggers an increase in cytosolic pH ( pHc ) , possibly through a rise in ATP levels . The V-ATPase responds to pHc by assembling and transducing the cellular signal through distinct GTPases , ultimately leading to enhanced Ras/PKA and TORC1 activity . As a result , cells adapt growth in response to carbon source availability [21 , 22] . Besides fermentable sugars , additional signals , such as osmotic stress and high extracellular pH , also influence V0-V1 assembly levels [22–24] . Interestingly , increasing evidence suggests that V-ATPase activity is required for regulating cell survival in both yeast and higher eukaryotes [25–29] . To better understand the mechanisms by which Sch9 regulates cell physiology in yeast , we performed a genome-wide synthetic genetic array ( SGA ) screening and identified mutants that require Sch9 function for their growth and survival . Gene ontology ( GO ) analysis revealed the V-ATPase as one of the most significant hits . Further analysis showed that Sch9 physically interacts with the V-ATPase , thereby influencing V-ATPase assembly/disassembly in response to glucose availability , while receiving input from TORC1 . Importantly , we show that the interaction of Sch9 with the V-ATPase is required to allow proper control of both pHc and vacuolar pH ( pHv ) , and we found that particularly pHv is important to determine longevity .
As only a few direct substrates are known for Sch9 [30–32] , we performed a genome-wide SGA analysis to discover additional functions and targets . To this end , the sch9Δ mutant was mated with the library of non-essential deletion strains and double deletion mutants were scored for a synthetic growth phenotype . GO analysis on the identified genes ( S1 Table ) revealed several functional classes for which a role of Sch9 had already been established , such as transcription , protein synthesis , mitochondrial function and cellular amino acid biosynthesis [3 , 32 , 33] , validating the approach . Interestingly , our screening also identified numerous new genes displaying a genetic interaction with SCH9 as only for a small subset of these genes ( ± 15% ) a putative interaction with SCH9 has been predicted by BioGRiD ( S1 Table ) . Two GO categories that were significantly enriched in our screening , but not represented in BioGRID are connected to vesicular trafficking and V-ATPase function ( S2 Table and Fig 1 ) . We first investigated whether the synthetic interaction between SCH9 and genes encoding proteins involved in vacuolar protein sorting arises from a defect in vesicular trafficking in the sch9Δ mutant . To this end , we monitored the localization of several marker proteins known to be sorted to the plasma membrane or vacuole by one of different trafficking routes ( S1 Fig ) [34] . Fig 2 ( Fig 2A and Fig 2B ) shows that the soluble hydrolase carboxypeptidase Y ( CPY ) was correctly sorted to the vacuolar lumen in the sch9Δ mutant , where it was processed to its mature form . Interestingly , deletion of SCH9 results in increased abundance of CPY ( Fig 2A and S2A Fig ) , and , in contrast to vacuolar protein sorting mutants [35] , CPY was not secreted from the cell in the sch9Δ strain ( S2B Fig ) , additionally confirming the CPY pathway is not impaired . The CPY receptor Vps10 , which is recycled from the late endosome ( LE ) to the trans-Golgi network ( TGN ) after CPY dissociation [36] , localizes to punctate endosome and Golgi compartments in both WT and sch9Δ mutant cells , suggesting that retrograde transport from LE to TGN is not compromised in the sch9Δ strain ( S2C Fig ) . Mutants affecting early endosome function mislocalize the v-SNARE protein Snc1 . In agreement with previously data [37] , most of the GFP-Snc1 fluorescence was present at the plasma membrane in both WT and sch9Δ cells and only a fraction of the protein was observed in internal cellular structures ( S2D Fig ) . This result indicates that the secretion of proteins to the cell surface , as well as the recycling of proteins from the cell surface to the TGN is not impaired in the sch9Δ mutant . Additionally , the successful staining of internal membranes of sch9Δ cells with FM4-64 indicates that SCH9 deletion does not impair endocytosis nor impact vacuolar morphology in exponentially growing cells ( Fig 2B and 2D ) . Proteins are also transported to the vacuole by non-endosomal routes . Indeed , the alkaline phosphatase ( ALP ) route directly transports proteins from the TGN to the vacuole [38] . Moreover , the selective cytoplasm-to-vacuole-targeting ( Cvt ) and non-specific autophagy pathways deliver cytosolic proteins to the vacuole [39 , 40] . As can be seen in Fig 2 ( Fig 2C and 2E ) , the vacuolar hydrolases ALP , encoded by PHO8 , and Ape1 are processed to their active form in sch9Δ cells , suggesting that Sch9 does not play an essential role in the ALP or Cvt pathway , respectively . In line with this , Sch9 did not influence GFP-Pho8 localization to the vacuolar membrane ( Fig 2D ) . Previous studies have implicated TORC1 , Sch9 and PKA in negatively regulating autophagy [8 , 41] . We used the Pho8Δ60 and GFP-Atg8 processing assays to monitor the delivery and lysis of autophagic bodies in the vacuolar lumen [42] . Although we observed a small increase in basal and starvation induced autophagic flux in the sch9Δ mutant ( Fig 2F and S2E Fig ) , this small difference is unlikely to explain the observed synthetic growth defects when combining the deletion of SCH9 with that of genes encoding proteins involved in vesicular trafficking . Moreover , none of the ATG genes were retrieved in our SGA screening . Collectively , our results show that Sch9 does not directly control vesicular trafficking pathways for protein transport to the vacuole , the plasma membrane , or for endocytosis . Another GO category that was significantly enriched in our screening is connected to the vacuolar proton pump ( Fig 1 ) . Since the V-ATPase is a key regulator of pH homeostasis [43] and since the majority of Sch9 localizes to the vacuolar membrane , where also the V-ATPase is found [17 , 44] , we investigated whether the deletion of SCH9 affected pH . As a first indication , we measured the ability to acidify the extracellular medium upon re-addition of glucose to glucose-starved cells . Consistent with a potential role in maintaining pH homeostasis , we found that cells lacking SCH9 displayed a reduced glucose-activated proton export similar as been reported for cells lacking a functional V-ATPase ( Fig 3A ) [43] . Next , we monitored the effects on pHc . We could not observe a difference in pHc between WT and sch9Δ cells during fermentative growth ( Fig 3B ) , although sch9Δ cells maintained their neutral pHc longer due to slower growth and , consequently , later depletion of glucose . However , after the diauxic shift , the pHc of sch9Δ cells dropped below that of WT ( Fig 3B ) . Similarly , a more acidic pHc for the sch9Δ mutant was observed when cells were starved for carbon ( Fig 3C and S3A Fig ) . In line with previous data [45] , subsequent re-addition of glucose to WT starved cells resulted in a rapid acidification of the cytosol followed by an alkalization to neutral pH within 3 minutes after the glucose pulse . In contrast , the sch9Δ mutant displayed a retarded recovery of pHc after glucose re-addition as it reached neutral pH values only 5 minutes after the pulse ( Fig 3C ) . Whereas the rapid acidification step is believed to be caused by initiation of glycolysis , subsequent alkalization of the cytosol is the result of coordinated activation of the plasma membrane H+-ATPase Pma1 and the vacuolar V-ATPase . Hence , similar to sch9Δ cells , the pHc of mutants with a dysfunctional V-ATPase also recovers more slowly after glucose deprivation [43] . Thus , the data described above are all consistent with a functional link between Sch9 and the V-ATPase . Because Sch9 is a known effector of the nitrogen-responsive TORC1 complex , we asked whether nitrogen starvation , similarly to glucose starvation , may influence pHc in a Sch9-dependent manner . Interestingly , pHc remained unchanged following nitrogen starvation in both WT and sch9Δ cells ( Fig 3D and S3B Fig ) . Because of the high-throughput nature of our SGA screen , we decided to manually validate the genetic interaction between Sch9 and the V-ATPase using tetrad dissection . Remarkably , all mutants that combined the deletion of SCH9 with the deletion of a vma subunit displayed a synthetic sick phenotype ( Fig 4A and S4 Fig ) . A quantitative measure of the synthetic fitness defect was obtained by measuring colony sizes using Image J ( Fig 4B and S5A Fig ) . Consistent with the genetic interaction , we observed a severe deteriorated growth phenotype of these mutant strains on fully supplemented medium , even when this medium was buffered at pH 5 to fully support growth of the V-ATPase mutants ( Fig 4C and 4D , S5B–S5E Fig ) . Interestingly , our analysis confirmed the partial functional redundancy of the V0 subunit a isoforms [17] , as it required the simultaneous deletion of VPH1 and STV1 to observe a synthetic growth phenotype in combination with the deletion of SCH9 ( Fig 4 and S5 Fig ) . Taken together , we established a negative genetic interaction between SCH9 and the V-ATPase complex and show that this is a general phenomenon that cannot be attributed to a specific V-ATPase subunit or sector . To gain insight into the molecular mechanisms underlying the genetic interaction , we sought processes that are regulated by both Sch9 and the vacuolar proton pump . To this end , we assayed growth on media known to impair growth of either the sch9Δ or V-ATPase deficient mutants [9 , 14] . Similar growth defects were observed for both sch9Δ and various V-ATPase mutants when they were exposed to osmotic stress or grown on media with high calcium concentrations . In addition , both types of mutants were unable to grow on non-fermentable carbon sources and on YPD medium containing 60 mM CaCl2 buffered at pH 7 . 5 ( Table 1 and S6 Fig ) . Interestingly , as both Sch9 and V-ATPase activity are required for tolerance to rapamycin and manganese , two substances known to affect TORC1 signaling [6 , 46] , our results point towards a functional relationship between TORC1 and the V-ATPase . Unlike V-ATPase deficient mutants , however , the sch9Δ mutant is tolerant to zinc and high extracellular pH , but the additional deletion of SCH9 does not restore growth of the V-ATPase deficient mutants on these two media ( Table 1 ) . On the other hand , the deletion of SCH9 appears to cause enhanced sensitivity of the stv1Δ or vph1Δ strains to osmotic stress and elevated extracellular calcium , and to partially impair growth on non-fermentable carbon sources . In general , it seems that phenotypes which rely on the H+ pumping ability of the V-ATPase are only mildly influenced by the deletion of SCH9 , while mostly the phenotypes that are not readily attributable to the vacuole acidifying function of the V-ATPase , i . e . the non-canonical functions , appear to be affected by loss of Sch9 . Taken together , these results show that the sch9Δ mutant has a partial vma- phenotype and suggest that Sch9 may somehow regulate the V-ATPase . One of the best studied phenotypes associated with the loss of Sch9 is the extension of chronological lifespan ( CLS ) [2 , 12 , 13] . Although V-ATPase activity has been implicated in regulating yeast ageing [25 , 29] , not much is known about the effect on CLS when the V-ATPase function is abrogated in S . cerevisiae . Hence , we determined the CLS of mutants lacking SCH9 and/or the V-ATPase subunit encoded by VMA2 . To this end , cells were grown in complete synthetic medium with 2% glucose and viability of cells was measured 8 days after they entered stationary phase . In line with previously published data [2 , 11] , we observed an increased lifespan for sch9Δ cells as compared to WT . In contrast , the vma2Δ mutant displayed a significantly reduced viability ( Fig 5A and S7A Fig ) . Interestingly , the CLS decreased dramatically in the vma2Δsch9Δ mutant even when compared to vma2Δ mutant alone . This suggests that when V-ATPase activity is compromised , Sch9 is important for maintaining cell viability . Very similar results were obtained when we extended our analysis to other V0 and V1 subunits ( S8A and S8B Fig ) . Thus , the role of Sch9 in lifespan determination is highly dependent on the presence of a functional V-ATPase . As especially superoxide anions are detrimental to survival and as both Sch9 and V-ATPase activity have been implicated in oxidative stress resistance [11 , 26] , we assessed the levels of endogenous superoxide anions using dihydroethidium ( DHE ) during chronological ageing . We found that in stationary phase cells the level of this reactive oxygen species ( ROS ) was elevated in the vma2Δ mutant when compared to WT cells and this difference became more pronounced as cells aged ( Fig 5B and S7B Fig ) . In sch9Δ cells , the level of ROS also increased during ageing , but here the amount of ROS was always significantly lower than in WT cells . In agreement with the reduction in CLS , the additional deletion of SCH9 in the vma2Δ mutant resulted in a striking increase in cells showing DHE staining , indicating that superoxide-induced oxidative stress may represent one of the main factors contributing to the rapid ageing of this mutant . Indeed , only a minor fraction of the vma2Δsch9Δ cells stained with DHE did not accumulate SYTOXgreen at day 8 in stationary phase ( S8C Fig ) . Moreover , the fraction of cells displaying SYTOXgreen staining but no accumulation of superoxide anions was negligible in all investigated strains ( < 0 . 2% ) . It has been shown that extracellular acidification is an important extrinsic factor affecting CLS [47–50] . Hence , we also determined CLS of the WT and mutant strains in complete synthetic medium buffered to pH 5 . 5 . As shown , buffering of the medium indeed reduced mortality significantly as the WT and single sch9Δ and vma2Δ strains maintained their viability during the first week of chronological ageing , while there was only a small drop in survival for the vma2Δsch9Δ mutant ( Fig 5C and S7C Fig ) . Again , an inverse correlation was seen with the ROS levels measured under the same conditions ( Fig 5D and S7D Fig ) . However , when cells were allowed to age for a longer period , it was once more evident that also in buffered medium Sch9 promoted ageing in case of a functional V-ATPase , while it supported survival when V-ATPase function was compromised ( Fig 5E ) . Of note , we also measured the pH of the buffered medium in the aged cultures . We noticed enhanced acidification of the culture medium when cells were lacking Sch9 , while the medium pH of aged WT and vma2Δ cells was higher and similar . Thus , the pH of the medium cannot explain why the role of Sch9 in regulating longevity switched from pro-ageing to pro-survival upon impairment of the V-ATPase ( Fig 5F ) . The amino acid composition of the growth medium can affect yeast CLS [51 , 52] , with methionine availability having a highly significant impact [53] . Indeed , several studies demonstrated that genetic or dietary restriction of methionine promotes longevity [54 , 55] . Because our strains are all in the BY4741 background and thus contain a deletion of MET15 , and since we retrieved MET6 and MET22 from the genome-wide SGA screening ( Fig 1 and S1 Table ) , we wondered whether methionine availability would differentially influence the CLS of our strains . To this end , strains were aged in non-buffered synthetic medium containing different concentrations of methionine . For WT and sch9Δ mutant cells , lifespan decreased with increasing methionine supply , while for cells with a dysfunctional V-ATPase , i . e . the vma2Δ and vma2Δsch9Δ cells , survival was better in medium containing the standard 20 mg/L methionine than in medium containing lower or higher concentrations of the amino acid ( Fig 5G and S7E Fig ) . These data confirm that CLS depends on methionine availability and is determined in part by V-ATPase function [55] . Nonetheless , independent of methionine availability , the deletion of SCH9 still extended longevity , while it reduced longevity when combined with disruption of the V-ATPase activity . Again , a tight correlation between survival and superoxide levels could be observed for all methionine concentrations tested ( Fig 5H and S7F Fig ) . Since our data indicate that Sch9 impacts on pH homeostasis and since several studies have linked yeast ageing to V-ATPase activity and vacuolar acidification [25 , 29 , 56] , we also measured pHv in WT and mutant strains to determine whether this could explain the apparent differential roles of Sch9 in regulating CLS . We used the pH-sensitive fluorescent dye BCECF-AM and performed measurements in cells growing exponentially on glucose , as well as in glucose-starved cells . As compared to WT cells , the sole deletion of SCH9 was associated with a significant drop in pHv in both conditions , indicative for enhanced V-ATPase activity ( Fig 5I ) . Remarkably , when the SCH9 deletion was introduced in the strain lacking Vma2 , it caused an increase in pHv . This effect was only apparent in cells growing on glucose , suggesting that especially under these conditions Sch9 controls pHv also independently of the V-ATPase . Because the changes in pHv correlated well with the CLS profiles for the strains studied , our data are in line with a model in which vacuolar acidity dictates cellular longevity . Both Vph1-containing V-ATPase complexes and Sch9 are known to locate at the vacuolar membrane during fermentative growth . Hence , we reasoned they might physically interact . Prior to studying this interaction , we identified conditions that influenced the intracellular localization of Sch9 or the assembly state of the V-ATPase . In agreement with previous work [33] , GFP-Sch9 was found to be enriched at the vacuolar membrane in exponentially growing cells , but a significant portion dissociated from the vacuolar membrane upon glucose starvation ( Fig 6A ) . In contrast , the protein kinase remained stably associated with the vacuolar membrane upon nitrogen starvation and rapamycin treatment ( Fig 6A ) , as well as in V-ATPase deficient mutants ( Fig 6B ) , indicating that the intracellular localization of Sch9 is specifically regulated by C-source availability . Concerning V-ATPase assembly , we found that the V-ATPase was fully assembled in both WT and sch9Δ cells during exponential growth , as well as upon nitrogen starvation and rapamycin treatment ( Fig 6C and 6E , S9A Fig ) . However , when sch9Δ cells were subjected to glucose starvation a significant fraction of Vma5-RFP remained localized with Vph1-GFP at the vacuolar membrane , as indicated by fluorescence intensity profile plots ( Fig 6D and 6F , S9B Fig ) and by the Pearson’s coefficient ( R ) ( S9C Fig ) . Thus , the disassembly of the V-ATPase is apparently hampered in sch9Δ cells , which may explain why the cells have a lower pHv as described above . Since microscopic analyses did not allow quantifying an absolute value of assembled V-ATPase complexes , we further studied the V-ATPase assembly state and the putative interaction of Sch9 with the V-ATPase by co-immunoprecipitation ( co-IP ) . Accordingly , Fig 7A shows that in exponentially growing cells a strong interaction between the V1 and V0 sectors , and between the V-ATPase and HA6-Sch9 could be observed . Upon glucose depletion , both interactions weakened considerably , but were rapidly restored by re-supplementation of glucose . In contrast , nitrogen deprivation did not impact on either interaction ( Fig 7B ) ; at most there was a slight decrease in the interaction between Vma1 and Sch9 . As this interaction was not strengthened by the subsequent supplementation of nitrogen , the minor decrease cannot be attributed to a starvation effect . Concerning V-ATPase assembly levels in WT and sch9Δ cells , the results in Fig 7C indicate that in exponentially growing cells , both the deletion of SCH9 , or the treatment of WT cells with rapamycin , significantly increases V-ATPase assembly as compared to untreated WT cells . Because the effect of rapamycin is comparable to that triggered by the deletion of SCH9 , the data suggest that Sch9 functions downstream of TORC1 to modulate V-ATPase assembly ( Table 2 ) . In line with our microscopy data , the absence of Sch9 also significantly lowered the amount of V-ATPase that disassembled upon glucose-starvation . This effect is only partially mimicked when WT cells are treated with rapamycin , suggesting that Sch9 may facilitate glucose starvation-induced V-ATPase disassembly to some extent independently of TORC1 . Importantly , glucose depletion still triggered V-ATPase disassembly in the absence of the Sch9 , indicative that the role of this kinase is only modulatory . To confirm our data , we repeated the co-IP experiments using strains expressing different Sch9 mutants . First , we monitored the effect of glucose availability on V-ATPase assembly in a strain expressing the analog-sensitive sch9as allele [33] , the activity of which can be blocked using the ATP-analog 1-NM-PP1 . As compared to our previous data with WT cells , even without inhibitor more V-ATPase remained assembled in the sch9as strain upon glucose-starvation , but consistent with the data obtained for the sch9Δ mutant , more V-ATPase assembly was observed when Sch9 activity was blocked by 1-NM-PP1 , and this independently of whether the cells were exponentially growing or glucose starved ( S9D Fig ) . Next , we tested the requirement of TORC1-dependent Sch9 phosphorylation by comparing rapamycin-induced V-ATPase assembly in WT and sch9Δ mutant strains complemented with either wild-type Sch9 , the Sch95A or the phosphomimetic Sch92D3E alleles [6] . As expected , expression of the wild-type Sch9 allele in the sch9Δ mutant restored the V-ATPase assembly state to WT levels and rendered it again sensitive to rapamycin ( Fig 7D ) . In contrast , rapamycin did not significantly affect V-ATPase assembly upon expression of the Sch9 phospho-mutants . While maximal assembly was obtained in the presence of Sch95A , similar as seen for the empty vector control , an intermediate level was found in case of the Sch92D3E allele . The latter indicates that this TORC1-independent Sch92D3E allele may not be fully functional in downstream signaling as noted before [57] . Nonetheless , when combined , our results suggest that Sch9 integrates input from TORC1 to influence the glucose-dependent assembly state of the V-ATPase .
By conducting a genome-wide SGA screening , we defined a global SCH9 genetic interaction network in yeast and , as such , identified numerous new genes that may function as Sch9 effector or act in pathways connected to Sch9 . Among these hits were several genes involved in modulating vacuolar biogenesis and function . However , we could not find evidence that Sch9 is involved in regulating trafficking routes that deliver material for vacuolar biogenesis or degradation . Instead , we found this protein kinase to influence pHc , pHv and extracellular acidification . We also demonstrated that Sch9 interacts with V-ATPase subunits to modulate the assembly state of the latter in function of nutrient availability , thereby integrating input from TORC1 and a yet undefined glucose-dependent sensor . Our findings are consistent with observations made in a recent study that coupled vacuolar biogenesis and functioning to cell growth and cell cycle progression [58] . This study demonstrated that Vph1 and components of the TORC1 complex are delivered to the vacuolar membrane early during vacuole biogenesis , while Sch9 is only recruited at a later stage . Moreover , this study reported that Sch9 , along with TORC1 , signals the cell-cycle machinery that a functional vacuole is present . Interestingly , Sch9 is thereby not only activated by TORC1-dependent phosphorylation , but also by additional signals that require a functional vacuole [58] . That Sch9 is involved in determining cell growth and division was already known for some time , but it remained mainly connected to a dynamic network that couples nutrient availability with ribosome biosynthesis [33 , 59] . Consistent with its prominent role in nutrient storage , the vacuole has emerged as central player regulating nutrient signaling pathways in both yeast and mammals [22 , 60] , though only few studies have begun to unravel the underlying molecular basis . For instance , Young et al . ( 2010 ) conducted a genome-wide screening to identify inositol auxotrophy mutants . The authors concluded that the drop in pHc triggered by glucose starvation releases the transcriptional repressor Opi1 from a lipid-sensor complex in the ER , which then translocates to the nucleus to repress the Ino2/4 transcription factors and as such many phospholipid metabolic genes [61] . Another genome-wide screening examined pHc and cell division rate during fermentative growth [62] . This screening retrieved several mutants that could be classified in different categories depending on their pHc-growth rate relationship . Both screenings not only identified various players and potential sensors involved in pHc signaling , but also provided additional links with Sch9 . Indeed , more than 20% of the genes retrieved by each screening overlapped with our SGA screening ( S10 Fig and S1 Table ) . As such , Sch9 seems to emerge as a key player connecting inositol and lipid metabolism with pHc , V-ATPase and growth . Whether this connection relates to the sphingolipid-dependent function of Sch9 [7 , 63] or the PI ( 3 , 5 ) P2-dependent activation of the V-ATPase [24] and vacuolar recruitment of Sch9 [64] needs to be investigated in more detail , but most likely additional mechanisms are at play . We also compared the data from our SGA screening with those obtained by the Cardenas group who performed a genetic screening for synthetic interactions with TOR1 [65] . Albeit the latter allowed to link Tor1 signaling with vacuolar functions , the overlap between both screenings was mainly restricted to genes encoding cytoplasmic and mitochondrial ribosomal proteins ( S1 Table ) . This may indicate that Sch9 does signal also independently of TORC1 , as suggested before [3 , 8 , 9] . One well-established phenotype of the sch9Δ strain is its increased survival during stationary phase [2 , 12] , which is partly due to increased respiration and expression of mitochondrial oxidative phosphorylation subunits [11 , 66] . Consistently , CLS extension by deletion of SCH9 can be blocked and even reversed to lifespan shortening by the additional deletion of respiratory genes , by introducing the SCH9 deletion in rho0 strains that lack functional mitochondria or by pregrowing cells under a different nutritional regime [66–68] . We now report that Sch9 can either extend or shorten CLS depending on the presence of a functional V-ATPase and the vacuolar acidity . This is in line with a previous study that connected CLS to the V-ATPase and autophagy-dependent vacuolar acidification under conditions of methionine restriction [55 , 56] . Another study demonstrated that CLS extension by methionine restriction requires activation of the retrograde response pathway to regulate nuclear gene expression in function of mitochondrial activity [54] . Interestingly , our SGA-screening confirmed a genetic link between SCH9 with RTG2 , encoding a sensor for mitochondrial dysfunction , RTG3 , encoding a key mediator of retrograde signaling , and with the methionine metabolism genes MET6 and MET22 . Hence , the question arises whether both vacuolar acidification and mitochondrial functioning are part of the same regulatory scenario determining longevity . At least for the control of replicative lifespan this seems to be the case . Here , vacuolar acidification is required to maintain proper pH-dependent vacuolar amino acid storage and this prevents age-induced mitochondrial dysfunction [25] . Furthermore , a systematic gene deletion analysis confirmed that several vacuolar mutants , including V-ATPase mutants , affect mitochondrial functions and display similar phenotypes as mitochondrial petite mutants [69] . Given the previously published link between Sch9 and respiratory capacity [11 , 66] and the data presented in this paper connecting Sch9 with the V-ATPase and vacuolar pH , it is well possible that Sch9 is part of a system that monitors vacuolar and mitochondrial function in order to sustain growth and lifespan . Such monitoring system may be quite complex as evidenced by a study that demonstrated additive effects of methionine , glutamic acid and glucose availability on yeast longevity [53] . Interestingly this study also attributed a role to Sch9 in the sensing of methionine and glucose , while implicating Gcn2 , a conserved protein kinase that links amino acid sensing with global protein synthesis , in the sensing of glutamic acid [53] . As Vam6/Vps39 regulates the formation of the vacuolar-mitochondrial contact sites involved in lipid transfer [70 , 71] , and contributes to the activation of TORC1 [44] , it could play an important regulatory role in this monitoring system . In contrast to Sch9 , the localization of Tor1 in yeast is not regulated by glucose availability [72] . However , it has been shown that the essential TORC1 subunit Kog1 is transiently sequestered in cytoplasmic stress granules upon heat stress [73] and in cytoplasmic foci in a Snf1-dependent manner upon glucose starvation [72] . As such , the movement of Kog1 in and out of these so-called Kog1-bodies determines the formation of TORC1 . Once reconstituted at the vacuolar membrane , the activity of TORC1 is controlled via Vps-C complexes and the amino acid sensing EGO complex ( EGOC ) , the yeast functional counterpart of mammalian Rag-Ragulator [74–76] . Besides being a downstream effector of TORC1 , a role of Sch9 in the control of EGOC has not been reported to our knowledge . However , such a role can be suspected given the data we now present on the contribution of Sch9 in regulating V-ATPase assembly and thereby vacuolar acidity . The latter is important for the vacuolar degradative capacity and the pH-dependent storage of amino acids in the vacuolar lumen [25] . In this context , it has been suggested that the Rag GTPases of EGOC could , in addition to mediating cytoplasmic amino acid signals [77] , also sense the vacuolar amino acid load through amino acid transport across the vacuolar membrane [74–76] . In addition , and similar to the mammalian system , the Rag GTPase Gtr1 was found to interact with the V-ATPase raising the possibility that the proton pump itself could be involved in the activation of the GTPase [22 , 60] . As depicted in Fig 8 , the consequence of the above is that Sch9 can be part of a feedback loop that keeps V-ATPase and TORC1 activity in balance during growth . The idea of an Sch9-dependent feedback control of TORC1 is not new , because it was already proposed in a previous study that analyzed the effectors by which TORC1 controls the transcription of ribosomal protein and ribosome biogenesis genes [57] . Moreover , both TOR complexes have already been proposed to function in feedback loops to maintain cellular homeostasis [78] . Besides connections with TORC1 , the V-ATPase was also identified as signaling intermediate linking C-source availability and pHc with activation of PKA via the GTPase Arf1 [21 , 22] . Thus , by regulating V-ATPase assembly , Sch9 would also be upstream of the Ras/PKA pathway . It is known for some time that Sch9 is implicated in the Ras/PKA pathway [79] , but only recently it was reported that Sch9 indirectly regulates the phosphorylation of the PKA regulatory subunit Bcy1 via the MAP kinase Mpk1 in a TORC1 dependent manner [80] . Whether this means that Arf1 signals through Mpk1 to control PKA activity needs to be investigated . Importantly , it is known that enhanced PKA activity prevents glucose-induced disassembly of the V-ATPase [81] and therefore also this signaling route is most likely subjected to feedback control . Several mechanisms have been proposed to control V-ATPase assembly [15 , 82] . Of these , the Vph1-specific interactor RAVE ( Regulator of ATPase of Vacuoles and Endosomes ) might be a good candidate to mediate Sch9 control on V-ATPase assembly , especially since our SGA screening retrieved the gene encoding the central RAVE component Rav1 [83] . However , another possible scenario comes from the observation that during exponential growth on glucose the deletion of SCH9 was associated with enhanced vacuolar acidification , but when the disruption of SCH9 was introduced in the strain lacking an active V-ATPase vacuolar alkalization was observed . The reason for this is not known , but it may indicate that Sch9 affects proton exchange by vacuolar antiporters like the Na+/H+ and K+/H+ exchangers Nhx1 and Vnx1 , both known to play a role in pH homeostasis [84 , 85] . If Sch9 would control such antiporters , it would provide a mechanism by which the kinase regulates V-ATPase assembly in response to glucose . Indeed , pH-dependent alterations in the N-terminal cytoplasmic domain of Vph1 has been proposed as possible mechanism governing glucose-dependent reversible assembly of the V-ATPase [86] . This model would explain the observed synthetic genetic interaction between SCH9 and the genes encoding the V-ATPase . Moreover , it is also in line with the observation that mammalian PKB co-localizes with , phosphorylates and inhibits the cardiac sarcolemmal Na+/H+ exchanger Nhe1 following intracellular acidosis [87] . This raises again the question whether Sch9 is the genuine orthologue of the mammalian orthologue of PKB/Akt [88 , 89] or whether it combines this role with an S6K function in yeast [6] .
S . cerevisiae strains and plasmids are listed in S4 and S5 Tables , respectively . All strains used in this study are derived from the BY4741 series . Cells were grown at 30°C in YPD ( 1% yeast extract , 2% bactopeptone , 2% glucose ) or synthetic defined medium ( Formedium; 0 . 5% ammonium sulfate , 0 . 17% yeast nitrogen base , amino acids , 2% glucose ) . When indicated , the culture medium was buffered to the specified pH with either MES , sodium citrate or MOPS buffer . For nitrogen starvation experiments , cells were made prototrophic by introducing auxotrophy complementing plasmid ( s ) . For short-term nutrient deprivation , exponentially growing cells were washed with and further grown in starvation medium . For re-stimulation , cells were supplemented with a final concentration of 2% glucose or 0 . 2% glutamine . For serial dilution growth assays , stationary phase cells were diluted to an OD600nm of 1 and 10-fold serial dilutions were spotted . For growth curve analysis , cultures were grown for 48h and diluted to the same density . OD600nm was measured every two hours in a Multiscan GO Microplate Spectrophotometer ( Thermo Scientific ) . Diploids were generated by crossing the sch9Δ mutant ( JW 04 039 ) with single deletion strains derived from the BY4741 Yeast Knock-out Collection ( EUROSCARF ) and incubated at room temperature on solid sporulation medium ( 1% potassium acetate , 1 . 5% ager ) for 5–6 days . A small amount of sporulated cells was resuspended in water containing 0 . 02 mg/ml lyticase and incubated for 10–15 min at room temperature . Next , tetrads were dissected on a YPD plate using a micromanipulator ( Singer instruments ) . After 3–5 at 30°C , the genotypes of the germinated spores were analyzed based on the segregation of the genetic markers , and/or by PCR analysis . Only deletion mutants with a BY4741 genotype ( MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) were used in subsequent experiments . The S . cerevisiae deletion strain collection , constructed in the BY4741 background , was obtained from EUROSCARF ( Frankfurt , Germany ) . SGA screening was performed as described previously [90] , using the sch9::NATMX4 strain as bait . Colony sizes of single and double mutants were scored by visual inspection . Double mutant strains that displayed an aggravated colony size as compared to the respective single deletion strain and the single sch9Δ mutant were retained as potential candidates for a synthetic genetic interaction with SCH9 . Several interesting candidate genes involved in protein sorting and vacuolar function were manually confirmed using tetrad dissection ( see above ) . After three to four days at 30°C , colony sizes resulting from individual spores were measured using ImageJ software ( NIH ) . Briefly , average colony surface areas were determined using the particle analyzer function for at least seven independent colonies for each genotype . Mutants were categorized as having a negative genetic interaction with SCH9 when the relative colony size of the double mutant was less than the product of the relative colony sizes of the corresponding single mutants . To quantify the extent of co-localization , the Pearson correlation coefficient was generated using the ImageJ plugin JACoP . Quantifications were performed on different independent experiments , with at least 30 cells analyzed in total . Fluorescence intensity profile plots were created using the Plot Profile function of ImageJ . Precultures grown overnight in YPD or minimal medium lacking uracil ( Sch9 mutants ) were inoculated in fresh YPD medium buffered at pH 5 with 50 mM MES and grown till exponential phase . Next , half of the culture was treated with 200 nM rapamycin ( Sigma-Aldrich ) for 30 min and subsequently starved for glucose in the presence of rapamycin . The untreated half was further grown for 30 min and subsequently starved for glucose . For V-ATPase assembly analysis by sch9as inhibition , precultures were diluted in YPD medium ( pH 5 , 50 mM MES ) with or without 300 nm 1-NM-PP1 ( Merck-Millipore ) and grown for 6 hours , after which cultures were starved for glucose in the absence or presence of the inhibitor . After treatment and/or starvation , cells were collected by centrifugation at 4°C , washed with ice-cold PBS , snap frozen in liquid nitrogen and stored at -80°C . Protein extraction was performed by bead beating in buffer A ( 40 mM Hepes-NaOH [pH 7 . 5] , 120 mM NaCl , 1 mM EDTA , 0 . 3% CHAPS , 50 mM NaF , 10 mM β-glycerophosphate ) supplemented with complete protease inhibitor tablets without EDTA ( Roche ) . Extracts were cleared by centrifugation and incubated overnight at 4°C with anti-Vma1 or anti-Vph1 ( Abcam , 8B1 and 10D7 ) . Next , samples were incubated with magnetic anti-IgG beads ( Invitrogen ) for 30 min and beads were washed four times with buffer A . Proteins were eluted by boiling in SDS-sample buffer and subjected to immunoblotting . Relative V-ATPase assembly was quantified by calculating the ratio of Vph1 IPed with anti-Vma1 ( assembled V0V1 complexes ) vs Vph1 levels IPed with both antibodies ( free V0 and assembled V0V1 ) [20] . Cells were pregrown to stationary phase in non-buffered fully supplemented medium containing 2% glucose . Next , stationary phase cells were diluted to an OD600nm of 0 . 1 in fresh medium and grown for 48h , which was set as time zero . For standard CLS experiments , cells were aged in non-buffered fully supplemented medium ( ~ pH 5 . 5 ) containing 2% glucose . Buffered CLS experiments were performed in fully supplemented medium buffered at pH 5 . 5 with 100 mM MES . For CLS experiments with varying methionine concentrations , methionine was added at the indicated concentration to minimal medium lacking methionine . Of note , a standard concentration of 20 mg/L methionine was used throughout all other experiments . At various time points , cell death was measured by flow cytometry ( Guava easyCyte 8HT , Merck Millipore ) using SYTOXgreen or propidium iodide ( Molecular Probes ) . Alternatively , cell survival was measured by clonogenictiy . To this end , the amount of cells/μl was determined by flow cytometry and 250 cells were plated on YPD agar plates . Subsequently colony forming units were counted and values are displayed as percentage of viable cells . ROS levels were measured via DHE staining and subsequent flow cytometry analysis . Collected flow cytometry data were processed and quantified with FlowJo software . Cytosolic pH was measured in prototrophic yeast cells expressing pHluorin [45] grown in low fluorescence medium ( loflo; Formedium ) containing 2% glucose , buffered at pH 5 with 25 mM sodium citrate . Fluorescence emission was recorded at 510 nm using a FLUOstar OPTIMA microplate reader ( BMG labtech ) with excitation at 390 nm and 470 nm . For starvation experiments , log phase cells were washed twice with starvation medium buffered at pH 5 and fluorescence was measured every 5 min at 30°C for 1h . For pulse experiments , 2% glucose or 0 . 2% glutamine was administered to starved cells . For growth curve analysis , stationary phase cultures were re-inoculated at the same density in fresh loflo medium and monitored every hour for pHc and OD600nm . Calibration was performed by incubating digitonin permeabilized cells in citric acid/Na2HPO4 buffers of different pH values . Vacuolar pH was measured as described previously with minor modifications [43] . Briefly , yeast cells were grown to log phase in fully supplemented loflo medium containing 2% glucose , buffered at pH 5 with 50 mM MES and labelled with 50 μM BCECF-AM ( Thermo Scientific ) . Next , cells were washed twice in growth medium with or without 2% glucose and fluorescence was recorded for 30 min at 30°C using a Fluoroskan Ascent FL Microplate Fluorometer and Luminometer ( Thermo Scientific ) . Fluorescence emission was recorded at 538 nm after excitation at 440 nm and 485 nm . Calibration curves were constructed for each strain in every experiment using the calibration mixture described by Brett et al . [84] , except that the ionophores ( monensin and nigericin ) were omitted . The acidification of the culture medium was monitored using the pH indicator bromocresol green sodium salt ( BCG; Sigma-Aldrich ) . For glucose-induced acidification of the medium , cells were grown to exponentially phase in fully supplemented medium containing 2% glucose , buffered at pH 5 . Next , cells were washed with glucose starvation medium and resuspended at an OD600nm of 0 . 1 in starvation medium containing 0 . 01% BCG . The absorbance of the medium ( 595 nm ) was monitored for 1h in a Multiscan GO Microplate Spectrophotometer . Medium acidification was initiated by the addition of 2% glucose and changes in absorbance over time were recorded . For the measurement of growth media pH of ageing cultures , cells were pelleted and BCG was added to the supernatants at a final concentration of 0 . 01% . A calibration curve was used to convert the measured absorbance to pH values . Autophagy was monitored using the Pho8Δ60 and GFP-Atg8 processing assay as described previously [91] . Concerning the Pho8Δ60 assay , the generation of p-nitrophenol from p-nitrophenyl phosphate ( Sigma-Aldrich , N9389 ) ] was monitored in pho8Δ and pho8Δsch9Δ mutant strains harboring the PHO8Δ60 gene by measuring absorbance at 405 nm using a Beckman DTX880 plate reader ( Molecular Devices ) . Specific activities were calculated as nmol p-nitrophenol/min/mg protein . Data are the mean of at least four independent transformants . Concerning the GFP-Atg8 assay , TCA protein extracts were prepared from WT and sch9Δ strains harboring the GFP-Atg8-expressing plasmid and equal amounts of proteins were resolved on a 10% SDS-PAGE . Blots were probed with anti-GFP ( Roche Diagnostics ) . The cytoplasm-to-vacuole pathway was monitored using the prApe1 processing assay as described previously [39] . Briefly , WT and sch9Δ cells were harvested , proteins precipitated using the TCA method and equal amounts were loaded on a 10% SDS-PAGE . After western blotting , membranes were probed with anti-Ape1 ( kindly provided by Dr . Klionsky ) . Missorting of CPY was measured by a colony overlay assay as described previously [35] . Briefly , cells were grown to stationary phase and spotted on fully supplemented medium at the indicated OD600nm . Plates were placed at 30°C for 4-6h and overlaid with a nitrocellulose membrane . After ± 24h of growth at 30°C , the membrane was washed several times with distilled H2O and TBS buffer containing 0 . 1% Tween-20 , and subjected to immunoblotting with anti-CPY ( Molecular probes , 10A5 ) . V-ATPase assembly was investigated by co-localization of pRS315-Vph1-GFP ( gift from Robert C . Piper ) with Vma5-RFP . To this end , we constructed WT and sch9Δ strains expressing a chromosomally encoded , RFP-tagged version of Vma5 by crossing sch9::NATMX4 with RD157 [21] . Correct localization of Sch9 in V-ATPase deficient mutants was examined by co-transforming cells with pRS415-GFP-Sch9 and pRS316-mCherry-Pho8 [92] . Plasmids expressing fusion proteins that served as marker for vesicular compartments were generous gifts ( S2 Table ) . All images were generated using a Leica DM 4000B fluorescence microscope ( Leica Microsystems ) equipped with a Leica DFC 300G camera . Protein extraction and western blot analysis were performed as described previously [63] . Protein concentrations were determined using the Bradford method ( Bio-Rad ) or the Pierce 660 nm protein assay ( Thermo Scientific ) . Equal amounts of protein were mixed with SDS-sample buffer and resolved on a SDS-PAGE gel . Either anti-ADH2 ( Merck Millipore , AB15002 ) or anti-PGK1 ( Molecular probes , 22C5 ) were used as loading controls . The ECL method was used for detection and blots were visualized using a UVP Biospectrum Multispectral Imaging System . Signals were quantified by densitometry using UVP VisionWorks LS software ( VWR ) . Unless stated otherwise , the results shown are mean values and standard deviations displayed as error bars . For other experiments , representative results are shown . The appropriate statistical tests were performed using GraphPad Prism . Significances: * p < 0 . 05 , ** p < 0 . 01 , *** p < 0 . 001 .
|
The evolutionary conserved TOR complex 1 controls growth in response to the quality and quantity of nutrients such as carbon and amino acids . The protein kinase Sch9 is the main TORC1 effector in yeast . However , only few of its direct targets are known . In this study , we performed a genome-wide screening looking for mutants which require Sch9 function for their survival and growth . In this way , we identified multiple components of the highly conserved vacuolar proton pump ( V-ATPase ) which mediates the luminal acidification of multiple biosynthetic and endocytic organelles . Besides a genetic interaction , we found Sch9 also physically interacts with the V-ATPase to regulate its assembly state in response to glucose availability and TORC1 activity . Moreover , the interaction with the V-ATPase has consequences for ageing as it allowed Sch9 to control vacuolar pH and thereby trigger either lifespan extension or lifespan shortening . Hence , our results provide insights into the signaling mechanism coupling glucose availability , TORC1 signaling , pH homeostasis and longevity . As both Sch9 and the V-ATPase are highly conserved and implicated in various pathologies , these results offer fertile ground for further research in higher eukaryotes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"chemical",
"compounds",
"vacuoles",
"carbohydrates",
"organic",
"compounds",
"glucose",
"membrane",
"proteins",
"physiological",
"processes",
"fungi",
"homeostasis",
"amino",
"acids",
"cellular",
"structures",
"and",
"organelles",
"proteins",
"chemistry",
"methionine",
"cell",
"membranes",
"yeast",
"sulfur",
"containing",
"amino",
"acids",
"biochemistry",
"organic",
"chemistry",
"cell",
"biology",
"genetic",
"screens",
"gene",
"identification",
"and",
"analysis",
"physiology",
"monosaccharides",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"organisms"
] |
2017
|
The yeast protein kinase Sch9 adjusts V-ATPase assembly/disassembly to control pH homeostasis and longevity in response to glucose availability
|
The phenotype of the spontaneous mutant mouse hop-sterile ( hop ) is characterized by a hopping gait , polydactyly , hydrocephalus , and male sterility . Previous analyses of the hop mouse revealed a deficiency of inner dynein arms in motile cilia and a lack of sperm flagella , potentially accounting for the hydrocephalus and male sterility . The etiology of the other phenotypes and the location of the hop mutation remained unexplored . Here we show that the hop mutation is located in the Ttc26 gene and impairs Hedgehog ( Hh ) signaling . Expression analysis showed that this mutation led to dramatically reduced levels of the Ttc26 protein , and protein-protein interaction assays demonstrated that wild-type Ttc26 binds directly to the Ift46 subunit of Intraflagellar Transport ( IFT ) complex B . Although IFT is required for ciliogenesis , the Ttc26 defect did not result in a decrease in the number or length of primary cilia . Nevertheless , Hh signaling was reduced in the hop mouse , as revealed by impaired activation of Gli transcription factors in embryonic fibroblasts and abnormal patterning of the neural tube . Unlike the previously characterized mutations that affect IFT complex B , hop did not interfere with Hh-induced accumulation of Gli at the tip of the primary cilium , but rather with the subsequent dissociation of Gli from its negative regulator , Sufu . Our analysis of the hop mouse line provides novel insights into Hh signaling , demonstrating that Ttc26 is necessary for efficient coupling between the accumulation of Gli at the ciliary tip and its dissociation from Sufu .
The Hedgehog ( Hh ) signaling pathway plays critical roles in embryonic development , wound healing , and tumorigenesis [1]–[4] . It is activated when the receptor protein Patched-1 ( Ptch1 ) binds to one of the secreted Hh lipoproteins , Sonic Hh ( Shh ) , Indian Hh , or Desert Hh [5] , [6] . The Hh-Ptch1 interaction affects cell proliferation , differentiation , and patterning by regulating three Gli transcription factors ( Gli1-3 ) [7] , [8] , and the extent of Gli activation is dictated by the ratio of the activator and repressor forms of Gli proteins in the cell . When Hh is absent , the full-length Gli3 protein ( Gli3-F ) is processed into a shorter repressor form ( Gli3-R ) , which strongly suppresses the expression of target genes [9] . The repressor form of Gli2 ( Gli2-R ) , in contrast , has only a minimal effect on transcription , and is formed inefficiently from full-length Gli2 ( Gli2-F ) [10] . Gli1 has no repressor form; it is regulated transcriptionally through activation of the other two Gli proteins [11] . Hh signaling activates both Gli2-F and Gli3-F and blocks their processing into repressors [9] , [10] . Although data on the full range and importance of various posttranscriptional modifications of Gli are still emerging [12]–[16] , it is clear that a crucial step in the activation of Gli2-F and Gli3-F is their dissociation from Sufu [17] , [18] . The primary cilium is the Hh signaling center of the mammalian cell . In the absence of Hh proteins , Patch1 is localized to this structure , where it inhibits the activity of the seven-span transmembrane protein Smoothened ( Smo ) [19] . Activation of the Hh pathway causes Ptch1 to exit the cilium and Smo to enter [19] , [20] . Smo enhances the ciliary import of Gli-F proteins ( Gli-Fs ) by inhibiting protein kinase A [21] , [22] , after which Gli-Fs are transported to the tip . This trafficking of Gli-Fs to the ciliary tip is required for their dissociation from Sufu [17] , [18] , and thus for activation of the Gli transcription factors [23]–[25] . Ciliary trafficking is facilitated by IFT particles , whose core proteins are organized into complexes A and B [26] . Complex B interacts physically with the kinesin-2 motor to mediate anterograde transport ( towards the ciliary tip ) [27] , [28] . Complex A and cytoplasmic dynein 2 are necessary for retrograde transport . Mutations in the gene that encodes cytoplasmic dynein 2 lead to the production of shortened cilia and reduced Hh signaling [29] , [30] . The complete absence of subunit Ift144 of IFT complex A likewise results in stumpy cilia and decreased Hh signaling , whereas a hypomorphic Ift144 allele and the null alleles of two other genes encoding components of complex A ( Ift122 , Ift139 ) are associated with the formation of swollen cilia and enhanced Hh signaling [31]–[33] . With regard to mutations affecting complex B , depending on the subunit involved , defects can include a lack of ciliogenesis , the formation of short cilia , or the dysregulation of Ptch1 and Smo trafficking [24] , [34]–[39] . All pathogenic mutations in complex B genes analyzed to date prevent or reduce Gli trafficking to the ciliary tip and inhibit Hh pathway activation . Two mechanisms by which complex B defects impair Hh signaling have been identified: indiscriminate interference with ciliary trafficking due to structural changes in the axoneme [40] , or selectively dysregulation of re-localization of the Hh signaling proteins Ptch1 and Smo [34] . In an effort to gain additional insight into Hh signaling , we searched publicly available mouse lines with unidentified gene mutations for phenotypic signs of reduced Hh signaling . We selected the hop mouse for further analysis based on its preaxial polydactyly , hopping gait , hydrocephalus , and male sterility [41]–[47]; preaxial polydactyly also occurs in Gli3+/− mice [48] , and a hopping gait has been described in Gli1−/−; Gli2+/− mice [49] . Furthermore , although Hh signaling and primary cilia had not been examined in hop mice previously , their motile cilia had been reported to be abnormal . Firstly , their sperms lack flagella , explaining the male sterility phenotype [43] . Secondly , although their epithelia in the trachea , oviduct , and ependyma form motile cilia , approximately 40% of these lack outer dynein arms [43] . Because ciliary beating at the apical side of ependymal cells is critical for the normal flow of cerebrospinal fluid [50] , the partial lack of outer dynein arms in ependymal cilia likely explains the hydrocephalus of hop mice . The hop mutation ( also known as hydrocephalic-polydactyly , hpy [51] ) arose spontaneously in an unirradiated mouse colony of the Radiobiology Unit of the Medical Research Council at Harwell more than 40 years ago [42] . Nevertheless , the gene harboring this mutation remained unidentified prior to our study . We used positional cloning to localize the hop mutation to the Ttc26 gene , which encodes a component [52] of IFT complex B . Based on the association of Ttc26 with IFT complex B , we expected to find that the Ttc26 mutation leads to the formation of abnormally short primary cilia , and to below-normal levels of Hh-triggered Gli accumulation at the ciliary tip . Surprisingly , neither proved true . Instead , Ttc26 was required for effective dissociation of Gli from Sufu . This finding suggests that Ttc26 protein is necessary for at least one step in the Hh pathway that lies downstream of Gli trafficking to the ciliary tip .
Although the gene affected by the hop mutation was not identified in previous studies , it had been localized to mouse chromosome 6 [47] . We hypothesized that the breeding history of the hop mouse line could facilitate further genetic mapping because the hop mutation had been transferred from an undefined genetic background onto the BALB/c background at The Jackson Laboratory , through repeated backcrosses . We analyzed chromosome 6 of hop mice for 15 SNPs that are almost completely BALB/c specific , as they have been detected in very few other strains ( see Table S1 ) . This approach identified a non-BALB/c region on chromosome 6 between SNPs rs36592952 and rs30852324 ( Figure 1A ) , encompassing 142 protein-coding RefSeq genes . We selected 3 of these genes for testing ( Figure 1A ) , based on their known association with the ciliome [53] , [54] . Sequence analysis of the candidate genes from the hop line showed that one , Ttc26 , contained a C-to-A point mutation that changes a tyrosine-encoding codon to a stop codon in exon 15 ( Figure 1B ) . This nonsense mutation is predicted to truncate the Ttc26 protein 125 amino acids from its C-terminus ( Figure 1C ) . We used Western blotting to examine expression of the wild-type Ttc26 protein ( Ttc26wt ) as well as the hop genome-encoded Ttc26 ( Ttc26hop ) . Each was readily detected in HEK293 cells transfected with the corresponding construct , and Ttc26hop was ∼13 kDa smaller ( Figure 1D ) . Immunoblotting also revealed the presence of endogenous Ttc26wt in airway epithelial cells and the testes of wild-type mice , but Ttc26hop was not detected in the equivalent samples from hop mice ( Figure 1D ) . We hypothesized that the premature stop codon in the endogenous Ttc26hop mRNA may trigger nonsense-mediated decay ( NMD ) , but that the transfected and intronless Ttc26hop evades degradation because NMD is not activated by a premature stop codon in the absence of downstream exon-exon junctions [55] . Real-time RT-PCR experiments revealed that , in mouse embryonic fibroblasts ( MEFs ) from the hop line , expression of the endogenous Ttc26hop mRNA was reduced 5 . 7-fold compared to that of the Ttc26wt mRNA in control MEFs ( Figure S1 ) . These results strongly suggest that the hop mutation is located within the Ttc26 gene , and that it leads to reduced expression of the Ttc26 protein . We found that each homozygous Ttc26 mutant mouse ( n = 82 ) in our hop colony had preaxial polydactyly ( Figure 2B ) . Thus , the previously reported polydactyly phenotype [44] , [47] was fully penetrant in this line . However , we observed that most litters included fewer hop/hop mice than expected based on Mendelian ratios . The genotyping of 126 newborn mice from heterozygous breeding pairs confirmed that the ratio of hop/hop mice was significantly lower than the expected 25% ( 33 wild-type [26 . 2%] , 75 heterozygous [59 . 5%] , and 18 homozygous mutant [14 . 3%]; χ2 test p = 0 . 017 ) . In contrast , when embryos were harvested from heterozygous breeding pairs on embryonic day ( E ) 10 . 5 , the ratio of hop/hop mice was close to 25% ( 27 wild-type [26 . 2%] , 51 heterozygous [49 . 5%] , and 25 homozygous mutant [25 . 7%]; χ2 test p = 0 . 96 ) . Thus , homozygosity for the Ttc26 mutation is associated with partial lethality between E10 . 5 and birth . The surviving homozygous Ttc26 mutant mice were smaller than control littermates ( Figure 2A ) , consistent with a previous characterization of the hop mouse line [44] . One of the best-studied examples of Shh-regulated patterning besides digit formation in the limb is cell-type specification in the neural tube . In this structure , neuronal fates are dictated by the local concentration of Shh , which decreases gradually from its source in the notochord and floor plate [28] , [56] , [57] . We examined the pattern of neuronal specification in the ventral neural tubes of hop/hop and control mice based on the expression of three markers of neuronal cell types: FoxA2 , Nkx2 . 2 , and HB9 . Immunofluorescence detection of FoxA2 at E10 . 5 revealed that the number of FoxA2-positive cells ( which require the highest Shh concentration for their specification ) was reduced in the neural tubes of hop/hop mice compared to that in their heterozygous littermates ( Figures 2C and 2D ) . Furthermore , in the hop/hop mice the Nkx2 . 2-expressing V3 interneurons were shifted to the ventromedial region ( Figure 2D ) , and the ventral edge of the HB9-expressing motoneuron area was located abnormally close to the ventral border of the neural tube ( Figure 2D ) . Thus , the Ttc26 mutation is associated with patterning defects that are characteristic of reduced Hh signaling . Defects in the primary cilium often lead to hearing loss , polycystic kidney disease , and retinopathy [58] . Testing of the auditory brainstem responses ( ABR ) of hop/hop and heterozygous control mice to broad-band sound stimuli of various intensities revealed that the homozygous animals were hearing impaired ( Figure 2E and 2F ) ; however , the severity of the hearing impairment was variable . Histological examination of the cochlear cross sections of hearing impaired hop/hop mice did not identify pathological changes other than reduced thickness of the bony labyrinth ( Figure S2 ) . Furthermore , visualization of the bundles of stereocilia in whole-mount preparations of organ of Corti samples from hop/hop mice showed that the planar orientation of hair cells was not altered ( Figure S3 ) . Thus , unlike the lack of several other ciliary proteins [59] , [60] , the Ttc26 defect of hop mice impairs hearing through mechanisms other than the dysregulation of planar polarity in hair cells . Histological examination of the kidneys and retinas of 1 year old hop/hop mice also did not reveal abnormalities ( Figure S4 ) . Collectively , these results indicate that the Ttc26 mutation is associated with some – but not all – of the pathological changes that are typically linked to dysfunction of the primary cilium . Although Ttc26 had been co-purified with IFT complex B and was recently classified as a subunit of this complex [52] , [61] , [62] , its ability to interact directly with proteins had not been examined . To gain insight into Ttc26 function , we screened mouse and human cDNA libraries for Ttc26wt-binding proteins , using the yeast two-hybrid method . These screens identified a single Ttc26-interacting protein: the IFT complex B subunit Ift46 . Next , we tested whether the C-terminus of Ttc26 is required for this protein-protein interaction , using the two-hybrid assay in yeast co-transformed with Ift46 and Ttc26wt or Ift46 and Ttc26hop . Because Ift46 was fused to a Gal4 activator domain and the Ttc26 proteins were fused to a Gal4 DNA-binding domain , the Ift46-Ttc26 interaction was predicted to reconstitute the active Gal4 transcription factor . We visualized Gal4 activity by supplementing the yeast plates with X-α-Gal , which is metabolized into a blue product by a Gal4-induced enzyme . This test showed Gal4 activation in yeast co-transformed with Ift46 and Ttc26wt , but not in yeast co-transformed with Ift46 and Ttc26hop ( Figure 3A ) . Thus , the Ttc26 C-terminus is required for binding to Ift46 . We used immunoprecipitation to test whether Ift46 and Ttc26 can interact in mammalian cells . HEK293 cells were transfected with HA-tagged Ift46 and either flag-tagged Ttc26wt or flag-tagged Ttc26hop . The cells were lysed , and Ttc26 and the interacting proteins were pulled down from the lysates using an anti-flag antibody . Immunoblot analyses of the proteins in these samples confirmed that Ift46 interacted efficiently with Ttc26wt ( Figure 3B ) but not with Ttc26hop , supporting the idea that the Ttc26 C-terminus is important for this interaction ( Figure 3B , see quantification in Figure S5 ) . Thus , even if the endogenous Ttc26hop protein is expressed in the hop mice at levels below the detection limit of our Western blots , its ability to interact with Ift46 is severely impaired . Defects in most subunits of IFT complex B cause either a complete lack of ciliogenesis or the formation of short cilia , depending on the affected subunit [27] . We therefore evaluated cilium formation in the MEFs of hop/hop and wild-type mice , visualizing the ciliary marker proteins acetylated-α-tubulin ( AαT ) and Arl13b by immunofluorescence . We normalized the number of visualized cilia to the number of ToPro-3-stained nuclei . These experiments revealed that the hop mutation did not affect the percentage of ciliated MEFs ( Figure 3C ) . Furthermore , it did not result in the production of short cilia , in either MEF cultures ( Figure 3D ) or the mesenchyme of hop/hop embryos ( Figure S6 ) . In fact , the hop mutation was associated with a slight increase in cilium length ( Figure 3D and Figure S6 ) . Next , we evaluated the expression of Ttc26 and Ift46 in the cilia of control and hop/hop MEFs by immunofluorescence . In control MEFs , Ttc26 was detected at the base of most cilia and at the tip of ∼20% of cilia ( Figure 3E ) ; the specificity of the immunostaining was confirmed by the lack of Ttc26 signal in the cilia of hop/hop MEFs . The expression of Ift46 was not different in the cilia of control and hop/hop cells ( Figure 3F ) . Collectively , these results show that the Ttc26 mutation of hop mice does not inhibit the formation of primary cilia or the ciliary localization of Ift46 . The patterning defects in the limbs and neural tube of hop/hop mice suggested that the Ttc26 mutation impairs Hh signaling . To evaluate the functionality of the Hh pathway , we transduced control and hop/hop MEFs with an adenovirus that encodes a previously described Gli-responsive reporter gene [63]–[65] , and measured reporter expression following 48-h treatment with Shh-conditioned medium . Gli reporter signal was normalized to the expression of GFP from a separate expression cassette in the adenoviral vector . This experiment revealed that Shh-induced expression of the Gli reporter was dramatically reduced in the hop/hop MEFs compared to wild-type controls ( Figure 4A ) . Next , we tested the response of control and hop/hop MEFs to the chemical agonist SAG , which activates Smo directly and induces Shh signaling independently of Ptch1 [66] . Again , induction of the Gli reporter was much lower in hop/hop MEFs than in their control counterparts ( Figure 4A ) . Thus , the hop mutation impairs Hh signaling downstream of Ptch1 . We also used the Gli reporter assay to assess whether the Ttc26 mutation was the cause of the Hh signaling defect in hop/hop cells . MEFs from hop/hop mice were co-transduced with the Gli reporter-encoding virus and either a Ttc26wt- or Ttc26hop-encoding adenovirus , and hop/hop MEFs transduced with only the Gli reporter-encoding virus served as a negative control . Western blot analysis confirmed that the Ttc26 constructs were expressed in the transduced cells ( Figure S7A ) , and immunofluorescence microscopy showed that the heterologously expressed Ttc26wt and Ttc26hop were imported into the cilium ( Figure S7B ) . Results of the reporter assay demonstrated that heterologous expression of Ttc26wt restored the SAG-dependent induction of the Gli reporter to nearly wild-type levels ( Figure 4B ) , but that overexpression of Ttc26hop had no significant effect ( Figure 4B ) . These data indicate that the Ttc26 mutation is the cause of the Hh signaling defect in the hop mouse line . Activation of Gli3-F is followed rapidly by its proteasomal degradation [17] , [18] . We therefore used Western blotting to examine whether the Ttc26 mutation was associated with a defect in Gli3 processing . In hop/hop MEFs , the Gli3-F levels changed only minimally following SAG treatment , whereas in wild-type MEFs they declined significantly ( Figure 4C and 4D ) . In addition , we found that the ratio of Gli3-F to Gli3-R was high in non-stimulated hop/hop MEFs ( Figure 4C and 4E ) , suggesting that the Ttc26 mutation also impaired the processing of Gli3-F into Gli3-R . These data are consistent with a cilium-dependent Hh signaling defect in hop/hop cells , because Gli3-F transport through the cilium is required for both the activation of Gli3-F and efficient production of Gli3-R [23] , [67] . We next probed the Hh pathway upstream of Gli3 processing in hop/hop cells by assessing the ciliary localization of Smo . Immunofluorescence experiments showed that SAG treatment led to an increase in the amount of Smo in the cilia of both hop/hop and wild-type MEFs ( Figure 5A ) . Thus , the Ttc26 mutation of hop mice did not block entry of Smo into the cilium . Next we used immunofluorescence to evaluate accumulation of the Gli protein at the ciliary tips of wild-type and hop/hop MEFs . Because Gli3 is degraded soon after stimulation with SAG ( Figure 4C ) , its accumulation was measured after short treatments ( i . e . 1 and 2 h , Figure 5B and 5C ) . In the case of the more stable Gli2 protein , accumulation was measured after both short ( i . e . 2 h , Figure S8 ) and long ( i . e . 48 h , Figure 5D and 5E ) incubations with SAG . The results revealed that the Ttc26 mutation did not block the SAG-induced transport of either Gli2 or Gli3 to the ciliary tip . To evaluate whether our immunofluorescence approach was suitable for detecting intermediate changes in the quantity of ciliary Gli2 , we measured its accumulation at the ciliary tip after treating control and hop/hop MEFs with low and high concentrations of SAG . We found that high concentration of SAG ( 400 nM ) led to a greater increase of the Gli2 signal at the tip than low concentration of SAG ( 1 nM ) , in both control and hop/hop MEFs ( Figure 5E ) . Moreover , the Gli2 signal at the ciliary tip was stronger in hop/hop MEFs treated with 400 nM SAG than in wild-type MEFs treated with 1 nM SAG ( Figure 5E ) , yet Gli reporter induction was milder in the former ( Figure S9 ) . Thus , the Ttc26 mutation disrupts the correlation between the amount of Gli2 accumulated at the ciliary tip and the transcriptional output of the Hh pathway . A subset of the cellular pool of Sufu protein that forms complexes with Gli is also transported to the ciliary tip following activation of the Hh pathway [7] . To evaluate whether the hop mutation affected Sufu accumulation in the cilium , we measured the intensity of Sufu immunofluorescence at the ciliary tip of hop/hop and control MEFs after treating them with SAG and DMSO ( control ) for 2 days ( Figure S10 ) . Our results showed that the SAG-induced accumulation of Sufu at the ciliary tip was not affected by the hop mutation , supporting the notion that the transport of Gli-Sufu complexes in the cilium was unimpaired . Since the transport of Gli-Sufu complexes to the ciliary tip is followed by their dissociation [17] , [18] , we evaluated this signaling step in hop/hop and wild-type cells using an immunoprecipitation approach . MEFs were treated with SAG or DMSO ( control ) for 5 hours , at which point the Sufu-associated proteins were pulled down from the cell lysates using an anti-Sufu antibody . To prevent the degradation of Gli3 following its dissociation from Sufu , we treated the SAG-stimulated cells with the proteasome inhibitor bortezomib . Western blot analysis of the immunoprecipitated fractions revealed that , in the cases of both Gli3 and Gli2 , dissociation from Sufu was reduced in the SAG-treated hop/hop vs . wild-type MEFs ( Figure 5F and 5G ) . Thus , the Ttc26 deficiency in hop mice led to a general decrease in the dissociation of Gli proteins from Sufu .
In the present study , we show that the Hh signaling defect of hop mice is caused by a nonsense mutation in the Ttc26 gene , which encodes a component of IFT complex B . Our analysis of the Hh pathway in hop/hop cells indicates that the signaling defect lies downstream of the accumulation of Gli at the ciliary tip , but upstream of its dissociation from Sufu . How does the deficiency for Ttc26 function lead to impaired dissociation of the Gli-Sufu complex ? Although the simplest interpretation of our data is that Ttc26 is an organizer of molecular interactions that lead to the dissociation of Gli from Sufu , this seems unlikely because neither our yeast two-hybrid screen nor previous high-throughput screens detected physical interactions between Ttc26 and Gli or Ttc26 and Sufu [68]–[70] . We therefore posit that Ttc26 facilitates Gli-Sufu dissociation indirectly , potentially by affecting the localization of other ciliary proteins . This hypothesis is supported , albeit indirectly , by the known role of Ttc26 in the motile cilium . Specifically , Ttc26 appears to be required for the correct localization of a group of proteins that form the inner dynein arm in the motile cilium; this is suggested by the fact that in hop mice approximately 40% of cilia in the trachea , ependyma , and oviduct lack inner dynein arms [43] , and by the recent discovery of an association between the Ttc26 mutation and a reduction in the levels of inner dynein-arm proteins in the flagella of the green alga Chlamydomonas reinhardtii [52] . Alternatively , it is possible that the hop mutation slows both the anterograde and retrograde transport of Gli proteins , because a balanced defect in the bidirectional transport would potentially temper the dissociation of Gli from Sufu without altering the amount of Gli at the ciliary tip . Although this alternative hypothesis cannot be ruled out , tracings of IFT particles in the flagella of the Ttc26 mutant C . reinhardtii indicate that the velocity of IFT is not affected by the lack of Ttc26 [52] . Thus , the absence of Ttc26 is not likely to alter the structure of the primary cilium to the extent that the movement of motor proteins along the axoneme is impaired . Some of the phenotypic features of hop mice have been described in various animal models of Gli protein deficiency . For example , Gli1−/−; Gli2+/− mice have a hopping gait [49] , and Gli3+/− mice have preaxial polydactyly [48] . Thus , defective regulation of Gli proteins is the most likely cause of the hopping gait and polydactyly in the hop mouse . Nevertheless , the phenotype of hop mice is milder than that of Gli2−/− and Gli3−/− mice , which do not survive after birth [48] , [49] . Our detection of residual induction of the Gli reporter gene in SAG-treated hop/hop MEFs ( Figure 4A ) is consistent with the relatively mild phenotype of hop mice . The residual Hh pathway activation in hop/hop cells suggests that Ttc26 is not absolutely necessary for Hh signaling . Alternatively , hop could be a hypomorphic mutation . The “modest” ( 5 . 7-fold ) downregulation of the Ttc26 mRNA in hop/hop MEFs supports the notion that hop could be hypomorphic; however , the expression of the encoded Ttc26hop protein was reduced much more dramatically ( Figure 1D ) . This difference in the extent of downregulation of Ttc26hop transcript and protein is compatible with NMD-dependent inhibition of Ttc26hop expression . During NMD , the premature stop codon-containing mRNAs are not degraded immediately after the completion of pre-mRNA splicing; rather , their degradation occurs after a single round of pioneering translation . The temporal lag between splicing and degradation leads to the steady-state abundance of aberrant mRNAs at levels which are “only” 3- to 10-fold below the expression of their wild-type counterparts [55] , [71] . Our inability to detect truncated Ttc26 protein in hop/hop cells could reflect the very low protein output of the pioneering translations of Ttc26hop mRNA molecules . Among the various mouse models that involve deficiencies in complex B subunits , the orpk and gt mice exhibit the most hop-like phenotypes . These mice carry hypomorphic alleles of two complex B protein-encoding genes , Ift88 and Ift80 , respectively . Homozygosity for the Ift88Orpk and Ift80gt alleles leads to postnatal growth retardation , preaxial polydactyly and incomplete pre-weaning lethality [37] , [72] , [73] . In the hop and Ift80gt/gt mice , the Hh signaling defects are not accompanied by pathological changes in the kidney . In the Ift88Orpk/Orpk mice , by contrast , the Hh signaling defect is accompanied by polycystic kidney disease . Cyst formation correlates with deformities in the cilia of Ift88Orpk/Orpk and Ift80gt/gt mice , with the Ift88Orpk/Orpk genotype – but not the Ift80gt/gt genotype – being associated with the shortening of primary cilia . Thus , the absence of polycystic kidney disease in the hop mouse is consistent with the lack of cilium shortening in hop/hop MEFs . Although ciliary trafficking of Gli has not been examined in the Ift88Orpk/Orpk and Ift80gt/gt cells , the ciliary localization of Smo has been found to be dysregulated in Ift88Orpk/Orpk MEFs [74] . Thus , the orpk mutation appears to affect the Hh pathway further upstream than the hop mutation . The Ttc26 mutation led to an elevated hearing threshold in the hop mice; however , unlike the absence of many other ciliary proteins , the Ttc26 defect did not disrupt the planar polarity of outer hair cells ( Figure S3 ) . Furthermore , histological analysis of the middle ear of hop/hop mice did not reveal signs of inflammation ( Figure S11 ) , a frequent consequence of defects in the motile cilia . Because a complete lack of Hh signaling has been shown to cause agenesis of the middle ear ossicles and the cochlear duct [75] , [76] , we speculate that reduced Hh signaling in hop mice could potentially impair hearing through as yet undetected alterations in the structures of the middle ear ossicles or the expression of cochlear genes . The biological relevance of Ttc26 was recently evaluated in zebrafish [52] , [77] , and comparison of the zebrafish and mouse models of Tc26 deficiency reveals both similarities and differences in Ttc26 function . In both species the motile cilia are defective , as indicated by reduced ciliary beating in Ttc26 knockdown zebrafish [52] , [77] and the partial loss of inner dynein arms in the hop mouse [43] . However , abnormal patterning has not been reported in the Ttc26 knockdown zebrafish , and thus Ttc26 may not be necessary for Hh signaling in fish . This notion is supported by the recent finding that the morpholino-dependent knockdown of various subunits of the IFT complexes in zebrafish leads to minimal Hh signaling defects but severely deformed cilia [78] , [79] . Another difference between the two animal models is the impact of the Ttc26 deficiency on the length of cilia . In the Ttc26 knock-down zebrafish the motile cilia are shorter than normal [52] , [77] , whereas in the hop mouse the lengths of neither the motile [43] nor the primary cilia ( Figure 3 ) are generally decreased – the only exception being the sperm flagellum [42] . This discrepancy in cilium length cannot be explained simply by species differences in ciliogenesis , because the shRNA-mediated knockdown of Ttc26 in mouse cell lines also leads to the formation of short cilia [77] , [80] . The unique feature of the hop cells is the presence of a premature stop codon towards the end of the coding region in the Ttc26 mRNA . Therefore , we suggest that very low expression of the truncated Ttc26 protein could be responsible for the lack of cilium shortening in the hop mouse . Models describing IFT function in Hh signaling have developed rapidly over the past 10 years . After key Hh signaling molecules were detected in the primary cilium , IFT defects were proposed to affect the Hh pathway by causing structural changes in the axoneme , a model supported by the finding that a combination of anterograde and retrograde trafficking defects results in milder structural and signaling anomalies in the cilium than does either in isolation [40] . This model was further developed with the discovery that , in mice deficient for the IFT complex B subunit Ift25 , the ciliary structure is intact but ciliary trafficking of Ptch1 and Smo is dysregulated [34] . This indicated that the contribution of complex B to Hh signaling is not limited to maintenance of the ciliary microtubule track . The detailed analysis of a series of mouse lines carrying various hypomorphic and null alleles of the complex A subunit Ift144 has also suggested that changes in the ciliary structure alone do not fully explain the Hh signaling defects of the mutant mice [31] . The results we present here show that the Ttc26 component of IFT complex B is necessary for efficient coupling between the ciliary accumulation of Gli and its activation . We thus propose that the Ttc26 defect of hop mice reveals a novel role for IFT complex B in Hh signaling , downstream of the maintenance of ciliary structure and the facilitation of Smo trafficking .
Mice were euthanized according to the current AVMA guidelines . Experimental procedures were approved by the Animal Care and Use Committee of the University of Iowa ( protocol#: ACURF 1303050 ) . Hop mice ( BALB/cByJ genetic background ) were obtained from The Jackson Laboratory . The hop allele was mapped by sequencing 15 PCR-amplified genomic regions that contain BALB/c specific SNPs ( SNP accession numbers and PCR primers are listed in Table S1 ) . The transcripts of candidate genes were RT-PCR amplified from testis RNA , using the PCR primers listed in Table S2 , and sequenced without subcloning . Our routine genotyping procedure detected the single nucleotide difference between the hop and WT alleles based on the elimination of a Bpu10I restriction site from Ttc26 by the hop mutation . In brief , a 618-bp long genomic segment containing the hop mutation site was PCR amplified using the PCR primers 5′-dTACTGCTTTTGAGGAGACTAGGG-3′ and 5′-dGGATGATGGAACTAGTCACGGG-3′ , the reactions were digested with Bpu10I ( New England BioLabs ) , and the digestion products were resolved on 1% agarose gels ( Figure S12 ) . Kidneys and eyes from ∼1 year old mice were fixed , paraffin-embedded , sectioned , and stained with hematoxylin and eosin . Fore and hind limbs from newborn mice were stained with Alcian Blue and Alizarin Red S as previously described [81] . Hematoxylin and eosin stained cochlear sections and phalloidin-Alexa Fluor 488 stained organ of Corti samples were prepared as previously described [82] . The ABR thresholds of mice were measured at postnatal day 21–28 , using a previously described open-field system and broadband click stimuli [83] . The Glix8-luciferase reporter cassette was kindly provided by Dr . Michael K . Cooper ( Vanderbilt University Medical Center , Nashville , TN ) ; all other constructs were generated from mouse RNA . The Pfu DNA polymerase was used under standard reaction conditions , with primers listed in Table S3 , to amplify the entire coding regions of the Ift46 , Ttc26wt , and Ttc26hop transcripts , the 5′ coding region of the Shh mRNA ( 1–594 nucleotides ) , and the entire Glix8-luciferase reporter cassette . The amplified DNA fragments were subcloned into yeast expression vectors ( pGBKT7 and pGADT7 ) , the mammalian expression vector pcDNA3 . 1 , and two adenoviral shuttle vectors ( pacAd5-CMV and the promoterless pacAd5 ) . Adenoviral particles were generated by the Gene Transfer Vector Core of the University of Iowa and by ViraQuest Inc . ( North Liberty , IA ) . The Glix8-luciferase encoding adenovirus contained a PGK-EGFP expression cassette; all other viral vectors lacked GFP . HEK293 cultures were maintained and transfected as previously described [84] . Shh-conditioned medium was harvested from HEK293 cells 3 days after transfecting them with the Shh-encoding plasmid . The harvested medium contained 2% FBS ( Hyclone ) , and it was used in MEF cultures after 5-fold dilution with DMEM . The Matchmaker Gold Yeast Two-Hybrid system was used , according to the manufacturer's instructions ( Clontech ) , to screen human testis , mouse embryo , and universal mouse cDNA libraries ( Clontech ) for genes whose products interact with Ttc26wt . Verification of the Ift46-Ttc26wt interaction was carried out in Y2HGold yeast ( Clontech ) co-transformed with plasmids encoding Ift46 ( in pGADT7 vector ) and either the WT or mutant form of Ttc26 ( in pGBKT7 ) , using the Yeastmaker Yeast Transformation System ( Clontech ) . Following transformation , the yeast was spread on X-α-Gal-supplemented double dropout medium lacking leucine and tryptophan . Pregnant mice were euthanized at E10 . 5 . Embryos were removed from the amniotic cavity , fixed in 4% PFA for 4 h at 4°C , cryoprotected in 30% sucrose solution , and embedded in Optimal Cutting Temperature compound . Transverse cryosections through the lumbar region were re-fixed with pre-chilled 4% PFA for 10 min at RT , permeabilized with 0 . 1% Triton X-100 for 10 min , blocked with 5% normal goat serum ( Sigma ) and incubated with antibodies against FoxA2 ( Abcam; 1∶250 dilution ) , HB9 ( Developmental Studies Hybridoma Bank , University of IA; 1∶30 dilution ) , Nkx2 . 2 ( Developmental Studies Hybridoma Bank , University of IA; 1∶10 ) , acetylated α-tubulin ( 1∶1000 , Sigma ) , or Arl13b ( 1∶500 , Protein Tech ) . Secondary antibodies were labeled with Alexa fluor 488 and Alexa fluor 568 ( Invitrogen , 1∶500 ) , and fluorescence was visualized using a confocal microscope ( LSM-510 , Carl Zeiss Inc . ) . MEFs were isolated from E10 . 5 embryos as described previously [85] . In brief , decapitated and eviscerated embryos were pressed through 18-gauge needles twice , and pipetted onto 0 . 2% gelatin-coated plates in high-glucose DMEM containing 10% FBS , penicillin ( 100 units/ml ) , streptomycin ( 100 µg/ml ) , and 4 mM L-glutamine . MEF cultures were maintained for 4–6 passages . For immunofluorescence experiments , MEFs were seeded onto glass coverslips and cultured to near confluency . Cilium formation was induced by serum starvation ( 0 . 4% FBS in DMEM ) for 48 h . When SAG treatment was used , cells were serum starved for 16–24 h prior to addition of the SAG-containing medium . Cultures were fixed in 4% PFA for 15 min , permeabilized with 0 . 2% Triton X-100 for 7 min , and blocked in 5% normal donkey serum ( Sigma ) , 5% normal goat serum ( Sigma ) , or 1% BSA , depending on the primary antibody used ( see Table S4 ) . The sources , catalogue numbers , and dilutions of the primary antibodies against acetylated α-tubulin , Arl13b , Ttc26 , Ift46 , Smo , Gli2 , Gli3 , and Sufu are also listed in Table S4 . The secondary anti-mouse , anti-rabbit , and anti-goat antibodies ( 1∶500 , Invitrogen ) were labeled with Alexa fluor 488 or Alexa fluor 568 . For analysis of the percentage of ciliated MEFs , cells were incubated with the nuclear stain ToPro-3 ( 1∶2000; Invitrogen ) for 10 min immediately before the slides were mounted . Images were obtained using a confocal microscope ( LCM-510 , Carl Zeiss Inc . ) . Intensity of the Gli signal at the ciliary tip ( 400-pixel area ) was measured using the ZEN software ( Carl Zeiss Inc . ) . Cilium length was measured in 4% PFA-fixed and acetylated α-tubulin-stained samples at 63× magnification ( 0 . 03 µm×0 . 03 µm pixel size ) , by line segment tracing and spline fitting in Image J . MEFs were seeded onto glass coverslips and plastic dishes in DMEM containing 10% FBS . Approximately 16 h after seeding , the indicated adenoviral particles were added to MEFs at 40 multiplicity of infection ( MOI ) , in DMEM containing 1 . 8% FBS and polybrene ( 2 . 75 µg/ml ) . Cells were incubated with adenoviruses for 4 h and then allowed to recover for 4 h in 10% FBS-containing DMEM . The Hh pathway was activated by incubating MEFs with SAG ( 1–400 nM ) in 0 . 4% FBS-containing DMEM for 48 h . Control cultures were incubated with DMSO ( 0 . 02% ) instead of SAG . The transduced MEFs were used for immunostaining , protein extraction , and luciferase assay . Control and adenovirus transduced MEFs were incubated with SAG ( 1–400 nM ) or DMSO ( 0 . 02% ) in 0 . 4% FBS-containing DMEM for 48 h before lysis with Reporter Lysis Buffer ( Promega ) according to the manufacturer's protocol . Cell lysates were cleared by brief centrifugation , and luciferase activity in the supernatants was measured using the Luciferase Assay System ( Promega ) following the manufacturer's instructions . Luminescence was quantified with a Victor3 1420 Multilabel Plate Counter ( Perkin Elmer ) . To facilitate the evaluation of relative transduction efficiency , the Gli reporter-encoding adenovirus was engineered to contain a PGK-EGFP expression cassette . GFP fluorescence in the supernatants of cell lysates was measured with the Victor3 1420 Multilabel Plate Counter and was used for normalization of luciferase activity . MEFs were serum starved for 24 hours before treatment with 400 nM SAG or 0 . 02% DMSO ( control ) for 0–5 h in DMEM plus 0 . 4% FBS . Cells were lysed in a previously described buffer [18] containing 50 mM Tris ( pH 7 . 5 ) , 300 mM NaCl , 2% NP-40 , 0 . 25% deoxycholate , 10 mM N-ethylmaleimide , 1 mM DTT , 10 µL/mL P8340 ( Sigma ) , 1 mM PMSF , 10 µg/mL chymostatin , and PhosSTOP Phosphatase Inhibitor Cocktail ( Roche ) . When bortezomib ( 2 µM ) was added to the cell culture medium , its concentration was maintained in the lysis buffer . The cell lysates were passed through insulin needles ∼5 times and cleared by centrifugation . Aliquots from the supernatant were used directly for Western blotting , or were used for immunoprecipitation . In the latter case , the samples were incubated with 7 µg of an anti-Sufu antibody ( Santa Cruz , sc-28847 ) overnight at 4°C , and immunocomplexes were pulled down using the Dynabeads Protein G Immunoprecipitation kit ( Invitrogen ) . The pulled-down and input fractions were immunoblotted using the following antibodies: anti-Gli3 ( R&D , AF3690 ) , anti-Gli2 [86] ( kindly provided by Dr . Jonathan T . Eggenschwiler , University of Georgia , Athens , GA ) , anti-Sufu ( Santa Cruz , sc-10933 ) , and anti-ß-actin ( Santa Cruz , sc-1616 ) . For the FL-Ttc26 and HA-Ift46 co-immunoprecipitation experiments , lysis buffer ( Buffer A ) consisted of PBS ( pH 7 . 2 ) , 5 mM EDTA , 0 . 5% Triton X-100 , 10 µL/mL P8340 , 1 mM PMSF , and 10 µg/mL chymostatin . Transfected HEK293 cells were scraped into ice-cold Buffer A and sonicated ( 40% amplitude , 2×6 s at 4°C ) . The lysates were cleared by centrifugation , and aliquots of the supernatant were used directly as “input control” during Western blotting , or were used for immunoprecipitation . In the latter case , the samples were incubated with 7 µg of a monoclonal anti-flag antibody ( Sigma , M2 clone ) and immunocomplexes were pulled down using protein G agarose beads as previously described [84] . The pulled-down and input fractions were immunoblotted using the following antibodies: anti-HA ( Abcam , ab9134 ) and anti-flag ( Abcam , ab1162 ) . Airway epithelial cells were harvested from 5–6 mouse tracheas by pronase digestion , as described previously [87] . Protein was extracted from airway epithelial cells , testes , Ttc26-transfected HEK293 cells , and Ttc26-transduced MEFs using Buffer A . Protein extracts were immunoblotted with anti-Ttc26 ( Novus Biologicals , NBP1-84034 ) and anti-ß-actin ( Santa Cruz , sc-1616 ) antibodies . Total RNA from serum-starved MEFs was isolated using the TRIzol reagent ( Invitrogen ) , and reverse transcribed using SuperScriptIII ( Invitrogen ) . Real-time PCR was performed using PerfeCTa SYBR Green Fastmix ( VWR ) , a Mastercycler ep realplex PCR machine ( Eppendorf ) , and the following primers: Ttc26 forward 5′-dTGGCCAGGAAATGGGTTCAAGG-3′ and reverse 5′-dACTAGCTGATCCTCCTACCAACTG -3′ , Gapdh forward 5′-dCGTCCCGTAGACAAAATGGT-3′ and reverse 5′-dGAATTTGCCGTGAGTGGAGT-3′ . The relative mRNA expression values were determined using the ΔΔCT method [88] .
|
The Hedgehog ( Hh ) signaling pathway determines pattern formation in many developing tissues , e . g . , during digit formation in the limbs , by regulating proteins of the Gli family . Activation of these proteins requires their transport to the tip of the primary cilium ( an antenna-like sensory structure of the cell ) , and subsequent dissociation from their negative regulator , Sufu . Little is known about the mechanism underlying this dissociation . To gain new insights into Hh signaling , we analyzed the mutant mouse hop-sterile ( hop ) , whose developmental defects suggest that the primary cilia are dysfunctional . We discovered that the hop mutation lies in the Ttc26 gene , and that levels of the encoded protein are low in hop mice . Normal Ttc26 was found to bind to Intraflagellar Transport ( IFT ) complex B , a structure essential for building the cilium and moving proteins towards its tip . Nevertheless , unlike previously characterized mutations that affect IFT complex B , hop did not interfere with either the formation of primary cilia or the accumulation of Gli at their tips , but rather with dissociation of Gli from Sufu . Our results provide novel insights into Hh signaling , demonstrating that efficient coupling between Gli's accumulation at the ciliary tip and its dissociation from Sufu depends on Ttc26 .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"morphogens",
"signal",
"transduction",
"developmental",
"biology",
"embryonic",
"pattern",
"formation",
"cell",
"biology",
"molecular",
"development",
"biology",
"and",
"life",
"sciences",
"morphogenesis",
"cell",
"signaling",
"pattern",
"formation",
"cell",
"fate",
"determination",
"hedgehog",
"signaling"
] |
2014
|
A Mutation in the Mouse Ttc26 Gene Leads to Impaired Hedgehog Signaling
|
There are 10× more bacterial cells in our bodies from the microbiome than human cells . Viral DNA is known to integrate in the human genome , but the integration of bacterial DNA has not been described . Using publicly available sequence data from the human genome project , the 1000 Genomes Project , and The Cancer Genome Atlas ( TCGA ) , we examined bacterial DNA integration into the human somatic genome . Here we present evidence that bacterial DNA integrates into the human somatic genome through an RNA intermediate , and that such integrations are detected more frequently in ( a ) tumors than normal samples , ( b ) RNA than DNA samples , and ( c ) the mitochondrial genome than the nuclear genome . Hundreds of thousands of paired reads support random integration of Acinetobacter-like DNA in the human mitochondrial genome in acute myeloid leukemia samples . Numerous read pairs across multiple stomach adenocarcinoma samples support specific integration of Pseudomonas-like DNA in the 5′-UTR and 3′-UTR of four proto-oncogenes that are up-regulated in their transcription , consistent with conversion to an oncogene . These data support our hypothesis that bacterial integrations occur in the human somatic genome and may play a role in carcinogenesis . We anticipate that the application of our approach to additional cancer genome projects will lead to the more frequent detection of bacterial DNA integrations in tumors that are in close proximity to the human microbiome .
Lateral gene transfer ( LGT ) is the transmission of genetic material by means other than direct vertical transmission from progenitors to their offspring , and has been best studied for its ability to transfer novel genotypes between species . LGT occurs most frequently between organisms that are in close physical proximity to one another [1] . Human somatic cells are exposed to a vast microbiome that includes ∼1014 bacterial cells that outnumber human cells 10∶1 [2] . Considering that ( a ) some human cells are in a constant and intimate relationship with the microbiome , ( b ) eukaryotes have widespread LGT from bacteria [3] , ( c ) bacteria in vitro can transform the mammalian genome [4] , and ( d ) viruses integrate into the human genome and cause disease [5] , [6] , we sought to investigate if LGT from bacteria to human somatic cells may be a novel mutagen and play a role in diseases associated with DNA damage like cancer . Previous studies have examined LGT from bacteria to humans that would result in vertical inheritance . During the original sequencing and analysis of the human genome , 113 proteins putatively arising from bacterial LGT were initially identified [7] . This was later refuted by an analysis that demonstrated that the number of putative LGTs is dependent on the number of reference genomes used in the analysis suggesting that the proteins found exclusively in both bacteria and humans at that time were due to the small sample size of genomes sequenced , instead of LGT [8] . A subsequent phylogenetic analysis of LGT in the human genome overlooked comparisons with all prokaryotes [9] . Both analyses only focused on full length genes , missing any smaller LGTs or LGT of non-coding DNA . In addition , by focusing on consensus genome sequences , these analyses focused on LGT to the germ line and ignored somatic cell mutations . While LGT to the germ line can affect future generations and potentially the evolution of our species , LGT to somatic cells has the potential to affect an individual as a unique feature of their personal genome . Some eukaryotes have extensive vertically inherited LGT despite potential barriers such as the nucleus , the immune system , and protected germ cells . DNA continues to be transferred from mitochondria and chloroplasts into the eukaryotic nucleus . These organelles originated from α-proteobacteria and cyanobacteria , respectively [10] . LGT from bacteria to eukaryotes , including animals , is also quite widespread [11]–[13] , particularly from endosymbionts [3] . Wolbachia endosymbionts infect up to 70% of all insects [14] , with ∼70% of examined , available invertebrate host genomes containing gene transfers [15] . The amount of genetic material transferred ranges from 100 bp [15] , [16] to bacterial genome sized LGTs [15] , [17] , [18] . One of the best studied examples of LGT from bacteria to eukaryotes is LGT to plants from the bacteria Agrobacterium tumefaciens . A . tumefaciens uses a type IV secretion system to inject bacterial proteins and its tumor inducing plasmid into plant cells [19] . Through illegitimate recombination , the plasmid integrates into the plant genome , and plasmid encoded transcripts are produced using endogenous eukaryotic promoters [20] , [21] . The corresponding proteins create a specific carbon source for A . tumefaciens and promote the formation of plant tumors [19] , [22] . Therefore , A . tumefaciens creates a tumor environment that promotes the bacteria's own growth . A . tumefaciens has been shown to transform a variety of plant and non-plant cells including human cells in vitro [22] , [23] . The bacteria Bartonella henselae has also been shown to transform human cells in vitro . Bartonella henselae is a human opportunistic pathogen that causes cat-scratch disease [24] . B . henselae and B . quintana are the only known bacteria to cause bacillary angiomatosis , the formation of benign tumors in blood vessels [24] , [25] . A recent study demonstrated the ability of Bartonella henselae to integrate its plasmid into human cells in vitro through its type IV secretion system [26] . Bacterial plasmids have also been engineered to integrate autonomously in vertebrate genomes using the phiC31 integrase . A phiC31 integrase-containing plasmid was first shown to integrate into human cells in vitro [27] at a pseudo-attP site that does not disrupt normal gene functions . The plasmid also integrates into mice in vivo after hydrodynamic tail-vein injection [28] and can yield a properly expressed protein that rescues a mouse knockout phenotype [28] . One of the key mechanisms by which some viruses promote carcinogenesis is through their integration into the human genome , causing somatic mutations [29]–[31] . In the early 20th century viruses were suggested as a transmissible cause of cancer . However , it was not until the mid-1960s that the capability of viruses to promote human cancer was fully recognized [29] . The majority of viral-associated human cancers are related to infection with human papillomaviruses ( HPV ) , hepatitis B and C viruses , and Epstein-Barr virus . Together these viruses are associated with ∼11% of the global cancer burden [32] . In 2002 , cervical cancers resulted in ∼275 , 000 deaths , of which HPV had integrated into ∼90% of these cancers [33] . Almost all cancers associated with Hepatitis B virus ( HBV ) have the virus integrated into tumor cells [34] . Most of the observed HBV integrations have been isolated as a single occurrence from a single patient [6] . However , a few recurrent integrations into genes promoting tumor formation have been identified , such as the integration of HBV into the human telomerase reverse transcriptase gene [35] , [36] . These mutations can result in altered gene expression and promote carcinogenesis . The advent of next generation sequencing has facilitated the investigation of how and where these viruses integrate into the human genome with unprecedented resolution and accuracy . In a recent study , next generation DNA and RNA sequencing identified HBV integrations in liver cancer genomes and concluded that the HBV integrations disrupted chromosomal stability and gene regulation , which was correlated with overall shortened survival of individuals [6] . Using publicly available sequence data from the human genome project , the 1000 Genomes Project , and The Cancer Genome Atlas ( TCGA ) , we examined bacterial DNA integration into the human somatic genome , particularly tumor genomes . Here we show that bacterial DNA integrates in human somatic genomes more frequently in tumors than normal samples . These data also support our hypothesis that bacterial integrations occur in the human somatic genome and may lead to altered gene expression .
Human DNA for genome sequencing is typically isolated from one of three sources: sperm , blood , or cell lines created by transforming collected cells . Most of the data presented here from the Trace Archive and 1000 Genomes project were collected from the latter two . Systematic comparisons of the integration rate based on tissue source is not possible because the metadata on source can be missing , internally inconsistent , or at odds with publications of the data . However , it is important to consider that some of the data arises from cell lines . Cell lines may be more permissive to LGT from bacteria . Cell lines are used frequently because once they are generated they can be maintained in the laboratory allowing greater access to materials by more researchers . On the other end of the spectrum , transfers of bacterial DNA in sperm cells could be inherited by a subsequent generation . In contrast , transfers in blood cells would generate somatic mutations that would not be inherited . In addition , if a transfer occurs in a terminally differentiated cell its fate within the individual would even be limited . Somatic mutations are frequently overlooked in genome sequencing as there may be only a single instance within the sequenced population of cells that is lost in the consensus-built genome assembly . Therefore , we examined all available human sequence traces for evidence of LGT to somatic cells . Previously , we had developed a pipeline for rapidly identifying LGT between Wolbachia and its hosts by using NUCMER [37] ( Figure 1A ) . BLASTN against NT was used to further validate such transfers . Using this pipeline , 8 of the 11 hosts of Wolbachia endosymbionts that were examined were found to have evidence of LGT between the endosymbiont genome and the host chromosome [15] . In five of these hosts , we were able to successfully characterize every LGT we attempted to validate using standard laboratory techniques [15] . The other three hosts were not examined further . Given our prior success with the NUCMER-based pipeline , we used it to search for LGT in the somatic cells of humans . We searched 113 , 046 , 604 human shotgun Sanger traces from 13 sequencing centers and >8 individuals with 2 , 241 bacterial genomes using NUCMER ( Figure 1A ) . All reads were subsequently searched against NT with BLASTN ( Figure 1A ) and manually curated to identify ( a ) reads containing non-overlapping matches to human and bacteria sequences ( Table S1 ) and ( b ) read pairs where one read matched human and the other matched bacteria ( Table S2 ) . These searches revealed a total of 680 traces that contain significant non-overlapping similarity to both bacteria and human sequences ( Figure 1Aa , Table S1 ) . There are also 319 identified clones that contain sequences with similarity to both bacteria and human sequences ( Figure 1Ab , Table S2 ) . For example , 40 traces and 220 clones contain bacterial fragments with best blast matches to Lactobacillus spp . when NT was the database . These matches were found to be distributed across an entire Lactobacillus genome ( Figure 1B ) and could not be assembled . The lack of coverage/redundancy across the LGT junctions may be indicative of somatic cell transfers . As an example , one such trace is illustrated that disrupts a gene encoding an antigen found in squamous cell carcinomas [38] ( Figure 1CD ) . The trace containing this junction does not show evidence of an artifact ( e . g . two clones being sequenced simultaneously ) ( Figure 1E ) . Laboratory artifacts can lead to sequences resembling bacteria-eukaryote somatic cell LGT . Errors can occur in clone or sequence tracking , such that traces are assigned to the wrong project , or through contamination of plasmid preparations that leads to two sequences being generated simultaneously . Some cases of these were identified and systematically culled . For example , reads with matches to E . coli were systematically eliminated because of the high potential for artifactual contamination of genomic DNA in plasmid sequencing preparations . Similarly , all matches involving Erythrobacter were eliminated since a set of traces submitted by one center were found to contain two sequences—one for human and one for Erythrobacter likely owing to systematic contamination of the culture stocks or the plasmid preparations . When two templates are present the resulting read will switch between the two templates as the relative signal between the templates changes resulting in a consensus read call that resembles LGT . However , such artifacts are not readily apparent for any of the putative LGTs described her since the sequences span multiple plates , libraries , and runs and show no evidence of two templates ( Table S1 , S2 ) . Ligation of bacterial DNA to human genomic DNA during library construction can also result in chimeric clones with a single clone with a bacterial insert and a human insert . This would be observed as a low percentage of bacteria-human mate pairs relative to bacteria-bacteria mate pairs . For example , if 1 in every 100 , 000 clones contains two inserts , as opposed to the single insert wanted/expected , one would expect a chimeric clone with both a human and bacterial insert would occur no more than 1/100 , 000 , or 0 . 001% . Considering that human sequences greatly outnumber bacterial sequences , we would expect clones with bacteria and human inserts to occur much less frequently than human-human chimeras and that the number of bacteria-human chimeras will be almost solely based on the amount of bacterial DNA in the samples . We would also anticipate that if 0 . 001% of bacterial reads are found in bacteria-human chimeric clones then 0 . 001% of human reads will be found in human-human chimeric clones and be discordant in the human genome . However , we find that the percentage of reads or read pairs supporting integration relative to the number of human mate pairs is higher than one would anticipate or has been measured previously . The average percentage of bacteria-human mate pairs compared to bacteria-bacteria mate pairs is ∼6% ( 319 highly curated bacteria-human clones/5 , 280 minimally curated bacteria-bacteria clones ) , meaning 6% of the bacteria sequences are attached to human sequences . If the bacteria-human sequences were the result of artifactual chimeras , we would expect that 6% of the human sequences should also be erroneously attached to non-adjacent human sequences . This level of artifact chimerism would undermine assembly as well as results regarding human genome structural variation . To the contrary , one such structural variation study found that <1% of the mate pairs were discordant with the reference human genome [39] using some of the same genome sequencing data used here . While it would be prudent to measure the human-human chimerism rates across all the data to compare to the bacteria-human chimerism rates , the lack of a strict ontology for the metadata precludes this . Specifically , it is difficult to determine the exact nature of the pertinent data needed ( i . e . sequencing strategy and insert size ) for such an analysis . In order to extend this observation to next generation sequencing data , we created a pipeline ( Figure 2 , Figure S1 ) to identify Illumina paired end reads that consist of one bacterial read and one human read in the 1000 genomes and TCGA datasets . This is analogous to identifying bacteria-human mate pairs with NUCMER above ( Figure 1A , left side ) . The first round of filtering uses BWA [40] to map the paired end reads to the human reference and the completed bacterial genomes in the RefSeq database . BWA was run with the default parameters such that the number of differences is dependent on the read length; for example a 50-bp read has 3 differences allowed [40] . BWA was designed to align short query sequences against much longer reference genomes with great efficiency . It was chosen as the initial screen because it could efficiently process very large datasets quickly . After BWA identified a small subset of the paired end reads that support bacterial integration , BLASTN was used to validate each read of the pair as specific for bacteria or human using the larger NT database . Subsequently , a lowest common ancestor ( LCA ) approach [41] was used to assign operational taxonomic units ( OTUs ) to each read using either the best BLASTN matches to NT or all of the results of BWA searches against the completed bacterial genomes in RefSeq . As expected , the level of taxonomic assignment possible was largely dictated by the sequence variation in the reference sequences used , as seen with a comparison of sequences with similarity to the 16S rRNA gene and what is known about the variable and conserved regions of that gene . ( Figure S2 ) . The results of BWA-based and BLAST-based LCA assignment methods each have their nuances but the results were very similar and parsimonious with a phylogenetic analysis ( Figure S3 ) . Problems were identified with using BLAST searches against NT due to eukaryotic whole genome sequencing projects that likely contain contigs from the microbiome ( Figure S3 ) . As such , the BWA-based LCAs are presented here . Regardless , even when specific ( e . g . strain level assignments ) OTUs should never be deemed definitive and should merely be considered an approximation of the taxonomy of the sequence . The blast-based assignments and subsequent analysis is available in tables and in an interactive interface for the 1000 genomes data ( Table S3; http://lgt . igs . umaryland . edu/1000genomes ) and TCGA data ( Table S4; http://lgt . igs . umaryland . edu/tcga ) . To calibrate our pipeline , we reconstructed the known HPV integration in HeLa cells using available RNA-based Illumina sequence data [42] . The HeLa cell line has a well-documented integration of HPV into chromosome 8 as well as constitutive expression of the viral oncogenes E6 and E7 [31] , [42]–[44] . Previously , PathSeq was used to identify 25 , 879 HPV reads in the HeLa transcriptome ( 0 . 25% of the total reads analyzed ) [42] . Using the same transcriptomics data , our pipeline identified a similar number of 28 , 368 paired-end reads ( 0 . 55% of the total read pairs ) with both reads mapping to HPV . Furthermore , our pipeline identified 6 , 333 reads ( 0 . 12% of the total read pairs ) supporting integration of HPV into the human genome . These paired end reads span the viral integration site , with one read mapping to HPV and the other read mapping to the human genome ( Figure 3 ) . As expected , the reads supporting the HPV integration into the human genome flanked the constitutively expressed E6 and E7 viral oncogenes . The human portions of these paired end reads reside in the known tandem HPV integration site on chromosome 8 between 128 , 240 , 832–128 , 241 , 553 bp [33] , [45] , [46] . Using this pipeline on 3 . 15 billion Illumina read pairs from the 1000 Genomes Project available as of February 2011 , 7 , 191 read pairs supported bacterial integration into the somatic human genome after BLASTN validation , removal of PCR duplicates , and a low complexity filter . The integrations have up to 5× coverage on the human genome . Of the 484 individuals examined , 153 individuals have evidence of LGT from bacteria with 1 individual having >1000 human-bacteria mate pairs and 22 individuals having >100 such pairs . On average , 47 human-bacteria mate pairs were identified in these individuals with putative somatic LGT ( median = 2; maximum = 1360 ) . These putative somatic cell LGTs were identified in data from all five centers that contributed data to this release . Bradyrhizobium was the most common OTU identified in the reads supporting LGT , with Bradyrhizobium sp . BTAi1 being the most common strain-level OTU . The Bradyrhizobium-like reads were distributed across an entire reference Bradyrhizobium genome ( Figure S4A ) similar to what was observed for Lactobacillus sequences in the Trace Archive data ( Figure 1B ) . BTAi1 is a strain that is unusual in its ability to fix nitrogen and carry out photosynthesis . Therefore , some may consider the presence of BTAi1-like sequences in humans unusual . However , our understanding of what bacteria exist in the body is limited . Most of the samples containing Bradyrhizobium-like reads were from the Han Chinese South ( CHS ) population and were sequenced by the Beijing Genomics Institute ( BGI ) . OTUs associated with bacterial integration that were detected in only one center may be viewed suspiciously , and several , including this one , were observed . However , population level differences in the diet , life style , and microbiome of the different populations examined could also lead to this result . The CHS study is an example of the difficulties in ascertaining the source of the material sequenced . The study information in the SRA states that lymphoblastoid cell lines were used ( SRP001293; http://trace . ncbi . nlm . nih . gov/Traces/sra/sra . cgi ? study=SRP001293 ) , but the sample information states that blood was used ( http://trace . ncbi . nlm . nih . gov/Traces/sra/sra . cgi ? view=samples ) . Two OTUs—Propionibacter acnes and Enterobacteriaceae—were detected in samples from all five centers . P . acnes is a common skin bacteria that is associated with acne . It is thought to contaminate genomic DNA preparations either from laboratory workers or during sample collection . Whether bacterial DNA arises from contaminants or the microbiome , laboratory artifact chimeras in Illumina whole genome shotgun sequencing that resemble bacterial integrations can occur ( a ) during PCR amplification steps in library construction or ( b ) from over-clustering on the flow cell [47] . The other OTU found across all five centers is a family level assignment of Enterobacteriaceae , which includes Escherichia coli . While next generation sequencing no longer relies on plasmid-based clones , they do use ligation steps and recombinant enzymes isolated from E . coli . Therefore , it is quite possible that low levels of E . coli DNA could be introduced with the enzyme preparations . Because both E . coli and P . acnes DNA in samples could arise from contamination of the samples , out of an abundance of caution they were excluded from all analyses . However , we note that this may be a conservative approach given that other Enterobacteriaceae may be found in the samples besides E . coli and both E . coli and P . acnes could contribute to bacterial integration . Given that the putative LGTs detected are likely some combination of real LGT and laboratory-based artifacts of reads from the microbiome , we sought to establish a metric by which the two could be differentiated . Given the short length of these reads , our analysis of next generation sequencing data focused solely on Illumina paired end data , identifying putative bacterial integrations when one read mapped to human and one to bacteria . Due to the length of the reads , chimeric reads could not be identified with BWA ( e . g . a 50-bp read that had 25-bp mapping to a bacteria and 25-bp mapping to human could not be identified with BWA because it would remain unmapped ) . Given the sole use of paired end data , reads from the microbiome were defined as those where both reads only map to a bacterial genome . This is , however , an oversimplification since any integration of bacterial DNA larger than the library insert size is likely to generate such reads . Regardless , the microbes that contribute to putative LGT are just a subset of the microbes present ( Figure S5 ) . If junctions of bacteria-human read pairs are merely artifacts , one would anticipate that they form in the same proportion relative to the contaminating DNA . However , this was not observed ( Figure S5 ) . Each OTU could be binned into one of two categories based on the difference between the composition of the microbiome and the LGT reads: ( A ) one where the contribution of the specific bacteria relative to the total population of bacteria is higher in the reads supporting LGT and ( B ) one where the contribution of the specific bacteria relative to the total population is higher in the reads coming from the microbiome . One would anticipate that the former would contain bacteria participating in real LGT , since the proportion of reads with putative LGT is higher while the latter would represent the level of artifactual chimeras from contaminating DNA . This cannot be examined on a per sample basis since most samples have a limited amount of bacterial DNA . However , when the data is aggregated across the entire project ( Figure 4A ) , the bacteria do in fact fall into either of these two categories . As expected , bacteria in the families of Propionibacterineae and Enterobacteriaceae fall into category B , along with Xanthomonadaceae . In contrast , Bradyrhizobiaceae falls into category A . In a preliminary analysis , the phage λ was observed to fit into category A . In the above analysis , it is not observed because λ , a bacteriophage , has similarity to sequences with an NCBI taxonomy of “cloning” and “expression vector” that are excluded with our final criteria . However , if we specifically include the λ reads , λ falls within category A ( Figure 4B ) . The reads map only to a small portion of the λ phage , specifically ranging in coverage from 50×–250× on both sides of a HindIII site . It is possible that this is a contaminant as λ is commonly used in research labs . For instance , an excised gel slice may have been contaminated with a λ fragment from an adjacent lane containing a λ ladder . However , this is not consistent with having reads on both sides of the same HindIII site . If the slice was contaminated with two ladder fragments , we would anticipate equal numbers of reads at two additional sites reflecting both ends of the fragment , which was not observed . We could not reconstruct , with in silico digestions of common and uncommon restriction endonucleases , a scenario that explains our observation and reflects what is known about laboratory artifacts in genome sequence data . Should this integration of λ in the human genome be validated , it is intriguing since a phiC31 integrase-containing plasmid has already been shown to integrate into human cells in vitro [27] at a pseudo-attP site . If prophage can integrate naturally into the human genome , they may also be capable of producing virions that would serve as an immune defense against certain bacteria . To further explore the relationship between bacterial integrations and laboratory artifacts , we sought to establish the mutation rate across each dataset as well as within subsets . The Trace Archive and 1000 Genomes data are derived from terminally differentiated blood samples , where integrations are expected to occur in a single generation . In the Trace Archive data [7] , [45] , [48] a total of 680 traces contain significant non-overlapping similarity to both bacteria and human sequences and 319 clones contain both bacteria and human sequences ( Tables S1 and S2 ) . From this data , an integration rate was measured as 680 integrations in 113 , 046 , 604 reads per a single generation , or 6 . 02×10−6 integrations/generation . While this may be considered an overestimate due to known laboratory artifact chimeras that result from cloning , it may also be an under-estimate as reads deposited in the Trace Archive are often cleansed of reads believed , but not proven , to be from bacterial contaminants . In the Illumina reads from the 1000 Genomes Project [49] , 7 , 191 read pairs supporting integration were detected in 3 , 153 , 669 , 437 paired reads sequenced yielding a remarkably similar mutation rate of 2 . 28×10−6 integrations/generation assuming the mutations happen in a single generation . This mutation rate would reflect both integrations as well as the formation of laboratory artifactual chimeras . To establish the contribution of the laboratory artifacts , we examined putative integrations involving OTUs of Propionibacterium . If reads with this OTU arose from contamination , then any bacteria-human read pairs would arise from laboratory artifacts . Of the 845 , 260 , 743 read pairs in runs containing putative integrations and/or reads attributed to the microbiome with a Propionibacterium-level OTUs , 191 read pairs represented putative integrations , yielding a mutation rate of 2 . 26×10−7 , or 10-fold lower than that for the entire dataset . A similar analysis of λ , which may represent true integrations for the reasons outlined above , reveals 554 reads supporting integration out of 404 , 243 , 537 read pairs , or a mutation rate of 1 . 37×10−6 , which is 6-fold higher than the Propioinibacterium rate . Coverage across a bacterial integration would provide greater evidence of its validity and would be observed when more than 1 unique read is present at a single site . Uniqueness of the reads was assessed with PRINSEQ after concatenating the two reads together and identifying if they are identical . Such identity of both sequence and insert size suggests that the pair are either PCR or optical duplicates formed during library construction or sequencing , respectively , and that should be counted only once . If coverage of unique read pairs supporting LGT across the human genome can be observed , it may suggest clonal expansion of a population with the LGT and support that they were formed biologically in vivo rather than through laboratory-based artifact formation in vitro . When the analysis is limited to putative LGT with >1× coverage on the human genome , only 275 read pairs support somatic cell LGT . The most predominant bacterial species level OTU , with 100 read pairs , is Stenotrophomonas maltophilia , an emerging opportunistic pathogen of the respiratory and blood systems of immunocompromised individuals [50] . The Stenotrophomonas-like reads were evenly distributed across the bacterial genomes ( Figure S4B ) . Reads supporting S . maltophilia-like LGT were detected in two individuals in the study of Utah residents with Northern and Western European ancestry ( CEU ) sequenced at the Max Planck Institute of Molecular Genetics ( MPIMG ) . One individual had the majority with 97 of these read pairs . While read pairs with >1× coverage were only detected in two samples from one site , when the coverage limit was relaxed , 450 read pairs with a S . maltophilia level OTU were detected in both the CEU and CHS studies and from both MPIMG and BGI . While compelling , given the low coverage , the data from the 1000 Genomes Project is inconclusive in the absence of experimental validation . Yet , in terminally differentiated cells , like blood cells that are routinely sequenced , somatic cell LGT cannot be validated because the transfer sequenced was destroyed in the process of sequencing and is likely the only copy that exists . Transfers could occur in progenitor cells but as they are typically well protected , it is less likely . Furthermore , extensive coverage is not expected for the same reason . In several cases , we could identify coverage that further supports the validity of these reads but these instances were quite limited . In addition , much of the 1000 Genomes data examined are from the first pilot study that only generated 0 . 5–4× coverage of the genomes . Lastly , much of the DNA for the 1000 Genomes Project is derived from cell culture , not directly from blood cells . There is an opportunity for LGT to happen in cell culture that would not necessarily be biologically relevant . Therefore , we sought to validate these results further by examining data from cancer samples in TCGA . From 7 . 05 trillion bases of Illumina paired-end sequencing data in TCGA , 691 , 561 read pairs support bacterial integration into the somatic human genome ( Table 1 ) . The integrations into the human genome have >100× sequencing coverage ( Figure S6 ) . TCGA contains sequencing data of tumor samples as well as normal tissue . Strikingly , while only 63 . 5% of TCGA samples analyzed were from tumors , the tumor samples contained 99 . 9% of reads supporting bacterial integration . Furthermore , while the majority of normal samples had no read pairs supporting integrations , the majority of tumor samples had >10 reads supporting integrations ( Figure 5 ) . However , these numbers may be biased by what was sequenced in each category ( Table 1 ) . For example , the two cases with extensive LGT lack matched normal samples and were both RNA-Seq studies . Acute myeloid leukemia ( LAML ) was identified as the cancer type with the highest number of reads supporting integrations . This blood-derived cancer had 665 , 676 read pairs supporting putative integrations . Unfortunately , no normal samples were available in this data release for comparison . After identifying reads supporting bacterial integrations , we increased our stringency by requiring integrations to be supported by >4× unique coverage on the human genome . Implementing this criterion reduced the number of putative integrations to 90 , 726 paired end reads . When a genus level bacterial OTU could be identified , it was most frequently Acinetobacter with 31% of the reads ( Figure S7 ) . Moraxellaceae was the largest family level OTU ( 36% ) , which includes Acinetobacter . As with the 1000 Genomes data , a broader diversity of OTUs are observed in the microbiome reads than in the reads supporting LGT . The samples can be binned into one of five categories based on the microbiome ( Figure S7C ) . Intriguingly , one of those categories lacks bacterial integration ( Figure S7D , blue ) and another has an extensive diversity of bacterial integrations across many OTUs ( Figure S7D , dark green ) . Of the 90 , 726 reads supporting bacterial integrations into the human genome , 57 , 826 of those reads can map to the Acinetobacter baumannii genome ( NC_010611 ) ( Figure 6 ) . Within the Acinetobacter baumannii genome , reads were frequently detected in the rRNA operon ( 57 , 487 reads in 5 , 279 bp ) ( Figure 6 ) . Across the entire dataset , integration of rRNA was observed most frequently . For example , 68% of the reads attributed to the microbiome in LAML samples were from bacterial rRNA and 32% were from bacterial coding sequences ( CDSs ) ( Table 2 ) . Yet , 99% of the bacterial reads in LAML read pairs supporting bacterial integration were from rRNA and only 1% were from CDSs ( Table 2 ) . In LAML , not only was there a preference for what bacterial DNA was integrated but also for the location of integration . Reads supporting bacterial DNA integration were detected more frequently in the human mitochondrial genome ( Figure 6A , 41 , 852 reads in 16 . 6 kbp ) than in the human nuclear genome ( 48 , 874 reads in 2 . 86 Gbp; p<2×10−16 , Chi-squared test ) . The reads supporting integration were uniformly distributed across the entire mitochondrial genome with 10 , 085 unique read start sites ( p = 0 . 27 , thus rejecting the hypothesis that they are not random , Kolmogorov-Smirnov test , Figure 6BC ) . This is important because one might anticipate that bacterial rRNA preferentially integrates into the mitochondrial rRNA , but this was not observed . This also cannot be attributed to similarity between the Acinetobacter rRNA and the mitochondrial rRNA . There was no similarity detected between Acinetobacter rRNA and the human mitochondrial rRNA , or any other human sequence , as assessed by a BLASTN search of human genomic and transcriptomic sequences in NT with the Acinetobacter rRNA ( data not shown ) . There also was no correlation observed between the amount of mitochondrial sequence in the sample and the number of integrants detected ( Spearman rank coefficient p = 0 . 0681 ) . The stomach adenocarcinoma ( STAD ) cancer type had the second highest number of reads supporting bacterial integrations at 11 , 013 . In the analysis of HBV integrations in human liver tumors , a criterion of a cluster of two read pairs was successfully applied to identify viral integrations in whole genome sequencing data with a validation success rate of 82% [6] . When a similar threshold requiring >1× coverage across the read ( meaning at least two unique read pairs support the integration ) , the read count was still highest in LAML , followed by STAD , breast invasive cancer , and ovarian serous cancer . If the stringency is further increased , STAD samples contained 223 paired end reads with >4× coverage along the corresponding portion of the human genome . This high level of coverage lends great support for bacterial DNA integration . Unfortunately , STAD does not have normal matched samples for comparison in this data release . The most common OTU ( 32% ) for the bacterial integrations in STAD arose from the Pseudomonas spp . and related taxonomic units ( Figure 7B ) . Approximately 6% of all reads supporting integration were more specifically assigned to the bacterial OTU Pseudomonas fluorescens . Of the 223 reads identified as bacterial integrations with >4× coverage on the human genome , 188 could be mapped to P . fluorescens ( NC_012660 ) . Of those , 184 mapped ( 98% ) to the P . fluorescens rRNA operon ( Figure S8 ) and only 4 integrations mapped to protein coding regions . P . aeruginosa has previously been shown to have a promoting effect on gastric tumorigenesis in rats receiving an alkylating agent [51] . Putative integration of DNA most likely of Pseudomonas origin has also been observed in the CBMI-Ral-Sto cell line in a study of NotI sites [52]; those Pseudomonas-like sequences have similarity to the ones we describe here ( Figure S3J ) . While Helicobacter pylori has been associated with the development of stomach cancer [53] only 2 Helicobacteraceae reads were identified supporting bacterial DNA integration , across all of the samples , and only 221 reads pairs with a Helicobacteraceae OTU were identified from the microbiome despite the presence of 15 Helicobacter pylori genomes in our reference dataset . A clustering analysis of the microbiome reads separates the STAD tumor microbiome profiles into two clusters ( Figure 7C ) . The tumors without integrations have a profile that is predominantly Enterobacteriaceae . However , the tumor samples with integrations have a distinct cluster of their own ( Figure 7D ) in which Pseudomonadaceae is the dominant OTU with a low proportion of Enterobacteriaceae . Although only one source site contributed to the sequencing of tumors with integrations , that same center also contributed samples of the other cluster . Furthermore , not all samples with a microbiome that is predominantly composed of members of the Pseudomonadaceae family had evidence of bacterial integration . Most of these paired end reads supporting bacterial integration in the human genome were in five nuclear human genes ( Figure 8A ) : TMSB10 , IGKV4-1 , CEACAM5 , CEACAM6 , and CD74 . Four of these five integrations into STAD are in genes known to be up-regulated in gastric cancer , specifically CEACAM5 , CEACAM6 , TMSB , and CD74 [54]–[57] . An expression analysis reveals that genes with bacterial integration were all up-regulated relative to the average transcript level ( Figure 9 ) . Integration in genes up-regulated in stomach cancer ( as opposed to those where transcription is down-regulated or abolished ) is parsimonious with detecting integrations in tumor RNA , as we are unlikely to identify integrations that abolish transcription or transcript stability .
Through this extensive analysis of several large human genome sequencing projects , we present evidence supporting LGT from bacteria to the human somatic genome . In terminally differentiated cells , we expect and observe that putative LGTs are detected consistently at low levels . Examination of clonally expanding tumors reveals many more transfers , as we would expect from a rapidly expanding population of cells . In all of the cases examined , the composition of the microbiome across the samples is different from the composition of bacterial DNA integrated into the human genome . When only the regions on the human genome with >4× coverage are examined , a pattern emerges of integration in the mitochondria for LAML and five genes in STAD . Remarkably , in STAD , four of those five genes have previously been shown to be implicated in cancer [54]–[57] . Together we believe this presents a compelling case that LGT occurs in the human somatic genome and that it could have an important role in human diseases associated with mutation . While it is possible that these LGT mutations may play a role in carcinogenesis , it is also necessary to consider that they could simply be passenger mutations . The rapidly proliferating tumor cells may be more permissive to LGT from bacteria due to mutations in tumor suppressor genes or down regulation of DNA repair pathways . As a result of clonal expansion , rare mutations may be amplified throughout the tumor . Based on our analysis , it is impossible to determine if the LGTs have a causal role in cancer , or are simply a byproduct of carcinogenesis . Likewise , while it is possible that the bacteria are causing mutations that benefit the bacteria , it is equally plausible that this occurs by random chance , or some combination of the two . If the mutations occur by random chance , mutations that induce carcinogenesis will be selected for over time within a local population of cells . This may explain why we observe low levels of LGT across the entire genome with increased coverage in specific genes in the STAD and LAML samples . In contrast , mutations that would benefit the bacteria would include those that create a micro-environment that promotes bacterial growth . This may explain why similar mutations , both in location and bacterial integrant , are observed in multiple individuals ( Figure 8 ) . While the extensive coverage across these putative integrations in multiple samples is strong support for bacterial integration being present in human tumors , we recognize the concern that such bacterial/human read pairs may arise merely from chimeric DNA generated during library construction . We pursued obtaining specimens for validation or establishing collaborations to accomplish this validation with TCGA investigators . Unfortunately , the combination of patient consent and access policy precludes the possibility of experimental validation on these samples by researchers that lack an IRB tied to a grant award from NCI/TCGA . As our funding is from the NIH New Innovator Program this was not possible . Collaborating with current TCGA investigators was also pursued but was found to be explicitly forbidden . However , we anticipate the future successful validation of these results by researchers with access to samples and the proper authorization . However , further analyses suggest that these are not laboratory artifacts . If chimeras arise in library construction , they should increase as the prevalence of bacterial DNA/RNA increases . Therefore , we evaluated the possibility of a correlation between the number of read pairs arising from the Pseudomonas-like DNA and putative LGT read pairs for these six STAD samples . The Spearman-rank correlation between these values was not significantly different from zero ( P = 0 . 19 ) , indicating no correlation between the abundance of reads from the bacteria genome and reads supporting integration of Pseudomonas-like DNA in the human genome for these six samples . Overall , no relationship was observed between bacterial integrations and ( a ) the microbiome composition , ( b ) human transcript abundance , or ( c ) mitochondrial transcript abundance . Yet , to further examine laboratory artifact chimerism in these samples , the distribution of the insert sizes for paired reads was compared between representative LAML samples , STAD samples , and Neisseria meningitidis whole genome sequencing project samples ( Figure S9 ) . The mappings with N . meningitidis were used to establish that ∼0 . 22–0 . 29% of reads were outside this distribution when the reads were mapped to the assembled genome for that exact strain . For comparison , 0 . 94–1 . 12% of reads were outside this distribution when reads mapped to a divergent genome from the same species ( Table S5 ) . The same percentage values for paired reads only mapping to bacteria in the STAD samples ranged from 0 . 06–0 . 60% while those for LAML ranged from 0 . 65–0 . 87% ( Table S5 ) . Given that the database we searched against was limited to only those with complete genomes , it is highly unlikely that the bacterial DNA sequenced through the TCGA is from the same strain that has a genome deposited in RefSeq . Therefore , we anticipate values should lie between 0 . 22–1 . 12% as is seen in the N . meningitidis controls . We observed that bacterial read pairs in the TCGA data fell outside the distribution less frequently ( 0 . 06%–0 . 60% ) . While this percentage is used as a proxy for laboratory artifactual chimerism , bacterial genomes are known to be fluid with genome rearrangements happening in single growths that would result in the same outcome . When these results are taken together , there is no indication that there is a higher level of chimerism in the bacterial DNA of TCGA samples than is normally observed . Furthermore , this level of chimerism would not explain 4× or 150× coverage across the bacterial integrations that are discussed here considering that PCR duplicates were removed . Of note , high coverage chimerism in bacterial samples would lead to an inability to properly assemble the corresponding genomes , which is not observed in microbial genome sequencing projects . We note , however , that laboratory artifact chimeras could be detected in TCGA samples with whole genome amplification . As such these samples were eliminated from further analysis beyond what is presented in Table 1 . In ovarian cancer , numerous read pairs that would normally support integrations were detected in both tumor and normal samples ( Table 1 ) . Upon further examination , all of the putative integrations involved E . coli DNA and are likely chimeras formed during the whole genome amplification used for these samples and that formed between human genomic DNA and small pieces of E . coli DNA introduced with recombinant enzymes . Further support that the putative integrations in LAML and STAD samples are not laboratory artifacts comes from the fact that reads supporting integrations were detected 672× and 13 . 2× more frequently , respectively , in these cancer samples than in representative non-cancer samples ( Table 1 ) . This is expected if such mutations were part of the clonally expanding tumor . Across all samples , there are 1 , 033 reads supporting integrations in the normal samples with 13 , 392 , 142 , 331 read pairs sequenced , yielding an estimated integration frequency of 7 . 7×10−8 . In contrast , 690 , 528 read pairs support integrations in tumor samples out of 42 , 533 , 195 , 146 paired reads sequenced , yielding an integration frequency of 1 . 6×10−5 , or 210× higher . Even when compared to the highest integration rate assessed in normal samples , which was 6 . 02×10−6 in the Trace Archive data , the aggregate rate across all cancer samples is still >2 . 5-fold higher . While the integration rate in cancers is 210× higher than that in normal samples across the TCGA , this comparison is not directly between matched tumor and normal pairs since normal samples were only present for OV , GBM , and BRCA . However , many different types of normal samples can be used in cancer studies and therefore other comparisons besides matched pairs are quite valid . In fact , no one type of normal sample may be perfect for all experiments . For example , a small piece of adjacent breast tissue determined to be non-cancerous by a pathologist would be considered the normal specimen for breast cancer [58] . These samples are often taken from the margins of tumors when they are resected during surgery . In that case , it's possible they could have cancer characteristics not evident by histology [59] , [60] . In OV , blood-derived samples were collected as normal samples from some patients , while others had normal tissue collected . In GBM , only blood-derived samples were collected as normal samples . In other cancer studies , skin tissue from patients prior to treatment may be used [61] . Some blood cancers lack a normal sample because the cancer originates in the bone marrow . Therefore , all of the patient's blood contains cancerous cells [62] . In this instance either blood from healthy individuals [63] or blood taken from the patient once in complete remission [64] may be used as a normal sample . Unfortunately , the STAD and LAML samples of greatest interest here for driving the dramatically increased integration rate in the tumor samples also lack normal matched samples in this data release . Given the lack of normal matched samples , and that blood samples from healthy individuals are frequently used as normal samples for studying types of leukemia [63] , it is informative to compare LAML to normal samples from OV , GBM , and BRCA or 1000 Genomes data . Of note , the normal samples for OV , GBM , and BRCA have integration rates of 1 . 1×10−7 , 2 . 3×10−8 , and 1 . 2×10−7 ( Table 1 ) respectively , while the integration rate of samples from the 1000 Genomes project was 2 . 3×10−6 . Of these , the BRCA mutation rate is most relevant to STAD and LAML as all three are RNA-based sequencing . Comparing these , LAML samples have an integration rate 672× higher than the integration rate for the BRCA normal samples ( Table 1 ) . Even if the LAML cancer samples are compared to the normal samples with the highest integration rate , those in the Trace Archive , the integration rate for LAML is still almost 14× higher . While the overall integration frequency of cancer samples is 1 . 6×10−5 , or 210× higher than the normal integration rate of 7 . 7×10−8 ( Table 1 ) , the LAML integration rate is the main driver of the increased frequency . Most tumor types do not have an increased integration rate relative to normal samples ( Table 1 ) . Another main contributor to the significant increase of integrations in cancer samples is STAD , which has an integration rate of 1 . 6×10−6 and is 13 . 2× higher than the integration rate for the BRCA normal samples ( Table 1 ) . Considering STAD is in close proximity to the microbiome , normal stomach tissue would better reflect this exposure to the microbiome , including an increased likelihood of bacterial integration . That means it would be particularly informative if available . Unfortunately , this release of the TCGA lacks STAD normal samples or any other normal samples with constant exposure to the microbiome . This prevents us from determining the rate of integration in non-cancer cells with an abundant microbiome . Further work is needed to resolve differences in the integration rate between normal samples that have constant contact with the microbiome and those that do not . The majority of the bacterial integrations detected were between an Acinetobacter-like organism and the mitochondrial genome . Acinetobacter spp . are known to invade epithelial cells and induce caspase-dependent and caspase-independent apoptosis [65] . Uptake of apoptotic bodies and caspase-dependant DNA fragmentation is known to facilitate LGT between mammalian cells [66] , including LGT of oncogenes [67] . While we present this as bacterial integration into the mitochondrial genome , it is possible that mitochondrial DNA is integrating into an Acinetobacter-like genome . However , despite the numerous complete Acinetobacter genomes sequenced , mitochondrial DNA has not been detected in the genome of an Acinetobacter isolate . Human mitochondria have an essential role in many key cellular processes such as the generation of cellular energy , production of reactive oxygen species , and initiation of apoptosis . The accumulation of somatic mutations in the mitochondrial genome has been implicated in carcinogenesis [68] . For instance , mutations in the mitochondrial cytochrome oxidase subunit I ( COI ) gene contribute to the tumorigenicity of prostate cancer through an increased production of reactive oxygen species [69] . The LGT from bacteria , such as Acinetobacter , to the mitochondria may be generating novel mutations in the mitochondrial genome and therefore influencing tumor progression . The integrations we identified in STAD frequently appear to be in , or near , the untranslated region ( UTR ) of known proto-oncogenes . In this case , these proto-oncogenes are genes known to be up-regulated in cancers . Despite occurring in or near UTRs , this does not reflect similarity in these sequences . The mappings are specific as observed by both the BWA matches and BLAST searches against NT . While CEACAM5 and CEACAM6 are paralogs , they are sufficiently diverged to be resolved . We postulate that these putative integrations have mutated a repressor binding site and have induced over-expression leading to carcinogenesis . While this is a tempting speculation , it needs to be experimentally verified . In chromosome 2 , one STAD sample had an integration site in the second exon of thymosin β10 ( TMSB10; Figure 8A ) while another integration site was found in IGKV4-1 . The TMSB10 gene has been shown by SAGE to be up-regulated in gastric tumors and confirmed with Northern blots [54] . On chromosome 19 , integrations were identified in CEACAM5 and CEACAM6 of STAD tumors ( Figure 8BC ) . The same integration site in CEACAM5 was detected in two separate samples while a third sample had a similar integration in CEACAM6 . CEACAM proteins were initially identified as prominent tumor-associated antigens in human colon cancer [55] . Approximately 50% of human tumors show over-expression of CEA family proteins [56] . CEACAM5 and CEACAM6 mediate cell adhesion by binding to themselves and other CEACAM family members [55] . Over-expression disturbs ordered tissue formation in 3D tissue culture and leads to increased tumor formation in mice [55] . On chromosome 5 , integration sites were identified in STAD tumors in different portions of CD74 with three samples having an integration in the 5′-end of the gene and one of those samples having a second integration in the 3′-UTR of CD74 ( Figure 8D ) . CD74 initiates antigen presentation as well as signaling cascades that result in cell survival . Therefore it is not surprising that while its regulation is tightly controlled in normal tissues , it has increased expression in many cancers including gastrointestinal carcinomas and precancerous pancreatic lesions [57] . Importantly , and significantly , we only identified integrations meeting our criteria in these 4 tumor-associated genes and one other immune-related gene . We did not first look at all known oncogenes and try to find bacterial integration with these criteria , nor did we look at oncogenes and try to explain why they are up-regulated . These four oncogenes merely emerged as those having such integrations . While there is an association between bacterial DNA integration and up-regulation of these genes , it is important to note that LGT is not associated with the most abundant bacterial transcripts . Such a result would be expected if the read pairs were merely laboratory-based artifactual chimeras generated during library construction . While these human transcripts are up-regulated in the tumors when compared to other tumors , in at least two cases they are not the most abundant transcripts . In fact , in the 143 STAD samples , if we examine the most abundant transcript , it is most frequently annexin A2 ( Table 3 ) , which was not identified as having a bacterial integration . Using our search criteria , we find no evidence of human-bacteria chimeras in any of the most abundant transcripts ( Table 3 ) that would suggest such sequences arise from laboratory artifacts . If we , instead , focus only on the abundance of the four up-regulated genes above and on the ten samples where we identified bacterial integration in these genes , we see no clear pattern that would correlate LGT with transcript abundance . In CEACAM5 , which has the most bacterial integrations , and CEACAM6 , they are >75% less abundant than the most abundant transcript in that sample ( Table S6 ) . In addition , there are between 35 and 95 transcripts that are more abundant depending on the sample examined ( Table 4 ) . Furthermore , multiple samples have bacterial integrations in CEACAM6 , but not the more abundant thymosin β10 . In fact , thymosin β10 is more abundant in all of these samples , yet we detect integrations in thymosin β10 only in one of the samples ( Table 4 ) . If these genes were somehow primed to participate more in forming chimeras ( e . g . through sequence similarity between the bacteria and human genes or by having an altered 5′-cap ) , one would expect that putative integrations would be frequently associated with thymosin β10 , and this is not observed . Identification of both sides of an integration is powerful evidence that these are not merely laboratory artifacts . In the Trace Archive data , such clones would contain a read with non-overlapping similarity to human and then bacterial sequences and the other read would have similarity to a human read . These clones would contain a bacterial sequence flanked on both ends with human sequence in a single clone . Additionally , they should not be detected as frequently as clones with one bacterial read and one human read . Consistent with this we find 2 clones , out of 319 clones , with these characteristics . In the 1000 Genomes projects where the coverage of all reads sequenced across the human genome was often less than 1× coverage , we were unable to accurately find both sides of the integration . In LAML , the integrations were primarily found randomly distributed around the human mitochondrial genome . While many putative pairs of paired reads can be identified that may constitute both sides of the integration , the large number of putative transfers and the presence of multiple mitochondrial genomes precludes this assignment of pairs flanking integrations with any confidence . It is unlikely that both sides of the integrations would be found in the STAD RNA sequencing project because the integrations were found to be in the 5′-UTR and the remaining piece would be quite small relative to the library insert size and be declining in abundance due to the nature of sequence data at the ends of transcripts . If we examine the bacterial portion of the transcript , it is frequently rRNA . There are at least two possible explanations for this observation , including that ( a ) rRNA is easier to detect in samples because regions in the rRNA gene are conserved across all bacteria , and ( b ) bacterial rRNA actually integrates more frequently . The former can be excluded as a possibility since in LAML 68% of the microbiome reads were from rRNA yet 99% of the LGT reads were from rRNA . This suggests that bacterial rRNA actually integrates more frequently than other RNA . Integration of bacterial rRNA is consistent with our understanding of the nucleotides recognized by the human innate immune system . Unmethylated CpG DNA [70] and mRNA [71] from bacteria are both recognized by the innate immune system , but at least some rRNA is not [71] , [72] . Some rRNA is detected by the immune system [72] , possibly explaining why not all bacterial rRNA mutagenizes the human genome . As such , immune response may prevent integration through DNA or mRNA intermediates , but be permissive to the integration of some rRNA . There is also a precedent for integration of rRNA into animal genomes that suggests the mechanism of bacterial integration . SINE elements are derived from tRNA [73] , 7SL rRNA [74] , and 5S rRNA [75] and are integrated via retrotransposition using endogenous retrotransposon machinery . It seems plausible that bacterial rRNA and tRNA may also be integrated using the same machinery . However , the mechanism by which DNA/RNA enters the human cell is not as readily apparent . There continue to be several barriers to the description of LGT using only genome sequencing data . The prevailing paradigm is to assume laboratory artifacts when other experimental evidence is lacking . Maintaining this status quo ensures that LGT in eukaryotes will continue to be overlooked and under-estimated . There is a notion that this is necessary in order to avoid LGT from being described inappropriately . This notion , as well as high profile erroneous reports of LGT in humans and other animals ( e . g . [7] , [8] , [76]–[78] ) , has had a chilling effect on the field . Ironically though , experimental validation of LGT is usually in the form of PCR amplification ( e . g . [15] , [17] , [79] , which is also the potential source of such artifacts in current sequencing protocols . While PCR amplification is an independent validation of capillary sequencing , it is not an independent validation of next generation sequencing data . One way chimeras are introduced in Illumina sequencing data is during sequencing library preparation through cDNA synthesis for RNA samples and PCR amplification for both RNA and DNA samples [47] . Yet validation of LGT would occur through cDNA synthesis and/or PCR amplification . Except for Northern blots , most experimental RNA work proceeds through a cDNA synthesis step making that step difficult to avoid . Regardless , experiments that include cDNA synthesis or PCR amplification should not be considered independent validations of next generation sequencing data . Arguably , such experimental validation is not necessary with newer and more sophisticated methods like those used here . One of the most prominent reasons for needing experimental validation of genome sequencing has been due to errors made by assembly algorithms . Such errors result in the erroneous joining of two pieces of a genome into one piece with little sequence support ( e . g . a single read spanning a small segment with limited similarity ) . These errors could be assessed by examining the assemblies themselves for coverage and read quality that would suggest missassembly in a region . However , few researchers had access to that assembly data and it was often limited to the generators of the assemblies . While such files could be deposited in NCBI's Assembly Archive [80] , this was infrequently done . For example , as of April 01 , 2013 , there were only 193 bacteria and 31 eukaryotes with assemblies in the Assembly Archive while there were 18 , 756 bacteria and 3 , 017 eukaryote with genome projects registered at NCBI . Researchers without access to such assembly data have needed to experimentally validate the sequence/structure of specific contigs in genome sequences . In our experience , if the underlying assembly was examined for well supported junctions , one was 100% successful in subsequent experimental validation of such bacterial integrations into animal genomes using just the assembly data . Therefore , we designed our current analysis in a manner that does not rely on assembly . It relies instead on sequence read mapping with an emphasis on coverage , indicating higher support across junctions of bacterial and human DNA . Populations of human cells have a constant , intimate relationship with the human microbiome . With that comes a potential for LGT that could be analogous to disease-causing DNA insertions by transposons , retroviruses , or mitochondria . Although chronic inflammation is increasingly implicated as a mechanism for cancer development following bacterial infection , proto-oncogene disruption by bacterial DNA could provide yet another mechanism . A well-established model for bacterial disease induced through somatic cell LGT was described many years ago , namely A . tumefaciens induced crown gall disease in plants . As nature often repeats itself , the results presented here indicate a similar situation may be applicable to humans and warrant targeted research projects aimed at identifying LGT from the microbiome to human somatic cells . Taken together , putative integrations of bacterial DNA in human tissues , including tumors , can be detected with next-generation sequencing . Such integrations were detected 210× more frequently in tumor samples than normal samples . Putative integration sites in known cancer-related genes were identified with >4× coverage on the human genome . With the currently available datasets , such integrations are most frequently detected between bacterial rRNA and cancer samples from acute myeloid leukemia and stomach adenocarcinoma . While it is tempting to speculate that integration of bacterial DNA may cause cancer , particularly given the detection of integrations in oncogenes that are over-expressed in these samples and the detection of the same integrations in multiple individuals , further carefully controlled experiments are needed , but now justified .
The 113 , 046 , 604 human shotgun sequencing traces in the NCBI Trace Archive as of March 11 , 2009 , were compared to all the bacterial genomes available on the NCBI genomes ftp site on November 11 , 2010 . Initial matches between these two datasets were identified with NUCMER using the MAXMATCH parameter [37] . A data subset was then created of the human traces with positive matches and all other reads from that clone using the XML available from NCBI parsed with custom scripts . This data subset was searched against NT using BLASTN [81] . The output of these BLAST searches was parsed to identify bacterial DNA linking to human DNA either directly or within a clone . The corresponding chromatograms hosted at NCBI and the wwwblast results against NT for 2 , 871 sequence pairs were inspected manually to remove poor quality sequences , vector contaminants , and low complexity sequence matches resulting in a curated set of putative integrations ( Table S1 , S2 ) . Importantly , the traces found to contain an integration boundary within the trace may also contain an integration boundary measured within the clone . In this way , the two counts are not exclusive of one another . Illumina sequences were downloaded from the 1000 Genomes Project that were in the NCBI Short Read Archive as of November 2010 and from the TCGA in the NCBI dbGap between September 18 , 2011 , and September 20 , 2011 . All reads were mapped to both the human genome ( hg19 ) and all the bacterial genomes available on the NCBI genomes ftp site on November 11 , 2010 using the short read mapper BWA [40] with the default parameters . Using custom scripts , pairs of reads were identified as spanning integrations when only one read mapped to the human genome and its mate mapped to a bacterial genome . Unless otherwise noted , paired reads spanning junctions that were identified in the initial BWA screen were screened for uniqueness , low complexity , and taxonomy . Low complexity sequence and duplicate reads were removed using PRINSEQ [82] . For low complexity filtering , the DUST method with an entropy cutoff of 7 was applied to each read in a pair separately . A pair is considered low-complexity if either read is considered low complexity . Duplicate reads were flagged by concatenating the two reads together in a pair and running the PRINSEQ derep function to find exact duplicates and the reverse complements of exact duplicates ( flag 14 ) . After low complexity and duplicate screening , both bacterial and human reads were searched against NCBI's NT database using BLASTN with an e-value cutoff of 10−5 . Reads identified as bacterial in the initial BWA screen were required to match bacteria in NT and not have a best match to human . The bacterial half of all putative LGT's was remapped against all complete bacterial genomes in RefSeq individually . These mappings were used to assign an OTU based on the LCA . The microbial composition was examined using Krona plots [83] as well as heat maps generated in the R software package . BWA computes were executed using the CloVR virtual machine [84] . The CloVR virtual machines were deployed in parallel on the Data Intensive Academic Grid ( DIAG ) cloud infrastructure . Data staging , output retrieval and cluster management was accomplished using CloVR's Vappio software package . Reads identified as putative bacterial reads in either the microbiome or lateral gene transfer were mapped using BWA with default parameters against all complete bacterial genomes in RefSeq . The LCA is calculated based on the congruent taxonomy for all genomes with mappings . The use of RefSeq limits the taxonomic assignments available to only those with complete genomes . However , the use of genomic sequences , as opposed to all deposited sequences in NT , ensures that the taxonomic assessment of the database sequence is correct . For reads assigned to the microbiome , once the LCA is calculated , the most specific taxonomic assignment is used as the bacterial OTU ( Figure S10 ) . Circular figures were generated with Circos [85] using putative LGT reads filtered using the method described above . Down sampling of the data to 5% for Figure S4A , 0 . 5% for Figure 6A and 2% for Figure 6BC was needed to successfully draw the purple linkages . For Figure 3 the data was not blast-verified in the same manner as the bacterial integration data as there are significant amounts of HPV integrant sequence data in the database with human listed as the source in the taxonomy . While this is correct , it stymied the blast validation . Therefore , reads were blast validated to confirm that they were HPV – human , but they could also be human – human in this screen with one of the human reads arising from HPV since many HPV reads exist in reference databases with the taxonomy assignment of Homo sapiens . Each read pair was assigned an average coverage value measured along the human mapping that was used to hone in on integrations with increased coverage . This value is obtained by running samtools mpileup [86] on the human read for each read pair indicating an integration . Coverage was calculated separately for each sequencing run . If a human read was assigned a value of >4× coverage , it had at least five unique reads aligning to that region on the human genome . Reads on the integration site cannot be mapped with BWA , but would be adjacent to reads supporting that integration . The RNA-Seq reads were mapped against hg19 with BWA and the reads per kilobase of gene per million human mapped reads ( RPKM ) was calculated as using the predicted transcriptional start and stop sites available from the UCSC annotation . The ratio was calculated by dividing the RPKM for a given gene in a given run by the average RPKM for that gene across all runs . The log2ratio was used for the expression analysis presented in Figure 9 . Nine randomly selected reads supporting bacterial integrations were searched against the NT database using BLASTN [81] with an expected threshold of 10−11 and with uncultured bacterial sequences removed using the BLAST interface . Each read and its first hit for each high scoring pair was aligned in a multiple sequence alignment using ClustalW [87] with default settings . This multiple sequence alignment was then used to draw a phylogenetic tree using PhyML [88] with default settings and 1000 bootstraps . The most likely tree and bootstrap support values from PhyML were visualized using FigTree ( http://tree . bio . ed . ac . uk/software/figtree/ ) . Statistical modeling and correlation analysis was performed using the R package ( v 2 . 7 . 2 ) .
|
There are 10× more bacterial cells in the human body than there are human cells that are part of the human microbiome . Many of those bacteria are in constant , intimate contact with human cells . We sought to establish if bacterial cells insert their own DNA into the human genome . Such random mutations could cause disease in the same manner that mutagens like UV rays from the sun or chemicals in cigarettes induce mutations . We detected the integration of bacterial DNA in the human genome more readily in tumors than normal samples . In particular , extensive amounts of DNA with similarity to Acinetobacter DNA were fused to human mitochondrial DNA in acute myeloid leukemia samples . We also identified specific integrations of DNA with similarity to Pseudomonas DNA near the untranslated regulatory regions of four proto-oncogenes . This supports our hypothesis that bacterial integrations occur in the human somatic genome that may potentially play a role in carcinogenesis . Further study in this area may provide new avenues for cancer prevention .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome",
"sequencing",
"sequence",
"analysis",
"genome",
"complexity",
"metagenomics",
"genome",
"evolution",
"host-pathogen",
"interaction",
"biology",
"genomics",
"evolutionary",
"biology",
"genomic",
"evolution",
"microbiology",
"computational",
"biology",
"bacterial",
"pathogens"
] |
2013
|
Bacteria-Human Somatic Cell Lateral Gene Transfer Is Enriched in Cancer Samples
|
Chromatin immunoprecipitation followed by high throughput sequencing ( ChIP-Seq ) has been successfully used for genome-wide profiling of transcription factor binding sites , histone modifications , and nucleosome occupancy in many model organisms and humans . Because the compact genomes of prokaryotes harbor many binding sites separated by only few base pairs , applications of ChIP-Seq in this domain have not reached their full potential . Applications in prokaryotic genomes are further hampered by the fact that well studied data analysis methods for ChIP-Seq do not result in a resolution required for deciphering the locations of nearby binding events . We generated single-end tag ( SET ) and paired-end tag ( PET ) ChIP-Seq data for factor in Escherichia coli ( E . coli ) . Direct comparison of these datasets revealed that although PET assay enables higher resolution identification of binding events , standard ChIP-Seq analysis methods are not equipped to utilize PET-specific features of the data . To address this problem , we developed dPeak as a high resolution binding site identification ( deconvolution ) algorithm . dPeak implements a probabilistic model that accurately describes ChIP-Seq data generation process for both the SET and PET assays . For SET data , dPeak outperforms or performs comparably to the state-of-the-art high-resolution ChIP-Seq peak deconvolution algorithms such as PICS , GPS , and GEM . When coupled with PET data , dPeak significantly outperforms SET-based analysis with any of the current state-of-the-art methods . Experimental validations of a subset of dPeak predictions from PET ChIP-Seq data indicate that dPeak can estimate locations of binding events with as high as to resolution . Applications of dPeak to ChIP-Seq data in E . coli under aerobic and anaerobic conditions reveal closely located promoters that are differentially occupied and further illustrate the importance of high resolution analysis of ChIP-Seq data .
Since its introduction , chromatin immunoprecipitation followed by high throughput sequencing ( ChIP-Seq ) has revolutionized the study of gene regulation . ChIP-Seq is currently the state-of-the-art method for studying protein-DNA interactions genome-wide and is widely used [1]–[5] . ChIP-Seq experiments capture millions of DNA fragments ( in length ) that the protein under study interacts with using random fragmentation of DNA and a protein-specific antibody . Then , high throughput sequencing of a small region ( ) at the end or both ends of each fragment generates millions of reads or tags . Sequencing one end and both ends are referred to as single-end tag ( SET ) and paired-end tag ( PET ) technologies , respectively ( Figure 1A ) . Standard preprocessing of these data involves mapping reads to a reference genome and retaining the uniquely mapping ones [6] , [7] . In PET data , start and end positions of each DNA fragment can be obtained by connecting positions of paired reads [8] . In contrast , the location of only the end of each DNA fragment is known in SET data . The usual practice for SET data is to either extend each read to its direction by the average library size which is a parameter set in the experimental procedure [7] or shift the end position of each read by an estimate of the library size [9] . Then , genomic regions with large numbers of clustered aligned reads are identified as binding sites using one or more of the many available statistical approaches [6] , [7] , [9]–[11] ( the first step in Figure 1C ) . Currently , the SET assay dominates all the ChIP-Seq experiments despite the fact that PET has several obvious , albeit less studied , advantages over SET . In PET data , paired reads from both ends of each DNA fragment can reduce the alignment ambiguity , increase precision in assigning the fragment locations , and improve mapping rates . This is especially advantageous for studying regulatory roles of repetitive regions of genomes [12] , [13] . Although many eukaryotic genomes are rich in repetitive elements , PET technology has not been extensively used with eukaryotic genomes [8] , [14] . One of the main reasons for this is that ChIP-Seq data is information rich even when the repetitive regions are not profiled [15] and that the PET assay costs times more than the SET assay . Put differently , given a fixed cost , PET sequencing results in a lower sequencing depth compared to SET sequencing . In contrast to eukaryotic genomes , prokaryotic genomes are highly mappable , e . g . , of the Escherichia coli ( E . coli ) genome is mappable with reads . This decreases the higher mapping rate appeal of the PET assay for these genomes . In this paper , we systematically investigate advantages of the PET assay from a new perspective and demonstrate both experimentally and computationally that it significantly improves the resolution of protein binding site identification . Improving resolution in identifying protein-DNA interaction sites is a critical issue in the study of prokaryotic genomes because prokaryotic transcription factors have closely spaced binding sites , some of which are only to apart from each other [16]–[19] . These closely spaced binding sites are considered to be multiple “switches” that differentially regulate gene expression under diverse growth conditions [17] . Therefore , identification and differentiation of closely spaced binding sites are invaluable for elucidating the transcriptional networks of prokaryotic genomes . Although many methods have been proposed to identify peaks from ChIP-Seq data ( reviewed in [20] ) , such as MACS [9] , CisGenome [6] , and MOSAiCS [10] , these approaches reveal protein binding sites only in low resolution , i . e . , at an interval of hundreds to thousands of base pairs . Furthermore , they report only one “mode” or “predicted binding location” per peak . More recently , deconvolution algorithms such as CSDeconv [21] , GPS [22] ( recently improved as GEM [23] ) , and PICS [11] have been proposed to identify binding sites in higher resolution . However , these methods are specific to SET ChIP-Seq data and are not equipped to utilize the main features of PET ChIP-Seq data . Although a relatively recent method named SIPeS [24] is specifically designed for PET data and is shown to perform better than MACS paired-end mode [9] , our extensive computational and experimental analysis indicated that this approach is not suited for identifying closely located binding events . To address these limitations , we developed dPeak , a high resolution binding site identification ( deconvolution ) algorithm that can utilize both PET and SET ChIP-Seq data . The dPeak algorithm implements a probabilistic model that accurately describes the ChIP-Seq data generation process and analytically quantifies the differences in resolution between the PET and SET ChIP-Seq assays . We demonstrate that dPeak outperforms or performs competitively with the available SET-specific methods such as PICS , GPS , and GEM . More importantly , dPeak coupled with PET ChIP-Seq data improves the resolution of binding site identification significantly compared to SET-based analysis with any of the available methods . Generation and analysis of factor PET and SET ChIP-Seq data from E . coli grown under aerobic and anaerobic conditions reveal the power of the dPeak algorithm in identifying closely located binding sites . Our study demonstrates the importance of high resolution binding site identification when studying the same factor under diverse biological conditions . We further support our findings by validating a small subset of our closely located binding site predictions with primer extension experiments .
The factor is responsible for transcription initiation at over 80% of the known promoters in E . coli [25] . combines with RNA polymerase to bind promoter sequences typically containing two consensus elements located at and upstream of the transcription start site [18]; thus a binding site spans about upstream from the transcription start site . Many E . coli genes contain multiple promoters , and much transcriptional regulation by oxygen as well as by other stimuli occurs by selection of one or a subset of the possible promoters in concert with binding of activators and repressors ( e . g . , ArcA and FNR for regulation by oxygen [17] , [19] ) . Understanding such regulation requires knowledge of precisely which promoters are used in a given condition . Therefore , the highest possible accuracy of ChIP-signal mapping will allow the best determination of promoter binding by -RNA polymerase holoenzyme . We generated both PET and SET ChIP-Seq data for factor from E . coli grown under aerobic ( ) and anaerobic ( ) conditions in glucose minimal media on the HiSeq2000 and Illumina GA IIx platforms . We used these experimental data for comparisons of PET and SET assays and evaluation of our high resolution binding site detection method dPeak throughout the paper . Figure 1B displays PET and SET ChIP-Seq coverage plots for the promoter region of the cydA gene under the aerobic condition . The height at each position indicates the number of DNA fragments overlapping that position . The cydA promoter contains five known binding sites separated by to [25] . As evidenced in Figure 1B , coverage plots for PET and SET appear almost indistinguishable visually . To further understand the appearance of peaks that multiple binding events in this region would result in , we simulated PET and SET data with parameters matching to those of this region . Figures S1A , B , C in Text S1 display SET and PET coverage plots of this region when it harbors one and three binding events . These plots support that when binding events are in close proximity with distances less than the average library size , they appear as uni-modal peaks regardless of the library preparation protocol ( Figure S1C in Text S1 ) . We next evaluated two peak callers , MACS [9] and MOSAiCS [10] , both of which are specifically developed for SET data , on our SET and PET experimental datasets ( Table S1 in Text S1 ) . Both methods identified broad regions and the median widths of MACS peaks were to times larger than those of the MOSAiCS peaks . Detailed comparison of the MACS and MOSAiCS peaks revealed that each MACS peak on average has to MOSAiCS peaks ( Table S2 in Text S1 ) . Next , we evaluated the number of annotated binding events from RegulonDB [25] ( http://regulondb . ccg . unam . mx/ ) in each of the MACS and MOSAiCS peaks and found that MACS peaks , on average , had to annotated binding events whereas MOSAiCS peaks had to . Overall , we did not observe any differences in the peak widths of the PET and SET assays with MOSAiCS whereas MACS peaks from PET data tended to be wider than those of the SET data . These findings indicate that the potential advantages of the PET assay for elucidating closely located binding sites are not simply revealed from visual inspection and by analysis with methods developed specifically for SET data . Hence , deciphering the advantages of PET over SET for high resolution binding site identification warrants a statistical assessment . Next , we developed a generative probabilistic model and an accompanying algorithm , dPeak , that can specifically utilize local read distributions from SET and PET assays . This algorithm enabled unbiased evaluation of the SET and PET assays using our E . coli SET and PET ChIP-Seq data . dPeak requires data in the form of genomic coordinates of paired reads ( for PET ) or genomic coordinates of reads and their strands ( for SET ) obtained from mapping to a reference genome . For computational efficiency , dPeak first identifies candidate regions ( i . e . , peaks ) that contain at least one binding event and considers each candidate region separately for the prediction of number and locations of binding events ( the first step of Figure 1C ) . Either two-sample ( using both ChIP and control input samples ) or one-sample ( only using ChIP sample when a control sample is lacking ) analysis can be used to identify candidate regions . For this purpose , we utilize the MOSAiCS algorithm [10] which produced narrower peaks than the MACS algorithm [9] in our ChIP-Seq datasets ( Table S1 in Text S1 ) . In each candidate region , we model read positions as originating from a mixture of multiple binding events and a background component ( the third step of Figure 1C ) . dPeak infers the number of binding events and the read sets corresponding to each binding event within each region . It iterates the following two steps for each candidate region . First , it assigns each read to a binding event or background , based on the positions and strengths of the binding events . Then , the position and strength of each binding event are updated using its assigned reads . In practice , the number of binding events in each candidate region is unknown a priori . Hence , we consider models with different numbers of binding events and choose the optimal number using Bayesian information criterion ( BIC ) [26] . We constructed generative probabilistic models for binding event components and a background component for each of the PET and SET data by careful exploratory analyses of multiple experimental ChIP-Seq datasets . Diagnostic plots of the fitted models ( Figure S3 in Text S1 ) indicate that the dPeak model fits ChIP-Seq data well . dPeak has two unique features compared to other peak deconvolution algorithms ( Table S3 in Text S1 ) . First , it accommodates both SET and PET data and explicitly utilizes specific features of both types . Second , it incorporates a background component that accommodates reads due to non-specific binding . Consideration of non-specific binding is critical because the degree of non-specific binding becomes more significant as the sequencing depths get larger . An additional unique feature of dPeak is the treatment of unknown library size for SET data . As discussed earlier , to account for unknown library size , each read is either extended to or shifted by an estimate of the library size in most peak calling algorithms [20] . This estimate is often specified by users [7] , [10] or estimated from ChIP-Seq data [9] , [11] . Currently available algorithms with the exception of PICS use only one extension/shift estimate for all the regions in the genome . However , our exploratory analysis of real ChIP-Seq data and the empirical distribution of the library size from PET data ( Figure S2A in Text S1 ) indicate that using single extension/shift length might be suboptimal for peak calling ( data not shown ) . In order to address this issue , dPeak estimates optimal extension/shift length for each candidate region . Comparison of empirical distribution of the library size from PET data with the estimates of the region-specific extension/shift lengths indicates that dPeak estimation procedure handles the heterogeneity of the peak-specific library sizes well ( Figures S2B , C , D in Text S1 ) . This advancement ensures that dPeak is well tuned for deconvolving SET peaks , which then enables an unbiased computational comparison between the SET and PET assays . We compared dPeak with two competing algorithms , GPS [22] and PICS [11] , for analysis of SET ChIP-Seq data . We did not include the CSDeconv algorithm [21] in this comparison because it is computationally several orders of magnitude slower than the algorithms considered here . We utilized the synthetic ChIP-Seq data which was previously used to evaluate deconvolution algorithms [22] . In this synthetic data , binding events were generated by spiking in reads from predicted CTCF binding events at predefined intervals [22] without explicitly implanting binding sequence motifs . Therefore , we also excluded GEM [23] , which capitalizes on motif discovery to infer positions of binding events , from this comparison and used additional computational experiments below to perform comparisons with GEM . The synthetic data from [22] consisted of 1 , 000 joint ( i . e . , close proximity ) binding events , each with two events , and 20 , 000 single binding events . We assessed performances of algorithms on these two sets separately . Figure 2A shows the sensitivity of each algorithm at different distances between the joint binding events . Here , sensitivity is the proportion of regions for which both of the two true binding events are correctly identified . dPeak outperforms other methods across all considered distances between the joint binding events and especially for closely located binding events separated by less than the average library size of . When the distance between the joint binding events is about , dPeak is able to identify both binding events in of the regions whereas neither PICS nor GPS can detect both binding events in more than . Further investigation indicates that PICS merges closely spaced binding events into one event too often ( Figure S4 in Text S1 ) . We also found that GPS estimates the peak shape incorrectly when ChIP-Seq data harbors many closely located binding events ( Figure S5 in Text S1 ) . Furthermore , the sensitivity of GPS also decreases significantly when the distance between joint binding events increases . A closer look at the results reveals that GPS filters out too many predictions for joint binding events . To ensure that increased sensitivity of dPeak is not a result of increased number of false predictions , we evaluated positive predictive value ( fraction of predictions that are correct ) of each method . Specifically , we plotted the number of binding events predicted by each algorithm at different distances between the joint binding events in Figure 2B . Since there are two true binding events in each region , two predictions at every distance correspond to perfect positive predictive value . dPeak on average generates more than one prediction and does not over-estimate the number of binding events when the distance between joint events is less than the average library size . This result confirms that the higher sensitivity of dPeak in Figure 2A is not due to increased number of predictions . In contrast , PICS and GPS on average generate only one prediction for closely located binding events , which recapitulates the conclusions from Figure 2A . In summary , dPeak outperforms state-of-the-art deconvolution methods across different distances between joint binding events , especially when the distance between the binding events is less than the average library size . Next , we evaluated the sensitivity and positive predictive value of the three methods on 20 , 000 candidate regions with a single binding event using the additional synthetic data from [22] ( Table S4 in Text S1 ) . Average number of predictions per region with at least one predicted binding event and the corresponding standard errors are as follows: dPeak ( ) , PICS ( ) , GPS ( ) . Overall , dPeak slightly over-estimates the number of binding events for regions with a single binding event , and hence PICS is slightly better than dPeak in positive predictive value for these regions . However , as revealed by our joint event analysis , this conservative approach of PICS severely under-estimates the number of binding events when multiple events reside closely . In contrast , GPS significantly under-estimates the number of binding events for the regions with a single binding event since it filters out too many predictions and does not result in a prediction for of the regions . In addition , it over-estimates the number of binding events across regions for which it produces at least one prediction . Comparisons in these two scenarios with and without joint binding events indicate that dPeak strikes a good balance between sensitivity and positive predictive value for both cases . Once we developed dPeak as a high resolution peak detection method for both SET and PET data , we implemented simulation studies to evaluate the PET and SET assays for resolving closely spaced binding events in an unbiased manner . Although SIPeS [24] supports PET ChIP-Seq data , we excluded it from the comparison of PET and SET ChIP-Seq datasets due to its poor performance ( Section 16 of Text S1 ) . We generated simulated PET and SET ChIP-Seq data with two closely spaced binding events and evaluated the predictions of these two data types with dPeak ( Section 11 of Text S1; Figure S7 in Text S1 ) . Figure 2C plots the sensitivity of dPeak as a function of distance between the joint binding events and number of reads for both the PET and SET settings . Note that we evaluated sensitivity up to the distance of because we used windows to determine whether a binding event is correctly identified and as a result , results for the distance less than could be misleading . When the distance between the events is at least as large as the average library size ( ) , the sensitivity using PET and SET data are comparable . However , as the distance between joint binding events decreases , the sensitivity using SET data decreases significantly . In contrast , PET ChIP-Seq retains its high sensitivity even for binding events that are located as close as . As the number of reads decreases , sensitivity for both PET and SET data decreases . When there are only DNA fragments ( i . e . , reads ) per binding event , sensitivity for PET data also decreases as the distance between joint binding events decreases . However , even in this case , sensitivity of PET data is still significantly higher than that of SET data with much higher number of reads . Figure 2D displays the number of binding events predicted by dPeak at different distances between joint binding events when reads correspond to each binding event for both PET and SET data and evaluates positive predictive value . Results are similar for higher number of reads ( data not shown ) . With PET ChIP-Seq , dPeak accurately chooses the number of binding events by BIC out of a maximum of five binding events at any distance between the joint binding events . In contrast , SET ChIP-Seq predicts less than two binding events when the distance between the events is less than . We present additional simulation results in Section 10 of ( Figure S6 in Text S1 ) . These simulations reveal that even for cases with single binding events , PET has a slight advantage over SET because it predicts the location of the binding event more accurately . Specifically , PET data always provides higher resolution compared to SET data regardless of the strength of the binding event , which we measure by the number of DNA fragments associated with the event . For example , for a binding event with DNA fragments , the average distance between the predicted and true binding events is with a standard deviation of in the PET data whereas it is with a standard deviation of in the SET data . Note that although this simulation procedure is based on the assumptions of dPeak model for PET data , our exploratory analysis and goodness of fit ( Figure S3A in Text S1 ) show that these assumptions hold well in the real PET ChIP-Seq data and therefore , these results have significant practical implications for real ChIP-Seq data . Lower sensitivity of the SET compared to PET data is mainly driven by the loss of information due to unknown library size . We describe this information loss by two concepts named invasion and truncation ( Figure 3A ) . Top diagram of Figure 3A depicts two closely spaced binding events and a DNA fragment that is informative for the first binding event ( in red ) in the PET data . Invasion refers to over-estimation of the library size and extension of the read to a length longer than the true one . Equivalently , in the shifting procedure , this corresponds to shifting the read more than necessary . As a result , the read extended to the estimated library size covers both of the closely spaced binding events in the SET data and becomes uninformative or less informative for the binding event it corresponds to . Bottom diagram of Figure 3A also depicts two closely spaced binding events and illustrates truncation which we define as under-estimation of the library size . In this case , the displayed DNA fragment is long and spans both binding events ( in red ) . Therefore , it contributes to estimation of both binding events in the PET data . In contrast , the read extended to estimated library size only covers the first binding event in the SET data and , as a result , its contribution to the first binding event is overestimated whereas its contribution to the second binding event is underestimated . We evaluated the frequency by which fragments with invasion and truncation arise in SET data with a simulation study . Our results ( Table S5 in Text S1 ) indicate that as high as and of the fragments for a typical peak region can be subject to invasion and truncation with the SET assay . Figures 3B , C display the probabilities of invasion and truncation , respectively , of a DNA fragment as a function of the distance between binding events and the variance of the library size . The analytical calculations are based on the dPeak generative model ( Section 12 of Text S1 ) . Probabilities of invasion and truncation are higher for closely spaced binding events , especially when the library size is shorter than the estimated library size ( in this case ) . In Figure 3B , the probability of invasion decreases for very closely spaced binding events , i . e . , when the distance between two binding events is less than . As the distance between the binding events decreases , most DNA fragments cover both binding events and the configuration in the first diagram of Figure 3A is unlikely to occur . Hence , there is already insufficient information to predict two binding events even in PET data and relative loss of information ( i . e . , invasion ) in SET data is insignificant . These concepts describe how information on binding events can be lost or distorted by the incorrect estimation of the library size in the SET data . Analytical calculations based on the dPeak generative model show that invasion and truncation influence closely located binding events the most , especially when the library size is not tightly controlled , i . e . , exhibit large variation ( Figures 3B , C ) . We compared the performance of PET and SET sequencing for factor under the aerobic condition by generating a ‘quasi-SET data’ by randomly sampling one of the two ends of each paired reads in PET data and comparing binding events identified from both sets . In order to match number of reads with SET data for fair comparison , only the half number of paired reads was used to construct PET data . Comparison with the quasi-SET data controlled for the differences in the sequencing depths of the original PET and SET samples in addition to the biological variation of the replicates . We then evaluated the dPeak predictions from the PET and SET analyses using the factor binding site annotations in the RegulonDB database as a gold standard . Because a significant number of promoter regions lack RegulonDB annotations , we evaluated the sensitivity based on the regions that contain at least one annotated binding site . This corresponds to binding sites in candidate regions that MOSAiCS identified . Of these regions , harbor only a single annotated binding event . For the regions with more than one annotated binding event , the average distance between binding events is . dPeak analysis of the SET data identifies only of the annotated binding events . In contrast , analysis of PET data with dPeak detects of the annotated binding sites . Figure 4A displays average sensitivity as a function of the average distance between annotated binding events for the regions with at least two RegulonDB annotations . A linear line is superimposed to capture the trend for both data types . Notably , the lower sensitivity of SET compared to PET is mainly due to closely located binding events . We also compared prediction accuracies of the PET and SET assays for the regions that harbor a single annotated binding event . Figure 4B displays resolutions , which we define as the minimum of distances between predicted and annotated positions of binding events , achieved by the PET and SET assays . Median resolutions are ( IQR = ) and ( IQR = ) for PET and SET , respectively . This result indicates that positions of binding events can be more accurately predicted with the PET assay compared to SET even for regions with a single binding event . To further examine the accuracy of the dPeak predictions , primer extension analysis was performed to map the transcription start site for eight genes ( Figures S10–S13 in Text S1; Table S7 in Text S1 ) . dPeak analysis of the PET ChIP-Seq data predicts two closely spaced binding sites in the upstream of each of these eight genes with the distance between predictions ranging to . Seven of these predictions are not annotated in RegulonDB and thus represent potential novel transcription start sites . A transcription start site was detected within of ( ) of these binding site predictions ( Figure 5A and Table 1 ) , further supporting the accuracy of the dPeak PET predictions . We treated these validated sites as a gold standard and evaluated the performance of each deconvolution algorithm for these regions . Figure 5B depicts that dPeak with PET ChIP-Seq data attains significantly higher resolution compared to SET-based analysis regardless of the deconvolution algorithm used ( p-values of paired t-tests between dPeak using PET data and each of the other methods using SET data are ) . dPeak with SET ChIP-Seq data has a resolution comparable to or better than those of the competing algorithms . GPS is not included in this plot because it provides significantly worse resolution compared to other methods ( Figure S9C in Text S1 ) . Genome-wide comparisons using the RegulonDB transcription start site annotations as a gold standard also lead to a similar conclusion , supporting the notion that PET-analysis with dPeak provides the best resolution ( Figures S9A , B in Text S1 ) . Figures 4C and 4D display two representative peak regions from these analyses . Figure 4C illustrates two binding events in the promoter regions of sibD and sibE genes separated by . In this case , two peaks are easily distinguishable just by visual inspection and the predictions using both PET and SET data are comparably accurate . Note that although these two binding events are visually distinguishable , standard applications of MACS and MOSAiCS identify this region as a single peak . Widths of MOSAiCS and MACS peaks for this region are and , respectively . MACS identifies the position of the right binding event as the “summit” of this region ( position ) . Figure 4D displays the promoter region of yejG gene , where the distance between the two experimentally validated binding events is only . In this case , dPeak application to PET data correctly predicts the number of binding events as two and identifies the locations of these events within of the validated sites . In contrast , all of the SET-based analyses with the deconvolution algorithms ( PICS , GPS , GEM ) incorrectly predict one binding event located in the middle of the two experimentally validated binding sites . High resolution identification of binding sites is especially important for differential occupancy analysis where a protein of interest is profiled under different conditions . Given the high agreement between the dPeak algorithm and experimentally validated transcription start sites at a subset of promoter regions , we set out to identify differential promoter usage between the aerobic and anaerobic growth conditions by profiling the E . coli factor . Results from the dPeak analysis of the aerobic and anaerobic PET data are summarized in Figure 5C both in the region ( i . e . , peak ) and binding event levels . We identified peaks and dPeak binding events that were common between the and conditions . Interestingly , only peaks were unique to the condition but dPeak analysis identified -specific binding events . Similarly , we identified peaks unique to the condition while dPeak analysis resulted in -specific binding events . We used the SET ChIP-Seq data from additional biological replicates under both conditions as independent validation of the results . This independent validation using SET data identified of the binding events identified by dPeak using PET ChIP-Seq data ( of the common events , of the -specific binding events and of the -specific binding events ) . Table S8 in Text S1 further summarizes these results by cross-tabulating the number of predicted binding events in each peak across the two conditions . It illustrates that there are indeed many peaks with at least one binding event in each condition and different number of binding events across the two conditions . Figure S14 in displays an example of closely located binding sites that are differentially occupied between aerobic and anaerobic conditions in PET ChIP-Seq data . These results suggest that dPeak analysis identified many unique binding events that could not be differentiated in the peak-level analysis .
High resolution identification of binding sites with ChIP-Seq has profound effects for studying protein-DNA interactions in prokaryotic genomes and differential occupancy . We evaluated PET and SET ChIP-Seq assays and illustrated that PET has considerably more power for deciphering locations of closely spaced binding events . Our data-driven computational experiments indicate that when the distance between binding events gets smaller than the average library size , SET analysis have notably less power than the PET analysis . Furthermore , PET provides better resolution than SET even when a region harbors a single binding event . We developed and evaluated the dPeak algorithm , a model-based approach to identify protein binding sites in high resolution , with data-driven computational experiments and experimental validation . dPeak is currently the only algorithm that can utilize both PET and SET ChIP-Seq data and can accommodate high levels of non-specific binding apparent in deeply sequenced ChIP samples ( Table S3 in Text S1 ) . Our data-driven computational experiments and computational analysis of experimentally validated binding sites indicate that it significantly outperforms the currently available PET ChIP-Seq peak finder SIPeS [24] . Application of dPeak to E . coli ChIP-Seq data under aerobic and anaerobic conditions revealed that although many peaks identified by standard application of popular peak finders might appear as common between the two conditions , a considerable percentage of these may harbor condition-specific binding events . The high-resolution binding sites identified by dPeak could be combined with start-site mapping or consensus-sequence identification to assign transcriptional orientation to the binding sites . The advantages of using the dPeak algorithm are not limited to the study of prokaryotic genomes . Applications in eukaryotic genomes include identification of the exact locations of binding motifs when multiple closely located consensus sequences reside in a peak region , studies of cis regulatory modules ( CRM ) , and refining consensus sequences . Figure S16 in Text S1 displays an example application of dPeak for differentiating among multiple closely located GATA1 binding sites with consensus WGATAR within a ChIP-Seq peak region critical for erythroid differentiation in mouse embryonic stem cells ( data from [27] ) . CRM studies investigate relationships between spatial configurations of binding sites of multiple transcription factors and gene expression . Relative orders , positions , and distances of binding sites of multiple factors and their relative strengths are key factors in CRM studies [28] . Because dPeak facilitates identification of binding sites of transcription factors in high resolution from ChIP-Seq data , it can enable construction of complex interaction networks among diverse factors across multiple growth conditions . We evaluated the performance of dPeak on eukaryotic genome ChIP-Seq data that GPS and PICS were optimized for . Figure S17 in Text S1 shows the performance comparison results for transcription factor GABPA profiled in GM12878 cell line from the ENCODE database . It indicates that dPeak performs comparable to or outperforms GPS and PICS . In the case of sequence-specific factors with well-conserved motifs such as the GABPA factor , we observed that dPeak prediction can be further improved in a straightforward way by incorporating sequence information . Figure S17 in Text S1 illustrates that dPeak with incorporated sequence information performs comparable to GEM and identifies the GABPA binding sites with high accuracy . Recently , ChIP-exo assay [29] , a modified ChIP-Seq protocol using exonuclease , has been proposed as a way of experimentally attaining higher resolution in protein binding site identification . Because the ChIP-exo protocol is new and relatively laborious , there are not yet many publicly available ChIP-exo datasets . We utilized ChIP-exo of CTCF factor in human HeLa-S3 cell line [29] and compared their binding event predictions with dPeak predictions on SET ChIP-Seq data of CTCF in the same cell line . Figure S18 in Text S1 illustrates that dPeak using SET ChIP-Seq data provides higher resolution than ChIP-exo data and that dPeak can be readily utilized for ChIP-exo data analysis . Furthermore , it also indicates that dPeak performs comparable to or outperforms currently available methods such as GPS and GEM for both ChIP-exo and SET ChIP-Seq data . Although the real power of the ChIP-exo technique will be revealed as more ChIP-exo datasets are produced and compared with ChIP-Seq datasets , our results with the currently available data suggest that analyzing ChIP-Seq data with powerful deconvolution methods such as dPeak might perform as well as ChIP-exo . We implemented dPeak as an R package named dPeak . dPeak utilizes the fast estimation algorithm we developed and parallel computing . Analysis of the data ( ∼1 , 000 candidate regions , each with ∼2 , 300 reads on average ) using our current sub-optimal implementation of dPeak takes about minutes using CPUs ( ) when up to binding events are allowed in each candidate region , while it takes about minutes to run PICS and GPS ( also using 20 CPUs ) . Similarly , analysis of human ENCODE POL2-H1ESC data ( ∼14 , 000 candidate regions , each with reads on average ) takes about minutes for dPeak , while it takes and minutes for GPS and PICS , respectively . dPeak is currently available at http://www . stat . wisc . edu/ ~chungdon/dpeak/ and will be contributed to public repositories such as Bioconductor [30] and Galaxy Tool Shed [31] upon publication .
All strains were grown in MOPS minimal medium supplemented with glucose [32] at and sparged with a gas mix of and ( anaerobic ) or , , and ( aerobic ) . Cells were harvested during mid-log growth ( of using a Perkin Elmer Lambda Spectrophotometer ) . WT E . coli K-12 MG1655 ( , , ) was used for the experiments ( Kiley lab stock ) . ChIP assays were performed as previously described [33] , except that the glycine , the formaldehyde , and the sodium phosphate mix were sparged with argon gas for minutes before use to maintain anaerobic conditions when required . Samples were immunoprecipitated using of RNA Polymerase antibody from NeoClone ( W0004 ) . For ChIP-Seq experiments , of immunoprecipitated and purified DNA fragments from the aerobic and anaerobic samples ( one biological sample for both aerobic and anaerobic growth conditions ) , along with of input control ( two biological replicates for anaerobic Input and one biological sample for aerobic Input ) , were submitted to the University of Wisconsin-Madison DNA Sequencing Facility for ChIP-Seq library preparation . Samples were sheared to during the IP process to facilitate library preparation . All libraries were generated using reagents from the Illumina Paired End Sample Preparation Kit ( Illumina ) and the Illumina protocol “Preparing Samples for ChIP Sequencing of DNA” ( Illumina part # 11257047 RevA ) as per the manufacturer's instructions , except products of the ligation reaction were purified by gel electrophoresis using SizeSelect agarose gels ( Invitrogen ) targeting fragments . After library construction and amplification , quality and quantity were assessed using an Agilent DNA 1000 series chip assay ( Agilent ) and QuantIT PicoGreen dsDNA Kit ( Invitrogen ) , respectively , and libraries were standardized to . For PET ChIP-Seq data , cluster generation was performed using an Illumina cBot Paired End Cluster Generation Kit ( v3 ) . Paired reads , run was performed for each end , using v3 SBS reagents and CASAVA ( the Illumina pipeline ) v 1 . 8 . 2 , on the HiSeq2000 . For SET ChIP-Seq data , cluster generation was performed using an Illumina cBot Single Read Cluster Generation Kit ( v4 ) and placed on the Illumina cBot . A single read , run was performed , using standard SBS kits ( v4 ) and SCS 2 . 6 on an Illumina Genome Analyzer IIx . Base calling was performed using the standard Illumina Pipeline version 1 . 6 . Sequence reads were aligned to the published E . coli K-12 MG1655 genome ( U00096 . 2 ) using the software packages SOAP [34] and ELAND ( within the Illumina Genome Analyzer Pipeline Software ) , allowing at most two mismatches . PET experiments yielded million ( M ) and mappable paired 36mer reads and SET yielded and mappable 32mer reads for aerobic and anaerobic conditions , respectively . Control input experiments , generated with SET sequencing , resulted in and mappable 32mer reads for the aerobic and anaerobic conditions , respectively . Raw and aligned data files are available at ftp://ftp . cs . wisc . edu/pub/users/keles/dPeak and are being processed by GEO for accession number assignment . For PET data , if a DNA fragment ( paired reads ) belongs to -th binding event , we model its leftmost position conditional on its length as Uniform distribution between and , where is the position of -th binding event . Lengths of DNA fragments , , are modeled using the empirical distribution obtained from actual PET data . For SET data , if a read belongs to -th binding event , we model its end position conditional on its strand as Normal distribution . Specifically , if a read is in the forward strand , its end position is modeled as Normal distribution with mean and variance . end positions for reverse strand reads are modeled similarly with Normal distribution with mean and variance . Parameters and are common to all binding event components in each candidate region . Strands of reads are modeled as Bernoulli distribution . Background reads are assumed to be uniformly distributed over the candidate region that they belong to . Parameters are estimated via the Expectation-Maximization ( EM ) algorithm [35] . Additional details on the dPeak model and the estimation algorithm for the PET and SET settings are available in Sections 2 and 3 of Text S1 . We compared the sensitivity and the number of predictions of dPeak with those of PICS [11] , GPS [22] , and GEM [23] . Sensitivity is the proportion of regions for which both of the two true binding events are correctly identified . A binding event is considered as ‘identified’ if the distance between the actual binding event and the predicted position is less than . Note that we chose a more stringent criteria than the used by GPS for defining true positives because is not high enough resolution for prokaryotic genomes . For the PICS algorithm , we used the R package PICS version 1 . 10 , which is available from Bioconductor ( http://www . bioconductor . org/packages/2 . 10/bioc/html/PICS . html ) . For the GPS algorithm , we used its Java implementation version 1 . 1 from http://cgs . csail . mit . edu/gps/ . In the performance comparisons using ChIP-Seq data , we also incorporated GEM , a recently modified and extended version of GPS , which incorporates genome sequence of the peaks to improve binding event identification . For the GEM algorithm , we used its Java implementation version 0 . 9 from http://cgs . csail . mit . edu/gem/ . We downloaded the synthetic data used for the method comparisons from http://cgs . csail . mit . edu/gps/ and its description is provided in Supplementary information of the GPS paper [22] . This synthetic data consists of “chrA” with 1 , 000 regions that harbor two closely spaced binding events and “chrB” to “chrK” with a total of 20 , 000 regions with a single binding event . We evaluated performances of the methods on joint and single binding event regions separately so that we could assess sensitivity and specificity for each of these cases . Candidate regions for dPeak were identified using the conditional binomial test [6] with a false discovery rate of by applying the Benjamini-Hochberg correction [36] . These regions were also explicitly provided to the GPS and GEM algorithms as candidate regions . Candidate regions for PICS were identified using the function segmentReads ( ) in the PICS R package ( default parameters ) . Default tuning parameters were used during model fitting for all the methods . We considered distances between binding sites ranging from to which characterize the typical binding event spacing in E . coli . We generated and assigned DNA fragments to each of two binding events as follows . For each DNA fragment , we drew the length ( ) from the distribution of library size , , estimated empirically from the actual PET ChIP-Seq data and group index ( ) from multinomial distribution with parameters ( , ) . Then , for given a library size and group index ( ) , leftmost position of the paired reads ( ) was generated from Uniform distribution between and , where is the position of -th binding event . Rightmost position was assigned as . SET data was generated by randomly sampling one of two ends from each of these paired reads . For the SET analysis , average library size was assumed to be . Then , only half of the total number of paired reads was used to construct PET data , in order to match number of reads with SET data for fair comparison . In addition , we randomly assigned DNA fragments to arbitrary positions within the candidate region to generate non-specific binding ( background ) reads . The sensitivity and the number of predictions were summarized over simulated datasets generated by this procedure . A binding event was considered as ‘identified’ if the distance between the binding event and the predicted position is less than . We repeated these PET versus SET analyses by comparing all the PET data with SET data constructed from selecting one of two ends of each read pair and obtained little or no change in the results ( data not shown ) . We identified candidate regions , i . e . , peaks with at least one binding event , using the MOSAiCS algorithm [10] ( two-sample analysis with a false discovery rate of ) . In each candidate region , we fitted the dPeak model , which is a mixture of binding event components and one background component ( Figure 1C ) . In the current analysis , up to five binding event components ( ) were considered . The optimal number of binding events was chosen with BIC for each candidate region . We utilized top of the predicted binding events from each condition for the comparison between the aerobic and anaerobic conditions . Overall conclusions remained the same when the full set of predicted binding events are considered . Total RNA was isolated as previously described [37] . Oligonucleotide primers ( Table S7 in Text S1 ) were labeled at the end using []ATP ( ) and T4 polynucleotide kinase ( Promega ) followed by purification with a G25 Sephadex Quick Spin Column ( GE ) . Labeled primer ( ) was annealed with total RNA in and extended with avian myeloblastosis virus reverse transcriptase ( Promega ) as described by the manufacturer , except that actinomycin D was present at [38] . Primer extension experiments were implemented for spr ( RNA ) , dcuA ( RNA ) , serC ( RNA ) , aroL ( and RNA for and , respectively ) , yejG ( RNA ) , hybO ( RNA ) , ybgI ( RNA ) , and ptsG ( RNA ) . A dideoxy sequencing ladder was electrophoresed in parallel with the primer extension products on a 8% ( ) polyacrylamide gel containing urea . In cases where the transcription start site could be assigned to one of two nucleotides , preference was given to the purine nucleotide . The dPeak algorithm is implemented as an R package named dpeak and is freely available from http://www . stat . wisc . edu/~chungdon/dpeak/ . We will commit dpeak to Bioconductor ( http://www . bioconductor . org ) and Galaxy Tool Shed ( http://toolshed . g2 . bx . psu . edu ) upon publication .
|
Chromatin immunoprecipitation followed by high throughput sequencing ( ChIP-Seq ) is widely used for studying in vivo protein-DNA interactions genome-wide . Current state-of-the-art ChIP-Seq protocols utilize single-end tag ( SET ) assay which only sequences ends of DNA fragments in the library . Although paired-end tag ( PET ) sequencing is routinely used in other applications of next generation sequencing , it has not been much adapted to ChIP-Seq . We illustrate both experimentally and computationally that PET sequencing significantly improves the resolution of ChIP-Seq experiments and enables ChIP-Seq applications in compact genomes like Escherichia coli ( E . coli ) . To enable efficient identification using PET ChIP-Seq data , we develop dPeak as a high resolution binding site identification algorithm . dPeak implements probabilistic models for both SET and PET data and facilitates efficient analysis of both data types . Applications of dPeak to deeply sequenced E . coli PET and SET ChIP-Seq data establish significantly better resolution of PET compared to SET sequencing .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
dPeak: High Resolution Identification of Transcription Factor Binding Sites from PET and SET ChIP-Seq Data
|
The FLP enzyme catalyzes recombination between specific target sequences in DNA . Here we use FLP to temporally and spatially control gene expression in the nematode C . elegans . Transcription is blocked by the presence of an “off cassette” between the promoter and the coding region of the desired product . The “off cassette” is composed of a transcriptional terminator flanked by FLP recognition targets ( FRT ) . This sequence can be excised by FLP recombinase to bring together the promoter and the coding region . We have introduced two fluorescent reporters into the system: a red reporter for promoter activity prior to FLP expression and a green reporter for expression of the gene of interest after FLP expression . The constructs are designed using the multisite Gateway system , so that promoters and coding regions can be quickly mixed and matched . We demonstrate that heat-shock-driven FLP recombinase adds temporal control on top of tissue specific expression provided by the transgene promoter . In addition , the temporal switch is permanent , rather than acute , as is usually the case for heat-shock driven transgenes . Finally , FLP expression can be driven by a tissue specific promoter to provide expression in a subset of cells that can only be addressed as the intersection of two available promoters . As a test of the system , we have driven the light chain of tetanus toxin , a protease that cleaves the synaptic vesicle protein synaptobrevin . We show that we can use this to inactivate synaptic transmission in all neurons or a subset of neurons in a FLP-dependent manner .
Every widely-used genetic model organism can be manipulated to express genes introduced by the experimenter . In C . elegans it is simple to create a transgene by injecting DNA containing a complete genomic region . In most cases the transgene will be expressed in its native temporal and spatial pattern and will rescue the mutant phenotype . However , if the researcher would like to test the function of a gene at a specific time or in a specific tissue of the worm , one would need a very specific promoter . Due to a limited set of promoters available , it is often impossible to express a gene of interest in a specific cell . More importantly , there is only one temporally inducible promoter available – the heat-shock promoter . This promoter has been a workhorse of the field , but has a major drawback because it is expressed ubiquitously . Three techniques have been developed that provide more precise temporal and spatial control by making gene expression dependent on two independently controllable events . The Chalfie laboratory has developed one solution by expressing the gene product as two complementary halves . In this case , the full gene product is reconstituted only in the cells that express both promoters . When expressed under the control of two different overlapping promoters , the complete protein is only reconstituted in a small number of cells . They have demonstrated that a two-part GFP can be used to label specific cells [1] and that a two-part caspase can be used to kill specific cells [2] . Their technique can also be applied to the temporal control of gene expression . If one of the promoters is a heat-shock inducible promoter , specific cells can be killed on command . The limitation of the two-part system is that it requires gene products that can reconstitute activity from two halves . A second combinatorial control technique relies on temperature-dependent degradation of the mRNA of the target gene . This method was independently proposed by several groups , but made practical by Getz , Xu and Fire ( A . Fire personal communication ) [3] . The nonsense mediated decay ( NMD ) pathway specifically degrades mRNAs with long 3′ untranslated regions containing many introns . Transgenes can be engineered with long 3′ UTRs so that their mRNAs are targeted for degradation . Strains with temperature-sensitive mutations in NMD components cannot degrade these mRNAs at the restrictive temperature . Thus , the transgene is more strongly expressed at the restrictive temperature ( when the NMD system is not functioning ) than at the permissive temperature ( when NMD is actively degrading aberrant mRNAs ) . This system gives some degree of control over expression levels , although there is a moderate background level of expression even in the “off” state , which has limited its use . Bacaj and Shaham have developed a method to add heat-shock control to a transgene expressed under a tissue-specific promoter[4] . The method uses a mutant background in which the heat-shock response is defective . By rescuing the mutant defect with tissue-specific promoters , a tissue-specific heat shock response is generated . This method requires using the hsf-1 mutant background and gene expression from the heatshock promoter is still acute . Ideally , a combinatorial expression system would be in the wild-type strain and provide a permanent change in the genotype of the cells of interest . Here we describe a method that uses FLP recombinase to control transgene expression in C . elegans . In this configuration , the transgene is expressed at the combinatorial intersection of two different promoters: either two spatially restricted promoters , or a spatially restricted and temporally controlled promoter .
The site specific recombinases Cre and FLP have been used in many systems to control gene structure and expression [5]–[10] . These enzymes align tandem copies of the target sequence , perform site-specific recombination , and remove the sequence between the targets as a circular DNA molecule . If the intervening sequence disrupts expression , removal by recombination will allow the transgene to be activated . We designed an “off cassette” , composed of a putative transcriptional terminator , that could be placed between a promoter and a coding region to disrupt expression of the coding region ( Figure 1 ) . Expression of FLP recombinase will excise the cassette as a circular DNA molecule . This rearrangement will place the promoter adjacent to the downstream coding regions , converting the transgene to the “on” state . Thus , expression is dependent on both the promoter driving the coding region and the promoter driving expression of FLP . The constructs are designed to provide a fluorescent readout in either the ‘off’ or ‘on’ state . The FRT-flanked “off cassette” contains the mCherry coding sequence followed by the 3′ genomic region from the let-858 gene . The red-fluorescent mCherry protein acts as a reporter for the promoter activity of the transgene , verifying that the transgene is present and expressing in the expected cell types prior to FLP-induced recombination . The let-858 3′ genomic region provides the poly-adenylation signal for mCherry mRNA as well as a putative transcriptional terminator , preventing expression of downstream sequences . GFP acts as a reporter for transcriptional read-through or reinitiation of transcription in the let-858 genomic region . Recombination of the FRT sequences removes the mCherry coding sequence and the terminator and brings a GFP coding region under control of the promoter , to indicate that the FLP reaction was successful . The coding region for any gene of interest can be fused in frame to the 3′ end of the GFP sequence , thus providing FLP-inducible expression of that protein . To speed assembly of constructs and to make use of genome reagents generated by other laboratories , we based our constructs on the Multisite Gateway™ in vitro recombination system from Invitrogen [11] , [12] . These vectors allow rapid , modular construction of plasmids using a site-specific recombinase from the bacteriophage lambda ( Figure 2A ) . The recombinase target sequences are designed to allow pairwise recombination between specific DNA sequences . The standard multisite system uses three libraries of entry vectors: a promoter library , a cDNA library , and transcriptional terminator library ( to be recombined into slots 1 , 2 or 3 , respectively ) . Individual components from these libraries can be selected and mixed with a destination vector to generate a desired combination of promoter , cDNA and terminator in a single reaction . Many reagents are already available for the construction of C . elegans expression constructs using this system . The C . elegans promoterome consortium has cloned the 5′ regulatory regions of approximately 6 , 000 different C . elegans genes into Gateway ‘entry’ vectors compatible with the slot 1 entry vectors [13] . The ORFeome project from the Vidal laboratory has cloned cDNAs from approximately 11 , 000 genes into slot 2 entry vectors [14] . Because the promoterome constructs are designed to recombine directly to ORFeome constructs , it was not possible to introduce the off-cassette between them . Instead , we built vectors that have the off-cassette in either the promoter slot ( slot 1 ) ( Figure 2B ) or in the cDNA slot ( slot 2 ) ( Figure 2C ) . The former arrangement requires construction of a promoter-FRT construct , but is compatible with the existing ORFeome library . The latter requires placing the ORF to the third slot , but is compatible with the existing promoterome library . The ORFeome-compatible format requires that the “off-cassette” be cloned into entry vectors containing various promoters . We generated several potentially useful promoter-“off-cassette” entry clones ( Figure 2B , Table 1 ) . These plasmids can then be recombined with any ORFeome clone to produce a large number of different open reading frames under the same FLP-inducible promoter sequence . This arrangement is particularly useful when one is primarily interested in expressing a number of different proteins in a particular cell or tissue . For the promoterome-compatible constructs , we placed the off-cassette into the position usually occupied by the ORF of interest . We then used standard cloning methods to place several ORFs of interest into slot 3 entry vectors ( Figure 2C , Table 2 ) . Any of the set of promoterome clones can be recombined with these clones to express a single ORF in a wide array of tissues in a FLP-dependent manner . This arrangement is particularly useful when one is determining the focus of gene rescue or inactivating different groups of cells . As a proof of principle , we produced two FLP-inducible constructs that would express GFP-tagged histone in different muscle cells . The first construct was in the ORFeome compatible configuration described in Figure 2B . We used a pharyngeal muscle promoter ( Pmyo-2 ) followed by the “off-cassette” in the first Gateway slot . We added the HIS-11 open reading frame in the second slot and an unc-54 3′ polyadenylation site in the third slot ( Figure 3A–D ) . We injected this plasmid together with a plasmid encoding FLP-recombinase under the control of the hsp-16 . 48 heat shock promoter [15] and a lin-15 ( + ) co-injection marker to produce a line of transgenic worms . As expected , these worms expressed diffuse red fluorescence in their pharyngeal muscles with no apparent GFP fluorescence ( Figure 3A , B ) . The lack of GFP fluorescence confirms that the let-858 3′ genomic region functions as expected to prevent read-through into the downstream gene . We then exposed the worms to a one hour , 34° heat shock and imaged the worms 2 hours , 3 hours and 15 hours later . Although no GFP was apparent at 2 hours , the heat shock-induced expression of the GFP::HIS-11 fusion protein was visible at three hours ( Figure S1 ) and was strong at 15 hours ( Figure 3C , D ) . From roughly 200 animals observed under a dissecting microscope , every worm examined exhibited green nuclei in muscle cells expressing mCherry . We counted the GFP positive nuclei in five randomly selected adult worms 22 hours after a 34° heat shock . We found strong expression in 98% ( 123/125 ) of major pharyngeal muscle cells ( pm3-pm8 , 25 nuclei per worm ) . Determining expression in pm1 and pm2 is complicated , since these cells are surrounded by the pm3 muscle cell ( see http://www . wormatlas . org/handbook/fig . s/alim9 . jpg ) . Bright mCherry fluorescence obscured expression of the cassette in these cells before heatshock . Thus , we could not determine whether the myo-2 promoter was driving expression of the transgene . After heatshock we did not score GFP expression in any pm1 nuclei ( 0/15 ) and only identified weak expression in 7/15 pm2 nuclei . In summary , robust expression due to recombination was observed in all cells in which the transgene was unequivocally expressed . The second construct was in the promoterome-compatible configuration described in Figure 2C . We generated this construct by recombining a myo-3 promoterome plasmid in the first position , the FRT cassette was placed into the second position and a HIS-11 ORF cloned in front of the unc-54 3′ genomic region in the third position . This construct should express the GFP::HIS-11 fusion in the body muscles of the worm after FLP expression . There was diffuse red but no green fluorescence in the body wall muscles prior to heat shock ( Figure 3E , F ) , but strong induction of nuclear-localized GFP 15 hours after a 1 hour exposure to 34° heat shock ( Figure 3G , H ) . Using a dissection microscope to survey a large number of animals , GFP was detected in body muscle nuclei in all transgenic animals . We examined 5 animals in greater detail and observed expression in body wall muscles , vulval muscles and the anal depressor muscles in all animals , demonstrating that FLP works in all major body muscle types . If all of the copies of the transgene are recombined , mCherry expression should not occur after FLP expression . However , in all cells examined ( using the myo-2 , myo-3 and unc-47 promoters ) , we never saw substantial loss of mCherry expression even after induction of the GFP fusion protein . Although the half-life of mCherry in worm cells is not known , the half life of GFP in body muscle cells is greater than 24 hours [16] . Thus , perdurance of mCherry could be a source of this remaining expression . It is also possible that several copies of the transgene on the extrachromosomal array are refractory to recombination . This could be due to the chromatin structure of the repetitive arrays , or damage to the arrays that occurred during their formation . One important application for the FLP-on method is to silence neurotransmission in specific neurons in a temporally-controlled manner . The tetanus toxin light chain is a highly specific protease that recognizes and cleaves synaptobrevin [17] . Since synaptobrevin is one of the three SNARE-class proteins required for calcium-mediated release of neurotransmitter [18] , expressing tetanus toxin eliminates the ability of a neuron to signal through chemical synapses . The high specificity of tetanus toxin preserves all other functions of the neuron , including electrical coupling through gap junctions . Temporal control of tetanus toxin expression is important for two reasons . First , synaptic transmission is essential for development . Animals lacking acetylcholine neurotransmission ( in cha-1 mutants ) or all synaptic neurotransmission ( in unc-13 deletion alleles ) are arrested in the first larval stage . Thus , loss of synaptic transmission in at least some neurons may lead to broad developmental defects that blind the investigator to functions for a neuron in the adult . Using the FLP-on method , expression of tetanus toxin can be activated after the developmental requirement for a neuron . Conversely , early loss of neuronal function can lead to developmental compensation in the nervous system . For example , when particular sensory neurons were ablated in males in the L3 stage the nervous system compensated for their loss; by contrast ablation of these same neurons in the L4 stage led to behavioral abnormalities in the adult [19] . The ability to silence these neurons in adulthood allows one to assay the function of the synaptic connectivity of a neuron in an existing developmentally-normal circuit . To test if tetanus toxin induction could inactivate neurotransmission , we designed an ORFeome-compatible tetanus toxin expression construct . The tetanus toxin sequence was inserted into slot 2 , and the FRT-flanked terminator was placed in slot 1 after the GABA-specific neuron promoter Punc-47 . unc-47 encodes the vesicular GABA transporter required to fill synaptic vesicles with GABA , and is expressed in all GABA neurons . We chose this promoter because loss of GABA function produces two distinct phenotypes: a locomotory phenotype and a defecation motor program defect . Animals lacking GABA neurotransmission exhibit a distinctive locomotory defect [20] . These animals cannot back when touched on the head but rather execute an accordion-like “shrinking” response . This symmetrical contraction of the body muscles occurs due to the lack of contralateral inhibitory inputs from the GABA motor neurons . Prior to heat shock , transgenic worms exhibited normal movement . Animals were exposed to heat shock at 34° for one hour . 24 hours later the animals exhibited a clear shrinking phenotype ( Video S1 ) . The structure of the GABA neurons was not affected by toxin expression ( Figure S2 ) , suggesting that the toxin was simply silencing synaptic transmission in these neurons . As expected the shrinking phenotype was associated with the presence of the transgene: 99 of 100 shrinker animals carried the Pmyo-2::GFP extrachromosomal array marker . The presence of nonshrinking animals in the population was due to loss of the extrachromosomal array: 29 of the 30 non-shrinker animals had lost the Pmyo-2::GFP transgene marker . The one non-shrinking animal carrying the array lacked the mCherry marker in the VD and DD motor neurons , demonstrating that this animal was a somatic mosaic which lacked the array in the motor neurons . GABA function is also required for the motor program of the defecation cycle [21] , [22] . The defecation motor program requires the AVL and DVB GABA neurons to stimulate contraction of the enteric muscles via a GABA-gated cation channel [23] . Prior to heat shock , transgenic worms expressing mCherry in the AVL and DVB GABA neurons had wild-type enteric muscle contractions during the defecation cycle ( n = 10 worms , 10 cycles per worm , Figure 4 ) . After heat shock , transgenic animals lacked the enteric muscle contractions during the defecation cycle ( Figure 4 ) , as expected for loss of GABA neurotransmission . Because AVL and DVB are partially redundant , these data suggest that FLP function must be greater than 95% effective . In addition , we were able to see induction of GFP-tagged tetanus toxin by confocal microscopy ( not shown ) . Tetanus toxin expression is continuous using this FLP-on construct; thus , unlike direct heatshock-induced expression of the toxin , the behavioral change is permanent .
FLP-dependent gene expression will have two general uses in C . elegans: to provide spatial specificity and temporal specificity for gene expression . Because it provides expression within the spatial overlap of two promoters , the method essentially squares the number of transgene expression patterns now available . In many cases , especially in the nervous system , this can restrict expression to single cells of the worm . FLP recombinase can also be used to provide temporal specificity for gene expression . For most purposes temporal control is best provided by activation of the heatshock promoter . Thus , heatshock-driven expression of FLP recombinase will confer temporal specificity to any promoter . This will be particularly useful for expression of dominant negative or constitutively activated gene products that may kill cells before their effects can be assayed . Moreover , acute expression can outflank the criticism most often thrown at genetic analysis –that homeostatic mechanisms will compensate for chronic genetic changes . In this way the heatshock FLP-on method and the cell-specific rescue of hsf-1 method developed by the Shaham laboratory are similar . In the FLP-on method , expression is permanently activated , whereas in the hsf-1 rescue method , expression is acute and depends on the length of the heatshock response . Depending on the circumstances and the gene product being expressed , one method may be more advantageous than the other . For the neuroscientist , FLP-on constructs provide a method for analyzing the role of a neuron in a circuit by killing a specific cell , inactivating the cell , or activating the cell . By combining our system with the Chalfie split caspase system ( Table 1 ) , cells can be killed at the intersection of three promoters . These three promoters could each provide a spatial component , giving extremely tight spatial control to potentially address the few single neuron types that have escaped the two-promoter system . Alternatively , one of the three could provide temporal inducibility , adding heat shock control onto many two-promoter single-cell killing experiments . Silencing the cell can allow specific dissection of the chemical synapses present in the system while leaving the gap junction connections in the network intact . In addition , leaving the cell in place will minimize any developmental perturbations of the circuit that might be caused by removing a neuron by the cell death pathway . In addition to eliminating a cell entirely , or silencing the chemical neurotransmission in a cell , single cells can be electrically silenced or activated on command by expressing halorhodopsin , a light activated chloride pump protein [24] , or channelrhodopsin , a light activated cation channel protein [25] , at the intersection of two spatial promoters . For the developmental biologist , FLP-on constructs can provide temporal control of gene expression so that the role of a gene during different developmental periods can be evaluated . This application is limited by the time delay required for FLP expression , recombination and gene expression . We observed a three hour delay from the end of heat shock to the expression of Pmyo-2::GFP . FLP regulation will also be useful for analyzing promoter expression patterns by permanently marking descendents of cells that have expressed a promoter . Combined with the known cell lineage of C . elegans , the expression pattern can quickly pinpoint the expression pattern of a transgene that may come on very briefly and in only a few cells in the embryo . Using traditional GFP reporters , such expression might be missed entirely , or if it is detected it might be very difficult to unambiguously identify the expressing cell among the dividing cells of the embryo . This technique could be combined with forward genetic screens . A FLP-dependent histone GFP reporter ( Table 1 ) will easily identify mutant backgrounds in which a gene is transiently misexpressed during development . In conclusion , FLP-dependent excision of a transcriptional terminator provides a simple way to make expression of a transgene dependent on the activity of two promoters . Depending on the promoters used , FLP-on constructs can confer combinatorial spatial or temporal control of gene expression in C . elegans . We anticipate that the combination of the wide availability of the Gateway reagents and the imagination of the C . elegans community will yield many new applications .
pWD157 ( slot 2 TeTx ) : Tetanus toxin light chain was PCR amplified from CMV-LC-Tx ( Heiner Niemann ) using primers containing attB1 and attB2 tails GGGGACAAGTTTGTACAAAAAAGCAGGCTTAATGCCGATCACCATCAACAACTTC and GGGGACCACTTTGTACAAGAAAGCTGGGTTTAAGCGGTACGGTTGTACAGGTT and recombined with the attP1 and attP2 sites in the slot 2 donor vector pDONR221 ( Invitrogen ) using the BP recombination reaction . pWD170 ( slot 3 TeTx ) : Tetanus toxin light chain was PCR amplified from CMV-LC-Tx using primers GTATGCCGATCACCATCAACAAC and TTAAGCGGTACGGTTGTACAGG and cloned as a blunt fragment into pMH472 using SrfI . pMH472 is a slot 3 entry vector containing a SrfI site followed by two stop codons and then the unc-54 3′ UTR . pPD119FRTRFPGFP: A fragment of mRFP1 was PCR amplified and cloned between the SalI and SmaI sites in pPD118 . 33 . pPD118 . 33 is a Pmyo-2::GFP plasmid ( gift of Andrew Fire ) . Tandem FRT sites [8] were cloned at the junctions as SalI-BamHI and MluI-SmaI double-stranded oligonucleotides . pWD176 ( Pmyo-2-FRT-mCherry-terminator-FRT-GFP-terminator ) : pPD119FRTRFPGFP was modified to replace mRFP with mCherry . An MluI-KpnI double-stranded oligonucleotide containing an FRT fragment in a different frame was used to replace the second FRT in pPD119FRTRFPGFP . The plasmid was then cut with BamHI and Mlu and a PCR fragment containing mCherry followed by the let-858 3′ end was ligated in as a BamHI-BssHII digested fragment . This produced a myo-2 promoter::FRT::mCherry terminator::FRT::GFP::unc-54 3′ UTR plasmid . pWD177 ( slot 1 Pmyo-2-FRT-mCherry-terminator-FRT ) : The Pmyo-2::mRFP FRT cassette::GFP fragment from pPD119FRTRFPGFP was PCR amplified using primers containing attB4 and attB1 tails GGGGACAACTTTGTATAGAAAAGTTGCTTGCATGCCTGCAGGTCGAGG and GGGGACTGCTTTTTTGTACAAACTTGTTTTGTATAGTTCGTCCATGCCATG and recombined with the attP4 and attP1 sites in the slot 1 donor vector pDONR P4-P1R ( Invitrogen ) using the BP recombination reaction to make pWD159 . The mRFP cassette was replaced with mCherry using a BamHI-XhoI fragment from pWD176 . This plasmid was sequenced with primers T7 , M13fwd , mCh r1: CTTTCACTTGAAGCTTCCCATCCC , GFP-I-r2: CTCCAGTGAAAAGTTCTTCTCC , and GFP-I-r1: TTGTGCCCATTAACATCACC . pWD178 ( slot 2 FRT-mCherry-terminator-FRT ) : The mRFP FRT cassette::GFP fragment from pPD119FRTRFPGFP was PCR amplified using primers containing attB1 and attB2 tails GGGGACAAGTTTGTACAAAAAAGCAGGCTTACGAAGTTCCTATTCTCTAGA and GGGGACCACTTTGTACAAGAAAGCTGGGTTTTTGTATAGTTCGTCCATGCC and recombined with the attP1 and attP2 sites in pDONR221 ( Invitrogen ) using the BP recombination reaction . The mRFP cassette was replaced with mCherry using a BamHI-XhoI fragment from pWD176 . This plasmid was sequenced with primers T7 , M13fwd , GFP-I-r2: CTCCAGTGAAAAGTTCTTCTCC , and GFP-I-r1: TTGTGCCCATTAACATCACC pWD179: The 1 . 2 kb unc-47 promoter was PCR amplified from plasmid pKS4 . 1 ( K . Schuske ) using primers: CGAACGCATGCGGATCCCGGAACAGTCGAAAG and CGAACGTCGACGCATCTGTAATGAAATAAATGTGACGCTG . This sequence was inserted into pWD159 as an Sph-Sal fragment . The mRFP cassette was replaced with mCherry using a SalI-BstZ17I fragment from pWD176 . This plasmid was sequenced with primers T7 , M13fwd , mCh r1: CTTTCACTTGAAGCTTCCCATCCC , GFP-I-r2: CTCCAGTGAAAAGTTCTTCTCC , and GFP-I-r1: TTGTGCCCATTAACATCACC . pWD180: The 1 . 2 kb rab-3 promoter was PCR amplified from N2 genomic DNA using primers: CGAACGCATGCATCTTCAGATGGGAGCAGTGG and CGAACGTCGACGCATCTGAAAATAGGGCTACTGTAGAT . This DNA was inserted into pWD159 as an Sph-Sal fragment . The mRFP cassette was replaced with mCherry using a BamHI-XhoI fragment from pWD176 . This plasmid was sequenced with primers T7 , M13fwd , mCh r1: CTTTCACTTGAAGCTTCCCATCCC , GFP-I-r2: CTCCAGTGAAAAGTTCTTCTCC , and GFP-I-r1: TTGTGCCCATTAACATCACC . pWD195 ( slot 2 Histone 2B ) : The his-11 ORF was PCR amplified using primers containing attB1 and attB2 tails GGGGACAAGTTTGTACAAAAAAGCAGGCTTACCACCAAAGCCATCTGCCAAGG and GGGGACCACTTTGTACAAGAAAGCTGGGTATTACTTGCTGGAAGTGTACTTGG using pGH42 ( G . Hollopeter ) as template . The DNA fragment was recombined with the attP1 and attP2 sites in pDONR221 ( Invitrogen ) using the BP recombination reaction . pWD198 ( Pmyo-3-FRT-mCherry-FRT-GFP-Histone ) : A multisite LR reaction was performed using pEntry[4-1] Pmyo-3 ( Open Biosystems ) , pWD178 , pGH42 ( G . Hollopeter ) , and pDEST R4-R3 ( Invitrogen ) . pWD199 ( Punc-47-FRT-mCherry-FRT-GFP-TeTx ) : A multisite LR reaction was performed using pWD179 , pWD157 , pMH473 ( M . Hammarlund ) , and pDEST R4-R3 ( Invitrogen ) . pMH473 is an attP2-attP3 entry clone carrying the unc-54 3′ polyadenylation site . pWD200 ( Pmyo-2-FRT-mCherry-FRT-GFP-Histone ) : A multisite LR reaction was performed using pWD177 , pWD195 , pMH473 and pDEST R4-R3 ( Invitrogen ) . pWD203 ( slot 3 Caspase C-terminus ) : The caspase 3 C-terminal fragment , leucine zipper and unc-54 3′ UTR from TU#813 [2] was PCR amplified using primers: GGGGACAGCTTTCTTGTACAAAGTGGGAAGTGGTGTTGATGATGACATGGCG and GGGGACAACTTTGTATAATAAAGTTGCCATAGACACTACTCCACTTTC and BP cloned into pDONR P2R-P3 ( Invitrogen ) . pWD204 ( slot 3 Caspase N-terminus ) The caspase 3 N-terminal fragment , leucine zipper and unc-54 3′ UTR from TU#814 [2] was PCR amplified using primers containing attB2 and attB3 tails: GGGGACAGCTTTCTTGTACAAAGTGGGAATGGCTAGCGCACAGCTGGAGAAG and GGGGACAACTTTGTATAATAAAGTTGCCATAGACACTACTCCACTTTC and BP cloned into pDONR P2R-P3 ( Invitrogen ) . pWD79-2RV ( Phsp-16-48:FLP ) : A PCR fragment containing the FLP coding sequence from pOG44 ( Stratagene ) was cloned as an MluI-NheI fragment into pJL44 ( J . L . Bessereau ) . pJL44 contains the Phsp-16-48 heat-shock promoter and the glh-2 3′ UTR . The FLP coding sequence in pOG44 contains a point mutation which was repaired using a PCR fragment from the FLP coding sequence cloned into pBR322 ( Makkuni Jayaram ) . An artificial intron was introduced into the FLP coding sequence at the EcoRV site using a double-stranded oligo: GTAAGTTTAAACATATATACTAACTAACCCTGATTATTTAAATTTTCAG . pWD172 ( slot 2 FLP-stop ) : The FLP ORF was PCR amplified from pWD79-2RV using primers containing attB1 and attB2 tails: GGGGACAAGTTTGTACAAAAAAGCAGGCTTAATGCCACAATTTGGTATATTATGT and GGGGACCACTTTGTACAAGAAAGCTGGGTATTATATGCGTCTATTTATGTAGGATG and BP cloned into pDONR221 ( Invitrogen ) . This version of FLP contains a stop codon at the native C terminus . pWD173 ( slot 2 FLP-no stop ) : The FLP ORF was PCR amplified from pWD79-2RV using primers containing attB2 and attB3 tails: GGGGACAAGTTTGTACAAAAAAGCAGGCTTAATGCCACAATTTGGTATATTATGT and GGGGACCACTTTGTACAAGAAAGCTGGGTATATGCGTCTATTTATGTAGGATG and BP cloned into pDONR221 ( Invitrogen ) . This version of FLP does not contain a stop codon , and can be used to make C-terminally tagged proteins . Gateway BP and LR in vitro recombination reactions were carried out according to manufacturer instructions . Strains used in this study: The wild type is Bristol N2 . EG3251 unc-25 ( e156 ) III . EG4859 oxEx1099 was made by injecting: pWD198 ( Pmyo-3-FTF-GFP-Histone ) at 5 ng/ul , pWD79-2RV ( Phsp-16-48:FLP ) at 45 ng/ul , pPD118 . 33 ( Pmyo-2::GFP ) ( A . Fire ) at 1 ng/ul , and pL15EK ( lin-15 ( + ) ) [26] at 50 ng/ul into N2 animals . EG4860 oxEx1100 was made by injecting: pWD199 ( Punc-47-FTF-GFP-TeTx ) at 5 ng/ul , pWD79-2RV ( Phsp-16-48:FLP ) at 45 ng/ul , pPD118 . 33 ( Pmyo-2::GFP ) ( A . Fire ) at 1 ng/ul , pL15EK ( lin-15 ( + ) ) [26] at 50 ng/ul into N2 animals . EG4866 lin-15 ( n765ts ) X oxEx1101 was made by injecting: pWD200 ( Pmyo-2-FTF-GFP-Histone ) at 2 ng/ul , pWD79-2RV ( Phsp-16-48:FLP ) at 45 ng/ul , pL15EK ( lin-15 ( + ) ) [26] at 50 ng/ul into MT1642 lin-15 ( n765ts ) . ‘FTF’ symbolizes the off cassette composed of FRT-mCherry-terminator-FRT .
|
Genes turn on and off as a natural part of development . The nematode C . elegans has been an important model system for studying the roles of genes in animal development and physiology . However , worm researchers have had a limited toolkit for controlling gene activation . These drawbacks have been particularly restrictive when studying the function of a gene that has different roles in several cell types or at different times in development . Here we describe a way to turn any gene on at a specific time in specific cells . We provide a set of mix-and-match reagents that give researchers a way to quickly build new combinations of regulatory elements . These reagents will allow researchers to express a single gene in a wide array of temporal or spatial patterns , or to serially express many genes in a single cell . As a proof of principle , we made an artificial worm gene composed of a neurotoxin that would block neurotransmission . When we activated the gene in a small number of neurons in adult animals , these cells ceased to function . We anticipate that this new technique will find a wide variety of uses by the C . elegans community .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"molecular",
"biology/recombination",
"neuroscience"
] |
2008
|
Gene Activation Using FLP Recombinase in C. elegans
|
Clonorchiasis , caused by the liver fluke Clonorchis sinensis , remains a serious public health issue in Asia , especially in China , and its relationship with cholangiocarcinoma has highlighted the importance of C . sinensis infection . Proteins containing tandem repeats ( TRs ) are found in a variety of parasites and , as targets of B-cell responses , are valuable for the serodiagnosis of parasite infections . Here , we identified a novel C . sinensis-specific antigen , Cs1 , containing TRs , and investigated its diagnostic value , other immunological properties , and tissue distribution . A partial Cs1 cDNA sequence was cloned by screening an adult C . sinensis cDNA expression library . The full-length Cs1 cDNA was obtained by 5′ rapid amplification of cDNA ends . The deduced Cs1 protein consists of a signal peptide and five TRs of 21 amino acids . The recombinant Cs1 ( rCs1 ) was constructed and purified . rCs1 showed higher sensitivity ( 94 . 3% ) and specificity ( 94 . 4% ) than the C . sinensis excretory–secretory products ( ESPs ) according to ELISA of 114 serum samples . Native Cs1 was identified in C . sinensis ESPs and crude antigens of adult C . sinensis by western blotting using an anti-rCs1 monoclonal antibody . ELISA of recombinant peptides of different Cs1 regions demonstrated that the TR region was immunodominant in Cs1 . Immunohistochemistry and confocal microscopy revealed that Cs1 is located in a granule-like structure surrounding the acetabulum of C . sinensis adults that has not previously been described . We identified a novel C . sinensis-specific TR protein , Cs1 , which is an antigen of high serological significance , compared with C . sinensis ESPs . The deduced features of Cs1 show a unique structure containing TRs and a signal peptide and the TR region is immunodominant in Cs1 . This provides a basis for targeted screens of other antigens . The novel structure in which Cs1 is located also deserves further investigation .
Clonorchiasis is a food-borne parasitic disease caused by infection with the liver fluke Clonorchis sinensis , which is mainly endemic in China , South Korea , northern Vietnam , and parts of Russia . It was conservatively estimated that in 2004 around 15 million people were infected , of whom an estimated of 13 million are in China [1–4] . C . sinensis infection causes liver and biliary diseases . An increased risk of developing cholangiocarcinoma , a malignant tumor that arises from the bile ducts , is the most severe clinical manifestation . C . sinensis was classified as a Group 1 biological carcinogenic agent ( carcinogen ) by the International Agency for Research on Cancer in 2009 [5] . Clonorchiasis is an urgent public health problem in most endemic areas [6 , 7] , and is included in the control programs of neglected tropical diseases by the World Health Organization . For many years the diagnosis of clonorchiasis has been primarily based on fecal examination , using the Kato–Katz method and the formalin–ether technique , which are labor-intensive and time-consuming , especially for mass screening in the field . A variety of immunological and serological approaches , including enzyme-linked immunosorbent assay ( ELISA ) and indirect fluorescence antibody tests , have been used as supplementary tests . The current ELISA is a reliable diagnostic test for clonorchiasis that uses crude extracts or excretory–secretory products ( ESPs ) of adult worms [8–10] . C . sinensis ESPs are thought to be superior to crude extracts , showing a sensitivity of 93 . 1% [8] . However , it is difficult to produce sufficient amounts of ESPs . Thus , the serodiagnostic applicability of several recombinant proteins ( 7-kDa protein , CsLPAP , CsEF-1α , 28-kDa cystein protease , and 26-kDa and 28-kDa glutathione S-transferases ) from C . sinensis worms has recently been evaluated [11–16] . These recombinant proteins show a range of sensitivities and specificities for the serodiagnosis of clonorchiasis , but are not sufficient to replace crude extracts or ESPs . Thus , further research is needed to identify more effective serological antigens . To determine whether alternative C . sinensis-specific antigens could be used , we serologically screened a C . sinensis expression library using pooled sera from clonorchiasis patients . This identified a novel C . sinensis-specific cDNA that we named Cs1 . This cDNA encodes a protein with a signal peptide and tandem repeats ( TRs ) . We also characterized the biochemical and immunological properties and immunolocalization of Cs1 protein .
The animal experiment was reviewed and approved by the Animal Welfare & Ethics Committee of the National Institute of Parasitic Diseases , Chinese Center for Disease Control ( NIPD , China CDC ) ( Permit No: IPD-2009-14 ) ( S1 Fig ) and followed the National Guidelines for Experimental Animal Welfare ( MOST of People’s Republic of China , 2006 ) . Ethical clearance for the collection and analysis of human samples was obtained from the Ethical Review Committee of the NIPD , China CDC ( Permit No: 20120826 ) ( S2 Fig ) . Written informed consent for participation in this study was obtained from all clonorchiasis patients and healthy controls ( all subjects were adults ) . Archived human sera used in this study were obtained from the serum samples library of the NIPD , China CDC . C . sinensis metacercariae were obtained from naturally infected Pseudorasbora parva captured from endemic areas in Guangxi Province , China , as previously described [17] , and were orally administered to New Zealand white rabbits . Adult worms were collected from rabbit bile ducts 6 weeks post-infection . ESPs were prepared as follows . Adult worms were cultured in Tyrode’s solution with penicillin ( 100 U/ml ) and streptomycin ( 100 μg/ml ) . The culture supernatant was collected every 24 h , centrifuged at 1000 × g for 10 min at 4°C , aliquoted and stored at −80°C . Crude antigens of C . sinensis adult worms were prepared as previously described [8] . A total of 114 serum samples were collected from patients with clonorchiasis ( egg-positive as detected by the modified Kato–Katz technique ) ( n = 35 ) , schistosomiasis japonica ( n = 15 ) , paragonimiasis westermani ( n = 15 ) , or cysticercosis ( n = 13 ) from areas of China where each parasite is endemic , and from healthy individuals without parasitic infections ( n = 36 ) from Guangxi Province , China ( an C . sinensis endemic area ) . All sera were stored at –80°C until used . An adult C . sinensis cDNA expression library was constructed as previously described [17] . The cDNA library , 1 . 1 × 106 pfu , was mixed with Escherichia coli XL1-Blue and cultured on a LB-agar plate . The plate was overlaid with a nitrocellulose ( NC ) membrane ( Amersham , UK ) , which was treated previously with 10 mM isopropyl-D-thiogalactoside ( IPTG ) , and then incubated at 37°C for 4 h . The membrane was further incubated for 3 h with pooled sera of five clonorchiasis patients diluted 1:100 at room temperature ( RT ) , followed by incubation with the goat anti-human IgG alkaline phosphatase-conjugated secondary antibody ( Sigma , USA ) diluted 1:2 , 000 . A color signal was developed with the addition of 5-bromo-4-chloro-3-indolyl phosphate and nitro blue tetrazolium ( Sigma , USA ) solution . Positive clones were purified through secondary and tertiary screenings using the same sera . These clones were then excised in vivo to single-stranded phagemids by employing ExAssist helper phage ( Stratagene , USA ) , and converted to double-stranded plasmids according to the manufacturer’s instructions . The positive clones were sequenced and analyzed , and sequence homology was searched in NCBI using BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) . A partial cDNA sequence of Cs1 was obtained through screening the adult C . sinensis cDNA expression library . To determine the full-length Cs1 cDNA sequence , the 5′ RACE System for Rapid Amplification of cDNA Ends ( Invitrogen , USA ) was used to amplify Cs1 transcripts from mRNAs of adult worms , using GSP1: 5′-TGCGCACCATCCGCATCG-3′ for cDNA synthesis , GSP2: 5′-GATGTGCTCGAGCCTGAAG-3′ and AAP primer ( see instruction manual ) for PCR amplification . PCR products were analyzed by electrophoresis on a 1% agarose gel and then cloned into the pGEM-T Easy Vector ( Promega , UK ) and sequenced . Cs1 and other serologically screened genes ( reported in our previous studies [18–21] ) from the adult C . sinensis cDNA expression library were analyzed with Tandem Repeats Finder ( http://tandem . bu . edu/trf/trf . html ) as previously described [22] to determine whether they are TR genes . However , TR domains in the deduced proteins were manually identified because some of the TR sequences in these genes did not match with coding regions . The biochemical characteristics of their deduced amino acid sequences were analyzed to determine: ( i ) the molecular mass , isoelectric point , and amino acid composition using ProtParam ( http://web . expasy . org/protparam/ ) , ( ii ) the presence of a signal sequence using the SignalP 4 . 1 Server ( http://www . cbs . dtu . dk/services/SignalP/ ) , and ( iii ) the presence of a transmembrane domain using the TMHMM Server v . 2 . 0 ( http://www . cbs . dtu . dk/services/TMHMM/ ) . The coding region of Cs1 ( the N-terminal signal peptide sequence was omitted ) was amplified by PCR using the positive phagemid corresponding to Cs1 ( pBlueScript-SK-Cs clone 5 ) as the template , and forward primer 5′-CCGACATGTCTGAGGACATTTTAG-3′ and reverse primer 5′-CCCAAGCTTTGATATGATTCTTCGTAGAATT-3′ . These primers have PciI and HindIII sites , respectively , at their 5′-ends ( underlined ) . The specific PCR product was purified and digested with PciI and HindIII ( all the restriction endonucleases used in this study were from New England Biolabs , USA ) , and recombined into the pET28a expression vector ( Promega , USA ) digested with NcoI and HindIII . The construct was confirmed by DNA sequencing and then transformed into BL21 ( DE3 ) -competent cells ( TIANGEN , China ) . The expression of recombinant Cs1 ( rCs1 ) was induced by IPTG ( 1 mmol/L ) . rCs1 was purified under non-denaturating conditions from culture supernatant using an Ni-NTA affinity column ( QIAGEN , USA ) according to the manufacturer’s instructions . The efficiencies of rCs1 expression and purification were analyzed by 12% sodium dodecyl-sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) . The TR region and two flanking regions ( the N-terminus not including the signal peptide , and the C-terminus plus the TR region because the C-terminus is too short to be expressed ) of Cs1 were amplified by PCR , using PET28a-rCs1 as the template , and specific primer sets as follows: TR region , 5′-CATGCCATGGAATGTGACCCTGAATCAG-3′ and 5′-CCGCTCGAGAATCTCACCTTGCGGTTT-3′ , N-terminus ( not including the signal peptide ) , 5′-CCGACATGTCTGAGGACATTTTAG-3′ and 5′-TTTTCCTTTTGCGGCCGCGTCACATTCTCCTTTCACTG-3′ , C-terminus plus TR region , 5′-CATGCCATGGAATGTGACCCTGAATCAG-3′ and 5′-CCGCTCGAGTGATATGATTCTTCGTAGAATT-3′ ( restriction sites are underlined ) . The amplified PCR products ( Cs1TR and Cs1C+TR ) were digested with NcoI and XhoI , respectively , and then inserted into pET28a digested with the same restriction enzymes . Cs1N was digested with PciI and NotI , and then inserted into pET28a digested with NcoI and NotI . Recombinant peptides ( named rCs1C+TR , rCs1N , and rCs1TR , respectively ) were expressed and purified as for rCs1 . The diagnostic value of rCs1 was evaluated by ELISA ( rCs1-ELISA ) and compared with that of C . sinensis ESPs ( ESP-ELISA ) . Briefly , 96-well plates were coated with ESPs ( 2 . 5 μg/well ) or rCs1 ( 0 . 5 μg/well ) in 0 . 05 M bicarbonate buffer , pH 9 . 6 ( 100 μl/well ) overnight at 4°C . Human sera were reacted with proteins at a 1:100 dilution for 1 h at 37°C . After washing with phosphate-buffered saline/0 . 05% Tween 20 ( PBST , pH 7 . 4 ) , goat anti-human IgG conjugated with horseradish peroxidase ( Sigma , USA ) was incubated at 1:10 , 000 for 1 h at 37°C . All experiments were carried out in duplicate . The cutoff value was set as the mean value plus two standard deviations of the absorbance of negative control samples . Sensitivity is represented by Se = TP/ ( TP+FN ) , where TP ( true positive ) is the number of sera from individuals infected with C . sinensis above the cutoff value , and FN ( false negative ) is the number of sera from infected individuals below the cutoff for the conserved peptide . Specificity is represented by Sp = TN/ ( TN+FP ) , where TN ( true negative ) is the number of sera from un-infected individuals below the cutoff , and FP ( false positive ) is the number of sera from these samples with reactivity . The serological reactivity of rCs1C+TR , rCs1N , and rCs1TR were valued using the same method . Monoclonal antibodies were produced in BALB/c mice using rCs1 as an antigen as previously described [16 , 23 , 24] . Briefly , 8-week-old female BALB/c mice were each immunized with 50 μg rCs1 in Freund’s complete adjuvant and boosted twice with the same amount of the mixture of incomplete adjuvant every 2 weeks later . The final boost was given intravenously without adjuvant . Spleen cells were then isolated from the immunized mice 3 days later and fused to SP2/0 plasmacytoma cells . Hybridoma culture supernatants were screened by ELISA . ELISA-positive hybridoma cell lines were cloned at least 3 times by limiting dilution prior to large-scale production . The cloned hybridoma cells , 5×106 in number , were inoculated intraperitoneally into the BALB/c mouse and IgG antibody was purified from ascites by protein G agarose ( Thermo scientific , USA ) . The ascites of mice injected with SP2/0 plasmacytoma cells was prepared as a negative control ( SP2/0 ) . ESPs or crude antigens of C . sinensis adult worms were subjected to 12% SDS-PAGE , then electrotransferred onto a polyvinylidene fluoride ( PVDF ) membrane ( Millipore , USA ) at 100 V for 1 h in a Trans-Blot transfer cell ( Bio-Rad , USA ) . The membrane was incubated with an anti-rCs1 monoclonal antibody ( mAb; Cs1-2-6-3 ) at 1:200 dilution , and subsequently in horseradish peroxidase-conjugated goat anti-mouse IgG ( 1:5 , 000; Sigma , USA ) . Finally , the color was developed with diaminobenzidine substrate solution . To determine the localization of Cs1 in C . sinensis adult worms , they were fixed in 4% paraformaldehyde , embedded in paraffin , and sliced at 6–7 μm thicknesses . Paraffin sections were deparaffinized , rehydrated , and then restoration heat-treated . The sections were incubated with anti-rCs1 mAb ( Cs1-2-6-3 ) diluted 1:20 in PBS for 2 h at 37°C , with SP2/0 used as a negative control . The sections were washed three times with PBS and then incubated with fluorescein isothiocyanate-conjugated anti-mouse IgG ( Sigma , USA ) for 1 h at 37°C in the dark . After washing , sections were observed under a fluorescence microscope ( Olympus BX51 ) . To further determine the 3D localization of Cs1 , C . sinensis adult worms were leaf-shaped in 10% neutral formalin overnight at 4°C , and then washed twice with 5% sucrose/PBS , twice with PBS , three times with 5% TritonX-100/PBS , and three times with 1% TritonX-100/PBS . The worms were then blocked with 3% bovine serum albumin/PBS for 1 h at RT , and incubated with anti-rCs1 mAb ( Cs1-2-6-3 ) diluted 1:50 in PBS for 1 . 5 h at RT; SP2/0 was used as a negative control . The worms were washed three times with 1% TritonX-100/PBS and then incubated with Alexa Flour 488 ( AF488 ) -conjugated anti-mouse IgG ( Sigma , USA ) for 1 h at RT in the dark . After washing three times with 1% TritonX-100/PBS , worms were counterstained with propidium iodide ( PI , 10 μg/ml , with 10 μg/ml RNase A ) for 0 . 5 h at 37°C in the dark . After washing three times with 1% TritonX-100/PBS , worms were observed under a laser confocal microscope ( Nikon C2 ) . Images were captured and analyzed using reconstruction software ( NIS-Elements ) . Statistical analyses were performed using SPSS for Windows , version 19 . 0 ( SPSS Inc . , Chicago , IL ) . The chi-square test was used for the analysis of significance . The statistical significance was defined as P < 0 . 05 .
A total of 44 positive clones were obtained through screening the adult C . sinensis cDNA expression library using pooled sera from five clonorchiasis patients . These clones were sequenced and divided into six categories according to their sequence similarities ( S1 Table ) . Two encoded previously well-characterized antigens , GRCSP ( glycine-rich antigen 1 ) and cysteine proteinase , three categories were reported in our previous studies [18–21] , and the remaining category , Cs1 , is reported here . The full-length Cs1 cDNA sequence was obtained by 5′-RACE and consists of 733 nucleotides , with a coding sequence from 23–619 that encodes a putative protein of 198 amino acids . No orthologue was found in the Opisthorchis viverrini genome archived in the GenBank database . BLAST results showed that Cs1 has a very low level of similarity ( ≤11% ) with other genes , indicating that it is novel and C . sinensis-specific . The deduced Cs1 protein is composed of a signal peptide ( MGMKPQLVYFIFIQLVTAECLA ) and five complete TRs of 21 amino acids ( DPESDGAVADDAPPSQVKPQG ) ( Fig 1A ) . The Cs1 cDNA sequence was submitted to GenBank ( accession number: HM236312 . 1 ) . The PCR product of the Cs1 coding region produced a 550 bp band by agarose gel electrophoresis ( Panel A in S3 Fig ) . rCs1 was expressed mainly in the supernatant of E . coli , with an apparent molecular weight of 45 kDa ( Fig 1B ) . ELISA was used to compare the diagnostic value of rCs1 ( rCs1-ELISA ) with that of C . sinensis ESPs ( ESP-ELISA ) . A total of 114 serum samples were assayed , including sera from patients with clonorchiasis ( n = 35 ) , schistosomiasis japonica ( n = 15 ) , paragonimiasis westermani ( n = 15 ) , and cysticercosis ( n = 13 ) , and from healthy individuals ( n = 36 ) . rCs1-ELISA showed a lower cut-off compared with ESP-ELISA . Moreover , rCs1-ELISA showed 6 . 7% cross-reactivity with Paragonimus westermani , while ESP-ELISA showed 60 . 0% cross-reactivity with Paragonimus westermani ( P < 0 . 01 ) . Overall , rCs1-ELISA showed 94 . 3% ( 33/35 ) ( 95% CI: 90 . 0% ~ 98 . 6% ) sensitivity and 94 . 9% ( 75/79 ) ( 95% CI: 90 . 9% ~ 98 . 9% ) specificity , compared with 88 . 6% ( 31/35 ) ( 95% CI: 82 . 8% ~ 94 . 4% ) and 86 . 1% ( 68/79 ) ( 95% CI: 79 . 7% ~ 92 . 5% ) , respectively , for ESP-ELISA ( Fig 2 ) . The Cs1 protein was predicted to have a signal peptide and was predicted to be secreted by the parasite . Western blotting showed that rCs1 was recognized by the anti-rCs1 monoclonal antibody ( mAb; Cs1-2-6-3 ) . Cs1-2-6-3 recognized the native Cs1 protein of 45 kDa in ESPs and crude antigens of C . sinensis adult worms ( Fig 3 ) . These results indicate that Cs1 is a component of both the ESPs and crude antigens of C . sinensis . To examine whether the TR region is immunodominant in the Cs1 protein , the complete TR region , the N-terminus ( without signal peptide ) , and the C-terminus plus TR region of Cs1 were amplified ( Panel B in S3 Fig ) and then expressed as recombinant peptides ( rCs1TR , rCs1N and rCs1C+TR ) , with apparent molecular weights of 28 kDa , 19 kDa , 30 kDa , respectively ( Fig 1C–1E ) . These proteins were then evaluated by ELISA using the pooled sera from patients with clonorchiasis ( n = 10 ) , and pooled sera from healthy individuals ( n = 10 ) as a negative control . rCs1TR and rCs1C+TR both showed positive reactions ( P/N > 2 . 1 , P: patient , N: negative control ) , while rCs1N showed a negative reaction ( P/N < 2 . 1 ) . These results indicate that the TR region is immunodominant in the Cs1 protein . The PI value of the Cs1 protein is 4 . 07 . The TR amino acid sequence of Cs1 is DPESDGAVADDAPPSQVKPQG . These 21 amino acids include five negatively-charged residues ( four D , one E ) and one positively-charged residue ( one K ) . These results indicate that the TR region in Cs1 protein has an abundance of strongly acidic amino acids . To determine the tissue distribution of Cs1 , immunohistochemistry and confocal microscopy were performed on C . sinensis adult worms using an anti-rCs1 mAb ( Cs1-2-6-3 ) . Sections of C . sinensis adult worms were probed with Cs1-2-6-3 ( Fig 4A and 4B ) or SP2/0 ( Fig 4C and 4D ) . A strong positive reaction was observed in the cells in a granulate , gland-like structure surrounding the acetabulum of C . sinensis adult worms , and a moderately positive reaction was seen in the outer tegument of the acetabulum ( Fig 4A and 4E ) . Confocal microscopy results further confirmed that Cs1 is located in the cells surrounding the acetabulum of adult C . sinensis worms , and showed it to have a dish-like structure ( Fig 5 ) .
In the present study , we identified a novel C . sinensis-specific TR protein for the serodiagnosis of clonorchiasis . The deduced Cs1 sequence ( 198 amino acids ) is composed of a signal peptide and five TRs of 21 amino acids . No orthologue was found in the genome of O . viverrini , which has a very close relationship to C . sinensis , in the archived GenBank database . BLAST results also showed that Cs1 has low sequence similarity ( ≤ 11% ) with other genes , indicating that Cs1 is novel and C . sinensis-specific . This result is consistent with previous findings , which demonstrated that many TR proteins are genus- or species-specific [25] . Proteins containing TRs , defined here as proteins consisting of two or more copies of a pattern of amino acids , have been found in a variety of protozoan parasites , including Plasmodium [26 , 27] , Leishmania [22 , 28–30] , Trypanosoma [31 , 32] , and Fasciola hepatica [33] . These TR proteins often serve as targets of B cell responses . With respect to C . sinensis , some currently validated antigens are also TR proteins , including GRCSP ( glycine-rich antigen ) [34] , CsPRA ( proline-rich antigen ) [35] , and pBCs31 [36] . Cs1 is a TR protein containing five complete copies of “DPESDGAVADDAPPSQVKPQG” . Its serodiagnostic value was evaluated by indirect ELISA with a total of 114 human serum samples , and compared with that of C . sinensis ESPs . Cs1 showed high sensitivity and specificity , which was similar to those of C . sinensis ESPs . However , C . sinensis ESPs showed 60% cross reactivity with P . westermani , whereas the cross reactivity of Cs1 was 6 . 7% ( P < 0 . 01 ) . rCs1-ELISA also showed a lower cut-off compared with ESP-ELISA . ESPs are well-known as serodiagnostic antigens , but our present results indicate that rCs1 may be a potential substitute of ESPs for the diagnosis of clonorchiasis . To further evaluate the potential of Cs1 for serodiagnosis of opisthorchiasis , ELISA was used to compare Cs1 ( rCs1-ELISA ) with C . sinensis ESPs ( ESP-ELISA ) using serum samples from patients with opisthorchiasis viverrini ( n = 5 ) . rCs1-ELISA showed no cross-reactivity with O . viverrini , while ESP-ELISA showed 60 . 0% cross-reactivity with O . viverrini ( P < 0 . 01 ) . This result indicates that Cs1 is very specific for C . sinensis and could be used to discriminate between clonorchiasis and opisthorchiasis . The fact that no orthologue of Cs1 was found in the genome of O . viverrini also validates this conclusion . Our studies also demonstrated that the TR region was immunodominant in the Cs1 protein . This is consistent with previous reports demonstrating that proteins containing TR domains are often B cell antigens , and that antibody responses toward TR domains are dominant in humans infected with certain parasites . The overall immunogenicity of proteins harboring TRs is high , as is the antigenicity of epitopes contained within these repetitive units [37–40] . These units may provide additional antigen-presenting sites and antibody-combining sites , which increase the overall immunogenicity and the antigenicity of TR proteins . The Cs1 TR region has an abundance of strongly acidic amino acids , including five negatively-charged and one positively-charged residue . These results are consistent with previous findings showing that B cell epitopes are rich in negatively-charged residues Asp ( D ) and Glu ( E ) , positively-charged residues Lys ( K ) , Arg ( R ) , and His ( H ) , non-polar aliphatic residues Gly ( G ) and Pro ( P ) , polar non-charged residues Asn ( N ) and Gln ( Q ) , and the aromatic residue Tyr ( Y ) [37 , 38 , 41] . The expected size of Cs1 itself is around 20 kDa , but the recombinant or the native protein in crude preparations is much bigger than that . It is not unusual for the molecular weight of a protein on an SDS PAGE gel to be different from its predicted size . In many cases , this molecular weight difference is attributed to chemical modifications of the protein . However , the contribution of chemical modifications was ruled out because the recombinant protein was expressed in a prokaryotic expression system . Another possible reason is that the protein existed as a multimer . However , in theory , multimers should be dissociated by SDS-PAGE . The most probable reason is that Cs1 is an acidic protein with a predicted isoelectric point of 4 . 07 , which resulted in a much larger molecular weight on an SDS-PAGE gel compared with its predicted size . The three truncated fragments of Cs1 ( i . e . rCs1N , rCs1TR , and rCs1C+TR ) also exhibited much larger molecular weights compared with those predicted ( 6 . 6 kDa , 11 . 9 kDa , and 14 . 2 kDa , respectively ) . They are also all acidic proteins with predicted isoelectric points of 4 . 61 , 3 . 64 , and 4 . 07 , respectively . These results are consistent with previous findings showing that proteins rich in negatively-charged residues Asp ( D ) and Glu ( E ) have drastically retarded gel mobility[42–45] . All six antigen categories identified from the adult C . sinensis cDNA library were analyzed for common features . Surprisingly , all six contained signal peptides , and all except cysteine proteinase contained TR domains ( S1 Table ) . This indicates that proteins with TRs and signal peptides may be potent antigens in C . sinensis , and potentially in other parasites . These features provide a basis for future targeted screens of entire proteomes based on the likelihood of seroreactivity . With respect to Cs1 , the features of TR content , SignalP > 0 . 7 , and PI < 5 were also validated by studies in other organisms [39 , 40] . In conclusion , we have cloned and expressed Cs1 , and assessed its serological properties . We also demonstrated that the repetitive domains of TR proteins are strong serological antigens , and may be useful for the serodiagnosis of C . sinensis . The unknown granule-like structure surrounding the acetabulum of C . sinensis adult worms deserves further study to help understand the biological functions of Cs1 .
|
Clonorchiasis is a neglected tropical disease . The major factor that prevents the effective management of clonorchiasis is a lack of effective diagnostic tools . Proteins containing tandem repeats ( TRs ) , which have been found in a variety of parasites , are known targets of B-cell responses and can be useful for the serodiagnosis of parasite infections . Here we identified a novel C . sinensis-specific cDNA , which we named Cs1 . This cDNA encodes a protein that has a unique structure , containing TRs and a signal peptide . A recombinant Cs1 protein ( rCs1 ) was expressed and purified . rCs1 showed a high sensitivity and specificity in enzyme-linked immunosorbent assays , and lower cross-reactivity with Paragonimus westermani compared with C . sinensis excretory–secretory products . Our results also indicated that the TR region was immunodominant in the Cs1 protein . Immunohistochemistry and confocal microscopy revealed that Cs1 was located in a granule-like structure surrounding the acetabulum of adult worms that has not been previously described in C . sinensis . These results show that Cs1 is a promising antigen for serodiagnosis of clonorchiasis and its features provide a basis for future targeted screens of entire proteomes based on the likelihood of seroreactivity .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"cdna",
"library",
"screening",
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
"helminths",
"tropical",
"diseases",
"parasitic",
"diseases",
"animals",
"trematodes",
"clonorchis",
"sinensis",
"foodborne",
"trematodiases",
"neglected",
"tropical",
"diseases",
"molecular",
"biology",
"techniques",
"dna",
"dna",
"libraries",
"immunologic",
"techniques",
"research",
"and",
"analysis",
"methods",
"proteins",
"flatworms",
"immunoassays",
"clonorchiasis",
"molecular",
"biology",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"biochemistry",
"helminth",
"infections",
"eukaryota",
"post-translational",
"modification",
"nucleic",
"acids",
"library",
"screening",
"clonorchis",
"genetics",
"biology",
"and",
"life",
"sciences",
"signal",
"peptides",
"organisms"
] |
2018
|
Cs1, a Clonorchis sinensis-derived serodiagnostic antigen containing tandem repeats and a signal peptide
|
Synapse remodeling is an extremely dynamic process , often regulated by neural activity . Here we show during activity-dependent synaptic growth at the Drosophila NMJ many immature synaptic boutons fail to form stable postsynaptic contacts , are selectively shed from the parent arbor , and degenerate or disappear from the neuromuscular junction ( NMJ ) . Surprisingly , we also observe the widespread appearance of presynaptically derived “debris” during normal synaptic growth . The shedding of both immature boutons and presynaptic debris is enhanced by high-frequency stimulation of motorneurons , indicating that their formation is modulated by neural activity . Interestingly , we find that glia dynamically invade the NMJ and , working together with muscle cells , phagocytose shed presynaptic material . Suppressing engulfment activity in glia or muscle by disrupting the Draper/Ced-6 pathway results in a dramatic accumulation of presynaptic debris , and synaptic growth in turn is severely compromised . Thus actively growing NMJ arbors appear to constitutively generate an excessive number of immature boutons , eliminate those that are not stabilized through a shedding process , and normal synaptic expansion requires the continuous clearance of this material by both glia and muscle cells .
The wiring of the nervous system , from initial axon sprouting to the formation of specific synaptic connections , represents one of the most dramatic and precise examples of directed cellular outgrowth . Developing axons navigate sometimes tortuous routes as they seek out the appropriate target cells . Once in their target area , interactions between axons and their potential targets are extremely dynamic , attempts are made to identify appropriate postsynaptic partners , and initial synaptic contacts are established [1] , [2] , [and reviewed in 3] . A next critical step in the formation of functional neural circuits is the remodeling of initial patterns of connectivity . To facilitate the elaboration and refinement of developing neural circuits synaptic partners often remain highly responsive to their environment and add or eliminate synaptic connections [4] , [5] , frequently in an activity-dependent fashion , presumably to fine-tune connectivity to specific activity patterns . After the axons have found their partners , two distinct mechanisms can drive the developmental reorganization of synaptic connectivity: intercellular competition between cells for common targets ( reviewed in [4] , [5] ) , and the addition/elimination of synapses within a single arbor in response to the physiological demands of the signaling unit [6]–[8] . The former mechanism dictates the circuit “wiring diagram” by defining precisely which subsets of cells will communicate through synaptic contacts; while the latter , in contrast , modulates the strength of connectivity between specific pre- and postsynaptic cells after circuits are assembled . Early in nervous system development an excessive number of axonal projections and synaptic connections are initially established . What then ensues is cell–cell competition between neurons innervating the same target for limiting target-derived cues or sites of innervation during synaptogenesis . Appropriate synaptic contacts are then strengthened and exuberant processes are destabilized and eliminated through activity-dependent mechanisms [5] , [9] . For example , at the mammalian neuromuscular junction ( NMJ ) muscles are initially innervated by more than one motor input . However , through a process of intercellular competition for motor endplates , all but one motor input are eliminated , with the “losers” retracting wholesale from the motor endplate [2] . Likewise , at the retinotectal projection in frogs , retinal axons initially establish a rough topographic map with substantial overlap between branches . However , these local synaptic terminals ultimately compete for target space and through activity-dependent modulation of synapse stabilization the spatial map of synaptic inputs is ultimately refined to a highly selective subset of inputs [10] . In the intercellular competition model the elimination of exuberant inputs ( the “losers” ) can entail large-scale elimination of axon branches , and perhaps smaller scale pruning of individual synaptic contacts . During axon and synaptic pruning in mammals and Drosophila entire axon branches are destabilized , degenerate , and are then cleared from the central nervous system by engulfing cell types ( reviewed in [5] ) . Similarly , recent work has shown that excessive motorneuron inputs at the mammalian NMJ also become destabilized , detach from the motor endplate , and undergo axosome shedding . In this process local Schwann cells processively engulf motorneuron terminals in a distal to proximal direction and constitute the force that drives retraction bulbs toward the parent arbor during input elimination [11] . Ultimately , this mechanism results in a reduction of the total number of cells supplying synaptic input to the target cell . In the second and mechanistically distinct mode of synapse remodeling , individual synaptic contacts are added or removed from a single arbor to strengthen or weaken synaptic input to the target cell . Such changes are generally elicited by changes in the target size or neural activity . For example , Drosophila motorneurons have established synaptic contacts with specific embryonic muscle cells by the end of embryogenesis [12] . At subsequent larval stages individual arbors , along with the target muscle itself , grow in size ∼100-fold [7] , [8] . This coordinate increase in muscle size and synaptic contacts at motorneuron terminals serves to increase synaptic input from the motorneuron as needed to drive activation of the expanding muscle fiber . Similar mechanisms appear in place to modulate the balance of neural input versus target cell size in mammals: at the mammalian adult bulbocavernous muscle , testosterone manipulation lead to increases or decreases in muscle size , and these changes were accompanied by respective expansion or shrinkage of the postsynaptic region of the NMJ , respectively [6] . Here we explore the in vivo dynamics of synaptic expansion in motorneuron arbors at the Drosophila NMJ . We show in live preparations that the addition of new synapses during normal synaptic growth entails a large amount of shedding of presynaptic membranes in the form of small debris and a subpopulation of undifferentiated synaptic boutons ( ghost boutons ) that failed to mature . This process is distinct from intercellular competition , as none of the motorneuron terminals are eliminated . Rather , this mechanism appears to regulate the final size of the terminal arbor . We find that the formation of presynaptic debris ( this report ) and ghost boutons [13] are modulated by neural activity , as acute stimulation of motor inputs leads to increased appearance of these structures . Intriguingly , presynaptic debris and the subpopulation of ghost boutons that become detached from the parent arbor appear to be actively cleared from the NMJ as they disappear over developmental time . We show that glia dynamically invade the NMJ and phagocytose presynaptically shed debris , and that ghost boutons are engulfed or degraded primarily by muscle cells . Loss of phagocytic function in glia or muscle cells through manipulating the Draper signaling pathway ( a key engulfment signaling pathway ) results in an accumulation of presynaptic debris or ghost boutons at the NMJ and a severe reduction in NMJ expansion , indicating that continuous clearance of shed presynaptic debris and/or ghost boutons is essential for normal synaptic growth . Thus glia and muscles work together to sculpt connectivity at developing NMJ arbors , clearing multiple types of shed presynaptic structures that are inhibitory to the formation of new synaptic boutons .
In insects , α-HRP antibodies cross-react with neuron-specific membrane antigens [14] likely by binding to carbohydrate moieties present in a number of neuronal membrane proteins , including the cell adhesion molecules Fasciclin ( Fas ) I and II [15] . Consistently , at the Drosophila larval NMJ α-HRP antibodies labeled the entire presynaptic arbor ( Figure 1Ai ) . However , we also noticed the presence of HRP-immunoreactive puncta at the postsynaptic junctional region , beyond the presynaptic membrane ( Figures 1Ai , 1Aii , arrows ) . These puncta also labeled with antibodies to FasII and did not appear to be connected to the presynaptic arbor ( Figures 1Aiii , Aiv ) . We wondered whether the HRP and FasII-positive postsynaptic staining might correspond to postsynaptic muscle structures , or whether the puncta might be derived from the presynaptic arbor . To distinguish between these possibilities , we expressed a membrane tethered green fluorescent protein ( GFP; UAS-mCD8-GFP ) in motorneurons using the motoneuron-specific Gal4 driver OK6-Gal4 [16] . We found that the postsynaptic HRP puncta were exactly colocalized with the presynaptically derived GFP signal ( Figure 1D , arrow ) , suggesting that the HRP puncta are likely membrane fragments derived from presynaptic boutons . The presynaptically derived mCD8-GFP puncta were also observed by imaging through the cuticle of intact ( undissected ) larvae using a spinning disk confocal microscope , indicating that they are naturally occurring and not an artifact of the dissection or sample preparation ( Figure 1E , arrows ) . The nature of the presynaptically derived puncta was examined using a number of synaptic markers . Cysteine string protein ( CSP ) and Synapsin ( Syn ) are presynaptic vesicle proteins that associate with the readily releasable and the reserve pool of synaptic vesicles respectively [17] , [18] . We found that the postsynaptic HRP puncta colocalized with CSP ( Figure 1B , arrows and inset ) , but not with Syn immunoreactivity ( Figure 1C ) . The presence of CSP in the HRP puncta further validates the idea that these puncta are presynaptic in origin . Labeling with antibodies against the active zone marker Bruchpilot ( Brp ) did not reveal immunoreactivity at the postsynaptic HRP-positive puncta ( unpublished data ) . Together these results suggest that during NMJ development the motorneuron sheds membrane fragments ( here referred to as presynaptic debris ) . Based on the presence of CSP but not Syn , the absence of Brp and the presence of FasII , we propose that presynaptic debris might arise from the perisynaptic bouton region . Studies in many systems have suggested that the state of a mature synapse is the result of a dynamic equilibrium between growth and retraction [19] . Therefore , to determine what conditions lead to the shedding of presynaptic debris , we attempted to perturb this equilibrium by inducing activity-dependent synaptic growth [13] . Previous studies at the larval NMJ show that an acute increase in activity induces a de novo formation of new synaptic boutons . In particular , spaced cycles of stimulation , consisting of either K+-induced depolarization , high frequency nerve stimulation , or light gating of neuronally expressed channelrhodopsin-2 ( ChR2 ) , induce rapid structural changes at the NMJ . These changes include an increase in the number and length of dynamic presynaptic filopodia ( synaptopods ) and the number of undifferentiated boutons ( ghost boutons ) containing synaptic vesicles but lacking active zones and postsynaptic proteins [13] . Imaging of intact larvae also showed that synaptopods and ghost boutons were naturally occurring structures observed even in unstimulated preparations albeit at low frequency [13] . In our experiments we expressed ChR2 in motorneurons using OK6-Gal4 and stimulated the motorneurons of intact larvae with 5 cycles of spaced light stimulation as previously described [13] . Body wall muscles were then dissected either 30 min or 18 h after the stimulation was complete and labeled with anti-HRP . As a control , we used unstimulated larvae expressing ChR2 in motorneurons but not subjected to the light pulses . Notably , we found that the total area occupied by particles of presynaptic debris around the NMJ was significantly increased 30 min after the end of spaced stimulation ( Figure 1F–1I ) , indicating that acute stimulation of neural activity resulted in an increase in presynaptic debris at the NMJ . Allowing NMJs to recover for 18 h after stimulation resulted in debris returning to wild-type levels ( Figure 1I ) , suggesting the presence of an active mechanism to eliminate presynaptic debris from the NMJ . We conclude that presynaptic debris are normally present at the NMJ and conditions that lead to synaptic growth result in a transient increase in the amount of presynaptic debris , thus shedding of debris is associated with NMJ growth . We also conducted time-lapse imaging of identified NMJs from live intact larvae expressing ChR2 in motorneurons using C380-Gal4 [20] . These larvae also contained fluorescent markers that allowed us to simultaneously image the pre- and the postsynaptic compartment . In particular , these larvae expressed UAS-mRFP in motorneurons to visualize the presynaptic NMJ arbor and mCD8-GFP::Sh in muscles using the myosin heavy chain ( MHC ) promoter [21] to visualize the postsynaptic NMJ region . In the MHC-mCD8-GFP::Sh transgene , the GFP is fused to the last ∼150 C-terminal amino acids of the Shaker K+ channel isoform containing a Discs-Large ( DLG ) PDZ binding site , and thus it is targeted to the postsynaptic region allowing its visualization in vivo [21] . These larvae were subjected to spaced stimulation with light as above , and the same NMJ imaged for 5–15 min at different intervals . Between imaging intervals larvae were returned to the food . As previously reported [13] , we found that ghost boutons were present and some of these became stabilized and recruited postsynaptic label . However , we also observed that many of these ghost boutons did not recruit postsynaptic label and disappeared over time ( Figure 2A , arrow and inset in right panel ) . The presence of presynaptic debris in normal animals , the enhancement of presynaptic debris deposition upon spaced stimulation , and the elimination of some of the newly generated ghost boutons after spaced stimulation suggest that NMJ development involves the continuous shedding of certain presynaptic membrane compartments . Furthermore , the lack of accumulation of these components over developmental time , suggest that they may be actively removed from the NMJ . To determine if presynaptic debris might originate from the breakdown of ghost boutons that failed to become stabilized and disappeared , we followed the fate of ghost boutons that became detached from the presynaptic arbor and presynaptic debris . In these experiments , identified NMJs from larvae expressing ChR2 and mCD8-GFP in motorneurons were repeatedly imaged through the cuticle as above following spaced stimulation . We found that on several occasions , as ghost boutons detached , debris appeared in the position of the ghost bouton stalk and around the ghost bouton , suggesting that ghost boutons can degenerate directly into presynaptic debris ( e . g . , Figure 2B and 2C; ghost boutons are marked by white arrows and debris by black arrowheads ) . In some samples we were able to directly image the disintegration of ghost boutons into smaller fragments ( Video S1 ) . However , in other cases , stalks simply disappeared without leaving debris , and detached ghost boutons became smaller and vanished from the NMJ without leaving any obvious debris ( Figure 2D and 2E , white arrows ) . Interestingly , not all presynaptic debris appeared to derive from ghost boutons and their stalks—we also observed the appearance and disappearance of presynaptic debris at NMJ regions devoid of ghost boutons ( Figure 2E , black and pink arrowheads ) , suggesting that presynaptic debris can be generated independently from ghost boutons . In summary , presynaptic debris can apparently arise directly from the breakdown of ghost boutons , or , alternatively may be derived directly from the presynaptic arbor without participation of ghost boutons . The very low levels of presynaptic debris and ghost boutons observed here in unstimulated larvae and the removal of the extra debris formed upon stimulation , suggested that as NMJs develop , presynaptic membrane debris and disconnected ghost boutons are actively cleared from the NMJ . Signal transduction mechanisms mediating the engulfment of neuronal debris are beginning to be elucidated [22] . Most prominent , the engulfment receptor Draper ( Drpr; Ced-1 in Caenorhabditis elegans ) is involved in the engulfment of neuronal cell corpses during programmed cell death , the pruning of mushroom body neuron arbors during fly metamorphosis , and in the phagocytosis of injured axons in the fly olfactory system [23]–[26] . We therefore used draper mutants as a tool to block the activity of local engulfing cell types and assayed the effects of loss of Draper function on clearance of shed presynaptic debris and disconnected ghost boutons from the larval NMJ . Strikingly , we found that draper mutant NMJs were highly abnormal , with the presence of unusually large and irregularly shaped boutons and with a marked reduction in the number of glutamatergic type Ib boutons ( Figure 3A , 3B , and 3F ) . Close examination of the NMJs in these mutants revealed that there was also a dramatic increase in the amount of presynaptic debris ( Figure 3C–3E , arrows , 3H ) and number of ghost boutons ( Figure 3E , arrowheads , 3G ) . Interestingly , we also found that third instar draper mutant larvae had reduced larval motility in behavioral assays ( Figure S1 ) , suggesting that the accumulation of presynaptically shed material may adversely affect neuromuscular function . Thus , in the absence of Draper function NMJs develop abnormally and presynaptic debris and ghost boutons accumulate at high levels . These observations suggest that an engulfing cell type might invade , or be a resident component of , the NMJ , and phagocytose shed presynaptic material . In the fly nervous system Draper is expressed in glia where it has crucial roles in engulfment activity [23]–[26] . To determine if Draper was also present in glial cells at the NMJ , we used α-Draper antibodies [24] . Surprisingly , in addition to its localization in peripheral glia that wrap around motor nerves ( Figure 4A ) , we found that Draper immunoreactivity was present at the postsynaptic region of every synaptic bouton in colocalization with the Drosophila PSD-95 homolog DLG ( Figure 4C ) . This immunoreactivity was specific to Draper , as it was virtually eliminated in draper null mutants ( Figure 4B and 4D ) . The above observation was surprising , since in contrast to vertebrate NMJs , where terminal Schwann cells completely cover the NMJ [27] , at the glutamatergic Drosophila larval NMJ terminal glia have not been reported to cap the synaptic arbor [28] , [29] . Instead , NMJ arbors are buried within the muscle surface , which wraps around the boutons forming a layered system of membranes , the subsynaptic reticulum ( SSR ) [30] , [31] . Previous studies have suggested that at the larval NMJ peripheral glia ensheath the segmental nerve , but for the most part , their membranes terminate at the axon branch point or at the first synaptic bouton closest to the branch point [29] . The presence of Draper surrounding the entire NMJ led us to reexamine the organization of glial cell membranes at the NMJ and their relationship to synaptic boutons . For these experiments we expressed a membrane tethered GFP ( mCD8-GFP ) in peripheral glia , using Gliotactin-Gal4 ( Gli-Gal4 ) , and HRP-labeled NMJs from abdominal segments 3 and 4 were systematically examined in fixed preparations . We found that in the majority of cases glial membranes deeply invaded the NMJ ( Figure 5 ) , presumably invading the space between the presynaptic motorneuron terminal and the SSR . Some NMJs ( 2%–40% on average depending on the specific NMJ ) , particularly those innervating dorsal muscles , appeared completely covered by glial membranes ( Figure 5A and 5E; covered NMJs ) . A majority ( 80%–100% ) of NMJs were associated with lamellipodia-like glial extensions that contacted several boutons ( Figure 5A–5C , and 5E ) . Glia also extended thin filopodia-like processes that contacted synaptic boutons at the same NMJ branch or that exited the branch and interacted with synaptic boutons from a different NMJ branch ( Figure 5Av and 5Bv ) . Glial membrane processes were also observed in association with muscle regions around the NMJ that were completely devoid of synaptic boutons ( Figure 5Aiv and 5Civ–v ) . A small percentage ( ∼7% ) of glial extensions had an elliptical appearance and terminated in bulbous structures of variable size ( Figure 5Div–v and 5E ) . These bulbous structures sometimes surrounded a synaptic bouton ( Figure 5Dv , arrowhead ) . In some NMJs ( 11%–33% ) glial membranes did not invade the NMJ and muscle , and terminated at the nerve branch-point before synaptic boutons ( Figure 5Bi–iii; blunt ended ) . Interestingly , the pattern of glial extensions was not stereotypic and showed a high degree of variability among segments and identified muscles from different individuals . This observation suggests that the glial processes are likely to extend and retract in a dynamic fashion . This possibility was examined by live imaging preparations expressing mCD8-GFP in peripheral glia with Gliotactin-Gal4 . We found that glial processes were indeed at the NMJ , and extended or retracted within a period of minutes ( Video S2 ) . These observations indicate that glial cells at the larval NMJ have previously unappreciated dynamics , and that they establish multiple transient associations with the NMJ . However , our studies of Draper localization at the NMJ demonstrated that Draper is present at every NMJ and surrounding each synaptic bouton ( Figure 4C ) . Thus , the extension of glial membranes is unlikely to account for Draper localization at the entire NMJ , raising the possibility that muscles might also contribute to NMJ Draper localization . In draper mutants , there were some changes in the distribution and frequency of glial extensions . Glial extensions that covered the entire NMJ ( covered NMJs ) were absent or drastically reduced in frequency , and there were also changes in the distribution and frequency of gliobulbs ( Figure S2 ) . In contrast , there was a strong increase in the frequency of blunted projections ( i . e . , those that end close to the nerve branch point and do not interact with synaptic boutons ) , and a normal level of lamellipodia-like extensions ) . These observations suggest that in the absence of Draper function some glial membranes do not extend properly into the NMJ . Thus positive signaling through Draper , perhaps in response to cues released by presynaptic debris , may directly regulate a subset of glial membrane movements at the NMJ . To address the possibility that Draper might function both in glia and muscle to sculpt the NMJ we selectively expressed a Draper-RNAi designed to knockdown all Draper isoforms in glia or muscles using cell-specific Gal4 strains . RNAi knockdown of Draper in either muscle or glia resulted in a reduction in the number of synaptic boutons , which was not significantly different from the draper null mutant ( Figure 6E ) . This indicates that the removal of Draper from either cell type is sufficient to interfere with NMJ growth . Remarkably , however , downregulating Draper in muscle versus glia had a different consequence for the deposition of presynaptic debris and the appearance of detached ghost boutons . RNAi knockdown of Draper in glia resulted in an increase in presynaptic debris to an extent similar to the draper null mutant ( Figure 6C and 6G ) . However , no significant increase in the number of detached ghost boutons was observed ( Figure 6F ) . If glial extensions are primarily involved in engulfing presynaptic debris , we predicted that we should find HRP positive debris within the glial extensions . We found that this was indeed the case . We found several instances in which glial terminals formed bulb-like structures that contained anti-HRP immunoreactive puncta within ( Figure 6D , arrowheads ) . In contrast , downregulating Draper in muscle resulted in an increase in the number of ghost boutons ( Figure 6B and 6F ) , but the level of presynaptic debris was similar to wild type ( Figure 6B and 6G ) . Expressing Draper RNAi in motorneurons did not affect the number of boutons , ghost boutons , or the levels of presynaptic debris ( Figure 6E–6G ) . These results support the idea that Draper functions both in muscle and glia , and that the function of Draper in each cell has some degree of specialization . While glial Draper appears to function in removing presynaptic debris , muscle Draper appears to remove ghost boutons fated for elimination . Importantly , these observations also provide the first evidence that muscle cells fulfill a phagocytic function at the NMJ . Previous studies have shown that the PTB-domain protein dCed-6 functions downstream of Draper [23] . Therefore , we used RNAi knockdown of dCed-6 in muscle or glia as a second approach to blocking glial and muscle engulfment activity . As in draper mutants , downregulating dCed-6 in either muscle or peripheral glia resulted in significant decrease in the number of synaptic boutons ( Figure 7A–7D ) . In contrast , no effect was observed when dCed-6-RNAi was expressed in motorneurons ( Figure 7D ) . Similar to Draper RNAi knockdown , expressing dCed-6-RNAi in muscles or glia had differential consequences for the appearance of presynaptic debris versus ghost boutons . Decreased levels of dCed-6 in muscles led to an increase in the number of ghost boutons , but had no influence in the deposition of presynaptic debris ( Figure 7B , 7E , and 7F ) . Downregulating dCed-6 in glia , on the other hand , led to a significant increase in presynaptic debris deposition , but the number of ghost boutons remained unaltered ( Figure 7C , 7E , and 7F ) . These results are consistent with the notion that dCed-6 functions downstream of Draper during the development of the NMJ . Further , they support the model that both muscle and glia contribute differentially to the clearance of debris versus ghost boutons at the NMJ . The draper gene gives rise to three different Draper isoforms , each with a unique combination of intracellular and extracellular domains ( Figure 8A ) . Draper-I bears 15 extracellular EGF repeats , whereas Draper-II and -III only contain five [24] . In their intracellular domains , all isoforms contain a potential dCed-6 binding site ( NPXY ) , but the Shark binding site is only present in Draper-I and -II . To determine which of the isoforms might be involved in NMJ development , we first carried out reverse-transcription PCR ( RT-PCR ) of body wall muscles . Interestingly , we found that Draper-I and III , but not Draper-II were expressed at the neuromuscular system ( Figure 8A and 8B ) . Therefore , we carried out rescue experiments by expressing Draper-I or -III in muscles or glia in a draper null mutant background . None of the Draper isoforms completely rescued the decrease in bouton number observed in the drpr null ( Figure 8C ) . This is consistent with the observations with cell-specific Draper-RNAi expression , showing that Draper functions both in muscle and glia , and that downregulating Draper in either cell is sufficient to decrease bouton number to an extent similar to the draper null mutant alone . In the case of ghost boutons , expressing Draper-I in glia or Draper-III in muscle completely rescued the mutant phenotype ( Figure 8D ) . However , expressing Draper III in glia or Draper I in muscle also resulted in substantial but incomplete rescue . For the deposition of presynaptic debris , only expressing Drpr-I in glia completely rescued the phenotype , but partial rescue was also observed when Drpr-III was expressed in muscle ( Figure 8E ) . These data provide conclusive evidence that the phenotypes we observe in draper null mutant NMJs indeed map to the draper gene , and that the phenotypes we observe in draper mutants can be significantly rescued by resupplying Draper in glia or muscle cells ( Figure 8F ) . The incomplete rescue of some of the phenotypes by specific isoforms might represent redundant functions by these isoforms , a requirement for multiple isoforms for complete rescue , or simply result from increased Draper expression in transgenic animals .
During larval development , the NMJ is continuously increasing the size and number of synaptic boutons . This expansion serves as a compensatory mechanism to preserve synaptic strength , despite the massive growth of muscle cells [32] . Our studies provide evidence that normal NMJ growth includes the constitutive shedding of presynaptic membranes . The presynaptic origin of HRP-positive debris was demonstrated by labeling motorneuron membranes with genetically encoded mCD8-GFP , which consistently labeled the debris , by the observation that in some cases ghost boutons that detached from the main arbor disintegrated into debris , and by the finding that the debris also contained presynaptic proteins , such as CSP . Thus , synaptic debris might contain synaptic vesicles or vesicle membrane remnants that failed to be recycled . Interestingly , Brp , an active zone marker [33] , was absent from the debris . This absence might reflect its degradation , or alternatively , the derivation of presynaptic debris from periactive regions of the NMJ . Indeed , FasII , which is localized at periactive zones [34] was also present in presynaptic debris . Acute spaced stimulation of the larval NMJ leads to the formation of dynamically extending and retracting synaptopods , and to the appearance of ghost boutons [13] . While some ghost boutons differentiate by acquiring active zones and postsynaptic proteins [13] , here we found that others lost their connection with the presynaptic arbor and were specifically removed . What happens to ghost boutons that detach from the main arbor ? In most cases we found that detached ghost boutons rapidly disappeared from the NMJ . On the basis of our finding that suppressing engulfing action in muscle leads to the accumulation of ghost boutons , we propose that these are engulfed directly by muscle cells ( Figure 8F ) . In other cases we found that ghost boutons , along with the stalk by which they were initially attached to the main arbor , would degenerate into smaller fragments resembling presynaptic debris . Thus at some level , ghost boutons also appear to be able to disintegrate into presynaptic debris . That presynaptic debris and ghost boutons are unique cellular remnants is also argued by the fact that they are differentially engulfed by glia and muscle cells , respectively ( Figure 8F ) . Nevertheless , the detachment and elimination of ghost boutons we describe represents a simple and newly defined mechanism for the removal of excessive synapses formed by individual innervating motorneurons . This process might also serve as a mechanism for rapid stabilization of new synaptic boutons during , for example , periods of increased synaptic or locomotor activity ( see below ) [13] , [35] , [36] . The functional significance of shedding presynaptic debris remains unclear . Manipulations that promote rapid synaptic growth , such as acute spaced stimulation , lead to an increase in presynaptic debris suggesting that its production is associated with synaptic growth . While some presynaptic debris appears to be derived from the breakdown of disconnected ghost boutons , we also observed the de novo formation of presynaptic debris in the absence of any ghost boutons . Thus , presynaptic debris is likely directly shed by motorneuron endings . Presynaptically shed debris might derive from dynamically extending synaptopods , whose formation is dramatically enhanced by increasing neural activity [13] . However , in live preparations demonstrating robust synaptopod growth we have yet to directly observe the formation of debris following synaptopod expansion or retraction ( Gorczyca M , Ashley J , Fuentes-Medel Y , unpublished data ) . The presence of presynaptic debris might highlight the extremely dynamic nature of synapse addition in vivo . Two important mechanisms appear to operate during NMJ expansion . First , the NMJ is shaped by a homeostatic mechanism that maintains synaptic efficacy despite larval muscle growth [32] . Second , the NMJ has the ability to respond to acute changes in activity and sensory experience with rapid modifications in synaptic structure and function . Well-fed larvae placed in a substrate devoid of food show an increase in synaptic strength within 30 min [35] , and spaced stimulation induces robust synaptic growth within 2 h [13] . It is tempting to speculate that presynaptic shedding is the byproduct of a mechanism designed to ensure rapid and efficient changes in synaptic performance . For example , the initiation of synaptic bouton formation might be a continuous process . This pool of synaptic boutons might reach an immature stage and if not subsequently stabilized by activity or other signals they might be shed and removed . Such a mechanism would provide a continuous supply of immature boutons ready to stabilize if rapid growth becomes essential . Glial cells have a key role in the removal of axonal debris and neuronal cell corpses from the central nervous system [22] , [37] , but mounting evidence also implicates glial cells in the elimination of synaptic inputs . In mammals microglia rapidly spread along neurites of injured motorneurons and displace synaptic inputs through synaptic stripping [38] . At the mammalian NMJ , terminal Schwann cells are also active participants in the activity-dependent elimination of exuberant motorneuron inputs by apparently pinching off fragments of retracting terminals [11] . Here we describe a novel mechanism by which glia , through their phagocytic clearance of shed synaptic debris , can sculpt synaptic connectivity within a single arbor and ultimately modulate the growth of nerve terminals . The formation of shed presynaptic material appears to be autonomous and not require the engulfing action of glial cells since presynaptic debris and ghost boutons accumulate at high levels in draper mutants . Notably , muscle cells collaborated with glia in the removal of shed presynaptic membranes and thus also helped to sculpt the growing NMJ . These observations provide a new view on the role of muscle cells in regulating synaptic growth: muscle cells are not simply postsynaptic target cells that give and receive synaptogenic signals; they are also phagocytes at the NMJ and through engulfing shed presynaptic material can help shape synaptic connectivity . Why has such presynaptic material not been previously described at the well-studied Drosophila NMJ ? This is likely due to the fact that we have assayed NMJ morphology for the first time in engulfment mutants . Even in wild type a very low level of presynaptic debris ( this report ) and a small number of ghost boutons [13] is observed . However in draper mutants or knockdown animals we observe their dramatic accumulation , which is reminiscent of the process of cell corpse engulfment after apoptotic cell death . Cell corpses are rapidly engulfed during development and thus very few are observed in wild-type animals . In contrast , they accumulate at significant levels in animals with reduced cell corpse engulfment activity , such as C . elegans ced-1 or ced-6 mutants [39] . We found that glial cells extended membrane processes that deeply invaded the NMJ . These cellular interactions were highly dynamic , as demonstrated by our time-lapse imaging , and by the high variability in the extent and type of glial membrane projections we found at the NMJ . Some projections were in the form of thin gliopods that associated with boutons within a branch or that extended across branches . Others resembled flat lamellipodia that associated with synaptic boutons or with the muscle . Given the requirement for glial Draper in the removal of synaptic debris , it is tempting to speculate that glial membranes are continuously and dynamically surveying the NMJ for the presence of synaptic debris , which is then engulfed . Consistent with this notion , we found several examples of glial membranes extending away from the arbor and overlapping with presynaptic debris . We also found that in some cases , HRP positive fragments were found associated with bulbous structures formed by the glial projections , suggesting that glia can engulf presynaptic debris . We also observed glial membrane projections that had the form of boutons , sometimes draping over an entire bouton , or extending well beyond the terminal bouton . While the function of these structures remains unclear we envisage at least two potential roles . First , these might represent glial extensions actively engulfing ghost boutons , although this would be predicted to be a rare event since our cell-type specific analyses argue that muscle cells are primarily responsible for clearance of ghost boutons . Second , these extensions , along with the additional types described above that extend beyond axonal arbors into the muscle , could be physically opening up space in the muscle cell for new bouton formation or process extension . Interestingly , we found that in draper mutants both disconnected ghost boutons and presynaptic debris accumulated , and this accumulation had a negative effect on NMJ expansion and bouton morphology . Moreover , synaptic growth appeared to be highly sensitive to both types of shed presynaptic material since the accumulation of either ghost boutons or presynaptic debris ( when engulfment activity was blocked in muscles or glia , respectively ) led to reductions in bouton growth similar to that seen in draper null mutants . As mentioned above , shed material might contain important signaling factors that potently stimulate or inhibit new synapse formation . If , for example , presynaptic debris contains molecules that inhibit synaptogenesis , the accumulation of such material would be expected to negatively regulate synaptic growth . Perhaps a similar type of inappropriate modulation of synaptogenesis by the membrane fragments of pruned terminals also accounts for their rapid clearance from the central nervous system after degeneration . Drosophila glial cells also engulf neuronal cell corpses and pruned or degenerating axons . Each of these targets is generated by a unique degenerative molecular cascade: cell corpses are produced by canonical apoptotic cell death pathways [40] , pruned axons undergo degeneration through a ubiquitin proteasome-dependent mechanism [41] , and severed axons undergo Wallerian degeneration via Wlds-modulated mechanisms [26] . Despite their unique pathways of production , each is engulfed by glia through Draper-dependent mechanisms , implying that these engulfment targets autonomously tag themselves with molecularly similar “eat me” cues . Our observations that mutations in draper led to accumulation of presynaptic debris and detached ghost boutons suggests that these new glial/muscle engulfment targets also produce similar cues for phagocytic cells to promote their destruction . If so , these data argue that all the necessary machinery essential for tagging membrane fragments for engulfment are present in a ghost bouton or fragment of presynaptic membrane . Importantly , while a lack of glial-mediated clearance of several targets has been observed in vivo—cell corpses , pruned axons or dendrites , and axons undergoing Wallerian degeneration—almost nothing is known about phenotypic consequences of a lack of glial engulfment function in the nervous system . Here we demonstrate that failure of glia and muscle to clear presynaptically derived material negatively regulates synaptic growth . In conclusion our studies demonstrate that the process of synaptic growth includes a significant degree of membrane/synaptic instability , and that growing terminals are constantly sloughing off undifferentiated boutons and fragments of membrane . Our observations demonstrate that growing NMJs generate an excess number of undifferentiated synaptic boutons and that only a fraction becomes stabilized and drive the assembly of the postsynaptic apparatus . Exuberant synapses that have failed to form successful postsynaptic contacts are shed , and cleared from the NMJ by glia and muscle cells . The presence of such a pool ensures a continuous supply of nascent synapses available for use to rapidly increase input into the muscle if dictated by dynamic changes in signaling at the NMJ .
The following fly strains were used for this study: draperΔ5 and UAS-Draper-RNAi [26] , UAS-dCed-6-RNAi [23]; Repo-Gal4 ( a gift from B . Jones ) , Gli-Gal4 [42] , OK6-Gal4 [16] , C57-Gal4 and C380-Gal4 [20] , UAS-mCD8-GFP [43] UAS-myrRFP ( Bloomington Stock Center ) , MHC-mCD8GFP-Sh [21] , and UAS-ChR2 [44] . UAS-Draper-I and UAS-Draper-III were generated by M . A . Logan and will be described in detail elsewhere ( MAL and MRF , unpublished data ) . For larval motility assays , larvae were cultured at 25°C , wandering third instar larvae were collected , briefly washed in distilled water , transferred to the center of a square agar plate , and covered with a transparent lid . After 30 s , total larval movement was followed for 1 min under red light conditions , 60% humidity , at 25°C . Third instar Drosophila larvae were dissected in calcium free saline [45] and fixed for 10 min with nonalcoholic Bouin's solution unless otherwise noted . Primary antibodies were used at the following dilutions: α-Draper , 1∶5 , 000 [24]; rabbit α-DLG , 1∶20 , 000 [46]; mouse α-DLG , 1∶500 ( clone 4F3 , Developmental Studies Hybridoma Bank , DSHB ) ; α-CSP , 1∶100 [47]; α-Synapsin , 1∶10 ( a gift from E . Buchner; [48]; α-Fas II , 1∶3000 [46]; α-GFP , 1∶200 ( Molecular Probes ) ; nc82 ( α-Brp ) , 1∶100 ( DSHB ) ; FITC or Texas red-conjugated α-HRP 1∶200 ( Jackson Immunoresearch ) . Secondary antibodies conjugated to FITC , Texas Red , or Cy5 ( Jackson Immunoresearch ) were used at a concentration of 1∶200 . Samples were imaged using a Zeiss Pascal confocal microscope and analyzed using the Zeiss LSM software package and ImageJ . To study the organization of glial membranes at the NMJ we fixed larval body wall muscle preparations of controls and draper mutants expressing mCD8-GFP in glia using the Gli-Gal4 strain for 15 min in 4% paraformaldehyde fix , and double stained the preparations with Texas Red conjugated α-HRP 1∶200 ( Jackson Immunoresearch ) and α-GFP ( Molecular Probes ) . Glial membrane extensions at identified body wall muscle NMJs from abdominal segments A3 and A4 were scored individually as “blunt ended” ( glial membranes terminated at the branch point ) , “covered” ( glial membranes completely ensheathed the NMJ ) , “gliobulbs” ( glial extensions terminated in a bulbous structure ) , “gliopods” ( small finger-like glial membrane projections ) , and lamellipodia ( glial membranes formed flat extensions that partially covered the NMJ ) . The percentage of NMJs containing the above types of glial membranes projections was calculated from 20 hemisegments for controls , and 15 hemisegments for draperΔ5 mutants . Presynaptic debris was scored from type Ib boutons at muscles 6 and 7 , abdominal segment A3 . This quantification was performed using images of α-HRP labeled NMJs that were acquired with identical confocal settings , and the amount of debris scored blindly according to a subjective scale of 0–3 . Number of NMJs analyzed are ten to 12 per sample ( from six animals ) . To score presynaptic debris after spaced stimulation , intact larvae expressing channelrhodopsin-2 in motorneurons were subjected to spaced light stimulation as in ( Ataman et al . [13] ) , fixed at 2 h ( 1 . 5 h stimulation , 30 min rest ) ( n = 18 for stimulated samples , n = 12 for unstimulated controls ) , and 18 h after stimulation ( n = 6 for stimulated samples , n = 6 for unstimulated controls ) , and stained with α-HRP antibodies . Confocal images of NMJs at muscles 6 and 7 ( A2 and A3 ) were acquired with identical settings , and two 8-µm diameter circles at the postsynaptic region of each NMJ branch were selected for analysis using NIH Image software . The number of synaptic boutons and ghost boutons were quantified at muscles 6 and 7 ( A3 ) from preparations double stained with α-HRP and α-DLG ( n≥10 NMJs per genotype ) . Data were represented in histograms as the average±SEM . Statistical significance of the data was obtained in pair-wise comparisons using the Student's t-test . Live imagining of larvae was performed on either intact or dissected preps as Ataman et al . [13] . Briefly intact larvae were anesthetized using Sevoflurane ( Baxter ) and the dorsal muscles were then imaged through the cuticle using a 40× 1 . 2 NA objective on an Improvision spinning disk confocal microscope . Larvae were examined live by expression of UAS-mCD8GFP in motor neurons ( pre-Gal4 ) or glia ( gli-Gal4 ) . Increased activity was induced in these larvae by expression of UAS-Channelrhodopsin2 , and exposure to a pulsed 491-nm LED paradigm described in Ataman et al . [13] and Figure 1H . Larvae were examined every hour , every 4 h , or at 18-h intervals depending on the experiment . In order to visualize the debris , samples were converted to rainbow gradient color , and then contrast enhanced until the main arbor was saturated , as the debris is much dimmer than the presynaptic membrane . Live imaging of glia was also performed in dissected preps , as Ataman et al . [13] . Briefly , larvae were dissected in 0 . 1 mM calcium Drosophila HL3-saline , and imaged on a Zeiss Pascal Confocal ( Carl Zeiss ) using either 25× or 40× water immersion objectives . Total RNA was isolated from third instar body wall muscle preparations with Trizol ( Invitrogen ) and purified using the RNeasy Mini Kit ( QIAGEN ) . First strand cDNA was synthesized using Superscript II ( Invitrogen ) enzyme and oligo ( dT ) 12–18 primer ( Invitrogen ) . PCR was performed using the following Draper isoform specific primers to detect expression of Draper-I , Draper-II , or Draper-III: DrprIuECDF ( 5′-GGGTCCCCTATGTGATATGC-3′ ) and DrprIuECDR ( 5′-TTGTAGCACTCGCAGCTCTC-3′ ) ; DrprIIuF ( 5′-GAAAATATATAGCAAGATTTTGTTTCC-3′ ) and DrprIIuR ( 5′-TTCGTGTTGTCGAAGCACTC-3′ ) ; DrprIIIuF ( 5′-GTCATTAGACTTTTACACAGG c-3′ ) and DrprIIIuR ( 5′-CTAGCGTATAGAATCAGAC-3′ ) . Plasmids containing the Draper isoforms ( pUAST-DraperI , pUAST-DraperII , and pUAST-DraperIII ) were used as controls for PCR amplification . PCR program was as follows: denature at 95°C for 1 min , anneal at 56°C for 30 s , extension at 72°C for 30 s ( 30 cycles total ) . PCR products were run on a 0 . 8% agarose gel and visualized by ethidium bromide stain .
|
The synapse is the fundamental unit of communication between neurons and their target cells . As the nervous system matures , synapses often need to be added , removed , or otherwise remodeled to accommodate the changing needs of the circuit . Such changes are often regulated by the activity of the circuit and are thought to entail the extension or retraction of cellular processes to form or break synaptic connections . We have explored the precise nature of new synapse formation during development of the Drosophila larval neuromuscular junction ( NMJ ) . We find that growing synapses are actually quite wasteful and shed significant amounts of presynaptic membranes and a subset of immature ( nonfunctional ) synapses . The shedding of this presynaptic material is enhanced by stimulating the activity of the neuron , suggesting that its formation is dependent upon NMJ activity . Surprisingly , we find presynaptic membranes are efficiently removed from the NMJ by two surrounding cell types: glia cells ( a neuronal ‘support cell’ ) , which invade the NMJ , and the postsynaptic muscle cell itself . Blocking the ability of these cells to ingest shed presynaptic membranes dramatically reduces new synapse growth , suggesting that the shed presynaptic material is inhibitory to new synapse addition . Therefore , our data demonstrate that actively growing synapses constantly shed membrane material , that glia and muscles work to rapidly clear this from the NMJ , and that the combined efforts of glia and muscles are critical for the proper addition of new synapses to neural circuits .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/neuronal",
"signaling",
"mechanisms",
"neuroscience/neuronal",
"and",
"glial",
"cell",
"biology"
] |
2009
|
Glia and Muscle Sculpt Neuromuscular Arbors by Engulfing Destabilized Synaptic Boutons and Shed Presynaptic Debris
|
In Arabidopsis , ultraviolet ( UV ) -B-induced photomorphogenesis is initiated by a unique photoreceptor UV RESISTANCE LOCUS 8 ( UVR8 ) which utilizes its tryptophan residues as internal chromophore to sense UV-B . As a result of UV-B light perception , the UVR8 homodimer shaped by its arginine residues undergoes a conformational switch of monomerization . Then UVR8 associates with the CONSTITUTIVELY PHOTOMORPHOGENIC 1-SUPPRESSOR OF PHYA ( COP1-SPA ) core complex ( es ) that is released from the CULLIN 4-DAMAGED DNA BINDING PROTEIN 1 ( CUL4-DDB1 ) E3 apparatus . This association , in turn , causes COP1 to convert from a repressor to a promoter of photomorphogenesis . It is not fully understood , however , regarding the biological significance of light-absorbing and dimer-stabilizing residues for UVR8 activity in photomorphogenic UV-B signaling . Here , we take advantage of transgenic UVR8 variants to demonstrate that two light-absorbing tryptophans , W233 and W285 , and two dimer-stabilizing arginines , R286 and R338 , play pivotal roles in UV-B-induced photomorphogenesis . Mutation of each residue results in alterations in UV-B light perception , UVR8 monomerization and UVR8-COP1 association in response to photomorphogenic UV-B . We also identify and functionally characterize two constitutively active UVR8 variants , UVR8W285A and UVR8R338A , whose photobiological activities are enhanced by the repression of CUL4 , a negative regulator in this pathway . Based on our molecular and biochemical evidence , we propose that the UVR8-COP1 affinity in plants critically determines the photomorphogenic UV-B signal transduction coupling with UVR8-mediated UV-B light perception .
Light is a critical environmental stimulus that regulates a number of developmental and physiological processes of living organisms . In sessile plants , perception of light is the initial and decisive step in light signaling transduction , and is achieved by several groups of photosensory receptor proteins . Phytochromes sense far-red and red light [1] , [2] . Cryptochromes and phototropins perceive blue and ultraviolet ( UV ) -A light [3] , [4] , [5] , [6] . In Arabidopsis thaliana , UV RESISTANCE LOCUS 8 ( UVR8 ) has recently been identified as a photoreceptor that detects UV-B ( 280 to 320 nm ) light [7] . Long-wavelength and low-fluence UV-B induces plant photomorphogenic development that is physically characterized by the inhibition of hypocotyl elongation , flavonoid accumulation , and UV-B stress tolerance [8] , [9] , [10] , [11] . UVR8 was originally isolated as a UV-resistance gene , having been shown to contribute to the UV-B-induced flavonoid accumulation and UV-B protection [12] . Transcriptomic analyses have revealed that UVR8 positively orchestrates UV-B signaling specifically under photomorphogenic UV-B [13] . Later , a series of functional studies have disclosed that UVR8 exhibits a number of features characteristic of photoreceptors , including a broad-range loss of UV-B responsive gene expression in the uvr8 null mutant [13] , [14] , the enrichment of aromatic residues in UVR8 protein and UV-B-induced conformational change of UVR8 [7] . Despite these insights , however , the exact process by which UVR8 mediates UV-B light perception remained unclear until two research groups independently reported the structure of UVR8 [15] , [16] . Without the presence of UV-B light , UVR8 appears as a symmetric seven-bladed-β-propeller homodimer that is stabilized by arginines primarily Arg 286 and Arg 338 . These arginine residues shape intramolecular cation-π interactions with their surrounding tryptophan residues , among which Trp 285 and Trp 233 act as the internal UV-B chromophore . Consequently , unlike phytochromes and cryptochromes , UVR8 is devoid of external small molecules as chromophore . Upon UV-B irradiation , the dark-state dimer of UVR8 is monomerized as a result of the disruption of the intramolecular cation-π interactions and the intermolecular hydrogen bonds mediated by Arg 286 and Arg 338 [7] , [15] , [16] . This structural conversion , which takes place in seconds , is a major determinant for UVR8 to sequester CONSTITUTIVELY PHOTOMORPHOGENIC 1-SUPPRESSOR OF PHYA ( COP1-SPA ) core complex ( es ) from the CULLIN 4-DAMAGED DNA BINDING PROTEIN 1 ( CUL4-DDB1 ) E3 apparatus . Ultimately , this complex reorganization enables COP1 to act as a positive regulator in the UV-B-induced photomorphogenesis by facilitating the stability and activity of a photomorphogenesis-promoting transcription factor ELONGATED HYPOCOTYL 5 ( HY5 ) [7] , [17] . Reversibly , upon the elimination of UV-B irradiation , REPRESSOR OF UV-B PHOTOMORPHOGENESIS 1 ( RUP1 ) and RUP2 , two UVR8-interacting proteins , might disrupt the physical contact of UVR8 and COP1 , so that UVR8 dimerization can be regenerated [7] , [15] , [16] , [18] , [19] , [20] . However , the exact biological significance of key residues in UVR8 has not been fully determined to date . Here we take advantage of site-directed mutagenesis to generate UVR8 variant proteins in Arabidopsis , and demonstrate the pivotal roles of two light-absorbing tryptophans , W233 and W285 , and two dimer-stabilizing arginines , R286 and R338 in UVR8-initiated UV-B-induced photomorphogenesis . We also characterize two constitutively active forms of UVR8 , UVR8W285A and UVR8R338A , whose photobiological activity is enhanced when the repressor CUL4 is suppressed . Overall , our molecular and biochemical evidence has supported that the intrinsic affinity of UVR8-COP1 critically determines the efficiency of photomorphogenic UV-B signal transduction coupling with UVR8-mediated UV-B light perception .
In order to assess the roles of key residues in UVR8 , we generated six UVR8 variants in two groups based on their functional classification via site-directed mutagenesis . The first group , which included UVR8W233A , UVR8W233F , UVR8W285A and UVR8W285F , and the second group , which included UVR8R286A and UVR8R338A , were designed to interrupt UVR8's perception of UV-B light and dimer stabilization respectively ( Figure S1A ) . As yeast has been widely used as an efficient system to determine the conformational status of UVR8 and its interaction with other proteins in response to UV-B [7] , [21] , [22] , we first introduced wild-type and these six mutated UVR8 ( mUVR8 ) into yeast . Using SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) followed by immunoblot analyses , we found that UVR8WT was dimeric under −UV-B and monomeric under +UV-B ( Figure 1A ) , while UVR8W233F , UVR8W285A and UVR8W285F were constitutively monomeric , monomeric and dimeric respectively ( Figure 1A ) . This is consistent with the results reported previously [7] . The other UVR8 variants , UVR8W233A , UVR8R286A and UVR8W338A appeared as monomers irrespective of UV-B treatment ( Figure 1A ) . These results demonstrate that in yeast , in addition to the dimer-stabilizing arginines , R286 and R338 , the light-absorbing tryptophans , W233 and W285 , critically contribute to the dimer-to-monomer switch of UVR8 upon UV-B irradiation . We next performed a series of yeast two-hybrid assays in order to examine the effects of the UVR8 mutations on the interaction between UVR8 and COP1 . While COP1 only interacted with UVR8WT under +UV-B , it interacted with UVR8W233A , UVR8W233F , UVR8W285A , UVR8R286A and UVR8R338A under both −UV-B and +UV-B . The interaction between UVR8W285F and COP1 was barely observed ( Figure 1B ) . These results suggest that the UVR8-COP1 interaction in yeast requires UVR8 to be in its monomeric form . Furthermore , previous studies have also proposed that RUP1 and RUP2 interact with UVR8 independent of UV-B irradiation [19] . Specifically , it has been suggested that these two proteins mediate UVR8 redimerization and disrupt UVR8-COP1 interaction , so as to facilitate the inactivation of the photoreceptor [18] . In yeast , RUP1 was found to constitutively interact with UVR8W233A , UVR8W233F , UVR8W285A and UVR8R286A , but was not observed to interact with UVR8WT , UVR8W285F and UVR8R338A . Differently , RUP2 was observed to interact with the wild-type UVR8 and all the mutant UVR8 proteins ( Figure S1B ) . These results collectively suggest that light perception and conformational change of UVR8 are both major determinants of its interaction with key players of UV-B-induced photomorphogenesis . To investigate the biological activity of these UVR8 variants in Arabidopsis , we introduced wild-type and mutated UVR8 fused with yellow fluorescent protein ( YFP ) driven by the native UVR8 promoter into the uvr8 null mutant uvr8-6 background . As expected , proUVR8-YFP-UVR8/uvr8-6 ( YFP-UVR8WT ) was found to express the transgenic UVR8 protein at a level comparable to the endogenous UVR8 protein in Col under −UV-B and +UV-B ( Figure S2A ) . It was able to rescue uvr8-6 in UV-B-induced hypocotyl growth and anthocyanin accumulation ( Figures 2A , 2B and 2C ) which are characteristic physiological responses to photomorphogenic UV-B [14] , [23] . We next examined these two responses in the six transgenic UVR8 variants expressing YFP-UVR8 proteins at equivalent levels to that in YFP-UVR8WT ( Figure 2D ) . The hypocotyl shortening in all the variants failed to reach the shortening degree detected in YFP-UVR8WT . Interestingly , YFP-UVR8W285A and YFP-UVR8R338A displayed shorter hypocotyl than YFP-UVR8WT under −UV-B , while YFP-UVR8R338A instead of YFP-UVR8W285A showed further shortened hypocotyl under +UV-B ( Figures 2A and 2B ) . Furthermore , compared with YFP-UVR8WT , reduced anthocyanin accumulation was found in YFP-UVR8W233A , YFP-UVR8W233F , YFP-UVR8W285F and YFP-UVR8R286A under both −UV-B and +UV-B . In contrast , enhanced anthocyanin accumulation was detected in YFP-UVR8W285A and YFP-UVR8R338A under −UV-B . YFP-UVR8R338A was even able to accumulate anthocyanin under +UV-B , though at a level lower than that in YFP-UVR8WT ( Figure 2C ) . These observations showed that all the mutations interfered with UV-B-induced photomorphogenesis , though to varying degrees . The mutations in UV-B-absorbing residues resulted in more severe phenotypic defects than those in UVR8-dimerizing residues , suggesting the importance of the sequential action of UV-B light perception before UVR8 monomerization . Meanwhile , a specific activity of UVR8W285A and UVR8R338A is indicated by the constitutive short hypocotyl and high anthocyanin content found in YFP-UVR8W285A and YFP-UVR8R338A ( Figures 2B and 2C ) . It is well known that differential transcriptomic regulation is orchestrated by photomorphogenic UV-B in Arabidopsis [9] , [13] . Using 4-day-old seedlings grown under −UV-B and +UV-B , we examined the expression pattern of several UV-B-responsive marker genes , EARLY LIGHT-INDUCIBLE PROTEIN 2 ( ELIP2 ) , UDP-GLYCOSYLTRANSFERASE 84A1 ( UGT84A1 ) and CHALCONE SYNTHASE ( CHS ) . The UV-B-induced activation of these genes observed in YFP-UVR8WT was largely impaired in YFP-UVR8W233A , YFP-UVR8W233F , YFP-UVR8W285A , YFP-UVR8W285F and YFP-UVR8R286A , while the activation was readily detected in YFP-UVR8R338A though it showed a reduced induction of UGT84A1 . Again , we noticed YFP-UVR8W285A and YFP-UVR8R338A as these two variants accumulated higher transcript levels of these genes than YFP-UVR8WT under −UV-B ( Figures 3A , 3B and 3C ) . In addition to these marker genes , key regulators of UV-B specific signaling , including the positive regulators COP1 and ELONGATED HYPOCOTYL 5 ( HY5 ) are also transcriptionally governed by photomorphogenic UV-B [24] , [25] . We found that the accumulation of COP1 mRNA was diminished in all UVR8 variants with the exception of YFP-UVR8R338A ( Figure 3D ) . Similarly , though the accumulation of HY5 mRNA was observed in YFP-UVR8W233F and YFP-UVR8R338A , it was limited in YFP-UVR8W233A , YFP-UVR8W285A , YFP-UVR8W285F and YFP-UVR8R286A ( Figure 3E ) . At the post-transcriptional level , COP1 protein was clearly induced in YFP-UVR8WT , YFP-UVR8W233F and YFP-UVR8R338A , slightly increased in YFP-UVR8W233A under +UV-B , and relatively high under both −UV-B and +UV-B in YFP-UVR8W285A . However , the accumulation of COP1 protein mediated by photomorphogenic UV-B was scarcely detected in YFP-UVR8W285F and YFP-UVR8R286A ( Figures 3F and 3G ) . A similar pattern was observed for the case of HY5 protein accumulation ( Figures 3F and 3G ) . Taken together , the altered expression profiles of these UV-B responsive genes suggest that these residues responsible for UV-B light perception and UVR8 dimerization are crucial for UVR8 activity in the transcriptional control of UV-B signaling at a molecular level . Downstream of UVR8-COP1 , RUP1 and RUP2 contribute to the negative feedback regulation of UV-B-induced photomorphogenesis . Given that RUP1 and RUP2 are known to be induced by photomorphogenic UV-B dependent on UVR8 , COP1 and HY5 [19] , it was noteworthy that the accumulation of RUP1 and RUP2 mRNA was apparently reduced in all UVR8 variants except UVR8R338A ( Figures 4A and 4B ) . As early UV-B responsive genes , RUP1 and RUP2 were activated in a temporal manner in response to photomorphogenic UV-B to balance UV-B specific signaling [19] . Using 4-day-old seedlings grown under −UV-B and then transferred to +UV-B for various periods of time , we found that the temporal induction of RUP1 and RUP2 by photomorphogenic UV-B was apparently observed in YFP-UVR8WT , but was retarded in all the UVR8 variant lines ( Figures 4C and 4D ) . It is worth pointing out that the transcript levels of RUP1 and RUP2 were elevated within 1 hours of UV-B irradiation to a peak and then fell back in YFP-UVR8WT , whereas they continued to rise to a lower peak particularly in YFP-UVR8W285A , YFP-UVR8R286A and YFP-UVR8R338A over the 12-hour UV-B treatment ( Figures 4C and 4D ) . These results suggest that RUP1 and RUP2 fail to be activated in our UVR8 variants , and thus do not establish the repressive transcriptional modules required for balanced UV-B signaling . Overall , none of the UVR8 variants are functionally equivalent to YFP-UVR8WT , which was consistent with our phenotypic observations . In order to better understand the biochemical activity of these residues in vivo , we investigated the efficiency of UV-B light perception , UVR8 monomerization and the formation of UVR8-containing complex in all of our UVR8 variant plants . By measuring UV-B absorbance at 310 nm , the central wavelength of our photomorphogenic UV-B condition , we found that Col and YFP-UVR8WT exhibited a strong ability to sense UV-B . In contrast , mutations in either residue of the internal chromophore , W233 or W285 , led to a complete loss of UV-B absorption . YFP-UVR8R286A and YFP-UVR8R338A retained the ability to sense UV-B , but at significantly diminished levels , which suggests that the disruption of the homodimeric interface has a negative impact on the full activity of UVR8 to perceive UV-B light ( Figure 5A ) . In SDS-PAGE analysis , we found that in response to photomorphogenic UV-B , while YFP-UVR8WT switched from dimer to monomer , none of YFP-UVR8 variant proteins showed a comparable conformational change . YFP-UVR8W233A , YFP-UVR8W233F , YFP-UVR8W285A , YFP-UVR8R286A and YFP-UVR8R338A were monomeric , while YFP- UVR8W285F was dimeric ( Figure 5B ) . Thus the conformational profiles of wild-type and variant UVR8 observed in Arabidopsis were consistent with those found in yeast . We next examined the endogenous association of UVR8 variants and COP1 by using in vivo co-immunoprecipitation ( co-IP ) assays . In agreement with previous studies [7] , [14] , YFP-UVR8WT co-immunoprecipitated a high level of COP1 specifically under +UV-B . However , independent of UV-B , YFP-UVR8W285F and YFP-UVR8R286A scarcely co-immunoprecipitated COP1 , while YFP-UVR8W233F and YFP-UVR8W233A co-immunoprecipitated very low levels of COP1 . YFP-UVR8W285A and YFP-UVR8R338A co-immunoprecipitated medium levels of COP1 under −UV-B , while the latter was also observed to co-immunoprecipitate more COP1 under +UV-B ( Figure 5C ) . These results reveal that the monomeric conformation is not sufficient for UVR8 to associate with COP1 in vivo . The relatively close association of YFP-UVR8W285A and YFP-UVR8R338A with COP1 without UV-B treatment indicates that a specific activity of UVR8 might be produced by W285A or R338A mutation . As both cop1-4 and uvr8-6 suffer from diminished gene expression , hypocotyl growth , anthocyanin accumulation and acclimation in response to photomorphogenic UV-B , COP1 and UVR8 share a high degree of functional similarity in photomorphogenic UV-B signaling [14] , [25] . We found that YFP-UVR8WT/cop1-4 phenocopied cop1-4 ( Figure 6A ) , clearly demonstrating that COP1 acts genetically downstream of UVR8 . The observation that YFP-UVR8WT failed to rescue cop1-4 ( Figure 6A ) also suggests that the function of UVR8 is dependent on COP1 . Though CUL4 works in concert with COP1 in darkness , it functionally disassociates from COP1 and plays a negative role in UV-B-induced photomorphogenesis [17] . Since cul4cs exhibited no obvious defect in hypocotyl growth under UV-B [17] , we consistently found that YFP-UVR8WT/cul4cs phenocopied YFP-UVR8WT and cul4cs ( Figure 6A ) . We have previously proposed that the UVR8-COP1 interaction mediated by UV-B enables a reorganization of COP1 complexes and eventually results in a functional switch of COP1 from a repressor to a promoter of photomorphogenesis [17] . Both the enhanced photomorphogenesis of YFP-UVR8W285A and YFP-UVR8R338A under −UV-B ( Figures 2A , 2B and 2C ) and the persistent binding of UVR8W285A and UVR8R338A ( Figures 1B and 5C ) to COP1 prompted us to examine the development of all the UVR8 variants in darkness . In addition to Col , uvr8-6 and YFP-UVR8WT , YFP-UVR8W233A , YFP-UVR8W233F , YFP-UVR8W285F and YFP-UVR8R286A all showed typical skotomorphogenic responses , demonstrating long hypocotyls , closed cotyledons and apical hooks . In contrast , YFP-UVR8W285A and YFP-UVR8R338A displayed open cotyledons , suggesting that these two UVR8 variants were capable of inducing constitutive photomorphogenesis irrespective of their exposure to light ( Figure 6B ) . We then examined light-regulated gene expression , and found highly accumulated transcripts of CHS , RIBULOSE BISPHOSPHATE CARBOXYLASE SMALL CHAIN 1A ( RBCS1A ) , CHLOROPHYLL A/B BINDING PROTEIN 3 ( CAB3 ) and ELIP2 , in YFP-UVR8W285A and YFP-UVR8R338A ( Figure 6C ) . Though UVR8W233A , UVR8W233F and UVR8R286A had constitutive physical interactions with COP1 in yeast ( Figure 1B ) , their affinity with COP1 in vivo was much weaker than that of UVR8W285A and UVR8R338A ( Figure 5C ) , indicating that conversion of COP1's function might require a threshold level of UVR8-COP1 interaction . We have also pointed out that in response to UV-B , monomerized UVR8 might sequester COP1 from the CUL4-DDB1 based E3 apparatus [17] . In darkness , compared with YFP-UVR8W285A/uvr8-6 and YFP-UVR8R338A/uvr8-6 , YFP-UVR8W285A/cop1-4 and YFP-UVR8R338A/cop1-4 mimicked cop1-4 ( Figure 6D ) , which is consistent with our conclusion that UVR8 functions in a COP1-dependent manner . YFP-UVR8W285A/cul4cs and YFP-UVR8R338A/cul4cs exhibited enhanced constitutive photomorphogenesis with decreased hypocotyl length ( Figure 6D ) . These results indicate that the reduced CUL4 protein abundance might facilitate the release of an increased amount of COP1 from CUL4-DDB1 to allow an improved association between UVR8 and COP1 , and in turn achieve a highly switched function of COP1 in promoting photomorphogenesis .
The molecular framework of UV-B-induced photomorphogenesis has been gradually established over the past ten years . For example , both the identification of UVR8 as a UV-B photoreceptor and the subsequent structural analysis of recombinant UVR8 have recently led to a proposed mechanism of UVR8-dependent UV-B signaling initiation [7] , [15] , [16] . A series of UVR8 variant proteins have been generated to further demonstrate that UVR8 exploits its own light-absorbing tryptophans and dimer-stabilizing arginines to perceive light and initiate protein conformational changes , respectively [15] , [16] . UVR8W233F , UVR8W285A and UVR8W285F are lack of the ability to perceive UV-B in vitro [16] . UVR8W285A is at least partially monomeric and interacts with COP1 . UVR8W285F , on the other hand , is dimeric and unable to interact with COP1 in yeast , plant and mammalian cells [7] , [15] , [16] , [22] , [26] . UVR8R286A and UVR8R338A are monomeric and display an obviously diminished perception of UV-B light in vitro [16] . In Arabidopsis , mutations in UVR8's tryptophan residues have been pointed out to result in the physical impairment of photomorphogenic UV-B responses [22] . However , the exact mechanism driving this hindered response remains unknown . Moreover , it is far less understood concerning the biological significance of light-absorbing and dimer-stabilizing residues in UVR8 , as well as the signaling process that connects UV-B light perception , UVR8 monomerization and subsequent signaling events including the organization of UVR8-COP1-SPA complex ( es ) . In our study , we generated transgenic plants expressing UVR8 variant proteins under UVR8's native promoter , based on the site-directed mutagenesis of two light-absorbing tryptophans and two dimer-stabilizing arginines . These variant proteins drove varied levels of UV-B-induced photomorphogenesis . All the variants were observed to impair both UVR8's activity to perceive UV-B light and its ability to undergo a dimer-to-monomer transition ( Figures 5A and 5B ) , suggesting the reciprocal impacts by these residues involved in the same intramolecular interaction network . More severe phenotypic defects were observed in UV-B-absorbing variants than those in UVR8-dimerizing variants ( Figures 2A , 2B and 2C ) , confirming that UV-B light perception precedes UVR8 monomerization to launch UV-B signaling . In addition , the unchangeable conformational status of the UVR8 variants ( Figure 5B ) abolished the dimer-monomer-dimer cycling of UVR8 , and further disturbed the balance in UV-B signaling . Molecularly , all the mutations caused an altered hierarchy of UV-B responsive gene expression ( Figures 3 and 4 ) . They failed to accurately establish the promotive module formed by the UVR8-COP1-HY5 core pathway and the negative transcriptional feedback mediated by RUP1 and RUP2 , leading to inadequate and unbalanced photomorphogenic UV-B responses . Taken together , the roles of light-absorbing tryptophans and dimer-stabilizing arginines in UVR8 are intrinsically coordinated for UVR8 activity in UV-B-induced photomorphogenesis . The direct interaction between UVR8 and COP1 takes place rapidly following UV-B light perception and UVR8 monomerization [7] . It requires UVR8 to be in its monomeric form [7] whereas physically monomeric UVR8 is not sufficient for the formation of the UV-B-dependent COP1 complex and to mediate photomorphogenesis in response to UV-B in plants ( Figures 2A and 5C ) . Though this point has been previously articulated [22] , the molecular mechanism underlying this empirical observation is still unknown . This discrepancy serves to indicate that factors downstream of UV-B light perception and UVR8 monomerization might essentially govern the progression of UV-B signaling . In terms of the transcriptome reprogramming induced by photomorphogenic UV-B , cop1-4 resulted in the loss of transcriptional responses of a broader range of genes than uvr8-6 [14] . While UVR8's ability to mediate UV-B-induced photomorphogenesis was observed to be dependent on COP1 ( Figure 6A ) , COP1 did not appear to significantly influence UVR8 conformation [7] , [20] . These data collectively indicate that COP1 is at least as essential as UVR8 in photomorphogenic UV-B signaling , if not superior to UVR8 , due to its role in the formation of UVR8-COP1-SPA complex ( es ) . By using the native promoter of UVR8 to drive UVR8 variants in plants , we selected those transgenic lines expressing comparable levels of UVR8 variant proteins ( Figure 2D ) , in order to stringently analyze each variant in parallel , particularly to examine the in vivo association intensity between UVR8 and COP1 ( Figure 5C ) . As a result , we propose that a threshold level of the in vivo association between UVR8 and COP1 is critically required for photomorphogenic UV-B signaling output , founded on the following evidence . Firstly , UVR8 monomers , rather than UVR8-COP1 interaction , were detected in −UV-B-treated plant and yeast cells expressing wild-type UVR8 ( Figures 1 , 5B and 5C ) . Secondly , the in vivo levels of UVR8-COP1 association correspond with the physiological and molecular features of the transgenic UVR8 variant lines ( Figures 2A and 5C ) . Only those UVR8 variants that possess high affinity with COP1 in plants , namely YFP-UVR8WT under +UV-B , and YFP-UVR8W285A and YFP-UVR8R338A under −UV-B and +UV-B in our study , result in photobiological activity in specific light contexts ( Figures 5C , 2A and 6B ) . Thirdly , once the affinity between UVR8 and COP1 is conditionally increased , such as the situation in cul4cs that might release more COP1 for UVR8 to interact with , the photobiological activity of UVR8 is ultimately enhanced ( Figure 6D ) . In agreement with our hypothesis , a most recent report has presented that comparing with the overexpressed wild-type UVR8 ( UVR8-OX ) , UVR8W285A leads to improved photomorphogenesis and UV-B tolerance by increased COP1 binding affinity , though UVR8W285A and UVR8-OX express equivalent UVR8 proteins [27] . Though our UVR8 variants do not demonstrate equivalent patterns of interaction with COP1 in yeast and plants ( Figures 1B and 5C ) , this discrepancy can most likely be explained by the fact that the yeast two-hybrid system is devoid of any other factors that might influence the UVR8-COP1 interaction . The variation in the UVR8 variants' affinity for COP1 observed in plants may be due to a wide variety of factors . For example , UVR8 monomer variants might undergo protein folding processes in plants that are distinct from those in yeast , given that each mutated residue locates specifically in the intramolecular interaction network ( Figure S1A ) . Similarly , the protein levels of endogenous COP1 vary amongst the different UVR8 monomer variants ( Figure 3G ) . Finally , a number of the other proteins present in vivo might interact with COP1 and/or UVR8 in manner that either enhances or hinders the in vivo contact of UVR8 and COP1 . Further research is required , however , in order to fully disentangle and elucidate the impact of the great complexity in vivo . Two constitutively active forms of UVR8 , UVR8W285A and UVR8R338A have been uncovered in our study . Though they differed in the phenotypic features of UV-B-induced photomorphogenesis , they both displayed constitutive interaction with COP1 ( Figure 5C ) and photomorphogenic development in darkness ( Figure 6B ) . It is worth pointing out that a previous report did not find constitutive photomorphogenesis in GFP-UVR8W285A [22] whereas a recent independent study reached a consensus with ours by showing the constitutive photomorphogenesis in UVR8W285A [27] . Structure of dimeric UVR8 has revealed that W285 , the UV-B chromophore , is located at the center of the strong cation-π and π-π interaction network at the dimer interface , while R338 , responsible for dimer stabilization , is located at the edge of the interaction network [16] . It is suggested that W285 is more essential to the function of UVR8 than R338 . Hence it is reasonable that YFP-UVR8W285A failed to respond to photomorphogenic UV-B while YFP-UVR8R338A was still able to , in terms of UV-B light perception , UV-B-induced UVR8-COP1 association and gene expression , and eventually photomorphogenic development ( Figures 2A–C , 3A–C , 5A and 5C ) . On the other hand , in the absence of UV-B , YFP-UVR8W285A and YFP-UVR8R338A associated with a sufficient amount of COP1 , and thus promoted hypocotyl growth , anthocyanin accumulation and gene activation under −UV-B ( Figures 2A–C , 3A–C and 5C ) , and even photomorphogenesis in darkness ( Figures 6B and 6C ) . As reported , other gain-of-function alleles of photoreceptors were also produced via point mutations in their photosensory regions , such as the GAF domain tyrosine mutants of phytochromes ( PHY ) , PHYAY242H and PHYBY276H [28] , and the photolyase-related ( PHR ) domain glycine mutants of cryptochromes ( CRY ) , CRY1G380R , CRY2G377R [29] . PHYBY276H was proposed to interact with COP1 in a light-independent manner to diminish the degradation of HY5 by COP1 [28] . CRY1G380R was found to co-localize in nucleus with COP1 to promote the nuclear exclusion of COP1 in darkness [29] . In contrast to its role in the repression of photomorphogenesis induced by far-red and visible light [30] , COP1 is known to promote UV-B-induced photomorphogenesis [25] . Upon UV-B irradiation , COP1 rapidly interacts with UVR8 in nucleus [7] , and switches from degrading to stabilizing HY5 [17] , [31] . The constitutive photomorphogenesis in YFP-UVR8W285A and YFP-UVR8R338A is not resulted from a loss in COP1 protein abundance that was observed in cop1-4 ( Figure S3 ) . Therefore , the way UVR8 modulates COP1 dramatically differs from the activity repression of COP1 by cryptochromes or phytochromes . Furthermore , UVR8 is involved in diverse developmental processes in plants . Photomorphogenic UV-B signaling displays crosstalk with circadian regulation [32] , and it also controls leaf morphogenesis [33] , [34] , drought tolerance [35] and plant immune response [36] . Beyond the area of plant researches , UVR8 has also been implemented in optical control of protein interactions [26] , [37] and multi-chromatic expression regulation [38] in mammalian cells . Thus , the characterization of constitutively active UVR8 variants should serve to elucidate the mechanism of UV-B specific signaling in plants and advance protein engineering pertinent to a variety of medical applications .
The wild-type Arabidopsis thaliana used in this study is of the Columbia ( Col ) ecotype . Some of the mutants and transgenic lines used in this study were described previously: cop1-4 [39] , uvr8-6 [14] , cul4cs [40] , proUVR8-YFP-UVR8/uvr8-6 , proUVR8-YFP-UVR8W285A/uvr8-6 and proUVR8-YFP-UVR8W285F/uvr8-6 [17] . The vectors for UVR8 variant transgenic lines , proUVR8-YFP-mUVR8/uvr8-6 , were generated using the QuikChange site-directed mutagenesis kit ( Stratagene ) . The primers are listed in Table S1 . These transgenic lines were prepared using floral dipping method [41] . The Arabidopsis materials were grown as described previously [24] . The seeds were surface-sterilized and sown on solid Murashige and Skoog medium supplemented with 1% sucrose for biochemical assays or with 0 . 3% sucrose for phenotypic analysis , and cold treated at 4°C for 4 days . For photomorphogenic UV-B treatment , seedlings were grown at 22°C under continuous white light ( 3 µmol·m−2·s−1 , measured by LI-250 Light Meter , LI-COR Biosciences ) supplemented with Philips TL20W/01RS narrowband UV-B tubes ( 1 . 5 µmol·m−2·s−1 , measured by TN-340 UV-B Light Meter , China ) under a 350-nm cutoff ( half-maximal transmission at 350 nm ) filter ZUL0350 ( −UV-B; Asahi spectra , USA ) or a 300-nm cutoff ( half-maximal transmission at 300 nm ) filter ZUL0300 ( +UV-B; Asahi spectra , USA ) . For the assay in yeast , the vectors of LexA fused wild-type and mutated UVR8 were transformed into the yeast strain EGY48 ( Clontech ) . Total proteins were extracted from transformants in Yeast Protein Extraction Reagent ( Thermo ) , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) , and 1× complete protease inhibitor cocktail ( Roche ) , and then kept on ice under −UV-B ( 3 µmol·m−2·s−1 of white light ) or +UV-B ( 3 µmol·m−2·s−1 of white light and 1 . 5 µmol·m−2·s−1 of UV-B ) for 20 min . Added with 4× loading buffer containing 250 mM Tris-HCl ( pH 6 . 8 ) , 2% SDS , 20% β-mercaptoethanol , 40% glycerol , and 0 . 5% bromophenol blue , the samples were subjected to immunoblot analysis without boiling . The assay in plants was performed as previously described [17] . Total proteins were extracted from 4-day-old Arabidopsis seedlings grown under −UV-B or +UV-B in protein extraction buffer containing 50 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM EDTA , 10% glycerol , 0 . 1% Tween 20 , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) , and 1× complete protease inhibitor cocktail ( Roche ) . Then the cell extracts were kept on ice under exactly the same condition ( −UV-B or +UV-B ) as where the seedlings were grown for 30 min . Added with 4× loading buffer containing 250 mM Tris-HCl ( pH 6 . 8 ) , 2% SDS , 20% β-mercaptoethanol , 40% glycerol , and 0 . 5% bromophenol blue , the samples were subjected to immunoblot analysis without boiling . The respective combinations of vectors were cotransformed into the yeast strain EGY48 ( Clontech ) containing the reporter plasmid p8op::LacZ . Transformants were grown under −UV-B ( 3 µmol·m−2·s−1 of white light ) and +UV-B ( 3 µmol·m−2·s−1 of white light and 1 . 5 µmol·m−2·s−1 of UV-B ) on proper dropout plates containing X-gal ( 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside ) for blue color development . Hypocotyl length was measured as previously described [24] . For each line grown under −UV-B or +UV-B for 4 days , hypocotyl length was analyzed in three biological replicates . In each replicate , at least 30 Arabidopsis seedlings were measured . The relative hypocotyl length was presented as the percentage of the hypocotyl length under +UV-B with respect to that under −UV-B ( % of −UV-B ) . For each line grown in darkness , hypocotyl length was analyzed using at least 30 Arabidopsis seedlings . The quantification of hypocotyl length was performed by ImageJ ( http://rsb . info . nih . gov/ij/ ) . Anthocyanin was extracted and quantified as previously described [42] . Briefly , Arabidopsis seedlings were harvested and placed into extraction solution ( 18% 1-propanol and 1% HCl ) , and boiled for 3 minutes . Then the mixture was left in darkness for at least 3 hours at room temperature . After a brief centrifugation to pellet the tissue debris , the supernatant was removed and diluted with the extraction solution . The anthocyanin content was presented as A535−2 ( A650 ) g−1 fresh weight . Total RNA was extracted from 4-day-old Arabidopsis seedlings grown under −UV-B or +UV-B using the RNeasy plant mini kit ( Qiagen ) . Reverse transcription was performed using SuperScript II first-strand cDNA synthesis system ( Invitrogen ) according to the manufacturer's instructions . Real-time qPCR analysis was performed using SYBR Premix Ex Taq ( Takara ) with Applied Biosystems 7500 Real-Time PCR System . Each experiment was repeated with three independent samples , and RT-PCR reactions were performed in three technical replicates for each sample . The primers are listed in Table S1 . Total proteins was extracted from 4-day-old Arabidopsis seedlings grown under −UV-B and +UV-B in protein extraction buffer containing 50 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM PMSF , and 1× complete protease inhibitor cocktail ( Roche ) . Absorbance at 310 nm of plant total proteins adjusted to equal concentration and total amount were measured . Each experiment was repeated with three independent samples . 1 mg of total proteins was extracted from 4-day-old Arabidopsis seedlings in protein extraction buffer containing 50 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM EDTA , 10% glycerol , 0 . 1% Tween 20 , 1 mM PMSF , and 1× complete protease inhibitor cocktail ( Roche ) . The extracts were incubated with 8 µl anti-GFP antibodies ( Invitrogen ) coupled with 25 µl Dynabeads Protein G ( Invitrogen ) for 3 hours at 4°C under the same condition ( −UV-B or +UV-B ) as where the seedlings were grown . Then the dynabeads were washed three times by protein extraction buffer . Next the precipitates were eluted into 100 mM Glycine ( pH 2 . 5 ) and 100 mM NaCl , and immediately neutralized by 2 M Tris-HCl ( pH 9 . 0 ) and 100 mM NaCl , and finally concentrated using Strataresin ( Stratagene ) before immunoblot analysis . Primary antibodies used in this study were anti-COP1 and anti-RPN6 [40] , anti-HY5 [31] , anti-GFP ( Invitrogen ) and anti-UVR8 [17] antibodies .
|
Higher plants are able to sense and interpret diverse light signals to modulate their growth . In response to long-wavelength and low-intensity ultraviolet-B ( UV-B ) light , plants establish photomorphogenic development and stress acclimation . UV RESISTANCE LOCUS 8 ( UVR8 ) is a unique UV-B photoreceptor that triggers photomorphogenesis in Arabidopsis thaliana . However , the signaling process following UV-B light perception by plants is not fully understood . In this study , by generating transgenic UVR8 variants in Arabidopsis , we have extensively analyzed the biological significance of key residues in UVR8 for UV-B-induced photomorphogenesis . Furthermore , by engineering and characterizing two constitutively active UVR8 variants , we have provided the biochemical insight that the in vivo association between UVR8 and CONSTITUTIVELY PHOTOMORPHOGENIC 1 ( COP1 ) critically determines the photomorphogenic UV-B signaling output .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"plant",
"science",
"signaling",
"plant",
"growth",
"and",
"development",
"molecular",
"development",
"plant",
"genetics",
"biology"
] |
2014
|
Photoactivated UVR8-COP1 Module Determines Photomorphogenic UV-B Signaling Output in Arabidopsis
|
The transcription factor DAF-16/forkhead box O ( FOXO ) is a critical longevity determinant in diverse organisms , however the molecular basis of how its transcriptional activity is regulated remains largely unknown . We report that the Caenorhabditis elegans homolog of host cell factor 1 ( HCF-1 ) represents a new longevity modulator and functions as a negative regulator of DAF-16 . In C . elegans , hcf-1 inactivation caused a daf-16-dependent lifespan extension of up to 40% and heightened resistance to specific stress stimuli . HCF-1 showed ubiquitous nuclear localization and physically associated with DAF-16 . Furthermore , loss of hcf-1 resulted in elevated DAF-16 recruitment to the promoters of its target genes and altered expression of a subset of DAF-16-regulated genes . We propose that HCF-1 modulates C . elegans longevity and stress response by forming a complex with DAF-16 and limiting a fraction of DAF-16 from accessing its target gene promoters , and thereby regulates DAF-16-mediated transcription of selective target genes . As HCF-1 is highly conserved , our findings have important implications for aging and FOXO regulation in mammals .
Recent studies in various model system have revealed multiple evolutionarily conserved genes and genetic pathways important for longevity [1–6] . One of the best characterized longevity determinants is the forkhead box O ( FOXO ) family of transcription factors , which function as major effectors of the insulin/insulin-like growth factor ( IGF ) -1-like signaling ( IIS ) cascade . The IIS pathway is highly conserved and has been shown to modulate longevity in Caenorhabditis elegans , Drosophila , and mice [7] . Activation of the insulin/IGF receptor tyrosine kinases triggers a kinase cascade , involving the phosphoinositide 3-kinase ( PI3K ) and the serine/threonine kinases AKT-1 and AKT-2 , which culminates in the cytoplasmic sequestration and inhibition of the FOXO transcription factors [7] . In addition to aging , IIS is also critical for regulating development , metabolism , and stress response . In C . elegans , reduced signaling of the IIS pathway , such as that caused by mutations in the IIS receptor daf-2 or the PI3K age-1 , results in a dramatic increase in lifespan , heightened resistance to a wide variety of environmental stress stimuli , and altered metabolism and development [8 , 9] . Loss of daf-16 , the C . elegans homolog of FOXO , completely suppresses all the phenotypes associated with IIS deficiency in C . elegans [10–12] , indicating daf-16 to be the major effector of IIS in worms . From C . elegans to mammals , DAF-16/FOXO is emerging as a master regulator that is capable of responding to diverse environmental stimuli and coordinating development , metabolism , and stress response [1 , 13] . In addition to the IIS pathway , DAF-16/FOXO also responds to many other signaling cascades . Recent findings reveal that different signals induce distinct modifications of DAF-16/FOXO , which can impact the expression level , subcellular localization , and/or transcriptional activities of DAF-16/FOXO , leading to expression changes of selective DAF-16/FOXO target genes and specific cellular responses [13 , 14] . Mammalian FOXOs have been shown to regulate the expression of antioxidant enzymes , gluconeogenic enzymes , cell cycle regulators , and apoptotic genes [13] . Similarly , C . elegans DAF-16 regulates the expression of a large number of target genes , including those involved in metabolism , stress response , and immunity [15–17] . DAF-16 is thought to promote survival and longevity by mounting a robust response to various stresses , infection , and toxic compounds . Therefore , the precise control of DAF-16 transcriptional activities is a key regulatory step for longevity determination . Similar to other DNA binding transcription factors , one way for DAF-16/FOXO to achieve specificity in gene regulation depends on its functional interactions with transcriptional co-regulators . Recent findings have revealed several nuclear factors that cooperate with DAF-16/FOXO to regulate gene expression . In mammals , FOXOs can be deacetylated by the protein deacetylase SIRT1 [18–22] . SIRT1-mediated deacetylation leads to promotion of FOXO activation of stress response genes and concurrent inhibition of FOXO-regulated apoptotic genes [18] . Interestingly , SIRT1 homologs in yeast ( Sir2 ) , C . elegans ( sir-2 . 1 ) , and Drosophila ( dSir2 ) are important longevity determinants as their over-expression results in longevity increase [23] . In C . elegans , SIR-2 . 1 forms a protein complex with DAF-16 [24 , 25] and requires DAF-16 activity to modulate lifespan [26] . Furthermore , the nuclear factor SMK-1 and the β-catenin homolog BAR-1 have recently been shown to promote DAF-16-mediated transcription [27 , 28] . C . elegans lacking smk-1 or bar-1 show shortened lifespan , similar to that of worms lacking daf-16 . BAR-1 cooperates with DAF-16 to elicit proper oxidative stress response [28] , and SMK-1 is important for the roles of DAF-16 in both lifespan modulation and specific stress response [27] . Whereas SIR-2 . 1 , SMK-1 , and BAR-1 represent putative positive regulators of DAF-16 in C . elegans , nuclear factors that negatively regulate DAF-16 are largely unknown . Since DNA binding transcription factors are often regulated by the interplay between positive and negative regulators , the characterization of DAF-16 negative regulators will be essential for the further elucidation of DAF-16 regulation . In this paper , we report that the C . elegans homolog of host cell factor 1 ( HCF-1 ) represents a new longevity determinant and functions as a negative regulator of DAF-16 . HCF-1 belongs to a family of highly conserved proteins [29] . Loss of hcf-1 in C . elegans induces substantial lifespan extension of up to 40% and robust resistance to specific stress stimuli . For hcf-1 to modulate lifespan and stress response , it requires the activity of daf-16 . In delineating the mechanism by which HCF-1 regulates DAF-16 , we found HCF-1 to be a ubiquitously expressed nuclear protein that physically associates with DAF-16 . Moreover , loss of hcf-1 led to increased recruitment of some DAF-16 to its target gene promoters and altered expression of a subset of DAF-16-regulated genes . Given the genetic and biochemical data , we propose that HCF-1 modulates lifespan and stress responses by forming a complex with DAF-16 and restricting the recruitment of a fraction of DAF-16 to its target gene promoters , thereby regulating DAF-16-mediated transcription of specific target genes . Considering that HCF-1 and DAF-16 are both highly conserved through evolution , our findings suggest that HCF-1 likely also regulates FOXO activities and is important for aging in diverse organisms .
In a recent genome-wide RNA interference ( RNAi ) screen for new longevity genes [30] , we found that RNAi knock down of the hcf-1 gene consistently caused C . elegans to live ∼20%–30% longer than control RNAi treated worms ( Figure 1A; Table 1 ) . C . elegans hcf-1 encodes a protein that is highly conserved through evolution , but its biological function is just beginning to be elucidated . Mammalian HCF-1 was first identified to be the host cell factor essential for stabilizing the transcriptional complex involving the herpes simplex virus ( HSV ) VP-16 transcription factor [29] . Mammalian HCF-1 has subsequently been shown to play key roles in cell cycle progression , both at the G1/S transition , and at M phase and cytokinesis [31–33] . In eliciting its diverse biological roles , mammalian HCF-1 acts by binding to and regulating many different transcription and chromatin factors and assembling appropriate protein complexes for context-dependent gene expression regulation [29] . C . elegans HCF-1 has been shown to complement some of the transcriptional role of mammalian HCF-1 [34] and to also be required for proper cell cycle progression in worms ( Figure S2 ) [35] . Our data ascribe a new longevity function for HCF-1 . To further investigate a role of hcf-1 in C . elegans longevity , we obtained two mutant alleles ( ok559 and pk924 ) [35] of hcf-1 and examined their lifespan . The hcf-1 ( ok559 ) mutant has an in-frame deletion that should truncate the N-terminal half of the protein , and the hcf-1 ( pk924 ) mutant has a large deletion that should result in a frame shift leading to an early stop codon , and likely represents a null mutant ( Figure S1 ) [35] . As expected on the basis of our observation with the hcf-1 RNAi worms , we detected up to 40% lifespan extension in the hcf-1 mutants compared to wild-type worms ( Figure 1B; Table 1 ) . In general , the hcf-1 ( pk924 ) mutant lives slightly longer than the hcf-1 ( ok559 ) mutant ( Table 1 ) , consistent with the possibility that the pk924 allele is a more severe mutation . We confirmed that the prolonged lifespan associated with the hcf-1 mutants is due to hcf-1 deficiency by demonstrating that expression of a C-terminal green fluorescent protein ( gfp ) -tagged hcf-1 transgene ( hcf-1::gfp ) was able to partially restore normal lifespan in the hcf-1 ( pk924 ) mutant ( Table S2 ) . To further characterize HCF-1 in C . elegans , we examined the expression pattern of HCF-1 in worms . Using an affinity-purified polyclonal HCF-1 antibody in immunostaining assays , we observed prominent HCF-1 staining in the nuclei of most , if not all , somatic and germline cells in wild-type worms ( Figure 2 ) . The HCF-1 expression pattern detected in wild-type worms is highly specific because the hcf-1 ( pk924 ) and hcf-1 ( ok559 ) mutants only showed background fluorescence when they were examined using identical immunostaining conditions ( Figure 2 ) ( unpublished data ) . The nuclear localization of HCF-1 was consistently observed from embryo through larval and adult stages ( Figure 2 ) ( unpublished data ) [35] . Moreover , we observed similar ubiquitous nuclear expression in worms expressing a low-copy number of a functional hcf-1::gfp transgene ( Figure S3 ) . Our results indicate that HCF-1 is ubiquitously expressed in the nucleus of C . elegans under normal culture condition . Mammalian HCF-1 is also predominantly a nuclear protein [36] and its nuclear localization is thought to be important for its role in gene expression regulation . To characterize how hcf-1 modulates C . elegans lifespan , we asked whether hcf-1 may genetically interact with any known longevity factors in C . elegans . Because DAF-16/FOXO is one of the best characterized longevity determinants , we tested the epistatic relationship between hcf-1 and daf-16 . We created double mutants containing the hcf-1 ( pk924 ) or hcf-1 ( ok559 ) and the null daf-16 ( mgDf47 ) mutations . We found that the daf-16 ( mgDf47 ) ;hcf-1 ( pk924 ) or daf-16 ( mgDf47 ) ;hcf-1 ( ok559 ) double mutant had a lifespan indistinguishable from that of the daf-16 ( mgDf47 ) single mutant , which is ∼20% shorter than wild-type worms [37] ( Figure 1C; Tables 1 and S1 ) . We obtained similar results with hcf-1 ( ok559 ) mutant worms treated with daf-16 RNAi ( Table S1 ) . Our results indicate that hcf-1 requires the activity of daf-16 to modulate longevity in C . elegans and suggest that hcf-1 may be a novel upstream regulator of daf-16 . Given the epistatic relationship between hcf-1 and daf-16 , we wondered whether hcf-1 modulates C . elegans lifespan by functioning in the IIS pathway , a well-established upstream regulator of daf-16 . To test this , we examined the genetic interactions between hcf-1 and two major components of the IIS pathway: daf-2/insulin/IGF receptor and age-1/PI3K . We reasoned that if hcf-1 normally affects C . elegans lifespan by acting in the IIS pathway , then loss of hcf-1 would not have a major impact on the longevity of worms already lacking IIS signaling . We created double mutants containing the hcf-1 ( pk924 ) or hcf-1 ( ok559 ) and either the daf-2 ( e1370 ) or the age-1 ( mg44 ) mutations . Consistent with previous findings [11] , the daf-2 ( e1370 ) temperature-sensitive mutant showed an ∼2-fold increase in lifespan compared to wild-type worms at the nonpermissive temperature 25 °C ( Figure 1D; Tables 1 and S1 ) . Interestingly , the daf-2 ( e1370 ) ;hcf-1 ( pk924 ) or daf-2 ( e1370 ) ;hcf-1 ( ok559 ) double mutant lived considerably longer than either the daf-2 ( e1370 ) or hcf-1 ( pk924 ) or hcf-1 ( ok559 ) single mutant ( Figure 1D; Tables 1 and S1 ) , and exhibited a lifespan increase that is greater than the sum of the effect for the two single mutations . We obtained similar results with the age-1 ( mg44 ) ;hcf-1 ( ok559 ) double mutant . age-1 ( mg44 ) is a null mutant and , when maintained as a homozygous strain , exhibits an unconditional dauer arrest phenotype [10 , 38] . To avoid this , we collected age-1 ( mg44 ) or age-1 ( mg44 ) ;hcf-1 ( ok559 ) homozygous mutant adults born from age-1 ( mg44 ) /+ or age-1 ( mg44 ) /+;hcf-1 ( ok559 ) parents for lifespan analysis . These age-1 ( mg44 ) and hcf-1 ( ok559 ) ;age-1 ( mg44 ) worms completely lacked zygotic age-1 expression; however , they inherited sufficient maternal age-1 message to develop normally [38] . Similar to previous results [38] , the age-1 ( mg44 ) zygotic null mutant worms lived much longer than wild-type worms ( Figure 1E; Table 1 ) . The age-1 ( mg44 ) ;hcf-1 ( ok559 ) double mutant worms lived considerably longer than either the age-1 ( mg44 ) or hcf-1 ( ok559 ) single mutant ( Figure 1E; Table 1 ) , and exhibited a lifespan increase that is even greater than the sum of the effect for the single mutations . RNAi knock down of daf-2 or age-1 in the hcf-1 ( ok559 ) mutant gave similar results ( Table S1 ) . The genetic results described here indicate that loss of hcf-1 and loss of IIS act synergistically to extend lifespan in C . elegans and suggest that hcf-1 likely functions in a pathway in parallel to IIS . Although the hcf-1 ( pk924 ) mutation is a putative null mutation , the daf-2 ( e1370 ) mutation is a temperature sensitive mutation , and a small amount of maternal AGE-1 protein may have persisted in the age-1 ( mg44 ) ;hcf-1 ( ok559 ) double mutant , it remains possible that loss of hcf-1 increases lifespan by further decreasing daf-2 signaling . Since germline proliferation has been implicated in C . elegans lifespan modulation , we wondered whether the extended lifespan of the hcf-1 mutant is related to the brood size defect of this mutant ( Figure S2 ) [35] . In this regard , we tested the lifespan of the double mutant glp-1 ( e2141 ) ;hcf-1 ( pk924 ) . The glp-1 ( e2141 ) mutant completely lacks germline cells at the nonpermissive temperature 25 °C and is long-lived [39] . If the hcf-1 mutant is long-lived due to its partial defect in germline proliferation , then we would expect hcf-1 deficiency not to affect the lifespan of worms completely lacking germline cells . We found that the glp-1 ( e2141 ) ;hcf-1 ( pk924 ) double mutant lived much longer than the glp-1 ( e2141 ) or hcf-1 ( pk924 ) single mutants at 25 °C ( Figure 1F; Table 1 ) . Therefore , loss of hcf-1 can continue to extend the lifespan of worms that completely lack germline cells and are sterile , suggesting that hcf-1 has a function in lifespan modulation that is beyond its role in germline and brood size regulation . Since DAF-16/FOXO is well known to regulate stress responses [5 , 40 , 41] , we tested whether the hcf-1 mutants may exhibit differential response to environmental stress stimuli . To assay for a response to acute oxidative stress , we challenged wild-type or hcf-1 mutant adult worms with a high-dose of paraquat , a superoxide-inducing agent , and monitored their survival . We found that the hcf-1 ( pk924 ) mutant worms were considerably more resistant to the paraquat treatment compared to wild-type worms at multiple time points throughout the experiment ( Figure 3A ) . Moreover , the paraquat resistance of the hcf-1 ( pk924 ) mutants was dependent on daf-16 , as the daf-16 ( mgDf47 ) ;hcf-1 ( pk924 ) double mutant was sensitive to paraquat , similar to that of the daf-16 ( mgDf47 ) single mutant ( Figure 3B ) . Interestingly , the hcf-1 ( ok559 ) mutant worms had survival kinetics very similar to that of wild-type worms in the paraquat assay , suggesting that the hcf-1 ( ok559 ) mutant , while long-lived , was not more resistant to a high dose of paraquat treatment compared to wild-type worms . No HCF-1 protein is detected in the hcf-1 ( pk924 ) mutant , whereas some truncated protein accumulates in the hcf-1 ( ok559 ) mutant ( Figure S1 ) . Therefore , it is possible that resistance to a high level of oxidative stress only becomes apparent when HCF-1 is completely lost . To assay for a response to heavy metal stress , we challenged wild-type or hcf-1 mutant adult worms with cadmium [42] and monitored their survival . Similar to that observed with the paraquat assay , the hcf-1 ( pk924 ) mutant worms were more resistant to the cadmium exposure compared to wild-type worms at multiple time points ( Figure 3C ) . The cadmium resistance of the hcf-1 ( pk924 ) mutant was also daf-16-dependent ( Figure 3C ) . To assay for a response to heat stress , we shifted adult wild-type and hcf-1 mutant worms to 35 °C and monitored their survival . We found that the hcf-1 mutants and wild-type worms behaved very similarly throughout the time course of the heat shock treatment ( Figure 3D ) . As previously reported [43] , the age-1 ( hx546 ) mutant worms survived much longer and the daf-16 ( mgDf47 ) mutant worms died much faster than wild-type worms at 35 °C . We therefore concluded that loss of hcf-1 did not result in altered response to acute heat shock . Taken together , our results indicate that loss of hcf-1 results in worms that are resistant to paraquat and cadmium exposure , but not to heat shock , suggesting that hcf-1 is required for specific stress response . Considering that DAF-16 also plays a key role in dauer formation and fat metabolism in C . elegans [10 , 44 , 45] , we tested whether the hcf-1 mutants exhibit any dauer or fat phenotypes . We found that the hcf-1 ( ok559 ) mutant exhibits no dauer phenotype , whereas the hcf-1 ( pk924 ) null mutant exhibits a weak dauer exit phenotype . When monitored at 25 °C , a typical temperature for testing a strong dauer phenotype , both hcf-1 mutants developed normally , whereas the daf-2 ( e1370 ) mutant formed 100% dauer ( Table 2 ) . When monitored at 27 °C , a temperature commonly used to test for a weak dauer formation phenotype , neither hcf-1 mutant behaved differently from wild-type worms ( Table 2 ) . Lastly , we tested the daf-2;hcf-1 double mutants at 22 °C , which represents a well-established sensitized condition [46] for assaying a weak dauer recovery phenotype . Single and double mutant worms harboring the daf-2 ( e1370 ) mutation were incubated at the semi-nonpermissive temperature of 22 °C . Under this condition , the daf-2 ( ts ) worms enter dauer for ∼3 d and then recover to become reproductive adults . We found that the hcf-1 ( pk924 ) mutation prevented dauer exit , whereas the hcf-1 ( ok559 ) mutation had no effect ( Table 2 ) . These results indicate that loss of hcf-1 is associated with a weak dauer phenotype . Using the vital dye Nile Red , which stains lipid droplets in worms and represents a sensitive way to monitor fat storage [47] , we did not detect any substantial differences in fat storage between hcf-1 mutants and wild-type worms ( Figure S4 ) . Our genetic data suggest that hcf-1 acts upstream of daf-16 to affect C . elegans lifespan and stress responses . Since increased DAF-16 nuclear localization and transcriptional activities have been shown to extend lifespan in C . elegans , we tested whether HCF-1 may regulate the localization , expression level , and/or transcriptional activities of DAF-16 . Using transgenic worms expressing GFP-fused DAF-16 to monitor the subcellular localization of DAF-16 , we did not detect any altered DAF-16 localization in hcf-1-deficient worms ( Figure 4A ) . Using quantitative reverse transcription PCR ( qRT-PCR ) and immunoblotting to examine the RNA and protein expression levels of daf-16 and the major components of the IIS pathway , including daf-2 , age-1 , and akt-1 , we observed no obvious differences in their expression levels in the hcf-1 ( ok559 ) and hcf-1 ( pk924 ) mutants compared to wild-type worms ( Figure 4B ) ( unpublished data ) . Taken together , our results suggest that HCF-1 is not likely to affect the subcellular localization or the expression level of DAF-16 . To test whether HCF-1 may affect the transcriptional activities of DAF-16 , we measured the message levels of DAF-16-regulated genes in the hcf-1 mutant and wild-type worms using qRT-PCR . sod-3 , which encodes an iron/manganese superoxide dismutase , is one of the best characterized DAF-16 target genes [17 , 40] and its transcription is directly up-regulated by DAF-16 [48] . Interestingly , we found that the RNA level of endogenous sod-3 was significantly elevated 3–4-fold in both the hcf-1 ( ok559 ) and hcf-1 ( pk924 ) mutants as compared to wild-type worms ( Figure 5A ) . The elevated expression of sod-3 in the hcf-1 mutant worms is completely dependent on daf-16 because in the daf-16 ( mgDf47 ) ;hcf-1 ( ok559 ) double mutant , the level of sod-3 expression remained low and was similar to that seen in the daf-16 ( mgDf47 ) single mutant worms ( Figure 5A ) . In corroboration of our qRT-PCR results , we observed elevated levels of GFP expression in Psod-3::gfp transgenic worms , which express a GFP reporter driven by the sod-3 promoter , in hcf-1 ( pk924 ) mutant background ( Figure S5 ) . To investigate whether hcf-1 may generally affect the transcriptional activities of DAF-16 , we surveyed additional DAF-16-regulated genes as reported in previous microarray studies [17] . The previous studies have focused on DAF-16 targets that are responsive to IIS and we verified that all the genes we chose to test exhibit daf-2/daf-16 responsiveness under our assaying conditions ( Table 3 ) . Among the 11 DAF-16-activated genes examined , we found that in addition to sod-3 , the expression levels of mtl-1 , which encodes a metallothionein , and F21F3 . 3 , which encodes an farnesyl cysteine-carboxyl methyltransferase , showed a statistically significant , greater than 2-fold up-regulation in the hcf-1 mutants compared to wild-type worms ( Figure 5A; Table 3 ) . The elevated expression of mtl-1 in hcf-1 mutant worms is completely dependent on daf-16 ( Figure 5A; Table 4 ) and that of F21F3 . 3 is partially dependent on daf-16 ( Figure 5A; Table 4 ) . Among the six DAF-16-repressed genes examined , we found that the expression level of C32H11 . 4 , which encodes a protein of unknown function , showed a statistically significant , greater than 2-fold down-regulation in the hcf-1 mutants compared to wild-type worms ( Figure 5A; Table 3 ) . Similar to that of F21F3 . 3 , the repressed expression of C32H11 . 4 is partially dependent on daf-16 ( Figure 5A; Table 4 ) . The partial dependency suggests that additional factors might cooperate with DAF-16 to regulate F21F3 . 3 and C32H11 . 4 expression in the hcf-1 mutants . We also noticed that the expression of hsp-16 . 1 , which encodes a heat shock protein , and fat-5 , which encodes a delta-9 fatty acid desaturase , was significantly up-regulated and repressed , respectively , in the hcf-1 mutants ( Table 3 ) . However , the altered expression of fat-5 and hsp-16 . 1 in the hcf-1 mutants did not require daf-16 ( unpublished data ) . These results suggest that although hsp-16 . 1 and fat-5 are robust DAF-16 targets in response to daf-2 signaling ( Table 3 ) , they do not appear to be significantly regulated by DAF-16 in response to hcf-1 deficiency . Moreover , whereas fat-5 is activated by DAF-16 in response to reduced daf-2 signaling , its expression was repressed in the hcf-1 mutant in a daf-16-independent manner . Many of the DAF-16 downstream genes we surveyed are likely regulated by multiple different transcription factors . For example , hsp-16 . 1 is likely also regulated by the heat shock factor HSF-1 . It is possible that in the hcf-1 mutants , different transcription factor ( s ) play a major role in mediating the expression change of hsp-16 . 1 and fat-5 , and DAF-16 only plays a minor role or is not involved in their gene expression regulation . The expression of the remaining DAF-16 target genes either did not show a significant change in the hcf-1 mutants , or their expression change was less than 2-fold . Consistent with our genetic data in which hcf-1 appears to act in parallel to daf-2 signaling to affect lifespan , the expression of sod-3 and mtl-1 was synergistically up-regulated in the daf-2 ( e1370 ) ;hcf-1 ( pk924 ) double mutant compared to either daf-2 ( e1370 ) or hcf-1 ( pk924 ) single mutant ( Figure 5B; Table 5 ) . Taken together , our results suggest that hcf-1 is only able to affect the expression of selective DAF-16 target genes . Moreover , our data indicate that hcf-1 inactivation leads to daf-16-dependent up-regulation of three different DAF-16 activated genes and daf-16-dependent down-regulation of a DAF-16 repressed gene , suggesting that hcf-1 normally participates in the inhibition of DAF-16 transcriptional activity . Importantly , the dependence of hcf-1 on daf-16 to elicit gene expression changes correlates with the requirement of daf-16 in hcf-1-mediated lifespan modulation , suggesting that the ability of HCF-1 to regulate DAF-16 transcriptional activity is likely linked to its role in longevity . Considering that C . elegans HCF-1 is normally localized to the nucleus , and that its mammalian homolog has a known role in gene expression regulation , we hypothesize that HCF-1 modulates C . elegans lifespan by regulating the transcriptional activity of DAF-16 at a subset of target genes that are particularly important for stress response and longevity assurance . We next examined how HCF-1 might affect the transcriptional activity of DAF-16 . In mammalian cells , HCF-1 is thought to regulate gene expression by binding to various transcription and chromatin factors [33 , 49–51] . In C . elegans , DAF-16 localizes to both cytoplasm and nucleus under normal culture condition , and HCF-1 appears predominantly nuclear in most cells ( Figure 2 ) [35 , 37 , 52] , suggesting that DAF-16 and HCF-1 co-localize in the nucleus ( Figure S6 ) . We therefore tested whether C . elegans HCF-1 may physically associate with DAF-16 . Since we did not have an antibody that could detect endogenous DAF-16 robustly , we performed co-immunoprecipitation ( co-IP ) experiments using worm strains that lack endogenous DAF-16 , but carry in low-copy number a functional gfp-fused daf-16 transgene ( daf-16 ( mu86 ) ;muIs71 ) [37] . We found that when an affinity-purified HCF-1 antibody was used to immunoprecipitate HCF-1 from extracts of the daf-16::gfp transgenic worms , DAF-16::GFP was co-immunoprecipitated ( Figure 6B ) . This interaction appeared specific because when co-IP experiments were done using extracts of daf-16::gfp worms that also harbor the hcf-1 ( ok559 ) deletion ( daf-16 ( mu86 ) ;hcf-1 ( ok559 ) ;muIs71 ) , we were not able to immunoprecipitate HCF-1 or to co-immunoprecipitate DAF-16::GFP ( Figure 6B ) . Furthermore , using identical co-IP conditions , we did not recover the irrelevant Psod-3::GFP ( a GFP reporter driven by the sod-3 promoter ) upon HCF-1 immunoprecipitation , indicating that the protein–protein interaction detected between DAF-16::GFP and HCF-1 is not likely mediated by the GFP tag . We obtained similar results using reciprocal co-IP experiments ( Figure 6A ) . Our results suggest that HCF-1 is able to form a specific protein complex with DAF-16 in C . elegans . To further elucidate the molecular mechanism by which HCF-1 regulates DAF-16-mediated transcription , we performed chromatin immunoprecipitation ( ChIP ) experiments to test how HCF-1 might affect DAF-16 enrichment on target gene promoters . For the ChIP experiments , we employed daf-16::gfp worms ( daf-2 ( e1370 ) ;daf-16 ( mgDf47 ) ;daf-16::gfp ) and anti-GFP immunoprecipitation to capture DAF-16 . As previously reported [48] , DAF-16 was enriched at the promoter of sod-3 when the IIS pathway was inactivated ( Figure 7A ) . We also examined the DAF-16 target gene mtl-1 and found that DAF-16 was enriched on its promoter ( Figure 7A ) . Under the same ChIP conditions , we did not detect HCF-1 enrichment at the sod-3 or mtl-1 promoters , but detected great enrichment of HCF-1 at the promoter of efl-1 ( Figure 7B ) . efl-1 is the C . elegans E2f1 gene; in mammalian cells , HCF-1 is highly enriched at the promoter region of E2f1 [53] . Thus , our results demonstrate a conserved role of HCF-1 at the efl-1/E2f1 promoter in worms , and suggest that HCF-1 is not likely to present at sod-3 or mtl-1 promoters . Considering our co-IP results showing that HCF-1 and DAF-16 physically associate in worms ( Figure 6 ) , our ChIP results suggest that the HCF-1/DAF-16 complex is probably not present at the promoters of DAF-16 target genes . However , it remains possible that HCF-1 is a component of a large protein complex that associates with DAF-16 at target gene promoters , and our cross-linking conditions cannot capture the presence of HCF-1 at those promoters . For the other hcf-1-regulated , daf-16-dependent genes ( Table 4; F21F3 . 3 and C32H11 . 4 ) , we either did not identify a putative DAF-16 binding element or did not detect DAF-16 enrichment at the promoter regions we surveyed , suggesting that they might not be DAF-16 direct targets . We next tested whether the enrichment of DAF-16 on sod-3 or mtl-1 promoters would be affected when hcf-1 was RNAi depleted . As a control , we showed that the enrichment of HCF-1 on the efl-1 promoter was substantially reduced when hcf-1 was knocked down by RNAi . Interestingly , under the same RNAi conditions , we consistently observed an enhanced enrichment of DAF-16 to the sod-3 and mtl-1 promoters in multiple independent trials ( Figure 7A ) . These results suggest that in the absence of HCF-1 , more DAF-16 is able to localize to the promoters of its target genes .
Our study has revealed the C . elegans homolog of HCF-1 to be an important longevity determinant and transcriptional regulator of DAF-16 . Our data indicate that HCF-1 is necessary for maintaining normal lifespan and stress response in C . elegans , as loss of hcf-1 results in mutant worms with substantially extended longevity and heightened resistance to specific stress stimuli . In modulating C . elegans lifespan and stress response , hcf-1 completely depends on the activity of daf-16 , but likely acts independently of the IIS pathway . In elucidating the mechanism by which HCF-1 regulates DAF-16 , we showed that HCF-1 is a ubiquitously expressed nuclear protein and forms a protein complex with DAF-16 in C . elegans . Interestingly , in the absence of hcf-1 , greater enrichment of DAF-16 at its target gene promoters is observed and more robust DAF-16-mediated regulation of selective transcriptional targets is detected . On the basis of our results , we propose that HCF-1 modulates C . elegans lifespan and stress response by acting as a novel negative regulator of DAF-16 . Normally , HCF-1 associates with DAF-16 and limits a fraction of DAF-16 from accessing its target gene promoters . In the absence of HCF-1 , more DAF-16 is released to localize to target gene promoters to confer greater transcriptional regulation of selective target genes ( Figure 8 ) . Altered expression of this subset of target genes likely contributes to the stress resistance and prolonged longevity phenotypes observed in the hcf-1 mutant worms . Our results ascribe a new longevity and stress response function to the highly conserved transcriptional regulator HCF-1 . Mammalian HCF-1 was first identified as a major host cell factor required for HSV VP16-induced immediate early gene transcription [29] . In addition to its role in HSV infection , HCF-1 is also essential for cell cycle progression . Studies have revealed that mammalian HCF-1 is required for appropriate transition from G1 to S phase , and also proper progression of M phase and cytokinesis [31–33] . Importantly , mammalian and C . elegans HCF-1 share conserved functions . C . elegans HCF-1 is able to stabilize the VP16-induced complex; moreover , C . elegans hcf-1 mutants produce small broods and exhibit low penetrance of embryonic lethality ( Figure S2 ) [35] , both phenotypes consistent with a role of HCF-1 in cell proliferation . Furthermore , C . elegans hcf-1 mutant embryos show low penetrance of mitotic and cytokinetic defects [35] . As HSV is a human specific virus , it is thought that VP16 likely mimicked a cellular interaction with HCF-1 and co-opted human HCF-1 for productive HSV lytic infection . Given the well-established and conserved role of HCF-1 in cell cycle control , it is interesting to consider whether the cell proliferation function of HCF-1 is linked to its role in longevity and stress response . In adult C . elegans , the only proliferative tissue is the germline . Whereas a defect in germline stem cell proliferation is known to cause lifespan increase , our genetic studies showed that hcf-1 deficiency can continue to extend the lifespan of worms completely lacking germline ( Figure 1F; Table 1 ) , suggesting that the longevity function of hcf-1 is likely not linked to its role in cell proliferation . The high degree of functional conservation between C . elegans and mammalian HCF-1 suggests that mammalian HCF-1 likely also participates in stress response and longevity determination . Therefore , HCF-1 may very well represent a new universal longevity determinant . Our model proposes that HCF-1 affects lifespan and stress response by forming a protein complex with DAF-16 and regulating DAF-16 recruitment to target gene promoters and DAF-16-mediated gene transcription . This model is consistent with the known roles of mammalian HCF-1 . For VP16-induced immediate early ( IE ) gene expression , binding of HCF-1 to VP16 is thought to help recruit the activating Set1/Ash2 histone methyltransferase complex to IE-gene promoters [29] . For its role in G1/S transition , mammalian HCF-1 has been found to recruit the Set1/Ash2 histone methyltransferase activating complex to E2F1 and the Sin3 histone deacetylase repressive complex to E2F4 at the appropriate times of the cell cycle , which likely helps to reinforce the activating or repressive functions of the respective E2F family members [33] . The role of HCF-1 in regulating the transcription factor Miz-1 is particularly relevant to this study . HCF-1 has been shown to physically associate with Miz-1 and antagonize Miz-1-mediated transactivation by interfering with the association of Miz-1 and the histone deacetylase P300 [49] . Furthermore , HCF-1 , via its various functional motifs , has been shown to mediate protein–protein interactions with a large number of polypeptides , including transcription factors LZIP/Luman , Zhangfei , HPIP , Sp1 , and GABPβ , protein phosphatase PP1 , and cell-death protein PDCD2 [29] . HCF-1 is emerging as an extremely versatile scaffolding protein , capable of binding to many different transcription and chromatin factors via its different conserved motifs , and assembling appropriate protein complexes for proper context-dependent gene expression regulation [33 , 49–51] . Our qRT-PCR results suggest that HCF-1 is only able to regulate the DAF-16-mediated transcription of a select group of previously identified DAF-16 target genes . Considering that the DAF-16 target genes we surveyed were previously determined to be responsive to daf-2/IIS , and since our genetic studies suggest that hcf-1 and daf-2 might act in parallel pathways and converge onto daf-16 ( Figure 1 ) , it is not surprising that some of the gene expression changes caused by hcf-1 deficiency would be distinct from that caused by reduced daf-2 signaling . It can be argued that HCF-1 might represent a weaker regulator of DAF-16 compared to DAF-2 , and its effect on some of the DAF-16-regulated genes might simply be missed in our analysis as it is likely to be much weaker than that in the daf-2 mutant ( Table 3 ) . Whereas a weaker effect model is possible , we favor the model that HCF-1 represents a gene-specific regulator of DAF-16 . In our analysis , we noticed that the impact of hcf-1 deficiency on DAF-16 target gene regulation is not always much weaker than reduced daf-2 signaling . For instance , the expression fold change of F21F3 . 3 in hcf-1 ( - ) was comparable with that in daf-2 ( - ) ( ∼5-fold versus ∼11-fold ) . On the other hand , there are genes , e . g . , dod-3 and dod-24 , whose expression change in daf-2 ( - ) is as great as that of sod-3 or mtl-1 ( ∼100-fold ) , however , unlike sod-3 and mtl-1 , their gene expression did not show a significant change in hcf-1 ( - ) . Future whole genome expression profiling experiments will provide a global view of whether HCF-1 acts as a gene-specific negative regulator of DAF-16 . Considering that HCF-1 and DAF-16/FOXO are highly conserved across species , it is very likely that mammalian HCF-1 also conserves the function of FOXO regulation . Whereas C . elegans only has one daf-16 gene , mammals have four FOXO genes . Context specific regulators of FOXOs are critical in ensuring specificity on gene expression regulation and subsequent biological responses [14] . Our findings in C . elegans raise the intriguing possibility that mammalian HCF-1 represents a new regulator of one or more of the FOXO proteins . An important next question is how HCF-1 might achieve its specificity in influencing DAF-16 transcriptional targets . A simple hypothesis is that the association between HCF-1 and DAF-16 may be regulated on the basis of upstream stimulus . For example , signals that induce altered subcellular localization of HCF-1 or DAF-16 may disrupt the HCF-1/DAF-16 interaction . Both HCF-1 and DAF-16 has been shown to shuttle between the nucleus and cytoplasm under specific conditions [14 , 37 , 54–56] . In addition , DAF-16/FOXO and HCF-1 proteins have been shown to be extensively modified post-translationally [14 , 37 , 52 , 57–59] . It is possible that different modifications on HCF-1 and/or DAF-16 will substantially affect their association . Lastly , the specificity toward target genes can also be conferred at the level of the transcriptional complex assembly . Under this scenario , additional co-regulators of DAF-16 will likely come into play . Whereas HCF-1 represents the only nuclear negative regulator of DAF-16 known in C . elegans , several DAF-16 nuclear positive regulators have been reported , including SIR-2 . 1 [24] , SMK-1 [27] , and BAR-1 [28] . Future research to elucidate the interplay among the various DAF-16 co-activators and HCF-1 will greatly advance our understanding of the mechanistic details of how DAF-16 transcriptional activities can be appropriately regulated . Our data indicate that an important function of HCF-1 may be to modulate responses to specific environmental stress stimuli . Interestingly , only the hcf-1 ( pk924 ) allele showed resistance to paraquat and cadmium treatment , and neither of the hcf-1 mutant alleles demonstrated altered response to heat shock . These results suggest that a general heightened response to a wide-range of environmental stresses is not likely to account for the lifespan increase observed in the hcf-1 mutant worms . However , it is important to also consider that in the stress assays , worms were challenged with a high dose of an acute stress , which is very different from the low level of chronic stress worms experience as they grow old in longitudinal assays . The involvement of hcf-1 in stress response nicely fits with the overall theme that HCF-1 is a gene-specific transcriptional regulator of DAF-16 . It is well established that distinct stress stimuli are able to induce DAF-16/FOXO to regulate different target genes [13 , 14] . We propose that the main role of HCF-1 is to help fine tune the regulation of a subset of DAF-16-regulated genes to modulate survival under specific conditions . Taken together , we showed that HCF-1 is essential for longevity maintenance and that it functions as a negative regulator of DAF-16 in C . elegans . As HCF-1 and DAF-16/FOXO are highly conserved from C . elegans to mammals , our findings have important implications for FOXO regulation and longevity determination in diverse organisms .
The strains used in this paper were as follow: wild-type N2 , daf-16 ( mgDf47 ) , daf-2 ( e1370 ) , age-1 ( hx546 ) , sqt-1 ( sc13 ) age-1 ( mg44 ) /mnC1 ( a gift from Catherine A . Wolkow , National Institute of Aging ) , glp-1 ( e2141 ) , hcf-1 ( ok559 ) ( generated by the C . elegans Gene Knockout Consortium ) , hcf-1 ( pk924 ) ( a gift from Winship Herr , University of Lausanne , Switzerland ) , daf-16 ( mgDf47 ) ;xrIs87[daf-16α::gfp::daf-16b , rol-6 ( su1006 ) ] ( DAF-16::GFP ) [52] , daf-16 ( mu86 ) ;muIs71[daf-16a::gfp/bKO , rol-6 ( su1006 ) ] ( DAF-16::GFP ) [37] , and muIs84[Psod-3::gfp] [60] . The hcf-1 ( ok559 ) allele was outcrossed five times and the hcf-1 ( pk924 ) allele was outcrossed three times with the N2 strain in our lab prior to phenotype analyses . The following double mutant strains were constructed using standard genetic methods: daf-16 ( mgDf47 ) ;hcf-1 ( ok559 ) , daf-16 ( mgDf47 ) ;hcf-1 ( pk924 ) , daf-2 ( e1370 ) ;hcf-1 ( ok559 ) , daf-2 ( e1370 ) ;hcf-1 ( pk924 ) , sqt-1 ( sc13 ) age-1 ( mg44 ) /mnC1;hcf-1 ( ok559 ) , glp-1 ( e2141 ) ;hcf-1 ( pk924 ) , daf-16 ( mu86 ) ;muIs71[daf-16a::gfp/bKO , rol-6 ( su1006 ) ];hcf-1 ( ok559 ) , daf-16 ( mu86 ) ;muIs71[daf-16a::gfp/bKO and hcf-1 ( pk924 ) ;sur-5::gfp , muIs84[Psod-3::gfp];hcf-1 ( pk924 ) . All strains were cultured using standard methods [61] . Unless otherwise stated , NGM plates were seeded with E . coli OP50 as the food source . For RNAi lifespan assays , RNAi bacteria were grown in Luria broth with 50 μg/ml ampicillin at 37 °C for 10–16 h , seeded onto NGM plates containing 2 or 4 mM IPTG , and induced at room temperature for about 6 h [62] . Worms were allowed to lay eggs overnight on RNAi plates at 16 °C , and the progeny were grown on RNAi plates at 25 °C until they developed into young adult stage . The young adult worms were then transferred onto RNAi plates seeded with 3-fold concentrated RNAi bacteria that contained 50 μg/ml FUDR to prevent the growth of progeny . For lifespan assays using NGM plates seeded with OP50 bacteria , worms were allowed to lay eggs over-night at 16 °C , and the progeny were grown on NGM plates at 25 °C till young adult stage . The young adult worms were then transferred onto NGM plates that contained 50 μg/ml FUDR and seeded with 3-fold concentrated E . coli OP50 . For lifespan assays involving daf-2 ( e1370 ) and daf-2 ( e1370 ) ;hcf-1 ( pk924 ) or daf-2 ( e1370 ) ;hcf-1 ( ok559 ) worms , progeny were allowed to grow at 16 °C and shifted to 25 °C after the L3 larval stage to avoid the constitutive dauer arrest phenotype associated with the daf-2 ( e1370 ) mutant [63] . For all lifespan assays , worms were aged at 25 °C . Worms were scored every day or every other day , and those that failed to respond to a gentle prodding with a platinum wire were scored as dead . Animals that bagged , exploded , or crawled off the plate were considered as censored . We defined the day when we transferred the young adult worms as day 0 of adult lifespan . Statistical analysis was done using the SPSS software and p-values were calculated using the log-rank test . All the lifespan experiments were repeated at least two independent times . A GFP-fused hcf-1 plasmid ( Phcf-1::hcf-1::gfp ) was created by inserting a genomic fragment that contains ∼500 bp upstream of the predicted ATG of hcf-1 and the entire predicted coding region of hcf-1 into the pPD95_77 plasmid . Transgenic worms were created by microparticle bombardment as previously described [64] . The Phcf-1::hcf-1::gfp plasmid was co-bombarded with the pJKL702 [unc-119 ( + ) ] plasmid into the unc-119 ( ed4 ) mutant worms to obtain the strain unc-119 ( ed4 ) ;rwIs3[Phcf-1::hcf-1::gfp , unc-119] . Multiple independent integrated transgenic lines were examined . Each single L4 worm was allowed to lay eggs and transferred to a fresh NGM plate every 24 h until it completed egglay . The number of eggs laid and the number of hatched worms were counted . A total of five worms were used for each strain . Nile red staining was performed as previously described [47] . Unseeded NGM plates were coated with 0 . 025 μg/ml final concentration Nile red that had been resuspended in acetone and diluted in H2O . The Nile red was allowed to diffuse through NGM overnight . The plates were then seeded with OP50 bacteria and left at room temperature overnight . Gravid adult worms were allowed to egglay onto the plates overnight , and progeny allowed to develop to young adult stage . Worms were then monitored using a fluorescent microscope ( Leica DM 5000B ) and images were captured using a Hamamatsu ORCA-ER camera and the OpenLab Software . All stress assays were performed as previously described [41–43] . For the paraquat assay , gravid adult worms of each strain tested were allowed to lay egg on NGM plates seeded with OP50 for 2–3 h to produce relatively synchronous populations of progeny . The progeny were allowed to develop at 25 °C , and when they reached young-adulthood , FUDR was added to the plates at a final concentration of 50 μg/ml to prevent the growth of progeny . At day 2–3 of adulthood , worms were washed off the NGM plates and rinsed with M9 buffer three times to remove the OP50 bacteria . Approximately 30 adults were dispensed into each well of a 24-well culture plate containing 300 μl of 200 mM paraquat ( Sigma ) in M9 buffer . Triplicate wells were used for each strain , and the experiment was repeated at least two independent times . Worms in the paraquat buffer were scored every 2–3 h for survival . Worms that failed to respond to gentle prodding were scored as dead . For the CdCl2 assay , synchronized day 2 adult worms were collected as described above and washed by K-medium . Worms were then put into each well of a 24-well culture plate containing 600 μl K-medium with 18 mM CdCl2 at 20 °C . Triplicate plates for each strain were scored for each time point indicated . Worms that failed to respond to gentle prodding were scored as dead . For the heat shock assay , synchronous populations of worms were grown as described above . Day 2 adult worms grown on OP50-NGM plates were shifted to 35 °C . Duplicate or triplicate plates for each strain were scored for each time point . Because the scoring was done at room temperature , once the worms were pulled from 35 °C and scored for survival , they were discarded to avoid the complication of recovery from heat shock during the time of scoring . Dauer assays were performed as previously described [46] . Worms at the L4 stage were allowed to lay egg at16 °C over night . The resulting progeny were allowed to develop at the indicated temperature . At ∼96 h ( 25 °C or 27 °C ) or ∼120 h ( 22 °C ) after egg lay , the number of dauers and adult worms on each plate were scored . Replica plates were scored for each strain . The dauer assays were repeated two to three times . daf-16 ( mgDf47 ) ;xrls87 transgenic strain was used for DAF-16::GFP localization assay [52] . Worms at the L4 larval stage were picked onto RNAi plates and allowed to lay egg at 16 °C for one day . Progenies were exposed to the RNAi bacteria at 16 °C for 5 d until they became gravid adults . The DAF-16::GFP signal was then monitored using a fluorescent microscope ( Leica DM 5000B ) , and images were captured using a Hamamatsu ORCA-ER camera and the OpenLab program . Worm fixation and immunostaining were performed as previously described [65] . In brief , worms were immobilized and compressed between two polylysine coated slides and snap frozen in liquid nitrogen . The cuticle was removed by quickly separating two frozen slides . Worms were then quickly fixed in pre-chilled methanol at −20 °C . Incubation with primary and secondary antibodies and washes were done in TBS buffer at room temperature . Fluorescence signal was monitored using a fluorescent microscope ( Leica DM 5000B ) , and images were captured using a Hamamatsu ORCA-ER camera and the OpenLab program . Immunoprecipitation and immunoblotting were carried out as described [66] . In brief , for immunoprecipitation experiments , worm extracts were made from mixed staged worms by sonication of worms in lysis buffer and subsequent removal of debris by centrifugation . The appropriate antibody was incubated with worm extract at 4 °C overnight followed by incubation with protein A slurry ( Pierce ) at 4 °C for 3∼6 hrs . The protein A beads were then washed with lysis buffer for 6 times . The bound proteins were eluted by boiling in 2× sample buffer , separated on a SDS gel , transferred onto a nitrocellulose membrane , and followed by standard ECL detection . To generate polyclonal antiserum against C . elegans HCF-1 , bacterial recombinant S-tagged fusion protein containing full-length HCF-1 was purified using the S-Tag Thrombin Purification kit ( Novagen ) and used as an antigen to immunize guinea pigs . The crude anti-HCF-1 antiserum was subsequently purified using S-tagged HCF-1 . In brief , the crude anti-HCF-1 antiserum was incubated with purified S-tagged HCF-1 immobilized on nitrocellulose membrane ( BA85 Protran BioScience ) . Poorly bound proteins were removed by multiple washes in TBS and PBS buffer , and the bound anti-HCF-1 antibody was recovered by subsequent elution . For immunostaining , affinity purified anti-HCF-1 antibody was used as the primary antibody ( 1:500 ) and anti-guinea pig conjugated with Cy3 ( Jackson ImmunoResearch Laboratories , 1:200 ) was used as the secondary antibody . For GFP immunostaining , anti-GFP ( goat , Rockland ) was used as the primary antibody ( 1:1000 ) and anti-goat conjugated with FITC ( Jackson ImmunoResearch Laboratories , 1:400 ) was used as the secondary antibody . For immunoprecipitation , affinity purified anti-HCF-1 and anti-GFP ( rabbit , Clontech ) antibodies were used . Antibodies for immunoblotting include: anti-DAF-16 ( cC-20 ) ( goat , Santa Cruz ) , anti-HCF-1 , anti-GFP ( rabbit , Clontech ) , anti-ACTIN ( mouse , Chemicon ) , anti-goat ( Rockland ) , anti-mouse ( Santa Cruz ) , anti-guinea pig ( Jackson ImmunoResearch Laboratories ) , and anti-rabbit ( Rockland ) . Synchronized late L4 staged worms were used for RNA isolation . All the worms for qRT-PCR were grown at 25 °C except that in the experiments using daf-2 ( e1370 ) mutants , worms were shifted from 16 °C to 25 °C for 8 h prior to harvest . Total RNA from ∼10–15 μl of worm pellet was isolated using Tri-reagent ( Molecular Research Center , Inc . ) [67] . cDNAs were synthesized with random hexamers using SuperScript III First-Strand Kit ( Invitrogen ) . qRT-PCR reactions were performed using iQ SYBR Green Supermix ( BIO-RAD ) and the MyiQ Single Color Real-time PCR Detection System ( BIO-RAD ) . The qRT-PCR conditions were: 95 °C for 3 min , followed by a 40-cycles of 10 s at 95 °C and 30 s at 60 °C . Melting curve analysis was performed for each primer set at the end to ensure the specificity of the amplified product . For qRT-PCR , act-1 was used as the internal control , and the RNA level of each gene of interest was normalized to the level of act-1 for comparison . The qRT-PCR experiment was repeated at least three times using independent RNA/cDNA preparations . The data were pooled and analyzed using Student's t-test . The qRT-PCR primers for act-1 are: forward primer: 5′-CCAGGAATTGCTGATCGTATGCAGAA-3′; reverse primer: 5′-TGGAGAGGGAAGCGAGGATAGA-3′ ( product length: 133 bp ) . Primers for sod-3 are: forward primer: 5′-CCAACCAGCGCTGAAATTCAATGG-3′; reverse primer: 5′-GGAACCGAAGTCGCGCTTAATAGT-3′ ( product length: 127 bp ) . Primers for mtl-1 are: forward primer: 5′-atggcttgcaagtgtgactg-3′; reverse primer: 5′-cacatttgtctccgcacttg-3′ ( product length: 56 bp ) . Primers for fat-5 are: forward primer: 5′-tggtgaagaagcacgatcag-3′; reverse primer: 5′-aagcagaagattccgaccaa-3′ ( product length: 125 bp ) . Primers for M02D8 . 4 are: forward primer: 5′-atttgccaacaaacatgcaa-3′; reverse primer: 5′-ggtccacgatggtgttgtct-3′ ( product length: 87 bp ) . Primers for ges-1 are: forward primer: 5′-agcaacaaggaagggtcgta-3′; reverse primer: 5′-ccgatgatctccgaaatgaa-3′ ( product length: 120 bp ) . Primers for lys-7 are: forward primer: 5′-gcgggttattgtgcagtttt-3′; reverse primer: 5′-tcaattccgagtccagcttt-3′ ( product length: 114 bp ) . Primers for K09C4 . 5 are: forward primer: 5′-tggaattgaaccgactattgc-3′; reverse primer: 5′-gcaaatggcacaagaacaaa-3′ ( product length: 106 bp ) . Primers for dod-3 are: forward primer: 5′-AAAAAGCCATGTTCCCGAAT-3′; reverse primer: 5′-GCTGCGAAAAGCAAGAAAAT-3′ ( product length: 137 bp ) . Primers for F21F3 . 3 are: forward primer: 5′-CCGATTCGTTCCTTTTGAAG-3′; reverse primer: 5′-ACAACCGAATGTTCCAATCC-3′ ( product length: 138 bp ) . Primers for hsp-16 . 1 are: forward primer: 5′-GCAGAGGCTCTCCATCTGAA-3′; reverse primer: 5′-GCTTGAACTGCGAGACATTG-3′ ( product length: 85 bp ) . Primers for hsp-12 . 3 are: forward primer: 5′-GCCATTCCAGAAAGGAGATG-3′; reverse primer: 5′-CGTTTGGCAAGAAGTTGTGA-3′ ( product length: 93 bp ) . Primers for C32H11 . 4 are: forward primer: 5′-TTACTTCCCATCGCCAAAGT-3′; reverse primer: 5′-CAATTCCGGCGATGTATGAT-3′ ( product length: 117 bp ) . Primers for F35E12 . 5 are: forward primer: 5′-TCTCGAAGCCAACAAGTTCA-3′; reverse primer: 5′-TTTCACGGGATCCGTATTTC-3′ ( product length: 78 bp ) . Primers for T16G12 . 1 are: forward primer: 5′-CAATGGGAGCTCACTTCGAT-3′; reverse primer: 5′-TCATCGGCAAGAAGAGTCAA-3′ ( product length: 138 bp ) . Primers for dod-24 are: forward primer: 5′-TGTCCAACACAACCTGCATT-3′; reverse primer: 5′-TGTGTCCCGAGTAACAACCA-3′ ( product length: 138 bp ) . Primers for F08G5 . 6 are: forward primer: 5′-tggacaacccagatatgcaa-3′; reverse primer: 5′-gtatgcgatggaaatggaca-3′ ( product length: 111 bp ) . Primers for dod-22 are: forward primer: 5′-ttgttggtcccaagttcaca-3′; reverse primer: 5′-aagaacttcggctgcttcag-3′ ( product length: 132 bp ) . Primers for daf-16 are: forward primer: 5′-ccagacggaaggcttaaact-3′; reverse primer: 5′-attcgcatgaaacgagaatg–3′ ( product length: 149 bp ) . Primers for daf-2 are: forward primer: 5′-cggtgcgaagagaggatatt-′; reverse primer: 5′-tacagaggtcgccgttactg-3′ ( product length: 97 bp ) . Primers for age-1 are: forward primer: 5′-agtggattcggaaacaatgc-3′; reverse primer: 5′-ggaatcgatcgacactttca-3′ ( product length: 135 bp ) . Primers for akt-1 are: forward primer: 5′-tcaccgatgcgatattgtct-3′; reverse primer: 5′-aactccccaccaatcaacac-3′ ( product length: 82 bp ) . ChIP was performed as previously described with slight modifications ( P . Kolasinska-Zwierz , I . Latorre , and J . Ahringer , personal correspondence ) [48] . In brief , ground frozen worm powder was crosslinked using 1% formaldehyde in PBS buffer and subjected to sonication . Immunoprecipitation was performed as described above . The protein-DNA complexes were then eluted from protein A beads and treated with RNase A and proteinase K . Precipitated DNA fragments were purified and subjected to qPCR analysis . The qPCR Primers for ChIP are as followed . Primers for sod-3 are: forward primer: 5′-TTTTCAAACCGAAAATTGACC-3′; reverse primer: 5′-CAAAGACCTCATCAACAGCAA-3′ . Primers for mtl-1 are: forward primer: 5′-ggccaccctcttttatcaca-3′; reverse primer: 5′-tcaaattgagctgccttcttc-3′ . Primers for efl-1are: forward primer: 5′-ttttatcttctcattcaagcgaaa-3′; reverse primer: 5′-gagacaatgggaaaggtgga-3′ . Primers for Chromosome IV noncoding region are: forward primer: 5′-CTCTTCATTTTGTTCCTGTGTTTTCC-3′; reverse primer: 5′-GAAGGCGGCGGTAATTGTTG-3′ .
The wormbase ( WB , http://www . wormbase . org ) gene ID for hcf-1 is WBGene00001827 .
|
One of the key molecules that modulate longevity in evolutionarily diverse organisms is the transcription factor DAF-16/FOXO . Despite its importance in aging and other biological processes , how DAF-16/FOXO activity is regulated in the nucleus is largely unknown . We report a new player important for aging modulation , the nematode homolog of host cell factor 1 ( HCF-1 ) , and show that it functions as a negative regulator of DAF-16 . In worms , HCF-1 inactivation extends lifespan up to 40% and increases resistance to specific stress stimuli . To affect lifespan and stress response , HCF-1 requires the activity of DAF-16 . We show that the HCF-1 protein is expressed in the nucleus and partners with DAF-16 in worms . Furthermore , we demonstrate that loss of HCF-1 results in elevated levels of DAF-16 at the promoters of its target genes and altered expression of a subset of DAF-16-regulated genes . We propose that HCF-1 modulates longevity and stress response by binding to DAF-16 and preventing the transcription factor from accessing its target gene promoters , thereby regulating the expression of DAF-16 target genes . As HCF-1 is highly conserved , our findings have important implications for aging and FOXO regulation in humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"genetics",
"and",
"genomics"
] |
2008
|
Caenorhabditis elegans HCF-1 Functions in Longevity Maintenance as a DAF-16 Regulator
|
Genome dynamics of pathogenic organisms are driven by pathogen and host co-evolution , in which pathogen genomes are shaped to overcome stresses imposed by hosts with various genetic backgrounds through generation of a variety of isolates . This same principle applies to the rice blast pathogen Magnaporthe oryzae and the rice host; however , genetic variations among different isolates of M . oryzae remain largely unknown , particularly at genome and transcriptome levels . Here , we applied genomic and transcriptomic analytical tools to investigate M . oryzae isolate 98-06 that is the most aggressive in infection of susceptible rice cultivars . A unique 1 . 4 Mb of genomic sequences was found in isolate 98-06 in comparison to reference strain 70-15 . Genome-wide expression profiling revealed the presence of two critical expression patterns of M . oryzae based on 64 known pathogenicity-related ( PaR ) genes . In addition , 134 candidate effectors with various segregation patterns were identified . Five tested proteins could suppress BAX-mediated programmed cell death in Nicotiana benthamiana leaves . Characterization of isolate-specific effector candidates Iug6 and Iug9 and PaR candidate Iug18 revealed that they have a role in fungal propagation and pathogenicity . Moreover , Iug6 and Iug9 are located exclusively in the biotrophic interfacial complex ( BIC ) and their overexpression leads to suppression of defense-related gene expression in rice , suggesting that they might participate in biotrophy by inhibiting the SA and ET pathways within the host . Thus , our studies identify novel effector and PaR proteins involved in pathogenicity of the highly aggressive M . oryzae field isolate 98-06 , and reveal molecular and genomic dynamics in the evolution of M . oryzae and rice host interactions .
Rice ( Oryza sativa ) is one of the most important staple food crops for more than half of the global human population [1] . However , the rice production is severely impacted by the blast disease caused by the fungus Magnaporthe oryzae , despite the cultivation of various disease-resistance cultivars [2 , 3 , 4] . One of the reasons is that new cultivars often lose their resistance within a few years of introduction . It is thought that genetic variability occurs rapidly within pathogen populations , especially in regions with high genome plasticity , and that the pathogenic traits such as secreted pathogen avirulence ( AVR ) /effectors evolve rapidly to counteract plant defense [5 , 6 , 7 , 8] . M . oryzae primarily infects rice but can also infects wheat or other small grains [9] . Recent advances in genetic and genomic technology have allowed M . oryzae to be a tractable model for studying the plant-microbe interaction [10] . Jantasuriyarat and colleagues used large-scale expressed sequence tag ( EST ) sequencing to profile gene expression at the early stage of the M . oryzae-rice interaction and identified four genes to be involved in the interaction out of 13 , 570 uniESTs [11] . Approximately 100 pathogenesis-related protein genes specific to infection were identified in other studies using EST analysis [10] , and four biotrophy-associated secreted ( BAS ) proteins were also found in M . oryzae using microarray [4] . More recently , several studies have identified the presence of an infection structure , known as the biotrophic interfacial complex ( BIC ) , that is involved in mediating the delivery of pathogen effectors into the rice host cytoplasm [4 , 12] . Interestingly , studies have also revealed that genome plasticity is closely linked to host-pathogen interaction specificities . Using genome-wide DNA polymorphism existed between M . oryzae field isolate Ina168 and isolate 70–15 , one of the progenies from a cross between a weeping lovegrass isolate and the archetypical rice isolate Guy11 [13 , 14 , 15] , three novel AVR genes were identified [16] . The presence of these AVRs correlates to a 1 . 68 Mb sequence in Ina168 that is absent from the assembled genome sequence of 70–15 [16] . Studies of two other field isolates , P131 and Y34 , also revealed slightly larger genome contents with more genes . It is suggested that the presence of these isolate-specific genes play diverse roles , including conidiation , vegetative growth , or virulence [17] . Genome studies of additional M . oryzae field isolates and identification of novel AVR effectors will help us to further address the mechanism of pathogen and host coevolution . Here , we characterized the genome of the dominant blast isolate 98–06 on several different rice cultivars cultivated in Southeast China ( S1 Table ) . We found that 98–06 contains seven known AVRs , corresponding to its wide field adaptability . AVR PWL1 , PWL2 , Avr-Pita , Avr1-CO39 , and ACE1 are highly polymorphic due to point mutations while AVR Avr-Pia and Avr-Pii are also polymorphic , but with complete deletions . AvrPiz-t and Avr-Pik allele ( D ) were also found and conserved . In addition , we found that isolate 98–06 contains 1 . 43 Mb of isolate-specific sequences encoding 238 isolate-specific genes , in comparison to isolate 70–15 . In addition , genome-wide expression profiling revealed the presence of a defense network in rice and major expression patterns of pathogenesis-related genes during the M . oryzae-rice interaction . Moreover , we predicted 134 candidate effectors from 1 , 732 putative secreted proteins and provided evidence to demonstrate that IUG6 and IUG9 are novel effectors .
The genome of the M . oryzae isolate 98–06 was sequenced using Illumina high-throughput sequencing technology . Four different insert-size libraries ( 500 , 350 , 5 , 000 , and 10 , 000 bp ) were generated , which represent 36 . 8- , 38 . 2- , 31 . 9- , and 27 . 6-fold coverage assemblies spanning 1 , 560 , 1 , 618 , 1 , 350 , and 1 , 167 Mb , respectively . The sequence coverage that was approximately six-fold greater than that of P131 and Y34 [17] , provided a near perfect assembly . The sequence reads were assembled into 1 , 161 contigs and placed into 284 scaffolds with a combined length of 42 . 1 Mb using SOAPdenovo [18 , 19] . The N50 and maximum lengths of scaffolds were 1 , 530 and 3 , 511 kb , respectively ( Table 1 ) . The sequences have been deposited at GenBank under the submission number of JRBC00000000 . When the scaffolds of isolate 98–06 were aligned with the assembled genome of isolate 70–15 , 98 . 83% of the genome was conserved . However , 98–06 contains 1 . 4 Mb isolate-specific sequences that are dispersed throughout the genome ( Fig 1 , S2 Table ) . Interestingly , blocks of the reverse-alignment sequences and chromosomal breakpoints were found at regions near the telomeres ( S1 Fig ) . A total of 14 , 019 genes including 1 , 732 secreted protein genes were identified from the annotated isolate 98–06 genome ( Table 1 ) . The average length of predicted proteins is ~470 amino acids , and the predicted genes comprise 46% of the assembly genome . In all , the 98–06 genome possesses more genes than isolates 70–15 [15] , P131 , Y34 [17] , FJ , or HN [20] . Further analysis revealed that 238 genes are unique when compared with 70–15 ( 49 genes if compared with P131 and Y34 ) , and of these 238 genes , 10% encode secreted proteins while 69% have no significant homologs in GenBank ( S3 Table ) . We conducted a Clusters of Orthologous Groups ( COG ) study and found that some of these specific genes are associated with general biological functions , including secondary metabolism and energy generation processes ( S2 Fig ) . Intriguingly , isolate-specific genes appear to be mainly located at some of the chromosomal ends . To identify possible gene families , we clustered the predicted proteins of isolates 98–06 , 70–15 , P131 , and Y34 using the OrthoMCL algorithm . A total of 52 , 177 proteins were grouped into 13 , 236 clusters , with each cluster containing at least two putative protein orthologs/paralogs ( S3 Fig , S4 Table ) . The mean gene number of each cluster for 98–06 , 70–15 , P131 , and Y34 was 1 . 11 , 1 . 04 , 1 . 02 , and 1 . 02 , respectively . Among these clusters , 12 . 86% of all 14 , 019 genes in 98–06 had at least one paralog , while 6 . 28% , 3 . 27% , and 3 . 08% of all genes for 70–15 , P131 , and Y34 respectively ( S4 Table ) . These results suggested that there are more paralogs in 98–06 than in the other isolates . Meanwhile , there are 455 , 310 , 165 , and 150 gene families with at least two paralogs in 98–06 , 70–15 , P131 , and Y34 , respectively . Comparative analysis indicated that 227 gene families are expanded in 98–06 ( S4 Table ) , which is consistent with its larger genome content . Repeated sequences and transposable elements ( TEs ) often account for a large portion of genomes and therefore are important in genome annotation . Using multiple approaches , we found that 9 . 3% of the 98–06 genome consists of repetitive sequences . This is consistent with other fungi in which repetitive sequences comprise ≤ 10% proportion of the genomes [21] . However , transposable elements predicted by RepeatMasker [22] are more abundant in isolate 98–06 ( 2 , 776 ) than in isolates 70–15 ( 2 , 297 ) , P131 ( 2 , 055 ) , or Y34 ( 2 , 322 ) ( S5 Table ) . In a follow-up examination of the larger number of genes ( up to 1 , 732 ) that encode putative secreted proteins in 98–06 , 132 genes containing putative signal peptide sequences were disrupted by TEs ( S6 Table ) . Proteins secreted by fungal pathogens during host colonization are generally referred to as effectors [23 , 24] , which are often less than 200 amino acids in length and cysteine-rich . Therefore , we principally focused on small secreted proteins less than 200 amino acids in length , and identified 645 effector candidates out of 1 , 732 secreted proteins in 98–06 ( S7 Table ) . These candidates , including known AVR genes Avr-Pik ( allele D ) and AvrPiz-t , have an average cysteine content of 3 . 11% in comparison to 1 . 26% of all 1 , 732 putative secreted proteins . Isolate 98–06 is known to exhibit an incompatible interaction with various rice cultivars harboring 12 known resistance ( R- ) genes: Pi7 , Pi1 , Pik , Pik-m , Pi20 , Pi9 , Pita2 , Piz-t , Pik-p , Pish , Piz5 , and Pik-h ( S1 Table ) , and the Pi7 , Pi1 , Pik , Pik-m , Pi-kp , and Pik-h are alleles that could all recognize Avr-Pik ( allele D ) . We focused on the remaining 643 ( 645–2 = 643 ) secreted proteins that provide a rich repertoire for the identification of novel effectors . Despite that conserved motifs such as the RxLR domain of oomycetes [25] were not found in some M . oryzae effectors , the M . oryzae effectors are often planta-specific and secreted proteins [4] . To study the host-pathogen interaction , we performed RNA sequencing ( RNA-Seq ) for six stages: mycelium ( MY ) and conidial infection at 0 h , 8 h , 24 h , 48 h , and 72 h post inoculation ( CO-0h to CO-72h ) . After discarding low-quality raw reads , we obtained 12 . 8~13 . 7 million clean reads from each of the six samples , and aligned these reads against the reference genes and genomes ( S8 Table ) . Almost all of the genes in isolate 98–06 and rice ( O . sativa ) were transcribed ( 11 , 075 and 26 , 452 , respectively , S9 Table ) . The majority of the differentially expressed genes ( log2Ratio ≥1 and GFOLD ( 0 . 01 ) >1 ) in M . oryzae were up-regulated during host-pathogen interaction in comparison to conidial infection at 0 h , suggesting a strong interaction between M . oryzae and rice ( S4 Fig ) . To confirm the RNA-Seq profiles , qRT-PCR was conducted on nine randomly selected M . oryzae and rice genes that were either induced or repressed . The average match between RNA-Seq and qRT-PCR data of the original sequenced samples was 30 of 41 ( 73% ) ( S5 Fig ) , indicating basic consistency between the two approaches . In addition , the average match between qRT-PCR of another independent sampling and the original sampling , or the RNA-Seq , was 76% and 63% , respectively ( S5 Fig ) . There also appear certain correlations among the sequencing data of samples belonging to different stages , as distinct from the comparative RNA-Seq between wild type and mutant . Moreover , the expression patterns of several previously characterized genes , such as MoACTIN; MoAP1 , MoVAM7 , and MoBAS1 [4 , 26 , 27] , in the transcriptome ( see S10 Table ) were similar to previous studies . These validation tests rectified to certain degree the potential limitation resulting from lack of sample duplication in RNA-Seq analyses . Since plant hormone responses play an important role in the host defense network during the rice-pathogen interaction [28] , we examined the expression of genes involved in salicylic acid ( SA ) , jasmonic acid ( JA ) , ethylene ( ET ) , and mitogen-activated protein kinase ( MAPK ) signaling cascades . Most of the genes in the SA and ET signaling pathways were up-regulated , meanwhile , JAZ genes that act as transcriptional repressors of JA [29] were induced after 48 hpi . A comparison of aggregate expression levels at 0 hpi versus 8 , 24 , 48 and 72 hpi in planta is shown in Fig 2 . Transcription for the most of the MAPK genes was increased during infection . In addition , several WRKY transcription factors and pattern recognition receptors ( PRRs ) FLS2 [30] and EFR were also significantly induced at 48 hpi ( Fig 2 ) . These findings revealed that multiple host defense signaling pathways were involved in the response to the infection of isolate 98–06 . Finally , to investigate how the M . oryzae genetic program is deployed during infection , we explored 64 known genes relevant to pathogenicity and 10 known effectors of M . oryzae in our interaction transcriptome ( S10 Table ) , and determined their expression patterns by the clustering affinity search technique ( CAST ) assay through MultiExperiment Viewer 4 . 6 software package [31] . The distance metric is the default Pearson correlation , and the threshold affinity value is 0 . 8 . Fourteen clusters were generated , and the cluster-a with the most members ( 23 genes ) was illustrated , which provided the major expression pattern of pathogenesis-related genes ( Fig 3A , a; S10 Table ) . The expression pattern-a represents high expression throughout the infection process , with the exception of sharp reduction at 24 hpi . In general , the virulence factors were up-regulated with different waves of expression during infection ( Fig 3A ) . Interestingly , another expression pattern-b for effectors was also distinguished ( Fig 3A and 3B; S10 Table ) . In transcriptome analysis described above , two genes MoVAM7 and MoSSO1 encoding soluble N-ethylmaleimide-sensitive factor attachment protein receptor ( SNARE ) were also identified . SNARE proteins mediate intracellular transport that is an essential biological process in fungi [32] . Consistent with this , MoVam7 and MoSso1 were found to be important in effector secretion and pathogenesis [27 , 33] . We gathered 21 SNARE genes and 35 other endocytosis-related ( ER ) genes through bioinformatics analysis , and found five expression patterns of SNARE genes and eight patterns of ER genes using the CAST method ( S6 Fig; S11 and S12 Tables ) . It is surprising that the key expression patterns of SNARE and ER genes are similar to the pathogenicity pattern-a , suggesting that this typical pathogen–host interaction pattern could be used to explore novel pathogenicity genes . In a broader definition , effectors are specific types of proteins secreted into the plant by pathogens to interfere with plant defenses , and they most likely play other roles in promoting infection as well [34] . To date , 15 effectors were identified that include nine Avr effectors , six newly identified effectors including four BAS proteins , Slp1 , and MC69 . In this analysis , AvrPiz-t , BAS1 , BAS3 , BAS4 , and SLP1 genes were clustered into expression pattern-b ( Fig 3A ) , indicating that the expression is highly inducible during the biotrophic invasion . Hierarchical clustering ( HCL ) analysis also showed two distinct expression patterns that are similar to pattern-a and pattern-b , respectively ( Fig 3B ) . Since Avr-Pik and AvrPiz-t genes are highly expressed during infection , and studies of two M . oryzae isolate-specific sequences and Verticillium dahliae indicated that lineage-specific genomic regions are enriched in genes encoding new effectors [35] , we explored the expression patterns of 645 small candidate effectors in 98–06 through CAST assay . 134 candidate effectors were found to be co-regulated with Avr-Pik and AvrPiz-t ( Fig 4; S13 Table ) . Six known effectors BAS1 , BAS2 , BAS3 , BAS4 , SLP1 , and PWL1 were among these candidates , providing a compelling argument for the presence of additional effectors among these candidates . Many bacterial and fungal pathogen effectors can suppress innate plant immunity , particularly that triggered by pathogen-associated molecular patterns [34 , 36 , 37] . For example , M . oryzae AvrPiz-t suppresses the BAX-mediated programmed cell death in tobacco leaves [38] . Among 134 putative secreted proteins identified , most have no predicted functions , including several 98–06 isolate-unique genes ( IUG ) that were not found in 70–15 . We queried the nucleic acid sequences against the genomic sequences of P131 and Y34 and found that IUG6 ( Mo_GLEAN_10000617 ) and IUG9 ( Mo_GLEAN_10000765 ) are specific to 98–06 . To identify Iug6 and Iug9 functions , we used a PVX-based high-throughput transient plant expression system in Nicotiana benthamiana . We also included three randomly selected non-specific proteins Nup1 , Nup2 , and Nup3 ( MGG_07900 , MGG_08024 , and MGG_04546 ) for controls . We first removed the signal peptides of IUG6 , IUG9 , NUP1 , NUP2 , and NUP3 to enable the genes to be expressed stably in plant cells before cloning into the PVX vector pGR106 . Infiltration of N . benthamiana leaves with Agrobacterium tumefaciens cells carrying pGR106:IUG6 , pGR106:IUG9 , pGR106:NUP1 , pGR106:NUP2 , pGR106:NUP3 , and the negative control pGR106:GFP did not cause any obvious cell-death symptoms ( S7 Fig ) , whereas obvious cell death was observed in N . benthamiana leaves infiltrated with A . tumefaciens cells carrying pGR106:BAX . N . benthamiana leaves infiltrated with A . tumefaciens cells harboring IUG6 , IUG9 , NUP1 , NUP2 , and NUP3 genes 24 h prior to infiltration with the pGR106:BAX-harboring cells did not produce symptoms ( Fig 5A ) . The expression of BAX was detected at 48 h after infiltration ( Fig 5B ) , which ruled out the possibility that BAX failed to express . Iug6 , Iug9 , Nup1 , Nup2 , and Nup3 all conferred BAX cell-death suppression activity . By analogy , additional effectors may also present in these 134 candidate effectors . Presence or absence polymorphisms of these 127 candidate effectors ( minus six known effectors and one failed to be amplified by PCR ) were tested by PCR using 29 M . oryzae field isolates collected from China ( Fig 5C ) . The candidate effectors showed different segregation patterns . Among them , IUG6 , IUG9 , and NUP2 have varied presence in 29 field stains , whereas NUP1 and NUP3 are detected in all isolates ( Fig 5C ) . A recent report indicates that the presence or absence of putative secreted proteins shows a good correlation with AVR genes in M . oryzae [16] . Since half of the characterized blast AVR effector genes are not present in the isolate 70–15 genome , it may suggest that gene gain or loss events could be a major factor in the evolution of AVR effectors [39] . Functional characterization of IUGs may reveal novel effectors or other factors significant in the host-pathogen interaction . We thus further characterized the functions of IUG6 and IUG9 , along with IUG17 , IUG18 , IUG34 , and IUG37 . We found that IUG6 and IUG9 are located near the chromosomal ends ( S14 Table ) , which is consistent with the hypothetic rapid development of novel effectors in plastic regions [40] . IUG17 ( Mo_GLEAN_10000632 ) and IUG18 ( Mo_GLEAN_10001404 ) are predicted to each contain the aminoglycoside phosphotransferase ( APH ) domain ( 145–223 amino acids ) and the RelA_SpoT domain ( 96–234 amino acids ) , while IUG34 ( Mo_GLEAN_10000877 ) and IUG37 ( Mo_GLEAN_10002374 ) are predicted to be associated with pathogenicity-related expression pattern through CAST assays . We also generated respective deletion mutants in 98–06 using the hygromycin-resistance marker gene and complemented the mutants with the respective wild type gene alleles including the endogenous promoter of approximately 1-kb ( S8 Fig ) . Disruption of IUG6 and IUG9 , as well as IUG18 , resulted in defects in vegetative hyphal growth and virulence , whereas disruption of IUG17 , IUG34 , and IUG37 had no obvious effects on colony morphology , conidiation , or virulence ( S9 Fig ) . Iug6 is relatively specific to M . oryzae , since only one homolog was identified in the fungus Gaeumannomyces graminis ( GL385397 . 1 , 53% identity , E = 4e-17 ) . Iug9 homologs can be found in Colletotrichum higginsianum ( CCF43986 . 1 , 63% identity , E = 7e-29 ) and C . graminicola ( EFQ33033 . 1 , 63% identity , E = 4e-26 ) . In contrast , Iug18 contain the RelA_SpoT domain that is well conserved among other filamentous fungi . Microsynteny analysis revealed that all three genes are located at the genomic inserted regions comparable to 70–15 ( S10A Fig ) . However , the parallel result was unfavorable , probably because the laboratory strain 70–15 was generated from a cross between rice and weeping lovegrass isolates [13 , 14] . To exclude the possibility that this was an assembly gap leading to gene deletion in the other three isolates , we amplified the genes by PCR from isolates 70–15 , Guy11 , P131 , and Y34 and performed a Southern blotting analysis ( S10B Fig ) . Expression of these three genes was validated by qRT-PCR , which also showed that IUG6 and IUG9 exhibit high expression levels during the pathogen-host interaction ( S11 Fig ) . The Δiug6 , Δiug9 , and Δiug18 mutants were examined for phenotypes including conidiogenesis . Conidiation in 10-d-old cultures of the iug6 mutant was reduced dramatically , by ~17-fold , compared with WT ( Fig 6B ) . The Δiug9 and Δiug18 mutants showed approximately 14% and 32% reduction in conidiation on SDC medium ( Fig 6B ) . Microscopic observations showed that Δiug6 , Δiug9 , and Δiug18 mutants produced significantly fewer conidia than the WT strain ( Fig 6A ) . To determine whether IUG6 , IUG9 , and IUG18 affect the expression of conidiation-related genes including MoCOM1 , MoHOX2 , MoCON7 , MoCOS1 , and MoSTUA [41 , 42 , 43 , 44 , 45] , we measured and found that their expression was decreased ( Fig 6C ) . To determine whether Δiug6 , Δiug9 , and Δiug18 have defects in pathogenicity , conidial suspensions ( 5 x 104 spores /ml ) were sprayed onto 2-wk-old susceptible rice seedlings ( CO-39 & LTH ) . Only small , necrotic-like dark brown spots were observed in Δiug6-infected rice leaves in comparison to controls ( Fig 7A and 7B ) . When the lesions were excised , surface sterilized with 70% ethanol for 1 min , and incubated with light and humidity on 4% water agar for 2 days , as described in a previous study [46] , no fungal growth or conidia occurred . The Δiug9 and Δiug18 mutants also resulted in a reduction in disease symptoms on rice 7 days after inoculation ( Fig 8A ) . The mean lesion density per unit area of the mutants was significantly lower than that of WT ( Fig 8B ) . Disease symptoms of three approximately 6 cm long rice blades from the same parts of plants infected by Δiug9 mutant or WT were also quantified using a ‘lesion-type’ scoring assay [9] , which showed that lesion types 4 and 5 ( severe , coalescing ) were rarely produced by the Δiug9 mutant ( Fig 8C ) . In addition , fungal DNA in rice was significantly lower in infection by Δiug9 and Δiug18 mutants than that by the WT as determined by M . oryzae 28S rDNA quantitation [47] ( Fig 8D ) . Finally , we performed a detailed phenotypic analysis to investigate the infectious hyphae within the host cells . At 24 hpi in barley epidermal cells and 48 hpi in rice leaf sheaths , both the WT and the Δiug6 mutant strain formed normal appressoria , but the invasive hyphae ( IH ) of the WT strain freely expanded into host cells . On the other hand , appressoria of Δiug6 failed to develop any penetration structures among 50 examined infection sites in rice and barley , respectively ( Fig 7C and 7D ) . We also analyzed invasive hyphal growth of Δiug9 and Δiug18 mutants at 100 appressorial penetration sites in barley tissues by rating the hyphal growth into four types ( type I , no penetration; type II , with penetration peg; type III , with a single invasive hypha; type IV , with extensive hyphal growth ) after inoculation with the spore suspensions for 30 hours ( Fig 8E ) . 75% of penetration sites of WT showed III or IV invasive growth type , by contrast 42% of cells were limited in type I invasive hyphal growth in the Δiug9 mutant , and 77% penetration sites of the Δiug18 mutant showed type II or III hyphal growth . A similar result was also observed in rice leaf sheaths , with the WT and complemented strains displaying faster hyphal growth extension to neighboring cells while the Δiug9 and Δiug18 mutants showing lower hyphal growth limited to one cell ( Fig 8F ) . To further characterize these Iug proteins , we expressed IUG6:GFP , IUG9:GFP , and IUG18:GFP fusion genes under the control of their native promoters in Guy11 , respectively . GFP fluorescence was observed in the conidial septum expressing Iug6:GFP and Iug9:GFP , while faint fluorescence was seem in the cytoplasm of conidia expressing Iug18:GFP ( S12 Fig ) . We next detected whether any GFP signals can be detected in the biotrophic interfacial complex ( BIC ) . To suppress host immunity during biotrophic intracellular growth , fungal effectors of M . oryzae are secreted and accumulated at the BIC or more generally within the Extra-Invasive Hyphal Membrane ( EIHM ) [4] . When transformants invaded rice sheath cells at ~27 hpi , the fluorescence of Iug6 can be observed in BIC ( Fig 9A ) . Microscopy of the secreted Iug9:GFP protein showed fluorescence that outlines the primary hyphae and BICs at ~27 hpi ( Fig 9B ) , similar to the BIC localization control ( Fig 9C ) , AvrPiz-t:GFP [48] . Both findings suggested that Iug6 and Iug9 could be delivered into the rice cytoplasm and accumulated in BICs to facilitate biotrophic invasion . To validate the signal peptide prediction of Iug6 and Iug9 , we used an assay based on the method previously described by Oh et al . [49] . The constructs with the signal peptides of Iug6 and Iug9 allowed the invertase secretion-deficient yeast strain YTK12 to grow on YPRAA medium , and this invertase activity was confirmed by inclusion of the triphenyl tetrazolium chloride dye ( S13 Fig ) . These results showed that the signal peptides of IUG6 and IUG9 are indeed functional . We further transformed the IUG6 gene into the virulent isolate Guy11 and used the Δiug9 mutant for pathogenicity comparison tests in four resistant rice cultivars: IRBLz5-CA ( Pi-z5 ) , IRBLsh-S ( Pi-sh ) , IRBL20-IR24 ( Pi-20 ) , IRBLta2-Re ( Pi-ta2 ) , and susceptible cultivar LTH . All of the transformants showed disease symptoms ( S14 Fig ) , excluding the possibility that IUG6 and IUG9 are conventional AVR genes corresponding to these specific resistance genes . However , further studies are underway to address whether or not IUG6 and IUG9 are unknown avirulence genes that are involved in the evolutionary host-pathogen arms race . As Iug6 and Iug9 can suppress the BAX-mediated programmed cell death in tobacco leaves , we investigated whether their expression affects the transcription of the rice defense-related genes . When infected with Guy11 over-expressing IUG6 and IUG9 , the pathogenicity of these transformants is not obviously improved , but the expression of PR1a and Cht1 in rice was significantly less than that caused by Guy11 following qRT-PCR analysis ( Fig 10B ) . Similarly , the expression levels of PR1a and Cht1 in rice by infection with isolate 98–06 were also significantly lower than that infected with Guy11 at 24 hpi or 8 hpi , with delayed peak expression . The expression patterns of PR1a and Cht1 in rice when infected with isolates over-expressing IUG6 or IUG9 is similar to that by isolate 98–06 , with somewhat lowered peak expression . PR1a and Cht1 are respective SA and ET signaling marker genes . SA is involved in establishing basal defenses , effector-triggered immunity , and systemic acquired resistance in many dicotyledonous species [50 , 51] , as well as in modulating redox balance and protecting rice plants from the oxidative stress caused by M . oryzae [52] . Our above interaction transcriptome analysis also suggested that SA and ET signaling pathways might function positively on rice basal defense against M . oryzae . We hypothesized that Iug6 and Iug9 might target unknown factors in rice , leading to suppression of the SA and ET signaling and promotion of biotrophy .
M . oryzae is known for its natural genetic variation in the field , resulting in emergence of new epidemics threatening world food supply . Comparative analyses of multiple new epidemics not only steadily improved the assembly and annotation and the identification of variations in the M . oryzae genome , but also open a door to explore new virulence mechanism of the pathogen for effective blast control [2 , 9] . In this study , we reported the genome and interactive transcriptome analyses of isolate 98–06 that is known to be one dominant field isolate containing as many as seven AVR genes ( Avr-Pik/km/kp , AvrPiz-t , Avr-Piz5 , Avr-Pita2 , Avr-Pish , Avr-Pi20 , and Avr-Pi9 ) . Among them , Avr-Pik/km/kp [16] , AvrPiz-t [38] , and Avr-Pi9 [53] were characterized . Avr-Pik/km/kp ( allele D ) and AvrPiz-t were also identified in our study . Our analysis indicated that isolate 98–06 contains an extra 1 . 4 Mb of genomic sequences not present in 70–15 , while a whole-genome dot-plot alignment of 98–06 and P131 suggested good genome synteny ( S15 Fig ) . To examine whether the extra genomic region is present in other isolates , we searched the genomes of isolates P131 and Y34 that found that 49 genes remain unique to isolate 98–06 . COG classification revealed that , while the functions for most of these genes were unknown , those identified exhibit functions relevant to general characteristics , secondary metabolites , and energy metabolism ( S2 Fig ) . Yoshida et al . [16] observed that presence or absence of effector gene polymorphisms are often associated with unstable genomic regions near the chromosome ends . It is therefore hypothesized that isolate-specific regions located at the chromosomal ends supply new effectors and pathogenicity-related factors to drive genome evolution . The dynamic adaptation of M . oryzae may be primarily achieved by deletion or recovery of AVR genes [54] . Such hypotheses indicate that further genome sequencing of multiple M . oryzae isolates is warranted for characterizing rice pathogenic strains , as key information can be discovered only by exploring beyond the “core” genomes [16] . One good example is that the extensive chromosomal rearrangement in asexual fungus V . dahliae establishes dynamic , lineage-specific regions that provide new effectors as a general mechanism of adaptation [35] . Although sexual reproduction can occur in M . oryzae under laboratory conditions , it has not been observed in nature . Such asexual organisms are often considered to be less flexible than sexual organisms [55 , 56] , so that the expanded genome is likely to represent examples of evolutionary tradeoffs , as the cost of maintaining the extra DNA is counterbalanced by the functional advantages it confers [57] . Since horizontal gene transfer ( HGT ) enables the acquisition of new genes and functions [57] , we hypothesize that some of these isolate-specific genes might also be acquired by HGT to drive the evolutionary process . Based on the genome data of isolate 98–06 , our transcriptome analysis provides a further molecular view of M . oryzae and rice gene expression during infection . The major expression patterns of M . oryzae genes relevant to pathogenicity remain high throughout the infection process , with the exception of a sharp decline at 24 hpi ( Fig 3A ) . The decreased expression pattern might due to low levels of transcript detection at 24 hpi , with only 2 , 426 genes ( M . oryzae ) compared with at least 5 , 161 genes for other infection stages . This is consistent with the previous discovery that expression of known pathogenicity genes was unchanged or down-regulated at 36 hpi [4] . We should point out that our RNA-Seq analyses were carried out without sample duplication that could result in potentially biased interpretation of the transcriptome data . Nevertheless , the result is agreeable with that from validation of several selected genes through qRT-PCR . Effectors are known key pathogenicity determinants that modulate plant innate immunity and enable disease development in plant-pathogen interactions [58] . Several AVR genes have been previously isolated by map-based cloning , genetic association analysis , or loss-of-function approaches [7 , 16 , 59 , 60 , 61 , 62] . Most of these effectors are small secreted proteins lacking homology to known proteins [63 , 64 , 65] . In characterizing the isolate 98–06 genome , we found 645 small candidate effectors with an average 3 . 11% cysteine content from 1 , 732 putative secreted proteins . Biotrophic invasive-specific expression is one of the best indicators for identifying new blast effectors [65] . Based on the gene expression data , we found that BAS1 , BAS3 , BAS4 , SLP1 , and AvrPiz-t effectors all show the similar expression pattern during infection ( Fig 3A ) . Based on the expression of Avr-Pik and AvrPiz-t , 134 candidate effectors were further explored using hierarchical clustering ( Fig 4 ) , including six characterized effectors BAS1 , BAS2 , BAS3 , BAS4 , SLP1 , and PWL1 . Among them , two isolate-unique effectors Iug6 , Iug9 , and three non-isolate unique proteins Nup1 , Nup2 , Nup3 were selected to show a similar function in blocking plant immunity ( Fig 5 ) . Our characterization of three IUG genes indicates that these 134 putative effectors were a rich source for additional effector identification . In our study , a high frequency in suppression of cell death induced by BAX in tobacco leaves was observed that may also be associated with effects of heterologous gene expression or the unfolded protein response ( UPR ) [66] . Therefore , additional studies are necessary to further characterize these effector candidates . Our functional analysis of IUG6 and IUG9 indeed showed that they are important for full virulence expression of isolate 98–06 . In addition , IUG6 and IUG9 characterization revealed the gene absence/presence polymorphism among 29 field stains ( Fig 5C ) , similar to six AVR ( Avr-Pita1 , Avr-Pik , Avr-Pia , Avr-Pii , PWL2 , and ACE1 ) genes that exhibited a prevalence of presence/absence polymorphisms among 62 M . oryzae isolates [67] . The similarity is consistent with that IUG6 and IUG9 might be AVR effectors . Functional characterization indicated that Iug6 and Iug9 , as well as Iug18 , are important for mycelial growth , sporulation , and pathogenicity ( Fig 7 and Fig 8 ) . Xue et al . [17] have also observed that several randomly selected isolate-specific genes play important and diverse roles , some affecting virulence while others affecting conidiation and vegetative growth . There are , however , several secreted proteins that are required for pathogenicity , such as MoMpg1 , MoEmp1 , MoMhp1 , MoMsp1 , MC69 , and Slp1 [62 , 68 , 69 , 70 , 71] . MC69 , which is potentially localized in BIC , is essential for successful appressorial penetration and pathogenicity [68] . Slp1 suppresses chitin-induced host defense responses , thereby facilitating rapid spread of the fungus within the host [71] . Targeted gene disruption of BAS1 , BAS2 , and BAS3 did not show any effects on pathogenic phenotypes , whereas BAS4 disruption did not provide any viable transformants [4] . Furthermore , over-expressing IUG6 or IUG9 in Guy11 can suppress the expression of PR1a and Cht1 in rice , which are marker genes of SA and ET pathways , respectively . These results are also supportive of the effector role for Iug6 and Iug9 . Meanwhile Iug18 appears to be a PaR protein based on the absence of a secretion signal peptide . Gene deletion and pseudogenization , silencing , amino acid replacements , protein chimerization , and new gene addition are all important factors for altering virulence in filamentous plant pathogens [57] . Our microsyntenic and Southern blotting analyses showed that IUG6 , IUG9 , and IUG18 are unique to isolate 98–06 compared to 70–15 and Guy11 ( S10 Fig ) . Iug18 is present in several isolates belongs to the Rel-Spo like protein superfamily . Blast search indicates that Iug6 and Iug18 have homologs in G . graminis , while Iug9 has a homolog from C . higginsianum . An emerging question would be how some M . oryzae isolates obtained these extra genes and where these genes come from . An attractive model was proposed to explain how M . oryzae regains deleted avirulence effector genes , in which parasexual exchange of genetic material enabled the recovery of ‘lost’ genes in asexual lineages [54] . A comprehensive population study revealed that the Avr-Pita gene has experienced a number of translocations in M . oryzae and related Pyricularia species , most likely as the consequence of its recovery by lateral transfer ( HGT ) [54] . Another example of translocation was recently reported , in which the ToxA gene was transferred from Stagonospora nodorum , a wheat pathogen , to another ( Pyrenophora tritici-repentis ) [72] . All these findings point out the likely source of these IUG genes . On the other hand , it has been reported that M . oryzae has evolved two distinct secretion systems to deliver apoplastic and cytoplasmic effectors , which outline the entire invasive hyphae ( IH ) or accumulate in the BIC [33] . Like AvrPiz-t , Iug6 and Iug9 proteins are accumulated at BICs ( Fig 9 ) , suggesting that they have high possibility to be translocated into rice cell as effectors . Fungal apoplastic effectors are invariably Cys rich , but cytoplasmic effectors accumulated in BICs are not necessarily Cys rich [73] . Especially , Iug6 , a small , secreted protein with four Cys residues accumulating in BICs , is similar to other known Cys-rich blast effectors like Bas2 and AvrPiz-t [4 , 38] . Effector genes are also often located near the telomeres , which tend to evolve at higher rates than the rest of the genome [10 , 64 , 65] . IUG6 and IUG9 are located on the subtelomeric regions of chromosome II and chromosome I , respectively ( S14 Table ) , revealing that they could be the result of rapid adaptation to environmental conditions . This discovery further promotes the hypothesis that isolate-specific regions of chromosomal plasticity serve as facilitators of genome evolution in M . oryzae . Overall , this study provides a systematic genomic and interaction transcriptome analysis of the dominant rice blast field isolate 98–06 . Based on bioinformatics and functional analysis , two novel effectors Iug6 and Iug9 and a pathogenicity-related gene IUG18 were characterized . This knowledge prompts the hypothesis that the isolate-unique genes beyond the “core” genome may act as a source for M . oryzae adaptation to the environment . Our analyses will facilitate further study of the roles of effectors and molecular mechanisms of pathogenesis and pathogen-host evolution .
The genome of M . oryzae 98–06 isolate was sequenced using a whole-genome shotgun approach . The genomic Illumina paired-end libraries were constructed with insertion size of 350 bp , 500 bp , 5 kb , and 10 kb , respectively , and sequenced at the Beijing Genomic Institute ( BGI , China ) . The short reads of sequencing data ( 135-fold coverage ) were assembled into genome sequence using SOAP denovo ( version 1 . 05 , http://soap . genomics . org . cn/soapdenovo . html ) , and then reads are mapped to contigs for partial assembling and filling gap through paired-end and overlap relationship between the reads [22 , 74] . The M . oryzae 70–15 genome sequence version 6 was downloaded from the Broad Institute ( http://www . broadinstitute . org/annotation/genome/magnaporthe_grisea/MultiDownloads . html ) . The P131 and Y34 genome sequences were downloaded from the NCBI Genome Database ( http://www . ncbi . nlm . nih . gov/genome ) . CEGMA software was used to predict the core genes of isolate 98–06 , which is based on the eukaryotes conservative gene database . We used the core gene set as the training set to predict genes by the Augustus and SNAP softwares [75 , 76] . In addition , we used the Homolog software to predict genes by using Magnaporthe oryzae isolate 70–15 as the reference sequence [77] . The Glean software was then employed to integrate the above results . Gene functions were predicted by comparison with the NCBI NR protein database and the KEGG [78] , COG [79] , SwissProt [80] , GO [81] databases . Protein domain was predicted using SMART database . Membrane and sub-cellular localization domains were predicted by TMHMM 2 . 0 [82] , SignalP4 . 0 [83] , and NLStradamus [84] . Nucleotide sequences of the predicted isolate 98–06 genes were compared separately with the genomic sequences of P131 , Y34 , and 70–15 isolates with TBLASTN [85] . Homologous genes with sequence identities of 100% , 80–100% , and 50–80% were defined as identical , similar , and divergent , respectively , while those below 50% were considered non-homologous . Amino acids of 52 , 177 proteins from all four isolates were compared with each other using TBLASTN ( version 2 . 2 . 23 ) [85] , and orthologs/paralogs families were clustered through OrthoMCL ( version 1 . 4 ) [74] . We used three programs ( RepeatMasker , RepeatProteinMasker , and Denovo ) to predict transposon sequences [22] . We also used TRF ( Tandem Repeat Finder ) to identify tandem repeat sequences [86] . To search for genes disrupted by TEs , unique flanking sequences of TEs were used to search against 98–06 genes . Masked genome sequences of the 98–06 isolates were compared with the MUMMER package [87] to construct chromosome sequences for isolate 98–06 based on isolate 70–15 data . The putative secreted proteins were identified using several prediction algorithms . TargetP 1 . 1 ( http://www . cbs . dtu . dk/services/TargetP/ ) was used to predict the cleavage sites of the predicted presequences with the ‘‘Perform cleavage site predictions” option . SignalP 3 . 0 ( http://www . cbs . dtu . dk/services/SignalP-3 . 0/ ) was used to predict signal peptide cleavage sites . Transmembrane helices were predicted using TMHMM 2 . 0 ( http://www . cbs . dtu . dk/services/TMHMM-2 . 0/ ) . Proteins that contain signal peptide cleavage sites but not transmembrane helices were selected as putative secreted proteins . Proteins of less than 200 amino acids in length were retained , and the average percentage of cysteine content was calculated . Three-week-old rice plants ( cv . CO-39 ) were inoculated with M . oryzae isolates at 1 x 108 spores /ml . The inoculated plants were placed in a sealed plastic box in the dark for 24 h at 25°C , and leaf tissues were collected at 0 h , 8 h , 24 h , 48 h and 72 h after inoculation . Mycelia were grown in shaking culture in complete medium for 36 h at 28°C , 150 rpm and harvested . Total RNA was extracted using the Invitrogen kit as described previously [88] . RNA was extracted from samples that were the mixtures of three independent experiments . Poly ( A ) mRNA was isolated from total RNA using oligo ( dT ) magnetic beads ( Invitrogen , Carlsbad , CA , USA ) . Using RNA as templates , random hexamer primed cDNA synthesis was performed using reverse transcriptase ( Invitrogen ) . Second-strand cDNA was synthesized using RNase H ( Invitrogen ) , DNA polymerase I ( New England Biolabs ) , dNTPs and buffer . DNA was purified using the QIAquick PCR extraction kit and ligated to sequencing adaptors following end repairing and Ploy ( A ) addition . Finally , the cDNA libraries were loaded onto the flow cell channels of an Illumina HiSeqTM 2000 platform for paired-end 90 bp x 2 sequencing at the Beijing Genomics Institute ( BGI ) , Shenzhen , China [89] . After discarding low-quality raw reads , the clean reads from each library were assembled to M . oryzae and rice genomes separately , and gene sequences were annotated using SOAP2 [90] . Gene expression levels were measured in the RNA-Seq analysis as reads per kilobase of exon model per million mapped reads ( RPKM ) [91] . Differentially expressed genes and their corresponding P-values were determined using the recent GFOLD algorithm , which could give more stable and biological meaningful gene ranking in comparison with other methods especially for single biological replicate experiments [92] . Fold changes ( log2Ratio ) were estimated according to the normalized gene expression level in each sample . We used the absolute value of log2Ratio ≥1 and GFOLD ( 0 . 01 ) >1 as the threshold to judge differentially expressed genes . For KEGG pathway analysis [93] , all the differentially expressed genes in the pathways were examined to uncover common expression patterns . For hierarchical clustering , Pearson’s correlation coefficient and Spearman’s rank were used to measure similarities between gene expression profiles and between samples , respectively . The heat map of the clustered genes and samples was generated by complete linkage . For RT-PCR and quantitative real time RT-PCR ( qRT-PCR ) , 5 mg of total RNA was reverse transcribed into first-strand cDNA using the oligo ( dT ) primer and M-MLV Reverse Transcriptase ( Invitrogen ) . The qRT-PCR reactions were performed following previously established procedures [82] . RNA-Seq expression profiles were validated by quantitative RT-PCR . Primer pairs used in this section are listed in S15 Table . All strains were cultured on complete medium ( CM ) agar plates at 28°C [69] . The M . oryzae isolate 98–06 was used as the wild type strain for transformation in this study . Protoplasts were prepared and transformed as previously described [94] . Conidiation assay was performed also as previously described [95] . Mycelia were harvested from liquid CM for genomic DNA and RNA extraction . Vegetative growth was measured on CM , minimal medium ( MM ) , straw decoction and corn medium ( SDC ) and oatmeal medium ( OM ) plates for 7 days at 28°C [27] . The radial growth was measured after incubation for 7 days and then photographed . All experiments were repeated three times , each with three replicates . Plant infection and injection assays were performed as previously described by spraying 4 ml of conidial suspensions ( 5 x 104 spores /ml in 0 . 2% gelatin ) on rice [96] . In order to distinguish resistant levels , spore suspensions were adjusted to 1 x 106 spores/ml for AVR candidate pathogenicity tests . For microscopic observation , rice was inoculated with 100 μl of conidial suspension ( 5 x 104 spores /ml ) on the inner leaf sheath epidermal cells . After 48 h incubation under humid conditions at 28°C , leaf sheaths were collected and observed under a microscope . All experiments were repeated three times . To generate the IUG6 gene replacement vector pCX62 , approximately 1 kb upstream and 1 kb downstream fragments were amplified with primer pairs ( S15 Table ) . The resulting PCR products were ligated to the hygromycin resistance cassette released from pCX62 , as previously described [97] . Putative mutants were screened by PCR and confirmed by Southern blotting analysis . To complement the Iug6 mutant , a DNA fragment including the putative promoter and the coding sequence was amplified and inserted into pYF11 ( bleomycin resistance ) by homologous recombination in Saccharomyces cerevisiae . The plasmids were extracted and transformed into E . coli competent cells , and then the plasmids with correct inserts were transferred into protoplasts , as previously described [97] . The same approach was used to generate mutants for isolate-specific genes IUG9 , IUG17 , IUG18 , IUG34 , and IUG37 . The primer pairs used are listed in S15 Table . To observe secretion in rice cells , the coding sequences of IUG6 , IUG9 , and AvrPiz-t with their native promoters were fused with GFP in pYF11 . To generate over-expression transformants of IUG6 and IUG9 in Guy11 , the coding sequences of IUG6 and IUG9 driven by the ribosomal protein P27 promoter were inserted into pYF11 , respectively . DNA primers are also listed in S15 Table . The yeast signal trap system is based on vector pSUC2 , which carries a truncated invertase gene , SUC2 , lacking both the initiation Met and the signal peptide [98] . DNA fragments coding for the signal peptides of Iug6 and Iug9 were PCR amplified and introduced into pSUC2 using EcoRI and XhoI restriction sites to create in-frame fusion with the invertase ( primers listed in S15 Table ) . The pSUC2-derived plasmids were then transformed into the invertase negative yeast strain YTK12 through lithium acetate method [98 , 99] . Following transformation , yeast cells were plated on CMD-W ( minus Trp ) plates , and positive colonies were transferred to fresh CMD-W plates and incubated at 30°C for two days . For invertase secretion , positive colonies were replica plated on YPRAA plates , supplemented with raffinose instead of sucrose . Growth occurs only when the invertase is secreted . The invertase enzymatic activity was measured by the reduction of TTC to insoluble redcolored triphenylformazan as described previously by Oh et al [49] . The IUG and NUP genes without signal peptides were amplified using combinations of primers for the PVX assay . The amplified fragments were cut and ligated into the PVX vector PVX::HA [100 , 101] before introduction into A . tumefaciens strain GV3101 by electroporation [102] . The PVX::HA transformants were selected using Tetracycline ( 12 . 5 μg /ml ) and Kanamycin ( 50 μg /ml ) . Individual colonies were verified by PCR . For infiltration of PVX::HA and PVX::flag ( negative control GFP ) into leaves , recombinant strains of A . tumefaciens were grown in LB medium plus 50 μg /ml Kanamycin for 48 h , harvested , and infiltrated as previously described by Yu et al [103] . The experiment was repeated three times with each assay consisting of three plants each with three leaves inoculations . Green fluorescent protein fluorescence in rice cells was captured using an LSM 710 laser scanning microscope with a 40 x objective lens ( Carl Zeiss ) , with an excitation 480 ± 10 nm and an emission 510 ± 10 nm . The genome sequence data of 98–06 was deposited at the NCBI Genome Database ( http://www . ncbi . nlm . nih . gov/assembly ) under the accession number JRBC00000000 . RNA-Seq reads were deposited at the GenBank SRA database under sample number SRS692257 and experiment number SRX689727 . The GenBank accession numbers for IUG6 , IUG9 , and IUG18 are KM522919 , KM522920 , and KM522921 , respectively . The information of other genes with their reference gene ID in 70–15 and gene location in the 98–06 genome is provided in S9 Table .
|
Genetic variations in pathogens , such as the causal agent of rice blast Magnaporthe oryzae , often lead to circumvention of disease-resistance cultivars . Previous genome-wide analyses of model organisms suggest that pathogen effectors are also rapidly evolving , especially in regions with high genome plasticity . However , genetic variations among different isolates remain largely unknown in M . oryzae , particularly at the genome and transcriptome levels . In this study , we provided a systematic genomic and interaction transcriptome profile for a dominant rice blast field isolate , resulting in identification of 134 candidate effectors . Two effectors , Iug6 and Iug9 , and one pathogenicity-related ( PaR ) gene product , Iug18 , were subjected to functional characterization . We found that Iug6 and Iug9 are located in the biotrophic interfacial complex ( BIC ) and their overexpression leads to suppression of defense-related gene expression in rice , while Iug18 appears to be a novel PaR protein . Our studies support the hypothesis that isolate-unique genes may serve as a source of genetic variability in the M . oryzae population encountering different environments . Our studies also facilitate further understanding of effectors and genomic variations in pathogenicity of M . oryzae .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Global Genome and Transcriptome Analyses of Magnaporthe oryzae Epidemic Isolate 98-06 Uncover Novel Effectors and Pathogenicity-Related Genes, Revealing Gene Gain and Lose Dynamics in Genome Evolution
|
Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures , and reveal what external signals will trigger desired changes of large-scale pattern . Despite recent advances in bioinformatics , extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation . The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex , self-regulating pattern to emerge . To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning , it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes . For example , planarian regeneration has been studied for over a century , but despite increasing insight into the pathways that control its stem cells , no constructive , mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations . We present a method to infer the molecular products , topology , and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic , surgical , and pharmacological experiments . We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together , our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration . This method provides an automated , highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form .
Advances in developmental biology and regenerative medicine require a mechanistic understanding of the generation and repair processes that construct and repair complex anatomical structures [1] . For example , a salamander can regenerate complete limbs , eyes , tails , and jaws [2]; a tail grafted to its flank will , within a few months , becomes re-patterned into a structure more appropriate to its new location—a limb [3 , 4] . During metamorphosis , tadpole faces with very abnormal organ positions become transformed into normal frog faces , as each organ undergoes evolutionarily-novel movements to ensure that it ends up in the right position relative to the others [5] . Planarian flatworms regenerate their complex body from almost any surgical amputation , and cease new growth and remodeling when their correct body pattern has been restored [6] . Learning to understand and harness these high-order pattern control programs is of high importance not only to basic developmental and evolutionary biology , but also underlies the roadmap to transformative advances in regenerative medicine , birth defects , and synthetic bioengineering . High-resolution genetic analyses are revealing an increasing number of regulatory genes , while developmental and regenerative research is producing a rich dataset of in vivo experimental manipulations and their resultant morphological phenotypes [7] . Unfortunately , our ability to understand and manipulate 3-dimensional patterning outcomes has not kept pace . A fundamental gap exists between the gene products experimentally identified as necessary for producing a morphological phenotype , and a mechanistic regulatory network that would be sufficient to explain exactly how and why a complex morphology is generated in the precisely correct size , shape , and orientation [8–10] . There exist individual examples of models that incorporate geometry [11–19] and attempt to understand the dynamics of patterning [20–29] , but the most prevalent arrow diagrams derived from genetic experiments largely do not specify , constrain , or explain the remarkable geometry and regenerative regulation of biological systems . Finding the mechanisms responsible for a given set of anatomical phenotypic data remains a significant challenge due to the non-linearity of many biological processes [30] . The increasing deluge of genetic data does not generally result in constructivist models that truly explain dynamic morphogenesis of living structures because it is simply too hard for human scientists to invent a model with all of the appropriate higher-order patterning properties . Indeed each additional dataset on patterning outcomes from some perturbation makes it more difficult , not easier , to come up with a model that matches all of the results . Thus , there is a clear need for automated tools to assist in the discovery of mechanistic models that explain the ever-increasing set of functional phenotypic results in the scientific literature on developmental and regenerative biology [1] . Tremendous progress has been made in developing bioinformatics tools for the reverse-engineering of dynamical models of regulatory networks from microarrays and quantitative PCR gene expression profiling data [31–43] as well as of metabolic networks from time-series concentration data [44–46] . However , these approaches produce models lacking spatial information and are not applicable to patterning and morphological experimental data . Indeed , inferring characterized regulatory networks from experimental resultant spatial patterns is exceedingly challenging due to the difficulties in robustly quantifying phenotypic data [47] , evaluating spatial-temporal models with these data [48] , and automatically characterizing known and unknown products and their underlying complex , non-linear interactions resulting in the desired patterning behavior [49 , 50] . The gene circuit method [51–53] and subsequent automated approaches [54–63] have successfully reverse engineered a complex dynamical regulatory network from spatial data: the gap gene network controlling Drosophila blastoderm patterning . However , these methods are still limited to quantitative 1-dimensional gene expression data and are not amenable for morphological phenotypes resulting from surgical manipulations and genetic and pharmacological treatments that are common in developmental and regenerative biology . No tools yet exist for mining the published datasets of experimental morphological data in regeneration and developmental biology . The complexity of anatomical and morphological data , the elaborate surgical , genetic , and pharmacological perturbation experiments , and the lack of methods to formalize in a mathematical language these data prevent us from reverse-engineering the key regulatory networks in development and regeneration . In consequence , the discovery of mechanistic regulatory networks has not kept pace with the increasing generation of phenotypic data from perturbation experiments . For example , despite over 100 years of focused attention , no quantitative model has been found that reproduces more than a few of the main features of the rich functional dataset on planarian regeneration [64] . We have learned much about the molecular pathways regulating stem cell decision-making [65 , 66] , but the understanding of axial polarity , morphogenesis , and persistent changes to the bodyplan [67] still lacks constructivist models . In order to make use of the ever-increasing data on patterning outcomes of genetic , pharmacological , and surgical experiments , bioinformatics must be extended to anatomy and pattern formation . We present here an automated method for the discovery of regulatory networks explaining the morphological patterning results from surgical , genetic , and pharmacological perturbation experiments ( Fig 1 ) . Our system integrates a formalization of the published results in planarian regeneration , a versatile in silico simulator in which the patterning properties of any regulatory network can be quantitatively tested in a regeneration assay , and a machine learning module that evolves networks whose patterning behavior optimally matches the dataset of planarian results . We demonstrate that regulatory networks comprising specific biological products can be automatically inferred from phenotypic morphological data resulting from functional experiments by an evolutionary computation process . The formalized experimental descriptions of surgical manipulations , genetic and pharmacologic treatments , and resultant phenotypes are used to infer the necessary and sufficient molecular products , their interactions , and the spatial and temporal dynamics of a regulatory network explaining the given set of phenotypic experiments . In inferring regulatory networks from phenotypic experimental data , unambiguous mathematical formalisms must be used to describe the relevant characteristics of the experimental dataset to explain ( Fig 1 ) . To this purpose , we used a functional mathematical ontology with an adequate level of abstraction for the formalization of developmental and regenerative experiments [47] . In contrast to ontologies based on natural language , our functional ontology uses mathematical language for unambiguously describing the experimental procedures as a hierarchy of elemental actions and their morphological outcomes as a set of interconnected body regions ( head , trunk , and tail ) . Thus , the formalized experimental procedures can be reliably performed in a simulator in silico , and the phenotypes of the formalized experiments are amenable to automated comparison with the predictions of models . Using an evolutionary algorithm search module , our system discovered the first quantitative , constructive model that predicts the main features of planarian regeneration .
We developed a generalized method to infer regulatory networks from a set of formalized , morphology-based experiments ( Fig 2 ) . Focusing on the planarian regeneration data [68] for the first proof-of-principle , our goal was to identify a regulatory network that could be executed on every cell in a virtual worm such that the patterning outcomes of simulated experiments would match the published data . Based on evolutionary computation principles [69] , the algorithm maintains an evolving population of candidate regulatory networks for searching the space of possible networks . The algorithm searches simultaneously for the necessary products , topology , specific regulatory interactions , and parameters of the regulatory networks , which are implemented as a non-linear system of partial differential equations . Nodes in the regulatory network can represent either signaling products or special products with a phenotypic meaning specific to the dataset ( head , trunk , and tail regions in the worm ) . The initial population of candidate regulatory networks is made of simple networks with random regulations and parameters . New regulatory networks are created in each generation , by combining two existent ( parent ) regulatory networks from the current population and probabilistically adding and removing products and regulations , and altering their parameters ( see methods section ) . The population is cyclically updated replacing old regulatory networks with the new regulatory networks that better fit the experimental dataset . The algorithm stops when a regulatory network is found that perfectly reproduces the same resultant phenotypes in all the experiments as formalized in the input dataset . Our system uses in silico experiments equivalent to the in vivo experiments formalized in the dataset to evaluate the predictive ability of candidate regulatory networks . For this , we implemented a simulator capable of performing the same kind of experiments formalized in the dataset , including surgical manipulations and genetic and pharmacological perturbations . An experiment stored in the dataset is simulated using a specific regulatory network in two stages: the wild-type morphology stage where the regulatory network can reach a stable state and the experimental stage where the resultant phenotypes are obtained . During the first stage , the product concentrations are initialized and the system of partial differential equations with this initial condition is numerically solved for a constant time interval . Phenotypic products are initialized to match the morphological regions pattern ( head-trunk-tail ) of the formalized wild-type morphology , while the signaling products are set to a continuous parameter value automatically found by the inferring method for each product . The second stage proceeds by applying the surgical manipulations and pharmacological treatments . Surgical manipulations change the system boundaries , while genetic and pharmacological treatments alter specific parameters of the differential equations corresponding to the perturbed products . Next , the new system of partial differential equations with the new initial condition and boundary is numerically solved for an additional constant time interval . The final state represents the resultant phenotype corresponding to the simulated experiment . Thus , each candidate network model is tested in a virtual worm , under simulated experiments , to determine its patterning properties in each case . Then , to determine the quality of a candidate regulatory network , the algorithm compares the resultant phenotypes from the simulation of each experiment with real published data in our planarian database [47 , 70] . To quantitatively ascertain the predictive quality of each model ( how well it matches the available data ) , we calculate a composite error score representing how well each experiment’s final pattern matches the known result of such an experiment in real planaria . For this purpose , we implemented a phenotypic distance metric that measures how different any two morphological phenotypes are [71] . The metric calculates the average differences between the phenotypic product concentrations of the two phenotypes . The predictive error of a regulatory network is then calculated as the average phenotypic distance between the resultant phenotypes from the simulated experiments and those corresponding to the formalized experimental dataset . Using this algorithmic approach , we inferred novel regulatory networks ( Fig 3 and S1–S6 Movies ) explaining the experimental data presented in a selection of key papers [72–79] studying the head-versus-tail regeneration decision making in the planarian flatworms S . mediterranea and D . japonica . First , we formalized datasets containing the surgical manipulations , pharmacological and genetic treatments , and their resultant experimental phenotypes for each of the selected papers . We next applied the method individually to each dataset to infer the subjacent regulatory networks explaining the experimental data presented on each of the papers . For each dataset , the algorithm found a complete system of differential equations ( S1 File ) that represent a regulatory network explaining the dynamical regeneration of the correct position , 2D shape , and proportions of the head , trunk , and tail regions of all the experimental phenotypes in each dataset . Remarkably , without any prior knowledge of genetic expression patterns or regulatory interactions among genes , but using only the pharmacological , genetic , and surgical experimental perturbations and the position , shape , and proportions of their morphological outcomes ( encoded as head , trunk , and tail regions ) , the algorithm discovered the correct known regulatory pathways of several signaling mechanisms ( Fig 3 ) . For example , the algorithm discovered the Wnt/β-catenin canonical regulation ( Fig 3C and 3D ) , the inhibition of head structures and promotion of tail structures by β-catenin ( Fig 3A , 3C and 3D ) , the inhibition of β-catenin by both APC ( Fig 3A ) and notum ( Fig 3C ) , and the cryptic lack of posterior tissue re-specification ( remaining as trunk ) due to the knock-down of wnt1 and notum ( Fig 3C ) , hh ( Fig 3D ) , or wnt1 and hh ( Fig 3D ) . In addition , several novel regulatory interactions and unidentified products were detected as necessary for the correct prediction of the experiments in the datasets . Fig 4 shows two experiments performed in silico using the regulatory network discovered from the search of the model in Fig 3A . The concentration dynamics during both experiments are shown for a selection of locations in the virtual worm . In the control experiment ( Fig 4B ) , no genetic or pharmacological perturbation was applied to the worm , resulting in the regeneration of the correct head-trunk-tail pattern . However , when β-catenin is blocked in the second experiment ( Fig 4C ) , the same regulatory network predicts the regeneration of a double-head worm , which is the exact phenotype resulting from the experiments in vivo . The discovered regulatory network also predicted the known role of APC inhibiting β-catenin , which explains the resultant double-tail phenotype after APC ( RNAi ) ( Fig 3A ) . Multiple knock-downs in the wnt1/wnt11-5 regulatory pathway are necessary to perturb the resultant phenotype from a trunk fragment [75] ( wnt1 and wnt11-5 were known as wntP-1 and wntP-2 , respectively [80] ) . When we applied the automated method to this dataset , the resultant model found consisted in a redundant modular network ( Fig 3B ) . Fig 5 illustrates the experiments in this dataset performed in silico with the network automatically discovered . The regulatory network presents both wnt1 and wnt11-5 activating the regeneration of tail and inhibiting the regeneration of head , and both of them activated by an unknown common product . Due to this redundancy in the network design , the knock-down of either wnt1 or wnt11-5 results in the same phenotype than the control: the correct head-trunk-tail pattern . However , when both wnt1 and wnt11-5 are simultaneously knocked down , the regenerated phenotype is then a double-head worm , similarly to the phenotypes obtained in vivo . The inferring method iteratively produces regulatory networks that better predict the experiments in the dataset . Fig 6 shows a selection of candidate regulatory networks generated during the search of the model in Fig 3C ( see S1 File for the system of equations for each regulatory network ) . The initial random regulatory networks ( generation 0 ) usually cannot reproduce any of the resultant phenotypes in the dataset , neither maintain the wild type morphology pattern . New candidate regulatory networks are generated by randomly combining previous networks and performing random changes , additions , and deletions , including nodes representing knocked-down genes in the experiments or unknown nodes found de novo . Incrementally , the new candidate networks can explain a higher number of experiments , and the final regulatory network can correctly explain all the experiments in the dataset . The time to converge to a satisfactory regulatory network depends on the complexity and quantity of the experiments included in the input dataset ( Fig 7 ) . The inferring algorithm is intrinsically parallel , since the simulation and evaluation of a population of candidate regulatory networks can be done independently . Using a parallel implementation of the algorithm in a computer cluster , the time to find a regulatory network from knock-down experiments ranged from an average of one hour for four-experiment datasets to seven hours for eight-experiment datasets . Datasets with experiments blocking the diffusion of a product averaged four hours . The dataset with three classical cut experiments averaged a time to find of 21 hours , suggesting a higher difficulty in inferring de novo all the unknown components in the regulatory network . We can visualize the evolution of regulatory networks during a search process by tracking the error of the best network ( lowest error ) in the population and its complexity ( the sum of the number of products and number of regulatory interactions in the network ) over time ( S1 Fig ) . The graphs show how the initial population contains networks with low complexity and high error . Gradually over time , the error of the networks improves , while their complexity increases , until a network with zero error is found by the algorithm . During the search process , regulatory networks can evolve products and regulations that do not participate directly or indirectly in the regulation of phenotypic products , and hence do not affect the dynamics of the phenotypic products ( S2 Fig ) . These auxiliary products are not included during the simulation of a regulatory network , but they can evolve independently through neutral mutations , and be reused at later generations by the search algorithm . We next applied the algorithm to a combined experimental dataset comprising all the selected head-versus-tail planarian regeneration papers to determine whether our approach could identify a comprehensive regulatory network of planarian regeneration ( Fig 8 and S7 Movie ) . Remarkably , after 42 hours , the algorithm returned the discovered system of equations ( S1 File ) representing a regulatory network that correctly predicts all 16 experiments included in the dataset . The network comprises seven known regulatory molecules inferred from knock-down experiments , one unknown gap junction-permeable diffusible product inferred from a gap junction blockage experiment , and two unknown general regulatory products . This automatically inferred regulatory network represents the most comprehensive model of planarian regeneration found to date , the only known model that mechanistically explains anterior-posterior polarity determination in planaria under many different functional experiments , and the first patterning model discovered from morphological outcomes by an automated method—a new successful application towards the augmenting of scientific discovery with artificial intelligence [81–83] . In order to characterize the two unknown regulatory products identified by the algorithm , we searched for products with similar interactions in public molecular interaction databases . Using the MiMI database [84 , 85] , we extracted all the known products ( in Homo sapiens ) interacting with the products that were predicted to regulate node ‘b’ ( β-catenin and hh; see Fig 8 ) and found hnf4 as the only common product interacting with both of them . This is thus an excellent candidate for node ‘b’ , and a homolog for this gene has already been found in planaria [86] . For node ‘a’ , we used the STRING database [87] , and identified the Frizzled family of receptors as commonly interacting ( with the highest confidence score of 0 . 9 ) with β-catenin , wnt1 , and wnt11 . Indeed , several Frizzled protein homologs have been already identified in planaria [72]; since phenotypes for each individual Frizzled gene product have not yet been uncovered by loss-of-function analyses in the literature ( suggesting redundancy ) , our network’s node ‘a’ likely represents the regulatory actions of several of these family members as a group . We next tested whether some regulatory pathways were robustly found by the search method—consistently discovered by independent evolutionary searches . To this end , we performed multiple runs of the method with the same comprehensive set of experiments . These searches resulted in three different regulatory networks that can correctly reproduce the complete set of experiments ( Fig 9A–9C; A being the most parsimonious network that was presented in Fig 8 ) . All the regulations shared by these three networks are seen together in a common subnetwork ( Fig 9D ) . Remarkably , 14 regulatory interactions were consistently found by the search method , suggesting that these relationships are the most important interactions explaining the comprehensive dataset of experiments . Finally , we tested the robustness and predictive ability of the regulatory networks found by our automated method in an experiment designed to test the predictive value of the discovered models for data they had never seen ( not included in the search process ) . We omitted three key experiments from the comprehensive dataset , and used the automated method to find a regulatory network that could correctly reproduce this reduced ( partial ) dataset ( Fig 10A and 10B ) . Crucially , the found regulatory network correctly predicted the outcomes of these three novel experiments—the model correctly explained the outcomes that were not known to it during the search ( Fig 10C ) . These results validate the ability of the automated search method to find regulatory networks capable of not only explaining the resultant phenotypes from the experiments performed in vivo included in the learning dataset , but also of predicting the resultant phenotypes from novel experiments . We conclude that the networks uncovered by this system have predictive value for novel results , in addition to helping to understand existing data from which they were extracted .
Our system addresses the gap between the wet-lab discovery of genetic regulatory interactions and an understanding of the dynamic patterning behavior of regenerative systems . Comprising ( 1 ) a formalized database of functional patterning outcomes in the planarian literature , ( 2 ) a simulator in which any ( human- or computer-derived ) regulatory model can be evaluated for fit to known anatomical data , and ( 3 ) a network discovery machine learning method , this system is a first step towards a new bioinformatics of shape . These three modules are integrated into a workflow designed to help human scientists discover mechanistic , constructivist models that optimally match the ever-growing dataset of regeneration data . Our results demonstrate the discovery of regulatory networks directly from formalized experimental morphological data with the use of an automated computational algorithm—the first automated linkage of morphological output and molecular-genetic underpinnings . With no prior information beyond the input dataset of functional outcomes of surgical , genetic , and pharmacological experiments , the method is capable of identifying the necessary biochemical products and their regulations and parameters that form a system of partial differential equations explaining the resultant phenotypes from the dataset . The networks discovered by our system represent immediately testable hypotheses for the control algorithms underlying regeneration . Our method improves the current state of the art for reverse-engineering dynamic regulatory networks in several areas . Foremost , our method is the first to be applicable to data containing morphological outcomes and surgical perturbations , which is essential for the regeneration field . Current methods are limited to inferring networks from dimensionless gene expression profiling data or 1-dimensional expression data resulting from genetic perturbations . In contrast , our method is flexible enough to extract regulatory networks directly from resultant 2-dimensional morphological patterning outcomes and to process a wide array of experimental perturbations , including surgical manipulations , pharmacological treatments , and genetic knock-downs . To this end , we implemented a whole-body developmental simulator that differs from current approaches [88–91] in that the input is a formalization of both the experimental surgical and genetic perturbations to perform and the dynamical regulatory model to test . This allows our method to be applicable to the reverse engineering of regulatory networks from the morphological outcomes of developmental systems , previously out of the range of automated inference methods , including the large experimental dataset of regenerative model organisms lacking mechanistic dynamical explanations . Importantly , our method can infer regulatory networks containing not only the specific products and genes perturbed in the input experimental dataset , but also discover completely de novo unknown products detected as necessary to explain the resultant phenotypes: predict their existence , functional roles in the network , and properties of interaction with known molecular components . This makes our approach applicable to even datasets with perturbations affecting unknown mechanisms , as well as datasets lacking all the experimental perturbations necessary to explain all the experimental data . In this way , our method can infer regulatory pathways not apparent from the input dataset and novel interactions not reported in the literature , whose yet-to-be-characterized products can be identified from the multiple interactome databases available in the literature—these inferences then serve as predictions of the model which can be empirically tested . We are currently implementing an automated method to characterize such unknown products . The inferred regulatory networks by our method contain more versatile regulatory interactions than previous approaches . Due to their capability to model a diverse set of biological regulations , we employed Hill functions to model the regulation between two products . Using two parameters per interaction ( the Hill coefficient and the disassociation constant ) , the model can accommodate a richer variety of non-linear interactions compared to linear and one-parameter non-linear functions . Furthermore , the regulatory networks inferred by our method improve current approaches by permitting different types of aggregated interactions between multiple regulations for a single product , such as necessary regulations ( both regulators are required ) , sufficient regulations ( any regulator is enough ) , or any combination of them . The high flexibility of the inferred regulatory networks makes our method a very versatile approach . The discovered regulatory networks reveal several interesting properties of the inference method . Networks matching many functional experiments that quantitatively and qualitatively explain regeneration of anatomical polarity—which had eluded human scientists—could be discovered in acceptable time by an automatic search performed by a computer . Surprisingly , the fully parameterized regulatory networks that were identified by this process are not highly complex tangles , but are similar in complexity to qualitative models proposed by human scientists in the literature and thus readily understandable . Moreover , the discovered networks contain only a few to-be-identified gene products , which facilitate their identification from known interactome data . Indeed , we could manually identify the two gene unknown products found by our method using publicly available molecular interaction databases . Currently , we are developing an automated method to facilitate the characterization of such products . Precise predictions can readily be made from the simulation of novel experiments with the discovered networks , guiding in the design of the best next set of experimental manipulations . The comprehensive model of planarian regeneration reverse-engineered by our method represents the first quantitative model able to recapitulate regeneration under genetic knock-downs , pharmacological treatments , and surgical manipulations . Unlike conventional arrow diagrams derived from molecular genetic experiments , this system identifies models that not only include necessary components ( without which regeneration cannot occur normally ) , but are fully-specified as a constructive model showing which dynamics are sufficient to give rise to the remarkable pattern homeostasis of planaria . Most models of regeneration are based on generalized mechanisms and do not consider the specific dynamic regulatory mechanisms or network topology necessary to precisely recapitulate the observed patterning phenotypes [92–96] . Meinhardt’s pioneering work on the mechanisms of pattern formation represents the only dynamic models of planarian regeneration proposed to date , based on reaction-diffusion mechanisms and able to recapitulate the head-versus-tail polarity regeneration and midline formation [23 , 64 , 97 , 98] . However , this approach was purely numerical as a proof of the general dynamic mathematical principles , without characterizing any of the regulatory products , and hence accounting only for surgical amputations . Our model was inferred directly from experimental data and includes particular genetic regulatory components able to precisely predict genetic and pharmacological interventions in addition to surgical manipulations . Hence , the models inferred with our method can be used to predict the morphological outcomes in specific genetic knock-downs . The method can identify those interactions most strongly implied by the dataset , by performing multiple searches and extracting the common pathways found in the resultant set of regulatory networks . Interestingly , the consensus model found in this way includes most of the genetic regulations of head vs . tail planarian regeneration published in the field to date , as well as novel genetic regulations only discovered recently in other model organisms , such as the inhibition of wnt by notum [99] . Furthermore , the method can be used as a generable protocol for automatically finding the less-universal regulatory interactions inferred from the data , and for automatically suggesting additional perturbations for in vivo experimental testing . Importantly , the robustness of the method to infer predictive regulatory networks was validated with a subtraction control test , which successfully produced a regulatory network that not only predicted all the experiments in the dataset used during the search , but also predicted the exact resultant phenotypes from a set of new in vivo experiments that were not part of the search process . In summary , these results validate the capacity of our method to reverse engineer robust regulatory networks with a high predictive power . Although our method has produced the most comprehensive model of planarian regeneration to date , it contains several limitations . We have restricted our experimental formalization and simulation to 2-dimensional spatial data; thus , the discovered models do not yet address the regulatory mechanisms necessary to specify the dorsoventral axis patterning in planaria [100–103] , or the detailed patterning of individual internal organs . In addition , the discovered models are deterministic , and do not account for the stochasticity shown by some partial penetrance phenotypes . Adding a stochastic component to such equations does not represent any technical difficulty; however , the computational requirements of the method to quantify the frequency for each of the possible resultant phenotypes in each experiment would increase by several-fold . Because the basic paradigm is fundamentally very flexible , future work will address these limitations , leading to further improvements in the ability to reverse engineer models that are more complete , including specific modeling of the numerous cellular mechanisms that physically implement such outcomes , such as cell migration , division , differentiation , or apoptosis . Our approach is broadly applicable to any model system whose experimental procedures and anatomical outcomes can be formalized [104 , 105] and can readily be extended to other problems in morphogenesis , including embryonic development or the programmed self-assembly of hybrid systems such as bioinspired robots [106–109] . The models discovered with this method allow the identification of the key mechanisms and the major regulatory products , including those directly perturbed during the experiments as well as-yet unidentified necessary products , explaining the resultant experimental phenotypes . Such models are required for the identification of intervention strategies to produce desired changes in large-scale shape , for birth defects , regenerative medicine , or synthetic bioengineering research . Our method represents a proof of principle towards the use of evolutionary search and quantitative spatial simulation to help constructively understand complex morphological outcomes in embryogenesis , regeneration , and synthetic bioengineering .
We created the input datasets of formalized experiments for the automated search algorithm with the software tool Planform [70] . Planform uses a functional ontology based on mathematical graphs [47] , a set of interconnected nodes [110] , to unambiguously describe the main characteristics of the morphology , including the overall shape and the location of specific phenotypic regions ( head , trunk , and tail regions in the worm ) . Experimental procedures are described in the ontology as a nested set of basic operations , including amputations , cuts , genetic/pharmacological perturbations , and their parameters . Using the graphical user interface , we created a separated dataset with the phenotypic experiments presented in each of the main publications of head-versus-tail planarian regeneration [72–79] , and an additional dataset including all the experiments together . To apply an automated discovery system capable of finding complex spatial and temporal dynamic networks , we modeled the behavior of gene , protein , and metabolite regulatory network with a system of nonlinear partial differential equations ( PDE ) . Products can act as intercellular signals or be confined intracellularly , decay with time , and be activated or inhibited by other products in the regulatory network . Each product can be regulated by several other products , where interactions can be combined in either a necessary or sufficient fashion . A regulatory network is made of phenotypic products and signaling products . Phenotypic products represent phenotypic regions in the organism , and as such cannot regulate other products . In this way , morphological features of the phenotype are abstracted as a single product , resulting in inferred regulatory networks centered on the signaling mechanisms and not the molecular details to form specific morphological features . For example , full-body worm phenotypic data are formalized using head , trunk , and tail regions , whereas the inferred networks employ corresponding specific products representing head , trunk , and tail outcomes . Signaling products can regulate other products , and they can represent the product of specific genes ( such as β-catenin or wnt1 ) inferred from the perturbation experiments in the dataset , or be found de novo as necessary by the search algorithm . In addition , special products are used to model specific aspects of an experiment . In particular , we implemented a wound signal product , which is produced in the area adjacent to a surgical cut during an experiment . Each equation in the system models the production rate of a product as the linear relation between a production term , a decay term , and a diffusion term . The production term is modeled with a combination of Hill functions , a widely-used nonlinear model of biochemical interactions and genetic regulation [111] . Each Hill function models the activation or repression of a product by another product ( including itself ) . A product can be regulated by several regulatory interactions simultaneously , and these interactions can be grouped in a necessary ( both regulators are required to produce the regulated product ) , sufficient ( one regulator is enough to produce the regulated product ) , or any combination of them . Sufficient interactions are grouped together in a max operator , while necessary interactions are grouped together in a min operator; the set of sufficient interactions is considered as a necessary interaction by itself , and hence it is included inside the min operator . The rate or production is modulated by a production constant , which multiples the result of the combined Hill functions regulation . Products decay in an exponential fashion . Thus , the decay term is modeled with a decay constant multiplying the product current concentration . Intercellular signaling mechanisms are essential in the regulation of developmental and regenerative processes . We modeled the propagation of intercellular signals as a diffusion term in the differential equation , modulated by a diffusion constant . This allows the implementation of products that can propagate intercellularly , carrying signals regulating other products . The diffusion constant of a product can be zero , in which case the product is considered exclusively intracellular . The following equation illustrates a model of the production of product a as regulated by two necessary products ( activator b and inhibitor c ) and two sufficient products ( activator d and inhibitor e ) : ∂a∂t=ρamin ( bη1α1η1+bη1 , α2η2α2η2+cη2 , max ( dη3α3η3+dη3 , α4η4α4η4+eη4 ) ) −λaa+Da∇2a where ρa is the production constant , ηi are the Hill coefficients , αi are the dissociation constants in the Hill functions , λa is the decay constant , and Da is the diffusion constant . In summary , each product of a regulatory network is defined according to four parameters ( production , decay , and diffusion constants , and the initial concentration value ) , while each regulatory interaction is defined with three parameters ( Hill coefficient , dissociation constant , and whether the regulation is necessary or sufficient ) . The values of all the parameters are automatically inferred by the search algorithm . We implemented a simulator able to load morphological phenotypes and perform surgical , genetic , and pharmacological experiments formalized with the functional ontology . The simulator takes as input a formalized experiment with the functional ontology and a regulatory network described with a system of PDEs . The simulator outputs the resultant morphology after numerically integrating the PDE system and performing in silico the formalized experiment . Due to the dynamic boundaries of a developmental and regenerative simulated organism , we implemented an Euler finite difference method [112] to integrate the set of PDEs corresponding to a regulatory network . The simulator performs an experiment in two stages . During the first stage , the original wild-type morphology is loaded into the simulator , the initial product concentrations are set according to the model , and the regulatory network defined in the PDE system is integrated for a fixed amount of time . This first stage allows the dynamical system to converge into a steady state , which will be used for the initial state in the second stage . A formalized morphology and regulatory network is loaded into the simulator by setting the initial concentration value for every product in the regulatory network . The concentration of phenotypic products ( head , trunk , and tail in the worm dataset ) is initialized according to the corresponding phenotypic regions . For example , the positions corresponding with a head region will be initialized with head product concentration of 1 . 0 and trunk and tail product concentrations of 0 . 0 , whereas the positions corresponding with a trunk region will be initialized with trunk product concentration of 1 . 0 and head and tail product concentrations of 0 . 0 . Signaling products are initially set homogenously according to either a numerical parameter for each product between 0 . 0 and 1 . 0 stored in the model or indicating a configuration similar to a phenotypic product . During the second stage of the simulation , the surgical manipulations and genetic and pharmacologic perturbations are performed and the PDE system is integrated for another fix amount of time . The final state of the system is the resultant morphology of the in silico experiment . Surgical manipulations change the boundary of the system and set the concentration of all products outside of the new boundaries to zero . A genetic knock-down ( RNAi ) will eliminate all the activation regulations of the corresponding product . Pharmacological treatments ( octanol ) will set the corresponding product diffusion constant to zero , simulating a block of gap junction channels . To calculate the error ( predictive power ) of a regulatory network , the resultant phenotypes of the simulated experiments using the network are scored by comparing them with the resultant phenotypes from the physical experiments . For this end , we implemented a distance metric between phenotypes . Wild type planarians can vary their size by about an order of magnitude due to feeding and starvation [113] , a common situation during regenerative experiments where worms may lack the ability to feed . In consequence , we made the distance metric between phenotypes tolerant to small variations between phenotypes . More precisely , the phenotypic metric is invariant in scale , for which the phenotypes are first centered and scaled before comparison . In addition , we included a concentration tolerance parameter ( ε ) and a radius tolerance parameter ( r ) within the metric , as defined below , which even out small differences between phenotypes . The goal of the search algorithm is to find regulatory networks that produce stable phenotypes , and not transient states that are only temporally similar to the resultant phenotypes of the physical experiments . To bias the search towards stable networks , we included a concentration change penalty that is applied when the maximum concentration change in the last time step of a simulation is higher that certain parameter threshold ( μ ) . We then define a Euclidian distance between two locations a and b that measures the squared averaged distance between a set p of phenotypic products within a tolerance ε: ∥a−b∥ε=∑pϵP ( ( [p]a−[p]b ) 2−ε ) + where [p]a and [p]b are the concentrations of product p in the locations a and b , respectively . Then , we define a distance metric between two phenotypes A and B of size w-h as the mean logarithmic minimum distance between every location a of phenotype A and every location b inside a radius r from a of phenotype B: d ( A , B ) =1w⋅h∑i=1w∑j=1hlog ( 1+minδ , θ∈ ( −r , r ) ∥ai , j−bi+δ , j+θ∥ε ) Finally , we define the error of a regulatory network model M for a set E of n experiments as: error ( M , E ) =1n∑i=1n ( d ( ΨeiM , Pei ) + ( ΔeiM−μ ) + ) where ΨeiM is the resultant phenotype of simulating experiment ei with the model M , Pei is the resultant phenotype from the physical experiment ei , ΔeiM is the average concentration change in the last time step of simulating experiment ei with the model M , and μ is the penalty concentration change threshold . The error of a network is calculated with the set of experiments formalized in the input dataset , plus an additional experiment with no surgical manipulation or perturbation to assure that a discovered regulatory network maintains the correct wild type morphology in the absence of any perturbation . Having an automated measurement of the error of a given regulatory network model for a set of formalized experiments , we then implemented an optimization method to search for models that minimize the error . Our method is flexible enough to find the parameters , the topology , and the necessary products of the network . We employed an evolutionary algorithm [114] approach to search for regulatory networks , where a population of candidate networks evolve in parallel until a network with zero error is found . The initial population comprises random networks with random parameters and regulations between the phenotypic products , the wound product , and the genetic and pharmacological perturbed products from the set of experiments to search . New regulatory networks are produced from existing ones through crossover and mutation operators . A crossover mixes randomly two networks to produce two new networks . Products that are in common between the two networks are copied to the new networks randomly , each network receiving one of each product , while products not shared are distributed randomly between the two new networks . Products are copied to a new regulatory network together with their regulatory links . If the regulatory product of a copied link does not exist in the new network , it is substituted randomly by another regulatory product . Mutations alter the regulatory network randomly . Each product or link parameter can be substituted by a random value with 1% probability . Products and links are duplicated with 1% probability . After duplicating a product , a new regulatory link and a new regulator link is created for the new product . After duplicating a link , the regulated and regulator products are chosen randomly . Products and links can be deleted with 1 . 5% probability , except phenotypic and perturbed products in the experiments , which cannot be deleted . These evolutionary parameters are not optimized; however , a higher probability of deletion with respect duplication is necessary to bias the evolution towards simpler networks and prevent bloating [115] . The evolutionary algorithm stops when a network with zero error is found and the complexity ( number of products and links ) of the simpler network with zero error have not been decreased for a certain number of generations . This extra evolutionary time is used to simplify the best network found , since the mutation operators are biased towards simpler networks . Since new regulatory networks in a population can be simulated and evaluated independently , we implemented a parallel version of our evolutionary algorithm in a cluster computer using 256 cores . We used an island distribution approach [116] , which improves performance and preserves genetic diversity by using many independently-evolving subpopulations . We used 32 parallel subpopulations with 64 regulatory networks each . For every 250 generations on average , all subpopulations are randomly paired and their regulatory networks are shuffled randomly; this compensates the trend for a single suboptimal regulatory network to saturate a single subpopulation . We used the deterministic crowding selection method [117] with 75% crossover , 1% parameter change mutation , 1% duplication mutation , and 1 . 5% deletion mutation . All the parameters in the regulatory network can vary in the range ( 0 , 1 ) , except the Hill coefficient , which can vary in the range ( 1 , 5 ) . To calculate the regulatory network error , we used an Euclidian distance tolerance ε of 0 . 1 , a distance comparison radius r of 2 , and a penalty concentration change threshold of 10–4 . We used 250 extra generations in the criteria to stop the algorithm after a network with zero error is found . The simulation and search method was implemented in C++ using the Standard Library and the Eigen library ( http://eigen . tuxfamily . org ) . Visualizations used the Qt libraries ( The Qt Company Ltd . ) and the Qwt library ( Uwe Rathmann and Josef Wilgen ) . The software is freely available at http://www . daniel-lobo . com/planarianmodels .
|
Developmental and regenerative biology experiments are producing a huge number of morphological phenotypes from functional perturbation experiments . However , existing pathway models do not generally explain the dynamic regulation of anatomical shape due to the difficulty of inferring and testing non-linear regulatory networks responsible for appropriate form , shape , and pattern . We present a method that automates the discovery and testing of regulatory networks explaining morphological outcomes directly from the resultant phenotypes , producing network models as testable hypotheses explaining regeneration data . Our system integrates a formalization of the published results in planarian regeneration , an in silico simulator in which the patterning properties of regulatory networks can be quantitatively tested in a regeneration assay , and a machine learning module that evolves networks whose behavior in this assay optimally matches the database of planarian results . We applied our method to explain the key experiments in planarian regeneration , and discovered the first comprehensive model of anterior-posterior patterning in planaria under surgical , pharmacological , and genetic manipulations . Beyond the planarian data , our approach is readily generalizable to facilitate the discovery of testable regulatory networks in developmental biology and biomedicine , and represents the first developmental model discovered de novo from morphological outcomes by an automated system .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration
|
Bioinformatics plays a key role in supporting the life sciences . In this work , we examine bioinformatics in Jordan , beginning with the current status of bioinformatics education and research , then exploring the challenges of advancing bioinformatics , and finally looking to the future for how Jordanian bioinformatics research may develop .
Previously published studies on the development of bioinformatics in several countries [1–7] inspired us to investigate the status of bioinformatics in Jordan . Bioinformatics is a vast multidisciplinary field that has developed computational tools to analyze and manage constantly growing amounts of biological data [8] . The increasing importance of integrative approaches to understanding biology and a burgeoning interest in personalized medicine make bioinformatics essential in many life science projects around the world [9] . In Jordan , rapid progress in life science research and healthcare has created a demand for bioinformatics [10 , 11] , especially with a rapidly growing set of biological data repositories [12] . In this work , we investigate the current status of bioinformatics education and research in Jordan , summarize the number of Jordanian bioinformatics publications and compare this with those of other Arab countries , analyze the proportion of bioinformatics reports within the Jordanian biomedical literature , and discuss future directions and opportunities for expansion of bioinformatics in Jordan .
There are no devoted bioinformatics institutes in Jordan , but there are at least four centers that feature bioinformatics as an important part of their research and education portfolios . The Princess Haya Biotechnology Center ( PHBC ) was established in 2013 at the Jordan University of Science and Technology ( JUST ) to support research in biotechnology at the national and regional levels . The PHBC envisions leading genomics , proteomics , metabolomics , and bioinformatics in Jordan . The PHBC provides training in all these areas , including bioinformatics . The Synchrotron-light for Experimental Science and Applications in the Middle East ( SESAME ) is another center supporting bioinformatics research and education in Jordan . SESAME is not a bioinformatics-specialized center , but with its research portfolio that includes biology , archaeology , and the medical sciences , bioinformatics plays a fundamental role that is recognized and supported at SESAME . The Jordanian Society for Microbial Biodiversity ( JSMB ) focuses on the study and conservation of microbial biodiversity . Like PHBC and SESAME , JSMB makes extensive use of bioinformatics and has a bioinformatics research program to support investigations of microbial diversity . Finally , the International Academy of Pathology–Arab Division ( IAP–AD ) is a center for research that also recognizes the important supporting role of bioinformatics in pathology . Jordan has hosted several conferences and workshops that were either devoted to or have featured bioinformatics . For example , the Department of Biotechnology and Genetic Engineering of Philadelphia University ( Jordan ) , in collaboration with the Institute of Genetic and Genomic Studies at the University of Tübingen ( Germany ) , organized the First German-Jordanian Meeting: Molecular Genetics of Human Diseases in 2015 . The focus of the conference was on modern techniques in molecular biology and the diagnosis of genetic diseases , particularly those that cause nondevelopmental mental retardation in some families in Jordan . Given this theme , presentations on bioinformatics tools and approaches were an important part of this conference . Bioinformatics also was featured heavily in the International Conference on Ethics & Biomedical Informatics organized by the College of Pharmacy at JUST in collaboration with the University of Miami Bioethics Program and Responsible Conduct of Research ( RCR ) Program . The first International Conference on Cancer Care Informatics , held in 2018 in Amman , Jordan , also prominently featured bioinformatics , including the workshop Bioinformatics for Cancer Studies . The conference was organized by King Hussein Cancer Foundation and , in partnership with the University of the West of England , Bristol , UK , featured six keynote speakers that included bioinformatics experts . Currently , there are no dedicated bioinformatics degree programs in Jordan . Instead , largely isolated courses in bioinformatics are offered within computer science and information technology departments . Table 1 illustrates the course name , the department they are offered under , and the university name . In many cases , instructors who teach these courses are not practicing bioinformaticians . To advance the field and to train scientists in bioinformatics in Jordan , a dedicated bioinformatics degree program with faculty who are specialists in bioinformatics must be established . We used the query terms ( “next-generation sequencing , ” “computational biology , ” “bioinformatics , ” genomic , ” and “in silico” ) used by the authors in [7] to search the Scopus [13] and PubMed [14] databases for the period from 2004–2018 and filtered these hits to include only authors affiliated with Jordanian institutions . As seen in Fig 1 , there was a sharp rise in the number of publications from Jordanian universities and research centers with keywords that matched these search terms that identify publications relevant to bioinformatics . The number of bioinformatics-related publications authored by scientists affiliated with Jordanian institutions was compared to the number of publications authored by scientists from other Middle Eastern countries with an economy relatively close to Jordan’s based on World Bank ( http://www . worldbank . org/ ) classification [15] . These countries are Lebanon , Egypt , and Iraq . As seen in Fig 2 , Jordan lags behind Egypt and Lebanon in the number and growth of publications in bioinformatics . We also used the PubMed database to compare the growth in Jordanian bioinformatics publications relative to the growth in publications in the broadly defined field of biomedical sciences . These results , which are shown in Fig 3 , indicate that the growth of Jordanian bioinformatics publications lags far behind the growth in biomedical publications .
In this section , we discuss challenges to the development of bioinformatics in Jordan: 1 . There is a lack of scientists in Jordan with expertise in bioinformatics . The absence of even a single dedicated degree program in bioinformatics is at least partially to blame . For whatever reason , the need to strengthen bioinformatics to support the expansion of the life sciences in Jordan has gotten little attention . 2 . Short-term , targeted educational programs , such as workshops and train-the-trainer courses , are rare in Jordan . These programs have the potential to spark interest in bioinformatics in Jordanian researchers and students . More educational programs must be instituted if bioinformatics is to flourish in Jordan . 3 . Much of the needed infrastructure for bioinformatics such as high-performance computers , operating and information systems , and software is lacking in Jordan . Expanding this computing infrastructure would go a long way to increasing the development and application of bioinformatics in Jordan [16] . 4 . The funding needed to develop robust bioinformatics research and education programs is difficult to obtain in Jordan . Funding needs to be expanded to have a chance of developing a robust bioinformatics program in the country .
As outlined above , there are significant challenges to expanding the scope and quality of bioinformatics research in education in Jordan . Here , we offer some suggestions for meeting these challenges . 1 . Short-term training , workshops , and conferences: Opening more opportunities for training in bioinformatics may be one of the easiest steps to improving the state of bioinformatics in Jordan . Short training courses delivered face to face or online should be offered more frequently for current bioinformatics researchers and for those interested in learning about the field . To design such short courses , we suggested taking the benefits from the case studies shown in [12 , 17 , 18] . In order to find sustainable solutions , we suggest finding train-the-trainer courses that aim to keep lecturers and researchers updated in this dynamic field will also be important . Such courses can be set up in collaboration with international bioinformatics institutes . Similarly , increasing the number of conferences that focus solely on bioinformatics or feature it as an important component will provide valuable opportunities for education and peer-to-peer networking among Jordanian scientists , as well as increasing international collaborations . These steps can support the competencies needed for the number of researchers working in several fields related to bioinformatics in the country . As reported by The Higher Council for Science and Technology ( HCST ) , Jordan currently has around 10 , 519 PhD researchers working in several research fields , with 1 , 003 in Information Technology , 282 in Biology , 339 in Mathematics and Statistics , 656 in Physics , and 1 , 307 in the medical fields . 2 . Creation of bioinformatics degree programs: The lack of bioinformatics degree programs in Jordan is an obvious impediment to expanding the field . Comprehensive bionformatics degree programs at the undergraduate and graduate levels must be created to train Jordanian scientists . As a start , BS- , MS- , and PhD-degree–granting programs patterned after respected programs at United States and European universities need to be established in at least one of Jordan's major centers of learning . 3 . Develop and enhance the courses curricula: Introducing bioinformatics courses into computer science and life sciences curricula would help develop multidisciplinary scientists who are proficient in using bioinformatics and who will be able to advance bioinformatics . Funding to provide doctoral and postdoctoral research fellowships in bioinformatics is another critical element to move Jordanian bioinformatics forward . These fellowships should be part of the strategic plan for the Jordanian Higher Education Ministry . 4 . Investment in computing power and data storage: Bioinformatics development benefited from open sources that are publicly available [19] . Several of these tools are cloud-based tools , with most of the computations being processed in the cloud platforms [20] . Since there is poor information and communications technology infrastructure in some areas in the country , educational and research institutes can invest in Free and Open-Source Software ( FOSS ) resources to improve bioinformatics knowledge and enhance the education platforms . For data-intensive applications in terms of processing power and storage space , the supercomputer infrastructures , such as the IMAN1 project , can be utilized . 5 . Foster national and international collaborations: Programs that encourage collaborations between bioinformatics researchers within Jordan and between Jordanian researchers and international colleagues will spur the development of bioinformatics in the kingdom . We suggest funding a national bioinformatics network that is similar to the Asia Pacific Bioinformatics Network ( APBioNet ) . We also recommend an increased role for SESAME as a center for bioinformatics research and as a host for conferences and workshops to bring together Jordanian bioinformaticists and international bioinformatics researchers . Finally , Jordanian scientists should be encouraged to engage with international organizations that promote bioinformatics , such as the International Society for Computational Biology ( ISCB ) , the Global Organization for Bioinformatics Learning , Education & Training ( GOBLET ) , and Bioinformatics Club for Experimenting Scientists ( Bioclues ) . 6 . Increased funding for bioinformatics: Of course , increases in funding are demanded by scientists in every discipline . But with a documented paucity of Jordanian bioinformatics publications relative to those of other Arab nations , coupled with the essential supporting role that bioinformatics plays in so many fields , the case for increased funding for bioinformatics is compelling .
There are significant challenges and opportunities in furthering the development of bioinformatics in Jordan . The good news is that the current challenges of limited educational opportunities , relatively little attention from Jordanian funding agencies for bioinformatics , and few conferences that bring visitors who are leaders in bioinformatics into Jordan are all relatively easy to remedy . The strong support for biomedical research , coupled with the essential role of bioinformatics in many areas of biomedicine , bodes well for strengthening bioinformatics research and education in the near term in Jordan .
|
Bioinformatics is an important multidisciplinary field for many life sciences . Mathematics , statistics , computer science , and the life sciences join together to breathe life into the discipline of bioinformatics . In Jordan , rapid progress in life science , research , and healthcare have created a demand for the bioinformatics field . Because of the importance of bioinformatics and the limited attention to it in Jordan , a need arises to convince stakeholders about supporting this field in the country . We discuss the current status of bioinformatics efforts in education and research in Jordan and investigate the challenges affecting the development of bioinformatics in the country . We present future directions and the opportunities for further development of the discipline . This work is a core step towards a capacity-building plan to attract bioinformatics experts to support bioinformatics research and education in the near term in Jordan . We believe that the current challenges of limited educational opportunities , relatively little attention from Jordanian funding agencies for bioinformatics , and few conferences that bring visitors who are leaders in bioinformatics into Jordan demand focused attention to remedy .
|
[
"Abstract",
"Introduction",
"Current",
"status",
"Challenges",
"to",
"the",
"growth",
"of",
"bioinformatics",
"in",
"Jordan",
"Future",
"directions",
"Conclusion"
] |
[
"jordan",
"geographical",
"locations",
"database",
"searching",
"scientists",
"genome",
"analysis",
"science",
"and",
"technology",
"workforce",
"research",
"and",
"analysis",
"methods",
"perspective",
"sequence",
"analysis",
"computer",
"and",
"information",
"sciences",
"genomics",
"sequence",
"alignment",
"bioinformatics",
"people",
"and",
"places",
"professions",
"asia",
"science",
"policy",
"careers",
"in",
"research",
"database",
"and",
"informatics",
"methods",
"computer",
"software",
"genetics",
"population",
"groupings",
"biology",
"and",
"life",
"sciences",
"computational",
"biology"
] |
2019
|
Bioinformatics in Jordan: Status, challenges, and future directions
|
Myelodysplastic syndromes ( MDS ) are triggered by an aberrant hematopoietic stem cell ( HSC ) . It is , however , unclear how this clone interferes with physiologic blood formation . In this study , we followed the hypothesis that the MDS clone impinges on feedback signals for self-renewal and differentiation and thereby suppresses normal hematopoiesis . Based on the theory that the MDS clone affects feedback signals for self-renewal and differentiation and hence suppresses normal hematopoiesis , we have developed a mathematical model to simulate different modifications in MDS-initiating cells and systemic feedback signals during disease development . These simulations revealed that the disease initiating cells must have higher self-renewal rates than normal HSCs to outcompete normal hematopoiesis . We assumed that self-renewal is the default pathway of stem and progenitor cells which is down-regulated by an increasing number of primitive cells in the bone marrow niche – including the premature MDS cells . Furthermore , the proliferative signal is up-regulated by cytopenia . Overall , our model is compatible with clinically observed MDS development , even though a single mutation scenario is unlikely for real disease progression which is usually associated with complex clonal hierarchy . For experimental validation of systemic feedback signals , we analyzed the impact of MDS patient derived serum on hematopoietic progenitor cells in vitro: in fact , MDS serum slightly increased proliferation , whereas maintenance of primitive phenotype was reduced . However , MDS serum did not significantly affect colony forming unit ( CFU ) frequencies indicating that regulation of self-renewal may involve local signals from the niche . Taken together , we suggest that initial mutations in MDS particularly favor aberrant high self-renewal rates . Accumulation of primitive MDS cells in the bone marrow then interferes with feedback signals for normal hematopoiesis – which then results in cytopenia .
Myelodysplastic syndromes are clonal disorders which are characterized by ineffective hematopoiesis , peripheral cytopenia and a high risk of disease progression towards acute myeloid leukemia ( AML ) [1]–[3] . They arise from an aberrant HSC that gains growth advantage over normal hematopoiesis resulting in clonal expansion [4] , [5] . The pathogenesis of this disease is still unclear , and no curative treatment has been developed with the exception of stem cell transplantation [6]–[8] . So far , research has particularly focused on cell-intrinsic modification of MDS cells: mutations and molecular aberrations have been identified which seem to increase proliferation of the malignant clone [9] , [10] . On the other hand , defects might also emerge as a result of an abnormal microenvironment [11]–[13] . Mesenchymal stromal cells ( MSCs ) show intrinsic growth deficiency in MDS [14] and fail to support hematopoiesis [13] . It has been suggested that MDS is also associated with increased apoptosis rates of normal bone marrow cells [3] , [15] . So far , the mechanisms that suppress normal hematopoiesis remain unclear , as there is no evidence that the bone marrow niche is completely filled by the malignant clone [4] , [16] . Self-renewal and differentiation of HSCs need to be tightly controlled according to the physiological needs [17] . For this purpose , feedback signals may either be derived from the immediate bone marrow microenvironment or by systemically released factors . The highest self-renewal rate is expected for the long-term repopulating HSCs ( LT-HSCs ) which predominantly remain dormant under steady-state conditions [18] . Yet , self-renewal and differentiation are also prerequisites of short-term repopulating stem cells ( ST-HSCs ) , multipotent progenitor cells ( MPPs ) , committed progenitor cells ( CPCs ) and precursors [19] , [20] . In analogy , cells derived from the aberrant MDS clone may also display a hierarchy of self-renewal and differentiation: this is in line with the concept of cancer stem cells – or tumor initiating cells – which then reveal further differentiation and heterogeneity [21] , [22] . It is generally anticipated that proliferation rates are higher in malignant cells . On the other hand , several mutations seem to affect the self-renewal in MDS [23] , [24] – yet , this is difficult to study under in vivo conditions . Mathematical modeling is a powerful tool to study interaction of different cell types and the impact of feedback signals [18] , [25] , [26] . Based on the biological context several models have been proposed to study the impact of feedback signals on system stability and regenerative properties . Theoretical and experimental studies on the olfactory epithelium [27] , [28] as well as theoretical considerations of self-renewing cell lineages [29] demonstrate the necessity of feedback signals for system stability and efficient regeneration . We have recently proposed mathematical models describing activation of the HSC-pool upon hematopoietic stem cell transplantation ( HSCT ) . These models indicated that feedback signals for self-renewal and proliferation are important . In particular , the increased self-renewal rates of immature cells facilitate efficient hematopoietic reconstitution [18] , [30] . Similar results have been obtained for the olfactory epithelium [27] . Subsequently , we have shown that patient serum obtained during aplasia after HSCT has impact on hematopoietic progenitor cells ( HPCs ) in vitro: it significantly increased proliferation , maintenance of the primitive immunophenotype and expansion of colony forming units ( CFUs ) [31] . These findings supported the notion that systemically released factors contribute to regulation of stem cell function . In the current work , we conceived a mathematical model to simulate development of MDS with particular focus on self-renewal and proliferation of the aberrant clone . MDS is a very heterogeneous disease . Furthermore , multiple mutations contribute to a complex clonal hierarchy during disease progression and many parameters are so far not well defined in specific cellular subpopulations . In this regard , we aimed for a conceptional approximation how the malignant clone interferes with normal hematopoiesis – irrespective of specific MDS subtypes or hematopoietic cell lineages as well as multiple mutation scenarios . Existence of the proposed feedback signals was then substantiated using serum of MDS patients .
The use of all human materials was performed after written consent and according to the guidelines approved by the local Ethic Committees: CD34+ cells were isolated from umbilical cord blood ( CB; Permit Number: EK187/08; RWTH Aachen University ) ; CD34+ cells and MSCs were also isolated from bone marrow ( BM ) during surgical intervention ( Permit Numbers: EK300/13 and EK128/09; RWTH Aachen University ) ; and serum from MDS patients or healthy controls was collected in Düsseldorf and Aachen , respectively ( Permit numbers: 2972 and EK206/09 ) . The mathematical model developed in this study considers interaction of normal hematopoiesis and myelodysplastic cells in the bone marrow . It is based on a previously proposed model of the hematopoietic system [18] that was extended to describe dynamics of aberrant clones , as in MDS development [26] . The model is based on a system of ordinary differential equations describing the flux of cells through different maturation stages for both normal and malignant cells . The structure of the model is depicted in Figure 1 and a detailed description of the model is given in Text S1 . CD34+ cells were isolated from fresh umbilical cord blood using the human CD34 Micro Bead Kit ( Miltenyi Biotec GmbH , Bergisch-Gladbach , Germany ) as described before [31] . Alternatively , CD34+ cells were isolated from human bone marrow aspirate from the femur obtained during orthopaedic surgery . MSCs were isolated from the caput femoris and cultured as described before [32] , [33] . For co-culture experiments , we have used MSCs of passage 3 to 6 ( 10–15 population doublings ) . Serum samples from 57 MDS patients and 5 healthy controls were obtained from the Department of Hematology of Heinrich Heine University in Düsseldorf . Additionally , serum of 12 healthy controls was obtained from the Department of Gynaecology at RWTH Aachen University . Generation of serum was performed as described in detail before [31] . Relevant patient data are summarized in Table 1 in Text S1 . Hematopoietic progenitor cells were expanded for up to seven days as described previously [34] in StemSpan culture medium supplemented with 10 ng/mL stem cell factor ( SCF; PeproTech GmbH , Hamburg , Germany ) , 20 ng/mL thrombopoietin ( TPO; PeproTech ) , 10 ng/mL fibroblast growth factor 1 ( FGF-1; PeproTech ) and 10 µg/mL heparin ( Roche GmbH , Mannheim , Germany ) [32] . For co-culture experiments , addition of cytokines was not performed as MSCs alone activate proliferation . Culture medium was always supplemented with 10% serum of individual MDS patients or control samples as described in our previous work [31] . Freshly isolated CD34+ cells ( either from CB or BM ) were labelled with carboxyfluorescein diacetate N-succinimidyl ester ( CFSE; Sigma-Aldrich , Hamburg , Germany ) to monitor cell divisions as previously described [34] . After five days , CFSE intensity was measured by flow cytometry . For immunophenotypic analysis , cells were stained with CD34-allophycocyanin , CD133-phycoerythrin and CD45-V500 and analyzed using a FACS Canto II ( BD ) [32] . Further details on immunophenotypic analysis are provided in Text S1 . Colony forming unit ( CFU ) frequency was determined to estimate culture expansion on HPCs . In brief , 12 , 500 CD34+ cells were grown for seven days in StemSpan medium supplemented with SCF , TPO , FGF , heparin and 10% patient serum . The progeny was harvested and analyzed in the CFU-assay as described before [31] . Concentrations of SCF , TPO and FGF in patient serum were determined with RayBio Human ELISA Kits ( RayBiotec , Norcross , GA , USA ) according to the manufacturer's instructions . Concentration of erythropoietin ( EPO ) was measured by the laboratory diagnostic center of RWTH Aachen University with a chemoluminescent-immunometric assay ( IMMULITE 1000 EPO ) . All results are expressed as mean ± standard deviation ( SD ) or ± standard error of the mean ( SEM ) . To estimate the probability of differences , we have adopted the two-sided Student's T-test . Probability value of p<0 . 05 denoted statistical significance .
We propose a mathematical model to address the relevance of self-renewal and proliferation rates for MDS development . The model describes interaction of 1 ) normal hematopoietic cells , which progress along long-term repopulating stem cells ( LT-HSCs ) , short-term repopulating stem cells ( ST-HSCs ) , multipotent progenitor cells ( MPPs ) , committed progenitor cells ( CPCs ) , precursors and mature cells ( Figure 1A ) , with 2 ) cells of the MDS clone which progress through analogous steps of differentiation except for mature cells ( MDS-LT-HSCs , MDS-ST-HSCs , MDS-MPPs , MDS-CPCs and dysplastic precursors; Figure 1B ) . We assume that proliferation is regulated in normal and malignant cells by feedback signals acting on all developmental stages - it is inversely correlated with the number of mature cells in peripheral blood ( PB ) . On the other hand , we assume that self-renewal is regulated by cellular density in a virtual stem cell niche occupied exclusively by the more primitive cells in the marrow – it is inversely correlated with the number of cells in the three more primitive compartments ( LT-HSCs , ST-HSCs , MPPs , MDS-LT-HSCs , MDS-ST-HSCs , and MDS-MPPs; Figure 1C ) . A wide range of values of each parameter has been examined . The simulations consistently demonstrate that high self-renewal of MDS-initiating cells is crucial for MDS development . Only if MDS-LT-HSCs have a higher self-renewal potential than normal LT-HSCs , they eventually outcompete healthy hematopoiesis . In contrary , increased proliferation of MDS-cells alone is not sufficient . Notably , we have assumed that the proliferation rate of MDS-HSCs is lower than in normal LT-HSCs ( maximal cell division rate every 100 versus every 50 days , respectively ) - even then the MDS clone gains predominance if the self-renewal rate is higher than in normal LT-HSCs ( maximal self-renewal rate 90% versus 70% , respectively ) . Nevertheless , high proliferation rates in MDS cells - although not required for establishment of the disease – would accelerate expansion of a cell population if self-renewal is also increased . Our results indicate that increased self-renewal is most essential for MDS , whereas an additional increase of proliferation accelerates the impairment of hematopoiesis . MDS is usually a slow progressive disease which occurs particularly in elderly people . Simulated examples with input parameters derived from our previous work [18] , [35] demonstrated that clonally derived MDS cells may increase over approximately 15 to 17 years without clinically relevant changes in bone marrow or blood counts ( Figure 2A ) . After 17 years , the BM will contain about 1 . 66×10−6% MDS-LT-HSCs , 0 . 39% MDS-ST-HSCs , 1 . 12% MDS-MPPs , 5 . 38% MDS-CPCs and 8 . 15% dysplastic progenitors . Then , within few years , the number of mature cells in PB drops significantly ( Figure 2B ) . Correspondingly , the percentage of normal hematopoietic cells in the bone marrow declines ( Figure 2C ) . The simulated dynamics of disease development are as follows: 1 ) Initially , a single MDS cell expands very slowly due to higher self-renewal compared to normal LT-HSCs . 2 ) Consequently , the number of cells in the bone marrow niche increases which leads , via feedback signaling , to reduced self-renewal of cells in the niche . 3 ) This indirectly results in suppression of normal hematopoiesis and cytopenia . 4 ) The low number of mature cells triggers proliferation of normal and malignant cells and thereby enhances disease progression ( Figure 2D ) . In this model , we consider apoptosis rates of mature cells and of dysplastic progenitors only . However , due to the increasing number of dysplastic precursors which die within 10 days , the percentage of apoptotic cells in the bone marrow increases to 5 . 6% ( under the assumption that apoptosis takes 24 h; Figure 2E ) . Alternatively , we modeled MDS including further maturation of MDS cells and high apoptosis on the level of committed progenitors . We assume that MDS derived mature cells have higher apoptosis rates than normal mature cells ( half-life time only 16 h ) , which is in line with higher apoptosis rates in bone marrow and peripheral blood observed in MDS patients [3] , [15] , [36] . These simulations lead to qualitatively similar results: in all cases , enhanced self-renewal of disease initiating cells is crucial for establishment of the disease . This indicates that increased apoptosis is compatible - but not required - for MDS development in our approach ( Figure 1 in Text S1 ) . The percentage of CD34+ cells in healthy bone marrow , low-risk MDS , and high-risk MDS was 1 . 4±0 . 2% , 3 . 4±0 . 7% and 7 . 8±1 . 9% , respectively ( Figure 2 in Text S1 ) . In our model , we assume that LT-HSCs , ST-HSCs , and MPPs , as well as MDS-LT-HSCs , MDS-ST-HSCs , and MDS-MPPs correspond to CD34+ cells – they are not pure stem cell fractions but they are all influenced by the self-renewal signal . The percentage of primitive cells is compatible with dynamics of the mathematical model , but it rapidly increases over time . It has been previously suggested that the percentage of blasts , defined as CD117+ or CD34+ cells , has prognostic value for survival [2] , [37] . In this regard , it might be speculated that high-risk MDS is characterized by higher cell-intrinsic self-renewal . Based on our mathematical model , we assumed that serum of MDS might comprise signaling molecules related to the systemic feedback which stimulate proliferation of CD34+ cells . These cells can be expanded in vitro – particularly if co-cultured with MSCs – but this is associated with further loss of stemness ( Figure 3 in Text S1 ) . We isolated serum of 57 MDS patients and 12 healthy controls . CB-derived CD34+ cells were then stained with CFSE and cultured in parallel with culture media supplemented with 10% of individual serum samples . After five days , the cells were analyzed by flow cytometry ( Figure 3A ) . Overall , proliferation rate of CD34+ cells , and hence dilution of CFSE , was significantly higher in MDS serum ( p = 0 . 007 ) . When we subdivided MDS patients into high risk ( sAML , RAEBI and RAEBII ) , low risk ( RCMD and RCMD-RS ) , CMML I , and 5q chromosomal deletion increased proliferation was particularly observed using serum of low-risk MDS ( p = 0 . 041; Figure 3B ) . These results were reproduced with all patient sera using HPCs of three different cord blood samples . Especially serum derived from leukopenic and anemic patients enhanced proliferation of HPCs ( p = 0 . 05 and p = 0 . 004 , respectively ) , whereas this trend was less pronounced with serum from thrombopenic patients ( Figure 4 ) . However , under co-culture conditions with MSCs , the growth-supporting effect of MDS serum was obscured by the overall growth-stimulation of stromal cells , even though we did not use cytokines in these experiments ( Figure 4 in Text S1 ) . MDS is rather observed in elderly patients and it is conceivable that age-matched HPCs respond differently to feedback signals . Therefore , we performed two additional experiments with HPCs from adult bone marrow using all patient sera . In analogy to our results with CB-derived HPCs , BM-derived CD34+ cells revealed significantly higher proliferation if stimulated with serum from MDS-patients ( del ( 5q ) : p = 0 . 0005; high-risk MDS: p = 0 . 0007; low-risk MDS: p = 0 . 0019; Figure 5 in Text S1 ) . Overall , the results support the notion that the number of mature cells is inversely correlated with the proliferative effect of patient serum - which is in agreement with our model . Computer simulations demonstrated that our mathematical model recapitulates clinical observations under the assumption that the feedback signal for self-renewal decays if malignant cells accumulate in the stem cell niche . Therefore , we reasoned that MDS patient serum might also impair maintenance of the primitive immunophenotype in vitro . To this end , we have only measured expression of CD34 and CD133 in CB-HPCs which underwent five cell divisions to exclude bias by proliferation . In fact , CD34 and CD133 expression was moderately decreased with MDS serum ( Figure 3B ) . In contrast , expression of CD45 was not influenced by MDS serum . A similar effect was also observed using BM-HPCs ( Figure 5 in Text S1 ) . Although effects of MDS serum on immunophenotype were rather moderate , they are in agreement with the proposed decrease of the self-renewal signal . The impact of MDS serum was further analyzed with regard to maintenance of colony forming units ( CFUs ) : CD34+ CB-HPCs were cultured in vitro for 7 days , and this was performed in parallel with medium supplemented with 10% of individual serum samples . The cells were then reseeded in methylcellulose medium and 12 to 14 days later , colony types and numbers were detected . Comparing MDS serum and control serum , no significant differences were found in colony-initiating cells ( CFU-G , CFU-M , CFU-GM , and CFU-GEMM ) . Only the number of erythroid colonies ( BFU-E and CFU-E ) was significantly increased when exposed to serum of MDS patients ( p = 0 . 004 and p = 0 . 02 respectively; Figure 5A ) . Thus , colony assays provide no support for the presence of circulating factors in MDS patient serum that increase colony formation initiated by the most primitive hematopoietic progenitors . The proposed feedback signals may involve growth factors . Therefore , we have analyzed serum levels of stem cell factor ( SCF ) , thrombopoietin ( TPO ) and fibroblast growth factor ( FGF ) which support expansion of CD34+ cells in vitro [32] , and of erythropoietin ( EPO ) which stimulates hematopoietic differentiation . Concentrations of SCF , TPO and FGF were higher in MDS serum than in control serum , but this trend did not reach statistical significance ( Figure 6 in Text S1 ) . However , the EPO-concentration was significantly higher in MDS patient serum and this is in line with previous reports ( Figure 5B ) [12] , [38] .
Our study indicates that increased self-renewal of MDS-initiating cells is the most critical parameter to initiate MDS development . This may also explain why the disease seems to be stem cell derived as stem cells already reveal relatively high self-renewal rates . The central question in this process is the nature of feedback signals regulating hematopoiesis . Our models suggest that cure of MDS would only be achieved if the self-renewal rate can be specifically down-regulated in the malignant cells - particularly in the tumor-initiating MDS-LT-HSCs . Therefore , better understanding of the MDS-niche interaction is crucial to identify new therapeutic targets .
|
Myelodysplastic syndromes are diseases which are characterized by ineffective blood formation . There is accumulating evidence that they are caused by an aberrant hematopoietic stem cell . However , it is yet unclear how this malignant clone suppresses normal hematopoiesis . To this end , we generated mathematical models under the assumption that feedback signals regulate self-renewal and proliferation of normal and diseased stem cells . The simulations demonstrate that the malignant cells must have particularly higher self-renewal rates than normal stem cells – rather than higher proliferation rates . On the other hand , down-regulation of self-renewal by the increasing number of malignant cells in the bone marrow niche can explain impairment of normal blood formation . In fact , we show that serum of patients with myelodysplastic syndrome , as compared to serum of healthy donors , stimulates proliferation and moderately impacts on maintenance of hematopoietic stem and progenitor cells in vitro . Thus , aberrant high self-renewal rates of the malignant clone seem to initiate disease development; suppression of normal blood formation is then caused by a rebound effect of feedback signals which down-regulate self-renewal of normal stem and progenitor cells as well .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"cell",
"cycle",
"and",
"cell",
"division",
"cell",
"processes",
"hematologic",
"cancers",
"and",
"related",
"disorders",
"mathematical",
"computing",
"mathematics",
"stem",
"cells",
"cell",
"growth",
"mesenchymal",
"stem",
"cells",
"adult",
"stem",
"cells",
"computer",
"and",
"information",
"sciences",
"cell",
"potency",
"animal",
"cells",
"stem",
"cell",
"niche",
"computing",
"methods",
"myelodysplastic",
"syndromes",
"hematopoietic",
"progenitor",
"cells",
"hematology",
"hematopoietic",
"stem",
"cells",
"cell",
"biology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"molecular",
"cell",
"biology",
"hematopoiesis"
] |
2014
|
Feedback Signals in Myelodysplastic Syndromes: Increased Self-Renewal of the Malignant Clone Suppresses Normal Hematopoiesis
|
Tuning curves characterizing the response selectivities of biological neurons can exhibit large degrees of irregularity and diversity across neurons . Theoretical network models that feature heterogeneous cell populations or partially random connectivity also give rise to diverse tuning curves . Empirical tuning curve distributions can thus be utilized to make model-based inferences about the statistics of single-cell parameters and network connectivity . However , a general framework for such an inference or fitting procedure is lacking . We address this problem by proposing to view mechanistic network models as implicit generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves . A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable . Recent advances in machine learning provide ways for fitting implicit generative models without the need to evaluate the likelihood and its gradient . Generative Adversarial Networks ( GANs ) provide one such framework which has been successful in traditional machine learning tasks . We apply this approach in two separate experiments , showing how GANs can be used to fit commonly used mechanistic circuit models in theoretical neuroscience to datasets of tuning curves . This fitting procedure avoids the computationally expensive step of inferring latent variables , such as the biophysical parameters of , or synaptic connections between , particular recorded cells . Instead , it directly learns generalizable model parameters characterizing the network’s statistical structure such as the statistics of strength and spatial range of connections between different cell types . Another strength of this approach is that it fits the joint high-dimensional distribution of tuning curves , instead of matching a few summary statistics picked a priori by the user , resulting in a more accurate inference of circuit properties . More generally , this framework opens the door to direct model-based inference of circuit structure from data beyond single-cell tuning curves , such as simultaneous population recordings .
Neural responses in many brain areas are tuned to external parameters such as stimulus- or movement-related features . Tuning curves characterize the dependence of neural responses on such parameters , and are a key descriptive tool in neuroscience . Experimentally measured tuning curves often exhibit a rich and bewildering diversity across neurons in the same brain area , which complicates simple understanding ( e . g . , see [1–4] ) . This complexity has given rise to a tendency towards biased selections of minorities of cells which exhibit pure selectivites , and have orderly and easily interpretable tuning curves . As a result the biological richness and diversity of tuning curves in the full neural population is often artificially reduced or ignored . On the theoretical side too , many network models feature homogeneous populations of cells with the same cellular parameters and with regular synaptic connectivity patterns . Neural tuning curves in such models will naturally be regular and have identical shapes . New theoretical advances , however , have highlighted the computational importance of diverse tuning and mixed selectivity , as observed in biological systems [3 , 5] . Furthermore , diversity and heterogeneity can be produced in mechanistic network models which either include cell populations with heterogeneous single-cell parameters ( see e . g . , Ref . [2] ) , or connectivity that is partly random and irregular despite having statistical structure and regularity ( see , e . g . , Ref . [4–10] ) . However , a general effective methodology for fitting such models to experimental data , such as heterogeneous samples of biological tuning curves is lacking . A related central problem in neural data analysis is that of inferring functional and synaptic connectivity from neural responses and correlations . A rich literature has addressed this problem [11–16] . However , we see two shortcomings in previous approaches . First , most methods are based on forward models originally developed in statistics that are primarily inspired by their ease of optimization and fitting to data , rather than purely by theoretical or biological principles . Second , in the vast majority of approaches , the outcome is the estimate of the particular connectivity matrix between the particular subset of neurons sampled and simultaneously recorded in a specific animal [11–16] . Post-hoc analyses may then be applied to such estimates to characterize various statistical properties and regularities of connectivity [12 , 16] . However , such statistical properties are , in most cases , the object of scientific interest , as they generalize beyond the specific recorded sample . Examples of such statistical properties are the dependence of connection probability between neurons on their physical distance [17] or preferred stimulus features [18] . Another example is the degree to which neuron pairs tend to be connected bidirectionally beyond chance [19] . A methodology for model-based inference of such circuit properties directly from simultaneously or non-simultaneously recorded neural responses is lacking . Here we propose a methodology that is able to fit theoretically motivated circuit models to recorded neural responses , and infer model parameters that characterize the statistics of connectivity or of single-cell properties . Conceptually , we propose to view network models with heterogeneity and random connectivity as generative models for the observed neural data , e . g . , a model that generates diverse tuning curves and hence implicitly models their ( high-dimensional ) distribution . The generative model is determined by a set of network parameters which specify the distribution of structural circuit variables like individual synaptic connections or single-cell biophysical properties . In this picture , the particular realization of the connectivity matrix or of biological properties of particular neurons are viewed as latent variables . Traditional , likelihood-based approaches such as expectation-maximization or related approaches need to fit or marginalize out ( e . g . , using variational or Monte Carlo sampling methods ) such latent variables , conditioned on the particular observed data sample . Such high-dimensional optimizations or integrations are computationally very expensive and often intractable . Alternatively , one could fit theoretical circuit models by approaches similar to moment matching , or its Bayesian counterpart , Approximate Bayesian Computation [20 , 21] . In such approaches , one a priori comes up with a few summary statistics , perhaps motivated on theoretical grounds , which characterize the data objects ( e . g . , tuning curves ) . Then one tunes ( or in the Bayesian case , samples ) the model parameters ( but not latent variables ) so that the few selected summary statistics are approximately matched between generated tuning curve samples and experimental ones [4] . This approach will , however , generally be biased by the a priori choice of the fit summary statistics , and does not exploit all the information available in the data for inferring circuit properties . A suite of new methods have recently been developed in machine learning for fitting implicit generative models [22–24] , i . e . , generative models for which a closed or tractable expression for the likelihood or its gradient is not available . Here , we will demonstrate that a specific class of such methods , namely Generative Adversarial Networks ( GANs ) [23 , 25 , 26] , can address the above problems . In particular , compared to methods such as moment matching , our proposed approach fits the entire high-dimensional data distribution in a much more unbiased and data-driven manner and without the need to choose a few summary statistics a priori . As we will show , this results in a more accurate and robust inference of circuit properties . In addition to inferring circuit parameters , this approach also allows for a more unbiased model comparison: one can simply simulate the competing circuit models , after fitting them to training data , and compare their goodness of fit , possibly to unseen data including new stimulus conditions not covered in the tuning curves used for training . The rest of this article is organized as follows . We start the Results section by introducing the conceptual view of circuit models as implicit generative models for tuning curves . We then introduce the GAN framework . Next , we present the results of applying GANs to fit and infer the parameters of two recent influential circuit models from theoretical neuroscience . In Experiment 1 we fit a feedforward model of motor cortex to an experimental dataset of hand-position tuning curves , and compare the match between the distributions of empirical tuning curves and those generated by the models fit using our proposed method and traditional moment matching . In Experiments 2 and 3 we apply the method to fit a recurrent network model of visual cortex to a simulated dataset of stimulus-size tuning curves generated by a ground-truth circuit model . In Experiment 2 we show that the fit model captures the statistics of observed tuning curves very accurately . In Experiment 3 , we assess the accuracy of circuit parameter identification using our proposed method , and discuss factors ( such as the kind of tuning curves used for inference ) that affect it . In the Discussion we conclude by discussing areas for extension and improvement of our proposed methodology , and broader potential applications of it . The details of our experiments , algorithms , and the models are given in Materials and methods; the source code for all implemented examples is available from https://github . com/ahmadianlab/tc-gan under the MIT license .
We consider mechanistic network models of the type developed in theoretical neuroscience , informed by knowledge of biological mechanisms and network anatomy , or by computational principles . We limit ourselves to networks evolving according to feedforward or recurrent firing rate equations ( examples include Ref . [2 , 4–10 , 27 , 28] ) , although the methodology is extendable to spiking networks as well ( possibly with slight modifications to ensure differentiability ) . In the general presentation of this subsection we focus on recurrent rate networks . Abstractly , the neural responses in such networks evolve according to a dynamical system that has the following general structure ( for a concrete example see the model in Experiment 2 below , which is governed by Eq ( 5 ) ) : d v t d t = F ( v t ; I , W , γ ) . ( 1 ) Here , vt is the vector of the state variables of the network’s neurons at time t ( components of vt may , e . g . , include the firing rate , membrane voltage and other “fast” state variables of individual neurons or synapses ) and F is a vector field on state space which is differentiable with respect to its arguments . The matrix W is the partially disordered synaptic connectivity matrix ( which can include both the recurrent , as well as feedforward external connections ) , and γ is the vector of possibly heterogeneous single-cell biophysical constants ( e . g . , membrane time-constant , spiking threshold potential , parameters of input-output nonlinearity , etc . ) . Finally , the vector I is the external input to the network which can represent stimuli or state-dependent modulators; we let I depend on a discrete index variable s denoting the stimulus or experimental condition ( or discretized parameter ) . I ( s ) can in general be time-dependent , but here we assume it is stationary for simplicity . We also assume that I ( s ) is deterministic , and that all quenched randomness in network structure is captured by W and γ . In order to exploit tuning curve datasets to constrain mechanistic network models , and in the process make model-based inferences about circuit properties , we propose to view network models of the above type as generative models for tuning curves . This view , which we will expound below , is graphically summarized in Fig 1 . ( Even though here we take trial-averaged tuning curves as the ultimate functional output of the model , this is not central to our proposed view; as discussed at the end of the Discussion , other quantities characterizing neural activity such as pairwise correlations or higher-order population statistics can augment or replace tuning curves in general applications . ) Given a choice of the non-dynamical variables W , γ and I ( s ) , and the initial condition v ( t = 0 ) ( t = 0 can , e . g . , be the stimulus onset time ) , the network dynamics can be simulated to compute the full temporal trajectory of all neural state variables . From this simulated trajectory , the average response , r ¯ ( s ) , of a designated “probe” neuron in the network during a given “response interval” can be calculated in each condition s ( this is the time-average of a certain component of vt for t in the response interval ) . The vector x ≡ ( r ¯ ( 1 ) , ⋯ , r ¯ ( S ) ) is then the tuning curve of that neuron , containing its responses in S different stimulus conditions which we assume are present in the training data . ( Note that once a network model is trained , it can be applied to stimuli other than those used for training . ) Thus , for networks with deterministic dynamics considered here , there is a deterministic mapping between the tuple of network’s structural variables , ( W , γ ) , and the tuning curve of a given network neuron ( we assume fixed initial conditions , vt=0 , or otherwise ignore the dependence of output on them ) . We call this mapping f ( see Fig 1 ) . Note that γ and W typically have very large dimensions; for a network of N neurons , γ and W have on the order of N and N2 components , respectively . In the proposed methodology , these large sets of structural and physiological network constants should be viewed as latent variables rather than model parameters that are fit to data . We are interested in cases in which these heterogeneous high-dimensional vectors are sampled from statistical ensembles ( distributions ) that capture the structured regularities as well as disordered randomness in single-cell properties and network connectivity . Consider a statistical ensemble described by a parameterized distribution P θ ( W , γ ) . Through the deterministic map , f , between ( W , γ ) and x , this distribution in turn induces a distribution , P θ ( x ) , over the tuning curve x , which is also parameterized by θ . The model’s parameter vector , θ , which is typically low-dimensional ( see the examples in Experiment 1–3 subsections below ) , determines the network’s statistical structure and constitutes the parameters that we would like to fit to data . Components of θ can control , e . g . , the average strength or spatial range of synaptic connections , or the mean and dispersion of biophysical single-cell properties . Traditional likelihood-based methods infer the circuit properties , encapsulated in θ , by maximizing the likelihood function P θ ( ( x i ) i = 1 N ) ≈ ∏ i P θ ( x i ) given a dataset of tuning curves ( x i ) i = 1 N . However , for cases of interest , the mapping f is typically very complex and practically cannot be inverted ( even though it can be relatively cheaply simulated in the forward direction ) ; therefore in practice P θ ( x ) cannot be computed explicitly . Moreover , most likelihood-based methods are based on expectation-maximization-like algorithms; in the expectation step , such algorithms have to infer the high-dimensional latent variables ( W , γ ) , which is a highly expensive computation . In the next subsection we discuss how recently developed methods in machine learning , in particular generative adversarial networks , can be used to fit generative models of the above type , for which the parametrized data distribution , P θ ( x ) , is only implicitly defined . All that is required in those approaches is the generative process that , given a random seed , generates a tuning curve ( or a set of tuning curves ) , via a function that is differentiable with respect to θ . More formally , in such frameworks the generative model , or the “generator” , is characterized by a parametrized function Gθ which , given an input vector , z , of random noise variables that have a fixed or standard distribution , outputs a tuning curve x = Gθ ( z ) . This is almost identical to the case of mechanistic circuit models described above , with two technical differences . First , the generative network process , as described above and in Fig 1 , is captured by the function f which does not directly depend on the model parameters , θ . Instead , it is the inputs to this function , namely ( W , γ ) , which implicitly depend on θ , as they are sampled from P θ ( w , γ ) . Thus the second difference is that the inputs to f , i . e . , the network’s structural variables ( W , γ ) , do not have a fixed standard distribution , but rather a distribution dependent on θ . However , we can use the so-called “reparametrization trick” [22] , to remedy this mismatch and cast the circuit-model-based generative processes of interest to us in the required form . To this end , we will formulate the sampling of ( W , γ ) from their statistical ensemble via a deterministic function or mapping , gθ , parametrized by θ , that receives the fixed-distribution noise variables z as input . For example , a synpatic weight , w , with a gaussian distribution parametrized by its mean , μ , and variance , σ2 , can be generated by the function gθ ( z ) ≡ μ + σz where z is sampled from the standard ( zero-mean , unit-variance ) normal distribution , and θ = ( μ , σ ) in this case . We provide biologically relevant examples of gθ in Materials and methods ( see Eqs ( 17 ) – ( 19 ) , ( 24 ) and ( 26 ) ) . Note that in the typical application of interest to us , while θ is low-dimensional , z has high dimensionality , on the order of the dimensions of ( W , γ ) . The full generator function Gθ is then simply the composition of f and gθ: Gθ ( z ) ≡ f ( gθ ( z ) ) ( see Fig 1B ) . In other words , first the standard noise variables z and network parameters θ together determine the full particular realization of network structure . The network is then simulated and a tuning curve ( or a set of tuning curves ) is generated . The function f is typically differentiable , and for many statistical ensembles of interest the function gθ is ( or can be closely approximated by a ) function that is differentiable with respect to θ . Then Gθ will also be differentiable in θ . As we describe in the rest of the article , with this formulation , we can use methods like generative adversarial networks to fit mechanistic neuronal circuit models to datasets of tuning curves . Generative Adversarial Networks ( GANs ) are a framework for training generative models developed by the deep learning community [23 , 29 , 30] . The GAN approach is powerful because it is applicable to implicit generative models , e . g . , the mechanistic networks discussed in the previous subsection , for which evaluating the likelihood function or its gradient is intractable . Another advantage of GANs ( in the context of the previous subsection ) is that , unlike typical likelihood-based methods , they fit the model parameters θ directly , skipping the computationally costly step of inferring the high-dimensional latent variables , namely the particular realization of network connectivity matrix W or single-cell constants γ . Note that unlike the particular realization of the connectivity matrix between the experimentally sampled cells , the model parameters θ , which characterize the statistics of connectivity and single-cell properties , are generalizable and of direct scientific interest . All that is required in the GAN approach is a generative process that , given a random seed , generates a sample data object , via a function that is differentiable with respect to θ . While in machine learning applications the generated data object is often an image ( and the generative model is a model of natural images ) , in our case it will be a tuning curve , formalized in the Introduction as the vector x ∈ R S containing the trial-averaged responses of a probed network neuron in S different experimental conditions ( e . g . , S different values of a stimulus parameter ) . In a GAN there are two networks: a “generator” and a “discriminator” . The generator implements the generative model and generates sample data objects , while the discriminator ( which can be a classifier ) distinguishes true empirical data objects from “fake” ones generated by the generative model . Conceptually , the discriminator and generator compete: the discriminator is trained to better distinguish real from fake data , and the generator is trained to fool the discriminator . More formally , the generator is characterized by a parametrized function Gθ which , given an input vector of random noise variables z that have a fixed distribution , outputs a sample data object x = Gθ ( z ) . As we saw in the previous subsection , the process of generating a tuning curve by mechanistic neuronal circuit models can also be formulated in this manner ( see Fig 1 ) . In that case , the vector z provides the random seed for the generation of the full network structure ( i . e . , all synaptic weights and single-cell biophysical constants ) , given which the network is simulated to generated an output tuning curve x . We note that while in our applications the generator parameters θ usually correspond to physiological and anatomical parameters with clear mechanistic interpretations , in typical machine learning usage the structure of the GAN generator ( e . g . , a deconvolutional deep feedforward network , generating natural images ) and its parameters may have no direct mechanistic interpretation ( see Materials and methods , under “Differences with common machine learning applications” , for further discussion of this point ) . The second network in a GAN is the discriminator . Mathematically , the discriminator is a function D w , parametrized by w , that receives a data object ( in our case , a tuning curve ) as input , and outputs a scalar . D w is trained so that its output maximally discriminates between the real data samples and those generated by Gθ . The generator is in turn trained to fool the discriminator . If the discriminator network is sufficiently flexible , the only way for the generator to fool it is to generate samples that effectively have the same joint distribution as real data . When D w is differentiable in its input and parameters and Gθ is differentiable in its parameters , training can be done using gradient-based algorithms . GANs can nevertheless be difficult to train and many techniques have been developed to improve their training . In this work we employ the Wasserstein GAN ( WGAN ) approach which has shown promise in overcoming some of the shortcomings of the traditional GAN approach [25 , 26] . ( We note , however , that traditional GANs could also be used for the types of application we have in mind . ) The WGAN approach is mathematically motivated by minimizing the Wasserstein or earth mover’s distance between the empirical data distribution and the distribution of the generator output [25] . The Wasserstein distance provides a measure of similarity between distributions which ( unlike e . g . , the Kullback-Leibler divergence , which is the distance minimized in maximum-likelihood fitting ) exploits the metric or similarity structure in the data space R S . In the context of WGANs , the discriminator can be viewed as yielding a scalar measure or “summary statistic” for a ( typically high-dimensional ) data object or tuning curve . A single , fixed summary statistic D ( x ) can be used to measure the divergence between two distributions as the difference between the expectation of D ( x ) under the two distributions . However , two distributions can lead to the same average D ( x ) and yet be completely different in other respects . For example , consider the case of orientation tuning curves for visual cortical neurons . An example D is the function that receives an orientation tuning curve as input and outputs the half-width of that tuning curve ( which measures the strength of orientation tuning ) . A generative model may produce orientation tuning curves that on average are narrower ( or broader ) than the average empirical tuning curve . However , even when a model matches the data distribution of tuning curve widths , its generated tuning curves may look very different from true ones along other dimensions ( e . g . , along the average height or maximum response dimension , or in the variance of tuning widths ) . One interpretation of the WGAN methodology is that instead of looking at data objects ( tuning curves ) along a fixed dimension using a fixed scalar measure , it optimizes that measure or probe to maximally distinguish between model-generated vs . empirical data objects . ( In other flavors of GAN , the discriminator has other useful interpretations and can provide an estimate of the density of generator output in data space relative to the true data distribution; see Materials and methods , under “Alternatives to WGAN , and alternative views of GANs” . ) Let S be the class of all possible “smooth summary statistics” that can be used to characterize tuning curves; more technically , S is taken to be the set of all scalar functions of tuning curves , D : R S → R , that are Lipshitz-continuous with a Lipshitz constant less than one ( if D is differentiable , the latter condition is equivalent to constraining the gradient of D to have norm less than one everywhere ) . Interestingly , by the Kantorovich-Rubinstein duality [25 , 31] , the earth mover’s distance , d ( ρ , ν ) , between two distributions , ρ ( x ) and ν ( x ) , can be expressed as the difference between the expectations of a maximally discriminating smooth summary statistic under the two distributions: d ( ρ , ν ) = max D ∈ S | E x ∼ ρ [ D ( x ) ] - E x ∼ ν [ D ( x ) ] | . ( 2 ) In our applications , one distribution ( say ν ) is the data distribution , and the other ( say ρ ) is the output distribution of Gθ which we would like to move closer to the data distribution . This can thus be done by iterating between improving D to maximize | E x ∼ ρ [ D ( x ) ] - E x ∼ ν [ D ( x ) ] | , and improving Gθ to minimize it . To obtain a practical algorithm that in this manner approximately minimizes the earth mover’s distance , Eq ( 2 ) , the expectations over the two distributions are approximated by mini-batch sample averages , and the discriminator class S is approximated by single-output feedforward neural network ( parametrized by weight vector w ) with input-output gradients of norm less than one ( such networks form a proper subset of S; in practice , the gradient norm is only forced to be close to one ) . This gives rise to an adversarial algorithm in which the discriminator D w and the generator Gθ are trained by iteratively alternating between minimizing the following two loss functions Loss D ( w , θ ) = E z [ D w ( G θ ( z ) ) ] - E x [ D w ( x ) ] + ( Gradient Penalty ) ( 3 ) Loss G ( w , θ ) = - E z [ D w ( G θ ( z ) ) ] , ( 4 ) where Eq 3 is minimized with respect to the discriminator’s parameters ( w ) , and Eq 4 is minimized with respect to the generator’s parameters ( θ ) . Here E x and E z denote averages over a batch of empirical data samples and samples from the standard noise distribution , respectively ( thus E z [ D w ( G θ ( z ) ) ] is the same as the average of D w ( x ) when x is sampled from the generator output , instead of the empirical data distribution ) . The “Gradient Penalty” term forces the gradient of D w to be close to one ( see Materials and methods , under “Conditional Generative Adversarial Networks” , for details ) . Finally , a penalty term PenaltyG ( θ ) can be added to the generator loss , Eq ( 4 ) , as a regularization term for generator parameters . In this subsection and the next two , we illustrate the GAN-based model fitting approach by applying it to two previously published mechanistic circuit models developed in theoretical neuroscience to explain various nonlinear features of cortical responses . As our first example we take a feedforward model of primary motor cortex ( M1 ) tuning curves proposed by Ref . [4] . The tuning curves describe the response tuning of M1 neurons as a function of 3D hand position and posture ( supination or pronation ) . The model was proposed as a simple plausible mechanism for generating the significant complex nonlinear deviations of observed monkey M1 tuning curves from the classical linear model [34] . We used the “extended” model of Ref . [4] with small modifications ( see Materials and methods subsection “Feedforward Model of M1” ) . In particular , we did not model response tuning with respect to hand posture ( we did this in order to increase the size of the dataset by combining hand-position tuning curves from both posture conditions to allow for proper cross-validated testing of model fits ) . The model is a two-layer feedforward network , with the input layer putatively corresponding to the parietal reach area or to premotor cortex , and the output layer modeling M1 ( see Fig 2 ) . The input layer neurons’ activations are given by 3D Gaussian tuning curves defined on the hand position space . The receptive field centers formed a fine regular grid , but their widths varied randomly across the input layer , and were sampled independently from the uniform distribution on the range [σl , σl + δσ] ( see Fig 2A ) . The feedforward connections from the input to output layer are random and sparse , with a connection probability of 0 . 01 . In our implementation of this model , the strength of the nonzero connections were sampled independently from the uniform distribution on the range [0 , J] . The response of an output layer neuron is given by a rectified linear response function with a threshold . The thresholds were allowed to be heterogeneous across the M1 layer , and were sampled independently from the uniform distribution on the range [ϕl , ϕl + δϕ] . Thus in total the model has five trainable parameters θ = ( σl , δσ , J , ϕl , δϕ ) . Using the WGAN framework , we fit the five model parameters to a dataset of experimentally measured monkey M1 tuning curves recorded by Ref . [4] and available online . With hand-posture conditions ignored , the tuning curves in this dataset describe the trial-averaged responses of a given M1 neuron in 27 experimental conditions corresponding to the monkey holding its hand in one location out of a 3 × 3 × 3 cubic grid of 3D spatial locations . ( We ignored the hand-position label by blindly mixing hand-position tuning curves across pronation and supination conditions , as if they belonged to different neurons . ) We randomly selected half of the hand-position tuning curves ( n = 257 ) to be used for training the model , and used the other half ( n = 258 ) as held-out data to evaluate the goodness of model fits . The results showing the performance of the fit model are summarized in Fig 3 . The figure shows the data and trained model histograms for four test statistics or measures characterizing the tuning curves or responses: firing rate ( across the 27 conditions ) , coding level , i . e . , the fraction of conditions with rate significantly greater than zero ( which we took to mean larger than 5 Hz ) , the R2 or coefficient of determination of the optimal linear fit to the tuning curve , and the complexity score . Ref . [4] defined the complexity score of a tuning curve to be the standard deviation of the absolute response differences between nearest neighbor locations on the 3 × 3 × 3 lattice of hand positions ( the tuning curve was first normalized so that its responses ranged from −1 to 1 ) . The last two statistics , the R2 and the complexity score , are of particular theoretical interest , as they provide two measures for the degree of irregular nonlinearity in the tuning curve and thus deviation from the classical linear model of M1 tuning curves [34] . Therefore , Ref . [4] fit their model by matching the mean and standard deviation of these two statistics between model and M1 data . By contrast , as described in the previous subsection , the WGAN , employed here , optimizes the discriminatory statistic , D , and ( for complex enough discriminator networks ) seeks to fit the entire joint multi-dimensional ( in this case 27-dimensional ) tuning curve distribution . We measured the mismatch between the model and data distributions for each of the four test statistics using the Kolmogorov-Smirnov ( KS ) distance . As shown in Fig 3B , the goodness of fit improves fast during training . The goodness-of-the-fit of the distributions of complexity score and R2 ( Fig 3C and 3D ) are comparable to those obtained in Ref . [4] by grid search . It is also notable that the trained model fits the distribution of firing rates and coding levels ( Fig 3E and 3F ) quite well . In Ref . [4] , the authors chose to individually normalize the model and data tuning curves so that their shape , but not their overall firing rate scale , was fit to data . We did not normalize the tuning curves , but as described above added a tunable parameter , J , to the model that scales the feedforward connection strengths . We found that with this addition and without normalization , the model is actually capable of accounting for the variability of firing rates across neurons and conditions as well . In the original model of Ref . [4] the thresholds were chosen separately for each output layer neuron such that the coding level of its response matched that of a randomly selected recorded M1 neuron . By contrast , in order to have all structural variability in the model in a differentiable form , we did not fit individual thresholds , but allowed them to be randomly distributed and only fit the two parameters of their distribution , ϕl and δϕl . Even though we did not match coding levels neuron-by-neuron by adjusting individual neural thresholds , the model was able to match the distribution of neural coding levels in the dataset , without the need for tuning individual thresholds . The second network model we consider is a recurrent model of local cortical circuitry , the Stabilized Supralinear Network ( SSN ) , which has found broad success in mechanistically explaining a range of nonlinear modulations of neural responses and variability by sensory context or attention , in multiple cortical areas [8 , 35 , 36] . The SSN is a recurrent rate network of excitatory ( E ) and inhibitory ( I ) neurons which have a supralinear rectified power-law input-output function , f ( u ) = k [ u ] + n ( where u is the total input to a cell , k > 0 and n > 1 are constants , and [u]+ = max ( 0 , u ) denotes rectification ) . The dynamical state of the network of N neurons is the vector of firing rates r ∈ R N which is governed by the differential equation τ d r d t = - r + k [ W r + F I ( s ) ] + n , ( 5 ) where W and F denote the recurrent and feedforward weight matrices ( with structure described below ) , the diagonal matrix τ = Diag ( ( τ i ) i = 1 N ) contains the neural relaxation time constants , τi , and I ( s ) denotes the stimulus input in condition s ∈ {1 , … , S} . We consider a topographically organized version of SSN with a one-dimensional topographic map which could correspond , e . g . , to a one-dimensional reduction of the retinotopic map in primary visual cortex ( V1 ) . We adopt this interpretation here , and simulate the fitting of an SSN model of V1 to a dataset of V1 grating-size tuning curves , which are commonly used to characterize surround suppression [8 , 37 , 38] . The model has a neuron of each type , E and I , at each topographic spatial location . For the i-th model neuron , we denote its type by α ( i ) ∈ {E , I} and its topographic location by xi ( which ranged from −0 . 5 to 0 . 5 on a regular grid ) . In many cortical areas the statistics of local recurrent connectivity , such as connection probability and average strength , systematically depend on several factors , including the types of the pre- and post-synaptic neurons , the physical distance between them , or the difference between their preferred stimulus features [17 , 18] . We made a choice for the distribution of Wij ( the connection strength from neuron j to neuron i ) that accounts for such dependencies in our topographic network , and also respects Dale’s principle [39 , 40] . In order to make the GAN method applicable , another criteria was that Wij are differentiable with respect to the parameters of their distribution . The most relevant aspects of the distribution of Wij for the network dynamics are its first two moments ( equivalently , mean and variance ) . We assumed that Wij’s are independent with uniform distributions on the range [〈Wij〉 − δWij/2 , 〈Wij〉 + δWij/2] , with a mean , 〈Wij〉 , and width , δWij , that depend on the pre- and post-synaptic types and fall off with the distance between the pre- and post-synaptic neurons over characteristic length scales . More precisely we chose the fall off to be Gaussian , and set ⟨ W i j ⟩ = ± J ¯ a b exp ( - ( x i - x j ) 2 2 σ a b 2 ) ( 6 ) δ W i j = δ J a b exp ( - ( x i - x j ) 2 2 σ a b 2 ) ( 7 ) where a = α ( i ) and b = α ( j ) are the E/I types of the neurons i and j , respectively . The 2 × 2 matrices J ¯ a b , δJab , and σab constitute 12 trainable model parameters; they control the average strength , the degree of random heterogeneity , and the spatial range of the recurrent horizontal connections between different E/I cell types , respectively . All parameters are constrained to be non-negative , and we also enforced the constraint J ¯ a b ≥ δ J a b / 2; the sign on the right side of Eq ( 6 ) , which is positive or negative , when the presynaptic cell type b is E or I , respectively , enforces the correct sign for Wij according to Dale’s principle . The feedforward input , FI ( s ) , to the SSN is composed of the stimulus-independent feedforward connectivity F and the topographically structured visual stimulus I ( s ) . We chose the matrix F to be square and diagonal and hence I ( s ) to be N-dimensional . To model size tuning curves , we let the visual input I ( s ) in condition s only target neurons in a central band of the topographic grid roughly extending from location −bs/2 to bs/2 ( see Fig 2B ) , as a simple model of visual input from a grating with diameter bs . We included quenched random heterogeneity in the diagonal feedforward weights by sampling Fii independently from the uniform two-point distribution on [1 − V , 1 + V]; thus V controls the degree of disorder in the feedforward inputs to network cells . We modeled size tuning curves based on the sustained response ( defined as the time averaged firing rate during the “sustained response” period; see Materials and methods ) of a subset of SSN neurons driven by stimuli of different diameters or sizes . In experiments , typically the grating stimulus used to measure a neuron’s size tuning curve is centered on the neuron’s receptive field . To model this stimulus centering , we let the model output , r ¯ ( s ) , in condition s be the sustained response of the excitatory neuron at the center of the topographic grid ( which is also the center of the stimulus with size bs ) . Note that in general , the tuning curves in random SSN’s show variability across neurons as well as across different realizations of the network for a fixed output neuron . Furthermore , when N is large , the variability across the tuning curves of neurons with topographic locations sufficiently near the center often approximates variability of the center neuron across different network realizations with different z . Although we do not do so here , this self-averaging property may in principle be exploited for more efficient training of the model . We used the GAN framework to fit the 12 recurrent connectivity parameters and one feedforward parameter of the SSN model , θ = ( J ¯ a b , δ J a b , σ a b , V ) , to simulated data . The latter consisted of 2048 size tuning curves generated from a “ground truth” SSN with the connectivity parameters listed in Table 1 . ( We used a slight reparametrization of θ in the actual WGAN implementation; see Eq ( 28 ) . ) All other model parameters were the same between the ground truth and trained SSN models ( see Materials and methods for details ) . To quantify the goodness of fit between the data and trained model distributions of tuning curves , as in the previous section , we compare the distributions of four test statistics or measures characterizing the size tuning curves: preferred stimulus size , maximum firing rate , the suppression index , and normalized participation ratio . The preferred stimulus size is the stimulus size eliciting the largest response or maximum firing rate . The suppression index for a neuron ( or tuning curve ) measures how much the response of that neuron is suppressed at large stimulus sizes relative to response to its preferred size . Finally , the normalized participation ratio measures the fraction of tested stimulus sizes that elicit a response close to the maximum response . ( See Materials and methods for the precise definition ) . Note that while to fit the model we used size tuning curves containing responses to stimuli with S = 8 different sizes , for testing purposes , we generated tuning curves from the trained and ground-truth SSN’s using a larger set of stimulus sizes . In particular , the tuning curves constituting the “true data” used for the tests in Fig 4 were not part of the training dataset , but were newly generated from the ground-truth model , and should thus be considered held-out test data . Moreover , they included responses to stimuli of sizes that were not covered in the tuning curves used in training . Fig 4C and 4D provide comparisons of the distributions of these tuning curve attributes under the trained and ground truth SSN models . As in Experiment 1 , we measured the mismatch of these distributions using the Kolmogorov-Smirnov ( KS ) distance . The KS distance for all distributions becomes very small as a result of learning ( Fig 4B ) , reflecting the close fit of all test statistics from the trained model with the data ( Fig 4C and 4D ) . ( Note that since the generator was not directly trained to minimize any of the above KS distances , there is no reason why they should decrease monotonically during learning . ) Although moment matching can capture the overall shape of the one-dimensional ( marginal ) distributions of some of the test statistics ( e . g . , see Fig 4D for preferred size ) , it fails to capture some details in higher-dimensional joint distributions . For example , moment matching underestimates the density of the joint distribution of the suppression index and peak rate between the two peaks . By contrast , the WGAN faithfully captures this joint distribution ( Fig 4C ) . We emphasize again that such summary statistics ( and their distributions ) did not directly play a role in the WGAN fit; instead , as discussed in subsection “Generative Adversarial Networks” above , the WGAN’s discriminator network automatically discovers the relevant optimal statistic , and in this way fits the full high-dimensional tuning curve distribution , and in particular the low-dimensional distributions plotted in Fig 4C and 4D . In the point of view adopted in this paper , the distribution of neural tuning curves serves as a probe into the network structure; the richer the tuning curve dataset , the stronger the probe . Richness of tuning curve data can correspond to at least two different factors . One factor is the richness of stimuli , or the breadth and dimensionality of the region of stimulus parameter space covered in the tuning curves . A second factor is the degree to which each neuron’s tuning curve is associated with other functional or anatomical information such as cell type , preferred stimulus , topographic location , etc . When the probe is not sufficiently strong , it may not serve to fully uncover the network structure as encoded in model parameters . When a model class is at all capable of capturing the observed tuning curve distribution , it may be the case that models within that class but with widely different parameters are equally capable of fitting the tuning curve distribution . In such a scenario the model parameters will not be uniquely identifiable using the available tuning curve data . For example , in Experiment 2 we were able to train an SSN to accurately capture the size tuning curve distribution . However , in many runs the parameters of the trained SSN failed to match the parameters of the ground-truth SSN that had generated the training dataset . This failure is , however , not a failure of the WGAN training algorithm , as the training was consistently successful in capturing the size tuning curve distribution very well . The failure is partly due to the relative poverty of the tuning curve data used . In that example , the dataset contained only the size tuning curves of excitatory and centered neurons ( neurons with receptive field or topographic location at the center of the stimulus ) . Moreover , stimulus parameters other than size , such as contrast , were not varied in the tuning curves . We thus set out to investigate whether enlarging the tuning curve data along some of the mentioned lines can allow for accurate identification of the network parameters using the GAN methodology . We experimented with including identified inhibitory cell tuning curves in the training data , as well as size tuning curves for offset cells with topographic location not at stimulus center . However , we found that only including offset tuning curves was sufficient to enable parameter identification . We will report the results of this experiment here; in this training dataset we included the size tuning curves of neurons with the following possible offsets from stimulus center: ( x i p ) p = 1 O = ( 0 , 1 / 16 , 1 / 8 , 1 / 4 , 3 / 8 ) . To fit the SSN to the enriched dataset , here we employed the conditional WGAN ( cWGAN ) algorithm . As explained in the subsection Conditional GANs above , in cWGAN the discriminator and generator depend on a set of “condition” variables in addition to their inputs in the original WGAN . Here , we provided the topographic offset , x i p , of the cells as the conditional input . The cWGAN formalism allows for using tuning curves of neurons for which only some of the offsets are measured . In particular , it allows for exploiting off-center size tuning curves ( which are commonly discarded ) towards system identification . We compare the quality of fits using the cWGAN and moment matching respectively ( Fig 5 ) . Although moment matching produces a reasonable fit to the distribution of tuning curves for the individual features we explored ( Fig 4B ) , the GAN approach significantly outperforms moment matching at parameter identification ( Fig 5 ) . This is summarized in the relative error plots in Fig 5A and 5D; relative error was measured using the symmetric mean absolute percentage error ( sMAPE ) , defined in Eq ( 44 ) of Materials and methods . In particular , moment matching severely misestimates the δJab’s ( see Fig 5E ) , which control the heterogeneity in recurrent horizontal connections . On the other hand , cWGAN was successful at identifying parameters with less than 10% error , and the fit of the summary statistic distributions was excellent . The results demonstrated in Fig 5 are robust . Fig 6 shows a histogram of percent error ( quantified by sMAPE , Eq ( 44 ) ) for multiple moment matching and cWGAN fits , performed using a wide range of hyperparameters ( see Materials and methods , under “Hyperparameters for parameter identification experiment” ) , including different learning rates for the generator , and for the discriminator in the cWGAN case . To provide a fair comparison of performance between the two methods , in Fig 6 we used an impartial termination criterion which is agnostic to the hyperparameters ( see Materials and methods , under “Stopping criterion and performance metric” ) , for both cWGAN and moment matching . In all cases the cWGAN outperforms moment matching . We observed that successful cWGAN fits required particularly small generator learning rates . By contrast , in other examples ( not reported ) in which the tuning curve dataset was richer , and inhibitory neuron tuning curves were also observed by the discriminator , trainings with ten times larger generator learning rates successfully identified the parameters . We also observed that moment matching was able to identify the parameters in those easier cases . More generally , we speculate that harder problems ( i . e . , those with poorer training data ) , such as in Fig 6 , may require a smaller WGAN generator learning rate for accurate parameter identification .
In our experiments we used both conditional and non-conditional GANs . When using a non-conditional GAN as in Experiments 1 and 2 , the discriminator only receives as input the discretized tuning curve in the form x ≡ ( r ¯ ( s ) ) s = 1 S . Here , the index s denotes the stimulus condition , corresponding to combinations of different stimulus parameter values . In this formulation of the tuning curve , the relationship between stimulus parameters and the index s is thus lost to the discriminator . In particular , the discriminator is blind to the metric or similarity structure in the stimulus parameter space ( e . g . , it does not explicitly know whether two different components of x encode responses to very similar or widely different stimuli ) , and therefore cannot directly exploit that structure in discriminating between true and generated tuning curves . Another drawback of this formulation is that all neurons in the dataset must have been recorded in all stimulus conditions; when there are several stimulus parameters , however , recording each neuron in all conditions for many trials becomes experimentally prohibitive . On the other hand , non-conditional GANs are advantageous in allowing the generator to learn the joint distribution of single-neuron responses across the entire stimulus parameter space; in particular , it enables to fit the marginal distribution of “global” tuning curve features that depend jointly on responses at different values of multiple stimulus parameters . The conditional GAN approach provides a complementary scheme for describing the tuning curve , i . e . , the relationship between neuronal responses and stimulus parameters . In this case , different values of a subset of stimulus parameters are implicitly represented by the different components of x . However , x ( and its component responses ) depend also on another subset of stimulus parameters that are provided explicitly as inputs to the discriminator ( as well as the generator ) , in the form of conditional GAN’s condition variables c ( more generally , c need not be limited to subsets of stimulus parameters , and can , e . g . , also denote a neuron’s cell type or preferred stimulus parameters ) . Since the value of these parameters is directly provided to the discriminator , the latter is not entirely blind to stimulus similarity structure . On the other hand , the conditional GAN framework only fits the conditional distributions of x at different values of c . This is beneficial in that we now do not need to record each neuron across all values of c . However , by the same virtue , the framework is blind to correlations of single-cell responses at different values of c across neurons , and may not fit the distribution of single-neuron tuning curve features that depend jointly on responses to stimuli with different c-values . By allowing a trade-off between capturing the joint distribution of single-neuron responses across the entire parameter spaces vs . handling a heterogeneous dataset with missing data , the conditional GAN provides additional flexibility in fitting theoretical models to diverse neuronal data . As with any gradient-based training method , it is possible for a GAN to become stuck in suboptimal local minima for the generator ( or the discriminator ) . It is further an open question whether GAN training will always converge [29 , 41 , 42] . As research in GANs and non-convex optimization advances this issue will be improved . For now avoiding this pitfall will be a matter of the user judging the quality of fit after the fit has reasonably converged . Starting the gradient descent algorithm with several different initial conditions for generator parameters can also help , with some initializations leading to better final fits . Apart from the above general problems , when the generator is a recurrent neural network ( RNN ) , other problems may arise within each step of gradient descent . When the generator output is based on a steady-state ( fixed point ) of the RNN , as was the case in our SSN experiment , a potential issue is lack of convergence to a stable fixed point for some choices of recurrent network parameters . In our experiment with SSN , we initialized the generator network in such a way that it initially had a stable fixed point for almost all realizations of z . For the SSN this would generically be the case when recurrent excitation ( which has destabilizing effects ) is sufficiently weak . Hence initializations with small JEE and δJEE are good choices . In addition , a relatively large SSN size N improves the stability issue because random quenched fluctuations in total incoming synaptic weights are relatively small when the number of presynaptic neurons is large . Thus , for large networks , for a given choice of network parameters , θ , either the network converges to a stable fixed point for almost all z , or almost never does . To avoid entering parameter regions leading to instability during training , we added the additional regularizing term Eq ( 32 ) to the generator loss . We found that the addition of this term is crucial for the success of the algorithm . An additional problem particular to optimizing GANs is mode collapse ( also known as mode dropping ) , in which some modes of a multi-modal dataset are not represented ( or in the worst case only one mode is represented ) in the generative model output [29 , 43 , 44] . Mode collapse is an example of underfitting . The work presented here did not suffer from mode collapse , likely because of the highly structured models employed . Nevertheless , other applications may suffer from the problem of mode collapse . Many approaches have been explored to prevent mode collapse , and we do not give a comprehensive review , but instead cite a selection of interesting approaches . The WGAN itself , which is employed here , is believed to alleviate mode collapse to some degree [25] . Other more sophisticated approaches exist , including the addition of a mutual information maximizing regularizer between model output and the latent variables [45] . One particularly elegant and effective approach is the PacGAN which provides two or more independent , concatenated generator or data samples to the discriminator so that a model suffering from mode collapse will be recognizable from its lack of diversity [46] . Another possible problem is that of overfitting . In the context of generative model training , extreme overfitting corresponds to the generative model approximately memorizing the individual samples in the training data . In our experiments , this would have corresponded to the trained circuit model only generating a finite set of possible tuning curves , namely the samples seen during training . GANs are prone to sample memorization when their generator and discriminator have high complexity or expressivity . For typical circuit models in neuroscience , however , such as the examples we considered here , the output ( e . g . , tuning curve ) distribution is expected to have a smooth density ( in contrast to a discrete distribution with support on or near the training-set tuning curves ) almost everywhere in parameter space; such a generator can never memorize a finite sample of tuning curves . In fitting generative models it is possible for models with widely different parameters to result in nearly identical output distributions . In our case , this corresponds to cases in which networks with widely divergent connectivity or single-cell parameters nevertheless generate very similar tuning curve distributions . In such cases it would be impossible to make precise inferences about network parameters ( e . g . , connectivity statistics ) using tuning curve data . This problem is exacerbated for the moment matching method , which discards information in the data by reducing the tuning curve distribution to a few moments . In comparison , the problem should generally be less severe for the GAN method which tries to fit the entire distribution . Irrespective of the fitting method , however , there is no general reason why the distribution of a relatively low-dimensional output of the model , such as the tuning curve with respect to one stimulus parameter , would provide sufficient information for constraining all circuit parameters . Fortunately there is nothing in our approach that prevents one from applying it to datasets of tuning curves with respect to several stimulus parameters , or tuning curves of multiple cell types . The general expectation is that the higher the dimension of the stimulus parameter space underlying the tuning curves in the training data , the more identifiable the network parameters become . For example , in the case of our SSN experiments , we first trained the generative model using only tuning curves with respect to stimulus size . In the parameter identification experiment ( Experiment 3 ) , we enriched the size tuning curve dataset by adding center-offset neurons , and additional sampling conditions by adding inhibitory cell tuning curves . The result was an improvement in the robustness and accuracy of model parameter identification . With datasets of sufficiently rich tuning curves , the GAN-based method provides a promising way to infer biophysical networks parameters , such as those governing connectivity statistics . This also has deep implications for experimental design: the standard approach of using optimal stimuli ( in the case of our SSN example , gratings with no center offset ) , or only focusing on excitatory neurons may produce datasets that are insufficiently rich to allow for model-based inference of all circuit parameters of interest . In particular , a framework like GANs can in principle be used to design experiments , i . e . , optimally choose the stimulus conditions and quantities to be recorded , to maximize the identifiability of the parameters of a given model . The current work can be extended along several directions . In addition to the GAN framework , a suite of other methods have also been developed recently in machine learning for fitting generative models [23] . Examples include variational autoencoders [22 , 47] , and hierarchical and deep implicit models [24] . These methods can also be fruitful for fitting circuit models from neuroscience and inferring circuit parameters . Recent progress in unifying these approaches [48–50] can further inform their future applications . Of special note is the Bayesian methodology of Ref . [24] which in addition to point estimates for parameters , also yields estimates of their posterior uncertainty ( or more generally , their approximate posterior distribution ) . In the current study , we took the output of the mechanistic circuit model to be tuning curves composed of single-cell trial-averaged sustained responses . But the conceptual framework explained in the beginning of Results and the GAN methodology can also be used to constrain mechanistic network models using data featuring the temporal dynamics of neural activity and higher-order statistics of trial-to-trial variability . For example , the network model can be fit not only to match the distribution of tuning curves encoding trial-averaged single-cell sustained responses , but rather the entire peristimulus time histogram or also the distribution of noise correlations between cell pairs in some cortical area , extracted from simultaneously recorded neural activity . Lastly , although we focus here on applications to neuroscience and neuronal networks , the proposed framework can potentially serve to fit mechanistic models from other corners of biology , in order to infer the structure of other kinds of biological networks from functional data . For example , the framework can potentially be used to infer the structure of gene regulatory networks from data on the expressions of one or a few genes in different environments .
Here we provide the complete expressions for the loss functions used in the conditional WGAN ( cWGAN ) method and a pseudocode for this algorithm . The non-conditional WGAN setup was described in subsection Generative Adversarial Networks of Results . cWGAN’s are similarly composed of a generator and a discriminator , but now both the descriminator D w and generator Gθ depend on a “condition” argument or input , c , in addition to their primary inputs [33] . The condition variable c can be discrete or continuous and can range over a set of possibilities C . When there is only one possibility for c ( in which case this argument can be dropped ) we recover the original ( non-conditional ) WGAN . The discriminator and generator loss functions for cWGAN are given by Loss D ( w , θ ) = E z , c [ D w ( G θ ( z ; c ) ; c ) ] - E x , c [ D w ( x ; c ) ] + ( Gradient Penalty ) ( 8 ) Loss G ( w , θ ) = - E z , c [ D w ( G θ ( z ; c ) ; c ) ] + Penalty G ( θ ) , ( 9 ) respectively . Here E x , c denote the average over a batch of data samples , x , together with the conditions , c , at which they were recorded . Similarly , E z , c denotes averaging over a batch of noise variables z , sampled from their fixed distribution , and conditions c , sampled from their empirical distribution . Note that z and c are treated as independent random variables . The “Gradient Penalty” term forces the gradient of D w ( with respect to its first argument ) to be close to one; following the recipe of Ref . [26] we set it to Gradient Penalty = λ E z , x , c , ϵ [ ( ‖ ∇ D w ( ϵ x + ( 1 - ϵ ) G θ ( z ; c ) ; c ) ‖ 2 - 1 ) 2 ] ( 10 ) where ϵ is a random variable with uniform distribution on [0 , 1] , and the gradient ∇ is taken with respect to the first argument of D w , and not the condition c or parameters w . Finally , the term PenaltyG ( θ ) in the generator loss denotes possible generator model dependent regularization terms . We did not include any such term for the feedforward network example presented in Experiment 1 . The PenaltyG ( θ ) for the recurrent SSN example is described in Eq ( 32 ) of Materials and methods . A stochastic gradient descent algorithm for cWGAN based on these loss functions is shown in Algorithm 1 . Algorithm 1: Improved cWGAN algorithm based on Ref . [26] . For all models , we use n D = 5 . For update method , we either use Adam with β1 = 0 . 5 , β2 = 0 . 9 , ϵ = 10−8 [26] or RMSProp with ρ = 0 . 9 , ϵ = 10−6 . For the feedforward model , we use α D = α G = 0 . 001 , m = 30 , γ = 0 , PenaltyG ( θ ) = 0 , and Update D = Update G = Adam . The generating processes are considered normal always and the test† always passes . For the SSN α D = 0 . 02 , αG = 10−4 , m = 128 , γ = 0 . 001 , PenaltyG ( θ ) = Eq ( 32 ) . The discriminator is updated only if generating processes ( Gθ ( z ) ) pass a test† for “normality” ( see Eq ( 33 ) ) . We use always RMSProp for the generator in the SSN experiments . We use RMSProp for the discriminator in Fig 4 , Adam in Fig 5 , and aggregate the results with both RMSProp and Adam in Fig 6 . Input: data distribution P r , the gradient penalty coefficient λ , the number of discriminator iterations per generator iteration n D , the batch size m , update methods Update D ( · ) , UpdateG ( · ) , learning rates α D , αG , weight decay hyperparameter γ , and the initial discriminator w0 and generator θ0 parameters . θ ← θ0; w ← w0; while θ has not converged do repeat for i = 1 , … , m do Sample real data ( x , c ) ∼ P r , latent variable z ∼ p ( z ) , a random number ϵ ∼ U[0 , 1] . ; x ˜ ← G θ ( z ; c ) ; x ^ ← ϵ x + ( 1 - ϵ ) x ˜ ; Loss D ( i ) ← D w ( x ˜ ; c ) - D w ( x ; c ) + λ ( ‖ ∇ x ^ D w ( x ^ ; c ) ‖ 2 - 1 ) 2 ; end if generating processes are normal† then w ← Update D ( ∇ w 1 m ∑ i = 1 m Loss D ( i ) , w , α D ) - γ w ; end until n D updates are tried; Sample latent variables ( z ( i ) ) i = 1 m ∼ p ( z ) for a mini-batch . ; Sample conditions from real data ( · , c ( i ) ) i = 1 m ∼ P r for a mini-batch . ; θ ← Update G ( - ∇ θ ( 1 m ∑ i = 1 m D w ( G θ ( z ( i ) ; c ( i ) ) ) + Penalty G ( θ ) ) , θ , α G ) ; end In this subsection , we provide a quick review of some of the alternatives to the WGAN loss functions , Eqs ( 3 ) and ( 4 ) , developed in the GAN literature , which can be useful in computational biology applications ( for a more comprehensive review of GANs see [29] ) . We also point out an alternative view of GANs inspired by the energy-based framework for unsupervised learning [51 , 52] . The original GAN developed in [23] was framed as a minimax or zero-sum game in which the generator and discriminator competed by respectively minimizing and maximizing the same loss: Loss G ( w , θ ) = - Loss D ( w , θ ) = E x [ log D w ( x ) ] + E z [ log ( 1 - D w ( G θ ( z ) ) ) ] . ( 11 ) Given this form for the loss , the discriminator , D w ( x ) , can be thought of as a binary classifier that estimates the conditional probability of the true or empirical category ( vs . “fake” or G-generated category ) given the observation x . In this case , for a fixed generator whose output has distribution Pθ ( x ) , the theoretically optimal discriminator is given by D * ( x ) = P true ( x ) P true ( x ) + P θ ( x ) , where Ptrue ( x ) is the true data distribution or density . In that sense , a sufficiently expressive and well-trained discriminator ( using Eq ( 11 ) ) learns the ratio of the model likelihood , Pθ ( x ) , to the true data density . Moreover , for this optimal discriminator solution the loss Eq ( 11 ) reduces to the Jensen-Shannon ( JS ) divergence ( up to additive and multiplicative constants ) between Ptrue ( x ) and Pθ ( x ) [29] . Thus the generator is theoretically trained to minimize the JS distance . By comparison , as noted in subsection “Generative Adversarial Networks” of Results , WGANs theoretically minimize the Wasserstein or earth-mover’s distance between Ptrue ( x ) and Pθ ( x ) . Both of these divergence or distance measures are in contrast to the Kullback-Leibler ( KL ) divergence DKL ( Ptrue‖Pθ ) which is effectively minimized in classical maximum-likelihood estimation of the generator ( or equivalently , of its parameters θ ) . Correspondingly , others have modified the generator-loss so that , given the theoretically optimal discriminator , it reduces to the KL divergence , or other divergences [53–56] . For example , to minimize the KL divergence , [56] used the same discriminator loss as in Eq ( 11 ) , in conjunction with the modified generator loss Loss G ( w , θ ) = E z [ f ( D w ( G θ ( z ) ) ) ] ( 12 ) where f ( u ) = u 1 - u exp ( u 1 - u ) . As we mentioned in the Discussion , Ref . [24] developed a GAN-like framework for variational Bayesian estimation of implicit generative models , in which a discriminator-like network was trained to estimate the generator’s likelihood function . However , this Bayesian framework goes beyond maximum-likelihood estimation: Ref . [24] developed a three-network framework in which , in addition to the ( implicit ) generative model and discriminator ( which estimates the generator’s likelihood ) , a third network is trained to provide an approximation to the Bayesian posterior over θ ( as in more general variational Bayesian approaches [22 , 57] ) . Finally , we mention energy-based GANs ( EBGANs ) , which are inspired by the energy-based framework for learning [51 , 52] . EBGANs use the following loss functions for the generator and the discriminator: Loss D EBGAN ( w , θ ) = E x [ D w ( x ) ] + E z [ ( m - D w ( G θ ( z ) ) ) + ] ( 13 ) Loss G EBGAN ( w , θ ) = E z [ D w ( G θ ( z ) ) ] ( 14 ) where ( x ) + = max ( 0 , x ) denotes rectification , m > 0 is a positive margin parameter , and D w is constrained to be non-negative ( see [58] ) . EBGANs are in part motivated by an alternative view of the role of the discriminator in GANs . In the viewpoint expressed in subsection “Generative Adversarial Networks” of Results , the discriminator is thought of as a flexible and trainable objective function that is used for training the generator . In this interpretation , the generator is key and the discriminator is auxiliary . However , an alternative viewpoint is also possible in which the discriminator is key and the generator is auxiliary ( see appendix B of [58] ) . In this view , which is suggested by the energy-based framework for learning [51 , 52] , the discriminator learns the relative density of the data distribution . More precisely , it is thought of as an energy function that is shaped during training to be low in regions of high data density , and high elsewhere . This is achieved by minimizing a loss functional during learning; Eq ( 13 ) is an example of such a loss functional . Imagine for simplicity that true data lie on a subspace or manifold in the data space . The loss functional is designed such that true data samples serve to “push down” the energy function ( i . e . , increase the probability density ) on the data manifold , while another mechanism is used to “pull-up” the energy function ( i . e . , reduce the probability density ) outside that manifold . In simple unsupervised learning methods such as principle component analysis the pull-up of energy is implicitly achieved due to the rigidity of the energy function itself ( see [52] ) . But when the energy function is sufficiently flexible , an explicit term in the loss functional is needed to pull it up outside the true data manifold . Fake or simulated data points , referred to as contrastive samples , can be used for energy pull-up . In Eq ( 13 ) , the first and second term serve to push up and pull down the energy function , D w , respectively . Accordingly , in the alternative viewpoint of GANs , the generator is viewed as merely providing such contrastive samples to the discriminator . As explained below , in contrast to many applications of unsupervised learning , the generator is indeed central in our intended applications , and we therefore focused on the first interpretation of GANs in subsection “Generative Adversarial Networks” of Results . Nevertheless , unsupervised learning of generative models is often carried out with the end-goal of learning a useful representation of observed data , which can , e . g . , serve to compress or reduce the dimensionality of data . In our case this corresponds to dimensionality reduction of tuning curves . Estimating the density of observed data is a related end-goal of unsupervised learning , which can , e . g . , serve data restoration ( e . g . , image denoising ) applications . Even though these were not the applications motivating the current study ( see the next subsection ) , they do constitute potential applications of GANs in computational biology . The alternative view of the role of discriminator can be more advantageous in such settings . In most applications of GANs in machine learning and artificial intelligence , the generative model is an artificial neural network ( e . g . , a deconvolutional deep feedforward network ) , and that network’s individual connection weights constitute the generator’s trainable parameters , θ . In most such applications , these parameters are not objects of interest on their own and may not be mechanistically meaningful or interpretable . Similarly , the development of generative models in such domains is not necessarily concerned with capturing the true , physical mechanisms underlying the modeled data . There and in other unsupervised learning settings , the end goal is to achieve a generator that produces realistic data objects ( e . g . , images ) , or to accurately estimate the data density or its support ( which can serve for dimensionality reduction or denoising of data ) . In such domains , the discriminator itself may be of central importance as it can potentially estimate the relative density of data objects ( see the previous subsection for a discussion of this viewpoint ) . By contrast , in applications of primary interest to us , it is the generator that is of primary interest . The core of the generator is a circuit model , developed independently of the method used to fit it ( in our case GANs ) , with the scientific goal of uncovering the circuit mechanisms in a neural or biological system . In particular , in our applications , the generator parameters θ typically correspond to physiological and anatomical parameters with clear mechanistic interpretations . Correspondingly , the circuit model is highly structured , with that structure strongly informed a priori by biological domain knowledge and the scientific desire for parsimony . This allows for post-training tests of the model that go beyond testing the fit to held-out data of the same type as training data , and may not be conceivable in many machine learning applications . For example , one can imagine training an SSN circuit model on size-tuning curves ( as we did in Experiment 1–3 ) but test it using stimuli with varying strengths or contrasts and compare the generated distribution of contrast-response function against data . Alternatively , one can feed the trained SSN dynamical noise and then compare the statistics of temporal neural variability ( such as pairwise noise correlations ) against empirical data . In such tests of generalization , the data-space itself changes ( and not just the data points in it ) between training and testing , and therefore a discriminator trained on one space simply cannot generalize to the other; the link between the two data-spaces is solely provided by the generative mechanistic circuit model . The promise of a faithful scientific model of a brain network is , in principle , to capture all such neural data at least approximately; the above scenarios can thus be used as strong tests for such models . To perform such tests faithfully and quantitatively , a strong fitting procedure is necessary . The model of primary motor cortex ( M1 ) tuning curves proposed by Ref . [4] is a two-layer feedforward network , with an input layer , putatively corresponding to the parietal reach area or to premotor cortex , and an output layer corresponding to M1 ( see Fig 2 ) . Ref . [4] introduced their model in two versions , a basic one , and an “extended” version . We have used their enhanced version with small modifications noted in Experiment 1 and below which allow our approach to be used . In particular , we did not model response selectivity to hand posture ( supination or pronation ) , and ignored that label in the dataset; i . e . , we blindly mixed hand-position tuning curves across pronation and supination conditions , as if they belonged to different neurons . We further simplified the dataset by removing spatial scale information in the positions of target hand locations , which varied slightly between experimental sessions , by rescaling the distance between adjacent hand position to be 1 . We randomly selected half of the hand-position tuning curves to be our training dataset , and used the other half as held-out data to evaluate the goodness of model fits ( presented in Fig 3 ) . The input to the feedforward network is the 3D hand position xs , with s ∈ {1 , ⋯ , 27} indexing the 3 × 3 × 3 grid of possible target locations . The input layer neurons have Gaussian receptive fields defined on the 3D hand position space . The activation , hi ( s ) of neuron i in the input layer with receptive field centered at xi is thus h i ( s ) ∝ exp ( - 1 2 σ i 2 ‖ x s - x ¯ i ‖ 2 ) in condition s ( when the hand is at xs ) . Across the input layer , the Gaussian centers xi form a fine cubic grid that interpolates and extends ( by 3 times ) the 3 × 3 × 3 stimulus grid along each dimension . Whereas Lalazar et al . [4] used a grid with 100 points along each axis , we reduced this to 40 to allow faster computations ( we checked that changing this resolution beyond 40 only weakly affects the results ) . Across the input layer , the receptive field widths , σi , were randomly and independently sampled from the uniform distribution on the range [σl , σl + δσ] . This can be expressed by writing σ i = σ l + z i σ δ σ where z i σ are uniformly distributed on [0 , 1] and independent for different i’s across the input layer . The feedforward connections from the input to output layer are sparse and random , with a connection probability of 0 . 01 . In our implementation of this model , the strength of the nonzero connections were sampled independently from the uniform distribution on the range [0 , J] . Since output layer neurons are independent , it suffices to describe the model with a single output neuron . If we denote the connections received by this neuron from the i-th input layer neuron by J i , we can thus write: J i = J M i z i J where z i J’s are sampled independently from the standard uniform distribution on [0 , 1] , and Mi is a binary 0/1 mask that is nonzero with probability 0 . 01 . The response of the output layer neuron is given by a rectified linear response function with threshold ϕ . The threshold ϕ was sampled uniformly from the range [ϕl , ϕl + δϕ] . Equivalently , ϕ = ϕl + zϕ δϕ with zϕ a standard uniform random variable . The model thus has five trainable parameters θ = ( σl , δσ , J , ϕl , δϕ ) ( listed in Table 2 ) . For a choice of θ , the collection of quenched noise variables , z = ( z ϕ , ( z i σ ) i = 1 40 3 , ( z i J ) i = 1 40 3 , ( M i ) i = 1 40 3 ) , fully determine the network structure: all input layer receptive field sizes , individual feedforward connection strengths , and output layer neural thresholds for a particular network realization . The response r ¯ ( s ; z , θ ) of an output neuron in condition s ( for s ∈ {1 , ⋯ , 27} ) is thus given by r ¯ ( s ; z , θ ) = [ ∑ i = 1 40 3 J i h i ( s ) - ϕ i ] + ( 15 ) where h i ( s ) = 1 Z ( s ) exp ( - 1 2 σ i 2 ‖ x s - x ¯ i ‖ 2 ) ( 16 ) J i = J M i z i J ( 17 ) ϕ = ϕ l + z ϕ δ ϕ , ( 18 ) σ i = σ l + z i σ δ σ , ( 19 ) z ϕ , z i J , z i σ ∼ i i d U [ 0 , 1 ] ( 20 ) M i ∼ i i d Bern ( 0 . 01 ) ( 21 ) where [u]+ = max ( 0 , u ) denotes rectification , and Z ( s ) is a normalizing factor such that ∑i hi ( s ) = 1 . Crucially , the network’s output , G θ ( z ) ≡ ( r ¯ ( s ; z , θ ) ) s = 1 27 , is differentiable with respect to each component of θ , we can thus use the output gradient with respect to model parameters to optimize the latter using any variant of the stochastic gradient descent algorithm . Note that Eqs ( 17 ) – ( 19 ) constitute an example of the “sampler function” gθ ( z ) introduced in the subsection “Mechanistic network models as implicit generative models” of Results ( here the vector of synaptic weights J i corresponds to W , while the vector ( ϕ , σ ) capturing single-cell properties corresponds to γ ) . Here we provide the technical details of the simulations , fit and analysis of the Stabilized Supralinear Network ( SSN ) model of the experiments in Experiment 1–3 . The SSN is a recurrent network of excitatory ( E ) and inhibitory ( I ) neurons . The dynamical state of the network is the vector of firing rates r ( t ) of the N network neurons . The rate vector is governed by the differential equation τ d r d t = - r + f ( W r + F I ( s ) ) , ( 22 ) where W and f denote the recurrent and feedforward weight matrices ( with structure described below ) , the diagonal matrix τ = Diag ( ( τ i ) i = 1 N ) contains the neural relaxation time constants , τi , and I ( s ) denotes the stimulus input in condition s , with s ∈ {1 , … , S} . The key feature of the SSN is the input-output nonlinearity of its neurons , which in the original model is a supralinear rectified power-law function: f ( u ) = k [ u ] + n ( [u]+ = max ( 0 , u ) and n > 1 and k > 0 are constants ) . During the training of the model inside the fitting algorithm , however , the model may explore non-biological regions in parameter space that may lead to divergence of model firing rates . To tame such divergences and enforce numerical stability during training , we modified the neural input-output nonlinearity in the model as follows . We let f ( u ) be a rectified power-law in the biologically relevant range , but smoothly connected it to a saturating branch at very high rates . More precisely we took f ( u ) = { k [ u ] + n if u < u 0 r 0 + ( r 1 - r 0 ) tanh ( n r 0 r 1 - r 0 u - u 0 u 0 ) otherwise ( 23 ) where k = 0 . 01 , n = 2 . 2 , r0 = 200 Hz , r1 = 1000 Hz , and u0 = ( r0/k ) 1/n . In our SSN examples , we chose to have all random structural variability ( which is the source of heterogeneity manifesting in tuning curve shapes ) occur in the connectivity matrices W and f . As described in Experiment 2 ( see Fig 2 ) , we experimented with SSN models with one-dimensional topographic structure , on which the structure of W and f depend . The model has a neuron of each type , E and I , at each topographic spatial location; for M topographic locations , the network thus contains N = 2M neurons . Below , for the i-th neuron , we denote its type by α ( i ) ∈ {E , I} and its topographic location by xi . We let xi’s range from −0 . 5 to 0 . 5 on a regular grid . The statistical ensemble for W was described in Experiment 2: the random variability of the matrix elements Wij’s was taken to be independent with uniform distribution , with mean and range that depend on the pre- and post-synaptic cell types and topographic distances . More precisely , for each instance of the model we generated W via W i j = ς b ( J a b < + z i j δ J a b ) exp ( - ( x i - x j ) 2 2 σ a b 2 ) , a = α ( i ) , b = α ( j ) ( 24 ) z i j ∼ i i d U [ 0 , 1 ] ( 25 ) where ςb = 1 or −1 if b = E or I , respectively , and U[0 , 1] denotes the uniform distribution on the interval [0 , 1] . Thus the average weight is 〈 W i j 〉 = J ¯ a bexp ( - ( x i - x j ) 2 / ( 2 σ a b 2 ) ) , where we defined J ¯ a b = J a b < + δ J a b / 2 , while the standard deviation SD [ W i j ] = 1 2 3 δ J a b exp ( - ( x i - x j ) 2 / ( 2 σ a b 2 ) ) . All parameters , J a b < , δJab , σab were constrained to be non-negative ( which is equivalent to the constraints J ¯ a b ≥ δ J a b / 2 ≥ 0 and σab ≥ 0 , for the alternative parameterization using J ¯ a b , δJab , σab ) . The first two constraints ( together with the sign variable ςb in Eq ( 24 ) ) ensure that any realization of Wij satisfies Dale’s principle [39 , 40] . We chose the feedforward weight matrix , f , to be diagonal with weights having independent random heterogeneity across network neurons . More precisely , for a network of N neurons , f was an N × N diagonal matrix generated via F = Diag ( ( 1 + z i F V ) i = 1 N ) ( 26 ) z i F ∼ i i d U ( [ - 1 , 1 ] ) ( 27 ) where the binary random variables z i F are sampled independently and uniformly from [−1 , 1] . Our recurrent and feedforward connectivity ensemble is thus characterized by 13 non-negative parameters ( enumerated in Table 3 ) : the parameter V that controls the degree of disordered heterogeneity in feedforward weights , as well as the elements of the three 2 × 2 matrices J a b < , δJab , σab ( where a , b ∈ {E , I} ) , which control the average strength , disordered heterogenetity , and spatial range of recurrent horizontal connections . These constituted the parameters θ = ( J a b < , δ J a b , σ a b , V ) a , b ∈ { E , I } , ( 28 ) that were fit using the WGAN ( or moment matching ) . ( Because enforcing the non-negativity constraints on the set ( J a b < , δ J a b , σ a b , V ) a , b ∈ { E , I } is easier than enforcing the constraints on the set ( J ¯ a b , δ J a b , σ a b , V ) a , b ∈ { E , I } introduced in Experiment 2 , we used the parametrization of Eq ( 28 ) in our WGAN implementation . ) While these parameters described the statistics of connectivity , a specific realization of the network is determined by the high-dimensional fixed-distribution random variables of the GAN formalism , z , in addition to θ . The former is composed of the N2 independent , standard uniform random variables ( z i j ) i , j = 1 N , and the N independent random variables ( z i F ) i = 1 N which are sampled uniformly from [−1 , 1] . Also note that Eqs ( 24 ) and ( 26 ) constitute an example of the sampler function , gθ ( z ) , introduced in the subsection “Mechanistic network models as implicit generative models” of Results . In our example experiments , we simulated the fitting of an SSN model of V1 to datasets of stimulus size tuning curves of V1 neurons [8] . As a simple model of the visual input to V1 evoked by a grating of diameter b , the stimulus input to neuron i was modeled as I i ( b ) = A σ ( l - 1 ( b / 2 + x i ) ) σ ( l - 1 ( b / 2 - x i ) ) , ( 29 ) where σ ( u ) = ( 1 + exp ( −u ) ) −1 is the logistic function , A denotes the stimulus strength or contrast . Thus , the stimulus targets a central band of width b centered on the middle of the topographic grid ( see Fig 2B ) . The parameter l determines the smoothing of the edges of the stimulated region . For training the model , we chose the sizes from a set of S = 8 different sizes ( 0 , 1/16 , 1/8 , 3/16 , 1/4 , 1/2 , 3/4 , 1 ) ( measured in units of the total length of the network ) . Letting bs denote the size in stimulus condition s ( s ∈ {1 , ⋯ , S} ) , the I ( s ) of Eq ( 22 ) and Experiment 2 is given by I ( s ) = I ( bs ) , with a slight abuse of notation . The output of the SSN , considered as a generative model for tuning curves , are the size tuning curves of a subset of network neurons which we call “probe” neurons . We define the tuning curve of these neurons in terms of their sustained responses evoked by different stimuli . Thus given a specific realization of the SSN , for each stimulus s ∈ {1 , … , S} , we first calculate the sustained network response vector by the temporal average between t1 and t2 r ¯ ( s ) = 1 T ∑ k = 1 T r ( t 1 + k Δ t ) ( 30 ) where Δt is the Euler integration step and T = ( t2 − t1 ) /Δt . We choose t1/Δt = 200 and t2/Δt = 240 to balance the computational cost and the accuracy for approximating the true steady-state . Given the sustained network response r ¯ ( s ) and an a priori selected set of O probe neurons with indices i = ( i1 , i2 , … , iO ) ( the probe neurons can equivalently be defined by their types and topographic locations ) , we define the output of the SSN generative model ( the GAN generator ) to be the vector G θ ( z ; c = x i p ) = ( r ¯ i p ( s ) ) s = 1 S . ( 31 ) Here , x i p denotes the topographic location of the p-th probe neuron , and we have now made the dependency of the output on the quenched noise variables , z , and model parameters explicit . We treated the probe neuron’s topographic location , x i p , as the condition c in conditional WGAN ( cWGAN ) . In this paper , we only probed excitatory neurons , i . e . , α ( ip ) = E . In experimental recordings , typically the grating stimulus used to measure a neuron’s size tuning curve is centered on the neuron’s receptive field . To model this stimulus centering , we always set the first probe neuron i1 to be at the center of the topographic grid ( i . e . , x i 1 = 0 ) , which was the center of the stimulus . In Experiment 2 we only fit the model to the size tuning curves of “centered” excitatory neurons . Since in that case there was only one probe neuron ( or cWGAN condition ) , we denoted the model output more succinctly by Gθ ( z ) , dropping its second argument . By contrast , in Experiment 3 we included tuning curves of neurons with offset receptive fields ( topographic locations ) in the training dataset and employed a conditional WGAN . Note that tuning curves for networks such as the SSN described here which have partially random connectivity , show variability across neurons as well as across different realizations of the network for a fixed probe neuron . When the network size N is large , typically a local self-averaging or “ergodicity” property is expected to emerge: the empirical distribution , in a single network realization , across the tuning curves of neurons of the same type and with nearby topographic locations should approximate the distribution across different z for a neuron of pre-assigned index ( i . e . , type and location ) . Although we did not do so in our experiments , one may exploit this ergodicity for more efficient training and testing of the model by sampling multiple nearby sites from each connectivity matrix realization . Given a dataset of size tuning curves , we would like to find model parameters , θ = ( J a b < , δ J a b , σ a b , V ) a , b ∈ { E , I } , that produce a matching model distribution of tuning curves . In this paper we constructed a simulated training dataset of size tuning curves using a “ground truth” SSN model , with parameters θtruth given in Table 1 . All other model parameters were the same between the ground truth and trained SSN models , and had the values: N = 402 , k = 0 . 01 , n = 2 . 2 , τE/Δt = 20 , τI/τE = 1/2 , A = 20 , l = 2−5 . During training , according to Algorithm 1 , every time Gθ ( z; x ) was evaluated , we simulated the trained SSN using the forward Euler method for t2/Δt = 240 steps ( see also Eq ( 30 ) ) . The gradients of the generator output or the generator loss with respect to parameters θ were calculated by standard back-propagation through time ( BPTT ) . To avoid numerical instability , the parameters J a b < , δ J a b , σ a b were clipped at 10−3 during training . To exclude extremely large ( non-biological ) values , we also clipped them below 10 . We also clipped V to bound it within the interval [0 , 1] . ( These upper bounds can be thought of as imposed Bayesian priors on these parameters . ) Moreover , during training , the SSN may be pushed to parameter regions in which , for some realizations of the quenched noise variables z , the network does not converge to a stable fixed point . Since an implicit model assumption of the SSN is to model sustained responses by stable fixed points of the model with rates in the biological range , dynamical non-convergennce and very high rates can be ( strongly ) penalized . We encouraged the firing rate of the all SSN neurons to uniformly remain below a permissive threshold of 200 Hz , by adding the following penalty term to the generator loss Penalty G ( θ ) = η N T m ∑ j = 1 m ∑ k = 1 T ‖ [ r ( t 1 + k Δ t ; z ( j ) ) - 200 ] + ‖ 1 ( 32 ) where m is the size of the mini-batch in the gradient descent , T = ( t2 − t1 ) /Δt is the time window for calculating the sustained response Eq ( 30 ) , and η = 100 is the weight of the penalty relative to the WGAN generator loss . This penalty , together with the modified neural input-output nonlinearity descrbied in ( Eq ( 23 ) ) , ameliorated diverging solutions of SSN dynamics and the resulting extremely large or small generator gradients . Once the SSN network produces large output rates , it disrupts the learning in the discriminator . Furthermore , Eq ( 32 ) alone can fix such behavior of SSN without relying on the discriminator to learn to adapt to new extremely strong inputs ( which are the SSN’s output rates ) . Thus , we find that it is better to skip such generator output samples for stabilizing learning through the GAN framework . Namely , we update the discriminator parameters for a mini-batch only if Penalty G ( θ ) < 1 ( 33 ) where the bound 1 is rather arbitrary . We observed that for many successful trainings , such large firing rates never occur . In order to encourage convergence to a fixed point , we also tried penalizing large absolute values of the time derivative dr/dt in the window [t1 , t2] . However , we found empirically that the penalty Eq ( 32 ) was sufficient to allow the training algorithms to find parameters for which the network converged with high probability to a fixed point . We looked at the goodness of fit of model outputs by comparing the distributions of several scalar functions or “summary statistics” of tuning curves . We give the precise definitions of these statistics here . For the feedforward network model of M1 tuning curves , in Experiment 1 we compared the data and model histograms of four test statistics or measures characterizing the hand-location tuning curves , defined as follows . Let r ¯ ( s ) denote the tuning curve , i . e . , the trial average firing rate in condition s , with s ∈ {1 , ⋯ , 27} indexing the hand location from among the 3 × 3 × 3 cubic grid of target locations in the experiment of Ref . [4] ) . The average firing rate was simply 1 27 ∑ s = 1 27 r ¯ ( s ) . The coding level of a tuning curve was defined as Coding Level = 1 27 ∑ s = 1 27 Θ ( r ¯ ( s ) - 5 Hz ) ( 34 ) i . e . , the fraction of conditions with rate larger than 5 Hz ( Θ ( ⋅ ) denotes the Heaviside function ) . The R2 denoted the coefficient of determination of the optimal linear fit to the tuning curve , i . e . , R 2 = 1 - ∑ s = 1 27 ( r ¯ ( s ) - L ( s ) ) 2 ∑ s = 1 27 r ¯ ( s ) 2 ( 35 ) where L ( s ) = r0 + m ⋅ xs is the optimal linear approximation to r ¯ ( s ) ( where r0 and m are the coefficients of the linear regression ) . Finally , following Ref . [4] , the complexity score of a tuning curve was defined as in Eq ( 36 ) . Complexity Score = SD [ | r ¯ ( s ) - r ¯ ( s ′ ) | max s ( r ¯ ( s ) ) - min s ( r ¯ ( s ) ) | | x s - x s ′ | = 1 ] ( 36 ) where SD denotes standard deviations . To quantify the goodness of fit between the outputs of the ground truth and trained SSN models in Experiment 2 , we compared the distributions of four test statistics characterizing the size tuning curves: preferred stimulus size , maximum firing rate , the suppression index , and the normalized participation ration , as defined below . While to fit the model we used size tuning curves containing responses to stimuli with S = 8 different sizes , bs , in the set ( 0 , 1/16 , 1/8 , 3/16 , 1/4 , 1/2 , 3/4 , 1 ) , for testing purposes and to evaluate the above measures , we generated tuning curves from the trained SSN using a larger set of stimulus sizes ( denoted by b below ) . Letting r ¯ ( b ) denote the size tuning curve ( i . e . , r ¯ ( b ) is the sustained response of the center excitatory neuron to the stimulus with size b ) , the maximum firing rate is maxb r ¯ ( b ) , and the preferred size is arg maxb r ¯ ( b ) . The suppression index is defined by Suppression Index = 1 - r ¯ ( max ( b ) ) max b ( r ¯ ( b ) ) , and measures the strength of surround suppression . Finally , the normalized participation ratio ( related to the inverse participation ratio [59] ) is defined by Normalized Participation Ratio = 1 n b ( ∑ b r ¯ ( b ) ) 2 ∑ b r ¯ ( b ) 2 ( 37 ) and measures the fraction of all tested sizes that elicited responses comparable to the maximum response . The most studied application of GANs is in producing highly structured , high-dimensional output such as images , videos , and audio . In those applications , mathematical structures such as translational symmetry in the data space ( for images ) is exploited to design complex and structured discriminators such as deep convolutional networks . It has also been noted that the discriminator network should be sufficiently powerful so that it is capable of fully capturing the data and model distributions [29 , 43 , 44] . In our application , the outputs of the generator are comparatively lower-dimensional objects , with less complex distributions . Furthermore , developing a new discriminator architecture exploiting mathematical structure in the tuning curve space such as metric and ordering in the stimulus space is beyond the scope of this paper . In this work we used relatively simple discriminator networks . Nevertheless care is needed in designing discriminators; a function D that is too simple can preclude the fitting of important aspects of the distribution . For example , if a linear function D were used in the WGAN approach it would result in a fit that matches only the average tuning curve between model and data , and ignores tuning curve variability altogether . For the M1 feedforward model , we used a dense feedforward neural network as the discriminator D , with four hidden layers of 128 rectified linear units and a single linear readout unit in the final layer . The discriminator network weights were initialized to uniformly random weights with Glorot normalization [60] . We do not use any kind of normalization or parameter regularization other than the WGAN penalty term in Eq ( 4 ) ( i . e . we set PenaltyG ( θ ) to zero in this example ) . For the SSN recurrent model , we used dense feedforward neural networks with four hidden layers and with layer normalization [61] as recommended for WGAN in Ref . [26] . The discriminator network used in the experiments of Figs 4 and 5 had 128 and 64 neurons in each hidden layer , respectively . In the training experiments underlying Fig 6 , we used networks with 32 to 128 neurons in each layer; as indicated by the WGAN histogram , all choices consistently performed well . We note that simple dense feedforward neural networks without normalization do not work well for SSN due to numerical instability in long-running training . It was important , however , not to apply the layer normalization in the first ( input ) layer , as the mean and variance across stimulus parameters of the turning curves are valuable information for the discriminator which would be discarded by such a normalization . We also used weight decay [62] for all parameters to stabilize the learning . We initialized all neural biases to 0 and initialized all weights as independent standard normal random variables , except in the input layer . For the input layers , we used the same initialization as in the M1 feedforward model’s discriminator . To provide a benchmark for our proposed GAN-based method , we also fit the SSN using moment matching [63] . We define a generic moment matching loss as L 0 ( θ ) = 1 D ∑ d = 1 D [ w 1 , d ( m d ( θ ) - μ d ) 2 + w 2 , d ( s d ( θ ) - σ d ) 2 ] . Here , D = S × O , where S is the total number of stimulus conditions and O the number of neurons whose firing rates are probed in the SSN , and d indexes the combination of stimulus condition and probe neuron . md ( θ ) and sd ( θ ) are the mean and variance of response in combination d , across a mini-batch of 32 model-generated sample tuning curves , respectively , while μd and σd are the empirical mean and variance of this response , across the full training dataset of 2048 size tuning curves . The wi , d are the weights given to each moment , and are hyperparameters of the moment matching method . We tried the following options uniform scaling : w 1 , d = ( 1 D ∑ c = 1 D μ c ) - 2 , w 2 , d = ℓ w 1 , d 2 , ( 38 ) element-wise scaling : w 1 , d = ( μ d + ε ) - 2 , w 2 , d = ℓ w 1 , d 2 , ( 39 ) relative scaling : w 1 , d = ( μ d + ε ) - 2 , w 2 , d = ℓ ( σ d + ε ) - 2 , ( 40 ) where ℓ controls weight of the variance with respect to the mean and ε = 10−3 is a regularization constant to avoid division by zero . In our preliminary experiments , we found that element-wise Eq ( 39 ) and relative Eq ( 40 ) scalings are better than uniform scaling Eq ( 38 ) . Thus , we only used element-wise Eq ( 39 ) and relative Eq ( 40 ) scalings in results presented here . For fitting the SSN , the same reasons for encouraging dynamical stability as in the WGAN case hold . Thus , we added the penalty term as defined in Eq ( 32 ) and minimized the loss L ( θ ) = L 0 ( θ ) + Penalty G ( θ ) . ( 41 ) For most of the trainings , large firing rates yielding PenaltyG ( θ ) > 0 rarely occured . The generator parameters are updated using Adam ( a variant of stochastic gradient descent ) with the hyperparameters β1 = 0 . 5 , β2 = 0 . 9 , ϵ = 10−8 as in Algorithm 1 . We use the learning rate 0 . 001 unless specified . To compare learned results between the GAN and moment matching and across different hyperparameters on an equal footing , we defined a condition for terminating learning as follows . First , we stop training at the first generator update step , n0 , in which the speed-of-change of the generator parameters , as evaluated by ‖ 1 ν ∑ n = n 0 - ν + 1 n 0 θ ( n ) - θ ( n - κ ) κ ‖ 1 ( 42 ) becomes smaller than a tolerance threshold of 0 . 01 . Here θ ( n ) is the vector of generator parameters at the nth generator update , κ controls the timescale at which the speed is computed , and ν is the size of the moving average window . ‖x‖1 denotes the L1-norm of the vector x , i . e . , the sum of absolute values of its components; thus the condition Eq ( 42 ) ensures that the speed-of-change of all model parameters are small , i . e . , they have approximately converged . To obtain the final result ( estimated parameters ) of learning , we then compute the average of the generator parameter θ ( n ) in the ω steps leading to step n0 θ ^ = 1 ω ∑ n = n 0 - ω + 1 n 0 θ ( n ) . ( 43 ) For comparing results across different generator learning rates , αG , we used κ = ν = ω = α G - 1 . As the metric of performance , we use the so-called symmetric mean average percent error ( sMAPE ) of the estimated generator parameters θ ^ relative to the ground truth parameters θtruth which were used to generate the training dataset . That is we let sMAPE ( θ ) = 100 % 1 13 ∑ i = 1 13 | θ ^ i - θ i truth | | θ ^ i + θ i truth | / 2 ( 44 ) where 13 is the number of parameters . The performance in any learning run is then computed by the sMAPE ( θ ^ ) of the final result , Eq ( 43 ) , of that run obtained using the above termination procedure Eq ( 42 ) . Note that this termination criterion is used only for Fig 6 and not in Fig 5 , where we plotted the learning curve over a broader range for demonstration purposes . For the WGAN-based fits shown in Fig 6 we picked combinations of hyperparameters from the following choices: the generator learning rate was αG = 10−4 , 2 × 10−4 , or 4 × 10−4 , the number of neurons in each discriminator hidden layer was 32 , 64 or 128 , discriminator update rule was Adam or RMSprop . For the moment matching fits the hyperparameter choices were: the learning rate was 10−4 , 2 × 10−4 , 4 × 10−4 , 10−3 , 2 × 10−3 , or 4 × 10−3 , the weight of variance λ = 0 . 01 , 0 . 1 , 1 , and the moment scaling was element-wise Eq ( 39 ) or relative Eq ( 40 ) . We implemented our GAN-based method and moment matching in Python using Theano [64] and Lasagne [65] . Our implementation is available from https://github . com/ahmadianlab/tc-gan under the MIT license .
|
Neurons in the brain respond selectively to some stimuli or for some motor outputs , but not others . Even within a local brain network , neurons exhibit great diversity in their selectivity patterns . Recently , theorists have highlighted the computational importance of diverse neural selectivity . While many mechanistic circuit models are highly stylized and do not capture such diversity , models that feature biologically realistic heterogeneity in their structure do generate responses with diverse selectivities . However , traditionally only the average pattern of selectivity is matched between model and experimental data , and the distribution around the mean is ignored . Here , we provide a hitherto lacking methodology that exploits the full empirical and model distributions of response selectivites , in order to infer various structural circuit properties , such as the statistics of strength and spatial range of connections between different cell types . By applying this method to fit two circuit models from theoretical neuroscience to experimental or simulated data , we show that the proposed method can accurately and robustly infer circuit structure , and optimize a model to match the full range of observed response selectivities . Beyond neuroscience applications , the proposed framework can potentially serve to infer the structure of other biological networks from empirical functional data .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"neural",
"networks",
"neuroscience",
"mathematics",
"network",
"analysis",
"distribution",
"curves",
"neuronal",
"tuning",
"research",
"and",
"analysis",
"methods",
"statistical",
"distributions",
"curve",
"fitting",
"computer",
"and",
"information",
"sciences",
"mathematical",
"functions",
"animal",
"cells",
"mathematical",
"and",
"statistical",
"techniques",
"probability",
"theory",
"cellular",
"neuroscience",
"cell",
"biology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences"
] |
2019
|
Inferring neural circuit structure from datasets of heterogeneous tuning curves
|
Despite significant frequencies of lateral gene transfer between species , higher taxonomic groups of bacteria show ecological and phenotypic cohesion . This suggests that barriers prevent panmictic dissemination of genes via lateral gene transfer . We have proposed that most bacterial genomes have a functional architecture imposed by Architecture IMparting Sequences ( AIMS ) . AIMS are defined as 8 base pair sequences preferentially abundant on leading strands , whose abundance and strand-bias are positively correlated with proximity to the replication terminus . We determined that inversions whose endpoints lie within a single chromosome arm , which would reverse the polarity of AIMS in the inverted region , are both shorter and less frequent near the replication terminus . This distribution is consistent with the increased selection on AIMS function in this region , thus constraining DNA rearrangement . To test the hypothesis that AIMS also constrain DNA transfer between genomes , AIMS were identified in genomes while ignoring atypical , potentially laterally-transferred genes . The strand-bias of AIMS within recently acquired genes was negatively correlated with the distance of those genes from their genome’s replication terminus . This suggests that selection for AIMS function prevents the acquisition of genes whose AIMS are not found predominantly in the permissive orientation . This constraint has led to the loss of at least 18% of genes acquired by transfer in the terminus-proximal region . We used completely sequenced genomes to produce a predictive road map of paths of expected horizontal gene transfer between species based on AIMS compatibility between donor and recipient genomes . These results support a model whereby organisms retain introgressed genes only if the benefits conferred by their encoded functions outweigh the detriments incurred by the presence of foreign DNA lacking genome-wide architectural information .
The evolutionary histories of genes within bacterial genomes have long been shown to be highly incongruent [1]; [2–4] . Horizontal Gene Transfer ( HGT ) between species enables bacteria to acquire and potentially utilize any gene that it encounters in the biosphere , thus catalysing exploration of novel niches , the evolution of pathogenicity , or responses to environmental stressors in manners beyond the capabilities of their ancestors . While the amount of transferred DNA inferred in individual genomes varies depending on methodology for detection , the age limit for distinguishing between acquired and native genes , and the taxa involved , the fraction of bacterial genomes resulting from recent transfer is very large , ranging from 20% to 80% of the genome [5–7] . Yet despite the preponderance and pervasiveness of this genetic admixture [8 , 9] , members of higher taxonomic groups share large degrees of genotypic and phenotypic similarity [4 , 10] which belie the potential for genome homogenization between groups afforded by such rampant transfer . This cohesion within groups indicates that more closely related bacterial groups are more likely to exchange genes successfully [9] , resulting in genotypic similarity due to shared pathways for gene trafficking , rather than a common pool of unchanging ancestral genes . Two mechanisms could result in the preferential use of gene donors: either bacteria are predominantly exposed to incoming DNA from closely-related taxa , or genes from related taxa are preferentially retained following their introduction [11 , 12] . For example , similarity in GC content [11] or ecological niche [4 , 13 , 14] between inferred donor and recipient genomes are proposed to influence HGT success . While organisms dwelling in the same environment likely have increased opportunities for gene exchange ( owing to the increased rate of both direct or indirect encounters among organisms in closer proximity ) and carry genes which are useful in that setting; these communities contain many unrelated taxa and do not necessarily bias gene transfer towards related members . Given the paucity of genes recalcitrant to HGT [15] , these factors alone are insufficient to reconcile the disparity between the scope and frequency of gene transfer , its role in promoting niche invasion , and overall levels of similarity among higher taxonomic groups of bacteria . Any benefits conferred by horizontally acquired genes that favor their retention must exceed any detriments imparted by the integration of incompatible foreign DNA into an evolutionarily coadapted genome . We have previously drawn attention to molecular mechanisms by which integrated DNA can negatively impact recombinant survival [8] . This constraint centers on the role of Architecture IMparting Sequences ( AIMS ) , strand-biased repetitive elements which act during DNA segregation . The improper distribution of these sequences in newly-acquired genes should disrupt AIMS-based genome architecture , and thus negatively impact cellular fitness; such genes would be preferentially lost if the encoded functions were insufficiently beneficial to overcome this detriment . If AIMS were shared among more closely-related taxa , they could reinforce cohesion within bacterial clades by counter-selecting gene acquisition from distantly-related taxa which do not have the sequences in congruent distributions . This makes AIMS distinct from other conserved features in chromosomes such as gene orientation , rRNA location , Chi sites or Ter sequences which will impose selective constraints but do not have the qualities of abundance or variation between taxa that would arbitrate the success of transfer events . AIMS form the basis of an architecture present in nearly all bacterial genomes [16 , 17] . Chromosomes are immense polymers with embedded instructions that direct faithful replication , repair , defense and segregation [18] . AIMS are identified as strand-biased octamers which , unlike simple strand-biased sequences such as chi [19] , increase in abundance and degree of strand-bias with proximity to the replication terminus ( Fig 1 ) [16] . This pattern suggests that selection for AIMS function would be maximal at the replication terminus ( Fig 1 ) [16] . AIMS are proposed to aid in processes such as DNA replication , repair and segregation [16]; for example , FtsK Orienting Polar Sequences ( KOPS ) are AIMS that assist the directional loading of the FtsK translocase , which pumps chromosomes trapped in division septa into the proper daughter cells [20–24] . The functions of most AIMS are unknown , and AIMS serve as surrogates for the true targets of selection . Detrimental effects of changing AIMS from permissive ( on leading strand ) to non-permissive ( on lagging strand ) orientations have been observed in E . coli [25] . Suites of AIMS are similar in sequence among more closely-related taxa [16 , 26] , suggesting that clades of bacteria share AIMS architectures . We propose that disruption of genome-wide AIMS organization will have deleterious effects . For example , inversions restricted to a single chromosome arm can place potentially large numbers of AIMS into nonpermissive orientations; therefore , we predict that the size and frequency of inversions will be correlated with distance from the replication terminus , as inversions close to the terminus would place AIMS in their nonpermissive orientations where selection for their function is the greatest . Similarly , insertion of foreign DNA will be detrimental if AIMS in the recipient organism are not strand-biased in the donor genome , thereby precluding introgressed fragments from bearing AIMS in predominantly permissive orientations . We predict that the degree to which newly-acquired DNA carries AIMS in their permissive orientation will also be negatively correlated with distance from the terminus . If so , then these results would validate the role of AIMS in promoting gene transfer among organisms wherein AIMS are shared , or at least strand-biased , among members of the same clade . Herein , we demonstrate that these predictions are validated and propose a framework for interspecific gene transfer based on AIMS compatibility .
AIMS are identified as degenerate octamers with three properties: ( i ) they are strand-biased , with more instances appearing on leading strands than on lagging strands , ( ii ) their abundance on leading strands increases on both chromosome arms with distance from the replication origin ( proximity to the replication terminus or telomere ) , and ( iii ) their degree of strand-bias also increases with distance from the replication origin . The increase in strand-bias and abundance with proximity to the terminus reflects selection for this gradient as it cannot be explained by mutational processes [27] . Oligomers identified with these properties often fall into groups of related or overlapping octamers , likely reflecting selection on a longer , degenerate sequence . However , small numbers of sequences with these properties may arise by stochastic factors alone . To identify sets of potential AIMS which minimize the number of sequences arising by stochastic processes , we first identified replication breakpoints in bacterial genomes using a Markov approach ( see Methods ) since AIMS are strand biased and required known replication breakpoints to identify; the breakpoints were classified as either a replication origin or terminus so that the majority of genes are transcribed from leading strands [28 , 29] . The location of the terminus was refined and validated using the locations of putative dif sites [30]; the predicted termini and the annotated dif sites were very close ( S5 Table ) , providing confidence that both the replication origin and terminus were predicted accurately . Recently-recombined regions were identified by comparison with closely-related genomes and removed , leaving the ancestral sequences whose properties reflect consistent mutational biases . The numbers of AIMS-like oligomers were identified in this ancestral backbone using a range of criteria , including different degrees of overall strand-bias and different degrees of increase in abundance with proximity to the replication terminus ( S1 Dataset ) . As expected , the numbers of potential AIMS decrease as the criteria for their selection become more stringent . To determine what fraction of oligomers reflects selection for function ( true AIMS ) , the same process was implemented on the backbone genomes after the positions of 10 kb segments were randomized within chromosome arms . This randomization preserved overall strand-bias , but eliminated any result of a gradient of selection from origin to terminus; putative “AIMS” identified within such randomized genomes would be the result of stochastic factors alone . As expected , fewer putative AIMS are identified in randomized genomes as compared to genuine genomes ( Fig 2 ) . Suitable selection criteria are defined as those wherein the numbers of putative AIMS are at least 10-fold higher in the genuine genome as compared to those identified in randomized genomes so that at least 91% of the octamers identified in genuine genomes are true AIMS , reflecting selection rather than stochastic processes . In this way , we are confident that the sets of AIMS we identified reflect the action of selection , with minimal numbers of confounding sequences . If the distribution of AIMS is maintained by selection , then genome rearrangements which disrupt these distributions will be counter-selected . Inversions are reported to be non-random with respect to the origin and terminus [31] . Inversions that do not include either the replication origin or terminus will move AIMS that were formerly in their permissive orientations into their nonpermissive orientations , and thus should be counter-selected . Therefore , we predict that inversions observed in extant genomes will become both smaller and less frequent with proximity to the replication terminus , where selection for AIMS function is maximal ( Fig 1 ) . We identified inversions in 159 pairs of genomes from 43 families representing 17 divisions of bacteria ( S1 and S2 Tables ) ; inversions that included the replication origin or terminus were ignored as they do not affect the strand-bias of AIMS . Genes were identified using the annotation provided; orthologous genes were identified as reciprocal best BLAST hits , where genes were aligned over >85% of their length . Inversions were identified as groups of orthologous genes that had been reversed in orientation relative to proximal , otherwise syntenic genes in a closely related genome ( see Materials & methods ) . In total , 634 unique inversions were identified; inversion positions were defined as the percentage of genome distance from the replication terminus to the center of the inversion , averaged between the two genomes compared . The distribution of inversions within bacterial chromosomes shows a clear and unambiguous relationship with respect to the replication terminus ( Fig 3 ) . As predicted by the distribution of AIMS , the number of inversions observed in genome alignments is strongly positively correlated with distance from the replication terminus ( Fig 3B; R = 0 . 86 ) . Moreover , the length of observed inversions is also strongly positively correlated with the distance from the replication terminus ( Fig 3C; R = 0 . 92 ) . Taken together , six times as much inverted DNA is found near the replication origin as compared to the replication terminus ( Fig 3A; R = 0 . 97 ) . In addition to typifying the data set as a whole , this pattern is evident within subsets of genomes with different properties . For example , inverted DNA is clearly lacking from the region of the replication terminus in different taxonomic groups including Actinobacteria , α-proteobacteria , γ-proteobacteria , δ , ε-proteobacteria , and Firmicutes ( S1 Fig ) , in genomes from low ( 35% ) to high ( 75% ) %GC ( S1 Fig ) , and in genomes ranging from 2 MB to 9 . 5 MB in size ( S1 Fig ) . Only small , AT-rich genomes failed to show a positive relationship between the amount of inverted DNA and distance from the terminus ( S1 Fig ) ; these organisms are primarily intracellular parasites whose genomes show weak purifying selection and high rates of chromosomal rearrangement [32 , 33] , which would occlude any pattern we would hope to detect . Rather than reflecting constraints imposed by AIMS , the decrease of inversion size and frequency with proximity to the replication terminus could reflect a preference for the individual genes to be transcribed from a particular strand [34 , 35] . For example , highly-expressed genes are more often transcribed from leading strands , thus avoiding collisions between DNA- and RNA-polymerases . If highly-expressed genes were found preferentially near the terminus , our results would be observed . To test this hypothesis , we used the degree of codon selection as a surrogate metric for average level of gene expression [36] . We calculated codon usage bias using four separate metrics within 12 representative genomes from 5 divisions of bacteria . In most genomes , codon usage bias was not correlated with distance from the replication terminus ( S6 Table ) ; in the few genomes which show weak effects , codon usage bias increased with proximity to the replication origin , not the replication terminus ( S6 Table ) . This is unsurprising , as highly-expressed genes in many organisms are found close to the replication origin , likely because of the higher average ploidy numbers there [19 , 37 , 38] . Therefore , we reject the hypothesis that inversions are avoided near the terminus because the genes in that region are more highly expressed . Alternatively , the dearth of inversions in the terminus region could reflect a gradient in the distribution of the small , repeated sequences that catalyze inversion formation [39–41] . To test this , we examined the spacing between adjacent inverted pentamers , hexamers and heptamers within each chromosome arm and regressed the average spacing for 10kb intervals against distance of the interval from the terminus ( S6 Table ) . While these oligomer lengths are not equal to those observed for spontaneous inversion join points [41] , their greater numbers allow for a more robust analysis while being able to capture any trend that would impact the slightly longer repeats observed . The distribution of the oligomers we examined showed no change in abundance near the replication terminus ( S6 Table ) ; therefore , we reject the hypothesis that inversions form at different rates , or at different sizes , near the replication terminus . Lastly , inversions may form with equal likelihood across the chromosome arm , but could be counter-selected near the replication terminus if operons there were longer , so that spontaneous inversions would be more likely to disrupt transcription units in that region . To test this hypothesis , we regressed operon length and number of genes per operon against distance of the operon from the terminus . There was no significant association with either metric in any of our 12 representative genomes ( S6 Table ) . Therefore , we conclude that inversions would not disrupt transcription units to a greater degree near the replication terminus . Taken together , these analyses can find no relationship between the likelihood of inversion and distance from the replication terminus for any factor aside from the distribution of AIMS within bacterial genomes . Therefore , we conclude that these intragenomic rearrangements are counter-selected because they disrupt AIMS distributions . Aside from AIMS , Ter sites in enteric bacteria are localized in proximity to the replication terminus [42–44] . Ter sites are longer and less abundant than AIMS , and serve to stall DNA polymerases travelling away from the replication terminus [45] . Inversion of individual Ter sites is highly detrimental as an inverted Ter site interrupts DNA replication before it is completed [46 , 47] . Analogous Rtp sites in Bacillus species also block retrograde replication and cannot be inverted [46–50] . Unlike highly abundant and nearly ubiquitous AIMS , Ter and Rtp sites are uncommon in the few genomes in which they are observed . To determine if the presence of known Ter-like sites could produce the distribution of inversions we observed , we simulated the random generation of inversions within a 4 . 5 MB genome that contained varying numbers of Ter-like sites placed in a gradient from replication origin to terminus . To simulate selection , simulated inversions containing a Ter-like site were considered nonpermissive and removed from the simulated data set . Each simulation was performed 100 , 000 times ( Fig 4 ) . For the actual number of Ter sites within the E . coli genome ( <20 ) , no impact on the distribution of inversions within chromosome arms was detected ( Fig 4 ) . To constrain inversions to the degree observed in genuine data , simulated genomes required ~1600 Ter-like sites to be placed in a positional gradient on each chromosome arm ( ~3200 per genome ) . This abundance of Ter-like sites is not consistent with the abundance of known Ter or Rtp sites , but is consistent with the abundance of AIMS . Therefore , we conclude that known low-abundance Ter-like sites could not have produced the distribution of inversions we observed . Just as AIMS distributions counter-select intragenomic rearrangements , we predict that intergenomic rearrangements that disrupt AIMS distributions will also be counter-selected . Upon insertion , newly-arrived DNA will contain AIMS in permissive and nonpermissive orientations at approximately equal frequencies . Inserted DNA should see minimal selection for AIMS function near the replication origin ( Fig 1 ) , so that acquired regions will show little strand-bias for AIMS . Selection for AIMS function increases with proximity to the replication terminus ( Fig 1 ) ; therefore , we expect insertions which introduce AIMS in nonpermissive orientations to be removed more aggressively with proximity to the terminus . As a result , insertions in this region should bear AIMS in predominantly permissive orientations as seen , for example , in the abundance of KOPS ( a subclass of AIMS ) in prophages in Salmonella and E . coli [51 , 52] . To test this hypothesis , we identified 17 , 096 insertions totalling 36 , 434 , 039 bp of transferred DNA in 177 completely sequenced bacterial genomes ( recipients ) ( S3 and S4 Tables ) . As described above , AIMS were identified in recipient genomes which lacked these insertions; that is , AIMS were identified in the backbone genome without considering their distribution in newly-acquired genes . We then enumerated the AIMS in permissive and nonpermissive orientations within each newly-acquired region . The strand-bias of AIMS within acquired regions was plotted against distance of the region from the replication terminus for all insertions in our dataset ( Fig 5 ) . Two conclusions can be drawn from these data . First , AIMS are strand-biased within DNA regions acquired by gene transfer even in the origin-proximal region of the chromosome . Second , a strong correlation was observed ( R2 = 0 . 98 ) , whereby the strand-bias of AIMS increased for insertions located near the replication terminus . If the analysis is limited to inserted regions up to 8 kb in length , the same pattern is observed ( S2 Fig ) . Therefore , it is extremely unlikely that this pattern reflects the analysis of regions of native DNA that have been misannotated as “genes” and thus not identified in sibling strains or sister species . As was true for the distribution of inversions above , this pattern was evident regardless of the taxonomy , genome size or nucleotide composition of the recipient genome ( S3 Fig ) . We do not believe this reflects a process whereby DNA with permissive AIMS preferentially inserts near the terminus; rather , we surmise that insertions bearing nonpermissive AIMS have been counter-selected , and thus are observed less frequently , in the terminus region . The selection for AIMS function near the replication origin , while weaker than selection near the terminus , was still sufficient to counter-select fragments bearing AIMS in predominantly nonpermissive orientations , thus increasing average strand-bias of AIMS even in this location . If the AIMS within inserted DNA arose from mutational processes after those genes’ acquisitions , then the strand-bias of AIMS within inserted DNA should increase with the length of time those sequences have dwelled in their recipient genomes . We used the average Ks between the most closely-related genomes bearing vs . lacking the insertion as a surrogate measure for the age of the insertion . We found that the increase of strand-bias of AIMS within terminus-proximal insertions is not a function of the average age of the insertion ( S3 Fig ) ; therefore , we conclude that the increase of strand-bias of AIMS towards the replication terminus does not reflect the action of mutation following the introduction of the foreign DNA . To estimate the fraction of insertions that were removed due to selection for AIMS function , we analyzed genomes of γ-proteobacteria; the dif site locations in these taxa were most reliable , so that AIMS strand-bias on insertions near the terminus was most accurate . We compared insertions in the terminus region , where selection for AIMS function is expected to be strongest , to insertions in the origin region , where selection is weakest . For each region , the normalized cumulative length of the fragments was plotted , ordering fragments by the strand-bias of the native-genome’s AIMS within the fragment ( Fig 6 ) . In both chromosomal regions , acquired fragments bore AIMS predominantly in the permissive orientation; this is evident from the paucity of fragments with AIMS strand-bias less than 50% . As expected , the strand-bias of AIMS in fragments inserted near replication termini is even more pronounced ( Figs 5 and 6 , gray curve ) , differing significantly from the distribution of strand bias within origin-proximal fragments ( P < 10−16 , Kolmogorov-Smirnov test ) . Using this cumulative distribution curve , we can estimate the fraction of fragments in the terminus region , relative to the origin region , that have been lost due to selection for AIMS function; this is accomplished by subtracting the areas under the normalized cumulative distribution curves . This analysis shows that at least 17 . 4% of fragments inserted near the replication terminus , relative to the replication origin , have been removed due to selection for AIMS function . This is , of course , an underestimate of the fraction of insertions lost due to selection for AIMS function because ( a ) the sets of fragments analyzed include very large numbers of genes that are recently acquired and have not yet been subject to selection [at least 90% of identified insertions[53] , and ( b ) the absence of fragments with AIMS below 50% in the origin region indicates that selection for AIMS function has led to loss of fragments in the origin region as well . Even so , it demonstrates that selection for AIMS function imposes a significant and measurable barrier to the long-term persistence of inserted DNA in bacterial genomes . Because AIMS provide a mechanism by which gene acquisition is constrained , they may act to bias overall gene flow between organisms of different taxonomic groups . Genomes will be more likely to acquire novel genes from donor taxa wherein the recipient genome’s AIMS are strand-biased ( Figs 5 and 6 ) . We posit that sets of AIMS found in any individual genome will be more likely to be strand-biased in genomes of related taxa , e . g . , taxa in the same family or division; AIMS in the recipient taxon likely evolved function from simple strand-biased oligomers that were present in the common ancestor of both donor and recipient genomes . If so , then gene exchange would be more permissible between members of the same taxonomic group , and be more constrained between members of different taxonomic groups ( Fig 7 ) . Genomes would be more compatible for transfer if AIMS in a recipient genome are strand-biased in a donor genome . To test this hypothesis , we identified AIMS in 119 taxa representing at least 54 families ( some families were unknown ) in 12 divisions; these were designated as potential recipient genomes . We then examined the degree of strand-bias for each set of AIMS within 1146 potential donor genomes , including taxa both closely- and distantly-related to each potential recipient genome . For each donor genome , the average strand-bias of oligomers which acted as AIMS in the 119 recipient taxa was assessed for 10 kb segments . Fig 8 shows representative data for the Escherichia coli and Bacillus subtilis genomes acting as potential recipients . In each case , the recipient genome’s AIMS were more strand-biased within more closely-related potential donor genomes ( Fig 8 ) . For recipients in each of the twelve divisions analyzed , donors from the same division were more compatible than donors in different divisions , and donors from the same family were always more compatible than donors from different families in the same division ( Table 1 ) . Because successful HGT events are more likely to involve donor genomes with compatible AIMS ( Fig 7 ) , these data support the hypothesis that AIMS will counter-select HGT events from more distantly-related taxa . Thus , these data suggest that donor taxa from the same division ( or family ) would introduce DNA fragments with AIMS in the permissive orientations more often than donor taxa from different divisions ( or families ) . Horizontal gene transfer is a powerful source of genetic and physiological change in bacteria . It has been suggested that genotypic and phenotypic cohesion is observed at higher taxonomic levels in bacteria despite rampant HGT [4 , 10 , 54] . While Gogarten et al . [9] proposed that this cohesion could reflect barriers to HGT between organisms in different higher taxonomic groups , no mechanisms had been identified . Here , we propose that any benefit conferred by an introduced gene must offset any detriment incurred by its integration into the genome; such detriments would arise if the incoming DNA fragment contained AIMS in preferentially non-permissive orientations . Our data demonstrate that AIMS likely constrain both intragenomic and intergenomic rearrangements , that substantial numbers of introduced genes were eliminated due to their failure to have AIMS in the permissive orientations , and that genomes within higher taxonomic groups are more compatible for gene transfer than genomes outside those groups due to donor genomes bearing recipient genomes’ AIMS as strand-biased oligomers . Thus , selection for the preservation of AIMS-based genome architecture provides a much-needed mechanism for the preferential transfer of genes among organisms of higher taxonomic groups . This , in turn , provides a mechanism whereby genotypic and phenotypic similarities among taxa within higher taxonomic groups do not reflect ancestral characteristics , but rather more frequent gene exchange .
All genome sequences were retrieved from GenBank; genes were defined using the annotations provided . Orthologues in strains of the same species were identified as reciprocal best BLAST hits where ( a ) encoded proteins exceeded 70% similarity or encoded structural RNAs exceeded 90% identity , and ( b ) >85% of coding sequences were aligned . A consensus sequence of 5’-RNTKCGCATAATGTATATTATGTTAAAT was used to locate putative dif sites in γ-proteobacterial genomes . A consensus sequence of 5’- AGNATGTTGTAACTAA was used to locate Ter sites in the E . coli genome . All analyses were performed using DNA Master version 5 . 23 , available from cobamide2 . bio . pitt . edu . The replication origins and termini were identified using the relative abundance of strand-biased pentamers . Possible intergenic locations of the replication origin and terminus were permuted across the genome , creating two potential chromosome arms . The relative frequency of pentamers was quantified within each of the three reading frames of protein-coding genes as , fijklm , r=∑r∑i∑j∑k∑l∑mP ( Bm|Tijkl ) ( 1 ) where r is the reading frame , ijklm are five consecutive nucleotide positions , T is the specific tetramer at position ijkl , Bm is the identity of the base at position m , and P ( B|T ) is the probability of base B given tetramer T . Values are summed across all 3 reading frames and all 4 nucleotides . The difference in pentamer frequencies Δ was calculated as the sum of the squared differences between genes on putative leading vs . lagging strands: Δ=∑r∑i∑j∑k∑l∑m ( fijklm , r , Lead−fijklm , r , Lag ) 2 ( 2 ) The replication breakpoints were identified as those locations that maximized Δ , the differences in relative , frame-specific pentamer frequencies between genes predicted to be transcribed on leading vs . lagging strands . The two breakpoints were assigned as the replication origin or terminus so that the number of genes transcribed away from the replication origin was maximized . The positions of the termini were validated using the locations of known dif sites , which are found at replication termini [30] . This validation also demonstrated that replication breakpoints identified using pentamer distributions were more robust than those identified using GC skew . The final dataset used only genomes with curated dif sites [55 , 56] , further substantiating the origins identified using the method described . Inversions were identified in organisms with at least 97% 16S rRNA similarity; inversions were evident within a backbone of syntenic genes as regions where gene orientations were reversed relative to adjacent genes . Using uppercase and lowercase letters to represent genes transcribed from the leading and lagging strands , respectively , genes DEF would be inverted if region ABCDEFGHJ were organized as ABCfedGHJ in a sister taxon . We ignored potential rearrangements where flanking genes lacked synteny and thus may represent translocations or xenologous insertions . Inversions including the replication origin or terminus were ignored as these do not invert AIMS . The midpoint of each inversion was used to calculate distance from the terminus , normalized as a percentage of the total genome length and averaged between the two genomes . In identifying inversions among multiple taxa , inversion identified in multiple comparisons were counted only once . Genes likely to have been acquired by horizontal gene transfer were identified as those lacking an orthologue in the genomes of a sister species as well as multiple strains of the same species , where the closest homologue in a conspecific strain encoded a protein with < 40% similarity . The absence of the gene in multiple strains increases the likelihood that the gene was a novel acquisition rather than a parallel loss . The location of the insertion was quantified as the percentage of the genome length of the midpoint of the insertion from the replication terminus . AIMS were identified in genomes in which horizontally transferred genes had been identified and removed from the sequence as above . AIMS were identified as 8-mer sequences with increased abundance , as well as increased strand-bias , in the 25% of the genome near the replication terminus relative to the values observed for the 60% of the genome near the replication origin [16] . Degenerate octamers are useful surrogates for detecting selection on longer sequences whose direct detection is not robust; longer sequences are generally too infrequent to allow reliable measures of changes in abundance across the chromosome . The thresholds for increase in skew and abundance were established for each genome such that the number of observed AIMS in genuine genomes exceeded the numbers identified in resampled genomes by at least 10-fold . Resampled genomes were constructed by randomly rearranging 40 kb segments within each chromosome arm , thus preserving leading and lagging strand-bias . Sets of AIMS included those that ( a ) were highly abundant , but had weaker increase in strand-bias near the terminus , and ( b ) were less abundant but with strong increase in strand-bias near the terminus . The final sets of AIMS used herein are outlined in S6 Table . To examine the number of Ter sites required to decrease the occurrence of inversions near the replication terminus , simulated Ter sites were inserted in a simulated genome where inter-Ter distance increased linearly with distance from the terminus . Simulated inversions were then generated at random within the genome , where the distribution of inversion size was modelled after those seen in genuine data; simulated inversions were discarded ( counter-selected ) if they included a simulated Ter site . To determine the compatibility for gene exchange between genomes , we measured the strand-bias of a recipient genome’s AIMS within a donor genome . Biases were measured within randomly chosen 10 kb segments of potential donor genomes; this method allows us to determine the AIMS composition of DNA fragments in a donor genome without the need to predict its replication origin or terminus . Instances of each of the recipient genome’s AIMS were counted on the Watson ( NW ) and Crick ( NC ) strands of each donor DNA fragment; the strand-bias of each AIMS ( SBi ) was calculated as SBi=SupremumofNW/ ( NW+NC ) andNC/ ( NW+NC ) . ( 3 ) The mean strand-bias of recipient AIMS in a donor genome ( SBi¯ ) was calculated as the mean strand-bias for 1000 randomly chosen 10 kb donor fragments . The overall compatibility between genomes X and Y ( CXY ) was calculated as CXY=∑iSBi¯*Ni/∑iNi ( 4 ) where Ni is the abundance of AIMS i in the recipient genome . Values are summed across all AIMS in the recipient genomes . Thus , compatibility represents a mean bias of a recipient genome’s AIMS in the donor genome , weighted for the abundance of the AIMS in the recipient genome . We do not weight the contributions of individual AIMS by their strand bias in the recipient genome since this is a function of both selection and mutational bias .
|
The potential success of horizontal gene transfer events is historically equated to the benefits conferred by encoded products . Here we show that gene transfer events are observed less frequently if the introduced genes disrupt important patterns of genomic information , suggesting that this disruption would confer an unacceptable cost . As a result , gene transfer events are less likely to be successful if the potential donor genomes have incompatible genome architecture . Because more distantly-related genes are less compatible , chromosome architecture serves as a mechanism to bias gene transfer events to those involving closer relatives , thereby providing a mechanism for the genotypic and phenotypic cohesion of higher taxonomic groups .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"discussion",
"Materials",
"&",
"methods"
] |
[
"bacteriology",
"horizontal",
"gene",
"transfer",
"chromosomal",
"inversions",
"microbiology",
"gene",
"transfer",
"dna",
"replication",
"materials",
"science",
"bacterial",
"genetics",
"dna",
"microbial",
"genetics",
"oligomers",
"microbial",
"genomics",
"materials",
"by",
"structure",
"bacterial",
"genomics",
"chromosome",
"biology",
"chromosomal",
"aberrations",
"comparative",
"genomics",
"biochemistry",
"cell",
"biology",
"nucleic",
"acids",
"evolutionary",
"processes",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"genomics",
"evolutionary",
"biology",
"computational",
"biology"
] |
2018
|
Chromosome architecture constrains horizontal gene transfer in bacteria
|
All genomes require a system for avoidance or handling of collisions between the machineries of DNA replication and transcription . We have investigated the roles in this process of the mTERF ( mitochondrial transcription termination factor ) family members mTTF and mTerf5 in Drosophila melanogaster . The two mTTF binding sites in Drosophila mtDNA , which also bind mTerf5 , were found to coincide with major sites of replication pausing . RNAi-mediated knockdown of either factor resulted in mtDNA depletion and developmental arrest . mTTF knockdown decreased site-specific replication pausing , but led to an increase in replication stalling and fork regression in broad zones around each mTTF binding site . Lagging-strand DNA synthesis was impaired , with extended RNA/DNA hybrid segments seen in replication intermediates . This was accompanied by the accumulation of recombination intermediates and nicked/broken mtDNA species . Conversely , mTerf5 knockdown led to enhanced replication pausing at mTTF binding sites , a decrease in fragile replication intermediates containing single-stranded segments , and the disappearance of species containing segments of RNA/DNA hybrid . These findings indicate an essential and previously undescribed role for proteins of the mTERF family in the integration of transcription and DNA replication , preventing unregulated collisions and facilitating productive interactions between the two machineries that are inferred to be essential for completion of lagging-strand DNA synthesis .
The mitochondrial genome and its expression are essential to maintain oxidative phosphorylation ( OXPHOS ) , a central metabolic process in higher eukaryotes . OXPHOS failure during development leads to developmental arrest in a diverse range of metazoans , including both insects [1] , [2] and vertebrates . In the mouse , for instance , ablation of genes required for mitochondrial DNA ( mtDNA ) maintenance results in lethality at embryonic day 8–9 [3]–[5] . OXPHOS dysfunction also underlies many pathological states in humans [6] . Elucidation of the mechanisms of faithful mitochondrial genome maintenance and expression is therefore of both developmental and medical relevance [6] . In metazoans , mtDNA replication has been most extensively studied in mammals , where several competing models have been proposed . The strand-displacement model [7] , originally based on imaging and end-mapping studies ( see also [8]–[10] ) , contrasts with the evidence from two-dimensional neutral agarose gel electrophoresis ( 2DNAGE ) analyses [11]–[16] , supporting various types of strand-coupled replication . In the strand-displacement model , leading-strand synthesis initiates in the major non-coding region ( NCR ) , at a site designated as the origin of heavy-strand synthesis ( OH ) [12] , [13] . It then proceeds two-thirds of the way around the circle until reaching the site designated as the origin of light-strand synthesis ( OL ) . Synthesis of the two strands on this model is asynchronous , but continuous on both strands , i . e . without a need for Okazaki fragments . 2DNAGE was developed almost three decades ago , to separate and characterize branched from linear DNA [17] . It has been widely used to analyze replication intermediates , starting in 1987 with the yeast 2 µ plasmid [18] , and subsequently in hundreds of other publications . The method is considered definitive for inferring replication mode and origins , termination sites , fork barriers and molecular recombination ( for review see [19]–[23] ) . 2DNAGE has indicated the existence of two classes of strand-coupled replication intermediate in mammalian mtDNA , which have been suggested to reflect alternate modes of replication that may operate in parallel . In the unidirectional RITOLS mode ( RNA Incorporation Throughout the Lagging Strand ) , a provisional lagging-strand , consisting of RNA segments derived from processed transcripts , is established as the replication fork proceeds [14] . This RNA is then replaced by DNA in a subsequent maturation step , in which lagging-strand DNA synthesis is initiated at one or more preferred sites , including OL . RITOLS shares many features with the strand-displacement model , the only major difference being that the latter postulates that the parental strand displaced during heavy-strand replication remains single-stranded until the light-strand initiates . The second type of replication intermediate detected by 2DNAGE is composed fully of dsDNA , whose structure implies bidirectional initiation of replication across a wider origin zone , stretching beyond the NCR . However , termination at OH means that this mode of replication is also effectively unidirectional [11] , [16] . Mitochondrial DNA replication in Drosophila melanogaster , based both on early TEM [24] , [25] and more recent 2DNAGE analyses [26] , also involves two replication modes . The majority of replication intermediates are composed entirely of dsDNA , with no evidence of extensive RNA incorporation . Their structure implies unidirectional strand-coupled DNA synthesis , commencing in the NCR , with an initiation site as inferred previously by end-mapping [27] . A minority of replicating molecules retain a region of single-strandedness encompassing the rRNA gene locus just downstream of the origin , indicative of delayed lagging-strand completion in this limited part of the genome . Also of note was the inference of specific replication pause regions through which the replication fork travels more slowly , based on the pronounced accumulation of replication intermediates containing fork structures therein . The two major pause regions of the mitochondrial genome [26] correspond approximately with zones of convergence of oppositely transcribed blocs of genes ( Fig . 1A ) . The coding region of metazoan mtDNAs shows a highly compact organization , with little or no non-coding sequences between genes . Typically , genes are encoded on both strands , a type of organization that unavoidably risks encounters between the transcription and replication machineries , which compete for the same template . As in other genetic systems , these processes should be subject to regulation , in order to minimize and resolve potential conflicts , including both co-directional and anti-directional collisions between the two molecular machineries . Defects in this collision regulation have been shown to cause abortive DNA synthesis , mutagenesis and genomic instability in a wide range of organisms [28]–[33] . In E . coli , for example , transcription termination is essential for the maintenance of genome integrity [34] , by minimizing the generation of double-strand breaks arising from replication-fork collapse . A recent report has documented the importance of a machinery to regulate replication pausing caused by collisions with transcription complexes [35] . The mitochondrial transcription termination factor ( mTERF ) family comprises a set of mitochondrial DNA-binding proteins with diverse , documented roles in mitochondrial gene expression [36] , [37] . The key structural feature of these proteins is the presence of multiple TERF motifs ( I–IX ) , which have been shown , at least in the case of human MTERF1 and MTERF3 , to form left-handed helical repeats that create a superhelical DNA-binding domain [38] , [39] . mTERF family members have been implicated in the regulation of transcriptional initiation [4] , [40] , [41] as well as attenuation [40] , [42] , [43] , and have also been shown to participate in mitoribosome assembly and translation [44]–[47] . In the mouse , Mterf3 and Mterf4 are essential genes [4] , [45] , while Mterf2 is not [41] . Human MTERF1 terminates transcription bidirectionally in vitro at its major binding site downstream of the rRNA genes [48]–[50] , but manipulation of its activity in cultured cells or knockout mice has rather modest effects on transcript levels [43] , [51] , whose physiological significance , if any , is unknown . Four proteins of this family have been identified in Drosophila , of which the best characterized is mTTF ( CG18124 ) . mTTF binds two sequence elements in Drosophila mtDNA [42] , each located at the junctions of convergently transcribed blocs of genes ( see Fig . 1A ) . Its binding facilitates transcriptional termination bidirectionally in vitro and is required for transcriptional attenuation in vivo [52] , [53] . The amount and activity of mTTF therefore influences the steady-state levels of mitochondrial RNAs whose coding sequences lie between the mTTF binding sites and the putative promoters [52] . Knockdown of an insect-specific paralog of mTTF , mTerf5 ( CG7175 ) , was found to have opposite effects on transcript levels to knockdown of mTTF , despite the fact that mTerf5 binds to the same sites in mtDNA in an mTTF-dependent manner [54] . As DNA binding proteins with an established role in the regulation of transcription , mTERF family members are strong candidates for mediating conflicts between the replisome and transcription complexes . Moreover , MTERF proteins may have multiple roles in mtDNA metabolism , considering that alterations in the levels of MTERF1 or its homologs MTERF2 ( MTERFD3 ) and MTERF3 ( MTERFD1 ) were reported to modulate the levels of paused replication intermediates in cultured human cells [55] , [56] . The sea urchin mTERF protein mtDBP has been demonstrated in vitro to have contrahelicase activity [57] . This feature , commonly seen in replication termination proteins , is shared also by the mammalian nuclear rDNA transcription terminator TTF-1 , which has been suggested to regulate entry of the replication machinery into an actively transcribed region [58] . The possible correspondence of the mTTF binding sites in D . melanogaster mtDNA with the regions of replication pausing identified in our earlier study suggests that mTERF family proteins could be considered as candidates for a similar role . To test the possible involvement of mTTF and mTerf5 in mtDNA replication , we investigated their effects on mtDNA metabolism after manipulation of their expression by RNAi , both in cultured cells and in vivo . Here we show that both factors are required for normal mtDNA topology and maintenance . Lack of either ( or both ) resulted in developmental arrest at L3 larval stage . mTTF knockdown led to the accumulation of nicks , dsDNA breaks and recombination junctions . 2DNAGE demonstrated stalled and reversed replication forks over broad zones surrounding the mTTF binding sites , and an accumulation of aberrant replication intermediates with extended segments of RNA/DNA hybrid , indicating a failure to complete lagging-strand DNA synthesis . Knockdown of mTerf5 had an opposite effect on mtDNA replication intermediates , bringing about an increase in replication pause strength when compared to wild-type , a decrease in fragile replication intermediates containing single-stranded segments , and the disappearance of species with even the short segments of RNA/DNA hybrid that we were able to detect in wild-type cells . Because of their opposing but essential roles in mtDNA expression and synthesis , we propose that the balance of these two mTERF family members facilitates the orderly and productive passage of oppositely moving replication and transcription complexes , preventing collisions that would otherwise result in abortive replication and loss of genome integrity .
Replication pauses are revealed as discrete spots on arcs of replication intermediates resolved by 2DNAGE [17] , [59] . The two major replication pause regions of D . melanogaster mtDNA were previously localized to approximately 1/3 and 2/3 of genome length from the replication origin , located in the NCR [26] . In order to map these pause sites more precisely , we conducted 2DNAGE on overlapping short restriction fragments , in a size range considered optimal for resolution on the standard two-dimensional gel system , i . e . 3–5 kb [60] . These analyses revealed the pause sites as the expected discrete spots ( Figs . 1 , S1 , red arrows ) , lying on standard Y-arcs which are characteristic of non-origin fragments through which a replication fork passes unidirectionally ( see [19]–[23] , [60] for full explanations of the signals seen on 2DNAGE ) . Within the ∼50 bp resolution of the method , and based on multiple digests ( Fig . S1 ) , each pause maps precisely to one of the two binding sites for mTTF in the genome , namely at the ND1/tRNASer ( UCN ) gene boundary ( here designated bs1 ) and the tRNAPhe/tRNAGlu gene boundary ( bs2; see Fig . S1C for explanation of mapping ) . The HindIII fragment beyond bs2 , encompassing the remainder of the coding region , did not reveal any discrete pause signals . However , an enhanced signal relative to that seen in the adjacent ClaI fragments was evident at the start of the Y arc in this fragment ( blue arrow ) , suggestive of a more diffuse replication slow-zone at the origin-proximal end of this fragment , consistent with previous data [26] . Treatment with the single strand-specific nuclease S1 had no effect on the migration of replication intermediates in any of the fragments tested , consistent with the previous inference that DNA replication in these regions is fully strand coupled [26] . The coincidence of replication pauses with the previously mapped binding sites for mTTF suggests a role for this protein in mtDNA maintenance . To investigate this we used dsRNA-based RNA interference to knock down mTTF in S2 cells . A ∼70% decrease in mTTF mRNA levels ( Fig . S1A ) resulted in altered mitochondrial transcript levels consistent with the previous report by Roberti et al . [52] . Depending on their location within the transcription map , transcripts were either upregulated ( e . g . cytochrome b mRNA ) , downregulated ( e . g . 16S rRNA and COX2 mRNA ) , or little altered ( e . g . ND5 mRNA ) ( Fig . 2A ) . Furthermore , in untreated cells , transcript levels of a given strand were observed to decrease markedly as the mTTF binding sites are successively traversed within the transcription unit ( Fig . S1B ) , consistent with the proposed role of mTTF as a transcriptional attenuator ( although this may also be influenced by differential RNA stability ) . Next we analyzed mtDNA levels in cells knocked down for mTTF , using three different methods: qRT-PCR ( Fig . 2B ) , PicoGreen staining of mtDNA nucleoids ( Fig . 2C ) and Southern blotting of both digested ( Fig . S1C , D ) and undigested mtDNA ( Fig . 2D ) . qRT-PCR indicated that mtDNA levels fell to approximately 20% of control levels following 4–5 days of mTTF knockdown ( Fig . 2B ) , whereas mtDNA levels were unchanged in untreated cells or cells treated with an inert dsRNA against GFP . The intensity of PicoGreen staining after 5 d of mTTF knockdown ( Fig . 2C ) was also greatly diminished compared with control cells treated with the dsRNA against GFP and similar to the effect of dsRNA treatment targeted against genes with well-established roles in mtDNA synthesis , such as tamas ( encoding the catalytic subunit of DNA polymerase gamma ) or CG5924 ( the Drosophila homologue of the mammalian mitochondrial helicase Twinkle ) . The relative amount of intact mtDNA detected by Southern blot was also diminished by mTTF knockdown ( Fig . 2D ) , with a progressive disappearance of the supercoiled form relative to genome-length linear molecules ( Fig . 2D ) . The total amount of mtDNA detectable by Southern hybridization after digestion with a restriction enzyme was also diminished ( Fig . S2C , S2D ) . Analysis of full-length mtDNA by 2DNAGE revealed a relative increase both of recombination structures and broken replication replication intermediates ( Fig . 2E: see [26] for a full explanation of the arcs revelaed by 2DNAGE of Drosophila mtDNA digested with restriction enzymes curring once in the genome ) . Recombination structures linking two whole copies of the genome following such linearization are most easily revealed in the Bsp1407I digest , where the characteristic X-arc that they form ( see [19]–[23] , [60] ) is well resolved from termination and dY structures . Their accumulation was most prominent after 3 d of knockdown of mTTF ( Fig . 2E , red arrows ) . Broken replication intermediates , arising from scission of one branch at or near the origin , migrate on or close to a standard Y-arc , instead of a bubble , double-Y or eyebrow arc ( see Figs . 2E , 2F , S3 ) . They are normally found only at a low-level in control cells , but are generated in material from control cells by treatment with S1 nuclease , which cuts the region that remains single-stranded in some replicating molecules , extending from the replication origin across the rRNA gene locus ( see Fig . 6 of [26] , panels from which are reproduced here in Fig . S3 for comparison ) . After mTTF knockdown , these broken intermediates were much more abundant , and further treatment with S1 nuclease had no effect ( Fig . 2F , red arrows ) . The characterstic eyebrow arc seen in the Bsp1407I digest , resulting from non-digestion in the partially single-stranded region , was already absent , consistent with systematic strand-breakage in this region following mTTF knockdown ( Fig . 2E , blue arrows ) . Roberti et al . [52] earlier found no significant effect on mtDNA levels from 3 days of mTTF knockdown , but using a different dsRNA . To clarify this inconsistency and exclude possible off-target effects , we repeated the experiment using either the same dsRNA as Roberti et al . [52] or our own custom-designed dsRNA . mtDNA levels were decreased by ∼80% at day 5 in both cases , although the dsRNA used by Roberti et al . [52] produced its effects more slowly , with only a 15% drop in mtDNA levels at day 3 ( Fig . S2E ) . To investigate the effects of mTTF knockdown on mtDNA maintenance in the whole organism , we expressed a ( hairpin ) dsRNA transgene targeted on mTTF , using the ubiquitous and constitutive daughterless-GAL4 ( da-GAL4 ) driver . We confirmed that the parental RNAi line ( itself homozygous viable ) produced normal numbers of progeny with a wild-type phenotype when mated to flies not expressing da-GAL4 . We also confirmed that RNA interference in vivo produced ∼90% knockdown of mTTF at the mRNA level at larval stage ( Fig . S4A ) , which was seen also at the protein level ( Fig . S4B ) . mTTF knockdown larvae gained weight more slowly than wild-type larvae of the same genetic background ( Fig . 3A ) . More than 90% of individuals failed to develop beyond the L3 larval stage although larval weight exceeded the critical range for developmental progression ( Fig . 3A , [61]] . None of the few aberrant pupae progressed to the late pupal stages . The persistent larval stage lasted approximately 30 days , during which the larvae became progressively inactive and then died . Mitochondrial RNA levels were altered in a similar manner as in mTTF knockdown cells , e . g . COX2 mRNA was decreased , whereas cytochrome b mRNA was elevated ( Fig . 3B ) . Mitochondrial DNA copy number failed to increase as typically occurs during wild-type development , remaining at 40% of the wild-type level 3 days after hatching ( Fig . 3A ) . During the persistent larval stage , mtDNA copy number steadily declined to approximately 25% of the maximum level observed in wild type L3 larvae , 25 days after hatching . A similar accumulation of broken replication intermediates was observed in mTTF knockdown larvae as in S2 cells , e . g . as revealed by NdeI digestion ( Fig . 3D , red arrows; compare with Figs . 2F , S3 ) . The control strain ( w1118 ; da-GAL4/+ ) displayed an identical pattern of replication intermediates to that described previously for the Oregon-R wild-type strain ( Fig . 4 of [26]] , ruling out any confounding effect of genetic background . The observed drop in mtDNA copy number and topological changes following mTTF knockdown prompted us to characterize mtDNA replication intermediates in more detail , in cells and larvae knocked down for mTTF . In each of the two ClaI fragments that contained the mTTF binding sites , the discrete spots corresponding to replication pauses were observed to fade out and spread over a wider region of the Y-arc , during 4 days of mTTF dsRNA treatment of S2 cells ( Fig . 4A , red arrows ) . After 4 days of treatment , the proportion of this novel material migrating along the Y-arc , relative to the unit-length fragment , was significantly increased compared to day zero for both bs1 and bs2 . Concomitantly we observed a transient increase in cruciform DNA species , particularly a subclass of Holliday junction-like molecules ( Fig . 4A , blue arrows ) . This is consistent with the increase in recombinational forms linking two full copies of the genome seen after 2–3 days of RNAi following digestion with restriction enzymes that cut once in the genome ( Fig . 2E , red arrrows ) . Spreading of the pauses ( Fig . S5 , red arrows ) , with accumulation of recombination junctions ( Fig . S5 , blue arrows ) was seen in mTTF knockdown larvae , although to a lesser extent than in mTTF knockdown cells . Stalled replication forks have a tendency to regress along the template and , under some conditions , can adopt a “chickenfoot” structure around a Holliday-like junction [62] , [63]; ( see Figs . 4B and S6 ) . If such fork reversal is relatively limited , the species formed would still migrate close to a classical Y-arc . However , they should become sensitive to nucleases targeting Holliday junctions [64] . To test this , we treated mtDNA with the bacterial cruciform-cutting enzyme RusA . This removed substantially more material from the region of the Y-arc in cells knocked down for mTTF compared to control cells ( Fig . 4B , red arrows; see also Fig . S7 for side-by-side comparisons of gels at equivalent exposures ) . This supports the idea that mTTF knockdown resulted in the accumulation of regressed replication forks containing Holliday-like junctions , which may be considered a signature of replication stalling . Note the decrease of the recombination structures ( blue arrows in Fig . 4B ) migrating on the X-arc , following RusA treatment , thus confirming the functionality of the enzyme in this experiment . Our findings are consistent with the idea that bound mTTF provides a natural barrier to fork progression , avoiding unregulated replication stalling that might arise , for example , from collisions of the replication and transcription machineries . Since mTTF is already known to promote transcriptional termination , we reconsidered the issue of the role of RNA in Drosophila mtDNA replication . Previous 2DNAGE analyses indicated that mtDNA replication intermediates in D . melanogaster were fully double-stranded [16] , except for the rRNA locus , which exhibited single-strandedness in a minority of molecules . Restriction sites across the remainder of the genome were completely digestible , indicating that extensive regions of RNA/DNA hybirid , such as seen in RITOLS replication intermediates in vertebrates [12] , [13] , [65] , were absent , although the presence of limited patches of RNA/DNA hybrid could not be excluded . We investigated the issue further by treating mtDNA , after restriction digestion , with RNase H , which digests regions of RNA hybridized to DNA , thus modifying the migration pattern of RITOLS-type replication intermediates . This analysis revealed a prominent , novel arc , migrating just below the Y-arc ( Fig . 5 , red arrows ) , whose trajectory is consistent with the presence of one or more short segments of ssDNA , arising from the enzymatic removal of RNA from some replicating molecules . Other species detectable by 2DNAGE were essentially unaffected by RNase H treatment , indicating that the novel arc arose from material previously not resolved on this gel system , which is consistent with the clear increase in signal seen after RNase H treatment ( Fig . S8 ) . The nature and trajectory of the novel arc released by RNase H treatment differed markedly after knockdown of mTTF . The forms migrating just below the standard Y-arc ( Fig . 5 , red arrows ) , were replaced by a much shallower sub-Y arc , extending beyond the limit of the fragment analysed ( Fig . 5 , blue arrows ) . Its trajectory is consistent with much more extensive ssDNA regions ( i . e . much longer segments of RNA/DNA hybrid prior to RNase H treatment ) than in the replication intermediates that formed the sub-Y arc generated by RNase H treatment in untreated cells . To test whether the mTTF partner protein mTerf5 antagonizes the effects of mTTF on replication as well as on transcription , we investigated the effect of mTerf5 knockdown on mtDNA copy number in S2 cells ( Fig . 6A ) . We observed a substantial depletion of mtDNA to a similar extent ( ∼70% ) , and with similar kinetics , as mTTF knockdown , although there was no cross-reaction between the two dsRNAs used ( Fig . S9 ) . Simultaneous knockdown of both factors in S2 cells produced a small initial increase in mtDNA copy number , followed by a gradual decline to the same low level as produced by knockdown of either factor alone , after 5 days of treatment . In the developing fly , mTerf5 knockdown using each of three independently isolated RNAi lines driven by da-GAL4 , produced the same phenotype as mTTF knockdown , i . e . a persistent larval stage with failure of pupariation . The congruent phenotype effectively excludes off-target effects as an explanation . Simultaneous knockdown of both factors in the developing fly also yielded this phenotype . Despite the fact that mTerf5 knockdown produced similar effects on mtDNA copy number and development as mTTF knockdown , 2DNAGE analysis of mtDNA from mTerf5 knockdown cells revealed different effects on the pattern of replication intermediates . We observed enhanced replication pausing at both mTTF binding sites ( Fig . 6C: for comparison based on gels of equivalent exposure , see Fig . S10 ) . The broken intermediates that accumulated when mTTF was knocked down were absent ( compare Fig . 6B with Fig . 2F , shown alongside in Fig . S3B ) , and treatment with S1 nuclease failed to release such intermediates in comparable amounts as in control cells ( Fig . 6B , Fig . S3B ) . Treatment with RNase H had no discernible effect ( compare Fig . 6C with the corresponding digests of Fig . 5 ) . mTerf5 knockdown thus had opposite effects on replication intermediates as mTTF knockdown , appearing to enhance replication pausing at specific sites and shifting the balance of mtDNA replication intermedaites towards those composed fully of dsDNA , as opposed to those with patches of RNA/DNA hybrid or single-strandedness .
mTTF and mTerf5 were previously shown , using RNAi , to have reciprocal effects on transcription . Here we investigated their roles in mtDNA maintenance , using a similar strategy . Both factors were essential for mtDNA copy number maintenance , but had opposing effects on mtDNA replication . These findings allow us to propose a model whereby these factors co-operate to facilitate the productive interaction between oppositely moving replication and transcription complexes on the same template , thus contributing to the maintenance of genomic fidelity . Contrary to a previous report [52] , our data demonstrate that mTTF is required in vivo to maintain mtDNA levels . The different findings are attributable to the kinetics of action of the dsRNAs used in the two studies . The effects on transcription were broadly similar [36]: the minor differences are most likely due to early changes in mtDNA levels compounding those on RNA . The apparent drop in steady-state transcript levels as the mTTF sites are successively traversed reflects the organization of the mitochondrial genome , but makes no obvious sense for the equimolar supply of polypeptides belonging to any given OXPHOS complex . The transcription termination activity of mTTF might therefore serve primarily a different role , such as in DNA replication , with effects on mitochondrial transcripts being accommodated ( post- ) translationally . The developmental arrest at larval L3 stage caused by deficiency of mTTF or mTerf5 is a phenotype shared by knockdown of many genes for mitochondrial functions , including those encoding mitochondrial transcription factor 2 ( mtTFB2 ) , single-strand binding protein ( mtSSB ) and CCDC56 , required for the assembly of cytochrome c oxidase [66]–[68] . Whether it is a direct result of OXPHOS deficiency or of deranged developmental signaling remains to be determined . Although we previously found no evidence for RNA-containing mtDNA replication intermediates in Drosophila [26] , finer scale analysis indicated the presence of short patches of RNA scattered around the mitochondrial genome , based on the prominent sub-Y arcs seen on 2DNAGE after treatment with RNase H . Standard Y-arcs , which were already present before RNase H treatment , also remained after the treatment . There was a clear and reproducible increase in total signal in the resolving portion of 2D gels following treatment with RNase H . Logically , this material must have been released by the specific action of the nuclease , from high molecular-weight complexes or tangles previously unable to enter the gel , This is supported by similar observations on human heart mtDNA [69] , much of which remained trapped in the well upon 2DNAGE , unless treated with suitable nucleases and/or topoisomerases to disrupt tangles visualized also by electron microscopy . We infer that mtDNA replication intermediates in Drosophila must consist of two classes , as in vertebrates . One class appears to be composed entirely of dsDNA , and is represented by the standard Y-arcs seen both before and after RNase H treatment . The second class , akin to the RITOLS intermediates seen in vertebrates [12] , [13] , contains tracts of RNA/DNA hybrid , except that here such tracts must be very short , so that RNase H generates a novel sub-Y arc which migrates close to the trajectory of the standard Y-arc . Short segments of RNA hybridized to replicating DNA may be covalently joined to longer transcripts , forming the complex tangles unable to enter gels unless released by RNase H treatment . The Y-like structure of the products , and the fact that they were created , not destroyed by RNase H , indicates that they are not simple intermediates of transcription , DNA repair or recombination . These observations raise the issue of whether transcription and DNA replication can occur simultaneously on the same template and , if so , whether this association is obligatory . The existence of a population of mtDNA molecules only able to enter agarose gels after treatment with a ribonuclease strongly suggests that these are molecules engaged in active transcription . After RNA removal , they migrate along 2DNAGE arcs expected for an iterative set of replication intermediates , strongly supporting the idea that replication and transcription can take place on the same template . Those replication intermediates that can be resolved on 2D gels without ribonuclease treatment may represent a distinct subset of replicating molecules , in which transcription is prevented . Resolving these issues will require the development of novel methods for metabolic labeling and analysis of replication and transcription intermediates . Knockdown of mTTF or mTerf5 produced specific and reciprocal effects on mtDNA synthesis . Lack of mTTF caused random stalling and fork regression , whilst decreasing those molecules specifically paused at the binding site itself . RusA treatment confirmed the presence of Holliday-like junctions , a signature of fork reversal associated with stalling due to replication impediments [63] , and proposed as a necessary intermediate in replication repair [70] . The logical explanation for replication stalling in the present case is random collisions with the transcriptional machinery , as observed in other systems [29] , [30] , [71] , such as the rDNA locus in yeast . The formation of Holliday-like chicken-foot structures at stalls of this type has not been reported previously , but our observation of an increase in X-form species containing recombination junctions after 2–3 days of mTTF knockdown suggests that stalling creates substrates for a recombinational repair and/or restart machinery . The observed mtDNA depletion and shift in topology indicates that such processes are unable to support the completion of replication sufficiently to maintain mtDNA copy number . The concomitant accumulation of broken replication intermediates , akin to those that can be created in material from unmanipulated cells by S1 nuclease treatment , indicates that the ssDNA region in the rRNA locus was systematically broken , suggesting that it was more pervasive or persistent than in control cells . Finally , a novel class of putative replication intermediates was observed to accumulate , inferred to contain more extensive RNA segments , based on the generation of shallower sub-Y arcs by RNase H . These replaced the forms with only short RNA segments , that were seen in control cells . Conversely , mTerf5 knockdown produced opposite effects , namely enhanced pausing at the mTTF binding sites , a decrease in replication intermediates broken at the rRNA locus and disappearance of the RNA-containing species . Thus , whereas mTTF is required for physiological pausing , mTerf5 allows paused replication to resume . Additional enzymatic treatments , as well as the use of in gel-digestion [72] , heat denaturation prior to second dimension electrophoresis [73] , [74] , and electron microscopy , will be needed to reveal the detailed structural differences between replicating molecules paused naturally by mTTF/mTerf5 , and those arising from unregulated or persistent stalling in their absence . Some of the effects of mTTF knockdown on mtDNA replication could be indirect , e . g . resulting from altered transcript levels . However , a failure of replication due to primer insufficiency would lead to the progressive disappearance of shorter replication bubbles , rather than the accumulation of broken termination intermediates . Evidence of a role for preformed transcripts in RITOLS replication of mammalian mtDNA , via the bootlace mechanism [13] , [14] , suggests the possibility that mTTF deficiency might impair DNA replication by distorting the relative abundances of different processed transcripts that must be incorporated during fork progression . However , this would not explain the accumulation of random collision products . Thus , we favor a more direct role for mTTF in DNA replication . The effects of mTTF and mTerf5 knockdown imply that RNA incorporation , replication-fork pausing and lagging-strand synthesis are related phenomena . RNA incorporated via the bootlace mechanism is one possible source of primers for the synthesis of lagging-strand DNA , although other mechanisms of lagging-strand initiation are consistent with RITOLS [9] . Our data suggest that proteins belonging to the mTERF family are crucial factors in execution and/or regulation of such a process , at least in Drosophila , as illustrated in Fig . 7 . The proposed model postulates that the balance of mTTF and mTerf5 nurses the productive interaction of replication and transcription machineries moving in opposite directions , and that replication pausing is vital for ensuring the incorporation of RNA transcripts into replication intermediates at the replication fork ( Fig . 7 ) . Capture of a new bootlace , resulting from the arrival of a transcription complex that undergoes termination , is also proposed to be essential for the priming of lagging-strand DNA synthesis , not only at the immediate site of mTTF/mTerf5 binding , but also further downstream , as the replication fork progresses . The prevention and/or regulation of collisions between the transcription and replication machineries is indispensable for all genetic systems [30] , [75] , to avoid knotting of the daughter molecules [76] , the generation of recombinogenic ends [77] and other types of genomic instability [29] . Proteins that perform this function are well documented in other systems , for example in the rDNA of both fungi [78] , [79] and mammalian cells [80] , although these proteins ( Fob1 in S . cerevisiae , Reb1 in S . pombe and TTF1 in mammals ) are unrelated to the mTERF family and to each other . Thus , there is both a precedent and a rationale for mTTF and mTerf5 to integrate transcription and DNA replication . However , the many cases of mitochondrial proteins having multiple functions , e . g . Ilv5 , Aco1 and RNase P [81]–[83] , means that it cannot be excluded that mTTF and mTerf5 regulate transcriptional and replication independently . The two proteins may also be considered as an example of an antagonistic pair that together control a specific process , a type of regulation widespread in biological systems . An intriguing parallel is provided by the related helicases Rrm3 and Pif1 , which exert opposing effects on DNA synthesis at the replication fork barrier of Saccharomyces cerevisiae rDNA [84] . Unlike mTTF and mTerf5 , Rrm3 and Pif1 do not bind DNA sequence-specifically , but can recognize and process unusual DNA structures in G+C-rich regions [85] . Although mTTF and mTerf5 are not themselves helicases , they may recruit an antagonistic helicase pair that act in a similar manner to Rrm3 and Pif1 , or may confer alternate properties on a single helicase .
S2 cells [86] were cultured in Schneider's Medium ( Sigma-Aldrich ) at 25°C . Cells were passaged every 3–4 d at a density of 0 . 5×106 cells/ml . Standard Drosophila strains , plus the mTTF RNAi line 101656 and mTerf5 RNAi lines 2899 , 2900 and 107227 from the Vienna Drosophila RNAi Center ( VDRC ) , were cultured as described previously [87] . Gene-specific dsRNAs were synthesized from templates created from S2 cell cDNA by a nested PCR strategy , which introduced the T7 promoter sequence on both sides of each final amplicon ( see Table S1 for primer sequences ) . S2 cells were transfected with 4 µg of each dsRNA added to 0 . 5 ml of culture medium , and grown for the times indicated in figures and legends . Where transfections were to be continued for >3 d , cells were passaged every 3 d and fresh dsRNA was added . For visualization of nucleoids , dsRNA against Tfam was added for the final 2 d , as described in SI , Nucleoids were detected by fluorescence microscopy , after staining with Quant-iT™ PicoGreen ( Invitrogen ) . Nucleic acids for mtDNA copy-number analysis , 2DNAGE and Q-RT-PCR were isolated from S2 cells , adult flies , larvae or purified mitochondria thereof using variants of standard methods . In general , 2DNAGE used total nucleic acids isolated from sucrose density gradient-purified mitochondria ( see SI ) . Q-RT-PCR to measure RNA levels was performed essentially as described earlier [52] , using cDNA prepared by random priming or , where indicated , by gene- and strand-specific primers as detailed in Table S1 . Assays always included three or more independent biological replicate samples , with normalization to the transcript of nuclear gene RpL32 . Relative quantitation of mtDNA content was performed similarly , using total DNA as template , plus primers for mitochondrial 16S rDNA ( Table S1 ) , also with normalization to RpL32 . Standard one-dimensional electrophoresis used 0 . 6% agarose gels in TBE buffer . 2DNAGE and blot-hybridization were conducted essentially as described earlier [13] , using slightly different conditions for resolving large and small DNA fragments ( see SI ) . For details of restriction digests and treatment with DNA modifying enzymes see SI . Radioactive probes for specific fragments of Drosophila mtDNA were generated by PCR , with [α-32P]-dCTP ( Perkin-Elmer , 3000 Ci/mmol ) in the reaction mix ( see Table S1 for primers ) . For further details , see Text S1 .
|
All genomes require a system for preventing collisions between the machineries of DNA replication and transcription . We have investigated the roles in this process of two proteins of the mTERF ( mitochondrial transcription termination factor ) family in Drosophila . These factors , mTTF and mTerf5 , share common binding sites in the mitochondrial genome , which we found to coincide with sites of replication pausing . Knockdown of either factor by RNA interference resulted in mtDNA depletion and developmental arrest . mTTF knockdown decreased site-specific replication pausing , but led to an increase in random stalling and regression of replication forks , with impaired synthesis of the lagging strand . This we attribute to random collisions with the transcriptional machinery . Conversely , mTerf5 knockdown led to enhanced replication pausing at mTTF binding sites . These findings indicate an essential and previously undescribed role for proteins of the mTERF family in the integration of transcription and DNA replication , preventing unregulated collisions and facilitating productive interactions between the two machineries that are inferred to be essential for completion of lagging-strand DNA synthesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Mitochondrial Transcription Terminator Family Members mTTF and mTerf5 Have Opposing Roles in Coordination of mtDNA Synthesis
|
Stress-induced transposition is an attractive notion since it is potentially important in creating diversity to facilitate adaptation of the host to severe environmental conditions . One common major stress is radiation-induced DNA damage . Deinococcus radiodurans has an exceptional ability to withstand the lethal effects of DNA–damaging agents ( ionizing radiation , UV light , and desiccation ) . High radiation levels result in genome fragmentation and reassembly in a process which generates significant amounts of single-stranded DNA . This capacity of D . radiodurans to withstand irradiation raises important questions concerning its response to radiation-induced mutagenic lesions . A recent study analyzed the mutational profile in the thyA gene following irradiation . The majority of thyA mutants resulted from transposition of one particular Insertion Sequence ( IS ) , ISDra2 , of the many different ISs in the D . radiodurans genome . ISDra2 is a member of a newly recognised class of ISs , the IS200/IS605 family of insertion sequences .
Stress-induced transposition has been an attractive notion for some time since it is potentially important in creating diversity to facilitate adaptation of the host to severe environmental conditions . One common major stress is DNA damage . This induces a variety of responses including changes in expression of numerous genes [1]–[4] , cell cycle arrest [5] , [6] , induction of bacterial prophages [7]–[8] and , by generating diversity , can also aid development of processes such as bacterial pathogenicity and virulence [9] . Several studies have focused on DNA damage-induced transposition in bacteria but have not yet provided a coherent mechanistic scenario . This interest presumably stemmed directly from capacity of UV-irradiation to promote lysogenic induction [7] . Indeed , although IS10 transposition was shown to be induced by UV light in an SOS-dependent pathway [10] , the precise mechanism has not been elucidated . A complex relationship between the SOS response and Tn5 transposition has emerged from contradictory reports [11]–[12] . More recently , activation of Tn7 transposition into regional hotspots by double-strand breaks , has suggested a relationship between Tn7 transposition and DNA repair [13] , but direct evidence is still missing . Finally , numerous host factors can modulate transposition in E . coli in response to stress [14] , but their specific roles are presently unknown . Here we identify and demonstrate the molecular basis of a strong radiation-stimulated response of transposition in the irradiation resistant Deinococcus radiodurans . D . radiodurans has an exceptional ability to withstand the lethal effects of DNA-damaging agents , such as ionizing radiation , UV light and desiccation ( for reviews , see [15] , [16] , [17] ) . High radiation levels result in genome fragmentation and reassembly in a process which generates significant amounts of single stranded ( ss ) DNA [18] . In addition to this extraordinary ability to reassemble its genome , the capacity of D . radiodurans to withstand irradiation also raises important questions about the mechanisms involved in the response to and repair of radiation-induced mutagenic lesions . A recent study analysed the mutational profile in the thyA gene following doses of 10 kGy of γ- or 600 J m−2 of UV-irradiation . The majority of thyA mutants were due to a single insertion of one particular IS belonging to the IS200/IS605 family: ISDra2 [19] ( originally named IS8301 [20] ) . While some mutants , presumably resulting from point mutations or small insertions or deletions , retained the length of the wild-type gene , the many other resident ISs unrelated to ISDra2 made only small contributions to the mutant pool despite their presence in significant numbers ( see www-IS . biotoul . fr ) in the D . radiodurans R1 genome sequence [21] . The importance of the contribution of ISDra2 to mutagenesis is further underlined by its low genomic copy number in the standard R1 ATCC 13939 strain used in our studies as judged by a combination of whole genome hybridization and sequencing [19]: 1 complete and 1 inactive degenerate ISDra2 copy ( in contrast to the published D . radiodurans genome sequence which revealed 7 complete and one partial ISDra2 copy [21] ) . Since another member of the IS200/IS605 family , IS608 from Helicobacter pylori , uses obligatory ssDNA intermediates [22] , it seemed possible that the signal which triggers ISDra2 transposition is the very event which leads to genome reassembly: the formation of ssDNA . We have explored the properties and behaviour of ISDra2 ( ISDra2F; Figure 1A ) in D . radiodurans and have identified the mechanism by which ISDra2 transposition is triggered by radiation . Using a genetic system to detect two principal transposition steps , transposon excision and insertion , we show that ISDra2 , like other IS200/605 family members , requires TnpA but not TnpB for both and that insertion occurs 3′ to a specific pentanucleotide ( as deduced from genome analyses [20] ) . We demonstrate genetically that both steps are significantly increased following host cell irradiation . We also show that the entire TnpA-catalysed transposition cycle including excision and insertion depends strictly on single strand DNA substrates in vitro . Finally , using a PCR-based approach , we demonstrate that , in vivo , exposure to γ-irradiation stimulates excision of the single genomic copy of ISDra2 from the genome in the form of a DNA circle . These events are closely correlated with the initiation of the process leading to genome reassembly from chromosomal fragments , which occurs mainly through a mechanism generating long stretches of single stranded DNA [18] .
IS200/IS605 family members often carry 2 genes , tnpA , encoding the transposase and tnpB , a gene of unknown function ( Figure 1A ) . As shown for the related IS608 [23] , the transposition cycle occurs in two principal steps: excision from the donor backbone in the form of a single strand circle and subsequent insertion into a suitable DNA target [22] . These steps were monitored for ISDra2 transposition in D . radiodurans using a genetic assay . TnpA and/or TnpB were expressed in trans from a plasmid , pGY11559 , under control of the IPTG-inducible Pspac promoter [19] ( Table S1 ) . For excision , the unique active ISDra2 copy ( ISDra2F: D . radiodurans loci DR1651-DR1652 ) was replaced either by a derivative , ISDra2-113 , retaining functional IS ends but in which tnpA and tnpB were replaced by a CamR cassette , or by ISDra2-103 , similar to ISDra2-113 but retaining tnpA controlled by its own promoter ( Figure 1A ) . The resident ISDra2F was first replaced by a TetR cassette and ISDra2-113 ( or ISDra2-103 ) was inserted by homologous recombination at the unique 5′TTGAT3′ target site ( see below ) present in the tetA gene . IS excision restores a TetR phenotype ( Figure 1B ) . These constructions are described in detail in Figure S1 . Ectopic TnpA expression in this system induced ISDra2-113 excision at a frequency of 2×10−3 ( Table 1 ) . This was not increased when IPTG was added to the medium , to induce TnpA expression . Similar experiments in which Pspac was used to drive a lacZ gene in plasmid pGY11556 ( Figure S2 ) showed that addition of IPTG resulted in a 143-fold increase in β-galactosidase activity . This suggests that sufficient transposase would be produced by escape synthesis from Pspac to ensure transposition and that activity is limited by the supply of a correct DNA substrate . Activity obtained with ectopic TnpB alone ( <1×10−9 ) was similar to the background levels observed with the empty vector plasmid ( <4 . 8 10−9 ) clearly demonstrating that TnpA is absolutely required for ISDra2 excision while TnpB is dispensable . Similar results were obtained with ISDra2-103 ( Figure 1A ) , in which tnpA was in its natural location in the IS sequence and expressed from its own promoter ( Table 1 ) . To measure transposon insertion , both non-targeted and targeted approaches were used . In both cases , we measured insertion in the subpopulation of cells in which the transposon ISDra2-113 had excised from its initial locus and had inserted elsewhere in the genome as judged respectively by the reconstitution of the TetR gene and retention of the CamR marker carried by the IS derivative . In the non-targeted approach , the proportion of CamR among TetR clones , reflecting the frequency of spontaneous insertion , was about 10−2 in strain GY13120 ( Figure 1B , top ) expressing TnpA ectopically , compared to <10−9 in the absence of TnpA . These insertions were true transposition events since they had occurred 3′ to the pentanucleotide , 5′TTGAT3′ ( Table S2 ) , the sequence preceding the left end of ISDra2 in all genomic loci identified [20] . In the targeted approach , the degenerate ISDra2 copy ( ISDra2* loci DR0177-DR0178 ) was replaced with the target sacB gene from Bacillus subtilis , coupled to a hygromycin resistance cassette to assist the construction ( Figure 1B , bottom , and Figure S1 ) . As in other bacteria [24] , [25] , sacB expression in the presence of sucrose is lethal for D . radiodurans ( data not shown ) and inactivation of sacB , for example , by IS insertion , confers a sucrose resistant phenotype . Moreover , sacB contains 10 copies of the insertion site 5′TTGAT3′ ( 9 on one strand and 1 on the complementary strand; Figure S3A ) . For this analysis , it was considered prudent to include a transcriptional terminator ( Term116 ) downstream of the cat gene of ISDra2-113 to avoid possible interference of sacB transcription from the strong Pspac promoter with expression of the cat gene ( Figure 1A and 1B; Figure S1 ) . By imposing a triple selection for TetR , CamR and SucR , we were able to directly collect clones in which ISDra2-103Term116 had excised from its resident site ( TetR ) and inserted into sacB ( SucR ) . The nucleotide sequence of the SucR mutants confirmed that each had ISDra2-113 inserted into one of the 5′TTGAT3′ target sequences ( Figure S3A and Figure 3B ) . Together , these results demonstrate that TnpA alone is sufficient for both transposon excision and insertion . Using this genetic system , we then measured the excision frequency of ISDra2-113 and ISDra2-103 following exposure to UV- ( 600 J m−2 ) or γ-irradiation ( 5 kGy ) . Both treatments increased excision frequencies about 10-fold ( Table 1 ) . To determine whether γ-ray irradiation also stimulates the insertion step of transposition , we measured the insertion frequency of ISDra2-103 ( Term116 ) expressing TnpA from its own promoter ( to remain as close to natural conditions as possible ) ( Figure 1A ) into sacB . The frequency of TetR colonies and of SucR CamR TetR colonies in cells irradiated with 5 kGy of γ-rays and in non-irradiated cultures was measured in the tester strain GY13174 carrying ISDra2-103 ( Term116 ) . The TetR frequency , which monitors the excision step , rose from 2 . 32×10−3 to 1 . 85×10−2 after γ- irradiation ( Table 1; average values of 10 independent experiments ) . The frequency of SucR CamR TetR colonies , representing the overall transposition frequency into the reporter gene , also increased from 3 . 76×10−10 to 1 . 8×10−8 after γ- irradiation ( Table 2 ) . Thus , both excision and insertion of the ISDra2 derivatives were stimulated by irradiation . This stimulation is unlikely to result from an increase in tnpA expression after irradiation since strain GY14310 in which the coding region of tnpA was replaced by the coding region of lacZ in ISDra2-104 ( Figure 1A ) showed no detectable increase in β-galactosidase activity at 0 , 30 , 60 , 120 or 180 min post-irradiation ( Figure S2 and data not shown ) . Since the related TnpA from IS608 uses obligatory single-stranded DNA substrates in in vitro transposition reactions [22] , we suspected that the stimulation of ISDra2 excision and insertion might be linked to the formation of single-stranded DNA during repair of DNA damage which would supply the appropriate substrate . To confirm that ISDra2 TnpA is active only on single-strand DNA substrates , we used an in vitro system , developed for the related IS608 , to investigate TnpA-catalysed cleavage and strand transfer [22] , [26] . IS608 transposition reactions are strand specific and use , by definition , the top strand . Recombination reactions recapitulating transposon excision and donor joint formation were performed . For this , the ISDra2 tnpA gene was cloned with a C-terminal His6 tag under control of a plac promoter ( Materials and Methods ) and the protein was purified as described previously [23] . The C-terminal His-tagged TnpA from D . radiodurans was active since it catalysed ISDra2-113 excision in vivo in a tester strain expressing TnpA-His6 under the control of Pspac promoter in plasmid pGY13505 ( data not shown ) . The DNA substrate was a 59 nt single strand DNA fragment including the first 39 nt of ISDra2 LE carrying a subterminal secondary structure which serves as the TnpA binding site ( Burgess-Hickman et al . , in prep ) and a 20 nt 5′ flank with the conserved pentanucleotide target sequence ( 5′TTGAT3′ ) ( Figure 2A ) . Incubation of the 5′ end-labelled fragment ( Figure 2B , lane 1 ) with purified TnpA in the presence of Mg2+ generated a cleaved 5′ end-labelled donor flank fragment of 20 nt ( Figure 2B , lane 2 ) . When mixed with an unlabelled 63 nt ss DNA fragment composed of the terminal 43 nt of RE ( including the secondary structure ) and a 3′ 20 nt flank , an additional fragment of 40 nt representing the joined donor flanks was generated ( Figure 2B , lane 3 ) . In contrast , double stranded LE ( Figure 2B , lane 4 ) was neither cleaved nor underwent a strand transfer reaction with the unlabelled RE ( Figure 2B , lane 5 ) . Similar results were obtained using 5′ end-labelled RE and unlabelled LE: cleavage and strand transfer were strictly dependent on ss subtrates ( Figure 2B ) . We were also able to recapitulate the integration reaction in vitro using an end-labelled target DNA and an unlabelled RE-LE junction ( Figure 2B ) . Thus , the ISDra2 transposase is active on single- but not double-stranded substrates and is capable of cleavage of both LE and RE and of strand transfer to generate the donor joint and the RE-LE junction . To investigate the relationship between irradiation and induction of ISDra2 transposition , we analysed the kinetics of γ-irradiation-triggered ISDra2-113 excision directly from the D . radiodurans chromosome . For this , we isolated genomic DNA before and at different times after γ-irradiation and subjected samples to PFGE analysis following NotI digestion ( Figure 3B ) and to PCR analysis ( Figure 3A , 3C and 3D ) . Primer pairs P1+P2 , complementary to the tetA flanks of ISDra2-113 ( Figure 3A ) , should generate a 2120 bp fragment when ISDra2-113 is inserted into tetA and a 500 bp fragment when the donor backbone is sealed following ISDra2-113 excision . IS circle junction formation was monitored using primers P3+P4 , complementary to the subterminal IS region ( Figure 3A ) , by the appearance of a 260 bp product representing abutted LE and RE . Total NotI-digested genomic DNA ( Figure 3B ) showed complete fragmentation immediately following irradiation ( compare lanes 1 and 2 ) . Reassembly , evidenced by the gradual reappearance of distinct NotI fragments , could be detected at about 2 h post-irradiation while the complete regenerated banding pattern exhibited by the non-irradiated sample ( lane 1 ) occurred after 3 h ( lane 6 ) . PCR analysis of these samples using the P1+P2 primer pair revealed the presence of the full length IS together with a low but significant quantity of donor joint prior to irradiation resulting from rejoining of the DNA flanks following IS excision ( Figure 3C , lane 1 ) . This indicates that IS excision occurs during normal growth of the host strain as might be expected as tnpA expression is under control of Pspac . Importantly , a significant increase in the level of the donor joint ( Figure 3C ) occurred over the 4 . 5 h post-irradiation period . We note that the progressive disappearance of the full length IS does not necessarily reflect its excision . In order to visualize the donor joint product , the PCR conditions were adjusted ( short extension times; see Materials and Methods ) to favour amplification of shorter donor joint fragment . It is probable that the reduction in the intensity of full length IS is due to competition in the reaction mixture from the increasing concentration of the short donor joint . In addition , the P3+P4 primer pair revealed the gradual appearance between 1 and 1 . 5 h post irradiation of the IS junction species ( Figure 3D ) . The identity of the donor joint and the IS junction was verified by sequencing . The IS junction generated by P3+P4 was cloned into a plasmid carrying CamR ( unable to replicate in D . radiodurans ) which was then introduced by transformation into a strain expressing ectopic TnpA . Ten independent CamR clones were analysed using inverse PCR and each was shown to carry an insertion 3′ to a 5′TTGAT3′ target sequence , demonstrating that the IS junction generated by P3+P4 is active in transposition ( data not shown ) . Although we did not use quantitative PCR , care was taken to include identical quantities of DNA in each reaction and , to a first approximation , the results indicate that both the donor joint and the transposon junction were formed with very similar kinetics . Products started to accumulate at a time which coincided with the end of DNA degradation and probably with the start of the ESDSA pathway generating long stretches of single stranded DNA [18] . These results therefore strongly suggest that the factor which triggers ISDra2 transposition is the formation of single stranded DNA during D . radiodurans genome assembly .
D . radiodurans has been the object of much interest due to its astonishing capacity to resist high levels of radiation [15] , [17] and to the genome fragmentation and reassembly processes essential for its survival after irradiation [18] . Clearly such exceptional properties might influence the behavior of mobile genetic elements within the genome and perhaps reveal new and interesting regulatory mechanisms . One hint that this might be the case came from the observation that transposition of one IS , ISDra2 , into the thyA gene was apparently increased by high levels of γ- or UV-irradiation [19] . ISDra2 was the only insertion sequence of the 12 different IS family members present in the D . radiodurans genome to behave in this way . We show that ISDra2 transposition is specifically triggered during the process of reassembly of the D . radiodurans genome which is associated with recovery from irradiation damage . ISDra2 belongs to the IS200/IS605 family ( see www-is . biotoul . fr ) and the paradigm of this family , IS608 , transposes by excision of a single strand circular DNA intermediate which can then insert into a single strand DNA target [22] . Our results demonstrate that ISDra2 also exhibits a strict requirement for single stranded DNA in vitro ( Figure 2 ) . Furthermore , we show genetically that both ISDra2 excision ( detected by the restoration of an intact tetA gene ) and insertion ( into the reporter sacB resulting in resistance to sucrose ) require TnpA , and that insertion occurs 3′ to the specific pentanucleotide sequence , 5′TTGAT3′ , found adjacent to the left end of all naturally occurring genomic ISDra2 copies [20] . Moreover , we observe a 50 to 60-fold increase in the overall frequency of transposition following UV or γ-irradiation resulting from stimulation of the two transposition steps: a 8-fold increase in excision and a 6-fold increase in insertion of the IS circle transposition intermediate ( Table 1 ) . The overall transposition stimulation factor is in accord with the 50- and 100-fold induction of in vivo transposition of wild-type ISDra2 into the thyA gene by UV- and γ-irradiation respectively [19] . Monitoring tnpA promoter activity with the lacZ reporter gene demonstrated that the radiation-triggered ISDra2 transposition was not due to a specific induction of TnpA expression . Moreover , a same stimulatory effect of irradiation was observed whether TnpA was expressed from its natural promoter or from an external IPTG-inducible Pspac promoter . Importantly , using a physical PCR-based approach , we also demonstrate that ISDra2 excision , reclosure of the chromosomal DNA donor joint and formation of a transposon joint with abutted left and right ends ( consistent with a circular form of ISDra2 ) occur within irradiated D . radiodurans cells . Both rejoined donor DNA flanks and the LE-RE junction begin to accumulate after 90 min of post-irradiation incubation . This correlates with the end of the degradation of damaged DNA and the start of DNA double strand break repair processes such as extended synthesis dependent strand annealing ( ESDSA ) that generates high concentration of single stranded DNA [18] , [27] . Induction of transposition by exposure to environmental stress has often been tacitly assumed . However , there has been only limited supporting evidence for the idea that transposition can be induced efficiently by environmental insults and the sparse data available are generally based on indirect genetic assays [10] , [12]–[13] , [28]–[32] . These data should be revisited using more powerful technologies now available . The idea of environmentally-induced transposition has also arisen from analysis of the growing number of available complete prokaryote genome sequences . These have identified many bacterial and archaeal species in which the number of ISs is dramatically high . They include different Shigellae species [33] , Bordetella pertusis [34] , Yersinia pestis [35]–[36] Lactobacillus [37] , Sulfolobus solfataricus [38] among many others . While it is attractive to imagine that this is the result of transposition bursts induced by an environmental trigger , a strong alternative has been to invoke stochastic transposition together with the formation of population bottlenecks produced in small isolated populations to fix such mutational events ( insertions ) [34] . Irradiation-triggered transposition described here is a response to an extreme set of environmental conditions which transiently generates large quantities of a substrate ( single strand DNA ) favoring transposition of ISDra2 but not of the other , unrelated , ISs present in the D . radiodurans genome . This response would result in movement of the single “top strand” IS copy generating mutational diversity while retaining the inactive bottom strand copy . Insertion of the single-stranded circle intermediate and replication of the bottom strand would increase the copy number of the IS and disperse it throughout the genome . Moreover , D . radiodurans possesses an efficient natural competence system whose regulation remains to be explored . In view of the fact that such processes in other bacterial species occur using single strand DNA intermediates , transformation may be instrumental in assuring spread of this single strand transposable element [39] . Thus radiation-triggered ISDra2 transposition might additionally generate diversity through participation of DNA containing newly transposed ISDra2 copies in intercellular transformation . An increase in the ISDra2 copy number exposed to a single gamma irradiation cycle has been observed previously ( see Figure S2 in [19] ) . Moreover , Islam et al [20] characterized the distribution of ISDra2 in different laboratory D . radiodurans strains and found that its copy number varied from strain to strain: 7 copies were identified in the published D . radiodurans R1 genome sequence , 6 in the MR1 strain , 21 in the KD8301 strain but only one in KR1 , the parent strain of KD8301 and in the reference strain ATCC 13939 used here . Unfortunately , the history and the intermediate strains are not available and no conclusions can be drawn concerning the factors which contributed to the gain or loss of ISDra2 copies in the genome of these strains . The expansion of transposon copy number with accompanying ectopic sequence homology raises the question whether this would be a detriment to the extensive chromosome repair process after heavy irradiation . Precise reconstitution of a shattered genome through ESDSA involves annealing of 20–30 kb single-stranded DNA overhangs . Zahradka et al ( [18] ) speculated that short blocks , 1–2 kb , of dispersed repetitive sequences present in the genome of D . radiodurans would not compromise the accuracy of repair through ESDSA . These authors pointed out that annealing only a limited repeated sequence block within two long non-complementary single-stranded overhangs could not readily link two fragments together . However , such ectopic sequence homology might stimulate chromosomal rearrangements through other DNA double strand break repair pathway such as single strand annealing ( SSA ) with a potential of increasing the plasticity of the genome under very adverse conditions . Since this class of newly recognized transposable elements require single stranded DNA as both substrate and target site , any process which generates single strand DNA such as replication , mismatch repair and transcription , might lead similarly to limited induction of transposition and also provide suitable insertion sites . In view of the extremely widespread occurrence of members of this IS family in nature , this type of mechanism could be important in regulating transposition activity , interfacing transposition with host physiology and cell cycle and in creating genomic diversity as has been recently shown for the IS200/605 family member , IS1541 , whose insertion allows the Yersinia pestis host to escape from adaptive immune responses and plague immunity [9] .
Bacterial strains are listed in Table S1 . E . coli strain DH5α was the general cloning host and strain SCS110 was used to propagate plasmids prior to introduction into D . radiodurans via transformation[40] . All D . radiodurans strains were derivatives of strain R1 ( ATCC 13939 ) . TGY2X liquid medium and TGY plates [41] were used for D . radiodurans and Luria-Bertani ( LB ) broth for E . coli strains . Media were supplemented with the appropriate antibiotics used at final concentrations of: chloramphenicol 20 µg ml−1 for E . coli and 3 µg ml−1 for D . radiodurans; spectinomycin 40 µg ml−1 for E . coli and 75 µg ml−1 for D . radiodurans; tetracycline 2 . 5 µg ml−1 for D . radiodurans; hygromycin 50 µg ml−1 . 5% ( w/v ) sucrose was supplemented to isolate the D . radiodurans sacB inactivated mutants . Transformation of D . radiodurans with genomic DNA , PCR products , or plasmid DNA was performed as described [41] . Plasmid DNA was extracted from E . coli using the QIAprep Spin miniprep kit ( Qiagen ) . D . radiodurans chromosomal DNA was isolated as described previously [19] . PCR reactions were carried out with Phusion DNA Polymerase ( Abgene ) . Inverse PCR was performed as follows: genomic DNA was digested with NarI , purified by Phase Lock Gel procedure ( Eppendorf ) , and then ligated using T4 DNA ligase . After ethanol precipitation , the ligated circular DNA was used as a template with primers P3 and P4 described in Table S1 . The iPCR products were then directly sequenced with LEext and REext by Genome Express ( Grenoble , France ) . Oligonucleotides used are listed in Table S3 . PCR reactions used for analysis of kinetics of irradiation-induced ISDra2 excision were performed as follows: PCR was carried out in a final volume of 50 µl with using 0 . 5 Units of DyNazyme EXT DNA polymerase ( Finnzymes ) and 200 µM of each dNTP . To detect the donor joint , PCR analysis using P1+P2 primer pair was performed under the following conditions: 94°C for 3 min , 30 cycles of 94°C for 45 s , 60°C for 45 s , and 72°C for 20 s; and finally 72°C for 10 min . To detect the RE-LE junction , PCR analysis using P3+P4 was carried out as follows: the PCR of the first round was performed with 1 µg of genomic DNA under the following conditions: 94°C for 3 min , 30 cycles of 94°C for 45 s , 55°C for 45 s , and 72°C for 10 s , and finally 72°C for 10 min . The second round of PCR used a 15 µl aliquot from round 1 as template and was done under the same conditions than round 1 . For plasmid pGY13224 expressing tnpA and tnpB from a Pspac promoter , the coding sequences of the two genes were amplified by PCR using primers DraF ( tagged with EcoRV ) and DraR ( tagged with XhoI ) . After cleavage , the PCR fragment was cloned into pGY11559 between the SwaI and XhoI sites . For plasmid pGY13203 expressing tnpA from the Pspac promoter , the D . radiodurans ISDra2 tnpA gene was amplified by PCR using the primer pair DraF/DraX and D . radiodurans R1 genomic DNA as template . The product was cloned into plasmid pGY11559 between the EcoRV and XhoI sites . For plasmid pGY13204 expressing tnpB from the Pspac promoter , the N-terminal part of tnpB was amplified using the primer pair 1651F/1651R , digested with NdeI and BsaI and ligated to the 10885-bp NdeI-BsaI fragment from pGY13224 . For plasmid pGY13507 expressing sacB from the Pspac promoter , the B . subtilis sacB gene was amplified with the primer pair NdeUPsacB/XhoDwnsacB and cloned into pGY11559 between the NdeI and XhoI sites . For plasmid pGY11556 expressing the E . coli lacZ from the Pspac promoter , pGY11559 was digested with BglII and XhoI and ligated to the BglII-XhoI fragment from pGY11540 containing lacZ fused to the Pspac promoter . Irradiated cultures were diluted in TGY2X to an A650 = 0 . 3 and incubated at 30°C . At different post-irradiation incubation times , samples ( 5 ml ) were taken to prepare DNA plugs as described [42] . The DNA in the plugs was digested for 16 h at 37°C with 60 units of NotI restriction enzyme . After digestion , the plugs were subjected to pulsed field gel electrophoresis for 28 hours at 10°C using a CHEF MAPPER electrophoresis system ( Biorad ) with the following conditions: 5 . 5 V/cm , linear pulse of 40 s , and a switching angle of 120° ( −60° to +60° ) . Individual CamR TetS colonies purified from GY13111 or derivatives of GY13115 strain expressing in trans TnpA , or TnpB or no protein were inoculated into 3 ml of TGY2X supplemented with spectinomycin when required and grown to an A650 of 1–2 . The bacterial cultures were washed in 10 mM MgSO4 , resuspended in the same buffer to an A650 = 1 . Half of the resuspension was kept on ice , and the second half was exposed to UV light at a dose rate of 3 . 5 J m−2 s−1 in Petri dishes . For γ-irradiation , the cultures were grown to an A650 = 1 , then concentrated 30-fold in TGY2X and irradiated on ice with a 137Cs irradiation system ( Institut Curie , Orsay , France ) at a dose rate of 41 . 8 Gy min−1 . UV- , γ- or non-irradiated cells were diluted in TGY2X to an A650 = 0 . 3 and grown to stationary phase . Determination of the total number of viable cells was performed on TGY plates and excision of ISDra2 derivatives from tetA gene was selected on TGY plates containing tetracycline . Colonies were counted after 3–4 days of incubation at 30°C . The frequencies of the excision event per viable cell from 10 independent experiments were used to calculate the mean values and the standard deviations . 5 individual CamR TetS colonies purified from GY13174 strain expressing ISDra2-103 were inoculated into 10 ml of TGY2X supplemented with hygromycin and grown to an A650 of 1 . 5 . The bacterial cultures were concentrated 30-fold in TGY2X . 100 µl of the resuspension was kept on ice and the rest was γ-irradiated as described above . After dilution , γ- or non-irradiated cells were grown to stationary phase . Excision of ISDra2-103 from tetA and insertion into sacB gene were selected on TGY plates containing tetracycline , or tetracycline , chloramphenicol and sucrose , respectively . Colonies were counted after 3–4 days of incubation at 30°C . The insertion frequencies per viable cell from 5 independent experiments were used to calculate the mean values and the standard deviations . Replacement of the tnpA coding region of ISDra2-103 with the lacZ coding region to generate ISDra2-104 ( Figure 1A ) was performed as follows: the PtnpA::lacZ fusion was amplified by the joining PCR method [43]; see Table S3 ) . The resulting lacZ fusion , the accompanying chloramphenicol resistance cassette and the right end of ISDra2-103 were then inserted into the tetA gene of strain GY13109 using the tripartite ligation method [44] . The resulting strain GY14310 was selected for CamR and the insertion of the lacZ fusion by homologous recombination was confirmed by diagnostic PCR . LacZ gene was expressed under the Pspac promoter in strain GY14312 containing plasmid pGY11556 . Expression of the lacZ reporter gene was detected in D . radiodurans colonies formed on TGY plates containing 5-bromo-4-chloro-3-indolyl-β-D-galactoside ( X-gal ) at 40 µg/ml . β-galactosidase activity was measured as previously described [41] . TnpA was purified from E . coli K12 MC1061 endA carrying TnpA-His6 expression plasmid pGY13503 ( Table S1 ) following induction with 0 . 5 mM IPTG as previously described [25] . Reactions were performed by 45 min incubation of 20 fmol of a 5′-32P-labelled oligonucleotide and 1 pmol of the same oligonucleotide unlabelled with or without 10 pmol unlabelled recombining oligonucleotide , 0 . 5 µg of poly-dIdC and 20 pmol TnpA-His6 at 37°C in a final volume of 16 µl in 20 mM HEPES ( pH 7 . 5 ) , 2 . 5% DMSO , 200 mM NaCl , 5 mM MgCl2 , 1 mM TCEP , 20 µg/ml BSA and 10% glycerol . The reactions were terminated by addition of 0 . 1% SDS followed by 15 min of incubation at 37°C and separated on a 10% denaturing sequencing polyacrylamide gel . The gel was analysed by phosphorimaging .
|
Induction of transposition in prokaryotes under cell stress conditions is potentially important in creating diversity facilitating adaptation to severe environments . In Deinococcus radiodurans , the most radiation-resistant organism known , despite abundance of resident insertion sequences ( IS ) , transposition of a single IS , ISDra2 , was found to be strongly induced by irradiation . We show that both steps involved in transposition , IS excision , and insertion , increase significantly following host cell irradiation and , using PCR analysis of genomic DNA , that exposure to γ-irradiation stimulates massive excision of the single genomic ISDra2 copy as a DNA circle and reclosure of the empty site . These events are closely correlated with the initiation of the process leading to genome reassembly from chromosomal fragments , which occurs mainly through a mechanism generating long stretches of single-stranded DNA . Consistent with this , we also demonstrate a requirement for single strand DNA substrates in TnpA-catalysed cleavage and strand transfer in vitro . Since we find no evidence for irradiation-induced expression of the ISDra2 transposase , we infer that transposition is triggered by the increase in its single-strand DNA substrate . The potential impact on genome reassembly and in creating genome host diversity by triggering transposition in this way is discussed .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"genetics",
"and",
"genomics",
"microbiology",
"molecular",
"biology"
] |
2010
|
Irradiation-Induced Deinococcus radiodurans Genome Fragmentation Triggers Transposition of a Single Resident Insertion Sequence
|
Analysis of HIV-1 gene sequences sampled longitudinally from infected individuals can reveal the evolutionary dynamics that underlie associations between disease outcome and viral genetic diversity and divergence . Here we extend a statistical framework to estimate rates of viral molecular adaptation by considering sampling error when computing nucleotide site-frequencies . This is particularly beneficial when analyzing viral sequences from within-host viral infections if the number of sequences per time point is limited . To demonstrate the utility of this approach , we apply our method to a cohort of 24 patients infected with HIV-1 at birth . Our approach finds that viral adaptation arising from recurrent positive natural selection is associated with the rate of HIV-1 disease progression , in contrast to previous analyses of these data that found no significant association . Most surprisingly , we discover a strong negative correlation between viral population size and the rate of viral adaptation , the opposite of that predicted by standard molecular evolutionary theory . We argue that this observation is most likely due to the existence of a confounding third variable , namely variation in selective pressure among hosts . A conceptual non-linear model of virus adaptation that incorporates the two opposing effects of host immunity on the virus population can explain this counterintuitive result .
The molecular evolution and adaptation of the human immunodeficiency virus ( HIV ) within infected individuals is exceptionally fast . This evolution is generated by a combination of high rates of mutation and recombination , large population sizes and short generation times , and has important consequences for the outcome and treatment of HIV infection [1] . For example , HIV is able to persist within hosts by evading host humoral and T-cell immune responses through the repeated generation and fixation of immune escape mutations . In addition , the evolution of resistance to anti-viral drugs represents a significant problem in HIV treatment . Several approaches have been taken to quantify and understand the dynamics of HIV molecular evolution during infection . Experimental estimates of the virus’ mutation rate suggests that it can generate ~1 . 4x10-5 mutations per nucleotide site per replication event [2] . Evolutionary analyses of HIV gene sequences sampled longitudinally during infection indicate that the nucleotide substitution rate of the virus is approximately constant but varies among genome regions , ranging from 10−2 to 10−3 substitutions per nucleotide site per year [3–5] . Positive natural selection during HIV infection has been typically inferred using dN/dS ratios [6 , 7] , as well as by methods based on allele frequency changes [7 , 8] , and these studies sometimes suggest that viral adaptation is associated with the time taken for disease symptoms to progress to AIDS [7 , 8] or rate of immune escape [9] . However , the interpretation of dN/dS ratios obtained from within-host viral populations are not straightforward due to the presence of transient polymorphisms [10 , 11] and therefore alternative approaches to studying viral adaptation are valuable . Williamson [12] introduced a method to estimate an absolute rate of viral molecular adaptation , and reported that the C2-V5 region of the HIV env gene undergoes approximately 3 adaptive fixations per year during infection . To date , most studies of the evolutionary dynamics of HIV during infection have examined infection in adults and many are based on the same cohort of nine untreated patients [5 , 12] . However , significantly different clinical features characterize HIV infection in children , including a faster rate of disease progression ( i . e . AIDS symptoms occur earlier ) , substantially higher viremia ( levels of HIV in the blood can exceed 100 , 000 RNA copies per ml in pediatric infection ) and a slower decline in viremia after initial infection compared to adult infections [13] . The clinical course of HIV infection in children also varies by age of infection and transmission route and , because infection takes place in patients with a developing immune system , a large variation in immune responses among patients is observed [14–16] . Recently it has been shown that development of broadly neutralizing antibodies ( bNAb ) in HIV-infected infants occurs early in infection and is relatively common [17] . Moreover , in spite of the major role that HLA class I polymorphisms play in determining adult HIV disease progression , a recent study has found that HLA alleles that are protective for adult infections are comparatively weak in HIV-infected children [18] . To better understand the dynamics of viral adaptation during pediatric HIV infection , we estimate the rate of within-host viral adaptation among a cohort of 24 children that acquired the virus through perinatal transmission . For each patient env gene sequences were sampled over 2 to 4 years of infection and were complemented by clinical measurements of viral load and CD4+ T-cell counts . Importantly , these measurements enable us to test associations between viral adaptation and the rate of disease progression , and to explore the determinants of variation in viral adaptation rate among infections . For both HIV and hepatitis C virus infection it has been suggested that disease progression is associated with measures of viral genetic diversity and viral adaptation [7 , 8 , 11 , 12 , 19] . Interpretation of these associations is often limited by implicit assumptions of linear relationships among viral population size , diversity , adaptation , and immune selection . It is likely that the majority of adaptive molecular evolution detected in HIV env sequences is the result of viral escape from humoral immune responses . The importance of humoral immunity for the long-term control of viremia is supported by the observation that depletion of B cells during human or primate infections leads to dramatic increases in viral load [20–22] . Although it is known that cytotoxic T-lymphocyte ( CTL ) responses also play an important role in restricting HIV replication , CTL escape mutations are likely few and each occur once during infection . In contrast , the idea that humoral responses drive reciprocal and recurrent adaptive selection during infection is supported experimentally [23] , by genetic analysis [9] and by theoretical models [24] . To estimate absolute rates of molecular adaptive evolution during HIV infection we employ a statistical framework [25–27] that is based on the classic McDonald-Kreitman test for positive selection [28] and on subsequent work [12 , 29] . Our approach has been specifically developed for rapidly evolving viruses and relaxes assumptions that are not reasonable for these populations: previous work has shown that methods related to the McDonald-Kreitman test can be made more robust when applied to viral populations by taking into account multiple mutations at a given site [26] and by relaxing the unrealistic assumption that all polymorphisms are selectively neutral [25] . However , a continuing weakness of the framework is that it does not account for sampling error when counting the number of polymorphic and fixed sites in the alignment . This is particularly important for data sets with small numbers ( 2 to 50 ) of sequences per time point , including the data investigated here . To address this problem , we extend the framework by introducing a probabilistic model that incorporates binomial sampling error when calculating mutational site frequencies . A more detailed summary of the theoretical background of our approach can be found in the Materials and Methods section . Notably , we discover a strong negative correlation between viral population size and the rate of molecular adaptation , which is the opposite of the relationship predicted by standard models of molecular evolution ( in which fixation rates are proportional to the product of population size , per capita mutation rate , and fixation probabilities; e . g . [30] ) . We suggest a simple non-linear model that incorporates the counteracting effects of host immune responses on viral adaptation during infection provides a parsimonious explanation for this observation .
The viral sequences investigated here come from a cohort of 24 HIV-infected children recruited between 1986 and 1992 for the New York City Perinatal Transmission study [31 , 32] . Detailed information about sample collection and sequencing methods are given elsewhere [31 , 32] . The infections were acquired at or very close to the time birth . A quarter of these patients received no treatment during the study , while the remaining patients were treated with Zidovudine and/or Didanosine for a part of the study ( S1 Table ) . The sequences available for analysis represent approximately 360 nt of the V3 region of HIV-1 envelope gene ( positions 6963–7328 relative to the HXB2 genome ) . A heteroduplex mobility assay ( HMA ) was used to screen PCR clones for sequence variants per time point , so sequences may be slightly more variable compared to a perfectly random sample [33 , 34] . The sequences were manually aligned using Se-Al [33 , 35] . We estimated rates of adaptation from viral gene sequences obtained at the first and last sampling times for each patient . The first sampling time was on average ~ 2 months after birth ( range 0 to 7 months ) and the last sampling time was on average ~ 25 months after birth ( range 8 to 55 months ) . For the first time point , between 1 and 15 sequences were obtained per patient , while for the last time point 3 to 15 sequences were sampled per patient . Measures of viral load and CD4+ T-cell count were also available for each patient , at an average of 5 time points per patient . The mean CD4+ and log viral load per patient were calculated by linearly interpolating between each measured value , then calculating the average value of the resulting piecewise linear function between the first and last sampling times . Furthermore , each patient was placed into one of four disease progression categories based on the CD4+ T cell counts and a clinical diagnosis of AIDS ( S1 Table ) . In order of increasing clinical severity these categories are ( i ) slow non-progressors ( ii ) moderate non-progressors , ( iii ) moderate progressors and ( iv ) rapid progressors . Interestingly , viral loads are not noticeably different during periods of anti-viral drug therapy ( for more details see Table S1 in Carvajal-Rodriguez et al . [6] ) . To estimate absolute rates of molecular adaptive evolution during HIV infection we employ a statistical framework [25–27] that has been developed specifically for rapidly evolving viruses . This approach is based on the classic McDonald-Kreitman test for positive selection [28] and on subsequent work [12 , 29] . A brief introduction to the methodology is given here; further details of implementation and validation are provided in [25 , 26] . To infer natural selection , two sets of homologous gene sequences are required: a ‘main’ alignment and an ‘outgroup’ alignment . In the context of estimating adaptive evolution in rapidly evolving viruses , these two alignments correspond to the viral population being sampled at two different time points ( Fig 1A ) . In the HIV-1 infected patients studied here , the main alignment comprises sequences from a later sampling time , while the outgroup alignment represents sequences from an earlier sampling time , during the acute phase of infection . Given the limited viral genetic diversity observed at the earlier sampling time , the outgroup alignment can be effectively replaced by a single consensus sequence ( Fig 1B ) . In other words , sequences from the earlier time point represent the ancestral viral population . Sequences from the later sampling time are then compared to the consensus sequence from the first sampling time , and each nucleotide site is classified according to its observed frequency in the population ( i . e . its derived site-frequency ) . We further infer whether the derived mutation represents a nonsynonymous or synonymous change with respect to the consensus sequence at the first sampling time ( Fig 1C ) . To ensure consistency in terminology and equations with previous work [20] we refer to nonsynonymous mutations as ‘replacement’ and synonymous mutations as ‘silent’ . By comparing the main and ancestral alignments , each site in the main alignment is defined as invariant ( no polymorphism and identical to the ancestral nucleotide ) , fixed ( no polymorphism and different to the ancestral nucleotide ) , or polymorphic . If the site in the main alignment is polymorphic , then the ancestral alignment is used to define which nucleotides are ancestral and which are derived . Rules based on fractional counting are used when three or more nucleotides are present at a site . For example , if we observe a two-state polymorphic site in the main alignment that does not include the ancestral nucleotide , the most parsimonious explanation for this site is that an earlier fixation event occurred which was then followed by another mutation at the same site . The classic McDonald-Kreitman test ( i . e . assuming the infinite sites model ) would treat this site as a polymorphism , leading to an underestimation of the number of fixation events . In contrast the fractional counting method treats this site as equally representing both a fixation and a polymorphism . Further details about the counting algorithm can be found in Bhatt et al [26] . Suppose that the main alignment consists of N viral gene sequences , K nucleotides in length . If Di denotes the number of derived nucleotides at site i in the main alignment , then the estimated frequency of the derived nucleotide at that site is simply Di/N . However , this estimate has a large binomial variance when sample size is small; the true frequency of a site that appears ‘fixed’ in a sample of 5 to 10 sequences may be considerably less than one . Similarly , a site that appears invariant in the main alignment ( i . e . Di = 0 ) may actually be polymorphic in the study population . If pi denotes the true frequency of the derived state at site i , then we model the probability of pi given N and Di using a Beta-Binomial Bayesian model . The ancestral ( N-Di ) and derived ( Di ) site-frequencies are dichotomous random variables for which the canonical likelihood function is the Binomial distribution . We model the prior distribution of the Binomial parameter pi as a Beta[1 , 1] distribution ( equivalent to a unit uniform distribution ) . The resulting normalized posterior distribution is therefore described by conjugacy as a Beta distribution with the form: P ( pi|N , Di ) = ( N+1 ) ! ( N−Di ) ! Di ! piDi ( 1−pi ) N−Di ( 1 ) The probability that pi lies between the interval u and v is therefore an integral over the posterior Beta distribution within the range {u , v}: P ( u<pi<v|N , Di ) = ( N+1 ) ! ( N−Di ) ! Di ! [∫uvpiDi ( 1− pi ) ( N−Di ) dp] ( 2 ) Hence the expected number of sites with a derived nucleotide frequency between u and v is f^u , v=∑i=1KP ( u<pi<v|N , Di ) ( 3 ) The values u and v define a ‘site-frequency range’ that contains fu , v sites . Since f0 , 1 = K the interval [0 , 1] can be split into any number of non-overlapping site-frequency ranges . Note that this means that site frequencies are estimated for all sites , including invariant sites , not just for polymorphic sites . The expected number of sites in each range can be calculated separately for silent ( synonymous ) and replacement ( non-synonymous ) sites ( Fig 1C ) . Polymorphic and fixed sites in the main alignment are classified as silent or replacement by direct comparison with the ancestral alignment . Invariant sites are classified as silent , replacement , or undefined using a fractional approach based on the codon degeneracy inherent in the genetic code ( see S2 Table ) . If ρu , v and σu , v define the expected number of replacement and silent sites with a frequency between u and v , then σu , v=∑i=1Ksi . P ( u<pi<v|N , Di ) ( 4 ) ρu , v=∑i=1K ( 1−si ) . P ( u<pi<v|N , Di ) ( 5 ) Where si and ( 1-si ) represent the probabilities of a site being silent or replacement , respectively ( see [26] ) . Thus , if the sampled sequences contain S silent sites and R replacement sites then σ0 , 1 = S , ρ0 , 1 = R and S+R = K . Following the theoretical and empirical analyses in [25] , three site-frequency ranges are defined in this study: “low frequency” ( 0%-15% ) , “mid frequency” ( 15%-75% ) and “high frequency” ( 75%-100% ) . The expected number of silent and replacement sites in each range were calculated using eqs 4 and 5 . The expected number of silent sites in the low , mid and high frequency ranges are denoted σl , σm and σh , and the number of replacement sites in the same ranges are denoted ρl , ρm , and ρh ( Fig 1C ) . If silent mutations and mid-frequency polymorphisms are selectively neutral , and deleterious mutations are confined to the low frequency range , then the expected number of adaptive sites ( αh ) can be estimated as: αh=ρh ( 1−σhρh . ρmσm ) ( 6 ) This is identical to equation 1 in [25] except that the silent and replacement counts are estimated probabilistically using the Beta-Binomial sampling model ( eqs 4 and 5 ) . The number of adaptive sites can be converted to a per-codon rate of molecular adaptation by dividing by the number of codons in the sequence alignment and the time elapsed between the two sampling points ( see Fig 1A ) . The assumptions on which the estimator in eq 6 is based were explored in [25] and appear to be robust for rapidly evolving viruses provided that viral effective population sizes are sufficiently large ( > = 500 ) . Recent estimates of the effective population size of HIV population within infected individuals ( and which do not rely on a neutral coalescent model ) are in the range of ~105 , strongly indicating that the molecular evolution of HIV is likely to be dominated by deterministic rather than stochastic forces [36] . The term in brackets in eq 6 represents an estimate of the fraction of replacement sites in the high frequency range that are driven by positive selection . The term ρmσm denotes the ratio of replacement-to-silent sites in the mid site-frequency range , which we refer to as the ‘neutral’ ratio and provides the baseline against which polymorphism in other site frequency ranges is compared . To assess statistical uncertainty a bootstrapping approach was undertaken using the procedure outlined in [25] . To verify that sequences generated after HMA screening are suitable for estimating adaptation rates , we analyzed a comparable within-host HIV-1 dataset that was generated without any HMA screening [5] . We emulated the effects of HMA-screening on this dataset by replacing all sets of sequences with >99% sequence identity by a single representative sequence . Site-frequencies and adaptation rates were then estimated from the original and screened datasets using the methods described above ( S1 Fig ) . As expected , the number of low-frequency polymorphisms in the HMA-screened dataset was underestimated ( S1A Fig ) . However , adaptation rates were similar between the two datasets ( S1B Fig ) , likely because our method explicitly ignores low frequency sites . Although screening led to a slight underestimation of adaptation rate in a few patients ( p1 , p2 , and p3 ) the effect is small compared to estimation error ( S1B Fig ) and therefore our results are qualitatively robust .
Although the sets of virus gene sequences analyzed here are modest in size in comparison to some recent studies [42 , 43] , the cohort we analyzed is unusual in that ( i ) it represents pediatric rather than adult HIV-1 infections , ( ii ) informative data on viral load and the outcome of infection was available for each patient , and ( iii ) there were sufficient numbers of patients in each disease category to permit statistical comparison . We argue that two opposing consequences of immune selection on viral molecular evolution have led to an unexpected inverse relationship between the rate of viral adaptation and population size . Specifically , the counteracting effects of host immune responses upon viral abundance and viral selection coefficients can explain the pattern observed in Fig 2 . This is the first time that the non-linear model of viral adaptation first formalized in Grenfell et al . [41] has been used to explain patterns in empirical data . This study highlights the benefits of re-analyzing previously published data sets when new methods of analysis become available . For example , Carvajal-Rodriguez et al . [6] also investigated the same pediatric HIV cohort using dN/dS based methods , yet did not find a significant relationship between viral adaptation and disease progression . Measuring adaptive evolution by estimating the rate at which natural selection fixes beneficial mutations is complementary to , and has some advantages over , alternative approaches methods that estimate dN/dS ratios , or which estimate selection coefficients . First , the absolute rates of viral adaptation obtained here can be interpreted directly , whereas the correct interpretation of dN/dS ratios in the context of within-host virus evolution is uncertain [10] . Per-year adaptation rates are directly comparable among different populations and even among species . Second , estimation of mutational selection coefficients often requires parametric population genetic models that make strong assumptions about population demography or the mode of selection [44 , 45] , which , if not correct , could lead to misleading results . In contrast , net rates of viral adaptation can be estimated without making any assumptions about the population and selection dynamics in the studied population , which may be very complex for rapidly evolving viruses including HIV-1 [46] . Third , even when such values can be estimated , their general relevance is unclear because fitness and selection coefficients are typically defined relative to the environment in which they are measured . The virus’ immune environment will vary significantly , both among hosts and through time within each host , making quantitative comparison of selection coefficients very difficult except under highly controlled experimental conditions , such as growth in cell culture [47] . However , it is important to note that the site frequency-based approach used here cannot identify the specific codons that are under positive selection and therefore other methods , such as dN/dS , should be used when that is the question of interest . Advances in HIV treatment necessitate the re-analysis of published HIV data in order to understand the virus’ evolutionary behavior , even when those data were generated using older sequencing techniques such as HMA screening . Modern highly active anti-retroviral therapy ( HAART ) reduces viremia to low or undetectable levels in most cases and it would be unethical to recruit new cohorts of chronically infected patient without providing them with treatment . Consequently , data sets that predate the widespread use of HAART , such as the pediatric cohort analyzed here , provide an irreplaceable source of information about the natural ecology and evolution of HIV during infection . The negative relationship between net viral adaptation rate and population size discovered here has consequences for the interpretation and prediction of the outcome of pediatric HIV-1 infection . Specifically , if childhood HIV infections do indeed lie on the left hand side of the model in Fig 3 , then potential interventions that aim to boost humoral “immune responses” will likely lead to an increased , not decreased , rate of viral adaptation , despite generating a lower viral load . This could mean that the benefits of any such intervention are short lived , unless the intervention itself can adapt on the same timescale as the viral population . Lastly , the model shown in Fig 3 suggests that adult HIV infections should be further towards the right ( regions B and C ) , as immune responses are stronger on average than for pediatric infection . As a consequence , we predict either no relationship , or a positive one , between rate of viral adaptation and viral load for adult HIV-1 infection . Our re-analysis of the adult HIV cohort from Shankarappa et al [5] matches this prediction because it shows no association between viral adaptation rate and viral load among patients ( S3 Fig ) . However , the results in S3 Fig should be interpreted with caution because that cohort contains far fewer patients and the range of viral load values is narrower , both of which will act to reduce statistical power to detect a trend . It has also been previously noted that there is a positive relationship between adaptation rate and disease progression in HCV infections [10] . This indicates that variation in within-host adaptation rates in HCV are most likely explained by region C in Fig 3 , where increasing immune selection on the viral population could lead to clearance of the infection . Understanding the effects of different host immune responses on HIV evolution during infection is also important to vaccine design and treatment . Since historic ( i . e . pre-HAART ) data sets mostly represent HIV env sequences , it is difficult to investigate viral adaptation in other genomic regions . Thus our study was restricted to examining how variability in env adaptation rates among patients is explained by viral load . However , it is clear that viral adaptation outside of env ( e . g . escape from CTL immune responses [48] ) is important in determining variation in viral replication and disease progression . Therefore our results should not be used to support the inverse argument , i . e . that env adaptation rates explain variation in viral load . In order to fully understand the relative importance of different host immune responses in shaping viral load additional sequence data that represent genes other than env is required .
|
Since some common approaches to the study of molecular adaptation may not be optimal for answering questions regarding within-host virus evolution , we have developed an alternative approach that estimates an absolute rate of molecular adaptation from serially-sampled viral populations . Here , we extend this framework to include sampling error when estimating the rate of adaptation , which is an important addition when analyzing historical data sets obtained in the pre-HAART era , for which the number of sequences per time point is often limited . We applied this extended method to a cohort of 24 pediatric HIV-1 patients and discovered that viral adaptation is strongly associated with the rate of disease progression , which is in contrast to previous analyses of these data that did not find a significant association . Strikingly , this results in a negative relationship between the rate of viral adaptation and viral population size , which is unexpected under standard micro-evolutionary models since larger populations are predicted to fix more mutations per unit time than smaller populations . Our findings indicate that the negative correlation is unlikely to be driven by relaxation of selective constraint , but instead by significant variation in host immune responses . Consequently , this supports a previously proposed non-linear model of viral adaptation in which host immunity imposes counteracting effects on population size and selection .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"and",
"Discussion"
] |
[] |
2016
|
Faster Adaptation in Smaller Populations: Counterintuitive Evolution of HIV during Childhood Infection
|
The necessity of a venous blood collection in all dengue diagnostic assays and the high cost of tests that are available for testing during the viraemic period hinder early detection of dengue cases and thus could delay cluster management . This study reports the utility of saliva in an assay that detects dengue virus ( DENV ) –specific immunoglobulin A ( Ig A ) early in the phase of a dengue infection . Using an antigen capture anti-DENV IgA ( ACA ) ELISA technique , we tested saliva samples collected from dengue-confirmed patients . The sensitivity within 3 days from fever onset was over 36% in primary dengue infections . The performance is markedly better in secondary infections , with 100% sensitivity reported in saliva samples from day 1 after fever onset . Serum and salivary IgA levels showed good correlation ( Pearson's r = 0 . 69 , p<0 . 001 ) . Specificity was found to be 97% . Our findings suggest that this technique would be very useful in dengue endemic regions , where the majority of dengue cases are secondary . The ACA-ELISA is easy to perform , cost effective , and especially useful in laboratories without sophisticated equipment . Our findings established the usefulness and reliability of saliva for early dengue diagnosis .
Dengue is one of the most prevalent mosquito-borne diseases in humans . This disease is best controlled by regular Aedes source reduction activities . However , total eradication of Aedes in a densely populated urban area where the vector has established itself is a daunting task . Dengue control must include prompt control response to dengue clusters , and early and reliable diagnosis of cases is critical to this effort , which aims to halt the DENV transmission . There has been progress in recent years in the development of dengue diagnostic tools , resulting in the availability of suitable tests for each stage of the disease . Specific detection of dengue viral ribonucleic acid ( RNA ) using real-time reverse transcription ( RT ) polymerase chain reaction ( PCR ) is widely utilized to diagnose and serotype dengue infections in the early phase of the disease [1]–[7] . These techniques , while rapid and effective in providing early dengue diagnosis , are costly and require trained personnel to perform . It is thus only currently available in a limited number of clinical laboratories . The more recent development of DENV non-structural protein 1 ( NS1 ) antigen detection in the Enzyme-linked immunosorbant assay ( ELISA ) and rapid lateral flow platform has offered clinical laboratories an effective tool for early diagnosis during the febrile phase of the disease [8]–[13] . The detection of anti-DENV immunoglobulin M ( IgM ) is the most widely used serological assay in dengue diagnosis [14]–[21] . However , anti-DENV IgM is usually detected 5 to 6 d after the onset of fever and thus could result in a delay in diagnosis . Moreover , it can persist for more than 8 mo [20] , [22] , [23] , and in dengue-endemic countries such as Singapore , the detection of IgM in a febrile patient does not necessarily indicate an acute dengue infection . The requirement for analysis of paired samples collected at least 7 d apart , for definitive diagnosis , could delay intervention efforts . Unfortunately , the necessity of a venous blood collection in all available dengue diagnostic assays and the high cost of the tests that are available for the viraemic period hinder the early detection of cases and clusters . Phlebotomy in needlephobic febrile individuals , especially children , can be challenging , and the tendency to forgo a dengue blood test is high . We have therefore worked toward saliva-based techniques that could address the early phase of the disease . Saliva is known to be rich in IgA , the concentration of which is 100 times greater than that of IgM and 14 times greater than IgG , and should thus serve as a good target for early diagnosis [24] . Usage of salivary IgG for diagnosis and epidemiological studies has been described before [24]–[26] . The use of serum anti-DENV IgA as a diagnostic marker has previously been explored . Groen et al . [27] described the simultaneous increase of DENV-specific IgA and IgM in dengue patients and reported that IgA was short-lived compared to IgM [27] . An antibody-capture IgA ( AAC ) ELISA was used . Using the same technique , subsequent studies showed that anti-DENV IgA typically appeared after IgM did and was thus not suitable for dengue diagnostics [15] , [23] , [28] . The use of salivary IgA for disease detection has also been reported for Human Immunodeficiency Virus , Hepatitis A and B , Measles , Mumps , and Rubella [29]–[33] . In this prospective study , we developed a protocol that allows saliva to be used for anti-DENV IgA detection . The technique , antigen-capture anti-DENV IgA ( ACA ) -ELISA , not only increased the sensitivity of DENV-specific IgA detection , it also reduced the total test time to 90 min , when compared with a previously published IgA assay .
The Environmental Health Institute ( EHI ) is a national public health laboratory that functions as a licensed diagnostic laboratory , with an ISO9001 accreditation , as well as a research laboratory . Three suites of characterized samples , collected in Singapore , were used in this study . WHO criteria for dengue confirmation was adhered to for the determination of dengue status in the following samples: The first ( A ) comprised saliva and sera collected from 10 healthy volunteers as well as dengue-confirmed patients for optimization of the protocol . The sera from healthy volunteers were previously confirmed to be dengue negative via DENV RT-PCR and PanBio IgM Capture ELISA , and their negative anti-DENV IgA status was ascertained in both saliva and sera using a previously reported DENV Antibody Capture IgA ELISA ( AAC-ELISA ) [23] . The samples from dengue-confirmed patients consisted of saliva and sera sequentially collected from five patients during the acute phase of their disease . The dengue status of these patients was confirmed by RT-PCR on sera collected in the first 72 h and subsequent sero-conversion as demonstrated by IgM assays . These five sets of saliva and sera samples were also previously confirmed to be DENV IgA positive in AAC-ELISA . Samples in this suite were used as reference samples to establish the ACA-ELISA . The second suite of samples ( B ) , for evaluation of the newly developed ACA-ELISA protocol , consisted of saliva and sera obtained from 69 DENV-PCR-confirmed patients through three consecutive collections . The first collection was within 72 h after fever onset , second collection around 3 d after first collection , and third collection within 21 d after fever onset . IgM tests , performed on all three collections of each patient , demonstrated sero-conversion of each of the patients , thus confirming their dengue status . Of the 69 patients , 37 ( 53 . 3% ) had DENV1 infections , 4 ( 5 . 8% ) had DENV2 , and 28 ( 40 . 6% ) had DENV3 , as determined by RT-PCR on the first collection samples [34] . Of the 69 PCR-positive patients from suite B samples , there were 33 primary infections and 36 secondary infections . A primary dengue infection was characterized by first collection serum ( first 72 h ) being positive for DENV PCR but PanBio Indirect IgG ELISA negative . The absence of DENV-specific IgG in the acute phase of a dengue infection is indicative of primary dengue infection [35]–[37] . A secondary dengue infection was characterized by the first collection sample being concurrently positive for DENV PCR and IgG . These samples were collected through a research project ( EDEN ) from April 2005 to December 2006 [34] and were used for evaluation of the established ACA-ELISA protocol . The third suite ( C ) , serving as a specificity test of the ACA-ELISA , comprised three consecutive collections of saliva and sera from 75 DENV PCR-negative febrile patients ( EDEN ) . Collected in the same manner as suite B , they were DENV RT-PCR negative patients , and IgM tests revealed no sero-conversion among all 75 cases . Oracol saliva collection swab ( Malvern Medical Development Lid , UK ) was used for saliva collection . A standardized saliva collection protocol was used such that active saliva secretion was obtained . Patients were instructed to swab in a scrubbing manner their inner upper and lower cheeks 10 times each on both sides and place the swab under their tongues for 1 min . Saliva samples , together with blood , were transported on ice , processed within the same day of collection , and stored at −80°C until testing . Use of samples in suite A was approved by NEA's Bioethics Committee ( IRB004 . 1 ) . Use of samples in suites B and C was approved by the National Healthcare Group Internal Review Board ( DSRB B/05/013 ) . Written informed consent was obtained from all participants . DENV cell culture lysate antigens used in ACA-ELISA were prepared using a DENV 2 strain ( SS194Y02 ) of the Cosmopolitan genotype , isolated from a dengue patient locally , according to the method previously described [38] , and viral titre was determined via plague assay [39] . A single batch of cell lysate was prepared and utilized for this entire study . Checkerboard dilution was performed using anti-DENV IgA positive and negative samples from suite A as reference to optimize the ACA-ELISA for serum and saliva usage . In brief , the 96-well plate maxisorp plates ( Nunc , Denmark ) were coated with 100 µl/well of pan-DENV monoclonal antibodies ( Mab; 1 . 15 mg/ml; Immunology Consultants Laboratory , USA ) diluted at 1∶500 in sodium bicarbonate ( pH 9 . 5 ) and incubated either overnight at 4°C or 1 h at 37°C . After blocking the plate with dilutent buffer ( 5% skim milk containing 0 . 05% Tween-20 ) , virus lysate ( 2 . 14×106 pfu/ml ) in diluent buffer was added to each well and incubated at 37°C for 1 h . The plate was then washed six times using washing buffer ( 1X PBS-0 . 05% Tween-20 ) . Either 100 µl of test serum at 1∶100 in diluent buffer or 100 µl of saliva at 1∶5 dilution was added to each well . In each plate , two positive controls , two negative controls ( DENV negative human sera or saliva ) , and one plate control ( no sera or saliva added ) were included . The two positive controls were either two anti-DENV IgA positive sera ( titre of 1∶256 ) or two anti-DENV IgA positive saliva samples ( titre 1∶10 ) . After 1 h of incubation at 37°C , the plate was washed again six times , and 100 µl of 1∶4000 rabbit anti-human IgA conjugated with horse-radish peroxidase ( HRP; Dako , Denmark ) was added to each well and then incubated for 30 min at 37°C . Following incubation , the plate was then washed again six times , and 100 µl of tetra-methyl-bencidine ( TMB; Sigma , USA ) was added to each well and incubated for 5 min at room temperature . Further color development was stopped using 100 µl 0 . 5 M sulphuric acid , and absorbance was measured at 450 nm against a reference filter at 620 nm . The same batch of controls , reagents , and dengue lysate was used for this entire study . All IgA assays for evaluation of the protocol were performed by a single analyst within a day , without masking the results of the reference tests . Interval between sample collection and testing ranged from 1 mo to 1 y , during which samples were kept frozen at −80°C . All IgM and IgG assays were performed using PanBio IgM Capture ELISA and PanBio Indirect IgG ELISA . IgM , IgG assays , and RT-PCR ( 7 ) were conducted by five trained personnel of the EHI Diagnostics Unit , licensed by the Ministry of Health . While PCR tests were performed within a day of collection of samples , antibody tests on sera and saliva were performed either on the same day or within 1 mo , during which samples were frozen at −80°C . Data analysis , including calculations of correlation coefficients and standard error of proportion , was carried out using Microsoft Excel 2002 and Statistical Package for Social Sciences ( SPSS ) 17 . 0 . Sensitivity and its 95% confidence intervals were provided as estimates of the effectiveness of the in-house ACA-ELISA . Standard error of proportion was calculated using the formula √ [p ( 1−p ) /n] .
The cutoff point of saliva IgA assays was determined with suite A of saliva samples from healthy volunteers . Mean ± standard deviation optical density ( OD ) values of the negative controls were determined . A sample was considered negative when the OD value was less than the mean value for the negative control plus two standard deviations , equivocal when the OD value exceeded the mean value for the negative control plus 2 standard deviations but less than 3 standard deviations and considered positive when the OD value is above 3 standard deviations . Suite B saliva and serum samples , consisting of three consecutive collections from each dengue-confirmed patient , were assayed for anti-DENV IgA using ACA-ELISA . All the serum samples were also tested with PanBio Capture IgM ELISA . Serum and salivary anti-DENV IgA levels showed good correlation ( Pearson's r = 0 . 69; p<0 . 001 ) . Figure 1 shows the sensitivities of ACA-ELISA on saliva and sera , compared to IgM Capture ELISA on sera . ACA-ELISA on saliva had an overall sensitivity of 70% in the first 3 d after fever onset and subsequently rose to over 90% between 3 and 8 d after fever onset . The same technique on sera gave similar results . More interestingly , the sensitivity of ACA-ELISA in saliva was higher than that of IgM Capture ELISA on sera , which detected only 10% of the dengue-confirmed patients after 1 to 3 d of fever , and only rose to around 90% after day 6 of fever . The specificity of the ACA-ELISA test was also found to be high at 97% . Among the 75 DENV-negative patients in suite C , only one patient tested positive , at day 7 and 27 , respectively . The data of the ACA-ELISA were further analyzed with respect to primary ( n = 33 ) and secondary ( n = 36 ) infections . The sensitivity of the technique is detailed in Figure 2A ( primary infection ) and 2B ( secondary infection ) . Among the 33 primary cases , the sensitivity of ACA-ELISA on saliva was 36% in the first 3 d and rose to 86% in the second collection ( 3 to 5 d ) . The number of samples collected during 6 to 8 d was small . Nevertheless , the results showed that at the early phase of the disease , the sensitivity of ACA-ELISA on saliva was clearly higher than those of ACA-ELISA and IgM Capture on sera ( 15% and 6% in the first 3 d after fever onset , respectively ) . Interestingly , ACA-ELISA tested on the saliva and sera of 36 secondary cases yielded sensitivities of 100% and 94% , respectively , in the first 3 d of fever and continued to allow detection at this high rate in second and third collection ( 3 to 34 d ) . In contrast , IgM Capture ELISA on sera of secondary cases gave a detection sensitivity of 14% in the first 3 d and rose to 88% only by day 6 .
This study has demonstrated the potential use of saliva for early dengue diagnostics . The sensitivity of ACA-ELISA was 70% to 92% within the first 8 d from onset of fever . Data from our diagnostic unit have revealed that dengue patients in local settings visit the primary health care physicians at an average of 3 . 5 d from onset of fever [13] . Ninety-five percent of the patients would visit their doctors within 0 . 95 to 6 . 03 d after fever onset . Under this setting , we have found that our in-house RT-PCR offers 72 . 5% specificity and the most sensitive commercial NS1 assay offers 81 . 7% [40] . Therefore , ACA-ELISA on saliva is comparable to these early diagnosis tests and offers the additional advantages of non-intrusive sampling and easy cost-effective laboratory procedures . It is not surprising that among the secondary dengue infections , IgA was detected in both the saliva and sera of all individuals as early as day 1 after fever onset , while detection in primary cases was delayed . This is due to the presence of IgA memory cells from the previous dengue infection that was triggered by the secondary DENV exposure—not unlike the early IgG responses in secondary dengue cases . This has also been observed in previous studies [15] , [25] , [41] , [42] . Due to the difference in sensitivity between primary and secondary cases , the sensitivity of the ACA-ELISA in a population will be highly dependent on the proportion of secondary cases in the cohort . About 50% of all dengue cases in Singapore are secondary [34] . Under this circumstance , we demonstrated about 70% sensitivity in the first 3 d of fever . In a population with a higher rate of secondary cases , the sensitivity could potentially be higher . There are two possible explanations to the early detection of IgA even in primary cases . Firstly , inaccuracy in reporting of the onset dates , due to insensitivity to mild fever or bias in recalls , may contribute to the situation . Secondly , intrinsic incubation periods and response time vary among individuals . Dengue patients are infected 2 to 14 d before fever onset , a period which may have allowed IgA ( and IgM ) production in some individuals . Early detection of IgM for some individuals is also evident for dengue and chikungunya [43] , [44] . It is highly likely that in the very early phases of dengue , IgM and IgA are present in very low levels , and time of detection lies in the sensitivity of the technique used . Previous studies , using AAC-ELISA , reported low sensitivities when using salivary IgA , in contrast to our findings [15] , [24] , [25] , [45] . Two strategies were designed in this study to overcome the limitation seen in previous studies . Firstly , saliva collection protocol in this study was designed to allow for the collection of actively secreted saliva . Previous studies on DENV-specific IgA in saliva used passively secreted saliva in their evaluation . Prior to this study , a comparison of the two saliva collection protocols had revealed that , in the same individuals , actively collected saliva yielded higher levels of DENV-specific IgA than passively collected ones ( unpublished data , Yap G ) . Secondly , ACA-ELISA was designed to eliminate binding competition from high levels of non-specific IgA that are normally present in mucosal secretions to protect one from infectious diseases . A previously published technique , AAC-ELISA , captured all IgA in the first step , followed by subsequent differentiation of DENV-specific IgA from the pool of IgAs . In the event of a dengue infection , anti-DENV IgA may represent only a small proportion of IgA in saliva . The low detection rate of anti-DENV IgA could be due to preexisting non-DENV-specific IgA out-competing anti-DENV IgA . To circumvent the limitation , ACA-ELISA was designed to capture all anti-DENV antibodies in its first step , followed by detection of anti-DENV IgA . In the early phase of a primary dengue infection , DENV-specific IgM and IgG are present in low levels , and coupled with the enrichment of IgA in saliva , the approach is expected to increase the detection sensitivity . The high sensitivity of the technique could also explain the observations that in the early phases of the disease in primary cases , DENV IgA could be detected earlier than DENV IgM , appearing to go against classical immunology . The capture IgM ELISA used in this study , like the AAC-ELISA , was expected to pick up only elevated levels of DENV IgM . The difference thus lies on the sensitivity of the techniques used . The slightly higher sensitivity in saliva compared to that in serum is likely due to the dimeric structure of the secretory IgA , which could increase the amplification of signal output from the ELISA . Even though the antigen used in ACA-ELISA of this study was DENV2 and not a tetravalent antigen , the ACA-ELISA is effective in detecting IgA illicited by the three serotypes circulating in Singapore during the study period , as DENV IgA , like IgM , is cross-reactive with all four serotypes [46] . This is supported by a study that demonstrated no significant differences in the sensitivity of ACA-ELISA when DENV2 is replaced by a tetravlent antigen ( unpublished data , Yap G ) . A multi-country study is ongoing to evaluate the test in various epidemiology settings , particularly to establish its performance in settings with other circulating DENV genotypes and other diseases that illicit cross-reacting antibodies , which may impact its performance . Potential limitation in specificity can be circumvented using recombinant antigen specific to DENV . This study suggests the potential of the saliva ACA-ELISA for dengue diagnosis . It eliminates the need to collect blood from dengue-suspected patients , is painless , is non-intrusive , and reduces the risk of needle stick injury . Moreover , the ELISA-based technique is simple and cost effective . Patients , especially the very young and the old , will be more willing to undergo a dengue test . Together , these benefits can potentially improve surveillance and early detection of cases , which in turn can translate to prompt dengue control effort . Due to its high sensitivity among secondary dengue infections , this technique could be very useful in highly endemic areas where the majority of the dengue cases are secondary .
|
The importance of laboratory diagnosis of dengue cannot be undermined . In recent years , many dengue diagnostic tools have become available for various stages of the disease , but the one limitation is that they require blood as a specimen for testing . In many incidences , phlebotomy in needle-phobic febrile individuals , especially children , can be challenging , and the tendency to forgo a dengue blood test is high . To circumvent this , we decided to work toward a saliva-based assay ( antigen-capture anti-DENV IgA ELISA , ACA-ELISA ) that has the necessary sensitivity and specificity to detect dengue early . Overall sensitivity of the ACA-ELISA , when tested on saliva collected from dengue-confirmed patients ( EDEN study ) at three time points , was 70% in the first 3 days after fever onset and 93% between 4 to 8 days after fever onset . In patients with secondary dengue infections , salivary IgA was detected on the first day of fever onset in all the dengue confirmed patients . This demonstrates the utility of saliva in the ACA-ELISA for early dengue diagnostics . This technique is easy to perform , cost effective , and is especially useful in dengue endemic countries .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"virology/diagnosis",
"infectious",
"diseases/viral",
"infections",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases"
] |
2011
|
Use of Saliva for Early Dengue Diagnosis
|
The temporal and stationary behavior of protein modification cascades has been extensively studied , yet little is known about the spatial aspects of signal propagation . We have previously shown that the spatial separation of opposing enzymes , such as a kinase and a phosphatase , creates signaling activity gradients . Here we show under what conditions signals stall in the space or robustly propagate through spatially distributed signaling cascades . Robust signal propagation results in activity gradients with long plateaus , which abruptly decay at successive spatial locations . We derive an approximate analytical solution that relates the maximal amplitude and propagation length of each activation profile with the cascade level , protein diffusivity , and the ratio of the opposing enzyme activities . The control of the spatial signal propagation appears to be very different from the control of transient temporal responses for spatially homogenous cascades . For spatially distributed cascades where activating and deactivating enzymes operate far from saturation , the ratio of the opposing enzyme activities is shown to be a key parameter controlling signal propagation . The signaling gradients characteristic for robust signal propagation exemplify a pattern formation mechanism that generates precise spatial guidance for multiple cellular processes and conveys information about the cell size to the nucleus .
Cascades of covalent protein modification cycles convey signals from cell-surface receptors to target genes in the nucleus . Each cycle consists of two or more interconvertible protein forms , for example , a phosphorylated and unphosphorylated protein , and an active , phosphorylated protein signals down the cascade . In eukaryotes , post-translational protein modifications include phosphorylation of Tyr , Thr and Ser residues , ubiquitylation , acetylation or sumoylation of Lys , methylation of Arg and Lys , and other modifications [1] . Every protein modification cycle is catalyzed by two opposing enzymes , such as a kinase and phosphatase for ( de ) phosphorylation cycle , ubiquitin ligase and deubiquitylating isopeptidase for ( de ) ubiquitylation , and methyltransferase and amine oxidase demethylase for ( de ) methylation . Well known examples of signaling cascades include mitogen activated protein kinase ( MAPK ) cascades , small GTPase cascades and coagulation cascades in blood clotting [2]–[4] . Instructively , although a MAPK cascade is usually referred to as a 3-tier pathway , in fact , the cascade encompasses five or more layers , which sequentially activate each other [5] . While signaling cascades were studied experimentally and theoretically for more than half a century , most studies disregarded the spatial aspects of signal propagation , considering one or more well-mixed compartment ( s ) with no variation in spatial dimensions . The stationary and temporal behavior of protein modification cascades was extensively analyzed , starting from pioneering numerical simulations by Stadtman and Chock [6] and followed by a theoretical exploration of steady-state input-output responses for a signaling cycle by Goldbeter and Koshland , who coined the term ultrasensitivity [7] . Depending on the degree of saturation of opposing enzymes in a modification cycle , the response curve for either interconvertible form varies from a merely hyperbolic to an extremely steep sigmoidal function [7] . Subsequent work showed that an increase in the number of layers in a cascade can further increase the sensitivity of the target to the input signal [8] , [9] . Although the dynamics of temporal responses of signaling cascades to a sustained , decaying or pulse-chase stimulation received less attention , the major types of temporal responses have been described . Depending on the cascade architecture and kinetic parameters , the sustained input can evoke sustained , transient [10] , [11] or more complex , bistable [12] , [13] and oscillatory responses [14]–[16] . An exponentially decaying input , which approximates the activity of a receptor after stimulation by a step function , causes a transient cascade response [17] . Yet , despite important breakthroughs in understanding the input-output relationships and temporal dynamics of information processing , we currently lack sufficient theoretical and experimental insights into spatial propagation of signals generated by protein modification cascades [18] , [19] . External signals received at the plasma membrane have to propagate across the cell to reach their targets , and , therefore , protein diffusion and active transport can change quantitative and qualitative aspects of output signaling by protein cascades [20]–[26] . In fact , signaling cascades are spatially distributed in living cells . Often , activating signals are only present on the cell membrane where activated receptors and small G-proteins ( such as Ras and Rap that activate MAPK cascades ) reside , whereas inactivating signals ( carried out by phosphatases in MAPK cascades ) are distributed throughout the cytoplasm . The concept of protein activity gradients that arise from the spatial separation of opposing enzymes in a protein modification cycle was proposed fairly recently [27] . For a protein phosphorylated by a membrane-bound kinase and dephosphorylated by a cytosolic phosphatase , Brown & Kholodenko predicted that there can be a gradient of the phosphorylated form , with high concentration close to the membrane and low within the cell [27] . This prediction was confirmed after a few years , when biosensors based on fluorescence resonance energy transfer enabled the discovery of activity gradients of the small GTPase Ran [28] , microtubule-binding protein stathmin [29] , the yeast MAPK Fus3 [30] , and , very recently , the anaphase phosphorylation gradient of Aurora B kinase , which , as a part of the chromosome passenger complex controls microtubule attachments to kinetochores and the late stages of cell division [31] . Precipitous gradients of phosphorylated kinases can impede information transfer from the plasma membrane to distant cellular locations , such as the nucleus . In the Ras/Raf/Mek/ERK ( MAPK ) cascade , Ras , Raf and , partially , MEK activation is localized to the plasma membrane , whereas MEK and ERK deactivation by phosphatases occurs in the cytoplasm . Calculations [32] , [33] and the experimental data [30] suggest that gradients of phosphorylated MEK and ERK can be steep , when the phosphorylated signals are terminated by phosphatases at the distances of 3–5 µm and greater from the plasma membrane . Despite recent work on the long-range signal transfer by phosphoprotein waves [33] , the quantitative understanding of how activity gradients spread in the space by the subsequent levels of signaling cascades is still lacking . It is not understood how the spreading of phosphorylation signals depends on the number of cascade levels ( stages ) and how the gradients of phosphoproteins along the cascade are controlled by the kinetic properties of the kinases and phosphatases . The previous work suggested that having more layers in a cascade would spread the phosphorylation signal to span ever increasing distances from the activation source [14] , [27] . The present paper shows that this simplistic view should be improved , and if the ratio of the phosphatase and kinase activities is above a certain threshold , the propagation of the phosphorylation signal stalls in the space . We demonstrate that a signaling cascade can produce a set of steady-state activation profiles that precipitously decay at different locations for different levels of the cascade . We determine analytically these locations , relating them with the kinetics of component reactions in signaling cascades . These activation profiles provide the localization information , which then can be used by other signaling pathways to regulate a range of cellular processes . We determine the features and conditions of the signal propagation and investigate the effect of saturation of activation and deactivation reactions on the generation of positional information .
Spatial separation of opposing enzymes , such as kinase and phosphatase , are hallmarks of protein modification cascades , including MAPK cascades . Here we consider a cascade of protein modification cycles , where each cycle consists of inactive and active forms of a signaling protein , and the active form catalyzes the activation of the protein at the next level down the cascade ( Fig . 1 ) . Although our analysis applies to any kind of protein modification , for convenience we will use the terminology of protein phosphorylation and dephosphorylation and a cascade of protein kinases as an example . The initial diffusible kinase in the cascade is activated exclusively at the plasma membrane by the membrane-bound receptor or small GTPase , whereas downstream kinases are phosphorylated in the cytoplasm by an active , diffusible kinase of the upstream level . A phosphorylated kinase is dephosphorylated in the cytoplasm by an opposing phosphatase at each cascade level . A simplified model , which neglects protein sequestration effects [34] , [35] , describes the signaling system in terms of the concentrations of the phosphorylated protein , , and the concentration of its unphosphorylated form , , at each level n of the cascade . The spatio-temporal dynamics of the phosphorylated kinases are governed by the following reaction-diffusion equations: ( 1 ) where D is the diffusion constant , and and are the phosphorylation and dephosphorylation rates , catalyzed by kinases and phosphatases , respectively . The equation for lacks the phosphorylation rate term , as the first level kinase is phosphorylated solely at the membrane . Spherical symmetry simplifies analysis of signaling in three dimensions , as the protein concentrations become functions of the radial distance and time only [27] . For simplicity , we neglect curvature effects and further consider a one-dimensional reaction-diffusion system with the Cartesian spatial coordinate x and the first kinase activated at the pole x = 0 ( the plasma membrane ) . For this kinase , the diffusive flux at the membrane ( x = 0 ) equals the surface phosphorylation rate , , and zero at the opposite pole , x = L . For kinases at the subsequent levels , there is no diffusive flux at either pole , which gives the following boundary conditions for Eq . ( 1 ) : ( 2 ) When diffusivities ( D ) of the phosphorylated and unphosphorylated forms are equal , and de novo protein synthesis and degradation are negligible on the time scale considered , the total protein abundance at each cascade level , , is constant across the cell [19] , ( 3 ) Assuming that the kinases and phosphatases follow Michaelis-Menten kinetics , at each level the phosphatase rate depends on the phosphorylated form concentration , , whereas the kinase rate depends on the concentration of the unphosphorylated protein , , and the concentration of the active kinase , , at the immediately preceding level , ( 4 ) Here , , and are the catalytic constant ( turnover number ) and the maximal rates . and are the Michaelis constants of the kinase and phosphatase at the n-th level [14] . Note that , in contrast with downstream levels , the surface rates and have the same dimension as the diffusion flux ( e . g , m·sec−1 ) . It is convenient to use the normalized protein concentrations . Dividing Eqs . ( 1 ) – ( 4 ) by , and bearing in mind that the unphosphorylated fractions are given by , we simplify the description of the cascade dynamics as follows ( 5 ) Here are the phosphatase activities ( known as the apparent first-order rate constants for a linear kinetic domain ) , and for n = 2 , … , N , are the kinase activities , where the maximal kinase rate is for n = 2 , … , N . The dimensionless ( normalized ) Michaelis constants of the phosphatases and kinases are and , respectively . The enzyme activities and determine the temporal scale of chemical reactions , indicating how fast phosphorylation and dephosphorylation occur , whereas the normalized Michaelis constants and characterize how far these reactions are from saturation ( unsaturated reactions correspond to , ) .
In this section we study the case when the total concentrations are small compared to the Michaelis constants , or in terms of the non-dimensional parameters , . From Eq . ( 5 ) , it follows that the evolution of the concentration of the active component at the first level is given by ( 6 ) where we used the notation , and , for the partial derivatives with respect to time t and the spatial coordinate x . The stationary solution to Eq . ( 6 ) reads , ( 7 ) where , , and . The distance defines the characteristic length scale for the gradient of the first kinase activity . When the spatial domain is large , that is , the activation profile of the first kinase decays almost exponentially , . Note , that regardless of the particular kinetics of the activation at the membrane , the steady-state profile of the first level kinase always decays nearly exponentially for large domains [27] . Using typical values of D = 5 µm2 s−1 , and as in Ref . [14] , the characteristic length scale can be estimated as . In addition to solving Eq . ( 5 ) numerically , we will explore analytically how the kinase activation profiles spread from the cell membrane into the cell interior . To simplify the analysis , we will further assume that the phosphatase activities for n = 1 , … , N and the kinase activities for n = 2 , … , N do not vary over different cascade levels ( although the values of the maximal rates , Michaelis constants and the total protein concentrations can be unique for each individual level ) . Non-dimensionalizing Eq . ( 5 ) by using the characteristic length scale 1/α , as , and the temporal scale , , we obtain ( omitting primes ) ( 8 ) where is a key parameter equal to the ratio of the phosphatase and kinase activities ( the ratio of deactivation and activation rates for a general case ) . The parameter indicates the strength of the membrane signal that determines the phosphorylation level of the first kinase at the membrane , as . We will first examine numerical solutions of Eq . ( 8 ) with the initial conditions , n = 1 , … , N on a domain of size L = 100 and . These numerical solutions are shown in Fig . 2 for six consecutive cascade levels . Note that the initial conditions and the signal strength do not affect the qualitative behavior of solutions ( see the supporting information , Figs . S1 and S2 ) . For γ = 0 . 1 , the concentration profiles propagate into the domain , moving to the right , until stationary profiles are attained ( Fig . 2D ) . The stationary concentration profiles propagate more deeply into the spatial domain as n increases , i . e . at higher levels of the cascade . A similar behavior is found for other values of γ<1 . For γ>1 we find a different scenario ( see Fig . S3 ) . In this case , the concentration profiles remain localized to the region near the left boundary , and their amplitudes decay dramatically for the consecutive levels . Thus , in this case the signal does not propagate through the domain and does not reach the right boundary . Examples of the asymptotic , steady state solutions are shown in Fig . 3 for a range of the ratios γ of the phosphatase and kinase activities . When γ<1 , the width of the profiles increases for smaller γ , while for γ>1 larger values of γ result in a faster decay with the concentration profiles localized to the left boundary . Note that the stationary solution for the first level is independent of γ . In general , there is no simple analytical expression for the stationary solutions of Eq . ( 8 ) , but we can gain some insight considering the behavior of these solutions within a range where . This approximation is always valid for the tail of the spatial distribution , that is for sufficiently large x , but when γ>1 , it can be satisfied over the whole domain . In this case , we can rewrite Eq . ( 8 ) in the stationary state as ( 9 ) This is a system of second order linear differential equations , which can be solved successively to obtain where is a polynomial of order ( explicit expressions are given in Text S1 ) . Figs . 3C and 3D ( dashed lines ) illustrate that for γ>1 the analytical solution of Eq . ( 9 ) agrees well with the numerical results . Importantly , for γ substantially less than 1 , we can determine the propagation length for successive activation profiles by using Eq . ( 9 ) near the tail of the distribution . Since the approximation is not valid near the boundary x = 0 , the coefficients of the polynomial cannot be exactly calculated ( Text S1 ) . However for small values of γ , when the activation signal spreads far enough for the tail region , the dominant contribution to the solution comes from the largest order term in which can be exactly obtained ( see Text S1 ) , ( 10 ) These solutions are shown ( dotted lines ) in Fig . 3A and B for different values of γ . Thus Eq . ( 10 ) gives a simple analytical approximation to describe the tail of each stationary profile . We will use this approximation to obtain the propagation length of the activation profile at different levels of the cascade . We formally define the propagation length of the steady state solution , , as the coordinate where the concentration falls below a certain threshold . Using Eq . ( 10 ) we have , which gives an implicit equation for the length , , that can be expressed by the Lambert W function [36] ( or simply solved numerically ) , ( 11 ) where the index −1 denotes the solution branch of the Lambert function corresponding to the values of . In the original dimensional units this length corresponds to , hence the spatial spread of signaling by each phosphorylated kinase is well characterized by the propagation length Ln . Figure 4 shows that if the concentration profiles for the successive cascade levels are shifted to the left by the distance given by Eq . ( 11 ) with , the resulting profiles converge to a single curve . Thus , Fig . 4 demonstrates that the steady state profiles have flat plateaus , which start from the left boundary . The length of each plateau , where the concentration is almost constant , increases with the cascade level , n . The plateau is followed by a transition region where the concentration decreases to zero . The shape of the curve corresponding to the transition region is asymptotically independent of n , with faster convergence to this asymptotic form for smaller values of γ . We can also determine the maximum active concentration ( termed the maximal signaling amplitude ) for that corresponds to the plateau region . Since the concentration field in the plateau region is flat , the second derivative can be neglected . Assuming that n is sufficiently large , is independent of n , and using Eq . ( 8 ) , we obtain , ( 12 ) Note that a positive solution for only exists for γ<1 , that is consistent with the observation that the signal cannot propagate into the spatial domain when γ>1 . Assuming the same profile shape for the different levels , we can use Eqs . ( 11 ) and ( 12 ) to estimate the total amount of active component for small values of γ . The value presents the stationary cascade activation response integrated over the space . At each level this spatial integral response should be proportional to the propagation length of the signal , . The asymptotic expansion of Ln is proportional to , thus the total active component of the different cascade levels depends on γ according to the functional form . This indicates that the difference in the total activated concentration at consecutive levels is , hence there is a constant step size between consecutive levels proportional to which agrees with the numerical results shown in Fig . S4 . In this section we consider a more general case given by Eqs . ( 5 ) that allows for saturation kinetics . Following the rescaling as in the previous section , the dynamics of the concentrations is described as follows , ( 13 ) For large values of and , Eq . ( 13 ) reduces to Eq . ( 8 ) . As in the previous section , we consider a cascade with similar properties for all levels and assume , . Note , that in contrast with the unsaturated case , we now make an additional assumption that the degrees of saturation of kinases and phosphatases do not depend on the cascade level , which implies that the corresponding Michaelis constants should change nearly proportionally to the total protein concentrations at each level . Simulations show that for saturable kinetics , the final steady states are not affected by the initial conditions , similarly as above ( Fig . S5 ) . Although the behavior of the phosphorylation profiles is found to be qualitatively similar to the case of non-saturable kinetics , it depends not only on the phosphatase/kinase activity ratios ( γ ) , but also on the degree of saturation . For fixed values of mi and ma , depending on the ratio γ , the stationary concentration profiles down the cascade either decay with n , or propagate into the spatial domain covering increasing distance with an increase in n . However , in contrast to the previous section where , the threshold value separating these two different behaviors is different from unity and depends on the Michaelis constants and . Figs . 5 and 6 show examples of the steady state activation profiles for γ = 0 . 1 and γ = 10 for different values of and . Note , that when deactivation reactions saturate at low values , whereas activation reactions have higher values , the signal may propagate even for γ greater than 1 , whereas it can decay for γ much less than 1 , for the opposite relation between the Michaelis constants . To illustrate the threshold behavior of the signal propagation in the parameter space , we consider two cases of large and large separately , each corresponding to systems where one of the opposing reactions is far from saturation . Fig . 7 shows phase diagrams of the system indicating the boundary between the decaying and propagating signals on the γ , plane for a fixed value of , and on the γ , plane for , respectively . For , Fig . 7A shows that for large values of , the threshold between propagating and decaying signals is γ = 1 in agreement with the linear case discussed in the previous section , but for , much lower values of γ are necessary for the propagation . On the other hand , if ( Fig . 7B ) , we obtain propagation for all γ<1 , however the saturation of the inactivating reaction extends the threshold to larger values of γ when is small . To obtain an analytical approximation for the boundary between the two regimes of decaying or propagating signals , as in the previous section , we assume that a propagating signal produces a set of stationary concentration profiles with a flat plateau region on the left side of the domain and the concentration converges to a constant for large n . The existence of non-zero asymptotic concentration , , is a prerequisite for the efficient signal propagation . Neglecting the second derivative at x = 0 from Eq . ( 13 ) we obtain ( 14 ) We will first assume that the phosphatases are far from saturation , , whereas the kinases can be saturable . Since , we can simplify Eq . ( 14 ) to obtain ( 15 ) We can readily see that Eq . ( 15 ) has a solution within only if ( 16 ) When the ratio γ of opposing enzyme activities satisfies Eq . ( 16 ) , the solution of Eq . ( 15 ) is . The dashed line in Fig . 7A represents the curve given by Eq . ( 16 ) , that agrees well with the numerical results . When the kinases in the cascade are far from saturation , , from Eq . ( 14 ) we obtain the following equation for , ( 17 ) The right hand side of Eq . ( 17 ) is a parabola with a maximum at where its value is . However , when , the maximum is reached for negative concentration values , and in this case the maximum for positive concentrations is 1 . Thus , Eq . ( 17 ) only has a positive solution within the interval [0 , 1] if ( 18 ) The dashed lines in Fig . 7B represent the curves and γ = 1 as given by ( 18 ) . Another regime of qualitatively different behavior is the case when both reactions are saturated , i . e . . The condition for signal propagation in this case cannot be obtained analytically from the plateau solution of Eq . ( 14 ) as in the previous cases , since this only gives an approximation for an unstable solution that is not relevant for the steady state distribution . Numerical simulations show that the signal propagation is restricted to smaller and smaller values of the activation ratio as the parameters are reduced . This is shown in Fig . 8 . Note , that even in the case when the saturation constants are the same , mi = ma , the threshold for signal propagation is smaller than one .
Cascades of protein modification cycles form the backbone of many signaling pathways , such as MAPK and GTPase cascades , which integrate signals from numerous plasma-membrane receptors and transmit information to distant cellular targets , including the nucleus [4] , [14] , [37] . A hallmark of these signaling cascades is the spatial separation of activation and deactivation processes to different cellular compartments [19] , [32] . In such spatially distributed cascades , the first signal transducer can be activated at the cell membrane by a membrane-bound enzyme , e . g . , a kinase or a guanine nucleotide exchange factor for GTPase cascades , and deactivated in the cytosol by an opposing enzyme , e . g . , a phosphatase or a GTPase activating protein [18] . If a subtle balance between the rates of activating and deactivating enzymes , e . g . , kinases and phosphatases , is not properly maintained , the phosphorylated kinase concentrations can drop even in close proximity to the activation membrane source , and the phosphorylation signals decay before reaching the targets . The results of the present paper have identified the general conditions for the robust signal propagation and determined when the activation signal stalls near the membrane for an arbitrary number of consecutive layers in a cascade . The signals that spread through a cascade generate a set of stationary activation profiles . When the ratio of the deactivation and activation rates , γ , is small , these activation profiles have almost identical plateau levels for successive kinases and shifted in the space relative to each other by a roughly constant distance towards the center of the cell . We expressed analytically the amplitude and the width of successive activation profiles that spread the signals into the cell interior and examined the effect of saturation of the reaction rates on the propagation of the phosphorylation signals . This precipitous descend in the signals at different distances from the plasma membrane provides digital , switch-like localization cues that are more structured and robust than the information carried by a concentration gradient that emerges in a single ( de ) phosphorylation cycle [38] . Importantly , we found that the control of the spatial signal propagation is dramatically different from the control of transient temporal responses for spatially homogenous cascades [17] , [39] . Whereas the persistence of transient activation in the spatially uniform cascade depends mainly on the phosphatase activities [17] , our results show that the spatial spread of activation from the membrane into the cell is determined by the ratio of the kinase and phosphatase activities and by their degree of saturation . Likewise , the maximal amplitude of propagating activation profiles depends on the activity ratios of phosphatases and kinases and not mainly on the kinases , as the amplitude of the temporal responses for spatially homogenous cascades [17] . More complex spatial patterns of active kinases are generated when the ratios of phosphatase and kinase activities are different along the cascade ( see Fig . S6 ) . The spatial structure of activity gradients also strongly depends on the size and shape of the cell [24] . An interesting consequence of the activity profiles produced by spatially distributed cascades is that at different cascade levels , the kinase activities at the nuclear membrane change with the cell size . As the cell grows , the distance between the cell membrane and nucleus increases , and consequentely the activity of proteins at the boundary is turned off one by one for increasing cascade levels . This suggests a signaling mechanism that conveys information about the cell size to the nucleus . This mechanism may play a role in the control of cell division cycle . The step structure of the concentration profiles ensures that a sharp change in concentration takes place when the cell reaches a certain size , representing a robust digital switch-like signal . In this work we have not considered the effects of feedback and feedforward loops , which may lead to more complex spatial structures and temporal dynamics [19] . In fact , it has recently been shown that bistability in protein phosphorylation cascades generates phosphoprotein waves that propagate from the surface deep into the cell interior [33] . For the cascade levels localized to the cytoplasm , if a downstream kinase stimulates the activation of the upstream kinase ( directly or via a regulatory circuit in the cytoplasm ) , a resulting bistable switch generates a trigger wave that propagates with nearly constant amplitude and velocity [33] . However , although such a wave relays the signal over increasingly long distances , it also destroys the positional information delivered by the successive activation profiles for monostable cascades , which we analyzed here . It is instructive to compare spatially distributed reaction cascades to other reaction-diffusion systems exploited in mathematical models of biological phenomena . Traveling front or pulse solutions in reactions with multiple steady states or with excitable dynamics ( e . g . , the Hodgkin-Huxley model ) produce concentration distributions that propagate in space with a constant speed , but rarely generate heterogeneous spatial structures at steady states [40] . On the other hand , the classical mechanism of morphogenesis based on the Turing instability leads to the formation of stationary concentration patterns driven by different diffusivities of the reacting species [41] . Although often this condition is not satisfied for biological systems , the Turing mechanism has been suggested to explain the formation of skin pigmentation patterns [42] , hair follicle distribution [43] and other biological patterns . Because of their translational symmetry , periodic Turing patterns are not always suitable for providing positional information . The concentration distribution generated by a spatially distributed reaction cascade can provide a simple and robust spatial pattern , in which the distance from the source ( e . g . , cell membrane ) is encoded into the local concentrations . There is an important distinction between the Turing patterns and patterns originated in spatially extended protein cascades considered here . Heterogeneous Turing patterns arise spontaneously due to diffusion driven instability and symmetry breaking , transforming an initially homogeneous spatial distribution [41] , [44] . The spatial patterns considered here involve the initial non-homogeneity of the media , which is brought about by the spatial separation of the opposing activator and deactivator enzymes that localize to different cellular structures , namely the membrane and cytoplasm [45] . This type of reaction-diffusion mechanism may also play a role at larger scales in the development of multi-cellular systems where positional information and growth guide cell proliferation and differentiation events .
|
Living cells detect environmental cues and propagate signals into the cell interior employing signaling cascades of protein modification cycles . A cycle consists of a pair of opposing enzymes controlling the activation and deactivation of a protein , where the active form transmits the signal to the next cascade level . A crucial challenge in cell and developmental biology is to understand how these cascades convey signals over large distances and how spatial information is encoded in these signals . With the advent of advanced imaging techniques , there has been emerging interest in understanding signal propagation in cells and tissues . Based on a simple cascade model , we determine the conditions for signal propagation and show how propagating signals generate spatial patterns that can provide positional information for various cellular processes .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[
"biophysics/cell",
"signaling",
"and",
"trafficking",
"structures"
] |
2009
|
Positional Information Generated by Spatially Distributed Signaling Cascades
|
Autophagy , an ancient and highly conserved intracellular degradation process , is viewed as a critical component of innate immunity because of its ability to deliver cytosolic bacteria to the lysosome . However , the role of bacterial autophagy in vivo remains poorly understood . The zebrafish ( Danio rerio ) has emerged as a vertebrate model for the study of infections because it is optically accessible at the larval stages when the innate immune system is already functional . Here , we have characterized the susceptibility of zebrafish larvae to Shigella flexneri , a paradigm for bacterial autophagy , and have used this model to study Shigella-phagocyte interactions in vivo . Depending on the dose , S . flexneri injected in zebrafish larvae were either cleared in a few days or resulted in a progressive and ultimately fatal infection . Using high resolution live imaging , we found that S . flexneri were rapidly engulfed by macrophages and neutrophils; moreover we discovered a scavenger role for neutrophils in eliminating infected dead macrophages and non-immune cell types that failed to control Shigella infection . We observed that intracellular S . flexneri could escape to the cytosol , induce septin caging and be targeted to autophagy in vivo . Depletion of p62 ( sequestosome 1 or SQSTM1 ) , an adaptor protein critical for bacterial autophagy in vitro , significantly increased bacterial burden and host susceptibility to infection . These results show the zebrafish larva as a new model for the study of S . flexneri interaction with phagocytes , and the manipulation of autophagy for anti-bacterial therapy in vivo .
Macroautophagy ( hereafter referred to as autophagy ) is an intracellular degradation process by which cytosolic materials are delivered to the lysosome . The canonical autophagy pathway involves the initiation and elongation of double-membrane autophagosomes to sequester cargo , and this process requires 36 autophagy-related ( ATG ) proteins conserved from yeast to man [1] . Autophagy has diverse functions in important cellular processes such as development , aging and inflammation , and is also linked to a wide range of disease states including microbial infection [2] , [3] . By binding to ATG8 family proteins and delivering them to recognized cargo , autophagy receptors can mediate selective targeting of intracellular bacteria to autophagy [4] , [5] . p62 ( sequestosome 1 or SQSTM1 ) is a well-characterized autophagy receptor [6] and belongs to a newfound category of pattern recognition receptors called SLRs ( sequestosome 1/p62-like receptors ) linking autophagy to innate immunity [7] . Discovered almost 10 years ago , bacterial autophagy has been highlighted as a fundamental host cell response to bacterial invasion in vitro by degrading intracellular pathogens including Shigella flexneri [8] , Listeria monocytogenes [9] , Salmonella Typhimurium [10] and Mycobacterium tuberculosis [11] . Since then , research in the field has exploded , revealing that some pathogens may avoid autophagy-mediated degradation while others may exploit the autophagy machinery for intracellular survival [12] , [13] . However , few in vivo studies have been performed and , as a result , the consequence of bacterial autophagy on disease outcome remains obscure . S . flexneri are human-adapted Escherichia coli that have gained the ability to invade the colonic mucosa , causing inflammation and diarrhea . The intracellular lifestyle of this pathogen has been well-studied in vitro , and Shigella has recently gained recognition as a paradigm of bacterial autophagy [8] , [12]–[16] . Once in the cytosol , the actin-based motility of Shigella is counteracted by septin cage-like structures that target bacteria to p62-mediated autophagy [14] , [15] . Septins are GTP-binding proteins that form higher-order structures including filaments and rings , and are viewed as a distinct component of the cytoskeleton [17] . The precise role of septins in autophagy is unknown , yet work has shown that septins may help to scaffold the autophagy machinery around actin-polymerizing bacteria [14] , [15] . Now , a major issue is to demonstrate the significance of these molecular and cellular events in vivo using relevant animal models . To explore the innate immune response to Shigella , several infection models have been useful [18] , [19] , however , these mammalian models remain poorly suited to image the cell biology of Shigella infection in vivo . The zebrafish has recently emerged as a non-mammalian vertebrate model to study the development and function of the immune system [20] , [21] . It is a genetically tractable organism , sharing many immune pathways and cell types with mammals [22] . The natural translucency of zebrafish larvae enables non-invasive in vivo imaging of individual cells and microbe-phagocyte interactions at high resolution throughout the organism [23]–[32] . While zebrafish larvae have been used to study infection by many different bacteria ( [23]–[32] , reviewed in [33] ) , including E . coli [26] , L . monocytogenes [24] and M . marinum [25] , Shigella infection has not yet been studied in this model . Considering that , at the cellular level , the infectious process for S . flexneri is similar to that of L . monocytogenes and M . marinum , i . e . , these bacteria escape from the phagocytic vacuole to the cytosol where they form actin tails or are recognized by autophagy [15] , [34]–[36] , we chose to investigate the outcome of experimental shigellosis and the role of autophagy in zebrafish larvae . We first established that Shigella is pathogenic for zebrafish larvae , and characterized the lethal dose and kinetics of the infection following microinjection . Inoculated S . flexneri were rapidly engulfed by macrophages and neutrophils , and these events could be captured in real time , highlighting a scavenger role for neutrophils in eliminating infected macrophages and non-immune cell types that have failed to control Shigella infection . Strikingly , the number of both macrophages and neutrophils dramatically decreased in larvae unable to control Shigella proliferation , and leukocyte depletion was associated with bacteremia preceding the death of the larvae . We also observed that intracellular S . flexneri could escape to the cytosol , induce septin caging and be targeted to autophagy in vivo . We then used Shigella infection of the zebrafish to study the role of bacterial autophagy in vivo , and showed that the depletion of p62-mediated autophagy significantly increased bacterial burden and zebrafish mortality . These data highlight the zebrafish model to study S . flexneri interaction with phagocytes in vivo . Moreover , the zebrafish constitutes a valuable system to develop new strategies aimed at pathogen clearance by manipulation of anti-bacterial autophagy .
The infection cycle of Shigella in human cells is well understood . After internalization , Shigella escape from the phagocytic vacuole to the cytosol where they may be recognized by septin cages and autophagy [14] , [15] . As Shigella is not a natural pathogen of fish and grows optimally at 37°C , initial experiments sought to determine whether the hallmarks of the Shigella infection cycle - invasion , phagosome escape and cytoskeleton reorganization- could be reproduced at 28°C , the optimal growth temperature of zebrafish . To test this , we first established a zebrafish fibroblast-like cell line ( ZF-AB; see Material and Methods ) that we could infect in vitro . The septins are a family of proteins required for the autophagy of Shigella in human cells , and SEPT7 in particular is essential for mammalian septin function [17] . Pan et al . reported a sept7 orthologue in zebrafish [37] , and our own search of the most recent zebrafish genome assembly ( zv9 , www . ensembl . org ) and EST databases identified three genes encoding proteins highly similar to the human protein ( Fig . S1A ) , with predicted molecular weights of 48–49 kDa . This close similarity indicated that an antibody raised against human SEPT7 was likely to cross-react with the zebrafish proteins . Indeed , using a human SEPT7 antibody , Western blotting of zebrafish lysates revealed proteins at 49 and 44 kDa ( Fig . 1A and S1B ) , and immunofluorescent labeling of ZF-AB cells revealed septin filaments partially overlapping with the actin cytoskeleton ( Fig . 1B ) , in agreement with what is known in mammalian cells [38] , [39] . We then infected ZF-AB cells with S . flexneri M90T . As observed with the human epithelial cell line HeLa infected at 28°C ( Fig . S1C ) and at 37°C [14] , [15] , Shigella invaded ZF-AB cells and induced the formation of septin cages ( Fig . 1B ) , demonstrating that Shigella can enter the cytosol of zebrafish cells and be targeted to autophagy . These results strongly suggest that the virulence factors required for Shigella invasion , escape from the phagocytic vacuole and replication in the cytosol can be expressed and are functional at 28°C , the most commonly used temperature for zebrafish rearing . We next sought to establish whether S . flexneri could cause disease in zebrafish swimming larvae . Larvae aged 72 h post fertilization ( hpf ) were microinjected intravenously ( iv ) near the cloaca ( ugo ) with a range of doses of wild-type S . flexneri ( Fig . 1C ) , and their survival at 28°C was assessed by regular observation with a stereomicroscope . For Shigella doses 103 CFU or less , there was 100% survival of the infected larvae; in contrast , a typical inoculum of 4×103 Shigella resulted in the death of most larvae within 48 hours post infection ( hpi ) ( Fig . 1D ) . Of note , larvae injected with a comparable ( or even higher ) inoculum of type III secretion system ( T3SS ) deficient ( T3SS− ) Shigella ( ΔmxiD strain; Fig . S1D ) or the closely related , but non-pathogenic , E . coli K12 ( data not shown and [26] ) always survived for the entire time of observation . We followed the progression of infection using two approaches . First , to monitor the replication of bacteria in the host , we measured the amount of viable bacteria within larvae at various time points by plating serial dilutions of homogenates of euthanized animals onto bacterial culture dishes . As shown in Fig . 1E , the survival of the larvae was closely associated with the clearance of the inoculated bacteria . Larvae inoculated with a low dose ( <103 CFU ) progressively cleared bacteria , with a burden reduced by >90% within 48 hrs . In contrast , in larvae inoculated with a high dose , S . flexneri numbers increased ∼30fold in the first 24 h; by 48 hpi , most larvae were dead , although in a minority of survivors the bacterial load declined . During infections using comparable or higher inocula of T3SS− Shigella ( Fig . S1E ) or E . coli ( data not shown and [26] ) , bacterial numbers always decreased over time , with only a few viable bacteria able to persist for 48 h at highest doses . As a second approach to follow the progression of infection , larvae were infected with sublethal or lethal inocula of fluorescent S . flexneri M90T , i . e . GFP-Shigella , and were analyzed by fluorescence microscopy . Large clusters ( ∼102 bacteria or more ) were detectable using a fluorescence stereomicroscope ( Fig . 1F , see also Fig . 2 below ) , while smaller groups and single bacteria could be viewed at higher magnifications using widefield or confocal fluorescence microscopy ( see below e . g . , Fig . 3 , 4 ) . At 4 hpi with a sublethal inoculum of S . flexneri , bacterial aggregates , probably associated to phagocytes [26] , [40] , were observed mostly in the caudal hematopoietic tissue ( CHT ) near the injection site , as well as in the more distant common cardinal vein over the yolk sac; the majority of the bacteria were cleared from the blood ( Fig . 1F , see also Fig . S2 below ) . The situation was relatively similar in larvae inoculated with a lethal inoculum at 4 hpi , except for the higher number of bacterial foci observed in the CHT and over the yolk sac ( Fig . 1F see also Fig . S2 below ) . However , the infection course by 24 hpi was radically different between the sublethal and lethal inocula . At this time point , larvae that received a sublethal inoculum had almost all cleared the infection , showing few foci of Shigella mainly located near the injection point in the caudal part of the larvae . In contrast , larvae that received a lethal inoculum showed a massive infection at 24 hpi , with Shigella abundant in the blood ( i . e . , bacteremia ) and in the tissues near the site of injection ( Fig . 1F , see also Fig . S2 below ) . We observed that larvae that controlled the bacterial proliferation within 24 hpi usually survived . By comparison , inoculation of fluorescent T3SS− Shigella or E . coli yielded an initial distribution of bacteria ( at 4 hpi ) very similar to that of wildtype Shigella . However , neither T3SS− Shigella ( data not shown ) nor E . coli ( data not shown and [26] ) were proliferating and bacterial numbers always decreased over time ( in agreement with CFU enumeration , Fig . S1E ) . These data strongly suggest that virulence factors required for Shigella pathogenesis are expressed during infection of zebrafish larvae at 28°C . Actin tail and septin cage formation are critically dependent upon expression of Shigella virulence factors icsA ( T3SS independent ) and icsB ( T3SS dependent ) [14] , [15] . By extracting RNA from zebrafish larvae infected with Shigella , the expression of icsA and icsB were clearly detected at 4 , 12 and 24 hpi ( Fig . S1F ) . In agreement with observations from survival assays ( Fig . 1D ) , CFU enumerations ( Fig . 1E ) and live confocal fluorescence microscopy ( see below , e . g . , Video S1 , S3 , S5 ) , these data indicate that Shigella virulence factors are expressed at 28°C during infection of zebrafish larvae . They also show that zebrafish larvae are susceptible to S . flexneri , suggesting their applicability to study the innate immune response to Shigella infection . To analyse macrophage and neutrophil behaviour in vivo upon Shigella infection , transgenic zebrafish larvae ( 3 dpf ) harboring red macrophages ( Tg ( mpeg1:Gal4-FF ) gl25/Tg ( UAS-E1b:nfsB . mCherry ) c264 , herein referred as mpeg1:G/U:mCherry ) or red neutrophils ( Tg ( lyz:dsRed ) nz50 , herein referred to as lyz:dsRed ) were inoculated with sublethal or lethal inocula of GFP-Shigella , and macrophages and neutrophils upon Shigella infection were followed in vivo using widefield fluorescence microscopy . Strikingly , the number of both macrophages and neutrophils appeared to decrease strongly in larvae unable to control Shigella proliferation , and leukocyte depletion was associated with bacteremia preceding larval death ( Fig . 2A , S2 ) . To more precisely quantify macrophages and neutrophils , we fixed larvae at 4 or 24 hpi , stained them with anti-dsRed and anti-GFP antibodies to label leukocytes and GFP-Shigella respectively , and counted the number of labeled macrophages and neutrophils per larva ( Fig . 2B ) . Macrophage counts significantly decreased both in lethally ( P<0 . 001 ) and sublethally ( P<0 . 001 ) infected animals , suggesting that macrophages undergo cell death in vivo following Shigella ingestion . The number of neutrophils also significantly declined in lethally inoculated larvae ( P<0 . 001 ) , with an almost total neutrophil depletion by 24 hpi . In contrast , larvae controlling Shigella infection did not show a decrease in neutrophil number ( P>0 . 05 ) . To analyse in detail Shigella-phagocyte interactions in vivo , transgenic 3 dpf zebrafish larvae harboring red macrophages ( mpeg1:G/U:mCherry ) or red neutrophils ( lyz:dsRed ) were inoculated with sublethal doses of GFP-Shigella , and Shigella-phagocyte interactions were captured immediately thereafter using high resolution fluorescence confocal microscopy . Although only one of the two phagocyte populations was labelled in each of these experiments , non-labelled cells containing pooled GFP-Shigella could generally be assigned to the other phagocyte population in a non-ambiguous manner , based on their localization and motility [24] , [26] . As soon as 20 min post infection ( the earliest possible time point in our experimental setup ) , we observed that most blood borne bacteria were stuck on , or were already engulfed by , macrophages and few interacted with neutrophils ( Fig . 3A , Video S1 , data not shown ) , as previously observed using non-pathogenic E . coli [26] or Listeria [24] . However , unlike T3SS- Shigella ( Fig . S3A , Video S2 ) or E . coli [26] which is degraded inside macrophages , we observed that wildtype Shigella were able to survive in macrophages and then the infected macrophages would burst ( Fig . 3A , Fig . S3B , Video S1; see also Video S3 ) . By contrast , neutrophils having phagocytosed similar amounts of Shigella quickly degraded the engulfed bacteria , as indicated by the diffuse GFP staining that transiently accumulated in these cells ( Fig . 3B , Video S4; see also Fig . 3A , Video S1 ) . It is known that in vitro human macrophages infected by Shigella undergo cell death [41] . We also observed this phenomenon in zebrafish macrophages in vivo ( Fig . 3C , Video S5; see also Video S4 ) . To further investigate cell death induced upon Shigella infection , we performed TUNEL labeling of infected larvae and detected host cell DNA damage , a hallmark of cell death . Infected leukocytes were clearly labeled by TUNEL ( Fig . S3C ) . Strikingly , we found that macrophages dead from Shigella infection were frequently engulfed by patrolling neutrophils , suggesting a newfound scavenger role for neutrophils during Shigella infection ( Fig . 3D , Video S6; see also Video S4 ) . In support of our live imaging studies , analysis by electron microscopy also highlighted that membrane and DNA from infected , dead cells are engulfed by leukocytes in vivo ( Fig . S3D , S3E ) . It is also well known that Shigella are able to actively invade non-immune cell types such as epithelial cells [42] . In zebrafish , invasion of some muscle fibers near the point of inoculation was observable from 5hpi ( Fig . 3E , Video S7 ) . We observed that Shigella were able to replicate within non-immune cells , which died after a few hours , releasing debris and bacteria that were quickly cleared by neutrophils ( Fig . 3E , Video S7 ) . We never observed bacterial invasion and/or replication in vivo with T3SS- Shigella ( data not shown ) nor E . coli ( data not shown and [26] ) . Collectively , these results demonstrate that , in vivo , Shigella is able to survive and replicate in macrophages and in some non-immune cells , ultimately leading to their death . Our results also highlight a scavenger role for neutrophils in engulfing and eliminating infected macrophages and non-immune cell types that have failed to control Shigella infection . We observed that in some cells ( e . g . , such as the macrophage or the muscle fiber displayed in Fig . 3D or 3E ) Shigella seemed to occupy the entire cell ( rather than a compartmentalized vacuole ) strongly suggesting bacterial invasion of the cytosol . Because this cannot be fully ascertained from these live images , we fixed and labeled infected larvae for confocal microscopy to investigate the recruitment of septin cages to GFP-Shigella , a hallmark of cytosolic invasion [15] . In every larva infected ( at 4 or 24 hpi ) , some GFP-Shigella were surrounded by SEPT7 cage-like structures . Confocal microscopy and deconvolution was used to obtain a high-resolution image of fluorescently-labeled assemblies and showed that septin structures assembled into cages around individual Shigella ( Fig . 4A ) , in complete agreement with dimensions obtained from Shigella-septin cages measured in human cells [15] . Thus , in vivo , Shigella are able to invade the cytosol where they may be directed to autophagy by septin caging . In mammalian cells , ATG8/LC3 is the marker widely used to define autophagosomes [43] , [44] . Zebrafish Lc3 is an ATG8 homologue , and to confirm the targeting of bacteria to autophagy in zebrafish we infected transgenic GFP-Lc3 zebrafish larvae with Shigella . GFP-Lc3 was clearly recruited to intracellular Shigella in vivo ( Fig . 4B , Video S8 ) . Ultrastructural analysis of Shigella-infected zebrafish by electron microscopy confirmed the sequestration of cytosolic bacteria by autophagosomes ( Fig . 4C , Video S9 ) , classically observed as a double membrane structure [43] , [44] . We also observed the in vivo recruitment of septin cages ( Fig . S4A ) and GFP-Lc3 ( Fig . S4B , Video S10 ) to intracellular M . marinum , a natural fish pathogen previously shown in vitro to be recognized septin caging and autophagy [14] , [15] . Taken together , the recruitment of autophagy markers to intracellular bacteria suggest the applicability of zebrafish larvae to study the manipulation of anti-bacterial autophagy . Though autophagy is considered a crucial aspect of innate immunity to Shigella , this has not yet been tested in vivo [12] , [13] . We proceeded to test the role of autophagy in the zebrafish response to Shigella infection . p62 is an autophagy receptor critical for autophagic recognition of bacteria in vitro , and is required for septin caging [14] , [15] . Our analysis of the zebrafish genome revealed a single orthologue of p62/sqstm1 ( Fig . S5A ) . We thus designed a morpholino to knockdown p62 in zebrafish larvae ( Fig . 5A ) . Fish depleted of p62 developed until 72 hpf with a survival rate similar to controls ( ∼80% ) , although we observed a slight developmental delay ( data not shown ) . We then injected p62 morphants ( i . e . , zebrafish larvae injected with p62 morpholino oligonucleotides ) with a sublethal inoculum of Shigella and observed that p62 knockdown significantly reduced host survival upon Shigella infection ( P = 0 . 01; Fig . 5B ) . To understand the cause of increased death in p62 morphants , we examined the bacterial burden in these fish . CFU counts clearly showed that p62 knockdown during Shigella infection prevents bacterial clearance ( Fig . 5C ) . T3SS- Shigella and E . coli may not escape to the cytosol of cells that have engulfed it , and are not expected to be targeted to autophagic degradation in vivo . Thus , as a control , we infected p62 morphants with T3SS- Shigella or E . coli . In contrast to infection using wildtype Shigella , we observed that the knockdown of p62 had no impact on growth of T3SS- Shigella nor E . coli , nor did it impact zebrafish survival ( Fig . S5B–S5E ) . These data suggest that p62-mediated autophagy is critical for the control of bacteria able to escape to the cytosol . We tested whether the inflammatory cytokine response to Shigella was modulated by p62 knockdown . Experiments suggested that the inflammatory response was stronger in p62 morphants inoculated with Shigella ( data not shown ) , yet these results may reflect the increased bacterial burden of p62 morphants compared to controls . Using confocal microscopy , we observed that in the absence of p62 , initial bacterial engulfment by phagocytes ( macrophages and neutrophils ) was not affected; yet uncontrolled Shigella proliferation ensued , accompanied by the ultimate lysis of leukocytes and death of the infected larvae ( Fig . 5D ) . These observations indicated that , as compared to control fish , p62 morphants are not able to restrict Shigella proliferation and , as a result , they are more susceptible to Shigella infection . We evaluated septin caging in p62 morphants . In every larva infected ( at 4 or 24 hpi ) , septin recruitment to Shigella was clearly reduced ( Fig . 5E ) , in agreement with previous in vitro work showing that septin cage assembly is more efficient once the process of autophagy has been initiated [14] , [15] . Together , these results strongly suggest that p62-mediated autophagy is a critical component of anti-Shigella defense in vivo . Rapamycin is an inhibitor of mTOR , a serine/threonine protein kinase that regulates a wide range of cellular responses and is a suppressor of autophagy [45] . Autophagy is classically activated in vitro by treating cells with rapamycin , and in agreement with previous work [46] , [47] , we observed that critical autophagy components in zebrafish larvae can respond to rapamycin , i . e . , p62 is degraded and LC3-II accumulates ( data not shown ) . Importantly , we saw no effect of rapamycin ( at the dose we applied throughout this study ) on the development of uninfected larvae , in agreement with a previously published study showing that rapamycin has no discernible effect on zebrafish development past 76 hpf [48] . The stimulation of autophagy by rapamycin is generally viewed as helping to clear bacterial infection [2] , [49] . To test this in vivo , we infected zebrafish larvae with a sublethal dose of Shigella and immediately after infection , treated these fish with rapamycin . Surprisingly , rapamycin treatment in zebrafish during Shigella infection significantly reduced host survival ( P = 0 . 001; Fig . 6A ) , albeit at a slower rate than observed for p62 morphants ( Fig . 5B ) . To understand the cause of increased death due to rapamycin treatment , we examined the bacterial burden in these fish . CFU counts showed that rapamycin treatment in zebrafish during Shigella infection may result in more bacterial growth ( Fig . 6B ) . Using fluorescence stereomicroscopy , we confirmed that dying larvae were overwhelmed with GFP-Shigella ( data not shown ) . We tested if an impairment of the inflammatory response could explain the enhanced susceptibility of rapamycin-treated fish; however , il1b induction was not found to be modulated by rapamycin in vivo ( Fig . 6C ) . Moreover , rapamycin treatment of the T3SS- Shigella- or E . coli-infected larvae did not affect zebrafish survival nor bacterial load ( Fig . S6A–S6D ) , indicating that rapamycin-treated fish are still able to mount an innate immune response . Taken together , these results show that rapamycin treatment of bacterial infection can lead to harmful effects in vivo , unlike what may be expected from studies performed in vitro . This suggests possible difficulties in therapeutic modulation of autophagy to resolve bacterial infection , until more specific activators of autophagy are discovered .
We describe here the successful establishment of a new S . flexneri infection model system , the larval zebrafish , which allows a detailed real-time analysis of host-pathogen interactions in vivo . Similar to other bacteria such as L . monocytogenes [24] , Bacillus subtilis [23] , S . Typhimurium [50] and Staphylococcus aureus [31] , both S . flexneri and E . coli are quickly cleared from the bloodstream by phagocytes , notably macrophages to which they readily stick . However , while T3SS- Shigella or E . coli are rapidly degraded by phagocytes [26] , we found that Shigella may persist and replicate inside macrophages , eventually killing them; by contrast , neutrophils that have engulfed similar amounts of Shigella efficiently kill the bacteria . We also observed that Shigella is able to invade and replicate inside non-immune cells , and cytosolic invasion of Shigella was evident from the formation of septin cages and Lc3-positive autophagosomes . Phagosome escape is known to be critical for the virulence of L . monocytogenes and M . marinum in the zebrafish [24] , [25] , and , as shown here , seems also critical for the virulence of S . flexneri in the zebrafish . The zebrafish model of S . flexneri infection displays several interesting features . We show that pathogenesis of Shigella in zebrafish larvae in vivo is strictly dependent upon its T3SS . Previous work has shown that Shigella virulence factors may be expressed at temperatures lower than 37°C [51] , and in agreement with this , we detected the expression of icsA and icsB in zebrafish larvae infected at 28°C . We have also shown that the zebrafish larva constitutes a relevant model of human infection as regards to host-phagocyte interactions . Strikingly , the numbers of both macrophages and neutrophils dramatically decreased in larvae unable to control Shigella proliferation , and leukocyte depletion was associated with bacteremia preceding the death of the larvae . Neutrophil depletion has also been observed upon infection of zebrafish larvae with Salmonella [50] and Staphylococcus [52] , upon infection of mice with Listeria [53] , and may emerge to be a critical correlate of bacterial overgrowth . In humans neutropoenia is clinically predictive of failure to resolve infection , and is associated with bacteraemia that leads to a worse prognosis . The control of infection by neutrophils will deserve careful attention in future studies . Pioneering studies performed in vitro suggested that Shigella may induce apoptosis in human macrophages [41] . More recent work has shown that Shigella-infected macrophages undergo caspase-1-mediated cell death , termed pyroptosis , which is a pathway of programmed cell death associated with an inflammatory response [54] . We have characterized cell death during Shigella infection in vivo . By live imaging we have followed the fate of individual leukocytes infected with Shigella in real time ( e . g . , Video S1 , S3 , S5 ) . By in situ analysis of fixed larvae , we have clarified that some infected leukocytes are dying based on labelling of DNA strand breaks ( TUNEL; Fig . S3C ) . Using EM , we have observed that infected leukocytes may undergo morphological changes associated with pyroptosis , e . g . , obvious plasma membrane rupture ( Fig . S3B ) . Macrophages dead from Shigella infection were frequently engulfed by patrolling neutrophils , suggesting an unexpected role for neutrophils during Shigella infection . This is comparable to a recent report for M . marinum [55] . Moreover , our live imaging studies have revealed a new scavenger role for neutrophils in clearing cell debris and live released bacteria from dying infected cells in vivo . The zebrafish model will be particularly useful to investigate these different host cell responses to infection , as well as to dissect the role of implicated Shigella effectors , in vivo . Autophagy is recognized as a crucial defense mechanism against intracytosolic bacteria [2] , [12] , [13] . In mammalian cells in vitro , virulent S . flexneri bacteria escape from the phagosome and invade the cytosol of cells where they are recognized by septin cages and autophagy [14] , [15] . Yet our understanding of autophagy during bacterial infection would benefit from an experimental model in which the sequence of host-pathogen interactions resulting in autophagy can be validated and dissected in vivo . The optical accessibility of zebrafish larvae allowed us to image Shigella-septin caging in the infected organism , an achievement that has never before been accomplished using mammalian host models . Septin recruitment to intracytosolic Shigella was consistently detectable in infected larva , and promises to be an exciting tool to investigate the role of the cytoskeleton in autophagy in vivo . To complement evidence that septin cages entrap bacteria targeted to autophagy in vivo , we have shown in vivo recruitment of GFP-Lc3 to Shigella by confocal microscopy ( Fig . 4B for analysis of fixed samples and Video S8 for live imaging ) . In addition , we have obtained ultrastructural analysis of Shigella autophagosomes by EM ( Fig . 4C ) , and clearly show the cytosolic sequestration of Shigella in vivo by double membrane vacuoles . By confocal microscopy ( Fig . S4A for analysis of fixed samples and Video S10 for live imaging ) , we have shown in vivo recruitment of septin caging and GFP-Lc3 to M . marinum , a natural fish pathogen previously shown to be recognized septin caging and autophagy in vitro . All of these observations fully validate the zebrafish as a new model for the in vivo study of bacterial autophagy . p62 is a well-characterized autophagy receptor and is increasingly recognized as a critical component of innate immunity ( reviewed in [12] , [13] ) . In agreement with this , we show that p62-mediated bacterial autophagy is crucial to control bacterial pathogenesis in vivo . On the other hand , we also observed that rapamycin , while stimulating autophagy as expected [46] , [47] , may increase bacterial replication and decrease zebrafish survival . Interestingly , in vitro treatment of Hela cells with rapamycin for 1–4 hrs was observed to have no significant effect on Shigella [56] . In our model , leaving zebrafish larvae in rapamycin for longer periods of time ( e . g . , 5 days ) may lead to harmful effects in vivo; this that will require attention in future studies . In agreement with data obtained using Shigella ( Fig . 6A , 6B ) , we have observed that rapamycin treatment of Listeria-infected zebrafish may not promote host survival or bacterial clearance ( Fig . S6E , S6F ) . This effect of rapamycin could be due to processes other than autophagy , for the rapamycin target mTOR affects many other cellular processes [45] , yet these data clearly serve as an important warning for the therapeutic implications of rapamycin treatment . Overall , these data strongly suggest that zebrafish survival depends on the appropriate autophagic response to control intracellular bacterial infection . To conclude , we show that the zebrafish larva represents a valuable new host for the analysis of S . flexneri infection . Interactions between bacteria and host phagocytes can be imaged at high resolution in vivo , and the zebrafish model should prove useful for understanding the cell biology of Shigella infection . It may become possible in the future to observe S . flexneri phagosome escape , actin tail formation and septin caging in vivo in real time by developing new transgenic zebrafish lines . Here , we use zebrafish larvae to investigate the role of bacterial autophagy in host defense at the whole organism , cellular and single-cell level , and observed that the perturbation of autophagy can adversely affect host survival in response to Shigella infection . Future work will visualize and characterize the molecular determinants of autophagy during microbe-cell interactions in vivo , using several different bacterial and viral pathogens .
Animal experiments conducted at the Institut Pasteur were performed according to European Union guidelines for handling of laboratory animals ( http://ec . europa . eu/environment/chemicals/lab_animals/home_en . htm ) and were approved by the Institut Pasteur Animal Care and Use Committee and the Direction Sanitaire et Vétérinaire de Paris under permit #A-75-1061 . Animal experiments performed at Imperial College were performed according to the Animals ( Scientific Procedures ) Act 1986 , and were fully approved by the Home Office ( Project license: PPL 70/7446 ) . Wild-type AB purchased from the Zebrafish International Resource Center ( Eugene , OR ) , and the Tg ( lyz:dsRed ) nz50 , Tg ( mpeg1:Gal4FF ) gl25 , Tg ( mpeg1:Gal4FFgl25/UAS:kaede ) , Tg ( UAS-E1b:nfsB . mCherry ) c264 , and GFP-Lc3 transgenic zebrafish lines have been previously described [46] , [57]–[59] . Eggs were obtained by marble-induced spawning , bleached according to protocols described in The zebrafish book [60] , and then kept in Petri dishes containing Volvic source water supplemented with 0 . 3 µg/ml of methylene blue and , from 24 hours post fertilization ( hpf ) onwards with 0 . 003% 1-phenyl-2-thiourea ( Sigma-Aldrich ) to prevent melanin synthesis . Embryos were reared at 28°C or 24°C according to the desired speed of development; infected larvae were always kept at 28°C . All timings in the text refer to the developmental stage at the reference temperature of 28 . 5°C [61] . Larvae were anesthetized with 200 µg/ml tricaine ( Sigma-Aldrich ) during the injection procedure as well as during the in vivo imaging . Fibroblast-like cells were derived from AB zebrafish as follows . After a zebrafish ( AB strain ) has been sacrificed by overexposure to eugenol , a dorsal slice comprising skin , muscle and the dorsal fin was taken and trypsinised under constant mild shaking for 5 min . The supernatant was collected in modified MacPherson Stoker Eagle's medium ( Eurobio ) supplemented with 10% fetal calf serum ( FCS ) , 100 IU/mL penicillin and 100 mg/mL streptomycin . The cell suspension was centrifuged for 5 min at 1000 g , and cells were resuspended in culture medium . The rainbow trout fibroblast cell line RTG-2 was then used as feeder , and zebrafish cells were grown on a monolayer of RTG2 for almost one year . At each passage , the co-culture was kept for 1–2/3 weeks at 20°C then put for a few days at 30°C; at this temperature , trout cells died and the remaining zebrafish cells used to seed the next passage over a new RTG-2 monolayer . After one year , ZF-AB cells started to be able to grow without feeder . Once established , fibroblast-like cells derived from AB zebrafish ( ZF-AB ) cells were cultured in minimum essential medium plus GlutaMAX ( Invitrogen ) supplemented with 1 mM sodium pyruvate ( Invitrogen ) , 0 . 1 mM nonessential amino acid solution ( Invitrogen ) , and 10% FCS . Zebrafish cells were grown at 28°C . 1–1 . 5×105 ZF-AB or HeLa cells were plated on glass coverslips in 6-well plates ( Techno Plastic Products ) and used for experiments 48 h later . Cells on coverslips were fixed for 15 min in 4% paraformaldehyde and then washed with 1× PBS and processed for immunofluorescence ( IF ) . After 10 min of incubation in 50 mm ammonium chloride , cells were permeabilized for 4 min with 0 . 1% Triton X-100 and then incubated in 1× PBS . Incubation with primary or secondary antibodies was performed in 1× PBS . Vectashield hard set mounting medium with DAPI ( Vector Laboratories ) or mounting medium for IF ( Interchim ) was used . Shigella was added to cells using 400 µl of growth ( A600 nm = 0 . 6 ) or using 200 µl of overnight growth ( A600 nm = 0 . 6 ) from single colony , and was diluted in minimum essential medium and added directly to cells for imaging analyses . Bacteria and cells were centrifuged at 700× g for 10 min at 21°C and then placed at 28°C for 30 min , washed with minimum essential medium , and incubated with fresh gentamicin-containing complete medium ( 50 µg/ml ) for 4 h , after which they were washed with 1× PBS and fixed and processed for IF . Images were acquired on a fluorescence inverted microscope Axiovert 200 M ( Carl Zeiss MicroImaging , Inc . ) equipped with a cooled digital charge-coupled device camera ( Cool SNAPHQ , Photometrics ) driven by Metamorph Imaging System software ( Universal Imaging Corp ) . Bacterial strains used in this study were wild-type invasive of S . flexneri serotype 5a ( M90T; BUG 2505 ) [15] , M90T expressing green fluorescent protein ( GFP ) ( GFP-Shigella; BUG 1908 ) [15] , dsRed [62] or mCherry ( pFPV 25 . 1 mCherry construct ) , T3SS− noninvasive variant ( ΔmxiD ) expressing dsRed [62] , E . coli K12 bacteria expressing dsRed [26] , and M . marinum M strain expressing GFP or dsRed [15] . In the case of E . coli , infections were performed as previously described [24] , [26] . S . flexneri were cultured overnight in trypticase soy , diluted 80× in fresh trypticase soy , and cultured until A600 nm = 0 . 6 . M . marinum M-GFP and M-DsRed were cultured at 30°C in Middlebrook 7H9 ( BD Biosciences ) supplemented with 0 . 2% glycerol , 0 . 05% Tween 80 and 10% ADC Enrichment ( Fisher Scientific ) , diluted 48 h prior to infection in fresh media , and cultured until OD600 nm = 0 . 6 . For injection of zebrafish larvae , bacteria were recovered by centrifugation , washed , resuspended at the desired concentration in PBS . In the case of M . marinum , bacteria were also passaged through a 26 gauge needle to dissociate bacterial clumps and homogenize the bacteria prior to infection . 3 day post-fertilisation ( dpf ) anesthetized zebrafish larvae were microinjected intravenously ( iv ) with 0 . 5–2 nl of bacterial suspension as described previously [24] . The exact inoculum was checked a posteriori by injection in a water drop and plating onto LB agar . Infected larvae were transferred into individual wells ( containing 1 ml of Volvic water in 24-well culture plates ) , incubated at 28°C and regularly observed under a stereomicroscope . Infections with a quantified standard dose were performed at least 3 times per strain of bacteria . Survival curves with graded doses as depicted were repeated at least 3 times . At the indicated times , animals were anesthetized , rinsed , and collected in 30 µl of sterile water . The animals were lysed in 200 µl of 0 . 4% Triton X-100 and homogenized through a 26-gauge needle ( five up-and-down sequences ) . Serial dilutions of the homogenates were plated onto LB agar , and CFU were enumerated after 24 h of incubation at 37°C; only colonies with the appropriate morphology and color were scored . Anesthesized zebrafish larvae were fixed for 2 h at RT or overnight at 4°C in 4% paraformaldehyde with 0 . 4% triton , then washed with PBS 0 . 1% tween , and processed for IF . In brief , after 20 additional minutes of PBS 1% triton , larvae were washed 3×5 minutes in PBS 0 . 4% triton , then incubated in blocking solution: PBS 1× supplemented with 10% sheep serum , 1% DMSO , and 0 . 1% tween , for 1 hour . Primary antibodies were diluted in blocking solution and were applied to larvae overnight at 4°C . To remove primary antibodies , larvae were washed 4×15 min in PBS +0 . 1% tween . After washes , larvae were placed in blocking solution for 1 h at RT . Secondary antibodies were diluted in block solution and were applied to larvae overnight at 4°C . To remove secondary antibodies , larvae were washed 4×15 min in PBS 0 . 1% Tween . Fluorescently labeled larvae were then cleared by progressive transfer to 80% glycerol . Quantification of macrophages and neutrophils numbers on fixed and labeled transgenic reporter larvae was performed as following . Briefly , brightfield , dsRed and GFP images of whole fixed larvae were taken using a Leica Macrofluo Z16 APOA ( zoom 16∶1 ) equipped with a Leica PlanApo 2 . 0× lens , and a Photometrics CoolSNAP HQ2 camera . Images were captured using the Metavue software version 7 . 5 . 6 . 0 ( MDS Analytical Technologies ) . Then , pictures were analyzed and positive cells for red fluorescence counted using the ImageJ software version 10 . 2 ( developed by the National Institute of Health ) . Neutrophils numbers were obtained with lyz:dsRed larvae , macrophage numbers with mpeg1:G/U:mCherry larvae . Counts shown in Fig . 2B are numbers of leukocytes per image . Examination of septin recruitment in vivo was performed using a confocal microscope ( Zeiss LSM510 ) . Larvae were infected with GFP-Shigella in the mesenchyme near the caudal vein or in the hindbrain , and fixed and labeled for microscopy 4 or 24 hpi . Z-stacks of infected larvae were acquired for each sample using a 63× or 100× objective and a slice increment of 0 . 2 µm . These stacks were then deconvoluted ( Huygens software ) and analysed using ImageJ according to previously established criteria [14] , [15] , [63] . To perform whole-body in vivo imaging , anaesthetized zebrafish larvae were oriented and immobilized in 1% low melting point agarose in 60 mm plastic bottom Petri dishes ( up to 9 fish mounted and imaged per dish ) , then covered with 2 ml Volvic water containing tricaine as described previously [26] . Transmission and fluorescence widefield imaging was done using a Nikon Biostation IM–Q S2 equipped with a DS-Qi camera . Imaging was typically performed at 26°C with a 10× ( NA 0 , 5 ) dry objective . Multiple-field Z-stacks with a 10 µm Z-step were acquired every 30 minutes . To perform high resolution confocal live imaging , injected larvae were positioned in 35 mm glass-bottom dishes ( Inagaki-Iwaki ) . To immobilise the larva in the dish a 1% low-melting-point agarose solution covering the entire larva was used . The immobilised larvae were then covered with 2 ml Volvic water containing tricaine . Confocal microscopy was performed at 23–26°C using a Leica SPE inverted microscope and a 40× oil immersion objective ( ACS APO 40×1 . 15 UV ) as previously described [26] . A Leica SP8 confocal microscope equipped with two PMT and an Hybrid detector ( HyD ) and a 20× oil immersion objective ( HC PL APO CS2 20×/0 . 75 ) was used to live image infected larvae ( represented in Figure S4B , Video S8 , Video S10 ) . The 4D files generated by the time-lapse acquisitions were processed , cropped , analysed and annotated using the LAS-AF Leica software . Acquired Z-stacks were projected using maximum intensity projection and exported as AVI files . Frames were captured from the AVI files and handled with Photoshop software to mount figures . AVI files were also cropped and annotated with ImageJ software , then compressed and converted into QuickTime movies with the QuickTime Pro software . For standard ultrastructure analyses , as described in [24] , [64] , anesthetized embryos were fixed in 0 . 5% glutaraldehyde in 200 mM sodium cacodylate buffer for 2 h , washed in buffer and secondarily fixed in reduced 1% osmium tetroxide , 1 . 5% potassium ferricyanide for 60 min . The samples were washed in distilled water and stained overnight at 4°C in 0 . 5% magnesium uranyl acetate , washed in distilled water and dehydrated in graded ethanol , infiltrated with propylene oxide and then graded Epon/PO mixtures until final embedding in full Epon resin in coffin moulds ( allowing different orientations ) and polymerised at 56°C overnight . Semi-thin survey sections were cut and stained and final ultrathin sections ( typically 50–70 nm ) and serial sections were collected on Formvar coated slot grids and stained with Reynold's lead citrate and examined in a FEI Tecnai electron microscope with CCD camera image acquisition . RNA was extracted from snap-frozen larvae using Trizol ( Invitrogen ) . cDNA was obtained using M-MLV H- reverse-transcriptase ( Promega ) with a dT17 primer . Quantitative PCR was then performed on an ABI7300 thermocycler ( Applied Biosystems ) using SYBR green reaction power mix ( Applied Biosystems ) . The following pairs of primers were used: EF1α ( GCTGATCGTTGGAGTCAACA and ACAGACTTGACCTCAGTGGT ) ; IL1b ( GAGACAGACGGTGCTGTTTA and GTAAGACGGCACTGAATCCA ) . Quantifications were performed on triplicate wells , and taking into account the previously measured yield of the reaction as described in [65] . To normalize cDNA amounts , we used the housekeeping gene EF1α transcripts [66] . S . flexneri M90T was injected into the hindbrain ventricle of zebrafish larvae and incubated at 28°C for 4 h ( high dose ) , 12 h ( medium dose ) and 24 h ( medium dose ) . RNA was extracted from 10 snap-frozen larvae per timepoint using Trizol ( Invitrogen ) . cDNA was obtained using QuantiTect Reverse Transcription Kit ( Qiagen ) . PCR was then performed using a PTC-225 Poltier Thermal Cycler ( MJ Research ) and RedTaq reaction mix ( Applied Biosystems ) . The following pairs of primers were used: icsA ( AATCAATAAGGGCACGTTCG and TCGCCATCTGTATCATTCCA ) ; icsB ( GCATCGGTACAGCCAAAAAT and GTATGAGTGGCAAGCGTTGA ) . Rabbit polyclonal antibodies used were anti-SEPT7 ( R170 ) [15] , anti-p62 ( Cliniscience , PM045 ) , anti-dsRed ( Clontech Laboratoires ) , anti-elongation factor 1-α ( EF1α; GeneTex ) ; to label GFP chicken polyclonal and mouse monoclonal antibodies anti-GFP ( Abcam ) were used . Secondary antibodies used were Cy3- , Cy5- ( Jackson ImmunoResearch Laboratories ) , Alexa Fluor 488- , or Alexa Fluor 546-conjugated goat anti-rabbit , anti-chicken or goat anti-mouse ( Molecular Probes ) . F-actin was labeled with Alexa Fluor 488- , 546- , or 647-phalloidin ( Molecular Probes ) . Whole-mount immunohistochemistry of infected zebrafish cells was performed using standard protocols [14] , [15] . For immunoblotting , total cellular extracts were blotted with the above-mentioned antibodies followed by peroxidase-conjugated goat anti-mouse or anti-rabbit antibodies ( Biosys Laboratories ) . Anti-EF1α was used throughout as a loading control . Proteins were run on 8 , 10 or 14% acrylamide gels . Antisense morpholino oligonucleotides were obtained from GeneTools ( www . gene-tools . com ) . After thawing , morpholinos are heated at 65°C for 10 min to ensure complete dissolution , and diluted to the desired concentration ( typically , 250 to 500 µM , for a total injected amount of 4 ng ) in morpholino buffer ( 120 mM KCl , 10 mM Hepes pH 7 . 2 ) containing 0 . 1% phenol red . Injections were performed into 1-cell embryos at 1 nL per embryo . Control ( TACCAAAAGCTCTCTTATCGAGGGA , with no known target on the zebrafish genome ) and p62-specific ( CACTGTCATCGACATCGTAGCGGAA , targeting the start AUG codon ) morpholinos were used . For rapamycin treatment , protocols were adapted from [46] . Larvae were treated with rapamycin ( Calbiochem ) for 12 h ( 50 nM ) , and treatment was continued throughout infection . Significance testing for performed by Log Rank test ( survival curves ) , Student's t test ( on log values of CFU counts ) , or by ANOVA with Bonferroni posttest ( leukocyte counts ) . The level of significance is shown as follows: ns , P>0 . 05; * , P<0 . 05; ** , P<0 . 01; *** , P<0 . 001 .
|
Autophagy , an ancient and highly conserved intracellular degradation process , is viewed as a critical component of innate immunity because of its ability to deliver cytosolic bacteria to the lysosome . However , a complete understanding of the molecules and mechanisms restricting cytosolic bacteria has not been obtained , and the role of bacterial autophagy in vivo remains poorly understood . Shigella flexneri are human-adapted Escherichia coli that have gained the ability to invade the colonic mucosa , causing inflammation and diarrhea . The intracellular lifestyle of this pathogen has been well-studied in vitro , and Shigella has recently gained recognition as a paradigm of bacterial autophagy . We show that the zebrafish larva represents a valuable new host for the analysis of S . flexneri infection . Interactions between bacteria and host phagocytes can be imaged at high resolution in vivo , and the zebrafish model should prove useful for understanding the cell biology of Shigella infection . We use zebrafish larvae to investigate the role of bacterial autophagy in host defense , and observed that the perturbation of autophagy can adversely affect host survival in response to Shigella infection . Therefore , the zebrafish constitutes a valuable system to develop new strategies aimed at pathogen clearance by manipulation of anti-bacterial autophagy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[
"animal",
"models",
"zebrafish",
"cellular",
"structures",
"gram",
"negative",
"model",
"organisms",
"immune",
"cells",
"cytoskeleton",
"immunity",
"immunologic",
"subspecialties",
"innate",
"immunity",
"immunity",
"to",
"infections",
"immunology",
"host-pathogen",
"interaction",
"biology",
"microbiology",
"molecular",
"cell",
"biology",
"bacterial",
"pathogens"
] |
2013
|
The Zebrafish as a New Model for the In Vivo Study of Shigella flexneri Interaction with Phagocytes and Bacterial Autophagy
|
To guide control policies , it is important that the determinants of influenza transmission are fully characterized . Such assessment is complex because the risk of influenza infection is multifaceted and depends both on immunity acquired naturally or via vaccination and on the individual level of exposure to influenza in the community or in the household . Here , we analyse a large household cohort study conducted in 2007–2010 in Vietnam using innovative statistical methods to ascertain in an integrative framework the relative contribution of variables that influence the transmission of seasonal ( H1N1 , H3N2 , B ) and pandemic H1N1pdm09 influenza . Influenza infection was diagnosed by haemagglutination-inhibition ( HI ) antibody assay of paired serum samples . We used a Bayesian data augmentation Markov chain Monte Carlo strategy based on digraphs to reconstruct unobserved chains of transmission in households and estimate transmission parameters . The probability of transmission from an infected individual to another household member was 8% ( 95% CI , 6% , 10% ) on average , and varied with pre-season titers , age and household size . Within households of size 3 , the probability of transmission from an infected member to a child with low pre-season HI antibody titers was 27% ( 95% CI 21%–35% ) . High pre-season HI titers were protective against infection , with a reduction in the hazard of infection of 59% ( 95% CI , 44%–71% ) and 87% ( 95% CI , 70%–96% ) for intermediate ( 1∶20–1∶40 ) and high ( ≥1∶80 ) HI titers , respectively . Even after correcting for pre-season HI titers , adults had half the infection risk of children . Twenty six percent ( 95% CI: 21% , 30% ) of infections may be attributed to household transmission . Our results highlight the importance of integrated analysis by influenza sub-type , age and pre-season HI titers in order to infer influenza transmission risks in and outside of the household .
Three to five millions severe illnesses and 250 , 000 to 500 , 000 deaths worldwide are due to the influenza virus each year [1] . To guide control policies , it is important that the determinants of influenza transmission are fully characterized . Such assessment is complex because the risk of influenza infection is multifaceted . For each individual , it depends on immunity that was acquired naturally or via vaccination; but also on the level of exposure to influenza the individual has in the community or in the household , which may vary by season , household and individual . Here , from the analysis of original data and relying on new and innovative statistical methods , we ascertain in a unifying and integrative framework the relative contribution of variables that influence these different mechanisms . This task is challenging because both protection and exposure are imperfectly characterized; and uncertainties about one may affect estimates for the other . For example , for haemagglutination-inhibition ( HI ) assays which are extensively used in the approval process for influenza vaccines [2] , [3] , it is generally accepted that a HI titer of 1∶40 is associated with a 50% reduction in the risk of infection [4] , [5] . However , it has long been acknowledged that HI titers are only an imperfect correlate of protection . For example , in 2009 , the proportion of elderly people estimated to be protected against H1N1pdm09 influenza was much higher than had been suggested by pre-pandemic HI titers [6] . In the first study that characterized the protective effect of HI titers , Hobson et al [4] used a challenge design to ensure all subjects in the study had the same level of exposure to influenza; but such approach is expensive and can only be applied to healthy adults . In non-experimental settings , however , it is harder to control for heterogeneity in individual exposures to influenza due to the difficulty to track down all potential sources of infection . Case-ascertained household transmission studies have been extensively used to quantify exposure in the household setting [7]–[13] . In this design , community-based influenza cases , that are labelled as index cases , are recruited via primary care practices or outpatient clinics . Symptoms of the index case and their household members are then monitored for one to two weeks following symptoms onset in the index case; virological samples may also be collected . However , since the follow-up of each household starts with an influenza case , this approach cannot be used to reliably quantify exposure from the community or to estimate the relative contributions of households and the community in the general epidemic . Furthermore , as index cases must have sufficiently severe symptoms to make contact with a healthcare provider and then have sufficiently high viral loads to be detected by laboratory tests for influenza , there may be a selection bias towards more infectious cases , which may lead the probability of transmission in the household to be overestimated . An alternative , less common design offers a more representative view of the role of households in influenza transmission . It is based on a cohort of households that are recruited prior to an epidemic and followed up during the epidemic [14] , [15] . Although the timing and source of infection is typically unobserved , collection of serum samples at baseline and after the epidemic makes it possible to determine serologically which subjects were infected . Statistical methods exist to estimate from such data the probability of transmission from other household members and from the community [16]–[18] . However , they become cumbersome and numerically intractable as the number of categories of individuals ( e . g . child/adult or low/intermediate/high HI titers ) or the size of the social unit of interest ( e . g . here households ) increase [17] , [19] . As a consequence , to our knowledge , it has never been possible to evaluate the protective effect associated with HI titers in such a framework , preventing a more integrated analysis of the determinants of influenza transmission . Here , from the analysis of the large Ha Nam household cohort study [20] conducted from 2007 to 2010 in Vietnam and relying on new and innovative statistical methods [19] , we ascertain in a unifying and integrative framework the protective effects associated with HI titers and age , along with the relative contributions of households and the community in influenza transmission . Differences by subtype are also investigated . The analysis makes it possible to ascertain potential biases in case-ascertained household transmission studies which are extensively used for early assessment at the start of influenza pandemics [8] , [10]–[12] . The analysis also documents influenza household transmission in South East Asia , which has received somewhat less attention than in Western countries [9] , [15] , [21]–[23] .
Samples were collected from a household-based cohort of 940 participants in 270 households in a single community in semi-rural northern Vietnam as previously described [20] . None of the participants had ever received influenza immunisation . Participants aged 5 years or older were asked to provide serial blood samples at times when national influenza surveillance data indicated that influenza circulation was minimal . The samples described here were collected over a period of three consecutive influenza seasons , from December 2007 through April 2010 . Serological samples were collected between 1st–7th December 2007 ( bleed 1 ) , 9th–15th December 2008 ( bleed 2 ) , 2nd–4th June 2009 ( bleed 3 ) , and on the 3rd April 2010 ( bleed 4 ) . This provided three sets of paired samples either side of an influenza transmission season: 548 paired samples for season 1 ( 2008 ) , 501 paired samples for season 2 ( Spring 2009 ) , and 540 paired samples for season 3 ( Autumn 2009 ) . In season 1 , the influenza A virus strains detected in the cohort through ILI surveillance were A/H1N1/Brisbane/59/2007-like and A/H3N2/Brisbane/10/2007-like; in season 2 , they were A/H1N1/Brisbane/59/2007-like and A/H3N2/Perth/16/2009-like; and in season 3 , it was A/H1N1/California/7/2009-like . There was co-circulation of influenza B Yamagata lineage and Victoria lineage in both season 1 and season 2 , with a predominance of Yamagata lineage in season 1 and Victoria lineage in season 2 . For each season and subtype , analysis was restricted to households with at most 1 individual for whom paired serum samples were missing . Influenza hemagglutination inhibition ( HI ) assays were performed according to standard protocols [WHO 2011 manual] . The seasonal influenza A viruses used were isolated from participants' swabs or from swabs taken from patients presenting in Ha Noi in the same season and propagated in embryonated hen's eggs or in MDCK cells ( ATCC ) . A reference antigen supplied by WHO ( A/H1N1/California/7/2009-like ) was used to assess season 3/pandemic sera . A single influenza B virus isolated from a participant during 2008 was used to assess serum for both the first and second seasons . The virus had a titer of 320 with B/Wisconsin/1/2010 ( Yamagata ) reference antisera and of <10 with B/Brisbane/60/2008 ( Victoria ) antisera . Each virus was first assessed for haemagglutination of erythrocytes from chickens , guinea pigs and turkeys then titrated with optimal erythrocytes . Serum was treated with receptor destroying enzyme ( Denka Seiken , Japan ) then heat inactivated and adsorbed against packed erythrocytes . Eight 2-fold dilutions of serum were made starting from 1∶10 and incubated with 4 HA units/25 µl of virus . Appropriate erythrocytes were added and plates read when control cells had settled . Virus , serum and positive controls were included in each assay . Pre- and post-season sera were tested in pairs . Each serum was tested in a single dilution series . The HI titer was read as the reciprocal of the highest serum dilution causing complete inhibition of RBC agglutination , partial agglutination was not scored as inhibition of agglutination . If there was no inhibition of HI at the highest serum concentration ( 1∶10 dilution ) the titer was designated as 5 . Influenza virus infection was defined as a ≥4-fold increase in antibody titer from pre-season to post-season titers , with post season titers ≥40 . For the purposes of analysis low , intermediate , and high pre-season HI titers were defined as ≤1∶10 , 1∶20–1∶40 , and ≥1∶80 respectively . Data were collected for 3 different seasons s = 1…3: 2008 ( s = 1 ) , Spring 2009 ( s = 2 ) and Autumn 2009 ( s = 3 ) . We classify the influenza virus into 4 different categories v = 1…4: seasonal A ( H1N1 ) ( v = 1 ) ; seasonal A ( H1N1 ) ( v = 2 ) ; seasonal B ( v = 3 ) ; pandemic A ( H1N1 ) ( v = 4 ) . A set of k = 1…K households are under study . Household k ( = 1…K ) is of size nk . Individuals are categorized in two types: children i . e . aged ≥5 to ≤15 y . o . and adults . A subject may be infected by influenza subtype v in the community ( i . e . outside the household ) or by another household member . Here , we define a generic model for the occurrence of these events . During season s , the probability that subject i from household k has contacts in the community that would lead to infection by influenza subtype v is defined as . The force of infection from the community is modelled as:where measures the force of infection for subtype v during season s , captures the susceptibility of adults relative to children ( i . e children are the reference group ) and captures the effect of pre-season titers , with 3 categories low ( ≤10 ) , intermediate ( 20–40 ) and high ( ≥80 ) ( reference category: ≤10 ) . The probability that subject i gets infected if household member j is infected is defined as withwhere measures the transmission rate as a function of household size nk ( the rate can be inversely proportional to nk [7] or independent of nk , depending on model variant ) , captures the infectivity of adults relative to children ( i . e . children are the reference group ) . It is challenging to estimate parameters of the transmission model from final size data because the chains of transmission are not observed . Here , we consider a simplified version of the approach developed by Demiris and O'Neill [19] to tackle the problem . A household of size n is represented by a random directed graph with n vertices ( Figure 1 ) . Each vertex represents a household member . Edges are added to represent the unobserved chain of transmission . Two types of edges are possible . If there is an edge between subject j and subject i , it means that subject i is infected if subject j gets infected . If there is an edge between the community and subject i , it means that subject i gets infected . For a given digraph , it is possible to derive the likelihood function [19] . However , since the chains of transmission are unobserved , different configurations for the edges of the digraph may be consistent with the final size data ( Figure 1 ) . The digraph is therefore considered as ‘augmented data’ [24] . The joint posterior distribution of parameters and augmented data is explored by Markov chain Monte Carlo sampling . The algorithm explores the set of digraphs consistent with the data and estimates therefore correctly capture uncertainty about the digraph ( see Text S1 for technical details ) . We use a Uniform prior U ( [0; 10 , 000] ) for all parameters except those characterising relative infectivity or relative susceptibility ( i . e . to a reference group ) . For this latter class of parameters , following [8] , we choose a log-Normal prior LN ( 0 , 1 ) . This prior satisfies the invariance condition that for example the ratio ( adult susceptibility/child susceptibility ) has the same prior as the ratio ( child susceptibility/adult susceptibility ) . In particular , it gives equal probabilities to the relative susceptibility of children versus adults being larger or smaller than 1 . Since the households under study represent only a fraction of households in the study area [20] , we assume here that households are independent of each other . The assumption of independence , which is standard in this type of analysis [8] , [14] , [16] , [25] , [26] , substantially reduces the computational burden compared with that of the more general model of Demeris et al [19] . A simulation study was carried out to investigate the performances of the statistical approach . Once the model structure has been defined and methods to estimate the parameters of the model from that data are available , different model variants may be considered . For example , the effect of pre-season HI titers may be the same for all subtypes , may vary by subtype , by age group etc… Here we consider a large number of possible model variants . Each of them is fitted to the data and we determine the model variant that provides the best fit to the data . This model comparison exercise is essential to better characterize key dependencies in household transmission . We use the Deviance Information Criterion ( DIC ) for model comparison [27] . The smaller the DIC , the better the model . A DIC difference of 5 is considered to be a substantial improvement . For each variable of interest , we explore the following variants: In general , no satisfying version of the criterion exists for data augmentation frameworks such as the one used here [28] . This is because the likelihood of the observed data is not available . To solve this problem , we use importance sampling [29] to estimate the likelihood of the observed data and be able to derive the DIC . The likelihood is derived as follows . For each household , we simulate N = 2 , 000 epidemics in the household . The contribution of a household to the likelihood is then equal to the proportion of simulations where simulated infection statuses in the household perfectly match the observed ones ( to avoid computational issues of likelihoods equal to zero , we assume that the sensitivity Se and specificity Sp of the diagnostic is not perfect , i . e . Se = 0 . 999 and Sp = 0 . 999 ) . In order to estimate the proportion of influenza cases that may be attributed to household transmission , we simulate epidemics in households where i ) all parameters are drawn from the posterior distribution and ii ) all parameters are drawn from the posterior distribution except the within household transmission rate which is assumed to be null . The case counts difference between i ) and ii ) gives the proportion of cases that may be attributed to household transmission . For each pair of case-household contact in the dataset , we calculate the associated probability of transmission under the assumption that the case was the first or the only infected in the pair and derive the average household transmission probability across all pairs . We compare the observed final size distribution with the one simulated with parameters drawn from the posterior distribution . The research was approved by the institutional review board of the National Institute of Hygiene and Epidemiology , Vietnam; the Oxford Tropical Research Ethics Committee , University of Oxford , UK; and the Ethics Committee of the London School of Hygiene and Tropical Medicine , UK . All participants provided written informed consent .
Between 140 and 155 households ( 439–502 subjects including 95–121 children and 344–393 adults ) were eligible for analysis , depending on the season and subtype . The average household size was 2 . 9 . Of all the model variants explored in our extensive model comparison exercise , Figure 2 summaries the characteristics of the model that had the best fit based on the DIC . The best fitting model had the following properties . The community risk of infection of children with low pre-season titers varied both with the subtype and the season ( Figure 2A ) . It was minimum for H3N2 in 2008 and maximum for A ( H1N1 ) pdm09 in Autumn 2009 . The DIC substantially worsened if the community risk of infection of children varied with the subtype but was assumed to constant from one season to the next ( ΔDIC = 24 . 7 ) . We found that high pre-season titers were protective against infection , with a reduction in the hazard of infection of 59% ( 95% CI , 44%–71% ) for intermediate titers ( 20–40 ) and 87% ( 95% CI , 70%–96% ) for high titers ( ≥80 ) ( Figure 2B ) . DIC substantially worsened if the number of titer categories was reduced to 2 ( ΔDIC = 20 . 8 ) or if pre-season titers were not accounted for ( ΔDIC = 44 . 0 ) . Even after correcting for pre-season titers , we found that adults had half the risk of acquiring infection in the household compared to children ( reduction in the hazard of infection of adults relative to children: 50%; 95% CI 32%–63% ) ( Figure 2C ) . Adding an age effect for each subtype did not improve the fit ( ΔDIC = 0 . 2 ) . Distinguishing pandemic versus seasonal influenza only provided a marginal improvement to the DIC ( ΔDIC = −4 . 0 ) ( reduction in the hazard of infection of adults relative to children for seasonal influenza: 41% , 95% CI 15%–58%; reduction in the hazard of infection of adults relative to children for pandemic influenza: 68% , 95% CI 42%–82% ) . Assuming the effect of age varied by subtype did not improve the fit ( Figure S1; ΔDIC = 0 . 2 ) . Ignoring the effect of the age of the subject on the risk of infection substantially worsened the fit ( ΔDIC = 37 . 7 ) . Assuming that infectivity changed with the age of the case did not improve the fit ( ΔDIC = 13 . 2 ) . Assuming the effect of pre-season HI titers could change with age , we found that a rise in HI titers had a slightly more pronounced effect on children than on adults ( Figure S2 ) . However , the fit of this model was not as good as that of our best fitting model ( ΔDIC = 6 . 9 ) . The probability of transmission from an infected individual to another household member was 8% ( 95% CI , 6% , 10% ) on average , and varied with pre-season titer , age and household size . In a households of size 3 , the probability of transmission from an infected individual to a child with low , intermediate and high pre-season titers was estimated to be 27% ( 95% CI 21%–35% ) , 12% ( 95% CI , 8% , 17% ) and 4% ( 95% CI , 1% , 9% ) , respectively . These probabilities dropped to 15% ( 95% CI 9%–23% ) , 6% ( 95% CI 4%–11% ) and 2% ( 95% CI 0–5% ) , respectively , if the recipient was an adult . As has been found in studies of households in Western developed countries [7] , [8] , the best fitting model assumed that household transmission hazard decreased with increasing household size . Ignoring this dependency worsened the fit substantially ( ΔDIC = 40 . 7 ) . After correcting for these variables , estimating an effect of subtype on the probability of transmission in the household did not improve the fit ( ΔDIC = 13 . 1 ) . We estimated that 26% ( 95% CI: 21% , 30% ) of cases may be attributed to household transmission . Figure S3 shows the prevalence of infection along with the estimated contribution of household transmission by season and subtype ( NB: Figure 2A captures only partially variations in the prevalence of infection as the distribution of pre-season HI titers vary for each season and subtype and by age group ) . The fit of the model to the data was adequate ( Table 1 ) . In a simulation study we found all parameters could be estimated from the data and no important systematic bias was detected ( Table S1 ) . Out of 10 simulated datasets and 11 parameters , there was 94% probability that the simulation value was in the 95% CI .
We have characterised the determinants of transmission of seasonal ( H1N1 , H3N2 , B ) and pandemic H1N1pdm09 influenza from a household cohort study conducted in 2007–2010 in Vietnam . We estimated that the household Secondary Infection Risk ( proportion of household contacts infected by an index case , SIR ) was approximately 8% on average . This is broadly consistent with estimates of SAR derived from case-ascertained studies , when diagnosis of contact cases is based on RT-PCR laboratory confirmation ( median SIRPCR: 8%; range: 3% , 38%; n = 12 ) or on a clinical case definition of Febrile Acute Respiratory Illness ( median SARFARI: 11%; range: 3% , 37%; n = 18 ) [12] . Lau et al [12] also reported two estimates of the proportion of household contacts who seroconverted of 20% [30] and 27% [31] . As expected , these proportions are larger than 8% since they capture transmission from the index case but also from the community for the whole duration of the epidemic . The similarity between our estimates and those derived from case-ascertained studies validates the use of case-ascertained studies as a way to obtain representative estimates of influenza household transmission . Overall , we estimated that 26% ( 95% CI: 21% , 30% ) of influenza infections may be attributed to household transmission . This is consistent with other estimates in the literature [32] . We also estimated the risk factors for household transmission and the risk of infection . Pre-season titer and age had a strong impact on the risk of infection . An HI titer of 40 is generally accepted to give a 50% reduction in the risk of infection [5] . Here we found a slightly more subtle effect of pre-season titer , with the risk of infection decreasing incrementally with HI titer and the reduction being as high as 90% for HI titer ≥80 . Even after correcting for pre-season titers , we found that adults had half the risk of acquiring infection compared to children . This supports the idea that HI titer is an imperfect correlate of protection . There is growing evidence that antibodies directed at the stalk domain of the HA protein may be important mediators of protection that accumulates with repeated exposure to influenza viruses but which is not detectable by the HI assay [33] . Consistent with other studies [7] , [8] , [34] , we found that the household person-to-person transmission probability decreased with increasing household size . Ours is the only contemporary study to prospectively assess the transmission of influenza in a random selection of all households ( including those without children ) in an unimmunised community over multiple seasons . The use of a final-size model based on serology minimizes the under-ascertainment inherent in studies that detect only symptomatic cases . As such we believe these results are the best available assessment of the risk of acquisition of influenza in the household and the community . The earlier analysis of this dataset [20] simply reported empirical infection rates by age based on a four-fold or greater increase in HI titers between paired sera , and did not estimate any other transmission parameters nor influences on the probability of transmission . The analysis presented in this manuscript therefore adds substantial new insights including estimates of the probability of transmission from an infected individual to another household member , the proportion of infections acquired in the household and the community , and how the probability of infection is affected by pre-season HI titers , age and household size This study has some limitations . First , the HI assay has imperfect sensitivity and specificity [35] , [36] . As a consequence , the infection status of some individuals may be incorrectly classified . The use of microneutralization assay to detect pH1N1 seroconversions would have increased the sensitivity . The average number of households per season was relatively small ( about 150 ) . However , the study was run over 3 seasons and looked at multiple different subtypes ( H1N1 , H3N2 , B , H1N1pdm09 ) , for a total of 6 pairs season/subtype . This means that the amount of information contained in these data is roughly that of a study of 6×150 = 900 households run over 1 season and for 1 subtype . This explains why the credible intervals for most parameters are relatively narrow . We were unable to assess transmission risks in children aged less than 5 years , since serum samples were not obtained from these subjects . Here , we disentangled the relative contributions of households and the community in the risk of influenza infection . This was made under the assumption that households were independent of each other and that all individuals of an age group were exposed to the same risk of infection in the community . Although standard in such analyses [8] , [14] , [16] , [25] , [26] , in practice , the risk of infection in the community may have a spatial component , potentially leading to higher transmission rates between households that are close to each other . However , we were unable to test this assumption here since our dataset was not spatially structured . Estimating the effect of space on influenza transmission will be an important step forward . This can for example be done from the analysis of household serological cohort studies in which the spatial location of each household is be documented [37] . Ideally , one would like to integrate such analysis in the framework of Demiris and O'Neill [19] , so that the contributions of households and space can be characterized in a single and coherent framework . This is an important subject for future research . This study considerably extends previously limited evidence on influenza transmission in non-Western countries . It also validates the use of case-ascertained studies as a way to obtain representative estimates of influenza household transmission . This has important implications for early assessment of household transmission in future pandemics , as case-ascertained studies are the only household design that can be used close to real-time .
|
Influenza causes an estimated three to five million severe illnesses worldwide each year . In order to guide control policies it is important to determine the key risk factors for transmission . This is often done by studying transmission in households but in the past , analysis of such data has suffered from important simplifying assumptions ( for example not being able to account for the effect of biological markers of protection like pre-season antibody titers ) . We have developed new statistical methods that address these limitations and applied them to a large household cohort study conducted in 2007–2010 in Vietnam . By comparing a large range of model variants , we have obtained unique insights into the patterns and determinants of transmission of seasonal ( H1N1 , H3N2 , B ) and pandemic H1N1pdm09 influenza in South East Asia . This includes estimating the proportion of cases attributed to household transmission , the average household transmission probability , the protection afforded by pre-season HI titers , and the effect of age on infection risk after correcting for pre-season HI titers .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases",
"plant",
"science",
"medicine",
"and",
"health",
"sciences",
"population",
"modeling",
"infectious",
"disease",
"epidemiology",
"epidemiology",
"influenza",
"plant",
"pathology",
"biology",
"and",
"life",
"sciences",
"infectious",
"disease",
"modeling",
"computational",
"biology",
"viral",
"diseases",
"epidemiological",
"methods",
"and",
"statistics"
] |
2014
|
Determinants of Influenza Transmission in South East Asia: Insights from a Household Cohort Study in Vietnam
|
Human Hereditary Sensory Autonomic Neuropathies ( HSANs ) are characterized by insensitivity to pain , sometimes combined with self-mutilation . Strikingly , several sporting dog breeds are particularly affected by such neuropathies . Clinical signs appear in young puppies and consist of acral analgesia , with or without sudden intense licking , biting and severe self-mutilation of the feet , whereas proprioception , motor abilities and spinal reflexes remain intact . Through a Genome Wide Association Study ( GWAS ) with 24 affected and 30 unaffected sporting dogs using the Canine HD 170K SNP array ( Illumina ) , we identified a 1 . 8 Mb homozygous locus on canine chromosome 4 ( adj . p-val = 2 . 5x10-6 ) . Targeted high-throughput sequencing of this locus in 4 affected and 4 unaffected dogs identified 478 variants . Only one variant perfectly segregated with the expected recessive inheritance in 300 sporting dogs of known clinical status , while it was never present in 900 unaffected dogs from 130 other breeds . This variant , located 90 kb upstream of the GDNF gene , a highly relevant neurotrophic factor candidate gene , lies in a long intergenic non-coding RNAs ( lincRNA ) , GDNF-AS . Using human comparative genomic analysis , we observed that the canine variant maps onto an enhancer element . Quantitative RT-PCR of dorsal root ganglia RNAs of affected dogs showed a significant decrease of both GDNF mRNA and GDNF-AS expression levels ( respectively 60% and 80% ) , as compared to unaffected dogs . We thus performed gel shift assays ( EMSA ) that reveal that the canine variant significantly alters the binding of regulatory elements . Altogether , these results allowed the identification in dogs of GDNF as a relevant candidate for human HSAN and insensitivity to pain , but also shed light on the regulation of GDNF transcription . Finally , such results allow proposing these sporting dog breeds as natural models for clinical trials with a double benefit for human and veterinary medicine .
Inherited peripheral neuropathies are neurodegenerative diseases of the peripheral nervous system ( PNS ) , including the Charcot-Marie-Tooth diseases ( CMT ) and Hereditary Sensory and Autonomic Neuropathies ( HSANs ) [1] . They are categorized based on the degree of involvement of motor , sensory and/or autonomic nerve fibers . HSANs are rare peripheral neuropathies constituting a clinically and genetically heterogeneous group which phenotypes range from pure sensory involvement to variable levels of motor and autonomic disturbances [2] . The HSAN types can be subdivided into two groups based on their mode of inheritance , HSAN I being autosomal dominant disorders and HSAN II to V being recessive . The main symptoms correspond to a progressive degeneration of sensory and autonomic neurons causing insensitivity to pain and temperature in feet and hands , leading to ulcerative mutilations [3–5] . These lesions could result in severe tissue infections and/or osteomyelitis and may lead to amputation of the affected limbs . To date , the pathophysiologic mechanisms known to be implicated in HSAN include sphingolipid metabolism with mutations in SPTCL1 or SPTCL2 ( serine palmitoyl-transferase genes ) , vesicular transport ( RAB7 , IKBKAP ) or neurotrophic factors and their tyrosine kinase receptors ( NTRK1 , NGF-B ) [6–8] . Although 12 genes have been associated with HSANs until now , they only explain one third of the patients affected with HSANs , highlighting the fact that the genetic causes of HSANs remain only partially understood in humans [2] and that natural models of these diseases are needed . Interestingly , while neurological diseases such as HSANs are rare in humans , some dogs breeds spontaneously develop such diseases with high frequencies [9] . Indeed , recent breeding practices have created genetically isolated populations with large homozygous haplotypes and linkage disequilibrium ( LD ) blocks [10–12] . With this particular genetic structure of population , the dog model allowed the identification of new genes involved in rare diseases [13 , 14] , and thus constitutes an excellent model to study sensory neuropathies . Indeed , a large number of inherited motor and sensory neuropathies were reported in the dog species [9 , 15] . Since 1960’s , symptoms similar to human HSANs were described in four sporting breeds , with the first cases reported in German short-haired Pointers ( GP ) in 1964 [16 , 17] , then in English Pointers ( EP ) , English Springer Spaniel ( ESS ) and more recently in French Spaniel ( FS ) [16 , 18–21] . For these four breeds , the hallmarks of the disease are loss of pain sensation and feet ulceration . Symptoms naturally occur in puppies approximately four months old when they begin to lick and bite their paws . Affected dogs present an acral insensitivity to pain with , in the majority of cases , severe self-mutilations of the feet including claw loss , painless fractures , and digit amputation ( Fig 1A ) . No sign of autonomic disturbances are detected and motor abilities and proprioception are normal . The pathologic process thus seems to only affect sensory neurons , primarily their development , followed by progressive postnatal degeneration of unmyelinated fibers [18–20] . In addition , pedigree analyses in these breeds revealed a recessive autosomal mode of inheritance such as in human type II-V HSANs [2 , 21] . We thus investigated these affected breeds as a potential opportunity to identify new genes for unexplained cases of human HSANs . We first started the genetic study with the French Spaniel breed and identified one locus strongly associated with the disease , on canine chromosome 4 , and refined the locus by intersecting the haplotypes of the other affected breeds . Using this strategy , we highlighted a common homozygous haplotype of 1 . 8 Mb ( chr4:70 , 6–72 , 4Mb ) shared by all affected dogs in the four sporting breeds . Next , we performed targeted DNA sequencing of the locus in four affected and four unaffected dogs . We found 478 variants which were individually removed by Allele-Specific PCR except one located upstream of the GDNF gene ( Glial cell-Derived Neurotrophic Factor ) , a neurotrophic factor involved in neuronal development and adult neuronal survival [22–25] . More precisely , we showed that this variant is localized in the last exon of a canine long-non-coding RNA ( GDNF-AS ) that is transcribed in the opposite direction of GDNF . Comparative genomic analysis with the human orthologous genomic region led us to hypothesize that the mutation disrupts a regulatory region , such as an enhancer , controlling GDNF expression . We then performed quantitative PCR analyses and showed lower expression levels of both GDNF and GDNF-AS RNAs in affected dogs compared to unaffected dogs . By gel shift assays , we further showed that the mutation modified the affinity of a regulatory complex . Such findings highlight the GDNF/GDNF-AS partnership as a subtle regulatory mechanism and relevant candidates to search for human HSAN mutations and potentially to develop targeted therapies .
We first focused the genetic analysis on the French Spaniel breed . The collection of 173 blood samples with pedigree information allowed us to design a large pedigree and to confirm the monogenic recessive inheritance of the disease in this breed as previously described [21] . Affected dogs presented with severe self-mutilations with insensitivity to pain only in the feet ( Fig 1A ) . Neurological examinations ( proprioception , motor abilities , spinal reflex exams ) confirmed that pain perception became progressively normal above the knees and was not altered in the rest of the body . One affected dog had never shown self-mutilation marks leading us to consider insensitivity to pain as the first clinical sign for this disease . As described in Paradis et al , no signs of autonomic disturbances were detected by neurological exams [21] and motor abilities and proprioception were normal in all dogs . We first performed a genome-wide association study ( GWAS ) using a linear mixed-model method accounting for population stratification and relatedness , with 49 French spaniels ( 21 affected showing insensitivity to pain in their feet and 28 unaffected dogs ) ( Fig 1B ) . Following quality control of the genotyping data , 123 , 579 SNPs were retained for genetic mapping . This analysis identified a 3 Mb locus on canine chromosome 4 strongly associated with the disease , with significant p-values ( Wald test; p-value = 10−16; Table 1 ) and confirmed the association using a permutation test ( p100 , 000 = 10−5 ) ( Fig 1C ) . Since all affected French spaniels present a homozygous haplotype in this 3 Mb locus , we took advantage of the particular genetic structure of the dog population . Based on the hypothesis that sporting breeds share the same disease and founder mutation , we genotyped additional affected dogs in three sporting dog breeds: German Short-haired Pointer ( GP ) , English Pointer ( EP ) and English Springer Spaniel ( ESS ) . These breeds revealed a common homozygous haplotype block with French Spaniel ( FS ) confirming the founder effect between these four breeds . Recombination breakpoints in the FS and ESS reduced the critical interval to 1 . 8 Mb ( chr4:70 , 6–72 , 4Mb ) ( Fig 2 ) . This interval contains a dozen genes annotated in the dog and human orthologous genomic region including the GDNF gene ( Glial cell-Derived Neurotrophic Factor ) , which appeared as a new and good candidate for HSAN ( Table 1 ) . Indeed , GDNF codes a neurotrophic factor involved in neuronal development and adult neuronal survival [22–25] and was never associated with HSAN . However , Sanger sequencing did not reveal any mutation neither in the three exons of the transcripts , nor in the promoter sequence of GDNF . To identify the disease-associated variants ( InDel and SNP ) , we chose to enlarge the homozygous candidate region to 3 Mb ( 70 . 3 Mb to 73 . 3 Mb on CanFam3 assembly ) and performed a targeted sequencing ( Fig 2 ) . Four affected and four unaffected sporting dogs were thus sequenced with high coverage and depth ( coverage 25X: 99%; 10 X: 100%; mean depth: 374X; S1 Table ) . Within this 3 Mb interval , 478 variants were identified with the expected distribution between affected and unaffected individuals ( Tables 2 and 3 ) . After QC and filtering for known SNPs from the dbSNP database ( dbSNP build 139 ) , 156 variants still remained ( Table 4 and S2 Table ) . To exclude non-disease variants due to polymorphisms , we then chose to genotype dogs from a large number of unaffected breeds making the hypothesis that the rare allele present in affected dogs should not be found in unaffected breeds ( S3 Table ) . Using AS-PCR experiment and Sanger sequencing , we found only one SNP perfectly segregating with the disease and located in an intergenic region ~90 kb upstream of the GDNF gene ( chr4 . g . 70 , 875 , 561C>T ) . To validate this variant , we sequenced over 900 unaffected dogs from 130 different breeds and observed that the mutated allele was never detected in these dogs , thus excluding a polymorphism . We also checked the allelic distribution of this variant in a panel of 250 sporting dogs of known clinical status and we observed a perfect correlation between the clinical status of dogs and their genetic status , confirming the strong association of the variant with the disease in the analyzed sporting breeds . Interestingly , heterozygous dogs did not present any clinical signs and only one dog was homozygous mutated with only insensitivity to pain ( no acral mutilations ) . Even if it was lying in a not yet described genomic region , we considered this variant as potentially causal and further named it “AMS variant” for Acral Mutilation Syndrome variant . To unravel the genomic context surrounding the GDNF locus , we mapped the canine 3 Mb locus targeted for sequencing ( chr4:70 , 3–73 , 3 Mb ) onto the human genome ( GRCh38 ) using the LiftOver tools available on the UCSC genome browser [26 , 27] and the AutoGRAPH website [28] ( Fig 2 ) . The locus is well conserved between dog and human with 63 . 3% sequence identity with human region ( chr5:34 , 9 Mb-38 , 5 Mb ) and 53 . 1% reciprocally . At the syntonic level , most of the canine orthologous protein-coding genes harbor the same order but in the opposite orientation of transcription as compared to humans . Remarkably in human , a long non-coding RNA gene ( GDNF-AS ) is annotated upstream of GDNF [29] , transcribed in a divergent orientation , and close to the orthologous position of the canine AMS variant ( Fig 2 ) . To refine the annotation of the canine locus , we used 33 RNA sequencing data produced from 25 different tissues of seven breeds ( S4 Table ) . The sequencing data were provided by the Broad Institute and as part of a collaborative work of the LUPA consortium [30–32] . This extended annotation confirmed the presence of a multi-exonic RNA transcribed in the vicinity and in a divergent orientation of GDNF as in human ( Fig 3 ) . Next , to assess the protein-coding capabilities of each of the 12 isoforms of the new gene , we used three complementary programs ( CPC [33] , CPAT [34] , PLEK [35] ) and confirmed that all of the new transcripts are classified as probable non-coding RNAs ( See Methods and S1 File ) . Remarkably , the AMS variant is located in the last exon of the canine lincRNA , which is shared by all of the 12 isoforms ( Fig 3 ) . The lincRNA , that we named canine GDNF-AS , is weakly expressed in the 33 RNA-seq ( S5 Table ) and is mostly detected in the olfactory bulb ( RPKM = 0 . 849 ) and spleen ( RPKM = 0 . 732 ) . We also observed that the highest level of GDNF mRNA expression level is in spleen ( RPKM = 4 . 6 ) . It has been shown that lincRNAs transcribed from a bi-directional promoter could regulate the level of expression of the corresponding neighbor mRNAs [37–39] . We therefore computed the correlation of expression using the 33 expression data values ( RPKM ) between the canine GDNF-AS and the four closest protein-coding genes i . e . GDNF , WDR70 , NUP155 and C5orf42 ( S1 Fig ) . We found that GDNF-AS expression is highly correlated with GDNF ( Pearson correlation = 0 . 67 , p-value = 1 . 8 10−5 ) but not with the other protein-coding genes ( Pearson correlations = -0 . 10 , -0 . 03 and 0 . 08 for WDR70 , NUP155 and C5orf42 , respectively ) . These results , together with the close proximity of GDNF and GDNF-AS transcription start sites ( TSSs ) , reinforced the relationship between both genes and pointed toward the presence of a bi-directional promoter [37 , 40 , 41] . We next analyzed the sequence conservation of the dog genomic region encompassing GDNF/GDNF-AS ( chr4:70 , 8–71 Mb ) and showed that it matches the human genome ( chr5:37 , 8–37 , 9 Mb ) with 84 . 2% of nucleic acid identity ( Fig 3 ) . Interestingly , the sequence surrounding the AMS variant on the human genome overlaps with regulatory regions defined by ENCODE data [36 , 42] using typical histone marks such as monomethylation of histone H3 at lysine 4 ( H3K4me1 ) and acetylation of histone H3 at lysine 27 ( H3K27ac ) ( Fig 3 ) . These typical histone marks led us to hypothesize [43] that the AMS variant is located in a region which could control GDNF and GDNF-AS expression . To confirm relationship between GDNF and GDNF-AS , we performed quantitative real-time PCR analyses on Dorsal Root Ganglia ( DRG ) . Indeed , DRG are interesting , as it was previously recognized as harboring the predominant lesions in these breeds [18 , 19] and contain sensory neurons where neurotrophic factors are highly expressed [22 , 23 , 44–47] . We thus extracted RNAs from DRG of two affected and two unaffected dogs and characterized the expression levels of GDNF and GDNF-AS together with the neighboring genes WDR70 , NUP155 , and SLC1A3 . QRT-PCR revealed that the GDNF mRNA level was significantly decreased ( 60% ) in affected dogs ( t-test: p-value *<0 . 0001 ) . We also showed that the expression levels of the other genes of the locus were not altered ( Fig 4 ) . Finally , we also observed a strong decrease of the GDNF-AS expression level ( 80% ) in this tissue in agreement with the hypothesis that GDNF-AS and GDNF share a bi-directional promoter highlighted by correlated expression shown by RNA-seq and genomic analyses ( Fig 3 , S1 Fig and S5 Table ) . The comparative genomic analysis indicates that the region containing the AMS mutation corresponds to an enhancer element ( Fig 3 ) . Mutations in regulatory elements are already described in cancer [48 , 49] or in developmental disorders such as Van der Woude syndrome [50] or isolated pancreatic agenesis [51] . To test the hypothesis of a mutated regulatory element , we constructed reporter systems using a pTAL-Luc plasmid in which the wild-type and mutated sequences were linked to a firefly luciferase reporter gene with a promoter ( S2 Fig ) . While we detected a weak luciferase signal ( p-value < 0 . 05 ) showing potential enhancer activity with these clones in HeLa cells , no significant difference between cases and controls were observed . However , the distance between the AMS variant and the promoter is ~2kb in these constructs which is a short distance compared to the ~90kb of the genomic region . We thus hypothesized that a long-distance regulation could exist , explaining the qRT-PCR results . The AMS variant region should thus contain regulatory elements sequences . To test this hypothesis , we performed Electrophoretic Mobility Shift Assay ( EMSA ) . We first used increasing amounts of nuclear extract from HeLa cells with duplex of wild-type or mutated sequences . We showed a high-affinity specific interaction of a Nuclear Extract ( NE ) complex with the double-stranded oligonucleotide containing the wild-type sequence but a low-affinity with the sequence containing the AMS variant ( Fig 5A ) . In addition , the competition experiment showed that the complex binds preferentially on the wild-type sequence rather than on the mutated sequence . This result indicates that the affinity of the complexes formed on the two sequences is different , ( Fig 5B ) . The same results were obtained when using human neuroblastoma cell line NE ( SY5Y ) . These data indicate that the mutation prevents the binding of a nuclear complex supporting the hypothesis that the decrease expression of both GDNF and GDNF-AS observed in qRT-PCR experiments ( Fig 4 ) is due to a lack of activity of the mutated enhancer .
This study presents the identification of the mutation involved in a severe form of hereditary sensory autosomal neuropathy ( HSAN ) shared by four sporting breeds having a common history . The identification of a 1 . 8 Mb homozygous region on chromosome 4 by GWAS , with only one variant perfectly segregating with the disease , illustrates the advantages of the canine model in genetics . Since this variant is located 90 kb upstream of the neurotrophic factor GDNF , we hypothesized a regulatory effect of this variant on GDNF . We then showed a strong correlation between the presence of the mutation and the decreased expression levels of both GDNF and its divergent long non-coding RNA GDNF-AS . Finally , we demonstrated the impact of the mutation on the binding of a complex confirming the hypothesis of a regulatory element . With this work , we provide relevant arguments to explore GDNF/GDNF-AS partnership in human patients with unexplained HSAN . We started this project focusing our study on dogs with self-mutilations using a precise clinical questionnaire and we quickly detected that all dogs with self-mutilations also presented insensitivity to pain , not always reported . This important clinical sign led us to improve the genetic analysis . Indeed , we found additional affected dogs without self-mutilations but related to cases with self-mutilations . These dogs showed the “affected” homozygous haplotype , while owners did not detect the insensitivity to pain . This observation reflects the difficulty to diagnose insensitivity to pain in dogs , which contributed to the spreading of this severe disorder in the four related breeds [18 , 19 , 21] . This feature led us to consider self-mutilation , probably triggered by small fractures of toes or other injuries [21] , as a consequence of the insensitivity to pain . With a high coverage targeted sequencing of the locus ( 365x ) , we identified one variant perfectly associated with the phenotype , and absent in more than 900 unaffected dogs . This variant located upstream of the GDNF gene appeared as an excellent candidate . Indeed , GDNF ( glial cell-derived neurotrophic factor ) promotes the axon development and the survival of sensory neurons at different stages of the development in mice and chicken model [22–25] . Moreover , it has been shown in vitro that after injury , nearly 100% of the sensory neurons are rescued by the production or injection of GDNF [24 , 46 , 52 , 53] . We thus expected that this AMS variant could disturb the expression of GDNF leading to the decrease of the number of sensory neurons and then their death , as previously observed in affected dogs [18–20] . This feature could happen during the first months of life , which would correlate well with the early age of onset of this insensitivity to pain . The genomic analyses of the human and dog orthologous regions revealed the presence of long non-coding RNA transcripts in both species . One of them , GDNF-AS , described in human [29] , contains the same structure as the canine lincRNAs identified in this study and more likely share a bi-directional promoter with the GDNF gene . While the GDNF mRNA sequence is well conserved between human , mouse and dog , the canine GDNF-AS transcript sequences diverge significantly from the human sequences at the nucleotide level ( only 14 . 3% identity using pairwise alignment with longest isoforms ) and no GDNF-AS are yet annotated in mouse . However , the whole locus is well conserved at the syntonic and nucleotide levels between human and dog . Regarding shared lncRNAs functions between species in spite of low sequence conservation , several well-described lncRNAs ( e . g . Xist , TUNA and Hotair… ) exhibit conserved biological functions even though nucleotide conservation is only limited to small patches and their exon/intron structure is not maintained [54] . An interesting point is the fact that the AMS variant is located in the last exon of GDNF-AS , which is the only exon shared by all of the 12 isoforms annotated by our RNA-seq data . Strikingly , another neurotrophic factor , BDNF ( Brain Derived Neurotrophic Factor precursor ) , playing similar functions as GDNF , also presents a similar genomic feature than GDNF , with the presence of a lincRNA transcribed in the opposite direction . The parallel between BDNF and GDNF is interesting because they both stimulate the growth and differentiation of new neurons and support the survival of existing neurons in central and peripheral nervous systems [23 , 52 , 55] . Moreover , BDNF interacts with the TrkB receptor encoded by the NTRK2 gene , which has a paralog NTRK1 , already annotated as mutated in HSAN type IV [5 , 56 , 57] . In addition , it has been shown that the inhibition of BDNF-AS increases BDNF levels in vivo [38 , 58] . Modarresi et al . recently showed that the knock-out of GDNF-AS increases the expression level of GDNF in human HEK293T cells [59] . We could thus hypothesize that the GDNF and GDNF-AS partnership could fine-tune the expression of GDNF and that the AMS variant disturbs this relationship at a specific developmental stage [60] . In embryonic rats , dorsal root ganglia ( DRG ) neurons were described as potential target of GDNF [61 , 62] . In addition , a recent paper in human and rat confirmed that DRG are the main tissue where genes involved in sensory neuropathies are highly expressed [47] . Thus , the qRT-PCR analyses in canine DRG brought a relevant result: the AMS variant seems to affect a regulatory element . Indeed , while the AMS variant is located in the last exon of GDNF-AS , both GDNF and GDNF-AS transcripts are weakly expressed in affected dogs as compared to unaffected dogs ( Fig 4 ) . This important observation supports the implication of GDNF in the disease , and suggests that too low levels of GDNF in the peripheral nervous system prevent the maintenance of the integrity of sensory adult neurons [22–25] . Moreover , this observation led us to consider the hypothesis of a mutated regulatory element that could impair the activation of the promoter shared by GDNF and its lincRNA GDNF-AS . While we detected an enhancer activity , we did not find any difference of activity between the wild-type and the mutated sequences in the luciferase reporter assay . This is probably due to the fact that GDNF regulation is modulated by a long distance interaction between the region containing the AMS variant and the promoter . As the luciferase reporter assay did not validate this hypothesis , we checked if the AMS variant could inhibit the enhancer activity by preventing the binding of a transcription complex . The EMSA experiment clearly showed that a complex binding the wild-type sequence has less affinity for the mutated sequence . The fact that the mutation of a single nucleotide , in a 20-nucleotides sequence , inhibits the fixation of regulatory elements comforts the causal effect of the variant . It will be interesting to further investigate the nature of the complex as we noticed that the AMS variant introduces a change of one nucleotide in the conserved binding motif recognized by NEUROD1 ( S3 Fig ) . This transcription factor plays an important role during the neurogenesis by binding to regulatory elements of neuronal genes that are developmentally silenced by epigenetic mechanisms [65] . In this context , decreased binding of neurogenic transcription factors to the mutated enhancer in dog could explain the weak expression of GDNF/GDNF-AS partnership in neuropathies . While the complete knock-out GDNF-/- mouse model is not viable since they die at 1–1 . 5 days after birth [63 , 64] , the AMS variant is not lethal in dogs . In affected dogs , we showed that GDNF and GDNF-AS RNAs are still produced but at very low levels . With the functional analyses , we can conclude that the AMS variant affects the regulation of GDNF expression , which could explain the non-lethal effect of the mutation in homozygous dogs who only present insensitivity to pain . However , heterozygous dogs are not affected similar to GDNF+/- mice . Indeed GDNF+/- mice were normal , viable and indistinguishable within controls by visual inspection [64] . Comparison with the mouse model revealed another difference: in dogs , no sign of autonomic disturbances was detected by neurological exams [21] and no metanephric kidney or gastrointestinal tract disorders were observed contrary to what is reported in GDNF-/- ( knock-out ) mice [63 , 64] . These results confirm that GDNF acts in a dose-dependent manner as suggested by GDNF-/- mice model [63] . In conclusion , considering the parallel with BDNF function , our RNA expression analyses , and dog/human comparative genomic analyses , we hypothesize that the GDNF/GDNF-AS partnership represents a very precise regulation mechanism for the mRNA expression of GDNF . Indeed , since we showed that the mutation ( in the last exon of GDNF-AS ) dramatically diminishes both the expression of GDNF/GDNF-AS , by probably disrupting a regulatory element , the AMS variant most probably leads to a decrease of the GDNF expression , preventing a correct growth and maintenance of the small fibers from the knees down to the paws . To date , no mutation in the GDNF gene or in its regulatory regions has been described in human HSAN patients . This work illustrates the power of the canine model in genetics and shows the potential implication of GDNF in such neurological diseases , revealing the importance to explore this gene and its regulatory elements as serious candidates in human HSAN patients .
All dogs were client-owned and no harmful procedures were performed , so there was no animal experimentation according to the European legal definition ( Subject 5b and 5f of Article1 , Chapter I of the Directive 2010/63/UE of the European Parliament and of the Council ) . Blood and biopsies were obtained as part of routine clinical procedures for diagnostic purposes , approved by the CNRS ethical board ( France ) , and were sent by veterinarians and French Veterinary Schools . The biological samples were obtained from the ‘Cani-DNA_CRB’ , which is part of the CRB-Anim infrastructure , ANR-11-INBS-0003 ( http://dog-genetics . genouest . org ) . The work with dog samples was approved by the CNRS ethical board , France ( 35-238-13 ) for UMR6290 . Genomic DNA was extracted from 2 mL blood samples collected in EDTA , using the NucleoSpin Blood L kit ( Macherey-Nagel ) according to the manufacturer’s instructions . Epidemiological and clinical data were collected using a dedicated questionnaire for each affected dog . Dogs over three years old without any symptoms were considered unaffected . Complementary neurological diagnoses ( proprioception , motor abilities , spinal reflex exams , electromyography ) were done by neurologists , in the Veterinary Schools of Lyon ( France ) and St-Hyacinthe ( Canada ) . Using the Illumina Canine HD 173k ( BeadChip ) , genotyping was performed at the Centre National de Génotypage ( CNG; Evry , France ) in the frame of European LUPA project in 62 dogs: 49 French spaniels ( 21 affected and 28 unaffected dogs ) . This design contained 2 subpopulations: one from France and one from Canada , each including 3 small families ( parents + 2 sibling including one affected and one unaffected ) , all other dogs were unrelated to one another at the grandparent level . To reduce the 3 Mb locus , additional dogs were genotyped including 5 German Short-haired Pointers ( 1 affected dog , his unaffected brother and father , and 2 other unrelated unaffected dogs ) , 4 unrelated English Pointers ( 1 affected and 3 unaffected dogs ) and 4 unrelated English Springer Spaniels ( 1 affected , and 3 unaffected dogs ) . Using the PLINK software ( v1 . 06–1 . 07 ) [66] for SNP filtering ( minor allele frequency <0 . 01; call rate by marker and individual>75% ) , a dataset of 123 , 579 SNPs was obtained and analyzed . Genome-wide association study was performed using the software GEMMA v0 . 94 . 1 ( Genome-wide Efficient Mixed-Model Association ) [67 , 68] . The linear mixed-model method accounts for population stratification and relatedness between dogs , especially for small families . Genomic library sample preparation was performed using the Illumina paired-end library sample preparation kit ( Illumina Inc . , San Diego , CA , USA ) . Sample preparation was carried out by Integragen ( Evry , France ) according to the manufacturer’s instructions ( Agilent ) , using 4μg of genomic DNA . We used two unrelated affected dogs in French Spaniel ( FS ) , one affected German Shorthaired Pointer ( GP ) and one affected English Springer Spaniel ( S1 Table ) . For controls , we used father of one the two affected FS + one unrelated , one brother of the affected GP and one unrelated ESS . All these dogs were previously genotyped on the Illumina Canine HD 173k . For targeted sequencing , solution-based capture was performed using the Agilent SureSelect Target Enrichment System Kit . Repeated elements were not sequenced due to the limit of this technology , this is why we extended the size of the targeted locus to 3 Mb ( chr4: 70 . 3–73 . 3 Mb , CanFam3 ) . A custom panel of 75 bases cRNA primers was designed to sequence the 3 Mb locus based on the canine genome ( CanFam3 assembly ) in the candidate region on chromosome 4 . The targeted regions were covered by approximately 25 , 000 probes designed for 2x coverage ( i . e . , each base was covered by two different probes ) . Targets were pulled down via streptavidin magnetic beads , purified , and enriched through 13 cycles of PCR amplification . Samples were paired-end sequenced on an Illumina HiSeq2000 . Reads mapping was performed using Bowtie 2 [69] and variants were called using GATK 3 . 5 [70] . Candidate variants were then genotyped on 100 breeds , each breed corresponding to a pool of four DNA samples of unrelated dogs ( S3 Table ) . We also used small French Spaniel families previously genotyped , to create 12 different pools used as references to detect the presence of the rare allele . Genotyping was performed using an Allele Specific PCR method ( AS-PCR ) ( Integragen ) developed from the methodology described by Nazarenko and Myakishev [71 , 72] . SNP genotyping was performed on the Biomark ( Fluidigm ) with a microfluidic multiplex 96 . 96 dynamic array chip . We excluded all variants where rare alleles were found in minimum three different breeds ( minimum one dog by breed ) . All primers used were designed using Primer3plus ( www . bioinformatics . nl/primer3plus/ ) [73] . We re-sequenced the GDNF gene and the variant on 16 affected and 16 unaffected French Spaniel using the Type-it Microsatellite PCR kit ( Qiagen ) on C1000 and S1000 thermocyclers ( Bio-Rad ) . PCR products were cleaned using the Illustra Exostar-1 Step reagent ( Dutscher ) and sequenced by Sanger method using the BigDye Terminator v3 . 1 cycle sequencing kit ( Thermo Fisher Scientific ) . Electrophoresis of the products was realized on a 370 ABI sequencer . Sequences were analyzed using the PhredPhrap and Consed software’s [74–76] . The presence or absence of the variant was validated on 250 French Spaniel , German-Short-Haired Pointer , English Pointer and English Springer Spaniel ( 24 affected and 226 unaffected ) , and 900 dogs ( including the 400 dogs used for AS-PCR ) from 130 unaffected breeds among the 10 FCI ( Fédération Cynologique Internationale ) groups . To detect the GDNF-AS long non-coding RNA ( lincRNA ) , we analyzed 33 RNA sequencing data produced from 25 different tissues of 7 breeds ( S4 Table ) as a part of the LUPA consortium and the Broad Institute annotation efforts [31] . Each RNA sequencing data includes between 60 and 150 millions paired-end and strand-specific reads respectively , that were analyzed by Bowtie/Tophat2 and CUFFLINKS/CUFFMERGE v2 . 2 . 1 [69 , 77] revealing 245 , 276 transcripts belonging to 81 , 363 genes . The protein-coding capabilities of all novel transcripts ( i . e . not annotated in Ensembl ) was measured using three complementary programs CPC , CPAT , PLEK [33–35] ( S1 File ) . We quantified RNA expression at the gene-level using the HTSEQ-count program version 0 . 6 . 1 [78] where each gene is considered as the union of its exons . This gene-count based measure was then transformed in RPKM using the edgeR software version 3 . 14 . 0 [79] in order to normalize gene expression by both its effective length and the total number of mapped reads ( thus the sequencing depth ) in each of the 33 samples [80] . Therefore , each gene is assigned a vector of 33 points corresponding to the normalized expression of the gene in the 33 RNA-seq samples . Using the cor . test function of the R software ( https://www . r-project . org ) , we computed all pairwise Pearson correlations for the 5 representative genes in the AMS locus i . e . GDNF-AS , GDNF , WDR70 , NUP155 , and C5orf42 ( S1 Fig ) based on the 33 points . RNA was extracted from tissues , using the NucleoSpin RNA kit ( Macherey-Nagel ) according to the manufacturer’s instructions . We used two affected French Spaniel ( brother and sister ) and two unrelated dogs for controls , all already genotyped on the Illumina Canine HD 173k . Reverse transcription was performed on 1 μg of total RNA using the High-capacity cDNA Reverse Transcription kit ( Thermo Fisher Scientific ) , according to the manufacturer's instructions . The total Dorsal Root Ganglia cDNA of the canine GDNF and GDNF-AS were amplified and sequenced using two primer pairs ( S6 Table ) . qRT-PCR was performed on 1:80 diluted cDNA samples after pre-amplification with the TaqMan PCR master mix ( Thermo Fisher Scientific ) on the 7900HT Fast Real-Time PCR System ( Applied Biosystems ) using standard procedures . We used pre-designed primers for GDNF ( Thermo Fisher Scientific Reference: Cf03986046_g1 ) , WRD70 ( TFS Ref: Cf02651565_m1 ) , NUP155 ( TFS Ref: Cf02644933_m1 ) and SLC1A3 ( TFS Ref: Cf02702629_m1 ) and we also specifically designed other probes for GDNF-AS ( S6 Table ) . Canine PPIB ( Peptidylprolyl Isomerase B ) was used as the reference gene ( Thermo Fisher Scientific Reference: Cf02629556_m1 ) . Each sample was measured in triplicate and each qRT-PCR was carried out three times by different experimenters . Relative amounts of the transcript were determined using the ΔΔCt method [81] . Expression analyses were carried out on two tissues ( DRG L6 , DRG L7 ) and their results were pooled . We also pooled results from both affected dogs and both controls to increase the number of points . Using the t . test function of the R software ( https://www . r-project . org ) , we determined the significance of the variation of expression level for all genes . Clones were created to be centered on the AMS variant and correspond to a fragment of 2359 bp containing two SINE , two LINE , one LTR , and eight SNVs . PCR was performed from the genomic DNA of two dogs: one affected dog carrying the mutation and rare alleles for 8 SNVs , and one unaffected dog with the same haplotype as the dog genome reference . The PCR fragments were cloned into the pTAL Luciferase plasmid ( Clontech Laboratories , Inc . ) by DNA ligation or by homologous recombination using the Gibson assembly MasterMix ( New England , Biolabs ) . All primer sequences used to prepare these constructs are given in S6 Table . All constructs were verified by DNA sequencing . Hela cells were grown in high-glucose Dulbecco’s Modified Eagle’s Medium ( DMEM ) containing 10% fetal calf serum ( GIBCO ) according to the manufacturer’s instructions . Cells were transfected in triplicates with 250 ng of reporter plasmid and 50 ng of Renilla control vector ( pRL-Null from Promega ) using JetPEI ( Polyplus Transfection ) in 24-well plates . After 24 h , Firefly and Renilla luciferase activities were determined using the Dual-Luciferase Reporter Assay System ( Promega ) on a Veritas Microplate Luminometer ( Turner Biosystems ) . Enhancer assays of selected regulatory regions were run as previously described [82] . Firefly luciferase activities of individual transfections were normalized against Renilla luciferase activities . As positive control ( control + ) , we used a home-made construction with enhancer activity previously described by Sérandour et al [82] . HeLa nuclear extract were purchased from the Computer Cell Culture Center S . A . ( Belgium ) and prepared according to Dignam et al [83] . SY5Y cells were grown in 50% Dulbecco's Modified Eagle Medium ( DMEM ) and 50% Nutrient Mixture F-12 with 10% of fetal calf serum and 1% of antibiotics and maintained at 37°C and 5% CO2 . Cells were harvested using 1 ml of Lysis buffer containing protease inhibitors , 10 mM Tris-HCl ( pH 7 . 5 ) , 0 , 5% NP40 , 2 mM MgCl2 , 3 mM CaCl2 and 10% Glycerol and gently spun down . The supernatant was removed , and the cells were resuspended in the lysis buffer and gently spun down . The supernatant was removed and the cells were resuspended in 500ul of Extraction buffer containing protease inhibitors , 20 mM Hepes , 0 , 4 M KCL , 1 , 5 mM MgCl2 , 2 mM DTT , 0 , 5 mM PMSF and 20% Glycerol . The cells were left on ice for 30 min to swell and were then vortexed vigorously for 10 s once during the time on ice . The cells were centrifuged at 12 , 000 rpm , 15 min at 4°C . The supernatant was kept and the protein concentration was subsequently estimated by the BCA assay . Probe labeling and duplex formation: 10 pmoles of primers wild type sequence ( WT: 5’- TGTTGTCTTTGCTGCTGTCATGATGG-3’ ) and mutated sequence ( Mut: 5’- TGTTGTCTTTGCTACTGTCATGATGG-3’ ) were 5'-end labeled using T4-PNK ( Promega M4101 ) and 20μCi of 32P-g ATP . Labeled oligonucleotides were purified on a Sephadex G-25 column . Unlabeled complementary oligonucleotides were then annealed to form a WT-duplex or Mut-duplex in hybridization buffer ( 10mM Tris pH7 . 4; 6mM MgCl2; 50mM NaCl; 6mM B-Mercapto-Ethanol ) by incubation for 1 min at 95°C and gradually cool down to room temperature . Complete annealing of the radioactive probe was checked by electrophoresis on 20% native acrylamide gel . For complex formation , 40fmoles ( 15000cpm ) of duplex was incubated with various amount of nuclear extracts in a total volume of 10μl containing 25 mM HEPES ( pH 7 . 5 ) , 150 mM KCl , 5 mM dithiothreitol ( DTT ) , 10% glycerol , 1 μg of poly ( dI-dC ) . Incubation was performed for 30 min on ice prior to loading . For competition experiments , the mixture was preincubated for 30 min at 4°C with 2 pmoles of unradiolabeled competitor duplex , before addition of the radiolabeled duplex . Complexes were then fractionated onto a 5% polyacrylamide ( 38:2 ) gel with 2 . 5% ( v/v ) glycerol , 0 . 5 mM EDTA and 22 . 25 mM Tris-borate ( pH 8 . 3 ) by electrophoresis at 4°C in 0 . 5 mM EDTA , 0 . 25%glycerol and 22 . 25 mM Tris-borate ( pH 8 . 3 ) . Free and bound duplexes were visualized with a phosphoimager using the ImageJ software .
|
In this study , we present a canine neuropathy characterized by insensitivity to pain in the feet , sometimes combined with self-mutilation described in four sporting breeds . This particular phenotype has the clinical hallmarks of human Hereditary Sensory Autonomic Neuropathies ( HSAN ) . As we hypothesized that a monogenic recessive disorder was shared between these breeds , we performed a Genome Wide Association Study ( GWAS ) to search for the genetic causes and found one homozygous chromosomal region in affected dogs . High-throughput sequencing of this region allowed the identification of a point mutation upstream to the GDNF gene and located in the last exon of a long non-coding RNA , GDNF-AS . We confirmed the perfect association of this variant with the disease using more than 900 unaffected dogs that do not present with this mutation . Functional analyses ( qRT-PCR , EMSA ) confirmed that the mutation alters the binding of regulatory complex , leading to a significant decrease of both GDNF and GDNF-AS mRNA expression levels . This work in canine spontaneous forms of human neuropathies allowed the identification of a novel gene GDNF and its regulation mechanism , not yet described in human HSAN , opening the field of clinical trials to benefit both canine and human medicine .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"sequencing",
"techniques",
"genome-wide",
"association",
"studies",
"animal",
"types",
"vertebrates",
"pets",
"and",
"companion",
"animals",
"dogs",
"animals",
"mammals",
"long",
"non-coding",
"rnas",
"genome",
"analysis",
"mammalian",
"genomics",
"molecular",
"biology",
"techniques",
"rna",
"sequencing",
"zoology",
"research",
"and",
"analysis",
"methods",
"artificial",
"gene",
"amplification",
"and",
"extension",
"molecular",
"biology",
"genetic",
"loci",
"animal",
"genomics",
"biochemistry",
"rna",
"nucleic",
"acids",
"polymerase",
"chain",
"reaction",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"non-coding",
"rna",
"amniotes",
"computational",
"biology",
"organisms",
"human",
"genetics"
] |
2016
|
A Point Mutation in a lincRNA Upstream of GDNF Is Associated to a Canine Insensitivity to Pain: A Spontaneous Model for Human Sensory Neuropathies
|
All cells must adapt to rapidly changing conditions . The heat shock response ( HSR ) is an intracellular signaling pathway that maintains proteostasis ( protein folding homeostasis ) , a process critical for survival in all organisms exposed to heat stress or other conditions that alter the folding of the proteome . Yet despite decades of study , the circuitry described for responding to altered protein status in the best-studied bacterium , E . coli , does not faithfully recapitulate the range of cellular responses in response to this stress . Here , we report the discovery of the missing link . Surprisingly , we found that σ32 , the central transcription factor driving the HSR , must be localized to the membrane rather than dispersed in the cytoplasm as previously assumed . Genetic analyses indicate that σ32 localization results from a protein targeting reaction facilitated by the signal recognition particle ( SRP ) and its receptor ( SR ) , which together comprise a conserved protein targeting machine and mediate the cotranslational targeting of inner membrane proteins to the membrane . SRP interacts with σ32 directly and transports it to the inner membrane . Our results show that σ32 must be membrane-associated to be properly regulated in response to the protein folding status in the cell , explaining how the HSR integrates information from both the cytoplasm and bacterial cell membrane .
The heat shock response ( HSR ) maintains protein homeostasis ( proteostasis ) in all organisms . The HSR responds to protein unfolding , aggregation , and damage by the rapid and transient production of heat shock proteins ( HSPs ) and by triggering other cellular protective pathways that help mitigate the stress . Although the specific HSR is tailored to each organism , chaperones that mediate protein folding and proteases that degrade misfolded proteins are almost always included in the core repertoire of induced protein and are among the most conserved proteins in the cell . These HSPs maintain optimal states of protein folding and turnover during normal growth , while decreasing cellular damage from stress-induced protein misfolding and aggregation . Malfunction of the HSR pathway reduces lifespan and is implicated in the onset of neurodegenerative diseases in higher organisms [1]–[3] . In E . coli and other proteobacteria , σ32 mediates the HSR by directing RNA polymerase to promoters of HSR target genes [4]–[9] . Given the importance of this response and the necessity for a rapid but transient increase in expression of HSPs , it is not surprising that regulation of the HSR across organisms is complex . σ32 is positively regulated by a feed-forward mechanism in which exposure to heat melts an inhibitory mRNA structure enabling high translation of σ32 mRNA [10] , [11] and is negatively regulated by two feedback loops [12] mediated through members of the σ32 regulon ( Figure 1A ) . σ32 activity is coupled to the cellular protein folding state via a negative feedback loop executed by the two major chaperone systems , DnaK/J/GrpE and GroEL/S . There is extensive support for the model that free chaperones directly inactivate σ32 and that these chaperones are titrated by unfolded proteins that accumulate and bind chaperones during a HSR . Depletion of either chaperone system or overexpression of chaperone substrates leads to an increase in σ32 activity , and conversely , overexpression of either chaperone system decreases σ32 activity [13] , [14] . Inhibition is likely direct , as DnaK/J and GroEL/S bind σ32 in vitro and inhibit its activity in a purified in vitro transcription system [13] , [15]–[17] . σ32 stability is controlled by the inner membrane ( IM ) protease FtsH: deletion of the protease stabilizes σ32 [18]–[20] , and FtsH degrades σ32 in vitro , albeit slowly [18] , [20] . DnaK/J and GroEL/S also regulate stability , as their depletion leads to σ32 stabilization in vivo [13] , [14] , [21] , although this finding has not yet been recapitulated in vitro [22] . Despite the regulatory complexity of the current model , it inadequately addresses two issues that are central to our understanding of the circuitry controlling the HSR , motivating us to search for additional players in the response: ( 1 ) Exhaustive genetic screens for mutations in σ32 that result in misregulation have identified a small cluster of four closely spaced amino acid residues ( Leu47 , Ala50 , Lys51 , and Ile54 ) , of which three are surface exposed , as well as a somewhat distant fifth residue that abuts this patch in the folded σ32 structure . When these residues are mutated , cells have both increased level and activity of σ32 , indicating that this region is involved in a central process required for operation of the negative feedback loops that control both the activity and degradation of σ32 ( Figure 1A ) [23]–[25] . However , the phenotypes of these mutants are not recapitulated in vitro , where both FtsH degradation and chaperone-mediated inactivation of mutant and WT σ32 are experimentally indistinguishable [25] , [26] . Thus , we do not understand how this “homeostatic control region” of σ32 functions . ( 2 ) σ32 is thought to monitor the folding status of IM proteins as well as cytoplasmic proteins , but the mechanism for this additional surveillance is unknown . Their close connection is indicated because ( 1 ) the IM protease , FtsH , not only degrades σ32 , but also maintains quality control in the IM by degrading unassembled IM proteins; ( 2 ) induction of the HSR is a very early response to perturbations in the co-translational membrane-trafficking system that brings ribosomes translating IM proteins to the membrane [27]–[29]; and ( 3 ) IM proteins are significantly overrepresented both in the σ32 regulon [30] and in an unbiased overexpression screen for HSR inducers [30] . In this report , we identify the co-translational protein targeting machinery , comprised of the Signal Recognition Particle ( SRP; Ffh protein in complex with 4 . 5S RNA; Figure 2A ) and the SRP Receptor ( SR; FtsY ) , as a regulator of σ32 . We show that SRP preferentially binds to WTσ32 compared to a mutant σ32 with a defective homeostatic control region . We further show that a fraction of σ32 is associated with the cell membrane and that both the SRP-dependent machinery and the homeostatic control region of σ32 are important for this localization . Lastly , the regulatory defects in HSR circuitry caused by mutation of either the σ32 homeostatic control region or the co-translational targeting machinery are circumvented by artificially tethering σ32 to the IM . We propose that SRP-dependent membrane localization is a critical step in the control circuitry that governs the activity and stability of σ32 . Membrane localization is widely used to control σ factors , but this is the first case where the IM-localized state is used for dynamic regulation rather than as a repository for an inactive protein .
To identify additional players involved in activity control of σ32 , we carried out a genetic screen for transposon mutants with increased σ32 activity under conditions that inactivate σ32 in wild-type cells ( see Methods ) . To impose a condition that mimics the negative feedback control of σ32 , the DnaK/J chaperones were overexpressed from an inducible promoter at their chromosomal locus . Under these conditions , a σ32-regulated lacZ chromosomal reporter ( PhtpG-lacZ ) is expressed so poorly that cells do not make sufficient β-galactosidase to turn colonies blue on X-gal indicator plates . We screened for blue colonies , indicative of a defect in σ32 inactivation . A conceptually similar screen previously identified mutations in the DnaK/J chaperones—key negative regulators of the σ32 response [31] . In addition to re-identifying these components , we found an insertion in the promoter region of ftsY ( pftsY::Tn5 ) , located 39 bp upstream of the ftsY open reading frame . The pftsY::Tn5 strain had a 3- to 4-fold reduction in the level of FtsY , the SR , and a ∼7-fold increase in the activity and amount of σ32 relative to WT ( Table 1 ) . Defects were complemented by a plasmid carrying ftsY . Unlike WT , in the pftsY::Tn5 strain σ32 activity did not respond to increased chaperone expression . Upon chaperone overexpression in WT cells , the specific activity ( S . A . ) of σ32 fell to 0 . 3 , relative to that in cells growing without chaperone overexpression . In contrast , upon chaperone overexpression in pftsY::Tn5 cells , the S . A . of σ32 did not change , suggesting a defect in chaperone-mediated activity control in that strain ( Table 1 ) . This finding raised the possibility that the high activity of σ32 in pftsY::Tn5 resulted from disruption of activity control of σ32 , rather than reflecting a cellular response to accumulation of unassembled membrane proteins . We tested whether σ32 binds to either FtsY ( SR ) or to Ffh , the protein component of SRP . Ffh is a two-domain protein , comprised of an M-domain that binds the signal sequence and 4 . 5S RNA , and an NG-domain that binds to SR , the ribosome , and GTP ( Figure 2A ) . We first used co-immunoprecipitation analysis . Interacting proteins were immunoprecipitated with antibodies against either FtsY or Ffh and , following resolution on SDS-PAGE , antibodies against σ32 or σ70 were used to probe for the presence of these proteins . σ32 was detected in the immunoprecipitations ( Figure 2B , lanes 7 and 8 ) , and this signal was dependent on the presence of σ32 in the strain ( Figure 2B , lanes 1–4 ) . By contrast , σ70 , although much more abundant than σ32 in the cell , did not interact with either SRP or SR ( Figure 2B , Lanes 3 , 4 and 7 , 8 ) , indicating that interaction with SRP is not a general property of σs . It was not surprising that σ32 was co-immunoprecipitated with both SRP and SR , as the latter two components interact in vivo . To determine the direct binding partner of σ32 , purified Ffh and FtsY were resolved on SDS-PAGE , transferred to nitrocellulose , and incubated with purified σ32 . Antibodies against σ32 detected σ32 present at the molecular weight corresponding to Ffh but not SR ( Figure 2C ) . In a reciprocal experiment , purified σ32 was resolved on SDS-PAGE , transferred to nitrocellulose , and incubated with purified Ffh or SR . Ffh , but not SR , bound σ32 ( unpublished data ) . Similar studies did not reveal an interaction between σ70 and either Ffh or SR ( unpublished data ) . We determined which Ffh domain binds σ32 by partially-proteolyzing Ffh to produce an 18 kDa M-domain and a 38 kDa NG-domain , resolving the mixture by SDS-PAGE , transferring to nitrocellulose , and probing with σ32 . σ32 was detected at the position of full-length Ffh and the M-domain , but not at the position of the NG-domain ( Figure 2D ) , indicating that the M-domain contains the determinants mediating the σ32-interaction . We used in vivo crosslinking to validate the direct interaction of SRP ( Ffh+4 . 5S RNA ) and σ32 . We created a σ32 derivative with an N-terminal 6×HIS-tag and a photoreactive amino acid analog ( pBPA ) at amino acid position 52 ( 6×HIS-σ32T52pBPA; see Methods ) , which is active as WTσ32 in expression of the σ32 reporter PhtpG-lacZ ( activity is 150% that of WT; within the range of the variability of the assay; unpublished data ) . Following UV irradiation of whole cells , anti-Ffh immunoblotting of the whole cell lysate detected one predominant crosslinked product , which was dependent on UV-irradiation ( Figure 3A , lanes 1 and 2 ) and pBPA at position 52 ( Figure 3A , lanes 2 and 4 ) . This UV- and pBPA-dependent product was also detected with anti-σ32 immunoblotting ( Figure 3A , lane 6 ) . To determine whether the crosslinked product represented 6×HIS-σ32T52pBPA-Ffh , we determined whether this product was identified both by co-immunoprecipitation with anti-Ffh antisera ( Figure 3B ) and by affinity purification of 6×HIS-σ32T52pBPA on a TALON resin ( Figure 3C ) . Upon immunoprecipitation with anti-Ffh antisera , we detected a single higher molecular mass band , which reacted with both anti-Ffh ( Figure 3B , lane 2 ) and -σ32 ( Figure 3B , lane 6 ) . Upon affinity purification on a TALON resin , anti-Ffh identified the same predominant UV- and pBPA-dependent Ffh-containing crosslinked product ( compare Figure 3B and 3C , lane 2 ) . Importantly , no free Ffh was recovered following TALON purification , indicating that the recovery of the Ffh conjugate was mediated by the covalently linked 6×HIS-σ32 , rather than interaction with either the TALON resin or another protein . These results strongly suggest that σ32 directly interacts with Ffh in vivo . Although only a faint band was seen at the same position using anti-σ32 immunoblotting , this was likely a result of high background in this area of the gel , possibly because of extensive interaction between chaperones and σ32 ( Figure 3C , lanes 5–8 ) . The function of the homeostatic control region of σ32 is not known [25] . I54Nσ32 is a mutation located in this region is severely compromised in both activity and degradation control , but the mechanism responsible for this phenotype had not yet been determined [25] . We therefore compared the binding of WTσ32 and I54Nσ32 to SRP using gel filtration . We incubated WTσ32 or I54Nσ32 either alone or in combination with SRP and subjected the mixture to gel filtration . Analysis of the elution profiles demonstrated that most WTσ32 was shifted towards the higher molecular weight region in the presence of SRP , and additionally , a fraction of σ32 eluted at a higher molecular weight than that of SRP alone , indicative of an SRP–σ32 complex [compare A280 profiles of σ32 , SRP , and SRP-σ32 ( Figure 4A ) with immunoblotting for σ32 ( Figure 4B; rows 1 , 2 ) ] . σ32 present at a molecular weight between σ32 and SRP likely represents transient forms of the σ32–SRP complex . In sharp contrast , an interaction between I54Nσ32 with SRP was almost undetectable [compare A280 profiles of I54Nσ32 and SRP ( Figure 4A ) with immunoblotting for I54Nσ32 ( Figure 4B; rows 3 , 4 ) ] , indicating that I54Nσ32 bound more weakly to SRP than WTσ32 . Neither WTσ32 nor I54Nσ32 interacted detectably with Ffh , indicating that differential binding is dependent on the formation of SRP ( Ffh+4 . 5S RNA ) , the biologically relevant cellular species of Ffh . The biological function of SRP is co-translational protein targeting , leading us to test whether σ32 may be targeted to the IM through an SRP-dependent mechanism . Rapid degradation by FtsH normally keeps σ32 levels very close to the detection limit ( ∼20–50 molecules/cell; [8] ) , making reproducible detection following fractionation very difficult . Therefore , we performed fractionation experiments ( Figure 5 ) , either in cells expressing an enzymatically inactive mutant of the FtsH protease ( FtsH E415A ) or in cells lacking FtsH altogether ( ΔftsH ) . Approximately 44% of σ32 fractionated to the membrane in a ΔftsH strain , and this fraction was increased to ∼58% in the FtsH E415A strain , raising the possibility that FtsH itself may participate in retention of σ32 at the IM . As the β′ subunit of RNA polymerase , a known interaction partner of σ32 , also fractionated with the membrane , we next tested whether σ32 association with the IM was dependent on its association with RNA polymerase . To this end , we used σ32Δ21aa , which is defective in interacting with RNA polymerase [32] . We confirmed that σ32Δ21aa did not detectably interact with RNA polymerase ( Figure S1A , B ) . Yet endogenous WTσ32 and ectopically expressed σ32Δ21aa fractionated equivalently to the IM both in ΔftsH cells ( ∼39% ) and in FtsH E415A cells ( ∼58% ) ( Figure S2 ) , indicating that σ32 transited to the membrane independent of RNA polymerase . We next tested whether the pftsY::Tn5 mutation or the homeostatic control region mutation of σ32 disrupted membrane partitioning of σ32 . Both WTσ32 and ectopically expressed σ32Δ21aa were defective in partitioning to the IM in pftsY::Tn5 cells ( Figure 5 ) . To look at the effect of disrupting the homeostatic control region on membrane fractionation , we expressed I54Nσ32 as a σ32Δ21aa variant ( I54Nσ32Δ21aa ) . The size difference allowed us to compare I54Nσ32Δ21aa and WTσ32 in the same cells ( Figure S2 ) . Whereas WTσ32 exhibited normal fractionation , I54Nσ32Δ21aa showed a severe localization defect , comparable to that of pftsY::Tn5 cells ( Figure 5 ) . We conclude that σ32 targeting to the IM is dependent on both SRP/SR and the σ32 homeostatic control region . SecA is an ATP-fueled motor protein that recognizes signal peptides , drives the translocation of secreted proteins through the Sec translocon [33]–[37] , and collaborates with the SRP/SR for integration of a subset of IM proteins into the membrane [33] , [38] . We previously found that σ32 activity is increased in a SecA ( ts ) strain [39] . This observation motivated us to explore the relationship of SecA to IM trafficking of σ32 . Indeed , using a SecA ( ts ) mutant with general defects in protein export ( SecAL43P ) [40] , [41] , we observed that cells displayed a significant defect in membrane localization of σ32 ( Figure 5 ) , as well as increased σ32 activity ( [39] and unpublished data ) . In addition , purified SecA , resolved on SDS-PAGE and transferred to nitrocellulose , showed binding affinity for σ32 , suggesting that these two proteins interact ( Figure S3 ) . We conclude that SecA participates in trafficking of σ32 to the IM . SecY forms the core of the SecYEG IM translocon . This multidomain protein has a large cytoplasmic domain ( C5 ) that functionally interacts with SR [42] , SecA , and the ribosome [43]–[50] ( Figure 6A ) . We tested whether 10 previously described secY mutations located in various domains of SecY ( Figure 6A ) [51] perturb chaperone-mediated control of σ32 activity and trafficking of σ32 to the IM ( Figure 6B ) . All mutants had enhanced σ32 activity . This result was not surprising as secY mutants are expected to accumulate secretory protein precursors that titrate chaperones [52] . Importantly , four mutants ( secY124 , secY351 , secY40 , secY129 ) were also defective in chaperone-mediated control of σ32 activity ( Figure 6B ) , as indicated by a lack of down-regulation of σ32 activity in response to overexpression of one or both of the chaperone systems . We examined the secY351 mutant , which had both high σ32 activity and a significant defect in chaperone-mediated inactivation , and found it to be defective in IM trafficking of σ32 ( Figure 5 ) . secY40 and secY351 affect domain C5 ( Figure 6A ) , implicated in the interaction of SecY with SR , raising the possibility that this interaction is important for both homeostatic control and IM targeting of σ32 . Alkaline phosphatase is active only in the periplasm , where it forms the disulfide bonds necessary for its activity . Therefore , translational fusions to alkaline phosphatase ( PhoA ) lacking its own export signal are commonly used as an indicator of membrane targeting by the appended N-terminal sequence [53] . If the appended N-terminal sequence has either an export or insertion sequence , the fusion protein will exhibit alkaline phosphatase activity in vivo because it is partly transported to the periplasmic side of the membrane through the SecYEG translocon . Although σ32 has neither a membrane insertion nor an export sequence , it may contain a sequence that targets it to the cytosolic face of the IM . There is some evidence that the secretory apparatus can recognize the mature domains of exported proteins at low efficiency [54] . If so , proximity of PhoA to the translocon resulting from the IM targeting signal might enable transit of some fraction of PhoA to localize to the periplasmic side of the membrane , where it is active . By random insertion of the transposon probe TnphoA into rpoH , encoding σ32 ( see Materials and Methods ) , we found that a phoA fusion to the first 52 amino acids of σ32 ( N52-σ32-PhoA ) showed ∼10-fold greater PhoA activity than signal-less PhoA itself , indicating that the N-terminus of σ32 facilitates PhoA export ( Table 2 ) . Moreover , PhoA activity enhancement is dependent both on the SRP/SR-dependent trafficking system and on SecY , as both pftsY::Tn5 and secY351 decreased the PhoA activity ∼2-fold , whereas leaderless PhoA exhibited little response to these perturbations ( Table 2 ) . Thus , this assay is consistent with the idea that the N-terminus of σ32 carries an IM-trafficking sequence and that the targeting process is dependent on SRP and SecY . The I54Nσ32 mutant and mutants in the IM-targeting machinery ( pftsY::Tn5 , secA ( ts ) , secY351 ) were both defective in proper regulation of σ32 and in σ32 association with the IM . This convergence motivated us to test whether artificially tethering σ32 to the IM could restore homeostatic control . To this end , we exploited the bacteriophage Pf3 coat protein . With the addition of three leucine residues in its membrane-spanning region , 3L-Pf3 translocates spontaneously in an orientation-specific manner to the IM , where it inserts in an N-out/C-in orientation [55] . We modified rpoH ( encoding σ32 ) at its chromosomal locus to encode a σ32 variant with the 3L-Pf3 membrane-insertion signal attached to its N-terminus ( schematized in Figure S4A ) . Strains carrying 3L-Pf3-σ32 ( IM-WTσ32 ) or 3L-Pf3-I54Nσ32 ( IM-I54Nσ32 ) as their sole source of σ32 were viable , even though 99% of IM-WTσ32 was inserted in the membrane as judged by fractionation studies ( Figure S4B ) . Thus , σ32 functions when it is tethered to the IM . We determined whether IM-WTσ32 was subject to homeostatic control circuitry exhibited by WTσ32 . σ32 is maintained at a low level by FtsH degradation , and its activity is decreased by chaperone-mediated inactivation . Both phenotypes are evident by comparing the amount and activity of σ32 in a WT versus a ΔftsH strain . In a ΔftsH strain , the level of WTσ32 increases ∼30-fold because the major protease degrading σ32 is removed ( Table 3; Figure S5 [compare lanes 1 and 3]; and [25] ) . However , the activity of σ32 increases only 3-fold as a consequence of chaperone-mediated activity control , leading to a 10-fold reduction in the S . A . of σ32 in ΔftsH cells relative to that in WT cells ( Table 3 and [56] ) . Both the level and S . A . of WTσ32 and IM-WTσ32 were closely similar in a ΔftsH strain , indicating that the chaperone-mediated activity control circuit is active in IM-WTσ32 ( Table 3 and Figure S5 [compare lanes 3 and 4] ) . Additionally , the level of IM-WTσ32 was significantly lower in ftsH+ than in a ΔftsH strain , indicating that IM-WTσ32 was efficiently degraded by FtsH ( Table 3 and Figure S5 [compare lanes 2 and 4] ) . The presence of a contaminating band prevented absolute quantification of IM-WTσ32 levels via Western blot analysis ( Figure S5 ) . However , if the relative S . A . of IM-WTσ32 and WTσ32 are equivalent in the ftsH+ strain as we found in the ΔftsH strain , then the 2-fold decrease in activity of IM-WTσ32 relative to WTσ32 implies a slight increase in the rate of degradation of IM-WTσ32 relative to WTσ32 . Note that the 3L-Pf3 membrane-insertion tag itself is not a signal for FtsH degradation , as the stability of the FliA σ factor , which is closely related to σ32 , was unchanged when expressed as 3L-Pf3-FliA ( Figure S6 ) . In summary , both the chaperone-mediated activity control circuit and the FtsH-mediated degradation control circuit are active on IM-tethered σ32 . Next , we asked whether the forced and stable tethering of σ32 to the IM bypassed the regulatory defects of I54Nσ32 and the reduced-level SR mutant pftsY:::Tn5 . I54Nσ32 is degraded poorly by FtsH as its level was 11-fold higher than that of WTσ32 ( Table 3; Figure S5 [compare lanes 1 and 6] and [25] ) . I54Nσ32 also had compromised chaperone-mediated activity control as the high chaperone levels in this strain did not reduce the S . A . of I54Nσ32 ( Table 3; and [25] ) . In stark contrast , both degradation and activity control were restored when I54Nσ32 was converted to IM-I54Nσ32 . FtsH efficiently degraded the membrane-tethered variant: IM-I54Nσ32 was undetectable in ftsH+ cells but present at a high level in ΔftsH cells ( Table 3 and Figure S5 [compare lanes 5 and 7] ) . Additionally , IM-I54Nσ32 and IM-WTσ32 exhibited comparable reductions in relative S . A . of σ32 in ΔftsH cells ( Table 3 ) . Stable tethering of σ32 to the IM also bypassed the regulatory defects of pftsY::Tn5 as IM-WTσ32 in the reduced-level SR background was degraded and subject to chaperone-mediated activity control . Indeed , IM-WTσ32 behaved identically in WT and pftsY::Tn5 strains , exhibiting comparable σ32 activity at a protein level below detection ( Table 3 and Figure S5 [compare lanes 8 and 9] ) . Finally , IM-tethering relieved the growth defects of both I54Nσ32 ( Figure S7A and C ) and of pftsY::Tn5 ( Figure S7B , C , and D ) . In summary , stable tethering of σ32 to the IM restored both homeostatic control and normal growth to cells with a defective σ32 homeostatic control region and to cells with a compromised SRP/SR co-translational targeting apparatus .
Our work has led to a revised model of the HSR circuitry ( Figure 1B ) . σ32 first transits to the IM via an SRP/SR-dependent process and is then subjected to the chaperone-mediated activity control and FtsH-mediated degradation control that have been previously described . This revised model enables the homeostatic control circuit to integrate information on both cytosolic and IM status . Importantly , the efficiency of co-translational protein targeting depends on the cumulative effect of multiple SRP checkpoints including differences in cargo binding affinities , kinetics of SRP-SR complex assembly , and GTP hydrolysis [57] . Multiple checkpoints and the fact that SRP is sub-stoichiometric relative to translating ribosomes ( ∼1∶100; SRP molecules to translating ribosomes [58] ) may allow SRP to modulate the extent of IM-localization of σ32 during times of stress and/or increased protein flux . Thus , σ32 down-regulation through its localization to the membrane could be alleviated when the IM is disturbed or SRP is overloaded in assisting membrane protein biogenesis . This feed-forward mechanism allows the σ32 homeostatic control to sense the state of cytosolic and IM proteostasis before unfolded proteins accumulate to a significant extent . Interestingly , ffh ( encoding the protein subunit of the SRP ) is a σ32 regulon member as its expression increases at least 3-fold following induction of σ32 either by heat shock or by deletion of dnaK/J ( [30] and unpublished data ) . This could provide an additional connection between σ32 and protein flux to the IM . Finally , and more speculatively , given the demonstrated involvement of SecA in IM targeting of σ32 and its direct interaction with σ32 , the σ32 homeostatic control circuit may also monitor protein flux through SecA to the periplasm and outer membrane . The idea that the high activity of σ32 in the I54Nσ32 homeostatic control mutant and in SRP/SR mutants ( eg . pftsY::Tn5 ) results from σ32 mislocalization to the cytosol and consequent homeostatic dysregulation , rather than from chaperone titration by a buildup of unfolded proteins , is supported by our data . First , forced IM-tethering overcomes the inviability of the I54Nσ32 mutation in the ΔftsH strain background ( Table 3 ) , as well as the growth defects of I54Nσ32 and pftsY::Tn5 ( Figure S7 ) , suggesting that high expression of σ32 is aberrant and deleterious to cells , rather than required to remodel misfolded proteins . This is reminiscent of previous findings that reduced-function σ32 mutants suppress physiological defects of a ΔdnaK strain [59] and that overexpression of HSPs was deleterious to growth [13] , [60] . Second , secY mutants dysregulated in chaperone-mediated activity control were not distinguished by their extent of σ32 induction . This is contrary to the prediction of the chaperone titration model , which posits that secY mutants with the highest σ32 induction would have the highest level of unfolded proteins . These mutants would then be refractory to activity control because the additional chaperones resulting from chaperone overexpression would actually be needed to remodel the misfolded protein burden . We conclude that homeostatic dysregulation of σ32 results from σ32 mislocalization , rather than from the buildup of unfolded proteins . The molecular nature of IM-localized σ32 remains unclear . Prediction programs [61] , [62] do not detect either a signal peptide-like or transmembrane sequence in σ32 . We favor the idea that following transit to the IM , σ32 is maintained at the membrane via interactions with other proteins and/or lipid head groups during its short half-life in the cell ( 30–60″ ) . Indeed , we have already demonstrated interactions between σ32 and several membrane-associated or IM proteins , including SRP , SecA , and FtsH itself . Moreover , the chaperone systems regulating σ32 ( DnaK/J/GrpE and GroEL/S ) show partial distribution to the membrane [63]–[68] , whereas other potential membrane-associated protein partners have not yet been tested for σ32 interaction ( e . g . , SecY and additional members of the Sec machinery ) . Each of these proteins could result in partial membrane localization of σ32 , as was shown for FtsH where deletion of the protein decreased localization relative to cells with the protease-dead mutation FtsH E415A . Importantly , if σ32 is membrane associated via transient protein–protein and/or protein–lipid interactions , some σ32 may dissociate from the membrane during cell lysis , as was demonstrated for FtsY , another peripheral membrane protein [69] , [70] . Therefore , although we report that ∼50% of σ32 is membrane-associated , the fraction of σ32 that is actually IM-localized may be significantly higher . IM-associated σ32 may provide regulatory flexibility not possible for IM-tethered σ32 . For example , during times of high stress , σ32 may be able to dissociate from the membrane to escape homeostatic control . These excursions could be transient if SRP were able to transport σ32 posttranslationally , a possibility suggested by the fact that full-length , fully folded σ32 binds to SRP ( Figures 2 and 3 and Figure S1 ) . Additionally , IM-tethered σ32 is more rapidly degraded than IM-associated σ32 , suggesting that tethering makes σ32 a better FtsH substrate . This could diminish the ability of the cell to regulate the rate at which FtsH degrades σ32 , which is of physiological significance during temperature upshift [8] . The transient reduction in σ32 degradation following increased temperature contributes significantly to the rapid build-up of σ32 during heat shock [8] . Membrane localization is widely used to control σ factors [71] , [72] . The inactive B . subtilis SigK pro-protein is membrane inserted; cleavage of its N-terminal pro-sequence releases SigK [73] , [74] . Cleavage is coordinated with passage of a checkpoint in spore development to provide just-in-time SigK activity [75] . Additionally , many σ factors are held in an inactive state at the membrane by cognate membrane-spanning anti-σ factors and released as transcriptionally active proteins when stress signals lead to degradation of their anti-σ [71] , [76] . IM-localization of σ32 serves a conceptually distinct role as σ32 is equally active in the cytoplasm or at the IM . Instead , the localization process itself is the key regulatory step in two ways: localization is both regulated by protein folding status and is prerequisite for proper function of the homeostatic control circuit . The SRP-SR co-translational targeting system has an important role in maintaining proteostasis . SRP-SR minimizes aggregation and misfolding of the approximately 20%–30% of proteins destined for the IM , by making their translation coincident with membrane insertion . Our finding , that SRP/SR-mediated transit of σ32 to the IM is also critical for proper control of the HSR , points to a significant new regulatory role for the co-translational targeting apparatus in protein-folding homeostasis . This finding also raises important mechanistic questions . Our in vitro interaction results suggest a direct , but weak , interaction between full-length σ32 and the M-domain of SRP . The prevailing paradigm suggests that the M-domain interacts only with nascent polypeptides with particularly hydrophobic signal sequences . It is possible that σ32 is detected co-translationally , as the Region 2 . 1 N-terminal α-helical structure , which resembles a hydrophobic signal sequence , may be recognized by the SRP . Alternatively , we note that the SRP chloroplast homolog ( cpSRP54 ) has a dedicated posttranslational targeting mechanism for several fully translated membrane proteins [77] , and E . coli SRP , alone or in combination with additional accessory factors ( e . g . , other σ32 interactors , such as chaperones or SecA ) , may target mature σ32 to the membrane in vivo . It remains to be determined whether an interaction between full-length σ32 and SRP , or a novel co-translational targeting interaction by the SRP-SR system , mediates transit of σ32 to the membrane .
All strains used were derivatives of the E . coli K-12 strain MG1655 , CAG48238 [25] , [39] . For chaperone overexpression experiments , mutations were transduced with phage P1 into strains carrying chromosomal Para-groEL/S [78] or PA1/lacO-1-dnaK/J-lacIq [14] . Mutant alleles in secY [51] and secA [39] were transferred to various strain backgrounds through P1 transduction . The SecAL43P mutant used here is a SecA ( ts ) allele , with general defects in protein export [40] , [41] . For propagation and transfer of the R6K pir plasmid , pKNG101 , strains DH5σ λpir and SM10 λpir were used , respectively . Plasmids pET21a and pTrc99A were used as expression plasmids . For construction of pRM5 ( 6×HIS-rpoH ) , the rpoH gene was PCR-amplified from the chromosomal DNA of W3110 and cloned into the EcoRI-SalI sites of pTTQ18 [79] . Then , the T52amber mutation was introduced into pRM5 by site-directed mutagenesis , yielding pRM17 ( 6×HIS-σ32T52amber ) . pEVOL-pBpF ( Addgene ) carried evolved Methanocaldococcus jannaschii aminoacyl-tRNA synthetase/suppressor tRNA for incorporation of a photoreactive amino acid analog , p-benzoylphenylalanine ( pBPA ) , into the amber codon site . All strains were grown in LB medium . When required , antibiotics were added to the medium as follows: 100 µg/ml ampicillin , 30 µg/ml kanamycin , 20 µg/ml chloramphenicol , and 25 µg/mL streptomycin . Strain CAG48275 [25] , which is ΔlacX74 , contains the prophage JW2 ( PhtpG-lacZ ) , and a chromosomal dnaK/J locus driven from PA1/lacO-1 under control of lacIq [14] was grown in LB , induced with 1 mM IPTG to overexpress DnaK/J chaperones , treated with Tn5 , and plated at 30°C on X-gal indicator plates containing kanamycin to select for strains containing Tn5 . Blue colonies were picked and tested for higher σ32 activity and for feedback resistance to excess DnaK/J [25] . Tn5 insertion sites were determined by DNA sequencing . Overnight cultures ( LB medium ) were diluted 250-fold and grown to exponential phase ( OD600 = 0 . 05–0 . 5 ) . Samples were taken at intervals starting at OD600 = 0 . 05 , and σ32 activity was monitored by measuring β-galactosidase activity expressed from the σ32-dependent htpG promoter , as done previously [25] . The following proteins were purified essentially as described: 6×H-tagged , Strep-6×H-tagged , and untagged WTσ32 or I54Nσ32 [80] , FtsY , Ffh , 4 . 5S RNA [81] , and SecA [82] . Chaperones were removed from σ32 with an additional wash containing 10 mM ATP , 10 mM MgCl2 , and 25 uM of both peptides , CALLLSAARR and MQERITLKDYAM , synthesized by Elim Biopharmaceuticals , Inc ( Hayward , CA ) . Cells were grown to OD600∼0 . 35 in LB medium at 30°C , harvested , washed two times with 1× PBS , resuspended in Lysis Buffer ( 20 mM Hepes-KOH , 150 mM NaCl , 10 mM EDTA , 10% glycerol , pH 7 . 5 ) , and lysed by passing 4× through an Avestin EmulsiFlex-C5 cell homogenizer at 15 , 000 psi . Cellular debris was spun out and the supernatants were incubated with anti-Ffh or anti-FtsY antibodies at 4°C for 14 h by rotation . TrueBlot anti-Rabbit Ig IP Beads ( eBioscience ) were added and the supernatants rotated for an additional 2 h at 4°C . Immunocomplexes were isolated by centrifugation and washed 5× in Lysis Buffer without EDTA , and eluted in TCA Resuspension Buffer ( 100 mM Tris ( pH 11 . 0 ) , 3% SDS ) containing LDS Sample Buffer ( Invitrogen ) . Proteins were separated by 10% SDS-PAGE , analyzed by immunoblotting using anti-σ70 and anti-σ32 antibodies , and imaged using fluorescent secondary antibodies ( as described below ) . Detection of a direct protein–protein/domain interaction was carried out exactly as previously described [83] . Proteins were separated on 10% SDS-PAGE . Partially proteolyzed Ffh was obtained by incubating 400 µg of purified Ffh with 4 µg of Glu-C endopeptidase ( New England Biolabs ) at 25°C in 10 mM Na-HEPES ( pH 7 . 5 ) , 150 mM NaCl , 1 mM DTT , 10 mM MgCl2 , and 10% glycerol . An aliquot of the reaction was taken out at various times ( 0 , 5 , 10 , 15 , 30 , 45 , 60 , 120 , 180 , and 330 min ) and stopped by addition of 5× volume of 5× SDS-sample loading buffer . The samples were then analyzed by blot overlay with σ32 as the probe . In vivo crosslinking experiments were carried out essentially as described previously [84] . Strains of CAG48238 carrying pEVOL-pBpF were further transformed with pRM5 or pRM17 . Cells were grown at 30°C in L medium containing 0 . 02% arabinose and 1 mM pBPA , induced with 1 mM IPTG for 1 h , and UV-irradiated for 0 or 10 min at 4°C . For analysis of whole cell samples , total cellular proteins were precipitated with 5% trichloroacetic acid , solublized in SDS sample buffer , and analyzed by 7 . 5% SDS-PAGE and immunoblotting . Co-immunoprecipitations were carried out as follows: UV-irradiated cells were suspended in 10 mM Tris-HCl ( pH 8 . 1 ) and disrupted by sonication at 0°C . After removal of total membranes by ultracentrifugation , proteins were precipitated with 5% trichloroacetic acid , washed with acetone , and solubilized in buffer containing 50 mM TrisHCl ( pH 8 . 1 ) , 1% SDS , 1 mM EDTA . The samples were then diluted 33-fold with NP40 buffer ( 50 mM TrisHCl ( pH 8 . 1 ) , 150 mM NaCl , 1% NP40 ) . After clarification , supernatants were incubated with anti-Ffh antibodies and TrueBlot anti-Rabbit Ig IP Beads ( eBioscience ) at 4°C for 13 h with rotation . Immunocomplexes were isolated by centrifugation , washed 2 times with NP40 buffer and then once with 10 mM TrisHCl ( pH 8 . 1 ) , and dissolved in SDS sample buffer . Proteins were separated by 7 . 5% SDS-PAGE and analyzed by immunoblotting using anti-Ffh and anti-σ32 antibodies , TrueBlot anti-Rabbit IgG ( eBioscience ) , and Can Get Signal immunoreaction enhancer solution ( TOYOBO Life Science , Japan ) . For 6×HIS-tag affinity isolation , UV-irradiated cells were suspended in 10 mM Tris-HCl ( pH 8 . 1 ) containing 6 M urea and disrupted by sonication at 0°C . After clarification by ultracentrifugation , the soluble fraction was loaded onto the TALON resin ( TAKARA BIO , Inc . , Japan ) . After washing the resin with wash buffer ( 50 mM TrisHCl ( pH 7 . 0 ) , 300 mM KCl , 6 M urea , 20 mM imidazole ) , bound proteins were eluted with wash buffer containing 300 mM imidazole . Proteins were precipitated with 5% trichloroacetic acid , solublized in SDS sample buffer , and analyzed by 7 . 5% SDS-PAGE and immunoblotting . Purified proteins were run on a Superdex 200 PC 3 . 2/30 column , pre-equilibrated with Buffer A ( 20 mM Tris-HCl pH 8 . 0 , 150 mM NaCl , 10 mM MgCl2 , 2 mM DTT ) . Purified proteins or protein complexes were run with Buffer A at a flow rate of 40 µL/min , and collected fractions were analyzed by SDS-PAGE and immunoblotting for σ32 . SRP was formed by incubating purified Ffh with 1 . 5× molar excess of purified 4 . 5S RNA on ice for 10 min . To form SRP-σ32 complexes , 3 µM of purified WTσ32 or I54Nσ32 was mixed with 10× molar excess of SRP; proteins were incubated on ice for 30 min before analysis by gel filtration . A 52-σ32-Tn5PhoA fusion was initially isolated by random screening for PhoA+ clones on PhoA indicator plates—using a strain carrying a TnphoA transposon probe [85] on a low-copy plasmid and Plac-rpoH ( encoding σ32 ) on a multicopy plasmid . The fusion used in this article ( N52-σ32-PhoA lacking the transposon but containing the first 52 amino acids of WTσ32 ) was subsequently constructed by standard recombinant DNA techniques . Direct construction of fusions past amino acid 52 of σ32 was very unstable , precluding their analysis . Cells were grown to OD600 = 0 . 3–0 . 4 , harvested , and resuspended in ice-cold Buffer B ( 10 mM Tris-Acetate ( pH 7 . 4 ) , 10 mM Mg ( OAc ) 2 , 60 mM NH4Cl , 1 mM EDTA , supplemented with 1 mM PMSF ) to an OD600 of 15 . Cells were immediately lysed by passaging the extracts through an Avestin EmulsiFlex-C5 cell homogenizer at 15 , 000 psi , and subjected to low-speed centrifugation to remove cell debris and un-lysed cells . Membranes were collected by ultracentrifugation in an Optima benchtop centrifuge ( Beckman–Spinco ) with a TLA 100 . 3 rotor ( 60 min; 52 , 000 rpm; 4°C ) . The supernatant was saved as the soluble fraction , while the pellet was washed 3× with Buffer B and then resuspended in Buffer C ( 50 mM HEPES-KOH pH 7 . 6 , 50 mM KCl , 1 mM EDTA , 1 mM EGTA , 0 . 5% n-Dodecyl β-D-maltoside , and 5% glycerol ) . Both the soluble and membrane fractions were precipitated in trichloroacetic acid ( 13% vol/vol ) , incubated on ice for 30 min , and then overnight at 4°C . Precipitated proteins were then washed with ice-cold acetone and analyzed by SDS-PAGE and immunoblotted for σ32 ( Neoclone ) , β′ ( Neoclone ) , σ70 ( Neoclone ) , RseA [86] , and RuvB ( Abcam ) with fluorescent secondary antibodies ( LI-COR Biosciences ) used for detection . The percentage of σ32 in each fraction was determined by direct scanning and analyzing bands with ImageJ software ( National Institutes of Health ) . Cells were grown to OD600 = 0 . 35–0 . 45 , harvested , and resuspended in ice-cold Buffer D ( 50 Tris-HCl , pH 8 . 0 , 0 . 1 mM EDTA , 150 mM NaCl , and 5% glycerol ) to an OD600 of 20 . Lysozyme was added to 0 . 75 mg/mL and cells were incubated on ice for 30 min , followed by sonication , then subjected to low-speed centrifugation to remove cell debris and unlysed cells . Lysates were then incubated with pre-equilibrated , pre-blocked ( Buffer D containing 5% Bovine Serum Albumin , 0 . 1 mg/mL dextran ) Softag 4 Resin ( Neoclone ) overnight at 4°C . Bound proteins were washed 3× with Buffer D and eluted with 4× LDS NuPAGE Buffer ( Life Technologies ) . To collect lysates and eluted proteins , 0 . 05 µM of Strep-6×H-tagged σ32 was added as a loading and blotting control during analysis by SDS-PAGE and Western blotting against σ32 . The 3L-Pf3 genetic sequence was created by carrying out standard polymerase chain reaction using the following overlapping oligos: 5′-atgcaatccgtgattactgatgtgacaggccaactgacagcggtgcaagc-3′ , 5′-taccattggtggtgctattcttctcctgattgttctggccgctgttgtgctggg-3′ , 5′-aaagaattgcgctttgatccagcgaatacccagcacaacagcggccagaa-3′ , and 5′-aagaatagcaccaccaatggtagtgatatcagcttgcaccgctgtcagtt-3′ . The stitched oligos were then cloned using TOPO TA cloning ( Invitrogen ) and sequenced . To construct chromosomal 3L-Pf3-σ32 , PCR was carried out to stitch the 3L-Pf3 gene sequence flanked by the first 500 base pairs of the σ32 open reading frame and 500 base pairs upstream of the start codon , and subsequently cloned into the pKNG101 suicide vector . The 3L-Pf3 sequence was then integrated 5′ and in-frame with the chromosomal rpoH gene by double homologous recombination . Counterselection of sacB on pKNG101 was carried out on 10% sucrose media ( 5 g/L Yeast Extract , 10 g/L Tryptone , 15 g/L Bacto Agar , 10% sucrose ) [25] , [87] . Clones were sequenced to verify chromosomal integration of the 3L-Pf3 sequence in the correct reading frame . To construct pTrc99A expressing 3L-Pf3-FliA , flgM and fliA ( in that order ) were cloned as an operon , with the sequence 5′-ccgtctagaattaaagAGGAGaaaggtacc-3′ added between the two genes in the vector; the Shine-Dalgarno site is designated in uppercase . Two plasmids were created—one with just flgM and fliA , unmodified , and one where the 3L-Pf3 sequence was cloned 5′ to and in-frame with fliA . Clones were sequenced to verify correct sequences and proper reading frame . Expression was from the leaky pTrc promoter , and experiments were only carried out after fresh transformation into the parental CAG48238 strain . Levels of FliA were analyzed by SDS-PAGE and immunoblotting with antibodies against FliA ( Abcam ) . Cells were re-suspended in equal volumes of Buffer C , with the addition of trichloroacetic acid ( final 13% vol/vol ) , kept on ice overnight , and the precipitate collected by centrifugation . Pellets were washed with acetone and resuspended in 1× LDS NuPAGE Buffer ( Life Technologies ) . Serial dilutions of WT and mutant samples were loaded onto a polyacrylamide gel , and proteins transferred to nitrocellulose membranes . The blots were first probed with primary antibodies and then with anti-primary fluorescence-conjugated secondary antibody ( Licor ) . Immunoblots were scanned at the appropriate wavelengths for detection . Fold increase ( protein level experiments ) was estimated by comparison with a dilution series of samples from the WT strain . Fold decrease after addition of chloramphenicol ( protein stability experiments ) was determined by direct scanning and analyzing bands with ImageJ software ( National Institutes of Health ) .
|
All cells have to adjust to frequent changes in their environmental conditions . The heat shock response is a signaling pathway critical for survival of all organisms exposed to elevated temperatures . Under such conditions , the heat shock response maintains enzymes and other proteins in a properly folded state . The mechanisms for sensing temperature and the subsequent induction of the appropriate transcriptional response have been extensively studied . Prior to this work , however , the circuitry described in the best studied bacterium E . coli could not fully explain the range of cellular responses that are observed following heat shock . We report the discovery of this missing link . Surprisingly , we find that σ32 , a transcription factor that induces gene expression during heat shock , needs to be localized to the membrane , rather than being active as a soluble cytoplasmic protein as previously thought . We show that , equally surprisingly , σ32 is targeted to the membrane by the signal recognition particle ( SRP ) and its receptor ( SR ) . SRP and SR constitute a conserved protein targeting machine that normally only operates on membrane and periplasmic proteins that contain identifiable signal sequences . Intriguingly , σ32 does not have any canonical signal sequence for export or membrane-integration . Our results indicate that membrane-associated σ32 , not soluble cytoplasmic σ32 , is the preferred target of regulatory control in response to heat shock . Our new model thus explains how protein folding status from both the cytoplasm and bacterial cell membrane can be integrated to control the heat shock response .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Heat Shock Transcription Factor σ32 Co-opts the Signal Recognition Particle to Regulate Protein Homeostasis in E. coli
|
We have characterized the conformational ensembles of polyglutamine peptides of various lengths ( ranging from to ) , both with and without the presence of a C-terminal polyproline hexapeptide . For this , we used state-of-the-art molecular dynamics simulations combined with a novel statistical analysis to characterize the various properties of the backbone dihedral angles and secondary structural motifs of the glutamine residues . For ( i . e . , just above the pathological length for Huntington's disease ) , the equilibrium conformations of the monomer consist primarily of disordered , compact structures with non-negligible -helical and turn content . We also observed a relatively small population of extended structures suitable for forming aggregates including - and -strands , and - and -hairpins . Most importantly , for we find that there exists a long-range correlation ( ranging for at least residues ) among the backbone dihedral angles of the Q residues . For polyglutamine peptides below the pathological length , the population of the extended strands and hairpins is considerably smaller , and the correlations are short-range ( at most residues apart ) . Adding a C-terminal hexaproline to suppresses both the population of these rare motifs and the long-range correlation of the dihedral angles . We argue that the long-range correlation of the polyglutamine homopeptide , along with the presence of these rare motifs , could be responsible for its aggregation phenomena .
Polyglutamine ( polyQ ) diseases involve a set of nine late-onset progressive neurodegenerative diseases caused by the expansion of CAG triplet sequence repeats [1] . These repeats result in the transcription of proteins with abnormally long polyQ inserts . When these inserts expand beyond a normal repeat length , the affected proteins form toxic aggregates [2] leading to neuronal death . PolyQ aggregation takes place through a complex multistage process involving transient and metastable structures that occur before , or simultaneously , with fibril formation [3]–[9] . Experimental findings suggest that the therapeutic target for polyQ diseases should be the soluble oligomeric intermediates , or the conformational transitions that lead to them [9] , [10] , and not the insoluble ordered fibrils . These findings , common to all amyloid diseases [11] , have spurred efforts to understand the structural attributes of soluble oligomers and amyloidogenic precursors . The free energy landscapes of polyQ aggregates display countless minima of similar depth that correspond to a great variety of metastable and/or glassy states . The aggregation kinetics of pure polyQ have been described as a nucleation-growth polymerization process [4]–[6] , [12] , where soluble expanded glutamine requires a considerable time lag for the creation of a critical nucleus , which then readily converts into a sheet in the presence of a template [13] . However , the “time lag” seems to properly be associated with the formation of the fully aggregated precipitates , since soluble aggregates – sometimes called “protofibrils” – that form during the putative lag phase have been reported [14] , [15] . The variety of polyQ soluble and insoluble aggregates might correlate with the conformational flexibility of monomeric ( non-aggregate single-chain ) polyQ regions , which are influenced by the conformations of neighboring protein regions [4] , [16]–[18] . One striking example of this conformational wealth – and still a source of controversy– is given by the polyQ expansion in the N-terminal of the huntingtin protein that is encoded in the exon 1 ( EX1 ) of the gene . The N-terminal amino acid sequence consists of a seventeen , mixed residue sequence , the polyQ region of variable length , two polyproline regions of 11 and 10 residues separated by a region of mixed residues , and a C-terminal sequence . Toxicity develops after the polyQ expansion exceeds a threshold of approximately 36 repeats , leading to Huntington's disease . The flanking sequences have been shown to play a structural role in polyQ sequences , both in synthetic and natural peptides , and both in monomeric or aggregate form [4] , [16] , [17] , [19] . In particular , a polyproline ( polyP ) region immediately adjacent to the C-terminal of a polyQ region has been shown to affect the conformation of the polyQ region; the resulting conformations depend on the lengths of both the polyQ and polyP sequences [16] , [17] , [20] , [21] . In this work , we set out to obtain a conceptual and quantitative understanding of the role played by a polyP sequence that is placed at the C-terminal of a polyQ peptide , which is relevant for the understanding of the behavior of the EX1 segment in the huntingtin protein . Sedimentation aggregation kinetics experiments [17] show that the introduction of a sequence C-terminal to polyQ in synthetic peptides decreases both the rate of formation and the apparent stability of the associated aggregates . The polyP sequence can be trimmed to without altering the suppression effect , but a sequence is ineffective . There are no effects when the polyP sequences are attached to the N-terminal or via a side-chain tether [17] . These experiments were complemented with CD spectra for monomeric peptides , where the presence of polyP at the C-terminal of showed remarkable changes in the spectra . Analysis of their data led the authors to propose that addition of the C-terminal sequence does not alter the aggregation mechanism , which is nuclefated growth by monomer addition with a critical nucleus of 1 monomer ( for ) , but destabilizes both the -helical and the ( still unknown ) aggregation-competent conformations of the monomer . These experimental results were unexpected: although a single proline residue interrupting an amyloidogenic sequence can decrease the propensity of that sequence to aggregate [22] , [23] , Pro replacements in amyloidogenic sequences placed in turns or disordered regions do not alter the aggregate core [23] . Here , we consider monomeric polyQ and polyQ-polyP chains , and quantify changes brought about in the conformations of the polyQ sequences by the addition of the polyP sequences at their C-terminal . In order to assess these changes , one must first characterize the conformation of pure monomeric polyQ in water . Wildly diverse conformations have been postulated experimentally for monomeric polyQ , including a totally random coil , -sheet , -helix , and PPII structures . At present there is growing experimental evidence that single polyQ chains are mainly disordered [6] , [13]–[15] . The solvated polyQ disorder , however , is different from a total random coil or a protein denatured state . In particular , atomic X-ray experiments [18] show that single chains of polyQ ( in the presence of flanking sequences ) present isolated elements of -helix , random coil and extended loop . Single-molecule force-clamp techniques were used to probe the mechanical behavior of polyQ chains of varying lengths spanning normal and diseased polyQ expansions [24] . Under the application of force , no extension was observed for any of the polyQ constructs . Further analysis led the authors to propose that polyQ chains collapse to form a heterogeneous ensemble of globular conformations that are mechanically stable . Simulations results for the monomer conformation have also been contradictory [25]–[31] . It is interesting that in the search for soluble prefibrillar intermediates , an -sheet was proposed to play a role in polyQ toxicity [32] , [33] . In these molecular dynamics simulations , polyQ monomers of various lengths were found to display transient -strands of four residues or less . The authors proposed that fibril formation in polyQ may proceed through strands intermediates [33] . More recently , a molecular dynamics study of hexamers of in explicit water showed that -sheet aggregates are very stable ( more stable than -sheets ) [34] . These results strongly support the idea that -sheet may either be a stable , a metastable , or at least a long-lived transient , secondary structure of polyQ aggregates . Coming back to the monomeric polyQ conformation , further simulation evidence [35]–[38] supports the experimental findings that monomeric polyglutamine of various lengths is a disordered statistical coil in solution . The disorder is inherently different from that of denatured proteins and the average compactness and magnitude of conformational fluctuations increase with chain length [35] . In addition , the coils may present considerable -helical content [38] , but there are acute entropic bottlenecks for the formation of -sheets . The molecular dynamics results presented here for single polyQ and polyQ-PolyP chains consisting of , , , , , and glutamine residues are in qualitative agreement with the experimental and simulation results mentioned above: polyQ is primarily disordered , with non-negligible -helical content and a small population of other secondary structures including both and strands . The addition of polyP reduces the population of the region of Ramachandran plot [39] , and increases the population of and PPII Ramachandran regions for all PolyQ lengths . If one considers secondary structure motifs ( i . e . , hydrogen-bonds patterns in addition to dihedral angles ) , the addition of the polyP segment increases the populations of the PPII helices and turns , and decreases the -helical content of all peptides but ( which may have a protective effect against aggregation , as discussed later ) . The addition of polyP does not change the average radius of gyration of polyQ , but changes the radius of gyration distribution function for , that becomes dependent on the prolyl bond isomerization state . Most importantly , the addition of polyP decreases the population of small and strands , and and hairpins . Since the extended strands and hairpins in both and forms are found only in a small fraction of the structures , we used a novel statistical measure based on the odds ratio construction [40] to quantify to study the secondary structural propensities [41] , [42] , thereby learning about the possibility of the growth of such secondary structures under nucleation conditions . This study , also supported by more conventional linear correlation analysis , provides evidence that among all the peptides studied here , only exhibits a long-range correlation between all glutamine residue pairs that favors formation of both and -strands . This correlation is suppressed by the addition of only six proline residues to the C-terminal of the peptide , which suggests a mechanism in which nucleation starts at these scarcely populated secondary structures ( mainly , , and strands , as well as -hairpins and -hairpins ) and can only spread through positive correlations in polyQ peptides of approximately 40 residues or longer . This paper is organized as follows . The Methods section details our simulation methodology and analysis . Specifically , we discuss the generalized Replica Exchange scheme used here for enhanced sampling , the simulation details , our clustering techniques to identify the Ramachandran regions and the secondary structural motifs , and the odds ratio construction , used here to study the correlations between residues . In the Results section , we present our results with a focus on a statistical analysis of the equilibrium conformations based on ( i ) Ramachandran regions ( ii ) secondary structure ( iii ) correlation analysis and ( iv ) radius of gyration . A discussion of our results and a short summary of this work is given in the last section .
Room temperature , regular molecular dynamics ( MD ) simulations are often too computationally limited to carry out a full sampling of the conformational space of a biomolecular system and generate a reliable statistical ensemble . Thus , in order to deal with the sampling issue , we make use of a replica exchange scheme [43] , [45] . In the replica exhange molecular dynamics ( REMD ) [43] , [46] method , one considers several replicas of a system subject to some sort of ergodic dynamics based on different Hamiltonians , and attempts to exchange the trajectories of these replicas at a predetermined rate to increase the barrier crossing rates ( i . e . , decrease the ergodic time scale ) . One possibility is to successively increase the temperatures of the replicas [46] . This method , known as parallel tempering , is here referred to as Temperature REMD ( T-REMD ) . Another possibility [43] is to construct the replicas by adding a biasing potential to the original Hamiltonian that acts on some collective variable that describes the slow modes of the system that need “acceleration” . This method can be referred to as Hamiltonian REMD ( H-REMD ) . In practice , T-REMD is used to promote the barrier crossing events in a generic way but the use of H-REMD allows one to directly focus on specific slow modes of the system , such as the cis-trans isomerization of proline amino acids which involves a barrier of 10 to 20 Kcal/mol [47] . A combination of the two methods , known as Hamiltonian-Temperature REMD ( HT-REMD ) [41]–[44] provides for a practical way to reduce the computational costs associated with REMD sampling , since it facilitates the sampling by both means . In this work , we used the T-REMD and HT-REMD methods for polyQ and polyQ-polyP peptides , respectively . In the T-REMD method , one replica runs at room temperature and the rest of the replicas run at higher temperatures . Care must be taken with respect to the choice of the number of replicas and their temperatures . The performance of the setting can be checked by monitoring the exchange rate between the neighboring replicas ( i . e . , with closest temperatures ) as well as the ergodic time scale of the “hottest” replica . The equilibrium conformational ensemble is then generated by taking the structures at a predetermined rate from the trajectory of the replica at the lowest ( room ) temperature . In the HT-REMD method , the replicas have different biasing potentials . The biasing potential is usually described in terms of a collective variable , defined as a smooth function of the atomic positions . The corresponding free energy or potential of mean force ( PMF ) [48] , ( where the angular brackets denote the equilibrium ensemble average ) , provides for an ideal biasing potential . Indeed , if the biasing potential is exactly , then the probabilities of different values of the collective variable would all be equal , since there are no barriers present . Although the true free energy is typically unknown in advance , a roughly approximate is often sufficient to improve the sampling considerably in an H-REMD or HT-REMD setting . Such free energies can be computed in a variety of ways [48] . For the polyQ-polyP systems , some of the slow modes originate in the cis-trans isomerization of the prolyl bonds , that occur when polyproline is in solution . We have recently carried out extensive work on proline-rich systems [41] , [42] , [44] , [47] , [49] and can take advantage of the free energy profiles previously obtained for polyproline of various lengths [44] , calculated using the Adaptively Biased Molecular Dynamics ( ABMD ) [50] , [51] method . The ABMD method is an umbrella sampling method with a time-dependent biasing potential , which can be used in conjunction with the REMD protocol , by combining different collective variables and/or temperatures on a per-replica basis [43] , [50] . Currently , the ABMD method has been implemented into the AMBER v . 10 , 11 simulation package [52] . Details of the calculation of the polyproline potentials are given elsewhere [41] , [42] , [44] , [47] . The HT-REMD simulations proceeded in several stages . We recycled the previously computed free energies associated with a collective variable that “captures” the cis-trans transitions of the prolyl bonds of polyproline peptides of different lengths in implicit water at different temperatures . The collective variable used for these calculations is defined based on the backbone dihedral angle of prolyl bonds , ( here sum runs over all the prolyl bonds ) . The dihedral angle takes the values around and for cis and trans conformations , therefore can “capture” different patterns of the cis/trans conformations in any proline-containg peptide . The biasing potentials , transfered from our previous calculations were then refined for the polyQ-polyP peptides using similar simulation settings . Next , several additional replicas running at the lowest temperature were introduced into the setup . One of these replicas is completely unbiased , and therefore samples the Boltzmann distribution at . The other replicas , also at , are subject to a reduced bias ( i . e . , these biasing potentials are scaled down by a constant factor ) . The purpose of these “proxy” replicas is to ensure adequate exchange rates between the conformations , and thereby enhance the mixing [43] . Data was then taken from the unbiased replica at a suitable , predetermined rate . Simulations were carried out for the peptides with sequence ( denoted as ) and ( denoted as ) . These peptides include , , , , , , , , , , and . In each case , we refer to the glutamine and proline residues as and , respectively . The simulations were carried out using the AMBER [52] simulation package with the ff99SB version of the Cornell et al force field [53] with an implicit water model based on the Generalized Born approximation ( GB ) [54] , [55] including the surface area contributions computed using the LCPO model [56] ( GB/SA ) . For more simulation details , our implementation of the REMD scheme and a discussion of convergence issues , please see the Supporting Information ( Text S1 ) . We used the ( , ) dihedral angles ( see Fig . 1 for their definition ) to identify different regions [57] of the Ramachandran map [39] . Table 1 provides the corresponding definition for these regions . Although this delineates clear regions for the dihedrals of most residues , it turns out that the populations may overlap around the borders . In order to handle this situation , we used a clustering technique as explained in the Supporting Information ( Text S1 ) to classify the conformations , rather than strictly enforcing the sharp boundaries between the defined regions . Although the backbone dihedral angles of all the residues forming a right-handed -helix fall into the region of Ramachandran map , many of the residues in this region do not actually form -helices . As a matter of fact , several other secondary structural motifs , such as and helices as well as random coil and turn are characterized by or may involve backbone dihedral angles falling in the same region . An interesting example is provided by polyglutamine itself . It has been suggested recently [32]–[34] that an -sheet , whose backbone dihedral angles alternate between the and helical regions , can be a stable , metastable , or at least a long-lived transient secondary structure in oligomers . In general , for a residue to be considered to belong to a given secondary structure , it is not enough to identify the Ramachandran region of its dihedral angles . Thus , we used the secondary structure prediction program DSSP [58] , [59] that uses not only the backbone diheral angles , but also the inter-residual hydrogen bonding as well as the relative position of the C atoms to identify secondary structural motifs . For our peptides , the DSSP secondary structures with highest probabilities were: ( i ) helices , including and types , ( ii ) turns , including H-bonded turns and bends , ( iii ) coils . There are also isolated residues involved in bridges and extended strands , participating in the ladders with small probabilities . Since DSSP does not specifically identify isolated or strands ( i . e . , strands not H-bonded to another strand of their type ) or hairpins , we used a combination of H-bonding results from DSSP analysis and the Ramachandran regions from the clustering analysis to define and strands and hairpins . A strand is defined here as at least adjacent residues all falling into the region of Ramachandran plot . A strand is referred to as isolated if none of its residues is H-bonded . A hairpin is defined as two adjacent strands with a turn in between and at least one H-bond between the two strands . The turn between the two strands of a hairpin could be H-bonded or not and is of any length but it has to have the geometrical form of a turn , ( i . e . , identified as bend by DSSP ) . Each of the two strands has at least three adjacent residues in region to ensure the structure is relatively extended . At least one of these three residues are H-bonded to another residue in the other strand . We define an repeat as two adjacent residues , whose backbone dihedral angles alternate between and regardless of the order ( i . e . , this includes both and ) . An strand is formed from adjacent residues , involving alternating and repeats . In this definition , an strand is either or and an strand is either or but not . An isolated strand is defined as an strand not H-bonded to another strand , and the hairpin is defined as two adjacent strands with a turn in between and at least one H-bond between the two strands , similar to the hairpin . Another relatively extended secondary structure is PPII that is defined here as adjacent residues whose dihedral angles fall into the PPII region of Ramachandran plot . A PPII structure , is defined as a structure having adjacent PPII residues . A summary of these secondary structures is given in Table 1 . Finally , we determined the type of turn from both the DSSP analysis and our Ramachandran region clustering analysis . DSSP distinguishes between H-bonded turns and geometrical bends that do not involve any H-bonding . The DSSP analysis can be also used to identify and types based on the number of residues involved , which is 4 and 3 respectively . The dihedral angles of the two middle residues of turns ( i . e . the second and the third residues ) can be used to partition turns into more types such as I , I′ , II , II′ , etc . but we will only consider type I- that involves an sequence and the “other” type turns that involve other combinations of dihedral angles . Since the population of “other” combinations is relative small , we group these all together . To quantify how the secondary structures of Gln residues influence each other we made use of the odds ratio ( OR ) construction [40]–[42] . The OR is a descriptive statistic that measures the strength of association , or non-independence , between two binary values . The OR is defined for two binary random variables ( denoted as and ) as: ( 1 ) where is the joint probability of the event ( with and taking on binary values of 0 and 1 ) . For the purposes of this study , we can think of and as being some characteristic properties describing the conformations of different residues . For example , the variables could be assigned values of 1 or 0 depending on whether the backbone dihedral angles of corresponding residue falls into the region of Ramachandran plot or not . We denote this definition of OR as OR . Similarly one can define based on the involvement of residues in repeats . In this case , to define the of two given residues and , the probabilities are defined such that the variables and take the values 1 or 0 depending on whether or not the corresponding residue is involved in an repeat as defined in the last subsection . For instance , if and only if residue either is in the region and is neighboring a residue in the region , or it is in the region and is neighboring a residue in the region . Note that in general , to calculate the of two residues , dihedral angles of not only the two residues but also their neighbors are needed , i . e . , up to 6 residues could be involved . The usefulness of the OR in quantifying the influence of one binary random variable upon another can be readily seen . If the two variables are statistically independent , then so that . In the opposite extreme case of ( complete dependence ) both and are zero , and the OR is infinite . Similarly , for rendering . To summarize , an OR of unity indicates that the values of are equally likely for both values of ( i . e . , , and are therefore independent ) ; an OR greater than unity indicates that is more likely when ( and are positively correlated ) , while an OR less than unity indicates that is more likely when ( and are negatively correlated ) . It is convenient to recast the log of the OR in terms of free energy language . If one expresses the probability of the events in terms of a free energy : ( 2 ) then the ratio of probabilities translates into a free energy difference: ( 3 ) Clearly , the logarithm of the OR then maps onto the difference of those differences , i . e . , ( 4 ) For the case of statistically independent properties , ; otherwise , this quantity takes on either positive or negative values , whose magnitude depends on the mutual dependence between the two variables . The standard error in its asymptotic approximation is: ( 5 ) in which is the total number of independent events sampled . While this development may be perceived as purely formal , the use of an OR analysis couched in terms of free energy language provides for a useful and intuitive measure of the inter-residual correlations , as has been illustrated before [41] , [42] . In this work , our OR-based correlation analysis is supported by the conventional linear correlation analysis . We have used the correlation coefficient ( also know as cross-correlation or Pearson correlation ) of dihedral angles of glutamine residues to measure the correlation of glutamine residues in different situations . We emphasize that in the context of secondary structural propensities , the odds ratio analysis is more powerful than the correlation coefficient since it eliminates the noise associated with the dihedral angles . This noise may dominate the linear correlation results such that even substantial correlations may be completely ignored . The OR-based correlation analysis , combined with the clustering technique explained here takes into account both nonlinearity and multivariate components of amino acid correlations in a peptide chain , although in some particular cases a conventional univariate linear correlation may reveal a correlation as we will report in the results . In the context of this paper , the multivariate component is particularly evident when the correlation of repeats is considered , since this may involve and angles of up to six residues for each single odds ratio calculation .
Figure 1b shows the Ramachandran plot of a typical glutamine residue , for which the clusters in the different regions are computed according to the protocol described in the Methods section . Four clusters can be identified in these plots including PPII ( blue ) , ( yellow ) , ( gray ) , and ( pink ) . Figures S2 and S3 show the Ramachandran plots of all 40 glutamine residues of both and . Considering these , as well as similar plots for other peptides ( not shown here ) , we observe the following trends: ( i ) The dominant region of most residues is the cluster that is present in all residues , except for the glutamines immediately followed by a proline , for which this region is precluded; ( ii ) PPII and clusters are present in almost all residues; ( iii ) The cluster is present in more than half of the residues but its population is often very small; ( iv ) Compared to , displays regions with higher non- intensities , particularly for the cluster ( see , , , and ) . Figure 2 plots the percent population of the , PPII , and regions of glutamine residues ( top , middle and bottom rows , respectively ) in terms of the residue number . The left column shows results for [red] and [blue] and the right column for [red] and [blue] . Table 2 presents the population of the different Ramachandran regions ( averaged over all glutamine residues ) and the repeats , the secondary structure motifs , and the “extended structures” including hairpins . The residue populations in the Ramachandran plot show that , on average , 67–87 of the residues are in the region of the Ramachandran plot , 5–13 of the residues are in the PPII region and 5–17 of the residues are in the region . The PPII and regions are almost always equally probable , as can be seen in Figs . 2 , S2 , S3 . The lowest population belongs to the region , comprising only 3–6 although in certain residues it could be as high as 38% as , for instance , in in where the content of correlates with the presence of turns . The addition of P decreases the population of the Ramachandran region and increases that of the and PPII regions , while leaving the small population of approximately invariant . In peptides , proline residues are excluded from the statistical analysis so that only Q residue propensities are compared ( for instance , when we state that the average helical content of is 43% , it means that 43% of all Q residues are in a helix – the P residues are not counted in the statistic ) . Figure 2 shows that the populations of the PPII and regions are always higher at the two ends of the polyQ peptides , particularly at the C-terminal . When a short proline segment is added at the C-terminal of polyQ , the population of these regions in the neighboring glutamines increases even more . For peptides shorter than ( not shown here ) , the population of the PPII- region decreases in the middle of the peptide , but for ( red line ) we see a small peak in the middle of the peptide for both PPII and regions . In , we have two small peaks ( rather than a single peak ) centered around residues 13 and 25 for both the and PPII regions . The presence of the prolines at the C-terminal of a polyglutamine can drastically alter the population distribution . Fig . 2 shows that the few relatively wide peaks of the -PPII regions in both and are replaced by several narrow peaks of larger heights . Regarding the residues involved in repeats , one can see from Fig . 2e , f that the distribution of these repeats throughout these peptides depends both on the position of glutamine residues and the presence or absence of the C-terminal prolines although , as seen in Table 2 , the average content is similar ( 6–7% ) in all four peptides: , , , and . We note that the distribution of content in the peptide is mostly determined by the content as the content is abundant in these peptides and most residues are involved in an repeat . One can compare Fig . 2e , f with Figs . S2 , S3 and observe similar behaviour , i . e . , the residues with high content ( Fig . 2e , f ) have more intense clusters ( pink clusters in Figs . S2 , S3 ) . When one considers not only the backbone dihedral angles i . e . , the ( , ) regions occupied by individual glutamine residues , but also the inter-residual hydrogen bonding and the relative positions of the atoms , one can identify different secondary structures , particularly -helical segments in many of the sampled conformations . Short helices are also possible but the majority of the residues are either in a turn or a coil conformations according to both DSSP [58] , [59] and STRIDE [60] analysis . Figure 3 plots the helical , turn , and coil content of the individual glutamine residues against their residue numbers for , , , and . Figure 4 shows plots of select conformations of and peptides , as generated by VMD [61] using STRIDE [60] for the secondary structure assignment . Table 2 lists the population of helix , turn , and “other” secondary structures as obtained from DSSP , averaged over all residues . The “other” secondary structure category includes mainly what DSSP identifies as “loop or irregular” – sometimes called “coil” in other programs – but which may also include a very small population of other secondary structures such as extended strand and “isolated -bridge” . We use the protocols explained in Methods section to further identify these , as well as other extended structures ( Tables 2 and 3 ) . When the population of residues in the region is compared to the actual helical content , one realizes that the majority of the residues in the region do not form or any other type of helices . Many of these residues in the region are followed and/or preceded by a residue in a different Ramachandran region , such as , as discussed in the previous subsection , forming an repeat . Similarly an repeat does not necessarily form an strand . Table 2 gives the population of the structures ( or conformations ) having at least one segment in one of the extended conformation forms , as defined in Methods section , including and strands either in the isolated form of length 3 ( or length 4 in parenthesis ) or in the hairpin form as well as PPII structures of length 3 ( or length 4 in parenthesis ) . Note that unlike the other populations in part ( a ) and ( b ) in Table 2 , the population of extended secondary structures in part ( c ) is not averaged over the residues . Instead , we counted all the conformations having at least one such secondary structures in the polyQ portion of the molecule and divided this number by the total number of sampled conformations . These structures are less common than helices or turns , but they are possible and form a small subpopulation of the secondary structures . Indeed , one can see that a non-negligible portion of the structures has at least one such segment . In particular , isolated strands are quite common , although they may simply be considered as part of a random coil . The isolated and PPII strands form the second most populated extended structures . Similarly , these structures may also be considered as part of a random coil . However , and strands form extended structures that are unlikely to be considered random coil elements . Figure 4 shows some examples of isolated and adjacent extended structures in both and forms . Remarkably , among all the sequences presented here , has the highest percentage of extended structures . This peptide shows a significantly higher propensities for the extended structures , particularly the strands . The population of the structures having at least one -hairpin is almost 2% , and is higher than the number of structures having at least one -hairpin . However , the -hairpin rate is still the highest among all the peptides studied here . Adding the proline segment to the peptide reduces the chance of forming or extended structure dramatically , especially in the case of -hairpins and isolated strands of length four or more . However , PPII propensity is increased in the peptides of length by adding the proline segment . Table 3 gives more details on the helices and turns observed in the polyQ and polyQ-polyP structures . The helices are found mostly in the right-handed form except for and that favor helices due to their short length . This Table also shows the percentage of helical segments present in a given peptide . A helical segment is defined as a series of residues adjacent in the sequence whose secondary structure has been identified as helical by DSSP . Thus helical segments can have varying lengths , and the table lists the number of helical segments ( independent of their length ) . Thus , among conformations , 31% do not have any helical segment but when the prolines are added 99% form at least one helical segment ( in particular , 40% of the structures in have 3 helical segments ) . The addition of P to increases the helical content from 30% in to 43% in ( the highest helical content in all peptides ) , while the addition of polyP decreases the helical content in all other peptides . Comparing and structures , the population of the structures having more than one helix increases . The select and structures given in Fig . 4a , b illustrate various conformations , for which a statistical description is given in Figs . 2 , 3 and the Tables 2–3 . In particular , the left column of Fig . 3 indicates that adding a polyP segment to reduces the helical content but increases the coil content ( while the turn content stays the same ) . Instead , adding a polyP to ( right column of Fig . 3 ) results in an increase of the helical content in the N-terminal of , farther away from the polyP segment . The addition of to increases not only the number of helical segments but also their length , particularly in the N-terminal half . The population of the structures having short helices ( less than 7 residues ) is very similar in ( 26% ) and ( 27% ) but 72% of conformations have longer helices ( 7 residues or more ) as compared to only 43% in . Also 37% of the conformations have a helical segment longer than 9 residues while only 20% of conformations do . Adding the polyP segment generally increases the turn content ( both of and types ) , except for , where the total population of turns stays constant . The majority of turns are of I- type but there is a smaller population of other types of turns as well as turns . The increase in the -turn content of polyQ-polyP peptides can explain why adding the polyP to polyQ sometimes increases the content , as residues are involved in most of -turns . For instance , one finds more content in the residues of compared to but there are fewer residues in involved in repeats . There is no contradiction here as part of the content is involved in turns rather than -strands . Finally , Fig . 5 presents examples of ( rare ) extended conformations in the peptides . In particular , the figure shows hairpins and isolated strands , and hairpins and isolated strands . An odds ratio analysis based on the Ramachandran regions was conducted , and results summarized in Figures 6 and 7 for , , , , and peptides . We defined the OR as a function of sequence distance between two glutamine residues and . indicates an OR based on the region of Ramachandran plot . These figures display , for a better intuitive illustration . measures how the presence or absence of in the region can influence the presence or absence of in the region . Here , to reduce the end effects , only runs between and , with for and for . In Fig . 6a , shows higher correlation for than . In other words , would have a greater chance of forming strands if the population of residues increases . However the correlation range between the residues in both and is about since for there is no significant deviation from , the expected value for independent events . This situation changes with polymer length . in Fig . 6b has a correlation length of about , after which it quickly loses correlation ( it even becomes “anti-correlated” ) . Once again , exhibits unique behavior since does not decay to zero but oscillates around kcal/mol and more importantly , the oscillation does not seem to be damped by increasing ( ignoring the smaller values ) . This indicates a long-range correlation between the glutamine residues of . ( Oscillations can be seen for as well , but they are around zero ) . The results of the OR analysis can be further confirmed by conducting a direct correlation analysis on the angles of the glutamine residues . We used the correlation coefficient ( also known as cross-correlation or Pearson correlation ) as a measure of linear correlation between the angles of Gln residues of sequence distance , using the same protocol explained above for odds ratio analysis ( i . e . , omitting the end residues ) and verified the same unique behavior of . First , the dihedral angles were shifted degrees ( with the assumption of periodic boundary condition at ) , then the correlation coefficient of of the residues with a sequence distance , corr ( r ) , was calculated . Note that this correlation measure does not involve any clustering and ignores any dependence on the dihedral angle , however , it confirms the OR predictions . Although in general both and angles are needed to identify the Ramachandran region of an amino acid , the linear correlation analysis on angles is still able to detect a long-range , positive correlation for ( Figs . 6c , d ) . An OR-based correlation analysis for is illustrated in Fig . 6e , f . Here , a residue is considered to be an residue if it is involved in an repeat . In the case of and there is an even shorter positive correlation range ( compared to ) for both peptides , with a significant negative correlation when increases . shows a somewhat similar oscillatory behavior around a non-zero average , with negative troughs . Note that the Pearson correlation coefficient cannot be used here for the analysis ( in its univariate form ) due to the fact that the definition of an repeat is highly dependent on the dihedral angles of both adjacent residues , involving four residues in the correlation analysis instead of two . The angles are also quite important for the / distinction . Finally , Fig . 7 compares the behavior of OR-based in , , and peptides for . In there are differences between these different regions , but they all decay by increasing , as expected for short correlations . However , in we see an almost identical behaviour for all three Ramachandran regions . This clearly indicates that the dihedral angles of most of the glutamine residues are correlated in an indirect manner , influencing each other . We compared the of glutamine residues based on their distance and the correlation coefficients of their angles for . Fig . 7d shows that the two vary similarly for different and have a correlation coefficient of about 0 . 97 , suggesting that OR and corr are linearly correlated . In terms of the error estimate , we note that the estimated standard error for these calculations is different not only for different plots but also for different data points ( varying by ) in one plot . The latter is the result of having fewer samples with larger than shorter but the former is due to the difference between the population of secondary structures , the number of residues in each peptide , and the number of sampled conformations for each peptide . However , the standard error remains less than kcal/mol in most cases . In some exceptions in Fig . 6e , f the standard error could be as high as kcal/mol . Here we consider the statistical ensemble results concerning the radius of gyration and its distribution . The radius of gyration gives a simple and intuitive measure of the overall structure of the polyQ peptides as the collapsed ( stretched ) structures are associated with smaller ( larger ) values of . Table S1 gives the of the atoms of the Gln residues in and . The proline segments are not included in the calculation of so that the polyQ sequences are compared on equal footing . The averages are accompanied by the standard deviation that somewhat estimates the width of the distribution , if it is close to a normal distribution . The averages do not show much difference between and peptides . The standard deviation is also very similar between the two in most cases except for the case . Fig . 8a shows the distribution of [red] and [blue] peptides that is close to a normal distribution with a longer tail on the right as expected for a random-coil structure . is only slightly more compact . The normal distribution with a slightly longer tail as a characteristic distribution of random coil is seen for all of these peptides except for . Fig . 8b shows that although follows the same distribution , can be estimated as the sum of three distinct Gaussian distributions . We used the Marquardt-Levenberg [62] algorithm to estimate the probability distribution of as the sum of three Gaussian distributions ( see Fig . 8b ) , each representing one class of structures covering 24 , 44 , and 32 of the samples distributed around an of 11 . 41 , 13 . 65 , and 17 . 08 , respectively . The fitting resulted in a reduced smaller than , indicating that this model explains the probability distribution of well . Examining the structures of each class shows that the segment is responsible for this clear difference between the three classes . The structures distributed around , accounting for almost one third of the samples , have relatively stretched conformations ( see Fig . ) , and this correlates with the presence of all-trans prolyl bonds in . In these proline isomers , forms a rigid stretched helical segment , in contrast with a proline segment including one or more cis-isomers , particularly in the middle of the segment ( see Fig . ) . Table S1 shows the trans content of each of the prolyl bonds of as well as the population of the isomers with all-trans prolyl bonds . There is a clear difference between and the rest of proline-containing peptides in terms of cis-trans isomerization . Although , 73–77% of the residues are in trans conformation in the shorter peptides , only 12–23% of the structures are all-trans . In 60% of the structures are stretched all-trans conformations . What is more interesting is that the distribution of radius of gyration is meaningfully different for the all-trans proline sub-ensemble as shown in Fig . 4c . Green curve is the distribution of this sub-ensemble and magenta curve is the distribution , obtained from the rest of the structures ( i . e . , cis-containing polyP ) . Here we somewhat recognize four normal distributions . We use a similar method as explained above to fit these Gaussians . We find four clusters with 6 , 17 , 29 , and 48% of the population centered around 11 . 02 , 12 . 24 , 13 . 94 , and 17 . 27 respectively . The conclusion is that all-trans prolines increase the population of the stretched cluster considerably . This somewhat explains why we do not observe this partitioning of the clusters with proline segment in shorter peptides ( see Fig . 8a ) because in those cases the population of all-trans conformations is not large enough to affect the overall distribution . As the peptides grow with residue number , their structure becomes more collapsed . In particular , the average radius of gyration for is only about 1 . 1 Å larger than for . The inset in Fig . 8d illustrates the dependence of the radius of gyration on the length of the peptide . Assuming one can estimate using any pair of peptides such as and from . Fig . 8d gives examples of the estimated for different pairs of and : is given by the indices in the x axis and is ( cyan circles ) or ( yellow squares ) . There is an abrupt collapse of the structure ( ) on going from to .
Our atomistic simulations show the disordered nature of monomeric polyglutamine peptides , in agreement with experimental conclusions [6] , [13]–[15] and with previous all-atom MD simulations [35]–[38] . Our simulations are also in agreement with recent experiments [18] in that the monomeric polyQ is different from a total random coil or a protein denatured state , with a significant presence of short -helices . Therefore polyglutamine is a disordered peptide that is somewhat preorganized , containing short rigid segments [63] , [64] . Contrary to certain coarse-grained models [27]–[29] , [31] , our atomistic simulations provide no evidence for a large content in monomeric polyglutamines . We observed that the peptide forms an ensemble of mostly compact structures with an average radius of gyration only about 1 . 1 Å larger than that of . This agrees with the conclusions from single-molecule force-clamp experiments [24] that polyQ chains collapse to form a heterogeneous ensemble of globular conformations that are mechanically stable . For the radius of gyration of the shorter peptides , we observed an exponent slightly larger than that of a random-coil in a good solvent ( i . e . about 0 . 6 , [65] ) . However , we have not been able to simulate a large enough range of peptide sizes in order to get a good estimate of . This may not be necessary , since the simulations suggest that the radius of gyration does not follow a power law anyway ( see Fig . 8d ) . The addition of a short C-terminal proline segment to the peptide changes the distribution of the radius of gyration from a Gaussian-like function with a longer tail for larger – a characteristic of a random coil , seen also in all the other peptides studied here – to a combination of three distinct Gaussians . The way the proline segment affects the distribution is closely correlated with the cis-trans pattern of its prolyl bonds . An all-trans proline segment ( the most common pattern in ) results in the multi-modal distribution of Fig . 8 . Instead , proline isomers with cis bonds are abundant in shorter peptides which results in the normal distribution . We note that prolyl bond isomerization requires crossing barriers of 10–20 kcal/mol , which can only be accomplished with special enhanced-sampling techniques such as used here [44] , [47] , [49] . The addition of the polyP segment to polyQ introduces position dependent features among the Gln residues . This is readily seen in Fig . 3 . The fluctuations observed cannot be explained as “noise” resulting from sampling limitations . As explained in the previous section , sampling of independent data produces the same features , which suggests a sensitive dependence on the position of the residue in the sequence . Interestingly , polyP induces helix formation in the further residues in the N-terminal of , while creating more turns in the nearer Gln residues . As a result of the polyP addition , the overall -helical content of increases . This is in contrast with the shorter peptides in which the -helical content drops considerably by adding the polyP segment . Experimentally , it has been claimed that the addition of polyP to polyQ decreases the -helical content of polyQ for all polyQ lengths [17] . A superficial comparison might indicate that this is in contradiction with our results for . Our results are , however , in agreement with the experimental data , which is based on the CD spectra of these peptides . These CD spectra identify the distribution of individual backbone dihedral angles rather than the actual -helical content , a quantity not only dependent on the individual residues but also the way they are aligned . Our simulations are in total agreement with this observation as we see a decrease in the population of the cluster ( i . e . , the residues falling into the region of Ramachandran plot ) in all the peptides studied here , as we add a segment to the C-terminal ( Table 2 ) . As we have pointed out before [41] , [42] , care is needed in the interpretation of the CD data . Table 2 shows that the majority of the residues in the cluster are not involved in any form of helix in either polyQ or polyQ-polyP peptides , and while the helical content of all other peptides decreases , that of actually increases with the addition of . While this effect for cannot be ruled out as an defficiency of the force field , it is interesting to note that this would represent quite an effective way of neutralizing , since the rather stable helix will not be prone to aggregation . In addition to and helices , as well as and turns , one can identify a small but non-negligible population of extended secondary structures of and strands , particularly in the peptides . PolyP increases the -region content in the Ramachandran plot , but decreases the -strand content ( as explained before , several residues need to be adjacent in order to form a -strand ) . For , the addition of polyP dramatically decreases the content of , , and strands . On the other hand , relatively short PPII helices in polyQ form another extended secondary structure that happens to be more common in peptides than peptides for . The PPII strands do not form inter-residual hydrogen bonds ( hairpins , sheets ) and would not favor aggregation . In this work we used an odds ratio analysis to quantify the dependencies among certain properties of the molecules . Regarding the -strand formation in , the graph for in Fig . 6 shows a positive , long-range correlation in sequence distance . In other words , the chances of two glutamine residues falling into the region of the Ramachandran map correlate positively with each other , even if they are distant in the sequence . This long range correlation was not seen in any other peptide but . Interestingly , this long-range correlation for the peptide is not limited to the -region but it is also seen in other regions such as and PPII . In particular , scales for the , and PPII regions as shown in Fig . 7 . A linear correlation analysis on dihedral angle verifies the very same long-range correlation between glutamine residues of peptide , a correlation that is absent in other peptides studied here . This surprising phenomenon could be interpreted as the possibility of the growth of any of these secondary structures in the long polyQ peptides , especially if the conformation were “seeded” with a given secondary structure . In a polymeric form of polyglutamine , the nucleation of or strands could result in further growth of those strands or could induce growth in adjacent strands resulting in the the growth of or sheets . Interestingly , the “period” for the oscillations of is approximately 7–8 residues , which is also the optimal experimental extended chain length in an aggregate [7] . The populations of -strand , -strand , -hairpin , and -hairpin ( Table 2 ) decrease and the long-range correlations and are disrupted by the presence of the C-terminal proline residues in . For shorter peptides , the corresponding populations are much lower , and the correlations are short-ranged . Taken together , these results indicate that for ( but not for the shorter peptides ) nucleation could start in one of these strands or hairpins ( that can align two strands ) and then grow from there , favored by the positive correlations generated by the longer peptide . We can summarize the main findings of this work as follows: Our careful statistical analysis has revealed a wealth of very subtle effects that are far from obvious . Secondary structures such as helices , -sheets , -sheets , PPII helices , and coils have all been reported in the literature . The picture that is emerging is that if one can induce the nucleation of one of these structures , or provide a template for it , a long enough polyQ polymer or an aggregate will probably continue growing in the given conformation , even if it is not the absolute thermodynamic minimum . In this sense , the wealth of conformations of polyQ is reminiscent of the different phases that appear in ‘inorganic’ systems with short-range attractive interactions and long-range electrostatics interactions such as Langmuir monolayers or block copolymers , where kinetics effects also play a fundamental role in determining the final phase of the system . PolyQ is a very special homopeptide due to its long side changes and the dipoles at the ends . The van der Waals packing of the side chains provides the source of short-range attractive interactions , while the carboxamide groups provide the long-range dipolar interactions [34] . In this sense , the only other peptide that would exhibit similar behavior is asparagine , with one methyl group less in its side chain [34] . The “collapsed” random coil would just represent the frustration between different phases .
|
Nine neurodegenerative diseases are caused by polyglutamine ( polyQ ) expansions greater than a given threshold in proteins with little or no homology except for the polyQ regions . The diseases all share a common feature: the formation of polyQ aggregates and eventual neuronal death . Using molecular dynamics simulations , we have explored the conformations of polyQ peptides . Results indicate that for peptides ( i . e . , just above the pathological length for Hungtington's disease ) , the equilibrium conformations were found to consist primarily of disordered , compact structures with a non-negligible -helical and turn content . We also observed a small population of extended structures suitable for forming aggregates . For peptides below the pathological length , the population of these structures was found to be considerably lower . For longer peptides , we found evidence for long-range correlations among the dihedral angles . This correlation turns out to be short-range for the smaller polyQ peptides , and is suppressed ( along with the extended structural motifs ) when a C-terminal polyproline tail is added to the peptides . We believe that the existence of these long-range correlations in above-threshold polyQ peptides , along with the presence of rare motifs , could be responsible for the experimentally observed aggregation phenomena associated with polyQ diseases .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"physics",
"chemistry",
"chemical",
"biology",
"chemical",
"physics",
"biophysics"
] |
2012
|
Are Long-Range Structural Correlations Behind the Aggregration Phenomena of Polyglutamine Diseases?
|
Fasciola hepatica is the causative agent of fascioliasis , a disease affecting grazing animals , causing economic losses in global agriculture and currently being an important human zoonosis . Overuse of chemotherapeutics against fascioliasis has increased the populations of drug resistant parasites . F . hepatica cathepsin L3 is a protease that plays important roles during the life cycle of fluke . Due to its particular collagenolytic activity it is considered an attractive target against the infective phase of F . hepatica . Starting with a three dimensional model of FhCL3 we performed a structure-based design of novel inhibitors through a computational study that combined virtual screening , molecular dynamics simulations , and binding free energy ( ΔGbind ) calculations . Virtual screening was carried out by docking inhibitors obtained from the MYBRIDGE-HitFinder database inside FhCL3 and human cathepsin L substrate-binding sites . On the basis of dock-scores , five compounds were predicted as selective inhibitors of FhCL3 . Molecular dynamic simulations were performed and , subsequently , an end-point method was employed to predict ΔGbind values . Two compounds with the best ΔGbind values ( -10 . 68 kcal/mol and -7 . 16 kcal/mol ) , comparable to that of the positive control ( -10 . 55 kcal/mol ) , were identified . A similar approach was followed to structurally and energetically characterize the interface of FhCL3 in complex with a peptidic substrate . Finally , through pair-wise and per-residue free energy decomposition we identified residues that are critical for the substrate/ligand binding and for the enzyme specificity . The present study is the first computer-aided drug design approach against F . hepatica cathepsins . Here we predict the principal determinants of binding of FhCL3 in complex with a natural substrate by detailed energetic characterization of protease interaction surface . We also propose novel compounds as FhCL3 inhibitors . Overall , these results will foster the future rational design of new inhibitors against FhCL3 , as well as other F . hepatica cathepsins .
Fascioliasis or hepatic distomatosis , caused by the food-borne trematodes Fasciola hepatica and Fasciola gigantica , is considered one of the most important parasitic diseases , which constitutes a serious public health problem and has a significant veterinary relevance . Economically important animals affected by this disease include cattle , sheep and goats [1 , 2] . Fascioliasis symptoms are host-specific , but generally comprise reduced milk and wool yields , weight gains , and fertility [3] . Recently , the global burden of fascioliasis was calculated and it has been estimated that 2 . 6 million people are infected with Fasciola spp . [4] . Despite the economic losses as well as the negative impact on human health , chemotherapy is currently the only viable parasite control mechanism . Benzimidazoles , in particular triclabendazole , are the most commonly-used drugs . Their targets are both immature and mature forms of the parasite , but their continued use has led to drug resistance [5] . Therefore , the search for new strategies and target molecules for the development of novel fasciolicide drugs is urgently required . The most abundant molecules found in F . hepatica secretions are papain-like cysteine proteases , termed cathepsins , which are grouped in cathepsin L and B families [6 , 7] . They are secreted in vesicle packages by gastrodermal cells into parasite gut lumen , and then released into host tissues [8] . In recent decades , the role of these proteases has been widely studied due to their importance as potential targets for the treatment of many parasite infections [9] . Cathepsins are critical for the development and survival of the parasite within the mammalian hosts . They participate in the digestion of host components such as fibronectin , collagen and albumin , which facilitates parasite migration and feeding , and can also degrade immunoglobulins and T cell surface molecules , thereby promoting immune evasion [10–12] . These proteases have an active site formed by five subsites , i . e . , S3-S2-S1-S1’-S2’ , the substrate specificity being governed by S2 and S3 subsites [13] . An analysis of the residues comprising the S2 and S3 subsites in several members of the cathepsin L family , reveals the divergence within these subsites , in particular at positions that have the greatest influence on substrate recognition , i . e . , 61 , 67 , 157 , 158 and 205 ( papain numbering ) [6 , 14 , 15] . F . hepatica can regulate the differential expression of cathepsins during its life cycle . These expression patterns have been associated with the functional diversity of papain-like proteases [12 , 16 , 17] . Previous studies have detected cathepsin B ( FhCB ) and L3 ( FhCL3 ) secretion in early invasive-stage parasites [18] . The prevalence of cathepsin L-like activity after excystation was observed in in vitro assays [19] . Also , experiments with an RNAi derived from an FhCL1 gene fragment encoding a region conserved across the cathepsin L family , led to the induction of phenotypes with abnormal motility in F . hepatica newly-excysted juveniles ( NEJ ) and a significant reduction of rat intestinal wall penetration [20] . The predominant cathepsin , found by proteomic analysis in the NEJ excretion/secretion products , is procathepsin L3 ( proFhCL3 ) [21] . The zymogen form of this peptidase progressively changes to the mature enzyme during the first 48h of NEJ development , which is mainly involved in penetration and immune response evasion [18] . Additionally , partial protection against fascioliasis in rats was obtained using a recombinant form of FhCL3 [22] . These findings suggest that FhCL3 could be a potential target for new therapies against early stages of parasite infection . It is widely accepted that the interaction patterns between enzymes and their natural substrates provide insights for drug design [23 , 24] . Accordingly , some studies have been conducted to assess the substrate specificity of FhCL3 , as well as the role of some enzyme residues ( i . e . , H63 and W69 ) in the substrate-binding process [25] . It was also demonstrated the strong preference of this cathepsin for Pro and Gly residues at P2 and P3 sites , respectively , through the usage of positional scanning synthetic combinatorial libraries [25] . The previous finding was linked to the FhCL3 collagenolytic activity , since type I and type II collagens possess repeating Gly-Pro-Xaa motifs [26] . To date some in silico modeling tools have been applied to provide structural insights into the interaction of FhCL3 with peptidic substrates , as well as to predict the affinity of the enzyme for various peptides [27] . However , no previous detailed energetic analysis of the FhCL3-substrate interactions has been performed yet . Therefore , we believe that the latter is required not only to complement previous structural analyses , but also to establish at an atomic level the nature of the interactions present at the complex interfaces , as well as to quantify their energy contribution to the binding process . Here we carried out a thorough energetic study of the binding site of FhCL3 in complex with a peptidic substrate based on a homology model . Furthermore , an FhCL3 model and HuCatL crystal structure were used for Virtual Screening ( VS ) studies and compounds with higher selectivity for the former enzyme were subsequently selected according to their Autodock Vina energy-scores ( Svina ) [28] . Finally , binding affinities were estimated through MM-GBSA absolute binding free energy ( ΔGbind ) calculations [29–31] based on thermodynamic ensembles generated with molecular dynamics ( MD ) simulations .
The search for FhCL3 homologues ( UniProt: Q9GRW6 ) [32] was carried out in a non-redundant protein database using the PSI-BLAST at the NCBI server [33] . SAS [34] and MESSA [35] servers were additionally used against the Protein Data Bank ( PDB ) [36] to retrieve the most suitable template . Automated multiple sequence alignment ( MSA ) was performed with MUSCLE v3 . 8 . 31 [37] while Seaview v4 . 3 . 1 [38] and ClustalX v2 . 1 [39] were used to edit the MSA and to determine conserved residues . Finally , a homology model of FhCL3 was generated with Modeller v9 . 11 [40] using the three dimensional ( 3D ) structure of proFhCL1 C25G [14] ( PDB: 2O6X , sequence identity ~71% ) as a template , in accordance with previous works [21 , 25 , 27] . In order to provide insights into the binding mode of peptidic substrates to FhCL3 , a 3D model of this enzyme in complex with a specific peptide , ACE-AGPR↓NAA-NME , was also built . The procedure for obtaining the complex structure was the same reported by Robinson et al [27] . VS against the FhCL3 homology model and the HuCatL structure ( PDB: 2YJC ) was carried out with AutoDock Vina v4 . 0 software [28] . Synthetic lead compounds from HitFinder database of the Maybridge British company ( http://www . mybridge . com ) were selected for this study . The VS was run using software default settings , however , the number of energetically-degenerated poses was set to ten . During docking simulations , all rotatable bonds of each ligand were allowed to freely move around the bond axes , while the protein structure was kept fixed . The grid box used to define the screening was centered on the catalytic cysteine residue , i . e . , C25 of FhCL3 and HuCatL , employing AutoDockTools . Box dimensions X , Y and Z were set to 16 . 5 , 21 and 15 Å , respectively . To identify compounds with higher specificity for FhCL3 , hit selection was based on the relative affinity for FhCL3 and HuCatL obtained from ΔSvina values . Furthermore , these hits were submitted to the DrugMint server [41] for the selection of drug-like compounds based on the best probability scores . The most probable pose of each compound within the FhCL3 binding site was selected by visual inspection . Some criteria taken into account for pose selection were ( i ) the number of hydrogen bonds between the compound and the enzyme residues and ( ii ) the available information for other compounds containing some similar chemical groups in complex with papain-like proteases [42 , 43] . Docking protocol validation was carried out through the non-covalent re-docking of nitrile ( ( 2S , 4R ) -1-[1- ( 4-chlorophenyl ) cyclopropyl] carbonyl-4- ( 2-chlorophenyl ) sulfonyl-N-[1- ( iminomethyl ) cyclopropyl] pyrrolidine-2-carboxamide ) , a well-known HuCatL covalent inhibitor , into the active site of this protease [44] . Nitrile structure was taken from the crystal of HuCatL-nitrile complex solved at 1 . 14 Å ( 2YJC ) [44] . The 3D structures of the ligands were obtained from the SDF format using Babel [45] . Then , Avogadro [46] was used for ligand protonation at pH = 7 . 4 and for subsequent steepest-descents energy minimization using Generalized Amber Force Field ( GAFF ) parameters [47] . Minimized structures were then optimized at HF/6-31G* level using Gaussian 09 package [48] . Electrostatic potentials ( ESPs ) for the optimized structures were finally generated by single-point calculations in Gaussian 09 with HF/6-31G* method and Merz-Kollman ( MK ) scheme [49] . Partial atomic charges were fitted to the ESPs through the Restricted Electrostatic Potential ( RESP ) method [50] implemented in the Antechamber program [51] . Likewise , ligand atom types , bond and dihedral angles , atomic masses and bond lengths were obtained from GAFF using Antechamber [51] . EM of free FhCL3 and all protease-ligand complexes was performed using GROMACS v4 . 6 . 3 [52] with the AMBER99SB-ILDN force field for the enzyme [53] and GAFF for the ligands . Briefly , protonation states of the FhCL3 ionizable residues were determined at pH = 7 . 4 by using the PDB2PQR server [54] . All systems were solvated with explicit TIP3P water molecules [55] in a dodecahedral box whose edges were placed at a minimum distance of 1 nm from the solute surface , and neutralized by replacing water molecules with Na+ counter ions . EM was carry out by 50000 of steepest descents steps with a tolerance of 10 kJ/ ( mol·nm ) , to relax high energy interactions and steric clashes . Subsequently , the equilibration procedure was conducted in two steps: NVT and NPT ensembles keeping the solute heavy atoms restrained . During the 200 ps NVT equilibration , temperature was kept constant at 300 K using the velocity-rescale thermostat [56] . The subsequent 200 ps NPT equilibration was performed at a temperature of 300 K using the same temperature coupling algorithm and at a pressure of 1 bar with the Parrinello-Rahman barostat [57] . A time step of 2 fs was employed to integrate the equation of motion using the Leap-Frog algorithm [58] . Random initial velocities taken from the Maxwell-Boltzmann distribution were assigned to the atoms of each system at 300 K . Cutoff radii of 1 . 4 nm and 1 . 0 nm were established for the calculation of van der Waals and short-range electrostatic interactions , respectively . The Particle Mesh Ewald ( PME ) method [59] was employed to handle long-range electrostatic interactions . Periodic boundary conditions were used at the boundaries of the unit cell . Neighbor lists were defined by a cutoff radius of 1 . 0 nm and were updated every 10 fs . All bond lengths were constrained with the LINCS method [60] . The productive run time was 130 ns and 100 ns for FhCL3-ligand and FhCL3-substrate complexes respectively . System coordinates and initial velocities were taken from the NPT simulation output . Constant temperature and pressure of 300 K and 1 bar were maintained during the simulations with the velocity-rescale thermostat [56] and the Parrinello-Rahman barostat [57] , respectively . The integrator , cutoff radii , constraint algorithm etc . were identical to those used during the equilibration steps . MM-GBSA and the Molecular Mechanics/Poisson—Boltzmann Surface Area ( MM-PBSA ) are computationally efficient methods for estimating the binding free energy ( ΔGbind ) of protein-ligand complexes [29–31 , 61 , 62] . In these methods , ΔGbind is expressed through the well-known equation: ΔGbind=Gcomplex−[Gprotein+Gligand] ( 1 ) Each term on the right-hand side of the former equation is expressed as follows in the MM-GBSA and MM-PBSA formalism: G=EMM+Gsol−TS=EMM+[GGB/PB+GSA]−TS ( 2 ) EMM , is the energy of the complex , the ligand or the protein in the gas phase , calculated as the sum of the internal energy ( Einter ) , the van der Waals energy ( Evdw ) and the electrostatic energy ( Eelec ) as expressed below: EMM=Einter+Evdw+Eelec ( 3 ) The second term of Eq 2 , ΔGsolv , represents the free energy of solvation calculated by means of implicit-solvation models . This term is further decomposed into the sum of the polar solvation ( GGB/PB ) and the non-polar solvation ( GSA ) contributions ( Eq 4 ) Gsol=Gpol+Gnp=GGB/PB+GSA ( 4 ) In the Poisson-Boltzmann model ( PB ) , the polar contribution is computed through the well-known PB equation . On the other hand , in the case of GB models , the polar solvation component ( GGB ) is calculated through Eq 5 proposed by Still et al [63] . Even though the PB model is considered as a more rigorous approach , GB models are less computationally-demanding and often give fairly satisfactory predictions [61 , 64] . The term εw is the dielectric constant of the solvent ( e . g . water ) . i and j represent the solute atoms , being rij the distance between them , qi and qj , their partial charges , and Ri and Rj , their effective Born radii . The non-polar solvation contribution is calculated through Eq 6 , where SA stands for the solvent-accessible surface area of the solute . Coefficients γ and β are empirical constants with values of 0 . 0072 kcal/mol and 0 , respectively , for the GB models [65] . In this study , MM-GBSA free energy calculations were performed using MMPBSA . py module of AMBER12 [66] . The snapshots of the complex , the receptor and the ligand were extracted from a single desolvated trajectory . GBOBC1 model ( igb = 2 ) with mbondi2 radii [67] was used for estimating ΔGGB . Snapshots belonging to the equilibrated trajectory were used for the calculation of effective binding free energy ( ΔGeff ) , which comprises all the energy terms in the right-hand member of Eq 2 except for the entropy contribution . Additionally , the snapshots were considered as statistically independent from each other during ΔGeff calculations . Conformational entropy associated with ligand binding was estimated by Normal-Mode Analysis ( NMA ) [68 , 69] . For entropy calculations , seventy frames evenly extracted from the productive MD simulations were taken . Prior to normal-mode calculations the complex , the ligand and the receptor were subjected to 50000 cycles of EM using a distance-dependent dielectric constant of 4r ( r being the distance between atom pairs ) and a dmrs value of 10–4 kcal/ ( mol Å ) as the convergence criterion for the root-mean squared gradient . Per-residue effective free energy decomposition ( ΔGres ) was carried out in order to determine the more important residues involved in FhCL3-ligand and FhCL3-substrate interactions [70] . Also , pair-wise effective free energy decomposition was performed for the FhCL3-substrate complex [70] . The trajectories were analyzed with tools provided by GROMACS v4 . 6 . 3 package [52] . The Root Mean Square Deviation ( RMSD ) was calculated during the productive run with respect to both the starting and average structures . Visual Molecular Dynamics ( VMD ) v1 . 9 . 1 [71] was used to visualize trajectories and to convert the GROMACS MD trajectory format ( xtc ) into AMBER trajectory format ( crd ) . Hydrogen bonds established between each ligand and FhCL3 were calculated employing a donor-acceptor distance cutoff ≤ 3 . 5 Å and a donor-acceptor-hydrogen angle cutoff ≤ 30 degrees during the equilibrated productive trajectory . PYMOL v1 . 6 [72] and LigPlot [73] were used for visualization , and Gnuplot v4 . 4 for graphic analysis of time profiles . UniProtKB: Q9GRW6 Protein Data Bank ( PDB ) : 2O6X , 2YJC , 1PPP , 1CVZ , 9PAP and 1ATK
A 3D-model of FhCL3 was generated based on a MSA of twelve papain-like proteases ( S1 Fig ) and using the crystal structure of proFhCL1 C25G ( PDB: 2O6X ) [14] as a template . The assessment methods employed here confirmed the high quality of the 3D-model of FhCL3 ( S1 Table and S1–S4 Figs ) , thereby suggesting its suitability for further structure-based analyses . MD simulations in combination with MM-GBSA per-residue and pair-wise free energy decomposition were performed for the FhCL3-peptide complex . Convergence and stability of the MD simulation was monitored through the inspection of structural and energetic properties . RMSD values showed different time evolution when calculated for the heavy atoms of the whole complex and for those of the peptide ( Fig 1A ) . In this regard , the whole complex showed relatively stable RMSD values , whereas the peptide displayed structural fluctuations during the first 20 ns , indicating a delay on its stabilization into binding site . This difference is a consequence of the slight contribution of the peptide atoms to the global RMSD . Additionally , instantaneous ΔGeff values were calculated ( Fig 1B ) . It is noteworthy that the accumulated mean values of ΔGeff reached relatively stable values during the MD simulation . Overall , these results suggest that 20 ns is a suitable equilibration time and , therefore , the last 80 ns were used to calculate mean ΔGeff values . Ten residues , i . e . , Q19 , G23 , G25 , W26 , G67 , W69 , Y143 , T161 , H162 and W184 , of FhCL3 largely contribute to the substrate binding ( ΔGres≤-1 . 0 kcal/mol ) according to the predictions of the per-residue free energy decomposition protocol ( Fig 1C ) . Our results showed that most peptide-FhCL3 interactions are governed by non-polar contributions ( i . e . , mainly van der Wals interactions ) . It is worth noting that some residues widely conserved within the cathepsin L family , i . e . , Q19 , G23 , C25 , W26 , G67 , H162 and W184 ( S1 Fig ) are included within the previous list . Particularly , the catalytic residues C25 and H162 are hot-spots ( residues whose side chain contribute in more than 1 kcal/mol to ΔGeff ) which establish important pair-wise interactions with the peptide residues from the P2 to the P1’ site ( Table 1 ) . Q19 is another hot-spot with a large electrostatic per-residue contribution located within the oxyanion hole of the enzyme [74] . This residue strongly interacts with the P1’ site residue through the formation of a hydrogen bond ( S5 Fig ) . Likewise , G67 was predicted as an important residue for anchoring the substrate through the formation of a hydrogen bond , in this case , with the residue at P2 ( S5 Fig ) . Interestingly , equivalent interactions involving Q19 and G67 have been observed in the 3D structures of other papain-like proteases in complex with peptidomimetic compounds [75 , 76] . Finally , the side chain of W184 and , to a less extent , that of W26 establish favorable van der Waals interactions with residues at P1’-P3’ and P2 sites , respectively ( Table 1 and Fig 1C and 1D ) . Overall , our predictions are in agreement with the essential roles attributed to some of the previously-mentioned residues within the papain-like family . On the other hand , some non-conserved FhCL3 residues such as W69 , Y143 and T161 have the largest per-residue free energy contributions to the complex formation ( Fig 1C and 1D ) . W69 establishes strong van der Waals interactions with the substrate residues Ala ( P4 ) , Gly ( P3 ) and Pro ( P2 ) ( Table 1 ) . Interestingly , our predictions showed that the ring of Pro ( P2 ) adopts a nearly-perpendicular conformation with respect to the indol group of W69 ( Fig 1D ) , which precludes the stabilization through stacking interactions proposed before [21 , 25] . Probably , a crucial role of Pro at the P2 site , given its bend-inducing capacity , is to promote the appropriate conformation of the substrate backbone within the enzyme binding site , especially that of the residue at P3 , which strongly interacts with W69 ( Table 1 ) . In addition , the structural analysis showed the occurrence of close contacts between the backbone of residues at P4 and P3 sites with the indol ring of W69 . Therefore , the substitution of the previous substrate residues by bulky amino acids could disrupt the interface complementarity thereby reducing the binding affinity as has been proven at least for the P3 site [25] . All these predictions clarify from an energetic point of view the experimental results that established the importance of W69 in determining the enzyme specificity for Gly and Pro residues at P3 and P2 , respectively [21 , 25 , 27] . Of note , the MD simulation of the peptide-FhCL3 complex performed here also confirmed that the most representative rotameric configuration of W69 side chain in the bound state of the enzyme corresponds to that predicted by Corvo et al based on molecular modeling approaches [25] . This particular conformation partially occludes the S2 subsite and favors the interaction with Gly ( P3 ) as predicted through our energetic analysis ( Table 1 ) and also suggested before [25 , 27] . In the case of Y143 , it was predicted the formation of a hydrogen bond comprising its phenol group and the carbonylic oxygen of Asn ( P1’ ) ( S5 Fig ) , which , in addition to its van der Waals interaction with Ala ( P3’ ) , explains the large energy contribution of this residue ( Table 1 and Fig 1D ) . In general , we believe that the interaction with Y143 might enhance the specificity of ligands toward FhCL3 , given that this residue is not conserved within the papain-like family and also bears a hydroxyl group with the capacity of forming specific hydrogen bonds with the substrate . Additionally , we obtained that the backbone of T161 has the largest energy contribution to the complex formation ( Fig 1C and 1D ) , which mainly arises from the hydrogen bonds involving its carbonyl oxygen ( O ) and the amidic hydrogen atoms of residues at P1 and P1’ sites ( S5 Fig ) . Note that although position 161 is not conserved throughout the papain-like family [21] ( S1 Fig ) and its energy contribution to the substrate binding is large , at least for the complex analyzed here , its nature seems to be irrelevant . The latter stems from the fact that its interactions with the peptide residues are mediated mostly by its backbone oxygen atom rather than by its side chain hydroxyl group , which extends away from the interface in disagreement with previous suggestions [21] . Remarkably , equivalent interactions have been observed between D158 of papain and the amidic hydrogen atoms at the P2 site of petidomimetic inhibitors ( PDBs: 1PPP and 1CVZ ) [75 , 77] , thereby reinforcing our previous predictions . Finally , we predicted the low energy contribution of H63 to the substrate binding . Hence , this position is not likely to be involved in ligand binding . This result explains the little impact of the H63N mutation on the specificity substrate profiles of FhCL3 [25] . The VS protocol used for the identification of potential ligands of FhCL3 was first validated through the non-covalent re-docking of nitrile within the binding site of HuCatL . This inhibitor has the sulfone chemical group ( Fig 2A ) , which is common in many parasitic cysteine protease inhibitors [78 , 79] . Irreversible inhibition mechanism occurs through covalent bond formation with the thiolate of the catalytic cysteine [80] . The RMSD for the heavy atoms of the re-docked pose having the highest Svina value with respect to the experimental binding mode was only 3 . 4 Å ( Fig 2B ) . Additionally , hydrogen bonds between nitrile and residues G68 and D162 , were also reproduced in the predicted HuCatL-nitrile complex ( Fig 2C ) . Note that only non-covalent interactions were taken into account in the re-docking simulation , therefore , the FhCL3-nitrile pre-complex rather than the actual covalent complex was modelled here . Overall , this procedure provided a reasonable prediction of nitrile experimental binding mode which , in turn , validates our docking protocol . In order to reduce cross-inhibition between host and parasite targets , we took into account only the potentially-selective ligands for the parasitic enzyme . Through VS calculations , twelve compounds with relative binding free energy values lesser than -1 . 50 kcal/mol ( ΔSvina<-1 . 50 kcal/mol ) between FhCL3 and HuCatL complexes were identified ( S2 Table ) . Moreover , according to the DrugMint scores , only five compounds constitute potential drug-like non-peptidic inhibitors ( S2 Table ) . The names of these selective compounds and their Svina values are listed in Table 2 . Non-peptidic inhibitors are considered as the best strategy for in vivo inhibition in order to avoid degradation by proteases . In this sense , structure analysis suggests a common peptidomimetic scaffold among the selected compounds . Besides , they all possess a certain number of aromatic moieties , i . e . , phenyl , naphtalene and bencyl groups , which increases their hydrophobicity . Those moieties could establish favorable hydrophobic interactions with the non-polar residues of FhCL3 binding site ( Fig 3 ) . Interestingly , several cysteine protease inhibitors reported so far bear aromatic functional groups in their structures [43 , 81 , 82] . On the other hand , the heterocyclic rings , i . e . , triazole , pyrrol and isoxazole , present in some of the selected compounds , could establish polar interactions with various active site residues ( Fig 3 ) . Some of the selected compounds , i . e . , HTS12701 and RH0159 , share a thiomethylene-ketone moiety ( Fig 3A and 3E ) , which is present in various reversible inhibitors of cysteine proteases from Plasmodium falciparum and Leishmania donovani parasites [81] . The inhibitory mechanism of these compounds might involve the formation of transition-state-like hemithioacetal complexes with C25 at the protease active site [81] . On the other hand , the hydrazide moiety was observed in compounds HTS11101 and RH01594 ( Fig 3D and 3E ) . This moiety is frequently present in inhibitors of other parasite cysteine proteases [43 , 81] . Finally , in the case of BTB03219 , it can be highlighted the presence of an aryl_CF3 substituent , which could increase the affinity for FhCL3 through halogen bond formation ( Fig 3B ) , as has been reported for other cysteine proteases like HuCatL [44] . The VS protocol performed in this paper led to the identification of five compounds as possible inhibitors of FhCL3 . However , the refinement of docking results is needed to more accurately predict the binding modes of the compounds to the enzyme , as well as the absolute and relative binding free energy values of the complexes . In this sense , the combination of conformational space-exploring techniques and end-point free energy calculation methods such as MD simulations and MM-GBSA , respectively , constitutes a useful approach to assess the stability of protein-ligand complexes [83–85] . Prior to free energy calculations , the stability and convergence of MD simulations were monitored through per-frame ΔGeff time profiles . In this regard , fluctuations for the FhCL3-ligand complexes are shown together with accumulated mean values ( Fig 4 ) . ΔGeff values are quite variable for each snapshot , but the accumulated mean values become stable in most of cases . Besides , significant differences in the stabilization time for every complex are clearly observed ( Fig 4 ) . The latter may result from the fact that for some FhCL3-ligand complexes the initial structures predicted by docking were farther from their MD average structures than for others ( S6 Fig ) . Accordingly , the subsequent ΔGbind calculations and the analyses of binding determinants and hydrogen bond formation are based on snapshots collected after the equilibration time of productive MD simulations . To better understand the main energy components contributing to the formation of the different FhCL3-ligand complexes , we analyzed the MM-GBSA free energy components , i . e . , van der Waals , electrostatic , polar solvation , non-polar solvation and entropy ( TΔS ) contributions ( Table 3 ) . The results indicate that non-polar contributions ( ΔEvdw+ΔGSA ) dominated the binding process , because the complex formation reduces the Solvent Accessible Surface Area ( SASA ) and the enzyme binding site has hydrophobic-interacting residues , i . e . , W69 , V160 , V209 and W184 , which establish favorable van der Waals interactions with the different ligands . Conversely , the polar ( ΔEelec+ΔGGB ) and entropy terms have unfavorable contributions in all cases . MM-GBSA results show that HTS12701 is the compound with the best ΔGbind value ( -10 . 71 kcal/mol ) , followed by BTB03219 ( -8 . 16 kcal/mol ) , both of them similar to the positive control ( nitrile , -10 . 55 kcal/mol ) . Furthermore , Ki values calculated from theoretical ΔGbind values suggest that HTS12701 and BTB03219 bind the enzyme in the sub-micromolar and micromolar concentration ranges ( Table 3 ) , respectively , the former being predicted as a tight-binding inhibitor . The theoretical Ki values for the rest of the ligands predict their low affinity interactions with the enzyme . Interestingly , the comparison between both the average structure of the productive MD simulation and initial docking pose shows that compounds like HTS12701 and BTB03219 keep bound to FhCL3 active site through specific interactions like nitrile ( S6A–S6C Fig ) . Particularly , the former compound adopts a conformation during the MD simulation which places the thiomethylene ketone moiety closer to C25 , in agreement with the proposed binding mode of this compound series [81] . On the other hand , the remaining compounds , i . e . , SPB07884 , HTS11101 and RH01594 ( S6D–S6F Fig ) do not form stable complexes during simulation time , which explains their more unfavorable ΔGbind values . Finally , by comparing the MM-GBSA and Svina values we can observe changes in the ranking list of the selected compounds . However , the energy values lie within the similar ranges of free energy values ( from -11 kcal/mol to -8 kcal/mol ) for the best hits ( compare Tables 2 and 3 ) . As MM-GBSA is considered to be a more accurate method for estimating relative affinities [61 , 64 , 83] , its predictions were taken as the final criterion for hit ranking . In order to get insights into FhCL3-ligand interactions , the MM-GBSA approach was employed to decompose the binding effective free energy of the high-affinity complexes into per-residue contributions ( Fig 5 ) . This allowed us to identify the residues at the enzyme active site with significant energy contributions to the ligand binding . In general , we observed that some of these residues , e . g . G67 , V160 and T161 lie within the S2 and S3 subsites , indicating substrate-like binding modes of the ligands to FhCL3 . The subsequent decomposition of ΔGres into backbone and side chain energy contribution led to the identification of six critical ( warm/hot-spot ) residues , i . e . , Q19 , C25 , W69 , V160 , W184 and V209 , whose respective side chain energy contributions were larger than those of the backbone . The latter suggests the essential role of these specific residues in the formation of some FhCL3-ligand complexes ( Fig 5 ) . It is also worth noting the prevalence of per-residue non-polar energy contribution in all systems , in agreement with ΔGeff decomposition results shown in the previous section . Accordingly , most of the previously-mentioned residues are hydrophobic and , thus , can establish strong van der Waals interactions with compounds containing aromatic groups ( Fig 5 ) . For the FhCL3-nitrile complex , four energetically-relevant residues , i . e . , G67 , W69 , V160 , and T161 ( Fig 5 ) , were identified . W69 and V160 are likely to interact with hydrophobic moieties ( Fig 6A ) , while T161 forms a stable hydrogen bond with the N2 atom of nitrile , equivalent to that described before for the FhCL3-peptide complex ( Fig 6A ) . Overall , these results are consistent with a previous work which state that nitrile non-covalent interactions comprise the enzyme active site and , especially , the specificity substrate subsites ( S2 and S3 ) [44] . The complexes of FhCL3 with the best hits , i . e . , BTB03219 and HTS12701 , and nitrile share a group of common energetically-relevant residues , i . e . , G23 , C25 , W26 , G67 , V160 , T161and H162 ( Fig 6 ) . However , some other residues show significant differential energy contributions among the three complexes . For example , W69 , which largely contributes to the formation of FhCL3-nitrile and FhCL3-BTB03219 complexes , seems to be irrelevant for HTS12701 binding to the enzyme . A similar behavior was observed for G68 . Conversely , HTS12701 establishes strong interactions with Q19 and W184 , residues belonging to S1-S1’ subsites , not observed in the other two complexes . Both residues are conserved throughout the cathepsin L family , which suggests their essential role in substrate binding , as observed for the FhCL3-peptide complex analyzed before . Therefore , HTS12702 may display low selectivity toward the proteases of this family . On the other hand , BTB03219 preferentially interacts with residues of the S2–S3 subsites , which are believed to control the enzyme specificity . Hence , though less potent than HTS12702 , it is likely to be a more selective inhibitor . Note , however , that both compounds are predicted to display a roughly equivalent specificity for FhCL3 with respect to HuCatL , according to their respective ΔSvina values ( S2 Table ) . Further insight into the structural determinants for the interaction of nitrile , BTB03219 and HTS12701 with FhCL3 was obtained through the hydrogen bond and hydrophobic contact analysis at the interfaces of these complexes . For example , W69 establishes hydrophobic interactions with aromatic rings of both nitrile and BTB03219 , which is consistent with the large van der Waals energy contribution to the ΔGres values of this residue ( Fig 6 ) . Another residue showing favorable hydrophobic interactions with the three compounds is V160 , whose side chain interacts with the ligand hydrophobic moieties lying within the S2 subsite . On the other hand , the carbonyl oxygen atom ( O ) of T161 is involved in hydrogen bond formation with both BTB03219 and nitrile , which suggests the importance of this position in accommodating hydrogen donor groups within the S2 subsites . Interestingly , in this particular position the nature of the residue is irrelevant for protein-ligand interactions , as was also obtained for the FhCL3-peptide complex . Furthermore , Q19 at the S1’ subsite forms two alternative hydrogen bonds with acceptor nitrogen atoms of HTS12701 . Unlike the previous case , the nature of this residue is important , since the hydrogen bond involves the NE2 atom of its side chain . In fact , Q19 is part of the oxyanion hole of cysteine proteases and stabilizes the tetrahedral intermediate of protease-substrate complexes [74] . Finally , the carbonyl oxygen atoms of Q19 and G66 may form halogen bonds with the fluorine atoms of BTB03219 ( Fig 6 ) , thereby contributing to the affinity of this particular compound for FhCL3 . Interestingly , halogen bonds have been observed in the crystal structure of HuCatL in complex with nitrile and are believed to contribute to the binding process [44] . Similar analyses were carried out for the compounds with less favorable ΔGbind values ( S7 and S8 Figs ) . A close inspection of the representative structures of the FhCL3-ligand complexes also revealed that the side chain of W69 can adopt different rotameric conformations depending on the nature of the ligand bound to the enzyme ( Fig 5 and S7 Fig ) . Specifically , we observed that in the FhCL3-nitrile complex the side chain of W69 has a coaxial orientation with respect to the binding site , while in the other complexes it has a perpendicular conformation that partially occludes the S2 subsite , as obtained for the FhCL3-peptide complex analyzed before . It is worth saying that even though both rotameric conformations have been proposed before [25] , this is the first time that their occurrence is predicted through MD simulations of FhCL3 in complex with different ligands . Therefore , our results reinforce the importance of W69 side chain rotation for the accommodation of ligands with different shapes within the enzyme binding site , as suggested before [21 , 25] . Overall , we proposed some crucial interaction patterns between the selected compounds and FhCL3 . Finally , as expected , the best hits interact with residues previously characterized for the FhCL3-peptide complex , which indicates their substrate-like binding mode . The list of most favorable residues ( C25 , W26 , G67 , V160 , T161 and H162 ) for ligand interaction is roughly similar for most of the compounds analyzed here . Remarkably , there are some distinctive residues of FhCL3 mediating the interactions with the ligands through its side chains , i . e . , W69 and V160 , which have been identified as substrate-specificity determinants [21] . In the present study , a computational protocol consisting of VS , MD simulations , and binding free energy calculations was used to search for novel and selective inhibitors against FhCL3 . Additionally , the results obtained here enhanced our understanding of the binding determinants of this protease with peptidic substrates and organic ligands . Free energy calculation through a more accurate method , i . e . , MM-GBSA , proved to be a useful post-docking refinement tool , since a new ranking list different from that of VS , was finally obtained . The further decomposition of the overall binding free energies into individual energy terms indicated that the van der Waals interactions are the dominant force for substrate/ligands binding . Moreover , the decomposition of the binding free energy into per-residue contributions showed that the non-polar side chain of residue W69 establishes critical van der Waals interactions with the substrate and some ligands . This agrees with a previous work that highlights the importance of this residue for FhCL3 specificity [25 , 27] . Interestingly , we also observed that the side chain of this residue may adopt different conformations to accommodate different ligand groups within enzyme binding site . The previous results suggest that a flexible docking protocol allowing the rotation of the side chain of W69 would lead to the identification of more diverse scaffolds of FhCL3 ligands . Roughly six different residues , i . e . , C25 , W26 , G67 , V160 , T161 and H162 , were predicted as energetically-important for ligand and/or substrate anchoring inside the FhCL3 active site via hydrophobic and hydrogen bond interactions in almost all complexes . However , the nature of T161 , one of the residues with very large energy contribution , seems to be irrelevant , since its main interactions involved the backbone oxygen atom , suggesting that the variation of this residue within the S2 subsites of papain-like proteases may not necessarily affect the substrate binding . Overall , we proposed HTS12701 and BTB03219 as promising lead compounds that could be FhCL3 inhibitors . We expect that the structural insights obtained in this study will facilitate the design of novel inhibitors against FhCL3 .
|
Fascioliosis is considered an emerging disease in humans , causing important losses in global agriculture through the infection of livestock animals . The outcome of resistant parasites has increased the search for new drugs which may contribute to disease control . In recent decades , Fasciola cathepsins ( FhCs ) have been defined as the principal virulence factors of this parasite . Despite being in the same protein family , they have different specificities and , thus , distinct roles throughout the fluke life cycle . Differences in specificity have been attributed to a few variations in the sequence of key FhCs subsites . Currently , the structure-based drug design of inhibitors against Fasciola cathepsin Ls ( FhCLs ) with unknown structures is possible due to the availability of the three-dimensional structure of FhCL1 . Our detailed structural analysis of the major infective juvenile enzyme ( FhCL3 ) identifies the molecular determinants for protein binding . Also , novel potential inhibitors against FhCL3 are proposed , which might reduce host invasion and penetration processes . These compounds are predicted to interact with the binding site of the enzyme , therefore they could prevent substrate processing by competitive inhibition . The structure-based drug design strategy described here will be useful for the development of new potent and selective inhibitors against other FhCs .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"and",
"Discussion"
] |
[] |
2015
|
Insights into the Interactions of Fasciola hepatica Cathepsin L3 with a Substrate and Potential Novel Inhibitors through In Silico Approaches
|
In recent decades , numerous studies have sought to better understand the mechanisms underlying the compatibility between Biomphalaria glabrata and Schistosoma mansoni . The developments of comparative transcriptomics , comparative genomics , interactomics and more targeted approaches have enabled researchers to identify a series of candidate genes . However , no molecular comparative work has yet been performed on multiple populations displaying different levels of compatibility . Here , we seek to fill this gap in the literature . We focused on B . glabrata FREPs and S . mansoni SmPoMucs , which were previously demonstrated to be involved in snail/schistosome compatibility . We studied the expression and polymorphisms of these factors in combinations of snail and schistosome isolates that display different levels of compatibility . We found that the polymorphism and expression levels of FREPs and SmPoMucs could be linked to the compatibility level of S . mansoni . These data and our complementary results obtained by RNA-seq of samples from various snail strains indicate that the mechanism of compatibility is much more complex than previously thought , and that it is likely to be highly variable within and between populations . This complexity must be taken into account if we hope to identify the molecular pathways that are most likely to be good targets for strategies aimed at blocking transmission of the parasite through the snail intermediate host .
Schistosomes are the causative agents of schistosomiasis , which is one of the most important neglected human tropical diseases in the world . Schistosomes infect over 200 million people worldwide , causing both acute and chronic debilitating diseases [1 , 2] . There is no effective vaccine against schistosomes , and the treatment of schistosomiasis still relies on a single drug: praziquantel [3] . Praziquantel resistance can be easily selected experimentally [4] , and some human populations subjected to mass treatment now show evidence of reduced drug susceptibility [5] . Thus , we need alternate control strategies . Toward this end , researchers have sought to block disease transmission at the level of the snail that acts as the intermediate host . However , if we hope to identify target genes that may be used to develop new strategies aimed at disrupting the transmission of schistosomiasis , we must decipher the mechanisms through which snails and schistosomes interact . Over the past four decades , numerous investigators have sought to understand these mechanisms by focusing on the interaction between Biomphalaria glabrata and Schistosoma mansoni , which was chosen as a model system . The genetic determinism of the compatibility between B . glabrata and S . mansoni was clearly demonstrated by the C . S . Richards group in the 1970s [6 , 7] . Since then , several research groups have investigated the underlying molecular determinants using different laboratory strains of snails and schistosomes . Genetic studies of crosses between snail lines displaying compatible and incompatible phenotypes have revealed some candidate loci , including a gene cluster containing a super oxide dismutase ( SOD ) -encoding gene [8–10] and a genomic region containing genes putatively involved in parasite recognition [11] . Various transcriptomic comparisons have also been performed on other compatible and incompatible strains of snails and schistosomes [12–16] . These studies uncovered a series of candidate genes involved in recognition , effector , and signaling pathways that could contribute to the compatibility process ( see [17] for a recent review ) . Taken together , the previous reports clearly show that the success or failure of S . mansoni in infecting B . glabrata reflects a complex interplay between the host’s defense mechanisms and the parasite’s infective strategies . Little is known about the molecular variability playing of these molecular determinants underlying the compatibility; only one work has studied and shown the differential allelic expression of a SOD gene in different individuals of the predominantly resistant 13-16-R1 strain of B . glabrata [10] . The objective of the present work is to fill this gap by studying the molecular determinants of compatibility in different populations with varied compatibility phenotypes , in order to evaluate potential between-population differences in the compatibility mechanisms . To achieve this aim , we focused on molecular determinants known to be involved in snail/schistosome compatibility , and studied their expressions and polymorphisms in host and parasite isolates that differ in their compatibilities . We first studied the SmPoMucs ( polymorphic mucins from S . mansoni ) , which were initially identified by a comparative proteomic analysis of two strains of S . mansoni that differed in their compatibility towards the same mollusk strain [18] . SmPoMucs share the general features of mucins , including a N-terminal domain containing a variable number of tandem repeats and a conserved C-terminal domain [19] . These proteins are expressed only by larval-stage parasites during interactions with the snail intermediate host; they are produced and located in the apical gland of miracidia and sporocysts , and are characterized by high levels of glycosylation and polymorphism [19 , 20] . A detailed analysis of intra- and inter-strain SmPoMuc polymorphisms revealed that the diversification of these proteins has been driven by a complex cascade of mechanisms involving recombination between genes of the multigene SmPoMuc family ( 10 genes ) , epigenetic control of transcription , post-transcriptional regulation events , and post-translational modifications [20–22] . This yields a remarkably high degree of diversification from a limited set of genes , enabling each individual parasite to express a specific and unique pattern of SmPoMucs [20] . Functionally , these proteins are thought to play roles in the very early steps of infection [23] . Based on the above findings , it has been proposed that SmPoMucs could be crucial antigens in the snail-schistosome compatibility process . Then , we developed co-immunoprecipitation ( CoIP ) experiments that enabled us to identify putative SmPoMuc-interacting immune receptors of the snail [24] . We found that SmPoMucs form molecular complexes with the fibrinogen-related proteins ( FREPs ) of B . glabrata [25] . FREPs are highly polymorphic , with somatic diversification generating unique repertoires in individual B . glabrata [26] . Thus , we considered these proteins to be good candidates as molecular determinants on the snail side of the compatibility between B . glabrata and S . mansoni . This importance of FREPs in the compatibility process was confirmed by specific knockdown of FREP 3 in B . glabrata BS-90 snails , which are totally resistant to a specific laboratory strain of S . mansoni [27] . The knockdown snails lost 21 . 4% of their resistance to S . mansoni infection , suggesting that FREP 3 participates in recognition but is not the sole determinant . As FREP immune receptors and their SmPoMuc antigens are clearly involved in the compatibility process , we herein focused on these molecular determinants of the recognition process between the host and the parasite . We first characterized the compatibilities among all sympatric and allopatric combinations of four strains of S . mansoni ( two from Brazil , one from Venezuela , and one from Guadeloupe Island ) and four strains of B . glabrata ( from the same locations ) from South America and the Caribbean area . We then used targeted approach to analyze the expressions of SmPoMucs in Schistosomes and global transcriptomic approach on FREPs between the four strains of B . glabrata . The global transcriptomic analysis of snail strains also revealed large transcriptional differences , especially for the B . glabrata strain that showed the least compatibility when confronted with the studied schistosome strains . Global transcriptomic differences were observed among numerous genes involved in the different phases of the immune response . Based on our findings , we propose that the compatibility between B . glabrata and S . mansoni depends on a multistep process that involves both recognition and effector/anti-effectors systems .
As the objective of the present work was to evaluate the putative link between the expression patterns of SmPoMucs and FREPs and the compatibility between snails and schistosomes , we first established the compatibility levels of four strains of B . glabrata ( BgBAR , BgVEN , BgBRE , and BgGUA ) when confronted with four strains of S . mansoni ( SmLE , SmVEN , SmBRE , and SmGH2 ) . Compatibility was tested for all sympatric and allopatric combinations . All strain combinations displayed different compatibility levels ( Fig 1 ) . SmLE displayed the highest compatibility , showing a 96–100% prevalence of infection when confronted with the four B . glabrata strains . SmGH2 showed the least compatibility , exhibiting prevalences of 0–44% for allopatric combinations and only 60% when confronted with its sympatric mollusk . SmVEN was highly compatible ( 100% ) with its sympatric mollusk strain and with BgBRE and BgGUA , but it was less efficient when infecting the BgBAR snail strain ( 55% ) . SmBRE displayed an intermediate compatibility phenotype; it showed 100% prevalence for its sympatric mollusk BgBRE , and its compatibility ranged from 12% to 83% when confronted by the other mollusk strains . Our results for the intensity of infection followed similar trends , ranging from 1 to 8 . 4 parasites per infected snails ( Fig 1 ) . The infective capacities of the four parasite strains exhibited a gradient of compatibility in the following descending order: SmLE , SmVEN , SmBRE , and SmGH2 . Considering the mollusk strains , BgBAR was the least compatible; it exhibited prevalences of 0–55% when confronted with allopatric parasite strains and reached 96% when infected with its sympatric SmLE , but the intensity of infection never exceed 3 . 9 ( even for SmLE ) . BgVEN , BgBRE , and BgGUA showed very similar compatibility patterns , exhibiting complete compatibility ( prevalence , 100% ) when exposed to SmLE and SmVEN , but less compatibility with SmGH2 . BgGUA and BgVEN displayed very similar compatibility patterns , showing slightly more infectivity when confronted with SmBRE than SmGH2 . BgBRE was totally compatible with its sympatric schistosome , but displayed a very low compatibility with SmGH2 ( 4% ) . The intensities were generally high , largely between 4 . 7 and 8 . 4 . Our results indicate that BgBAR is the least compatible host strain , whereas BgVEN , BgBRE , and BgGUA are much more compatible and display quite similar compatibility phenotypes . We previously showed that the SmPoMucs are encoded by a multi-gene family of 10 members that can be divided into four paralogous sequence groups ( groups 1–4 ) [20] . Fig 2A shows the SmPoMuc cDNA structure shared by the different groups . Their 5’ regions comprise a variable number of tandem repeats corresponding to repetitions of exon 2 , whereas the 3’ regions ( exons 3 to 15 ) differ in their sequences , enabling the SmPoMucs to be divided into groups 1 through 4 . At the transcript level , only groups 1 , 2 and 3 . 1 have been detected to date [20] . SmPoMucs of groups 1 and 2 contain the same type of exon 2 ( r2 , 27 nucleotides ) that is repeated in the transcript structure , while those of group 3 contain a different type of repeated exon 2 . Most group 3 SmPoMucs have r1 as their repeated sequence , except for the subgroup denoted group 3 . 1 ( r1-r2 ) , which displays both types of exon 2 ( r1 and r2 ) in the gene structure . These recombined genes are present in the genomes of different schistosome strains , but appear to be expressed only in SmGH2 ( see also [19 , 20] ) . To analyze SmPoMuc transcript polymorphism , RNA was extracted from 11 individual sporocysts of each strain ( SmBRE , SmVEN , SmLE , and SmGH2 ) and subjected to nested RT-PCR . Consensus primers ( see Fig 2A for the positions of the utilized primers ) were used to amplify the complete coding sequence of all SmPoMucs in each individual . Fig 2B shows the SmPoMuc banding patterns obtained on agarose gels . We first examined inter-strain variability . Consistent with the results of previous studies performed using the same method [20] , the SmPoMuc banding patterns were highly different across the analyzed strains . To analyze this polymorphism at the protein level , we analyzed SmPoMucs by Western blotting . Proteins from 5000 individuals of each S . mansoni strain were resolved and detected with an anti-SmPoMuc antibody directed against a conserved region of the SmPoMucs [24] . As shown in Fig 2C , the SmPoMuc protein patterns differed across SmLE , SmVEN , SmBRE , and SmGH2 , each isolate expressed a specific SmPoMuc profile . With respect to intra-strain comparisons , the polymorphism was found to be very high at the transcript level . The SmBRE and SmGH2 banding patterns were obtained from a previous work [21] and the banding patterns of SmVEN and SmLE was obtained in the present work . For the 4 S . mansoni isolates , no two individuals display the same amplification profile ( Fig 2B ) . To more precisely characterize these patterns , we sequenced the amplicons obtained from each individual of the four S . mansoni strains . The results are shown in S1 Table . All individuals expressed multiple variants; some expressed only variants belonging to a single group of SmPoMucs , while others expressed variants from two or three groups . Within each group , the SmPoMuc transcript polymorphisms reflected: ( i ) a variable number of tandem repeats ( from 1 to 100 ) in the 5’ region; ( ii ) the occurrence of alternative and aberrant splicing in the 3’ region; and ( iii ) nucleic acid substitutions ( synonymous or non-synonymous ) in the 5’ and 3’ regions . These different mechanisms were previously reported [19 , 20] . We did not observe any clear link between the presence of a given variant and the compatibility level of the different strains , with one exception . A sub-group of variants belonging to SmPoMuc group 3 . 1 , named group 3 . 1 ( r1-r2 ) , was more abundant in the less compatible SmGH2 strain than in the other strains . This variant was found in six individuals of SmGH2 , but in only one individual each of SmBRE and SmVEN and no individual of SmLE . Interestingly , SmLE and SmVEN , which were the most infective strains , displayed a larger number of SmPoMuc variants ( 276 and 247 , respectively ) than the less infective SmBRE and SmGH2 strains ( 40 and 84 , respectively ) . It is interesting to note that all of the 647 variants sequenced in the present study were different . These results are shown in Fig 3 . The expression levels of SmPoMuc transcripts were assessed for each S . mansoni strain by RT-Q-PCR . Primers E11allgrFw and E14allgrRv were universal to all SmPoMuc genes , whereas the other utilized primers ( see Fig 4 ) allowed us to quantify the transcripts corresponding to the group 1 , group 2 , group 3 . 1 , and group 3 . 1 ( r1-r2 ) SmPoMuc genes . The positions of the utilized primers are indicated in Fig 4A . Relative expression ratios were calculated using α-tubulin as a reference gene ( Fig 4B ) . Our results revealed that the levels of SmPoMuc transcripts of all groups differed across the four schistosome strains . SmLE and SmBRE displayed the lowest levels of SmPoMuc transcripts , and SmVEN and SmGH2 expressed around 2 and 2 . 5 fold more SmPoMuc transcripts , respectively ( Mann-Whitney test: LE vs VEN P = 0 . 0033; LE vs GH2 P = 0 . 0079; BRE vs VEN P = 0 . 0021; BRE vs GH2 P = 0 . 0025 ) . With respect to the different SmPoMuc groups , the transcription patterns were quite similar between the strains: group 1 was weakly expressed in all cases , whereas group 2 and 3 . 1 transcripts were expressed at higher levels ( 2- to 15 fold more , depending on the group and strain ) . The group 3 . 1 ( r1-r2 ) SmPoMuc transcripts were highly expressed only in strain SmGH2 ( Mann-Whitney test: GH2 vs LE P = 0 . 0079; GH2 vs BRE P = 0 . 0025; GH2 vs VEN P = 0 . 0009 ) . Interestingly , the sum of the group-level expression data did not correspond to the SmPoMuc expression revealed by the SmPoMuc universal primers . This may reflect that some of the spliced variants of SmPoMucs revealed in a previous study [20] are amplified only with universal primers . As no specific primer could be designed to specifically amplify these spliced variants , we were unable to test this hypothesis . Until now , most of the experiments done on compatibility between Schistosomes and Biomphalaria snails were conducted using targeted Quantitative PCR or micro-array approaches to identified differentially represented transcripts following infection . In the present paper a more global and powerful approach was conducted to identify the differentially regulated transcripts or differential level of constitutive expression between snail strains . This global approach will also ensure a gene discovery effort without foreseeing the molecules involved compared to targeted approaches . To investigate such differences , four B . glabrata strains were used . The global transcript representation was analyzed by RNAseq and correlated with their compatibility phenotypes . BgBAR was the less compatible strain , while BgBRE , BgVEN and BgGUA present higher compatible phenotypes that were mostly similar ( Fig 1 ) . To compare the native immune potentials of the four Bg strains , we compared the biological replicates , BgBRE1 and BgBRE2 , to the other Bg strains . BgBRE strain was selected for duplicate sequencing because it is the strain for which we have the largest number of individuals in the laboratory . To identify the differentially represented transcripts in the transcriptomes of the different strains , we used the DEseq2 software comparing the duplicate of BgBRE strain to the others transcriptomes . Of the 117 , 269 transcripts of the BgBRE transcriptome , 6555 ( 5 . 6% ) were found to be differentially expressed between BgBRE and the three other B . glabrata strains . Fig 5A and 5B present the numbers of BgBAR , BgVEN , and BgGUA transcripts that were significantly over- and under-represented , respectively . BgBAR showed the highest number of differentially expressed transcripts ( 1257 over-represented and 3462 under-represented ) . In contrast , BgVEN and BgGUA showed differential expression ( both over- and under-representation ) of 2983 and 1613 transcripts , respectively . Of the 6555 modulated transcripts identified in the present study , 72% , 45% , and 25% were from BgBAR , BgVEN , and BgGUA , respectively . Based on our observations that BgBAR differed the most from the other strains in both its molecular phenotype and its compatibility phenotype , we decided to analyze the putative functions that were enriched among its differentially expressed genes . We carried out a Gene Ontology ( GO ) enrichment analysis on the differentially expressed transcripts that were uniquely over-represented ( 865 transcripts , Fig 5C ) and under-represented ( 2059 transcripts , Fig 5D ) in BgBAR relative to BgBRE . The over-represented transcripts of BgBAR corresponded to 85 biological processes representing 44 categories . Interestingly , eight categories ( red arrows in Fig 5C ) that contained 15% of the transcripts were grouped together by the REVIGO software into the biological process of “response to biotic and abiotic stress . ” One additional category ( 3% of the transcripts ) corresponded to the biological process of “reactive oxygen species metabolism . ” These two biological processes are clearly of interest in our study context . Indeed reactive oxygen species ( ROS ) have been widely shown to be involved in snail/parasite compatibility [8 , 28 , 29] . Reactive oxygen species ( ROS ) produced by the hemocytes of B . glabrata are known to play a crucial role in killing S . mansoni [30 , 31] . This process could be related to the level of H2O2 expressed by resistant snails associated with a higher expression of SOD genes ( copper/zinc superoxide dismutase ) [8 , 32] . The under-represented transcripts of BgBAR fell into 18 categories . Among them , only the “defense response” ( 3% of the transcripts ) seems likely to be related to immune function . Considering the previous results presented above , we decided to deepen our investigation of immune functions in the different Bg strains . We first identified genes bearing immune-relevant domains and examined their proportions in the studied strains . We referred to a previous study in which the whole transcriptome of BgBRE was screened for the presence of immune-relevant domains using Interproscan [33] . Using these data , we determined the proportion of the relevant transcripts that were differentially expressed in the strains studied herein . The results are shown in Fig 6A . Given the focus of the present paper and our interest in FREPs , we found it interesting that 3% and 10% of all transcripts bearing an “Ig-fold” domain were over and under-represented , respectively , in BgBAR . This particular domain is shared by a family of variable immunoglobulin and lectin domain-containing proteins [33]; notably , this family includes numerous FREPs . A large proportion of the transcripts corresponding to proteins containing other recognition domains , as well as immune signaling molecules and effectors , were also differentially represented between BgBRE and the other three strains . As expected , BgBAR had higher proportions of differentially expressed genes for most of the categories of putative immune-relevant genes . To study the immune function of the different snail strains more deeply , we constituted a B . glabrata immunome comprising 122 transcripts identified after immune challenge of B . glabrata or by comparative "omic" analyses of snail strains displaying different compatibility phenotypes towards trematodes ( S2 Table ) [11 , 17 , 34] . When we examined these immune-relevant molecules in BgBAR , BgGUA , and BgVEN versus BgBRE ( Fig 6B ) , we identified 37 differentially expressed transcripts . Nine of them were over-represented and 28 were under-represented: of the latter group , 14 were specifically under-represented in BgBAR . Again , BgBAR is the most different among the strains; the nine over-represented transcripts displayed their highest representation in BgBAR , while 20 of the 28 under-represented genes showed their lowest expression levels in this strain . The nine over-represented immune-relevant transcripts included the following: two FREP-encoding transcripts; a transcript containing a Aerolysin domain belonging to the epsilon toxin ETX/Bacillus mosquitocidal toxin MTX2 superfamily previously characterized as a biomphalysin-like protein shown to kill S . mansoni sporocysts [35]; and a transcript encoding an achacin-like , which suggests that BgBAR may have an enhanced ability to respond to antimicrobial stress [34 , 36] . The under-represented transcripts included the following: several transcripts corresponding to FREP family members; transcripts encoding different molecules of the CREP family , whose members are composed of immunoglobulin domain ( s ) followed by a C-type lectin domain [33]; and transcripts encoding several factors involved in extracellular matrix remodeling and cell migration ( e . g . , sco-spondin , ADAMTS , and VEGF receptor ) . As several FREPs ( available in GenBank databases ) were revealed in the above described transcriptomic analysis , we decided to exhaustively study all transcripts in the transcriptomes of BgBAR , BgVEN , BgBRE , and BgGUA that could belong to FREP family members . We selected transcripts corresponding to proteins that contain immunoglobulin ( IPR013783 ) and fibrinogen ( IPR002181 ) domains using the Blast2Go and/or Interproscan software packages . This selection process yielded 258 transcripts . Most of them were not full-length , which meant they could potentially represent molecules that contain fibrinogen or immunoglobulin domains but do not follow the classical organization of FREPs [1 or 2 IgSF domain ( s ) associate with a fibrinogen domain] . As our objective was to study FREPs-encoding transcripts , we retained all transcripts that contained contiguous immunoglobulin superfamily ( IgSF ) and fibrinogen ( FBG ) domains ( full-length or partial ) . In total , we retained 69 FREP transcripts . The 69 retained transcripts were translated , and the corresponding amino acid sequences were aligned with the Bg FREP protein sequences contained within GenBank . None was an exact match , but 30 of the 69 transcripts displayed only a few differences , and could thus be clearly assigned according to the GenBank FREP nomenclature [37] . The 38 remaining sequences could not be assigned to the established nomenclature . Next , we aligned the 69 sequences with the B . glabrata genome draft ( www . vectorbase . org/organisms/biomphalaria-glabrata; genome assembly version BglaB1 ) and assigned them to precise positions in the genome ( see supplementary S3 Table ) . The 69 transcripts corresponded to 24 different genomic loci . Several de novo assembled transcripts were validated by traditional Sanger sequencing of PCR products to confirm that this FREPs were not the result of wrong assemblies ( S1 Fig ) . For this study , we classified FREPs by their identity and assigned genomic locus . Any two FREPs that shared more than 85% identity were considered as homologous and were given the same letter ( designating FREP class ) ; however if they occupied different genomic loci , the letter is followed by a different number ( e . g . , C1 and C2 ) . We identified seven loci that encode FREPs with one IgSF domain ( grouped into classes A to F ) , and 16 loci corresponding to FREPs with two IgSF domains ( grouped into classes H to M ) . Finally B . glabrata genome assembly did not allow us to establish the number of IgSF domains for a last FREP class named “O” . After we characterized these 24 loci of FREPs , we used RNA-seq to analyze the representation of the corresponding transcripts in the four B . glabrata strains . We built a FREP transcriptome containing the 69 selected sequences , mapped the reads obtained from each snail strain to these sequences using the Bowtie2 software , and normalized the hit count values for each transcript using the upper quartile method . The normalized hit count values were then summed for transcripts belonging to the same class . The results ( Fig 7A ) showed that there was a high degree of heterogeneity in the representation of FREPs between classes and snail strains . For example , FREP A was 2000 fold more highly expressed than FREP E regardless of the snail strain . Between strains , the majority of the transcripts corresponding to FREPs containing one IgSF domain ( e . g . , FREPs A , D , C1 , and C2 ) were more highly represented in the less-compatible BgBAR strain . Fig 7B shows the summed transcript levels of FREPs containing one or two IgSFs for the four snail strains . The one-IgSF FREPs comprised 80% of the FREP transcripts expressed by BgBAR , but only 55% to 70% of those expressed in the three other snail strains . Moreover , BgBAR was found to globally express more FREPs than the other snail strains ( 13% , 48% and 52% more than BgGUA , BgVEN , and BgBRE , respectively ) ( Fig 7B ) . As most of the recovered FREP transcripts were only partial sequences , we could not align and compare the sequences of the FREP variants . Thus , to estimate their degree of polymorphism , we selected the longest transcript sequence for each of the 24 FREP loci and performed Blastn alignments with each of the B . glabrata transcriptomes ( before CD-Hit EST treatment; Blastn cutoff , 95% ) . The number of hits was further normalized by the total number of transcripts of each transcriptome . The results ( Fig 8A ) showed that the variant numbers differed between FREP loci at the intra-strain level . For example , FREPs B and I3 had 50 times more variants than FREP A in BgBRE , while FREP I2 had 30 times fewer variants than FREPs C1 and L in BgBAR . There were also many differences at the inter-strain level; BgBRE ( Fig 8B ) displayed the highest number of normalized variants ( 480 ) followed by BgGUA ( 409 ) , BgVEN ( 313 ) , and BgBAR ( 302 ) . FREP A , E , and M always presented a low number of variants , while FREP F , H , and I3 all had many variants , regardless of the strain ( Fig 8A ) . For the other FREP classes , the numbers of variants differed among the strains , with more variants seen for: FREPs C1 and L in BgBAR; FREPs B , F , I2 , I3 , and J2 in BgBRE; and FREPs K2 and K4 in BgGUA . For BgVEN , the FREP variants were distributed more or less homogeneously . Notably , although BgBAR had the fewest normalized FREP variants , it displayed the highest numbers of variants for the one-IgSF FREP A , C1 ( corresponding to FREP 2 in GenBank ) , and D ( corresponding to FREP 14 in GenBank ) . Thus , our results show that BgBAR , which is the least susceptible to S . mansoni presents the highest level of FREP expression and the lowest number of total FREP variants among the studied strains . Moreover , it expresses a higher proportion of FREPs containing one IgSF domain , and has the highest ratio of one-IgSF variants/all FREP variants .
FREPs and SmPoMucs are key molecular determinants of the compatibility process between B . glabrata and S . mansoni . Here , we examined the expression patterns and polymorphisms of these important factors in four host isolates and four parasite isolates that displayed different levels of compatibility with one another . Among the schistosomes , strain SmLE was clearly the most infective against the different snail strains , SmGH2 was the least infective , and the other two strains displayed intermediate compatibility patterns ( Fig 1 ) . Among the snails , strain BgBAR was clearly the least permissive , whereas the compatibility phenotypes of the other three strains were much higher and quite similar . In an effort to explain the compatibility differences among the different schistosome strains , we first analyzed the diversity and expression levels of SmPoMucs in the different strains . The most compatible strains , SmLE and SmVEN , expressed similarly high numbers of SmPoMuc variants ( around 270 and 250 , respectively; Fig 3 ) , while the two less compatible strains expressed fewer variants ( 45 and 75 for SmBRE and SmGH2 , respectively; Fig 3 ) . These results are clearly in agreement with the hypothesis of a matching phenotype [38] . Indeed , if a population of schistosomes is more diverse in their SmPoMuc patterns , they would be less prone to being recognized by a population of hosts that display a fixed number of FREP immune receptors . These results of SmPoMuc polymorphism analysis clearly agree with the theoretical results obtained from co-evolutionary models applied to plant and animal innate immunity [39] . In the multistep infectious interactions of parasites and hosts , a genetically explicit model revealed that polymorphism will be greater at recognition loci than at effector loci , and that host-genotype by parasite-genotype interactions are greater for the recognition phase than for the effector phase . Our results confirm these predictions , as we herein establish a link between SmPoMuc polymorphism and the compatibility phenotype . Additional results obtained by Nuismer and Dybdahl [39] also showed that , compared to the effector phase , the recognition phase contributes more to local adaptation . This also makes sense in the context of our observation that the compatibility level is generally higher for sympatric combinations . The expression level of SmPoMucs also appears to be relevant to the compatibility pattern . In terms of expression , SmVEN and SmGH2 expressed around two fold more SmPoMuc genes than the other two strains . Conceivably , a schistosome expressing more SmPoMucs will generate more SmPoMuc/FREP interactions at the parasite surface ( even given a fixed number of possible combinations ) , which will favor the recognition , the immune response and the killing of the parasite . This could explain why strain SmVEN expressed a similar number of variants compared with strain SmLE , but displayed a lower compatibility phenotype . Similarly , the expression level of SmPoMucs could explain why SmGH2 was less compatible than SmBRE , even though the former expressed more variants than the latter . Concerning now the comparison of the proportion of variants belonging to the different groups of SmPoMucs in the studied schistosome strains , it revealed that: ( i ) the expression level and the number of variants of group 1 SmPoMucs is always low; ( ii ) the expression levels of groups 2 and 3 SmPoMucs are very similar within each strain; ( iii ) the proportions of group 2 and 3 variants can differ according to the strain ( e . g . , group 3 was more diversified in strain SmLE ) ; and ( iv ) the group 3 of SmPoMucs , which containing intermingled repeats [19 , 20] , are only expressed at significant levels in strain SmGH2 . These differences in repeat types or numbers could also explain some of the compatibility differences between strains . However , our current understanding of the link between polymorphism , glycosylation status , and recognition by FREP lectins is too fragmentary to allow us to propose a more precise hypothesis . The SmPoMuc repeat number was shown to be related to the glycosylation status within a specific strain of schistosome [20] , but the glycosylation of different variants and its influence on FREP recognition remain to be investigated . Concerning the FREP contents of the studied snail strains , we first performed a global transcriptomic analysis; we then focused on immune genes , and finally on FREPs . As compatibility depends on molecules that are expressed constitutively by the host and the parasite [18] , we conducted our transcriptomic analysis on naive snails . Our global comparative analysis revealed that , as expected , the transcriptome of BgBAR ( which was much less permissive than the others ( Fig 1 ) showed the greatest differences with respect to the others . Among the differentially expressed transcripts of this strain , we observed enrichment of numerous genes involved in the immune response and responses to biotic and abiotic stimuli . Accordingly , we performed a more targeted analysis of a B . glabrata . immunome composed of 122 transcripts encoding proteins known to be involved in the immune recognition , immune signaling pathways , and effector functions of B . glabrata [17 , 40] . As expected , BgBAR had a higher proportion of genes that were differentially represented in the majority of these molecular classes and the comparison of the transcription levels of these genes in the different strains showed again that BgBAR is the most important differences . This strain exhibited both over-representation ( e . g . , FREP1 , FREP14 , an LBP-BPI 1 . 1 , an achacin , a biomphalysin-like , etc . ) and under-representation ( e . g . , FREP 3 . 1 M , FREP J1 , FREP K2 , a biomphalysin-like , a macin , grctm 2 , grctm 6 , proteases , etc . ) of transcripts . As some FREPs were differentially expressed between snail strains , we next analyzed the expression of all FREPs known to be present in the B . glabrata genome . Our results indicated that BgBAR expressed more FREPs than the three other strains , and that most of the FREPs containing one IgSF domain ( 5 of 7 ) were more highly expressed in BgBAR compared to the other strains . This suggests that this specific class of FREPs could play a key role in schistosome recognition and anti-schistosome defense . Indeed , this is consistent with a previous report showing that FREP 2 ( containing one IgSF domain ) interacts with SmPoMuc antigens [24] . Our analysis of FREP polymorphisms between and within strains showed that BgBRE and BgGUA had more variants than BgVEN and BgBAR , suggesting that the diversity of FREPs is not correlated with the compatibility level , as would be expected from an ongoing arms race between FREPs and SmPoMucs . We thus propose ( very speculatively ) that this absence of correlation could be a consequence of the recent introduction of the schistosome to the South American and Caribbean areas ( from which the tested snail strains were sampled ) through the Atlantic slave trade of the 16-19th centuries . This would have forced the schistosome to adapt to a new intermediate host of the same genus ( B . pfeifferi in Africa versus B . glabrata in the New World ) . The parasite needed to develop an evolutionary strategy to increase its compatibility with its new host , and thus diversified its SmPoMucs to escape recognition by FREPs . In this context , the diversification of FREPs in some snail strain could be linked to co-evolution with other pathogens living in sympatry with snails of the New World . Our present results clearly corroborate that FREPs and SmPoMucs seem to be molecular determinants of the compatibility between B . glabrata and S . mansoni . However , it is largely accepted that this compatibility process is a complex one that involves numerous genes engaged in an arms race ( see [41] for a recent review ) . Compatibility can be viewed as a multistep process through which the parasite escapes the recognition and effectors of the host . Although FREPs and SmPoMucs may play a crucial part in this recognition , other pattern recognition receptors ( e . g . , lectins ) and antigens have been suggested to be involved [24] . In terms of the effectors of the host and the anti-effector systems of the parasite , it has been shown that highly reactive chemical compounds derived from molecular oxygen ( ROS ) are crucial to the snail’s ability to defend itself against S . mansoni [42] , and the parasite has developed ROS scavenger systems to counter these molecules [29] . Additional candidate genes have also been recently implicated in the compatibility process between B . glabrata and S . mansoni; for example , the biomphalysin from B . glabrata have been shown to exert high cytotoxicity against S . mansoni sporocysts [35] , and a new family of putative immune receptors was identified using a RAD-seq approach [11] . The previous reports and present results therefore collectively suggest that the compatibility process is likely to be much more complex than previously thought . Indeed , our transcriptomic analysis of B . glabrata strains showed that the less permissive snail strain varied from the others in terms of immune-relevant transcripts that are involved not only in immune recognition , but also in signaling pathways and effector functions ( Fig 6 ) . In conclusion , the arms race between B . glabrata and S . mansoni has selected for diversified molecular repertoires that allow the parasite to counter the immune recognition system of the host . The extraordinary diversification that enables SmPoMucs to avoid recognition by FREPs illustrates the outcome of these evolutionary dynamics . The present findings together with other results obtained in recent decades show that the compatibility between B . glabrata and S . mansoni depends on multiple factors , including: ( i ) the genetics of the snail and the schistosome; ( ii ) the age of the snail; ( iii ) the previous interactions of the snail with schistosomes; and ( iv ) the ability of the environment to influence ( through epigenetic mechanisms ) the compatible/incompatible phenotypes of both partners ( see [41] for a recent review ) . To solve this complicated puzzle , we need to develop novel integrative approaches that combine comparative genomic , epigenomic , and transcriptomic approaches performed under different environmental conditions . This should enable us to identify relevant candidate genes whose functions could then be validated using CRISPR/Cas or RNAi methodologies . Given the variability of the mechanisms involved in compatibility , such studies must be undertaken on different snail and schistosome populations and strains . These ambitious approaches are absolutely necessary if we hope to identify the molecular pathways that are most likely to be good targets for strategies aimed at blocking transmission through the snail intermediate host .
Our laboratory holds permit # A66040 for experiments on animals from both the French Ministry of Agriculture and Fisheries , and the French Ministry of National Education , Research , and Technology . The housing , breeding and animal care of the utilized animals followed the ethical requirements of our country . The experimenter also possesses an official certificate for animal experimentation from both French ministries ( Decree # 87–848 , October 19 , 1987 ) . Animal experimentation follows the guidelines of the French CNRS . The different protocols used in this study have been approved by the French veterinary agency from the DRAAF Languedoc-Roussillon ( Direction Régionale de l'Alimentation , de l'Agriculture et de la Forêt ) , Montpellier , France ( authorization # 007083 ) . The four studied strains of S . mansoni parasites and the four corresponding sympatric snail strains of B . glabrata originated from South America and had been maintained in the laboratory using Swiss OF1 mice ( Charles River Laboratories , France ) as the definitive host . The four sympatric host/parasite combinations were designated BgVEN/SmVEN ( from Guaraca , Venezuela ) , BgGUA/SmGH2 ( le Lamentin , Guadeloupe ) , BgBRE/SmBRE ( Recife , Brazil ) , and BgBAR/SmLE ( Belo Horizonte , Brazil ) . Miracidia from the parasite strains ( SmBRE , SmLE , SmGH2 and SmVEN ) were recovered from infected mouse livers and intestines and transformed into sporocysts in vitro as previously described [19] . Compatibility trials between the strains of parasites and snails were conducted as previously described [43] . For all sympatric and allopatric combinations , we experimentally infected snails with 20 miracidia per snail , which was previously shown to yield a maximum infection rate regardless of the utilized strain [43] . Two weeks later , we assessed the prevalence ( percentage of infected snails ) and intensity ( number of developed parasites per infected snail ) of infection . Three independent experiments were performed . The data presented in the present manuscript correspond to the mean values obtained in a previous published work [43] and the data obtained from two other experiments performed in 2010 and 2012 . Five thousand sporocysts were collected and counted for each S . mansoni strain ( SmBRE , SmLE , SmGH2 , and SmVEN ) . The samples were ground with a pestle and vortexed , and proteins were extracted using UTC buffer ( 7 M urea , 2 M thiourea , 30 mM Tris , pH 8 . 5 , and 4% CHAPS ) for 2 h at room temperature . The samples were centrifuged at 10 , 000 g for 5 min , the supernatants were recovered , and the protein concentrations were estimated using a 2D Quant kit ( GE Healthcare Life Sciences ) . For each sample , proteins ( 8 μg ) were incubated with Laemmli buffer for 5 min at 99°C , resolved by 12% SDS-PAGE , and blotted on a nitrocellulose membrane ( Trans-blot Turbo; Bio-Rad ) . The membrane was blocked with 5% skimmed dry milk in TBST ( TBS containing 0 . 05% Tween 20 ) for 3 h at room temperature and incubated with the previously described anti-SmPoMuc [24] diluted 1/1000 in TBST overnight at 4°C . The membrane was washed three times in TBST ( 10 min each , room temperature ) , and then incubated with peroxidase-conjugated purified anti-rabbit IgG ( Sigma Aldrich ) diluted 1/5000 in TBST with 5% skimmed dry milk for 90 min at room temperature . The membrane was washed three times in TBST and once in TBS , and proteins were detected with a ChemiDoc MP Imaging system ( Bio-Rad ) using ECL reagents . As a loading control , we performed a parallel Western blot using anti-actin ( Thermo Scientific ) diluted 1/1000 and HRP-conjugated anti-mouse IgG ( Sigma Aldrich ) diluted 1/10 , 000 . Eleven sporocyts were recovered individually from each S . mansoni strain . RNA was isolated from each individual using a Dynabeads mRNA Direct Micro kit ( Ambion Life Technologies ) as previously described [20] . The RNA was reverse transcribed by adding the enzyme mix ( Superscript II , Invitrogen ) directly to the paramagnetic Dynabeads . The generated cDNA was recovered using the magnetic system , washed twice in 10 mM Tris ( pH 7 . 5 ) and SmPoMuc sequences were directly amplified using PCR and nested PCR , as previously described [20] . The obtained products were separated by 1% agarose gel electrophoresis . Each band was excised , purified , and cloned into pCR4-TOPO , and 100 clones were sequenced . Five thousand sporocysts from each S . mansoni strain were recovered and stored at -80°C . RNA was extracted using a Dynabeads mRNA Direct Micro kit ( Ambion Life Technologies ) according to the manufacturer’s instructions . Between the washing and elution steps of the RNA purification , an additional on-bead DNase treatment was performed using the TURBO DNA-free kit ( Ambion Life Technologies ) . Reverse transcription was performed using the Maxima H Minus First Strand cDNA Synthesis kit ( Thermo Scientific ) with a 1:1 mixture of oligo dT and random primers . Quantitative PCR amplifications were performed with 2 μl of 20-fold diluted cDNA and 0 . 5 μM of each primer in a final volume of 10 μl , using a LightCycler 480 SYBR Green I Master kit and a Light Cycler 480 II Real Time instrument ( both from Roche ) . An initial denaturation at 95°C for 12 min was followed by: 45 cycles of 11 sec denaturation at 95°C , 11 sec annealing at 60°C , and 19 sec elongation at 72°C; a melting curve step from 65 to 97°C with a heating rate of 0 . 11°C/sec and continuous fluorescence measurement; and a cooling step to 40°C . For each reaction , the cycle threshold ( Ct ) was determined using the 2nd derivative method of the LightCycler 480 Software release 1 . 5 ( Roche ) . PCR experiments were performed in triplicate ( technical replicates ) from three biological replicates . The mean value of Ct was calculated . Corrected melting curves were checked using the Tm-calling method of the LightCycler 480 Software release 1 . 5 . Results were normalized with respect to the α-tubulin gene , as previously described [22] , and ΔCt values were calculated . The primers used to analyze the different SmPoMuc groups are indicated in Fig 1A . RNA extraction , cDNA library construction , and Illumina SOLEXA sequencing were performed as previously described [33 , 44] . Briefly , total RNA was extracted from whole snail body tissues from 10 juvenile , 10 adult , and 10 old snails for each B . glabrata strain ( BgVEN , BgGUA , BgBRE , and BgBAR ) . Tissues were disrupted in liquid nitrogen and total RNA was extracted using the TRIzol reagent ( Life Technologies ) according to the manufacturer’s instructions . Equimolar amounts of RNA from juvenile , adult and old B . glabrata were combined to yield two pools of 30 individuals for BgBRE ( BRE1 and BRE2 ) and one pool of 30 individuals for each of the other strains ( BgBAR , BgVEN and BgGUA ) . Paired-end 72-bp cDNA libraries were generated using an mRNA-seq kit for transcriptome sequencing ( Solexa , Illumina ) on a Genome analyzer II platform ( Illumina ) . Three samples were multiplexed per lane . Library construction and sequencing were performed by MGX ( Montpellier Genomix , c/o Institut de Génomique Fonctionnelle , Montpellier , France ) . For library constructions cDNA fragments following RT-PCR amplification ranged from 220 to 500 bp ( average , 300 bp ) . The numbers of 72-bp reads obtained from BgBRE1 , BgBRE2 , BgBAR , BgGUA , and BgVEN were 99 , 316 , 948 , 73 , 000 , 210 , 116 , 679 , 444 , 100 , 640 , 190 and 111 , 178 , 786 , respectively . The reads that passed the Illumina quality filter were further cleaned via a previously described workflow , using the Galaxy server [33 , 44] . The 13 first and three last low-quality bases were then trimmed; yielding paired-end reads of 56 nucleotides each . Reads without a pair ( orphan reads ) were removed . The final high-quality libraries used for transcriptome assemblies contained 90 , 331 , 578 , 66 , 558 544 , 105 , 718 , 404 , 88 , 800 , 366 , and 85 , 918 , 982 reads for BgBRE1 , BgBRE2 , BgBAR , BgGUA , and BgVEN , respectively . De novo transcriptomic assemblies were performed using the Velvet version 1 . 2 . 02 software implemented by the python script provided by Oases version 0 . 2 . 06 , as previously described [33 , 44] . The resulting contigs were merged into unigene clusters using CD-HIT-EST version 4 . 5 . 4 A multiple k-mer assembly approach was applied to optimize the assembly . Four high-quality reference transcriptomes were produced , comprising 117 , 269 , 70 , 533 , 82 , 500 , and 79 , 664 transcripts for strains BgBRE , BgBAR , BgGUA , and BgVEN , respectively ( for transcriptome details see S4 Table ) . The transcripts were automatically annotated using Blast2GO version 2 . 4 . 2 . Quality reads ( Phred score >26 ) were aligned on the BgBRE transcriptome assembly using Bowtie2 ( v2 . 0 . 2; mapping quality score 255 ) on the Galaxy server ( http://bioinfo . univ-perp . fr ) [45] . The count of the reads mapped to each transcript was assessed using Hitcount to BAM ( SAM tools v0 . 1 . 18 . 0 ) . The obtained values were normalized by the upper quartile method [46] , which has been proposed to be more accurate than the RPKM method of normalization [47] . The DESeq2 software ( v2 . 12; http://www . bioconductor . org/packages/release/bioc/html/DESeq2 . html ) [48] was used under default settings to identify genes that were differentially expressed in the two biological snail sample duplicates ( BRE1 and BRE2 ) versus the other snail strain samples ( P<0 . 05 ) . Hierarchical ascending clustering ( HAC ) with Pearson correlation , which was performed using the Cluster 3 . 0 [49] and JavaTreeView software packages , was used to generate a heatmap for analysis of the transcript expression patterns ( log2 fold change ) . To detect biological processes that were significantly over- or under-represented in the BgBAR strain , we examined the transcripts found to be differentially expressed between BgBAR and BgBRE . Functional enrichment was assessed using the GoStatsPlus package in R ( https://github . com/davfre/GOstatsPlus ) . The enrichment was calculated for the 3 categories including of biological process ( BP ) , molecular function ( MF ) , and cellular component ( CC ) , and . The results were imported into REVIGO [50] for further analysis and visualization . Transcripts corresponding to potential FREP sequences were selected from transcriptomes on the basis of Blast2Go annotations or the presence of immunoglobulin and/or fibrinogen domains ( IPR013783 and IPR002181 , respectively ) , as detected by Interproscan . As most of the obtained sequences were not full length , transcripts encoding complete sequences or partial sequences with contiguous Immunoglobulin superfamily ( IgSF ) and fibrinogen ( FBG ) domains were selected to be sure to have a transcript belonging to a real FREP family member and not to GREP , CREP , FREM or IgSF or FBG containing molecules . The retained transcripts were translated to predicted amino acid sequences and aligned with those of reference FREP proteins obtained from GenBank using BioEdit version 7 . 1 . 3 . 0 . and were also aligned on the B . glabrata genome draft using the Blast tool available on the Vector Base website ( https://www . vectorbase . org/ ) . For quantitative analysis of FREPs , the reads from each B . glabrata strain were mapped to a reference FREP transcriptome ( FREP transcripts assembled from the transcriptomes of the four snail strains ) using Bowtie2 ( v2 . 0 . 2 ) . The mapped reads were counted per transcript and the values were normalized by the upper quartile method , as described above . To estimate the FREP polymorphism in each strain , the longest transcript from each FREP class was aligned against the B . glabrata transcriptomes obtained before CD-Hit EST treatment , using Blastn ( cutoff , 95% identity ) on the Galaxy server . The number of mapped hits was further normalized to the total number of transcripts for each B . glabrata transcriptome , and the level of polymorphism was calculated as follows for each FREP class and snail strain: normalized number of FREP variants = ( number of blast hits / total number of transcripts ) x 105 . Several de novo assembled transcripts were validated by traditional Sanger sequencing of PCR products . Briefly , depending of the strain , total RNA was reverse transcribed with random primers and RevertAid premium enzyme ( Thermo scientific ) . Two μl of the RT reaction was then used for PCR ( Advantage 2 PCR system , Invitrogen , Carlsbad , CA , USA ) with primers that were designed to specially target and amplify novel predicted transcripts , and amplicons were sequenced ( GATC Biotech , Konstanz , Germany ) . Sequences from Sanger sequencing and from computational assembly of de novo transcripts were aligned ( Supplementary S1 Fig ) . The obtained SmPoMuc sequences were deposited in GenBank under the following accession numbers: KX645102-KX645377 for SmLE , KX645378-KX645624 for SmVEN , EU676447-EU676459/EU676503-EU676530/EU676572-EU676583/EU676595-EU676625 for SmBRE , and EU676460-EU676502/EU676531-EU676571/EU676584-EU676594/EU676556-EU676626 for SmGH2 . The obtained FREP sequences were deposited in GenBank under the following accession numbers: KY024239 to KY024307 .
|
Schistosomiasis is the second most widespread human tropical parasitic disease after malaria . It is caused by flatworms of the genus Schistosoma , and poses a considerable threat for human health in numerous Asian , African and South American countries . The World Health Organization has set the goal of eradicating schistosomiasis by 2025 . However , no vaccine is available , and we currently have only one drug ( praziquantel ) that can effectively and efficiently treat the disease . As treatment by mass drug administration would enhance the risk of drug resistance in schistosome parasites , complementary strategies to fight this parasitic disease are urgently needed . Freshwater snails of the Biomphalaria genus act as intermediate hosts in the transmission of the schistosome species . Thus , learning more about the mechanisms of the interaction between these snails and the schistosomes could critically facilitate the identification of potential new candidate molecules that may be targeted to prevent schistosome transmission in the field .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"schistosoma",
"invertebrates",
"schistosoma",
"mansoni",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"helminths",
"parasitic",
"diseases",
"animals",
"parasitology",
"developmental",
"biology",
"gastropods",
"sporocysts",
"genome",
"analysis",
"snails",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"sequence",
"alignment",
"bioinformatics",
"life",
"cycles",
"molluscs",
"pathogenesis",
"host-pathogen",
"interactions",
"database",
"and",
"informatics",
"methods",
"transcriptome",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"computational",
"biology",
"organisms",
"parasitic",
"life",
"cycles"
] |
2017
|
A multistrain approach to studying the mechanisms underlying compatibility in the interaction between Biomphalaria glabrata and Schistosoma mansoni
|
Envenoming by coral snakes ( Elapidae: Micrurus ) , although not abundant , represent a serious health threat in the Americas , especially because antivenoms are scarce . The development of adequate amounts of antielapidic serum for the treatment of accidents caused by snakes like Micrurus corallinus is a challenging task due to characteristics such as low venom yield , fossorial habit , relatively small sizes and ophiophagous diet . These features make it difficult to capture and keep these snakes in captivity for venom collection . Furthermore , there are reports of antivenom scarcity in USA , leading to an increase in morbidity and mortality , with patients needing to be intubated and ventilated while the toxin wears off . The development of an alternative method for the production of an antielapidic serum , with no need for snake collection and maintenance in captivity , would be a plausible solution for the antielapidic serum shortage . In this work we describe the mapping , by the SPOT-synthesis technique , of potential B-cell epitopes from five putative toxins from M . corallinus , which were used to design two multiepitope DNA strings for the genetic immunisation of female BALB/c mice . Results demonstrate that sera obtained from animals that were genetically immunised with these multiepitope constructs , followed by booster doses of recombinant proteins lead to a 60% survival in a lethal dose neutralisation assay . Here we describe that the genetic immunisation with a synthetic multiepitope gene followed by booster doses with recombinant protein is a promising approach to develop an alternative antielapidic serum against M . corallinus venom without the need of collection and the very challenging maintenance of these snakes in captivity .
Envenomation by snakebite is a common and generally harmful , environmental and occupational neglected tropical disease that constitutes a highly relevant public health problem with worldwide mortality estimated to be around 50 , 000 deaths annually [1] . In the Americas , although most of the registered cases of snake envenomation are due to snakes from the Viperidae family [2] , accidents caused by members of the Elapidae family can also be severe or even lethal [3] . Distributed throughout the tropical and subtropical regions around the world , the Elapidae family consists of 325 species divided into 61 genera of potentially deadly neurotoxic venomous snakes that exhibit a wide range of sizes and are characterised by hollow and proteroglyphous fixed fangs through which venom is injected . The coral snakes are the only elapids found in the New World , being Micrurus the most diverse and abundant genus across Americas [4] . In Brazil , the envenomation accidents reported are mainly due to M . corallinus and M . frontalis , which occupy highly populated areas in central , south and southeast of the country [5] . For this reason , the immunisation of horses with equal amounts of M . corallinus and M . frontalis venoms is used at Butantan Institute for the production of the Brazilian coral snake antivenom [6] , which is the only accepted medical treatment for coral snakebite envenomation [7] . Micrurus spp . coral snakes have an average dry venom yield of 13 . 87 mg [8] , which results in the need of snake collections composed of numerous specimens in order to obtain sufficient amounts of venom for horse immunisation . On the other hand , due to characteristics such as fossorial habit , relatively small sizes and ophiophagous diet , it is very challenging to capture and keep these snakes in captivity , as survival rate rarely exceeds one year [9] . These limitations in maintenance , the small size of their venom glands and , consequently , the low yield of venom , have been the major factors jeopardising the production of the Brazilian antielapidic serum . Additionally , being snakebite a health problem that mainly afflicts the poorest regions of the world [10] , antivenom production holds very limited commercial value , which not only hinders its production by major pharmaceutical companies but also results in an increased shortage of antivenom . As a matter of fact , since 2003 , Pfizer/Wyeth , discontinued the manufacture of ANTIVENIN® , the only FDA-approved coral snake antivenom used for the treatment of accidents caused by Micrurus fulvius , a coral snake found in the southeastern USA . Furthermore , since 2008 , all of the 2003 antivenom lots have expired , culminating in critical situations of patients being intubated and ventilated while toxins wear off . Under these circumstances , there is an increase not only in the morbidity of these accidents , but also of registered cases of people dying as a result of antivenom shortage [11–13] . Another issue that should be addressed is that the venom glands of snakes produce a variety of proteins and biologically active peptides with only a small percentage of those molecules being actually responsible for the biological manifestations observed after envenomation . As a result , antivenoms contains antibodies against an extensive number of different proteins , irrespective of their toxicity or immunogenicity , leading to a reduction in the antivenom’s efficacy and to an increased probability of developing serum sickness reactions due to large volumes of equine proteins [14] . The development of an alternative , but still efficient immunising protocol for the generation of coral snake antivenom , with less reliance upon snake collection/maintenance and composed solely of toxin-specific antibodies , would , therefore , be ideal for the treatment of envenomation by coral snake bites . The use of recombinant coral snake toxins as immunogens would be a reasonable way to accomplish both issues but , although these molecules did induce an immune response that indicated the recognition of the native proteins , very complicated steps were required for protein refolding [15] . On the other hand , however , the use of DNA immunisation to evoke IgG antibody titres and protective responses for the production of snake antisera have also been described [16–19] . Furthermore , researchers from the Liverpool School of Tropical Medicine , UK , demonstrated that the genetic immunisation of mice with a multiepitope DNA string coding for the most antigenic epitopes of metalloproteinases from Echis ocellatus ( an African viper ) could be used for the generation of an antiserum that neutralised the toxicity of different African snakes [20] , similar to the responses observed when rabbits were immunised with recombinant toxins [21] . These observations not only indicate that the DNA immunisation is a plausible way of developing specific and neutralising antibodies against snake venoms with no need for recombinant protein expression and purification from heterologous organisms such as Escherichia coli , but alto indicates that these neutralising antisera could be developed by the genetic immunisation of animals with the most antigenic epitopes , only . In a previous work , after the transcriptomic analysis of M . corallinus venom gland , the predominant proteins in the venom were identified and five toxins that could represent good antigenic candidates were chosen for DNA immunisations , providing an initial evidence of the feasibility of this approach for an antielapidic sera development [22] . Among the proposed candidates , there are four three-fingered toxins ( 3FTx ) and one putative M . corallinus phospholipase A2 , which were selected based on the abundance of each transcript . The first antigen selected ( Ag1 ) is a 3FTx similar to a previously characterised as neurotoxin homolog 8 ( Nxh8 ) , which differs from most 3FTx as it shows an extra disulphide bond in the first loop [23] . The second one ( Ag2 ) refers to a more typical 3FTx and is homologous to the previously described Nxh7 , Nxh3 and Nxh1 neurotoxins [24] . The other two 3FTx ( Ag3 and Ag4 ) represent new identified proteins with similarity of no more than 50% to the sequences of 3FTx in the databanks . The fifth selected antigen candidate ( Ag5 ) corresponds to the putative M . corallinus phospholipase A2 ( PLA2 ) . In this work we describe the mapping , by the SPOT-synthesis technique [25] , of potential B-cell epitopes from these five putative toxins . These epitopes were then analysed through different in silico methods and used for the design of two multiepitope DNA strings for the genetic immunisation of female BALB/c mice . By the end of the immunisation period , animals were bled and sera were subjected to further analysis concerning its neutralisation capabilities .
The identification of potential B-cell epitopes from the five most abundant toxins that constitute the venom of Micrurus corallinus [22] was performed by the SPOT-synthesis technique [25] . For this procedure , overlapping pentadecapeptides , frameshifted by three residues and spanning the whole sequences of all these toxins were adsorbed into a cellulose membrane according to the protocol of Laune et al . [26] . The cellulose membranes were obtained from Intavis ( Koln , Germany ) ; fluorenylmethyloxycarbonyl amino acids and N-Ethyl ( hydroximino ) cyanoacetate were from Novabiochem . A ResPep SL/AutoSpot SL Automatic Spot synthesiser ( IntavisAG , Bioanalytical Instruments , Germany ) was used for the automated peptide synthesis in the membrane . After assembling the peptide sequences , the side-chain protecting groups were removed by treatment with trifluoroacetic acid . A membrane map of epitopes can be found in Fig 1 . For the identification of immunoreactive peptides , after an overnight blocking step with 3% bovine serum albumin ( BSA ) diluted in phosphate buffered saline with 0 . 05% ( v/v ) Tween-20 ( PBS-T ) , the SPOT membrane was probed with a 1:1000 dilution of a monospecific anti-M . corallinus horse antiserum ( whole IgG , kindly provided by the antivenom facility of Butantan Institute , São Paulo , Brazil ) . Antibody binding was detected with an alkaline phosphatase-conjugated anti-horse IgG ( Sigma Aldrich ) and detection was performed with 60 μL of MTT 0 . 12M ( methylthiazolyldiphenyl-tetrazolium bromide , Sigma Aldrich ) , 50 μL BCIP 0 . 16M ( 5-bromo-4-chloro-3-indolyl phosphate , Sigma Aldrich ) and 40 μL MgCl2 1M diluted in 10mL of citrate buffered saline , pH 7 . 0 ( 137 mM NaCl , 3 mM KCl , 10mM citric acid ) . The membrane was digitalised using a colour scanner ( ScanJet 3670 , Hewlett-Packard ) and subjected to densitometric intensity analysis with ImageJ—image processing software [27] . In order to eliminate unspecific binding of secondary antibody to spots , the membrane was also probed and detected with secondary antibody alone . A spot was considered immunoreactive when the relative density value obtained after incubation with both primary and secondary antibodies was higher than the value obtained by the incubation with the secondary antibody , alone . Two multiepitope DNA strings were designed based on the results obtained with the SPOT-synthesis technique [25] . One of them codes for the reactive epitopes associated with the four selected 3FTx while the other one codes for the reactive epitopes associated with the putative phospholipase A2 ( PLA2 ) toxin . All epitopes sequences were separated by a six amino acid residues linker and all cysteine codons were exchanged by serine codons in order to avoid the formation of disulphide bond-mediated protein multimerisation . The codon usage was optimised for both Mus musculus and Escherichia coli expression according to the Codon Usage Database ( Kazusa DNA Research Institute ) [28] . To facilitate further molecular cloning into expression vectors , a XhoI and a SfiI restriction sites were inserted at the 5’ region of each DNA string , while a PstI restriction site was inserted at the 3’ region of each DNA string . Theses DNA strings were synthesised by GeneArt® Gene Synthesis ( Thermo Fischer Scientific ) . The three-dimensional structure of all the five toxins described in this work have not been resolved yet . However , in order to obtain the approximated spatial localisation of reactive epitopes , we performed some protein structure homology modelling using the SWISS-MODEL workspace tool for molecular modelling [29–31] . Ramachandran plots [32] and QMEAN [33] scores were used for quality assessment and models were selected based on their Global Model Quality Estimation ( GMQE ) values [31] . Briefly , the Ag1 ( Nxh8 ) homology model was obtained based on the crystal structure ( PDB ID: 3nds ) of Naja nigricollis toxin alpha ( GMQE: 0 . 80 / Seq . identity: 54 . 10 / Seq . similarity: 0 . 48 ) . The Ag2 ( Nxh7/3/1 ) homology model was obtained based on the NMR structure ( PDB ID: 1nor ) of neurotoxin II from Naja naja oxiana ( GMQE: 0 . 78 / Seq . identity: 40 . 35 / Seq . similarity: 0 . 48 ) . The Ag3 ( 3FTx ) homology model was obtained based on the crystal structure ( PDB ID: 2h8u ) of Bucain , a cardiotoxin from the Malayan Krait Bungarus candidus ( GMQE: 0 . 89 / Seq . identity: 57 . 63 / Seq . similarity: 0 . 51 ) . The Ag4 ( 3FTx ) homology model was obtained based on the crystal structure ( PDB ID: 4iye ) of the green mamba , Dendroaspis angusticeps , ρ-Da1a toxin ( GMQE: 0 . 75 / Seq . identity: 40 . 00 / Seq . similarity: 0 . 40 ) . And , finally , the Ag5 ( PLA2 ) homology model was obtained based on the crystal structure ( PDB ID: 1yxh ) of a phospholipase A2 from Naja naja sagittifera ( GMQE: 0 . 82 / Seq . identity: 58 . 97 / Seq . similarity: 0 . 50 ) . Protein images were generated using DeepView ( Swiss PDB Viewer ) [34] . We also decided to evaluate the hydropathic profile and antigenic index of all antigens so we could analyse if the reactive epitopes are localised in highly hydrophilic and antigenic regions of their respective proteins . This would corroborate and reinforce the empirical results obtained by the SPOT-synthesis technique . The hydropathic characters of all proteins were evaluated using the Kate & Doolittle algorithm [35] while the antigenic index were computed using the Jameson & Wolf algorithm [36] . All images were created with Protean computer program ( DNASTAR Inc . Madison , Wisconsin , USA ) . The complete coding sequences corresponding to the mature portions of the five selected antigens were PCR amplified from a previously constructed cDNA library [22] . Both KpnI and XhoI restriction sites were included in the 5’ end of the forward primer while a NotI restriction site was included in the 5’ end of the reverse primer . Each one of the amplicons were cloned into the KpnI and NotI endonucleases sites of individual pSECTAG2A mammalian expression vectors ( Thermo Fischer Scientific ) . To avoid the expression of the c-myc epitope located at the 3’ region of the vector multi cloning site , a stop codon was introduced in the reverse primer . Alternatively , for the heterologous recombinant expression of toxins in Escherichia coli cells , the amplicons were also cloned into the XhoI and NotI restriction sites of a high-copy T7 promoter-based E . coli expression vector ( pAE ) [37] . Both multiepitope DNA strings were cloned either into the SfiI and PstI restriction sites of pSECTAG2A plasmids ( for the genetic immunisations protocols ) or into the XhoI and PstI restriction sites of pAE vectors ( for the heterologous recombinant expression in Escherichia coli cells ) . The correct transcription of toxins’ cDNA sequences by mammalian host cells was investigated by transiently transfecting COS-7 cells ( ATCC CRL 1651 ) , which were maintained in Dulbecco's Modified Eagle´s Medium ( DMEM; Life Technologies , USA ) supplemented with 2 mM L-glutamine , 100U⋅mL-1 penicillin , 100μg⋅mL-1 streptomycin , 0 . 25μg⋅mL-1 amphotericin B ( Thermo Fischer Scientific , USA ) and 10% foetal bovine serum ( FBS; Cultilab , Campinas , SP , Brazil ) . Individual pSECTAG2A vectors , cloned with the complete cDNA sequences of each toxin [22] , were used for the transient transfection of COS-7 cells using Lipofectamine 2000 ( Thermo Fischer Scientific , USA ) according to the manufacturer's instructions . Cells were washed three times with Phosphate Buffered Saline ( PBS ) 48 h after transfection and the medium was replaced with DMEM without FBS . After 24 h incubation , both medium and cells were collected—after centrifugation for 10 minutes at 3000 g—and stored at -20°C until use . After transfection , cells had been treated with Trizol ( Thermo Fischer Scientific , USA ) for the isolation of total mRNA , which were reverse transcribed using an oligo ( dT ) 20 primer ( Thermo Fisher Scientific , USA ) . Total cDNA was than subjected to PCR amplifications for the detection of toxins’ cDNAs . The heterologous toxin expressions by all COS-7 cells previously transfected were assessed by Western Blot analysis of cell extracts . For this , a SDS-PAGE with the cells extracts was performed and the proteins transferred to a nitrocellulose membrane . After an overnight blocking at 4°C with 10% ( v/v ) non-fat dry milk diluted in PBS-T , membrane was incubated with a 1:3000 dilution of monospecific anti-Micrurus corallinus horse antiserum ( kindly provided by the antivenom facility of Butantan Institute ) for 90 minutes at constant agitation at room temperature . Free , non-bound primary antibodies were removed with three 30 minutes’ washes in PBS-T . Goat anti-horse IgG-HRP antibodies ( Sigma Aldrich ) were used as secondary antibody at a dilution of 1:5000 . Membranes were probed with ECL Prime detection reagent ( GE Healthcare ) according to manufacturer’s instruction . Purified pSECTAG2A plasmid ( vehicle control ) , pSECTAG2A-ag1 , pSECTAG2A-ag2 , pSECTAG2A-ag3 , pSECTAG2A-ag4 , pSECTAG2A-ag5 , pSECTAG2A-3ftx-multiepitope , and pSECTAG2A-pla2-multiepitope were precipitated onto 1 . 6μm gold beads and coated on the inner surface of Tefzel ETFE Fluoropolymer resin tubing according to the manufacturer’s protocol ( BioRad Laboratories , Inc . ) . The final quantity of DNA/gold beads for each shot was adjusted to 1 μg of DNA / 0 . 5 mg Au . For the recombinant expression of toxins or multiepitope proteins , each one of the pAE plasmid constructions described before were introduced , by heat shock , into chemically competent Escherichia coli BL21 ( DE3 ) cells ( Thermo Fischer Scientific , USA ) , which were grown on Luria-Bertani ( LB ) medium and induced for three hours by the addition of 1mM isopropyl-1-thio-β-D-galactopyranoside ( IPTG ) when an OD600 ( optical density at 600nm ) of 0 . 6 was achieved . After the induction period , cells were collected by centrifugation and mechanically lysed by French Press ( Thermo Fischer Scientific , USA ) . Recombinant proteins were expressed as inclusion bodies and were solubilised with 20 mL of solubilisation buffer ( 8 M urea , 50 mM Tris-Cl , 5 mM β-mercaptoethanol , pH 7 . 4 ) . After complete solubilisation , recombinant proteins were purified by immobilised metal ion affinity chromatography ( IMAC ) . For this procedure , proteins were adsorbed on a 5mL column previously charged with Ni+2 and equilibrated with 5 column volumes ( CV ) of solubilisation buffer without β-mercaptoethanol . Washing procedures were performed with 5 CV of wash buffer ( 3M urea , 40mM imidazole , 150mM NaCl , 50mM Tris-Cl , pH 7 . 4 ) . Protein elution were accomplished with 5 CV of elution buffer ( 3M urea , 1M imidazole , 150 mM NaCl , 50mM Tris-Cl , pH 7 . 4 ) . Finally , before these expressed proteins could be used in immunisation regimens , imidazole was removed by simple dialysis against PBS buffer containing 3M urea ( to avoid protein precipitation ) . The purity and concentration of recombinant protein were evaluated and quantified by SDS-PAGE densitometry analysis with ImageJ—Image processing software [27] . For the determination of the total IgG titre from the different antisera or from antielapidic antivenom produced by Butantan Institute , 96-well microtitre plates were coated with either 100 μL of purified recombinant antigens ( 10 μg⋅ml-1 in Carbonate-Bicarbonate Buffer , pH 9 . 6 ) or with 100 μL of Micrurus corallinus venom ( 10 μg⋅ml-1 in Carbonate-Bicarbonate Buffer , pH 9 . 6 ) . After three washes with PBS-T , plates were blocked with 5% non-fat milk/PBS-T ( m/V ) at 37°C for 1 h . Serial dilutions of each serum in PBS-T were added to the wells and microtitre plates incubated for 1 h at 37°C . Bound antibodies were detected with a 1:5000 dilution of a commercial peroxidase-conjugated anti-mouse IgG ( Sigma Aldrich ) . Detection was performed with 8 mg o-phenylelediamine ( OPD ) diluted in 20 mL of 0 . 2 M citrate-phosphate buffer , pH 5 . 0 , in the presence of 10 μL of 30% H2O2 . Reaction was stopped by adding 50 μL of 4M H2SO4 to each well . Absorbances were measured at 492 nm and titres were determined as the highest dilution , in which an absorbance value ≥ 0 . 1 was observed . ELISA experiments were performed in simultaneous duplicates with all detection reaction being stopped at the same time . In order to evaluate the neutralisations capabilities of all experimental sera conceived during the immunisations protocols performed in this study , 3LD50 ( 21 μg ) [38] of Micrurus corallinus venom were diluted in physiological saline to a final volume of 100 μL and mixed with the 100 μL of each serum . All venom/antiserum mixtures were , then incubated at 37°C for a total of 30 min before being intraperitoneally administered to groups of five female Balb/c mice weighting around 20 g . Animals were monitored every 6 hours with the number of deaths being recorded until 48 h after injection . For positive and negative controls groups , 3LD50 of venom were also incubated with either 100 μL of Butantan’s antielapidic antivenom , 100 μL of monospecific anti-M . corallinus horse antiserum or with 100 μL of serum from naïve mice , respectively . All animal experimentation protocols were performed in conformity with the Ethical Principles on Animal Research of the Brazilian College of Animal Experimentation ( COBEA ) and were previously revised and approved by the Ethics Committee on Animal Research of Butantan Institute under identification number 657/09 .
Two multiepitope DNA-strings were designed by identifying reactive B-cell epitopes in four major three-fingered toxins and one phospholipase A2 from M . corallinus venom . For this , synthetic pentadecapeptides covering the entire amino acid sequences of these toxins were adsorbed on a nitrocellulose membrane ( Fig 1 ) , which was incubated with a monospecific anti M . corallinus horse antiserum and revealed with an alkaline phosphatase-conjugated goat anti-horse IgG as secondary antibody . Unspecific spots were identified by incubating the SPOT membrane with only the secondary antibody . A spot was considered immunoreactive when its relative density value after incubation with both primary and secondary antibodies was higher than its relative density value obtained after incubation with only the secondary antibody ( Fig 2 ) . Represented by spots 80/81 and 94/95/96/97 , two linear epitopes with amino acid sequences KICNFKTCPTDELRHCAS ( Epitope 1 ) and CAATCPTVKPGVNIICCKTDNSN ( Epitope 2 ) were identified in the primary structure of Ag1 ( Fig 2—Ag1 ) . Likewise , the densitometric analysis of spots associated with the primary structure of Ag2 showed a single epitope ( Fig 2—Ag2 ) represented by spots 68/69/70 and with amino acid sequence GCPQSSRGVKVDCCMRDKCNG ( Epitope 3 ) . In the same way , a single 27-mer epitope , represented by spots 116/117/118/119/120 and with amino acid sequence WKKGEKVSRGCAVTCPKPKKDETIQCC ( Epitope 4 ) was detected on Ag3 ( Fig 2—Ag3 ) and a single 24-mer epitope , represented by spots 139/140/141/142 and with amino acid sequence DDFTCVKKWEGGGRRVTQYCSHAC ( Epitope 5 ) was detected on Ag4 ( Fig 2—Ag4 ) . In the case of the putative PLA2 , two linear epitopes were detected ( Fig 2—Ag5 ) , represented by spots 21/22 and 44/45/46/47 . The amino acid sequences of these epitopes are , respectively , GAGGSGTPVDELDRCCKV ( Epitope 6 ) and AALCFGRAPYNKNNENINPNRCR ( Epitope 7 ) . Considering that the hydrophilic regions of a protein are precisely those that , in theory , are more exposed to the immune system and consequently have a higher reactivity when in contact with an anti-M . corallinus serum , we decided to compare the position of these epitopes within an antigenic index and a hydrophilic profile of their respective antigens . The results clearly demonstrate that all epitopes are positioned within the antigenic and hydrophilic regions ( Fig 2—Ags1–5 ) . Additionally , when these epitopes were mapped into the three-dimensional models we created , we could observe that these epitopes are occupying large accessible surface areas ( Fig 3 ) , corroborating the empirical results obtained by this epitope mapping technique . Furthermore , concerning the PLA2 3D model , it is also worth noting that despite the two detected epitopes are located in opposite sides in the primary structure of the protein , they are situated in the same spatial region of the protein , which strongly suggests that these peptides are , indeed , important for an effective immune response ( Fig 3E ) . Having identified the most reactive peptides from all the five selected neurotoxins , two synthetic multiepitope DNA strings were designed , as previously described , based on the amino acid sequence of those epitopes . One of them , named 3ftx , codes for all five reactive epitopes associated with the four 3FTx ( Fig 4 ) . The other one , named pla2 , codes for the two reactive epitopes associated with the PLA2 toxin ( Fig 5 ) . In both cases , cysteine codons were replaced by serine codons to avoid the formation of disulphide bond-mediated protein multimerisation . All epitopes were separated by a six residues linker and codons were optimised for both Mus musculus and Escherichia coli expression . Although most of the registered cases of snakebite envenomation are due to snakes from the Viperidae family , accidents involving members of the Elapidae family do occur . Additionally , coral snakes , which are the only elapids found in the New World , possess one of the most potent venom found in snakes , which tend to have significant neurotoxicity , inducing peripheral nervous system depression in a way similar to curare poisoning , with muscle paralysis and vasomotor instability . Actually , accidents caused by coral snakes could be very severe or even lethal [3] . Since the early observations from Calmette and Vital Brazil [46] , the only acceptable medical treatment for snakebite accidents is the administration of an antiserum generated by horse immunisation with snake venom . Here , for what concerns an antielapidic antivenom , due to a number of factors such as the small size of glands , fossorial habit and very low survival rates in captivity , the production of sufficient amounts of antivenom is jeopardised by the inadequate amount of venom available . In fact , there are already registered cases of patients being intubated and ventilated as a consequence of antivenom shortage in USA , leading to increased morbidity and mortality [11] . Under these circumstances , the development of a new and efficient procedure for coral snake antivenom development , with less reliance upon snake collection and maintenance , would be an important contribution for the treatment of coral snakebite accidents . In a recent work , the B-cell epitope mapping of M . corallinus antigens was described and showed promising results when these epitopes were used as peptide antigens [47] . Here , on the other hand , we describe the design and synthesis of two multiepitope DNA strings through the identification of linear B-cell epitopes of five major toxins ( four 3FTx and one PLA2 [22] ) from the venom of M . corallinus . When these multiepitope DNA strings were used for the genetic immunisation ( by GeneGun ) of mice , detectable levels of specific antibodies with partial ( 40% ) neutralisation capabilities in lethal dose assays were observed ( Fig 6B-i ) . Furthermore , when these multiepitope DNA strings were used for the expression and purification of recombinant multiepitope proteins , which , in turn , were administered to those previously genetically immunised groups of mice , not only the IgG antibody titres increased but a 60% neutralisation capability was also observed in lethal dose assays ( Fig 6B-ii ) , showing that both multiepitope DNA strings can be used for the generation of neutralising antibodies against M . corallinus toxins . These results also confirm that transcriptomic studies can provide potential targets for the development of neutralising antibodies and further studies concerning the characterisation of other B-cell epitopes , other formulations and immunisation protocols could help to improve venom neutralisation . At last , but not least , the fact that a neutralisation of 100% could not be observed does not disqualifies this approach as a promising alternative method for the development of an antielapidic antiserum . As a matter of fact , it is worth noting that all the neutralisation capabilities observed in this work were , as expected , intimately related with the antibody titres . Unfortunately , however , the total volume of sera withdrawn from immunised animals was not sufficient to obtain reliable quantities of purified immunoglobulins , what would be , indeed , an interesting outcome of this work .
|
Coral snakes are a group of deadly venomous snakes that exhibit a characteristic red , yellow/white , and black coloured banding pattern . Accidents involving these snakes tend to be very severe or even lethal , causing peripheral nervous system depression with muscle paralysis and vasomotor instability . The only acceptable medical treatment for snakebite accidents is the administration of an antivenom , generally produced by immunising horses with the snake venom . Nonetheless , for what concerns the antielapidic serum production in Brazil , the total amount of venom available for horse immunisations is insufficient . This is mainly due to the small size of coral snake glands , their underground life style , combined with its very low survival rates in captivity . Moreover , cases of patients being intubated and ventilated as a consequence of antivenom shortage in USA have also been registered . In this work , we present an alternative method for the development of antielapidic serum , which does not rely upon snake capture . This serum was produced by a heterologous DNA prime—with a multiepitope DNA string coding for the most reactive epitopes from the most abundant toxins of M . corallinus , a coral snake which occupy highly populated areas in Brazil—followed by recombinant multiepitope protein boost immunisation of mice .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"and",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"toxins",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"crystal",
"structure",
"immunology",
"condensed",
"matter",
"physics",
"vertebrates",
"animals",
"toxicology",
"toxic",
"agents",
"reptiles",
"dna",
"crystallography",
"antibodies",
"venoms",
"immune",
"system",
"proteins",
"solid",
"state",
"physics",
"proteins",
"recombinant",
"proteins",
"snakes",
"physics",
"biochemistry",
"nucleic",
"acids",
"physiology",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"dna",
"recombination",
"organisms"
] |
2016
|
A Heterologous Multiepitope DNA Prime/Recombinant Protein Boost Immunisation Strategy for the Development of an Antiserum against Micrurus corallinus (Coral Snake) Venom
|
Neuropilin 1 ( Nrp1 ) is a coreceptor for vascular endothelial growth factor A165 ( VEGF-A165 , VEGF-A164 in mice ) and semaphorin 3A ( SEMA3A ) . Nevertheless , Nrp1 null embryos display vascular defects that differ from those of mice lacking either VEGF-A164 or Sema3A proteins . Furthermore , it has been recently reported that Nrp1 is required for endothelial cell ( EC ) response to both VEGF-A165 and VEGF-A121 isoforms , the latter being incapable of binding Nrp1 on the EC surface . Taken together , these data suggest that the vascular phenotype caused by the loss of Nrp1 could be due to a VEGF-A164/SEMA3A-independent function of Nrp1 in ECs , such as adhesion to the extracellular matrix . By using RNA interference and rescue with wild-type and mutant constructs , we show here that Nrp1 through its cytoplasmic SEA motif and independently of VEGF-A165 and SEMA3A specifically promotes α5β1-integrin-mediated EC adhesion to fibronectin that is crucial for vascular development . We provide evidence that Nrp1 , while not directly mediating cell spreading on fibronectin , interacts with α5β1 at adhesion sites . Binding of the homomultimeric endocytic adaptor GAIP interacting protein C terminus , member 1 ( GIPC1 ) , to the SEA motif of Nrp1 selectively stimulates the internalization of active α5β1 in Rab5-positive early endosomes . Accordingly , GIPC1 , which also interacts with α5β1 , and the associated motor myosin VI ( Myo6 ) support active α5β1 endocytosis and EC adhesion to fibronectin . In conclusion , we propose that Nrp1 , in addition to and independently of its role as coreceptor for VEGF-A165 and SEMA3A , stimulates through its cytoplasmic domain the spreading of ECs on fibronectin by increasing the Rab5/GIPC1/Myo6-dependent internalization of active α5β1 . Nrp1 modulation of α5β1 integrin function can play a causal role in the generation of angiogenesis defects observed in Nrp1 null mice .
In vertebrates , the development of a hierarchically organized and functional vascular tree relies on the dynamic interaction of endothelial cells ( ECs ) with the surrounding extracellular matrix ( ECM ) , which is mediated by heterodimeric αβ integrin adhesive receptors [1] . During evolution , vertebrates have acquired an additional set of adhesion-related genes that regulate blood vessel assembly and function [2] . Among these genes , the ECM protein fibronectin ( FN ) and α5β1 integrin , the predominant FN receptor , have proven to be essential for embryonic vascular development and tumor angiogenesis [3] . Indeed , in vertebrate embryos FN is the earliest and most abundantly expressed subendothelial matrix molecule [3 , 4] . Endothelial α5β1 mediates cell adhesion to FN and the assembly of soluble FN dimers ( sFN ) into a fibrillar network [3] , which has also been implicated in branching morphogenesis [5] . The biological activities of integrins depend on the dynamic regulation of their adhesive function in space and time . In cells , integrins exist in different conformations that determine their affinities for ECM proteins [6] and are continuously endocytosed , trafficked through endosomal compartments , and recycled back to the plasma membrane [7 , 8] . Therefore , during vascular morphogenesis , real-time modulation of EC–ECM adhesion can result from two interconnected phenomena: the regulation of integrin conformation and traffic in response to extracellular stimuli [8 , 9] . Indeed , there is mounting evidence that pro- and antiangiogenic cues regulate blood vessel formation by modulating integrin function [1] . In this respect , the transmembrane glycoprotein neuropilin 1 ( Nrp1 ) , which is expressed in both neurons and ECs [10] , is remarkable because it was originally identified as a surface protein mediating cell adhesion [11] and then found to also act as a coreceptor for both pro- and antiangiogenic factors , such as vascular endothelial growth factor A 165 ( VEGF-A165 , VEGF-A164 in mice ) [12 , 13] and semaphorin 3A ( SEMA3A ) [14–20] , respectively . The extracellular region of Nrp1 contains two repeated complement-binding domains ( CUB domains; a1-a2 domains ) , two coagulation-factor-like domains ( b1-b2 domains ) , and a juxtamembrane meprin/A5/μ-phosphatase ( MAM; c ) homology domain . The Nrp1 intracellular region is only 50 amino acids in length , and its function is poorly characterized [21] . Through its b1-b2 domains , Nrp1 binds and potentiates the proangiogenic activity of VEGF-A165 , which contains the heparin-binding peptide encoded by exon 7 [13] . In addition , Nrp1 acts as the ligand-binding subunit of the receptor complex for the antiangiogenic SEMA3A [14–20] , whose sema and immunoglobulin-basic domains , respectively , bind the a1-a2 and b1-b2 domains of Nrp1 [21] . The MAM/c domain instead mediates the SEMA3A-elicited Nrp1 oligomerization that is required for SEMA3A biological activity [21] . Interestingly , the short cytoplasmic domain of Nrp1 is not required for SEMA3A signaling in neurons [22] . In addition , the extracellular b1-b2 domains of Nrp1 mediate heterophilic cell adhesion independently of VEGF-A165 and SEMA3A [11] . Nrp1 null mice display an embryonic lethal phenotype , characterized by dramatic vascular defects ascribed to impaired angiogenic sprouting [23] , branching [24] , or arterialization [25] that is significantly more severe than and/or qualitatively different from that of mice lacking either VEGF-A164 ( VEGF120/120 mice ) [26] or Sema3A [16] . Indeed , although Nrp1–/– embryos die in utero by 13 . 5 days postcopulation [23] , VEGF120/120 pups are recovered at birth at a normal Mendelian frequency [27] . Moreover , a major feature of Nrp1 null mutants , i . e . , the severe impairment of neural tube vascularization [23] , is not phenocopied by VEGF120/120 mouse embryos [26] . In addition , differently from Nrp1 null mice [23 , 24] , the vascular phenotype of Sema3a null mice is significantly influenced by the genetic background [16 , 28–30] . These findings suggest that the vascular defects caused by the loss of Nrp1 could be due to a VEGF-A164/SEMA3A-independent function of Nrp1 in vascular cells , such as adhesion to the ECM [31 , 32] . However , how Nrp1 regulates integrin-dependent EC linkages to the surrounding matrix is still obscure . Here , we shed light on the molecular mechanisms by which Nrp1 , via its short cytoplasmic domain and independently of VEGF-A165 and SEMA3A , specifically controls a biological function that is crucial for vascular development [3] , namely , α5β1-mediated EC adhesion to FN .
To understand the mechanisms by which Nrp1 modulates EC adhesion to different ECM proteins , we silenced the expression of Nrp1 in human umbilical artery ECs by RNA interference ( RNAi ) . Parenthetically , Nrp1 has been found to be expressed at higher levels in arteries than in veins [33] . Endothelial cells were transfected twice with either a pool of three different small interfering RNAs ( siRNAs ) targeting human Nrp1 ( sihNrp1 ) or control nontargeting siRNA ( siCtl ) . Twenty-four hours after the second transfection , Western blot analysis revealed that , in comparison with control cells , Nrp1 protein , but neither β-tubulin nor the Nrp1 interactor GAIP interacting protein C terminus , member 1 ( GIPC1 ) , was successfully silenced in sihNrp1 ECs ( Figure 1A ) . Next , we investigated the effect of Nrp1 silencing on EC adhesion to different ECM proteins . Fibronectin , vitronectin ( VN ) , and type I collagen ( COLL-I ) are typical constituents of the provisional angiogenic ECM [1 , 3] , whereas laminin ( LN ) isoforms are major components of the vascular basement membrane surrounding both immature and mature blood vessels [34] . Short-term ( 15 min ) adhesion assays showed that loss of Nrp1 greatly reduced EC adhesion to FN but not to VN , COLL-I , or LN ( Figure 1B–E ) , suggesting that positive modulation of cell adhesion by Nrp1 is not a general phenomenon [31] but rather a function restricted to specific ECM proteins , such as FN . Because FN polymerization by ECs has been suggested to participate in vascular morphogenesis [3] , we next examined the role of Nrp1 in the fibrillogenesis of endogenous FN . During FN matrix assembly , current models envisage the binding of sFN to surface integrins , thus causing the conversion of FN to a conformational form that favors fibril formation through interactions with other integrin-bound FN dimers [3] . Endothelial cells were cultured in a medium containing FN-depleted fetal calf serum , and accumulation of endogenous FN into fibrils was then detected by confocal immunofluorescence analysis . In comparison with control cells , sihNrp1 ECs were impaired in their ability to incorporate endogenous sFN into a dense fibrillar network 3 h after plating ( Figure 1F and 1G ) . Time-course real-time reverse transcription PCR ( RT-PCR ) and Western blot analyses revealed that the endogenous FN fibrillogenesis defect observed in sihNrp1 ECs was not due to a reduction in FN mRNA ( Figure S1A ) or protein ( Figure S1D ) . Hence , Nrp1 specifically promotes EC adhesion to FN and FN matrix formation . To start dissecting the mechanisms by which Nrp1 controls the interaction of human ECs with FN , we sought to compare the abilities of full-length and deletion constructs of mouse Nrp1 ( mNrp1 ) to rescue the adhesion and fibrillogenesis defects of sihNrp1 ECs ( Figure 2 ) . In particular , we investigated the role played by the extracellular and cytoplasmic moieties of Nrp1 . Indeed , the Nrp1 cytodomain , although dispensable for SEMA3A collapsing activity in neurons [22] , could signal in cultured ECs [35] . Moreover , the C-terminal SEA sequence of Nrp1 interacts with the PDZ domain of the endocytic adaptor protein GIPC1 [36] , whose knockdown during development results in altered arterial branching [37] . Therefore , we transduced sihNrp1 ECs with retroviral vectors carrying the hemagglutinin ( HA ) -tagged full-length ( mNrp1 ) and deletion mutants of murine Nrp1 ( Figure 2A ) , lacking either the C-terminal SEA amino acids ( mNrp1dSEA ) or the whole cytoplasmic domain ( mNrp1dCy ) . The sihNrp1 pool did not target any of the mNrp1 constructs , and immunoprecipitation experiments on membrane-biotinylated cell monolayers revealed that all three transmembrane proteins were efficiently exposed on the cell surface ( Figure 2B ) . In comparison to wild-type mNrp1 , both mNrp1dSEA and mNrp1dCy constructs were severely impaired in their abilities to rescue sihNrp1 EC defects in adhesion to FN ( Figure 2C ) and endogenous FN fibrillogenesis ( Figure 2D–F ) . Accordingly , only mNrp1 overexpression stimulated the adhesion of NIH 3T3 fibroblasts to FN , whereas neither mNrp1dSEA nor mNrp1dCy were active in this respect ( Figure 2G ) . Moreover , mNrp1 overexpression did not promote NIH 3T3 adhesion to VN ( Figure S2 ) , further supporting the concept that Nrp1 behaves as a substrate-specific enhancer of cell adhesion . Hence , it appears that the cytoplasmic domain of Nrp1 , in particular its SEA motif , which interacts with the endocytic adaptor GIPC1 [36] , is required for Nrp1 stimulation of EC spreading on FN and polymerization of endogenous FN . Opposing autocrine loops of VEGF-A [38–41] and SEMA3A [16 , 19 , 42 , 43] have been found in ECs both in vitro and in vivo . Therefore , we investigated whether the SEA motif and the full cytoplasmic domain of Nrp1 could be required for the modulation of EC adhesion to FN by VEGF-A165 and SEMA3A . Consistent with previous observations [16 , 18 , 20 , 44] , silencing Nrp1 completely blocked VEGF-A165-dependent stimulation ( Figure 2H ) and SEMA3A-dependent inhibition ( Figure 2I ) of human EC adhesion to FN . As expected , inhibition of cell adhesion to FN by SEMA3F , which signals through Nrp2 [20 , 21] , was not affected by Nrp1 knockdown ( Figure 2J ) . Moreover , similarly to what was observed for SEMA3A in neurons [22] , we found that the cytoplasmic domain of Nrp1 is entirely dispensable for both VEGF-A165 ( Figure 2H ) and SEMA3A ( Figure 2I ) activity on EC adhesion to FN , because all three mNrp1 constructs rescued sihNrp1 EC response to these factors with a similar efficiency . Thus , the Nrp1 SEA motif and cytodomain are required for Nrp1 modulation of EC adhesion to FN and sFN incorporation into fibrils but not for Nrp1 activity as a VEGF-A165 and SEMA3A coreceptor . α5β1 Integrin is the main FN receptor in ECs [1 , 3] , and by transmitting the actin-dependent tension to sFN , it triggers FN fibrillogenesis [45] . To elucidate whether Nrp1 stimulation of cell adhesion to FN was directly mediated by Nrp1 or was dependent on α5β1 integrin , CHO cells lacking ( CHO B2 ) or expressing ( CHO B2α27 ) the α5 integrin subunit were transfected with mNrp1 and allowed to adhere to FN . Overexpression of mNrp1 stimulated CHO cell adhesion to FN in the presence ( CHO B2α27; Figure 3A ) but not in the absence ( CHO B2; Figure 3B ) of α5β1 integrin . Therefore , Nrp1′s proadhesive activity on FN is nonautonomous and mediated by α5β1 integrin . We then examined whether in ECs Nrp1 could interact physically with α5β1 integrin . Lysates from ECs adhering on endogenous ECM were immunoprecipitated with an antibody ( Ab ) recognizing the FN receptor α5β1 and then blotted with anti-Nrp1 Ab . Nrp1 coimmunoprecipitated with α5β1 , and blotting Nrp1 immunoprecipitates with anti-α5β1-integrin Ab further confirmed the association between endogenous hNrp1 and α5β1 integrin in ECs ( Figure 3C ) . To better understand whether the Nrp1 cytoplasmic domain was required for the interaction with α5β1 integrin , lysates of NIH 3T3 fibroblasts overexpressing HA-tagged full-length or deletion constructs of mNrp1 and green fluorescent protein ( GFP ) -tagged α5 integrin subunit ( α5-GFP ) [46] were immunoprecipitated with anti-GFP Ab and then blotted with anti-HA Ab ( Figure 3D ) . We found that both the C-terminal SEA and the cytoplasmic domain of Nrp1 were fully dispensable for its interaction with α5β1 integrin . To understand the spatial and functional relationships between Nrp1 and α5β1 integrin in ECs , we first generated a monomeric red fluorescent protein ( mRFP ) -tagged mNrp1 construct ( mNrp1-mRFP ) that was then cotransfected with α5-GFP in ECs . Fluorescent confocal microscopy showed that at the plasma membrane of ECs adhering on FN mNrp1-mRFP was enriched in close proximity to , or even tightly intermingled with , α5-GFP-containing adhesion sites ( Figure 4A , arrows ) . Moreover , mNrp1-mRFP and α5-GFP fully colocalized in intracellular vesicles ( Figure 4A , arrowheads ) . Notably , immunofluorescence analysis of endogenous endothelial proteins confirmed the spatial links between hNrp1 and vinculin ( Figure 4B ) or α5β1 integrin ( Figure 4C ) at either adhesion sites ( Figure 4B and 4C , arrows ) or vesicular structures located in their proximity ( Figure 4C , arrowheads ) . The observation that Nrp1 and α5β1 colocalization was particularly apparent in intracellular vesicles indicated that these two molecules may associate at or near the point of endocytosis and that they may be internalized as a complex , which is then subsequently disassembled upon recycling to the plasma membrane . We have previously found that endosomal integrin complexes can be preserved by treating the cell with primaquine ( PMQ ) , a receptor recycling inhibitor , prior to lysis [47] . Therefore , we immunoprecipitated α5β1 integrin or Nrp1 from cells that had been treated with PMQ for 10 min and probed for the presence of the α5β1/Nrp1 complex by Western blotting . Pretreatment of the cells with PMQ greatly increased the coprecipitation of α5β1 integrin with Nrp1 and vice versa ( Figure 3C ) , indicating the likelihood that this complex is more stable in endosomes than at the plasma membrane . To further characterize the interaction between Nrp1 and α5β1 integrin , we measured fluorescence resonance energy transfer ( FRET ) in live NIH 3T3 cells transfected with α5-GFP alone or cotransfected with α5-GFP and mNrp1 tagged with the fluorescent protein Cherry , an improved version of mRFP ( mNrp1-Cherry ) . Total internal reflection fluorescence ( TIRF ) illumination [48] was used to selectively excite α5-GFP at the basal cell plasma membrane where ECM adhesions lie . Fluorescence resonance energy transfer was measured by fluorescence lifetime imaging microscopy ( FLIM ) [49] and was read out as a decrease in donor ( GFP ) fluorescence lifetime . We found that the α5-GFP fluorescence lifetime was significantly reduced in cells that coexpressed mNrp1-Cherry , indicating that FRET , and thus a close physical interaction , was occurring between α5β1 and Nrp1 at adhesion sites with an 11 . 5% FRET efficiency ( Figure 5 ) . Taken together , these data indicate that in living cells Nrp1 physically associates with α5β1 at or near sites of cell–ECM contact and that this interaction is likely maintained following internalization of the complex . The efficiency of cell adhesion and spreading on ECM is generally thought to be proportional to the amount of either active or total ( i . e . , active and inactive ) integrin at the cell surface [1 , 6] . We found that lack of Nrp1 did not alter the global amount of either total ( Figure 1A ) , as already reported [31] , or active α5β1 integrin , as recognized by the mouse monoclonal Ab ( mAb ) SNAKA51 [45] ( Figure S3 ) . Then , we analyzed whether Nrp1 could influence the amount of α5β1 integrin on the endothelial surface . Biotinylation experiments revealed that knocking down human Nrp1 did not diminish the surface levels of either total or active α5β1 integrin in sihNrp1 ECs ( Figure 6A ) , thus suggesting that a mechanism alternative to the control of integrin conformation should be responsible for Nrp1-dependent activation of α5β1 integrin function in ECs . On the basis of our observations that Nrp1 and α5β1 integrin colocalize in intracellular vesicles ( Figure 4A and 4C ) and that inhibition of recycling by PMQ increased the association of Nrp1 with α5β1 integrin ( Figure 3C ) , we decided to monitor the effect of Nrp1 knockdown on the internalization of total and active surface α5β1 integrin . Endothelial cells were surface-labeled with cleavable biotin at 4 °C and incubated at 37 °C for different times to allow internalization , and then biotin remaining on cell-surface proteins was cleaved at 4 °C [50] . Integrin internalization was quantified by immunoprecipitation of either total ( Figure 6A and 6B ) or active ( Figure 6A and 6C ) α5β1 integrin , followed by Western blot analysis with streptavidin . Notably , although endocytosis of the cell-surface pool of total α5β1 integrin ( i . e . , active plus inactive heterodimers ) was not detectably altered in sihNrp1 cells ( Figure 6A and 6B ) , knockdown of Nrp1 markedly reduced the quantity of active ( SNAKA51-positive ) α5β1 heterodimers internalized by ECs ( Figure 6A and 6C ) . Taken together , these data indicate that on the cell surface Nrp1 interacts with active α5β1 heterodimers at adhesion sites ( Figure 4A and 4C , arrows ) and acts to promote their internalization and localization to intracellular vesicles ( Figure 4A and 4C , arrowheads ) . To visualize the internalization and postendocytic trafficking of the α5β1/Nrp1 complex , we deployed the photoactivatable ( PA ) α5-GFP ( α5-PA-GFP ) probe that we had previously used to monitor α5β1 trafficking in human ovarian carcinoma A2780 cells [51] . However , the multitude of fluorescent vesicles travelling to and from the cell surface made it difficult to track the progress of individual α5β1 integrin transport vesicles . Therefore , we used TIRF to restrict the plane of activating fluorescence , such that only α5-PA-GFP present at or near the cell surface became photoactivated . Then we tracked the movement of this photoactivated fraction of α5β1 integrin using time-lapse epifluorecence microscopy . With this novel technique , α5β1 integrin was photoactivated almost exclusively at adhesion sites ( mostly fibrillar adhesions ) , where it colocalized with mNrp1-Cherry ( Figure 7 , arrows ) . Photoactivated α5β1 was then rapidly ( <6 s ) internalized and cotransported with mNrp1-Cherry in small endocytic vesicles ( Figure 7 , empty arrowheads , and Video S1 ) that moved away from the fibrillar adhesions . In addition , we found that α5β1 integrin turnover in ECM adhesions was unexpectedly very rapid ( Figure S4 and Video S2 ) , with the α5-PA-GFP signal leaving the adhesive sites , accumulating in vesicles , and disappearing by ∼45 s after photoactivation in approximately 50% of the adhesion sites and by ∼115 s in the remaining ones ( Video S2 ) . Having established that α5β1 integrin and Nrp1 are cointernalized at fibrillar adhesions , we wished to determine whether the integrin was then recycled from Nrp1-positive vesicles back to the plasma membrane . To address this , we aimed a pulse of 405-nm laser light at a “single point” corresponding to Nrp1-positive vesicles , leading to the immediate photoactivation of α5-PA-GFP integrin largely within the confines of these structures ( Figure 8 and Video S3 ) . During the following 80 s , fluorescence was lost from the photoactivated vesicle , and this was accompanied by a corresponding increase in integrin fluorescence at peripheral elongated structures that look like adhesion sites ( Figure 8 , red circle and white arrows ) . On the contrary , when the activating laser was aimed at a cell region devoid of Nrp1-positive vesicles , little or no photoactivation occurred ( Figure S5 and Video S4 ) , indicating that the α5-PA-GFP fluorescence detected in Figure 8 was indeed at mNrp1-Cherry vesicles and not at the plasma membrane above and below them . Taken together , these data indicate that α5β1 integrin and Nrp1 are cointernalized into intracellular vesicles , which are then rapidly returned or recycled to the plasma membrane . Interestingly , both the internalization and the recycling of Nrp1-associated α5β1 integrin occur at the site of adhesion to the ECM . In the eukaryotic early endocytic pathway , the small GTPase Rab5 is a rate-limiting component that regulates the entry of cargoes from the plasma membrane into the early endosome [52] . Hence , we analyzed the early endocytic steps of active α5β1 integrin in ECs cotransfected with mNrp1-mRFP and Rab5-GFP , which were incubated with the α5β1 integrin activation reporter mAb SNAKA51 for 30 min at 4 °C and then at 37 °C for different time points . Fluorescent confocal analysis indicated that , after 1–3 min of internalization at 37 °C , Nrp1 and active α5β1 integrin colocalized in early Rab5-positive vesicles near the EC plasma membrane ( Figure 9A ) . Accordingly , immunofluorescence analysis of endogenous endothelial proteins confirmed that hNrp1 and Rab5 colocalized in vesicles , many of which were located near adhesion sites ( Figure 9B , empty arrowheads ) , further supporting the view that Nrp1 can induce α5β1-mediated adhesion by promoting the preferential internalization of its active conformation into Rab5-positive early endosomes and the ensuing recycling to newly forming cell–ECM contacts . Next , to characterize the molecular mechanisms by which Nrp1 regulates the traffic of active α5β1 integrin , we evaluated the abilities of mNrp1 full-length and mutant constructs to rescue the integrin internalization defects that we observed in sihNrp1 ECs . Remarkably , only wild-type mNrp1 , but neither mNrp1dSEA nor mNrp1dCy construct , was able to rescue the sihNrp1 EC defects in the endocytosis of active α5β1 integrin ( Figure 6D ) . Therefore , in ECs the SEA motif of Nrp1 , which binds the endocytic adaptor GIPC1 [36] , is mandatory for Nrp1 stimulation of cell adhesion to FN ( Figure 2C ) , endogenous FN fibrillogenesis ( Figure 2D–F ) , and active α5β1 integrin endocytosis ( Figure 6D ) . The N-terminal portion of GIPC1 mediates its oligomerization , whereas its central PDZ domain can bind the C-terminal consensus S/T-X-Φ sequence of Nrp1 [36] , the α5 integrin subunit [53] , and the Rab5/Rab21 interactor protein APPL1 [54 , 55] . Thus , we theorized that as a result GIPC1 could support the Rab5-dependent early internalization of α5β1 integrin . To test this hypothesis , we silenced the expression of GIPC1 in human umbilical artery ECs by RNAi and examined its effect on α5β1 integrin traffic . Western blot analysis showed that , 96 h after the second transfection , GIPC1 protein , but not β-tubulin , was successfully silenced in sihGIPC1 ECs in comparison with control cells ( Figure 10A ) . Knockdown of GIPC1 in ECs dramatically reduced the amount of internalized total ( Figure 10C and 10D ) and active ( Figure 10C and 10E ) α5β1 integrin by ∼70% throughout the whole internalization assay , suggesting that indeed the interaction of α5β1 integrin with GIPC1 is crucial for the endocytosis and the proper functioning of this integrin . Accordingly , short-term adhesion assays showed that , in comparison with control cells , sihGIPC1 ECs adhered poorly to FN ( Figure 10B ) and much less efficiently assembled endogenous sFN into a fibrillar network ( Figure S1H ) in comparison with cells transfected with siCtl ( Figure S1G ) . The latter defect was not due to a reduction in FN mRNA or protein levels as demonstrated by real-time RT-PCR ( Figure S1B ) and Western blotting ( Figure S1E ) . Hence , within Nrp1 the extracellular domain mediates the association with α5β1 integrin , and the C-terminal SEA sequence allows the binding to the endocytic adaptor GIPC1 that stimulates the internalization and traffic of active α5β1 integrin , finally promoting EC adhesion to FN and FN fibrillogenesis . Because the C terminus of GIPC1 binds to the minus-end-directed motor myosin VI ( Myo6 ) that has also been involved in endocytosis [56] , we considered the hypothesis that Myo6 could cooperate with GIPC1 in promoting α5β1 integrin internalization . Interestingly , RNAi-mediated knockdown of Myo6 in human umbilical artery ECs ( Figure 10A ) resulted in a significant ( ∼70% ) impairment of active α5β1 integrin internalization ( Figure 10F and 10H ) , whereas the total integrin pool was only mildly affected ( ∼25%; Figure 10F and 10G ) . These data , together with the fact that sihMyo6 EC adhesion to FN was severely hampered ( Figure 10B ) , indicate that Myo6 cooperates with GIPC1 in the regulation of active α5β1 integrin endocytosis . Similarly to what we noticed after Nrp1 and GIPC1 knockdown , ECs in which Myo6 was silenced did not efficiently assemble an endogenous FN fibrillar network ( Figure S1I ) in comparison with cells transfected with siCtl ( Figure S1G ) . However , differently from what we observed in sihNrp1 and sihGIPC1 ECs , the endogenous FN fibrillogenesis defect seen in sihMyo6 ECs was due to an inhibition of FN1 gene transcription mRNA ( Figure S1C ) , which associated to a significant reduction of FN protein levels as well ( Figure S1F ) . Indeed , in addition to its role in cytoplasmic transporting and anchoring , Myo6 is also present in the nucleus , where it promotes the RNA-polymerase-II-dependent transcription of active genes [57] . Here we identify the FN1 gene as a new Myo6 transcriptional target and downstream effector that can bolster EC adhesion and motility .
Defects of developing blood vessels caused by Nrp1 gene knockdown in mice [23 , 24] are different from vascular malformations displayed by mice lacking either SEMA3A [16] or VEGF-A165 ( Vegf-a120/120 mice ) [26] . Furthermore , it has been recently reported that Nrp1 is required for EC responses to both VEGF-A165 and VEGF-A121 isoforms , the latter being incapable of binding Nrp1 on the EC surface [58 , 59] . Therefore , it is conceivable that the vascular abnormalities of Nrp1–/– mice could be due at least in part to the disruption of a VEGF-A165/SEMA3A-independent Nrp1 function . α5β1 Integrin and its ligand FN are key players in vascular development [3] . The data reported here support a model in which Nrp1 , through its cytoplasmic domain and independently of its activity as a SEMA3A and VEGF-A165 coreceptor , stimulates GIPC1/Myo6-dependent endocytosis and traffic of active α5β1 integrin , thus promoting EC adhesion to FN and FN fibrillogenesis . In rescue experiments , where we reintroduced full-length and mutant murine Nrp1 constructs in human ECs in which endogenous hNrp1 was simultaneously knocked down by RNAi , we showed that EC adhesion to FN and polymerization of endogenous sFN into fibrils depend on the cytoplasmic domain of Nrp1 , the C-terminal SEA motif representing the minimal sequence required to exert these functions . Importantly , as already shown for SEMA3A-elicited growth cone collapse in neurons [22] , we found that the cytoplasmic domain of Nrp1 is instead dispensable for VEGF-A165 stimulation and SEMA3A inhibition of EC adhesion to FN . Moreover , by using two CHO cell clones differing in the expression of α5β1 integrin , we demonstrated that Nrp1 alone does not directly mediate adhesion to FN and that it requires α5β1 integrin . Therefore , we conclude that in ECs , independently of VEGF-A165 and SEMA3A , Nrp1 stimulates α5β1-mediated adhesion to FN and endogenous FN fibrillogenesis via its cytoplasmic SEA motif [36] . This motif , similar to the C-terminal SDA sequence of the α5 integrin subunit [53] , selectively and specifically binds the PDZ domain of the homomultimeric endocytic adaptor GIPC1 . It is known that conformational activation of cell-surface integrins supports cell adhesion and spreading , whereas transition of integrins toward an inactive bent conformation causes cell de-adhesion and rounding up [1 , 60 , 61] . However , we observed that lack of Nrp1 does not result in the reduction of either active or total α5β1 integrin either at the cell surface or intracellularly . Rather , by combining biochemical analysis with conventional and TIRF/FLIM confocal microscopy , we found that at the plasma membrane Nrp1 is tightly associated with adhesion sites , where it physically interacts with α5β1 integrin . The complex formed between active α5β1 integrin and Nrp1 is then rapidly internalized into Rab5-positive endosomes in an Nrp1-dependent fashion . Interestingly , the integrin is then returned to the plasma membrane from Nrp1-containing vesicles , and this recycling event appears to be targeted to adhesive structures . In addition , although the extracellular domain of Nrp1 is sufficient for its interaction with α5β1 integrin , the C-terminal GIPC1-binding SEA sequence of Nrp1 is necessary for stimulating EC adhesion to FN . Accordingly , knocking down either GIPC1 or its interacting motor Myo6 results in a significant impairment of active α5β1 integrin endocytosis and EC adhesion to FN . Taken together , our data indicate that , during EC adhesion and spreading on FN , Nrp1 , through its extracellular domain , transiently interacts with active α5β1 integrin at adhesive sites and , via its cytoplasmic association with GIPC1 , enhances the early endocytosis and the ensuing recycling of active α5β1 integrin to newly forming adhesion sites ( Figure 11A ) . It is therefore likely that fast cycles of endocytosis from and recycling to ECM adhesions of active α5β1 integrin could allow real-time optimization of adhesion during EC spreading on FN . These conclusions are in line with the recent findings by Ivaska and colleagues [8 , 9] that found how endocytosis of β1 integrins , in addition to their established role in directional migration [7] , regulates cell adhesion and spreading as well . In particular , they reported that class V Rab GTPases ( for review , see [52] ) Rab21 and Rab5 directly bind to several integrin α subunits , α5 included , by interacting with the conserved membrane proximal region GFFKR , which interestingly has been previously implicated in conformational integrin activation [61] . It is thus conceivable that GIPC1 oligomers could favor α5β1 integrin endocytosis by bridging the α5 integrin subunit and the Rab5/Rab21 interactor APPL1 , finally stabilizing the interaction between these small GTPases and α5β1 integrin . This could represent a main functional feature distinguishing α5β1 from other integrin heterodimers not interacting with GIPC1 . Finally , the fact that by 2 min after activation α5-PA-GFP disappeared from preexisting adhesion sites into vesicles without a concomitant cell retraction suggests the existence of a steady endo-exocytic flow of ( active ) α5β1 integrins from and toward existing ECM adhesions as well ( Figure 11B ) . This mechanism could allow adherent cells to be always ready to rapidly exchange integrins among cell–ECM contacts in response to extracellular stimuli . Such a scenario is also compatible with a previous study by Ezratty and colleagues [62] and implies that disassembly of ECM adhesions could depend on an imbalance of endocytosis over recycling . Our observation that Myo6 siRNA severely impairs EC adhesion to FN and results in a significant reduction in the internalization of active α5β1 integrin suggests that Myo6 cooperates with GIPC1 ( Figure 11A and 11B ) and is compatible with the notion that Myo6 plays a role in the formation and transport of endocytic vesicles along F-actin microfilaments [56] . The decrease in FN mRNA that we noticed in sihMyo6 ECs is likely due to the lack of the transcriptional activity displayed by Myo6 in the nucleus [57] that could depend on a still not fully characterized actin–myosin-based mechanism of transcription [63 , 64] . Therefore , Myo6 can support EC adhesion and motility by promoting both active α5β1 integrin traffic ( Figure 11A and 11B ) and FN1 gene transcription ( Figure 11C ) . Additionally , these findings can have significant implications for the biology of α5β1-expressing human carcinomas [51 , 65] , in which Myo6 can be overexpressed and promote metastatic invasion [66–68] . In conclusion , we propose here that Nrp1 , in addition to and independently of its role as coreceptor for VEGF-A165 and SEMA3A , stimulates through its cytoplasmic domain the spreading of ECs on FN by increasing the Rab5/GIPC1/Myo6-dependent internalization of active α5β1 integrin . Nrp1 modulation of α5β1-mediated adhesion can play a causal role in the generation of angiogenesis defects observed in Nrp1 null mice . We anticipate that signaling pathways controlling Nrp1 expression in ECs could ultimately modulate the activity of α5β1 integrin . In particular , Nrp1 is a major target of the inhibitory Delta-like 4–Notch signaling pathway [69] that negatively regulates the formation of endothelial tip cells [10] . Higher expression of Nrp1 in tip ECs compared with that in stalk ECs of angiogenic sprouts could differentially modulate α5β1 integrin traffic , thus favoring tip cell adhesion and spreading on FN . Finally , both Nrp1 [70] and α5β1 integrin [71 , 72] are expressed in pericytes and vascular smooth muscle cells , which have been implicated in vascular remodeling by intussusceptive angiogenesis [73] . Further work is needed to assess whether Nrp1 is regulating α5β1 integrin function not only in ECs but also in pericytes and vascular smooth muscle cells .
Goat polyclonal anti-Nrp1 ( C-19 ) and rabbit polyclonal anti-β-tubulin ( H-235 ) were from Santa Cruz Biotechnology . Mouse monoclonal anti-human-Nrp1 ( MAB 3870 ) was from R&D Systems . Mouse monoclonal anti-FN ( MAB88904 ) and anti-αvβ3-integrin ( MAB1976 ) , goat polyclonal anti-α5β1-integrin ( AB1950 ) , rabbit polyclonal anti-α5-integrin ( AB1928 ) , rabbit polyclonal anti-α2-integrin ( AB1936 ) , and anti-α3-integrin ( AB1920 ) were from Chemicon . Mouse monoclonal anti-human-vinculin ( V9131 ) and rabbit polyclonal anti-Rab5 ( R4654 ) were from Sigma-Aldrich . Rat monoclonal anti-HA ( 3F10 ) was from Roche . Rabbit polyclonal anti-GFP ( A11122 ) and 4′ , 6-diamidino-2-phenylindole ( DAPI ) were from Molecular Probes . Goat polyclonal anti-GIPC1 ( ab5951 ) and rabbit polyclonal anti-Myo6 ( ab11096 ) were from Abcam . Streptavidin–horseradish peroxidase was from Amersham . Mouse monoclonal anti-active-α5-integrin , SNAKA51 , was previously described [45] . Human plasma FN was from Tebu-bio . Human plasma vitronectin , Engelbreth-Holm-Swarm murine sarcoma laminin , and calf skin collagen type I were from Sigma-Aldrich . Recombinant human VEGF-A165 was from Invitrogen . Recombinant human SEMA3A and mouse Sema3F were from R&D Systems . Sulfo-NHS-SS-Biotin was from Pierce . Hemagglutinin-tagged mNrp1 deletion constructs were generated by standard PCR protocols according to the Taq polymerase manufacturer's instructions ( Fynnzymes ) and using an HA-tagged version of full-length mNrp1 kindly donated by A . Püschel ( Westfälische Wilhelms-Universität , Münster , Germany ) as template . Cytoplasmic domains and the last three amino acids SEA were deleted using the following oligonucleotide primers: ( i ) 5′-cgccatggagagggggctgccgttg-3′ ( Fw ) ; ( ii ) 5′-ccaacaggcacagtacag-3′ ( Re1 ) to amplify the mNrp1 deleted of the cytoplasmic domain; ( iii ) 5′-gtaattactctgtgggttc-3′ ( Re2 ) to amplify the mNrp1 deleted of the three amino acids SEA . The corresponding PCR product was first thymidine–adenine ( TA ) -cloned into pCR2 . 1TOPO ( Invitrogen ) and subsequently subcloned in PINCO retrovirus or pAcGFP-N1 Vector ( BD Bioscience ) whose GFP coding sequence was previously substituted with the cDNA of mRFP , a kind gift of R . Tsien ( University of California , San Diego , CA ) . α5-GFP and Rab5-GFP constructs were kindly provided , respectively , by A . F . Horwitz ( University of Virginia , Charlottesville , VA ) and M . Zerial ( Max Plank Institute of Molecular Cell Biology and Genetics , Dresden , Germany ) . The α5-PA-GFP construct was previously described [51] . The day before oligofection , ECs were seeded in six-well dishes at a concentration of 10 × 104 cells/well . Oligofection of siRNA duplexes was performed according to the manufacturer's protocols . Briefly , human ECs were transfected twice ( at 0 and 24 h ) with 200 pmol of siCONTROL nontargeting siRNA ( as control ) , siGENOME SMART pools ( in the case of hGIPC1 and hMyo6 ) , or a mix of three ( in the case of hNrp1 ) siRNA oligonucletides ( Dharmacon ) . After 24 h ( in the case of hNrp1 ) or 96 h ( in the case of hGIPC1 or hMyo6 ) had passed since the second oligofection , ECs were lysed or tested in functional assays . In the case of hNrp1 , the single oligonucleotide sequences were: ( 1 ) 5′-AAUCAGAGUUUCCAACAUA-3′; ( 2 ) 5′-GAAGGAAGGGCGUGUCUUG-3′; ( 3 ) 5′-GUGGAUGACAUUAGUAUUA-3′ . Six-thousand ECs were resuspended in 0 . 1 ml of EBM-2 ( Clonetics ) with or without appropriate stimuli ( 50 ng/ml VEGF-A , 200 ng/ml SEMA3A , and 400 ng/ml SEMA3F ) and plated on 96-well microtiter plates ( Costar ) that were previously coated with ECM proteins at different concentrations and then saturated with 3% bovine serum albumin . After 15 min of incubation at 37 °C , cells were fixed in 8% glutaraldehyde and then stained with 0 . 1% crystal violet in 20% methanol . Cells were photographed with a QICAM Fast 1394 digital color camera ( QImaging ) and counted by means of Image-ProPlus 6 . 2 software ( Media Cybernetics ) . In adhesion assays with NIH 3T3 fibroblasts or CHO cells , Dulbecco's modified Eagle's medium was used . Endothelial cells were lysed in buffer containing 25 mM Tris-HCl , pH 7 . 6 , 100 mM NaCl , 0 . 15% Tween-20 , 5% glycerol , 0 . 5 mM ethylene glycol tetraacetic acid ( EGTA ) , and protease inhibitors ( 50 mg ml−1 pepstatin; 50 mg ml−1 leupeptin; 10 mg ml−1 aprotinin; 2 mM phenylmethanesulfonylfluoride ( PMSF ) ; 2 mM MgCl2 ) . Cells were lysed in buffer , incubated for 20 min on wet ice , and then centrifuged at 15 , 000g , 20 min , at 4 °C . The total protein amount was determined using the bicinchoninic acid ( BCA ) protein assay reagent ( Pierce ) . Equivalent amounts ( 1 , 200 μg ) of protein were immunoprecipitated for 1 h with the antibody of interest , and immune complexes were recovered on Protein G-Sepharose ( GE Healthcare ) . Immunoprecipitates were washed four times with lysis buffer , twice with the same buffer without Tween-20 , and then separated by SDS-PAGE . Proteins were then transferred to a Hybond-C extra nitrocellulose membrane ( Amersham ) , probed with antibodies of interest , and detected by an enhanced chemiluminescence technique ( PerkinElmer ) . Integrin traffic assays were performed as previously described by Roberts et al . [50] with minor modifications . Cells were transferred to ice , washed twice in cold phosphate-buffered saline ( PBS ) , and surface-labeled at 4 °C with 0 . 2 mg/ml sulfo-NHS-SS-biotin ( Pierce ) in PBS for 30 min . Labeled cells were washed in cold PBS and transferred to prewarmed EGM-2 at 37 °C . At the indicated times , the medium was aspirated , and dishes were rapidly transferred to ice and washed twice with ice-cold PBS . Biotin was removed from proteins remaining at the cell surface by incubation with a solution containing 20 mM sodium 2-mercaptoethanesulfonate ( MesNa ) in 50 mM Tris-HCl ( pH 8 . 6 ) , 100 mM NaCl for 1 h at 4 °C . MesNa was quenched by the addition of 20 mM iodoacetamide ( IAA ) for 10 min , and after other two further washes in PBS , the cells were lysed in 25 mM Tris-HCl , pH 7 . 4 , 100 mM NaCl , 2 mM MgCl2 , 1 mM Na3VO4 , 0 . 5 mM EGTA , 1% Triton X-100 , 5% glycerol , protease mix ( Sigma ) , and 1 mM PMSF . Lysates were cleared by centrifugation at 12 , 000g for 20 min . Supernatants were corrected to equivalent protein concentrations by BCA assay , and integrins were isolated by immunoprecipitation and analyzed by SDS-PAGE . Cells were placed in RNAlater solution ( Ambion ) , kept at 4 °C for 24 h , and frozen at −80 °C . After the cells were thawed on ice , total RNA was extracted following the manufacturer's recommended protocol ( SV Total RNA Isolation System , Promega ) . The quality and integrity of the total RNA were quantified by means of the RNA 6000 Nano Assay kit in an Agilent 2100 bioanalyzer ( Agilent Technologies ) . cDNAs were generated from 1 μg of total RNA using the High Capacity cDNA Reverse Transcription Kit ( Applied Biosystems ) . mRNA expression of FN and endogenous control genes , i . e . , 18S rRNA , glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) , and TATA binding protein ( TBP ) , was measured in the samples by real-time RT-PCR using TaqMan Gene Expression Assays run on an ABI PRISM 7900HT Fast Real-Time PCR System ( Applied Biosystems ) . The following assays were used: Hs00365058_m1 ( FN ) , Hs99999901_s1 ( 18S rRNA ) , Hs99999905_m1 ( GAPDH ) , and Hs00427620_m1 ( TBP ) . Three replicates were run for each gene for each sample in a 384-well format plate ( cDNA concentration 20 ng/well ) according the manufacturer's protocol . Between the three measured endogenous control genes , we chose TBP for normalization , identified by geNorm [74] . The experimental threshold ( Ct ) was calculated using the algorithm provided by the SDS 1 . 9 . 1 software ( Applied Biosystems ) . Ct values were converted into relative quantities using the method described here [75] . The amplification efficiency of each gene was calculated using a dilution curve and the slope calculation method [75] . Cells were plated on glass coverslips coated with 1 μg/ml FN ( TebuBio ) and allowed to adhere for 3 h . In addition , ECs cotransfected with mNrp1-mRFP and Rab5-GFP were then washed in PBS , incubated with 10 μg/ml SNAKA51 Ab in EBM-2 for 30 min at 4 °C , washed 3 times in PBS , transferred to prewarmed EGM-2 , and allowed to recover at 37 °C for 2 min to induce endocytosis . Cells were washed in PBS , fixed in 4% paraformaldehyde , permeabilized in 0 . 01% saponin for 10 min on ice , and incubated or not with the Alexa-Fluor-405-conjugated secondary antibody ( Molecular Probes ) for 1 h at room temperature . Cells were analyzed by using a Leica TCS SP2 AOBS confocal laser-scanning microscope ( Leica Microsystems ) . Immunofluorescence analysis was performed as previously described [16] . Small interfering RNA silencing was performed , and after the second oligofection , cells were seeded onto glass coverslips in six-well dishes at a concentration of 20 × 104 cells/well and left to adhere for 3 h in EGM-2 medium ( Clonetics ) containing FN-depleted serum . Cells were then washed with PBS and fixed with 3 . 7% paraformaldehyde for 20 min at room temperature . Next , cells were permeabilized in PBS containing 0 . 1% Triton X-100 on wet ice for 2 min and incubated with anti-FN Ab for 1 h at room temperature . After three washes , cells were incubated with anti-mouse Alexa Fluor 555 for 45 min at room temperature and subsequently with DAPI . Cells were finally examined using a Leica TCS SP2 AOBS confocal laser-scanning microscope ( Leica Microsystems ) . Fluorescence resonance energy transfer was detected using a Lambert Instruments fluorescence attachment ( LIFA ) on a Nikon Eclipse TE 2000-U microscope , with the same changes to the condenser as described above and a filter block consisting of a Z473/10 excitation filter , a Z 488 RDC dichroic mirror , and a HQ 525/50M emission filter . The light source was a modulated 473-nm laser diode , which allows , in combination with the modulated intensifier from the LIFA system , measurement of fluorescence lifetimes using frequency domain . The laser was brought into TIRF mode before acquiring the images for the lifetime analysis . Donor ( D ) lifetime , τ , was analyzed either in the presence or in the absence of the acceptor ( A ) , in adhesion sites , characterized by high donor concentrations , using the FLIM software ( version 1 . 2 . 1 . 1 . 130; Lambert Instruments , The Netherlands ) . Fluorescence resonance energy transfer efficiency ( E ) was calculated as E = 1 – ( τDA/τD ) . Lifetime τ was evaluated in four different areas ( 12 × 12 pixels ) of seven α5-GFP and seven α5-GFP/mNrp1-Cherry transfected NIH 3T3 cells . For statistical evaluation , results were analyzed with Student's t test . Total internal reflection fluorescence experiments have been performed on a Nikon Eclipse TE 2000-U microscope equipped with 60× and 100× 1 . 45 NA Nikon TIRF oil immersion objectives . The Nikon Epi-fluorescence condenser was replaced with a custom condenser in which laser light was introduced into the illumination pathway directly from the optical fiber output oriented parallel to the optical axis of the microscope . The light source for evanescent wave illumination was either a 473-nm diode , a 405-nm diode , or a 561-nm laser ( Omicron ) , with each laser line coupled into the condenser separately to allow individual TIRF angle adjustments . Each laser was controlled separately by a DAC 2000 card or a uniblitz shutter operated by MetaMorph ( Molecular Devices ) . A filter block consisting of an E480SPX excitation filter , a FF 495 dichroic mirror , and an ET 525/50M emission filter was used for activation of α5-PA-GFP with the 405-nm laser . After activation the filter was manually changed to a green/red dual filter block ( ET-GFP/mCherry from AHF Analysentechnik , Germany ) to allow simultaneous time-lapse acquisition of activated α5-PA-GFP and mNrp1-Cherry using 473- and 561-nm excitation . A Multi-Spec dual emission splitter ( Optical Insights , NM ) with a 595-nm dichroic and two bandpass filters ( 510–565 nm for green and 605–655 nm for red ) was used to separate both emissions . All cell imaging was performed with a Cascade 512F EMCCD camera ( Photometrics UK ) . Localized activation of α5-PA-GFP in mNrp1-Cherry-positive vesicles was done on a FV 1000 Olympus confocal microscope , using two-channel imaging and a separate SIM scanner for 405-nm activation [51] .
|
The vascular system is a hierarchical network of blood vessels lined by endothelial cells that , by means of the transmembrane integrin proteins , bind to the surrounding proteinaceous extracellular matrix ( ECM ) . Integrins are required for proper cardiovascular development and exist in bent ( inactive ) and extended ( active ) shapes that are correspondingly unable and able to attach to the ECM . Extracellular guidance cues , such as vascular endothelial growth factor and semaphorins , bind the transmembrane protein neuropilin-1 ( Nrp1 ) and then activate biochemical signals that , respectively , activate or inactivate endothelial integrins . Here , we show that Nrp1 , via its short cytoplasmic domain and independently of vascular endothelial growth factor and semaphorins , specifically promotes endothelial cell attachment to the ECM protein fibronectin , which is known to be crucial for vascular development . Notably , Nrp1 favors cell adhesion by associating with fibronectin-binding integrins and promoting the fast vesicular traffic of their extended form back and forth from the endothelial cell-to-ECM contacts . Binding of the Nrp1 cytoplasmic domain with the adaptor protein GIPC1 , which in turn associates with proteins required for integrin internalization and vesicle motility , is required as well . It is likely that such an integrin treadmill could act as a major regulator of cell adhesion in general .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology",
"cardiovascular",
"disorders"
] |
2009
|
Neuropilin-1/GIPC1 Signaling Regulates α5β1 Integrin Traffic and Function in Endothelial Cells
|
Evolution is driven by mutations , which lead to new protein functions but come at a cost to protein stability . Non-conservative substitutions are of interest in this regard because they may most profoundly affect both function and stability . Accordingly , organisms must balance the benefit of accepting advantageous substitutions with the possible cost of deleterious effects on protein folding and stability . We here examine factors that systematically promote non-conservative mutations at the proteome level . Intrinsically disordered regions in proteins play pivotal roles in protein interactions , but many questions regarding their evolution remain unanswered . Similarly , whether and how molecular chaperones , which have been shown to buffer destabilizing mutations in individual proteins , generally provide robustness during proteome evolution remains unclear . To this end , we introduce an evolutionary parameter λ that directly estimates the rate of non-conservative substitutions . Our analysis of λ in Escherichia coli , Saccharomyces cerevisiae , and Homo sapiens sequences reveals how co- and post-translationally acting chaperones differentially promote non-conservative substitutions in their substrates , likely through buffering of their destabilizing effects . We further find that λ serves well to quantify the evolution of intrinsically disordered proteins even though the unstructured , thus generally variable regions in proteins are often flanked by very conserved sequences . Crucially , we show that both intrinsically disordered proteins and highly re-wired proteins in protein interaction networks , which have evolved new interactions and functions , exhibit a higher λ at the expense of enhanced chaperone assistance . Our findings thus highlight an intricate interplay of molecular chaperones and protein disorder in the evolvability of protein networks . Our results illuminate the role of chaperones in enabling protein evolution , and underline the importance of the cellular context and integrated approaches for understanding proteome evolution . We feel that the development of λ may be a valuable addition to the toolbox applied to understand the molecular basis of evolution .
Protein evolution is central to adaptation and ultimately survival of all species [1] . Proteins evolve through mutation and selection , and given the marginal stability of their native state and the sensitivity of protein structure to mutation [2] , major questions arise concerning how the emergence of new functions is balanced with the destabilizing effect of mutations [3] , [4] . Importantly , the cell has an elaborate quality control machinery to target destabilized and misfolded proteins for either refolding or degradation [5] . The interplay between acceptance and selection of mutations and cellular protein quality control is a relatively unexplored factor in protein evolution . It is proposed that cellular factors , such as molecular chaperones , can stabilize mutant proteins [6] , thereby providing extrinsic robustness against mutations [7] [8] . However , a detailed understanding of the role of chaperones in proteome evolution is currently lacking . The evolution of protein coding genes is commonly described by the rates of non-synonymous substitutions , or amino acid changes , dN , and synonymous or silent substitutions , dS . Their rate ratio ω = dN/dS is widely used to detect the strength of positive or purifying selection on protein sequences [9] . These analyses have shaped our understanding of protein evolution , demonstrating for instance that sequences evolve at markedly varying rates [10] , [11] , [12] . Thus , highly expressed proteins evolve at a lower rate of amino acid changes dN [13] , likely because the cost of deleterious mutations leading to misfolding and aggregation increases with protein abundance . Consequently , highly expressed proteins are subject to stronger purifying selection to maintain high translational fidelity [14] , stability [15] , and solubility [16] . Despite the usefulness of ω = dN/dS to understand broad evolutionary selective pressures , it is more limited in quantifying how proteins evolve . Recent efforts to carefully integrate structural information have revealed distinct modes of evolution in buried and exposed residues [17] , [18] , [19] , [20] . However , detailed structural information is only available for a limited set of highly expressed and soluble proteins . Generally , biophysical and genetic analyses indicate that conservative ( C ) substitutions between more similar amino acids , e . g . from Leu to Ile , often have very little effect on a protein , whereas a non-conservative ( NC ) mutation is more likely to affect protein stability and function [21] . By amalgamating all non-synonymous substitutions into one rate dN , ω does not discriminate between the disparate effects of different amino acid mutations on protein structure and function , which is important for gaining further insight into the forces that shape protein evolution . Importantly , proteins are only marginally stable [2] and soluble [22] in the cell , and protein stability is a major constraint on protein evolution [23] , [24] . Indeed , mutations causing loss of protein stability and solubility are generally deleterious [25] . Furthermore , mutations that lead to new protein functions are often highly destabilizing [26] , [27] . This renders protein evolvability a balancing act between accepting beneficial substitutions and providing sufficient robustness against their deleterious effects . We hypothesized that incorporating the stability effect of mutations into an extended model of protein evolution may shed light onto how cellular factors influence the evolution of protein coding sequences . To this end , we developed an evolutionary parameter , λ , that directly infers the rate ratio of NC and C substitutions . Our rationale for λ is the demonstration in many experimental systems and biophysical analyses that NC mutations are generally more likely destabilizing than C mutations [21] , thus providing a good approximation for the stability effect of mutations given the constraints imposed by sequence-based evolutionary models ( see Methods ) . Supporting this assumption , we find that , unlike ω , λ can capture known instances of rapid and distinct evolution , namely , those of intrinsically disordered proteins and those acting as re-wired nodes in protein-protein interaction networks . In these cases , NC mutations appear to drive new protein interactions and thus novel functions . Importantly , analyses of λ in the proteomes of E . coli , S . cerevisiae , and H . sapiens revealed higher λ values in chaperone substrates , suggesting that chaperones globally promote the acceptance of NC substitutions . Taken together , our analysis suggests that the mutations that are both on average more destabilizing and more likely leading to novel protein interactions are also preferentially found in chaperone substrates . These findings obtained at a systems-level for three different organisms agree with individual biochemical studies indicating a capacitor role of specific chaperones in evolution [6] , [7] , [8] . Lastly , our analysis , showing that the re-wiring of protein interactions at the proteome level is linked to energetically costly protein quality control , suggests a cost-benefit trade-off in the evolvability of protein networks .
The 20 naturally occurring amino acids differ widely in their physicochemical properties . Consequentially , not all amino acid substitutions equally affect protein structure and function . More differentiated descriptions of protein evolution beyond the rate of non-synonymous substitutions dN are needed to better describe the forces shaping protein evolution . Initial classifications into “conservative” substitutions between more similar , and “radical” substitutions between more dissimilar amino acids focused for instance on charge and polarity [28] , [29] . Recent experimental and theoretical work underlined the pivotal role of protein stability as a major constraint on protein evolution [23] , [24] . We hypothesized that incorporating the stability effect of mutations into the computation of evolutionary rates may provide more nuanced insights into the evolution of protein coding sequences ( Figure 1A ) . To test this assumption , we derived a classification into conservative ( C ) and non-conservative ( NC ) substitutions hat reflects this important biophysical constraint . Because the effect of mutations on protein stability in vivo is highly contextual , depending on the local and global protein fold , and is not easily measurable in high-throughput , we predicted the stability effect of a large set of mutations for the S . cerevisiae proteome in silico [30] . We found very clear differences in the distributions of the predicted stability changes ΔΔG for different amino acids pairs ( Figure S1A ) . Computational models of protein evolution that are based on sequence information alone require a strict classification of mutations into either C or NC substitutions . We computed for each amino acid pair that is separated by one nucleotide substitution in the genetic code the fraction of mutations that was predicted highly destabilizing ( ΔΔG<−2 kcal/mol ) . Because a hard classification into C or NC substitution is of necessity an approximation that neglects the detailed structural context of mutations for that particular protein , we sought to define only the most frequently destabilizing mutations as NC ( Figure S1B ) . Because mutations that were predicted highly destabilizing in more than 20% of the cases clearly separate from the rest ( Figure S1B ) , we choose 20% as the best threshold . A more stringent threshold reduces the number of NC mutations ( Figure S1C , D ) , but increases the difference in predicted stabilities between C and NC substitutions ( Figure S1E ) . Too few NC mutations make it difficult to estimate evolutionary parameters due to diminishing sample sizes . We thus classified all amino acid changes into NC substitutions if they were predicted to be highly destabilizing more than 20% of the time , and as C otherwise ( see Methods ) . We further validated that this classification is in agreement with the established Blosum62 amino acid substitution matrix in that no NC mutation has a positive Blosum62 score . In our classification , NC substitutions are significantly more destabilizing than C substitutions ( Wilcoxon rank-sum test: p<<10−16 , standardized mean difference SMD = 0 . 96 , Figure 1B ) . Indeed , less than 10% of C mutations , but almost 50% of NC mutations were predicted to be highly destabilizing ( ΔΔG<−2 kcal/mol , Figure 1C ) . One caveat of our approach is that , while the grouping into classes of mutations reduces their dependence on the accuracy of individual predictions , our approach is prone to systematic biases in the stability predictions . We therefore compared our classification to established predictors of the phenotypic consequences of mutations , namely PolyPhen [31] and SNPs3D [32] , as well as fitness values for mutations from deep sequencing data [33] . Our analyses all support a significantly stronger negative phenotypic or fitness effect of NC substitutions compared to C substitutions ( Figure S2A–C ) , but simultaneously highlight the potential for novel data to improve these classifications . Thus , while our classification procedure is ad hoc and only represents one of several possible avenues to predict and integrate the stability effect of mutations , our approach appears to be useful for comparing NC and C as a proxy for incorporating the stability effect of mutations into an extended evolutionary model . We next implemented a Markov model of codon substitutions that directly estimates the rates of conservative substitutions dC , and non-conservative substitutions dNC in complement to the established rates dN and dS ( Figure 1A , S3A–D ) . As expected , dNC is on average lower than dC , reflecting stronger purifying selection on substitutions that are more likely destabilizing ( Figure 1D ) . In analogy to the evolutionary rate ratio ω = dN/dS , we defined the evolutionary parameter λ = dNC/dC . Whereas ω describes the relative rate of all selected mutations that result in an amino acid change , the new parameter λ informs on the relative partitioning of substitutions that are more or less likely destabilizing . Importantly , ω and λ are not correlated , thus independently describe orthogonal aspects of protein sequence evolution ( Figure 1E ) . Furthermore , because λ is not correlated to expression levels ( Figure S3E , F ) , this new parameter can shed light on factors that promote non-conservative substitutions even in highly expressed proteins that have overall low rates of amino acid changes . An interesting correlate and test case for our new evolutionary parameter λ is presented by the analysis of intrinsically disordered proteins ( IDPs ) . Disordered regions in proteins maintain a distinct composition of polar and charged amino acids , and IDPs are under tight cellular regulation to prevent their aggregation [34] [35] . In turn , intrinsic disorder is known to perform functionally important roles in molecular recognition [36] , and thus cellular interaction networks [37] . Protein disorder also plays a unique role in protein evolution . Disordered regions themselves evolve on average more rapidly , attributed in part to the lack of structural constraints [38] , [39] . Moreover , disordered regions have been shown to exhibit distinctly different amino acid substitution patterns with a much higher frequency of NC substitutions [40] . We thus next sought to evaluate the power of λ based on the more general classification of NC and C defined above , to describe the evolution of IDPs . We divided the S . cerevisiae proteome into three classes based on the percentages of residues that were predicted to be disorder , as described in [35]: S ( “highly structured” , 0–10% disordered ) , M ( “moderately unstructured” , 10–30% disordered ) , and U ( “highly unstructured” , >30% disordered ) . When controlled for expression levels , which appear as the strongest determinant of the evolutionary rate ratio ω , we found that ω is not well suited to detect differences in the evolution of proteins of different levels of structuredness ( Wilcoxon rank-sum test; S vs . M: p = 0 . 15 , SMD = 0 . 05; S vs . U: p = 0 . 19 , SMD = 0 . 11; M vs . U: p = 0 . 007 , SMD = 0 . 14; Figure 2A , S4A ) . In contrast , using λ , we found that structured proteins evolve at significantly lower λ than moderately unstructured proteins ( Wilcoxon rank-sum test: p<10−6 , SMD = 0 . 34 , Figure 2B ) . Unstructured proteins evolve at an even higher λ than moderately unstructured proteins ( Wilcoxon rank-sum test: p<10−13 , SMD = 0 . 43 , Figure 2B ) . Importantly , both ω and λ are computed for the full protein coding sequences . While ω is not correlated with disorder , λ is strongly correlated , and increases with the disordered content in proteins ( RS = 0 . 38 , Figure S4B , C ) . How does ω not depict the known accelerated evolution of disordered regions when controlled for expression levels ? To understand this discrepancy , we computed the Rate4Site scores [41] of the local sequence variability of disordered regions and their adjacent structured flanking regions . Our analysis revealed that , while unstructured stretches are indeed more variable , their structured flanking regions in contrast are very conserved ( Figure 2C ) . This observation suggests systematic compensatory selection in the flanking regions , likely to facilitate the variability of disordered regions [42] . This combination of variable and conserved regions often results in comparable rates of overall non-synonymous substitutions between structured and more disordered proteins , thereby obscuring the more rapid evolution of the disordered regions in IDPs . As a result , while ω is limited in its power to characterize the evolution of proteins with disordered regions , λ identifies the much higher rate of NC mutations linked to intrinsic disorder . This analysis indicates , even in the general parsimonious classification used here , that λ can quantify and contribute additional insights into the evolution of IDPs in complement to ω . Although NC mutations may not “destabilize” intrinsically disordered regions , they can still perturb protein dynamics , protein interactions and homeostasis . In response , intrinsically disordered proteins are under tight regulation and turned-over more rapidly ( Figure 2D ) [35] . This is energetically costly , but likely prevents their aggregation [43] , and non-specific interactions [44] . The documented evolutionary benefit of intrinsically disordered regions thus comes at the expense of selective protein quality control . Through an integrated analysis , we show in the following the importance of λ to decipher this trade-off . We next used λ to analyze the factors involved in the re-wiring of protein-protein interactions ( PPIs ) . Evolving a novel protein interaction has been suggested to present one of the easiest routes to acquiring a novel protein function [45] , and PPIs are re-wired substantially over the course of evolution [46] . Indeed , a single mutation may suffice for the design of novel protein interactions [47] [48] . Accordingly , generally only very few mutations are predicted to separate protein surfaces from interaction interfaces [49] . We thus hypothesized that λ might characterize the evolvability of protein interactions . To assess the relationship between λ and the evolution of novel PPIs , we extracted the consensus most highly re-wired proteins from the protein networks ( PN ) of S . cerevisiae and S . pombe ( Figure 3A , see Methods ) [50] . We found the most highly re-wired proteins to be highly connected and evolving at clearly lower ω than the less re-wired proteins ( Wilcoxon rank-sum test: p = 0 . 004 , SMD = 0 . 38; Figure 3B ) . Highly connected proteins are important and functionally constrained hubs in PNs , often highly expressed , and thus evolving more slowly when described by ω [51] . Strikingly , the highly re-wired proteins exhibit significantly higher λ ( Wilcoxon rank-sum test: p = 0 . 002 , SMD = 0 . 57; Figure 3B ) . Thus , despite an overall high degree of conservation , i . e . lower rates of overall amino acid changes , the highly re-wired proteins exhibit elevated relative rates of NC substitutions , as expected when acquiring or remodeling interaction partners . Indeed , the link between NC mutations and the evolution of novel protein interactions is supported by a recent scale of amino acid interaction propensities derived from a careful analysis of curated protein complexes [52] . NC substitutions are much more likely to comprise changes between amino acids with very different interaction propensities ( Fisher's exact test: p = 0 . 0002 , Figure 3C ) , underlining that NC mutations more profoundly change the interaction potential of a protein . Importantly , the connectivity in the S . cerevisiae PN negatively correlates much more strongly with dC than dNC , ( Figure 3D ) , and only very weakly with λ . Thus , the plasticity to accept NC substitutions is not constrained by network connectivity . We conclude that λ can shed light onto the evolution of protein interactions within cellular protein networks . We next used λ to better understand the cellular strategies that regulate NC substitutions . Besides loss-of-function mutations , organismal fitness is most impaired by the accumulation of destabilized , misfolded proteins leading to cytotoxic protein aggregates [5] . Because a higher λ indicates a higher relative rate of NC mutations , we asked whether cellular factors confer robustness against their potentially destabilizing effects . Chaperones , which promote folding and prevent aggregation [5] [53] , have long been proposed to buffer mutations and play important roles in protein evolution [54] , [55] , [56] . For instance , enhancing chaperone capacity through over-expression has been directly shown to promote enzyme evolvability [6] . Chaperones have been found to act both as genotypic and phenotypic capacitors [54] , [55] , [56] . However , if and how chaperones systematically influence genome and proteome evolution remains to be understood . In some studies , native chaperone substrates have been found to evolve , unexpectedly , more slowly as revealed by low ω [57] . This observation appears to contradict the idea that chaperones promote evolvability . However , chaperone clients are often highly expressed , and highly connected in protein interaction networks [58] , and thus may be functionally more constrained . This could explain why they evolve more slowly when judged by the ω parameter [51] . An analysis of site-specific evolutionary rates has found higher local sequence variability in GroEL clients compared to non-clients [59] . We here next examined in detail whether λ can detect and quantify a role for chaperones in protein evolution at a global , proteome wide level . We first took advantage of data from a directed evolution experiment to test whether λ can reproduce a known role for chaperones in protein evolution . The chaperonin GroEL is essential in most prokaryotes , and assists the post-translational folding of topologically complex and aggregation-prone proteins [60] . Over-expression of GroEL in E . coli promoted enzyme evolution and acquisition of novel functions [6] . Our analysis of the mutations selected in the directed evolution experiments by Tawfik and co-workers indicated a significantly higher fraction of accepted NC substitutions for proteins that evolved at higher GroEL expression level , suggesting that the chaperone helps buffer NC mutations ( Fisher's exact test: p = 0 . 005; Figure 4A ) . After validating our approach with this known and well–characterized example , we next extended our analysis to the evolution of known in vivo substrates of GroEL . Importantly , in the following section we systematically tested for an effect of chaperones on protein evolution by non-parametric regression analyses that estimate the contribution of the chaperone independently of other determinants such as levels of expression and disorder ( see Methods ) . We found that ω cannot distinguish between GroEL substrates and proteins that do not interact with GroEL in vivo ( p = 0 . 16 , Figure 4B ) . In contrast , GroEL substrates evolve at significantly higher λ than non-substrates ( p = 0 . 025 , Figure 4C ) . Thus , while here in this integrated analysis that also considers potential confounding factors such as expression levels and disorder the established evolutionary rate ratio ω fails to detect any influence of the chaperone on protein evolution , λ indeed finds a higher rate of NC substitutions in the GroEL substrates ( Figure 4C , S5A ) . Of note , bacterial proteomes have a low content of disorder compared to eukaryotes [61] , [62] ( Figure S5B ) , and almost all GroEL substrates are highly structured ( Figure S5A ) . In comparison to prokaryotic genomes , the role of chaperones in eukaryotes is less defined at a systems level . The heat-shock chaperone Hsp90 facilitates the maturation of many oligomeric complexes , as well as regulatory and signaling proteins , and also protects stress-denatured polypeptides [58] [63] ( Figure S5C ) . A recent very elegant study identified a large set of kinases in H . sapiens as Hsp90 substrates , classified according to their strong and weak dependence [64] . Strong substrates more stringently depend on chaperone assistance for folding . Weak substrates in turn interact more sporadically and do not necessarily require chaperone assistance . Hsp90 was found to promote protein evolutionary rates in strong substrates when assessed by dN or ω [64] . However , this analysis did not take into account expression levels [64] , which are known to strongly impact the evolutionary rate ω of proteins . Strong Hsp90 substrates are also significantly less abundant , which may offer an alternative explanation for their higher rate of non-synonymous mutations ( Figure S5D ) . Indeed , when taking into consideration the contributions of expression levels and disorder , we found that neither the strong , nor the weak Hsp90 substrates evolved at higher ω ( strong: p = 0 . 1; weak: p = 0 . 594; Figure 4D ) . However , strong and weak substrates evolved at higher λ . Notably , while the difference for the strong substrates is not significant ( p = 0 . 20 ) , the weak substrates evolved at significantly higher relative rates of NC substitutions ( p = 0 . 037; Figure 4E ) . We speculate that strongly dependent substrates may be more aggregation prone and labile , and therefore less accepting of NC mutations . Strong substrates may also already exhaust all catalytic capacity of the chaperone for their normal folding requirements . In contrast , weakly dependent substrates may be able to fold more easily , and for these proteins chaperones may confer excess robustness that can be exploited by evolutionary pathways . Accordingly , the weak substrates were reported to be more thermodynamically stable than the strong substrates [64] , which could explain why they can accommodate more destabilizing mutations , consistent with their higher λ . We next examined the substrates of a co-translationally acting chaperone . The Hsp70 SSB is the main ribosome-associated chaperone involved in the de novo folding of nascent polypeptides in S . cerevisiae ( Figure S5C ) [65] . Recent work quantified substrates that strongly and weakly associate with SSB co-translationally [65] , indicating that Hsp70 preferentially binds long and disordered nascent chains that may have difficulties to fold on their own [65] . Our analysis revealed that strong SSB substrates evolved at significantly lower ω ( p<10−16 , Figure 4F ) , while weak substrates evolved at similar ω compared to non-substrates ( p = 0 . 762; Figure 4F ) . Interestingly , strong substrates also evolve at lower λ ( p<10−16; Figure 4G ) . In contrast , weak SSB substrates evolved at significantly higher λ ( p = 0 . 035; Figure 4G , S5E ) . Similar to Hsp90 , the more stringent Hsp70 substrates are more folding-challenged and are likely to be most sensitive to mutation , which may explain the strong purifying selection on both the rate of non-synonymous as well as NC substitutions . Also similar to Hsp90 , weak substrates depend less stringently on chaperone assistance and may benefit more from the chaperone buffering capacity to accommodate additional NC substitutions . The observed difference between co-translationally acting SSB/Hsp70 and the post-translationally acting Hsp90 is that stringent Hsp70 substrates are characterized by both lower ω and λ , emphasizing the challenge of de novo folding in eukaryotes [53] . Of note , Hsp70/SSB substrates are enriched in proteins with long disordered stretches [65] . Thus , the contribution of chaperones to protein evolution is likely two-fold . On the one hand , chaperones manage the disordered proteome , likely preventing aggregation , thus promoting protein evolution indirectly by potentiating disordered regions . On the other hand , chaperones confer extra robustness to clients , especially to proteins that do not stringently depend on their assistance , and in this way directly promote their evolution . We also examined the substrates of another cotranslationally acting chaperone , the ATP-independent NAC complex , which bind to virtually all nascent chains as they emerge from the ribosome [66] . NAC does not significantly modify either λ or ω , highlighting that not all chaperones equally promote protein evolution . Our analyses underscore the power of λ to disentangle the influence of chaperones on proteome evolution . While the plasticity in evolving novel protein interactions presents a clear evolutionary advantage , the maintenance of intrinsically disordered proteins and chaperone assisted protein folding are energetically costly for the cell [67] . Most chaperones catalyze protein folding in an energy dependent manner , and the targeted degradation and rapid turnover of intrinsically disordered proteins comes at the expense of both increased proteolysis and biosynthesis . This poses the question of the cost-benefit trade-off of promoting NC mutations in the broader context of protein interaction networks . To extend our analysis beyond highly connected proteins , we analyzed the evolution of the most critical proteins in networks , hubs and low-connectivity bottlenecks ( Figure 5A ) [50] . Hubs are the most highly connected proteins , whereas lowly connected bottlenecks are often key connectors between network modules [68] . Given their functional prominence , they likely evolve under distinct selective pressures . Strikingly , both hubs and bottlenecks evolve at lower or similar ω compared to non-hub and non-bottleneck proteins respectively ( Wilcoxon rank-sum test; hubs: p = 0 . 0001 , SMD = 0 . 38; bottlenecks: p = 0 . 12 , SMD = 0 . 04; Figure 5B ) . In contrast , both hubs and bottlenecks display significantly higher distributions of λ compared to their counterparts ( Wilcoxon rank-sum test; hubs: p = 0 . 04 , SMD = 0 . 24; bottlenecks: p = 0 . 014 , SMD = 0 . 31; Figure 5B ) . Of note , highly connected proteins that are components of large and very conserved macromolecular assemblies , such as the ribosome , provide an exception to this trend ( Figure S6A , B ) . The lower relative evolutionary rate ω of hubs and bottlenecks can be rationalized given their prevalent high expression levels and functional constraints [50][68] . Highly connected hubs with several hydrophobic interfaces have to maintain protein solubility , thus reducing the overall number of accepted amino acid changes . On the other hand , those amino acid substitutions that become fixed in hubs and bottlenecks but not in non-hubs and non-bottlenecks appear to be biased towards NC substitutions , likely serving to maintain plasticity to evolve novel interactions . Our analysis indicates that the evolution of protein networks relies on NC changes in otherwise conserved proteins . This raises the question regarding the mechanisms that protect these proteins from destabilizing and deleterious effects . Remarkably , both hubs compared to non-hubs and bottlenecks compared to non-bottlenecks are more likely to be substrates of Hsp90 and Hsp70 , and contain long intrinsically disordered regions ( Figure 5C ) . The strongest determinants of λ between hubs and non-hubs are disorder and expression ( Figure S7A ) . We find that hubs are more disordered [69] , more highly expressed , and longer lived ( Figure S7B ) . This finding , observed for hubs of protein networks , deviates from the genome-wide trend whereby more disordered proteins are usually less expressed , and turned-over more rapidly [35] . This incongruity led us to examine whether chaperones provide an additional layer of regulation for this subset of unstructured proteins . Indeed , the Hsp70/SSB stabilizes disordered proteins that are highly connected in protein networks , which can explain this discrepancy [65] . Upon deletion of SSB , 50% of the hub proteins , but less than 10% of the non-hubs aggregate immediately ( Fisher's exact test: p<10−7 , Figure 5D ) [65] . Our analysis of bottlenecks indicated that these proteins are also preferentially intrinsically disordered and chaperone substrates ( Figure 5E ) . In contrast to hubs , we find that Hsp70/SSB directly promotes NC substitutions in bottlenecks ( Regression analysis: p = 0 . 033 , Figure 5F , S7C ) . However , bottlenecks are also short-lived and turned-over more rapidly ( Figure S7D ) , reflecting their functional role in the dynamic coordination of protein network modules . We conclude that the energy expenditure of engaging cellular protein homeostasis , i . e . buffering of NC mutations by molecular chaperones , stabilizing IDPs through chaperones or preventing their aggregation through regulated protein turnover , benefits the evolution of protein-protein interaction networks ( Figure 6 ) . The associated energetic cost appears to directly trade-off with the re-wiring of protein interactions in critical nodes of protein networks . Importantly , only an integrated analysis can reveal the complex interplay between cellular protein homeostasis and evolution at the proteome level .
To better understand how proteins evolve in the cellular context , including the role of chaperones and selective protein quality control on protein evolution at a global proteome level , we incorporated the stability effect of mutations into an extended evolutionary model by defining an evolutionary parameter λ that deconvolves the non-synonymous substitutions in proteins into non-conservative ( NC ) and conservative ( C ) amino acid changes . Because NC substitutions are more likely to affect protein stability , which is a major biophysical constraint on function [70] , λ allows to characterize several aspects of protein evolution that are inaccessible to the established evolutionary rate ratio ω . In support of our rationale , we found that λ can quantify known instances of rapid protein evolution . For instance , λ captures the distinct evolutionary patterns of intrinsically disordered proteins , which paradoxically ω does not detect . Of note , not all disordered regions are equally variable , and distinct functional roles have been attributed to conserved and flexible disorder [71] . We feel λ will become a useful tool to examine the evolution of proteins . The classification used here may be adapted and optimized using additional criteria to improve its usefulness to different problems . Thus , while some non-synonymous mutations will always be non-conservative and destabilizing , there may be species or compartment-specific aspects to consider in the classification of NC vs . C . Further refinements of the concept and implementation of λ may help capture specific aspects of protein evolution , for instance within specific organisms or organelles or even within classes of proteins , such as IDPs . We found the same set of NC substitutions to critically stand out in several independent paradigms: chaperone substrates and intrinsically disordered proteins show elevated rates of NC amino acid changes , and NC substitutions were strongly enriched in proteins that have evolved novel interactions as evident by both the comparison of large-scale protein interaction networks as well as an interaction propensity scale derived from a curated database of protein complexes . Thus , using λ we could capture the role of chaperones in conferring robustness against NC mutations , as well as the role of NC mutations in re-wiring of protein networks . Our findings that proteins most critical in protein interaction networks are both highly re-wired despite strong purifying selection , and preferentially supported by chaperones , suggests a general role of chaperones in protein network evolution . Proteins have tightly co-evolved with their cellular environment . By using well-curated sets of chaperone substrates , we find that weak chaperone dependence more strongly promotes protein evolution . Likely , stringently dependent substrates might already have exhausted all chaperone capacity for their normal folding requirements . The evolutionary advantage of weak chaperone dependence thus might also explain the low specificity observed in large and heterogeneous substrates sets for the Hsp70 and Hsp90 chaperone systems . The detailed differentiation between strong vs . weak substrate dependence demonstrates the power of λ to analyze this relationship at proteome level . The global link between the evolution of novel protein functions arising from NC mutations , and the buffering role of chaperones is in remarkable agreement with the analysis of accepted mutations from a directed enzyme evolution experiment focused on the bacterial chaperone GroEL [6] . Taken together , these results extend previous findings on the roles of chaperones in protein evolution including observations that they can act as genotypic and phenotypic capacitors [54] , [55] , [56] , buffer the destabilizing effect of mutations upon over-expression [6] , facilitate the divergence of gene duplicates [72] , and promote greater sequence variability [59] . Our results also fit well in the wider context of related work and theoretical considerations that mutational robustness is central to evolution and adaptation [73] . Protein complex formation itself has been found to stabilize the interaction partners , thus facilitating the evolution of protein networks [74] . In contrast , strong genetic drift alone has been suggested to promote interactome complexity [75] , but chaperones may be directly involved in facilitating genetic drift [59] . The finding that proteins that act as phenotypic capacitors , providing robustness against environmental as opposed to genetic perturbations , are also strongly enriched in network hubs [76] indicates a link between environmental and genetic buffering that merits further investigation . A detailed and mechanistic understanding of protein and proteome evolution will ultimately require contributions from many different approaches at different levels of resolution . Sequence-based evolutionary models will continue providing important insights and assistance in interpreting the increasing amounts of available sequencing data , offering a very efficient indication of distinct selective pressures . Cases of particular interest can be further analyzed with specialized algorithms to predict phenotypic consequences of individual mutations that may achieve higher resolution , especially in well-curated protein families and with available structural information . While in this work the definition of NC and C substitutions is based on predicted stability effects of mutations across the S . cerevisiae proteome , species–optimized metrics may increase the strength and significance of insight gained from λ in other organisms . The current classification into NC and C substitutions may become less accurate with increasing evolutionary distance from S . cerevisiae , raising the question of how λ changes with evolutionary distance . Similarly , optimized classifications may further increase the power of λ in the analysis of specific sets of proteins , such as IDPs or distinct sub-cellular localizations . To this end , we expect clear advances from novel targeted evolutionary approaches that more explicitly take aspects of protein biophysics and cellular processes into account . New technologies such as deep mutational scanning [77] present an exciting outlook for the experimental characterization in high-throughput of evolutionary constraints on proteins , as well as will provide the necessary data for more accurate classifications of NC and C substitutions independent of potential biases in prediction algorithms , and further improving models of protein evolution . Cost-benefit trade-offs are ubiquitous in organismal evolution . Our evolutionary parameter λ allowed us an integrated analysis that revealed a finely tuned interplay of costly protein homeostasis and beneficial evolution at proteome level . The direct relationship between chaperone assistance and the evolvability of protein networks is intriguing , and may explain the link between expanding chaperone systems with increasing proteome size and complexity [78] . Our work illustrates the importance of integrated as well as more specific and targeted approaches for understanding protein evolution . The increasing availability of biological sequence data provides unique opportunities to incorporate biophysical aspects of protein folding and function into extended evolutionary models , and decipher the cellular contributions to robustness [79] . Our results highlight the importance of considering the cellular context , and in particular the role of molecular chaperones , for understanding protein sequence evolution to achieve a much-needed integrated understanding of protein and proteome evolution .
Protein evolutionary rates and parameters were estimated by maximum likelihood from a Markov model of codon substitutions [9] , fitted to pairs of aligned orthologous coding sequences . Because of a shared common ancestor , thus time of divergence , evolutionary distances between pairs of orthologous sequences of closely related species can be interpreted as relative evolutionary rates . Our evolutionary model is described by the rate matrix , where qij is the instantaneous rate from codon i to j ( i≠j ) , The transition probability matrix is obtained by maximum likelihood estimation of the parameters t , κ , γ , and λ from pairs of aligned orthologous coding sequences by minimizing the log likelihood functionwhere nij is the number of sites , and πi is set to the average genome codon composition of the species compared , in analogy to the F61 model . t is the relative divergence time estimated from the relative distance of two orthologous input sequences . κ is the transversion-transition ration of all substitutions in the input sequences . λ is the ratio of the rates of non-conservative and conservative substitutions . Thus , λ indicates the relative partitioning between on average more and less likely destabilizing mutations given all amino acid changes . All calculations were implemented in Matlab , and the function fminsearch was used for maximum likelihood estimation . The conventional evolutionary rates dN and dS , and their ratio ω = dN/dS were computed with the Matlab function dndsml . An independent evolutionary count model based on the established approach by Nei and Gojobori [80] was employed to verify our results ( Figure S3A , B ) . Generally , the calculation of evolutionary rates is based on counting the number of synonymous , non-synonymous , and here also conservative and non-conservative mutations between aligned orthologs relative to the number of synonymous , non-synonymous , conservative and non-conservative sites in the two sequences , while correcting for multiple substitutions . The maximum likelihood approach explicitly models codon sequence evolution as a stochastic process , and estimates all critical parameters , including the transition/transversion ratio , simultaneously in a probabilistic manner from the sequences . The power of maximum likelihood approaches decreases for small datasets , thus maximum likelihood methods are often applied to larger phylogenies . The count model does not explicitly incorporate transition/transversion biases , but includes mutations that are more than one nucleotide substitution apart by averaging over all possible mutational pathways , and is more robust for small datasets , e . g . low counts of non-conservative mutations . Both approaches produce very similar results ( Figure S3A , B ) . Protein stability is a major constraint on protein evolution . The stability effect of mutations depends on the local and global protein fold . However , sequence-based evolutionary models require a clear classification of mutations . In addition , detailed evolutionary trajectories are not known from comparing pair-wise sequence alignments of orthologous sequences , i . e . it is generally not known which sequence has incurred the mutation . We thus chose a simple classification into more likely destabilizing ( non-conservative , NC ) and less likely destabilizing ( conservative C ) substitutions to approximate the stability effect of mutations and integrate it into sequence-based evolutionary models . The effect of mutations on protein stability was predicted from the amino acid sequences with the I-Mutant3 . 0 algorithm [30] for a representative set of ca . 3 , 000 , 000 mutations , reflecting 10% of all possible mutations for each protein and randomly sampled across the cytosolic S . cerevisiae proteome . This algorithm predicts the effect of mutations with support vector machines that have been trained on experimental data on protein stability . Predictions of stability effects of mutations achieve an accuracy of about 70% . We ranked all amino acid pairs , combined in both directions ( i . e Ala to Val and Val to Ala ) , based on how likely there were highly destabilizing ( ΔΔG<−2 kcal/mol ) . Of note , some mutations exhibit clear anisotropy in their mutational effects . For instance , while Ala to Val mutations is mostly neutral , Val to Ala mutations are very often predicted to be highly destabilizing . Val residues are often found in the tightly packed hydrophobic core of proteins , thus more sensitive to substitutions . Because the direction of the mutation is not normally known , we considered the combined effect between amino acid pairs . Mutations that were in less than 20% of their occurrences highly destabilizing were considered conservative ( C ) , and non-conservative ( NC ) otherwise . We validated that this is in full agreement with the widely used Blosum62 amino acid substitution matrix in that no NC mutation had a positive Blosum62 score . The conservative and non-conservative substitutions used in this analysis are listed in Table 1 . The limited accuracy of the stability predictions , and currently available data to validate them should be kept in mind . For our analysis of λ in S . cerevisiae , we started from 5025 known pairs of orthologous sequences between S . cerevisiae and S . paradoxus . We removed all alignments of pairs of orthologs if more than 1/3 of the positions in the alignments contained gaps , and required satisfactory convergence of the maximum likelihood in the computation of evolutionary rates ( lmax<−1000 ) . This lead to a curated dataset of 3495 pairs of orthologous sequences . Parameter estimation by maximum likelihood becomes inaccurate at very small sample sizes . To test for the robustness of our results , we analyzed protein evolutionary rates for proteins with at least 3 NC substitutions as estimated by maximum likelihood , and validated our findings by evolutionary rates computed by both the maximum likelihood and the count method of all sequences . Orthologs of E . coli and S . salmonella have diverged less , and we report the evolutionary rates from the count method . Similarly , orthologs of H . sapiens and M . musculus are more distant , and we report the evolutionary rates calculated by the count method , because these include also mutations that are more than one nucleotide substitution apart . Of note , the parameters from the maximum likelihood and count models are very highly correlated ( Figure S3A , B ) . Genomic sequences and ortholog assignments for S . cerevisiae ( Scer ) and S . paradoxus ( Spar ) were retrieved from the Broad Institute ( http://www . broadinstitute . org/regev/orthogroups ) . We also computed evolutionary rates from aligned orthologs of E . coli and S . salmonella , and of H . sapiens and M . musculus , which were obtained from the Ensembl database ( http://ensembl . org/biomart/martview/ ) . For orthologs of the more distant H . sapiens and M . Musculus we required at least 80 aligned codons as described in [14] . Alignments of the genetic sequences between orthologs were computed with ClustalW via the corresponding amino acid sequences . Protein abundances for S . cerevisiae were obtained from [81] , and cellular half-lives from [82] . S . cerevisiae mRNA expression levels were taken from [83] . Protein abundances for E . coli were retrieved from [84] , and the H . sapiens “whole organism integrated dataset” from the paxdb database ( www . pax-db . org ) . Intrinsic disorder was predicted with Disopred2 [85] . Classes of structuredness were defined based on the fraction of residues in a protein that was predicted to be disordered , and follows the definition reported in [35]: S ( 0–10% disordered ) , M ( 10–30% disordered ) , and U ( >30% disordered ) . Local sequence conservation and variability was computed with the program Rate4Site [41] . The GroEL-overexpression dependent mutations were extracted from [6] , GroEL substrates from [60] , Hsp70 substrates as well as protein aggregates upon Hsp70 deletion from [65] , and Hsp90 substrates in S . cerevisiae from [86] , and in H . sapiens from [64] . We pooled class II and III GroEL substrates to achieve a larger sample size . Because GroEL , SSB , and Hsp90 are cytosolic chaperones , we restricted the non-substrate control groups to the cytosolic proteomes of E . coli , S . cerevisiae , and H . sapiens respectively . The protein-protein interaction networks for S . cerevisiae and S . pombe were downloaded from the Biogrid database ( http://thebiogrid . org ) . To circumvent a systematic bias due to the very different coverage of the two networks , we extracted a consensus list of highly re-wired proteins as the consensus top 10% proteins in each network with the most “non-orthologous” interactions , i . e . interactions to proteins that do not have orthologs in the other species . We chose this procedure as a very conservative approach to infer re-wired protein interactions . If both binding partners have orthologous proteins in both species , but the exact edge is not present and both networks have very different depth and coverage , it is difficult to systematically know if this is because the edge is in fact not conserved , or simply because the edge was not detected in the much smaller network . In contrast , interactions to proteins that do not have an ortholog in the other organism must be species-specific , thus re-wired . Hubs are the top 10% most highly connected proteins in our study , and non-hubs the bottom 10% connected proteins . Bottlenecks are critical proteins that show a high betweenness centrality , i . e . concentrate the highest density of shortest paths [68] . In this study we only considered proteins as bottlenecks if they were in the top 10% of proteins with the highest betweenness centrality but not also hubs , and non-bottlenecks if they were in the bottom 10% of betweenness centrality . To systematically evaluate the contribution of chaperone assistance to protein evolutionary rates under consideration of confounding factors , we employed non-parametric regression analyses based on a kernel-smoothing approach that has explicitly been developed to test for the significance of categorical predictor variables . The test is fully data driven and robust to over-fitting through extensive cross-validation for smoothing parameter selection and bootstrapping to obtain the null distribution . We used this non-parametric regression model to predict λ and ω respectively by chaperone dependence , intrinsic disorder , and expression level . Predictor variables that did not improve the overall regression fit were excluded . The regressions of λ generally only relied on the input of chaperone dependence and disorder; we found expression to not explain λ , consistent with the lack of correlation between λ and expression levels . Correlation coefficients of regression fits ranged between R = 0 . 32 and R = 0 . 45 . Partial regression plots indicate the individual contributions of predictor variables . Error bars were derived from predicting λ and ω respectively only from individual predictor variables . The relatively large error bars for the contributions of chaperone dependence reflect the limited power of predicting a continuous variable ( λ or ω ) only from a categorical one ( chaperone substrate or not ) . This statistical approach is available from the freely available R package “NP” [87] . Differences between distributions of evolutionary rates were tested for statistical significance by Wilcoxon rank-sum tests , and significant enrichments of protein categories in hubs and bottlenecks by Fisher's exact test . Next to p-values , we report the standardized mean difference ( SMD ) as measure of effect sizes between distributions . All statistical analyses were performed in the statistics environment R ( www . r-project . org ) .
|
Evolutionary innovation through mutation is important for adaptation , thus ultimately survival of all species . Proteins , the main actors of cellular life , are generally only marginally stable in the cell and sensitive to mutations . This raises the question how the emergence of new functions is balanced with the destabilizing effect of mutations . Here , we incorporate biophysical knowledge on the stability effect of mutations into an extended model of protein evolution to better understand at the proteome level factors that promote non-conservative , more likely destabilizing mutations . Our analyses reveal a central role of molecular chaperones , specialized quality control enzymes that are known to promote protein evolvability by buffering the destabilizing effect of mutations in individual substrates . We find that chaperones support non-conservative mutations at the proteome level both directly , and through stabilizing intrinsically disordered proteins . We demonstrate how the main co- and post-translationally acting chaperones distinctly promote the same non-conservative mutations that characterize the re-wiring of protein interactions . Our results thus suggest how energetically costly protein quality control pathways can systematically promote the evolution of protein networks , and highlight the importance of considering the cellular context for understanding protein and proteome evolution .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"sequencing",
"techniques",
"sequence",
"analysis",
"biochemistry",
"protein",
"synthesis",
"protein",
"interactions",
"genome",
"evolution",
"proteins",
"protein",
"folding",
"chaperone",
"proteins",
"protein",
"structure",
"evolutionary",
"modeling",
"biology",
"and",
"life",
"sciences",
"molecular",
"biology",
"techniques",
"computational",
"biology",
"molecular",
"biology"
] |
2014
|
Interplay between Chaperones and Protein Disorder Promotes the Evolution of Protein Networks
|
Directing stem cell fate requires knowledge of how signaling networks integrate temporally and spatially segregated stimuli . We developed and validated a computational model of signal transducer and activator of transcription-3 ( Stat3 ) pathway kinetics , a signaling network involved in embryonic stem cell ( ESC ) self-renewal . Our analysis identified novel pathway responses; for example , overexpression of the receptor glycoprotein-130 results in reduced pathway activation and increased ESC differentiation . We used a systematic in silico screen to identify novel targets and protein interactions involved in Stat3 activation . Our analysis demonstrates that signaling activation and desensitization ( the inability to respond to ligand restimulation ) is regulated by balancing the activation state of a distributed set of parameters including nuclear export of Stat3 , nuclear phosphatase activity , inhibition by suppressor of cytokine signaling , and receptor trafficking . This knowledge was used to devise a temporally modulated ligand delivery strategy that maximizes signaling activation and leads to enhanced ESC self-renewal .
Self-renewal is one of the defining characteristics of embryonic stem cells ( ESCs ) [1] . This fate choice is influenced by ligand–receptor-mediated activation of intracellular signaling pathways . Significant work is being done to understand the signaling proteins and pathways that control self-renewal of ESCs , and an emerging picture is that these pathways influence self-renewal in a context-dependent and temporally modulated manner [2–4] . One such pathway is the Jak/Stat3 ( Janus kinase / signal transducer and activator of transcription-3 ) pathway [5] . Activation of Stat3 by phosphorylation at Tyr-705 results in induction of genetic programs that are sufficient for maintenance of self-renewal in mouse ESCs [6–8] . Understanding how Stat3 activation is controlled may be useful for controlling ESC self-renewal . Stat3 is activated by a variety of ligands from the interlukin-6 ( IL-6 ) –type family [9] . In mouse ESCs , Stat3 activation results from binding of leukemia inhibitor factor ( LIF ) to the LIF receptor and glycoprotein-130 ( GP130 ) , forming a heterodimeric receptor complex [10 , 11] . Jak-mediated Src homology-2 ( SH2 ) –domain phosphorylation of receptors leads to Stat3 recruitment to the receptor complex [12] , and its Tyr-705 phosphorylation and subsequent nuclear accumulation [13–25] . This pathway is under control of three main inhibitors , protein inhibitor of activated Stat3 ( PIAS3 ) , Src-2 homology containing phosphotyrosine phosphatase ( SHP2 ) , and suppressor of cytokine signaling ( SOCS3 ) . PIAS3 and SHP2 work to reduce Stat3 availability [26] and receptor activation [21 , 24–26] , respectively , and SOCS3 , which is under transcription control of Stat3 , inactivates activated receptors by binding to GP130 [26 , 27] . Activation of Stat3 is therefore influenced by a variety of intrinsic pathway components as well as receptor trafficking [28 , 29] . Understanding how this signaling is controlled presents a challenge which may be best addressed by mathematical modeling [30] . Previous attempts to model the Jak/Stat pathway have either focused on steady state responses or on capturing the transient activation profile of the pathway to understand its kinetics [31–35] . Examining the transient activation profile provides a larger dynamic range of signal activation , and is therefore more amenable to experimental investigation . Although several models have made predictions about the role of different signaling processes in Stat activation , little work has been done to systematically understand how different signaling events contribute to pathway control , and to experimentally validate model predictions . Furthermore , a lack of computationally feasible algorithms for assessing the importance of pathway structure on signaling behavior has prevented an examination of the signaling consequences of all possible intrapathway interactions . To address this limitation , we developed an in silico model of the Jak/Stat3 pathway to computationally screen and classify parameter interactions for their effects on the transient activation profile of Stat3 . In performing this global sensitivity analysis , we were able to predict and experimentally verify novel pathway dynamics such as a receptor-concentration–dependent switch in ligand sensitivity . Moreover , this approach allowed us to group pathway interactions into stimulatory- and inhibitory-signaling modules . By focusing our computational analysis on signaling kinetics , we were able to examine the consequence of pathway structure on the kinetics of ligand desensitization . These results led to predictive control of ESC self-renewal by modulating the frequency of ligand stimulation .
LIF-induced activation of Jak/Stat3 pathway was modeled using mass action kinetics for the network structure in Figure 1 . The equations for this SBML-compatible [36] system are included in Figure S1 , and Table S1 describes the parameters according to MIRIAM standards [37] . Trafficking was assumed to be similar for all surface receptors [38] . Flow cytometry ( see Figure S2 ) demonstrated that surface expression of LIFR and GP130 is unaffected by LIF addition , and that there is a basal level of receptor turnover , consistent with previous findings [26] . Model dependency on starting conditions [39] was determined ( see Figure S3 ) , and showed stability over a large range of values . Simulation results of transient pathway activation as a function of increasing LIF concentration are presented in Figure 2 , with exogenous LIF concentration shown in the first panel . In agreement with previous reports [40] , there was little difference in Stat3 activation between 500 and 1 , 000 pM LIF . At 500 pM LIF , LIFR and GP130 receptor complexes formed stabilized dimers in less than ten minutes . Cytoplasmic levels of Stat3 decreased by 4-fold during the first 15 min of LIF addition as Stat3 became phosphorylated and localized to the nucleus , consistent with its observed kinetics in other cells [41] . Even at 10 pM of exogenous LIF , the steady state levels of activated Stat3 were at 60% saturation , and significant changes in SOCS3 induction were observed . Simulations predicted a rapid activation and nuclear localization of Stat3 with a peak at 20–25 min , and a time to equilibrium of 2 h . Next , we experimentally validated model predictions . Model consistency with experimental output was determined by quantitative image analysis of mouse ESCs [1] . Figure 3A demonstrates how automated fluorescence microscopy combined with single cell image analysis was used to distinguish signaling in undifferentiated ESCs based on expression of stem-cell–maker Octamer-4 binding protein ( Oct4 ) . The velocity of receptor activation was determined by assessing the phosphorylation level of Jak ( Janus Kinase ) during transient LIF stimulation ( Figure 3B ) , and nuclear accumulation of Stat3 ( Figure 3C ) . Excellent agreement was observed between experimental results and model predictions . Analysis of the kinetics of SOCS3 transcription ( Figure 3D ) and nuclear accumulation of Tyr705 phosphorylated Stat3 ( Figure 3E ) further supported model predictions . Experimental results validated model predictions , suggesting that the model can be used to investigate the effects of perturbations in signaling parameters on Stat3 signaling kinetics . To determine the effects of changes in model parameters on Stat3 signaling , we simulated transient pathway behavior over a 40-fold range of values for each parameter to generate Stat3 activation surfaces ( see Figure S4 for a complete list ) . By examining the surfaces for local minima and maxima , which represent non-monotonic effects on Stat3 activation , we found that the only parameter that provided such a response was the production rate of GP130 receptors ( Figure 4A ) . The model predicted a biphasic response; a small increase in receptor expression resulted in an increase in Stat3 activation; while significant receptor overexpression attenuated activation . In silico analysis suggested that transient formation of LIFR and GP130 nonsignaling receptor heterodimers , and sequestering of LIFR by GP130 in the overexpressing cells , is responsible for the observed results , which is consistent with previous reports [28] . Removal of this ligand-independent interaction from the model eliminated the biphasic response to GP130 expression . To validate this prediction , we developed several ESC lines which overexpress the GP130 receptor to varying degrees ( Figure 4B and 4C ) . As predicted by the model ( Figure 4D ) , Stat3 activation increased upon an incremental GP130 overexpression but decreased at higher levels of receptor expression ( Figure 4E ) . To demonstrate that the overexpressed receptors were in fact functional , we examined Stat3 activation in response to IL-6 plus sIL-6R , which signals exclusively through GP130 receptors [25 , 26] . Cells expressing higher levels of GP130 exhibited higher levels of Stat3 activation upon IL-6 stimulation ( Figure 4F ) , confirming that the overexpressed receptors were functional . As expected from the correlation between Stat3 activation and cell fate processes , modest receptor overexpression appeared to enhance self-renewal in the absence of exogenous LIF , while greater levels of receptor expression increased differentiation ( Figure 4B and 4C ) . In addition to demonstrating the predictive power of this model , these results provide a range of parameter variations over which Stat3 activation is monotonic ( see Figure S5 ) . This allowed us to define a range of parameter values over which we could perform a global sensitivity analysis of parameter interactions . To determine how different parameters control signal propagation , and thereby impact ESC self-renewal , we performed a global sensitivity analysis ( GSA ) on Stat3 activation . Model parameters were varied by 5-fold , and the change in the activation profile of Stat3 was quantified using metrics such as Euclidean distance ( see Figure S6 for a complete list of metrics used ) . Sensitivity analysis of parameters in isolation ( Figure 5A ) showed that the pathway is more sensitive to SOCS3 inhibition than SHP2 or PIAS3 . However , parameter interactions have an important role in signal control and must be considered in this context . To do this , a GSA on Stat3 activation was performed for all two-parameter interactions . This approach can be used to cluster different interactions according to their impact on pathway output . Since rows corresponding to parameters group closer together if they interact similarly with other parameters , this approach allowed us to cluster groups of similarly interacting parameters . This approach provides a visual representation that is easy to interpret . The results of this analysis , presented in a clustergram ( Figure 5B ) , show that it is possible to distinguish parameters that contribute to pathway activation ( hatched circle ) or inhibition ( yellow circle ) , as well as to identify interesting pathway interactions . For example , simultaneously changing the nuclear export rate of Stat3 ( K16 , all rate constants described in Table S1 ) and the rate of docking of Stat3 on activated receptors ( K7 ) will influence Stat3 activation more significantly than either of these parameters in isolation or in combination with any other parameters . Our sensitivity analysis demonstrates that nuclear phosphatase activity , inhibition of SOCS3 , and Stat3 nuclear export most significantly influence Stat3 activation . These results were unaffected by how much parameters were changed ( see Figure S7 ) , and could be averaged over different fold-changes in parameter values ( see Figure S8 ) . To experimentally validate the GSA results , chemical inhibitors were used to specifically target different pathway activation steps ( reducing their corresponding rates by 5-fold ) and the resultant Stat3 activation profiles were compared with model predictions . For validation , we chose to target nuclear export of Stat3 ( K17 ) and receptor phosphorylation ( K5 ) because of the availability of specific chemical inhibitors and the ability to directly measure the reduction in each rate constant . A CRM-1–dependent nuclear export blocker , Leptomycin-B , was used to reduce phosphorylated-Stat3 nuclear export , and a Jak-specific inhibitor was used to reduce receptor complex phosphorylation . Dose responses of the inhibitors were produced to determine the exact concentrations required to reduce the corresponding rate constants by 5-fold ( see Figure S9 ) . As predicted by the model ( Figure 5C ) , experimental results ( Figure 5D ) show that a 5-fold reduction in surface complex activation by addition of Jak inhibitor will reduce levels of activated Stat3 , while a 5-fold reduction in nuclear export of phospho-Stat3 increases levels of activated Stat3 . If both inhibitors are used simultaneously , the upstream effect ( K5 ) will dominate the response . We also verified two other sensitivity analysis results involving SOCS3 transcription and receptor production ( K22 , K29 ) and SOCS3 translation and receptor production ( K25 , K29 ) . Model-predicted trends ( Figure 5E ) were observed experimentally ( Figure 5F ) . In this case , reduction of receptor production decreased the level of Stat3 activation , and reduction of SOCS3 production led to loss of signal attenuation . Knockdown of SOCS3 using siRNA [42] verified that it is responsible for signal attenuation ( see Figure S9 ) . Therefore , global sensitivity analysis was used to cluster cell-signaling steps based on intrapathway interactions and identified biologically relevant control nodes of Stat3 activation . Desensitization , which is the inability to respond to ligand restimulation [26] , may significantly limit cytokine-driven in vitro stem cell propagation [43] . Possible mechanisms of desensitization in the Jak/Stat3 pathway include receptor downregulation , SHP2- or SOCS3-mediated signal attenuation , and differential transport kinetics of STAT3 , but the relative importance of each interaction is unknown . As a first step to investigate this response , model-predicted trends of desensitization ( Figure 6A ) were experimentally verified ( Figure 6B ) , and showed that a minimum time of about 3 h is required after ligand stimulation for the cells to become fully responsive to the readdition of LIF . GSA of two successive Stat3 activation profiles was used to understand what pathway components control desensitization . This method distinguishes between desensitization and inhibition , since both successive activation profiles will be changed by inhibition , but only the reactivation profile is influenced by desensitization . The relative shift of the reactivation profile of Stat3 ( Figure 6C ) , and the sensitivity analysis ( Figure 6D ) , both showed that the decreased production of SOCS3 and a reduction of Stat3 nuclear export ( together or independently ) could delay the Stat3 reactivation profile . The main determinants of desensitization control , in order of importance , were SOCS3 , nuclear phosphatase , and nuclear export of Stat3 . Our analysis also suggested that receptor turnover rates influence desensitization . To determine if Jak/Stat3 pathway desensitization control could impact ESC self-renewal , we experimentally modulated the frequency of LIF-mediated Stat3 stimulation based on the period of cytokine supplementation . Dose responses of Stat3 activation and Oct4 expression at different LIF concentrations identified 10 pM LIF as the minimum concentration that maintains undifferentiated ( Oct4+ ) ESC cells and does not saturate Stat3 activation ( Figure 6E and 6F ) . We reasoned that small modulations of Stat3 above and below this level would reveal sensitivities to desensitization , and thus this concentration of LIF was cycled at two different periods to investigate the impact of desensitization on self-renewal of ESCs . According to our predictions , a 6-h LIF-supplementation period would provide sufficient time ( 3 h ) for Stat3 signaling to reach a minimum and for cells to lose their “memory” of previous LIF levels ( Figure 6G ) . In contrast , a 1-h LIF-supplementation period would result in an increased minimum level of activated Stat3 ( Figure 6E–6G ) due to desensitization-mediated effects . Consequently , the 1-h LIF cycle should maintain a higher percentage of Oct4+ cells . To experimentally test this prediction , periodic LIF supplementations were performed for a total of 72 h , and the impact of these manipulations on Oct4 expression in ESCs was determined . Results indicated that although total exposure to LIF was the same between these two conditions , the percentage of Oct4+ cells was higher in the 1-h periodic LIF stimulation condition ( Figure 6H and 6I ) . Therefore , the desensitization period may provide windows of opportunity for ESCs to lose memory of previous ligand concentrations and differentiate . Desensitization of the Jak/Stat3 pathway provides an example of how pathways kinetics influences ESC fate choices .
The Jak/Stat3 pathway , which is required for self-renewal of mouse ESCs , was modeled using a deterministic , lumped-parameter , differential-equation–based representation of mass action kinetics . Model-predicted trends of Stat3 activation and nuclear accumulation as well as Jak activation and SOCS3 expression were verified experimentally by quantitative imaging of adherent mouse ESCs . These results provided evidence for biological relevance of model predictions , and led to further investigation of pathway control . Transient Stat3 activation was used to understand how different signaling events influence pathway kinetics , and grouping of model parameter interactions determined the important intracellular signaling control modules . Sensitivity analysis of model parameters in isolation predicted that significant GP130 overexpression should reduce Stat3 activation . Experimental results of GP130 overexpressing cell lines confirmed predictions , showing decreased Stat3 activation with increased expression of GP130 receptors . Intact biological function of overexpressed receptors was confirmed by IL-6 stimulation of the cells . Based on model results , sequestering of LIFR by GP130 to form a nonsignaling heterodimer , which has been shown to exist transiently [12] , can account for this behavior . Higher differentiation was observed in the highest GP130 overexpressing cell lines in the absence of LIF ( Figure 4C ) . This response may be attributed either to reduced Stat3 activation from endogenously produced GP130 ligands [44] or possibly to increased activation of ERK by SHP2 through the GP130 receptors contributing to the response [25 , 45] . Global sensitivity analysis of the system was performed by considering two parameter interactions; this approach covered the entire solution space of these interactions and provided a computationally feasible method to perform this analysis compared with previous attempts [31] . Clustering ( Figure 5B ) demonstrated how different parameters interact to yield similar effects . It is noteworthy that sets of inhibitory and stimulatory signaling interactions appeared distributed across the signaling network . Points of sensitivity may represent important nodes of crosstalk between pathways , and nonintuitive pathway behavior could arise from the alteration of seemingly innocuous parameters . These possibilities may in part account for the observation that conserved signaling pathways affect a variety of cell processes in a cell-type and context-dependent manner . A number of other signaling pathways have recently been implicated in ESC self-renewal; this analysis provides a first step toward understanding this signaling crosstalk . GSA results were verified experimentally by specifically targeting signaling steps for which specific inhibitors were available . The impact of a reduction in the rates of nuclear export of Stat3- and Jak-mediated receptor activation in isolation and in combination were determined experimentally and corroborated model predictions . Interaction of two targets , which clustered together in the GSA , SOCS3 induction and receptor production , was verified , demonstrating that they synergize similarly . Although this sensitivity analysis focused on Stat3 activation due to its importance to self-renewal of mouse ESCs; the same analysis could be performed for any protein in the signal transduction pathway ( see Figure S10 ) . Based on GSA , nuclear export of Stat3 , nuclear phosphatase activity , and inhibition by SOCS3 were the most sensitive parameters for manipulating pathway output . Although other mechanisms of desensitization , in particular receptor desensitization or differential transport kinetics between phosphorylated and unphosphorylated receptors , are possible , our investigation points out a series of possible mechanisms through which desensitization can occur . More importantly , this approach allows for determination of importance of each mechanism to desensitization . Computational investigation of the dynamics of pathway desensitization to ligand stimulation was a particularly interesting application of GSA . In agreement with previous reports , SOCS3 , but neither SHP2 nor PIAS3 kinetics , could account for pathway desensitization [26] . The phenomena of desensitization is an interesting feature of the Jak-Stat signaling pathway , which lends itself to a computational approach for identifying methods of circumventing this inhibitory behavior . To do this , we predicted the effects of two different ligand-dosing frequencies on Stat3 activation and examined their consequences on ESC fate . Although both sets of conditions were exposed to the same overall amount of LIF , the conditions with the LIF cycle period of 1 h retained a higher percentage of Oct4+ cells . Therefore , knowledge of dynamic pathway behavior such as desensitization can be used to identify nonintuitive targets or cell culture manipulations to control stem cell behavior . In summary , directing stem cell fate requires knowledge of how intracellular signaling can be controlled . To do this , we used a combined computational and experimental approach to study the kinetics of the Jak/Stat3 pathway in mouse ESCs . Although such mass action kinetics models can be improved , for example by including more detailed reaction mechanisms and interpathway interactions , this approach utilized herein provided us with an appropriate representation of the network dynamics and has allowed identification of interesting behaviors and properties of this signaling pathway . Modeling predicted novel effects of signaling ( which were experimentally verified ) , and global sensitivity analysis led us to understand how this pathway is controlled . Based on computational predictions of desensitization kinetics , the self-renewal response of ESCs was modulated by controlling the frequency of ligand stimulation . This approach is useful for optimizing stem cell cultures by providing a ligand delivery regimen based on knowledge of intracellular signaling pathway kinetics . Our approach can be used to find key targets for control of intrapathway kinetics in stem cells . What is now required is to develop direct links between the nuclear concentration of activated Stat3 and cell fate decisions in individual cells . Ultimately , this requires insight into how cells convert graded stimuli into a threshold-based cell fate decision . Adapting the model presented herein to single cell signaling and integrating it with approaches to describe gene regulatory networks will yield a deep understanding of stem cell self-renewal .
The Matlab software package ( The Mathworks , http://mathworks . com ) was used for model development and implementation . Signal propagation starts with binding of LIF to its LIFR [10 , 11]; this is followed by recruitment of the glycoprotein 130 ( GP130 ) receptor to form a stabilized heterodimeric receptor complex on the cell surface . Transient homodimers of LIFR and GP130 are nonsignaling in this system [12] . LIF-mediated heterodimeric receptor complex formation leads to activation of receptor-associated Jaks , which phosphorylate the complementary SH2 domain of both LIFR and GP130 [24 , 25] . The transcription factor Stat3 then docks on the phosphorylated SH2 domains of the receptor heterodimer and is phosphorylated at Tyr705 by Jak [23] . In this model , we assumed pre-association of Jak with the receptors according to literature [21 , 22] , and we have assumed reversible kinetics as shown by double arrows in Figure 1 [20] . Stat3 is predominantly dimeric [17–19] and cytoplasmic [15 , 17] in its unphosphorylated form , and translocates to the nucleus upon phosphorylation by using the importin machinery , in particular importin α3–5 [15 , 24] . In addition to ligand-mediated nuclear import of Stat3 [15 , 16] , there is a basal level of nuclear–cytoplasmic shuttling of unphosphorylated Stat3 [15 , 17] , and dephosphorylation of phosphorylated nuclear Stat3 leads to its CRM-1 dependent nuclear export [14] . We modeled these events by reversible binding of Stat3 to activated receptor complexes ( rate constants K7 , K8 ) , nonreversible phosphorylation and dissociation of Stat3 from the receptor complex [13] ( K9 ) , followed by reversible nuclear translocation of both phosphorylated and unphosphorylated Stat3 . We used a set of lumped parameters for all nuclear transport events ( K13–17 ) where import rates are similar [34 , 38]; however , the export rate of phosphorylated Stat3 is lower , in accordance with previous work [14 , 41] . We optimized this profile by tuning the export rate constant of phosphorylated Stat3 based on the rise time of its activation profile as determined experimentally . Three important inhibitors of the Jak/Stat3 pathway are also implemented . SOCS3 is under transcription control of Stat3 [26 , 27] , binds to the activated receptor complex of LIF and GP130 by binding to the Tyr974 of the GP130 receptor in the complex , and renders it inactive by removing the ability of Jak to phosphorylate Stat3 [25 , 26] . Transcription control of SOCS3 by Stat3 and transport rates for the mRNA were modeled by using lumped parameters . Translated SOCS3 protein binds reversibly to the activated herterodimeric receptor complex and renders it inactive , and it dephosphorylates the receptor complex while remaining bound . The second inhibitor of the pathway , PIAS3 , has been shown to be present in both nuclear and cytoplasmic compartments , to bind directly to phosphorylated Stat3 , is believed not to be under the transcription control of Stat3 , and removes the DNA binding ability of phospho-Stat3 [26] . We modeled PIAS3 activity by two reversible binding steps in the cytoplasm and nucleus . Finally , the effect of SHP2 , which is the third main inhibitor of the pathway , was captured in the dephosphorylation of the Src-Homology-2 SH-2 domain phosphorylated receptor heterodimer complexes , using rate constant ( K6 ) . This is the suggested mode of action of SHP2 in this system [21 , 24–26] . Trafficking of the receptors GP130 and LIFR is also implemented in the model , and all surface receptors can be internalized with the same rate constant ( K28 ) , there is a fixed rate of receptor production using rate constant K29 , and internalized complexes do not signal and are degraded [11 , 28 , 29] . Using this system , we are able to capture the observed transient kinetics of the Jak/Stat3 pathway . Undifferentiated R1 were maintained in a mixture of 15% fetal bovine serum ( FBS ) ( 900–108; Gemini Bio-Products , http://www . gembio . com/ ) , 100 μM β-mercaptoethanol ( M6250; Sigma-Aldrich , http://sigmaaldrich . com ) , 2 mM L-glutamine ( 25030–081; Invitrogen , http://www . invitrogen . com ) , 0 . 1 mM nonessential amino acids ( 11140–050; Invitrogen ) , 1 mM sodium pyruvate ( 11360–070; Invitrogen ) , 50 U/mL penicillin and 50 μg/mL streptomycin ( 15140–122; Invitrogen ) in high-glucose Dulbecco's Modified Eagle's Medium ( DMEM ) supplemented with 500 pM murine leukemia inhibitory factor ( mLIF; ESG11–7; Chemicon , http://www . chemicon . com ) , on 0 . 2% gelatin-coated tissue-culture flasks . Single-cell suspensions were obtained by incubating the cells with 0 . 25% Trypsin-EDTA ( 25200–106; Gibco , http://www . invitrogen . com ) for 3 min , and 1 × 106 cells were plated in a T25 between subsequent passages , using only cells from passages 15 to 30 . In order to obtain adherent cells , special optic flat-bottom , clear 96-well plates ( 3614; Corning , http://www . corning . com ) were precoated with 1% Fibronectin ( F1141; Sigma-Aldrich ) in 0 . 02 % Gelatin at 37 °C and 5% CO2 for 24 h . Single-cell suspension of R1 ESCs was obtained as outlined above . Cells were plated in 200 ul per well of ESC culture media at a density of 1 . 5 × 104 cells per well of a 96-well plate , spun using appropriate plate holders at 220 G for 5 min , and incubated at 37 °C and 5% CO2 for 4 h in order to allow cell attachment . Thereafter , media was changed to ESC culture media without LIF and with FBS replaced with 15% knockout serum replacement ( KO media ) ( 10828–028; Invitrogen ) , and cells were incubated in this media at 37 °C and 5% CO2 for 24 h before LIF stimulation . LIF stimulation was performed by adding KO media containing 500 pM LIF at predefined time points and fixing the cells at time zero with 3 . 7% formaldehyde in phosphate-buffered saline ( PBS ) . For inhibitor studies , the inhibitors were added at the appropriate concentration in KO media at 200 ul/well for 4 h before LIF stimulation . Jak Inhibitor-1 ( 420099; Calbiochem , http://www . merckbiosciences . co . uk ) was used at concentration 5 nM as determined by dose response curves and Leptomycin-B ( L2913 , Sigma Aldrich ) was used at concentration 10 ng/ml ( see Figure S9 for dose responses ) , with appropriate controls containing equal concentration dimethyl sulfoxide ( DMSO ) ( D2650; Sigma-Aldrich ) . For intracellular staining , the cells are permeablized with 100 μl per well methanol for 3 min at room temperature and washed three times with 200 μl per well of PBS . To reduce nonspecific binding of antibody , 200 ul of 10% FBS in PBS is added to each well , and the plate was kept at 4 °C for 24 h . Antibody dilutions were performed in the same FBS in PBS composition . The following primary antibodies were used with 24-h incubation: anti-phospho-Stat3 ( tyr705 ) ( 9131; Cell Signaling Technology , http://www . cellsignal . com/ ) , anti-Oct3 ( Oct4 ) ( 611203; BD Bioscience , http://www . bdbiosciences . com/ ) , anti-phospho-Jak2 ( Tyr1007–1008 ) ( 3771; Cell Signaling technology ) , anti-LIFR ( Sc659-Santa Cruz Biotechnology , http://www . scbt . com/ ) , anti- GP130 ( Sc656-Santa Cruz Biotechnology ) , and anti-SOCS3 ( Sc9023-Santa Cruz Biotechnology ) . The following secondary antibodies were used with 2-h incubation: AlexaFluor 488 ( A-11034 ) , and AlexaFluor 546 ( A-11030; Molecular Probes , http://www . probes . invitrogen . com ) . Hoechst nuclear dye ( B2261; Sigma-Aldrich ) was used at 0 . 1 ug/mL . Cells were scanned using the ArrayScan VTI automated fluorescent microscope ( Cellomics , http://www . cellomics . com/ ) . Image analysis was performed using the vHSC View software provided by Cellomics . Object selection was performed based on Hoechst staining of nuclei , and images of appropriate channels ( for example , Oct4 and Phospho-Stat3 ) were analyzed to determine the average pixel intensity for each cell . Oct4 subpopulations were determined by fitting two Gaussian distributions ( OriginPro 7 . 5; OriginLab , http://www . originlab . com/ ) to log-transformed data of Oct4 fluorescence . All experiments were performed at least in triplicate , and at least 30 , 000 cells were imaged per data point . Cells were fixed after LIF addition at appropriate time points ( see Figure S2 ) using Reagent 1 containing 5 . 5% formaldehyde ( IM2389 , Immunotech , http://www . beckmancoulter . com ) in 2% FBS Hank's Buffered Salt Solution ( HBSS ) ( 14175–095-Gibco ) for 15 min . Permeablization ( in the case of permeablized cells ) was done in Reagent 2 containing 0 . 1% sodium azide ( IM2389 , Immunotech ) for 5 min , followed by incubation with the appropriate primary antibody for another 15 min . Primary antibodies that were used are listed above , and the secondary antibodies that were used are PE anti-mouse IgG1 ( 550083; BD Bioscience ) and Goat-anti-rabbit FITC ( 0833; Immunotech ) . GP130 cDNA was provided by Kishimoto et . al [41] . The PCR-amplified fragment was subsequently inserted into the pCAG-GFP vector provided by Dr . J . Draper [46] using the Xho1 and Not1 sites . The cloned product was sequenced . R1 ESCs were transfected by electroporation , and stable transfectants were selected using Puromycin resistance . Five cell lines ( RG1–RG5 ) were expanded and cell-surface GP130 overexpression was confirmed by both flow cytometry and Cellomics imaging . siRNA transfections were performed using Lipofectamine transfection reagent ( 18342–012; Invitrogen ) . siRNA for murine SOCS3 ( sense , 5′-GGAGCAAAAGGGUCAGAGGtt-3′; antisense , 5′-CCUCUGACCCUUUUGCUCCtt-3′ ) [42] was custom-manufactured ( 16211; Ambion , http://www . ambion . com ) . Transfection was performed on adherent cells in KO media without antibiotics , in a volume of 200 ul/well containing 160 ul of plating media , 1–50 pMol of siRNA , and 0 . 3–0 . 8 ul of Lipfectamine reagent . After dose response , the optimum condition was found to be 11 . 4 pmol of siRNA in 200 μl of plating media containing 0 . 4 μl of Lipfectamine , which was made according to manufacturer instructions ( 18342–012; Invitrogen ) . Cells were plated in the transfection reagent 24 h before readout .
|
Directing stem cell fate requires knowledge of how intracellular signaling pathways integrate environmental stimuli to make decisions to stay as stem cells ( self-renew ) or to differentiate into specific functional cell types . We developed and validated a computational model of signal transducer and activator of transcription-3 ( Stat3 ) pathway kinetics , a signaling network involved in mouse embryonic stem cell ( ESC ) self-renewal . Our analysis demonstrates that stem cell fate control is regulated by a distributed set of parameters that positively and negatively regulate Stat3 activation . We further demonstrate that we can take advantage of differences in the timing of signaling pathway activation and inhibition to design a strategy to deliver self-renewal stimuli to stem cells in a more efficient manner . Ultimately , the use of stem cells in biotechnological applications will require an in-depth understanding of how cells integrate diverse environmental stimuli to make cell fate decisions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"oncology",
"developmental",
"biology",
"mathematics",
"cell",
"biology",
"in",
"vitro",
"computational",
"biology",
"chemical",
"biology",
"animals",
"mus",
"(mouse)"
] |
2007
|
Sensitivity Analysis of Intracellular Signaling Pathway Kinetics Predicts Targets for Stem Cell Fate Control
|
The filoviruses , Marburg and Ebola , are non-segmented negative-strand RNA viruses causing severe hemorrhagic fever with high mortality rates in humans and nonhuman primates . The sequence of events that leads to release of filovirus particles from cells is poorly understood . Two contrasting mechanisms have been proposed , one proceeding via a “submarine-like” budding with the helical nucleocapsid emerging parallel to the plasma membrane , and the other via perpendicular “rocket-like” protrusion . Here we have infected cells with Marburg virus under BSL-4 containment conditions , and reconstructed the sequence of steps in the budding process in three dimensions using electron tomography of plastic-embedded cells . We find that highly infectious filamentous particles are released at early stages in infection . Budding proceeds via lateral association of intracellular nucleocapsid along its whole length with the plasma membrane , followed by rapid envelopment initiated at one end of the nucleocapsid , leading to a protruding intermediate . Scission results in local membrane instability at the rear of the virus . After prolonged infection , increased vesiculation of the plasma membrane correlates with changes in shape and infectivity of released viruses . Our observations demonstrate a cellular determinant of virus shape . They reconcile the contrasting models of filovirus budding and allow us to describe the sequence of events taking place during budding and release of Marburg virus . We propose that this represents a general sequence of events also followed by other filamentous and rod-shaped viruses .
Marburg virus ( MARV ) and Ebola virus , the two genera in the family Filoviridae , cause fulminant hemorrhagic disease in humans and nonhuman primates , resulting in high mortality rates [1] , [2] , [3] . Outbreaks of MARV disease in sub-Saharan Africa underline the emerging potential of this virus , which is classified as a highest-priority bioterrorism agent by the Centre for Disease Control [4] , [5] , [6] , [7] , [8] . The filoviruses are members of the order Mononegavirales and contain a single-stranded negative-sense RNA genome , which is encapsidated by the nucleoprotein ( NP ) . The MARV genome encodes seven structural proteins [9] , [10]: the polymerase ( L ) , VP35 and VP30 associate with NP to generate the helical nucleocapsid ( NC ) [11] , [12] , [13] . The viral glycoprotein ( GP ) , which is inserted in the viral envelope , mediates cell entry [14] , [15] . The major matrix protein VP40 plays a key role in virus assembly , and VP24 , the second matrix protein , is suggested to support the template function of the NC [12] , [16] , [17] , [18] . MARV infected cells develop viral inclusions in the perinuclear region [19] , [20] , [21] . These contain NC proteins and are most likely centres of NC assembly [22] . MARV particles bud from the plasma membrane ( PM ) of long filamentous cellular protrusions that contain parallel actin bundles and other markers of filopodia [23] . The released virus particle has a membrane envelope and contains an NC that is surrounded by the viral matrix protein VP40 . It is unclear how NCs are transported from viral inclusions to the PM , whether they adopt their virion conformation before , during , or after , transport , or where NCs associate with VP40 that is not co-transported with NCs but is necessary for budding . Released MARV particles appear filamentous , hooked , six-shaped or round by electron microscopy ( EM ) [24] but their three-dimensional ( 3D ) morphology is unclear . It is also unknown whether production of differently shaped viruses depends on different budding mechanisms and whether they differ in infectivity . Experiments to address these issues are complicated by the need to perform all infection experiments under BSL-4 containment conditions . The processes of assembly , budding and release of spherical viruses have been extensively studied [25] , [26] , [27] and it is well established that spherical enveloped viruses are produced by budding away from the cytoplasm in a process that is related and topologically equivalent to the formation of small vesicles in multivesicular bodies [28] , [29] . In contrast , the basic steps of assembly and release for large , filamentous , enveloped particles such as the filoviruses are poorly understood . EM studies have shown MARV and Ebola virus particles protruding perpendicularly from the cell [23] , [30] . These observations , together with similar findings of protruding intermediates of other important filamentous or rod-shaped viruses such as rabies virus [31] , influenza virus [32] , [33] , [34] , or vesicular stomatitis virus ( VSV ) [35] have led to the suggestion of a vertical “rocket-like” mode of budding . Ebola virus NCs have been seen associated parallel to the PM [36] , leading to the suggestion that a second , horizontal , “submarine-like” mechanism is the major mode of budding . This unusual mechanism has not been described for any other virus . In this study we use electron tomography ( ET ) to describe and analyse in 3D the structure of MARV budding intermediates and released viruses at different stages of infection . In contrast to conventional EM of ultrathin sections , ET allows complete MARV virions and budding structures to be studied in 3D . This permits unambiguous determination of virus morphology , dimensions , stage of budding and position relative to the infected cell . The samples are prepared by high-pressure freezing followed by embedding in resin and staining with heavy metals . Unlike cryo-ET of vitreous samples , this method is not appropriate for the study of high-resolution protein structure . Nevertheless , it gives excellent preservation of the features studied here such as membranes and protein assemblies including viral NCs [37] , [38] . It also has , for this particular purpose , substantial advantages over cryo-ET . The features of interest are imaged at much higher contrast , including at high tilt angles . The samples are also easier to handle and stable under the electron beam , allowing efficient screening , which facilitates the collection of larger datasets . Our observations suggest that interplay between the virus and the infection state of the cell determines virus morphology . Furthermore , the 3D data allow us to describe the sequence of steps that take place in infected cells during assembly and budding of filamentous virions , and to reconcile the horizontal and vertical models for filovirus budding as representing different snapshots of a single budding process .
To determine the time course of production of infectious MARV during a prolonged infection period , supernatants of HUH-7 cells , infected with MARV under BSL-4 conditions , were collected from day one to four post infection ( p . i . ) and tested for viral protein content , specific infectivity and virion morphology . The amounts of viral NP and VP40 released from the cells were measured by quantitative immunoblotting . This showed that viral protein release peaked between day one and two p . i . ( Figure 1A ) . The TCID50 of each supernatant was determined and normalized to the amount of released NP to estimate the specific infectivity per virus ( Figure 1B , grey areas ) . Specific infectivity was also at a maximum between day one and two p . i . . Virus morphology in the supernatants was monitored by EM and revealed that at the peak of viral protein production and specific infectivity , 80% of the virus particles displayed the characteristic filamentous morphology of the filoviruses , with the remainder appearing bent or round ( Figure 1B ) . Later in infection , the specific infectivity was lower and correlated with fewer filamentous particles and more round or bent particles ( Figure 1B ) , suggesting that infectivity can predominantly be attributed to filamentous virus . There was also an increase in the release of cellular vesicular material over time ( Table 1 ) . Quantitative EM analysis of infected adherent cells that were fixed and embedded in situ revealed that cell morphology also changed over time . At day 1 p . i . 95% of the observed cells appeared intact and displayed filopodia-like PM protrusions ( Figure 1C , D ) . The cytoplasm often contained densely stained viral inclusions ( Figure 1D ) and virions were readily seen in the immediate periphery of cell profiles; they were easily identified by means of their electron-dense stain and a rod-shaped NC and were frequently found ‘trapped’ between or underneath adherent cells ( Figure 1E ) . Over 90% of all observed virions around the cells were filamentous or hooked ( Figure 1F ) . In contrast , when the monolayer was fixed at 4 days p . i . , only 17% of the cell profiles were intact and 83% appeared vesiculated and resembled apoptotic cells ( Figure 1C , G ) . Most of the virus particles around such vesiculated cells were bent or round ( Figure 1F , H ) . The above characterisation indicates that production of fully infectious , filamentous virus is highest at 1–2 days p . i . , when the cells still have intact membrane profiles . This was therefore selected as an appropriate time point for studying assembly and budding of filamentous particles . To study the assembly and budding of MARV we performed ET on MARV-infected cells . ET allows the different steps of assembly and budding to be visualized in 3D and the dimensions of viral structures to be measured and analysed . Infected cell monolayers were fixed at 1 day p . i . , removed from the BSL-4 facility , processed for EM and cut into 300 nm thick sections in their in situ orientation . Dual-axis tilt series of the sections were acquired in the electron microscope , as described in Materials and Methods , and 3D reconstructions were computationally generated . Viral NCs could be readily identified in 3D reconstructions . They were found around viral inclusions , within the cytoplasm , associated with the PM , incorporated into budding viruses , and in released virus particles ( Figure 2 ) . We therefore used the NC itself as a convenient marker for identifying virus assembly intermediates . The 3D data allowed us to measure the length of complete viral NCs even when they were bent or tilted with respect to the sectioning plane . In the periphery of viral inclusions in the cytoplasm individual NCs could be found . They appeared as rod-shaped , striated structures that were more densely stained than the cytoplasm and were on average 711 nm long ( Figure 2A , Table 2 ) . NCs were frequently seen at the PM or in filopodia-like membrane protrusions , where they were associated with the membrane along their whole length . The length of PM-associated NCs was 707 nm ( Figure 2B , Table 2 ) , the same as that of cytoplasmic NCs . Viral budding structures localized predominantly to filopodia-like protrusions of infected cells , in agreement with previous data [23] . They appeared as filamentous finger-like extensions emerging either from the tip or the sides of filopodia-like protrusions ( Figure 2C-E ) . Each bud accommodated a rod-shaped NC that was surrounded by densely stained material and had the same length as intracellular and PM-associated NCs ( Table 2 ) . Of the budding structures reconstructed in 3D , 13% appeared as extrusion intermediates ( Table 2 ) . These structures contained a full length NC ( 729 nm ) that was only partially extruded ( Figure 2C ) : one end of the NC was tightly wrapped on all sides by the PM , whereas the other was attached to the PM along one side ( Figure 2C ) . The majority ( 87% ) of budding structures had the NC completely inserted into the finger-like membrane extension ( Figure 2D , E ) . Scission at the base of these fully extruded buds would lead to the release of filamentous virions ( Figure 2F and Video S1 ) . Most released viruses in the periphery of infected cells appeared as straight filaments in 3D reconstructions ( Figure 2F ) . Some filamentous viruses displayed one bent or buckled end giving rise to hooked or six-shaped particles ( Figure 2G and Video S1 ) . Filamentous viruses were on average 789 nm long and had a diameter of 88 nm ( Table 2 ) , in agreement with previous studies [20] , [24] . All virions had a membrane envelope and contained a rod-shaped NC that displayed the regular striated pattern previously described [12] , [20] . The volume between membrane envelope and NC was filled with a densely stained material , most likely the VP40 matrix protein , which appeared to link the envelope to the NC on all sides and along the whole length ( Figure 2F ) . 3D analysis revealed that NCs in all released virions had the same length , on average 735 nm , and appeared bent or kinked when particles were hooked or six-shaped ( Figure 2G , Table 2 ) . These length averages exclude a single outlier , which was a double-length filamentous virus with a double-length NC ( Table 2 ) . The presence of NCs of the same length in the cytoplasm , at the PM , in filopodia , in all budding structures and in filamentous viruses suggests that the NC assembles into a helix of a defined and final length prior to being transported to the PM . The 3D data also allowed us to closely examine both ends of reconstructed filamentous virions . This revealed morphological differences between the two virus ends: 19 out of 21 3D-reconstructed filamentous virions displayed a membrane bulge or hook at one end ( Figure 3A ) , while the membrane formed a round , intact hemisphere at the opposite tip ( Figure 3B and Table 2 ) . Only 2 out of 21 filamentous viruses had two round , intact tips . Virions with membrane distortions at both ends were never observed . Although we cannot exclude that such membrane distortions are due to chemical fixation , they suggest that a local instability of the viral membrane is specifically induced on one virus end . Thus , we made use of the fact that cells and extracellular viruses were fixed and examined in their in situ orientation . Measurement of the average distance of intact and distorted virus ends from the nearest cellular membrane revealed that intact virus ends were predominantly found at distances >250 nm away from a cellular membrane , whereas distorted virus ends were more frequently localized within 50 nm distance ( Figure 3C ) . This suggests that the membrane distortions are found at the rear end of filamentous viruses where scission took place ( also shown in Figure 2G and Video S1 ) . We also carried out ET of infected cells fixed at 4 days p . i . . At this time point the majority of cells exhibited heavily vesiculated membranes and produced rounded viruses of lower infectivity . The 3D analysis revealed that most viruses were roughly spherical in shape . They were generally found in close proximity to convoluted , vesiculating areas of the PM ( figure 4A ) and often surrounded by large numbers of cell-derived vesicles ( Figure 4A and Video S2 ) . This suggests that release of spherical viruses occurs rapidly and simultaneously with the shedding of other cell-derived vesicles . NCs within spherical virions were bent or kinked but never broken or segmented , as might be suggested by 2D EM of thin sections ( see , for example the 2D and 3D views of the spherical particle shown in Figure 4B ) . None of the virus particles were toroidal , as has previously been suggested [39] . Interestingly , kinked NCs in spherical viruses had a total length of 734 nm , the same length as NCs in filamentous virions ( Figure 4B , Table 2 ) and they were associated along one side with the viral membrane ( Figure 4B ) . These findings demonstrate that preassembled full-length NCs are packaged into each virion , irrespective of virus shape .
The analysis of MARV-infected cells by ET allowed us to take 3D measurements of NCs and revealed that NCs have uniform length at all stages in the virus assembly and budding process described here , from intracellular NCs to released virus particles . This suggests that the length of the complete viral genome and the number of nucleoproteins required to encapsidate the genome dictate the length of the intracellular NC prior to its transport to the PM . Once the NC has reached the PM , two contrasting mechanisms have been proposed for filovirus budding . In the “submarine-like” model , budding proceeds via lateral association of the NC with the membrane , followed by horizontal budding . In the “rocket-like” model , the NC is extruded vertically from the PM . The analysis of budding MARV particles presented here allows the sequence of steps resulting in filamentous virus budding and release to be described in 3D . Viral NCs are assembled in the cytoplasm ( Figure 5A ) and delivered in their full-length form to the PM , with which they associate laterally , along one side , for their entire length ( Figure 5B ) . Envelopment of the PM-associated NC is initiated at one end , and proceeds along the length of the NC ( Figure 5C ) until the nascent virion protrudes from the membrane , remaining attached at only one end ( Figure 5D ) . Only very few particles are seen which appear to be partly extruded , whereas larger numbers of NCs are either associated with the PM prior to extrusion or are fully extruded and protruding from the PM . This strongly suggests that extrusion is a rapid process in comparison with its initiation or the subsequent membrane scission event . Scission of the filamentous virus particle from the PM then takes place with a bud-neck shape which can have a circular cross-section . This is the same shape as the bud-neck which would be present in the budding of spherical enveloped virions , or in the budding of vesicles into a multi-vesicular body [40] . Release of a horizontally budding particle would require scission of a membrane neck with a non-circular cross-section . Our observations indicate that no such unusual scission mechanism needs to be proposed . Scission of the protruding bud leaves local membrane instability at the rear end of the virus particle ( Figure 5E ) , which may be exaggerated during sample preparation . In contrast , the front end of the filamentous particle is a well-defined hemisphere . The difference between the two ends could reflect a destabilization at the rear end of the particle induced by scission . Alternatively , the hemispherical front end of the particle may be stabilized by a structure involved in initiating envelopment , similar to the front end of bullet-shaped rhabdovirus particles [41] , [42] . The budding of filamentous MARV therefore proceeds via an NC that is laterally associated with the PM , and a vertically protruding bud . Any changes in the rates of individual steps in the budding process could dramatically alter the appearance of the budding structures in EM . For example , if the rate of scission were significantly increased , or the rate at which extrusion is initiated were dramatically decreased , then large numbers of NCs might be expected to collect horizontally under the PM , giving the appearance of a predominant “submarine” mode of budding [36] . If the rate of initiation of extrusion were significantly increased , or the rate of scission decreased ( for example by inhibiting recruitment or function of the cellular endosomal protein sorting system ) , then large numbers of protruding buds would accumulate , giving the appearance of a predominant “rocket-like” mode of budding . We suggest the contrasting appearance of Ebola virus and MARV infected cells in EM does not reflect different budding mechanisms , but rather different rates for the individual steps of the process presented here . These rates are likely to be both virus and cell type dependent . The sequence of steps in budding described here results in a very different mechanistic understanding of filovirus budding . Firstly , the proposed two budding modes , one vertical and one horizontal , can be reconciled as representing different snapshots of a single budding mechanism . Secondly , rather than filoviruses adopting a unique horizontal mode of budding not seen in other systems , the budding process can now be placed within the established framework of other cellular budding events . In the new model , budding is initiated by wrapping of one end of the NC with a hemispherical membrane , and completed by scission at a bud-neck with a classic round cross-section . These are the membrane shapes present during budding and scission events of cellular vesicles and spherical viruses . Thirdly , comparison of our data with published electron micrographs of filamentous virus budding structures suggests that other viruses may also follow the sequence of steps described here , and that rather than being unique , filovirus budding belongs within a more general budding mechanism also adopted by other rod-shaped viruses . For example , budding VSV , a rhabdovirus , can be found protruding perpendicular to cell membranes in infected cells , but the NC can also be seen underneath the cell membrane , with which it associates laterally along one side [35] . The rounded end of the virus , easily distinguished in VSV , seems to associate more tightly with the membrane [43] , and buds first . After budding , under certain preparation conditions , the rear of the virus is seen to show small membrane blebs or instabilities [41] similar in appearance to those described here for MARV . These striking observations are consistent with VSV following the same sequence of budding steps as MARV . A general assembly and budding mechanism for filoviruses and rhabdoviruses might represent a target for future antiviral drugs . After a prolonged period of infection , the released MARV particles have lower specific infectivity and the majority is roughly spherical in form . Like the filamentous virions , the spherical particles contain a single full-length NC , which is kinked in a number of places , but not broken , suggesting that these particles still contain a full-length viral genome . The NC is not tightly wrapped on all sides as it is in filamentous virions , but is associated with the membrane along one side for its entire length . This lateral association is also seen for intracellular NCs prior to extrusion from the PM . Since formation of spherical particles is paralleled by convolution of the PM and shedding of cellular vesicular material into the supernatant , we propose that spherical viruses are released by large-scale membrane instability at sites of budding , leading to vesiculation of viruses prior to extrusion . In this model , kinking of the NC might be induced by the forces which lead to invagination and vesiculation of the PM , though other factors could contribute , such as lack of a viral or cellular factor which rigidifies the NC . The interplay between the budding process and the dynamics or composition of the cellular membrane , as well as possible changes in the relative rates of the different steps of the budding process in different virus strains [44] , mutants [45] , cell types [46] or stages of infection , may also contribute to the variable morphology observed in other viruses . In summary , our observations reconcile the contrasting models of filovirus budding and allow us to describe the sequence of events taking place during budding and release of MARV virus . We propose that this represents a general sequence of events also followed by other filamentous and rod-shaped viruses . Furthermore , we demonstrate that virus shape is determined both by viral and cellular factors . The model for filamentous virus budding presented here raises a number of new questions . Do elements of the cytoskeleton or other cellular components play a specific role in mediating envelopment or budding of filamentous or spherical virus particles ? Is one end of the PM-associated NC specifically able to initiate the extrusion of the filament , as in rhabdoviruses , or is the directionality of the extrusion process random ? What is the organisation of the NC and VP40 matrix layers in the released virion ? What kind of arrangement of interactions between NC and VP40 occurs in the wrapping process during bud extrusion ? These and other questions must be addressed in future structural studies , and may have wider implications for the budding and assembly of filamentous viruses .
The HUH-7 human hepatoma cell line and Vero cells were maintained in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 10% fetal calf serum , l-glutamine and penicillin–streptomycin at 37°C under 5% CO2 . All work with infectious MARV was performed under BSL-4 conditions at the Institute of Virology in Marburg . The MARV Leiden strain , isolated in 2008 in Leiden , the Netherlands [47] , was propagated in Vero E6 cells and purified as described previously [48] . HUH-7 cells were infected with MARV with a multiplicity of infection of approximately 1 plaque-forming unit per cell for one to four days . At the indicated time points , cell culture supernatants were collected and used for viral infectivity assays ( see below ) , or viruses in the supernatants were purified by centrifugation over a sucrose cushion and fixed for 48 hours ( h ) with 4% paraformaldehyde in PBS for analysis of particle morphology ( see below ) . For EM and ET of infected cells , HUH-7 cells were grown on carbon-coated sapphire disks , infected as above and cell monolayers were fixed at the indicated time points in the cell culture dish with 4% paraformaldehyde/0 . 1% glutaraldehyde in 0 . 1M PHEM buffer ( 60 mM PIPES , 25 mM HEPES , 2 mM MgCl2 , 10 mM EGTA ) , pH 6 . 9 for 30 min , after which the fixative was replaced with 4% paraformaldehyde in 0 . 1M PHEM . Fixed cells were removed from the BSL-4 lab after 48 h of inactivation . Infectivity of MARV particles released into the supernatant of cells 1 to 4 days p . i . was assayed by a 50% tissue culture infective dose ( TCID50 ) assay: Vero cells were grown in 96-well plates to 30 to 40% confluence . Cells were inoculated in quadruplicate with 10-fold serial dilutions of supernatants of HUH-7 cells infected with the MARV Leiden strain for one to four days as described above . The assays were evaluated at 10 days p . i . . TCID50 values were calculated using the Spearman-Karber method [49] . Equal volumes of each supernatant were separated by SDS-PAGE followed by quantitative immunoblotting on a LiCor Odyssey system using a mouse monoclonal anti-NP antibody , and secondary antibodies , protocols and software ( Odyssey version 2 . 0 ) provided by the manufacturer . TCID50 values were normalized to NP levels detected in each supernatant , and the TCID50 value in the supernatant collected at day 2 p . i . was set to 100% . For quantification of virus morphology at different time points p . i . , fixed cell culture supernatants were purified by centrifugation over a sucrose cushion , pelleted for 30 min at 40 , 000 g , 4°C in a Beckman ultracentrifuge using a TLA-55 rotor . Pellets were embedded in 12% gelatin and prepared for EM as described previously [50] . 70 nm cryosections were obtained with a Leica EM UC6 microtome , FC6 cryochamber ( Leica Microsystems , Wetzlar , Germany ) and a diamond knife ( Diatome , DiS-Galetzka Weinheim , Germany ) . Thawed cryosections were counterstained with uranyl acetate as described elsewhere [51] . For EM and ET of infected cells , HUH-7 cells were grown on carbon-coated sapphire disks , infected and fixed as described above . Samples were high-pressure frozen with a BalTec HPM-010 and freeze substituted with 0 . 1% ( w/v ) uranyl acetate , 1% osmium tetroxide ( w/v ) and 5% ( v/v ) water in glass distilled acetone in a temperature-controlling device ( Leica EM AFS I ) . Cells were kept for 40 h at −90°C and warmed up to 0°C ( slope 5°C/h ) with an additional 3 h infiltration period at −30°C . Samples were washed three times with glass distilled acetone , infiltrated at room temperature with increasing concentrations of epoxy resin ( Glycidether 100 , Roth , Karlsruhe , Germany ) in acetone over 12 h , and polymerized at 60°C for 48 h . 150 nm and 300 nm sections were obtained with a Leica Ultracut UCT microtome and a diamond knife . Thin sections of virus and infected cells were examined on a FEI Morgagni 268 TEM equipped with a 1K side mounted CCD camera ( SIS , Muenster , Germany ) . Quantification of cell morphology and cell-associated virus morphology was carried out on 150 nm resin-embedded sections in a systematic random sampling manner [52] . Starting points for sampling were chosen randomly on each grid , and all grids were examined in the same systematic manner . At least three grid squares per EM grid and three EM grids per time point were sampled . Morphology of viruses from cell culture supernatants was quantified by EM in the same systematic random sampling manner on thawed 70 nm cryosections of virus pellets , purified and embedded as described above . At least 200 particles from three different grids were evaluated per sample . ET was carried out essentially as described elsewhere [53] . Dual axis tilt series from 300 nm sections were recorded on a FEI TECNAI TF30 microscope operated at 300kV ( 4K FEI Eagle camera; binned pixel size 0 . 77 nm or 1 nm on the specimen level ) over a −60° to 60° tilt range ( increment 1° ) and at a defocus of −0 . 2 µm . Tomograms were reconstructed using the IMOD software package ( version 3 . 12 . 20 ) [54] . 3D measurements of virus end distance , analysis of virus end morphology and 3D surface renderings were carried out using the AMIRA Visualisation Package ( version 5 . 2 . 0 , Visage Imaging , Berlin , Germany ) . In total , 68 3D reconstructions from three independent infection experiments were analysed , of which 48 of high image quality were used for detailed analysis and measurements . NCs in different samples displayed variable contrast , which resulted from differential access of electron-dense stain to individual cells and different subcellular regions during sample preparation . NC representations in Figures 2 and 4 and Video S1 and Video S2 were generated and displayed using the AMIRA EM package [55] and MatLab ( version 7 . 4 . 287 ) .
|
The filoviruses , Marburg and Ebola , cause lethal hemorrhagic fever and are highest-priority bioterrorism agents . Filovirus particles contain a rod-like nucleocapsid and are normally filamentous , though other shapes are seen . It is poorly understood how such large filamentous particles are assembled and released from infected cells . Here we have studied Marburg virus production in infected cells using electron tomography . This technique allows virus particles to be visualized in three dimensions at different stages during assembly . We find that in early stages of virus production , highly infectious filamentous viruses are produced , whereas after prolonged infection poorly infectious spherical viruses are released . We also define the sequence of steps in filamentous virus release . The intracellular nucleocapsid first travels to the plasma membrane of the cell , where it binds laterally along its whole length . One end is then wrapped by the plasma membrane and wrapping proceeds rapidly until the virus protrudes vertically from the cell surface . The rear end of the virus particle then pinches off from the cell . We propose that other important filamentous and rod-shaped viruses also follow this series of steps of assembly and budding .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/virion",
"structure,",
"assembly,",
"and",
"egress",
"cell",
"biology/morphogenesis",
"and",
"cell",
"biology",
"cell",
"biology",
"virology",
"infectious",
"diseases/viral",
"infections"
] |
2010
|
Electron Tomography Reveals the Steps in Filovirus Budding
|
Infection with the intracellular protozoan parasite Leishmania mexicana causes chronic disease in C57BL/6 mice , in which cutaneous lesions persist for many months with high parasite burdens ( 107–108 parasites ) . This chronic disease process requires host IL-10 and FcγRIII . When Leishmania amastigotes are released from cells , surface-bound IgG can induce IL-10 and suppress IL-12 production from macrophages . These changes decrease IFN-γ from T cells and nitric oxide production in infected cells , which are both required for Leishmania control . However , antibodies targets and the kinetics of antibody production are unknown . Several groups have been unsuccessful in identifying amastigote surface proteins that bind IgG . We now show that glycoinositol phospholipids ( GIPLs ) of L . mexicana are recognized by mouse IgG1 by 6 weeks of infection , with a rapid increase between 12 and 16 weeks , consistent with the timing of chronic disease in C57BL/6 mice vs . healing in FcγRIII-deficient mice . A single prominent spot on TLC is recognized by IgG , and the glycolipid is a glycosyl phosphatidylinositol containing a branched mannose structure . We show that the lipid structure of the GIPL ( the sn-2 fatty acid ) is required for antibody recognition . This GIPL is abundant in L . mexicana amastigotes , rare in stationary-phase promastigotes , and absent in L . major , consistent with a role for antibodies to GIPLs in chronic disease . A mouse monoclonal anti-GIPL IgG recognizes GIPLs on the parasite surface , and induces IL-10 from macrophages . The current work also extends this mouse analysis to humans , finding that L . mexicana-infected humans with localized and diffuse cutaneous leishmaniasis have antibodies that recognize GIPLs , can bind to the surface of amastigotes , and can induce IL-10 from human monocytes . Further characterization of the target glycolipids will have important implications for drug and vaccine development and will elucidate the poorly understood role of glycolipids in the immunology of infections .
Leishmania is an intracellular protozoan parasite that causes 2 million new infections yearly and is a major cause of death worldwide [1] . Drug toxicity and the development of resistance have made leishmaniasis an ever-challenging set of diseases [2] , [3] , [4] . While a vaccine is likely the best way to deal with leishmaniasis , development has been hampered by our lack of understanding of factors needed to induce long-lasting cell-mediated immunity . Infections in which antibodies are protective , caused by bacteria such as Streptococcus pneumoniae , and many viral infections such as hepatitis B , have yielded successful vaccines [5] , [6] . However , Leishmania are able to hide from antibodies in an intracellular location . When Leishmania amastigote stages , found in the mammalian host , are released from the cell to parasitize new host cells , the parasite is bound by antibodies and utilizes mechanisms to prevent lysis by complement [7] , [8] . In fact , not only are antibodies not helpful , they can be pathogenic [9] , [10] , [11] . The immune response to the better-studied L . major infection is well explained by the Th1/Th2 paradigm , with IFN-γ-associated Th1 responses being protective and IL-4-associated Th2 responses leading to susceptibility . Non-healing infections such as those caused by L . mexicana complex parasites do not fit well into this explanation [12] . Mice that lack IL-4 ( a key cytokine of Th2 responses ) have chronic infection with L . mexicana and L . amazonensis [13] , [14] , and mice that lack IL-12 ( the master cytokine driving Th1 responses ) are just as susceptible as wild-type B6 mice to L . mexicana [15] . We therefore looked for other explanations and found that that IL-10 is required for chronic disease with L . mexicana infection [14] . C57BL/6 ( B6 ) mice lacking IL-10 resolve infection with a protective IFN-γ response . IL-10 exerts multiple immunosuppressive functions such as decreasing antigen presentation to T cells , decreasing IL-12 production and inhibition of iNOS ( with nitric oxide being a required factor for killing of the parasite ) [16] . In addition , cell surface receptors for IgG , termed FcγRs , are required for chronic disease caused by L . mexicana complex parasites [9] , [14] . In particular we have shown a requirement for FcγRIII [16] and IgG1 [11] . The parasite is thus able to suppress the protective Th1 IFN-γ immune response through an IgG-FcγR pathway , utilizing the host's IgG response . Leishmania have a wide array of glycolipids called glycosyl phosphatidylinositols ( GPIs ) as membrane components . Many proteins such as the promastigote surface protease , gp63 , are inserted into the plasma membrane by GPI anchors rather than through trans-membrane protein domains ( Fig . 1 ) . The surface of the insect vector stage of the parasite ( the promastigote ) is covered with lipophosphoglycan ( LPG ) , which consists of a GPI core with a very large phosphoglycan repeat structure ( Fig . 1 ) . Small non-protein bound GPI molecules called glycoinositol phospholipids ( GIPLs ) are the most abundant glycolipids on the surface of the amastigote ( the mammalian host stage ) , and are potential antibody targets . EPiM3 is the most abundant GIPL in L . mexicana and likely is the molecule recognized by the mouse serum IgG , or is closely related to it in structure . EPiM3 has three mannose residues in a branched configuration [17] and is an isomer of the well-described glycolipid A from African trypanosomes , which has three linear mannose residues and a different lipid composition . Glycolipid A is the free-GPI precursor to the anchor of the variant surface glycoprotein ( VSG ) ; VSG is responsible for antigenic variation . The structures of Leishmania GIPLs have been determined for L . mexicana and several other species [17] , [18] , [19] . This analysis has shown species-specificity with L . mexicana , but not L . major , having ethanolamine-containing GIPLs [17] , [19]; and by contrast , L . major , but not L . mexicana , having terminal galactose residues on GIPLs [20] . It has been shown that humans with cutaneous leishmaniasis have antibody responses to promastigote GIPLs [21] , [22] , although analysis of antibody recognition of amastigote GIPLs has not previously been performed . It is clear that IgG in the serum of infected mice binds the surface of L . mexicana amastigotes and that this IgG can induce IL-10 from macrophages in vitro [16] and suppress IL-12 [23] , [24] . Many groups , using sensitive metabolic labeling methods , failed to find surface proteins on Leishmania amastigotes that are recognized by antibodies , despite prominent IgG responses to the parasite surface [17] , [25] , [26] . Abundant surface proteins such as gp63 , PSA2 , and mPPG found on the infective insect stage of the parasite ( metacyclic promastigote ) are downregulated in the mammalian host stage ( amastigote ) , as is LPG [17] , [27] . As promastigotes disappear within a few days of the sandfly bite , IgG responses to these promastigote molecules are not relevant to the chronic aspects of Leishmania infection , which involves only the amastigote stage of the parasite . When rabbits were infected with L . mexicana amastigotes , they produced antibodies that bind the surface of amastigotes . These surface-binding antibodies do not recognize proteins but instead bind to GIPLs [17] . In these studies , EPiM3 ( see Fig . 1 ) , the predominant amastigote GIPL ( 63% of GIPLs ) , was strongly recognized , and was the only parasite-derived glycolipid bound by rabbit IgG . EPiM3 is quite abundant , with roughly 2×107 molecules per amastigote [17] . Unfortunately , the understanding of disease and the tools needed to study the immunology of leishmaniasis are not yet available for rabbit infection . We therefore decided to use our well-characterized mouse model to investigate whether IgG specifically recognizes the GIPLs on L . mexicana amastigotes , and to confirm that these particular antibodies can bind to FcγR to induce IL-10 . We also began the process of identifying aspects of the structure of the immunodominant GIPL needed for antibody recognition . In addition we extended this work by determining that humans infected with L . mexicana also harbor anti-GIPL antibodies that can induce IL-10 from monocytes .
Female C57BL/6 mice ( 4–6 wks old ) were obtained from Jackson Laboratories ( Bar Harbor , ME ) . Sera were obtained from tail bleeds of mice anesthetized with isofluorane at various times post-infection , or from terminal bleeds at experiment termination . Sera were stored at −20°C before use . L . mexicana ( MNYC/BZ/62/M379 ) , L . major ( MHOM/IL/80/Friedlin ) , and L . amazonensis ( LTB004 ) promastigotes were grown at 27°C in Grace's medium ( pH 6 . 3 ) supplemented with 20% heat-inactivated FBS , 2 mM L-glutamine , 100 U/ml penicillin , and 100 µg/ml streptomycin . For infections , stationary-phase Leishmania promastigotes ( day 7 ) were washed three times in PBS and 5×106 parasites ( in 50 µl DMEM ) were injected into the hind footpad of mice . Stationary-phase promastigote cultures of L . mexicana or L . amazonensis ( day 7 ) were incubated at 33°C for 3 days to generate axenic amastigotes . Axenic amastigotes were passaged every 7–10 days at 1/100 into acidic Grace's medium ( pH 5 . 5 ) supplemented as above . “Washed membranes” were prepared from axenic amastigotes by hypotonic lysis as described previously [16] . Briefly , axenic amastigotes were washed in PBS and then hypotonically lysed at 109/ml in endotoxin-free water containing 0 . 1 mM N-tosyl-L-lysine-chloromethyl ketone ( TLCK; Sigma-Aldrich , St . Louis , MO ) and 1 µg/ml leupeptin ( Sigma-Aldrich ) for 5 min on ice . Then an equal volume of 0 . 1 mM TLCK , 1 µg/ml leupeptin , 20% glycerol was added and parasites were frozen at −80°C . Thawed lysate was washed in PBS ( 6100 g , 10 min , 4°C ) to remove soluble proteins and protease inhibitors , and resuspended at 109 cell equivalents/ml in PBS . This lysate was assayed for protein content by the bicinchoninic acid method ( Pierce/Thermo Fisher Scientific , Rockford , IL ) and brought to 1 mg/ml protein , aliquoted , and stored at −80°C . For some experiments , intracellular amastigotes devoid of mouse-derived surface antibodies were generated by infecting IC21 macrophages ( ATCC , VA ) . IC21 cells were grown in 300 ml RPMI 1640 supplemented with 10% heat-inactivated FBS , 2 mM L-glutamine , 100 U/ml penicillin , and 100 µg/ml streptomycin , 10 mM HEPES , 1 mM sodium pyruvate in 1900 ml tissue culture flasks ( T300; Becton Dickinson Labware , CA ) and were infected with L . mexicana stationary-phase promastigotes ( 2×108 parasites: 4 . 5×107 cells; MOI 4 . 4 ) for 4 days at 33°C with parasite isolation by disruption of the cells ten times through a 23G needle . Parasites were then washed , counted , and frozen at −80°C . L . mexicana lesion amastigotes were isolated from footpads of infected C57BL/6 mice by soaking the feet in 70% ethanol , chlorhexadine , and 70% ethanol ( 5 min each ) followed by removal of the skin and grinding the footpad with a tissue grinder in PBS with 200 U/ml penicillin , and 200 µg/ml streptomycin . Debris was removed with a low speed spin ( 5 min at 50 g ) and parasites were washed three times at high speed ( 15 min at 1900 g ) . Amastigotes were frozen at 5×107/ml in RPMI-1640 supplemented with 10% heat-inactivated FBS and 7 . 5% DMSO at −80°C and stored in liquid nitrogen . Leishmania GIPLs were purified as described by Winter et al [17] . Briefly , Leishmania ( promastigotes or amastigotes ) were washed in PBS and the pellet was extracted with chloroform∶methanol∶water ( 1∶2∶0 . 8 , v/v ) to precipitate proteins , and water was added to the supernatants to extract with chloroform∶methanol∶water ( 4∶8∶5 . 6 ) . The upper methanol/water-rich phase was dried under nitrogen and re-extracted using a Folch extraction with chloroform∶methanol∶10 mM KCl ( 8∶4∶3 ) ; the GIPLs partition into the lower chloroform/methanol-rich phase . Samples were dried under nitrogen and resuspended in methanol for TLC analysis . Bloodstream form Trypanosoma brucei glycosyl phosphatidylinositols ( GPIs; a kind gift of Dr . Kojo Mensa-Wilmot ) were extracted using chloroform∶methanol∶water ( 10∶10:3 ) , followed by extraction into the n-butanol phase of n-butanol/water ( 1∶1 ) . Anti-GIPL ELISAs were performed by drying 2×107 cell equivalents of GIPL extract onto PVC 96-well plates ( Becton Dickinson Labware ) . Plates were blocked with 5% newborn calf serum in PBS . Samples were applied and washes were performed using PBS/0 . 05% tween-20 . Biotinylated anti-mouse IgG1 and anti-IgG2a/c ( BD Bioscience , CA ) were used with detection by streptavidin-peroxidase ( Jackson ImmunoResearch , PA ) and ABTS substrate [2 , 2′-azino-bis ( 3-ethylbenzthiazoline-6-sulphonic acid ) ] . Uninfected control serum values were subtracted from infected serum values . Biotinylated 26H3T-B4 ( a mouse IgG1 mAb ) was detected with streptavidin-peroxidase with PBS control optical density subtracted from all results . Leishmania-specific IgG1 and IgG2a/c were assayed by ELISA using axenic amastigote “washed membranes” for capture , with detection as above for GIPL ELISAs . For human IgG ELISAs , biotin-goat anti-human IgG ( Jackson ImmunoResearch ) was used for detection . Standard methods were used to fuse splenocytes from an L . mexicana-infected B6 mouse with Sp2/0 cells ( ATCC ) to form hybridomas . Hybridoma supernatants were screened by ELISA using axenic amastigote “washed membranes” as capture reagent , and further screened using the GIPL ELISA . Positive clones were recloned at least twice by limiting dilution in 96-well plates . Monoclonal antibodies were prepared by growing cells in DMEM supplemented with 10% heat-inactivated ultralow IgG FBS ( Invitrogen , CA ) , 25 mM HEPES ( pH 7 . 4 ) , 50 µM 2-mercapto-ethanol , 2 mM L-glutamine , 100 U/ml penicillin , 100 µg/ml streptomycin , 1X non-essential amino acids ( Invitrogen ) , 1 . 14 mM oxaloacetate , 0 . 46 mM sodium pyruvate , and 8 µg/ml rHuman insulin ( Invitrogen ) in bioreactor bags ( Biovectra , Canada ) . IgG was precipitated using ammonium sulfate , dialysed against PBS , and purified with rProtein G agarose chromatography ( Upstate Biotech , NY ) according to the manufacturer's suggestions . IgG concentration was determined using the bicinchoninic acid method ( Pierce/Thermo Fisher Scientific ) . 26H3T-B4 mAb was biotinylated with a 25 molar excess of sulfo-NHS-LC-biotin ( Pierce/Thermo Fisher Scientific ) in PBS for 4 hours at room temperature and then separated from unreacted reagent using a spin concentrator ( 50 kDa cutoff; Vivascience , Germany ) with the equivalent of 1 . 2×106-fold dilution with PBS . Thin layer chromatography ( TLC ) was performed by spotting samples ( 2×107–2×108 cell equivalents ) on pre-baked silica gel HPTLC plates ( J . T . Baker , NJ ) with separation using chloroform∶methanol∶28%NH4OH∶1M NH4OAc∶water ( 180∶140∶9∶9∶23 ) in a pre-incubated glass TLC tank . Immunoblot techniques were modified from [28] . Dried plates were plasticized with 0 . 1% polyisobutylmethacrylate in hexane for 60 sec . , then dried , blocked by spraying with 1% bovine serum albumin ( BSA ) in water , and then layered with 1% BSA/PBS . Washes were performed using PBS . Appropriate dilutions of serum ( approx . 1/4 , 000 in PBS ) were layered on the plate in a humidified box , and antibodies were detected using alkaline phosphatase-labeled goat anti-mouse IgG ( Jackson ImmunoResearch ) and then Western Blue substrate ( Promega , WI ) . GIPL bands that reacted with mouse IgG appear purple and are detected using a flatbed scanner ( HP Scanjet 4370 ) . To visualize glycolipids , two TLC plates were run in parallel , and one plate was stained using 0 . 2% orcinol in water∶ethanol∶conc . H2SO4 ( 1∶15∶2 ) , which makes mauve spots when baked at 115°C for 15 min . Orcinol stained plates were also scanned on a flatbed scanner . Purified L . mexicana amastigote GIPLs were digested with four enzymes under the following conditions: 1 ) 360 U recombinant T . brucei GPI-PLC ( a generous gift of Dr . Kojo Mensa-Wilmot ) for 3 hours at 37°C in 50 mM Tris-HCl ( pH 8 . 0 ) , 5 mM Na2EDTA ( pH 8 ) , 1% Triton X-100 , and 1 M potassium glutamate , with an additional 360 U added with further digestion overnight [29]; 2 ) human GPI-PLD ( 8 µl human serum ) at 37°C for 2 . 5 hours in 50 mM Tris-HCl ( pH 7 . 4 ) , 10 mM NaCl , 2 . 6 mM CaCl2 , and 0 . 1% Triton X-100 , followed by 8 µl more human serum incubated for 2 . 5 hours , and a final 8 µl added with incubation overnight [30]; 3 ) jack bean α-mannosidase ( added as washed ammonium sulfate suspension , 50 U/ml final; Sigma-Aldrich , MO ) , with samples dissolved in 100 mM sodium acetate ( pH 5 . 0 ) , 0 . 1% taurodeoxycholate and incubated overnight at 37°C followed by repeat overnight digestion with the same amount of enzyme [30]; or 100 U bee venom phospholipase A2 ( PLA2 , Sigma-Aldrich ) in 25 mM HEPES pH 7 . 4 , 1 mM CaCl2 incubated for 3 hours at 37°C , with a second digestion as before [31] . After digestion , the GIPLs were partitioned into n-butanol [butanol phase of butanol/water ( 1∶1 ) ] before separation by TLC . To be sure that GIPLs do not run aberrantly on TLC due to residual detergent , samples were back extracted with water , and mock digestions were performed without enzyme , as a control . With PLA2 digestion , the aqueous phases were purified using a Sep-pak plus C18 reverse-phase syringe cartridge ( Waters Corp . , MA ) as described previously [31] . Briefly , the cartridge was washed with 10 ml methanol followed by10 ml water , and then the aqueous GPI-containing sample ( 400 µl ) was slowly applied . The cartridge was washed with 5 ml water , and then the GPIs were eluted with 5 ml methanol . Mouse IgG on the surface of amastigotes was measured using PE-rat anti-mouse IgG1 ( BD Bioscience ) , PE-goat anti-mouse IgG ( Invitrogen ) , and FITC-rat anti-mouse IgG2a ( BD Bioscience ) . Staining was performed for IgG1 and IgG2a/c in different tubes to avoid the need for compensation , and to remove the possibility of fluorescence overlap . When axenic amastigotes were opsonized ( 4°C , 30–60 min in 50 µl volume of IgG-containing serum dilution ) , surface IgG was similar to that of lesion-derived amastigotes . Human IgG was detected using PE-anti-human IgG ( BD Bioscience ) . Flow cytometry was acquired on a FACSCaliber or FACSCanto flow cytometer ( BD Biosciences ) and analyzed with CellQuest Pro ( BD Biosciences ) or FlowJo ( Tree Star , Inc . , OR ) on an Apple Macintosh computer . For monoclonal antibody binding to axenic amastigotes , one million parasites were incubated with antibody at varying concentrations in triplicate . Serum samples from Mexican patients infected with L . mexicana were generously provided by Dr . Ingeborg Becker of the Facultad de Medicina , Universidad Nacional Autónoma de México in Mexico City , Mexico . Four samples for each group were tested from localized cutaneous leishmaniasis ( LCL ) , a disseminated skin disease called diffuse cutaneous leishmaniasis ( DCL ) , and endemic controls . Anonymous random human serum that was excess material discarded from clinical samples from the Philadelphia Veterans Affairs Medical Center was used as “PA nl” and gave results similar to those of the endemic controls . Mouse bone marrow macrophages were prepared and infected at 10∶1 ( parasites: cells ) as previously described [16] . Axenic amastigotes were opsonized for 30 min . on ice with serum from infected mice , or other antibody preparations , and washed before infection of macrophages . Lipopolysaccharide ( LPS ) from Escherichia coli 0111:B4 ( Sigma-Aldrich , MO ) was added at 100 ng/ml . Purified anti-IL-10R ( 1B1 . 3a , a generous gift from DNAX ) was added to cultures at 9 µg/ml . Human blood monocytes , provided by the University of Pennsylvania Center For AIDS Research Immunology Core , were prepared by countercurrent elutriation ( >90% pure ) , rested overnight in 24-well plates at 2 . 5×105/well , and stimulated with LPS and/or opsonized parasites in the same manner as mouse macrophages . IRB approval was obtained to use these de-identified human cells . For murine macrophages and human monocytes , cells were incubated for 20 hours , supernatants were frozen , and then were assayed for IL-10 after thawing . All in vitro macrophage/monocyte stimulations were performed in quadruplicate . Supernatants were assayed for IL-10 by ELISA using an antibody pair ( for mouse IL-10 ) or a human IL-10 BDOptEIA kit ( for human IL-10 ) according to the manufacturer's recommendations ( BD Bioscience ) . Macrophage and monocyte cultures were incubated in quadruplicate with means and SE shown for all four . The kinetics of anti-GIPL antibody production was quantitated as above , with means and SE shown for 5–13 mice per group . Human serum experiments were performed with 4 patients each of uninfected Mexican controls , LCL , and DCL patients with mean and SE shown for the group . All experiments were performed at least twice with representative data shown . Monoclonal antibody binding to axenic amastigotes was performed in triplicate with geometric mean fluorescence intensities calculated and presented as mean and SE . A student T test was used to compare groups of mice , groups of patients , or replicate samples , with P values shown and P<0 . 05 considered significant . Animal studies were reviewed and approved by the Institutional Animal Care and Use Committee of the Philadelphia Veterans Affairs Medical Center and of the University of Pennsylvania , and were carried out in strict accordance with the “Guide for the Care and Use of Laboratory Animals” of the National Academy of Sciences , as well as all other U . S . government federal guidelines . Human serum samples and human random donor monocytes were de-identified , and therefore the protocol was deemed exempt from requirements for informed consent by the Institutional Review Boards of the Philadelphia Veterans Affairs Medical Center ( FWA #00001311 ) and the University of Pennsylvania ( FWA #00004048 ) . The IRB of the Universidad Nacional Autónoma de México in Mexico City , Mexico also approved the serum collection and use .
We have shown by ELISA that at early times there is an IgG1 response and later an IgG2a/c response in B6 mice to parasite antigens ( freeze-thawed Ag ) . Here we showed that this same kinetics occur with amastigote surface-binding IgG . Strong IgG1 and negligible IgG2a/c are seen at 9 wks post-infection , but both IgG1 and IgG2a/c to surface epitopes are present late in infection ( 27 wks ) ( Fig . 2A ) . The geometric mean fluorescence intensities ( GMFI ) are shown ( Fig . 2B ) , with significant differences between 9- and 27-wk IgG2a/c binding , but no difference in IgG1 binding at the two time points . We have already shown that IgG , when bound to L . mexicana amastigotes , can induce IL-10 from macrophages stimulated with lipopolysaccharide [16] , and that IL-10 is required for chronic disease caused by infection with this parasite [14] . Using methods previously described , we extracted a class of glycolipids called glycosyl phosphatidylinositols from L . mexicana amastigotes [17] and bound these to a polyvinyl chloride plate for use in an ELISA . We found that serum from L . mexicana-infected mice had IgG1 that specifically bound L . mexicana GIPLs , and that there was minimal binding from the serum of L . major-infected mice ( Fig . 3A ) . No anti-GIPL IgG was found in uninfected mice . A similar ELISA also detected anti-GIPL IgG2a/c in the serum of L . mexicana-infected mice , but at later times in infection ( not shown ) . Note that these IgG-recognized glycolipids will be referred to as GIPLs , as they have a GPI structure ( see below for more detailed structural analysis ) . We found that the GIPL reactivity of serum from 28 wk-infected mice was highly positively correlated with their parasite loads ( R2 = 0 . 97 , Fig . 3B ) . This could either be due to anti-GIPL antibodies having a pathogenic effect and leading to poorer control of parasites , or conversely a consequence of higher parasite loads inducing higher IgG titers . To examine this further , we serially bled mice throughout an infection and looked for correlations between parasite loads at both 16 wks and 29 wks with various parameters such as lesion size , and IgG1 and IgG2a/c directed against GIPLs and washed membranes . Note that this infection had larger variation in parasite burdens than usual ( for unknown reasons ) and was used because greater variation allowed for comparisons not possible if parasite burdens were very similar in all mice . We found that the anti-GIPL IgG1 response at 16 wks correlated well with the parasite load in the same mouse at 29 wks of infection ( R2 = 0 . 80 , Fig . 3C ) . Anti-GIPL IgG1 at 16 wks did not closely correlate with lesion size at 16 wks ( R2 = 0 . 25 , Fig . 3D ) , indicating that anti-GIPL antibodies and inflammation in the lesion are not closely tied . Because of the temporal relationship , we concluded that anti-GIPL IgG1 responses earlier in infection could potentially cause increased parasite burdens later in infection due to IL-10 suppression of a Th1 response . New data indicate that L . mexicana parasite loads do not increase early in infection , but only increase later once the IgG1-induced IL-10 is present ( Jude Uzonna , University of Manitoba , personal communication ) . Furthermore , there was no correlation between parasite burdens ( at 29 wks ) and IgG1 directed at L . mexicana amastigote “washed membranes” or between parasite burdens and IgG2a/c responses to GIPLs or “washed membranes” ( at 16 or 29 wks , data not shown ) . This implies that IgG1 antibodies to GIPLs may be more important in determining parasite loads ( through the FcγR-IL-10 pathway ) than antibodies directed against other parasite components , such as proteins , and more important than IgG2a/c antibodies to GIPLs or other parasite molecules . This is consistent with the hypothesis that there is a strong IgG1 antibody response directed against GIPLs , but not against surface proteins , presumably because surface proteins are masked in some way . Because GIPLs are present on the amastigote surface , whereas protein targets of antibodies may reside within the parasite , antibodies to GIPLs , but not proteins , can efficiently induce IL-10 through the FcγR pathway . When mice were bled throughout a course of L . mexicana infection , we found that IgG1 recognition of GIPLs increased rapidly between 10 and 20 wks of infection ( Fig . 4 ) , consistent with the time when chronic lesions plateau in size ( B6 mice ) rather than resolving in strains of mice that heal ( IL-10 KO and FcγRIII KO mice ) [17] , [27] . IgG2a/c that recognized GIPLs was also present ( Fig . 4 ) . Next we separated the GIPLs by thin layer chromatography ( TLC ) and probed with anti-sera from L . mexicana-infected mice . We found that a single spot was recognized in L . mexicana axenic amastigote GIPL extract and to a much lesser extent in stationary-phase promastigotes ( Fig . 5 ) . L . mexicana-infected mouse serum did not recognize GIPLs from promastigotes of L . major ( Fig . 5A ) , nor GIPLs from L . major amastigotes derived from IC-21 cells ( data not shown ) . Uninfected mouse serum gave no binding to amastigote GIPLs in several similar experiments ( data not shown ) . Staining with orcinol also demonstrated the strongly recognized GIPL molecule ( s ) in addition to other spots ( Fig . 5A ) . These additional spots are also glycolipids , but they are not recognized by antibodies in the infected mouse sera . On immunoblot , GIPLs from lesion amastigotes had similar reactivity to GIPLs from axenic amastigotes ( Fig . 5B ) . We next digested the L . mexicana glycolipids with enzymes that cleave glycosyl phosphatidylinositol ( GPI ) structures specifically , namely trypanosome GPI-PLC and human serum GPI-PLD , and found that binding of the immunodominant glycolipid by antibodies was abolished ( Fig . 6 ) . As these enzymes , which are GPI-specific , cleave the molecule of interest ( with the glycan lost into the aqueous phase which is not run on the TLC ) , the molecule in question must have a GPI structure , and therefore is a GIPL . See Fig . 1 for structures and enzymatic cleavage points . We found that Trypanosoma brucei glycolipids , such as glycolipid A ( an isomer of EPiM3 , see Fig . 1 ) , are not recognized by the anti-serum ( Fig . 6 ) . Glycolipid A , unlike EPiM3 , has a linear tri-mannose structure , so we decided to determine if a branched mannose is present in the recognized L . mexicana GIPL and is an important part of the recognized epitope . Digestion of the GIPLs with jack-bean α-mannosidase , which cleaves terminal α-linked mannose residues , resulted in a shift to a less hydrophilic ( greater relative mobility ) GIPL species , which is recognized by the anti-amastigote serum ( Fig . 6 ) . Therefore , the immunodominant GIPL has a branched mannose structure like EPiM3 . This experiment also shows that at least some reactivity is independent of the branched mannose structure , as both the original ( labeled A ) and de-branched ( labeled B ) structures are recognized by IgG . To help determine if the lipid portion of the GIPL is required for antibody binding , we digested L . mexicana amastigote GIPLs with bee venom phospholipase A2 ( PLA2 ) , which specifically cleaves the sn-2 fatty acid from glycolipids . Binding of the immunodominant GIPL by antibodies was greatly diminished by PLA2 digestion ( Fig . 7 ) , although digestion was incomplete . As lyso-glycolipids ( ones with only one fatty acid or fatty alkyl group and a free hydroxyl ) are significantly less hydrophobic than ones with two lipid moieties , and depending on the hydrophobicity/chain length of the lipids , can be lost in the aqueous phase during butanol-water partitioning [31] , we isolated the glycolipids from the aqueous phase using a Sep-pak C18 reverse phase syringe column [31] . Despite this maneuver , there was no antibody reactivity in the aqueous phase . This indicated either that the fatty acid itself is involved in binding , or at least that the conformation of the binding site on the glycolipid depends on the sn-2 fatty acid . In order better to assess the pathogenic nature of antibodies to GIPLs , we developed monoclonal antibodies that can bind GIPLs . They were first screened for the ability to bind L . mexicana “washed membranes” by ELISA and then were tested for GIPL binding . A representative IgG1 mAb ( 26H3T-B4 ) was found that efficiently binds the surface of amastigotes in a dose-response manner ( Fig . 8A ) , indicating that the GIPL target is accessible on the parasite surface . A mouse IgG1 isotype-control mAb did not bind the surface of amastigotes , with similar values to the no-antibody control . In addition , when biotinylated , this mAb bound GIPLs very well in an ELISA ( Fig . 8B ) , also demonstrating a dose-response relationship . We further demonstrated that the IgG1 mAb , when bound to amastigotes , induced IL-10 from murine bone marrow-derived macrophages ( Fig . 8C ) . Unopsonized parasites and those opsonized with uninfected B6 mouse serum showed no difference in IL-10 induction [11] . Thus antibodies to GIPLs can induce an IL-10 response from macrophages . Note that macrophages do not secrete much IL-10 when stimulated with immune complexes alone [32] . LPS is a convenient second signal , which is known not to induce much IL-10 in the absence of immune complexes . In vivo , it is likely that Leishmania-derived toll-like receptor ligands are important . For example the L . mexicana complex parasite L . pifanoi has a proteoglycolipid complex which , like LPS , acts through toll-like receptor 4 [33] . Human infection with L . mexicana can cause localized cutaneous leishmaniasis ( LCL ) , a disseminated skin disease called diffuse cutaneous leishmaniasis ( DCL ) , and very infrequently visceral disease or mucocutaneous disease . Although high antibody titers are seen in visceral leishmaniasis , antibody titers in cutaneous leishmaniasis are not generally sensitive , with visualization of parasites from skin scrapings or biopsies being the main diagnostic tool [34] . In order to determine if human infection leads to anti-GIPL antibodies , we obtained human sera from Mexican patients with L . mexicana infection and LCL or DCL . We analyzed these sera using the GIPL ELISA and found that patients with LCL , and to a greater extent those with DCL , have antibodies to GIPL molecules ( Fig . 9A ) . Background values from uninfected sera may represent low levels of natural IgG antibodies or non-specific binding . We also bound the sera from DCL and LCL patients to L . mexicana amastigotes and found that IgG was detectible on the parasite surface ( Fig . 9B and C ) , with all uninfected controls ( both endemic controls and one random serum sample from Pennsylvania ) having lower mean fluorescence ( Fig . 9D ) . Geometric mean fluorescence was significantly greater in DCL and LCL patients than in controls , but these groups were not significantly different from each other ( Fig . 9E ) . Thus L . mexicana-infected people have antibodies that bind the parasite surface and that bind GIPLs in an ELISA format . Similar results were obtained from true L . mexicana amastigotes derived from the IC21 macrophage cell line as with axenic amastigote-like forms ( data not shown ) . Human blood monocytes were stimulated with LPS and infected with L . mexicana axenic amastigotes that were opsonized with human sera from leishmaniasis patients , either with DCL or LCL . IL-10 production was higher ( P<0 . 05 ) when parasites were opsonized with all DCL sera and all LCL sera as compared with the mean value for four Mexican control sera or a Pennsylvania control serum ( Fig . 10 , right panel ) , although in one LCL patient this difference was small . Thus human IgG from both types of cutaneous leishmaniasis patients , when bound to amastigotes , can stimulate IL-10 production from human cells . No IL-10 was seen when cells were unstimulated and when infection with LCL-opsonized amastigotes was not accompanied by LPS stimulation . This extends what we have seen with mouse macrophages to the human disease state .
Antibodies are known to have a detrimental role in Leishmania infection . IgG bound to parasites can induce IL-10 from macrophages , and likely other cell types , via FcγRIII . This IL-10 , in turn , suppresses the immune response to the parasite by directly decreasing iNOS expression and thereby decreasing nitric oxide , which is needed for parasite clearance . In addition IL-10 down-regulates the Th1-associated IFN-γ response , which is required to activate infected cells to make nitric oxide and kill parasites . We have recently shown that IgG1 , in particular , is pathogenic in vivo in B6 mice , whereas IgG2a/c is not [11] . There have been some important gaps in our understanding of this mechanism . In particular it has been challenging to determine what the targets of these pathogenic antibodies are . Whereas much is known about surface proteins ( such as gp63 , PSA2 , and mPPG ) and a glycoconjugate , LPG , on the promastigote stage of the parasite , these are down-regulated when L . mexicana transforms into the intracellular amastigote stage . Because promastigotes rapidly disappear from the mammalian host , whereas antibodies do not appear for at least 4–6 wks of infection , antibodies to amastigote surface molecules are likely the most relevant in this protracted chronic infection . Several groups have failed to find surface proteins on the amastigote stage of the parasite that bind antibodies , and that could be responsible for initiating this pathway . Winter and colleagues identified a GIPL , EPiM3 ( see Fig . 1 for structure ) , that is abundant on the L . mexicana amastigote surface and that is recognized by rabbit antibodies that bind the amastigote surface [17] . We therefore investigated whether GIPLs are important ligands of antibodies in our well characterized mouse model . Using flow cytometry , TLC immunoblot , and an ELISA with unfractionated GIPLs , we have now shown that antibodies from L . mexicana-infected mice bind the surface of L . mexicana amastigotes and specifically recognize L . mexicana GIPLs . Presumably these antibodies can access parasites when the latter are released from the macrophage . In fact amastigotes isolated from footpad lesions are coated with antibodies , probably from the lesion fluid ( data not shown ) . There is species specificity , as antibodies from L . major-infected mice do not bind these same GIPLs , and sera from L . mexicana-infected mice do not recognize L . major GIPLs . The kinetics of appearance of these anti-GIPL antibodies is consistent with the generation of chronic disease in B6 mice , as compared with healing seen in IL-10 KO and FcγRIII KO mice . We used the variation in parasite loads we occasionally see in infections to demonstrate that there is a direct correlation between anti-GIPL IgG1 responses ( but not IgG2a/c responses ) and parasite burdens . In fact anti-GIPL IgG1 levels measured at earlier times predict higher parasite loads later in infection , suggesting that the anti-GIPL antibodies may induce higher parasite loads through IL-10 , rather than merely being caused by the higher parasite burdens . Direct evidence of pathogenicity of these antibodies is currently being studied . We have identified a number of properties of the immunodominant GIPL molecule ( s ) . The main recognized glycolipid has a GPI structure as it is digested by GPI-PLC and GPI-PLD , enzymes that are specific for GPI structures . In addition , like EPiM3 , but unlike trypanosome glycolipid A , the immunodominant GIPL has a terminal mannose present , likely part of a branched , rather than linear , tri-mannose structure . However , the molecule generated by jack bean α-mannosidase ( which removes terminal mannose residues ) , is still recognized by some , if not all of the antibodies in the polyclonal serum , and thus this branched mannose is not needed for IgG recognition . In addition , recognition was abolished by PLA2 digestion , which removes the sn-2 fatty acid . Others have found that the binding of proteins to glycolipids can depend on the lipid portion of glycolipids and on the local lipid environment . E . coli verotoxins ( VT1 and VT2c ) bind to a glycosphingolipid , globotriaosylceramide b3 ( Gb3 ) , with different affinities based on the fatty acid length on the ceramide , with VT1 and VT2c having different optimal lengths of fatty acid chains [35] . Furthermore the length of lipid chains and degree of unsaturation , as well as the surrounding phospholipid environment ( sphingomyelin/cholesterol vs . dipalmitoylphosphotidylcholine/cholesterol liposomes ) can alter binding of polyclonal antibodies to glycolipids such as cerebroside sulfate [36] , [37] . It is believed that the “exposure” of the glycan and its conformation may be determined by an interaction between the lipid portion of the glycolipids and the lipid bilayer or liposome environment . Thus our findings that the PLA2-digested GIPL ( a lyso-GIPL ) no longer bound antibodies ( Fig . 7 ) extends this body of knowledge of the interaction between antibodies and glycolipids and demonstrates that the recognition process is more complex than just carbohydrate recognition . Furthermore , we have generated mAbs to GIPLs . In particular an anti-GIPL-specific IgG1 ( 26H3T-B4 ) is able to bind isolated GIPLs and the surface of amastigotes . This mAb , when bound to amastigotes , induces IL-10 from mouse macrophages . This helps demonstrate the plausibility that anti-GIPL antibodies are important in the pathogenesis of L . mexicana disease in mice and will aid in future studies to directly show pathogenicity of anti-GIPL antibodies . We have also found that humans develop antibodies that recognize the surface of amastigotes and GIPLs when infected with L . mexicana , whether they have DCL or LCL . Furthermore human monocytes secrete IL-10 in response to immune complexes consisting of these human antibodies and L . mexicana amastigotes . This shows the potential relevance of our mouse studies to human disease . This opens the door to further analysis of the human response to GIPLs , and generates several important questions: Does human IgG recognize the same GIPL structures as mice ? Can we block this interaction and change the course of disease with a competing small molecule ? In mice , IgG1 is pathogenic but IgG2a/c appears not to be , and may be protective [11] . By generating a vaccine that induces competing antibody isotypes that are not pathogenic , can we protect against or treat L . mexicana infection ?
|
Leishmania mexicana is a single-celled parasite that causes chronic skin disease in humans and mice . Antibodies on the surface of parasites lead to the production of a protein called interleukin-10 ( IL-10 ) , which blocks an effective immune response needed to kill parasites and resolve skin lesions . In mice , IL-10 is required to maintain chronic , non-healing lesions . Parasite surface targets of these antibodies have not been identified . Using biochemical and immunologic techniques , we have shown that antibodies bind to parasite surface glycolipids ( molecules with sugars that are anchored to the membrane by lipids ) , rather than to protein targets . We have determined some basic structural features of these glycolipids and shown that antibodies to them bind the surface of parasites and can induce IL-10 from mouse cells . We have extended this work to humans by showing that people infected with this parasite also make antibodies that bind to these glycolipids and to the surface of parasites , and that can induce IL-10 from human white blood cells . Further characterization of these glycolipids may have important implications for the development of a drug or vaccine for this and related parasite infections , and may shed light on poorly understood immunologic pathways by which glycolipids induce antibody responses .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"host-pathogen",
"interaction",
"biology",
"microbiology"
] |
2013
|
Leishmania mexicana Infection Induces IgG to Parasite Surface Glycoinositol Phospholipids that Can Induce IL-10 in Mice and Humans
|
RNA polymerase I ( Pol I ) synthesizes ribosomal RNA ( rRNA ) in all eukaryotes , accounting for the major part of transcriptional activity in proliferating cells . Although basal Pol I transcription factors have been characterized in diverse organisms , the molecular basis of the robust rRNA production in vivo remains largely unknown . In S . cerevisiae , the multifunctional Net1 protein was reported to stimulate Pol I transcription . We found that the Pol I-stimulating function can be attributed to the very C-terminal region ( CTR ) of Net1 . The CTR was required for normal cell growth and Pol I recruitment to rRNA genes in vivo and sufficient to promote Pol I transcription in vitro . Similarity with the acidic tail region of mammalian Pol I transcription factor UBF , which could partly functionally substitute for the CTR , suggests conserved roles for CTR-like domains in Pol I transcription from yeast to human .
RNA polymerase I ( Pol I ) transcribes the precursor for three out of the four ribosomal RNAs ( rRNAs ) , which are essential components of ribosomes , required for cell growth and proliferation . In proliferating cells of S . cerevisiae ( hereafter called yeast ) , Pol I activity accounts for over 50% of the cellular transcriptional activity [1] . Robust rRNA production is supported by repetitive gene arrays at one or multiple chromosomal locations , collectively called the ribosomal DNA ( rDNA ) loci ( see Fig 1 upper panel , yeast rDNA ) . Furthermore , the specialized Pol I transcription machinery , including dedicated transcription factors , promotes efficient rRNA synthesis ( reviewed in [2 , 3] ) . In the past , many factors supporting Pol I transcription in vivo and in vitro have been identified in various organisms . However , it is still an open question how the observed high transcriptional output is mechanistically achieved . Basal mechanisms of Pol I transcription might be conserved since there appears to be a significant degree of similarity even between distantly related species . Both , mammalian and yeast promoters are arranged in two main domains including a core element ( CE , Fig 1A ) , required for transcription initiation in vitro , and an upstream ( control ) element ( UE in yeast ( Fig 1A ) , UCE in mammals ) , supporting activated transcription under certain conditions ( reviewed in [3] ) . Whereas promoter DNA sequences are unrelated , components of CE-binding complexes , the yeast core factor ( CF ) and the human Selectivity Factor 1 ( SL1 ) share functional and structural homology [4–7] . CF and SL1 are required to recruit the conserved initiation competent Rrn3-Pol I complex to the rDNA promoter [8 , 9] . Stable recruitment of CF to the rDNA promoter in yeast depends on the 6-subunit upstream activation factor ( UAF ) binding to the UE [10] . In analogy , the high mobility group ( HMG ) -box protein upstream binding factor ( UBF ) interacts with the UCE and stabilizes the SL1 complex at mammalian rDNA promoters [11 , 12] . UBF has , however , no reported homology with UAF components . Apart its function in Pol I pre-initiation complex ( PIC ) formation , UBF can bind the entire rDNA region transcribed by Pol I presumably to maintain an open chromatin structure [13 , 14] . In yeast , the HMG-box protein Hmo1 is a component of open rDNA chromatin [15 , 16] , and genetic data suggests functional conservation between distinct HMG-boxes in human UBF and Hmo1 [17] . Regulation of Pol I transcription by post-translational covalent modifications of proteins has been extensively described and characterized in mammals ( reviewed in [18 , 19] ) . Thus , the polymerase , Rrn3 , SL1 and UBF are targets of phosphorylation or acetylation , and many factors either attaching or removing specific modifications marks have been reported . Post-translational modifications occur in response to intra- or extracellular signals mainly implicated in the regulation of cell growth and proliferation . Like in higher eukaryotes , yeast ribosome biogenesis responds to different growth conditions ( reviewed in [20] ) . However , for this organism detailed knowledge about post-translational modifications regulating the activity of the basal Pol I transcription machinery is rather limited . Recently , yeast phosphatase Cdc14 , the orthologue of mammalian Cdc14B , has been proposed to downregulate Pol I transcription by de-phosphorylation of a polymerase subunit in late anaphase of the cell cycle [21] . This differs from observations in higher eukaryotes , where Cdc14B phosphatase activity is required for re-activation of Pol I transcription at the end of mitosis [22] . Likewise , both , the yeast NAD-dependent lysine deacetylase Sir2 , and its mammalian homologue SIRT1 have been differentially implicated in the regulation of rDNA transcription . While the mammalian SIRT1 protein deacetylates components of the Pol I transcription machinery [23] , yeast Sir2 rather targets RNA polymerase II ( Pol II ) transcription in rDNA repeats likely by acting on acetylated histones [24 , 25] . Therefore , although post-translational covalent modifications by homologous factors might influence rDNA transcription in yeast and mammals , it is unclear if the regulation by the respective modification marks is conserved . In yeast , Cdc14 and Sir2 associate together with the nucleolar protein Net1 , forming the “REgulator of Nucleolar silencing and Telophase” ( RENT ) complex [26–28] . Cdc14 and Sir2 interaction interfaces have been mapped to the N-terminal and central parts of full-length Net1 , respectively ( Fig 1B ) [29–31] . By interacting with Cdc14 , Net1 inhibits its phosphatase activity and sequesters the protein in the nucleolus . Presumably triggered by specific phosphorylation events Net1 releases Cdc14 in late anaphase . Cdc14 release in turn , leads to de-phosphorylation of nuclear and cytoplasmic substrates , which facilitate completion of anaphase , cytokinesis , and progression into G1 [32–36] . Net1 acts likely as a recruiting factor for Sir2 , to establish rDNA silencing of Pol II transcription within the intergenic sequence 1 and 2 ( IGS1+2 , Fig 1A , middle panel , also named NTS1+2 , for non-transcribed spacers 1 and 2 , in the literature ) [27 , 37–39] . Accordingly , main association sites for the RENT complex have been mapped to the 35S rDNA promoter region in IGS2 , and to the replication fork barrier sequence ( RFB ) in IGS1 at the 3’ end of the 35S rRNA gene ( Fig 1A ) [37] . RENT association with the RFB depends on the fork blocking protein Fob1 , presumably through interaction with the Net1 N-terminus [37 , 40] . Efficient association with the 35S rDNA promoter , instead , requires UAF [41] . A presumably RENT complex-independent function of Net1 in activating Pol I transcription was reported more than 15 years ago [42] . Thus , temperature sensitivity of net1 mutants , in which rRNA production was impaired , could be suppressed by overexpression of the Pol I initiation factor Rrn3 . Additionally , recombinant purified Net1 stimulated Pol I transcription in vitro . The molecular basis for these observations , however , remains far from being understood . In this study , we aimed at investigating how Net1 stimulates Pol I transcription . Strikingly , we discovered that the C-terminal 138 amino acids of Net1 harbored the Pol I transcription stimulating function . The C-terminal region ( CTR ) activated Pol I transcription outside the context of the full-length Net1 protein in vivo and was sufficient to stimulate Pol I transcription in vitro . The identification of the CTR of yeast Net1 as a Pol I transcription activator made it possible to discover similarities of this protein domain with the acidic tail region of the human Pol I transcription factor UBF . Our results are discussed in the light of apparent conservation of the Pol I transcription machineries in distantly related organisms .
We established haploid yeast strains , expressing a series of Net1 truncation mutants C-terminally fused to GFP from the endogenous NET1 locus ( Fig 2A , cartoon on the right , S1A Fig , western blot analysis ) . These yeast strains were subjected to growth analyses on solid medium and in liquid cultures ( Fig 2A and 2B; S1 Dataset ) . A strain expressing full-length Net1 protein fused to GFP grew as well as a NET1 wild-type strain ( Fig 2B and S1B Fig , compare NET1 with net-GFP ( 1–1189 ) ) , indicating that the GFP tag likely did not interfere with important functions of the protein . C-terminal truncation of the Net1 sequence by only 138 amino acids led to a significant slow-growth phenotype ( Fig 2A and 2B , compare net1GFP ( 1–1051 ) with net-GFP ( 1–1189 ) ) . Additional truncation affecting the integrity of the Sir2-binding domain of Net1 increased the doubling time ( Fig 2B , net1GFP ( 1–693 ) , ( 1–455 ) , and ( 1–341 ) ) . A strong growth defect , indistinguishable from that of a NET1 deletion strain , was observed when the C-terminal truncation affected the Cdc14-binding domain ( Fig 2A and 2B , compare net1GFP ( 1–233 ) with net1Δ ) . This suggested that the last 138 amino acids of Net1 ( hereafter referred to as C-terminal region , CTR ) harbor an important growth-supporting function . A mutant Net1 protein lacking the CTR ( hereafter referred to as Net1ΔCTR ) still contains the regions important for Net1 binding to Cdc14 and Sir2 in the context of the RENT complex ( Fig 1B ) [29–31] . In good agreement , both FLAG-tagged full-length Net1 ( Net1FLAG ) and Net1ΔCTR ( Net1ΔCTRFLAG ) fusion proteins co-precipitated Myc-epitope-tagged Cdc14 and Sir2 ( Cdc14myc , Sir2myc ) with similar efficiencies from whole-cell extracts ( Fig 2C , compare lanes 4 with 5 , and 10 with 11; S1C Fig , full membrane ) . As expected , FLAG antibody-mediated precipitation of Myc-tagged Cdc14 , or Sir2 , was not observed when whole-cell extracts were prepared from strains that did not express FLAG fusion protein ( Fig 2C , lanes 6 and 12 ) . We then examined if the CTR was required for the reported nucleolar localization of Net1 [26–28] . Live cell imaging of haploid and diploid yeast strains co-expressing Net1GFP fusion proteins and the nucleolar marker protein Nop56 fused to mCherry ( Nop56mCherry ) confirmed co-localization of both proteins in the crescent shaped yeast nucleolus ( Fig 2D and 2E ) . Net1ΔCTRGFP showed clear nucleolar localization in a diploid yeast strain expressing a NET1 wild-type allele ( Fig 2F ) . This strain was not compromised in growth ( not shown ) , indicating that the net1ΔctrGFP allele had no dominant negative effect . A haploid strain expressing Net1ΔCTRGFP was impaired in growth ( Fig 2A and 2B ) , and showed abnormal cell morphology ( Fig 2G , DIC ) , and de-localization of Nop56mCherry over the whole nucleus ( Fig 2G , mCherry ) . However , a subpopulation of Nop56mCherry preferentially co-localized with Net1ΔCTRGFP within a subnuclear compartment , likely the remainder of the nucleolus ( Fig 2G , GFP and merge ) . To analyze if the CTR is an independent functional domain , we tested whether expression of the CTR in trans could rescue the phenotypes observed in a net1Δctr strain . A cassette for constitutive overexpression of the CTR N-terminally fused to GFP ( GFPCTR ) under the control of the TEF2 promoter was stably integrated in the LEU2 locus . Co-expression of GFPCTR in trans fully restored wild-type growth in net1Δctr and net1 ( 1–455 ) strains , and largely suppressed the growth defect of a net1Δ strain ( Fig 3A and 3B; S1 Dataset ) . This indicated that the CTR and the first 455 amino acids of Net1 , containing the Cdc14 binding region ( but lacking the Sir2 interacting domain ) are sufficient to promote normal cell growth . Live cell imaging showed a preferential nucleolar localization of GFPCTR in all strains , although the overexpressed fusion protein also spread all over the cell ( Fig 3C and 3D; S2B and S2C Fig , compare panels mCherry and GFP ) . In net1 ( 1–455 ) and net1Δctr strains , overexpression of GFPCTR restored normal cell morphology as well as nucleolar localization of Nop56mCherry ( Figs 2G and 3D; S2A and S2B Fig compare panels DIC , and mCherry , respectively ) . However , cellular morphology was still significantly altered in a net1Δ strain overexpressing GFPCTR , in good correlation with the residual growth defect ( S2C Fig , DIC ) . As observed for expression of GFPCTR , expressing the CTR in fusion with a N-terminal FLAG-tag ( FLAGCTR ) from a chromosomally integrated cassette suppressed the growth defect of net1Δctr strains ( S2D Fig; S1 Dataset ) . Overexpression of the CTR did not affect the expression levels of Net1ΔCTR ( S2E Fig ) . Net1 and other RENT components associate with two rDNA regions , the 35S rDNA promoter region and the RFB at the 3’-end of the 35S rRNA gene [37] . To investigate if deletion of the CTR influenced this binding pattern , we performed Chromatin Endogenous Cleavage ( ChEC ) [43] and Chromatin Immuno-Precipitation ( ChIP ) analyses . Haploid yeast strains expressing either wild-type Net1 or Net1ΔCTR in fusion with micrococcal nuclease ( MN ) and a hemagglutinin ( HA ) epitope ( Net1-MNHA , ΔCTR-MNHA ) were created . For ChEC analyses , crude nuclei isolated from exponentially growing formaldehyde-crosslinked cells were treated with calcium to activate the MN . Cleavage events of the MNHA-fusion proteins were mapped to the indicated genomic regions by Southern blot analysis using the indirect end-labeling technique [44] . Net1-MNHA fusion protein mediated cleavages were observed both at the 35S rDNA promoter and at the RFB in good correlation with earlier results ( Fig 4A , lanes 1–4 and 9–12 ) [41] . Net1ΔCTR-MNHA-mediated cuts were strongly reduced at the 35S rDNA promoter region , whereas robust cleavages were still observed at the RFB ( Fig 4A , lanes 5–8 and 13–16 ) . In good agreement , Net1-MNHA but not Net1ΔCTR-MNHA co-precipitated 35S rDNA promoter fragments from cellular extracts in ChIP experiments ( Fig 4B , compare ChIP of fragment 2 , cartoon at the bottom for location of ChIP fragments in yeast rDNA ) . In contrast , co-precipitation of RFB fragments with Net1ΔCTR-MNHA was unaltered or even slightly enhanced , when compared to ChIP with Net1-MNHA ( Fig 4B , compare ChIP of fragments 5 , 6 ) . As expected , no significant co-precipitation of rDNA fragments bearing the 5S rDNA , 18S rDNA , and 25S rDNA regions with the fusion proteins was observed ( Fig 4B , ChIP of fragments 1 , 3 , 4 ) . Thus , it can be concluded that the CTR is required for interaction of Net1 with the 35S rDNA promoter but does not affect Net1 association with the RFB , which is mediated by Fob1 [37] . In good correlation , Fob1 association with the RFB was unaltered or even slightly increased in net1Δctr strains when compared with NET1 strains ( S3A and S3B Fig , compare lanes 9–12 with 13–15 , compare ChIP of fragments 5 , 6 ) . Cdc14 and Sir2 have rDNA association patterns very similar to that of Net1 [37 , 41 , 45] . Accordingly , Cdc14-MNHA mediated ChEC in a NET1 wildtype strain led to cuts within the 35S rDNA promoter region and at the RFB , in good correlation with enrichment of these regions in ChIP experiments ( Fig 4C , lanes 1–4 , and 13–16; Fig 4D , ChIP of fragments 2 , 5 , 6 ) . When expressed in a net1Δctr strain , Cdc14-MNHA-mediated cleavages at the 35S rDNA promoter after ChEC were strongly reduced and the co-precipitation of this region in ChIP experiments was abolished ( Fig 4C , lanes 5–8; Fig 4D , ChIP of fragment 2 ) . In contrast , ChEC and ChIP indicated that the interaction of Cdc14 with the RFB was very similar to that observed in the NET1 strain ( Fig 4C , lanes 17–20; Fig 4D , ChIP of fragments 5 , 6 ) . As for Cdc14-MNHA , Sir2-MNHA-mediated cleavages within the 35S rDNA promoter region in a net1Δctr strain were reduced when compared to cleavage events observed in a NET1 strain ( S3C Fig , upper panel , compare lanes 1–4 with 5–8 ) . As opposed to Cdc14-MNHA , however , Sir2-MNHA mediated cuts at the RFB in a net1Δctr strain were reduced , when compared with cleavages observed in a NET1 strain ( S3C Fig , lower panel , compare lanes1-4 with 5–8 ) . The impaired association of RENT complex components with the 35S rDNA promoter could be causative for the growth defect observed in the net1Δctr strain . Ectopic expression of FLAGCTR in net1Δctr strains restored wild-type growth ( Fig 3A and 3B ) . Thus , it was tested if the interaction of Cdc14 with the 35S rDNA promoter could be re-established in this condition . ChEC and ChIP experiments were carried out in a net1Δctr strain expressing Cdc14-MNHA from the endogenous locus and the FLAGCTR from a chromosomally integrated cassette . Expression of FLAGCTR resulted neither in significant Cdc14-MNHA-mediated cleavage at the 35S rDNA promoter in ChEC experiments , nor did it lead to co-precipitation of 35S rDNA promoter fragments in ChIP ( Fig 4C , lanes 9–12; Fig 4D , ChIP of fragment 2 ) . Co-expression of FLAGCTR did not affect Cdc14-MNHA association with the RFB ( Fig 4C , lanes 21–24; Fig 4D , ChIP of fragments 5 , 6 ) . In agreement with the assumption that Cdc14 is recruited to the rDNA via its interaction with the N-terminus of Net1 , very similar results were obtained in ChEC experiments with a FLAGCTR strain expressing Net1ΔCTR-MNHA ( S3D Fig , lanes 5–8 ) . The above experiments suggested , that the CTR plays a RENT complex-independent role supporting cellular growth , presumably by interacting with the 35S rDNA promoter . We therefore tested if the CTR harbors the Pol I transcription stimulating function of Net1 . Haploid yeast strains carrying a NET1 or a net1Δctr allele and co-expressing MNHA fusion proteins of the Pol I subunits Rpa43 or Rpa190 were subjected to ChEC and ChIP experiments . ChEC analyses revealed that Rpa43-MNHA and Rpa190-MNHA cleavage events at the 35S rDNA promoter region and at the 35S rDNA transcription termination site upstream of the RFB were reduced in net1Δctr strains , when compared to cleavages in NET1 strains ( Fig 5A compare lanes 1–4 with lanes 5–8 , and lanes 17–20 with lanes 21–24 ) . This pointed to a lower occupancy of Pol I molecules within the 35S rRNA gene region . ChIP experiments strongly supported this finding , since co-precipitation of 35S rDNA fragments with the tagged Pol I subunits was substantially impaired in net1Δctr strains when compared with ChIP in NET1 strains ( Fig 5B , ChIP of fragments 2–4 ) . Importantly , ChEC and ChIP experiments indicated that expression of FLAGCTR in trans could fully restore Pol I association with the 35S rDNA in net1Δctr cells to wild-type levels , in good correlation with the re-establishment of normal cell growth in these strains ( Fig 5A , compare lanes 9–12 , and 25–28 with lanes 13–16 , and 29–32 , respectively; Fig 5B , ChIP of fragments 2–4; S3 Table , doubling times ) . Since the CTR is required to recruit full-length Net1 to the 35S rDNA promoter ( Fig 4A ) , we further investigated if the isolated CTR expressed in trans may interact with this chromosomal region . ChEC experiments were performed in NET1 or net1Δctr strains , expressing the CTR with an N-terminal FLAG tag and C-terminally fused to MNHA ( FLAGCTR-MNHA ) , under the control of the TEF2 promoter from a cassette integrated in the LEU2 locus . As observed for GFPCTR and FLAGCTR , expression of the FLAGCTR-MNHA fusion protein suppressed the slow-growth phenotype of net1Δctr strains ( S3 Table ) . Similar FLAGCTR-MNHA-mediated cleavage events within the 35S rDNA promoter region were observed in both NET1 and net1Δctr strains ( Fig 6A , upper panels , lanes 1–4 , and 5–8 ) . FLAGCTR-MNHA-mediated cuts were also observed at the RFB and at further sites within IGS1 ( Fig 6A , lower panels , lanes 1–4 , and 5–8 ) . Additionally , FLAGCTR-MNHA-dependent cleavages were observed at accessible sites within intergenic sequences at other genomic loci , which were not cut by Net1-MNHA ( Fig 6B , lower panels , lanes 1–4 , and 5–12 ) . In accordance with the observed nuclear localization of GFPCTR ( Fig 3C and 3D; S2B and S2C Fig ) , ChEC in the FLAGCTR-MNHA-expressing strains eventually resulted in the complete degradation of genomic DNA , which did not occur in a Net1-MNHA-expressing strain ( Fig 6B , upper panels compare lanes 1–4 with lanes 5–12 ) . Thus , upon overexpression of the CTR , the protein is available for DNA interactions within the entire nucleus , including the 35S rDNA promoter . The above data are compatible with a model in which the CTR assists Pol I recruitment at the 35S rDNA promoter . To further test this hypothesis experimentally , we fused the FLAGCTR to the C-terminus of Rpa190 and the CF component Rrn7 in net1Δctr strains . Whereas expression of Rrn7-FLAGCTR could not significantly rescue the growth phenotype of net1Δctr strains , expression of Rpa190-FLAGCTR fully suppressed slow growth ( Fig 6C and 6D; S1 Dataset ) . The expression of CTR fusion proteins had no impact on growth in NET1 strains . This observation is in accordance with a Pol I-specific function of the CTR , but also indicates that simple tethering of the CTR to the 35S rDNA promoter may not be sufficient to obtain full stimulation of Pol I transcription . Previous work showed co-purification of Net1 with Pol I subunits [42 , 46] . Here , it was tested if the CTR of Net1 could associate with Pol I . Immuno-precipitation experiments using whole-cell extracts from a strain expressing Rpa43myc and FLAGCTR with an anti-Myc antibody showed enrichment of the Myc-tagged bait protein and co-precipitation of a minor sub-population of FLAGCTR in western blot analysis ( Fig 7A , lanes 1 and 3 ) . As expected , FLAGCTR was not precipitated from whole-cell extracts of strains expressing the untagged Rpa43 protein ( Fig 7A , lanes 2 and 4 ) . FLAGCTR in whole-cell extracts migrated as a diffuse band around 30 kDa in SDS-PAGE , despite an estimated molecular weight of 18 kDa . Net1 is known to be phosphorylated and several phosphorylation sites have been identified within the CTR in proteomic studies ( Fig 8A , asterisks mark described phosphorylation sites ) [32 , 47–50] . To investigate if the aberrant migration behavior of the CTR could be explained by phosphorylation , FLAGCTR was enriched from whole-cell extracts using an anti-FLAG antibody matrix . The immobilized protein was incubated in the absence or presence of different concentrations of lambda protein phosphatase ( λ PP ) . Western blot analysis revealed that the incubation with λ PP substantially increased the electrophoretic mobility of FLAGCTR ( Fig 7B , lanes 3–5; S4B Fig , full membrane ) suggesting that the CTR is phosphorylated in vivo . To investigate if the post-translational modification state of the CTR was subject to changes in vivo , whole-cell extracts were prepared from exponentially growing and stationary cells expressing FLAGCTR . Proteins were separated by SDS-PAGE and analyzed in a western blot . FLAGCTR in extracts from stationary cells had a higher mobility in SDS-PAGE than FLAGCTR in extracts from exponentially growing cells , suggesting alterations in the modification state ( Fig 7C , αFLAG , compare “exp” with “stat”; S4C Fig , full membrane ) . Ponceau-red staining of the immunoblot membrane indicated that the overall mobility of cellular proteins was not altered in the different extracts ( Fig 7C , Ponceau; S4C Fig ) . Ribosome biogenesis and Pol I transcription are downregulated when yeast cells grow to stationary phase [51 , 52] . Downregulation of Pol I transcription correlates with a decreased psoralen accessibility of the 35S rRNA gene region transcribed by Pol I [53–55] . As a control for downregulation of Pol I transcription in the above experiment , we subjected cells from the same cultures used for protein extraction to psoralen crosslinking analysis . In agreement with the expected alterations in Pol I transcription , psoralen accessibility of 35S rRNA genes was high in exponentially growing cells and negligible in stationary phase cells ( S4D Fig , compare lanes 4 and 5 ) . Recombinant full-length Net1 has been shown to stimulate promoter-dependent Pol I transcription in vitro [42] . We tested if the CTR could be sufficient to promote Pol I stimulation in a reconstituted system . The CTR was expressed in insect cells with a C-terminal tandem affinity purification ( TAP ) tag ( CTRTAP ) bound to IgG-sepharose and released as a calmodulin binding peptide ( CBP ) fusion protein ( CTRCBP ) by cleavage with tobacco etch virus ( TEV ) protease ( S4E Fig ) [56] . As observed for the CTR expressed in exponentially growing yeast ( Fig 7A–7C ) , purified recombinant CTRCBP with a predicted size of 20 kDa migrated with an apparent molecular weight of 32 kDa in SDS-PAGE ( Fig 7D; S4F Fig , lane1; S4E Fig , lane 3 ) . λ PP treatment prior to TEV-mediated release from IgG-Sepharose significantly increased the mobility of the recombinant protein in SDS-PAGE when compared with the mobility of the mock-treated CTRCBP , indicating that the protein was phosphorylated ( Fig 7D compare lane 1 with lane 2; S4F Fig , full gel ) . λ PP-treated and mock-treated CTRCBP were tested in promoter-dependent Pol I transcription in a minimal in vitro system consisting of Pol I purified from yeast and of recombinant Rrn3 and CF purified from E . coli [57] . The mock treated CTRCBP could stimulate promoter-dependent Pol I transcription in a concentration-dependent manner up to 6-fold , whereas the λ PP treated CTRCBP still led to a 2 to 3-fold stimulation ( Fig 7E , compare lanes 1 and 2 , with lanes 3–6 and lanes 7–10 ) . Additionally , recombinant full-length Net1TAP and Net1ΔCTRTAPfusion proteins were purified from insect cells ( S4G Fig ) and tested in parallel with another preparation of CTRCBP protein with and without λ PP treatment . The recombinant Net1CBP was the strongest activator of Pol I transcription , whereas Net1ΔCTRCBP still stimulated Pol I transcription to a similar extent as the purified CTRCBP ( S4H Fig ) . Thus , it is possible that Net1ΔCTR still partially supported Pol I transcription in vivo . In agreement with such hypothesis , residual Net1ΔCTR-MNHA-mediated cleavage at the 35S rDNA promoter could still be observed upon ChEC ( Fig 4A , lanes 5–8; S3D Fig , upper panel , lanes 5–8 ) . In contrast , ChEC analyses performed with cells from a Net1 ( 1–341 ) -MNHA expressing strain did not lead to significant cuts within the 35S rDNA promoter ( S3D Fig , upper panel , lanes 9–12 ) . Net1 ( 1–341 ) -MNHA mediated cleavage at the RFB region , instead , was comparable–although not identical—to cleavage mediated by Net1-MNHA or by Net1ΔCTR-MNHA ( S3D Fig , lower panels ) . Whereas the full-length Net1 is not conserved in higher eukaryotes , local sequence alignment using the tool “matcher” of the EMBOSS platform revealed 25% identical and 43 . 5% similar residues within the acidic tail region ( AR ) of human UBF1 Pol I transcription factor ( Fig 8A ) . An acidic serine/aspartate-rich sequence within the CTR showed the highest similarity with the UBF1 AR ( Fig 8A , underlined amino acids ) . Assuming , that the CTR is hyperphosphorylated within this sequence , the similarity between the two regions might be even greater . To test if the UBF1 AR could functionally substitute for the Net1 CTR , yeast strains were created expressing different derivatives of Net1 in fusion with MNHA ( Fig 8B ) . Full-length Net1 , Net1ΔCTR , or Net1 variants in which the CTR was replaced by the AR of human UBF1 , an N-terminally extended AR ( eAR ) , or a triplicated synthetic minimal VP16 activation domain ( VP ) , respectively were analyzed . The VP16 activation domain triggers high-level Pol II-dependent transcription when recruited to gene promoters by a specific DNA binding domain in higher eukaryotes [58] . The expression of the respective MNHA-fusion proteins in the above strains was verified by western blot analysis ( Fig 8C; S5A Fig , full membrane ) . Six independently obtained clones of each of these strains were subjected to growth analyses together with clones of a strain expressing an untagged Net1 protein as a reference control ( Fig 8D; S1 Dataset ) . Whereas all the strains expressing Net1 variants lacking the CTR grew slower than the Net1-MNHA or Net1-expressing strains , Net1ΔCTR-AR-MNHA and Net1ΔCTR-eAR-MNHA-expressing strains grew slightly but statistically significantly faster than Net1ΔCTR-MNHA-expressing strains . This indicated that the Net1-CTR and the UBF1-AR regions share a conserved function in promoting growth . In contrast , expression of Net1ΔCTR-VP-MNHA did not significantly rescue the net1Δctr growth phenotype in those experiments ( Fig 8D ) .
In this study we could attribute the Pol I stimulating function of the multi-functional yeast Net1 protein to its C-terminal 138 amino acids ( CTR ) . In good accordance with the presumed role of Pol I transcription in promoting cell proliferation , the CTR supported wild-type growth ( Figs 2A , 2B , 3A and 3B; S1 Dataset ) . The CTR was further required for normal cellular morphology and localization of the nucleolar protein Nop56 ( Figs 2G and 3D , S2A and S2B Fig ) . It is likely that the observed nucleolar de-localization of Nop56 in net1Δctr strains can be explained by impaired Pol I transcription . This would be in agreement with earlier observations showing that rRNA gene transcription by Pol I is an important determinant for proper localization of nucleolar components [59–61] . The nucleolar localization of Net1 correlates with its reported interaction with two different regions within the rDNA [37] . The CTR was dispensable for nucleolar localization of Net1 and for recruitment of Net1 and Cdc14 to the RFB element at the 3’-end of the 35S rRNA gene ( Figs 2G and 4; S3D Fig ) . The latter observations are in good agreement with the observation that only the N-terminal 341 amino acids of Net1 are needed for its interaction with Fob1 [62] , which in turn is needed for Net1 interaction with the RFB [37] . In contrast , the CTR was required for Net1 and Cdc14 association with the 35S rDNA promoter ( Fig 4; S3D Fig ) . Secondary structure prediction did not provide evidence for the presence of a DNA binding domain within the CTR , but rather suggested that this protein region might be intrinsically disordered ( S5B Fig ) . Thus , it is more likely that the CTR interacts with the promoter region in context of the DNA-bound protein ( RNA ) scaffold . Interestingly , interaction of Net1 with the 35S rDNA promoter was impaired in a uaf30Δ strain in which Pol I PIC formation is strongly affected [41] , indicating that Net1 may interact with PIC components . Candidate interaction partners could be the UAF subunits Rrn5 and Rrn10 , since they were found in a Cdc14 interactome study in which the entire RENT complex–including Net1—was purified [63] . Additionally , co-purification of many Pol I subunits with full-length Net1 was reported [37 , 42 , 64] , and here , co-purification of the CTR with the Pol I subunit Rpa43 was shown ( Fig 7A ) . A Pol I-related function of the CTR was further supported by the observation that the Pol I subunit Rpa190 in fusion with the CTR could suppress slow growth of net1Δctr strains ( Fig 6C and 6D ) . Net1 interaction with the 5’ end of the 35S rDNA is not restricted to the promoter region but extends into the external transcribed spacer 1 as revealed in ChEC and ChIP experiments ( Fig 4A; S3D Fig ) [37 , 41] . Such a trailing into the Pol I transcribed 35S rDNA resembles the association observed for the Pol I transcription factor Rrn3 [65] . Thus , the CTR might interact with promoter-bound factors and assist efficient PIC assembly , and possibly additional downstream events of the Pol I transcription process . The amino acid sequence analysis of the CTR revealed similarity with the AR of the human Pol I transcription factor UBF1 ( Fig 8A ) . However , it should be noted that the CTR amino acid sequence is of low complexity , mainly composed out of aspartate and serine residues . Additionally , we used the HHPred module from the free HH-suite software package [66 , 67] to detect homologous domains in other proteins which identified a very similar ( small ) set of RNA-binding proteins for both CTR and AR ( not shown ) . In our experiments , the AR of human UBF1 could at least partially substitute for the growth-supporting function of the CTR in the context of Net1 ( Fig 8D; S1 Dataset ) . However , the observed effects on growth were small . Accordingly , Net1- ( e ) AR-MNHA fusion proteins could not significantly restore cleavage at the 35S rDNA promoter in ChEC experiments ( S5C Fig ) . Several studies have provided evidence that the carboxy-terminus of UBF1 is important to support efficient Pol I transcription , probably by stabilizing the UBF1-SL1 complex at the rDNA promoter [68–70] . Interestingly , the AR of UBF1 is also required for proper nucleolar localization of UBF1 and preferentially localizes to the nucleolus when expressed in trans [71 , 72] , alike observations with the CTR of Net1 ( Fig 3C and 3D; S2 Fig ) . Phosphorylation of specific residues within the acidic tail have been suggested to increase UBF-SL1 interaction [38 , 39 , 73–75] , and UBF phosphorylation correlates well with active rRNA gene transcription in vivo [70 , 73 , 76–81] . Additionally , de-phosphorylation of UBF1 reduces its potential to stimulate promoter-dependent Pol I transcription in vitro [70 , 80] . Interestingly , a growth-dependent alteration in the post translational modification state could also be observed for the CTR , correlating with changes in Pol I transcription ( Fig 7C; S4C and S4D Fig ) . Furthermore , de-phosphorylation decreased CTR-mediated stimulation of Pol I transcription in vitro ( Fig 7E; S4H Fig ) . As a next step , it will be interesting to identify enzymes and pathways regulating the differential post-translational modification of the CTR . Two Cdc28 ( Cdk1 in human ) target sites reside within the CTR [32 , 48] , and many residues within the serine-rich region are predicted to be potential substrates for casein kinase 2 ( CK2; Fig 8A , predicted CK2 phosphorylation sites in bold [82 , 83] ) . Noteworthy , CK2 activity might have a function in the regulation of Pol I transcription [38 , 84 , 85] . The possibility that the CTR of Net1 functions in part similarly to the AR in UBF1 could point to further conserved mechanisms in Pol I transcription . As outlined in the introduction , the yeast HMG-box protein Hmo1 might also share functions with HMG-boxes 1 and 2 of UBF1 [17] . This could indicate that similar functional domains act in yeast and higher eukaryotes to support Pol I transcription but are organized in different polypeptides . Recent advances in structure determination of the Pol I transcription machinery have yielded great insights in the architecture of individual components as well as in the assembly of a minimal PIC ( reviewed in [86] ) . In the future , analyses using defined in vitro systems will further deduce structure-function relationships important to eventually understand the mechanism driving Pol I transcription . For faithful reconstitution of this process , it will be necessary to identify all of the participating factors in vivo . In this study , we identified another , likely conserved , component of the yeast Pol I transcription machinery .
Unless noted otherwise , standard techniques were used for cloning of plasmids , and transformation of yeast cells [87–89] . Information about oligonucleotides , plasmids , and yeast strains used in this study can be found in S1–S3 Tables , including details on plasmid and strain construction . Plasmid sequences are available upon request . For the individual experiments in this study , yeast cells were grown at the indicated temperatures in either YPD ( 2% w/v ) peptone , 1% ( w/v ) yeast extract , 2% glucose ) , YPAD ( YPD including 100mg/l adenine ) , or XYD medium ( YPD including 100mg/l adenine , 200mg/l tryptophan , and 10mM KH2PO4 ) . Exponentially growing cells were harvested by centrifugation , transferred to glass slides and covered with an agarose slice ( supplied with yeast nitrogen base , amino acids , and 2% glucose ) . Cells were observed at 20–22°C on an Axio Observer Z . 1 confocal spinning disk ( CSU-X1 ) microscope ( Yokogawa; Carl Zeiss ) using a Plan Apochromat 63×/1 . 40 oil differential interference contrast ( DIC ) M27 lens . Ten Z-stack images with an optical section spacing of 0 . 5 μm were acquired with a shutter speed of 200 ms using an AxioCam MRm camera ( Carl Zeiss ) . Microscopy data was processed using ImageJ ( National Institutes of Health , Bethesda , MD ) . Z-stacks of the region of interest ( fluorescence channels ) were projected using maximum-intensity projection . A single plane ( DIC channel ) was duplicated to visualize cell morphology . For ChEC and ChIP experiments yeast strains expressing different genes as fusion proteins with a C-terminal MN followed by a triple HA-tag from their endogenous genomic location were grown in YP ( A ) D at 30°C to a final OD600 of 0 . 5 . ChEC analyses were performed as previously described [15] . All DNA samples were digested with XcmI prior to agarose gel electrophoresis and Southern blot analysis ( see below ) . ChIP experiments were performed as described and analyzed by quantitative PCR [41] . Data was collected with a Rotor-Gene Q system ( Qiagen ) . The cycle threshold values and amplification efficiency ( E ) for a defined primer pair for all input and ChIP samples of an experimental dataset were determined with the Rotor-Gene 6000 software ( Qiagen ) using the comparative quantification method . The cycle threshold ( CT ) value correlates with the Rotor-Gene software “take-off” value ( tov ) . For each primer pair the efficiency ( E ) is calculated as an average from the fluorescence measurements for each individual sample . The percentage of co-precipitated DNA was determined by the formula %ChIP = ( ( 1+E ) tov ( ChIP ) / ( 1+E ) tov ( input ) ) *100 . Average and standard deviation errors for ChIP are derived from three independent ChIP experiments , each analyzed in triplicate qPCRs . The original qPCR data , including CT , “take off” and efficiency values , melting curve analysis , and mathematical operations are added as S2 Dataset . SDS–PAGE and Western blot analysis was performed according to standard procedures [90 , 91] . Gels were either stained with Coomassie blue for detection of proteins or transferred to nitro cellulose ( GE Healthcare ) or PVDF membranes ( Immobilon P , Roth ) for subsequent immuno-detection . Transfer to membranes was verified by staining with Ponceau S . S4 Table contains a complete list of antibodies used for detection . Secondary antibodies coupled to IRDye 800 were detected with an LI-COR Odyssey Infrared Imaging System ( LI-COR Bioscience ) . Secondary antibodies coupled to horse reddish peroxidase ( HRPO ) were visualized using BM Chemiluminescence Western Blotting Substrate ( POD , Roche ) , and a LAS-3000 Chemiluminescence Imager . Agarose gel electrophoresis and Southern blot analysis were performed as described [88] , with the exception that transfer of nucleic acids onto nylon membranes ( Positive Membrane , Qbiogen ) by capillary transfer was performed with 1 M ammonium acetate [92] . S5 Table contains a list of templates used for the synthesis of radiolabeled hybridization probes generated using the RadPrime DNA labeling system ( GE Healthcare ) . For preparation of native protein extracts yeast cells were grown overnight in XYD media at 25°C to an OD600 of 0 . 8 . For preparation of yeast whole-cell extracts ( WCE ) , cell equivalents corresponding to 10–20 OD600 were harvested by centrifugation ( 805 rcf , 2 min ) and washed once with ice-cold water . Lysis buffer ( 150 mM NaCl ( for IPs with Net1FLAG-fragments ) or 350 mM KCl ( IPs with Rpa43myc ) , 50 mM Tris-HCl pH 7 . 5 , 50 mM NaF , 5 mM EDTA , 0 . 1% IGEPAL CA-630 , 60 mM β-glycerol phosphate ) was added to yield a final volume of 150 μl cell suspension . After addition of 200μl of glass beads cells were lysed by shaking the mixture for 5 min at 4°C in a mixer mill ( Retsch ) . After removal of cell debris by two consecutive centrifugation steps of 10 min and 15 min ( 16000 rcf , 4°C ) , equal volumes of supernatant and 2× SDS-sample buffer were mixed and incubated for 10 min at 100°C . For immuno-precipitation of Myc-epitope tagged RNA polymerase I subunits , WCEs ( adjusted to a total volume of 450μl ) were incubated with anti-Myc antibodies ( clone 9E10 ) for 2h at 4°C . Thereafter , 50 μl of protein A–agarose slurry ( Santa Cruz Biotechnology Inc . ) were added , and the mixtures were incubated on a rotator for 2 h at 4°C . For immuno-precipitation of FLAG-tagged Net1-fragments , WCEs ( adjusted to a total volume of 450 μl ) were incubated with 45μl of α-FLAG-beads ( Sigma-Aldrich ) on a rotator for 3 h at 4°C . Beads were collected by centrifugation and washed three times with 500 μl lysis buffer . After the last washing step , beads were resuspended in 1x SDS sample buffer and incubated for 10 min at 100°C . After immuno-precipitation of FLAG-tagged CTR as described above , beads were collected by centrifugation and washed twice with 1ml of lysis buffer ( without β-glycerol phosphate ) and once with λ-phosphatase buffer ( 50 mM HEPES-KOH pH 7 . 5 , 5 mM DTT , 2 mM MnCl2 ) . Beads were split in different samples and treated in the absence ( control ) , or presence of 200 , 400 , and 2000 units of λ-phosphatase ( New England Biolabs ) in a total volume of 50μl λ-phosphatase buffer , and samples were incubated for 1 h at 37°C . Beads were collected by centrifugation and washed twice with 1 ml λ-phosphatase buffer . After the last washing step , beads were resuspended in 1x SDS-sample buffer and incubated for 10 min at 100°C . All samples were analyzed by western blot as described above . The data presented in Figs 2C , 7A and 7B are representative for at least three independent experiments . Single colonies of yeast strains were used to inoculate 3 ml of YPAD which were incubated for 2 days at 30°C under shaking . This culture was used to inoculate YPAD media to an OD600 not higher than 0 . 1 . Samples for denaturing protein extraction or trimethyl psoralen crosslinking analyses were withdrawn when the culture reached an OD600 of 0 . 5 ( exponential phase ) or 144 hours after this time point ( stationary phase ) . For protein analysis cell equivalents of 0 . 5–3 OD600 , were subjected to protein extraction as described elsewhere [93] . For trimethyl-psoralen crosslinking analysis , cell equivalents of 25 OD600 were treated as previously described [15] . All DNA samples were digested with EcoRI prior to agarose gel electrophoresis and Southern blot analysis ( see above ) . Purification of 6x histidine-tagged Rrn3 , or 6x histidine-tagged core factor from E . coli , as well as TAP-tagged RNA Pol I from yeast have been described previously [57] . Recombinant expression of different Net1-TAP fusion proteins in baculovirus infected S . frugipedia SF21 cells was achieved according to a protocol described in [94–96] . For TAP-tag mediated purification , 50x106 cells from a large scale infection were suspended in 30 ml TAP-lysis buffer ( 50 mM HEPES-KOH pH 7 . 5 , 2 . 5 mM Mg-acetate , 0 . 001% ( w/v ) Tween , 0 . 2 M KCl , 5 mM 2-mercaptoethanol , 1 mM PMSF and 2 mM benzamidine ) and whole-cell lysates were prepared as previously described [96] . To purify TAP tagged proteins from 30ml of extract were added to 75mg of magnetic beads ( BcMag , Bioclone Inc . ) coupled to rabbit IgGs ( Sigma ) prepared as described [97] . Further purification followed the protocol for TAP-tagged RNA Pol I from yeast [57] . Dephosphorylation of recombinant CTR-TAP fusion protein was performed similarly to the dephosphorylation of the FLAG-tagged CTR purified from yeast ( see above ) . In brief , after binding TAP-tagged CTR to IgG-coated magnetic beads ( see above ) , the affinity matrix was washed 3x with 1 ml TAP-lysis buffer , and 3x with 1 ml TAP-lysis buffer lacking PMSF and benzamidine . The magnetic beads were equilibrated with 1 ml λ-phosphatase buffer and split into two aliquots . Beads in each aliquot were suspended in a total volume of 50 μl λ-phosphatase buffer in the absence ( mock control ) or presence of 2000 u λ-phosphatase and incubated in a thermomixer ( Eppendorf ) for 1h at 30°C under shaking . The supernatant was discarded , and the beads were washed 3x with 1 ml TAP-lysis buffer lacking PMSF and benzamidine . Proteins bound to the beads were eluted by TEV cleavage as previously described [57] . This experiment has been repeated three times and a representative experiment is shown in Fig 7D . In vitro transcription analysis was performed essentially as previously described [57] , with minor changes . To allow formation of the initiation-competent Rrn3-Pol I complex , 5 nM RNA Pol I and 70 nM Rrn3 were incubated at 4°C for 60 min . The reaction was complemented with 10 nM CF , and 5 nM template DNA containing the 35S rDNA promoter . Additionally , potassium acetate was added to yield a constant salt concentration of 200 mM in the final transcription reaction . In some reactions affinity purified Net1 derivatives were included ( 5–20 nM for full-length Net1 , and Net1ΔCTR , and 20–100 nM for CTR or dephosphorylated CTR ) . The reaction was eventually adjusted to 25μl containing 20 mM HEPES-KOH pH 7 . 8 , 10 mM MgCl2 , 5 mM EGTA , 0 . 05 mM EDTA , 2 . 5 mM DTT , 0 . 2 mM ATP , 0 . 2 mM UTP , 0 . 2 mM GTP , 0 . 01 mM CTP including 1 . 25 μM α-32P-CTP . Samples were incubated for 30 min at 24°C , before the reaction was stopped and processed as reported [57] . The data presented in Fig 7E and S4H Fig are representative for at least three independent experiments .
|
The production of ribosomes , cellular factories of protein synthesis , is an essential process driving proliferation and cell growth . Ribosome biogenesis is controlled at the level of synthesis of its components , ribosomal proteins and ribosomal RNA . In eukaryotes , RNA polymerase I is dedicated to transcribe the ribosomal RNA . RNA polymerase I has been identified as a potential target for cell proliferation inhibition . Here we describe the C-terminal region of Net1 as an activator of RNA polymerase I transcription in baker’s yeast . In the absence of this activator RNA polymerase I transcription is downregulated and cell proliferation is strongly impaired . Strikingly , this activator might be conserved in human cells , which points to a general mechanism . Our discovery will help to gain a better understanding of the molecular basis of ribosomal RNA synthesis and may have implications in developing strategies to control cellular growth .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"phosphorylation",
"cell",
"physiology",
"gene",
"regulation",
"purification",
"techniques",
"regulatory",
"proteins",
"membrane",
"staining",
"dna-binding",
"proteins",
"fungi",
"transcription",
"factors",
"cellular",
"structures",
"and",
"organelles",
"extraction",
"techniques",
"protein",
"purification",
"research",
"and",
"analysis",
"methods",
"specimen",
"preparation",
"and",
"treatment",
"staining",
"protein",
"extraction",
"proteins",
"gene",
"expression",
"recombinant",
"protein",
"purification",
"ribosomes",
"yeast",
"biochemistry",
"rna",
"eukaryota",
"ribosomal",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"post-translational",
"modification",
"genetics",
"biology",
"and",
"life",
"sciences",
"non-coding",
"rna",
"organisms",
"cell",
"fusion"
] |
2019
|
The C-terminal region of Net1 is an activator of RNA polymerase I transcription with conserved features from yeast to human
|
Spinal Muscular Atrophy ( SMA ) is caused by diminished function of the Survival of Motor Neuron ( SMN ) protein , but the molecular pathways critical for SMA pathology remain elusive . We have used genetic approaches in invertebrate models to identify conserved SMN loss of function modifier genes . Drosophila melanogaster and Caenorhabditis elegans each have a single gene encoding a protein orthologous to human SMN; diminished function of these invertebrate genes causes lethality and neuromuscular defects . To find genes that modulate SMN function defects across species , two approaches were used . First , a genome-wide RNAi screen for C . elegans SMN modifier genes was undertaken , yielding four genes . Second , we tested the conservation of modifier gene function across species; genes identified in one invertebrate model were tested for function in the other invertebrate model . Drosophila orthologs of two genes , which were identified originally in C . elegans , modified Drosophila SMN loss of function defects . C . elegans orthologs of twelve genes , which were originally identified in a previous Drosophila screen , modified C . elegans SMN loss of function defects . Bioinformatic analysis of the conserved , cross-species , modifier genes suggests that conserved cellular pathways , specifically endocytosis and mRNA regulation , act as critical genetic modifiers of SMN loss of function defects across species .
Decreased Survival of Motor Neuron ( SMN ) protein function underlies most Spinal Muscular Atrophy ( SMA ) cases [1] . The SMN protein is ubiquitously expressed [2] , [3] , yet SMA pathology is remarkably specific . Patients lose spinal α-motorneurons and experience muscular dysfunction with atrophy . Mild cases result in slowly progressing muscular weakness , while severe cases dramatically perturb proximal neuromuscular function resulting in childhood death [4] . There is no effective treatment for SMA and at least 1 in 40 people in the US population are carriers of SMN loss of function disease alleles [5]–[7] . The SMN protein is a component of the well-characterized Gemin complex , which assembles splicing machinery in eukaryotes [8]–[10] . SMN also associates with β-actin mRNA during anterograde transport in neuronal processes suggesting a role for SMN in mRNA transport , sub-cellular localization and/or local translation [11]–[15] . In addition , SMN is found in post-synaptic densities and Z-discs of muscles along with other RNA processing proteins [11]–[19] . Roles for SMN in small nucleolar RNA ( snoRNA ) and microRNA ( miRNA ) pathways have also been suggested [20]–[22] . The relative contributions of SMN in these various compartments and the relative importance of SMN function in neurons and muscles for SMA pathology have been difficult to determine . Various tissue requirements for SMN function have been observed in different SMA model systems [23]–[27] . The diverse subcellular SMN localization and varied cellular requirements for SMN function suggest that this protein may act in multiple cellular compartments including the neuromuscular junction ( NMJ ) [28] . To determine in an unbiased fashion which cellular and molecular pathways are particularly relevant to SMA pathology , researchers have turned recently to genetic approaches in vertebrates and invertebrates . The identification of SMN loss of function modifier genes can reveal important biochemical pathways for SMA pathology . Studies in patients have already identified two genes that act as modifiers of SMA: SMN2 and Plastin 3 ( PLS3 ) . Two genes encode human SMN protein: SMN1 and SMN2 . The SMN1 gene encodes only full-length SMN protein while the SMN2 gene encodes two different transcripts; 10% of SMN2 transcripts encode a full-length SMN protein identical to the SMN1 gene product . However , due to a change in the splice consensus sequence , 90% of SMN2 transcripts contain a stop codon at the beginning of exon 7 and , therefore , encode a truncated protein ( called SMNdeltaEx7 or Δ7SMN ) of diminished function and stability [1] , [29]–[31] . Humans have various numbers of SMN2 genes; patients with more copies of SMN2 generally have later onset/less severe symptoms than patients with fewer copies of SMN2 . Decreased severity and delayed onset is usually attributed to increased full-length SMN levels from SMN2 in vivo [17] , [32]–[36] . PLS3 may modulate the severity of SMA . In several families , daughters who lack SMN1 and over-express PLS3 were remarkably unaffected [37] . PLS3 encodes a conserved calcium-binding , actin-bundling/stabilizing protein that is broadly expressed in various tissues including blood , muscles and neurons [38]–[40] . Loss of the yeast PLS3 ortholog , Sac6p , results in defective endocytosis [41] , [42] . Altering PLS3 levels modified SMN loss of function defects in zebrafish motorneurons consistent with results in human families and PLS3 co-precipitated with SMN from neuronal tissues [37] . However , increased PLS3 ( due to profilin knockdown ) did not decrease the defects in an SMA mouse model and it remains unclear how PLS3 might modify SMN neuromuscular defects [43] . Modifier genes identified in patient populations are clearly pertinent to SMA pathology . However , studies in humans are limited by kindred sizes and other considerations . As SMN orthologs are found in C . elegans and Drosophila melanogaster , it may be more efficient to identify SMA modifier genes in these powerful invertebrate models . SMN loss of function models have already been defined in C . elegans and Drosophila [18] , [25] , [44] , [45] . Loss of Drosophila Smn ( DmSmn ) causes larval lethality and NMJ defects; DmSmn function is required in neurons and muscles in flies [26] . Loss of C . elegans SMN-1 ( Cesmn-1 ) also causes neuromuscular function deficits followed by larval lethality [44] . Expression of Cesmn-1 in neurons dramatically restores neuromuscular function , whereas expression in muscles has little effect [44] . Given SMN conservation across species , genes that act as SMN loss of function modifiers in invertebrates could be important in SMA pathology in humans ( e . g . PLS3 ) [37] . In a recent study , twenty-seven P-element transposon insertion lines were identified in Drosophila that modified SMN loss of function defects , and a role for the TGF-beta pathway in SMN loss of function pathology was delineated [26] . However , it remains unclear for several P-element lines which Drosophila gene near the transposon insertion site is responsible for modulating SMN phenotypic defects . The Drosophila P-element lines carried an inducible GAL4-UAS that could drive either over-expression or antisense RNAi expression of neighboring genes depending on transposon insertion site . Additionally , insertion of the P-element itself might perturb gene function . Eliminating ambiguity regarding modifier gene identity would increase the utility of the Drosophila study . To explore the genetic circuitry affecting SMN activity in C . elegans , the Cesmn-1 ( lf ) growth defect phenotype was used as a metric in a rapid large-scale genetic screen . Growth may be affected by a variety of changes , such as body length and longevity . Subsequently , modifier genes were tested using a C . elegans behavioral assay , the pharyngeal pumping , which is likely more pertinent to SMN loss of function neuromuscular defects . In addition , to identify conserved invertebrate SMN modifier genes , we utilized previously described Drosophila assays to assess genetic interaction of DmSmn with Drosophila orthologs of C . elegans modifier genes . In the study by Chang and co-workers , the DmSmn lethal phenotype correlated with NMJ defects for virtually all DmSmn modifier genes , suggesting that lethality and neuromuscular bouton number are effective measures of genetic interaction with the Drosophila SMN ortholog [26] . Here , we define conserved genetic modifiers of SMN loss of function using C . elegans and Drosophila . We find that PLS3 orthologs act as SMN modifier genes in both invertebrate species . A genome-wide RNAi screen in C . elegans identified four new SMN modifier genes , including ncbp-2 and flp-4 , which also modify SMN loss of function defects in Drosophila . Candidate SMN modifier genes identified in a previous Drosophila screen were tested in C . elegans yielding twelve cross-species modifier genes . Examination of the literature for these genes suggested specific cellular pathways that are critical genetic modifiers of SMN function: endocytosis and RNA processing . These pathways may also be pertinent to SMN loss of function defects in patients with SMA .
To validate growth as an assay for SMN modifier gene identification , we first demonstrated that RNAi knockdown of Cesmn-1 or the invertebrate ortholog of Plastin 3 ( PLS3 ) altered Cesmn-1 ( lf ) growth . The C . elegans gene plst-1 ( PLaSTin ( actin bundling protein ) homolog-1 ) encodes a predicted protein similar to PLS3 . To knockdown gene function , C . elegans were reared on bacteria producing double stranded RNA corresponding to the gene of interest , a strategy known as ‘feeding RNAi’ [47] . Feeding RNAi decreases gene transcripts in most C . elegans tissues although knockdown in neurons is generally less effective than knockdown in muscles , germline , and other tissues [48]–[50] . Here , animals were reared for two generations on solid media and RNAi feeding bacteria corresponding to Cesmn-1 or plst-1 , allowing knockdown of maternal and zygotic transcripts ( Figure 1B ) . Bacteria containing the empty RNAi feeding vector were used as a negative control ( empty ( RNAi ) ) . An automated system was used to simultaneously measure growth and determine genotype for the progeny of +/Cesmn-1 ( lf ) animals ( Figure 1C ) . The COPAS BioSorter ( Union Biometrica , Holliston , MA ) measures C . elegans length as ‘time-of-flight’ , which is the time required for the animal to pass through the fluorescence-detection chamber [51] . Cesmn-1 ( lf ) homozygous animals do not express GFP while +/Cesmn-1 ( lf ) heterozygous animals express GFP and are longer than Cesmn-1 homozygous animals of the same late larval or adult stage . Animals smaller than the L2 larval stage were excluded from this analysis to avoid bacterial debris . The percentage of large adult animals was determined for each genotype and RNAi treatment . RNAi knockdown of Cesmn-1 decreased the percentage of large animals in both Cesmn-1 ( lf ) homozygous and +/Cesmn-1 ( lf ) heterozygous populations ( Table 1 , Rows 1 & 2 ) . Initially , it seems counter-intuitive that the defects of Cesmn-1 ( lf ) animals are exacerbated by Cesmn-1 ( RNAi ) . However , in this scenario , transcripts in both the somatic tissues and germline of +/Cesmn-1 ( lf ) heterozygous animals are targeted and , consequently , maternally-loaded Cesmn-1 transcript and protein are depleted in homozygous Cesmn-1 ( lf ) progeny , abrogating partially the observed maternal rescue . The ability of Cesmn-1 ( RNAi ) to exacerbate Cesmn-1 ( lf ) defects suggests that the effects of modifier genes can be assessed using RNAi feeding . Knockdown of the C . elegans PLS3 ortholog , plst-1 , increased the average length of the +/Cesmn-1 ( lf ) population , but did not significantly alter the average length of Cesmn-1 ( lf ) animals ( Table 1 , Rows 1 & 3 ) . Genetic interaction with plst-1 was further confirmed by using the plst-1 ( tm4255 ) mutant allele ( Table 2 ) . The average length of +/Cesmn-1 ( lf ) ;plst-1 ( tm4255 ) adult animals was significantly increased in relation to +/Cesmn-1 ( lf ) animals . In contrast , the average length of homozygous Cesmn-1 ( lf ) ;plst-1 ( tm4255 ) was not altered , recapitulating the results of plst-1 ( RNAi ) . Increased average adult length is an overall growth metric thzat may encompass a variety of changes; decreased plst-1 function , by RNAi or mutant allele , could increase length , cause sterility , and/or increase longevity in +/Cesmn-1 ( lf ) control animals . It appears that loss of Cesmn-1 function suppresses the effects of decreased plst-1 function ( i . e . increased length was not observed in Cesmn-1 ( lf ) ;plst-1 ( tm4255 ) homozygous mutant animals ) . The genetic and functional relationship between SMN and PLS3 bears further examination; as plst-1 and Cesmn-1 have opposing effects on the growth assay and since Cesmn-1 ( lf ) ;plst-1 ( tm4255 ) animals resemble Cesmn-1 ( lf ) single mutants , Cesmn-1 may act downstream of plst-1 in this growth assay [52] . To identify additional genes that modify SMN loss of function defects , a large-scale genome-wide screen for enhancers and suppressors of the Cesmn-1 ( lf ) growth defect was undertaken . The growth assay was adapted to a higher-throughput 96-well , liquid culture format and a previously described genome-wide C . elegans RNAi feeding library was used for gene knockdown ( Figure 2A ) [53] . Progeny of +/Cesmn-1 ( lf ) animals were reared for two weeks ( more than 2 generations ) on RNAi feeding bacterial strains before assessment of growth using the COPAS Biosorter [51] . To identify RNAi clones that specifically altered the growth of Cesmn-1 ( lf ) animals , a growth ratio of large to small animals was determined for each clone for Cesmn-1 ( lf ) and for +/Cesmn-1 ( lf ) genotypes . If the RNAi clone growth ratio was more than 2 standard deviations away from the mean for Cesmn-1 ( lf ) animals and within 0 . 7 standard deviations of the mean for +/Cesmn-1 ( lf ) animals in at least 40% of independent trials , then the corresponding gene was designated as an Cesmn-1 ( lf ) modifier ( Figure 2B ) . In the primary high-throughput screen , no suppressors were found , but four genes were identified as enhancers ( Figure 2B ) . RNAi knockdown of these genes exacerbated homozygous Cesmn-1 ( lf ) growth defects and did not significantly alter the growth of heterozygous +/Cesmn-1 ( lf ) animals: ncbp-2 , T02G5 . 3 , grk-2 , and flp-4 . ncbp-2 encodes the C . elegans Cap Binding Protein 20 ( CBP20 or Cbp20 ) ortholog [54] . T02G5 . 3 encodes a predicted protein of unknown function with no vertebrate orthologs based on BLAST analysis . grk-2 encodes one of two G-protein coupled receptor kinases . flp-4 encodes an FMRFamide family neuropeptide protein . The low number of modifiers identified in this screen versus the previous Drosophila screen may reflect the stringent criterion utilized here or the inefficiency of RNAi by feeding in neurons . To determine if decreased adult body length accounts for the enhanced Cesmn-1 ( lf ) growth defect upon knockdown of ncbp-2 , T02G5 . 3 , grk-2 and flp-4 , the average body length of Cesmn-1 ( lf ) young animals was determined ( Text S1 and Table S4 , top panel ) . Only ncbp-2 ( RNAi ) significantly reduced the average body length of Cesmn-1 ( lf ) animals suggesting that the enhanced growth defect caused by ncbp-2 ( RNAi ) could be attributed to the Cesmn-1 ( lf ) shorter body size . The other three enhancer genes may alter survival or growth as adult animals . SMA is a neuromuscular disease and , therefore , our objective was the identification of modifier genes that impact SMN neuromuscular function . We then examined the impact of Cesmn-1 growth modifier genes on Cesmn-1 loss of function neuromuscular defects using RNAi and , when available , loss of function alleles of modifier genes . A recent study from the Sattelle laboratory demonstrated that loss of Cesmn-1 function causes progressive defects in C . elegans neuromuscular function in pharyngeal pumping [44] . C . elegans feeds on bacteria and other microorganisms using a small , discrete subset of neurons and muscles contained in the pharynx ( Figure 3A ) [55] . Pharyngeal cell specification , neuronal development , and myoblast fusion is completed within hours of hatching [56] , [57] . The pharynx pumps continuously and symmetrically at over 250 beats per minute in wild type animals when food is present and larval pumping is interrupted only by molting under standard culture conditions . We confirmed a previous report [44] that in early larval stages , the pumping rates of Cesmn-1 ( lf ) animals are indistinguishable from control animals , but at later larval stages Cesmn-1 ( lf ) pumping rates drop ( Figure 3B ) . Cesmn-1 ( lf ) animals have progressive defects in pharyngeal pumping , which occur earlier than reported locomotion defects . At day 2 , 62% of Cesmn-1 ( lf ) animals are moving spontaneously , but pumping rates have dropped dramatically ( Figure 3B ) . Restoration of Cesmn-1 function in neurons almost completely restores pumping rates suggesting that Cesmn-1 is required in neurons for this behavior [44] . The efficacy of RNAi by feeding in this neuromuscular assay was assessed for Cesmn-1 and plst-1 using Cesmn-1 ( lf ) and +/Cesmn-1 ( lf ) animals . Animals were allowed to hatch on RNAi feeding plates and pumping rates were determined after three days . Either plst-1 ( RNAi ) or Cesmn-1 ( RNAi ) decreased Cesmn-1 ( lf ) pumping rates , but not +/Cesmn-1 ( lf ) pumping rates ( Figure 3C ) . In addition , plst-1 ( lf ) significantly decreased the pumping rates of Cesmn-1 ( lf ) animals , validating the genetic interaction of plst-1 with Cesmn-1 in the neuromuscular pharyngeal pumping assay ( Figure 3D ) . This exacerbation of Cesmn-1 loss of function defects by plst-1 manipulation is consistent with results in other organisms [37] . The ability of Cesmn-1 ( RNAi ) and plst-1 ( RNAi ) to alter pumping of homozygous mutant Cesmn-1 ( lf ) animals suggests that candidate modifier genes can be assessed using RNAi knockdown in this neuromuscular assay . The four modifier genes from the C . elegans growth screen were tested for function as Cesmn-1 neuromuscular modifier genes using the pharyngeal pumping assay . Results are summarized in Figure 4A . ncbp-2 ( RNAi ) and T02G5 . 3 ( RNAi ) enhanced and suppressed the pharyngeal pumping defects of Cesmn-1 ( lf ) animals , respectively; flp-4 ( RNAi ) and grk-2 ( RNAi ) had no significant effect compared to controls . We suggest that ncbp-2 and T02G5 . 3 are likely modifiers of Cesmn-1 ( lf ) neuromuscular defects based on RNAi results . RNAi knockdown of C . elegans genes by feeding is robust in virtually all cell types but can often be inefficient and can result in only partial loss of gene function , especially in the nervous system [58] , [59] . To address the specificity of the genetic modifiers , the RNAi results were confirmed by using mutant alleles when possible; alleles of ncbp-2 and T02G5 . 3 were not available . A grk-2 loss of function allele has been previously described , grk-2 ( rt97 ) [60] . Loss of grk-2 significantly enhanced the growth defects of Cesmn-1 ( lf ) animals ( Table 2 ) . Additionally , the pumping rates of grk-2 ( lf ) animals derived from hT2 parents were not significantly lower than those of control animals , but the average pumping rates of Cesmn-1 ( lf ) grk-2 ( lf ) double mutant animals were significantly lower than the pumping rates of either single mutant ( Figure 4B ) . This suggests that grk-2 loss enhances both Cesmn-1 ( lf ) growth and pharyngeal pumping defects . A grk-2 gain of function allele is not available and transgenes are unstable in +/Cesmn-1 ( lf ) animals ( unpublished results and [44] ) . To test the genetic interaction of flp-4 with Cesmn-1 , we identified a flp-4 loss of function allele , flp-4 ( yn35 ) , using PCR based screening techniques [61]–[65] . The flp-4 ( yn35 ) deletion removes all sequences encoding FLP-4 FMRFamide neuropeptides and likely causes a complete loss of flp-4 function ( [66] and C . Li , in preparation ) . Although flp-4 ( yn35 ) reduced the percentage of Cesmn-1 ( lf ) large animals in the growth assay , the difference was not statistical significant different ( Table 2 ) . Similar results were obtained using the pharyngeal pumping assay . The pumping rates of flp-4 ( lf ) animals were slightly lower , but not significantly different than control animals . Loss of flp-4 function decreased pumping rates of Cesmn-1 ( lf ) animals in five independent trials , but the difference was not statistically significant ( p = 0 . 236 , Figure 4B ) . Either flp-4 is not a bona fide modifier or flp-4 ( RNAi ) may act off-target decreasing the function of more than one of the 32 other C . elegans FMRFamide genes [67] . SMN modifier genes that are conserved across species would be of considerable interest . Three of the candidate genes identified in the C . elegans screen encode conserved proteins with clear orthologs in other species: grk-2 , flp-4 , and ncbp-2 . To determine if their orthologs modify SMN loss of function defects , we turned to the fruit fly Drosophila . Decreased function in the Drosophila SMN ortholog Smn ( DmSmn ) results in growth defects , early pupal arrest , and NMJ synaptic defects [26] . We utilized pre-existing Drosophila loss of function alleles and previously described Drosophila assays to assess genetic interaction of DmSmn with Drosophila orthologs of C . elegans modifier genes [18] , [25] , [26] . First , we determined if Fimbrin ( Fim ) , the Drosophila ortholog of PLS3 , modifies DmSmn loss of function defects in growth and NMJ assays . It has been shown that RNAi knockdown of DmSmn ( DmSmn RNAi ) results in 44% lethality in early pupal stages with 56% lethality at late pupal stages [26] . Loss of Fim alone does not cause larval or pupal lethality ( data not shown ) . Three Fim loss of function alleles were crossed into the DmSmn ( RNAi ) background and each accelerated death compared to DmSmn ( RNAi ) control animals ( Figure 5A ) . In Drosophila , loss of SMN function results in a dose-dependant decrease in process arborization at the NMJ and diminished numbers of synaptic specializations , termed synaptic boutons [26] . Boutons are visualized as coincident pre-synaptic synaptotagmin and post-synaptic Discs large protein immunoreactivity . The number of synaptic boutons found between Drosophila neurons and muscles provides a simple and readily quantifiable assessment of phenotypic severity . We determined if the Drosophila PLS3 ortholog Fim might also modify the NMJ defects of DmSmn . RNAi knockdown of DmSmn using the ubiquitous tubulin promoter ( TubGAL4;SmnRNAi ) modestly decreased synaptic innervation in Drosophila larvae ( reported as bouton numbers per muscle area , Figure 5B ) . Loss of Drosophila Fim function in Fimd02114 animals also modestly decreased bouton density . We found that effects of Fimd02114 and DmSmn knockdown were synergistic; bouton numbers were significantly decreased suggesting that loss of Fim function exacerbated DmSmn loss of function defects , being consistent with studies in vertebrate models of SMA [37] . These results suggest that PLS3 is a cross-species modifier of SMN function . Next , Drosophila orthologs of candidate SMN modifier genes from C . elegans were examined . Cbp20 and Fmrf were selected as Drosophila orthologs of ncbp-2 and flp-4 , respectively , based on similarity and Drosophila loss of function alleles were obtained . ( There are 32 genes in C . elegans encoding 32 FMRFamide-related neuropeptides , in contrast , three FMRFamide genes exist in Drosophila . There may be less redundancy in FMRFamide gene function in Drosophila [68] , [69] , [70] ) . Heterozygous loss of DmSmn function in +/Smn73Ao or +/Smnf01109 animals had no significant effect on bouton number as expected , Smn73Ao/Smnf01109 animals had dramatically decreased bouton numbers ( Figure 6 ) . Loss of one copy of Cbc20 or Fmrf modestly decreased synaptic bouton number compared to control animals . However , simultaneous loss of one copy of DmSmn and one copy of either modifier gene resulted in further synaptic bouton loss ( Figure 6 ) . The genetic interaction in trans-heterozygous animals is consistent with a strong genetic interaction between Smn and the two modifier genes . We were unable to obtain classical alleles of the grk-2 Drosophila ortholog . We conclude that Cbp20 and FMRFamide are conserved invertebrate enhancers of Smn loss of function defects and that this genetic interaction is conserved across species . A previous Drosophila screen identified twenty-seven P-element insertion lines that altered Drosophila SMN ( DmSmn ) loss of function defects [26] . Cross-species validation of these genes might also help elucidate conserved pathways that are critical in SMN loss of function pathology . However , several genes flanked the P-element insertion site for many of these modifier lines and the precise DmSmn modifier gene could not be unambiguously identified . Therefore , 40 candidate modifier genes were reported [26] . We identified the likely C . elegans orthologs for 32 of these 40 genes using reciprocal BLAST similarity searching ( Table S1 ) . The ability of these genes to modify Cesmn-1 ( lf ) growth defects was assessed by feeding Cesmn-1 ( lf ) and +/Cesmn-1 ( lf ) animals bacteria expressing the corresponding dsRNA; RNAi feeding clones were constructed for B0432 . 13 , dhs-22 and ugt-49 [53] . Twelve genes crossed species and modified Cesmn-1 ( lf ) defects in one or both C . elegans assays . Knockdown of seven C . elegans genes ( uso-1 , nhr-85 , egl-15 , atf-6 , ape-1 , kcnl-2 and nekl-3 ) orthologous to DmSmn modifier genes specifically enhanced Cesmn-1 ( lf ) growth defects , but did not significantly alter the percentage of large heterozygous +/Cesmn-1 ( lf ) animals . Knockdown of the C . elegans ortholog atn-1 significantly suppressed the growth defects of Cesmn-1 ( lf ) animals without altering the percentage of large +/Cesmn-1 ( lf ) siblings . Finally , C . elegans orthologs of two Drosophila genes were identified , whose genetic interaction with Cesmn-1 resembled the interaction of plst-1 with Cesmn-1: cash-1 and dlc-1 . RNAi knockdown of these two genes increased the percentage of large animals in the +/Cesmn-1 ( lf ) population without altering the Cesmn-1 ( lf ) population . Growth assay results for these ten genes are found in Table 1 ( Rows 4 through 13 ) , results for all orthologs tested can be found in Table S1 , and a discussion of modifier gene function is presented in Text S2 . For bona fide cross-species modifier genes , the impact of modifier genes on SMN loss of function defects should be conserved across species ( i . e . enhancer genes should enhance in both species ) . For six cross-species genes , the impact of modifier gene loss on DmSmn and Cesmn-1 loss of function defects was conserved as expected . Specifically , the enhancement of Cesmn-1 ( lf ) defects by RNAi knockdown of nhr-85 , egl-15 , and kcnl-2 was consistent with the effects of the corresponding Drosophila modifier genes on DmSmn [26]; the corresponding Drosophila insertion lines ( d09801 , f02864 , and d03336 ) enhanced DmSmn defects and the transposon insertion in these lines are predicted to decrease function . The results for Drosophila orthologs of C . elegans uso-1 and nekl-3 were also consistent across species . The exacerbation of Cesmn-1 ( lf ) growth defects observed after uso-1 ( RNAi ) or nekl-3 ( RNAi ) knockdown was consistent with the suppression of DmSmn defects observed after over-expression of the cognate Drosophila genes . There was also good concordance for the effect of actinin orthologs across invertebrate species . The d00712 Drosophila insertion line likely drives over-expression of the Drosophila gene Actinin ( Actn ) and enhances DmSmn defects [26] , [71] , [72] , while suppression of Cesmn-1 ( lf ) growth defects by RNAi knockdown of C . elegans atn-1 was observed here . For four genes , it is unclear if the results for Drosophila orthologs are concordant across species: atf-6 , ape-1 , dlc-1 and cash-1 . For atf-6 and ape-1 , the corresponding Drosophila transposons ( d05057 and d05779 ) are inserted into the 1st intron of one of the two transcripts predicted for the orthologous Drosophila genes; accordingly , these transposons may perturb gene function or may drive over-expression of the predicted 2nd shorter transcript . For the genes with complex genetic interactions with Cesmn-1 ( i . e . dlc-1 and cash-1 ) , the function of Drosophila orthologs ctp and CKA are likely decreased by Drosophila insertion lines f02345 and f04448 , which suppressed and enhanced DmSmn defects , respectively [26] . Overall , six of ten genes that modified DmSmn growth defects are clearly concordant with the C . elegans growth data , suggesting conserved roles as SMN loss of function modifiers . C . elegans orthologs of DmSmn modifier genes identified in the previous Drosophila screen [26] were also rescreened using the pharyngeal pumping assay . We found that RNAi knockdown daf-4 enhanced Cesmn-1 ( lf ) pharyngeal pumping defects , while knockdown of kncl-2 or nhr-25 suppressed Cesmn-1 ( lf ) pumping defects ( Table 3 , rows 3 through 5 ) . daf-4 encodes one of the C . elegans TGF-beta receptor subunits orthologous to Drosophila Wit ( Witless ) . In C . elegans , daf-4 and TGF-beta/Dpp pathway function is required for cell specification at numerous stages and for transit through the stress-resistant , long-lived dauer stage [73] . RNAi knockdown of daf-4 exacerbated Cesmn-1 ( lf ) pumping defects , consistent with the effect of TGF-beta pathway manipulation in Drosophila [26] . RNAi knockdown of two C . elegans genes diminished Cesmn-1 ( lf ) pumping defects: kcnl-2 and nhr-25 . kcnl-2 encodes a likely C . elegans SK channel subunit and nhr-25 is one of the two C . elegans proteins most similar to Drosophila Usp ( Ultraspiracle ) . No clear ortholog of Usp is found in the C . elegans genome . The corresponding Drosophila d00712 transposon insertion line likely drives over-expression of Usp resulting in enhancement of DmSmn defects [26] . This is consistent with C . elegans results . By contrast , the impact of SK/kcnl-2 loss in growth versus pumping assays is discordant . The d03336 transposon insertion is located in the SK gene , likely perturbs SK function , and enhances DmSmn growth and Drosophila NMJ defects [26] . This is consistent with kcnl-2 ( RNAi ) enhancement of C . elegans growth defects described above . The suppression of Cesmn-1 ( lf ) pumping defects observed here after kcnl-2 knockdown may reflect differences in the requirement for kcnl-2 function in neuromuscular tissue and/or the relative inefficiency of RNAi knockdown in neurons . To address the specificity of the invertebrate SMN modifier genes , the impact of their RNAi knockdown was examined on an unrelated pharyngeal pumping defective strain . Loss of egl-30 ( ad805 ) , which perturbs Gqα function in C . elegans , decreases their pharyngeal pumping rates [74] . RNAi knockdown did not significantly alter egl-30 pharyngeal pumping rates for any modifier gene ( Table S3 ) , suggesting that these genes are likely specific modifiers of SMN loss of function defects . Combined the results described here define eleven conserved genes that modify invertebrate SMN ortholog function in at least one assay in both C . elegans and Drosophila ( summarized in Table 4 ) . A subset of these cross-species modifier genes interact , directly or indirectly , with previously described neurological or neuromuscular disease proteins suggesting common neurodegenerative pathways may be at work ( i . e . ATF6 with VAPB/ALS8 or GPRK2 and SMN1 with FMRP ) [75]–[77] . To determine if specific cellular mechanisms could be implicated in SMN loss of function pathology , the published literature and public databases were examined for physical and/or functional interactions between cross-species SMN modifier genes , SMN and neuromuscular disease genes . A protein/genetic interaction map was assembled and is presented in Figure 7 with references . We note that genes implicated in endocytosis and mRNA translational regulation unexpectedly predominate in this interaction map . These two cellular pathways may be pertinent to SMN loss of function pathology .
Enormous effort over the last few decades has resulted in the successful identification of numerous neurodegenerative disease genes and the proteins they encode . However , in many cases there remains considerable controversy as to why perturbation of these genes results in neurodegeneration [78]–[81] . SMN plays a well-described and ubiquitous role in the Gemin complex and snRNP assembly [8]–[10] , yet SMA specifically affects neuromuscular function , motorneuron survival , and leads to muscle atrophy . Given this neuromuscular specificity , it seems likely that loss of SMN function impacts cellular pathways outside of the Gemin complex . In addition , given the complexity of cellular signaling pathways , genetic pathways that are not directly involved in SMN activity may impact SMN loss of function pathology . To identify SMN modifier pathways , we have used a genetic approach . Unbiased genetic screens are powerful tools as they utilize functional criteria for the identification of genes critical for cellular function . In the case of SMN loss of function , genetic screens can reveal conserved genes and pathways that are important for neuromuscular dysfunction and pathology independent of initial assumptions about the roles of SMN in neurons and muscles . The identification of hitherto unsuspected molecular pathways that modulate SMN neuropathology , directly or indirectly , is expected to widen the range of targets for SMA therapy development . Conserved genes that modify SMN loss of function defects in disparate species likely represent pathways that are important for SMN loss of function defects or pathology . C . elegans and Drosophila models have been used here to identify SMN loss of function modifier genes that ‘cross species’ . It is difficult to estimate how many modifiers of SMN loss of function were missed in the genome-wide C . elegans RNAi screen . ‘Growth’ encompasses a variety of factors; slow progression through the larvae stages , reduced growth in the adult stage , longevity , body size , different culture format ( liquid versus plates ) , or a combination of these . Additionally , identification of genetic modifiers for a null allele can be more challenging as compared to identification of genetic modifiers for partial loss of function alleles [82] . No genetic screen can identify all modifier genes pertinent to a pathway and important players can be missed ( e . g . miRNAs ) . Despite this , there is excellent concordance of modifier gene action in C . elegans and Drosophila . In most cases , genes that enhanced SMN loss of function defects in Drosophila also enhanced SMN loss of function defects in C . elegans and vice versa . This concordance suggests that the genetic relationships between SMN and these modifier genes are conserved across species . Orthologous genes are likely also important in SMN loss of function pathology in vertebrate species , as suggested by other invertebrate modifier screens that have identified conserved human disease-related genes and/or functional pathways [83]–[88] . Thus far , there are only two published human SMA modifier genes: Plastin 3 ( PLS3 ) and SMN2 . The role of SMN2 is clear as it provides a modicum of functional SMN protein . However , the role of PLS3 in SMA is controversial and it is unclear how PLS3 levels might modulate severity in SMA patients [37] , [43] . We find that invertebrate PLS3 orthologs act as modifiers in C . elegans and Drosophila models . This cross-species interaction of PLS3 and SMN both increases confidence in the invertebrate models and suggests that plastin-associated pathways are important for SMN function at a fundamental level in multiple contexts . In the bioinformatic analysis presented in Figure 7 , we independently identified two cellular pathways that connect multiple modifier genes with SMN: endocytosis and RNA processing/translational control pathways . Regarding the former , it is of note that the yeast ortholog of PLS3 , Sac6p , is a key player in endocytosis and Sac6p levels are critical when expanded polyglutamine neurodegenerative disease proteins are expressed in this system [42] , [89]–[91] . We suggest that 1 ) these two cellular mechanisms may be of particular importance in SMA pathology and 2 ) that unexpected and intimate connections exist between these two pathways . A pair of recently published studies found that the microRNA regulatory RISC complex and endocytosis are physically and functionally coupled in non-neuronal cells [92] , [93] . Interestingly , the RISC complex also contains Gemin complex proteins; the function of the Gemin and RISC complexes may be related , directly or indirectly [20] . We speculate that in normal animals , physically coupling the seemingly disparate pathways of endocytosis and local translational regulation may help coordinate synaptic activity and receptor signaling with protein translation during both synaptic development and neuron maintenance [94] . Defects in endocytosis have been suggested previously to play a pivotal role in neurodegenerative diseases in numerous scenarios . In such diseases , including SMA , perturbation of endocytosis may result in RNA translational control defects , or vice versa [92] , [93] . A recent study has demonstrated impaired synaptic vesicle release at the NMJs in severe SMA mice consistent with defects in synaptic vesicle endocytosis/recycling and/or defects in active zone organization [94] . Further studies are warranted to ascertain the interdependence of endocytosis with translational control pathways and to explore the relevance of these pathways in neurodegenerative disease .
Cesmn-1 ( lf ) homozygous strains cannot be maintained due to infertility; hT2 ( lethal ) [myo-2p::GFP]/Cesmn-1 ( lf ) ( hT2[bli-4 ( e937 ) let- ? ( q782 ) qIs48] ( I;III ) ) animals are fertile and were maintained using standard techniques [95] . The lack of homologous pairing for the rearranged chromosomes LGI and LGIII in hT2 animals likely results in increased maternal/zygotic expression of Cesmn-1 and other balanced genes [96] . As expected , we found that the progeny of hT2 animals were relatively resistant to Cesmn-1 ( RNAi ) compared to wild type control strains in our assays ( data not shown ) . Consequently , to keep the genetic background invariant , all animals were tested herein were the progeny of hT2 ( lethal ) [myo-2p::GFP] parents . The use of RNAi sensitive C . elegans mutant strains was avoided as their behavior is not normal in many assays ( Hart , unpublished observations ) and because SMN complex/Sm proteins have been implicated in miRNA pathways [20] , [97] , [98] . We note that RNAi knockdown is not always effective . To control for genetic background effects , animals tested in these studies were either heterozygous for hT2 balancer chromosome or progeny of hT2 parents unless otherwise noted . plst-1 ( tm4255 ) animals were obtained from the Japanese National Bioresource Project and were backcrossed four times before further study . The tm4255 allele is a 368 base pair deletion that removes one of the calponin-like , actin-binding homology ( CH ) domains; plst-1 ( tm4255 ) is likely a partial loss of function allele . To test the genetic interaction of plst-1 with Cesmn-1 , the backcrossed plst-1 ( tm4255 ) allele was used to create a double mutant with Cesmn-1 ( lf ) . The flp-4 ( yn35 ) deletion allele was isolated by PCR-based screening of EMS-mutagenized animals . The yn35 allele is a 928 base pair deletion that removes exon 3 of flp-4 gene along with 5′ sequences ( flanking sequences , ttctgaaaaacttttaataa and agctcgccgagccgagtctt ) [66] . The grk-2 ( rt97 ) loss of function allele was previously characterized [60] . Drosophila stocks were maintained on standard cornmeal/yeast/molasses/agar medium at 25°C . The mutations of Smn73Ao and Smnf01109 have been described previously [25] . Cbp20e02787 is a Piggy-Bac insertion mutation from the Exelixis collection . The insertion location is 5′ upstream and adjacent to the start codon of the Cbp20 transcript . FmrfKG1300 and Fim alleles are loss of function alleles ( Flybase ) . The line d03334 may have an unlinked lethal mutation on another chromosome . Fimd02114 and SmnRNAi; Fimd02114 have Tubulin:Gal4 in the background; this Gal4 transgene does not alter Smn defects ( data not shown ) . C . elegans orthologs of Drosophila and human genes were identified by BLAST searching at NCBI . When a clear ortholog was not identified by reciprocal BLAST analysis , the most similar C . elegans genes were generally tested . plst-1 corresponds to exons of predicted adjacent genes Y104H12BR . 1 and Y104H12BL . 1 based on similarity searching . T02G5 . 3 corresponds to exons of T02G5 . 3 , T02G5 . 2 , and T02G5 . 1 based on high-throughput cDNA sequencing and gene prediction programs [99] . New gene predictions have been reported to Wormbase . To assemble the interaction map in Figure 7 , literature pertaining to each modifier gene was examined at NCBI , AceView , C . elegans and yeast on-line databases ( Wormbase and SGD ) to identify functional or direct interactions between modifier genes and neurodegenerative disease genes . The L4440 vector [47] was used to clone PCR products corresponding to B0432 . 13 , dhs-22 and ugt-49 genes . Plasmids were transformed into the bacterial strain HT115 ( DE3 ) [47] , [49] . Primers used for cloning were: B0432 . 13 forward 5′-acaagctctcgacatcgctg-3′ , reverse 5′- ttaatcgccgcatcctcttg -3′; dhs-22 forward 5′-tatgctgtgcagaagcgaag-3′ , reverse 5′-ctgcttgattcctggtgtattc-3′; ugt-49 forward 5′-acgtggatgtagctgaatgg-3′ , reverse 5′- acgtgaagaacagcaacgaac-3′ . For analysis of modifier genes , animals were reared for two generations/5 days on plates spread with bacterial RNAi strains from the Ahringer or Vidal RNAi libraries [53] . RNAi clones corresponding to modifier genes in Table 4 were sequenced to confirm accuracy . The hlh-4 ( RNAi ) clone in the feeding library was incorrect . A Cesmn-1 ( ok355 ) ;hlh-4 ( tm604 ) double mutant strain was generated . hlh-4 ( tm604 ) did not affect the pharyngeal pumping rates of Cesmn- ( lf ) ( data not shown ) and hlh-4 was excluded from further analysis . Length and GFP fluorescence was determined using the COPAS Biosorter ( Union Biometrica , Holliston , MA ) and the percentage of large animals was determined for each genotype [51] . Three to six independent determinations were undertaken for each genotype/RNAi culture . Significant changes from empty ( RNAi ) were calculated for each RNAi/genotype using the two-tailed Mann-Whitney U test . The average pharyngeal pumping rates of animals were determined after 3 days ( at 25 , 25 and 20°C ) post-hatching on empty vector ( empty ( RNAi ) ) or candidate gene RNAi bacterial feeding strains . Animals were videotaped while feeding for 10 seconds with an AxioCam ICc1 camera on a Zeiss Stemi SV11 at 20 to 66× magnification . Movies were slowed before counting pumping rates . Pharyngeal grinder movements in any axis were scored as a pumping event . Average pumping rates ( ± standard error of the mean , S . E . M ) for each genotype/treatment were calculated independently in two to four separate experiments . The percent change in pumping rate on empty vector versus candidate gene RNAi was determined for each trial for both Cesmn-1 ( lf ) homozygous and +/Cesmn-1 ( lf ) heterozygous animals and used to calculate the mean , S . E . M , and significance . hT2 ( bli-4 ( e937 ) let- ? ( q782 ) qIs48[myo-2p::GFP] ) ( I;III ) animals were reared in liquid cultures in a 96-well plate format on RNAi feeding strains [53] . At least two independent cultures corresponding to each C . elegans RNAi feeding clone were established . Concentrated dsRNA expressing bacteria was added to cultures as necessary to prevent starvation . Cultures were maintained for 8 days at 25°C to generate sufficient animals for analysis . Length and fluorescence were determined using the COPAS BioSorter ( Union Biometrica , Holliston , MA ) . Data was exported to Excel ( Microsoft Corp . ) for analysis . Thirty-one clones were identified that modified the average length of Cesmn-1 ( lf ) animals relative to +/Cesmn-1 ( lf ) siblings in both trials . Four of these genes altered Cesmn-1 ( lf ) size relative to +/Cesmn-1 ( lf ) siblings in at least 40% of subsequent trials and these were selected as candidate modifier genes for neuromuscular analysis as described in the text . Three males and three virgin females were placed on fresh food at 25°C on day 1 . Eggs were collected for next 2 days ( Set 1 ) , and the parents transferred to fresh food . Eggs were collected for another 2 days ( Set 2 ) , and the parents discarded . The F1 animals were scored after 15 days , - on the 16th day for the first set , and the 19th day for the second set , from day 1 . White pupae were scored as early stage death and black pupae were scored as late stage death . Control crosses of tubGAL4:FL26B ( Smn RNAi ) out-crossed to the wild type strain were used as a control for every experimental set . Significance was determined by Chi-square analysis . Primary antibodies were used at the following dilutions: monoclonal anti-DLG ( 1∶500 ) ( Developmental Studies Hybridoma Bank ) , polyclonal anti-Synaptotagmin ( 1∶1000 ) ( a gift from Hugo Bellen ) . FITC- ( 1∶40 ) and Cy5- ( 1∶40 ) conjugated anti-rabbit and anti-mouse secondary antibodies were purchased from Jackson Immunoresearch Laboratories . Anti-Disc large used at 1∶100 ( Hybridoma ) and anti-HRP used at 1∶1000 ( Cappell ) . 3rd instar larvae were dissected and fixed for 5 minutes in Bouin's fixative . Stained specimens were mounted in FluoroGuard Antifade Reagent ( Bio-Rad ) , and images were obtained with a Zeiss LSM510 confocal microscope . Bouton numbers were counted based on the Discs large and Synaptotagmin staining in the A2 segment between muscles 6 and 7 or muscle 4 as indicated . The ratio of muscle area for the various genotypes was normalized to wild type . At least 10–12 animals of each genotype were dissected for the bouton analysis . The ANOVA multiple comparison test was used for statistical analysis .
|
Spinal Muscular Atrophy ( SMA ) is a common , untreatable , and often fatal neuromuscular disease predominately caused by reduced Survival Motor Neuron ( SMN ) protein function . Here , we use invertebrate models to identify and validate conserved genes that play a critical role in SMN loss of function neuromuscular defects . Decreased SMN function causes growth defects in the nematode Caenorhabditis elegans and in the fruit fly Drosophila melanogaster—as well as behavioral or synaptic connectivity defects between neurons and muscles , respectively . We found that a genetic modifier of SMA in patients , plastin , also affects SMN function in these invertebrate models . We undertook a genome-wide RNAi screen to identify genes whose perturbation alters the growth defects of C . elegans lacking SMN . These genes were validated in neuromuscular assays in nematode and fly models of SMA . Additionally , we used the C . elegans model to test SMN modifier genes previously identified in the Drosophila SMA model . Combined , these cross-species approaches identified fifteen genes that are important in both species when SMN function is decreased . Related mammalian proteins and the pathways in which they act ( including endocytosis and RNA transport/translational control ) are likely important players in SMA .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neurological",
"disorders/neuromuscular",
"diseases",
"neurological",
"disorders/spinal",
"disorders",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"neurological",
"disorders/neurogenetics",
"neuroscience/neurobiology",
"of",
"disease",
"and",
"regeneration"
] |
2010
|
Conserved Genes Act as Modifiers of Invertebrate SMN Loss of Function Defects
|
The vacuolating toxin VacA , released by Helicobacter pylori , is an important virulence factor in the pathogenesis of gastritis and gastroduodenal ulcers . VacA contains two subunits: The p58 subunit mediates entry into target cells , and the p34 subunit mediates targeting to mitochondria and is essential for toxicity . In this study we found that targeting to mitochondria is dependent on a unique signal sequence of 32 uncharged amino acid residues at the p34 N-terminus . Mitochondrial import of p34 is mediated by the import receptor Tom20 and the import channel of the outer membrane TOM complex , leading to insertion of p34 into the mitochondrial inner membrane . p34 assembles in homo-hexamers of extraordinary high stability . CD spectra of the purified protein indicate a content of >40% β-strands , similar to pore-forming β-barrel proteins . p34 forms an anion channel with a conductivity of about 12 pS in 1 . 5 M KCl buffer . Oligomerization and channel formation are independent both of the 32 uncharged N-terminal residues and of the p58 subunit of the toxin . The conductivity is efficiently blocked by 5-nitro-2- ( 3-phenylpropylamino ) benzoic acid ( NPPB ) , a reagent known to inhibit VacA-mediated apoptosis . We conclude that p34 essentially acts as a small pore-forming toxin , targeted to the mitochondrial inner membrane by a special hydrophobic N-terminal signal .
Helicobacter pylori is a gram-negative bacterium infecting the human gastric mucosa , causing gastritis and peptic ulcer and , in some cases , gastric cancer [1]–[5] . One of the major virulence factors of the bacteria is the vacuolating toxin VacA , a protein of about 90 kDa [1] , [6] , [7] . VacA forms hexameric or heptameric flower-shaped oligomers [8]–[11] . These contain a central cavity and are able to form an ion channel [12]–[17] . The VacA toxin , as it is released by the bacteria , is a hetero-dimeric protein , comprising the subunits p58 and p34 that stay associated by non-covalent interactions [7] , [18]–[20] . Similar to the subunits of A/B toxins , the two components have different functions in targeting and toxicity: The p58 subunit mediates binding to target cells . Following entry into host cells by endocytosis , the p34 subunit is essential to cause toxic effects [6] , [21] . The p34 subunit is a polypeptide of 319 amino acid residues [22] . The first 32 residues are uncharged and required both for VacA insertion into the plasma membrane of host cells and for toxicity [7] . Inside the target cells , p34 can be imported into mitochondria [23] , and mitochondria were shown to play an important role in the toxicity of the protein [24]–[29] . However , p34 does not reveal an obvious mitochondrial targeting signal , and nothing is known about the fate and the relevant molecular activities of p34 inside the mitochondria . p34 can dissipate the mitochondrial membrane potential , interfere with the mitochondrial energy metabolism and trigger apoptosis [23] , [24] , [30] , but the mechanism is unclear . In this study we therefore asked: How is p34 imported into mitochondria ? What is the mitochondrial targeting sequence ? To which subcompartment is p34 targeted , and what is the activity of p34 inside the mitochondria ? Combining biochemical and biophysical investigations , we found that the p34 subunit essentially acts as a small pore-forming toxin targeting the mitochondrial inner membrane by a peculiar import signal .
The p34 subunit of VacA is specifically imported into mitochondria , however it does not reveal an obvious mitochondrial targeting signal . To identify the segment that determines mitochondrial import , we designed hybrid proteins containing an EGFP moiety ( enhanced green fluorescent protein ) and investigated their distribution after expression in HeLa cells . Expression of p34-EGFP , a hybrid protein carrying the EGFP domain at the p34 C-terminus , causes cell death [23] , however we confirmed that an EGFP-p34 protein containing the EGFP domain at the N-terminus was targeted to mitochondria keeping the transfected cells intact ( Fig . 1A , upper panel ) . To determine the possible role of the hydrophobic p34 N-terminus in targeting , we tested a truncated construct comprising residues 37–319 of p34 fused to the EGFP domain . The construct stayed in the cytosol ( Fig . 1A , middle panel . ) Probably due to an affinity of the EGFP moiety [31] , the construct partially co-localized with the nuclei of the cells . On the other hand , we found that the 36 N-terminal residues of p34 were sufficient for targeting of the EGFP domain to mitochondria ( Fig . 1A , lower panel ) . Minor differences in the distribution of EGFP-p34 ( 1–319 ) and p34 ( 1–36 ) -EGFP suggest that the interactions of the N-terminal residues with the mitochondria may be facilitated by the authentic subunit . However , the N-terminal residues of p34 were essentially sufficient for targeting . The first 32 residues of p34 , followed by a lysine in position 33 , are non-charged or hydrophobic ( Fig . 1B ) . They appear to represent a novel type of a mitochondrial targeting signal . The N-terminus of p34 is both essential and sufficient for mitochondrial targeting . To investigate the interactions of p34 with mitochondria in more detail , we used an in vitro assay ( Fig . 2 ) . For this purpose , we synthesized 35S-labelled p34 in reticulocyte lysate . Under these conditions , proteins are synthesized in the presence of the cytosolic proteins that determine the import-competence of mitochondrial proteins in mammalian cells [32] . Both hydrophilic and membrane proteins can subsequently be imported into isolated mitochondria . In the absence of mitochondria , 20 µg/ml of proteinase K ( PK ) were sufficient to completely hydrolyze p34 at 0°C within 10 min ( Fig . 2A ) . We then incubated the 35S-labelled p34 with freshly isolated rat liver mitochondria at 25°C , the mitochondrial outer membrane protein Tom70 [33] was likewise synthesized in reticulocyte lysate and included as a control protein ( Fig . 2B ) . After an incubation of 10 min , the mitochondria were reisolated by centrifugation and incubated in the presence of increasing concentrations of the detergent digitonin and proteinase K ( PK , 50 µg/ml ) at 0°C . Tom70 and p34 associated with the mitochondria ( Fig . 2B , lane 1 ) . In the presence of proteinase K ( lane 2 ) , Tom70 was rapidly degraded while about 40% of the p34 was retained , indicating that p34 was transported across the mitochondrial outer membrane . High concentrations of digitonin were required to allow the protease to get access to the p34 ( Fig . 2B , lanes 3-6 ) . In most of the experiments , about 20% of the p34 initially added to the samples was imported to a PK-protected location within 10 min . Previous studies emphasized the relevance of distinct residues within the p34 N-terminus for VacA-induced cellular vacuolation [34] , [35] . We separately exchanged three of these residues against alanine ( P9A , G14A , K33A ) , but a substantial influence on the import efficiency was not observed ( Fig . 2C ) . Moreover , the import was not blocked by the uncoupling reagent valinomycin , demonstrating that the mitochondrial membrane potential was not required in this reaction ( Fig . 2C , lanes 2 vs . lanes 4 ) . To investigate the relevance of the N-terminal residues of p34 , we used the construct p34 ( 37–319 ) lacking the N-terminal sequence , or the hybrid protein p34 ( 1–35 ) -DHFR comprising the N-terminal 35 residues of p34 linked to a complete DHFR ( dihydrofolate reductase ) domain . The p34 part of this construct was easily degraded by proteinase K , similar to the authentic p34 ( Fig . 2D ) . The DHFR domain , however , was resistant even against high concentrations of the protease ( Fig . 2D ) . Both constructs were synthesized in reticulocyte lysate and incubated with isolated rat liver mitochondria ( Fig . 2E ) . The truncated version of p34 lacking the hydrophobic N-terminus was not imported ( Fig . 2E , p34[37–319] , lanes 1 and 2 ) , in agreement with the observation that in intact cells , the p34 ( 37–319 ) EGFP fusion protein did not co-localize with mitochondria ( Fig . 1A ) . In a parallel assay , we tested the construct p34 ( 1–35 ) -DHFR . The same samples also contained authentic DHFR . Upon incubation with mitochondria , p34 ( 1–35 ) -DHFR efficiently associated with the mitochondria , while the DHFR was removed ( Fig . 2E , lanes 3 vs . lane 4 ) . The associated p34 ( 1–35 ) -DHFR was partially protected against proteases , similar as the authentic p34 ( in Fig . 2C ) , but a fraction of about 35% was degraded to a fragment of slightly smaller size ( Fig . 2E , lane 5 ) . The observations show that the p34 N-terminus is necessary and sufficient for mitochondrial targeting of p34 both in vitro and in vivo . Since the p34 ( 1–35 ) -DHFR fusion protein was only partially transported across the outer membrane , the p34 N-terminus might be a weaker import signal as compared to conventional positively charged mitochondrial targeting sequences . However , similar fragments as with p34 ( 1–35 ) -DHFR were not observed with authentic p34 . The p34 N-terminus thus appears to be sufficient for efficient targeting and import of the complete p34 subunit . To act as a specific targeting signal , p34 should have the capability to interact with specific sites at the mitochondrial surface . These sites could be provided by mitochondrial outer membrane proteins . We pretreated rat liver mitochondria with trypsin at low concentrations , reisolated the mitochondria , and investigated if the import of p34 was affected . The rate of import was clearly reduced ( Fig . 2F ) . We used the same mitochondria to import porin , a mitochondrial outer membrane protein , and found that the rate of import was similarly reduced ( data not shown ) . Similar to endogenous mitochondrial proteins , import of p34 seems to be facilitated by proteins that are exposed at the mitochondrial surface . The effect was also observed with import of p34 ( 1–35 ) -DHFR ( Fig . 2G ) . In summary , the assays for import of p34 into rat liver mitochondria confirm that the p34 N-terminus can act as a mitochondrial targeting signal . The p34 N-terminus is sufficient to target specific recognition sites at the mitochondrial outer surface . The complete p34 is not only able to target mitochondria but to traverse the outer membrane and to accumulate inside the organelles . To identify the structures that are targeted by p34 , we used isolated yeast mitochondria as a model system ( Fig . 3 ) . To improve the import efficiency , we followed the observation that insertion of the VacA holotoxin into the plasma membrane is facilitated by an acid pre-treatment of the toxin [9] , [13] , [36] . We pre-incubated the lysate at pH 5 , however , the import experiments were subsequently carried out at pH 7 . 2 . The acid-pretreated p34 was completely degraded by proteinase K at a concentration of 20 µg/ml ( Fig . 3A ) . After incubation with isolated yeast mitochondria , a fraction of p34 was protected against degradation ( Fig . 3B , lanes 1 and 2 ) , indicating that p34 was imported into the organelles . To exclude an unspecific aggregation of p34 , we tested samples lacking mitochondria ( Fig . 3B , lanes 3 and 4 ) . Similar as with rat liver mitochondria , the import was independent of the mitochondrial membrane potential , as demonstrated by samples containing valinomycin ( Fig . 3B , lanes 5 and 6 ) . Import was also independent of mtHsp70 ( encoded by SSC1; [37] ) , the major heat shock protein of 70 kDa in the mitochondrial matrix ( Fig . 3B , lanes 7 and 8 ) . The import rate of p34 was significantly reduced if the yeast mitochondria were pretreated with trypsin , indicating that p34 similarly interacts with proteins at the surface of mitochondria from yeast and from mammalian cells ( Fig . 3C ) . We took advantage of the availability of yeast mutants that show defined defects in the mitochondrial import machinery , and we isolated mitochondria from several of these strains . Tom20 , a protein of the outer membrane , is the major receptor for import of endogenous proteins into mitochondria [32] , [38]–[40] . Comparing the rate of import into mitochondria of a tom20 deletion strain ( tom20Δ; [41] ) and the corresponding wildtype strain , we found that the import of p34 was significantly reduced ( Fig . 3D ) . The delay in import into mitochondria lacking Tom20 was also observed with p34 ( 1–35 ) -DHFR ( Fig . 3E ) . p34 and p34 ( 1–35 ) -DHFR were similarly affected , and the results resembled data obtained with porin , which was again included as a mitochondrial standard protein ( Fig . 3E ) . The experiments show that the N-terminal segment of p34 is able to recognize the import receptor Tom20 . Interestingly , import of p34 into mitochondria from a tom70 deletion mutant showed only minor effects ( Fig . 3E , right column ) . Tom70 is a second import receptor besides Tom20 and involved in the uptake of a subset of mitochondrial proteins [32] , [38] . p34 seems to specifically follow the Tom20 pathway for import . For import into the inner compartments of mitochondria , proteins have to pass the general import pore that is mainly formed by Tom40 [32] , [38] , [40] , [42] , [43] . Using the mutant tom40-4 [42] , we found that Tom40 was clearly involved in the import of p34 ( Fig . 3F ) . To enter mitochondria , p34 targets the same import pore as newly synthesized endogenous mitochondrial proteins . Where is p34 localized after import into mitochondria ? EGFP-p34 was previously detected by immuno gold labelling in the interior of HEp-2 cell mitochondria but the precise localization was unclear [23] . We imported radiolabelled p34 into isolated yeast mitochondria , disrupted the membranes by sonication , and separated the membrane vesicles by sucrose density centrifugation ( Fig . 3G and H ) . The peak fractions of the outer membrane protein Tom40 and the inner membrane protein Tim23 were clearly separated , p34 was found to co-fractionate with Tim23 . No significant amounts of p34 were detected in the outer membrane . In summary , we conclude that p34 targets the TOM complex in the outer membrane and subsequently accumulates in the mitochondrial inner membrane . A previous study reported that after insertion of VacA into membranes , most parts of the p34 subunit are protected against proteases [44] . However , structure and function of p34 are still unclear . To obtain purified p34 , we expressed p34 in Escherichia coli and isolated the protein by ammonium sulphate precipitation , hydrophobic chromatography and anion exchange chromatography ( Fig . 4A ) . CD spectra of the purified protein indicated a high content of β-strands and a very low probability to form α-helices ( Fig . 4B ) . The algorithm of the spectrapolarimeter calculated a content of 40–45% anti-parallel β-strands . The values are similar to results obtained with typical pore-forming β-barrel proteins [45] . A pore-forming activity of p34 is also indicated by the observation that an expression of p34 in HeLa cells entails a quick loss of the mitochondrial membrane potential [23] . Interestingly , using the program TMB hunt [46] , we calculated a probability of p34 to form a β-barrel protein of 99% . Therefore we asked: Is the p34 subunit able to form a channel in the absence of the p58 subunit , and does the purified p34 oligomerize independently of p58 ? We incubated purified p34 with the chemical cross-linking reagents DSS ( Disucciminidylsuberate ) or Sulfo-MBS ( Sulfo-m-maleimidobenzoyl-N-hydroxysulfo-succinimide ester ) , respectively ( Fig . 4C , lanes 1–3 ) . With both reagents we observed a series of cross-linking products , indicating that p34 formed homo-oligomers containing several subunits . The same pattern of cross-linking products was observed in the presence and in the absence of Triton X-100 ( Fig . 3C , lanes 3 and 4 ) , suggesting that similar complexes assemble in the absence and in the presence of membranes . To determine the size of the complexes we carried out a blue native electrophoresis ( BN-PAGE ) . In this system , the mobility of the proteins depends on their size , but also on their affinity for detergent molecules and for the Coomassie dye that is used to keep protein complexes in solution . In comparison to hydrophilic proteins , most membrane proteins show a reduced mobility [47] . Monomeric p34 showed a mobility similar to hydrophilic marker proteins of about 50 kDa ( Fig . 4D , lower panel ) . In the presence of 500 mM ε-aminocaproic acid , two different complexes of native p34 were resolved , corresponding to an apparent molecular mass of about 330 or 650 kDa , suggesting that native p34 assembles in complexes of 6–7 or 12–14 monomers , respectively ( Fig . 4 , upper panel ) . Complexes of similar size were found if derivatives of p34 were tested lacking the N-terminal 36 amino acid residues ( Fig . 4 , middle panel ) . The uncharged N-terminus is obviously dispensable for complex formation . To obtain higher amounts of the subunit , we purified a derivative of p34 containing a ( His ) 10-tag instead of the hydrophobic N-terminus ( p34 residues 37–319 connected to 10 histidine residues; Fig . 5A ) . Complex formation was then tested by size exclusion chromatography . In the presence of 10% glycerol , ( His ) 10-p34 eluted in several fractions , partially corresponding to very high molecular mass , probably due to a tendency to form aggregates ( Fig . 5B ) . From the Kav value of the most prominent peak fraction , we calculated a molecular mass of 193 kDa ( Fig . 5C ) . Since ( His ) 10-p34 is a protein of 32 . 2 kDa , we infer that the oligomers contained 6 subunits . To prevent aggregation of the protein , the chromatography was repeated in the presence of 2 M urea ( Fig . 5D and E ) . p34 eluted under these conditions in a single peak corresponding to a molecular mass of the hexameric complex . The hexamers showed an impressive stability , even in the presence of 4 M urea most of the complexes were retained , while a smaller fraction started to dissociate ( Fig . 5F and G ) . Following the observation that the p34 N-terminus was not required for complex formation , we also tested a construct additionally lacking a segment of 28 residues at the p34 C-terminus ( p34 residues 37–292 linked to a 10 histidine tag; Fig . 5H and I ) . In the presence of 4 M urea , most of the protein was stable . Only a small fraction dissociated and eluted in fractions corresponding to the molecular mass of the monomers ( Fig . 5I ) . The possible pore formation of p34 was tested directly by incorporation of purified p34 in artificial membranes and subsequent electrophysiological characterization ( Fig . 6 ) . Current recordings confirmed a low but significant conductivity and a high dynamics in gating behaviour ( Fig . 6A ) . A closer investigation of the current-voltage relationship revealed a conductance state of about 12 pS at 1 . 5 M KCl , pH 7 . 5 ( Fig . 6B ) . Under asymmetrical buffer conditions the channels showed a clear preference for anions , with a PCl/PK value of 19 ( data not shown ) . Similar electrophysiological characteristics were observed at pH 4 ( not shown ) . p34 obviously acts as an anion channel of low conductivity . Based on the main conductance state , the diameter of the p34 ion channel can be calculated to be between 0 . 5 and 1 . 5 Å ( assuming a cylindrical restriction zone of 1 nm with a five-fold higher resistance than the bulk medium ) . This dimension is sufficient to accommodate single chloride ions , but complex molecules such as organic acids should be excluded . Interestingly we found similar values for the p34 construct lacking the hydrophobic N-terminus ( p3436–311 ) ( Fig . 6C and D ) . At 1 . 5 M KCl we determined a conductance state of about 10 pS ( Fig . 6D ) , only slightly less as compared to the authentic p34 . Similar data were also obtained with p34 containing exchanges of single residues within the N-terminus ( P9A , G14A , K33A ) or an N-terminal extension of 12 amino acids ( s2 subtype , data not shown ) . It is therefore unlikely that the p34 N-terminus forms the ion channel . The conductivity of some pore-forming toxins is modulated by ATP [48] . However , using a fluorescent-labelled ATP derivative , we did not obtain any evidence of ATP binding to p34 ( not shown ) . Previous investigations demonstrated that the ion channel of VacA complexes can efficiently be blocked by NPPB ( 5-nitro-2-[3-phenylpropylamino] benzoic acid ) [14]-[49] . Moreover , it was shown that the proapoptotic effect of VacA can be blocked by a preincubation of the target cells with NPPB [26] . A pretreatment of HeLa cells in the presence of 200 µM NPPB was demonstrated to prevent the cytochrome c release that is induced if purified VacA is added to the cells [30] . We added NPPB at a final concentration of 100 µM to our assay and found that it inhibited the conductance of the p34 channel completely ( Fig . 6E and F ) .
In this study , we investigated targeting , mitochondrial import , the final location , and the function of the VacA p34 subunit of Helicobacter pylori . Because previous investigations on VacA were nearly exclusively carried out on the holotoxin [1] , [7] , [19] , only little was known about the specific features of the toxic p34 subunit . Our data indicate that p34 acts as a pore-forming protein in the mitochondrial inner membrane . It was previously shown that p34 targets mitochondria [23] , but the targeting signal and the import pathway of the subunit remained enigmatic . We found that the 36 N-terminal residues of p34 are both necessary and sufficient for mitochondrial targeting . This observation is surprising because a similar mitochondrial targeting sequence was not described for any endogenous mitochondrial protein [32] , [38] , [40] , [50] , [51] , or any other bacterial effector protein [52] . The 32 N-terminal residues of p34 , shown in Fig . 1B , are uncharged or hydrophobic , the first charged residue of the sequence is the lysine in position 33 . We found that the mitochondrial import receptor Tom20 is involved in the uptake of p34 , in agreement with studies showing Tom20 to interact with hydrophobic residues of precursor proteins [53] . Subsequent transport of p34 across the mitochondrial outer membrane is mediated by the general import pore formed by Tom40 . In this case , the length and hydrophobicity of the p34 N-terminus seem to determine the specificity for import into mitochondria . Data on protein traffic in plant cells have already shown that the specificity for different membranes can be determined by the length of a hydrophobic segment of the sequence [54] , [55] . Interestingly , it was demonstrated that a mitochondrial outer membrane protein can be changed to an inner membrane protein by an artificial increase in the mean hydrophobicity [56] . To our knowledge , the p34 subunit is the first example of an authentic protein that uses a hydrophobic N-terminus for targeting of the mitochondrial inner membrane . Membrane insertion of long hydrophobic peptides is membrane potential-independent [57] , and in fact we found that p34 is efficiently imported both in the presence and in the absence of the mitochondrial membrane potential . The p34 N-terminus is not only required for mitochondrial import but also for interactions of the VacA holotoxin with the plasma membrane during entry into the target cell [7] . The uncharged N-terminal sequence of the p34 subunit seems to confer both a general affinity for lipid bilayers , and specific targeting to mitochondria . The situation is reminiscent of the affinities of the outer membrane porin that similarly has a capability of spontaneous membrane insertion [58] but shows specific import into mitochondria depending on interactions with the mitochondrial TOM complex [42] . Some inner membrane proteins are first imported into the matrix compartment and subsequent insertion starts at the inner side of the membrane [32] , however , we did not obtain any evidence of p34 import into the matrix . Because import of p34 is independent of the mitochondrial membrane potential and of matrix mtHsp70 , it is more likely that p34 inserts at the outer surface of the inner membrane . The oligomeric state of the complete VacA toxin was characterized in several studies [8]–[11] . We find that the p34 subunit alone is able to form highly stable complexes of about 200 kDa , corresponding to the molecular mass of 6 subunits . The formation of these hexamers is independent of membrane insertion , raising the possibility that soluble p34 monomers may pass the TOM complex , reassemble in the mitochondrial intermembrane space and insert into the inner membrane after complex formation . Assembly pathways of this type are well documented for bacterial small pore-forming toxins [59] , [60] . A fraction of purified p34 formed oligomers of about 400 kDa in BN-PAGE ( Fig . 4D ) , raising the possibility that p34 hexamers may form double donut-like structures under appropriate conditions . This assumption is supported by data on the complete VacA toxin that similarly showed a formation of 12mers [9] , [10] . Previous data from a yeast two-hybrid assay had suggested that p58 may be essential for complex formation of p34 [18] . Our data on purified p34 demonstrate that this subunit is able to assemble autonomously , independently of the p58 component . The central part of p34 ( residues 37–292 ) is sufficient for the formation of stable hexamers . A crystal structure of the VacA p58 subunit was resolved [20] , [61] , but the structure of p34 has not yet been determined . Our CD spectra indicate that p34 has a content of >40% β-strands , probably in anti-parallel orientation . This value is similar to data reported for β-barrel proteins , although it does not exclude that the p34 β-strands may assemble in a different structure . The endogenous porin of human mitochondria , hVDAC1 , shows a content of 32-37% β-strands , depending on the experimental conditions [45] . A high content of β-strands is also a feature of classical small pore-forming toxins , as exemplified by the α-hemolysin of Staphylococcus aureus ( Gouaux , 1997 ) . Similar to α-hemolysin , also p34 is able to form an anion channel of low conductance . The p34 complex seems to act essentially as a small pore-forming toxin targeting the mitochondrial inner membrane . The pore-forming activity of the VacA holotoxin has been known for a long time [12]–[15] , [49] , [62] , but the role of the two subunits had not been clarified . Surprisingly , we found that p34 is not only able to form a channel in the absence of the p58 subunit , but the conductivity of the p34 pore , about 12 pS in 1 . 5 M KCl buffer , is very similar to the values that were previously reported for the VacA holotoxin [12] . Both VacA [14] , [62] and the purified p34 complex ( this study ) form an anion channel that can be blocked by the reagent NPPB , demonstrating that p34 is the essential pore-forming subunit of the toxin . Several observations demonstrated that in the VacA holotoxin , the conductivity of the channel is dependent on the p34 N-terminus . For example , a formation of membrane channels was not detected with VacA containing an amino acid exchange P9A or G14A [16] . Following this observation , a model was proposed , suggesting that the p34 N-termini within the VacA toxin adopt an α-helical structure and associate to form the ion-conducting channel [17] . Remarkably , the first X-ray structures of pentameric ligand-gated ion channels recently confirmed that the central ion-conducting channel of these proteins is indeed formed by α-helices while other parts have a high content of β-strands [63] . However , other data already indicated that the p34 N-terminus might not be essential for pore formation . A mutant VacA protein lacking residues 6–27 oligomerized properly and showed a conductivity similar to the wildtype protein [34] . Strikingly , the current was detectable only after a much longer delay than when compared with the wildtype VacA [34] . Similar effects were described for the s2 subtype of the VacA toxin which is produced by some strains of H . pylori . s2 subtype VacA carries an additional peptide of 12 hydrophilic residues at the N-terminus [1] , [7] . It forms membrane channels at a significantly reduced rate as compared to the abundant s1 type , but the channels exhibit similar anion selectivities [15] . Our experiments on the isolated p34 subunit indicate that the N-terminus is essential for mitochondrial targeting , but dispensable for assembly and for channel formation . We therefore regard it as unlikely that the p34 N-terminus is the channel-forming domain . Discrepancies between the data on the purified p34 complex and on the VacA holotoxin could be due to direct or indirect interactions between the N-terminus and the p58 subunit . Functional interactions with distant sites were reported for the N-terminus of Staphylococcus aureus α-hemolysin [64] . However , most effects of modifications in the p34 N-terminus , such as a reduced cell vacuolation [34] could be the consequence of an inhibition in membrane insertion rather than in conductivity . Strains producing the s2 subtype are less virulent as compared to other strains [65] . We found that the s2 subtype p34 was imported into mitochondria with similar efficiency as the common s1 subtype , and it showed the same conductivity ( data not shown ) . The observations confirm that the N-terminus is not relevant in determination of the channel properties . We assume that the reduced virulence of the corresponding H . pylori strains is due to effects in the pathway of the toxin from the plasma membrane to the mitochondria , in line with the observation that the N-terminus is primarily required for targeting in the cell . It is currently unclear if p34 separates from p58 after uptake of VacA by target cells . However , our data show that both the VacA holotoxin and the p34 subunit carry the same mitochondrial targeting signal . Moreover , they indicate that both the VacA holotoxin and p34 are able to form hexamers that act as anion channels of very similar conductivity . What are the physiological consequences if these anion channels are formed in the mitochondrial inner membrane ? It is difficult to asses the ionic equilibria inside mitochondria because the activities of the ions are largely determined by the extremely high protein content inside these organelles . A study on endosomal membranes suggested an interaction of VacA with pyruvate [66] , raising the interesting question if the toxic activity of p34 complexes may depend on a depletion of the mitochondria from organic acids and thus from energy substrates . However , the permeability for pyruvate that was reported for the purified toxin was extremely low [49] . Our own data on the purified and reconstituted p34 indicate an inner diameter of the ion channel of only 0 . 5 to 1 . 5 Å . These dimensions correspond to the requirements of a single chloride ion and the ion channel should be unable to mediate the diffusion of complex molecules . We conclude that p34 essentially interferes with the homeostasis of inorganic anions inside the mitochondria . An increasing number of studies already demonstrated a role of the VacA toxin as a mediator of apoptosis [23] , [25] , [26] , [30] , [67]–[69] , but the relation of the VacA toxin to the mechanism of apoptosis remained unclear . Modifications of the inner mitochondrial membrane potential seem to be the first sign of a VacA activity in relation to cell death [27] . Remarkably , the reagent NPPB that we found to efficiently block the conductance of the p34 ion channel also inhibits VacA-dependent cytochrome c release and apoptosis [26] , [30] . In the context of our data we suggest that ( i ) the anion conductivity described for the VacA holotoxin is due to the autonomous oligomerization and pore-formation of subunit p34 , ( ii ) p34 contains an N-terminal signal for targeting to the mitochondrial inner membrane , deletion of the N-terminus abolishes transport to the mitochondrial target membrane . ( iii ) The p34 N-terminus is dispensable for pore-formation . ( iv ) Both pore formation and inner membrane targeting are essential features of p34 toxicity . The p34 subunit of the Helicobacter pylori VacA toxin may serve as a model system in further investigations on the pathological consequences of an anion channel in the mitochondrial inner membrane .
HeLa cells were cultured in DMEM ( Bio-Wittaker , Verviers , Belgium ) with 5% fetal calf serum ( Bio-Media , Boussens , France ) , penicillin/streptomycin and 2 mM L-glutamine ( Gibco-BRL , Paisley , UK ) at 37°C in an incubator with 5% CO2 . For expression of EGFP-labelled proteins , the corresponding DNA fragments were cloned into the vectors pEGFP-C1 or pEGFP-N1 , respectively ( Clontech ) . The cells were transfected using Lipofectamine 2000 reagent ( InvitroGen ) . Immunofluorescence labelling was performed following a conventional protocol . Briefly , 24 h after transfection , cells grown on glass coverslips were rinsed with PBS and fixed for 15 min with a 3 . 7% paraformaldehyde solution . After a 4 min permeabilization in PBS+0 . 1% Triton X-100 , the coverslips were incubated in a 1∶100 dilution of a rabbit anti-Tom20 antibody ( Santa-Cruz ) . A secondary antibody raised in donkey and coupled to Cy3 ( Jackson Immunoresearch ) was subsequently used , together with DAPI ( Sigma ) . The coverslips were finally rinsed and mounted in Mowiol mounting medium ( Calbiochem ) prepared according to the manufacturer's instructions . Pictures were acquired on a TCS SP2 confocal microscope ( Leica ) equipped with a 63× HCX PL APO , NA = 1 . 40 objective ( Leica ) , under oil immersion . Following acquisition , the images were combined using Photoshop software ( Adobe ) . Plasmids for expression in HeLa cells: The constructs encoding EGFP-p34 ( 1–319 ) or p34 ( 1–319 ) -EGFP , respectively , were described previously ( [23]; the VacA sequence is available using the NCBI acc . no S72494 . 1 or GI∶619248 ) . To obtain 34 ( 37–319 ) -EGFP , the plasmid encoding p34 ( 1–319 ) -EGFP was cut with XhoI and EcoRI within the p34 sequence , and the gap was closed using a synthetic DNA fragment encoding residues 37–58 . For construction of p34 ( 1–36 ) -EGFP , a synthetic DNA fragment encoding the N-terminal 36 amino acids of p34 was introduced in pEGFP-N1 via the XhoI and BamHI site . In both cases , the synthetic DNA fragments were obtained by hybridization of two complementary oligonucleotides , encoding the desired sequence together with an initiating start codon and containing the appropriate restriction site overhangs for subsequent cloning . Plasmids for synthesis in reticulocyte lysate and for expression in Escherichia coli: For construction of p34 ( 37–319 ) , the plasmid pET28a-p34 ( 1–319 ) was used as template [23] . This plasmid contains a NcoI site upstream of the p34 coding sequence . An additional NcoI site was introduced upstream of the codon of residue 37 . By cutting with NcoI , the DNA segment corresponding to the N-terminal residues 1–36 was subsequently removed and the remaining plasmid was religated . For construction of p34 ( 1–35 ) -DHFR , a BamHI site was introduced directly downstream of the codon of residue 35 in the plasmid pET28a-p34 ( 1–319 ) . The main part of the p34 coding sequence was subsequently removed by cutting with BamHI and HindIII and substituted by a DNA fragment encoding the entire DHFR ( dihydrofolate reductase ) of the mouse . ( His ) 10-p34 ( 37–319 ) was constructed by insertion of the p34 sequence encoding residues 37–319 into the plasmid pET10N ( a modified pET19b plasmid; [70] ) . The DNA segment encoding p34 ( 37–319 ) was obtained from pET28a-p34 ( 1–319 ) by PCR , creating a NotI site upstream of the triplet encoding p34 residue 37 , and subsequent cutting with NotI and XhoI . The DNA segment was ligated into pET10N downstream of the sequence for the dekahistidine tag . Due to the NotI site , the histidine residues of ( His ) 10-p34 ( 37–319 ) and the glutamic acid of position 37 are connected by a linker of three alanine residues . Essentially following the same procedure , a DNA insert encoding p34 ( 37–292 ) was amplified from pET28a-p34 ( 1–319 ) by PCR , creating a NotI site in front of the codon of p34 residue 37 and a XhoI site after the codon of residue 292 . For import of radiolabelled proteins , rat liver mitochondria were used within 1 h after the isolation . The livers were obtained from animals of 80–120 g weight . Liver pieces were homogenized in buffer A ( 300 mM sucrose , 2 mM EGTA , 10 mM Tris-HCl pH 7 . 4 ) containing BSA ( 5 mg/ml , fatty acid free ) and 1 mM PMSF using a Dounce homogenizer . Cell debris was removed by centrifugation ( 500 g , 10 min , 4°C ) . The supernatant was centrifuged at 12 . 000 g for 6 min to obtain a crude fraction of mitochondria . The mitochondria were subsequently resuspended in 12 ml buffer A , and Percoll ( Sigma , P1644 ) was added to a final concentration of 5% ( v/v ) . The mitochondria were pelleted by centrifugation at 17 . 000 g for 10 min , washed once in buffer A , and eventually resuspended in buffer A at a final concentration of 10 mg protein/ml . Yeast strains were grown in YPG medium ( 1% ( w/v ) yeast extract , 2% ( w/v ) bacto-peptone , pH 5 . 0 , containing 3% ( v/v ) glycerol and mitochondria were subsequently isolated following standard procedures [71] . Radiolabelled proteins were imported into yeast and mammalian mitochondria following similar protocols [71] . The proteins were synthesized in rabbit reticulocyte lysate ( TNT T7 Coupled Reticulocyte Lysate System , Promega , L4610 ) in the presence of 35S-labeled methionine ( ICN Biomedical Research Products ) . For import into yeast mitochondria , the reticulocyte lysate containing p34 was preincubated with HCl ( final concentration 30 mM ) at pH 5–5 . 5 for 10 min at 25°C . ( With most preparations of yeast mitochondria , the efficiency of p34 import without acid pretreatment was very low . ) For import into mammalian mitochondria , the acid pretreatment was omitted . For protease-protection assays , the samples contained BSA buffer ( 3% [w/v] BSA , 80 mM KCl , 10 mM MOPS-KOH , pH 7 . 2 ) , 2 µl reticulocyte lysate , 2 mM NADH , 1 mM ATP , 20 mM potassium phosphate and 30 µg ( yeast ) or 40 µg ( rat liver ) mitochondrial protein in a total volume of 100 µl . The import reactions were carried out at 25°C . The samples were subsequently cooled on ice and proteinase K was added at a final concentration of 25 µg/ml . Following an incubation for 10 min at 0°C , the protease was inactivated by 2 mM PMSF ( phenylmethylsulfonyl fluoride ) and an additional incubation for 5 min at 0°C . To dissipate the membrane potential , valinomycin ( Sigma , V-0627 ) was used at a final concentration of 1 µM . Digitonin was used as described previously [72] . For preparation of membrane vesicles , mitochondria ( 2 mg protein in 200 µl SEM ) were mixed with 200 µl 0 . 6 M Sorbitol , 20 mM HEPES-KOH pH 7 . 4 and incubated for 5 min at 0°C . 2 . 6 ml 0 . 5 M EDTA , 20 mM HEPES-KOH pH 7 . 4 and 100 mM PMSF were added for swelling of the mitochondria . Following an incubation for 30 min at 0°C , a mixture of protease inhibitors was added . Swelling of the mitochondria was stopped by the addition of sucrose to a final concentration of 1 . 8 M and an additional incubation for 10 min . Membrane vesicles were formed by sonication using a sonifier ( Branson 250; duty cycle 70% , Output control 3 ) . Each sample was treated with 3 cycles of each 30 sec . sonification and 15 sec . , using a 3 mm Microtip ( Heinemann , Schwäbisch Gmünd ) . The suspension of vesicles obtained by sonification was centrifuged for 10 min at 16 . 000 g to remove residual mitochondria . The vesicles were collected from the supernatant by centrifugation at 160 . 000 g ( 30 min . , 4°C ) . The membranes were carefully resuspended in 400 µl 10 mM KCl , 5 mM HEPES-KOH pH 7 . 4 and 100 mM PMSF . The suspension was centrifuged for 10 min at 16 . 000 rpm×g to remove aggregates . The supernatant was applied on a step gradient of 0 . 85 , 1 . 1 , 1 . 35 and 1 . 6 M sucrose in 100 mM KCl , 5 mM HEPES-KOH pH 7 . 4 in a total volume of 11 ml , using Ultra-Clear centrifuge tubes ( Beckman , 14×95 mm , No . 344060 ) . The centrifugation was carried out for 16 h at 100 . 000×g using a SW41 rotor ( Beckman , 30 . 000 rpm , 4°C ) . 1 ml fractions were collected for TCA precipitation and SDS-PAGE . The proteins were subsequently transferred on nitrocellulose and polyclonal antisera were used for labelling of marker proteins . p34 was expressed in Escherichia coli , strain C43 ( DE3 ) , using the vector pET28a ( Novagen ) . After an induction by 1 mM IPTG in a culture of 3000 ml for 3 h at 37°C , the cells were harvested and then opened using a french press . Inclusion bodies containing p34 were recovered by centrifugation and washed once in 100 mM urea , 1% ( v/v ) Triton X-100 , 10 mM Tris/HCl pH 8 . 0 , 0 . 1% ( v/v ) mercaptoethanol , and subsequently three times in 1 M urea , 10 mM Tris/HCl pH 8 . 0 , 0 . 1% ( v/v ) mercaptoethanol . The inclusion bodies were eventually dissolved in 8 M urea , 100 mM Na2HPO4 , 1 mM EDTA , 10 mM Tris/HCl pH 8 ( PETurea , 5 ml/g cells ) , cell debris was removed by centrifugation . The supernatant was incubated for 30 min at 0°C with ammonium sulfate corresponding to a final concentration of 10% saturation . Precipitated proteins were removed and a fraction containing most of the p34 was precipitated from the solution by addition of ammonium sulfate at 30% saturation and 90 min incubation on ice . The precipitate was dissolved in PETurea buffer . The solution was diluted 1∶1 with 4 M urea , 1 M ammonium sulfate , 100 mM Na2HPO4 , pH 8 and applied to a column containing 3 ml Phenyl-Sepharose ( Amersham Pharmacia ) . The column was washed with fractions of decreasing concentrations of ammonium sulfate ( 500 mM , 300 mM ) and p34 was eventually eluted with 4 M urea , 100 mM Na2HPO4 , pH 8 . The solution was dialyzed over night against 2 M urea , 1 mM EDTA , 50 mM Tris/HCl pH 8 ( TEurea ) . The solution was then applied to a 3 ml DEAE-Sephacel column . p34 was easily eluted in subsequent washing steps using the TEurea buffer . Impurities were eventually eluted using TEurea buffer containing 1 M NaCl . Starting with 1 g E . coli cells , about 0 . 2 mg p34 was isolated . The truncated protein p34 ( 37–319 ) and derivatives containing single amino acid exchanges were purified following the same protocol . Derivatives of p34 ( comprising residues 37–319 or 37–292 ) containing a ( His ) 10-tag were isolated by Ni-NTA affinity chromatography . For expression , we used the E . coli strain C43 . The expression was induced by addition of 1 mM IPTG following standard conditions and continued for 4 h at 37°C . The cells were opened using the sonifier ( Branson 250; 2×1 . 30 min , 30% amplitude , 1 . 5 sec impulse , 0 . 5 pauses ) and dissolved in 8 M urea , 1 mM EDTA , 50 mM Tris/HCl pH 8 . 0 . Following a clarifying spin , the solution was applied to a 1 ml Ni-NTA column ( Histrap , Amersham Pharmacia ) using an Äkta-Prime system . ( His ) 10-p34 eluted in a gradient at concentrations between 50 and 200 mM imidazole . The eluate was dialyzed over night against 4 M urea , 1 mM EDTA , 50 mM Tris/HCl pH 8 or 2 M urea , 1 mM EDTA , 50 mM Tris/HCl pH 8 . The eluate was applied to a ‘Superose 6’ 10/300 or ‘Superose 12’ 10/300 column ( Amersham Pharmacia ) . About 20 mg ( His ) 10-p34 were obtained from 1 g E . coli cells . Samples of purified ( His ) 10-p34 were used to immunize two rabbits and to obtain polyclonal antisera . The molecular mass of the eluted oligomers was determined using standard marker proteins ( Amersham Pharmacia ) . The average elution position , Kav , was calculated using the equation Kav = ( Ve - V0 ) / ( Vt - V0 ) , with Ve representing the elution volume , V0 the void volume , and Vt the total column volume . For cross-linking , 30 µg p34 were dissolved in 0 . 5 ml 2 M urea , 1 mM EDTA , 10 mM MOPS , pH 7 . 2 . DSS ( Disucciminidylsuberate , Pierce Biotechnology Inc . ) was used at a final concentration of 50 µM , Sulfo-MBS ( Sulfo-m-maleimidobenzoyl-N-hydroxysulfo-succinimide ester , Pierce ) was added at a final concentration of 0 . 5 mM as described previously [72] . Blue native electrophoresis ( BN-PAGE ) was carried out according to published procedures [47] , [72] . Complexes of purified p34 ( 30 µg/lane ) , dissolved in 0 . 5% Triton X-100 , 10% Glycerol , 50 mM NaCl . 0 . 1 mM EDTA , PMSF 1 mM , 20 mM Tris-HCl pH 7 . 0 , were separated in gels containing 500 mM ε-aminocaproic acid ( EACA ) . Electrophysiological characterization of p34 was carried out using the planar lipid bilayer technique as detailed in ref . [73] . Briefly , purified urea solubilised p34 was applied directly below the bilayer in the cis chamber . An acid pretreatment of the protein was omitted . Buffer conditions were symmetrical with 1 . 5 M KCl , 10 mM Mops-Tris ( pH 7 . 0 ) in the cis/trans compartment or 1 . 5 M KCl , 10 mM Na-Acetat ( pH 4 . 0 ) in the cis/trans compartment . Two Ag/AgCl electrodes covered by 2 M KCl-agar bridges were inserted into each chamber with the trans chamber electrode connected to the headstage ( CV-5-1GU ) of a Geneclamp 500 current amplifier ( Axon Instruments ) and thus was the reference for reported membrane potentials . A solution of purified azolectin ( 60 mg/ml; Sigma type IV-S ) in n-decan ( purity >99% , Sigma ) was used to generate the planar lipid bilayers . Current recordings were carried out using a Digidata 1200 A/D converter . Data analysis was performed by self written Windows-based SCIP ( single-channel investigation program ) in combination with Origin 7 . 0 ( Microcal Software ) . Current recordings were performed at a sampling interval of 0 . 1 ms , filtered with a low-pass-filter at 2 kHz . Purified p34 was dialyzed against 8 mM N-Decyl-β-D-Maltopyranosid , 10 mM KCl , 20 mM K2HPO4/KH2PO4 , pH 7 . 0 . CD-spectroscopy and calculation of the secondary structure of p34 was performed as described in ref . [74] . Briefly , CD spectra were recorded using a Jasco J-810 spectrapolarimeter . All measurements were carried out in a quartz cuvette with an optical path length of 0 . 01 cm at room temperature . The scans ( n = 16 ) were averaged to improve the signal/noise ratio . Blank buffer spectra were collected and subtracted from the sample spectra .
|
VacA is a toxic protein produced by Helicobacter pylori , the bacteria that cause gastritis and ulcer diseases . p34 , the toxic component of VacA , is known to damage mitochondria , defined cell organelles in the target cells . However , both the mechanism of mitochondrial targeting and the toxic activity inside the mitochondria are unclear . In this study , we show that p34 carries a unique targeting signal that is different from all targeting signatures that were previously identified in endogenous mitochondrial proteins . Eventually , p34 seems to act as an anion channel in the mitochondrial inner membrane and thus to destroy the balance of salt ions in the organelles .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology/membranes",
"and",
"sorting",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"biophysics/membrane",
"proteins",
"and",
"energy",
"transduction"
] |
2010
|
Helicobacter pylori VacA Toxin/Subunit p34: Targeting of an Anion Channel to the Inner Mitochondrial Membrane
|
Polyomaviruses are a family of DNA tumor viruses that are known to infect mammals and birds . To investigate the deeper evolutionary history of the family , we used a combination of viral metagenomics , bioinformatics , and structural modeling approaches to identify and characterize polyomavirus sequences associated with fish and arthropods . Analyses drawing upon the divergent new sequences indicate that polyomaviruses have been gradually co-evolving with their animal hosts for at least half a billion years . Phylogenetic analyses of individual polyomavirus genes suggest that some modern polyomavirus species arose after ancient recombination events involving distantly related polyomavirus lineages . The improved evolutionary model provides a useful platform for developing a more accurate taxonomic classification system for the viral family Polyomaviridae .
Murine polyomavirus ( MPyV ) was discovered in the mid-1950s as a filterable infectious agent that could induce salivary tumors in experimentally exposed mice [2 , 3] . It was quickly established that the virus is potently carcinogenic , causing many different types of tumors ( Greek poly + oma ) in various experimental systems . When the first primate polyomavirus , simian vacuolating virus 40 ( SV40 ) , was discovered as an abundant contaminant in early poliovirus vaccines that had already been administered to millions of individuals , it posed significant cause for alarm ( reviewed in [4] ) . The ensuing rush to study the molecular biology of polyomaviruses provided a great wealth of insights into basic cell biology and the fundamental mechanisms of tumorigenesis ( reviewed in [5] ) . There is no conclusive evidence for productive transmission of SV40 among humans and it does not appear that the virus caused discernible disease in poliovirus vaccine recipients ( reviewed in [6] ) . However , SV40 is closely related to human JC and BK polyomaviruses ( JCV and BKV ) , both of which cause disease in immunosuppressed patients . JCV was discovered in a patient ( initials JC ) who was suffering from a lethal brain disease called progressive multifocal leukoencephalopathy ( PML ) [7] . BKV is rarely found in the brain , but causes serious kidney damage in up to 10% of kidney transplant recipients [8] . Conflicting reports suggest possible associations between JCV and BKV and additional human diseases , including prostate , colorectal , and kidney cancers [5 , 9] . A more recently discovered human polyomavirus , Merkel cell polyomavirus ( MCV ) , plays a key causal role in the development of a rare form of skin cancer , Merkel cell carcinoma [10] . Other recently discovered human polyomaviruses have been associated with a variety of disease states , ranging from thymic and lymphoid cancers to non-malignant skin dysplasias and vascular myopathy [11–14] . Efforts to discover additional human and animal polyomaviruses , and the conclusive establishment of further links to disease states , will undoubtedly remain highly active research areas for the foreseeable future . It has been difficult to achieve consensus on the development of systems for taxonomic classification of polyomaviruses . This is regrettable , in the sense that the availability of a robust classification scheme could help guide researchers and clinicians toward an understanding of where to expect biological similarities and differences among established and newly discovered polyomavirus species . A key barrier to the development of a consensus taxonomic scheme has been the lack of a clear model for the evolutionary history of polyomaviruses . Approaches to this question have been limited by the fact that known polyomavirus species are derived from a restricted subset of terrestrial vertebrates . In this study , we report our discovery of polyomaviruses in several species of fish . Searches of shotgun genomics datasets also revealed previously unknown polyomavirus-like sequences in a surprisingly wide variety of additional animals , including insects and arachnids . We make use of these new , highly divergent polyomavirus sequences to develop an evolutionary model that might account for the interrelationships of extant polyomavirus species .
In an effort to obtain more divergent polyomaviruses to use as reference points for understanding polyomavirus evolution , we sampled a variety of fish species . We have recently published a brief announcement describing the sequence of a polyomavirus found in samples of a perciform fish , black sea bass ( Centropristis striata ) [15] . In the current report , we present our discovery of another polyomavirus species found in a different perciform fish , the sharp-spined notothen ( Trematomus pennellii ) from McMurdo Sound ( Ross Sea , Antarctica ) . The predicted genetic organization of these viruses is shown in Fig 1 . We also report a previously unknown polyomavirus species found in a giant guitarfish ( Rhynchobatus djiddensis ) suffering from papillomatous skin lesions . Guitarfish are members of the subclass Elasmobranchii , which includes sharks and rays . Elasmobranchs and bony vertebrates are thought to have diverged during the Cambrian period , about half a billion years ago [16] . Although the guitarfish polyomavirus encodes the characteristic polyomavirus arrangement of major open reading frames ( Fig 1 ) , its 3 , 962 bp genome is substantially smaller than the 4 , 697 bp genome of bovine polyomavirus 1 , which had previously been the smallest known member of the family ( see S1 File ) . To confirm that the virus directly infected the giant guitarfish ( as opposed to an unknown environmental source ) , we performed in situ hybridization using a probe targeting the VP1 ORF . Hybridization signal was observed in small numbers of cells in resolving skin lesions ( S1 Fig ) , confirming that the virus directly infects guitarfish . We have recently reported the sequences of three polyomavirus species found in supermarket ground beef [17 , 18] . In a follow-up effort using the same methods , we sampled supermarket ground turkey , American bison , and lamb . Although no polyomaviruses were found in the turkey or bison samples , a single previously unknown polyomavirus species was identified in the ground lamb ( Ovis aries , sheep ) meat sample . In light of recent scandals identifying traces of horse meat in supermarket ground beef products [19] , the association of this virus with sheep should be considered tentative . In GenBank keyword searches we noticed that a genomic DNA segment of a South African social spider ( Stegodyphus mimosarum ) had been annotated as having a patch of sequence similarity to polyomavirus LT ( accession KK122585 ) . The apparent endogenized “fossil” LT gene , which is integrated into a putative spider transcription elongation factor locus , was inferred to have one frameshift mutation and one nonsense mutation . Polyomavirus protein sequences , including the novel fish polyomavirus LTs and a “resurrected” version of the social spider LT , were used to query translated nucleotide sequences in various NCBI databases . An additional fossil LT sequence was detected at a second locus in the social spider Whole Genome Shotgun ( WGS ) dataset . At least half a dozen fossil LT-like sequences could be detected in WGS entries for the common house spider ( Parasteatoda tepidariorum ) . A short ( 170 bp ) LT-like contig was identified in a third spider species , the Brazilian whiteknee tarantula ( Acanthoscurria geniculata ) . Nearly a dozen transcripts with clear similarity to LT proteins were found in the Transcriptome Shotgun Assembly ( TSA ) datasets for two primitive insects , Machillis hrabei and Meinertellus cundinamarcensis ( commonly called bristletails ) . More recently , some additional arthropod polyomavirus LT and VP1 transcripts have appeared in the TSA datasets for brown widow ( Latrodectus geometricus ) and cupboard spider ( Steatoda grossa ) . Accession numbers for these newer sequences are listed in the “fragments” tab of S1 File . Several polyomavirus-like sequences were also observed in TSA datasets for vertebrates , including a short VP1-like sequence in guineafowl ( Numida meleagris ) , a short LT-like fragment in Carolina anole lizard ( Anolis carolinensis ) , and an apparently complete set of spliced LT , VP1 , and VP2 transcripts in the TSA dataset for dark-eyed junco ( Junco hyemalis ) . The most important discovery in the WGS database was a single contig ( AXZI01204118 ) that appears to represent a nearly complete polyomavirus genome associated with Baja California bark scorpion ( Centruroides exilicauda ) . Extension of the contig using individual reads from the parent Sequence Read Archive ( SRA ) datasets revealed two variants ( ~92% identity ) of a circular non-integrated polyomavirus-like sequence . It thus appears that the individual animal used for the genome sequencing project happened to be productively infected with a polyomavirus . Although the complete , apparently episomal sequences show the usual organization of polyomavirus genomes , with highly divergent homologs of the standard LT and VP2 proteins ( Fig 1 ) , BLAST alignments using the inferred VP1 protein do not yield any convincing hits ( E values >0 . 5 ) . Computer-based modeling was used to investigate the possible structural conservation of the apparent LTs of the new fish and arthropod polyomaviruses . SV40 LT is divided into discrete structural domains that are thought to exist in a “beads on a string” configuration ( reviewed in [20] ) . The structures of individual LT domains have been solved [21 , 22] . The modeled structures of the scorpion and fish LT origin binding domain ( OBD ) , zinc finger domain , and ATPase domain each show a good fit with the known SV40 structures ( Fig 2 ) . A conservation map for the DNAJ and Zn-ATPase domains is shown in S2 Fig . These results confirm that the fish- and scorpion-derived sequences represent bona fide polyomavirus LT proteins . LT proteins typically carry an N-terminal domain with sequence and structural similarity to cellular DNAJ chaperone proteins . The domain is defined by a hallmark linear motif , HPDKGG . The guitarfish and scorpion viruses share this motif , and the N-terminal domains of their LT proteins can readily be modeled onto known DNAJ structures ( Fig 2 ) . In contrast , the predicted sea bass and notothen polyomavirus LT proteins lack HPDKGG motifs . The two viruses are unique among known polyomaviruses in their apparent lack of any sequences that can be modeled onto known DNAJ structures . The novel N-terminal domains of the two perciform fish LT proteins share only about 25% similarity to one another , show no clear similarity to any other known proteins or protein structures , and are predicted to be unstructured . A possible explanation could be that the LT DNAJ domain is a common ancestral feature that was lost during development of the perciform fish polyomavirus lineage . A phylogenetic tree was constructed for the complete LT protein sequences of examples of all currently known polyomavirus species and sub-genomic fragmentary sequences available prior to November , 2015 . The phylogenetic analyses also included putative LT protein sequences found in a pair of viral species that cause carcinomatosis in an Australian marsupial , the western barred bandicoot ( Perameles bougainville ) . The bandicoot viruses appear to have arisen after recombinant chimerization involving an unidentified polyomavirus and a member of a known group of marsupial-tropic papillomaviruses [23 , 24] . The apparently chimeric viruses encode a polyomavirus LT-like gene on one strand and genes for papillomavirus-like L1 and L2 capsid proteins on the other strand . Like the bandicoot viruses , a different apparently chimeric virus called Japanese eel endothelial cells-infecting virus ( JEECV ) encodes a protein with typical LT features , including an N-terminal DNAJ-like sequence domain [25] . A similar virus has recently been discovered in Taiwanese marbled eels [26] . Aside from the clear 2 . 1 kb LT gene , the remaining ~13 kb of the JEECV genome bears little similarity to sequences in GenBank . It thus appears that JEECV and the marbled eel virus arose through recombination between a bony fish-associated polyomavirus and a member of another DNA virus family that remains unidentified . Phylogenetic analysis of LT proteins ( Fig 3 ) shows distinct clades corresponding to fish- and arthropod-associated sequences , as well as the previously recognized mammalian Ortho and Almi clades [27 , 28] . Avian and bandicoot LT sequences together occupy a distinct clade . The appearance of the bandicoot virus LT protein sequences within this clade suggests that polyomaviruses with Avi-like early regions may infect modern marsupials . The avian and bandicoot LT proteins occupy a larger super-clade that loosely includes the newly identified fish-associated LT sequences . The LT protein sequences of the fish-associated polyomaviruses form a distinct clade that includes JEECV LT . Phylogenetic analyses of VP1 protein sequences ( Fig 4 ) reveal somewhat different patterns . In contrast to avian polyomavirus LT protein sequences , avian polyomavirus VP1 sequences are interspersed among mammalian Ortho VP1 sequences . Phylogenetic analyses of VP2 protein sequences ( presented in FigTree format in S4 File ) are concordant with the VP1 analysis in this regard . Members of the previously recognized Wuki clade [29] encode VP1 protein sequences that occupy a highly divergent clade that distantly encompasses fish-associated VP1 sequences , while the early regions of Wuki species encode Ortho- or Almi-LT-like genes . Thus , relative to “classic” Ortho polyomaviruses the Avi clade shows a highly divergent early region while the Wuki clade shows a highly divergent late region . In Fig 5 we illustrate a recombination scheme that could account for this strangely mixed phylogeny . The carboxy-terminal halves of Avi and bandicoot small T antigens ( sT ) show no linear sequence similarity to the sT proteins of Ortho or Almi polyomavirus species . In particular , Avi-type sT proteins lack highly conserved cysteine motifs that have recently been shown to coordinate iron-sulfur clusters in mammalian sT proteins [30] . It is also noteworthy that a conserved LXCXE motif ( thought to be involved in interactions with the pRb family of tumor suppressor proteins and suppression of innate antiviral immunity [31] ) is located on the shared sT/LT leader sequence in the Avi and bandicoot viruses , whereas the LXCXE motif is instead located in the second exon of LT in Ortho and Almi species . This suggests that Avi sT has a different evolutionary origin than Ortho/Almi sT . A possible explanation would be that Ortho/Almi sT arose after re-location of an ancestral cysteine motif-containing accessory gene into an N-terminal LT intron . For example , duplication of the scorpion polyomavirus ORF labeled “Agno” ( Fig 1 ) into the LT intron could roughly reproduce an Ortho/Almi sT-like arrangement . A prediction of this idea would be that some of the hypothetical accessory ORFs of fish and arthropod polyomaviruses may be metal-binding proteins with Ortho/Almi sT-like functions , such as manipulation of cellular protein phosphatase 2A proteins [32] . ALTO is a recently discovered accessory gene that is “overprinted” in the +1 frame of the second exon of LT [27 , 33 , 34] . Although the function of ALTO is unknown , it shows sequence similarity to the C-terminal transmembrane domain of the well-studied middle T antigen of MPyV . This suggests that ALTO might , like middle T , function by mimicking activated growth factor receptors ( reviewed in [35] ) . In their initial report demonstrating the existence of MCV ALTO , Carter et al . suggested that the gene might have first arisen in the Almi ( ALTO/middle T ) lineage after its divergence from the Ortho lineage . However , Carter and colleagues also noted that the ATG codon thought to initiate the translation of MCV ALTO and a hydrophobic sequence near the C-terminus of ALTO are partially conserved in other polyomaviruses outside the defined Almi clade . Puzzlingly , many recently discovered non-Almi polyomaviruses appear to have ALTO-like ORFs with lengths similar to some of the shorter examples of recognized Almi-LT ALTOs ( summarized in S1 File ) . For example , the two variants of the scorpion polyomavirus potentially encode 9 or 13 kD ATG-initiated proteins in the +1 frame of the second exon of their LT sequences ( see Fig 1 ) . Despite the fact that the new supermarket sheep meat-associated polyomavirus occupies the Ortho clade , the +1 frame of its LT second exon encodes a potential 10 kD ALTO-like protein . One conceivable explanation for these observations might be that ALTO-like ORFs are an ancient ancestral feature that has been lost in some polyomavirus lineages . A possible example of occult or remnant ALTO/MT-like genes might be found in the small clade of primate polyomaviruses that encompasses SV40 . Members of this group of viruses encode a short Met-initiated ORF in the +1 frame of the second exon of LT and a separate short downstream LT +1 frame ORF with a splice acceptor near its 5’ boundary ( S3 Fig ) . It will be important to experimentally test the hypothesis that polyomavirus species outside the Almi-LT group express LT +1 frame ORFs as functional accessory proteins . Three previously established [36–38] virus-host co-evolutionary models are summarized in simplified cartoon form in Fig 6 . In the strict co-divergence model , the rate at which viruses “speciate” from one another exactly matches the rate at which host animals speciate . A group of retroviruses known as foamy viruses are an example of a viral genus that may at least roughly follow this evolutionary model [39] . Many prior studies have established that the family Polyomaviridae , as a whole , does not conform to the strict co-divergence model [37 , 40–42] . In the co-divergence with host switching model ( middle panel of Fig 6 ) , viruses and hosts generally co-diverge , but viruses are occasionally productively transmitted between distantly related host animals . In the example , such events are reflected in finding closely related viral sequences in bats and great apes ( see light blue branches ) . Ebola and influenza viruses are familiar examples of viruses with clear evidence of occasional long-range host switching . The first known polyomavirus of birds was discovered in diseased budgerigar fledglings ( reviewed in [43] ) . Sequences >99% identical to the original budgerigar fledgling disease polyomavirus have subsequently been found in a surprisingly wide range of distantly related bird species [44–47] . Likewise , sequences nearly identical to goose hemorrhagic polyomavirus have been found in ducks [48 , 49] ( accession JF304775 ) . These prior findings are displayed as points close to the x-axis in Fig 7 . Although the findings indicate that the host-switching model shown in the middle panel of Fig 6 might be applicable to some avian polyomaviruses , an important caveat is that all documented instances of inter-species Avi polyomavirus transmission have involved captive animals . It thus remains uncertain whether Avi polyomavirus host-switching occurs in the wild over longer timescales . In contrast to Avi polyomaviruses , there are currently no examples of an individual polyomavirus species being found in more than one mammalian host ( Fig 7 ) . Most strikingly , there is no evidence of productive polyomavirus transmission between humans and any of the various polyomavirus-bearing animals we commonly live with or eat ( i . e . , budgerigars , canaries , geese , ducks , mice , rats , hamsters , capuchins , horses , cattle , sheep , caribou , or sea bass ) . The fact that the Rhesus macaque polyomavirus SV40 seems not to have gained a detectable foothold in the human population despite extremely widespread human exposure is also noteworthy in this regard . These observations suggest that the host-switching model is not generally applicable to mammalian polyomaviruses . In the intrahost divergence model ( right-hand panel of Fig 6 ) , viruses diverge from one another at a faster rate than host animal speciation . Ancient viral divergence events occurring within a single host animal lineage eventually give rise to separate viral clades that co-occupy a single animal species . The model does not invoke transmission of viruses between distantly related host animals , but could accommodate viral transmission between closely related animal species or subspecies . In the shown example , the dark blue and light blue lobes of the viral phylogenetic tree each internally resemble the phylogeny of host animals . More recent intrahost viral divergence events are reflected as distinct but closely related viral species found within a single host animal ( see dark blue branches ) . Herpesviruses and some retrovirus genera are well-documented examples of this form of viral evolution [50 , 51] . The phylogeny of mammalian polyomavirus species is qualitatively similar to the intrahost divergence model . In particular , the two Almi “Monominor” sub-clades ( defined as species that encode a recognizable large ALTO but lack VP3 [1 , 27] ) recapitulate the expected topology of the intrahost divergence model ( S2–S5 Files , S4 Fig ) . The intra-host divergence model predicts that homologs of polyomavirus species that occupy currently depauperate lobes of the tree , such as the small clade that encompasses only WU and KI , may ultimately be found in other mammals . This prediction is consistent with the recent discovery of two WU/KI-like polyomavirus species associated with two European vole genera [52] ( see Discussion ) . A 2007 study by Carroll and colleagues showed that the VP1 nucleotide sequences of a panel MPyV strains found in feral mice collected in various locations in the United States all exactly matched the sequence of an MPyV laboratory isolate propagated in culture since 1953 [53] . Likewise , recent avian polyomavirus isolates are nearly identical to the isolate originally discovered in budgerigar fledglings in 1981 [54] . The concept that individual polyomavirus lineages may remain perfectly static over historical timescales is also consistent with the fact that BKV , JCV , MCV , and TSV strains with nearly or exactly identical nucleotide sequences have repeatedly been isolated from people residing on different continents [55 , 56] . Historical sampling thus does not appear to be a tractable approach to measuring polyomavirus nucleotide sequence divergence rates . We set out to instead compare the observed divergence of different polyomavirus species to the estimated time of divergence of the host animals in which they were found . The analysis rests on the starting assumption that productive transmission of polyomaviruses between different mammal genera is rare or non-existent ( Fig 7 , S3 Fig , S5 File ) . In the intrahost divergence model , distantly related polyomaviruses found in closely related animals reflect ancient polyomavirus divergence events that occurred long prior to the divergence of the host animal pair . Under this scenario , data points in the top left quadrant of Fig 7 would give an artificially fast estimate of the rate of polyomavirus sequence divergence . Despite this caveat , it seems reasonable to assume that polyomavirus divergence events might sometimes happen to coincide with host animal speciation events . This would be reflected as the lowermost Ortho and Almi points in the scatter plot shown in Fig 7 . The arbitrary dashed line in the figure connects polyomavirus pairs that hypothetically happened to diverge from one another at about the same time that the host animal pair diverged . The slope of the line is consistent with the idea that at least some Ortho and Almi polyomavirus pairs cumulatively diverged by roughly 0 . 5% per million years , at least during the first 60 million years after divergence . This crude estimate is consistent with a more sophisticated phylogenetics-based Bayesian rate estimate by Krumbholz et al . of about 0 . 8% per million years ( 8 x 10−9 nucleotide substitutions per site per year ) for the protein-coding segments of Ortho polyomaviruses [57] . A separate observed-divergence analysis of LT and VP1 proteins suggests that the two genes have independently accumulated non-silent changes at comparable long-term rates ( S5 Fig ) . This rough result is also consistent with the more sophisticated prior work of Krumbholz and colleagues . We also performed additional computational analyses to further confirm the prior rate estimates of Krumbholz et al . These analyses focused on the phylogenetically tractable Monominor clade . A ParaFit analysis of the clade as a whole indicates that the null hypothesis that polyomaviruses evolved independently of their hosts can be rejected , with a p-value of 0 . 0258 . Based on the assumption that the separate Monominor A and B sub-clades arose after an ancient intrahost divergence event that pre-dated the first placental mammals , we performed separate ParaFit analyses on each Monominor sub-clade . These analyses indicate an even more confident rejection of the null hypothesis , with p-values of 1 x 10−4 and 8 x 10−4 for the A and B sub-clades , respectively . A BEAST analysis of concatenated LT and VP1 genes for the Monominor clade confirms that codon positions 1 and 2 evolve at a long-term rate of about 5 x 10−9 substitutions per site per year ( i . e . , 0 . 5% per million years ) , while codon position 3 evolves at a rate of about 2 x 10−8 substitutions per site per year . A time-resolved phylogenetic tree of the entire Monominor clade based on host phylogeny is shown in S5 File .
In this report , we propose a comprehensive theoretical framework for understanding the evolutionary history of the viral family Polyomaviridae . Our model suggests that the last common ancestor of arthropods and vertebrates harbored at least one polyomavirus . In the ensuing roughly half billion years , polyomaviruses appear to have accumulated genetic change at a remarkably slow cumulative long-term pace , in a pattern consistent with the intrahost divergence model diagrammed in Fig 6 . Qualitative comparisons of phylogenetic trees suggest the occurrence of ancient recombination events involving distantly related polyomavirus species . The intrahost divergence model also seems applicable to the evolution of papillomaviruses [58 , 59] . A striking difference between polyomaviruses and papillomaviruses is the much greater number of known papillomavirus types . Our model could explain this difference simply by postulating that papillomaviruses evolve ( and therefore undergo intrahost divergence ) at a slightly faster rate than polyomaviruses . This is consistent with the findings of Rector and colleagues , who used phylogenetic analyses to estimate that papillomaviruses diverge at an observed long-term rate of 2% per million years ( i . e . , slightly faster than our phylogenetics-based long-term rate estimates for polyomaviruses ) [57 , 60] . In the current classification system approved by the International Committee on Taxonomy of Viruses ( ICTV ) , all members of the family Polyomaviridae belong to a single genus , Polyomavirus . We have previously contributed to a proposal that the family be divided into three genera to be officially named Orthopolyomavirus , Avipolyomavirus , and Wukipolyomavirus [29] . A recent case study [61] helped us to appreciate a potential pitfall of the previously proposed taxonomic system . Clinical colleagues approached us about a lung transplant recipient whose lung-wash samples showed strong immunohistochemical reactivity with an antibody known to detect BKV and JCV LT proteins . Puzzlingly , the samples were negative for BKV and JCV by PCR . Although WU and KI were initially discovered in human respiratory samples [62 , 63] , we reasoned that the observed immunohistochemical staining was unlikely to represent cross-detection of WU or KI , since they occupy a different proposed genus than BKV and JCV . Hypothesizing that the sample might instead contain an undiscovered human polyomavirus related to BKV and JCV , we applied virion purification , random-primed RCA and deep sequencing methods . The deep sequencing revealed high levels of WU and no other polyomaviruses . With hindsight , we realize that a taxonomic system highlighting the close phylogenetic relationship between the LT proteins of BKV/JCV and WU/KI would have served us by suggesting the less time-consuming approach of performing simple WU/KI-specific PCR on the lung wash sample . In short , our failure to appreciate the now-apparent problem of inter-generic polyomavirus chimeras resulted in wasted effort . An established taxonomic approach to the problem of chimerization is to separately categorize each major gene product . The most familiar example is the classification of influenza virus hemagglutinin ( H ) and neuraminidase ( N ) genes ( e . g . , H1N1 , H5N1 , etc . ) . As an example of applying this type of approach to polyomaviruses , BKV could be described simply as an Ortho species , while WU could be described as an Ortho-LT/Wuki-VP1 species . Like the influenza virus classification system , this form of nomenclature could serve as a colloquial set of conventions operating as an adjunct to official ICTV classifications ( which can only be applied to entire organisms , as opposed to individual gene segments ) . Our proposed colloquial classification scheme is in conflict with a recent formal proposal currently being considered by the ICTV . The new ICTV proposal suggests classifying polyomaviruses into four official genera based solely on the phylogeny of LT proteins [64] . Although the proposal is appealingly simple , it suffers from the “chimera-blindness” described in the case study above . For example , the proposal fails to recognize that all seven members of proposed genus Gammapolyomavirus encode VP1 and VP2 proteins that are monophyletic with proposed genus Betapolyomavirus VP1 and VP2 proteins . We suggest that the slightly greater complexity of the colloquial “flu-style” classification system proposed in the current study is justified by its greater taxonomic accuracy . Since the new four-genus proposal would awkwardly preclude the use of the more accurate flu-style classification , we concur with Tao and colleagues’ recent argument [41] in favor of preserving the existing ICTV standard , under which all polyomavirus species would officially remain in a single genus , Polyomavirus . The intrahost divergence model predicts that multiple polyomaviruses with varying degrees of divergence will often be found within individual host animal species . Although we continue to favor the traditional cutoff of 81–84% identity across the entire viral genome for polyomavirus species distinctions , we note that this standard could be considered an arbitrary cutoff applied to a theoretically continuous variable . Since knowing the host animal species of origin appears to be of paramount importance for understanding polyomavirus evolution , we suggest that , in the future , it would be useful for new polyomavirus species names to reference the host animal species in which they were found . A possible problem with this approach is that , in some cases , a newly discovered virus might theoretically represent environmental contamination ( as opposed to productive infection of the sampled animal ) . Our model provides a rough “back-of-the-envelope” approach to this question . As a concrete example , two recently discovered polyomavirus species whose genome sequences differ by about 20% were found separately in common voles and bank voles [52] . These two host species are thought to have diverged about 10 million years ago [65] . The two rodent-associated polyomaviruses differ from their nearest previously known relatives , human polyomaviruses WU and KI , by about 50% . Primates and rodents diverged about 90 million years ago [66] . Given the rough consistency of the observed divergences of the two new viruses with the >0 . 5% per million year “rule of thumb” shown in Fig 7 , there seems to be no affirmative reason to suspect that the putative vole viruses originated in a non-rodent host . As polyomavirus phylogenetic trees become better populated , such guesswork could become increasingly confident . It will be interesting to learn whether any un-recombined examples of the hypothetical vertebrate Arche lineage infect modern mammals . Since the chimeric bandicoot papilloma/polyomaviruses appear to carry an Arche-LT , it seems possible that Australian marsupials would be a promising group of animals in which to search for un-recombined Arche polyomaviruses . Similarly , a small Almi-VP1-like contig from the TSA dataset for helmeted guineafowl ( Numida meleagris ) raises the possibility that some modern birds may harbor un-recombined examples of the Almi clade . It will also be important to search for additional polyomavirus species in wild Mus musculus , as well as other common laboratory animals , such as zebrafish , sea urchin , Caenorhabditis elegans , Xenopus laevis , and Drosophila melanogaster . In addition to providing experimentally tractable models for exploring polyomavirus/host interactions , discovering new polyomavirus species in any of these animal lineages would shed additional light on the seemingly languid evolution of this fascinating family of viruses .
Complete genome sequences of known polyomavirus species , as well as sub-genomic polyomavirus fragments , were downloaded from GenBank . Final database searches and downloads for sequences included in the shown phylogenetic analyses were performed on August 5 , 2015 . When necessary , the circular genome map was rearranged to comply with the convention that the initiator ATG of the Large T antigen ( LT ) CDS comprises the 5’ end of the antisense strand ( see genome maps in Fig 1 ) . In some instances , predicted splice sites or initiation codon annotations were altered based on alignments against other known polyomaviruses . MacVector 13 software was used to construct graphical maps . Polyomavirus sequences that share <85% genome-wide pairwise nucleotide identity with other polyomaviruses are traditionally considered to be distinct viral species [29] . A few exceptions to the species cutoff rule were made for polyomavirus genomes with ≥85% identity that were isolated from different animal species . Examples of this exception include LPV/Vervet3 and Vervet2/Baboon2/SA12 . Multiple representatives of each polyomavirus species were included in instances where different isolates with 85–95% identity could be found within the designated species . Each polyomavirus species was assigned a familiar nickname based on either a common name for the animal species with which it is associated or an established abbreviation ( e . g . , SV40 , BKV , JCV ) . As part of an ongoing Trematomus species physiology study in the Ross Sea , we sampled seven individual Trematomus pennellii ( common name: sharp-spined notothen ) caught using hook and line in McMurdo Sound during the summer field season of 2012–2013 . T . pennellii are benthic nototheniid fish with a maximum body length of ~24 cm and are endemic to the Southern Ocean at a typical depth of ~ 1–100 meters . Their range can extend as far as ~700 meters [67] . Approximately 1 g of stomach , gills , liver and skin from the seven fish were grouped and each sample type was homogenized in 20 ml of SM buffer ( 0 . 1 M NaCl , 50 mM Tris/HCl–pH 7 . 4 , 10 mM MgSO4 ) using a mortar and pestle , as previously described by Varsani et al . [68 , 69] . Extracted DNA was sequenced on an Illumina HiSeq 2000 sequencer at Macrogen Inc . ( South Korea ) and the paired-end reads de novo assembled using ABySS v1 . 5 . 2 [70] assembler ( kmer = 64 ) . In BLASTX [71] analyses , we identified a contig of ~6000 nt from the stomach sample that had similarity to polyomavirus LT . Based on this ~6000 nt de novo assembled sequence contig we designed abutting primers ( PES-F: 5’-GTC GAC TTC TGT GCT GAC GTG ACT GAG-3’; PES-R: 5’-AGG TCC AGC CAT CTT CGG TGT ATC ACT T-3’ ) to recover the complete circular DNA molecule encompassing the LT-like sequence . Using the abutting primer pair with KAPA Hifi Hotstart DNA polymerase ( Kapa Biosystems , USA ) we amplified the polyomavirus-like circular molecule using the following protocol: initial denaturation at 95°C for 3 min followed by 25 cycles at 98°C for 20 sec , 60°C for 15 sec , 72°C for 5min and a final extension at 72°C for 5min . We were able to recover the ~6 kb amplicon from the liver and the stomach samples and these were cloned into pJET1 . 2 plasmid ( ThermoFisher , USA ) , and Sanger-sequenced by primer walking at Macrogen Inc . ( Korea ) . The Sanger-sequences were assembled using DNAbaser v . 4 ( Heracle BioSoft S . R . L . , Romania ) . The complete genome of sharp-spined notothen ( Trematomus pennellii ) polyomavirus 1 ( 6219 nt ) was 100% identical in both the stomach and liver deep sequencing samples and has been deposited in GenBank ( accession KP768176 ) . Giant guitarfish ( Rhynchobatus djiddensis ) polyomavirus 1 ( GenBank accession KP264963 ) was detected using previously reported methods [72] in specimens from an aquarium animal suffering from proliferative skin lesions . The guitarfish polyomavirus was discovered alongside much higher levels of a member of a different DNA virus family . The sequence of the other virus , and details on the pathology of the guitarfish specimen , will be published in a separate report . Previously reported methods were used to discover sheep ( Ovis aries ) meat-associated polyomavirus 1 ( GenBank accession KP890267 ) in a sample of ground lamb meat purchased at a US supermarket [18] . Baja California bark scorpion ( Centruroides exilicauda ) polyomavirus 1 was initially identified in a TBLASTN [71] search of the NCBI Whole Genome Shotgun database ( WGS ) using the LT protein sequence of black sea bass polyomavirus as bait . A single contig , accession number AXZI01204118 , was curated back to the original reads ( Sequence Read Archive ( SRA ) accession number SRX476227 ) . The back-curation revealed that small segments were missing from the ends of the original contig . The SRA dataset contained at least three distinct viral sequence variants . The two most abundant variants were compiled separately . The putative LT intron ( where the original contig ends fell ) was an apparent polymorphic hotspot . The extensive variation in this portion of the polyomavirus genome could explain why the contig assembly process failed at this particular point . No chimeric reads ( potentially representing integration of the viral genome into the host animal’s DNA ) were detected , suggesting that both viral genomes were carried in an episomal form . Because current GenBank policies do not allow deposits of third-party sequence assemblies , the two scorpion polyomavirus sequences were instead deposited at EMBL ( accession numbers LN846618 and LN846619 ) . In the interest of clarity , this manuscript favors the use of host animal common names and avoids the extensive use of abbreviations . In our view , when abbreviations are necessary they should be short , easily inferred as representing the host animal species of origin , and , ideally , should serve as pronounceable “sigla” http://ictvonline . org/codeofvirusclassification_2012 . asp . We suggest that newly coined abbreviations should use a condensation of a common name for the host animal and “PyV” for polyomavirus . Examples of pronounceable abbreviations might be ShePyV1 for supermarket sheep meat-associated polyomavirus 1 or ChimPyV1 for Pan troglodytes verus polyomavirus 1 . Possible accessory proteins were detected by analyzing genome sequences for ORFs of at least 25 codons . Small T antigen ( sT ) was defined as an ORF encoding an ATG-initiated protein of at least 10 kD near the 5’ end of the LT gene . ALTO was defined as a >250 bp ATG-initiated ORF in the LT +1 frame located near the 5’ end of the LT exon encoding the helicase domain . In nearly all cases , the ALTO ORF overlaps the segment of LT encoding the putative pRb-interaction motif LXCXE . Agno was defined as an ORF encoding a >10 kD protein initiated from an ATG codon located upstream of the inferred VP2 ORF . An attempt was made to infer the LT-binding sites associated with the viral origin of replication . The “classic” Oris of SV40 and MPyV were defined as paired palindromic GRGGCY motifs adjacent to an A/T tract . Hypothetical Avi and fish Ori sequences were defined as paired palindromic YYTGSCA motifs adjacent to an A/T tract . A hypothetical arthropod Ori was defined as paired palindromic ATCACGYG motifs flanked on both sides by A/T tracts . The analyses of the Large T antigens ( LTs ) from scorpion , guitarfish and notothen polyomaviruses were performed using multiple bioinformatics tools from the psipred server , http://bioinf . cs . ucl . ac . uk/psipred/ ? disopred=1 [73] . In order to obtain models of high quality , the structural relationships between the novel LTs and previously solved protein structures were determined through fold recognition using pGenTHREADER and pDomTHREADER from the psipred server [74] . Matching structures with the highest scores were then selected as templates for predicting structures of the novel LTs . Models for DNAJ and OBD-Zn-ATPase were generated separately . All structures and models were visualized and compared using PyMOL ( The PyMOL Molecular Graphics System , Version 1 . 2r3pre , Schrödinger , LLC ) . MEME suite 4 . 10 . 0 http://meme . nbcr . net/meme/ [75] was used to facilitate the identification of possible palindromically arranged LT-binding motifs in candidate Ori regions . Inferred candidate motifs are indicated in the legend of Fig 5 . Curated polyomavirus sequence sets used in this work are posted at http://home . ccr . cancer . gov/Lco/PyVE . asp . The site includes annotated genomes for examples of all currently known polyomavirus species and compiled protein sequences . Initial exploratory phylogenetic analyses were performed using the Phylogeny . fr website http://phylogeny . lirmm . fr/ in “One Click” mode without Gblocks [76] . FigTree software v1 . 4 . 2 http://tree . bio . ed . ac . uk/software/figtree/ was used to display trees . Confirmatory analyses were performed by aligning sequences using MUSCLE [77] and manually editing the output . Maximum-likelihood phylogenetic trees ( with approximate likelihood branch support , aLRT ) were inferred using PHYML 3 [78] with LG+I+G as the best substitution model determined using ProtTest [79] . Branches with <80% aLRT branch support were collapsed . Confirmatory Bayesian phylogenetic analyses showed essentially identical tree topology . However , Bayesian phylogenetic trees for VP1 proteins showed poor support values . The results are consistent with a pending ICTV proposal http://talk . ictvonline . org/files/proposals/animal_dna_viruses_and_retroviruses/m/animal_dna_under_consideration/5637 . aspx . Because of their better bootstrap values , maximum-likelihood analyses were favored for the current study . Nucleotide divergence calculations were performed for individual sequence pairs using Sequence Demarcation Tool ( SDT ) version 1 . 2 in MUSCLE mode [80 , 81] http://web . cbio . uct . ac . za/~brejnev/ . Pairwise calculations were performed on discrete clades , specifically: the separate “Monominor” A and B sub-clades , the Ortho-LT clade ( excluding WU and KI ) , the “Blympho” clade ( which houses B-lymphotropic polyomavirus ( LPV ) and HPyV9 ) , and the two small clades that separately house TSV and Chimp3 . For Avi polyomaviruses , sequences found in the “fragments” tab of S1 File were included in the analysis . The analysis was performed in January 2015 and does not include polyomavirus sequences made public after that time . Estimates of the time to last common ancestor of animal species pairs were based on various references [82–87] . In most cases , the estimates were based primarily on sequence analyses , as opposed to fossil records . Estimates are consistent ( to within 10% ) with the “Expert Result” in Time Tree of Life http://www . timetree . org/ [65] . To ensure maintenance of codon information , nucleotide sequences for the VP1 and Large T coding regions were translated into protein sequences . The translated proteins were aligned using Mafft ( implementing the L-ins-I algorithm ) [88] . Next , the aligned protein sequences were reverse translated into nucleotide sequences . Finally , the individual alignments were concatenated into a supermatrix . To test for potential substitutional saturation [89 , 90] the index of substitutional saturation statistic was calculated for the supermatrix ( test implemented in DAMBE version 6 . 0 . 0 [91] ) . The results indicated that the observed saturation index of 0 . 5865 was smaller than the critical saturation index ( Iss . c = 0 . 8023 ) , suggesting that the sequences have experienced little substitutional saturation , thus conserving sufficient phylogenetic signal for phylogenetic reconstruction . PartitionFinder v1 . 1 . 1 was used to select the best-fit partitioning schemes and partition-specific substitution models under the Bayesian information criterion ( BIC ) [92] . PartitionFinder suggested the use of 4 different partitions [ ( Large T codon position 1 , VP1_CP1 ) , ( Large T_CP2 , VP1_CP2 ) , ( VP1_CP3 ) , and ( Large T_CP3 ) ] . All partitions were estimated to evolve under the General Time Reversible ( GTR ) model of nucleotide substitution with invariant sites ( I ) and Γ distributed rate variation among sites ( GTR+I+G ) . Parafit was used to formally test the hypothesis of coevolution between Monominor polyomaviruses and their associated hosts [93 , 94] . The null hypothesis ( H0 ) of the global test is that the evolution of polyomavirus species and the host animals in which they were found has been independent . The test , as implemented within the R package ( APE ) version 3 . 3 [95] requires two phylogenetic trees and the set of host-parasite association links . The host tree was constructed using phyloT ( available from http://phylot . biobyte . de/ ) . PhyloT uses NCBI taxonomy identification numbers to generate a phylogenetic tree . The obtained tree was manually edited to include branch lengths of unit length . MrBayes 3 . 2 . 6 [96 , 97] , as implemented within the CIPRES Science Gateway V . 3 . 3 [98] , was used to estimate the Monominor phylogenetic tree . The selected GTR+I+G substitution model was implemented . The analysis was run using two independent chains for a total chain length of one million iterations , with a sampling frequency every 1 , 000th step . Following a 10% burn-in , the tree was summarized . The GlobalParafit was estimated to be 3633 . 384 , with a p-value = 0 . 0258 ( based on 1 , 000 permutations ) , providing support in favor of co-speciation . The supermatrix described in the previous section was used for this analysis . The Bayesian analysis ( Beast 1 . 8 [99] ) as implemented within the CIPRES Science Gateway V . 3 . 3 [98] , was performed using linked substitution rates for the first and second codon positions ( CP12 ) , while allowing independent rates in CP3 . The uncorrelated lognormal relaxed molecular clock was used to accommodate rate variation among lineages . Monophyletic constraints were placed on the separate Monominor A and B clades . Based on the posterior distributions obtained for the host [84 , 100] , normal priors were imposed on specific nodes used to calibrate the evolutionary rates ( S5 File ) . Three independent Markov Chain Monte Carlo ( MCMC ) analyses were run for 10 million generations each , with samples from the posterior drawn every 1 , 000 generations . The first 10% of each run was discarded prior to the construction of the posterior probability distributions of parameters . Each analysis was run sufficiently long that effective sample sizes for parameters were >400 . The results from the three runs were combined to generate a maximum clade credibility tree and rate and divergence time summaries ( S5 File ) .
|
Polyomaviruses are a family of DNA-based viruses that are known to infect various terrestrial vertebrates , including humans . In this report , we describe our discovery of highly divergent polyomaviruses associated with various marine fish . Searches of public deep sequencing databases unexpectedly revealed the existence of polyomavirus-like sequences in scorpion and spider datasets . Our analysis of these new sequences suggests that polyomaviruses have slowly co-evolved with individual host animal lineages through an established mechanism known as intrahost divergence . The proposed model is similar to the mechanisms through with other DNA viruses , such as papillomaviruses , are thought to have evolved . Our analysis also suggests that distantly related polyomaviruses sometimes recombine to produce new chimeric lineages . We propose a possible taxonomic scheme that can account for these inferred ancient recombination events .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"sequencing",
"techniques",
"organismal",
"evolution",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"microbiology",
"invertebrate",
"genomics",
"viruses",
"dna",
"viruses",
"phylogenetic",
"analysis",
"genome",
"analysis",
"microbial",
"evolution",
"sequence",
"motif",
"analysis",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"genomics",
"medical",
"microbiology",
"microbial",
"pathogens",
"biological",
"databases",
"molecular",
"biology",
"polyomaviruses",
"viral",
"evolution",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"animal",
"genomics",
"dna",
"sequence",
"analysis",
"sequence",
"databases",
"virology",
"viral",
"pathogens",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"evolutionary",
"biology",
"genomic",
"databases",
"organisms"
] |
2016
|
The Ancient Evolutionary History of Polyomaviruses
|
Information on the growth rate and metabolism of microbial pathogens that cause long-term chronic infections is limited , reflecting the absence of suitable tools for measuring these parameters in vivo . Here , we have measured the replication and physiological state of Leishmania mexicana parasites in murine inflammatory lesions using 2H2O labeling . Infected BALB/c mice were labeled with 2H2O for up to 4 months , and the turnover of parasite DNA , RNA , protein and membrane lipids estimated from the rate of deuterium enrichment in constituent pentose sugars , amino acids , and fatty acids , respectively . We show that the replication rate of parasite stages in these tissues is very slow ( doubling time of ~12 days ) , but remarkably constant throughout lesion development . Lesion parasites also exhibit markedly lower rates of RNA synthesis , protein turnover and membrane lipid synthesis than parasite stages isolated from ex vivo infected macrophages or cultured in vitro , suggesting that formation of lesions induces parasites to enter a semi-quiescent physiological state . Significantly , the determined parasite growth rate accounts for the overall increase in parasite burden indicating that parasite death and turnover of infected host cells in these lesions is minimal . We propose that the Leishmania response to lesion formation is an important adaptive strategy that minimizes macrophage activation , providing a permissive environment that supports progressive expansion of parasite burden . This labeling approach can be used to measure the dynamics of other host-microbe interactions in situ .
A number of medically important bacterial , fungal and protozoan pathogens are associated with persistent chronic infections that can reactivate to cause acute disease long after initial infection [1–5] . With few exceptions [6] , very little is known about the growth rate or physiological state of these pathogens during chronic stages of infection , reflecting limitations in current methods for measuring microbial growth in situ . This information is crucial for modeling host-pathogen dynamics and developing therapies that target these stages . Leishmania spp are protozoan parasites that are associated with long-term chronic infections , as well as acute disease , ranging from self-healing cutaneous lesions to fatal visceral infections , in millions of people worldwide [7] . Infection is initiated by flagellated promastigote stages that are injected into the skin by a sandfly vector . Following their uptake by macrophages and other phagocytic cells , promastigotes differentiate to aflagellate amastigotes that proliferate in the phagolysosome compartment of these host cells [8 , 9] . A hallmark of all Leishmania infections is the formation of localized tissue lesions or granulomas composed primarily of infected and uninfected macrophages , at the site of the sandfly bite or in distal tissues such as the liver and spleen [10–12] . Depending on the Leishmania species involved and host genetics , lesion formation can be associated with immune control ( but usually not eradication of the parasite ) or parasite expansion and systemic infection . In murine models of infection , host resistance is associated with the development of a T-helper type 1 response , while lesion development occurs in susceptible animals that mount a T-helper type 2 response [13 , 14] . In contrast to our understanding of the host immune responses that underlie these different outcomes , very little is known about the growth rate or physiological state of Leishmania in these tissues . Transgenic parasite lines expressing luciferase or different fluorescent proteins have been developed and used to visualize parasite dynamics in vivo [15–18] . However , these approaches only provide a measure of net changes in parasite burden , which are determined by rates of parasite death and migration from infected tissues , as well as rates of replication . Furthermore , attempts to infer the physiological status of lesion amastigotes from transcriptomic and proteomic analyses have been hampered by the absence of conventional gene-specific transcriptional control in these parasites and the paucity of coordinated changes in the abundances of individual mRNA and proteins in different insect and mammalian-infective stages [19 , 20] . In this study we introduce the use of 2H2O labeling to measure Leishmania growth rate and metabolic activity in murine inflammatory lesions . In the presence of 2H2O , cells stably incorporate deuterium into a wide range of metabolites , which are subsequently incorporated into cellular macromolecules , providing a quantitative read-out of global rates of DNA replication , transcription , protein turnover and membrane lipid biosynthesis ( S1 Fig . , Fig . 1A ) [21 , 22] . 2H2O is easily and safely administered to animals for periods of weeks to months and rapidly equilibrates across all tissues [23] , making it suitable for measurement of slowly growing microbial populations in infected tissues . Using this approach , we show that amastigotes exhibit a constant , but very slow growth rate , in non-healing lesions and appear to enter into a distinct semi-quiescent metabolic state characterized by low rates of transcription and protein turnover . This quiescent state is distinct from that measured in non-dividing ( insect ) promastigote stages and may represent an adaptive response to a growth-restrictive intracellular microenvironment in granulomas . Using this approach we also identified parasite-specific metabolic pathways , such as polyunsaturated fatty acid biosynthesis that are up-regulated in situ . This approach has provided the first global analysis of the physiological state of the major mammalian-infective stage of Leishmania and is generally applicable to studying the in vivo growth and physiology of other microbial pathogens .
As previously reported [24] , cultivation of L . mexicana promastigotes stages in standard medium containing 5% 2H2O , leads to the deuterium-labeling of the deoxyribose ( dRib ) moiety of DNA ( Fig . 1B ) . The labeling of dRib occurs as a result of gluconeogenesis , various sugar phosphate isomerization/epimerization reactions , the pentose phosphate pathway and ribonucleotide reductase ( S1 Fig . ) , and the rate of incorporation of deuterium into promastigote DNA was growth-dependent . Specifically , the rate of labeling of exponentially growing promastigotes ( Prolog ) indicated a doubling time of 9 hours , concordant with cell counts , while no incorporation was observed in the DNA of non-dividing promastigotes ( Prostat ) ( Fig . 1B ) . Significantly , labeling of the deoxyribose moiety of DNA was not affected by supplementation of the culture medium with ribose or a range of nucleosides and nucleotides to the culture medium ( S2 Fig . ) , indicating that de novo synthesis of ribose/dRib is not affected by salvage pathways . Prostat were induced to differentiate to axenic amastigotes ( Amaaxenic ) by acidification of the medium and cultivation at elevated temperature . Following differentiation , Amaaxenic exhibited a doubling time of 4 . 2 days , substantially slower than Prolog ( Fig . 1B , C ) . These data support the notion that amastigote differentiation is associated with activation of the parasite stress responses and a reduction in maximum growth rate , independent of exogenous nutrient levels [24] . Having shown that 2H2O labeling can be used to quantitate rates of parasite replication; we extended this approach to directly measure amastigote proliferation in murine tissue lesions . BALB/c mice were infected with L . mexicana parasites and subsequently labeled with 2H2O following the appearance of cutaneous lesions . A constant level of 5% 2H2O in the body water was established by providing mice with a bolus of 100% 2H2O and subsequent inclusion of 9% 2H2O in the drinking water for up to several months ( S3 Fig . ) . Mice were culled at various time points and lesion amastigotes ( Amalesion ) isolated from infected tissues . Histological examination showed that these lesions primarily comprised heavily infected host cells ( with parasites in large communal vacuoles ) and no detectable necrosis ( S4 Fig . ) . Amalesion purified from these tissues were free of intact host cells or nuclei , as determined by DAPI staining , and were further treated with DNAse to remove any extracellular host DNA released during tissue disruption ( S5 Fig . ) . Contamination of parasite DNA with host DNA was estimated to be less than 20% as determined by direct quantitation of DNA ( S5 Fig . ) . Deuterium enrichment in parasite DNA-dRib , increased with time , reaching a maximum 15% enrichment ( EM1; excess molar fraction of M1 ) after about 40 days of labeling ( Fig . 1B ) . Based on the rate of enrichment , Amalesion were found to have a remarkably constant doubling time of ~12 days , irrespective of the age of the lesion when the 2H2O labeling was initiated ( ranging from 4 weeks to 4 months post-infection ) ( Fig . 1B , C ) . This growth rate is 32-fold slower than the maximum growth rate of promastigotes stages and approximately 4-fold slower than that measured for Amaaxenic ( Fig . 1C ) , indicating that parasite growth in the granuloma microenvironment is highly constrained . To investigate whether the slow growth of lesion amastigotes reflected growth-limiting conditions in the phagolysosome of infected macrophages , we measured amastigote replication in J774 macrophages . J774 macrophages were infected with L . mexicana promastigotes and internalized parasites allowed to differentiate to amastigotes , before cultures were incubated in the presence of 5% 2H2O . Infected macrophages were labeled for up to 20 days and amastigotes ( AmaMø ) isolated at various time points . Intracellular AmaMø were found to have a doubling time of 4 days ( Fig . 1B ) , comparable to that of Amaaxenic and substantially faster than observed in Amalesion ( Fig . 1B ) . These results suggest that the phagolysosomal compartment of non-activated macrophages is not growth limiting for amastigotes per se , and that additional factors in the lesion environment constrain parasite growth and/or induce a slow growth phenotype . Quantitative analysis of hematoxylin and eosin ( H&E ) stained sections of BALB/c lesions harvested at day 85 post-infection showed that the majority of granuloma macrophages harbored between 100–200 amastigotes/phagolysosome ( Fig . 1D ) . These values are in close agreement with parasite vacuole densities calculated assuming; ( i ) that each vacuole was established by a single parasite invasion event , ( ii ) a constant amastigote doubling time of 12 days and ( iii ) zero parasite death ( 120 parasites/vacuole ) . The presence of macrophages with fewer than 100 amastigotes/phagolysosome ( 28% of all infected macrophages ) may reflect influx of uninfected macrophages that have been infected for a shorter period of time , or a subpopulation of slower growing parasites . On the other hand , the presence of small number of hyper-infected macrophages ( up to 400 amastigotes/phagolysosome ) may reflect a subpopulation of amastigotes with a faster growth rate and/or the uptake of multiple promastigotes/amastigotes during the initial infection . Overall , these analyses indicate that parasite and host cell turnover in lesions is minimal and that a significant majority of the granuloma host cells were infected very early after injection of parasites . To further define the physiological state of Amalesion , we assessed the rate of incorporation of deuterium into the ribosyl moiety of RNA and proteinogenic amino acids in different parasite stages . Measurement of deuterium enrichment in the RNA ribosyl moiety primarily reflects ribosome biosynthesis , one of the most energy intensive processes in the cell [25] and is thus a measure of the metabolic state of a cell . As expected , Prolog exhibited the highest rate of RNA turnover , comparable to the rate observed for DNA synthesis ( Fig . 2A ) . Appreciable levels of RNA turnover were also observed in Prostat , confirming that this non-dividing stage remains transcriptionally and metabolically active ( Fig . 2A ) . Strikingly , all three amastigote stages ( Amaaxenic , AmaMø and Amalesion ) exhibited lower rates of RNA turnover than Prostat , providing direct evidence that amastigote differentiation is linked to a general shut-down of energy intensive processes ( Fig . 2A-C ) . The rate of protein synthesis/turnover provides another proxy for the metabolic state of a cell . As expected , deuterium was incorporated into a range of Leishmania proteinogenic amino acids via different transamination reactions and pathways of de novo biosynthesis [8 , 26] ( S6A Fig . ) . While maximum levels of deuterium enrichment in different amino acids varied , with highest levels of enrichment in alanine ( 11% EM1 ) and glutamate ( 9% EM1 ) , similar rates of protein turnover were calculated after normalization to maximum labeling , regardless of the amino acid used ( S6B Fig . ) . Both dividing and non-dividing promastigote stages exhibited higher rates of protein synthesis than Amaaxenic and Amalesion ( Fig . 3A-C ) . Interestingly , while the turnover of protein in Prolog and Amaaxenic stages occurred at approximately the same rate as DNA replication , protein turnover in Amalesion occurred at nearly twice the rate of DNA synthesis . Thus although Amalesion stages have the lowest absolute rates of protein turnover , the cellular proteome is turned over more times per cell division cycle than in other stages . The turnover of membrane phospholipids is intimately linked to cell division , organelle biogenesis and dynamic cellular functions , such as secretion and endocytosis . To investigate whether membrane turnover is reduced in Leishmania amastigotes we measured global rates of fatty acid turnover in different cultured and lesion-derived stages . Fatty acids are primarily incorporated into phospholipids , with little incorporation into other lipids ( such as triacylglycerol ) , providing a direct measure of membrane biogenesis . As with proteinogenic amino acids , the extent to which 2H-is incorporated into different Leishmania fatty acids varies , depending on the extent to which they are generated via de novo synthesis or scavenged from the media or host ( S7 Fig . , S8 Fig . ) . However , when rates of labeling were normalized to maximum labeling , similar rates of turnover were obtained regardless of the fatty acid measured . As expected , Prolog exhibited fastest rates of fatty acid turnover , while both Prostat and Amaaxenic exhibited turnover rates that were comparable to , or slightly faster , than determined rates of protein turnover , respectively ( Fig . 4 ) . Strikingly , Amalesion exhibited very low rates of fatty acid turnover ( t1/2 ~ 7 . 8 days , compared to 1 . 4 days in Amaaxenic ) , indicating a dramatic slow-down in global rates of membrane biogenesis in lesion stages . To further investigate whether the 2H-labeling of Amalesion fatty acids reflects de novo synthesis or uptake of 2H-labeled fatty acids from the host [8 , 27] , we measured the maximum levels of 2H-enrichment in both parasite and host fatty acids derived from plasma or lymph nodes after a prolonged period of labeling ( Fig . 5 , S9 Fig . ) . The labeling of C14:0 , C16:0 and C16:1 fatty acids was comparable in both Amalesion and serum samples , and somewhat lower than maximum levels of enrichment in Prolog , indicating that lesion amastigotes are largely dependent on salvage pathways for these fatty acids . In contrast , maximum 2H-enrichment in Amalesion C18:0 , C18:1 and C18:2 was higher than in equivalent host fatty acids . This was particularly pronounced for parasite C18:1 ( oleic acid ) and C18:2 ( γ-linoleic acid ) which were 2-fold or >50-fold more highly labeled than corresponding host fatty acids . The absence of 2H-enrichment in plasma C18:2 is consistent with the absence of Δ12 oleic acid desaturase in animals , while the elevated levels of labeling of amastigote C18:1 and C18:2 indicate that these fatty acid are predominantly synthesized by the parasite . Significantly , the rate of turnover of γ-linoleic acid was very similar to that Amalesion DNA providing additional support for the notion that these stages have a slow replication rate of ~12 days in vivo . Amalesion also contained higher levels of very long chain , polyunsaturated fatty acids , than cultured promastigotes ( S8 , S9 Figs ) . These included C20:4 n-6 ( arachidonic acid ) and C22:6 n-3 which were labeled to the same extent as the equivalent host fatty acids ( Fig . 5A ) . Because the additional 2H-enrichment in parasite γ-linoleic acid is not observed in these downstream fatty acids , they are most likely salvaged directly from the host cell ( Fig . 5B ) . Collectively , these results show that fatty acid/membrane turnover is dramatically reduced in lesion amastigotes . Notwithstanding their reduced requirements , these stages appear to be dependent on both salvage and de novo synthesis for maintaining fatty acid levels . In particular , our data suggest that they are likely to be critically dependent on the de novo synthesis of C18:2 , which is not synthesized by the host and appears to be depleted in the macrophage phagolysosome compartment .
Information on host-parasite dynamics within Leishmania-induced lesions is limited , reflecting the technical difficulty of measuring the growth rate or physiological state of parasites within these tissues . Estimates of parasite growth based on direct enumeration of parasite numbers or detection of transgenic parasite lines expressing luciferase or fluorescent proteins are limited in sensitivity and do not distinguish between dynamic changes in the rate of pathogen replication , death or migration out of infected tissues . These approaches also require the generation of transgenic parasite lines which may alter virulence phenotypes and in which expression of reporter proteins may vary . In this study we have utilized 2H2O labeling to explicitly measure the growth rate and other key physiological parameters of wild type L . mexicana amastigotes in non-healing cutaneous lesions . We show that lesion amastigotes replicate very slowly ( doubling time ~12 days ) throughout lesion development and appear to enter a distinct semi-quiescent state , characterized by low rates of transcription , protein turnover and membrane biogenesis . Significantly , the calculated rates of amastigote growth account for the increase in the total parasite burden in isolated lesions , as well as the mean parasite densities in the phagolysosomes of lesion macrophages , suggesting that parasite death and turnover in these lesions occurs to a minimal extent . These analyses also provide direct evidence that infected macrophages in L . mexicana-induced granulomas have a long life span . Specifically , based on the average parasite burden of infected macrophages ( 100–200 amastigotes/ phagolysosome ) it is likely that a significant majority of infected host cells in the lesion had been infected very early in the infection and have sustained intracellular parasites for >12 weeks . While it is possible that some macrophage death and turnover could be masked by direct transfer of parasite-laden vacuoles from a ruptured macrophage to a naive recipient host cell [28] , these findings are broadly consistent with a growing body of evidence suggesting that L . mexicana amastigotes repress a number of signaling pathways in macrophages , including those that activate apoptosis , autophagy or necrosis [29 , 30] . Overall , these findings suggest that L . mexicana-induced lesions are characterized by slow parasite growth and low macrophage turnover . We propose that the slow replication rate of intracellular amastigotes ( triggered in part by intrinsic amastigote differentiation signals ) , minimizes overgrowth of the phagolysosome and contributes to the long life-time of infected macrophages . Slow growth may therefore be a key factor in generating a stable , permissive tissue niche within which the parasite burden can progressively expand . A number of other bacterial , fungal and protozoan parasites induce granulomatous structures in their host , of which the most intensively studied are the pulmonary granulomas induced by Mycobacterium tuberculosis [31 , 32] . Our findings suggest that host-pathogen dynamics in the L . mexicana induced lesions differ substantially from those in M . tuberculosis granulomas in several respects . In particular , the M . tuberculosis-induced granulomas are generally characterized by high rates of macrophage infiltration and host cell death [31 , 32] . There is also increasing evidence that bacterial replication and turnover in these granulomas may be relatively high as a result of host-mediated bacterial killing and/or immune clearance [6 , 12 , 32 , 33] . This contrasts with the earlier view that M . tuberculosis bacilli have a low replication rate , and corresponding low death rates , leading to the observed plateauing in bacterial numbers during chronic infections [6 , 12 , 32 , 33] . These observations highlight marked differences in the way different pathogens adapt to , and potentially exploit the host’s attempt to wall off persistent infection with granulomas . Whether these differences are defined by intrinsic differences in pathogen growth rate remain to be determined . While a number of microbial pathogens are thought to switch to a quiescent or semi-quiescent state during long-term chronic phases of infection , relatively little is known about the physiological/metabolic state of these stages [3 , 4] . Here we show that slowly replicating lesion amastigotes strongly repress energy-expensive processes such as transcription , protein synthesis and membrane lipid turnover . The down-regulation of these processes in amastigotes was more pronounced than in non-dividing promastigotes , highlighting the fact that metabolic quiescence is not necessarily linked to growth rate and that non-dividing stages can remain metabolically active . It was notable , that repression of RNA synthesis was less pronounced than for either protein or fatty acid biosynthesis . Leishmania lack transcription factors and gene-specific transcriptional control [19] and a higher basal level of RNA turnover may be needed for post-transcriptional regulation of gene expression . The strong repression of protein synthesis in Amalesion ( 10-fold and 5-fold lower than in Prostat or Amaaxenic , respectively ) is consistent with recent studies demonstrating that promastigote to amastigote differentiation results in activation of the PERK kinase and phosphorylation of eIF2a , both of which regulate and repress protein translation and are required for amastigote virulence [34–36] . A general shut down in energy intensive processes , such as protein and fatty acid synthesis is also consistent with recent 13C-flux studies on isolated Amalesion , which identified a unique stringent metabolic response in these stages , characterized by reduced carbon utilization and more efficient mitochondrial catabolism of sugars and fatty acids [24] . Thus the physiological responses of amastigotes to the lesion microenvironment are complex and regulated at the level of DNA replication , transcription and protein synthesis , in addition to remodeling of central carbon metabolism . Entry into this semi-quiescent state is likely to be triggered by a number of factors . Both Amaaxenic and AmaMø exhibited slow maximum rates of growth ( doubling time 3 . 5–4 days ) and RNA turnover , suggesting that entry into this state is a hardwired response to elevated temperature and/or low pH used to induce differentiation in vitro . These findings also suggest that the phagolysosome of non-activated J774 macrophages are not restrictive for amastigote growth . On the other hand , there is evidence that the growth of L . major amastigotes in cutaneous lesions is restricted by the chronic production of sub-lethal levels of nitrous oxide [18] . However , L . mexicana amastigotes reside within larger communal phagolysosome compartments and are intrinsically more resistant to macrophage microbicidal processes , including nitric oxide or reactive oxygen species [14 , 37] . They also appear to have evolved additional mechanisms for inhibiting macrophage activation and nitric oxide production [38] . The slow growth rate of L . mexicana amastigotes in lesions may therefore reflect both intrinsic parasite responses , as well as adaptive responses to other host microbicidal processes , nutrient deprivation and/or physical stresses in this niche [24] . The high parasite inoculum used in our studies is expected to lead to rapid recruitment of macrophages and early induction of lesion development [39] . In contrast , infection of mice with a low dose inoculum is associated with a significant delay in lesion formation that is preceded by an exponential increase in parasite numbers [40] . In the L . major—C57BL murine model , this silent expansive phase is associated with an increase in parasite burden consistent with an apparent parasite doubling time of ~2 . 3 days [40] . This growth rate is similar to the replication rate we observed for L . mexicana amastigotes in non-activated J774 macrophages , raising the possibility that Leishmania amastigotes may switch between different growth states during acute and long-term chronic phases of infection . The possibility that amastigotes exhibit a range of growth rates within lesion was also suggested by the detection of a small number ( <20% ) of hyper-infected macrophages with more than 200 parasites/macrophage . The existence of distinct amastigote growth/physiological states is analogous to the situation in the sandfly vector . Initial colonization of the mid-gut of this host is mediated by ‘procyclic’ promastigotes that are rapidly dividing and generally more sensitive to a variety of physiological and nutritional stresses . These stages transition though a number of physiological states before differentiating to non-replicating , metacyclic promastigotes ( related to Prostat ) in the foregut [41] . Metacyclic promastigotes are resistant to a number of stresses ( including elevated temperature ) , suggesting that slow-growth represents a generalized response to elevated stresses in both the insect and mammalian hosts . Many microbial pathogens acquire essential nutrients or metabolites via a combination of salvage pathways or de novo synthesis , leading to a level of redundancy that complicates efforts to identify and validate drug targets . While administration of 2H2O to infected mice results in metabolic labeling of both parasite and host lipids , analysis of the relative rates of labeling of parasite/host pools can be used to infer the contribution of salvage versus de novo biosynthetic pathway to parasite lipid homeostasis . In particular , we found that 2H-enrichment in Amalesion C18 fatty acids was substantially higher than in the equivalent host fatty acids , indicating that they are synthesize de novo by intracellular amastigotes . This was particularly striking for C18:2 , which was labeled to a significant extent in Amalesion , but unlabeled in mouse plasma samples . The absence of labeling of plasma C18:2 was not surprising given that animals lack the Δ12 desaturase needed to synthesize linoleic acid and are dependent on uptake of this fatty acid in the diet . The high maximum labeling of C18:2 in Amalesion indicates that this fatty acid is limiting for parasite growth in the phagolysosome compartment and therefore that amastigotes may be dependent on their own Δ12 desaturase for intracellular growth [42 , 43] . On the other hand , Amalesion contained elevated levels of very long chain , polyunsaturated fatty acids , including C20:4 n-6 ( arachidonic acid ) , C22:4 n-6 ( adrenic acid ) , C22:5 n-6 ( osbond acid ) and C22:6 n-3 ( cervonic acid ) compared to promastigotes . These fatty acids were 2H-enriched to a higher level than parasite C18 precursors fatty acids and were labeled to the same extent as the equivalent host fatty acids , suggesting that they are derived primarily via salvage pathways . These findings add to accumulating evidence that Leishmania amastigotes acquire a range of lipids from the host cell [24 , 44 , 45] and suggest that salvage as well as de novo biosynthesis pathways are potential drug targets . In summary , we show that 2H2O labeling can be used as a universal labeling procedure to measure microbial growth , physiology and metabolism in their animal hosts . This approach is well suited for studying the in vivo growth and metabolism of other microbial pathogens .
L . mexicana promastigotes ( Prolog , Prostat ) were cultured in RPMI 1640 medium supplemented with 10% ( v/v ) heat-inactivated fetal calf serum ( FCS ) at 27°C . Axenic amastigote ( Amaaxenic ) were generated by adjusting the pH of the medium of stationary phase promastigotes ( day 5 , ProStat ) to pH 5 . 5 with 1 M HCl and addition of 10% FCS ( 20% v/v final ) , followed by incubation at 33°C for four days . Cultivated parasites stages ( Prolog , Prostat , Amaaxenic ) were cultured in medium supplemented directly with phosphate buffered saline- 2H2O ( 99 . 9% , Cambridge Isotopes ) to give a final concentration of 5% ( v/v ) 2H2O . J774 macrophages ( 4 × 106 ) were grown overnight in RPMI 1640 medium supplemented with 10% ( v/v ) FCS , penicillin and streptomycin at 33°C [46] , before being infected with L . mexicana Prostat at a MOI of 3 . Non-internalized parasites were removed after 4 hr by washing macrophages twice with fresh RPMI medium . After 3 days , the medium of infected macrophages was replaced with fresh medium containing 5% 2H2O and cells harvested at various time points ( ranging from 4 h and 16 days ) . For long term labeling experiments , the medium containing 5% 2H2O was replaced every 5 days . BALB/c mice ( 6 week old ) were infected with L . mexicana Prostat ( 106 in 50 μl PBS ) near the base of the tail and lesion size monitored as previously described [46] . Infected mice were injected intra-peritoneally with 2H2O ( 99% , 35 μl/g body weight ) containing 0 . 9% NaCl after the development of nascent lesions and serum 2H2O concentration subsequently maintained at 5% by supplementation of the drinking water with 9% 2H2O . 2H2O levels in the urine were routinely monitored as previously described [47] . To determine maximum labeling parasite metabolites ( EM1 ) , mice were labeled with 2H2O immediately after the infection and parasites harvested one month after the development of a granulomatous lesion . Cultured parasites stages were harvested with rapid metabolic quenching as previously described [48] and cell pellets ( triplicate samples ) were stored at -80°C prior to extraction . Infected J774 macrophages were metabolically quenched by chilling plates on ice and replacing the overlying culture medium with ice-chilled PBS . Infected macrophages were scraped from the plastic surface and lysed by repeated passage through a 25G needle ( x10 ) . After low speed centrifugation to remove host cell debris ( 60 g , 5 min , 4°C ) , amastigotes were recovered by sequential filtration through 5 μm and 3 μm pore filters and centrifugation ( 1500g , 10 min , 4°C ) of the filtrate . The parasite pellet was washed three times with chilled PBS and any residual host cell DNA , removed by treatment of the pellet with 1000 U of DNAase in PBS with 5 mM MgCl2 for 2 h at 33°C . Mice were culled humanely post-labeling ( 24 h to 150 days ) and granulomatous lesions excised and immediately chilled in cold PBS . All subsequent procedures were carried out at <4°C . The isolated tissue mass was disrupted by passage through a cell strainer and host cells lyzed by passage through a 27 G syringe needle ( x5 ) . Intact host cells and host cell debris were removed by centrifugation ( 60 g , 10 min , 4°C ) and released parasites harvested by centrifugation ( 1500 g , 10 min , 4°C ) . The pellet was washed three times with chilled PBS and the purity of the parasite extract confirmed by light microscopy . Samples for DNA analysis were treated with 1000 U of DNAase in PBS with 5 mM MgCl2 for 2 h at 33°C to remove any host cell DNA . BALB/c lesions ( 85 day post-infection ) were fixed with 10% paraformaldehyde in PBS , embedded in paraffin and tissue sections stained with hematoxylin and eosin ( H&E ) reagents . Nucleic acids were extracted , hydrolyzed and dephosphorylated and released ribosyl and deoxyribosyl sugars derivitized as previously described , with some modifications [23 , 49 , 50] . In brief , nucleosides in 250μl H2O were incubated in HCl ( 0 . 01 M , 1 . 84 ml ) and O- ( 2 , 3 , 4 , 5 , 6-pentafluorobenzyl ) hydroxylamine acetate ( PFBHA , 25 mg/ml , 20 μl ) , at 90°C for 3 h . Oximes of ribose and deoxyribose were extracted in ethyl acetate/hexane mix ( 1:1 v/v ) followed by pure ethyl acetate and the pooled organic phases were dried under nitrogen . Samples were silylated by sequential addition of ethyl acetate ( 20 μl ) and N , O-bis ( trimethylsilyl ) trifluoroacetamide reagent ( 40 μl BSTFA + 1% TMCS , Thermo scientific ) and incubation at 90°C for 1 h . The perfluorotritrimethylsilyl ( PFtriTMS ) derivatives of deoxyribose and ribose were analyzed by GC/MS in negative and positive chemical ionization mode ( NCI , PCI ) with methane as reagent gas . The fragments at m/z 530 and m/z 633 are abundant PCI fragments of deoxyribose and ribose , respectively , that correspond to the loss of CH4: [M+1–16]+ . All derivitized samples were analyzed by GC/MS using a DB5 capillary column ( J&W Scientific , 30 m , 250 μm inner diameter , 0 . 25 μm film thickness ) , with a 10 m inert duraguard . The injector insert and GC/MS transfer line temperatures were 270 and 250°C , respectively . The oven temperature gradient was set to: 70°C ( 1 min ) ; 70°C to 295°C at 12 . 5°C/min;295°C to 320°C at 25°C/min; 320°C for 2 min . All metabolites were identified based on GC-retention times and mass spectra of standards . The excess M1 fraction and the fraction of new cells/molecules were calculated as described [51] following the measurement of the M0 and the corresponding M+1 ion using selected ion monitoring ( SIM ) . Parental ions ( M0 ) were as follows: deoxyribose: PCI = 530 m/z , NCI = 525 m/z; ribose: PCI = 618 m/z , NCI = 452 m/z; alanine: PCI = 318 m/z; aspartate: PCI = 476 m/z; glutamate: PCI = 490 m/z; stearate: EI: 298 m/z , PCI = 327 m/z; oleate: EI = 296 , PCI = 325 m/z; linoleate: EI = 294 m/z , PCI = 323 m/z . The half-life ( t½ ) was determined by plotting data points on a log time ( days ) scale , fitting a straight line to the data points and solving the equation y = m*x + c for y = 0 . 5 . The t½ is given by exp ( log days ) . Total lipids were extracted in chloroform/methanol/water ( 1:2:0 . 8 v/v ) as described previously [48] . The organic phase was dried in a centrifugal evaporator and resuspended in chloroform/methanol ( 2:1 v/v , 30 μl ) and total fatty acids analyzed as their methylesters after addition of Meth-Prep II reagent ( 5 μl , Grace Davison , Alltech ) and direct injection and analysis by GC/MS in electron impact ( EI ) mode and positive chemical ionization mode . The delipidated protein pellets were hydrolyzed in 6 M HCl ( 200 μl , 110°C , 18 hr ) and insoluble material removed by centrifugation ( 16 , 100 g , 5 min , RT ) . The supernatant was dried under nitrogen and released amino acids converted to their TBDMS derivatives by addition of MTBSTFA-1% TBDMCS ( 30 μl ) and pyridine ( 30 μl , 60°C , 30 min ) prior to GC-MS analysis in positive chemical ionization mode as described above . BALB/c lesions ( 85 day post-infection ) were fixed with 10% paraformaldehyde in PBS , embedded in paraffin and tissue sections stained with hematoxylin and eosin ( H&E ) reagents . Slides were imaged using a Zeiss Axioplan microscope and digital computer images were recorded with a Zeiss camera control unit and the corresponding dedicated software . The numbers of amastigotes was counted in 8 sections of 2 lesions evaluating >1200 phagolysosome in total . The number of parasites/vacuole was calculated assuming that ( 1 ) the phagolysosome compartment is a sphere , ( 2 ) that the average cross sectional diameter of the phagolysosome is equivalent to the largest vacuolar sections seen in the tissue sections and ( 3 ) that parasites are primarily arranged around the periphery of the phagolysosome at equal density . The average cross sectional diameter of the parasite occupied PVs was found to be 25 . 6 μm , while the average diameter of intracellular amastigotes was 2 . 5 μm . To determine the maximum number parasites that fit in a vacuole section the ratio of the areas was calculated ratio of areas = areavacuoleareaparasite=104 . 9 and the maximum packing density of circles ( PDmax2 ) was calculated PDmax2 = π3×2=0 . 90689 to estimate that the maximum number of parasites in a vacuole section ( Ptheor , max2 ) is Ptheor , max2 = ratio of areas ×PDmax2=95 . 1 Similarly , we determined the ratio of the volumes ratio of volumes = volumevacuolevolumeparasite=1073 . 7 and used maximum packing density of spheres ( PDmax3 , Kepler conjecture ) PDmax3 = π2×3=0 . 74048 to determine that the maximum number of parasites in a vacuole ( Ptheor , max3 ) is Ptheor , max3 = ratio of volumes ×PDmax3=795 . 1 The number of parasites estimated to be present in the evaluated vacuoles ( Pvac , estim ) was then determined based on the number of parasites counted in a vacuole section ( Pcount ) as follows
|
Microbial pathogens can adapt to changing conditions in their hosts by switching between different growth and physiological states . However , current methods for measuring microbial physiology in vivo are limited , hampering detailed dissection of host-pathogen interactions . Here we have used heavy water labeling to measure the growth rate and physiological state of Leishmania parasites in murine lesions . Based on the rate of in situ labeling of parasite DNA , RNA , protein , and lipids , we show that the growth rate of intracellular parasite stages is very slow , and that these stages enter a semi-quiescent state characterized by very low rates of RNA , protein , and membrane turnover . These changes in parasite growth and physiology are more pronounced than in in vitro differentiated parasites , suggesting that they are induced in part by the lesion environment . Despite their slow growth , the parasite burden in these lesions progressively increases as a result of low rates of parasite death and host cell turnover . We propose that these changes in Leishmania growth and physiology contribute to the development of a relatively benign tissue environment that is permissive for long term parasite expansion . This approach is suitable for studying the dynamics of other host-pathogen systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Characterization of Metabolically Quiescent Leishmania Parasites in Murine Lesions Using Heavy Water Labeling
|
The genetic determination of eggshell coloration has not been determined in birds . Here we report that the blue eggshell is caused by an EAV-HP insertion that promotes the expression of SLCO1B3 gene in the uterus ( shell gland ) of the oviduct in chicken . In this study , the genetic map location of the blue eggshell gene was refined by linkage analysis in an F2 chicken population , and four candidate genes within the refined interval were subsequently tested for their expression levels in the shell gland of the uterus from blue-shelled and non-blue-shelled hens . SLCO1B3 gene was found to be the only one expressed in the uterus of blue-shelled hens but not in that of non-blue-shelled hens . Results from a pyrosequencing analysis showed that only the allele of SLCO1B3 from blue-shelled chickens was expressed in the uterus of heterozygous hens ( O*LC/O*N ) . SLCO1B3 gene belongs to the organic anion transporting polypeptide ( OATP ) family; and the OATPs , functioning as membrane transporters , have been reported for the transportation of amphipathic organic compounds , including bile salt in mammals . We subsequently resequenced the whole genomic region of SLCO1B3 and discovered an EAV-HP insertion in the 5′ flanking region of SLCO1B3 . The EAV-HP insertion was found closely associated with blue eggshell phenotype following complete Mendelian segregation . In situ hybridization also demonstrated that the blue eggshell is associated with ectopic expression of SLCO1B3 in shell glands of uterus . Our finding strongly suggests that the EAV-HP insertion is the causative mutation for the blue eggshell phenotype . The insertion was also found in another Chinese blue-shelled breed and an American blue-shelled breed . In addition , we found that the insertion site in the blue-shelled chickens from Araucana is different from that in Chinese breeds , which implied independent integration events in the blue-shelled chickens from the two continents , providing a parallel evolutionary example at the molecular level .
Avian eggshell coloration is the result of crypsis or mimetism and plays important roles in filtering solar radiation and strengthening the eggshell [1] . Blue eggshell color has been proposed as post-mating signals of female phenotypic quality to their mates and is related to fitness of the offspring due to the antioxidant of biliverdin , a predominant pigment for blue eggs [2] , [3] . Blue eggshells can be found not only in some wild birds , e . g . eastern bluebird [4] , blue-footed booby [5] , and pied flycatcher [6] , but also in domestic birds such as Japanese quail [7] , chickens [8] and ducks [9] . Brown and white are the two major eggshell colors in chickens . Protoporphyrin-IX , biliverdin , and biliverdin zinc chelate are the main pigments of the eggshell [10] and several blue egg laying breeds have been reported worldwide [11] , [12] . The Araucana , an indigenous breed from Chile , was the first chicken breed described to lay blue eggs [8] , and has been frequently used in genetic studies of the blue eggshell phenotype . In China , Dongxiang and Lushi chickens are representative breeds laying blue eggs and show dominant inheritance as that in Araucana . However , the blue eggshell phenotype has not been fixed in these three breeds which still produce brown eggs at low frequency . Blue eggshell color exhibits an autosomal dominant inheritance and eggs laid by homozygotes are a darker blue than those from heterozygotes ( Figure 1A ) . In 1933 , Punnett firstly reported that blue or green shell appearance of the Araucana was determined by a single genetic factor , traditionally denoted as oocyan ( O ) [8] . A series of linkage analysis involving O have been performed with O affirmatively mapped to the short arm of chromosome 1 [12]–[16] , and closely linked to ev1 and P which was identified as SRY ( sex determining region Y ) -box 5 ( SOX5 ) [12] , [13] , [16] , [17] . In the region around ev1 , two single nucleotide polymorphisms ( SNPs ) ( rs15297163 and rs15297165 ) were found to be highly associated with the blue eggshell phenotype [18] . A 1 . 8 Mb genomic interval harboring the O gene was defined in an F2 resource population [19] . The localization of the O was further refined to the vicinity of ss244244378 by linkage and association analysis [20] . The ss244244378 is very close to the two SNPs reported by Zhao et al . [18] with a physical distance of 0 . 12 Mb implies that the region around the three SNPs is mostly like to harbor the blue eggshell gene . Combined mapping information from traditional breeds and Chilean village chickens allowed the O to be fine mapped to two small regions ( Gga 1:67 . 25–67 . 28 Mb , Gga 1:67 . 28–67 . 32 Mb ) [21] . In the present study we found that the blue eggshell phenotype in chickens is caused by a retrovirus insertion in the 5′ flanking region of SLCO1B3 coding a membrane transporter OATP1B3 which is responsible for transporting amphipathic organic compounds including bile salt .
A linkage analysis was performed in an F2 resource population segregating for the O gene to refine the location of chicken blue shell gene in the present study . Eight molecular markers in the candidate region were used for linkage analysis ( Table S1 ) . By two-point analysis , the O gene was mapped in the region between marker L4 and L5 which were the closest flanking markers to O with recombination rate being both 0 . 02 ( LOD = 15 . 84 ) ( Figure 2 ) . Fifteen SNP markers between L4 and L5 were further genotyped in the F2 resource population to narrow the mapping region and the O was finally located in a ∼120 kb region from 67296991 bp to 67416784 bp on chromosome 1 on the UCSC chicken genome ( May 2006 assembly ) ( Table S1 ) and no recombination was found between the blue eggshell phenotype and the markers within the region . Totally , four genes ( SLCO1C1 , SLCO1B3 , LOC418189 and SLCO1A2 ) were found in the ∼120 kb interval by a MapView search ( http://www . ncbi . nlm . nih . gov/mapview/ ) ( Table S2 ) . The uterus is where pigment is secreted to eggshell . We performed expression analysis for the four candidate genes in the uterus of blue-shelled ( n = 16 ) and brown-shelled ( n = 16 ) Dongxiang hens by RT-PCR . We found that SLCO1B3 was the only gene expressed specifically in the uterus of blue-shelled Dongxiang chickens ( Figure 1B ) , thus we measured its expression in the uterus in 8 blue-shelled ( 4 O*LC/O*LC and 4 O*LC/O*N . O*LC is for the blue-shell allele in the two Chinese breeds , Dongxiang and Lushi Chicken and O*N denotes the non-blue-shell allele or wild-type allele ) and 4 brown-shelled ( O*N/O*N ) Dongxiang chickens , 6 blue-shelled ( 3 O*LC/O*LC and 3 O*LC/O*N ) and 6 brown-shelled ( O*N/O*N ) Lushi chickens , and 24 chickens from three brown-shelled and one white-shelled breeds ( O*N/O*N , 6 chickens per breed ) by real-time PCR . All blue-shelled chickens expressed the gene in the uterus while non-blue-shelled chickens did not ( Figure 1C ) . In addition , expression of SCLO1B3 was 2 to 3 fold higher in homozygous blue-shelled chickens than in heterozygous blue-shelled Dongxiang and Lushi individuals ( Figure 1C ) . Fluoscence labeled cDNA in situ hybridization demonstrated that the transcripts of SLCO1B3 were only expressed in the uterus of blue-shelled but not brown-shelled hens ( Figure 1D ) . These results suggest that SLCO1B3 is the causative gene for blue eggshell in the chicken . We found a SNP ( g . 67334934 G>T ) in exon 5 of SLCO1B3 gene by sequencing the coding region and the SNP presented complete association with the blue eggshell phenotype in the Dongxiang chicken by genotyping it in Dongxiang blue-shelled and brown-shelled chickens . With six heterozygous individuals produced by mating a homozygous Dongxiang blue-shelled male with a White Leghorn female , the allelic expression of SLCO1B3 gene was demonstrated by RT-PCR analysis and pyrosequencing . More than 95% of the transcripts expressed in the uterus originated from the T allele corresponding to the blue-shell allele ( Figure 1E ) . This means the expression of the gene is regulated by a cis-acting element . Surprisingly , its expression in liver is also allele specific , and ∼95% of the transcripts in liver come from the G allele which is non-blue-shell allele ( Figure 1F ) . We sequenced the genomic region of SLCO1B3 in order to reveal the potential causative mutation of the gene with 5 blue-shelled and 5 brown-shelled Dongxiang chickens . Twenty-one SNPs evenly covering the whole genomic region ( ∼24 kb ) of SLCO1B3 were taken for genotyping in 353 chickens from 3 blue-shelled breeds ( Araucana , Dongxiang and Lushi ) and 9 non-blue-shelled breeds . However , none of the SNPs was found to be in complete linkage disequilibrium with blue eggshell ( Table 1 ) . We subsequently cloned the 5′UTR ( GenBank accession number: JN381032 ) of SLCO1B3 by 5′ RACE in a blue-shelled ( O*LC/O*LC ) and a brown-shelled ( O*N/O*N ) Dongxiang chicken and an extra 24 bps were found at the beginning of 5′UTR end in blue-shelled Dongxiang chicken ( Figure S1 ) . We further sequenced 5 kb upstream of the promoter using 5 blue-shelled ( O*LC/O*LC ) and 5 brown-shelled ( O*N/O*N ) Dongxiang chickens . A ∼4 . 2 kb insertion adjacent to 5′UTR containing the extra 24 bps was found in the blue-shelled but not in the brown-shelled chickens . The sequence of the ∼4 . 2 kb insertion ( GenBank accession number: JF837512 ) represents an incomplete retrovirus and shows 95 . 8% identity with the sequence of the avian EAV-HP retrovirus ( EMBL accession number: AJ238124 ) [22] . A typical proviral structure consists of gag , pol and env flanked by long terminal repeat ( LTR ) , which are arranged in the order of 5′LTR-gag-pol-env-LTR3′ [22] . Here , the inserted retrovirus is absent of the whole pol gene and part of gag and env ( Figure 3A ) . The retrovirus was integrated into the blue-shelled chicken genome in an inverted orientation ( Figure 3B ) at Chr1: 67324641–67324642 . We also found that the EAV encompassed some promoter elements by sequence analysis , indicating its expression promotion activities ( Figure 3A ) . A wide-range survey of the EAV-HP insertion was performed in 705 chickens from 12 worldwide breeds and the F2 resource population ( Table 2 ) using diagnostic PCR test . The results that the EAV-HP insertion is completely associated with the blue eggshell phenotype provide strong evidence that the mutation is causative . In order to elucidate whether the blue-shelled chickens from China and Chile have the same origin for the genotypic mutation , we further sequenced the EAV-HP insertion regions in a homozygous Araucana and a homozygous blue-shelled Lushi chicken . The EAV-HP insertion was found in both samples and the alignments of Araucana and Lushi to Dongxiang blue-shelled chicken showed the identity of the inserted EAV-HP being around 97% . Interestingly , the insertion sites in Araucana are different from that in the two Chinese blue-shelled chickens . The break point for EAV-HP insertion in blue-shelled Araucana is located at 23 bp upstream to that in the two Chinese breeds ( Figure 3C ) . We sequenced the junction sites in homozygous blue-shelled chickens of Araucana ( n = 5 ) , Lushi ( n = 5 ) and Dongxiang ( n = 5 ) and confirmed the insertion sites in Dongxiang and Lushi are the same but different from that in Araucana . We also typed 21 SNPs in genomic region of SLCO1B3 in multiple breeds of blue-shelled and non-blue-shelled chicken breeds . It is obvious to see that the EAV-HP insertions in blue-shelled chickens from the two continents were embedded in two distinguished different haplotypes ( Table S3 ) , which supports independent integrations accounting for the blue shelled phenotypes .
In birds , eggshell color is a variable Mendelian trait . Colored eggshell could function as avoiding predation through either crypsis or aposematism , distinguishing from brood parasitism , reinforcing eggshell strength , regulating egg temperature , combating harmful solar radiation and sending sexually selected signal to males [2] , [23] . However , molecular mechanism of all kinds of eggshell color formation is poorly understood to date . Here , we demonstrate that a ∼4 . 2 kb EAV-HP insertion at upstream of SLCO1B3 is responsible for blue eggshell phenotype in the chicken . By linkage analysis , we fine mapped the O locus to a 120 kb region , where four candidate genes of SLCO1C1 , SLCO1B3 , LOC418189 and SLCO1A2 are located . These genes are all members of organic anion transporting polypeptides ( OATP , gene symbol SLCO ) . Functionally , the OATPs serve as membrane transporters that mediate a wide range of sodium-independent transport of amphipathic organic compounds , such as some endobiotic compounds of bile salts , eicosanoids , sterioids , thyroid hormones and some xenobiotic compounds of anionic oligopeptides , organic dyes , toxins , and drugs [24] . SLCO1B3 codes a membrane transporter OATP1B3 which is considered a liver-specific transporter and is highly expressed in liver where it transports a wide range of substrates including bile salts [24] , [25] . A genome-wide association study ( GWAS ) for serum bilirubin levels also showed SLCO1B3 is a plausible candidate gene responsible for changes in bilirubin levels in humans [26] . As blue egg is colored mainly by deposition of biliverdin on the eggshell and biliverdin is just one component of the bile salts , the expression of the SLCO1B3 in uterus could enhance transportation of biliverdin to eggshell . In this study , we found that SLCO1B3 is exclusively expressed in shell gland of uterus of blue-shelled chickens rather than in that of brown- or white-shelled chickens , which supports that the gene plays a pivotal role for coloration of blue eggs . Regulatory mutations demonstrate an important role for phenotypic diversity which may be explained by cis-acting elements [17] , [27]–[34] . The effect of endogenous retrovirus ( ERV ) on hosts is extensive . It can unfavorably influence certain production traits , i . e . egg production , egg weight and body weight [35] , induce lymphoid or erythroid leukosis and a variety of tumors [35] , and cause some phenotype variants , i . e . dilute coat color mutation [36] and hairless mutation in mice [37] , recessive white [38] , henny-feathering mutation [39] , and the sex-linked late-feathering mutation [40] in chickens and outheld wing mutation in Drosophila melanogaster [41] . ERV could alter splicing patterns of transcript to produce variants such as the recessive white mutation in the chickens [38] . ERV could also promote expression of genes in alternative tissues , which is associated with activity of LTR which contains promoter and/or enhancer sequences responsible for transcription of virus genes [22] and may induce expression of flanking genes . Avian lymphoid leukosis and the henny-feathering mutation are respectively related to activation of c-myc in B cell and aromatase in the extrogonadal tissues by LTR [35] , [39] . We found an EAV-HP inserted in 5′ flanking region of SLCO1B3 in reverse orientation . The LTR of EAV-HP could induce expression of the downstream gene ( SLCO1B3 ) by its bidirectional promoter activity [22] . Moreover , 5′UTR of SLCO1B3 transcripts from the blue-shell allele containing the 24 bp EAV-HP partial sequence implies that the expression of SLCO1B3 in blue-shelled chickens is closely related to the insertion . Blue eggshells are also seen in other avian species , such as domestic duck , Japanese quail and wild birds [4]–[7] , [9] . The genetic pattern of duck blue egg is similar to that of chicken , displaying a dominant phenotype determined by a single gene [42] . However , SLCO1B3 is not expressed in the uterus of blue-shelled and non-blue-shelled ducks , and the EAV-HP insertion was not found in the homologous region in duck ( Figure S2 , Primers in Table S4 ) . Thus , the causative gene for blue eggshell in ducks may be different from that in chickens . Moreover , the genetic pattern in the chicken is also different from that for Japanese blue-eggshell quail which arise from a recessive mutation ce [7] . Because there is no record showing that the two ancestral species of domestic chickens , red jungle fowl and grey jungle fowl [33] , lay blue eggs , we may conclude that the causative EAV-HP insertion for blue eggshell is a derived mutation in the domestic chicken . China and Chile are two countries reported for having indigenous blue-shelled chicken breeds . Araucana from Chile , Dongxiang and Lushi from China got the blue eggshell phenotype and were all bred for several hundred years . Analysis with mtDNA showed both Indo-European and Asian origins of Chilean and Pacific chickens and blue/green-shell trait in the Araucana did not originated from ancient pacific/pre-Columbian chickens [43] . It is noted in the present study that though all these blue-shelled chickens had the EAV-HP insertion , the EAV-HPs inserted into two different genomic sites in the 5′ flanking region of SLCO1B3 in the blue-shelled chickens from the two countries ( Figure 3C ) and the EAV-HP insertion in blue-shelled Araucana embedded in a haplotype which is distinctly different from the corresponding haplotype from blue-shelled Lushi and Dongxiang chickens ( Table S3 ) . Here , we provide unambiguous evidences that the genetic basis of blue shell phenotype in Araucana is different from that in Chinese blue-shelled breeds , indicating independent originations of the trait in different continents . Due to the blue eggshell mutation having been artificially selected for consumption and variable eggshell color types for human requirements , the separated insertion events present us another parallel evolution case at the molecular level under adaptive selection by humans .
Two Chinese indigenous blue-shelled chicken breeds , Dongxiang and Lushi , and an American blue-shelled breed , Araucana , were used in the present study . Dongxiang chicken is from Dongxiang town , Jiangxi province of China . It is characterized by blue eggshell , single comb and black feather . Historically Dongxiang chicken is selected for blue eggshell , however , the trait has not been fixed to date . Lushi chicken is another local breed laying blue-shelled egg from Lushi town , Henan province of China . Because Lushi chicken has not been systematically bred , some appearance traits , eggshell color , as well as feather color does not show homogeneity . Araucana is an indigenous breed from Chile of South America . Besides blue-shelled egg , two distinguishing characteristics of Araucana breed are rumpless and tufts of feathers which protrude from each side of neck . In the present study , Dongxiang chicken and Lushi chicken were collected from Jiangxi Hualv breed poultry conservation farm and Henan Sanmenxia Lushi chicken farm , respectively . The blood samples of Araucana were obtained from members of the Araucana Club of America . We also collected 9 non-blue-shelled chicken breeds including Red Jungle Fowl , White Leghorn , Rhode Island Red , Beijing You , Silkie , Tibetan , Luxi Game , Gushi and Dwarf ( a commercial layer line in China ) . A three-generation F2 resource family was constructed by crossing homozygous blue-shelled Dongxiang ( O*LC/O*LC ) males , which has been verified by a test cross , and brown-shelled Dongxiang ( O*N/O*N ) females . All F1 hens laid blue shelled eggs and individual egg color phenotypes were recorded for all 146 F2 hens . Six heterozygous ( O*LC/O*N ) hens were produced by mating one homozygous Dongxiang blue-shelled ( O*LC/O*LC ) male and one White Leghorn ( O*N/O*N ) female , and the progeny were used for pyrosequencing analysis . All animal research was approved by Beijing Administration Committee of Laboratory Animals under the leadership of the Beijing Association for Science and Technology , the approve ID is SYXK ( Beijing ) 2007–0023 . DNA was extracted from blood using standard phenol/chloroform method . RNA was extracted from the liver and uterus . All the tissue samples for RNA isolation were collected at 3 to 5 hours before the next expected oviposition . The F2 resource family was used for linkage analysis . A set of 8 markers covering the region anchored by GWAS and SOX5 , ev1 were used in the linkage analysis ( Figure 2 ) . Marker L1 and L4–L7 were adopted from previous reports [18] , [20] and L2 , L3 and L8 were mined from the chicken genome assemble ( Build 2 . 1 ) at http://genome . ucsc . edu/cgi-bin/hgGateway . Fifteen SNP markers ( L9–L23 ) between L4 and L5 were added to narrow the mapping region . Primers and genotyping methods for all markers were present in Table S1 . CRI-MAP 2 . 4 was used for linkage analysis [44] . The TWO-POINT option was used to calculate the recombination fractions between loci as well as corresponding LOD-scores . The CHROMPIC option was used to find unlikely double recombinants . Total RNA was extracted from the uterus using Trizol reagent ( TianGen , Dalian , China ) , followed by synthesis of cDNA from 2 µg of RNA using M-MLV reverse transcriptase ( Promega , CA , USA ) . Five pairs of primer were designed for the four candidate genes ( SLCO1C1 , SLCO1B3 , LOC418189 and SLCO1A2 ) and housekeeping gene GAPDH using Primer 5 . 0 for RT-PCR ( Table S4 ) and all primer pairs were designed to span an intron at least . Expression analysis of the four candidate genes was performed in the uterus of blue-shelled ( n = 16 ) and brown-shelled ( n = 16 ) Dongxiang chickens . RT-PCR amplification conditions were as follows: 94°C for 5 min , followed by 36 cycles of amplification ( 94°C for 30 s , 58°C for 30 s , 72°C for 20 s ) and one cycle of 72°C for 5 min . Expression of SLCO1B3 in the uterus was subsequently detected in a set of samples including homozygous blue-shelled ( O*LC/O*LC , n = 4 ) , heterozygous blue-shelled ( O*LC/O*N , n = 4 ) , and brown-shelled ( O*N/O*N , n = 4 ) Dongxiang chickens , homozygous blue-shelled ( O*LC/O*LC , n = 3 ) , heterozygous blue-shelled ( O*LC/O*N , n = 3 ) , and brown-shelled ( O*N/O*N , n = 6 ) Lushi chickens , and 4 non-blue-shelled breeds including White Leghorn O*N/O*N , n = 6 ) , Beijing You ( O*N/O*N , n = 6 ) , Silkies ( O*N/O*N , n = 6 ) , Tibetan chicken ( O*N/O*N , n = 6 ) by real-time PCR with a Bio-Rad CFX96 instrument ( Bio-Rad , CA , USA ) . Samples were run in triplicates using RealMasterMix ( SYBR Green I ) ( Tiangen , Dalian , China ) . PCR amplifications were carried out in a 20 µL reaction volume containing 1 . 2 pmol of each primer , 9 µL of 2 . 5×working concentration RealMasterMix and 1 µL of cDNA in following cycling conditions: 95°C for 2 min , followed by 40 cycles of 95°C for 10 s , 58°C for 10 s , 68°C for 10 s . GAPDH is used as endogenous reference gene to normalizing amounts of input cDNA , heterozygous blue-shelled Dongxiang ( O*LC/O*N ) group was designed as a calibrator . Fold change of every group related to the calibrator was calculated as described in Livak et al . [45] . Tissues ( the uterus and liver ) were collected from the six blue-eggshell heterozygotes . Total RNA was extracted from the uterus and liver with trizol ( Tiangen , Dalian , China ) . The RNA quality was controlled using NanoVue plus spectrophotometer ( GE Healthcare , USA ) . The first-strand cDNA synthesis used M-MLV ( Promega , CA , USA ) with the 18 hexamers . A fragment containing the SNP ( g . 67334934 G>T ) in exon 5 was amplified with forward ( CATGTTGCGAGGAATTGGTG ) and reverse ( TTCCTTAGCAAAATCGTCAAGATA ) primers . The relative expression of the two allele ( O*LC or O*N ) transcripts in heterzygotes was scored by analyzing the SNP ( g . 67334934 G>T ) by pyrosequencing . A pyro-seq primer ( CGTCAAGATAAGAGATGCC ) was used as the sequencing primer and all steps were performed according to manufacturer's protocol . All samples were analyzed in triplicates . The uterus from a 60-week-old egg laying blue-shelled hen and a non-blue-shelled hen were collected and fixed in 4% paraformaldehyde in phosphate buffered saline ( PBS ) for 24 hours at room temperature . Fixed uterus was embedded in irrigation solution PBS for six hours to eliminate 4% paraformaldehyde . Then slides were dehydrated in increasing concentrations of ethanol ( 50% , 70% , 80% , 90% , 95% for 1 . 5 hours each and 100% for 2 hours ) followed by transparentizing in two clearing agents respectively of xylene for 15 minutes each . After transparentizing , the slides were pretreated by the mixture of xylene and low melting paraffin for 30 minutes then were directly transferred into pure melting paraffin ( 58°C ) twice for 3 hours each . The cDNA probe 5′-AACTCTGGCTGAACGCATCT-3′ were labeled by 6-FAM and were synthesized from mRNA of SLCO1B3 ( XM416418 . 2 ) by Boxing Bio-engineering Limited Company ( Boxing , Guangdong , China ) . The in situ hybridization was then carried out according to the instruction of the FISH Detection Kit ( Boxing , Guangdong , China ) . Imaging was performed using a fluorescence microscope equipped with vision software . Twenty-four kilobases fragment ( GenBank accession No . JN020139 ) covering the whole SLCO1B3 was resequenced using a panel of ten birds from 5 blue-shelled ( O*LC/O*LC ) and 5 brown-shelled ( O*N/O*N ) Dongxiang chickens . Seventeen primer pairs used to generate overlapping PCR amplicons ranging from approximately 800 bp to 2000 bp in size were listed in Table S5 . The PCR amplifications were performed in a total volume of 50 µL containing 5 µL of 10×Taq polymerase buffer , 10 mmol of each deoxynucleotide triphosphate ( dNTP ) , 20 pmol of each primer , 2 . 5 U Taq DNA polymerase ( HT-biotech , Beijing , China ) , and 50 ng genomic DNA . All purified PCR products were directly sequenced in both directions using the same primers . The sequences were assembled and analyzed for polymorphisms using the ChromasPro 1 . 5 or BLAST program in UCSC ( http://genome . ucsc . edu/cgi-bin/hgBlat ? command=start ) . In order to analyze the 5′ and 3′ untranslated regions ( UTR ) of the SLCO1B3 gene , RACE experiments were performed on 2 µg total RNA extracted from the uterus of a homozygous blue-shelled ( O*LC/O*LC ) and a brown-shelled ( O*N/O*N ) Dongxiang chicken using 5′ and 3′-Full RACE Kit ( Takara , Dalian , China ) , according to the manufacturer's instructions . 5′ and 3′ UTR of SLCO1B3 gene transcripts were amplified by nested PCR with gene specific ( Table S4 ) and adaptor primers ( Table S4 ) for the first and second amplifications of 5′ and 3′ UTR respectively . First and second PCR amplifications were carried out in a 50 µL reaction volume containing 20 pmol of each primer , 5 µL of 10× LA PCR buffer ( Mg2+ plus ) , 2 . 5 U of LA Taq ( Takara , Dalian , China ) , 20 mM of each dNTP and 1–2 µL of cDNA or 1st PCR product . RACE products were cloned to pMD-18 vector ( Takara , Dalian , China ) , and then sequenced in both directions . A long-range PCR amplification with 1B3_5F & 5R primer pair ( Table S5 ) was performed in volumes of 50 µL containing 5 µL of 10× LA PCR buffer ( Mg2+ plus ) , 2 . 5 U of LA Taq ( Takara , Dalian , China ) , 20 mM of each dNTP , 20 pmol of each primer and 50 ng genomic DNA . The PCR condition was as follow: 94°C for 3 min followed by 33 cycles of 94°C for 30 s , 58°C for 30 s , 72°C for 5 min , and a final extension at 72°C for 10 min . The PCR product was completely sequenced using the other three pairs of bridging primers ( Table S5 ) besides the 1B3_5F & 5R . Twenty-one SNPs found in resequencing of SLCO1B3 were used to analyze the genetic variants of SLCO1B3 ( Table S6 ) . SNP markers were genotyped by iPLEX SEQUENOM MassARRAY platform ( Sequenom , CA , USA ) . This genotyping system used single-base extension reactions to create allele-specific products that are separated automatically and scored in a matrix-assisted laser desorption ionization/time of flight mass spectrometer . Primer design was performed using MassARRAY Assay Design software ( v3 . 1 ) according to Sequenom's instructions . Multiplex PCR amplification of amplicons containing SNPs of interest was performed using HotStart Taq Polymerase ( Qiagen , CA , USA ) with 12 ng genomic DNA . Assay data were analyzed using Sequenom TYPER software ( v3 . 4 ) . The retrovirus insertion was genotyped with a mix of three primers: the primer “test-nor-up” 5′- TTTGACCAGCGTAGATAA-3′ and “test-nor-down” 5′-ATGTTAGCAGTGTAGTTG-3′ were located in the wild type genomic sequence of SLCO1B3 , the primer “test-eav” 5′-TAGGTTCCGAACGCGATGT-3′ was located in the gag region of the inserted retroviral sequence ( Figure S3 ) . The PCR amplifications were preformed in a total volume of 25 µL containing 2 . 5 µL of 10×Taq polymerase buffer , 5 mmol of each deoxynucleotide triphosphate ( dNTP ) , 10 pmol of each primer , 1 . 25 U Taq DNA polymerase ( HT-biotech , Beijing , China ) , and 50 ng genomic DNA in the following condition: 94°C for 5 min , followed by 36 cycles of 94°C for 30 s , 58°C for 30 s , 72°C for 20 s , and a final extension at 72°C for 5 min . The PCR products was separated by 2% agarose gel electrophoresis , and the length of target fragment was 340 bp for test-nor-up and test-nor-down , and 425 bp for test-nor-up and test-eav , respectively ( Figure S3 ) . Two pairs of PCR primers ( EAVIS-1F , EAVIS-1R , EAVIS-2F and EAVIS-2R , Table S5 ) were designed for amplifying 5′ and 3′ end of EAV-HP junction regions . The PCR condition was as follow: 94°C for 3 min followed by 33 cycles of 94°C for 30 s , 57°C for 30 s and 54°C for 40 s , respectively , 72°C for 45 s , and a final extension at 72°C for 10 min . The PCR products were sequenced bidirectionally using the PCR primers .
|
The eggshell color of birds is of wide interest , but the molecular basis remained unknown until our discovery , reported here . The blue eggshell is found not only in wild birds but also in domestic fowls . In this study , we identified that blue eggshell in chickens from different geographical regions is caused by a ∼4 . 2 kb EAV-HP insertion in the 5′ flanking region of SLCO1B3 . The EAV-HP insertion in chicken is a derived mutation in domestic chickens . The genetic determination of blue eggshell in other birds requires further investigation . We also found that the EAV-HP insertions in the chickens from China and America were separate integration events , which presents us with a parallel molecular evolution example driven by artificial selection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"mutation",
"haplotypes",
"genetic",
"polymorphism",
"natural",
"selection",
"genetics",
"population",
"genetics",
"biology",
"population",
"biology",
"evolutionary",
"biology",
"genetics",
"and",
"genomics"
] |
2013
|
An EAV-HP Insertion in 5′ Flanking Region of SLCO1B3 Causes Blue Eggshell in the Chicken
|
Coherent angular rotation of epithelial cells is thought to contribute to many vital physiological processes including tissue morphogenesis and glandular formation . However , factors regulating this motion , and the implications of this motion if perturbed , remain incompletely understood . In the current study , we address these questions using a cell-center based model in which cells are polarized , motile , and interact with the neighboring cells via harmonic forces . We demonstrate that , a simple evolution rule in which the polarization of any cell tends to orient with its velocity vector can induce coherent motion in geometrically confined environments . In addition to recapitulating coherent rotational motion observed in experiments , our results also show the presence of radial movements and tissue behavior that can vary between solid-like and fluid-like . We show that the pattern of coherent motion is dictated by the combination of different physical parameters including number density , cell motility , system size , bulk cell stiffness and stiffness of cell-cell adhesions . We further observe that perturbations in the form of cell division can induce a reversal in the direction of motion when cell division occurs synchronously . Moreover , when the confinement is removed , we see that the existing coherent motion leads to cell scattering , with bulk cell stiffness and stiffness of cell-cell contacts dictating the invasion pattern . In summary , our study provides an in-depth understanding of the origin of coherent rotation in confined tissues , and extracts useful insights into the influence of various physical parameters on the pattern of such movements .
An epithelial sheet is comprised of a group of cells that are connected to each other via cadherin bonds to form a monolayer . Many experimental observations have demonstrated that cells in this network are persistently motile , and upon reaching a critical density show collective migration behavior [38] . Presence of front-rear polarity axis is known to be essential for migrating cells . This polarity axis manifests in migrating cells in different forms like: ( i ) increased actin activity in the front and formation of actin structures such as lamellipodia , ( ii ) localization of the microtubule organizing center ( MTOC ) at the front of the nucleus with microtubule growth towards the leading edge , ( iii ) gradients in cell-ECM adhesion , and ( iv ) establishment of front-rear gradients in the activity of GTPases such as Rac/Cdc42 [39] . Cell polarity is actively maintained and constantly steered by complex mechano-chemical processes governed by cell-cell and cell-ECM interactions [40 , 41] . A surprisingly simple upshot of these complex processes in terms of mechanical observables is that , in epithelial sheets such as MDCK tissue , the polarization of constituent cells is closely oriented with the principal direction of stress as well as with their average velocity [42 , 43] . Keeping these experimental observations in mind , we have utilized a simple model to explore how mechano-chemical properties of individual cells impact their collective behavior in confined epithelial sheets . For modeling the collective mechanics of cells , we have adopted a ‘cell center-based mechanics model’ with cells represented as discrete points at their center of mass [26 , 44 , 45] . As shown in Fig 1 , the whole epithelial tissue is represented as a continuous sheet with cell-cell cadherin junctions represented by simple harmonic springs [26 , 46] . Each cell is assumed to exert an attractive or repulsive force on its neighboring cells depending on the relative deformation of springs with respect to their undeformed length , a0 and stiffness , k . The force acting on any cell at any time , t , is the sum of the contributions of all the connecting neighbors . Thus , if ri represents the position of ith cell , the net force exerted on that cell by neighbors ( m , say ) is given by F i = ∑ j ∈ neighbor k ( | r j - r i | - a 0 ) e i j ( 1 ) where , e i j = ( r j - r i ) | r j - r i | represents the unit vector along the direction connecting the ith cell with its jth neighbor . Depending on the relative deformation of springs with respect to the natural length , the interaction potential can either be tensile or compressive . In order to avoid force transfer between distant neighbors , it is assumed that when the deformation of spring is greater than a threshold , dmax , no force transfer occurs between those two cells . For all our simulations , we took the value of dmax equal to 1 . 3 a0 [26] . Thus the value of spring stiffness for the entire range of deformation can be written as: k = 0 , if ( | r j - r i | - a 0 ) > d max . k t , if 0 ≤ ( | r j - r i | - a 0 ) ≤ d max . k c , if ( | r j - r i | - a 0 ) ≤ 0 . ( 2 ) In the above expression , kc and kt represent the bulk cell stiffness and the stiffness of cell-cell adhesions ( or cohesivity ) , respectively . Fig 1 ( e ) illustrates the attractive/repulsive force acting on each cell . The cells are allowed to exchange their neighbors , which are obtained by repeated Delaunay triangulation [25 , 44] . For a given set of cell centers , Delaunay triangulation provides a connectivity for cells that produces the least number of distorted triangles , i . e . , triangles with least shear strain . Delaunay triangulations are dual to Voronoi tessellations ( Fig 1 ( b ) and 1 ( c ) ) and the Voronoi polygon for a given cell center can be modeled to be the cell itself ( see Materials and Methods ) . In our model , cells are assumed to act as self propelled active particles [26] , with their inherent motility ( v0 ) representing the speed with which they move in the absence of any external force . The preferential direction of cell’s motion ( i . e . , polarization ) is represented by the vector p i ^ , which is a coarse-grained representation of the front-rear polarization in a motile cell [39] . As cells move over a viscous substrate with mobility μ , the drag force acting in the opposite direction of motion balances the internal forces . If ri is the position vector of ith cell , its velocity at time t can be written as: v i = d r i d t = v 0 p i ^ + μ F i ( 3 ) Similar to the procedure followed elsewhere [26] and as motivated earlier , we assume that the cell’s polarization vector tends to orient with its velocity vector as per the following equation: d p ^ i d t = ξ ( p ^ i × v ^ i . e ^ z ) p ^ i ⊥ ( 4 ) where v ^ i is the unit velocity vector and e ^ z is the unit vector perpendicular to the plane . The parameter ξ represents the polarization coordination constant determining the tendency of cell’s polarization to rotate and align with the velocity vector . We do not account for noise in our simulations [47] as we are primarily interested in a mean-field understanding of CAM , and noise is known to typically increase fluctuations in the system [25] .
Various theoretical studies modeling the behavior of cells on micro-patterned substrates have established the emergence of coherent rotation of cells under confined conditions [25 , 26] . Similar to these studies , our model also shows the emergence of a persistent mode of rotation for a group of cells ( N = 140 ) when confined on a circular substrate ( kc = kt = 10 , ξ = 1 , v0 = 1 , μ = 1 ) ( S1 Video ) . While the theory of active elastic systems attributes the onset of rotational motion to energy transfer to the lowest modes [50 , 51] , a systematic analysis of this phenomenon in the context of epithelial sheets remains to be performed . Using our model , we demonstrate that rotation is indeed the preferred mode of motion for tissues confined in circular geometries—this mode of CAM is very different than that observed in bacterial suspensions [20] ( also see S2 Text ) . Fig 2 ( c ) illustrates the quantification of this rotational motion in terms of mean vorticity of the system ( See Materials and Methods ) . After an initial transient mode , cells start to rotate steadily as evidenced by the constant value of the mean vorticity of the system . The onset of rotation depends on the parameter ξ , which reflects the tendency of the cell’s polarization to orient along its velocity ( Fig 2 ( a ) ) . The greater the value of ξ , higher is the tendency of polarization vector to reorganise and align along the velocity vector , resulting in faster initiation of coherent rotation of cells ( Fig 2 ( b ) ) . Fig 2 ( d ) emphasizes this by plotting the scalar product of polarization vector and velocity vector ( p ^ . v ^ ) as a function of time . From the figure it is seen that , as the value of ξ increases , coordination between p ^ and v ^ is builds up faster resulting in a faster approach to steady state of motion . We would also like to emphasize that , for larger values of ξ , the time scale for polarization evolution can be faster than the relaxation of a few long wavelength radial modes ( see S2 Text and S3 Video ) . In this case , some long wavelength radial modes can be sustained during the coherent rotation and the tissue can exhibit radial movements that are similar to those observed by Deforet et al . [24] . Additionally , as the confinement radius R for the tissue increases , these radial movements become prominent even at lower values of ξ ( S2 Text and S19 Video ) . This is because , larger the system size , lower is the stiffness of long wavelength radial modes , and hence slower is their decay . This behavior of increasing radial velocity for the tissue with increasing confinement size is also observed by Deforet et al . in their experiments ( see SI Fig . 4 of Ref . [24] ) . It was reported by Doxzen et al that , for tissues with confinement size greater than the velocity correlation length ( ≈200 μm ) , there was no onset of CAM within the observation window of around 48 hours [9] . However , we find from our simulations that irrespective of tissue size ( R ) , the tissue always reaches the steady state of coherent rotation ( see Fig 2 ) . In other words , we find that the steady state velocity correlation length is set by the size of the confined tissue . However , the time required to reach the steady state is higher for larger tissues ( see Figs 2 ( e ) , 2 ( f ) and S5 ) . This increase in the time required to reach the steady state may be attributed to the presence of a greater number of long wavelength modes for the larger system , as described above . The presence of these modes would interfere with the transfer of cellular motility to the rotational mode . We can reconcile our simulation results with the experimental observations by noting that , as the time required for setting the coherent motion is greater for larger tissues , the tissue is likely to be perturbed by certain unknown factors ( e . g . , cell proliferation ) in that additional time . The resulting mechanical and polarization perturbations may , therefore , further delay the onset of coherence with respect to the experimental time window , or make CAM infeasible . We predict that in the absence of perturbations , even a large confined tissue can undergo CAM . These predictions differ from the observation of finite velocity correlation lengths of around 10 cell lengths in unconfined tissues ( e . g . Refs . [42 , 48] ) , wherein different boundary conditions ( e . g . , leader cells , high cable tension , etc ) are likely to lead to qualitatively different behavior from that of confined tissues . Collectively , these results illustrate the effect of confinement in inducing coherent angular motion . Under in vivo conditions , such confinement may be provided by non-motile cells [35] possessing higher substrate frictions than motile cells ( see S4 Text and S3 and S4 Figs , S4–S11 Videos ) . Under these conditions , the efficiency of coherent motion is dictated by the ratio of substrate frictions between the two cell types . As the presence of a rotational mode of migration under confinement is well established by now , we focused our attention in understanding the characteristics of that motion in detail . Studies by Doxzen et . al . have shown that the movement of small circular tissues under confinement is similar to solid body rotations with angular velocity ω equal to 4 v 0 3 R , where R is the radius of circle [9] . Further , the linear relationship between velocity and radial distance for rotating cell collectives obtained by multiple research groups support the argument of solid body rotations [9 , 25] . However , what factors influence this solid-like tissue behavior has not been addressed . Here , we show that cell density is one such parameter dictating the nature of tissue behavior . As shown in S1 Video , at lower cell densities , system behaves as an elastic solid with negligible neighbor changes and a linear velocity versus radial distance relationship ( Fig 3 ( a ) ) . Increase in number of cells in the system while keeping the size R constant , i . e . , increase in cell density , leads to an interesting phenomena . Increase in cell density alters the nature of the velocity versus radial distance relationship and induces a transition from solid-like behavior ( N = 140 ) to that like a fluid ( N = 170 ) . Specifically , with increase in cell density , the linear velocity versus radial distance curve becomes more saturating . At the highest cell density ( N = 170 ) , the velocity plateaued to v0 = 1 at the edges . One of the probable reasons for this change is the large shear that the system experiences at such densities , as evident from the relative sliding of cells past each other ( S2 Video ) . Quantification of the shear strain rate ( ϵ ˙ x y ) from the rate of deformation tensor as ϵ ˙ x y = 1 2 ( ∂ u ∂ y + ∂ v ∂ x ) was performed to obtain additional insight into the magnitudes of shear experienced by the cells at various cell densities . A plot showing the variation of principal shear strain rate as a function of radial distance shows that with increase in cell number , the shear in the system also increases ( Fig 3 ( b ) ) . Collectively , the above numerical results indicate that the number density of cells alters the behavior of system; i . e , at lower cell densities , system behaves like an elastic solid and at higher cell densities , system becomes more fluid-like . While studying the effect of cell crowding on the nature of coherent rotation , we assumed that the motile cell speed or the fraction of motile cells is not modified by cell density . Consequently , we find that the mean speed of the cells in the tissue increases with cell density ( Fig 3 ( d ) ) . This finding follows from our observation in Fig 3 ( a ) wherein upon increase in cell density , the tissue fluidises , as a result of which more and more layers of the tissue move with speeds comparable to v0 = 1 . On the other hand , when the tissue behaves elastically ( for N = 140 ) , the tissue rotates as a rigid body with cell speed comparable to v0 at the edges , but significantly lower speed of cells in the interior . However , while studying the effect of cell density on velocity profile of the over-confluent tissue , the condition of contact inhibition observed experimentally [52] has not been taken into account . To mimic the condition of contact inhibition for a denser system , and reconcile the experimental observations of decrease in mean velocity with increase in number density [9] , we have considered the following cases: ( i ) due to crowding , the self-propelled speed of cells can be smaller on account of cells forming smaller lamellipodia [9] ( see Fig 4 ( a ) ) , or ( ii ) due to crowding , a fraction of cells are possibly not motile ( see S6 Fig ) . Both of these effects are feasible due to contact inhibition of motility in crowded tissues . For both cases , as expected , we observed reduction in mean cell speeds . Additionally , we can also see from Fig 4 ( b ) that the tissue shows fluidisation for value of v0 as low as 0 . 3; only at really low v0 = 0 . 1 does it recover back its elastic behavior . Thus , for appropriate values of v0 at large N , we can observe lower mean cell speeds , concurrently with a fluid-like behavior for the overall tissue . It appears from the above findings that N and v0 are two parameters that tune the solid to fluid transition in coherently rotating tissues . In order to gain insight into this transition , we first analytically obtain mechanical steady state of the tissue by modeling it as a homogeneous , linear , elastic solid . This continuum description seems reasonable when cell-cell connectivity in the tissue is maintained during coherent rotation [49] . For such a tissue constrained within a circular patch and with zero tangential tractions , we can obtain one particular steady state solution , based on the following assumptions: Due to ensuing radial symmetry , the confinement is expected to induce isotropic compression only , i . e . , σr = σθ = p , which should not interfere with the shear strain ( stress ) in the tissue due to motile forces . The easiest solution to visualize is a small elastic displacement ur and uθ superimposed on a rigid body rotation with angular speed ω . If this solution is indeed possible then , p ^ and cell velocities will be both aligned in the tangential direction . The continuum form of polarization evolution equation ( see Eq 4 ) would be , D p ^ D t = ξ ( p ^ × v ) . e z p ^ ⊥ ( 5 ) for the current model , where D/Dt represents the co-rotational material derivative for the elastic sheet [23 , 53] . We look at the steady state solution when p ^ would not vary temporally . The equation of equilibrium , respectively , in the radial and the tangential direction for the current elastic sheet with above conditions will be [54]: E h 2 ( 1 + ν ) ∂ 2 u r ∂ r 2 + 1 r ∂ u r ∂ r - u r r 2 + E h 2 ( 1 - ν ) ∂ ∂ r ∂ u r ∂ r + u r r = 0 ( 6 ) E h 2 ( 1 + ν ) ∂ 2 u θ ∂ r 2 + 1 r ∂ u θ ∂ r - u θ r 2 + v 0 μ s 1 - ω r v 0 = 0 ( 7 ) In the above set of equations E , ν and h are , respectively , the Young’s modulus , Poisson’s ratio , and thickness for the sheet—the connection between these values and the parameters used in the simulations is discussed in S1 Text . The parameters v0 and μs are the self-propelled speed and effective motility per unit area of the tissue . Since we presume that all cells are motile , v0 is the essentially same as the self-propelled motility value used in our simulations for the tissue . The parameter μs is related to the motility of single cell as μs ρ = μ , where ρ is the cell density , or number of cells per unit area of the tissue . The angular velocity ω is an unknown in this problem and can be obtained as follows . The equation of equilibrium for the tissue in the simplest form is: ∇ . σ + ρ μ ( v 0 - ω r ) t ^ = 0 . ( 8 ) Taking a cross product on both sides with r , the position vector with respect to the center , and integrating this over the entire area of the circle we get: e z . ∫ r × ∇ . σ d A + e z . ∫ ρ μ ( v 0 - ω r ) r × t ^ d A = 0 . ( 9 ) Since the tangential traction is zero , by design , on the boundary , the first term of this equation reduces to zero by the divergence theorem . The second term can be simplified , further , due to the presumed radial symmetry to give the following expression for ω: ω = 4 3 v 0 R , ( 10 ) where R is the radius of the confined tissue . This derivation is similar in essence to that done in Ref . [9] . Substituting this value for ω we can now solve the two equations subject to two boundary conditions: u r ( R ) = 0 ( confinement ) , ∂ ∂ r u θ r R = 0 ( shear traction ) . ( 11 ) Using these boundary conditions , and noting that , by symmetry ur ( 0 ) = uθ ( 0 ) = 0 , we get the following solution for the displacements ( with respect to the undeformed configuration ) u r = 0 u θ = v 0 ρ ( 1 + ν ) R 2 3 E h μ r R 2 r R - 2 ( 12 ) The internal shear τrθ ( per unit height h ) for the tissue is given as τ r θ = E h 2 ( 1 + ν ) ∂ u θ ∂ r - u θ r = v 0 R 3 μ s r R r R - 1 ( 13 ) The maximum value τmax of τrθ happens at r = R/2 and given as τ max = v 0 ρ R 12 μ and ϵ max = v 0 ρ R ( 1 + ν ) 12 μ E h ( 14 ) This simple expression , in combination with simulations , gives us significant insights into the variables and mechanisms that influence the behavior of the coherently rotating tissue . It can be seen from Eq 14 that the maximum shear strain in the tissue , assuming it to be linear , elastic , homogeneous , is directly proportional to both v0 and ρ ∼ N . Because shear strain governs transition from solid to plastic flow [55] , we expect the quantity v0 × N to govern the transition of the tissue from solid-like to fluid-like . Since we observed solid-like coherent rotation of the tissue for N = 140 and v0 = 1 , we can expect the tissue to behave in a similar manner for N = 170 , if the cell motile speed is taken as v0 = 140/170 ≈ 0 . 82 . It can , however , be seen from Fig 4 ( b ) , that even for values of v0 as low as 0 . 3 , the tissue still undergoes fluidisation . Only when v0 reduces to values lower than 0 . 1 , does the tissue recover back its solid-like behavior . This implies that there is possibly a density dependent shear threshold which controls the solid to fluid transition of the tissue . This can be achieved if confinement introduces a density dependent shear pre-strain in the tissue , thus apparently altering the critical threshold . The underlying mechanism of tissue fluidisation at higher densities can perhaps be understood from analysing the deformation pattern of cell triangles in the tissue . In our study , the cells , represented by their centers , are confined in a circle of a given radius R . The presence of confinement results in pre-straining of cell-cell connections ( springs ) . Unlike in a homogeneous material , due to discrete nature of this system , pre-straining by circular confinement is not uniform; instead , it leads to non-uniform deformation of the springs resulting in the presence of shear pre-strain in the system , which , by definition , implies distortion of the tissue . This distortion is further enhanced by the shear strain induced by the motile forces on the cells . Delaunay triangulation , which is used in our model to obtain/update the connectivity of cells , seeks to minimise distortion ( shear ) in the connecting triangles that form the tissue . Everything else remaining the same , crowding increases the amount of pre-strain , and hence the initial shear strain in the tissue . Thus a crowded tissue is more susceptible to connectivity update via neighbor changes ( i . e . , T1 transition ) , which is reflected as non-zero shear strain rate or fluidisation ( see S5 and S6 Texts for detailed analysis ) . It may be noted that , in order to provide a general and more realistic , continuum description of the tissue here , one may need a more sophisticated model [56] . This is beyond the scope of this paper , due to the difficulty in both obtaining appropriate rheology that is compatible with the discrete model , and obtaining an analytical solution . We hence , present a simple semi-analytical case of a simple Newtonian fluid to demonstrate coherent rotation for a fluidised tissue , and resort to the simulation results to make any contact with experiments ( see S3 Text for derivation ) . The analytical predictions of mean vorticity ( ω = 4 v 0 3 R in the case of solid-like and ω = v 0 R in the case of fluid-like ( calculation shown in S3 Text ) ) closely matched the simulation values , and predict a reduction in mean vorticity with increase in cell density ( Fig 3 ( c ) ) . As seen from the previous section , in addition to providing rotational velocity , the continuum modeling also gives us a simple expression for maximum shear strain ( stress ) in the tissue ( Eq 14 ) . This equation gives us further insights into the possible behavior of the tissue . For example , this expression predicts that a tissue with larger R has greater shear strain , and is hence more susceptible to cross over the critical strain threshold and exhibit fluidisation . To test this prediction , we performed simulations with increasing R , such that the number density of cells in the tissue was very close to the number density for the case R = 5 , N = 130 , where the tissue rotates as a solid . It can be seen from Fig 5 ( a ) and S19 Video that , though there is no fluidisation for R = 5 , for larger R , the tissue behaves in an increasing fluid-like manner—more and more layers of tissue were observed to move with velocity close to v0 = 1 . Thus , tissue can undergo fluidisation solely due to the influence of system size . The relatively larger values of cell speeds at lower radial distance is due to radial movement of cells ( see S19 Video ) , and is possibly related to the dominance of radial modes with increasing system size ( S2 Text ) . Thus , even though Eq 14 , does not exactly capture the tissue behavior with increasing system size , it provides us with pointers in the right direction , and concurrently exposes the shortcoming of describing the tissue as a solid-like material [9] . It can be noted from Eq 14 that , the shear strain is , as expected , inversely proportional to the tissue stiffness . This implies that tissues with stiffer cells ( kc ) and greater cell-cell cohesivity ( kt ) are less susceptible to cross over the critical strain threshold and more likely to exhibit solid-like behavior; the inverse would apply for tissues with softer cells . For the case R = 5 , N = 170 , increasing the stiffness kc for a tissue from 10 to 100 results in a transition from fluid-like to solid-like coherent rotation of the tissue ( Fig 5 ( b ) ) . Similarly decreasing the value of cell cohesivity ( kt ) also leads to fluid-like behavior of tissue . We can see from Fig 5 ( c ) that for N = 140 when kt = kc = 10 , then the velocity profile being linear is an indication of rigid body rotation ( as per the analytical solution for elastic solids shown in previous section ) . However , when kt is decreased from 10 to 1 while keeping kc = 10 , then it is clearly seen that the tangential velocity as a function of radial position has saturating profile ( as seen in the previous section for analytical solution for viscous fluid ) indicating fluidisation . Thus the stiffness and cohesivity of tissue cells can independently control the nature of coherent rotation for the confined tissue . While the above results of coherent rotation were obtained in circular geometries , it remains unclear if similar coherent rotation is also possible in non-convex geometries . Of the various non-convex geometries , annular rings are often observed in vivo in glands , ducts or tissues with lumen inside . Several studies have probed the collective behavior of cells inside annular geometries [25 , 57 , 58] . Since annular geometry represents the simplest non-convex geometry obtained from a circular shape , we next studied the coherence patterns in annular geometries and the influence of cell density . For this , simulations are done with outer and inner radius of annulus taken as 100 μm and 70 μm . Simulation with N = 100 , kc = 10 , kt = 10 , ξ = 1 , v0 = 1 , shows that here also , after a short initial transient mode , cells exhibit robust coherent rotation similar to that on circular geometries ( S12 Video ) . However , the pattern of coherent rotation is dictated by the stiffness of cell-cell adhesions . Specifically , for lower stiffness values ( kt ) , different cell layers in annular section may move in different directions ( S13 Video ) ; in contrast , for higher stiffness values of cell-cell connections , cells move in a robust manner after an initial breathing mode ( S12 Video ) . Next , to test the effect of cell density on mean vorticity , simulations were performed on annular geometries with varying annular thickness , t and constant outer radius ( R ) . For a constant number of cells ( N ) , varying t R leads to change in number density , and is hence expected to influence the pattern of coherent motion . Consistent with this , distinct behavior is observed for two different values of N . Fig 6 shows the plot of mean vorticity of system as a function of t R for N = 100 ( green curve ) and N = 140 ( blue curve ) ; the other parameters are kept the same as in previous simulations . As seen from the plot , it is seen that with increase in the thickness of annulus , the mean vorticity of the system also increases , which matches with the findings of Li and Sun [25] . In addition to that , we have also shown that for lower number of cells ( N = 100 ) , system behaves more like an elastic solid with minimum shearing between cell layers similar to that seen in the circular geometries . But it is interesting to note that for larger number of cells ( N = 140 ) , system behaves like an elastic solid at higher t R values . However , when the thickness of annular section decreases , cells become more compressed which leads to the fluidisation of system and as a result , for lower t R values , system behavior is more similar to viscous fluid . In order to have a better understanding , we have also calculated the analytical values of mean vorticity of system using the following equation: ω = 1 2 2 π ( R out v out - R in v in ) π ( R out 2 - R in 2 ) , ( 15 ) from Stokes theorem [53] . Here Rout and Rin are the outer and inner radius and vout and vin are the outer and inner velocities , respectively . As seen from Fig 6 , the analytical values of vorticity ( see earlier sections for analytical expressions for elastic and viscous calculation for vout and vin ) closely follow the computed values and illustrate the dependence of vorticity on t R values . Taken together , our findings on circular as well as annular geometry imply that for different confinement geometries , cell behavior can vary between that of a perfectly elastic solid and a complex fluid depending on cell density . In addition to illustrating the role of confinement in inducing coherent motion , our results also demonstrate the critical influence of cell density in dictating the pattern of coherent motion . While cell density can be experimentally controlled in in vitro experiments , under in vivo conditions , cell density is controlled by cell division—a factor that was not taken into account in our simulations . While several computational studies have tried to understand how cell division influences morphogenesis [59–61] , the sensitivity of coherent motion to cell division remains unexplored . Having demonstrated the robust influence of cell density in our simulations , we next probed the extent to which coherent motion is sensitive to changes in cell density effected by cell division events . Cell division can occur either synchronously ( i . e . , all cells divide at the same time ) or asynchronously ( i . e . , cells divide at different times ) . During early stages of embryo development , cells generally exhibit multiple fast synchronized division , accompanied by a transition stage and subsequent slow non-synchronized divisions , with different cells having different stages of cell cycle [62 , 63] . In vitro the cell cycle of individual cells can be synchronized by serum starvation [64] . To study the sensitivity of coherent motion to cell division , we probed how changes in the total number of cells in a confined geometry would influence the pattern of rotation . For these studies , an annular geometry was chosen , as such geometries are biologically relevant [37] . Further , both synchronized and asynchronized cell division were introduced into an already rotating system to perturb the steady state rotational motion . A total number of 40 cells , which are below the level of confluence , were confined in an annular substrate of outer radius 100 μm and inner radius of 70 μm , and allowed to reach a state of coherent rotation ( Fig 7 ( a ) ) . Once this state was reached , cells were allowed to divide either synchronously or asynchronously . For implementing asynchronous cell division , each cell in the system was initially assigned a random cell cycle number between 0 and 1 . In contrast , for synchronized division , the initial cell cycle number of all cells were set to 0 . The time interval for cell division was assumed as 24 hrs for all our simulations , and represents the time taken by any cell to reach its cell cycle from 0 to 1 . Any cell , on reaching a cell cycle number of 1 , underwent division to form two new daughter cells , provided the cell area was above some critical area ( see Materials and Methods ) . The two new daughter cells formed after division , were assigned equal and opposite polarization in random direction , and placed at a0/2 along the major principal axis of mother cell’s area ( Fig 7 ( a ) ) . For both synchronous and asynchronous division , cell division was stopped when the total cell number reached 80 . Interestingly , synchronous and asynchronous division were found to perturb coherent rotation to varying extents . Asynchronous division did not alter the direction of rotation , but only created some local disturbances , after which coherent rotation was fully established . This is clearly seen from the temporal profile of the mean vorticity ( Fig 7 ( a ) and 7 ( b ) ) where transient fluctuations in the mean vorticity quickly die down and cells continue to rotate coherently . In contrast , for the case of synchronous cell division , on several occasions , the direction of coherent rotation underwent a change as observed from the change in mean vorticity values ( Fig 7 ( c ) ) . Statistical analysis revealed that out of 200 independent simulations conducted , this reversal after synchronous cell division was observed for almost 60% of the cases , indicative of a preferential bias for the change in rotation ( S14 and S15 Videos ) . Together , these results suggest that while coherent rotation is insensitive to asynchronous division , synchronous division introduces a bias in the direction of rotation . However , the biological implication of this reversal remains to be established . Under in vivo conditions , the confinement assumed in our simulations , is generally provided by the surrounding extracellular matrix ( ECM ) . For example , all epithelial tissues are surrounded by the basement membrane , which helps to maintain tissue organization and prevents cell invasion . However , the basement membrane is breached by epithelial cells which turn cancerous . Cancer cells are known to invade both as single cells and collectively [65–67] . Since coherent rotation is sensitive to the properties of cell-cell contacts ( i . e . , kt and kc , respectively ) ( Fig 5 ) , we hypothesize that , the initial coherent rotation dictated by the properties of cell-cell adhesions has a distinct bearing on the eventual invasion pattern , when confinement is removed . To test this hypothesis , we have studied the invasion patterns formed when a coherently moving group of cells break their boundaries and invade to the surrounding matrix . For doing this , three conditions were chosen with the following combinations of kt and kc to mimic different properties of cells and cell-cell adhesions: kc = kt = 1 ( i . e . , soft ) , kc = 10 , kt = 1 ( i . e . , medium stiff ) , and kc = 10 , kt = 10 ( i . e . , stiff ) . The number of cells in each system was taken as 100 and the values of all other parameters were kept the same as that of other simulations . Once coherent rotation was set up in all the systems , the confinement was relaxed at t = 50 to allow for invasion . Consistent with our hypothesis , the combination of kc and kt were found to directly influence the nature of coherent motion ( Fig 8 ( a ) –8 ( c ) and S16–S18 Videos ) . For the soft and medium stiff systems , the extent of invasion ( i . e . , radial position as function of time ) remained the same . However , contrary to the ‘soft’ case where cells scatter in all directions , for the ‘medium stiff’ case , cells move radially outward as clusters which remain connected . For the ‘stiff’ case , cells continue to rotate even after the removal of confinement . Together , these results demonstrate that the nature of coherent motion set by the extent of cell-cell cohesivity dictates the invasion pattern when confinement is removed . Also , the persistent rotation of stiff cells with stiff adhesions even after the removal of boundary shows that even though confinement is essential for the emergence of coherent rotation , depending upon the properties of the system , the presence of a confinement is not mandatory condition for the cells to continue in their coherent motion .
Coherent rotation of cells is essential for a variety of physiological activities . In vitro studies have reported the occurrence of robust rotation of epithelial sheets under confinement . We developed a self propelled , cell center-based model to understand this phenomena . We have shown both numerically and analytically that , a two-way feedback between cell velocity and cell polarization , along with force transmission via cell-cell connections , is sufficient to produce persistent rotational mode of migration for cells under confined conditions . Though there are computational studies that demonstrate the possibility of CAM for tissues in confined geometries , we provide a logical and plausible explanation as to why this may happen . Similarly , we show the presence of additional hydrodynamic modes that deform the tissue in the radial direction , if the time-scale of relaxation of these modes is comparable to the time-scale of the orientation of polarization . We also predict that such radial modes are more likely to be observed for larger size tissues since they can sustain long wavelength and slow decaying radial modes , whose stiffness is inversely proportional to the tissue size—this finding is consistent with the experimental observations of Deforet et al . [24] . Our model predicts that irrespective of the size of the confinement , the cells can reach the state of coherent rotation . This implies that the velocity correlation length for the tissue can be as large as the system size . However , Doxzen et al . reported absence of coherent rotation for larger confinement size and linked this observation with the system size being larger than the experimentally observed correlation length of ≈10 cell lengths that was reported elsewhere [48] . This apparent contradiction regarding correlation length can possibly be explained as follows . Unlike confined tissues , the reported correlation length were obtained for systems having free boundaries . It is known that the presence of free boundaries leads to modifications in the tissue boundary conditions ( e . g . , leader cells , high cable tension , etc . ) [68] . These modifications may influence the velocity correlation lengths for the tissue , and therefore lead to qualitatively different behaviors when compared with that of confined tissues . We would also like to point out that for the same type of cells , velocity correlation length can be influenced by mechanical perturbations—time-increasing velocity correlation length of up to 450 μm was reported in Ref . [69] for migration cells on deformable substrates . Thus the correlation length need not be an inherent property of a tissue type , and can be influenced by mechanical perturbations ( such as confinement ) . In addition to these factors , perturbations in the form of cell division and cell death , can also influence the temporal dynamics of velocity correlation length of a confined tissue , and may be one of the reasons why coherent rotations were experimentally not observed for larger size tissues . Our results also demonstrate that a few experimentally controllable/observable variables—cell density ( N ) , motile speed ( v0 ) , system size ( R ) , cell stiffness ( kc ) and cell cohesivity ( kt ) —collectively tune the behavior of a coherently rotating tissue between that of an elastic solid and a complex fluid . We found that , in our model , tissue fluidisation is associated with increased shear strain in the system , which can be relieved by connectivity changes ( T1 transitions ) . Such T1 transitions that are dependent on shear deformation of cells , have been also observed/modeled in a very recent work by Etournay et al . [70] , thus providing biological relevance for our modeling . We found that upon increase in cell density the tissue showed an increasingly fluid-like behavior . This effect is not expected if the apparently radially symmetric confinement isotropically compresses the tissue . However , due to the discrete nature of the tissue , we observed that for greater cell density , the confinement induces larger distortion within the cells and makes the tissue more susceptible to fluidisation . Moreover , we also found that other factors such as increase in cell motility , increase in system size , and decrease in cell stiffness , which lead to increase in shear strains in the tissue , can make the tissue more vulnerable to fluidisation . Thus , our study identifies some of the potential parameters , which , as described above , could give rise to novel and hitherto unreported behavior for confined tissues . The basement membrane , which is found at the basal surface of epithelial cells is essential for tissue polarity , and maintains tissue structure by confining cells . In epithelial cancers , uncontrolled proliferation of cells leads to buildup of stress within the tissue . Subsequently , malignant cells breach the basement membrane and escape into the surrounding stroma . Cancer invasion through these matrices is dictated both by extrinsic factors ( e . g . , ECM density and organization ) and by intrinsic factors . Among the intrinsic factors , our findings implicate cell division and functional nature of cell-cell contacts as two parameters influencing the coherent motion , and the invasion process . Our studies show that synchronous division introduces a bias in the direction of rotation , with a reversal in the direction of rotation observed in nearly 60% cases . Whether or not this reversal has any significance in invasion remains to be established . EMT , or epithelial to mesenchymal transition , refers to the complex process whereby immobile epithelial cells lose their cell-cell adhesions and get converted into motile mesenchymal cells [71 , 72] . EMT is relevant both to normal embryonic development and in carcinogenesis . During EMT , downregulation of the cell-cell adhesion protein E-cadherin is accompanied by upregulation of mesenchymal cadherins like N-cadherin which favour forming of transient contacts [73] . However , EMT is not an all-or-nothing phenomenon and cells can exist in partial EMT states . Such states have been reported in carcinosarcomas [74] . In contrast to EMT , cancer cells are also known to exhibit collective cell migration , where E-cadherin-positive cell-cell contacts are maintained [75] . It is likely that both cell scattering and collective cell invasion are outcomes of alterations in the physical behavior of cell-cell contacts . This was evident from the scattering patterns observed in our simulations when confinement was removed . When adhesions were soft , cells scattered in all directions with the formation and breakage of transient adhesions . This mode of invasion was closer to that of single cell invasion . However , when adhesions were medium stiff , cells scattered as uniform-sized clusters indicative of a collective mode of invasion . Our results thus suggest that the different modes of invasion observed in different contexts are dictated by the strength of cell-cell adhesions . When adhesions were very strong , even upon removal of confinement , the cell layer expanded to release the confinement-induced compression , but continued to exhibit coherent rotation . Under this condition , activation of invasion as observed experimentally after removal of confinement [57] , is likely to involve mechano-chemical changes in the motility behavior of cells arising from the presence of free boundary . In conclusion , our framework of velocity-polarization coupling successfully recapitulates coherent motion in confined circular and annular geometries , demonstrates the influence of a few experimentally controllable variables—motile speed ( v0 ) , cell density ( N ) , cell stiffness ( k ) and system size ( R ) – in collectively dictating the pattern of coherent motion , and illustrates the effect of synchronous cell division on coherent motion . In addition , our model predicts the invasion patterns that arise due to coherent motion when confinement in removed . Future work can be focused on further improving the predictive power of our model by incorporating the effect of actomyosin contractility and substrate properties ( e . g . , stiffness ) .
For simulations , the scaling quantities for length , time and force are taken as a0 , a0/v0 and v0/μ respectively . Unless otherwise specified , values of a0 , v0 and μ are taken as 1 for all the simulations . The cells were represented by their centers , and their connectivity was obtained via Delaunay triangulation [44] , which produces least number of distorted triangles , i . e . , triangles with least shear strain [76] . Delaunay triangulations are dual to Voronoi tessellations ( see Fig 1 ( b ) ) and the Voronoi polygon for a given cell center can be modeled to represent the cell [44 , 77] . It is unrealistic to obtain the areas of boundary cells directly from tessellation , since the Voronoi polygon for these cells can have a vertex at infinity [76] . To circumvent this problem , a row of dummy points are inserted at the boundaries , just to create well defined polygons for visualization , but they do not contribute to the dynamics of the system . Even though cells are connected to each other by cell-cell connections to form an apparently solid tissue , based on the dynamic position of the cells , this connectivity is constantly updated using Delaunay triangulation and may result in cell neighbor changes within the tissue . These modification of neighbors can be interpreted as the so called T1 transitions , the specialised terminology for neighbor exchange in the context of foams and epithelia [78] ( also see S6 Text ) . Since confinement is experimentally shown to be essential for setting up coherent rotation , we model the soft confinement at boundaries by providing resistance of stiffness 3kc at the edges , which will apply force on any cell trying to cross the boundary and thus prevent them from escaping . To begin with , cell centers were randomly distributed inside a confined zone of given dimensions , and were allowed to equilibrate , such that the velocities of all the cells was near zero—the cell-cell connectivity was obtained by Delaunay triangulation , as described . Since we model cells as self propelled particles , they were assigned a uniform motility v0 in random directions of their polarization after the equilibration stage . This led to the evolution of position and polarization of cells thereby setting up of the dynamics of the system . After a short initial transient state with random motility , cells started to rotate coherently and reached a steady state of motion . However , it is to be noted that the current formulation does not account for the effect of any noise in the system . For solving the set of differential equations numerically , we adopted forward Euler scheme that was implemented in Matlab . After performing a detailed convergence study , a time-step of Δt = 0 . 001 was used for all the calculations . In order to quantify the angular motion of tissues , we calculated the mean vorticity of system derived from the antisymmetric part of velocity gradient matrix . Mean vorticity can be defined as ∫ ω d A ∫ d A , where ω = 1 2 ( ∂ u ∂ y - ∂ v ∂ x ) is the vorticity tensor , and u and v represent the velocity components in x and y directions at the time t . As already described , polarization of cells are initially randomly oriented . So it is logical to assume that there should not be any preferential bias in the direction of coherent rotation of cells . In order to verify this , a statistical analysis is carried out . Out of 100 independent simulations performed on both circular and annular geometry without cell division , almost equal number of clockwise and counter-clockwise rotations are obtained , which shows that there is no preferential bias in the system . Similarly , 100 independent simulations performed in an annular geometry with asynchronized cell division show no change in their direction of rotation after cell division . In order to check the statistical significance of the switch in the rotational direction on synchronous cell division , two sets of 200 independent simulations are carried out . For the first set of simulations , the polarization of daughter cells are assigned equal and opposite in random direction , while for the second case , their polarizations are completely random and independent . For both these cases , cells are observed to preferentially ( around 60% ) switch their direction of rotation after synchronous cell division , which indicate that the mechanical perturbations caused because of cell division may be the reason for the additional 10% bias in switch in direction .
|
Epithelial and endothelial cells that line various cavities and the vasculature in our bodies , are tightly connected to each other and exist as sheets . Upon confinement in two-dimensional geometries , these cells exhibit rotational motion , which has also been observed in vivo and implicated in physiological processes . However , how this rotational motion is achieved remains unclear . We show that a simple rule wherein preferred direction of motion ( i . e . , polarization ) of cells tends to align with the direction of their velocity is sufficient to induce such coherent movement in confined geometries . We also show that the number of cells within the confinement , the size of the tissue , cell motility and physical properties of the cell and cell-cell connections regulate this coherent motion , and the pattern of invasion when the confinement is relaxed .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Coherent Motion of Monolayer Sheets under Confinement and Its Pathological Implications
|
Plants have evolved sophisticated systems for adaptation to their natural habitat . In response to developmental and environmental cues , plants produce and perceive jasmonate ( JA ) signals , which induce degradation of JASMONATE-ZIM-Domain ( JAZ ) proteins and derepress the JAZ-repressed transcription factors to regulate diverse aspects of defense responses and developmental processes . Here , we identified the bHLH subgroup IIId transcription factors ( bHLH3 , bHLH13 , bHLH14 and bHLH17 ) as novel targets of JAZs . These bHLH subgroup IIId transcription factors act as transcription repressors and function redundantly to negatively regulate JA responses . The quadruple mutant bhlh3 bhlh13 bhlh14 bhlh17 showed severe sensitivity to JA-inhibited root growth and JA-induced anthocyanin accumulation , and exhibited obvious increase in JA-regulated plant defense against pathogen infection and insect attack . Transgenic plants overexpressing bHLH13 or bHLH17 displayed reduced JA responses . Furthermore , these bHLH factors functioned as transcription repressors to antagonize the transcription activators , such as MYC2 and the WD-repeat/bHLH/MYB complex , through binding to their target sequences . Coordinated regulation of JA responses by transcription activators and repressors would benefit plants by allowing fine regulation of defense and development , and survival in their frequently changing environment .
Plant hormones are essential for the regulation of plant growth , differentiation , development , reproduction , and survival [1]–[3] . Jasmonates ( JAs ) , a class of cyclic fatty acid-derived plant hormones originating from plastid membrane α-linolenic acid [4] , [5] , regulate diverse aspects of plant developmental processes [6] , such as seedling growth [7] , root development [8]–[10] , plant fertility [11]–[13] , trichome initiation [14] , [15] , pigment formation [14] , [16] , [17] , and senescence [18] . It is also well established that jasmonates act as key defense signals in regulation of various abiotic and biotic stress such as mechanic wounding [19] , [20] , arthropod herbivores and necrotrophic pathogens [21]–[24] and drought [25] , [26] . In response to external environmental signals and internal developmental cues , plants generate and perceive jasmonates ( JA ) signals to induce degradation of JASMONATE ZIM-Domain ( JAZ ) proteins [20] , [27]–[33] . As consequence of JAZs degradation , JAZ-targeted transcription factors will be relieved to subsequently regulate their downstream signal cascades and modulate respective JA responses . Current studies have identified several key transcription factors as direct targets of JAZ proteins [34]–[38] . The bHLH subgroup IIIe transcription factors ( MYC2 , MYC3 and MYC4 ) are targets of JAZs and play important roles in regulation of plant defense and developmental processes [39]–[41] . The R2R3-MYB transcription factors ( MYB21 , MYB24 and MYB57 ) , and the transcription complexes WD-repeat/bHLH ( TT8 , GL3 or EGL3 ) /MYB ( MYB75 or GL1 ) interact with JAZs to regulate JA-mediated male fertility , anthocyanin accumulation and trichome initiation , respectively [12] , [14] , [42] . Current research so far has not identified JAZ-targeted transcription repressors in JA pathway . In this study , we identified the bHLH subgroup IIId transcription factors ( bHLH3 , bHLH13 , bHLH14 and bHLH17 ) as new targets of JAZ proteins . These bHLH subgroup IIId transcription factors function redundantly to negatively regulate JA-mediated plant defense and development . Furthermore , these bHLH factors act as transcriptional repressors to suppress JA responses , which antagonize the previously reported transcription activators ( such as MYC2 and the WD-repeat/TT8/MYB75 complex ) through binding to their downstream target sequences . Coordinated regulation of JA responses by transcription repressors and activators would benefit plants for adaptation to their frequently changing environment .
To understand molecular basis of jasmonate action , we exhaustedly screened the Arabidopsis thaliana cDNA library using JAZ proteins ( JAZ1 and JAZ8 ) as bait in the yeast two-hybrid ( Y2H ) system , and identified several transcription factors [12] , [14] . We found that a bHLH transcription factor bHLH13 ( AT1G01260 ) also interacted with various JAZ proteins ( Figure 1A ) . Phylogenetic analysis showed that bHLH13 , together with bHLH3 ( AT4G16430 ) , bHLH14 ( AT4G00870 ) and bHLH17 ( AT2G46510/AtAIB ) , belongs to the subgroup IIId of the Arabidopsis bHLH family [43]–[45] . We further found that , in the yeast two-hybrid ( Y2H ) assays , all the bHLH subgroup IIId factors interacted with various JAZ proteins ( Figure 1A ) . To verify the interactions between JAZs and the bHLH3 , bHLH13 , bHLH14 or bHLH17 in planta , we performed bimolecular fluorescence complementation ( BiFC ) assays in leaves of Nicotiana benthamiana . As shown in Figure 1B , the strong signals of yellow fluorescent protein ( YFP ) in nucleus of N . benthamiana leaves were reconstructed by coexpression of JAZ1-nYFP ( the JAZ1 fused with N-terminal fragment of YFP ) with cYFP-bHLH3 , cYFP-bHLH13 , cYFP-bHLH14 and cYFP-bHLH17 , respectively . Strong YFP signals were also detected when JAZ10 was coexpressed with these transcription factors in the BiFC assays ( Figure 1B ) . These results suggested that these bHLH subgroup IIId factors interact with JAZs in planta . To investigate which domain of these bHLH factors is responsible for the interaction with JAZ proteins , we representatively divided the bHLH13 into the N-terminal fragment ( bHLH13NT ) containing the JAZ-Interaction-Domain ( JID ) [40] , and the C-terminal part ( bHLH13CT ) ( Figure 1C ) . As shown in Figure 1C , bHLH13NT , but not bHLH13CT exhibited interactions with various JAZ proteins . Consistent with the previous speculation [40] , these results suggest that the N-terminal fragment ( JID domain ) of the bHLH13 factor is responsible for the interactions with JAZ proteins . To further examine which domain of JAZ proteins is critical for interactions with the bHLH subgroup IIId factors , we divided JAZ8 into the N-terminal fragment ( JAZ8NT ) , the C-terminal part ( JAZ8CT ) , and the Jas domain only ( JAZ8Jas ) ( Figure 1D ) . Our results suggested that the Jas domain in JAZ8 is required for interactions with bHLH13 and bHLH17 ( Figure 1D ) . Interestingly , bHLH3 and bHLH14 were able to interact with JAZ8 , but not with the truncated fragments of JAZ8 ( JAZ8NT , JAZ8CT and JAZ8Jas ) ( Figure 1D ) , suggesting that the entire JAZ8 is required for interactions with bHLH3 and bHLH14 . Similarly , we found that the entire JAZ11 is required for interactions with bHLH3 and bHLH14 , while either of Jas domains in JAZ11 is sufficient for interactions with bHLH13 and bHLH17 ( Figure 1D ) . To examine expression patterns of bHLH3 , bHLH13 , bHLH14 and bHLH17 in plant tissues , we generated Arabidopsis plants transgenic for the GUS reporter driven by endogenous promoter of each bHLH factor . Histochemical staining of the GUS activity demonstrated that all these bHLH factors are expressed in various plant tissues ( Figure 2A ) , which is consistent with quantitative real-time PCR analysis ( Figure 2B ) and the public available data ( www . bar . utoronto . ca ) [43] . Interestingly , the COI1-dependent JA-induced gene expression was observed for bHLH13 and bHLH17 , but not for bHLH3 and bHLH14 ( Figure 2D ) . Further examination of subcellular localization indicated that bHLH3 and bHLH17 were nucleus-localized ( Figure 2C ) , whereas bHLH13 and bHLH14 were localized in both nucleus and cytoplasm ( Figure 2C ) . To investigate the function of the bHLH subgroup IIId factors , we identified Arabidopsis mutants for bHLH3 , bHLH13 , bHLH14 and bHLH17 with the T-DNA insertion into the exon ( for bHLH3 , bHLH14 and bHLH17 ) or the 5′UTR ( for bHLH13 ) ( Figure 3A ) . Quantitative real-time PCR analysis showed that the expression of the full length bHLH subgroup IIId gene was abolished ( for bHLH3 , bHLH14 and bHLH17 ) or obviously reduced ( for bHLH13 ) in their respective mutants ( Figures 3A and 3B ) . Observation of typical JA-regulated responses , including plant fertility , JA-inhibitory root growth and JA-induced anthocyanin accumulation , showed that no obvious differences were detected among wild-type , bhlh3 , bhlh13 , bhlh14 and bhlh17 single mutants ( Figure 3 , and data not shown ) . As bHLH3 , bHLH13 , bHLH14 and bHLH17 display high similarity at the amino acid level and belong to the same subgroup IIId of the Arabidopsis bHLH family [44] , we further generated double , triple and quadruple mutants for the bHLH subgroup IIId factors , through genetic cross among the bhlh3 , bhlh13 , bhlh14 and bhlh17 mutants , to investigate whether these factors function redundantly in regulation of JA responses . Interestingly , we found that the anthocyanin accumulation was gradually increased in the double mutant bhlh3 bhlh17 , the triple mutant bhlh3 bhlh13 bhlh17 , bhlh3 bhlh13 bhlh14 , bhlh3 bhlh14 bhlh17 , bhlh13 bhlh14 bhlh17 , and the quadruple mutant bhlh3 bhlh13 bhlh14 bhlh17 in response to MeJA treatment ( Figures 4A , 4C , and S1 ) . Consistent with the tendency of JA-induced anthocyanin accumulation ( Figures 4A and 4C ) , MeJA-induced expression of the anthocyanin biosynthetic genes , including DFR , LDOX and UF3GT [46] , was gradually increased in the double , triple and quadruple mutants ( Figure 4B ) . The quadruple mutant bhlh3 bhlh13 bhlh14 bhlh17 exhibited enhanced JA responses . They displayed obvious increase in JA-induced anthocyanin biosynthesis compared with wild-type control ( Figures 4A and 4C ) . The JA-inhibitory root growth analysis also showed that the quadruple mutant was more sensitive to JA inhibition of root growth ( Figures 4D and 4H ) . Observation of flowering time showed that the quadruple mutant also exhibited enhanced JA response: the quadruple mutant exhibited late flowering phenotype whereas the coi1-1 mutant plant flowered early ( Figures 4F and 4G ) . Consistent with the enhanced JA-responses , expression of JA-inducible marker gene VSP1 [7] was significantly increased in the quadruple mutant ( Figure 4E ) . Taken together ( Figures 3 and 4 ) , mutations in bHLH3 , bHLH13 , bHLH14 and bHLH17 caused enhanced JA responses , demonstrating that these bHLH subgroup IIId factors function redundantly to negatively regulate JA responses . Botrytis cinerea , a necrotrophic fungus that causes gray mold disease in many plant species [47] , induced severe wilting and high mortality in the Arabidopsis mutants coi1-1 [8] , [48] or aos1[49] , [50] . To investigate potential role for bHLH3 , bHLH13 , bHLH14 and bHLH17 in plant defense , we sprayed the B . cinerea spore suspension onto the quadruple mutant , wild-type and coi1-1 plants . As shown in Figure 5A and 5B , the quadruple mutant plants exhibited increased resistance against B . cinerea compared with wild type , whereas coi1-1 mutant plants displayed severe disease symptom , as revealed by disease severity and plant survival rate ( Figures 5C and 5D ) . Consistent with the increased defense response , the PLANT-DEFENSIN gene PDF1 . 2 [51] , antifungal gene THI2 . 1 [52] , defense gene ERF1 [53] and wound-inducible gene LOX2 [54] were highly induced in the quadruple mutant compared with wild type when treated with MeJA ( Figure 5E ) . Previous studies showed that JA induces plant susceptibility to the bacterium strain Pst DC3000 of Pseudomonas syringae pv . Tomato . The coi1-1 mutant is more resistant to the Pst DC3000 inoculation [8] , [55] , [56] ( Figures 5F and 5G ) . The quadruple mutant exhibited enhanced JA response in the Pst DC3000 inoculation assay: the quadruple mutant was more susceptible to the Pst DC3000 infection , whereas coi1-1 was resistant ( Figures 5F and 5G ) . To test whether the bHLH subgroup IIId factors regulate JA-mediated plant defense against insects , mature rosette leaves of wild-type , coi1-1 , and the quadruple mutant were fed to Spodoptera exigua , a globally-significant agricultural pest with a broad host range [21] . We found that S . exigua larvae consumed the majority of the coi1-1 leaves ( Figure 6A ) and grew rapidly ( Figures 6 B and C ) . However , the quadruple mutant leaves inhibited the growth of S . exigua larvae and showed reduced consumption by S . exigua ( Figures 6 A–C ) . When insects were given the choice of selecting among wild type , coi1-1 and the quadruple mutant in the three-choice test , the quadruple mutant attracted fewer S . exigua larvae ( ∼6% ) , while wild type and coi1-1 accommodated the majority of S . exigua larvae ( 23% for wild type , 71% for coi1-1 ) ( Figure 6D ) . In the two-choice test , the quadruple mutant attracted less larvae than wild type ( ∼28% for the quadruple mutant , ∼72% for wild type ) , while the wild-type attracted less larvae than coi1-1 ( 19% for wild type , ∼81% for coi1-1 ) ( Figure 6E ) . These results suggested that the quadruple mutant displays enhanced JA-regulated plant defense against insect . In summary , we demonstrated that the bHLH subgroup IIId factors ( bHLH3 , bHLH13 , bHLH14 and bHLH17 ) function as novel negative regulators of JA-mediated plant defense . Having demonstrated that the quadruple mutant exhibited enhanced JA responses ( Figures 4–6 ) , we further investigated whether overexpression of the bHLH subgroup IIId factors would lead to reduction in JA responses . As shown in Figure 7 , the bHLH17 overexpression lines , 17OE-2 and 17OE-4 with highly expressed bHLH17 transcripts ( Figure 7A ) , exhibited reduced JA responses compared with wild type , as indicated by reduction in JA-induced anthocyanin accumulation ( Figures 7B and 7C ) , JA-inducible anthocyanin biosynthetic gene expression ( Figure 7D ) , and JA-inhibitory root growth ( Figures 7F and 7G ) . Similar to the bHLH17 overexpression plants , the bHLH13 overexpression lines ( 13OE-3 and 13OE-7 ) also exhibited reduced JA responses ( Figure 7 ) . These overexpression lines also exhibited the coi1-like phenotype to flower early ( Figure 7H ) . Consistent with the decreased JA responses , JA-inducible gene expression of VSP1 , LOX2 , and PDF1 . 2 was reduced in the bHLH13 or bHLH17 overexpression lines ( Figure 7E ) . Taken together with data of the genetic and physiological analysis on the quadruple mutant and the overexpression transgenic lines ( Figures 4–7 ) , we demonstrated that the bHLH3 , bHLH13 , bHLH14 and bHLH17 function as novel negative regulators of JA responses . Having shown that the bHLH subgroup IIId factors ( bHLH3 , bHLH13 , bHLH14 and bHLH17 ) negatively regulat JA responses , we examined whether these bHLH subgroup IIId factors function as transcriptional repressors using the GAL4-DNA- binding-domain ( GAL4DB ) and its binding sites ( GAL4 ( 4X ) -D1-3 ( 4X ) -GUS ) -based protoplast transient expression system [57] , [58] . We found that the MYC2 functions as a transcription activator whereas JAZ1 acts as a negative regulator to inhibit the MYC2 activation activity in this transient expression system ( Figure 8A and 8B ) . In contrast , bHLH17 functions as a transcription repressor whereas JAZ1 acts as a negative regulator to inhibit the bHLH17 repression function: expression of GAL4DB-fused bHLH17 obviously repressed the activity of the GUS reporter ( Figure 8A and 8B ) ; furthermore , coexpression of JAZ1 inhibited the bHLH17 repression function and rescued the bHLH17-repressed GUS activity in a dosage-dependent manner ( Figure 8B ) . Similarly , we observed that GAL4DB-fused bHLH3 , bHLH13 or bHLH14 repressed the activity of GUS reporter , and that their repression function could be partially inhibited by JAZ1 ( Figure 8C ) . These results collectively suggest that these bHLH subgroup IIId factors act as transcriptional repressors , and that JAZ proteins interact with these transcription factors to attenuate their repression function . We further generated a reporter construct PDFR-LUC in which the LUC reporter was driven by the promoter of DFR ( Figure 9A ) , a direct target of the WD-repeat/TT8/MYB75 complex [46] , [59] , [60] . Consistent with previous data [61] , [62] , TT8 and MYB75 act as transcription activators to significantly induce expression of PDFR-LUC in the transient transcriptional activity assays ( Figures 9A and 9B ) . We further found that bHLH17 repressed the TT8/MYB75-activated PDFR-LUC expression in a dosage-dependent manner ( Figure 9B ) . Similarly , bHLH3 also repressed the TT8/MYB75-activated PDFR-LUC expression ( Figure 9C ) . Using similar approach , we generated a reporter construct PTAT1-LUC in which the LUC reporter was driven by the promoter of TAT1 ( Figure 9D ) , a direct target of MYC2 [63] . Consistent with previous data [63] , MYC2 acts as transcription activator to significantly induce expression of PTAT1-LUC in the transient transcriptional activity assays ( Figure 9E ) . Furthermore , we found that bHLH3 and bHLH17 were able to repress the MYC2-activated TAT1 expression ( Figures 9E ) . These results collectively suggest that the bHLH subgroup IIId factors antagonize transcription activators ( such as MYC2 , TT8 and MYB75 ) in JA pathway . The Y2H and BiFC assays detected no direct interactions between the transcription repressor ( bHLH3 , bHLH13 , bHLH14 or bHLH17 ) and transcription activator ( such as MYC2 or TT8/MYB75 ) ( Figures S2 , S3 , S4 ) , which excluded the possibility that the bHLH subgroup IIId factors antagonize the previously reported transcription activators via direct interactions . Previous studies showed the transcription activators TT8/MYB75 and MYC2 bind to and activate their respective target sequences , such as promoters of DFR and TAT1 [61]–[63] . Here we used chromatin immunoprecipitation ( ChIP ) -PCR assays to investigate whether the bHLH subgroup IIId factors antagonize these transcription activators through binding to their target sequences . The ChIP-PCR assays showed that DFR promoter sequence , spanning two G-box motifs ( CACGTG ) at the position from −182 to −154 , was highly enriched in the anti-myc-immunoprecipitated chromatin of the myc-bHLH3 transgenic plant , but not in the controls ( the anti-myc-pulled wild-type chromatin , the empty beads-pulled chromatin of wild-type or the myc-bHLH3 transgenic plant ) ( Figure 10A ) , demonstrating a direct binding of myc-bHLH3 to the promoter sequence of DFR . Using the similar approach , we also detected the association of myc-bHLH3 with promoter of TAT1 ( Figure 10B ) . Taken together , our results suggest that bHLH3 , bHLH13 , bHLH14 and bHLH17 factors function as transcriptional repressors to repress the JA responses . These transcriptional repressors antagonize the previously reported transcription activators through binding to their downstream target sequences .
The JAZ protéines [20] , [29] , [30] , through formation of a core repression complex with TOPLESS and the NOVEL-INTERACTOR-OF-JAZ [57] , are speculated to negatively regulate JA-mediated plant responses via interaction with and attenuation of their target transcription factors such as the bHLH subgroup IIIe transcription factors ( MYC2 , MYC3 , MYC4 ) [29] , [39]–[41] , the R2R3-MYB transcription factors ( MYB21 , MYB24 and MYB57 ) [12] , and the WD-repeat/bHLH ( TT8 , GL3 or EGL3 ) /MYB ( MYB75 or GL1 ) complexes [14] . Here , we identified four bHLH subgroup IIId transcription factors ( bHLH3 , bHLH13 , bHLH14 and bHLH17 ) as new targets of JAZ proteins ( Figure 1 ) . These transcription factors function as negative regulators to repress JA responses ( Figures 4–7 ) . Furthermore , our results showed that these negative regulators act as transcriptional repressors to antagonize the positive transcription factors through binding to their downstream target sequences ( Figures 8–10 ) . Coordinated regulation of JA responses by transcription repressors and transcription activators may benefit plants for adaptation to their frequently changing nature habitat . Genetic and physiological analysis on the single , double , triple and quandruple mutants ( bhlh3 , bhlh13 , bhlh14 , bhlh17 , bhlh3 bhlh17 , bhlh3 bhlh13 bhlh17 , bhlh3 bhlh13 bhlh14 , bhlh3 bhlh14 bhlh17 , bhlh13 bhlh14 bhlh17 , bhlh3 bhlh13 bhlh14 bhlh17 ) showed that these bHLH subgroup IIId transcripiton factors function redundantly to repress JA responses ( Figures 3–6 and S1 ) . The quandruple mutant bhlh3 bhlh13 bhlh14 bhlh17 exhibited significant increase in JA responses , while the single mutant bhlh3 , bhlh13 , bhlh14 or bhlh17 displayed no obvious alterations in the tested JA responses ( Figures 3–6 and S1 ) , though we cannot exclude possibility that some JA responses may be mildly altered in single mutants . As shown in Figure 2 C and D , bhlh3 showed a mild increase in JA-inducible anthocyanin accumulation . Recent study also showed that the single mutant bhlh17/jam1 ( another T-DNA insertion mutant of bHLH17/JAM1 ) displayed no obvious alteration in JA-inhibitory root growth , but exhibited the enhanced sensitivity in JA-inducible anthocyanin accumulation and defense against insect [64] . The functional redundancy among these bHLH3 , bHLH13 , bHLH14 and bHLH17 factors may result from their high similarity at amino acid level . It would be interesting to investigate whether and how these transcription factors exhibit homo- and heterodimerization to exert their redundant functions . In addition to their redundant function , it is not clear whether the bHLH3 , bHLH13 , bHLH14 and bHLH17 factors have distinct roles in regulation of JA responses . The bHLH13 and bHLH17 exhibited similar interaction patterns with 10 JAZ proteins , and with the Jas domain of JAZ8/JAZ11 ( Figure 1 ) . However , for bHLH3 and bHLH14 , they interacted with 7 JAZ proteins , and the full length of JAZ8/JAZ11 was required for interactions with bHLH3 and bHLH14 ( Figure 1 ) . Furthermore , the COI1-dependent and JA-induced gene expression was observed for bHLH13 and bHLH17 , but not for bHLH3 and bHLH14 ( Figure 2D ) . Interestingly , both bHLH3 and bHLH17 were nucleus-localized ( Figure 2C ) , while bHLH13 and bHLH14 were localized in both nucleus and cytoplasm ( Figure 2C ) . It remains to be elucidated whether these distinguished features would lead to distinct roles for these four transcription factors in JA pathway . Current studies so far have showed that bHLH17/AtAIB was positively involved in ABA signaling [43] . It is not clear whether the bHLH subgroup IIId factors play positive or negative roles in other signal pathways . Previous studies showed that MYC2 , and the WD-repeat/bHLH ( TT8 , GL3 or EGL3 ) /MYB ( MYB75 or GL1 ) complexes act as transcription activators which bind to and activate promoter sequences of their respective target genes , such as TAT1 [63] , and DFR [46] , [60] , [61] . We showed that the bHLH subgroup IIId factors act as transcription repressors ( Figure 8 ) , which bind to the promoter sequences of TAT1 and DFR ( Figure 9 and 10 ) , to antagonize the transcription function of MYC2 and MYB75/TT8 ( Figure 9 ) . It is interesting to investigate whether the bHLH subgroup IIId factors antagonize the activation function of MYC2 and MYB75/TT8 through competitive or concurrent binding to these target promoter sequences . Fernandez-Calvo et al . predicted that the activation domain is localized at the N-terminus of MYC2 [40] . It remains to experimentally investigate which domain in MYC2 ( or in the bHLH subgroup IIId factors ) is responsible for activation ( or repression ) of their target sequences . The GAL4DB-based protoplast transient expression system is a well-established method for determination of transcription activators and repressors [57] , [58] , [65]–[68] . MYC2 acts as a transcription activator in this transient expression system ( Figure 8A and 8B ) , which is consistent with previous observations [39] , [57] , [63] , [69] . Consistent with its activation function , the MYC2 is a key gene that positively regulates a vast array of JA-responsive genes and diverse aspects of JA responses , including root growth [22] , [70] , anthocyanin accumulation [71] , sesquiterpene synthase [72] , nicotine biosynthesis [73] , wound response [74] , oxidative stress tolerance [75] , and plant defense against both bacterial pathogen Pst DC3000 [40] and insects ( Spodoptera littoralis and Helicoverpa armigera ) [40] , [75] . Interestingly , the MYC2 gene was shown to negatively regulate defense against necrotrophic fungi ( B . cinerea and Plectosphaerella cucumerina ) and expression of the related genes , such as PDF1 . 2 [22] , [75] , [76] . We showed that the bHLH subgroup IIId factors act as transcription repressors ( Figures 8 ) , which antagonize the activation function of MYC2 and TT8/MYB75 ( Figure 9 ) , to negatively regulate all the tested JA responses , including root growth , flowering , anthocyanin accumulation , and plant defense responses against bacterial pathogen Pst DC3000 , necrotrophic pathogen B . cinerea , and insect S . exigua ( Figures 4–7 ) . Generation and characterization of the penta mutant myc2 bhlh3 bhlh13 bhlh14 bhlh17 would clarify whether mutations in the bHLH subgroup IIId factors are able to rescue the myc2-associated reduction of JA responses ( such as root growth , anthocyanin accumulation , wound response , and defense against bacterial pathogen and insects ) , and to additively or synergistically affect the myc2-associated increase of defense response against necrotrophic pathogen B . cinerea . It is speculated that , in response to JA signal , JAZ proteins are recruited by SCFCOI1 for ubiquitination and subsequent degradation . As a result , the JAZ-targeted transcription activators and repressors ( bHLH3 , bHLH13 , bHLH14 and bHLH17 ) are released to antagonistically and coordinately regulate their target genes ( such as TAT1 and DFR ) , which may further modulate expression of JA responsive genes essential for various JA responses ( Figure 11 ) . Plants live in fixed places and have to evolve sophisticated systems for adaptation to their frequently changing environment . The antagonistic and coordinated regulation of JA responses by transcription repressors and transcription activators may provide an important strategy for plant survival in their complicated nature habitat . It is possible that plants evolve this type of repressor-mediated negative regulation system to provide a fine feedback regulatory mechanism for avoiding exhausted and harmful excess JA responses .
The Arabidopsis thaliana mutant coi1-1 [77] was described as previously . bhlh3 ( CS428877/GK-301G05 ) , bhlh13 ( CS466724/GK-696A04 ) , bhlh14 ( CS27164/GT1193 . Ds3 . 05 . 24 . 99 . b . 288 ) and bhlh17 ( CS874647/SAIL_536_F09 ) were obtained from the ABRC or NASC . The double , triple and quadruple mutants of the bHLH subgroup IIId factors were generated through genetic crossing among bhlh3 , bhlh13 , bhlh14 and bhlh17 . Arabidopsis thaliana seeds were sterilized with 20% bleach , plated on Murashige and Skoog medium ( MS; Sigma-Aldrich ) , chilled at 4°C for 3 days , and transferred to a growth room under a 16-h ( 22–24°C ) /8-h ( 16–19°C ) light/dark photoperiod . Nicotiana benthamiana was grown in a growth room under a 16-h ( 28°C ) /8-h ( 22°C ) light/dark condition . The yeast two-hybrid screening method was described as previously [12] , [14] . For Y2H assay , all the CDS of JAZs , bHLH3 , bHLH13 , bHLH14 , bHLH17 , MYC2 , MYC3 , MYC4 , TT8 , GL3 , EGL3 , MYB75 , GL1 and TTG1 , and their domain derivatives were cloned into pLexA or pB42AD vectors . Primers used for the vector construction are presented in Table S1 . The method details for yeast transformation and interaction detection using EGY48 were described as previously [12] , [14] . Y2H images were taken 3 days after incubation at 30°C . Experiments were repeated three biological times . For BiFC assays , full-length coding sequences of Arabidopsis JAZ1 , JAZ10 , bHLH3 , bHLH13 , bHLH14 , bHLH17 , MYC2 , MYC3 , MYC4 , TT8 , GL3 , EGL3 , MYB75 and GL1 were cloned into the binary nYFP or cYFP vector through enzyme digestion sites ( KpnI/SalI ) or Gateway reaction with pDONR207 vector system ( Invitrogen ) [12] , [14] . Primer pairs for generation of constructs are listed in Table S1 . Agrobacterium strains with indicated nYFP or cYFP vector were incubated , harvested , and resuspended in infiltration buffer ( 0 . 2 mM acetosyringone , 10 mM MgCl2 , and 10 mM MES ) . Equal concentrations and volumes of Agrobacterium strains were mixed and coinfiltrated into N . benthamiana leaves by a needleless syringe . After infiltration , plants were placed at 24°C for 50 h before observation . The experiments were repeated three biological times . In Figure 2B , root , stem , rosette leaf , stem leaf and flowers were harvested for RNA extraction and subsequent reverse transcription . In Figures 2D and 7E , Arabidopsis seedlings were grown on MS medium for 11 days and then were treated with or without 100 µM MeJA for indicated time . In Figures 4B , 4E , 5E and 7D , Arabidopsis seedlings were grown on MS medium supplied with or without indicated concentration of MeJA for eleven days . These materials were harvested for RNA extraction and subsequent reverse transcription . Real-time PCR analyses were performed with the RealMasterMix ( SYBR Green I ) ( Takara ) using the ABI7500 real-time PCR system as described previously [14] . ACTIN8 was used as the internal control . The primers used for real-time PCR analysis are presented in Table S2 . All the experiments were repeated three biological times with similar results . For transient expression assay in Arabidopsis protoplast using the GUS reporter , the CDS of bHLH3 , bHLH13 , bHLH14 , bHLH17 and MYC2 were fused with the GAL4DB under control of 35S promoter . The coding sequence of JAZ1 was cloned into the pGreenII 62-SK vector under control of 35S promoter [78] . Primers used for plasmid construction were shown in Table S1 . Four copies of upstream GAL4 DNA binding sites ( GAL4 ( 4x ) -D1-3 ( 4x ) ) were used to drive the GUS gene generating the GUS reporter construct [58] , [68] . The internal control contains a firefly luciferase gene ( LUC ) under control of 35S promoter . Arabidopsis mesophyll protoplasts preparation and subsequent transfection were performed as described previously [79] . Relative GUS activity was normalized against the LUC activity . In the JAZ1 dosage effect test of Figure 8B , 0 . 2 , 1 , 5 or 10 µg JAZ1 plasmid was respectively used , while for other reporter and effectors , 10 µg plasmid was used . For transient transcriptional activity assay using the LUC reporter , the CDS sequences of MYC2 , TT8 , MYB75 , bHLH3 and bHLH17 were cloned into pGreenII 62-SK vectors under control of 35S promoter respectively . The ∼519 bp and ∼980 bp promoter sequences of DFR and TAT1 were amplified from genomic DNA and cloned into pGreenII 0800-LUC respectively [78] . All primers used for making these constructs are listed in Table S1 . After protoplasts preparation and subsequent transfection , firefly luciferase ( LUC ) and renillia luciferase ( REN ) activities were measured using the Dual-Luciferase Reporter Assay System ( Promega ) following the manufacturer's instructions . Relative firefly luciferase ( LUC ) activity was calculated by normalizing against the renillia luciferase activity . In the experiment of bHLH17 dosage effect in Figure 9B , 0 . 2 , 1 , 5 or 10 µg bHLH17 plasmid was respectively used , while for other reporter and effectors , 10 µg plasmid was individually used . All the experiments were repeated three biological times with similar results . The ChIP experiment was performed as described previously [80] using leaves of the 4-week-old myc-bHLH3 transgenic plants and the wild-type ( Col-0 ) plants treated with 100 µM MeJA for 40 minutes . Immunoprecipitation was performed using Mouse anti-MYC antibody and protein G agarose beads . Enrichment of promoter DNA was confirmed by qRT-PCR using ACTIN2 as normazlization control . Primers for 3′UTR region were used as negative control . Primers for the ChIP assays are listed in Table S2 . The experiments were repeated for three biological repeats with similar results . To generate Arabidopsis transgenic plants overexpressing bHLH13 and bHLH17 , the full-length coding sequences of bHLH13 and bHLH17 were amplified and cloned into the modified pCAMBIA1300 vector under control of 35S promoter through the SalI and SpeI sites . To generate Arabidopsis myc-bHLH3 transgenic plants , coding sequence of bHLH3 was cloned into pROK2-myc vectors for fusion with six myc tags . These constructs were introduced into Arabidopsis plants using the Agrobacterium-mediated floral dip method . Two representative transgenic lines for bHLH13 and bHLH17 respectively were displayed in the article . ∼2520 bp , ∼2500 bp , ∼1050 bp and ∼2570 bp promoter regions of bHLH3 , bHLH13 , bHLH14 and bHLH17 were respectively amplified and cloned into pCAMBIA1391Z vector to drive the GUS genes for generation of PbHLH3-GUS , PbHLH13-GUS , PbHLH14-GUS and PbHLH17-GUS . These constructs were transformed into Agrobacterium strains GV3101 , and transferred into Arabidopsis by floral dip methods . Seedlings , inflorescences and roots from the transgenic plants harboring PbHLH3-GUS , PbHLH13-GUS , PbHLH14-GUS or PbHLH17-GUS were used for histochemical staining of GUS . Histochemical staining for GUS activity assay was performed as described previously [18] . Coding sequences of bHLH3 , bHLH13 , bHLH14 and bHLH17 were respectively cloned into pEGAD vector for fusion with GFP under control of 35S promoter to generate the GFP-bHLH3 , GFP-bHLH13 , GFP-bHLH14 and GFP-bHLH17 constructs . The Agrobacterium containing the indicated constructs were resuspended in infiltration buffer ( 0 . 2 mM acetosyringone , 10 mM MgCl2 , and 10 mM MES ) , and infiltrated into N . benthamiana leaves by a needleless syringe . After infiltration , plants were placed at 24°C for 50 h before GFP observation . For anthocyanin measurement , 11-d-old Arabidopsis seedlings grown on MS medium with 0 , 5 , or 15 µM MeJA were measured as described previously [14] The anthocyanin content is presented as ( A535–A650 ) /g fresh weight . The experiment was repeated three biological times . Seeds were grown on MS medium with 0 , 5 , 20 or 50 µM MeJA , chilled at 4°C for 3 days , and transferred to the growth room . Root lengths of fifteen 11-d-old seedlings for each genotype and treatment were measured and presented . The experiment was repeated three biological times . Flowering time of plants , grown in soil under long day condition with 16-h ( 22–25°C ) /8-h ( 18–21°C ) light/dark photoperiod , was recorded as the number of days from germination to the first appearance of buds in the rosette center . The experiment was repeated three biological times . For Figures 5A and 5C , thirty-day-old plants were sprayed with Botrytis cinerea ( 106 spores/mL ) solved in 0 . 025% tween or with 0 . 025% tween as control , placed in dark at the appropriate temperature ( 22°C ) and high humidity ( 100% ) for 36 hours , and transferred to a growth room under the growth conditions of a 16-h-light ( 21 to 23°C ) /8-h-dark ( 16 to 19°C ) photoperiod with ∼40% humidity . Infection symptoms were recorded at 7-day after infection . Infection ratings from 0 to 3 were assigned to the inoculated plants ( 0 , no visible symptoms; 1 , weak symptoms; 2 , severe symptoms; 3 , dead plants ) . For Figures 5B and 5D , four-week-old plants were sprayed with Botrytis cinerea ( 107 spores/mL ) solved in 0 . 025% tween or with 0 . 025% tween as control , placed in dark at the appropriate temperature ( 22°C ) and high humidity ( 100% ) for 36 hours , transferred to a growth incubator under the growth conditions of a 16-h-light ( 21 to 23°C ) /8-h-dark ( 16 to 19°C ) photoperiod with high humidity ( >90% ) . Plant survival ratio were recorded at 9-day after infection . At least thirty plants from each genotype were used in each experiment . The experiment was repeated three biological times . Thirty-day-old plants were sprayed with Pseudomonas syringae pv tomato ( Pst ) DC3000 suspension containing 108 ( colony-forming units ) /mL bacteria ( OD600 = 0 . 2 ) with 0 . 02% Silwet L-77 or 0 . 02% Silwet L-77 as control . Infection symptoms were recorded at 3-day after infection . At least thirty plants from each genotype were used in each experiment . The bacterial population counts in the plant was determined as previously described [81] . The experiment was repeated three biological times . More than fifty mature rosette leaves with similar size from 4-week-old plants for each genotype were placed in one plastic Petri dishes ( 90 mm ) containing wet filter paper . The 10 third-instar S . exigua larvae ( ∼8 mg each ) were weighted , and reared on leaves in one Petri dish , for each genotype using five independent replicates . Two days after feeding , the weight of the 10 larvae were measured . The increase of average larval weight was recorded . In the two-choice test , five rosette leaves from 4-week-old plants for each genotype were placed intervally in a circle in plastic Petri dishes ( 90 mm ) containing wet filter paper . 40 newly hatched larvae were placed in the center of the Petri dishes for equal distance to the leaves . One day after incubation in the growth room , the numbers of larvae on leaves for each genotype were recorded . Four independent replicates were performed . In the three-choice test , three rosette leaves from 4-week-old plants for each genotype were placed intervally in a circle in plastic Petri dishes ( 90 mm ) containing wet filter paper . 40 newly hatched larvae were placed in the center of the Petri dishes for equal distance to the leaves . One day after incubation in the growth room , the numbers of larvae on leaves for each genotype were recorded . Six independent replicates were performed . The Arabidopsis Genome Initiative numbers for genes mentioned in this article are as follows: JAZ1 ( AT1G19180 ) , JAZ2 ( AT1G74950 ) , JAZ3 ( AT3G17860 ) , JAZ4 ( AT1G48500 ) , JAZ5 ( AT1G17380 ) , JAZ6 ( AT1G72450 ) , JAZ7 ( AT2G34600 ) , JAZ8 ( AT1G30135 ) , JAZ9 ( AT1G70700 ) , JAZ10 ( AT5G13220 ) , JAZ11 ( AT3G43440 ) , JAZ12 ( AT5G20900 ) , bHLH3 ( AT4G16430 ) , bHLH13 ( AT1G01260 ) , bHLH14 ( AT4G00870 ) , bHLH17 ( AT2G46510 ) , MYC2 ( AT1G32640 ) , MYC3 ( AT5G46760 ) , MYC4 ( AT4G17880 ) , TT8 ( AT4G09820 ) , GL3 ( AT5G41315 ) , EGL3 ( AT1G63650 ) , MYB75 ( AT1G56650 ) , GL1 ( AT3G27920 ) , TTG1 ( AT5G24520 ) , DFR ( AT5G42800 ) , LDOX ( AT4G22880 ) , UF3GT ( AT5G54060 ) , VSP1 ( AT5G24780 ) , THI2 . 1 ( AT1G72260 ) , PDF1 . 2 ( AT5G44420 ) , LOX2 ( AT3G45140 ) , TAT1 ( AT4G23600 ) , ERF1 ( AT3G23240 ) and ACTIN8 ( AT1G49240 ) .
|
Plants live in fixed places and have to evolve sophisticated systems for adaptation to their frequently changing environment . Plant hormones are essential for the regulation of these sophisticated systems which coordinately control plant growth , development , reproduction and defense . Jasmonates ( JAs ) , a new class of cyclic fatty acid-derived plant hormone , regulate diverse aspects of plant defense and developmental processes . In response to external environmental signals and internal developmental cues , plants rapidly produce and efficiently perceive JA signals , which regulate a dynamic regulatory network to activate various downstream transcription factors essential for appropriate plant defense and development . Here , we identified the bHLH3 , bHLH13 , bHLH14 and bHLH17 transcription factors as novel transcription repressors of JA signaling . The coordinated regulation of JA-mediated plant defense and development by transcription activators and repressors would improve the survival of plants in their natural habitat and adaptation to the frequently fluctuating environment .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"pests",
"plant",
"science",
"model",
"organisms",
"plant",
"and",
"algal",
"models",
"plant",
"growth",
"and",
"development",
"genetics",
"plant",
"genetics",
"plant",
"pathology",
"biology",
"arabidopsis",
"thaliana"
] |
2013
|
The bHLH Subgroup IIId Factors Negatively Regulate Jasmonate-Mediated Plant Defense and Development
|
The α-thalassemia/mental retardation X-linked protein ( ATRX ) is a chromatin-remodeling factor known to regulate DNA methylation at repetitive sequences of the human genome . We have previously demonstrated that ATRX binds to pericentric heterochromatin domains in mouse oocytes at the metaphase II stage where it is involved in mediating chromosome alignment at the meiotic spindle . However , the role of ATRX in the functional differentiation of chromatin structure during meiosis is not known . To test ATRX function in the germ line , we developed an oocyte-specific transgenic RNAi knockdown mouse model . Our results demonstrate that ATRX is required for heterochromatin formation and maintenance of chromosome stability during meiosis . During prophase I arrest , ATRX is necessary to recruit the transcriptional regulator DAXX ( death domain associated protein ) to pericentric heterochromatin . At the metaphase II stage , transgenic ATRX-RNAi oocytes exhibit abnormal chromosome morphology associated with reduced phosphorylation of histone 3 at serine 10 as well as chromosome segregation defects leading to aneuploidy and severely reduced fertility . Notably , a large proportion of ATRX-depleted oocytes and 1-cell stage embryos exhibit chromosome fragments and centromeric DNA–containing micronuclei . Our results provide novel evidence indicating that ATRX is required for centromere stability and the epigenetic control of heterochromatin function during meiosis and the transition to the first mitosis .
Heterochromatin formation in eukaryotic cells is essential for the maintenance of nuclear architecture , the control of gene expression and chromosome segregation [1]–[6] . In the mouse genome , constitutive heterochromatin consists of two closely related chromosomal sub-domains with distinct structure and function [7] , [8] . Centric heterochromatin , is epigenetically determined by deposition of the histone variant CENP-A ( centromere associated protein-A ) , contains several hundred kilobases of the 120 bp repeat unit of the minor satellite sequence and regulates the assembly of a single kinetochore on each sister chromatid required for microtubule attachment [9] , [10] . In contrast , pericentric heterochromatin comprises several megabases of the 234 bp repeat of the major satellite sequence and is marked by transcriptionally repressive histone modifications such as trimethylation of lysine 9 on histone H3 as well as chromatin remodeling proteins such as heterochromatin protein 1 ( HP1 ) and members of the SWI/SNF2 ( switch/sucrose non-fermenting 2 ) family including ATRX [7] , [10]–[12] . In several organisms including mammals , pericentric heterochromatin formation is essential to coordinate sister centromere cohesion and for the timely separation of individual chromatids during mitosis [2] , [3] , [7] , [10] . Importantly , members of the SWI/SNF2 protein family specifically recruited to pericentric heterochromatin are essential to maintain sister chromatid cohesion until the onset of anaphase in order to ensure accurate chromosome segregation . For example , chromatin-remodeling complexes such as SNF2h are essential to load the cohesin subunit RAD21 at the centromeres of human mitotic cells [13] . In addition , loss of HP1 from pericentric heterochromatin in mouse somatic cells deficient for the histone methyltransferase SUV39H1/2 disrupts centromeric cohesion [14] , [15] . ATRX associates with constitutive heterochromatin domains in human and mouse somatic cells , as well as with facultative heterochromatin of the inactive X chromosome in mouse somatic and trophoblast stem cells , suggesting a potential role in heterochromatin formation [11] , [12] , [16]–[18] . Therefore , while a growing body of evidence indicates that pericentric heterochromatin formation may have a significant impact on centromere cohesion during mitosis , little is known about this critical process during female mammalian meiosis . The centromeres of meiotic chromosomes exhibit unique structural and functional properties that are essential to coordinate dynamic molecular interactions with the microtubular network and ensure accurate chromosome segregation . For instance , cohesion along sister chromatids and at centromeres during metaphase I is required to coordinate homologous chromosome segregation whereas maintenance of sister centromere cohesion at the metaphase II stage is required for the timely separation of sister chromatids during anaphase II onset [19] , [20] . We have previously demonstrated that ATRX binds to pericentric heterochromatin in fully-grown mouse oocytes and that its functional ablation in vitro severely disrupts chromosome alignment at the metaphase II ( MII ) spindle [12] . Consistent with these studies , transient depletion of ATRX protein following siRNA transfection in HeLa cells resulted in chromosome congression defects during mitosis [18] . The abnormal chromosome alignment observed in mouse oocytes at the metaphase II stage [12] suggests that loss of ATRX function might interfere with proper chromosome segregation during meiosis and result in the potential transmission of aneuploidy to the developing conceptus . However , the molecular mechanisms of ATRX function during meiosis as well as the functional consequences of maternal ATRX ablation on chromosome stability in the early pre-implantation embryo remained to be determined . Here , we induced an oocyte-specific knockdown of the ATRX protein using a transgenic RNAi approach [21]–[23] . Our results indicate that ATRX plays a critical role in the functional differentiation of chromatin structure during oogenesis and underscore the importance of chromatin remodeling proteins in the control of chromosome segregation during meiosis .
Following pronuclear microinjection , a total of seven founder mice ( 3 females and 4 males ) were obtained showing successful transgene integration . Analysis of the F1 progeny indicated that three male founders ( #A-6 , #B-23 and #C-11 ) and two female founders ( #G-5 and #H-10 ) efficiently transmitted the transgene to their offspring ( Figure S1B ) . Microscopic examination of histological sections of gonadotropin stimulated and non-stimulated ovaries from control littermates and transgenic females revealed the presence of oocytes at different stages of growth and follicular maturation , indicating that ATRX ablation does not adversely affect oocyte growth ( Figure S1C ) . Consistent with our previous experiments [12] , ATRX ( red ) was found associated with bright DAPI stained heterochromatin foci as well as with the peri-nucleolar heterochromatin rim in control pre-ovulatory oocytes at the germinal vesicle stage where it partially co-localized with kinetochores detected by CREST ( calcynosis Raynaud's phenomenon esophageal dismotility , sclerodactyly and telangiectasia ) ( Figure 1A and 1B ) . Moreover , ATRX was also present at heterochromatin foci in surrounding somatic granulosa cells ( Figure 1A; thick arrow ) . In contrast , ATRX was undetectable at the germinal vesicle in >95% of transgenic oocytes of line #G-5 ( Figure S1D ) in which only somatic granulosa cells exhibited bright ATRX staining therefore confirming the oocyte-specific ablation of ATRX in our transgenic mouse model ( Figure 1A ) . The cytoplasmic signals for γ-tubulin staining ( green ) located near the germinal vesicle of transgenic oocytes remained unaffected , further attesting for the specificity of this approach ( Figure 1A; inset ) . Importantly , analysis of CREST signals in transgenic oocytes revealed that the kinetochore-associated proteins detected by this antiserum remain unaffected in the absence of ATRX function ( Figure 1B; inset ) . At the metaphase II stage , ATRX exhibits a prominent localization to the centromeres of meiotic chromosomes in 99 . 4% of control oocytes ( n = 86 ) . In contrast , the proportion of transgenic oocytes ( 0 . 9%; n = 93 ) with ATRX staining at pericentric heterochromatin in line #G-5 was significantly reduced ( P<0 . 001 ) indicating that expression of the Atrx-hairpin caused an effective reduction of ATRX protein levels in the majority of mature oocytes ( Figure 1C and 1D , insets ) . Moreover , comparison of mRNA expression in pools of transgenic and control metaphase II stage oocytes by quantitative real-time PCR demonstrated that the levels of Atrx transcripts in transgenic oocytes were drastically reduced and represent only 1 . 1% of the total Atrx transcript levels found in non-transgenic controls ( Figure 1E and 1F ) . These results indicate that the zona pellucida 3 ( ZP3 ) -driven Atrx-hairpin vector is a reliable tool for the selective ablation of the ATRX protein during oogenesis . To determine whether lack of ATRX function in female gametes influences fertility , we compared the average litter size of transgenic founder females ( #G-5 and #H-10 ) to a wild type littermate control ( #F-31 ) . Over a period of 7 months , the transgenic founder female #G-5 produced a total of 6 litters and had an average of 3 . 2 pups per litter , while founder female #H-10 produced only three litters with an average of 2 . 7 pups per litter ( Figure 2A ) . In stark contrast , we observed an average litter size of 12 . 7 pups per litter in a control littermate that produced a total of 7 litters during the observation period . Fertility parameters such as litter size strongly depend on the genetic background of the transgenic strain used [22] , [24] , [25] . To determine reproductive parameters in ATRX-RNAi females in more detail , we established backcrosses into C57BL/6 as well as outcrosses into the CF1 genetic background ( Figure S1E , S1F ) . As expected , both genetic backgrounds had an effect on the average litter size in both transgenic females as well as controls . However , fertility of transgenic females remained significantly ( P<0 . 05 ) reduced compared to controls regardless of the particular genetic background used ( Figure S1E , S1F ) . Analysis of meiotic maturation revealed no significant differences in the proportion of transgenic ova ( 71% ) that reached the metaphase II stage following culture of pre-ovulatory oocytes for 14 h compared with controls ( 68% ) , suggesting that functional ablation of ATRX during oogenesis had no effect on meiotic progression ( Figure 2B ) . However , lack of ATRX function during meiosis results in severe chromosome segregation defects in both in vitro matured as well as in vivo matured oocytes ( Figure 2C–2E ) . For example , compared to in vitro matured control oocytes ( n = 99 ) in which chromosomes were tightly aligned at the equatorial region of a bipolar metaphase II spindle ( Figure 2C ) , a high proportion ( 74 . 1%; P<0 . 001 ) of transgenic oocytes ( n = 96 ) exhibited a range of chromosome segregation defects including lagging chromosomes and loss of sister chromatid cohesion ( Figure 2D and 2F ) . Chromosome segregation defects were also detected in a high proportion of transgenic oocytes by live cell imaging ( Figure S3 and Videos S1 and S2 ) . Notably , in vivo matured MII oocytes collected from the oviduct of transgenic females , presented a significant increase of chromosomal abnormalities such as misaligned chromosomes , chromosome lagging and in some instances premature anaphase onset with formation of chromosome bridges ( Figure 2E , insets ) demonstrating that lack of ATRX protein during meiotic maturation in vivo and in vitro interferes with proper chromosome alignment at the metaphase II stage . Next , we determined whether lack of ATRX function during meiosis results in any potential numerical or structural chromosome abnormalities . Analysis of surface spread chromosomes from control oocytes ( n = 34 ) revealed that ATRX ( red ) associates with a large pericentric heterochromatin domain in contrast with the circumscribed localization of CREST signals to the kinetochore ( Figure 3A ) . As expected , no ATRX protein was detected in transgenic oocytes ( n = 35 ) . ATRX-deficient ova exhibit a normal chromosome configuration at the metaphase I stage ( Figure S2A ) . However , at metaphase II lack of ATRX function results in abnormal chromosome morphology associated with incomplete chromosome condensation as well as chromatid breaks in the majority of oocytes ( Figure 3B and 3C ) . At this stage , ATRX deficient oocytes ( n = 82 ) also exhibit a high incidence of aneuploidy ( 59 . 7% ) compared with only 7 . 4% observed in controls ( n = 66; P<0 . 001 ) . Cytogenetic analysis of methanol spread chromosomes obtained from in vivo matured oocytes revealed that chromosome non-disjunction resulting in hyperploidy ( Figure 3D ) was the most common type of chromosomal aberrations found in ATRX deficient oocytes , although the presence of single chromatids and premature anaphase II onset was also detected . Taken together , the different types of meiotic spindle and chromosome configuration observed in transgenic oocytes at the metaphase II stage suggest a critical role for ATRX in regulating chromosome segregation and the maintenance of chromosome stability during meiosis . Importantly , our results indicate that lack of ATRX function severely impairs female fertility . ATRX has been shown to exhibit a physical interaction with the transcriptional regulatory factor death domain associated protein ( DAXX ) at promyelocytic leukemia nuclear bodies ( PML's ) in both human and murine somatic cells [26]–[28] . However , the mechanisms responsible for recruiting DAXX to pericentric heterochromatin are not known . Therefore we determined whether ATRX is required to recruit DAXX protein to heterochromatin domains in the oocyte genome . Simultaneous analysis of the nuclear localization of DAXX and centromeric proteins detected by the CREST antiserum confirmed that DAXX associates with pericentric heterochromatin domains in control oocytes ( n = 74 ) at the germinal vesicle stage ( Figure 4A; arrows ) . Importantly , analysis of transgenic ova ( n = 84 ) revealed that in the absence of ATRX centromeric signals detected by CREST remain unaffected attesting for kinetochore integrity . However , DAXX protein fails to associate with pericentric heterochromatin domains in 81 . 3% of germinal vesicle stage oocytes ( P<0 . 005 ) ( Figure 4A and 4B ) . In contrast , no differences were observed on the patterns of histone H3 trimethylated on lysine 9 ( H3K9me3 ) in ATRX-deficient oocytes compared to controls ( Figure S2B ) suggesting that this hallmark epigenetic modification is upstream from ATRX binding to constitutive heterochromatin . These results demonstrate for the first time that functional ablation of ATRX affects the molecular composition of pericentric heterochromatin and that ATRX is required to recruit the transcriptional regulator DAXX to these nuclear domains in mammalian oocytes . To assess the mechanisms involved in the abnormal chromosome condensation observed in ATRX deficient ova , we determined the patterns of histone H3 phosphorylation at serine 10 ( H3S10ph ) , a chromosome-wide epigenetic modification associated with chromosome condensation [29] , [30] . Abnormal H3S10 phosphorylation has been associated with impaired chromosome condensation , chromatid cohesion and aneuploidy [30]–[32] . Notably , a significant proportion ( P<0 . 005 ) of transgenic metaphase II oocytes ( 85%; n = 73 ) exhibit reduced levels of H3S10 phosphorylation ( red ) at both centromeric heterochromatin and along the chromatids when compared to the chromosomes of control oocytes ( 13% n = 98; Figure 5A and 5B ) , while immunolocalization of CREST signals ( green ) at the kinetochores remained essentially unaffected ( Figure 5A , lower panel ) . Consistent with our analysis of transgenic oocytes at the GV stage , lack of ATRX function had no effect on the patterns of H3K9 trimethylation in metaphase II stage oocytes , demonstrating that ATRX knockdown is specifically associated with reduced phosphorylation of H3S10 without affecting the establishment of transcriptionally repressive chromatin modifications such as H3K9me3 ( Figure S2C ) . These results suggest that functional ablation of ATRX might interfere with the establishment and spreading of critical chromatin modifications responsible for proper chromosome condensation and chromatid cohesion during meiosis II . To study the dynamics of chromosome segregation during anaphase and determine the type of chromosomal defects transmitted to the embryo , metaphase II stage oocytes were parthenogenetically activated and late anaphase to telophase II oocytes were fixed and processed for immunochemical analysis using antibodies against ATRX ( red ) and β-tubulin ( green , Figure 6 ) . Following parthenogenetic activation , control oocytes ( n = 60 ) showed no evidence of chromosome abnormalities and sister chromatids segregated properly to opposing spindle poles ( Figure 6A ) . However , a significant ( P<0 . 05 ) proportion of transgenic oocytes ( 29 . 2%; n = 68 ) exhibited multiple chromosome fragments ( Figure 6B , arrows , and Figure 6E ) and , in extreme cases , the formation of multi-polar spindles with dispersion of genetic material throughout the cell ( Figure 6D , arrows ) . This suggests that lack of ATRX function during meiotic progression leads to chromosome instability as defined by the presence of numerical and structural chromosome abnormalities [33] and subsequent chromosome segregation defects during the transition to the first mitosis . To gain further insight into the potential mechanisms of aneuploidy , we analyzed in vitro fertilized zygotes during the first mitotic division by CREST immunochemistry ( green ) . Transmission of aneuploidy was clearly evident in transgenic zygotes as indicated by the presence of numerical chromosome abnormalities ( Figure 7A ) . Moreover , immuno-FISH experiments with CREST antiserum ( green ) and subsequent visualization of pericentric heterochromatin using a pan-centromeric DNA probe ( red ) revealed the presence of centromeric breaks ( Figure 7B lower panel , arrow and inset ) in a high proportion of in vitro fertilized transgenic zygotes ( 41 . 2%; n = 105 ) compared to controls ( 6 . 6%; n = 100 ) ( P<0 . 001; Figure 7-D ) . Moreover , we observed extensive stretching of centromeric heterochromatin domains in transgenic zygotes ( Figure 7-B , lower panel , arrowhead ) , indicating that chromosome breaks and structural aberrations occur within constitutive heterochromatin . This notion was further substantiated by a significant increase in the incidence ( 44 . 7%; n = 65 ) of illegitimate recombination events within centromeric heterochromatin in parthenogenetic ATRX-RNAi zygotes compared to controls ( 20 . 9%; n = 85 ) , which was reflected by the presence of sister chromatid exchanges ( red ) during the first DNA replication cycle ( P<0 . 005; Figure 7C , arrow ) . Interestingly , we also found evidence for the presence of centromeric breaks within the same chromosomal spreads ( arrowhead ) , which might give rise to acentric chromosomal fragments and the formation of micronuclei . Notably , compared to controls ( 5 . 6%; n = 90 ) , a higher proportion of in vitro fertilized transgenic 2-cell embryos ( 39 . 6%; n = 102 ) exhibited micronuclei ( Figure 7E , lower panel , arrow and inset ) . Moreover , virtually all micronuclei observed at this stage contained major satellite DNA corresponding to pericentric heterochromatin ( red ) along with euchromatic chromosomal fragments ( Figure 7E ) . However , in contrast to blastomere nuclei ( arrowheads , inset ) , kinetochore domains ( CREST , green ) were not detectable in micronuclei of transgenic 2-cell embryos ( Figure 7-F , arrow , inset ) demonstrating that most micronuclei originate from acentric chromosomal fragments rather than whole chromosomes . These findings provide strong evidence that lack of ATRX results in the transmission of aneuploidy and the occurrence of centromeric breaks leading to a high incidence of structural and numerical chromosome aberrations in the pre-implantation embryo . Surprisingly , despite the formation of micronuclei and centromeric instability , progression to the blastocyst stage was similar for transgenic and control embryos ( Figure 8A ) . Immunostaining of control pre-implantation embryos using antibodies against ATRX ( red ) revealed a characteristic localization pattern to DAPI-bright heterochromatic domains ( Figure 8B , upper panel ) . In transgenic embryos , however , ATRX protein became gradually detectable beginning at the 8–16 cell stage , presumably as a result of zygotic gene activation and a cessation of the RNAi effect ( Figure 8B , middle panel ) . Interestingly , micronuclei formation in transgenic embryos was prevalent throughout pre-implantation development and increased from 39 . 6% ( n = 67 ) at the 2-cell stage to 59 . 8% ( n = 83 ) at the blastocyst stage ( Figure 8B lower panel , arrows ) . Controls demonstrated micronuclei in significantly lower proportions at all developmental stages 5 . 7% ( n = 71 ) at the 2-cell stage to 17 . 3% ( n = 88 ) in blastocysts , respectively , ( Figure 8C ) , demonstrating that lack of ATRX in the female gamete results in severe chromosomal instability throughout pre-implantation development .
The underlying mechanisms that predispose the female gamete to a high incidence of aneuploidy during meiosis are not well defined . In this study , we provide evidence that maintenance of pericentric heterochromatin structure and function is essential for chromosome stability in the female germ line . Our findings indicate that the chromatin remodeling protein ATRX plays a critical role in heterochromatin formation and chromosome segregation in the mammalian oocyte . Lack of ATRX function during oocyte growth results in severe sub-fertility and has a profound effect on the molecular composition of pericentric heterochromatin as demonstrated by failure to recruit the transcriptional regulator DAXX to the chromocenters of transgenic pre-ovulatory oocytes . At the metaphase II stage , ATRX-deficient oocytes exhibit a high incidence of aneuploidy due to chromosome non-disjunction , presence of single chromatids as well as premature anaphase II onset . Notably , abnormal segregation was also associated with aberrant chromosome morphology , reduced phosphorylation of histone H3 at serine 10 ( H3S10ph ) and centromeric breaks , suggesting a role for ATRX in chromosome condensation-related events and centromere stability during meiosis . Collectively , our results revealed a critical pathway in which ATRX is required for recruitment of DAXX to pericentric heterochromatin in the mammalian oocyte genome . Moreover , our study indicates that the role of ATRX in chromosome segregation might be mediated through an epigenetic mechanism involving the maintenance of chromatin modifications associated with pericentric heterochromatin compaction and chromosome condensation . Importantly , the type of chromosome segregation defects found during meiotic progression of transgenic oocytes in vivo and in vitro are consistent with a role for ATRX in maintaining chromosome stability at metaphase II and during the transition to the first mitosis . In human and murine somatic cells , interaction of the ATRX protein with DAXX results in the formation of a novel transcriptionally repressive complex with chromatin remodeling activity [26]–[28] . In HeLa cells , the transcriptional regulator DAXX establishes a physical interaction with ATRX where it plays an active role in recruiting ATRX to promyelocytic leukemia bodies ( PML's ) [26]–[28] . In turn , DAXX accumulation at heterochromatic domains is associated with phosphorylation of ATRX [26] . However , the mechanisms responsible for recruiting DAXX to pericentric heterochromatin remained to be established . We now provide direct evidence that ATRX is required for the recruitment of DAXX to pericentric heterochromatin domains in the female germ line . The presence of normal histone trimethylation ( H3K9me3 ) in transgenic oocytes indicates that ATRX is downstream from this repressive mark but upstream from DAXX and hence provide novel insight on a critical pathway of heterochromatin assembly in the oocyte genome . Therefore , by recruiting transcriptional regulatory factors , ATRX plays a critical role in pericentric heterochromatin formation and in regulating the molecular composition of constitutive heterochromatin domains during oocyte growth . Notably , in stark contrast with somatic cells in which the association of DAXX with ATRX at pericentric heterochromatin is restricted to a brief period at the S phase of the cell cycle [26] , [28] , DAXX protein remains associated with ATRX at pericentric heterochromatin in the nucleus of fully-grown oocytes . Thus , in female germ cells the interaction between DAXX and ATRX is maintained beyond the S phase as mammalian dictyate oocytes are arrested at a stage equivalent to the G2/M transition , a process that might be of functional significance for the establishment of unique chromatin remodeling events in the oocyte genome . The functional consequences of ATRX ablation on centromere structure and function are not fully understood . However , our results indicate that at least one chromatin modification , phosphorylation of histone H3 at serine 10 ( H3S10ph ) , which is known to initiate at pericentric heterochromatin at the G2/M transition and subsequently distributes throughout the chromatids following chromosome condensation [29] , [34] , [35] , is dramatically reduced in ATRX deficient oocytes and therefore may contribute to the abnormal chromosome condensation observed in transgenic oocytes at the metaphase II stage . The underlying epigenetic mechanisms involved in this process are currently under investigation . However , recent studies indicate that H3S10ph is severely affected by factors known to disrupt DNA methylation in somatic cells [36] . Although ATRX does not contain a canonical DNA methyltransferase motif , DNMT3B and ATRX share a closely related plant homeodomain ( PHD ) -like zinc finger domain [37] , [38] and patients with ATRX syndrome exhibit alterations in DNA methylation at repetitive sequences [39] . Moreover , recent studies indicate that spontaneous mutations occurring at the PHD domain of several chromatin remodeling factors , including ATRX , may severely affect their ability to interpret specific epigenetic marks resulting in striking changes in chromatin structure [40] . Therefore , it is conceivable that lack of ATRX function might interfere with chromosome-wide H3S10 phosphorylation by inducing either a conformational change in chromatin configuration or an abnormal DNA methylation pattern at pericentric heterochromatin . In turn , H3S10ph is governed by members of the aurora kinase family , which are essential for the formation of a bipolar spindle and the recruitment of the condensin complex in order to ensure sister chromatid cohesion [31] . Hence , chromosomal defects in conjunction with global reduction of H3S10 phosphorylation in ATRX deficient oocytes suggest that pericentric ATRX might be critical for chromosome condensation and sister chromatid cohesion . Our studies indicate that abnormal chromatin modifications might contribute to chromosome instability in ATRX deficient oocytes and thus constitute a valuable model to unravel the potential involvement of abnormal chromatin modifications on the onset of aneuploidy in the female gamete . Consistent with our previous studies [12] , ATRX-deficient oocytes progress to the metaphase II stage , however , while chromosomes at metaphase I appear normal , more than 70% of transgenic oocytes at the metaphase II stage exhibit chromosome segregation defects including lagging chromosomes and to a lesser extent , premature separation of sister chromatids , suggesting a role for ATRX in chromosome segregation and sister chromatid cohesion . Chromosome congression defects have also been observed following transient depletion of ATRX in human somatic cells [41] , indicating that ATRX is important for proper chromosome segregation during mitotic as well as meiotic cell division in mammals . However , the higher frequency of chromosome non-disjunction observed in ATRX deficient oocytes ( our study ) compared to somatic cells ( 30% ) [41] might reflect subtle albeit important differences in ATRX function during meiosis and hence further studies are required to determine the molecular mechanisms predisposing ATRX deficient oocytes to a high incidence of chromosomal non-disjunction . Both cytogenetic analyses as well as live cell imaging studies indicate that chromosome segregation defects in ATRX deficient oocytes arise during the metaphase II stage . For example , the presence of a single misaligned chromosome might reflect a specific disruption of kinetochore-microtubule interactions underscoring a potential ‘crosstalk’ between centromeric and pericentric heterochromatin in the epigenetic control of centromere function in mammalian oocytes . Interestingly , this process may not be sufficient to trigger the spindle assembly checkpoint in ATRX deficient oocytes as evidenced by the lack of metaphase I arrest . However , it confers the oocyte with the potential to transmit aneuploidy to the early conceptus . On the other hand , the presence of single chromatids and premature anaphase II onset in transgenic oocytes matured in vivo suggest a potential role for ATRX in centromeric cohesion . Consistent with this notion , studies in human and murine somatic cells have recently demonstrated a role for ATRX in centromeric cohesion [41] . Interestingly , abnormal regulation of Atrx transcripts has also been observed in cohesin deficient cell lines obtained from patients with Cornelia de Lange Syndrome ( CdLs ) [42] . The presence of chromosome fragments containing major satellite DNA sequences during the transition to the first mitosis is indicative of centromere instability in the absence of ATRX and might result from microtubule tension during the transition to the first mitosis . Micronuclei formation is a prominent indicator of extensive chromosomal damage and identification of the type of micronuclei prevalent provides critical insight into the underlying mechanisms of aneuploidy . For example , micronuclei might correspond to small acentric fragments resulting from double strand break formation or , alternatively , to whole chromosomes induced by abnormal segregation during previous cell divisions [43] . We demonstrate here , that ATRX-depleted oocytes and embryos contain both numerical chromosome aberrations as well as centromeric break-associated micronuclei reflecting severe chromosome instability . In addition , pericentric heterochromatin of transgenic zygotes shows an increased rate of mitotic recombination events . Tandem repeats in centromeric satellite DNA are thought to form hot-spots for illicit sister chromatid exchanges requiring repressive heterochromatin marks to prevent changes in centromeric repeat sequence length and hence impairment of centromere function . For instance , increased rates of centromere mitotic recombination have previously been correlated with DNA hypomethylation in ES cells lacking the methyltransferases Dnmt3a and Dnmt3b underscoring the significance of epigenetic marks in the repression of mitotic sister chromatid exchanges [44] . In conclusion , our data identify ATRX as an essential epigenetic component of pericentric heterochromatin for the repression of centromere mitotic rearrangements , DNA breaks and chromosome missegregation and hence as an important guardian of centromere integrity and function . Chromosome segregation errors during meiosis lead to aneuploidy in approximately 10–25% of all human conceptions and human embryos are particularly susceptible to aneuploidy , which in the majority of cases is inherited through the female gamete [45] , [46] . Although the underlying mechanisms that promote a high incidence of aneuploidy are not fully understood , advanced maternal age is widely recognized as the single most important etiological factor for an increased risk of chromosomal non-disjunction and premature separation of sister chromatids [47] , [48] . The two mechanisms of aneuploidy most commonly observed in ATRX deficient oocytes , namely non-disjunction of whole chromosomes and premature separation of sister chromatids are also the major causes of aneuploidy in oocytes from women of advanced age [49] . Therefore ATRX deficient ova constitute an invaluable model to determine the molecular mechanisms involved in the onset of aneuploidy as a function of maternal age . Interestingly , transcriptome analyses conducted in oocytes from young and aged female mice revealed a significant reduction of Atrx mRNA levels [50] , [51] consistent with the notion that loss of ATRX function during reproductive senescence may contribute to the onset of aneuploidy in the female gamete . The reduced fertility observed in ATRX knockdown female mice underscores the importance of this model to determine the molecular mechanisms of aneuploidy and its effects on female fertility .
All animal experiments were approved by the institutional animal use and care committee of the University of Pennsylvania according to National Institutes of Health guidelines . A two-step cloning strategy was used to direct the insertion of an Atrx inverted repeat into a vector driving the expression of the enhanced green fluorescent protein ( EGFP ) under the control of a zona pellucida-3 gene promoter ( pZP3-intron-EGFP ) , a generous gift from Dr . R . Schultz [21] , [52] . Briefly , the Atrx coding sequence spanning exon 3 to exon 5 was amplified from mouse oocyte cDNA using the BglII and XbaI restriction site-containing primer pair “Atrx-fwd1” 5′-aga tct gga aaa taa caa gga aga ggg agc-3′ and “Atrx rev” 5′-tct aga acg cag tca cca agt cca gta gag-3′ . A shorter fragment was amplified using the primer pair “Atrx-fwd2” 5′-gga tcc cag agc cag tgc tga atg aag ac-3′ ( BamHI ) and “Atrx rev” . Both PCR products were individually sub-cloned into vector pCR4-TOPO ( Invitrogen , Carlsbad , CA ) before NotI/BglII-excision of the longer hairpin fragment and ligation into a BamHI/NotI-linearized pCR4-TOPO vector carrying the shorter fragment . Following XbaI digest , the inverted repeat was inserted into vector pZP3-intron-EGFP to generate pZP3-intron-EGFP-Atrx-hp ( Figure S1A ) . Microinjection of the construct into pronuclear stage embryos was conducted at the University of Pennsylvania's Transgenic Mouse Facility . Transgenic mice were identified by PCR using the primer pair “EGFPseq-F” 5′-aaa gac ccc aac gag aag cg-3′ and ”Atrx-fwd2” or by presence of a 301 bp amplicon originating from the SV40 intron as described previously [21] . Transgenic lines were established through outcrossing of transgenic founders with CF1 wild type mice . To test the fertility of transgenic founders ( C57BL/6/SJL/J ) , females were mated with CF1 males at a ratio of 1∶1 and housed together for a period of 7 months . CF1-outcrossed female offspring ( B6SJL/CF1 ) as well as C57BL/6-backcrossed female offspring ( n = 3–4/line ) and appropriate wild-type controls ( n = 6 for each experiment ) were mated with C57BL/6/SJL/J males at approximately 40 days of age and continuously housed at a ratio of 1∶1 for a period of 7 months . Litter size was recorded and is displayed as the average per transgenic line or control group . Fully-grown GV stage oocytes were recovered from 22–24 day old females 48 h post injection with 5 IU of pregnant mare serum gonadotropin ( PMSG; National Hormone and Peptide Program , NIDDK ) . Oocytes were collected in MEM medium supplemented with 3 mg/ml bovine serum albumin ( MEM/BSA; Sigma Aldrich , Inc . St . Louis , MO ) , and 10 µM Milrinone ( Sigma ) to prevent germinal vesicle breakdown . In vitro matured metaphase II oocytes were obtained following culture in fresh MEM/BSA media supplemented with 5% fetal bovine serum ( FBS , Hyclone , Logan , UT ) for 14 h under an atmosphere of 5% O2 , 5% CO2 and 90% N2 at 37°C . In vivo matured metaphase II oocytes were collected from the oviducts of superovulated females 14 h post human chorionic gonadotropin injection ( 5 IU; EMD Biosciences , Inc . La Jolla , CA ) . Early zygotes and pre-implantation stage embryos were obtained following in vitro fertilization and culture of in vivo derived eggs in KSOM medium as described [53] . In vivo matured metaphase II oocytes were parthenogenetically activated as described previously [54] and late anaphase/telophase II oocytes were fixed for chromosome and spindle analysis . Chromosomal spreads from cleavage stage embryos were obtained following exposure to 100 ng/ml colchicine for 4 h ( GIBCO , Life Technologies , Grand Island NY ) . Karyotyping was conducted on surface spread chromosomes following treatment with methanol/acetic acid ( MeOH/AA ) essentially as described [55] . Centromeric sister chromatid exchanges were detected through changes in the lateral asymmetry in the C-band region of chromosomes as revealed by incorporation of BrdU ( 5-bromo-2′-deoxyuridine ) during a single replication cycle [56] . Lateral asymmetry is thought to reflect an unequal distribution of thymidine residues between adjacent C-band regions of sister chromatids resulting in a strongly asymmetric fluorescent signal following incorporation and detection of BrdU nucleotides . These regions of asymmetrical brightness have been shown to consist of centromeric satellite sequences by chromosome orientation fluorescence in situ hybridization ( CO-FISH ) [56] , [57] and hence allow the visualization and quantification of centromere mitotic recombination events [44] . To analyze the incidence of centromeric sister chromatid exchanges in the ATRX-RNAi model , parthenogenetic transgenic and control zygotes were cultured in 250 µM BrdU from 6 to 24 h post activation ( hpa ) . In addition , colchicine ( 100 ng/ml ) was added to the culture media at 12 hpa to arrest chromosomes at the first mitotic division before surface spreading of chromosomes complements onto glass slides . Ovaries from transgenic females and control ( wild type ) littermates ( 26 days of age ) were dissected and fixed in Bouin's solution before processing for paraffin sectioning ( 5 µm ) and staining with hematoxylin and eosin according to standard procedures . For whole mount immunochemistry , maturing oocytes and pre-implantation embryos were washed in MEM/BSA and fixed in 4% paraformaldehyde ( PFA ) and 0 . 1% Triton X 100 for 20 minutes at 37°C . Analysis of chromosomal proteins was conducted on surface spread chromosomes prepared following exposure of zona-free oocytes to a solution of 1% PFA , 0 . 1% Triton X 100 as described [58] and subsequently blocked with PBS containing 1 mg/ml BSA and 0 . 01% Triton X 100 for 1 h at room temperature or overnight at 4°C . Polyclonal rabbit anti-ATRX and anti-DAXX antibodies were obtained from Santa Cruz Biotechnology , Inc . ( Santa Cruz , CA ) and used at a dilution of 1∶200 . Mouse anti-γ-tubulin was used at 1∶6000 and mouse anti-β-tubulin at 1∶400 ( Sigma ) . Human anti-CREST antiserum was a generous gift of Dr . W . Earnshaw and used at a concentration of 1∶5000 . Mouse anti-H3S10ph ( Histone 3 phosphorylated at serine 10 , 1∶1000 ) and rabbit anti-H3K9me3 ( Histone 3 trimethylated at lysine 9 , 1∶200 ) were purchased from Millipore ( Billerica , MA , USA ) and Abcam Inc . ( Cambridge , MA , USA ) , respectively . Incorporation of BrdU nucleotides was detected as previously described [16] . Immunodetection was performed using appropriate Alexa Fluor-conjugated secondary antibodies ( Molecular Probes , Eugene , Oregon , USA ) at a dilution of 1∶1000 for 2 h at room temperature . Samples were counterstained and mounted onto glass slides in mounting medium containing DAPI ( 4′ , 6-diamidino-2-phenylindole; Vecta Shield plus DAPI , Vector Laboratories , Inc . Burlingame , CA ) . Messenger RNA was isolated from denuded oocytes using Micro-Fast Track 2 . 0 kit ( Invitrogen ) and subsequently subjected to reverse transcription using oligo-dT primer and the Superscript II first strand synthesis system ( Invitrogen ) . The resulting cDNA was used for quantitative expression analysis of Atrx transcripts by real-time PCR using FastStart DNA master SYBR green I kit ( Roche , Indianapolis , IN ) on a Roche Light Cycler apparatus . Primer sequences were as follows: “Atrx-RT-Fwd” 5′-ctg cct tca cac act gga ttt tg-3′ and “Atrx-RT-Rev” 5′-cgg agt tca cca tca tct gct g-3′ , corresponding to sequences within exon 9 and exon 11 , respectively . Real-time PCR results were normalized against β-actin transcript levels as a housekeeping control ( primer pair: “β-actin-Fwd” 5′-gat atc gct gcg ctg gtc gtc-3′; “β-actin-Rev” 5′-acg cag ctc att gta gaa ggt gtg g-3′ ) . Time-lapse image acquisition was performed following microinjection of capped messenger RNA encoding a histone H2B-GFP fusion protein in fully-grown GV stage oocytes to visualize chromosomes as described [59] . Microinjected oocytes were cultured in the presence of 10 µM milrinone for 3–4 h to allow recombinant protein expression before in vitro maturation for 16 h in an oil-covered micro drop of M2 medium maintained at 37°C using an incubation chamber . Germinal vesicle breakdown and polar body extrusion was monitored by 2D time-lapse microscopy at 20 minutes intervals on a Leica TCS-SP5 laser scanning confocal microscope equipped with a 10x objective lens and 2 . 37x digital zoom . Chromosome segregation was analyzed by 3D time-lapse imaging following excitation of GFP protein with a 488 nm argon laser and detected using 505–550 nm emission filter . Live cell imaging data were analyzed by 3D reconstructions using LAS AF software ( Leica , Bannockburn , IL ) . Following immunochemistry , slides were processed for DNA-FISH analysis using a Cy3-conjugated Pan-centromeric probe ( Cambio Ltd . , Cambridge , UK ) , according to manufacturer's specifications and with the following modifications . Briefly , surface spread interphase nuclei and metaphase chromosomes were denatured in 70% formamide ( VWR International Ltd . , Poole , UK ) in 2X SSC at 85°C for 10 minutes and subsequently chilled in ice-cold 70% ethanol for 5 minutes . The pan-centromeric probe was denatured for 10 minutes at 85°C and incubated at 37°C for 1 h . Overnight hybridization was carried out in a humidified chamber at 37°C and stringency washes were conducted in a solution containing 50% formamide in 2X SSC as previously described [60] . Data presented as percentage values were analyzed by one-way analysis of variance ( ANOVA ) following arcsine transformation . Comparison of all pairs was conducted by the Tukey-Kramer HSD or Student's t-test using JMP Start Statistics ( SAS Institute Inc . , Cary , NC ) . Variation among individual replicates is indicated as the standard deviation ( s . d . ) . Differences were considered significant when P<0 . 05 and are indicated by different superscripts . Immunofluorescence and FISH image acquisition of paraformaldehyde or methanol/acetic acid fixed samples was performed at room temperature using Vectashield ( Vector Laboratories ) as mounting medium . Data analysis was conducted using a Leica DMRE fluorescence microscope ( Leica Microsystems , Inc . ) equipped with a HCX PLAN APO 40x/0 . 85 air , and with a PLAN APO 63x/1 . 20 water objective . Images were captured with a Leica DFC 350F camera using Openlab 3 . 1 . 7 . software ( PerkinElmer ) and image processing was performed using Photoshop 2 . 0 ( Adobe ) for linear adjustments and cropping of fluorescent images . No gamma adjustments were made .
|
The transmission of an abnormal chromosome complement from the gametes to the early embryo , a condition called aneuploidy , is a major cause of congenital birth defects and pregnancy loss . Human embryos are particularly susceptible to aneuploidy , which in the majority of cases is the result of abnormal meiosis in the female gamete . However , the molecular mechanisms involved in the onset of aneuploidy in mammalian oocytes are not fully understood . We show here that , the α-thalassemia/mental retardation X-linked protein ( ATRX ) is essential for the maintenance of chromosome stability during female meiosis . ATRX is required to recruit the transcriptional regulator DAXX to pericentric heterochromatin at prophase I of meiosis . Notably , lack of ATRX function at the metaphase II stage interferes with the establishment of chromatin modifications associated with chromosome condensation leading to segregation defects , chromosome fragmentation , and severely reduced fertility . Our results provide direct evidence for a role of ATRX in the regulation of pericentric heterochromatin structure and function in mammalian oocytes and have important implications for our understanding of the epigenetic factors contributing to the onset of aneuploidy in the female gamete .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/germ",
"cells",
"developmental",
"biology/embryology",
"molecular",
"biology/histone",
"modification",
"molecular",
"biology/centromeres",
"developmental",
"biology/aging",
"cell",
"biology/developmental",
"molecular",
"mechanisms",
"genetics",
"and",
"genomics/chromosome",
"biology",
"molecular",
"biology/chromosome",
"structure",
"molecular",
"biology/dna",
"methylation",
"genetics",
"and",
"genomics/epigenetics",
"developmental",
"biology/developmental",
"molecular",
"mechanisms",
"molecular",
"biology/chromatin",
"structure",
"molecular",
"biology/dna",
"repair"
] |
2010
|
Loss of Maternal ATRX Results in Centromere Instability and Aneuploidy in the Mammalian Oocyte and Pre-Implantation Embryo
|
Regeneration of lost tissues depends on the precise interpretation of molecular signals that control and coordinate the onset of proliferation , cellular differentiation and cell death . However , the nature of those molecular signals and the mechanisms that integrate the cellular responses remain largely unknown . The planarian flatworm is a unique model in which regeneration and tissue renewal can be comprehensively studied in vivo . The presence of a population of adult pluripotent stem cells combined with the ability to decode signaling after wounding enable planarians to regenerate a complete , correctly proportioned animal within a few days after any kind of amputation , and to adapt their size to nutritional changes without compromising functionality . Here , we demonstrate that the stress-activated c-jun–NH2–kinase ( JNK ) links wound-induced apoptosis to the stem cell response during planarian regeneration . We show that JNK modulates the expression of wound-related genes , triggers apoptosis and attenuates the onset of mitosis in stem cells specifically after tissue loss . Furthermore , in pre-existing body regions , JNK activity is required to establish a positive balance between cell death and stem cell proliferation to enable tissue renewal , remodeling and the maintenance of proportionality . During homeostatic degrowth , JNK RNAi blocks apoptosis , resulting in impaired organ remodeling and rescaling . Our findings indicate that JNK-dependent apoptotic cell death is crucial to coordinate tissue renewal and remodeling required to regenerate and to maintain a correctly proportioned animal . Hence , JNK might act as a hub , translating wound signals into apoptotic cell death , controlled stem cell proliferation and differentiation , all of which are required to coordinate regeneration and tissue renewal .
The regeneration of missing tissues requires tight coordination between stem cell proliferation , differentiation , and cell death . However , it remains unclear how these processes are integrated to generate a well-proportioned organism . We addressed this question using the freshwater planarian Schmidtea mediterranea , a popular model system in regeneration research . These animals stand out in housing a pluripotent cell population ( neoblasts ) throughout their lives [1]–[7] . Due to their pluripotent nature , neoblasts confer planarians with unmatched plasticity , allowing them to regenerate any body part within a few days and to continuously modulate their size in accordance with energy supply while sustaining physiological functions [8]–[12] . Planarians thus are a unique model in which to study the molecular processes that underlie regeneration in vivo . After any kind of amputation in planarians , the wound is closed by muscle contraction within a few minutes [13] , [14] . Subsequent signaling from the wound area triggers specific gene activation [15] , [16] , the induction of apoptotic cell death [17] and the controlled induction of neoblast proliferation and differentiation [18] , [19] . JNK is a stress-activated protein kinase ( SAPK ) that belongs to a large family of mitogen-activated protein kinases ( MAPKs ) and regulates essential cellular processes , such as stem cell proliferation , differentiation and programmed cell death , in response to stress [20] , [21] . As a stress indicator , JNK has been implicated in cell cycle regulation , where it ensures the controlled onset of mitosis [22] , [23] . Deregulation of JNK-mediated signaling has been demonstrated in a wide variety of human diseases , including neurodegenerative disorders , diabetes and cancer [20] , [24] . Transcriptional profile analyses have identified planarian orthologs of downstream effectors of the JNK pathway such as jun and fos as possible participants in neoblast maintenance [7] and in the wound response program [16] . Here we show that loss of function of the S . mediterranea JNK ortholog after RNA interference ( RNAi ) prevents the regeneration of missing structures . In response to wounding , JNK ( RNAi ) planarians exhibited decreased expression of wound-induced genes , a severe attenuation of the apoptotic response and acceleration of the dynamics of neoblast proliferation between G2- to M-phase transition . In pre-existing regions , the positive balance between cell death and stem cell proliferation was reversed , leading to improper remodeling and rescaling in JNK ( RNAi ) animals . Furthermore , JNK RNAi specifically interfered with the maintenance of body proportion during degrowth , but not growth , as only decreases in size were dependent on the activation of apoptosis . These findings point to JNK as an essential stress response element required for the integration and coordination of the apoptotic and proliferative responses triggered by tissue loss to ensure successful regeneration and tissue remodeling . Moreover , our results contribute to a novel aspect of regeneration: the importance of temporal control of the cell cycle progression of stem cells for balanced differentiation [25] .
We identified a single JNK ortholog in the S . mediterranea genome ( Smed-JNK ) ( Ref for genome , or weblink to Washington ) ( see alignment in Figure S1A ) . We performed RNAi of JNK to decipher its function during planarian regeneration ( Figure S1B-S1C ) . After head amputation , trunk fragments were unable to regenerate anterior structures such as the brain and the anterior digestive branch . Similarly , head fragments failed to regenerate medial and posterior structures , including pharynx and tail ( Figure 1A ) . Analysis of the pattern of several differentiated structures , such as brain branches ( gpas+ ) [26] , anterior chemoreceptors ( cintillo+ ) [27] and the visual system ( ovo+ and VC1+ ) [28] , [29] , revealed aberrant regeneration in the anterior blastema of JNK ( RNAi ) planarians ( Figure 1B ) . We next investigated whether this inability to regenerate was associated with a prior defect in polarity determination . In control animals , a few hours after injury , the expression of the polarity genes ( notum , sFRP1 , wnt1 ) is activated in the wound region , and is subsequently polarized and confined to the posterior or anterior midline regions [30]–[32] . The initial induction of these polarity genes is irradiation insensitive and thus stem cell independent , whereas the polarization of their expression domains relies on stem cell proliferation [31] , [32] . Whole-mount in situ hybridization ( WISH ) analysis revealed that the early expression of notum ( 18 hours ) and sFRP1 ( 24 hours ) in anterior wounds of JNK ( RNAi ) animals was indistinguishable from that of control animals ( Figure S2 ) [30]–[32] , as was the early expression ( 24 hours ) of wnt1 in both anterior and posterior wounds [31] ( Figure S2 ) . However , the subsequent polarized and confined expression of these genes was severely attenuated in JNK ( RNAi ) animals ( Figure S2 ) . Fading of the anterior expression of wnt1 , which occurs around 48 h after injury and is known to be stem cell independent [32] , was also observed in JNK ( RNAi ) animals ( Figure S2 ) . Thus , while the initial establishment of polarity by differentiated cells is JNK-independent , JNK is specifically required for the maintenance of polarity gene expression during later , stem cell-dependent stages . Together , these results demonstrate that JNK is required for proper regeneration of missing tissues independently of the initial specification of identity . Early wound-generated signaling has been proposed to mediate the onset of regeneration after injury [15] , [16] , however the underlying molecular mechanisms remain largely unknown . To assess a putative role of JNK signaling in wound healing , we analyzed the dynamics of wound closure and quantified the expression of the early wound-induced genes egrl1 and runt1 [15] , [16] after JNK RNAi . Although normal wound closure was observed in JNK ( RNAi ) animals , the blastema shape and size were aberrant ( Figure S3 ) , and expression of these early wound-induced genes was diminished ( Figure 2-S4 ) , indicating that JNK might be important for proper signal transmission or interpretation at the wound . Neoblast are the only mitotically active somatic cells in the adult planarian [33] . Double fluorescent whole-mount in situ hybridization ( FISH ) analysis of JNK and the neoblast marker histone 2B ( h2b ) [33] showed that JNK is expressed in h2b-negative differentiated tissues , such as the brain , and in h2b-positive neoblasts ( Figure 3A ) . Accordingly , JNK expression vanishes specifically in the subpopulation of proliferative cells after eliminating neoblasts by h2b RNAi [33] ( Figure S5A ) or by irradiation [34] ( Figure S5B ) . Expression analysis of h2b by FISH and qRT-PCR showed that the proportion of neoblasts in JNK ( RNAi ) animals was similar to that of controls ( Figure S5C-S5D ) . In agreement , sorting of the different planarian cell populations by FACS , a method by which planarian cells can be separated based on their DNA content and size [35] , [36] , showed no alterations in the proportion of actively cycling cells ( in the S or G2/M phase of the cell cycle; ×1 sub-population ) after JNK RNAi ( Figure S5E ) . Therefore , although JNK is expressed in neoblasts and essential for regeneration , it is not required for their viability . Amputation in planarians triggers two waves of neoblast proliferation in a temporally coordinated manner [18] , [19] , [37] , [38] . The first proliferative peak can be detected 6 hours after amputation in response to any type of injury , whereas the second peak , which appears 48 hours after amputation , is only observed after injuries that involve tissue loss and subsequent neoblast recruitment to the wound area [19] . We performed a thorough analysis of the mitotic response in the wound region after anterior amputation by quantifying pH3-positive neoblasts . Although JNK ( RNAi ) animals displayed a bimodal mitotic response after wounding , the temporal dynamic of the response was altered ( Figure 3B-S5F ) . During the first mitotic response ( 6 hours after amputation ) , we observed a significant increase in the number of neoblasts entering mitosis as compared with controls ( Figure 3B ) . Furthermore , the second mitotic peak in JNK ( RNAi ) animals was sharper and occurred 37 hours after amputation , 10 hours earlier than in control animals , indicating an earlier onset of mitosis . Finally , we observed an increase in the number of mitotic neoblasts 5 days after amputation in JNK ( RNAi ) animals ( Figure 3B ) . A similar alteration was observed in the wound region of JNK ( RNAi ) animals after posterior amputation ( Figure S5G ) . Quantification of the number of mitotic cells using an alternative method , a modified Gomori technique [18] , revealed an alteration in the mitotic profile of JNK ( RNAi ) animals comparable to that demonstrated by quantifying the number of pH3+ cells ( Figure S5H ) . To further characterize the alteration of the cell cycle dynamics after JNK RNAi , we labeled cells in the S-phase of the cell cycle by injecting planarians with the thymidine analogue chlorodeoxyuridine ( CldU ) before amputation . Detection of CldU labeling in cells expressing the neoblast marker piwi-1 [34] allowed us to specifically quantify the number of neoblasts that went through S-phase but did not start to differentiate at the time of fixation ( 16h post-CldU injection and 6h post-amputation ) . Quantification of CldU/piwi1+ cells , as well as of CldU+ cells , revealed no differences between the control and JNK ( RNAi ) animals ( Figure 3C ) , indicating that JNK is not required for normal progression through S phase . Conversely , quantification of CldU/piwi1+ cells positive for the mitotic marker pH3 ( CldU+/piwi1+/pH3+ ) corroborated the significant increase in mitotic cells in JNK ( RNAi ) animals ( Figure 3C ) . The maintenance of the total CldU+ cells shows that the increase of mitotic cells in JNK ( RNAi ) is not explained by a previous increase in S-phase cells , but by a shorter G2 phase and hence a faster entry into mitosis . FISH and qRT-pCR analysis of post-mitotic neoblast progeny markers - referred to as early ( NB . 32 . 1g ) and late ( Agat-1 ) division progeny genes [39] - revealed that the number of cells expressing these markers and their expression levels were unaltered between control and JNK ( RNAi ) animals in any of the regions analyzed ( Figures S6 ) . This suggests that the acceleration of the cell cycle of JNK ( RNAi ) neoblasts does not alter their capacity to exit the proliferative state and produce progeny . Altogether , these results indicate that , although JNK is expressed in neoblasts , it is not essential for their viability but for the control of their cell cycle length . Hence , in planarians , JNK controls the wound-induced proliferative response attenuating the transition between G2- to M-phase . Cell death is necessary for tissue remodeling during planarian regeneration [17] , [40] . In planarians , amputation triggers two peaks of apoptotic cell death , one 4 hours after injury , which is localized in the wound region , and a second peak 3 days after amputation that spreads throughout the organism [17] . These apoptotic responses are stem cell-independent and thus , occur almost exclusively in post-mitotic cells [17] . Using the TUNEL assay , we analyzed apoptotic cell death after anterior amputation in whole-mount preparations and tissue sections , and found that JNK RNAi prevents the activation of apoptosis both in regenerating and pre-existing regions ( Figure 4A-S7A-S7B ) . Analysis of mitotic rates during the regenerative process in pre-existing regions revealed an altered profile with a general increment in the number of mitotic cells in JNK ( RNAi ) animals with respect to control counterparts ( Figure 4C ) , an effect that was also observed after posterior amputation ( Figure S7C ) . Thus , in pre-existing regions in which remodeling is required , JNK ( RNAi ) planarians exhibited a complete inhibition of the apoptotic response , together with an increase in the rate of proliferation . This inversion of the balance between cell death and cell proliferation in JNK ( RNAi ) animals prevents the restoration of correct body proportion , as evidenced by their inability to re-adjust the position of pre-existing organs such as the pharynx as regeneration proceeds ( Figure 4B ) . The disruption of rescaling is not due to a complete inability to regenerate , as these defects were also observed in the less penetrant JNK ( RNAi ) phenotypes , which can at least partially regenerate anterior structures such as the brain ( Figure 4B ) . The lack of apoptosis in pre-existing regions should generate a larger number of cells than found in controls . To determine the fate of these cells we quantified the number of DAPI-stained epidermal cells . Interestingly , the number of epidermal cells was increased in the posterior epithelium of JNK ( RNAi ) versus control animals ( Figure 4D ) , suggesting that an increased number of cells might contribute to the epidermis , and possible other tissues . These findings indicate that JNK activity is essential to trigger wound-induced apoptotic cell death as well as the second systemic apoptotic response during planarian regeneration . Inhibition of JNK-dependent apoptosis and probably together with the increase in proliferative rates , prevents remodeling of pre-existing regions and causes the higher density of epithelial cells . Importantly , these results demonstrate that in planarians , the induction of neoblast proliferation might be independent of apoptotic cell death . Wound signaling induced by a simple incision without the loss of tissue causes cell responses different from that induced by amputation [19] . Healing of a simple incision is characterized by single apoptotic and mitotic peaks; secondary peaks are only observed in response to injury involving tissue loss [19] ( Figure 5A-5B-S8A ) . Wound-associated mitotic and apoptotic responses after a simple lateral incision did not differ between JNK ( RNAi ) and control animals ( Figure 5A-5B-S8A ) . Moreover , the expression of early response genes , normally triggered by this type of injury [15] , was unchanged in JNK ( RNAi ) animals ( Figure S8B ) . To determine the degree of injury that requires JNK-mediated regeneration we performed lateral amputations , removing small portions of tissue ( notching ) . As seen after complete amputation , we observed two temporally coordinated mitotic peaks in control animals ( Figure 5C-S8C ) . In JNK ( RNAi ) animals , both proliferative peaks were affected in the same manner observed after anterior amputations: the first mitotic peak was augmented and the second occurred 10 hours earlier than in controls ( Figure 5C-S8C ) . Analysis of the apoptotic rates in control animals revealed that lateral notches also induced two apoptotic responses: an early wound-related peak and a second more systemic peak , reflecting the need for remodeling after the loss of tissue . As observed after anterior amputation , JNK was essential to trigger an apoptotic response after notching ( Figure 5D ) and regeneration was impaired in JNK ( RNAi ) animals , as evidenced by the disrupted ventral nerve cord ( Figure S8D ) . Thus , in contrast to the healing process observed after simple incision , regeneration after notching requires JNK for proper cell cycle dynamics and apoptosis . Taken together , these findings demonstrate that JNK is only essential for responses that require the regeneration of missing tissues . Planarians undergo degrowth in the absence of nutrients while maintaining their physiological functions [8] . Degrowth is accompanied by an increase in cell death [17] , [41] and maintenance of the neoblast population and baseline rates of proliferation . During starvation-induced degrowth ( Figure S9A–S9B ) JNK RNAi resulted in the near to complete abolition of apoptotic cell death ( Figure 6A ) . However , baseline mitotic rates and neoblast population levels remained constant after JNK RNAi ( Figure 6B-S9C-S9D ) . Planarians exhibit allometric rescaling during their continuous size changes with positive rescaling of the post-pharyngeal region as compared with the pre-pharyngeal region [42] . We investigated whether the imbalance between apoptosis and proliferation in JNK ( RNAi ) animals undergoing degrowth altered rescaling . To this end , we analyzed proportionality by measuring the length of the posterior portion of the body ( from the pharynx anchoring to the tail tip ) relative to whole-body length ( Figure S9E ) . No decrease in the length of the posterior portion was observed in JNK ( RNAi ) versus control animals as starving proceeded ( 42 days vs . 56 days of starvation ) , indicating that rescaling is impaired when JNK-dependent apoptosis is prevented ( Figure 6C ) . Starvation-induced degrowth leads to a decrease in the number of neoblast progeny cells [41] and a coordinated reduction in the number of cells in pre-existing organs to accommodate to the adjustment in body size . We studied the effects of blocking apoptosis in degrowing JNK ( RNAi ) animals on the number of progeny cells and the morphogenesis of structures such as the epithelium , eyes and brain . The decrease in neoblast progeny observed in JNK ( RNAi ) animals was less pronounced than that seen in starved control animals ( Figure 6D-S9F ) . Quantification of the number of visual system cells ( VC1+ , opsin+ ) [29] , [43] , specific neuronal cell types ( GABAergic , gad+ [44]; octopaminergic , tbh+ [45] and serotoninergic , tph+ [46] ) and epithelial cells , revealed an increase in the density of all differentiated cell types tested in degrowing JNK ( RNAi ) animals ( Figure 6E-6F-6G-S9G ) . Moreover , analysis of the population of cintillo+ cells during starvation revealed a decreasing number of this anterior-specific cell type in degrowing control animals but not in degrowing JNK ( RNAi ) animals ( Figure 6H-S9H ) . Analysis of brain morphology in DAPI-stained tissue sections indicated that the increase in cell density in JNK ( RNAi ) animals was associated with a disruption of brain architecture ( Figure S9I ) . The process of degrowth is reversible; starved planarians return to their original size when feeding is reinstated [8] , [41] . A proliferative peak has been described 1 day after feeding , after which mitotic rates return to baseline levels [38] , [41] . Planarian growth is also accompanied by a decrease in apoptotic cell death to minimum levels [17] and an increase in the number of neoblast progeny cells [41] . During growth promoted by sustained feeding ( Figure S10A–S10B ) , JNK ( RNAi ) animals exhibited no changes in the rate of apoptotic cell death since minimal levels of apoptosis were also observed in growing control animals ( Figure 7A ) . Furthermore , growing JNK ( RNAi ) maintained neoblast population levels and baseline mitotic rates ( Figure 7B-S10C–S10D ) . In agreement with these observations , growing JNK ( RNAi ) planarians underwent normal rescaling ( Figure 7C-S10E ) . Moreover , we observed no differences in the number of neoblast progeny cells ( Figure 7D-S10F ) , density of the differentiated cells analyzed ( Figure 7E-7F-7G-S10G-S10H-S10I ) or in brain structure ( Figure S10J ) between growing JNK ( RNAi ) animals and controls . Taken together , these findings demonstrate that JNK is required to maintain body proportions and to remodel organs by the induction of apoptotic cell death specifically during degrowth when apoptosis is required to allow size reduction .
The coordination of cell proliferation , differentiation , and apoptosis is central to a number of physiological and pathological processes , including tissue homeostasis , development and cancer [47] . Planarians are a promising model organism for the in vivo analysis of the coordination of these processes . The molecular basis for the initiation of neoblast proliferation in response to injury remains largely unknown . It has been proposed that the first mitotic peak would correspond to the shortening of the G2/M transition of neoblasts that were in G2- or S-phase at the time of the injury [18] , [19] . Furthermore , given that the mitotic minimum between the two peaks is not due to the cessation of proliferative input , it has been proposed that the same early wound-induced signal triggers the two temporally distinct mitotic peaks [19] . These findings suggest that the two proliferative waves correspond to different sets of neoblasts: those in the G2- or S-phase that enter mitosis earlier and produce the first mitotic peak , and those still in G0/G1-phase that produce the second one , as they need to migrate and complete the cell cycle to enter mitosis [18] . Neoblast descendants from the first peak may also participate in the second peak . Our CldU labeling results showed that 6 hours after amputation , all pH3 positive cells were also positive for CldU ( pH3+/CldU+ ) , indicating that all the cells entering mitosis 6 hours post-amputation had been in S-phase during CldU labeling . The fact that we were unable to detect neoblasts in the G2-phase prior to amputation ( pH3+/CldU- ) supports the hypothesis that this G2 subpopulation does not significantly contribute to the first proliferative peak [48] . However , two factors must be considered: i ) these neoblasts would be the first ones reaching M-phase earlier since the first mitotic response already starts 3 hours after cutting [19] , [37] , and ii ) these neoblasts might have already started to enter mitosis during the previous stimulus induced by the CldU injection ( 10 hours before amputation ) . The role of JNK as a cell cycle checkpoint has been previously described in human cell cultures . JNK responds to stress stimuli by delaying the G1/S [49] and the G2/M transitions , thus temporally controlling the onset of mitosis [22] , [23] . Interestingly , our double CldU/pH3 labeling results showed an increase in mitotic cells in JNK ( RNAi ) animals while maintaining the number of cycling cells that have gone through S-phase . These results indicate that in planarians JNK attenuates cell cycle progression between G2- to M-phase rather than between G1- to S-phase transition . The sharper and earlier second mitotic peak seen in JNK ( RNAi ) versus control animals further supports compression of the cell cycle progression of neoblasts after JNK inhibition . The increase of mitotic neoblasts in JNK ( RNAi ) animals five days after anterior amputation may indicate that the mitotic response remains active due to failed execution of the regenerative response . A previous study on the role of JNK in the planarian species Dugesia japonica showed that treatment with the JNK inhibitor SP600125 blocks the entry of neoblasts into M-phase , leading the authors to propose a role for JNK in promoting the G2- to M-phase transition of neoblasts [50] . The lack of consistency between those findings and our own data might be due to species-specific differences or off-target effects by SP600125 , which can bind to a broad range of protein kinases [51] , [52] . Our data reveal a molecular mechanism in planarians that controls the onset of neoblast division in response to injury , specifically by the attenuation of the G2- to M transition . Injury triggers a plethora of signals that regulate the onset of regeneration . In planarians , early regeneration involves not only temporally controlled neoblast proliferation [18] , [19] , but also the induction of the expression of a repertoire of early genes [15] , [16] and an apoptotic response [17] , accompanied by tightly coordinated cell differentiation and identity specification ( Figure 8 ) . Our results demonstrate that in contrast to the previously described function of JNK during Drosophila imaginal disc regeneration [53] , [54] , wound closure in planarians is JNK-independent , probably because in planarians this is a predominantly mechanical process that does not require directed cell migration through actin cables and filopodial extensions . However , JNK is required during the first stages of regeneration to properly induce the expression of early wound-induced genes , the initiation of the wound-associated apoptotic peak and the controlled onset of mitosis ( Figure 8 ) . Due to the pleiotropy of JNK , we are not able to discern if JNK independently controls all these processes , or if they are functionally dependent . Both the wound-related expression of Runt1 , which has been directly linked with the specification of neural precursors [16] , and the production of ovo+ eye progenitors [28] were decreased after amputation in JNK ( RNAi ) animals as compared with controls . This decrease points to impaired production of neural progenitor cell types , as also suggested by the impaired regeneration of neural tissues in these animals . Regeneration after notching , a process that does not require anterior or posterior re-specification , was also defective in JNK ( RNAi ) animals . Given that early establishment of polarity was unaffected by JNK RNAi , these findings suggest that JNK is required for tissue regeneration of structures and the production of neural progenitor cells , independently of the initial identity specification . Expression of the post-mitotic neoblast progeny markers NB . 32 . 1g and Agat-1 is associated with the initiation of the differentiation process [39] . The maintenance of their expression in JNK ( RNAi ) planarians suggests that , although the cell cycle is altered , the neoblasts can exit their proliferative cycle and produce the progeny related to these specific markers . Alternatively , a reduction in the number of progeny cells related to defective differentiation in JNK ( RNAi ) animals might be compensated for the lack of death of these progeny cells , making the reduction impossible to detect . However , the nature of the cells expressing these progeny genes remains obscure . It has been shown that these genes do not specify a common state for all post-mitotic neoblasts [55] , and there is no evidence indicating that they mark a specific cell lineage . Whether induction of apoptosis or the control of mitotic onset is the most relevant JNK function during regeneration remains to be elucidated . On the one hand , several recent findings support a link between alterations in cell cycle progression and improper differentiation; shortening of the cell cycle can inhibit neuronal differentiation during Xenopus development [56] and enhance the differentiation of epidermal keratinocytes [57] . In planarians , JNK-mediated arrest of cell cycle progression may be required to provide a time window during which neoblasts embark upon specific programs of differentiation . In that sense , our data support the recently proposed “specialized” neoblast model [19] , [58] , which proposes that a pluripotent neoblast commences determination before becoming post-mitotic progeny , giving rise to specialized progenitors . On the other hand , wound-induced apoptosis has been shown to be essential for tissue regeneration since apoptotic cells release mitogenic factors to stimulate the proliferation of surrounding cells , a process known as compensatory proliferation [47] , [59] , [60] , which has been recently renamed “apoptosis-induced proliferation” [61] . Our results , however , indicate that apoptosis-induced proliferation might not occur during planarian regeneration . While JNK RNAi led to a reduction in apoptosis , a subsequent reduction in proliferation was not observed either in the blastema or during the remodeling of pre-existing tissues . The induction of neoblast proliferation in response to amputation or during tissue turnover thus appears to be independent of factors released by apoptotic cells , if present . Alternatively , given that compensatory proliferation is controlled by JNK during Drosophila wing disc development [62] , it is possible that no direct relationship between apoptosis and proliferation is observed in JNK ( RNAi ) animals because compensatory proliferation may also be JNK-dependent in planarians . Furthermore , it should be noted that JNK is required for the appropriate expression of the early-wound induced genes , probably also essential to launch regeneration [15] , [16] . Finally , our data demonstrate that JNK is required for appropriate regenerative responses elicited exclusively by tissue loss and not after simple wounding . Therefore , in agreement with previous studies demonstrating distinct roles of the two mitotic peaks ( at 6 h and 48 h ) depending on the degree of injury [19] and the requirement of Smed-follistatin specifically for the “missing-tissue response” [63] , our findings distinguish between the response to a small incision and that of an amputation . According to the role of JNK as a stress-activated protein to coordinate a complex response , JNK is exclusively required in large scale tissue recovery events , whereas mild tissue damage can be repaired in a JNK-independent manner . The ability to generate a proportioned organism after amputation depends on a carefully maintained balance between stem cell proliferation and differentiation to create new tissues , and cell death to eliminate unnecessary cells and re-shape organs [47] . Planarians respond to amputation by increasing neoblast proliferation and differentiation in regions close to the wound in order to regenerate the missing structures . In parallel , pre-existing regions decrease in size in order to adjust their proportions relative to the new whole-body size , mainly by augmenting cell death while maintaining baseline proliferative rates [17] , [41] , [64]–[66] ( Figure 8 ) . Similarly , proliferative rates are maintained during homeostatic changes in body size , whereas the control of cell death is the decisive shift . Our findings demonstrate that JNK is essential to trigger apoptotic cell death in planarians . In agreement with the essential role of apoptosis during planarian remodeling [17] , [40] , animals in which JNK-mediated apoptotic cell death was prevented were unable to relocate pre-existing structures and hence to restore body proportion not only after amputation , but also during starvation-induced degrowth ( Figure 8 ) . The pro-apoptotic role of JNK in vertebrates , in which JNK acts as a tumor suppressor gene , has been extensively documented [20] . Our results show that after JNK RNAi , the positive balance between cell death and cell proliferation required to decrease body size is reversed ( Figure 8 ) . However , we observed no overgrowths in JNK ( RNAi ) animals . A tempting explanation is that the lack of apoptosis results in an ectopic accumulation of certain cell types , as reflected by the increased density of differentiated cells , such as neural and epithelial cells , observed in degrowing JNK ( RNAi ) planarians . JNK RNAi during diet-induced changes in body size resulted in impaired rescaling only in planarians that were starved , and hence decreased in size . By contrast , animals that were growing while actively feeding exhibited normal body proportions . While downregulation of apoptosis is essential during active feeding so that planarians can grow , high rates of apoptosis are required during starvation . Our results show that JNK-dependent apoptosis is only required during degrowth , while proliferation is maintained during both conditions . These findings demonstrate that defective rescaling and the accumulation of neoblast progeny and differentiated cells in degrowing JNK ( RNAi ) planarians are a consequence of an inhibited apoptotic response . Remarkably , the number of cintillo+ cells was maintained during starvation in JNK ( RNAi ) animals , indicating that differentiated cells accumulated due to the lack of cell death , and not as a result of ectopic cell differentiation . Finally , our data indicate that apoptotic cell death is required during homeostatic tissue turnover not only to re-scale but also to re-shape organs , as the ectopic accumulation of differentiated cells in JNK ( RNAi ) animals might be associated with ultrastructural changes in the brain . That the maintenance of mitotic rates during gradual degrowth is independent of JNK supports the role of JNK as a molecular modulator of cell cycle progression specifically during regeneration , a process that requires the coordination of massive neoblast proliferation and differentiation . Similar to planarians , Drosophila imaginal discs undergo dynamic regulation of cell death and proliferation , as growth depletion in one compartment of the Drosophila wing disc results in increased apoptotic cell death and decreased proliferation in the adjacent compartment , reducing its size in order to maintain a properly proportioned organ [67] . It has been proposed that the combined autonomous and non-autonomous activity of p53 is fundamental in this process , as it exerts distinct effects in damaged and distant tissues . The generation of differential outputs from a single stress response element , based on proximity to the injury , may be a general mechanism to coordinate basic cellular responses in order to maintain proportionality during regeneration .
Planarians used in these experiments were from a clonal strain of the S . Mediterranea BCN-10 biotype and were maintained as previously described [68] . Planarians were starved for 1 week and were 4 to 6 mm in length when used for experiments . Smed-JNK fragments were identified from the S . mediterranea genomic contigs ( Washington University , St . Louis , USA ) . The following pairs of specific primers were used to clone a fragment of Smed-JNK: 5′-:GCTATTGGTTCCGGTGCACAAG-3′; 5′-:CGACCGAGATTCGTTGAAGTGG-3′ The corresponding full-length transcripts were amplified by rapid amplification of cDNA ends ( RACE ) using the Invitrogen GeneRacer Kit ( Invitrogen ) . Double-stranded RNAs ( dsRNAs ) were synthesized by in vitro transcription ( Roche ) as previously described [43] , and dsRNA microinjections performed as previously described [43] , following the standard protocol of a 3×32-nl injection of dsRNA for three consecutive days before amputation ( one round of injections ) . To obtain reliable gene interference , we performed three consecutive rounds of RNAi injections; an anterior amputation was performed after the first and third rounds . Control animals were injected with water or a double-stranded ( ds ) RNA of the green fluorescent protein ( GFP ) sequence . The same RNAi experimental design was used in all experiments but different types of injuries were induced ( incision , notching or amputation ) , as indicated in the text . For degrowth and growth experiments , animals were starved and injected with RNAi for three weeks . Subsequently , degrowing animals were maintained under starvation conditions while growing animals were fed every second day . The following pairs of specific primers were used to generate the dsRNA target gene: dsRNA against Smed-JNK , 5′-:GCTATTGGTTCCGGTGCACAAG-3′; 5′-: GGACGTCCTTTCGTGATCTAAGTCC-3′ Total RNA was extracted from a pool of five trunk fragments , six wound region fragments and six post-pharyngeal ( pre-existing ) region fragments of RNAi-treated planarians using TRIzol reagent ( Invitrogen ) . RNA samples were treated with DNase I ( Roche ) and cDNA was synthesized using a First-Strand Synthesis System kit ( Invitrogen ) . Real-time PCR was performed using SYBR Green ( Applied Biosystems ) in an ABI Prism 7900HT Sequence Detection System ( Applied Biosystems ) . Three samples were run in parallel for each condition . Data were normalized to the expression of the internal control ( UDP ) . Similar results were obtained using elongation factor 2 ( EF-2 ) as an alternative internal control . The following sets of specific primers were used: Smed-JNK mRNA , 5′-TCAACGAATCTCGGTCG-3′ , 5′-AGTGAGCTCTCTTTCATCAACC-3′; Smed-h2b mRNA , 5′-GAGAAAGTTGAACGGCCC-3′ , 5′-AAGATAATACGTACTTCAACGACG-3′; Smed-Agat-1 mRNA , 5′-GCCCAGAAAGACCATGC-3′ , 5′-GAGACAACCATTGAGAGCTG-3′; NB32 . 1g mRNA , 5′-CATCGCGCAACTTTTG-3′ , 5′-GTTTACGGAGAATGCCG-3′; Smed-UDP mRNA was detected using primers previously described in [69]: Smed-EF-2 , 5′-CGAGCCGGAAGATTTGTAT-3′ , 5′-TGGAGTCACTTGAATATCTCC-3′ . Intact planarians were X-irradiated at 96 Gy and fixed for in situ hybridization 1 day after irradiation . RNA probes were in vitro synthesized using Sp6 or T7 polymerase ( Roche ) and DIG- , FITC- ( Roche ) or DNP- ( Perkin Elmer ) modified ribonucleotides . RNA probes were purified by ethanol precipitation and the addition of 7 . 5 M ammonium acetate . For ISH , animals were fixed and then processed using an In situ Pro hybridization robot ( Abimed/Intavis ) , as previously described [70] . Hybridizations were carried out for 16 h at 56 C . Samples were observed using a Leica MZ16F microscope and images were captured with a Leica DFC300FX camera . For FISH , animals were fixed and processed as previously described [71] . Confocal laser scanning microscopy was performed using a Leica TCS 4D ( Leica Lasertechnik , Heidelberg ) adapted for an inverted microscope ( Leitz DMIRB ) . Images were processed using Fiji software [72] . The numbers of NB32 . 1g+ and Agat-1+ cells were quantified using the “Find maxima” plug-in in Fiji , maintaining a fixed noise tolerance of 100 and correcting by hand . opsin+ , gad+ , tbh+ , tph+ and cintillo+ cells were quantified by hand using the “multi-point selection“ tool in Fiji . egrl-1 and runt-1 images were captured with identical laser settings: 18 stacks were used to build the z-projection and the intensity of equivalent areas was quantified using the “Measure” plug-in in Fiji . Immunostaining was carried out as described previously [43] . The following antibodies were used: anti-synapsin ( anti-SYNORF1 , 1∶50; Developmental Studies Hybridoma Bank ) , anti-Smed-β-catenin2 ( 1∶1000; Chai et al . , 2010 ) and anti-phospho-histone H3 ( Ser10 ) ( D2C8 ) ( pH3 ) ( 1∶500; Cell Signaling Technology ) . Images were scanned , processed and quantified as described for FISH images . To avoid technical variance and obtain a reliable quantification of pH3+ cells , at least two independent experiments of RNAi and pH3 immunostaining were carried out for anterior amputation , incision , degrowth and growth experimental designs . Animals were fixed and stained for TUNEL as previously described [17] using the ApopTag Red In Situ Apoptosis Detection Kit ( CHEMICON , S7165 ) , with some modifications to increase permeability: between the fixation step with 4% formaldehyde in PBST , samples were incubated with ProteinaseK ( 20 µg/mL ) in PBSTx ( PBS with 0 . 3% Triton X-100 ) for 10 minutes at 37°C in a water bath while agitating by hand , and an additional reduction step was added after fixation [71] . Finally , samples were incubated overnight in terminal transferase enzyme at 37°C , and again overnight at 4°C with anti-dioxigenin-rhodamine . Images were scanned , processed and quantified as described for FISH images . To avoid technical variance and obtain a reliable quantification of TUNEL+ cells , at least two independent experiments of TUNEL staining were carried out for anterior amputation , notching , incision , degrowth and growth experimental designs . Animals were sacrificed in 10% n-acetyl cysteine in PBS , docked for 8 minutes at RT to remove mucous and fixed in 4% paraformaldehyde in PBS for 4 hours at 4°C . Paraffin embedding , sectioning and de-paraffinization were carried out as previously described [73] . Staining was performed according to the manufacturer's recommendations with the following modifications: sections were treated with ProteinaseK for 30 minutes and with TdT for 2 hours and were incubated in the anti-digoxigenin conjugate overnight at 4°C in a humidified chamber . Fixation and partial maceration of the animals , staining of nuclei and calculation of the mitotic index were carried out following a modified Gomori technique as previously described [18] . The dissociation of planarians , cellular labeling , and isolation of cells by FACS were performed as described previously [36] . After RNAi injection protocol , control and JNK ( RNAi ) animals were injected a single time 10 hours prior amputation as previously described in [74] . 6 hours after amputation animals were fixed as previously described in [74] for FISH with Smedwi-1 DIG probe . After FISH staining , animals were processed for anti-pH3 immunostaining as described above . Animals were then processed for CldU staining as previously described in [74] . Blocking and incubation with the CldU antibody were performed in 1%BSA/10%NGS in PBSTx .
|
Planarians , thanks to their extraordinary regenerative capacity , represent a unique model of animal regeneration . After amputation , new animals regenerate from each individual piece of tissue , leading Dalyell to describe them as “immortal under the edge of the knife” in 1814 . Planarians also continuously renew their tissues and adapt their size in accordance with nutritional supply . This amazing plasticity relies on the presence of a population of adult pluripotent stem cell , the neoblasts . However , little is known about the mechanisms that trigger cell responses , such as cell death and division , which are required to regenerate and maintain tissues and organs in response to injury or nutritional challenge . Here , we show that JNK acts as a hub in the coordination of these events . Specifically in response to tissue loss , JNK modulates the expression of wound-related genes , induces the elimination of unnecessary cells by apoptotic cell death and controls cell division in neoblasts . Loss of JNK function results in the deregulation of these processes and prevents regeneration . Moreover , we demonstrate that JNK-dependent apoptosis is crucial to generate proportioned organisms during tissue turnover . Our findings reveal a central mechanism in planarians that senses tissue loss and translates this information into cellular responses leading to regeneration and tissue renewal .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"gene",
"function",
"developmental",
"biology",
"growth",
"control",
"cell",
"fate",
"determination",
"organism",
"development",
"stem",
"cells",
"animal",
"cells",
"cell",
"biology",
"regeneration",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"morphogenesis",
"cell",
"differentiation",
"adult",
"stem",
"cells"
] |
2014
|
JNK Controls the Onset of Mitosis in Planarian Stem Cells and Triggers Apoptotic Cell Death Required for Regeneration and Remodeling
|
Insulin and its receptor are critical for the regulation of metabolic functions , but the mechanisms underlying insulin receptor ( IR ) trafficking to the plasma membrane are not well understood . Here , we show that Bardet Biedl Syndrome ( BBS ) proteins are necessary for IR localization to the cell surface . We demonstrate that the IR interacts physically with BBS proteins , and reducing the expression of BBS proteins perturbs IR expression in the cell surface . We show the consequence of disrupting BBS proteins for whole body insulin action and glucose metabolism using mice lacking different BBS genes . These findings demonstrate the importance of BBS proteins in underlying IR cell surface expression . Our data identify defects in trafficking and localization of the IR as a novel mechanism accounting for the insulin resistance commonly associated with human BBS . This is supported by the reduced surface expression of the IR in fibroblasts derived from patients bearing the M390R mutation in the BBS1 gene .
Insulin is critically involved in the regulation of glucose and lipid metabolism in various tissues ensuring the coordinated uptake and storage of the products of digestion . Insulin binding to its receptor initiates a myriad of intracellular events resulting in downstream activation of glucose uptake as well as glycogen , fatty acid , and DNA synthesis [1] . The insulin receptor ( IR ) is a dynamic molecule that moves through multiple cellular compartments throughout its lifecycle [2 , 3] . In its mature form , the IR resides in the plasma membrane as tetrameric proteins consisting of two extracellular , α-subunits , and two transmembrane , β-subunits . However , the molecular mechanisms underlying the trafficking of the IR to the plasma membrane remains largely unknown . Bardet Biedl Syndrome ( BBS ) is a highly pleiotropic autosomal recessive disorder associated with clinical features that are considered the cardinal manifestations: obesity , retinal degeneration leading to blindness , postaxial polydactyly , learning disabilities and defects in the urogenital tract [4 , 5] . BBS is also associated with increased susceptibility to other disorders including insulin resistance and type 2 diabetes . Indeed , diabetes mellitus and the associated impairments in glucose metabolism and insulin sensitivity are common among BBS patients and often manifest during childhood [4 , 6–8 , 9] . Notably , even when matched for pubertal stage and body composition , individuals with BBS were found to exhibit significantly elevated insulin levels than controls [9 , 10] . Important mechanistic advances have been made in recent years in understanding the function of BBS proteins . Eight proteins [BBS1 , BBS2 , BBS4 , BBS5 , BBS7 , BBS8 , BBS9 and BBS18 ( also known as BBIP10 ) ] were found to form a stable complex , the BBSome [11] . Three other BBS proteins ( BBS6 , BBS10 , BBS12 ) form another complex with CCT/TRiC family of group II chaperonins and mediate BBSome assembly [12 , 13] . Two additional BBS proteins , BBS3 ( ARL6 ) and BBS17 ( LZTFL1 ) interact with the BBSome and regulate its trafficking . In addition to the well-established role of BBS proteins in ciliary function , these proteins have been implicated in a number of cellular processes including intracellular trafficking , cell signaling and receptor trafficking [11 , 14–19] . In this study , we show that BBS proteins are necessary for the sorting of the IR and its cell surface expression . We found that the IR directly interacts with BBS17 and is present in a protein complex with the BBSome proteins . We further report that BBS proteins are required to maintain adequate levels of IR at the cell membrane and loss of BBS proteins leads to a reduction in the amount of IR at the cell surface . As a consequence , mice that lacks BBS proteins exhibit hyperglycemia , insulin resistance and blunted insulin-induced activation of IR signaling in insulin sensitive tissues ( liver , skeletal muscle and adipose tissue ) . Notably , insulin resistance in BBS mice appears intrinsic and independent from obesity . These data identify BBS proteins as critical regulators of glucose metabolism and insulin sensitivity through the control of IR trafficking to the cell membrane . These findings also point to defects in IR trafficking as a mechanism of insulin resistance associated BBS .
To investigate the possibility that BBS proteins are involved in IR trafficking , we first examined whether BBS proteins interact with the IR in a sucrose gradient fractionation experiment . Interestingly , a portion of the β subunit of the IR was identified in the same pool of proteins that contained the BBSome complex ( Fig 1A ) . This fraction also contained other known partners of the BBSome such as Ptc1 and Smo proteins [20] . Next , we performed a pairwise reciprocal immunoprecipitation assay to investigate whether BBS proteins interact directly with the IR . Among all the BBS proteins tested , a physical interaction between the IR and BBS17 was identified based on the ability of the endogenous IR and GFP- or Flag-BBS17 to pull down each other in cells ( Figs 1B and S1A ) . Similar results were obtained with the endogenous proteins ( IR and BBS17 ) using mouse tissue ( S1B Fig ) . Of note , shRNA-mediated reduction in the expression of BBS1 ( S1C Fig ) , a key component of the BBSome , disrupted the interaction between BBS17 and the IR ( Fig 1C ) . Together , these data indicate a direct interaction between BBS proteins and IR . To test directly the requirement of BBS proteins for IR localization to the plasma membrane , we examined the effect of knocking-down expression of BBSome subunits on IR surface levels . In cells , shRNA-mediated silencing of either Bbs1 or Bbs2 genes ( S1C and S1D Fig ) significantly reduced the amount of IR present in the plasma membrane ( Fig 2A ) . In contrast , there was no change in total IR protein expression . Next , we used mouse embryonic fibroblast ( MEF ) cells from mice bearing the M390R mutation in the Bbs1 gene . This missense mutation , changing a methionine to an argine at position 390 of the BBS1 protein , accounts for about 80% of BBS1 cases while BBS1 mutations account for about 25% of all BBS cases [21] . We previously reported that Bbs1M390R/M390R mouse model phenocopy BBS [22] . Relative to littermate controls , Bbs1M390R/M390R MEF cells exhibited a significant decrease in the membrane fraction of the IR without significant change in total IR protein ( Fig 2B ) . To assess whether this finding results from a specific defect in IR transport , we evaluated the levels of an unrelated membrane receptor , transferrin receptor . There was no significant difference in protein levels of transferrin receptor in the membrane fraction or total protein after Bbs1 or Bbs2 knock-down or in the Bbs1M390R/M390R MEF cells compared to control cells ( S2 Fig ) . The reduced amount of IR on the cell surface of Bbs1M390R/M390R MEF cells was further confirmed by immunohistochemistry . Using an antibody that recognizes an extracellular epitope of the α subunit of IR , we stained non-permeabilized MEF cells for IR on the surface of the cell . Notably , the intensity of the surface IR signal was significantly less in Bbs1M390R/M390R MEF cells as compared to control MEF cells ( Fig 2C and 2D ) . There was also a difference in the pattern of staining . Wild type MEF cells exhibit typical plasma membrane staining , while Bbs1M390R/M390R MEF cells have a more punctate pattern indicating clustering of the few IRs found on the surface . Similarly , the surface level of the IR was reduced in cells in which expression of BBS chaperonin complex proteins ( BBS6 and BBS12 ) were independently silenced using GFP-tagged shRNA ( S3 Fig ) . Importantly , fibroblast cells obtained from BBS1M390R/M390R patients also exhibited reduced surface expression of the IR ( Fig 2E and 2F ) . To test whether the altered trafficking of the IR can be rescued in BBS1M390R/M390R cells with a functional BBS1 protein we used an adeno-associated virus ( AAV ) vector expressing the wild type BBS1 protein reported previously [23] . The surface expression of the IR was recovered in the BBS1M390R/M390R cells treated with the AAV-Bbs1 ( Fig 2G ) demonstrating that the altered trafficking of the IR in these cells is due to loss of function of the BBS1 protein . BBS proteins and complexes have been implicated in the trafficking of multiple cellular receptors , such as somatostatin receptor 3 , dopamine receptor 1 , vasopressin receptor 2 , and neuropeptide Y family receptors , to cilia [16 , 17 , 19 , 24] . Thus , we tested the possibility that the IR may be trafficked to cilia . However , there was no overlap of the fluorescent labeling of the IR and cilia indicating that the IR is not targeted to the cilia ( S4 Fig ) . Nonetheless , we cannot fully exclude the possibility that the IR in the cilia is below the level of detection or is targeted to the cilia of other cell types . Our finding is consistent with the concept that the IR relies on the BBSome for targeting to the plasma membrane , rather than to cilia . Given the striking decrease in IR membrane levels caused by disruption of BBS proteins , we examined insulin sensitivity and glucose homeostasis in BBS mice . Consistent with a previous report [25] , we found that Bbs4-/- mice are hyperglycemic ( Fig 3A ) and hyperinsulinemic ( Fig 3B ) . Bbs2-/- and Bbs6-/- mice also displayed significantly elevated blood glucose and plasma insulin as compared to wild type littermates ( Fig 3A and 3B ) . Further analysis using glucose and insulin tolerance tests showed that Bbs2-/- , Bbs4-/- , and Bbs6-/- mice are glucose intolerant ( Fig 3C and 3D ) and insulin resistant ( Fig 3E and 3F ) . Next , we assessed whether the defects in glucose disposal and insulin action in BBS mice are associated with altered IR signaling ( using Akt activation as a read out ) in liver , skeletal muscle , and adipose tissue , the classic targets of insulin action [1] . In wild type mice , insulin treatment significantly increased the levels of phosphorylated Akt ( pAkt ) in all three tissues ( Fig 4A–4C ) . In contrast , insulin did not significantly increase pAkt levels in liver , skeletal muscle and adipose tissue from Bbs4-/- mice . Importantly , our data demonstrate that BBS mice recapitulate the defects in glucose metabolism and insulin sensitivity reported in BBS patients . To examine whether the metabolic defects in BBS mice are secondary to obesity , we studied lean BBS mice that had their body weights normalized by calorie restriction ( S5 Fig ) . Strikingly , the fasting insulin levels of calorie restricted Bbs2-/- , Bbs4-/- , and Bbs6-/- mice are significantly elevated ( Fig 5A ) indicating that the hyperinsulinemia associated with BBS is independent of obesity . Furthermore , insulin-induced Akt activation in the skeletal muscle , liver and adipose tissue was blunted in the calorie restricted Bbs4-/- mice relative to controls ( Fig 5B–5D ) . Thus , the defect in insulin receptor signaling in Bbs4-/- mice is not related to obesity , but rather due to loss of BBS4 protein . These findings are consistent with the demonstration that surgery-mediated weight loss failed to suppress the hyperinsulinemia or manage glycemia in BBS patients [26 , 27] . It is also interesting to note that even when matched for pubertal stage and body composition , individuals with BBS exhibit elevated plasma insulin levels relative to the control subjects , which is consistent with the notion that insulin resistance associated with BBS is a primary defect [9] . This notion is further supported by the inability of insulin to activate Akt in Bbs1M390R/M390R MEF cells ( Fig 6A ) . The Bbs1M390R/M390R MEF cells indicate the innate cellular response to insulin because they have never been exposed to either obesity or calorie-restriction . In addition , Bbs1M390R/M390R MEF cells provide further evidence from a model BBS that mimics human disease . Of note , BBS1M390R/M390R fibroblasts also exhibited blunted Akt activation in response to insulin ( Fig 6B ) . These findings are in line with the reduced membrane fraction of the IR described above in these same cells .
The current study establishes BBSome proteins ( BBS1 , BBS2 and BBS4 ) and by implication the BBSome as key mediators of IR trafficking to the cell membrane . Additionally , BBS6 and BBS12 which are not part of the BBSome but are needed for BBSome formation are implicated as well which is consistent with the role of these proteins in the BBSome assembly . Interestingly , the role of BBS proteins in IR trafficking appears not to involve localization to cilia , indicating a more generalized role in intracellular trafficking as has been shown for retrograde trafficking of the melanosome transport in bbs knockdown zebrafish models [18] . Our study also indicates that defects in IR trafficking and localization are major mechanisms of insulin resistance in BBS . Strikingly , very little is known about the molecular mechanisms underlying anterograde IR trafficking . It has been known that newly synthetized IR moves through multiple subcellular compartments of the cell before its insertion in the plasma membrane [2 , 3] . During this process the receptor undergoes various modifications including glycosylation which are important for its trafficking [28 , 29] . Here , we demonstrated that disruption of Bbs genes interfere with IR localization to the cell membrane . The precise steps and events by which the BBS proteins influence IR trafficking are not clear and will necessitate further studies . For example , a limitation of the current study is that it is unknown whether our finding of decreased IR in the plasma membrane is due to decreased trafficking of newly synthesized IR to the membrane as opposed to increased rate of loss of plasma membrane bound IR . Related to this and given that disruption of Bbs genes did not alter the overall expression levels of the IR it will be interesting to determine the cellular compartment where the receptor resides when the BBSome is impaired . Proteins other than BBS have been shown to influence the transport of the IR to the surface . For example , a previous study [30] demonstrated the importance of myotonic dystrophy protein kinase for IR targeting to the plasma membrane in skeletal muscle cells which may explain the high prevalence of insulin resistance in patients with myotonic dystrophy [31] . The relationship between BBS proteins and myotonic dystrophy protein kinase in IR trafficking remain to be determined . We considered the possibility that BBSome impairment interfere with overall transport of membrane receptors in a manner similar to what occur in cells infected with prions proteins [32] . However , the ability of the transferrin receptor to maintain its presence at the cell surface in the absence of BBS proteins indicates that overall transport of membrane receptors is not disrupted and that BBS proteins mediate the routing to the plasma membrane of specific receptors such as the IR . To clarify the relevance of BBS proteins to whole body glucose metabolism and insulin action , we studied several mouse models that lack various BBS genes . Similar to the phenotype reported in patients , BBS mouse models displayed type 2 diabetes phenotype as evidenced by the hyperglycemia and hyperinsulinemia . Furthermore , these mouse models exhibited glucose intolerance and insulin resistance . The defective IR signaling as indicated by the blunted insulin-induced Akt activation further corroborates the insulin resistance phenotype . Cells obtained from BBS patients also exhibited blunted Akt stimulation in response to insulin and have reduced IR surface expression . Together , these findings establish the significance of BBS proteins for insulin sensitivity and glucose homeostasis . These data also indicate that disruption of IR trafficking underlie insulin resistance in BBS . The recent demonstration by Lim et al . [33] that polymorphism in BBS genes such as BBS10 increase the risk of type 2 diabetes in a recessive state raise the possibility that BBS genes may contribute to the pathogenesis of common forms of type 2 diabetes perhaps through their role in IR handling . It should be noted that in contrast to our findings , a previous report by Marion et al . showed a paradoxical improvement in insulin sensitivity in Bbs12-/- mice [34] . Indeed , Bbs12 null mice were found to have enhanced adipogenesis , glucose tolerance and insulin sensitivity of adipose tissue whereas here we show that BBS12 is required for IR trafficking to the cell membrane . There is no clear explanation for the contrast between our findings and this previous report , but there are many factors that may have contributed including the heterogeneity of BBS and associated phenotypes , age and genetic background of the mice . Alternatively , BBS12 may have a function in adipocyte differentiation besides its typical role as part of the BBS chaperonin complex and mediating BBSome formation . In addition to their involvement in the BBS complexes individual BBS proteins could be engaged in other cellular tasks requiring further investigation . This possibility is supported by several evidences including the synergistic effects of suppressing bbs genes in zebrafish [35] , and the variability of the phenotypes of BBS mice [36] and patients carrying mutations in BBS proteins that belong either to the same complex ( e . g . chaperonin complex ) or related complexes ( e . g . chaperonin complex vs BBSome ) [37] . In conclusion , the current study indicates that BBS proteins regulate glucose metabolism and insulin sensitivity through its involvement in the trafficking of the IR to the cell membrane . These findings also indicate that insulin resistance associated BBS arise from defective localization of the IR .
Bbs1M390R/M390R , Bbs2-/- , Bbs4-/- , and Bbs6-/- mice were bred on mixed 129SvEv and C57B/6J backgrounds . As there was no evidence of sexual dimorphism , both male and female mice were used for each experiment . Wild type littermates were used as controls . Mice were housed at the University of Iowa Animal Care facility in a temperature and humidity controlled room on a 12 hour light/dark cycle with free access to food and water except in the calorie restriction experiment where food intake of BBS mice was restricted . At 6–8 weeks of age , which is before the onset of obesity , individually housed BBS mice were given 75–80% of the daily food intake of their littermate’s intake for 8 weeks as we reported previously [38 , 39] . All animal testing was performed based on guidelines set forth by the National Institutes of Health and approved by The University of Iowa Institutional Animal Care and Use Committee ( Protocols 1211242 and 1301003 ) . Euthanasia was performed with an overdose of anesthesia ( Ketamine/xylazine cocktail ) followed by the combination of thoracotomy and harvesting of vital organs ( heart , liver and/or brain ) . MEF were established from wild type and Bbs1M390R/M390R E13 . 5 embryos . Viscera , liver and heart were discarded from the embryos , and the remaining embryo was cut into fine pieces in the presence of Trypsin-EDTA ( Invitrogen ) . Further Trypsin-EDTA was added and the digested tissue was incubated in a 15 ml tube at 37°C , 95% humidity and 5% CO2 for 15 min . The digested tissue was mixed with Dulbecco’s Modified Eagle’s Medium ( DMEM , high glucose without sodium pyruvate ) , 10% heat-inactivated fetal bovine serum ( FBS ) , non-essential amino acids , and 50 U/ml penicillin/streptomycin ( Gibco ) and passed through a 10 ml pipette 5 times . MEFs were cultured in a 100 mm culture dish for experiments . All patients provided written , informed consent for this study , which was approved by the Institutional Review Board of the University of Iowa . Skin biopsies were collected and used for the generation of fibroblasts which were grown in DMEM with 10% FBS , and 1% sodium pyruvate at 37°C with 5% CO2 as described previously [40 , 41] . HEK293T cells were cultured in regular growth medium; DMEM supplemented with 5% ( v/v ) fetal bovine serum , and 1% ( v/v ) sodium pyruvate , at 37°C with 5% CO2 . Two μg of plasmid DNA encoding a Flag-tagged BBS17 protein [42] or shRNA against Bbs1 , Bbs2 , or control [38] on an LKO1 plasmid or GFP expressing Bbs6 and Bbs12-shRNA on an GIPZ plasmid was transfected into 70–80% confluent HEK293T cells in 60 mm dish with FuGENE 9 ( Roche ) according to manufacturer’s instructions . After 48 h incubation with the DNA/FuGENE mixture , cells were used for the surface proteins assays as described below or co-immunoprecipitation using endogenous IR or Flag-BBS17 for pull down or immunohistochemistry for surface IR alpha . Fibroblasts derived from control and BBS1 patients were infected with 1x105 PFU of AAV expressing green fluorescent protein ( GFP , used as control ) or Bbs1 for 48 h before processed for surface IRα immunostaining . Biotin labeling was performed as previously described [43] . Cells were placed on ice and washed with phosphate buffered saline ( PBS ) . EZ-link Sulfo-NHS-SS-Biotin ( Thermo Scientific ) was used to prepare biotinylation solution immediately before incubating cells for 30 min at 4°C . Cells were washed three times on ice and immediately lysed in lysis buffer ( 50 mM HEPES pH7 . 5 , 137 mM NaCl , 1% NP-40 , 0 . 25% Na-deoxycholate , 2 mM EDTA , 2 mM Na3VO4 , 10 mM NaF , 10% glycerol , protease inhibitor cocktail [Roche complete Mini , EDTA-Free] ) for 30 min at 4°C . The cell lysate was centrifuged at 14 , 000 g for 30 min at 4°C . Whole cell lysates were used for detection by immunoblot and for immunoprecipitation with Pierce Streptavidin UltraLink Resin ( Thermo Scientific ) overnight at 4°C to form Avidin-Biotin complex ( ABC ) . The ABC resin was washed in PBS three times , and precipitated proteins were analyzed by immunoblotting . Sucrose gradient assay was performed as previously described [13] . Briefly , protein lysates were centrifuged at 20 , 000 × g for 20 min . The supernatants were loaded onto a 20–60% sucrose gradient . The gradient was centrifuged at 100 , 000 × g for 14 h using a TH-660 rotor Thermo Scientific ( Asheville , NC ) . Two hundred-microliter fractions were taken from the top and precipitated by cold acetone . Precipitated samples were spun at 20 , 000 × g for 15 min . The pellets were dissolved in SDS-PAGE sample buffer and used for Western blotting . Mice were fasted overnight in a clean cage . On the morning of the test , mice were weighed and anesthetized with xylazine-ketamine injections . Once the mice were anesthetized , the jugular vein was cannulated for either vehicle ( physiological saline ) or insulin ( 5 U/kg ) injection . Mice were sacrificed after 15 min and liver , white adipose tissue pad and soleus muscle collected . Tissues were homogenized in lysis buffer ( 50 mM HEPES pH7 . 5 , 137 mM NaCl , 1% NP-40 , 0 . 25% Na-deoxycholate , 2 mM EDTA , 2 mM Na3VO4 , 10 mM NaF , 10% glycerol , protease inhibitor cocktail [Roche complete Mini , EDTA-Free] ) . The extracts were centrifuged at 14 , 000 g for 30 min at 4°C . Protein concentration of the obtained supernatant was measured using the Bradford protein assay [44] . The level of phospho-Akt and total Akt was determined by Western blotting . Proteins in whole tissue lysate were resolved by 9% acrylamide SDS-PAGE , and the proteins were then transferred to PVDF membranes . Membranes were blocked in 5% nonfat dry milk in Tris-buffered saline ( NaCl , KCl , Tris-base ) with 0 . 1% Tween-20 ( TBST ) for 1 h at 25°C then incubated with primary antibodies at 4°C overnight . Membranes were further incubated with secondary antibodies conjugated with horseradish peroxidase ( HRP ) for 2 h at 25°C . Visualization was performed with enhanced chemiluminescence ( ECL ) followed by autoradiography . Cells were seeded on cover slips . To induce cilia formation some cells were serum-starved for 48 h . Cells were then washed with PBS and fixed with 4% PFA for 15 min at room temperature . After washing fixed cells three times with PBS , cells were blocked in blocking solution ( 10% goat serum , 5% milk , 0 . 1% Triton X-100 in PBS ) for 1 hour at room temperature . Next , cells were incubated with primary antibodies against the IRα ( 1:250 , Santa Cruz ) or IRβ ( 1:250 , Santa Cruz ) with or without the anti-Ac-α Tubulin antibody ( 1:250 , Santa Cruz ) for 2 h at room temperature followed by 3 washes in PBS , for 5 min each . Cells were then incubated with secondary immunofluorescent antibodies , goat-anti-rabbit Alexa488 and goat-anti-mouse Alexa568 ( Invitrogen ) for 1 h at room temperature followed by washing as above . Finally cover slips were mounted using VectaShield mounting medium with DAPI . Images were visualized using confocal microscopy ( Zeiss 710 ) and analyzed using ImageJ software . Fasting blood glucose measurements were taken using a glucometer ( OneTouch Ultra ) with a blood sample taken from a tail snip . Insulin levels were measured using an ELISA kit ( Crystal Chem ) . The mice were fasted overnight followed by cardiac puncture for blood collection . Plasma was isolated using 0 . 5 M EDTA and centrifugation to separate plasma from red blood cells . Insulin concentration in the plasma was measured by radioimmunuassay using a commercially available kit ( Crystal Chem Inc . ) . Glucose tolerance test ( GTT ) and insulin tolerance test ( ITT ) were performed as previously described [45] . Four to five month old mice were fasted overnight ( GTT ) or for 5 h ( ITT ) in a clean cage . Body weight and basal blood glucose measurements were taken before intraperitoneal injection of glucose ( 2 g/kg ) or insulin ( 1 U/kg ) . Blood glucose measurements were assessed at multiple time points during the two-hour test . All data are expressed as means ± SEM . A two-way ANOVA was used to compare BBS mice to wild type ( WT ) littermates for GTT and ITT analyses and insulin signaling assay . The first factor was genotype , and the second factor was time point for GTT and ITT analyses , while the second factor was treatment for insulin signaling . Multiple-comparison testing following two-way ANOVA was performed using a Bonferroni t-test . One-way ANOVA was used to compare more than two groups , such as baseline blood glucose and plasma insulin . Rank-ANOVA was used whenever the data did not follow a normal distribution . A two-tailed p-value < 0 . 05 was considered significant for all analyses .
|
A main function of the hormone insulin in the body is to regulate metabolism of glucose . The hormone causes body cells in different organs and tissues to utilize glucose from the bloodstream , storing the excess amount . Insulin resistance which reflects the inability of insulin to properly regulate glucose metabolism is common in people with obesity and/or type 2 diabetes . This insulin resistance is strongly associated with cardiovascular disease and increases the risk of death . However , the reasons that account for this insulin resistance phenomenon are currently not well understood . Here , we show that Bardet Biedl Syndrome proteins are required for proper action of insulin . We found that cells or animals that are deficient in Bardet Biedl Syndrome proteins are unable to respond to insulin . These results provide an explanation why patients that carry mutations in the Bardet Biedl Syndrome genes are insulin resistant , and will potentially contribute to understand common human forms of insulin resistance .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Regulation of Insulin Receptor Trafficking by Bardet Biedl Syndrome Proteins
|
We herein describe the positional identification of a 2-bp deletion in the open reading frame of the MRC2 receptor causing the recessive Crooked Tail Syndrome in cattle . The resulting frame-shift reveals a premature stop codon that causes nonsense-mediated decay of the mutant messenger RNA , and the virtual absence of functional Endo180 protein in affected animals . Cases exhibit skeletal anomalies thought to result from impaired extracellular matrix remodeling during ossification , and as of yet unexplained muscular symptoms . We demonstrate that carrier status is very significantly associated with desired characteristics in the general population , including enhanced muscular development , and that the resulting heterozygote advantage caused a selective sweep which explains the unexpectedly high frequency ( 25% ) of carriers in the Belgian Blue Cattle Breed .
The Belgian Blue Cattle breed ( BBCB ) is notorious for its exceptional muscular development known as “double-muscling” . This extreme phenotype is due in part to an 11-bp loss-of-function deletion in the myostatin gene that has been fixed in the breed ( e . g . [1] ) , as well as to ongoing selection on as of yet unidentified polygenes influencing muscularity . As in other breeds , intense selection has substantially reduced the effective population size . Extensive reliance on artificial insemination ( AI ) , in particular , by allowing popular sires to have thousands of descendants , narrows the genetic basis . The concomitant increase in the rate of inbreeding causes recurrent outbreaks of recessive defects . Inherited defects that have lately afflicted the BBCB include the recently described Congenital Muscular Dystonias ( CMD ) I and II [2] . As a result of this peculiar demography of domestic animal populations , inherited defects generally involve unique “founder” mutations . Allelic homogeneity greatly facilitates positional identification using identity-by-descent ( IBD ) mapping , as recently demonstrated using the first generation high density SNP arrays for the bovine [2] . The genes underlying CMD I & II were readily mapped , and the causative mutations in the ATP2A1 and SLC6A5 genes identified . The widespread use of the resulting diagnostic tests allowed immediate and effective control of the corresponding pathologies . We herein report the positional identification of the mutation causing a novel , recently appeared defect referred to as Crooked Tail Syndrome ( CTS ) . The incidence of CTS has risen very suddenly in the BBCB , and 25% of animals now appear to be CTS carriers . We herein provide strong evidence for exacerbated muscular development of carriers of the CTS mutation , conferring “heterozygote advantage” underlying the selective sweep that raised the causative mutation to alarming proportions .
We recently established a heredo-surveillance platform operating in close collaboration with field veterinarians to rapidly identify emerging genetic defects . As part of these activities , 105 CTS cases were reported to the platform between November 2006 and November 2007 . In addition to the striking deviation of the tail ( equally likely to be dextro- or levo-rotatory ) , detailed clinical examination revealed three symptoms shared by all cases: ( i ) general growth retardation manifesting itself at approximately one month of age , ( ii ) abnormal skull shape manifested as a shortened broad head , and ( iii ) extreme muscular hypertrophy including a conspicuous outgrowth of the gluteus medius anchor . Additional symptoms were observed in a substantial proportion but not all cases: ( i ) spastic paresis of the hind limbs affecting either the quadriceps only ( 22% ) , or quadriceps and gastrocnemius ( 14% ) , often associated with straight hocks , ( ii ) short , straight and extended fore limbs ( 33% ) , and ( iii ) pronounced scoliosis with asymmetric development of the muscles of the back ( 20% ) . Figure 1 illustrates the corresponding symptomatology . We performed complete necropsy of a few selected cases but detected no additional obvious abnormalities . Moreover , radiological examination of crooked tails and scoliotic spines failed to reveal structural defects of the vertebrae ( data not shown ) . Although the defect is not lethal by itself , the most severe cases ( ∼25% ) were euthanized on welfare grounds . The surviving ∼75% nevertheless caused important economic losses to their owners as a result of growth retardation and carcass depreciation . We previously mapped the CTS locus to bovine chromosome 19 , in a 2 . 4 Mb interval shared homozygous-by-descent by the eight analyzed CTS cases [2] . To refine the map location of the CTS locus we genotyped the 105 reported CTS cases for five SNPs covering the 2 . 4 Mb interval ( Figure 2A ) . The SNPs were selected on the basis of the low population frequency of the disease-associated allele . Genotyping was achieved by first sequencing 35 pools of three animals , followed by individual sequencing of the pools revealing the presence of the major allele and therefore of one or more recombinants . This approach allowed us to confine the critical region to the 812 Kb rs29010018 - AAFC03034831 interval . It comprises seven annotated genes which were ranked on the basis of their perceived relevance with regard to the CTS condition . Coding exons were sequenced in an affected and a matched healthy control individual . During this process , we identified a 2-bp deletion in the ORF of the mannose receptor C type 2 ( MRC2 ) gene . The MRC2 gene encodes the 180 kDa Endocytic Transmembrane Glycoprotein ( Endo180 ) , one of the four members of the mannose receptor family [3] , [4] . Endo180 is a recycling endocytic receptor that is predominantly expressed in mesenchymal cells such as stromal fibroblasts and in the chondrocytes and osteoblasts/osteocytes in the developing bones , and is proposed to play a role in regulating extracellular matrix degradation and remodelling . It has C-type lectin activity , binds collagen and interacts with urokinase-type plasminogen activator receptor ( uPAR ) in a trimolecular cell surface complex with pro-urokinase plasminogen activator ( pro-uPA ) . The 180 kD Endo180 protein comprises an aminoterminal cysteine-rich domain of unknown function , a fibronectin type II domain which mediates collagen binding , eight C-type lectin-like domains ( CTLDs ) of which the second mediates Ca2+-dependent lectin activity , a stop-transfer signal anchoring this single-pass transmembrane protein in the membrane , and a carboxyterminal cytoplasmic domain allowing association with adaptor proteins in the clathrin coat . The mutation identified in CTS cases is located in exon 20 and deletes nucleotides 2904 and 2905 of the MRC2 cDNA ( c . 2904_2905delAG ) . It is predicted to append a frame-shifted 30-residue peptide to a truncated Endo180 receptor missing the CTLD6-8 domains , the stop-transfer signal and the cytoplasmic domain ( Figure 2B , C , D ) . As a result , the mutated protein should be unable to localize to the plasma membrane and mediate receptor-mediated endocytosis . We developed a 5′ exonuclease assay for the mutation and genotyped the 105 reported CTS cases . All proved to be homozygous for the c . 2904_2905delAG mutation . We then genotyped 1 , 899 healthy Belgian Blue animals . Unexpectedly , 24 . 7% of animals appeared to be carriers , without a single homozygous mutant ( p<10−12 assuming Hardy-Weinberg equilibrium ) . Taken together , these results allowed us to incriminate the c . 2904_2905delAG mutation as being causal and fully penetrant . C . 2904_2905delAG causes a frame-shift resulting in a premature stop codon in the 21st of the 30-exon MRC2 gene . Mutant mRNAs are therefore predicted to undergo NMD [5] . To test this , we first compared the levels of wild-type and mutant MRC2 mRNA in lung and skeletal muscle of a carrier animal by direct sequencing of RT-PCR products encompassing the deletion . As can be seen from Figure 3A , mutant mRNA was barely detectable . We then compared the levels of MRC2 mRNA in lung tissue of animals of the three genotypes using quantitative RT-PCR performed with primer sets targeting the 5′ and 3′ end of the mRNA respectively . Highly significant reductions in MRC2 mRNA levels were observed in carriers relative to homozygous wild-type individuals ( 75%±4% and 45%±6% of control values for the 5′ and 3′ systems respectively ) , while MRC2 mRNA levels in cases were less than 5% of homozygous wild-types ( Figure 3B ) . Both the allelic imbalance and qRT-PCR experiments thus supported degradation of the mutant transcripts by NMD . From the ten anti-human Endo180 antibodies tested by Western blotting , only one polyclonal rabbit antibody ( CAT2 ) detected the bovine Endo180 protein with sufficient specificity . The CAT2 antibody is directed against the last 19 amino acids of Endo180 of which the last 18 are perfectly conserved between human and cow [6] . CAT2 was thus predicted to allow recognition of the wild-type but not mutant Endo180 . As expected , no wild-type Endo180 was detected in lung tissue of CTS affected animals . In carrier animals , the levels of Endo180 protein were approximately half those observed in homozygous wild-types ( Figure 4 ) . Assuming that Endo180 is dosage sensitive , such reduction in the supposedly functional species might affect phenotype . The unusually high frequency of the CTS mutation in BBCB suggested that it might confer heterozygote advantage in this highly selected population . To test this hypothesis we estimated the effect of carrier genotype on 22 type traits evaluating muscularity , skeletal conformation , size and leg soundness , which are systematically recorded as part of the selection programs implemented in the BBCB . The analysis was conducted on 519 pedigreed bulls , including 148 carrier and 371 homozygous wild-type animals , using a mixed model including fixed effects of MRC2 genotype , year at scoring , body condition and age at scoring , as well as a random individual animal effect . Variance components and effects were estimated by restricted maximum likelihood ( REML ) analysis . Highly significant effects were obtained for the four categories of recorded traits ( Table 1 ) . CTS carrier animals were smaller , stockier and more heavily muscled . They had a thinner skeleton and more rounded ribs , which are characteristics of beef cattle . MRC2 genotype accounted for 3 . 6% , 3 . 6% and 2 . 6% for the genetic variance of height , muscularity and general appearance , respectively . These results strongly suggest that CTS carrier frequency is increased by selection programs applied in BBCB . To more directly demonstrate the occurrence of a selective sweep , we performed the following analysis . Examination of the available genealogies of the 105 affected individuals indicated that all of them trace back to Précieux , a popular AI sire , via both sire and dam . This suggested that Précieux , born in 1980 , was CTS carrier and that its extensive utilization in the mid eighties spread the CTS mutation in BBCB . Genotyping Précieux and three of his sons for the C . 2904_2905delAG mutation and the 60K Illumina chip , indeed demonstrated that he carried the CTS mutation embedded in the SNP haplotype shared homozygous-by-descent by the examined cases [2] . Thus , the vast majority of C . 2904_2905delAG mutations encountered in present-day BBCB animals , trace back to Précieux . We obtained DNA samples from all BBCB sires ( 174 ) born between 2003 and 2005 , whose semen had been commercialized by one of the ten major Belgian AI studs . Such AI sires are heavily selected for extreme muscularity . Examination of the pedigrees indicated that 160 of the 174 [2003–2005] AI sires were descendents of Précieux . The number of generations separating these sires from Précieux averaged 5 . 9 ( range: 3 to 8 ) . Genotyping the C . 2904_2905delAG mutation in this cohort identified 45 CTS carriers , all of them amongst the 160 descendents of Précieux . Assuming that the CTS mutation indeed underwent a selective sweep , 45 carriers out of the 160 Précieux descendants would be significantly higher than expected by chance alone . To verify this assumption we simulated the segregation of a mutation in the true genealogy of the 160 descendants of Précieux and counted the resulting number of carrier bulls . In these simulations , Précieux was systematically assumed to be carrier , while the frequency of the mutation in animals unrelated to Précieux varied from 0 to 0 . 05 . In the absence of selection ( i . e . if a carrier animal is equally likely to transmit either the mutation or the wild-type allele to anyone of its descendents ) , the probability to obtain 45/160 carriers was 0 . 0014 , 0 . 0023 and 0 . 0130 for mutation frequencies ( outside the Précieux lineage ) of 0 . 00 , 0 . 01 and 0 . 05 , respectively ( Table S1 ) . Thus we can confidently assert that the C . 2904_2905delAG mutation indeed underwent a selective sweep in the BBCB . To have some quantitative assessment of the intensity of the selective sweep , we repeated the “gene dropping” simulations while varying the degree of segregation distortion in favour of the mutant allele . Figure 5 shows the proportion of simulations yielding 45/160 carrier bulls as a function of the transmission probability of the CTS mutation from carrier parents to offspring . It can be seen that the outcome of 45/160 carrier bulls is most likely for a transmission rate between 0 . 62∶0 . 38 ( mutation frequency outside Précieux lineage of 0 . 05 ) and 0 . 67∶0 . 33 ( mutation frequency outside Précieux lineage ≤0 . 01 ) . The fact that all 105 CST cases traced back to Précieux both on the dam and sire side , indicates that the mutation frequency outside of the Precieux lineage is closer to 1% than to 5% . Thus , a carrier animal is approximately two times more likely to be selected than a non-carrier sib .
We herein describe a frame-shift mutation in the MRC2 gene causing the CTS syndrome in cattle . Clinical manifestations of CTS are dominated by skeletal and muscular anomalies . Skeletal symptoms including growth retardation , abnormally shaped legs and skulls , are perfectly compatible with the known involvement of MRC2 in regulating extracellular matrix degradation and remodeling and its strong expression in developing bone [7] . The muscular symptoms , including muscular hypertrophy , tail deviation and spastic paresis are more difficult to rationalize , although a role for the related mannose receptor in myoblast motility and muscle growth has been recently reported [8] . We cannot formally exclude the possibility that the muscular manifestations result from distinct sequence variants in linkage disequilibrium with the CTS mutation , although we favor the more parsimonious hypothesis of a single causative mutation . It is noteworthy that mice homozygous for a targeted deletion of MRC2 exons 2 to 6 have been generated in two independent laboratories [9] , [10] . Both laboratories reported that the mice were viable and fertile , although more recently a minor deficiency in long bone growth , bone mineral density and calvarial bone formation has been demonstrated [7] . Cells derived from these animals show a clear defect in collagen uptake and degradation . One reason for the more pronounced clinical manifestations in cattle than in mice may lie in the distinct nature of the murine and CTS MRC2 mutations . Cells isolated from the genetically modified mice express a mRNA species in which exon 1 ( containing the signal sequence ) is spliced in frame onto exon 7 ( containing CTLD2 ) , and in embryonic fibroblasts a truncated Endo180 protein missing the cysteine-rich , FNII and CTLD1 domains can be expressed [9] . However , little or no truncated protein is found in postnatal tissues from these knockout mice [10] , [11] . Alternatively it may be that there are distinct degrees of redundancy between members of the mannose receptor family in different species . Also the more striking phenotype in cattle may be due the different genetic background and particularly the fact that the studied animals were all homozygous for a MSTN loss-of-function mutation [1] . This hypothesis could be tested by mating the available MRC2 and MSTN knock-out mice . Whatever the reason , at this point bovine CTS may be the more informative model to decipher MRC2 function , and to assist in the identification of as of yet unidentified human pathological conditions resulting from MRC2 loss-of-function . We provide very strong evidence of phenotypic manifestations of the CTS mutation in carriers . This is more than likely reflecting dosage sensitivity for Endo180 , as NMD causes the mutant protein to be present at near undetectable levels , thus very unlikely to affect cellular function per se . Enhanced muscularity of CTS carriers has supposedly contributed greatly to the rapid increase of the CTS mutation in the BBCB . Indeed , we demonstrate that carrier animals have approximately two times more chance to be selected as elite sires than their non-carrier sibs . Note that this is the level of segregation distortion expected for a gene that accounts for ∼5% of the genetic variance for a trait with heritability of ∼25% and assuming a selection intensity of ∼2% ( Table S2 ) . Such selective sweep is reminiscent of the spread of other inherited defects in domestic animals as a result of advantageous traits exhibited by carriers . These include loss-of-function mutations of the porcine ryanodine receptor and equine skeletal muscle sodium channel alpha subunit gene causing , respectively , malignant hyperthermia and hyperkalaemic periodic paralysis in homozygotes , yet increased muscle mass in heterozygotes [12] , [13] , or of a FGFR3 mutation causing hereditary chondrodysplasia in homozygous sheep and increased size in the carriers [14] , [15] . A diagnostic test for the CTS mutation has been developed and already applied on more than 4 , 000 BBCB samples . The resulting information should have an immediate and positive impact on the incidence of CTS , and protect animals and breeders against the pathological condition and ensuing economic losses . This work is yet another illustration of the value of domestic animal populations in enriching the phenotype-genotype map . It adds to a recent list of positional cloning successes in poultry [16] , dog [17] , [18] , horse [19] and bovine [2] .
Coding exons of positional candidate genes were amplified from genomic DNA of a CTS case and a matched control using standard procedures . The primers used for the MRC2 gene are listed in Table S3 . PCR products were directly sequenced using the Big Dye terminator cycle sequencing kit ( Applied Biosystem , Foster City , CA ) . Electrophoresis of purified sequencing reactions was performed on an ABI PRISM 3730 DNA analyzer ( PE Applied Biosystems , Foster City , CA ) . Multiple sequence traces from affected and wild-type animals were aligned and compared using the Phred/Phrap/Consed package ( www . genome . washington . edu ) . A Taqman assay was developed to genotype the CTS mutation , using 5′-GCG CAA CAG CAC CAG AGA-3′ and 5′-CTC CCT ACC TTG TTC AGG AAC TG-3′ as PCR primers , and 5′-CTG CCG CCC AC[* *] GGG-3′ ( CTS ) and 5′-CTG CCG CCC AC[A G]G-3′ ( wild type ) as Taqman probes . Reactions were carried out on a ABI7900HT instrument ( Applied Biosystems , Foster City , CA ) using standard procedures . Total RNA was extracted from lung , heart and skeletal muscle of a two month old heterozygote c . 2904_2905delAG animal using Trizol ( Invitrogen ) . The RNA was treated with TurboDNase ( Ambion ) . cDNA was synthesized using SuperscriptTMIII First Strand Synthesis System for RT-PCR ( Invitrogen ) . A portion of MRC2 cDNA , encompassing the deletion , was amplified using MRC2 specific primers ( Table S4 ) . The PCR products were directly sequenced as described above . Total RNA from lung and skeletal muscle was obtained from animals of the three genotypes ( +/+ , +/CTS and CTS/CTS ) . After DNase-treatment ( Turbo DNA-free , Ambion ) , 500 ng total RNA was reverse transcribed in a final volume of 20 µl using the iScript cDNA Synthesis Kit ( Bio-Rad ) . PCR reactions were performed in a final volume of 15 µl containing 2 µl of 2 . 5-fold diluted cDNA ( corresponding to 20 ng of starting total RNA ) , 7 . 5 µl of 2× master mix prepared from the qPCR Core Kit for SYBR green I ( Eurogentec ) , 0 . 45 µl of 1/2000 SYBR green I working solution prepared from the qPCR Core Kit for SYBR green I ( Eurogentec ) , forward and reverse primers ( 250 nM each ) and nuclease free water . PCRs were performed on a an ABI7900HT instrument ( Applied Biosystems , Foster City , CA ) under the following cycling conditions: 10 min at 95°C followed by 40 cycles at 95°C for 15 sec and 60°C for 1 min . Two primer sets were used to test MRC2 expression ( MRC2_5′QRT_UP/DN and MRC2_3′QRT_UP/DN ) and seven genes were included as candidate endogenous controls: ( 1 ) Beta Actin ( ACTB ) , ( 2 ) Glyceraldehyde-3-phosphate Dehydrogenase ( GAPD ) , ( 3 ) Hypoxanthine Phosphoribosyltransferase 1 ( HPRT1 ) , ( 4 ) Ribosomal Protein Large P0 ( RPLP0 ) , ( 5 ) Ribosomal Protein S18 ( RPS18 ) , ( 6 ) Succinate Dehydrogenase Complex Subunit A Flavoprotein ( SDHA ) , and ( 7 ) Tyr-3- & Trp-5-Monooxygenase Activation Protein Beta ( YWHAB ) . After analyse of the results with geNorm [20] , the four following genes were selected as best endogenous controls: ACTB , RPLP0 , RPS18 and YWHAB . The corresponding primer sequences are given in Table S4 . All sample/gene combinations were analyzed in triplicate . Relative MRC2 expression levels , for the 5′ & 3′cDNA parts , in the samples of the three genotypes were computed using the qBase software package ( http://medgen . ugent . be/qbase/ ) ( Hellemans et al . , 2007 ) . A series of available antibodies directed against the human Endo180 were tested by Western blotting for cross reactivity with bovine Endo180 on commercial bovine aortic endothelial cells ( BAOEC , Cell Applications ) . A positive control corresponding to a lysate of MRC5 human fibroblast cell line expressing Endo180 was included in each experiment . The tested antibodies were the following: ( i ) seven mouse monoclonal antibodies ( for details see [21]–[23] ) , ( ii ) a rabbit polyclonal antibody ( DEX ) directed against the full length human protein [21] and ( iii ) two rabbit polyclonal antibodies ( CAT1 and CAT2 ) against a peptide from the human C-terminal cytoplasmic domain ( CATEKNILVSDMEMNEQQE ) conjugated to KLH [6] . After initial testing , only the CAT2 antibody was retained for further experiments . Flash-frozen skeletal muscle and lung tissues from animals of the three MRC2 genotypes ( see above ) were disrupted and homogenized with a tissue lyser system II ( Quiagen ) . Crude protein extracts were obtained and total protein concentrations determined using a colorimetric test ( Pierce BCA Protein Assay kit , Thermo Scientific ) . Fifteen µg were diluted in 15 µl final volume ( 1× SDS gel-loading buffer ) and loaded on a 5% stacking – 10% resolving Tris-glycine SDS-Polyacrylamide gel . Proteins were separated by electrophoresis at 120 V-250 mA during 3 hours , visualized by Coomassie blue staining , and electro-transferred overnight to Hybond P PVDF membranes ( GE Healthcare ) . Membranes were blocked with 5% skim milk in PBS-Tween 20 ( PBS-T ) followed by incubation with primary CAT2 antibodies ( 1∶200 ) in a total volume of 3 ml for 1 h30 min . After washing , the specific signal was detected by using Alkaline Phosphatase conjugated secondary rabbit antibodies ( Sigma ) following the instructions of the manufacturer . Phenotypes corresponded to 22 type traits related to muscularity , skeletal conformation , size and leg soundness that are systematically recorded in the BBCB [24] . These were analyzed using a mixed model including genotype at the MRC2 locus ( 2 ) , year at scoring ( 2 ) , body condition ( 4 ) as fixed effects , age at scoring as covariate ( quadratic regression ) , the additive genetic animal effect and the residual effect as random effect [25] . The number of animals in the relationship matrix was 6 , 356 . Variance components were estimated using the DFREML method ( Derivative-Free Restricted Maximum Likelihood ) [26] . The part of the genetic variance due to MRC2 genotype was estimated as the difference between the variance due to the animal model with and without MRC2 genotype in the model . The allele substitution effects ( contrast ) were calculated as the difference between the genotypic means ( +/+ and +/M ) obtained from the mixed model equations . We simulated the segregation of a heterozygous mutation from Précieux to its 160 [2003–2005] sire offspring . Variable parameter values were ( i ) the transmission rate of the mutation from carriers to their offspring ( 0 . 5 to 0 . 75 ) , ( ii ) the frequency of the mutation outside of the Précieux lineage . 10 , 000 simulations were conducted for each set of parameter values . Only non-affected genotypes were sampled from matings between heterozygous parents .
|
Livestock are being subject to intense artificial selection aimed at ever-increasing , sometimes extreme , production phenotypes . This is well-illustrated by the exceptional muscular hypertrophy characterizing the “double-muscled” Belgian Blue Cattle Breed ( BBCB ) . We herein identify a loss-of-function mutation of the bovine MRC2 gene that increases muscle mass in heterozygotes , yet causes skeletal and muscular malformations known as Crooked Tail Syndrome ( CTS ) in homozygotes . As a result of the “heterozygote advantage” , the MRC2 c . 2904_2905delAG mutation has swept through the BBCB population , resulting in as many as 25% carrier animals and causing a sudden burst of CTS cases . These findings highlight one of the risks associated with pushing domestic animals to their physiological limits by intense artificial selection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/disease",
"models",
"genetics",
"and",
"genomics/animal",
"genetics",
"genetics",
"and",
"genomics",
"genetics",
"and",
"genomics/comparative",
"genomics"
] |
2009
|
Balancing Selection of a Frame-Shift Mutation in the MRC2 Gene Accounts for the Outbreak of the Crooked Tail Syndrome in Belgian Blue Cattle
|
Ly6C+ inflammatory monocytes are essential to host defense against Toxoplasma gondii , Listeria monocytogenes and other infections . During T . gondii infection impaired inflammatory monocyte emigration results in severe inflammation and failure to control parasite replication . However , the T . gondii factors that elicit these monocytes are unknown . Early studies from the Remington laboratory showed that mice with a chronic T . gondii infection survive lethal co-infections with unrelated pathogens , including L . monocytogenes , but a mechanistic analysis was not performed . Here we report that this enhanced survival against L . monocytogenes is due to early reduction of bacterial burdens and elicitation of Ly6C+ inflammatory monocytes . We demonstrate that a single TLR11/TLR12 ligand profilin ( TgPRF ) was sufficient to reduce bacterial burdens similar to T . gondii chronic infection . Stimulation with TgPRF was also sufficient to enhance animal survival when administered either pre- or post-Listeria infection . The ability of TgPRF to reduce L . monocytogenes burdens was dependent on TLR11 and required IFN-γ but was not dependent on IL-12 signaling . TgPRF induced rapid production of MCP-1 and resulted in trafficking of Ly6Chi CCR2+ inflammatory monocytes and Ly6G+ neutrophils into the blood and spleen . Stimulation with TgPRF reduced L . monocytogenes burdens in mice depleted with the Ly6G specific MAb 1A8 , but not in Ly6C/Ly6G specific RB6-8C5 depleted or CCR2−/− mice , indicating that only inflammatory monocytes are required for TgPRF-induced reduction in bacterial burdens . These results demonstrate that stimulation of TLR11 by TgPRF is a mechanism to promote the emigration of Ly6Chi CCR2+ monocytes , and that TgPRF recruited inflammatory monocytes can provide an immunological benefit against an unrelated pathogen .
Toxoplasma gondii is an obligate intracellular Apicomplexan parasite that can infect nearly any nucleated cell of all warm blooded animals . Within warm blooded hosts , T . gondii replicates as a fast growing tachyzoite form , which disseminates throughout the body during acute infection . Over time and under immune pressure , the parasite differentiates into an encysted bradyzoite form within the central nervous system and muscle tissue , which establishes a life-long chronic infection . Approximately 30% of humans are infected with T . gondii but the infection may be asymptomatic in immunocompetent hosts . T . gondii infection is characterized by a highly polarized Th1 type immune response associated with production of IL-12 by dendritic cells ( DCs ) , neutrophils , and macrophages which drives T and NK cell production of IFN-γ , long regarded as the main mediator of acute and chronic defenses against the parasite [1] , [2] , [3] . One of the T . gondii proteins known to stimulate IL-12 production is T . gondii profilin ( TgPRF ) , which is required for parasite actin remodeling during host cell invasion and egress , and is also a ligand for TLR11 and TLR12 [4] , [5] , [6] , [7] . Another critical factor for innate defenses are a class of Gr-1+ Ly6C+ monocytes that produce nitric oxide ( NO ) and TNF-α , and are recruited in a CCR2 dependent manner in response to both oral and parenteral T . gondii infections [8] , [9] , [10] , [11] . MCP-1−/− and CCR2−/− mice do not recruit Ly6C+ monocytes to the lamina propria in response to oral infection , leading to a higher influx of neutrophils and death from intestinal necrosis and inflammation [8] , [9] . Similarly , MCP-1−/− and CCR2−/− mice fail to recruit inflammatory monocytes to the peritoneal cavity following i . p . inoculation leading to increased mortality and parasite burdens [10] . Thus , Ly6C+ monocytes are necessary for early control of T . gondii replication and to prevent immune pathology . However , the specific parasite factors that elicit Ly6C+ monocytes during T . gondii infection have not been identified . Ly6Chi monocytes are also recruited during infections with other protozoan and bacterial pathogens , including Listeria monocytogenes [12] , [13] , [14] , [15] , [16] . T . gondii sexual reproduction occurs exclusively in the intestines of the feline definitive hosts , making the rodents they prey on key intermediate hosts in the T . gondii lifecycle . T . gondii infection has been shown to alter rodent aversion to cat urine and fear avoidance behaviors in ways that increase the odds of predation and thus parasite reproductive success [17] , [18] . Previous studies have also reported that mice infected with T . gondii are more resistant to secondary infections with unrelated pathogens , including L . monocytogenes , Salmonella typhimurium , mengo virus , Cryptococcus neoformans , Besnoita jejuni , Moloney leukemia virus and Schistosoma monsoni [19] , [20] , [21] , [22] , [23] , [24] , which may also serve to increase predation . We have recently shown that stimulation with soluble T . gondii antigens ( STAg ) reduced viral titers and conferred a survival advantage in mice infected with highly pathogenic H5N1 avian influenza virus [25] , demonstrating that treatment with STAg can stimulate immunity against unrelated pathogens . In order to further investigate the mechanisms conferring this immunological benefit , we used a highly tractable L . monocytogenes infection model . L . monocytogenes is a Gram positive facultative intracellular bacteria commonly associated with outbreaks of the foodborne illness listeriosis . In mice , intravenous inoculation with L . monocytogenes causes highly predictable infection , involving both innate and adaptive immune responses that ultimately clear the bacteria [26] , [27] . Before the onset of adaptive immunity , bacteria replicate primarily in infectious foci within cells of the spleen and liver where innate immune responses are critical for controlling early bacterial growth to prevent dissemination and lethal systemic infection . Increased early bacterial burdens in the spleen and liver correlate with the severity and outcome of infection . Ly6Chi CCR2+ inflammatory monocytes mediate critical innate control of early bacterial replication . During L . monocytogenes infection , Ly6Chi CCR2+ cells emigrate from the bone marrow in a CCR2-dependent manor , and traffic to sites of bacterial infection to differentiate into CD11C+ TNF-α and inducible nitric oxide synthase ( iNOS ) producing DCs ( TipDCs ) that enhance bacterial clearance [12] , [15] , [28] . Emigration of Ly6Chi CCR2+ cells from the bone marrow is directed by MCP-1 and MCP-3 , which is mainly produced by non-hematopoietic cells during infection and can be produced by bone marrow mesenchymal stem cells ( BMSCs ) in response to circulating TLR ligands [12] , [16] , [29] , [30] . Accordingly , CCR2−/− mice have reduced numbers of circulating Ly6Chi monocytes , reduced numbers of TipDCs in the spleen and liver , reduced TNF-α production and are more susceptible to L . monocytogenes infection [12] , [15] , [16] , [28] . IFN-γ and TNF-α are essential to the innate response as mice lacking either cytokine rapidly succumb to L . monocytogenes infection [31] , [32] , [33] . In this study we show that chronic T . gondii infection or stimulation with STAg provides resistance against L . monocytogenes bacterial infection by reducing bacterial burdens in the major sites of bacterial replication , the spleen and liver . We also show that stimulation with the TgPRF is sufficient to induce this resistance independent of IL-12 , T and NK1 . 1+ cells but cannot completely overcome the requirement for IFN-γ mediated defenses . Most importantly , we show that TgPRF induces production of MCP-1 , which results in the trafficking of Ly6Chi CCR2+ inflammatory monocytes into the blood and spleen , and that CCR2-dependent recruitment of these cells is essential to the TgPRF-induced anti-bacterial response . These results demonstrate that stimulation of TLR11 by TgPRF is sufficient to promote recruitment of Ly6Chi CCR2+ inflammatory monocytes , and that these monocytes can provide and immunological benefit against other infections .
Previous research has shown that mice with a chronic T . gondii infection had greater survival or delayed time to death when challenged with a lethal L . monocytogenes infection [19] . Further experiments showed that this protective effect was not transferrable in the serum , and thus was likely a cell mediated response [34] . Although the specific bacterial burdens were not determined for the animals in these studies , early innate control of L . monocytogenes replication correlates well with severity of infection in mice: animals that maintain low bacterial numbers generally go on to clear the infection , whereas failure of innate immunity is associated with high numbers of bacteria , overwhelming sepsis and inevitable death . We hypothesized that the enhanced survival of T . gondii infected mice was due to innate control of L . monocytogenes replication . To test this hypothesis , we infected naïve and T . gondii chronically infected mice with a lethal inoculum of L . monocytogenes , and then we determined the number of viable bacteria in the spleens and livers 72 hours later . T . gondii-infected mice had significant ∼3 . 6 log reductions in bacterial burdens in the spleen ∼4 . 5 log reductions in the liver compared to uninfected controls ( Fig . 1A ) . In our experience , mice with bacterial burden less than 6 log10 CFU/g in the spleen and liver at 72 hours post infection typically remain asymptomatic and survive L . monocytogenes infection; whereas those with higher bacterial burdens usually succumb to infection . As the bacterial burdens in T . gondii infected mice were consistently less than 6 log10 CFU/g in both organs ( Fig . 1A ) , these results suggest that survival of T . gondii infected mice reported previously [19] was due to early reductions in the numbers or replication of L . monocytogenes bacteria . Our previous work with influenza virus [25] had shown that the protective effects of T . gondii infection could be replicated by treating mice with STAg , a non-infectious lysate of soluble antigens from sonicated T . gondii tachyzoites . STAg contains many T . gondii proteins , including profilin [5] , and previous work has shown that STAg can stimulate immune responses similar to those induced by live parasites , including induction of IL-12 , TNF-α , IFN-γ , IL-1β , IL-10 and MCP-1 in vivo or in vitro [35] , [36] , [37] . Consistent with these data , we observed increased levels of IL-12 , TNF-α , IFN-γ and MCP-1 in the serum of STAg-stimulated mice within 24 hours ( data not shown ) . We hypothesized that STAg treatment would reduce the bacterial burdens of L . monocytogenes infected mice as well as chronic T . gondii infection . Mice treated with 1 µl of STAg ( approximately 1 µg total protein ) 24 hours prior to infection with L . monocytogenes had ∼2 . 5 log reductions in bacterial burden in the spleens and ∼3 . 8 log reductions in the liver compared to PBS-treated controls ( Fig . 1B ) . These effects were similar to the reduction in bacterial burdens we observed in T . gondii infected mice ( Fig . 1A ) . STAg stimulated mice also experienced significantly less weight loss than PBS treated controls at 72 hours post infection ( Fig . 1B ) . STAg stimulation was effective for reducing bacterial burdens and weight loss when given 2 or 6 hours post L . monocytogenes infection , although the reduction in bacterial burdens began to decline at 6 hours ( data not shown ) . To determine if the protective components in STAg were protein or other molecules such as RNA or DNA , we subjected STAg to proteinase K digestion . Proteinase K-digested STAg did not reduce bacterial burdens in L . monocytogenes infected mice ( Fig . S1A ) , which suggested that the protective component ( s ) were protein . To identify the specific protein ( s ) , we subjected STAg to ammonium sulfate ( AS ) precipitation and assayed the fractions for their ability to reduce the bacterial burdens . The AS precipitation fraction containing the proteins that remained soluble at AS concentrations >60% reduced the bacterial burdens similar to STAg ( Fig . S1B ) . When we subjected these fractions to western blotting with antibodies against several T . gondii proteins , we saw TgPRF was present in the AS >60% fraction ( Fig . S1C ) . TgPRF is an actin-binding protein involved in parasite gliding motility , host cell invasion and egress , and is known for inducing IL-12 production through stimulation of TLR11 and TLR12 expressed on DCs and macrophages [4] , [5] , [6] . In order to determine if TgPRF was sufficient to confer protection against L . monocytogenes we stimulated mice with purified recombinant N-terminal his-tagged TgPRF ( rPRF ) ( Fig . 2A ) . Mice stimulated with 100 ng rPRF 4 hours prior to L . monocytogenes infection had a significant ∼3 . 4 log reduction in bacterial burdens in the spleen and ∼4 log reduction in the liver compared to PBS-treated animals ( Fig . 2A ) . rPRF-treated mice did not exhibit weight loss in contrast to PBS-treated controls which lost 17% of their starting weight by 72 hours post infection ( Fig . 2A ) . Because stimulation with rPRF was sufficient to reduce bacterial burdens similar to T . gondii infection ( Fig . 1A ) , we expected rPRF to enhance survival of L . monocytogenes infected mice in our model ( Fig . 2B ) . All ( 8/8 ) PBS-treated mice rapidly succumbed to L . monocytogenes infection within 7 days , with the majority of mice succumbing by day 5 . In contrast , 100% ( 8/8 for each group ) of mice stimulated with rPRF 4 hours prior to , or 4 hours after , L . monocytogenes infection survived for 30 days , at which point the experiment was terminated . These results demonstrate that rPRF-stimulation is sufficient to reduce bacterial burdens and confer a long-term survival advantage during L . monocytogenes infection . Although TgPRF can be recognized by TLR11 and TLR12 [5] , [6] , [7] , the ability of rPRF to reduce bacterial burdens was strictly dependent on TLR11 . In multiple experiments , TLR11-deficient ( TLR11−/− ) mice treated with 40-fold more protein ( 4 µg rPRF ) 4 hours prior to L . monocytogenes infection had no reduction in bacterial burden in either the spleen or liver compared to PBS-stimulated controls ( Fig . 2C ) . rPRF-stimulated TLR11−/− mice also showed equivalent weight loss as the PBS controls ( Fig . 2C ) . These results demonstrate that the effects of rPRF are dependent on recognition by TLR11 and that potential contaminants such as LPS do not contribute to the effect . To determine whether TgPRF was the major T . gondii factor in STAg responsible for the resistance to L . monocytogenes , we stimulated TLR11−/− mice with STAg . Doses of STAg up to 200 µl did not result in significant reductions in bacterial burdens or reduced weight loss during L . monocytogenes infection ( Fig . S2 and data not shown ) . We did observe modest but statistically significant reductions in bacterial burdens in the spleen ( ∼20-fold ) and liver ( ∼80-fold ) with 200 µl of STAg generated from twice the normal number of parasites , or 400 µg of protein ( Fig . S2 ) . These results were in contrast to WT mice , in which 1 µg of STAg reduced bacterial burdens by up 300-fold in the spleen and 6 , 000-fold in the liver ( Fig . 1B ) . It is possible that the TLR11 independent effects of STAg could be due to parasite derived TLR ligands such as nucleic acids and GPI moieties , or other parasite derived proteins . However , it is unlikely that such large doses of STAg , equivalent to material from 1 . 6×108 lysed parasites , are relevant during natural infection and thus TgPRF is likely to be the main factor in STAg responsible for the resistance to L . monocytogenes . STAg has been shown to induce cytokines and chemokines including IL-12 , TNF-α , IFN-γ , IL-1β , IL-10 and MCP-1 [35] , [36] , [37] . TgPRF has been shown to induce IL-12 by classes of DCs and macrophages , IFN-α by CD11c+ spleenocytes , and to promote IFN-γ production by NK1 . 1+ cells [6] . To determine if TgPRF could induce production of other anti-listerial cytokines , we stimulated mice with rPRF then analyzed serum 2 or 24 hours later . rPRF stimulation induced significant production of IL-12 and MCP-1 at 2 and 24 hours , and IFN-γ and TNF-α by 24 hours ( Fig . 3 ) . These results show that TgPRF can stimulate the production of multiple cytokines and chemokines in addition to IL-12 . IL-12 mediates defenses against T . gondii by inducing IFN-γ production from NK and T cells , which in turn helps to activate macrophage effector functions , enhancing antigen presentation , and by promoting the differentiation of Th1 cells [2] . IL-12 plays a similar and critical role in L . monocytogenes infections [26] , [27] . We hypothesized that the ability of rPRF to reduce the bacterial burdens would require IL-12 signaling . However , we determined that IL-12 signaling was not required for rPRF-induced resistance to L . monocytogenes infection using IL-12Rβ1 deficient ( IL-12Rβ1−/− ) mice ( Fig . 4A ) . Compared to PBS-treated controls , rPRF-treated IL-12Rβ1−/− mice had significant ∼2 . 6 log and ∼2 . 8 log reductions in bacterial burdens in the spleen and livers , respectively . rPRF-treated IL-12Rβ1−/− mice exhibited only mild weight loss of 1 . 5% , in contrast to PBS-treated controls which lost 14% of their weight . IL-12Rβ1 is also a component of the IL-23 receptor , so these results indicate that both IL-12 and IL-23 signaling are not required for rPRF-induced resistance to L . monocytogenes infection . IFN-γ is a critical mediator of innate defenses against both L . monocytogenes [31] , [32] and T . gondii [1] , [3] . Our previous work with STAg and influenza virus found that STAg-induced IFN-γ from NK cells was required to mediate protection against influenza virus [25] . To determine the role of IFN-γ mediated defenses in rPRF-induced protection against L . monocytogenes we treated IFN-γ deficient ( IFN-γ−/− ) mice with rPRF ( Fig . 4B ) then infected them with a low but lethal dose of L . monocytogenes , 200 CFU/animal , to account for the extreme susceptibility imposed by IFN-γ deficiency [31] , [32] . rPRF-treated IFN-γ−/− mice had a slight but statistically significant 6-fold reduction in bacterial burden in the spleen and 10-fold reduction in the liver compared to PBS-treated controls . Although the bacterial burdens were still high , rPRF-treated IFN-γ−/− mice experienced less weight loss than PBS-treated controls . While all rPRF-treated IFN-γ−/− mice did succumb to L . monocytogenes infection within eight days , the delay was significant relative to PBS-stimulated animals ( Fig . 4C ) . These results suggest that IFN-γ is at least partially required for rPRF-induced protection against L . monocytogenes and that rPRF stimulation cannot overcome the requirement for IFN-γ mediated defenses even at the low infectious doses used . The major sources of IFN-γ are T cells and NK cells . NK1 . 1+ cells are the critical source of IFN-γ for early defense against T . gondii [38] and for STAg-induced protection against influenza virus [25] . Similarly , the majority of IFN-γ during early L . monocytogenes infection is produced by NK1 . 1+ cells [39] , [40] . However , T cells can also produce IFN-γ in early responses to T . gondii [41] and L . monocytogenes infection [40] , [42] . To determine if either NK1 . 1+ or T cells were required for rPRF-induced for protection against L . monocytogenes , we created mice deficient in both T and NK cells by depleting Rag1 deficient ( Rag1−/− ) mice with PK136 ( anti-NK1 . 1 ) monoclonal antibody . In contrast to IFN-γ−/− mice , Rag1−/− NK1 . 1-depleted mice had no increase in susceptibility to L . monocytogenes infection and rPRF-stimulation was highly effective in Rag1−/− NK1 . 1-depleted mice infected with 6×104 CFU/animal , the same dose used for experiments with WT animals ( Fig . 4D ) . rPRF-treated mice had a ∼3 . 7 log reduction in bacterial burden in the spleen and ∼1 . 8 log reduction in the liver , compared to PBS-treated controls . rPRF-treated Rag1−/− NK1 . 1-depleted mice also did not show weight loss ( Fig . 4D ) . Similar results were observed with rPRF treatment in singly deficient Rag1−/− or wild-type NK1 . 1-depleted mice ( data not shown ) . These data suggest that neither T nor NK cells are required for rPRF-induced reduction in bacterial burdens and survival . However , in the absence of T and NK cells , mice may develop compensatory defense mechanisms , so it is possible the factors required in these animals are different than in WT mice . During L . monocytogenes infection , MCP-1 and MCP-3 signals promote emigration of TipDC precursors , Ly6Chi inflammatory monocytes , out of the bone marrow and into circulation in a CCR2-dependent manner [16] . Because serum levels of MCP-1 in rPRF-stimulated mice were significantly increased within 2 hours ( Fig . 3 ) and because T . gondii infection is also known to elicit a population of Ly6C+ monocytes via CCR2 [8] , [9] , [10] , we examined the ability of rPRF to promote emigration of Ly6Chi monocytes . Within four hours after rPRF stimulation , there was an ∼3 fold average increase in the frequency of CD11b+ Ly6Chi monocytes in both the blood and spleens of TgPRF stimulated animals ( Fig . 5A and B ) . The Ly6Chi monocyte population expressed CCR2 ( data not shown ) , consistent with an inflammatory monocyte and TipDC precursor populations described previously [9] , [10] , [11] , [12] , [13] , [14] , [15] . There was also an ∼2 . 7 fold average increase in the frequency of neutrophils ( CD11b+ Ly6Cint Ly6G+ ) in the blood and a ∼2 . 5 fold average increase in the spleens of rPRF stimulated mice ( Fig . 5A and B ) . To confirm that these results were specifically attributable to TgPRF , we measured monocyte and neutrophil recruitment in TLR11−/− mice . As expected , there was not an increase the percentage of Ly6Chi monocytes or neutrophils in TLR11−/− mice stimulated with 100 ng rPRF compared to PBS stimulated controls ( Fig . S3 ) , demonstrating the specificity of the TgPRF-TLR11 interaction in monocyte and neutrophil recruitment . Ly6Chi CCR2+ monocyte emigration from the bone marrow into circulation is CCR2-dependent [9] , [12] . To determine if Ly6Chi CCR2+ cells recruited in response to rPRF were essential for the reductions in bacterial burdens , we rPRF-stimulated CCR2 deficient ( CCR2−/− ) mice ( Fig . 6A ) . rPRF-stimulated CCR2−/− mice did not have large reductions in bacterial burdens compared to PBS-treated controls , 2-fold in the spleen and 10-fold in the liver . Although the reductions were statistically significant , they are not likely biologically relevant given the overall high burdens . In addition , both groups experienced equal weight loss . Although CCR2−/− mice have diminished levels of circulating Ly6Chi monocytes , they have increased numbers in the bone marrow at rest , and large numbers of activated TNF-α producing Ly6Chi monocytes accumulate in the bone marrow during infection [12] . Thus , the small reduction in bacterial burden we saw in rPRF-stimulated CCR2−/− mice could still be dependent on Ly6Chi CCR2+ monocytes , either by activation of a limited number of cells in circulation , or via soluble cytokines such as TNF-α produced by those cells restricted to the bone marrow . To deplete Ly6Chi CCR2+ monocytes , we treated mice with the anti-Gr-1 MAb RB6-8C5 , which recognizes a common epitope shared by Ly6C and Ly6G [43] . Depletion with MAb RB6-8C5 reduced neutrophils in the spleens of rPRF stimulated mice by ∼95% and inflammatory monocytes by ∼85% ( data not shown ) . We consistently observed that rPRF-stimulation did not offer any protection in RB6-8C5 depleted mice . There were no significant difference in bacterial burdens between rPRF- and PBS-stimulated mice in either the spleens or livers ( Fig . 6B ) , and both groups experienced equal weight loss ( Fig . 6B ) . Because TLR11 and TLR12 are expressed on macrophages and DCs [7] , which may express Ly6C and thus would be depleted by RB6-8C5 , we tested the ability of RB6-8C5 depleted mice to respond to profilin by measuring serum cytokine levels 2 hours post rPRF stimulation . rPRF-stimulated RB6-8C5 depleted mice produced significant amounts of IL-12 and MCP-1 ( Fig . S4 ) at levels similar to WT mice at the same timepoint ( Fig . 3 ) . rPRF-stimulated RB6-8C5 depleted mice also produced significant levels of TNF-α ( Fig . S4 ) . This suggests that the cell population required for recognition of profilin and production of MCP-1 is not subject to depletion by RB6-8C5 MAb . Because RB6-8C5 significantly depletes Ly6G+ neutrophils as well as Ly6Chi monocytes , we also depleted mice with the Ly6G specific MAb 1A8 [44] to establish the relative contribution of Ly6G+ cells . In contrast to CCR2−/− and RB6-8C5-depleted mice , 1A8-depleted rPRF-stimulated animals were consistently protected against L . monocytogenes infection ( Fig . 6C ) . rPRF-stimulation reduced bacterial burdens in the spleens of 1A8-depleted mice by ∼3 logs and in the livers by ∼2 . 3 logs , although bacterial burdens in the livers of all 1A8 depleted mice were highly variable . This observation along with the fact that rPRF stimulated CCR2−/− mice had a 10-fold reduction in liver bacterial burdens may indicate that neutrophils play a minor role in defense in this organ . rPRF-stimulated 1A8-depleted mice also did not show weight loss in contrast to PBS-stimulated controls which lost significantly more weight ( Fig . 6C ) . Together , these results indicate that although rPRF stimulates a large influx of Ly6Cint LyG+ neutrophils into the blood and spleen , these cells are largely dispensable for rPRF induced protection and reduction of bacterial burden in the spleen and liver . While Ly6G+ neutrophils may have a small contribution in the liver following rPRF-treatment , CCR2-dependent recruitment of Ly6Chi CCR2+ inflammatory monocytes plays the central and essential role in rPRF-induced clearance of L . monocytogenes .
In this study we investigated how chronic infection with T . gondii protects the rodent host against unrelated pathogens [19] , [20] , [21] , [22] , [23] , [24] , [25] . Because rodents are the primary reservoir for T . gondii , elucidating the key ligand/receptor interactions is essential for understanding host defense . Our work identifies TgPRF as a T . gondii factor that recruits inflammatory monocytes and demonstrates that stimulation of TLR11 or TLR11/TLR12 heterodimers provides an immunological benefit to a T . gondii-infected host against another pathogen . Stimulation with TgPRF results in production of MCP-1 and recruitment of Ly6Chi CCR2+ inflammatory monocytes and Ly6G+ neutrophils into the blood and spleen , although only Ly6Chi CCR2+ inflammatory monocytes and CCR2-signaling are essential to reduce bacterial burdens . These data have significant implications for our understanding of the biology of T . gondii infection and the evolutionary maintenance of TLR11 in rodents . Ly6Chi CCR2+ inflammatory monocytes were first identified in L . monocytogenes infection , where they differentiate into TipDCs at the sites of bacterial infection and are essential for early control of bacterial replication . Emigration of these cells out of the bone marrow is directed by the chemokines MCP-1 and MCP-3 and their receptor CCR2 [12] , [16] . Accordingly , CCR2 mice have diminished numbers of TipDCs in the spleen and are highly susceptible to L . monocytogenes infection [15] . Ly6Chi monocytes have also been implicated in defense against many other pathogens , including T . gondii . In both oral and parenteral T . gondii inoculation , Gr-1+ Ly6C+ monocytes are recruited to sites of infection and are critical for acute survival [8] , [9] , [10] , [11] . These cells have been shown to produce TNF-α and iNOS , and their emigration is dependent on MCP-1 and CCR2 consistent with inflammatory monocytes or TipDC precursor populations , although interestingly they do not appear to acquire CD11c [8] , [9] , [10] , [11] . MCP-1−/− and CCR2−/− mice , which fail to recruit inflammatory monocytes , have enhanced mortality , greater parasite burdens , and die of pathological inflammation and intestinal necrosis [8] , [9] , [10] , [11] . These studies show that Ly6C+ monocytes are essential for early control of T . gondii replication and to prevent immune pathology . However , the parasite factors that elicit Ly6C+ monocytes had not been identified . Here we identify TgPRF as a mechanism by which T . gondii can elicit emigration of a Ly6Chi CCR2+ inflammatory monocyte population and show that these cells are required for TgPRF to confer resistance to L . monocytogenes infection . In this study stimulation by TgPRF was associated with production of the CCR2 ligand MCP-1 but we did not examine production of other notable CCR2 ligands such as MCP-3 . Presumably MCP-3 is also involved in CCR2 dependent inflammatory monocyte recruitment during T . gondii infection as mortality and defects in monocyte recruitment and are less severe in MCP-1−/− than CCR2−/− mice [10] , although no specific studies have addressed the role of this chemokine . We also did not determine if TgPRF recruited monocytes acquire CD11c or differentiate into TipDCs during the context of L . monocytogenes infection . Stimulation with TgPRF also results in trafficking of Ly6Cint Ly6G+ neutrophils into the blood and spleen . Early work suggested that neutrophils were the major cells responsible for controlling the early growth and dissemination of L . monocytogenes [45] , [46] . These observations were based mainly on studies using an anti-granulocyte receptor-1 ( Gr-1 ) MAb , which is now known to recognize both neutrophils ( Ly6Cint Ly6G+ ) and non-neutrophil Ly6C+ cells , including subsets of monocytes , macrophages , DCs and lymphocytes [43] . Recent work has suggested that Ly6G+ neutrophils are largely dispensable for innate defenses [47] while others have shown that these cells contribute to significant anti-listerial defenses in the liver [48] , [49] . Consistent with these findings , we observed that 1A8 depleted mice are slightly more susceptible to L . monocytogenes than WT mice ( lethal dose 1×104 versus 6×104 CFU ) , although not as susceptible as CCR2−/− ( 8×103 CFU ) or RB6-8C5 depleted animals ( 200 CFU ) . The fact that rPRF-stimulated 1A8 depleted mice are resistant to L . monocytogenes infection demonstrates that rPRF-recruited Ly6G+ neutrophils are dispensable for TgPRF-induced protection . Rather , Ly6Chi CCR2+ inflammatory monocytes and TipDCs play the predominant role in TgPRF-mediated defenses . There are several mechanisms by which TgPRF recruited monocytes may contribute to early control of L . monocytogenes and that could also account for the requirement for IFN-γ . First , inflammatory monocytes may be directly bactericidal . Inflammatory monocytes recruited to the peritoneal cavity during T . gondii infection express iNOS and have enhanced parasite killing in vitro [11] , so it is reasonable to infer that TgPRF recruited monocytes would display enhanced activity against L . monocytogenes as well . However , rPRF treatment effectively reduced bacterial burdens in L . monocytogenes infected iNOS deficient mice ( data not shown ) suggesting that NO production is unlikely to be a primary mechanism of killing . The impaired protection we observed in IFN-γ−/− mice could be due to generalized defects in antimicrobial effector mechanisms dependent on IFN-γ that stimulation with TgPRF cannot overcome or because the development of Ly6Chi inflammatory monocytes into TipDCs and inflammatory DCs during L . monocytogenes and T . gondii infections is largely dependent on NK1 . 1+ cell derived IFN-γ [50] , [51] . Noncognate antigen driven proliferation and activation of memory T cells and innate NK cells could also mediate a degree of resistance dependent on IFN-γ and explain the IFN-γ dependence of TgPRF induced protection . Memory T cells can proliferate , produce IFN-γ and acquire effector cell functions during bacterial infection , which contributes to IFN-γ mediated defenses [52] , [53] , [54] . Activation is driven by IL-15 and IL-18 production by inflammatory monocytes and CD8α+ DCs , dependent on inflammasome activation , type I interferon and TLR priming [53] , [54] . TgPRF could contribute to induction of noncognate memory T cell responses by increases in the number of inflammatory monocytes or serving as the TLR-based priming signal via stimulation of TLR11 . Activation of transferred IFN-γ sufficient memory T cells mediated a ∼100-fold reduction in L . monocytogenes bacterial burden in IFN-γ-/- mice , but only modest 3-fold reduction in mice with intact IFN-γ responses [52] , [53] , [54] . Thus , it is unclear if the 2 , 500- to 30 , 000-fold reductions we describe in T . gondii infected or rPRF stimulated IFN-γ sufficient mice can be entirely attributed to cognate antigen independent induction of IFN-γ by memory T cells . Inflammatory monocytes can also induce IFN-γ production by NK cells [53] . Along these lines , TgPRF has been shown to stimulate IFN-γ production by NK1 . 1+ cells [6] and NK1 . 1+ derived IFN-γ is required for T . gondii induced protection against influenza [25] . In our model however , NK1 . 1+ cells do not appear to be essential for TgPRF mediated defenses against L . monocytogenes . The increased importance of NK cells in defense against influenza may be attributable to the comparatively increased role of NK cells in viral infections and killing of virus infected cells . All of these mechanisms are unable to fully account for the fact that stimulation with rPRF was able to reduce L . monocytogenes bacterial burdens in Rag1−/− NK1 . 1 depleted mice . It is possible that in the absence of T and NK cells , alternative mechanisms leading to production of IFN-γ may be induced . Neutrophils could be an important source of IFN-γ independent of T and NK cells in our model . Recent evidence has clearly shown that IFN-γ producing neutrophils are present in the peritoneal cavity during T . gondii infection of WT and TLR11−/− mice and are a biologically relevant source of IFN-γ [55] . Neutrophil derived IFN-γ is produced independent of IL-12 [55] , which is consistent with our results showing that neither T cells , NK1 . 1+ cells , nor IL-12 are required for TgPRF-induced resistance to L . monocytogenes . The fact that stimulation with TgPRF elicited a significant number of neutrophils suggests that IFN-γ producing neutrophils could provide a relevant source of non NK1 . 1+ derived IFN-γ in our model . Future studies will determine if TgPRF elicits these IFN-γ producing neutrophils during T . gondii infection of mice . The identification of TgPRF as a T . gondii factor that elicits Ly6Chi inflammatory monocytes and neutrophils is especially important for our understanding of T . gondii infection given that humans presumably lack functional TLR11 and TLR12 receptors for TgPRF , yet inflammatory monocytes are critical for innate defenses against T . gondii . In mice , Ly6C+ and Gr-1+ cells are recruited to sites of T . gondii infection in a CCR2 dependent manner and produce TNF-α and iNOS [8] , [9] , [10] , [11] . CCR2−/− and MCP1−/− mice fail to control parasite replication and are highly susceptible to both oral and parenteral T . gondii infection [8] , [9] , [10] . Lack of inflammatory monocytes is associated with severe inflammation at the sites of T . gondii infection , including increased numbers of neutrophils , intestinal necrosis and CNS pathology [8] , [9] , [10] . However , the beneficial versus detrimental role of TgPRF is unclear . Similar to mice lacking inflammatory monocytes , lack of TLR11 during systemic T . gondii infection is associated with inappropriate inflammation [56] , which suggests a role for TgPRF recruited monocytes in the regulation of systemic immunopathological responses . In contrast , recognition of TgPRF is detrimental during oral T . gondii infection , likely because gut commensal bacteria stimulate anti-parasitic immune responses [57] . WT , but not TLR11−/− , mice develop acute ileitis and liver pathology suggesting that additional parasite or bacterial factors may be sufficient to direct recruitment of inflammatory monocytes in the absence of TLR11 , but concurrent stimulation by gut microbes , TgPRF and other T . gondii molecules promotes overwhelming pathological inflammation . The detrimental effects of TgPRF recognition may also be due to TgPRF mediated recruitment of neutrophils , which lead to mucosal pathology [8] , [9] , [58] and contribute to parasite spread within the intestine [59] . Even so our work presented here shows that recognition of TgPRF and subsequent recruitment of inflammatory monocytes provides a host the benefit of innate defense against an unrelated pathogen . It is possible that carriage of T . gondii may have driven the maintenance of TLR11 specifically in rodent hosts due to this property , and that the interaction of TgPRF with TLR11 or TLR11/TLR12 heterodimers may be critical for this beneficial host-microbe interaction . Other microbes are known to confer symbiotic-like protection against unrelated pathogens . Latent infection with the murine γ-herpesvirus MHV68 and the β-herpes virus MCMV conferred protection against the bacterial pathogens L . monocytogenes and Yersinia pestis [60] . Protection resulted in increased survival and correlated with 100-fold reductions in L . monocytogenes burdens in the spleen and liver , similar to the results we observed with chronic infection by T . gondii and stimulation with TgPRF . MHV68 infection also confers enhanced resistance to influenza A virus infection associated decreased viral titers , similar to previous results we reported for T . gondii infection [25] , [61] . γHV68-induced protection against both L . monocytogenes and influenza was associated with elevated IFN-γ and increased numbers of activated macrophages with enhanced antibacterial activity [60] , [61] . These results suggest that herpes virus and T . gondii exploit similar mechanisms to enhance antibacterial innate immunity . Inflammatory monocytes and TipDCs play key roles in defense against several other pathogens . Ly6C+ monocytes are recruited in CCR2 dependent manner and help initiate protective T cell responses following infection with Mycobacterium tuberculosis , Leishmania major , and Cryptococcus neoformans [14] . The importance of Ly6C+ monocytes against C . neoformans infection may explain prior observations that chronic T . gondii infection confers a survival benefit during co-infection with this pathogen [21] . Ly6C+ monocytes have been shown to reduce Plasmodium chabaudi circulating parasitemia in a mouse model of malaria and to enhance clearance of West Nile Virus [14] . Inflammatory monocytes and TipDCs may play a more limited or even detrimental role in other infections . TNF-α and nitric oxide produced by TipDCs contribute to tissue injury and liver necrosis during infection with Trypanosoma brucei [14] . TipDCs are recruited to the bladder via CCR2 during uropathogenic E . coli infection but are dispensable for bacterial clearance [62] . Future studies will examine the role of TgPRF recruited inflammatory monocytes during T . gondii and other infections .
Animals were housed under conventional , specific-pathogen-free conditions and were treated in compliance with guidelines set by the Institutional Animal Care and Use Committee of the University of Wisconsin School of Medicine and Public Health ( IACUC ) , according to IACUC approved protocol number M01545 . This protocol adheres to the regulations and guidelines set by the National Research Council . The University of Wisconsin is accredited by the International Association for Assessment and Accreditation of Laboratory Animal Care . Unless indicated otherwise , all mice used in this study were on a C57BL/6 background and used at 6–8 weeks of age . Wild-type ( WT ) mice were purchased from National Cancer Institute – Harlan , Frederick , MD . IL-12Rβ1−/− ( 002984 , B6 . 129S1-Il2rb1tm1Jm/J ) , IFN-γ−/− ( 002287 , B6 . 129S7-Ifngtm1Ts/J ) , Rag1−/− ( 002216 , B6 . 129S7-Rag1tm1Mom/J ) , CCR2−/− ( 004999 , B6 . 129S4-Ccr2tm1Ifc/J ) mice were purchased from Jackson Laboratory ( Bar Harbor , ME ) . TLR11−/− mice were a generous gift from Felix Yarovinsky [5] and were rederived at the University of Wisconsin . A/J mice ( National Cancer Institute ) were used for protein purification experiments because they more susceptible to L . monocytogenes infection than C57BL/6 mice [63] , which allowed us to detect subtle changes in bacterial burdens in partially active fractions . All animals were housed and bred under specific pathogen free conditions at an AALAC accredited facility at the University of Wisconsin School of Medicine and Public Health . All experiments were conducted in accordance with an IACUC approved protocol . L . monocytogenes strain EGD was a kind gift from C . Czuprynski . Mice were anesthetized with an isofluorane vaporizer connected to an IVIS 200 imaging system ( Caliper Life Sciences , Hopkington , MA ) then infected via retro-orbital i . v . injection with an appropriate number of bacteria to cause lethal infection as indicated . Animals were monitored daily for clinical signs of disease ( ruffled fur , hunched posture , paralysis , etc . ) and were euthanized if moribund . At 72 hours post infection , weight loss and bacterial burdens ( CFU/g ) in the spleen and liver were determined . WT mice were infected with approximately 6×104 CFU ( ∼6 LD50's ) , which consistently resulted in death or euthanasia of 100% of control animals . TLR11−/− , IL-12Rβ1−/− , IFN-γ−/− , Rag1−/− NK1 . 1-depleted , WT RB6-8C5 ( Ly6C/Ly6G ) -depleted , CCR2−/− , and WT 1A8 ( Ly6G ) -depleted mice were infected with approximately 4×104 CFU , 1×104 CFU , 200 CFU , 8×104 CFU , 200 CFU , 8×103 CFU and 8×103 CFU respectively . These doses were chosen because they resulted in bacterial burdens and weight loss similar to lethally infected WT mice . 10 week old WT mice were injected i . p . with 250 tachyzoites of the T . gondii strain PruΔ . In order to increase the number of animals that survived greater than 30 days into chronic infection , T . gondii-infected and control uninfected mice were all fed a diet containing sulfadiazine ( 1 , 365 ppm ) and trimethoprim ( 275ppm ) ( TD . 06596 , Harlan Teklad , Madison , WI ) from days 9 through 14 post T . gondii infection , then returned to a normal diet on day 15 through the duration of the experiment . For experiments in WT mice , soluble T . gondii antigens ( STAg ) was made from sonicated tissue culture grown tachyzoites ( 4×108/ml ) essentially as described previously [25] and typically had a protein concentration of ∼1 mg/ml . For experiments in TLR11−/− mice , double the amount of parasites ( 8×108/ml ) were used . Purified recombinant his-tagged T . gondii profilin ( rPRF ) was a kind gift from F . Yarovinsky [5] . rPRF preparations used in this study had endotoxin levels of 6×10−4 EU or 1×10−2 EU per 100 ng dose ( estimated 0 . 06 and 1 pg endotoxin respectively ) as measured by LAL assay ( Pierce , Rockford , IL ) . Blood was collected from mice via the lateral tail vein 2 or 24 hours post stimulation with rPRF as indicated . Serum was frozen in aliquots at −80°C and then analyzed using a Mouse Inflammation Cytometric Bead Array kit ( BD Biosciences , San Jose , CA ) according to the manufacturer's instructions . Spleens were dissociated by mechanical disruption and digested with collagenase/dispase ( 20 ug/ml , Roche , Indianapolis , IN ) and DNAse I ( 300 ug/ml , Roche ) for 30 min at 37°C and passed through a 70 um cell strainer ( BD Biosciences , San Jose , CA ) . Heparinized blood was collected via cardiac puncture and RBCs were removed by dextran sedimentation . Remaining RBCs were lysed with ammonium chloride . Cells were stained at 4°C in PBS with Live/Dead Violet Fixable Stain kit ( Invitrogen , Carlsbad , CA ) , washed , then stained in PBS with 0 . 5 mM EDTA , 0 . 2% BSA , 0 . 09% azide , and 2% normal rat serum ( Jackson ImmunoResearch , West Grove , PA ) . Anti-mouse CD45 ( 30-F11 ) APC , anti-mouse CD11b ( M1/70 ) PerCpCy5 . 5 ( eBioscience , San Diego , CA ) , anti-mouse Ly6C ( AL-21 ) PE , anti-mouse CCR2 ( 475301 ) Fluorescein ( R&D Systems , Minneapolis , MN ) , and anti-mouse Ly6G ( 1A8 ) PE-Cy7 antibodies were purchased from BD Biosciences except as indicated . Anti-Rat/Hamster CompBeads ( BD Biosciences ) were used to set compensation . Data were collected on an LSRII cytometer ( BD Biosciences ) and analyzed with FlowJo 7 . 6 . 1 ( TreeStar , Ashland , OR ) . All antibodies used for depletions were purchased from BioXCell ( West Lebanon , NH ) . To deplete NK cells , mice were treated with the anti-NK1 . 1 MAb PK136 ( 250 µg/animal ) . To deplete both Ly6Chi inflammatory monocytes and Ly6Cint Ly6G+ neutrophils , mice were treated with anti-Gr-1 MAb RB6-8C5 ( 250 µg/animal ) which recognizes a common epitope shared by Ly6C and Ly6G [43] . To deplete only neutrophils , mice were treated with anti-Ly6G MAb 1A8 ( 250 µg/animal ) which has been shown to deplete neutrophils in the spleen , liver and blood [44] , [48] , [49] . All depletion treatments were administered in PBS via i . p . injection beginning 48 hours prior to stimulation with rPRF , then continued every 48 ( 1A8 ) or 96 ( PK136 and RB6-8C5 ) hours after for the duration of the experiment . Graphs and statistical analysis were made using Graph Pad Prism ( San Diego , CA ) . Graphs represent means and error bars represent standard deviation except where noted otherwise . Bacterial burden data and cytokine were analyzed with the two-tailed student's t-test , and survival data were analyzed using the Log-rank ( Mantel-Cox ) method . p-values are represented by asterisks in figures as follows: *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 , and ****p<0 . 001 . We consider all p-values <0 . 05 to be significant .
|
Toxoplasma gondii is an apicomplexan parasite that can infect all warm blooded animals , but rodent species are considered the primary reservoirs . Mice that are infected with T . gondii become more resistant to lethal infection with other pathogens . Ly6C+ inflammatory monocytes are innate immune cells that are critical for defense against T . gondii and other infections . Mice with defects in the ability to recruit inflammatory monocytes fail to control T . gondii replication and succumb to overwhelming inflammation . In this study we used a co-infection model to explain why T . gondii-infected mice are more resistant to the bacterium Listeria monocytogenes . We show that stimulation of the rodent specific Toll-like receptor TLR11 by the T . gondii ligand profilin can recruit inflammatory monocytes , and that these monocytes can protect the host against L . monocytogenes . These findings make profilin an important tool for the study of monocyte biology during T . gondii infection of rodents and are especially interesting given that TLR11 is nonfunctional in humans and other vertebrates .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacterial",
"diseases",
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"protozoan",
"infections",
"biology",
"and",
"life",
"sciences",
"immunology",
"microbiology",
"listeriosis",
"infectious",
"disease",
"control",
"parasitic",
"diseases",
"toxoplasmosis"
] |
2014
|
Toxoplasma gondii Profilin Promotes Recruitment of Ly6Chi CCR2+ Inflammatory Monocytes That Can Confer Resistance to Bacterial Infection
|
Intense spiking response of a memory-pattern is believed to play a crucial role both in normal learning and pathology , where it can create biased behavior . We recently proposed a novel model for memory amplification where the simultaneous two-fold increase of all excitatory ( AMPAR-mediated ) and inhibitory ( GABAAR-mediated ) synapses in a sub-group of cells that constitutes a memory-pattern selectively amplifies this memory . Here we confirm the cellular basis of this model by validating its major predictions in four sets of experiments , and demonstrate its induction via a whole-cell transduction mechanism . Subsequently , using theory and simulations , we show that this whole-cell two-fold increase of all inhibitory and excitatory synapses functions as an instantaneous and multiplicative amplifier of the neurons’ spiking . The amplification mechanism acts through multiplication of the net synaptic current , where it scales both the average and the standard deviation of the current . In the excitation-inhibition balance regime , this scaling creates a linear multiplicative amplifier of the cell’s spiking response . Moreover , the direct scaling of the synaptic input enables the amplification of the spiking response to be synchronized with rapid changes in synaptic input , and to be independent of previous spiking activity . These traits enable instantaneous real-time amplification during brief elevations of excitatory synaptic input . Furthermore , the multiplicative nature of the amplifier ensures that the net effect of the amplification is large mainly when the synaptic input is mostly excitatory . When induced on all cells that comprise a memory-pattern , these whole-cell modifications enable a substantial instantaneous amplification of the memory-pattern when the memory is activated . The amplification mechanism is induced by CaMKII dependent phosphorylation that doubles the conductance of all GABAA and AMPA receptors in a subset of neurons . This whole-cell transduction mechanism enables both long-term induction of memory amplification when necessary and extinction when not further required .
Traditionally , increased synaptic response is believed to reflect activity dependent synaptic plasticity , in which the association between different inputs is strengthened through synaptic-specific potentiation . We have previously shown that acquiring the skill to perform in a particularly difficult olfactory-discrimination task [1–4] results in a robust enhancement of excitatory and inhibitory synaptic connectivity to and within the piriform cortex that lasts for days after training [5–8] . The synaptic enhancement was observed few days after the rats were last trained and thus indicates long term induced synaptic modifications . In particular , we suggested that this increase in synaptic strength results from a process where in a subset of cells all AMPA and GABAA receptors double their strength through a whole-cell two-fold increase of their single channel conductance . This process results in a two-fold increase in the strength of all excitatory and inhibitory synapses in the cell [9] , and thus can support the previously observed increase in synaptic strength [5–8] . Using a preliminary study of a computational network model [9] we suggested that if this mechanism is induced in a group of cells that compose a memory-pattern , it can substrate as a memory specific amplification mechanism [9] . A memory amplification mechanism should largely increase the spiking response of the cells that compose the memory pattern when the memory pattern is active , and have a minor effect when not active . In this work we aim to computationally establish the role of this mechanism in memory amplification by studying its effect on the net synaptic current and on the spiking response of a single cell . Moreover , through characterization of the amplification properties such as speed and stimulus dependencies we aim to obtain a further characterization of the functional effect of this mechanism . The memory amplification model extends beyond the well-studied activity-dependent synaptic plasticity that was suggested to underlie memory formation in several key ways: ( 1 ) It amplifies the response of an already formed memory-pattern rather than forming a new association between the different inputs . ( 2 ) The increased synaptic excitation that underlies the memory amplification is not synapse specific and is mediated by the increased strength of virtually all the excitatory synapses in the cell . ( 3 ) The increase in synaptic strength is not mediated by an increased number of AMPA receptors , as was shown for LTP [10–12] , but rather is mediated by increased AMPA receptor channel conductance . ( 4 ) Importantly , unlike activity-dependent synaptic plasticity , memory amplification requires a parallel and similar increase in the strength of synaptic inhibition . These characteristics enable this mechanism to be independent of memory formation and thus enable the promotion of an already learned memory into a dominant memory . Several studies have shown that CaMKII-mediated phosphorylation of the AMPA channel in ser831 causes a two-fold increase in AMPA channel conductance [13–15] , which is similar to the increase found in our setting . A co-increase in excitation and inhibition is vital for whole-cell balanced amplification . This suggests that both increased excitation and increased inhibition are induced by a shared process for which CaMKII is a potential candidate that doubles the conductance of all AMPA and GABAA receptors in the cell . Indeed we recently showed that the increase in inhibitory and excitatory synaptic transmission that was induced by learning the task is CaMKII dependent and that this increase is the result of increased conductance of the GABAA and AMPA channels [7 , 8] . In these studies the blocker effect on each cell was measured by comparing the averaged synaptic event amplitude before and after the CaMKII blocker was applied . In the current study we further examined whether the task learning induced reduction in averaged synaptic event amplitude is mediated through the effect on memory amplification mechanism , namely through reversal of the two-fold multiplication of the strength of all GABAA and AMPA mediated events in a subset of cells . In these studies , miniature inhibitory and excitatory post-synaptic currents were recorded in trained and control animals before and after application of CaMKII blockers [7 , 8] . The generation of a miniature synaptic event is triggered by a spontaneous release of one vesicle in the presynaptic site which activates synaptic receptors that are densely packed in a nanodomain cluster opposite to the release site [16] . Each nanodomain cluster is 70 nm in length , contains 20 synaptic receptors and has an average distance of 250 nm from other nanodomain clusters in the same PSD [16] . The binding of the CaMKII molecule to the PSD in the cytoplasmatic site [17 , 18] enable it to phosphorylate the synaptic receptors . The nanoscale size of the cluster and the auto-phosphorylation characteristics of CaMKII can enable a single CaMKII molecule located in close proximity to phosphorylate all synaptic receptors in this cluster [19] , and thus to affect all synaptic receptors that mediate a single miniature synaptic event . The miniature synaptic events are spontaneously evoked and therefore do not reflect a pathway-dependent activity . This enables us to use these recordings to analyze the whole-cell effect of CaMKII and to study if this enhancement can be explained within the scope of the memory amplification mechanism . Moreover , while in the previous study [9] we indirectly inferred the multiplication mechanism based on between-groups comparison , in the current study , the learning specific effect of CaMKII blocker allows us to directly establish the induction of the memory amplification mechanism based on within-cell comparison . This enables us to further examine the segregated effect of learning and the induction of a cell-based two-fold multiplication through a whole-cell transduction mechanism . If CaMKII phosphorylation is indeed the molecular mechanism that underlies memory amplification , it is expected that in the selected subset of cells CaMKII blocker will induce a two-fold reduction in all AMPA and GABAA mediated synaptic responses , through a two-fold decrease in the conductance of all AMPA and GABAA receptors . Thus the memory amplification model has four testable predictions for both GABAA and AMPA mediated miniature synaptic events: ( a ) the effect of the CaMKII blocker will be prominent in a sub-group of cells . ( b ) In this subset of cells the effect will be mediated by a decrease in the single channel conductance . ( c ) The effect of the CaMKII blocker should correspond to a reduction of each event amplitude by a factor of two , and ( d ) the sub-group of cells that was affected by CaMKII blockers should be the sub-group of cells that was affected by task learning . In this work we first analyze the experimental data in depth , to test the predictions of the model . Then , using theory and single-cell biophysical simulations , we show how such a whole-cell increase in excitatory and inhibitory synaptic inputs can function as an instantaneous and linear amplifier of the cell spiking response . These findings establish the basis for the memory amplification mechanism; namely , applying a cell specific linear amplifier on a subset of cells that constitutes a memory-pattern can lead to amplification of this memory when activated .
In this part , we aim to explore the functional significance of whole-cell synaptic multiplication , in which the strength of all excitatory and all inhibitory synapses in a single cell was doubled . We previously showed in a preliminary study , using integrate and fire neurons , that such a whole-cell two-fold multiplication in synaptic strength functions as a memory amplification mechanism [9] . Here we attempt to bridge the gap between the simplified integrate and fire neuron that was used in the network simulations and a more realistic biophysical neuron . To that end , using biophysical simulations , we first characterize the effect of a whole-cell , two-fold increase in synaptic strength on the neurons' firing response . A detailed characterization of the effect of the whole-cell amplification mechanism on the number of spikes should enable a better understanding of its effect on memory amplification . An amplifier whose main effect on the single cell firing response is at the near-threshold level should have a different effect on memory amplification than an amplifier whose main effect is at the supra-threshold level . Moreover , an amplifier which instantaneously follows the changes in synaptic input is likely to exert a different effect than a history dependent amplifier . We used a single cell with a simplified morphology and realistic biophysical properties ( see Materials and Methods ) , where realistic post-synaptic excitatory and inhibitory responses were implemented along the basal and apical dendritic cylinders . In order to simulate synaptic bombardment as recorded in vivo we mimicked the firing of populations of inhibitory and excitatory pre-synaptic neurons using an independent random Poisson process such that the reversal potential of the net synaptic current ( see Material and Methods ) during background activity was about -37 mV [20] .
In this study we verified the hypothesis in which in a subset of cells , learning the task induced a whole-cell , CaMKII dependent process , in which all the inhibitory and excitatory synapses doubled their strength through a two-fold increase in the single channel current of the AMPA and GABAA receptors . All the predictions derived from this hypothesis were confirmed using two different blockers of CaMKII for both inhibition and excitation . Validation of the first and second predictions indicates that only a subset of the cells underwent a task learning induced CaMKII dependent phosphorylation that significantly increased the synaptic single channel current and as a consequence the average event amplitude . The clustered effect could not be explained using within-animal variation since for almost every cell in the affected cluster there was a cell from the same rat that was in the non effected cluster . Moreover , the recordings were taken from identified pyramidal neurons in layer II of the piriform cortex; therefore the clustering effect could not be ascribed to different types of cells being different [7 , 8] . The blocker effect was mainly apparent in cells from the trained group ( 30–40% of the cells ) , present to a lesser extent in the pseudo-trained group ( 0–10% ) and absent in the naïve group ( 0% ) . Thus , learning the task clearly induced the amplification process , even though the recordings were made 4–5 days after training ended . The third prediction stated that CaMKII blocker would reduce the amplitude of each event by a factor of two . This prediction required a uniform synaptic event-based effect rather than an average effect . We approached this issue by computationally reversing the effect of the CaMKII blocker on the histogram of event amplitudes through a uniform two-fold multiplication of the events amplitudes . In each cell in the affected cluster , this manipulation yielded a full computational reversal of the blocker effect , thus confirming that the CaMKII blocker reduced the amplitude of each synaptic event by a factor of two . Due to the moderate concentrations of CaMKII blockers , the blockers did not yield a full effect on the cells in the affected cluster ( see results ) . The effect of tatCN21 ( inhibition 147±7%; excitation 148±3% ) was significantly smaller than the effect of KN93 ( inhibition 165±9%; excitation 173±21% ) . The similar effect of each blocker on inhibition and excitation supports the notion that indeed the discrepancy between the expected effect of the twofold reduction and the experimentally observed effect is due to a partial effect of the blocker . For each cell , the fraction of blocker affected events was used when calculating the reversal of the blocker effect on the amplitude distribution . This reversed blocker effect cannot be attributed to the calculated fraction since when assuming different processes we did not achieve a full reversal of the blocker effect on the events amplitude histogram , even though the effect on the averaged amplitude was reversed . The fourth prediction stated that the cells that were affected by CaMKII blocker are the cells that were affected by the task learning; thus in these cells , there was a process that caused all the synaptic AMPA and GABAA receptors to multiply their single channel current by a factor of two . This prediction was confirmed by demonstrating that the single channel current in these cells was twice as high as in the controls , and that their amplitude distribution curve could be reconstructed by two-fold multiplication of all the events in the control group . The affected cells could be discriminated from the non- affected cells solely based on their single channel current , which was indicative of their difference before the CaMKII blocker was applied . In addition to demonstrating the task learning effect on the CaMKII blocker-affected cells , we argued that for inhibition , the multiplication process might account for virtually all the measured learning induced modifications of the synaptic events . We showed that the two-fold multiplication of the average inhibitory event amplitude in a randomly selected 35% of the cells could fully account for the learning induced modification on the cumulative frequency curve of all the cells . However we could not fully explain the learning induced modification in the cumulative frequency curve of excitatory events . Task learning induced a shift in the cumulative curve that could not be accounted for by the multiplication process in a subset of cells . Thus learning the task induced an additional process that caused an almost constant increase in the average excitatory events amplitude in all cells in the trained group . Phosphorylation of the AMPA receptor by CaMKII is known to double the single channel conductance of AMPA receptors [13–15] . These findings consolidate our basic premise that CaMKII induces a two-fold increase of the AMPA receptor conductance . Although a two-fold increase in the AMPA channel conductance as a result of CaMKII dependent phosphorylation is believed to be a universal property of the AMPA receptor , currently there are no other findings that demonstrate the induction of the memory amplification mechanism in other brain regions . However since the amplification mechanism is a whole-cell mechanism and thus appears to be independent of circuit property , the memory amplification mechanism is likely to be induced also on pyramidal cells in other brain regions . Future demonstration of the induction of this mechanism in other regions can further increase its impact , as learning is known to be supported by parallel modifications in several regions . As expected by a mechanism that is induced through whole-cell transduction mechanism , the reversal of the memory amplification mechanism using CaMKII blockers was rapid ( several minutes passed between the application of the drug in the perfusion solution and the effect of CaMKII blocker ) . In this set of experiments the recordings were made at a fixed interval of 4–5 days after training termination [7 , 8] . This interval allows us to suggest that the memory amplification mechanism comprise a long-term effect of learning , nevertheless it does not enable us to determine the time scale of the induction of this mechanism . Resolving this issue is significant for understanding the behavioral role of the amplification mechanism in different settings . Using simulations we have shown that whole-cell balanced amplification acts mainly via multiplicative amplification of the average and standard deviation of the net synaptic current; as such its effect is correlated with the size and the polarity of the net synaptic current . Moreover , we showed using simulations and analytical study that in the balanced state this modification leads to a linear amplification of the number of spikes with a fairly stable multiplication factor . This trait suggests that the amplification mechanism is a robust and stable mechanism for gain modulation . Quite a few studies have attempted to describe the mechanism of gain modulation . It has been shown that tonic inhibition creates a divisive operation during physiological excitation in which the cell is under continuous bombardment of synaptic activity [26–30] . In other studies [22 , 29 , 31–33] , it was shown that depolarizing the membrane potential in the presence of synaptic noise increases the response gain . In both mechanisms ( i . e . increase in tonic inhibition and depolarization of the membrane potential ) , the change has an additive effect on the input-output function , and in both , the amplitude of the baseline noise determines the shift from additive to multiplicative operations [26 , 31] . Indeed , theoretical studies [31 , 32] have shown that in the presence of noise , the firing frequency becomes a power-law function of the membrane potential but only for a limited range of values , and that this is primarily visible at low firing frequencies . Due to this limitation , these mechanisms cannot be considered a robust driver of gain modulation at supra-threshold conditions [34] . The mechanism we suggest here is inherently multiplicative and cell selective , and hence can cause robust cell specific gain modulation . We found that both the amplitude and the standard deviation of the net synaptic current were multiplied through a whole-cell balanced multiplication by approximately the same constant for all simulations . The simulations spanned a large space of several parameters , which effectively produced different initial conditions for the inhibition excitation ratio , and a different activation curve of the cell . We confirmed these results analytically and showed that in the balanced state whole-cell multiplication induces a linear amplifier on the number of spikes . Moreover , this analysis showed that when the cell is not in the threshold regime , the multiplication factor is fairly stable . Theoretical works suggested that gain modulation can be mediated by changes in intrinsic excitability [35 , 36] , and more specifically through modulation of the currents that mediate the firing frequency adaptation; however using simulations these studies indicated that the induced gain is not robust at lower firing frequencies [36] . Moreover , since pyramidal cells differ in their firing adaptation properties , the resulting gain modulation varies considerably across cells . Gain modulation mechanisms that act through modulation of the firing frequency adaptation are dependent on the past spiking activity of the cell . In the balanced regime spiking activity is inherently irregular and therefore a consistent gain modulation effect of such mechanisms can be achieved by averaging , or by long time integration of the spiking activity . Whole-cell balanced amplification modifies the synaptic input directly and as such is not dependent on past spiking activity . Moreover its underlying multiplicative process enables it to respond in particular to large deviations in the membrane potential . These characteristics enable it to act instantaneously to rapid changes in the synaptic input . We showed that whole cell balanced multiplication could reliably amplify the number of spikes in 75% of the voltage bumps that are caused by a transient , brief elevation of excitatory synaptic activity , despite ongoing differences in synaptic noise . These characteristics enable a reliable instantaneous amplification during transient periods of brief orchestrated elevations of the membrane potential that results from a brief external stimulus such as touch , sound and visual stimulation [25 , 37–39] . To summarize , to our knowledge the memory amplification mechanism is currently the only candidate for a real-time amplifier that creates a high impact also at highly supra-threshold values . While a mechanism that works through depolarizing the membrane potential is real time , it exerts its main impact mainly at near threshold , and mechanism that works through firing frequency adaptation is history dependent and thus does not react instantaneously to changes in the membrane potential . Amplification of a subset of cells that comprise a memory-pattern would primarily amplify the response of these cells when memory is activated . Due to the multiplicative nature , the net effect of the amplification during baseline activity is much smaller than when the cell is tuned to the input . This tendency is increased by the larger multiplication factor of the net synaptic current at membrane potentials that are considerably more depolarized than the GABAA reversal potential . This enables the amplification to have a large net effect mainly when the memory-pattern is activated and the cells receive large excitatory input . In this fashion , the amplification mechanism does not modify the information carried by the memory pattern , but only amplifies it . We showed previously [9] using a simple computational model that this mechanism induces selective memory amplification . The current work explains the underlying cellular basis of this property . A parallel increase in inhibition and excitation was previously suggested to function as a network-wide gain increase mechanism [40 , 41] . This effect is mediated by the non-specific effect of neuro-modulators which increases the membrane potential of both the excitatory and inhibitory neurons [41] . While such a mechanism can affect coding by generally impacting synaptic circuits between different regions [40] , the mechanism presented here is cell-specific and thus can affect coding by amplifying an input specific response . A pattern of cells that underlies a critical behavioral response should reliably respond to the input , even during brief periods . Increasing the reliability of eliciting a spike in each of these transient periods can create a consistent response in such states . We showed that the effect of whole-cell multiplication on the probability of eliciting a spike possesses the following properties ( a ) at threshold values the probability of eliciting a spike was increased nearly to one and ( b ) the increase in probability was prominent only if the ability of the event to elicit a spike was not zero . These properties enable whole-cell multiplication to induce reliable coding without modifying the information carried by the synaptic input . The whole-cell increase in synaptic strength observed after learning the task is not the result of homeostatic synaptic scaling [42 , 43]; Learning-induced increase in synaptic strength occurs when the spike firing rate is enhanced [44–46] rather than decreased [42 , 43] . Thus , while homeostatic synaptic scaling is a negative feedback control mechanism , learning-induced whole-cell synaptic strengthening appears to be a positive feedback mechanism . Moreover , while the memory amplification mechanism appears to induce a uniform scaling of all synapses , homeostatic synaptic scaling appears to cause a general increase in synaptic strength which does not appear to be uniform . Indeed , increase in synaptic strength that is supported by a pseudo binary increase in single channel conductance as we showed for the memory amplification mechanism allows such a uniform increase , while increase in synaptic strength which is supported by addition of synaptic receptors appears to be more general and less uniform [43] . An across-the-board increase in the strength of all excitatory synapses in a sub-group of cells cannot be explained by the classical learning theories that underlie memory formation in which only synapses relevant to the storage of a memory are strengthened . Moreover , while we show that doubling the channel conductance underlies the increase in synaptic strength; activity-dependent synaptic enhancement is thought to be mediated by an increase in the number of AMPA channel receptors [47 , 48] . Indeed , memory amplification should not modify the composition of the subset of cells that compose the memory and thus should be uniformly applied on all synapses in this subset . Moreover , since memory formation is tuned by the correlation between presynaptic and postsynaptic activity in each synapse , it should support a continuous increase in synaptic strength and thus can be implemented by addition of AMPA channels . In contrast , a linear amplification of a memory can be induced by multiplying its firing response by a single factor and thus can be implemented by a uniform two-fold increase of the synaptic strength . We presented a cell-specific amplification mechanism that can induce a memory selective amplification process when induced on a subset of cells that comprise a memory-pattern . This memory enhancement is independent of memory formation . A memory of vital importance needs to be enhanced to dominate subsequent behavior [49 , 50] . When the memory becomes less crucial it should be de-enhanced to balance its weight with the weights of other memories . The induction of whole cell multiplication via a whole cell transduction mechanism and its action through channel conductance enables it to be readily toggled-on when needed and off when no further needed . Such properties may underlie the extinction and relapse seen in our behavioral settings . Thus whole-cell CaMKII mediated phosphorylation can function as a “software switch” unlike a hardware switch that requires morphological modulation as was suggested in processes that accompanies memory formation .
In this study we have used data from previously published experiments in which training- induced modification of inhibitory [8] and excitatory [7] synaptic transmission was studied . For sake of clarity we present here a brief description of the animal training and the experimental recording setup . More details can be found in previously published works ( behavioral settings: [1 , 2 , 51]; Slice preparation and recordings: [7 , 8] ) Animal training: Rats designated to the trained group were rewarded with water when they chose the correct positive cue . Rats designated to the pseudo-trained group were rewarded with water when choosing any odor in a random manner , and rats in the naive group were left in their home cages and were water deprived only . Training consisted of 20 trials per day for each rat [2] . Two learning phases can be seen during training [1 , 6]: in the initial phase of "rule learning" , which requires 7–8 consecutive training days , the rat forms a strategy for executing the olfactory discrimination task , and in the second phase the rat exhibits an enhanced learning ability , and can learn novel odors within 1–2 training days . Once the rats have reached the criterion for olfactory discrimination , they were allowed to rest for 4–5 days and then brain slices were prepared . Recordings [7 , 8]: Whole cell recordings of spontaneous events where done in the presence of 1 μM tetrodotoxin and were performed 10–15 min after membrane rupture and lasted for up to 45–50 min . Miniature excitatory post-synaptic currents ( mEPSCs ) were recorded at holding potential of −80 mV causing miniature inhibitory synaptic events to be exceedingly small due to the small driving force and leaving most voltage-dependent channels closed and NMDA receptors seldom activated . To record GABAA-mediated miniature IPSCs ( mIPSCs ) , the recording electrode contained 140 mM cesium chloride solution in order to reach a reversal potential of 0 mV , yielding a strong GABAA-mediated currents at holding potential of −60 mV . The perfusion solution also contained DNQX ( 20 μM ) and APV ( 50 μM ) , to block glutamatergic synaptic transmission via AMPA receptors , thus allowing recording of pure IPSCs . CaMKII blockers: the CaMKII inhibitor KN93 ( 10 μM ) and the CaMKII cell-penetrating peptide inhibitor , tatCN21 ( 5 μM; GL Biochem , Shanghai Ltd , China ) were used in order to inhibit the active state of CaMKII . Estimate of the average single channel current and the average number of active channels were obtained using a peak-scaled non-stationary fluctuation analysis ( NSFA ) of synaptic events [13] . The NSFA was applied on the events that were electrotonically nearby ( 10–90% rise-times < 1 . 5 ms ) . Using Mini analysis software ( Synaptosoft Inc . ) , events ( 80–300 per each cell ) were scaled and aligned by their peak , and their decay phase was divided to 30 bins . The single channel current ( i ) and the number of channels ( N ) were calculated ( using Mini Analysis Software ) by fitting the theoretical relationship for the peak scaled variance ( σ2 ) after subtraction of the background variance σb2: σ2 ( t ) =i·I ( t ) –I ( t ) 2/N+σb2 Only cells in which the fitting of the equation yielded an R>0 . 85 were incorporated in the analysis . Under the assumption of a model in which the blocker effect ( averaged events amplitude before the blocker divided by the averaged events amplitude after the blocker ) on the event amplitude is a division by a factor of a , given that a fraction x of the events was affected by the blocker then the blocker effect y should be calculated as follows: y= ( 1−x ) +x*a Some algebra enables as to calculate the fraction of the events that was modified by the blocker ( x ) as x=y−1a−1 This study examined the effect of whole-cell balanced multiplication on the spiking activity of neurons , using computer simulations . We used a conductance based cell with simplified morphology and a realistic generation and adaptation of action potentials as was observed in recordings from pyramidal cells in the piriform cortex [2] . The uniform whole-cell property where the strength of all synapses is doubled makes whole-cell balanced amplification relatively insensitive to the locality of the synaptic responses and thus relatively independent of the precise morphology of the cells . This enabled us to implement the modeled cell morphology as a simplification of a pyramidal cell where the apical and basal dendritic trees were reduced to long cylinders connected to the soma [52] . Both the morphology and the passive properties were constructed such that the membrane decay time constant ( 12 ms ) and the size of a single excitatory synaptic current ( 1 mV ) would approximate previously measured parameters in-vitro [1 , 53] . In our model cell , the input resistance ( 114 MΏ ) was in the high range of previously reported values [1] , in order to compensate for the leak introduced by the sharp electrode used in these experiments . The currents generated by the excitatory and inhibitory synapses were modeled by an alpha function with realistic rise times and decay times . The excitatory and inhibitory synapses had characteristics of decay and rise times as measured for AMPA and GABAA receptor mediated currents [6] . 350 Excitatory and 350 inhibitory synapses were uniformly distributed along the apical and basal dendrites . The reversal potential of the net synaptic current resulted to a value of around -37mV , which similar to the value measured experimentally during UP-states [20] , and is theoretically expected at balanced state . In our simulations , we measured the reversal potential of the net synaptic current by measuring the amplitudes of the averaged net synaptic current at various holding potentials . The amplitudes of the excitatory synapses were chosen based on the excitatory miniature event amplitude distribution ( Fig 5B , naive ) and scaled to result in average amplitude of 70pA . The scaling factor reflects the ratio of the average miniature excitatory synaptic event ( mepsc ) amplitude and the average synaptic response to one presynaptic action potential in piriform neurons [54] . Since there is no definitive evidence that in general Hebbian learning also modifies the inhibitory synapses , for simplicity we modeled the inhibitory synapses as a uniform distribution where the average response yielded a synaptic current with amplitude of 100 pA . The activation frequencies of inhibitory and excitatory synapses differed across simulations and ranged from 2 to 5 Hz . These values resulted in a voltage standard deviation of 3 . 9±0 . 23 mV . The reversal potential of the net synaptic current was determined as the holding potential at which the net synaptic current averaged over 200 ms was amounted to zero . Although the tail of this distribution cannot be fully accounted by learning , a simple way to divide the population of synapses to strong and weak synapses is through a division of the miniature amplitude distribution curve . The synapses were divided into strong and weak based on the miniature amplitude distribution curve ( Fig 5A , weak synapses < 13 pA the Gaussian like section of the distribution; strong synapses > = 13 pA the tail of the distribution ) . When scaling values between zero and one , strong synapses had a relative conductance of 0 . 25–1 and weak synapses had a relative conductance of 0 . 04–0 . 25 . The rationale behind increasing the proportion of strong synapses is that in Hebbian learning synapses that connect co-active cells become stronger . Therefore when a cell participates in a learned Hebbian memory the proportion of strong synapses will increase . We divided the increase into four levels where the proportion of active strong excitatory synapses was 30% , 45% and , 60% . At rest , strong and weak synapses had the same activation probability . The increase in the proportion of strong synapses was generated by increasing the frequency of the strong synapses and decreasing the frequency of weak synapses . The decrease in the frequency of weak synapses and the increase in the frequency of strong synapses were done such that the total number of excitatory synaptic events was maintained constant . The whole-cell balanced amplification mechanism acts directly through modulation of the synaptic strength and is only indirectly dependent on intrinsic excitability . In this paper we aimed to understand the functional outcome of this mechanism and not address its quantitative effect on spike frequency . Therefore we did not do extensive modeling of the cell’s intrinsic properties . We modeled a regular spiking pyramidal cell that exhibits moderate adaptation [2] . We included voltage dependent currents that are known to participate in the generation of the action potential and the refractory period: transient ( inactivating ) sodium current gNa; delayed rectifier potassium current gK ( DR ) ; transient inactivating gK ( A ) ; In addition we included currents that participate in spike adaptation: muscarinic receptor suppressed potassium conductance gK ( M ) ; slow calcium dependent potassium conductance gK ( AHP ) . For the activation of the gK ( AHP ) we implemented a high threshold non- inactivating calcium conductance gCa ( H ) and calcium homeostasis mechanism . [Ca]i was elevated by the calcium current and decayed due to calcium homeostasis mechanisms according to the following equation: [Ca]i′=−Icadepth∙F∙4000∙107−[Ca]i−[Ca]oτ , where depth is the cell diameter , and τ is the time constant of [Ca]i removal . The equation describing muscarinic conductance was taken from [55]; the equations describing the rest of the active conductance were taken from [56] in a work that modeled a cortical pyramidal cell . The rate parameters were kept at the same values and conductance densities were varied to result in spiking activity that resembled a regular spiking pyramidal cell . The conductances that generate the action potential ( gNa; gK ( DR; gK ( A ) ) were located in the soma whereas the currents that underlie the after- hyperpolarization and the calcium current ( gk ( M ) ; gca ( H ) ; gk ( AHP ) ) were located in the proximal apical dendrite . The response of the cell to a step current was similar to the responses of cells as recorded in the piriform cortex , both in the characterization of action potential and their firing frequency adaptation ( Fig 2A; [1 , 2] ) . The mechanism of whole-cell balanced multiplication was implemented by multiplying the strength of all excitatory and inhibitory synapses . As was observed previously ( see Results ) , the increased strength was mediated by multiplying the conductance of both the excitatory and the inhibitory synapses by a factor of 2 . The default values for the simulation parameters are noted at Table 1 . The parameter space for Fig 7 was created by varying the amplitudes of the excitatory and inhibitory currents and their activation frequency . The average conductance of the AMPA receptor ranged from 0 . 6 and 10 nS and the average conductance of the GABAA ranged from 0 . 9–2 . 7 nS . For each value of the excitatory and inhibitory conductance the activation frequency of both synapses ranged from 2-5Hz . Only simulations that yielded an average membrane voltage of less than -55 mV were included . For each point in this parameter space , simulations were performed for the 4 activation levels of the strong synapses; for each level the average over ten trials was calculated and each trial was calculated over a period of 500ms . In this parameter space where both the average amplitude and activation frequency of the synapses varied , the net synaptic current before whole-cell balanced amplification was used as a uniform indicator of the synaptic strength . For Figs 9 and 10 the parameter space was created by varying the reversal potential of the leak current from -100 to -70 . This manipulation modified the averaged membrane potential during baseline activity from -62 mV to -75 mV , which effectively shifted the distribution of the baseline membrane potential relative to threshold and is equivalent to modification of the term θσ in Eq 2 . In addition , the strength of the excitatory and inhibitory synapses was varied by independent multiplication by factors that ranged from 0 . 6 to 2 , and the activation frequency of the inhibitory and excitatory synapses was co-modulated up to a factor of 3 . These manipulations are equivalent to modulating σ and I¯ . As the aim of these simulations was to yield instantaneous and not the average effect of whole cell multiplication , the results presented in this figure are from single traces . Simulations were conducted using the Neuron simulation program [57] .
|
Amplifying the strength of a neuronal assembly that underlies a behavioral choice can lead to a particularly long lasting dominant memory . We report experimental and theoretical evidence for a long-term mechanism that amplifies the response of a neuronal assembly which we termed “memory amplification mechanism” . The amplification mechanism is mediated by doubling the strength of all inhibitory and all excitatory synapses in the cell and is induced by whole-cell phosphorylation of all inhibitory and excitatory synaptic receptors in a subset of cells , via a process that is distinct from memory formation . Computationally , the inherent scaling of both excitation and inhibition yields a robust and stable amplifier of the neuron’s response . When such an amplifier is induced in a set of cells that compose a memory-pattern , it can selectively amplify the response of this memory . The memory amplification mechanism is independent from associative learning . Thus , while associative learning forms a memory that encodes new associations , the amplification mechanism can promote an already formed memory to a dominant memory .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"phosphorylation",
"learning",
"medicine",
"and",
"health",
"sciences",
"action",
"potentials",
"nervous",
"system",
"membrane",
"potential",
"social",
"sciences",
"electrophysiology",
"neuroscience",
"learning",
"and",
"memory",
"cognitive",
"psychology",
"mathematics",
"cognition",
"memory",
"distribution",
"curves",
"statistical",
"distributions",
"animal",
"cells",
"proteins",
"probability",
"theory",
"biochemistry",
"psychology",
"cellular",
"neuroscience",
"anatomy",
"synapses",
"cell",
"biology",
"post-translational",
"modification",
"physiology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"cognitive",
"science",
"neurophysiology"
] |
2017
|
Real Time Multiplicative Memory Amplification Mediated by Whole-Cell Scaling of Synaptic Response in Key Neurons
|
Understanding the genetic , structural , and biophysical mechanisms that caused protein functions to evolve is a central goal of molecular evolutionary studies . Ancestral sequence reconstruction ( ASR ) offers an experimental approach to these questions . Here we use ASR to shed light on the earliest functions and evolution of the glucocorticoid receptor ( GR ) , a steroid-activated transcription factor that plays a key role in the regulation of vertebrate physiology . Prior work showed that GR and its paralog , the mineralocorticoid receptor ( MR ) , duplicated from a common ancestor roughly 450 million years ago; the ancestral functions were largely conserved in the MR lineage , but the functions of GRs—reduced sensitivity to all hormones and increased selectivity for glucocorticoids—are derived . Although the mechanisms for the evolution of glucocorticoid specificity have been identified , how reduced sensitivity evolved has not yet been studied . Here we report on the reconstruction of the deepest ancestor in the GR lineage ( AncGR1 ) and demonstrate that GR's reduced sensitivity evolved before the acquisition of restricted hormone specificity , shortly after the GR–MR split . Using site-directed mutagenesis , X-ray crystallography , and computational analyses of protein stability to recapitulate and determine the effects of historical mutations , we show that AncGR1's reduced ligand sensitivity evolved primarily due to three key substitutions . Two large-effect mutations weakened hydrogen bonds and van der Waals interactions within the ancestral protein , reducing its stability . The degenerative effect of these two mutations is extremely strong , but a third permissive substitution , which has no apparent effect on function in the ancestral background and is likely to have occurred first , buffered the effects of the destabilizing mutations . Taken together , our results highlight the potentially creative role of substitutions that partially degrade protein structure and function and reinforce the importance of permissive mutations in protein evolution .
A central goal in studies of molecular evolution is to reveal the genetic , structural , and biophysical mechanisms by which protein functions have evolved [1]–[6] . Ancient proteins and DNA are seldom directly available , but the traces of their evolutionary history are found in their extant descendants [7] . Direct comparisons among present-day proteins can sometime yield insights into the sequence and structural mechanisms that underlie functional differences [8]–[11] . Such “horizontal” comparisons , however , cannot determine which protein features are ancestral and which are derived , so they are not suited to reconstructing the events that produced functional diversity [12] . Further , because the effect of a mutation on protein structure and function often depends on the residues present at other sequence sites [13]–[17] , studies of extant proteins may often be unsuited to revealing the effects of mutations in the historical backgrounds in which they occurred [12] . Ancestral sequence reconstruction ( ASR ) allows the forms and functions of ancient proteins to be studied experimentally . Beginning with an alignment of extant sequences , the maximum likelihood phylogeny and best-fit probabilistic model of evolution are inferred; the most likely ancestral sequence at any node – defined as the sequence with the highest probability of delivering all the observed extant sequences – can then be identified [18] . These ancestral protein sequences can be “resurrected” using gene synthesis and cell culture or in vitro expression systems and then characterized using the same methods typically applied to study extant proteins . This approach allows hypotheses about the ancestral and derived characteristics of proteins to be tested experimentally . It also allows the historical interval during which structure and function changed to be identified and the causal role of specific historical mutations in the ancestral background to be determined . The glucocorticoid and mineralocorticoid receptors ( GR and MR ) are paralogous hormone-regulated transcription factors that have served as useful models for studying protein evolution [15] , [16] , [19] . GR and MR have a modular domain structure that includes a well-conserved DNA-binding domain ( DBD ) and a moderately conserved ligand-binding domain ( LBD ) – which binds the hormone , changes conformation , and attracts coactivator proteins that potentiate transcription of nearby target genes; they also contain poorly conserved hinge and N-terminal domains . In most bony vertebrates , the intrinsic functions of the GR and MR LBDs differ in both specificity and sensitivity . GR is more specific , being activated by high doses of the adrenal hormone cortisol to regulate aspects of immunity , glucose metabolism , and the long-term stress response [20] , [21] . MR , in contrast , is activated by the adrenal mineralocorticoids aldosterone or deoxycorticosterone , as well as cortisol ( albeit with somewhat lower sensitivity ) , and primarily regulates osmotic homeostasis . GR is also considerably less sensitive than MR , often requiring concentrations several orders of magnitude higher for activation [19] , [22] , [23] . Some information is available on GR and MR evolution . The two paralogs descend by duplication from a single ancestral corticosteroid receptor ( AncCR ) , which existed in an ancient jawed vertebrate ∼450 million years ago , before the divergence of bony vertebrates from cartilaginous fishes ( Figure 1 ) [19] , [24] . Reconstruction and experimental analysis showed that AncCR , like the extant MRs , was extremely sensitive to both mineralocorticoids and glucocorticoids , and its structure was MR-like , as well [19] . Subsequent work revealed that GR's specificity for glucocorticoids evolved later in the lineage leading to bony vertebrates , after the divergence of cartilaginous fishes but before the split of ray-finned fish from the lineage leading to tetrapods and lobe-finned fish , due to a small specific set of historical mutations [15] , [16] , [19] ( Figure 1 ) . The evolutionary causes of GR's reduced hormone sensitivity are not known . In the little skate – the only cartilaginous fish studied to date – GR is a low-sensitivity , broad-spectrum receptor: like MR , it responds to both glucocorticoids and mineralocorticoids , but it is unique in requiring high concentrations of either type of hormone to activate it . The difference in receptor sensitivity between the GR and MR is thought to have physiological consequences: in several elasmobranch species , the same corticosteroids appear to regulate both stress and osmolarity [25]–[28] , and the highest titres are associated with stress conditions [29] , [30] . These observations suggest that GR regulates stress in response to high doses of hormones , while MR regulates osmolarity in response to much lower doses [23] . Based on these data , we hypothesize that GRs' reduced sensitivity to all hormones was an independent evolutionary event that occurred before cartilaginous fishes split from bony vertebrates , and before glucocorticoid specificity evolved in the GRs of bony vertebrates [15] , [19] . Here we report on experiments to test this hypothesis and determine the genetic , structural , and biophysical mechanisms by which GR's reduced hormone sensitivity evolved . We first resurrected the LBD of AncGR1 ( Figure 1 ) – the GR protein present in the common ancestor of bony and cartilaginous vertebrates and the earliest node after the GR-MR split – and then used functional assays , X-ray crystallography , site-directed mutagenesis , and computational predictions of biophysical parameters to dissect the mechanisms by which GR evolved . We show that after its initial birth by gene duplication , a small number of mutations that partially degraded its structure , stability , and function caused GR to become a novel low-sensitivity receptor .
Statistical confidence in ASR depends in part on taxon sampling in groups descending directly from the node of interest [31]–[33] . Although GR sequences are available from many bony vertebrates , only a single GR sequence from cartilaginous fishes has been previously sequenced . We therefore isolated additional GRs sampled from throughout the cartilaginous fishes and characterized the functions of their LBDs . Specifically , we isolated GRs from four elasmobranch species – the Atlantic sharpnose shark ( Rhizoprionodon terraenovae ) , brownbanded bambooshark ( Chiloscyllium punctatum ) , small-spotted catshark ( Scyliorhinus canicula ) , and the Atlantic stingray ( Dasyatis sabina ) – and one holocephalan , the elephant shark ( Callorhincus milii ) ( Figure 1 ) . We used a luciferase reporter gene expression assay to characterize the sensitivity of each LBD to four major corticosteroids present in elasmobranchs – 11-deoxycorticosterone ( DOC ) , corticosterone , 1alpha-hydroxycorticosterone , and 11-dehydrocorticosterone [34] , [35] . All elasmobranch GRs were low-sensitivity receptors activated by multiple corticosteroids , except for the D . Sabina GR , which did not activate transcription in the presence of any hormone . All hormone-activated cartilaginous fish GRs were most sensitive to DOC and corticosterone . The receptors had EC50 values ( the hormone concentration required to elicit half-maximal activation ) for these steroids in the 10−8 to 10−6 M range ( Figure 2 , Table S1 ) , typical of the EC50s of bony fish GRs for glucocorticoids but two to four orders of magnitude greater than AncCR or the MRs of bony vertebrates [19] . These observations are consistent with a model that after duplication of AncCR – which was highly sensitive to a broad array of corticoisteroids – GR evolved reduced sensitivity without a shift in specificity , explaining the observed characteristics of AncGR1 and the GRs of extant elasmobranchs; later – after elasmobranchs diverged from bony vertebrates – the narrower specificity for glucocorticoids that characterizes the GRs of present-day tetrapods and teleosts evolved ( Figure 1 ) . The new cartilaginous fish GR sequences were added to a dataset of 97 other steroid receptor sequences and aligned for phylogenetic analyses and ancestral sequence reconstruction . The maximum likelihood phylogeny was generally well supported and in agreement with previously published trees [19] , except for the placement of the agnathan receptors ( Figure S1 , Table S2 ) . The hypothesis that GR's functions are derived can be tested experimentally and by sequence analysis of evolutionary rates . This hypothesis predicts that the rate of amino acid evolution after duplication of AncCR should be faster in the lineage leading to the GRs than in that leading to the MRs , which retain the ancestral functions [3] . Branch lengths between two nodes represent the mean probability of substitution per site , which equals the product of evolutionary rate times time . The branch leading from AncCR to AncGR1 and the branch leading from AncCR to AncMR1 ( MR in the same ancestral species—the common ancestor of jawed vertebrates ) cover exactly the same period of time , so any authentic differences in length must be due to differences in evolutionary rate . As predicted , there are 36 differences between the AncCR and AncGR1 . 1 , compared to 16 between AncCR and AncMR1 , and the estimated amino acid replacement rate 2 . 25 times greater on the GR branch than on the MR branch ( Figure 1 ) , but this difference did not reach formal statistical significance ( p = 0 . 09 ) using a likelihood ratio test . To more decisively test the hypothesis that GR's functions changed between AncCR and AncGR1 , we used ancestral reconstruction . We inferred the sequence of AncGR1 assuming the best-fit model and integrating over plausible phylogenies weighted by their posterior probabilities [36] . The denser taxon sampling of this study was found to improve confidence in the inferred AncGR1 sequence compared to the previously published version , which was inferred from an alignment that included only a single cartilaginous fish [15] . The updated reconstruction , which we named AncGR1 . 1 , differs at 7% of sites from the original reconstruction ( AncGR1 . 0 ) , with a higher mean posterior probability across all sites ( 0 . 951 vs . 0 . 930 ) , a greater number of sites reconstructed with 100% posterior probability , and fewer sites reconstructed with plausible alternate states ( Figure S2 , Table S3 ) . Other previously reconstructed ancestral steroid receptor sequences , such as AncCR and AncGR2 ( the GR gene in the last common ancestor of ray- and lobe-finned fishes , including tetrapods ) [19] , were affected to a much lesser extent by including additional cartilaginous fish sequences . We then characterized the functions of the AncGR1 . 1 LBD by synthesizing a nucleic acid sequence that codes for it , subcloning that sequence into an expression construct , and assaying its sensitivity to the same suite of corticosteroids using the luciferase reporter assay . As predicted , we found that AncGR1 . 1 is activated by the same broad suite of hormones as AncCR , but markedly higher doses are required . For all ligands tested – including the classic mineralocorticoid DOC – AncGR1 . 1 was 25- to 530-fold less responsive to hormone than AncCR ( Figure 2 , Table S1 ) . These data allow us to trace on the phylogeny two separate shifts in the evolution of GRs from a high-sensitivity , promiscuous corticosteroid receptor: first , the evolution of reduced hormone sensitivity , and later a loss of sensitivity to mineralocorticoids ( Figure 1 ) . To determine whether AncGR1 . 1′s reduced sensitivity could be an artifact of uncertainty in the ancestral sequence reconstruction , we introduced plausible alternate states into the maximum likelihood ancestral sequence and repeated the experimental characterization . None of these contradicted the finding that AncGR1 . 1 has markedly reduced hormone sensitivity compared to AncCR ( Table S1 ) . Taken together , these observations indicate that reduced sensitivity evolved in the GR lineage after duplication of AncCR but before the split of cartilaginous from bony vertebrates , and this conclusion is robust to uncertainty about the ancestral reconstruction . We next sought to identify the genetic mechanisms that caused reduced hormone sensitivity to evolve . Because the shift in function occurred on the branch between AncCR and AncGR1 . 1 , the initial set of candidate mutations includes the 36 historical substitutions that occurred on this same branch . At 17 of these sites , the same derived state is present in both AncGR1 . 1 and AncGR1 . 0: AncGR1 . 0 is much more similar in sensitivity to AncCR than AncGR1 . 1 is ( Figure 2 ) , so substitutions at these sites are unlikely to represent the major-effect mutations . Of the 19 substitutions that are unique to AncGR1 . 1 , twelve represent biochemically conservative replacements ( e . g . , D/E , I/L , K/R , S/T ) . Only one of the others is in a position predicted to contact ligand based on the crystal structures of other steroid receptors [15] , [37]; this substitution ( A36G ) was previously tested in AncCR and found to have no significant effect on sensitivity to DOC or other corticosteroids [19] . We therefore prioritized the six remaining biochemically radical replacements as the best candidates for having caused the evolution of reduced sensitivity . We introduced each candidate mutation into the maximum likelihood ( ML ) AncCR background using site-directed mutagenesis and tested its effect on hormone sensitivity in the luciferase reporter gene assay with increasing concentrations of DOC . Two substitutions – V43A and R116H – markedly reduced AncCR's sensitivity to hormone , increasing the receptor's EC50 of DOC by at least two orders of magnitude to AncGR1 . 1-like values ( Figure 3 , Table 1 ) . The others had much weaker effects on sensitivity . The double mutant V43A/R116H was severely compromised , with an EC50 for DOC ∼10 , 000 times greater than AncCR and more than 50 times greater than even AncGR1 . 1 . These results indicate that V43A and R116H are large-effect historical mutations that are more than sufficient to recapitulate the evolution of the low-sensitivity AncGR1 . 1 . They also indicate that the effects of these two mutations on receptor sensitivity must have been partially buffered by additional substitutions that occurred during the same interval . To understand the structural basis of reduced GR sensitivity and identify other important substitutions , we purified AncGR1 . 1 expressed in E . coli and used X-ray crystallography to determine its atomic structure in complex with DOC at 1 . 95 Å resolution ( see Table S4 ) . AncGR1 . 1 adopts the classic steroid receptor active conformation [38] , consisting of three helical layers , an internal ligand cavity bounded by helices 3 , 5 , 6 , 7 , and 10 , well-defined ligand density within the cavity , and a surface for coactivator binding formed by helices H3 , H5 , and H12 [39] . The conformation of AncGR1 . 1 is very similar to the previously determined AncCR crystal structure , with a root mean square deviation ( RMSD ) in backbone atom position of only 0 . 66 Å ( Figure 4A ) , and most of the larger deviations are far from the ligand . The side chain identity of all residues within 4 Å of DOC are conserved except for A36G , which alters a ligand-contacting residue but has no discernible effect on hormone sensitivity [19] . These results indicate that AncGR1 . 1′s reduced sensitivity to hormone must be due to indirect mechanisms not involving contacts with the ligand , such as changes to intraprotein contacts that affect the stability of the protein-hormone complex . To understand the mechanisms by which mutations V43A and R116H reduced hormone sensitivity , we first examined the apparent roles of these residues in the AncCR and AncGR1 . 1 crystal structures . Position 43 faces inward on the middle of H3 , just above the ligand where H3 packs against H5 and forms part of the coactivator-binding cleft . In AncCR , Val43 packs tightly against neighboring hydrophobic residues , making van der Waals contacts with Leu72 , presumably stabilizing H3 and H5 , which participate in forming both the coactivator interface and the ligand pocket ( Figure 4B ) . In AncGR1 . 1 , the smaller side chain of Ala43 loses its van der Waals contacts to Leu72 , opening a small cavity in this region ( Figure 4C ) . The poor packing that results is expected to destabilize the receptor-ligand complex . Position 116 is situated on H7 , the opposite side of the protein from site 43 . In AncCR , R116 is a hub in a network of hydrogen bonds between H7 and residues in H5 and H6 ( Figure 4D ) . In AncGR1 . 1 , this hydrogen-bond network is much sparser , largely due to the replacement of Arg116 with His ( Figure 4E ) . The loss of favorable interactions presumably destabilizes these helices , the ligand pocket , and possibly the coactivator interface . We also noted that a third historical substitution in this region , Q113K , abolishes other hydrogen bonds in the same network as Arg116H . When we introduced Q113K into AncCR , it also reduced sensitivity , though its effect was considerably smaller than those of V43A and R116H ( Table 1 ) . The loss of favorable interactions in the atomic structure due to substitutions at sites 43 , 116 and 113 , together with the experimental finding that these mutations recapitulate the evolutionary decline in AncCR's hormone sensitivity , suggests that AncGR1's novel function – its reduced sensitivity to corticosteroids – evolved because of the partial degeneration of ancestral structures and functions . Because introducing mutations V43 and R116H together into AncCR reduces sensitivity to an extent greater than the historical difference between AncCR and AncGR1 , other historical substitutions during the same interval must have buffered the impact of these large-effect mutations . Of the remaining candidate mutations , one – C71S – occurred at a site already known to have a strong positive effect on receptor function in extant steroid hormone receptors: introducing serine at the homologous site in mammalian GRs ( F602S ) dramatically improves bacterial expression , solubility , and crystallization [15] , [16] , [37] , [40]–[2] . To test the hypothesis that the historical acquisition of Ser71 buffered the effect of mutations at sites 43 and 116 , we introduced mutation C71S into AncCR-V43A/R116H background . As predicted , this additional change improved sensitivity by ∼90-fold , yielding a receptor with DOC sensitivity similar to that of AncGR1 . 1 . In isolation , however , the C71S substitution has no discernable effect on AncCR sensitivity ( Figure 3 , Table 1 ) . The biophysical mechanism for this buffering effect is not clear . All previously crystallized corticosteroid receptors , ancestral and extant , have had Ser71 engineered into them to aid in protein expression and crystallization [15] , [16] , [37] , [40]–[2]; comparison to receptor agonist structures lacking Ser71 is therefore not possible . In both the AncCR and AncGR1 . 1 structures , this site is located on H 5 in the central core of the protein , just above the ligand-binding pocket , bordering a distinctive kink in H5 ( Figure 5 ) . Ser71 is adjacent to a highly solvated channel next to the hydrophobic core of the receptor , and a serine substitution would increase the hydrophilicity of the region compared to the ancestral cysteine . It appears that Ser71 in Chain B of AncGR1 . 1 might stabilize the receptor through direct and water-mediated hydrogen bonds that a cysteine would not form ( Figure 5 ) . This is not a strictly conserved mechanism , however , because in the structures of AncCR-C71S ( Figure 5 , inset ) and Chain A of AncGR1 . 1 , the polar Ser71 side chain occupies an alternate conformation: it interacts with water molecules in the channel , but the bond network varies among the structures . An alternate explanation for the buffering effect of C71S is that it may facilitate proper folding and solubility of the protein , an effect that could have a more beneficial effect on receptors with less stable native conformations , such as those carrying the V43A or R116H substitutions . To test the hypothesis that the evolutionary reduction in GR sensitivity was due to mutations that destabilized the receptor-hormone complex , we used a computational approach to predict the effects of historical GR mutations on the stability of AncCR . Using the AncCR:DOC crystal structure as a starting-point , we used FoldX software [43] to generate single and combination AncCR mutants , optimize the predicted structures , and calculate the predicted change in free energy of folding ( ΔΔG ) for mutant receptors . We calculated the distribution of stability effects for 29 single-substitution mutants and found that V43A and R116H are , as predicted , the most destabilizing substitutions , reducing stability by 1 . 62 and 2 . 81 kcal/mol , respectively ( Figure 6A , Table S5 ) . The buffering mutation C71S had a very weakly destabilizing effect ( 0 . 29 kcal/mol ) . The combination V43A/R116H is predicted to be extremely destabilizing in AncCR ( 5 . 31 kcal/mol ) ; addition of C71S to this background causes a slight additional decrease in stability ( 5 . 41 kcal/mol ) . We found a strong overall correlation between the predicted effects of mutations on protein stability and the observed reduction in receptor sensitivity to hormone ( r2 = 0 . 78; Figure 6B , Table 1 , Table S5 ) . The relationship is even tighter ( r2 = 0 . 89 ) when C71S-containing mutants are excluded . These results corroborate the hypothesis that V43A , R116H , and Q113K caused the evolution of reduced receptor sensitivity by destabilizing the protein-hormone complex , while C71S partially buffered the effects of these mutations through mechanisms not directly related to protein stability .
Our analyses allow a detailed description of the genetic , structural , and biophysical mechanisms by which AncGR1 evolved its derived function – sensitivity to only high concentrations of corticosteroid hormone – after duplication of an ancestral receptor that was sensitive to very low doses of the same hormones . The shift appears to have been driven primarily by two large-effect mutations that caused partial degradation of the receptor's structure and function . The mechanism for the functional change appears to be that these mutations compromised favorable hydrophobic interactions and hydrogen bonds in the ancestral protein , destabilizing the hormone-receptor complex . Although the combined effect of the two large-effect mutations is so great that they nearly abolish hormone sensitivity in the double mutant , a third mutation – which occurred during the same historical interval strongly buffers their effect . This buffering mutation causes no apparent effect on function in isolation; we therefore conjecture that this mutation occurred as a permissive mutation before V43A and R116H were established . Additional substitutions subsequently tuned the sensitivity of AncGR1 and its descendants . Our results do not allow us to determine the roles of selective and neutral processes in the fixation of these mutations , and the physiological significance of the GR's reduced sensitivity in the ancestral organism is unknown; it is possible , however , that the advent of a low-sensitivity GR , along with the high-sensitivity MR , allowed a greater degree of endocrine control by different doses of corticosteroids , as appears to be the case in extant elasmobranchs . Modulating the stability of a protein:ligand complex represents one way to alter the effective dose of ligand required to produce some specific quantity of active complex . During the evolution of AncGR1 , this outcome was achieved through mutations that disrupted favorable contacts among structural elements of the peptide itself , without directly affecting receptor-ligand contacts . Our findings add to a growing literature on the evolution of protein stability . Random mutations are more likely to reduce than increase protein stability [44]–[46] . Periods of mutation accumulation without purifying selection can quickly degrade proteins structures so they fall below the minimum threshold for folding and activity [13] , [14] , [47] , [48] . Although most proteins are marginally stable , a protein's precise distance from this threshold is dynamic , depending on the specific balance of stabilizing and destabilizing mutations that have occurred [13] , [49] . A stability threshold can also be relaxed by mechanisms such as the overexpression of chaperone proteins [50] or decreased selection for optimal protein function [14] . “Global suppressor” mutations that increase protein stability allow a greater number of destabilizing mutations to accumulate than would otherwise be allowed [51]–[53] , including those that generate novel functions [15] , [17] , [49] , [51] , [54]–[56] . Our observations indicate that destabilizing mutations can be buffered not only by permissive mutations that increase stability but also by those that affect protein structure and function via other biophysical mechanisms , such as folding or solubility [57] , [58] . The extreme reduction in stability conferred on AncGR1 . 1 by mutations at sites 43 and 116 are strongly buffered by historical mutation C71S , which does not affect function in isolation and is not predicted to alter protein stability . Previous studies in extant proteins have shown that introduction of a serine at the homologous site in rat and human GRs increases transcriptional activity and ligand affinity [59] and dramatically improves protein solubility and expression in bacterial cells [15] , [16] , [37] , [40]–[42] . It has been proposed that the serine maintains the receptor in an “agonist-like” conformation , preventing the collapse and aggregation of the LBD [60] . Effects on protein folding and aggregation may explain why C71S by itself is neutral in AncCR and yet , in the context of strongly destabilizing mutations , is required to maintain the active conformation and function . An alternative explanation is that C71S may play a local role in maintaining secondary structural elements and the spatial relations between them in the active conformation; the location of C71S on H5 , directly opposite where V43A packs against H5 , lends credence to this possibility . Additional experiments will be necessary to directly measure the effects of mutations at sites 43 , 71 , and 116 on the protein's biophysical properties . Our study highlights a creative role for partial loss-of-function mutations in the evolution of novel genes and gene functions . This aspect of GR evolution is related to the process described by Bridgham et al . [61] during post-duplication evolution of a different steroid receptor: in that case , a loss-of-function mutation abolished the modular LBD's ligand-activated transcriptional function and generated a competitive repressor that retained its ability to compete with its paralog for DNA and dimerization partners . The mechanism we observed during AncGR1 evolution , in contrast , involved a partial loss of activity , leading to densensitization of the receptor and a novel response to existing hormone levels . Steroid signaling relies on very precise molecular cues , and changes in receptor sensitivity can have noticeable effects on biological response [62] . After duplication of AncCR , MRs retained the ancestral receptor's sensitivity , while the evolution of reduced sensitivity in the GR created a distinctly different transcriptional regulator that responded only to high doses of hormone . These observations demonstrate how mutations that abolish or impair native protein functions can drive the evolution of novel functional roles after gene duplication [3] .
The little skate ( Leucoraja erinacea ) GR ligand-binding domain ( LBD ) was isolated previously using degenerate PCR and RACE with liver cDNA [19] . The skate GR protein sequence was used in a tblastn search of the elephant shark genome ( http://esharkgenome . imcb . a-star . edu . sg/ ) to identify its GR LBD , and gene-specific primers were designed to amplify the coding sequence from cDNA . All other cartilaginous fish GR LBDs were isolated by hemi-degenerate PCR from cDNA using a degenerate primer in the GR DNA-binding domain ( DBD ) in combination with a gene-specific primer for a ∼25 bp sequence conserved in the 3′-UTR of the elephant shark and skate ( 5′-TCATATGCACTACATATGGTTTACAGA-3′ ) . In total , GR LBDs were amplified using high-fidelity PCR from five cartilaginous fish species: elephant shark ( Callorhincus milii ) , Atlantic sharpnose shark ( Rhizoprionodon terraenovae ) , brownbanded bambooshark ( Chiloscyllium punctatum ) , small-spotted catshark ( Scyliorhinus canicula ) , and Atlantic stingray ( Dasyatis sabina ) . Template cDNA for PCR was graciously provided by B . Venkatesh ( C . milii and C . punctatum ) and B . S . Nunez ( R . terraenovae , S . canicula , and D . sabina ) . The conserved DNA- and/or ligand-binding domains of 97 steroid receptor protein sequences were aligned using Clustal X [63] . Maximum likelihood phylogenetics was performed using PhyML_aLRT [64] assuming the Jones model of evolution [65] and a four-category discrete gamma distribution of among-site rate variation , with the shape parameter estimated from the data; the JTT model was previously shown to be highly supported , with 100% posterior probability , when this and other models are compared in a Bayesian analysis [19] . Support at nodes was calculated as the chi-square statistic using an approximate likelihood ratio test [64]; the chi-square statistic represents 1-p , where p is the estimated probability that the given node would occur by chance alone . The maximum likelihood tree topology differed from previously published SR phylogenies with respect to the placement of jawless fish receptors [19]; to account for this uncertainty , ancestral receptor sequences were reconstructed over both the experimental and published trees weighted by their inferred posterior probability [36] . Ancestral states were inferred using PAML version 3 . 15 [66] and the ancestral reconstruction tool Lazarus [36] , given the sequence alignment , phylogenies , and the JTT model . For any ancestor relevant to our study , no site in the inferred sequence possessed amino acid states that differed between trees . A nucleic acid sequence coding for the LBD of the last common ancestor of all GRs ( AncGR1 . 1 ) was optimized for expression in mammalian cells , synthesized de novo ( Genscript , Piscataway , NJ ) , and characterized as described below . Rates of protein evolution were analyzed in HyPhy [67] . A likelihood ratio test ( LRT ) was used to compare the relative branch lengths from the last common GR/MR ancestor ( AncCR ) to the last common ancestors of all GRs ( AncGR1 . 1 ) or MRs ( AncMR1 ) . Under the null hypothesis of equal rates , all branch lengths were unconstrained and optimized independently; under the alternate hypotheses , the branch leading from AncCR to AncGR1 was constrained to have the same length as that leading from AncCR to MR1 ( MR in the ancestor of all jawed vertebrates ) . The likelihood ratio of alternate and null models was determined and a p-value calculated using a chi-squared distribution with one degree of freedom . LBDs were cloned as fusion proteins into a pSG5-Gal4DBD expression vector ( gift of D . Furlow ) and cotransfected using Lipofectamine and Plus Reagents ( Invitrogen , Carlsbad , CA ) with a UAS-driven luciferase reporter gene ( pFRluc ) into mammalian cell culture ( CHO-K1 ) , and grown in phenol red-free α-MEM plus 10% dextran-charcoal-stripped fetal bovine serum ( Hyclone , Logan , UT ) . Cells were incubated with transfection reagents for four hours , after which they were treated with fresh medium; after recovery , cells were treated in triplicate with hormone or vehicle control , and incubated for one day . Reporter expression was measured using Dual-Glo ( Promega , Madison , WI ) and dose-response relationships analyzed using Prism4 ( GraphPad , La Jolla , CA ) . Site-directed mutagenesis was carried out using QuickChange II ( Stratagene , La Jolla , CA ) and clones verified by DNA sequencing . Plausible alternate states were defined as non-maximum likelihood amino acid states with posterior probability >0 . 20; we reasoned that residues that are present in one or more extant high-sensitivity receptors are the most likely to increase receptor sensitivity , whereas those that are present only in low-sensitivity receptors are unlikely to confer high sensitivity . Each such alternate state was introduced singly into the ML AncGR1 . 1 , and the experimental characterization was repeated . AncGR1 . 1 was subcloned into the pMCSG7-MBP-His expression vector , transformed into BL21 ( DE3 ) pLysS cells , and grown to an OD600 of 0 . 8–1 . 0 . Cultures were induced with 0 . 1 µM IPTG plus 50 µM of the steroid 11-deoxycorticosterone ( DOC ) and grown overnight at 16°C . Purification of AncGR1 . 1 was performed using nickel affinity chromatography and a sizing column . Pure AncGR1 . 1 was concentrated to 3 . 7 mg/mL and dialyzed into a crystallization buffer consisting of 20 mM Tris , pH 6 . 5 , 150 mM NaCl , 5% glycerol , 50 µM CHAPS , and 50 µM hormone ( DOC ) . Multiple sparse matrix screens were set with AncGR1 . 1 protein using a Phoenix crystallization robot ( Art Robbins Instruments , Sunnyvale , CA ) ; hits formed at 22°C from the Salt Rx screen ( Hampton Research , Aliso Viejo , CA ) . Crystals were optimized at 22°C in hanging drop diffusion plates with: 2 . 5–2 . 8 M sodium acetate trihydrate , pH 7 . 0 , 0 . 1 M BIS-TRIS propane , pH 7 . 0 , and a small peptide designed from the TIF2 Box3 steroid receptor coactivator protein . Crystals were soaked in a cryoprotectant solution containing 20% glycerol and flash-frozen in liquid nitrogen . Data was collected to 1 . 95 Å resolution at the South East Regional Collaborative Access Team ( SER-CAT ) at the Advanced Photon Source ( Argonne National Laboratory ) and data was processed and scaled with HKL2000 [68] . Initial phasing of the AncGR1 . 1 plus DOC structure was determined using molecular replacement of the AncCR with DOC ( 2Q3Y ) ; model building and refinement of the structure was carried out using COOT version 0 . 5 [69] and REFMAC [70] in the CCP4 suite [71] . The root mean square deviation ( RMSD ) , a measure of the overall similarity between protein backbones , was calculated using CaspR [72] . Interpretation was focused on Chain B , which displayed lower overall b-factors and had fewer crystal-packing contacts . Coordinates have been deposited in PDB with accession 3RY9 . We used FoldX version 3 . 0 Beta 4 [43] to predict protein stability of AncCR and its mutational variants , using the empirical AncCR structure ( PDB 2Q3Y , which contains an engineered C71S mutation to facilitate expression and crystallization ) as a template . The receptor was optimized using the ‘Repair PDB’ function with sites Q39 and S76 fixed , as these side chains moved considerably in the absence of ligand ( which is not modeled in FoldX ) . The ancestral state Cys71 was re-introduced into the AncCR sequence , and the structure was energy-minimized . GR substitutions were analyzed singly or in combination using the ‘Build Model’ function , and the change in protein stability estimated as the average difference in the free energies between the maximum likelihood and mutant AncCR structures ( ΔΔG ) for five runs . Several substitutions were excluded from the dataset because they generated errors ( “segmentation fault” at sites Y27R , Q213K , and K246Q ) , bordered gaps in the electron density of the AncCR structure ( A171V , K173R , and N175G ) , or contacted ligand ( A36G ) .
|
A central question in molecular evolution is how changes in the genetic , structural , and biophysical properties of proteins generate new functions . Ancestral sequence reconstruction ( ASR ) allows long-extinct proteins to be resurrected and characterized in the laboratory and allows the mechanisms for evolutionary shifts in protein functions to be studied experimentally . We used ASR to study the earliest functions and evolution of the glucocorticoid receptor ( GR ) , a hormone-activated transcription factor and critical regulator of vertebrate physiology . We reconstructed the first GR ancestor and showed that this ancient gene evolved dramatically reduced sensitivity to hormone shortly after its birth by gene duplication some 450 million years ago , a function that persists in GRs to this day . Using site-directed mutagenesis , X-ray crystallography , and computational predictions of protein stability , we found that the shift to reduced GR sensitivity was driven by two large-effect mutations that destabilized the receptor-hormone complex . The combined effect of these mutations is so strong that a third mutation , apparently neutral in the ancestral background , evolved to buffer their degenerative effects . Our results suggest a creative role for mutations that partially degrade protein form and function and highlight the importance of interactions between mutations in evolutionary processes and protein functions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetic",
"mutation",
"gene",
"function",
"mutation",
"phylogenetics",
"protein",
"folding",
"protein",
"structure",
"molecular",
"genetics",
"divergent",
"evolution",
"forms",
"of",
"evolution",
"proteins",
"biology",
"evolutionary",
"systematics",
"biophysics",
"physics",
"mutagenesis",
"biochemistry",
"gene",
"identification",
"and",
"analysis",
"genetics",
"gene",
"duplication",
"evolutionary",
"biology",
"evolutionary",
"processes",
"genetics",
"and",
"genomics"
] |
2011
|
Mechanisms for the Evolution of a Derived Function in the Ancestral Glucocorticoid Receptor
|
The causal agent of Huanglongbing disease , ‘Candidatus Liberibacter asiaticus’ , is a non-culturable , gram negative , phloem-limited α-proteobacterium . Current methods to control the spread of this disease are still limited to the removal and destruction of infected trees . In this study , we identified and characterized a regulon from ‘Ca . L . asiaticus’ involved in cell wall remodeling , that contains a member of the MarR family of transcriptional regulators ( ldtR ) , and a predicted L , D-transpeptidase ( ldtP ) . In Sinorhizobium meliloti , mutation of ldtR resulted in morphological changes ( shortened rod-type phenotype ) and reduced tolerance to osmotic stress . A biochemical approach was taken to identify small molecules that modulate LdtR activity . The LdtR ligands identified by thermal shift assays were validated using DNA binding methods . The biological impact of LdtR inactivation by the small molecules was then examined in Sinorhizobium meliloti and Liberibacter crescens , where a shortened-rod phenotype was induced by growth in presence of the ligands . A new method was also developed to examine the effects of small molecules on the viability of ‘Ca . Liberibacter asiaticus’ , using shoots from HLB-infected orange trees . Decreased expression of ldtRLas and ldtPLas was observed in samples taken from HLB-infected shoots after 6 h of incubation with the LdtR ligands . These results provide strong proof of concept for the use of small molecules that target LdtR , as a potential treatment option for Huanglongbing disease .
The rapid expansion of Huanglongbing ( HLB; also known as “citrus greening” ) disease caused a crisis in the citrus industry worldwide , with no solution visible in the near future . Experts estimate that without pro-active measures , the citrus industry in affected areas ( like Florida ) will be significantly reduced within 2–10 years . As such , it is critical to further our understanding of the metabolic and regulatory pathways in the causal agent ‘Candidatus Liberibacter asiaticus’ ( ‘Ca . L . asiaticus’ ) , to facilitate the discovery of new means of prevention and/or treatment for HLB . Various treatment methods , including large scale field applications of penicillin and streptomycin , have been thoroughly examined and resulted in little success [1] . Although not applicable to field studies , thermotherapy ( incubation of living plants in chambers at 40°C for 48 h ) has been proposed for use in nurseries [2] . Despite all these efforts , current methods to control the spread of HLB are still limited to the removal and destruction of infected trees . The causal agent of this devastating disease , ‘Ca . L . asiaticus’ , is an unculturable bacterium . The inability to culture these species has greatly hindered progress toward the identification of therapeutic targets , and the development of viable treatment options . Furthermore , comparative genome analyses did not identify genes with predicted virulence functions ( toxins ) , or specialized secretion systems ( pathogenicity determinants ) in the genome of ‘Ca . L . asiaticus’ . These analyses did , however , provide valuable insight into the putative mechanisms of gene regulation . Transcription factors , as defined by the Cluster of Orthologous Groups , constitute less than 2% of the ‘Ca . L . asiaticus’ genome , while in S . meliloti , another member of the Rhizobiaceae family , it comprises 6% of the genome . As a consequence , a small number of transcription factors may control several metabolic pathways . Therefore , we hypothesized that inactivation of a single transcription factor could result in pleiotropic effects , including decreased persistence within the host . CLIBASIA_01180 ( renamed LdtR ) , is a homolog of the multidrug resistance regulator MarR . This regulator is encoded upstream of CLIBASIA_01175 , a predicted L , D transpeptidase ( renamed LdtP ) involved in cell wall remodeling . Peptidoglycan ( PG ) modifications have been observed in Gram-positive and Gram-negative bacteria , and often occur in response to environmental changes . The bacterial pathogens Neisseria gonorrhoeae and Listeria monocytogenes modify their PG residues to evade detection by the host immune system , and increase tolerance to stress [3] , [4] . The PG structure consists of alternating N-acetylglucosamine ( NAG ) and β- ( 1-4 ) -N-acetylmuramic acid ( NAM ) residues . A peptide stem linked to the NAM residue mediates the cross-link to other units in the growing PG , forming a three-dimensional mesh-like architecture that confers structural strength and rigidity to the cell wall [5] . The PG of A . tumefaciens and S . meliloti is highly cross-linked ( 64% ) , with the muropeptide NAM-L-alanine , D-glutamic acid , DL-diaminopimelic acid , D-alanine being the most frequent [6] . The goal of this study was to characterize and assess the biological importance of LdtR and LdtP , and their role in the persistence of ‘Ca . L . asiaticus’ within citrus hosts . We used a biochemical approach to identify small molecules that modulate the expression and activity of the LdtR transcription factor . As ‘Ca . L . asiaticus’ is yet to be cultured , we used two of its closest culturable phylogenetic relatives , Sinorhizobium meliloti and Liberibacter crescens , as models to assess the biological role of LdtR and LdtP . We also developed a model using ‘Ca . L . asiaticus’ infected shoots , to validate LdtR as an effective target for the design of new therapeutics .
The ldtR gene encodes the only MarR family member of transcriptional regulators in the genome of ‘Ca . L . asiaticus’ psy62 ( ldtRLas ) . It shares high amino acid sequence identity to proteins found in all Rhizobiaceae family , including: ‘Ca . L . solanacearum’ CLso-ZC1 ( 89% ) , Liberibacter crescens BT-1 ( 73% ) , Sinorhizobium meliloti 1021 ( 70% ) , Agrobacterium tumefaciens F2 ( 74% ) , A . radiobacter K84 ( 71% ) , Rhizobium leguminosarum bv . viciae 3841 ( 71% ) , and Hoeflea phototrophica DFL-43 ( 65% ) . The genomic arrangement of ldtRLas was also similar to that of its orthologs ( Fig . 1 ) ; however , none of these proteins has previously been characterized . ldtRLas is encoded by the minus strand . CLIBASIA_01185 is encoded 341 bp upstream of ldtRLas , on the plus strand . This gene encodes for a putative delta-aminolevulinic acid dehydratase ( hemB ) involved in tetrapyrrole biosynthesis [7] . Downstream of ldtRLas , on the minus strand , is ldtPLas , which contains both a YkuD L , D-transpeptidase domain ( pfam03734 ) and a peptidoglycan binding domain ( pfam01471 ) , suggesting that it likely acts as an L , D-transpeptidase . Biotinylated probes were generated to contain the intergenic region of CLIBASIA_01185 and ldtRLas ( PldtR: −395 to +47 , positions are relative to ldtRLas translation start site ) , as well as the putative promoter region of ldtPLas ( PldtP: −248 to +79 , relative to the ldtPLas translation start site ) . EMSA analysis of the interaction between LdtRLas and PldtR or PldtP , revealed higher binding affinity for PldtP , with 50% binding achieved at 100 nM ( Fig . 2A ) . With increasing concentrations of LdtRLas , a higher molecular weight oligomer was also observed . Size exclusion chromatography indicated that LdtRLas is a stable dimer in solution with an observed molecular weight of 39 kDa ( Fig . S1 ) . Taken together , these results suggest that there is either a second binding site within the ldtP promoter , or LdtRLas may further oligomerize upon binding to DNA . To confirm the location of LdtR binding , competitor experiments were conducted using unlabeled DNA probes ( Table 1 ) . The largest probe ( CD-1 ) contains the whole sequence used in EMSA ( from −248 to +79 ) . Probe CD-2 contains LdtRLas binding site surrounded by promoter elements ( −139 to +79 ) . Probe CD-3 was designed to contain only the protected site I identified by DNase I footprinting ( −118 to −74 ) , while probe CD-4 does not contain the LdtRLas binding site ( −21 to +58 ) . The addition of probe CD-1 or CD-2 resulted in a similar decrease in the intensity of the shifted bands ( Fig . 2B ) . This effect was further enhanced in the presence of probe CD-3 . No competition was observed with probe CD-4 . These results indicate that LdtRLas may have two binding sites within the ldtP the promoter . The DNA binding sequence for LdtRLas in the promoter region of ldtPLas was identified by DNase I footprinting . The protected site consists of 18 nucleotides ( ATATTCCTTGTATTTTAA , ldtP_1 ) on the minus strand ( Fig . 2C ) , upstream of the predicted −35 box . Immediately downstream from the protected site , a 15 nt DNase I-hypersensitivity region was identified , which may correspond to a DNA bending site ( Fig . 2C ) . Analysis of the DNA sequence upstream of the hypersensitivity region indicated the presence of a second binding site; however , the binding sequence is broken into two segments separated by 9 nt ( ATATTTCTT-n9-GTGATTTAA , ldtP_2; Fig . 2D ) . A putative binding site was identified in the promoter region of ldtRLas with a similar disruption ( ldtR_1 , Fig . S2E ) . This sequence displays a separation of 6 nt between each segment , which may explain the lower affinity of LdtRLas for PldtR ( Fig . 2A ) . To determine the residues required for LdtR binding , the three binding sites ( ldtP_1 , ldtP_2 , and ldtR_1 ) were compared , and a position specific frequency matrix was constructed ( Fig . 3A ) . Using plasmid pBS6 as a template , site directed mutagenesis was performed on residues that showed high conservation as follow: M1 ( −111 ) AT→GG , M2 ( −108 ) TT→GG , M3 ( −104 ) TT→GG , M4 ( −102 ) GT→AG , and M5 ( −99 ) TtT→GtG . EMSA experiments were then conducted with the mutated binding sites . Decreased LdtRLas binding was observed with probes PldtP_M1 and PldtP_M5 ( Fig . 3B ) . Mutations M2 , M3 , and M4 did not affect LdtRLas binding ( Fig . 3B ) . To determine the mode of regulation for LdtRLas , we generated several lacZ fusions using Bacillus subtilis as a model strain , since all the genes under study are absent from its genome . This system allows the study of transcriptional fusions by inserting a single copy of the gene into a non-essential chromosomal locus ( thrC ) . The putative promoter regions of CLIBASIA_01185 , ldtRLas , and ldtPLas were fused to the lacZ gene , resulting in strains BS1 ( PCLIBASIA_01185 ) , BS3 ( PldtR ) and BS5 ( PldtP ) . All three promoters were found to have very low activity ( 1 . 1±0 . 7 , 0 . 1±0 . 03 and 3 . 8±0 . 07 AU , respectively; Fig . 4 ) . In the presence of ldtRLas , increased expression of lacZ was observed in strains BS4 ( PldtR-ldtRLas ) and BS6 ( PldtR-ldtRLas-PldtP ) ( 16 . 4±0 . 02 and 36 . 3±0 . 2 AU , respectively; Fig . 4 ) . No expression was observed in strain BS2 ( harboring ldtRLas and PCLIBASIA_01185 ) . These results confirmed that LdtRLas is a transcriptional activator of ldtRLas and ldtPLas , while it does not regulate CLIBASIA_01185 . The in vivo specificity of LdtRLas binding to PldtR-ldtRLas-PldtP was tested in strains BS6M1 and BS6M5 , harboring mutations M1 or M5 on the PldtP binding site 1 . β-galactosidase activity was significantly reduced ( p<0 . 001 ) by 55% and 47% , for BS6M1 and BS6M5 respectively , when compared to the wild type promoter ( Fig . 4 ) . These results positively correlated with the reduced binding of LdtRLas to probes PldtP_M1 and PldtP_M5 in EMSA experiments ( Fig . 3B ) , confirming the specificity of the LdtRLas binding site . Inactivation of L , D-transpeptidases have been shown to induce morphological changes , resulting in decreased rigidity of the cell wall [8] . As ‘Ca . L . asiaticus’ has yet to be cultured , a model strain was used to study the biological role of LdtR and LdtP . Due to its close phylogenetic relationship to ‘Ca . L . asiaticus’ , and the availability of genetic tools , S . meliloti was chosen . Prior to in vivo experiments , SMc01768 ( named LdtRSmc ) was purified and confirmed to bind to its own promoter region , as well as to the promoter region of the ldtPLas homolog , SMc01769 ( named LdtPSmc; Fig . S3 ) . Insertional mutants of ldtPSmc and ldtRSmc were constructed in S . meliloti ( strains SMP1 and SMP2 , respectively; Table 2 ) by homologous insertion of pSMP1 and pSMP2 in ldtPSmc and ldtRSmc , respectively . In strain SMP1 , ldtPSmc was disrupted at 498 nt from the ATG start codon . In strain SMP2 , ldtRSmc was disrupted 29 nt from ATG start codon . Analysis of crystal violet-stained cells revealed the SMP1 and SMP2 mutants had a shortened rod-type phenotype ( short-cell ) , when compared to the wild type S . meliloti . However , they did not show growth defects in liquid cultures ( doubling time or final OD600 , data not shown ) . Scanning electron microscopy was used to verify and quantify these morphological changes . Electron micrographs confirmed the average length of SMP1 ( 1 . 16 µm±0 . 15 ) and SMP2 ( 1 . 15 µm±0 . 14 ) mutants to be significantly shorter ( 30% , p<0 . 005 ) than wild type cells ( 1 . 65 µm±0 . 20 ) ( Fig . 5A–C ) . To determine if modifications in the cell wall composition would affect tolerance to osmotic stress , a seven-fold serial dilution of each strain was spot plated in the presence of sucrose ( 0 . 3 M ) or NaCl ( 0 . 4 M ) . Increased sensitivity to osmotic stress was observed in strain SMP1 ( 1 . 8×107±3 . 9×106 and 1 . 4×106±5 . 4×105 CFU/ml , for sucrose and NaCl respectively ) , and SMP2 ( 1 . 6×107±7 . 1×105 and 1 . 1×106±7×105 CFU/ml , for sucrose and NaCl respectively ) , when compared to the wild type strain ( 2 . 2×108±2 . 5×107 and 7 . 1×106±6 . 9×105 CFU/ml , for sucrose and NaCl respectively; Fig . 6A ) . These results were significantly different for sucrose ( p<0 . 05 ) but not for NaCl . Higher concentrations of NaCl or sucrose were toxic for all strains ( data not shown ) . To establish a link between elevated sensitivity to osmotic stress , and the regulation of gene expression by LdtRSmc , β-glucuronidase activity ( encoded by the uidA gene ) was measured in S . meliloti ( Fig . 6B ) . Strain SMP3 was constructed by inserting the uidA reporter gene downstream of ldtPSmc ( no disruption to ldtRSmc or ldtPSmc ) . Strain SMP3 was used as a reporter strain to determine the expression of uidA in a wild type phenotype . In the presence of NaCl , the β-glucuronidase activity was induced in a concentration-dependent manner in strains SMP3 and SMP1 ( Fig . 6B ) . Induction of β-glucuronidase activity was dependent on the presence of LdtRSmc . In absence of the regulator ( strain SMP2 ) , no changes in the expression of the reporter gene were observed . These results confirm the role of LdtRSmc as an activator of ldtRSmc and ldtPSmc transcription in response to osmotic stress . To determine if the tolerance to osmotic stress could be recovered by the addition of ldtR , strain SMP2 ( ldtR mutant ) was transformed with plasmid pSMP4 carrying ldtRLas ( strain SMP2B ) , and analyzed for sensitivity to osmotic stress . Strain SMP2A ( carrying the empty pBBR1MCS-5 plasmid [9] ) served as a control . Increased tolerance to osmotic stress was observed in strain SMP2B ( 6 . 1×106±5 . 7×105 and 3 . 6×106±7 . 6×105 CFU/ml , for sucrose and NaCl respectively p<0 . 05 ) , when compared to SMP2A ( 7 . 2×105±5 . 8×104 and 5 . 7×105±1 . 0×105 CFU/ml , for sucrose and NaCl respectively ) ( Fig . 6C ) . These results suggest that LdtRLas is directly involved in tolerance to osmotic stress by recognizing similar promoter elements in PldtP of S . meliloti . Further in silico analyses in S . meliloti revealed the presence of LdtRLas binding sites upstream of the ldtP −35 sequence , in agreement with the arrangement of LdtR binding sites in L . crescens and ‘Ca . L . asiaticus’ ( Fig . S2 ) . To determine if the addition of ldtPLas could recover the tolerance to osmotic stress , strain SMP2 ( ldtR mutant ) was transformed with pSMP5 carrying ldtPLas ( SMP2C ) . Increased tolerance to osmotic stress was observed in strain SMP2C ( 1 . 5×107±2 . 6×106 and 8 . 0×106±2 . 1×106 CFU/ml , for sucrose and NaCl respectively , p<0 . 05 ) when compared to SMP2A ( 7 . 2×105±5 . 8×104 and 5 . 7×105±1 . 0×105 CFU/ml , for sucrose and NaCl respectively , ( Fig . 6C ) . These results indicate that LdtP from S . meliloti and ‘Ca . L . asiaticus’ are functionally homologous . Taken together these findings confirm that the decreased tolerance to osmotic stress observed in strain SMP2 , was due to the absence of LdtRSmc transcriptional activity . A fluorescence based small molecule screening assay [10] was used to identify chemical scaffolds that may interact with the transcription factor LdtRLas . We utilized a library containing 196 biologically relevant small molecules [11] , [12] and the Prestwick Chemical Library , which contains 1 , 200 small molecules [10] . Small molecules that induced a shift in the melting temperature ( ΔTm ) of LdtRLas , by more than two degrees , were considered as positive “hits” . The chemicals with the strongest destabilizing effect were hexestrol ( ΔTm = −2 . 5±0 . 5°C ) , diethylstilbestrol ( ΔTm = −4 . 5±0 . 9°C ) , and benzbromarone ( ΔTm = −2 . 0±0 . 3°C ) , while oxantel pamoate ( ΔTm = 2 . 0±0 . 2°C ) was found to greatly increase the stability of the protein ( Fig . S4 ) . A change in thermal stability does not guarantee a biologically relevant interaction; therefore , each of the compounds was tested on the ability to modulate the PldtP: LdtRLas interaction . All of the identified chemicals decreased the DNA binding activity of LdtRLas in a concentration-dependent manner ( Fig . 7 ) . Benzbromarone had the strongest effect and disrupted the PldtP: LdtRLas interaction at 50 µM . Oxantel pamoate completely impaired the PldtP: LdtRLas interaction at 250 µM , where only partial disruption of the complex was observed with hexestrol and diethylstilbestrol . The chemical scaffold of the strongest destabilizing agents ( benzbromarone and hexestrol ) served to identify other natural compounds such as resveratrol and phloretin . It was found that resveratrol decreased binding at 250 µM , while phloretin disrupted the PldtP: LdtRLas interaction at 100 µM , consistent with molecules having physiological relevance ( Fig . 7 ) . To determine the specificity of each ligand that decreased PldtP: LdtRLas interaction , EMSA experiments were carried using a MarR homolog ( LVIS0553 ) , in the presence or absence of each chemical . The PLVIS0553: LVIS0553 interaction was previously found to be modulated by the presence of novobiocin [10] . As expected none of the identified ligands for LdtRLas affected the binding of LVIS0553 to its cognate promoter ( Fig . S5 ) . We hypothesized that chemicals that modulate binding of the transcription factor would result in phenotypic abnormalities , similar to those observed in ldtR mutants of S . meliloti . The toxicity of each chemical was determined and sub-lethal concentrations were used for these experiments ( Table 3 ) . As expected , the addition of increasing concentrations of each chemical ( 25 µM phloretin , 25 µM benzbromarone , or 1 µM hexestrol ) resulted in a pronounced decrease in cell size in S . meliloti ( Fig . 8A ) . Quantitative assessments of the cell size were conducted in wild type S . meliloti cells grown in the presence of 25 µM phloretin . The addition of phloretin resulted in a significant decrease of 27% in the cell size ( 1 . 20 µm±0 . 18 , p<0 . 005; Fig . 5D ) when compared to the wild type ( 1 . 65 µm±0 . 20; Fig . 5A ) . These results are in agreement with the decrease in cell size observed for the SMP1 and SMP2 mutants ( Fig . 5B and C ) . Confirmatory studies were performed in L . crescens BT-1 . L . crescens is a close relative of ‘Ca . L . asiaticus’ that was recently isolated from mountain papaya , and can be cultured under laboratory conditions . In addition , the complete genome of L . crescens BT-1 has been sequenced [13] , and the homolog of ldtRLas ( B488_10910 , named ldtRLcr ) identified . The chemicals ( 50 µM phloretin , 50 µM benzbromarone , or 25 µM hexestrol ) that induced the “short-cell” phenotype in S . meliloti modulated the activity of ldtRLcr , resulting in a similar phenotype in L . crescens BT-1 ( Fig . 8B ) . To test if the phenotype induced by the presence of the chemicals correlated with changes in the expression of the ldtRLcr and B488_10900 ( named ldtPLcr ) , the mRNA levels were determined . L . crescens was grown to exponential phase , in presence or absence of the small molecules . Modest , but highly reproducible decreases of 45 . 4±8 . 9 , 62 . 5±7 . 7 , and 37 . 5±11 . 5 percent in ldtPLcr expression , were observed upon growth in the presence of 25 µM phloretin , 50 µM benzbromarone , or 25 µM hexestrol , respectively . These results confirmed the role of the small molecules in modulating the activity of LdtRLcr , in vivo . Based on the pivotal role of peptidoglycan in counteracting the effects of osmotic pressure , we hypothesized that the downregulation of ldtR and ldtP , by chemicals that impair LdtR activity , will result in decreased tolerance to osmotic stress . The S . meliloti wild type-phenotype strain , SMP3 , was used to evaluate the effect of the small molecules , on the ability to grow under osmotic stress conditions . The cells were grown in the presence or absence of phloretin or benzbromarone with increasing concentrations of NaCl ( Table 3; Fig . 9A ) . In the presence of the small molecules , strain SMP3 showed a severe decrease in tolerance to NaCl . At NaCl concentrations as low as 50 mM , a decrease in growth was observed in the presence of phloretin or benzbromarone ( 50 and 30% , respectively ) . Under these conditions , β-glucuronidase activity was determined . Induction of ldtRSmc and ldtPSmc expression , in response to high concentrations of NaCl , was overturned in presence of phloretin or benzbromarone ( Fig . 9B ) . These results are in agreement with the decrease tolerance to osmotic stress observed in presence of the small molecules . Since genetic tools are not available yet to manipulate L . crescens , we determined the effect of the addition of chemicals , at sublethal concentrations , on the ability to tolerate high concentrations of NaCl or sucrose . It was establish that the maximal concentration of NaCl and sucrose that L . crescens tolerate is 150 and 200 mM , respectively ( Fig . S6 ) . The effect of increasing concentrations of the small molecules was tested on the ability to tolerate NaCl or sucrose . The addition of phloretin , benzbromarone , or hexestrol ( 50 , 100 , or 25 µM , respectively ) , did not affect the growth of L . crescens in control conditions . Conversely , in the presence of NaCl or sucrose , L . crescens displayed increased sensitivity to all chemicals tested ( Fig . 10 ) . Together , these results indicate that in S . meliloti and L . crescens , tolerance to osmotic stress is in part mediated by changes in the peptidoglycan crosslinking , which can be manipulated by the addition of small molecules that modulate mRNA levels through LdtR activity . Based on these results , we designed an in vitro model to test the effectiveness of these chemicals . Shoots were collected from a single HLB-symptomatic Valencia Orange ( C . sinensis ) tree , infected with ‘Ca . L . asiaticus’ . Previous studies have reported greater numbers of viable ‘Ca . L . asiaticus’ cells in the sieve elements of young , asymptomatic leaves , collected from new flushes [14] . All leaves used for this study were collected from new flushes on highly symptomatic branches . Nine leaves were collected for each treatment and control group . Samples were then incubated for 6 or 24 h ( with or without chemical ) . Since ‘Ca . L . asiaticus’ still remains elusive to culture under laboratory conditions , we followed the transcriptional activity of the 16S RNA gene and the L10 ribosomal protein ( encoded by the rplJ gene ) as viability parameters . The amplification values were normalized to the plant gene cox2 and are expressed relative to the control ( incubated without chemical ) samples . After 24 h of incubation , significant differences were observed in samples treated with small molecules . Expression of the 16S RNA gene was repressed in samples treated with hexestrol and phloretin [39 . 7±9 . 8 ( p<0 . 05 ) and 55 . 9±9 . 5 ( p<0 . 005 ) percent decrease , respectively] , while benzbromarone showed the strongest effect , with 90 . 9±6 . 1 percent decreased expression ( p<0 . 005 ) ( Fig . 11A ) . A similar trend was observed for the expression of rplJ , with a decreased expression of 94 . 2±2 . 3 , 94 . 6±2 . 9 , and 97 . 6±1 . 5 percent for phloretin , hexestrol , and benzbromarone , respectively ( p<0 . 005 ) ( Fig . 11B ) . After a short period of incubation ( 6 h ) no significant changes were observed ( data not shown ) . The effect of the chemicals on the expression of the specific genes ldtRLas and ldtPLas was then determined in the infected leaves . The expression values are calculated relative to the 16S RNA gene , to assess the specificity of the chemicals to target genes . Phloretin showed a strong effect ( 88 . 1±1 . 2 percent decrease ) on the expression of ldtRLas after 6 h of incubation , while benzbromarone displayed similar decreased expression values after 6 or 24 h ( 78 . 9±1 . 3 and 80 . 5±1 . 1 percent , respectively , p<0 . 005; Fig . 11C ) . The expression of ldtPLas showed constant and incremental repression values over time . Hexestrol and benzbromarone reached maximal values of 93 . 7±0 . 8 and 94 . 2±0 . 9 , respectively , while phloretin showed a maximal value of 84 . 8±3 . 8 percent decrease ( p<0 . 005 ) ( Fig . 11D ) . These results indicate that the small molecules tested act specifically on the ldtRLas activator . We hypothesize that in ‘Ca . L asiaticus’ , expression of LdtP is increased in response to osmotic stress , allowing persistence of the bacteria within the phloem of the tree . As such , the regulation of ldtP expression through inactivation of LdtR with small molecules represents a direct means of influencing osmotic stress tolerance , and survival of ‘Ca . L asiaticus’ within the host .
‘Ca . L . asiaticus’ is frequently exposed to changes in osmotic pressure , due to variations in phloem sap composition . Sucrose concentrations in the phloem can vary significantly ( between 0 . 5 and 30% w/v , corresponding to 15 mM and 880 mM , respectively ) depending on plant species , tissue , time of day , and season [15] , [16] . Consequently , bacterial pathogens that replicate in the phloem must continuously respond to changes in osmotic pressure . In this context , L , D transpeptidase activity is critical , as these enzymes are directly involved in cell wall biosynthesis and remodeling in response to stress conditions . In this report , we identified and characterized a regulon from the citrus pathogen ‘Ca . L . asiaticus’ , involved in peptidoglycan remodeling . These results represent the first regulatory system functionally analyzed for this pathogen . Included in this regulon is ldtR , a member of the MarR family of transcriptional regulators , and ldtP , a predicted L , D-transpeptidase . The genomic context of ldtRLas was conserved among members of the Rhizobiacea family . As such , the two closest phylogenic relatives of ‘Ca . L . asiaticus’ , S . meliloti and L . crescens , were used to study the phenotypic effects of L , D-transpeptidase inactivation , and the physiological conditions that contribute to the expression of the ldtR regulon , since ‘Ca . L . asiaticus’ is yet to be cultured . The highly conserved nature of ldtR suggests a similar mechanism of regulation among these members of the Rhizobiacea family; however , the response to ligands may vary due to the different lifestyle of each species . L , D-transpeptidases ( E . C . 2 . 3 . 2 . 12 ) mediate the substitution of 4→3 ( D-Ala4 to mDAP3 ) crosslinks , generated by the penicillin binding protein D , D-transpeptidase , to 3→3 ( mDAP3 to mDAP3 ) crosslinks . This pattern of L , D-transpeptidation represents 80% of the crosslinks observed in the cell walls of stationary phase M . tuberculosis cells [17] . Similar results were observed in other microorganisms , including E . coli and V . cholerae [8] , [18] . These observations suggest that transpeptidation is an active process in stationary phase cells , which may be critical for adaptation and tolerance to environmental stress . In M . tuberculosis , increased cell wall transpeptidation was positively correlated with increased transcription of LdtM1 during nutrient starvation [17] , [19] . Interestingly , our results in L . crescens indicate that ldtPLcr and ldtRLcr are expressed throughout the growth phases , when cultured under laboratory conditions . However , a comparative analysis of the ‘Ca . L . asiaticus’ transcriptome revealed that ldtR expression was five times higher in samples obtained from infected trees , when compared to samples collected from infected psyllids ( an alternate host and insect vector of ‘Ca . L . asiaticus’ ) [20] . These results suggest that in ‘Ca . L . asiaticus’ , transcription of Ldt-associated genes may be triggered by the high osmotic pressure generated by the phloem sap . These data , in combination with previous reports of the large proportion of 3→3 crosslinks in the muropeptides of Rhizobiales , suggest that LdtP may be involved in both housekeeping activities and stress response . To further explore the LdtR regulatory mechanism , Bacillus subtilis was used as a heterologous host . Interestingly , we found that LdtR acts as a transcriptional activator of the ldtR and ldtP genes . Although the majority of MarR proteins act as transcriptional repressors , several MarR transcriptional activators have been described . In S . meliloti , the MarR family member ExpG binds to the ExpADGE operon to activate expression of the galactoglucan biosynthesis genes [21] . Similarly , PntR and PenR , from Streptomyces arenae and S . exfoliatus , respectively , activate synthesis of the pentalenolactone antibiotic [22] . Interestingly , all of these regulators bind AT-rich sequences similar to the binding sequence identified for LdtR [21]–[23] . This high degree of conservation could represent a common feature among binding sequences for MarR members that act as transcriptional activators . In S . meliloti , changes in cell morphology ( short-cell phenotype ) were induced by the mutagenesis of ldtR and ldtP . Similar changes in cell morphology have been described for S . meliloti and Rhizobium spp in response to the accumulation of compounds such as glycine , which decreases the extent of crosslinks [24]–[26] . A similar short-cell phenotype was also observed in V . cholerae , following the accumulation of D-amino acids in the media [18] . Analysis of the ‘Ca . L . asiaticus’ genome revealed no homologs of the transpeptidases involved in these activities , however , a glutamate and alanine racemase were identified . These enzymes contribute to fluctuations in the concentration of D-amino acids . The potential involvement of LdtR in the regulation of these genes may explain the phenotypic changes observed in ldtR mutants . The direct or indirect involvement of LdtR in the regulation of these racemases is currently under examination . Based on the biological relevance of the ldtR regulon , we identified small molecules ( phloretin , benzbromarone , and hexestrol ) that decreased binding of LdtR to its cognate promoters , resulting in decreased expression of ldtP and ldtR . In L . crescens , decreased gene expression in presence of these small molecules was positively correlated with decreased tolerance to osmotic stress . Furthermore , in S . meliloti , the addition of phloretin , benzbromarone , or hexestrol resulted in morphological changes ( short-cell phenotype ) similar to those observed in ldtR and ldtP mutants . Consequently , we reasoned that chemical manipulation of LdtRLas activity will reduce long term survival and persistence of the pathogen in infected citrus trees . Thus , we designed an in vitro model using sweet orange leaves infected with ‘Ca . L . asiaticus’ , to validate the effect of these chemicals . In samples treated with the small molecules , a significant decrease in ldtR and ldtP expression was observed , confirming the specific effect of these chemicals in ‘Ca . L asiaticus’ . The use of a specific target is essential for the development of an effective therapeutic treatment . Modulation of cell wall transpeptidation has been used as a therapeutic treatment for recalcitrant microorganisms , such as Mycobacterium tuberculosis [27] . In contrast , current efforts towards the treatment of Huanglongbing disease are focused primarily on the use of “broad spectrum” treatments ( i . e . penicillin , streptomycin , and thermotherapy ) . This study provides strong proof of concept for the use of small molecules that target LdtRLas , as a potential treatment option for Huanglongbing disease .
Bacterial strains and plasmids are listed in Table 2 . Escherichia coli and Bacillus subtilis strains were grown in Luria-Bertani ( LB ) medium at 37°C . S . meliloti cells were grown at 30°C in either LB medium or M9 minimal medium with glucose . When required , the media was supplemented with gentamicin ( 30 µg ml−1 ) , ampicillin , ( 100 µg ml−1 ) , or chloramphenicol ( 170 µg ml−1 ) for E . coli; neomycin ( 100 µg ml−1 ) , gentamicin ( 30 µg ml−1 ) , and streptomycin ( 250 µg ml−1 ) for S . meliloti; or with erythromycin ( 1 µg ml−1 ) for B . subtilis . L . crescens BT-1 was cultured at 25°C , with moderate agitation ( 150 rpm ) , in modified BM7 media [13] containing 1% Brain Heart Infusion ( Difco Laboratories , Detroit , MI ) , 15% Fetal Bovine Serum ( Sigma-Aldrich , St . Louis , MO ) , 30% TMN-FH insect medium ( Sigma ) , α-Ketoglutaric acid ( 2 mg ml−1 ) , ACES ( 10 mg ml−1 ) , and potassium hydroxide ( 3 . 75 mg ml−1 ) , at pH 6 . 9 . Sodium chloride ( 0–200 mM ) or sucrose ( 0–300 mM ) was added to the growth media to induce osmotic stress . All antibiotics and chemicals were purchased from Sigma-Aldrich . Standard methods were used for chromosomal DNA isolation , restriction enzyme digestion , agarose gel electrophoresis , ligation , and transformation [28] . Plasmids were isolated using QIAprep Spin Miniprep Kit ( Qiagen , Valencia , CA ) , and PCR products were purified using Qiaquick purification kits ( Qiagen ) . For protein expression and purification , ldtR gene was amplified from ‘Ca . L . asiaticus’ str . psy62 or S . meliloti 1021 chromosomal DNA via PCR , and then cloned into the p15TV-L plasmid as described previously [10] . Protein expression and purification was performed as previously described [10] . Concisely , the His-tagged fusion proteins were overexpressed in E . coli BL21-Star ( DE3 ) cells ( Agilent Technologies , Santa Clara , CA ) . The cells were grown in LB medium at 37°C to an OD600 = 0 . 6 and expression induced with 0 . 5 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) . After addition of IPTG , the cells were incubated with shaking at 15°C overnight . The cells were harvested and resuspended in binding buffer ( 500 mM NaCl , 5% glycerol , 50 mM Tris pH 8 . 0 , 5 mM imidazole , 0 . 5 mM TCEP ) , and stored at −80°C . The thawed cells were lysed and passed through a French Press . The lysate was clarified by centrifugation ( 30 min at 17 , 000 rpm at 4°C ) and applied to a metal chelate affinity-column charged with Ni2+ . After the column was washed , the protein was eluted from the column using elution buffer ( binding buffer with 250 mM Imidazole ) . The hexa-histidine tag was then cleaved from the protein by treatment with recombinant His-tagged TEV protease . The cleaved protein was then resolved from the cleaved His-tag and the His-tagged protease by passing the mixture through a second Ni2+-column . The purified proteins were dialyzed against 10 mM Tris pH 8 . 0 , 500 mM NaCl , 0 . 5 mM TCEP , and 2 . 5% glycerol . Finally , the proteins were aliquoted and stored at −80°C . Purified LdtR protein was screened against a library of 160 intracellular compounds [11] at a final concentration of 100 µM , or against the Prestwick chemical library of 1152 compounds ( Prestwick Chemical , France ) at a final concentration of 1 . 3 µg/mL , using fluorometry as previously described [11] , [29] . LdtR was diluted to a final concentration of 30 µM in 100 mM Tris pH 8 . 0 , 150 mM NaCl . SYPRO orange was added to a final concentration of 5× . 25 µL aliquots of protein solution containing the chemical compounds were placed in duplicate into 96 well plates ( Bio-Rad , Hercules , CA ) and heated from 25°C to 80°C at the rate of 1°C per minute . A real-time PCR device ( iCycler IQ , Bio-Rad ) was used to monitor protein unfolding by the increase in the fluorescence of the fluorophor SYPRO Orange ( Life Technologies , Grand Island , NY ) . Fluorescence intensities were plotted against temperature for each sample well and transition curves were fitted using the Boltzmann equation using Origin 8 software ( Northampton , MA ) . The midpoint of each transition was calculated and compared to the midpoint calculated for the reference sample . If the difference between them was greater than 2 . 0°C , the corresponding compound was considered to be a “hit” and the experiment was repeated to confirm the effect in a dose dependent manner . Figure S4 shows the melting curves obtained for LdtRLas without chemicals or in presence of the selected hit chemicals . The chemicals that were not selected displayed melting curves similar to the one observed for the control . Gel shift assays for LdtR were performed using aliquots of protein purified and concentrated according to the procedures described above . Fragments of the ldtR , and ldtP promoters were generated by PCR using biotin prelabeled ( 5′-end ) primers ( Table 1 ) , then purified using QIAquick spin columns ( Qiagen ) . Incubation mixtures for EMSA ( 20 µL ) contained 1 ng of a 5′-labelled DNA probe , 50 mM Tris-HCl pH 7 . 2 , 150 mM KCl , 10 mM MgCl2 , 0 . 01% Triton ×100 , 12 . 5 ng/µL of both Poly ( dI-dC ) and Poly ( dA-dT ) nonspecific competitor DNAs , purified LdtR protein ( 0–400 nM ) , and ligand ( 0–1 mM ) when indicated . After incubation for 20 min at 37°C , samples were separated on 6% acrylamide-bisacrylamide nondenaturing gels in 0 . 5× Tris borate-EDTA buffer , pH 8 . 3 ( TBE ) . Electrophoresis was performed at 100 V using ice-cold 0 . 5× TBE as a running buffer . DNA was then transferred from the polyacrylamide gel to a Hybond-N+ membrane ( GE Healthcare , Pittsburgh , PA ) by electroblotting at 250 mA for 45 min in a semidry transfer . Transferred DNA was cross-linked for 15 min using a UV cross-linker equipped with 312 nm bulbs . Biotin labeled DNA was detected using a Phototope-Star Detection Kit ( New England Biolabs , Ipswich , MA ) . Membranes were exposed to Kodak X-ray film . For the EMSA competitions assays , different fragments of the promoter regions were synthesized using PCR , or by annealing of primers as previously described [10] ( Table 1 ) . Protection assays were performed on both minus and plus strands using 5′-6FAM or 5′-VIC labeled probes generated by PCR using primers described in Table 1 . The protection assay contained the same components used for EMSAs , except that 5 ng µl−1 PldtP labeled probe , 6 µM LdtRLas , 0 . 5 mM CaCl2 , 2 . 5 mM MgCl2 , and 0 . 025 U of DNase I ( New England Biolabs ) were added into 200 µL of reaction . The mix was incubated for 20 min at 37°C , and ended by adding 50 mM EDTA pH 8 . 0 . The corresponding digestion reaction without LdtR was included as a control . The digested DNA and the sequencing reaction products were analyzed at the Plant and Microbe Genomics facility , Ohio State University , Columbus , using a 3730 DNA analyzer . The protected regions were identified using GeneMapper software ( Life Technologies ) , as previously described [30] . The transcription start site of ldtR and ldtP genes from ‘Ca . L . asiaticus’ and L . crescens were determined by a modified 5′RACE-PCR protocol . Cultures of B . subtilis BS6 ( for ‘Ca . L . asiaticus’ ldtR and ldtP ) and L . crescens were grown to exponential phase as described above . The total RNA was extracted using the RiboPure-Bacteria kit ( Ambion , Austin , TX ) following the manufacturer's protocol . 2 . 5 µg of each RNA was first treated with 20 U of the Calf intestine alkaline phosphatase ( New England Biolabs ) for 1 h to remove the 5′-PO4 from degraded RNAs followed by a phenol∶chloroform∶isoamylalcohol precipitation . The RNAs were further treated with 2 . 5 U of Tobacco acid pyrophosphatase ( Epicentre Biotechnologies , Madison , WI ) for 1 h to remove the 5′-cap from mRNAs . The CIP/TAP RNAs were then ligated to the Oligo_RACE_RNA adapter ( Table 1 ) . The synthesis of the first strand of cDNAs were carried out using primers described in Table 1 , with the SuperScript II Reverse Transcriptase ( Invitrogen ) and according to the manufacturer's protocol . The cDNAs were amplified by PCR using Oligo_RACE_Fw and LdtRLas_RACE_Rv or LdtPLas_RACE_Rv for ‘Ca . L . asiaticus’ . Similarly , Oligo_RACE_Fw and LdtRLcr_RACE_Rv or LdtPLcr_RACE_Rv were used for L . crescens ( Table 1 ) . The PCR fragments were cloned using the StrataClone Blunt PCR cloning kit ( Agilent Technologies ) , following the manufacturer's protocol . The clones were sequenced and ldtR and ldtP transcriptional start sites determined . 100 µl of protein samples were prepared using 10 mM Tris pH 8 . 0 , 500 mM NaCl , and 10 µM LdtRLas . The sample was incubated 20 min on ice and then injected onto a prepacked Superose 12 10/300 GL gel filtration column ( GE Healthcare ) , connected to a LCC-501 plus ( GE Healthcare ) , and equilibrated with 10 mM Tris pH 8 . 0 and 500 mM NaCl . Filtration was performed in a flow rate of 0 . 5 ml/min at 4°C . The eluted protein was monitored continuously for absorbance at 280 nm using a UV-M II monitor ( GE Healthcare ) . Blue dextran 2000 was used to determine the void volume of the column . A combination of protein molecular weight standards , including IgG ( 150 kDa ) , BSA ( 66 kDa ) , Albumin ( 45 kDa ) , Trypsinogen ( 24 kDa ) , Cytochrome C ( 12 . 4 kDa ) , and Vitamin B12 ( 1 . 36 kDa ) was also applied to the column under the same conditions . The elution volume and molecular mass of each protein standard was used to elaborate a standard curve for further determination of the molecular weight of the proteins under study . The theoretical molecular weight of LdtR was calculated from the amino acid sequence using the Compute pI/Mw tool at the ExPASy Proteomics Server ( http://ca . expasy . org/tools/pi_tool . html ) . Plasmid pDG1663 [31] was used for the transcriptional analysis of ldtR expression . Plasmids pBS1 , pBS2 , pBS3 , pBS4 , pBS5 , and pBS6 described in Table 2 , were constructed using primers listed in Table 1 . To this end , the PCR fragments were cut with HindIII and BamHI restriction enzymes , and ligated into pDG1663 previously digested with the same restriction enzymes . The recombinant clones selected in E . coli DH5α were confirmed by sequencing with primer pDGseq9_Fw . Plasmids pBS6M1 , pBS6M2 , pBS6M3 , pBS6M4 , and pBS6M5 were constructed by site-directed mutagenesis in pBS6 using the QuikChange Site-directed Mutagenesis kit ( Agilent Technologies ) . The primers used are listed in Table 1 . The transfer of plasmids pBS1 , pBS2 , pBS3 , pBS4 , pBS5 , pBS6 , pBS6M1 , pBS6M2 , pBS6M3 , pBS6M4 , and pBS6M5 into B . subtilis 168 was carried out by natural competence [32] . The new generated strains are listed and detailed in Table 2 . The integration into the thrC locus was confirmed via extraction of B . subtilis genomic DNA using DNeasy Blood and Tissue kit ( Qiagen ) , followed by PCR with primers pDGseq9_Fw and pDGseq10_Rv ( Table 1 ) . For the β-galactosidase assays , B . subtilis cells were grown at 37°C in LB medium until reached an OD600 of 0 . 3 ( mid-exponential phase ) . Cells were collected and washed twice with 0 . 9% NaCl , and permeabilized with 1% toluene in Z-buffer ( 60 mM Na2HPO4 , 40 mM NaH2PO4 , 10 mM KCl , 1 mM MgSO4 , 50 mM β-mercaptoethanol ) [33] . β-galactosidase activity was assayed by following the catalytic hydrolysis of chlorophenol red-β-D-galactopyranoside ( Sigma-Aldrich ) . The absorbance at 570 nm was read continuously using a Synergy HT 96-well plate reader ( BioTek , Winooski , VT ) . β-galactosidase activity , expressed as arbitrary units ( AU ) , was calculated using the slope of absorbance curve normalized with the initial cell density . The assays were performed in triplicates . Promoter fusions to the uidA reporter gene , as well as ldtRSmc and ldtPSmc disruption mutants , were generated using plasmid pVMG [34] . pVMG is a modified version of plasmid pVO155 , containing a multiple cloning site upstream of a promoterless β-glucuronidase ( uidA ) reporter gene [35] . For the generation of recombinant strains , a ∼400 bp region of the target gene ( −378 to +29 of ldtRSmc , +104 to +498 of ldtPSmc , and +932 to +1332 of ldtPSmc , for pSMP2 , pSMP1 , and pSMP3 , respectively ) was amplified by PCR using the primers detailed in Table 1 . The amplified fragments were inserted into the SpeI and AgeI restriction sites upstream of uidA , in pVMG . The resultant plasmids were propagated in DH5α and mobilized into S . meliloti 1021 via triparental mating , using helper plasmid pRK600 [36] . Transconjugants were selected on M9 sucrose-neomycin plates and their correct insertion confirmed by sequencing , using primers upstream of the original fragment used for cloning into pVMG and primer Gus_Seq_Rv , located 204 bp inside uidA reporter gene ( Table 1 ) . For complementation assays , the complete sequence of ldtRLas gene was amplified by PCR using primers LdtRLas_EcoRI_Fw and LdtRLas_BamHI_Rv , while ldtPLas sequence was amplified using primers LdtPLas_KpnI_Fw and LdtPLas_EcoRI_Rv ( Table 1 ) . The DNA fragments were inserted into pBBR1MCS-5 plasmid , previously digested with the corresponding restriction enzymes , generating plasmids pSMP4 ( ldtRLas ) and pSMP5 ( ldtPLas ) . The recombinant plasmids were selected in DH5α and confirmed by sequencing using universal M13 primers ( Table 1 ) . Plasmids pBBR1MCS-5 , pSMP4 , and pSMP5 were mobilized into S . meliloti SMP2 via triparental mating , using helper plasmid pRK600 [36] . Transconjugants were selected on M9 sucrose-neomycin-gentamicin plates . For the β-glucuronidase assays , S . meliloti cells were grown in M9 minimal media , supplemented with NaCl , phloretin , or benzbromarone when indicated , until reached late-exponential phase . Cells were collected and washed twice with 0 . 9% NaCl , and permeabilized with 1% toluene in Z-buffer ( 60 mM Na2HPO4 , 40 mM NaH2PO4 , 10 mM KCl , 1 mM MgSO4 , 50 mM β-mercaptoethanol ) as previously described [33] . β-glucuronidase activity was measured by means of the hydrolysis of 4-nitrophenyl β-D-glucuronide substrate ( Sigma-Aldrich ) . The absorbance at 405 nm was read continuously using a Synergy HT 96-well plate reader ( BioTek ) . β-glucuronidase activity was expressed as µM of p-nitrophenol generated per min , normalized with the initial cell density . The assays were performed in triplicates . L . crescens cells were cultured in broth with hexestrol ( 25 µM ) , phloretin ( 50 µM ) , or benzbromarone ( 50 µM ) when required . The cells were collected by centrifugation at 4°C when OD600 = 0 . 3 ( mid-exponential phase ) . Total RNA was subsequently isolated with RiboPure-Bacteria ( Ambion ) in accordance with the manufacturer's protocol . cDNAs were synthesized with the Superscript first-strand synthesis kit ( Life Technologies ) in accordance with the manufacturer's instructions and stored at −80°C prior to use . Real-time quantitative PCR ( qRT-PCR ) was carried out in a iCycler IQ apparatus ( Bio-Rad ) using Platinum SYBR Green qPCR SuperMix for iCycler ( Life Technologies ) in accordance with the manufacturer's recommended protocol . Primers used for the qRT-PCR are described in further detail on Table 1 . The RNA polymerase sigma factor rpoD , 50S ribosomal protein L10 , 50S ribosomal protein L12 genes , ( B488_13350 , B488_08460 , B488_08450 , respectively ) , and 16S ribosomal RNA were used as internal controls . To test resistance to NaCl and sucrose , S . meliloti cells were grown in LB media to exponential phase ( OD600 = 1 . 0 ) . Serial dilutions were made and 4 µl was spot plated . Plates were prepared to contain 0 . 4 M NaCl or 0 . 3 M sucrose . In L . crescens the effect of chemical inactivation of LdtR on the stress tolerance was tested by following growth ( as increased optical density ) on liquid cultures . The morphology of different strains of S . meliloti ( Table 2 ) was visualized by scanning electron microscopy using a Hitachi S-4000 FE-SEM apparatus ( ICBR Electron Microscopy Core Lab , University of Florida , FL ) . S . meliloti 1021 strain , grown in the presence or absence of 25 µM phloretin , as well as SMP1 and SMP2 mutants , were cultured until exponential phase ( OD600 = 1 . 0 ) in LB media , as described above . Prior fixation , the cells were centrifuged 3 min at 8 , 000 rpm and the pellets washed twice with 1× PBS buffer . Finally , the cells were treated with 1 mL of Trump's fixative solution for 20 min at room temperature , and post-fixed in 1% osmium tetroxide followed by dehydration in graded ethanol concentrations , following Electron Microscopy Core Lab recommended procedures . For the statistical analysis , the size of 10 cells per strain per field was determined ( 6 fields per strain ) . The statistical significance of data obtained from SEM ( cell size ) and stress resistance assays ( CFU/ml ) , was determined using a Student's t-test . qRT-PCR statistical significance was assessed using a two-tail P-value , calculated with the Mann–Whitney nonparametric test .
|
The rapid expansion of Huanglongbing disease ( HLB ) has caused a severe crisis in the citrus industry , with no solution visible in the near future . The causative agent , ‘Candidatus Liberibacter asiaticus’ , is an unculturable bacterium under common laboratory conditions , which has made it difficult to gain understanding of this pathogen . Here we used a biochemical approach to identify new chemicals that could be used for the treatment of this devastating disease . These chemicals target a specific transcription factor ( LdtR ) in ‘Ca . Liberibacter asiaticus’ . When bound to LdtR , the chemicals inactivate the protein , which disrupts a cell wall remodeling process that is critical for survival of the pathogen when exposed to osmotic stress ( i . e . within the phloem of a citrus tree ) . Several model strains were used to confirm that the newly identified transcription factor ( LdtR ) and its regulated genes ( ldtR and ldtP ) confer tolerance to osmotic stress . The results presented in this study provide strong proof of concept for the use of small molecules that target LdtR , as a potential treatment option for Huanglongbing disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"bacteriology",
"biochemistry",
"gram",
"negative",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"host-pathogen",
"interactions",
"medical",
"microbiology",
"small",
"molecules",
"microbial",
"pathogens",
"biology",
"and",
"life",
"sciences",
"microbiology",
"chemical",
"biology",
"bacterial",
"pathogens",
"pathogenesis"
] |
2014
|
The Transcriptional Activator LdtR from ‘Candidatus Liberibacter asiaticus’ Mediates Osmotic Stress Tolerance
|
An important unresolved problem associated with actomyosin motors is the role of Brownian motion in the process of force generation . On the basis of structural observations of myosins and actins , the widely held lever-arm hypothesis has been proposed , in which proteins are assumed to show sequential structural changes among observed and hypothesized structures to exert mechanical force . An alternative hypothesis , the Brownian motion hypothesis , has been supported by single-molecule experiments and emphasizes more on the roles of fluctuating protein movement . In this study , we address the long-standing controversy between the lever-arm hypothesis and the Brownian motion hypothesis through in silico observations of an actomyosin system . We study a system composed of myosin II and actin filament by calculating free-energy landscapes of actin-myosin interactions using the molecular dynamics method and by simulating transitions among dynamically changing free-energy landscapes using the Monte Carlo method . The results obtained by this combined multi-scale calculation show that myosin with inorganic phosphate ( Pi ) and ADP weakly binds to actin and that after releasing Pi and ADP , myosin moves along the actin filament toward the strong-binding site by exhibiting the biased Brownian motion , a behavior consistent with the observed single-molecular behavior of myosin . Conformational flexibility of loops at the actin-interface of myosin and the N-terminus of actin subunit is necessary for the distinct bias in the Brownian motion . Both the 5 . 5–11 nm displacement due to the biased Brownian motion and the 3–5 nm displacement due to lever-arm swing contribute to the net displacement of myosin . The calculated results further suggest that the recovery stroke of the lever arm plays an important role in enhancing the displacement of myosin through multiple cycles of ATP hydrolysis , suggesting a unified movement mechanism for various members of the myosin family .
Myosin II , the conventional myosin responsible for muscle contraction , generates mechanical force by interacting with actin filament . Our understanding of this actomyosin motor has greatly increased by X-ray analyses of myosin structures [1]–[3] and by electron microscopy ( EM ) of actomyosin complex [4]–[7] . These structural observations have led to the widely held lever-arm hypothesis [2] , [3] , in which the change in the nucleotide state in the myosin head is amplified through allosteric communication for rotating the lever-arm region of myosin to exert mechanical force . X-ray and EM data of static protein structures do not , however , provide direct information on how the motor works dynamically . Dynamical behaviors have been observed in single-molecule experiments ( SMEs ) [8]–[14] , among which the Yanagida group [11] , [13] analyzed the fluctuating motion of a single subfragment-1 ( S1 ) of myosin and supported the alternative Brownian-motion hypothesis [15] . In this hypothesis , the myosin head stochastically moves along the actin filament with a regular step size of 5 . 5 nm , which corresponds to the diameter of actin subunit , in both directions toward the plus and minus ends of the actin filament during a single cycle of ATP hydrolysis . In this stochastic walk or effective Brownian motion , the frequency of steps toward the plus end is considerably higher than that of steps to the minus end . This biased Brownian motion enables the search for a stable binding site on the filament , which pulls the filament to exert mechanical force [11] , [13] . The thermal Brownian fluctuation of the myosin molecule should also cause the stochastic fluctuation in the direction of lever-arm swing . Even with such Brownian fluctuation of conformation , the lever-arm hypothesis implies that the net displacement of myosin is limited by the allowed angular range of the lever arm . In contrast to this narrow distribution , the net displacement of myosin stochastically varies under the Brownian-motion hypothesis and its distribution is broad and changes flexibly depending on the load applied to the system . These two hypotheses should accordingly show a clear difference in predicting the flexibility and load dependence of the system [16] . In addition , for myosin V , a non-conventional myosin responsible for vesicle transport , SME measurements [17]–[19] have clearly shown that the Brownian motion of the leading head of myosin in searching for the binding location on the actin filament significantly contributes to force generation together with the lever-arm pushing mechanism at the trailing head of myosin . A key issue in understanding the mechanism of actomyosin motors is thus to clarify how and to what extent lever-arm swing and Brownian motion contribute to force generation [16] , [20] , [21] . In this study , we address this problem by in silico observations of the system composed of a single head ( S1 ) of myosin II and an actin filament . Analyses of the kinetic cycle of interactions between myosin II and actin filament [22] should help to resolve this problem: ( 1 ) Myosin ( M ) strongly binds to actin filament ( A ) when no nucleotide is bound to myosin to form the rigor state ( A . M ) . When ATP binds to myosin ( A . M . ATP ) , the myosin detaches from the actin filament ( M . ATP ) . After the bound ATP is hydrolyzed into ADP and Pi , the complex M . ADP . Pi binds to actin to form the weakly bound state ( A . M . ADP . Pi ) , which is transformed to the strongly bound state by the release of Pi ( A . M . ADP ) and ADP to reach the rigor state again . From observed structures of myosin with various nucleotide analogs [2] , [3] , [23] , it is plausible to assume that the lever arm of myosin in M . ATP and M . ADP . Pi is in the pre-stroke position and the lever arm in other states is in the post-stroke position; therefore , processes 4 and 2 in Eq . 1 should correspond to lever-arm stroke during force generation and the recovery stroke , respectively . Further detailed comparison among kinetic states and structures , however , has raised a question regarding the application of the lever-arm hypothesis [3] . From various observed myosin structures , it is noted that the opening/closure of the nucleotide binding pocket , the lever-arm positioning , and the closure/opening of the 50 kDa cleft of myosin are correlated with one another [23] , [24] ( See Fig . 1 for an example structure of S1 of myosin II ) . The resolved structures have shown that Pi in M . ADP . Pi makes the nucleotide binding pocket closed , which tends to maintain the lever-arm in the pre-stroke position and the 50 kDa cleft open . Given that the closure of the 50 kDa cleft has been reported to be necessary for the strong binding of myosin to actin [4]–[7] , it is reasonable to assume that M . ADP . Pi weakly binds to actin . The weak binding of M . ADP . Pi to actin has been suggested by kinetic [25]–[27] and structural [28]–[30] measurements . However , for myosin to exert a force using the lever-arm mechanism , myosin must strongly bind to actin before the occurrence of the lever-arm swing . This problem in applying the lever-arm hypothesis may be solved if it is assumed that the 50 kDa cleft of A . M . ADP . Pi is closed , although the pre-stroke open-cleft structure is stable in M . ADP . Pi [31] . If myosin adopts the pre-stroke closed-cleft structure , it should strongly bind to the actin filament , and the subsequent occurrence of the lever-arm swing on the release of Pi should generate mechanical force . The pre-stroke closed-cleft structure may be possible when this structure is stabilized by specific myosin-actin interactions . Although considerable effort has been devoted to detecting the pre-stroke closed-cleft structure [32] , there is no direct evidence for its existence thus far [33] . In this study , we develop a theory on kinetic process , which is a dynamical energy landscape theory of actomyosin , without relying on the assumption of a stable pre-stroke closed-cleft structure of myosin . We assume that myosin interacting with actin tends to adopt one of structures observed in previous experiments . We also assume that the structure of myosin with a given nucleotide-binding state shows fluctuating transitions among these conformations , such as are shown by many allosteric proteins in the population-shift or conformation-selection mechanism of allostery [34] , [35] . In our previous studies , the theoretical models of movement of myosin S1 were discussed [36] , [37] . Molecular dynamics simulation was performed to investigate myosin with the nucleotide-free post-stroke closed-cleft structure [37] and it was shown that the electrostatic interactions at the actin-myosin interface should lead to a globally biased energy landscape of myosin movement toward the strong-binding site on the actin filament and that the stochastic movement of weakly binding myosin follows the gradient of this landscape in the course of relaxation from weak- to strong-binding states; therefore , the relaxation process reproduces the biased Brownian motion observed in SMEs [11] , [13] . However , to investigate the roles of this simulated behavior in the kinetic cycle of Eq . 1 , as noted in [37] , we need to extend this method to cases in which the energy landscape is not fixed , but is dynamically changing according to changes in nucleotide state and conformation . In dynamical energy landscape theory , multiple kinetic states , corresponding to different stages of chemical reactions or other different conditions , are considered and the dynamical switching among landscapes in these states is analyzed [38]–[45] . Here we consider the multiple kinetic states appearing in the course of force generation , called “actomyosin states . ” Figure 2 shows the kinetic network among actomyosin states considered in this study . Actomyosin states shown in Fig . 2 are defined by both the conformation and nucleotide state of myosin . We assume that myosin in actomyosin states tends to adopt conformations similar to those observed in X-ray or EM data . Myosin in A . M . ADP . Pi should adopt the pre-stroke open-cleft conformation ( Mpre ) that is modeled by the X-ray structure of myosin with an ADP . Pi analog , and myosin in A . M . ADP should adopt the post-stroke open-cleft conformation in the X-ray data ( Mpost ) . Myosin in A . M should adopt the post-stroke closed-cleft conformation ( Mclosed ) obtained by fitting the EM image in the rigor state . See the Methods section for more details on the definitions of these model conformations . In the present study , we distinguish the weakly bound Mclosed from the strongly bound Mrigor in the rigor state . Although both Mclosed and Mrigor have a post-stroke closed-cleft conformation , water molecules that hydrate myosin should be expelled from the interface with actin in transition to the rigor state , which is expressed in the model by a transition from Mclosed to Mrigor . It should be noted that in A . M . ADP in the absence of Pi , switch-I and switch-II regions of myosin are not bound to the ligand , and therefore , the post-stroke position of the lever arm and the closed 50 kDa cleft are expected to be energetically stable . The open 50 kDa cleft structure , however , should be entropically favorable to form both the post-stroke open-cleft structure and post-stroke closed-cleft structure in A . M . ADP . Therefore , we consider that A . M . ADP fluctuates between A . Mpost . ADP and A . Mclosed . ADP . Though the post-stroke open-cleft structure Mpost has been often referred to as the “near-rigor” or “post-rigor” conformation that appears after leaving the rigor state [46] , the post-stroke open-cleft structure is a representative structure of the ADP-bound myosins , and there is no evidence against the appearance of this structure before the rigor state is reached . Therefore , we use Mpost as a structure expected in A . M . ADP . Similarly , we consider that both Mpost and Mclosed appear in A . M . The electron paramagnetic resonance data have shown that coupling between the nucleotide state and conformation is not rigid [47] . We , therefore , assume that myosin with a given nucleotide state can adopt conformations that are expected to appear in the next or in the previous step of ATP hydrolysis as pre-existing or post-existing conformations in the conformation-selection mechanism of allostery . We consider A . Mpost . ADP . Pi to be the pre-existing conformation ( the conformation expected to be found in the ADP bound state ) . In the ADP-bound state , we consider A . Mpre . ADP to be the post-existing conformation ( the conformation expected in the ADP . Pi bound state ) . The rigor state is reached through the conformation-selection mechanism by selecting the pre-existing Mclosed conformation in the weakly bound state . It is assumed that the concentration of ADP or Pi in solution is so low that reverse reactions in steps of ATP hydrolysis are negligible . Thus , we have the network of transitions as shown in Fig . 2 . We also assume that the strongly bound state A . Mrigor is stable , and hence , we do not consider the spontaneous loosening of binding from A . Mrigor to A . Mclosed . For individual actomyosin states , we calculate the free-energy landscape which determines the movement of the myosin head in each of these states . Free-energy landscapes of myosin movement and actin-myosin binding are derived using a coarse-grained model of actomyosin , which represents proteins as chains connecting beads of carbons ( s ) . Forces acting among s of myosin are derived from the Gō-like potential [48] , [49] , which stabilizes the model myosin structure , Mpre , Mpost or Mclosed . Nucleotide and Mg ion bound to myosin are represented as particles of all nonhydrogen atoms . In this way , different actomyosin states are represented using different Gō-like potentials and different models of nucleotide and Mg . The potential consistently used among actomyosin states is the Gō-like potential for actin , which stabilizes the EM structure of actin filament [50] . As inter-protein interactions , we introduce electrostatic interactions , which are represented by Debye-Hückel potentials , and van der Waals interactions , which are represented by the Lennard-Jones type potentials . Using these potentials , we perform the Langevin molecular dynamics simulation . The setup of the simulation is shown in Fig . 3 . An S1 domain of myosin II , comprising a heavy chain , an essential light chain ( ELC ) , and a regulatory light chain ( RLC ) , is placed on the actin filament , which extends along the -axis with its plus-end facing the positive direction . The angle around the -axis is denoted by . The actin filament is connected to the spatially fixed points by springs . By mimicking the setup of the SME [11] , the tip of the myosin lever-arm is connected by springs to a line running parallel to the actin filament . Myosin can move freely along this line without any bias either toward the or direction . By monitoring the position of the center of mass of the myosin motor domain ( MD ) during simulations , we calculate the free-energy landscape in the two-dimensional space of and using the weighted histogram analysis method ( WHAM ) [51] with umbrella potentials . See the Methods section for the simulation details . Using the free-energy landscapes thus calculated , the movement of myosin II on the surface of an actin filament is simulated by the stochastic motion of a point at the center of mass of the myosin motor domain . Motion of this point along the free-energy landscape of each state is simulated using the value of the free energy in the Metropolis algorithm . The transition between different actomyosin states because of nucleotide-state change or lever-arm swing is simulated by dynamical switching between free-energy landscapes . Therefore , the point representing the position of myosin moves along the calculated free-energy landscapes and stochastically jumps among them . In this way , we shed light on roles of both the lever-arm swing , occurring during transitions among landscapes , and the biased Brownian motion along individual landscapes .
In Fig . 4 , free-energy landscapes of actin-myosin interaction in states A . Mpre . ADP . Pi , A . Mpost . ADP , and A . Mclosed are shown as functions of . In addition , the one-dimensional free-energy landscapes obtained by projecting the two-dimensional landscapes onto the -axis are shown . The calculated free-energy landscapes are almost periodic in the direction because of the helical nature of the EM structure of the actin filament with approximate helical pitch nm . In states with the open 50 kDa cleft structure , A . Mpre . ADP . Pi ( top , Fig . 4 ) and A . Mpost . ADP ( middle , Fig . 4 ) , the landscapes have multiple basins located at an interval of 5 . 5 nm , corresponding to the diameter of the actin subunit . These basins are separated by the low free energy barrier of 1–2 , which should be easily overcome by thermal noise . The lowest free-energy minima on the landscape of A . . ADP . Pi and A . Mpost . ADP are positioned at nm and nm , respectively , as shown in Fig . 4 . A large difference from the above two landscapes is found in the landscape of the A . Mclosed state with a closed 50 kDa cleft structure ( bottom , Fig . 4 ) . The landscape has an array of basins at positions separated by the size of actin subunit , 5 . 5 nm , with a global gradient toward the strong binding site at . This prominent feature can be ascribed to complementary matching between the closed-cleft structure of myosin and the actin filament with a heterogeneous distribution of electric charges on its surface . The shear motion between upper and lower 50 kDa subdomains should also contribute to the complementary matching between myosin and actin in A . Mclosed [52] . The arrangement of valleys in the direction is also notable . In A . Mclosed the angle difference between adjacent basins is considerably smaller than the angle expected from the helical structure of the filament , . This narrow distribution of basins results from the interplay among the myosin-actin interactions and the restraints on the motion of myosin and actin . The disagreement between the actin-subunit arrangement and the basin distribution indicates that myosin binds with different orientations to the actin surface in different basins , a difference that should lead to the difference in free energy among these basins . Thus , the strong gradient of the free-energy landscape is coupled with a narrow distribution of basins in the landscape . The one-dimensional free-energy landscapes in seven states in Fig . 2 are compared in Fig . 5 . It is found that the difference in conformation more significantly affects the free-energy landscape than the difference in the nucleotide state . The corresponding two-dimensional landscapes are shown in Fig . S1 . We do not consider myosin movement in A . Mrigor , and therefore , the calculation of free-energy landscape in the A . Mrigor state is omitted . From Figs . 4 and 5 , we can deduce the behavior of myosin through kinetic transitions in Fig . 2 . A myosin head landing on the actin filament should be attracted to the valley in the free-energy landscape of A . Mpre . ADP . Pi . It should weakly bind there and widely fluctuate among multiple basins of landscapes in A . Mpre . ADP . Pi or A . Mpost . ADP . The most populated - region of the myosin head in A . Mpre . ADP . Pi , A . Mpost . ADP , or other states is the region of high free energy in A . Mclosed . ADP and A . Mclosed landscapes . Thus , after releasing Pi or ADP . Pi , the myosin begins to relax to the more stable low free-energy position at the larger by moving along the actin filament . This movement associates jumps among minima with a regular spacing of approximately 5 . 5 nm . The above scenario of myosin movement can be verified by Monte Carlo ( MC ) simulation . The diffusive motion of the myosin head is simulated by the motion of a point representing the position of the center of mass of the myosin motor domain on the calculated two-dimensional free-energy landscape using the Metropolis algorithm . The trial movement of a point is generated as a step on the lattice with mesh size , where nm and . This trial is accepted when the free-energy change induced by the trial movement is . When , the trial is accepted with probability and rejected with probability . A similar method was used to simulate the movement of kinesin head along the surface of a microtubule [39] . We extend this method by applying it to the problem of multiple landscapes . A point representing the center of mass of the myosin motor domain diffuses along a landscape and jumps from on one landscape to the same on the other landscape with probabilities defined by rates in Fig . 2; with and with . Values of with and with represent chemical reactions and large-scale conformational change , respectively , which should have a 1–10 ms timescale . As discussed in the Methods , Monte Carlo steps ( MCS ) should correspond to several ms or longer , and hence , with and with should be – . For simplicity , we use either of two values , or . Given that Mpre . ADP . Pi , Mpost . ADP , and Mclosed have been observed in the X-ray and EM analyses , the actomyosin states A . Mpre . ADP . Pi , A . Mpost . ADP , and A . Mclosed should be relatively stable . In the following , values of and are chosen to stabilize the A . Mpre . ADP . Pi state as for , , , , and , and for , , , , and . is the rate of the process of hydrophobic matching between surfaces of proteins and should be faster than the large conformational change of proteins . We accordingly use the value . The calculated results are robust against changes in this parametrization . See Fig . S2 for the results of other choices of values for and . After the transition from one landscape to the other , the point representing the center of mass of the myosin motor-domain continues to diffuse on the new landscape . Such successive transitions and diffusions are terminated when the trajectory reaches the A . Mrigor state . We assume that this termination is the transition from the lowest free-energy valley of the landscape of A . Mclosed at to A . Mrigor with the rate . See the Methods for more details on the MC simulation . We should note that this MC calculation is based on the approximation that processes occurring during the transition between states , namely the lever-arm swing or chemical reactions , can be decoupled from the motions of actin and myosin within each state . This decoupling should be validated when we can assume separation of timescales among the process between states and motions within states . To evaluate the validity of this assumption , simulations of the coupled processes of transition , conformational fluctuation , and diffusive motion are necessary . A more elaborate molecular dynamics model that allows the examination of such dynamic coupling among processes is being developed [53] , and we leave the application of that model to the motor problems as a future project . Along the MC trajectory , myosin that has begun to interact with the actin filament at an arbitrary position is attracted and weakly bound to the free-energy valley in the A . M . ADP . Pi state , but the position of the myosin largely fluctuates along the -axis while it stays in the weak-binding state . After reaching the A . Mclosed . ADP or A . Mclosed state , the myosin begins to show the biased Brownian motion . Because the closure of the 50 kDa cleft should promote the release of ADP from myosin , we assume that the lifetime of A . Mclosed . ADP is short , and thus , the persistent motion appears in the A . Mclosed state . In the A . Mclosed state , Brownian motion is composed of steps with a regular width of 5 . 5 nm , and shows both the forward and backward stepping , but is biased toward the forward direction ( Fig . 6A ) . This biased Brownian motion is terminated when myosin reaches the rigor state . The distribution of myosin displacement was monitored when the large positional fluctuation in the weak-binding state was reduced on the start of the displacement [11] . To compare this measurement , we monitored the simulated displacement after the system enters into the A . Mclosed state by calculating , where and are the position of the myosin motor domain in the rigor state and that at the time when the system enters the A . Mclosed state , respectively . in distinguishes the different strong-binding sites which are almost periodically positioned along the helical actin filament . Shown in Fig . 6B is the calculated distribution of , which consists of two parts , i . e . , the major and minor parts . The major part is biased toward with multiple peaks separated by 5 . 5 nm . The major part of the distribution represents the trajectories that reach . The minor part is the distribution of trajectories that reach the strong-binding site at the periodic location . In previous SMEs [11] , [13] , the displacement of S1 has been monitored at RLC near the tip of the lever arm . As will be discussed in the subsection Contribution of the lever-arm swing , the most probable value of the -coordinate near the tip of the lever arm , , is approximately 5–7 nm greater than the -coordinate of the center of mass of myosin motor domain , , in A . Mclosed . Therefore , for comparison with the distribution of displacement of the lever-arm tip , the distribution of Fig . 6B should be shifted by several nanometers in the positive direction . With this correction , the simulated distribution of Fig . 6B reproduces the results of SME of Fig . 5 in [13] . The distribution of Fig . 6B is also consistent with the SME reported earlier [9] although the data has been differently interpreted [9] by disregarding the minor part of the observed distribution . The distributions of simulated with different parameterizations of kinetic rates are compared in Fig . S2 , showing that the results are insensitive to differences in these parameters . The lever-arm swing upon the kinetic transitions in the present scheme also contributes to force generation by displacing the lever-arm tip . Fig . 7 shows the position of the center of mass of the myosin motor domain and the position in RLC near the lever-arm tip represented by ( Fig . 7A ) , and the free-energy landscapes drawn on the plane of in the A . Mpre . ADP . Pi state ( Fig . 7B , left ) and in the A . Mclosed state ( Fig . 7B , right ) . In A . Mpre . ADP . Pi , myosin only weakly binds to actin to make the free energy insensitive to the angle of myosin to the actin surface . The free-energy basin accordingly spreads in the direction of the axis . In A . Mclosed , in contrast , the free-energy basin is localized at locations with 5–7 nm reflecting the post-stroke position of the lever-arm tip . From Fig . 7B , from estimated difference in location of free-energy basins in two landscapes , the net displacement of the lever-arm tip ( A . Mclosed ) ( A . Mpre . ADP . Pi ) is 10–16 nm , in which the contribution of the biased Brownian motion ( A . Mclosed ) ( A . Mpre . ADP . Pi ) is 5 . 5–11 nm and the contribution of the lever-arm swing is 3–5 nm . The finite width of the distribution of is noteworthy because of the stochastic nature of the diffusive motion , and some width of the contribution of the lever-arm swing due to the fluctuating position in the A . Mpre . ADP . Pi state should also be noted . Although the simultaneous measurement of and in SME has not yet been reported , it is important to acquire high resolution data for and to check the validity of the discussed mechanism . As shown in the previous subsections , the biased Brownian motion of myosin arises from the global gradient of the free-energy landscape of A . Mclosed . Two crucial factors involved in this gradient are ( i ) the close contact of myosin and actin surfaces , which is allowed to occur only when the 50 kDa cleft of myosin is closed , and ( ii ) the attractive electrostatic interactions through the contact between myosin and actin [37] . In the following , we show that this contact is formed through the conformational flexibility of actin and myosin . Some regions of heavy and light chains of myosin are structurally disordered and not determined by X-ray analysis . These disordered regions are spread over N-terminal region ( residue number , 1–3 ) , loop 1 ( residue number , 205–215 ) , loop 2 ( residue number , 627–646 ) , loop 3 ( residue number , 572–574 ) , converter ( residue number , 732–737 ) , and several regions in the ELC and RLC . In addition , the structure of the N-terminus of actin subunit is inconsistent between X-ray crystallography [54] and EM [50] , indicating that this part is also disordered in solution . The importance of loop 2 , loop 3 , and the N-terminus of actin subunit to actin-myosin binding was shown in our previous molecular dynamics simulation [55] . In Fig . 8A four landscapes are compared with different degrees of allowed fluctuations in these regions . In one landscape , all of the disordered regions fluctuate without the guidance of the Gō-like potential , whereas in the other landscapes , the Gō-like potentials stabilizing the reference structures are assumed to regulate the fluctuation of these regions . The fluctuation of the myosin-actin surface is indispensable for the generation of the global gradient of the landscape ( Fig . 8A ) . When loop 2 and loop 3 structures of myosin are more rigid in the simulation , the global gradient of the landscape considerably decreases , a consequence that should diminish the bias in the Brownian motion . We also find that the flexibility of the N-terminus of actin subunit enhances the biased Brownian motion; with the less flexible N-terminus of actin subunit , the barrier between the minima becomes higher , to allow the rigid N-terminus works to hinder for the diffusive motion of myosin . It would be interesting to investigate these theoretical predictions by observing the movement of myosin by following the introduction of mutations that rigidify the structure of myosin loop regions or of the N-terminus of actin . The role of electrostatic interactions was investigated in our previous studies [37] , [55] by changing the concentration of ions in solution and introducing mutations to change the charge distribution in the model . In addition , in the present simulation , the global gradient in the free-energy landscape decreases by increasing the concentration of counter ions , a result consistent with the experimentally observed decrease in the efficiency of the actomyosin motor in an in vitro motility assay [56] ( Fig . 8B ) . In this study , the simulated results predict that this decrease in motor efficiency should be observed not only for the ensemble of actomyosins but also for SME .
Two major assumptions in the present study are that ( i ) myosin tends to adopt the conformations determined by X-ray and EM observations , and ( ii ) myosin fluctuates among these conformations as allosteric proteins fluctuate in the conformation-selection mechanism of allostery . With these assumptions , we found that free-energy landscapes for myosin movement along actin are different in the weak-binding and strong-binding states , necessarily leading to a difference in the most stable positions for myosin , which we called the weak-binding and the strong-binding sites . This difference in binding location forces myosin to move from the weak-binding to the strong-binding sites , according to the kinetic change from weak-binding to strong-binding states . We found that the free-energy landscape of this movement has a global gradient from the weak-binding to strong-binding sites; therefore , myosin shows the biased Brownian motion toward the strong-binding site as has been observed in SME . We also found that this Brownian motion is concomitant with lever-arm swing; therefore , the nm displacement due to the lever-arm swing and the nm displacement due to the biased Brownian motion are coupled with each other to contribute to the net displacement of myosin . The simulated biased Brownian motion explains the SME data , and the theoretical results predicted that the biased Brownian motion and the underlying free-energy landscape are modified by mutagenesis to change the structural rigidity of myosin loop regions or the N-terminus of actin subunit . It is also predicted that the bias in the Brownian motion is weakened due to the increase in counter-ion concentration . The displacement of S1 due to the combined effects of the biased Brownian motion and lever-arm swing arises from the dynamically changing free-energy landscape . This dynamical free-energy landscape also suggests an intriguing scenario on the mechanism by which myosin binds to actin when the S1 domain is connected to the S2 domain and further to the light meromyosin ( LMM ) domain . In Fig . 9 , the movement of myosin with S2 in a cycle of ATP hydrolysis is illustrated . Here , one of two myosin heads is shown to emphasize the movement in an ATP cycle . Myosin weakly binds to actin in the A . M . ADP . Pi state at ( Fig . 9A ) , begins to move in the direction in the A . Mclosed state ( Fig . 9B ) , reaches the strong-binding site at , and enters the strongly bound rigor state ( Fig . 9C ) . When myosin binds ATP , myosin detaches from actin and the myosin motor domain changes its orientation through the recovery stroke ( Fig . 9D ) . Here , we emphasize the positive role of the recovery stroke . Because the myosin S1 is connected to S2 and LMM , the recovery stroke of the lever arm in a detached state from the actin filament does not shift the position of S2 or LMM but rather shifts the orientation of the motor domain , as illustrated in Fig . 9D . After ATP hydrolysis , the myosin begins to bind to actin . Here , we can expect that the myosin head searches for the next binding site with the swinging motion of S1 and S2 domains ( Fig . 9E ) . With this swinging search with the motor domain oriented as in Fig . 9D , the myosin should have higher binding affinity to the binding site in the next helical pitch rather than to the position in the previous ATP cycle ( Fig . 9F ) . Subsequently , after the release of Pi and ADP , the myosin moves toward the strong-binding site . In this way , the recovery stroke should enhance the net displacement of the myosin head through the multiple cycles of ATP hydrolysis . This suggested mechanism of myosin II movement is similar to that of processive motion of myosin V . The leading head of myosin V after ATP hydrolysis searches for a binding site to the actin filament through the swinging motion of the neck domain [17] , [18] . Because the motor domain has its orientation changed by the recovery stroke in myosin V [57] , the binding affinity of myosin to actin is enhanced in the forward more than that in the backward direction along the actin filament [57] , [58] . The importance of the motor domain orientation on binding to the actin filament has also been suggested for myosin VI [59] . The present results showed that loop 2 of myosin is the key element in causing the biased Brownian motion . Given that loop 2 of myosin V is longer than loop 2 of myosin II , with a larger number of positive charges , the biased Brownian motion may also contribute to the processive motion of myosin V . As shown by a recent SME [19] , the leading head of myosin V finds a position to bind to the actin filament through its random swinging motion , also called the Brownian search-and-catch motion . It will be interesting to investigate whether the leading head of myosin V searches for the strong-binding site through the biased Brownian motion , as discussed in the present study , at the final step of this Brownian search-and-catch process by moving along the actin filament . The simulated result presented in this paper based on dynamical energy landscape theory is consistent with the observed structural features of myosin and actin , reproduces the SME data , predicts the effects of conformational flexibility and electrostatic interactions , and further suggests a unified mechanism for different members of myosin family . We found that the displacement of myosin head during a cycle of ATP hydrolysis is variable with respect to both the contribution of lever-arm swinging and the biased Brownian motion . These variances should be changed by the load applied to the myosin head in ways characteristic of these contributions . Such dynamically flexible responses should affect the dynamical behaviors of muscle and cardiac systems [60] . The comparisons among these system behaviors , SMEs , and in silico observations should open further avenues to understanding the dynamical physiological phenomena .
We constructed three structural models , Mpre , Mpost , and Mclosed of myosin S1 , each of which comprises a heavy chain , an essential light chain and a regulatory light chain . We assumed that Mpre , Mpost , and Mclosed are structures of myosin obtained from chicken skeletal muscle in accordance with single-molecule experiments [11] , [13] . Mpre is the pre-stroke open-cleft structure of myosin with the bound analog of ADP and Pi . Because the X-ray structure of myosin of chicken muscle with the analog of ADP and Pi is not yet available , we constructed Mpre using scallop myosin with ADP and VO4 ( PDB code: 1QVI ) [61] by homology modeling . Using the sequence alignment between sequences of myosin from chicken skeletal muscle and 1QVI and using the structure of 1QVI as a template , Mpre was computationally constructed with the software MODELLER [62] . A vanadium atom , V , was replaced with a phosphorus atom , P , to give Pi . Parts of the myosin structure that were missing because of disorder in the template structure 1QVI were treated as flexible parts in Mpre fluctuating without guidance of the Gō-like potential in the model . Mpost is the post-stroke open-cleft structure of myosin with the bound analog of ADP . Mpost was constructed using chicken skeletal myosin without nucleotide ( PDB code: 2MYS ) [1] , and its binding with ADP was modelled using scallop myosin with ADP ( PDB code: 2OTG ) [63] . We treated the missing parts in 2MYS ( chicken skeletal myosin without any nucleotide ) as flexible parts in Mpost fluctuating without guidance of the Gō-like potential . Mclosed is the post-stroke closed-cleft structure extracted from the structure determined by fitting the electron-microscope image of actomyosin complex [7] . Because Mclosed appears in the course of relaxation from the weak to the strong actin-binding states in our simulation scheme , we assumed that structures of loops and other flexible regions of Mclosed are not fixed as in the rigor state . We therefore treated the missing parts in 2MYS as the flexible parts in Mclosed fluctuating without guidance of the Gō-like potential , unless otherwise noted . We assumed that actin filament is obtained from rabbit skeletal muscle , in accordance with the single-molecule experiment [11] . A structural model of actin filament was represented as a complex of 26 subunits and was reconstructed from the X-ray structure ( PDB code: 2ZWH ) by positioning the adjacent subunit with 166 . 4 rotation and 2 . 759 nm translation [50] . The actin filament constructed in this way shows the 3 . 2 rotation for the translation of 13 subunits , amounting to 35 . 867 nm . We represented this structure as one exhibiting helical symmetry with approximate helical pitch 35 . 9 nm . These structural models , Mpre , Mpost , , and the model of actin filament , were used as reference structures for the Gō-like potentials ( see below ) . Each polypeptide chain in the system was represented with residue-level coarse graining as a chain of connected beads of atoms . Bound ligands , Mg+ADP+Pi for the A . M . ADP . Pi states , Mg+ADP for the A . M . ADP states , were represented by all nonhydrogen atoms , whereas the nucleotide-free A . M states lacked bound ligand atoms . The total potential energy of the actomyosin system , , is given by ( 2 ) where is the interaction potential within myosin ( including the bound ligands ) and within the actin filament , is the interaction potential between myosin and the actin filament , and is the restraint potential on the lever-arm tip of myosin and on the subunits of the actin filament . As shown in the following , the reference structure defined above is the minimum-energy structure in the interaction potential given by ( 3 ) The bond-angle potential is ( 4 ) where the bond angle is defined as the angle formed by three successive residues , , and , and the superscript 0 hereafter denotes the values of variables in the reference structure . The dihedral angle potential is given by ( 5 ) where is defined as the dihedral angle formed by the four successive residues , , , and . With respect to the contact interactions , all residue pairs are classified as either native or nonnative using the reference structure; for residue pair and within the same chain , if at least one pair of nonhydrogen atoms are within 4 . 5 Å from each other in the reference structure with , the pair and is considered a native pair . Given that there are multiple subunits within a myosin or an actin filament , residue pairs between different subunits also interact with each other by the contact potential . For the pair and across different subunits in myosin or actin filament , if at least one pair of nonhydrogen atoms are within 4 . 5 Å from each other in the reference structure , the pair is considered a native pair . Otherwise , a pair within the same chain or a pair across different subunits is a nonnative pair . Contact potential is given by ( 6 ) and is given by ( 7 ) where ( 8 ) is given by ( 9 ) where ( 10 ) The constants , , , and were defined as 6 . 67 kcal/mol/rad2 , 1 . 6710 kcal/mol , 8 . 3310 kcal/mol , 3 . 3310 kcal/mol and 1 . 33 kcal/mol/Å2 , respectively . The cutoff distance was set to be 4 . 0 Å . These definitions of intramyosin or intraactin potential , including the relative strengths of bond angle , dihedral and contact potentials , are similar to those of the Gō-like model [48] , [49] , except for the following modifications . Bond length between adjacent residues along the polypeptide chain was constrained to by the RATTLE algorithm [64] , instead of the spring potential , to ensure the stability of the Langevin dynamics simulation . The contact potential at or was replaced by the spring-like potential to avoid instability in the numerical integration of the Langevin equation . Ligand contact potential was given by the spring-like potential , ( 11 ) The pair of ligand atoms located within 4 . 5 Å from each other in the reference structure interact with each other by this potential . In addition , if at least one of the atoms in the amino acid residue and an atom in the ligand are located within 4 . 5 Å from each other in the reference structure , that residue also interacts with the ligand atom by this potential . was 6 . 67 kcal/mol/Å2 . The interaction at the interface between myosin and actin , , is similar to that in our previous study [37] , and is composed of electrostatic and van der Waals interactions: ( 12 ) The electrostatic interactions were expressed by the Debye-Hückel potential as ( 13 ) where or is the charge of the amino acid residue ( for Asp and Glu , for Lys and Arg , and for His ) or the charge of the atom in the ligand ( for each of the the three oxygen atoms in ADP , for each of three oxygen atoms in Pi , and for Mg ) . The parameters were defined as Å , kcal Å/mol , and = 59 . 3 Å . The van der Waals interactions were given by the 12-6 type Lennard-Jones potential ( 14 ) with ( 15 ) where the potential at was replaced by the spring-like potential . The parameters were = 0 . 015 kcal/mol , = 8 . 0 Å , and = 1 . 33 kcal/mol/Å2 . To mimic the experimental setup of the single-molecule experiment [11] , we applied spatial restraints to myosin and the actin filament , respectively , as ( 16 ) The tip of the myosin lever-arm ( residue number 830–843 in the heavy chain and residue number 1–83 in the regulatory light chain ) was restrained with the curtain-rail potential ( 17 ) where was 0 . 2 kcal/mol/Å2 . The -axis runs parallel to the center line of the reference structure of actin filament , and and are coordinates perpendicular to the -axis . We assumed that the curtain-rail runs helically around the actin filament so that the whole system has the same helical symmetry as the reference structure of the actin filament , a 3 . 2 rotation for each 35 . 867 nm . We therefore used ( 18 ) with . The rotation of 3 . 2 per 35 . 867 nm is so small that the helical arrangement of the curtain-rail is visually indistinguishable from the straight line along the -axis . However , this slightly helical arrangement aids rapid numerical convergence in WHAM by assuring the periodicity of the entire system . In accordance with the single-molecule measurement [11] , no force is applied with respect to the movement of the lever-arm tip of myosin along the curtain-rail . All residues in the actin filament were restrained by the potential ( 19 ) where was defined as kcal/mol/Å2 . We performed the Langevin molecular dynamics simulation to sample the conformational ensemble of the actomyosin complex . The integration scheme is that of Honeycutt & Thirumalai [65] , with the particle mass = 1 . 0 , temperature = 300 K , time step = 0 . 0175 , and friction coefficient = 0 . 005 . We defined the two-dimensional coordinate system around the actin filament ( , ) , where is the angle around the -axis . The position of the center of mass of the motor domain ( residue number 1–780 ) of the myosin head is denoted by ( , ) . Umbrella sampling was used to enhance sampling at the high-free-energy region on the ( , ) plane . The region of nm nm and was divided into blocks , where nm . We applied the umbrella potential to enhance the sampling of myosin located in each of the 60 blocks as ( 20 ) where nm ( ) and ( ) . The constants and were defined as kcal/mol/Å2 and 10 kcal/mol/rad2 . For each of 60 umbrella potentials , we performed 12 independent runs of Langevin dynamics for steps and the data acquired in the first half of the run were discarded . From the data obtained with each of 60 umbrella potentials , we generated the histogram of ( , ) with the bin size of nm and . We subsequently combined these data by WHAM [51] to calculate the -dependent free-energy landscape and the two-dimensional free-energy surface on the plane . When we used only the 60 sets of the data sampled with 60 different umbrella potentials , the resulting landscape was not periodic because of the boundary effect . To avoid this numerical error , we replicated the whole data using the helical symmetry of the system as ( 21 ) with , and 2 , so that in total five sets of data were used . We confirmed that copying twice in both directions , resulting in five repeats of the same data , yielded a sufficiently periodic free energy landscape . In each MCS with a time scale of , both a change in the actomyosin state and diffusion in the plane can occur . The procedures in one MCS were as follows: ( i ) the actomyosin state changed with probability or in Fig . 2 , which is defined to be considerably smaller than unity . We assumed that the rates of transitions among actomyosin states are fast ( ) or slow ( ) except for the transition from A . Mclosed to A . Mrigor , where represents the inverse of an MCS . The lifetime of each actomyosin state is determined by whether the rates of approach to or departure from that state are fast or slow . Parameters were chosen to lengthen the lifetime of the A . Mpre . ADP . Pi state: fast for , , , , and , and slow for , , , , and . See Fig . S2 for the other choices of parameter values . ( ii ) If the transition to the different actomyosin state was not chosen , the diffusion of the myosin head on the two-dimensional free-energy landscape in the current actomyosin state was chosen . The trial movement of the myosin head was represented by motion along a lattice with mesh size . At each trial , the movement of myosin in the direction was chosen with probability , whereas movement in the direction was chosen with probability ( see below for the value of ) . ( iii ) The trial move was defined by selecting either of two sites in the direction chosen in ( ii ) with probability 0 . 5 . ( iv ) The free-energy difference accompanying the trial move was calculated , and the trial was accepted when or with the probability when , and otherwise rejected . The value of was determined as follows . One step in the direction is the displacement of nm , whereas one step in the direction is the displacement of nm with . Assuming that the Brownian motion of the myosin head is isotropic in the two-dimensional plane of , the average distance after steps is , which yields . The diffusion constant of the freely diffusing myosin head can be roughly estimated as by considering the myosin head as an ellipsoid moving sidewise with semi-major axes of 8 nm and semi-minor axes of 2 . 5 nm in water with viscosity 0 . 89 pNnsnm at 300 K [36] . This value can be used to estimate , the time scale of an MCS . Because the average distance after time steps is given by , we have , which gives ns . Because this value is obtained by assuming the free diffusion of myosin , we should note that this estimate of should give a lower limit . With this estimate , the trajectory of 104 steps as shown in Fig . 6A has a time scale of several milliseconds or longer .
|
Myosin II is a molecular motor that is fueled by ATP hydrolysis and generates mechanical force by interacting with actin filament . Comparison among various myosin structures obtained by X-ray and electron microscope analyses has led to the hypothesis that structural change of myosin in ATP hydrolysis cycle is the driving mechanism of force generation . However , single-molecule experiments have suggested an alternative mechanism in which myosin moves stochastically in a biased direction along actin filament . Computer simulation serves as a platform for assessing these hypotheses by revealing the prominent features of the dynamically changing landscape of actin-myosin interaction . The calculated results show that myosin binds to actin at different locations of actin filament in the weak- and strong-binding states and that the free energy has a global gradient from the weak-binding site to the strong-binding site . Myosin relaxing into the strong-binding state therefore necessarily shows the biased Brownian motion toward the strong-binding site . Lever-arm swing is induced during this relaxation process; therefore , lever-arm swing and the biased Brownian motion are coupled to contribute to the net displacement of myosin . This coupling should affect the dynamical behaviors of muscle and cardiac systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"chemistry",
"molecular",
"complexes",
"cell",
"biology",
"molecular",
"dynamics",
"biophysics",
"theory",
"biology",
"and",
"life",
"sciences",
"chemistry",
"physical",
"sciences",
"computational",
"biology",
"molecular",
"cell",
"biology",
"biophysics",
"molecular",
"biology",
"biophysical",
"simulations"
] |
2014
|
Coupling of Lever Arm Swing and Biased Brownian Motion in Actomyosin
|
The stress of living conditions , similar to infections , alters animal immunity . High population density is empirically considered to induce prophylactic immunity to reduce the infection risk , which was challenged by a model of low connectivity between infectious and susceptible individuals in crowded animals . The migratory locust , which exhibits polyphenism through gregarious and solitary phases in response to population density and displays different resistance to fungal biopesticide ( Metarhizium anisopliae ) , was used to observe the prophylactic immunity of crowded animals . We applied an RNA-sequencing assay to investigate differential expression in fat body samples of gregarious and solitary locusts before and after infection . Solitary locusts devoted at least twice the number of genes for combating M . anisopliae infection than gregarious locusts . The transcription of immune molecules such as pattern recognition proteins , protease inhibitors , and anti-oxidation proteins , was increased in prophylactic immunity of gregarious locusts . The differentially expressed transcripts reducing gregarious locust susceptibility to M . anisopliae were confirmed at the transcriptional and translational level . Further investigation revealed that locust GNBP3 was susceptible to proteolysis while GNBP1 , induced by M . anisopliae infection , resisted proteolysis . Silencing of gnbp3 by RNAi significantly shortened the life span of gregarious locusts but not solitary locusts . By contrast , gnbp1 silencing did not affect the life span of both gregarious and solitary locusts after M . anisopliae infection . Thus , the GNBP3-dependent immune responses were involved in the phenotypic resistance of gregarious locusts to fungal infection , but were redundant in solitary locusts . Our results indicated that gregarious locusts prophylactically activated upstream modulators of immune cascades rather than downstream effectors , preferring to quarantine rather than eliminate pathogens to conserve energy meanwhile increasing the “distance” of infectious and target individuals . Our study has obvious implications for bio-pesticides management of crowded pests , and for understanding disease epidemics and adaptiveness of pathogens .
Similar to pathogenic infection , the stress of living conditions alters animal immunity . Changes in population density usually induce polyphenic transition and variations in the immune response of animals [1] . The locust , globally notorious agricultural pests that have been controlled with fungal biopesticides for decades , exhibits phenotypic changes during solitary or gregarious phases in response to low or high population densities , respectively [2] . Differences in morphology , behavior and physiology have been observed between the two locust phases . Gregarious locusts ( Schistocerca gregaria ) have longer life spans than solitary locusts in response to treatment with a lethal fungal biopesticide ( Metarhizium anisopliae var . acridum ) [3] . The altered susceptibility of gregarious locusts through changes in population density potentially enhance the risk of fungal biopesticide resistance and provide an ideal model system for investigating the environmental factors that modulate the phenotypic immunity of locusts . High population densities increase the risk of infection by individuals through contact and injury from cannibalism , which require relatively higher investments into host immunity [4] , [5] , [6] . However , several gregarious insects display low total hemocyte counts and decreased phenoloxidase ( PO ) activity , previously postulated to have arisen from the increasing the “distance” between the susceptible individuals and infected individuals , due to the gap filled by more healthy individuals after crowding , resulting in a relatively lower risk of infection [7] . This assumption from Watve's model depends on the increasing Euclidean or behavioral distance between the susceptible individuals and infected individuals [8] , [9] , [10] . That is , any physiological or behavioral characteristics of host ( such as rapid wound healing after injury or cannibalism , allogrooming after pathogen attachment to body surfaces and other hygienic activities ) beneficial to inhibiting output of pathogens particles from infectious individuals and increasing the “distance” between hosts ensures the health of crowded individuals . Previous observations of altered immunity in high-density populations were largely incomprehensive because the prophylactic investment in immunity could be emphasized either on behavioral defenses [11] or physiological defense , displaying the defense strategies from pathogens elimination to the control of pathogens proliferation , spread and damages [12] , [13] , [14] , [15] . Therefore , it is essential to find indicators for the evaluation of density dependent prophylaxis either in physiological or behavioral defenses . Unlike behavioral defenses , physiological defenses are largely depend on distinctive , efficient , and dedicated immune responses . When fungi germinate on insect integument and penetrate into their hemocoel , fungal molecular patterns ( mostly β-1 , 3-glucans ) and virulent factors ( PR1 ) from the growing hyphae are detected by insect immune surveillance molecules such as glucan recognition proteins ( GNBP/GRP ) and persephone ( PSH ) . In response to the fungal invasion , the insect immune system initiates series of defenses such as humoral melanization , fungal β-1 , 3-glucan degradation , assembly of attack complex and production of intracellular antimicrobial peptides ( AMPs ) [16] , [17] . The relatively simple immune system of invertebrates allows for the comprehensive observation of resource allocation in immune cascades and for further understanding of the host defense strategy in response to changes in population density . Previous studies have attempted to understand desert locust phenotypic immunity by comparing the differences in behavioral fever , hemocyte counts , bacterial lysis and melanization [3] . However , the migratory locust modulated behavioral phase changes through different molecules comparing with desert locusts in response to population density changes [18] , [19] , [20] , [21] . Moreover , the detailed molecular mechanisms underlying the phenotypic response of either in the desert or the migratory locust to M . anisopliae infection are rarely observed [22] , [23] . Our previous work found that locust innate immune genes of the Toll and IMD pathway existed in the expressed sequence tag ( EST ) library . In addition , the software for de novo short reads assembly from RNA-sequencing ( RNA-seq ) without genome references were successfully developed [24] , [25] . These fundamental studies provide a molecular basis for understanding the phenotypic resistance of locusts to M . anisopliae . With the knowledge that the fat body is the major insect organ for immune responses and energy metabolism [26] , [27] , RNA-sequencing transcriptome analysis was used to investigate the mRNA expression profiles in fat bodies of basal and M . anisopliae-infected two phases of the migratory locusts ( Locusta migratoria ) , in order to determine whether increased population density induces phenotypic immunecompetence in locust . The differentially expressed transcripts involved in phase changes and resistance to M . anisopliae were determined via de novo assembly in combination with expression abundance calculations using Trinity software [25] . The full-length cDNAs of DETs were confirmed and their expression patterns were determined at the transcriptional level using quantitative real-time polymerase chain reaction ( PCR ) . The target genes were knocked down via the dsRNA method to analyze their effects on phenotypic resistance to fungal infection and in modulating immune cascades . The present study revealed important information for understanding how resistance against M . anisopliae is modulated at the molecular level by density-dependent prophylaxis .
Fungal inoculation ( M . anisopliae , IMP003 ) was performed under the protonum to avoid septic injury ( Figure 1A ) . Analyzing the variables that affect locust survival , indicated that sex ( χ2 = 0 . 598 , P = 0 . 439 ) and body weight ( χ2 = 1 . 69 , P = 0 . 193 ) had negligible effects on locust survival , whereas the phase variable ( solitary to gregarious phase ) increased 2 . 6 times hazard ratio ( 95%CI 0 . 259–0 . 590 , χ2 = 22 . 581 , P<0 . 001 ) of locust survival ( Table S1 ) . Adult solitary and gregarious locusts exhibited significantly different life spans ( χ2 = 25 . 959 , P<0 . 001 ) under Kaplan-Meier analysis ( Figure 1B ) . The mean life span of the gregarious locusts was 1d to 2d longer than that of solitary locusts ( gregarious locusts , 7 . 5±0 . 2; solitary locusts , 6 . 0±0 . 3 ) . The lifespan of the migratory locust was correlated with the phase change rather than gender or body weight . The M . anisopliae infection bioassay confirmed that gregarious locusts have longer life spans than the solitary locusts . To investigate candidate genes involved in locust phenotypic immunity , we used RNA-seq transcriptome analysis to observe the responses to fungal infections of solitary and gregarious locusts . After sequencing >140 million pair-end reads of fat body samples before and after infected , Trinity software was used for the de novo assembly of transcripts and fat body samples from infected gregarious locusts was used as reference transcripts ( N50 = 967 bp , longest transcripts = 12 , 677 bp ) to align reads for calculating DETs ( Table S2 ) . After DETs detected were by DEGseq software , hierarchical clustering analysis of total DETs ( 3 , 418 DETs , q-value<0 . 05 , P<0 . 001 ) indicated that the two phases of the locust resisted the M . anisopliae infection with different strategies ( Figure 2A ) . Before M . anisopliae infection , gregarious locusts highly expressed immune molecules including pattern recognition proteins ( PRPs ) , serine proteases , serine protease inhibitors ( serpins and pacifastin ) , reactive oxygen species ( ROS ) inhibitors ( peroxiredoxin ) and cellular surface molecules ( CD-like proteins ) ( Data S1 ) . In response to M . anisopliae infection , the solitary locusts increased the expression of genes related to behavior , proteolysis , protease inhibition , oxidation/reduction and signal transduction , but the gregarious locusts already had increased the expression of these genes ( Figure 2B , 2C and Data S1 ) . M . anisopliae stimulated expression of at least twice the numbers of genes in the solitary locust than in gregarious locust as determined by two independent software analysis ( Figure S1 and S2 , Table S3 and S4 ) . In dual detection pathway of fungal infection [28] , we found that prophylaxis immunity of gregarious locusts focused on upstream modulators of the pathway triggered by GNBPs ( Figure 2D ) . Interestingly , expression of cellular surface molecules ( CD-like proteins ) was increased after M . anisopliae infection . The drosomycin-like specific anti-fungal peptide transcripts were not observed in the locusts before and after fungal infection . This could be due to our cutoff limit of 180 bp in assembling reference transcripts . Although various insect immune molecules were previously investigated in altered immunity [29] , [30] , [31] , prophylactic expression of PRPs ( GNBPs and PGRPs ) rather than downstream products such as antimicrobial peptides lead us to understand the underlying mechanisms of GNBPs in modulating phenotypic fungal-resistance at top of immune cascades . We identified three gnbp homologous genes from de novo assembled transcripts and confirmed their full-length cDNA sequences by using rapid amplification of cDNA ends ( RACE ) method . De novo assembly of Trinity software successfully discerned the homologous transcripts that were consistent with the RACE results . The subsequent phylogenetic analysis of the deduced amino acid sequences showed that locust GNBP1 and GNBP3 are recognition proteins whereas GNBP2 is a putative glucanase protein ( Figure S3 ) . Quantitative real-time PCR of selected genes from immune cascades was performed to confirm the consistency ( r2 = 0 . 85 ) of differential expression with transcriptome analysis ( Table S5 ) . The data revealed significant differential expressions of gnbp1 ( n = 18 , two tailed P<0 . 001 ) , gnbp3 ( n = 18 , two tailed P<0 . 001 ) , and pgrp-sa ( n = 15 , two tailed P<0 . 001 ) between the two locust phases without M . anisopliae infection ( Figure 3A ) . Although attacin production was already considered as specific for insect immune defense , the differential expression of toll and downstream attacin were not detected in fat bodies between the two locust phases . The toll receptor , a cellular membrane protein that triggers intracellular responses to fungal infection , showed no significantly different expressions between the two locust phases while cactus for inhibiting downstream immune effectors production was highly presented ( n = 12 , two tailed P<0 . 001 ) in fat body of gregarious locusts ( Table S5 ) . In addition , there was no significant difference in the expression of map3k4 in response to temporary stress between the two locust phases . The phenotypic expression of pacifastin-like proteins was consistent with a previous observation in the two locust phases ( Data S1 ) [32] , [33] . Although solitary locusts showed enhanced mRNA expression of GNBP1 in fat bodies compared to gregarious locusts ( Figure 3A ) , further immunoblot analysis indicated that circulating GNBP1 was at a low level in hemolymph ( Figure 3B and S4 ) despite the presence of signal peptide at N-terminal sequence . To investigate the differential expression of gnbp3 in translation level , we used a sandwich enzyme-linked immunosorbent assay ( ELISA ) to detect circulating GNBP3 by coating antibodies against C-terminal fragment to the plate and labeling antibodies against N-terminal fragment with HRP . ELISA demonstrated that GNBP3 circulated at higher levels in the hemolymph of gregarious locusts ( Figure 3C ) . To discern the roles of locust GNBPs in response to M . anisopliae infections , we performed immunoblotting assays to determine the distribution of locust GNBPs in immune tissues . GNBP1 was only detected in hemocytes , and was almost undetectable in hemolymph , fat bodies , and midgut . However , GNBP3 was constitutively expressed in most immune tissues such as fat bodies , midgut , hemocytes , and hemolymph ( Figure S4 ) . Following the injection of pathogen-associated molecular patterns ( PAMPs ) laminarin ( mostly β-1 , 3-glucan for simulating fungal infection ) into hemocoel for 3 h to12 h , the expression of gnbp1 remarkably increased around 30-fold in fat bodies , but injection of lipopolysaccharide or peptidoglycans ( LPS ) only slightly altered the expression of gnbp1 ( Figure 4A , left panel ) . Surprisingly , gnbp3 with a high level of expression in fat bodies showed little response to the PAMPs injections ( laminarin , peptidoglycan or LPS ) ( Figure 4A , right panel ) . After fungal conidia injection , the GNBP1 in the fat body increased remarkably after 6 h to 9 h , but GNBP3 expression responded minimally to the conidia injection ( Figure 4B ) . Moreover , we found circulating GNBP1 in hemolymph was also induced by conidia injection but this was not seen with GNBP3 ( Figure S5 ) . Through the use of an immunofluorescence assay we were able to demonstrate that both GNBP1 and GNBP3 in locust hemolymph are able to bind conidial cell wall ( Figure 5A ) . Moreover , Locust attacin , that dramatically responds to peptidoglycan injection , was also induced by fungus-associated molecular pattern ( laminarin , mostly β-1 , 3-glucan ) ( Figure S6 ) . We successfully knocked down GNBPs protein expression by RNAi method ( Figure S7 ) , which allowed us to observe their effect on attacin expression . The transcriptional level of attacin was significantly reduced by gnbp3 knockdown ( n = 12 , P<0 . 001 , Mann-Whitney U test ) ; however , gnbp1 knockdown also suppressed the transcriptional level of attacin ( n = 12 , P<0 . 001 , Mann-Whitney U test ) ( Figure 5B ) . These results showed that circulating GNBP3 was crucial for the activation of attacin transcription induced by laminarin . Interestingly , GNBP1 affected attacin expression through as yet unknown mechanisms . Furthermore , the alignment of amino acid sequences showed that a proline-rich sequence ( total 32 amino acid residues comprising11 proline residues which commonly acts as a “stick arm” for protein-protein interactions ) was inserted into two domains ( CRD and GH16 ) of GNBP3 ( Figure S8 ) [34] . These proline-rich sequences are also inserted into the GNBPs of Bm-GNBP3 , Bm-GNBP1 ( Bombyx mori ) and Dm-GNBP3 ( D . melanogaster ) , except for inducible GNBPs , such as Dm-GNBP2 and Bm-GNBP2 , which have shorter inserted sequences and fewer proline residues ( Figure S9 ) . Locust GNBP1 , but not GNBP3 , retained an N-terminal fragment after humoral protease treatment ( Figure 5C ) . Thus , the proteolysis susceptible GNBP3 increased the instability of assembled GNBP3-complex . After laminarin challenge , knocking down of gnbp3 expression led to a non-detectable melanization in hemolymph ( data not shown ) , indicating that GNBP3 is essential for the initiation of melanization . Insects' innate immune responses to fungal infection are activated by GNBPs and PSH proteolysis pathways [28] . In our transcriptome and molecular functional analysis , prophylactically driven expression of GNBP3 in gregarious locusts indicated that GNBP3-dependent pathway could have a crucial impact on locust phenotypic resistance to fungal infection . When adult male locusts were injected with 20 µg of dsRNA that targeted gnbps , the expression levels of gnbp1 and gnbp3 were reduced to around 6% and 15% , respectively , after 48 h ( Figure S10 ) . Knockdown of gnbp3 in gregarious locusts significantly reduced the mean survival time to 6 . 0±0 . 3 days compared with GFP RNAi control ( 7 . 0±0 . 2 days ) ( χ2 = 7 . 480 , PGNBP3 vs . GFP = 0 . 006 , Log-rank test ) . Knockdown of gnbp1 in gregarious locusts did not cause significant changes in life span ( χ2 = 0 . 67 , P GNBP1 vs . GFP = 0 . 796 ) ( Figure 6A ) . Moreover , silencing of gnbp1 or gnbp3 in solitary locusts did not significantly affect their life spans ( χ2 = 0 . 743 , PGNBP1 vs . GFP = 0 . 389; χ2 = 0 . 076 , PGNBP3 vs . GFP = 0 . 783 ) ( Figure 6B ) . The significant extension of the life span in gregarious locust but not in solitary locusts indicated that the GNBP3-dependent immune defenses are involved in the phenotypic resistance of locust to M . anisopliae .
The large scale devastation of crops by locusts is largely the result of formation of gregarious phase by migratory locust in response to high population density . Moreover , the prophylactic immunity of gregarious locusts reduces the susceptibility to fungal biopesticide . Our comprehensive transcriptome analysis revealed that gregarious migratory locusts selectively increased molecules of immune cascades including PRPs , inhibitors ( serpins ) and counteragents ( peroxiredoxin ) of ROS rather than activate the entire immune pathways to produce specific effectors ( e . g . AMPs ) , improving our understanding of the molecular dynamics in insect prophylactic immunity . The benefit of emphasizing on the upstream of immune cascades could be to deposit sufficient products of immune responses ( eg . melanin ) onto the surface of pathogens whilst conserving resources for the production of specific effectors for migration . This defense strategy of coating pathogens to decrease their ability to obtain nutrition from the insect hemolymph leads to an inhibition of pathogens proliferation and production of infective particles . Hence , we suggested that a high level of PRP ( GNBP3 ) allowed gregarious locusts to adapt a tolerance strategy [14] , [15] of quarantining the fungal pathogens rather than through their direct elimination . The employment of this strategy reduced the output of M . anisopliae conidia from infectious individuals , increased the probability of stochastic extinction , presumably by increasing the “distance” between infected and healthy individuals ( Figure 7 ) , which has been proposed as an explanation for density dependent prophylaxis [7] based on the Watve and Jog's model [8] . This effect of prophylactic immunity possessed by gregarious locusts is highly similar to an “immunized” status in the herd immunity . We found that a proline-rich sequence in locust GNBP3 , involved in activating fungal defenses , was widely expressed in other species ( Figure S9 ) . Despite little knowledge about proline-rich sequences in hemolymph proteins recruitment [17] , [34] , insect GNBP3 was proven to activate humoral and cellular immune responses with the ability to specifically recognize and bind to pathogens in a manner similar to mammalian antibodies . Therefore , due to the versatile roles in immune defenses [17] , [35] , [36] , [37] , insect pattern recognition proteins are considered to be good indicators for the assessment of immune defense . Interestingly , GNBP3 assembled attack complexes on fungal pathogens were unstable because of their susceptibility to proteolysis ( Figure 5C ) , this resulted in the exposure of fungal PAMPs and caused the recurrent immune activation on the surface of M . anisopliae . However , GNBP1 induced by the PAMPs was resistant to proteolysis and therefore is likely to attenuate immune cascades by shielding PAMPs with stable GNBP1-PAMPs complexes , preventing the overstimulation of the immune response in locusts . After M . anisopliae infection , solitary migratory locusts increased the expression of immune genes as well as behavioral genes ( eg . chemosensory protein ( CSP ) , juvenile hormone ( JH ) metabolic enzymes and neuroparsin ) that play important roles in migratory locust gregarization [38] . We also found that the basal level of these genes in gregarious locusts was higher than that in solitary locusts ( Data S1 ) . Moreover , previous studies have found that gregarious desert locusts had an enhanced behavioral fever to resist M . anisopliae infection [3] , [39] . These results implied that investment into behavioral defenses were part of prophylactic immunity in the arms race between host and pathogens . Thus , more investigations on behavioral defenses at genetic level are required to interpret immune prophylaxis in non-social species , such as studies have the potential to provide the clues to understanding social prophylaxis in eusocial insect immunity [40] , [41] , [42] , [43] , [44] . Prophylaxis immunity has been observed in various species and could provide the answer to improved resistance to pathogens at high population densities . However , previous paradoxical observations from restricted investigations limit our understanding of density dependent prophylactic immunity , and have resulted in the polarized sentiments between physiological and ecological immunologist [45] . With the genome wide analysis of locust transcripts , here we found that gregarious migratory locusts presented high levels of circulating PRPs but not AMPs , which suggested a selection of a tolerance strategy for inhibiting pathogen spread and for increasing the “distance” between infected and susceptible individuals that together improved the immune defense of gregarious locusts . This has obvious implications for managing insect pests with high population densities by using entomopathogens-based bio-pesticides , and can help us better understand the prophylaxis immunity in host adaptation of parasites as well as disease epidemics in crowded populations .
The migratory locusts were collected from North China plain ( Huanghua , Hebei Province ) and gregarious locust model were established by rearing in large , well-ventilated , cages ( 25 cm×25 cm×25 cm ) at densities of 200 to 300 insects per cage for more than 10 generations . A solitary locust model was established by rearing gregarious locust under physical , visual and olfactory isolation . These conditions were achieved by using a ventilated cage ( 10 cm×10 cm×25 cm ) with charcoal-filtered compressed air for more than 10 generations , which maintained the phase-traits for reversible phase transition . Both gregarious and solitary cultures were reared under a 14∶10 light/dark photo regime at 30±2°C and on a diet of fresh greenhouse-grown wheat seedlings and wheat bran [18] , [46] . The adult locusts were analyzed at 3 d to 5 d after molting when male and female nymphs were previously separated at the end of 5th instar period to avoid mating . Peptidoglycans , lipopolysaccharides ( LPS ) and laminarin were injected at a dose of 20 , 80 , and 100 µg/insect , respectively , according to previous studies [47] , [48] , [49] . The chemicals were purchased from Sigma unless otherwise indicated . At 4 d after molting , locust individuals were inoculated via standard procedures as previously described [50] . Briefly , the locusts from each phase were inoculated with 2 µL peanut oil ( Sigma , USA ) containing 1×106 conidia ( IMP003 , M . anisopliae [51] ) under the pronotum . This method is noninvasive , and the peanut oil control has negligible effects on mortality [52] . The locusts were maintained in individual containers under a 12 h∶12 h light: dark circle at 22°C to 25°C , and were fed and assessed for mortality twice daily . The survival curves were compared using Kaplan-Meier and Cox's proportional hazards model was used for assessing variables that affect locusts survival . The threshold of P value was adjusted by Bonferroni correction . SPSS 13 . 0 software was used in all statistical analyze . To investigate the prophylactic immunity and responses to M . ansopliae infection in the two locust phases , we used high throughput sequencing ( HTS ) platform ( HiSeq 2000 ) to analysis genes expression in pre- and post-fungal infected gregarious and solitary locust . At 6 d to 7 d ( TL50 of solitary and gregarious locust respectively ) after inoculation of 1×106 conidia ( M . anisopliae ) or peanut oil ( mean life of solitary or gregarious locusts , respectively ) , three replicates for each samples ( 25 individuals/sample ) were pooled for analysis ( GC: gregarious locust control , GI: gregarious locust infected , SC: solitary locust control , and SI: solitary locust infected ) . 8 µg of total mRNA from each sample was used to construct libraries with an Illumina kit v2 . After paired-end sequencing , the raw reads were assembled by Trinity software ( version 2011-08-20 ) to obtain reference transcripts due to no published locust genome data . Downstream analysis of alignment ( Bowtie ) and abundance estimation ( RSEM ) were performed using the utility package in Trinity software ( version 2011-08-20 ) . The differential expressed transcripts were analyzed using DEseq and EdgeR software [53] , [54] . Finally , Blast2Go was used to annotate and enrich the DETs ( P<10−6 ) [55] . The raw reads of 4 samples are available for download form the NCBI SRA server ( accession number: SRA054168 ) . Hemolymph was immediately collected in 1 mL of ice-cold saline and then centrifuged at 1000× g at 4°C for 3 min to separate the hemocytes , which were washed thrice with 1 mL of locust saline . The insect tissues ( fat body and midgut ) were dissected immediately after hemolymph collection . All samples were directly frozen in liquid nitrogen until RNA and protein sample preparation . RNA was extracted using Trizol ( Invitrogen , USA ) according to the manufacturer's instructions . Proteins were sequentially extracted in Trizol according to the manufacturer's instructions . The protein pellets were weighed and completely dissolved overnight at 100 µg/µL in loading buffer containing 2 M thiourea , 7 M urea , 1% sodium dodecyl sulfate ( SDS ) , 1% dithiothreitol ( DTT ) , and 100 mM Tris HCl ( pH 7 . 0 ) ( rehydration buffer for two-dimensional electrophoresis , Bio-Rad ) . Cell-free protein concentration of hemolymph was measured with the bicinchoninic acid ( BCA ) method ( Pierce , USA ) . RACE experiments were performed to obtain the full-length of cDNAs ( gnbp1 , gnbp2 , and gnbp3 ) to examine the RNA-seq assembly quality . A BD SMART RACE cDNA Amplification Kit was used to amplify the 5′ - and 3′ -ends of locust Gram-negative bacteria-binding protein ( gnbp ) cDNAs according to the manufacturer's protocol . The fragment of the target sequence was assembled using CONTIGEXPRESS software . Finally , the sequence was verified by sequencing the full-length PCR amplification products inserted into the pGEM-T Easy vector . To observe locust gnbp family classification , the derived protein sequences of GNBPs were identified using a BLAST search against other species . All GNBP sequences ( without signal sequences ) were aligned using ClustalX 1 . 83 , and the alignment data was used to construct phylogenetic tree with MEGA 4 . 0 . The tree was rooted on B . circulans β- ( 1 , 3 ) -glucanase ( AAC60453 ) . One thousand replicates were used to calculate the bootstrap values , and branches were collapsed with a 50% consensus rule . To observe target genes expression , qPCR experiments were performed according to the standard protocols for thermocyclers ( Stratagene and Roche , USA ) . Four biological replicates ( 3 to 5 individuals per replicate ) were pooled for three parallel technical replicates analysis . The mRNA of each sample was extracted from dissected tissues using Trizol . The ratios of OD260/OD280 were then measured . The cDNA was synthesized from 2 µg of total RNA with MLV reverse II system ( Promega , USA ) . The primers were designed based on PRIMER 5 . 0 ( also see Table S6 ) . The actin sequence of L . migratoria ( GenBank accession no: AF370793 ) were used for internal control and the target gene sequences cloned into pGEM-T Easy for standard curve calibration . The specificity of amplification was confirmed through melting curve analysis . Values were represented as the mean ( ±SE ) , and the statistical significance was determined by using Mann-Whitney U test with SPSS 13 . 0 software . To obtain the detectors for GNBPs proteins , we recombinantly expressed the highly antigenic fragments of the locust GNBPs . The antigenicity of the proteins was analyzed with an online tool ( http://imed . med . ucm . es/Tools/antigenic . pl ) . The recombinant proteins that contained the highly antigenic truncated sequences ( GNBP1 JF915523 ORF: N terminal 125–808 bp , C-terminal 1103–1411 bp; GNBP3 JF915525 ORF: 95–901 bp ) were expressed using the Invitrogen pET-28a vector in E . coli . The proteins were then purified with Ni-Sepharose media ( GE Healthcare , USA ) . After 12 weeks of immunization , the rabbit polyclonal antibodies sera were purified by amino sulfate precipitation and tested for specificity using the recombinant full-length GNBPs from SF9 cells . The cross-reactive antibodies were absorbed by immobilized rGNBPs ( SF9 cell-expressed ) on CNBr-activated Sepharose 4B ( GE Healthcare , USA ) . To obtain positive control of ELISA and immunoblotting assay , GNBPs expressed in SF9 cell culture supernatant were used to test antibody cross reactivity . The gnbps , including their signal sequences , were cloned into pFastbac , and were shuttled into DH10bac bacteria . The plasmids were transfected into SF9 cells according to the manufacturer's instructions ( Invitrogen , USA ) . After three rounds of infection , a 2 . 4×107 pfu/mL titer of virus was added to 1 L SF9 cell culture for infection ( Multiplicity of Infection , MOI = 1 . 2 ) . The secreted GNBP proteins in the supernatant liquid were collected at time points of 48 , 72 and 96 hours . The cell debris were removed by centrifugation at 12 , 000× g for 5 min , and the samples were concentrated 10-fold and buffer-exchanged into locust saline by centrifuging at 18 , 000× g for 15 min and at 4°C in an Ultra-15 tube ( Millipore , USA ) . To reduce the GNBPs proteins level in locust , we used RNAi method to knockdown the gnbps expression . Templates for dsRNA preparation were PCR-derived fragments between two T7 promoter sequences . The fragments of each gene were: gfp ( nucleotides ORF35-736 , GenBank accession L29345 ) , gnbp1 ( ORF22-437 , GenBank accession JF915523 ) , and gnbp3 ( ORF38-534 , GenBank accession JF915525 ) . The single-stranded RNA fragments were synthesized using a T7 transcription kit ( Promega , USA ) . The annealed dsRNA were purified by ethanol precipitation then were dissolved in sterilized locust saline buffer at 5 µg/µL until use . Finally , each insect was injected with 20 µg of the dsRNA for experiments . To investigate the locust GNBPs capability of binding to fungi cell wall , adult locusts' hemolymph was collected after injected conidia 9 hours . Hemocytes and injected conidia were dispersed on poly-lysine-coated glass slides and then fixed with 1% PEG-8000 in 95% ethanol for 45 min . Cells were then permeabilized with 0 . 1% Triton X-100 , 1% bovine serum albumin and phosphate-buffered saline ( PBS ) for 1 hour , following blocked with TBS containing 5% skim milk for 4 hours at room temperature . The samples were incubated overnight with fluorescein isothiocyanate ( FITC ) -conjugated polyclonal antibodies with 400-fold dilution in TBS containing 0 . 1% Tween-20 and 5% skim milk . After staining with 4′6-diamidino-2-phenylindole ( DAPI ) in PBS for 15 min , the samples were washed five times and sealed in 50% glycerol in PBS for observation under a confocal microscope ( LSM710 , Carl Zeiss ) . To observe locust GNBPs proteins expression , protein samples were prepared using Trizol after mRNA extraction according to the manufacturer's instructions . The samples ( each lane for 6 individuals and loaded 10 µg/lane ) were loaded , separated using sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) , and transferred onto polyvinylidene difluoride ( PVDF ) membranes ( Bio-Rad , USA ) . Membranes were blocked in Tris-buffered saline with 5%Tween-20 ( TTBS ) containing 5% slim milk for 4 hours at room temperature , then probed with primary antibodies ( 1∶4 , 000 dilution ) at 4°C overnight , washed 3×10 minutes in TTBS , incubated for 1 hour with secondary antibodies ( HRP-conjugated mouse anti-rabbit monoclonal antibody; Sigma A1949 , USA ) at room temperature , and visualized under an enhanced chemiluminescence system ( Bio-Rad , USA ) or recorded on X-ray films ( Kodak , USA ) . We used locust glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) for internal control of proteins amounts . The polyclonal antibodies against locust GNBP1 , GNBP3 and GAPDH were previously purified and stored in PBS ( containing 50% glycerol ) at −80°C until use . To investigate the locust circulating GNBPs in hemolymph , polyclonal antibodies against the C-terminal of GNBP1 was dissolved in pH 9 . 5 carbonate buffers at a concentration of 500 ng/mL and coated onto plates ( Costar 92592 , USA ) overnight at 100 µL/well . The plates were washed three times with TTBS , incubated with 200 µL/well of 20% fetal calf serum at 37°C for 2 h , and then washed 3 times with TTBS . Samples with same total proteins amounts ( gregarious: n = 9 and solitarious: n = 15 ) were applied at 100 µL/well and incubated at 37°C for 1 hours . Finally , plates were washed three times with TTBS , incubated with 100 µL/well of horse radish peroxidase ( HRP ) conjugated polyclonal antibodies against N-terminal of GNBPs ( 1∶1000 dilution ) to detect the positive signals . The reaction was stopped with 50 µL/well of 2 mol/L sulfuric acid . The OD450 values were recorded for further statistical analysis . To examine locust GNBPs resistance to hemolymph protease , the hemolymph from laminarin-stimulated locusts ( n = 12 ) was collected and immediately diluted tenfold with ice-cold saline . Hemocytes were removed by centrifugation at 1 , 000× g for 3 minutes , and hemolymph was incubated at 25°C for 1 h . The incubated samples were centrifuged at 12 , 000× g for 20 minutes to collect the supernatant liquid and clot pellets . All samples were denatured , and were then loaded into gels ( 10 µg/lane ) for SDS-PAGE separation . After electrophoresis , the proteins in gels were transferred onto a PVDF membrane for immunoblot analysis . The primary polyclonal antibodies against GNBPs C-terminal fragment were applied at 1∶3 , 000 dilution , and the secondary monoclonal antibody ( A1949 , Sigma , USA ) were applied at 1∶12 , 000 to detect target proteins through chemiluminescence .
|
The wide application of fungal biopesticides for insect management has led to concerns over the development of biopesticide resistance . The migratory locust , a globally notorious agricultural pest , has density-dependent phase changes between solitary and gregarious states . The gregarious locusts displayed longer life spans than solitary locusts after biopesticide Metarhizium anisopliae infection . We analyzed prophylactic immunity of the locusts in phase change adaptation by transcriptome analysis . Gregarious locusts optimized immunity by investing more in molecules of upstream immune cascades including pattern recognition proteins , anti-oxidation proteins , protease inhibitors and serine protease . High levels of pattern recognition proteins guided deposition of immune products onto pathogens reducing growth , proliferation and transmission . This prophylactic immunity of gregarious locusts emphasized on quarenteening M . anisopliae pathogens in early infection , which decreased individuals' infection risk in a population and avoids disease epidemics . Pest outbreaks mostly occur in high population densities , thereby , diminishing entomopathogen biopesticide efficiency . Our results provide an insight to an organism's “enhanced” immunity induced by population densities and inspires new paradigms to understand biopesticide tolerance and disease epidemics in the future .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"pesticides",
"genome",
"sequencing",
"genome",
"expression",
"analysis",
"immunity",
"pest",
"control",
"innate",
"immunity",
"immunology",
"biology",
"genomics",
"microbiology",
"host-pathogen",
"interaction",
"genetics",
"and",
"genomics",
"agriculture"
] |
2013
|
Altered Immunity in Crowded Locust Reduced Fungal (Metarhizium anisopliae) Pathogenesis
|
Trypanosoma cruzi , the agent of Chagas disease , is a protozoan parasite transmitted to humans by blood-sucking triatomine vectors . However , and despite its utmost biological and epidemiological relevance , T . cruzi development inside the digestive tract of the insect remains a poorly understood process . Here we showed that Gp35/50 kDa mucins , the major surface glycoproteins from T . cruzi insect-dwelling forms , are involved in parasite attachment to the internal cuticle of the triatomine rectal ampoule , a critical step leading to its differentiation into mammal-infective forms . Experimental evidence supporting this conclusion could be summarized as follows: i ) native and recombinant Gp35/50 kDa mucins directly interacted with hindgut tissues from Triatoma infestans , as assessed by indirect immunofluorescence assays; ii ) transgenic epimastigotes over-expressing Gp35/50 kDa mucins on their surface coat exhibited improved attachment rates ( ~2–3 fold ) to such tissues as compared to appropriate transgenic controls and/or wild-type counterparts; and iii ) certain chemically synthesized compounds derived from Gp35/50 kDa mucins were able to specifically interfere with epimastigote attachment to the inner lining of T . infestans rectal ampoules in ex vivo binding assays , most likely by competing with or directly blocking insect receptor ( s ) . A solvent-exposed peptide ( smugS peptide ) from the Gp35/50 kDa mucins protein scaffolds and a branched , Galf-containing trisaccharide ( Galfβ1–4[Galpβ1–6]GlcNAcα ) from their O-linked glycans were identified as main adhesion determinants for these molecules . Interestingly , exogenous addition of a synthetic Galfβ1–4[Galpβ1–6]GlcNAcα derivative or of oligosaccharides containing this structure impaired the attachment of Dm28c but not of CL Brener epimastigotes to triatomine hindgut tissues; which correlates with the presence of Galf residues on the Gp35/50 kDa mucins’ O-glycans on the former but not the latter parasite clone . These results provide novel insights into the mechanisms underlying T . cruzi-triatomine interplay , and indicate that inter-strain variations in the O-glycosylation of Gp35/50 kDa mucins may lead to differences in parasite differentiation and hence , in parasite transmissibility to the mammalian host . Most importantly , our findings point to Gp35/50 kDa mucins and/or the Galf biosynthetic pathway , which is absent in mammals and insects , as appealing targets for the development of T . cruzi transmission-blocking strategies .
Chagas disease , caused by the protozoan parasite Trypanosoma cruzi , is a life-long , debilitating illness of major significance in Latin America , and an emergent threat to global public health [1] . T . cruzi is a very adaptable organism , able to infect a wide range of mammals and more than 140 species of blood-sucking triatomine bugs that act as transmission vectors in endemic areas . This plasticity likely stems from a complex population structure , made up of multiple strains showing remarkable genetic and phenotypic diversity that were grouped into 6 evolutionary lineages termed TcI to TcVI [2] . Development of T . cruzi in the insect is a quite complex process that begins when bloodstream trypomastigotes are ingested by triatomines upon blood-feeding on an infected mammal ( reviewed in [3] ) . Parasites able to survive the harsh conditions of the insect crop differentiate into replicative epimastigote forms . These progress on to the triatomine midgut , where they bind to the luminal surface epithelium and/or to the perimicrovillar membranes secreted by the underlying epithelial cells . This attachment is a critical step for effective vector colonization and , accordingly , it involves a multiplicity of parasite surface molecules [4–7] . Upon reaching the insect hindgut , i . e . rectal ampoule and Malpighian tubules , epimastigotes undergo various morphological and biochemical changes that accompany their transformation into metacyclic trypomastigotes ( reviewed in [8] ) . These forms bring the infection into mammals when deposited on the skin or mucosa together with insect excreta during a blood-meal . However , and despite its utmost biological and epidemiological relevance , the mechanisms regulating T . cruzi metacyclogenesis are poorly understood . Several groups have shown that it may be stimulated by nutritional starvation as well as by different environmental cues such as cAMP , metabolic stressors or pH shifts [8] . Epimastigote adhesion to the highly hydrophobic triatomine rectal cuticle is a pre-requisite for this transformation , and in vitro studies using highly defined conditions and artificial hydrophobic surfaces as insect tissue surrogates have shown that improvements in the conditions for parasite attachment to the substrate strictly correlate with an increase in metacyclogenesis [9 , 10] . Electron microscopy studies revealed a prominent role for T . cruzi surface molecules , and particularly those localizing to the flagellum tip , during the first steps of substrate attachment . This initial and presumably low-affinity contact is followed by modifications on the parasite body and the formation of stabilizing , desmosome-like structures of unknown composition underneath its plasma membrane [8 , 11] . The entire outer surface of T . cruzi , including the parasite body , the flagellum and the flagellar pocket , is covered with glycosylphosphatidylinositol ( GPI ) -anchored glycoconjugates [12 , 13] . They include glycoinositolphospholipids ( GIPLs ) [14] , and large families of developmentally regulated glycoproteins , such as mucins , mucin-associated surface proteins ( MASPs ) and Gp85/trans-sialidases [12 , 15–17] . Mucins expressed by both epimastigotes and metacyclic trypomastigotes ( henceforth Gp35/50 kDa mucins ) are quite heterogeneous molecules that run on SDS-PAGE as double or triple bands in the relative molecular mass range of ~35–50 kDa depending on the parasite strain [15 , 18] . The genes coding for their polypeptide backbones , termed TcSMUG S , define a family of ~20–50 members bearing just a few , mostly conservative polymorphisms and/or differences in the length of the threonine-rich repeats among them [16 , 19–22] . TcSMUG S genes were also shown to be extremely conserved among T . cruzi strains [16 , 19–22] . Upon processing of the N-terminal signal peptide and the C-terminal GPI-anchoring signal , the average predicted molecular mass for the TcSMUG S polypeptides would be ~7 kDa , with threonine representing up to 50% of the residues [16 , 20] . TcSMUG L genes , on the other hand , code for GPI-anchored polypeptides bearing high sequence similarity to TcSMUG S ones , though they undergo different glycosylation and are restricted to the surface of epimastigote forms [20 , 21] . A particular feature of the O-type oligosaccharide chains from Gp35/50 kDa mucins is that they are α-linked to threonine residues in the TcSMUG S polypeptide core via N-acetylglucosamine ( GlcNAc ) instead of N-acetylgalactosamine ( GalNAc ) as found in mammalian mucins . Depending on the parasite strain , up to 20% of these GlcNAcα residues may remain unsubstituted . Alternatively , they may be elongated , and even branched , with various units of ( mostly ) β-galactose in different types of linkages , thus leading to a quite complex assortment of oligosaccharides ( reviewed in [12 , 23] ) . This heterogeneity suggests deficiencies in the ‘streamlining’ of the parasite O-glycosylation machinery , which may affect the efficiency of GlcNAc addition to different threonine residues and/or the elongation/termination of individual oligosaccharides . In fact , as many as eight different oligosaccharides have been identified in the Gp35/50 kDa mucins of some parasite strains [12 , 23] . Concurrent expression of multiple TcSMUG S polypeptides bearing slight polymorphisms may also contribute to increase Gp35/50 kDa mucins’ diversity [16 , 20 , 21] . In addition to intra-strain heterogeneities , structural ( and hence functional ) variations among Gp35/50 kDa mucins from distinct parasite strains have been extensively underscored [12 , 15 , 18 , 23 , 24] . These differences are mostly attributed to variations in the profile of glycosyltransferases , which indeed constitute a complex family in the T . cruzi genome [25] . Remarkably , and in addition to β-galactopyranose ( βGalp ) residues , TcI parasite strains display β-galactofuranose ( βGalf ) units in the oligosaccharides of their Gp35/50 kDa mucins [12 , 23] . The T . cruzi Tulahuen strain classified as TcVI was also shown to bear βGalf units on its Gp35/50 kDa mucins O-glycans [26] . However , several Tulahuen-derived clones displayed TcI-like features [27–29] , suggesting that the original ‘strain’ may have contained a mixture of parasite genotypes . Importantly , the presence or absence of βGalf units in the oligosaccharides of Gp35/50 kDa mucins is more likely explained by inter-strain differences in Galf transferase activities rather than by variations in Galf biosynthesis [30] . In fact , non-TcI strains that do not bear βGalf units in the O-linked oligosaccharides of Gp35/50 kDa mucins are still able to decorate their GIPL glycan cores with βGalf residues [31] . Cumulative evidence point to Gp35/50 kDa mucins as key determinants for T . cruzi insect-to-mammal host switching . In particular , they were shown to contribute in the recognition , signaling and invasion of mammalian host cells/tissues by metacyclic trypomastigotes [15 , 24 , 32–35] . Their linked glycans , particularly upon sialylation of terminal βGalp units on the parasite surface , seem critical in all these phenomena . However , little is known about the role ( s ) played by Gp35/50 kDa mucins during T . cruzi development within the insect vector . Solely based on their abundance , i . e . up to 106−7 molecules per parasite [15] , and high resistance to proteases in vitro [18] , they were proposed to play a protective role against digestive enzymes in the triatomine crop . In this work , we used epimastigote ex vivo binding assays together with different biochemical and genetic approaches to explore the interactions established by these molecules along the triatomine digestive tract .
Fifth-instar nymphs of Triatoma infestans ( Hemiptera: Reduviidae ) , the most important triatomine vector in the Southern cone countries of South America [2] , were obtained from a long-standing colony reared by the Centro de Referencia de Vectores , Dirección de Enfermedades Transmisibles por Vectores–Ministerio de Salud de la Nación ( Santa María de Punilla , Córdoba , Argentina ) , where they were fed on hens weekly . Insects were immediately used for experimental purposes upon arrival to the IIB-INTECh , i . e . ~12–15 days after their last non-infectious blood-meal . T . cruzi clones used in this study were CL Brener ( TcVI ) , derived from CL strain , isolated from T . infestans in Brazil and Dm28c ( TcI ) , derived from Dm strain , isolated from Didelphis marsupialis in Venezuela [36 , 37] . Epimastigote forms were grown at 28°C in brain-heart tryptose ( BHT ) medium supplemented with 10% ( v/v ) Fetal Calf Serum ( FCS ) , as described [38] . FLAG-tagged versions of TcSMUG S , TcSMUG L and TSSA-CL genes have been described [21 , 39–41] . All of them were subcloned into the T . cruzi expression vector pTEX-OMNI [41] . For parasite transfection , exponentially growing epimastigotes ( 3 x 108 ) from either CL Brener or Dm28c clone were harvested , washed with phosphate-buffer saline ( PBS ) , transferred to a 0 . 2 cm gap cuvette ( Bio-Rad ) with 10 μg of purified DNA and electroporated as described [41 , 42] . Antibiotic selection ( 500 μg/mL G418 , Gibco Laboratories ) was sustained over time once stably transfected populations were obtained . Wild-type epimastigote forms ( 1–3 x 109 ) were delipidated by chloroform/methanol/water ( 5:10:4 v/v/v ) treatment and then subjected to successive butan-1-ol/water partitions as described [43] . Aliquots of fractions enriched in glycoconjugates were resolved by SDS-PAGE , transferred to nitrocellulose membranes ( GE Healthcare ) , and subjected to a slightly modified version of the periodate-Schiff staining technique . Briefly , blots were incubated in the dark for 1 h in 0 . 1 M acetic acid containing 10 mM sodium periodate . After extensive washings with PBS , membranes were incubated for 5 min in 15 mM glycerol solution followed by 2 h-incubation in the dark with 5 mM biotin hidrazide ( Sigma ) . Membranes were extensively washed with PBS , blocked with PBS supplemented with 0 . 1% ( v/v ) Tween 20 and 5% ( w/v ) Bovine Serum Albumin ( BSA ) , incubated with HRP-conjugated streptavidin ( 1:5 , 000; Sigma ) for 1 h and developed using Super Signal West Pico chemiluminescent substrate ( Thermo Scientific ) . Epimastigote forms ( 1 . 5 x 106 ) were washed , blocked in PBS 10% ( v/v ) FCS , and incubated with mouse monoclonal antibody ( mAb ) anti-FLAG ( clone M2 , Sigma , 1:200 dilution ) , in an ice-water bath followed by Alexa Fluor-conjugated secondary antibodies ( 1:500 dilution ) ( Molecular Probes ) . After several washes with PBS , parasites were resuspended in 300 μL of PBS containing 4% ( w/v ) p-formaldehyde ( PBS 4% PFA ) , extensively washed with PBS and analyzed using FACS CyFLOW Partec and FloMax software as described [41] . Propidium iodide uptake was evaluated by flow cytometry as described [44] . Epimastigote IIF assays using mAb anti-FLAG , and acquisition and processing of images were done as described [41] . For Western blot analysis , total extracts from 1 . 5 x 107 parasites were resolved into SDS-PAGE ( 12 . 5% gels ) and transferred onto nitrocellulose membranes . For dot blot assay , 2 μl of extracts prepared with buffer A ( see below ) corresponding to 2 x 106 parasites , or appropriate dilutions in PBS , were spotted onto nitrocellulose membranes and let dry for 10 min [45] . Both kinds of blots were blocked with PBS containing 0 . 1% ( v/v ) Tween 20 and 1% ( w/v ) BSA , and reacted with the indicated anti-Gp35/50 kDa mucins’ mAb ( all of them were used at 1:5 , 000 dilution ) or antiserum followed by IRDye800CW- or IRDye680LT-conjugated secondary antibodies , and signal intensities were quantified using an Odyssey laser-scanning system ( Li-Cor Biosciences ) . Rabbit polyclonal anti-FLAG antibody ( Sigma ) and rabbit antiserum to T . cruzi glutamate dehydrogenase were used as described [21] . Pellets of transgenic epimastigotes over-expressing different FLAG-tagged products were resuspended ( at 5 x 108 per mL ) in ice-cold buffer A ( 150 mM NaCl , 50 mM Tris . HCl pH 7 . 6 , 1 mM EDTA , 0 . 1% ( v/v ) Nonidet P40 , 1% ( v/v ) Triton X-100 , and 1 mM PMSF ) and incubated on ice for 1 h . After centrifugation , supernatants were incubated with 25 μL of M2 clone mAb anti-FLAG-Sepharose ( Sigma ) , and both the flow-through and retained fractions were evaluated by Western blot . Samples resuspended in buffer A as above were normalized by anti-FLAG dot blot assays . Fractions containing similar amounts of ‘FLAG equivalents’ were incubated with sections of the digestive tracts of freshly dissected T . infestans collected 10 days after a non-infectious blood meal . Following washes with PBS , tissues were fixed with PBS 4% PFA for 30 min , blocked with PBS 10% BSA for 1 h and processed for IIF assay . Briefly , tissue samples were washed and incubated for 1 h with mAb anti-FLAG clone M2 in PBS 5% BSA ( 1:500 dilution ) , washed with PBS , and incubated for 1 h with secondary Alexa Fluor-conjugated antibodies in PBS 5% BSA ( 1:500 dilution ) . Samples were extensively washed with PBS and mounted with FluorSave Reagent ( Sigma ) . All these procedures were carried out at room temperature . Images were obtained with an IX-81 microscope attached with a FV-1000 confocal module; the objective was a PLAN APO 60X NA 1 . 42 oil immersion and the acquisition software used was FV 10-ASW 3 . 1 ( all from Olympus Life Sciences , Japan ) . Images were treated using ImageJ 1 . 45s Software ( NIH , USA ) for final presentation . The same procedure was followed to assess binding of native Gp35/50 kDa mucins . In this case , however glycoconjugate-rich samples were partially purified from wild-type epimastigotes by butan-1-ol/water partitions as described above , and IIF assays were developed using mouse mAb 3F5 ( at 1:500 dilution ) as primary antibody . Peptides were custom synthesized by GenScript . Their sequences were derived from the predicted N-terminal region of mature TcSMUG S ( VEAGEGQDQTC , smugS peptide ) and TcSMUG L ( AVFKAAGGDPKKNTTC , smugL peptide ) products , respectively [21] . The C-terminal cysteine residue , not present in TcSMUG S/TcSMUG L sequences , was included for eventual peptide coupling purposes . Additional peptides displaying the scrambled sequences of either the smugS peptide ( ADEVQDEGQTTT , smugSsc peptide ) or the smugL peptide ( AAGGVFDKAKPKTTT , smugLsc peptide ) were used as controls . Stock solutions ( at 10 mg/mL ) of all of these peptides were prepared in PBS . The following carbohydrates were chemically synthesized as indicated and used for the adhesion assays: GlcNAcα-benzyl ( Bn ) ( compound 1 ) [46] , Galfβ1-6GlcNAcα-Bn ( compound 2 ) [47]; Galpβ1-6GlcNAcα-Bn ( compound 3 ) [48]; Galfβ1-4GlcNAcα-Bn ( compound 4 ) [49]; Galfβ1-3GlcNAcα-Bn ( compound 5 ) [47]; Galpβ1-4Glcβ-Bn ( compound 6 ) , Galfβ1–4[Galpβ1–6]GlcNAcα-Bn ( compound 7 ) [48]; Galpβ1–2[Galpβ1–3]Galpβ-Bn ( compound 8 ) [50]; Galpβ1-3Galpβ1–6[Galfβ1–4]GlcNAcα-Bn ( compound 9 ) [51]; Galpβ1–2[Galpβ1–3]Galpβ1–6[Galfβ1–4]GlcNAcα-Bn ( compound 10 ) [52]; Galpβ1–2[Galpβ1–3]Galpβ1–6[Galfβ1-2Galfβ1–4]GlcNAcα-Bn ( compound 12 ) [53] , Galfβ1-2Galfβ1-4GlcNAcα-Bn ( compound 13 ) [54] , Galpβ1-2Galfβ1-4GlcNAcα-Bn ( compound 14 ) [54] . The synthesis of pentasaccharide Galpβ1-3Galpβ1–6[Galfβ1-2Galfβ1–4]GlcNAcα-Bn ( compound 11 ) will be described elsewhere . Oligosaccharides were used as Bn glycosides , because they are more stable than the free sugars and we reasoned that the lipophilic benzyl group may improve hydrophobic interactions . All of them were readily solubilized in PBS at 20 mM ( stock solutions ) . Stationary epimastigotes were suspended in BHT to a density of 104 cells/mL . The content of intermediate forms and/or metacyclics in these parasite suspensions were in the range of 10–16% for CL Brener lines and 15–19% for Dm28c lines . Samples ( 200 μL ) of this parasite suspension together with T . infestans midguts or rectal ampoules , freshly dissected and washed in PBS , were placed in 96-well microplates and incubated for 30 min at 25°C . When indicated , insect tissues were previously incubated ( 30 min , 25°C ) in PBS supplemented with the indicated peptide ( at 0 . 1 μg/mL [6] unless otherwise stated ) or synthetic carbohydrate ( at 20 nM [4] , unless otherwise indicated ) . Treated tissues were then washed in fresh PBS and immediately added with 200 μL of the above mentioned parasite suspension . After incubation ( 30 min , 25°C ) , insect tissue preparations were spread onto glass slides to expose their inner surface and the number of attached parasites was counted . A Zeiss microscope with reticulated ocular was used for counting parasites attached to 100 randomly chosen epithelial cells in 10 different fields of each tissue preparation . For each experimental group , 10 insects were used and experiments were performed in triplicate . Results were analysed using ANOVA and Tukey's tests using the Graph Pad Prism 6 software . Even though it is thought that strains of T . cruzi are able to effectively complete their life cycles , albeit with differential efficiency , in most triatomine vectors [55] , to our knowledge , infection of T . infestans with the Dm28c clone has not been reported . To address this issue , which may have otherwise imposed certain restrictions in our conclusions , we carried out epimastigote in vivo infection assays ( S1 Fig ) . Briefly , fifth-instar nymphs of regularly fed T . infestans , which had been starved for 15 days after the last ecdysis , were fed on artificial bloodmeal apparatus with a mixture of heat-inactivated heparinized rabbit blood and Dm28c culture epimastigotes ( 1 x 109 parasites in 40 mL of blood ) as previously described [6] . At days 14 and 28 post-feeding , the entire digestive tracts consisting of anterior midgut ( stomach ) , posterior midgut and rectum of 20 insects were dissected and homogenized in 500 μL of PBS and the number of infected insects as well as the number of parasites ( epimastigotes + metacyclics + intermediate forms ) /infected triatomine in each homogenate was determined using a Neubauer hemocytometer .
As a first step to explore the role of Gp35/50 kDa mucins in T . cruzi-triatomine interplay , we generated transgenic epimastigotes ectopically expressing a FLAG-tagged TcSMUG S gene . Transgenic lines ( henceforth , TcSMUG S ox lines ) were developed into Dm28c and CL Brener , two T . cruzi clones belonging to extant parasite evolutionary lineages and showing differences on the carbohydrate composition of their Gp35/50 kDa mucins’ O-glycans . In particular , Dm28c ( TcI ) is a ‘Galf-containing’ clone whereas CL Brener ( TcVI ) is a ‘Galf-lacking’ clone [12 , 23] . Expression of recombinant Gp35/50 kDa mucins in both genetic backgrounds was initially evaluated by non-permeabilising flow cytometry assays using mAb anti-FLAG . As shown in S2 Fig , FLAG-tagged Gp35/50 kDa mucins were displayed on the surface of both TcSMUG S ox lines , compatible with the proper processing of their predicted sorting signals [42] . IIF assays further supported this surface localization , and revealed that recombinant Gp35/50 kDa mucins are arranged on the membrane of transgenic epimastigotes following a rather patchy distribution ( Fig 1A ) . Clustering and/or aggregation of native mucins ( and other surface molecules ) on membrane micro-domains has been described in different developmental stages of T . cruzi [13 , 21 , 41 , 56 , 57] . To further characterize recombinant Gp35/50 kDa mucins , lysates from TcSMUG S ox parasites were prepared , subjected to anti-FLAG-affinity chromatography , and the different fractions analyzed by Western blot . FLAG-reactive species migrated in reducing SDS-PAGE as a broad smear ranging from 34 to 45 kDa in CL Brener and from 34 to 60 kDa in Dm28c ( Fig 1B ) . Since FLAG-tagged Gp35/50 kDa mucins were expressed upon a unique TcSMUG S trans-gene , it can be concluded that intra-clone heterogeneities were due to differences in their post-translational processing , particularly glycosylation . ‘Bound’ and ‘flow-through’ fractions were next probed with mouse mAbs 2B10 and 10D8 , which recognize βGalp- and βGalf-based glycotopes , respectively , on Gp35/50 kDa mucins [18] . Western blots showed that recombinant ( enriched in the ‘bound’ fraction ) and native ( enriched in the ‘flow-through’ fraction ) Gp35/50 kDa mucins exhibited rather indistinguishable electrophoretic mobility ( Fig 1C ) . Most importantly , they also revealed that both kinds of glycoprotein display the same pattern of recognition by mAbs directed to Gp35/50 kDa mucins’ glycotopes . Briefly , those expressed by Dm28c epimastigotes bore βGalf- and βGalp-based glycotopes whereas those expressed by CL Brener parasites exhibited βGalp-based structures but lacked βGalf-based glycomarkers ( Fig 1C ) . Together , these data indicate that recombinant Gp35/50 kDa mucins expressed by TcSMUG S ox epimastigotes undergo similar post-translational processing and surface display than endogenous molecules and , hence constitute suitable tools for carrying out functional studies . To estimate the extent of Gp35/50 kDa mucins over-expression in transgenic epimastigotes , comparative Western blot assays were carried out on CL Brener and Dm28c wild-type and TcSMUG S ox lines . These blots were revealed with mAb 3F5 , which recognizes a conserved peptide epitope on Gp35/50 kDa mucins [18] , and with an antiserum to T . cruzi glutamate dehydrogenase ( GDh , Fig 2A ) . The ratio between both signals ( 3F5/GDh ) was calculated by densitometric analyses . As shown in the lower panel of Fig 2 , an increase ( ~25–35% in CL Brener and ~100% in Dm28c ) in the overall Gp35/50 kDa mucins’ content of TcSMUG S ox lines as compared to parental lines was recorded . These differences are most likely attributed to FLAG-tagged molecules . For ex vivo binding assays , stationary growing epimastigotes were incubated with different sections of the digestive tract ( intestine or rectum ) of freshly dissected T . infestans , and the number of parasites attached per 100 insect cells determined by light microscopy . As shown in Fig 2B , epimastigotes from TcSMUG S ox lines exhibited a significant increase ( ~2–3 fold ) on the ex vivo attachment to insect rectal ampoules as compared to wild-type counterparts . To evaluate a possible non-specific effect of transfection and/or growing the parasites under constant drug pressure on this phenotype , we evaluated in parallel the adhesion of parasite lines over-expressing a FLAG-tagged trypomastigote small surface antigen ( TSSA ) molecule . TSSA is a T . cruzi glycoprotein restricted to the surface of bloodstream trypomastigote forms [29] , involved in adhesion/signaling of non-macrophagic mammalian cells [39 , 41] . Development of TSSA ox lines in Dm28c and CL Brener genetic backgrounds , as well as the evaluation of surface display of FLAG-tagged TSSA molecules in these transgenic parasites were done as described above ( S2 Fig ) . TSSA ox epimastigotes , expressing roughly equivalent amounts of native Gp35/50 kDa mucins ( Fig 2A ) , displayed similar binding rates to T . infestans hindgut tissues than parental parasites ( Fig 2B ) . Overall , and even though we cannot formally rule out the possible contribution of additional surface adhesin ( s ) whose expression/processing may become deregulated in TcSMUG S ox parasites , these data strongly suggest a role for surface-associated Gp35/50 kDa mucins in mediating the interaction between T . cruzi epimastigotes and the rectum of T . infestans . To directly assess the binding of recombinant Gp35/50 kDa mucins to triatomine hindgut tissue , total extracts from TcSMUG S ox and TSSA ox epimastigotes of both genetic backgrounds were prepared and the relative concentration of FLAG-tagged glycoproteins on these lysates calculated by dot blot assays ( S2 Fig ) . Fractions containing similar amounts of ‘FLAG-equivalents’ were then incubated with T . infestans hindgut tissues and processed for IIF assays using mAb anti-FLAG . As shown in Fig 2C , FLAG-tagged Gp35/50 kDa mucins expressed by either Dm28c or CL Brener transgenic lines displayed significantly improved binding as compared to FLAG-tagged TSSA products , which in turn yielded equivalent signals than those recorded for negative controls . We next assessed the capacity of native Gp35/50 kDa mucins to adhere to triatomine hindgut tissues . To that end , glycoconjugates were purified in bulk from wild-type epimastigotes of Dm28c or CL Brener clone following a standard butan-1-ol extraction protocol . As shown in Fig 2D , periodate-Schiff staining of this material revealed a broad smear ranging from 34 to 60 kDa that most likely corresponded to Gp35/50 kDa mucins and , just below , a duplet of bands that may correspond to NETNES , a small glycoprotein identified and characterized by Macrae et al [58] . Upon incubation of this material with T . infestans hindgut tissues , direct binding of Gp35/50 kDa mucins was evaluated by IIF assays using mAb 3F5 . In line with recombinant Gp35/50 kDa mucins data ( Fig 2C ) , native Gp35/50 kDa mucins from both Dm28c and CL Brener parasite clones interacted with the inner lining of T . infestans rectal ampoules ( Fig 2E ) . Specificity of the signals was assessed using T . infestans tissue samples processed for mAb 3F5-based IIF assays in the absence of T . cruzi purified glycoconjugates ( Fig 2E ) . Similar ex vivo binding assays were carried out using freshly dissected triatomine midguts instead of hindgut tissues . In this case , however , surface over-expression of Gp35/50 kDa mucins did not have an effect on the attachment of epimastigotes ( Fig 3A ) . As shown , the number of TcSMUG S ox parasites attached per 100 insect midgut cells was rather indistinguishable than those recorded for TSSA ox lines or wild-type counterparts . As a putative positive control for this experiment , we evaluated the attachment of epimastigotes over-expressing FLAG-tagged TcSMUG L molecules ( TcSMUG L ox lines ) . TcSMUG L are T . cruzi glycoproteins bearing high sequence similarity to TcSMUG S polypeptides , though they undergo different glycosylation , i . e . they are not acceptors of sialic acid , and are restricted to the surface of epimastigote forms [20 , 21] . As previously reported by our group [6] , a synthetic peptide derived from the conserved N-terminal region of TcSMUG L molecules adhered to the luminal endothelium of Rhodnius prolixus midguts . Transgenic TcSMUG L ox lines were developed into Dm28c and CL Brener as above , and the expression and surface display of FLAG-tagged TcSMUG L molecules assessed by non-permeabilizing flow cytometry ( S2 Fig ) . When evaluated in ex vivo binding assays , TcSMUG L ox lines exhibited significantly increased attachment ( ~6–8 fold ) to T . infestans midgut tissues as compared to TcSMUG S ox , TSSA ox or wild-type lines ( Fig 3A ) . Unfortunately , differences in the direct binding of recombinant TcSMUG L glycoproteins and recombinant Gp35/50 kDa mucins to T . infestans midgut preparations could not be properly assessed due to the non-specific reactivity displayed by our developing system ( mAb anti-FLAG/secondary antibody to mouse IgG ) towards these insect tissues ( Fig 3B ) . Notwithstanding this , and together with published data [6] , results of this section support a role of TcSMUG L molecules , but not Gp35/50 kDa mucins , in mediating T . cruzi parasites anchoring to triatomine midgut tissues . The molecular determinants involved in Gp35/50 kDa mucins’ adhesion to T . infestans rectal ampoule were next explored by carrying out ex vivo epimastigote binding assays in the presence of compounds that may act as competitors . To that end , a panel of oligosaccharides were chemically synthesized as described in Materials and Methods . They include different mono- to hexasaccharides found in the O-linked glycans of Gp35/50 kDa mucins obtained as α-benzyl glycosides ( compounds 1 , 4 , 7 , and 9–14 ) ; the trisaccharide 8 , which is part of oligosaccharides 10 and 12 , as β-benzyl glycoside; and appropriate controls ( Fig 4A ) . T . infestans hindgut tissues were individually pre-incubated with these carbohydrates ( at 20 nM ) for 30 min , washed with PBS and then used to perform epimastigote ex vivo binding assays as before . As shown in Fig 4B , αGlcNAc-Bn , equivalent to the common reducing end of Gp35/50 kDa mucins’ O-linked oligosaccharides , did not interfere with epimastigote binding . Neither did disaccharides containing αGlcNAc in different linkages to βGalp or βGalf units ( compounds 2–5 ) , nor the lactose derivative ( compound 6 ) , used as control ( Fig 4B ) . In contrast , the branched trisaccharide Galfβ1–4[Galpβ1–6]GlcNAcα-Bn ( compound 7 ) , yielded significant inhibition ( Fig 4B ) . Different linear and branched trisaccharides , some of which were also identified in the glycans of Gp35/50 kDa mucins ( compounds 8 , 13 and 14 ) presented negligible effect ( Fig 4B ) . These results indicated a specific inhibitory effect for compound 7 and , together with the fact that this compound bears the ‘negative’ Galfβ1-4GlcNAcα ( compound 4 ) and Galpβ1-6GlcNAcα ( compound 3 ) motifs , suggested that both its constituent carbohydrates and its branched structure contribute to its inhibitory effect . Most notably , compound 7 was able to exert an inhibitory effect on the binding of Dm28c but not on CL Brener epimastigotes to the insect rectal ampoule ( Fig 4B ) , strongly suggesting that it is competing with similar structures , i . e . Galfβ1–4[Galpβ1–6]GlcNAcα motifs , present in Dm28c Gp35/50 kDa mucins’ glycans but absent in CL Brener ones . In line with this , additional assays showed that branched tetrasaccharide ( compound 9 ) , pentasaccharides ( compounds 10 and 11 ) , and hexasaccharide ( compound 12 ) , all of them bearing the Galfβ1–4[Galpβ1–6]GlcNAcα motif , also interfered with the interaction between Dm28c , but not CL Brener , epimastigotes and T . infestans hindgut tissues ( Fig 4B ) . Though not significantly different , compounds 9–12 showed a slight increase in their inhibitory capacity as compared to compound 7 ( Fig 4B ) . The inhibitory effect of compound 7 could not be attributed to non-specific effects on parasite motility and/or viability ( S3 Fig ) and was dose-dependent ( Fig 4C ) , further supporting that its mode of inhibition is by blocking a potential ligand-receptor interaction involved in Dm28c epimastigote-T . infestans rectal ampoule recognition . Interestingly , if this compound was added after incubation of the parasites with the hindgut tissue , its inhibitory effect was not observed ( Fig 4D ) . The fact that CL Brener Gp35/50 kDa mucins lack βGalf-based glycotopes and thereby Galfβ1–4[Galpβ1–6]GlcNAcα-bearing structures on their O-linked glycans [12 , 23] , suggested the existence of additional molecular determinant ( s ) underlying their adhesion to T . infestans hindgut tissues ( Fig 2 ) . To address this issue , we tested the inhibitory potential of a synthetic peptide ( smugS peptide ) from the mature N-terminal region of TcSMUG S proteins on ex vivo epimastigote binding assays . Topological reconstructions indicate that this region , which cannot undergo glycosylation , protrudes from the sugar-coated structure of Gp35/50 kDa mucins ( schematized in Fig 5A ) [20 , 21] . As controls , we used a peptide spanning the ‘corresponding’ N-terminal sequence of TcSMUG L glycoproteins ( smugL peptide ) and peptides spanning a scrambled version of either sequence ( smugSsc and smugLsc peptide ) ( Fig 5A and 5B ) . As shown in Fig 5C and 5D , attachment of epimastigotes to T . infestans rectal ampoule could be partially counteracted ( in a dose-dependent manner ) by preincubation with the smugS peptide , but not with PBS or control peptides . At variance with the compound 7 ( Fig 4B ) , the smugS peptide displayed a similar inhibitory effect on the binding of both CL Brener and Dm28c epimastigotes ( Fig 5C and 5D ) , which correlates with its high degree of conservation among TcSMUG S deduced polypeptides from different parasite strains [20 , 21] . Most interestingly , the inhibitory effect of a mixture containing both the smugS peptide and compound 7 was significantly higher than those recorded for the individual reagents ( Fig 5E ) , suggesting they are interacting with different molecular partners on triatomine hindgut tissues . As shown for compound 7 , the inhibitory capacity of the smugS peptide could not be attributed to non-specific effects on parasite motility and/or viability ( S3 Fig ) . To further assess the specificity of the latter results , we also tested the effect of synthetic peptides on epimastigote-T . infestans midgut interaction . In this case , and in line with transgenic parasite data ( Fig 3 ) and previous results [6] , the smugL peptide but neither the smugS nor the scrambled peptides significantly interfered with epimastigote adhesion ( Fig 5F ) . Overall , these data indicate that the smugS peptide from the mature N-terminal region of the TcSMUG S scaffold polypeptide and the Galfβ1–4[Galpβ1–6]GlcNAcα motif from the O-linked glycans are molecular determinants of Gp35/50 kDa mucins’ adhesion to the triatomine rectal ampoule .
The T . cruzi taxon comprises multiple strains showing remarkable genetic and phenotypic diversity [2] . In susceptible triatomine models , this diversity translates into large variations in the rate of parasite proliferation and/or transmissibility [59–62] . Although underexplored , the cellular basis underlying such differences are thought to be related to the dissimilar capacity of parasite strains to withstand the action of haemolytic factors , antimicrobial peptides and/or the insect gut microbiota [61 , 63–68] . Alternatively , or additionally , these variations may be related to the dissimilar profile of interactions established by different parasite strains with receptor ( s ) along the digestive tract of triatomines . In line with this framework , we herein show that natural variations in the O-glycosylation of Gp35/50 kDa mucins modulate T . cruzi epimastigote anchoring to the triatomine rectal ampoule , which may in turn lead to differences in parasite differentiation/transmissibility to the mammalian host . At variance with all of the epimastigote surface receptors characterized so far [4–7] , including the TcSMUG L glycoproteins analyzed in this work , Gp35/50 kDa mucins display ‘tropism’ towards triatomine hindgut rather than midgut tissues . Experimental evidence supporting this conclusion could be summarized as follows: i ) native and recombinant Gp35/50 kDa mucins directly bind to the internal cuticle of T . infestans rectal ampoules; ii ) transgenic epimastigotes over-expressing Gp35/50 kDa mucins on their surface coat display an exacerbated ex vivo attachment to such tissues; and iii ) chemically synthesized compounds derived from Gp35/50 kDa mucins are able to specifically interfere with epimastigote attachment to T . infestans rectal ampoule , most likely by competing with or directly blocking epimastigote surface-insect receptor ( s ) pairing . By means of competition assays , two different adhesion determinants were identified in Gp35/50 kDa mucins: the smugS peptide , derived from the mature N-terminus of TcSMUG S protein scaffolds , and the Galfβ1–4[Galpβ1–6]GlcNAcα motif , derived from their O-linked glycans . Both compounds display an additive inhibitory effect , strongly suggesting that they engage different receptors on the inner lining of T . infestans hindgut tissues . Interestingly , the Galfβ1–4[Galpβ1–6]GlcNAcα motif impaired the attachment of Dm28c ( TcI ) but not of CL Brener ( TcVI ) epimastigotes to triatomine rectal ampoules; which is in line with the distribution of Gp35/50 kDa mucins bearing this trisaccharide across the T . cruzi taxon [12 , 23] . Topological reconstructions suggest that the smugS peptide is ideally suited for the engagement of counter-receptors on insect tissues . Upon being tethered to the outer layer of the parasite membrane by a C-terminal GPI motif and undergoing extensive glycosylation in the secretory pathway , the threonine-rich region of surface Gp35/50 kDa mucins is predicted to adopt a rigid , ‘stalk-like’ structure . This architecture , in turn , projects the outermost and non-glycosylated N-terminal region of the TcSMUG S polypeptides above the parasite glycocalix . Variations on this theme , aimed at improving the exposition of peptidic ligand-binding domains of surface molecules by means of heavily glycosylated regions have been proposed for TcSMUG L glycoproteins [6] as well as for other T . cruzi surface molecules [16 , 69] and a multiplicity of yeast flocculins and adhesins [70] . βGalf residues have been shown to play a role in maintaining membrane/cell wall physiology and/or in enhancing the virulence of Leishmania and certain pathogenic bacteria and fungi [71 , 72] . Our current findings suggest that Galf residues are also major forces driving the interaction between T . cruzi surface glycoconjugates and the digestive tract of triatomine vectors . In line with this , it is worth mentioning that the involvement of Galf residues on the T . cruzi-triatomine interplay has been previously proposed by Nogueira et al [4] . Briefly , these authors have shown that biochemically purified GIPLs from epimastigotes of the Y strain ( TcII ) adhere to R . prolixus midgut tissues . Treatment of such GIPLs with diluted trifluoroacetic acid partially abolished their binding capacity , thereby suggesting a role of terminal non-reducing Galf residues decorating GIPLs’ glycan on triatomine midgut recognition . Whether T . cruzi GIPLs are also able to interact with triatomine hindgut tissues is being currently explored . However , and even in the case they do , the contrasting structures of the Galfβ1–4[Galpβ1–6]GlcNAcα motif and the GIPLs’ glycan core ( in which Galf residues are linked to α-mannopyranose units instead of α-GlcNAc residues [14] ) , along with the fact that Gp35/50 kDa mucins do not bind to T . infestans midgut endothelium argue against the possibility that GIPLs and Gp35/50 kDa mucins may be using the same triatomine molecular partner ( s ) . The synthetic molecules and transgenic parasites developed here , along with recently released triatomine genomes [73 , 74] , may provide helpful tools to identify the interacting partner ( s ) of Gp35/50 kDa mucins ( and eventually of T . cruzi GIPLs ) on the inner lining of the triatomine rectal ampoule . Overall , the most parsimonious hypothesis to explain our results would imply that the smugS peptide and Galfβ1–4[Galpβ1–6]GlcNAcα motifs on Gp35/50 kDa mucins engage with different insect counter-receptors during the initial steps of T . cruzi epimastigote binding to T . infestans hindgut tissues . In the latter case , such counter-receptor ( s ) is/are expected to display lectin-like properties . The occurrence of digestive lectins recognizing carbohydrate-based motifs has been demonstrated in other vector-borne protozoa and viruses [75–77] . Moreover , a pioneer work , not further pursued , described a series of putative lectins with diverse monosaccharide specificities in different regions of the digestive tract of R . prolixus [78] . The sheer number of Gp35/50 kDa molecules on the epimastigote surface [15] , together with the clustering effect achieved by means of their organization on membrane micro-domains [13 , 56] and/or by the presence of several oligosaccharides carrying the Galfβ1–4[Galpβ1–6]GlcNAcα motif in a single TcSMUG S polypeptide may increase the overall affinity/avidity of these interactions , as shown in other models [79 , 80] . Supporting this idea , our data show that epimastigote adhesion , mediated by ‘aggregating’ surface adhesion determinants , cannot be disrupted if the ‘non-aggregating’ Galfβ1–4[Galpβ1–6]GlcNAcα-Bn compound is added after incubation of the parasites with the triatomine hindgut tissue . Other epimastigote surface molecules , including GIPLs and/or NETNES ( although no Gal residues were found linked to this molecule [58] ) may also contribute to initial parasite-hindgut interaction , which should be later on strengthened by the formation of desmosome-like structures of unknown composition underneath the epimastigote plasma membrane [8 , 11] . Upon metacyclogenesis , parasites readily detach from the triatomine rectal ampoule cuticle , a process that is supposed to occur because of changes on the composition of their surface coat [8 , 81] . Differentiation to metacyclic forms also correlates with an up-regulation of surface trans-sialidase activity , and with a massive sialylation of terminal βGalp units in Gp35/50 kDa mucins’ oligosaccharides [12] . In this framework , a regulatory effect of sialic acid incorporation on the adhesion properties of Galfβ1–4[Galpβ1–6]GlcNAcα-containing glycans might be proposed . Despite continuous efforts , the prospects for the development of effective vaccines and/or appropriate drugs for large-scale public-health interventions against Chagas disease are still clouded by substantial scientific and socioeconomic challenges [82] . In this scenario , development of novel drugs , novel drug targets and/or novel strategies to control parasite transmission are urgently needed . Based upon our findings , we propose Gp35/50 kDa mucins and/or Galf biosynthesis as appealing targets for intervention . Since adhesion to the insect hindgut strictly correlates with epimastigote differentiation into infective forms , the development of compounds able to interfere with this interaction and their subsequent delivery into triatomines by transgenic and/or paratransgenic technologies [83–85] is expected to have an impact into T . cruzi vector transmissibility and , hence in Chagas disease epidemiology .
|
Chagas disease , caused by the protozoan Trypanosoma cruzi , is a life-long and debilitating neglected illness of major significance to Latin America public health , for which no vaccine or adequate drugs are yet available . In this scenario , identification of novel drug targets and/or strategies aimed at controlling parasite transmission are urgently needed . By using ex vivo binding assays together with different biochemical and genetic approaches , we herein show that Gp35/50 kDa mucins , the major T . cruzi epimastigote surface glycoproteins , specifically adhere to the internal cuticle of the rectal ampoule of the triatomine vector , a critical step leading to their differentiation into mammal-infective metacyclic forms . Ex vivo binding assays in the presence of chemically synthesized analogs allowed the identification of a solvent-exposed peptide and a branched , galactofuranose ( Galf ) -containing trisaccharide ( Galfβ1–4[Galpβ1–6]GlcNAcα ) as major Gp35/50 kDa mucins adhesion determinants . Overall , these results provide novel insights into the mechanisms underlying the complex T . cruzi-triatomine interplay . In addition , and since the presence of Galf-based glycotopes on the O-glycans of Gp35/50 kDa mucins is restricted to certain parasite strains/clones , they also indicate that the Galfβ1–4[Galpβ1–6]GlcNAcα motif may contribute to the well-established phenotypic variability among T . cruzi isolates . Most importantly , and taking into account that Galf residues are not found in mammals , we propose Gp35/50 kDa mucins and/or Galf biosynthesis as appealing and novel targets for the development of T . cruzi transmission-blocking strategies .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"microbiology",
"cloning",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"protozoan",
"life",
"cycles",
"animals",
"developmental",
"biology",
"protozoans",
"molecular",
"biology",
"techniques",
"epimastigotes",
"insect",
"vectors",
"digestive",
"system",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"proteins",
"life",
"cycles",
"molecular",
"biology",
"insects",
"rectum",
"disease",
"vectors",
"arthropoda",
"gastrointestinal",
"tract",
"biochemistry",
"mucin",
"trypanosoma",
"cruzi",
"trypanosoma",
"eukaryota",
"anatomy",
"biology",
"and",
"life",
"sciences",
"protozoology",
"species",
"interactions",
"organisms"
] |
2019
|
Trypanosoma cruzi surface mucins are involved in the attachment to the Triatoma infestans rectal ampoule
|
HIV-1 latency remains a formidable barrier towards virus eradication as therapeutic attempts to purge these reservoirs are so far unsuccessful . The pool of transcriptionally silent proviruses is established early in infection and persists for a lifetime , even when viral loads are suppressed below detection levels using anti-retroviral therapy . Upon therapy interruption the reservoir can re-establish systemic infection . Different cellular reservoirs that harbor latent provirus have been described . In this study we demonstrate that HIV-1 can also establish a silent integration in actively proliferating primary T lymphocytes . Co-culturing of these proliferating T lymphocytes with dendritic cells ( DCs ) activated the provirus from latency . Activation did not involve DC-mediated C-type lectin DC-SIGN signaling or TCR-stimulation but was mediated by DC-secreted component ( s ) and cell-cell interaction between DC and T lymphocyte that could be inhibited by blocking ICAM-1 dependent adhesion . These results imply that circulating DCs could purge HIV-1 from latency and re-initiate virus replication . Moreover , our data show that viral latency can be established early after infection and supports the idea that actively proliferating T lymphocytes with an effector phenotype contribute to the latent viral reservoir . Unraveling this physiologically relevant purging mechanism could provide useful information for the development of new therapeutic strategies that aim at the eradication of HIV-1 reservoirs .
Combined antiretroviral therapy ( cART ) is able to suppress the HIV-1 plasma RNA load in patients to undetectable levels . The treatment , however , does not lead to complete virus eradication . Even after many years of successful cART the virus can rebound from latently infected cellular reservoirs and re-establish systemic infection upon therapy interruption [1]–[5] . Proviral latency is an effective strategy to sustain long-term infection by evading the immune system as long as viral antigens are not expressed and presented . Cells latently infected with HIV-1 remain a formidable barrier towards virus eradication and therapeutic attempts to purge these reservoirs have thus far been unsuccessful [6]–[8] . The pool of latent proviruses is established early during infection and forms a steady source of integrated proviral DNA lasting a lifetime for infected individuals [9] , [10] . Early onset of cART reduces the size of the viral reservoir but does not prevent its formation [11] . HIV-1 establishes latent proviral integration mainly in T lymphocytes , but viral reservoirs in monocytes and dendritic cells have also been described [12]–[16] . How the reservoir in memory T lymphocytes is established remains unclear . Infection of quiescent memory T lymphocytes is inefficient due to incomplete reverse transcription and integration [17] , [18] . Linear non-integrated cDNA is rapidly degraded with a half life of approximately 1 day , suggesting that de novo infection of memory T lymphocytes is unlikely to play a major role in formation of this long-lived viral reservoir [18] . However , it has been shown that cytokine stimulation of quiescent T lymphocytes can increase the HIV-1 infection efficiency by boosting reverse transcription and integration processes without inducing cell proliferation or up-regulation of cellular activation markers [19]–[24] . These integrated HIV-1 proviruses are transcriptionally insufficiently active to support the production of new viral particles and the resting T lymphocyte may thus become part of the long-lived latent reservoir . An alternative hypothesis for the formation of the latent reservoir is that actively proliferating T lymphocytes become infected with a transcriptionally silent provirus [25]–[27] . This latently infected proliferating T lymphocyte will not be recognized by the immune system and the proliferating cell can revert to a memory T lymphocyte , thus contributing to the long-lived viral reservoir . We and others previously demonstrated that silent HIV-1 proviral integrations occur in T cell lines [28]–[35] . In this study , the presence of latent proviruses in primary proliferating T lymphocytes was studied . To show that silent integration does not equal a defective provirus , one should demonstrate that the provirus can be purged out of latency . Conventional anti-latency treatments , such as TNFα that is effective in T cell lines , had no effect on the latent provirus in actively proliferating primary T lymphocytes , in agreement with the results of other groups [36] , [37] . Therefore , an alternative anti-latency treatment was explored . Co-culturing of the actively proliferating T lymphocytes with dendritic cells ( DCs ) was found to trigger a robust activation of the latent provirus . Our results demonstrate that a natural mechanism based on cell-cell contact can purge HIV-1 from latency and support the idea that actively proliferating T lymphocytes contribute to the latent viral reservoir . Understanding the natural mechanisms that activate latent HIV-1 provirus may lead to novel intervention therapies to overcome latency .
To study HIV-1 proviral latency , the transcriptionally silent provirus must be distinguished from a defective provirus . This can be achieved by purging the silent provirus out of latency and measuring production of viral proteins such as the major structural protein Gag or its CA-p24 domain . We previously developed a latency assay and demonstrated that TNFα , which is a strong activator of the transcription factor NF-κB , could purge HIV-1 out of latency in the SupT1 T cell line [28] , [35] . We reported that HIV-1 frequently establishes latent infection in these actively dividing T cells . Here we used this assay to test several known anti-latency drugs on primary PHA-activated T lymphocytes . As expected , treatment of HIV-1 infected SupT1 cells with TNFα yielded a 3-fold increase in the percentage of CA-p24 positive cells , but no such effect was observed in primary T lymphocytes ( Fig . 1A ) . The phorbol esters prostratin and PMA can indirectly increase HIV-1 transcription via activation of the protein kinase C ( PKC ) signaling route [38] , [39] . Treatment of infected SupT1 cells with prostratin increased the percentage of CA-p24 positive cells 1 . 5-fold , whereas no or even a small negative effect was observed for primary T lymphocytes ( Fig . 1B ) . PMA treatment reduced the percentage of CA-p24 positive primary T lymphocytes while it did not change the percentage of CA-p24 positive SupT1 cells ( Fig . 1C ) . Activation of the PKC route by stimulating the T cell receptor ( TCR ) with the cross-linker phytoheamagglutinin ( PHA ) increased the number of CA-p24 positive SupT1 cells 1 . 7-fold , but like PMA reduced the percentage of CA-p24 positive primary T lymphocytes ( Fig . 1D ) . Other activators of latent HIV-1 provirus in T cell lines include histone deacetylase ( HDAC ) inhibitors , such as sodium butyrate ( NaBut ) and trichostatin A ( TSA ) [40] , [41] . These compounds prevent deacetylation of histone tails thereby creating a more open DNA chromatin conformation and this improves the accessibility of the HIV-1 promoter in the long terminal repeat ( LTR ) for transcription factors . In SupT1 cells , both NaBut and TSA activated latent provirus 2-fold when used at the highest concentration , but the compounds had no effect on the percentage of CA-p24 positive primary T lymphocytes ( Fig . 1D and E ) . DMSO and ethanol , used to dissolve NaBut and TSA , did not affect transcriptional activity of latent provirus ( Fig . 1F ) . These results indicate that many of the known anti-latency drugs can indeed purge HIV-1 out of latency in the SupT1 T cell line but not in primary proliferating T lymphocytes . Thus , either PHA-activated primary T lymphocytes do not harbor latent HIV-1 infections , or the anti-latency drugs used at the indicated concentrations are not sufficient to activate HIV-1 provirus from latency in these primary cells . Dendritic cells ( DCs ) regulate T and B cell responses via cell-cell contact in combination with secretion of specific cytokines and chemokines [42] , [43] . To investigate if a more physiological cell-based stimulus could activate HIV-1 from latency in primary T lymphocytes , the cells were co-cultured with immature monocyte-derived dendritic ( DCs ) . The HIV-1 infected T lymphocyte culture was split 24 h after infection into a mock treated culture and a co-culture with DC ( Fig . 2A ) . In the latency assay new rounds of virus replication and virus transmission from DC to T lymphocyte are prevented by the fusion inhibitor T1249 . The cells were harvested after 24 hours , stained for intracellular CA-p24 and analyzed by flow cytometry . The percentage of CA-p24 positive T lymphocytes increased significantly from 2 . 2% in the control culture to 5 . 2% upon co-culture with DCs ( Fig . 2B ) . This 2 . 4-fold activation shows that HIV-1 can frequently establish a latent provirus early after infection of PHA-activated T lymphocytes and DCs can induce proviral gene expression from the silent provirus to re-initiate virus production ( Fig . 2C ) . Similar results were obtained with CD3/CD28-activated T lymphocytes instead of PHA-activated T cells ( Fig . S1 ) . To confirm that the PHA-activated T lymphocytes have an effector phenotype they were stained with different antibodies to detect immune phenotype markers by flow cytometry . The PHA-activated T lymphocytes expressed low levels of CD69 , CD127 and CCR7 and high levels of CD25 and CD45RO , as expected of effector T lymphocytes ( Fig . S2A ) . For most markers the expression level , measured with the mean fluorescent intensity ( MFI ) , did not change as a result of virus production , except for a significant increase in CD25 and CD45RO expression in the CA-p24 positive T lymphocytes compared to CA-p24 negative cells ( Fig . S2B ) . The increase in CD25 expression was even more pronounced in the CA-p24 positive T lymphocytes that were co-cultured with DCs ( Fig . S2C and S2D ) . To investigate whether DCs induce apoptosis of the HIV-1 infected T lymphocytes , the cells were analyzed for the presence of the phospholipid phosphatidylserine ( PS ) , an early apoptosis marker . Co-culturing of T lymphocytes with DCs slightly increased the percentage of PS positive cells but this was observed for the complete T lymphocyte population and not specifically for the CA-p24 positive cells , demonstrating that increased CA-p24 expression is not caused by the onset of apoptosis ( Fig . S3 ) . To investigate whether contact with DCs also enhances the virus production per cell , we inspected the MFI of CA-p24 positive T lymphocytes . The MFI of DC versus mock treated cells was compared and the ratio of the two values was calculated ( Fig . 2D ) . There was no significant difference , indicating that virus production per individual T lymphocyte does not increase . To investigate the total virus production in the cell culture , secreted CA-p24 was measured in the culture supernatant with ELISA . Co-culturing of the T lymphocytes with DCs increased the extracellular CA-p24 production in the culture supernatant 2-fold , but this was not significantly different from the extracellular CA-p24 measured in the mock treated culture ( Fig . 2E ) . When the extracellular CA-p24 production was corrected for the increased number of intracellular CA-p24 positive cells , no nett difference was observed ( data not shown ) . These results demonstrate that DCs induce more T lymphocytes to produce HIV-1 but that virus production per cell does not change . The conventional anti-latency drugs that can activate provirus from latency in SupT1 cells are insufficient to activate latent provirus in primary T lymphocytes . However , latent provirus in T lymphocytes can be activated upon co-culture with DCs . To investigate whether DCs can also activate HIV-1 from latency in the SupT1 T cell line , HIV-1 infected SupT1 cells were mock treated or co-cultured with DCs for 24 hours . The percentage of CA-p24 positive SupT1 cells increased from an average of 2 . 7% in the mock treated culture to 4 . 1% in the co-culture with DCs . This 1 . 5-fold increase is not significantly different , as underscored by the comparison to the 4 . 7-fold activation upon TNFα treatment ( Fig . 3 ) . These combined results indicate that distinct pathways seem to trigger activation of latent provirus in SupT1 T cells versus primary T lymphocytes . In the latency assay DCs are added to T lymphocytes 24 hours after a single round HIV-1 infection to allow for completion of the reverse transcription and integration processes . Several control experiments were performed to investigate if DCs influence delayed reverse transcription or integration processes rather than transcriptional activation of latent HIV-1 provirus . First , the integrated DNA copy numbers were analyzed . Infected T lymphocytes were co-cultured with DCs or mock treated and an aliquot of the cultures was analyzed for CA-p24 expression by flow cytometry , which showed the expected 3-fold induction as the percentage of CA-p24 positive cells increased from 2 . 8% in the mock treated culture to 8 . 6% in the DC co-culture ( Fig . 4A ) . The remaining cells were pelleted , subjected to proteinase K treatment , and the HIV-1 DNA copy number was analyzed with a TaqMan assay that detects the number of integrated HIV-1 copies with primers binding to repetitive chromosomal Alu segments in combination with primers specific for HIV-1 DNA . The T lymphocytes co-cultured with DCs appeared to have higher integrated viral copy numbers compared to the mock treated culture , but this trend was not statistically significant ( Fig . 4B ) . To further monitor the effects on integration , the latency assay was done in the presence or absence of the integrase inhibitor Raltegravir , which was added at the start of the DC co-culture . Raltegravir caused a small but significantly reduction of the DC-mediated provirus activation . Nevertheless , the 2-fold induction of latent provirus remained , showing that DCs can influence the HIV-1 integration process but also activate latent provirus ( Fig . 4C ) . To eliminate the effect on the early events of the HIV-1 replication cycle , the latency assay was repeated 9 days after infection . At this time all reverse transcription and integration processes should be completed . To allow for prolonged culturing , the T lymphocytes were activated with beads coated with CD3 and CD28 antibodies instead of PHA and infected according to the standard latency assay , except that the culture was split into 3 cultures on day 2 post infection . The first culture was mock treated , the second co-cultured with DC's and the third was maintained for 1 week . The mock treated and DC co-cultured T lymphocytes were harvested after 24 hours and analyzed by flow cytometry . On day 9 post infection the third culture was split into two cultures; one mock treated and one co-cultured with DC's . Both were harvested after 24 hours and analyzed by flow cytometry . The percentage CA-p24 positive T lymphocytes increased 2-fold by DC co-culture executed on either day 2 or day 9 post infection ( Fig . 4D ) . These results show that PHA-activated T lymphocytes still harbor latent provirus at one week after infection and that this provirus can become transcriptionally active upon T lymphocyte contact with DCs . To unequivocally show that PHA-activated T lymphocytes harbor latent provirus the latency assay was performed with HIV-1 infected CD4 expressing T lymphocytes . Productively infected T lymphocytes down-regulate CD4 expression at the cell surface via the viral Nef protein , one of the early HIV-1 proteins , whilst latently infected T lymphocytes retain normal CD4 expression levels [44] . To study if DCs induce gene expression of truly latent provirus , PHA-activated T lymphocytes ( mixed population ) were infected with HIV-1 according to the latency assay . A single day after infection half of the T lymphocyte culture was used to select CD4 expressing cells with magnetic beads ( CD4 selected , Fig . 5A ) . To determine selection efficiency , the percentage of CD4 expressing cells was compared by flow cytometry analysis . In a representative experiment starting with a cell population of which 50% expressed CD4 , we could enrich up to 94% ( Fig . 5B ) . As expected , the CD4 selected cells exhibited decreased CA-p24 positivity ( Fig . 5C ) . Both mixed and CD4 selected T lymphocytes were co-cultured with DCs or mock treated for 24 hours , and the CD3 positive T lymphocyte population was analyzed for CD4 and CA-p24 expression by flow cytometry ( Fig . 5D ) . Co-culturing the infected CD4 selected T lymphocytes with DCs induced the percentage of CA-p24 positivity to increase while the cells that became CA-p24 positive lost CD4 expression ( Fig . 5E and data not shown ) . The average 2 . 6-fold DC-mediated activation of latent provirus in the CD4 selected T lymphocytes was similar to the 2 . 5-fold activation in the whole population T lymphocyte population ( Fig . 5F ) . Thus , the latent HIV-1 provirus in PHA-activated T lymphocytes – assayed by unaffected CD4 expression – is sensitive to transcriptional activation upon T lymphocyte DC contact . To investigate possible differences in establishment and activation of latent proviruses between individuals , DCs and T lymphocytes were isolated from six healthy blood donors and tested in the latency assay . The percentage of mock treated CA-p24 positive T lymphocytes ranged from 1 . 7% for donor B to 5 . 6% for donor D ( Fig . 6A ) . Despite such differences in infection rate , latent provirus activation by stimulation with autologous DCs was apparent for all donors . The DC-mediated fold activation from latency ranged from 2-fold for donor A to 3 . 5-fold for donor D ( Fig . 6B ) . Next , we investigated whether latent HIV-1 proviruses in T lymphocytes could be activated by co-culturing with allogenic DCs . Infected T lymphocytes from donor D and E were mock treated or co-cultured with DCs from donor D or E . Both autologous and allogenic DCs similarly increased the percentage of CA-p24 positive T lymphocytes by 3-fold ( Fig . 6C ) . It has previously been shown that activation of latent HIV-1 in quiescent memory T lymphocytes can be achieved by activating the cells via TCR stimulation with CD3/CD28 specific antibodies or using IL-2 or ionomycin [45]–[47] . Since the T lymphocytes in our latency assay are activated via TCR stimulation with PHA prior to HIV-1 infection , we tested whether further TCR stimulation could increase the number of CA-p24 positive cells . Reactivation with CD3/CD28 antibodies did not increase the percentage of CA-p24 positive cells ( Fig . 7 ) . Treating the T lymphocytes with IL-2 or ionomycin , which activates the NFAT transcription factor , also had no significant effect on the percentage of CA-p24 producing cells . The T lymphocytes used in this study are all TCR stimulated and still harbor latent provirus that can be activated by DCs . This demonstrates that TCR stimulation is not sufficient to activate all latent provirus in proliferating T lymphocytes . The C-type lectin DC-SIGN is a cell surface molecule expressed on DCs that facilitates HIV-1 infection of T lymphocytes in cis or in trans [48] , [49] . Furthermore , DC-SIGN can induce HIV-1 transcription in DCs themselves [50] . To investigate whether DC-SIGN is also involved in the activation of latent HIV-1 provirus in primary T cells , the HIV-1 infected T lymphocytes were mock treated or co-cultured with DCs in the presence or absence of the C-type lectin competitor mannan ( Fig . 8A ) . Addition of mannan did not influence DC-mediated provirus activation . To further study a possible role for DC-SIGN , T lymphocytes were co-cultured with either Raji or modified Raji cells expressing DC-SIGN ( Raji-DC-SIGN ) . DCs triggered a 3-fold activation , whereas both the Raji and Raji-DC-SIGN cells could not purge provirus out of latency ( Fig . 8B ) . The combined results indicate that DC-SIGN is most likely not involved in DC-mediated HIV-1 activation from proviral latency . To investigate the requirements for DC-mediated activation of latent provirus , we first studied the effect of co-culturing the T lymphocytes with increasing numbers of DCs ( ratio DC∶T; 1∶150–1∶1 , 5 ) . The CA-p24 positive T lymphocytes increased from an average of 1 . 4% in the mock treated culture to an average of 2 . 5% in the DC co-culture at a ratio of 1∶150 , yielding a 1 . 8-fold activation of latent provirus . Increasing DC numbers enhanced proviral activation to 4 . 8% CA-p24 positive T lymphocytes , representing 3 . 5–fold activation with 1 DC per 1 . 5 T lymphocytes ( Fig . 9A ) . To investigate the role of DC-secreted components , cell-free DC culture supernatant was added to infected T lymphocytes . As a control , cell-free culture supernatant of HEK 293T cells was used . The DC-supernatant induced a 2 . 2-fold activation whereas HEK 293T supernatant showed no activation ( Fig . 9B ) . This result demonstrates that the DC culture medium contains ( a ) DC-secreted factor ( s ) activating latent HIV-1 provirus . This is not mediated by IL-4 or GM-CSF that are used to differentiate the monocytes into DCs as both cytokines did not induce activation of the latent provirus ( Fig . S4 ) . Co-culturing of infected T lymphocytes with freshly washed DCs , thus removing soluble factors , induced a 2 . 5-fold increase in percentage of CA-p24 positive cells , while washed DCs combined with DC supernatant gave the strongest ( 3 . 7-fold ) activation ( Fig . 9C ) . This demonstrates that a combination of DCs and their secreted factors induce the strongest activation . Next , we investigated if the activation of latent provirus can be inhibited by blocking the DC-T lymphocyte cell-cell interaction . Since TCR-stimulation does not have an effect on the latent provirus in the PHA-activated T lymphocytes ( see Fig . 1 ) and activation of latent provirus is induced by both autologous and allogenic DCs ( see Fig . 6 ) , we chose to use antibodies that specifically target the general adhesion molecules ICAM-1 , ICAM-2 or ICAM-3 ( Fig . 9D ) . The presence of ICAM antibodies did not have an effect on the percentage of CA-p24 positive T lymphocytes in the absence of DCs ( left part of the panel ) . The co-culture with DCs increased the percentage of CA-p24 positive T lymphocytes 5-fold . Blocking ICAM-1 interactions between the DC and T lymphocyte significantly reduced the activation by 75% to 2-fold . Addition of ICAM-2 or ICAM-3 antibodies , on the contrary , did not inhibit the DC-mediated increase in CA-p24 positive T lymphocytes . Together , these results demonstrate that both cell-cell contact between DC and T lymphocyte and ( a ) secreted DC factor ( s ) induce activation of latent HIV-1 provirus .
We previously developed a fast and relatively simple method to study HIV-1 latency in T cell lines and showed that the virus can be purged out of latency with TNFα , genistein and 5-Aza [28] , [35] . In this study , our latency assay was used to demonstrate that HIV-1 can establish a latent proviral integration in primary PHA-activated T lymphocytes . Conventional anti-latency drugs ( TNFα , NaBut , Prostratin , TSA , PMA or PHA ) , were not sufficient to activate the latent provirus but the virus was purged out of latency by co-culturing the T lymphocytes with dendritic cells ( DCs ) . Co-culturing with DCs , activated gene expression from the latent HIV-1 provirus in the T lymphocytes by 2- to 5-fold . This may seem modest in comparison to other latency models [20] , [29] , [45] , [47] , [51] , [52] , but we note that in other studies the latently infected cells are usually selected , either by clonal selection or by specific outgrowth . In contrast , our latency assay is performed on the bulk culture without any form of selection . The obtained 2- to 5-fold activation means that for every virus-producing lymphocyte there are 1 to 4 lymphocytes that harbor a silent provirus that can be activated by DCs . In fact , since we cannot rule out that some latent proviruses are unresponsive to DC-activation , the actual latent reservoir may be underestimated . In resting memory T lymphocytes , activation of the latent provirus is predominantly mediated via cellular activation with αCD3/CD28 ( TCR-stimulation ) , IL-2 or via activation of the NFAT transcription factor with ionomycin [36] , [45]–[47] . These stimuli did not have an effect on the silent provirus in activated T lymphocytes , most likely because the transcription factors involved are already active . This observation illustrates that the establishment of a silent provirus is not necessarily due to the absence of certain transcription factors , which was proposed to be the major cause for the establishment of latently infected quiescent memory lymphocytes [36] . Importantly , this also shows that the molecular mechanism leading to the activation of latent HIV-1 can differ between different T lymphocyte subsets , depending on their activation status . A remaining question is whether HIV-1 latency is caused by silencing of a transcriptionally active provirus or the result of silent proviral integration . If silent integration occurs , the infected proliferating T lymphocyte will not be recognized by the immune system , favoring the transition to a latently infected memory cell . Our results showing that DCs can activate latent HIV-1 already 2 days after the initial infection support previous observations obtained in T cell lines that the latent phenotype is established immediately after provirus integration and is not due to down-regulation of an initially transcriptional active provirus [28] , [29] , [34] . We have not yet fully characterized the molecular interactions required for the DC-mediated HIV-1 activation from latency . However , our results indicate that both ( a ) secreted DC-component ( s ) and the cell-cell interaction between DC and T lymphocyte trigger activation of latent provirus . HIV-1 activation did not require autologous T lymphocytes and DCs , suggesting that the cell-cell interaction is not self-restricted . Blocking the DC-T cell interaction with anti-ICAM-1 antibodies strongly inhibited the activation of latent provirus . ICAM-1 is expressed on the DC and binds to leukocyte function-adhesion molecule-1 ( LFA-1 ) on the T lymphocyte , an interaction that is important for the process of antigen presentation [53]–[55] . We and others previously showed that the interaction of LFA-1 and ICAM-1 , but not ICAM-2 and ICAM-3 , is crucial for efficient HIV-1 transmission and that DC subsets that express higher levels of ICAM-1 transmit HIV-1 more efficiently [56]–[58] . It would be interesting to investigate if DC subsets with higher ICAM-1 levels are also better activators of latent provirus . Further characterization of the cellular contact molecules that can purge HIV-1 from latency and testing their efficiency on latently infected memory T lymphocytes is of great interest , as our results strongly suggest that cellular activation via TCR-stimulation is not sufficient to activate all latent proviruses . Purging the provirus from latency via physiologically relevant cell-cell interactions supports the idea that the viral reservoir is dynamic . In this study we show that immature monocyte-derived DCs can activate latent HIV-1 in activated T lymphocytes . Marini et al . demonstrated that mature monocyte-derived DCs can activate latent provirus in resting memory T lymphocytes [47] . If these cell-cell contacts activate latent virus in vivo , local outbursts of virus production can occur despite suppressive therapy . However , the situation in vivo is more complex as mature myeloid DCs have been reported to inhibit productive infection by inducing latency [59] . This indicates that different DC subsets may have opposing effects on viral latency . Thus , the cellular HIV-1 reservoir could be activated or silenced depending on the type of DC that is encountered . We are currently investigating the influence of different DC subsets ( myeloid and plasmacytoid ) on latent HIV-1 provirus in proliferating T lymphocytes . In this study we demonstrate that HIV-1 can establish a silent integration in actively proliferating T lymphocytes . The latently infected T lymphocytes will escape immune-surveillance as long as no viral peptides are expressed and presented on the cell surface . Although proliferating T lymphocytes are generally short-lived such that their contribution to the total HIV-1 reservoir will be limited , these cells can return to the resting state of memory T lymphocyte and thereby contribute to the long-lived viral reservoir . How important this interaction is in vivo and whether it can be used in ‘shock and kill’ approaches [60] , [61] for the complete eradication of the HIV-1 reservoir in patients remains to be studied .
HEK 293T cells were grown as a monolayer in Dulbecco's minimal essential medium ( Gibco , BRL , Gaithersburg , MD ) supplemented with 10% ( v/v ) fetal calf serum ( FCS ) , 40 U/ml penicillin , 40 µg/ml streptomycin and nonessential amino acids ( Gibco , BRL , Gaithersburg , MD ) at 37°C and 5% CO2 . The human T lymphocytic cell line SupT1 ( ATCC CRL-1942 ) was cultured in advanced RPMI 1640 medium ( Gibco BRL , Gaithersburg , MD ) supplemented with 1% ( v/v ) FCS , 20 mM glucose , 40 U/ml penicillin , and 40 µg/ml streptomycin . The Raji cell line and Raji-DC-SIGN cells were cultured in RPMI 1640 medium ( Gibco , BRL , Gaithersburg , MD ) containing 10% FCS . The immature monocyte-derived dendritic cells ( DCs ) were prepared as previously described [62] . In short , human peripheral blood mononuclear cells ( PBMCs ) were isolated from buffy coats ( Central Laboratory Blood Bank , Amsterdam , The Netherlands ) by use of a Ficoll gradient . Monocytes were subsequently isolated with a CD14 selection step using a magnetic bead cell sorting system ( Miltenyi Biotec GmbH , Bergisch Gladbach , Germany ) . Purified monocytes were cultured in RPMI 1640 medium containing 10% FCS and differentiated into DCs by stimulation with 45 ng/ml interleukin-4 ( rIL-4; Biosource , Nivelles , Belgium ) and 500 U/ml granulocyte macrophage colony-stimulating factor ( GM-CSF; Schering-Plough , Brussels , Belgium ) on day 0 and 2 , and used on day 6 . The remaining PBMCs were controlled by PCR for the absence of the CCR5 Δ32 allele and frozen in multiple vials . When required , the PBMCs were thawed , activated with phytohemagglutinin ( PHA , Remel , 5 µg/ml for 2 days , 2 µg/ml for 3 days activation ) or CD3/CD28 immunomagnetic beads ( ratio cell∶beads 1∶1 for 3 days , Dynal , Invitrogen ) and cultured in RPMI medium supplemented with 10% FCS and recombinant IL-2 ( rIL-2 , Novartis ) at 100 U/ml . On day 2 of culture , CD4+ T lymphocytes were enriched by depleting CD8+ T lymphocytes using CD8 immunomagnetic beads ( Dynal , Invitrogen ) . The CD4+ T lymphocytes were cultured for 3 days in RPMI medium with rIL-2 and 10% FCS . Plasmid DNA encoding the CXCR4-using HIV-1 LAI primary isolate [63] was transiently transfected in HEK 293 T cells with the calcium phosphate method as described previously [64] . Virus supernatant was harvested 2 days after transfection , sterilized by passage through a 0 . 2 µm filter and stored in aliquots at −80°C . The concentration of the virus stocks was determined by CA-p24 ELISA . Culture supernatant was heat inactivated at 56°C for 30 min in the presence of 0 . 05% Empigen-BB ( Calbiochem , La Jolla , USA ) . The CA-p24 concentration was determined by twin-site ELISA with D7320 ( Biochrom , Berlin , Germany ) as capture antibody and alkaline phosphatase-conjugated anti-CA-p24 monoclonal antibody ( EH12-AP ) as detection antibody . Quantification was performed with the lumiphos plus system luminescence reader ( Lumigen , Michigan , USA ) in a LUMIstar Galaxy ( BMG labtechnologies , Offenburg , Germany ) . Recombinant CA-p24 produced in a baculovirus system was used as standard . Mannan ( Sigma ) was used at a final concentration of 40 µg/ml . Raltegravir ( ISENSTRESS/MK-0518 ) was obtained through the AIDS Research and Reference Reagent Program and used at the final concentration of 50 µg/ml . The fusion inhibitor T1249 ( WQEWEQKITALLEQAQIQQEKNEYELQKLDKWASLWEWF ) was obtained from Pepscan ( Therapeutics BV , Lelystad , The Netherlands ) and used at a final concentration of 0 . 1 µg/ml . PHA ( Remel ) , PMA ( Sigma ) , Prostratin ( Sigma-Aldrich ) , TNFα ( Invitrogen ) , Trichostatin A ( TSA , Sigma ) and Sodium butyrate ( NaBut , Aldrich ) were used at the indicated concentrations . Recombinant IL-2 ( Novartis ) was used at a final concentration of 100 U/ml and ionomycin ( Sigma-Aldrich ) at 100 µg/ml . αCD3/CD28 beads ( Dynal , Invitrogen ) were used at a ratio of 1 bead per cell . For intracellular CA-p24 measurement we used the RD1- or FITC-conjugated mouse monoclonal α-CA-p24 ( clone KC57 , Coulter ) . For CD3 staining the APC- or FITC-conjugated α-CD3 ( BD Bioscience ) was used . Annexin V-APC ( BD Pharmingen ) was used to stain for the early apoptosis marker phosphatidyldserine . To characterize the T lymphocyte immunophenotye , α-CD69-PE ( BD Bioscience ) , α-CD45-RA-PE ( Pharmingen ) , α-CD25-FITC ( BD ) , α-CD45-RO-FITC ( DAKO ) , α-CD127-PE ( BD Pharmingen ) , and α-CCR7-PE ( BD Pharmingen ) were used . For DC staining purified α-CD83-APC ( BD Bioscience ) , α-CD86-PE ( BD Pharmingen ) , α-HLA-DR-PerCPCy5 ( BD Bioscience ) , α-CD14-FITC ( BD Bioscience ) and α-DC-SIGN-PE ( R&D Systems ) antibodies were used . To block DC-T lymphocyte cell-cell ( ICAM ) interactions in the co-culture , α-CD54 ( Peli Cluster; the Netherlands ) , α-CD102 ( RD Systems ) or α-CD50 ( Immunotech ) antibodies were used . HIV-1 infected cells were used in the latency assay as described previously [35] . In short , PHA- or CD3/CD28-activated CD4+ T lymphocytes ( 1 . 0 or 2 . 0×106 cells ) were infected with HIV-1 ( 20 ng CA-p24 ) . Free virus was washed away after 4 hours and the cells were cultured with the fusion inhibitor T1249 to prevent new infections . At 24 hr after infection the CD4+ T lymphocytes ( 1 . 5×105/well ) were either mock treated , treated with anti-latency drugs or co-cultured with DCs , Raji or Raji-DC-SIGN cells ( 0 . 5×105/well ) . After another 24 hr , the cells were harvested and intracellular CA-p24 was detected by FACS flow cytometry . Virus production was also determined by measuring extracellular CA-p24 in the culture supernatant by ELISA . The percentage of CA-p24 positive cells in the treated culture was divided by the percentage of CA-p24 cells in the mock treated culture and used as a measure for proviral latency ( fold activation ) . The CD4+ T lymphocytes were co-cultured with autologous DCs unless when co-cultured with allogenic DC's ( as indicated in the figure legend ) . To block cell-cell interactions between T lymphocyte and DC antibodies specific for human ICAM-1 ( CD54 ) , ICAM-2 ( CD102 ) or ICAM-3 ( CD50 ) were added to the co-culture at the final concentration of 10 µg/ml . To select CD4 expressing cells in the HIV-1 infected culture a magnetic bead cell sorting system was used according to the manufactures instructions ( Miltenyi Biotec GmbH , Bergisch Gladbach , Germany ) . One Way ANOVA and student T test ( 2-tailed ) were used to evaluate if observed differences between groups are significant ( Graphpad Prism , version 5 ) . P values * = p<0 . 05 , ** = p<0 . 01 , *** = p<0 . 001 . Cells were fixed in 4% formaldehyde for 10 minutes at room temperature and subsequently washed with FACS buffer ( PBS supplemented with 1% FCS ) . The cells were permeabilized with BD Perm/Wash buffer ( BD Pharmingen ) and antibody staining was performed in BD Perm/Wash or FACS buffer for 1 hr at 4°C . Excess of unbound antibody was removed and the cells were analyzed on a BD FACSCanto II flow cytometer with BD FACSDiva Software v6 . 1 . 2 ( BD biosciences , San Jose , CA ) in FACS buffer . The live population was defined based on forward/sideward scatter and analyzed for CD3 and intracellular CA-p24 positivity . Gate settings were fixed between samples for each experiment . To measure the membrane phospholipid phosphatidylserine as a marker for early apoptosis , the cells were stained with Annexin V prior to cell fixation . The DC phenotype ( negative for CD14 , low levels of MHC class II ( HLA-DR ) , CD83 and CD86 and high levels of DC-SIGN ) was confirmed by FACS flow cytometry [57] . TaqMan assay was used to quantify the number of HIV-1 DNA copies in infected cultures . In summary , cells were resuspended in Tris-EDTA ( 10 mM pH 8 . 3 ) containing 0 . 5 units/µl proteinase K ( Roche Applied Science ) , incubated for 1 hr at 56°C and 10 min at 95°C and directly used for quantitative PCR amplification . The number of input cells was determined using TaqMan reagents for quantification of β-actin DNA ( AB , Applied Biosystems ) according to the manufacturer's instruction . To quantitate integrated proviral DNA copy numbers a pre-amplification was done with primers detecting the repetitive Alu sequence [65] in combination with primers specific for the HIV-1 LTR . The pre-amplified DNA was subsequently quantified by real-time PCR as previously described [66] .
|
Combination therapy can suppress the viral load in HIV-1 infected individuals to undetectable levels , but does not lead to complete virus eradication . Even after many years of successful therapy the virus is still present in long-lived cells as a latently integrated provirus . HIV-1 can re-establish systemic infection from this reservoir when therapy stops . Purging attempts in patients have been unsuccessful and HIV-1 latency remains a formidable barrier to virus eradication . Different cellular reservoirs that harbor latent HIV-1 proviruses have been described to consist mainly of resting memory T lymphocytes . Yet how this reservoir in memory T lymphocytes is established is still unclear as infection of these cells is very inefficient . In this paper we demonstrate that HIV-1 can establish a latent provirus in activated effector T lymphocytes . We observed that for every virus producing cell there is at least one other cell harboring a latent provirus , illustrating that latent infections occur frequently . Proliferating T lymphocytes are generally short-lived and their contribution to the total cellular reservoir thus seems limited . However , these activated T lymphocytes can revert into resting memory T lymphocytes and become part of the long-lived viral reservoir .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"viral",
"immune",
"evasion",
"viral",
"persistence",
"and",
"latency",
"virology",
"biology",
"microbiology"
] |
2013
|
Dendritic Cell-induced Activation of Latent HIV-1 Provirus in Actively Proliferating Primary T Lymphocytes
|
CELADEN was a randomized placebo-controlled trial of 50 patients with confirmed dengue fever to evaluate the efficacy and safety of celgosivir ( A study registered at ClinicalTrials . gov , number NCT01619969 ) . Celgosivir was given as a 400 mg loading dose and 200 mg bid ( twice a day ) over 5 days . Replication competent virus was measured by plaque assay and compared to reverse transcription quantitative PCR ( qPCR ) of viral RNA . Pharmacokinetics ( PK ) correlations with viremia , immunological profiling , next generation sequence ( NGS ) analysis and hematological data were evaluated as exploratory endpoints here to identify possible signals of pharmacological activity . Viremia by plaque assay strongly correlated with qPCR during the first four days . Immunological profiling demonstrated a qualitative shift in T helper cell profile during the course of infection . NGS analysis did not reveal any prominent signature that could be associated with drug treatment; however the phylogenetic spread of patients’ isolates underlines the importance of strain variability that may potentially confound interpretation of dengue drug trials conducted during different outbreaks and in different countries . Celgosivir rapidly converted to castanospermine ( Cast ) with mean peak and trough concentrations of 5727 ng/mL ( 30 . 2 μM ) and 430 ng/mL ( 2 . 3 μM ) , respectively and cleared with a half-life of 2 . 5 ( ± 0 . 6 ) hr . Mean viral log reduction between day 2 and 4 ( VLR2-4 ) was significantly greater in secondary dengue than primary dengue ( p = 0 . 002 ) . VLR2-4 did not correlate with drug AUC but showed a trend of greater response with increasing Cmin . PK modeling identified dosing regimens predicted to achieve 2 . 4 to 4 . 5 times higher Cmin . than in the CELADEN trial for only 13% to 33% increase in overall dose . A small , non-statistical trend towards better outcome on platelet nadir and difference between maximum and minimum hematocrit was observed in celgosivir-treated patients with secondary dengue infection . Optimization of the dosing regimen and patient stratification may enhance the ability of a clinical trial to demonstrate celgosivir activity in treating dengue fever based on hematological endpoints . A new clinical trial with a revised dosing regimen is slated to start in 2016 ( NCT02569827 ) . Furthermore celgosivir’s potential value for treatment of other flaviruses such as Zika virus should be investigated urgently . Trial Registration: ClinicalTrials . gov NCT01619969
Dengue fever is a mosquito-borne viral illness that is endemic in tropical regions around the world , with an estimated 96 million cases of dengue illness annually [1] . Dengue is one of 17 neglected tropical diseases that the World Health Organization ( WHO ) has identified for priority attention due to its disproportionate impact on global health , with cases reported from over 100 countries [2] . Singapore maintains an aggressive mosquito control program [3] , with spending by the National Environment Agency approaching US$50 million annually . These efforts have successfully driven the proportion of households harboring the Aedes mosquito , the vector for dengue , to historic lows of less than 1% . Yet , in the last decade , the incidence rates have continued to climb , with the highest rate recorded in 2013 of 404 . 9 cases per 100 , 000 with 8 deaths [4] . Currently , there are no approved drugs for dengue . Vaccine development has been underway since the 1970s [5] , an extraordinarily challenging effort because immunity to one serotype does not confer protection against the others . Furthermore , a phenomenon known as antibody-dependent enhancement ( ADE ) posits that antibodies to one serotype from a previous dengue infection increases the risk of more serious forms of the illness , dengue hemorrhagic fever ( DHF ) or dengue shock syndrome ( DSS ) [6 , 7] . Indeed , the proportion of DHF among patients with secondary dengue is much higher than those with primary dengue [8 , 9] . Therefore , vaccine development has proceeded under the premise that a vaccine must protect against all four dengue serotypes; otherwise , potentially more serious outcomes may ensue if the subject achieves only partial immunity . Sanofi’s tetravalent dengue vaccine CYD-TDV achieved 56% and 65% efficacy in Phase 3 field studies in Southeast Asia and Latin America , respectively . Protection was serotype dependent with protective efficacy for DENV 2 of 35% and 42% in the two trials [10 , 11] . In December 2015 , a number of countries , namely Mexico , Brazil , and the Philippines , approved CYD-TDV for use as a dengue vaccine . However , the modest vaccine efficacy , particularly in patients with no previous history of dengue infection , the higher hospitalization rate among vaccinated children younger than 9 years in the third year of follow-up [12 , 13] , and the extended timeframe required to implement large-scale vaccination programs underscores a continuing need to discover and develop dengue drugs that can be used alongside vaccines . Since 2008 , five randomized , controlled trials of dengue drugs have been completed [14–18] All adopted a strategy of repositioning , or using drugs for which human safety data were already available from approved drugs or from trials of other clinical indications . Celgosivir , an inhibitor of alpha-glucosidase , a host enzyme required for proper processing of viral surface glycoproteins , has been given to hundreds of patients in HIV and HCV trials , but further pharma-driven development of celgosivir was discontinued because approved drugs for those indications had better or equivalent efficacy in Phase 2 trials [19 , 20] . When tested in cell-based assays and an animal model of dengue infection , celgosivir demonstrated submicromolar activity and prevented death in mice infected with an otherwise lethal dose of virus [21 , 22] . The strong preclinical pharmacology results motivated the conduct of a Phase 1b randomized , double-blind , placebo-controlled trial in 50 adult dengue patients ( CELADEN , NCT01619969 ) . Although the trial did not meet the primary endpoints of lowering viremia or fever [17] , examination of data for secondary endpoints , presented here , as well as additional studies using a mouse model of infection [23] have provided insights for a new Phase 2a clinical trial with an altered regimen of celgosivir ( NCT02569827 ) . The analysis presented here may also be informative for the design of other drug trials for dengue fever .
Celgosivir was synthesized according to US patent 5 , 017 , 563 [24] by selective C-acylation of castanospermine ( MedChem 101 LLC ) . A suspension of castanospermine and bis ( tributyl tin ) oxide ( 1:2 molar ratio ) in toluene ( 30 vol ) was refluxed under argon for 3 h . The solution was cooled down to -17°C , then butyryl chloride ( 1 . 8 molar excess ) was added dropwise over a 10 min period . The mixture was stirred at room temperature for 2 h . Absolute EtOH was added , and the mixture was stirred for 30 min , followed by addition of 1 . 5M HCl/EtOH solution ( 2-fold molar ratio to butyryl chloride ) . The mixture was stirred over night ( 18 h ) at room temperature then for 1 h at +3°C . The precipitate was filtered , washed with hexane , and dried in vacuo . The compound was recrystallized to obtain a product with > 99% purity by HPLC . Celgosivir was synthesized , purified , capsuled in 100 mg doses , and packaged into blister packs at a GMP facility , Dalton Pharma Services ( Toronto , Ontario , Canada ) . USP pregelatinized maize starch was prepared in identical capsules and blister packs for placebo . Patient samples were obtained from a randomized , double-blind and placebo-controlled proof-of-concept trial ( CELADEN ) in Singapore to assess the efficacy and safety of celgosivir in patients with dengue fever [17] . The Trial Protocol is included here again as S1 Text . The Consort flowchart showing the CELADEN Trial Profile was published previously [17] and is included in this manuscript as Fig 1 . The inclusion and exclusion criteria for CELADEN Trial was described previously and is included here as S1 Fig . The Dengue Duo ( SD Diagnostics ) point-of-care diagnostic kit was used to screen for dengue infection . It consists of two tests , one for serum NS1 and the other for dengue immunoglobulins ( IgM and IgG ) . Fifty dengue patients identified by Dengue Duo Diagnostics and with fever >38°C for less than 48 hr were randomly assigned ( 24 to celgosivir , 26 to placebo ) , of which 14 had DENV-1 , 32 had DENV-2 and 4 had DENV-3 infection . The patients were housed in the clinical trial facility at the Singhealth Investigational Medicine Unit for five days during the acute illness period and returned to the study center for follow-up examinations on study days 7 , 10 and 15 . Immunoglobulin M antibody capture or dengue IgG indirect ELISA ( Panbio Diagnostics , Providence RI ) were performed on baseline samples to identify primary and secondary infection status as described previously [17] . The study was approved by the Singapore Health Services’s Centralized Institutional Review Board ( CIRB Ref:2012/025/E ) —and monitored independently by the Singapore Clinical Research Institute ( SCRI ) , a publicly funded clinical research organization ( CRO ) [17] . All subjects between the ages of 21–65 years provided written consent to participate in the inpatient trial as described in detail in our previous publication [16] . It should be noted that all deviations to the study progress during the trial was provided to the Health Sciences Authority of Singapore and also to an independent data safety monitoring board . The CELADEN trial was registered with www . ClinicalTrials . gov , number NCT01619969 . The method for determination of viral RNA by quantitative polymerase chain reaction ( qPCR ) is described elsewhere [17 , 22] . Viral load reduction ( VLR ) was defined as the difference between viremia ( determined by qPCR ) at enrollment and at each study day . The primary endpoint was the mean VLR between study days 2 and 4 inclusive ( VLR2-4 ) , which is mathematically equivalent to the area under the curve ( AUC ) of the VLR curve between those days divided by the number of days . A plaque assay was also performed to measure viremia , as described in [22 , 25] . The plaque assay measures the number of virus particles capable of productive infection , unlike the qPCR assay that measures RNA of all viral particles , regardless of replication competency . Hematology and clinical chemistry assays were standard assays performed in the clinical diagnostic laboratories of Singapore General Hospital . A sandwich-based ELISA assay using DENV-2 protein for capture and goat anti-human antibody conjugated with horseradish peroxidase as the detecting antibody . Patient sera and positive and negative human control sera were diluted 1:100 in serum diluent prepared using PBS with 0 . 5% nonfat dry milk . The plates were developed with tetramethylbenzidine as the substrate [26] . PK samples were collected prior to the first dose and at 23 , 25 , 47 , 49 . 5 , 71 , 74 and 95 hr after the first dose , representing 4 trough and 3 peak levels . Urine was collected in 12-hr periods , the volume recorded , and a sample reserved for drug assays . Celgosivir and castanospermine concentrations were determined on a LC/MS/MS system ( Hewlett Packard 1100 with Applied Biosystems API 3200 MSMS ) . N-dodecyl-deoxynojirimycin was the internal standard for celgosivir , and 6 , 7 dihydroxyswainsonine was the internal standard for castanospermine . The LC column was a Waters Atlantis HILIC Silica column; the mobile phase consisted of 23% 20 mM Ammonium acetate pH 5 . 0 in pump A and 77% acetonitrile in pump B for 7 min then changed to 60% in pump A for 3 . 9 min . The LC eluent was connected directly to a Sciex API 3200 triple-quadrupole MS equipped with electrospray ionizing ion source without splitting . The quadrupoles were operated with unit resolution in the positive ion multiple reaction monitoring mode . The assay had a lower limit of quantitation ( LLoQ ) of 10 ng/mL and a linear response over the range of 10 to 2000 ng/mL . Concentration-time profiles were analyzed with Phoenix WinNonlin v6 . 3 using a one-compartment model with first-order absorption and elimination ( Model 3 ) and weighting by the inverse of predicted concentration . Two samples were excluded from PK analysis: one was a trough sample that had a concentration nearly 5 times higher than the other trough samples from the same patient . The other was a peak sample that had a concentration more than 10 times lower than the other peak samples from the same patient . Both concentrations were more than 3 standard deviations from the mean of the other patients’ samples at the same time point . Body weight , age , sex , and renal clearance were evaluated as covariates . Renal clearance was estimated from serum creatinine levels , patient demographics , and the Crockcroft-Gault formula . We performed simulations of several dosing regimens being considered for a follow-on trial of celgosivir in adult dengue subjects . These were performed with Model 3 using fitted parameters for the population of patients in the CELADEN trial . The primary virological endpoint of the trial was the mean virological log reduction between study days 2 and 4 ( VLR2-4 ) . To explore the relationship between VLR2-4 and PK , patients’ exposure ( Cmin , Cmax or AUC ) was subdivided into 3 quantiles: zero exposure ( placebo ) , low drug exposure ( lower quantile ) and high drug exposure ( upper quantile ) , and the distribution of VLR2-4 in each group was graphed . Patient plasma samples , drawn at 24 , 48 , 72 and 120 hr after the first dose , were analyzed for 41 cytokines and chemokines using the Human cytokine panel 1 ( Merck Millipore cat no . MPXHCYTO-60K-14 ) as per manufacturer’s protocol . Briefly , samples were diluted 1:2 . 5 with RPMI + 10% fetal calf serum and loaded onto a Millipore Multiscreen BV 96-well filter plate . Serial dilutions of cytokine standards were prepared in parallel and added to the plate . Milliplex Cytokine beads were vortexed for 30 sec . and 25 μl was added to each well with culture supernatants . Samples were then incubated on a plate shaker at 600 rpm in the dark at room temperature for 2 hr . The plate was applied to a Millipore Multiscreen Vacuum Manifold , washed twice with 50μl of assay buffer ( PBS , pH7 . 4 , 1% BSA , 0 . 05% Tween20 , 0 . 05% sodium azide ) , and each well resuspended with 75μl assay buffer . Twenty-five μl of biotinylated Anti-Human Multi-Cytokine Reporter was added to each well . The plate was incubated on a plate shaker at 600rpm in the dark at room temperature for 1 . 5 hr . Streptavidin-Phycoerythrin was diluted 1:12 . 5 in assay buffer , and then 25μl was added directly to each well . The plate was again incubated on a plate shaker at 600rpm in the dark at room temperature for 30 minutes . Twenty-five μl of stop solution ( 0 . 2% ( v/v ) formaldehyde in PBS , pH 7 . 4 ) was added to each well and incubated at room temperature for 5 minutes . The plate was then applied to the vacuum manifold and each well resuspended in 125μl assay buffer and shaken for 1 minute . Assay plates were read with Flexmap 3D systems ( Luminex Corp , Austin , TX , USA ) . Cytokine concentrations were calculated using Bio-Plex Manager 6 . 0 software with a 5 parameter curve fitting algorithm applied for standard curve calculations . Correlations between log10 plaque forming units ( pfu ) and log10 viral RNA copy numbers measured using qPCR at each day were evaluated by the Pearson correlation coefficient . Fisher’s exact test was used to compare the number of negative plaque assays ( zero pfu ) between celgosivir and placebo groups by day and between primary and secondary dengue . Student’s t-test for two independent samples was used to compare VLR2-4 between primary and secondary dengue . The t-test was also used to compare celgosivir and placebo-treated secondary dengue patients’ platelet nadirs and differences between the maximum minus the minimum hematocrit . Covariate dependencies on PK parameters were evaluated by linear regression and deemed statistically significant if the 95% confidence interval of the slope excluded zero . The Kruskal-Wallis test was performed to evaluate the relationship between exposure ( quantiles of Cmin , Cmax , and AUC ) and VLR2-4 . Two-way repeated ANOVAs were used to analyze the effects of time and treatment on the Luminex analyte concentrations . Graphing and statistical evaluations were performed with R version 2 . 15 . 2 , Graphpad Prism v 5 . 0d or SAS statistical software . Next-generation whole-genome sequencing of DENV samples isolated from blood at study days 1 , 2 , 3 and 4 was performed as described previously [27 , 28] . Viral RNA was extracted from human sera using the QIAamp Viral RNA Mini Kit ( Qiagen ) , and cDNA synthesis for each serotype was carried out with the Maxima H Minus First Strand cDNA Synthesis Kit ( ThermoFisher Scientific ) using serotype-specific primers designed to bind to the 3’ end of the viral genome . The entire DENV genome was PCR-amplified in 6 overlapping fragments , each approximately 2 kb in length with the PfuUltra II Fusion HS DNA Polymerase ( Agilent Technologies ) . Table 1 lists the number of patient samples that were extracted and Table 2 lists the primer sequences used for the dengue serotypes . PCR products were gel-extracted and purified using the Qiagen Gel Extraction Kit ( Qiagen ) . For each sample , equal amounts of all PCR-amplified fragments were combined and sheared on the Covaris S2 sonicator ( Covaris ) to achieve a peak size range of 100–300 bp ( shearing conditions: duty cycle—20%; intensity—5; cycles per burst—200; time—110 seconds ) . Samples were purified with the Qiagen PCR Purification Kit ( Qiagen ) and their quality assessed on the Agilent 2100 Bioanalyzer with a DNA 1000 Chip ( Agilent Technologies ) . Library preparation was performed with the KAPA Library Preparation Kit ( KAPA Biosciences ) . After end-repair , A-tailing , and adapter ligation , ligated products in the 200–400 bp range were gel-extracted with the Qiagen Gel Extraction Kit ( Qiagen ) . Samples were subjected to 14 PCR cycles to incorporate multiplexing indices and quantified using the Agilent Bioanalyzer . Samples were then diluted to 10 nM and pooled . Paired-end multiplexed sequencing ( 2 x 76 bp reads ) [29] of libraries was performed on the Illumina HiSeq ( Illumina ) at the Genome Institute of Singapore . FastQC [FastQC: A quality control tool for high throughput sequence data ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) ] was used to check the quality of the reads from Illumina-generated FASTQ files . Trim Galore ! was used to trim and filter the reads with minimum quality cutoff of 20 and minimum read length of 35 bp . The consensus genome for the sample at time point one was generated using the bam2cons_iter . sh script from the Viral Pipeline Runner ( ViPR , available at https://github . com/CSB5/vipr ) , which uses the Burrows-Wheeler Aligner to perform iterative mapping of paired-end reads to the reference [29] . DENV genome of samples isolated from later time points was then mapped against the consensus , generated based on the maximum frequency of the nucleotide at a given position , using BWA-MEM v0 . 7 . 5 aligner . Picard Tools v1 . 95 [30] were used to remove PCR duplicates and base calibration , and indel realignment was done by GATK v3 . 3 . SNVs for each sample were detected using LoFreq2 software [31] , which incorporates base-call quality scores as error probabilities into its model to distinguish SNVs from the average sequencing error rate , and assigns a p-value to each position ( Bonferroni-corrected p-value > 0 . 05 ) . As LoFreq has previously been applied to DENV datasets , and its SNV predictions on these datasets have been experimentally validated down to 0 . 5% allele frequency [27 , 32] , we filtered the SNPs with a threshold of coverage of >1000 and allele frequency of >0 . 5% . SNVs that were located within primer sequences were discarded . An in-house R script was used to group the samples and count the number of SNVs occurring at each genomic position . The dN/dS analysis , mutation density ( SNVs per 100 bp ) and all statistical tests were also performed in R . For identification of mutational cold spot , SNVs from groups of samples were pooled and then scanned for windows ( minimum size of 40 ) with a depletion of SNVs ( binomial test; Bonferroni corrected p-value < 0 . 05 ) . Full genomes of DENV strains isolated from treatment and placebo patients were analyzed for each DENV type independently , along with DENV genomes originating from Asia that were retrieved from NCBI GenBank . Multiple sequence alignments were performed using MAFFT [33] . Maximum likelihood phylogenetic trees were constructed using RAxML applying the General time reversible ( GTR ) model with gamma distributed rates across sites ( GTR+γ ) [34] . Trees were visualized and annotated using FigTree v1 . 5 ( http://tree . bio . ed . ac . uk/software/figtree/ ) .
All the 50 patients recruited to the study who tested positive on the Dengue Duo NS1 screening kit and recruited to the study had virologically confirmed dengue [17] ( Fig 1 ) . On days 1 , 2 and 3 , there was strong and significantly positive correlation between viremia measured by qPCR and the plaque assay ( Fig 2A–2C ) although by day 3 , only 66% of the samples had positive viremia by the plaque assay . By day 4 , the correlation was weaker , but still significant ( Fig 2D ) , with only 34% of samples having positive viremia by the plaque assay compared to 98% by qPCR . By day 5 , only two samples were positive for dengue virus by plaque assay ( Fig 2E ) . There was no treatment effect when comparing the frequency of negative plaque assay results for celgosivir and placebo groups . The kinetics of viral clearance ( qPCR ) was faster in secondary dengue than in primary dengue ( Fig 3 ) . VLR2-4 for patients with secondary dengue ( -2 . 25 ± 1 . 04 ) was significantly lower ( p-value 0 . 002 ) than for those with primary dengue ( -1 . 46 ± 0 . 70 ) ; the difference ( 95% CI ) was 0 . 79 ( 0 . 30 , 1 . 29 ) . On days 3 and 4 , there were a significantly higher number of negative plaque assays in secondary dengue patients compared to primary dengue patients ( Day 3: p-value<0 . 001 , OR = 10 . 8 , 95% CI 2 . 75 to 42 . 4; Day 4: p-value = 0 . 029 , OR = 5 . 79 , 95% CI 1 . 13 to 29 . 6 ) . Celgosivir was rapidly converted to castanospermine in vivo , presumably by endogenous esterases , as expected from previous animal and human studies [19]; 83% of the samples had no quantifiable levels of parent drug above the lower limit of quantification . Observed mean castanospermine Cmin and Cmax were 430 ng/mL ( 2 . 23 μM ) and 5730 ng/mL ( 30 . 2 μM ) , respectively ( Table 2 ) . Mean ( ± sd ) oral clearance ( CL/F ) was 132 ( ± 28 ) mL/min . The mean volume of distribution ( V/F ) was 28 . 2 ( ± 9 . 1 ) L , and half-life was 2 . 5 ( ± 0 . 6 ) hr . Using PK parameters from compartmental modeling , the predicted mean plasma concentration profile during the entire dosing period is shown in Fig 4 . The data showed that the actual drug concentrations remained above 400 ng/mL during the dosing period when mean viremia levels started from greater than 6 logs and declined by more than 4 logs ( Fig 3 ) . The target concentration of 400 ng/mL was the trough concentration in mice treated with celgosivir using a dosing regimen ( 50 mg/kg bid for 5 days ) that protected all animals from otherwise lethal dengue infection [22] . Body weight , age and sex were not significant covariates on clearance or volume of distribution ( Fig 5A–5D and 5F ) . On the other hand , castanospermine clearance and renal ( creatinine ) clearance were significantly correlated ( Fig 5E ) . Drug in the urine was >99% castanospermine , and urinary recovery was 80% ± 17% . Although only one dose was evaluated , a 3-fold range of exposure was obtained in observed Cmin and a two-fold range in Cmax and predicted AUC ( Table 3 ) . There was a subtle trend for lowered viremia ( VLR2-4 ) with increasing Cmin ( Fig 6 ) that was not evident with AUC . This is in line with the concept of maintaining a serum concentration above a minimum inhibitory concentration for optimizing antimicrobial/antiviral drug therapy . Although there was a trend between VLR2-4 and quantiles of exposure of Cmin , and Cmax , neither one was significantly correlated ( Fig 6 ) . The PK parameters obtained from model fitting were used to predict Cmin , Cmax and AUC for other dosing regimens ( Fig 7 ) . Simulations predicted that 150 mg given 8 hourly ( total daily dose of 450 mg ) would increase steady-state Cmin by 2 . 4-fold , decrease steady-state Cmax by 20% and increase daily AUC by only 13% compared to the regimen used in CELADEN . Doses of 200 mg every 8 hr or 150 mg every 6 hr ( total daily dose of 600 mg ) would achieve increases in steady-state Cmin by 3 . 2- and 4 . 5-fold , respectively with only a modest 33% increase in daily AUC . The profiles for platelet count and hematocrit in the celgosivir and placebo groups are illustrated in Fig 8A and 8B . These show that the curves for the treatment and control groups are almost exactly superimposed . However , because the celgosivir group had a much higher proportion of secondary dengue ( 13/24 = 54% ) than the placebo group ( 5/26 = 19% ) , and secondary dengue patients typically have a greater decrease in platelets and higher increase in hematocrit ( 7 , 8 , 21 and 22 ) , the comparable profiles are suggestive that celgosivir had some benefit in secondary dengue . When only secondary dengue cases were compared , a trend toward better outcomes in the celgosivir treated group is discernible ( Fig 8C and 8D ) . Platelet nadir and the difference between the maximum and minimum hematocrit for secondary dengue patients are shown in Fig 8E and 8F . The differences were not significant but are in the direction of benefit for celgosivir . Due to the small numbers of patients in this subgroup , caution is warranted not to over-interpret these trends . Numerous studies have demonstrated that the magnitude and quality of systemic immune response during febrile dengue illness is linked to pathological disease progression . It has been hypothesized that the expression of proinflammatory cytokines from innate and adaptive immune effector cells plays a critical role in the cell activation , apoptosis , and vascular permeability characteristics of dengue hemorrhagic fever [35 , 36] . A comprehensive analysis of circulating cytokines and chemokines was undertaken to assess the systemic impact of celgosivir treatment on the immune status of acutely infected patients . Fig 9A shows the concentration of circulating analytes at all time points for all patients in the trial . Longitudinal analysis of plasma cytokine concentrations ( Fig 9B ) demonstrated that drug treatment led to a qualitative shift in circulating cytokine and growth factor concentrations during the course of infection . Significant increases in IL-13 and PDGF-AA concentrations were observed in celgosivir-treated patents relative to placebo-treated patients indicating an increase in Th2 polarizing cytokines with time in this group . In support of this interpretation , drug treatment led to a corresponding decrease in circulating levels of IFNγ and TGFα . This qualitative shift from a Th1 to Th2 profile in patients receiving treatment may be reflective of a larger shift in T-cell polarization during the course of treatment as observed in other antiviral treatments [37] . Due to the limited number of samples for DENV3 , NGS analysis was performed only on DENV1 and DENV2 isolates . Overall , our deep sequencing data shows positional variance throughout the DENV genome for placebo and celgosivir-treated patients ( S2 and S3 Figs ) . Single nucleotide variants ( SNVs ) in each DENV population were called with the LoFreq variant calling algorithm . As in other studies [28 , 31] , the majority ( 80% average across all samples ) of SNVs identified in our data set were transitions ( S4 Fig ) . For both DENV1 and DENV2 , the bulk of the SNVs detected were present evenly in both treated and untreated populations , indicating some level of non-specific selective pressure . However , significant difference in selection pressure or genetic drift was observed between the different treatments for any of the viral genes ( S1 Table ) . Interestingly , the average SNV density for each dengue gene was lower for celgosivir-treated patients compared to placebo-treated control ( S2 Table ) . No mutational hotspots were detected in any of the conditions for both DENV serotypes . Consistent with a previous DENV1 study [32] , cold-spots were detected in NS3 of DENV1 from placebo-treated samples ( P-tp3 ) . More cold-spots were also detected for celgosivir-treated samples ( C-tp1 and C-tp2 ) than placebo samples ( P-tp1 and P-tp2 ) for DENV1 ( S5A Fig ) . For DENV2 , coldspots were detected mostly in NS3 and NS5 for placebo-treated samples . For celgosivir-treated samples , coldspots show a different profile from that of DENV1 and only exists in the NS5 region ( C-tp1 and C-tp3 ) ( S5B Fig ) . Phylogenetic analysis of the whole genome sequences of treatment and placebo samples revealed the co-circulation of multiple DENV lineages in our study ( Fig 10 ) . In particular , for each DENV serotype , the treatment and placebo samples were derived from multiple genotypes . Five independent lineages of DENV 2 were detected in this study , which belonged to two major DENV 2 genotypes ( Cosmopolitan and Asian ) . While the majority of DENV 2 samples were of the Cosmopolitan genotype we observed that they were derived from lineages that have diverged many years ago , however both these lineages have been previously detected in Asia during 2004–2012 . Two lineages of DENV 1 and DENV 3 were also detected in our samples , respectively , that belonged to different genotypes . Samples obtained from treatment and placebo patients were inter-dispersed among all lineages–represented by blue and red labeled strains in each of the lineages , except in DENV 1 genotype IV and DENV 3 Genotype I where only one sample type was detected ( Fig 10 ) . We were unable to assess the statistical significance of sample type for each of the lineages due to small samples numbers among the lineages . However , these results suggest that the high genetic diversity of dengue may be a confounding factor in the endpoint analysis of this drug trial . Currently , it is not known if the genetic changes prevalent between the different lineages would have an effect on the action of celgosivir , or related anti-dengue drugs in early phase development such as UV-4 , which also target ER alpha glucosidases [38] .
Although CELADEN did not meet its primary endpoint of lowering viremia or fever , assessment of the PK and pharmacodynamics provides valuable insights and lessons for the design of future dengue drug trials , not only of celgosivir but of other dengue antivirals as well . An important objective of early phase clinical trials is to identify dose regimen ( s ) that are safe and tolerable and that show some evidence of pharmacological activity . Previous clinical experience in hundreds of subjects ( healthy volunteers , HIV and HCV patients ) established that celgosivir’s maximally tolerated dose ( MTD ) is 400 mg qd ( once a day ) for 12 weeks [20] . A small trial in HCV patients ( N = 43 ) reported asymptomatic , reversible increases in creatine kinase ( 19% for 200 mg qd , 42% for 200 mg bid and 80% for 400 mg qd ) , suggesting that a divided dose would be better tolerated than a single daily dose of the same amount of total drug [19] . Among CELADEN patients who received celgosivir , the mean observed trough castanospermine concentration was 430 ng/mL ( 2 . 3 μM ) . This was comparable to the corresponding Cmin in the mouse model ( 400 ng/mL ) where 50 mg/kg bid for 5 days protected all animals from dengue-related death when treated immediately after infection , and was far more effective than 100 mg/kg qd [22] . When treatment in mice was delayed by 24 and 48 hr post-infection , survival rates were lower at 75% and 50% , respectively [21] . In dengue patients , symptoms do not arise until several days after infection . Therefore , it may be necessary to achieve higher trough drug concentrations to overcome the delay in treatment after becoming infected . By decreasing the dosing interval from 12 to 8 or 6 hr , PK simulations demonstrate that 2 . 4- to 4 . 5-fold increase in Cmin are achievable with only a 13% to 33% increase in total dose . Indeed , AG129 mice treated at peak viremia with a four-times-daily regimen had a significantly reduced viremia compared to untreated animals in a mouse viremia model using clinical isolates of DENV2 [23] . Drug clearance was significantly correlated with creatinine clearance , and 80% of the drug was recovered in the urine , indicating that renal clearance is the dominant elimination pathway , as was previously reported from animal studies [21] . While all patients in the trial had serum creatinine levels well below the exclusion criterion of 165 μmol/l , retrospective creatinine clearance calculations indicated that one patient who had a serum creatinine of 114 μmol/ fell in the moderate renal impairment category [39] . Although this issue did not specifically result in any AE or SAE in CELADEN , our current PK analysis suggests a greater awareness of renal function for future trials . DENV cleared significantly more rapidly in patients with secondary dengue compared to primary dengue , confirming a previous finding [40] . Future trials of antiviral drugs for dengue drugs that use viremia as an endpoint may need to take this into account when calculating trial size in order to achieve adequate power . Although an ideal case Target Product Profile [41] for a dengue drug should be efficacy in both primary and secondary dengue , it is possible that for some early phase proof-of-concept studies , previous infection status could either be an inclusion criteria or a stratification variable to achieve balanced groups with respect to this parameter . In the clinical management of dengue , platelet count is closely monitored for signs of incipient bleeding and progression to more serious DHF . Hemoconcentration is monitored through changes in hematocrit to decide whether to administer intravenous fluids and which type of fluid should be given [42] . For Proof of Concept and early stage human clinical trials , it is worth considering other parameters such as imaging leakage as an alternative early biomarker for clinical outcome in assessing potential drugs to treat dengue fever [43] . By gathering evidence to promote these biomarkers or hematology laboratory results to surrogate or valid clinical endpoints , it will become easier to evaluate other dengue drugs in the future . The NGS analysis to obtain critical information on nucleotide changes did not reveal any suggestion that rapid development of resistance to celgosivir is an explanation for the low efficacy observed in our trial . However it does reinforce the need for a panel of strains that are representative for the various genotypes of DENV serotypes 1–4 to be examined as part of preclinical evaluation for efficacy , especially when the drug targets a host enzyme . Interestingly recent in vitro biochemical studies have confirmed that celgosivir’s mechanism of action is via inhibition of ER α-gulcosidases and also showed that the antiviral activity of celgosivir in primary human macrophages in vitro , inhibits DENV secretion with an EC50 of 5 μM [44] . These studies do lend support to celgosivir as a potential drug for detailed clinical analysis . Celgosivir’s efficacy on zika virus ( ZIKV ) has not been reported but given its close relationship with dengue virus within the flavivirus membership , it is possible that it may inhibit ZIKV using a similar mechanism . ZIKV , like DENV is spread by Aedes aegypti mosquitoes but its current association with widespread numbers of Guillain-Barre syndrome and microcephaly in babies born to mothers infected during the first trimester of pregnancy is unique and a cause of much concern [45] . Several lines of evidence suggest a trend towards a pharmacological effect of celgosivir in dengue patients . Celgosivir patients with secondary dengue had a slightly less decline in platelets . Only 1 of 13 ( 7 . 6% ) of the secondary dengue patients treated with celgosivir had a platelet nadir below 20 , 000/μL compared to 2 of 5 ( 40% ) in the placebo group . Secondary dengue patients treated with celgosivir also had a smaller difference between maximum and minimum hematocrit than secondary dengue patients in the placebo group . Other evidence of a trend towards pharmacologic activity include ( i ) the significantly lower level of TNFα in celgosivir-treated patients compared to placebo controls during the early stage of treatment , independent of prior infection status and ( ii ) enhanced NS1 clearance in secondary infection following celgosivir treatment [17] . In conclusion , lessons derived from the analysis of the secondary endpoint data of the CELADEN trial are a useful guide for future dengue drug trials . PK analyses and simulations show that alternate dosing regimens can achieve several-fold increases in steady-state trough concentrations with the same or modestly higher total dose . The difference in viremia kinetics for primary and secondary dengue underscores the need for balanced arms with respect to infection status in future trials . Pathological endpoints such as HCT and vascular leakage may be potentially better surrogate endpoints than viremia in assessing activity of dengue antivirals . We note that CELADEN was not powered to determine statistically significant differences in secondary endpoints . Such analyses are subject to inflated Type I error and merit cautious interpretation . However taken together , the trends in pharmcological activity are consistent with increasing celgosivir exposure . Since PK simulations of further divided doses suggest a potentially large increase in Cmin with a modest increase in AUC , which is supported by recent animal data [23] , we plan to evaluate a revised dosing regimen of celgosivir in a Phase 2a to start in 2016 ( NCT02569827 ) .
|
Dengue virus is currently threatening 40% of the world’s population . An approximately 60% efficacious vaccine has been registered for use in Mexico , Brazil , the Philippines , Paraguay and El Salvador , but there are no approved antiviral treatments available . We have shown that celgosivir , an endoplasmic reticulum alpha glucosidase inhibitor , has submicromolar activity against all 4 serotypes of dengue virus ( DENV ) and also efficacious in mouse model of infection . The strong preclinical pharmacology motivated the conduct of an investigator-initiated , Phase 1b randomized , double-blind , placebo-controlled trial of celgosivir in 50 adult dengue patients . Although the trial did not meet the primary endpoints of lowering viremia or fever , the safety profile of the drug prompted extended hematological , pharmacokinetic , immunological and viral sequence profiling . Here we report several non-significant trends of pharmacological effect of celgosivir on platelet count , hematocrit , and NS1 clearance in secondary dengue patients . In addition , pharmacokinetic modeling identified an alternate dosing regimen that is predicted to achieve a 4 . 5-fold increase in minimum drug concentrations during treatment ( Cmin ) with only a modest increase in overall dose . A new Phase 2a clinical trial with an optimized dosing regimen of celgosivir ( ClinicalTrials . gov number NCT02569827 ) is scheduled to start in the latter part of 2016 .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"blood",
"cells",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"cytokines",
"body",
"fluids",
"dose",
"prediction",
"methods",
"animal",
"models",
"of",
"disease",
"blood",
"counts",
"immunology",
"tropical",
"diseases",
"microbiology",
"developmental",
"biology",
"pharmaceutics",
"molecular",
"development",
"platelets",
"neglected",
"tropical",
"diseases",
"research",
"and",
"analysis",
"methods",
"animal",
"models",
"of",
"infection",
"infectious",
"diseases",
"animal",
"cells",
"animal",
"studies",
"dengue",
"fever",
"hematology",
"immune",
"system",
"hematocrit",
"viremia",
"anatomy",
"blood",
"cell",
"biology",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"viral",
"diseases",
"drug",
"therapy"
] |
2016
|
Extended Evaluation of Virological, Immunological and Pharmacokinetic Endpoints of CELADEN: A Randomized, Placebo-Controlled Trial of Celgosivir in Dengue Fever Patients
|
Escherichia coli O157∶H7 is a human enteric pathogen that causes hemorrhagic colitis which can progress to hemolytic uremic syndrome , a severe kidney disease with immune involvement . During infection , E . coli O157∶H7 secretes StcE , a metalloprotease that promotes the formation of attaching and effacing lesions and inhibits the complement cascade via cleavage of mucin-type glycoproteins . We found that StcE cleaved the mucin-like , immune cell-restricted glycoproteins CD43 and CD45 on the neutrophil surface and altered neutrophil function . Treatment of human neutrophils with StcE led to increased respiratory burst production and increased cell adhesion . StcE-treated neutrophils exhibited an elongated morphology with defective rear detachment and impaired migration , suggesting that removal of the anti-adhesive capability of CD43 by StcE impairs rear release . Use of zebrafish embryos to model neutrophil migration revealed that StcE induced neutrophil retention in the fin after tissue wounding , suggesting that StcE modulates neutrophil-mediated inflammation in vivo . Neutrophils are crucial innate effectors of the antibacterial immune response and can contribute to severe complications caused by infection with E . coli O157∶H7 . Our data suggest that the StcE mucinase can play an immunomodulatory role by directly altering neutrophil function during infection . StcE may contribute to inflammation and tissue destruction by mediating inappropriate neutrophil adhesion and activation .
Enterohemorrhagic Escherichia coli ( EHEC ) of serogroup O157∶H7 is an emerging human diarrheal pathogen associated with numerous food-borne outbreaks in the U . S . Infection of the colon by EHEC causes mild diarrhea that proceeds to bloody colitis and can be acquired following ingestion of fewer than 100 organisms [1] . In 15% of childhood cases , EHEC gastroenteritis progresses to the more serious hemolytic uremic syndrome ( HUS ) , characterized by red blood cell fragmentation , low platelet count , and acute renal failure . HUS can cause severe kidney damage and have outcomes ranging from full recovery to death [2] . EHEC virulence factors include the locus of enterocyte effacement , which confers the ability to form attaching and effacing lesions , and the phage-encoded Shiga toxin , which causes termination of protein synthesis in the microvascular endothelium leading to cell death and tissue destruction [3] . Efforts to study the pathogenesis of EHEC are complicated by the lack of a suitable animal model that fully recapitulates human disease . Although models of EHEC-induced diarrhea in rabbits or injection of purified components leading to HUS-like symptoms in mice and baboons have been described , no model exists that follows the natural progression from EHEC infection to development of HUS [4]–[6] . The immune response is involved in the development of HUS , but it is less clear how the interaction between bacteria and the host immune system influences early EHEC disease progression . Colonic damage can include hemorrhage and edema within the lamina propria with focal necrosis and neutrophil influx [7] , but leukocyte infiltration into the intestinal lumen occurs in only ∼50% of EHEC cases and is rarely severe [2] . Patients who progress to HUS demonstrate clear indicators of an inflammatory response with neutrophil involvement , and increased circulating blood leukocytes are correlated with development of disease [8] , [9] . Increased levels of interleukin-8 ( IL-8 ) and complexed elastase are found in the blood [10]–[12] . Children with HUS demonstrate infiltration of monocytes and neutrophils into the kidney glomeruli [13] , [14] . The onset of HUS occurs 5–7 days after initiation of diarrhea , and it has been suggested that inappropriate immune cell activation in the gut could lead to renal pathology and explain the lag time in the development of disease [15] . The majority of EHEC isolates in the United States carry the 92 kb pO157 virulence plasmid . Carriage of the plasmid is associated with increased incidence of hemorrhagic colitis , HUS , and colonization of the bovine recto-anal junction mucosa [2] , [16] , [17] . pO157 encodes StcE ( Secreted protease of C1-esterase inhibitor ) , a type II-secreted , 95 kDa zinc-dependent glycoprotease that is produced during EHEC infection [18] , [19] . StcE recognizes O-glycan-induced protein conformations in order to cleave the protein backbone of mucin-type glycoproteins [L . Walters , unpublished , [18] , [20]] . Mucins are large glycoproteins that coat numerous surfaces in the body and play important roles in cell-cell interactions within the immune system . CD43 and CD45 are large mucin-type glycoproteins expressed exclusively and abundantly on the surface of nearly all hematopoeitic cells including neutrophils [21] , [22] . CD45 is a protein tyrosine phosphatase that can exist as several isoforms which vary primarily in the length of the terminal O-glycosylated portion of the extracellular domain . Dephosphorylation of Src family kinases by CD45 regulates the signaling threshold in T cells [21] . Little is known about CD45 function on neutrophils , but it may modulate chemotaxis and the oxidative burst [23] , [24] . CD43 possesses a large extracellular domain that contains 60–80% of its total molecular mass in sialylated O-glycans . Extensive glycosylation causes the protein to assume a rod-like conformation that protrudes ∼45 nm from the cell surface [25] . The combination of steric hindrance , negative charge , and relative abundance on the cell surface provides an anti-adhesive force [26] , and CD43-deficient leukocytes demonstrate increased adhesion in vitro and in vivo [22] , [27]–[29] . The intracellular domain of CD43 interacts with cytoskeletal linker proteins , allowing neutrophils and T cells to cluster CD43 at the rear of the cell , or “uropod” , during adhesion and migration . This removes anti-adhesive force from the leading edge to promote adhesion and/or migration , while providing a useful anti-adhesive force at the uropod [30]–[34] . In this study we report the interaction of the StcE protease with CD43 and CD45 on the neutrophil surface . StcE altered neutrophil function via both cleavage-dependent and cleavage-independent effects . Proteolytic activity of StcE led to increased neutrophil oxidative burst production , while binding of StcE was sufficient to increase neutrophil adhesion , leading to impaired migratory capacity . We propose that the interaction between StcE and CD43 prevents the sialoglycoprotein from providing crucial anti-adhesive force , preventing uropod-mediated detachment leading to impaired migration . Oxidative burst production and migration defects leading to increased neutrophil retention could contribute to tissue destruction and inflammation , as well as bacterial evasion of the immune response . Interaction with neutrophil surface mucins by StcE might therefore represent a novel way of dysregulating the immune response during EHEC disease .
StcE binds to and aggregates cells of the Jurkat leukemic T cell line and binds to the undifferentiated HL-60 promyelocytic cell line [18] . To determine if StcE interacted with neutrophils , primary human neutrophils isolated from whole blood and neutrophil-like differentiated HL-60 cells ( dHL-60s ) were treated with StcE or a proteolytically inactive StcE point mutant , E435D [18] , [35] . Binding was detected by flow cytometry using polyclonal anti-StcE antibody . Both wild-type StcE and E435D bound to neutrophils ( Figure 1A ) and dHL-60s ( data not shown ) . We next sought to identify the surface determinant responsible for this interaction . The cell-bound mucin-like glycoproteins CD43 ( leukosialin ) and CD45 ( leukocyte common antigen ) were previously identified as StcE substrates on Jurkat T cells ( unpublished data ) , and we investigated whether these proteins served as ligands for StcE on the neutrophil surface . As we were unable to immunoprecipitate StcE using available antibodies , we performed direct precipitations with StcE fused to a chitin binding domain ( StcE-CBD ) and bound to chitin beads ( CB ) . StcE precipitated both CD43 and CD45 from dHL-60 lysates ( Figure 1B ) . Staining of precipitation reactions for total glycoprotein or total protein did not reveal other significant binding partners , suggesting that CD43 and CD45 are the major relevant binding partners on the neutrophil surface ( Figure S1 ) . The pulldown assays described above were performed in the presence of EDTA , which inhibits the metalloprotease activity of StcE . We next investigated whether CD43 and CD45 could be proteolytically cleaved by StcE . Intact dHL-60s were treated with purified , endotoxin-free StcE or proteolytically inactive E435D and analyzed by immunoblottting . Recognition by the anti-CD43 L10 monoclonal antibody ( mAb ) , which recognizes an extracellular epitope near the N-terminus , completely disappeared upon treatment with StcE but not E435D ( Figure 2A ) . An antibody to the intracellular domain of CD43 , sc-7052 , recognized a StcE-cleaved product of ∼28 kDa ( Figure 2B ) , consistent with a fragment containing the intracellular and transmembrane domains . No L10-reactive cleavage product of any size appeared in the supernatant ( Figure 2A ) , suggesting that the extracellular domain of CD43 was degraded while leaving the intracellular and transmembrane domains intact . Selective cleavage of mucin-like domains is consistent with StcE activity toward other known substrates [18] . Flow cytometric analysis confirmed that presence of the CD43 extracellular epitope recognized by L10 was reduced on the surface of primary human neutrophils following treatment with StcE but not E435D ( Figure 2C ) . We examined potential cleavage of CD45 using two antibodies . The HI30 mAb recognizes an epitope in the CD45 extracellular domain that is found in all isoforms . Treatment of dHL-60s with StcE resulted in a small size shift from 180 kDa to ∼165 kDa in the band recognized by HI30 ( Figure 2D ) . This suggested that StcE cleaved within the very N-terminal O-glycosylated extracellular portion of CD45 . CD45RO is the major isoform present on the neutrophil surface . We next examined cleavage of CD45 using the UCHL1 mAb , which is specific for CD45RO , suggesting that it binds somewhere in the O-glycosylated extracellular region that defines this isoform . Flow cytometric analysis of neutrophils demonstrated no change in staining of total CD45 with HI30 , ( Figure 2E ) , while staining of CD45RO with UCHL1 was reduced following StcE treatment ( Figure 2F ) . This suggested that StcE cleaves within the membrane-distal , O-glycosylated extracellular portion of CD45 but does not degrade the protein further . CD43 and CD45 are found uniquely on the surface of immune cells and may be important for neutrophil function [21] , [22] . To determine if StcE modulates neutrophil function , we examined the effect of StcE treatment on the oxidative burst using a flow-based assay for hydrogen peroxide and superoxide production . Treatment with StcE increased the respiratory burst in the absence of other stimuli ( Figure 3A and 3B ) , while neutrophils treated with proteolytically inactive E435D did not differ significantly from control samples . These findings suggest that StcE modulates neutrophil oxidative function through its protease activity . Both CD43 and CD45 have been suggested to contribute to the neutrophil oxidative burst , and we did not identify the specific mediator of this effect [24] , [33] . Because CD43-deficient leukocytes are more adherent , we examined whether removal of the CD43 extracellular domain by StcE altered the ability of human neutrophils to adhere to the extracellular matrix . Treatment with StcE significantly increased neutrophil adhesion on a fibrinogen ( Fbg ) -coated surface in a dose-dependent manner ( Figure 3C ) . Surprisingly , proteolytic activity of StcE was not required , as binding by E435D induced a similar phenotype . Treatment with either protein resulted in a 2–3 fold increase in adhesion , similar to stimulation with fMLP , which served as a positive control . As proteolytic activity of StcE was not essential to increase adhesion , we sought to confirm that binding activity of E435D was responsible for this effect . Purified protein was heat-inactivated , which eliminates both binding and proteolysis by StcE and controls for the presence of heat-stable contaminants in the protein preparation . Heat-inactived StcE ( hiStcE ) had no effect on neutrophil adhesion . E435D is proteolytically inactive but retains substrate binding activity , unlike hiStcE , indicating that binding is the minimal function necessary to induce neutrophil adhesion . To account for the possibility that StcE and E435D increased adhesion by serving as a bridge between CD43 or CD45 and the extracellular matrix , binding of StcE to Fbg was tested by ELISA . StcE alone did not bind appreciably to Fbg ( Figure S2 ) , suggesting that effects were specific to interaction of StcE and E435D with the neutrophil surface . CD43 is localized to the uropod during neutrophil adhesion and migration , removing its anti-adhesive force from the front of the cell [30] . The observation that both StcE and E435D induced neutrophil adhesion led us to hypothesize that protein binding to CD43 could interfere with its anti-adhesive function even in the absence of cleavage . This hypothesis is consistent with reports that antibodies to CD43 can induce clustering of the protein at the uropod and increase cell adhesion [30] , [32] . We therefore examined how treatment with StcE and E435D affected CD43 localization in adherent neutrophils via confocal immunofluorescence microscopy . Although the anti-StcE antibody exhibited some background staining of vehicle-treated cells , we observed specific and diffuse surface staining of bound StcE ( Figure 4A ) , consistent with flow cytometry data . StcE-treated neutrophils demonstrated reduced membrane staining for the CD43 extracellular domain with L10 , consistent with results of immunoblotting and flow cytometry . No change in localization of the CD43 intracellular domain as detected by sc-7052 was observed , confirming that it remained intact and membrane-associated ( Figure 4B ) . In contrast , treatment of neutrophils with E435D caused readily observable relocalization of CD43 . Both extracellular and intracellular staining revealed clustering of CD43 , and E435D staining was co-localized with the CD43 extracellular domain ( Figure 4 ) . Immunofluorescence staining for total CD45 revealed no change in localization induced by StcE or E435D ( data not shown ) . These data suggest that E435D promotes neutrophil adhesion by clustering CD43 to the uropod and removing its anti-adhesive force from the rest of the cell membrane . To further confirm that E435D bound to native CD43 on the neutrophil surface , we used flow cytometry to investigate the ability of E435D to compete with the L10 mAb for binding to the extracellular domain . Increasing concentrations of E435D led to decreased L10 staining ( Figure S3 ) , indicating that E435D bound to CD43 and blocked antibody accessibility . Reduction of L10 binding by StcE was evident at a much lower protein concentration , demonstrating that cleavage by StcE was more efficient than blocking with E435D in preventing L10 antibody binding . Together these findings suggest that while StcE causes loss of CD43 from the neutrophil surface , E435D clusters CD43 in the uropod , leading to removal of anti-adhesive force from the cell membrane and providing an alternative mechanism by which binding alone can induce neutrophil adhesion . Neutrophil adhesion regulates the development of cell polarity and is required for cells to become migration-competent , but too much adhesion can interfere with migration [36] . We examined how the StcE-induced increase in adhesion affected migratory capabilities of human neutrophils using transwell assays . In the absence of chemoattractant , StcE treatment had no effect on migration across transwell inserts ( Figure 5A ) . In the presence of fMLP as a chemoattractant in the lower chamber , treatment with StcE or E435D caused a significant , 1 . 7-fold reduction in migration across the filters . Consistent with the results of adhesion experiments , binding but not proteolytic activity of StcE was required to inhibit neutrophil migration , and heat-inactivated protein had no effect . Circulating leukocytes that detect chemotactic signals first adhere to the vasculature and then transmigrate across the endothelium in order to reach effector sites . To verify that results obtained with purified Fbg extended to interactions with the endothelium , migration experiments were conducted using monolayers of primary human lung microvascular endothelial cells ( HMVEC-L ) . Both StcE- and E435D-treated neutrophils demonstrated decreased migration across HMVEC-L toward fMLP ( Figure 5B ) . These findings confirmed that the defect in neutrophil migration results from the action of StcE on the neutrophil surface and is not specific to the migratory barrier . We further examined the effect of StcE on neutrophil migration using time lapse microscopy . The adhesion and migration experiments described above were conducted with PBS as a vehicle control . Although the experimental media contained 5% serum , the formal possibility remained that increased total protein concentration was responsible for the effect of StcE or E435D addition . We therefore evaluated migration in the presence of an equivalent concentration of human serum albumin ( HSA ) , and cell behavior was identical to vehicle treatment ( data not shown and Video S1 ) . Neutrophils were treated with HSA , StcE or E435D on a Fbg-coated surface and non-directional migration was imaged in the absence or presence of interleukin-8 ( IL-8 ) . Consistent with adhesion and transmigration assays , StcE-treated and E435D-treated neutrophils demonstrated increased adhesion and were visibly impaired in their migratory capabilities ( Figure 6A and Videos S2 and S3 ) . IL-8 treatment caused an increase in random migration of neutrophils ( Video S4 ) , and both StcE and E435D reduced migration even in the presence of IL-8 ( Figure 6A and Videos S5 and S6 ) and fMLP ( data not shown ) . Quantitation of neutrophil migration was performed on IL-8 treated samples , as these had comparable numbers of adherent cells for control and experimental conditions . Treatment with StcE or E435D significantly reduced neutrophil migration velocity compared to HSA control ( Figure 6B ) . Although they traveled shorter distances over time , StcE- and E435D-treated neutrophils did not appear deficient in production of forward protrusions . However , cells seemed unable to retract their rearward edge and move forward , suggesting that StcE interfered with neutrophil migration by preventing de-adhesion of the uropod . During migration , StcE-treated neutrophils displayed striking morphological differences , with formation of elongated tails at the uropod ( Figure 6B ) . E435D treatment also resulted in elongated morphology , but the phenotype was not as severe . Analysis of cell length confirmed that unstimulated , StcE-treated neutrophils were significantly longer than control cells , while E435D treatment did not cause a significant difference in cell length ( Figure 6C ) . Both StcE-treated and E435D-treated neutrophils exhibited increased cell length in the presence of IL-8 , although the increase was not significant compared to IL-8 stimulated controls ( Figure 6C ) . Together our findings suggest that both StcE and E435D interfere with CD43-based anti-adhesion to alter adhesion and migration , but do so via different mechanisms dependent on binding and cleavage of cell surface mucins ( StcE ) or binding alone ( E435D ) . Zebrafish share many features of the mammalian immune system and have recently been utilized as a model to study neutrophil migration and chronic inflammation [37]–[39] . We took advantage of the zebrafish model to examine the effects of StcE on neutrophil-mediated inflammation in vivo . Zebrafish embryos at 3 days post-fertilization ( dpf ) were wounded in the ventral tail fin in the presence of a bath of StcE protein . At this stage in development , neutrophils are normally located in the caudal hematopoeitic tissue and circulating in the bloodstream . Wounding of the tail fin induces neutrophil recruitment to the wound , and resolution of this response is generally observed after 24 hours [37] . Wounded embryos were fixed after six or 24 hours and localization of myeloperoxidase ( mpo ) , a neutrophil-specific marker , was examined by immunofluorescence microscopy . Differences in neutrophil recruitment were not observed six hours after wounding ( data not shown ) . At 24 hours , treatment with StcE caused visible neutrophil mislocalization ( Figure 7A ) , resembling chronic inflammation recently observed in zebrafish mutants [39] . Neutrophil mislocalization was quantified by counting neutrophils present in the fin , confirming that treatment with StcE caused a significant increase in number of mislocalized neutrophils ( Figure 7B ) . HiStcE had no effect on neutrophil localization . The inflammation-like phenotype observed in zebrafish embryos suggests that StcE may affect neutrophil motility and trafficking in vivo to regulate inflammatory responses .
In this study , we report cleavage of CD43 and CD45 on the human neutrophil surface by StcE , a secreted glycoprotease of E . coli O157∶H7 . We found that StcE exerted both cleavage-dependent and cleavage-independent effects on neutrophil migration and activation , suggesting that it may modulate the immune response during infection . We have previously reported specific cleavage of mucin-type O-glycoproteins by StcE [18] , [20] . CD43 and CD45 share characteristics of these substrates and were the major StcE ligands on the neutrophil surface . We found that StcE cleaved specifically within the O-glycosylated domains of these proteins . The majority of the CD43 extracellular domain is heavily O-glycosylated , and the resultant negative charge and steric hindrance serve to inhibit non-specific cell-cell interactions . StcE degraded this domain , leading to loss of anti-adhesive function . In contrast , the majority of the CD45 extracellular domain is N-glycosylated , and the N-terminal portion , which varies in length by differential splicing , is O-glycosylated . StcE cleaved within this terminal portion , leaving the majority of the protein intact . It is not known what effect the terminal O-glycosylated region has on function of the intracellular phosphatase domain , but cleavage of this region by StcE could promote inhibitory dimerization . In order to fight infection , neutrophils must leave the bloodstream to reach effector sites . Cells first adhere to the vascular endothelium and then migrate across the endothelial cell layer and through the tissues by sensing and responding to chemoattractant gradients [40] . Adhesion is required to initiate this process , but excessive adhesion can inhibit migration [36] . Neutrophils counter the anti-adhesive function of CD43 by shedding it from their surface when they become activated [34] , [41] . The remaining surface-associated CD43 is redistributed to the uropod at the cell rear [30] , [31] , [42] , where it may provide useful anti-adhesive force . Treatment with StcE led to increased neutrophil adhesion that interfered with random migration as well as chemotaxis across filters and endothelial monolayers . Surprisingly , this effect was cleavage-independent . The proteolytically inactive mutant , E435D , caused similar effects to StcE , suggesting that binding was the minimal function required . Our data suggest that removal of CD43 anti-adhesion is the mechanism by which StcE and E435D interfere with migration , although we cannot rule out a role for CD45 . StcE degraded the extracellular domain of CD43 , while E435D clustered CD43 at the uropod and blocked antibody binding to the extracellular domain . Cleavage of CD43 by StcE reduces anti-adhesive force at the promigratory leading edge , but also relieves the anti-adhesive force in the uropod that could promote rear detachment . E435D , like crosslinking antibodies , induces CD43 relocalization to the uropod , reducing anti-adhesive force at the leading edge . Binding of E435D may also mask the negative charge of CD43 and interfere with anti-adhesion at the rear , causing a similar outcome to StcE via a slightly different mechanism . Increased adhesion can be induced by crosslinking antibodies to CD43 [30] , and it is unclear if this results from simple masking of the protein or because antibody binding transduces a pro-adhesive signal . Our results support the hypothesis that the anti-adhesive function of CD43 at the uropod is an important component of cellular migration . Although it has been proposed that CD45 may regulate chemotactic signaling in neutrophils [23] , only recently has it been suggested that CD45 may be directly important for cell adhesion . Shivtiel and colleagues found that bone marrow mononuclear cells deficient in CD45 were more adherent to fibronectin as a result of increased activation of β1 integrins [43] . It is possible that CD45 signaling may be important for neutrophil adhesion and that interaction with CD45 contributes to StcE-mediated effects on adhesion and migration . It is unknown whether StcE cleavage of the terminal O-glycosylated portion of CD45 will affect its signaling capacity . Investigation of the effect of StcE on CD45 signaling is a potential topic for future study . The observation of a cleavage-independent function for StcE in neutrophils parallels findings with C1-esterase inhibitor ( C1-INH ) , another StcE substrate . StcE-cleaved C1-INH retains its ability to inhibit the complement cascade , and E435D is equally capable of potentiating C1-INH function . StcE binds to C1-INH and the bacterial surface simultaneously , increasing the local concentration of C1-INH and protecting the cell from complement-mediated lysis [35] . If binding of StcE to its substrates is sufficient , what is the purpose of proteolytic activity ? Proteolysis may be more important for some substrates than others . For example , cleavage of intestinal mucins might be required to promote colonization , whereas only binding is necessary to affect activity of C1-INH and CD43 . Alternatively , proteolytic activity of StcE might be dispensable for its interaction with all substrates but provide enhanced turnover . StcE displays high affinity and low turnover of C1-INH and MUC7 [44] , and it is possible that proteolysis provides a mechanism for it to detach from one substrate molecule in order to bind another . This would facilitate interaction of the same StcE protein molecule with different substrates , allowing it to interact with multiple glycoproteins during infection . Proteolytic activity was not completely dispensable to modulate neutrophil function , as the activities of E435D and StcE were not identical . Active StcE more potently induced an elongated cell morphology during neutrophil migration than did E435D , suggesting that cleavage was more efficient than binding in preventing rear detachment . This could be explained if binding of E435D to CD43 blocked charge repulsion , but removal of the CD43 extracellular domain by StcE eliminated both charge repulsion and steric hindrance . Furthermore , StcE exerted a cleavage-dependent effect on neutrophil oxidative burst production . The specific substrate mediating these effects was not identified , but CD45 is an attractive candidate because it has previously been reported to modulate production of the oxidative burst [24] . CD43 may also play a role in oxidative burst production , although evidence for this is less clear [33] . Regardless of the exact mechanism , the finding that StcE increased the oxidative burst in a cleavage-dependent manner provides further evidence that it may play an immunomodulatory role during infection . Neutrophils are the first responders to bacterial infection , and pathogens have strategies to combat this response that include inhibition of chemoattractant receptors and inactivation of C5a and IL-8 [45] . To our knowledge , proteolysis of cell surface mucins by StcE represents a novel mechanism for altering neutrophil function . Respiratory burst production and the ability to migrate are crucial capabilities of neutrophils in fighting infection , and alteration of these functions by StcE may lead to a dysregulated immune response during EHEC infection . Neutrophils isolated from children with HUS are more adherent to the vascular endothelium [46] , and StcE could contribute to this phenotype . The effect of StcE on neutrophil migration was both rapid and persistent . Increased adhesion and impaired migration were evident after 30 minutes of StcE treatment . After 210 minutes , fewer StcE-treated neutrophils had migrated across transwell filters , suggesting this was not a transient defect that could be overcome with time . The longevity of the effect further suggests that the interaction between StcE and neutrophils may be physiologically relevant . Whether alteration of neutrophil function leads to pro- or anti-inflammatory outcomes may depend on the site of activity and presence of other stimuli . Neutrophils that remain stuck to the endothelium may be unable to migrate into the intestine in response to infection , and StcE may thus protect the bacteria from clearance by the host immune response . The observed decrease in neutrophil migration across endothelial cell monolayers supports the conclusion that StcE could impair migration out of the vasculature and into the intestine . Inhibition of complement activation at sites of infection by StcE-localized C1-INH would reduce production of the chemotactic C5a fragment , further contributing to a migration defect . Alternatively , neutrophils that are stuck to the endothelium may contribute to inflammation and tissue destruction , as seen in the kidneys during HUS . Zebrafish treated with StcE exhibited neutrophil mislocalization to the tissues that resembled recently described inflammatory mutants , lending credence to this hypothesis . The fact that mislocalization occurred at a late but not early time point after wounding may be explained by accumulation of inflammatory signals from neutrophils that are retained in the tissues , leading to progressive neutrophil infiltration and retention over time . Enhancement of the neutrophil oxidative burst by StcE could further contribute to inflammatory tissue damage in the intestines and during HUS as a result of inappropriate neutrophil retention . The pO157 plasmid is associated with EHEC disease incidence and severity , suggesting that plasmid-encoded genes might contribute to pathogenesis [2] , [3] . The plasmid-encoded StcE protein has a dedicated type II secretion system , is co-regulated with known virulence factors , and is produced in detectable amounts during infection , making it a likely virulence candidate . In a recently described disease model of EHEC in rabbits , mutation of the type II secretion system led to decreased colonization , and the authors conclude that lack of StcE secretion might contribute to this defective colonization [47] . Potential contributions of StcE to virulence including inhibition of complement-mediated lysis and promotion of pedestal formation have been described [18] , [20] . Cleavage of CD43 and CD45 by StcE on the neutrophil surface , leading to increased adhesion , defective migration , increased respiratory burst production , and overall dysregulation of the immune response , may provide another mechanism by which StcE enhances the progression of disease caused by enterohemorrhagic E . coli . Moreover , as CD43 and CD45 are expressed on many cells of the immune system , it is unlikely that the immunomodulatory activity of StcE is limited to neutrophils .
For studies with neutrophils from human subjects , informed consent was obtained from healthy donors at the time of blood draw with approval of the University of Wisconsin-Madison Center for Health Sciences Human Subjects committee . Recombinant StcE and E435D protein were expressed and purified as described [20] . Endotoxin was removed using Endotrap blue columns ( Lonza ) according to manufacturer's instructions . Samples were evaluated by Lonza Endotoxin Testing Services , and endotoxin levels were routinely <1 EU/mL . Dulbecco's Phosphate-Buffered Saline with Ca2+/Mg2+ ( DPBS+/+ , Mediatech ) of an equivalent volume to soluble protein was used as a vehicle control . Heat-inactivated StcE ( hiStcE ) was produced by incubation at 65°C for 10 minutes ( Grys 2006 ) . Interleukin-8 ( IL-8 ) , f-Met-Leu-Phe ( fMLP ) , fibrinogen ( Fbg ) , bovine serum albumin ( BSA ) , and phorbol myristate acetate ( PMA ) were purchased from Sigma . Human serum albumin ( HSA ) was from ZLB Bioplasma AG ( Berne , Switzerland ) . Phycoerythrin ( PE ) -conjugated anti-CD43 clone L10 and FITC-conjugated anti-CD45 clone HI30 were obtained from Caltag ( Invitrogen ) . The anti-CD43 C-terminal domain antibody ( sc-7052 ) was obtained from Santa Cruz Biotechnology . PE-conjugated anti-CD45RO clone UCHL1 was obtained from Ebioscience ( San Diego , CA ) . EM-grade 16% paraformaldehyde ( PFA ) was from Electron Microscopy Sciences ( Hartfield , PA ) and 16% formaldehyde was from Polysciences ( Warrington , PA ) . Peripheral blood neutrophils were purified from human blood using Polymorphprep according to manufacturer's recommendations ( Nycomed , Sheldon , UK ) . HL-60 cells ( ATCC ) were maintained in Iscove's Modified Dulbecco's Medium ( IMDM ) according to ATCC guidelines . HL-60 cells were differentiated ( dHL-60s ) as previously described [48] . Primary human lung microvascular endothelial cells ( HMVEC-L ) were maintained in EGM-2MV media according to manufacturer's instructions ( Lonza ) . For transmigration assays , cells were seeded on collagen-coated 3 µm pore , 0 . 33 cm2 polycarbonate transwell inserts ( Costar ) at a density of 1×105 cells . For cleavage reactions , 1×106 dHL-60s in IMDM were treated with 1 µg/mL StcE , E435D or vehicle control for 30 min at 37°C , 5% CO2 , followed by separation of supernatants and cell pellets . Supernatants were precipitated with 10% trichloracetic acid ( TCA ) on ice . Samples were separated by SDS-PAGE , transferred to nitrocellulose or PVDF and immunoblotted using standard methods [49] . L10 and HI-30 antibodies were used at 1∶500 and sc-7052 was used at 1∶200 . Samples were detected by enhanced chemoluminescence ( ECL ) using Immobilon HRP substrate ( Millipore ) . For direct precipitation of binding partners , StcE was expressed with an uncleavable intein-chitin binding domain fusion tag ( CBD ) in vector pTYB11 and purified using the IMPACT system as previously described [20] . Purified StcE-CBD bound to chitin beads ( CB ) was incubated with 1×107 lysed dHL-60s . CB alone served as a negative control , and samples were processed using standard methods for co-immunoprecipitations [49] . Neutrophils ( 1×106/mL ) were treated with 1 µg/mL StcE or E435D ( unless otherwise indicated ) or vehicle control in EGM-2MV for 30 min at 37°C , 5% CO2 . Cells were blocked in DPBS+/− with 0 . 05% BSA and 0 . 01% HSA and incubated with primary antibody per manufacturer instructions . Samples were read using an LSRII flow cytometer ( Becton Dickinson ) . For the flow oxidative burst ( OB ) assay , neutrophils ( 2 . 5×106 ) were labeled with dihydrorhodamine 123 ( DHR ) ( Molecular Probes ) as described [50] , [51] and treated with 5 µg/mL StcE or E435D , vehicle control , or 30 ng/mL PMA for 30 min at 37°C , 7 . 5% CO2 . Oxidation by H2O2 and O2− of DHR to rhodamine was measured as fluorescence of live cells in the FITC channel . Data were analyzed using FlowJo ( Treestar ) and individual values from five independent experiments were combined and analyzed by one-way ANOVA using GraphPad Prism . Neutrophils ( 1×105/mL ) in EGM-2MV were plated on Fbg-coated ( 10 µg/mL ) glass coverslips for 30 min at 37°C , 5% CO2 in the presence of 20 µg/mL StcE , E435D or vehicle control , and further incubated for 10 min in the presence of 100 nM fMLP . Samples were fixed in 1% PFA in DPBS , post-fixed in 1% formic acid , and permeabilized in 0 . 1% Triton-X 100 ( Sigma ) . Primary antibodies were used at 1∶200 ( anti-StcE ) , 1∶100 ( L10 and HI30 ) , or 1∶25 ( sc-7052 ) . Goat anti-rabbit Alexa Fluor 633 , goat anti-mouse Alexa Fluor 488 , and rabbit anti-goat Alexa Fluor 488 secondary antibodies were used at 1∶200 , and samples were mounted in ProLong Gold antifade reagent with DAPI ( Molecular Probes ) . Coverslips were imaged using a 63× oil immersion lens on a Zeiss LSM510 confocal microscope . Data were obtained and analyzed using LSM 5 Image Software ( Zeiss ) . Non-tissue culture-treated dishes were coated with 10 µg/mL Fbg , and 5×105 neutrophils were plated in the presence of 8 . 33 µg/mL StcE or E435D or vehicle control in EGM-2MV for 30 min at 37°C , 7 . 5% CO2 . 1 . 25 nM IL-8 or 100 nM fMLP was included for the last 5 or 10 min as indicated . Dishes were placed in The Box closed system ( Life Imaging Services , Reinach Switzerland ) at 37°C and imaged on an Olympus IX-70 inverted microscope ( Olympus America ) using a 20× phase objective . Images were collected using a Coolsnap fx cooled charged-coupled device ( CCD ) video camera ( Photometrics , Huntington Beach , CA ) and captured into Metaview v6 . 2 ( Universal Imaging Corp . , Downingtown , PA ) every 15 s for 10 min . To obtain measurements of cell velocity , cell centroids were tracked for the first 21 frames using MetaMorph v7 . 0r2 . For quantitation of cell length , all cells in two random frames were measured using MetaMorph . Means of at least three independent experiments were combined and analyzed by one-way ANOVA using Prism with Dunnett posttest . Neutrophils were fluorescently labeled as described [52] and brought to 2×106/mL in EGM-2MV . 50 µL of cell suspension were added to StcE , E435D , hiStcE , or vehicle control serially diluted in 50 µL EGM-2MV in a Fbg-coated 96-well black plate ( Greiner , Kremsmuenster , Upper Austria ) . Cells were allowed to adhere for 40 min at 37°C , 7 . 5% CO2 . Positive control cells were treated with 100 nM fMLP for the final 10 min . Samples were washed 3 times and the fluorescence of remaining adherent cells measured using a Gemini EM microplate spectrofluorometer ( Molecular Devices ) with excitation/emission at 485/530 nm . A standard curve was included on each plate , and linear regression was performed with Prism to determine number of neutrophils adhered in each well . Relative adhesion was calculated by normalizing the number of adherent cells to the average values for vehicle control in the absence of fMLP . Means of at least three independent experiments performed in duplicate were combined and analyzed by two-way analysis of variance ( ANOVA ) using Prism with Bonferroni post test . Neutrophil migration was determined using transwell assays essentially as described ( Lokuta 2005 ) . 3 µm transwell filters were coated with 2 . 5 µg/mL Fbg or a monolayer of HMVEC-L , and 4×105 calcein-AM-labeled neutrophils were placed in the top chamber with 25 µg/mL ( filters ) or 50 µg/mL ( monolayers ) StcE , E435D , hiStcE , or vehicle control . EGM-2MV alone or containing 100 nM fMLP was placed in the bottom chamber and samples were incubated at 37°C , 5% CO2 for 210 min . 50 mM EDTA was added to the lower chamber , transwells were removed and the fluorescence of migrated cells quantitated as described for adhesion assays . Numbers of transmigrated cells were normalized to vehicle control with chemoattractant , and means of at least three independent experiments were combined and analyzed by one-way ANOVA using Prism with Bonferroni post test . Zebrafish were bred and maintained as previously described ( Mathias 2006 ) . At 3 days post fertilization ( dpf ) , zebrafish embryos were anesthetized in 3 mL embryo water ( E3 ) containing 0 . 1 mg/mL tricaine and 25 µg/mL StcE , hiStcE , or vehicle control . Zebrafish were wounded in the dorsal tail fin with the tip of a 25 gauge needle , and the bath replaced with protein treatment in E3 without tricaine . After 24 hours at 28°C , zebrafish were fixed and stained for whole-mount immunofluorescence as previously described ( Mathias 2006 ) . Rabbit polyclonal anti-myeloperoxidase antibody and goat anti-rabbit Alexa Fluor 488 ( Molecular Probes ) were used at 1∶500 . Images were acquired with a Nikon SMZ-1500 zoom microscope with epifluorescent illumination using MetaMorph software . For quantitation of inflammation , images of individual fish were compiled and blinded , and the number of neutrophils in the dorsal and ventral fin caudal to the yolk sac were counted . Means of four independent experiments were combined and analyzed by one-way ANOVA using Prism with Dunnett's post test . Swissprot ID numbers for proteins described in the text are as follows: StcE ( O82882 ) ; CD43 ( P16150 ) , CD45 ( P08575 ) .
|
Enterohemorrhagic Escherichia coli ( EHEC ) poses a significant threat to the U . S . food supply , causing foodborne gastrointestinal disease in humans that can progress to hemolytic uremic syndrome ( HUS ) , a potentially fatal kidney disease . Research suggests that EHEC strains are growing more virulent , resulting in a higher incidence of hospitalization and development of HUS from recent produce-associated outbreaks . Although immune dysregulation is a feature of HUS disease , the specific mechanisms contributing to altered immune function require investigation . Furthermore , the contribution of the immune response to early intestinal disease is not known . StcE is a secreted protease of EHEC that is expressed during infection and may contribute to virulence via cleavage of mucin-like glycoproteins . In this study , we define mucinase activity toward glycoproteins on the surface of human neutrophils and find that StcE alters neutrophil activity by interacting with these proteins . StcE affected crucial neutrophil functions including oxidative burst production and migration . The effects of StcE were both cleavage-dependent and cleavage-independent , providing insight into a novel mechanism for mediating neutrophil function via mucin interactions . Our study reports an immune-modulating role for a potential EHEC virulence factor and provides a possible explanation for altered neutrophil phenotypes observed during E . coli O157∶H7-induced disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"microbiology/immunity",
"to",
"infections",
"microbiology/innate",
"immunity",
"immunology/immunity",
"to",
"infections",
"infectious",
"diseases/bacterial",
"infections",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"microbiology/medical",
"microbiology",
"infectious",
"diseases/gastrointestinal",
"infections"
] |
2009
|
Modulation of Neutrophil Function by a Secreted Mucinase of Escherichia coli O157∶H7
|
Pulmonary mycoses are often associated with type-2 helper T ( Th2 ) cell responses . However , mechanisms of Th2 cell accumulation are multifactorial and incompletely known . To investigate Th2 cell responses to pulmonary fungal infection , we developed a peptide-MHCII tetramer to track antigen-specific CD4+ T cells produced in response to infection with the fungal pathogen Cryptococcus neoformans . We noted massive accruement of pathologic cryptococcal antigen-specific Th2 cells in the lungs following infection that was coordinated by lung-resident CD11b+ IRF4-dependent conventional dendritic cells . Other researchers have demonstrated that this dendritic cell subset is also capable of priming protective Th17 cell responses to another pulmonary fungal infection , Aspergillus fumigatus . Thus , higher order detection of specific features of fungal infection by these dendritic cells must direct Th2 cell lineage commitment . Since chitin-containing parasites commonly elicit Th2 responses , we hypothesized that recognition of fungal chitin is an important determinant of Th2 cell-mediated mycosis . Using C . neoformans mutants or purified chitin , we found that chitin abundance impacted Th2 cell accumulation and disease . Importantly , we determined Th2 cell induction depended on cleavage of chitin via the mammalian chitinase , chitotriosidase , an enzyme that was also prevalent in humans experiencing overt cryptococcosis . The data presented herein offers a new perspective on fungal disease susceptibility , whereby chitin recognition via chitotriosidase leads to the initiation of harmful Th2 cell differentiation by CD11b+ conventional dendritic cells in response to pulmonary fungal infection .
Pulmonary mycoses , ranging from invasive fungal infection to severe asthma with fungal sensitization , affect millions of people worldwide [1 , 2] . Fungi inhabit a multitude of ecological niches , and consequently , humans continuously encounter potentially pathogenic fungi in the environment . Subsequent disease is determined by the size of the innoculum , virulence of the microbe , and immune status of the host . In particular , CD4+ helper T ( Th ) cell subsets are critical mediators of the immune response to fungal exposure . Interferon-γ from Th1 cells and interleukin ( IL ) -17 from Th17 cells contribute to protective immunity via classical activation of macrophages and neutrophil recruitment , respectively [3] . Conversely , Th2 cell production of IL-4 , IL-5 , and IL-13 impels eosinophilia , alternative macrophage activation , mucus and IgE production , and airway obstruction [4] . These type-2 responses drive fungal-associated allergies and positively correlate with invasive fungal disease severity [4] . Although a fair amount is known about type-2 responses and their downstream consequences , the basis of Th2 cell induction associated with pulmonary mycosis is less well defined . Antigen presentation by an immune cell bearing major histocompatibility II ( MHCII ) is required for naïve Th cell priming and differentiation . Thus , a cellular intermediate must coordinate Th2 cell induction . Professional antigen presenting cells direct Th cell fate , and inflamed lungs contain several ontologically distinct immune cells with this potential capability [5] . The precise leukocyte subset responsible for priming Th2 cells , as well as the location that this event occurs , whether at the site of infection or within secondary lymphoid tissue , has not been comprehensively investigated . Furthermore , specific features of the infection that lead to Th2 cell lineage commitment remain largely unexplored in the context of pulmonary fungal infection . While some models attribute induction of type-2 responses to protease cleavage of host proteins and wound repair of lung injury [6] , many microbes that elicit Th2 cell responses produce chitin [7] . Chitin is a polysaccharide composed of polymeric N-acetylglucosamine . The rigidity of chitin is utilized in the cell wall of fungi as well as the exoskeleton of arthropods and filarial sheath of parasitic worms . Higher organisms rely on keratin for similar structural purposes , and as a result , vertebrates do not synthesize or store chitin . These differences allow an opportunity for the vertebrate immune system to detect chitin-containing pathogens as foreign [8 , 9] . While chitin detection may prove beneficial to the host in the context of parasitic infection [10] , we hypothesize that inappropriate or dysregulated Th2 responses instigated by recognition of chitin promotes fungal pathogenesis . Chitinases are a pivotal component of the host response to chitinous organisms [11] . Chitin is positioned beneath layers of mannans and glucans in the fungal cell wall , thus secreted host chitinases are needed to penetrate the wall matrix and make chitin fragments available to host surveillance [12] . Mammals encode two functional chitinases , chitotriosidase ( Chit1 ) and acidic mammalian chitinase ( AMCase ) [11] . A naturally occurring allele of CHIT1 renders the enzyme inactive [13] , and these mutations have been associated with susceptibility to parasitic worm infection in humans [14] . Likewise , AMCase has been linked to eosinophilia [15] and alternative macrophage activation [16] in mouse models of pulmonary allergy . Consequently , we reasoned that mammalian chitinases could be necessary for efficient host recognition of fungal chitin and subsequent Th2 cell priming . Using an inhalation model of Cryptococcus neoformans infection and novel reagents to detect Cryptococcus-specific Th cells , we unravel the basis for Th2 cell induction in response to pulmonary fungal infection . We report profound accumulation of detrimental Th2 cells in the lungs of infected mice . We additionally show that lung-resident CD11b+ interferon regulatory factor ( IRF ) 4-dependent conventional dendritic cells present antigen to Th cells and drive potent Th2 differentiation at the site of infection in the lungs . Surprisingly , our results demonstrate that an excess of fungal chitin , as well as digestion of chitin via Chit1 , and not AMCase , lead to chitin detection , Th2 cell accumulation , and enhanced disease . Lastly , we observed increased Chit1 activity in humans with confirmed fungal infections , reinforcing the relevance of Chit1 in human disease . This study offers novel insights into the cellular source of antigen presentation and molecular basis of chitin recognition via Chit1 that underlies deleterious Th2 cell formation during pulmonary mycosis .
We utilized a murine model of pulmonary cryptococcosis to investigate Th cell priming during fungal infection . Upon inhalation , Cryptococcus neoformans establishes a robust lower respiratory tract infection that causes tissue damage and ultimately leads to mortality from pulmonary complications and dissemination resulting in meningoencephalitis . To distinguish Th cell responses to infection from non-specific wound healing Th2 cell responses , we generated a recombinant peptide-major histocompatibility class II ( pMHCII ) tetramer that enabled identification of C . neoformans antigen-specific Th cells . The pMHCII tetramer contains a 13 amino acid peptide from an immunodominant cryptococcal protein , chitin deacetylase 2 ( Cda2 ) ( Table 1 ) [17] . The Cda2-MHCII tetramer labeled a population of antigen-experienced ( i . e . CD44+ ) Th cells , but it did not stain non-activated ( i . e . CD44− ) Th cells from C . neoformans infected mice or CD44+ Th cells from naive mice ( Figs . 1A , S1 for flow cytometry gating ) . In addition , mice infected with a C . neoformans mutant ( cda2Δ ) that lacks Cda2 protein expression [18] had marked reductions in Cda2-MHCII tetramer binding cells ( Fig . 1A ) . Though Cda2 contains a dominant CD4+ T cell epitope , cross-reactivity to other closely related cryptococcal proteins likely account for the remaining tetramer binding Th cells generated during infection with cda2Δ ( Table 1 ) . Taken together , these studies show the Cda2-MHCII tetramer reliably identified antigen-specific CD4+ T cells produced in response to C . neoformans infection . We characterized the immune response in the lung and lung-draining mediastinal lymph node ( MLN ) to determine the relative contributions of each site to CD4+ T cell subset differentiation . Pulmonary cryptococcal infection resulted in a progressive accumulation of Cda2-MHCII-specific T cells in the lungs that predominately expressed the Th2 cytokines IL-5 and/or IL-13 ( Fig . 1B-D ) . In addition , Th2 cytokines IL-5 , IL-13 , and CCL5 were among the most abundant cytokines present in infected lung homogenates ( Fig . 1E ) , and eosinophils , a downstream correlate of type-2 cytokines , represented an overwhelming majority of the bulk leukocyte population in the lungs ( S2 Fig . for flow cytometry gating , S3A Fig . ) . In contrast , the Cda2-MHCII-specific Th2 cell response within the MLN ( Fig . 1F-H ) was significantly lower than the response observed in the lungs , and eosinophils comprised an insubstantial component of the lymph node resident leukocytes ( S3 Fig . ) . These findings collectively suggest the local inflammatory environment in the lung may shape the differentiation and/or promote the selective expansion of Th2 cells . Due to the seemingly contradictory roles of Th2 cells in beneficial wound healing responses and harmful allergic disease [19] , it is not entirely clear whether Th2 cells simply correlate with or cause disease associated with fungal infection . To test the causal relationship of Th2 cells with disease severity in this model , we augmented the endogenous Th2 cell response to fungal infection using IL-2 cytokine/antibody complex treatment . IL-2 can be targeted to the high affinity IL-2 receptor to enhance Th cell proliferation by conjugating IL-2 cytokine with anti-IL-2 antibody to form IL-2 cytokine/antibody complexes [20 , 21] . Since Th2 cells generated during pulmonary cryptococcal infection expressed high levels of the alpha chain of the high affinity IL-2 receptor ( CD25 ) ( Fig . 2A ) , we sought to use IL-2 complexes to boost the Th2 cell response . The wildtype strain of C . neoformans , KN99α , induces an extremely aggressive infection that leaves little room to increase the Th2 cell response . Consequently , we used an attenuated strain of C . neoformans , gpr4Δgpr5Δ ( attenuation explained below/Fig . 5; deficient in production of large , chitinous cells ) . Treatment with IL-2 complexes increased Th2 cell numbers compared with similarly infected mice receiving antibody or cytokine alone ( Fig . 2B ) . Th2 cells and cytokines were also elevated in lung homogenates from infected mice treated with IL-2 complexes ( Fig . 2C&D ) . In addition to Th2 cells , Regulatory T ( Treg ) cells can be expanded by IL-2 complex treatment [20 , 21] ( S4A Fig . ) . This increase of Treg cells in mice treated with the IL-2 complex could theoretically suppress protective Th cell responses and allow Th2 cells to predominate the response . However , IL-2 complex treatment did not affect Th1 or Th17 cell numbers ( Fig . 2B ) and only minimal changes in IFNγ cytokine ( Fig . 2C ) and monocyte accumulation ( Fig . 2D ) were observed , showing IL-2 complex treatment did not eliminate the effector activity of protective Th1 cells ( Fig . 2D ) . Instead , Schulze et al . [22] showed Treg cells suppress Th2 cells during cryptococcal infection ( S4B–S4C Fig . ) , suggesting the increase in Treg cells due to IL-2 complex treatment would actually limit Th2 cell accumulation in this system . Hence , IL-2 complexes can be used to augment the Th2 cell response during pulmonary fungal infection and assess the relationship between Th2 cells and fungal disease . If Th2 cells promote disease , we hypothesized that increasing the Th2 response should accelerate death during infection with the virulence-attenuated strain , gpr4Δgpr5Δ . IL-2 complex treatment increased the Th2 cell response to levels even higher than the fully virulent KN99α infection ( Fig . 2E ) . Treatment with IL-2 complexes also greatly reduced the survival time of infected mice ( Fig . 2F ) without affecting pulmonary fungal burden ( Fig . 2G ) . Uninfected mice treated with the same regimen of IL-2 complexes survived more than 30 days and remained healthy ( Fig . 2F ) , indicating the IL-2 complex treatment targeted detrimental cells that were only present during infection . IL-2 treatment of infected mice also induced obvious lung pathology consistent with increased Th2 activity , noted by increased metaplasia of the bronchiolar epithelium and mucous obstruction of the airways ( Fig . 2H ) . En masse , these data indicate that Th2 cells exacerbate pulmonary disease during fungal infection . The diminished Th2 response in the MLN compared to the lung led us to question whether lymphoid priming was required for Th2 cell induction during pulmonary fungal infection . Fms-like tyrosine kinase 3 ligand ( Flt3L ) is a differentiation factor for several hematopoietic cell subsets , and genetic deletion of Flt3L causes defects in antigen presenting cell traffic between the site of infection and secondary lymphoid organs [23] . Flt3L deficient mice infected with C . neoformans neither experienced mediastinal lymphadenopathy ( Fig . 3A ) nor elicited a polyclonal Th2 response in the MLN ( Fig . 3B ) . Surprisingly , the Th2 cell response in the lungs after C . neoformans infection was unaffected by Flt3L deficiency compared to wildtype animals ( Fig . 3C ) , indicating lymphoid priming is not required for pulmonary Th2 cell accumulation . To determine the immune cell intermediate that primes Th2 cells in the lungs , we relied on the fact that Th cells are MHCII restricted . Three leukocyte subsets that express MHCII exist in the lungs of mice infected with C . neoformans: monocytes , CD11c+ cells , and B cells ( Fig . 4A ) . Of these , CD11c+ cells are the most abundant in the lungs during cryptococcal infection ( Fig . 4A ) . Consequently , we interrupted the specific interaction between CD11c+ cells and Th cells by generating mice with conditional deletion of MHCII in cells that express CD11c ( CD11c-cre MHCII fl/fl ) ( Fig . 4B ) . Unlike NOD/SCID/Rag mice that fail to generate mature Th cells , naïve CD11c-cre MHCII fl/fl mice produced an equivalent number of Th cells as naïve wildtype mice ( Fig . 4C ) , showing the peripheral Th cell compartment remained intact in CD11c-cre MHII fl/fl mice . Thus , conditional deletion of MHCII on CD11c+ dendritic cells allowed specific disruption of the interaction between the dendritic cells and the Th cells in the periphery . Pulmonary Th cell expansion during cryptococcal infection was completely abolished in CD11c-cre MHCII fl/fl mice ( Fig . 4C-D ) . Consequently , MHCII-bearing CD11c+ cells prime antigen-specific Th cells in the lungs of mice infected with C . neoformans . CD11c+ cells are a heterogeneous group of macrophages and several dendritic cells ( DC ) subsets in the lungs [24] . Therefore , we sought to discern the specific lineage of the CD11c+ antigen presenting cell that is responsible for pulmonary Th2 cell induction using an unbiased forward genetic screen of mouse lines genetically deficient in various CD11c+ subsets or their ability to interact with Th cells via MHCII ( S5A Fig . ) . Lysozyme M ( LysM ) -cre MHCII fl/fl ( macrophages and granulocytes [25] ) , BATF3−/− ( CD103+ conventional dendritic cells [5] ) , and CCR2−/− ( monocytes and monocyte-derived dendritic cells [26] ) mice generated robust antigen-specific Th2 responses during cryptococcal infection ( S5–S6 Fig . ) . Only mice deficient in CD11b+ conventional dendritic cells , abrogated using CD11c-cre IRF4 fl/fl mice , experienced blunted Th2 cell accumulation with cryptococcal infection ( Fig . 4E-H ) . Therefore , our exhaustive search revealed lung-resident CDllc+ CDllb+ IRF4-dependent conventional DC ( referred to as CD11b+ conventional DC ) are uniquely required for Th2 cell induction in response to pulmonary cryptococcal infection . Existing evidence and our data show CD11b+ conventional dendritic cells are capable of inducing both Th17 and Th2 cell responses to pulmonary fungal infection [27]; therefore , these DC are not inherently programmed to specify a single Th cell lineage . Determination of Th2 cell fate by these lung resident DC must require higher order detection of specific features of the fungal infection . Many chitin-containing pathogens , as well as asthma/allergy models using purified chitin , evoke type-2 immunity [9 , 16 , 28] . Consequently , the striking Th2 cell response to pulmonary fungal infection prompted us to explore the role of chitin as a Th2 cell adjuvant . Maintaining chitin homeostasis is critical for cell wall integrity and microbe vigor . Chitin synthases that regulate cell wall chitin deposition are often essential for fungal viability or are part of a redundant pathway [29] . As a result , studies that attempt to correlate loss-of-function mutations in chitin synthesis genes with modulation of the host response and attenuation of virulence would be challenging to interpret . Whether loss of Th2 cells and alterations in disease were due to a decrease in chitin , a loss of cryptococcal fitness/growth , or unmasking of other antigens due to modifications of the fungal cell wall or capsule would not be easily distinguishable [30] . In lieu of testing the requirement of chitin in Th2 cell induction , we exploited a natural property of C . neoformans to determine if increased fungal chitin was sufficient to expand Th2 cell formation . Approximately 20% of wildtype cryptococcal cells ( KN99α ) recovered from the lungs of infected mice increase in diameter from <10 μm to 15–100 μm [31–33] . Previous studies have shown these enlarged cells , known as titan cells , exhibit increased thickness of the fungal cell wall [32] . Using the fluorescent dye calcofluor white to measure chitin content in individual cells by epifluorescence microscopy ( Fig . 5A-B ) , or at the population level with flow cytometry ( S7A Fig . ) , we found the large C . neoformans titan cells contained more chitin , at a higher density , than the typical sized cells . C . neoformans produces several enzymes that deacetylate chitin to form chitosan [18 , 30] . Biochemical analyses additionally revealed that the amount of chitosan produced by titan cells and typical size cells did not differ , whereas chitin was significantly more abundant in the titan cells ( Fig . 5C ) . Therefore , enhanced chitin content accompanied cell size increases during formation of cryptococcal titan cells . To control for the relative effects of cell size and chitin content on Th2-mediated disease , we utilized several mutants of C . neoformans . A strain with targeted deletions in G protein-coupled receptor ( gpr ) 4 & gpr5 produces >95% typical sized cells in vivo [34] , and these cells retain normal amounts of chitin ( Fig . 5A ) . Deletion of the transcription factor Rim101 ( rim101Δ ) abolishes titan cell production [34 , 35] , yet the typical sized cells have increased expression of chitin synthesis genes and elevated chitin content [36 , 37] ( Fig . 5A ) . Using these mutants , we were able to dissociate cryptococcal cell size from cell wall chitin as well as manipulate the total amount of chitin present during infection ( Table 2 ) . We examined the impact of alterations in chitin content in C . neoformans on Th2 cell accumulation in the lungs . Antigen-specific Th cell priming and Th2 cell differentiation were reduced in mice infected with the low chitin gpr4Δgpr5Δ strain compared to infections with both the high chitin KN99α ( due to titan cell production ) and rim101Δ strains ( Fig . 5D-E ) . The Cda2-independent polyclonal Th2 cell response to gpr4Δgpr5Δ infection was also significantly lower than the responses to KN99α and rim101Δ infections ( S7B Fig . ) , indicating the defect in antigen-specific Th2 cell accumulation to gpr4Δgpr5Δ infection was not due to differential expression of cda2 between the strains . Finally , the secreted Th2 cytokines IL-5 , IL-13 , and CCL5 present in lung homogenates from mice infected with gpr4Δgpr5Δ were significantly reduced compared to KN99α and rim101Δ infections ( Fig . 5F ) . Of note , secreted Th1 and Th17 cytokines , interferon-γ and IL-17A , did not concomitantly increase in response to gpr4Δgpr5Δ infection ( Fig . 5F ) . Thus , Th cells not receiving strongly polarizing Th2 signals in the gpr4Δgpr5Δ infection failed to acquire an alternate Th1 or Th17 cell differentiation fate . Since KN99α and rim101Δ strains contribute more total chitin to the infection than the gpr4Δgpr5Δ strain ( Table 2 , S7A Fig . ) , these data demonstrate that chitin abundance and not cell size positively correlated with Th2 cell response intensity . Uncontrolled factors affected by the gpr4Δgpr5Δ or rim101Δ mutations could correlate with chitin levels and independently contribute to Th2 cell accumulation . To directly test if purified chitin can increase Th2 cell numbers , mice were infected with gpr4Δgpr5Δ , and <10 μm chitin particles were co-administered into the lungs . Chitin treatments partially rescued the attenuated Th2 cell response ( Fig . 5G ) . Importantly , this effect was observed in the clonal population of antigen-specific cells , and therefore , chitin increased the potency of Th2 cell induction during C . neoformans infection . We examined the correlation between chitin , Th2 cell accumulation , and disease severity in our experimental model of cryptococcal infection . Infections with the high chitin strains , KN99α and rim101Δ , significantly hastened the time to death relative to mice infected with gpr4Δgpr5Δ ( Fig . 5H ) . Interestingly , all strains had equivalent pulmonary colony forming units at 14-days post-infection , indicating the differences in disease were not simply due to a failure to control the infection ( Fig . 5I ) , but rather paralleled the total amount of chitin present during infection ( Table 2 ) . In summary , both chitin production by C . neoformans and Th2 cell accumulation directly correlated with exacerbation of lethal fungal disease . We next investigated host intrinsic factors that could influence detrimental Th2 cell responses to chitin—specifically the mammalian chitinases , AMCase and Chit1 . pH-sensitive differences in enzyme activity allow for an assessment of the contribution of each enzyme to chitin cleavage . Consistent with published reports , recombinant AMCase cleaved 4-methlylumbelliferone chitotriose across a broad pH range , whereas recombinant Chit1 was only active at less acidic pH ( Fig . 6A ) [38] . Chitinase activity in lung homogenates from infected mice was significantly elevated compared with uninfected animals at pH 2 and pH 5 ( Fig . 6A ) . Furthermore , lung homogenates from infected mice genetically deficient in Chit1 or AMCase both showed decreased cleavage of the fluorescent chitin substrate ( Fig . 6A ) . These data indicate both chitinase enzymes are active during pulmonary fungal infection , and genetic abolishment of these enzymes can be used to understand the effect of chitin degradation on Th2-mediated fungal pathogenesis . To test the hypothesis that Th2 cell-associated disease depends on chitinases , we infected wildtype , Chit1−/− , and AMCase−/− mice with C . neoformans and quantified the Th2 cell response . Despite no differences in pulmonary fungal burden at 14-days post-infection ( S8A Fig . ) , Th2 cells were 10-fold less abundant in the lungs of infected Chit1−/− mice compared with wildtype controls ( Fig . 6B-C ) , and this trend was consistent with all the strains of C . neoformans tested ( S8B Fig . ) . Conversely , AMCase deficiency did not impact Th2 cell quantities after cryptococcal infection ( Fig . 6B-C ) . Furthermore , Chit1 deficiency and not AMCase deficiency also significantly extended the survival of mice infected with C . neoformans relative to age matched , wildtype animals ( Fig . 6D-E ) . The loss of Th2 cell accumulation with Chit1 deficiency was responsible for attenuation of disease , because the use of IL-2 complexes to boost Th2 cell numbers ( Fig . 6C ) and associated cytokines ( Fig . 6F ) also hastened lethal disease in Chit1−/− mice compared with infection-matched , untreated Chit1−/− controls ( Fig . 6D ) . Similar to our previous studies using the IL-2 complex , Th1 cytokine production in the Chit1−/− mice was only minimally affected by the IL-2 complex treatment ( Fig . 6F ) . Thus , the presence of Chit1 , and not AMCase , positively influences Th2 induction and subsequent disease . Chitin receptors in plants bind chitin oligomers [39] , and chitin polymer size also influences the mammalian immune response to chitin [40 , 41] . Since appropriately sized chitin fragments could result from chitin digestion by Chit1 , we used heterogeneous-length chitin and highly purified chitin heptamers to understand the effect of Chit1-associated degradation of chitin on Th2 cell accumulation . Although treatment with heterogeneous-length chitin augmented Th2 cell induction in wildtype animals infected with gpr4Δgpr5Δ cells ( Fig . 5F ) , it did not increase Th2 cell numbers in Chit1−/− mice ( Fig . 6G ) . Conversely , inoculation with mass equivalent amounts of chitin heptamers boosted the Th2 cell response in C . neoformans-infected animals with Chit1 deficiency ( Fig . 6G ) , revealing the requirement for Chit1 in chitin polymer recognition can be bypassed by providing exogenous chitin fragments . Therefore , our data demonstrate a role for Chit1 in the chitin cleavage pathway that leads to Th2 cell accumulation . We next examined how chitin fragments influence the upstream pathway of Th2 cell induction . To test the hypothesis that DC interact directly with chitin fragments , we cultured primary pulmonary leukocytes from infected mice in the presence of R-phycoerithrin-fluorophore conjugated chitin heptamers ( RPE-GN7 ) or unbound REP-streptavidin ( RPE-SA ) . While RPE-GN7 labeled a subset of B cells that have been shown to bind chitin [9] , RPE-GN7 did not adhere to conventional CD11b+ DC ( S9A Fig . ) . Thus , we did not detect direct binding of chitin heptamers to the conventional DC . Furthermore , conventional CD11b+ DC stimulated ex vivo with PMA + ionomycin produced an important Th2 cell differentiation cytokine , IL-4 , yet this DC subset did not express IL-4 upon stimulation with chitin heptamers ( GN7 ) ( S9B Fig . ) . Combined , these data suggest the CD11b+ conventional dendritic cells may not sense chitin levels directly . Pulmonary epithelial cells respond to chitin [42] and secrete several Th2-inducing alarmins , thymic-stromal lymphopoietin ( TSLP ) , IL-25 , and IL-33 [28 , 43 , 44] . As a result , alarmin receptors on DC could potentially mediate the indirect recognition of chitin , leading to Th2 cell polarization during pulmonary fungal infection . We found CD11b+ conventional DC from the lungs of fungal infected mice expressed high levels of TSLP receptor , but not receptors for IL-25 or IL-33 ( S9C–S9D Fig . ) , indicating this DC subset is capable of sensing TSLP generated by epithelial cells . Taken together , our data suggest Th2 cell induction by conventional CD11b+ DC appears to involve an indirect recognition of chitin oligomers . The importance of Chit1 in promoting Th2 cell-mediated disease in an experimental model of cryptococcosis prompted us to investigate the relevance of chitotriosidase activity in human fungal disease . Blood samples were collected from human donors: 46 Ugandan patients presenting at the hospital for the first time with AIDS and 38 similar AIDS patients experiencing acute cryptococcal disease ( S1 Table ) . We analyzed chitotriosidase and AMCase enzymatic activity in plasma from each group as described for the mouse lung homogenates . Chitin substrate cleavage at pH = 5 was significantly elevated in plasma from AIDS patients with cryptococcal infection when compared to AIDS patients without cryptococcal infection ( Fig . 6H ) . Comparatively low levels of enzymatic activity were detected at pH = 2 for each group , indicating that chitotriosidase and not acidic mammalian chitinase is the predominate chitinase produced by humans with cryptococcal infection ( Fig . 6H ) . To determine if the difference in chitotriosidase activity was due to an inherent propensity or deficiency in chitotriosidase expression , we used cryptococcal lysate antigens to stimulate chitinase production in whole blood samples from the human donors . Stimulation of whole blood from all patients induced robust chitotriosidase activity relative to unstimulated samples , and chitinase activity did not differ between the groups ( Fig . 6H ) , indicating all human donors had equivalent capacity to produce chitotriosidase . Chitinase activity was not detectable in pure cryptococcal culture supernatants or cryptococcal lysate antigens ( S10 Fig . ) , and as a result , the chitinase activity detected in the assays with human samples was not due to Cryptococcus-derived chitinases . Taken together , we conclude that fungal antigen induces chitotriosidase activity in humans experiencing cryptococcosis .
An association between pulmonary fungal exposure and allergic Th2 inflammation is well established [1 , 3] . Fungal proteases [45] and fungal chitin [46] impact the innate immune responses underlying allergic inflammation , but elements of Th2 cell induction are enigmatic . Using an experimental model of pulmonary cryptococcosis , we demonstrated that inhalation of C . neoformans establishes a robust pulmonary infection , and the potent antigen-specific Th2 cell accumulation required lung resident CD11b+ conventional DC . Since these CD11b+ conventional DC can stimulate Th2 or Th17 cell differentiation in response to C . neoformans and Aspergillus fumigatus exposure respectively [27] , these DC must interpret specific features of the infection to direct Th2 cell fate . To this end , we found cryptococcal chitin and exogenous administration of chitin particles correlated with increased Th2 cell accumulation . We further showed that chitotriosidase activity was highest in mice and humans infected with C . neoformans , and Chit1 was necessary for efficient Th2 cell induction and disease in our murine model of cryptococcosis . Taken together , these data indicate the host response to fungal chitin is an important factor that enhances Th2 cell production during pulmonary fungal infection . Our findings narrow an important gap in the mechanism of pattern recognition of fungal chitin in the lungs ( Fig . 7 ) . We have shown Chit1 functions as a “gatekeeper” in making chitin fragments available to host surveillance , thereby promoting Th2 cell accumulation and disease . Additionally , the use of pharmaceutical grade chitin heptamers to augment Th2 cell responses establishes a new minimum component for chitin recognition in vertebrates . While we did not detect direct interactions between the CD11b+ conventional DC and labeled chitin ex vivo , other systems have shown chitin binding to the mannose receptor and subsequent recognition by TLR9 and/or NOD2 [47] . Alternatively , pulmonary allergy models demonstrate that lung epithelial cells recognize chitin fragments and produce the necessary alarmins for Th2 cell induction: TSLP , IL-25 , and IL-33 [43 , 44 , 48] . In our model , CD11b+ conventional DC express high levels of TSLPR . Finally , natural IgM has been shown to bind fungal carbohydrates , including chitin , and facilitate the interaction of DC and fungal carbohydrates [9] . Signals , such as alarmins or antibody-chitin complexes , could be received by CD11b+ conventional DC to direct Th2 cell differentiation via IL-4 or another novel pathway . A major impediment in understanding Th cell responses to pulmonary fungal infection has been the lack of reagents to detect antigen-specific Th cells . Antigen-specific reagents are particularly important when examining Th2 responses , because Th2 cells can be induced by the wounding that occurs during infection . Our ability to track endogenously derived , antigen-specific Th cells with pMHCII tetramers [49] allowed us to present for the first time that Th2 cells produced in response to fungal pathogens are not part of a generalized wound healing response but are fungal-antigen specific . Unlike T cell receptor transgenic approaches , pMHCII tetramers permitted us to monitor the population of infection-specific Th cells in the polyclonal repertoire , while maintaining physiologic precursor frequency and clonal expansion . Thus , we are able to examine the Th cell response during the natural course of infection and keep all other variables constant . The availability of these pMHCII tetramers will undoubtedly empower cryptococcal researchers and accelerate the field of fungal immunology . The use of IL-2 complexes allowed us to conveniently and reliably augment the Th2 cell response to further understand unappreciated elements underlying Th2-mediated disease . This strategy is amenable to any host or microbial genetic model , which facilitates direct comparisons . Also , this gain-of-function approach permitted us to test the sufficiency of Th2 cells to exacerbate disease . These data , combined with loss-of-function studies by other groups [50 , 51] , alter the longstanding paradigm that susceptibility to lethal fungal disease is traditionally viewed as a breakdown in protective immunity . This paradigm is supported by the higher prevalence of invasive fungal disease in immunosuppressed individuals , including people living with HIV/AIDS , cancer patients undergoing chemotherapy , and solid organ transplant recipients . However , we propose that in addition to the lack of a protective response , an independent development of a harmful Th2 cell response further exacerbates disease . This is particularly important in the the case of human cryptococcocosis were a compromised immune system not only lacks sufficient quantities of lymphocytes to resolve the fungal infection , but the residual Th cell repertoire is plastic and detrimentally influenced by the microbe [52–55] . A subset of innate lymphoid cells ( ILC ) produce the Th2 cytokines , IL-5 and IL-13 [56] , and these so-called ILC2 have been shown to contribute to allergic airway disease [4] . While IL-2 complex treatment dramatically increases Th2 cell accumulation and enhances pulmonary disease , ILC numbers are not affected by IL-2 complex treatment in our model . Likewise , CD25+ ILC2 exist in the lungs under homeostatic conditions [57] , yet uninfected mice exhibit no ill effects of IL-2 complex treatment . However , the developmental relationship of ILC2 to lymphocytes , combined with the lack of lineage markers expressed by ILC , make it challenging to separate the relative effects of Th2 cells and ILC2 in driving immunopathology . Thus , our conclusion that Th2 cells are vital mediators of disease does not categorically exclude the participation of ILC2 in this process . CD11b+ conventional DC are an ontologically distinct mononuclear phagocyte subset that require IRF4 for maturation [5] . Although it has been suggested that this subset is also functionally unique in programing specific Th cell differentiation , building evidence seems to indicate a plastic role of these cells in Th cell induction . CD11b+ conventional IRF4-dependent DC have been shown to coordinate Th2 cell priming following protease inoculation and worm infection in the skin [58] , as well as house dust mite extract installation in the lungs [59] . However , several research groups have found the same DC subset , using identical host genetic systems , can control Th17 cell differentiation in the gut under homeostatic conditions [60] and the lungs after fungal infection [27] . While these observations infer that CD11b+ conventional DC are plastic , these cells may have different functions in the skin and gut compared to the lungs that could explain their role in priming Th17 and Th2 cell responses . Our findings showing Th2 priming by C . neoformans , combined with work by Schlitzer et al . showing IRF4-dependent DC are capable of priming Th17 cells in response to A . fumigatus to pulmonary fungal infection , highlight a fatal flaw in the notion that DC subsets are inherently specialized to control Th cell lineage fate . As a result , we explored an alternative hypothesis that higher order signals ( e . g . chitin recognition ) are required for pulmonary CD11b+ conventional DC to promote Th2 cell priming to fungal infection . Gene deletions in microbes can cause pleiotropic phenotypes , as seen in the rim101Δ strain ( 35 ) that can complicate interpretation of the effects of these mutations on host responses . However , the data obtained from the mutants utilized in this study support the conclusion that fungal chitin promotes Th2-mediated disease for several reasons . First , equivalent fungal burden in the lungs at 14 days post-infection indicates these mutations do not confer an inherent survival advantage or disadvantage for the fungus , and any effect the mutants have on leukocyte accumulation or disease is not simply driven by antigen load . Second , Rim101 transcriptionally controls many elements associated with cryptococcal virulence , including pH responses , encapsulation , cell enlargement , and iron sequestration [61] . While individual mutations in each of these pathways should result in a loss of virulence , the rim101Δ mutants paradoxically exhibit equivalent or accelerated disease [36] . Altered expression of cell wall synthesis genes combined with unmasking of the cell wall has been posited [36 , 37] , and in particular , our data implicate cell wall chitin in enhancing fungal pathogenesis associated with rim101 deficiency . More importantly , we view rim101Δ as an essential control for the effect cryptococcal cell size has on Th2 cell formation more than an independent test of the hypothesis that fungal chitin drives Th2 cell priming . Thus , we needed a strain of C . neoformans that produces mostly small cells but retained elevated chitin density to offset the loss of elevated chitin density with titan cell deficiency . At a minimum , these data prove that large cryptococcal cell size is not driving robust Th2 cell accumulation . Finally , due to the structural similarities , as well as the interdependent synthetic pathways , it is extremely challenging to decouple how chitin and chitosan separately impact a complex biological system . Although we cannot rule out immunomodulatory effects of chitosan in our experiments with C . neoformans mutants , we have confirmed chitin alone , when provided as an adjuvant , is sufficient to augment Th2 cell accumulation . Interpretation of the Cryptococcus chitin mutant data in the context of our additional data regarding host recognition of exogenous chitin builds a compelling argument that fungal chitin promotes detrimental Th2 cell induction . Acidic mammalian chitinase has been previously implicated in innate type-2 responses [62] . However , a head-to-head comparison of the effect of both mammalian chitinases , chitotriosidase and acidic mammalian chitinase , has never been performed in any model , much less in the context of Th2 cell responses to fungal infection . Surprisingly , chitotriosidase , and not acidic mammalian chitinase , influenced Th2 cell accumulation and disease during pulmonary fungal infection . While no direct explanation exists , the different patterns of expression of AMCase and Chit1 could explain our results . AMCase is produced by a number of cells including several leukocyte subsets and pulmonary epithelial cells , whereas Chit1 expression is restricted to mononuclear phagocytes , including DC [38 , 63] . Since these DC are the main coordinators of antigen presentation to CD4+ T cells , the close proximity of chitin degradation and recognition likely allows the DC to efficiently influence the fate of CD4+ T cells during pulmonary fungal infection . Furthermore , the varying pH-dependent enzymatic activity of Chit1 and AMCase suggest these enzymes may function in disparate anatomical and subcellular compartments that may bias their participation in response to pulmonary fungal infection . Considering Th2 cell responses likely evolved to resist parasitic infection [10] , the asymmetric global distribution of CHIT1 alleles [64] combined with the data presented herein offers a unique perspective on why individuals from tropical regions with endemic parasites tend to experience frequent and severe mycosis [65–69] . Ethnic groups historically residing in regions with highly prevalent parasites like Strongyloides tend to maintain functional CHIT1 , whereas populations from more temperate climates with lower endemic parasitic burdens frequently possess enzymatically inactive CHIT1 alleles [64] . Perhaps continuous fungal exposure in the absence of parasitic encounters provides sufficient negative selection pressure to eliminate functional CHIT1 alleles from these populations ( e . g . Europe ) . Conversely , ethnic groups historically residing in tropical areas ( e . g . Africa ) that maintain functional CHIT1 alleles may have enhanced protection against common parasitic exposures , while these same individuals experience exacerbation of Th2-mediated fungal disease [67–69] . In conclusion , we elucidated a novel mechanism of Th2 cell induction during fungal infection . CD11b+ DC , and importantly recognition of chitin via Chit1 , drive the generation of deleterious Th2 cells responding to pulmonary fungal infection . Next generation anti-fungal treatments should not only block fungal growth , but should also target the host immune response . A recent trial used IFNγ in combination with traditional anti-fungal therapy to promote beneficial immune responses . This treatment improved cryptococcal clearance , yet it had no significant impact on patient survival [70] . Our study suggests treatments that additionally aim to suppress the pathologic Th2 response , perhaps through chitotriosidase inhibition , may be necessary to improve clinical outcomes . Ultimately , the coordinated efforts of microbiologists , immunologists and infectious disease physicians will enable personalized medicine approaches that effectively combat lethal fungal infections by inhibiting fungal growth , promoting beneficial host responses , and dampening pathologic inflammation .
This study was approved by the institutional review boards of the University of Minnesota , Makerere University , and the Uganda National Council of Science and Technology . Written informed consent was obtained from all human participants prior to inclusion in the sutdy , and all data were de-identified [71] . All animal experiments were done in concordance with the Animal Welfare Act , U . S . federal law , and NIH guidelines . Mice were handled in accordance with guidelines defined by the University of Minnesota Institutional Animal Care and Use Committee ( IACUC ) protocol numbers 1010A91133 and 1207A17286 and University of Massachuesets IACUC protocol number A-1802 . All mice used in this study were derived from a C57BL/6 background . C57BL/6J , LysM-cre ( B6 . 129P2-Lyz2tm1 ( cre ) Ifo/J ) , CD11c-cre ( B6 . Cg-Tg ( Itgax-cre ) 1-1Reiz/J ) , MHCII loxP ( B6 . 129X1-H2-Ab1tm1Koni/J ) , Batf3 −/− ( B6 . 129S ( C ) -Batf3tm1Kmm/J ) , CCR2 −/− ( B6 . 129 ( Cg ) -Ccr2tm2 . 1Ifc/J ) , B6 . 129 ( Cg ) -Foxp3tm3 ( DTR/GFP ) Ayr/J ) mice were purchased from Jackson Laboratories ( Bar Harbor , ME ) and Flt3L−/− ( C57BL/6-flt3Ltm1Imx ) were purchased from Taconic ( Hudson , NY ) . Crosses were performed when necessary to generate the mouse strains used in this study , as indicated in S2 Table . Chit1 −/− [72] mice were infected and processed in the laboratory of Kirsten Nielsen . AMCase−/− [73] mice were infected and processed in the laboratory of Stuart Levitz per MTA stipulations . All mice were housed in specific pathogen–free conditions . Cryptococcus neoformans var . grubii strains were streaked on yeast peptone dextrose ( YPD ) agar plates and incubated for 2 days at 30°C . YPD broth was inoculated with colonies from the aforementioned plate and incubated for 16 hours at 30°C with gentle agitation . The inoculum was prepared by pelleting the culture , washing 3 times with phosphate buffered saline ( PBS ) , and resuspending in PBS at a concentration of 2x106 cells/mL . All strains used in this study were on a KN99α genetic background , and the complemented strains have wildtype phenotypes [18 , 34 , 35] . A well established intranasal pulmonary aspiration model of cryptococcosis was used for this study [74] . 6–8 week old , sex-matched mice were anesthetized with pentobarbitol or isoflurane . 5x104 cryptococcal cells in 25 μL of PBS was placed on the nares of each mouse , and the mice aspirated the inoculum into the lower respiratory tract . Finally , the mice were suspended by their incisors for 5 minutes and subsequently placed upright in their cage until regaining consciousness . For survival studies , ten mice per group were infected as described above . Animals were monitored for morbidity and sacrificed when endpoint criteria were reached . Endpoint criteria were defined as 20% total body weight loss , loss of 2 grams of weight in 2 days , or symptoms of neurological disease . Nine amino acid peptides from Cda2 were selected using a MHCII loading algorithm [75] . pMHCII tetramers were produced as previously described [76] . In short , biotinylated pMHCII monomers were expressed in Drosophila melanogaster S2 cells and isolated from culture supernatants by affinity chromatography . Streptavidin-Phycoerythrin ( Prozyme ) was added to pMHCII monomers at a 4:1 molar ratio . Finally , tetramer formation was assessed by western blot analysis . Lung leukocytes were isolated as previously described [77] . Briefly , lungs were excised and minced to generate approximately 1 mm3 pieces . The lung mince was incubated in HBSS ( Invitrogen , Grand Island , NY ) + 1 . 3 mM EDTA solution for 30 min at 37°C with agitation , and then transferred to RPMI-1640 ( Invitrogen ) medium supplemented with 5% Fetal Bovine Serum ( FBS ) ( Invitrogen ) and 150 U/ml type I collagenase ( Invitrogen ) and incubated for 1 h at 37°C with agitation . The cells were passed through a 70 μm filter , pelleted , and resuspended in 44% Percoll-RPMI medium ( GE Life Sciences , Pittsburgh , PA ) . A Percoll density gradient was created ( 44% top , 67% bottom ) , and the samples were centrifuged for 20 min at 650 x g . The leukocytes at the interface were removed , washed 2 times with RPMI medium , and resuspended in PBS + FBS at a concentration of 107 cells/ml . CD4+ T cells were enriched using a Dynabeads CD4+ T Cell Negative Isolation Kit ( Life Technologies , Grand Island , NY ) per manufacture’s instructions . After enrichment , ∼106 cells were suspended in 200 μL of restimulation buffer ( RPMI + 10% FBS + 1% Penicillin/Streptomycin + 5 μg Brefeldin A ) with ( stimulated ) or without ( unstimulated ) 10 ng phorbol myristate acetate ( PMA ) and 50 ng ionomycin . After 6 hours , the cells were washed and immediately prepared for flow cytometry . 5 μg of murine IL-2 cytokine ( Biolegend ) and 25 μg of clone JES6-1A12 anti-IL-2 antibody ( Bio X Cell , West Lebanon , NH ) were added to 100 μL of PBS at room temperature . Each mouse received intraperitoneal injections of IL-2 complexes every other day beginning at 5 days post-infection . Samples were incubated for 5 minutes with CD16/32 antibody ( Biolegend ) to block the Fc receptor and prevent nonspecific antibody binding . 25nM Cda2-tetramer was added to the sample and incubated at 25°C for 1 hour in the dark . Samples were surface-stained at 4°C for 30 minutes with the following antibodies ( see gating S1 Fig . ) : CD4 ( RM4-5 , BV605 , Biolegend ) , CD8 ( 53-6 . 7 , APC-eFluor 780 , eBioscience , San Diego , CA ) , CD11b ( M1/70 , PE-Cy5 , eBioscience ) , CD11c ( N418 , PE-Cy5 , eBioscience ) , B220 ( RA3-6B2 , PE-Cy5 , eBioscience ) , and CD44 ( IM7 , Alexa Fluor 700 , Biolegend ) . The cells were then incubated in Foxp3 Transcription Factor Buffer ( eBioscience ) at 4°C for 30 minutes . The cells were labeled with antibodes against the following intracellular antigens: Foxp3 ( FJK-16s , FITC , eBioscience ) , IL-5 ( TRFK5 , APC , Biolegend ) , IL-13 ( eBio13A , eFluor 450 , eBioscience ) , IL-17A ( TC11-18H10 . 1 , BV650 , Biolegend ) , and IFNγ ( XMG1 . 2 , PE-Cy7 , eBioscience ) . 1:200 antibody concentrations were used for surface staining , and 1:100 antibody concentrations were used for intracelluar staining . For data acquisition , events from the entire sample ( 500 , 000–1 , 000 , 000 ) were collected on a BD FACSCanto II flow cytometer ( BD Biosciences , San Jose , CA ) , and the data were analyzed with FlowJo X ( Tree Star Inc . , Ashland , OR ) . To account for cell loss during CD4+ T cell enrichment and mitogen restimulation , several calculations were performed . Total leukocyte numbers were determined by hemacytometer count after Percoll density gradient separation . 2 . 5% of the sample was stained with the following antibodies: Ly6G ( RB6-8C5 , APC-eFluor 780 , eBioscience ) , Ly6C ( HK1 . 4 , eFluor 450 , eBioscience ) , CD11b ( M1/70 , BV650 , Biolegend ) , CD11c ( N418 , BV605 , Biolegend ) , NK1 . 1 ( PK136 , AF700 , Biolegend ) , CD3 ( 17A2 , PE-Cy5 , Biolegend ) , CD4 ( RM4-5 , FITC , Biolegend ) , CD19 ( 6D5 , PE-Cy7 , Biolegend ) , Sca1 ( D7 , APC , Biolegend ) , and Siglec F ( E50-2440 , PE , BD Biosciences ) . Cells were identified as the following ( see gating S2 Fig . ) : Th cells = CD11b- CD3+ CD4+ . Eosinophils = CD11b+ CD11c- Siglec F+ . Innate lymphoid cells = lineage- Sca1+ . Dendritic cells and macrophages = CD11b+ CD11c+ Siglec F- . B cells = Siglec F- CD11b- CD11c- CD19+ . Natural killer cells = Siglec F- CD11c- NK1 . 1+ . Neutrophils = CD11b+ CD11c- Ly6G+ . Monocytes = CD11b+ Ly6C+ Siglec F- . CD8+ T cells = Siglec F- CD11b- CD3+ CD4- . The proportion of CD4+ T cells was determined by flow cytometry , and this percentage was multiplied by the total number of lung leukocytes to calculate the number of CD4+ T cells per pair of lungs . The number of CD4+ T cells in the unstimulated , CD4+ T cell enriched sample was calculated after flow cytometric analysis , as described in the previous paragraph . The number of CD4+ T cells recovered in the unstimulated , enriched sample was divided by the total number of CD4+ T cells to calculate the CD4+ T cell isolation efficiency . Cell death due to mitogen restimulation was calculated by dividing the number of CD4+ T cells recovered in the stimulated sample by the number of CD4+ T cells recovered in the unstimulated sample . The number of Cda2+ Th2 cells was determined by dividing the number of Cda2+ IL-5 and/or IL-13+ positive cells by the CD4+ enrichment and cell viability indices . Dendritic cell subsets were determined using the following antibodies and gating strategy . CD3 ( 17A2 , PE-Cy5 , Biolegend ) , CD19 ( 6D5 , PE-Cy5 , Biolegend ) , Siglec F ( E50-2440 , APC , BD Biosciences ) , CD64 ( X54-5/7 . 1 , PE , Biolegend ) , MHCII ( M5/114 . 15 . 2 , AF700 , Biolegend ) , CD11c ( N418 , BV605 , Biolegend ) , CD11b ( M1/70 , BV650 , Biolegend ) , CD103 ( 2E7 , e450 , eBioscience ) , FcεRI ( MAR-1 , PE-Cy7 , Biolegend ) , TSLPR ( PE , R&D Systems ) , IL-25R ( 9B10 , PE , Biolegend ) , IL-33Rα ( DIH9 , PE , Biolegend ) , IL-4 ( BVD6-24G2 ) , PE , Biolegend ) , and GN7-PE [78] . See gating S5B Fig . Dump = CD3+ , CD19+ , Siglec F+ . Monocyte-derived DC = Dump- , CD64+ , CD11c+ , MHCII+ , CD11b+ , CD103- , FcεRI+ . CD103+ cDC = Dump- , CD64- , CD11c+ , MHCII+ , CD103+ , CD11b- . CD11b+ cDC = Dump- , CD64- , CD11c+ , MHCII+ , CD11b+ , CD103- . While CD103 , CD11b , FcεRI , and CD64 surface markers allow convenient detection of each DC subset , it is unclear whether other developmentally and/or functionally unrelated cells can express similar markers . Consequently , genetic blockade in the developmental pathways of each DC subset were assumed to be completely penetrant , notwithstanding the persistence of cells expressing surrogate markers for each ontological subset . Lungs from mice 14-days post-infection were excised , snap frozen in liquid nitrogen , and homogenized in 3 mL of T-PER ( Thermo Fisher Scientific ) with Complete Protease Inhibitor Cocktail ( Roche , Indianapolis , IN ) . The lung homogenate was pelleted , and the supernatant was collected and stored at 80°C until analysis . Samples were diluted 1:4 in assay buffer immediately before processing . Cytokines were quantified using Luminex technology according to manufacturer instructions ( Bio-Rad , Hercules , CA ) . Lungs were removed from mice 14 days post-infection , perfused via the right ventricle with cold PBS , inflated with 10% formalin ( Thermo Fisher Scientific , Rockford , IL ) , and placed in a container of 10% formalin . Tissues were dried with organic solvent , embedded in parafin , sectioned , and stained with hematoxylin and eosin , before images were captured . Cryptococcal cells were isolated from the lungs after enzymatic digestion and density gradient separation , as described above . Cells were fixed with 3 . 7% formaldehyde and stored at 4°C until analyzed . The cells were standardized to 1x106 cells/mL and stained for 2 minutes at 25°C with 1 μg/mL of Calcofluor White ( Sigma Aldrich , St . Louis , MO ) [79] . Cells were washed and immediately processed with an epifluorescence microscope ( Axio Imager M1 , 40X/0 . 6 lens , Zeiss Filter Set 02 , Axio Cam MRc5 , Axiovision 4 . 8; Carl Zeiss , Inc . , Munich , Germany ) . ImageJ software ( NIH . gov ) was used to calculate fluorescence intesity per pixel . For flow cytometry , large and typical sized cells were first gated by forward scatter properties to distinguish size . Chitin/chitosan content was then determined by 405 nm laser excitation and fluorescence detection at ∼450 nm . Biochemical chitin/chitosan quantification was adapted from Banks et al . [30] . Purified titan cell ( >15 μm ) and typical sized cell ( <15 μm ) samples collected from infected mice were each divided into two aliquots: one treated with acetic anhydride to fully acetylate the chitin/chitosan polymer and the other was left untreated . 5 μl of purified Streptomyces griseus chitinase ( 5 mg/ml in PBS ) was added to hydrolyze chitin to N-acetylglucosamine ( GlcNAc ) and samples were incubated for 3 days at 37°C . For colorimetric determination of GlcNAc , the Morgan-Elson method was adapted for microplate readers . Chitinase-treated samples were incubated with 0 . 27 M sodium borate ( pH 9 . 0 ) and heated at 99 . 9°C for 10 minutes . Immediately upon cooling to room temperature , freshly diluted 10X DMAB solution ( 10 g p-dimethylaminobenzaldehyde in 12 . 5 ml concentrated HCl and 87 . 5 ml glacial acetic acid ) was added , followed by incubation at 37°C for 20 minutes . Absorbance at 585 nm was recorded for each sample . Standard curves were prepared from stocks of 0 . 2 to 2 . 0 mM of GlcNAc ( Sigma ) . The amount of GlcNAc was calculated as mol/g cells ( dry weight ) . The acetylated samples contained chitin plus chitosan , and the untreated sample contained chitin . The difference between the two measurements estimated the amount of chitosan . Chitin was prepared as previously published [42] . Chitin from shrimp shells ( Sigma Aldrich ) was pulverized with a mortar and pestle . 12 . 5N HCl was added , and the slurry was incubated at 40°C for 30 minutes . Chilled 10N NaOH was added until a neutral pH was attained . The sample was centrifuged at 2x104 g for 5 minutes , the supernatant was decanted , and the sample was suspended in deionized water ( dH2O ) . This step was repeated 3 times followed by a wash in ethanol . The sample was pelleted , suspended in dH2O , and filtered through a 10μm membrane ( EMD Millipore , Billerica , MA ) . The solution containing <10 μm chitin was dried with a SpeedVac ( Thermo Scientific , West Palm Beach , FL ) . The powder was weighed and resuspended in PBS to make a concentration of 50 mg/mL ( i . e . 10X ) . Endotoxin was measured by Limulus amebocyte lysate assay ( Associates of Cape Cod , East Falmouth , MA ) and was found to be less than 0 . 03 EU/mL . Chitin heptamers were purchased from Carbosynth ( Berkshire , UK ) . Mice were anesthetized and allowed to aspirate 125 μg of chitin suspended in 25 μL of PBS into the lungs at 0 , 5 , and 10 d . p . i . Pulmonary leukocytes from wildtype mice 14 days post-infection with KN99α were cultured and stimulated ex vivo for 5 hours with PMA + ionomycin ( as previously described for Th cells ) + 125 μg of chitin heptamers + golgi stop , or golgi stop alone ( unstimulated ) before processing by flow cytometry . CycLex Acidic Mammalian Chitinase ( AMCase ) and Chitotriosidase ( Chit1 ) Fluorometric Assays ( MBL International , Woburn , MA ) were used to detect chitinase activity . In brief , each of the following were added to pH 2 and pH 5 buffers containing 4-Methylumbelliferyl Chitotriose: 25 ng of recombinant AMCase , 25 ng recombinant Chit1 , 10 μL of mouse lung homogenate , 10 μL of human plasma , 10 μL of lysate antigen , or 10 μL of culture supernatant of KN99α grown in YPD . The samples were incubated at 37°C in a Synergy H1 Microplate Reader ( Biotek , Winooski , VT ) and 360 nm excitation/450 nm emission readings were obtained every 2 minutes . The relative fluorescent units ( RFU ) at 1 hour of incubation were compared to the RFU of serial dilutions of 4MU standard , and the molar concentration of cleaved chitin was calculated . The assay was performed as previously described [55] . Cell wall antigens were prepared from Cryptococcus neoformans , strain KN99α . The cells were flash frozen in liquid nitrogen , combined with glass beads , and vortexed vigorously for 2 hours at 4°C to disrupt the cells . The insoluble fraction ( i . e . , cell wall ) was analyzed for protein concentrations ( bicinchoninic acid protein assay; Thermo Fisher Scientific , Rockford , IL ) . Endotoxin levels in all antigen preparations were undetectable ( <0 . 06 U/ml ) by Limulus amebocyte lysate assay ( Associates of Cape Cod , East Falmouth , MA ) . Whole-blood samples were obtained from AIDS patients at screening for the Cryptococcus Optimal Timing of Anti-retroviral Therapy Trial in Sub-Saharan Africa [71] . Peripheral blood samples from each subject were drawn into lithium heparin tubes , diluted 2-fold with PBS , and dispensed into a tissue culture plate . Cell wall antigens containing 5 μg of protein were added to the wells , and PBS was used as the “unstimulated” control . The plates were incubated at 37°C in 5% CO2 for 20 hours . After incubation , the plasma was separated from the cells and stored at 4°C until chitinase activity analysis . P-values for pairwise comparisions were by Mann-Whitney U with Bonferroni adjustments for multiple comparisons . Global tests were by Kruskal-Wallis ANOVA . Surival curves were compared with Mann-Whitney tests . Power calculations were performed to assess appropriate sample size for all experiments . P-values ≤ 0 . 05 were considered statistically significant . All statistics and graphs were processed with Prism 6 ( GraphPad Software , La Jolla , CA ) .
|
Humans often inhale potentially pathogenic fungi in the environment . While CD4+ helper T ( Th ) cells are required for protection against invasive disease , a subset of Th cells , called Th2 cells , are associated with increased mortality and allergy/asthma morbidity . Our study aimed to unravel the cellular and molecular basis of pulmonary Th2 cell induction in response to lethal infection with Cryptococcus neoformans . Antigen-presenting cells coordinate naïve Th cell priming and differentiation , but the precise leukocyte responsible for Th2 cell expansion to pulmonary cryptococcal infection has not been determined . Using an experimental mouse model of pulmonary cryptococcosis , we show that a subset of lung-resident dendritic cells is uniquely required for Th2 cell induction . We additionally sought to identify the molecular signal received by the host that allows dendritic cells to selectively induce Th2 cells . Since parasites and fungi elicit Th2 cell responses and both produce chitin , a molecule not found in vertebrates , we hypothesized that recognition of fungal chitin is a determinant of fungal disease . Here , we demonstrate that C . neoformans chitin and the host-derived chitinase , chitotriosidase , promote Th2 cell accumulation and disease . These findings highlight a promising target of next generation therapies aimed at limiting immunopathology caused by pulmonary fungal infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Chitin Recognition via Chitotriosidase Promotes Pathologic Type-2 Helper T Cell Responses to Cryptococcal Infection
|
Congenital disorders of glycosylation ( CDG ) are a group of rare metabolic diseases , due to impaired protein and lipid glycosylation . In the present study , exome sequencing was used to identify MAN1B1 as the culprit gene in an unsolved CDG-II patient . Subsequently , 6 additional cases with MAN1B1-CDG were found . All individuals presented slight facial dysmorphism , psychomotor retardation and truncal obesity . Generally , MAN1B1 is believed to be an ER resident alpha-1 , 2-mannosidase acting as a key factor in glycoprotein quality control by targeting misfolded proteins for ER-associated degradation ( ERAD ) . However , recent studies indicated a Golgi localization of the endogenous MAN1B1 , suggesting a more complex role for MAN1B1 in quality control . We were able to confirm that MAN1B1 is indeed localized to the Golgi complex instead of the ER . Furthermore , we observed an altered Golgi morphology in all patients' cells , with marked dilatation and fragmentation . We hypothesize that part of the phenotype is associated to this Golgi disruption . In conclusion , we linked mutations in MAN1B1 to a Golgi glycosylation disorder . Additionally , our results support the recent findings on MAN1B1 localization . However , more work is needed to pinpoint the exact function of MAN1B1 in glycoprotein quality control , and to understand the pathophysiology of its deficiency .
Congenital Disorders of Glycosylation ( CDG ) are a group of genetic diseases , due to deficient protein and lipid glycosylation [1] . To date , over 60 distinct disorders have been described , comprising a very broad range of phenotypes [2] . However , the search for the culprit gene in an unsolved CDG case can be very laborious , due to its heterogeneous clinical presentation and the extensive list of candidate genes . Over the last few years , massive parallel sequencing techniques have permitted to identify the underlying genetic defect in a growing number of diseases . In case of CDG , exome sequencing led to the discovery of 7 novel disorders in less than 2 years [3] . N-glycosylation of proteins is a meticulously orchestrated process occurring in the cytosol , the endoplasmic reticulum ( ER ) and the Golgi apparatus . Responsible for the modification of secreted and transmembrane proteins , N-glycosylation is considered as one of the most widespread forms of glycosylation . A prerequisite for export of a protein out of the ER and its further transport through the secretory pathway is the adaptation of a native folding state [4] . However , protein folding is inherently error prone . Therefore , a quality control system has evolved to ensure that only properly folded proteins reach the plasma membrane . Glycoproteins unable to acquire a correct conformation will be recognized as terminally misfolded and marked for degradation by demannosylation of their glycan moiety . The α ( 1 , 2 ) -mannosidase , MAN1B1 , catalyzes the removal of the terminal mannose residue from the middle branch of Man9GlcNAc2 , hence generating a degradation signal corresponding to Man8GlcNAc2 isomer B [5] . Initially , MAN1B1 was predicted to function as an ER resident protein , based on the localization of its yeast orthologue Mns1p [6] . Also the overexpressed recombinant human orthologue was found to reside within the ER [7] . However , recent studies indicate that the endogenous MAN1B1 localizes to the Golgi apparatus of mammalian cells , implying that quality control is not confined to the ER , but stretches throughout the secretory pathway [8] . In this new model , MAN1B1 functions not only as a checkpoint for misfolded proteins escaping the ER , but also as a lectin retrieving these proteins back to the ER prior to their degradation by the 26S proteasome . Interestingly , mutations in MAN1B1 have been recently associated with non-syndromic autosomal recessive intellectual disability ( NS-ARID ) [9]–[10] . However , in the present study we show that MAN1B1 deficiency is associated with N-glycosylation disorders and causes a CDG-II syndrome . In addition , our findings suggest that MAN1B1 plays a role in protein quality control at the level of the Golgi apparatus .
To identify the causative gene in an unsolved CDG-II case , we performed whole exome sequencing by using the SeqCap EZ Human Exome Library v2 . 0 and the Illumina HiSeq2000 . A total number of 29 , 518 , 319 reads were retrieved . 89% of the reads generated passed CASAVA quality filters , were unique , and mapped to the reference genome . The mean target coverage was 56 , with 83% of the target covered at least 20-fold . Quality filtering led to a total of 29 , 437 variants , of those 11 , 026 were nonsynonymous . 702 variants had a frequency lower than 1% in the 1000 Genomes project . 8 homozygous variants and 132 compound heterozygous variants fitted with a recessive disease model . From this 140 variants , 4 were retained based on a Polyphen-2 score higher than 0 . 8 . Out of this list , 2 were homozygous and 2 compound heterozygous ( Table 1 ) . The variant detected in MAN1B1 ( MIM 604346 ) encoded the c . 1225T>C ( p . S409P ) mutation in a homozygous state . The sequencing depth of this variant was 22 reads . On the basis of the described function of MAN1B1 , this gene was the best candidate from the list . Using Sanger sequencing , we confirmed the presence of the homozygous missense mutation in exon 8 of MAN1B1 . This serine residue is highly conserved . The software tools SIFT , PolyPhen-2 and Project HOPE predicted the mutation to be damaging ( Figure S1 , Figure S2 ) . The serine 409 is located within an alpha helix of the protein . We hypothesize that the mutation would impair MAN1B1 function by either breaking or kinking this helix . Sequencing of MAN1B1 in a cohort of individuals with unsolved CDG-II allowed us to identify 6 other cases with MAN1B1 mutations . Case 2 is compound heterozygous for the same missense mutation c . 1225T>C ( p . S409P ) in exon 8 , and the nonsense mutation c . 172G>T ( pE58X ) in exon 1 . Case 3 was found to carry the homozygous missense mutation c . 1000C>T ( p . R334C ) in exon 7 ( Figure S1 ) . Cases 4 . 1 and 4 . 2 are homozygous for the same missense mutation as case 3 , namely p . R334C . The arginine residue at position 334 is located within an alpha helix of the protein and strictly conserved ( Figure S2 ) . Furthermore it is localized at the active site pocket of the protein , where it interacts with the substrate via hydrogen bonding . The conversion to a cysteine might then alter the electrostatic interactions necessary for binding of the substrate in the active site . Case 5 was found to carry a homozygous 2 bp deletion c . 1833_1834delAG ( p . T611del ) , causing a frameshift that results in an alternative protein , missing many of the residues required for substrate recognition . Case 6 carried a heterozygous 4 bp deletion in intron 9 ( c . 1445+2delTGAG ) . Sequencing of full MAN1B1 cDNA did not only confirm splicing of exon 9 due to the 4 bp deletion , but also revealed a heterozygous loss of exon 4 . To prove that this event on cDNA was due to a deletion of exon 4 , a long template PCR on genomic DNA stretching from exon 2 to exon 5 was performed . The PCR product revealed a shorter fragment of approximately 3 , 000 bp instead of the estimated 10 , 000 bp , confirming the presence of a heterozygous deletion of exon 4 ( data not shown ) . Further sequencing of the boundaries showed a deletion of 6 , 453 bp ( c . 465+1460_620+527del ) . The 4 bp deletion in intron 9 was located on the maternal allele . The deletion of exon 4 occurred de novo on the paternal allele ( Figure S1F ) . The index case ( P1 , Figure 1A ) is a 16 years old girl of Portuguese origin . Head circumference and length at birth were situated at percentile 97 . The girl presented with psychomotor retardation and hypotonia . There was slight facial dysmorphism . A brain MRI performed at the age of 7 , showed cerebellar hypoplasia with vermian atrophy . To date , she presents severe psychomotor retardation and obesity . Case 2 ( P2 , Figure 1B , 1C ) is a 12 years old boy . He was born as the first child of healthy , non-consanguineous parents of Portuguese origin . Developmental delay was noted at the age of 18 months . He presented with hypotonia and slight facial dysmorphism . Fingers were long and thin . Brain MRI was normal . To date , he displays moderate mental retardation ( IQ 43 ) and obesity ( BMI>>P97 ) . Periods of auto-aggressive behaviour and repetitive movements occur . Case 3 ( P3 , Figure 1D ) is an 11 years old girl of Turkish origin . She presented with delayed motor development at the age of 3 years . Brain MRI was normal . She showed slight facial dysmorphism , hypermobility of the joints and skin laxity . To date she presents with mild mental retardation , short stature ( length<P3 ) , truncular obesity ( weight at P50 ) and macrocephaly ( head circumference at P97 ) . Case 4 . 1 ( P4 . 1 , Figure 1E ) and case 4 . 2 ( P4 . 2 , Figure 1F ) are siblings of Turkish origin . Parents are consanguineous ( first cousins ) . Case 4 . 1 is a 24 years old woman . She came under medical attention because of mild psychomotor retardation and hypotonia . Brain MRI was normal . She showed slight facial dysmorphism , hypermobility of the joints and skin laxity . She presented an important scoliosis , which was corrected by surgery at the age of 18 . To date , she presents mild mental retardation ( IQ 62 ) and truncular obesity . Case 4 . 2 is a 18 years old man . He presented with psychomotor retardation and hypotonia . Brain MRI was normal . There was only slight facial dysmorphism and skin laxity . To date , he presents mild mental retardation ( IQ 69 ) and obesity . Case 5 ( P5 , Figure 1G , 1H ) is a 13 years old boy . He was born at term as the first child of healthy , non-consanguineous parents of Belgian origin . He presented with hypotonia and slight dysmorphic features . Fingers were long and thin . Hypermobility of the joints and skin laxity were noticed , as well as a pectus excavatum . Dilatation of the aortic root was seen on cardiac ultrasound . Brain MRI performed at the age of 3 months was normal . At the age of 5 years he was diagnosed with epilepsy ( absences ) . To date , he presents mild mental retardation and is able to use simple language . Growth and weight evolution are normal , though he displays truncular fat accumulation . Case 6 ( P6 , Figure 1I , 1J ) is a 12 years old girl . She was born as the first child of healthy , non-consanguineous parents of Belgian origin . Reduced fetal movements were observed during pregnancy . After birth , she was diagnosed to have a muscular ventricular septum defect ( VSD ) . Spontaneous closure occurred . Developmental delay was notified at the age of 18 months . She displayed hypotonia and a delay in motor development . There was slight facial dysmorphism , hypermobility of the joints , and skin laxity . Fingers were long and thin . At the age of 5 , she was diagnosed with an autism spectrum disorder and mild mental retardation ( IQ 52 ) . Brain MRI showed multiple , small white matter lesions . Rapid bone maturation and early puberty occurred because of overweight . Clinical features of all patients are furthermore compared in Table 2 . Capillary zone electrophoresis ( CZE ) of serum showed a type 2 transferrin pattern with an increase of trisialotransferrin ( representing 31 to 41% of the transferrin isoforms , normal range: 1–8% ) in all affected cases ( Table S1 ) . Isoelectrofocusing of serum transferrin ( sTF-IEF ) confirmed these results ( Figure 2 ) . This categorized them as CDG-II . The type 2 sTF-IEF pattern pointed to an N-glycan remodelling defect . Therefore , we investigated the Golgi glycosylation in more detail and determined the structures of the N-linked glycans on serum glycoproteins from controls and affected cases by using mass spectrometry ( Figure 3 ) . In MAN1B1-deficient individuals ( P2 , P5 ) , we observed the accumulation of the hybrid-type glycan structure NeuAc1Hex6HexNAc3 ( m/z 2 , 390 ) , present together with the corresponding fucosylated NeuAc1Hex6HexNAc3dHex1 ( m/z 2 , 564 ) . We also observed a significant increase of the sialylated NeuAc1Hex5HexNAc3 ( m/z 2 , 186 ) with respect to the control . This glycan also appeared to be present together with the corresponding fucosylated one ( NeuAc1Hex5HexNAc3dHex1; m/z 2 , 360 ) which was not present in the control . In MAN1B1-CDG cases , there was also a marked increase of Hex6HexNAc2 ( m/z 1 , 783 ) , but no significant variations of Hex5HexNAc2 ( m/z 1 , 579 ) . These results were confirmed in a second group of MAN1B1-CDG cases ( P1 , P4 . 1 , P4 . 2 and P6 ) that were analysed independently ( Figure S3 ) . We noticed the presence of hybrid-type glycan structures NeuAc1Hex5HexNAc3 and NeuAc1Hex6HexNAc3 . These glycans appeared to be present together with the corresponding fucosylated NeuAc1Hex5HexNAc3dHex1 and NeuAc1Hex6HexNAc3dHex1 , respectively . These four species were not detected in controls . Because the N-glycans abnormalities suggested a Golgi glycosylation defect , Golgi morphology was investigated . Staining with the Golgi markers TGN46 and GM130 detected significant alterations in Golgi morphology in fibroblasts of all affected cases , with marked dilatation and fragmentation ( Figure 4 ) . We used quantitative real-time PCR ( qPCR ) to investigate the effects of the mutations on MAN1B1 expression by comparing MAN1B1 cDNA levels amplified from mRNA isolated from control and MAN1B1-deficient fibroblasts , cultured in the presence or absence of the translation inhibitor puromycin . Compared to controls , most of the MAN1B1-deficient individuals presented an expression increased 1 . 22 to 1 . 49 fold compared to the wild-type MAN1B1 transcript . Case 2 , who harbours the nonsense mutation p . E58X , showed an expected reduced expression level of 40% compared to control . Case 6 showed an expression level of 2% compared to control , likely because the deletions on both alleles lead to instability of the transcript ( Figure 5A , left panel ) . Culturing control and MAN1B1-deficient fibroblasts in presence of puromycin did not markedly alter these expression levels ( Figure 5A , right panel ) . To further investigate the effects of the mutations on MAN1B1 , we performed immunoblotting using control and MAN1B1-deficient fibroblasts . As shown in Figure 5B , an extremely low level of endogenous MAN1B1 was detected in affected cases . The steady-state level of MAN1B1 in P3 was reduced to 24% , suggesting that the protein was slightly more stable than in the other affected individuals . We then compared the subcellular localization of MAN1B1 in control and MAN1B1-deficient fibroblasts by confocal microscopy . In accordance with the data recently published by Pan and coworkers [8] , the endogenous MAN1B1 showed a clear perinuclear Golgi-like distribution ( Figure 5C and Figure S4 , first row ) rather than a typical diffuse ER localization . As expected from the western blot analysis , some of the MAN1B1-deficient individuals did not show any staining . A faint Golgi localization could be seen in some of the affected cases , despite the largely predominant Golgi staining for giantin ( Figure 5C , Figure S4 ) . As mentioned above , MAN1B1 displayed a perinuclear Golgi distribution . Double labelling of control cells with MAN1B1 and the rough ER marker PDI showed a clear separation of the two proteins ( Figure 6A ) . A similar pattern was obtained regarding the distribution of both MAN1B1 and the ER protein GRP78 , suggesting that the mannosidase was located apart from the ER compartment ( data not shown ) . In order to gain insights into the precise MAN1B1 subcellular localization , we examined its distribution in the Golgi and the ER-Golgi intermediate compartment ( ERGIC ) . MAN1B1 showed a high level of colocalization with both the trans-Golgi marker GPP130 and the cis-Golgi marker giantin . Only weak colocalization with the ERGIC marker ERGIC-53 was noted ( Figure 6A ) . The intensity of the different protein markers was plotted against the distance , using the RGB profiler plugin from Image J ( Figure 6B ) . The level of colocalization was quantified as a percentage of the area occupied by the colocalized spots from the total labelled spots , based on Pearson's coefficient . These results showed a much higher degree of colocalization for MAN1B1 versus GPP130 ( 80%±3 ) or giantin ( 84%±2 ) , than for MAN1B1 versus ERGIC-53 ( 41%±4 ) , which is consistent with the qualitative data . These results strongly suggest that MAN1B1 is widely distributed within the Golgi apparatus and is hardly or not detectable in the ER . The impact of MAN1B1 on Golgi glycosylation was studied in MAN1B1-deficient fibroblasts . The cells of individuals mutated in MAN1B1 were further investigated by metabolic labelling . MAN1B1 is reportedly known to be the key enzyme responsible for the trimming of Man9GlcNac2 species to Man8GlcNAc2 species [5] . To address the potential deleterious effect of the mutations on MAN1B1 function , a structural analysis of the N-linked protein glycans was performed . Therefore , cells were pulse-labelled with 2-[3H] mannose and chased for 2 h . In healthy controls as well as in MAN1B1-deficient cells , Man8GlcNAc2 , Man9GlcNAc2 and Glc1Man9GlcNAc2 structures were found after the pulse period ( Figure 7A , 7B and Figure S5A ) . As expected , when the control glycoproteins were chased for 2 hours , an increase of Man8GlcNAc2 species was observed indicating that the Man9GlcNAc2 species can serve as substrate for further trimming by ER/Golgi mannosidases . In contrast , only a slight increase of Man8GlcNAc2 species was seen in MAN1B1-deficient cells ( Figure S5B ) . The processing efficacy from Man9 to Man8 was hence estimated for both healthy controls and MAN1B1-deficient cells . Figure 7C shows that this processing efficacy was estimated to 100%±6 . 9 in control cells but ranged between 40 . 1%±5 . 9 to 43 . 6%±2 . 2 for MAN1B1-deficient cells . Surprisingly , this value was much lower for P1 cells , with an average efficacy of 17 . 7%±3 . 8 . These results strongly suggest that in MAN1B1-deficient cells , a delay in the trimming of Man9GlcNAc2 species occurs . For statistical relevance , we compared the mean processing efficacy of the 3 control cell lines to the mean processing efficacy of all 4 MAN1B1-deficient cell lines ( Figure 7D ) . We conclude that the deficiency of processing from Man9GlcNAc2 species to Man8GlcNAc2 species is not complete , since this value remains of 36 . 1%±12 . 8 ( p value<0 . 001 ) in cells of affected individuals ( Figure 7D ) . This is likely due to the action of other ER/Golgi mannosidases or alpha-mannosidase like proteins such as EDEM proteins , which enable trimming of Man9 to Man8 species in mammalian cells .
MAN1B1 encodes the endoplasmic reticulum mannosyl-oligosaccharide 1 , 2-alpha-mannosidase ( ERManI ) , which cleaves the terminal mannose from the middle branch of Man9GlcNAc2 , then producing Man8GlcNAc2 isomer B . It is believed to play a key role in glycoprotein quality control , by targeting terminally misfolded proteins for ERAD . The capacity to distinguish between terminally misfolded proteins and properly folded intermediates as appropriate ERAD substrates is indeed known to be one of the early steps in protein quality control . Several studies in both yeast and mammalian cells already provided evidence that the removal of terminal mannose residues from asparagine-linked Man9GlcNAc2 glycan structures generates a signal for ERAD [11] . However , the exact function of MAN1B1 is still unclear . In the present study , we definitely link mutations in MAN1B1 to pathology that presents as a deficiency of glycosylation . The first patients with MAN1B1 deficiency were detected in a study on NS-ARID [9] . We identified MAN1B1 mutations in 7 cases with unsolved CDG-II . All patients presented a certain degree of intellectual disability , but also had facial dysmorphism and obesity . Furthermore , some of them displayed joint hypermobility and skin laxity . Dysmorphic features were similar for all cases , i . e . downslanting palpebral fissures , hypertelorism , large low set ears , a hypoplastic nasolabial fold and a thin upper lip . These clinical characteristics correspond to the phenotype of the formerly published patients , although the author assigned the dysmorphism to family traits rather than to the disease [9] . Overall , individuals with MAN1B1-CDG present a mild phenotype , which is in concordance with studies in Saccharomyces cerevisiae , since the yeast orthologue MNS1 is not essential for growth [12] . The mild disease resulting from MAN1B1 deficiency however contrasts with another defect of N-glycan trimming , which is caused by mutations in the glucosidase I ( MOGS ) gene . A single case of MOGS deficiency has been reported so far , characterized by dysmorphism and hypotonia , leading to the death of the affected infant [13] . We believe that MAN1B1 deficiency belongs to the relatively frequent CDG . In our cohort of solved CDG-II cases , the occurrence of MAN1B1-CDG is indeed over 25% , more frequent than TMEM165-CDG , COG5-CDG or COG7-CDG ( 14% respectively ) . From the overall number of solved CDG cases ( CDG-I plus CDG-II ) in our database , MAN1B1-CDG is the fifth most frequently encountered CDG type ( 1 . 9% ) , right behind SRD5A3-CDG ( 2 . 6% ) or PMI-CDG ( 2 . 8% ) . PMM2-CDG and ALG6-CDG are the most frequent ( 69 . 9% and 8 . 5% , respectively ) . The genetic results were supported by a significant decrease in steady-state levels of the MAN1B1 protein . Enzymatic activity was measured indirectly by pulse-chase experiments with 2-[3H] mannose , which showed a significant delay in the trimming of Man9GlcNAc2 to Man8GlcNAc2 . The trimming efficiency in MAN1B1-deficient cells was still up to 36% . This activity is likely due to the action of other ER/Golgi class I mannosidases or alpha- mannosidase-like proteins such as EDEM proteins , cleaving the terminal mannose residue at a lower rate . Former studies localized the overexpressed recombinant MAN1B1 protein within the ER or the ERGIC . The S . cerevisiae homologue of MAN1B1 , Mns1p , was originally demonstrated to function as an ER-resident protein [14] . The human MAN1B1 was then identified and cloned on the basis of its close sequence homology with MNS1 and was therefore predicted to localize and function in the ER [7] [15] , despite a more than 50% sequence homology with Golgi α ( 1 , 2 ) -mannosidases IA , IB an IC [15] . We could clearly demonstrate that the endogenous MAN1B1 shows a Golgi distribution in primary skin fibroblasts . Further determination of its localization within the organelle revealed an equal distribution throughout the Golgi apparatus . This result confirms the initial observation of Sifers and coworkers , who used a panel of highly specific mAbs to demonstrate that human endogenous ERManI is O-glycosylated and localized in the Golgi apparatus [8] . As expected , only a faint or even no Golgi localization could be seen in MAN1B1-deficient cells . One can wonder why the MAN1B1 protein , which is assumed to play a pivotal role in ERAD substrate generation , is physically separated from the ER . Traditionally , MAN1B1 was believed to function as a molecular clock in ER quality control . It was speculated that the slow processing activity of MAN1B1 determined the time needed for a protein to acquire its native folding state . In case of a terminally misfolded protein , futile folding cycles would provide MAN1B1 prolonged access to the glycan , resulting in its demannosylation and thereby marking the protein for proteasomal degradation [16] . However , recent findings indicate that the quality control system is not confined to the ER but stretches throughout the secretory pathway . In this model , MAN1B1 operates as a checkpoint within the Golgi apparatus , recycling misfolded proteins that escaped ERAD back to the ER , by interacting with the COP-I machinery . It is assumed that demannosylation of the glycan moiety will prevent the recycled protein from reentering the folding cycle after its retrieval to the ER . Instead , its degradation signal will be recognized by ER resident lectins , leading to proteasome mediated disposal of the misfolded protein [8] . How does MAN1B1 deficiency leads to disease ? The purpose of quality control is to minimize the level and toxicity of misfolded proteins within the cell . So , failure of a checkpoint to recognize ERAD substrates might have serious repercussions on cell function . However , patients with MAN1B1-CDG present only a mild phenotype . Hence , one could assume that additional checkpoints exist in the Golgi apparatus . This hypothesis is supported by the observation that other Golgi α ( 1 , 2 ) -mannosidases were shown to be involved in glycoprotein quality control . In case of MAN1B1 deficiency , these Golgi mannosidases might be recruited for post-ER surveillance at the expense of their normal processing activity , leading to a delay in the trafficking of glycoproteins through the secretory pathway . If so , a relative accumulation of glycoproteins will occur within the Golgi apparatus prior to their impaired secretion . Storage of proteins will then activate a stress response increasing the capacity of the Golgi cisternae . The fact that MAN1B1 deficient cells display an altered Golgi morphology , characterized by dilatation and fragmentation of the cisternae , could support this hypothesis . However , this suggestion of a Golgi storage disease is in conflict with the model raised by Pan and coworkers , rather implying an increased secretion of misfolded glycoproteins in MAN1B1-depleted cells [8] . Furthermore , we believe that part of the phenotype might be explained by these Golgi abnormalities . First of all , disruption of Golgi morphology could explain the glycosylation deficiency observed in MAN1B1-deficient patients , by affecting the stability of key proteins responsible for the creation of the microenvironment necessary for proper Golgi glycosylation . Next , some of the observed clinical features ( skin laxity , joint hypermobility ) are also described in patients with COG5- , COG7- and ATP6VOA2-CDG . Interestingly , MAN1B1-deficient cells , as well as ATP6V0A2- and COG-deficient cells , show an altered Golgi morphology [17]–[18] . Furthermore , impaired secretion and intracellular retention of tropoelastin were already proven to result in skin laxity and joint hypermobility in patients with ATP6V0A2-CDG , implying that a similar mechanism could cause skin laxity in MAN1B1-deficient patients [19] . Finally , since the secretory pathway plays also an important role in neurotransmission and endocrine regulation , one can hypothesize that impaired protein secretion underlies the intellectual disability and obesity in our patients . In conclusion , we have identified MAN1B1 deficiency as a relatively frequent CDG-II , since the occurrence of MAN1B1-CDG is over 25% in our cohort of unsolved CDG-II cases . Overall , the clinical picture is mild , comprising not only intellectual disability , but also truncal obesity and facial dysmorphism , hence defining MAN1B1-CDG as a syndrome . Furthermore , our results confirm that MAN1B1 is indeed localized to the Golgi apparatus . We hypothesize that part of the phenotype is linked to the disruption of Golgi morphology . Though , more work is needed to pinpoint the exact function of MAN1B1 in glycoprotein quality control and to understand the pathophysiology of its deficiency .
Research on patients' cells was prospectively reviewed and approved by the Ethical Committee of the University Hospital of Leuven . PLOS consent forms were obtained for publication of clinical pictures . Capillary zone electrophoresis ( CZE ) and isoelectric focusing of serum transferrin ( sTF-IEF ) were performed as previously described [20] . Primary fibroblasts from patients and controls were grown from a skin biopsy and cultured in Dulbecco's modified Eagle medium DMEM/F12 ( Life Technologies ) supplemented with 10% fetal bovine serum ( Clone III , HyClones ) at 37°C under 5% CO2 . For the index case , mutation analysis was achieved by whole exome sequencing . Genomic DNA was sheared by sonication , platform-specific adaptors were ligated , and the resulting fragments were size selected . The library was captured using the SeqCap EZ Human Exome Library v2 . 0 ( Roche NimbleGen ) , and 2× 76 bp paired-end reads were generated on a HiSeq2000 ( Illumina ) . Reads that did not pass Illumina's standard filters were removed prior to alignment . Remaining reads were aligned to the reference human genome ( hg19 ) , using the CASAVA pipeline . Quality filtering was applied by excluding variants found in less than 5 reads and variants detected in less than 15% variant reads . Synonymous variants were excluded . To retrieve the quality-filtered private variants , those with a higher frequency than 1% in the 1000 Genomes project were excluded . Based on recessive inheritance , a subsequent prioritization was applied using the following criteria: a minimum of 80% variant reads for potential homozygous variants and between 20 and 60% for compound heterozygous variants . Variants with a Polyphen-2 score of less than 0 . 8 were filtered out . Total RNA was isolated from the primary fibroblasts of the patient and control cells using the RNeasy Kit ( Qiagen ) . The amount of extracted RNA was quantified using a NanoDrop spectrophotometer ( Thermo Scientifics ) and the purity of RNA was checked by the ratio of the absorbance at 260 and 280 nm . 2 µg of purified total RNA was then subjected to reverse transcription with the First-Strand cDNA synthesis Kit ( GE Healthcare ) following the manufacturer's instructions . For PCRs , cDNAs obtained after reverse transcription were diluted 1∶5 . Genomic DNA was extracted from white blood cells or fibroblasts from the patients and controls . Primers ( available on demand ) were designed to amplify the different exons of the MAN1B1 gene ( GenBank accession number NM_016219 . 4 ) , including at least 50 bp of the flanking intronic regions . Consecutive primers within intron 3 and intron 4 were designed to find deletion breakpoints . Standard cDNA- and gDNA-based PCR reactions were based on 1 µl DNA in a total volume of 25 µl , 0 . 2 µl Platinum Taq polymerase ( Invitrogen ) . Standard reaction conditions were 3 min at 95°C , then 10 cycles of 30 sec at 95°C , 30 sec at 65°C ( −1°C each cycle ) , 2 min at 72°C , followed by 25 cycles of 30 sec at 95°C , 30 sec at 55°C and 2 min at 72°C . The reaction was finished by an incubation of 5 min at 72°C . A long template PCR stretching from exon 2 to exon 5 was performed using the Expand Long Template PCR System ( Roche Applied Science ) . The PCR reaction was based on 4 µl DNA in a total volume of 50 µl , 0 . 75 µl Expand Long Template Enzyme Mix , 5 µl of Buffer 2 , a final 300 nM concentration of each primer and a final 500 µM concentration of dNTPs . Reaction conditions were 2 min at 95°C , then 10 cycles of 10 sec at 95°C , 30 sec at 65°C , 8 min at 68°C , followed by 25 cycles of 15 sec at 94°C , 30 sec at 65°C and 8 min ( +20 sec each cycle ) at 68°C . The reaction was completed with a final elongation of 7 min at 68°C . For the sequencing of the resulting PCR product , the BigDye Terminator Ready reaction cycle sequencing kit v . 3 . 1 ( Applied Biosystems ) was used . Analysis of the results was performed on an ABI3100 Avant ( Applied Biosystems ) . The glycans from total plasma N-glycoproteins were released as previously described [21]–[22] . Analysis of N-linked glycans on plasma glycoproteins was carried out by matrix-assisted laser desorption ionization time-of-flight mass spectrometry ( Ciphergen ) essentially as described for MALDI-TOF-MS , and performed in a positive linear ion mode [21] . The reference samples were anonymous plasma samples from patients without CDG and without a lysosomal storage disease . For cases P1 , P4 . 1 , P4 . 2 and P6 , the lab of P . Mills and colleagues performed the analysis . For cases P2 , P3 and P5 , the lab of L . Sturiale and colleagues performed the analysis . Anti-MAN1B1 monoclonal antibodies used for indirect immunofluorescence were kindly provided by the lab of Richard N . Sifers and colleagues ( Baylor College of Medicine , Houston , USA ) . Monoclonal anti-MAN1B1 antibodies used for western blotting were from Novus Biologicals . Polyclonal antibodies anti-GPP130 and anti-Giantin were purchased from Covance , polyclonal antibodies anti-PDI from Cell Signalling , polyclonal antibodies anti-ERGIC53 from Sigma-Aldrich and polyclonal anti-GRP78 BiP antibodies from Abcam . 30 µg of proteins were analysed by SDS-PAGE and immunoblotted onto Hybond-ECL nitrocellulose membrane ( Amersham Biosciences UK limited ) , with the indicated antibodies . Cells were rinsed twice with ice-cold phosphate buffer saline ( PBS ) and then lysed for 5 min on ice in cell lysis buffer ( 25 mM Tris-HCl , 150 mM NaCl pH 7 . 6 , supplemented with 1% Triton X-100 , 1% sodium deoxycholate and 0 . 1% SDS ) . Proteins were quantified by using the Micro BCA Protein BSA Assay Kit ( Fischer Scientific ) . Signals were detected using the Western Lightning-ECL , Enhanced Chemiluminescence Substrate ( Perkin Elmer ) according to the manufacturer's instructions . Signal detection was performed by autoradiography and quantified with the ImageQuant LAS 4000 ( GE Healthcare ) , using the ImageQuant LAS 4000 Control Software for analysis . Cells were grown on glass coverslips , washed once with PBS and fixed by incubation for 30 min with 4% paraformaldehyde in 0 . 1 M sodium phosphate buffer ( pH 7 . 2 ) at room temperature . The coverslips were rinsed three times with PBS for 5 min . The fixed cells were then permeabilized with PBS containing 0 . 5% Triton X-100 for 5 min and washed three times with PBS . Next , the fixed cells were incubated for at least 60 min with a blocking solution containing 0 . 1% Triton X-100 ( Sigma-Aldrich ) , 1% BSA ( Roche Applied Science ) , and 5% normal goat serum ( Life Technologies ) in PBS . After blocking , the cells were incubated overnight at 4°C with primary antibodies diluted in the previously described blocking solution . The cells were then washed again three times , followed by incubation with the Alexa 488- or Alexa 568-conjugated secondary antibodies ( 1∶500 , Life Technologies ) for 1 h at room temperature in the dark . Immunostaining was detected through an inverted Leica SP5 spectral microscope with a 63× oil immersion lens at room temperature . Data were therefore collected using the LAS 6000 AF software and finally processed in Adobe Photoshop 7 . 0 ( Adobe systems ) . Colocalization was quantified using the colocalization plugin of ImageJ . The channel ratio was always set at 90% . PCRs were performed for the MAN1B1 gene ( GenBank NM_016219 . 4 ) and the house-keeping gene HPRT ( Hypoxanthine PhosphoRibosylTransferase , GenBank NM_000194 ) , which was used as an endogenous control for normalization . PCR primers were designed using Primer 3 software . All the primers were synthesized by Integrated DNA Technologies . PCR primers used are the followings: MAN1B1 ( 5′-CTGTGGATCCCCGCCCGGAA-3′ and 5′-TCAGATGCACTGGTGTGCCCTGT-3′ ) and HPRT ( 5′-GCCAGACTTTGTTGGATTTG-3′ and 5′-CTCTCATCTTAGGCTTTGTAT TTTG-3′ ) . PCRs ( 20 µl ) were performed using 2× LightCycler 480 SYBR Green I Master ( Roche Applied Science ) with 2 µl of a 1∶5 cDNA dilution and 300 nM final concentration of each primer . Data were analysed using the LightCycler 480 Software ( Roche Applied Science ) . The comparative threshold cycle method described by Livak and Schmittgen was used to quantify the results [23] . In addition , serial dilutions were used to create standard curves for relative quantification , and the expression of the MAN1B1 transcript was normalized to HPRT expression . Fibroblasts ( 8×106 cells per labelling ) were grown overnight in a 175 cm2 tissue culture flask . After 24 h , cells were preincubated for 45 min in 0 . 5 mM glucose and then pulse-radiolabelled for 2 h with 150 µCi of 2-[H3]-mannose ( 16 Ci . mmol−1 , Amersham Biosciences ) at the same glucose concentration . For chase experiments , the radioactive culture medium was replaced by a medium containing 5 mM glucose and 5 mM mannose . After metabolic labelling or chase period , cells were scraped with 1 . 1 ml MeOH/H2O ( 8∶3 ) followed by the addition of 1 . 2 ml CHCl3 . Sequential extraction of oligosaccharide material was performed as previously described [24] . Glycoproteins fraction obtained at the end of the sequential extraction were digested overnight at room temperature with trypsin ( 1 mg . ml−1; Sigma-Aldrich ) in 0 . 1 M ammonium bicarbonate buffer , pH 7 . 9 . The resulting glycopeptides were treated with 0 . 5 U PNGase F ( Roche Applied Science ) in 50 mM phosphate buffer , pH 7 . 2 for 4 h to release the oligosaccharides from the peptides . They were subsequently desalted on Bio-Gel P2 columns and eluted with 5% acetic acid . The oligosaccharides were afterwards separated by HPLC on an amino derivated Asahipak NH2P-50 column ( 250×4 . 6 mm; Asahi ) applying a gradient of acetonitrile/H2O ranging from 70∶30 over 50∶50 over 90 min at a flow rate of 1 ml per min . Oligosaccharides were identified on the basis of their retention time , compared to standard glycans , as previously described [24] . Elution of the labeled oligosaccharides was monitored by continuous β-counting with a flo-one β detector ( Packard ) . HPLC chromatograms were analysed using the ProFSA software ( Perkin Elmer ) . To compare the percentage of specific oligomannoside structures , a fixed amount ( 50 , 000 dpm ) of radioactivity was injected into HPLC . The counts in each peak were calculated on the basis of the peak area and normalized against the total number of counts in the injected samples . Moreover , the differences in the number of mannose residues between the different oligomannoside structures were taken into account . Hence , the radioactivity associated to the Man5 species was multiplied by 9/5 to be comparable to the radioactivity associated to the Man9 species .
|
Glycosylation concerns the synthesis of sugar chains , their addition onto proteins and/or lipids , and their subsequent modifications . The resulting glycoproteins serve many critical roles in metabolism . The importance of this pathway is illustrated by a group of diseases called Congenital Disorders of Glycosylation ( CDG ) . To date , over 60 distinct disorders have been described . In the present study , we demonstrated that mutations in MAN1B1 , a gene formerly linked to non-syndromic intellectual disability , cause CDG . We described 7 patients with similar clinical features ( developmental delay , intellectual disability , facial dysmorphism and obesity ) , defining MAN1B1-CDG as a syndrome . Furthermore , we confirmed that the MAN1B1 protein is localized into the Golgi apparatus instead of the endoplasmic reticulum , where it was assumed to reside for many years . Moreover , we showed that mutations in MAN1B1 lead to alterations of the Golgi structure .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
MAN1B1 Deficiency: An Unexpected CDG-II
|
Mature human B cells infected by Epstein-Barr virus ( EBV ) become activated , grow , and proliferate . If the cells are infected ex vivo , they are transformed into continuously proliferating lymphoblastoid cell lines ( LCLs ) that carry EBV DNA as extra-chromosomal episomes , express 9 latency-associated EBV proteins , and phenotypically resemble antigen-activated B-blasts . In vivo similar B-blasts can differentiate to become memory B cells ( MBC ) , in which EBV persistence is established . Three related latency-associated viral proteins EBNA3A , EBNA3B , and EBNA3C are transcription factors that regulate a multitude of cellular genes . EBNA3B is not necessary to establish LCLs , but EBNA3A and EBNA3C are required to sustain proliferation , in part , by repressing the expression of tumour suppressor genes . Here we show , using EBV-recombinants in which both EBNA3A and EBNA3C can be conditionally inactivated or using virus completely lacking the EBNA3 gene locus , that—after a phase of rapid proliferation—infected primary B cells express elevated levels of factors associated with plasma cell ( PC ) differentiation . These include the cyclin-dependent kinase inhibitor ( CDKI ) p18INK4c , the master transcriptional regulator of PC differentiation B lymphocyte-induced maturation protein-1 ( BLIMP-1 ) , and the cell surface antigens CD38 and CD138/Syndecan-1 . Chromatin immunoprecipitation sequencing ( ChIP-seq ) and chromatin immunoprecipitation quantitative PCR ( ChIP-qPCR ) indicate that in LCLs inhibition of CDKN2C ( p18INK4c ) and PRDM1 ( BLIMP-1 ) transcription results from direct binding of EBNA3A and EBNA3C to regulatory elements at these loci , producing stable reprogramming . Consistent with the binding of EBNA3A and/or EBNA3C leading to irreversible epigenetic changes , cells become committed to a B-blast fate <12 days post-infection and are unable to de-repress p18INK4c or BLIMP-1—in either newly infected cells or conditional LCLs—by inactivating EBNA3A and EBNA3C . In vitro , about 20 days after infection with EBV lacking functional EBNA3A and EBNA3C , cells develop a PC-like phenotype . Together , these data suggest that EBNA3A and EBNA3C have evolved to prevent differentiation to PCs after infection by EBV , thus favouring long-term latency in MBC and asymptomatic persistence .
Epstein-Barr virus ( EBV ) is a human gamma-herpesvirus that infects about 95% of the world’s adult population . Although infection is nearly always benign , on rare occasions it is etiologically associated with various human cancers , including Burkitt lymphoma ( BL ) , diffuse large B cell lymphoma ( DLBCL ) , Hodgkin’s lymphoma , and post-transplant lymphoproliferative disease ( PTLD ) . EBV is also the primary cause of acute infectious mononucleosis , aka glandular fever [1–3] . Although EBV can be found in epithelial , natural killer ( NK ) , and T cells , it preferentially infects mature B cells . Infection of primary human CD19+ve B lymphocytes ex vivo results in their activation , growth , and sustained proliferation so that lymphoblastoid cell lines ( LCLs ) are established . These cells have a B-blast–like phenotype , carry EBV genomes as extra-chromosomal episomes , and express a limited number of viral latency-associated factors; these include 6 nuclear proteins ( EBNA1 , EBNA2 , EBNA3A , EBNA3B , EBNA3C , and EBNA-LP ) , 2 membrane-associated proteins ( LMP1 and LMP2 ) , 2 small non-coding RNAs , and several microRNAs ( reviewed in [2 , 4] ) . This pattern of viral gene expression is known as the latency III or growth program . Infection of mature B cells in vivo is thought to initiate a similar program of activation and growth , but only transient proliferation , because under the influence of CD4+ve T helper ( Th ) lymphocytes these B cells differentiate in the lymphoid structures called germinal centres ( GC ) to form a pool of memory B cells ( MBC ) carrying latent EBV [5–8] . These EBV genome–containing , long-lived MBC can then enter the peripheral circulation and , expressing no viral proteins , form a major site of lifelong EBV persistence . To complete the life cycle , reactivation of EBV into a program of gene expression that leads to replication and release of virions is thought to occur in a small number of B cells differentiating into antibody-secreting plasma cells ( PCs ) [9] . The EBNA3 latency-associated proteins are a family of 3 large ( approximately 160kD ) EBV nuclear antigens expressed from 3 genes arranged in tandem within a complex transcription unit only functional in B cells ( reviewed in [10] ) . All 3 proteins can act as transcription factors that regulate host gene expression [11] . They do not bind DNA directly , but , via contacts with cellular cofactors , are largely targeted to chromatin at gene promoters and/or distal regulatory elements [12–15] . Often , regulation of transcription involves long-distance chromatin interactions ( chromosome ‘looping’ ) between promoter and enhancer elements mediated by EBNA3 proteins [16–19] . EBNA3A and EBNA3C have been widely reported to reduce expression of various cell cycle regulatory factors found in latently infected B cells . For instance , EBNA3A and EBNA3C together repress expression of the pro-apoptotic BCL2-family member BIM and the senescence-inducing cyclin-dependent kinase inhibitor ( CDKI ) p16INK4a—2 tumour suppressors that would otherwise contribute to an oncogenic stress response resulting from virus infection driving unscheduled DNA synthesis and cell proliferation ( reviewed in [20] ) . In addition , EBNA3A and EBNA3C directly repress transcription of the p16INK4a-related CDKI , p15INK4b [21] and—by inducing precursors of oncogenic microRNAs miR-221 and miR-222—inhibit expression of CIP/KIP family CDKIs , p57KIP2 and p27KIP1 [19] . Furthermore , EBNA3A- and EBNA3C-mediated repression of transcription of genes encoding the related INK4 CDKIs p16INK4a ( CDKN2A ) and p15INK4b ( CDKN2B ) is associated with binding of these viral factors to chromatin in and around the gene loci and subsequent deposition of repressive histone marks by polycomb protein complexes [21] . By targeting these various genes , operationally EBNA3A and EBNA3C can behave as oncoproteins , but these same functions facilitate early MYC-driven cell proliferation soon after infection [10] . In contrast , EBNA3B is not necessary to establish or maintain LCL proliferation [10] and can act as a tumour suppressor in a humanized-mouse model and in some human tumours—at least in part—by facilitating immune cell trafficking and T cell surveillance [22] . Of all the mammalian CDKIs , there is one specifically linked to B cell function: the INK4 family member p18INK4c , encoded by the CDKN2C gene . Although biochemically and structurally p18INK4c very closely resembles p16INK4a , functionally it appears to be specialized for differentiation [23 , 24] . The requirement for p18INK4c in PC differentiation was first indicated by a significant reduction of antibody secretion in p18INK4c-deficient mice during primary and secondary immune responses . It was confirmed when ectopic p18INK4c expression was shown to rescue PC differentiation in vitro [24 , 25] . Consistent with these observations , reduced proliferation and concurrent differentiation towards a PC phenotype correlates with increased p18INK4c expression in mitogen-activated CD19+ve human B cells [26] . Moreover , increased amounts of p18INK4c ( relative to its preferred target , the cyclin-dependent kinase CDK6 ) are linked to differentiation in other cell lineages , including osteoclasts and myeloid cells [23] . There is general agreement that in vivo , differentiation of B cells to PCs can be initiated from various cell populations , including antigen-activated extra-follicular B cells ( e . g . , marginal B cells and B1 cells ) , from GC B cells , and from circulating MBC [27–29] . In each case the genome-wide transcription patterns of the B cells and derived PCs are significantly different , indicating that there is substantial reprogramming of gene expression networks as differentiation proceeds [28] . This is a very complex process involving many factors , but there is an emerging consensus of opinion that at least 3 transcriptional regulators are essential for differentiation to occur efficiently ( reviewed in [28 , 30–32] ) . These factors are B lymphocyte-induced maturation protein-1 ( BLIMP-1 ) , interferon-regulatory factor 4 ( IRF4 ) , and a specifically spliced form of X-box binding protein-1 ( XBP-1 ) . Although EBV-transformed LCLs generally express high levels of IRF4 [33 , 34] , they express little BLIMP-1 or XBP-1 ( see below ) . It is the lack of sufficient BLIMP-1 that probably explains why EBV-activated B cells generally remain self-renewing blasts and fail to undergo default terminal differentiation to antibody-secreting PCs unless additional endogenous or paracrine signals are applied . Until now , the relationship between EBV and the B cell differentiation specialized CDKI p18INK4c has not been reported . Considering EBNA3A and EBNA3C are well-established transcriptional regulators of CDKI , it was therefore of particular interest to investigate whether they also regulate p18INK4c and subsequently to determine whether EBNA3A and EBNA3C play a specific role in the regulation of B cell-to-PC differentiation—a role directly attributed to p18INK4c . Here , using novel EBV recombinants encoding conditional versions of both EBNA3A and EBNA3C , we show that when these latent proteins are non-functional , mature human B cells infected ex vivo show elevated expression of 2 key regulators of PC differentiation , p18INK4c and BLIMP-1 . Chromatin immunoprecipitation sequencing ( ChIP-seq ) and chromatin immunoprecipitation quantitative PCR ( ChIP-qPCR ) data are consistent with EBNA3A and EBNA3C binding to regulatory elements and directly epigenetically repressing the CDKN2C ( p18INK4c ) and PRDM1 ( BLIMP-1 ) genes . Furthermore , cell surface marker analysis indicates a PC–like phenotype when EBNA3A and EBNA3C are inactive . We suggest a major physiological function of EBNA3A and EBNA3C is to block terminal differentiation to PCs , the default pathway in B cells normally activated by antigens without help from T cells .
To assess the involvement of the EBNA3 proteins in regulation of p18INK4c we infected CD19+ve peripheral B cells with an EBV recombinant from which the whole EBNA3 locus was deleted ( EBNA3KO ) ( Fig 1A; [11] ) . We saw reproducibly—using B cells from independent donors—that 15–20 days post-infection ( pi ) there was a substantial increase in the expression of p18INK4c , compared to cells infected with the prototypical lab strain ( referred to hereafter as ‘WT’ B95 . 8-BAC ) , where all EBNA3 proteins are expressed ( Fig 1B ) . Relative expression of the control housekeeping gene ALAS1 was unaffected by the absence of the EBNA3 proteins ( Fig 1C ) . With substantial increases in p18INK4c expression seen in all donors when the entire EBNA3 locus is deleted , it is likely an EBNA3 protein , or a combination of EBNA3 proteins , is responsible for repression of p18INK4c expression . As indicated above , the currently available data suggest that EBNA3B is unlikely to be involved in the regulation of p18INK4c or in the B cell transformation/immortalization process , with EBNA3A and EBNA3C widely considered the principle EBV-mediated regulators of CDKI [11 , 22] . Therefore , to focus on the relative contributions of EBNA3A and EBNA3C , and to formally exclude EBNA3B playing a role in the context of the triple EBNA3KO EBV , we constructed a recombinant virus in which EBNA3A and EBNA3C could be co-regulated , while EBNA3B remained constitutively active ( see schematic in Fig 2A ) . The 3A3CERT2 virus—that expresses functional EBNA3A and EBNA3C only in the presence of the activating ligand ( 4-hydroxytamoxifen [HT] ) of the modified-oestrogen receptor—was constructed using bacterial artificial chromosome ( BAC ) technology and its genomic structure was confirmed ( see Materials and methods ) . LCLs were established using CD19+ve B cells from 2 independent ‘buffy coats’ from anonymous donors ( 3A3CERT2 LCL D1 and 3A3CERT2 LCL D2 ) infected with 3A3CERT2 and cultured in the continuous presence of HT . When HT was removed , with the cells washed in fresh medium and re-cultured , the EBNA3 fusions were sequestered in the cytoplasm [35–37] . Western blot analyses of these cells showed that the stability of the EBNA3A- and EBNA3C-ERT2 fusion proteins was dependent on HT in the growth-medium ( Fig 2B ) . EBNA3A- and EBNA3C-ERT2 proteins have an increased molecular weight due to the ERT2-fusion ( Fig 2B ) . Expression of other EBV latency-associated proteins—including EBNA3B—was unaffected by this switch in EBNA3A/EBNA3C functionality . The expression of latency-associated proteins in 3A3CERT2 LCLs is comparable with those in ‘WT’ B95 . 8 LCLs with the exception of EBNA-LP . However , EBNA-LP levels have been shown to be donor dependent [19 , 38] , and although levels of this protein are lower in ‘WT’ B95 . 8 LCLs , they are comparable between 3A3CERT2 LCLs grown with or without HT . Consistent with the results from these established LCLs , examining freshly infected primary B cells 20 days pi , grown either with or without HT , revealed similar patterns of latent protein expression ( see Fig 2C for example ) . This confirms our recombinant 3A3CERT2 virus has simultaneous conditional expression of both EBNA3A and EBNA3C , while maintaining expression of other latency-associated proteins . In order to establish whether EBNA3A and/or EBNA3C regulate the expression of the p18INK4c-encoding gene CDKN2C in newly infected B cells , an experiment was performed using cells from a single donor infected with EBV recombinants conditional for EBNA3A ( 3AERT2 ) , EBNA3C ( 3CERT2 ) , or both proteins ( 3A3CERT2 ) . Cells were cultured with or without HT , from the day of infection for up to 30 days . Samples of cells were harvested at the time of infection and after 10 , 15 , 20 , and 30 days and mRNA extracted for analysis by reverse transcription quantitative PCR ( RT-qPCR ) . There was little difference in the level of p18INK4c mRNA whether EBNA3A was functional ( 3AERT2 +HT ) or inactive ( 3AERT2 -HT ) ( Fig 3A ) . Similar analysis of 3CERT2-infected cells suggested that non-functional EBNA3C ( -HT ) results in a small but sustained increase in p18INK4c mRNA ( Fig 3B ) . However , by far the most striking increase in p18INK4c mRNA was seen when both EBNA3A and EBNA3C were inactive after infection ( 3A3CERT2 -HT ) ( Fig 3C ) . These data were consistent in magnitude and kinetics with experiments using the triple knockout virus ( Fig 1B ) . The results were consistent and reproducible in infections of B cells derived from 3 independent donors ( Fig 3D–3F ) . We conclude that inactivating EBNA3A alone has very little effect on p18INK4c expression , and inactivating EBNA3C alone has a modest but reproducible effect . However , inactivating EBNA3A and EBNA3C simultaneously leads to a very robust activation of p18INK4c expression during the first 3 weeks after infection , confirming EBNA3A/EBNA3C-mediated regulation of CDKN2C . Analysis of control housekeeping genes ( e . g . , ALAS1 ) showed no change in the mRNA level ( S1A–S1C Fig ) , but similar analysis of p16INK4a mRNA confirmed what has been widely reported previously [38–40]; that is , inactivating EBNA3C or , to a lesser extent , EBNA3A leads to increased p16INK4a expression . Inactivating both EBNA3A and EBNA3C together produced an additive increase in p16INK4a mRNA ( see S1D–S1F Fig ) . Analysis of p15INK4b mRNA from the 20 days pi samples showed that inactivation of both EBNA3A and EBNA3C induced a substantial increase in p15INK4b expression ( S2A Fig ) , indicating that CDKN2A and CDKN2B are co-regulated . Because inactivation of EBNA3A and EBNA3C produced a marked increase in the expression of p18INK4c mRNA after infection with EBV , and this CDKI has been definitively linked to PC differentiation ( see Introduction ) , we asked whether BLIMP-1 , the key transcriptional regulator of this pathway , was also a target of EBV . RNAs from the cells used in the experiments shown in Fig 3 , were subjected to RT-qPCR using primers specific for BLIMP-1 mRNA . This showed BLIMP-1 is regulated in a very similar way to p18INK4c ( Fig 4 ) . In particular , BLIMP-1 mRNA expression after infection with recombinants in which both EBNA3A and EBNA3C were inactivated ( 3A3CERT2 -HT ) were greatly increased ( Fig 4C and 4F ) , similar to p18INK4c mRNA ( compare Figs 3F and 4F ) and comparable to the results using EBNA3KO virus ( Fig 4G ) . Inactivation of EBNA3A ( 3AERT2 -HT ) had a very modest but reproducible effect ( Fig 4A and 4D ) , whereas inactivation of EBNA3C alone ( 3CERT2 -HT ) had very little effect on BLIMP-1 expression ( Fig 4B and 4E ) . We conclude that EBV , via the transcription factors EBNA3A and EBNA3C , regulates at least 2 critical cellular genes involved in PC differentiation: p18INK4c and BLIMP-1 . The genes encoding p16INK4a and p15INK4b are epigenetically repressed in established LCLs [10 , 38] . However , if conditional EBNA3C is inactivated by the removal of the activating ligand HT from the growth medium , the repressed state of the CDKN2A locus is reversed and the levels of p16INK4a RNA and protein increase ( Fig 5A and 5E; [38 , 39] ) . The transcription of various other genes regulated by EBNA3C ( e . g . , COBLL1 and AICDA; [12 , 17] ) or EBNA3A and EBNA3C ( e . g . , BIM/BCL2L11; [41] ) are also reversibly regulated . In order to determine whether p18INK4c and BLIMP-1 are regulated by EBNA3A and EBNA3C in a similar way , LCLs derived from donors D1 , D2 , and D3 cells were investigated . LCLs established with 3A3CERT2 virus were continuously cultured in HT ( +HT ) for >60 days , then half the cells were cultured with no HT ( HT WASH ) . Unlike the expression of p16INK4a and p15INK4b mRNA that was de-repressed within 30 days without HT ( Fig 5A and S2B Fig ) , similar analyses consistently showed no increase in p18INK4c or BLIMP-1 mRNA ( Fig 5B and 5C ) , suggesting repression of these 2 differentiation-associated loci by EBNA3A and EBNA3C is not reversible under similar conditions . As expected , expression of the control housekeeping gene ALAS1 was unaffected by the inactivation of EBNA3A and EBNA3C ( Fig 5D ) . Analysis by western blotting confirmed the same trend at a protein level ( Fig 5E ) . To determine how soon this repression becomes irreversible , CD19+ve primary B cells from an independent donor were infected with the 3A3CERT2 recombinant EBV and the activating ligand ( HT ) was added at the time of infection . HT was then washed out from the medium after 4 or 12 days . By 12 days pi , repression of both p18INK4c and BLIMP-1 can no longer be reversed—that is , cells exposed to active EBNA3A/EBNA3C for 12 days appear to be committed to self-renewal rather than differentiation ( Fig 6A–6C; a second independent donor is shown in S3 Fig ) . If HT is removed from the population 4 days after infection there can be partial activation of p18INK4c and BLIMP-1 , suggesting that EBNA3A and EBNA3C act earlier in some cells ( Fig 6D–6F and S3D–S3F Fig ) . Taken together , these data demonstrate EBV commits to EBNA3A and EBNA3C mediated irreversible repression of p18INK4c and BLIMP-1 within 12 days of primary B cell infection . Interrogating a recent ChIP-seq analysis performed on LCLs established with EBV-recombinants expressing epitope-tagged EBNA3A or EBNA3C revealed multiple binding peaks on chromatin for both EBNA3A and EBNA3C distributed across both the CDKN2C ( p18INK4c ) and PRDM1 ( BLIMP-1 ) loci ( Fig 7 and Fig 8; [15] ) . At the CDKN2C locus ( Fig 7A ) both the raw sequence reads and peaks identified by the high stringency model-based analysis of ChIP-seq ( MACS ) algorithm revealed 4 substantial EBNA3C binding sites ( BS1-4 ) , of which BS1 spans the predicted transcription start site ( TSS; -490 to +44 ) for p18INK4c mRNA . EBNA3C appears to bind BS1 and BS2 very efficiently , but considerable EBNA3C is also found at a discrete intronic site , BS3 . Overall , the distribution of EBNA3A appears similar , but the binding is probably slightly less efficient—with only 1 MACS peak being called , at BS3 . To verify EBNA3A and EBNA3C occupancy there , binding of tagged EBNA3A ( EBNA3A-TAP ) and tagged EBNA3C ( EBNA3C-TAP ) at these loci were determined in LCLs by ChIP-qPCR . These data ( Fig 7B and 7C ) confirm that BS1 and BS2 ( both located proximal to the p18INK4c TSS ) are the predominant binding sites . The ChIP-seq raw sequence reads [15] for the PRDM1 locus show that of the 6 peaks ( BS1-6 ) identified by MACS algorithm binding both EBNA3A and EBNA3C , the intronic peak ( BS5 ) is most prominent ( Fig 8A ) . ChIP-qPCR confirmed that BS5 is by far the most efficient binding site for both EBNA3A and EBNA3C ( Fig 8B and 8C ) . We therefore conclude that EBNA3A and EBNA3C are targeted to chromatin within the genes encoding p18INK4c and BLIMP-1 , indicating a direct repressive role . On the basis of all the data presented above ( and previous reports [12 , 15 , 38 , 41] ) , we assumed that EBNA3A and EBNA3C , by binding to the chromatin in the CDKN2C and PRDM1 genes , would produce changes in histone modifications on local chromatin . At several EBNA3A/EBNA3C target genes , repression of transcription has been shown to correlate with the repressive histone mark H3K27me3 that is normally catalysed by the Enhancer of Zeste Homologue-2 ( EZH2 ) , an essential component of the polycomb repressor complex 2 ( PRC2 ) [41 , 42] . In order to determine whether the levels of H3K27me3 correlate with the repression of CDKN2C and PRDM1 by EBNA3A and EBNA3C , 3A3CERT2-infected CD19+ve B cells ( grown with or without HT ) were harvested 20 days pi and subjected to further ChIP analysis . When EBNA3A and EBNA3C are active ( 3A3CERT2 +HT ) , H3K27me3 occupancy is increased across both genes—in particular at sites proximal to each TSS ( Fig 9A ) . Moreover , analysis of an established LCL confirmed a significant steady-state distribution of H3K27me3 when EBNA3A and EBNA3C are active ( Fig 9B ) . In order to confirm that the histone mark H3K27me3 , and therefore PRC2 , is not only present but also involved in p18INK4c and BLIMP-1 repression in LCLs , we made use of the small molecule GSK126 , a specific inhibitor of EZH2 [43] . Two independent LCLs ( LCL WT D1 and LCL WT D2 ) were treated with the inhibitor for 20 days and harvested for RNA and protein extraction . The level of p18INK4c ( Fig 9C ) and BLIMP-1 mRNA ( Fig 9D ) increased in both LCLs after EZH2 inhibition . Analysis of protein levels by western blot also confirmed the de-repression of BLIMP-1 , and to a lesser extent p18INK4c , and that the GSK126 treatment effectively abrogated the total level of H3K27me3 in LCL ( Fig 9E ) . As expected , proliferation in GSK126-treated cells is reduced , with EBNA3A and EBNA3C protein expression modestly increased ( S4 Fig ) . Taken together , we conclude that PRC2 and the repressive histone modification H3K27me3 are involved in EBNA3A and EBNA3C mediated repression of p18INK4c and BLIMP-1 . Considering both p18INK4c and BLIMP-1 are associated with PC differentiation , we wanted to assess whether expression of these factors in EBNA3A/EBNA3C-null cells were physiologically comparable to levels expressed in PCs and therefore sufficient to drive differentiation . As multiple myeloma cell lines are derived from neoplasms arising from terminally differentiated PCs , we utilized the myeloma/plasmacytoma-derived cell line U266 as a PC model [44 , 45] . We assessed levels of p18INK4c and BLIMP-1 by RT-qPCR and western blotting in U266 alongside 3A3CERT2-infected cells ( +/-HT ) from multiple donors at 20 days pi ( Fig 10 ) . Levels of p18INK4c mRNA in 3A3CERT2-infected cells with non-functional EBNA3A and EBNA3C are lower but comparable to U266 levels ( depending on the donor ) and consistently higher than when EBNA3A and EBNA3C are active ( Fig 10A ) . On the other hand , levels of BLIMP-1 in these cells far exceed that seen in U266 across all donors ( Fig 10B ) . Western blotting and quantification by densitometry confirmed these RNA changes in p18INK4c and BLIMP-1 translate to protein , with respective levels consistent between RNA and protein expression in U266 and 3A3CERT2-infected cells ( Fig 10C and 10D ) . BLIMP-1 is a DNA-binding zinc finger transcription factor and many of its target genes have been characterized [30 , 46 , 47] . To determine whether increasing BLIMP-1 was functional in our cells and transcriptionally activating target gene XBP-1 , and repressing the target genes LMO2 and SPIB , we extended our mRNA analysis of 3A3CERT2-infected cells . Consistent with a physiological increase in functional BLIMP-1 , and concurrent p18INK4c increase , XBP-1 transcription consistently increases around 4-fold when EBNA3A and EBNA3C are inactivated , exceeding levels seen in U266 , which are comparable to EBV-infected cells with functional EBNA3A and EBNA3C ( Fig 10E ) . Furthermore , transcription of the BLIMP-1–repressed genes LMO2 and SPIB is substantially reduced when EBN3A and EBNA3C are inactivated , reaching levels as low as U266 cells ( Fig 10F and 10G ) , indicating that the activated BLIMP-1 retains its repressive activity . LMO2 and SPIB transcriptional repression and XBP-1 activation were followed during the first 30 days after infection of primary CD19+ve B cells with the 3A3CERT2 virus +/- HT , and were found to be consistent with BLIMP-1 activation and comparable to the phenotype seen after infection with EBNA3KO virus relative to ‘WT’ ( B95 . 8-BAC ) infection ( S5 Fig ) . Alongside BLIMP-1 , the transcription factor IRF4 is also implicated in the B cell-to-PC differentiation pathway ( See Introduction , [34] ) . Western blots showed that newly infected B cells and established LCLs express substantial amounts of IRF4—as much , if not more , than in U266 . The level of expression is unaffected by EBNA3A/EBNA3C functionality ( S6 Fig ) . Therefore , we have found that in cells lacking functional EBNA3A and EBNA3C , factors required for PC differentiation are induced to levels comparable to , or greater than , levels in U266 cells . The PC–like phenotype of 3A3CERT2 EBV-infected cells with non-functional EBNA3A and EBNA3C was further studied and the presence of PC surface markers was assessed by flow cytometric analysis ( FCA ) . There is widespread agreement that the defining characteristics of PCs are expression of CD138 ( aka Syndecan-1 ) and elevated levels of CD38 , alongside expression of CD27 and down-regulation/absence of CD20; therefore , these markers where our focus [48–50] . Approximately 20% of infected cells lacking EBNA3A and EBNA3C express surface markers consistent with a PC–like phenotype at day 20 pi when defined as CD138+ and CD38high ( Fig 11; a second independent donor is shown in S7 Fig ) . However , overall there is a notable shift in expression of both CD138 and CD38 in EBNA3A/EBNA3C-null cells , compared to those where the proteins are functional , indicating the majority of cells are responding to the absence of EBNA3A and EBNA3C ( Fig 11B and 11C ) . We also found a distinct shift in expression in other PC surface markers , with CD27 levels elevated in cells with non-functional EBNA3A and EBNA3C , exceeding those found in our PC control U266 , alongside distinct reduction in CD20 , approaching levels seen in U266 ( Fig 11D and 11E and S7D and S7E Fig ) . As cells with non-functional EBNA3A/EBNA3C at 20 days pi appear to express a plasma cell–like phenotype , we utilized FCA to assess whether they were producing immunoglobulin ( Ig ) , a hallmark of PCs . We induced Ig-producing PC in vitro through exposure to CD40-ligand and IL21 ( described by [51] ) as a second PC model to assess levels of IgG and IgM in EBNA3A/EBNA3C-null cells , as U266 cells do not produce these Igs [44 , 52] . We confirmed p18INK4c and BLIMP-1 were activated in these cells; however , as with U266 , levels were greater in cells with inactive EBNA3A and EBNA3C ( S8 Fig ) . Using FCA we found a distinct shift in IgG expression in cells lacking EBNA3A/EBNA3C , with a notable population reaching expression levels comparable to induced PCs ( Fig 12; a second independent donor is shown in S9 Fig ) . Furthermore , there is a notable shift in IgM levels with increased expression in all cells lacking EBNA3A and EBNA3C , with overall levels exceeding that of the induced control across both infections but with the high-producing 3A3CERT2 population exhibiting similar levels as the high producers of the CD40-L/IL21–treated cells ( Fig 12B and S9B Fig ) . Additionally , using immunohistochemistry to analyse 3A3CERT2-infected cells ( grown with or without HT ) at 20 days pi from an independent donor , we have identified IgG positive cells , with a PC–like morphology , in cells with non-functional EBNA3A and EBNA3C ( S10A Fig ) . These were observed approximately 3 times more frequently in cells with inactive EBNA3A/EBNA3C compared to EBV-infected cells expressing functional EBNA3A/EBNA3C ( S10B Fig ) . Taken together , these results show that in the absence of EBNA3A and EBNA3C , EBV-infected cells express an increasingly PC–like phenotype , with evidence of elevated IgG and IgM production , further indicating that EBNA3A and EBNA3C function to prevent PC differentiation .
We have provided an unambiguous link between the EBV transcription factors EBNA3A and EBNA3C , and the suppression of a transcription program that normally drives activated B cells towards the phenotype of terminally differentiated PC . Deletion or biochemical inactivation by manipulation of the EBNA3A and EBNA3C genes in the virus genome produces recombinant EBVs that , when used to infect CD19+ve B cells ex vivo , are unable to produce ‘immortalized/transformed’ LCLs . Such viruses appear to have no gross defects that become manifest during the early phase of rapid B cell polyclonal expansion described by Luftig and colleagues ( [20 , 53] ) . However , around 15 days pi , in cells where both EBNA3A and EBNA3C were functionally incapacitated , we saw substantially elevated levels of the CDKI p18INK4c and the major PC differentiation transcriptional regulator BLIMP-1—unlike cells expressing functional EBNA3A and/or EBNA3C . Our results suggest that for maximal repression there is cooperation between EBNA3A and EBNA3C , but that each alone might exert repressor activity , in a donor dependent manner . This is consistent with reports of cooperative activity that have been described for other EBNA3A/EBNA3C-regulated genes [10 , 12 , 14 , 16 , 38 , 54] . The observation that these EBNA proteins can also be co-precipitated from cell extracts and the multiple reports that EBNA3A and EBNA3C commonly co-localize across the genome of LCLs ( Figs 7 and 8; [10 , 14–16 , 41 , 55] ) support this hypothesis . However , a precise biochemical description of the mechanism ( s ) responsible for cooperation has yet to be determined . As both p18INK4c and BLIMP-1 induce a post-mitotic state , it is not surprising that they have both been identified as tumour suppressor proteins that are commonly compromised by mutation or silencing in B cell lymphoma—including EBV-negative DLBCL [56–59] . In EBV-positive DLBCL it is possible that EBNA3A and EBNA3C are responsible for this inactivation and , in this setting , contribute to oncogenic progression . We cannot rule out the possibility that EBNA3A and EBNA3C directly regulate additional genes required for differentiation . However , finding ( by ChIP-seq and ChIP-qPCR ) that both proteins are bound to chromatin at regulatory elements in the vicinity of the CDKN2C and PRDM1 loci has driven our working hypothesis that these cellular regulators are primary targets and that EBNA3A/EBNA3C-binding to local chromatin results in the recruitment of factors that initiate and sustain the chromatin state responsible for reducing transcription . These data , and those described in many other reports , are consistent with EBNA3A and EBNA3C binding to regulatory elements , followed by the removal of activation-associated histone marks ( e . g . , H3/H4KAc ) , concomitant chromosomal conformational changes , and finally further covalent histone modifications by polycomb protein complexes ( particularly H3K27me3 , catalysed by the EZH2 component of PRC2 ) . Polycomb-mediated modifications then fix this state into a stable epigenetic form of repression [12 , 18 , 60] . This sequence of events , with EBNA3A and EBNA3C initiating polycomb-mediated repression by 12 days pi—but not being required to sustain the state—could explain why repression of p18INK4c and BLIMP-1 was not reversed by the inactivation of EBNA3A and EBNA3C , but can be reversed by the inhibitor GSK126 reducing global H3K27me3 ( compare Figs 5 , 6 and 9 ) . Once the PRC2 machinery is in place , active EBNA3A and EBNA3C appear to be unnecessary at these specific loci to silence transcription in the infected cells and their progeny . Although it is beyond the scope of this study , it will be of great fundamental interest to determine what specific feature ( s ) of the CDKN2C ( p18INK4c ) and PRDM1 ( BLIMP-1 ) loci determine why they remain repressed after ablation of EBNA3A and EBNA3C , while simultaneously other EBNA3A/3C targets—including BCL2L11 ( BIM ) and CDKN2A ( p16INK4a ) —are reactivated . Additionally , it will be of interest to determine whether this reversibility is related to requirements of gene expression during the sustained infection . We believe that the biological aim of EBNA3A/EBNA3C-mediated epigenetic repression of p18INK4c and BLIMP-1 is to suppress PC differentiation following EBV infection . This was confirmed through experiments showing cells with inactive EBNA3A and EBNA3C progress along a PC differentiation–like pathway following infection with EBV . We determined that physiological levels of both p18INK4c and BLIMP-1 in EBNA3A/EBNA3C-null cells are sufficient to drive PC differentiation with expression comparable to , or exceeding , those in 2 PC models: U266 cells and in vitro induced PCs . With differentiation , associated BLIMP-1 target genes responded as predicted to elevated BLIMP-1 transcription levels . Furthermore , phenotypic consequences of increasing levels of differentiation factors were identified by FCA , with elevated levels of markers characteristic of PCs and increased IgG and IgM production in cells with inactive EBNA3A and EBNA3C at 20 days pi . It is likely complete differentiation of cells with inactive EBNA3A and EBNA3C into terminally differentiated PCs is being prevented by additional consequences of EBV infection , alongside the consequences of inactive EBNA3A and EBNA3C . This likely includes reduced proliferative capacity and rising levels of the CDKI p16INK4a leading to cell cycle arrest ( S1 Fig , [39] ) . It would therefore be interesting to establish if p16INK4a-null cells with non-functional EBNA3A and EBNA3C can terminally differentiate into PCs . To our knowledge , p18INK4c and BLIMP-1 are the first genes to be identified as epigenetically repressed by EBNA3 proteins , where sustained repression subsequently becomes independent of the EBNA3 proteins . This stable and heritable regulation involving PRC2 does not require continuous expression of EBNA3A and EBNA3C , which we believe relates to establishment of EBV latency in MBC [5] . When EBV infects any type of quiescent , mature human B cell , the consensus of opinion is that it becomes activated , grows , and within a few days begins to proliferate rapidly [20] . In vivo , if helper T cells or T cell–derived factors are available , these cells can differentiate into GC-like B cells , and as they progress through the GC , viral gene expression is sequentially silenced . Cells exit the GC as MBC which do not express viral proteins , and form the site of viral latency and persistence [6] . Usually , in the absence of EBV infection , when antigen-activated B cells have no T cell help , B cells can enter a default differentiation pathway to become plasmablasts , then Ig-secreting PCs [28]; however , it has yet to be established what happens if T cell help is unavailable in the context of EBV infection . We propose EBV has evolved a coordinated mechanism to favour long-term latency in MBC by preventing this PC differentiation pathway , whereby the transcription factors EBNA3A and EBNA3C specifically restrain the transcription of 2 critical regulators of PC differentiation—p18INK4c and BLIMP-1 . It is likely EBV has evolved to ensure transient expression of EBNA3A and EBNA3C during early EBV infection that is sufficient to irreversibly repress these PC differentiation factors as a mechanism to avoid of spontaneous PC differentiation of EBV-infected cells as EBNA3 genes are silenced during progression through the GC . This therefore favours memory B cell differentiation and long-term viral persistence and latency ( summarized in Fig 13 ) . Finally , although the in vitro model system described here should be ideal for investigating how the switch from latency to the EBV lytic cycle and PC differentiation are coupled , we have been unable to shed light on this unifying feature of EBV biology . EBV replication occurs in rare cells undergoing PC differentiation in the tonsils of healthy carriers [9] and recently BLIMP-1 was demonstrated to induce EBV reactivation in B cells by activating transcription from the lytic switch promoters , R and Z [61] , providing a molecular link between the 2 phenomena; however , this was found to occur in only a subset of B cells . A prediction , based on our experiments described above , was that in the conditional EBNA3A/EBNA3C model , expression of the EBV lytic-switch protein BZLF1 ( and mRNA ) would echo p18INK4c and BLIMP-1 activation , with an inverse correlation between EBNA3A/EBNA3C function and EBV lytic activity . However , we found no consistent correlation between inactivating EBNA3A and EBNA3C , p18INK4c and BLIMP-1 activation , and BZLF1 mRNA or protein expression . Donor-dependent behaviour , or incomplete terminal differentiation of EBNA3A/EBNA3C-null cells , may be responsible for this . On the other hand , it may result from the rarity in which EBV reactivation occurs [9] , of which a contributing factor may be difficulty in overcoming the EBNA3A and EBNA3C mediated stable repression of p18INK4c and BLIMP-1 . Unfortunately , exploring the biochemistry linking activation and differentiation is very challenging and beyond the scope of this current study . In summary , we have formally established a link between the transcription regulatory activities of EBV and the B cell-to-PC differentiation process , and provided unique insights into the molecular mechanisms involved . We have shown that through epigenetic repression of the differentiation factors p18INK4c and BLIMP-1 , EBNA3A and EBNA3C suppress the PC differentiation pathway following EBV infection , thus favouring establishment of long-term EBV latency in MBC .
The buffy coat residues used in this study for the isolation of CD19+ve primary B cells were purchased from the UK Blood Transfusion Service . As these were derived from anonymous volunteer blood donors , no ethical approval is required . The HT-sensitive oestrogen receptor ERT2 used to construct EBNA3A-ERT2 [10] was used to construct EBNA3C-ERT2 fusion proteins in the B95-8 EBV background . These fusions were recombined into a B95-8 BAC lacking EBNA3C or all the EBNA3s , respectively , using methods previously described [10 , 38 , 62] to produce BACs containing either EBNA3C-ERT2 ( EBNA3C-ERT2 ) independently or both EBNA3A-ERT2 and EBNA3C-ERT2 ( EBNA3A/3C-ERT2 ) . Restriction digest and pulse field gel electrophoresis were used to confirm BAC structures . Cells were routinely cultured at 10% CO2 and 37°C in RPMI 1640 medium supplemented 10% foetal calf serum ( FCS ) , penicillin , and streptomycin . Cells were routinely seeded at 3 x 105/ml 1 day before harvesting . The activating ligand HT was added to 400 nM and EZH2 inhibitor ( GSK126 ) to 4 μM , where indicated . Both these supplements were added to cultures every time fresh medium was added to the cells . Where indicated , medium from cultures containing HT was exchanged for fresh media noted as ‘wash’ cell populations . U266 multiple myeloma cells were a kind gift from Professor Anastasios Karadimitris ( Imperial College London , UK ) . Recombinant viruses EBNA3KO , ‘wild type’ ( WT , B95-8-BAC ) [11] , EBNA3A-ERT2 [10] , EBNA3C-ERT ( this study ) , and EBNA3A/EBNA3C-ERT2 ( this study ) , were produced and titred as described previously [38] . Primary B cells were isolated from PBLs obtained from anonymous buffy coat donors ( UK Blood Transfusion Service ) by centrifugation over Ficoll . CD19 microbeads were used for magnetic separation of purified B cells using an autoMACS separator ( Miltenyi Biotec ) . Virus amounts were normalised across infections by Raji green units ( RGU ) [38] and cells were cultured initially in 15% FCS , reduced to 10% after 30 days . RNA was isolated from cells using an RNeasy mini kit ( Qiagen ) with DNAse digestion as per the manufacturer’s instructions . Reverse transcription of RNA into cDNA was performed using Superscript III First Strand Synthesis Supermix for qRT-PCR ( Invitrogen ) . Ten nanograms of cDNA was run per qPCR reaction using Platinum SYBR Green qPCR Supermix UDG kit ( Invitrogen ) , and performed on an ABI 7900HT real-time PCR machine . The comparative Ct ( ΔΔCt ) method was used to calculate relative mRNA expression with the housekeeping gene GNB2L1 used as an endogenous control . Gene expression is relative to either uninfected primary B cells or LCLs grown with HT as indicated . Primer sequences for this study are listed in S1 Table . Error bars in figures are the standard deviation from 3 triplicate qPCR replicates for each mRNA sample . SDS polyacrylamide gel electrophoresis and western blotting was performed as described previously [10 , 17 , 39] . Antibodies used in this study are listed in S2 Table . ChIP assay and qPCR analysis were performed as described previously [12] . Antibodies and sequences of primers used in these assays are listed in S2 Table and S3 Table , respectively . For FCA , cells were harvested , washed once in PBS , and then incubated with 1 ml PBS with 0 . 5 μl Fixable Viability Stain 780 for 10 minutes at room temperature . Cells were then washed once in PBS , once in PBS 1% BSA , and then stained for cell surface markers in PBS 1% BSA for 30 minutes at 4°C by resuspending in 100 μl 1% PBS/BSA containing the conjugated antibodies anti-CD20-AF700 ( 5 μl ) , anti-CD27-BV605 ( 1 μl ) , anti-CD38-PE-Cy7 ( 2 . 5 μl ) , and anti-CD138-APC ( 5 μl ) . For Ig analysis , following viability staining ( as above ) cells were additionally fixed using a Cytofix/Cytoperm Fixation/Permeabilization Kit ( BD Biosciences ) as per the manufacturer’s instructions . Cells were then stained for intracellular Ig in PBS 1% BSA for 30 minutes at 4°C by resuspending in 100 μl PBS 1% BSA containing the conjugated antibodies anti-IgG-PE and IgM-BV605 . Following staining , cells were washed twice in PBS and fluorescence was measured on a Fortessa B flow cytometer . Gating was used to exclude dead cells and doublets , and analysis and mean fluorescence intensity calculation was performed using FlowJo software . All antibodies were purchased from BD Biosciences . Representative ancestry gating strategies and FCS files can be found at osf . io/97zrj . PCs were induced essentially as described previously [51] . Briefly , CD19+ve primary B cells were purified , and CD40-ligand ( Enzo Life Sciences ) and IL21 ( Preprotech ) were added to cell culture at a final concentration of 50 ng/ml and 100 ng/ml , respectively . Cells were harvested at 7 days post induction . Cytospins of 1 x 105 cells were fixed in methanol/acetone , rehydrated in PBS , and stained for IgG as described previously [26] . IgG positive cells were counted [ ( HT+ 6/210 , 14/391 ) , ( HT- 22/206 , 43/500 ) ] . Cell proliferation assays were performed as described previously [39] . Briefly , cells were harvested after 2 hours in the presence of 5 μM of EdU and stained with Live/Dead Fixable Violet Cell Stain . Following ethanol fixation and rehydration , click chemistry was used for EdU labelling before staining with FxCycle Far Red to stain for DNA content . Fluorescence was measured on an LSR II flow cytometer; dead cells and doublets were excluded from analysis .
|
Epstein-Barr virus ( EBV ) infection can cause several types of cancer associated with its major target in humans , the mature B cell . Furthermore , EBV is one of the most potent transforming agents ever identified , producing—in vitro—‘immortal’ B lymphoblastoid cell lines ( LCLs ) with outstanding reliability . However , the near-symbiotic relationship between EBV and its natural host ( >95% of human adults are asymptomatically infected ) provides a powerful argument that this gamma-herpesvirus did not primarily evolve to be a harmful tumour-causing virus . Consistent with this , we show here that 2 of the potentially oncogenic viral proteins ( EBNA3A and EBNA3C ) have evolved not to facilitate oncogenic progression but to block plasma cell differentiation in EBV-activated B cells . Specifically , they act to interrupt the gene regulation network that drives activated B cells to become terminally differentiated , quiescent plasma cells , thus allowing for sustained regeneration of virally infected B cells . EBNA3A and EBNA3C achieve this by epigenetically inhibiting expression of cellular genes essential for the differentiation pathway; these include the cyclin-dependent kinase inhibitor p18INK4c and the transcription factor BLIMP-1 . This favours the establishment of EBV latency in long-lived memory B cells and therefore helps maintain a ubiquitous , generally asymptomatic infection in human populations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"gene",
"regulation",
"pathogens",
"regulatory",
"proteins",
"immunology",
"microbiology",
"dna-binding",
"proteins",
"cell",
"differentiation",
"dna",
"transcription",
"viruses",
"developmental",
"biology",
"dna",
"viruses",
"transcription",
"factors",
"epigenetics",
"herpesviruses",
"chromatin",
"white",
"blood",
"cells",
"epstein-barr",
"virus",
"animal",
"cells",
"chromosome",
"biology",
"medical",
"microbiology",
"proteins",
"gene",
"expression",
"microbial",
"pathogens",
"recombinant",
"proteins",
"antibody-producing",
"cells",
"biochemistry",
"cell",
"biology",
"b",
"cells",
"viral",
"pathogens",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"organisms"
] |
2017
|
EBV epigenetically suppresses the B cell-to-plasma cell differentiation pathway while establishing long-term latency
|
TNF-alpha plays an important role in trypanocidal mechanisms and is related to tissue injury . This cytokine has been detected in the heart of human chagasic patients where it is associated with tissue damage . This study investigated whether TNF-alpha levels and the presence of genetic polymorphisms are associated with the presence of T . cruzi infection and/or with the development of the cardiac form in chronic chagasic patients . Genomic DNA of 300 subjects from an endemic area was extracted and analyzed by PCR using specific primers . TNF-alpha was assayed in culture supernatants by ELISA . An association was observed between the absence of the TNF-238A allele and negative serology . Furthermore , seropositive individuals carrying the TNF-238A allele produced significantly higher TNF-alpha levels without stimulation ( p = 0 . 04 ) and after stimulation with LPS ( p = 0 . 007 ) and T . cruzi antigens ( p = 0 . 004 ) . The present results suggest that the polymorphism at position -238 influences susceptibility to infection and that this allele is associated with higher TNF-alpha production in seropositive individuals .
Chagas disease is an important chronic infection caused by the protozoan Trypanosoma cruzi . The disease continues to be a major public health problem in most Latin America countries , affecting around 9 million people [1] . In addition , Chagas disease is an emerging health problem in non-endemic areas because of the increasing migration of individuals [2] . Chagas disease has two successive phases . The acute phase is usually asymptomatic or characterized by the presence of fever , discomfort , tachycardia and high parasitemia [3] , [4] . Manifestations of the acute disease resolve spontaneously in about 90% of infected individuals even if the infection is not treated with trypanocidal drugs . About 60–70% of these patients will never develop clinically apparent disease . These patients have the indeterminate form of chronic Chagas disease , which is characterized by positivity for antibodies against T . cruzi in serum , a normal 12-lead electrocardiogram ( ECG ) , and normal chest , esophagus and colon exams . The remaining 30–40% of patients will subsequently develop a determinate form of chronic disease: cardiac , digestive ( megaesophagus and megacolon ) , or cardiodigestive [2] . Geographic variations exist in the severity and prevalence of the clinical forms of Chagas disease , but the reasons for this clinical and epidemiological heterogeneity are unknown [5] . Possible causes include variations in the genetic constitution of the host , especially genes related to the immune system since the latter is involved in the control of parasitism and in tissue injury . In Chagas disease , like in other parasitic diseases , the causal factor ( i . e . , the infectious agent ) is necessary but often insufficient for clinical manifestation of the disease [6] . The complexity of the host-parasite relationship in Chagas disease suggests the involvement of different components of the immune system . The genes for tumor necrosis factor-alpha ( TNF-alpha ) and lymphotoxin-alpha , which are located in the MHC III region on chromosome 6 , are closely linked to the HLA class I and class II genes [7] . TNF-alpha , which is mainly produced by monocytes and activated T cells , plays an important immunoregulatory role [8] . This cytokine contributes to the pathogenesis of Chagas disease due to its role in both trypanocidal mechanisms and tissue injury . Production of this cytokine at high levels has been demonstrated in experimental models during the acute phase [9] and its function as an inducer of iNOS is important for the control of parasite growth [10] , [11] . TNF-alpha has been detected in the heart tissue of experimentally infected animals [12] , as well as in inflammatory exudate cells of human chagasic myocarditis [13] . These findings suggest that individual differences in TNF-alpha production may be responsible for the variation among individuals . These differences are the result of polymorphisms present in the general population . Polymorphisms in the promoter region of the TNF-alpha gene have been known for a long time and might be involved in the control of the expression of this gene [14] , [15] . The presence of these polymorphisms has been associated with susceptibility to certain inflammatory and infectious diseases [16]–[18] . Recent studies have investigated the role of TNF-alpha gene polymorphisms in Chagas disease [19]–[22] , but none of those studies evaluated a population from an endemic area or used the clinical assessment criteria established here . The present study investigated TNF-alpha production by peripheral blood cells and whether the presence of G substitutions at positions -238 and -308 are associated with the presence of T . cruzi infection and/or the development of the cardiac clinical form in chronic chagasic patients .
The study was performed in the municipality of Água Comprida ( 20°3′23″S , 48°6′31″W at 540 m above sea level ) , situated in the Vale do Rio Grande , southern region of Triângulo Mineiro , Minas Gerais State , Brazil . The town was endemic for Chagas disease and was included in the first National Campaign against T . cruzi that started in 1975 . Epidemiological and entomological data demonstrated the interruption of vector transmission of the parasite to humans in 1999 , and an international commission certified Brazil to be free of transmission in 2005 [23] , [24] . A total of 300 unrelated individuals agreed to participate in the study . Only subjects who were 25 years or older were included ( mean age: 51 . 2±14 . 4; range: 25–91 years ) , since they corresponded to the youngest seropositive individuals of the sample studied . Serological screening for anti-T . cruzi antibodies showed that 25 . 6% of these subjects were positive and 74 . 5% were negative [25] . All HIV-seropositive individuals were excluded from the study . The treatment criteria did not exclude any patient . The study was approved by the Research Ethics Committee of Universidade Federal do Triângulo Mineiro , Brazil ( protocol 343 ) . All individuals provided written informed consent . The presence or absence of T . cruzi infection was evaluated by passive hemagglutination ( Salck Laboratory , São Paulo , Brazil ) , enzyme-linked immunosorbent assay ( Abbott , Brazil ) and indirect immunofluorescence using fluorescein isothiocyanate ( FITC ) -conjugated to rabbit anti-human IgG ( Sigma ) . The assays were performed according to manufacturer instructions and the results are expressed quantitatively . A subject who presented at least two positive tests was defined as positive [25] . Patients infected with T . cruzi were submitted to clinical examination , electrocardiography and chest , esophagus and colon contrast X-ray exams for classification into the cardiac , digestive , mixed , or indeterminate form [26] , [27] . Patients with the cardiac form were classified according to the Criteria Committee of the New York Heart Association [28] . Appropriate statistical analysis was not possible because of the small sample size . Peripheral blood samples ( 20 mL ) were collected with a vacuum system using heparin as anticoagulant . Peripheral blood mononuclear cells ( PBMC ) were separated by density centrifugation on a Ficoll-Hypaque gradient ( Pharmacia ) according to manufacturer recommendations . After separation , the cells were washed by centrifugation in RPMI medium ( Gibco ) and resuspended in RPMI medium supplemented with 5% fetal bovine serum ( Gibco ) , 2 mM L-glutamine ( Gibco ) , 50 mM 2-mercaptoethanol ( Merck ) , and 40 µg/mL gentamicin . PBMC ( 2×106 cells/well ) were cultured in the presence of 5 µg/mL T . cruzi ( strain Y ) antigens , 2 µg/mL Salmonella typhimurium lipopolysaccharide ( LPS ) ( Sigma ) , and 5 µg/mL phytohemagglutinin ( PHA ) ( Sigma ) . The plates were incubated at 37°C in a 5% CO2 atmosphere for 48 h . The culture supernatants were collected and stored at −70°C . For TNF-alpha titration , microplates ( Nunc ) were sensitized overnight with anti-TNF-alpha mAb ( Pharmingen ) . Nonspecific binding was prevented by incubating the plates with 2% BSA ( Sigma ) in PBS . The plates were incubated overnight with 100 µL of the culture supernatants in PBS diluted 1∶2 , 2% BSA , and recombinant human TNF-alpha ( Pharmingen ) . The plates were then washed four times with PBS and 0 . 05% Tween 20 and incubated with biotinylated anti-TNF-alpha mAb ( Pharmingen ) for 2 h , followed by washing and incubation for 2 h with streptavidin-conjugated alkaline phosphatase . Finally , the plates were washed four times and enzymatic activity was developed by incubating the plates with p-nitrophenyl phosphate ( Sigma ) . Absorbance was read at 405 nm in a microplate reader ( BioRad ) . The sensitivity limit of the test was 10 pg/mL . The mass of red and white blood cells was submitted to osmotic lysis using Tris-EDTA lysis buffer ( 20∶5 ) consisting of 1 M Tris-HCl and 0 . 5 M EDTA , pH 8 . The samples were centrifuged at least three times at 1334×g for 15 min at controlled room temperature ( approximately 27°C ) . For DNA extraction , the samples were treated by two different techniques depending on the amount of red blood cells remaining in the leukocyte pellet to guarantee the best quality DNA . A commercially available kit ( DNAzol , Gibco ) was used for samples containing few red blood cells and phenol-chloroform extraction was used for samples containing higher amounts of red blood cells . The samples were resuspended in water and DNA was analyzed by 1% agarose gel electrophoresis . Polymorphisms in the promoter region of the TNF-alpha gene were determined by PCR amplification , followed by digestion with appropriate restriction enzymes . For TNF-238G/A [15] , PCR was carried out in a volume of 30 µL containing 11 . 70 µL sterile and filtered Milli-Q water , 3 . 0 µL 10× buffer without MgCl2 ( Gibco ) , 1 . 0 µL 50 mM MgCl2 ( Gibco ) , 3 . 0 µL 2 mM dNTPs ( Invitrogen ) , 3 . 0 µL of each primer ( 40 nmol 238F: 5′ GGT CCT ACA CAC AAA TCA GT 3′ , and 43 . 2 nmol 238R: 5′ CAC TCC CCA TCC TCC TCC CTG GTC 3′ ) ( Gibco ) , 0 . 30 µL ( 500 units , 5 U/µL ) Taq DNA polymerase ( Gibco ) , and 5 . 0 µL genomic DNA at a concentration of 20 µg/mL . The following PCR conditions were used: 5 min at 95°C for initial denaturation and 35 cycles at 95°C for 1 min ( denaturation ) , 55°C for 45 s ( primer annealing ) , and 72°C for 45 s ( extension ) , followed by a final extension step at 72°C for 3 min . Next , RFLP was performed using 0 . 1 µL AvaII ( 10 , 000 U/mL ) ( New England Biolabs ) , 1 . 0 µL NE 4 buffer provided with the restriction enzyme , 0 . 4 µL sterile and filtered Milli-Q water , and 8 . 5 µL of the PCR product in a final volume of 10 µL . The samples were incubated for approximately 18 h at controlled room temperature ( approximately 27°C ) . The digestion products were analyzed on 12% polyacrylamide gel ( 37∶5∶1 ) stained with 1% silver nitrate . Digestion of the PCR products from patients homozygous for the TNF-238A allele ( -238A/A ) generated only one 71-base pairs ( bp ) fragment , whereas those from patients homozygous for the TNF-238G allele ( -238G/G ) were completely digested ( 51 and 20 bp ) . All three fragments ( 71 , 51 and 20 bp ) were present in heterozygous patients . For TNF-308G/A [14] , the PCR mixture contained 11 . 70 µL sterile and filtered Milli-Q water , 3 . 0 µL 10× buffer without MgCl2 ( Gibco ) , 1 . 0 µL 50 mM MgCl2 ( Gibco ) , 3 . 0 µL 2 mM dNTPs ( Invitrogen ) , and 3 . 0 µL of each primer ( 38 nmol -308F: 5′ AGG CAA TAG GTT TTG AGG GCC AT 3′ , and 45 nmol -308R: TCC TCC CTG CTC CGA TTC CG 3′ ) ( Gibco ) , 0 . 30 µL ( 500 units , 5 U/µL ) Taq DNA polymerase ( Gibco ) , and 5 . 0 µL genomic DNA at a concentration of 20 µg/mL in a final volume of 30 µL . The following PCR conditions were used: 5 min at 95°C for initial denaturation , followed by 35 cycles at 95°C for 1 min ( denaturation ) , 52°C for 45 s ( primer annealing ) , and 72°C for 45 s ( extension ) , followed by a final extension step at 72°C for 3 min . For RFLP , the mixture contained 0 . 1 µL NcoI ( 10 U/µL ) ( Gibco ) , 1 . 5 µL REACT 3 buffer provided with the restriction enzyme , 10 . 5 µL sterile and filtered Milli-Q water , and 3 . 0 µL of the PCR product in a final volume of 12 . 1 µL . The samples were incubated for approximately 18 h at 37°C . The digestion products were analyzed on 10% polyacrylamide gel ( 37∶5∶1 ) stained with 1% silver nitrate . Digestion of the PCR products from patients homozygous for the TNF-308A allele generated only one 107-bp fragment , whereas those from patients homozygous for the TNF-308G allele were completely digested ( 87 and 20 bp ) . All three fragments ( 107 , 87 and 20 bp ) were present in heterozygous patients . The following antibodies purchased from BD Pharmingen were used: FITC anti-CD8 , FITC anti-CD14 , and PE-Cy5 anti-CD4 . PE anti-TNF-alpha and appropriate isotype controls were used . All of these antibodies were used according to manufacturer instructions . PBMC were recovered from 48 h cell cultures in the presence of T . cruzi antigens or medium alone . The cells were transferred ( 2×105 cells/tube ) to 5 mL polystyrene tubes ( Falcon® ) and washed once with cold buffer ( PBS-5% BSA ) by centrifugation at 400×g for 10 min at 20°C . Cell pellets were resuspended in 100 µL PBS-BSA buffer and reacted with FITC anti-CD8 plus PE-Cy5 anti-CD4 mAb or FITC anti-CD14 for surface labeling . After 30 min of incubation in the dark at 4°C , the samples were washed three times in buffer by centrifugation at 300×g for 5 min at 20°C and fixed and permeabilized with freshly prepared Fixation/Permeabilization Working Solution ( Pharmingen ) for 30 min at 4°C , followed by two washed in 2 mL buffer . After the last wash , the cell pellets were resuspended in 100 µL 1X Permeabilization Buffer and reacted with PE anti-TNF-alpha at 4°C in the dark . After 30 min of incubation , the cells were washed twice with 2 mL 1X Permeabilization Buffer and then resuspended in 1% paraformaldehyde in Dulbecco's PBS ( Sigma ) for analysis . A total of 20 , 000 events/tube were acquired using a FACScalibur® flow cytometer ( Becton Dickson ) . The Cell Quest™ software provided by the manufacturer was used for data acquisition and analysis . Genotype and allele frequencies were analyzed statistically by the chi-square test . The strength of association was estimated using odds ratios and 95% confidence intervals ( CI ) . The Mann-Whitney or Kruskal-Wallis test was used to analyze the association of TNF-alpha levels with genotypes , allele frequency , serology , and clinical forms . The unpaired t-test was used to analyze the association of intracellular TNF-alpha expression with the cardiac or indeterminate form . Analysis was performed using the StatView software , version 4 . 57 ( Abacus Concepts , USA ) . The level of significance was set at 5% ( p<0 . 05 ) .
A total of 300 individuals were included in the study , 168 ( 56% ) with positive serology for T . cruzi and 132 ( 44% ) with negative serology . All subjects were from the same endemic area . A total of 214 subjects were genotyped to position -238 . Of these , 100 individuals were seropositive for T . cruzi and 114 were seronegative . All 300 individuals were genotyped to position -308 . Of these , 168 individuals were seropositive for T . cruzi and 132 were seronegative . Clinical classification was possible in 119 of the 168 subjects infected with T . cruzi . Sixty-six ( 55 . 46% ) had the cardiac form and 53 ( 44 . 54% ) had the indeterminate form . Only 39 and 66 patients with the cardiac form were genotyped to positions -238 and -308 , respectively . Among the indeterminate patients , 36 were genotyped to position -238 and 53 to position -308 . Clinical manifestations were only compared between patients with the cardiac and indeterminate forms . Seronegative individuals produced higher levels of TNF-alpha than seropositive subjects without stimulation ( Mann-Whitney , p = 0 . 0003 ) and seropositive individuals produced more TNF-alpha than seronegative individuals after PHA stimulation ( Mann-Whitney , p = 0 . 01 ) ( Figure 1A ) . TNF-alpha levels were significantly higher in patients with the indeterminate form than in cardiac patients without stimulation ( Mann-Whitney , p = 0 . 01 ) , after LPS stimulation ( Mann-Whitney , p = 0 . 005 ) , and after stimulation with T . cruzi antigen ( Mann-Whitney , p = 0 . 01 ) ( Figure 1B ) . Comparison of seronegative individuals with cardiac and indeterminate patients showed that cardiac patients produced higher levels of TNF-alpha than seronegative individuals without stimulation ( Mann-Whitney , p = 0 . 0002 ) and after LPS stimulation ( Mann-Whitney , p = 0 . 005 ) ( Figure 1B ) . Moreover , indeterminate patients produced higher levels of TNF-alpha than seronegative individuals after PHA stimulation ( Mann-Whitney , p = 0 . 03 ) ( Figure 1B ) . Analysis was performed between genotypes and infection determined by serology . Table 1 shows the distribution of the TNF-238G/A and TNF-308G/A genotypes . No association was observed between genotypes and serology ( X2 , TNF-238G/A p = 0 . 08; TNF-308G/A p = 0 . 41 ) . When the TNF-238AA and TNF-238GA genotypes were grouped as allele A presence and the genotype TNF-238GG was considered to be allele A absence , the data verified that allele A absence was more frequent among seronegative individuals ( X2 , p = 0 . 03 ) ( Table 2 ) . An odds ratio of 1 . 846 was observed ( CI = 1 . 057 to 3 . 223 ) . Table 3 shows the distribution of the TNF-238G/A and TNF-308G/A genotypes only among individuals with positive serology divided into the cardiac and indeterminate clinical forms . No significant associations were observed ( X2 , TNF-238G/A p = 0 . 28; TNF-308G/A p = 0 . 64 ) . There were no differences in TNF-alpha levels produced by seropositive and seronegative individuals carrying different genotypes of the TNF-alpha polymorphisms position -238 ( Figure 2 ) and position -308 ( data not shown ) . On the other hand , higher levels of TNF-alpha were observed in individuals carrying the TNF-238A allele after LPS stimulation ( Mann-Whitney , p = 0 . 045 ) when all subjects were analyzed together ( Figure 3A ) . No differences were observed when the individuals were grouped according to negative serology ( data not shown ) . Interestingly , when seropositive individuals were analyzed alone , higher TNF-alpha levels were observed in those carrying the TNF-238A allele without stimulation ( Mann-Whitney , p = 0 . 04 ) , after stimulation with T . cruzi antigen ( Mann-Whitney , p = 0 . 004 ) , and LPS stimulation ( Mann-Whitney , p = 0 . 007 ) ( Figure 3B ) . The presence of intracellular TNF-alpha was analyzed by flow cytometry in 48-h cultured cells . In the absence of stimulation , 1 . 18% and 1 . 83% of CD8+ cells were positive for TNF-alpha in patients with the cardiac and indeterminate forms , respectively . In CD4+ cells , the rate of TNF-alpha production was 2 . 60% in cardiac patients and 1 . 713% in indeterminate patients . No association was observed between the cell type and clinical form presented by the patient ( data not shown ) . After stimulation with T . cruzi antigens , 28 . 47% and 38 . 80% of CD8+ cells were positive for TNF-alpha in patients with the cardiac and indeterminate forms , respectively . In CD4+ cells , positivity for TNF-alpha was 2 . 78% for cardiac patients and 6 . 54% for indeterminate patients ( Figure 4 ) . This difference was statistically significant ( unpaired t-test; p = 0 . 044 ) . Two levels of TNF-alpha production were observed in CD14+ cells , low and high levels . In cardiac patients , 63 . 36% and 3 . 76% of CD14+ cells were classified as low and high producers , respectively . These percentages were 29 . 96% and 0 . 70% in patients with the indeterminate form ( Figure 4 ) .
The immune response plays an important role in the control of T . cruzi infection . TNF-alpha is an important cytokine involved in parasite control during the acute phase [9]–[11] . However , there are few studies in the literature discussing its role during the chronic phase . Some studies have suggested the involvement of TNF-alpha in the development of the cardiac form [13] , [29] , [30] , but its role in the control of parasite growth in humans and the consequent development of specific antibodies has been little studied . The present study conducted on individuals ( control group and infected individuals ) from an endemic area in the central region of Brazil ( Água Comprida ) provided important results . This area was included in the first national campaign against Chagas disease that started in 1950 [31] . These characteristics result in homogeneous population exposition and similar environmental and social conditions , thus reducing possible confounding factors . Higher TNF-alpha levels were produced by seronegative individuals without stimulation , indicating that individuals who did not acquire the infection are able to produce TNF-alpha spontaneously . High basal levels of TNF-alpha may improve the host defense against T . cruzi , possibly by modulating the expression of iNOS and adhesion molecules involved in rolling , adhesion and extravasation during inflammatory events in response to T . cruzi invasion [10]–[12] . Higher levels of TNF-alpha were observed in seropositive individuals compared to seronegative subjects . This finding might be due to polyclonal expansion in response to nonspecific stimulation . Analyzing the development of the clinical form , TNF-alpha levels were significantly higher in patients with the indeterminate form than in those with the cardiac form . Comparison of cardiac patients with the control group showed higher levels of TNF-α in the cardiac group without stimulation and after LPS stimulation . Furthermore , indeterminate patients produced higher level of the cytokine after PHA stimulation . This fact suggests that TNF-alpha production by PBMC might be more important for the ability of the cytokine to control parasite growth than for promoting tissue damage or the development of heart lesions . It is important to point out that in the present study most patients classified as having the cardiac form did not exhibit severe heart involvement such as heart failure . Severe and end-stage Chagas disease has been associated with high levels of TNF-alpha [29] , [30] , [32] . Our results differ from those reported by Ferreira et al . [29] and Talvani et al . [30] . However , a previous study from our group analyzing plasma TNF-alpha levels was unable to show differences between the clinical forms of chronic Chagas disease [33] . TNF-alpha plays a potential dual role , controlling parasite growth or promoting tissue damage . Moreover , TNF-alpha is able to stimulate IL-10 synthesis [34] , a regulatory cytokine that contributes to the control of inflammation . A recent experimental study demonstrated that the blockage of TNF-alpha with Etanercept enhances left ventricular dysfunction in T . cruzi-induced chronic cardiomyopathy [35] . In this respect , the delicate balance between the ability of TNF-alpha to control parasite growth and to promote tissue damage may be responsible for human resistance/susceptibility to Chagas disease . Not only the level of this cytokine , but the cells involved in its production and the elapsed time after interaction with the parasite should be investigated to improve the current understanding of the role of TNF-alpha in Chagas disease . Regarding TNF-alpha gene polymorphisms , this study provided evidence of an association between the absence of allele A at position -238 and seronegativity . We observed that individuals carrying the TNF-238A allele produce higher levels of TNF-α than those without the allele . This result suggests that the TNF-alpha polymorphism affects gene expression and that this effect depends on the cell population and on the strength of the stimulus since no differences were observed after PHA stimulation . Moreover , infected individuals carrying the TNF-238A allele produced higher levels of TNF-alpha than those without allele A . This result suggests that the -238 polymorphism exerts its potential function in infected subjects , probably as a result of clonal expansion and immunoregulatory mechanisms established during infection . The effect of the polymorphism might be more pronounced under these conditions . No significant association was established for the -308 polymorphism . A higher proportion of the TNF-238A allele among patients when compared to controls was demonstrated for various other infectious diseases such as infection with Chlamydia trachomatis , although these findings were not statistically significant [36] . Another study reported a higher bacteriological index in patients with leprosy carrying the TNF-238A allele [37] . Homozygous carriers of the TNF-238A allele were more frequent among patients with psoriasis compared to controls [17] . In chronic and active hepatitis C , the TNF-238A allele was more frequent in the group of patients than in controls [18] . Other studies on Chagas disease were conducted in Peru , Mexico and Brazil . The first study analyzed 87 healthy controls and 85 individuals seropositive for anti-T . cruzi antibodies from an endemic area in Peru . No significant differences were observed between patients and controls or between asymptomatic individuals and patients with cardiomyopathy for either polymorphic region [19] . The second study compared 54 individuals seropositive for Chagas disease and 169 controls from the Mexican population . A higher frequency of the TNF-308A allele was observed in chagasic patients compared to the control group . Furthermore , the TNF-308A allele was more frequent among patients with cardiopathy compared to asymptomatic individuals [20] . However , these differences in the results might be attributed to ethnic variations among different populations and to differences in the study design and patient selection criteria . The third study analyzed 42 patients with severe ventricular dysfunction . Patients carrying the TNF-308A allele presented a significantly shorter survival time than those carrying other alleles [21] . The same group compared 166 chronic Chagas disease patients with cardiomyopathy to 80 asymptomatic patients , but observed no significant association with TNF polymorphisms [22] . However , none of the studies conducted in Brazil investigated the -238 gene polymorphism or included subjects from endemic areas . The present study differs from previous investigations because all individuals were from a region of high endemicity , with a greater probability of a more homogenous condition of exposure and accurate clinical analysis after some years of infection , permitting a sufficient time interval for the development of the clinical forms of the disease . This is advantageous in relation to the study's characteristics , though it represents a disadvantage due to the possible death of patients with more severe forms of the disease . Furthermore , in the present study , functional analysis showed that PBMC from seropositive individuals carrying the TNF-238A allele produced significantly higher levels of TNF-alpha when stimulated with LPS and T . cruzi antigens and in the absence of any stimulus . Other investigators observed higher TNF-alpha production by LPS-stimulated PBMC in individuals homozygous for the TNF-308A allele [38] . Studies using gene construction strategies in which the promoter region of TNF was ligated to the luciferase gene have also shown an increased production of TNF-alpha levels among individuals with the TNF-308A allele [39] , [40] . A repressor-binding site was identified at position -238 [41] . Thus , it is possible that in certain cell types , the presence of the polymorphism reduces affinity for the repressor , with a consequent increase in transcription [42] . These data indicate that the polymorphism at this position may influence gene expression and may help explain the association between the presence of allele TNF-238A and higher TNF-alpha production observed in this study . Flow cytometry analysis indicated that CD14+ and CD8+ cells are the major source of TNF-alpha after antigen stimulation . The number of CD14+ T lymphocytes expressing TNF-alpha was significantly higher in cardiac patients , whereas the number of CD4+ lymphocytes producing this cytokine was high in patients with the indeterminate form . The stimulation of CD8+ T lymphocytes with exogenous antigens , including T cruzi lysate antigens , under similar culture conditions has been demonstrated previously [43] , [44] . The importance of TNF-alpha as an activator of the mechanisms involved in parasite elimination [12] and in tissue injury [10] , [11] has been clearly demonstrated in experimental models by biological blockage of the cytokine or in genetically modified animals . Studies on humans have associated TNF-alpha with phenomena of tissue injury during the chronic phase of infection [13] . The present results demonstrate an association between gene polymorphisms and infection . In conclusion , the present results suggest that the TNF-238A allele exerts a significant effect on human susceptibility to infection . Furthermore , seropositive individuals carrying the TNF-238A allele produce higher levels of TNF-alpha after antigen and polyclonal stimulation , suggesting that the presence of the allele is associated with higher TNF-alphaproductionIn addition to its trypanocidal activity , TNF-alpha seems to trigger regulatory networks , such as the induction of IL-10 [34] , that permit parasite escape . The development of clinical forms of Chagas disease may implicate other genes involved in the delicate balance of immune response .
|
Chagas disease is an important parasitic disease that has no cure . The pathogenesis of the disease is still not completely understood . Studies using candidate genes are important to better understand the differences between individuals that lead to such heterogenous disease . TNF-alpha is a cytokine involved in the control of parasitemia during the acute phase and in cardiac injury during the chronic phase . The TNF-alpha gene is located in an important region of the MHC and its polymorphisms are associated with many parasitic and infectious diseases such as cerebral malaria and leishmaniasis . These studies are important since they were conducted in the same regions and involved populations leaving in the same conditions . The present study shows that patients with the indeterminate form produce higher levels of TNF-alpha than cardiac patients . The data suggest a control mechanism between inflammatory and regulatory cytokines . In addition , the presence of the TNF-238G allele contributes to the development of negative serology . We show that CD8+ T lymphocytes and macrophages are the main cells producing TNF-alpha . This study is an important contribution to explain the pathogenesis of Chagas disease .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"immunology/genetics",
"of",
"the",
"immune",
"system",
"immunology/immunity",
"to",
"infections"
] |
2011
|
Genetic and Functional Role of TNF-alpha in the Development Trypanosoma cruzi Infection
|
Mitotic repression of rRNA synthesis requires inactivation of the RNA polymerase I ( Pol I ) -specific transcription factor SL1 by Cdk1/cyclin B-dependent phosphorylation of TAFI110 ( TBP-associated factor 110 ) at a single threonine residue ( T852 ) . Upon exit from mitosis , T852 is dephosphorylated by Cdc14B , which is sequestered in nucleoli during interphase and is activated upon release from nucleoli at prometaphase . Mitotic repression of Pol I transcription correlates with transient nucleolar enrichment of the NAD+-dependent deacetylase SIRT1 , which deacetylates another subunit of SL1 , TAFI68 . Hypoacetylation of TAFI68 destabilizes SL1 binding to the rDNA promoter , thereby impairing transcription complex assembly . Inhibition of SIRT1 activity alleviates mitotic repression of Pol I transcription if phosphorylation of TAFI110 is prevented . The results demonstrate that reversible phosphorylation of TAFI110 and acetylation of TAFI68 are key modifications that regulate SL1 activity and mediate fluctuations of pre-rRNA synthesis during cell cycle progression .
Posttranslational modification of transcription factors is critical for cell cycle progression in a unidirectional and reversible manner . Cell cycle-dependent oscillation of transcriptional activity is governed by a complex network of regulatory proteins and signaling pathways that respond to various intra- and extracellular stimuli by influencing the activity and tertiary structure of proteins , controlling subcellular distribution , and regulating interactions with other proteins . Global repression of gene expression starts at prophase and is accompanied by release of most transcriptional regulators from mitotic chromatin [1–3 , 4] . Mitotic switch-off of cellular transcription involves inactivation of key components of the transcription machinery . For class II genes , components of the basal transcription apparatus are inactivated by mitotic phosphorylation , including TAF subunits of TFIID [4 , 5] , the cdk7 subunit of TFIIH [6 , 7] and the heptapeptide repeats of the carboxy-terminal domain ( CTD ) of RNA polymerase II [8] . For class III genes , inactivation of TFIIIB causes repression of RNA polymerase III ( Pol III ) transcription [9–11] . With regard to transcription by RNA polymerase I ( Pol I ) , the nucleolar structure undergoes extensive changes at the onset of mitosis , and rDNA transcription ceases between pro-metaphase and telophase [3] . While most nucleolar proteins disperse throughout the mitotic cell after breakdown of the nuclear envelope , some components of the Pol I transcription machinery , including UBF and TTF-I , remain associated with nucleolus organizer regions ( NORs ) to bookmark active rDNA repeats [11 , 12] . Consistent with post-translational modification of basal transcription factors controlling cell cycle-dependent fluctuations of gene expression , mitotic silencing and reactivation of rDNA transcription upon mitotic exit has been shown to be governed by reversible phosphorylation of the promoter selectivity factor SL1 [13] . SL1 is a multiprotein complex comprising the TATA-box binding protein ( TBP ) and five TBP-associated factors ( TAFIs ) , TAFI110 , TAFI68 , TAFI48 , TAFI41 , and TAFI12 [14–17] . At the onset of mitosis , Cdk1/cyclin B , the kinase that triggers early mitotic events , e . g . chromosome condensation , nuclear envelope breakdown and spindle pole assembly , phosphorylates TAFI110 . This phosphorylation impairs the interaction between SL1 and UBF , thus attenuating the assembly of pre-initiation complexes at the rDNA promoter [13 , 18] . Upon exit from mitosis , rDNA transcription is restored , yet the mechanisms that restore transcriptional activity are poorly characterized [19] . In this study , we have investigated the molecular mechanisms that cause reversible mitotic inactivation of SL1 at the onset of mitosis and relieve transcriptional silencing at the end of mitosis . Consistent with prior studies showing that the phosphatase hCdc14B regulates progression through mitosis by counteracting mitotic phosphorylation by Cdk1/cyclin B [20] , hCdc14B dephosphorylates TAFI110 , thus promoting its reactivation as cells exit mitosis . Notably , though phosphorylation of TAFI110 by Cdk1/cyclin B is necessary , alone it is not sufficient for mitotic inactivation of rDNA transcription . Previous studies have established that another SL1 subunit , TAFI68 , is acetylated by PCAF , acetylation of TAFI68 stabilizing binding of SL1 to the rDNA promoter [21] . Here we show that deacetylation of TAFI68 by the NAD+-dependent deacetylase SIRT1 is also crucial for mitotic inactivation of SL1 . SIRT1 becomes enriched in nucleoli at the onset of mitosis and deacetylates TAFI68 , which in turn weakens SL1 binding to rDNA and impairs transcription complex assembly . Thus both phosphorylation of TAFI110 by Cdk1/cyclin B and deacetylation of TAFI68 by SIRT1 are required for repression of Pol I transcription during mitosis . The finding that both Cdk1 and SIRT1 modulate the activity of SL1 underscores the functional significance of reversible modification of SL1 in linking cycle progression to regulation of rDNA transcription .
In asynchronous cells , TAFI110 ( TAF1C ) is constitutively phosphorylated at two tryptic peptides ( labeled a and b in Fig 1A ) . In mitotic cells , a third peptide ( labeled c ) is phosphorylated by Cdk1/cyclin B , phosphorylation of peptide c correlating with mitotic inactivation of SL1 and transcriptional repression [13] . Phosphoamino acid analysis using 2-dimensional electrophoresis along with amino acid standards of phosphorylated serine , threonine and tyrosine showed that Cdk1/cyclin B phosphorylates TAFI110 at threonine , phosphorylation being reduced by the Cdk inhibitor roscovitine ( S1A Fig ) . Two-dimensional phosphopeptide mapping experiments revealed that peptide c co-migrates with a synthetic peptide ( SQQHpTPVLSSQPLR ) that is phosphorylated at threonine 852 ( Fig 1A and S1B Fig ) , suggesting that phosphorylation of T852 is causally involved in mitotic inactivation of SL1 . Sequence alignment revealed that T852 as well as adjacent amino acids are conserved in TAFI110 from different vertebrates ( S1C Fig ) . Regarding the phosphatase that counteracts mitotic phosphorylation of T852 , we hypothesized that hCdc14B , the phosphatase that regulates Cdk1/cyclin B activity and progression through mitosis in mammals [20] , could remove the inhibitory phosphate from T852 . Indeed , recombinant hCdc14B dephosphorylated peptide c comprising T852 , but not peptides a or b ( Fig 1A , right ) . In vitro protein pull-down assays showed specific association of hCdc14B with hTAFI110 ( Fig 1B ) . Moreover , co-immunoprecipitation experiments demonstrate that hCdc14B interacts with hTAFI110 in vivo ( Fig 1C ) , supporting that hCdc14B is the phosphatase that removes the inhibitory phosphate from TAFI110 . These results identify hTAFI110 as a novel substrate of hCdc14B , revealing that hCdc14B counteracts Cdk1/cyclin B-mediated phosphorylation of SL1 . To examine whether hCdc14B is capable to overcome mitotic repression of Pol I transcription , we performed in vitro transcription assays using extracts from M-phase cells . As reported before [18 , 19] , extracts from mitotic cells are transcriptionally inactive ( S1D Fig ) . Transcriptional repression was relieved if extracts were depleted from Cdk1/cyclin B by bead-bound p13suc1 , underscoring the importance of Cdk1/cyclin B in mitotic repression of Pol I transcription ( Fig 1D , lanes 1 , 2 ) . Transcriptional activity was also restored if transcription was performed in conditions that prevent Cdk1/cyclin B-dependent phosphorylation and thus inactivation of SL1 . If ATP was replaced by the non-hydrolysable ATP analogue AMP-PNP ( adenylyl-imidotriphosphate ) or by inclusion of the kinase inhibitor DMAP ( 6-dimethylaminopurine ) , transcriptional repression was relieved ( Fig 1D , lanes 3–10 and S1E Fig ) . Addition of calf intestine alkaline phosphatase ( CIAP ) or Cdc14B relieved transcriptional repression in ATP-containing reactions , reinforcing that Cdc14B-dependent dephosphorylation of TAFI110 at T852 reactivates Pol I transcription at the exit from mitosis . Nucleoli disassemble during mitosis and many nucleolar proteins are released into the cytoplasm [3] . However , UBF remains bound to rDNA , thus bookmarking rDNA for resumption of transcription upon mitotic exit ( Fig 1E and S1F Fig; see also ref . [22] ) . Both in yeast and mammals , Cdc14B is sequestered in nucleoli during interphase and activated both during mitosis and DNA damage upon release from nucleolar chromatin [20 , 23] . In accord with these observations , we found that in asynchronous cells hCdc14B was preferentially bound to intergenic spacer sequences separating individual rDNA repeats ( 5’- and 3’-IGS ) . Binding of hCdc14B to rDNA was abrogated in mitotic cells ( Fig 1E , S1F and S1G Fig ) , reinforcing that Cdc14B is inactivated during interphase by confinement to the intergenic spacer , and is released from nucleolar chromatin during mitosis . Significantly , UBF and histone H3 remained associated with rDNA in M-phase cells . Together , the results suggest that release from rDNA enables hCdc14B to dephosphorylate SL1 , a step that is required for resumption of rDNA transcription when cells re-enter the cell cycle . To prove that hCdc14B-dependent dephosphorylation of SL1 is required to activate rDNA transcription at the exit from mitosis , we assayed the activity of immunopurified SL1 in a reconstituted transcription system . SL1 from asynchronous cells stimulated transcription up to 8-fold ( Fig 2A , lane 3 ) , while the same amount of SL1 from mitotic cells was inactive ( Fig 2A , lane 4 ) . The activity of mitotic SL1 was restored by addition of hCdc14B , demonstrating that dephosphorylation of T852 by hCdc14B relieves Cdk1/cyclin-dependent mitotic inactivation of SL1 ( Fig 2A , lanes 5 , 6 ) . If Cdk1/cyclin B-mediated phosphorylation of TAFI110 is the only mechanism that inactivates SL1 and represses Pol I transcription during mitosis , then mutation of T852 should prevent mitotic inactivation of SL1 . To test this , we established HeLa cell lines that stably overexpress Flag-tagged wildtype TAFI110 or a mutant in which T852 has been replaced by alanine ( TAFI110/T852A ) . Both wildtype and mutant TAFI110 assembled into proper TBP-TAF complexes , indicating that replacement of T852 by alanine does not affect the interaction of TAFI110 with other SL1 subunits ( Fig 2B ) . Moreover , both wildtype and mutant TAFI110 bound with similar efficiency to the rDNA promoter , supporting that phosphorylation of T852 has no impact on DNA binding ( Fig 2C ) . To further corroborate these results , we compared rDNA occupancy of Pol I , UBF and SL1 in synchronized HeLa cells expressing Flag-TAFI110 or Flag-TAFI110/T852A . While UBF remained associated with rDNA throughout the cell cycle [22 , 24] , binding of SL1 and Pol I was decreased in mitotic cells ( Fig 2D ) . Decreased rDNA occupancy of Pol I and SL1 was observed in mitotic cells regardless of whether wildtype or mutant TAFI110 were overexpressed , indicating that the assembly of Pol I pre-initiation complexes was impaired both in cells overexpressing wildtype TAFI110 or mutant TAFI110/T852A . To confirm that rDNA transcription is switched-off during mitosis in cells expressing mutant TAFI110 , we pulse-labeled nascent RNA with FUrd and visualized nascent transcripts that co-localize with UBF at mitotic NORs ( Fig 2E ) . Similar amounts of FU-labeled transcripts were synthesized in interphase cells , regardless of whether wildtype or mutant TAFI110 was expressed . Surprisingly , though the level of ectopic TAFI110/T852A was about 2–3 fold higher than that of endogenous TAFI110 ( S2A Fig ) , no nascent transcripts were visible in mitotic cells expressing mutant TAFI110/T852A . The observation that the phosphorylation-deficient mutant repressed rDNA transcription as efficiently as wildtype TAFI110 reveals that phosphorylation of TAFI110 by Cdk1/cyclin B is necessary but not sufficient for mitotic repression of rDNA transcription . As phosphorylation of TAFI110 was not sufficient for mitotic inactivation of SL1 , we reasoned that other posttranslational modifications contribute to inactivation of SL1 at the entry into mitosis . Previous work has established that TAFI68 ( TAF1B ) , a TBP-associated factor that is structurally related to the general transcription factor TFIIB [25] , is acetylated by the histone acetyltransferase PCAF at two lysine residues , K438 and K443 [26] . Acetylation was shown to augment the DNA-binding activity of TAFI68 and activate rDNA transcription [21] . Mutation of both lysine residues abolished PCAF-dependent acetylation of TAFI68 ( S3A and S3B Fig ) . The correlation between acetylation and DNA binding efficiency of TAFI68 implies that deacetylation of TAFI68 impairs transcription complex assembly . In a previous study we have shown that PCAF-dependent acetylation of TAFI68 was counteracted by SIRT1 [21] , the founding member of the Sirtuin family of NAD+-dependent histone deacetylases . In support of SIRT1 interacting with SL1 , immobilized SIRT1 associated with TAFI110 , TAFI68 and TBP , subunits of the SL1 complex ( Fig 3A , top ) . Consistently , endogenous SIRT1 was co-immunoprecipitated with ectopic TAFI68 ( Fig 3A , bottom ) . No association of TAFI68 was observed with either SIRT6 or SIRT7 , highlighting the specificity of TAFI68 binding to SIRT1 ( Fig 3B ) . This result reveals that the two nucle ( ol ) ar Sirtuins SIRT1 and SIRT7 serve opposing functions , SIRT7 activating rDNA transcription in cycling cells [27 , 28] , while deacetylation of TAFI68 by SIRT1 being required for mitotic repression of rDNA transcription . Accordingly , acetylation of TAFI68 was decreased in prometaphase- compared to G1/S-arrested cells , supporting that cell cycle-dependent fluctuations of SL1 acetylation are involved in mitotic repression of rRNA synthesis ( Fig 3C ) . Consistent with SIRT1 targeting TAFI68 , in vitro deacetylation of TAFI68 by SIRT1 required the presence of NAD+ ( Fig 3D ) . Likewise , in vivo acetylation of TAFI68 was increased if cells were treated with nicotinamide ( NAM ) , a competitive inhibitor of NAD+-dependent deacetylases ( Fig 3E ) . Knockdown of SIRT1 further increased acetylation , proving that SIRT1 rather than another member of the Sirtuin family deacetylates TAFI68 . Next , we examined whether SIRT1-dependent deacetylation of TAFI68 contributes to mitotic inactivation of SL1 . For this , we compared rDNA occupancy of SL1 ( TBP , TAFI110 and TAFI68 ) , Pol I ( RPA116 ) and UBF in prometaphase cells expressing wildtype or mutant TAFI110 ( Fig 3F ) . As expected , rDNA occupancy of UBF was comparable in both cell lines and was not affected by NAM-dependent inhibition of SIRT1 activity . In contrast , treatment with NAM similarly increased binding of SL1 regardless of whether cells expressed wildtype or mutant TAFI110 . This indicates that deacetylation of TAFI68 by SIRT1 rather than phosphorylation of TAFI110 weakens the association of SL1 with the rDNA promoter , which leads to partial displacement of SL1 from rDNA in early mitosis . Consistent with acetylation of TAFI68 being required for binding of SL1 and transcription complex formation , rDNA promoter occupancy of Pol I was elevated in NAM-treated mitotic TAFI110/T852A cells , indicating that under these conditions transcriptional repression was partially relieved . Thus , unphosphorylated TAFI110 and acetylated TAFI68 are required for the assembly of productive Pol I transcription initiation complexes . Together , these results imply that mitotic repression of Pol I transcription is brought about by a dual mechanism . Deacetylation of TAFI68 by SIRT1 weakens the association of SL1 with rDNA , while phosphorylation of TAFI110 by Cdk1/cyclin B impairs the interaction with UBF , leading to mitotic repression of , rDNA transcription . In asynchronous cells the majority of SIRT1 resides in the nucleoplasm and is excluded from nucleoli . In prophase cells , however , SIRT1 transiently localizes in nucleoli , co-staining with UBF and Pol I ( Fig 4A , dashed circles ) . Prophase cells have an intact nuclear envelope but show chromosome condensation and are positive for the mitotic histone mark H3-pSer10 . Consistent with UBF bookmarking mitotic NORs [22 , 29] , UBF remained bound to NORs throughout mitosis . Co-localization of SIRT1 and UBF was confined to prophase and was not detected at later stages of mitosis ( Fig 4B ) . Enrichment of SIRT1 at NORs preceded repression of Pol I transcription , monitored by visualization of NOR-associated nascent RNAs at different stages of mitosis . Consistent with Pol I transcription being switched-off during mitosis , no nascent RNA was detected at UBF-specific foci from prometaphase to telophase ( Fig 4C ) . Next we monitored nascent pre-rRNA levels in mitotic HeLa cells expressing Flag- TAFI110/WT or TAFI110/T852A in the absence or presence of the Sirtuin inhibitor NAM . The rationale of this experiment was to find out whether mitotic inactivation of SL1 would be relieved and pre-rRNA synthesis restored if both phosphorylation of T852 and deacetylation of TAFI68 were prevented . Immunofluorescence analysis of fluorouridine ( FUrd ) -labeled RNA revealed low but significant levels of nascent transcripts at mitotic NORs in NAM-treated cells that express TAFI110/T852A ( Fig 4D ) . In contrast , there was no nascent RNA visible at mitotic NORs of HeLa cells expressing wildtype hTAFI110 , regardless of whether the cells were treated with NAM or not . The finding that SL1 was not inactivated if both Cdk1/cyclin B-dependent phosphorylation of hTAFI110 and SIRT1-dependent deacetylation of TAFI68 were prevented highlight the functional significance of both activities in mitotic repression of Pol I transcription .
At mitosis , there is substantial reorganization of chromosomal architecture as cells prepare to exit the G2-phase of the cell cycle and enter the prophase of mitosis . This reorganization of nuclear structure is accompanied by a global shut-off of transcriptional activity . Transcription by all three classes of nuclear DNA-dependent RNA polymerases stops by mid-prophase and resumes in late telophase [1 , 2] . Mitotic repression of rDNA transcription correlates with perturbation of nucleolar structure and dispersion of most nucleolar proteins . However , basal factors required for transcription initiation are maintained on metaphase chromosomes [3 , 12 , 24 , 30] , thus marking rRNA genes for rapid assembly of pre-initiation complexes and resumption of rRNA synthesis in G1-phase . Mitotic repression of Pol I transcription is brought about by phosphorylation of TAFI110 , the large subunit of the basal Pol I-specific transcription factor SL1 by Cdk1/cyclin B [13 , 18] . Phosphorylation of TAFI110 at threonine 852 impairs the capability of SL1 to interact with UBF , thereby abrogating the assembly of transcription-competent initiation complexes [13] . Here we show that phosphorylation of TAFI110 at threonine 852 is counteracted by the phosphatase Cdc14B , which regulates progression through mitosis [20] . Cdc14B is sequestered during interphase in the nucleolus by association with intergenic spacer sequences that separate individual rDNA transcription units . At prometaphase , Cdc14B is released from rDNA , allowing dephosphorylation of TAFI110 and resumption of rRNA synthesis in early G1-phase [19] . These results suggested that Cdc14B-dependent dephosphorylation of TAFI110 is the molecular switch that reactivates SL1 at the exit from mitosis . Surprisingly , however , transcription was also repressed in mitotic cells that express the phosphorylation-deficient mutant TAFI110/T852A , indicating that phosphorylation of T852 by Cdk1/cyclin B is not the only mechanism that inactivates SL1 during mitosis . In addition to phosphorylation of TAFI110 , repression of rDNA transcription upon entry into mitosis involves deacetylation of another SL1 subunit , TAFI68 , by the NAD+-dependent deacetylase SIRT1 . TAFI68 is acetylated by the histone acetyltransferase PCAF , acetylation promoting the association of SL1 with the rDNA promoter [21] . The functional significance of TAFI68 acetylation , however , remained obscure . We found that mitotic repression of transcription was alleviated in the presence of nicotinamide , a competitive inhibitor of NAD+-dependent deacetylases . Moreover , PCAF-dependent acetylation of TAFI68 was counteracted by SIRT1 , which is transiently enriched in nucleoli at prophase . This identifies TAFI68 as novel substrate of SIRT1 , SIRT1-dependent deacetylation of SL1 reinforcing mitotic shut-off of Pol I transcription . In addition , SIRT1 is known to deacetylate the euchromatic histone mark H4K16Ac , and to facilitate loading of histone H1 and the condensin I complex , which promotes facultative heterochromatin formation and thereby contributes to chromosome integrity and stability during mitosis [31–33] . Our finding that SIRT1 deacetylates TAFI68 is in accord with numerous studies demonstrating that the deacetylase activity of SIRT1 targets histones , chromatin regulators , and a world of nonhistone substrates , including metabolic enzymes , transcription factors , cytoskeleton proteins , and many others [34] . Like other transcription factors , such as p53 , acetylation of TAFI68 increases site-specific DNA binding activity . Accordingly , deacetylation by SIRT1 weakens the association of SL1 with rDNA [21] . Nucleolar enrichment of SIRT1 and decreased rDNA transcription correlates with increased dynamics of the Pol I transcription machinery at mitotic NORs determined by FRAP measurements [24 , 35] . Thus , cells regulate rDNA transcription complex formation by reversible acetylation of TAFI68 , acetylation of TAFI68 increasing and deacetylation by SIRT1 decreasing DNA binding of SL1 . At the entry into mitosis two posttranslational modifications , i . e . , phosphorylation of TAFI110 and deacetylation of TAFI68 , inactivate SL1 , thereby attenuating pre-initiation complex formation ( Fig 5 ) . These results reveal that fine-tuned reversible acetylation and phosphorylation of TAFIs is an effective means to regulate SL1 activity and mediate fluctuations of Pol I transcription during cell cycle progression .
Cells cultured according to standard conditions ( ATCC ) were transfected with Fugene6 , Lipofectamin 2000 ( Invitrogen ) , or calcium phosphate . Clonal HeLa cell lines that stably express Flag-tagged wildtype hTAFI110 or mutant hTAFI110/T852A were selected in the presence of G418 ( 750 μg/ml ) . To knockdown SIRT1 , cells transfected with specific shRNA expression plasmids ( Sigma ) were selected in the presence of puromycin ( 1 μg/ml ) and analyzed after 5–6 days . HeLa and U2OS cells were synchronized at G1/S with thymidine ( 2 mM , 23 h ) , released for 8 h , and arrested in prometaphase with nocodazole ( 80 ng/ml ) . To inhibit SIRT1 activity , cells were treated for 5 h with 5–10 mM nicotinamide ( NAM ) . Plasmids encoding SIRT1 , SIRT6 , SIRT7 , hCdc14B , TBP , and individual TAFIs have been described [15 , 20 , 21 , 27 , 28] . cDNA encoding hTAFI110 ( accession number NM_005679 ) was cloned into pRc/CMV ( Invitrogen ) . Threonine at position 852 of hTAFI110 , and lysine residues at position 438 and 443 of hTAFI68 were converted into alanine or arginine by site-directed mutagenesis . Primers used for PCR-mediated mutagenesis are listed in S2 Table . Antibodies against UBF [36] , RPA194 and RPA116 [37 , 38] and TAFIs [15] have been described . Anti-TBP antibodies ( clone 3G3 ) were provided by Laszlo Tora . Antibodies against the Flag epitope ( F3165 ) , actin , BrdU ( BU-33 ) were from Sigma , anti-acetylated lysine from Cell Signaling ( 9441 ) , anti-UBF ( sc-13125 ) and anti PCAF ( H-369 ) from Santa Cruz , anti-SIRT1 ( 07–131 ) , anti-histone H3-pSer10 ( 06–570 ) from Millipore , anti-GFP from Abcam , anti-histone H3 from Diagenode and anti-Cdc14B from Zymed . Immunofluorescence was performed as described [39] . The secondary antibodies were conjugated to Cy2 , Cy3 , or FITC ( Dianova ) , Alexa Fluor 488 or Alexa Fluor 555 ( Molecular Probes ) . To visualize nascent RNAs , cells grown on poly-L-lysine coated coverslips were labeled with 2 mM fluorouridine ( FUrd ) for 20 min , fixed with 2% paraformaldehyde , permeabilized with methanol , incubated with the respective antibodies and stained with fluorophore-coupled secondary antibodies . DNA was stained with Hoechst 33342 . Images were visualized with a Zeiss microscope ( Axiophot ) using a 40×1 . 3 oil immersion Plan-Neofluar magnifying objective ( Carl Zeiss ) , captured with a device camera ( DS-Qi1Mc; Nikon ) and processed with NIS-Elements software ( version BR 3 . 10; Nikon ) . Images were quantified images using ImageJ and calculated as described [40] . Confocal laser scanning microscopy ( CLSM ) was done with LSM META 510 ( Zeiss ) . Immunofluorescence images were quantified using ImageJ as described [40] . HEK293T or HeLa cells expressing epitope-tagged proteins were lysed in buffer AM-600 ( 600 mM KCl , 20 mM Tris-HCl [pH 7 . 9] , 5 mM MgCl2 , 0 . 2 mM EDTA , 10% glycerol , 0 . 5 mM DTT ) supplemented with 0 . 1% NP-40 , protease inhibitors ( Roche Complete , PMSF ) , and HDAC inhibitors ( 500 nM TSA , 5 mM sodium butyrate , 10 mM nicotinamide ) . Lysates were incubated for 4 h at 4°C with mouse or rabbit IgGs bound to protein A/G-agarose ( Roche , Dianova ) or with M2 anti-Flag beads ( Sigma ) . After washing in lysis buffer , tagged proteins were eluted in buffer AM-300 supplemented with 0 . 1% NP-40 and Flag-peptide ( 20 μg/100 μl ) . GST-tagged proteins were expressed in E . coli and affinity-purified on Glutathione-Sepharose 4B ( GE healthcare ) . Flag-tagged UBF was isolated from Sf9 cells [36] . GFP-tagged proteins were bound to GFP-Trap ( ChromoTek ) in buffer AM-300 containing 0 . 1% NP-40 , Roche complete and HDAC inhibitors . Flag-tagged UBF and PCAF were isolated from baculovirus-infected Sf9 cells as described [36 , 21] . 2 μg of GST or GST-tagged proteins immobilized on Glutathione-Sepharose 4B were incubated with in vitro synthesized 35S-labeled proteins for 4 h at 4°C in 120 mM KCl , 20 mM Tris-HCl [pH 7 . 9] , 5 mM MgCl2 , 0 . 2 mM EDTA , 10% glycerol , 0 . 2% NP-40 and protease inhibitors ( Roche Complete , PMSF ) . After washing , bead-bound proteins were analyzed by SDS-PAGE and PhosphorImaging . Endogenous SL1 was immunopurified from HeLa nuclear extracts with anti-TBP antibody ( clone 3G3 ) to Dynabeads , mouse IgGs were used as control [15] . To monitor the interaction of Flag-tagged hTAFI68 with GFP-tagged Sirtuins , cells were lysed in buffer containing 300 mM KCl , 20 mM Tris-HCl [pH 7 . 9] , 5 mM MgCl2 , 0 . 2 mM EDTA , 0 . 5% Triton X-100 and protease inhibitors ( Roche Complete , 0 . 5 mM PMSF ) . After capture on GFP-trap beads ( 4 h , 4°C ) , associated hTAFI68 was monitored on Western blots . To examine the association of TAFI110 with Cdc14B , Flag-tagged hTAFI110 co-expressed with GFP-hCdc14B in HEK293T cells was bound to M2-agarose in the same buffer containing 120 mM KCl , and co-purified GFP-hCdc14B was visualized on immunoblots . Co-immunoprecipitation of Flag-tagged hTAFI110 with endogenous TAFI68 and TBP was performed in the presence of 250 mM KCl . In vitro transcription reactions contained 40 ng of linearized plasmid pHrP2 comprising human rDNA sequences from -410 to +378 ( with respect to the transcription start site ) and 40 μg of extract from HeLa cells [13] . Depletion of Cdk1/cyclin B from mitotic extracts with immobilized p13suc1 has been described [18] . SL1 was immunopurified from extracts with anti-TBP ( 3G3 ) antibodies immobilized on anti-mouse IgG Dynabeads . Flag-tagged UBF was immunopurified from baculovirus-infected Sf9 cells [37] . The SL1-responsive reconstituted transcription system contained 5 μl of Pol I ( MonoS fraction ) , 3 μl of TIF-IA ( Q-Sepharose fraction ) [41] , and 10 ng of UBF . After incubation for 1 h at 30°C , transcripts were purified and analyzed by gel electrophoresis and PhosphorImaging . Cells were fixed with 1% formaldehyde ( 10 min , RT ) , quenched with 0 . 125 M glycine , and sonicated to yield 250–500 bp DNA fragments . After 5-fold dilution with IP dilution buffer ( 0 . 01% SDS , 1 . 1% Triton X-100 , 1 . 2 mM EDTA , 16 . 7 mM Tris-HCl [pH 8 . 0] , 167 mM NaCl ) and preclearing with protein A/G Sepharose , lysates were incubated overnight with the respective antibodies , and protein-DNA complexes were captured on protein A/G Sepharose followed by washes in low salt buffer ( 150 mM NaCl , 50 mM Tris-HCl [pH 8 . 0] , 5 mM MgCl2 , 1% Triton X-100 ) , high salt buffer containing 500 mM NaCl , followed by LiCl buffer ( 250 mM LiCl , 10 mM Tris-HCl [pH 8 . 0] , 5 mM EDTA , 0 . 5% Na-deoxycholate , 0 . 5% Triton X-100 ) and TE buffer . After reversal of the crosslink and proteinase K digestion , DNA was purified and analyzed by qPCR ( Roche LightCycler480 ) . Precipitated DNA was calculated as the percentage of DNA in the immunoprecipitates compared to input DNA . The primers used for PCR are listed in S1 Table . Recombinant TAFI110 was immunopurified from HEK293T cells overexpressing FLAG-tagged TAFI110 and radiolabeled in vitro by incubation with extracts from mitotic HeLa cells or immobilized Cdk1/cyclin B in the presence of 32P-ATP . After digestion overnight at 37°C with trypsin ( 5 μg , Promega , sequencing grade ) in 50 mM ammonium bicarbonate and lyophilisation , the peptides were resolved on cellulose thin-layer plates by electrophoresis for 25 min at 1000V in 1% ( w/v ) ammonium carbonate ( pH 8 . 9 ) , followed by ascending chromatography in a buffer containing 62 . 5% isobutyric acid , 1 . 9% n-butanol , 4 . 8% pyridine , and 2 . 9% acetic acid [13 , 41] . Phosphoamino acid analysis was performed according to Boyle et al . [42] .
|
In metazoans , transcription is arrested during mitosis . Previous studies have established that mitotic repression of cellular transcription is mediated by Cdk1/cyclin B-dependent phosphorylation of basal transcription factors that nucleate transcription complex formation . Repression of rDNA transcription at the onset of mitosis is brought about by inactivation of the TBP-containing transcription factor SL1 by Cdk1/cyclin B-dependent phosphorylation of the TAFI110 subunit , which impairs the interaction with UBF and the assembly of pre-initiation complexes . Here we show that hCdc14B , the phosphatase that regulates Cdk1/cyclin B activity and progression through mitosis , promotes reactivation of rDNA transcription by dephosphorylating TAFI110 . In addition , the NAD+-dependent deacetylase SIRT1 becomes transiently enriched in nucleoli at the onset of mitosis . SIRT1 deacetylates TAFI68 , another subunit of SL1 , hypoacetylation of TAFI68 destabilizing SL1 binding to the rDNA promoter and impairing transcription complex assembly . The results reveal that modulation of SL1 activity by reversible acetylation of TAFI68 and phosphorylation of TAFI110 are key modifications that mediate oscillation of rDNA transcription during cell cycle progression .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Cooperative Action of Cdk1/cyclin B and SIRT1 Is Required for Mitotic Repression of rRNA Synthesis
|
In experimental cultures , when bacteria are mixed with lytic ( virulent ) bacteriophage , bacterial cells resistant to the phage commonly emerge and become the dominant population of bacteria . Following the ascent of resistant mutants , the densities of bacteria in these simple communities become limited by resources rather than the phage . Despite the evolution of resistant hosts , upon which the phage cannot replicate , the lytic phage population is most commonly maintained in an apparently stable state with the resistant bacteria . Several mechanisms have been put forward to account for this result . Here we report the results of population dynamic/evolution experiments with a virulent mutant of phage Lambda , λVIR , and Escherichia coli in serial transfer cultures . We show that , following the ascent of λVIR-resistant bacteria , λVIR is maintained in the majority of cases in maltose-limited minimal media and in all cases in nutrient-rich broth . Using mathematical models and experiments , we show that the dominant mechanism responsible for maintenance of λVIR in these resource-limited populations dominated by resistant E . coli is a high rate of either phenotypic or genetic transition from resistance to susceptibility—a hitherto undemonstrated mechanism we term "leaky resistance . " We discuss the implications of leaky resistance to our understanding of the conditions for the maintenance of phage in populations of bacteria—their “existence conditions . ”
The viruses of bacteria and archaea , phage , for brevity and generality , are touted to be the most abundant organisms on Earth [1] . Research with phage , and particularly but not exclusively those that infect E . coli , have played a major role in the development of the contemporary concept of the gene [2] , the demonstration that DNA is the genetic material [3] , "breaking" the genetic code [4] , understanding the regulation of gene action [5] , and the development of classical ( restriction-endonuclease-based ) [6] and contemporary CRISPR-Cas ( clustered regularly interspaced short palindromic repeats , CRISPR-associated proteins ) –based genetic engineering [7] . Phage have been used to prevent and treat bacterial infections [8] , an enterprise that , in response to concerns about mounting resistance to antibiotics , is being resurrected in various ways [9–12] . Phage are also significant as a source of contamination for the bacteria employed in processing of dairy products [13 , 14] , as well as other industrial applications [15] . Despite the abundance and ubiquity of phage in natural bacterial and archaeal communities , including the enteric microbiomes of humans [16 , 17] , together with all that we know about their structure , genetics , molecular biology , and mechanisms of replication , relatively little is known about the ecology and ecological role of phage in bacterial communities . What are the ecological , genetic , and evolutionary conditions necessary for phage to be maintained in bacterial populations ? In the words of Allan Campbell , what are their "existence conditions" [18] ? Do phage regulate the densities of their host populations of bacteria and archaea ? What is the role of phage in determining the distribution and abundance of different species and genotypes of bacteria in their natural communities ? While the answers to these questions may be unknown for natural populations , these questions have been addressed with mathematical models , as well as experiments with bacteria and lytic ( “virulent” ) phage in continuous and serial passage cultures . In theory , if the densities of susceptible host populations are sufficiently high , lytic phage can become established and regulate the densities of these populations under a broad set of conditions [18–20] , and this has been observed experimentally [21 , 22] . However , as a consequence of mutation to resistance or , in the case of CRISPR-Cas , acquired immunity , bacteria upon which the lytic phage cannot replicate eventually emerge , and these experimental bacterial populations become limited by resources rather than phage [22–30] . While the lytic phage may eventually be lost in such cultures [26 , 31] , in most cases they are maintained and coexist with bacteria resistant or immune to them for extensive periods of time [32] . Several mechanisms have been proposed to account for how lytic phage are maintained in populations dominated by bacteria upon which they cannot replicate: ( i ) the resistant bacteria are at a strong selective disadvantage . As a result of this disadvantage , a minority population of phage-susceptible bacteria continues to be maintained in a population dominated by resistant cells , and supports replication of the phage [20 , 22 , 33 , 34] . ( ii ) There is an indefinite coevolutionary arms race between the bacteria and the phage . The emergence and ascent of bacterial resistance to phage selects for host range phage mutants that can grow on the resistant bacteria . This , in turn , selects for new mutations that generate bacteria resistant to the newly evolved phage and so on [26 , 35] . ( iii ) The habitat is heterogeneous . A small population of susceptible bacteria ( refuge ) is maintained in a subhabitat that is inaccessible to the phage and continues to provide susceptible cells in cultures dominated by resistant bacteria [36] . In addition to these mechanisms , a hypothesis was presented by Max Delbrück more than 70 years ago [37] to account for how , in cultures of resistant bacteria “contaminated with virus , ” the phage continued to be maintained . He postulated that susceptible cells were continually “thrown off” by mutation and , by replicating on these susceptible cells , the phage are able to maintain their population in a community dominated by bacteria upon which they cannot replicate . Delbruck did not explore in the necessarily quantitative way the conditions under which this mechanism could account for the maintenance of the phage or test this hypothesis experimentally . It was , however , clear that he found this mechanism more appealing than lysogeny , which could have also explained the continued presence of phage in a population of seemingly resistant cells . Far more recently , Weissman and colleagues explored the conditions under which this mechanism can account for the continued maintenance of phage in populations dominated by immune bacteria carrying CRISPR-Cas [31] . General theoretical consideration and direct experimental verification of this hypothesis are , however , still lacking . The results of our computer simulations and experiments using E . coli and a virulent mutant of phage λ ( λVIR ) provide compelling evidence in support of the hypothesis that high rates of genetic and/or phenotypic reversion from resistance to susceptibility , a phenomenon we term “leaky resistance , ” is the dominant if not the unique mechanism responsible for continued maintenance of λVIR in populations dominated by λVIR-resistant E . coli .
To generate hypotheses and to facilitate the design and interpretation of the results of experiments , we use a simple , mass action mathematical model of population and evolutionary dynamics of bacteria and phage in a liquid culture . This model is a version of that described in [19] modified for serial transfer , rather than a continuous ( chemostat ) culture . We consider two populations of bacteria: a susceptible population N ( cells per mL ) , upon which the phage can replicate , and a resistant population NR ( cells per mL ) , to which the phage does not adsorb . As in [38 , 39] , we assume that the bacteria grow at rates equal to the product of their maximum growth rates , v and vR ( per cell per hour ) , for susceptible and resistant bacteria , respectively , and a Monod function: ψ ( R ) = R/ ( k + R ) , where R is the concentration ( μg per mL ) of the limiting resource and k is the “Monod constant , ” corresponding to the concentration of the resource ( μg per mL ) , at which the bacteria grow at a half of their maximum rate . As in [39] , we assume that the bacteria take up the resource at a rate jointly proportional to their density , their growth rate , and a conversion efficacy parameter , e ( μg per cell ) . Lytic phage , which are present at density V ( particles per mL ) , adsorb to the susceptible bacteria according to the law of mass action at a rate equal to the product of their densities and a rate constant δ ( per hour per mL ) [19] . For simplicity , we disregard the latent period and assume that the infected bacteria are killed instantaneously , liberating β-1 phage particles ( the burst size minus the one phage particle lost by adsorption ) . At rates μN and μR ( per cell per hour ) , respectively , susceptible bacteria become resistant to the phage ( NS→NR transition ) and resistant bacteria become susceptible ( NR→NS transition ) . To account for the decline in the rates of metabolism as bacteria approach stationary phase , we assume that the rates of transition as well as the rate at which the phage adsorb to the bacteria decline with the concentration of the limiting resource by multiplying the rates parameters by ψ ( R ) . With these definitions and assumptions , the rates of change in concentration of the resource and densities of bacteria and phage are given by a set of coupled differential equations: dRdt=-e⋅ψ ( R ) ⋅ ( v⋅N+vr⋅Nr ) ⏟resourceuptake ( 1 ) dNdt=v⋅ψ ( R ) ⋅N⏟growth-δ⋅ψ ( R ) ⋅N⋅V⏟phagelysis+ ( μR⋅NR-μN⋅N ) ⋅ψ ( R ) ⏟transition ( 2 ) dNRdt=vR⋅ψ ( R ) ⋅NR⏟growth+ ( μN⋅N-μR⋅NR ) ⋅ψ ( R ) ⏟transition ( 3 ) dVdt=δ⋅ψ ( R ) ⋅N⋅V⋅ ( β-1 ) ⏟phagelysis ( 4 ) where ψ ( R ) = R/ ( k + R ) . In our simulations , we assume the existence of a refuge density ref ( cells per mL ) for the susceptible bacteria , perhaps wall populations [36] , below which the phage cannot adsorb to them . To simulate the serial transfer protocol used in our experiments , the density of the bacteria and phage populations as well as the concentration of the resource are reduced every 24 hours by a factor of d = 0 . 01 . At the same time , a defined amount C ( μg per mL ) of fresh resource is added . For convenience , we assume a culture volume of 1 mL . In Fig 1 we present the results of numerical simulations with the parameters in the range estimated for Lysogeny broth ( LB ) medium . In the absence of transition from susceptibility to resistance ( μN = 0 ) , the phage density rapidly increases and that of the susceptible bacteria declines to the refuge level ( Fig 1A ) . In the course of a serial transfer , in which one hundredth of the culture is transferred to fresh medium every 24 hours , the bacteria continue to be maintained at the refuge level , with the concentration of the resource being only slightly lower than the initial concentration at each transfer . Thus , the density of the bacterial population is limited by the phage . A very different result obtains if we allow for transition from susceptibility to resistance ( μN = 5 × 10−6 ) , without yet considering transition in the opposite direction ( μR = 0 ) . A population of bacteria resistant to the phage is generated and ascends , whilst that of the susceptible bacteria declines ( Fig 1B ) . The concentration of the resource at the end of each transfer approaches zero , indicating that the resource , rather than phage , limits the bacterial population . Despite that susceptible bacteria are present , the rate of phage infection is too low for the phage to maintain their population . In other words , the phage are lost because the extent to which they are produced in each transfer is lower than the rate required to overcome the serial 100-fold dilutions . Once transition from resistance to susceptibility is allowed , the phage can be stably maintained in a population dominated by resistant bacteria because of the existence of a sufficiently large subpopulation of susceptible cells ( Fig 1C ) . However , to achieve stable phage maintenance , the transition rate from resistance to susceptibility has to be noticeably large ( at the order of 10−5 per cell per hour or greater ) . Below this rate of transition from resistance to susceptibility , the density of susceptible bacteria is too low for the phage to stably maintain its population . It deserves to be noted that because of inaccuracies in estimating the parameters and the many simplifying assumptions of the model , the value of the threshold transition rate above which the phage can be maintained is , at best , approximate . In addition to the “leaky resistance” mechanism described here , at least three other mechanisms could , in principle , lead to stable phage maintenance in populations dominated by bacteria upon which the phage cannot replicate . While these mechanisms are not mutually exclusive and could , in principle , act together in some populations , in the following section we describe reasons why these alternative mechanisms are unlikely to play a significant role in our experiments .
If the population and evolutionary dynamics of bacteria and phage in natural communities reflect what is observed in experimental populations ( like those studied here and many other investigations ) , we would have to conclude that viruses play little or no role in regulating the population densities of their host bacteria . On the other hand , bacterial and phage populations in nature are complex systems . As a result of environmental and population heterogeneity , different mechanisms allowing for phage maintenance in simple experimental cultures likely interact to produce the ecological and evolutionary dynamics observable in natural populations . Indeed , evidence of adaptation of viruses to resistant hosts [28 , 57] as well as coevolutionary dynamics [58 , 59] can frequently be observed in natural populations . The mechanism of “leaky resistance” described here could allow phage to persist in a population dominated by resistant bacteria for periods of time necessary for host range mutations to appear [49] , thus playing an important role in facilitating coevolutionary dynamics . Loss of immunity was recently proposed as the mechanism responsible for stable coexistence of a lytic phage 2972 and Streptococcus thermophilus with CRISPR-Cas–mediated immunity to this phage [31] . The concept of immunity differs from that of resistance in that immune bacteria neutralize the phage genetic material once it enters the cell , whereas resistant bacteria prevent injection of the phage genetic material in the first place . The model Weissman and colleagues used in this study was similar to that employed here and also derived from that in [19] . If we substitute resistance for immunity , the conclusions of Weissman and colleagues are analogous to those presented here; if the rate of transition from phage resistance to susceptibility is sufficiently high , lytic phage will be stably maintained in communities dominated by bacteria upon which the phage cannot replicate . The rate of transition from immunity to susceptibility was not directly measured in Weissman and colleagues [31] but rather inferred from the rate of loss of CRISPR-Cas in plasmid transfer experiments , with Staphylococcus epidermidis bearing CRISPR-Cas–mediated immunity to the plasmid [60] . In this study , we provide direct evidence showing that a high rate of transition from phage resistance to susceptibility is the main mechanism accounting for stable maintenance of phage in populations dominated by bacteria upon which they cannot replicate . Jacque Monod is reported to have quipped: “What is true for E . coli is true for elephants . ” While we certainly appreciate this bacteriocentric form of inductive inferences , we will not argue that the results reported with our simple models and experiments with E . coli and λVIR are general for all bacteria and phage in laboratory culture , much less in natural communities . On the other hand , high rates of transition between resistant and susceptible states were previously observed for Pseudomonas aeruginosa grown in the presence of phage PP7 [61] . For P . syringae , it was shown that some resistant mutants can maintain populations of phage ϕ6 stably despite the absence of a significant fitness cost and with no evidence of coevolution [62] . Is a high rate of transition from resistance to susceptibility observed here for E . coli and λVIR responsible for the stable maintenance of viruses in these experiments and those with other bacterial and phage species ? This leaky resistance hypothesis can be readily tested with any species of bacteria and phage that can be cultured in vitro . The first step in testing this hypothesis is to establish experimental cultures in which phage are maintained despite resistant cells emerging and dominating the bacterial population . If leaky resistance is the mechanism responsible for the maintenance of the phage in these experiments , without subsequent host range mutation , the phage should be able to become established and be maintained in populations derived from these resistant clones . Particularly striking in this study is the observation that genetic changes are occurring at rates several orders of magnitude greater than that anticipated by classical point ( base change ) mutation [63] . High rates of mutation caused by insertion and removal of either short duplications or IS elements can thus play an important role in the ecology and evolution of bacteria . Recent experiments also support this perspective [34 , 64] .
Bacterial cultures were grown at 37 °C in either M9M ( M9 salts [248510 , Difco] supplemented with 0 . 4% maltose [6363-53-7 , Fisher Scientific] , 1 mM MgSO4 , 0 . 1 mM CaCl , and 0 . 2% Thiamine [B1] ) or LB ( 244620 , Difco ) . All E . coli strains used in our experiments were wild-type K12 derivatives of the parent strain MG1655 . The K12 and E . coli B6 strains used to test for evolution of host range mutations ( K12 malT− , K12 ompF− , K12 lamB− , B6 lamB− ) were obtained from [49] . Phage lysates were prepared from single plaques at 37 °C in LB medium alongside wild-type MG1655 . Chloroform was added to the lysates and the lysates were centrifuged to remove any remaining bacterial cells . The λVIR strain used in these experiments was obtained from Sylvain Moineau . The construction of λKAN is described in [42] . The 12 resistant mutants ( W1–W12 ) were picked from 12 LB soft agar plates , in which independently grown cultures of E . coli MG1655 were plated as a lawn and λVIR was spotted on top of the plates . Bacteria and phage densities were estimated by serial dilution in 0 . 85% saline followed by plating . The total density of bacteria was estimated on LB hard ( 1 . 6% ) agar plates . To estimate the densities of λKAN lysogens , cultures were plated on LB agar with 25 μg/mL kanamycin ( AppliChem Lot# 1P0000874 ) . To estimate the densities of free phage , chloroform was added to suspensions before serial dilution . These suspensions were mixed with 0 . 1 mL of overnight LB-grown cultures of wild-type MG1655 ( about 5×108 cells per mL ) in 3 mL of LB soft ( 0 . 65% ) agar and poured onto semihard ( 1% ) LB agar plates . Bacteria were tested for resistance by spotting about 25 μL of a lysate ( >108 plaque-forming units [pfu]/mL ) on LB soft agar lawns with about 108 bacteria . Susceptibility to λVIR was noted as clear zones . Failure to see zones was interpreted as evidence for resistance . There is , however , a caveat to this procedure . If 1% or less of the cells in the lawn were susceptible to the phage , there would be no visible plaques ( S6 Fig ) . All serial transfer experiments were carried out in 10-mL cultures grown at 37 °C with vigorous shaking . The cultures were initiated by 1:100 dilution from 2-mL overnight cultures grown from single colonies . Phage were added to these cultures to reach the initial density of approximately 106 pfu/mL . At the end of each transfer , 100 μL of each culture were transferred into flasks with fresh medium ( 1:100 dilution ) . Simultaneously , 100-μL samples were taken for estimating the densities of colony-forming units ( cfu ) and pfu , as described above . Overnight LB cultures of the W1–W12 strains were grown from single colonies . These cultures were diluted 1:100 in 2 mL of fresh LB and λKAN was added to reach the density of approximately 5×105 pfu/mL . The dynamics of lysogen formation were followed by sampling 100 μL at regular intervals and plating the diluted samples at regular time intervals on LB agar with 25 μg/mL kanamycin . We estimated the rate of mutation to λVIR resistance as follows: 12 independent 10-mL cultures of MG1655 , each initiated with about 1E4 cells , were grown overnight in LB to stationary phase . The next day , 100 μL of these overnight cultures was added to 3 mL LB and allowed to grow for 1 hour at 37 °C . One hundred milliliters of these cultures was added to 1 mL of a high titer ( >109 pfu/mL ) λVIR lysate . The mixture was incubated for 45 minutes at 37 °C . While these cultures were incubating , the viable cell densities in their cultures were estimated by serial dilution and plating . After 45 minutes of incubation with the phage , 200 μL of the mixture was spread on LB agar to estimate the number of resistant bacteria . The estimate of the mutation rate was made according to the protocol described in [51] , using the median number of mutants in the fluctuation experiment and the fraction of the total cultures plated . We estimated the rate of mutation from Mal- to Mal+ as follows: for strains W1 , W3 , and W9 ( all Mal− ) , 12 independent 2-mL LB cultures of each were initiated with about 1E4 cells in 12-well macrotiter plates . These cultures were grown overnight at 37 °C . Subsequently , 1 mL of these cultures was sampled , and the residual LB medium was removed by centrifugation . The pelleted cells were then resuspended in 1 mL of 0 . 85% saline . This process was repeated three times . The total cell densities of E . coli in these washed cultures were estimated by serial dilution and plating on LB agar . The washed cultures were then diluted 1/10 in saline , and 100 μL from each independent culture was spread onto M9M agar . The mutation rate to Mal+ was estimated with the protocol in [51] , using the median cfu on the M9M agar and the fraction of the total culture plated . The lamB and malT genes were amplified from individual colonies using the fw_lamB , rv_lamB and fw_malT , rv_malT primers ( Table 3 ) , respectively , in a standard PCR reaction with Taq DNA Polymerase ( Sigma ) . The PCR products were then purified using the GenElute Gel Extraction kit ( Sigma ) and sequenced . The malT gene was sequenced using primers fw_malT , rv_malT , s1_malT , s2_malT , s3_malT , s4_malT . The lamB gene was sequenced using primers fw_lamB , rv_lamB , s1_lamB , s2_lamB . This primer arrangement covers the sequence of both genes at least twice . Mutations were identified by comparing the sequences of the mutants to the corresponding sequences of wild-type E . coli MG1655 . The parameters critical for the interaction of λ and E . coli used in this study were estimated in independent experiments in LB medium . The maximum growth rates of E . coli and the 12 resistant mutants were measured by Bioscreen as described in [10 , 65] . Phage burst sizes ( β ) were estimated with one-step growth experiments [66] in a manner similar to [67] . Adsorption of λ to E . coli was estimated as described in [19] . The procedure for estimating the probability of lysogeny and the rate of spontaneous lysogen induction are presented in [42] .
|
While it is clear that bacteriophage abound in bacterial communities , their role in the ecology and evolution of these communities remains poorly understood . Fundamental questions remain unanswered , such as , are phage regulating the population densities of their host bacteria ? And how are virulent phage maintained in bacterial communities , following the seemingly inevitable evolution of resistant bacteria ? Here we present a theoretical and experimental investigation to provide evidence for a new mechanism for maintaining phage in populations dominated by resistant bacteria . This mechanism , which we term “leaky resistance , ” is based on a high rate of either phenotypic or genetic transition from resistance to susceptibility .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"bacteriology",
"organismal",
"evolution",
"microbial",
"mutation",
"bacteriophages",
"microbiology",
"viruses",
"mutation",
"genetic",
"predisposition",
"lysogeny",
"microbial",
"evolution",
"population",
"biology",
"viral",
"replication",
"population",
"metrics",
"point",
"mutation",
"bacterial",
"evolution",
"phenotypes",
"virology",
"genetics",
"biology",
"and",
"life",
"sciences",
"population",
"density",
"evolutionary",
"biology",
"genetics",
"of",
"disease",
"organisms"
] |
2018
|
Leaky resistance and the conditions for the existence of lytic bacteriophage
|
Toxoplasma gondii is an intracellular parasite that infects a wide range of warm-blooded species . Rats vary in their susceptibility to this parasite . The Toxo1 locus conferring Toxoplasma resistance in rats was previously mapped to a region of chromosome 10 containing Nlrp1 . This gene encodes an inflammasome sensor controlling macrophage sensitivity to anthrax lethal toxin ( LT ) induced rapid cell death ( pyroptosis ) . We show here that rat strain differences in Toxoplasma infected macrophage sensitivity to pyroptosis , IL-1β/IL-18 processing , and inhibition of parasite proliferation are perfectly correlated with NLRP1 sequence , while inversely correlated with sensitivity to anthrax LT-induced cell death . Using recombinant inbred rats , SNP analyses and whole transcriptome gene expression studies , we narrowed the candidate genes for control of Toxoplasma-mediated rat macrophage pyroptosis to four genes , one of which was Nlrp1 . Knockdown of Nlrp1 in pyroptosis-sensitive macrophages resulted in higher parasite replication and protection from cell death . Reciprocally , overexpression of the NLRP1 variant from Toxoplasma-sensitive macrophages in pyroptosis-resistant cells led to sensitization of these resistant macrophages . Our findings reveal Toxoplasma as a novel activator of the NLRP1 inflammasome in rat macrophages .
Toxoplasma gondii is an obligate intracellular parasite , for which different host species or strains within a species display variable susceptibilities . Different Toxoplasma strains also differ in virulence within the same host , suggesting variation in effectors among parasite strains and/or their impact in various hosts . Host innate immunity is known to play a critical role in susceptibility to infection . In mice , for example , resistance to Toxoplasma infection is critically dependent on the induction of IL-12 , which subsequently induces IFN-γ , the main mediator of toxoplasmicidal activities ( for review , see [1] ) . Rats , like humans , are quite resistant to Toxoplasma infection when compared to mice . However varying levels of resistance also exist among rat strains . The resistance of the Lewis ( LEW ) strain is characterized by total clearance of the parasite , failure to develop cysts and the absence of a strong antibody response . Fischer ( CDF ) and Brown Norway ( BN ) rats , however , are susceptible to chronic infection and develop transmissible cysts in their brain and muscle tissue [2] , [3] . Resistance in rats is a dominant trait and is linked to myeloid cell control of parasite proliferation [2] , [3] . Linkage analyses of LEWxBN F2 progeny was previously used to map Toxoplasma resistance in rats to a single genetic locus , termed Toxo1 , within a 1 . 7-cM region of chromosome 10 [2] . We noted that this locus overlaps with the locus that controls rat and macrophage sensitivity to the anthrax lethal toxin ( LT ) protease . Inbred rat strains and their macrophages exhibit a perfectly dichotomous phenotype in response to LT: animals either die rapidly ( <1 h ) or exhibit complete resistance to the toxin [4] . Only macrophages from LT-sensitive rat strains undergo rapid caspase-1 dependent death ( pyroptosis ) . The HXB/BXH recombinant inbred ( RI ) rat collection , developed from the SHR/Ola and BN-Lx congenic parental strains [5]–[7] , with opposing LT sensitivities , was used to map anthrax toxin susceptibility to a single locus at 55 . 8–58 . 1 Mb of rat chromosome 10 . SNP analyses and sequence correlation to phenotype implicated the inflammasome sensor Nlrp1 ( nucleotide-binding oligomerization domain , leucine-rich repeat protein 1 ) as the likely susceptibility locus . NLRP1 is a member of the NLR cytosolic family of pathogen-associated molecular pattern molecule ( PAMP ) sensors , the activation of which leads to recruitment and autoproteolytic activation of caspase-1 , followed by cleavage and release of the proinflammatory cytokines IL-1β and IL-18 . NLR-mediated activation of caspase-1 is typically accompanied by rapid death of macrophages through a process known as pyroptosis ( for review see [8] , [9] ) . NLRP1 sequences from 12 inbred rat strains show a perfect correlation between sensitivity and the presence of an N-terminal eight amino acid ( aa ) LT cleavage site [4] , [10] . Proteolytic cleavage by LT activates the NLRP1 inflammasome in rat macrophages leading to rapid caspase-1 dependent cell death ( pyroptosis ) and cytokine processing [10] . We hypothesized that the Toxo1 locus could be Nlrp1 as the macrophage is an important carrier of the parasite [11] , [12] and inflammasome-mediated pyroptosis of this cell could impact in vivo parasite dissemination . The recent association of polymorphisms in the human NLRP1 gene with susceptibility to congenital toxoplasmosis , evidence that P2X ( 7 ) receptors influence parasite proliferation in mouse cells , and the finding that IL-1β responses in Toxoplasma infected human monocytes are dependent on caspase-1 and the inflammasome adaptor protein ASC all suggest that the inflammasome plays a role in determining the outcome of Toxoplasma infection in humans and mice [13]–[15] . Our results indicate that rat strain macrophages exhibit dichotomous susceptibilities to Toxoplasma-induced rapid lysis and associated cytokine processing in a manner correlated with NLRP1 sequence . We go on to show that Nlrp1 knockdown in Toxoplasma-sensitive macrophages protects against this cell death while overexpression of certain variants of the gene in resistant macrophages can sensitize these cells to the parasite-induced pyroptosis . Our findings establish Toxoplasma as the second known activator of the inflammasome sensor NLRP1 and suggest a mechanism of host resistance involving activation of this sensor .
All animal experiments were performed in strict accordance with guidelines from the NIH and the Animal Welfare Act , approved by the Animal Care and Use Committee of the National Institute of Allergy and Infectious Diseases , National Institutes of Health ( approved protocols LPD-8E and LPD-22E ) and the MIT Committee on Animal Care ( assurance number A-3125-01 ) . Ultra-pure lipopolysaccharide ( LPS ) , nigericin ( Calbiochem/EMD Biosciences , San Diego , CA and Invivogen , San Diego , CA ) , 3- ( 4 , 5-dimethyl-2-thiazolyl ) -2 , 5-diphenyl tetrazolium bromide ( MTT ) ( Sigma , St Louis , MO ) , Mycalolide B ( Wako USA , Richmond , VA ) were purchased . LT consists of two polypeptides , protective antigen ( PA ) and lethal factor ( LF ) . Endotoxin-free LF and PA were purified from B . anthracis as previously described [16] . Concentrations of LT refer to equal concentrations of PA+ LF ( ie , LT 1 µg/ml is LF+PA , each at 1 µg/ml ) . Brown Norway ( BN/Crl; BN ) , Fischer CDF ( F344/DuCrl; CDF ) , Lewis ( LEW/Crl; LEW ) , Spontaneously Hypertensive Rat ( SHR/NCrl; SHR ) and Sprague Dawley ( SD ) rats ( 8–12 weeks old ) were purchased from Charles River Laboratories ( Wilmington , MA ) and used as source of bone marrow . Certain experiments utilized F344/NTac rats from Taconic Farms ( Germantown , NY ) . The recombinant inbred ( RI ) rat strains HXB1 , HXB15 and HXB29 are derived from the progenitor strains BN-Lx and SHR/Ola [5]–[7] . The microsatellite marker genotypes and linkage maps used in mapping LT sensitivity using the HXB/BXH RI collection have been described [4] . Tachyzoites from Type I ( RH ) and Type II ( 76K or Prugniaud [PRU] ) strains expressing luciferase and GFP from the plasmid pDHFR-Luc-GFP gene cassette [17] were used for most experiments . The following strains ( haplogroup/type in parentheses ) were used in a survey of effects on rat macrophages: GT1 ( I ) , ME49 ( II ) , DEG ( II ) , CEP ( III ) , VEG ( III ) , CASTELLS ( IV ) , MAS ( IV ) , GUY-KOE ( V ) , GUY-MAT ( V ) , RUB ( V ) , BOF ( VI ) , GPHT ( VI ) , CAST ( VII ) , P89 ( IX ) , GUY-DOS ( X ) , VAND ( X ) , Cougar ( XI ) , RAY ( XII ) , WTD3 ( XII ) . All parasite strains were routinely passaged in vitro in monolayers of human foreskin fibroblasts ( HFFs ) at 37°C in the presence of 5% CO2 , spun and washed prior to quantification by hemocytometer counts . In some experiments , Mycalolide B ( 3 µM , 15 min ) or DMSO was used to pretreat isolated parasites prior to washing in PBS ( 3× ) before infections . The viability of these Mycalolide B- or DMSO-treated parasites was assessed in each experiment by adding them to a monolayer of HFFs and staining for STAT6 activation induced by the parasite secreted rhoptry kinase ROP16 . Mycalolide B-treated parasites were able to secrete ROP16 but could no longer invade . In other experiments parasites were lysed using cell lysis solution ( Abcam , Cambridge , MA ) to assess LDH activity . Parasite viability and health differed from experiment to experiment , accounting for variations in experimental results that are reflected in standard deviations for pooled studies . BMDMs were cultured in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 30–33% L929 cell supernatants as previously described [18] , [19] , or with minor modification ( 20% fetal bovine serum , 50 µg/ml penicillin and 50 µg/ml streptomycin ) . NLRP1-expressing HT1080 or macrophage BMAJ lines and their growth conditions have been previously described [10] . The c-myc tagged rat caspase-1 gene was synthesized by GeneArt ( Regensburg , Germany ) and cloned into pcDNA ( 3 . 1 ) + vector for expression in HT1080 cells by transfection with TurboFect ( Fermentas , Glen Burnie , MD ) using manufacturer's protocols . HA-tagged LEW and CDF NLRP1 expressing constructs used in BMDM nucleofection experiments have been described [10] . Endotoxin-free control vector or various NLRP1 expressing constructs were purified ( Endofree kit , Qiagen , Germantown , MD ) and nucleofected ( 1 . 2–3 . 0 µg/1×106 cells/nucleofection ) into rat BMDMs using the Amaxa Nucleofector ( Lonza , Walkersville , MD ) ( kit VPA-1009 , program Y-001 ) . Nucleofections were performed at −24 , −36 , −48 , and −72 h prior to infections with parasite . Toxicity and viability assays were modified from previously described methods [18] , [19] . Briefly , animal-derived BMDMs with or without LPS priming 0 . 1 µg/ml , 1 h ) were infected with Toxoplasma at various multiplicities of infection ( MOIs ) or treated with anthrax LT ( 1 µg/ml ) and cell viability was assessed at different time points by one of three methods . 1 ) MTT staining ( 0 . 5 mg/ml ) was performed as previously described [18] , [19]; 2 ) MTS ( [3- ( 4 , 5-dimethylthiazol-2-yl ) -5- ( 3-carboxymethoxyphenyl ) -2- ( 4-sulfophenyl ) -2H-tetrazolium ) was used to measure viability with the CellTiter 96 AQueous One Solution Cell Proliferation Assay ( Promega , Madison , WI ) according to manufacturer protocol ; 3 ) Lactate dehydrogenase ( LDH ) release assays were performed in select experiments according to manufacturer protocol ( Roche Diagnostics , Mannheim , Germany ) . For luciferase assays , cells were lysed in 1× Lysis Reagent ( Promega ) and luciferin ( Caliper Life Sciences , Hopkinton , MA ) added prior to luciferase activity readings . In all experiments culture supernatants were removed for cytokine measurements by ELISA ( R&D Systems , Minneapolis , MN and Abnova Corporation , Walnut , CA ) or Western blotting , with or without concentration using Amicon filters ( 3000 Molecular weight cutoff ) ( Millipore , Billerica , MA ) . Cell lysates were made from infected cells as previously described [18] , [19] . Anti-rat IL-1β ( Abcam or Santa Cruz BT , Santa Cruz , CA ) , anti-rat IL-18 ( Santa Cruz BT ) or anti-HA antibody ( Roche Diagnostics ) were used as primary antibodies . Secondary IR-dye conjugated or HRP-conjugated antibodies were from Rockland ( Gilbertsville , PA ) , Licor Biosciences ( Lincoln , NE ) or Jackson Immunoresearch ( West Grove , PA ) . Immun-Star Western C substrate ( BioRad , Hercules , CA ) and a charge-coupled device camera ( Chemidoc XRS , Biorad ) or the Odyssey Infrared Imaging System ( Licor Biosciences ) was used for Western visualization depending on the secondary antibody used for detection . For select microscopy studies phase contrast images of MTT-stained cells were acquired on a Nikon Eclipse TE2000-U microscope without cell fixation followed by fluorescence image collection for the same field . For other fluorescence microscopy studies nucleofected cells were plated on poly-lysine ( Sigma , St . Louis , MO ) treated coverslips prior to infection and fixed ( 4% paraformaldyde , Electron Microscopy Sciences , Hatfield , PA ) , with or without permeabilization ( 0 . 1% TritonX-100 ) . Immunostaining was with anti-HA antibody ( Roche Diagnostics ) and Alexa Fluor 594 secondary antibody ( Invitrogen ) . For immunofluorescence staining of surface antigen ( SAG ) -1 or assessment of STAT6 phosphorylation , cells were fixed ( 3% formaldehyde ) and permeabilized ( 0 . 2% TritonX-100 or 100% ethanol ) followed by staining with a rabbit polyclonal antibody against human pSTAT6 ( Santa Cruz BT , Santa Cruz , CA ) or rabbit polyclonal antibody against Toxoplasma surface antigen ( SAG ) -1 . Alexa Fluor 594 secondary antibodies were used for detection as has been described [20] . NLRP1 knockdown was achieved by two methods . First , siGENOME SMARTpool siRNA set of four , targeting rat Nlrp1a ( D-983968-17 , D-983968-04 , D-983968-03 , D-983968-02; target sequences of GGUCUGAACAUAUAAGCGA , CCACGGUGUUCCAGACAAA , GCAUUACGUUCUCUCAUGU , GCAGUACGCAGUCUCUGUA ) and siGENOME non-targeting siRNA pool ( D-001206-14-05 , target sequences of UAAGGCUAUGAAGAGAUAC , AUGUAUUGGCCUGUAUUAG , AUGAACGUGAAUUGCUCAA , UGGUUUACAUGUCGACUAA ) were obtained from Thermo Sciences-Dharmacon ( Pittburgh PA ) . siRNA pools were nucleofected ( 200 nM ) into rat BMDMs ( day 5 or 6 of differentiation ) using the Amaxa Nucleofector ( Lonza , Walkersville , MD ) ( kit VPA-1009 , program Y-001 ) at −24 , −36 , −48 , and −72 h prior to infection . Alternatively , on day 2 of differentiation BMDMs were infected with high-titer lentivirus ( Broad Institute RNAi consortium ) encoding shRNA against target sequence TGATCTACTATCGAGTCAATC designed against murine Nlrp1b with high homology ( 18 out of 21 nucleotides , perfect seed sequence identity ) to rat Nlrp1a or the control shRNA with sequence ( GCTTATGTCGAATGATAGCAA or GTCGGCTTACGGCGGTGATTT ) . Puromycin selection ( 6 µg/ml ) of lentivirus infected cells , followed by qPCR analysis ( Nlrp1a primers were 5′CATGTGATTTGGACCTGACG′3 , 5′TCTTTGCCTGCAAGTTTCCT′3 , actin primers were 5′GTCGTACCACTGGCATTGTG′3 , 5′CTCTCAGCTGTGGTGGTGAA′3 ) verified knockdown . Expression of Nlrp1a was normalized against actin expression levels . SNP and haplotype analyses for the HXB , SHR , F334 and LEW rats were performed based on data and genome analysis tools at the Rat Genome Database ( RGD ) , Rat Genome Database Web Site , Medical College of Wisconsin , Milwaukee , Wisconsin ( http://rgd . mcw . edu/ ) . Any gene within the region fine mapped using the above haplotype analysis that contained at least one non-synonymous SNP was identified using Ensembl's Biomart engine and the rat short variation ( SNPs and indels ) ( Rnor_5 . 0 ) dataset . We then used the variant distribution tool on the RGD website to identify which SHR strain genes contained at least one SNP difference from F344 and BN strains . Nucleotide positions correspond to the RGSC3 . 4 assembly . Further fine mapping analyses were performed by whole transcriptome sequencing and novel SNP identification . RNA ( Qiagen RNeasy Plus kit ) was isolated from unprimed and LPS-primed ( 100 ng/ml ) LEW and SD BMDMs or LPS-primed BN BMDMs . mRNA purified by polyA-tail enrichment ( Dynabeads mRNA Purification Kit , Invitrogen ) was fragmented into 200–400 bp , and reverse transcribed into cDNA before Illumina sequencing adapters ( Illumina , San Diego , CA ) were added to each end . Libraries were barcoded , multiplexed into 5 samples per sequencing lane in the Illumina HiSeq 2000 , and sequenced from both ends ( 60 bp reads after discarding the barcodes ) . Sequences were mapped to the Rat genome ( rn4 ) using Bowtie ( 2 . 0 . 2 ) [21] and Tophat ( v2 . 0 . 4 ) [22] . To identify SNPs from the RNAseq data in the interval fine mapped above , Bam files were processed with samtools ( 0 . 1 . 16 , r963:234 ) mpileup function , with rn4 as reference sequence . Read pileups were processed across all five samples using VarScan . v2 . 2 . 11 and the mpileup2snp function ( parameters: –min-coverage 2 –min-reads2 1 –min-var-freq 0 . 01 –p-value 0 . 05 –variants ) . Resulting variant positions were annotated using UCSC Genome Browser's “Variant Annotation Integrator” . SNPs identified between 5 samples ( 2 SD , 2 LEW , 1 BN ) were filtered for concordance and homozygosity between the two independent LEW samples and BN having the same nucleotide as the reference genome ( which is from BN ) , and subsequently filtered for non-synonymous SNPs where LEW differed from BN and SD . It should be noted that not all known LEW SNPs in Nlrp1 are discovered using this procedure as the N-terminal NLRP1 region contains a stretch of eight amino acids that differ between LEW and BN and our procedure for mapping reads to the genome does not allow for that many mismatches . Similar problems lead to underreported Nlrp1 SNPs in the RGD website .
The Toxo1 locus on chromosome 10 , which controls rat resistance to toxoplasmosis , maps within a region containing the inflammasome sensor Nlrp1 gene . NLRP1 was previously shown to control rat macrophage sensitivity to pyroptosis by the anthrax protease LT . Sequencing of twelve inbred rat strains revealed five highly homologous variants , two encoding NLRP1 protein sensitive to LT-mediated cleavage activation ( NLRP1variant 1 , 2 ) , and three which encode LT-resistant proteins ( NLRP1variant 3 , 4 , 5 ) ( Figure 1A ) . We noted that rat strains encoding NLRP1variant 1 , 2 historically support parasite proliferation in myeloid cells while rat strains encoding NLRP1variant 5 do not . [2] . Therefore we investigated whether macrophages from rats expressing different NLRP1 variants also differed in inflammasome activation and pyroptosis upon parasite infection . Inflammasome activation was assessed by monitoring cell death and cleavage of pro-IL-1β ( 37 kD ) with subsequent secretion of mature active IL-1β ( 17 kD ) . We infected BMDMs from LT-sensitive CDF , BN or SD ( NLRP1variant 1 , 2 ) rat strains and LT-resistant LEW and SHR ( NLRP1variant 5 ) rat strains with luciferase-expressing Type I ( RH ) and Type II ( 76K , or PRU ) Toxoplasma strains at various MOIs . BMDM viability measurements showed that NLRP1variant 5-expressing macrophages underwent a rapid cell death after Toxoplasma infection starting at 3 h and completed by 24 h whereas the majority of the NLRP1variant 1 , 2 −-expressing macrophages remained viable and supported Toxoplasma growth even 24 h after infection ( Figure 1B–D ) . The parasite itself did not contribute significantly to MTT or LDH signals ( Figure S1 , panels A , B ) and DAMPs from lysed host cells also did not induce cell death ( Figure S1 panels C , D ) . Results were unaltered when cells were pre-treated with LPS ( 100 ng/ml ) prior and throughout infection ( Figure S1 panels C , D ) . Fischer F344/NTac ( NLRP1 variant 2 ) macrophages also showed resistance similar to that of Fischer CDF macrophages ( data not shown ) . Both NLRP1variant 1 , 2 and NLRP1variant 5 -expressing macrophages were fully responsive to nigericin-induced NLRP3 activation ( Figure S2 and [18] ) , indicating fully functional inflammasome assembly and caspase-1 function in these rat strains . We next tested macrophages from three rat strains ( HXB1 , HXB15 and HXB29 ) from the HXB/BXH recombinant inbred ( RI ) rat collection previously used to map LT sensitivity [4] . These strains have chromosome 10 crossover points closely flanking the Nlrp1 locus , as indicated by SNP analyses . We found that macrophages from the RI strain HXB1 , an LT-resistant strain , were sensitive to Toxoplasma Type I ( RH ) and Type II ( 76K ) infection-induced lysis while the macrophages from the other two strains , which are LT-sensitive , were resistant to parasite induced rapid death ( Figure 1E ) . These rats allowed us to reduce the Toxo1 locus from the previous 54 . 2 Mbp–61 . 8 Mbp region to 54 . 2 Mbp–59 . 2 Mbp ( Figure S3 ) . We performed SNP and haplotype analyses for the CDF ( F344/Crl ) , F344/NTac , BN ( all strains with macrophages resistant to Toxoplasma-induced lysis ) and the SHR strain ( a strain with macrophages sensitive to Toxoplasma-induced lysis ) and further narrowed the region determining resistance to 55 . 3–59 . 2 Mbp ( between SNPs rs63997836 and rs106638778 ) ( Figure S3 ) . This region contained 133 genes of which 21 contained non-synonymous SNPs that were present in F334 and/or SHR rats , where genotype correlated with Toxoplasma resistance phenotype . To further narrow down the list of possible candidate genes , we performed whole transcriptome sequencing on BMDM from the LEW ( pyroptosis-sensitive macrophages ) , BN ( pyroptosis-resistant macrophages ) and SD ( pyroptosis-resistant macrophages ) strains . We determined which genes were expressed in unstimulated and LPS-stimulated LEW BMDM ( which are sensitive to parasite induced pyroptosis under both conditions ) , and contain SNPs that correlate with the resistance phenotype . Sixty-five of the 133 genes in the fine-mapped region were expressed ( fragments per kilobase of transcript per million mapped reads >2 ) but only five of these contained non-synonymous SNPs that distinguished LEW from SD/BN ( Dataset S1 and Figure S4 ) . Although there were also differences in gene expression levels between LEW and SD/BN macrophages , none of the genes were expressed higher ( 1 . 5 fold ) in both the non-stimulated and LPS stimulated LEW macrophages compared to the SD/BN macrophages ( Dataset S1 ) . By combining all analyses , we were able to narrow down the possible candidate genes to Aurkb ( Aurora kinase B1 , 55 . 7 Mbp , 1 SNP ) , Neurl4 ( neutralized homolog 4 , 56 . 7 Mbp , 1 SNP ) , Cxcl16 ( chemokine C-X-C ligand 16 , 57 . 3 Mbp , 1 SNP ) and Nlrp1 ( 6 SNPs ) . Figure 2 summarizes the above described mapping steps . Of these four genes , Nlrp1 was the most likely candidate to be Toxo1; it contained the highest number of non-synonymous SNPs and is a known activator of the inflammasome . Our fine-mapping analyses combined with the established perfect correlation between sensitivity to Toxoplasma induced macrophage cell death and the NLRP1 N-terminal sequence in inbred and RI rats [4] , which was in turn inversely correlated to rat resistance to chronic , transmissible Toxoplasma infection suggested that the Toxo1 locus could be the Nlrp1 gene . A survey of Toxoplasma strains that are genetically distinct from the archetypal I , II and III strains [23] , [24] showed that they all induced NLRP1 variant-dependent rapid cell death ( Figure 3 ) . Because cell death was consistently dependent on MOI , we tested whether parasite invasion was required for cell death , as Toxoplasma can secrete effectors from its rhoptry organelle directly into the host cytoplasm . Parasites treated with Mycalolide B , a drug that blocks invasion but allows for secretion of microneme and rhoptry contents , attached but were unable to kill BMDMs , indicating that macrophage sensitivity to cell death was invasion-dependent ( Figure 4A ) . Mycalolide B did not affect the viability of parasites or their ability to secrete rhoptry contents as verified by the observation that every cell with an attached mycalolide-B-treated parasite also had protein kinase ROP16 activation of STAT6 ( Figure S5 ) . Because Toxoplasma needs host cells for replication and the parasite replicates equally well in fibroblasts from different rat strains [2] , we hypothesized that rapid macrophage cell death prevents Toxoplasma replication . We therefore investigated parasite proliferation in BMDMs from the different rat strains . Toxoplasma burden , as measured by bioluminescence , was significantly higher in infected NLRP1variant 1 , 2 -expressing BMDMs than NLRP1variant 5 -expressing cells ( Figure 4B , 4C ) . This difference was independent of Toxoplasma strain but perfectly correlated with NLRP1 sequence and continued to increase over time only in the cell death-resistant BMDMs from Toxoplasma susceptible rat strains ( Figure 4C ) . Similarly , GFP signal indicative of parasite load was higher in resistant cells from these rat strains ( data not shown ) . Parasite proliferation was independent of LPS-priming ( data not shown ) and more parasites/vacuole were detected in NLRP1variant 1 , 2 -expressing macrophages compared to Nlrp1variant 5 -expressing cells ( Figure 4D ) . Although only ∼10% of sensitive LEW ( NLRP1variant 5 ) BMDMs were intact after 24 h of infection ( Figure 4E left panels ) , 90% of these surviving cells contained single parasites ( Figure 4E right panels ) . Nearly 100% of resistant SD , BN or CDF ( NLRP1variant 1 , 2 ) BMDMs were intact after 24 h , and >60% of those infected contained multiple parasites per vacuole ( Figure 4D , 4E ) . To determine if parasites released from lysed cells were viable , we measured the parasite's ability to reinvade macrophages by adding an antibody specific for the Toxoplasma surface protein , SAG1 , to the medium of pre-infected BMDMs . We found that ∼35% of intracellular parasites in the sensitive LEW BMDMs were coated with the SAG1 antibody while only 5% were coated in resistant cells , demonstrating that some fraction of parasites released from rat BMDMs that rapidly lyse remain viable and capable of re-invasion ( Figure S6 ) . We verified that SAG-1 was not shed upon invasion by immunofluorescence , where 100% of parasites were stained for SAG1 when infected SD BMDMs were fixed and permeabilized at 18 h post-infection ( Figure S6 ) . Supernatants from lysed Toxoplasma-sensitive BMDMs also did not contribute to the rapid pyroptosis of resistant macrophages ( Figure 4F ) or alter parasite proliferation within these cells ( Figure 4G ) . To investigate whether Toxoplasma infection induced maturation and secretion of IL-1β and IL-18 in an NLRP1 sequence-dependent manner , we measured secreted levels of these cytokines in the different rat strains . In the absence of LPS priming , Type II strain-infected BMDMs did not produce IL-1β ( data not shown ) , but low levels of IL-18 were measurable by 6 h ( PRU ) and 24 h ( 76K ) of infection in an NLRP1 variant-dependent manner . Thus in the unprimed situation , both 76K and PRU produced a much higher response in the LEW macrophages ( expressing NLRP1variant 5 ) when compared to infection of CDF macrophages ( expressing NLRP1variant 2 ) with the same Type II strain ( Figure 5A ) . After LPS-priming , high levels of IL-1β and IL-18 secretion also correlated with NLRP1 sequence and macrophage sensitivity to rapid lysis ( Figure 5B , 5C ) . Furthermore , the HXB1 ( NLRP1variant 5 ) , HXB15 and HXB29 ( NLRP1variant 1 ) RI strains also produced IL-1β after infection in a manner correlated with NLRP1 sequence and macrophage sensitivity to Toxoplasma ( Figure 5D ) . No IL-1β or IL-18 release was measurable from uninfected controls at any time point for any of the experiments shown in Figures 5A–D ( data not shown ) . If parasites were treated with Mycalolide B , there was a significant reduction in cytokine production ( Figure 5E ) indicating that parasite invasion was necessary for inflammasome activation . Finally , cleavage of IL-1β and IL-18 was detected in cell lysates from LPS-primed , 76K or PRU-infected LEW , but not infected CDF and SD BMDMs , and cleavage correlated with cytokine secretion ( Figure 5F ) . Nigericin activation of the NLRP3 inflammasome in both Toxoplasma-sensitive ( LEW , NLRP1variant 5-expressing ) and CDF or SD ( NLRP1variant1-expressing ) BMDMs confirmed previous findings that no general defect in the caspase-1 pathway was present in rats ( Figure S1 , 5F and [18] ) . Together these findings indicate a perfect correlation between sensitivity to Toxoplasma-induced macrophage cell death , decreased parasite proliferation , IL-1/IL-18 processing , rat resistance to Toxoplasma infection and NLRP1 sequence [4] , suggesting that the Toxo1 locus could be assigned to the Nlrp1 gene . We utilized two methods to knock down expression of rat Nlrp1 ( designated as Nlrp1a in the rat genome ) to determine if NLRP1 mediates Toxoplasma-induced rat macrophage pyroptosis . First , an siRNA nucleofection approach was utilized . Only 20–35% of rat BMDMs can be transfected with this method , as assessed by control nucleofections with GFP expression vector and confirmed in parallel nucleofections in our current studies ( data not shown ) . We found that there was a significant protection against LEW macrophage death in cells transfected with Nlrp1 siRNA , compared to control siRNA , under conditions where 100% of BMDMs succumbed ( Figure 6A and 6B ) . The 20–30% difference in viability was correlated with the number of successfully transfected cells , as reflected by the all-or-none nature of the protection in individual cells assessed by microscopy ( Figure 6A , inset ) . Surviving LEW BMDMs remaining attached after longer periods of infection were verified to contain dividing GFP-expressing Toxoplasma gondii by fluorescence microscopy ( Figure 6C , D ) , and viability was verified by MTT-staining ( Figure 6D , left panel ) . Nonsurviving cells were completely detached from monolayers . A second method of knockdown by lentiviral delivery of a homologous mouse Nlrp1b shRNA was used to achieve a 2 . 2-fold reduction in Nlrp1 expression compared to controls infected with a scrambled shRNA . Expression of Nlrp1 was assessed by qPCR and standardized against actin levels ( Figure 6E ) . Knockdown correlated with increased parasite proliferation and a higher number of vacuoles with more than one parasite ( ∼60% ) , compared to the macrophages treated with a scrambled control ( 35% ) ( Figure 6F ) . Host cell viability was also increased by 30% in the shRNA knockdown condition ( Figure 6G ) . We next overexpressed HA-tagged NRLP1variant2 and NLRP1variant 5 constructs [10] in rat BMDMs by nucleofection to test if this alters susceptibility to parasite-induced pyroptosis . The efficiency of transfection ranged from 25–40% in BMDMs in individual nucleofections ( as assessed by monitoring of a co-transfected GFP construct in control cells ) . The LEW BMDMs did not gain resistance when transfected with the resistant CDF NLRP1variant2 , but were sensitized to treatment with anthrax LT , confirming expression of the CDF NLRP1variant2 in a subpopulation of nucleofected cells ( Figure S7 ) . There was a significant sensitization to parasite-induced pyroptosis in CDF cells transfected with the LEW NLRP1variant5 ( Figure 6H , Figure S7 ) , while these cells remained almost 100% susceptible to rapid lysis by LT ( Figure S8 ) . Microscopy confirmed cell death for both Toxoplasma-infected CDF cells expressing LEW NLRP1variant5 and LT-treated LEW cells expressing the CDF NLRP1variant2 ( Figure 6I ) . These results confirm that the LEW NLRP1variant 2 -mediated sensitivity to Toxoplasma is dominant , much in the manner the resistance of LEW rats to the parasite was previously shown to be a dominant trait [2] . They also re-confirm that the sensitivity to anthrax LT , mediated by the CDF NLRP1variant2 is a dominant trait . Interestingly , fibroblast HT1080 lines expressing these rat NLRP1 constructs [10] were not sensitized to Toxoplasma-induced pyroptosis even when transiently transfected and confirmed to express caspase-1 along with NLRP1 ( Figure S8 , panel A ) . These results confirmed that a macrophage cofactor or the macrophage cellular environment is required for parasite-induced pyroptosis . Furthermore , infection of mouse macrophage cell lines stably expressing rat NLRP1 constructs also did not result in sensitization to Toxoplasma ( Figure S8 , panel B ) , suggesting the presence of other factors in murine macrophages , or the BMAJ macrophage cell line , that result in a dominant resistance to pyroptosis or the absence of a factor needed for interaction with rat NLRP1 and subsequent pyroptosis . All tested mouse macrophages from any inbred strain , to date , have been resistant to Toxoplasma-induced pyroptosis ( data not shown and Figure S8 , panel C ) . The competition of endogenous murine NLRP1a and NLRP1b proteins for co-factors required for pyroptosis in the mouse macrophage may explain this resistance . Together , the results presented in this work indicate that Nlrp1 expression contributes to the ability of BMDMs from rats resistant to Toxoplasma infection to control parasite replication , most likely because of its role in mediating Toxoplasma-induced macrophage pyroptosis .
The Toxo1 locus that controls rat susceptibility to toxoplasmosis [2] was previously mapped to a region of rat chromosome 10 containing the inflammasome sensor Nlrp1 . In this work we identify Toxoplasma as a novel pathogen activator of the NLRP1 inflammasome . Until this work , anthrax LT was the only known activator of this inflammasome sensor [4] , [10] , [25] . We now demonstrate that like LT , rapid Toxoplasma-induced rat macrophage cell death is a pyroptotic event for which sensitivity correlates to NLRP1 sequence . Type I , Type II and a variety of genetically diverse T . gondii strains induce rapid pyroptosis in macrophages derived from inbred rats expressing NLRP1variant 5 , while macrophages from BMDMs expressing NLRP1variant 1 , 2 are resistant to the parasite . This is the inverse of what is known for LT , where NLRP1variant 1 , 2 confers sensitivity [4] . In rats , macrophage sensitivity to Toxoplasma-induced cell death inversely correlates with whole animal resistance to infection . Rat strains historically susceptible to chronic Toxoplasma infection ( e . g . , CDF , BN , SD; NLRP1variant 1 , 2 ) have pyroptosis-resistant macrophages whereas resistant rats that cure infection ( e . g . , LEW , SHR; NLRP1variant 5 ) harbor macrophages that undergo parasite-induced pyroptosis . This suggests that the ability of the macrophage to allow parasite proliferation and possibly dissemination is linked to resistance to parasite-induced macrophage pyroptosis . Similar findings were previously described for mouse Nlrp1b-mediated control of anthrax infection . Mice resistant to Bacillus anthracis have macrophages expressing Nlrp1b variants which confer macrophage sensitivity to anthrax LT , and resistance is linked to the IL-1β response induced by toxin [26] , [27] . The idea of control of parasite proliferation at the macrophage level is supported by findings that macrophages are among the first cell types to be infected when an animal ingests Toxoplasma cysts or oocysts [11] , [12] and innate immune cells are used to traffic from the site of infection to distant sites such as the brain [28] . In parallel to the consequences for parasite proliferation after NLRP1 activation , the pro-inflammatory cytokines , IL-1β and IL-18 , which are substrates of caspase-1 , are cleaved and released following inflammasome activation . We demonstrate that these events only take place after infection of pyroptosis-sensitive macrophages in a manner correlating with NLRP1 sequence . It is possible that the release of these cytokines of the innate immune system could also play a role in controlling toxoplasmosis . IL-18 was at one time known as “IFN-inducing factor” and the role of IFN-γ in resistance to Toxoplasma is extensively documented ( for review see [1] , [29] ) . Treatment of resistant LEW rats with anti-IFN-γ antibodies does not reverse resistance but results in a much stronger antibody response , while anti-IFN-γ antibody treatment in susceptible rats causes an increase in parasite burden [3] . Altogether these findings suggest that IL-18 , ( through actions by IFN-γ ) could be important for inhibition of Toxoplasma replication in rats , but that the cytokine's actions do not necessarily prevent parasite dissemination . On the other hand , it is important to note that as Toxoplasma can replicate and form cysts in many cell types that do not undergo pyroptosis , macrophage death may play a role strictly in dissemination . Thus , we suggest the combined consequences of inflammasome activation , macrophage cell death and IL-1/IL-18 secretion , on both dissemination and parasite proliferation , may ultimately result in resistance to Toxoplasma . The only difference between the NLRP1 proteins from Toxoplasma-resistant and Toxoplasma-sensitive inbred strains is an 8 aa polymorphic region in the N-terminus of the protein , in a region of unknown function [4] . LT cleaves NLRP1variant 1 , 2 proteins to activate this sensor and induce pyroptosis , while NLRP1variant 5 is resistant to cleavage [10] . How Toxoplasma activation of NLRP1 varies between rat strains based on an 8 aa sequence difference is unclear . The similar induction of pyroptosis we observed with numerous Toxoplasma strains suggests that the factor activating NLRP1 is unlikely to be parasite strain specific , or at least is conserved among multiple strains . One logical hypothesis is that the parasite-encoded effector molecule responsible for activation of NLRP1 is , like LT , a protease , but one which targets the LT-cleavage resistant sequence found in NLRP1variant 5 . Toxoplasma secretes a large number of proteases [30]–[35] . It is unlikely that such a secreted protease could be derived from the rhoptries , because rhoptry secretion into the host cell was not sufficient to induce cell death . To date , we have been unable to observe any cleavage of NLRP1 in Toxoplasma infected fibroblasts which overexpress an HA-tagged variant of the protein ( data not shown ) . It has also been recently shown that Toxoplasma can secrete effectors post invasion beyond the parasitophorous vacuole membrane [36] and these could be candidate effectors for NLRP1 activation . An alternative hypothesis to the parasite causing direct cleavage of NLRP1 is that the N-terminal polymorphic region of rat NLRP1 affects this protein's interaction with a different host ‘sensor’ acting as adaptor for the inflammasome , much in the manner described for the NLRC4/NAIP5/NAIP6 inflammasome recognition of flagellin [37] , [38] . This unknown adaptor would interact with Toxoplasma or its effectors in all macrophages but may be limited by its ability to interact with the N-terminus of NLRP1variant 1 , 2 in rat BMDMs , or alternatively it could act as a direct inhibitor with specificity for these variants . The likelihood of a proteolytic activation of NLRP1 is also reduced when considering the finding that mouse ortholog NLRP1b proteins harbor an LT-cleavage site similar to rat proteins [25] but are highly resistant to Toxoplasma-induced pyroptosis in a manner independent of NLRP1b sequence or LT sensitivity ( Figure S8 ) . Furthermore , mouse macrophages could not be sensitized by rat NLRP1 overexpression . This finding was in contrast to the sensitization of the same cells to LT-mediated cell death [10] , suggesting resistance of mouse macrophages to Toxoplasma-induced pyroptosis was dominant to any NLRP1-mediated effect , or ( less likely ) that co-factors required for parasite-mediated activation were only present in rat cells . Alternatively , the endogenous Toxoplasma non-responsive NLRP1a and NLRP1b proteins in mouse macrophages could compete in a dominant manner with expressed rat NLRP1 for co-factors required for pyroptosis . Interestingly , human NLRP1 does not contain an LT cleavage site in its N-terminus ( for review see [39] ) . Instead human NLRP1 contains a pyrin domain required for association with the adaptor protein ASC [40] , which does not appear to play a role in NLRP1-mediated rodent cell death [41] , [42] . SNPs prevalent in this N-terminal region of human NLRP1 have been correlated with the severity of human congenital toxoplasmosis [14] . In those studies , knockdown of NLRP1 in human monocytic lines led to reduced cell viability after Toxoplasma infection , perhaps by allowing uncontrolled division of the parasite . Unlike our findings in rat cells , a protective role for human NLRP1 against macrophage death was suggested . It seems likely that the cell death observed in these human cell studies , which occurred over a period of days , differs from NLRP1-mediated rapid pyroptosis of rat cells , which occurs over a period of hours . Future studies are required to determine the mechanism of NLRP1 action in human cells . In summary , we have established that Toxoplasma gondii is a new activator for the NLRP1 inflammasome . The identification of T . gondii as the second pathogen to activate the NLRP1 inflammasome raises the question whether this parasite activates the sensor via a novel mechanism , or whether proteolytic cleavage is required , in a manner similar to anthrax LT .
|
Inflammasomes are multiprotein complexes that are a major component of the innate immune system . They contain “sensor” proteins that are responsible for detecting various microbial and environmental danger signals and function by activating caspase-1 , an enzyme that mediates cleavage and release of the pro-inflammatory cytokines , IL-1β and IL-18 . Toxoplasma gondii is a highly successful protozoan parasite capable of infecting a wide range of host species that have variable levels of resistance . Rat strains have been previously shown to vary in their susceptibility to this parasite . We report here that rat macrophages from different inbred strains also vary in sensitivity to Toxoplasma induced lysis . We find that NLRP1 , an inflammasome sensor whose only known agonist is anthrax LT , is also activated by Toxoplasma infection . In rats there is a perfect correlation between NLRP1 sequence and macrophage sensitivity to Toxoplasma-induced rapid cell death , inhibition of parasite proliferation , and IL-1β/IL-18 processing . Nlrp1 genes from sensitive rat macrophages can confer sensitivity to this rapid cell death when expressed in Toxoplasma resistant rat macrophages . Our findings suggest Toxoplasma is a new activator of the NLRP1 inflammasome .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"parastic",
"protozoans",
"immunity",
"innate",
"immunity",
"toxoplasma",
"gondii",
"immunology",
"protozoology",
"biology",
"microbiology"
] |
2014
|
Inflammasome Sensor NLRP1 Controls Rat Macrophage Susceptibility to Toxoplasma gondii
|
In the mammary gland , genetic circuits controlled by estrogen , progesterone , and prolactin , act in concert with pathways regulated by members of the epidermal growth factor family to orchestrate growth and morphogenesis during puberty , pregnancy and lactation . However , the precise mechanisms underlying the crosstalk between the hormonal and growth factor pathways remain poorly understood . We have identified the CUB and zona pellucida-like domain-containing protein 1 ( CUZD1 ) , expressed in mammary ductal and alveolar epithelium , as a novel mediator of mammary gland proliferation and differentiation during pregnancy and lactation . Cuzd1-null mice exhibited a striking impairment in mammary ductal branching and alveolar development during pregnancy , resulting in a subsequent defect in lactation . Gene expression profiling of mammary epithelium revealed that CUZD1 regulates the expression of a subset of the EGF family growth factors , epiregulin , neuregulin-1 , and epigen , which act in an autocrine fashion to activate ErbB1 and ErbB4 receptors . Proteomic studies further revealed that CUZD1 interacts with a complex containing JAK1/JAK2 and STAT5 , downstream transducers of prolactin signaling in the mammary gland . In the absence of CUZD1 , STAT5 phosphorylation in the mammary epithelium during alveologenesis was abolished . Conversely , elevated expression of Cuzd1 in mammary epithelial cells stimulated prolactin-induced phosphorylation and nuclear translocation of STAT5 . Chromatin immunoprecipitation confirmed co-occupancy of phosphorylated STAT5 and CUZD1 in the regulatory regions of epiregulin , a potential regulator of epithelial proliferation , and whey acidic protein , a marker of epithelial differentiation . Collectively , these findings suggest that CUZD1 plays a critical role in prolactin-induced JAK/STAT5 signaling that controls the expression of key STAT5 target genes involved in mammary epithelial proliferation and differentiation during alveolar development .
In the mammary gland , development of an extensive ductal network during puberty and formation of lobuloalveolar units during pregnancy are critical events required for lactation . These complex developmental processes are regulated by a variety of signaling cues , including the steroid hormones 17β-estradiol ( E ) and progesterone ( P ) , the peptide hormone prolactin ( PRL ) , and the epidermal growth factor ( EGF ) family of growth factors [1] . During pregnancy and lactation , E , P , and EGF family members act in concert with PRL to induce alveologenesis , a process in which ductal epithelial cells undergo extensive proliferation and secretory differentiation [2 , 3] . Circulating levels of PRL rise during pregnancy and promote proliferation and differentiation of the mammary epithelium in preparation for lactation [4–7] . The prolactin receptor ( PRLR ) is a trans-membrane protein belonging to the cytokine receptor superfamily [8] . Binding of PRL to PRLR triggers signaling events through the JAK/STAT5 pathway [9 , 10] . Janus tyrosine kinase 1 ( JAK1 ) and janus tyrosine kinase 2 ( JAK2 ) , associated with PRLR , are rapidly phosphorylated upon PRL binding . Signal transducer and activator of transcription 5 ( STAT5 ) , which is phosphorylated following JAK activation , undergoes dimerization and localizes to the nucleus [9 , 11–13] . The tyrosine phosphorylation of STAT5 is essential for DNA binding and transcriptional regulation [10] . Activated STAT5 binds directly to the GAS motif ( TTCnnnGAA ) at target genes to regulate their transcription and promote proliferation and/or differentiation of the mammary epithelium during distinct phases of mammary gland development [11] . It was reported that PRL signaling through JAK2/STAT5 activates cyclin D1 transcription and nuclear accumulation in proliferating mammary epithelial cells [14] . Furthermore , STAT5a has been shown to regulate transcription of other mitogenic factors , such as the EGF family member epiregulin [15 , 16] . Terminal differentiation of the mammary gland is defined by the expression of milk protein genes in preparation for lactation . STAT5 controls the expression of several of these genes , including whey acidic protein ( Wap ) and β-casein ( Csn2 ) , to induce functional differentiation of the alveolar epithelial cells [11 , 12 , 17–20] . These observations established that STAT5 signaling is essential for proliferation and differentiation of the mammary gland . Ample evidence exists to suggest integrated effects of PRL and EGF receptor ( ErbB ) -mediated signaling pathways during mammary gland development . Binding of specific EGF ligands induces differential heterodimerization of ErbB family receptors to stimulate specific intracellular signaling pathways , thereby accounting for the varied effects of an activated receptor . Upon EGF administration , STAT5 is activated to a similar degree as seen with PRL treatment [3 , 20] . Furthermore , active ErbB4 was shown to induce phosphorylation of STAT5 in the mammary epithelium [21] . ErbB4 ( -/- ) mice exhibit disrupted alveologenesis and a dramatic reduction in the expression of Wap and further investigation revealed that STAT5 phosphorylation is lost [21] . These findings pointed to a possible link between signaling via EGF family receptors and STAT5 activation to control alveolar proliferation and differentiation , although the precise molecular basis of this crosstalk remains unclear . This study reports that CUZD1 is a novel mediator of PRL and EGF signaling in mammary epithelial proliferation and differentiation during pregnancy . This protein , also known as ERG1 , Itmap1 , or UO-44 , was originally identified in our laboratory as an E-regulated gene in the rodent uterine epithelium and later reported in other tissues [22–25] . CUZD1 contains a zona-pellucida ( ZP ) -like domain and two tandem CUB ( Complement subcomponent /C1s , Uegf , Bmp1 ) motifs ( S1A Fig ) . There is presently little information concerning the functional significance of these motifs , although their presence is often noted in molecules involved in developmental processes [26 , 27] . The mouse Cuzd1 gene shares strong sequence identity with its human ortholog , indicating functional conservation across species ( 25 ) . Using a Cuzd1 ( -/- ) mouse model and a combination of in vivo and in vitro approaches , we investigated the molecular pathways that are controlled by Cuzd1 in the mammary gland and uncovered a novel mechanism linking CUZD1 to the PRL and EGF family growth factor signaling pathways that guide epithelial proliferation and differentiation in the mammary gland during pregnancy .
We examined the expression of CUZD1 in the mammary glands of Cuzd1 ( +/- ) and Cuzd1 ( -/- ) mice at different stages of development: pubertal ( 5 weeks ) , late pregnancy ( D18 ) and early lactation ( L2 ) . Immunofluorescence ( IF ) analysis of CUZD1 revealed no detectable expression in mammary tissue of Cuzd1 ( -/- ) mice during development ( Fig 1B , 1D and 1F ) . CUZD1 was detected in the developing ductal epithelium of Cuzd1 ( +/- ) mice at puberty ( Fig 1A ) . CUZD1 immunostaining was also observed in both cytoplasmic and nuclear compartments of the ductal and alveolar epithelial cells of Cuzd1 ( +/- ) mammary glands during alveologenesis at late pregnancy ( Fig 1C ) . Prominent nuclear staining was seen during lactation ( Fig 1E ) , indicating that CUZD1 may play a critical role during mammary gland development , particularly during pregnancy and lactation . To investigate the functional role of CUZD1 in mammary gland development , we created Cuzd1 ( -/- ) mice in which this gene is deleted from the mouse germ line by homologous recombination using mouse embryonic stem cells ( S1B Fig ) . The efficiency of gene deletion was confirmed by PCR analysis of genomic DNA ( S1C Fig ) and northern blot analysis of Cuzd1 mRNA expression ( S1D Fig ) . The Cuzd1 ( -/- ) females were fertile and delivered normal size litters . However , the majority of pups from Cuzd1 ( -/- ) dams died within 72 h of parturition and it was observed that they had insufficient milk in their stomachs . Almost all pups survived and grew normally when they were transferred to a foster dam immediately after birth . These results indicated that the Cuzd1 ( -/- ) dams fail to produce an adequate amount of milk . To further examine the phenotypic defects in the Cuzd1 ( -/- ) mice , morphological analyses of whole mounts of mammary glands were performed at different stages of development . In comparison to their Cuzd1 ( +/- ) littermates , the expansion of the epithelial tree in Cuzd1 ( -/- ) mice was delayed at puberty ( 6-weeks old ) ( Fig 2A , a and b ) . However , smooth muscle actin ( SMA ) and E-cadherin staining of Cuzd1 ( -/- ) mammary glands at puberty indicate that there are no structural abnormalities in the cap or body cells of the terminal end buds ( S2A Fig , a-d ) [28–29] . The extent of ductal branching was modestly reduced in adult mutant females at estrous stage ( 10-weeks old ) ( Fig 2A , c and d ) . During early pregnancy , mammary glands of mutant mice exhibited a severe deficiency in tertiary branching ( Fig 2A , e and f ) and impaired alveolar development during late pregnancy ( Fig 2A , g and h ) and lactation ( Fig 2A , i and j ) . Histological analysis of lactating Cuzd1 ( -/- ) mammary glands revealed sparsely distributed alveolar units with disrupted epithelial structure in comparison to their Cuzd1 ( +/- ) littermates ( Fig 2B , a-d ) . Collectively , these results indicated that the impairment in alveolar differentiation in Cuzd1 ( -/- ) females during pregnancy and lactation leads to the deficiency in milk production . The impaired alveolar development in Cuzd1 ( -/- ) mammary glands raised the possibility that CUZD1 is involved in the control of epithelial cell proliferation . To test this possibility , we monitored the mammary epithelial proliferation in Cuzd1 ( -/- ) mice and Cuzd1 ( +/- ) littermates during puberty and lactation . We employed IHC analysis using an antibody against Ki67 , a widely used marker for cellular proliferation . As expected , extensive cell proliferation was observed in the mammary ductal epithelia of non-pregnant pubertal Cuzd1 ( +/- ) mice ( Fig 3A , a ) . There was a significant reduction in the number of proliferating ductal epithelial cells in the mammary glands of pubertal Cuzd1 ( -/- ) mice ( Fig 3A , b ) . The difference in Ki67 positive cells at puberty is quantified in Fig 3A , c . When epithelial proliferation was assessed in the lactating mammary gland , we again observed a dramatic decline in epithelial proliferation in Cuzd1 ( -/- ) mice ( Fig 3A , d and e ) . During puberty and pregnancy , Cuzd1 ( -/- ) females maintained normal serum levels of E , P and PRL ( S2B Fig ) , indicating that tissue intrinsic factors rather than systemic hormonal disruptions caused by the loss of Cuzd1 are responsible for this defect in mammary gland proliferation . These results demonstrated that Cuzd1 plays a critical role in regulating side-branching and alveolar morphogenesis in female mice during pregnancy and lactation , in part by influencing pathways involved in mammary epithelial proliferation . To identify the pathways downstream of CUZD1 , a microarray analysis was performed to compare the gene expression profiles of mammary epithelial cells isolated from Cuzd1 ( -/- ) mice and their Cuzd1 ( +/- ) littermates on day 18 of pregnancy . This microarray identified 411 transcripts that were altered ( >2-fold ) in the Cuzd1 ( -/- ) epithelium compared to the Cuzd1 ( +/- ) epithelium ( GEO Accession GSE30939 ) . Prominent among the 377 down-regulated transcripts were mRNAs encoding three members of the EGF family , neuregulin-1 ( Nrg1 ) , epiregulin ( Ereg ) and epigen ( Epgn ) . Interestingly , no significant alteration was detected in the expression levels of transcripts of several other EGF-family growth factors , such as amphiregulin ( Areg ) , epidermal growth factor ( Egf ) , heparin binding epidermal growth factor ( Hbegf ) , neurgulin-2 ( Nrg2 ) , neuregulin-3 ( Nrg3 ) and neuregulin-4 ( Nrg4 ) . Gene expression changes of EGF family ligands were confirmed using real-time RT-PCR and analyzed for statistical significance ( Fig 3B ) . Furthermore , IHC analysis of EPGN and NRG1 at lactation day 2 showed a substantial decline in these EGF ligands in Cuzd1 ( -/- ) mice ( Fig 3C , b and d ) . These data indicate that the deletion of Cuzd1 results in reduced expression of a specific subset of EGF family ligands in the mammary epithelium during late pregnancy . Binding of EGF ligands to ErbB receptors results in their activation via auto-phosphorylation of critical tyrosine residues , which subsequently serve as docking sites for downstream signaling molecules [30] . While EREG binds to both ErbB1 and ErbB4 , EPGN acts primarily via ErbB1 . NRG1 binds to ErbB3 as well as ErbB4 . We therefore , examined whether the observed alterations in the expression levels of Nrg1 , Ereg and Epgn in the mammary tissue affected the ErbB receptor-mediated signaling . Mammary gland sections obtained from mice during late pregnancy were subjected to IHC , using antibodies directed against specific phosphorylated tyrosine residues critical for activation of ErbB1 ( Tyr 1068 ) , ErbB2 ( Tyr 877 ) , and ErbB4 ( Tyr 1056 ) . Abundant activating phosphorylation of ErbB1 , ErbB2 , and ErbB4 was observed in mammary epithelia of Cuzd1 ( +/- ) mice , consistent with the proliferative activity seen in this tissue ( Fig 3D , a , c , and e ) . In contrast , pErbB1 and pErbB4 were markedly reduced in the Cuzd1 ( -/- ) epithelium ( Fig 3D , b and d ) . Interestingly , phosphorylation of ErbB2 was not affected in the Cuzd1 ( -/- ) epithelium ( Fig 3D , f ) . No alteration was observed in the total protein levels of ErbB1 , ErbB2 , and ErbB4 in mammary epithelia of these mice ( Fig 3D , insets ) . Collectively , these results indicated that CUZD1 is necessary for the production of the EGF family ligands , NRG1 , EREG , and EPGN , which then function through ErbB receptor-mediated signaling pathways to control epithelial proliferation in the mammary gland during alveolar development . We used HC11 cells , a non-transformed mammary epithelial cell line derived from pregnant mice , to examine the cell autonomous role of Cuzd1 [31] . A lentiviral expression vector harboring a full-length cDNA encoding Cuzd1 or LacZ ( control ) was integrated into HC11 cells to generate stable cell lines which express constitutively elevated levels of Cuzd1 ( HC11-Cuzd1 ) or β-galactosidase ( HC11-LacZ ) ( S3A and S3B Fig ) . When HC11-Cuzd1 cells were subjected to a BrdU incorporation assay , they exhibited significantly higher rates of proliferation compared to control HC11-LacZ cells ( Fig 4A ) . These data provided evidence that Cuzd1-dependent mechanisms indeed promote proliferation of mammary epithelial cells . We next investigated whether CUZD1 controls the proliferation of HC11 cells by regulating the expression of the EGF growth factors . First , we examined the effects of Cuzd1 overexpression on the expression of the EGF family members . Significantly higher levels of Epgn , Ereg and Nrg1 transcripts were detected in HC11-Cuzd1 cells as compared to the HC11-LacZ cells ( Fig 4B ) . Conversely , siRNA-mediated attenuation of Cuzd1 mRNA expression in HC11 cells led to a marked reduction in the levels of Nrg1 , Ereg , and Epgn mRNAs without significantly altering the levels of mRNAs encoding other EGF family ligands ( Fig 4C ) . To determine which ErbB receptors play a role in CUZD1-induced cell proliferation , we performed a knock down of ErbB receptors 1–4 in HC11-Cuzd1 cells using gene-specific siRNAs ( S4 Fig ) . Knock down of ErbB1 , ErbB3 and ErBb4 resulted in a decrease in HC11-Cuzd1 cell proliferation as measured by a BrdU incorporation assay ( Fig 4D ) . We next wanted to determine if the loss of Cuzd1 , and therefore the loss of specific EGF family ligands , led to a reduction in mammary epithelial cell proliferation . Using siRNA , we knocked down Cuzd1 in HC11 mammary epithelial cells and supplemented with EPGN or NRG1 ligands . EPGN and NRG1 were both able to partially rescue proliferation of HC11 cells as compared to the ligand treated control ( Fig 4E ) . Altogether , these data strongly support the concept that CUZD1 controls the production of specific EGF family growth factors , which act via ErbB1 , ErbB3 and ErbB4 to induce mammary epithelial cell proliferation . To further elucidate the molecular mechanism of CUZD1 , we attempted to identify the cellular factors that interact with it . To achieve this goal , we created HC11 cells stably over-expressing recombinant FLAG epitope-tagged CUZD1 ( HC11-3xFLAG-Cuzd1 cells ) . Soluble extracts of these cells were subjected to co-immunoprecipitation using a FLAG antibody . The immunoprecipitated proteins were recovered and submitted for mass spectrometry . The LC/MS identified peptide fragments corresponding to multiple potential interaction partners of CUZD1 , including JAK1 and JAK2 , protein arginine methyltransferase 5 and phosphoribosyl pyrophosphate synthetase 1 . Since JAK1/2 signaling and subsequent STAT5 phosphorylation is critical for mammary gland development , we focused on the interactions between JAK1 , JAK2 , and CUZD1 . Co-immunoprecipitation of JAK1 and JAK2 from HC11 cell lysates was confirmed using an IP for endogenous CUZD1 and Western blot analysis ( Fig 5A ) . Interestingly , we also detected a signal for phosphorylated STAT5 in the HC11 cell immunoprecipitates ( Fig 5A ) . The presence of this complex of proteins was also confirmed using Western blot in HC11-3xFLAG-Cuzd1 cells ( S5A Fig ) . Although STAT5 was not identified as an interacting partner of CUZD1 in our proteomic analysis of the immunoprecipitate , it is conceivable that CUZD1 interacts directly with JAK1/JAK2 , which exist in a larger cytosolic complex with STAT5 . In response to signaling by hormones , such as prolactin , activation of JAK1/2 leads to activation of the transcription factors STAT5a and STAT5b , which control mammary epithelial cell proliferation and differentiation during alveologenesis [7 , 32] . Though both STAT5a and STAT5b are present in the mammary gland , STAT5a is the dominant form phosphorylated and localized to the nucleus during pregnancy and lactation [18 , 33] . We examined the status of the activating STAT5 phosphorylation ( Tyr 694 ) in the mammary glands of Cuzd1 ( -/- ) mice at day 18 of pregnancy by IHC analysis . Total STAT5 protein levels were unchanged in Cuzd1 ( -/- ) mice compared to Cuzd1 ( +/- ) ( Fig 5B , a and b ) . However , we observed a striking loss of STAT5 phosphorylation in the mammary epithelia of these mice , whereas abundant pSTAT5 was present in the mammary epithelium of Cuzd1 ( +/- ) littermates ( Fig 5B , c and d , e and f ) . STAT5 is known to directly regulate the expression of Wap and Csn2 , two milk proteins secreted by differentiated epithelial cells [8 , 19] . We postulated that the loss of STAT5 phosphorylation impairs STAT5-dependent gene expression , leading to the observed deficiency in milk production in Cuzd1 ( -/- ) females . To test this notion , we analyzed the gene expression levels of Wap and Csn2 . The levels of Wap and Csn2 transcripts were indeed markedly reduced in the mammary glands of Cuzd1 ( -/- ) females during lactation ( Fig 5C ) . These results formed the basis of our hypothesis that CUZD1-mediated signaling through JAK/STAT5 controls mammary epithelial cell differentiation . To further understand the functional significance of the interaction of CUZD1 with the JAK/STAT5 pathway , we examined phosphorylation and localization of STAT5 and expression of direct transcriptional targets of pSTAT5 in HC11-Cuzd1 cells in response to PRL treatment . In this experiment , the HC11-Cuzd1 cells were treated with vehicle or PRL and STAT5 phosphorylation/localization was analyzed using immunocytochemistry . We observed that pSTAT5 immunostaining was dramatically enhanced in HC11-Cuzd1 cells ( Fig 5D , c and d ) relative to HC11-LacZ ( Fig 5D , a and b ) cells upon PRL treatment and , as expected , it was localized predominantly in the nucleus . PRL treatment of HC11-Cuzd1 cells did not result in a marked alteration in total STAT5 levels ( Fig 5D , e and f ) . The enhanced STAT5 phosphorylation observed in HC11-Cuzd1 cells as compared to HC11-LacZ cells was also confirmed via Western blotting ( S5B Fig ) . These data are consistent with the concept that CUZD1 promotes PRL signaling by enhancing STAT5 phosphorylation and activation . To investigate the role of CUZD1 in the PRL signaling pathway in vivo , virgin , pubertal Cuzd1 ( -/- ) and Cuzd1 ( +/+ ) ( wild type ) mice were treated with E , P and PRL for 3 consecutive days to stimulate proliferation and differentiation of the mammary epithelium . To examine the gross morphological changes in the mammary epithelium following this hormonal treatment , whole mounts of mammary glands were performed . Compared to the vehicle control , Cuzd1 ( +/+ ) mice treated with E , P and PRL exhibited initiation of alveolar development ( Fig 6A , a and c ) . Conversely , Cuzd1 ( -/- ) mice displayed a markedly reduced response to E , P and PRL treatment compared to vehicle control ( Fig 6A , b and d ) . Interestingly , we observed an elevated CUZD1 expression and nuclear localization in the mammary epithelium of Cuzd1 ( +/+ ) mice treated with E , P and PRL compared to vehicle-treated controls ( Fig 6B , a and c ) . As expected , this induction was absent in Cuzd1 ( -/- ) mice ( Fig 6B , b and d ) . We also observed a robust phosphorylation of STAT5 and its nuclear localization in mammary epithelia of mice treated with E , P and PRL , which was absent in Cuzd1 ( -/- ) mice ( Fig 6C , a-d ) . Consistent with data obtained in cell lines , the expression of EREG , a direct target of STAT5 , which is induced in wild-type mice upon treatment with E , P , and PRL , was absent in Cuzd1 ( -/- ) mice ( Fig 6D , a and b ) . Overall , these data support the concept that CUZD1 is necessary for transduction of PRL signaling through the JAK/STAT pathway to induce mammary epithelial gene expression during hormone-induced alveologenesis . CUZD1 has no nuclear localization sequence or DNA binding domain , but we observed that it was translocated to the nucleus upon stimulation with serum . We hypothesized that CUZD1 could be moving into the nucleus in association with pSTAT5 . To investigate this possibility , HC11-3xFLAG-Cuzd1 cells were treated with a PRL/FBS/EGF cocktail , FBS , or a vehicle control to induce nuclear translocation of pSTAT5 . Dual immunostaining was then performed to examine the cellular locations of pSTAT5 and CUZD1 . In cells treated with the vehicle control , pSTAT5 and CUZD1 remained largely cytoplasmic ( Fig 7A , a , d , and g ) . Upon stimulation with FBS or PRL/FBS/EGF , pSTAT5 and CUZD1 were colocalized in the nucleus ( Fig 7A , b-c , e-f , and h-i ) . Previous studies reported that STAT5 binds directly to regulatory regions of Ereg and Wap genes to regulate their transcription ( 15 , 16 ) . We observed that the expression of Ereg and Wap genes were up-regulated upon treatment with PRL , and their expression was further elevated in Cuzd1-overexpressing HC11 cells ( Fig 7B ) . Based on the protein structure of CUZD1 ( S1A Fig ) , there is no indication that CUZD1 binds to DNA , but we wanted to determine if CUZD1 and STAT5 remained in a complex when STAT5 is bound to DNA . To investigate this , we performed a ChIP re-ChIP using a STAT5-specific antibody followed by precipitation with anti-FLAG ( M2 ) resin . Enrichment of regulatory elements in specific GAS motifs of Ereg , Wap , and Csn2 indicated that CUZD1 remains bound to STAT5 in the nucleus when STAT5 is acting as a transcription factor ( Fig 7C ) . In single ChIP experiments , we confirmed STAT5 binding at in the Ereg , Wap , and Csn2 GAS sequences ( S6 Fig ) as well as enrichment of these regulatory elements when we immunoprecipitated the FLAG-CUZD1 fusion protein ( S6 Fig ) . The authenticity of this result was confirmed by the absence of enrichment of the Wap , Csn2 and Ereg GAS sites when a FLAG ChIP was performed in HC11-Cuzd1 cells in which CUZD1 is not flag-tagged ( S6 Fig ) . Collectively , our results indicated that , upon PRL-induced activation , the CUZD1-STAT5 complex translocates to the nucleus and interacts with target genes to bring about changes in gene expression that critically promote mammary epithelial cell proliferation and differentiation ( Fig 8 ) .
E , P and PRL act in concert with the EGF family growth factors to govern mammary gland development during pregnancy and lactation [3 , 13] . In this study , we provide evidence that CUZD1 is a novel regulator of STAT5 signaling in the steroid-primed mammary epithelium . Loss of Cuzd1 expression in mammary epithelial cells prevented in vivo phosphorylation of STAT5 , resulting in a severe impairment in mammary epithelial proliferation and differentiation , which disrupts alveologenesis and prevents milk production during lactation . A molecular link between CUZD1 and STAT5 phosphorylation has emerged from our study . Immunoprecipitation of CUZD1 from mammary epithelial cells followed by mass spectrometric and Western blot analyses revealed that CUZD1 is physically associated with several proteins , including JAK1/JAK2 and STAT5 . Importantly , increased CUZD1 expression augmented PRL-induced phosphorylation as well as nuclear translocation of STAT5 . The precise nature of CUZD1’s association with the JAK1/JAK2/STAT5 complex and the mechanism by which it promotes STAT5 phosphorylation are presently unclear . It is conceivable that CUZD1 potentiates JAK/STAT signaling downstream of PRLR activation by acting as an adaptor protein that aids in the recruitment of STAT5 to the PRLR/JAK complex . It may also act in an accessory role in stabilizing/enhancing phosphorylation of STAT5 by JAKs . Precedence for this hypothesis is based on literature describing the roles of effector proteins that alter signaling through this complex [34] . For example , c-Src has been shown to propagate PRL initiated JAK/STAT signaling in normal mammary tissue [35] . Additionally , caveolin-1 ( Cav-1 ) has been shown to inhibit the STAT5 signaling pathway by competitively binding to the tyrosine kinase domain of JAK2 , preventing interaction and subsequent activation of STAT5 [36] . Female mice lacking Prlr , Jak2 , and Stat5 are characterized by severe defects in mammary lobuloalveologenesis during pregnancy and lack of milk production during lactation [14 , 20 , 33 , 37–39] . The Cuzd1 ( -/- ) mice phenocopy the mammary defects observed in these mice during pregnancy and lactation , lending further support to the concept that CUZD1 is functionally linked to the components of the PRLR/JAK/STAT5 pathway during lobuloalveolar development and lactation . The CUZD1 protein is localized in both cytoplasmic and nuclear compartments of mammary epithelial cells . When the mammary epithelial cells are grown in the absence of serum , CUZD1 is predominantly localized in the cytoplasm . Stimulation of these cells with media containing serum triggers nuclear translocation of CUZD1 . This result is also recapitulated by adding a combination of PRL , EGF and serum to these cells . Since CUZD1 lacks a nuclear localization motif or a DNA binding domain , we predicted that its translocation to the nucleus is dependent on association with a transcription factor . Indeed , our results are consistent with the view that CUZD1 translocates to the nucleus in association with pSTAT5 . We further demonstrated that CUZD1 is recruited along with pSTAT5 to the regulatory regions of key target genes , such as Ereg and Wap . It is plausible that EREG contributes to CUZD1-mediated epithelial proliferation and alveolar expansion during pregnancy and lactation , as Ereg is a direct transcriptional target of STAT5 and has been implicated in promoting growth and survival of breast cancer cells [40–42] . Our study showed that CUZD1 controls the production of a subset of EGF family growth factors , EREG , NRG1 , and EPGN , in mammary epithelium during pregnancy . Mice lacking Nrg1 display pronounced defects in mammary alveologenesis with condensed alveoli and impaired alveolar outgrowth during pregnancy [43 , 44] . Development of mutant mouse models showed that ErbB1 , ErbB2 , and ErbB3 play important roles in mammary ductal growth and fat pad penetration [45–48] . ErbB4 ( -/- ) mammary glands exhibited severe defects in alveolar proliferation and differentiation during pregnancy and lactation [21] . Cuzd1 ( -/- ) mammary glands showed impaired activation of ErbB1 and ErbB4 during pregnancy and lactation . These results are in accord with the hypothesis that the CUZD1-regulated growth factors , NRG1 , EREG and EPGN , act primarily through ErbB1 and ErbB4 to exert their effects mainly during alveolar development . Consistent with this concept , there is a remarkable similarity between the mammary gland phenotypes of ErbB4 ( -/- ) and Cuzd1 ( -/- ) females . In summary , our findings support a model in which CUZD1 is a downstream mediator of PRL that enhances the signaling pathway through STAT5 during proliferation and differentiation of the mammary epithelium ( Fig 8 ) . CUZD1 impacts mammary epithelial proliferation and differentiation during pregnancy and lactation . It promotes production of a specific subset of the EGF-like ligands , NRG1 , EREG and EPGN , which control alveolar development . These growth factors primarily function through ErbB1 and ErbB4 to regulate the proliferation and differentiation of mammary epithelial cells . Further analysis of the molecular mechanisms by which CUZD1 integrates the pathways regulated by STAT5 and the EGF family growth factors will improve our understanding of the molecular networks that underlie PRL regulation of normal mammary gland development .
Mice were maintained in the designated animal care facility at the University of Illinois , according to institutional guidelines for the care and use of laboratory animals . All experimental procedures involving mice were conducted in accordance with National Institutes of Health standards for the use and care of mice . The animal protocol describing these procedures was approved by the University of Illinois Institutional Animal Care and Use Committee ( IACUC ) . The IACUC approval number for this protocol is 16026 . This approval is valid until August 22 , 2019 . To generate the vector for homologous recombination , about 16-kb mouse genomic DNA containing eight exons of mouse Cuzd1 was sequenced and intron-exon boundaries were analyzed . A 4 . 0-kb BamH I-Kpn I fragment containing the 1st and 2nd exons and a 2 . 0-kb BamH I-EcoR I fragment containing part of 6th exon was cloned into Scrambler A and B site of pKO Scrambler NTKV-1901 targeting vector , respectively . Correct targeting resulted in deletion of gene sequence containing exons III-VI spanning the first and second CUB domains of CUZD1 protein and replaced with a neomycin resistance gene ( NEO ) ( S1B Fig ) . The construct was linearized and electroporated into embryonic stem ( ES ) cells . ES clones were selected by G418 and screened by Southern blot analysis employing a 335-bp 5’-end probe , a 390-bp 3’-end probe and a 450-bp internal probe respectively . The ES clone with appropriate homologous recombination was selected for blastocyst injection and chimaeras were generated with heterozygous ES cell lines . Heterozygous male mice were backcrossed to wildtype C57BL/6 female to generate the Cuzd1-null mice with pure genetic background . Progeny were genotyped by using PCR assay that identified both mutant and wild-type alleles and Southern blotting analysis with 5’-end probe ( S1B Fig ) . HC11 cells were grown in RPMI-1640 supplemented with 5% ( v/v ) fetal bovine serum , 1x Penicillin-Streptomycin , 10 ng/ml EGF and 5 μg/ml insulin . In certain experiments , 2% charcoal-stripped calf serum was used . HC11 cells were treated with 10nm E , 10μm P and/or 50μm PRL . Ovariectomized mice were treated with vehicle controls ( oil or saline ) , 1ng E and 1mg P ( subcutaneous ) and/or 50μg/g bw of PRL ( intraperitoneal ) . The inguinal mammary glands were dissected out , spread onto a glass slide and fixed in a 1:3 mixture of glacial acetic acid/100% ethanol . After hydration , slides were stained as described previously [50] . Following mounting , images were captured using bright field dissecting microscope . Paraffin-embedded mammary tissues were sectioned and subjected to IHC as described previously [49] . Rabbit polyclonal antibodies against a peptide antigen containing amino acids SSPNYPKPHPEL of mouse CUZD1 were generated in our laboratory . IHC was performed on mammary tissue sections , using primary antibodies and bound primary antibodies were detected with either horseradish peroxidase ( HRP ) - or fluorescent label-conjugated secondary antibodies . Sections were counterstained with hematoxylin or dapi and mounted . Cells were fixed in a 3 . 7% formalin solution at room temperature for 15 min followed by washing with PBS . The cells were permeabilized by 0 . 25% Triton X-100 in PBS for 10 min , and nonspecific binding of antibodies was blocked with 5% donkey serum for 1 h at room temperature . Cells were incubated with primary antibodies overnight at 4°C . Labeling was visualized with fluorescent label-conjugated secondary antibodies and slides were mounted in Prolong GOLD and cured for 24 h before imaging . The images of immunohistochemical staining were captured by using a Leica DM2500 light microscope fitted with a Qimaging Retiga 2000R camera ( Qimaging ) . Immunofluorescence imaging was performed on a Leica 700 confocal microscope . These images were minimally processed on ADOBE Photoshop version 8 . Pooled inguinal mammary glands from three mice ( Cuzd1 ( +/- ) or Cuzd1 ( -/- ) ) were minced into small pieces and incubated with DMEM: F12 containing 100 U/ml hyaluronidase and 1 . 5 mg/ml collagenase at 37°C for 2 h accompanied by shaking at 110 RPM . Following neutralization of enzyme activity with 5% FBS , the homogeneous cell mixture was centrifuged and the cell pellet was washed several times with PBS . Purified epithelial cells were frozen in liquid nitrogen and stored at –80°C . Total RNA was prepared from these cells and hybridized to Affymetrix mouse arrays ( GeneChipMouse Genome 430 2 . 0 array ) containing probes that represented ~14 , 000 known genes . They were processed and analyzed according to Affymetrix protocol . Although the microarray analysis was performed using pooled mammary glands , we further confirmed gene expression changes , using RNA samples isolated from independent batches of epithelial cells isolated from Cuzd1 ( +/- ) or Cuzd1 ( -/- ) glands and analyzing the expression of selected genes by real-time PCR followed by statistical analyses . As shown in Fig 3B , several transcripts corresponding to the EGF family ligands were indeed differentially expressed in a manner similar to that predicted by the microarray analysis . The microarray data were deposited in the publicly available GEO database with GEO Accession GSE30939 . For qPCR , total RNA was extracted from purified mammary epithelium or cultured HC11 cells using Trizol RNA purification kit , according to manufacturer’s instructions and subjected to qPCR using gene specific primers . Primer sequences are provided in S1 Table . Relative mRNA levels were plotted after normalization to the loading control 36B4 . The error bars represent the relative gene expression ± the standard error from three or more independent trials . Data were analyzed using a student’s t-test and * indicate p-values < 0 . 05 . HC11 cells were transfected with siRNA against Cuzd1 or control siRNA ( non-targeting ) , using Lipofectamine-RNAimax reagent following manufacturer’s protocol . Briefly , lipofectamine was mixed with siRNA , and allowed to form siRNA-liposome complexes , which were then added to HC11 cells at 60% confluency . After 24 h , the transfection was repeated again . Cells were harvested 48 h after the second transfection , total RNA was isolated and analyzed by qPCR using gene-specific primers . HC11-3xFLAG-Cuzd1 cells were cultured with FBS , EGF and PRL for 6h , lysed and samples were precleared before immunoprecipitation ( IP ) . The IP was done using anti-FLAG M2 or a mouse IgG control resin ( according to manufacturer’s directions ) and the captured proteins were eluted using 3xFLAG peptide . Samples were boiled in SDS buffer and analyzed by standard Western blotting . Directly following IP , protein samples were submitted to the Mass Spectrometry Laboratory at the University of Illinois at Urbana-Champaign . Liquid chromatography ( LC ) /mass spectrometry ( MS ) proteomic data were analyzed using Mascot ( Matrix Science ) and results were sorted by protein score . ChIP assays were performed using the EZ-ChIP kit ( Millipore ) according to the manufacturer's instructions with minor modifications . Anti-FLAG M2 affinity gel ( Sigma , A2220 ) and anti-STAT5 antibody ( Santa Cruz , sc-835 ) were used overnight at 4°C to immunoprecipitate flag-CUZD1 and STAT5 , respectively . Normal mouse IgG ( Santa Cruz , sc-2027 ) immunoprecipitation served as a negative control . Protein/DNA complexes were eluted , crosslinks were reversed and purified DNA was analyzed for enrichment in sequences of interest using qPCR .
|
In the mammary gland , genetic circuits controlled by the hormones , estrogen , progesterone and prolactin , act in concert with pathways regulated by members of the epidermal growth factor family to orchestrate growth and morphogenesis during puberty , pregnancy and lactation . We have identified CUZD1 as a novel mediator of prolactin signaling in the steroid hormone-primed mouse mammary gland during pregnancy and lactation . Cuzd1-null mice exhibited a striking impairment in ductal branching and alveolar development during pregnancy , resulting in a subsequent defect in lactation . Administration of prolactin failed to induce proliferation of the mammary epithelium in Cuzd1-null mice . Protein binding studies revealed that CUZD1 interacts with downstream transducers of prolactin signaling , JAK1/JAK2 and STAT5 . Additionally , elevated expression of Cuzd1 in mammary epithelial cells stimulated phosphorylation and nuclear translocation of STAT5 . CUZD1 , therefore , is a critical mediator of prolactin that controls mammary alveolar development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"phosphorylation",
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"maternal",
"health",
"obstetrics",
"and",
"gynecology",
"reproductive",
"physiology",
"endocrine",
"physiology",
"epithelial",
"cells",
"women's",
"health",
"pregnancy",
"animal",
"cells",
"proteins",
"endocrinology",
"gene",
"expression",
"lactation",
"exocrine",
"glands",
"biological",
"tissue",
"stat",
"signaling",
"breast",
"tissue",
"biochemistry",
"signal",
"transduction",
"anatomy",
"cell",
"biology",
"post-translational",
"modification",
"physiology",
"genetics",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"mammary",
"glands",
"cell",
"signaling"
] |
2017
|
CUZD1 is a critical mediator of the JAK/STAT5 signaling pathway that controls mammary gland development during pregnancy
|
The functional significance of electrical rhythms in the mammalian brain remains uncertain . In the motor cortex , the 12–20 Hz beta rhythm is known to transiently decrease in amplitude during movement , and to be altered in many motor diseases . Here we show that the activity of neuronal populations is phase-coupled with the beta rhythm on rapid timescales , and describe how the strength of this relation changes with movement . To investigate the relationship of the beta rhythm to neuronal dynamics , we measured local cortical activity using arrays of subdural electrocorticographic ( ECoG ) electrodes in human patients performing simple movement tasks . In addition to rhythmic brain processes , ECoG potentials also reveal a spectrally broadband motif that reflects the aggregate neural population activity beneath each electrode . During movement , the amplitude of this broadband motif follows the dynamics of individual fingers , with somatotopically specific responses for different fingers at different sites on the pre-central gyrus . The 12–20 Hz beta rhythm , in contrast , is widespread as well as spatially coherent within sulcal boundaries and decreases in amplitude across the pre- and post-central gyri in a diffuse manner that is not finger-specific . We find that the amplitude of this broadband motif is entrained on the phase of the beta rhythm , as well as rhythms at other frequencies , in peri-central cortex during fixation . During finger movement , the beta phase-entrainment is diminished or eliminated . We suggest that the beta rhythm may be more than a resting rhythm , and that this entrainment may reflect a suppressive mechanism for actively gating motor function .
Human motor behaviors such as reaching , grasping and speaking , are executed and controlled by the somatomotor regions of the cerebral cortex , which are located immediately anterior and posterior of the central sulcus . This peri-central system is known to manifest a 12–20 Hz electrical oscillation known as the beta rhythm [1] , [2] , which has long been known to have an inverse relation to sensory processing [3] and motor production [4] , [5] , [6] . This beta rhythm is decreased during movement initiation and production [7] , [8] , [9] , [10] ( illustrated in Figures 1&2 ) , and also during motor imagery [11] . Following cessation of movement , there is a rebound augmentation in beta amplitude , specific to peri-central somatomotor and somatosensory cortex [12] , [13] . With movement disorders , variations in the amplitude of the brain surface beta rhythm are differentially associated with specific motor diseases [14] . Classically , diseases in the brain have been approached in terms of local pathology . For example , a stroke that abolishes the blood supply to the precentral gyrus , or a precentral tumor can produce paralysis [15] . However , we are beginning to understand that some are diseases of brain dynamics , which involve functionally specific brain areas , but are ultimately a result of dysfunctional interactions . Dysfunctional inter-regional dynamics have been explored using measures of cortical metabolic activity ( fMRI and PET ) , in studies of epilepsy [16] , Alzheimer's disease [17] , depression and other neuropsychiatric disorders [18] , [19] , and , most relevant for the present study , motor disease [20] , [21] , [22] . However , these measures of brain metabolism are tied to metabolic timescales ( e . g . at least 5–10 s ) ; in contrast , the computational neural network dynamics occur at the faster timescales over which cognition and behavior are modulated ( 50–250 ms ) . Aspects of macroscale and network physiology at these timescales in the brain can now be captured by changes in brain surface electrical potentials . Despite the association between the beta rhythm and motor function , it is not known whether the rhythm plays an active role in altering the computations taking place in somatomotor cortex , or whether it is epiphenomenon of cortical state changes determined by other mechanisms . Here we present new evidence for the role of the beta rhythm in organizing somatomotor function by quantifying the relation between rhythmic population activity on the time-scales of tens of milliseconds in sites across the lateral cerebral cortex . Recent work has shown that the phase of the beta rhythm in the local field potential is correlated with the firing time of individual neurons in primary motor cortex [23] , [24] , [25] . Here we use subdural electrocorticography ( ECoG ) at the brain surface to examine this relationship for neural population activity ( Figure 1 ) . From these ECoG recordings , a correlate of mean instantaneous firing rate across the population of neurons beneath the electrode can be extracted in the form of broadband spectral power [26] , [27] , [28] , [29] . Changes in this extracted broadband activity have been demonstrated to capture movement-related and visual cortical changes at the 50–100 ms timescales on which the faster aspects of cortical processing take place [30] , [31] ( Figures 2 , 3 ) . In this study , we examined the properties of the beta rhythm at rest during simple visual fixation and during a basic finger movement task . By documenting how broadband spectral changes vary with the phase of underlying motor beta rhythm , we show that local cortical activity has a robust entrainment on the phase of the beta rhythm , specifically in peri-central motor areas . This relation of the beta rhythm to local neuronal activity is a property of the “resting brain”: it is present during fixation and when resting , and is selectively diminished during movement , along with the amplitude of the beta rhythm . We hypothesize that this phenomenon , here observed for the peri-central beta rhythm , may be one example of a general motif for cortical-subcortical circuits , and that this motif will be observed with different brain rhythms across cortical areas and behavioral states .
Results were obtained from ECoG recordings in 14 human subjects ( see Methods ) . As shown in Figure 1E , the averaged power spectral density ( PSD ) of the ECoG potential during movement and rest exhibits a general broadband 1/fχ shape , with superimposed rhythms that deviate from this 1/fχ structure at particular low frequencies [28] , [31] . During motor behavior , these averaged PSDs commonly reveal decreases in power in the beta rhythm , and an increase in the broadband power ( Figures 1E , 2A&J ) [8] , [9] . The spatial distribution of the sites showing beta rhythm change is relatively large , covering pre- and post-central cortex ( Figure 1F ) . In contrast , the broadband spectral power changes are localized to specific sites representing the digit that is moved ( Figure 1G ) . To illustrate the temporal and spatial specificity of the broadband power , Figure 2 shows the spectral changes for two finger movements observed at two adjacent cortical sites . During thumb movement ( top half ) the PSD shows the increase in broadband power and decrease in beta rhythms ( Figure 2A ) . The PSD can be naively decoupled using a variant of principal component analysis ( Methods ) to show how the powers in different frequency bands vary with respect to one another . The spectra during movement and rest are reconstructed without the 2nd–4th principal spectral components ( PSCs ) in Figure 2B . This reconstruction shows that during movement power increases across all frequencies , including low frequencies . Conversely , the spectrum reconstructed for only the 2nd–4th PSCs ( Figure 2C ) shows that the change is specific to narrowband frequencies in the β-rhythm range . Thus , changes in the power spectrum of the ECoG potential can be understood as a combination of a band-limited ( rhythmic ) component and a broadband ( non-rhythmic ) component . The time-course of spectral changes relative to thumb movement are shown in averages aligned with movement onset ( Figure 2D–G ) . A time-varying estimate of broadband spectral change was obtained by projecting the PSD at each time point onto the coefficient vector of the first principal spectral component ( Figures 2 , 3 ) . The resulting pattern of broadband change over time approximates the time-varying amplitude function in the power-law relationship: . Broadband spectral change was isolated from the continuous dynamic spectrum , applied to only the 1st PSC , and averaged with respect to the first thumb movement following each cue ( Figure 2D ) . Note that broadband power begins to change 50–100 ms prior to the onset of thumb movement . The average time-varying PSD , scaled as a percentage of mean power at each frequency ( Figure 2E ) shows the inverse relation of beta and high frequency power as a function of time relative to movement . Note that the change in β activity begins prior to the broadband spectral change . The drop in beta power with movement is illustrated in Figure 2F . The specificity of this site for thumb movements is shown by the greater increase in broadband power compared to movement of other digits ( Figure 2J ) . In contrast , all digit movements were associated with a similar drop in beta power relative to rest ( Figure 2H , I ) . The lower half of Figure 2 documents similar results for movement of the index finger in recordings from an adjacent site , separated by 1 cm from the thumb site . While the first movement in a set is sometimes associated with the largest amount of cortical change , this was not reliably the case , likely because movement frequency varied across epochs within and across subjects . Emerging work , using primary motor and somatosensory microECoG arrays during supervised pacing of repeated finger movement , shows that cortical activity attenuation with repeated movement is robust , and is a function of movement rate [32] . Figure 3 demonstrates that , when examined across the cortex , broadband spectral changes during movement are highly localized and somatotopically distinct for different fingers ( A ) . In contrast , changes in beta power are broadly distributed and essentially the same for any finger movement . The instantaneous broadband power shows a clear temporal correlation with individual finger movements ( C ) ; note that the broadband trace obtained at each site correlates with the position of only one of the fingers and not the others . The spatial overlap of changes for particular pairs of digits is summarized in Figure 3B . Overlap ( B ) is quantified by the proportion of sites showing similar changes with movement of the two digits ( Methods ) , and is larger for the beta power ( near complete overlap ) than broadband power ( largely distinct spatially ) . Similar overlap patterns are documented for the other eight subjects as well ( D ) . Broadband spectral changes show largely non-overlapping representation between digits , while the distribution of change in the beta rhythm overlaps almost completely in all cases . Digit-specific electrodes were typically 1 cm from one another ( also see Figures S5 , S6 , S7 , S8 , S9 , S10 in Text S1 ) . The relation between the instantaneous broadband power and phase of ECoG signal at different frequencies is documented in Figure 4 . The analysis is based on recordings obtained during finger movements and rest ( B ) . The dynamic broadband ( C ) obtained from the raw ECoG potential ( D ) shows distinct fluctuations related to index finger movement . The ECoG can be filtered with a simple wavelet to obtain a temporally varying estimate of the narrow band-passed signal , as shown for the 3 Hz rhythm in ( E ) . The phase of this signal can be used to extract associated snippets of the broadband signal ( H ) and obtain a phase-triggered average of the broadband power ( I ) . Repeating this process for all frequencies between 1 and 50 Hz yields the “Phase coupling palette” shown in Figure 4J , which documents the phase relation for all frequencies . The predominant pattern for the range 10–30 Hz shows a tendency for brain activity , as measured by broadband power , to increase just prior to the surface-negative phase ( ) and decrease just prior to the surface positive phase ( ) of the beta rhythm . This modulation of broadband amplitude by rhythm phase is also called “rhythmic entrainment” , “phase-amplitude coupling” , and “nested oscillation” in this and other manuscripts [27] , [33] , [34] . It is important to note that , for broadband coupling to low frequency rhythm , the portions of the broadband that contribute to the coupling measurement will be those with amplitudes that can track the low frequency phase ( e . g . for a 10 Hz rhythm , only the portions of broadband >20 Hz will be contributing to the coupling measure between the two ) . To quantify how rhythmic entrainment with the beta band is modulated with behavior , we summarized the main relationship with a “coupling vector” , as shown in Figure 5 . We captured the phase-amplitude coupling across a range of low-β band frequencies ( 12–20 Hz ) , rather than at a single frequency , using the Hilbert transform . As described in the Methods , a “coupling vector” in the complex plane ( D ) succinctly represents the magnitude , Zmod , and phase , φc , of the broadband activity phase-coupling with the 12–20 Hz rhythm ( C ) . This coupling vector can be derived for individual behavioral epochs consisting of specific finger movements and rest ( E–G ) , as well as for single trials within epochs ( H–J ) . The global distribution of trial-to-trial coupling vectors across all movement epochs ( H ) reveals heterogeneous coupling for the different behaviors . A mean coupling vector ( ) may be obtained for each behavior , and its statistical significance computed as illustrated in ( I ) for rest and in ( J ) for thumb movement . The statistics are obtained from the distribution of the projections of individual movement or rest epoch coupling vectors onto the mean vector direction ( e . g . , upper right histogram in [I] ) . The Zmod values for the different behaviors ( L ) show significantly larger entrainment for rest than for any of the movements . A similar difference is seen for the values of the motor rhythm amplitudes ( K ) . The fact that this cortical site ( A ) is specific for thumb representation is shown by the larger Z-score of log broadband values ( M ) . Similar phase entrainment is present at many cortical sites , as shown in Figure 6 for the baseline fixation task . The phase coupling palettes ( C ) show distinct differences , with the largest beta rhythm modulation in the dorsal peri-central region ( F , G ) . The average phase palettes computed for different cortical regions ( G ) show robust phase modulation in the Dorsal Roladic region , with similar but weaker patterns in the Ventral Rolandic , Anterior Frontal and Posterior Parietal sites . The same relationships are seen in the regional palettes combined for nine subjects ( Figure 7A ) . Overall , the 12–20 Hz ( β ) coupling is strongest peri-centrally , whereas 4–8 Hz ( θ ) coupling is ubiquitous . The average coupling for each region is summarized by the histograms for the θ and β frequency bands ( 7C & E , resp . ) . Note that the preferred phase of coupling is significantly different for 4–8 Hz ( θ , preferred phase −0 . 75π ) compared to 12–20 Hz ( β , preferred phase 0 . 81π , significant p<106 , by unpaired resampling from electrodes/bands that independently have significant coupling – each at p<0 . 05 after Bonferroni correction of t-test on projected coupling values for that electrode ) . When the preferred phase of coupling is compared between brain regions , there are some very weak , but significant , differences , for both a 4–8 Hz and 12–20 Hz range ( see Figure S16 in Text S1 ) . A detailed picture of the phase-coupling motifs and changes with different finger movements for different cortical sites in a representative subject ( #4 ) is shown in Figure 8 . Note that the region of strongest absolute coupling with beta during rest epochs runs along the central sulcus . The spatial extent of significant change in coupling ( P ) lies within both the region of largest coupling during rest ( Q ) and also within the region of significant change in 12–20 Hz rhythm amplitude ( O ) . The spatial extent of significant broadband change ( K ) also lies within these regions . Figure 9 shows the movement-related change in rhythmic modulation of broadband cortical activity during index movement relative to rest in different areas . Two subjects are illustrated in A and B , and the results for nine subjects are summarized in G–I . These data again indicate that the greatest change in beta rhythm coupling occurs in the dorsal precentral region . The phase amplitude coupling palettes in Figures 4 , 6 , 7 , and 8 demonstrate a strong and significant modulation of broadband spectral change with the phase of low frequency rhythms in the δ/θ/β ranges . The variability of the coupling strength across different sites on the lateral cortical surface , and across different behavioral states ( movement , rest , visual fixation ) is also clear . Often the broadband spectral change is modulated with more than one rhythm at a single electrode site , and multiple couplings are superimposed in the coupling palettes . The predominant phase couplings for different frequency ranges are different . The structural relationship between modulation of local activity by the β-rhythm and β-amplitude is not simple . In subjects 1–9 ( finger movement task ) , there are 484 electrode sites after rejecting ones with artifacts or epileptic activity . Of these , 297 sites exhibit significant coupling during rest epochs ( e . g . “Zmod sites” = 297 , p<0 . 01 , uncorrected for multiple comparisons - 4 . 8 expected by chance ) . There is a significant decrease in the 12–20 Hz range amplitude in 142 ( e . g . “β sites” = 142 , p<0 . 01 , uncorrected for multiple comparisons - 4 . 8 expected by chance ) . Amongst these 142 , 98 ( 69% ) are sites with significant coupling during rest ( e . g . “β sites” that are “Zmod sites” = 98 ) . Therefore , these two types of measurement are not spatially correlated in a simple way . These experiments pose the issue of the basic relationship ( if any ) between the amplitude of the β-rhythm ( 12–20 Hz ) and the coincident strength of rhythmic modulation . On an epoch-by-epoch basis , β amplitude can be compared parametrically with strength of modulation via a standard Pearson correlation . One hypothesis would be that the degree of rhythmic modulation is determined by a simple relationship with the β-amplitude: with a task-related decrease in β-amplitude , there would be a corresponding decrease in modulation . In order to test this , the epoch-by-epoch correlation was measured between β-amplitude and ( projected ) modulation strength , performed independently for movement epochs and rest epochs ( Illustrated in Figure S17 in Text S1 ) . During rest epochs , there is a clear correlation in many cases . Of the 297 “Zmod sites” , there was significant correlation at 71 sites ( p<0 . 01 , uncorrected for multiple comparisons - 3 . 0 expected by chance; 2 were negatively correlated ) . Of the 142 “β sites” , 33 showed significant epoch-by-epoch correlation during rest periods ( p<0 . 01 , uncorrected for multiple comparisons - 1 . 4 expected by chance; 3 were negatively correlated ) . Not surprisingly , 30 of the 33 were at “β sites” that were also “Zmod sites” . Only 3 of the 341 sites that were either “Zmod sites” or “β sites” showed significant correlation during movement epochs ( p<0 . 01 , uncorrected for multiple comparisons - 3 . 4 expected by chance , none were at sites which were correlated for rest epochs ) . The conclusions that can be drawn from this are that 1 ) there is a parametric relationship between β amplitude and broadband modulation by β phase in the resting cortex at many loci , and 2 ) this relationship is not seen in the active cortex . One interpretation of this is that during movement , these cortical areas are in a lower amplitude regime of β , and that in this lower power regime , the 12–20 Hz amplitude attributed to the rhythmic activity is small compared to the stochastic 1/f portion of the signal , so any parametric relationship disappears ( see illustration in Figure S17 in Text S1 ) . This interpretation is supported by the epoch-to-epoch correlation seen during rest at the 30 of 71 sites with modulation during rest that also show movement-associated shift in β amplitude . However , that interpretation does not account for the 41 of 71 sites with a significant correlation , selectively during rest and not movement , but without a significant movement-associated shift in β-amplitude . In two subjects it was possible to correlate the ECoG recordings with fMRI measures of the BOLD signal , using registration techniques described in the Methods . Finger movement generates a robust BOLD activation , primarily focused in the pre- and post-central gyri , as well as smaller outlying activations ( Figure 10 ) . The topography of BOLD changes overlapped the most with the topography of shift in the β rhythm ( 10B , G ) . The location of broadband ECoG change with thumb movement also coincided with the largest BOLD changes ( 10A , F ) . The least overlap with BOLD occurred with phase-amplitude modulation ( 10C , H ) . Topographies of the resting phase-amplitude modulation and the movement-associated change in phase-amplitude modulation had similar overlap with the topography of BOLD change ( 10D , I ) . To determine the extent to which β-rhythms at neighboring cortical sites are generated by common versus independent sources , we examined the pairwise phase coherence of the β rhythms across sites . In contrast to many studies in which the absolute value of phase coherence ( or mean-squared phase coherence ) is used , we retain the phase-shift between sites . If there is a strong , coherent rhythm in a large fraction of the electrode array , then the act of re-referencing will introduce false phase coherence , π out of phase . When this occurs , it can be revealed by examining the phase of the phase coherence . Additionally , different motifs in phase coherence , such as those which would be found with propagating waves [25] , would be revealed . Figure 11A plots the phase coherence relative to a chosen reference site at which broadband power was maximally correlated with movement of the index finger . The figure shows that phase coherence of the β rhythm is spatially distributed over neighboring sites . Furthermore , by projecting each phase coherence value ( i . e . each phase coherence vector in the complex plane ) onto the strongest phase coherence value ( i . e . a reference vector ) we can measure the extent to which phases are aligned among the coherent sites . This projection procedure facilitates the identification of variation in phase across different cortical sites ( Figure 11B&C ) . The spatially coherent rhythms are gyrally delineated along the pre-central and post-central gyri . The phase coherence measure either decreases in magnitude , or “skips” in phase as sulci are crossed . Thus the phase coherence motifs for the motor rhythm with M1 obey sulcal boundaries . As shown in Figures 11 , 12 , and in Figure S19 in Text S1 , this is true across all 9 subjects . The spatial pattern of phase coherence in the 4–8 Hz theta range likewise shows anatomic delineation , but with a different spatial pattern than the 12–20 Hz beta rhythm ( see Figure S20 in Text S1 ) . Responses evoked by pair-wise cortical stimulation between adjacent electrodes was systematically documented in four subjects . The sites where stimulation evoked hand movement were all in the pre-central gyrus , and all were coincident with the sparse subset of sites that exhibited significant broadband spectral change for different finger movement types ( Figures S7 , S8 , S9 , S10 in Text S1 ) .
The major new findings of this study show that cortical population activity , as reflected in broadband power of the ECoG potentials , is significantly modulated with the phase of different rhythmic elements of the cortical surface potential . These relations are comprehensively documented in the “phase-coupling palettes” from various sites on the lateral cortex , which show how the average value of the instantaneous broadband power varies with the phase of a wide range of low frequency rhythms . Notably , local cortical activity , revealed by broadband spectral change , is entrained on the 12–20 Hz beta rhythm in peri-central cortex . When second-to-second changes in entrainment are examined during epochs of movement and rest , there is selective decrease in rhythmic entrainment during movement epochs . The phase-coupling palettes show particularly strong phase modulation at sites in sensorimotor cortex ( Figures 6–8 ) . Consistent with this evidence , neural recordings in monkeys show that action potentials of many motor cortex neurons tend to be entrained at particular phases of local rhythms [23] , [24] , [25] . In one study about 2/3 of precentral neurons fired about 30 degrees prior to the peak negativity of the local field potential oscillations [20–40 Hz] , independently of cortical depth [24] . This is consistent with our finding that broadband ECoG power , which reflects aggregate population spiking activity , peaks prior to the surface negativity phase ( ) of local beta rhythm ( Figures 5 , 13 ) . The features in phase-coupling palettes for dorsal pre- and post-central cortex are clearly more robust than those for other cortical sites , including ventral Rolandic cortex ( Figures 6–8 ) . Nevertheless , the palettes averaged over each of these other regions also show similar features during baseline fixation ( Figures 6–7 ) . Interestingly the palettes for the lateral brain surface ( Figure 7C , 8A ) do not show coupling in the gamma range ( 30–50 Hz ) , although phase coupling in this range has been observed with ECoG in the occipital cortex [30] . Also , unlike findings in occipital cortex [35] , the α ( ∼8–12 Hz ) range most often appears to reflect a transition between discrete motifs in beta coupling and theta coupling , rather than representing a distinct entity in and of itself . Put plainly , we do not see ubiquitous entrainment by an 8–12 Hz “mu” rhythm as might have been anticipated by EEG studies . It is worth noting that it is not necessary to extract a broadband time series in order to see these effects . The coupling motifs can also be seen , albeit less clearly , if broadband spectral change is approximated by band-pass filtered high frequency power as is common in other studies ( e . g . with “high gamma” power , illustrated in Figures S3 , S4 in Text S1 ) [33] . The neural mechanism underlying the continuous “diagonal bands” visible in the lower frequencies ( δ , θ , α bands ) of the phase-coupling palettes may reflect a process in which broadband power is elevated at a fixed time lag relative to the peak of a periodic voltage trace ( illustrated in Figure S14 in Text S1 ) . This fixed time lag would then appear as a continuously varying phase lag ( diagonal band ) across different low-frequency ranges . The periodicity of the voltage determines the base frequency , and the “slopes” of the bands ( vertical or angled ) are a function of the degree to which both the potential and the broadband are sinusoidal versus a periodic “pulse train” . In other cases , the superposition of multiple rhythms , each of which has a different preferred phase , may give the appearance of a sloped palette ( seen clearly in Figure 8 E , G , and J ) . The movement-related cortical activity reflected in the extracted broadband is localized to the pre-central gyrus and is spatially quite sparse , showing distinct separate cortical sites for thumb , index , and little fingers in most subjects ( Figure 3 , Figures S5 , S6 , S7 , S8 , S9 , S10 in Text S1 ) . Figure 3B/D shows that the overlap between different digits is very small when quantitatively measured . In comparison , single unit studies in the monkey have shown that M1 neurons firing with different fingers are distributed in a largely overlapping fashion [36] , [37] . Our findings indicate that these overlapping digit representations nonetheless exhibit a dominant and well-delineated finger representation when cortical activity is summed over the entire neuronal population . Comparable somatotopic organization does not exist for the beta rhythm ( 12–20 Hz ) , which is broadly distributed , spanning both the pre- and post-central gyri , and is ubiquitously reduced during movements of any of the fingers . This movement-associated decrease in the β-rhythm exhibits near-complete spatial overlap for different finger movements ( Figures 1F , 3 , 8O , 9B/E , 10B/G ) . Moreover , the beta rhythms at neighboring sites are largely coherent ( Figures 11&12 ) , which suggests a common mechanism that is inversely modulated related to movements of different fingers . For over a century , cortical rhythms recorded from the brain surface have been known to fluctuate with behavior [38] . However , the physiological origin of these rhythms is poorly understood , and their possible role in brain function remains a topic of debate . The inverse relation between motor activity ( including imagery ) and the appearance of beta activity in Rolandic cortex has led to the hypothesis that beta , like alpha , is a “resting rhythm” , passively emerging when neural circuits are not actively engaged in computations that desynchronize their activity . In this paradigm , the frequency of this rhythm is determined by the resonant properties of the neural networks that are recruited; these circuits would also include subcortical networks . An alternative and potentially exciting hypothesis proposes that beta rhythms reflect an active mechanism that synchronizes neural populations and suppresses cortical information processing [30] . In such a “suppression through synchronization” hypothesis the input from a “synchronizing locus” in thalamus projects diffusely to an inhibitory population of neurons that , in concert , inhibit pyramidal neurons at their basal dendrites and soma ( Figure 13 ) . While our data are potentially consistent with either of these hypotheses , we detail this “suppression through synchronization” hypothesis for further consideration . What might be the function of these beta rhythms in motor cortex ? Perhaps they play a dynamically suppressive role as follows: feed-forward inputs to the large , computationally important pyramidal neurons of motor cortex ( Betz cells ) are mediated via synaptic inputs to superficial layers , although their somas are in layer 5 [39] . The complex dendritic computations performed as these superficial inputs combine to influence the somatic potential [40] , could be interrupted by peri-somatic inhibitory inputs that are rhythmically synchronized across the entire population of Betz cells . Consistent with this idea , selective blockade of inhibitory GABA-A receptors and gap-junctions in layer 5/6 abolishes the beta rhythm [41] , and selective GABA agonism with benzodiazepines selectively potentiates the amplitude of the peri-central beta rhythm [42] . At the macroscale of the ECoG , the motor beta-rhythm is spatially synchronized in peri-central cortex ( Figures 11&12 ) , where it entrains neural population activity ( Figure 7 ) , and transiently releases this entrainment during movement ( Figure 9 ) . Such synchronous population activity would prevent the generation of the complex spatio-temporal patterns of activity among pyramidal neurons that are necessary for the execution and coordination of movement . When present , the rhythm keeps the motor cortex in an idling , dynamically “ready” , state – the timings of cortico-cortical distal dendritic inputs are entrained on the population-synchronized GABAeric proximal interneuron input . This scenario is consistent with the fact that broadband measurement of averaged activity from the 105 neurons beneath each electrode is entrained with beta-rhythm of the ECoG ( Figure 13 ) . In this way , weak but synchronized interneuron input might keep motor cortical activity in a “dynamically suppressed” state , where neural activity can quickly transition into an active “processing state” when the synchronized input stops . The inputs of the synchronizing locus to motor cortex likely arise in thalamus as part of a well described cortical to sub-cortical feedback circuit ( see illustration in Figure S15 in Text S1 ) . Motor cortex , in addition to its brainstem and spinal projections , also projects to striatum and sub-thalamic nucleus , and these loci follow “direct” and “indirect” pathways to the globus pallidus ( pars interna ) , which , in turn , project to nuclei in the thalamus . The thalamic nuclei close a recurrent feedback loop by sending diffuse input to motor cortex in layer 5 [39] , [43] . Supporting this is the observation that the beta rhythm is present at each step of the circuit [8] , [9] , [43] , [44] , [45] , [46] , [47] , and physiologic or pharmacologic intervention at the level of the cortex [48] , striatum [49] , [50] , pallidum [51] , subthalamic nucleus [52] , [53] , or thalamus [54] , disrupts the beta rhythm throughout the circuit . Furthermore , disruptions in this circuit are correlated with specific motor disease , and interruptions of these loci provide specific therapeutic improvement in the disease with concomitant decrease in beta rhythm [53] , [55] . But what generates and drives the beta frequency throughout this circuit ? Perhaps the timescale of the beta rhythm reflects the loop time it takes for impulses to travel around the cortical-subcortical circuit , although this is challenged by fact that there are various loop times for different circuits , and also because the resonant frequency of distributed cortical oscillations is not determined by conduction times between synchronized sites [56] . Alternatively , the beta rhythm may be a “cortically local” product of the recurrent network of excitatory and inhibitory neurons within motor cortex . This latter hypothesis is supported by the fact that carbachol applied to rodent cortical slices generates beta-range rhythms in the absence of the rest of the motor circuit [57] . However , there is also evidence against the local-generation hypothesis . When the motor representation of both sides of the body is re-mapped to one hemisphere following perinatal stroke ( and the beta rhythm is already present at this stage [58] ) , the laterality of the beta rhythm is preserved in adulthood , despite the fact that the local representation is not [59] . Finally , an intermediate hypothesis is that the beta oscillation is generated by a pacemaker at a specific subcortical site , but then appears throughout the circuit due to propagation from this site . Using modeling and carbachol injection into the intact rodent , Kopell et al provide evidence that the striatum is the origin of these rhythms [50] . Lesion studies in the cat suggest that the ventral tegmentum generates the beta rhythm [60] . While our study does not directly address the origin of the beta rhythm to distinguish between these hypotheses , our findings do document the functional importance of the beta rhythm as reflected by robust modulation of population-scale broadband activity with beta rhythm phase . Our ECoG recordings help explain why the beta rhythm is so robust in electroencephalographic recordings from outside the head; namely , it is spatially synchronous across the pre- and post-central gyri , and so this coherent beta rhythm is augmented with respect to background by spatial averaging ( Figure 11&12 ) . Furthermore , the different states of the surface rhythm may represent switching between stable modes observed in ongoing surface oscillations . In contrast with the beta rhythm , the broadband spectral change that accompanies movement is asynchronous at the local level [28] and unrelated across cortical regions , so it is washed out in spatial averaging [61] ( see heuristic illustration and back-of-the-envelope calculation in Figure S11 in Text S1 ) . The observation that administration of a benzodiazepine GABA agonist produces an increase in magnetoencephalographic peri-central beta range power [42] is potentially consistent with our hypothesis that synchronized thalamic projections to peri-central inhibitory interneuron populations suppress motor cortex . This would suggest that the muscle relaxant effect of benzodiazepines might be mediated by augmenting the synchronizing effect underlying the beta rhythm and suppressing corticospinal output to somatic motor neurons . In functional magnetic resonance imaging ( fMRI ) , the BOLD signal reflects temporally-averaged cortical metabolism of all types ( excitatory , inhibitory , glial , etc ) . A prior study with fMRI and ECoG during simple hand movement showed that the most robust fMRI changes are parametrically associated with 65–95 Hz ECoG power ( an approximation of our broadband ) , specifically in pre-central motor cortex [62] . Outside of this central hand region , a weaker and more spatially diffuse BOLD change was found that correlated with beta-range changes ( see Figure 10 ) . Our findings in this study may provide an explanation for this: the pre-central BOLD peak reflects the shift in local cortical activity in the engaged brain area , while the surrounding BOLD change reflects the increased metabolism in adjacent disengaged but also disinhibited cortex . This study establishes that motor cortical populations are phase-modulated with the beta rhythm , both during long rest periods ( minutes of fixation ) as well as during brief ( 1–2 seconds ) rest periods within a dynamic finger movement task . During periods of movement , the amplitude of the beta rhythm decreases and this phase-modulation is also diminished . We believe that this reflects a robust mechanism that is fundamental to brain function . Similar experiments in patients with motor disease might reveal how perturbations to this mechanism disrupt cortical function . A “suppression through synchronization” hypothesis explains how diffuse cortical inputs originating from a thalamic locus might allow large regions of the cortex to be functionally suppressed . The expected utility of selectively engaging brain areas results in the dynamic reallocation of metabolic resources , and mediates the coordinated engagement of task-relevant and task irrelevant brain circuits .
All patients participated in a purely voluntary manner , after providing informed written consent , under protocols approved by the Institutional Review Board of the University of Washington at the Seattle site , and the ethical committee of the Universitair Medisch Centrum Utrecht ( in accordance with the Declaration of Helsinki 2004 ) at the Utrecht site .
|
We have long known that there are rhythmic oscillations in the mammalian brain . Although the power in these rhythms changes during behavior , their relevance for brain function has been something of a mystery . In this study , the particular role of rhythms in human motor cortex was studied both during rest and during engagement in different finger movements . Arrays of electrocorticographic electrodes were placed directly on frontal , temporal , and parietal brain surfaces to measure the electrical potential . These measurements reveal that movement of different fingers produces spatially focal and finger-specific changes in local neuronal population activity , along with spatially diffuse and finger-nonspecific decreases in 12–20 Hz β-rhythm power . Apart from these power changes , there are independent interactions between the β-rhythm and local cortical activity . During rest , 5–20% of the variation in local cortical activity is due to entrainment on the phase of the β-rhythm over the pre- and post-central gyri , but the interaction is significantly diminished during movement . This shifting entrainment suggests that the β-rhythm is not simply a background process that is suppressed during movement , but rather that the β-rhythm plays an active and important role in motor processing .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"circuit",
"models",
"motor",
"systems",
"computational",
"neuroscience",
"biology",
"neuroscience",
"neurophysiology"
] |
2012
|
Human Motor Cortical Activity Is Selectively Phase-Entrained on Underlying Rhythms
|
Previous studies have shown that wild-type human telomerase reverse transcriptase ( hTERT ) protein can functionally replace the human papillomavirus type 16 ( HPV-16 ) E6 protein , which cooperates with the viral E7 protein in the immortalization of primary keratinocytes . In the current study , we made the surprising finding that catalytically inactive hTERT ( hTERT-D868A ) , elongation-defective hTERT ( hTERT-HA ) , and telomere recruitment-defective hTERT ( hTERT N+T ) also cooperate with E7 in mediating bypass of the senescence blockade and effecting cell immortalization . This suggests that hTERT has activities independent of its telomere maintenance functions that mediate transit across this restriction point . Since hTERT has been shown to have a role in gene activation , we performed microarray studies and discovered that E6 , hTERT and mutant hTERT proteins altered the expression of highly overlapping sets of cellular genes . Most important , the E6 and hTERT proteins induced mRNA and protein levels of Bmi1 , the core subunit of the Polycomb Group ( PcG ) complex 1 . We show further that Bmi1 substitutes for E6 or hTERT in cell immortalization . Finally , tissue array studies demonstrated that expression of Bmi1 increased with the severity of cervical dysplasia , suggesting a potential role in the progression of cervical cancer . Together , these data demonstrate that hTERT has extra-telomeric activities that facilitate cell immortalization and that its induction of Bmi1 is one potential mechanism for mediating this activity .
Cell immortality is a hallmark of cancer cells [1] and the high-risk oncogenic HPVs encode two major transforming genes , E6 and E7 , which are required for the immortalization of human primary genital keratinocytes [2] , [3] . These two oncogenes are uniformly retained and expressed in cervical cancers and their continued expression is required for the cells to retain the tumorigenic phenotype [4] , [5] , [6] , [7] , [8] . The E6 and E7 proteins were initially identified as targeting the p53 and Rb tumor suppressor pathways in host cells , thereby disrupting cell cycle controls [5] , [6] , [7] , [8] . E7 stimulates the cell cycle via its ability to bind and inactivate the cellular Rb protein while E6 binds to p53 , leading to its degradation via the proteosomal pathway [5] , [6] , [7] , [8] . In addition to p53 degradation , E6 induces telomerase activity in epithelial cells [6] , [9] , [10] . Telomerase is a specialized reverse transcriptase that synthesizes the telomeric repeat DNA sequences at the ends of chromosomes [11] . The absence of telomerase activity in most normal human cells results in the progressive shortening of telomeres with each cell division [12] , [13] , ultimately leading to chromosomal instability and cellular replicative senescence [12] , [14] . For this reason , telomere shortening is thought to represent the “mitotic clock” that determines cellular lifespan . In contrast to most human somatic cells , approximately 90% of immortalized and cancer cells express telomerase activity and consequently maintain minimal , stable telomeres and indefinite proliferative potential [15] . Therefore , telomerase activation is considered a critical event in the process of cell immortalization . Recent studies indicate that telomerase may assist in bypassing two separate events which block the continuous replication of primary human cells: mortality stage 1 ( M1 , replicative senescence ) followed by mortality stage 2 ( M2 , crisis ) [16] . In some cells , especially those with decreased function of the p16/Rb pathway , telomerase activity is sufficient to bypass both M1 and M2 blockades and to stabilize and elongate telomeres [17] , [18] , [19] , [20] . Studies have demonstrated that activation of telomerase by E6 is critical for cell immortalization by HPV [17] , [21] . E6 executes this increase in telomerase activity by multiple mechanisms [8] , [22] , [23] , [24] , [25] , [26] . While increased hTERT is required for viral-mediated cell immortalization [8] , [17] , [21] , our previous studies demonstrated that telomeres erode in HPV-expressing keratinocytes similar to normal keratinocytes [10] , suggesting that the role of hTERT overexpression in cell immortalization might involve functions additional to those in telomere elongation . Evidence is accumulating that hTERT has important non-canonical functions . For example , mTERT has been ascribed roles in altering apoptotic responses [27] , [28] , tumor formation in mice [29] , [30] , stem cell migration and renewal [30] and chromatin remodeling [31] . The Artandi laboratory has shown that mTERT can not only augment breast cancer development in mice , but also can regulate the transcription of genes responsive to the Wnt/β-catenin pathway [30] . Smith et al demonstrated that in human mammary epithelial cells ( hMECs ) telomerase modulates expression of growth-controlling genes , including epidermal growth factor receptor ( EGFR ) [32] . Vascular endothelial growth factor ( VEGF ) and fibroblast growth factor ( FGF ) also appear to be induced by hTERT in fibroblasts , along with many other targets [33] , [34] . Interestingly , the majority of these data have been recapitulated with an hTERT mutant that is catalytically-inactive , suggesting that these non-canonical roles of hTERT are independent of the reverse transcriptase function . Although hTERT has been shown to be a key player in cellular immortalization , in many cases it does not immortalize alone [17] . Interestingly , the Bmi1 protein has been shown to cooperate with hTERT in immortalization and to induce hTERT mRNA [35] , [36] . Bmi1 is the central protein in polycomb repressive complex 1 ( PRC1 ) . The Polycomb group ( PcG ) complex of proteins act through remodeling chromatin to silence hundreds of genes and have been implicated in controlling cell fate , development , and cancer [37] , [38] . In the current study , we used quantitative assays to measure telomerase activity and telomere length following transduction of foreskin keratinocytes by E6/E7 , hTERT/E7 and mutant hTERT/E7 . These activities were correlated with the ability of the various hTERT mutant proteins to immortalize cells . Our studies indicate that a telomerase-independent activity of hTERT collaborates with E7 in the immortalization of primary human cells . To elucidate the underlying mechanism , whole genome expression profiling was performed in keratinocytes expressing E6 , hTERT , or a catalytically inactive hTERT mutant ( hTERT-D868A ) . Increased expression of Bmi1 mRNA was observed in this screening and follow-up experiments indicate that Bmi1 appears to be a functional component of hTERT- or E6-mediated cell immortalization and that its expression further increases during cancer progression .
Immortalized cells generally do not display the phenotypic properties of cancer cells ( e . g . growth in soft agar or tumor formation in nude mice ) without further gain of genetic changes or transduction of additional genes [5] , [8] , [39] . To determine if changes in telomerase activity might contribute to the differences between immortalized and tumorigenic cervical cells , we compared the levels of telomerase activity for cells immortalized by the HPV-16 E6/E7 genes to those found in 3 cervical cancer cell lines ( SiHa , HPV16 positive; HeLa , HPV-18 positive; C33A , HPV negative ) . The E6/E7 immortalized cell lines ( HFK E6/E7 at population doubling ( PD ) 90 and HEC E6/E7 at PD 98 ) exhibited similar levels of telomerase activity as the three cervical cancer lines , indicating that further increases in telomerase are not required for progression to malignancy ( Figure 1A ) . While an early hypothesis for HPV-mediated cell immortalization suggested that telomerase induction by E6 maintained telomere length [8] , [9] , [17] , our previous studies showed that E6/E7 expressing cells continued to shorten telomeres even in the presence of induced telomerase activity [10] . To quantify these changes in telomere length , we used a PCR-based assay to screen E6/E7 immortalized cells and cervical cancer cell lines . At PD 2 , HFKs had long average telomere lengths with a T/S ratio of 1 . 0 ( Figure 1B ) . Since the approximate length of telomeres in early passage HFKs is 10 kb , the T/S ratio can be converted into telomere length ( where 1 . 0 T/S ratio equals 10 kb telomere length ) . At PD 18 , HFKs had a T/S ratio of 0 . 6 or 6 kb size . Immortalized , PD 96 HFK E6/E7 cells , which have bypassed crisis , had amongst the shortest telomeres ( Figure 1B; T/S ratio of 0 . 2 , or 2 kb length ) . These short telomeres were also seen in all three cervical cancer cell lines , including the HPV-negative cancer cell line , C33A . Our data suggest that E6/E7-immortalized cells continue to degrade their telomeres until they reach a length of 2 kb , at which point they become stabilized and equivalent in length to telomeres in cervical cancer cell lines . The kinetics of passage-dependent shortening or degradation of telomeres during cell immortalization by E6/E7 were also studied ( Figure 1C ) . By PD 74 , HFK cells expressing E6/E7 achieved their shortest telomere length , after which telomere length became stable . An hTERT protein that was epitope-tagged at its C-terminus ( hTERT-HA ) retained telomerase activity , but alone was unable to elongate telomeres or immortalize HFFs or SV40 transformed epithelial cells [40] , [41] , [42] . To test the functions of wild-type hTERT and hTERT-HA in human keratinocytes , HFKs were co-transduced with vectors expressing E7 and the hTERT proteins . As expected , wild-type hTERT cooperated with E7 to immortalize HFKs , while hTERT or E7 alone were unable to immortalize HFKs . Surprisingly , hTERT-HA also immortalized HFKs in collaboration with E7 [26] , indicating that telomere maintenance is not critical for hTERT/E7 immortalization . The functionality of E7 was verified by demonstrating that the Rb protein level was significantly decreased in all E7 expressing cells ( Figure S1 ) . To verify that the hTERT-HA mutant generated telomerase activity in HFKs , Telomeric Repeat Amplification Protocol ( TRAP ) assays were performed . HFKs transduced with hTERT or hTERT-HA alone , or in combination with E7 , exhibited similar levels of telomerase activity ( Figure 2A ) . HFKs with E7 alone did not exhibit significant telomerase activity . Consistent with our earlier results , Figure 2B illustrates that telomeres lengthened during immortalization of the hTERT/E7 cells , but shortened in the hTERT-HA/E7 cells ( Figure 2B ) . The preceding experiments indicate that cell immortalization is independent of telomere lengthening and raise the possibility that other telomere-related functions of hTERT were involved in this process . To evaluate this possibility , we therefore examined the immortalizing activity of additional hTERT mutants that were known to be catalytically inactive ( hTERT-D868A ) [43] or had impaired recruitment to telomeres ( hTERT N+T ) [42] , [44] . Similar to hTERT-HA , both the hTERT-D868A and hTERT N+T mutants were able to immortalize keratinocytes in conjunction with E7 ( Figure 3A ) . Cells immortalized by the hTERT N+T mutant exhibited similar levels of telomerase activity as cells immortalized by E6/E7 , so decreased telomerase activity could be ruled out as a potential mechanism ( Figure 3B ) . The results with hTERT-D868A were even more significant . Cells immortalized by this defective mutant were as efficient at cell immortalization as wild-type hTERT ( Figure 3A ) , despite the complete lack of telomerase activity in early passage keratinocytes ( Figure 3B ) . Immunofluorescence studies demonstrated that hTERT-D868A exhibited a similar expression level and localization as wild-type hTERT ( Figure S2 ) . Thus , the catalytic activity of hTERT and its ability to elongate telomeres is dispensable for the immortalization of keratinocytes with E7 . Another unexpected finding was that cells immortalized by the telomerase-defective hTERT-D868A mutant exhibited high telomerase activity at late passages ( Figure 3C ) in contrast to the lack of telomerase at early passages ( Figure 3B ) . This led us to ask whether hTERT proteins , including hTERT-D868A activate the endogenous hTERT promoter . To test this , we transfected wild type or mutant hTERT proteins along with an hTERT core promoter construct into HFK . The data from luciferase reporter assays demonstrated that neither wild type hTERT nor mutant hTERT-D868A activated the hTERT promoter ( Figure 4A ) . This is consistent with the lack of endogenous telomerase activity in early-passage cells transduced with the hTERT-D868A mutant ( Figure 3B ) . HPV E6 was used as positive control in this experiment ( Figure 4A ) . We also confirmed that both wild type and mutant hTERT proteins were biologically active in this assay and able to activate the cyclin D promoter ( Figure 4B ) as described previously [30] . Together , these findings suggest that inactive hTERT proteins ( in collaboration with E7 ) can mediate transit through crisis ( the M2 phase of cell ) , but that continued cell proliferation correlates with increased endogenous hTERT expression , identical to what is observed in cells immortalized by the E6 protein . The implications of these findings are considered in the Discussion . Since previous studies have defined extra-telomeric functions of hTERT , we attempted to identify a potential telomere-independent mechanism to explain the ability of the inactive hTERT to immortalize cells , with a specific focus on cellular gene expression . Given the conflicting reports of hTERT on gene expression in various model systems [32] , [33] , [34] , [45] , [46] , [47] , [48] , it was important that we examined hTERT effects in primary keratinocytes . We therefore stably expressed E6 , wild-type hTERT ( hTERTwt ) , or hTERT-D868A in primary HFKs and conducted array-based whole genome expression analysis ( Figure S3 , and Dataset S1 ) . Because E6 is a known activator of the hTERT protein [8] , expression changes shared by hTERT and E6 could represent hTERT-dependent E6 targets . As expected , significant changes in mRNA expression in E6 cells were also seen in cells with wt hTERT ( 1379 of 6991 , or 20% of E6 changes with fold change >1 . 33 and p-value <0 . 01 ) ( Figure 5A ) . More than half of the wt hTERT changes ( 58% , 1379/2359 ) were also seen in E6 cells , suggesting that changes seen in hTERT-expressing cells are also altered by E6-expressing cells , possibly through an hTERT-dependent pathway . To pursue whether the mRNA expression changes seen in wt hTERT cells were dependent on changes in telomere biology , we also expressed the catalytically inactive mutant hTERT-D868A in primary HFKs . A total of 2359 mRNA probe sets were altered in wt hTERT HFKs compared to 5467 changes in hTERT-D868A HFKs ( Figure 5B ) . Interestingly , 2077 of the 2359 ( 88% ) of the RNA probes altered by wt hTERT were also altered by hTERT-D868A . Thus , the gene expression alterations seen following hTERT expression are largely independent of reverse transcriptase function . 1258 changes in mRNA expression were shared by wt hTERT , hTERT-D868A , and E6 ( Figure 5C , Dataset S1 ) . Thus , additional considerations were required to focus our study ( Figure 5C ) . The wt hTERT/hTERT-D868A overlapping gene set was submitted for analysis using Database for Annotation , Visualization and Integrated Discovery ( DAVID ) [49] . Hierarchical clustering of the 2077 catalytically-independent changes was used to identify 408 gene clusters that were visualized as a heatmap [50] ( Figure S4 ) . Based on enrichment scores , genes associated with “Chromosome Organization” , “Chromatin Organization” , and “Chromatin Modification” were grouped with the SP-PIR Keyword “Chromatin Regulator” ( Dataset S2 ) . Of particular interest in the enriched chromatin regulation cluster was the gene Bmi1 , which exhibited significant increases in two overlapping probe sets ( Figure 5D , probe set IDs L13689 and NM_005180 ) . We verified by RT-PCR that E6 , wt hTERT and hTERT-D868A expression led to 5–7 fold increases in Bmi1 transcript levels ( Figure 5E ) . More important , these three genes also increased Bmi1 protein expression ( Figure 6A , B ) . To further validate the RT-independent ability of hTERT to induce Bmi1 , we analyzed two additional mutated hTERT constructs that lacked telomerase activity . Both of these telomerase mutants increased Bmi1 transcript levels , similar to hTERT-D868A mutant ( Figure S5 ) , further substantiating the telomerase-independent activity involved in Bmi1 induction . Since Bmi1 expression is increased acutely by hTERT , we investigated whether Bmi1 levels remained increased in late-passage HFKs immortalized by hTERT/E7 . We doubly transduced HFKs with hTERT and E7 and propagated the cells beyond the time when they would normally enter crisis . Bmi1 protein was shown to be increased in early passage ( 28 population doublings , PD 28 ) hTERT/E7 immortalized HFKs and remained high after serial passaging ( PD 204 ) ( Figure 6C ) . We also confirmed increased Bmi1 protein expression in hTERT/E7 immortalized HFKs by immunohistochemistry ( Figure 6D ) . Compared to control HFKs , Bmi1 was significantly increased in the nuclei of early passage ( PD42 ) and late passage ( PD220 ) hTERT/E7 immortalized HFKs ( Figure 6D ) . Together , these data indicate that Bmi1 protein is not only increased acutely by hTERT in primary keratinocytes but that its increased expression is maintained in late passage immortalized cells . Given the correlative data between hTERT and Bmi1 expression and a previous study showing that Bmi1 immortalizes mammary and oral epithelial cells [51] , we speculated that Bmi1 might substitute for hTERT and immortalize human keratinocytes . To test this , we transduced HFKs with Bmi1 and E7 together or separately , as well as with empty vector . Cells were passaged to determine the growth rate and lifespan of these cell populations ( Figure 7A ) . As expected , the HFKs infected with empty vector alone failed to reach 25 population doublings . Introducing E7 or Bmi1 alone extended lifespan by approximately 15 population doublings , as previously described for E7 [8] . However , cell immortalization ( >50 population doublings ) was observed only when Bmi1 and E7 were co-expressed ( Figure 7B ) . It is noteworthy that the Bmi1/E7 cells also exhibited shortened telomere length , similar to E6/E7 cells . Thus , these data indicate that Bmi1 , like hTERT and E6 , is able to cooperate with E7 in the immortalization of HFKs . Although Bmi1 was increased in immortalized , non-tumorigenic HFKs , we queried whether its expression might be altered in cervical cells and during the progression to cervical cancer . We therefore first examined Bmi1 expression in immortalized and tumorgenic cervical cell lines . Consistent with our data in HFKs , expression of E6/E7 in primary human ectocervical cells ( HECs ) leads to immortalization , enhances endogenous hTERT expression , and increases in both Bmi1 mRNA and protein ( Figure 8A , Figure S6 ) . Bmi1 protein levels also showed increases in the tumor-derived , telomerase-positive HeLa cancer cell line ( Figure 8A , 1A ) . These data indicated that Bmi1 is increased in HPV-immortalized and tumorigenic cervical cells . Furthermore , immunohistochemical ( IHC ) staining of cervical cancer tissue specimens demonstrated increased Bmi1 levels in invasive lesions ( Figure 8B ) . Given the evidence demonstrating increased expression of Bmi1 in immortalized cells and tumorigenic cervical cancer cells and the observation that even invasive cervical tumors overexpress Bmi1 , we examined whether Bmi1 expression correlated with the severity of cervical cancer progression . Bmi1 levels were evaluated by IHC in 21 cervical tissues identified by pathological review as either normal , precancerous , or cancerous lesions . Bmi1 was observed in modest amounts in the nucleus of cells in the basal and immediate suprabasal epithelium in normal cervical tissue ( Figure 8D ii ) , CIN1 , and CIN2 ( Figure 8D iv , vi ) . However , a striking increase in staining was observed in the epithelial layers of CIN3 and invasive carcinoma ( Figure 8D viii , x ) . For cases of invasive cervical carcinoma , Bmi1 staining was specific to cancerous lesions ( Figure 8D x ) . To quantify Bmi1 expression , intensity and positivity scores were determined ( Figure 8C ) with the mean and standard deviation shown ( Figure 8E ) . CIN3 and invasive carcinoma show combined scores that are significantly higher than both CIN1 and CIN2 ( CIN3 vs . CIN1 , p = 0 . 0083; CIN3 vs . CIN2 , p = 0 . 0372; invasive vs . CIN1 , p = 0 . 0012; invasive vs . CIN2 , p = 0 . 0130; p values as calculated by student t-test ) . These data indicate that Bmi1 expression does indeed correlate with the degree of cervical dysplasia and with progression to cancer .
Overall our findings demonstrate that a novel extra-telomeric and non-catalytic function ( s ) of hTERT contributes to cell immortalization by hTERT and E7 in human keratinocytes . These findings not only define new properties of hTERT that contribute to cell immortalization , but they potentially modify our concept of the mechanism by which E6 is mediating cell immortalization . Using our current and published data , we have constructed a summary table listing the properties of HFK expressing the various hTERT mutants and HPV oncogenes ( Table S1 ) . While we have shown that E6 and E7 are required for the efficient cell immortalization of primary cells , one study has shown that E7 immortalizes human keratinocytes at a very low efficiency when cultured in serum-free synthetic medium [8] , [52] and another study has shown that wild type HPV16 E6 as well as an natural mutant E6 are able to immortalize human keratinocytes [53] . However , it is important to note that when E6 and E7 are used alone , an obvious “crisis” period or “flat” phase of cell growth is observed , indicating that cell immortalization is infrequent and arises from a small subpopulation of cells . Most likely additional genetic or epigenetic changes are required for escape from “crisis” . Although we have also noted that E7 is highly efficient for immortalizing keratinocytes without a “crisis” period of time when co-cultured with feeders or conditioned medium ( Liu , X . , et al . unpublished data ) , this is best explained by the ability of feeder cells or conditioned medium to induce telomerase [54] . A basic tenet of cell immortalization is that hTERT reverse transcriptase activity is essential for maintaining or elongating telomeres , thus allowing for continued cell replication [11] . However , our early studies showed that E6-induced telomerase activity could be dissociated from telomere maintenance [10] . Supporting this hypothesis , the current study clearly indicates that immortalized cells exhibit similar levels of telomerase activity , yet telomeres shorten during cell passaging and stabilize at late passages ( Figure 1B , 1C ) . We have also demonstrated the same pattern of telomere length in keratinocytes immortalized by a Rho kinase ( ROCK ) inhibitor [55] , [56] , [57] . These data suggest that extra-telomeric functions of telomerase or hTERT play a role during cell immortalization independent of telomere maintenance . Several reports indicate that hTERT-HA fails to elongate telomeres and immortalize human fibroblasts [41] , [42] or HA1 cells [40] despite a high level of telomerase activity . Surprisingly , we found that hTERT-HA reproducibly and efficiently immortalized HFK cells in cooperation with HPV E7 [26] . Even more interesting , these immortalized HFK cells had short telomeres ( Figure 2B ) . We also observed the same results with the hTERT N+T mutant , which is positive for telomerase activity , but negative for telomere recruitment and elongation ( Figure 3 ) . Here , we demonstrate in keratinocytes that telomerase activity is not required , as the catalytically-defective hTERT-D868A retains its ability to immortalize HFKs in cooperation with E7 . We have also shown that immortalization is independent of the telomere lengthening function of hTERT . Telomere lengthening is predominantly carried out by telomerase , but can also occur via the alternative telomere lengthening ( ALT ) pathway [58] . However , the exact mechanisms of telomere maintenance or elongation remain elusive . Studies have suggested that many mechanisms , including enzymatic activity , telomere-capping , and recombination , may play roles in the final stabilization of telomeres in immortalized and human cancer cells [42] , [58] , [59] , [60] , [61] , [62] , [63] . A number of non-canonical functions for hTERT have been reported in literature , and the list is increasing rapidly [27] , [28] , [29] , [30] , [31] , [33] , [34] , [47] , [48] , [64] . This led us to pursue whole genome expression studies to probe altered signaling pathways in primary cells expressing hTERT . Surprisingly , our data have shown that 88% of the genes altered by wild-type hTERT are also altered in the same direction by hTERT-D868A ( Figure 5B ) . Somewhat surprisingly , hTERT-D868A regulates about twice as many genes as wild-type hTERT , suggesting that elimination of the catalytic function of hTERT actually augments non-canonical functions . It is critical to note that bypass of the Hayflick limit by enzymatic-defective hTERT mutants is accompanied by the global induction of many cellular genes , including endogenous hTERT ( Figure 3C ) . This induction of endogenous hTERT is not due to the direct , acute transactivation of endogenous hTERT by mutant hTERT ( Figure 3A and 4A ) . Rather the endogenous hTERT activation is part of the larger number of gene sets that are increased during transit through M1/M2 restriction points . A very recent study [47] demonstrates the existence of a splice-variant of hTERT ( Δ4-13 ) containing an in-frame deletion of exons 4 through 13 that encode the catalytic domain of telomerase . This variant is expressed in telomerase negative normal cells and tissues as well as in transformed telomerase positive cell lines and in cells that employ an alternative method to maintain telomere length . The overexpression of the Δ4-13 significantly elevated the proliferation rate of several cell types without enhancing telomerase activity , while decreasing the endogenous expression of this variant using siRNA technology reduced cell proliferation . The expression of the Δ4-13 variant stimulates Wnt signaling . This is the first report that a naturally occurring hTERT splice variant that lacks telomerase activity exhibits an ability to stimulate cell proliferation , supporting our conclusions that non-canonical hTERT functions contribute cell immortalization . We have also used real-time RT-PCR to validate more than 20 genes that were altered greater than two-fold as determined by microarray analysis . The RT-PCR results were virtually identical to the microarray data , and several of these genes are critical regulators of keratinocyte growth , apoptosis , and differentiation . Bmi1 was one such target of hTERT . Our microarray data also demonstrated that E6 and hTERT increased the expression of RB and ROCK1 mRNA in HFKs ( Dataset S2 ) and this increased expression was confirmed by real time RT-PCR . This induction of RB is presumably counteracted by the activity of the E7 protein . There are similar parallel events in the cooperation of the E6 and E7 proteins in cell immortalization and transformation [6] , [7] , [8] . For example , these two genes seem to have evolved both complementary and opposing functions that are necessary to prevent senescence and/or apoptosis . For example , while E7 stabilizes p53 protein , E6 degrades this tumor suppressor protein . Similarly , while E6 stabilizes RB protein , E7 inactivates and destabilizes it . The yin-yang regulation of E6/E7 functions and telomerase and RB/16 pathways may be critical for fine tuning the growth and differentiation of keratinocytes as well as for regulating the viral replication cycle . Bmi1 has been identified as a marker of cancer progression in a number of carcinomas , including those derived from the nasopharynx , breast , pancreas , and other sites [65] , [66] , [67] . Equally important , hTERT has been identified a potential universal cancer target , since it is up-regulated in most cancers [15] , [68] . Beyond identification of Bmi1 in our genetic screen , hTERT and Bmi1 have been linked in several previous reports . PcG components Bmi1 and SIRT1 have been shown to be altered in hTERT-expressing urothelial cells [45] , and Bmi1 has been shown to induce endogenous telomerase in human mammary epithelial cells [35] . Besides Bmi1 , other chromatin remodeling complex members have also been associated with the non-telomere effects of hTERT , including transcriptional regulation of Wnt targets by binding BRG1 , a Trithorax group protein ( TrxG ) [30] . Our data links hTERT expression to changes in Bmi1 , suggesting that there is an hTERT-Bmi1 signaling pathway . In this study , we have shown that Bmi1 overexpression occurs prior to full transformation since both hTERT-expressing HFKs and multiple types of telomerase-positive immortalized cells ( Figure 5E , Figure 6 , & Figure 8A ) overexpress Bmi1 . The above Bmi1 findings may have clinical relevance . Our in vivo studies reveal a differential expression of Bmi1 in carcinoma in situ and invasive carcinomas compared to preneoplastic lesions ( Figure 8B–E ) . Indeed , Bmi1 mRNA expression is increased in cervical cancer compared to corresponding noncancerous tissues [69] . Additionally , Bmi1 overexpression has been significantly correlated with tumor size , clinical stage , and regional lymph node metastases in cancers of the cervix [70] . Another PcG protein , EZH2 , was also recently shown to be up-regulated in high grade squamous cervical intraepithelial lesions ( HSILs ) compared to normal cervical epithelium [71] , further implicating chromatin remodeling changes in tumor initiation and progression . While Bmi1 appears to be a significant contributor to cell immortalization , it is also obvious that other genes detected in the mRNA expression screen may also contribute to this process . Indeed , the known anti-apoptotic activity of hTERT might also be expected to assist in the bypass of cellular senescence .
pLXSN vector and pLXSN-16E6 , pLXSN-16E7 , pLXSN-16E6E7 were as described previously [10] , [21] , [22] , [72] . pBABE-puro-hTERT , pBABE-puro-hTERT-N+T [42] , pBABE-puro-hTERT-D868A [43] were gifts from Dr . Elizabeth Blackburn , pBABE-puro-hTERT-HA [40] from Dr . Robert Weinberg , and pCLMSCV-puro-Bmi1 [36] from Dr . Tohru Kiyono . Other hTERT mutants were made using the QuikChange XL Site-Directed Mutagenesis Kit ( Stratagene , La Jolla , CA ) . An N-terminal double Flag epitope tag was added to hTERT using a PCR insertion method . SD3443 retrovirus packaging cells were transfected with pLXSN vectors or pBABE-puro vectors described above using LipofectAmine 2000 ( Invitrogen ) as instructed . Culture supernatants containing retrovirus were collected 48 hours after transfection . Primary human foreskin keratinocytes ( HFKs ) and human foreskin fibroblasts ( HFFs ) were isolated and cultured from neonatal foreskins as described58 . Primary human ectocervical keratinocytes ( HECs ) were derived from fresh cervical tissue similarly and obtained after hysterectomy for benign uterine diseases . Standard trypsinization procedures were used to isolate the keratinocytes , which were cultured in serum-free keratinocyte medium supplemented with 50 µg/ml of bovine pituitary extract and 25 ng/ml of recombinant epidermal growth factor ( Invitrogen ) . The cells were cultured in serum-free keratinocyte growth media ( Invitrogen ) supplemented with gentamycin ( 50 µg/ml ) . Primary HFKs , HFFs , and HECs were transduced with amphotropic pLXSN retroviruses expressing HPV-16E6 , E7 , or both E6 and E7 and/or pBABE-puro retroviruses expressing hTERT or its mutants ( see above ) . Retrovirus-transduced cells were selected in G418 ( 100 µg/ml ) for 5 days and/or puromycin ( 2 ug/ml ) for 3 days . Resistant colonies were pooled and passaged every 3–4 days ( 1∶4 ratio for HFKs and HECs , 1∶8 ratio for HFFs ) . HeLa , C33A , SiHa cells were maintained in complete DMEM medium . All cells were cultured on plastic tissue culture dishes or flasks . To prepare cells for immunocytochemistry , cells were pelleted and then fixed with 4% paraformaldehyde solution overnight and resuspended in HistoGel ( Richard-Allan Scientific ) at a ratio of 1∶1 per volume . The gel matrix was processed through graduated alcohols and Clear-Rite 3 ( Richard-Allan Scientific ) for paraffin embedding using the Leica ASP300 system ( Leica Microsystems , Wetzlar , Germany ) . Paraffin sections were cut at 5 µm and mounted on Superfrost Plus slides ( Fisher Scientific ) . Patient samples were acquired through the Histopathology and Tissue Shared Resource at the Lombardi Comprehensive Cancer Center ( Washington , DC ) . Twenty one cervical tissues were acquired which represented different pathological stages–one normal tissue core and five tissue cores for each of the following pathological stages: Cervical Intraepithelial Neoplasia Stage 1 ( CIN1 ) , CIN2 , CIN3 or carcinoma in situ , and invasive cervical carcinoma . Human keratinocytes and fibroblasts were lysed and analyzed by Q-TRAP [72] with SYBR Green Supermixture ( Bio-Rad ) . A standard curve was produced for the real-time Q-TRAP assay using serially diluted HeLa cell extracts . All samples were run in triplicate . Genomic DNA was extracted from cells using Qiagen DNeasy Blood & Tissue Kit . Average telomere length was assessed by a modified method of the real-time PCR–based telomere assay [73] . Briefly , the telomere repeat copy number to single gene copy number ( T/S ) ratio was determined using the Bio-Rad IQ5 thermocycler in a 96-well format . Five nanograms of genomic DNA was subjected to PCR reactions with Bio-Rad SYBR Green Super mixture . The primers for telomere length and HBG1 ( a single copy gene ) were as below: Tel-1 5′ CGGTTTGTTTGGGTTTGGGTTTGGGTTTGGGTTTGGGTT-3 , Tel-2 5′-GGCTTGCCTTACCCTTACCCTTACCCTTACCCTTACCCT-3′; HBG1 5′- TGTGCTGGCCCATCACTTTG , HBG2 5′- ACCAGCCACCACTTTCTGATAGG-3′ . The reactions proceeded for 1 cycle at 95°C for 5 min , followed by 41 cycles at 95°C for 15 s , 60°C for 45 s . All samples for both the telomere and HBG1 reactions were done in triplicate . In addition to the samples , each 96-well plate contained a six-point standard curve from 0 . 0 , 0 . 2 , 1 . 0 , 5 . 0 , 25 . 0 , 125 . 0 ng using genomic DNA ( telomere length 10 . 4 kb ) from Roche Telo-kit . The T/S ratio ( dCt ) for each sample was calculated by normalizing the average HBG1 Ct value from the average telomere Ct value . 1×105 telomerase-negative HFKs were seeded onto 24-well plates and grown overnight . Transient transfections were performed using LipofectAmine 2000 reagent ( Invitrogen ) according to the protocol provided by the manufacturer . Cotransfections were performed using 0 . 5 ug of a core hTERT or Cyclin D1 promoter reporter plasmids and 50 ng of each expression vector as indicated ( HPV16E6 , hTERTwt or hTERT-D868A ) or empty vectors as control for basal promoter activity . Cells also were cotransfected with 2 ng of the pRL-CMV plasmid ( Promega ) , which contains the Renilla reniformis luciferase gene as a transfection control . Firefly and Renilla luciferase activities were measured 24 hr after transfection using the Dual luciferase reporter assay system ( Promega ) . hTERT , hTERT-D868A , HPV E6 or the pBP vector was stably expressed in primary HFKs . Cells were grown on 100 mm tissue culture dishes ( BD Falcon ) to confluency before harvesting RNA with 1 mL TRIzol Reagent according to manufacturer's protocol . DNAse treatment was performed ( Ambion , Austin , TX ) . RNA was sent to MOGene , LC ( St . Louis , MO ) for microarray analysis . E6 , hTERT , and hTERT-D8686A were run separately against the pBP on a two-color Agilent whole human genome slide with a 4 x 44K format . A total of six comparative arrays were run- hTERT , hTERT-D868A , or E6 vs . empty vector and run with a duplicate for dye swap . RNA was amplified using the Agilent Low Input Linear Amplification kit ( Agilent Technologies , Santa Clara , CA ) , and then labeled with either cyanine-5 or cyanine-3 using the ULS RNA Fluorescent Labeling Kit ( Kreatech Biotechnology , Amsterdam , The Netherlands ) . 825 ng each of labeled c-DNA was hybridized overnight at 65°C in an ozone-free room to protect the label . All washes and hybridization conditions followed were consistent with the Agilent processing manual ( protocol version 4 . 0 ) . Arrays were scanned using an Agilent C scanner and extracted using the Agilent Feature Extraction software 10 . 7 . 1 ( Agilent Technologies , Santa Clara , CA ) . Initial data analysis was performed by MOGene using the Rosetta Luminator software ( Agilent ) . Expression arrays were submitted to DAVID Bioinformatics Resources 6 . 7 ( NIAID , NIH ) for Functional Annotation Clustering [49] . Using the MultiExperiment Viewer v4 . 8 ( TM4 Microarray Software Suite , Rockville , MD ) and data from the four hTERT comparative arrays , a heat map was constructed with the cluster of interest [50] . SuperScript III Reverse Transcriptase kit ( Invitrogen ) was used to perform reverse transcription PCR ( RT-PCR ) , as previously described [72] . Reactions were annealed and analyzed using a Bio-Rad iCycler and accompanying software ( Bio-Rad Laboratories ) . Primer sets used include the following: Bmi1-F: 5′ TGCCCAGCAGCAATGACTGT3′ Bmi1-R: 5′ GTCCATCTCTCTGGTGACTGATCTTC3′ GAPDH-F: 5′ TCTCCTCTGACTTCAACAGC3′ GAPDH-R: 5′ GAAATGAGCTTGACAAAGTG3′ Stable cell lines were lysed in 2X SDS gel electrophoresis sample buffer . Proteins were separated on a 4–20% Tris-glycine gradient gel ( Invitrogen ) and electrophoretically transferred to an Immobilon-P PVDF membrane ( Millipore ) . The membranes were blocked in 5% dry milk-PBST and incubated with pRb antibody ( 1∶1000 , Cell Signaling ) , hTERT ( 1∶1000 , Y182 , Epitomics ) , Bmi1 ( 1∶200 , F6 , Millipore ) , P53 ( 1∶1000 , Pab 1801 , Santa Cruz ) , and HPV16-E7 ( 1∶1000 , ED17 , Santa Cruz ) ; and a secondary antibody with HRP conjugation and detected by chemiluminescence ( anti-rabbit IgG or anti-mouse IgG; Santa Cruz Biotechnology ) . Equal protein sample loading was monitored using an anti-β-actin ( 1∶5000 , Sigma ) or anti-GAPDH ( 1∶2000 , FL-335 , Santa Cruz ) . The membranes were visualized by using Western Blotting Chemiluminescence Luminol Reagent ( Santa Cruz ) . Immunocytochemistry of HFK cell pellets and immunohistochemistry of cervical tissue was performed for Bmi1 . Five micron sections from formalin fixed , paraffin embedded tissues were de-paraffinized with xylenes and rehydrated through a graded alcohol series . Heat induced epitope retrieval ( HIER ) was performed by immersing the tissue sections at 98°C for 20 minutes in 10 mM citrate buffer ( pH 6 . 0 ) with 0 . 05% Tween . Immunohistochemical staining was performed using the VectaStain Kit from Vector Labs according to manufacturer's instructions . Briefly , slides were treated with 3% hydrogen peroxide for 10 minutes . Endogenous biotin was blocked using an avidin/biotin blocking kit from Invitrogen . The slides were then treated with 10% normal goat serum and exposed to primary antibodies for Bmi1 ( 1∶200 , F6 , Millipore ) for 1 hour at 22°C . Slides were exposed to appropriate biotin-conjugated secondary antibodies ( Vector Labs ) , Vectastain ABC reagent and DAB chromagen ( Dako ) . Slides were counterstained with Hematoxylin ( Fisher , Harris Modified Hematoxylin ) at a 1∶17 dilution for 2 minutes at RT , blued in 1% ammonium hydroxide for 1 minute at 22°C , dehydrated , and mounted with Acrymount . Consecutive sections with the omitted primary antibody were used as negative controls . To quantify expression of immunohistochemical staining , slides were subjected to a randomized , blinded scoring performed by a board-certified clinical pathologist . Combined scores were calculated by adding the intensity score and positivity scores . Mean and standard deviation of combined scores were calculated . A subset of slides was scored multiple times to demonstrate reproducibility . Each case received an intensity score from 0–3 ( 0 = negative , 1 = weak , 2 = moderate , 3 = intense ) and the percentage of positive cells was recorded , which was converted to a tiered positivity score ( 0 = less than 10% , 1 = 11–49% , 2 = 50–74% , 3 = 75–100% ) . HFKs were transfected with either wild-type hTERT or an hTERT mutant , and then grown on sterile glass cover slips , fixed in 4% ( wt/vol ) paraformaldehyde , and labeled with the primary and secondary antibodies . The following primary antibodies were used: anti-hTERT ( Rockland 1∶500 dilution ) and anti-hTERT serum from rabbit immunized with KLH conjugated Ac-CSRKLPGTTLTALEAAANPAL-amide ( aa1104-1123 ) . The secondary antibodies , AlexaFluor 488 donkey anti-mouse IgG and AlexaFluor 555 donkey anti-rabbit IgG ( Invitrogen ) were used at a concentration of 5 µg/mL . A Zeiss Axioskop microscope and a HAMAMATSU ORCA-ER Digital Camera were used for visualization and microphotography . The HFK cells were prepared from human neonatal foreskins at Georgetown University Hospital , normally these tissues are de-identified and discarded . The cervical tissue samples from the Histopathology and Tissue Shared Resource at the Lombardi Comprehensive Cancer Center were anonymized . These protocols ( 2002-021 and 1992-048 ) have been approved by the Georgetown University Institutional Review Board . BMI1 ( NM_005180 ) , BRG1 ( NM_003072 ) , EGFR ( NM_005228 ) , EZH2 ( NM_004456 ) , FGF2 ( NM_002006 ) , HPV16 E6 ( NP_041325 ) , HPV16 E7 ( NP_041326 ) , hTERT ( NM_198253 ) , P16 ( NM_058195 ) , RB ( NM_000321 ) , SIRT1 ( NM_012238 ) , TP53 ( NM_000546 ) , VEGF ( NM_003376 ) .
|
The human papillomaviruses ( HPVs ) are critical elements in the etiology of cervical cancer , as well as several other human cancers . The E6 protein , in combination with the E7 protein of these viruses , immortalizes epithelial cells and increases the expression of the hTERT protein . In the current study we show that the enzymatic activity of hTERT is not required for cooperating in cell immortalization . We further demonstrate that hTERT proteins increase the expression of the Bmi1 protein , which is also capable of cooperating in cell immortalization . We anticipate that these findings will stimulate new studies of telomerase in HPV biology , cancer etiology , and stem cell reprogramming .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology",
"viruses",
"and",
"cancer",
"biology",
"microbiology"
] |
2013
|
HPV16 E7 Protein and hTERT Proteins Defective for Telomere Maintenance Cooperate to Immortalize Human Keratinocytes
|
Hematophagous mosquitoes serve as vectors of multiple devastating human diseases , and many unique physiological features contribute to the incredible evolutionary success of these insects . These functions place high-energy demands on a reproducing female mosquito , and carbohydrate metabolism ( CM ) must be synchronized with these needs . Functional analysis of metabolic gene profiling showed that major CM pathways , including glycolysis , glycogen and sugar metabolism , and citrate cycle , are dramatically repressed at post eclosion ( PE ) stage in mosquito fat body followed by a sharply increase at post-blood meal ( PBM ) stage , which were also verified by Real-time RT-PCR . Consistent to the change of transcript and protein level of CM genes , the level of glycogen , glucose and trehalose and other secondary metabolites are also periodically accumulated and degraded during the reproductive cycle respectively . Levels of triacylglycerols ( TAG ) , which represent another important energy storage form in the mosquito fat body , followed a similar tendency . On the other hand , ATP , which is generated by catabolism of these secondary metabolites , showed an opposite trend . Additionally , we used RNA interference studies for the juvenile hormone and ecdysone receptors , Met and EcR , coupled with transcriptomics and metabolomics analyses to show that these hormone receptors function as major regulatory switches coordinating CM with the differing energy requirements of the female mosquito throughout its reproductive cycle . Our study demonstrates how , by metabolic reprogramming , a multicellular organism adapts to drastic and rapid functional changes .
The ability of multicellular organisms to maintain metabolic homeostasis and respond to changing energy requirements during development , reproduction and stress represents an essential adaptation critical for survival and evolutionary success . Thus , it is important to decipher regulatory mechanisms coordinating metabolic pathways; understanding these mechanisms in organisms facing extreme and fluctuating energy demands is particularly valuable . Female mosquitoes , which are obligatory blood feeders , serve as disease vectors [1] . Pathogens , taking advantage of this blood dependency , use mosquitoes as vectors spreading serious human diseases . Despite continuing efforts and advances in insect control , mosquitoes pose an enormous threat , killing over a million people each year . The situation is aggravated by the lack of effective vaccines , fast growing insecticide resistance , social complexities and ecological changes [2] . A detailed understanding of the reproductive biology of the mosquito may provide vital information to take us a step closer to more effective vector-control strategies . Hematophagous mosquitoes possess numerous distinct physiological features that play a critical role in the stunning environmental adaptations of these disease vector insects . These include a powerful system of odorant receptors , an extremely efficient host-seeking behavior , adaptations for blood feeding and digestion , ability to excrete large amounts of solutes , and rapid egg development [3] . Hematophagy puts extremely high energy demands on a female mosquito at different stages throughout its reproductive cycle . Therefore , metabolic pathways must be synchronized with energy needs of a reproducing female mosquito . However , regulatory mechanisms governing temporal coordination of metabolism at the molecular level have not been well understood in mosquitoes . Each reproductive cycle of a female mosquito is divided into two phases , which are governed by alternating titers of two major insect hormones—a sesquiterpenoid juvenile hormone ( JH ) and a steroid hormone 20-hydroxyecdysone ( 20E ) . JH guides the female mosquito development from the adult eclosion from pupae to blood feeding . During this JH-controlled post eclosion ( PE ) phase , which lasts 3–5 days , a female mosquito matures and prepares itself for events associated with subsequent blood feeding , while actively seeking hosts . Ingestion of blood leads to dramatic events in a female mosquito , including digestion of a huge meal , powerful excretion , a high level of gene expression and rapid egg maturation . During this post blood meal ( PBM ) phase , a female mosquito faces an intense degree of metabolic activity . 20E is the major regulator of the PBM phase of the female mosquito reproductive cycle , and its action is mediated by a nuclear receptor , the Ecdysone Receptor ( EcR ) [4] . The mosquito fat body serves as the nutrient sensor organ , detecting the nutrients derived from a blood meal and blood-derived nutrients are utilized for the production of YPPs in fat body cells [1] . In this study , we investigated whether the two major regulators of female mosquito reproductive cycles , JH and 20E , are involved in the temporal coordination of CM throughout the female mosquito reproductive cycle . Our results show that the JH receptor , Met , and the EcR synchronize CM with energy requirements of a reproducing female mosquito . Deciphering the regulatory mechanisms governing mosquito CM has shed light on adaptations of an organism dealing with intense energetic stress .
We characterized transcript abundance of genes encoding CM enzymes in the fat body of the female Aedes aegypti mosquito throughout the first reproductive cycle . For this analysis , we used two time course fat body microarray transcriptomes spanning the entire first gonadotrophic cycle; one that encompassed eight time points from 6h to 72h PE , and a second one covering nine time points from 3h to 72h PBM ( S1 and S2 Tables ) . Both transcriptomes were obtained from custom-made Agilent microarray chips that contained probe sets corresponding to 15 , 321 genes in the A . aegypti genome [5] . DEG sets during PBM were calculated by comparing transcripts from each of the nine time points with that at 72h PE using the same filtering criteria as those for the PE genes [5] . CM gene transcripts were abundant during the first 24h PE , while later , by 72h PE , there was a considerable decline in their levels ( Fig 1 and S1A Fig ) . Following a blood meal , most CM genes exhibited significant up-regulation reaching their maximal expression by 36h PBM , dropping back to early PE levels by 72h PBM ( Fig 1 and S1A Fig ) . Overall , the second wave of the CM gene activity during the PBM phase was considerably higher than the first during the PE phase . The genes encoding glycogen/sugar metabolism ( 10 of 17 enzyme coding genes ) and glycolysis ( 13 of 28 enzyme coding genes ) exhibited particularly pronounced fluctuations in their expression levels ( Fig 1 and S1B and S1C Fig ) . The genes encoding the citrate cycle exhibited a similar trend , but to a lesser extent ( Fig 1 ) . To authenticate our microarray analysis results , transcript levels of genes encoding key enzymes of CM pathways in fat body samples were determined using real-time PCR ( qPCR ) ( Fig 2A and S2 Fig ) . In agreement with microarray data , the transcript of the glycogen phosphorylase gene ( GLY ) encoding the key glycogen degrading enzyme was high at the beginning of the PE phase , but its expression at 72h PE was dramatically reduced ( Fig 2A ) . This is in contrast to a small decrease in the mRNA levels of genes encoding the enzymes involved in glycogen biosynthesis—glycogen synthase ( GYS ) ( S2 Fig ) . These gene dynamics suggest predominance of glycogen accumulation during the PE phase . During the PBM phase , the GLY transcript was greatly elevated at 36h PBM ( Fig 2A ) , while that of the GYS gene showed only a moderate increase , suggesting a trend opposite to PE in the utilization of sugar reserves during the PBM stage ( S2 Fig ) . Transcripts of genes encoding enzymes for trehalose metabolism , trehalose-6-phosphate ( TPS ) and trehalose-6-phosphatase ( TPP ) and trehalase ( TREA ) ( Fig 2A and S2 Fig ) , the enzymes responsible for transforming glucose to trehalose , declined during the PE phase . During PBM , each of these three genes had a dramatic peak of expression at 36h PBM ( Fig 2A and S2 Fig ) . Transcript levels of nine glycolytic genes , determined using qPCR , were in agreement with microarray data showing differential PE and PBM expression of these genes ( Fig 2A and S2 Fig ) . Our analysis included three genes encoding the rate-limiting enzymes of glycolysis—hexokinase ( HEX ) , phosphofructokinase ( PFK ) , pyruvate kinase ( PYK ) ( Fig 2A and S2 Fig ) . Although they followed a similar expression trend during the mosquito reproductive cycle , the levels of their relative expression differ significantly ( Fig 1 ) . PYK catalyzes the final glycolytic irreversible step generating pyruvate and ATP [6] . Strikingly , the PYK gene expression was highly elevated during the mosquito gonadotrophic cycle , particularly during the PBM stage when its transcript increased over 200-fold , while PFK transcript increased more than 125 folds ( Fig 2A ) . This suggests a dramatic acceleration of the glycolytic flux after blood feeding . Lactate dehydrogenase ( LDH ) is the enzyme that catalyzes conversion of pyruvate to lactate , and this reaction supplies NAD+ [7] . Notably , there was a 12-fold elevation in the LDH gene transcript level by 6h PBM , suggesting a drastic increase in the generation of lactate immediately after a blood meal ( Fig 2A ) . Using antibodies that recognize respective Aedes CM enzymes at the protein level ( S3 Table ) , we performed western blot analyses of samples from 6h , 24h , 72h PE and 6h , 36h , 72h PBM developmental time points . Protein levels for the major glycogen-utilizing enzyme , GLY , and two key glycolytic enzymes , PGM and PYK , were high until 24h PE , after which there was a drop , at 72h PE . During the PBM period , all three proteins were in abundance at 36h ( Fig 2B ) . HEX , the enzyme that catalyzes the first step in glycolysis , converting glucose to glucose-1-P , showed a weak accumulation at 72h PE in female mosquito fat bodies . However , this HEX protein could be detected at a much higher level at 36h PBM . Protein levels of all four tested enzymes declined during the late PBM phase ( 72h PBM ) ( Fig 2B ) . Overall , our western blots demonstrate that the protein levels of glycolysis and glycogen/sugar metabolic enzymes exhibit periodic changes throughout the mosquito reproductive cycle that correlate with the transcript abundance of their respective genes . To find out whether sugar reserves correlated with fluctuating levels of CM enzymes at the gene and protein levels in the female mosquito fat body , we undertook thorough quantitative measurements of stored and circulating sugars during the female mosquito reproductive cycle . In a newly eclosed female mosquito , the glycogen level was relatively low but increased significantly by 24h PE ( about 150% of that of 6h PE ) and was maintained at a similar level for the rest of the PE developmental phase ( Fig 3A ) . A blood meal , however , triggers glycogen depletion and by 24h PBM its level dropped to about half that of the late PE phase mosquitoes . The glycogen level increased by 72h PBM , but was still lower than that of 72h PE mosquitoes ( Fig 3A ) . In order to visualize the glycogen content in situ , we used Periodic acid/Schiff ( PAS ) staining of fixed female adult mosquito fat bodies . Glycogen content was at a detectable level at 6h PE . Consistent with our colorimetric measurements of glycogen , there was a significant increase in PAS positive signal in the 24h PE fat body . Using this staining method , however , the highest glycogen level was observed at 72h PE ( Fig 3A ) . Correlating well with glycogen level measurements , PAS staining showed that glycogen content was less at 6h PBM than at 72h PE . Staining also revealed that the glycogen levels were moderately increased from 6h to 72h PBM in the fat body ( Fig 3A ) . We then measured the levels of circulating sugars using gas chromatography—mass spectrometry ( GC-MS ) . Trehalose level increased during the PE phase , reaching its maximum at 72-78h PE ( about 3-fold increase in comparison with that at 0-6h PE ) ( Fig 3B ) . A blood meal triggers depletion of trehalose and by 24h PBM its concentration dropped to about half that of late-stage PE mosquitoes . During late PBM , the level of trehalose increased and returned to original level by 72h PBM ( Fig 3B ) . Although trehalose is the major form of the circulating sugar in insects , glucose and fructose function as additional circulating sugars found in the hemolymph [7 , 8] . During the late PE phase , there was approximately 10- and 20-fold increase in glucose and fructose levels , respectively ( Fig 3B ) . Blood feeding resulted in a decrease in the levels of these two sugars until 36h PBM , after which it was restored back to PE levels by 72h PBM ( Fig 3B ) . Triacylglycerols ( TAG ) represent another important energy storage form in the mosquito fat body . During the PE phase , the change in TAG level was delayed compared with that of glycogen . The TAG level was relatively low from 6 to 24h PE , but increased by 72h PE ( Fig 3C ) . During PBM phase , the TAG levels in the fat body dropped significantly at 6h PBM reaching its lowest level by 72h PBM ( Fig 2C ) . Adenosine triphosphate ( ATP ) serves as a major indicator of energy consumption by an organism [7] . To evaluate energy utilization in female mosquitoes throughout the reproductive cycle , we measured ATP levels using high performance liquid chromatography ( HPLC ) . The ATP level was high in newly eclosed female mosquitoes at 6h PE , declining thereafter , and by 72-78h PE its level was only 50% of that of 6h-old mosquitoes ( Fig 3D ) . However , the ATP level increased during the PBM phase , reaching a peak at 48h PBM , which was higher than that at 72h PE ( Fig 3D ) . To provide further insight into the CM dynamics in reproducing female mosquitoes , we used GC-MS to measure several intermediary metabolites ( IMs ) of glycolysis and the citrate cycle . Overall , this analysis revealed that IM profiles correlated with those of CM enzymes , exhibiting two pronounced waves at the PE and PBM phases , respectively ( Fig 4 ) . Glucose-6-phosphate represents the first key IM of the glycolytic pathway that also serves as a precursor for glycogen/sugar metabolism and pentose-phosphate pathways . During the PE phase , the level of glucose-6-phosphate was reduced 2-fold by 72-78h ( Fig 4A ) . This metabolite also showed a significant drop in its level immediately after a blood meal , at 6h PBM , and its level remained low throughout the PBM phase , being elevated only by 72h PBM ( Fig 4A ) . The level of the next glycolytic IM , fructose-6-phosphate , exhibited a 2-fold reduction by 72–78 h PE , but it was elevated at 6h PBM , maintaining its high level throughout the rest of the PBM phase ( Fig 4A ) . There was a dramatic reduction in the level of pyruvate , the terminal product of glycolysis during the PE phase . However , more than a 100% increase of the pyruvate level was observed at 6h PBM , reflecting an increase in the glycolytic flux following the blood intake . The pyruvate level remained high until 36h PBM , declining thereafter ( Fig 4A ) . Our transcript data analyses demonstrated a reduction in the mRNA level of the gene encoding LDH , the enzyme catalyzing transformation of pyruvate to lactate , at the end of PE period ( Fig 1 ) . Accordingly , GC-MS measurements of lactate showed a pronounced drop in its level late PE . Moreover , there was an elevation in the lactate level during the PBM phase corresponding to the rise in the expression of this gene ( Fig 4A ) . The citrate cycle is the key pathway used for energy production in all aerobic organisms . Pyruvate serves as an essential precursor for the citrate cycle , and its availability along with activity levels of citrate cycle enzymes determines the final outcome . The latter can be determined by measuring concentrations of citrate cycle IMs . The GC-MS analysis revealed considerable differences in PE and PBM profiles of citrate cycle IMs , which reflects contrasting energetic requirements of the female mosquito during these two phases of the reproductive cycle ( Fig 4B ) . The level of citrate , the first IM of the citrate cycle , exhibited a significant reduction over the PE phase , while it was highly elevated at 6h PBM . Succinate and fumarate exhibited more moderate fluctuations . Malate , however , had PE and PBM profiles similar to those of citrate ( Fig 4B ) . To investigate the role of JH in regulation of CM during PE phase , we topically applied JH III onto newly eclosed female mosquitoes and investigated the effect of this treatment 20h later . The application of JHIII caused a premature drop in abundance of CM gene transcripts ( S3A Fig ) . At the same time , there was a significant elevation in the levels of glycogen and glucose compared with control untreated mosquitoes ( S3B Fig ) . Our previous data indicated that the JH receptor Met plays a central role in regulating JH-mediated gene expression in the fat body of the PE female mosquito [5] . Met silencing has been shown to inhibit ovarian follicle growth as well as result in the reduction of the egg number [5 , 9] . We examined the transcriptome obtained from the fat body of Met RNAi-depleted females and analyzed the response of CM genes . The CM gene transcripts were enriched among upregulated gene cohorts of the iMet transcriptome ( S4A Fig ) . The transcripts of genes belonging to glycogen/sugar metabolism and glycolysis were particularly upregulated , while those of the citrate cycle were elevated to a significantly lesser degree ( Fig 5A and S4 Table ) . Next , we silenced Met by RNA interference ( RNAi ) in female mosquitoes ( iMet ) at 24h PE and analyzed transcript levels of CM genes 4 days later using qPCR . We measured mRNA levels of four glycogen/sugar metabolism genes—GLY , TPS , TPP , and TREA—in the Met-depleted background and found these genes to be considerably induced ( Fig 5B and S4C Fig ) . Six glycolytic enzyme coding genes , including the rate limiting PYK and HEX , were significantly upregulated in the iMet mosquito fat body ( Fig 5B and S4C Fig ) . We also tested the key rate-limiting enzyme PFK but found no effect of Met , consistent with microarray results ( S4C Fig ) . Western blot analysis showed a substantial accumulation of enzymes for glycogen/sugar metabolism and glycolysis at the protein level in fat bodies of Met-silenced female mosquitoes ( Fig 5C ) . These results demonstrate a dramatic effect of Met RNAi knockdown on CM gene and protein levels , suggesting that the JH receptor plays a critical role in CM regulation . The glycogen levels were significantly reduced in Met-silenced female mosquitoes ( Fig 6A and 6B ) . A dramatic depletion of glycogen reserves in fat bodies of Met-silenced female mosquitoes was confirmed by means of PAS staining ( Fig 6A ) . Circulating hemolymph sugars—trehalose , fructose and glucose—significantly declined in abundance in Met-depleted female mosquitoes ( Fig 6B ) . Like the sugar reserves , TAG levels also declined after Met depletion ( Fig 6C ) . Met RNAi depletion resulted in elevated ATP levels , showing an increase in energy consumption in these mosquitoes ( Fig 6D ) . We then measured levels of pyruvate and lactate , the metabolic end products of glycolysis , both of which were significantly elevated with Met depletion , indicating that Met affects the glycolytic flux . However , citrate , succinate and malate , IMs of the citrate cycle , showed no noticeable fluctuations in response to Met depletion ( Fig 6E ) . Our data show that CM is severely compromised in Met-silenced female mosquitoes . This effect of Met RNAi silencing clearly demonstrates that Met functions as a major regulatory switch of CM during the PE phase of the gonadotrophic cycle . 20E and the Amino Acid ( AA ) /Target of Rapamycin pathway have been implicated in regulating vitellogenic events in female mosquitoes [10 , 11 , 12] . To test whether AAs and 20E affect CM gene expression in the female fat body , we used an in vitro tissue culture assay in which fat body tissue isolated from mosquitoes at 72h PE was incubated in the presence of AAs and/or 20E [11 , 13] . Incubation of the fat body in AA-containing medium elevated transcript abundance of PYK and GLY , while addition of 20E to this medium resulted in a further rise of their levels ( S5A Fig ) . To investigate whether these regulatory factors were also involved in controlling CM metabolism during the PBM phase , female mosquitoes 72PE were injected with 20E and AAs . The expression of GLY and PYK were upregulated as a result of the simultaneous application of AAs and 20E; LDH was responsive to AAs but not 20E , while GYS to neither of these two regulators ( S5B Fig ) . In agreement with in vitro experiments , AAs elevated LDH transcript abundance , but 20E had little effect . Thus , 20E and AAs play different roles in the regulation of CM genes . In in vivo experiments utilizing application of 20E and AAs , glycogen and glucose levels decreased when both AAs and 20E were given , mimicking the status of these sugars in PBM mosquitoes ( S5C Fig ) . 20E is the principal hormone governing PBM reproductive events in female mosquitoes . EcR silencing has been reported in mosquitoes with EcR knockdown resulting in reduced ovarian follicular length [14] , and egg numbers as compared to the controls ( S6A Fig ) . Therefore , we investigated whether EcR plays a role in controlling CM . We silenced EcR using dsRNA to a common EcR region ( iEcR ) in female mosquitoes at 24h PE , blood fed them 4 days later , and analyzed transcript levels of CM genes at 36h PBM using qPCR ( S6B Fig ) . Expression of TREA and TPP genes encoding enzymes of glycogen/sugar metabolism were transcriptionally suppressed at 36h PBM as a result of EcR silencing ( S6C Fig ) . Representative glycolytic genes—HEX , PFK and PYK—were controlled by the EcR in a similar manner ( Fig 7A and S6C Fig ) . GLY , PGM and GPI followed the same trend . In contrast , GYS and LDH were not affected by EcR RNAi silencing . This is in agreement with the lack of 20E effect on expression of these genes described above . This transcriptional alteration was also reflected in enzyme protein levels , although the effects were milder in the case of proteins ( Fig 7B ) . EcR dsRNA treatment resulted in an increase in the fat body glycogen 36h PBM as revealed by means of PAS staining ( Fig 7C ) . An increase in circulating sugars was observed in EcR-depleted female mosquitoes; in particular , the levels of glucose and fructose were highly elevated , reflecting an inability of these mosquitoes to utilize sugars ( Fig 7D ) . Both glucose and fructose levels increased by greater than 3-fold at 36h PBM as result of EcR knockdowns . EcR dsRNA treatment resulted in an increase in TAG levels and a drop in the ATP levels 36h PBM ( Fig 7E and 7F ) . To examine whether EcR promotes CM , PBM , via altering the glycolytic flux , we measured the IM levels downstream of glycolysis . Consistent with their inability to catabolize sugar reserves , EcR-silenced mosquitoes showed accumulation of early intermediates of the glycolytic pathway—glucose-6-phosphate and fructose-6-phosphate ( Fig 7G ) . There was also a considerable build-up of lactate ( Fig 7G ) . The level of pyruvate declined slightly , while that of citrate decreased considerably in these mosquitoes . These results clearly pointed to the fact that EcR is a critical regulator of CM during the PBM phase of the gonadotrophic cycle in female mosquitoes . PEPCK is an essential enzyme in maintaining glucose homeostasis and as such pays important role in response to stress and starvation [15 , 16 , 17] . The microarray and qPCR analysis has revealed that the level of the PEPCK gene transcript was high at the beginning of the PE phase , but it was dramatically reduced by 72h PE ( Fig 1 and S7A Fig ) . In agreement with these data , the expression of the PEPCK gene was inhibited by the application of JH in vivo and activated by Met RNAi silencing , indicating negative regulation of this gene by the JH/Met in PE phase ( S7B and S7C Fig ) . In contrast to most CM genes , activation of which reached maximum at 36h PBM , the PEPCK gene was highly upregulated immediately after a blood meal in Aedes females ( Fig 1 and S7D Fig ) . Significantly , both in vivo and in vitro tissue culture experiments have shown that AAs play a key role in activating this gene expression ( S7E and S7F Fig ) . However , these experiments have shown that 20E is not involved in regulation of this gene expression ( S7E and S7F Fig ) . Furthermore , EcR RNAi silencing did not affect its transcript levels ( S7G Fig ) . The PPP consists of oxidative and non-oxidative branches [18] . In contrast to other CM pathways , the genes encoding PPP enzymes of both branches were transcriptionally active throughout the PE phase and downregulated during the PBM phase ( Fig 1 ) . In the PPP oxidative branch , glucose-6-phosphate is utilized for the synthesis of ribose-5-phosphate , with glucose-6-phosphate dehydrogenase being a rate-limiting enzyme [18] . In this respect , it was of particular interest that genes encoding glucose-6-phosphate dehydrogenase ( G6PD ) and ribose-5-phosphate isomerase A ( RPIA ) were sequentially activated during the PE phase ( Fig 1 ) . G6PD also reduces NADP+ to NADPH that is utilized in lipid biosynthesis [18] . In Aedes female mosquitoes , the expression of the gene encoding G6PD is regulated by Met ( Fig 5A ) . G6PD RNAi depletion resulted in decreased TAG levels , suggesting that Met-dependent control of this enzyme contributes to fat metabolism in the mosquito fat body ( Fig 8A ) . Transketolase ( TAL ) , which is the rate-limiting enzyme of the non-oxidative PPP branch [18] , exhibited a higher expression level during the first 24h PE , while its expression was downregulated during the PBM phase ( Fig 1 ) . We used qPCR to examine the transcript abundance of genes encoding RPIA and TAL , representatives of the oxidative and non-oxidative PPP branches , respectively . This analysis confirmed the transcriptome data showing an elevation of transcript abundance of these two PPP genes at late PE phase and a decrease at 36h PBM ( Fig 8B ) . RNAi depletion demonstrated that Met was an activator of expression of these genes , while EcR had no effect ( Fig 8C ) . Moreover , in vitro fat body assay experiments confirmed the lack of 20E effect on TAL and RPIA expression ( Fig 8D ) .
Throughout each gonadotrophic cycle , females of hematophagous mosquitoes undergo drastic physiological changes , shifting from nectar feeding and host seeking to blood utilization and rapid egg development . We show here that these changes are accompanied by CM reprogramming to support the dramatically different functional requirements of a reproducing female mosquito . To accommodate this reprogramming , the female mosquito fat body , which is the metabolic center , undergoes a particularly remarkable transformation . Our transcriptome and qPCR analyses have demonstrated that the expression of genes encoding CM enzymes in this tissue was synchronized with the two phases of the gonadotrophic cycle , responding to the varying energy requirements of the reproducing female mosquito . Protein levels of enzymes involved in glycolysis and glycogen/sugar metabolism exhibited periodic changes throughout the mosquito reproductive cycle that correlated with the transcript abundance of their respective genes . Levels of stored and circulating sugars revealed their periodic accumulation and depletion in response to changing energy requirements throughout . These sugar levels were concurrent with transcript levels of genes encoding glycogen/sugar metabolism enzymes . Metabolomics analysis provided further evidence that the CM dynamics were entirely different during the PE and PBM phases of the female mosquito gonadotrophic cycle . Moreover , these data corroborated with the existence of a link between levels of CM gene expression and IMs . In addition , our analysis has revealed that the timing and regulation of the PPP was different from other CM pathways , suggesting its pivotal role in metabolic homeostasis of the female mosquito . Overall , our analyses suggest that the temporal coordination of CM in female mosquitoes occurs to a large degree at the gene level . We have demonstrated here that Met functions as a major regulatory switch that governs metabolic reprogramming during the PE phase of the female mosquito gonadotrophic cycle . Our data suggest that Met acts at the genomic level , affecting expression of CM genes , thus determining the PE CM reprogramming . The majority of genes involved in CM , including those of glycogen/sugar , glycolysis and the citrate cycle , were greatly elevated in Met-silenced female mosquitoes , while several genes encoding the PPP were downregulated . The JH receptor Met belongs to the bHLH-PAS family of heterodimeric transcription factors , proteins that respond to environmental or physiological signals and are involved in mediating multiple cell responses including metabolism and cancer [19 , 20] . The genomic action of Met has been established [21]—Met forms heterodimers with other bHLH-PAS factors in a JH-dependent manner and activates target genes via interaction with E-box motifs in their regulatory regions [22 , 23 , 24 , 25] . However , Met is also involved in the JH-mediated repression hierarchy . While the gene activation by Met appears to be direct , its repressive action requires intermediate factors [5] . In addition to a significant effect on CM enzymes at the transcript level , Met RNAi silencing caused elevation of glycolytic flux and depletion of stored and circulating sugars . Ingestion of blood leads to dramatic events in a female mosquito . Our transcriptomics and metabolomics analyses have revealed that there is an immediate change in the CM status following blood feeding . The TREA transcript increase and the drop in the trehalose level at 6h PBM suggest an early onset of trehalose utilization for glycolysis . Likewise , there was a dramatic rise in the LDH transcript level as early as 3h PBM followed by a lactate spike . The citrate cycle IMs also exhibited early sharp increases at 6h PBM . This instant elevation of glycolysis to maintain high levels of glycolytic intermediates occurs prior to the rise of the 20E titer in the female mosquito , indicating that it is regulated by factors other than this hormone . Indeed , we found that the early PBM response is likely controlled by amino acids . In the in vitro fat body culture assay , the LDH gene transcript was elevated in response to amino acids , but downregulated by 20E . The role of the amino acid/TOR pathway in vitellogenic PBM events has been established [10] . The mosquito fat body serves as the nutrient sensor organ detecting signaling amino acids derived from a blood meal [1 , 26] . Here , we have uncovered the role of amino acids in regulating CM at the early PBM stage . PEPCK gene activation by AAs occurs at the onset of blood feeding , the time of the ingestion of a huge amount of food in a form of blood . This physiological state imposes an enormous energy requirement on a female mosquito that is needed for the rapid excretion of a large volume of fluid and digestion of a massive blood meal . It appears that a high elevation of the PEPCK gene expression that correlates with these events is essential for maintaining circulating sugar homeostasis and represents an adaptation of mosquito CM to hematophagy . The PBM stage is the apex of the gonadotrophic cycle , when a female mosquito utilizes a huge blood meal and rapidly develops over a hundred eggs just within 48h . We show here that there is a stunningly high level of the CM activity during the middle of the PBM stage , particularly of glycolysis . The genes encoding the rate-limiting glycogen and glycolytic enzymes , such as GLY , PFK and PYK , were upregulated 40 to over 100-fold by 36h PBM . Apart from providing substrate for energy production , the major function of aerobic glycolysis is to maintain high levels of glycolytic intermediates to support anabolic reactions in rapidly dividing cells [6] . Our analysis of CM IMs showed that the glycolytic flux was extremely elevated at the PBM stage . 20E is the major hormone controlling events of the PBM stage of the female mosquito reproductive cycle , and its action is mediated by the heterodimer of EcR and the insect RXR homologue Ultraspiracle , both of which are members of the nuclear receptor superfamily [4] . Nuclear receptors are a specialized family of ligand-bound or unliganded transcription factors that play central roles in regulating development , growth and metabolism [27] . In female mosquitoes , the 20E regulatory hierarchy is responsible for the YPP gene expression in the fat body [28 , 29 , 30] . Our results further suggest that control of CM occurs mainly at the gene level , and EcR is an important regulator of these genes during dramatic increases in CM . In rapidly developing Drosophila melanogaster larvae , CM is temporally coordinated by the estrogen-related receptor ( ERR ) [8] . This nuclear receptor alters the expression of genes encoding metabolic pathway enzymes , thus playing the role of a metabolic switch . Whether ERR plays a similar role in the female mosquito and its mode of interaction with EcR in synchronizing CM during the PBM stage requires further study . In summary , we have presented a comprehensive analysis of CM dynamics in the female mosquito during the reproductive cycle . We show that such metabolism is tightly correlated with the rapidly changing physiological conditions of this organism . Our transcriptomics and metabolomics studies have revealed the association of expression of genes encoding CM pathways and IMs . Our analyses have identified that Met is the key regulatory switch responsible for temporal coordination of CM during the PE phase of the female mosquito gonadotrophic cycle . We also show that 20E/EcR and amino acids play different roles in CM regulation . Further molecular analysis of these metabolic regulatory pathways may lead to the implementation of metabolism-based methods to prevent mosquito-borne disease transmission .
The mosquito A . aegypti Rockefeller strain was raised as described previously [11 , 31] . Adult mosquitoes were fed water and 10% sucrose solution continuously . All procedures for vertebrate animal use were approved by the Institute of Zoology Animal Care and Use Committee . Sample sets from 12 independent mosquito populations were analyzed for every experimental condition . Six mosquitoes per sample point were washed in PBS buffer , frozen in liquid nitrogen , grounded in 400 μl pre-cooled 90% MeOH and then incubated for 1 h at -20°C [32] . Following centrifugation and debris removal , a second extraction step with 60% MeOH was performed . The supernatant was vacuum dried for 1 h and incubated with 40 μl O-methoxylamine hydrochloride ( 20 mg/ml saturated in pyridine ) for 1 h at 37°C . Then , 50 μl MSTFA reagent was added to the samples , which were then incubated for 30 min at 37°C , with shaking , and finally diluted with 400 μl n-hexane and transferred to the auto sampler vials for the next step . GC-MS analysis was performed following a standard protocol using Agilent 7890 GC coupled with a 5975N series mass selective detector ( MSD ) . The following temperature steps were used: initial temperature of 75°C for 1 min , 5°C /min ramp to 250°C for 5 min , 5°C/min ramp to 320°C for 3 min . A 1-μl sample was injected in split-less mode at 250°C with helium carrier gas flow set at 1 ml/min . A HP-5MS column with a 5-m-long guard column was used for the analysis . Chromatogram acquisition , peak de-convolution and library searches were performed using Agilent MSD Chemstation software . Metabolites were identified using authentic chemical standards analyzed on the same system . Glycogen assays were performed as described previously [33 , 34] . SpectraMax Plus384 was used for detection . Six independent biological samples , with six adult female mosquitoes per sample , were used for each experimental condition . Following centrifugation , samples were transferred into 96-well plates , incubated with Free Glycerol Reagent ( Sigma ) , and assayed using SpectraMax Plus384 . For TAG measurements , six mosquitoes were homogenized in 100 μl PBST containing 0 . 5% Tween-20 and incubated at 70°C for 5 min . Then , the samples were incubated with Triglyceride Reagent ( Sigma ) and assayed colorimetrically . For ATP measurements , six mosquitoes were homogenized in extraction buffer ( 6 M guanidine-HCL , 100 mM Tris , 4 mM EDTA ) and boiled for 5 min . After centrifugation , the supernatant was filtered via PTFE membrane for HPLC assays performed using an Agilent 1100 HPLC coupled with DVD detector , following a published protocol [35] . On the chromatogram , ATP peaks were identified by utilizing retention time of standards ( Molecular Probes , 911734 ) . Total glycogen , TAG , and ATP concentrations were normalized to endogenous protein level of the samples , determined using Bradford assays ( BioRad , 500–0201 ) . For histochemical analysis of fat body glycogen content , staining and visualization were performed as previously described [36] . The abdomen was separated from the rest of the body and fixed in 4% paraformaldehyde at 4°C overnight . Each sample was then dehydrated with increasing concentrations of ethanol , embedded in paraffin , and sectioned into 3- to 5-μm slices . Abdominal fragments were stained according to the periodic acid Schiff ( PAS ) method ( Sigma , 395B ) and observed under a Nikon Ni-E microscope . dsRNA synthesis was performed as previously described [5] . The bacterial luciferase gene was used to generate control iLuc dsRNA . A Nanoliter 2000 injector ( World Precision Instrument ) was used to introduce corresponding dsRNA into the thorax of cold-anesthetized mosquito females 24h PE . The specificity of gene knockdown was characterized by a 50–70% decrease in transcript abundance of target genes ( S3B and S4A Figs ) . All primers used for making dsRNA are listed in S5 Table . To test the effect of JH , 0 . 5 μl of JH ( 10 μg/ml JH in acetone as solvent ) or acetone was topically applied to newly eclosed female mosquito abdomens . The females were examined 20h post treatment as previously described [24] . For metabolite measurements , samples were collected 20h post JH treatment . To test the effect of 20E , 0 . 5 μl 10−6 M 20E was injected along with amino acids into 72h PE female mosquitoes . Mosquitoes were examined 20h post treatment . Experiments were performed in triplicates under the same condition . Total RNA samples were prepared under three different conditions , and fat bodies were dissected from abdomens of 10–15 individual mosquitoes . qPCR reaction was performed on the MX3000P system ( Stratagene , CA ) using SYBR green PCR Master Mix ( Tiangen , Beijing ) . Thermal cycling conditions were: 94°C , 5 s; 59°C , 20 s; and 72°C , 20 s . Quantitative measurements were performed in triplicate and normalized to the internal control S7 ribosomal protein mRNA for each sample . Primers used for qPCR are listed in S5 Table . Eight mosquito fat bodies were homogenized in 100 μl of breaking buffer by pellet pestle , as described previously [37] . Aliquots of whole mosquito protein samples were resolved on 4–15% gradient SDS-polyacrylamide gels ( Bio-Rad ) and transferred to PVDF membranes ( Invitrogen ) . After blocking , the membranes were incubated overnight with the primary antibody at 4°C ( S3 Table ) . As loading control , an antibody against β-actin ( Sigma ) was used . DEG datasets from PE and PBM time course microarray were utilized to reconstruct the expression profiles of the genes involved in metabolism . Complete linkage hierarchical clustering was performed using the hclust function in R [5] . Discrete clusters were obtained by cutting the resulting dendrogram with the cutree function using a visually determined height value . Orthologous groups and pathway information , based on the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) , were downloaded from the database [38] and used in this study . An enrichment analysis was used to detect the significance of alteration of each metabolic pathway , and p values were calculated based on hyper-geometric tests , as described previously [39] . In all other experiments , statistical significance was defined by a p value < 0 . 01 , as evaluated using paired-end , two-tailed , student's t-tests ( Graphpad 5 . 0 ) . Comparisons were made between time points/ treatments and the controls and significant differences were indicated in the graphs . All quantitative data are reported as mean ± SD . In vitro fat body culture experiments were performed as previously described [11 , 26] . Female mosquito abdominal walls with adhered fat body tissue were incubated in a culture medium under various conditions . In the culture medium lacking amino acids , an equal molar amount of mannitol was supplemented to compensate for changes in osmotic pressure [26] . 20E was added to the culture medium supplemented with a complete set of amino acids [26] . To mimic a natural rise in the 20E titer , the tissue was first incubated with 5 x 10−8 M of this hormone for 4h and then in the presence of 10−6 M for 4 h . Total RNA was then isolated and transcript abundance was analyzed using qPCR . The experiment was repeated three times under the same conditions .
|
Mosquitoes transmit numerous devastating human diseases due to their obligatory hematophagy that is required for the efficient reproduction . Metabolism must be synchronized with high energetic needs of a female mosquito for host seeking , blood feeding and rapid egg development . Each reproductive cycle is divided into two phases that are sequentially governed by juvenile hormone ( JH ) and 20-hydroxyecdysone . During the pre-blood meal phase , the JH receptor Methoprene-tolerant ( Met ) controls carbohydrate metabolism ( CM ) pathways and its RNA interference ( RNAi ) silencing caused up-regulation of CM enzymes at the transcript and protein levels activating glycolytic flux and depletion of storage and circulating sugars . During the second , post blood meal phase , CM was regulated by the ecdysone receptor EcR and its RNAi silencing had a dramatic effect opposite to that of Met RNAi . Thus , we show that Met and EcR function as regulatory switches coordinating carbohydrate metabolism with energetic requirements of the female mosquito reproductive cycle .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Temporal Coordination of Carbohydrate Metabolism during Mosquito Reproduction
|
Besides protein-coding mRNAs , eukaryotic transcriptomes include many long non-protein-coding RNAs ( ncRNAs ) of unknown function that are transcribed away from protein-coding loci . Here , we have identified 659 intergenic long ncRNAs whose genomic sequences individually exhibit evolutionary constraint , a hallmark of functionality . Of this set , those expressed in the brain are more frequently conserved and are significantly enriched with predicted RNA secondary structures . Furthermore , brain-expressed long ncRNAs are preferentially located adjacent to protein-coding genes that are ( 1 ) also expressed in the brain and ( 2 ) involved in transcriptional regulation or in nervous system development . This led us to the hypothesis that spatiotemporal co-expression of ncRNAs and nearby protein-coding genes represents a general phenomenon , a prediction that was confirmed subsequently by in situ hybridisation in developing and adult mouse brain . We provide the full set of constrained long ncRNAs as an important experimental resource and present , for the first time , substantive and predictive criteria for prioritising long ncRNA and mRNA transcript pairs when investigating their biological functions and contributions to development and disease .
The mammalian genome displays a complex and extensive pattern of interlaced transcription of protein-coding genes and thousands of non-coding RNA ( ncRNA; see Materials and Methods for definitions ) loci [1] . Exons from ncRNA loci may overlap on the same ( sense ) , or opposite ( antisense ) , strand with exons from other transcripts , including those from protein-coding genes . They may also be contained within introns of other transcripts . Other ncRNAs are transcribed from bidirectional promoters: their transcriptional events , and those for neighbouring transcripts from the opposite strand , are initiated in close genomic proximity . Several recent studies investigated whether cis-antisense , intronic , or bidirectional ncRNAs regulate the transcription of protein-coding genes whose loci they overlap [2] , [3] . These report complex relationships between the expression profiles of ncRNAs and their overlapping protein-coding genes in adult mice . Further investigations , however , are clearly needed to investigate other types of ncRNAs , in particular intergenic and long ( >200 nt ) ncRNAs transcribed from outside protein-coding loci , and those expressed during development . If most long ncRNAs convey biological functions , then what these molecular mechanisms are remain almost completely unknown . For the few with established mechanisms a general theme has emerged of them acting as transcriptional regulators of protein-coding genes ( reviewed in [4] ) . For many such ncRNAs , the genomic location of their transcription has proved key to their mechanism . When promoters of non-coding and coding transcripts are closely juxtaposed on the chromosome , for example , then transcriptional events initiated from them may be coupled . This has been shown to occur following chromatin remodelling of chromosomal domains [5]–[7] , or because of collisions between transcriptional machineries processing along sequence in close proximity [8] , or because of transcriptional interference when transcription proceeds through a promoter sequence thereby suppressing transcription initiation from it [8] . Other long ncRNAs are cis-regulators of transcription via indirect means involving their participation in ribonucleoprotein complexes [9] , [10] . Other long ncRNAs , such as NRON or 7SK , act in trans: they regulate the expression of target genes or gene products from chromosomes other than the ones from which they are transcribed [11]–[13] . Cis-regulation by ncRNAs of protein-coding gene transcription is well-established in imprinting [14] and for developmental genes , such as Dlx5 and Dlx6 [9] , yet these represent transcriptional events that overlap on the genome . By way of contrast , we sought statistical evidence that pairs of adjacent , yet distinct , coding and non-coding loci often give rise to separate transcripts with similar spatiotemporal expression patterns indicative of positive co-operativity of transcriptional regulation . ( Of course , negative co-operativity by , for example , transcriptional interference is also likely . However , such instances tend to be harder to establish experimentally owing to low levels of ncRNA expression . ) We considered that if evidence of transcriptional co-operativity were to be forthcoming then specific pairs of coding and noncoding transcripts could be prioritised for experimentation . In such studies , it is important to demonstrate that long ncRNAs and mRNAs are transcribed exclusively from separate promoters . Otherwise , similarities in their expression profiles may not represent distinct transcriptional events but instead single transcripts spanning both coding and noncoding exons . We recently demonstrated several evolutionary signatures of functionality for a large set of mouse long ncRNAs and their promoters [15] . These long ncRNA sequences are largely full-length [16] , map to genomic loci lying outside of protein-coding gene models and consequently are unlikely to act as antisense transcripts of a neighbouring gene locus . Although some of these ncRNAs may result from uncoordinated and inconsequential transcription , evidence of transcriptional regulation [17] and constraints on splicing motifs [15] cannot be explained by such transcriptional ‘noise’ . We were interested in whether long intergenic ncRNAs are located randomly with respect to protein-coding genes . If not , this might suggest a trend for long ncRNAs to act in cis with neighbouring protein-coding genes . To improve our chances of detecting non-uniformities of chromosomal location , we considered long ncRNAs whose genomic sequences are evolutionarily constrained and thus are more likely to be functional . If long ncRNAs possess , in general , cis-regulatory roles , one might expect their transcribed genomic regions to lie in proximity to their functionally-linked protein-coding genes , and their tissue expression profiles to be similar . Finally , it might also be expected that functional long ncRNAs would tend to be linked to certain subsets of protein-coding genes that convey particular biological functions . We investigated this cis-regulatory hypothesis for a set of 659 evolutionary constrained long ncRNAs and found large-scale and experimental evidence for co-regulation of non-coding and protein-coding transcript pairs . For the first time , we show that these constrained long ncRNAs are not evenly distributed on the genome but rather tend to be concentrated near to genes with similar expression patterns and from particular functional classes . These findings immediately provide new and unbiased criteria for prioritising long ncRNAs for experimental investigation . Hundreds of constrained long ncRNAs can now be targeted for detailed examination , specifically those that either ( i ) are expressed in the brain during development and are transcribed in proximity to transcription factor genes , or ( ii ) are expressed outside of the CNS in adult individuals and that lie adjacent to signalling genes .
We started by analysing 3 , 122 long ncRNAs transcribed from intergenic regions ( see Materials and Methods ) that , when considered together , exhibit evolutionary constraint [15] . Among these ncRNAs , we then identified 659 long ncRNAs that individually show evidence of constraint ( hereafter termed constrained long ncRNAs ) : individually , their mouse-human nucleotide substitution rate is significantly ( p<2 . 5×10−2 ) suppressed relative to rates for neighbouring transposable elements ( Figure 1A; see Materials and Methods ) . As expected from these suppressed rates , many of these constrained long ncRNA loci ( for example , AK034244 , AK034417 , AK039739 , and AK048867 ) are alignable to the genomes of more distantly-related species , such as chicken . Henceforth , we focus on these 659 constrained ncRNAs since they are more likely to be functional , and less likely to represent random transcriptional events . Indeed , this is consistent with constrained ncRNAs being more frequently supported by CAGE ( Cap-analysis gene expression ) tag evidence [1] , [18] than are non-constrained ncRNAs ( 319/659 , 48% versus 537/1932 , 28% , respectively; p<10−4 , χ2-test ) . Suppression of nucleotide substitution rates for these 659 ncRNAs would be compatible with functional roles for their underlying genomic DNA sequences , rather than their transcripts , for example if their transcription elongation remodels chromatin structure thereby causing conserved DNA sequence motifs to become more accessible to transcription factors . Evidence that the RNA transcript itself is often functional comes from the significant 2 . 4- to 2 . 8-fold over-representation of predicted stable RNA secondary structures within constrained ncRNAs ( p<10−4 ) ( Figure 1B ) ; 178 of 659 constrained long ncRNAs contain at least one predicted RNA secondary structure . A previous study [2] also proposed that a large proportion ( 39% ) of brain-expressed ncRNAs contain predicted RNA secondary structures . Figure S1 illustrates three such likely functional ncRNA molecules ( AK082637 , AK082142 and AK032637 ) , each expressed in the developing mouse brain , which contain predicted RNA secondary structures . In summary , ncRNA sequences that have most frequently experienced purifying selection of substitution , duplication and insertion or deletion mutations ( Figure 1B ) tend to possess a higher than expected proportion of predicted folded RNA structures . Next , we investigated whether long ncRNA loci tend to be transcribed adjacent to protein-coding genes associated with particular sets of molecular functions . If so , we reasoned that such pairings might reflect neighbouring non-protein-coding and protein-coding transcripts that act by regulating each other's transcription . For this study , long ncRNAs derived from mouse brain ( see Materials and Methods ) were considered separately from other long ncRNAs since their genomic sequences are more frequently conserved , and thus more likely to show conserved functions ( Table S1 ) . More specifically , brain-expressed long ncRNAs exhibit a significantly greater proportion of bases aligned to orthologous human sequence than long ncRNAs derived from other tissues ( p = 2×10−4; Kolmogorov-Smirnov two-sided test ) . In support of our cis-regulation hypothesis , we find that the 239 brain-expressed ncRNA loci are not evenly distributed along the mouse genome . Instead , they exhibit significant preferences ( ∼2 to 3-fold enrichments ) to be closest to protein-coding genes from two functional classes , namely genes that are involved either in transcriptional regulation or in nervous system development ( Figure 2A ) . Importantly , these functional associations were significant only for the set of long ncRNAs that are expressed in the developing mouse brain ( ∼2 to 7-fold enrichments; Figure 2B ) , and thus were absent for the set of long ncRNAs expressed in the adult brain . For these studies , results are highly significant ( p<10−3 ) and a low number of chance associations is expected ( estimated number of false discovery observations = 0 . 08 annotations ) . These statistical tests took care to account for variations arising from known chromosome-specific and G+C biases ( see Materials and Methods ) . Long ncRNAs expressed outside of the brain , on the other hand , exhibit a strong and significant ( ∼2-fold increase; p<10−3 ) tendency to be transcribed adjacent to protein-coding genes involved in protein kinase-mediated signalling pathways ( Figure 3A ) . This particular preference is apparent for transcripts expressed only in adult , but not in the developing , brain . Finally , the bias for long ncRNA loci to be transcribed adjacent to genes encoding transcription regulators holds true for transcripts that are expressed in developing non-brain , as well as brain , tissues ( Figure 3B ) . Next , by comparing promoter sequences of these long ncRNA loci , predicted using CAGE clusters [18] , to those of neighbouring protein-coding genes , we found evidence that the ncRNAs tend to be expressed in limited tissue repertoires , whereas their partner protein-coding genes tend to be expressed more widely . Only a third of constrained long ncRNAs have CpG-associated promoters ( 107 of 319 ) , compared with 72% of all protein-coding genes [19] , and thus most are expected to be expressed in a limited repertoire of tissues . By contrast , promoters of protein-coding genes that neighbour long ncRNA loci are depleted in TATA-promoters ( data not shown ) , instead belonging predominantly to the Broad class [18] which are often associated with CpG islands and with housekeeping or brain-specific genes [20] . Furthermore , the initiator ( Inr ) element or Cap motif [18] of these neighbouring protein-coding genes is composed mainly of PyPu dinucleotides ( CA , CG and TG; Figure S2 ) which are known to be associated with high-expression levels , whereas for the long ncRNAs it is mainly PuPu ( GA and GG; Figure S2 ) , which is favoured in rarely-expressed transcripts [18] . Finally , we investigated whether the tissue specificity of protein-coding genes differed according to whether their genomically adjacent long ncRNA loci are evolutionarily constrained or are expressed in the brain . For this we took advantage of a relative entropy ( RE; Kullback-Leibler distance ) measure based on the distribution of CAGE tags from different tissues [21] . We found that protein-coding genes located adjacent to brain-expressed and constrained long ncRNA loci exhibit significantly higher tissue specificity ( median RE = 0 . 63 ) than coding genes either adjacent to unconstrained long ncRNA loci ( median RE = 0 . 45 ) or adjacent to constrained long ncRNA loci expressed in non-brain tissues ( median RE = 0 . 52 ) ( Kolmogorov-Smirnov test , p≤0 . 05 ) . These results are thus consistent with transcription of constrained ncRNAs during brain development often regulating transcription of genomically adjacent protein-coding transcription factor genes in a tissue-specific manner . A prediction of this model is that neighbouring protein-coding and ncRNA transcripts are more likely to be expressed in the same tissue than by chance alone . Upon testing this prediction we found that brain-expressed long ncRNA loci did indeed show a 2 to 3-fold significant tendency to neighbour protein-coding genes that are highly expressed in brain-associated tissues , particularly during mouse development , and specifically in the vomeronasal organ or olfactory bulb ( p<10−3 , EFDR = 0 . 05 entries; Figure 4A; Table S2 ) . Genes expressed in three other central nervous system tissues ( namely , frontal cortex , dorsal striatum and amygdala ) also show associations with brain-expressed ncRNA loci , albeit at levels that are only marginal significant ( p-value<10−2 , EFDR = 0 . 53; Table S3 ) . By way of contrast , ncRNA loci expressed in non-brain tissues have , as expected , a significant preference to be located next to protein-coding genes that are highly expressed outside of the central nervous system ( Figure 4B; Table S2 ) . These findings again point to functional interactions between genetically-linked pairs of non-coding and protein-coding transcripts . Genetic interactions between adjacent coding and non-coding transcripts might be reflected in a preference for their transcription in sense ( same ) or antisense ( opposite ) directions . Indeed , brain-expressed ncRNA loci and their adjacent protein-coding genes strongly exhibit a preference for transcription in sense ( 73% , p<10−10 ) ; a similar , but weaker , significant tendency was observed for constrained ncRNAs expressed outside of the brain ( 56% , p = 0 . 01 ) ( Table S1 ) . The ncRNA we considered are transcribed from largely intergenic loci and are mainly full-length in sequence . Nevertheless , these biases in sense-transcription may be explained if their sequences are also contained within alternative transcripts from protein-coding gene loci . This possibility was explored , and eventually discounted , following investigation of twelve pairs of closely neighbouring non-coding and coding gene loci ( see below ) . We were also able to discount a model involving a ‘rippling’ of transcription across neighbouring loci [22] ( see Discussion ) . Constrained long ncRNA loci thus exhibit preferences to be transcribed on the same strand as adjacent protein-coding genes that are expressed in similar tissues and that often function as transcription regulators . To test this model experimentally by in situ hybridisation , we selected 6 pairs of ncRNA and mRNA , transcribed from adjacent genomic loci , whose ncRNA transcripts were identified originally from embryonic or neonatal mouse brain libraries . These pairs were chosen essentially at random , except that they were required to be transcribed in the same orientation in order to test experimentally for read-through transcripts between coding and non-coding loci ( see below ) . Experimental evidence for independent promoters for individual ncRNAs and genes was provided by CAGE tags ( Figure 5 ) . Note that because these experiments investigated expression at developmental time-points , relevant data from the Allen Brain Atlas are not available . Across a range of embryonic and postnatal time-points , all 6 ncRNA and protein-coding gene pairs tested display overlapping expression patterns in the CNS ( Figure 5A–5C and Figure S3 ) . For example , co-expression of Slitrk1 with AK049627 ( Figure 5A ) , and Vangl2 with AK082938 ( Figure 5B ) , were maintained throughout mouse development , from E11 . 5 to E17 . 5 . For the transcription factor Zic4 , however , embryonic expression was highly localised to the spinal cord and regions of the forebrain , whereas the paired ncRNA was ubiquitously expressed ( Figure S3 ) . At postnatal time-points , Rbms1 was co-expressed together with its paired ncRNA AK149041 at low levels throughout the brain , but most notably in the Purkinje cells of the cerebellum , from P12 ( Figure 5C ) to adulthood ( data not shown ) . In addition , both Meis1 and Grik2 were expressed at very low levels at P12 apart from in the cerebellar granule cell layer; their respective ncRNAs were also only detectable in the same population of cells ( Figure S3 ) . Similarly , at random , we chose an additional 6 protein-coding and non-coding pairs for which the ncRNA was initially identified in the brains of adult mice . Available data [23] also indicate expression for each of these 6 protein-coding genes within the specific sub-region of the adult brain from which its partner ncRNA transcript was originally derived . Of the 6 adult-expressed protein-coding gene partners , all were detectable in the brain by in situ hybridisation; of these , the expression patterns of 5 overlapped with those of their adjacent non-coding RNAs ( Figure 6 and Figure S3 ) . Extensive evidence was available from CAGE tags that each long ncRNA we examined represented a transcript that was independent of the upstream protein-coding gene ( Figure 5 and Figure 6; Table S3 ) . Nevertheless , we decided to investigate whether any long cDNAs derive from transcriptional read-through of a single transcript spanning the 3′ UTR of a neighbouring protein-coding gene locus and the ncRNA locus . If so , this might explain our previous observations of co-expression and transcription in sense . We performed RT–PCR experiments for 8 ncRNAs whose intergenic distance to the closest protein-coding gene was less than 25 kb . Results showed that in all but one case no such read-through transcript could be identified from within the particular tissues and/or at the specific time-points used to generate the in situ hybridisation data ( Figure S4 ) . Next , we used 5′ RACE experiments to confirm the transcription start sites that are expected from these ncRNAs' database sequences . Importantly , for this we obtained sequence only from the same brain tissue and at the specific developmental timepoint in which we had shown , by in situ hybridisation , expression of the relevant ncRNA . Using a method specific for full-length , capped mRNA species , products of the expected sizes and sequences were amplified for 11 of the 12 selected ncRNAs ( Figure S4 ) . The one exception in these 5′ RACE experiments ( an exception , also , for the RT–PCR experiments ) was the Add2/AK013768 pair; these experiments identified an Add2 splice variant transcript with an extended 3′ UTR spanning the entire AK013768 ncRNA sequence . Indeed , this variant transcript ( accession NM_008601 ) had been identified previously as being brain-specific [24] and thus represented a positive-control in our experiments . One ncRNA , AK162901 , whose genomic locus lies adjacent to Adr could not be detected by RT–PCR , 5′ RACE or in situ hybridisation . Aside from these two examples , our data demonstrate that the overlapping in situ hybridisation patterns for 10 out of the 12 ncRNAs tested cannot be derived simply from 3′ UTR extensions of these protein-coding genes; instead , they represent independent transcriptional units that are expressed in the nervous system .
Instead of ‘transcriptional noise’ , the enrichment of predicted RNA secondary structures in constrained ncRNAs ( Figure 1B ) , the comparable expression levels of presumably stable ncRNA and protein-coding transcripts ( Figure 5 , Figure 6 ) , and ncRNAs' increasing constraint moving away from protein-coding sequence [15] all point to the RNA sequences themselves conveying diverse regulatory functions . Previously , we also demonstrated that splice site dinucleotides of mouse long ncRNAs are better conserved to human and to rat than expected by chance [15] . An example of canonical GT-AG splice site consensus sequence motifs that are conserved to human and to rat lies within the 5′ of mouse AK090266 , a long ncRNA locus transcribed bidirectionally with Cited1 , a regulator of CBP/p300-dependent transcriptional responses . Long ncRNAs with predicted RNA secondary structures may be processed to form smaller functional RNAs . Evidence for widespread processing of long ncRNAs remains scant [25] , [26] although some of the set we examined ( including AK080813 , for example , which harbours the mmu-mir-568 microRNA sequence ) may yet be shown to be precursors of smaller trans-acting molecules . The annotated functions of the adjacent protein-coding genes are consistent with the general functional biases observed among non-coding and coding transcript pairs . Some of the genes assayed encode known transcriptional regulators ( Rbms1 , Mitf , Zic4 ) , some possess functions in the developing CNS ( Vangl2 , Slitrk1 , Gabrb1 , Zic4 ) and some , when disrupted , are associated with disease ( Vangl2 , Slitrk1 , Mitf , Gabrb1 , Add2 , Zic4 ) [27]–[34] . Given their sequence conservation and predicted RNA secondary structures , it is likely that mutations within constrained long ncRNAs will be deleterious , although whether such deleterious variants would often manifest as observable phenotypes remains to be determined . We have conservatively identified 659 constrained long and intergenic ncRNAs that appear the most likely to be functional , as opposed to being transcriptional noise . Nevertheless , many ncRNA sequences for which we could not detect constraint may yet be functional . For example , Evf2 , which is known to act as a Dlx-2 transcriptional coactivator [9] , and Neat1 ( AK159400 ) , which is essential for the structure of nuclear paraspeckles [35] , are each not considered as being under constraint in our analysis . Our inability to detect constraint in some functional ncRNA sequences is , in part , owing to the low amount of functional sequence within them: the average proportion of a ncRNA locus that can be identified as being under constraint is approximately 5% [15] . In addition , because we are estimating constraint between mouse and human sequence , lineage-specific ncRNAs such as mouse B2 [36] , [37] will be overlooked by our approach . Co-expression and genomic co-localisation of these non-coding and coding locus pairs is consistent with their transcriptional co-regulation in cis . Our studies were not intended to investigate the genetic action of non-coding gene loci in trans or over long physical distances , although some long ncRNAs may act in trans if their predicted secondary structures are the targets of transcriptional regulatory RNA-binding proteins . Instead , we focused our attention on cis-regulatory coding and noncoding gene partners because the mechanisms of long ncRNA loci , when known , often are exerted over short-ranges ( reviewed in [4] ) , and because many such loci lie in very close proximity to protein-coding genes [15] , [38] . These cis-regulatory long ncRNAs , as for other molecular types such as proteins or ‘housekeeping’ RNAs , are likely to convey a broad spectrum of molecular functions . For some , it will be their transcription driving chromatin remodelling that regulates the transcription of neighbouring ( and not necessarily adjacent ) protein-coding genes [6] , [39] , perhaps by facilitating access to enhancers and promoters for transcriptional machinery molecules . This is of particular relevance to transcription factor genes since their genomic loci and flanking regions tend to be replete in conserved noncoding sequence [40] , [41] . In other cases , long ncRNAs may ‘coat’ double-stranded DNA as it appears to do in epigenetic gene silencing , or it may suppress transcription of the neighbouring protein-coding gene by transcriptional interference ( reviewed in [4] ) . These three possibilities are consistent with stronger sequence conservation within these ncRNAs' promoters than in their transcripts' sequences [1] , [15] . Long ncRNAs may also bind DNA-bound factors that expedite or suppress transcription of adjacent loci . One possibility that we considered initially , and then discarded , is that transcription of these ncRNAs is an inconsequential result of neighbouring ‘intermediate-early’ protein-coding genes ( IEGs ) being transcribed [22] . However , long ncRNA loci in our data set are depleted , rather than enriched , within IEGs and their immediate 100 kb up- and downstream flanking sequence ( no overlap; p = 0 . 57 for enrichment; IEGs from [22] ) . We considered one further explanation of the close vicinity of long ncRNA and transcription factor gene loci . This supposes that the ncRNA promoter is one of the downstream targets of the transcription factor , perhaps participating in a feedback or feedforward loop thereby regulating the level of transcription factor expression . Nevertheless , our observations that transcription factor genes are expressed at higher levels and in a greater range of tissues than their genomically neighbouring ncRNA loci argue that it is their promoters , and not those of the long ncRNAs , that are the downstream targets . The well-characterized regulatory ncRNAs to date convey a broad variety of functional roles . Thus , the molecular mechanisms of the long ncRNAs presented here are not expected to proceed only in one regulatory model . Nevertheless , our findings are consistent with mechanisms by which long ncRNA loci provide subtle and tissue-specific regulatory control over neighbouring protein-coding gene loci . This is because these long ncRNA loci tend to be transcribed at low levels and in restricted numbers of tissues , whilst their neighbouring protein-coding loci are mainly transcribed at higher levels and more broadly , in greater numbers of tissues . The importance of our findings concerns the insights they provide into the extensive , yet unannotated , mammalian transcriptome . In the midst of the large amount of the un-annotated transcriptome , these insights allow an objective prioritization of long ncRNA loci that are likely to regulate expression of adjacent protein-coding transcriptional regulators in the brain . They will thus be critical in the design of experiments seeking to investigate the large number of non-coding transcripts , reported by the ENCODE project [42] and by others [1] , [43]–[46] , whose functions remain virtually all unknown . The ncRNA transcripts , and annotations relating to expression , constraint , copy number variation and predicted RNA secondary structures , are provided in Table S5 and Table S6 .
We considered a set of 3 , 122 long intergenic ncRNAs derived from mouse cDNA libraries [1] , [47] . These ncRNAs have been purged of those containing long open-reading frames , they are virtually exclusively located outside of protein-coding gene models ( 3% overlap such models but are on the complementary strand ) and , as shown elsewhere , they are enriched in sequence that is constrained with respect to nucleotide substitution and to insertion or deletion [15] . After removing 62 overlapping ncRNAs ( see below ) , this set was further divided according to the transcript's spatiotemporal expression and the degree of constraint on nucleotide substitutions . Specifically , ncRNAs were divided into those derived from brain tissues and non-brain tissues , and further into those showing ( or not showing ) evidence of constraint in mouse-human comparisons ( see below ) . ncRNAs derived from multiple tissues such as head and whole body ( 469 ) were not considered further . Overall , 1 , 932 ncRNAs were classified as non-constrained; these include transcripts whose evolution is indistinguishable from neutrality , as well as mouse transcripts with insufficient numbers of aligned positions ( <500 bp ) , when compared to orthologous human sequence , to allow reliable estimation of evolutionary rates . Of these non-constrained ncRNAs , 579 are known to be expressed in the brain . Overall , 255/659 of constrained and 523/1 , 932 of non-constrained transcripts were supported by CAGE tag clusters ( TCs ) [1] , [18] lying within 100 bp of their transcriptional start site . ncRNA data sets are listed according to constraint in Table S5 . To determine tissue specificity of protein-coding genes we employed the relative entropy ( RE; Kullback-Leibler distance ) measure based on the distribution of CAGE tags from different tissues [21] . Protein-coding genes were selected whose tag cluster contained more than 30 CAGE tags . The Kolmogorov-Smirnov test was used to investigate whether two RE data sets may reasonably be assumed to sample the same distribution . Each ncRNA was assigned a tissue and a developmental stage according to information present in its cDNA library entry [1] , [47] . In 62 instances , multiple ncRNAs mapped to the same genomic locus . In all but three of these cases the multiple ncRNAs were derived from a single tissue . In these three exceptional cases , all ncRNAs were derived from non-brain tissues . By excluding ncRNA loci expressed in head and whole body cDNA libraries , we further classified ncRNAs into two tissue classes and two developmental stage classes: ( i ) those expressed in one of 15 CNS tissues ( brain , cerebellum , corpora quadrigemina , corpus striatum , cortex , diencephalon , hippocampus , hypothalamus , medulla oblongata , olfactory brain , pituitary gland , spinal cord , spinal ganglion , sympathetic ganglion and visual cortex ) defined as brain-derived ncRNAs , ( ii ) those expressed in one or more of 45 different tissues from outside the CNS , ( iii ) those expressed during embryonal or neonatal development , and ( iv ) those expressed in adult mice . Nucleotide substitution rates between orthologous mouse-human aligned sequences were estimated and compared with local rates estimated from local ancestral repeats ( ARs ) as described elsewhere [15] . To accurately estimate substitution rates , we only considered ncRNAs' alignments exceeding 500 bp in length . Local ARs had to fulfil two criteria as described in [15] , viz . ( i ) no overlap with its local ncRNA , and ( ii ) minimal length of 100 bp , and additionally: ( iii ) no overlap with indel-purified segments ( IPSs ) ( identified at a false discovery rate ( FDR ) of 10% [48] in order to exclude any selectively purified sequence ) , and ( iv ) a location within 500 kb up- and downstream of the ncRNA neighbouring region to ensure a similar local mutation rate . To determine whether a specific ncRNA exhibits a significantly suppressed substitution rate ( dRNA ) compared to the expectation under neutrality , we estimated the local neutral rate by randomly sampling local ARs in 1 , 000 iterations . Local ARs that fulfilled the above criteria were selected randomly and concatenated until the total ungapped alignment length of these AR sequences exactly matched the length of the aligned fraction of the ncRNA sequence . Subsequently , the average substitution rate ( ) of these concatenated AR sequences was estimated . A ncRNA was considered to have been subject to a significant degree of purifying selection if fewer than 25 of the 1 , 000 dARs values were less than dRNA ( i . e . p<0 . 025 ) . Use of the mean dARs value was justified owing to these values being normally distributed ( data not shown ) . In total , 659 ncRNAs derived from brain or elsewhere were inferred to have been subject to significant levels of purifying selection , with a false discovery rate ( FDR ) less than 0 . 025 ( 16 expected cases ) . To assess whether long ncRNA segments S are significantly associated with functional annotations among genomic elements E within a subset of the genome I , while accounting for any G+C–content biases and chromosome-specific biases , we applied a randomization procedure [15] . This compares , within I , a defined set of genomic segments S against multiple randomized sets of segments S′ , which are chosen to have the same genomic overlap within G+C-stratified subsets of I and within each chromosome , and to have a matched length distribution . The set S and sets S′ are compared with respect to their overlap with a specified fixed set of intervals E that are associated with a particular annotation . To obtain accurate p-values , simulation runs were performed 10 , 000 or 100 , 000 times . This procedure was applied to five annotation sets E: ( i ) indel-purified segments identified at a FDR of 10% [48]; ( ii ) PhastCons multispecies' conserved elements [49]; ( iii ) EvoFold predictions of RNA secondary structure [50]; ( iv ) non-overlapping human copy number variants ( CNVs ) [51] and ( v ) non-overlapping human segmental duplications [52] . I was defined as intergenic sequences located between ENSEMBL-annotated protein-coding genes [53] . To account for the ascertainment bias in case ( iii ) , resulting from EvoFold searching for RNA structure only within conserved sequence , we restricted I to those intergenic sequences that are multiply aligned to genomic sequences of five or more vertebrate species in the 8-way MultiZ alignments [54] , and exhibit overlap with PhastCons multispecies conserved elements; this filtering procedure is similar to that used in the EvoFold pipeline ( Petersen JS , pers . comm . ) . If not otherwise stated , data were obtained from the UCSC Genome Browser Database [55] . Association studies ( i ) to ( v ) that were significant resulted in p-values<2×10−4 and experimental false discovery rate ( EFDR ) values<10−3 . We assessed whether the functional categories of those protein-coding genes that are nearest to the genomic loci from where the ncRNAs are transcribed sample the functions of all genes randomly . For this , we considered Gene Ontology ( GO ) [56] annotations associated with these nearest protein-coding Known Genes ( based on UniProt , RefSeq and GenBank mRNA ) [55] . To test for expression associations , we used GNF Gene Expression Atlas data of all 61 non-cancer mouse tissues [57] by mapping the Locus Link identifier to Known Genes . A gene was classified as being highly expressed in a tissue if its expression exceeded the median calculated across these 61 tissues by 8-fold or more . We assigned a non-coding transcript to its closest known protein-coding gene i if it overlapped with this protein-coding gene's “territory” , defined as nucleotides that are closer to gene i than they are to the most proximal up- and down-stream protein-coding genes i+1 and i−1 . The territory of overlapping protein-coding genes constitutes the maximal region both genes occupy until the mid-distance to the next most proximal genes . The sampling procedure outlined above ensures that systematic variations in territory size , resulting from variations in gene density , will not result in biased outcomes from the association test ( although the power to detect these associations will be affected ) . To discount significant GO and GNF associations for annotations that occur at low frequency , which otherwise would lead to high FDRs , we only considered GO and GNF terms each with an associated territory covering at least 1% of the genome ( resulting at p<10−3 in EFDR = 0 . 08 and EFDR = 0 . 05 , respectively ) . By applying these significance thresholds , we tested whether protein-coding genes of a particular GO category are enriched close to ncRNAs derived from different classes ( see above ) . In particular , when considering constrained and brain-derived ncRNAs that are expressed ( i ) in adult mice or ( ii ) during mouse development , we found significant associations for ( ii ) but not for ( i ) . It is notable that distributions of distances from a ncRNA to its closest protein-coding gene for these two classes are not significantly different ( p = 0 . 2 , Kolmogorov-Smirnov test ) . To determine whether there is a preference for ncRNAs to be transcribed in the same ( sense ) or opposite ( antisense ) direction relative to their neighbouring protein-coding genes , we used the defined genomic coordinates of Known Genes as described above . ncRNAs that overlap two gene territories or that coincide with a territory containing overlapping genes transcribed on both strands were discarded . We separately counted those ncRNAs transcribed in sense , and those in antisense , orientations , and tested the null hypothesis that the directions of transcription of a ncRNA transcript and its neighbouring protein-coding gene are not associated ( and binomially distributed with p = 0 . 5 ) . The high and counts justify the use of a normal approximation to the binomial distribution . Fragments of each target of approximately 400 bp were amplified by RT–PCR from mouse whole brain cDNA or by PCR from genomic DNA and cloned into pCR4-TOPO ( Invitrogen ) ; primer sequences are available on request from the authors . Probes for the protein-coding genes were designed to represent transcripts from all annotated splice variants . Dioxygenin-labeled riboprobes were synthesized using the appropriate RNA polymerase for both the anti-sense and sense strands . Mouse brain and whole embryos were frozen in OCT ( VWR ) on dry ice , and 10 µm parasagittal cryosections were cut and mounted on positively charged slides . Adjacent sections were hybridized to probes for each protein-coding gene and corresponding ncRNA with sense strand probes used as a negative control in all cases . Hybridizations and signal development were performed as previously described [58] , with all slides developed for 24 hours prior to mounting and microscopy . For both RT–PCR and 5′ RACE experiments , tissue from C57BL/6 mice was obtained from wild-type 56 day old adults or from the developmental stage at which expression of the ncRNA had been observed by in situ hybridisation . Total RNA was purified using the RNeasy Midi kit ( Qiagen ) and subsequently DNAse treated as recommended . For RT–PCR , cDNA was synthesized using Expand Reverse Transcriptase ( Roche ) and amplified with 35 cycles using Expand Hi-Fidelity Polymerase ( Roche ) . 5′ RACE was performed using a RNA Ligase-Mediated RACE ( RLM-RACE ) method . Briefly , total RNA was de-phosphorylated with alkaline phosphatase to select for full-length transcripts , followed by treatment with tobacco acid pyrophosphatase and ligation of a RACE adaptor primer ( 5′ GCUGAUGGCGAUGAAUGAACACUGCGUUUGCUGGCUUUGAUGAAA ) to the newly decapped mRNA . After reverse transcription with Expand Reverse Transcriptase ( Roche ) , cap-specific products were amplified with Expand Long Template polymerase ( Roche ) using a reverse primer approximately 350 bp from the predicted transcription start site of each ncRNA and a forward primer specific for the RACE adaptor ( 5′ GCTGATGGCGATGAATGAACACTG ) . An aliquot of each reaction was then used as a template with a nested ncRNA and nested forward primer ( 5′ GAACACTGCGTTTGCTGGCTTTGATG ) . Amplified products were cloned into the pCR4-TOPO or pCR-XL-TOPO TA-cloning vectors ( Invitrogen ) and sequenced . Optimal amplification conditions were determined by adjusting the annealing temperature in all cases .
|
Virtually all of the eukaryotic genome is transcribed , yet far from all transcripts encode protein . Very little is known about the functions of most non-coding transcripts or , indeed , whether they convey functions at all . Among all such transcripts , we have chosen to consider long non-coding RNAs ( ncRNAs ) that are transcribed outside of known protein-coding gene loci . Our approach has focused on mouse long ncRNAs whose genomic sequences are conserved in humans , and also on ncRNAs that are expressed in the brain . This conservation might reflect the functionality of the underlying DNA , rather than the ncRNA , sequence . However , this cannot fully explain the concentration of predicted RNA structures in these ncRNAs . These long ncRNAs also tend to be transcribed in the genomic neighbourhood of protein-coding genes whose functions relate to transcription or to nervous system development . These observations are consistent with the positive transcriptional regulation in cis of these genes with nearby transcription of ncRNAs . This model implies co-expression of protein-coding and noncoding transcripts , a hypothesis that we validated experimentally . These findings are particularly important because they provide a rationale for prioritising specific ncRNAs when experimentally investigating regulation of protein-coding gene expression .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/genomics",
"genetics",
"and",
"genomics/comparative",
"genomics",
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/gene",
"expression",
"evolutionary",
"biology/genomics",
"neuroscience/neurodevelopment"
] |
2009
|
Genomic and Transcriptional Co-Localization of Protein-Coding and Long Non-Coding RNA Pairs in the Developing Brain
|
Plants have evolved pathogen-associated molecular pattern ( PAMP ) -triggered immunity ( PTI ) and effector-triggered immunity ( ETI ) to protect themselves from infection by diverse pathogens . Avirulence ( Avr ) effectors that trigger plant ETI as a result of recognition by plant resistance ( R ) gene products have been identified in many plant pathogenic oomycetes and fungi . However , the virulence functions of oomycete and fungal Avr effectors remain largely unknown . Here , we combined bioinformatics and genetics to identify Avr3b , a new Avr gene from Phytophthora sojae , an oomycete pathogen that causes soybean root rot . Avr3b encodes a secreted protein with the RXLR host-targeting motif and C-terminal W and Nudix hydrolase motifs . Some isolates of P . sojae evade perception by the soybean R gene Rps3b through sequence mutation in Avr3b and lowered transcript accumulation . Transient expression of Avr3b in Nicotiana benthamiana increased susceptibility to P . capsici and P . parasitica , with significantly reduced accumulation of reactive oxygen species ( ROS ) around invasion sites . Biochemical assays confirmed that Avr3b is an ADP-ribose/NADH pyrophosphorylase , as predicted from the Nudix motif . Deletion of the Nudix motif of Avr3b abolished enzyme activity . Mutation of key residues in Nudix motif significantly impaired Avr3b virulence function but not the avirulence activity . Some Nudix hydrolases act as negative regulators of plant immunity , and thus Avr3b might be delivered into host cells as a Nudix hydrolase to impair host immunity . Avr3b homologues are present in several sequenced Phytophthora genomes , suggesting that Phytophthora pathogens might share similar strategies to suppress plant immunity .
Plant innate immunity , which has been continuously refined by challenges from a diversity of pathogens during evolution , employs at least two defense systems in response to pathogen attacks [1] . One is basal defense through host receptor recognition of conserved pathogen-associated molecular patterns ( PAMPs ) , termed PAMP-triggered immunity ( PTI ) . In most cases , the basal defense response can successfully prevent infections from becoming established . However , successful pathogens deliver secreted proteins ( effectors ) to suppress basal defense . Therefore , plants have developed a second immunity system that relies on plant resistance ( R ) protein perception of specific pathogen effectors and is called effector-triggered immunity ( ETI ) . Pathogen effectors recognized by plant R proteins have historically been termed avirulence ( Avr ) effectors [1] , [2] . ETI usually results in faster and stronger plant resistance responses , including programmed cell death ( PCD ) , reactive oxygen species ( ROS ) accumulation , and induction of plant hormone-mediated signal pathways [1] . Thus , the interaction between host R proteins and pathogen Avr effectors can determine the outcome of an infection . Many plant R genes encode polymorphic proteins characterized by nucleotide binding ( NB ) and leucine-rich repeat ( LRR ) domains . These R proteins are located inside the cell , indicating that Avr effectors are usually delivered into plant cells by pathogens [2] , [3] . A motif , RXLR ( Arg-any-Leu-Arg ) , was identified in the N-terminus of Phytophthora effectors such as P . sojae Avr1b and P . infestans Avr3a [3] , [4] , [5] , [6] and was experimentally shown to be a host-targeting motif that could translocate effectors into host cells [7] , [8] , [9] . Recent evidence has shown that Phytophthora RXLR effectors could enter host cells without pathogen machinery by binding to the host external lipid phosphatidylinositol-3-phosphate ( PI3P ) , suggesting that the RXLR-PI3P-mediated cell-entering mechanism is used by pathogens to deliver effectors into host cells [10] . Interestingly , nearly all of the Avr effectors identified from oomycetes so far carry the RXLR motif . These Avr effectors include Avr1b , Avr1a , Avr3a/5 , Avr3c , and Avr4/6 from P . sojae; Avr-blb1 , Avr-blb2 , Avr2 , Avr3a , and Avr4 from P . infestans; and ATR1 and ATR13 from the downy mildew pathogen Hyaloperonospora arabidopsidis [3] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] . Although a recently cloned H . arabidopsidis avirulence effector , ATR5 , does not employ a strict RXLR motif [21] , RXLR motifs are still considered to be characteristic signatures of oomycete Avr effectors . Based on the genome sequencing of several oomycete plant pathogens , a large number of RXLR effector candidates were predicted . Altogether , more than 1200 RXLR effector candidates were predicted in the genomes of P . sojae , P . infestans , P . ramorum , and H . arabidopsidis [5] , [6] , [22] , [23] . The RXLR motif and the large collection of predicted RXLR effector candidates provide a good resource for Avr effector identification . Avr effectors are assumed to be important virulence factors that function by manipulating host immunity . However , only a few fungal and oomycete effectors have been functionally characterized [1] , [2] . In the fungal pathogen Cladosporium fulvum , the Avr effector Avr2 is a cysteine protease inhibitor , and Avr4 acts as a chitin-binding protein that protects C . fulvum against host chitinases [24] , [25] . The Avr-Pita effector is a zinc-dependent metalloprotease that is required by the fungus Magnaporthe oryzae for full virulence on rice [26] , [27] . The polyketide synthase activity of M . oryzae Ace1 is required for avirulence [27] . In the flax rust Melampsora lini , AvrP123 effectors contain a Kazal protease inhibitor signature , but the activity has not been verified [28] . Recently , Fusarium oxysporum f . sp . lycopersici Avr1 was found to suppress Avr2- and Avr3-triggered ETI in tomato [29] . Avr1b and many other effectors from P . sojae could abolish cell death triggered in plants by BAX ( a mammalian pro-apoptotic factor ) , by the PAMP infestin1 ( INF1 ) and by several effectors [30] , [31] . Downy mildew H . arabidopsidis Avr effectors ATR1 and ATR13 could enhance bacterial virulence on susceptible hosts , while some ATR13 alleles could suppress bacterial PAMP-triggered callose deposition or reduce PAMP-triggered ROS production [32] . Phytophthora infestans Avr3a could interact with and stabilize host U-box E3 ligase CMPG1 , preventing it from being degraded by the proteasome system and subsequently blocking triggered cell death triggered by INF1 [33] . However , the virulence mechanism of most oomycete and fungal Avr effectors remains to be further explored . Here , we identify a new oomycete Avr effector , Avr3b , from P . sojae that is recognized by soybean plants containing the R gene Rps3b . Biochemical assays validate the bioinformatic prediction that Avr3b has ADP-ribose/NADH pyrophosphorylase activity . Avr3b contributes to virulence through suppression of plant immunity and that suppression is dependent on enzyme activity . However the enzyme activity of Avr3b is not required for recognition by Rps3b-containing plants .
Briefly summarized , previous studies have described the following features of Avr effectors [12] , [13] , [14] . ( 1 ) The transcripts of Avr effectors are detectable in avirulent strains; ( 2 ) all of the Phytophthora Avr effectors cloned so far possess the RXLR motif; and ( 3 ) many Avr proteins are polymorphic . Previous genetic studies also indicated that Avr3b segregates as a single dominant gene [34] . Thus , we hypothesized that Avr3b was likely encoded by an infection-expressed RXLR effector that is polymorphic among P . sojae strains . To identify expressed RXLR effector candidates in avirulent strains , we examined transcription of potential RXLR effector genes in P . sojae using digital gene expression ( DGE ) , Affymetrix arrays , and expressed sequence tags ( ESTs ) . The DGE method resulted in the identification of 81 expressed RXLR effector genes [35] . A BLAST search in the P . sojae EST database identified 16 expressed RXLR effector genes . After the addition of 59 previously identified expressed RXLR effector genes from microarray analysis [12] , [31] , a total of 119 RXLR effectors were detected as expressed in the P . sojae Avr3b avirulent strain P6497 ( Table S1 ) . Genomic and mitochondrial DNA RFLP results delineated P . sojae strains into four major genetic lineages [36] . Phytophthora sojae isolates P6497 , P7064 , P7074 , and P7076 are representative isolates from each lineage . In addition to the published genome sequence of P6497 , the re-sequencing of three P . sojae lineages ( P7064 , P7074 , and P7076 ) has recently been completed [31] . Investigation of the 131 expressed effector sequences among these lineages revealed that six effectors ( Avh20 , Avh113 , Avh238 , Avh258 , Avh288 , and Avh307 ) showed a sequence polymorphism pattern consistent with the Avr3b phenotype; namely , identical in P6497 and P7074 ( Avr3b avirulent strains ) but variant in P7074 and P7076 ( virulent strains ) ( Table 1 ) . Furthermore , Avh6 showed a presence/absence polymorphism between avirulent and virulent strains ( Table 1 ) . Thus , these seven effectors were selected as Avr3b candidates for further investigation . To determine whether candidate effectors might match Avr3b , we conducted genetic mapping to identify effectors that co-segregated with the Avr3b avirulence phenotype . First , we crossed P6497 and P7076 , and identified 3 F1 individuals . All the 3 F1 individuals showed avirulence on Rps3b soybeans . Then , one F1 individual was selected for self-crossing , resulting in a total of 71 F2 individuals ( P . sojae is diploid and homothallic , and wild-type strains show low levels of heterozygosity , hence segregation for most loci occurs in the F2 generation ) . Avirulence assays of these progeny on soybean plants carrying resistance gene Rps3b distinguished 51 avirulent and 20 virulent F2 individuals . The observed F2 population segregation ratio for Avr3b fit a 3∶1 ratio ( χ2 = 0 . 38 , p >0 . 05 ) , suggesting that the Avr3b avirulence phenotype is conferred by a single dominant allele , consistent with a previous report [34] . Cleaved amplified polymorphic ( CAP ) DNA markers were designed based on the sequence polymorphisms of the candidate effector genes ( Table S2 ) . The Avh307 genotype was found to perfectly match ( 0% recombination ) the Avr3b avirulence phenotype among the 71 F2 progeny ( Figure 1A ) . In contrast , Avh238 ( 8 . 3% recombination ) , Avh288 ( 29% ) , Avh20 ( 33% ) , Avh6 ( 40% ) , Avh113 ( 35% ) , and Avh258 ( 31% ) did not match . Avh238 and Avh307 are genetically linked and are located on the same genome sequence scaffold , 713 kb apart ( Avh238 , scaffold_3: 2234002–2234580; Avh307 , scaffold_3: 1520669–1521613 ) ( Figure 1B ) . Examination of 1 . 06 Mb of DNA sequence surrounding Avh307 revealed another six RXLR effector genes ( Avh309 , Avh308 , Avh9 , Avh198 , Avh340 , and Avh302 ) in this region ( Figure 1B; Table S3 ) . Analysis by RT-PCR of all of these effector candidates revealed that only Avh309 and Avh340 were expressed in P6497 during infection ( Table S3 ) . However , neither Avh309 nor Avh340 displayed sequence polymorphisms . The predicted effector gene Avh308 displayed polymorphisms but was not expressed during infection . To generate a more detailed genetic map of the Avr3b locus , we identified another two markers ( CAPSAvh307_ds6k and CAPSAvh9_us10k ) in the vicinity of Avh307 . The marker CAPSAvh307_ds6k ( scaffold_3:1515512–1516432 ) is located 6 kb downstream of Avh307; this marker also co-segregated with the Avr3b avirulence phenotype without recombination in our 71 F2 progeny . The marker CAPSAvh9_us10 k ( scaffold_3: 1424151–1424781 ) is located 96 kb upstream of Avh307; this marker showed 4 . 4% recombination with the Avr3b avirulence phenotype ( Figure 1B ) . Thus , of the eight RXLR genes examined in this region , Avh307 emerged as the best candidate for Avr3b ( Table S3 ) . Oomycete Avr effectors often show significant sequence polymorphisms among field populations [12] , [13] , [14] . We examined Avh307 polymorphisms among 20 P . sojae field isolates from China . As shown in Figure 1C , Avh307 from each of the 16 avirulent strains encoded a 315 amino acid protein identical in sequence to Avh307P6497 . However , the four virulent isolates encoded a protein identical to Avh307P7076 which is truncated to 230 amino acids by a premature stop codon ( Figure 1C ) . Outside of the truncated region , an additional 46 amino acid substitutions and two deletions were present in Avh307P7076 compared to Avh307P6497 . In addition to a secretory signal peptide and RXLR and dEER motifs , the sequence of the Avh307P6497 allele contains a W motif [30] , and a nuclear diphosphate hydrolase ( Nudix ) motif , G5XE7XREUXEEXGU ( Conserved Domain Database ID: cd04666 , E-value = 1 . 52e-07 ) ( Figure 1C ) . The Avh307P7076 allele retained all of these motifs but with polymorphic sites in the dEER- , Nudix- , and W-motifs , as illustrated in Figure 1C . The proteins encoded by P . sojae Avr genes trigger cell death when they are expressed in or enter into soybean cells containing their matching soybean Rps proteins . To test whether Avh307 alleles could trigger cell death in the presence of Rps3b , we conducted a soybean transient expression experiment by co-bombardment of soybean hypocotyls to measure cell death [37] . Plasmid constructs carrying Avh307 alleles encoding mature proteins without native signal peptides were delivered by particle bombardment into soybean hypocotyls , along with a beta-glucuronidase ( GUS ) reporter gene to measure cell survival . The Avh307P6497 allele triggered cell death in the presence of Rps3b , but not in the absence of Rps3b , as evidenced by a strong reduction in GUS staining . This experiment was performed on two different soybean lines carrying the Rps3b gene and on control plants without Rps3b ( Figure 1D ) . In contrast , expression of Avh307P7076 did not reduce GUS staining in the presence of the Rps3b gene , nor in its absence ( Figure 1D ) . To further confirm our results , we performed a double-barreled bombardment which could compare cell death in a more direct fashion [30] . These data confirmed that expression of the Avh307P6497 allele strongly triggered cell death on Rps3b soybeans , but no reaction on near-isogenic soybean lines that do not contain Rps3b ( Figure S1 ) . Avh307P7076 , the virulence allele , did not induce significant cell death on Rps3b plants . Thus , both kinds of co-bombardment assays provided functional evidence that Avh307 is Avr3b , and that Avh307P6497 and Avh307P7076 are avirulence and virulence alleles , respectively , of Avr3b . To explore how Avr3b may be involved in P . sojae-soybean interactions , we examined the Avr3b transcriptional profile at different stages of infection and among different isolates . To define the transcriptional pattern of Avr3b , we isolated total RNA from mycelia , zoospores , cysts , germinating cysts , and from infected susceptible soybean leaves at 6 hours post-infection ( hpi ) , 12 hpi , and 24 hpi . Real-time RT-PCR data indicated that Avr3b transcript levels are significantly elevated during infection ( Figure 2A ) . Compared with in vitro grown mycelia , the Avr3b transcript level was elevated 20-fold , 150-fold , and 50-fold in the cyst , germinating cyst , and 24-hpi stages , respectively . These data indicate that Avr3b is strongly induced in pre-infection structures and during infection . P . sojae Avr effector genes Avr1b , Avr1a , and Avr3a all showed transcriptional polymorphisms among P . sojae strains [12] , [13] , [14] . To test for transcriptional polymorphisms of Avr3b , we compared Avr3b transcript levels in the germinating cyst and 24-hpi stages between P . sojae isolates P6497 ( avirulent ) and P7076 ( virulent ) using real-time RT-PCR . In P6497 compared to P7076 , Avr3b transcript levels were five times higher at the germination cyst stage and three times higher at the 24-hpi stage ( Figure 2B ) , indicating that Avr3b exhibits significant transcriptional polymorphism . However , despite the relatively lower expression level , transcripts of the Avr3b virulence allele remain detectable , indicating that it might be required during infection . Several oomycete avirulence effectors can suppress aspects of plant immunity [8] , [32] , [38] . To determine whether Avr3b interferes with plant immunity , we transiently expressed recombinant Avr3bP6497 ( without a signal peptide ) fused with a FLAG tag in N . benthamiana using agroinflitration with a potato virus X ( PVX ) vector . Total protein samples were isolated from agroinfiltrated plant leaves and subjected to western blot analysis , employing an anti-FLAG monoclonal antibody as a probe . The results show expression of a protein of the expected size ( 34-kDa ) in the tissue infiltrated with the Agrobacterium cells carrying the FLAG:Avr3bP6497 construct ( Figure 3A ) , consistent with expression of FLAG:Avr3bP6497 in N . benthamiana . Two Phytophthora pathogens of N . benthamiana , P . capsici and P . parasitica , were used to the challenge the infiltrated tissue . After 48 hours following Agrobacterium infiltration , we inoculated the infiltrated regions with an agar plug ( 5×5 mm ) containing freshly grown P . capsici and P . parasitica mycelia , then evaluated disease development . On leaves infiltrated with strains carrying a control gene ( GFP ) , the diameter of the disease lesions was approximately 0 . 7 to 0 . 8 cm at 36 hpi; however , on leaves infiltrated with strains carrying Avr3bP6497 the lesion diameter expanded to 1 . 3 to 1 . 4 cm , as shown in Figure S2 . To more precisely measure P . capsici infection , we measured the ratio of P . capsici DNA to N . benthamiana DNA using real-time PCR to determine the Phytophthora biomass in the infected plant tissues ( Figure 3B ) . The data show that the P . capsici/N . benthamiana biomass ratio was significantly higher in Avr3bP6497-infiltrated leaves ( 38% ) than in GFP leaves ( 24% ) ( P value < 0 . 01 ) . Both the lesion size data and the biomass data suggest that Avr3b expression in planta enhanced the susceptibility of N . benthamiana to Phytophthora . To explore possible mechanisms behind the increased Phytophthora susceptibility , trypan blue and diaminobenzidine ( DAB ) staining were performed to examine infected plant tissues for the distribution of hyphae and for ROS production , respectively . At 16 hpi , a higher density of infected mycelium was observed in Avr3bP6497-transformed tissues ( Figure 3C ) , consistent with the real-time PCR data showing greater P . capsici biomass in Avr3bP6497-expressing tissues . Furthermore sporangia had formed within the infected regions of Avr3bP6497-expressing leaves , whereas none were found within the infected regions of GFP-expressing leaves ( Figure 3C ) . In contrast , there was more extensive trypan blue staining of plant cells in infected tissues expressing the GFP control than in those expressing Avr3bP6497 , suggesting that more defense-related cell death may have occurred in those tissues . Substantially less DAB staining of 16-hpi plant tissues was observed in infected regions of Avr3bP6497-expressing leaves compared to the GFP control , suggesting that ROS accumulation in response to infection was significantly reduced in the Avr3bP6497-expressing tissue ( Figure 3D ) . Overall , transient expression of Avr3bP6497 in N . benthamiana leaf tissue resulted in a greater degree of Phytophthora infection with less ROS accumulation and less cell death , compared to the GFP control . To examine the distribution of Avr3b homologs in other oomycete species , we performed a BLAST similarity search in several oomycete genome databases . A search of the P . sojae genome identified another 12 predicted genes that had the Nudix motif . A total of 15 Nudix hydrolases were found in the P . infestans genome; four of these hydrolases ( PITG05846 , PITG06308 , PITG15679 , and PITG15732 ) had a signal peptide and RXLR motif . The P . ramorum genome encoded a total of 14 Nudix hydrolases; three ( PrAvh165 , PrAvh268 , and PrAvh281 ) were RXLR effectors . Only one RXLR Nudix hydrolase ( ID: 102433 ) was defined in the JGI P . capsici genome . We also searched other plant and animal oomycete databases , including H . arabidopsidis , Pythium ultimum , and Saprolegnia parasitica without finding any predicted RXLR Nudix hydrolase effectors , suggesting that this gene family might be present only in the Phytophthora genus . Thus , an Avr3b family composed of nine members from four Phytophthora species was identified ( Table S4 ) . Sequence alignment of Avr3b family members plus two characterized Nudix hydrolases ( AtNUDT7 from Arabidopsis and ScYSA1 from Saccharomyces cerevisiae ) revealed that all of these sequences contain the conserved residues of the Nudix hydrolase motif ( GX5EX7REUXEEXGU ) ( Figure 4A ) . The Nudix motif is located at the C-terminus of each of the oomycete effectors . The motif is located at the extreme C-terminus in many family members including P . capsici Pc102433 , P . infestans PITG05846 , PITG06308 , PITG15679 , and the three P . ramorum Nudix hydrolase effectors , a pattern similar to that of Avr3bP7076 ( Figure S3 ) . However , in Avr3bP6497 and PITG15732 the motif is closer to the middle of the protein , which is more similar to AtNUDT7 and ScYSA1 . To examine whether Avr3b has Nudix hydrolase activity , and to identify possible substrates for it , we expressed FLAG:Avr3bP6497 ( with the FLAG tag fused to the Avr3b N-terminus ) in N . benthamiana ( attempts to produce active protein in E . coli were unsuccessful ) . Western blot data showed a clear 34∼35 kDa band which suggested the recombinant protein was expressed . As a positive control , we fused AtNUDT7 from Arabidopsis with a FLAG tag . We then conducted immuno-precipitation using anti-FLAG M2 affinity gel . After immuno-precipitation , western blotting of the elution samples showed clear signals , indicating that , Avr3b protein had been recovered ( Figure 4B ) . To test the hydrolase activity of the precipitated FLAG:Avr3bP6497 protein and its possible substrates , relative hydrolase activity was measured as a ratio of FLAG:Avr3bP6497 samples over immuno-precipitated GFP , using a series of nucleotide derivatives as substrates . The results showed that FLAG:AtNUDT7 and FLAG:Avr3bP6497 immunoprecipitates could both hydrolyze ADPR and NADH , as shown in Figure 4C and Table S5 . The AtNUDT7 showed higher activity than Avr3bP6497 . To confirm Avr3bP6497 that is a nudix hydrolase , and to validate that the hydrolase activity in the immunoprecipitates was due to Avr3b , we mutated Avr3bP6497 at four conserved residues ( R220Q , E221Q , E225Q , E226Q simultaneously , Figure 4A ) to generate a non-functional Nudix mutant [39] , called Avr3bQQQQ . The hydrolase activity of immunoprecipitated Avr3bQQQQ was significantly weaker than Avr3bP6497 , on either ADPR or NADH , and not significantly greater than the GFP control ( Figure 4C ) . Besides ADPR and NADH , other nucleotide derivatives including NADPH , NAD , Ap4A , FAD and Coenzyme A were also examined as possible substrates for Avr3bP6497 . The Avr3bP6497 immunoprecipitate caused weak but significant hydrolysis of all these derivatives except NAD , whereas there was no significant hydrolysis by the Avr3bQQQQ immunoprecipitate , except possibly of NADPH ( Table S5 ) . In our assay , the hydrolase data from AtNUDT7 ( Figure 4C ) , a positive control , is consistent with previous reports [40] . Together , these data are consistent with the hypothesis that Avr3bP6497 contains ADP-ribose and NADH pyrophosphorylase activity . To measure the elevation of total nudix hydrolase activity in the plant tissue resulting from Avr3bP6497 expression , we examined the hydrolase activity of total plant extracts . Avr3bP6497 , Avr3bQQQQ , GFP and AtNUDT7 were transiently expressed in N . benthamiana by using Agrobacterium infiltration . Western blotting of total plant extracts confirmed expression of the proteins ( Figure 4D ) . Consistent with the results from the immunoprecipitated protein , the extracts from Avr3bP6497-expressing plant tissue showed significantly elevated hydrolase activity with NADH and ADPR than the control whereas expression of Avr3bQQQQ did not significantly elevate the hydrolase activity ( Figure 4E ) . No significant elevation of hydrolase activity against NADPH , NAD , Ap4A , FAD or Coenzyme A could be detected in total extracts from Avr3bP6497- and Avr3bQQQQ-expressing tissue ( Table S5 ) . Thus , in planta expression of Avr3bP6497 elevated the ADP-ribose/NADH pyrophosphorylase activity in the plant tissue by approximately two-fold . To examine the relationship between Avr3b's Nudix motif and its virulence and avirulence activities , we fused a FLAG tag to the N-terminus of Avr3bP6497 , Avr3bQQQQ , Avr3bP7076 ( virulence allele ) , Avr3b16-174 ( N-terminus of Avr3bP6497 ) , and Avr3b170-315 ( C-terminal of Avr3bP6497 ) ( Figure 5A ) . These proteins were then transiently expressed in N . benthamiana leaves using agroinfiltration . Western blots confirmed that all of the proteins were expressed in planta with the expected sizes ( Figure 5B ) . Next , N . benthamiana leaves expressing each protein were challenged with P . capsici and then stained with DAB to test for suppression of ROS production . Expression of Avr3bP6497 , Avr3bP7076 , and Avr3b170-315 all noticeably reduced ROS generation , whereas , expression of Avr3b16-174 and Avr3bQQQQ failed to do so , similar to expression of the GFP control ( Figure 5C ) . To measure the elevation in hydrolase activity caused by expression of the Avr3b proteins , total proteins from leaves expressing each construct were assayed with ADP-ribose and NADH as substrates . Protein extracts from leaf tissue expressing Avr3bP6497 , Avr3bP7076 , and Avr3b170-315 exhibited significantly elevated ADP-ribose and NADH pyrophosphorylase activity whereas leaves expressing Avr3b16-174 did not ( Figure 5D , 5E ) . Avr3bQQQQ expression resulted in no significant elevation in ADP-ribose pyrophosphorylase activity , and a weak but not statistically significant elevation in NADH pyrophosphorylase activity ( Figure 5D , 5E ) . To determine the effect of each mutant on susceptibility to P . capsici infection , real-time PCR was used to quantify the pathogen biomass in infected plant tissues expressing each construct . The pathogen biomass was significantly higher in tissues expressing Avr3bP6497 , Avr3bP7076 , and Avr3b170-315 ( p <0 . 01 ) than in tissues expressing the GFP control , but was not significantly higher in tissues expressing Avr3b16-174 or Avr3bQQQQ ( p >0 . 05 ) ( Figure 5F ) . To quantitate the avirulence activity of each Avr3b allele and mutant , the double-barreled version of the co-bombardment assay was used . This assay allows precise quantitation of cell death triggered by a construct by comparing the number of blue spots on one side of a leaf with the number produced by a parallel empty vector bombardment on the other side of the leaf [29] . Consistent with the qualitative assay results shown in Figures 1D and S1 , Avr3bP6497 , but not Avr3bP7076 , significantly reduced the cell survival ratio to around 40% on leaves containing Rps3b , whereas no significant effector-triggered cell death was observed in control leaves lacking Rps3b ( Figure 5G ) . Transient expression of Avr3bQQQQ and Avr3b170-315 also reduced the cell survival ratio to around 40% in leaves with Rps3b but not in those without Rps3b . On the other hand , Avr3b16-174 did not trigger significant Rps3b-dependent cell death ( Figure 5G ) . Therefore the avirulence activity of Avr3b ( i . e . triggering of Rps3b-dependent cell death ) required the C-terminal half of the protein , containing the Nudix motif , but did not require the Nudix motif to be intact . To more directly test the contribution of Avr3b , to P . sojae virulence , transient silencing of Avr3b expression in P . sojae was carried out . In vitro synthesized Avr3bP6497 dsRNA was introduced into P . sojae P6497 , and 16 lines were recovered . Avr3b transcript levels were determined in germinating cysts from two biological replicates of each line using Real-Time PCR . In five lines ( T5 , T7 , T9 , T10 , T13 ) levels of the Avr3bP6497 transcript were significantly reduced ( 14%∼29% ) compared to P6497 ( Figure 6A ) . In five non-silenced lines ( T1 , T2 , T3 , T12 , T16 ) levels of the Avr3bP6497 transcript were consistently similar to P6497 . In six lines , Avr3b transcript levels were not consistent between the two biological replicates; these lines were not examined further . Zoospore inoculation of etiolated soybean hypocotyls was performed on susceptible soybean cultivar Williams . The disease lesion length was measured to quantify the virulence of Avr3b-silenced lines at 36 hpi , as shown in Figure 6B . The lesion lengths generated by the Avr3b-silenced lines were significantly smaller than those of the non-silenced lines and of P6497 ( P <0 . 01 ) . Thus , the silencing of Avr3b expression in P6497 significantly impaired virulence . To further elucidate the virulence mechanism of Avr3b , we tested if Avr3b could suppress ETI in soybean using a double-barreled bombardment protocol [31] . This experiment involves measuring ETI-associated cell death triggered by P . sojae effector Avr1b in the presence of soybean R gene Rps1b , in the presence or absence of Avr3b . The side-by-side comparison of cell death triggered by co-bombardment of Avr1b alone compared to Avr1b+Avr3bP6497 on Rps1b soybean indicated that Avr1b-triggered cell death was strongly suppressed in the present of Avr3bP6497 as shown in Figure 7A . To quantify suppression , and to determine whether the Nudix motif is required for suppression , an indirect and a direct assay were carried out ( Figure 7B ) . For the indirect assay , the number of GUS-expressing spots surviving in the presence of Avr1b on Rps1b soybean was measured relative to a parallel GUS-only control in the presence or absence of Avr3bP6497 or Avr3bQQQQ . For the direct assay one barrel delivered DNA encoding Avr1b + Avr3b ( Avr3bP6497 or Avr3bQQQQ ) and the other barrel delivered DNA encoding Avr1b alone . The results of both the direct and the indirect assays showed that the survival of GUS-spots from Avr1b + Avr3bP6497 was significantly higher than from Avr1b alone , confirming that Avr3b could suppress Avr1b-triggered cell death . The survival of GUS-spots from Avr1b + Avr3bQQQQ was significantly less than Avr1b + Avr3bP6497 and not significantly higher than from Avr1b alone indicating that Avr3bP6497 suppression of Avr1b mediated cell death is dependent on the nudix motif activity .
Oomycete and fungal avirulence effectors form a highly diverse class of proteins , with few if any common sequence signatures . Therefore , the cloning of most oomycete and fungal Avr effectors has relied heavily on genetic mapping and/or genomic subtraction techniques . The discovery of the RXLR host-targeting signal motif in oomycetes has provided a rapid way to identify new Avr effector candidates in conjunction with mapping or screening strategies . For instance , RXLR effector prediction and high-throughput functional screening aided in the discovery of the P . infestans genes Avr3a , Avr-blb1 , and Avr-blb2 [11] , [16] . By combining RXLR effector prediction with transcriptional patterns , two RXLR effectors encoded by the P . sojae avirulence genes Avr3a and Avr3c were identified [12] , [14] . Here , in addition to RXLR effector prediction and transcriptional data , sequence polymorphism analysis [41] was also used for the discovery of the P . sojae Avr effector Avr3b . This powerful combination of methods could significantly accelerate the identification of additional oomycete and fungi avirulence effectors . The avirulence allele of Avr3b , Avr3bP6497 , encodes a protein with an intact RXLR motif , a W-motif [5] , [30] , a Nudix hydrolase motif and two cysteine residues . In the virulence allele , Avr3bP7076 , a premature stop codon creates a 90-amino-acid C-terminal deletion that removes one cysteine and places the Nudix motif at the extreme C-terminus of the protein . In the non-deleted region , sequence variation results in 46 amino acid substitutions and two deletions , making Avr3b the most polymorphic Avr effector among all reported P . sojae Avr genes [12] , [13] , [14] , [15] . Transcripts from Avr3bP7076 accumulate at a significantly lower level than from Avr3bP6497 . However , it is unlikely that Avr3bP7076 is a pseudogene because transcripts for this gene are detectable and in planta transient expression of Avr3bP7076 resulted in pyrophosphorylase enzyme activity and suppression of plant immunity . Most RXLR effectors in Phytophthora have diverged in sequence to such a degree that the identification of orthologous effectors across species is often difficult or impossible . This is likely a result of the rapid diversification of RXLR effectors caused by the evolutionary host-pathogen arms race [3] , [6] , [18] , [23] , [42] . However , P . sojae Avr3b has one ortholog in P . capsici , three in P . ramorum , and four in P . infestans , forming an Avr3b-like Nudix RXLR effector family . Sequence alignment of the Avr3b family members shows that most of the sequence similarity occurs in the C-terminal region of the Nudix hydrolase motif ( Figure S3 ) . The key residues for Nudix hydrolase activity are conserved , consistent with enzyme activity being required for normal function . Avr3b from P . sojae has two cysteine residues , a feature that is uncommon in intracellular RXLR effectors but is not unusual for apoplastic effectors from oomycetes or fungi . However the positions of the cysteine residues are not conserved in other Avr3b-like effectors . Whether these cysteine residues are required for avirulence or virulence activities needs further investigation . With regard to expression of the Avr3b orthologs , microarray data shows that two P . infestans Avr3b-like effectors PITG06308 , PITG15679 are up-regulated by 2 . 5 and 2 . 2 fold at 2 dpi , compared to mycelium stages , respectively [23] , suggesting Avr3b-like effectors might also function during P . infestans infection . The orthologs' conservation among Phytophthora species suggests that they that provide a common virulence mechanism . However , this remains to be tested . Recently evidence has shown that P . sojae , P . infestans , P . capsici , M . oryzae , M . lini and other pathogen effectors can enter inside plant cells , presumably to promote virulence [7] , [8] , [9] , [10] , [43] , [44] . Discrete targeting motifs have been identified in these effectors that are required for their translocation into host cells [10] , [43] , [45] , [46] . Like previously reported oomycete avirulence effectors [3] , [11] , [12] , [14] , [15] , [16] , [17] , [18] , [20] , Avr3b has a signal peptide leader and an RXLR motif at its N-terminus . In our assays , expression of Avr3b in soybean or N . benthamiana cells without its signal peptide resulted in avirulence and virulence activities , suggesting that Avr3b normally acts inside plant cells; thus it should be able to enter into plant cells during infection . The RXLR motif was also conserved in the Avr3b-like Nudix RXLR effector family . Except for PITG15732 from P . infestans ( RFLR ) and PrAvh268 from P . ramorum ( RSLH ) , all the other Avr3b-like Nudix RXLR effectors have the sequence “RSLR” ( Figure S3 ) . The dEER motif that is associated with the RXLR motif was not as highly conserved as the RXLR motif in the Avr3b family . We failed to identify any Avr3b-like Nudix RXLR effectors in the genome of H . arabidopsidis , which has a reduced set of RXLR effectors [22] or in the genome of P . ultimum , which has no other RXLR effector genes [47] . Sequencing of additional oomycete pathogen genomes will be required to confirm whether this family is only present in Phytophthora species . Nudix hydrolases are a family of pyrophosphatases containing the highly conserved Nudix motif GX5EX7REUXEEXGU . The family is widely distributed in many organisms , including viruses , bacteria , Archaea , and eukaryotes [39] . Nudix hydrolases catalyze the hydrolysis of a variety of nucleoside diphosphate derivatives linked to a second moiety with varying degrees of specificity [48] . The substrates of Nudix hydrolases identified so far include di- and triphosphates and their oxidized forms , dinucleoside polyphosphates , nucleotide sugars , NADH , coenzyme A , and the mRNA cap [39] , [48] . In the genome of the model plant Arabidopsis , 29 putative Nudix hydrolases have been identified , and the substrates of a few Arabidopsis Nudix hydrolases have been characterized [49] . Like Avr3b , the Arabidopsis Nudix hydrolases AtNUDT2 , AtNUDT7 , and AtNUDT6 use both ADP-ribose and NADH as substrates [40] , [50] . Interestingly , among these hydrolases , AtNUDT7 was reported to be a pathogen-responsive gene whose induction depends on plant defense regulatory genes EDS1 and PAD4 [51] . Furthermore , AtNUDT7 knockout mutants showed enhanced resistance against Pseudomonas syringae and H . arabidopsidis , suggesting that ADP-ribose/NADH pyrophosphatases may act as negative regulators of plant immunity to pathogenic bacteria and oomycetes [51] . Therefore , it is reasonable to suppose that pathogen effectors like Avr3b might mimic negative regulators of plant immunity such as AtNUDT 7 to repress plant defense . Recently , a few putative type-three secretion system ( TTSS ) effectors with the Nudix motif were identified from the plant pathogenic bacterium Ralstonia solanacearum [52] , suggesting that this pathogen might also translocate Nudix proteins into plant cells as virulence factors . However , those TTSS effectors were not tested for enzyme activity nor was their contribution to virulence measured . Recombinant Avr3b expressed in planta elevated NADH and ADP-ribose pyrophosphatase activities in plant extracts , converting NADH to a reduced form of nicotinamide mononucleotide ( NMNH ) plus AMP , and ADP-ribose to AMP plus ribose 5-P . The NADH and ADP-ribose pyrophosphatase activity co-purified with Avr3b protein in immunoprecipitation experiments , and the activity was not present when the nudix motif mutant Avr3bQQQQ was expressed . Therefore , it is very likely that Avr3b protein has NADH and ADP-ribose pyrophosphatase activity . However , because Avr3b was not purified to homogeneity , we cannot absolutely rule out an indirect stimulation and co-purification of a plant Nudix hydrolase with wildtype Avr3b in these experiments . NADH/NAD+ turnover plays an important role in maintaining the ROS balance . Recent reports have shown that AtNUDT7 and AtNUDT2 modulate redox homeostasis in response to biotic or abiotic stress by nucleotide recycling from free ADP-ribose molecules [40] , [53] , [54] , [55] . Thus , we hypothesize that inside plant cells Avr3b can suppress pathogen-triggered ROS accumulation by reducing NADH content and recycling nucleotides from ADP-ribose . However , these mechanisms remain to be further investigated . Rps3b recognizes the presence of Avr3b through the Avr3b C-terminal region ( 170 to 315 aa ) but not the N-terminal region ( 16 to 174 aa ) ( Figure 5G ) . This is consistent with previous reports indicating that the C-terminal sequences of most P . sojae , P . infestans and H . arabidopsidis avirulence effectors show signs of positive selection [11] , [30] , [31] , [56] . In contrast to other avirulence effectors , the Avr3bP6497 C-terminal region contains an identifiable enzyme domain , a Nudix hydrolase domain . In this study , we generated a mutant version of the protein , Avr3bQQQQ , that apparently lacks Nudix enzyme activity but nonetheless could still trigger a defense response in the presence of Rps3b . This result suggests that Rps3b recognition might not depend on Nudix hydrolase activity . However , Avr3bQQQQ retains residual hydrolase activity . Thus we can not rule out the possibility that Avr3b avirulence activity also required Nudix activity because the threshold for Avr3b-mediated avirulence activity is likely to be much lower than for its virulence activity . Due to the numerous polymorphic sites present within the Avr3b170-315 domain , the particular residues responsible for Avr3b-Rps3b recognition cannot readily be discerned . Only Avr3b mutants retaining Avr3b ADP-ribose and NADH pyrophosphorylase activity could suppress ETI and increase Phytophthora biomass in infected plant tissue , suggesting that the enzymatic activity is required to promote Phytophthora virulence . Phytophthora genomes encoded hundreds of RXLR effector genes , suggesting many of these effectors might be redundant in function [57] . For examine , either overexpression or silencing of P . sojae Avr gene Avr3a/5 does not significantly effect P . sojae virulence on susceptible soybean [19] . Overexpression of Avr1b in P . sojae makes transformants more aggressive on soybean [30] , but the effector is naturally silenced in some isolates [13] . On the other hand silencing of Avr3a impaired P . infestans pathogenicity on N . benthamiana [33] , and silencing of Avh172 and Avh238 in P . sojae also impaired virulence [31] , suggesting that these effectors are essential virulence factors . In this paper , transient silencing of Avr3bP6497 compromised the virulence of transformant recipient strains on susceptible soybean cultivar , identifying Avr3b also as an essential virulence factor . This is consistent with our finding that both Avr3b virulence and avirulence alleles are transcribed and both proteins retain Nudix enzyme activity . In conclusion , Avr3b plays dual roles ( avirulence and virulence ) in the P . sojae - soybean interaction . The virulence allele Avr3bP7076 may be considered to be a compromise between host recognition pressures and pathogen fitness .
Detailed information about the P . sojae strains used in this study is listed in Table S6 . The P . capsici ( Pc263 ) and P . parasitica ( P24-3 ) strains were obtained from the Phytophthora species collection at Nanjing Agricultural University . All of these isolates and the P6497× P7076 F1 progeny and F2 progeny used in this paper were routinely maintained on 2 . 5% vegetable ( V8 ) juice medium at 25°C in the dark [58] . Phytophthora sojae mycelia , zoospores , and cysts were prepared as previously described [59] . Cysts germinating at 25°C for 6 hours were used for this study . For RNA samples , infection assays with P . sojae were performed by sandwiching P . sojae mycelium between pairs of soybean leaves [60] . The mycelium was removed at 6 hpi , and the infected soybean leaves were collected at 12 hpi and 24 hpi . All of the collected samples were immediately frozen in liquid nitrogen and stored at −80°C until used for RNA isolation . Soybean ( Glycine max ) cultivar Williams ( rps ) , Williams isoline L88–1479 ( Rps3b ) , and PRX146–36 ( Rps3b ) from the collections at Nanjing Agricultural University and Northeast Agricultural University ( Harbin , China ) were used to score the virulence of P . sojae cultures . Williams isoline L77–1863 ( Rps1b ) was from the collection at the Virginia Bioinformatics Institute . Etiolated soybean seedlings were grown in vermiculite soaked with water at 25°C without light for 4 days before harvest for co-bombardment . For light-grown soybeans , ten soybean seeds were sown in 10 cm pots ( a minimum of three pots per isolate ) containing a soil ( 70% ) and vermiculite ( 30% ) mix soaked with water . Soybeans were grown in a greenhouse with a 16-hour photoperiod at 25°C . Soybeans were grown for 7 days for virulence assays , or for 13 to 14 days for use in co-bombardment . Phytophthora sojae cultures were grown on 0 . 9% ( v/v ) V8 agar plates 5 to 7 days prior to light-grown plant inoculations . The virulence of P . sojae cultures was scored in exactly the same manner as that previously described [14] . A minimum of three independent replicates of the disease assay were performed for each P . sojae culture tested . N . benthamiana plants were grown at 25°C with a 16 hr photoperiod in a greenhouse in styrofoam cups containing disinfected soil . Plants of 5 to 6 weeks old were used for agroinfiltration . In total , 395 predicted RXLR effectors in P . sojae P6497 were previously predicted [42] . For DGE expression data , RNA samples were collected at Nanjing Agricultural University , and sequencing and analysis were performed at Beijing Genomics Institute . Affymetrix microarray data were previously reported [12] , [31] . All RXLR sequences were used as queries for BLAST searches of the P . sojae EST unigene database ( http://vmd . vbi . vt . edu ) [61] , with an E-value cutoff at e−20 . We identified a total of 131 RXLR effectors that are expressed in P . sojae P6497 . P . sojae 454 genome sequencing data ( P7064 , P7074 , and P7076 ) [31] were accessed from Virginia Microbial Database ( http://vmd . vbi . vt . edu ) . Nudix homologs were identified in two ways: genome annotation searching and BLAST searching . The P . sojae , P . capsici and P . ramorum homolog searches were performed at the Joint Genome Institute database ( http://genome . jgi-psf . org ) , the H . arabidopsidis search was performed at Virginia Microbial Database , and the P . infestans search was conducted at the Broad Institute P . infestans database ( http://www . broadinstitute . org ) . For Pythium ultimum genome searching , the genome sequence was downloaded from the Pythium genome database available at http://pythium . plantbiology . msu . edu and a local BLAST search was conducted . SignalPv3 . 0 ( http://www . cbs . dtu . dk/services/SignalP ) was used for secretion signal peptide prediction . Protein domain and motif analyses were conducted using the NCBI conserved domain database ( http://www . ncbi . nlm . nih . gov/Structure/cdd/cdd . shtml ) and Motif Scan ( http://myhits . isb-sib . ch/cgi-bin/motif_scan ) . Sequence alignment was performed using BioEdit2005 ( http://www . mbio . ncsu . edu/bioedit/bioedit . html ) . The Avr3b-like family member sequences have been deposited into NCBI GenBank with accession numbers ( Table S7 ) . F1 hybrids were derived from P6497 ( Avr3b ) × P7064 ( avr3b ) crosses . Oospores from F1 hybrids were produced as described [14] . Single germinating oospores were harvested for DNA isolation . DNA isolation was performed using a fast DNA isolation kit ( Axygen Biotech , China ) . Of 79 germinating oospores , three F1 hybrids were confirmed using two different CAP DNA markers corresponding to polymorphisms within the predicted genes Avh20 and Avh238 ) . One of the F1 hybrids was cultured on 2 . 5% ( v/v ) V8-medium plates for F2 progeny oospore generation . A total of 71 F2 progeny were produced to establish an expanded mapping population to test Avr3b candidate effectors . For each F2 individual , virulence was scored on Williams ( rps ) and L88–1479 ( Rps3b ) plants , and DNA samples from mycelia were also isolated for genotyping . CAP markers for seven Avh genes ( Avh238 , Avh307 , Avh113 , Avh258 , Avh20 , Avh288 , and Avh6 ) were designed based on polymorphisms between P6497 and P7076; details are provided in Table S2 . The genotype of the candidate gene and the virulence type of the F2 progeny were compared . For Avr3b allele sequencing in different strains , the specific primers Avh307 MF , Avh307 MR , Avh307 MFa , and Avh307 MFb were used for PCR amplification and sequencing . Primer sequences are presented in Table S2 . The Avr3bP6497 and Avr3bP7076 genes , excluding regions encoding the predicted signal peptide sequences , were amplified using specific primers and cloned into a 35S promoter-derived plant expression vector pFF19 using BamH I and Sph I restriction sites ( primers sequences are shown in Table S8 ) . Similarly , constructs for bombardment of Avr3bP6497-derived mutants ( Avr3b16-174 , Avr3b170-315 and Avr3bQQQQ ) were also generated . Co-bombardment and transient expression assays for Avr3bP6497 and Avr3bP7076 on etiolated soybean hypocotyls were performed as described [37] . Leaves were photographed using a digital camera system ( V12 , Zeiss Germany ) . For Avr3b-derived mutant avirulence assay and Avr3b suppression of Avr1b ETI assay , double-barreled particle bombardment assays on soybean leaves were performed as previously described [30] . Both hypocotyl co-bombardment and double-barreled particle bombardment were conducted using a Bio-Rad ( USA ) He/1000 particle delivery system . For each paired shot , the logarithm of the ratio of the spot numbers of the test construct to that of the control was calculated . The log ratios obtained from the Rps3b and non-Rps3b leaves were then compared using the Wilcoxon rank-sum test . To monitor Avr3b transcript profiling in P . sojae P6497 by real-time RT-PCR , total RNA samples from mycelia , zoospores , cysts , germinating cysts , and infected plant tissue samples were extracted using a PureLink RNA Mini Kit ( Invitrogen USA ) . For Avr3b transformants , germinating cysts was prepared at 9 and 11 days after transformation . The RNA samples were isolated from these germinating cysts ( two biological replicates for each transformants ) . The first-strand cDNA was synthesized using Superscript II reverse transcriptase ( Invitrogen ) following the manufacturer's directions . For Avr3b transcript profiling analysis , SYBR green real-time RT-PCR assays were carried out . Primer pairs ( Table S8 ) were designed at the identical region between Avr3bP6497 and Avr3bP7076 . Two P . sojae housekeeping genes were selected as endogenous controls , namely actin ( JGI Gene ID: 109046 ) and the molecular chaperone HSP70 superfamily gene ( JGI Gene ID: 144810 ) . PCR reactions ( 20 µL ) included 20 ng cDNA , 0 . 2 µM of each primer , and 10 µL SYBR Premix ExTaq ( TaKaRa Inc . , Dalian , China ) . Reactions were performed on an ABI PRISM 7300 fast real-time PCR system ( Applied Biosystems , USA ) under the following conditions: 95°C for 30 s , 40 cycles of 95°C for 5 s , and 60°C for 31 s; followed by 95°C for 15 s , 60°C for 1 min , and 95°C for 15 s to obtain melt curves . The expression of each gene relative to average Ct values of the two housekeeping genes ( Ct = Ctgene - CtHKaverage ) was determined and analyzed using ABI 7300 System Sequence Detection Software Version 1 . 4 [62] . Sequences corresponding to Avr3bP6497 and Avr3bP7076 with secretion signal peptides replaced by a FLAG tag sequence were amplified from P6497 and P7076 genomic DNA using high-fidelity DNA polymerase ( TaKaRa , Inc . ) with primers described in Table S8 . The PVX vector pGR107 for N . benthamiana transient expression assay was isolated using a plasmid spin column small isolation kit ( Axygen Biotech . China . ) . For the Avr3bP6497 mutant , a method similar to that used for vector construction was used . The Avr3bQQQQ mutant was generated with additional primers ( Table S8 ) by overlapping PCR . High-fidelity PCR products were sub-cloned into pGR107 pre-digested by the restriction enzyme Sma I . Recombinant binary vectors were maintained and propagated in the Escherichia coli strain JM109 , grown in the presence of 50 µg/mL kanamycin . The recombinant binary vectors were transformed into the Agrobacterium tumefaciens strain GV3101 by electroporation . After growing at 28°C on LB agar plates supplemented with 50 µg/mL kanamycin as selective agents for 2 days , individual Agrobacterium colonies were verified with PCR using vector primers . Agrobacterium tumefaciens was grown in LB broth cultures supplemented with 25 µg/mL kanamycin for 2 day at 28°C with constant shaking . The cultures were centrifuged at 4 , 000 rpm for 4 min in a tabletop centrifuge . The pellet was resuspended in 1 mL of induction medium ( 10 mM MES , 10 mM MgCl2 , and 150 mM acetosyringone , pH = 5 . 6 ) . The final concentration of Agrobacterium cells was adjusted to OD600 = 0 . 4 . Leaves of N . benthamiana were infiltrated with Agrobacterium cultures using a blunt syringe . The infiltrated plants were then kept in a greenhouse for 72 hours prior to Western blot , immuno-precipitation , or virulence assays . Agrobacterium-infiltrated N . benthamiana plants were grown in a greenhouse for 48 hours , and transformed leaves were then detached and maintained on half-strength MS medium in a petri dish . Next , 2 . 5% V8 juice agar plugs ( 0 . 5×0 . 5 cm ) infested with fresh P . capsici or P . parasitica mycelia were inoculated onto the infiltrated regions . The diameter of the disease lesion was photographed and measured at 36 hpi . Total DNA isolated from P . capsici-infected regions ( 2×2 cm ) was isolated at 36 hpi . Real-time PCR was used to quantify the ratio of host to pathogen ratio DNA sequences , employing primers specific for the N . benthamiana and P . capsici housekeeping actin genes ( Table S8 ) . Three independent biological replicates were conducted . Diseased plant tissues at 16 hpi were stained by trypan blue and DAB according as described [63] . Quantification DAB was performed by analyzing DAB staining image by a combination of Photoshop and Quantity One . GFP was considered to be 100% standard . Total plant protein was isolated as described [63] for Western blot and immuno-precipitation assays . The protein samples were quantified using a BioPhotometer ( Eppendorf , Germany ) . A standard sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) protocol was used for protein separation . The amount of total protein loaded per gel lane varied from 80 µg to 120 µg depending on each experiment . Proteins were transferred onto polyvinylidene difluoride ( PVDF ) membranes using a semi-wet apparatus ( Bio-Rad ) according to the product instructions . Western blotting was performed as a standard protocol . Anti-FLAG monoclonal antibody ( Sigma-Aldrich ) and anti-mouse IgG-peroxidase conjugate ( Beyotime Biotech , China ) were used as the primary and secondary antibodies . The membrane was treated with Chemiluminescent Peroxidase Substrate-1 ( Thermo Scientific Pierce , No . 34080 , USA ) for 5 min . The membrane was briefly drained and exposed to BioMax ( Kodak , USA ) light film several times ( depending on results ) for exposure signal development . For immuno-precipitation , anti-FLAG M2 affinity gel ( Sigma-Aldrich ) was used . The gel suspension ( 40 µL ) was transferred into a fresh 1 . 5 mL test tube and centrifuged in a pre-cooled rotor at 6000 g for 30 s . The resin was washed twice with 1 mL TBS ( 50 mM Tris HCL , 150 mM NaCl , pH = 7 . 4 ) , and all washing buffer was removed . Total protein lysate was clarified by centrifugation ( 10000 g , 2 min ) then 1000 µL of the supernatant was added to the washed resin , and the mixture was incubated using a roller shaker for 4 hours at 4°C . The suspension was centrifuged at 6000 g and the supernatant was removed . The remaining resin was washed three times with 1 mL of TBS . A total of 100 µL elution buffer ( 0 . 5 M Tris-HCl buffer , 150 ng/µL 3xFLAG , pH = 7 . 2 ) was then added to the resin , and the samples were incubated using a roller shaker for 10 min at room temperature . The resin was centrifuged for 30 s at 6000 g , and the supernatants were transferred to fresh tubes previously loaded with 10 µL of 0 . 5 M Tris HCl and 1 . 5 M NaCl , pH 7 . 4 . The purified protein samples were used for gel electrophoresis and enzyme activity assay . A general method for the Nudix hydrolase activity assay was performed to test for Avr3b enzymatic activity [40] , [53]; a 50-µL reaction mixture contained 50 mM Tris-HCl , pH 8 . 5 , 5 mM MgCl2 , 1 mM dithiothreitol , 2 units of calf alkaline phosphatase , 2 mM substrate ( ADP-ribose , NADH , FAD , or Ap4A; Sigma-Aldrich ) , and certain amount of protein samples . Equal amount of protein ( 5 µg for purified protein , 2 mg for total plant protein extract ) was loaded for hydrolase activity assay in each reaction . After incubation for 1 hr at 37°C , the reaction was stopped by adding 150 µL of 1 N H2SO4 . For color development , 100 µL of water and 700 µL of a freshly made mixture containing 600 µL of 3 . 4 mM ammonium molybdate and 100 µL of 570 mM ascorbic acid was used . The reaction tubes were incubated in a 45°C dry bath for 10 min for color development and then cooled on ice . The solutions were measured using the Du-640 spectrophotometer ( Beckman , USA ) at A820 The reaction without protein was used as a blank control for each substrate . For direct assay of purified proteins , immuno-precipitation eluate was directly added into enzymatic assay reaction mixture . For hydrolase activity assay of total plant protein extract , two regions per leaf transiently expressing FLAG:GFP and FLAG:Avr3bP6497 were created in N . benthamiana leaves using Agrobacterium infiltration . A total of 0 . 1 g of GFP- or Avr3b-infiltrated leaf tissue was collected from each leaf . For both kinds of protein preparations , the relative hydrolase enzyme activity was calculated as a ratio of Avr3b extract ( A820 reading ) over GFP extract ( A820 reading ) . For NADH and ADPR , 17 and 21 biological replicates were performed , respectively . For FAD , Ap4A , NAD and NADPH , 4 biological replicates were conducted . Avr3b in vitro dsRNA synthesis was carried out as described [64] . Primers AVH307-T7F/R with T7 promoter sequence added to the 5′ end of both primers ( see Table S8 ) were used to amplify a C-terminal specific Avr3b DNA fragment from the Avr3bP6497 allele . The PCR product was cloned into pMD19 vector ( TaKaRa ) and sequenced . AVH307-T7F/R primers were also used to generate Avr3b in vitro dsRNA by using the Megascript RNAi kit ( Ambion AM1626 ) . A total of around 200 µg dsRNA was obtained as measured by spectrophotometry . P . sojae transient transformation was performed as described [30] , [59] with a few modifications [32] . About 100 µg of Avr3b dsRNA was added into 1 mL MMg solution ( 0 . 4 M mannitol , 15 mM MgCl2 , 4 mM MES , pH 5 . 7 ) containing 5000–10000 P . sojae P6497 protoplasts . After transformation , the regenerated protoplasts were suspended in liquid pea agar ( 40 °C ) containing 0 . 5 M mannitol . The visible colonies could be observed after 36 h incubation at 25 °C . A total of 16 single colonies were randomly selected and propagated on V8 agar plates for RNA extraction and virulence assays .
|
Phytophthora , a group of notorious oomycete pathogens , damages a very wide range of crop , vegetable , pasture and horticultural plants , generating great losses to agricultural production annually . Disease outcomes between plants and Phytophthora pathogens often depend on whether plants carry resistance ( R ) gene-encoded receptors than recognize the presence of pathogen avirulence ( Avr ) effectors . Previous studies identified a conserved host-targeting motif , RXLR ( arginine , any , leucine , arginine ) , common to several Phytophthora Avr effectors . The genome sequencing of several Phytophthora species including P . infestans ( potato late blight pathogen ) and P . sojae ( soybean root rot disease pathogen ) , resulted in the identification of a large reservoir of RXLR-carrying effector candidates . In this paper we identified an RXLR-carrying protein from P . sojae as Avr effector Avr3b based on genetic mapping , sequence polymorphisms , and transient expression . Avr3b carries a Nudix hydrolase motif at its C-terminus and enhances Phytophthora virulence . Biochemical assays revealed that Avr3b is a pyrophosphorylase with ADP-ribose and NADH as its preferred substrates . Furthermore , the enzymatic activity is required for Avr3b to promote virulence but is not required for recognition by Rps3b .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"science",
"plant",
"microbiology",
"plant",
"biology",
"plant",
"pathogens",
"microbial",
"pathogens",
"plant",
"pathology",
"biology",
"microbiology"
] |
2011
|
Phytophthora sojae Avirulence Effector Avr3b is a Secreted NADH and ADP-ribose Pyrophosphorylase that Modulates Plant Immunity
|
Yaws , caused by Treponema pallidum ssp . pertenue , is a neglected tropical disease closely related to venereal syphilis and is targeted for eradication by 2020 . Latent yaws represents a diagnostic challenge , and current tools cannot adequately distinguish between individuals with true latent infection and individuals who are serofast following successful treatment . PCR on blood has previously been shown to detect T . pallidum DNA in patients with syphilis , suggesting that this approach may be of value in yaws . We performed real-time PCR for Treponema pallidum ssp . pertenue on blood samples from 140 children with positive T . pallidum Particle Agglutination ( TPPA ) and Rapid Plasma Reagin ( RPR ) tests and 7 controls ( negative serology ) , all collected as part of a prospective study of yaws in the Solomon Islands . All samples were also tested by a nested PCR for T . pallidum . 12 patients had clinical evidence of active yaws whilst 128 were considered to have latent yaws . 43 children had high titre rapid plasma reagins ( RPRs ) of ≥1:32 . PCR testing with both assays gave negative results in all cases . It is possible that the failure to detect T . pallidum ssp . pertenue in blood reflects lower loads of organism in latent yaws compared to those in latent infection with T . pallidum ssp . pertenue , and/or a lower propensity for haematogenous dissemination in yaws than in syphilis . As the goal of the yaws control programme is eradication , a tool that can differentiate true latent infection from individuals who are serofast would be of value; however , PCR of blood is not that tool .
Yaws , caused by infection with Treponema pallidum ssp . pertenue[1] , is targeted for eradication by 2020[2] . The organism is closely related to Treponema pallidum ssp . pallidum , the causative agent of venereal syphilis . The mainstay of the World Health Organization ( WHO ) eradication strategy is mass drug administration ( MDA ) of azithromycin in endemic communities . A significant concern for the implementation of this strategy is the development of resistance to azithromycin . Two mutations in the 23s rRNA gene have been associated with azithromycin resistance and treatment failure in syphilis[3] . Resistance to azithromycin and treatment failure are now seen frequently in syphilis in a number of regions , including North America , Europe , Asia and Australasia[4–6] , but have not been observed in low income settings where use of macrolide antibiotics for other diseases is less common[7] . As in venereal syphilis , latent infection also occurs in yaws . There are estimated to be 5–6 latently infected cases for each case of active disease , and these individuals may relapse for up to 5 years[8 , 9] . There is a theoretical risk that azithromycin may be less effective in treating latent infection because the bacterium is less metabolically active . A second related issue in latent yaws is the inability to differentiate between individuals with latent yaws who are at risk of relapse , and individuals who have been successfully treated but remain sero-positive . The gold-standard test for latent infection is the Rabbit-Infectivity Test ( RIT ) , but availability of this technique is limited to highly specialized research laboratories[10] . PCR has been shown to have a diagnostic yield of up to 40% in latent syphilis , depending on the sample used[11] . The diagnosis of latent yaws is further complicated by the finding that other organisms may be responsible for ulcers in individuals seropositive for yaws[12] . The development of a molecular technique to detect T . pallidum ssp . pertenue would allow the differentiation of true latent yaws from individuals with serofast status , and might also facilitate the detection of azithromycin resistance in latent yaws if it were to appear . The aim of this study was to assess whether it is possible to use nested PCR and/or real-time PCR to detect T . pallidum ssp . pertenue DNA in the blood of individuals with latent yaws .
Samples were collected as part of a large prospective study of the epidemiology of yaws in the Solomon Islands . The survey methodology has been described elsewhere[13] . Briefly , we recruited a total of 1 , 497 children from 30 randomly-selected households in each of 25 randomly selected clusters in each Western and Choiseul provinces of the Solomon Islands , collected information on symptoms and treatment of yaws , and performed a standardised examination of the skin . Venepuncture was performed for collection of both a serum sample for serological testing and a whole blood sample; the latter was stored in a PAXgene DNA tube ( Qiagen Inc . , Valencia , CA ) , which maintain DNA integrity for up to 2 weeks at ambient temperature . Samples were transferred to Honiara National Referral Hospital within 5 days and stored at -20°C . Serum samples were shipped on dry ice to London School of Hygiene and Tropical Medicine ( LSHTM ) . Whole blood samples were shipped on dry ice to the Centers for Disease Control and Prevention ( CDC ) . For this study , paired sera and PAXgene blood samples from 147 children ( 9 . 8% of total study population ) were used for serology and PCR analysis . Serum samples were tested by Treponema pallidum particle agglutination ( TPPA; Mast Diagnostics , Merseyside , UK ) . In individuals with a positive TPPA , a quantitative rapid plasma reagin test ( RPR; Deben Diagnostics , Ipswich , UK ) was performed . DNA was manually extracted from 1–1 . 2ml PAXgene samples at CDC , using the QIAamp DNA Blood Midi Kit ( Qiagen Inc . ) and tested in a real-time quadriplex PCR containing primers and TaqMan probes targeting the tp0858 gene , two areas of the tprI ( tp0620 ) gene and the human RNase P gene to monitor for PCR inhibition [14] . This PCR has been reported to successfully identify T . pallidum ssp . pertenue from skin lesions of primary and secondary yaws and differentiate it from the other two subspecies . The quadriplex PCR amplification was performed as described previously [14] . Briefly , 10–20 μL of DNA was used in a reaction containing a final concentration of 1x PerfeCTa MultiPlex qPCR SuperMix ( Quanta Biosciences Inc . , Gaithersburg , MD ) . All reactions were performed using the Rotor-Gene Q real-time PCR instrument ( Qiagen Inc . ) with the following conditions: initial hold at 95°C for 4 min followed by 50 cycles of 95°C for 20 sec and 60°C for 1 min . A no-template control and custom synthesized DNA fragments with unique genetic signatures in tp0858 or tprI ( insert size is ~250 to 400 bp ) cloned into pIDTSmart plasmids ( Integrated DNA Technologies , Coralville , IA ) were used as positive controls . In addition , each run also included genomic DNA from a lesion sample that previously tested positive for T . pallidum ssp . pertenue . A nested PCR consisting of outer primers for a conventional PCR and inner primers for a real-time PCR was used to specifically amplify one of two alleles of the 23S rRNA gene ( tp0226 ) of T . pallidum ssp . pertenue . The outer PCR which amplifies a 1602-bp fragment was performed using a final concentration of 200 nM each of the sense ( 5’–GTACCGCAAACCGACACAG ) and antisense ( 5’–GCGCGAACACCTCTTTTTAC ) primers[4] . Reactions were carried out in a 50 μL volume containing 200 μM of dNTPs , 1x PCR buffer , 1 . 25 U of Takara Ex Taq polymerase ( Clontech Laboratories , Inc . , Mountain View , CA ) , and 20 μL of DNA in an Applied Biosystems GeneAmp PCR system using the following conditions: initial hold at 94°C for 3 min followed by 45 cycles of 94°C for 1 min , 63°C for 1 min , and 68°C for 2 min , with a final extension cycle at 68°C for 10 min . A no-template control and purified genomic DNAs from T . pallidum ssp . pallidum strain Nichols and T . pallidum ssp . pertenue strain CDC2575 were included in each run . The inner real-time PCR which amplifies a 185-bp fragment of the outer PCR amplicon above was performed using primers and TaqMan probes as described previously [3] . Briefly , 1 μL of outer PCR amplicon was used in a 25 μL reaction containing a final concentration of 1x PerfeCTa MultiPlex qPCR SuperMix . All reactions were performed using the Rotor-Gene Q real-time PCR instrument with the following conditions: initial hold at 95°C for 4 min followed by 50 cycles of 95°C for 20 s and 60°C for 1 min . The real-time PCR controls used were the same as those for conventional PCR above . Blood samples were defined as being PCR-negative for T . pallidum ssp . pertenue if an amplification curve or a cycle threshold ( Ct ) value was not observed after real-time PCR , provided all positive control DNA samples gave the expected results . The analytical sensitivities of the real-time PCR and nested PCR were determined by testing a 10-fold serial dilution of purified genomic DNA from the T . pallidum Nichols strain . As the yield of PCR positivity on blood was uncertain we pragmatically elected to test all seropositive samples with a dual TPPA and RPR ( titer ≥1/4–128 ) positivity , and a small number of dual positives with an RPR titer <1/4 and seronegative controls . We deliberately over-represented individuals with high titre positive serology as these individuals are more likely to be truly infected with T . pallidum ssp . pertenue rather than having serofast serology . Individuals were considered to have latent yaws if they had reactive serology with no current clinical manifestations of active disease , and active yaws if they had skin lesions consistent with yaws and reactive serology . For the purposes of this analysis , we defined a low titre RPR result as an RPR of <1/32 and a high titre RPR result as an RPR of ≥1/32 . All analyses were performed in Stata 13 . 1 ( Statacorp , Texas ) . Written informed consent was obtained from the head of each household , who was the parent or guardian of children enrolled in the study , and assent was obtained from all children . Ethical approval for the study was granted by the ethics committees of the Ministry of Health and Medical Services in the Solomon Islands , LSHTM in the UK , and the CDC in the US .
Of the 1 , 497 children that were enrolled in the baseline survey , paired sera and PAXgene blood samples from 147 children were tested [13] . The median age of children included was 9 years ( IQR 7–12 ) and 90 ( 61% ) were male . 12 ( 8 . 2% ) children had reactive serology ( reactive RPR and TPPA ) and a skin lesion consistent with active yaws , and 128 ( 87% ) had reactive serology without evidence of active disease and were classified as latent yaws . The remaining 7 ( 5% ) children had non-reactive serology , and their samples were included as negative controls for PCR ( Supplemental Data 1 ) . The prevalence of active yaws in the population included here was significantly higher than in the overall baseline survey population ( 3 . 7% , p = 0 . 012 ) [13] , consistent with our deliberate oversampling for this study of individuals with reactive serology . Of individuals included 97 children ( 66% ) had a low titre RPR , and 43 children ( 29% ) had a high titre RPR . All blood samples tested negative for T . pallidum ssp . pertenue with the real-time quadriplex PCR . Nested-PCR amplification also was negative for all samples . The internal control ( human RNase P gene ) was amplifiable from all blood samples . The limit of detection ( LOD ) of the real-time PCR was determined to be approximately10 to 100 genomic copies of the T . pallidum Nichols strain per reaction . The real-time PCR had an efficiency of 90% and a coefficient of correlation ( R2 ) of 0 . 98 . The nested PCR products that were derived from serial 10-fold dilutions all had very low threshold cycle ( Ct ) values as compared to the non-nested real-time PCR assay , but the LOD remained the same . Although the LOD of both assays was 10 to 100 genomic copies , both assays did occasionally detect samples with <10 genomic copies .
In this study we were unable to detect T . pallidum ssp . pertenue DNA in the blood of patients with latent or clinical yaws infection who were diagnosed on the basis of dual seropositivity . We extracted DNA from a larger volume of blood ( 1–1 . 2mL ) compared to studies of syphilis where ≤ 500 μL blood was used[15 , 16] , which might have been expected to increase the diagnostic yield . By contrast , T . pallidum DNA has been detected in up to 40% of individuals with latent syphilis [11] . The real-time quadriplex PCR we used initially has a detection limit of 10 to 100 genomic copies[14] . Subsequent testing with a real-time PCR modified into a nested PCR was also negative in all the samples , suggesting that a lack of sensitivity of the real-time quadriplex PCR does not explain our findings . In addition , amplification of the human DNA control in all samples ruled out the possibility of PCR inhibitors and showed that the integrity of the DNA was preserved . Nested PCR is presumed to be more sensitive than real-time PCR although there is limited published data to support this . The nested-PCR assay and real-time PCR we tested had the same detection limit; however , it is possible that nested PCR might detect T . pallidum DNA more frequently in samples with <10 genomic copies as suggested by the low Ct values versus real-time PCR but this remains to be investigated . Haematogenous dissemination of spirochaetes is thought to be responsible for the development of late stage disease in treponemal infection , suggesting that patients are at least temporarily spirochaetaemic . In fact , previous studies have shown that PCR can be used to detect treponemal DNA from the blood at every stage of syphilis[16] . Severe late stage cardiovascular and neurological manifestations are well recognised in untreated syphilis . Whilst some studies have suggested that neurological and cardiovascular manifestations may rarely occur in yaws[17] , these findings are difficult to interpret given the historical difficulty in differentiating T . pallidum ssp . pallidum and T . pallidum ssp . pertenue . It is possible that compared to T . pallidum ssp . pertenue , T . pallidum ssp . pallidum is more virulent and associated with more frequent spirochaetaemia , and that our results reflect this difference . In our study we used a clinical and serological definition of yaws . It is increasingly recognised that other organisms may cause ulcers in individuals in yaws endemic communities[12 , 18] . Whilst this might have resulted in some individuals who truly had latent yaws as being classified as having active disease it does not alter our finding that we were unable to detect T . pallidum ssp . pertenue from any of the individuals in this study . An attenuated clinical phenotype of yaws has been described in the Solomon Islands with little advanced tertiary disease seen [19] . Although the cause of attenuated disease is not known , it might be possible that the strain of T . pallidum ssp . pertenue in the Solomon Islands is less invasive than that seen in other countries such as Ghana where late stage disease is still seen . Studies should be conducted in other countries where yaws is endemic to confirm our findings . Our findings have implications for surveillance strategies in the WHO yaws eradication campaign . Relapses of latent disease could be responsible for onward transmission to previously uninfected contacts , but current tools can not differentiate individuals who are serofast from individuals with true latent infection and who therefore require treatment . As the goal of the yaws control programme is eradication , a tool that could differentiate true latent infection from individuals who are serofast would be of value in a post-MDA setting , where clinical and serological surveys are recommended to identify individuals who require re-treatment . Our findings suggest that PCR of blood is unlikely to be useful in that context . Whilst azithromycin resistance has not yet been reported in T . pallidum ssp . pertenue , it is now well-established in T . pallidum ssp . pallidum and monitoring for its emergence will be important to yaws programmes in the post-MDA phase . We had hoped that PCR of blood samples would allow monitoring for azithromycin resistance in individuals with latent yaws . As we were not able to amplify T . pallidum ssp . pertenue DNA from blood samples , we were not able to apply PCR testing for mutations associated with azithromycin resistance . In conclusion , we were unable to detect T . pallidum ssp . pertenue DNA in the blood of individuals with latent yaws . The development of improved diagnostic tools should be a high priority for the yaws eradication campaign .
|
Yaws is a bacterial infection closely related to syphilis . The WHO has launched a worldwide campaign to eradicate yaws by 2020 . For each clinically apparent case , many close contacts are infected but do not show clinical signs , which is called latent yaws . Currently , diagnosis for these patients relies on the detection of antibodies using syphilis serology . Reliance on detection of antibodies for diagnosis of latent yaws is problematic as the test may remain positive with a low titre despite successful treatment . This makes the interpretation of surveillance data in a post-mass treatment setting difficult for programme managers . In syphilis , PCR can detect T . pallidum ssp . pallidum in the blood of patients with latent disease . We used similar techniques to try and detect the yaws bacterium in the blood of patients seen as part of a study of yaws in the Solomon Islands . Although many people had positive antibodies for yaws consistent with latent yaws , we were unable to detect the bacterium in the blood . This may reflect the lower virulence of the yaws bacterium compared to syphilis . Currently available molecular techniques are therefore not able to aid programme managers in conducting post-mass drug administration surveillance for latent yaws . The development of alternative diagnostic tests should be considered .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Failure of PCR to Detect Treponema pallidum ssp. pertenue DNA in Blood in Latent Yaws
|
Hepatitis C virus ( HCV ) RNA is synthesized by the replicase complex ( RC ) , a macromolecular assembly composed of viral non-structural proteins and cellular co-factors . Inhibitors of the HCV NS5A protein block formation of new RCs but do not affect RNA synthesis by pre-formed RCs . Without new RC formation , existing RCs turn over and are eventually lost from the cell . We aimed to use NS5A inhibitors to estimate the half-life of the functional RC of HCV . We compared different cell culture-infectious strains of HCV that may be grouped based on their sensitivity to lipid peroxidation: robustly replicating , lipid peroxidation resistant ( LPOR ) viruses ( e . g . JFH-1 or H77D ) and more slowly replicating , lipid peroxidation sensitive ( LPOS ) viruses ( e . g . H77S . 3 and N . 2 ) . In luciferase assays , LPOS HCV strains declined under NS5A inhibitor therapy with much slower kinetics compared to LPOR HCV strains . This difference in rate of decline was not observed for inhibitors of the NS5B RNA-dependent RNA polymerase suggesting that the difference was not simply a consequence of differences in RNA stability . In further analyses , we compared two isoclonal HCV variants: the LPOS H77S . 3 and the LPOR H77D that differ only by 12 amino acids . Differences in rate of decline between H77S . 3 and H77D following NS5A inhibitor addition were not due to amino acid sequences in NS5A but rather due to a combination of amino acid differences in the non-structural proteins that make up the HCV RC . Mathematical modeling of intracellular HCV RNA dynamics suggested that differences in RC stability ( half-lives of 3 . 5 and 9 . 9 hours , for H77D and H77S . 3 , respectively ) are responsible for the different kinetics of antiviral suppression between LPOS and LPOR viruses . In nascent RNA capture assays , the rate of RNA synthesis decline following NS5A inhibitor addition was significantly faster for H77D compared to H77S . 3 indicating different half-lives of functional RCs .
Direct-acting antivirals ( DAAs ) targeting the hepatitis C virus ( HCV ) include specific inhibitors of the NS3/4A protease/helicase , the NS5B RNA-dependent RNA polymerase and the NS5A protein . Combination therapies with two or more DAAs can result in a sustained virological response ( SVR ) in most infected persons and have revolutionized treatment of chronic hepatitis C in the USA and other developed countries . Inhibitors targeting NS5A are a key component of antiviral regimens currently used in the clinic . These include ledipasvir , daclatasvir , ombitasvir , elbasvir and velpatasvir . Next generation NS5A inhibitors in clinical development include , ruzasvir , pibrentasvir and odalasvir . NS5A inhibitors were originally identified by screening libraries of compounds for antiviral activity in cell-based screening assays [1] . NS5A was identified as the target of this class of drug by streptavidin pulldown of a biotinylated inhibitor from lysates of HCV-infected cells and also by sequence analysis of drug-resistant replicons . Initially , the mode of action of this class of drug was unclear since NS5A has no known enzymatic activity and its structure is only partly characterized . Furthermore , NS5A is a multifunctional protein that participates in several processes in the viral life cycle . Recent studies have shed some light on the mode of action of NS5A inhibitors but molecular mechanisms remain incompletely characterized . Studies of NS5A inhibitors in vitro [2] and in vivo [3] suggest a dual mode of action with inhibition of both viral RNA synthesis and virion assembly . The molecular mechanisms underlying NS5A inhibitor blockade of RNA synthesis have been studied in greatest detail but much remains uncharacterized . HCV RNA genomes are synthesized by multi-protein replicase complexes ( RCs ) composed of viral and cellular proteins in association with the membranous web , a virus-induced organelle composed of remodeled ER membranes . Interestingly , NS5A inhibitors do not inhibit RNA synthesis directly but rather inhibit formation of new RCs [2] in part by blocking biogenesis of the membranous web [4]–a process mediated by the interaction of NS5A with the host lipid kinase phosphatidylinositol-4 kinase IIIα ( PI4K-IIIα ) [5] . In our previous studies using the genotype 1a H77S . 3 virus , we noted a potent but partial inhibition of RNA synthesis by NS5A inhibitors at early time points following addition of antiviral drug to infected cells [2] . In assays that measured viral RNA synthesis , residual viral RNA abundance , and viral polyprotein synthesis , kinetics of antiviral suppression were slower for NS5A inhibitors compared to other classes of DAA such as protease or polymerase inhibitors . Earlier studies using genotype 1b replicon-bearing cells also noted slow kinetics of antiviral activity for NS5A inhibitors compared to other classes of DAA [6] . These data suggest a model where NS5A inhibitors do not block RNA synthesis from pre-existing RCs , but rather prevent formation of new RCs . In contrast to these findings , recent studies using a Gaussia luciferase- ( GLuc- ) expressing virus based on the genotype 2a JFH-1 clone indicated that NS5A inhibitors block GLuc expression faster than protease or polymerase inhibitors [7] . The difference between this and previous studies may be explained by the use of the highly replicative JFH-1 . To better understand the biology underlying the different kinetic responses of JFH-1 and H77S . 3 to NS5A inhibitors , we examined a panel of viruses that can replicate in cell culture . Kinetics of reporter virus decline following addition of NS5A inhibitors to infected cell cultures varied according to the sensitivity of the viral RCs to lipid peroxidation ( LPO ) . Lipid peroxidation resistant ( LPOR ) viruses such as JFH-1 [8] and H77D [9] showed a more rapid decline compared to lipid peroxidation sensitive ( LPOS ) viruses such as H77S . 3 [10] following treatment with NS5A inhibitors . Further studies capitalized on two isoclonal gt1a cell culture infectious viruses: the LPOS virus H77S . 3 and the LPOR virus H77D . To understand differences between LPOS and LPOR viruses , we developed a mathematical model of intracellular RNA dynamics following treatment with DAAs . The different kinetics of decline following NS5A inhibitor treatment can be explained by a model in which the functional RC half-life is shorter for LPOR viruses compared to LPOS viruses . The model was validated using a nascent RNA capture assay to directly monitor the effect of NS5A inhibitors on RNA synthesis . Overall , our results show that the half-life of RCs assembled by different viruses varies widely .
Our previous studies show that when cells infected with the gt1a HCV clone H77S . 3 are cultured with NS5A inhibitors , the decline in intracellular viral RNA abundance and new viral RNA synthesis is relatively slow compared to other classes of DAA such as protease and polymerase inhibitors [2] . These unique kinetics of RNA synthesis inhibition suggested a model where NS5A inhibitors block assembly of new RCs but are unable to inhibit RNA synthesis from pre-existing RCs in infected cells . In contrast to our findings , other studies using reporter viruses based upon the gt2a HCV clone JFH-1 found that NS5A inhibitors resulted in more rapid decline in RNA abundance compared to other classes of DAA [7] . To resolve this discrepancy , we examined kinetics of antiviral suppression for H77S . 3 or JFH-1/QL carrying a GLuc reporter gene . Strikingly different kinetics of antiviral suppression were observed between H77S . 3/GLuc2A ( Fig 1A ) and JFH-1/QL/GLuc2A ( Fig 1B ) following treatment with the NS5A inhibitor elbasvir . These data suggest that the discrepancies between previous studies [2 , 7] represent a genuine difference in the response to NS5A inhibitors between the two viruses and are not simply a result of differences in experimental systems used . The mode of action is likely to be the same for both viruses: NS5A inhibitors block new RC formation but do not block RNA synthesis from pre-existing RCs . The faster kinetics of inhibition for JFH-1/QL/GLuc2A suggest that pre-existing RCs become non-functional faster for JFH-1 compared to H77S . 3 . One simple explanation for the difference in kinetics is that JFH-1/QL is genotype 2a whereas H77S . 3 is genotype 1a . However , an alternative explanation for the difference in kinetics is differential sensitivity to lipid peroxidation: the JFH-1 replicase is LPOR while the replicase of H77S . 3 ( and most other HCV clones ) is LPOS . To distinguish between these possibilities , the kinetics of antiviral suppression by the NS5A inhibitor elbasvir were measured by GLuc assays using a panel of virus genomes including those with LPOS RCs ( H77S . 3 , N . 2 ) and those with LPOR RCs ( H77D , HJ3-5 , JFH-1/QL ) [9] . Differences between the LPOS vs LPOR viruses can be best visualized by plotting the maximum % inhibition ( Emax ) against time ( Fig 1C ) . Following addition of elbasvir to infected cell cultures , kinetics of antiviral suppression for LPOS viruses resembled H77S . 3 whereas kinetics of antiviral suppression for LPOR viruses resembled JFH-1/QL . Another measure that illustrates the different responses of the two classes of virus to NS5A inhibitor is time until Emax = 50% ( shown in Table 1 ) . Previous studies have suggested that sensitivity to different classes of antivirals and kinetics of antiviral suppression is influenced by replicative fitness [11] . In partial agreement with this study , we find that GLuc expression from highly replicative LPOR virus strains is inhibited faster than for moderately replicative LPOS virus strains ( compare kinetics of antiviral suppression in Fig 1C with relative virus fitness measurements in Fig 1D ) . However , virus fitness is a continuous variable whereas sensitivity or resistance to LPO is a discrete property of the virus-encoded RC . The different virus strains shown in Fig 1C show kinetics of antiviral suppression by elbasvir that fall into two discrete categories: LPOR or LPOS . An intriguing difference in kinetics of inhibition was observed for H77S . 3/GLuc2A and H77D/GLuc2A ( compare Fig 1A and 1E ) , two isoclonal genotype 1a viruses . H77D is a LPOR virus derived from H77S . 3 , a LPOS virus [9] . H77D differs from H77S . 3 by only 12 amino acids localized through the non-structural proteins ( Fig 2A ) but replicates to at least 10-fold higher levels than H77S . 3 [9] . Only two of the amino acids that differ between H77S . 3 and H77D lie within NS5A: 2204 in low complexity sequence I ( LCSI ) and 2416 in domain III . Both are distant from the binding site of NS5A inhibitors in domain I . Amino acids 2204 and 2416 ( from the N-terminus of the polyprotein ) are isoleucine and aspartic acid in H77S . 3 and serine and glycine in H77D . Following elbasvir treatment , replication of H77S . 3/GLuc2A carrying I2204S and a D2416G mutations in NS5A was suppressed with kinetics similar to H77S . 3/GLuc2A , not H77D/GLuc2A ( Fig 2B ) . The kinetics of antiviral suppression were not influenced by differences in fitness resulting from the substitutions ( Fig 2C ) . These data indicate that differences in kinetics of antiviral suppression between H77S . 3 and H77D are not caused simply by the differences in NS5A sequence between the two viruses . The minimum number of adaptive mutations required to convert H77S . 3 to a LPOR virus was previously shown to be 8 plus the removal of the key H77S . 3 adaptive mutation S2204I in NS5A [9] . The resulting virus is LPO resistant but replicates to levels that are too low to support kinetic analyses of antiviral suppression . An additional compensatory mutation in NS4B ( G1909S ) confers higher replication levels [9] that permitted kinetic analyses . The LPOR genome H77S . 3/IS/8mut/GS declined with kinetics identical to H77D ( S1 Fig ) . Further efforts to dissect the amino acid differences between H77S . 3 and H77D showed that changes in NS3 , NS4B and NS5B are not responsible for the different kinetics of GLuc decline following NS5A inhibitor treatment ( S1 Fig ) . Previously , detailed analyses showed that an A1672S substitution in NS4A of H77S . 3 was a key determinant of resistance to LPO by mutating this position back to alanine from serine in the H77S . 3IS/8mut/GS genome shown in Fig 2 [9] . However , introduction of an S1672A substitution into H77S . 3IS/8mut/GS results in replication levels that are too low to support detailed kinetic analyses of GLuc . Overall , we conclude that differences in kinetics of GLuc decline following NS5A inhibitor addition required multiple amino acid substitutions in concert ( possibly including the A1672S change in NS4A ) . This is in agreement with previous studies that demonstrated that multiple amino acid substitutions contribute to the LPO sensitivity/resistance phenotype [9] . Also , the difference in kinetics of GLuc decline following NS5A inhibitor addition tracks closely with the LPO sensitivity/resistance phenotype of the replicase . Importantly , the different rates of GLuc activity decline following NS5A inhibitor treatment depend on the combination of amino acid differences across the non-structural proteins that form the replicase . In cell culture , H77S . 3 and other LPOS viruses replicate to lower levels than LPOR viruses such as H77D or JFH-1 . Addition of the lipophilic antioxidant vitamin E to cell culture medium can boost replication of H77S . 3 by 10-fold but has no effect on H77D [9] . In Fig 1 , cells infected with LPOS virus strains were grown in medium containing vitamin E . We hypothesized that the slower rate of decline observed for H77S . 3 following NS5A inhibitor treatment was due to a protective effect of vitamin E . However the presence or absence of vitamin E in cell culture medium did not affect the rates of GLuc decline of H77S . 3/GLuc2A or H77D/GLuc2A following addition of NS5A inhibitor ( S2 Fig ) . This indicates that RC half-life is not dependent upon the presence or absence of lipid peroxidation itself . There can be a number of reasons for the different rates of decline in GLuc production by cell cultures infected with the H77S . 3/GLuc2A and H77D/GLuc2A viruses following NS5A inhibitor treatment . These include ( i ) faster packaging and export of H77D genomic RNA from infected cells , ( ii ) faster turnover of H77D-infected cells , ( iii ) faster degradation of H77D RNA compared to H77S . 3 , ( iv ) faster turnover of functional H77D RCs . Faster export of H77D is unlikely to be responsible for different rates of GLuc decline following elbasvir treatment since NS5A inhibitors efficiently block virus assembly and release . To formally rule out this possibility , an FFU assay was used to monitor infectious virus production following NS5A inhibitor addition . Rates of inhibition of virus production were not different for H77S . 3 and H77D following elbasvir addition ( S3 Fig ) . Thus differences in rate of export of viral RNA from infected cells are not responsible for different rates of GLuc inhibition . LPOR viruses such as H77D and JFH-1 replicate to higher levels compared to LPOS viruses such as H77S . 3 [9] . These higher replication levels result in a greater degree of cell cycle arrest and a higher frequency of apoptosis in infected cells [12] . To explore the possibility that H77D/GLuc2A-infected cells proliferate more slowly or are lost at a faster rate from infected cells cultures , cell viability and proliferation was compared for Huh-7 . 5 cells following electroporation with H77S . 3/GLuc2A , H77D/GLuc2A or the non-replicating H77S/AAG/GLuc2A ( S4 Fig ) . In a WST-1 cell proliferation assay , infected cells grew more slowly compared to mock-infected cells . By 72 h after plating , the lower viability/growth rate of H77D/GLuc2A was apparent by WST-1 assay . Importantly , no difference was observed between the growth rate of cells infected with H77S . 3 and H77D at 24 and 48 h after plating . These data suggest that differences in cell viability/proliferation are not responsible for different rates of virus decline following addition of NS5A inhibitors . The genomes of H77S . 3 and H77D are very similar with only 12 non-synonymous and 1 synonymous nucleotide changes . Nonetheless , it is possible that these changes result in differences in RNA half-life . To examine this possibility , kinetics of GLuc decline following sofosbuvir treatment were measured for both H77D/GLuc2A and H77S . 3/GLuc2A . Sofosbuvir is a potent nucleotide inhibitor of the NS5B RdRP and blocks new HCV RNA synthesis so declines in GLuc should be proportional to the rate of ( + ) strand RNA loss . In the absence of new RNA synthesis , the rate of ( + ) strand loss from infected cells is determined by a combination of export from the cell and RNA decay , mediated primarily by the cellular 5’ exonuclease Xrn1 [13] . No difference was observed in the kinetics of GLuc decline between the two viruses following addition of sofosbuvir ( Fig 3A ) . In contrast , there was a difference in the rate of GLuc decline between the two viruses following addition of either Compound 23 ( Fig 3B ) , a selective small molecule inhibitor of the host lipid kinase PI4K-IIIα [14] , or the cyclophilin inhibitor SCY-635 [15] ( Fig 3C ) . Like NS5A inhibitors , PI4K-IIIα inhibitors and cyclophilin inhibitors both block membranous web formation and as a consequence , inhibit formation of new RCs [11 , 16–18] . Although protease inhibitors block formation of new RCs , no difference was observed in the rate of GLuc decline between the H77S . 3 and H77D following addition of the PI boceprevir ( Fig 3D ) . This is consistent with previous studies showing that PIs such as boceprevir can block multiple functions of NS3/4A including NS3-dependent functions required for RNA synthesis in preformed replicase complexes [2 , 19] . Taken together , these data suggest that differences in kinetics of antiviral suppression by NS5A inhibitors between H77S . 3 and H77D reflect differences in half-life of functional RC rather than RNA half-life . In cell cultures that are infected with GLuc-expressing reporter HCVs , the GLuc activity in the culture medium represents an indirect measure of intracellular RNA abundance . To better understand the biological basis underlying the differences between rates of decline for different viruses following NS5A inhibitor treatment , we developed a mathematical model of RNA dynamics in HCV-infected Huh7 . 5 cells . To describe the HCV RNA dynamics under different DAAs , we extended previous HCV replication models [3 , 20] to consider the dynamics of the replicase complex as well as three key positive strand viral RNA species in the model ( see Methods ) . At the same time , we kept the complexity of the model at a minimum by explicitly considering only the key processes that we study using DAAs ( in contrast to previous detailed HCV intracellular models [21] ) , such that these key processes can be tested and quantified . Analysis of the model showed that the inhibition data of GLuc activity under elbasvir , sofosbuvir and Compound 23 treatment allows a reliable estimate of the half-life of RCs ( see S1 Text ) . We then fitted the model to the inhibition data and found that the model well describes the patterns shown in the data from both H77S . 3 and H77D viruses under all three treatments ( S5 , S6 , S7 , S8 , S9 and S10 Figs ) . We estimated the rate at which RC becomes nonfunctional and degraded to be 0 . 20 and 0 . 07 hour-1 , corresponding to functional RC half-lives of 3 . 5 and 9 . 9 hours , for H77D and H77S . 3 , respectively ( Table 2 ) . For elbasvir treatment , H77S . 3 has a higher EC50 value than H77D , whereas for sofosbuvir or Compound 23 treatment , the EC50 values are estimated to be similar for the two variants ( Table 2 ) . Overall , our parameter estimation procedure confirms that the half-lives of functional RCs are drastically different for H77S . 3 and H77D variants . There exists a notable delay in the decline in the level of the GLuc-reporter protein upon elbasvir treatment for H77S . 3 in contrast to that measured for H77D ( Fig 4 ) . Virus particle assembly is rapidly blocked following NS5A inhibitor addition . This results in a transient increase in intracellular RNA levels , since RNA is still being synthesized from preexisting RCs but does not get assembled into virus particles and exported . Modeling results suggest that when H77S . 3 assembly is blocked by elbasvir , some of this RNA is redirected from sites of assembly to ribosomes in the cytosol ( where they can act as templates for polyprotein synthesis ) , leading to the transient increase in the H77S . 3 GLuc activity immediately upon elbasvir treatment observed in the data ( Fig 4 and S5 Fig ) . In contrast , for H77D , this rate of redirection is estimated to be very low , suggesting H77D +RNAs at blocked sites of assembly may not be redirected to act as templates for polyprotein synthesis . This low rate of redirection combined with a higher rate of RC degradation lead to an immediate decrease in H77D GLuc activity upon elbasvir treatment . To further test this model and directly monitor decreases in functional RC following NS5A inhibitor treatment , a nascent RNA capture assay combined with qRT-PCR with HCV-specific primers was used . This assay allows measurement of HCV RNA synthesis within different time intervals following addition of DAA . In agreement with our previous studies [2] , inhibition of RNA synthesis was potent ( i . e . , achieved with low drug concentrations ) but only partial at early time points following addition of NS5A inhibitor . Importantly , striking differences were observed between H77S . 3 and H77D in the rate of decline of RNA synthesis following elbasvir treatment ( Fig 5A and 5B ) . For H77D , elbasvir inhibited new RNA synthesis by greater than 50% within 4 h after addition of drug . In contrast , in H77S . 3-infected cells , levels of RNA synthesis inhibition did not exceed 50% until later than 8 h following NS5A inhibitor addition . These measurements strongly support the estimates of RC half-life predicted using the modeling approach . During HCV RNA synthesis , the positive strand genome acts as a template for a complementary negative strand RNA that can form a double stranded RNA ( dsRNA ) with the genome . This dsRNA has been proposed to act as a replication intermediate from which new positive strand genomes are synthesized by strand displacement [22] . In HCV-infected cells , dsRNA-containing foci are considered accurate markers for sites of RNA synthesis . Following addition of NS5A inhibitors to HCV-infected cell cultures , the number of dsRNA foci per cell was decreased for both viruses but the number of dsRNA foci per cell declined faster for H77D compared to H77S . 3 ( Fig 5C ) . These data further support the conclusion that differences in kinetics of antiviral suppression by NS5A inhibitors between H77S . 3 and H77D reflect differences in half-life of functional RC .
Assuming that an NS5A inhibitor binds to NS5A and blocks formation of new membrane-protected RCs soon after its addition to infected cell cultures , the rate of decline of RNA synthesis should reflect the rate of turnover of existing functional RCs . In this study , we have demonstrated that RNA synthesis by LPOR viruses such as H77D show a faster decline in RNA synthesis following treatment with NS5A inhibitors , cyclophilin inhibitors or PI4KIIIα inhibitors compared to LPOS viruses such as H77S . 3 . Resistance to LPO is linked to robust viral RNA replication in cell culture . The ability of JFH-1 to grow in cell culture without adaptive mutations is unusual among HCV isolates from patient samples . Only a handful of other HCV isolates have been shown to infect and replicate in cultured cells and have usually required adaptive mutations . For the majority of HCV strains , the RC is highly sensitive to endogenous LPO [9 , 23] . Treatment with lipid soluble antioxidants such as vitamin E protects against LPO and boosts RNA replication of most isolates of HCV even those lacking cell culture adaptive mutations . In contrast , the robust replication of JFH-1 is insensitive to LPO and lipid soluble antioxidants . HCV replication can promote LPO through interactions of viral proteins with mitochondria [24] . The sensitivity of the HCV replicase to LPO has been proposed as a mechanism by which the virus can auto-regulate its own replication to limit tissue injury , maintain a reduced immune profile and thus persist in the host [9 , 25] . Sensitivity of HCV RNA replication to LPO is complex but maps genetically to several transmembrane or membrane proximal amino acids in NS4A and NS5B . If NS5A inhibitors block formation of new RCs but do not affect preformed RCs , the rate of RNA synthesis decline following addition of inhibitor to infected cells must reflect the decay rate of existing RCs . These data thus suggest that LPOR viruses must have a shorter functional RC half-life compared to LPOS viruses such as H77S . 3 . It is noteworthy that differences between H77S . 3 and H77D in kinetics of RNA synthesis inhibition by NS5A inhibitors are determined by HCV amino acid sequences in transmembrane regions of non-structural proteins . These differences could impact the interaction of non-structural proteins with host membranes to impact the stability of the functional RC . The kinetics of antiviral suppression following addition of elbasvir to cell cultures infected with H77S . 3 were not affected by the presence of Vitamin E in the cell culture medium ( S2 Fig ) . This suggests that lipid peroxidation itself does not affect functional RC half-life . Instead the short RC half-life ( 3 . 5 h ) is an intrinsic feature of the LPOR viruses while the longer RC half-life ( 9 . 9 h ) is an intrinsic feature of the LPOS viruses . The difference in RC half-life between the LPOS and LPOR viruses is likely determined by amino acid differences in the NS proteins ( as shown here for H77S . 3 and H77D ) . It is interesting to speculate that these differences impact the interactions of the NS proteins either with each other , with cellular proteins , or with host membranes in the macromolecular assembly of the RC . At first glance , it is perhaps surprising that the RCs of robustly replicating viruses ( such as H77D or JFH-1 and variants ) appear to have a shorter half-life than less replicative viruses ( exemplified by H77S . 3 but also including most wild type viruses ) . In infected cells , the HCV RC is localized to the virus-induced organelle known as the membranous web . The membrane structure allows compartmentalization of virus replication and limits recognition of pathogen-associated molecular patterns by host pattern recognition receptors such as RIG-I [26 , 27] . There is evidence that the components of the nuclear pore complex are recruited to the membranous web and act to gate access of macromolecules to the component vesicles [26 , 28] . Biosynthetic machinery such as ribosomes are excluded from the membranous web [26] . If there is a component of the RC that is limiting and excluded from the lumen of membranous web vesicles after formation , it is possible that each RC can only replicate a fixed number of genomes before that limiting component is exhausted . High replication rates ( as observed for H77D and JFH-1 ) may result in faster depletion of limiting components and a shorter replicase half-life . If RCs are considered as nano-machines one might expect greater “wear and tear” on highly active machines compared to less active machines . The viral proteins that make up the RC must undergo movements during RNA synthesis ( e . g . helicase and polymerase translocating along the RNA template ) and such movements are accompanied by dissipative processes or protein friction [29] . This process has been described for protein systems in vitro [30] but the consequences of molecular wear are typically masked by synthesis of new molecules in cellular systems . In HCV infected cells , the membrane-associated RCs are continuously generated at spatially distinct sites [31] . The blockade of membranous web biogenesis by NS5A inhibitors effectively unmasks the decay rate of the membrane-protected HCV RC in infected cells and highlights differences between LPO sensitive and resistant viruses .
Huh7 . 5 human hepatoma cells ( Apath LLC , Brooklyn , NY ) were used for all experiments and maintained in DMEM with 10% FBS , 100U/ml Penicillin/Streptomycin and 1X Glutamax ( all Gibco/Life Technologies ) . Cell culture-infectious viruses used in this study have all been described previously and include: genotype 1a viruses H77S . 3 [10] and H77D [9]; the genotype 1b virus N . 2 [9]; the genotype 2a virus JFH-1/QL , a derivative of JFH-1 [8 , 32] containing a Q221L mutation in the NS3 helicase; the genotype 1a/2a chimera HJ3-5 that encodes the core–NS2 coding sequence of genotype 1a H77S in the background of JFH-1 with compensatory mutations in E1 and NS3 [33] . H77S/AAG is a replication incompetent variant of the cell culture-infectious H77S virus that carries a mutation in the NS5B RNA-dependent RNA polymerase [34] . Viruses carrying a Gaussia luciferase ( GLuc ) reporter gene have all been described previously and include: H77S . 3/GLuc2A [10] , H77D/GLuc2A , N . 2/GLuc2A , JFH1/QL/GLuc2A and HJ3-5/GLuc2A [9] . Diagrams with the positions of cell culture adaptive mutations in these genomes are shown in S11 Fig . The NS5A inhibitor elbasvir was a gift from Merck Research Laboratories ( Kenilworth , NJ ) . The nucleoside analog NS5B RdRP inhibitor sofosbuvir was purchased from Chemscene ( Monmouth Junction , NJ ) . We developed an ordinary differential equation ( ODE ) model to keep track of the dynamics of positive strand HCV RNAs ( +RNAs ) that are available for translation in the cytosol/ER ( T ) , +RNAs available for replication in the membranous web ( R ) , or +RNAs associated with the lipid droplets for assembly and export ( A ) . We also keep track of the dynamics of the replicase complex ( C ) . The amount of extracellular Gaussia luciferase ( GLuc ) protein is represented by G . See Fig 6 for a schematic . The following equations describe the model: dTdt=θR−σT+ηA−δT dRdt=σT−θR− ( 1−εSOF ) ( 1−εELB ) ( 1−εC23 ) r ( 1−CCmax ) R+ ( 1−εSOF ) αC−kR−δR dCdt= ( 1−εSOF ) ( 1−εELB ) ( 1−εC23 ) r ( 1−CCmax ) R−μC dAdt=kR−ηA−δA− ( 1−εELB ) ρA dGdt=wT−dG εD=[D]EC50 , D+[D] where D ∈ {ELB , SOF , C23} In this model , +RNAs are transported from the cytosol/ER to the membranous web at rate σ and from the membranous web ( R ) to the cytosol/ER ( T ) at rate θ . +RNAs associated with the lipid droplets ( A ) can dissociate from the lipid droplets and be transported to the cytosol/ER to serve as templates for translation at rate η . The +RNAs in the membranous web ( R ) associate with NS5A and other viral and host proteins to form the replicase complexes ( C ) . Within the replicase complex , negative strand RNAs ( -RNAs ) are synthesized to form double-strand HCV RNAs and then serves as templates for +RNA synthesis . The formation of replicase complex and RNA synthesis are assumed to occur at rate r . Since there is a maximum number of replicase complexes that can be formed in a cell [27] , we use the term r ( 1−CCmax ) to describe the rate of replicase complex formation , where Cmax is the carrying capacity of replicase complexes . +RNAs ( R ) are produced from the replicase complex at rate α , and then they can be shuttled to lipid droplets ( to become A ) at rate k . The +RNAs associated with the lipid droplets ( A ) are assembled and packaged into virions and exported extracellularly at rate ρ . For simplicity , we assume that all +RNAs degrade at rate δ and the replicase complex becomes non-functional and degraded at rate μ . GLuc proteins are translated from +RNAs and secreted into the extracellular medium at rate w , and are degraded at rate d . The impact of DAAs are modeled using the term ( 1 − εD ) , where D represents the NS5A inhibitor elbasvir ( ELB ) , the nucleotide analog sofosbuvir ( SOF ) or Compound 23 ( C23 ) , and εD is the efficacy of the drug treatment at blocking its targeted processes . Elbasvir blocks the formation of new replicase complex ( the process modeled by the term r ( 1−CCmax ) R ) and viral assembly ( the process modeled by the term ρA ) , whereas Compound 23 only blocks the formation of new replicase complexes . Sofosbuvir blocks the synthesis of both + and—strand HCV RNAs , which are described by the terms αC and r ( 1−CCmax ) R , respectively . We use an Emax model to describe the relationship between the drug concentration , [D] , and its efficacy: εD=[D]EC50 , D+[D] , where EC50 , D is the drug concentration needed for a half maximal response of an HCV strain . In addition , we assume that there is a pharmacological delay in the action of sofosbuvir [35] , τSOF: the effective drug concentration is 0 when t < τSOF , and it becomes the concentration used in the experiment when t > τSOF .
|
Inhibitors targeting the HCV NS5A protein are a key component of highly effective interferon-free combination therapies for chronic hepatitis C . Despite their high potency against HCV , the precise details of their mode of action are poorly understood . They are known to block assembly and release of virus particles from infected hepatocytes , resulting in a rapid drop in viral RNA in the blood . Additionally they block formation of intracellular membrane structures that are the site of viral RNA synthesis in infected hepatocytes . By preventing membrane remodeling , NS5A inhibitors effectively block formation of new RCs within the cell . Following addition of NS5A inhibitors to infected cell cultures , the kinetics of antiviral suppression were found to vary between different HCV strains , independent of specific differences in NS5A sequence . Using an integrated experimental and mathematical modeling approach , we provide evidence that the rate of decline of viral RNA abundance in infected cells treated with NS5A inhibitors is determined by the stability or half-life of the functional HCV RC .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"nucleic",
"acid",
"synthesis",
"microbial",
"mutation",
"pathology",
"and",
"laboratory",
"medicine",
"hepacivirus",
"pathogens",
"biological",
"cultures",
"microbiology",
"mathematical",
"models",
"viruses",
"rna",
"viruses",
"cell",
"cultures",
"rna",
"synthesis",
"chemical",
"synthesis",
"research",
"and",
"analysis",
"methods",
"lipids",
"medical",
"microbiology",
"mathematical",
"and",
"statistical",
"techniques",
"microbial",
"pathogens",
"hepatitis",
"c",
"virus",
"hepatitis",
"viruses",
"viral",
"replication",
"biosynthetic",
"techniques",
"biochemistry",
"rna",
"lipid",
"peroxidation",
"nucleic",
"acids",
"flaviviruses",
"virology",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2017
|
NS5A inhibitors unmask differences in functional replicase complex half-life between different hepatitis C virus strains
|
Chromosomal fusion plays a recurring role in the evolution of adaptations and reproductive isolation among species , yet little is known of the evolutionary drivers of chromosomal fusions . Because sex chromosomes ( X and Y in male heterogametic systems , Z and W in female heterogametic systems ) differ in their selective , mutational , and demographic environments , those differences provide a unique opportunity to dissect the evolutionary forces that drive chromosomal fusions . We estimate the rate at which fusions between sex chromosomes and autosomes become established across the phylogenies of both fishes and squamate reptiles . Both the incidence among extant species and the establishment rate of Y-autosome fusions is much higher than for X-autosome , Z-autosome , or W-autosome fusions . Using population genetic models , we show that this pattern cannot be reconciled with many standard explanations for the spread of fusions . In particular , direct selection acting on fusions or sexually antagonistic selection cannot , on their own , account for the predominance of Y-autosome fusions . The most plausible explanation for the observed data seems to be ( a ) that fusions are slightly deleterious , and ( b ) that the mutation rate is male-biased or the reproductive sex ratio is female-biased . We identify other combinations of evolutionary forces that might in principle account for the data although they appear less likely . Our results shed light on the processes that drive structural changes throughout the genome .
The number of chromosomes is one of the most fundamental features of a eukaryotic genome . Chromosome number varies , both between closely related species and within species , and such variation can contribute to divergent adaptation and speciation [1–5] . Shifts in chromosome number typically result from a reciprocal translocation between two acrocentric chromosomes , bringing together two linkage groups ( “fusions” as reinterpreted by [6] ) or by splitting a metacentric chromosome into two ( “fissions” ) . Although genetic drift , selection for changes in recombination rate , and meiotic drive are thought to play a role [7 , 8] , the evolutionary forces that allow fusions and fissions to fix within a population remain obscure . Sex chromosome evolution offers a unique glimpse into these forces . The X and Y chromosomes of male-heterogametic species ( as in mammals ) and the Z and W chromosomes of female-heterogametic species ( as in birds ) differ in many aspects of their evolutionary environments . While Y and W chromosomes are often thought to be evolutionarily similar , Y chromosomes spend all of their evolutionary history in males , while W chromosomes spend none . X and Z chromosomes also differ: X chromosomes spend 1/3 of their evolutionary history in males , while Z chromosomes spend 2/3 of their history in males . Consequently , the four types of sex chromosomes vary in how selection acts on them , in their effective population sizes , in their mutation rates , and in how meiotic drive acts on them [9–12] . All of these factors might play a role in the evolution of chromosomal rearrangements , and so differences in rates of rearrangement among sex chromosomes offer clues to what evolutionary conditions favor changes in genome structure . Structurally , sex chromosomes are the most rapidly evolving parts of the genome in many groups of animals [2 , 11 , 13–15] . In some taxa , such as fishes and squamate reptiles , both XY and ZW sex determination is found among closely related species ( and even among populations within a species ) [14 , 16] . Further , fusions between sex chromosomes and autosomes are relatively easy to detect from karyotypic data , and a large number of such fusions have been discovered [2 , 17] . Thus there are many phylogenetically independent events , providing the opportunity to test whether fusions involving the four different types of sex chromosomes are equally likely to occur and/or establish within a species . A fusion between a sex chromosome and an autosome is usually detected because it creates an odd number of chromosomes in one sex ( Fig 1 ) [2 , 18] . With XY sex determination , a Y-autosome fusion creates an X1X2Y system , with the unfused homologue segregating as a neo-X chromosome . Likewise , X-autosome fusions generate XY1Y2 systems , Z-autosome fusions generate ZW1W2 systems , and W-autosome fusions generate Z1Z2W systems . These neo-sex chromosome systems can often be identified by light microscopy , without molecular cloning or linkage mapping . This has enabled cytogenetic studies to identify many species with sex chromosome-autosome fusions [2 , 19–22] . These data have yet to be used to estimate rates of different types of sex chromosome-autosome fusions . Three main evolutionary forces have been thought to be important to the establishment of fusions . The first is direct selection . While chromosome rearrangements are often considered deleterious [1 , 23] , chromosomal translocations may alter the expression of genes near the breakpoint [18 , 24] , which may sometimes be beneficial [3 , 5] . A second mechanism that has been proposed to establish fusions is sexually antagonistic selection at an autosomal locus [25] . A fusion with a sex chromosome can cause an allele that is beneficial in one sex to spend most or all of its evolutionary life in that sex . Meiotic drive is a third force . During female meiosis in animals , one of the meiotic products goes into the egg , while the others are discarded in the polar bodies . In some species , female meiotic drive preferentially transmits fused chromosomes to eggs , while unfused chromosomes go into polar bodies [26 , 27] . This situation favors X-autosome fusions because they experience female meiosis in two out of every three generations . In other species , female meiotic drive preferentially transmits unfused chromosomes , which selects against X-autosome fusions [21] . Limited data suggests that male meiosis in mammals can also favor the transmission of fused chromosomes [28 , 29] . While these evolutionary forces are known to affect the spread of sex chromosome-autosome fusions , it is unknown how they shape the relative establishment rates of fusions with different sex chromosomes . We begin this study by analyzing a large new data set that includes information on the sex determination system and karyotypes across the tree of life [17] . We focus on fishes and squamate reptiles because these taxa include many independent origins of XY and ZW systems [19 , 20] , allowing us to assess differences in the rates of fusions . We find that Y-autosome fusions become established at a much higher rate than any of the other three types of sex chromosome-autosome fusions . This then motivates us to develop an integrated body of analytic models that predict the relative establishment rates for the different types of fusions . The models incorporate a large number of potentially important factors: deleterious and beneficial fitness effects , sexually antagonistic selection , female meiotic drive , genetic drift , sex-biased mutation rates , and biased sex ratios . We find that the data cannot be explained by models of selection unless there is also some mechanism generating a difference between the sexes , including sex-biased mutation rates , biased sex ratios , or sex-specific selection ( including meiotic drive ) . A particularly plausible explanation is that fusions are slightly deleterious , fix by drift , and occur more frequently in males .
We begin by analyzing the patterns of chromosome fusions in vertebrates , based on our recent compilation of sex chromosome data [17] . Hereafter , we refer to the fusion between a Y chromosome and an autosome as Y-A fusion , and similarly for other sex chromosomes . Examining the raw counts ( Table 1 ) , two interesting patterns emerge . First , there are more species with Y-A fusions ( 101 species ) than with X-A fusions ( 27 species ) . The excess of Y-A fusions over X-A fusions is particularly strong in fishes and squamate reptiles , while the numbers are closer to equality in mammals ( Table 1 ) . Second , sex chromosomes in XY lineages are more often fused than those in ZW lineages ( Table 1 ) . In fishes , 41% ( 45/109 ) of XY species have fused sex chromosomes , whereas only 5% ( 2/38 ) of ZW species do ( Fisher’s exact test P < 0 . 001 ) . In reptiles , 33% ( 40/120 ) of XY species have fusions , whereas only 3% ( 6/240 ) of ZW species do ( Fisher’s exact test P < 0 . 001 ) . Such counts , however , do not take into consideration the phylogenetic relationships among species . To assess the relative rates of the establishment of fusions , we mapped fusion status onto the phylogenetic trees of fishes ( Fig 2 ) and squamate reptiles ( Fig 3 ) . This resulted in datasets containing 163 species of fishes and 261 species of squamate reptiles . We then estimated transition rates between the chromosomal states using Markov chain Monte Carlo ( MCMC ) methods ( see Methods for details ) . We first examined whether XY and ZW systems differ in the rate of fusions . In fish , 98 . 6% of the posterior probability density suggests that fusions occur at a higher rate in XY than in ZW lineages ( Fig 4 ) . In squamates , 99 . 9% of the posterior probability density supports this conclusion ( Fig 4 ) . These analyses are based on a reduced model where fissions were allowed to occur at an equal rate in XY and ZW systems , although similar results are obtained if we allow both fusion and fission rates to differ between sex determining systems ( S1 and S2 Figs ) . We next asked if fusion rates differ for the four types of sex chromosomes ( see S1 Text ) . We found that Y-A fusions establish at a higher rate than other sex chromosomes , even when accounting for the shared evolutionary history among taxa ( S3 Fig for fish and S4 Fig for squamates ) . To evaluate the plausibility of various mechanisms to explain the excess of fusions involving Y chromosomes , we compared the rate of establishment of different sex chromosome-autosome fusions under various evolutionary scenarios . The core results are derived in S1 Text , where we present expressions for the rates at which fusions with the four types of sex chromosomes are established . These results follow the standard population genetic practice ( e . g . , [9] ) of modeling establishment rates as the product of the rate of appearance and fixation probability for mutations of interest ( here fusions ) , explicitly allowing for sex-biased mutation rates and biased sex ratios . To facilitate comparison to the data , we focus on the establishment rates for Y-A , Z-A , and W-A fusions relative to the rate of X-A fusions . We begin by studying the neutral case , where selection is absent . We allow , however , for sex-biased mutation rates and biased sex ratios among breeding individuals . We then ask how these neutral results are altered by the three main evolutionary forces thought to impact the rate of fusions: direct selection , meiotic drive , and sexually antagonistic selection .
A major finding in our study is that Y-autosome fusions occur more frequently than other sex chromosome fusions in vertebrates , particularly in fishes and squamate reptiles . In amphibians , only one species in the database has multiple sex chromosomes , and it involves a Y-A fusion ( Table 1 ) . Because mammals and birds have only male heterogametic ( XY ) and female heterogametic ( ZW ) systems , respectively , we cannot use these taxa to conduct phylogenetic tests of the association between fusions and XY or ZW systems . We note , however , that there are many more known mammalian species with fusions , but only three avian species ( Table 1 ) . These data are consistent with our conclusion that fusions occur at a higher rate in XY than in ZW lineages . Interestingly , however , mammals have roughly as many species with X-A fusions as with Y-A fusions . This suggests that evolutionary forces acting on fusions in mammals may be different from those in fish and reptiles . In particular , the form of female meiotic drive appears to vary among mammals , with drive favoring fused chromosomes in some species and unfused chromosomes in [26 , 27] . This leads to a pattern in which species with X-A fusions tend to have metacentric chromosomes ( i . e . , drive generally favors fused chromosomes ) , while species with Y-A fusions tend to have acrocentric chromosomes ( i . e . , drive generally favors unfused chromosomes ) [21] . It is necessary to further examine the correlation between the frequencies of acrocentric ( or metacentric ) chromosomes and the types of fusions in many taxa . Invertebrates provide a promising system for further phylogenetic analyses , with sex chromosome variation in several groups [2 , 13 , 17 , 38] . In Diptera there are seven ZW species 986 XY species , and 42 XO species in the Tree of Sex database [17] . Among these , there is a preponderance of fusions involving the Y: six Y-A fusions , one X-A fusion , and one species with both . Looking across all the invertebrates in the Tree of Sex database , there are many more cases of Y-A fusions ( 247 species ) than X-A fusions ( 32 species ) , W-A fusions ( 8 species ) , and Z-A fusions ( 4 species ) ; an additional 69 species have both X-A and Y-A fusions . While these data are consistent with the idea that Y-A fusions establish at a higher rate among invertebrates , a proper phylogenetic analysis is needed . A recent analysis of jumping spiders found only Y-A fusions ( involving between four and seven independent events ) among species that had both X and Y chromosomes [2 , 22] . Several X-A fusions were also identified , but these occurred only in species lacking a Y . Similar analyses in other groups of invertebrates promise to shed more light on sex chromosome evolution . Our theoretical analyses clarify the conditions under which fusions involving the Y chromosome are more likely to become established . Interestingly , several plausible explanations fail to account for the data . Neutral fusions could account for an excess of Y-A over X-A fusions if fusions arise more often in males , but under such conditions the theory predicts that Z-A fusions should also be common , which contradicts the data ( Table 1 , Figs 2 and 3 ) . Likewise , beneficial fusions cannot explain the data , as they would tend to favor the accumulation of fusions involving the X or Z , which provide more abundant targets for new fusions than the Y or W . Furthermore , hypotheses in which fusions are established because they capture sexually antagonistic alleles also fail , because the smaller population sizes of Y and W sex chromosomes decreases the rate at which these types of fusions arise , counterbalancing the advantage they gain when capturing sexually antagonistic alleles . To account for the preponderance of Y-A fusions thus requires more complicated explanations , involving both selection and sex biases . We consider three plausible explanations below . Other evolutionary forces not considered in this study may be important to the evolution of sex chromosome-autosome fusions . For example , we ignored inbreeding and spatial structure in our models . We also did not consider fusions that capture alleles held polymorphic by heterozygote advantage , but the fate of fusions is unaffected by such loci [25] unless there is inbreeding [44] . Furthermore , it is plausible that fusions may be more likely to involve some sex chromosomes for reasons that are independent of sex . For example , Y and W chromosomes often accumulate repetitive elements [13 , 38] , which could make them more prone to fusion through nonhomologous recombination . X-A and Z-A fusions may also appear more ephemeral because the neo-Y and neo-W chromosomes that they generate could be lost without substantial fitness reductions due to masking in the hemizygous sex , leading to a loss of the multiple sex chromosome systems that we have used to detect fusions . Alternatively , the Y and W may be less likely to be captured by a fusion when they are diminutive in size relative to the X and Z . Similarly , direct selection on fusions may be chromosome specific . For example , deletions and changes to gene expression may be less problematic on degenerated Y and Z chromosomes . While our analytical results allow for mutation rates and fitness effects to depend on the specific chromosome involved ( S1 Text ) , our figures and conclusions were drawn assuming that there were only sex-specific and not chromosome-specific effects . As more data emerge about chromosome-specific mutation rates and selection , the analytical results can guide refinements to these conclusions .
We compiled lists of species with multiple sex chromosome systems ( X1X2Y , XY1Y2 , ZW1W2 , and Z1Z2W systems ) from the Tree of Sex database [17] . Although X1X2Y systems ( or ZW1W2 systems ) can also arise from species with XO ( or ZO ) systems through a reciprocal translocation between an X ( or a Z ) and an autosome [2 , 20] , XO or ZO systems are rare in vertebrates [17] ( Table 1 ) . In addition , although fission of sex chromosomes can also create multiple sex chromosome systems [2 , 20] , such fissions are also rare in vertebrates [18 , 20 , 21] . We therefore focus this discussion on fusions , although the data analysis allowed fissions as well as fusions ( S1 Text ) . We address two questions with our empirical analyses . First , do Y-A ( W-A ) fusions occur at different rates than X-A ( Z-A ) fusions ? Second , are there differences in rates of fusion between male and female heterogametic lineages ? For both questions , we first simply tabulated the numbers in the database and computed Fisher’s exact test . This ignores phylogenetic non-independence but allowed us to use all of the available data . To gain a better estimate of the rates at which fusions with different chromosomes get established , we fit phylogenetic models to the fusion data . We first matched sex chromosome systems from the fish dataset to a recent time-calibrated phylogeny of teleosts [45] , containing 7811 species ( we note that a small number of species were removed from the published phylogeny due to errors discovered after publication; M . Alfaro , personal communication ) . We matched the data of sex chromosome systems from squamates to the squamate phylogeny [46 , 47] using genetic data from 4161 species . In order to maximize overlap between the trait data and the species , we used an approximate matching algorithm for unmatched species: 1 ) retain all species that occur in both the tree and the dataset; 2 ) replace an unmatched species in the tree with a randomly selected unmatched species in the dataset from the same genus as long as this did not result in more than two representatives from the genus ( this assumes monophyly of genera but avoids determining node order for nodes not in the original trees ) . We then pruned down the phylogeny down to those tips with data assignments . In a first set of analyses , we fit a four-state Markov model ( following [48] ) : 1 ) male heterogametic unfused; 2 ) male heterogametic fused; 3 ) female heterogametic unfused; 4 ) female heterogametic fused . We assumed that the probability of a fusion or fission event did not depend on whether the sex chromosomes were highly differentiated ( heteromorphic ) or not ( homomorphic ) . To reduce model complexity , we first identified parameters for which little information exists in the data and that are similar biologically to other model parameters . We then used likelihood ratio tests to determine whether keeping these parameters distinct significantly improved the likelihood of the observed data ( see S1 Text for details ) . We fit the best supported models using a MCMC approach , as implemented in the diversitree R package [49] , to estimate the posterior probability that XY fusions occurred at a greater rate of ZW fusions . We set broad exponential priors on all parameters ( mean = 0 . 05 ) . We ran the MCMC for 50 , 000 generations and removed the first 10 , 000 for burn-in . To accommodate auto-correlation between parameters , we calculated the difference between the rate of XY fusion and ZW fusion across the posterior distribution . In a second set of analyses , we repeated these procedures , considering X-A , Y-A , Z-A , and W-A fusions separately . Code to reproduce all empirical analyses is available at https://github . com/mwpennell/fuse .
|
Chromosome number is a basic feature of the eukaryotic genome that has important consequences for recombination , segregation , and other processes . Despite a century of research on the evolution of karyotype , however , we still have little understanding of the evolutionary forces that enable chromosomal fusions and fissions to become established . Here , we compare the rates of chromosomal fusions between sex chromosomes ( X , Y , Z , and W chromosomes ) and autosomes . We find that these fusions more frequently involve the Y chromosome than other sex chromosomes in fishes and squamate reptiles . To account for these observations , we conduct theoretical analyses and find that the most likely explanation for this pattern is that fusions have deleterious effects , and further that mutation rates and/or sex ratios are biased . Improving our knowledge of the evolutionary mechanisms driving sex chromosome-autosome fusions provides a richer understanding of the forces that shape chromosomes generally .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Y Fuse? Sex Chromosome Fusions in Fishes and Reptiles
|
Malaria and schistosomiasis often overlap in tropical and subtropical countries and impose tremendous disease burdens; however , the extent to which schistosomiasis modifies the risk of febrile malaria remains unclear . We evaluated the effect of baseline S . haematobium mono-infection , baseline P . falciparum mono-infection , and co-infection with both parasites on the risk of febrile malaria in a prospective cohort study of 616 children and adults living in Kalifabougou , Mali . Individuals with S . haematobium were treated with praziquantel within 6 weeks of enrollment . Malaria episodes were detected by weekly physical examination and self-referral for 7 months . The primary outcome was time to first or only malaria episode defined as fever ( ≥37 . 5°C ) and parasitemia ( ≥2500 asexual parasites/µl ) . Secondary definitions of malaria using different parasite densities were also explored . After adjusting for age , anemia status , sickle cell trait , distance from home to river , residence within a cluster of high S . haematobium transmission , and housing type , baseline P . falciparum mono-infection ( n = 254 ) and co-infection ( n = 39 ) were significantly associated with protection from febrile malaria by Cox regression ( hazard ratios 0 . 71 and 0 . 44; P = 0 . 01 and 0 . 02; reference group: uninfected at baseline ) . Baseline S . haematobium mono-infection ( n = 23 ) did not associate with malaria protection in the adjusted analysis , but this may be due to lack of statistical power . Anemia significantly interacted with co-infection ( P = 0 . 009 ) , and the malaria-protective effect of co-infection was strongest in non-anemic individuals . Co-infection was an independent negative predictor of lower parasite density at the first febrile malaria episode . Co-infection with S . haematobium and P . falciparum is significantly associated with reduced risk of febrile malaria in long-term asymptomatic carriers of P . falciparum . Future studies are needed to determine whether co-infection induces immunomodulatory mechanisms that protect against febrile malaria or whether genetic , behavioral , or environmental factors not accounted for here explain these findings .
Malaria and schistosomiasis , caused by the protozoan Plasmodium and the trematode helminth Schistosoma , respectively , impose tremendous public health burdens in tropical and subtropical countries . Whereas malaria afflicts ∼210 million people annually , with ∼0 . 6 million malaria deaths in 2012 caused primarily by Plasmodium falciparum in sub-Saharan Africa [1] , Schistosoma infects ∼240 million people annually , with >90% of cases occurring in Africa [2] . In humans , schistosomiasis manifests as chronic inflammation around schistosome eggs that are embedded within host tissues . Specifically , urogenital schistosomiasis , caused by Schistosoma haematobium , affects the ureteral or bladder wall and can lead to hematuria-induced anemia , urogenital deformities , bladder cancer , and diminished health-related quality of life [3] . The substantial epidemiological overlap of these two parasitic infections invariably results in frequent co-infections [4] . The challenges facing the development of a highly effective malaria vaccine have generated interest in understanding the interactions between malaria and co-endemic helminth infections , such as those caused by Schistosoma , that could impair vaccine efficacy by modulating host immune responses to Plasmodium infection [5] . Both malaria and schistosomiasis are endemic to Mali , a landlocked country in West Africa with a population of 14 . 9 million . Intense , seasonal transmission of malaria occurs over much of the country , with ∼2 . 1 million malaria cases reported in 2012 [1] . Malaria control strategies include distribution of insecticide-treated bed nets , indoor residual spraying , intermittent preventative therapy , and active case detection of febrile cases at the community level [1] . From 2004–2006 , the overall S . haematobium prevalence in Mali was 38 . 3% but varied widely by region [6] , and attempts to control the disease with mass drug administration ( MDA ) with praziquantel have been ongoing since 2005—initially through the Schistosomiasis Control Initiative and then as part of an integrated , national Neglected Tropical Disease ( NTD ) control program [7] . In co-endemic settings such as Mali , the impact of S . haematobium and P . falciparum co-infection on the risk of clinical malaria remains unclear . Independent studies have shown that S . haematobium co-infection can either correlate positively [8] , [9] or negatively [10]–[12] with P . falciparum parasite density . Although baseline S . haematobium infection decreased the risk of febrile malaria in a prospective cohort study of Malian children [10] , it did not alter malaria risk in a malaria vaccine efficacy trial of Kenyan children in which all children received curative treatment immediately prior to the surveillance period [13] . One possible explanation for this discrepancy is confounding by asymptomatic P . falciparum carriage at enrollment , which has been associated with a decrease in the subsequent risk of febrile malaria [14] , [15] and likely accounted for a significant proportion of children in the Malian study [10] but not the Kenyan study [13] . Additional factors that have been shown to associate with both urogenital schistosomiasis and malaria while possibly affecting subsequent malaria outcomes are co-infection with helminths other than S . haematobium [13] , [16] , iron-deficiency anemia [17]–[20] , and contextual factors related to geography and ecology [9] , [21] , [22] . To clarify the relationship between urinary schistosomiasis and malaria , we evaluated the effect of baseline S . haematobium mono-infection , asymptomatic P . falciparum carriage ( baseline P . falciparum mono-infection ) at the end of the six-month dry season , and co-infection with both parasites on the risk of febrile malaria in a prospective cohort study of Malian children and adults living in an area where both diseases are co-endemic . Individuals diagnosed with urogenital schistosomiasis were treated with praziquantel within 6 weeks of enrollment , prior to the peak of the malaria transmission season . We adjusted for possible confounders of malaria risk , including age , sickle cell trait ( HbAS ) , anemia , and spatial factors as determined by distance from home to river and residence within a cluster of high S . haematobium transmission .
The Ethics Committee of the Faculty of Medicine , Pharmacy and Dentistry at the University of Sciences , Techniques , and Technology of Bamako , and the Institutional Review Board of the National Institute of Allergy and Infectious Diseases , National Institutes of Health approved this study ( ClinicalTrials . gov identifier: NCT01322581 ) . Written , informed consent was obtained from adult participants and from the parents or guardians of participating children . The study was conducted in the village of Kalifabougou , Mali , which is located 40 km northwest of Bamako , Mali . Kalifabougou is in the savanna ecoclimatic zone where annual rainfall is 800–1 , 200 mm per year . Among its inhabitants , Bambara is the predominant ethnic group , and ∼90% of residents engage in subsistence farming . Malaria transmission is intense and seasonal , occurring from June through December , with the vast majority of malaria cases caused by P . falciparum [23] . Schistosoma haematobium is also endemic in this region of Mali , with peak transmission occurring during the dry season from January through March when temporary water sources serve as ideal breeding sites for snails , which are the intermediate hosts for schistosomes . Schistosomiasis control in Kalifabougou is done primarily via case treatment and MDA with praziquantel as part of a national integrated NTD control program [7] . Overall S . haematobium prevalences in nearby communes were 12 . 9% in Kati in 2005 ( data from the Malian national NTD control program ) and 6% in Kambila in 2006 [15] . The study population has been previously described [23] , [24] . Enrollment procedures are summarized in Figure 1 . In July 2010 , prior to the start of this study , we conducted a village-wide census of the Kalifabougou study site and determined the total population to be 4 , 394 . Using the complete census data , we then randomly sampled census ID numbers in an age-stratified manner ( age 3 months to 25 years ) and invited these individuals or their parents/guardians to be screened for participation in the study . Of the 857 individuals who were invited , 747 ( 87% ) agreed to be screened for eligibility . Of the 747 individuals who were screened for eligibility , 695 ( 93% ) met the inclusion and exclusion criteria and were enrolled in May 2011 . Exclusion criteria at enrollment included a hemoglobin level <7 g/dL , axillary temperature ≥37 . 5°C , acute systemic illness , underlying chronic disease , use of antimalarial or immunosuppressive medications in the past 30 days , or pregnancy . Notably , only 29 individuals ( 4% of all individuals screened ) were excluded on the basis of fever . Baseline hemoglobin values , measured by a HemoCue analyzer , were used to determine anemia status based on WHO criteria [25] . As part of MDA [7] , [26] , all residents >5 years of age received albendazole , ivermectin , and praziquantel in March 2011 ( prior to enrollment ) and only albendazole and ivermectin in October 2011 . During the scheduled clinic visits , blood was collected by finger prick every two weeks to prepare dried blood spots on filter paper . Detection of asymptomatic Plasmodium infection by PCR was done retrospectively at the end of the surveillance period . Detailed methods for PCR detection have been described [23] . Plasmodium positive samples were identified as P . falciparum , P . malariae , or both ( mixed infections ) . For each participant , PCR was performed on blood samples in chronological order from enrollment onwards until the first P . falciparum infection was detected . Geographic coordinates of the study participants' place of residence and the major communal buildings , main roads , and large streams in Kalifabougou were determined using GeoXM global positioning system ( GPS ) receivers ( Trimble ) . Mapping and determination of distances were performed using ArcView 8 . 0 software ( Esri ) and QGIS version 2 . 0 . 1 ( http://www . qgis . org/; map provider: glovis . usgs . gov ) . Differences in the baseline characteristics between the S . haematobium positive and negative groups ( Table 1 ) and attrition rates were assessed by Fisher's exact test . Linear trends in proportions were assessed by the Cochran-Armitage trend test , whereas differences in means were assessed by Welch's t test . The likelihood ratio test [29] was used to identify high-transmission spatial clusters for S . haematobium , P . falciparum , or both parasites at the time of enrollment ( May 2011 ) . The Kaplan-Meier survival curve was used to estimate the probability of remaining free of clinical malaria during the surveillance period , and the log-rank test was used to compare the survival curves of different subgroups . The Cox proportional hazards model was applied to evaluate the differences in the risk of febrile malaria between the four subgroups: uninfected ( reference group ) , S . haematobium mono-infection , P . falciparum mono-infection , and co-infection with S . haematobium and P . falciparum . The Cox model includes the following potential confounding variables ( age and distance are continuous ) : age ( per year increase ) , closest distance from home to river ( largest stream in Kalifabougou; per 100 m increase ) , HbAS , mild anemia at baseline , residence within a S . haematobium high-transmission cluster and presence of a metal roof on the participant's home . We also explored a model in which S . haematobium mono-infections at baseline were stratified as light ( <10 eggs per 10 ml urine ) or heavy ( >10 eggs per 10 ml urine ) but saw no significant difference in risk between the two groups . Thus , S . haematobium mono-infection was treated as binary covariate for all subsequent regression analyses . Moreover , an interaction term between anemia and S . haematobium infection was included in the model given the differential risk of malaria between S . haematobium infected individuals with and without anemia . The effect of S . haematobium and/or P . falciparum infection on log-transformed parasite density ( asexual parasites/µl ) during first malaria episodes was assessed by multiple linear regression with the following independent variables: HbAS , residence in a S . haematobium high-transmission cluster , and anemia as categorical variables; and log transformations of age and distance from clinic as continuous variables . Missing data were assumed to be missing at random . Statistical significance was defined as a 2-tailed P value of <0 . 05 . Spatial analyses were performed in SaTScan version 9 . 2 ( http://www . satscan . org/ ) . All other analyses were performed in R version 3 . 0 . 2 ( http://www . R-project . org ) .
Of 695 individuals enrolled , 616 ( 89% ) provided blood and urine samples for P . falciparum and S . haematobium diagnosis , respectively ( Figure 1 ) . Of these , 62 ( 11% ) were microscopy positive for S . haematobium , 293 ( 48% ) were PCR positive for P . falciparum at enrollment , and 39 ( 6 . 3% ) individuals were co-infected with both parasites . Individuals with heavy S . haematobium infections ( >9 eggs/10 ml of urine , n = 13 ) were no more likely to be co-infected with P . falciparum than those with light infections ( 1–9 eggs/10 ml of urine , n = 49; odds ratio 1 . 4; 95% confidence interval [CI] , 0 . 33–7 . 2; P = 0 . 75 , by Fisher's exact test ) . Contemporaneous P . falciparum asexual parasite densities by microscopy were similar in both heavy and light S . haematobium infections ( mean 140 parasites/µl blood; 95% CI , 1 . 6–280; mean 290 parasites/µl blood; 95% CI , −24–600; P = 0 . 37 , by Welch's t test ) . Consistent with their recent anti-helminth treatment via MDA , only 31 ( 5 . 1% ) of individuals had other helminthic infections at enrollment by stool microscopy with a single S . mansoni infection and 30 infections with the non-pathogenic intestinal helminth Hymenolepis nana . For real-time PCR diagnosis of additional helminth infections , only subsets of available samples were analyzed given the overall negative findings ( Table 1 ) . Additional baseline characteristics are shown in Table 1 . Sex , HbAS , presence of mild anemia at enrollment , and presence of other helminthic infections were similarly distributed between S . haematobium infected and uninfected individuals ( Table 1 ) . The proportion of children with baseline S . haematobium infections increased with age ( χ2 = 44 . 6 , P<0 . 001 by Cochran-Armitage test for trend; Table 1 ) . Individuals infected with S . haematobium were more likely to reside furthest away from both the health clinic and the main river in Kalifabougou ( top tertile of distance from home to clinic or river ) and were twice as likely to be infected with P . falciparum at enrollment by Fisher's exact test ( unadjusted odds ratio = 2 . 0 , P = 0 . 02; Table 1 ) . Of the 616 individuals who provided initial samples for this study , 560 ( 91% ) completed follow up from May 2011 to January 2012 . Among the 56 individuals who did not complete the study , 6 individuals ( 11% ) had a clinical malaria episode with one death due to cerebral malaria . Those who remained free of malaria were censored at their last visit . The most common reasons for withdrawing were extended travel outside the study area ( 50% ) and refusal of further blood draws ( 43% ) . Three women withdrew due to pregnancy . The attrition rate was highest in adults ( 3 months–2 years: 9% , 3–6 years: 8% , 7–8 years: 6% , 9–10 years: 9% , 11–17 years: 10% , 18–25 years: 40%; P<0 . 001 ) . There was an increase in the attrition rate among individuals who were S . haematobium-infected at the time of enrollment ( uninfected: 7% , P . falciparum mono-infection: 9% , S . haematobium mono-infection: 22% , co-infected: 15%; P = 0 . 056 ) . Gender , spatial measures , sickle cell trait , anemia , and roof type were similarly distributed between those who did and did not complete the study . Geographical clustering may explain the disproportionate increase in S . haematobium infections and co-infections in areas furthest way from the clinic and river . We used SatScan as a tool for identifying geographical clusters that can be used as a proxy for unmeasured confounders related to S . haematobium infection and polyparasitism in regression models . The spatial distribution of PCR-positive P . falciparum infections , S . haematobium infections , co-infections , and uninfected controls at enrollment is shown in Figure 2 . In May 2011 , there was significant clustering of S . haematobium infected and co-infected individuals in an area centered ∼3 km north of the health clinic ( 28 cases , n = 94 , relative risk [RR] = 4 . 57 , P<0 . 0001; 27 cases , n = 158 , RR = 6 . 51 , P<0 . 0001 , respectively ) . Both clusters overlapped substantially; therefore , only the co-infection cluster is shown in Figure 2 . PCR-positive P . falciparum infections clustered significantly in two areas centered ∼4 . 6 km east-northeast ( 35 cases , n = 41 , RR = 1 . 90 , P<0 . 001 ) and ∼3 . 3 km west-southwest ( 12 cases , n = 12 , RR = 2 . 15 , P = 0 . 04 ) of the clinic ( Figure 2 ) . Estimating the risk of P . falciparum blood-stage infection prospectively can be used as a surrogate of P . falciparum exposure [30] . Thus , we assessed the time to the first PCR-positive P . falciparum infection in individuals who began the study without P . falciparum infection and found no difference in the median time to P . falciparum PCR positivity between the S . haematobium uninfected and infected groups ( 89 days [95% confidence interval , CI , 81–96 days]; 92 days [95% CI , 83–125 days] , respectively , P = 0 . 6 , Figure 3A ) . Given that asymptomatic P . falciparum carriage has been shown to affect the risk of febrile malaria [14] , [15] and associates with S . haematobium infection [8] , [9] , [11] , we estimated the risk of febrile malaria in individuals with 1 ) baseline S . haematobium mono-infection , 2 ) baseline P . falciparum mono-infection , 3 ) co-infection with both P . falciparum and S . haematobium , and 4 ) neither infection ( uninfected ) . In the unadjusted analysis ( Figure 3B ) , pairwise log-rank test between the uninfected group ( median time to first malaria episode , 152 days [95% CI , 143–169 days] ) and the 3 infected groups revealed significant delays in time-to-first malaria episode with P . falciparum mono-infection ( median time not reached , P<0 . 001 ) and co-infection ( median time not reached , P<0 . 001 ) but not with S . haematobium mono-infection ( median time not reached , P = 0 . 054 ) . After adjustment for age , distance from home to river , HbAS , anemia , residence in the S . haematobium high-transmission cluster , and roof type in the Cox proportional hazards model , the protective effect of baseline P . falciparum mono-infection on febrile malaria persisted ( hazards ratio [HR] = 0 . 71 , 95% CI 0 . 55–0 . 92 , P = 0 . 01; reference group: uninfected , Table 2 ) . Baseline co-infection with P . falciparum and S . haematobium associated with enhanced protection from febrile malaria ( HR = 0 . 44 , 95% CI 0 . 22–0 . 90 , P = 0 . 02; reference group: uninfected , Table 2 ) , but the difference was not statistically significant relative to P . falciparum mono-infection ( HR = 0 . 62 , 95% CI 0 . 31–1 . 3 , P = 0 . 19; reference group: P . falciparum mono-infection ) . Subset analysis of only individuals who were confirmed as negative for other helminth infections by stool PCR ( Table 1 , n = 142 ) revealed a similar association between co-infection and reduced malaria risk ( HR = 0 . 20 , 95% CI 0 . 06–0 . 71 , P = 0 . 01; reference group: uninfected ) . Increased distance from the river was an independent predictor of malaria protection , while age , HbAS , and residence within the S . haematobium high-transmission cluster were associated with a non-significant trend towards reduced malaria risk at the parasite density threshold of ≥2500 asexual parasites/µl ( Table 2 ) . Metal roof houses have been previously shown to associate with reduced malaria risk , especially when they represent well-constructed housing [31] , [32] as they do in Kalifabougou . However , we did not see any association between presence of a metal roof and malaria protection . Hazard ratio estimates of malaria risk using secondary definitions of malaria episodes ( i . e . parasite density thresholds of any parasitemia , ≥500 , and ≥5000 asexual parasites/µl ) are shown in Table 2 . We found a significant interaction between anemia and S . haematobium infection , as the protective effect of S . haematobium was apparent only in individuals without anemia ( Figure 3C ) . Inclusion of an interaction term between anemia and the two S . haematobium infected groups in the Cox model strengthened the association between baseline co-infection with protection from febrile malaria ( HR = 0 . 14 , 95% CI 0 . 034–0 . 57 , P = 0 . 006; reference group: uninfected , Table 3 ) , and notably , co-infection was significantly more protective than P . falciparum mono-infection ( HR = 0 . 19 , 95% CI 0 . 05–0 . 80 , P = 0 . 02; reference group: P . falciparum mono-infection ) . Multiple linear regression analysis of parasite density at the first febrile malaria episode revealed that increasing age and asymptomatic P . falciparum carriage were strong negative predictors of parasite density , whereas light S . haematobium mono-infection ( 1–9 eggs/10 ml urine ) had no effect on parasite density at the first malaria episode ( Table 4 ) . Interestingly , individuals with heavy S . haematobium mono-infection ( ≥10 eggs/10 ml urine ) only suffered from febrile malaria episodes with parasite densities of <500 parasites/µl , and among those episodes , heavy S . haematobium negatively predicted parasite density ( Table 4 ) . Baseline co-infection with both P . falciparum and S . haematobium was a significant , independent predictor of lower P . falciparum parasite density at all definitions for malaria except for the malaria case definition ( ≥2500 asexual parasites/µl ) ( Table 4 ) .
Investigating the relationship between different parasitic infections in co-endemic communities at the population level is challenging due to the possibility of confounding by unknown variables that co-associate with both diseases . The interaction between P . falciparum and S . haematobium is one relationship where evaluating confounders may help explain inconsistent findings in the literature . In this prospective cohort study of malaria risk , we accounted for several possible confounders and observed that S . haematobium infection enhances protection from febrile malaria in individuals with asymptomatic P . falciparum carriage . In this study , asymptomatic P . falciparum carriers were more likely to be co-infected with S . haematobium at enrollment , which corroborates previous cross-sectional studies [8] , [9] , [11] . Prospective , univariate analysis demonstrated that both baseline P . falciparum and S . haematobium mono-infections predict protection from malaria , with stronger significance for P . falciparum than S . haematobium ( Figure 3B ) , possibly due to a difference in statistical power ( i . e . there was a low number of S . haematobium mono-infections ) . Taken separately , both of these findings are consistent with previous studies done in areas of seasonal malaria transmission [10] , [14] , [15] . Notably , co-infection with both parasites conferred greater protection from subsequent febrile malaria ( Figure 3B ) , a finding that , to our knowledge , has not been reported elsewhere . To further investigate this finding , we performed an adjusted analysis of malaria risk in which we included covariates that could potentially affect malaria outcomes based on prior studies ( age , HbAS , anemia ) or that were differentially distributed between individuals with and without S . haematobium at baseline ( age , distance from home to river , residence within a high-transmission cluster ) . In the adjusted model , asymptomatic P . falciparum carriage and co-infection , but not S . haematobium mono-infection , independently predicted protection from febrile malaria ( Tables 2 and 3 ) . Given that we stopped screening individuals for gastrointestinal helminths by stool PCR at only 23% of the cohort due to completely negative findings ( Table 1 ) , it is possible that baseline co-infection with these and other helminths among unscreened individuals confounded our findings . However , the malaria-protective effect of S . haematobium and P . falciparum co-infection persisted even when the same regression analysis was restricted to stool-negative individuals ( n = 142 ) , suggesting that co-infections with gastrointestinal helminths are unlikely to confound our interpretation of the data . Our data demonstrates that heavy S . haematobium infections at baseline predict lower P . falciparum parasite densities at the first malaria episode , suggesting a potential negative interaction between the two parasites . However , we did not observe differences in malaria risk by intensity of S . haematobium infection , perhaps due to the low prevalence of heavy S . haematobium infections in our study ( 2 . 1% ) , a finding consistent with recent praziquantel MDA . The above findings may help reconcile the disparity between two previous prospective cohort studies of S . haematobium infection and malaria risk . A study conducted in Mali reported an association between baseline S . haematobium infection and protection from malaria attacks but did not differentiate between mono-infection and co-infection [10] . Conversely , S . haematobium mono-infection did not influence malaria risk in a malaria vaccine efficacy study conducted in Kenya in which all children were cleared of P . falciparum with anti-malarials immediately prior to surveillance [13] . It is important to note , however , in both our study and the Kenyan study , the frequencies of S . haematobium mono-infections were small ( 3 . 7% and 8% , respectively ) , suggesting limited power to detect a difference in malaria risk for this group . Indeed , with only 23 cases of S . haematobium mono-infections , we had a 32% probability of detecting a hazard ratio of 0 . 62 or smaller at a 2-sided significance level of 0 . 05 . While we might have detected more light infections had we examined more than one urine specimen per individual or used a more sensitive molecular diagnostic assay [33] , it is evident that additional studies with larger sample sizes , perhaps in an area of higher S . haematobium prevalence , are needed to better address whether S . haematobium mono-infection confers protection from febrile malaria per se and also if infection intensity might affect malaria risk . A plausible mechanism of how co-infection enhances the protection conferred by asymptomatic P . falciparum carriage against febrile malaria is suggested by prior studies which demonstrated increased production of the anti-inflammatory cytokine IL-10 in co-infected individuals relative to individuals infected with only P . falciparum by either analysis of circulating plasma cytokines [34] or after in vitro stimulation of peripheral blood mononuclear cells with P . falciparum schizont extract [35] . In addition , we have observed that P . falciparum-inducible IL-10 responses are upregulated in asymptomatic children with chronic P . falciparum infections [36] . Thus , S . haematobium infection could further augment anti-inflammatory responses induced by asymptomatic P . falciparum infection , thereby blunting the risk of fever upon subsequent P . falciparum infections . Curiously , baseline co-infection predicted protection from febrile malaria despite treatment with the anti-schistosomal agent praziquantel ( Figure 3B ) . This suggests that the putative immunomodulatory effects of S . haematobium persist for an unknown period of time following clearance of S . haematobium . Although speculative , it is plausible that co-infection induces epigenetic changes that maintain an anti-inflammatory environment—a mechanism described in a mouse model of S . mansoni infection at the level of alternatively activated macrophages [37] . However , several alternative possibilities could also explain the protective association of co-infection with malaria risk despite praziquantel therapy . Individuals who were infected at baseline may simply be at the highest risk for re-infection after treatment . An important limitation of our study is that we were not able repeat surveillance for urogenital schistosomiasis to determine the re-infection frequencies in our cohort . Since we also did not confirm parasite clearance after administration of praziquantel , modulation of host responses by persistent S . haematobium infection due to ineffective killing of juvenile worms ( a known limitation of praziquantel ) or complete drug failure remains a possibility . The latter is less likely given that , to our knowledge , there have been no reports of praziquantel failure in Mali since the initiation of MDA . An immunomodulatory effect of praziquantel per se is another possibility , but this is not supported by our data given the more modest protection seen in individuals who received praziquantel for S . haematobium mono-infection during the same time period as co-infected individuals ( Table 2 ) . Lastly , unknown genetic , behavioral , or environmental factors that co-associate specifically with co-infected individuals and reduced malaria risk may be confounding the findings of this study . We were intrigued by the observation that those individuals living the furthest from the clinic and river were more likely to be infected with S . haematobium at baseline and hypothesized that geographical clustering may explain this finding . Indeed , spatial cluster analysis of infections at the time of enrollment clearly demonstrated a significant cluster of S . haematobium-infected and co-infected individuals in an area north of the clinic , where a striking 28 of the 62 ( 45% ) S . haematobium cases reside . Here , we used SatScan solely for identifying geographical clusters of disease that can then be used as a proxy for unmeasured confounders in regression models , noting that the spatial statistic employed by SatScan operates ideally when disease information is known for all households rather than a sampling of households . Nevertheless , these findings support a previous study in Kenya which found that intense infections of P . falciparum and S . haematobium clustered together within a subset of individuals even after the authors controlled for behavioral factors related to exposure to both parasites , implicating host susceptibility factors as the reason for this phenomenon [9] . By including residence in the S . haematobium transmission cluster as a covariate in our models , we were thus able to adjust for geospatial factors related to S . haematobium infection that we could not directly measure , such as host susceptibility factors , unmapped water sources serving as co-infective reservoirs , and micro-heterogeneity of malaria exposure , the last which has been shown to occur at sites similar to Kalifabougou [38] , [39] . However , comparable rates of P . falciparum blood-stage infection between individuals with and without baseline S . haematobium infection ( Figure 3A ) would suggest that heterogeneity of malaria exposure is less likely to be a confounder of S . haematobium infection and malaria risk . Reduced malaria risk was seen almost exclusively in S . haematobium-infected individuals without anemia ( Figure 3C ) , and a significant interaction between the two variables was observed in one model of malaria risk ( Table 3 ) . These findings may be related to the intensity of S . haematobium infection , as more severe egg burden has been associated with increased hematuria and anemia [40] as well as increased P . falciparum density [9] . We did not observe an association between infection intensity and anemia in our study , although this may simply be a reflection of the low prevalence of heavy S . haematobium infections in our study . More likely , anemia may simply be a marker of malnutrition [41] , which has been independently associated with increased malaria risk [42] and thus may minimize any malaria-protective effect conferred by S . haematobium infection . Potential sources of bias are worth noting . Although we randomly sampled in an age-stratified manner from the entire village-wide census , we enrolled only healthy , afebrile individuals . Thus , we could have excluded individuals with symptomatic infections ( including P . falciparum and/or S . haematobium infection ) , and it is possible that these individuals would be the most susceptible to subsequent malaria episodes . However , since only 4% of screened individuals were excluded due to fever , the potential for bias would be minor . An addition source of bias may be from the higher attrition rates among the S . haematobium-infected individuals ( 18% ) , as these individuals may have developed malaria had they remained in the study . This potential bias is mitigated by the fact that 55% of the S . haematobium-infected individuals who withdrew from the study were adults and thus would be less likely to have a malaria episode , and three individuals completed more than 6 months of follow up prior to withdrawal . In summary , we conducted a prospective cohort study to investigate the relationship between S . haematobium and P . falciparum infection and the risk of febrile malaria that accounted for several biological and contextual variables . We observed that S . haematobium co-infection is associated with enhanced protection from febrile malaria in long-term asymptomatic carriers of P . falciparum . Future studies are needed to investigate whether co-infected individuals share other genetic , behavioral , or environmental factors not included here that may explain this association . In addition , further studies are needed to understand the immunological state induced by co-infection and its impact on clinical outcomes of P . falciparum infection .
|
The parasitic diseases malaria and schistosomiasis are tremendous public health burdens , each affecting over 200 million people worldwide with substantial geographic overlap in sub-Saharan Africa . Understanding how schistosomiasis influences the human immune response to Plasmodium , the agent of malaria , can be important for developing effective malaria vaccines . Past studies have tried to determine if infection with Schistosoma haematobium , which causes urinary schistosomiasis , affects the number of febrile attacks from malaria caused by Plasmodium falciparum in communities where the diseases overlap , but the findings have been inconsistent . Here , we examined 616 healthy people from a village in Mali for symptomless infections with S . haematobium and treated those with infections . We then followed them over a single malaria-transmission season of 7 months during which we diagnosed and treated all febrile malaria attacks . After the season , we examined archived blood collected at enrollment to look for occult P . falciparum infection . The study revealed that people who were infected with both parasites at the beginning of the season were better protected from the malaria attacks than those who were uninfected or infected with either parasite alone . Further studies are needed to confirm these findings and to determine the biological basis for this phenomenon .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"clinical",
"research",
"design",
"tropical",
"diseases",
"plasmodium",
"falciparum",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"parasitology",
"observational",
"studies",
"research",
"design",
"protozoans",
"cohort",
"studies",
"neglected",
"tropical",
"diseases",
"zoology",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"malarial",
"parasites",
"helminth",
"infections",
"schistosomiasis",
"biology",
"and",
"life",
"sciences",
"malaria",
"organisms"
] |
2014
|
Co-infection of Long-Term Carriers of Plasmodium falciparum with Schistosoma haematobium Enhances Protection from Febrile Malaria: A Prospective Cohort Study in Mali
|
Enterococcus faecalis is an opportunistic pathogen frequently isolated in clinical settings . This organism is intrinsically resistant to several clinically relevant antibiotics and can transfer resistance to other pathogens . Although E . faecalis has emerged as a major nosocomial pathogen , the mechanisms underlying the virulence of this organism remain elusive . We studied the regulation of daughter cell separation during growth and explored the impact of this process on pathogenesis . We demonstrate that the activity of the AtlA peptidoglycan hydrolase , an enzyme dedicated to septum cleavage , is controlled by several mechanisms , including glycosylation and recognition of the peptidoglycan substrate . We show that the long cell chains of E . faecalis mutants are more susceptible to phagocytosis and are no longer able to cause lethality in the zebrafish model of infection . Altogether , this work indicates that control of cell separation during division underpins the pathogenesis of E . faecalis infections and represents a novel enterococcal virulence factor . We propose that inhibition of septum cleavage during division represents an attractive therapeutic strategy to control infections .
Enterococci are Gram-positive commensal bacteria colonizing the gastrointestinal tract of humans . They are opportunistic nosocomial pathogens that can cause a wide range of life-threatening infections in immunocompromised patients or following antibiotic-induced dysbiosis [1] . The emergence of enterococci as nosocomial pathogens relies on the capacity of these bacteria to colonize the host and to grow in a wide range of harsh conditions ( e . g . , in the presence of bile salts or in iron-depleted environments ) [2] . Enterococci are intrinsically resistant to multiple antibiotics , such as cephalosporins and several aminoglycosides , and can also acquire resistance to glycopeptides . Vancomycin-Resistant Enterococci ( VRE ) represent a major problem in clinical settings as they can potentially transfer resistance genes to other pathogens such as Staphylococcus aureus [3 , 4] . Two enterococcus species , Enterococcus faecium and Enterococcus faecalis are the most clinically relevant [5] . E . faecium infections are caused by a particular subset of clones specifically found in hospital settings that share several acquired mobile genetic elements [6] . In contrast , the E . faecalis strains responsible for hospital-acquired infections are also found in healthy individuals and genes associated with virulence are not only exclusively present in clinical isolates [7] . How this organism can cause infections therefore remains poorly understood . In the present work , we study the regulation of daughter cell separation during cell division and explore the impact of this process on pathogenesis . We previously revealed that in E . faecalis , one peptidoglycan ( PG ) hydrolase with N-acetylglucosaminidase activity ( named AtlA ) is dedicated to septum cleavage to allow separation of daughter cells at the end of division [8] . Using a combination of in vitro experiments and sets of isogenic strains , we describe multiple mechanisms controlling the activity of AtlA . We show that control of septum cleavage during growth underpins the formation of diplococci and short chains , a property critical to cause lethality in the zebrafish model of infection .
In vitro enzymatic assays using recombinant E . faecalis PG hydrolases indicated that AtlA specific activity is 20- to 30-fold lower than the activity of the N-acetylmuramidase AtlB [9] . We hypothesized that a high level of AtlA expression could explain the predominant role of this enzyme in septum cleavage . To test this hypothesis , we compared the amount of AtlA and AtlB produced during growth . We generated two strains producing His-tagged AtlA and AtlB proteins expressed under their own promoters ( PatlA::atlA-his and PatlB::atlB-his ) . Culture samples were harvested at the end of exponential growth ( OD600 = 1 ) and His-tagged AtlA and AtlB were detected by western blotting using an anti-histidine serum ( Fig 1A ) . Unlike His-tagged AtlA , AtlB was barely detectable ( Fig 1A , lanes 2–5 and lanes 6–9; see arrowhead on Fig 1A ) . Next , we tested whether increasing the level of expression of AtlB could enable this peptidoglycan hydrolase to cleave the septum efficiently . We built a strain producing an AtlB-His tagged protein expressed under the atlA promoter ( PatlA::atlB-his ) , thereby replacing the AtlA open reading frame by AtlB . As expected , expression of atlB-his under the control of the atlA promoter increased the production of AtlB-His to levels similar to AtlA-His ( Fig 1A , compare lanes 2–5 with lanes 10–13 ) . The impact of AtlB production on septum cleavage was analyzed by flow cytometry to measure bacterial chain lengths , as previously described [8] . Increasing the production of AtlB to levels similar to those of AtlA was not sufficient to shorten bacterial cell chains ( Fig 1B ) . The cell chain length of the PatlA::atlB-his strain was not significantly different from that of the ΔatlA strains ( P>0 . 05; n = 3 ) . This result indicated that the relatively low production level of AtlB does not account for the low septum cleavage activity of this enzyme . This prompted us to explore the enzymatic properties of AtlA and their impact on cell separation . Previous studies indicated that truncation of the AtlA N-terminal domain ( residues 54 to 172 ) only had a marginal impact on the activity of the recombinant enzyme tested in vitro against whole PG molecules ( sacculi ) as a substrate [10] . We sought to re-investigate the contribution of the N-terminal domain to AtlA activity using an in vitro assay ( as described in [8] ) to specifically measure septum cleavage . A recombinant AtlA protein ( residues 54 to 737 ) harboring an N-terminal 6-Histidine tag and a Tobacco Etch Virus ( TEV ) protease site at the end of the N-terminal domain was expressed in Escherichia coli and purified ( Fig 2A , lane 1 ) . Following cleavage with TEV protease , truncated AtlA was recovered by metal affinity chromatography ( Fig 2A , lanes 2 to 4 ) . In agreement with previous results , the specific activity of the truncated AtlA ( AtlAΔN; 488 . 2 ± 155 . 6 ΔOD600/nmole/min ) was slightly higher than that of AtlA ( 252 . 5 ± 27 . 4 ΔOD600/nmole/min ) when assayed against whole sacculi ( **P = 0 . 0018; n = 9; Fig 2B ) . However , using the specific septum cleavage assay , AtlAΔN was more than 10-fold active than the full-length AtlA enzyme . Whilst 1 . 6 ± 1 . 2 pmoles of AtlAΔN were sufficient to disperse 50% of the cell chains , 18 . 2 ± 2 . 0 pmoles of AtlA were required for a similar septum cleavage activity ( ***P = 0 . 0008; n = 3; Fig 2C ) . We further explored the contribution of the AtlA N-terminal domain in septum cleavage during growth . We built a recombinant strain producing AtlA with a truncated N-terminal domain ( atlAΔN ) ( Fig 3A ) . However , as the parental strain mostly forms diplococci and short chains ( 2–4 cells ) , we anticipated only a limited reduction in bacterial cell chain length . To see a more pronounced reduction in bacterial chain length , we analyzed the impact of the N-terminal truncation in a strain forming longer chains . We suspected that truncating the C-terminal domain of AtlA would impair binding of this enzyme to its substrate and its activity , hence leading to the formation of longer chains . We therefore constructed atlA1-4 , a strain producing an AtlA variant with only 4 C-terminal LysM repeats instead of 6 . AtlA proteins produced by recombinant strains were detected in culture supernatants by Western blot using polyclonal antibodies raised against the catalytic domain of AtlA , indicating that AtlA proteins with the expected molecular weights were produced and secreted in all cases ( S1A Fig ) . The distribution of cell chain lengths measured by flow cytometry was in agreement with our in vitro experiments . Truncation of the N-terminal domain led to the formation of shorter chains as indicated by a significant shift towards lower forward scattered light values ( Fig 3B ) . This conclusion was supported by two pairwise comparisons ( i ) between the cells producing full-length AtlA ( WT; FSC = 46 . 38 ± 0 . 38 ) and its N-terminally truncated counterpart ( atlAΔN; FSC = 40 . 32 ± 0 . 99 ) ( **P = 0 . 0015; n = 3 ) and ( ii ) between the cells producing AtlA with 4 C-terminal LysM repeats ( atlA1-4; FSC = 204 . 45 ± 5 . 71 ) and its N-terminally truncated counterpart ( atlA1-4ΔN; FSC = 80 . 33 ± 1 . 99 ) ( ***P = 0 . 0001; n = 3 ) . The reduction of cell numbers per chain in the mutants producing a truncated AtlA protein was confirmed by light microscopy ( S1B and S1C Fig ) Altogether , these results showed that the N-terminal domain of AtlA negatively controls the septum cleavage activity of this enzyme . The N-terminal domain of AtlA contains a high proportion of threonine and serine residues ( 28% and 12% , respectively; S1D Fig ) . This property prompted us to test whether this domain can be O-glycosylated . To purify AtlA produced by E . faecalis , a recombinant strain expressing a C-terminally 6His-tagged AtlA protein and a TEV site between the N-terminal and catalytic domains was constructed by allele exchange ( Fig 4A ) . Cell surface proteins were extracted with 8M urea and His-tagged AtlA protein was purified by metal affinity chromatography . Two major bands of 75 kDa and 62 kDa matching the expected molecular weights of the full-length and N-terminally truncated AtlA , respectively , were detected ( Fig 4B , lane 1 ) . A carbohydrate moiety was detected on the full-length AtlA protein , but absent on the truncated AtlA ( Fig 4B , lane 2 ) , suggesting that glycosylation occurred at the N-terminal domain of AtlA . To confirm this hypothesis , exponentially growing cells were harvested and incubated in the presence of TEV protease . This treatment released a glycosylated polypeptide matching the apparent molecular weight of the N-terminal domain ( Fig 4C , lanes 2 and 3 and Fig 2A , lane 3 ) . No glycosylated polypeptide was detected when the protease was omitted , therefore indicating that the N-terminal domain of AtlA is glycosylated . In Gram-positive bacteria , two glycosyl transferases named GtfA and GtfB have been shown to be essential for surface protein glycosylation [11–13] . We used allele exchange to inactivate two putative glycosyl transferase homologs ( gtfA and gtfB; EF2891 and EF2892 in E . faecalis V583 ) sharing the same glycosyl transferase domain ( PFAM PF00534 ) . Following incubation of cells harboring an in-frame deletion of the gtfAB locus in the presence of TEV protease , no glycosylated peptide could be detected ( Fig 4C , lanes 4–6 ) . Altogether , these results show that the N-terminal domain of AtlA is glycosylated and that this posttranslational modification requires the glycosyltransferases gtfAB . Next , we explored the impact of AtlA glycosylation on septum cleavage during growth by measuring the bacterial chain length of gtfAB mutants by flow cytometry ( Fig 4D ) and light microscopy ( S2 Fig ) . We compared the cell chain lengths of E . faecalis JH2-2 ( WT ) forming mostly diplococci and short chains ( 2–4 cells ) and atlA1-4 , producing AtlA1-4 lacking two LysM modules ( 6–12 cells ) with the chain length of their ΔgtfAB counterparts . Pairwise comparisons of cell chain length by flow cytometry revealed that ΔgtfAB mutants formed shorter chains than parental strains , thus indicating that the lack of glycosylation enhanced AtlA septum cleavage activity . The cell chains of the parental strain ( WT; FSC = 46 . 09 ± 0 . 43 ) were longer than those from the ΔgtfAB mutant ( FSC = 42 . 04 ± 0 . 66; **P = 0 . 0017; n = 3 ) . A more pronounced difference was measured between the strain expressing the glycosylated C-terminally truncated AtlA ( atlA1-4; FSC = 200 . 87 ± 5 . 71 ) and its non-glycosylated counterpart ( atlA1-4ΔgtfAB; FSC = 101 . 66 ± 1 . 47 ) ( ***P = 0 . 0002; n = 3 ) . When introduced to the ΔatlA genetic background , the deletion of the gtfAB operon did not significantly reduce the bacterial chain length ( FSC = 397 . 68 ± 5 . 37 versus 392 . 95 ± 11 . 37 , respectively; P>0 . 05 ) . Together with light microscopy analyses ( S2C and S2D Fig ) , these results indicate that AtlA glycosylation mediated by gtfAB impairs septum cleavage . Substrate recognition by the catalytic domain of peptidoglycan hydrolases is an important factor modulating enzymatic activity [14] . The fact that AtlA is dedicated to septum cleavage can therefore be underpinned by the recognition and cleavage of a specific peptidoglycan structure present at the septum . We hypothesized that if such is the case , the N-acetylglucosaminidase activity of AtlA should be essential for septum cleavage . To explore this possibility , we constructed a gene replacement vector encoding an allele of atlA with a catalytic domain flanked by two restriction sites ( NcoI and BglII ) . These two sites were used to swap the N-acetylglucosaminidase domain of AtlA for the catalytic domains of E . faecalis N-acetylmuramidase AtlB [8] , Staphylococcus aureus N-acetylmuramoyl-L-Alanine amidase Atl [15] or Streptococcus thermophilus D , L endopeptidase Cse [16] ( Fig 5A and S3 Fig ) . AtlA alleles encoding variants with distinct peptidoglycan cleavage specificities ( Fig 5B ) were introduced on the chromosome by gene replacement and expressed as a single copy under the atlA promoter . Western blot analyses indicated that all AtlA variants were expressed at similar levels , except AtlACse , which was subject to proteolysis ( S3C Fig ) The septum cleavage activity in each strain was analyzed using flow cytometry . Cell chain length of the ΔatlA deletion mutant was used as a reference to define the forward scattered light value corresponding to maximal ( 100% ) chain length . Introduction of the NcoI and BamHI restriction sites had a limited impact on the size of the cell chains ( 11 . 68 ± 1 . 35% of the ΔatlA mutant chains versus 10 . 59 ± 1 . 36% of the ΔatlA mutant for the parental JH2-2 strain ) . All strains expressing AtlA variants with altered enzymatic specificity formed shorter chains than the ΔatlA mutant in exponential phase ( Fig 5C ) . This result indicated that the peptidoglycan cleavage specificity of AtlA is not an essential property of the enzyme for septum cleavage . The forward scattered light measurements corresponding to the strains expressing AtlA variants compared to the ΔatlA mutant ranged from 28 . 71 ± 5 . 38% of the ΔatlA mutant for the strain expressing AtlB with a muramidase activity to 53 . 63 ± 1 . 3% of the ΔatlA mutant for the strain expressing AtlA with an endopeptidase activity . The relatively higher forward scattered light values associated with this strain could be due to the proteolysis of the chimeric protein ( S3C Fig ) . The chain lengths of strains expressing chimeric proteins were all significantly shorter than those of the ΔatlA mutant ( **P<0 . 01; n = 3 ) . To test the contribution of the LysM domain of AtlA ( LysMA ) to septum cleavage , we compared the septum cleavage activity of proteins containing this domain or the LysM domain from AtlB ( LysMB ) using flow cytometry . Four recombinant proteins were produced in E . coli and purified: the full-length AtlA and AtlB proteins ( without a signal peptide ) and derivatives containing a swapped LysM domain ( AtlAB and AtlBA; Fig 6A and 6B; S4 Fig ) . In agreement with our previous work , AtlA septum cleavage activity was much higher than that of AtlB . Whilst 4 . 6 ± 1 . 3 pmoles of AtlA reduced the cell chain length of a ΔatlA mutant by 50% in 15 minutes at 37°C , 100-fold more AtlB was not sufficient to produce the same effect . When AtlA LysMA was replaced by LysMB , the septum activity of the enzyme decreased 17-fold indicating that LysMA is critical for optimal septum cleavage . Swapping LysMB for LysMA in AtlB led to a septum cleavage activity comparable to that of AtlA ( Fig 6C ) . The contribution of individual LysM repeats to the septum cleavage activity was investigated . A set of strains expressing AtlA with 1 to 5 LysM repeats ( henceforth referred to as atlA1 to atlA1-5 strains ) was constructed by allele exchange ( Fig 6D ) . Western blot and zymogram analyses ( S4A and S4B Fig ) showed that all strains produced AtlA proteins with the expected size and in similar amounts , except for the atlA1strain in which lower amounts were detected . AtlA activity decreased as LysM repeats were truncated . Next , we measured the impact of LysM truncations on the septum cleavage activity by measuring the cell chain length . Flow cytometry analyses revealed that sequential truncation of LysM modules led to a stepwise increase of cell chain length ( Fig 6E and S4C Fig ) . This result indicated that optimal septum cleavage requires the full-length LysM domain , each module providing an additive contribution to AtA enzymatic activity . We investigated whether the formation of long chains in E . faecalis has an impact on virulence using the zebrafish model of infection [17] . We compared the lethality induced by the wild-type OG1RF strain to that of an in-frame atlA deletion mutant OG1RF ( ΔatlA ) forming long chains . In the ΔatlA mutant , each chain ( equivalent to 1 CFU ) can contain several viable cells . To eliminate this bias , ΔatlA chains were sonicated to mechanically separate cells ( S5A and S5B Fig ) [18] and establish the number of cells per CFU . This information was then used to inject the same number of cells ( but different CFU numbers ) for each strain . Zebrafish embryos were infected 30h post fertilization ( hpf ) and survival was monitored over the following 90h . One of three independent experiments is shown in Fig 7A whilst the results for three biological replicates are shown in S5C and S5D Fig . Injection of ca . 1 , 000 OG1RF cells killed between 60% and 77% of larvae ( n = 79 ) depending on the experiment . In contrast , the ΔatlA mutant only killed 4% to 20% of larvae ( n = 88 ) , showing a significant reduction in virulence ( ***P<0 . 001 for the experiment shown in Fig 7A ) . At this stage , we envisaged that this difference could be attributed to several factors: ( i ) the increased chain length of the ΔatlA mutant , ( ii ) ( an ) other alteration ( s ) in cell surface properties ( like in the Streptococcus mutans atlA mutant; [19] ) or ( iii ) a defect in biofilm formation [20] . To specifically investigate the contribution of bacterial chain length to virulence , we subjected the ΔatlA mutant to mild sonication . This treatment is dispersing bacterial chains ( S5A Fig ) whilst it does not alter viability ( Dubee et al . , 2011 ) , virulence ( S6 Fig ) or subsequent bacterial growth rate ( S7 Fig ) . Sonication of the ΔatlA mutant thus allowed us to compare cells with an identical genetic background , differing only by the size of their cell chains . This treatment restored the virulence of the mutant to similar levels as the wild-type strain ( P = 0 . 455; Fig 7A ) , killing between 46% and 59% of larvae ( n = 90 ) . Previous work revealed that phagocyte evasion is a critical step for E . faecalis pathogenesis in the zebrafish [17] . We therefore quantified phagocytosis in zebrafish larvae infected with bacteria expressing the green fluorescent protein ( GFP ) ( Fig 7B ) . Confocal microscopy images were used to measure bacterial uptake by phagocytes labelled with anti L-plastin antibodies . To specifically investigate the impact of the chain forming phenotype on phagocytosis , we compared the uptake of long and short bacterial chains formed by the ΔatlA mutant before and after sonication ( ΔatlAS ) . The ratio between green fluorescence area inside to outside phagocytes was significantly higher for the ΔatlA mutant ( **P = 0 . 0098; n = 7 ) . A significant difference was also measured when we compared the uptake of the ΔatlA mutant to that of wild-type ( *P = 0 . 0438; n = 8 ) . As expected , no difference was detected between wild-type and ΔatlAS cells . Representative images used to quantify uptake are shown as an example ( Fig 7B ) . Bacteria forming long chains were mostly found inside phagocytes ( 96% of total fluorescence area ) as opposed to diplococci and short chains corresponding to wild-type and ΔatlAS cells ( 75% and 60% respectively ) . Death rates were the same when immunocompromised zebrafish ( n>20 per group ) were infected with the wild-type OG1RF strain or with the ΔatlA mutant . In both cases , injection of 1250 cells in pu . 1 morphants led to more than 90% mortality within 20h post infection ( Fig 7C and S5E Fig ) . This result suggested that the impaired virulence of the long chain-forming ΔatlA mutant is due to the fact that this strain is unable to evade phagocytosis during infection . To make a direct comparison of the uptake of wild-type and ΔatlA cells by phagocytes , an in vitro assay was performed using monocyte-derived macrophages obtained from peripheral blood mononuclear cells from healthy volunteers ( Fig 7D ) . Phagocytic uptake was quantified following labeling of bacterial cells with pHrodo-S-ester , a pH-sensitive dye displaying increased fluorescence in low-pH compartments of phagosomes [21] . In each pairwise comparison , ΔatlA long chains were more efficiently phagocytosed ( P = 0 . 0024 over 7 biological replicates ) ( Fig 7D ) . Collectively , our results indicate that formation of short chains by E . faecalis is a critical property enabling this bacterium to evade phagocytosis during pathogenesis .
The formation of diplococci and short chains is a distinctive property of E . faecalis that was originally reported over a century ago [22] . This typical morphology results from the separation of daughter cells by the N-acetylglucosaminidase AtlA [8] . Here , we show that multiple mechanisms are in place to control septum cleavage by AtlA . We further demonstrate that the formation of diplococci and short chains is crucial for the virulence of this opportunistic pathogen . Cells with impaired cell separation are more prone to phagocytosis and can no longer cause infection . We propose that the control of cell chain length is a novel virulence factor that links cell division and pathogenesis in E . faecalis . Using a specific assay to measure septum cleavage by flow cytometry , we showed that two post-translational modifications , both occurring on the N-terminal domain of AtlA contribute to down-regulate the activity of this enzyme . Our hypothesis is that the N-terminal domain , predicted to be disordered ( http://prdos . hgc . jp ) , sterically hinders the catalytic activity of AtlA . By extension , glycosylation of the N-terminal domain is expected to further impair PG recognition and cleavage by the catalytic domain . Truncation of the N-terminal domain by extracellular proteases thus ensures optimal activity of the enzyme once it has reached its substrate at the cell surface . N-terminal truncation of the N-terminal domain occurs during growth and can be detected by zymogram [8 , 10] . It is tempting to assume that the proteolytic cleavage of the AtlA N-terminal domain is primarily mediated by the metalloprotease GelE , which has been associated with formation of short chains in E . faecalis [23] . However , the diplococcal state of E . faecalis is not restricted to strains producing GelE , indicating that other proteases can process AtlA . An example is E . faecalis JH2-2: although this strain is deficient for the production of GelE , it forms diplococci and very short chains . Interestingly , the genes essential for AtlA glycosylation were previously shown to catalyze the production of diglucosyl-diacylglycerol [24] . This implies that ( an ) other glycosyl transferase ( s ) mediate ( s ) the direct glycosylation of AtlA . Further investigations are required to identify the corresponding enzyme ( s ) . This task appears relatively difficult given the functional redundancy of glycosyl transferases . The E . faecalis V583 genome encodes 15 putative enzymes that could be responsible for AtlA glycosylation . Our results showed that in the absence of AtlA , the gtfAB deletion has no impact on septum cleavage . Altough we cannot formally rule out that glycosylation of other surface proteins can modulate cell separation , this effect ( if any ) is limited . The identification and characterization of GtfAB substrates awaits further analysis . Another open question deals with the degree of AtlA glycosylation . It is possible that the extent of AtlA glycosylation varies during growth or in response to environmental cues . Recent studies have explored the impact of PG structure on substrate recognition and cleavage by the catalytic domain of PG hydrolases . The N-acetylglucosaminidase LytB , the S . pneumoniae functional homolog of E . faecalis AtlA , requires fully acetylated GlcNAc moieties for cleavage . A substrate-assisted catalytic mechanism involving anchimeric assistance by the C2-acetamido group of the GlcNAc moiety is likely to underpin this requirement [25] . Another example is the pneumococcal autolysin LytA , in which several amino acids in the vicinity of the catalytic residues contribute to positioning of the substrate in the catalytic cleft so that the scissile bond is at an optimal distance from the catalytic residue [26] . Our work revealed that the N-acetylglucosaminidase domain of AtlA is not essential for septum cleavage . However , swapping the AtlA catalytic domain for another domain results in a lower septum cleavage activity . One possibility is that in E . faecalis , AtlA has evolved to optimally recognize and cleave the local PG structure at the septum . Recent work in E . coli and B . subtilis suggested that septal PG is enriched in “denuded” glycan strands resulting from N-acetylmuramoyl-L-alanine amidase activity [27] . Whether denuded glycan strands represent an optimal substrate for AtlA remains to be tested . It is expected that the lack of peptide stems will increase the binding activity of the LysM domain [28] . Thus , measurements of the N-acetylglucosaminidase activity of AtlA against glycan chains and chains substituted by peptide stems should be carried out with the catalytic domain in isolation to uncouple binding of the AtlA enzyme to its substrate from catalysis . Our previous work revealed that AtlA LysM motifs can fold independently and do not interact , thus suggesting that they behave as “beads on a string” [28] . This model implied that instead of forming a quaternary structure , LysM repeats bind PG in a cooperative manner . Here , we showed that septum cleavage increases with the number of LysM motifs , with the formation of diplococci requiring the presence of all six repeats . The model strain V583 encodes twelve proteins with LysM domains; four contain two repeats , seven contain a single repeat , AtlA being the only one with six repeats . Bearing in mind that the formation of diplococci or short chains by E . faecalis is critical for virulence , our results suggest that the lifestyle of this organism as a commensal has favored the emergence of multimodular LysM domains in AtlA . During infection , the size of bacterial cells has a major impact on recognition by the immune system . One example is the cording morphology of mycobacteria , which correspond to snake-like structures formed by the end-to-end and side-to-side aggregation of bacilli [29] . The formation of large bacterial aggregates impairs phagocytosis and is required for virulence [30] . Another well-documented strategy to escape host immunity is the formation of filaments after invasion of epithelial cells by uropathogenic E . coli ( UPEC ) [31] . Inhibition of septation enhances resistance to phagocytosis and increase survival rates of UPEC and other pathogens in the host [32] . The exact mechanism by which filamentation inhibits uptake and killing by phagocytes is unclear . In vitro experiments using anisotropic polystyrene particles and alveolar macrophages revealed that the point of contact between phagocytes and particles is critical for phagocytosis initiation [33] . It has therefore been proposed that the increased cell length in filamentous bacteria reduces the probability of macrophages to encountering the cell poles that stimulate the formation of the phagocytic cup [34] . This early step in the uptake by immune cells appears to be a limiting factor , the internalization speed of filaments itself being similar to that of smaller particles [34] . In contrast , minimization of bacterial cell chains has been described as a strategy to overcome host immunity in Streptococcus pneumoniae [35] . The effect of cell chain length in S . pneumoniae involves subversion of complement-mediated opsonophagocytosis . Interestingly , our assay using monocyte-derived macrophages indicated that in the absence of complement , cell chains can be recognized and readily engulfed by phagocytes . Unlike UPEC filaments , the long chains of enterococci are pretty flexible and often form turns ( S5B Fig ) . This is expected to generate a contact point with phagocytes that will favor cytoskeleton remodeling to form the phagocytic cup [34] . Impaired septum cleavage is primarily expected to restrict the capacity of the bacteria to disseminate and multiply in the host and is not expected to have any impact on clearance by phagocytes . In the context of a systemic infection in zebrafish larvae , diplococci are circulating in the bloodstream . The formation of long chains limits the dissemination of the bacteria , hence increasing their probability of encountering immune cells . We propose that the sequestration of E . faecalis inside the phagocytes prevents cell multiplication and release of the metalloprotease GelE that is essential to cause tissue damage and host death [17] . E . faecalis is a common nosocomial pathogen associated with a wide range of infections that can be life-threatening . It was recently shown to promote the growth of other microorganisms during polymicrobial infections [36] . This study suggests that targeting the enzymatic activity of AtlA , the autolysin dedicated to septum cleavage , represents a novel therapeutic strategy to eradicate E . faecalis .
Monocyte-derived macrophages ( MDM ) were isolated from whole blood from healthy volunteers at the Sheffield Royal Hallamshire hospital with written informed consent prior to inclusion in the study , as approved by the South Sheffield Research Ethics Committee ( 07/Q2305/7 ) [37] . All samples were anonymised . Animal work was carried out according to guidelines and legislation set out in UK law in the Animals ( Scientific Procedures ) Act 1986 under Project License PPL 40/3574 . Ethical approval was granted by the University of Sheffield Local Ethical Review Panel . Bacterial strains and plasmids used in this study are described in Table 1 . E . coli was grown at 37°C in Brain Heart Infusion ( BHI ) broth or agar 1 . 5% ( w/v ) supplemented with 200ug/ml erythromycin ( for pGhost9 derivatives ) and 100ug/ml ampicillin ( pET derivatives ) . E . faecalis strains were grown in BHI broth or agar at 37°C , unless otherwise stated . When necessary , the medium was supplemented with 30 μg/ml erythromycin . London wild type ( LWT ) zebrafish [38] were provided by the aquarium facility at the University of Sheffield . Embryos were maintained in E3 medium at 28°C according to standard procedures previously described [39] . Phagocyte-depleted embryos were obtained following injection of phosphorodiamidate morpholino oligomers against pu . 1 as previously described [40] . Cells were grown to mid-exponential phase ( OD600~0 . 3 ) and harvested by centrifugation ( 5 , 000 x g for 10 min at room temperature ) . Bacteria were resuspended in filtered phosphate buffer saline ( 150 mM Na2HPO4 , 20 mM KH2PO4 , 150 mM NaCl [pH 7 . 5] , PBS ) and transferred to microcapillary pipettes . Embryos at 30 hours post fertilization ( hpf ) were anaesthetized , dechorionated , embedded in 3% ( w/v ) methylcellulose and injected individually with 2nl of a cell suspension corresponding to ca . 1 , 000 cells as previously described [40] . The number of cells injected was checked before and after each series of injections with a given strain . Zebrafish embryos were monitored at regular intervals until 90 h post infection ( hpi ) . Zebrafish larvae were fixed 1 . 5h post infection with 4% paraformaldehyde ( m/v ) at 4°C overnight and washed four times in PBS supplemented with 0 . 4% ( v/v ) TritonX 100 and 1% ( v/v ) DMSO ( PBS-TxD ) . Samples were blocked in 5% ( v/v ) sheep serum in PBS-TxD for 1h at room temperature followed by one wash in PBS-TxD . Cells were incubated at 4°C overnight with primary antibodies ( anti-L-plastin 1:400 , gift from Paul Martin , University of Bristol ) . After four washes in blocking solution , embryos were incubated with secondary antibodies ( goat anti-rabbit IgG , Alexa Fluor 647 conjugate , Life Technologies ) for 2h at room temperature . Larvae were washed four times with PBS-TxD and fixed again with 4% paraformaldehyde ( m/v ) for 30 min at RT . Immunolabelled embryos were mounted using 0 . 8% ( m/v ) agarose in E3 medium and imaged with a confocal microscope . Immunolabelled embryos were immersed in 0 . 8% ( w/v ) low melting point agarose in E3 medium and mounted flat on FluoroDish™ ( World Precision Instruments Inc . ) . Images were collected using a DMi8 confocal microscope ( Leica ) . Image acquisition was performed with the Volocity software and the images were processed with ImageJ 1 . 49v software . Bacterial phagocytosis was quantified using an ImageJ custom script called Fish Analysis v5 which can be obtained from http://sites . imagej . net/Willemsejj/ or via ImageJ updater . All bacteria were identified based on their ( GFP , Channel 1 ) fluorescence . Subsequently , the fluorescence intensity of the phagocytes ( Alexa 647 , Channel 2 ) surrounding the phagocytosed bacteria was measured . The phagocytosed bacteria had high fluorescence intensity of Channel 2 and the cut off of 2 was used to discriminate the phagocytosed from non-phagocytosed bacteria . The area of phagocytosed bacteria was compared with the area of non-phagocytosed bacteria and their ratio was calculated . Monocyte-derived macrophages ( MDM ) were isolated from whole blood from healthy volunteers . Peripheral blood mononuclear cells were isolated by Ficoll Plaque ( GE Healthcare ) density centrifugation . To differentiate PBMC into monocyte-MDM 2×106 PBMC/ml were plated in RPMI 1640 media ( Lonza ) with 2 mmol/l L-glutamine ( Gibco BRL ) containing 10% human AB serum ( First Link ( UK ) LTD ) in 24-well plates ( Costar ) . After 24h , non-adherent cells were removed , and adherent cells were cultured in RPMI with 10% heat-treated fetal bovine serum ( FBS; Lonza ) in 5% CO2 at 37°C to give a final concentration of approximately 2×105 MDM/ml at day 14 [37] . E . faecalis strains were grown to OD600 = 0 . 6 and stored in frozen aliquots at -80°C . Viable counts were determined upon thawing and used to calculate volumes necessary to give desired multiplicity of infection ( MOI ) . Bacteria were labelled with pHrodo dye ( pHrodo Red , succinimidyl ester , Invitrogen ) as previously described [41] . Briefly , bacteria were washed in phosphate-buffered saline before being incubated with 10 . 2 μM pHrodo in 0 . 1M sodium bicarbonate pH8 . 3 for 30min at 37°C protected from light . Excess dye was washed off before MDM were challenged with pHrodo labelled bacteria at MOI = 100 for 4 hours at 37°C . Cells were then fixed in 2% paraformaldehyde and fluorescence ( Ex/Em 560/585nm ) measured on a Varioskan Flash multimode reader ( Thermo Scientific ) . Relative fluorescence values ( RFU ) of cell only wells were subtracted from readings to control for autofluorescence . A similar strategy was followed to construct all plasmids for allele replacement except pGABhis , for which the whole insert was synthesized and cloned into pGhost9 using XhoI and EcoRI restriction sites . For pGAAhis , pGBBhis , pGDN , pGtfAB , pGAtlA1 , pGAtlA1-2 , pGAtlA1-3 , pGAtlA1-4 , pGAtlA1-5 , two homology regions were amplified and fused by overlap extension using PCR [42] . A 5’ homology region ( referred to as H1 ) was amplified with oligonucleotides H11 ( sense ) and H12 ( antisense ) . It was fused to a 3’ homology region ( referred to as H2 ) amplified with oligonucleotides H21 ( sense ) and H22 ( antisense ) . The assembled PCR fragment flanked by two restriction sites was digested and cloned into pGhost9 similarly digested . Oligonucleotide sequences and restriction sites used for cloning are described in S1 Table . For pGAtlA* , three homology regions ( H1 , H2 , H3 ) were fused by overlap extension . The resulting plasmid contains a catalytic domain flanked by NcoI and BamHI sites . The NcoI-BamHI fragment encoding the N-acetylglucosaminidase activity of AtlA was swapped for NcoI-BamHI fragments encoding catalytic domains with distinct catalytic activities to produce pGAtlA-Cse , pGAtlA-AtlB and pGAtlA-Ami generated by PCR ( see Supplemental Experimental Procedures for oligonucleotide sequences ) . The sequences of the chimeric proteins encoded by these plasmids are described in S3 Fig . Isogenic derivatives of E . faecalis JH2-2 were constructed by allele exchange using the procedure previously described [8] . Briefly , pGhost derivatives were electroporated into JH2-2 and transformants were selected at a permissive temperature ( 28°C ) on BHI plates with erythromycin . To induce single crossover recombination , transformants were grown at a non-permissive temperature ( 42°C ) in the presence of erythromycin . The second recombination event leading to plasmid excision was obtained after 5 serial subcultures at 28°C without erythromycin . The last overnight subculture was plated at 42°C without erythromycin . A clone harboring a double crossover mutation was identified by PCR and Southern blot hybridization . To construct double mutants JH2-2 atlA1-4ΔN and JH2-2 atlA1-4ΔgtfAB , the deletion of two LysM repeats was introduced in JH2-2 atlAΔN and JH2-2 ΔgtfAB backgrounds using the pGatlA1-4 plasmid ( Table 1 ) . pET2818 was used as an expression vector to produce C-terminally His-tagged recombinant proteins . To construct pET-AtlATEV , a cleavage site recognized by the Tobacco Etched Virus ( TEV ) protease was introduced by PCR by fusing two amplified products ( named H1 and H2 ) . For pET-AtlAB and pET-AtlBA , a DNA fragment encoding the N-terminal domain of AtlA or AtlB ( referred to as H1 ) was fused to a DNA fragment encoding the LysM domain of AtlA or AtlB ( referred to as H2 ) . The resulting fragments were cut by NcoI and BamHI and cloned into pET2818 that had been similarly digested . Specific oligonucleotides used for each construct are described in S1 Table . The sequences of recombinant proteins expressed in E . coli are described in S3 Fig . E . coli BL21 ( DE3 ) cells harboring pET-derivatives were grown to an optical density at 600 nm ( OD600 ) of 0 . 7 and production of recombinant proteins was induced by addition of 1 mM isopropyl-β-D-thiogalactopyranoside . The cells were harvested 4h after induction , resuspended in buffer A ( 50 mM Tris-HCl [pH 8 . 0] containing 300 mM NaCl ) and sonicated ( 5 times 30 sec at 20% output using a Branson Sonifier 450 ) . Soluble proteins were recovered after centrifugation ( 45 , 000 x g , 20 min at 4°C ) , loaded onto Ni2+-nitrilotriacetate agarose resin ( Qiagen GmbH , Hilden , Germany ) , washed with 10 mM imidazole in buffer A and eluted with 300 mM imidazole in buffer A . Recombinant his-tagged proteins were further purified by size exclusion chromatography on a Superdex75 HR 26/60 column ( Amersham biosciences , Uppsala , Sweden ) equilibrated with PBS . The fractions were analyzed by SDS-PAGE and pooled . Protein concentration was estimated by measuring the absorbance , using a theoretical extinction coefficient at 280 nm ( http://www . expasy . org ) . Proteins were kept frozen at -80°C in PBS supplemented with 25% glycerol . AtlB stocks were available from previous studies [8] . Proteins from supernatants were prepared from exponentially growing cultures ( OD600~0 . 4 ) . Supernatants were precipitated by addition of TCA ( 10% v/v final ) . After 10 min on ice , proteins were recovered by centrifugation ( 15 , 000 x g , 10 min at room temperature ) , washed in 100% acetone , dried and resuspended in SDS-PAGE loading buffer ( 1ml/equivalent OD600 = 50 ) . For the detection of His-tagged AtlA and AtlB produced under the control of the atlA promoter , proteins were prepared from cultures in late exponential phase ( OD600~1 ) . One ml of culture ( containing both cells and supernatant ) was transferred to a tube containing 250μL of glass beads ( 100μm in diameter , Sigma ) . Cells were mechanically disrupted using a FastPrep device ( six cycles of 40 sec at maximum speed with 5 min pauses between cycles ) . Loading buffer was added to the protein samples and equivalents of 40 , 20 and 10 μl of the cultures were separated on a 10% SDS-PAGE . Proteins were transferred to a nitrocellulose membrane . After a blocking step for 1h at room temperature in Tris buffer saline ( TBS , 10mM Tris-HCl pH7 . 4 , 150mM NaCl ) supplemented with tween-20 ( 0 . 025% , v/v ) and milk ( 2% , m/v ) , the membrane was incubated with rabbit polyclonal anti-AtlA antibodies raised against the catalytic domain of AtlA ( 1:1 , 000 dilution ) or polyclonal anti-His antibodies ( 1:2 , 000 dilution; Ebioscience ) . Proteins were detected using goat polyclonal anti-rabbit antibodies conjugated to horseradish peroxidase ( Sigma ) at a dilution of 1:20 , 000 and clarity Western ECL Blotting Substrate ( BioRad ) . Proteins from the supernatant were separated on a 10% SDS-PAGE containing Micrococcus luteus autoclaved cells as a substrate ( final OD600 = 2 ) . After electrophoresis the gel was rinsed in distilled water and proteins were renatured at 37°C in a buffer containing 50mM Tris-HCl ( pH7 . 5 ) and 0 . 1% ( v/v TritonX-100 ) . Cells were grown overnight without agitation at 37°C . Cells were diluted 1:100 into fresh broth ( OD600 ~0 . 02 ) and grown in standing cultures to mid-exponential phase ( OD600~0 . 2 to 0 . 4 ) . Bacteria were diluted 1:100 in phosphate buffer saline filtered on a 0 . 22μm pore size membrane ( Millex-GV syringe filter unit , Millipore ) to eliminate salt crystals which could interfere with measurements and analyzed by flow cytometry using Millipore Guava Easy Cyte system . Light scatter data were obtained with logarithmic amplifiers for 20 , 000 events . To measure the septum cleavage activity of recombinant proteins , the OG1RF ΔatlA mutant was grown to exponential phase ( OD600 = 0 . 2 ) , collected by centrifugation , and bacterial chains were resuspended in filtered PBS containing various concentrations of recombinant proteins . Cell size distribution was determined by flow cytometry after 15 min of incubation at 37°C . Relative logs of forward scattered light values ( FS log ) were collected for 5 , 000 events and expressed as a percentage of the control strain incubated in the absence of enzyme . Cells were grown to mid-exponential phase ( final OD600~0 . 3 ) and fixed with 1 . 6% paraformaldehyde in PBS for 30min at RT . After fixation , bacteria were washed twice in distilled water and mounted onto poly-L-lysine coated slides and imaged using a DeltaVision deconvolution microscope equipped with an UplanSApo 100x oil ( NA 1 . 4 ) objective and a Photometrics Coolsnap HQ CCD camera . ImageJ software was used to optimize contrast and to count the numbers of cells per chain . All experiments reported in this study correspond to at least three biological replicates . Statistical analyses were performed using GraphPad Prism version 6 . 0e . Comparisons between survival curves were made using the log rank ( Mantel-Cox ) test . Median FSC values were compared using a two-tailed , unpaired Student’s t test with Welch’s correction . Comparison of OG1RF and ΔatlA derivative uptake in vitro by MDM was carried out using a paired Student’s t test . Comparison of uptake by zebrafish macrophages was carried out using an unpaired non-parametric Dunn’s multiple comparisons test . The number of cells per chain was compared using a non-parametric Mann-Whitney U test . Statistical significance was assumed at P values below 0 . 05 .
|
Enterococcus faecalis is a commensal bacterium that colonizes the gastrointestinal tract of humans . This organism is an opportunistic pathogen that can cause a wide range of life-threatening infections in hospital settings . Despite the identification of several virulence factors , the mechanisms by which E . faecalis evades host immunity and causes infections remains poorly understood . Here , we explore how the formation of diplococci and short cell chains , a distinctive property of E . faecalis , contributes to pathogenesis . We describe several mechanisms that control the activity of AtlA , the enzyme dedicated to septum cleavage during division . Using a combination of in vitro assays and flow cytometry analyses of E . faecalis mutants , we show that AtlA activity is regulated by several mechanisms . We reveal that during pathogenesis , AtlA activity is critical for overcoming the host immune response . In the absence of AtlA , the long cell chains of E . faecalis mutants are more susceptible to phagocytosis and can no longer cause lethality in the zebrafish model of infection , thus indicating that control of cell chain length is a novel virulence factor in E . faecalis . This work highlights a link between cell division and pathogenesis and suggests that cell separation represents a step that can be targeted to control bacterial infections .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"blood",
"cells",
"flow",
"cytometry",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"enterococcus",
"infections",
"pathogens",
"immunology",
"cell",
"processes",
"microbiology",
"vertebrates",
"animals",
"animal",
"models",
"osteichthyes",
"bacterial",
"diseases",
"model",
"organisms",
"experimental",
"organism",
"systems",
"enterococcus",
"glycosylation",
"phagocytes",
"bacteria",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"spectrum",
"analysis",
"techniques",
"white",
"blood",
"cells",
"fishes",
"animal",
"cells",
"proteins",
"medical",
"microbiology",
"microbial",
"pathogens",
"enterococcus",
"faecalis",
"recombinant",
"proteins",
"phagocytosis",
"spectrophotometry",
"biochemistry",
"zebrafish",
"cytophotometry",
"cell",
"biology",
"post-translational",
"modification",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"glycobiology",
"organisms"
] |
2017
|
Bacterial size matters: Multiple mechanisms controlling septum cleavage and diplococcus formation are critical for the virulence of the opportunistic pathogen Enterococcus faecalis
|
Asthma and chronic obstructive pulmonary disease ( COPD ) exacerbations are commonly associated with respiratory syncytial virus ( RSV ) , rhinovirus ( RV ) and influenza A virus ( IAV ) infection . The ensuing airway inflammation is resistant to the anti-inflammatory actions of glucocorticoids ( GCs ) . Viral infection elicits transforming growth factor-β ( TGF-β ) activity , a growth factor we have previously shown to impair GC action in human airway epithelial cells through the activation of activin-like kinase 5 ( ALK5 ) , the type 1 receptor of TGF-β . In the current study , we examine the contribution of TGF-β activity to the GC-resistance caused by viral infection . We demonstrate that viral infection of human bronchial epithelial cells with RSV , RV or IAV impairs GC anti-inflammatory action . Poly ( I:C ) , a synthetic analog of double-stranded RNA , also impairs GC activity . Both viral infection and poly ( I:C ) increase TGF-β expression and activity . Importantly , the GC impairment was attenuated by the selective ALK5 ( TGFβRI ) inhibitor , SB431542 and prevented by the therapeutic agent , tranilast , which reduced TGF-β activity associated with viral infection . This study shows for the first time that viral-induced glucocorticoid-insensitivity is partially mediated by activation of endogenous TGF-β .
Exacerbations of asthma and chronic obstructive pulmonary disease ( COPD ) are commonly associated with airway viral infection , including respiratory syncytial virus ( RSV ) , human rhinovirus ( RV ) and influenza A virus ( IAV ) [1 , 2] . RSV infection is a major cause of acute respiratory disease ( i . e . bronchiolitis ) , especially in infants and the elderly [3–5] . Most children are infected by RSV at least once by 2 years of age [3] . RSV infection in children does not elicit long-term immunity , and the adaptive immunity following natural infection is poorly protective even in adults . Thus , re-infection occurs throughout life , even with the identical RSV strain [6 , 7] . Severe RSV infection in infancy may result in Th2 and Th17-biased responses , that influence allergic airway inflammation[6] . Approximately 50% of the children who had severe RSV bronchiolitis were subsequently diagnosed with asthma [8 , 9] . In addition to RSV , RV and IAV are also commonly detected in patients with asthma and COPD exacerbations [2 , 10 , 11] . During cellular infection , the viral pathogen-associated molecular patterns ( PAMPs ) , such as viral single-stranded ( ss ) RNA , double-stranded ( ds ) RNA , dsRNA-like structures ( panhandles ) , the 5’ triphosphate structure of viral RNA or some unidentified RNA structures , are detected by pattern recognition receptors , including toll-like receptors ( TLRs ) , retinoic acid-inducible gene ( RIG ) -1-like receptors ( RLRs ) , and nucleotide-binding oligomerization domain ( NOD ) -like receptors ( NLRs ) [1 , 12–14] . Activation of these innate immune receptors induces secretion of primary anti-viral mediators , including Type-I and -III interferons ( IFN-α/β and IFN-λ ) to combat the viral infection [12] . Simultaneously , respiratory viral infection induces the secretion of an array of other pro-inflammatory cytokines and chemokines to recruit inflammatory cells to the site of infection to facilitate viral clearance . The infiltrating inflammatory cells also release inflammatory mediators that may induce tissue damage and compromise function [12 , 15] . There is no effective therapeutic strategy for either RSV or RV infection except for supportive care , including hydration and oxygenation . Development of effective vaccines is challenging due to the immature infant immune system in early-life RSV infection , and to the large number of RV serotypes [5 , 16 , 17] . The approved antiviral drugs for the treatment of IAV , the M2 ion channel blocker ( amantadine and rimantadine ) and neuraminidase inhibitors ( zanamivir , oseltamivir and peramivir ) , are associated with adverse effects or have limited efficacy , respectively . The IAV vaccine is updated annually; however , it still gives limited protection [18] . The most commonly used anti-inflammatory drugs for asthma and COPD exacerbations are glucocorticoids ( GCs ) . However , the majority of clinical studies have found that respiratory viral infection responds inadequately to the anti-inflammatory actions of either inhaled or systemic GCs [19–25] . Moreover , the effect of GCs on virus-induced cytokine secretion is controversial . GCs have been shown to inhibit RSV infection-induced interleukin ( IL ) -8 and macrophage inflammatory protein ( MIP-1 ) secretion from neutrophils [26] , and IL-11 production by lung fibroblasts [27] . However , GCs have been shown to have no effect on the RSV-induced release of IL-8 and MIP-1 during infection of either Hep-2 epithelial cells or primary bronchial epithelial cells [28] . The mechanism by which the inflammation associated with respiratory viral infection is unresponsive to GCs treatment remains unclear . Airway epithelium is a key target for both GC activity and GC resistance [29 , 30] . Infection with RSV or RV has been shown to impair GC transactivation in alveolar epithelial A549 cells , bronchial epithelial BEAS-2B cells , and in submerged primary bronchial epithelial cells [31–36] . However , there is limited understanding of the underlying mechanism of viral-induced GC resistance in epithelial cells . Upon viral infection , airway epithelial cells produce an array of pro- and anti-inflammatory cytokines and chemokines including the type-I and -III interferons ( IFN-α/β and IFN-λ ) , TNFα , IL-4 , IL-8 , IL-13 , IL-17 , CCL3 and RANTES [37] , some of which have been shown to interfere with GC action in epithelial cells: TNFα inhibits GC transactivation in A549 cells and BEAS-2B cells [38]; IL-17 induces GC insensitivity in the human bronchial epithelial cell line , 16HBE14o- [39]; and , IFN-λ-induced JAK/STAT signaling activation is insensitive to GC action in A549 cells and air-liquid interface ( ALI ) differentiated primary human bronchial epithelial cells ( HBECs ) [40] . Viral infection of airway epithelial cells with RSV [41] , RV [42 , 43] , or IAV [44 , 45] also results in increased expression , secretion , and activity of the pleiotropic growth factor transforming growth factor-β ( TGF-β ) . Moreover , endogenous TGF-β enhances RSV replication by induction of cell cycle arrest in an autocrine manner [41] , and increases RV replication by suppression of type I/III IFN expression [42 , 43] . Importantly , our group recently found that TGF-β causes a profound impairment of GC activity in A549 cells , BEAS-2B cells and in ALI-HBECs [46 , 47] . We therefore hypothesize in the current study that viral-infection induced glucocorticoid insensitivity in epithelial cells is due to activation of endogenous TGF-β . We provide evidence that autocrine activation of TGF-β mediates the GC insensitivity induced by RSV , RV , and IAV infection in epithelial cells . Moreover , we also examined the anti-allergic agent tranilast , which has been widely used in Japan and South Korea [48 , 49] . Tranilast has therapeutic effects in many conditions including inflammation , renal fibrosis , autoimmune disorders and cancer . It has been reported that tranilast inhibits the expression and activity of TGF-β in different cell types [50–52] . We show that tranilast inhibits the expression and activity of TGF-β in epithelial cells , and provide the first evidence that TGF-β modulators may be suitable novel therapeutics to restore sensitivity to GC actions during viral infection .
Budesonide ( 0 . 01-100nM ) induced a concentration-dependent increase in the expression of the selected GC-inducible genes . The expression of most of the genes assessed was markedly impaired by RSV infection at a multiplicity of infection ( MOI ) of 0 . 1 virus units/cell for 48 hours ( Fig 1 ) . Genes impaired in this manner included those encoding glucocorticoid-inducible leucine zipper ( GILZ ) , which is an anti-inflammatory/anti-proliferative gene; epithelial sodium channel-α subunit ( ENaCα ) , which regulates the airway fluid levels by absorbing Na+ ions; α-1 antichymotrypsin ( SERPINA3 ) , which inhibits the activity of proteases; cyclin-dependent kinase inhibitor 1C ( CDKN1C ) , which is a cell cycle negative regulator; pyruvate dehydrogenase kinase isozyme 4 ( PDK4 ) , which decreases glycolytic metabolism; and the potassium channel shab-related subfamily B member 1 ( KCNB1 ) , which regulates epithelial electrolyte transport . We examined the effect of viral infection on maximum GC response; therefore 100nM budesonide was used for the following gene expression experiments based on the concentration-response curves . Suppression of budesonide-induced glucocorticoid response element ( GRE ) activity was also observed in BEAS-2B cells infected with MOI 0 . 1 RSV for 48 hours ( S1 Fig ) . Budesonide at 1nM was used for the GRE activity study , as we previously showed that GRE activity requires lower concentrations of GCs to reach the maximum response compare to the concentrations for GC-inducible gene expression . Although RSV infection did not influence cell viability , it decreased the total cell number ( S2 ( A ) Fig ) . The intracellular expression of RSV A2 strain N gene mRNA , measured as an index of viral load , was unaffected by budesonide ( S3 Fig ) . The expression of GC-inducible proteins ENaCα and the promyelocytic leukemia zinc finger ( PLZF , a transcriptional repressor in control of cell proliferation and differentiation ) was also examined . We found that RSV infection clearly impaired the expression of budesonide-induced ENaCα and PLZF protein ( Fig 2 ) . As respiratory viral infection is a major trigger of exacerbations of asthma or COPD , we therefore investigated the effect of RSV infection in BEAS-2B cells , which had been pretreated with budesonide for 24 hours or 4 hours , to emulate the sequence of exposure for asthma or COPD patients who are on GC therapy at the time of viral infection . We found that budesonide-induced expression of GILZ , ENaCα and PLZF mRNA was significantly impaired by subsequent RSV infection . Moreover , budesonide-induced expression of PLZF protein was significantly reduced by subsequent RSV infection ( Fig 3 ) . RSV infection significantly increased the expression of TGF-β1 mRNA in BEAS-2B cells ( Fig 4 ) . The cells were pre-incubated for 30 min with the TGF-β receptor ( activin-like kinase 5 ( ALK5/ TGFβR1 ) ) selective inhibitor , SB431542 ( 1μM using concentration validated in previous studies [46 , 47] ) to ascertain the activity of the endogenous TGF-β [53] . TGF-β-inducible gene PAI-1 ( plasminogen activator inhibitor-1 ) was used as a consistent marker for TGF-β activity . Inhibition of ALK5 attenuated the RSV-induced mRNA expression of PAI-1 in BEAS-2B cells [54] . Moreover , PAI-1 can also be induced by GC in different cell types [55 , 56] . We found , as expected , that budesonide ( 100 nM ) significantly induced the expression of PAI-1 mRNA , and further enhanced the induction by RSV infection ( Fig 4 ) . TGF-β impairs glucocorticoid function in both bronchial epithelial cells ( BEAS-2B cell line and primary ALI-HBECs ) and pulmonary epithelial cells ( A549 cell line ) [46 , 47] . The impairment of glucocorticoid action in these cell types was found to be dependent on activation of the TGF-β receptor kinase ALK5 [46 , 47] . Inhibition of ALK5 using SB431542 ( 1μM ) completely prevented RSV infection—impaired GRE activation in BEAS-2B cells ( S1 Fig ) . Moreover , inhibition of ALK5 using SB431542 completely prevented or significantly attenuated the RSV infection ( 48 hours ) -induced impairment of budesonide-induced expression of GILZ , ENaCα and SERPINA3 , with little effect on CDKN1C expression levels ( Fig 2 ) . However , SB431542 did not influence the total cell number ( S2B Fig ) , or the intracellular expression of RSV A2 strain N gene ( S3A Fig ) . Similar findings were obtained by pretreating the cells with a structurally distinct ALK5 inhibitor GW788388 ( 1μM ) [53] ( S4 Fig ) . In addition , transfection of ALK5-targeted siRNA resulted in more than 50% knockdown of ALK5 protein expression ( S5A Fig ) . A concomitant impairment of ALK5 activity was confirmed by measurement of TGF-β-induced phosphorylation of Smad2 , which is a critical downstream signaling molecule for TGF-β/ALK pathway ( S5B Fig ) . Importantly , transfection of ALK5 siRNA showed similar effects to the ALK5 inhibitors in restoring GC sensitivity . Infection of BEAS-2B cells with IAV at MOI 0 . 1 ( Fig 5A ) , RV at MOI 1 ( Fig 5B ) for 48 hours , or treatment of the cells with Poly ( I:C ) ( 10 μg/ml ) for 24 hours ( Fig 5C ) , impaired budesonide ( 100nM ) or dexamethasone ( 30nM ) -induced GILZ expression . Inhibition of ALK5 using SB431542 ( 1μM ) prevented IAV , RV or Poly ( I:C ) impairment of GC-induced GILZ expression . Moreover , inhibition of ALK5 attenuated the IAV , RV or Poly ( I:C ) -induced PAI-1 mRNA expression Again , budesonide ( 100 nM ) significantly increased PAI-1 expression ( Fig 5 ) . However , treatment of BEAS-2B cells with RLR ( RIG-1 and MDA-5 ) ligands Poly ( I:C ) ( HMW ) /LyoVec ( 0 . 01–1μg/ml ) had no effect on dexamethasone ( 30nM ) -induced GILZ and ENaCα expression ( S6 Fig ) . RSV infection is known to activate a variety of intracellular signaling cascades . A number of the underlying kinases are involved in non-canonical TGF-β signaling pathways , including p38MAPK , ERK1/2 , JNK , Akt and NFkB . We found that RSV infection induced the phosphorylation of ERK1/2 kinase in BEAS-2B cells , and pretreatment of the cells with SB431542 showed a trend to decrease the phosphorylation of ERK1/2 ( Fig 6A ) . We therefore further investigated the involvement of the ERK1/2 kinase using the MEK1/2 inhibitor U0126 at 1 μM , at concentration validated in previous studies [46 , 47] . Pre-incubation of the cells with U0126 ( 1μM ) reduced the induction of TGF-β and PAI-1 mRNA during RSV infection and attenuated the RSV infection impairment of budesonide-induced expression of GILZ mRNA ( Fig 6B ) . Stimulation of BEAS-2B cells with the TLR3 ligand Poly ( I:C ) ( 10 μg/ml ) also induced EKR1/2 phosphorylation ( S7 Fig ) . We next examined whether the viral infection-impaired GC action was mediated by activation of TLR3 using targeted siRNA . Transfection of TLR3-targeted siRNA induced a knockdown of approximately 70% , which was stable throughout the experimental period ( 72 hours ) ( Fig 7A ) . We found that knockdown of TLR3 largely inhibited both RSV and RV-induced TGF-β expression ( Fig 7B ) , and prevented the viral infection impairment of dexamethasone-induced gene expression ( Fig 7C and 7D ) . TGF-β-induced impairment of glucocorticoid action was partially attributed to attenuated nuclear translocation of GRα in the A549 cell line [47] , although this was not observed in the BEAS-2B cell line [46] . We investigated the potential relevance of delayed or reduced GRα translocation , or changes in the level of GRα in RSV-infected BEAS-2B cells . Analysis of BEAS-2B cell cytoplasmic and nuclear extracts indicated that whilst SB431542 did not affect the total GRα protein expression level , 24 hours budesonide treatment ( 100nM ) significantly reduced the expression of GRα protein ( Fig 8A ) . However , neither the expression level nor the GRα subcellular distribution was influenced by RSV infection ( Fig 8C ) . Immunoreactive-GRα was detected in both cytoplasmic and nuclear compartment in vehicle-treated cells . The immunoreactive-GRα level was increased in the nuclear compartment in response to budesonide treatment . However , the localization of GRα in the presence of budesonide was not affected by RSV infection ( Fig 8B ) . The anti-allergic agent tranilast inhibits the expression and activity of TGF-β in different cell types [50–52] . Importantly , this agent has few and only mild side-effects and is well tolerated [49] . We therefore examined the effects of tranilast at a concentration ( 100μM ) within the range detected in plasma ( 30–300μM ) after oral administration of a therapeutic dose [48] , to ascertain its impact on GC impairment by RSV infection in epithelial cells . We found that tranilast inhibited RSV infection-induced mRNA expression of TGF-β1 and PAI-1 ( Fig 9A ) . Pre-incubation of BEAS-2B cells with tranilast prevented/attenuated RSV infection impairment of budesonide-induced mRNA expression of GILZ , ENaCα , PDK4 and CDKN1C ( Fig 9B ) , whilst it did not affect the intracellular expression of RSV A2 strain N gene ( S3B Fig ) . Expression of the anti-viral cytokines IFN-α , IFN-β , IFN-λ1 ( IL29 ) and IFN-λ2 ( IL28A ) , and the pro-inflammatory cytokines IL-8 and IL-6 were measured . We found that RSV infection markedly induced the mRNA expression of IFN-β , IL29 and IL28A in BEAS-2B cells , whilst modest up-regulation of expression of IFN-α mRNA was observed . None of these expression levels were influenced by budesonide ( Fig 10 ) . RSV infection also induced marked expression of pro-inflammatory cytokines , including IL-8 and IL-6 mRNA in BEAS-2B cells . Treatment of the cells with budesonide ( 100nM ) attenuated the RSV infection-induced expression of IL-8 and IL-6 mRNA by more than 80% . Inhibition of ALK5 using SB431542 ( 1μM ) prior to RSV infection did not affect IL-8 expression . However , SB431542 ( 1μM ) reduced the RSV infection-induced IL-6 expression by 30% ( S8A Fig ) . Interestingly , pre-incubation of the cells with tranilast ( 100μM ) , a modulator of TGF-β production and activity , reduced RSV infection-mediated expression of both IL-8 and IL-6 . Co-treatment with budesonide further inhibited IL-8 expression ( S8B Fig ) Primary HBECs were cultured at air-liquid interface ( ALI-HBECs ) to reach the criteria for ALI differentiation , including TEER values of at least 200 Ω . cm2 to ascertain the formation of functional tight junctions; increased mRNA expression of Tektin-1 ( a marker for ciliated cells ) and MUC5AC ( a marker of goblet cells ) ; and visible cilia and mucus on the differentiated cells [46] . RSV infection increased expression of TGF-β1 and the TGF-β-inducible gene PAI-1 mRNA ( Fig 11 ) . The PAI-1 expression was reduced by pre-incubation of the cells with SB431542 ( 1μM ) or tranilast ( 100μM ) for 1 hour prior to RSV infection . RSV infection impaired the dexamethasone-induced mRNA expression of GILZ and ENaCα . Importantly , the impairment of the expression of the genes was prevented by SB431542 or tranilast ( Fig 11 ) .
Respiratory viral infection-induced acute bronchiolitis and asthma/COPD exacerbations are worldwide health problems , with a substantial disease burden in the young , the elderly , in adults with chronic lung disease and patients who are immunocompromised [3 , 4] . Inhaled or oral glucocorticoids are standard treatments for asthma and COPD , but GCs are generally not effective for treating exacerbations of asthma and COPD , and other inflammatory complications of respiratory virus infection . In this study , we identified that endogenous TGF-β is expressed , induced TGF-β-like activity , increases PAI-1 expression in RSV , RV or IAV-infected bronchial epithelial cells , contributing to the viral infection-induced GC insensitivity . We also showed that treatment of epithelial cells with the anti-allergic agent tranilast reduced the expression and activity of TGF-β , and restored GC sensitivity ( Fig 12 ) . RSV infection impaired GRE activity and the expression of GC targeted genes , including GILZ and ENaCα in the BEAS-2B bronchial epithelial cell line . GILZ is a GC-responsive gene that mediates anti-inflammatory effects of GCs in T cells , macrophages and also epithelial cells [57] . Impairment of GILZ expression by viral infection dampens the anti-inflammatory activity of GCs . ENaC channels in lung epithelial cells regulate the airway surface fluid levels . The attenuation of ENaCα expression by viral infection leads to excess fluid and recurrent infections in the lung [58] . As the primary ALI-HBECs are necessarily cultured in hydrocortisone-containing medium , the response to synthetic GC stimulation is modest . Nevertheless , RSV infection-induced GC activity impairment was also observed in ALI-HBECs . Importantly , we found that RSV infection-impaired expression of GC-responsive gene ( ENaCα and PLZF ) translates to similar patterns of change in protein levels . This impairment of GC action may explain the lack of clinical effectiveness of GC treatment in RSV-infected patients . Interestingly , we found that RV and IAV also induced TGF-β-like activity , observing increased PAI-1 expression and TGF-β-dependent impairment of GC activity in BEAS-2B cells . Thus , GC resistance is likely a common response to a respiratory viral infection . The viral pathogen-associated molecular patterns are detected by pattern recognition receptors , including TLRs , RLRs and NLRs , expressed in or on respiratory epithelial cells . We found that GC impairment was also induced by the TLR3 agonist , poly ( I:C ) , a synthetic analog of dsRNA . Poly ( I:C ) stimulation also activates RLRs ( RIG-1 and MDA-5 ) . However , activation of RLRs with Poly ( I:C ) HMW/LyoVec did not affect the GC actions . RSV and IAV comprise negative-sense ssRNA genome viruses , classified as a paramyxovirus and orthomyxovirus , respectively . RV is classified as a picornavirus and has a positive-sense ssRNA . RSV and RV generate dsRNA intermediates during viral replication cycles , which activate TLR3 [59–61] . IAV does not generate abundant levels of dsRNA in the infected cells . However , TLR3 is thought to recognize as yet unidentified RNA structures during IAV infection [13] . In order to validate that impairment of GC action with each of these viruses is through activation of TLR3 in the infected cells , we chose to knockdown TLR3 using targeted siRNA . Whilst TLR3 expression was reduced approximate 70% , the viral infection-induced TGF-β expression and GC impairment were prevented . These data strongly suggest that viral infection-impaired GC action is at least partially mediated by activation of TLR3 ( Fig 12 ) . Engagement of TLR3 activates multiple transcription factors including NF-κB , mitogen-activated protein kinases ( MAPKs ) , and members of the interferon regulatory factor ( IRF ) family , which induce the expression of inflammatory cytokines and type I/III IFNs . Since both viral infection and poly ( I:C ) stimulation induces secretion of various cytokines [62] , it is conceivable that the GC impairment was mediated by the release of soluble factors that act in an autocrine manner . Some of these cytokines ( such as TNFα , IFNγ , IL4 , IL13 and IL17 ) have been reported to interfere with GC action in epithelial cells [38–40] . Viral infection induces expression and secretion of TGF-β in epithelial cells [41 , 42] . Our group has recently shown that TGF-β impairs GRE-dependent transactivation in different epithelial cell types [46 , 47] . Moreover , we found that TGF-β was more potent , had a more rapid onset and shows a greater extent of GC impairment than the combination of TNFα , IL-4 and IL-13 . We now show that RSV infection induces TGF-β expression and activity in the BEAS-2B cell line and ALI-HBECs . Moreover , we found that RSV infection-induced TGF-β expression was mediated by phosphorylation of ERK1/2 , at least partially through activation of TLR3 . Inhibition of ERK1/2 activation significantly attenuated the impairment of GC activity by RSV . Inhibition of ALK5 with SB431542 tended to reduce the phosphorylation of ERK1/2 . However , we have shown previously that TGF-β-induced GC impairment was not induced by activation of ERK nor other established canonical or non-canonical pathways . A novel TGF-β-inducible mechanism is implicated in the modulation of GC action [46] . Therefore , we believe the contribution of ERK1/2 activation to the viral infection-induced GC action occurs upstream of TGF-β expression , in signaling emerging from activation of TLR3 ( Fig 12 ) . Viral infection induces cytopathic effects reducing cell viability . The cytopathic effect is both time and inoculation dose ( MOI ) dependent . RSV infection decreased the cell numbers compared to the uninfected cells . However , under the conditions of RSV infection in the present study there were no detectable effects on cell viability , suggesting that viral induced GC impairment is not secondary to reduced cell viability . We found that inhibition of the type I TGF-β receptor ALK5 activity did not impact the viral reduction in cell numbers or viral replication . Interestingly , inhibition of ALK5 activity prevented/attenuated the RSV-impaired GRE activity and the expression of its targeted genes , including GILZ and ENaCα , which suggests that blockade of TGF-β activity increases the GC-mediated anti-inflammatory action and airway fluid regulation . Viral infection induced activation of the TGF-β/ALK5 pathway and subsequent impairment of GC action was further confirmed by knockdown of ALK5 using targeted siRNA . Thus , autocrine TGF-β contributes to the viral infection-induced GC insensitivity in airway epithelial cells , identifying TGF-β signaling as a target for inhibition that can potentially restore GC sensitivity during RSV infection ( Fig 12 ) . Moreover , we found that poly ( I:C ) or viral infection-impaired GC activity was shown after 24–48 hours incubation or infection . A similar latency period has been reported by another group showing that poly ( I:C ) or RV infection-decreased GC activity only became apparent after a period of hours and reached maximum in 24–48 hours post-treatment [36] . The incubation period fits our conclusion with regard to the time required for dsRNA generation by viral replication , TLR3 activation-induced TGF-β expression and activity . We found that RSV infection also reduced on-going responses to budesonide . This latter experimental design is of relevance to therapeutic patterns in asthma and COPD and offers a potential explanation of the exacerbations upon respiratory viral infection . We suggest that viral infections not only induce inflammatory pathways that are intrinsically insensitive to GC , but also that the asthmatic or COPD inflammation previously controlled by GC is compromised by infection induced TGF-β activity . RSV infection impaired most of the GC-responsive genes assessed . However , we also found GCs might have beneficial effects in regulation of RSV-induced cytokine expression , as budesonide inhibited RSV-induced mRNA encoding the pro-inflammatory cytokines IL-8 and IL-6 , without interfering with the production of IFNs or viral replication . The mechanism of tranilast-inhibition of TGF-β production and activity was unclear . It is likely acting differently from SB431542 , but has in common with SB431542 suppression of TGF-β expression and activity . Interestingly , we have shown for the first time that tranilast , but not SB431542 , markedly inhibited the RSV-induced expression of IL-8 and IL-6 , which suggests the potential for additional beneficial anti-inflammatory activities mediated by tranilast , when used as an anti-allergic agent . The molecular mechanisms of TGF-β impairment of GC-action in epithelial cells have been extensively studied [46 , 47] . The impairment was unrelatedto either the GRα protein level , or to the GRα nuclear translocation in BEAS-2B cells . The GC impairment by TGF-β requires activation of ALK5 . However , the signal transduction downstream of ALK5 could not be associated with any known canonical or non-canonical pathways [46 , 47] . Similar results have been found with RSV infection-impaired GC action that GRα protein expression or GRα nuclear translocation were not influenced by RSV infection . Moreover , inhibition of ALK5 did not affect the expression of GRα protein level . Current evidence suggests a novel non-canonical signaling pathway being activated . Hypothesis-free approaches , such as proteomics and functional genomics , are being used to further examine the signaling mechanisms subserving GC resistance induced by TGF-β [29] . TGF-β activates a variety of signaling cascades regulating many cellular processes . Global inhibition of TGF-β activity therefore engenders many adverse effects , including excessive inflammation and risk of autoimmunity [56 , 63] . Further investigation of the novel signaling mechanism underlying the GC impairment by TGF-β and its more selective targeting may restore GC sensitivity during respiratory viral infection , whilst avoiding the adverse effects that are associated with complete inhibition of TGF-β activity . TGF-β modulators may be an alternative means to restore GC activity without undue adverse effects . The anti-allergic agent tranilast is reported to inhibit the expression and activity of TGF-β in different cell types [50–52] . Importantly , it has few and only mild side-effects and is well tolerated [49] . We found that tranilast inhibits the expression and activity of TGF-β in both BEAS-2B cells and ALI-HBECs . Intriguingly , we show that pre-incubation with tranilast prevented the GC impairment by RSV infection . Further establishing the effectiveness of tranilast in viral infection would support the use of TGF-β modulators for the prevention/treatment of GC insensitivity occurring during RSV infection-induced bronchiolitis or asthma/ COPD exacerbations . In summary , exacerbations of asthma or COPD associated with respiratory viral infection are resistant to the anti-inflammatory actions of GCs . We identified autocrine TGF-β as a key mediator of the GC impairment . Our studies show for the first time that modulation of TGF-β activity is a potential strategy for restoring the GC sensitivity during viral infection and for prevention of viral exacerbation of chronic airway diseases .
BEAS-2B bronchial epithelial cells ( ATCC , Manassas , VA , USA ) were cultured as described [47] , seeded at 5×104 cells/cm2 in 24 well plates , T-75 flask or chamber slides in Dulbecco’s modified Eagle’s media ( DMEM ) containing 5% vv-1 heat-inactivated FBS , 15 mM HEPES , 0 . 2% vv-1 sodium bicarbonate , 2 mM L-glutamine , 1% vv-1 non-essential amino acids , 1% vv-1 sodium pyruvate , 5 IU·mL-1 penicillin and 50 mg·mL-1 streptomycin , and incubated overnight at 37°C in air containing 5% CO2 . The cells were then inoculated with RSV at a multiplicity of infection ( MOI ) of 0 . 1 TCID50 ( 50% tissue culture infectious dose ) per cell for 1 hour , and incubated for up to 48 hours . The GC transactivation was assessed by incubating the cells with budesonide ( Bud , 0 . 01-100nM ) for the last 24 hours to measure the glucocorticoid response element ( GRE ) activity , or for the final 4 hours , to measure the mRNA expression of the GC-inducible genes . In some experiments , budesonide was added to BEAS-2B cells for 24 hours or 4 hours prior to RSV infection and it was re-added after 1 hour RSV inoculation . The mRNA expression of the GC-inducible genes and also protein were examined after RSV infection for up to 48 hours . Primary HBECs were purchased from Lonza ( Waverley , Australia ) and cultured using B-ALI Bulletkit ( Lonza ) according to the manufacturer’s instructions . The cells were differentiated for more than 21 days at air-liquid interface on fibrillar collagen-coated 24-well Corning Transwell 0 . 4μm pore polyester membrane cell culture inserts ( Sigma-Aldrich , MO , USA ) as described [46] . Cell differentiation was confirmed through measurement of trans-epithelial electrical resistance ( TEER ) and visualization of beating cilia . RSV at a MOI of 0 . 1 or control culture medium was added onto the apical surface of the cells , which were inoculated for 1 hour , and then incubated for up to 48 hours . Dexamethasone ( Dex , 100nM ) was applied to the basolateral side of the cells for 5 hours to assess the GC transactivation by measuring the mRNA expression of the GC-inducible genes . Human RSV , prototype A2 strain ( ATCC VR-1540 ) was cultured in Hep2 cells ( also from the ATCC ) . Viruses were inoculated into Hep2 cells , and incubation continued until a cytopathic effect was observed . Supernatant was removed and the Sucrose-Phosphate-Glutamate-Albumin ( SPGA ) stabilizer solution was added to the cells . The virus was harvested by scraping the cells and centrifugation the cell suspension at 1 , 000g for 15 min . clarified supernatants were snap frozen and stored at -80°C RSV was titrated by serially diluting the newly generated stock virus ( 1/103−1/108 ) and then inoculating Hep2 cells in flat-bottomed 96-well plates ( 2 . 5×104 cells/well ) . Viral titer was determined by TCID50 assay , defined as the quantity of virus which induces detectable cytopathic effects in 50% of the infected cells after 3–5 days , and was calculated according to Reed and Muench [64] . Human rhinovirus , RV16 strain ( ATCC VR-283 ) was cultured in Ohio-HeLa cells ( a kind gift from Dr . Reena Ghildyal ) . The virus was harvested by scraping the cells without removing the infection media . The cell suspension was centrifuged at 3 , 000g for 15 min . The viral titer was titrated by using the same methodology as RSV , but in Ohio-Hela cells . Influenza A virus , HKx31 strain ( also known as X-31 , a virus strain of H3N2 subtype , a kind gift from Dr . Sarah L . Londrigan ) was cultured in the allantoic cavity of 10-day old embryonated chicken eggs ( Research Poultry , Research , Victoria , Australia ) and titrated on Madin-Darby canine kidney ( MDCK ) cells ( ATCC ) by standard procedures and expressed as plaque forming units ( PFU ) /ml as previously described [65] . BEAS-2B cells for transfection were seeded in 24-well plates overnight . Cells were co-transfected with pGRE-SEAP and pGL3 control plasmids using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) , as previously described [46 , 47] . Transfected cells were inoculated with RSV at a MOI of 0 . 1 or control medium for 1 hour , and incubated for 24 hours prior to the addition of Bud ( 1 nM ) or vehicle for further 24 hours . The 24 hour time point for Bud-induced GRE activity was selected based on our previous studies [46 , 47] . Supernatants were collected for measurement of secreted SEAP using a chemiluminescence kit ( Roche Applied Science , NSW , Australia ) as described [46] . Pre-validated siRNA targeting ALK5 and TLR3 ( Invitrogen ) was transfected using Lipofectamine RNAiMAX ( Invitrogen ) as described previously [66] . BEAS-2B cells were seeded in 6-well plates overnight . Cells were pre-incubated with SB431542 ( 1μM ) for 30 min prior to RSV infection at MOI of 0 . 1 or control medium for 3 hours , 24 hours and 48 hours for assessment of intracellular kinase phosphorylation . To assess changes in total GRα , ENaCα and PLZF expression , budesonide ( 100nM ) was added to the cells following 48 hours RSV infection ( MOI 0 . 1 ) for the last 2 hours or the last 24 hours . In some experiments , PLZF expression was measured after treatment of the cells with budesonide for 4 hours prior to RSV infection for 48 hours . Rabbit polyclonal antibody ( pAb ) anti-phospho-ERK1/2 ( Thr202/Tyr205 ) and rabbit monoclonal antibody ( mAb ) anti-Erk1/2 ( Cell Signaling ) was used to measure the ERK1/2 activation . Rabbit pAb anti-GRα ( Santa Cruz Biotechnology ) was used to measure GRα expression . Mouse monoclonal antibody anti-PLZF and goat polyclonal antibody anti-αENaC ( Santa Cruz Biotechnology ) were used to measure the expression of PLZF and ENaCα . The expression level of GAPDH protein ( Rabbit pAb; Abcam , Cambridge , UK ) was used as a reference control for normalization to account for variation in protein loading . Western blotting was performed as described [66] . Band intensities were quantified by densitometry using the image J program ( 1 . 48v , National Institute of Health , USA ) . BEAS-2B cells were seeded in a T-75 flask for isolation of cytosolic and nuclear fractions , or in an 8 chamber slide for immunofluorescence staining . Cells were infected with RSV at MOI of 0 . 1 or control culture medium for 46 hours prior to addition of Bud ( 100nM ) for 2 hours . GRα localization was then determined by subcellular fractionation followed by western blot analysis as described [47] . In separate experiments , immunofluorescence was used to monitor GRα localization with the DAPI-stained nucleus [47] . Cell viability was assessed using the Trypan blue exclusion method , as described [66] . BEAS-2B cells were seeded in 24-well plates overnight . Cells were pre-incubated with SB431542 ( 1μM ) for 30 min prior to RSV infection at MOI of 0 . 1 , RV infection at MOI of 1 , IAV infection at MOI of 0 . 1 , or control medium for 44 hours prior to addition of Bud ( 0 . 01-100nM ) or Dex ( 30nM ) for 4 hours . The 4 hour time point for mRNA expression of the GC-inducible genes was chosen based on our previous studies [46 , 47] . In some experiments , tranilast ( 100μM ) or U0126 ( 1μM ) was added 30 min prior to RSV infection . Cells were also treated with TLR3 agonist , polyinosinic-polycytidylic acid ( poly ( I:C ) ; 10μg/ml ) ; or RLRs ligands , Poly ( I:C ) ( HMW ) /LyoVec ( 0 . 01–1μg/ml ) for 24 hours prior to addition of Dex ( 30nM ) for 4 hours . The mRNA extraction and reverse transcription were performed as previously described [46] . An ABI Prism 7900HT sequence detection system ( Applied Biosystems ) was used to quantitatively analyze the level of gene expression as previously described [47] . The generation of specific PCR products was confirmed by dissociation curve analysis . 18S ribosomal RNA ( 18S rRNA ) was used as a housekeeping gene . RSV N gene mRNA expression level was determined by the standard curve on the basis of known TCID 50 virus stock . Primer sequences ( Table 1 ) were KiCqStart pre-designed primers from Sigma-Aldrich , or obtained from the literature , or designed using Primer Express software ( Applied Biosystems , Mulgrave , Australia ) with mRNA sequences from the National Centre for Biotechnology Information ( http://www . ncbi . nlm . nih . gov ) . Data are expressed as the mean ± SEM . Reported n values represent number of experiments repeated or number of primary cultures used . All data were statistically analyzed using GraphPad Prism 5 . 0 for Windows ( GraphPad Software , San Diego , CA ) . One-way or two-way analyses of variance ( ANOVA ) with Bonferroni’s post hoc test were used to analyze the data . A P value less than 0 . 05 was considered statistically significant .
|
In this study , we investigate how respiratory viral infection interferes with the anti-inflammatory actions of glucocorticoid ( GC ) drugs , which are a highly effective group of anti-inflammatory agents widely used in the treatment of chronic inflammatory airway diseases , including asthma and chronic obstructive pulmonary disease ( COPD ) . Exacerbations of both asthma ( “asthma attacks” ) and COPD are often caused by viral infection , which does not respond well to GC therapy . Patients are often hospitalized placing a large burden on healthcare systems around the world , with the young , elderly , and those with a poor immune system particularly at risk . We show that viral infection of airway epithelial cells causes increased expression and activity of transforming growth factor-beta ( TGF-β ) , which interferes with GC drug action . Importantly , we have shown for the first time that inhibiting TGF-β activity in the airways could serve as a new strategy to prevent and/or treat viral exacerbations of chronic airway diseases .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"respiratory",
"infections",
"pathology",
"and",
"laboratory",
"medicine",
"viral",
"transmission",
"and",
"infection",
"gene",
"regulation",
"pathogens",
"microbiology",
"orthomyxoviruses",
"pulmonology",
"epithelial",
"cells",
"viruses",
"chronic",
"obstructive",
"pulmonary",
"disease",
"rna",
"viruses",
"small",
"interfering",
"rnas",
"influenza",
"a",
"virus",
"animal",
"cells",
"medical",
"microbiology",
"gene",
"expression",
"microbial",
"pathogens",
"biological",
"tissue",
"biochemistry",
"signal",
"transduction",
"rna",
"cell",
"biology",
"anatomy",
"influenza",
"viruses",
"nucleic",
"acids",
"virology",
"viral",
"pathogens",
"tgf-beta",
"signaling",
"cascade",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"epithelium",
"non-coding",
"rna",
"cell",
"signaling",
"organisms",
"signaling",
"cascades"
] |
2017
|
Glucocorticoid Insensitivity in Virally Infected Airway Epithelial Cells Is Dependent on Transforming Growth Factor-β Activity
|
Perceptual decisions are thought to be mediated by a mechanism of sequential sampling and integration of noisy evidence whose temporal weighting profile affects the decision quality . To examine temporal weighting , participants were presented with two brightness-fluctuating disks for 1 , 2 or 3 seconds and were requested to choose the overall brighter disk at the end of each trial . By employing a signal-perturbation method , which deploys across trials a set of systematically controlled temporal dispersions of the same overall signal , we were able to quantify the participants’ temporal weighting profile . Results indicate that , for intervals of 1 or 2 sec , participants exhibit a primacy-bias . However , for longer stimuli ( 3-sec ) the temporal weighting profile is non-monotonic , with concurrent primacy and recency , which is inconsistent with the predictions of previously suggested computational models of perceptual decision-making ( drift-diffusion and Ornstein-Uhlenbeck processes ) . We propose a novel , dynamic variant of the leaky-competing accumulator model as a potential account for this finding , and we discuss potential neural mechanisms .
A number of computational models have been proposed to account for binary decisions based on fluctuating evidence ( see Computational Method for a quantitative description of the models ) . One of these models , the drift–diffusion model ( DDM; [7 , 19 , 30 , 31] employs two accumulators racing each other to a decision criterion . Each accumulator integrates the difference between the evidence in favor of the hypothesis it represents and the evidence favoring the competing hypothesis; as shown in Fig 1a , this can be implemented via feed-forward inhibition [31] . According to this model , for experimentally controlled interrogation paradigms ( in which the response-time is controlled by the experimenter ) , when the stream of evidence terminates , the decision is determined in favor of the most active accumulator . While this "standard" diffusion model predicts uniform ( i . e . , flat ) temporal weighting , a number of diffusion model variants were proposed that can generate either primacy or recency . A first variant of the DDM , assumes the presence of an upper absorbing boundary [17] , which terminates the evidence-integration and generates an implicit decision when the first of the two accumulators reaches a decision criterion . This corresponds to the idea that evidence-accumulation is a resource-demanding process and therefore once a certain degree of "confidence" in the decision is accumulated the observer stops accumulating evidence and prepares a response . A second diffusion variant replaces the upper absorbing boundary with a reflecting one [32] , which corresponds to nonlinear saturation processes on the neural firing rate; in this model the integration process continues even when this boundary is reached , but accumulators are not allowed to exceed it . A third , more sophisticated variant involves two boundaries , an upper-absorbing one , and a lower-reflecting one ( [33]; see Fig 1c ) . Here the upper boundary ( implicitly ) terminates the decision as in DDM-variant1 , while the lower boundary corresponds to the neural constraint imposed in the LCA ( see below ) , that firing-rates cannot become negative . As we will show below ( see also [32] ) , the introduction of absorbing boundaries in the DDM results in primacy , while the introduction of reflecting boundaries results in recency . The temporal weight profile of the combined , reflecting/absorbing boundary model has not been investigated yet ( see Computational Results section ) . Another group of perceptual-choice models assumes competitive interactions between cell assemblies that correspond to the choice alternatives . Examples of such models include the leaky-competing accumulator model ( LCA; [3] ) and the attractor-choice model [34–36] . We will focus here on the LCA , as it was examined in more detail with regard to temporal weighting , however similar investigations could be pursued with attractor models . The LCA consists of two accumulators , one for each alternative , which compete with each other via lateral inhibition and are subject to decay ( or leak ) of activation . Here like in the standard drift-diffusion model , the evidence is integrated without a boundary in interrogation paradigms , and the decision is determined in favor of the accumulator whose activation is the highest at the end of the integration interval . Importantly , in the LCA , the ratio between lateral inhibition and leak determine the shape of the temporal weighting profile . As we illustrate in Fig 2 , all the variants of the DDM and the LCA predict that regardless of parameters’ values , the temporal weighting profile is one of three: i ) flat ( unbiased ) , ii ) monotonically decreasing ( primacy ) , or iii ) monotonically increasing ( recency ) . The DDM with an absorbing boundary predicts primacy ( Fig 2a; red line ) , since the accumulation process terminates upon reaching the decisional criterion , even when additional evidence is presented later [17] . Conversely , the DDM with a reflecting boundary predicts recency ( Fig 2a; blue line ) , since early information arriving before the bound has been reached is lost [20] . For the DDM with combined upper-absorbing and lower-reflecting boundaries we also obtain monotonic weights ( recency or primacy , depending on which boundary is closer to the starting activation ) . For the LCA model , when inhibition dominates over leak early information biases the accumulation process , resulting in primacy ( Fig 2b; red line; similar predictions take place in the attractor model; [35 , 36] ) , while when leak dominates over inhibition , early information decays , resulting in a recency bias ( Fig 2b; blue line; see also [18] ) . When leak and inhibition are equal , both effects are counterbalanced resulting in a flat temporal weighting profile ( Fig 2b; black line ) . Thus , both models predict a monotonic pattern of temporal weighting , independent of parameter-values . To summarize , we have shown that flat and monotonic ( primacy- or recency-biased ) temporal weights can arise in two computational models that account for the mechanism by which observers integrate evidence and trigger decisions . The aim of the experimental study was to test how the temporal weight profile depends on the duration of the evidence . As we will show , the results provide a challenge to all the models described above .
Participants’ performance ( accuracy-Pc and mean-RT ) did not differ between baseline and perturbed-signal conditions [Pc_Baseline = 76 . 1%; Pc_Perturbed = 76 . 5%; t ( 12 ) = 0 . 44; p = 0 . 66; RT_Baseline = 506 ms; RT_ Perturbed = 512 ms; t ( 12 ) = -0 . 36; p = 0 . 72] , as well as between congruent and incongruent trials , when averaged across all time-windows [Pc_Cong = 76%; Pc_Incong = 77%; t ( 12 ) = -1 . 28; p = 0 . 23; RT_Cong = 508 ms; RT_Incong = 514 ms; t ( 12 ) = -0 . 31; p = 0 . 77]; as we show below , the difference in accuracy between congruent and incongruent perturbations varies across time-windows . In order to determine the extent of temporal integration of evidence we first examine the dependency of the accuracy on the duration of the evidence ( trial-duration ) . We observe that the accuracy improved with trial-duration over the full 1–3 sec interval [Pc_1 = 72 . 93%; Pc_2 = 76 . 7%; Pc_3 = 79 . 64%; repeated measures ANOVA f ( 2 , 24 ) = 11 . 68; p = 0 . 0003] , suggesting that participants integrate the perceptual evidence [7 , 19] . Note , that an increase in accuracy with trial-duration can also be accounted for by a model , which is not based on evidence-integration , but rather on comparison of independent samples of evidence ( the momentary difference between the disks’ brightness level ) to a criterion . For example , a ‘probability summation’ model , in which the decision corresponds to the first sample that reaches the criterion [38] , will predict increase in accuracy with trial-duration , because the probability that a sample that supports the correct response will be first to reach the criterion increases as the amount of samples increases . Nonetheless , we show in S1 Text and S1 Fig , that the predictions of these two alternative classes of models regarding accuracy in the 3 perturbation conditions ( congruent , incongruent and baseline ) qualitatively diverge in our perturbation-based experimental design . The probability summation model predicts that accuracy on congruent trials will be highest , intermediate in baseline trials , and lowest on incongruent trials . Conversely , integration-based models predict that discrimination accuracy will remain constant across the 3 conditions—this latter prediction is corroborated by the data ( S1 Fig ) . While modest ( ~10% ) , this extent of integration , extending for 30 samples , corresponding to 3 seconds of noisy evidence , is predicted by the model we propose below . This exceeds by almost an order of magnitude , the temporal extent observed in the moving dot paradigm ( about 420 ms; [17]; but see [18] ) , as well as the capacity of about 4 samples of evidence , suggested by some studies of experience based decisions ( [27]; but see [9] ) . Note however , that the model does not assume a perfect integration over the 3 sec interval , but rather is subject to leak and lateral inhibition . Nevertheless , it accounts well for the increase in accuracy over the range we tested . A fit to the observed duration-accuracy function using an exponential decay function: y = ( a-0 . 5 ) * ( 1-exp ( -x/T ) ) + 0 . 5; where T is the integration time-constant , reveals that the effective integration time-constant is T ≈ 700 ms . Interestingly , post-interrogation response-times ( RT; measured from stimulus offset until response ) were slower for longer trials ( 3-sec ) , as compared to the 1- and 2-sec trials [RT_1 = 487ms; RT_2 = 480ms; RT_3 = 565ms; ANOVA f ( 2 , 24 ) = 3 . 61; p = 0 . 04] , indicating that participants did not prepare their response in advance of the evidence termination . If they did so , one would expect faster RT at longer duration , since a larger fraction of the trials may have reached an implicit decision [17] . We quantified temporal integration biases , using two measures: i ) a behavioral index of the influence of the perturbation on choice probability as compared to baseline , as a function of its temporal window , given by: Temporal Biasi = 12* ( Congruent Accuracyi Baseline Accuracy+ Baseline Accuracy Incongruent Accuracyi ) ; where i denotes the temporal window ( similar results are obtained when using Temporal Biasi = Congruent Accuracyi − Inongruent Accuracyi ) ; and ii ) a logistic regression on observed choices with each of the temporal window’s average signal as predictors ( defined as the difference between the two brightness levels ) . As the two measures give identical conclusions , we will focus here on the behavioral measure . As shown in Fig 4 , we find a temporal main effect across durations in the behavioral index [ANOVA f ( 4 , 48 ) = 3 . 38; p = 0 . 016] . Post-hoc comparisons reveal that information presented in the first window is more influential than information presented in the second window [t ( 12 ) = 3 . 63; p = 0 . 003] and in the third window [t ( 12 ) = 2 . 36; p = 0 . 036] . All other comparisons are not significant . When analyzing the temporal-weights for the different trial-durations , we observe a primacy bias in the short trials [1-sec: ANOVA f ( 4 , 48 ) = 3 . 87; p = 0 . 008; t ( 12 ) = 3 . 58; p = 0 . 004; window-1 as compared to window-2; 2-sec: ANOVA f ( 4 , 48 ) = 2 . 82; p = 0 . 035; t ( 12 ) = 3 . 17; p = 0 . 008; window-1 as compared to window-2; similar results were also obtained for the logistic regression coefficients; see also S2 Fig , for the logistic regression coefficients of the different durations using 200-ms windows] . These results corroborate the identification of a primacy-bias in previous perceptual studies [17 , 18] and generalize their conclusions to additional class of stimuli . An unexpected pattern emerges when the temporal weighting profile is analyzed for the expanded , 3-sec trials: under this duration we find that participants exhibit a non-monotonic temporal weighting profile [ANOVA f ( 4 , 48 ) = 2 . 58; p = 0 . 049] . The influence of the signal arriving at the 1st temporal window was higher than in the 2nd window [t ( 12 ) = 2 . 6; p = 0 . 023 , while the influence of the signal arriving at the 5th ( and final ) temporal window was also higher than in the 2nd window [t ( 12 ) = 2 . 49; p = 0 . 029; see Fig 4]—a pattern which is inconsistent with the predictions of either of the two models that were offered to account for temporal weighting ( see simulation studies in the Introduction section ) . Importantly , this non-monotonic pattern is not the result of averaging two monotonic patterns ( primacy and recency ) , as 9 out 13 participants show numerical trend of non-monotonic weighting ( i . e . , both window-1 and window-5 are more influential than window-2 ) at the individual level . While the fraction of participants whose temporal weight at window-2 is lower than at window 1 and 5 , is significantly higher than expected by chance [1/4 , where 4 indicates the possible relations between the 3 windows; Chi-Square ( 1 df ) = 13 . 564; p = 0 . 0002] , the identity of these critical windows was selected based on the data , and thus cannot provide valid statistical test . For that reason , we conducted two additional experiments , in which we sought to replicate this unexpected pattern of temporal weighting by presenting solely 3-sec trials ( Experiment-2; N = 10 ) , as well as randomly varying trial-durations in order to additionally ensure that the observed non-monotonicity is not due to participants’ fatigue from repetitive trials of the same duration ( Experiment-3; N = 10 ) . On the basis of the previous results , we predict that the temporal weights for 3 sec evidence trials will be non-monotonic , with higher weights at window 1 and 5 , compared with window-2 . Experiment-2 ( N = 10 ) was identical to Experiment-1 , with the exception that only 3-sec trials were presented ( each participants underwent a total of 900 trials ) . Experiment-3 ( N = 10; no overlap of participants between experiments ) , was identical to Experiment-1 , with the sole exception that trial-durations ( 1 , 2 or 3 seconds ) were randomized rather than blocked . The temporal weights observed for the 1 and 2-sec trials in Experiment-3 were similar to those observed in experiment-1 , both indicating a primacy bias [1-sec: t ( 9 ) = 2 . 75; p = 0 . 022; window-1 as compared to window-2; 2-sec: t ( 9 ) = 2 . 21; p = 0 . 027; one-tail; window-1 as compared to window-2] . Thus , we have replicated the finding that under short presentation times perceptual decisions are primacy biased . The temporal weighting profile of the 3-sec trials did not differ between experiment-2 and experiment-3 [ANOVA of Weighs X Experiment F ( 4 , 36 ) = 0 . 5; p = 0 . 73] , and is therefore reported below collapsed across both experiments ( for the weighting functions observed in each experiment separately , see S3 Fig ) . As in Experiment-1 , we find a non-monotonic temporal weighting profile in the 3-sec trial duration: information in the 1st temporal window was more influential than information in the 2nd window [t ( 19 ) = 2 . 26; p = 0 . 036;] , while the influence of the 5th window was also higher than the 2nd one [t ( 19 ) = 2 . 31; p = 0 . 033; Fig 5] . At the individual level , 13 of the 20 participants show this non-monotonic pattern ( Chi Square compared with 1/4 ( 1 df ) = 17 . 07; p = 0 . 0001 ) . Taken together , the results of Experiments 1–3 suggest that when participants engage in expanded perceptual judgments , they exhibit a non-monotonic temporal weighting profile , although this pattern is inconsistent with the predictions of either the drift-diffusion or the LCA models . Below , we account for this result by proposing a dynamic variant of the LCA model , in which the leak and inhibition parameters change during the trial . To investigate whether temporal biases deteriorate the accuracy of the decisions , we have calculated for each individual ( collapsed across the 3 experiments; N = 33 ) his or her overall temporal bias in the 3-sec condition , by summing the absolute deviations of the behavioral temporal index from one ( which represents an unbiased weight ) across the temporal windows ( when this measure is zero it represents an unbiased temporal weighting ) . We ran a Pearson correlation between this measure of individual bias and the participant’s accuracy and found that the more an individual is temporally biased , the lower is his or her accuracy ( r = -0 . 5; p = 0 . 003; Fig 6 ) . Thus , biased temporal weighting had a deteriorating effect on accuracy of about roughly 15% ( from 85% to 70%; cf . [17] ) . The temporal weight profile that was presented above raises a challenge for the existing computational models of perceptual decisions , which can account only for monotonic profiles ( primacy or recency-based profiles; Fig 2 ) . Here we propose an extension of the LCA model that can account for the data we have presented . The dynamic LCA ( DLCA ) is an extension of the LCA model , in which the leak and the inhibition parameters change with time . As time within the evidence-integration process progresses , it is assumed that leak increases and inhibition decreases . As the LCA has two opposite domains of temporal weights , primacy ( for inhibition dominance ) and recency ( for leak dominance; [3 , 40] ) , this dynamic change shifts the integration mechanism between the two domains , allowing a more rich temporal profile; as we show below , the two factors do not cancel each other but rather result in non-monotonic weights . We defer the discussion of potential neural mechanism to the Discussion section , as well as the discussion of functional considerations that motivate the DLCA . The detailed formulation of the model ( and of 3 extant models ) is presented in the Computational methods . We have compared 4 alternative models: Note , that the inputs to the models are the raw luminance values . However , these inputs may undergo a non-linear transformation in the visual system ( e . g . power-law or logarithmic ) , which can , under certain conditions , alter the behavior of the models ( for example , see 11 , for a discussion on how transience in the input may mimic leakage ) . Nonetheless , such transformations are unlikely to cause artificial shift from monotonic weights into non-monotonic ones . In order to validate this assumption , we ran an additional logistic regression analysis on the 3-sec data , using non-linear transformed data , in which i ) Input = ( 10*I ) ^0 . 8; or ii ) Input = log ( I*1000 ) . We find that the obtained logistic regression weights are almost identical to the weights obtained using non-transformed inputs . We fitted each model to participants' responses in the 3-sec trials and also applied the generalization criterion method [41 , 42] by using the model parameters to make predictions for the 1–2 sec trials . For each model , given a set of parameters , we generated 1000 simulations for each trial ( i . e . , for the actual displayed stochastic external input , I1 and I2 ) , in which only the internal noise varied , and calculated the model probability ( given parameters and inputs ) to select Left ( Pl ) or , Right ( 1-Pl ) . We assigned likelihood ( of the data , given model , inputs and parameters ) for each trial , by using the observed decision in that trial ( Likelihood = P ( D ) ; D = {L , R} ) . The likelihood for the entire data was calculated by multiplying the likelihood for the separate trials ( adding the Log Likelihoods ) . Finally , parameters were estimated by maximizing the likelihood term using an exhaustive grid search ( see S1 Table for description of the parameter-space of each model ) . The random number generator in all simulations as well as iterations over the grid was initialized with a random seed . We have compared the four models described above in accounting for the response the participants made in each trial of the 3 sec condition , using the maximal likelihood method and the Bayesian information criterion ( BIC ) which penalizes for the number of free-parameters . The BIC is given by: -2 * ln ( maximal likelinhood ) + k * ln ( n ) ; where k is the number of free parameters and n is the number of observations . Both maximal likelihood and BIC comparisons strongly favor the DLCA model followed by the LCA and the absorbing boundary DDM ( see Table 1 ) . In Fig 7 , we used the best-fitting parameters of the three leading models’ in order to show their predictions for the behavioral temporal-weighting index and for the logistic regression weights [42] . We did so by simulating each model’s “responses” for the actual presented stimuli , and then subject these simulated responses to the temporal-weight analyses described above . As expected , we find that while the DLCA predicts non-monotonic temporal weighting patterns , the LCA ( model III ) and the absorbing-boundary DDM ( model I ) produce a recency-biased pattern and are thus unable to account for the observed temporal weighting profile ( Fig 7 ) . The reason that the dynamic LCA accounts for the non-monotonic temporal weight profiles is the following: At the start of the trial , the model operates in an inhibition dominant regime that is primacy-biased . As time progresses , however , leak increases and the model becomes recency-biased . Crucially for accounting for the temporal weight data , this shift towards recency is not homogenous ( as in Fig 2 ) . Rather , both early and late evidence affect the decision more than intermediate evidence . We can understand this pattern as resulting from a dynamic effect: early evidence has high impact , because it pushes the LCA into one of two attractor states ( strong response-competition at the start ) . As times progresses , and the competition weakens , there is growing chance for new evidence to trigger a switch to the other attractor . This chance is stronger , however , for evidence that arrives later ( 2–3 sec ) , than for evidence that arrives in the 1–2 sec . Thus , in addition to the non-monotonic weights in 3-sec trials , the model predicts two additional important results ( see Fig 8 ) : i ) for shorter temporal stimuli ( <2 sec ) the temporal weight is monotonic ( primacy ) ; ii ) for longer temporal stimuli ( e . g . , 5-sec ) , the temporal weights will again become monotonic , yet recency-biased . The latter results from the fact that with further increasing time ( increased leak and reduced response inhibition ) the attractors are destabilized , and thus the early history becomes irrelevant to the decision , that is affected by the late evidence only . Hence—in the DLCA the weights interact with trial-duration: as the duration of the input increases , recency increases at the expense of primacy since with additional integration time the influence of early-accumulated information diminishes , due to elevated leak . To conclude , the DLCA not only accounts for the non-monotonic weights in the 3 sec condition ( note that the parameters were selected to fit those trials ) , but with the same parameters it predicts a monotonic primacy based weighting pattern at shorter durations ( 1–2 sec ) , as well as a monotonic recency based weight pattern at longer durations ( 5-sec; see below ) . Moreover , the DLCA model , with these parameters , also predicts the observed increase in accuracy with trial-duration ( Fig 9 ) .
Although the DLCA model was set up to account for the non-monotonic weights at 3 sec ( Fig 8 ) , it was able to predict , based on the same parameters ( no extra fitting ) , the primacy-biased weights observed in the 2-sec trials ( Fig 9 ) and the monotonic recency-biased weights on 5-sec trials ( S5 Fig; see also [10 , 50] ) . While we find the DLCA account appealing , we believe that future research is needed to further corroborate its predictions and to probe its neurophysiological mechanism . In particular , we acknowledge it as a tentative model , which will need to be evaluated in relation to additional accounts . One such account , which we consider as a potential candidate , involves a dual-mechanism: participants may accumulate perceptual evidence in a primacy-biased manner , as suggested by computational models of decision making that trigger initial decisions [17 , 18 , 35 , 36] , but in addition , rely on information available in short-term visual working memory [51 , 52] , which is recency biased [53 , 54] , to override the initial decisions . This account can potentially predict concurrent primacy ( as a result of the accumulation process ) and recency at longer stimulus duration , if the short-term memory-trace contains only information from the last second of the stimulus . Future research should explore additional alternative accounts for the complex duration-dependent temporal weighting function . For example , a diffusion framework , in which the evidence is integrated between two reflecting ( and possibly collapsing ) boundaries and in which the decision is determined by the last boundary that has been reached may account for non-monotonic weighting under certain assumptions . The rationale here is that with short stimuli there is enough time only to reach the boundary once , but with longer decisions reversals can take place resulting in recency . While the motivation for the DLCA , presented above is based on functional considerations , such as interpolating between competitive and independent race mechanisms , it is possible to speculate about potential underlying neural mechanisms . One such possibility is the effect of neural adaptation , either at the synaptic or neuron level [55–60] . Accordingly , the reduction in inhibition is a direct outcome of neural adaptation for inhibitory circuits ( this may be implemented via excitatory projections to interneurons ) , while the increase in leak is the indirect outcome of the reduction in recurrent self-excitation , which balances part of the neural leak [3] . According to this , as time progresses , the time constant of the evidence integration decreases , while the mechanism becomes less competitive , closer to an accumulator or race model [8 , 61] . An alternative neural mechanism , may involve neuromodulators that reduce the impact of recurrent connections compared to the inputs ( e . g . , Acetylcholine; [62] ) . To conclude , we have carried out a study of the time-course of evidence integration over a time scale of 1–3 sec ( 10–30 events ) . The results indicate an extended temporal integration , with temporal weights that are primacy biased on shorter duration , but U-shaped at longer durations . We have presented a computational model accounting for these results and discussed potential neural implementations , and alternative mechanisms . Future work will be needed to compare between these alternative models and also to determine whether this non-monotonic pattern extends to other perceptual and value-based domains , such as averaging of visual properties ( e . g . , [10 , 63] ) and numerical-integration [12 , 29 , 64] , as well as preference-formation ( e . g . , [6] ) and legal-decisions ( e . g . , [65] ) .
All procedures and experimental protocols were approved by the ethics committee of the Psychology department of Tel Aviv University ( Application 743/12 ) . All experiment were carried out in accordance with the approved guidelines . 13 volunteers participated in Experiment 1 . All participants were undergraduate students recruited through the Tel Aviv University Psychology Department’s participant pool , were naive to the purpose of the experiment and were awarded either course credit or a small financial compensation ( 40 NIS; equivalent to about $10 ) . All participants had normal ( or corrected-to-normal ) vision . Stimuli were generated Matlab and were presented on a gamma-corrected ViewSonic ( Walnut , CA ) 17-in . CRT monitor viewed at a distance of 41 cm ( participants rested their head on a chin rest ) . The screen resolution was set to 1 , 024 × 768 pixels , and the monitor had a refresh rate of 60 Hz . Stimuli consisted of two brightness-fluctuating round disks ( each 50 mm in diameter ) . The disks were presented 40 mm right and left to a central 10 X 10 mm white fixation cross ( Fig 3a ) . At each time-frame ( 100 ms ) , each of the disk’s brightness level was sampled from a normal distribution with either high or low mean ( all distributions had a standard deviation of 0 . 15; the distributions’ means depended on the experimental condition—see Stimulus Condition , below ) . The first frame had two grey disks ( brightness level = 0 . 2 ) and the last frame included white masks ( brightness level = 1 ) , in order to prevent steep changes in brightness onset and afterimages respectively . These frames were discarded from further analyses and are not included in the calculation of the trials’ duration . The participants were asked to watch the evidence streams ( 1–3 sec ) and to indicate at the end of each trial , the disk with the higher overall brightness value , using one of two designated keyboard keys ( ‘M’ for right-disk , ‘Z’ for left-disk ) . An incorrect response was followed by an auditory feedback . An illustration of a single typical trial is depicted in Fig 3 . Following a 50-trial practice session , participant underwent 1080 trials , divided into 18 experimental blocks . Between each experimental block participants received a self-paced break . The entire experiment duration was approximately 90 minutes . The experiment consisted of 3 possible trial durations: 1 , 2 , or 3 seconds , corresponding to 10 , 20 or 30 frames , and the stimulus duration was blocked . ( Blocks had 60 trials and block order was randomized and counterbalanced between participants ) . 20% of the trials ( randomly determined ) were baseline trials and the rest were perturbed trials . In baseline trials , either the left or the right ( random between trials ) disk’s brightness level was sampled from a high-value Gaussian distribution ( Mean = 0 . 75 ) , while the other disk’s brightness was sampled from a low-value distribution ( Mean = 0 . 6 ) ( dotted blue and red lines , in Fig 3b and 3c , respectively ) . On the rest of the trials ( 80% ) a perturbed signal was delivered in 1 ( random ) out of 5 equal-duration temporal windows ( see Fig 3b and 3c for an illustration of the perturbation procedure ) . The perturbed signal consisted of a stronger separation between the means of the underlying distributions ( Perturbed_high = 0 . 85; Perturbed_low = 0 . 45 ) . The perturbation was randomly either congruent ( 40%; Fig 3b ) or incongruent ( 40%; Fig 3c ) with the correct response in order to make sure that even if participants detected the perturbed-signal , it was not indicative of the correct response . Since the signal-perturbation manipulation altered the overall signal as compared to baseline trials ( increasing it on congruent trials and decreasing it on incongruent ones ) , we have equated this deviation by assigning compensatory signal ( negative on congruent trials and positive on incongruent trials ) to the remaining temporal windows ( Fig 3b and 3c ) . For each temporal window , the compensatory signal was evenly divided between the two disks , so that the brightness level of the disk representing the correct response decreased ( increased ) when the equating signal was negative ( positive ) , while the opposite change took place for the brightness level of the disk representing the incorrect response . This procedure ensured that the overall signal was kept constant for baseline and perturbed trials ( both congruent and incongruent ) of a given duration . In other words , trials of same duration had an equal overall signal , regardless of whether they were baseline or perturbed trials . However , the distribution of the signal varied between baseline ( even distribution ) , congruent ( strong signal in the perturbed window; weak signal in the rest of the temporal windows ) and incongruent ( opposite ( incongruent ) signal in the perturbed window; strong ( congruent ) signal in the rest of the temporal windows ) trials . Thus this design predicts a Null effect of the perturbation ( compared to baseline ) for evidence integration mechanisms which give uniform/flat weights . Deviations from such Null effect can then indicate temporal weights .
|
An important process that supports decision-making is the integration of evidence over time , which optimizes decision quality by enhancing the signal to noise ratio . The nature of this process depends critically on the weight given to evidence across time: which information has more impact—early , intermediate or late ? We used a novel psychophysical technique , which relies on differential temporal dispersion of evidence . This technique allowed us to extract the temporal weights people assign to the flow of evidence . We find that in decisions that are based on relatively short streams of evidence , people gave stronger weight to early information ( primacy ) . Surprisingly , however , with longer streams of evidence , people assigned higher weights to early and late evidence , while underweighting intermediate evidence . This non-monotonic pattern of evidence integration is not predicted by the existing models of decision-making , posing a challenge to current theories . We propose a novel model that accounts for non-monotonic weighting , based on a change in leak and response-competition with integration-time , and we discuss potential neural mechanisms .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"decision",
"making",
"engineering",
"and",
"technology",
"numerical",
"integration",
"signal",
"processing",
"experimental",
"design",
"social",
"sciences",
"neuroscience",
"simulation",
"and",
"modeling",
"research",
"design",
"mathematics",
"cognition",
"vision",
"inhibitions",
"research",
"and",
"analysis",
"methods",
"behavior",
"psychology",
"signal",
"to",
"noise",
"ratio",
"biology",
"and",
"life",
"sciences",
"sensory",
"perception",
"physical",
"sciences",
"cognitive",
"science",
"numerical",
"analysis"
] |
2016
|
Non-monotonic Temporal-Weighting Indicates a Dynamically Modulated Evidence-Integration Mechanism
|
The dynamics of filopodia interacting with the surrounding extracellular matrix ( ECM ) play a key role in various cell-ECM interactions , but their mechanisms of interaction with the ECM in 3D environment remain poorly understood . Based on first principles , here we construct an individual-based , force-based computational model integrating four modules of 1 ) filopodia penetration dynamics; 2 ) intracellular mechanics of cellular and nuclear membranes , contractile actin stress fibers , and focal adhesion dynamics; 3 ) structural mechanics of ECM fiber networks; and 4 ) reaction-diffusion mass transfers of seven biochemical concentrations in related with chemotaxis , proteolysis , haptotaxis , and degradation in ECM to predict dynamic behaviors of filopodia that penetrate into a 3D ECM fiber network . The tip of each filopodium crawls along ECM fibers , tugs the surrounding fibers , and contracts or retracts depending on the strength of the binding and the ECM stiffness and pore size . This filopodium-ECM interaction is modeled as a stochastic process based on binding kinetics between integrins along the filopodial shaft and the ligands on the surrounding ECM fibers . This filopodia stochastic model is integrated into migratory dynamics of a whole cell in order to predict the cell invasion into 3D ECM in response to chemotaxis , haptotaxis , and durotaxis cues . Predicted average filopodia speed and that of the cell membrane advance agreed with experiments of 3D HUVEC migration at r2 > 0 . 95 for diverse ECMs with different pore sizes and stiffness .
Cell migration in the three dimensional extracellular matrix ( ECM ) plays a crucial role in a wide variety of biophysical processes , such as wound healing , morphogenesis , angiogenesis , tumor growth , and cancer invasion [1 , 2] . Migration dynamics in 3D are significantly different from those observed on 2D ECM surfaces [3] . Cells invade into the ECM , extend filopodia into the gel , and degrade and remodel the surrounding ECM . Cells sense the direction and magnitude of complex cues and exhibit multifaceted responses , such as chemotaxis , haptotaxis and durotaxis responses from the 3-D extracellular environment . Filopodia play many important roles in interacting with ECM . Detailed mechanisms of filopodia dynamics have previously been studied for 2D behaviors: filopodia traction dynamics , including motor-clutch mechanism at the filopodial shaft , frictional slippage , and load-and-fail phenomena at hard and soft ECMs [4]; filopodial protrusion due to actin polymerization and depolymerization [5–7]; the formation of focal complexes ( FCs ) [8 , 9] and retraction force generation at the filopodial tip [10]; and filopodium buckling instability [11] . In contrast to their behavior on 2D surfaces , filopodia in 3D must penetrate through and interact with the surrounding ECM fibers , probing for an open space or gap in the ECM fibers to extend . When they encounter an ECM fiber , they bind to it via FCs , and subsequently generate traction forces to pull the cell membrane forward . This complex process and the intricate interactions that occur between the filopod and the ECM fibers remain poorly understood . How are these multifaceted activities coordinated to allow first the filopod and eventually the whole cell to penetrate into the 3D ECM ? What are the essential features of 2D filopodia dynamics needed to predict 3D migration ? To address these questions , we have conducted experiments to probe the detailed interactions as a single filopodium penetrates into a 3D gel matrix , and constructed a computational model to predict coordinated filopodia behaviors and cell-ECM interactions in 3D . Our experimental observations using 2D and 3D time-lapse data revealed that filopodia a ) crawl or slide along ECM fibers near the tip , b ) tug on the fibers to generate local forces , causing deformations within the surrounding gel , and c ) probe the local fiber network and coordinate their multiple activities: protrusive outgrowth , retraction , and contraction . To explain these behaviors we have built a computational model in which the crawling/sliding of filopodial tips along fibers is described as a continuous process of forming and rupturing focal complexes ( FCs ) between integrins distributed along the filopodial shaft and ligands on the ECM fibers . The strength of FCs is determined by binding kinetics and other factors , including the local stiffness and porosity of the ECM . Each filopodium alternates between different modes of behavior depending on the properties of FCs . We have established simple rules to describe the switching and coordination of multifaceted filopodia behaviors . The model reproduced not only our experimental data , but also many of the previously reported behaviors , including frictional slippage , and load-and-fail phenomena at hard and soft ECMs [4] . Predicting the filopodia-ECM interactions and the resultant penetration of the whole cell into a 3D gel matrix requires a detailed model of the ECM . We have constructed a network model of cross-linked ECM fibers , with specified pore size and fiber stiffness to match the experimental measurements of bulk elastic moduli . This fiber network consists of straight fibers having both extensional and bending stiffnesses [12 , 13] . The ECM fiber network degrades due to matrix metalloproteinases ( MMPs ) [14 , 15]; the expression of proteolytic enzymes at the cell membrane [16] dissolves the fiber , ruptures the fiber , or ruptures the cross-link [17 , 18] . Over the ECM network , chemo-attractants diffuse and react to the cell . Furthermore , the mechanics of the cell membrane and nucleus as well as acto-myosin contraction and actin stress fiber formation are all coupled dynamics inside the cell and the filopodium that must be integrated with the filopodial / ECM dynamics . Previously , many modeling approaches in the areas of cell migration have already provided insights into processes of cell-adhesion and cancer cell invasion . Such approaches include a coarse-grained Langevin dynamics model of lamellipodium protrusion by actin polymerization [19] , an invadopodia penetration model with the effect of crosslink on ECM degradation [17] , a force-based , individual-based modeling framework that links single cell migration and ECM fibers through contract guidance and matrix remodeling [18] , a multi-scale model of dynamic of cell colonies [20] , and a cell invasion model in fiber networks and confined microchannels using extended cellular Potts model ( CPM ) [21] . Our modeling approach exhibits both mechanistic and chemical interactions of 3-D ECM with filopodial and cellular membrane structures since the degradation of ECM is more essential as the cell penetrates into stiffer and denser ECM [22] . Mechanistic interactions of ECM induce local compaction [23] , migration and remodeling of individual ECM fibers [24] . On the other hand , chemical interactions of ECM guide dynamic variations of filopodial orientations due to growth factor gradients in the ECM gel as well as the degradation of ECM fibers due to the secretion of focalized proteolysis at the cellular membrane [25] . As further evidence for the model , we also find the densification of ECM fibers in surroundings of the filopodia at both simulation and experiment since filopodia create a substantial traction force and corresponding ECM fibers are considerably deformed . In addition , our modeling approach is developments of previous our modeling works of intracellular mechanics; an integrative cell migration model incorporating FA dynamics , cytoskeleton and nucleus remodeling , actin motor activity , and lamellipodia protrusion was developed for predicting 1 ) cell migration behaviors on 3D curved surfaces , such as cylindrical lumens in the 3D ECM [26] , and 2 ) cell spreading and migration behaviors on micropatterns and planar substrates with various fibronectin coating concentrations [27] . Integrating these we predicted the average speeds at which the filopodium and cell penetrate into the ECM , obtaining excellent agreement ( r2 = 0 . 995 ) with experiment data for diverse pore sizes and gel moduli . To our knowledge , no cell invasive model that takes into account penetration of the cell into a 3D ECM environment has previously been reported that reflects both the cell-ECM and filopodia-ECM interactions .
To create the overall computational model , we integrate four modules , each capturing a different physical aspect influencing migration: 1 ) filopodia penetration dynamics [4] ( Fig 1A ) ; 2 ) intracellular mechanics , including formation of FAs and actin stress fibers ( SFs ) , and remodelling of cellular and nuclear membranes [26 , 27] ( Fig 1B ) ; 3 ) reaction-diffusion mass transfer [14 , 15] ( Fig 1C ) ; and 4 ) dynamics of ECM fiber networks [12 , 13] ( Fig 1D ) . In particular , it should be noted that FAs are different from FCs in that FAs ( 1–5 μm in size ) are formed on the cellular membrane with a long turnover time > 5 minutes , but FCs ( ~0 . 5 μm in size ) are formed on the filopodial membrane with a short turnover time < 5 minutes [28] . To incorporate viscoelastic behaviors in cellular membrane , line elements of actin cortex in the cellular membrane can be modeled using Kelvin-Voigt model ( a spring and a dashpot together in parallel ) ( Fig 1B ) . The detailed equations that govern each of these dynamical processes are summarised in Table 1 , and the list of simulation parameters are also summarised in Table 2 . We identified and modelled four phases of filopodial dynamics . Three resemble the phenomena previously reported: an outgrowing phase due to protrusive polymerization of actin [5–7]; a retractile phase due to zero or weak FC forces at the filopodial tip and fast myosin motor activities along the filopodial shaft [9]; and a contractile phase due to strong FC forces at the filopodial tip and slow myosin motor activities along the filopodial shaft [4 , 9] ( Fig 1A ) . The fourth phase , which we refer to as the “tugging phase” , begins with the formation of FCs near the tip of the filopodium . The point of attachment between a filopodial tip and a nearby ECM fiber migrates along the fiber , a tension builds up between them as the filopodium moves , and the bond either ruptures or results in the generation of a significant traction force , depending on the strength and spatiotemporal properties of the FC formation . This phase plays a critical role in switching among the other phases and coordination of the diverse filopodial dynamics , leading to either success or failure of cell migration depending on local ECM conditions . First , in the outgrowing phase , polymerization of actin filaments generates a force FP , if on the filopodial membrane [29] . The average magnitude of the maximum protrusive actin pressure is in the range of 5–10 nN/μm2 . It has been reported that a few nN of force can exerted by a few hundred actin filaments per μm2 , which implies that each filament contributes on the order of 10–20 pN [30] . Thereby , the imposed maximum magnitude of FP , if~ 2 nN ( FP , maxf ) is reasonable since the diameter of single filopodium is assumed to be 300 nm consisting of >30 actin filaments . It should be noted that FP , if is exerted only at the tip of the filopodium . To incorporate a chemotactic response from the 3D ECM environment , the direction of FP , if is predicted from the gradient of the VEGF concentration ( CVEGF ) at the tip of the filopodium . Thus , FP , if is given by FP , if=FP , maxfn^VEGF , if where n^VEGF , if is a unit normal vector parallel to the gradient of CVEGF ( Fig 1E ) . Second , the contractile phase arises from the acto-myosin contraction . At contractile phase , we assume that a bundle of actin microfilaments is assembled by actin-myosin II interactions [31] . It has been observed that veil ( or lamellae ) advances following filopodial pulling ( or contractile ) motion , a process in which two kinds of adhesion mechanisms with collagen fibers in the ECM take place , i . e . , FCs formation at the tip of filopodial shaft and FAs formation at the root of filopodium [32] . These two kinds of adhesions at both tip and root of the filopodium are required for gaining stable traction forces from the surrounding collagen fibers in the ECM , and for transmission of the force to allow the advancement of the cellular membrane via the filopodial contractile motion . Here , stable traction forces indicate sufficiently large magnitude to withstand the acto-myosin contractile force . In addition , for the condition of the veil advancement , the focal complex force at the filopodial tip must be stronger than the focal adhesion force at the veil . Otherwise , weak complex force leads to the retractile phase . Therefore , we model filopodia penetration dynamics at the contractile phase by taking into account the complex myosin motor activity and FCs formation at the tip of filopodial shaft . Third , the retractile phase is also arises from the acto-myosin contraction in a similar manner of the contractile phase . However , the retractile phase is induced by weak traction forces from the surrounding ECM , which result in fast , oscillatory ‘load-and-fail’ traction dynamics during the retractile phase ( S1 Fig and S1 Video ) . That is , the reconnection of FCs at the tip allows a filopodium to repeatedly probe the surrounding ECM fibers , but the filopodium retracts immediately if it experiences weak traction forces at the tip [10] . Finally , we have found through experiments that there exists another phase in filopodial dynamics: the tugging phase ( S2 Video ) . The tip of a filopodium apparently crawls or slides along a nearby ECM fiber or multiple fibers ( Fig 2A ) . We have observed that the binding site of FCs moves along the ECM fibers and that the bound ECM fibers are pushed or pulled by the filopodial tip . The adhesive force of FCs is sufficient to prevent the filopodium from retracting . This crawling or sliding can be viewed as a continuous process during which FCs form and rupture as they move along the ECM fibers , as depicted in Fig 2B . Here , we build a computational model to predict the continuous formation and rupture of FCs along the ECM fibers based on binding kinetics between integrins on the filopodial membrane and collagen molecules in ECM fibers . This technique is similar to the one used to predict the formation of a FA at a cellular membrane that interacts with ligands in the ECM fibers [27] . The FC force at the k-th filopodial node is given by FFC , kf=nbk κLR ( Lb−λ ) n^R , kf ( 1 ) where nbk is the number of integrin-collagen bonds , κLR is the spring constant of a single ligand-receptor bond ( ~1 pN/nm ) [33] , Lb is the average stretched length of the ligand-receptor bonds , λ is an unstressed length of bonds ( ~30nm ) [34] and n^R , kf is a unit vector at the local surface of the k-th filopodial node toward the bonding site between the j-th and j+1-th nodes in the i-th fiber ( Fig 2B ) . ( Lb-λ ) represents the stretched distance from the equilibrium . From Fig 2B , this intersection position , that is , the root location of receptor and ligand bonds ( xL , i ) between xije and xij+1e , is given by xL , i=xkf+Lbn^R , kf=xkf−hpn^R , kfn^w⋅n^R , kf ( 2 ) where hp is the gap between the k-th filopodial membrane node and ECM fiber node , and n^w is a vector normal to the curved surface of the cylindrical fiber , defined by ( xi j+1e−xi je ) × ( ( xkf−xi je ) × ( xi j+1e−xi je ) ) and shown in Fig 2B . We utilize Bell’s model to simulate the stochastic nature of bond rupture and formation . Bell’s equation for the kinetic dissociation rate is defined by koff=koff0exp[κLR ( Lb−λ ) xbkbT] , where koff0 is the kinetic dissociation rate ( 1 s-1 ) under unstressed condition with an equilibrium distance λ , xb is the separation distance between the bound state and the transition state ( 0 . 02 nm ) , kb is the Boltzmann constant , and T is the absolute temperature [35] . In addition , kbTxb represents the maximum rupturing force exerted on single molecule of ligand-receptor bond ( ~200 pN ) . During the dynamic process , the filopodium switches between these four phases . Depending on the strength of the FC force , the length of the filopodium , the duration of the current phase , and other factors , transitions between phases are determined . This can be modelled as a discrete state transition network detailed in S2 Fig . Briefly , the outgrowing phase switches to the tugging phase when FCs are formed within a specified time , otherwise it switches to the retraction phase . In addition , the formation of FCs is assumed to occur when hp is less than 100 nm , and is described by a stochastic process due to binding kinetics between receptors and ligands on the surface of ECM fiber . Monte Carlo simulation methods have been established for various ligand-receptor binding kinetics in the literature [27] . Each focal FC consists of a bundle of ligand-receptor bonds ( Fig 2B ) , each of which ruptures and binds stochastically . Let Pb be the probability with which a single receptor binds to a ligand on the ECM fiber during a time interval Δt . pb=1−exp ( −konΔt ) ( 3 ) kon=kfAL ( CL−Cb ) ( 4 ) where kf is the forward reaction rate ( 1 molecule−1 s−1 ) , Cb represents the density of bound ligands , CL the original density of the ligands ( molecules area−1 ) , and AL is the local area of a single fiber associated with the integrin node under consideration . Note that ( CL-Cb ) represents the number of unbound ligands available for bonding in the vicinity of the integrin node . In simulations , values of ALCL were identically set to be 300 molecules per a ECM fiber node in three ECM fibers network models . Once in the tugging phase , the strength of the FCs is tested ( rupture test ) , and the phase switches to the contractile phase if the bond does not rupture and the tension rises beyond a specified threshold level at the filopodial tip ( 3–4 nN/μm ) [36] . If it ruptures , it switches back to the outgrowing phase . The formation of FCs is restricted to the proximal tip of the filopodia , since it is known that veil advance typically results from the formation of FCs proximal to the tip of the filopodial shaft [32] ( Fig 1A and 1E , S3 Video ) . The filopodial model is geometrically composed of NAM compartments of acto-myosin ( AM ) assemblies; the first compartment is attached to the root of filopodium and the last compartment is connected to the tip of filopodium ( Fig 3A and 3B ) . We model filopodial contractile motion in a manner characterized by that of actin stress fibers ( SF ) [27] . The stiffness of an AM assembly is variable , κAM , j=EAMAAMLAM , j1 , j=1 . . NAM where EAM is the Young’s modulus of AMs [37] , AAM is the average cross-sectional area of AMs in a filopodium and LAM , j1 is an unstressed length of a single compartment of the j-th AM . The length of each compartment contracts at both ends according to the myosin II sliding rate , vm , j . Therefore , dLAM , j1dt=−2vm , j . Furthermore , it is known that myosin motors slides on actin filaments in opposite directions of FC forces at the filopodial tip [4] . That is , an increasing elastic load from the ECM is transmitted through FCs at the tip of the filopodium , through the AM assembly , to the myosin motors , which , in turn , decreases the myosin speed ( vm ) . It has been known that myosin sliding velocities affected by ECM stiffness , that is , myosin sliding speed is high on the soft gel but it is low on the hard gel [4] . In addition , measurements on few or many myosin molecules by optical trap [38] reported that the force-velocity relationship for these molecules is significantly similar to that of whole muscle [39] or muscle fibers [40 , 41] . To incorporate these characteristics into the filopodia dynamics , we adopt the force-velocity relationship for the whole muscle [39] into the following equation: vm=vm0Fstall−FTRFstall+cmFTR where vm0 is the sliding rate of myosin in the absence of load ( 10 nm/s ) [42] , Fstall is the stall force of 1 nN , cm is a dimensionless myosin parameter of 0 . 1 , and FTR is the sensed elastic force from the ECM at the tip of filopodium . It should be noted that the speed of myosin become zero when FTR exceeds Fstall , and linear relationship of force-velocity can be tuned to that of the nonlinear relationship by increasing the value of cm more than 1 . The total elastic energy stored in the AMs in the filopodium is given by HAM=∑j=1NAM[κAM , j2 ( dAM , j−LAM , j1 ) 2] ( 5 ) where dAM , j represents the distance of the j-th contractile AM compartment under tension ( Fig 3B ) . Using the virtual work theory , forces due to contractile myosin motor activity at the j-th node of filopodial shaft is given by FAM , jf=−∂HAM∂xjf=−κAM , j ( dAM , j−LAM , j1 ) ∂dAM , j∂xjf+κAM , j+1 ( dAM , j+1−LAM , j+11 ) ∂dAM , j+1∂xj+1f . ( 6 )
It has been reported that distinct load-fail behaviour and frictional slippage behaviour of filopodia at both soft and hard gels can be explained by using the motor-clutch mechanism at the filopodial shaft [4] . Furthermore , the speed of the filopodia retraction is highly dependent on the stiffness of ECM surrounding the filopodia tip: as the stiffness of the ECM increases , the retrograded flow of actin at the filopodial shaft becomes faster , but the traction stress generated in the ECM becomes lower [4] . In the current stochastic model of filopodia penetration dynamics , these filopodia behaviours have been reproduced and their mechanisms have been elucidated as a dynamic 3D interplay between filopodia traction and ECM remodelling ( S5–S7 Videos ) . Filopodia mechanically interact with ECM fibers to cause gel compaction and fiber remodelling [24] ( S19 Video ) . In turn , the local stiffness of the ECM , which is modulated by the filopodia activities , influences the contractile/retractile behaviours of the filopodia via actin motor activities . Here , the contractile motion differs from the retractile motion in that the contractile motion leads to the generation of traction fields in the ECM surrounding the filopodia via the motor-clutch mechanism , while the retractile motion results in the relaxation of filopodia as well as the relaxation of ECM fibers . Furthermore , these distinct behaviours of filopodial motion have been confirmed using a 3D cell migration microfluidic assay in which 200 nm-sized fluorescent beads are embedded into the collagen gel . As a result , beads move towards the extending filopodium tip during the tugging and contractile phases , and away from the filopodium during retractile motion ( S13 and S14 Videos ) . The ECM stiffness is known to increase as the concentration of crosslinking molecules increases [49] . On the other hand , an increase in the number of crosslinks leads to a decrease in the ECM pore size . In our current study , three kinds of ECM fiber network models were built with pore sizes of 0 . 5 , 1 . 0 and 1 . 5 μm and single fiber diameters of 28 , 34 and 41 nm , respectively , based on mechanical properties experiments [43] . The line stiffness of a single fiber ( =EfeAf/Lfe0 ) with a pore size for the three different ECM models were calculated to be 0 . 880 , 0 . 908 and 1 . 2 pN/nm , respectively . Although these values are within the range of soft substrate whose stiffness is less than 1 pN/nm , apparent values of stiffness for the fiber network at the local area of 1 μm2 are significantly increased to the range of harder substrate whose stiffness is more than 10 pN/nm as pore size becomes smaller and more crosslinks are added . To promote cell migration in a 3D ECM fiber network with the smallest pore size , degradation of the ECM fiber network is required [50] . For comparison we simulated filopodial penetration models with no degradation of the ECM fiber network , that is , MT1-MMP deficient cell model ( S16 Video ) . This resulted in inhibition of the cell invasion into the ECM fiber network , compared to that observed for deep cell invasion model that incorporates filopodia penetration dynamics and ECM fiber network degradation ( S7–S10 Figs and S2 Text and S17 Video ) . Thus , our simulated results reveal that the degradation of ECM fiber network plays an important role in filopodia penetration dynamics during both tugging and contractile phases . Without ECM degradation , filopodia can still grab ECM fibers; however , the lamella , where the root of the filopodial shaft is connected to the cellular membrane , can hardly penetrate through the ECM fiber network . With the addition of the component of degradation by MMP-2 , our ECM fiber network model is able to simulate more complex situations; local fiber network , where MMP-2 is diffused from the cellular membrane , gradually becomes less stiffer since degraded fiber network is decomposed into multiple elements of single fibers whose stiffness are less than 1 pN/nm . Thereby , the lamella can easily penetrate through the degraded ECM fiber network . Filopodia dynamics is very complex , and many other factors are likely to contribute to these dynamics . We have applied the Bell model to our filopodial focal complex model to rupture bonds between integrins and collagen molecules with force , and it is known as the ‘slip bonds’ as the ligand should slip out of the binding pocket more rapidly under higher tensile force [35] . As results , probability of rupturing an individual bond becomes higher and the lifetime of its bond becomes shorter under higher tensile force ~200 pN . On the other hand , some cell-ECM interactions have been recently known to indicate ‘catch bonds’ behavior [51 , 52]; the lifetime of catch bonds show biphasic distributions under applied force , which implies that the lifetime of catch bond takes a maximum at a critical value of applied force . It is of interest how catch bonds likely to influence filopodia dynamics . There was an interesting work of stochastic models comparing behaviors between ‘catch bond’ and ‘slip bond’ in relation to actin retrograde flow and corresponding traction stresses [53] . In their work , in case of ‘slip bond’ , bond fraction was rapidly decreased as actin retrograde speed was increased . However , in case of ‘catch bond’ , bond fraction takes a maximum at actin retrograde speed of 5 nm/s . Overall , ‘catch bond’ case showed higher bond fraction than ‘slip bond’ case at the range of actin retrograde speed > 5 nm/s . Based on these results , filopodia dynamics with ‘catch bonds’ is likely influence longer lifetime focal complex formations than that with ‘slip bonds’ . Furthermore , filopodia dynamics with ‘catch bonds’ is likely influence stronger traction fields in ECM than that with ‘slip bonds’ . The initial goal of current model was aimed at understanding how filopodia penetration dynamics plays an important role in 3D cell invasion into an ECM fiber network . The ultimate goal of the future cell model will be the development of the model like a realistic cell by capturing complicated morphology changes the cell . However , comparisons of cellular morphologies between the simulated cell models and experimentally measured HUVECs were limited in current cell model because morphologies of simulated cells were rounded or oval shapes , but those of experimentally measured cells were very elongated . To our knowledge , this discrepancy was resulted from two assumptions of the model; 1 ) the number of nodes in the cellular membrane was set to be 549 , and 2 ) maximum number of clustered integrins per a node was set to be 100 . Therefore , the direction of the future cell migration model will be to improve the morphology of cell model by increasing the number of nodes > 10 , 000 as the size of a mesh size is close to molecular size of ~80 nm , and decreasing the number of clustered integrins . In addition , all diffusion coefficients and some secretion rates of biochemical concentrations in the current model were assumed to be identical for three ECM fiber models . However , in fact , heterogeneous diffusion coefficients and secretion rates of biochemical concentrations should be considered in the future cell migration model since recent experimental observations have indicated that diffusion coefficient of bovine serum albumin ( BSA ) is decreased as the ECM is stiffer and a pore sizes of ECM network is reduced more [22] . Furthermore , the future cell model can be extended to more complex models of cancer metastasis including a collective migration mediated by both cell-cell and cell-ECM adhesions , epithelial-mesenchymal transition ( EMT ) -mediated mesenchymal cell migration , amoeboid migration in ECM [54] .
The structural mechanics and intracellular mechanics are other key mechanisms involved in cell invading into 3D gel . Here , we formulate them by extending the previous dynamic model for cell migration on curved surfaces [26] , and cell migration and spreading on planar micropatterns [27] . The essential equations in the model include: 1 ) an equation for FA dynamics based on Monte-Carlo simulations of ligand-receptor bonds , 2 ) two equations for deformations of double elastic membranes: an outer cell membrane and an inner nuclear membrane , 3 ) an equation describing the contractile motion of actin stress fibers , which is extended from FAs on the cortical surface to the nuclear membrane , and 4 ) lamellipodium protrusion by actin polymerization [19] with a constant force of 300 pN . The detailed description of the equations in the model can be found in previous works [26 , 27] . Among them , the major extension in the mechanobiological dynamics model presented here is FAs dynamics in 3-D ECM fiber network model ( Fig 1B ) . The FA force acts between the i-th integrin node on the cellular membrane and points of ECM fibers where the extension of the unit vector normal to the cellular membrane interacts with the nearest point of ECM fibers . To generate the computational model for ECM fiber network , a 3D cubic structure ( 50 × 50 × 50 μm ) was initially drawn using AUTOCAD software . This structure was made in a stereolithography ( STL ) file format . Then , the surface geometry of the STL file was imported into a commercial CFD software package ( STAR CCM++ , CD-adapco ) to build tetrahedral grids for the model . As a preprocess , further refinements to the STL file , including surface triangulation , tetrahedral volume meshing , and optimization for computational stability , were carried out to build three different ECM fiber network models with pore sizes of 0 . 5 , 1 , and 1 . 5 μm ( S11A Fig ) . These preprocessed tetrahedral grids for three different ECM fiber network models with pore sizes of 0 . 5 , 1 , and 1 . 5 μm consisted of ~ 3 . 0×106 , 3 . 8×105 , and 1 . 3×105 elements , respectively . Finally , these grids were modified to construct components of ECM fiber network , such as elastic fibers and crosslinks ( S11B Fig ) . Here , red and yellow nodes in these grids represent crosslink and fiber nodes , respectively . To construct the fiber network , each line between two red nodes was divided by Ndiv fiber segments and two crosslink segments that consist of Ndiv +1 fiber nodes ( yellow ) . Here , values of Ndiv for ECM fiber network models with pore sizes of 0 . 5 , 1 , and 1 . 5 μm were respectively set to be 1 , 2 , and 3 with an assumption that the density of collagen molecules along all fibers in three ECM fiber network models was identical ( 300 collagen molecules per a fiber node ) , and the distance of a crosslink segment was set to be 30 nm . After segmentations of fibers , one set of fiber segments were randomly made to connect to coaxial neighbouring sets of fiber segments under a condition when the angle between two connected sets of fiber segments was above 60° ( S11C Fig ) . In addition , the number of connected fibers at the i-th crosslink node was set to be Pf ( Nifs2 ) , where Pf is an initial ratio of forming fibers at the i-th crosslinks node ( 0 . 7 ) , and Nifs is a total number of linked fiber segments at the i-th crosslinks node . We assume the ECM fiber network to be composed of elastic ECM fibers and crosslinks , which make strong bonds between adjacent fibers [13] . The elastic energy stored in the ECM fiber network can be expressed in terms of the stretching and bending properties of the constituent fibers . The stretching modulus of a fiber is given by κf , se ( =EfeAf ) , where Efe and Af ( =πrf2 ) are the Young’s modulus ( 1 MPa ) and the cross-sectional area of a single fiber , respectively . The bending modulus of a fiber is given by κf , be ( =EfIf ) , where If ( =πrf4/4 ) [55] . The stretching elastic energy of the j-th segment of the i-th fiber is given as a function of the difference between the stressed ( Li je ) and unstressed ( Li je0 ) lengths , and the bending elastic energy as the one of stressed ( θi je ) and unstressed ( θi je0 ) angles at the j-th node between two segments in the i-th fiber ( Fig 7 ) . The total elastic energy in the i-th ECM fiber in the network can be expressed as following: Hf , ie=κf , se2∑j=1Nie ( Li je−Li je0 ) 2Li je0+κf , be2∑j=1Nie ( θi je−θi je0 ) 2Li je0 . ( 8 ) Here , it should be noted that the elastic energy at the j-th node in the i-th fiber is summed only for coaxial neighbouring nodes . Similarly , the elastic force at the j-th node in the i-th fiber , FE , i je , can be derived by using the virtual work theory: FE , i je=−∂Hf , ie∂xi je=−κf , se∑k=jj+1 ( Li ke−Li ke0 ) Li ke0∂Li ke∂xi je−κf , be∑k=j−1j+1 ( θi ke−θi ke0 ) Li ke0∂θi ke∂xi je ( 9 ) where θike=cos−1 ( t^i , k⋅t^i , k+1 ) , t^i , k and t^i , k+1 are tangential unit vectors at the k and k+1-st segments in the i-th fiber , respectively , and ∂θf , i ke∂xi je=−11− ( t^i , k⋅t^i , k+1 ) 2 ( ∂t^i , k∂xi je⋅t^i , k+1+t^i , k⋅∂t^i , k+1∂xi je ) . To incorporate the degradability factor into the ECM fiber network and its nonlinear behaviour under mechanical responses , we consider that each crosslink node comprises crosslink molecules , such as amino acids , that can rupture . Fig 7B shows an example of connectivity between the i-th crosslink and the two neighbouring fibers . We model the degradability of ECM fiber network by considering detachment events among the i-th crosslink node and its neighbouring fibers , and the degradability of ECM fiber network depends on a local value of the ECM integrity ( IECM , i=CECM , i/CECM , i0; 0≤IECM , i≤1 ) ( see S18 Video ) . Here , CECM , i and CECM , i0 are concentrations of the i-th ECM node at present and initial states ( 10μM ) , respectively . The number of uncrosslinked ( or degraded ) fibers at the i-th crosslink node , Niuf , is calculated as Niuf= ( 1−IECM , k ) Ni0f , where Ni0f is an initial number of crosslinked ECM fibers at the i-th crosslinks node . To incorporate chemical interactions of 3D ECM with filopodia and cellular membranes ( Fig 1C ) , four distinct dynamics associated with chemotaxis , proteolysis , haptotaxis , and degradation are modelled . Seven reaction-diffusion equations for concentrations of VEGF , MMP-2 , TIMP-2 ( S7 and S8 and S9 Figs ) , MT1-MMP , a ternary complex of MT1-MMP:TIMP-2:proMMP-2 [14] , ligands ( or collagen molecules ) and ECM are numerically solved using Finite Volume Method ( FVM ) [56] . Constitutive equations for the seven biochemical concentrations are summarised in Table 3 . In particular , MT1-MMP and TIMP-2 secretions at the membrane near the roots of filopodia are modelled as source terms [25] . Cell migration simulations were carried out using a fourth order Rosenbrock method based on an adaptive time-stepping technique for integrating ordinary differential equations with the convergence criterion <10−4 . The ordinary differential equations of cell and filopodia models were numerically coupled to solve for unknown variables associated with the mesh node position vectors for both cell membrane , nucleus membrane , transduce layer , and filopodial membrane ( see Table 1 ) . In particular , cell membrane and transduce layer were coupled with the viscoelastic actin cortex using kelvin-voigt model ( Fig 1B ) . To solve two coupled ordinary differential equations in the CC and CT modules ( Table 1 ) numerically , these equations should be converted with respect to vectors {dxicdt , dxitdt}T as followings: ( dxicdtdxitdt ) =1CcCt+Ccort ( Cc+Ct ) ( Ct+CcortCcortCcortCc+Ccort ) ( FFA , ic+FE , ic+FL , ic+FT , icFE , it+FSF , it+FT , it ) i=1 , ⋯ , Nc . ( 10 ) For cell migration simulation the Rosenbrock method outperforms the standard Runge—Kutta method which requires a relatively large number of iterations . Furthermore , the Rosenbrock method consumes less computing time by using adaptive time-step control that ranges from 10−3 s to 10−2 s in the present work . Thus , it is suitable for simulating transient cell migratory behaviours over 1 hour . For computations of ECM fiber networks , numbers of ECM fiber nodes were ranged from 30 , 000 to 234 , 000 depending on pore sizes of ECM model . As pore sizes of ECM fiber network models are smaller , computations of these models become more expensive . To solve ECM fiber network models effectively , computational domains of ECM fiber models were set within a radius of 15 μm at both the centre of cellular membrane and filopodial tip . As results , three ECM fiber network models ( see Fig 4A , 4B and 4C ) were visualized as half spheres . These computational domains were updated every 10 seconds of physical time as the cell interacts with ECM fibers dynamically . One practical issue in computing finite mesh geometric models is to check geometrical compatibility . As the coordinates of cell membrane , filopodial membrane , and ECM fibril nodes are updated based on the equations of motion , geometrically incompatible situations occur occasionally in the configurations of the cell membrane mesh and that of the filopodia in relation to the ECM fibril surface . For example , some cell membrane nodes intersect with the fibril fiber , and the filopodia also intersects with the fibril fiber . These incompatible situations must be checked in every computational cycle , and necessary corrections such as contact forces ( elastic repulsive ) must be made [26 , 57] . The methods for microfluidic sprouting experiments were previously described in detail [58] . Briefly , GFP-expressing HUVEC ( Angio-Proteomie ) were cultured in EGM2-MV growth medium ( Lonza ) and used in experiments at passage 6 . PDMS microfluidic devices were bonded to glass coverslips and channels were coated with PDL ( Sigma ) . The central gel region was filled with 2 . 5 mg/ml Collagen I solution before incubating for 30 min at 37°C to polymerize . The NaOH concentration of the Collagen I solution was varied to control the pH of the solution during polymerization . After polymerization of the gel , HUVECs were seeded in the medium channel by introducing 40 μl of cell suspension at 2×106 cells/ml . After 24 h , medium was replaced with EGM2-MV supplemented with 50 ng/ml VEGF ( peprotech ) and 250 nM S1P ( Sigma ) . After 12 h , individual cells sprouting into the collagen gel were imaged at 60x magnification using a confocal laser-scanning microscope ( FV-1000 , Olympus ) . Collagen fibers were simultaneously visualized on the same instrument by collecting the reflected light ( confocal reflectance microscopy ) . During imaging , cells were kept humidified at 37°C and 5% CO2 . For imaging analysis , speeds of both filopodial tip and root was measured using a 3D image analysis tool ( IMARIS , Bitplane ) .
|
Cell invasion into a 3D ECM requires substantial cellular traction forces as well as the degradation of ECM . We are interested in how filopodia gain traction forces from the surrounding collagen fibers in the degradable ECM . Thereby , to create the overall computational model , we integrated four modules , each capturing a different physical aspect influencing migration: 1 ) filopodia penetration dynamics; 2 ) intracellular mechanics; 3 ) reaction-diffusion mass transfer; and 4 ) structural mechanics of ECM fiber networks . We successfully compared our model with experiments of 3D HUVEC migration for diverse ECMs with different pore sizes and stiffness . Finally , our model reveals the degradation of ECM fiber network plays an important role in filopodia penetration dynamics during both tugging and contractile phases .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Model"
] |
[] |
2015
|
Cell Invasion Dynamics into a Three Dimensional Extracellular Matrix Fibre Network
|
The molecular genetic mechanisms of sex determination are not known for most vertebrates , including zebrafish . We identified a mutation in the zebrafish fancl gene that causes homozygous mutants to develop as fertile males due to female-to-male sex reversal . Fancl is a member of the Fanconi Anemia/BRCA DNA repair pathway . Experiments showed that zebrafish fancl was expressed in developing germ cells in bipotential gonads at the critical time of sexual fate determination . Caspase-3 immunoassays revealed increased germ cell apoptosis in fancl mutants that compromised oocyte survival . In the absence of oocytes surviving through meiosis , somatic cells of mutant gonads did not maintain expression of the ovary gene cyp19a1a and did not down-regulate expression of the early testis gene amh; consequently , gonads masculinized and became testes . Remarkably , results showed that the introduction of a tp53 ( p53 ) mutation into fancl mutants rescued the sex-reversal phenotype by reducing germ cell apoptosis and , thus , allowed fancl mutants to become fertile females . Our results show that Fancl function is not essential for spermatogonia and oogonia to become sperm or mature oocytes , but instead suggest that Fancl function is involved in the survival of developing oocytes through meiosis . This work reveals that Tp53-mediated germ cell apoptosis induces sex reversal after the mutation of a DNA–repair pathway gene by compromising the survival of oocytes and suggests the existence of an oocyte-derived signal that biases gonad fate towards the female developmental pathway and thereby controls zebrafish sex determination .
The existence of two differentiated sexes is common among animals and yet the mechanisms that determine sex are amazingly diverse . Among vertebrates , for instance , some species use primarily genetic factors and others rely on environmental factors to cause embryonic gonads to become testes or ovaries . Genetic sex determination ( GSD ) includes monogenic as well as polygenic systems , and in monogenic systems the sex-determining gene is usually found on sex chromosomes that evolved from a pair of autosomes after acquiring a novel sex-determining allele ( reviewed in [1] ) . Mammals have an XX/XY sex chromosome system with males as the heterogametic sex , but birds and many reptiles have a ZZ/ZW sex chromosome system with females as the heterogametic sex . Among fish , both sex chromosome systems have been described [2]–[7] . In environmental sex determination ( ESD ) , factors in the environment , such as temperature , control sexual fate [2] . GSD and ESD have long been thought of as distinct mechanisms , but recent data show regulation by both genetic and environmental factors within a single species [8] . In such species , the integration of genetic and environmental factors ultimately tips the bipotential gonads towards the male or the female fate ( reviewed in [9] ) . For example , in medaka , a teleost fish with an XX/XY sex determination system , high temperatures can sex reverse XX females [10] . Despite the vast diversity of primary sex-determining mechanisms , genes downstream in the sex determination pathway appear to be broadly conserved among vertebrates . It has been suggested that during evolution , different species recruited different downstream genes to be the major sex-determining gene , sometimes relatively recently , and that changes at the top of the sex-determining pathway appear to be better tolerated than changes at the bottom of the pathway because they are less likely to have deleterious effects [11] . In mammals , the Y chromosome gene SRY ( Sex determining region Y ) is at the top of the sex determination cascade [12]–[16] and acts as a genetic switch that triggers the bipotential gonad to initiate the male pathway ( reviewed in [17] ) . SRY however , does not appear to exist beyond therian mammals [18] . In several groups , including mammals , Dmrt1 ( doublesex and mab-3 related transcription factor 1 ) is a downstream gene in the male sex-determination pathway , but in medaka ( Oryzias latipes ) , a duplicated copy of dmrt1 ( called DMY or dmrt1by ) is the major sex-determining gene [19] , [20] and recent work has shown that dmrt1 is required for testis development in chickens [21] . Interestingly , dmrt1by is absent in most Oryzias species [22] , showing that the upstream regulators of sex determination can change rapidly . Teleost fish show a broad diversity of sex determining mechanisms that range from genetic to environmental , from monogenic to polygenic , and from hermaphroditism to gonochorism ( two distinct sexes ) [2] . Zebrafish , like many other teleosts , have no obvious heteromorphic sex chromosomes [23]–[25] . Adult zebrafish have two differentiated sexes , but have been described to develop initially as juvenile hermaphrodites because all juveniles develop gonads with immature oocytes regardless of their definitive sex [26]–[28] . Zebrafish juvenile gonads contain immature oocytes that progress through oogenesis in about half of the individuals , which become females , but that degenerate in the other half of the individuals , which become males [26]–[28] . Oocytes begin to degenerate in a window of time ( 20–30 days post-fertilization ( dpf ) ) that lasts several days and varies among individuals and among rearing conditions [26]–[31] . Because the sex of the zebrafish gonad drives secondary sex characters , gonadal sex determines the definitive sex of the fish . Zebrafish depleted of germ cells develop as infertile males [31]–[34] and it has been shown that the presence of germ cells is essential to maintain female fate in zebrafish [31] . We do not yet know , however , the primary genetic mechanisms that cause some zebrafish to become females and others to become males . To broaden our knowledge of the genetic mechanisms involved in zebrafish sex determination , we studied a fancl zebrafish mutant that develops exclusively as male . Fanconi Anemia complementation group L ( Fancl , OMIM 608111 ) , along with 12 other Fanconi Anemia proteins , facilitates cellular responses to a variety of stresses , including signals of DNA damage and apoptosis [35] , [36] and belongs to the Fanconi Anemia/BRCA DNA repair pathway . In humans , mutations in any of these Fanconi genes can cause Fanconi Anemia ( OMIM 227650 ) , a recessive disease characterized by bone marrow failure , high risk of acute myeloid leukemia , development of squamous cell carcinomas of the head and neck , and developmental abnormalities in many organs including gonads , which causes hypogonadism , impaired gametogenesis , defective meiosis and sterility [37] , [38] . Fancl is a member of the Fanconi Anemia core complex with a Plant Homeo Domain ( PHD ) that mono-ubiquitinates Fancd2 and Fanci [39] , [40] , which co-localize with BRCA1 and BRCA2 proteins in nuclear foci to stimulate DNA repair . A severe allele of human FANCL causes a clinical phenotype that includes hematopoietic and skeletal abnormalities that are similar to , or more severe than , those typically observed in patients suffering from a defect upstream in the Fanconi Anemia pathway ( H . Joenje , personal communication ) . We previously identified the zebrafish ortholog of the human FANCL gene [41] . Here we show that fancl homozygous mutants develop solely as males and that the absence of fancl mutant females is not due to female-specific lethality but to female-to-male sex reversal . Results demonstrated that the sex reversal of fancl mutants is not due to the absence of germ cells , but to an abnormal increase of germ cell apoptosis that compromises survival of developing oocytes and masculinizes the gonads . We found that reducing germ cell apoptosis by introducing a Tp53 ( p53 or tumor protein p53 ) mutation rescues the fancl sex reversal phenotype , and that many double mutants develop ovaries and become females . These results suggest the model that oocytes normally must progress through meiosis to signal the gonadal soma to maintain female development , and point to Tp53-mediated apoptosis of germ cells as a factor that could be targeted by environmental or genetic signals to modify zebrafish sex determination .
A zebrafish fancl mutant ( allele HG10A , accession number AB353980 ) was generated by insertional mutagenesis in a Tol2 transposon-mediated enhancer trap screen [42] . Cloning and sequencing of genomic DNA surrounding the insertion revealed that the Tol2 construct was inserted into exon 12 of fancl , thereby disrupting the coding region of the PHD finger domain ( Figure 1A and 1B ) , which is essential for Fancl function [39] . To determine whether the HG10A Tol2 insertion disrupts fancl transcription , we performed reverse transcriptase-PCR experiments on cDNA isolated from testes of a homozygous fanclHG10A mutant adult . To learn if the Tol2 insert formed part of the fanclHG10A transcript , we designed a forward primer in exon1 and a reverse primer in the insertion ( F1 and R1 in Figure 1A ) . The sequence of the PCR product revealed a fanclHG10A transcript that contained the Tol2 construct inserted after codon Q318 in exon 12 ( arrowhead in Figure 1B line 2 ) . This insertion is predicted to insert seven novel amino acid residues and to introduce a premature stop codon ( asterisk in Figure 1B line 2 ) , resulting in the loss of 41 of the 57 residues of the PHD finger domain . This loss eliminates the crucial tryptophan-337 ( W , double underlined in the wild type ( WT ) in Figure 1B line 1 ) that is conserved in all PHD finger-type E3 ligases , as well as histidine-330 and five of the seven cysteines ( H and C , underlined in Figure 1B line 1 ) that are highly conserved in PHD finger domains [39] , [43] , [44] . To test if fancl HG10A mutants could produce fancl transcripts with an intact PHD domain due to elimination of the Tol2 insertion , we amplified the region encoding the PHD domain using primers flanking the Tol2 insertion ( primers F2 in exon-11 and R2 in exon-13 , Figure 1A ) . RT-PCR experiments revealed that fancl HG10A mutants lacked the expected 232 base pair ( bp ) PCR-product corresponding to the intact PHD domain found in wild-type siblings ( WT in Figure 1C ) , but instead possessed a PCR-product of smaller size ( 174 bp ) ( fancl in Figure 1C ) . Cloning and sequencing of the F2-R2 products revealed that the small band from fancl mutants was a variant transcript that lacked both the first half of exon 12 and the Tol2 insertion ( fanclΔTol2 in Figure 1B line 3 ) . This fanclΔTol2 variant resulted from the joining of exon-11 to the second half of exon-12 due to a splice acceptor site that is newly created at the junction of the Tol2 insertion ( Figure 1B line 3 ) . The absence of the first half of exon-12 in the fanclΔTol2 transcript introduced a frameshift that generated an early stop codon ( asterisk in Figure 1B line 3 ) leading to a predicted truncated protein lacking the entire PHD domain . These results show that homozygous fancl HG10A mutants have two variant transcripts , both of which encode products lacking an intact PHD finger domain shown to be essential for the ubiquitination function of Fancl [39] . To characterize the fancl HG10A phenotype , we crossed fancl +/HG10A heterozygotes ( called fancl+/− below ) , and after genotyping the progeny by PCR , observed that all fancl HG10A/HG10A homozygous mutants ( called fancl−/− below ) developed exclusively as males , even though their wild-type and heterozygous siblings developed about as many females as males . Two alternative hypotheses could explain the lack of homozygous fancl mutant females: female-specific lethality or female-to-male sex reversal . To distinguish between these two hypotheses , we crossed female fancl+/− heterozygotes to male fancl−/− homozygotes . We raised 211 progeny to adulthood , determined their phenotypic sex according to sexually dimorphic characters including the color of the anal fin and body shape , and finally scored their fancl genotypes by PCR . Under normal conditions , this cross should give 50% heterozygotes ( about half of which should be female ) , and 50% homozygous mutants ( about half of which should be female ) , expecting a 1∶1∶1∶1 ratio of heterozygous females to heterozygous males to homozygous mutant females to homozygous mutant males . The fancl female death hypothesis predicts a 1∶1∶0∶1 ratio , or 66% heterozygotes and 33% homozygous mutants , but the sex reversal hypothesis , predicts a 1∶1∶0∶2 ratio , or equal proportions ( 50%∶50% ) of homozygous mutants ( all male ) and heterozygotes ( males plus females ) . Resulting genotypes revealed 46 fancl+/− females: 62 fancl+/− males: 0 fancl−/− females: 103 fancl−/− males , which showed that about half of the progeny were fancl homozygous mutants ( 103/211 , 49% ) and the other half were heterozygous for the fancl mutation ( 108/211 , 51% ) ( Figure 2 ) . These results had strong statistical support ( chi-square likelihood ratio = 0 . 794 , p-value >0 . 1 ) , thus ruling out the hypothesis that homozygous fancl mutant females died . Results , however , were consistent with the hypothesis that animals that otherwise would have become females developed as males due to female-to-male sex reversal . Sex distributions within each genotype confirmed our previous observations that all fancl homozygous mutants developed as males ( n = 103 , 100% ) , and while approximately half of fancl heterozygous siblings developed as males ( n = 62 , 57% ) , the other half developed as females ( n = 46 , 43% ) ( Figure 2 ) . These scores showed strong statistical support for the hypothesis that fancl mutants experienced female-to-male sex reversal ( chi-square likelihood ratio = 73 . 946 , p-value<0 . 0001 ) . To exclude the possibility that some of the fancl mutants could have ovaries despite their external male phenotypic characters , we dissected the gonads of adult fancl homozygous mutants ( n = 45 ) , heterozygous females ( n = 11 ) and heterozygous males ( n = 29 ) . In all cases , we found a perfect match between external sexual characters and gonadal sex . These results ruled out the possibility that fancl mutants masqueraded as males externally while having female gonads . We conclude that the HG10A Tol2 insertion into fancl induced a female-to-male sex reversal phenotype in zebrafish . Because germ cells play a fundamental role in controlling female sex determination in zebrafish [31] , [32] , we wondered if fancl could play a role in zebrafish germ cell development . To address this question , we first tested whether fancl is expressed in germ cells of wild-type zebrafish . We analyzed the expression pattern of fancl by in situ hybridization on sections of gonads at seven developmental time points encompassing representative stages of gonad development ( Figure 3 ) , including sexually undifferentiated and presumptively still bipotential gonads ( e . g . 10 , 17 and 23 days post-fertilization ( dpf ) ) ; transitioning gonads ( e . g . 26 dpf ) , sexually determined but still immature gonads ( 33 and 37 dpf ) , and mature adult gonads ( 6 months post-fertilization ) . Results showed no detectable fancl expression in undifferentiated wild-type gonads at 10 dpf ( data not shown ) , but weak expression signal appeared in immature gonads at 17 dpf and 23 dpf ( arrows in Figure 3A and 3B ) . In transitioning gonads at 26 dpf , fancl expression increased in developing germ cells ( arrows in Figure 3C and 3D ) , and signal was clearly detected in the ooplasm of oocytes in the ovary-like gonad ( arrow in Figure 3C ) . At 33 dpf and 37 dpf , immature gonads showed a clear morphology of ovaries or testes , and fancl expression signal was maintained in developing oocytes and spermatocytes ( arrows in Figure 3E–3H ) . In adult gonads , fancl expression remained restricted to germ cells , but remarkably , the intensity of the detected signal differed depending on the stage of germ cell differentiation ( Figure 3I and 3J ) . In ovaries , the weak fancl signal detected in early stage IB oocytes ( eIB in Figure 3I ) contrasted with the obvious strong signal in the ooplasm of late stage IB oocytes ( lIB in Figure 3I ) . This result suggests that oocytes up-regulate fancl transcription before they transition into stage II . At later stages of oogenesis , fancl signal became less intense as oocytes progressed through oogenesis ( Figure 3I ) . This reduction in staining intensity may be due to the dilution of transcript as oocytes increase in volume when cortical alveoli ( also known as cortical granules in non-fish species ) appeared in the ooplasm ( stage II ) and yolk began to accumulate ( stage III ) ( Figure 3I ) . We detected low levels of fancl transcript at late stages of oocyte maturation ( stage IV ) , suggesting that fancl is part of the maternal load of messenger RNA transcripts stored in the egg and passed along to embryos . This result agrees with our detection of fancl transcripts by RT-PCR and in situ hybridization experiments even at early developmental stages before the embryonic transcription machinery becomes active [41] . In testes , fancl expression appeared in spermatocytes ( sc in Figure 3J ) , but not in more advanced stages of spermatogenesis , including spermatids and sperm ( sp in Figure 3J ) . This result revealed the stage-specific expression of fancl during spermatogenesis . The finding that fancl was expressed in zebrafish germ cells during the time-window critical for gonad differentiation and sex determination ( 17 to 33 dpf ) and was up-regulated in early stages of gametogenesis is consistent with the hypothesis that Fancl plays a specific role in germ cell development and suggests that its disruption might lead to the female-to-male sex reversal phenotype displayed by fancl mutants . Because zebrafish depleted of germ cells by dead end ( dnd ) morpholino ( MO ) knockdown [45] , [46] develop exclusively as males [31] , [32] , and even though adult fancl mutants are fertile , we wondered if the female-to-male sex reversal of fancl mutants could be related to extremely low numbers of germ cells during stages of sex determination in juvenile mutants , or at least in those that otherwise would have developed as females and had been reversed to males . To answer this question , we performed gene expression analyses comparing gonads of fancl homozygous mutants ( fancl ) , wild-type sibling controls ( WT ) and dnd-MO knockdown animals ( dnd ) at key stages in sex determination: 19 dpf ( Figure 4A–4I ) , 26 dpf ( Figure 4J–4X ) and 33 dpf ( Figure 4Y–4M' ) . Expression of the germ cell specific marker vasa [47] revealed the presence of germ cells in gonads of all fancl mutants sectioned ( n = 15 ) ( Figure 4D , 4P , 4S , 4E' , and 4H' ) and sibling controls ( n = 13 ) ( Figure 4A , 4J , 4M , 4Y , and 4B' ) , while all germ-cell depleted animals injected with dnd-MO ( n = 16 ) lacked vasa signal ( Figure 4G , 4V , and 4K' ) . The presence of substantial numbers of germ cells in all fancl mutants tested even at early stages of gonad development rules out the possibility that the near absence of germ cells is the cause of the female-to-male sex reversal in fancl mutants . Because all fancl mutants developed as males , we wondered if fancl mutants embark upon the male pathway from the beginning of gonad development , or whether they follow a normal bipotential pathway of development that later derails exclusively to the male pathway . To address these alternatives , we used the expression of cyp19a1a ( cytochrome P450 family 19 subfamily A polypeptide 1a ) and amh ( anti-Mullerian hormone ) , which are the earliest sex-specific somatic gonadal cell markers known for ovary and testis , respectively , to monitor development before gonads were sexually differentiated at the morphological level [29] , [31] , [48] . In 19 dpf undifferentiated gonads , somatic cells of fancl mutants , as well as those of wild-type controls and dnd-MO animals , expressed both the female marker cyp19a1a and the male marker amh ( Figure 4B , 4C , 4E , 4F , 4H , and 4I ) . This result showed no indication that fancl mutant gonads were developing abnormally , which suggests that fancl mutant gonads initially embark upon the normal bipotential pathway of development , and later derail into the male pathway . The fact that individual gonads in both fancl mutants and WT siblings expressed both cyp19a1a and amh , as did animals lacking germ cells , suggests that the onset of expression of these somatic cell markers is independent of germ cell derived signals . These results extend to a much earlier age than previously noted ( 19 dpf versus 35 dpf [31] ) the time at which gonads depleted of germ cells express amh . At 26 dpf , different individual WT juveniles showed different degrees of sexual differentiation , suggesting that this age is within the transitional period of sex determination . Some WT animals had gonads with few oocytes , low expression of cyp19a1a and up-regulation of amh ( Figure 4K and 4L ) , while others had gonads with many developing oocytes , up-regulation of cyp19a1a and absence of amh signal ( Figure 4N and 4O ) . In contrast to WT sibling controls , at 26 dpf , all fancl mutants had gonads with no ooctyes or just a few small oocytes , and most of them ( 4 out of 5 ) lacked expression of cyp19a1a and showed up-regulation of amh ( Figure 4Q and 4R ) . Most juvenile fancl mutants at 26 dpf , therefore , had completed the transitional period of sex determination , and had embarked on the male pathway . Only one of the five fancl mutants analyzed retained a remnant of a few cyp19a1a-expressing cells despite the presence of a considerable number of amh-expressing cells ( Figure 4T and 4U ) ; this animal was probably still transitioning towards the male pathway . In 26 dpf dnd-MO animals , all gonads were depleted of germ cells , and like fancl mutants , showed no cells or few cells expressing cyp19a1a and many cells up-regulated for the male marker amh ( Figure 4W and 4X ) . Therefore , most fancl mutants and dnd-MO animals tipped the fate of the bipotential gonad towards the male pathway earlier than WT controls . At 33 dpf , WT juveniles had already passed the transitional period of sex determination . Males had immature testes with no oocytes , no cyp19a1a-expressing cells and many cells with high levels of amh expression ( Figure 4Z and 4A' ) , and females had immature ovaries , with cyp19a1a-positive somatic cells surrounding oocytes but no amh-expressing cells ( Figure 4C' and 4D' ) . In contrast to WT sibling controls , at 33 dpf , most fancl mutant gonads ( 6 of 8 ) showed clear testes morphology , including the absence of cyp19a1a expression and up-regulation of amh expression ( Figure 4F' and 4G' ) . Interestingly , we found two fancl mutants that still had some oocytes; in contrast to WT controls , however , these individuals showed low cyp19a1a expression and high amh signal ( Figure 4I' and 4J' ) , which would be expected if these two fancl mutants were putative females that were in the process of sex-reversing to males . At 33 dpf , all fancl ( Figure 4G' and 4J' ) and dnd-MO animals ( Figure 4M' ) showed the typical male-specific up-regulation of amh . In contrast to 33 dpf dnd-MO animals , all of which lacked cyp19a1a expression ( Figure 4L' ) , fancl mutants that still retained some oocytes showed low levels of cyp19a1a expression ( Figure 4I' ) . These results would be expected if the presence of oocytes is essential to maintain cyp19a1a expression , and suggested the hypothesis that the female-to-male sex reversal of fancl mutants is due to abnormal development of oocytes that leads to a failure of somatic cells of the gonad to maintain cyp19a1a expression and to down-regulate amh expression . Because the Fanconi Anemia/BRCA system is involved in the repair of damaged DNA , such as that originating in meiotic recombination , we hypothesized that oocyte development is altered in fancl mutants . To test this hypothesis , we performed a histological analysis of fancl and wild-type gonad sections stained with hematoxylin and eosin at different stages of development to follow the progression of germ cells through meiosis ( Figure 5 ) . At 19–22 dpf , WT sibling controls and fancl homozygous mutants had undifferentiated gonads with no obvious morphological differences between genotypes . Gonads of both genotypes contained stage IB perinucleolar oocytes ( arrows in Figure 5A and 5B ) , as indicated by the presence of nucleoli at the periphery of the nuclei [49] . Shortly after the beginning of stage IB , chromosomes decondense and form lampbrush chromosomes [50] , which occurs during the diplotene stage of meiosis I as the synaptonemal complex dissolves and recombination nodules keep homologous chromosomes together [51] . We define “early” perinucleolar oocytes ( epo ) as stage IB oocytes that have not yet decondensed their chromosomes , and “late” perinucleolar oocytes ( lpo ) as stage IB oocytes that have already formed lampbrush chromosomes and entered the diplotene stage of meiosis I . Gonads of fancl ( 10 individuals ) and WT siblings ( 10 individuals ) at 19–22 dpf both had early ( epo in Figure 5A and 5B ) but not late stage IB oocytes , indicating that at this time , oocytes had not yet entered the diplotene stage of meiosis I in either genotype . At 26 dpf ( Figure 5C–5F ) , most WT controls ( 7 of 9 individuals ) showed late perinucleolar oocytes that had progressed through meiosis from early to late stage IB ( lpo in Figure 5C ) , in which lampbrush chromosomes were visible , indicating that recombination had completed and oocytes had already entered the diplotene stage of meiosis I [51] . In contrast to WT , most fancl mutants ( 11 of 12 ) lacked oocytes at late stage IB ( Figure 5F ) , indicating that oocytes in fancl mutants failed to progress through meiosis to the diplotene stage . Only one of the twelve fancl mutants showed late stage IB oocytes ( lpo in Figure 5D ) , and this individual also contained pyknotic cells ( pc in Figure 5D ) , some of which were identifiable as oocytes and some of which were of unclear origin due to their advanced stage in the process of degeneration . The fancl mutants that lacked oocytes ( 11 of 12 ) also had numerous pyknotic cells ( pc in Figure 5F ) , and showed groups of spermatogonia ( sg in Figure 5F ) , which were also found in WT animals ( sg in Figure 5E ) that had gonads with a testis-like morphology . The difference between fancl and WT controls became accentuated at 32 dpf ( Figure 5G–5I ) . At 32 dpf , all fancl gonads lacked oocytes and had become immature testes with spermatogonia and spermatocytes ( sg and sc in Figure 5I ) , but only about half of WT siblings had immature ovaries with late stage IB oocytes ( lpo in Figure 5G ) while the other half had immature testes ( Figure 5H ) . At adult stages ( Figure 5J–5L ) , consistent with results observed at 32 dpf , all fancl mutants lacked oocytes and had mature testes filled with germ cells at different stages of spermatogenesis ( Figure 5L ) . In contrast , half of the WT controls had mature ovaries filled with oocytes at different stages of oogenesis ( Figure 5J ) , and the other half had mature testes ( Figure 5K ) . This analysis of developmental histology revealed that in fancl mutants , oocytes failed to progress through meiosis and rarely reached the diplotene stage . Interestingly , in contrast to wild types , we observed abundant pyknotic cells in all fancl mutant gonads at 26 dpf ( pc in Figure 5D and 5F ) , suggesting that the absence of oocytes in older fancl mutants could be related to increased germ cell apoptosis associated with the failure to complete meiosis . To examine whether germ cell apoptosis could be the cause of both the abnormally high number of pyknotic germ cells in fancl juvenile gonads and the absence of oocytes at late stage IB , we used immunoassay to examine the activation of Caspase-3 , an early marker of apoptosis [52] , [53] . We scored the number of Caspase-3-positive cells in 70 gonadal cross-sections in each of 12 individuals: six wild-type sibling controls ( Figure 6A ) and six fancl homozygous mutants ( Figure 6B ) at 25 dpf , a stage within the time-window critical for sex determination . The morphology of the Caspase-3-positive cells detected in the immunoassay ( shown in red in Figure 6B ) , and the subsequent staining of the same slides with hematoxylin and eosin ( data not shown ) confirmed that the Caspase-3-positive cells were germ cells and not somatic cells , and corroborated our earlier finding that germ cells that appeared to be pyknotic in our histological analysis are indeed apoptotic cells . In many cases , the shape and size of the apoptotic Caspase-3-positive cells was appropriate for oocytes , however , we cannot rule out the possibility that some Caspase-3-positive cells might be undifferentiated gonial cells ( oogonia or spermatogonia ) . Results revealed that the average number of apoptotic germ cells in gonads of fancl−/− mutants was almost three fold higher than in gonads of wild-type sibling controls ( Figure 6C ) ( t-test p = 0 . 0058 , statistically significant at the p = 0 . 01 level ) . Therefore , these results suggest the hypothesis that the absence of oocytes in fancl mutants is caused by increased apoptosis of germ cells , especially oocytes , which ultimately leads to the sex reversal phenotype observed in fancl mutants . The hypothesis that the female-to-male sex reversal of fancl mutants is caused by increased germ cell apoptosis predicts that blocking apoptotic pathways should rescue the sex reversal phenotype . Because tumor protein Tp53 ( alias p53 ) is an important activator of apoptosis [54] , we can inhibit apoptosis in fancl mutants by introducing a tp53 mutation into the fancl mutant line . To generate double mutants , we crossed a zebrafish female carrier of the hypomorphic mutation tp53M214K [55] to a male homozygous fancl mutant , identified double heterozygotes ( fancl+/HG10A;tp53+/M214K called fancl+/−;tp53+/− below ) among F1 progeny by PCR , and in-crossed double heterozygotes to obtain an F2 population containing double homozygous mutants . Among the F2 raised to adulthood , 44/171 ( 25 . 7% ) , or about a quarter , were fancl−/− homozygous mutants . Among these 44 fancl−/− homozygous mutants , 15 were also tp53−/− homozygous mutants , from which 11 developed as females and four as males ( Figure 7A ) . All of the fancl homozygous mutant siblings ( n = 29 ) that were either homozygous wild type for tp53+/+ ( n = 8 ) or heterozygous for the tp53+/− mutation ( n = 21 ) developed exclusively as males ( Figure 7A ) . This result shows that the female-to-male sex reversal phenotype characteristic of fancl mutants was rescued in fancl−/−;tp53−/− doubly homozygous mutants ( Figure 7A ) . The sex-ratio scores observed in the three genotypes showed strong statistical support ( chi-square likelihood ratio = 32 . 088 , p-value<0 . 0001 ) for the hypothesis that the presence of females in fancl−/−;tp53−/− double mutants and the absence of females in the other tp53 genotypes ( fancl−/−;tp53+/− and fancl−/−;tp53+/+ ) is linked to the tp53 genotype . Histological analyses of fancl−/−;tp53−/− females corroborated the conclusion that external female sex characteristics were accompanied by ovaries filled with normal oocytes at all stages of development similar to fancl+/+; tp53+/+ wild-type female siblings ( Figure 7B and 7C ) . To determine whether the tp53 mutation rescues fancl sex reversal phenotype by reducing germ cell apoptosis , we studied the activation of Caspase-3 in histological sections of fancl homozygous mutants that were either homozygous for the tp53−/− mutation ( n = 5 ) or wild-type for the tp53+/+ mutation ( n = 5 ) at 25 dpf , a critical stage for sex determination ( Figure 7D and 7F ) . Counts of Caspase-3-positive cells of 70 gonadal cross-sections per animal in these ten animals showed that double homozygotes ( fancl−/−;tp53−/− ) had an average number of apoptotic germ cells approximately three fold lower ( Figure 7E and 7F ) than their fancl−/− mutant siblings that were homozygous wild-type for the tp53+/+ mutation ( Figure 7D and 7F ) ( t-test p = 0 . 1032 , approaching statistical significance given the small sample size ) . These results support the hypothesis that the tp53 mutation rescues the fancl female-to-male sex reversal phenotype by decreasing the number of apoptotic germ cells , thereby counteracting the abnormally high frequency of apoptotic germ cells observed in fancl homozygous mutants . This result is consistent with the hypothesis that the fancl mutation causes the female-to-male sex reversal phenotype by increasing germ cell apoptosis during a critical time for sex determination .
Fancl protein helps mediate cellular responses to a variety of stresses , especially DNA damage and apoptosis [36] . Mutations in human FANCL lead to Fanconi Anemia ( FA ) [39] , a disease of bone marrow failure , enormous risks of cancer , and hypogonadism and impaired fertility ( reviewed in [38] ) . Likewise , the most consistent FA phenotype in murine FA gene knockout models ( e . g . Fancc , Fancg , Fanca , Fancd1 , Fancd2 ) , is hypogonadism , impaired gametogenesis and infertility ( reviewed in [56] ) . Our work shows that the disruption of fancl in zebrafish causes homozygous mutants to develop exclusively as males due to female-to-male sex reversal rather than female-specific lethality . This is the first demonstration , to our knowledge , that a mutation in a Fanconi gene can cause female-to-male sex reversal . Our work revealed expression of fancl in germ cells during zebrafish gonad differentiation , which is consistent with a role of Fancl in germ cell development . Other species , such as mouse , also express fancl in their germ cells [57] , [58] , suggesting a conserved role of Fancl in vertebrate germ cell development . Previous work had shown exclusive male development in zebrafish lacking germ cells due to total loss-of-function of dead end , nanos , ziwi , or zili [31]–[34] , [59] . We demonstrate here , however , that germ cells are present throughout the entire life in all individuals homozygous for the fancl mutation , which rules out the possibility that male development in fancl mutants that otherwise would have become females is due to lack of germ cells . Work presented here shows specifically that the mere presence of germ cells is insufficient to feminize gonads , but rather , it suggests that oocytes passing through meiosis are essential to support differentiation of ovaries . Our results are in agreement with previous suggestions that zili mutants all become phenotypic males probably due to the lack of oocytes at week 4 during the window of sex determination rather than due to the total loss of germ cells at week 8 [59] . Homozygous fancl mutants , in which germ cells are always present , provide a useful tool to better understand the role of germ cell-soma signaling that tips gonad fate towards the male pathway . Comparison of sex-specific gonadal markers among fancl mutants , WT controls and dnd-MO animals , which lack germ cells , reveals that the onset of expression of the female marker cyp19a1a and the early male marker amh in individual undifferentiated gonads at 19 dpf is similar in all genotypes . This result supports the conclusion that the onset of early somatic makers is independent of germ cell signaling [31] . These results also show that undifferentiated gonads of fancl mutants initially develop as normal bipotential “juvenile ovaries” containing oocytes at early stage IB with no obvious histological differences from gonads of WT controls . During the critical time-window for sex determination in zebrafish ( e . g . 26 dpf ) , however , fancl mutant gonads become morphologically different from wild-type gonads . Wild-type animals have perinucleolar oocytes that progress through meiosis from early stage IB to late stage IB with obvious lampbrush chromosomes , indicating that recombination is complete and oocytes are at the diplotene stage of meiosis I , in which homologous chromosomes begin to separate but remain attached at chiasmata [51] . In contrast to wild types , most fancl mutants lack late stage IB oocytes , indicating that oocytes fail to progress beyond pachytene stage , when recombination occurs , and do not enter diplotene . Our results show that the levels of fancl transcripts are regulated during the process of gametogenesis because fancl expression up-regulates in oocytes transitioning from early to late stage IB ( Figure 3I ) . Consistent with this result , a large-scale gene expression profiling study of developing ovaries in trout found fancl in a group of many genes that were over-expressed when the first oocyte meioses were observed [60] . In fancl zebrafish mutants , the failure of oocytes to transition from early to late stage IB suggests that Fancl might promote the successful progression of oocytes through meiosis I or the survival of meiotic oocytes . The FA pathway is apparently involved in meiosis because in mouse , Fanca is expressed in pachytene spermatocytes and Fanca knockout mice have elevated rates of mis-paired meiotic chromosomes and increased germ cell apoptosis [37] . Whether this effect on meiosis depends on the known role of FA proteins in homologous recombination in somatic cells [61] or some other aspect of meiosis is as yet unknown . The failure of oocytes to progress through meiosis in fancl mutants correlates with the observation that most mutant gonads do not express the female somatic marker cyp19a1a , but instead up-regulate the male somatic marker amh . Interestingly , we found a few fancl mutants with some late stage IB oocytes accompanied by expression of cyp19a1a , but also showing high expression levels of amh; we interpret these animals as females whose progress towards ovary development was being derailed due to the mutation of fancl . These results would be expected if oocytes are essential to maintain cyp19a1a expression . We hypothesize that in juvenile fancl mutants , the absence of oocytes progressing through meiosis alters oocyte signaling to the soma that maintains the female program . Without this signal , somatic cells do not maintain the expression of cyp19a1a , do not suppress amh expression , and as a result , gonads do not become ovaries but instead become masculinized and form testes . It is likely that this signal arising from meiotic oocytes is essential for somatic pre-granulosa cyp19a1a-expressing cells to proliferate and to differentiate as mature granulosa cells . In mammals , it has been suggested that meiotic oocytes reinforce ovarian fate by antagonizing the testis pathway [62] , [63] . Studies on gonadal somatic cell lineages in mice and medaka , have shown that granulosa cells of the ovary and Sertoli cells of the testis develop from a common precursor [64]–[66] . It is possible that mammalian meiotic oocytes reinforce the ovarian pathway by preventing granulosa cells from trans-differentiating into Sertoli-like cells , because the loss of oocytes in mammals induces maturing follicular cells ( or pre-granulosa cells ) to acquire Sertoli-like cells characteristics [67] . We hypothesize that the action of meiotic oocytes in preventing pre-granulosa cells from trans-differentiating into Sertoli-like cells is an ancestral function that has been conserved in mammals and fishes . Although our experiments do not address the question of whether somatic cells trans-differentiate in fancl mutant gonads , our results are consistent with the hypothesis that fancl mutants , which lack oocytes at the diplotene stage of meiosis , can not prevent the trans-differentiation of pre-granulosa cyp19a1a-expressing cells into Sertoli-like amh-expressing cells . This hypothesized mechanism could explain the disappearance of cyp19a1a-expressing cells and the maintenance and proliferation of amh-expressing cells in fancl mutant gonads that results in gonad masculinization . Future transcription profiling analyses comparing wild-type animals and fancl mutants lacking oocytes will help to identify genes involved in oocyte-soma signaling essential for ovary development . We observed that the loss of oocytes in fancl mutants during the time-window of sex determination ( 25 dpf ) is accompanied by an abnormal increase of Caspase-3-mediated apoptosis of germ cells compared to wild-type siblings . This result suggests the hypothesis that the disappearance of meiotic oocytes in fancl mutants is due to an increase in germ cell apoptosis , which provides a cellular mechanism for the female-to-male sex reversal phenotype of fancl mutants . To test this hypothesis , we suppressed cell death in fancl mutants by making them homozygous for a tp53 mutation . We show that the reduction of apoptosis in fancl−/−;tp53−/− double mutants is sufficient to promote the survival of developing oocytes and to rescue the female-to-male sex reversal phenotype of fancl mutants . Our result showing that only fancl−/−;tp53−/− double mutants developed any females , while their fancl−/−;tp53+/− and fancl−/−;tp53+/+ sibling controls developed exclusively as males , indicates that the amount of germ cell apoptosis alters sex determination in fancl mutants . The double mutant experiments further show that Tp53 activity mediates increased apoptosis associated with the fancl mutation . Doubly homozygous fancl−/−;tp53−/− rescued females were fertile and developed normal ovaries full of oocytes maturing through all stages of oogenesis . Active Caspase-3 results show that the amount of germ cell apoptosis is lower in double homozygous fancl−/−;tp53−/− individuals than in their fancl−/−;tp53+/+ mutant sibling controls , which further supports the hypothesis that the abnormal increase of apoptosis in fancl mutants that compromises the survival of meiotic oocytes is the mechanism responsible for the female-to-male sex reversal . We did not notice a sex ratio biased towards females in the tp53M214K mutant line . This allele , however , is hypomorphic , and may possess levels of apoptosis compatible with the male pathway . This conclusion is supported by our finding that a few fancl−/−;tp53−/− double mutants developed as males . An alternative explanation is that mechanisms of apoptosis independent of Tp53 might occur in male gonads that promote oocytes to disappear in developing testes . Our finding of increased germ cell apoptosis in fancl zebrafish mutants is consistent with the increase of apoptosis in a variety of cell types reported in Fanconi Anemia knockout mice . For instance , Fanca−/− , Fancc−/− , and Fancg−/− knockout mice show increased apoptosis of hematopoietic or neuronal cells , which might lead to a progressive loss of stem and progenitor cells [68]–[70] . Bone marrow failure in children with Fanconi Anemia is attributed to excessive apoptosis and subsequent failure of the hematopoietic stem cell compartment ( reviewed in [56] ) . Interestingly , Fanca−/− knockout mice also show increased male germ cell apoptosis [37] , suggesting that a role of the FA network related to apoptosis of germ cells might be a conserved feature in fish and mammals . Young Fancl−/− knockout mice , in contrast to fancl mutant zebrafish , do not show sex reversal but initially develop as sterile males and sterile females . Fancl−/− knockout male mice – but significantly , not Fancl−/− knockout female mice – can recover fertility and become fertile adult males . These results suggest that Fancl is necessary for germ cell proliferation in mouse embryos and for the maturation of oocytes , but not for the proliferation or maturation of spermatogonia in adulthood [58] . In zebrafish , the fact that fancl mutant males are fertile and that fancl−/−;tp53−/− rescued females are also fertile indicates that Fancl function is not essential for the maturation of zebrafish spermatogonia and oogonia to become sperm or mature oocytes , but rather that Fancl function affects specifically germ cell survival . The loss of oocytes progressing through meiosis in fancl mutants suggests that Fancl function is involved in the survival of developing germ cells through meiosis , and that when Fancl is mutated , developing oocytes cannot survive due to an inappropriate increase of Tp53-dependent germ cell apoptosis . This idea is consistent with the fact that genetic deletion of Tp53 can rescue the TNF-alpha dependent apoptosis caused by accumulation of the pro-apoptotic protein kinase PKR resulting from a mutation of the human FANCC gene [68] , reviewed in [56] . Therefore , inappropriate activation of Tp53-dependent apoptosis might be a common mechanism affecting cell survival in both zebrafish and human after alteration of the FA network . Given the fundamental similarity of the cellular mechanisms of the FA pathway in zebrafish and humans , the screening of small molecule libraries for compounds that can rescue the sex-reversal phenotype of zebrafish fancl mutants might identify compounds of therapeutic importance for Fanconi Anemia patients . Our analysis of zebrafish fancl mutants suggests a model in which oocyte survival regulated by Tp53-mediated apoptosis is a central element that can tip gonad fate towards the male or the female pathway ( gradient red box in Figure 8 ) . Zebrafish develop initially as juvenile hermaphrodites , and have immature ovaries during the juvenile stage regardless of their definitive sex [26]–[28] . This immature ovary is bipotential , and expresses both female ( cyp19a1a ) and male ( amh ) specific markers ( Figure 8A ) [29] , [31] , [48] . During the fate decision period , some wild-type animals up-regulate cyp19a1a and suppress amh expression ( Figure 8B ) thereby tipping the fate of the gonad towards the female ovarian pathway ( Figure 8C ) . Complementarily , other wild-type individuals suppress cyp19a1a and up-regulate amh expression ( Figure 8D ) and gonad fate tips towards the male testis pathway ( Figure 8E ) . In this work , we show that oocyte survival is crucial to maintain the female gene expression profile of somatic cells that is essential for ovary development . In wild-type zebrafish , juvenile bipotential gonads contain immature oocytes at early stage IB ( [49]; and this work ) . In transitional stages , gonads that become ovaries possess oocytes that progress through meiosis to late stage IB and reach diplotene , where they arrest for the remainder of oocyte development [49] . In fancl−/− homozygous mutants , loss of oocytes at or before diplotene likely alters signaling from germ line to the soma , leading to loss of cyp19a1a expression , failure to down regulate amh expression , and consequent masculinization of the gonads to form testes ( Figure 8G ) . The cyp19a1a gene encodes aromatase , the enzyme that converts testosterone to estrogen . It is known that aromatase is critical for female fate in zebrafish because pharmacological treatments with the aromatase inhibitor fadrozole masculinizes gonads [71]–[73] and because , complementarily , treatments with estrogen ( estradiol ) down-regulate amh expression and feminize the gonad [74] . We hypothesize that the apoptotic loss of oocytes in fancl mutants causes cyp19a1a gene expression to disappear and leads to the failure to maintain aromatase levels , which results in failure to produce and sustain high estrogen levels in the gonad , causing gonads to abandon the female fate and instead , enter the testis developmental program . The presence of oocytes appears to be important for sex determination not only for zebrafish , but also for medaka . In contrast to zebrafish , in which all individuals begin oogenesis , in medaka only XX females start oogenesis while XY males suppress oogenesis and all germ cells remain undifferentiated ( reviewed in [75] ) . A feature common to both species is that the number of developing oocytes is a key feature that tips undifferentiated gonads towards an ovary fate ( [31] , [75] and this work ) . In medaka , the partial removal of PGCs can reduce the number of developing oocytes below a threshold necessary for female development [76] . In addition , medaka hotei mutants , which have aberrant oocyte development [77] , fail to maintain cyp19a1a expression and gonads develop into testes . Therefore , the survival of developing oocytes appears to be important for sex determination in both zebrafish and medaka . These considerations support the hypothesis that when the number of oocytes exceeds a threshold , sexual fate tips towards the female pathway , and alternatively , when the oocyte number fails to exceed that threshold , the sexual fate tips towards the male pathway , as we observed in zebrafish fancl mutants . In zebrafish , presumptive juvenile males had more TUNEL signal in germ cells than presumptive females had suggesting the hypothesis that oocyte apoptosis could be the mechanism of testicular and ovarian differentiation in zebrafish [27] . Consistent with this hypothesis , analysis of ziwi null mutants showed that total loss of germ cells by apoptosis caused ziwi mutants to develop exclusively as sterile males [34] . Our results show that Tp53-mediated germ cell apoptosis is a mechanism that can tip gonad fate towards the female or male pathway , at least in fancl mutants . Because environmental factors such as high temperature ( Figure 8H ) or endocrine-disrupting chemical treatments can also increase oocyte apoptosis and cause sex reversal [71]–[73] , it is plausible to suggest that the integration of genetic and environmental factors converge to modify the levels of Tp53-mediated germ cell apoptosis , which affect oocyte survival during the critical time window to determine the sexual fate of the gonad , and ultimately alter zebrafish sex determination .
Animals were handled in accordance with good animal practice as defined by relevant animal welfare bodies , and the University of Oregon Institutional Animal Care and Use Committee approved all animal work ( Animal Welfare Assurance Number A-3009-01 , IACUC protocol #08-13 ) . The zebrafish fancl mutation ( HG10A; GenBank accession AB353980 ) was generated by insertional mutagenesis by Tol2 transposon-mediated enhancer trap [42] . The tp53 mutant line tp53zdf1 causing the amino acid substitution M214K was obtained from ZIRC ( http://zebrafish . org/zirc/home/guide . php ) [55] . Genotyping of tp53 animals was performed as described [55] . Genetic nomenclature follows guidelines from ZFIN ( http://zfin . org/zf_info/nomen . html ) . The full-length zebrafish fancl cDNA was previously described [44] ( GenBank accession AY968598 ) . Primer pairs used to amplify the fancl wild-type or mutant alleles were: WT_F:CTGGTCTTTATTGACTGTAATGGC; WT_R:TAGATAAGCTCCAGATTTGGCTTG; Mutant_F:GTCAGCCCATCCAGATCAGCAG; Mutant_R:CATGACGTCACTTCCAAAGGACC . PCR conditions were: 5′94°C; 32 cycles of: 30″94°C , 30″55°C , 1′72°C; followed by 10′72°C . Sizes of PCR-amplified bands: Wild type: 479 bp; Mutant: 370 bp . Total RNA isolation from dissected adult testes and cDNA synthesis were performed as described [41] . Primers used for reverse transcriptase-PCR ( RT-PCR ) experiments were: F1:GACGGCTTCATCACAGTGCTG; R1:CATGACGTCACTTCCAAAGGACC; F2:GAACCCTGACTGCACTGTCCTAC; R2:GCTTTGGCGACTGGTTGGCAGAC . PCR conditions were: F1-R1: 3′94°C; 40 cycles of: 30″94°C , 30″58°C , 1′30″72°C; followed by 10′72°C; F2-R2: 3′94°C; 37 cycles of: 20″94°C , 30″60°C , 45″72°C; followed by 10′72°C . Sizes of PCR-amplified bands: F1-R1: 1239 bp F2-R2: 232 bp . To obtain animals lacking germ cells , wild-type zebrafish embryos from the AB strain were injected at the 1–2 cell stage with antisense morpholino oligonucleotide ( Gene Tools , Oregon ) directed against dead end as described [46] . Sibling non-injected embryos and a fraction of dnd MO-injected embryos were fixed at 24 hours post-fertilization to confirm the presence or absence of germ cells by whole-mount in situ hybridization using vasa probe as described [47] . Animals were reared and collected under standard conditions [78] . In situ hybridization experiments on zebrafish cryosections were performed as described [29] . Adjacent sections of gonads were obtained by placing three consecutive sections of the gonad on three different slides . Probes for amh and cyp19a1a were made as described [29] and probe for vasa was made from its 3′end as described [47] . A fancl cDNA fragment of 786 nt containing the PHD domain ( nucleotides 646-1431 of AY968598 ) was cloned in TOPO vector ( Invitrogen ) and used to synthesize DIG-labeled riboprobe ( Boehringer Mannheim ) . For gonad histology , euthanized animals were fixed in Bouin's fixative for about 24–48 hours and washed repeatedly in 70% ethanol . Animals were dehydrated and embedded in paraffin , sectioned at 7 microns , and stained with hematoxylin and eosin . Animals were fixed at 25 dpf in 4% PFA ON at 4°C , dehydrated , embedded in paraffin , and sectioned at 7 microns . Apoptotic cells were detected by immuno-fluorescence using anti-active Caspase-3 as primary antibody ( 1∶200 , BD Pharmingen ) and Alexa-Fluor594 goat anti-rabbit as secondary antibody ( 1∶1000 , Invitrogen ) following an immuno-histochemical protocol ( S . Cheesman , personal communication ) . Gonads were screened for positive signal by DIC-fluorescence microscopy . The number of positive cells in gonads of fancl and wild-type animals was scored in 840 sections: 70 sections containing gonads per each animal ( n = 12 ) .
|
Zebrafish has become an important model for understanding vertebrate development and human disease , yet the genetic mechanisms that regulate gonad fate to determine zebrafish sex remain elusive . In this work , we describe a mutation in the fancl gene that causes zebrafish to develop exclusively as male due to female-to-male sex reversal . Fancl is a member of the Fanconi Anemia/BRCA pathway involved in the repair of damaged DNA . We find that the sex-reversal phenotype is caused by an abnormal increase of programmed germ cell death during the critical period for zebrafish sex determination in which oocytes progress through meiosis . This abnormal increase in germ cell death compromises oocyte survival , gonadal somatic cells do not maintain the female gene expression profile , gonads become masculinized to testes , and mutants develop into fertile males . Remarkably , we show that the introduction of a mutated allele of the tp53 ( p53 ) tumor suppressor gene into fancl mutants rescues the sex-reversal phenotype by reducing germ cell death . We conclude that Tp53-mediated germ cell death alters gonad fate selection in fancl mutants by compromising oocyte survival , possibly by eliminating a hypothesized oocyte-derived signal , which alters sex determination in zebrafish .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/germ",
"cells",
"evolutionary",
"biology/animal",
"genetics",
"genetics",
"and",
"genomics/animal",
"genetics",
"marine",
"and",
"aquatic",
"sciences/genetics,",
"genomics,",
"and",
"barcoding",
"genetics",
"and",
"genomics/gene",
"expression",
"marine",
"and",
"aquatic",
"sciences/evolutionary",
"biology",
"cell",
"biology/developmental",
"molecular",
"mechanisms",
"hematology/anemias",
"evolutionary",
"biology/developmental",
"molecular",
"mechanisms",
"developmental",
"biology/developmental",
"molecular",
"mechanisms",
"cell",
"biology/gene",
"expression"
] |
2010
|
Sex Reversal in Zebrafish fancl Mutants Is Caused by Tp53-Mediated Germ Cell Apoptosis
|
Hypertension is a heritable and major contributor to the global burden of disease . The sum of rare and common genetic variants robustly identified so far explain only 1%–2% of the population variation in BP and hypertension . This suggests the existence of more undiscovered common variants . We conducted a genome-wide association study in 1 , 621 hypertensive cases and 1 , 699 controls and follow-up validation analyses in 19 , 845 cases and 16 , 541 controls using an extreme case-control design . We identified a locus on chromosome 16 in the 5′ region of Uromodulin ( UMOD; rs13333226 , combined P value of 3 . 6×10−11 ) . The minor G allele is associated with a lower risk of hypertension ( OR [95%CI]: 0 . 87 [0 . 84–0 . 91] ) , reduced urinary uromodulin excretion , better renal function; and each copy of the G allele is associated with a 7 . 7% reduction in risk of CVD events after adjusting for age , sex , BMI , and smoking status ( H . R . = 0 . 923 , 95% CI 0 . 860–0 . 991; p = 0 . 027 ) . In a subset of 13 , 446 individuals with estimated glomerular filtration rate ( eGFR ) measurements , we show that rs13333226 is independently associated with hypertension ( unadjusted for eGFR: 0 . 89 [0 . 83–0 . 96] , p = 0 . 004; after eGFR adjustment: 0 . 89 [0 . 83–0 . 96] , p = 0 . 003 ) . In clinical functional studies , we also consistently show the minor G allele is associated with lower urinary uromodulin excretion . The exclusive expression of uromodulin in the thick portion of the ascending limb of Henle suggests a putative role of this variant in hypertension through an effect on sodium homeostasis . The newly discovered UMOD locus for hypertension has the potential to give new insights into the role of uromodulin in BP regulation and to identify novel drugable targets for reducing cardiovascular risk .
Hypertension is a major cardiovascular risk factor with a global prevalence of 26 . 4% in 2000 , projected to increase to 29 . 2% by 2025 , and is the leading contributor to global mortality[1] , [2] . While epidemiologically BP is a trait continuously associated with an increased risk of cardiovascular mortality and morbidity , clinical risk assessment is necessarily based on a predefined threshold at which the quantitative BP phenotype is converted into a binary trait ( hypertension ) [3]–[6] . The main justification for large scale efforts to determine the genetic underpinnings of BP regulation is to identify new pharmacological targets for BP reduction while advancing our understanding of blood pressure regulation . This in turn could lead to novel prevention strategies to reduce the growing public health burden of hypertension-related cardiovascular disease [2] , [7] . Systemic blood pressure ( BP ) is determined primarily by cardiac output and total peripheral resistance , which are controlled by a complex network of interacting pathways involving renal , neural , endocrine , vascular and environmental factors . So far , the search for common variants affecting BP has identified thirteen loci from two large meta-analyses consortia , with each association explaining only a very small proportion of the total variation in systolic or diastolic blood pressure ( SBP or DBP; ∼0 . 05–0 . 10% , approximately 1 mmHg per allele SBP or 0 . 5 mmHg per allele DBP ) [8] , [9] . The sum of rare and common genetic variants robustly identified so far through linkage and genome wide association studies explain only 1–2% of the population variation in BP and hypertension . These data suggest the existence of more undiscovered blood pressure related common variants . Cross-sectional studies of the general population have required extremely large sample sizes to detect such small effect sizes [10] . In this paper we explored an alternative strategy to increase power , using cases and controls drawn from the extremes of the BP distribution , and detected a novel locus associated with hypertension . We then validated this association using large-scale population and case-control studies , where similar extreme criteria for selection of cases and controls have been used . As the locus was related to uromodulin , a protein exclusively expressed intrarenally , we tested for dependency of the association on renal function ( eGFR ) and urinary excretion of uromodulin . Finally , we tested associations with cardiovascular outcomes .
The demographic characteristics of the discovery and validation cohorts are presented in Table 1 and Table S1 respectively . The results of the GWAS in the discovery sample are presented in Figure 1 . The observed versus expected p-value distributions ( quantile-quantile plots ) are shown in Figure 2 . The top hit was rs13333226 with the minor G allele associated with a lower risk of hypertension ( OR [95%CI]: 0 . 6 [0 . 5–0 . 73]; p = 1 . 14×10−7; Figure 3 ) and we selected this for validation in two stages ( Figure S1 , Table 2 and Table 3 ) . In the first stage we genotyped rs13333226 in the MONICA/PAMELA population samples ( in which we also genotyped an additional top 88 SNPs – Table S2 ) and in the larger MDC and MPP validation case-control populations . For the stage 1 validation , we had 9 , 827 cases and 8 , 694 controls and the combined analysis showed the minor G allele to be associated with a lower risk of hypertension ( 0 . 87 [0 . 82–0 . 92]; p = 3 . 6×10−6 ) after adjustment for age , age2 and BMI . Combined analysis of the 89 SNPs genotyped in the MONICA/PAMELA with the discovery cohort showed rs13333226 ( p = 3 . 86×10−7 ) and rs4293393 ( p = 3 . 30×10−7 , r2 = 0 . 996 ) were the top SNPs . In stage 2 analysis which included 10 , 018 cases and 7 , 847 controls , the results were similar with the G allele associated with a lower risk of hypertension ( 0 . 86 [0 . 81–0 . 92]; p = 1 . 0×10−5 ) . Combining stage 1 and stage 2 cohorts increased the strength of association ( 0 . 86 [0 . 83–0 . 90]; p = 1 . 61×10−10 ) . There was no evidence of heterogeneity across the stage 1 or stage 2 samples or the combined stage 1 and 2 samples as tested by the Q statistic ( p>0 . 05 ) . Merging stages 1 and 2 with the discovery samples yielded the strongest association signal for rs13333226 ( 0 . 85 [0 . 81–0 . 89]; p = 1 . 5×10−13 ) with some evidence of heterogeneity ( Q statistic p value = 0 . 04 ) introduced by the discovery cohort ( Table 2 , Figure 4A and 4B , Figure S2 ) . This is probably due to the fact that the discovery cohort was ascertained using more extreme criteria than the replication cohorts . In the 13 , 446 individuals with eGFR measurements available , the strength of association of rs13333226 with hypertension was identical after correcting for eGFR and the effect sizes remained unchanged ( unadjusted for eGFR: OR [95%CI] = 0 . 90[0 . 83;0 . 96] , p = 0 . 004; after eGFR adjustment: OR [95%CI] = 0 . 89[0 . 83;0 . 96] , p = 0 . 003 ) and there was no evidence of heterogeneity across the study samples ( Table 3 , Figure 4C and 4D ) . We examined association of rs13333226 with continuous blood pressure measurements in the entire Global BPgen , MPP and MDC cohorts ( n = 79 , 133 ) . Each copy of the G allele of rs13333226 is associated with 0 . 49 mmHg lower SBP ( p = 2 . 6×10−5 ) and 0 . 30 mmHg lower DBP ( p = 1 . 5×10−5 ) . The direction of continuous trait effect is consistent with the odds of hypertension . The SNP rs13333226 is in close proximity to the uromodulin transcription start site at −1617 base pairs ( Figure 3 ) . We studied the association between rs13333226 genotypes and different phenotypes including urinary uromodulin , in 256 hypertensive individuals from the BRIGHT cohort and 110 participants from the population-based HERCULES study . Univariate analyses showed that the G allele was associated with lower excretion of uromodulin in both the BRIGHT and HERCULES studies ( Table 4 and Table 5 ) . Each copy of the G allele was associated with 0 . 2 mg/mmol lower urinary uromodulin corrected for urine creatinine in BRIGHT study ( p = 0 . 007 ) . Each copy of the G allele was also associated with 4 . 6 ml/min/1 . 73 m2 higher eGFR ( p = 0 . 005 ) in the BRIGHT cohort . In HERCULES , however , a higher creatinine clearance in GG individuals did not attain statistical significance . In both studies the association of rs13333226 with urinary uromodulin levels persisted on multiple regression analysis adjusting for sex , urine sodium and eGFR ( p<0 . 001 ) . In BRIGHT , GG carriers were found to have a significantly lower fractional excretion of sodium ( p = 0 . 032 ) . In the smaller HERCULES sample this also occurred , though short of statistical significance . However , in HERCULES urinary uromodulin was positively associated with urinary sodium excretion ( p = 0 . 025 ) and fractional excretion of endogenous lithium ( r2 = 0 . 19 , p = 0 . 045 ) . Overall , BRIGHT and HERCULES data suggest that low urinary uromodulin is associated with higher sodium reabsorption , and that this occurs at the proximal tubular level . In the small GRECO cohort , urinary uromodulin concentration ( p = 0 . 004 ) and 24 hour uromodulin excretion ( p = 0 . 002; Wilcoxon′s signed ranks test ) were found to be significantly increased after a high sodium intake ( Table 6 ) . The G allele was associated with lower uromodulin excretion only on low sodium diet ( p = 0 . 002 ) . Finally , we evaluated the clinical significance of our findings by testing whether the low BP associated allele may protect against development of cardiovascular events during long-term follow-up at the population level . Among 26 , 654 subjects from the entire population based MDC study [11] who were free from prior cardiovascular events at baseline , 2 , 750 individuals developed cardiovascular events ( CVD ) during 12 years of follow-up . We found each copy of the G allele to be associated with a 7 . 7% reduction in risk of CVD events after adjusting for age , sex , BMI and smoking status ( H . R . = 0 . 923 , 95% CI 0 . 860–0 . 991; p = 0 . 027 ) . When SBP ( H . R . = 0 . 936 , 95% CI 0 . 872–1 . 005; p = 0 . 067 ) or SBP and DBP ( H . R . = 0 . 937 , 95% CI 0 . 873–1 . 005; p = 0 . 069 ) were added to the Cox regression model , the directionality and risk remained almost identical .
We have identified and validated a SNP upstream of the uromodulin ( UMOD ) gene whose minor allele is associated with a lower risk of hypertension . The associated SNP ( rs13333226 ) is in close proximity to the uromodulin transcription start site at −1617 base pairs . There is only one previous candidate gene study of UMOD and hypertension . This study tested rs6497476 , located in the 5′ region of the UMOD gene ( −744 bp from UMOD transcriptional start point ) and showed the minor allele with a lower risk of hypertension in a Japanese population , but it did not reach statistical significance [12] . This SNP is correlated with rs13333226 in the Japanese HapMap population ( r2 = 0 . 91 ) and shows the same directionality of effect . A recent genome scan for chronic kidney disease ( CKD ) [13] has found the minor T allele at rs12917707 , −3653 bp upstream from the UMOD transcription start site to be associated with a 20% reduction in risk of CKD . This association was consistent after adjusting for major CKD risk factors including SBP and hypertension . This SNP -rs12917707 is perfectly correlated ( r2 = 1 in HapMap CEU ) with rs13333226 . Our data show the minor allele of rs13333226 is associated with increased eGFR ( beta = 3 . 6 , p = 0 . 012 ) , but adjustment for eGFR in our meta-analyses did not alter its association with lower risk for hypertension . This suggests that the UMOD locus is independently associated with hypertension . We also show an association of rs13333226 with long term cardiovascular outcomes with a relatively small attenuation of the relationship after SBP/DBP adjustment . This suggests UMOD may have an influence on cardiovascular disease at least partly independent of BP . However , our conditional analyses are limited by the fact that single point measures of BP and eGFR may not truly represent the lifetime effect of the genetic variant on these traits . Therefore , we cannot exclude that rs13333226 may exert its effects on hypertension and cardiovascular disease , at least partly through its effects on renal function and blood pressure , respectively . The UMOD gene encodes the Tamm Horsfall protein ( THP ) /uromodulin , a glycosylphosphatidylinositol ( GPI ) anchored glycoprotein . It is the most abundant tubular protein in the urine , which is expressed primarily in the thick ascending limb of the loop of Henle ( TAL ) with negligible expression elsewhere [14] , [15] . We show in the BRIGHT , HERCULES and GRECO ( low sodium diet ) that the minor allele of rs13333226 ( associated with a lower risk of hypertension ) is consistently associated with lower urinary uromodulin excretion . This effect was lost when GRECO subjects were given a high sodium diet . We also show in BRIGHT and HERCULES that the G allele and lower urinary uromodulin are associated with lower fractional excretion of sodium and lower fractional excretion of endogenous lithium , indicating increased sodium reabsorption at the proximal tubular level . While the association of lower blood pressure and increased sodium reabsorption may appear counterintuitive , an increased sodium reabsorption by the proximal tubule may simply be the consequence of an increased sodium load because of increased GFR , or a compensatory reaction to a primary decrease in distal reabsorption . In absence of information on sodium intake in individuals in BRIGHT and HERCULES , we cannot exclude that the lower fractional sodium excretion in carriers of the G allele simply reflects a low dietary sodium intake . The exclusive expression of uromodulin in TAL , where physiologically crucial mechanisms of sodium handling are located , suggests that alterations of some of these mechanisms in G allele carriers may underlie their lower risk of hypertension . However , functional studies are needed to clarify the renal mechanisms by which the UMOD gene may affect hypertension and renal sodium handling . In the context of our findings it is of interest to note that UMOD mutations ( in exons 4 and 5 ) are implicated in monogenic syndromes such as familial juvenile hyperuricemic nephropathy , autosomal-dominant medullary cystic kidney disease [MCKD2] and glomerulocystic kidney disease ( GCKD ) ( MIM603860 , MIM162000 , MIM609886 ) [16]–[18] . In previous small studies , urinary uromodulin levels were found to be decreased in older subjects and in subjects with renal impairment [19] , [20] . In renal disease patients , uromodulin excretion was reduced in proportion to the extent of renal damage , and was a marker of distal tubular sodium reabsorption , but in these studies , the effects of BP on uromodulin were inconsistent [21] , [22] . The TAL , where UMOD is selectively expressed is also the site where mutations of tubular transporters have resulted in rare Mendelian high or low BP syndromes [23] . Furthermore , recent data from Lifton's group demonstrated that heterozygous mutations in SLC12A3 ( encoding the thiazide-sensitive Na-Cl cotransporter ) , SLC12A1 ( encoding the Na-K-Cl cotransporter NKCC2 ) , and KCNJ1 ( encoding the K+ channel ROMK ) discovered in the general population have been associated with lower BP and a 60% reduction in the development of hypertension [24] . Our strategy of using extremes of BP distribution has led to the discovery of a gene variant that could not be discovered when a less stringent case-control definition was used [10] . For example , in stage 1 Global BPgen samples ( n = 34 , 433 ) , the p values for association of rs13333226 with SBP and DBP were 0 . 0077 and 0 . 0099 respectively indicating that rs13333226 would not have been selected for validation as the p-value threshold for follow-up genotyping in that study was p<10−5 . Also , in Global BPgen study when the top 8 SNPs that attained genome wide significance for continuous BP were tested for association with hypertension , four of the eight SNPs had 0 . 01<p≤0 . 10 with odds of hypertension in directions consistent with the continuous trait effect . As effect size of the risk allele of rs13333226 is comparable to the effect sizes of the previous robust association signals for blood pressure[8] , [9] , we think that using an extreme case-control strategy successfully enabled the discovery of a locus that previous GWAS meta-analysis failed to detect possibly due to the cost imposed by multiple testing correction . The main limitation of our study is that the functional studies were performed on three different populations – hypertensive , population-based and dietary sodium intervention samples . The renal and blood pressure measurements were measured at single time-points and are not entirely representative of genotype-phenotype effects which occur over prolonged time periods . On the other hand , definitions of the extreme hypertension and extreme normotension in the discovery cohort are based on very robust data . Subjects with extreme hypertension were chosen from an intervention trial in which blood pressure was measured after a wash-out period during which all antihypertensive therapy was discontinued before randomization , whereas normotensive controls were chosen from a population followed up for 10 years and who remained free of cardiovascular disease and antihypertensive treatment throughout this period . Therefore , we think that the newly discovered UMOD locus for hypertension has the potential to give unique insights into the mechanisms of high blood pressure , and identify novel drugable targets .
All studies were approved by institutional ethics review committees at the relevant organizations . All participants provided informed written consent . To identify novel susceptibility loci for hypertension , we used an extreme case-control design . Hypertensive cases had to have at least two consecutive BP measurements of ≥160 mmHg systolic and ≥100 mmHg diastolic , with the diagnosis made before age 63 years . We identified 2 , 000 cases in the Nordic Diltiazem study ( NORDIL ) [25] . These hypertensive subjects represent approximately the top 2% of the BP distribution in the Swedish population . Two thousand control subjects were drawn from the Malmö Diet and Cancer study ( MDC , n = 27 , 000 ) [26] who had a SBP≤ 120 mmHg and DBP≤ 80 mmHg . Controls had to be at least 50 years of age and free from cardiovascular events ( coronary events and stroke ) during 10 years of follow up [27] and not on any antihypertensive medication . The controls derived from the MDC population represented the lower 9 . 2% of the BP distribution and with the selection for low cardiovascular risk , can be considered as hyper-controls . In both NORDIL and MDC , BP was measured in the recumbent position after 5–10 minutes rest using a manual sphygmomanometer . Rigorously phenotyped samples minimize case/control misclassification , and the potential advantage of an extreme case/control design is greater power to detect variants associated with BP and hypertension , for a given total sample size and total genotyping cost . For the validation we used phenotypic definitions ( extreme SBP/DBP thresholds ) to closely match our discovery samples . The BP measurements in all the cohorts were based on the average of at least 2 measurements obtained when the subject was seated and after rest for at least 5 minutes . The BP criteria were slightly modified as most validation cohorts were general population cohorts . Cases: Individuals less than 60 years of age with SBP ≥140 mmHg or DBP ≥90 mmHg or current treatment with antihypertensive or BP lowering medication commenced before age 60 years . Controls: Individuals with SBP ≤120 mmHg and DBP ≤80 mmHg , at least 50 years of age , and free from any BP lowering medication . If age ≤50 years , then the criteria were slightly modified to SBP ≤115 mmHg and DBP ≤80 mmHg and free from BP lowering medications . The validation cohorts were the MONItoring trends and determinants of CArdiovascular diseases ( MONICA ) /Pressioni Arteriose Monitorate E Loro Associazioni ( PAMELA ) studies ( 894 cases/746 controls ) from Northern Italy [6] , [28] , 1956 cases/1057 controls from the 2002–2006 follow-up exam of the Malmö Preventive Project ( MPP ) [29] and 6977 cases/6891 controls from the Malmö Diet and Cancer study [11] ( MDC; non-overlapping with discovery samples ) , 509 cases/209 controls from The Netherlands Study of Depression and Anxiety study ( NESDA ) [30] and ten cohorts from a collaboration with the Global BPgen consortium [9] . Analyses reported here are distinct from those previously published [9] , because they use phenotypic definitions to match our discovery samples . The combined sample size of the discovery and validation cohorts is 39 , 706 individuals ( 21 , 466 cases and 18 , 240 controls ) . Estimated glomerular filtration rate ( eGFR ) was calculated using the Modification of Diet in Renal Disease ( MDRD ) Study equation [31] . We studied functional associations of the top SNP in a hypertensive cohort and a population cohort with extensive urine phenotypes and one interventional study of low and high sodium intake with extensive measurements of sodium balance . The British Genetics of Hypertension ( BRIGHT ) study [32] is a hypertension case-control study . Case inclusion criterion was a diagnosis of hypertension ( >150/100 mmHg ) prior to 50 years of age . Exclusion criteria included BMI>35 , diabetes , secondary hypertension or co-existing illness . 24-hour urine collection was available for all the cases with measurements of urinary sodium , potassium , creatinine and microalbuminuria . We measured urinary uromodulin in 256 hypertensive subjects . Groningen Renal Hemodynamic Cohort Study Group ( GRECO ) : The GRECO protocol comprises integrated measurement of renal hemodynamics and extracellular volume as applied in an ongoing series of studies in healthy subjects [33] , [34] . For the current analysis 64 healthy adult males were included ( mean age = 23 years ) , who had been studied after two seven-day periods: the first 7 days on a low sodium diet ( LS , 50 mmol Na+ per day , balance verified by repeated 24 h urine ) , the second 7 days on a high-sodium diet ( HS , 200 mmol Na+ per day ) . Hypertension Evaluation by Remler and CalciUria LEvel Study ( HERCULES ) is a substudy of the population-based CoLaus study ( www . colaus . ch ) from Lausanne Switzerland [35] , [36] . A random sample of 411 CoLaus participants , aged 38–78 years , underwent ambulatory BP monitoring and 24 hour urine collection . The phenotypes available include 24-hour urine collection with measurement of creatinine clearance , endogenous lithium clearance , urinary sodium , potassium and uric acid excretion and microalbuminuria . We measured urinary uromodulin in 110 participants of this study . Urinary uromodulin was measured in duplicate in 24 hour urine samples using a commercially available ELISA ( MD Biosciences , Zürich , Switzerland ) as recommended by the manufacturer . The range of assay is 9 . 375–150 ng/mL and sensitivity <5 . 50 ng/mL . The inter-assay coefficient of variation was 11 . 9% . Urinary uromodulin levels were corrected for urine creatinine before analysis . The genomewide association study ( GWAS ) samples were genotyped using Illumina 550K Single and Illumina 610 Quad V1 BeadChip ( Illumina , Inc . , San Diego , CA , USA ) . We included 551 , 629 SNPs common to both the Single and Quad chips , for analysis . SNPs with a minor allele frequency ( MAF ) <1% or in significant Hardy-Weinberg disequilibrium ( P<1×10−7 ) in pooled samples were removed leaving 521 , 220 SNPs for analysis . We assessed population structure within the data using principal components analysis as implemented in EIGENSTRAT [37] to infer continuous axes of genetic variation . After data quality control for unspecified sex ( 5 subjects removed ) , relatedness/duplicates ( 68 individuals removed ) , multidimensional scaling plot outliers ( 33 individuals removed ) , genetic outliers - which are defined as individuals whose ancestry is at least 6 s . d . from the mean on one of the top ten axes of variation on principal component analysis ( 388 individuals removed ) and genotyping success of <95% ( 92 individuals removed ) , genotype information from 1 , 621 cases and 1 , 699 controls ( 1 , 510 males and 1 , 810 females ) was available for analysis . Untyped SNPs were imputed using IMPUTE v1 [38] with data from the August 2009 release of CEU phased haplotypes from Pilot 1 of the 1000 Genomes Project NCBI Build 36 ( dbSNP b126 ) as the reference panel ( from https://mathgen . stats . ox . ac . uk/impute/impute_v1 . html ) . The probability threshold used for calling an imputed genotype was 0 . 9 . Association analysis was performed using SNPTEST [38] taking into account uncertainty in imputation . In the GWAS samples , we tested each SNP for association using an additive genetic model and logistic regression with adjustment for significant ancestry principal components [37] to correct for population stratification . There was still a slight overall inflation of test statistics , with a genomic control inflation factor ( λ ) of 1 . 07 ( Figure 2 ) . All results are presented after application of genomic control to correct for this residual inflation [39] . Additionally two logistic regression analyses were performed , with adjustment for age , age2 , sex and BMI and with adjustment for age , age2 , sex , BMI and eGFR . Multiple linear regression was used to test association between genotype and urinary uromodulin levels , functional parameters like GFR , extracellular volume etc . with relevant covariates . In the GRECO study , as the numbers of GG genotypes were small , AG and GG were combined for analysis . Non-normally distributed traits were tested using the non-parametric Kruskal Wallis test . In validation samples , SNPs were tested for association using logistic regression , with adjustment for ancestry principal components where available to correct for population stratification . Meta-analysis of the combined discovery and validation results was conducted using an inverse-variance weighted ( fixed-effects ) meta-analysis . In the meta-analysis , a genomewide significance threshold of 5×10–8 corresponding to a P value of 0 . 05 with a Bonferroni correction for 1 million independent tests was considered a priori as genomewide significant [40] . The associations between the validated SNP and SBP and DBP were analysed separately in the Stage 1 samples of the Global BPgen consortium ( n = 34 , 433 ) and in the overall MDC ( n = 27 , 000 ) and MPP ( n = 17 , 700 ) cohorts [9] , [26] , [29] . The results were combined using fixed-effect inverse variance weighted meta-analysis . Continuous SBP and DBP were adjusted for age , age2 , body mass index and any study-specific geographic covariates in sex-specific linear regression models . In individuals taking antihypertensive therapies , blood pressure was imputed by adding 15 mm Hg and 10 mm Hg for SBP and DBP , respectively [9] , [41] . We performed multivariable Cox proportional hazards models to examine the association between biomarkers and incident events . ( myocardial infarction , stroke , coronary death ) . Two models , one adjusted for age , sex , BMI , SBP and smoking status and another adjusting for age , sex , BMI , SBP , DBP and smoking status were analysed . We confirmed that the proportionality of hazards assumption was met . The results are presented as hazard ratios and 95% confidence intervals per copy of the G allele . Survival analysis was performed using SPSS version 13 . 0 for Windows ( SPSS Inc ) .
|
Hypertension is the leading contributor to global mortality with a global prevalence of 26 . 4% in 2000 , projected to increase to 29 . 2% by 2025 . While 50%–60% of population variation in blood pressure can be attributable to additive genetic factors , all the genetic variants robustly identified so far explain only 1%–2% of the population variance indicating the presence of additional undiscovered risk variants . Using an extreme case-control strategy , we have discovered a SNP in the promoter region of the uromodulin gene ( UMOD ) to be associated with hypertension ( minor allele protective against hypertension ) . We then validated this association using large-scale population and case-control studies , where similar extreme criteria for selection of cases and controls have been used ( 21 , 466 cases and 18 , 240 controls ) . As the locus was related to uromodulin , a protein exclusively expressed in the kidneys , we show that the association is independent of renal dysfunction . We also show preliminary evidence that the SNP allele which is protective against hypertension is also protective against cardiovascular events in 26 , 654 Swedish subjects followed-up for 12 years . The newly discovered UMOD locus for hypertension has the potential to give unique insights into the role of uromodulin in BP regulation and to identify novel drugable targets .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cardiovascular",
"disorders/hypertension",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"genetics",
"and",
"genomics/complex",
"traits"
] |
2010
|
Genome-Wide Association Study of Blood Pressure Extremes Identifies Variant near UMOD Associated with Hypertension
|
Trachoma is a progressive blinding disease initiated by infection of the conjunctiva with Chlamydia trachomatis . Repeated infections are thought to cause chronic inflammation , which drives scarring , leading to in-turning of the eyelids . The relationship between C . trachomatis , clinical inflammation and scarring development in children is not fully understood due to a paucity of longitudinal studies with infection data at frequent follow-up . This longitudinal cohort study took place in northern Tanzania . Children aged 6–10 years at baseline were eligible for inclusion . Participants were visited every three months for four years . Clinical signs and conjunctival swabs for C . trachomatis detection by qPCR were collected at each time-point . Conjunctival photographs from baseline and final time-points were graded and compared side-by-side to determine scarring incidence and progression . Of the 666 children enrolled in the study , outcome data were obtained for 448 . Scarring progression was detected in 103/448 ( 23% ) children; 48 ( 11% ) of which had incident scarring and 55 ( 12% ) had progression of existing scarring . Scarring was strongly associated with increasing episodes of trachomatous papillary inflammation ( TP ) . Weaker associations were found between episodes of C . trachomatis infection and follicular trachoma ( TF ) with scarring progression in unadjusted models , which were absent in multivariable analysis after adjusting for inflammation ( multivariable results: C . trachomatis p = 0 . 44 , TF p = 0 . 25 , TP p = <0 . 0001 , age p = 0 . 13 , female sex p = 0 . 05 ) . Individuals having TP at 30% or more of the time-points they were seen had an odds ratio of 7 . 5 ( 95%CI = 2 . 7–20 . 8 ) for scarring progression relative to individuals without any TP detected during the study period . These data suggest that the effect of infection on scarring progression is mediated through papillary inflammation , and that other factors contributing to the development of inflammation , in addition to C . trachomatis infection , may be important in driving conjunctival scarring progression in children . The addition of TP as a measure in trachoma control programs would provide an indication of the future risk of developing scarring sequelae .
Sight loss from trachoma , the leading infectious cause of blindness , is the end result of an inflammatory-scarring process . Starting from early childhood , people growing-up in a trachoma endemic community may be repeatedly exposed to ocular challenge with Chlamydia trachomatis , the causative organism . This is thought to trigger inflammatory responses that lead to conjunctival scarring in some individuals[1] . As a result of conjunctival scarring the eyelids ( entropion ) and eyelashes ( trichiasis ) turn in , scratching the ocular surface and resulting in corneal opacification[1] . These complications of scarring usually develop during adulthood . Trachoma control rests on the SAFE Strategy: Surgery for trichiasis , Antibiotic treatment to treat C . trachomatis infection , Facial cleanliness and Environmental improvements to reduce transmission . Endemic countries and the international community have set the ambitious target of 2020 for the elimination of trachoma as a public health problem[2] . There is no specific treatment to halt the progression of scarring , beyond controlling the infection . Around 3 . 2 million people are estimated to have trichiasis and 1 . 9 million of these are blind or have severe visual impairment[3] . Currently WHO estimates that 158 million people live in districts that require A , F and E interventions[4] . Nearly 90% of these people live in Sub-Saharan Africa . Longitudinal data sets documenting the incidence or progression of conjunctival scarring trachoma are limited[1] . Such studies are complex and can take many years to complete . In this paper we report the clinical signs and infection results of children who were followed up every three months for four years . The aim of the study was to investigate the risk factors associated with scarring incidence and progression in Tanzanian children . We investigated the association between scarring progression and clinical signs of inflammation , C . trachomatis infection , age and sex , in order to strengthen the evidence base that supports trachoma control programs .
This study was reviewed and approved by Ethics Committees of the Tanzania National Institute for Medical Research , Kilimanjaro Christian Medical University College and the London School of Hygiene & Tropical Medicine . It adhered to the tenets of the Declaration of Helsinki . The study was explained in detail in Kiswahili or Maasai; written informed consent from a parent or legal guardian was necessary for enrollment . We recruited a cohort of children from three neighboring villages in northern Tanzania . Two villages were in Siha district located in Kilimanjaro region and one was in Longido district located in Arusha region . They were assessed every three months for four years , totaling 17 time-points . The communities and recruitment have been previously described in detail[5] . These communities are predominantly comprised of Maasai people . Children aged between 6 and 10 years at baseline ( February 2012 ) , who were normally resident in the villages , were eligible for inclusion . This restricted age group was chosen as we anticipated that younger children may not have manifest incident / progressive conjunctival scarring during the four years of follow-up . A census was conducted and eligible children enrolled . At each time-point all available children were examined by an experienced ophthalmic nurse . The eye was first anaesthetized with preservative-free proxymetacaine hydrochloride 0 . 5% eyedrops . The left upper eyelid was everted and tarsal conjunctiva examined ( using x2 . 5 loupes and torch ) for signs of trachoma and graded using the 1981 WHO ‘FPC’ detailed grading system[6] . This grading system corresponds to the WHO Simplified Trachoma Grading System: F2/F3 equates to Trachomatous Inflammation-Follicular ( TF ) , and P3 to Trachomatous Inflammation-Intense ( TI ) [7] . “Clinically Active Trachoma” was defined as presence of TF and/or TI . We also consider that both P2 and P3 represent clinically significant papillary inflammation , and refer to this as “TP”[8] . High resolution photographs ( Nikon D90 camera with 105mm Macro lens ) were taken of the conjunctiva for independent grading . Two conjunctival swab samples were collected ( Dacron polyester , Puritan Medical Products Company , Maine ) at each time-point . The first was placed in RNAlater ( Thermo Fisher , UK ) and the second was stored dry . Clinical swabs and air control swabs were collected and stored as described previously[5] . Samples were stored on ice in the field and were transferred to a -80°C freezer upon return to the laboratory later the same day . Following approval from the Ministry of Health ( MoH ) and in collaboration with the district eye coordinators the SAFE strategy was implemented in study villages by the study field team . Education was provided regarding facial cleanliness and environmental improvements and free trichiasis surgery was offered . Azithromycin mass drug administration ( MDA ) was administered by the study team , according to WHO guidelines , in August 2012 , August 2013 and August 2014 . In mid-2015 , one of the three villages from Longido district , which had a persistently elevated TF prevalence , received a further round of MDA . This was delivered by the local MoH team as part of the district-wide distribution . The other two villages , which are in the neighboring district ( Siha ) , were not re-treated as the district-wide prevalence was below the treatment indication threshold , and these two villages had shown a good response to the three rounds of MDA . At the first time-point , DNA was extracted using the PowerSoil DNA Isolation Kit ( Mo Bio Laboratories , USA ) from swab samples stored in dry tubes and C . trachomatis was detected by droplet digital PCR , as previously described[5 , 9] . At all subsequent time-points , DNA was extracted from samples stored in RNAlater using the Norgen RNA/DNA purification kit ( Norgen Biotek ) and C . trachomatis was detected by triplex quantitative PCR ( qPCR ) for chlamydial chromosomal ( omcB ) and plasmid ( pORF2 ) genes and a human endogenous control gene ( RPP30 ) , as described previously[10] . Time-point 2 Norgen-extracted samples were tested by both detection methods and the kappa score for agreement was 0 . 84 . Samples were tested in duplicate and were defined as C . trachomatis positive if RPP30 and pORF2 and/or omcB amplified in <40 cycles in one or both replicates . We used photographic grading to determine whether there was either development of incident scarring in previously un-scarred conjunctiva , or increase in pre-existing scarring . Conjunctival photographs from baseline ( time-point 1 ) were compared to the final time-point ( time-point 17 ) . For individuals not seen at time-point 1 , the image from time-point 2 was used for their baseline . Similarly , if an individual was not seen at time-point 17 , the image from time-point 16 was used as their final time-point . The images were assessed by an ophthalmologist experienced in using a detailed scarring grading system[11] . Baseline and final photographs were compared side-by-side to produce the main binary outcome variable of overall “scarring progression” , defined as evidence of either incident scarring or worsening of pre-existing scarring . For further sub-analyses , we subdivided individuals with “no scarring progression” into ( 1 ) no scarring at either baseline and final; ( 2 ) scarring unchanged between baseline and final . We subdivided individuals with “scarring progression” into ( 3 ) incident scarring ( no scarring at baseline and new scarring at final ) ; ( 4 ) increasing scarring ( some scarring at baseline and more at final ) . All field data were managed in Access ( Microsoft ) . Data were merged and analyzed in STATA v14 . The total number of time-points at which participants were seen varied due to absence or refusal . We excluded from the analysis individuals who were seen on fewer than four occasions or did not have outcome data ( time-point 16 or 17 assessments ) . A proportion variable was generated for each of TF , TP and C . trachomatis infection: number of time-points with each factor as a proportion of the total number of time-points that individual was seen . Proportions were subsequently categorized . Separate mixed effects logistic regression models were used to determine the association between ( 1 ) TP , ( 2 ) TF , and ( 3 ) C . trachomatis infection with sex and baseline age , using data from all time-points in the longitudinal dataset . Mixed effects regression was also performed to assess the relationship between ( 1 ) TF and C . trachomatis infection , and ( 2 ) TP and C . trachomatis , again using data from all time-points in the longitudinal dataset ( adjusting for age at baseline and sex ) . These analyses were limited to the 448 individuals with outcome data . To identify risk factors for scarring progression , analysis was initially performed using logistic regression to assess the association between categorized proportions of TF , TP or C . trachomatis infection and overall scarring progression ( either incident scarring in those without scarring at baseline or progression of pre-existing scarring ) . Each of these were initially included as exposures separately in a logistic regression using scarring progression as the outcome variable and adjusting for age at baseline and sex . Following this , all three were included in a final multivariable model ( adjusting for baseline age and sex ) , and likelihood ratio tests were performed between models including versus excluding each exposure to determine its overall P value . The analyses were subsequently repeated to identify risk factors for incident scarring and progression of pre-existing scarring separately . In the first set of univariable and multivariable analyses ( using the same exposures as above ) the analysis was restricted to individuals without scarring at baseline ( incident scarring versus no scarring ) . In the second set , analysis was restricted to individuals with scarring at baseline ( progression of existing scarring versus no progression of existing scarring ) . Chlamydial load was calculated by extrapolating from a standard curve . OmcB in copies/μl was log10 transformed to normalize the distribution . In the longitudinal dataset random effects linear regressions were performed to look for associations between a ) chlamydial load and scarring progression ( adjusting for MDA-period , age at baseline and sex ) , and b ) chlamydial load and age at time-point ( in years ) in C . trachomatis positive individuals ( adjusting for sex and MDA-period ) . Age at time-point was split into four groups; <7 . 5 years , ≥7 . 5 - <10 years , ≥10 - <12 . 5 years , and ≥12 . 5 years . A random effects linear regression was then performed to assess for association between chlamydial load , age group and progression , including an interaction term between age group and progression in order to determine whether the association between chlamydial load and progression was modified by age . MDA-period ( pre-MDA , post first MDA , post second MDA , post third MDA ) was included in the model to adjust for confounding .
The participant flow is shown in Fig 1 . There were 666 potentially eligible children and 616 enrolled . Fifty either refused or were absent . We excluded 57 who were examined on less than four occasions , and 111 without scarring progression outcome data ( no time-point 16 or 17 assessment ) . This left 448 in this analysis , who were seen at a median of 15 time-points ( 1st– 3rd quartiles = 13–16 , S1 Fig ) . The demographic characteristics of the entire cohort were described in the baseline report[5] . Of the 448 children included in this analysis , 242 ( 54 . 0% ) were female , mean age at baseline was 6 . 8 years , and 438 ( 97 . 8% ) were Maasai . Of the 218 children not included , 92 ( 42 . 2% ) were female ( OR = 1 . 61 , 95%CI = 1 . 16–2 . 23 , p = 0 . 004 ) , mean age at baseline was 7 . 4 years ( OR = 0 . 86 , 95% CI = 0 . 79–0 . 93 , p<0 . 0002 ) and 214 ( 98 . 2% ) were Maasai . Younger children and females were therefore more likely to be included in this study analysis . Antibiotic coverage of the 448 children included in scarring progression analysis was 355 ( 79 . 2% ) in 2012 , 374 ( 83 . 5% ) in 2013 and 338 ( 75 . 4% ) in 2014 . The estimated community-wide MDA coverage in 2012 , 2013 and 2014 were 68 . 7% , 42 . 9% and 72 . 9% , respectively . Of the 448 participants , 240 ( 53 . 6% ) had TF ( F2/F3 ) , 185 ( 41 . 3% ) had TP ( P2/P3 ) ) , and 248 ( 55 . 4% ) had clinically active trachoma ( F2/3 and/or P3 ) at one or more time-point . The prevalence of TF and TP is shown for each time-point in Fig 2 . There was a significant reduction in inflammatory disease following MDA , although TP prevalence was particularly high at time-point 6 . Examination of inflammation and infection prevalence by village revealed that this peak in TP was found in only two of the three villages ( “A” and “C” ) , and did not appear to correlate with infection ( S2 Fig ) , indicating that it may have been driven by non-chlamydial infection . The number of individuals with categorized proportions of time-points ( none , <10% , 10–19% , 20–29% , 30%+ ) that they were found to have ( 1 ) TF , ( 2 ) TP and ( 3 ) C . trachomatis infection is shown in Table 1 . At baseline , 93 ( 20 . 8% ) had some degree of conjunctival scarring . The odds of TF were estimated to be higher in females ( OR = 1 . 49 , 95%CI = 1 . 05–2 . 11 , P = 0 . 025 ) and lower with each additional year of age ( OR = 0 . 65 , 95%CI = 0 . 59–0 . 71 , p<0 . 0001 ) in the longitudinal dataset . The odds of TP were also estimated to reduce with age ( OR = 0 . 79 , 95%CI = 0 . 71–0 . 88 , p<0 . 0001 ) but the evidence of an association with sex was much weaker ( OR = 1 . 39 , 95%CI = 0 . 92–2 . 09 , p = 0 . 119 ) . There was little difference between children included and those excluded in the analysis in terms of sex and baseline TF/TP/infection although those excluded tended to be slightly older on average than those included ( mean baseline age 7 . 4 vs 6 . 8 , p<0 . 001 ) . C . trachomatis was detected in 219/448 ( 48 . 9% ) at one or more time-points . The prevalence of infection is shown for each time-point in Fig 2 . The proportion of time-points that each individual had infection is shown in Table 1 . The median proportion of time-points infected among the 219 individuals who had C . trachomatis detected on at least one occasion was 12 . 5% , which was equivalent to ~2 time-points if someone had been seen on all 17 visits . Infection prevalence declined following each round of MDA , however at time point 10 ( 9 months after second MDA ) it had increased and at time-point 14 ( 9 months after third MDA ) infection prevalence had returned to pre-MDA levels ( 10–15% ) . Infection prevalence dropped again by time-points 16 and 17 . Further examination of infection and clinical sign prevalence in each of the three villages showed that the majority of infection and TF at later time-points were found in only village “C” ( S2 Fig ) . Village “C” is located in a different administrative district , which ( unlike the other two villages ) was eligible for and received MoH administered MDA treatment in July-2015 . This was subsequently followed by a further drop in infection , TF and TP prevalence in village “C” at time-points 16 and 17 ( S2 Fig ) . In a mixed effects logistic regression of infection at any time-point , female sex ( OR = 1 . 7 , 95%CI = 1 . 25–2 . 34 , p = 0 . 001 ) and younger baseline age ( OR = 0 . 82 , 95%CI = 0 . 75–0 . 99 , p<0 . 0001 ) were significantly associated with C . trachomatis infection . Overall , there was a strong association between C . trachomatis infection and TF ( OR = 11 . 6 , 95%CI = 8 . 9–15 . 0 , P<0 . 0001 ) and TP ( OR = 9 . 6 , 95%CI = 7 . 1–12 . 8 , P<0 . 0001 ) in the longitudinal dataset ( adjusted for baseline age and sex ) . The odds ratios for TP and TF as predictors of C . trachomatis infection ( adjusted for TP/TF , age at baseline and sex ) at each time-point were generally similar to or slightly higher after the initiation of MDA , however confidence intervals were much wider ( S3 Fig ) . Overall , scarring progression was observed in 103/448 ( 23 . 0% ) participants ( Table 2 ) . There were 307 ( 68 . 5% ) who had no scarring; 38 ( 8 . 5% ) with unchanged scarring; 48 ( 10 . 7% ) with incident scarring; and 55 ( 12 . 3% ) with increasing scarring . The relationships between scarring progression and proportion of time-points when C . trachomatis was detected or signs of inflammation ( TF or TP ) were seen , adjusting only for age at baseline and sex , are shown in Table 3 . In these models strong evidence was found of an association between progression and both TP and TF , but the association between infection and progression was weaker . There was also evidence of a greater risk of progression in females compared to males . In a multivariable model ( Table 4 ) for scarring progression ( retaining infection , TF , TP , age at baseline and sex ) , the strong relationship between increasing proportion of time-points with TP and scarring progression remained . Female sex was marginally associated . There was no association with either TF or infection , suggesting that the associations between TF and infection with scarring were mediated through TP . The analysis was repeated , restricted to individuals with ( a ) no scarring at baseline , and ( b ) some scarring at baseline , in order to differentiate between factors associated with incident scarring and progression of pre-existing scarring , respectively . In the unadjusted models there was evidence for associations between episodes of C . trachomatis infection , TF and TP and incident scarring ( S1 Table ) . In the multivariable model however , only TP was significantly associated with incident scarring , again suggesting that the effect of infection and TF was mediated through TP ( S2 Table ) . Neither infection , TF nor TP were significantly associated with progression of pre-existing scarring in either the unadjusted or adjusted models . There seemed to be a trend for increasing risk of progressive scarring with increasing episodes of TP , however the evidence for this effect was weak , it should be noted that the sample size for these sub-analyses was small . These data from children with pre-existing scarring did not demonstrate that additional episodes of C . trachomatis infection were associated with further progression of scarring . Female sex was associated with an increase in pre-existing scarring but not with incident scarring . There were no associations with age . Bacterial load in C . trachomatis positive individuals was equivalent between people with and without scarring progression , using data from all time-points ( adjusting for age at baseline , sex and pre/post-MDA period ) ( OR = 1 . 1 , 95% CI = 0 . 86–1 . 42 , p = 0 . 45 ) . There was evidence of an association between age at time-point ( in years ) and infection load among C . trachomatis positive individuals , with lower loads in older individuals ( OR = 0 . 91 , 95%CI = 0 . 86–0 . 97 , p = 0 . 004 ) , adjusting for sex and MDA period . Bacterial load in C . trachomatis positive progressors and non-progressors was plotted across different age groups ( derived from age in years at that time-point ) to determine whether the association between scarring progression and load varied by age . In the oldest age group , progressors had a slightly higher infection load relative to non-progressors , Fig 3 , which was supported by evidence for an interaction between age group and progression in their association with bacterial load ( p = 0 . 012 ) . The model including the interaction explained the data better than the model without the interaction ( p = 0 . 016 ) .
Progressive scarring trachoma was strongly associated with papillary inflammation in this longitudinal study . C . trachomatis infection was no longer associated with scarring progression after adjustment for TP , suggesting that the effect of infection is mediated through TP , and that other factors contributing to TP in addition to C . trachomatis infection are important determinants of disease progression . Further research is required to understand what these factors are; they might include other ocular or non-ocular infections , genetic variation in host immune responses or environmental factors . Females were at greater risk of C . trachomatis infection , clinical inflammation and scarring progression . The addition of TP as an indicator for trachoma control programs might provide a more accurate marker for the risk of disease progression and of the need for future trichiasis interventions , which are likely to be needed for many years to come in this community .
|
Trachoma is the leading cause of preventable blindness worldwide and is targeted for elimination as a public health problem by 2020 . The natural history of trachoma is not completely understood however . We conducted a four-year longitudinal study in a trachoma-endemic area of northern Tanzania with detailed follow up every three months . In the four-year study period , nearly one quarter of children developed progression of conjunctival scarring , despite three rounds of annual mass drug administration ( MDA ) for trachoma control . Disease progression was strongly associated with increasing proportion of episodes with conjunctival papillary inflammation ( TP ) , and only weakly associated with Chlamydia trachomatis infection and trachomatous inflammation–follicular ( TF ) . Analysis revealed that associations between infection and TF with scarring progression were mediated through TP , and that other factors causing individual differences in TP were also contributing to scarring progression . These data have significant implications for trachoma control . We hypothesise that in individuals who have previously experienced ocular C . trachomatis infection , TP is the primary driver of scarring progression . The addition of TP to trachoma surveillance programs would provide an indicator for active disease progression in the community and a more accurate guide to the need for future trichiasis interventions .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"inflammatory",
"diseases",
"children",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"chlamydia",
"trachomatis",
"pathogens",
"immunology",
"tropical",
"diseases",
"microbiology",
"bacterial",
"diseases",
"research",
"design",
"age",
"groups",
"signs",
"and",
"symptoms",
"eye",
"diseases",
"sexually",
"transmitted",
"diseases",
"chlamydia",
"neglected",
"tropical",
"diseases",
"bacteria",
"bacterial",
"pathogens",
"families",
"research",
"and",
"analysis",
"methods",
"infectious",
"diseases",
"chlamydia",
"infection",
"inflammation",
"medical",
"microbiology",
"microbial",
"pathogens",
"immune",
"response",
"longitudinal",
"studies",
"people",
"and",
"places",
"diagnostic",
"medicine",
"ophthalmology",
"biology",
"and",
"life",
"sciences",
"population",
"groupings",
"trachoma",
"organisms"
] |
2019
|
Progression of scarring trachoma in Tanzanian children: A four-year cohort study
|
A recombinant cysteine proteinase from Leishmania ( Leishmania ) infantum chagasi ( rLdccys1 ) was previously shown to induce protective immune responses against murine and canine visceral leishmaniasis . These findings encouraged us to use rLdccys1 in the immunotherapy of naturally infected dogs from Teresina , Piauí , a region of high incidence of visceral leishmaniasis in Brazil . Thirty naturally infected mongrel dogs displaying clinical signs of visceral leishmaniasis were randomly divided in three groups: one group received three doses of rLdccys1 in combination with the adjuvant Propionibacterium acnes at one month interval between each dose; a second group received three doses of P . acnes alone; a third group received saline . The main findings were: 1 ) dogs that received rLdccys1 with P . acnes did not display increase of the following clinical signs: weight loss , alopecia , onychogryphosis , cachexia , anorexia , apathy , skin lesions , hyperkeratosis , ocular secretion , and enlarged lymph nodes; they also exhibited a significant reduction in the spleen parasite load in comparison to the control dogs; 2 ) rLdccys1-treated dogs exhibited a significant delayed type cutaneous hypersensitivity elicited by the recombinant antigen , as well as high IgG2 serum titers and low IgG1 serum titers; sera from rLdccys1-treated dogs also contained high IFN-γ and low IL-10 concentrations; 3 ) control dogs exhibited all of the clinical signs of visceral leishmaniasis and had low serum IgG2 and IFN-γ levels and high concentrations of IgG1 and IL-10; 4 ) all of the dogs treated with rLdccys1 were alive 12 months after treatment , whereas dogs which received either saline or P . acnes alone died within 3 to 7 months . These findings illustrate the potential use of rLdccys1 as an additional tool for the immunotherapy of canine visceral leishmaniasis and support further studies designed to improve the efficacy of this recombinant antigen for the treatment of this neglected disease .
Zoonotic visceral leishmaniasis ( VL ) is caused by Leishmania ( Leishmania ) infantum chagasi in Mediterranean , Middle-East , Asian countries , and Latin America and dogs are the main domestic reservoirs of this zoonosis which has resulted in an annual incidence of 40 , 100–75 , 000 new human cases [1] , [2] . A high human VL incidence has been reported in Brazil mainly due to disease urbanization as a consequence of human migration from rural areas and ineffective vector and reservoir control [3]–[6] . Canine VL control is based on either treatment or euthanasia of infected animals . However , treatment of canine leishmaniasis with drugs successfully used for human VL shows low efficacy and induces the development of parasitic resistance to these drugs [7]–[10] . The WHO thus strongly recommends that the same drugs should not be used for treatment of dogs and humans in a same area [2] . On the other hand , euthanasia of infected dogs is often unacceptable for ethical and social reasons . Furthermore , the elimination of infected dogs has shown controversial results in Brazil [11] , [12] . These issues led to the search of immunotherapy as a treatment alternative for canine VL . The administration of L . ( L . ) infantum chagasi extracts associated with the conventional chemotherapy of naturally infected dogs resulted in a significant reduction in infectivity [13] . Similar results were observed in dogs infected with L . ( L . ) infantum chagasi that displayed a significant parasite burden reduction after treatment with autoclaved L . ( L . ) major antigens and heat killed Mycobacterium vaccae administered in conjunction with Glucantime [14] . The healing efficacy of some vaccine candidates has also been tested . Treatment of infected dogs with purified L . ( L . ) infantum chagasi LiF2 antigen in combination with Glucantime led to the disappearance of clinical signs and a 100% cure rate [15] . Dogs naturally infected with L . ( L . ) infantum chagasi and treated with the recombinant vaccine Leish-110f formulated with the adjuvant MPL-SE associated with Glucantime showed clinical improvement , parasitological cure and increased survival [16] . Recent data supported the effectiveness of this recombinant vaccine for the treatment of mild cases of canine VL [17] . The immunotherapeutic potential of the Leishmune vaccine alone or in association with chemotherapy for canine VL treatment has also been demonstrated [18]–[20] . A recombinant cysteine proteinase from L . ( L . ) infantum chagasi , rLdccys1 was previously shown to be an useful immunological marker for different VL stages in humans and dogs , and to offer an appropriate diagnostic tool for human and canine VL [21]–[23] . Furthermore , immunization with either rLdccys1 or the gene Ldccys1 , which encodes the cysteine proteinase , induced significant protection against L . ( L . ) infantum chagasi infection mediated by a predominant Th1 response in a murine model of VL [24] . In that study rLdccys1 was administered with P . acnes , a Gram-positive bacillus , known to induce a prevalent Th1 immune response in mice [25] , [26] . In earlier studies we used P . acnes as an adjuvant to immunize BALB/c mice with native Ldccys1; this resulted in a predominant Th1 response and a significant protection against L . ( L . ) infantum chagasi challenge [27] . These results encouraged us to evaluate the immunotherapeutic potential of rLdccys1 plus P . acnes for naturally infected dogs from Teresina , Piauí , a state in Brazil with a high incidence of VL [28] .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( http://www . cobea . org . br ) . The protocol was approved by the Committee on the Ethics of Animal Experiments of the Institutional Animal Care and Use Committee at the Federal University of São Paulo ( Id # CEP 1540/11 ) . A total of thirty dogs of different breeds , ages and sex were provided by the Zoonosis Control Center in Teresina , Piauí , in Brazil , a VL endemic area . The animals were kept in a thin-screened kennel in the Faculdade NOVAFAPI within Teresina , Piauí state and fed commercially balanced animal food ( Cherokee , PRT , Brazil ) . Drinking water was provided ad libitum . Diagnostic procedures were performed in the Department of Parasitology and Microbiology at the Federal University of Piauí . All dogs had been pre-treated with anti-tick , anti-scabies and anti-helminthic drugs and had been vaccinated against Parvovirus , Adenovirus type II , Distemper , rabies ( Defensor , Pfizer , EUA ) , Parainfluenza and Corona viruses and Leptospirosis ( Vanguard Plus , Pfizer , EUA ) . Animals diagnosed with distemper , ehrlichiosis , and babesiosis were excluded from the study . Animals with severe renal failure ( creatinine and urea values higher than 2 . 0 mg/dl and 35 mg/dl , respectively ) and pancytopenia ( total leucocyte number lower than 6 , 000 and number per mm3 of erythrocytes and platelets lower than 5 . 5×103 and 200 , respectively ) were not included in the study . All animals selected for immunotreatment tested positive for leishmaniasis in parasitological , serological and biochemical assays , had typical clinical signs of visceral leishmaniasis and were never treated for canine leishmaniasis . Diagnosis was grounded on positive bone marrow aspirate cultures , and ELISA assays using an L . ( L . ) chagasi amastigote extract and rLdccys1 as antigens . Additional laboratory studies included: complete blood cell counts , biochemical assays ( creatinine , urea , alkaline phosphatase , aspartate aminotransferase , alanine aminotransferase , albumin , total protein , glucose , and direct and indirect bilirubin ) . Clinical parameters evaluated were: alopecia , anorexia , apathy , cachexia , hyperkeratosis , size of lymph nodes , ocular secretion , onychogryphosis , skin lesions and weight loss . Microtiter plates ( high binding , Costar , Corning Incorporated , Corning , New York , USA ) were coated with either 100 ng/well of L . ( L . ) infantum chagasi lysates from amastigotes isolated from spleens of L . ( L . ) infantum chagasi-infected hamsters as described elsewhere [29] or with 200 ng/well of rLdccys1 in coating buffer ( 0 . 05 M Na2CO3/NaHCO3 , pH 9 . 6 ) . The plates were incubated overnight at 4°C and then blocked with 5% powdered skim milk in PBS for 1 h . Canine sera were diluted 1∶500 , added to the plate and incubated for 2 h at room temperature . After three washes with 0 . 05% Tween 20 in PBS , peroxidase labeled antibodies specific to canine IgG diluted 1∶500 were added to the plate for 1 h at room temperature . The plates were then washed three times with 0 . 05% Tween 20 in PBS , and the reaction developed with 0 . 5 mg/ml o-phenylenediamine in 0 . 05 M sodium citrate , pH 4 . 5 , containing 0 . 03% H2O2 . The reaction was stopped by adding 4 N H2SO4 , and the absorbance was measured at 492 nm in a Multiskan MS Plate Reader ( Labsystems Oy , Helsinki , Finland ) . The cut-off values were calculated by adding two S . D . values to the mean absorbance of 20 dog normal sera . The PCR product corresponding to the ORF of the Ldccys1 gene previously obtained [21] was subcloned into the Bam HI and Eco RI restriction sites of the pHis-parallel 3 expression vector in frame with an amino-terminal six histidine tag [30] . The recombinant plasmids were used to transform E . coli BL21 ( DE3 ) . Protein expression was carried out by inoculating 500 ml of LB medium containing 100 µg/ml ampicillin with a 25 ml overnight bacterial culture . The suspension was kept in a rotatory shaker at 37°C until reaching log phase ( Abs 600 nm = 0 . 6 ) , and protein expression was then induced with 0 . 2 mM IPTG for a further 3 h at 37°C . After growth , the recombinant bacteria were pelleted at 4 , 000× g for 10 min , and the recombinant antigen was then purified from the insoluble inclusion bodies by affinity chromatography using a Ni-NTA Superflow agarose matrix ( QIAGEN ) , according to Skeiky et al . [31] . Purified protein was analyzed by SDS-PAGE and Western blotting using a previously described monoclonal antibody directed against a cysteine proteinase of 30 kDa from L . ( L . ) amazonensis ( MoAb 2E5D3 ) that cross reacts with an antigen of 30 kDa from L . ( L . ) infantum chagasi amastigotes [27] , [32] . P . acnes was obtained from Instituto Adolfo Lutz , São Paulo , S . P . , Brazil and cultured as previously described [33] . Briefly , the bacteria were grown in anaerobic medium ( Hemobac , Probac , São Paulo , S . P . , Brazil ) for 3 days at 37°C and washed by centrifugation . The resulting pellets were suspended in 0 . 9% saline and subjected to continuous water vapor for 20 min at 120°C . The protein concentration of the suspension was determined by the Bradford method [34] . The treatment protocol was performed with 30 mongrel dogs selected by the parameters described in the ‘Animals’ section . All selected dogs were positive for leishmaniasis in parasitological , serological and biochemical assays and displayed clinical signs of VL . After selection they were randomly divided into three groups of 10 dogs each: one group received three subcutaneous doses of 500 µg rLdccys1 plus 500 µg P . acnes as an adjuvant on the back at a one month interval; the second group received three doses of P . acnes alone; and the third group received PBS . Half of the dogs from each group were followed by monthly clinical examinations until natural death , while the other half were euthanized three months after the end of treatment . The development of the clinical signs of VL was described by a score that quantifies the number of signs and discriminates mild from severe signs . The scoring system for clinical signs was based on the diameters of small ( score = 1 ) , and large lymph nodes ( score = 2 ) . For mild and severe weight loss were given scores of 3 and 4 , respectively . For alopecia , onychogryphosis , cachexia , apathy , anorexia , hyperkeratosis , ocular secretion and skin lesions were attributed a score of 3 . The dogs were evaluated at time zero , when they received the first dose , and monthly thereafter until two months after the end of treatment ( month 4 ) . Clinical evaluation was performed by a veterinarian which was blinded to the treatment groups evaluated . Animals were looked for side effects of the recombinant antigen such as local pain , local swelling , vomit and diarrhoea . These signs were followed for 10 days after each antigen injection . Parasite burden was evaluated in the spleens of all dogs enrolled in the study immediately after animal death using the limiting dilution assay , as previously described [35] , and parasite numbers were determined from the highest dilution at which promastigotes could be grown . Briefly , spleens were aseptically excised , weighed and approximately 1–1 . 5 g of spleen tissue was collected from the mid area and minced into small pieces with sterile scissors within a sterile Petri dish . The tissue was homogenized in 1 ml of PBS and further diluted in 199 medium ( Gibco ) containing 4 . 2 mM NaHCO3 , 40 mM HEPES , 1 . 0 mM adenine , 5 µg/ml hemin , 15% fetal bovine serum and 2% male human urine to obtain a final concentration of 1 mg/ml . Serial dilutions ranging from 1 to 1×10−6 were prepared in the same medium under sterile conditions in 96 wells micro plates ( Costar plates; Corning , Inc . , Corning , NY ) . After incubation for 10 days at 26°C , plates were examined using an optical microscope at 3-day intervals . The reciprocal of the highest dilution that was promastigote positive was considered to be the concentration of parasites in the spleen tissue processed and the total parasite load was calculated by multiplying this value by the total spleen weight . Delayed-type hypersensitivity assays ( DTH ) were performed by intradermal injection of rLdccys1 ( 10 µg ) into the inner surface of the right thigh . As a negative control , each animal received an injection of PBS into the inner surface of the left thigh . The induration diameter was measured by use of a caliper after 24 , 48 and 72 h , and each time the values of the saline control were subtracted from the reaction due to the rLdccys1 antigen . Skin reactions with diameter equal or larger than 5 mm were considered positive . The DTH assays were carried out at time zero and one month after animals received the third dose of rLdccys1 . Specific anti-rLdccys1 antibodies IgG , IgG1 and IgG2 isotypes were evaluated by ELISA at time zero and one month after administration of each dose of rLdccys1 in microtiter plates coated with 200 ng/well of rLdccys1 according to the protocol described in “ELISA assays for dog screening” section except that canine sera were diluted 1∶200 . Peroxidase labeled antibodies specific to canine IgG were diluted 1∶500 and to IgG1 or IgG2 isotypes ( Bethyl Laboratories , Inc . , Montgomery , TX , USA ) were diluted 1∶2000 and added to the plate for 1 h at room temperature . The plates were then washed and the reaction developed as described above . Lymphokine concentrations were measured in dog sera at time zero and one month after administration of each dose of rLdccys1 using a double-sandwich ELISA assay ( Quantikine Canine IFNγ and IL-10 ) ( R&D Systems ) . Microtiter plates ( high-binding Costar plates; Corning , Inc . , Corning , NY ) were coated overnight at 4°C with specific mAb , which was directed to each lymphokine tested and used at 100 ng/well . After washing with 0 . 05% Tween 20/PBS ( PBS/T ) and blocking with PBS/T containing 5% skim milk for 2 h at 37°C , 100 µl of dog sera diluted 1∶2 were added to wells . Standard curves were generated using recombinant canine IFNγ and IL-10 . After incubation overnight at 37°C , plates were washed with PBS/T , and a second antibody specific to each lymphokine was added ( biotinylated antibody diluted 1∶250 ) . After 60 min at 37°C , the plates were washed three times in 0 . 05% Tween 20 in PBS , and the reaction was developed with 0 . 5 mg/ml of o-phenylenediamine in 0 . 05 M sodium citrate , pH 4 . 5 , containing 0 . 03% H2O2 . The reaction was stopped by adding 4N H2SO4 , and the absorbance was measured at 492 nm in a Multiskan Plate Reader ( Labsystems Oy , Helsinki , Finland ) . Serum concentrations higher than the minimal values obtained from the respective standards were considered to be positive . One-way ANOVA and Student's t-test were used to determine the significant differences between groups by use of GraphPad Prisma ( version 5 . 0 ) and P values smaller than 0 . 05 ( P<0 . 05 ) were considered significant . The Pearson correlation coefficient was also calculated by use of GraphPad Prisma ( version 5 . 0 ) .
Figure 1A shows that at time zero , selected dogs from each of the groups exhibited low DTH values , whereas one month after the end of treatment the DTH values were significantly higher in dogs that received rLdccys1 compared to controls . The production of total IgG at time zero and one month after administration of each dose of rLdccys1 is shown in Figure 1B , while dosages of IgG1 and IgG2 are illustrated in Figure 1C . Starting from the first dose , there was a significant increase of IgG production in the sera of dogs treated with the recombinant antigen . Starting from the second dose , there was also an increase of IgG production in sera from control dogs , although this increase was lower than that observed in animals treated with rLdccys1 . The characterization of IgG subclasses showed that there was a significant increase of IgG2 in animals treated with rLdccys1 , whereas IgG1 production was significantly reduced after the second and third doses . In contrast , there was constant production of IgG1 in controls during all treatment time and a significant reduction of IgG2 starting from the first dose . IFN-γ and IL-10 concentrations were measured by ELISA in dog sera . Figure 2 shows that one month after the first dose there was a low but significant IFN-γ secretion in animals treated with the recombinant antigen . Furthermore , an increased concentration of this lymphokine was observed after the second and third doses of rLdccys1 , while a low IL-10 concentration was detected in these animals . In contrast , the control dogs exhibited a low IFN-γ concentration in all periods analyzed , as well as a significant IL-10 increase after they received the third dose of either P . acnes alone or saline . These data indicate that the treatment with the recombinant antigen led to the activation of Th1 responses . The evolution of clinical signs of VL is shown in Figure 3 and Table S1 . At time zero , animals from the three groups exhibited a similar clinical score average ( saline , 6 . 2; P . acnes , 4 . 9; rLdccys1 plus P . acnes , 6 . 3 ) . Control animals exhibited a significant increase of clinical signs that indicate disease progression two months after the end of treatment . In contrast , there was no increase of clinical signs in dogs treated with rLdccys1 , indicating that they were able to control the disease development . It is also important to emphasize that clinical signs implicated in the severity of canine VL , such as weight loss , cachexia and anorexia , were observed only in control dogs ( Table S1 ) . The safety of the recombinant antigen was also evaluated . The number of dogs showing pain ( 25% ) increased significantly from the first to the third dose ( P<0 . 001 ) . The pain after each treatment dose lasted for 48 h . Local swelling was the most common adverse effect observed ( 67% ) reaching an average diameter of 5 cm and no significant differences among doses 1 , 2 and 3 were noted . Furthermore , in all dogs the local swelling reaction was transient and decreased 24 h after each dose , disappearing five days after injection . Dogs did not vomit or present diarrohea . The correlation between lymphokine production and clinical score averages 30 days after the end of treatment with rLdccys1 is shown in Figure 4 . The correlation between the production of IFN-γ and the clinical score averages is negative because there was a significant increase of IFN-γ followed by low clinical scores in dogs treated with rLdccys1 , whereas in the controls the low secretion of IFN-γ was correlated with an increase in clinical signs ( Figure 4A ) . In contrast , there is a direct correlation between IL-10 production and the clinical score average . A significant increase in IL-10 and clinical score averages was observed in controls , while there was a decrease in IL-10 secretion followed by low clinical scores in dogs treated with rLdccys1 ( Figure 4B ) . The survival of controls and dogs treated with rLdccys1 was followed until the natural death of half of the L . ( L . ) chagasi-infected animals . Most of the dogs that received saline died between 2 . 6 and 4 . 4 months after the end of treatment . Among these animals , one was observed to have the lowest clinical score average and survived until 6 months after treatment . Among the dogs treated with P . acnes , three died between 2 . 8 and 5 . 3 months , while two of them survived 6 . 6 and 6 . 7 months after treatment . In contrast , dogs treated with rLdccys1 died after 12 . 3 to 14 . 2 months , with a mean survival time two times higher than the controls ( Figure 5A ) . Figure 5B shows the parasite load evaluated by limiting dilution analysis in dog spleens after death . Animals treated with rLdccys1 exhibited a seven-log reduction in parasite burden compared to controls that received either saline or P . acnes alone . The other half of the dogs that were euthanized three months after the end of treatment showed a lower parasite burden compared to animals followed until natural death . However , the parasite burden reduction in rLdccys1-treated dogs was not significantly different between euthanized and non-euthanized animals ( data not shown ) .
The main problems that face the treatment of leishmaniasis are toxicity , high cost and parasitic resistance to leishmanicidal drugs currently available on the market . This scenario is even more pronounced in the case of canine VL due to the low efficacy of the drugs currently in use for treatment of infected dogs [36] . All of these issues have pointed to the immunological stimulation as an attractive option for the treatment of leishmaniasis [37] . With this rationale , the potential of a recombinant cysteine proteinase of L . ( L . ) infantum chagasi , rLdccys1 , for the treatment of canine VL was investigated in this study . The usefulness of this recombinant antigen was first demonstrated in screening naturally infected dogs selected for this study . Similar values were observed when either rLdccys1 or L . ( L . ) infantum chagasi extracts were used as antigens in ELISA assays for evaluation of humoral responses ( data not shown ) . These findings corroborate previous results that demonstrated the high sensitivity and specificity of rLdccys1 for the diagnosis of canine and human VL [21] , [23] . In this study , a significant increase of DTH responses was observed after treatment with rLdccys1 , supporting our previous results on the use of rLccys1 to distinguish between asymptomatic and symptomatic canine VL [23] . Data on antibody production showed an increase of total IgG in all animal groups during the treatment; however this increase was higher in the rLdccys-treated dogs . Furthermore , rLdccys1-treated dogs had increased IgG2 production and decreased IgG1 , while control animals exhibited lower antibody levels with predominance of IgG1 . Although the IgG1 and IgG2 subclasses have been used as more reliable indicators of the CVL status than total IgG [38] , recently , the functional characterization of canine IgG subclasses raised doubts about the correlation of IgG subclasses with Th1 or Th2 responses [39] . However , our findings are compatible with those that showed high levels of IgG1 anti-Leishmania antibodies associated with the development of clinical signs in L . ( L . ) infantum chagasi-infected dogs , while IgG2 antibodies appear to be associated with asymptomatic infection [40] . Furthermore , protective responses in dogs vaccinated with the recombinant A2 protein appear to be associated with increased levels of total IgG and IgG2 but not with those of IgG1 anti-A2 antibodies [41] . Our data on DTH and humoral responses in rLdccys1-treated dogs are also compatible with those reported in dogs infected with L ( L . ) infantum chagasi subjected to immunotherapy with the Leishmune vaccine [18] , [20] . The treatment with rLdccys1 resulted in a significant increase of IFN-γ , reduction in IL-10 , and significantly less progressive clinical disease than control groups . In contrast , the control dogs presented the opposite profile of these lymphokines and a significant increase of clinical signs until four months after treatment . Indeed , a significant negative correlation was found between the number of clinical signs and IFN-γ production in dogs treated with rLdccys1 , whereas a positive correlation was observed between the production of IL-10 and an increase of clinical signs in control dogs . These results strengthen the implication of IFN-γ and IL-10 in control and progression , respectively of canine VL . The induction of Th1 cells producing IFN-γ , IL-2 and TNF-α has been associated with protection against canine VL [42] , [43] . The activation of macrophages by IFN-γ to kill intracellular amastigotes via the L-arginine nitric oxide pathway is the main effector mechanism involved in the protective immune responses of dogs infected with L . ( L . ) chagasi infantum [44] , [45] . In contrast , IL-10 has been correlated with disease progression and an increase in IL-10 levels; this was observed in the spleens of dogs naturally infected with L . ( L . ) infantum chagasi [46] . IL-10 mRNA transcripts were detected in Con A-stimulated PBMC derived from dogs with VL clinical signs [43] , [47] . It is worth noting that among the rLdccys1-treated dogs , the first animal that died exhibited a higher clinical score mean at screening , as well as a lower level of serum IFN-γ one month after the end of treatment ( data not shown ) . These findings indicate that the effectiveness of the rLdccys1 treatment is dependent on the disease progression at the time of inclusion in the study . It is possible that sick animals with lower levels of IL-10 respond better to antigen stimulation . Similar results were observed in dogs developing severe VL that did not respond to Leish-111f vaccine treatment [17] . These considerations point to the treatment of asymptomatic dogs with rLdccys1 and a potential more pronounced Th1 response in these animals . It is also important to emphasize the choice of the adjuvant used in the present study . P . acnes treatment elicits a type-1 ( Th1 ) immune response involving IL-12 and IL-18 that induces IFN-γ release , enhancement of the IgG2a switch and Th2 expansion inhibition [25] , [26] . Administration of killed P . acnes as an adjuvant increased the resistance to infection by Trypanosoma cruzi [48] . In leishmaniasis , the treatment with P . acnes led to the control of L . ( L . ) major infection in BALB/c mice [49] . Murine vaccination with the A2 antigen from L . ( L . ) donovani plus P . acnes resulted in a mixed Th1 and Th2 response with a predominance of Th1 responses after the homologous challenge , as well as a significant parasite burden decrease in immunized animals [50] . Our previous data on immunization of BALB/c mice with either the native or recombinant Ldccys1 plus P . acnes followed by challenge with L . ( L . ) infantum chagasi also showed a predominant Th1 response and a significant parasite burden decrease in immunized animals [24] , [27] . Immunization of dogs with rLdccys1 plus P . acnes also resulted in a significant protection against L . ( L . ) infantum chagasi infection ( unpublished data ) . The participation of CD8+ lymphocytes in protective immune responses triggered in dogs treated with rLdccys1 was not addressed in this study but cannot be overlooked . Analysis of the predicted amino acid sequence of the L . ( L . ) infantum chagasi Ldccys1 gene cloned previously showed one potential MHC class I epitope for CD8+ lymphocytes in addition to two MHC class II epitopes [21] . The involvement of CD8+ lymphocytes in canine VL has been demonstrated [51] and increased levels of these cells appear to be the major phenotypic feature of asymptomatic disease [52] . Enhanced expression of CD8+ lymphocytes was also observed in L . ( L . ) infantum chagasi-infected dogs after treatment with the Leishmune vaccine and resulted in a significant reduction of VL clinical signs and parasite burden levels [18] , [20] . Immunotherapy with rLdccys1 increased the survival time of L . ( L . ) infantum chagasi-infected dogs . Comparable results were reported following the use of Glucantime and the recombinant Leish-110f vaccine for treatment of dogs naturally infected with L . ( L . ) infantum chagasi [16] . Contrary to our findings , animals subjected to that immunotherapeutic protocol had a reduced number of deaths . Nevertheless , treated animals were followed until 180 days after treatment , while in our study animals were monitored until their natural death , and all of the rLdccys1-treated dogs were alive up until one year after treatment . Use of the Leish-110f vaccine in infected dogs treated for longer period of time also resulted in lower death rates compared to our results [17] . Again , this study was different from our treatment protocol because the authors emphasized the advantage of a weekly vaccine schedule over antigen administration with 3 or 4 weeks intervals [17] . Higher survival rates were also observed in dogs treated with the Leishmune vaccine [18]–[20] . It is important to highlight that the differences among previous studies on immunochemotherapy and the present study , including treatment schedules , adjuvant , geographical factors , and disease status of the dogs , hinder the comparison with our data . It is still important to mention that only symptomatic dogs with a high parasite burden were selected for our study and presumably treatment with rLdccys1 of animals with lower parasite burden may lead to improved results . Despite the high parasitism levels found within the infected dogs enrolled in the present study , there was a significant lesser parasite burden in dogs treated with rLdccys1 compared to controls . Nevertheless , dog cure and a pronounced decrease of vector infectivity are desirable goals in regions of high VL endemicity . In this context , we believe that the use of booster doses of rLdccys1 associated to allopurinol , the drug recommended by WHO to treat CVL [2] , are promising to improve the effectiveness of treating CVL with this recombinant antigen . In conclusion , our findings showed the potential of rLdccys1 as an additional tool for immunotherapy of canine VL and support further studies to evaluate its healing efficacy with a larger number of dogs , as well as in different regions of VL incidence .
|
Visceral leishmaniasis ( VL ) is an important public health problem and dogs are the main domestic reservoirs of zoonotic VL which has resulted in an annual incidence of 40 , 100–75 , 500 new human cases . Because canine VL chemotherapy is limited by the low efficacy of drugs currently used for human VL treatment , immunotherapy may provide a viable alternative . We used a recombinant cysteine proteinase from L . ( L . ) infantum chagasi , rLdccys1 , in combination with the adjuvant P . acnes for the treatment of naturally infected mongrel dogs from Teresina , Pauí a state in Brazil that has a high incidence of VL . Dogs treated with rLdccys1 showed a significant delayed type hypersensitivity reaction against the recombinant antigen and displayed high serum concentrations of IgG2 and IFN-γ and low concentrations of IgG1 and IL-10 . Immunotherapy with rLdccys1 resulted in no increase of the clinical signs of canine VL and an extensive reduction of spleen parasite load . Furthermore , all of the dogs treated with rLdccys1 survived for at least 12 months after treatment , whereas those that received either saline or P . acnes alone died within 3 to 7 months . These findings support the potential of rLdccys1 immunotherapy as an additional option for the treatment of canine VL .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"immunity",
"immunology",
"microbiology",
"biology",
"parasitology",
"immunotherapy"
] |
2014
|
Use of a Recombinant Cysteine Proteinase from Leishmania (Leishmania) infantum chagasi for the Immunotherapy of Canine Visceral Leishmaniasis
|
Despite decades of use of control programs , schistosomiasis remains a global public health problem . To further reduce prevalence and intensity of infection , or to achieve the goal of elimination in low-endemic areas , there needs to be better diagnostic tools to detect low-intensity infections in low-endemic areas in Brazil . The rationale for development of new diagnostic tools is that the current standard test Kato-Katz ( KK ) is not sensitive enough to detect low-intensity infections in low-endemic areas . In order to develop new diagnostic tools , we employed a proteomics approach to identify biomarkers associated with schistosome-specific immune responses in hopes of developing sensitive and specific new methods for immunodiagnosis . Immunoproteomic analyses were performed on egg extracts of Schistosoma mansoni using pooled sera from infected or non-infected individuals from a low-endemic area of Brazil . Cross reactivity with other soil-transmitted helminths ( STH ) was determined using pooled sera from individuals uniquely infected with different helminths . Using this approach , we identified 23 targets recognized by schistosome acute and chronic sera samples . To identify immunoreactive targets that were likely glycan epitopes , we compared these targets to the immunoreactivity of spots treated with sodium metaperiodate oxidation of egg extract . This treatment yielded 12/23 spots maintaining immunoreactivity , suggesting that they were protein epitopes . From these 12 spots , 11 spots cross-reacted with sera from individuals infected with other STH and 10 spots cross-reacted with the negative control group . Spot number 5 was exclusively immunoreactive with sera from S . mansoni-infected groups in native and deglycosylated conditions and corresponds to Major Egg Antigen ( MEA ) . We expressed MEA as a recombinant protein and showed a similar recognition pattern to that of the native protein via western blot . IgG-ELISA gave a sensitivity of 87 . 10% and specificity of 89 . 09% represented by area under the ROC curve of 0 . 95 . IgG-ELISA performed better than the conventional KK ( 2 slides ) , identifying 56/64 cases harboring 1–10 eggs per gram of feces that were undiagnosed by KK parasitological technique . The serological proteome approach was able to identify a new diagnostic candidate . The recombinant egg antigen provided good performance in IgG-ELISA to detect individuals with extreme low-intensity infections ( 1 egg per gram of feces ) . Therefore , the IgG-ELISA using this newly identified recombinant MEA can be a useful tool combined with other techniques in low-endemic areas to determine the true prevalence of schistosome infection that is underestimated by the KK method . Further , to overcome the complexity of ELISA in the field , a second generation of antibody-based rapid diagnostic tests ( RDT ) can be developed .
Schistosomiasis remains as a major worldwide public health problem . Since it is a disease of poverty and limited sanitary facilities , the disease has proved difficult to control for centuries [1] . Schistosomiasis afflicts low-income populations in tropical and subtropical regions with varying levels of morbidity and mortality and has a significant socioeconomic impact [2] . Estimates suggest that approximately 290 million people are affected in 78 countries around the world , especially in Sub-Saharan Africa , Asia , and South America [3] . Brazil has the highest burden of disease in the Americas and infection is caused by S . mansoni [4] . During the past 40 years , Brazil has developed an extensive history regarding the fight against schistosomiasis . Integrated control measures , such as investments in basic sanitation and hygiene , improvement in the population’s income levels and quality of life , and chemotherapy have had considerable success in terms of reducing prevalence , transmission and parasite loads [5] . The prevalence in Brazil was estimated at 1% by the National Schistosomiasis and Soil-transmitted Helminth Infection Survey ( INPEG ) , conducted between 2010 and 2015 [5] . Despite this significant reduction in prevalence , the disease has acquired a new epidemiological profile . Currently , Brazil has multiple endemic areas where chronically infected patients have low-intensity infections ( number of eggs per gram of feces , EPG , <100 ) [5–8] . The continuous distribution of disease remains mainly in the Northeast and Southeast regions of the country . Focal transmission , followed by acute infection , has also been reported as a result of migration of infected individuals ( rural tourism and urbanization ) [5 , 9–11] . In this new epidemiological scenario , infected individuals are very unlikely to be detected with routine parasitological methods . Since praziquantel ( PZQ ) mass drug administration is not conducted in Brazil , the main strategy to control and eliminate the disease is diagnosis and treatment of active cases [4 , 12] . As recommended by WHO , diagnosis of schistosomiasis continues to be detection of schistosome eggs in stools by microscopic examination using the KK technique [13] . The KK method is low-cost and suitable for detection of medium and high-intensity infections , i . e . > 100 EPG . However , it has poor sensitivity for detection of low-intensity infections that are seen in residents living in low-endemic areas ( <10% prevalence , <100 EPG ) [6–8 , 14 , 15] . As consequence , many true positive individuals are missed , generating significant underestimation of prevalence and shortcomings on control programs . Previous studies in Brazil demonstrated that prevalence has been underestimated by a factor of 2–4 , due to the inability of the KK method to detect low-intensity infections [6–8 , 16 , 17] . The failure to diagnose infected individuals contributes to continuation of S . mansoni infection , followed by contamination of the environment and maintenance of transmission . If the goal of elimination is a priority for the WHO [1 , 9] , new and more sensitive methods need to be applied to achieve it . The development of new methods that have the ability to accurately diagnose low-intensity infections was outlined in the WHO’s plans focusing on elimination of schistosomiasis as a public health problem [9 , 18 , 19] . In this regard , molecular and immunological techniques have proven to be more sensitive and promising for identifying infected individuals that are negative by KK coproscopy results [8 , 16 , 17 , 20–22] . Significant progress has been seen in the development of antigen-based rapid diagnostic tests ( RDT ) , as their assembly is user-friendly in the field . The immunochromatographic point-of-care ( POC ) test that detects circulating cathodic antigen ( CCA ) in urine has been commercially available since 2008 [23 , 24] . Although POC-CCA has been suggested to be a suitable substitute for KK in S . mansoni prevalence mapping [24–27] , its performance is still debatable in low-endemic areas [28–30] . Most studies validating POC-CCA were conducted in Africa , whereas few ( 10 ) studies were conducted in Brazil , which has a significantly different prevalence and morbidity profile . In contrast to Africa where low-intensity infections range from 1–100 EPG , most infections in Brazil are denoted as < 25 EPG [6 , 7 , 14 , 22 , 29 , 31–35] . Furthermore , the KK method was used as a reference standard during the validation of POC-CCA in Africa . However , it is not sensitive enough to serve as a gold standard [28] . Indirect techniques based on detection of antibodies have high sensitivity in detecting low-intensity infections and are capable of identifying loads of 1 EPG [17 , 21 , 36–41] . In endemic settings , antibody-based methods present low specificity and are not indicated as single use tests . However , their use as screening tool combined with parasitological evaluations has decreased false-negative cases seen when only utilizing 2 KK slides in endemic settings [16 , 21 , 39 , 40] . The indirect diagnostics can also detect pre-patent infections from individuals returning from schistosomiasis endemic areas . As antibodies to the parasite develop during the first weeks of infection , they can be detected before eggs are produced and released in the feces . In clinical practice , positive serology in KK negative people from non-endemic countries is usually sufficient to prescribe treatment with PZQ [10 , 42 , 43] . Antibody-based methods have been re-evaluated in order to improve detection of S . mansoni infection in endemic populations . As an alternative to enhance the specificity of assay , some studies focus on detection of specific antigens [44–47] . Crude antigens , such as soluble eggs antigens ( SEA ) and worm antigens ( SWAP ) , are frequently used , but they can exhibit low-sensitivity and cross-reactivity with different helminths [48 , 49] . Ideally , antibody detection should be performed using a specific , purified schistosome component or a schistosome-derived recombinant protein as the immunodiagnostic target . A combination of proteomic and serological analyses have served as promising experimental approaches for screening new biomarkers in the diagnostic field [50–52] . However , there is a limited number of serological-proteomic studies involving Schistosoma spp . and most of them are related to searching for vaccine candidates using animal models [52–57] . Only one immunoproteomic analysis related to S . mansoni and human samples has been performed to date , but it focuses on the search for vaccine candidates [57] . In the present work , we adopted immunoproteomic analysis to identify a new antigen candidate to be applied in schistosomiasis diagnosis . As antibodies against schistosome eggs have been considered useful antigens for the diagnosis of schistosomiasis [37 , 43 , 58] , we screened soluble egg extracts ( SEE ) by two-dimensional western blotting ( 2D-WB ) . To achieve higher specificity , we compared native SEE extracts to those oxidized by sodium metaperiodate ( SMP ) , in order to exclude antigens whose epito pes were glycan-based , since they denote high cross-reactivity among different helminths . Moreover , we analyzed the potential of the new target ( MEA ) as recombinant antigen for detecting individuals with low-intensity infection by ELISA .
The present study was approved by the Ethics Committee of the Research Center Rene Rachou/Fiocruz under the following number: 893 . 582 11/2014 and by the National Brazilian Ethical Board under the following number: 14886 . Before any research activities , the local health authorities were contacted and they agreed to collaborate with the researchers from different institutions . All enrolled participants were required to sign an informed consent form . Parents or legal guardians signed the informed consent when minors less than 18-years old were involved . When the parasitological results were positive , the relevant individuals were informed and received free oral treatment at the local health clinic . Schistosomiasis: PZQ ( 50 mg/kg for adults and 60 mg/kg for children ) ; intestinal helminths: albendazole ( 400 mg ) ; protozoan parasites: metronidazole ( 250 mg/2x/ 5 days ) . All procedures involving animals were conducted in compliance with the Manual for the Use of Animals/FIOCRUZ and approved by the Ethics Committee on the Use of Experimental Animal ( CEUA–FIOCRUZ ) license number LW-31/15 . Recombinant antigen rMEA was evaluated for the ability to diagnose S . mansoni infection by antigen-specific IgG ELISA ( rMEA-IgG-ELISA ) . Optimization of the protocol and dilution of reagents were determined by titration . Flat bottom plates ( Maxisorp NUNC ) were coated 100 μl/well with rMEA 1μg/mL in 0 . 05 M carbonate bicarbonate buffer pH 9 . 6 and incubated at 4°C for 16 h . The plates were washed six times in PBS with 0 . 05% Tween 20 ( PBS-T ) and blocked by addition of 300 μl/well of 2 . 5% skim milk in PBS-T at 37°C for 2 h . After additional washing , 100 μl/well of individual serum diluted 1:100 in PBS was added to the plate in duplicate and incubated at RT for 2 h . The plates were washed , and peroxidase conjugated anti-human IgG antibody was then added to wells at a dilution of 1:60 , 000 in PBS-T at RT for 1 h . After more washes , plates were developed using 3 , 3' , 5 , 5'-tetramethylbenzidine ( TMB , Sigma ) . The reaction was stopped after 10 min of incubation in the dark with 50 μL of sulfuric acid . The optical density ( OD ) was determined by an automatic ELISA reader ( Multiskan , Thermo Scientific ) , using a filter at 450 nm . Analyses were performed using Open Epi , version 3 . 03 and GraphPad Prism , version 5 . 0 . In order to evaluate the performance of rMEA-IgG-ELISA , a reference standard was established , which included all positive results ( visible eggs ) from any of the parasitological methods used ( KK and SG ) . Normal distribution of the data was verified by the Shapiro-Wilk test . To compare the means for non-normal distribution , the Mann-Whitney test was used with a p-value ≤ 0 . 05 considered significant . Receptor Operating Characteristic curves ( ROC curves ) were used to calculate area under curve ( AUC ) , sensitivity , specificity and the cut-off points between positive ( group 1 ) and negative groups ( group 5 ) . The AUC indicates the probability of accurately identifying true positives , where one could distinguish between non-informative ( AUC = 0 . 5 ) , less accurate ( 0 . 5<AUC≤ 0 . 7 ) , moderately accurate ( 0 . 7<AUC≤ 0 . 9 ) , highly accurate ( 0 . 9<AUC<1 ) and perfect tests ( AUC = 1 ) [64] . Positive predictive values ( PPV ) , Negative Predictive Values ( NPV ) and overall accuracy ( ACC ) was determined by the following formula: PPV = number of true positives/ ( number of true positives + number of false positives ) ; NPV = number of true negatives/ ( number of true negatives + number of false negatives ) and ACC = ( number of true positives + number of true negatives ) / ( number of true positives + true negatives + number of false positives + number of false negatives ) . The McNemar’s test was used to analyze categorical variables . To evaluate the degree of concordance between the different methods , the kappa index ( κ ) followed the categorization for Landis and Koch ( 1972 ) : <0 poor , 0 . 00–0 . 20 slight , 0 . 21–0 . 40 fair , 0 . 41–0 . 60 moderate , 0 . 61–0 . 80 substantial and 0 . 81–1 . 00 almost perfect . The relationship between the intensity of infection ( EPG ) determined by parasitological tests and the IgG-ELISA ( OD ) was examined by the Spearman correlation test .
The 2D-PAGE provided good resolution of spots in pH range with minimal streaking . In order to identify the antigens recognized by antibodies in pooled sera , a corresponding 2D-PAGE was performed in parallel so that WB ( native and SMP-oxidized ) could be performed to exclude any variation that might arise from the use of different antigen preparations ( Fig 1 ) . In native 2D-WB ( Fig 1B ) , 23 immunoreactive spots were recognized by the pooled infected sera from S . mansoni . No difference in recognition was seen between chronic and acute sera ( groups 1 and 4 ) . From 23 spots , 22 spots were simultaneously recognized by STH-positive sera ( group 3 ) and 10 spots were recognized by negative sera ( group 5 ) . One single spot , number 5 ( indicated by white arrow on Fig 1 ) , was exclusively recognized by infected patients ( acute and chronic ) and was not recognized by the STH-infected and health individuals . Spot 5 was detected by antibodies in the pooled 180-day post-treatment sera ( group 2 ) . The immunoblot and homologous stained gel were aligned , and the 23 spots matched and excised for LC/MS analysis ( Fig 2 ) . The identification of spots is presented in Table 1 . The 23 spots were resolved into 12 proteins . LC/MS analysis revealed instances in which different spots were derived from the same protein: for example , spots 11 , 17 , 22 , and 23 are all secretory glycoprotein k5 . It was observed that , in some cases , there was no direct correlation between the amount of protein in the SEE protein extract and its antigenicity level . Although most of the immunoreactive spots recognized by infected serum were visible in the corresponding 2D-PAGE , there were highly immunoreactive spots that were barely visible in stained gels ( e . g . , spot 1 ) . Spots 10 , 19 , 20 and 21 were not identified due to low abundance . Most identified proteins were related to housekeeping proteins . These include structural/muscle proteins , enzymes ( mostly components of the glycolytic pathway ) and chaperone proteins . To evaluate the presence of glycosylated epitopes on the 23 immunoreactive spots , 2D-WB was performed using SMP-treated membranes ( Fig 1C ) and then compared to the native one ( Table 2 ) . After oxidation , only 12/23 spots maintained immunoreactivity , indicating they potentially have protein epitopes . From these 12 spots , 11 spots cross-reacted with the STH-positive sera ( group 3 ) and 10 spots cross-reacted with negative sera ( group 5 ) . Spot number 5 was uniquely recognized by S . mansoni-infected groups in chronic and acute phase ( group 1 and 4 ) and was not recognized in uninfected groups ( group 3 and 5 ) . Furthermore , there was an observed decrease in immune recognition of spot 5 in 180-day post-treatment sera ( group 2 ) compared to the corresponding chronic sera at baseline ( group 1 ) in the SMP experiment . Spot 5 , approximately 40 kDa and pI 7 . 0 , was identified as MEA and chosen for further evaluation in immunodiagnostic assays . Selection was based on: 1 ) single identification in infected S . mansoni individuals ( group 1 and 4 ) , 2 ) absence of cross-reaction in S . mansoni uninfected individuals ( group 3 and 5 ) , 3 ) recognition after SMP treatment ( potential presence of immunogenic peptides and feasibility for bacterial production ) and 4 ) apparent decrease of reactivity intensity by 180 days post-treatment ( group 2 ) . The rMEA was expressed by IPTG induction in E . coli . The size from recombinant construction was predicted in Expasy Software including the histidine tag ( https://www . expasy . org/proteomics/protein_structure ) corresponding to 43 kDa . As shown in Fig 3 , the purified protein was present in the gel and the corresponding western blotted anti-histidine tag . To validate the recombinant proteins , the purified material was sent for MS analysis by Shotgun . The results showed 98 . 6% of abundance was related to native MEA ( Smp_049250 . 1 ) , confirming its identity . We evaluated the antigenicity from rMEA using serum from S . mansoni infected individuals from endemic areas and non-infected healthy individuals ( NEG ) . The rMEA maintained the recognition pattern from native form , which was recognized by positive but not negative sera . Also , no unspecific bands were visualized in WB experiment . These data confirmed the correct purification of rMEA and the presence of antigenic epitopes in the in vitro prokaryote expression ( Fig 4 ) . Once we demonstrated potential for diagnostic application , rMEA was evaluated for detection of IgG by ELISA . The ROC was carried out to estimate the cut-off and performance indices ( sensitivity , specificity , PPV , NPV and AUC ) for rMEA-IgG-ELISA . The samples from group 1 ( positive from endemic area ) ( Table 3 ) and group 5 ( negative from health donors ) were used as reference . The average intensity of infection of 93 individuals from group 1 was 5 . 4 EPG , calculated by the geometric mean of the number of EPG ( GMEPG ) in examination of 2 grams of feces . The AUC demonstrated a high power of discrimination between the groups ( AUC = 0 . 95 ) . The cut-off was 0 . 232 which was selected based on the best overall accuracy ( ACC = 87 . 8% ) . Significant IgG reactivity against rMEA was observed in S . mansoni infected individuals in comparison with negative healthy donors and those negative from endemic areas ( Fig 5 ) . The sensitivity was 87 . 10% and specificity was 89 . 09% with PPV and NPV of 93 . 1% and 80 . 33% respectively . The agreement between rMEA-IgG-ELISA and the reference method determined by 24 slides of KK and 2 procedures of SG showed substantial concordance ( κ = 0 . 75 ) . When the current adopted KK ( 2 slides ) was compared , it demonstrated a fair concordance ( κ = 0 . 32 ) with a significant difference between the positivity rates ( McNemar’s test , p < 0 . 0001 ) . The positivity rate from rMEA-IgG-ELISA ( 58 . 8% , 87/148 ) and the reference method ( 62 . 8% , 93/148 ) showed no significant difference ( McNemar’s test , p = 0 . 24 ) ( Table 4 ) . K-K identified 36/93 positive cases ( 38 . 7% sensitivity ) , yielding 57 false negative results . The rMEA-IgG-ELISA was able to identify 81/93 infections , of which 75 were low-intensity infections ( < 100 EPG ) . From the group harboring extremely low-intensity infections ( ≤ 10 EPG ) , the immunoassay identified 56/64 ( 87 . 5% sensitivity ) , of which 27 had intensity of infection at 1 EPG ( Table 5 ) . rMEA-IgG-ELISA determined 12 false negative results , which the egg burden varied from 1 to 99 EPG . There was not a significant positive correlation between IgG levels ( OD ) and egg burden ( EPG ) by Spearman rank test ( r = 0 . 024 , p = 0 . 8167 ) . From 80 stool negative individuals from the endemic area , rMEA-IgG-ELISA identified 41 as positive .
Advances in development of new schistosomiasis diagnostic methods are necessary for low prevalence/low-intensity infections [1] . In the majority of Brazilian endemic areas , transmission is maintained by individuals having low level infections that are undiagnosed by analysis of 2 slides of KK in a single stool sample , as recommended by WHO [5 , 9] . Despite extensive efforts over several years , the search for sensitive and specific diagnostics for schistosomiasis is ongoing . Diagnosis using antibodies [37 , 45 , 46] , antigens [65 , 66] or DNA [8 , 16 , 67] show high sensitivity , but reduced specificity compared to egg-based methods [7 , 14 , 48] . Improvements on immunoassays have been the most studied aspect of mapping , due to promising ability to detect individuals with low-intensity infections undiagnosed by standard KK [29 , 37 , 45 , 65 , 66 , 68–70] . In addition , they have paved the way for less laborious rapid tests that are useful both in communities in endemic areas and at point-of-care facilities [66 , 71] . The POC-CCA is the antigen-based method most recently evaluated to be part of WHO guidelines . This immunochromatographic RDT has been commercially available since 2008 and it is based on detection of CCA in urine samples . As this antigen is released into the circulation by adult schistosomes , its detection levels can indicate an active infection [24] . The POC-CCA has shown good performance in Africa and has been proposed as a substitute for the KK method based on its estimated higher sensitivity and operational advantages , especially in highly endemic areas [68] . However , in low-endemic areas , especially in Brazil where extreme low-intensity infections ( 1–25 EPG ) are predominant , the results are controversial and more evaluation is needed before the test is released for general use [6–8 , 30] . The issues surrounding adopting POC-CCA in Brazil are related to 1 ) inadequate estimation of sensitivity and specificity of the POC-CCA , due to the absence of adoption of a highly sensitive method as a reference standard [7 , 14 , 32–34]; 2 ) high number of individuals incorrectly diagnosed ( false positive and false negative ) due to interpretation of trace as positive or negative [6 , 14 , 29 , 31]; 3 ) the low sensitivity to detect infections with low parasite loads [7 , 14 , 22] . In a recent study , in order to improve the performance of POC-CCA in low-endemic areas , urine samples were 10-fold concentrated by lyophilization and analysis of 2 grams of feces ( 24 K-K slides plus 2 SG ) served as a reference method . After the additional step , the trace became positive in parasitological positive cases but remained as trace in parasitological negative cases increasing the sensitivity of POC-CCA from 6% to 56% [31] . More validation is going on in order to reduce the time-consuming lyophilization step ( 34 h ) by using 30 kDa-filter centrifugation ( 50 min ) , thereby allowing the test to be more suitable for large-scale evaluations [29] . As none of the diagnostic tests used currently provide 100% accuracy , sequential or simultaneous multiple tests are applied to address mapping , monitoring of interventions , assessment of cure rates and disease surveillance [6–8 , 14 , 15 , 17 , 71] . Antibody-based immunodiagnostics are particularly useful for detecting low-intensity infections . Since antibodies to the parasite develop during the first weeks after infection , they can be detected before eggs yielding higher sensitivities . Due to greater sensitivity than parasitological methods , these tests allow for detection of infections with loads as low as 1 EPG [22 , 36 , 37 , 40] . The use of antibody detection in low-endemic areas has been successfully applied but it is limited to use as screening tests or as a complementary tool to parasitological evaluation [16 , 21 , 37–40 , 72] . This is due to the inability to accurately differentiate between active infection , past infection , and reinfection; and also because of antibody cross-reactivity with different helminth species [48] . In terms of increasing the specificity of antibody-based immunodiagnostics , the search for new antigens has been proposed [44 , 45 , 73 , 74] . Most of the antigens described in the literature are related to the crude extracts ( SEA and SWAP ) which have a complex source , require time-consuming purification steps , and vary greatly on accuracy and reproducibility [37 , 48] . Molecular cloning and the expression of recombinant proteins represent a reliable alternative for generating enough amounts of well-defined antigens for use in immunodiagnostic assays . For these reasons , the goal of the present study was to identify antigenic targets using immunoproteomic analysis and validate their performance for detection of low burden individuals as an initial step towards development of recombinant protein-based immunodiagnostics . Our work was the first serological-proteomic study conducted with egg extracts from S . mansoni and human samples . It included diversified sets of sera allowing for a more rational search for highly specific diagnostic molecules . Through the immunoproteomic approach , we identified 12 different immunogenic proteins from egg extracts . Other Schistosoma spp . serological-proteomic studies using human samples have been conducted . Mutapi et al . ( 2005 ) used serum from infected individuals with S . hematobium to screen adult worm antigens in 2D-PAGE to identify suitable antigens for diagnostic purposes . Twenty-six immunoreactive protein spots were identified and investigated [56] . The unique study related to S . mansoni and human samples involved searching for vaccine candidates using worm extracts . Ludolf et al . ( 2014 ) identified 47 different immunoreactive proteins from worm antigens using sera from positive and negative endemic individuals . One of them , the eukaryotic translation elongation factor , uniquely reacted with naturally resistant residents from endemic areas and was considered a potential vaccine candidate [57] . Our results showed that 23 immunoreactive spots , resolved into 12 different proteins , were strongly recognized by pooled sera from S . mansoni-infected individuals . No differences were found between acute and chronic samples . Currently , differentiation between the two stages of infection is based on clinical and epidemiological data . Differentiating them by serological diagnosis could contribute to the establishment of adequate protocols for treatment of infected patients and detection of new foci or infection cases in tourists . However , this work did not identify proteins specific for different stages of infection . Some studies initially pointed out antigens , such as SmRP26 and KLH , with the potential to discriminate between the acute and chronic phase , however , there was no reproducibility in subsequent evaluations [75–77] . de Assis et al . ( 2016 ) evaluated the recognition of 92 proteins in sera from positive ( acute and chronic phase ) and negative individuals by using protein microarrays . Fifty antigens were recognized by sera samples in the acute and chronic phase . From these , 4 antigens were differentially recognized between the acute and chronic phase and will be further evaluated in the standardization and validation of new differential methods for the diagnosis of different infection stages [78] . Differential recognition was not found between the infected group and post-treatment group . Antibodies remain present in serum following treatment of infected individuals , making it difficult to differentiate between current and previous infections [48] . The persistence of antibodies after treatment impairs post-treatment monitoring , which could be resolved by means of a differential diagnosis using an antigen specific for that phase . Mutapi et al . ( 2005 ) , using a similar approach to this work , but with S . haematobium infections , identified 5 exclusively immunoreactive proteins in serological post-treatment samples . The presence of new antigens at this stage was related to the release of these antigens after parasite death and exposure to the host’s immune system [56] . In order to analyze if the antigenicity from proteins was carbohydrate dependent , we screened the extract after SMP treatment . Periodate oxidation alters glycan structures from glycoproteins and therefore eliminates their ability to be detected by anti-glycan antibodies [62] . This finding has strong implications for selection of a more specific target and choice of appropriate vectors to express recombinant candidates for the development of diagnostic tests . Polyparasitism is common in endemic areas and glycans are the most shared and most immunogenic fractions among helminth species [60 , 79 , 80] . Alarcon de Noya et al . ( 2000 ) demonstrated that after oxidation of egg extracts , the specificity from IgG-ELISA in detecting S . mansoni-infected individuals increased from 73% to 97% due to reduction of cross-reactivity with other parasites [49] . In this study , from 23 spots recognized in native SEE extract by the S . mansoni-positive group , 22 cross-reacted with STH-positive individuals . After the oxidation step , the number of spots recognized by the S . mansoni-positive group decreased to 12 , indicating the influence of carbohydrate moieties on the antigenicity of proteins . The treatment with SMP did not influence the antigenicity from spot 5 ( MEA ) , the only protein which maintained recognition in the S . mansoni-positive group and was absent in STH-positive groups . This suggests the presence of protein epitopes and enabled the selection of a prokaryotic system for production of recombinant MEA . Glycoproteins with carbohydrate-dependent antigenicity require eukaryotic expression due their ability to undergo post-translation modifications , such as glycosylation . This system is less attractive for diagnosis purposes as it is more laborious , complex and expensive to adopt [81] . MEA was selected to be evaluated as an immunodiagnostic for schistosomiasis because it was the only antigen that was recognized by S . mansoni-infected patients , but was not recognized by negative individuals and those infected with other STH in native and SMP evaluations . MEA , also known as Smp40 , is one of the 40 most abundant proteins secreted by the eggs [79 , 82 , 83] . MEA is a chaperone and shares homology with the family of heat shock proteins . It is involved in the protection of miracidia from oxidative stress , denaturation , and aggregation of proteins [79] . In the study by Nene et al . ( 1986 ) , when western blots were probed with serum raised against a Smp40 fusion protein , the Smp40 could be detected in adults , cercariae , schistosomulum and egg stages [84] . van Balkon et al . ( 2005 ) also demonstrated the MEA is present on tegmental and stripped worms protein fractions [85] . In humans , MEA has been described to initiate a strong T-cell response , which is associated with reduced granuloma formation [86 , 87] . The potential for diagnostic application of MEA was observed in study by Ludolf et al . ( 2014 ) . MEA was selected by immunoproteomic analysis of adult worm and its recombinant form demonstrated immunoreactivity against samples from chronic individuals using western blotting [57] . In this study , rMEA was recognized by sera from infected endemic individuals and was not recognized by sera from negative non-endemic individuals in WB analysis . Since this antigen proved to be promising in preliminary WB , we evaluated the performance of rMEA in the detection of antibodies IgG by ELISA . rMEA-IgG-ELISA performed significantly better than the currently adopted KK ( 2 slides ) for detection of low-intensity infections . When compared to a reference standard ( 24 KK + 2 SG ) , the test showed sensitivity of 87 . 10% and specificity of 89 . 09% represented by AUC = 0 . 95 . On other hand , the KK performed by 2 slides exhibited a sensitivity of 38 . 71% and 57 false negative cases . These 57 misdiagnosed individuals were verified have 1–10 EPG , which is indicative of the majority of cases of schistosome infection in Brazil [6–8 , 22 , 29 , 31] . These individuals would not receive treatment , possibly develop serious forms of infection , and contribute to maintenance of transmission . Differently , rMEA-IgG-ELISA identified 56 from 64 cases from group having ≤ 10 EPG , demonstrating its high sensitivity for identifying extreme low intensity infections . Even though POC-CCA has been encouraged for use , as it is based on direct detection , it presents the same low sensitivity as KK ( 2 slides ) to detect low burden infection . In two different studies from Brazil , regardless if traces were considered positive or negative , the POC-CCA sensitivities only ranged from 14–47% compared to a reference standard . Further , around one third of positive individuals misdiagnosed by POC-CCA in these studies had loads 1–10 EPG [7 , 14] . As observed here , other studies have demonstrated the ability of ELISA to identify low burden individuals missed by 2 KK analysis in low-endemic areas in Brazil , as well as have demonstrated a good correlation compared to an improved reference method . IgG-ELISA-SWAP showed 90% sensitivity/specificity and Kappa index 0 . 85 when compared to 18 K-K slides [37] . The study by Oliveira et al . ( 2005 ) [40] demonstrated 98% sensitivity and 97 . 7% specificity for IgM-ELISA . Both studies identified low burden individuals undiagnosed when 1 K-K slide was used . Although crude extract antigens can be used for ELISA , such assays would require infrastructure to maintain the parasite cycle and the complexity of large-scale production and standardization . In respect to use of S . mansoni recombinant proteins , the previous studies also reported high levels of sensitivity and specificity similar to rMEA for detecting low-intensity infections . The recombinant CCA showed 100% sensitivity and 96% specificity by IgG detection in chronic individuals using magnetic microspheres without false-negative results [47] . The IgG-ELISA using recombinant 200-kDa tegumental protein demonstrated 90% sensitivity and 93 . 3% specificity with a strong correlation with egg burden in the same set of individuals [74] . El Aswad et al . ( 2011 ) showed sensitivity and specificity of 89 . 7% and 100% , respectively , using the recombinant calreticulin and cercarial transformation fluid in ELISA [88] . The rMEA-IgG-ELISA determined that a number of negative residents from endemic areas were positive . In endemic regions , residents are continuously exposed to parasite infection and parasite antigens; many have high titers of antibodies without being infected , leading to a large-number of false positive results [37] . The false positive issue has been reported in other studies discussing single test immunoassay and why a single immunodiagnostic assay may not be appropriate for epidemiological surveys [58 , 88–90] . Even though the high performance of immunoassays indicates them as alternatives to the standardized K-K in terms of preventing false negative results , the presence of false positives can yield significant over-treatment , making them not optimized for single use tests . On the other hand , combined approaches have been successful in diagnostic screening , whereby individuals are initially tested for the presence of antischistosomal antibodies and then those with positive results are confirmed by copro-microscopy techniques . In Brazil , this combination has led to accurate diagnoses and help inform treatment decisions [16 , 17 , 21] . In the work presented here , we demonstrated that the immunoproteomic approach was successful in selecting a good candidate for use in the diagnosis of schistosomiasis , as confirmed by 2D-PAGE and western blotting analysis . Although rMEA was capable of detecting low parasite burden infections that were undiagnosed by 2 slides of K-K , the sensitivity and specificity were 87 . 10% and 89 . 09% , respectively , and there was not a significant correlation between the IgG absorbance and the egg burden . Our results indicate that the use of MEA in indirect immunoassays can be valuable when used as a screening tool during epidemiological surveys , followed by more specific assays for a robust parasitological evaluation . To overcome the complexity of ELISA in the field , a second-generation of antibody-based RDTs has already been proposed , as well as the detection of antigen together in a multiplex strip on a reader [66] . Accordingly , new RDTs platforms should take better advantage of antibodies for the specific detection of protein epitopes to be an alternative method to distinguish active infections .
|
Schistosomiasis remains a serious global public health problem . Detecting parasite eggs in patient stool samples using the KK method is the standard diagnostic recommended by the World Health Organization ( WHO ) for infection by S . mansoni . As a result of intensive control strategies , many previously high-endemic areas are now considered low-endemic areas and the KK method does not function well in low-endemic areas and therefore cannot be considered the gold standard . Thus , a new emphasis on strategies to accurately diagnose low-intensity infections was outlined in a plan from the WHO focusing on elimination of disease as a public health problem . Successful diagnoses and treatment of infected individuals may result in eradication of low-burden transmitters and consequently contribute to interruption of disease transmission . In this regard , immunological techniques have proven to be more sensitive and promising for identifying low-intensity infections where KK may be negative . The identification of antigens is the initial step for developing new immunodiagnostic assays . In this study , we used sets of pooled human sera samples from controls with acute and chronic infections to identify new target antigens via proteomic screening . Using these approaches , we initially identified 12 different egg proteins in S . mansoni-infected individuals ( acute and chronic phase ) . A single antigen , identified as MEA , was shown to be highly specific as this antigen was not recognized by sera from negative patients or patients infected with other STH . The recombinant MEA protein functioned in an ELISA as a highly sensitive and specific antigen to detect patient IgG-antibodies . Recombinant MEA performed significantly better to detect low-intensity infections ( 1 egg per gram of feces ) than the KK method using 2 slides . Therefore , we were able to use a proteomic screening approach to identify a potential new candidate antigen for development of far more sensitive diagnostic assays . Further diagnostic assays employing the MEA could be useful tools on their own or in combination with other methods for diagnosis of schistosome infection in populations living in extreme low-intensity endemic areas of Brazil .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2019
|
Serological proteomic screening and evaluation of a recombinant egg antigen for the diagnosis of low-intensity Schistosoma mansoni infections in endemic area in Brazil
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.